Andy Masley

What I get out of effective altruism

Mon, 21 Sep 2026 13:44:14 GMT

This is the first post in a series on the core things I get out of the cluster of ideas that effective altruism (EA) offers as a package. EA has been thrust into the national spotlight, and I’ve been publicly associated with it for half a decade, after lurking on it from afar for the decade before. This is what I’d want to say to a smart friend who wanted to know the high-level deal with EA, beyond the sales pitch but without any jargon. This first post is a very high-level explanation of the big story EA tells about the world, and why I think it’s useful, but only as one mental tool among many. Among other things I want to explain to people what the underlying connection is between bed nets, animal welfare, and AI.

The effective altruist story of the world

The core story effective altruism tells about the world, which was already there in the drowning child thought experiment that a lot of the movement grew from, is that the ways we go about doing good, whether through our donations or our careers, are often strongly determined by what we believe will get us social status or otherwise help us get ahead. There’s good reason to think our higher-level thinking itself evolved to help us win social games, so when we set out to find “how to do good,” a lot of what we’re actually doing is picking up on subtle social vibes around different causes, trying to suss out what the “good” (often meaning high-status) people work on and believe in.

This is not inherently bad or useless. Status systems communicate a lot of useful information. Society often gives status to things because they really are useful, just not in ways that can be clearly articulated to every person affected by the push and pull of what we give status to. So social status behaves a lot like a market, with the behavior of billions of people together determining how much status different things get, without any central planner making the final decision. A lot of our inclination to do good is really an inclination to respond to these market incentives. And that’s good! It keeps society running, helps us live together, and often helps us respond to subtle human needs that can’t be clearly articulated through direct argument. When I say status is like a market, I mean both that markets are phenomenally efficient and that status isn’t always bad. The status we give our friends is not meaningless evolutionary noise, just like the money a local baker receives for a cupcake isn’t the full story of what happened in the act of making it.

But like any market, this one has deep failures and missed opportunities. Our sense of how to do good was built for the small bands of early humans we evolved in, who couldn’t meaningfully affect the world far away from them. We now live in a hyper-globalized world of over 8 billion people, and can affect almost any of them by donating to the right places. We also have unbelievable sci-fi control over the world, to the point that we could bathe the entire planet in nuclear fire if we wanted to. Among other things, we now farm hundreds of billions of animals every year. Despite our strong evolved intuition that the world will mostly stay the way it was when we grew up, the last 200 years have seen constant, previously unimaginable technological change, and the near future might bring new technologies that upend our safety at least as much as nuclear weapons did a mere 80 years ago. In this environment, our evolved intuitions around goodness and status are much blunter instruments than they used to be for figuring out where we could actually do the most good. To rely on them completely because they feel right and normal and good, and to never be willing to override what immediately feels virtuous or what your peers (or society more broadly) are encouraging you to do, seems at the very least like an unserious way of living, a way of not fully thinking through the situation we’re in. It seems like a form of playing pretend. EA is one of many ways I try to think more seriously about ethics and complicate my immediate sense of what’s good.

A metaphor I like for two types of thinking is serial vs. parallel processing. In serial processing, you follow logic from premises to conclusions, step by step. In parallel processing, you “vibe it out.” You pick up on subtle, inarticulable aspects of the world and respond to them. When you do a math or physics problem, you’re thinking serially. When you pick up on a social cue or sense you’re in danger, you’re thinking in parallel. Most of our thinking is actually an incredibly complex mix of both. Importantly, we didn’t really evolve to do consistent, thorough serial thinking without having it easily thrown off by the vibes of parallel processing. Thinking step by step is much more cognitively demanding. As Dennett says in Consciousness Explained:

Conscious human minds are more-or-less serial virtual machines implemented—inefficiently—on the parallel hardware that evolution has provided for us.

In the evolutionary environment, serial thinking about morality probably wasn’t very useful. Social cohesion was a matter of life and death, and relied on members of the group not randomly getting some idea in their heads that seemed logical and right but didn’t fit what the group needed from them. Our desire to look virtuous to our peers was heavily selected for, regardless of its effects on the wider world, which we had little ability to affect anyway. How would evolution even select for what “had good effects on the world” far beyond us?

Of course, our moral intuitions have also evolved alongside society. Modern people’s moral intuitions are wildly different from those of people thousands of years ago, and it’s not a coincidence that they now line up with a lot of very complex ways of behaving that happen to make a complex society function remarkably well. Our general respect for and good treatment of strangers, and the corresponding ability to trade with and benefit from people very different from us who live far away and well outside our immediate networks, is a relatively new and miraculous development in the history of humanity. But society has changed, and keeps changing, so much that even our newer moral intuitions often can’t tell apart the very different levels of impact we can have on the world. They’re still too blunt for our hyper-globalized, hyper-technological situation.

So to circle back to effective altruism, this is the core story it tells about the world:

Our thinking about ethics and about what we should do is often heavily structured around what will give us social status. This isn’t necessarily bad, because social status is like a complex market, which can both communicate a lot of useful information and incentivize a lot of pro-social behavior. But this market is likely to have gigantic failures, places where it drastically misjudges where we could do the most good, because our moral intuitions ultimately come from a very different situation, where we lived in small, close-knit bands in a world we mostly couldn’t affect, rather than in a world of 8 billion people where each of us can affect lives on the other side of the planet with a simple donation. While our moral intuitions and status systems have evolved to keep up with our changing situation, the world has changed so fast in the last few hundred years that there are likely massive gaps between our moral sense of what we can do and what we can actually do. There are lots of other serious belief systems and ways of investigating the world that have been phenomenally successful at finding truth (including what looks like moral truth), and we should defer to them (people into EA are quite deferential to scientific and philosophical consensus where it exists). But the new world we’re in is so strange and vast, and people are so averse to thinking step by step about doing good, that other moral market failures likely persist. We should try our best to find them, compare them, and act on the best ones.

Wouldn’t be a post about EA without this graph

Getting more concrete

It’s rare for people to actually try to think step by step about what they’re doing overall.

A lot of our core beliefs about ourselves and the world are surprisingly surface-level. One of the most humbling experiences is sitting down with a blank Google Doc and trying to type out a detailed explanation of some belief that’s important to you and guides a lot of your life, or what you’re ultimately trying to do with your career, or your life plan in general, or why you donate to the charities you do. Things you believed you “think about a lot” might turn out to be a few slogans you repeat in your head over and over while you’re at the grocery store. Our own minds are pretty opaque to us. We often think we’re doing deep, premise-to-conclusion serial thinking about the problems we care about, when in fact we’re just basking in the warm glow of status we get from turning simple slogans over in our heads. A lot of my own “serious thinking about economics” looks more like me offhandedly thinking “Markets communicate information. Hayek! I’m so smart” and feeling great about it while I wait in line at Trader Joe’s. My most popular post was basically a long meditation on how we do this when learning about the world, and how to avoid it.

This doesn’t make sense if you think of the world as a sequence of rational, thought-out decisions by actors carefully weighing every trade-off. It does make sense if you think of human society as mostly guided by markets for social status, where our behavior and thinking are strongly pulled toward whatever makes us feel we’re doing or believing the right thing, and careful serial thinking about what we’re doing is much more demanding and unpleasant. When we do think step by step, we regularly bump up against our own failures or lack of knowledge or seriousness, all things our brains are hard-wired to avoid.

But doing it also often yields a surprising amount of useful insight.

There are many ways of thinking serially about the world (my favorites being physics and analytic philosophy) that have been remarkably successful at overriding our longstanding moral and factual beliefs and getting us to better behavior or deeper moral truth. The specific goal of EA is to scout out the most important of these that are still left to find, the ones that seem robustly good after wrestling with all the reasons we have to be uncertain, and that merely require us to think step by step about how to live coherently with what we value in this new sci-fi world.

This is a good time to come back to the drowning child argument for giving much more to global charity. Here’s the argument as Peter Singer originally wrote it:

I begin with the assumption that suffering and death from lack of food, shelter, and medical care are bad. I think most people will agree about this, although one may reach the same view by different routes. I shall not argue for this view. People can hold all sorts of eccentric positions, and perhaps from some of them it would not follow that death by starvation is in itself bad. It is difficult, perhaps impossible, to refute such positions, and so for brevity I will henceforth take this assumption as accepted. Those who disagree need read no further.
My next point is this: if it is in our power to prevent something bad from happening, without thereby sacrificing anything of comparable moral importance, we ought, morally, to do it. By “without sacrificing anything of comparable moral importance” I mean without causing anything else comparably bad to happen, or doing something that is wrong in itself, or failing to promote some moral good, comparable in significance to the bad thing that we can prevent. This principle seems almost as uncontroversial as the last one. It requires us only to prevent what is bad, and to promote what is good, and it requires this of us only when we can do it without sacrificing anything that is, from the moral point of view, comparably important. I could even, as far as the application of my argument to the Bengal emergency is concerned, qualify the point so as to make it: if it is in our power to prevent something very bad from happening, without thereby sacrificing anything morally significant, we ought, morally, to do it. An application of this principle would be as follows: if I am walking past a shallow pond and see a child drowning in it, I ought to wade in and pull the child out. This will mean getting my clothes muddy, but this is insignificant, while the death of the child would presumably be a very bad thing.
The uncontroversial appearance of the principle just stated is deceptive. If it were acted upon, even in its qualified form, our lives, our society, and our world would be fundamentally changed. For the principle takes, firstly, no account of proximity or distance. It makes no moral difference whether the person I can help is a neighbor’s child ten yards from me or a Bengali whose name I shall never know, ten thousand miles away. Secondly, the principle makes no distinction between cases in which I am the only person who could possibly do anything and cases in which I am just one among millions in the same position.
I do not think I need to say much in defense of the refusal to take proximity and distance into account. The fact that a person is physically near to us, so that we have personal contact with him, may make it more likely that we shall assist him, but this does not show that we ought to help him rather than another who happens to be further away. If we accept any principle of impartiality, universalizability, equality, or whatever, we cannot discriminate against someone merely because he is far away from us (or we are far away from him). Admittedly, it is possible that we are in a better position to judge what needs to be done to help a person near to us than one far away, and perhaps also to provide the assistance we judge to be necessary. If this were the case, it would be a reason for helping those near to us first. This may once have been a justification for being more concerned with the poor in one’s town than with famine victims in India. Unfortunately for those who like to keep their moral responsibilities limited, instant communication and swift transportation have changed the situation. From the moral point of view, the development of the world into a “global village” has made an important, though still unrecognized, difference to our moral situation. Expert observers and supervisors, sent out by famine relief organizations or permanently stationed in famine-prone areas, can direct our aid to a refugee in Bengal almost as effectively as we could get it to someone in our own block. There would seem, therefore, to be no possible justification for discriminating on geographical grounds.
From Famine, Affluence, and Morality

This thought experiment is, among other things, a way to snap you awake into the hyper-globalized world we’re in, out of the still very strong moral illusion that we live in small hunter-gatherer bands and can only affect the people right in front of us. In reality, there are enormous numbers of invisible levers floating in front of each of us that we can pull at any time, each with some effect on other people’s lives, and some with unimaginably larger effects than others. These levers go undetected by the status games around us, and at times even by the more complex and nuanced ethical systems that have arisen to keep pace with modernity, because the world has just moved too fast for most people to intuitively sense how much real impact they can have now.

One of these levers is donating to specific malaria charities, which can reliably prevent one death from malaria for roughly every $5,000 donated.

This fact, that each of us could save a real person’s life for a meaningful but still small fraction of our annual income (the median American household makes $87,000 per year), seems at first like one interesting moral claim among many, but once you think through its implications, a lot of our behavior looks kind of absurd. For example, the ongoing Russian invasion of Ukraine has been an unimaginable catastrophe, with around 600,000 people killed since the war began. I don’t want to cheapen what’s happened, and to be clear, I’m not saying “the money spent on this war should have been donated to charity instead.” Instead, consider that the budget of the school district I taught in (Fairfax County, admittedly one of the largest in the country) is about $4 billion per year. 600,000 x $5,000 = $3 billion. So politicians in Fairfax County, Virginia could get together and say “Hey all, we’re going to double the taxes you pay into the school system for exactly one year, which will be a lot, and donate all of it to anti-malaria and similarly effective charities, but by doing this our one county will prevent an entire Ukraine war’s worth of deaths in a single year, just spread around the world.” If a single American county had the opportunity to prevent a major war’s worth of deaths with one policy change for one year, that would be major news! We wouldn’t treat it as one interesting decision among many. “Oh, this one random county could’ve prevented a whole war’s worth of deaths, but decided not to. They want to focus on infrastructure!” That would be pretty shocking.

And yet we all do have this collective opportunity, and it mostly goes undiscussed. When I bring it up, people often treat it as one interesting fact among many, go on with their days, and get morally fired up about things that don’t seem nearly as consequential, and that I don’t think even they would believe are nearly as consequential if they spent some time writing out how those things compare to malaria. But people often just never think to do these comparisons at all. If they took the time to seriously consider how they use their resources, I think they would often come to different conclusions. My claim here isn’t that malaria is the one big cause every smart person needs to care about, only that it’s evidence that many people won’t even take the first step in seeking out ethical market failures unless really pushed. Malaria looks like a cause that would get a lot, lot, lot more attention if people actually sat down and thought step by step about what they’re doing with their charitable donations. It wouldn’t fade into the background as one interesting problem among many. For a lot of people, donating to fight malaria would seem much more urgent than most other things they could donate to, for the same reason it would seem drastically important for a single county to prevent a major war if it could.

There are of course problems with this thought experiment. Malaria kills around 600,000 people every year, so that’s the very maximum malaria charities could possibly prevent. And malaria charities hit diminishing returns. It gets harder to prevent each additional death once the easier cases are covered, so the cost per life would rise well before we got anywhere near that many people saved in a year. But there are a lot of other ways to spend $5,000 that save or come close to saving a life (malaria is just one of the causes GiveWell directs money to). Once malaria crosses some cost threshold, you can divert money to the next cheapest way of saving a life, and a single county in Virginia can still prevent a major war’s worth of deaths in a year.

Individuals, and large groups of individuals, could make moves like this whenever they’d like, each preventing at least one unnecessary death per year by donating to the right places, and collectively preventing major wars’ worth of them. Yet it often doesn’t get talked about at all. When people first hear the basic argument for “effective” giving, that we should direct our donations wherever they save the most lives, their reaction is often “That’s so obvious, why would you even need a special name for that?” It may seem obvious, but almost no one does it, anywhere. Walk around where you live and, as you pass people or homes, ask yourself “What are the odds this person has actually sat down to work out where their donations would go the farthest in saving lives or otherwise doing good? Have they compared the options available to them?” For the vast majority of people I’ve spoken to, the answer seems to be no, even though a few hours spent comparing the options would probably let the same amount of money change vastly more real people’s lives. Why not? Why doesn’t this happen?

There are a lot of reasons, but it’s useful to keep in mind that most people aren’t invested in thinking step by step about their donations, rigorously following premises to conclusions without first sussing out how those conclusions fit into their social sense of what will make them seem important. Thinking step by step is often arduous, we didn’t evolve to do much of it, and we often don’t even do it when planning our own careers!

Source

If you talk to people about donating to malaria bed nets, they’ll often quickly exit the act of thinking through the problem with you, and let their parallel-processing sense that “this isn’t actually one of the cool things to care about right now” take over. They’ll come up with strange justifications you know they wouldn’t later endorse. They’ll repeat simple slogans they haven’t thought much about. Some of my more left-wing friends have told me bed nets are bad because “we need systematic change” instead, but then don’t go on to criticize similar one-off donations to people in need when they’re local.

This doesn’t always happen, to be clear. People also give thoughtful answers about why it makes sense to donate elsewhere, or not to donate at all. I’m not saying that people’s beliefs about malaria nets are a sign they’re ideologically asleep. What does strike me is that so many people, who spend so much of their lives guided by specific modern moral systems, treat the fact that we could collectively prevent major wars’ worth of deaths about the same as any other random fact about the world, and won’t follow along with thinking through what it implies about how we should behave, or will come up with on-the-spot justifications for not donating to effective causes that they probably wouldn’t endorse with a little more thought. Again, this suggests there could be massive, simple ethical market failures created by our status systems that most big ideologies today miss, and that could be found by just applying serial thinking, of writing out a Google Doc of what we actually know about the options available to us. EA is about trying to find those market failures with serial thinking, and to work out which look most promising to work on and which a lot of different moral views converge on once the facts are in front of them (or in slogan form, “using reason and evidence to do the most good we can with our time and resources”).

Another key part of seeking out ethical market failures is that they should look reliably good from a lot of different ethical perspectives. The idea that you ought to prevent someone from dying of malaria when the cost to you is a few minutes of thinking carefully about your donations seems like a very strong reason to do it whether you’re a utilitarian or a deontologist, a Christian or a Muslim. This isn’t to say there’s some objective morality written into the universe that we believe we can find, only that we want to take actions that look good under a lot of different possible moral systems, and it’s surprisingly easy to use serial thinking to find actions that look “robustly” better than others, meaning clearly good across a wide range of ethical assumptions.

So to recap: the core of EA is the idea that simply applying step-by-step thinking to the ways you do good (which, again, is rare, because we mostly didn’t evolve to do it and are often socially incentivized against it) can yield new options that look so robustly better for our values that letting them fade into the background of everything else we do looks ridiculous, and that people should just take this step and do things like simple cost-benefit analysis of the charities they donate to. When we do, we often find that the best things we could do are hidden from us, can have thousands of times the impact we care about (like saving lives) compared to what we were doing before, and look surprisingly compelling from a lot of different basic moral perspectives. So, as just some guy in DC, I can reliably prevent someone from dying of malaria every year because I’m careful about comparing where to donate. If I weren’t, it’d be extremely easy for my charitable giving to never prevent a single death.

If you want a sense of how this serial thinking looks in practice, a good place to start is the 80,000 Hours problem profiles, which guide you step by step through what they see as the most important problems in the world to work on. Reading a lot of writing like this in the 2010s, which tried to walk rational people through the full comprehensive deal with complex global problems without relying on social passwords or going along with the coolest things to work on at the time, and which was one of the few places online that actually tried to put numbers on all these different interventions, had a big influence on how I write. I credit a lot of my own blog’s success to the style I picked up from the blogs I was lurking on back then.

The main cause areas of EA (global health, animal welfare, AI safety, biosecurity) grow out of this intuition. Each is based on a plausible idea that doesn’t get much social status, so it doesn’t get considered much, and that also has the potential to be massively, robustly important:

People in rich countries like the United States can save the lives of people in poor countries with modest donations to the right charities, which often drastically outperform local charities in lives saved per dollar.
Animals aren’t just automatons, and may have complex forms of happiness and suffering that matter morally.
Humanity could last a very long time, so if we go extinct or civilization collapses, this seems like a uniquely large loss.
AI might gain most of the economically relevant abilities of the human mind sometime this century, and the consequences could be insane. (This one has gone mainstream.)

Each of these is complicated, and brilliant people disagree with them. But there are also (especially for animals) very strong social biases against even considering them in the first place, which leaves open the possibility of a massive market failure. At best, a lot of people react with “Oh wow, neat idea” and keep these in their pockets as talking points to bring up every now and then. A big goal of effective altruist thinking is to not do that, to not collect neat ideas and fold them into your current status game, but to behave as if they’re really real, as if they reflect something actually happening in the world that ought to shake you into behaving at least somewhat differently, and to act the way your future or past self would want you to if you suddenly discovered you really lived in a world like this. To then reason through what else we can know and do about the problem, and try our best to act on it under uncertainty instead of letting it become another neat conversational move.

Behaving as if the world exists beyond our status games

I worked at a summer camp when I was a teenager. Once, a camper crossing a bridge suddenly gripped the railing and started breathing heavily. A counselor asked what was wrong, and the kid turned to him and said “This is all REAL.”

“What? Yeah.”

“No, really, like us being here right now, and the bridge, and the world. It’s all REAL.”

I think this is a relatively common experience. A lot of the time, the world can feel somewhat fake. We go through the motions of our lives and a lot of what we see starts to feel like a dream, or like some kind of rehearsal while we wait for the life where things are real to start. It can take effort to remember that the faces we pass every day have lives just as complex as ours, never mind that the rest of the world is actually happening too, with billions of people in it. Sometimes the sense that our lives are real, that this is all really happening, suddenly floods us, or comes to us gradually as we live more in line with what we actually think is valuable.

For a lot of people moved by the basic EA story of the world, I think a key shared intuition is that thinking step by step about how to do good is a way of letting the world beyond our status games actually reach us, of taking it seriously as really existing out there beyond our little games, and of trying our best to work out how to live most coherently with our values in a world far bigger than the part of it we can see. Of letting the full unimaginable complexity and depth of the world beyond our friends briefly flood us, and try to act and respond seriously after being so overwhelmed. To not laugh it off because it doesn’t perfectly align with the social status detectors in our brains, and instead to follow the real facts about it to whatever conclusions they support about how we ought to behave, to do the best we can with the actual state of the world beyond our little corner of it. To put numbers on things, to compare the costs of saving abstract people far away, is to treat those people as really existing, whether or not we ever hear the specific story that would make us feel for them. Someone just as real as a close friend could get to live out their life because of your decision to donate to a malaria charity.

Another motivation is to behave like adults, like how our past selves would have wanted adults to behave. The idea “you should carefully compare the charities you donate to to figure out which will have the best effect on the world” seems so obvious that you’d expect most adults to do it, and yet these “adults” mostly don’t exist. The world is reliably more nuanced, but also more goofy, than you immediately expect, and its full goofiness can often be revealed by just following a chain of thought like “are people actually taking the incredibly easy opportunities to impact the world in line with their values?”

I also find that a lot of people who are into effective altruism, myself included, have at some point felt burned by a moral flare-up we were part of. Things that gave us so much meaning, that seemed so important in the moment, that turned out to be a game we were playing with ourselves and the people around us, that wouldn’t have survived much serial thinking at all. They maybe wouldn’t have withstood even a single Google Doc page of clear premise-to-conclusion thinking about what we were trying to do. As you get older, you start to look out for what you won’t regret, for what will let you feel at home with yourself later on. There’s a deep appeal in using serial thinking not just to access the world, but to live coherently with what your past and future self would’ve hoped you’d do. To gain some distance from any one specific cause you care about, and to invest more of your emotions into the act of adjusting, over time, toward what you actually think is worthwhile. You want to live sincerely.

Over the past 10 years, the world has felt more and more prone to mass movements and moral panics. While EA has generated a lot of weird behavior itself, it has also in some ways been a useful ideological haven from the constant demands to get maximally fired up about the thing of the moment, which usually dissipates into nothing shortly after. Some people have developed strange defense mechanisms against being captured by these panics, leaning hard into nihilism or performative cruelty to show they won’t be mind-hacked. I think this gives way too much power to the sources of the mind-hacking, and I’d rather calmly and technocratically assess the claims from an emotional distance.

This worldview only goes so far

Obviously, this story is also incredibly dangerous. Any story that tells you that you can easily ascend beyond your local status games should be treated with extreme suspicion, because humans are much more social and status-oriented than this simple story implies, and what’s waiting for you beyond your immediate status games can be much worse. Those games exist in part to protect us from new and worse social dynamics that can warp our thinking, be exploited by charming sociopaths, or lead us to believe the normal ethical rules constraining everyday people don’t apply to us anymore. While I’ve loved, and been excited to represent, large parts of the network of people who identify with EA, it doesn’t surprise me at all that loose associations of people organized around the idea that we should investigate the ways we’re taught to do good and try to transcend them through reason have at times led to everything from stultifying groupthink to outright cataclysm. Nothing written into this worldview prevents people who believe it from committing horrible evil, or from being led astray by the strange, invisible new status systems that exist in these peripheral social spheres. Holding views that normal society doesn’t reward can itself turn into a status symbol, and into a status market of its own. EA at its worst can also fall into what Dan Williams calls “naive realism,” where we believe we’ve simply ascended to the brute ethical truth of the world and can easily perceive it.

So whenever I write about what I think is so special about effective altruism, I’m not going to hype up the community. I think the community has collectively saved at minimum tens of thousands of lives and done incredible good for animals, and I increasingly feel proud of a lot of our work in AI safety, but I also see horrible decisions continuing to be made that harm people into the idea, and the world more broadly.

I’ve always approached EA with a pretty heavy epistemic conservatism, and a wariness of abstract human reason. The piece of writing that had the single biggest effect on the way I think was Charles Sanders Peirce’s Some Consequences of Four Incapacities, which notes:

Philosophy ought to imitate the successful sciences in its methods, so far as to proceed only from tangible premisses which can be subjected to careful scrutiny, and to trust rather to the multitude and variety of its arguments than to the conclusiveness of any one. Its reasoning should not form a chain which is no stronger than its weakest link, but a cable whose fibers may be ever so slender, provided they are sufficiently numerous and intimately connected.

I can’t follow a lot of people in EA when their thinking gets too abstract. But I don’t think it’s possible to engage deeply with EA arguments and come away honestly dismissive of the idea that EA has stumbled on some profound ethical problems that deserve way more help than they’re getting, or of the more general point that there’s disturbingly little serial thinking in the world about how to do good (or even how to pick a career). One of my favorite ethics papers is Evan Williams’s “The Possibility of an Ongoing Moral Catastrophe,” which lays out the case that, just given human history, we are very likely in the middle of unseen, gigantic moral catastrophes right now, exactly the kind we’re often stunned that past people lived alongside without trying to solve or even thinking about much. EA is one specific tool for finding these and trying to act on them, and it needs to be surrounded by a lot of outside moral and epistemic rules to keep you from drifting into the more dangerous dynamics of believing you’ve ascended above mere human social games. Here, my personal rules for not going ideologically crazy have come in handy.

It seems obvious to me that a central tragedy of the human condition is that two things are true at once: we are almost certainly living with and tolerating horrific ethical catastrophes that we would act on if we could see them clearly, and attempting to rise above our social situation to identify and solve those invisible catastrophes is itself unspeakably dangerous, carrying the threat of ideological extremism to the point of cults, groupthink, or a flattening of our intellectual and emotional engagement with the world. I think the only way to be morally serious is to try to occupy the emotionally difficult middle ground between these extremes, to allow ourselves to be deeply troubled by counterintuitive moral arguments, and to act on them when the evidence and reasoning are overwhelming, even if that violates our social sense of what the cool good people do. But also to stay intensely wary of, and somewhat apart from, the social and intellectual dynamics we land in when we follow these arguments.

I’ve seen effective altruism as an amazing and dangerous button to press. It scrambled my brain a bit and complicated most of the ethical narratives I used to take as given, and of all the clusters of ideas that could have done that to me, I feel especially proud of the decisions I’ve made as a result of this one, and of the balance I’ve struck with the story it tells. Among other things, I’m sure I wouldn’t have had the success I’ve had with this blog without the tools for thinking I picked up from EA writing. Even my data center stuff came out of the basic observation that applying some simple serial thinking and math to a widely discussed problem (like whether it’s an environmental disaster to use ChatGPT) can, if done well, take you far past the public debate, because way more people are just vibing it out and repeating slogans than you might think.

More broadly, the donations I’ve made, the brilliant people I’ve helped get careers in DC policy areas that I still think are some of the most important in the world, and even some of how I try to talk to friends directly and serially instead of running on social autopilot, all trace clearly back to the ways the EA story of the world disrupted my background sense of status and importance. But I’m also happy I didn’t chase status within EA scenes in ways that would’ve warped my thinking and made me much more confident about AI, animals, or global health than is warranted. And I’ve seen disastrous decisions made in the name of EA, and emotionally stunting social dynamics among true believers, which have reinforced my own epistemic conservatism and my wariness of straying far from my otherwise pretty ordinary ethics. Right now this middle ground, where EA is one important idea but deathly serious idea among many, feels like the most authentic way I can use it. I can’t use it to structure my life or give me a deep sense of meaning, but I can tap into it for a few very broad stories about the world, and after wrestling with the arguments for a long time, occasionally see a massive problem coming a decade early. And I definitely can’t turn away from the basic arguments, go back to my cozy human-scale world, and pretend I never saw them.

My goal in writing more about EA is not to make anyone like us more as people, trust us with power, or put on a lightbulb shirt and show up to an EA conference. Instead, I’d like the aspects of EA that I think are so valuable, and that have shaped my thinking for the last 15 years, to be more available in the general intellectual buffet that smart people choose from to interpret the world. I’d like people to encounter, be challenged by, and use the very best mental tools I’ve personally pulled out of the sprawling intellectual ecosystem EA has spawned, without feeling that they need to give any status to the people (including me) who happened to get into this cluster of ideas in the first two decades of its existence. I think the EA cluster of ideas could have a much bigger positive effect if it were used by a much larger number of people. The specific scene that happened to get into these ideas early is mostly a coincidence, though it also carries deathly important lessons about both the promise and the danger of believing you can use reason and evidence to override the status games around you and do the most good.

Don’t treat surface-level metaphors about AI as if they describe fundamental reality

Sat, 19 Sep 2026 03:17:20 GMT

A privilege of being a physics major is learning just how strange fundamental reality is compared to the concepts we developed to make sense of it. As far as we know, all the stuff we’ve ever run into or interacted with can ultimately be written out like this:

In a more legible form (already an imperfect metaphor), this says that all ordinary matter is some combination of these particles (which are really ripples in underlying fields), acting on each other through fundamental forces:

If you put them together in juuuust the right way, you eventually get Antarctica, evolution by natural selection, or tables. These things are our imperfect maps and metaphors for the physical reality underneath, but we often come to believe they’re foundational entities in the world, as if the lamp I see in my room shares some fundamental lampness with all other lamps, and at the heart of reality there are neatly sorted categories like “lamp” or “continent” or “human.” The category of “lamp” is useful, but it would be a mistake to think that lamps are a fundamental part of reality, and that there will be 100% clean and predictable ways all lamps behave that I can infer from any one of them.

A frustration I regularly have in debates about AI is when people act as if the words we use about it are getting at fundamental categories of reality itself, even when they’re clearly especially imperfect, pragmatic metaphors we use to predict our human-scale world. In these moments I kind of want to grab the other person and announce to them that the concepts we use to think about the world in general are just very imperfect social conventions we use to try our best to find these basic but fallible patterns in the incomprehensible complexity lurking beyond our perception. This is obvious in most other circumstances, but it somehow often gets lost in debates about the future of AI.

One example that drives me crazy is the statement that AI is “just a tool.” People seem to infer from this that AI is and always will be completely inert, like a hammer, and that it’s the person using it or designing it who’s responsible for anything bad that happens. This just assumes away the possibility that AI could do things the user doesn’t want, or that the designer didn’t expect and had no way of foreseeing. How do people arrive at this conclusion? By studying how AI actually works? It looks to me that instead they mostly get there because AI, like tools, is something created by humans to be used by humans to solve problems, or that it’s not a literal biological animal. I don’t see anything in how AI models are trained that implies they’ll always behave the way we want, like a hammer does, but this metaphor seems to convince a lot of people that they will. Why?

When people say this, I worry that they believe they’ve accessed some real part of fundamental reality called “tools” and discerned that AI has this same tool-ness as hammers. It seems obvious to me that the word “tool” is a useful but wildly imperfect social game we’re playing to try to make sense of reality. There’s no fundamental tool-ness at the heart of reality. Anything from a rock to a nuclear weapon can, in the right circumstances, be called a “tool.” In some sense a vaccine is a “tool,” but some vaccines contain live pathogens that, in rare cases, behave in ways nobody wants. Does the fact that the vaccine is a “tool” tell us much about how the pathogen inside it will behave? Or that no one could ever have a bad reaction to it unless the person who made or administered it intended that to happen? It seems like the fact that the vaccine shares some properties with other tools doesn’t automatically tell us anything about other unrelated properties it might have. I need some clear additional reason for the fact that a vaccine is a “tool” to imply that it shares any one property with other tools. I can’t just say “Well it’s a tool, so therefore like a hammer it will only cause problems if the person using it intends it to.” That’s obviously silly! Yet in conversations about AI, moves like this regularly fly.

I would think that “AI is just a tool” is meant as a stepping stone to a real debate about whether it’s controllable the way other tools are, but instead most people who say it seem to want to frame the entire conversation around any and all evidence that AI has some inherent property of tool-ness that other things in the universe share. If it’s created by people, that gets counted as evidence that it has this tool-ness, and therefore evidence that it will be inert and controllable. I don’t believe this kind of property actually exists, so this step can’t be taken. It’s all just atoms and the void! So I don’t believe that a fact like “it was created by people” can tell us much about whether AI has some other, unrelated property, like “it will always be easy for the user to control,” just because the completely different objects we call “tools” happen to have both. Drawing this inference seems about as reasonable as saying that because candlesticks aren’t made the way weapons are, a candlestick can never be a weapon. It assumes there’s some part of fundamental reality that literally always connects how a thing was made to what it can do, and I just don’t see the connection.

This has also come up for me in a lot of conversations about when it’s right or wrong to “anthropomorphize” AI. I agree that because AI writes as if it’s human, it’s often tempting, and wrong, to ascribe qualities of the human mind to it that it doesn’t currently have. But critics of anthropomorphizing AI often go way farther and imply that “human” is itself a fundamental category of reality that a system either belongs to or doesn’t, and that because AI isn’t human, it will never be useful to describe it in anthropomorphic terms. This seems completely mistaken to me. I don’t think “human” is a fundamental category of reality either. What we experience as human is the very specific result of incredibly complex systems of biology, neural wiring, and social and economic interaction, each of which is itself made up of simpler nonhuman systems working together in nonhuman ways to give rise to entities like us. To say that we ourselves are human is itself a useful simplification of the complex nonhuman processes that work together to make us up. When I attribute intent to another person, I’m not actually saying “There is some fundamental property of the universe called ‘intent’ that this person is experiencing.” What I’m actually saying is “The complex patterns in this person’s brain and behavior line up in such a way that saying they ‘intend’ to do something helps me predict what they’ll do and experience.” Everything we consider human is a pattern that emerges from simpler nonhuman processes, and the words we use to describe ourselves are imperfect metaphors that help us imperfectly predict and react to those more complex processes in ourselves and others.

Our minds don’t actually work like our folk theories of psychology suggest, and yet our folk language about them still often yields good predictions. Because anthropomorphic language is ultimately a useful fiction imposed on a much more complex process, there are times when it might also be a useful fiction to impose on AI. I think saying an AI “filled out a form” is a useful description of what it did. It often, but not always, yields the same true predictions about the AI’s behavior as it would about a person’s. Similarly, the most recent models check a lot of the boxes in my own definition of what it means to “think.” I don’t think AIs have first-person experience, but they seem to form world models, move step by step through chains of reasoning, and manipulate words in ways that seem impossible without granting that they basically “understand” them. Is it wrong to say that AIs “think”? I think this debate should focus entirely on the pragmatic question of how saying so would affect our predictions about AI. It’s bad to say AI can “think” if it communicates to the other person that the AI is very humanlike, has first-person experience like we do, or has complex emotions and intentions like ours. But it seems useful to say AI can “think” if it communicates that AIs can work with language the way we can and draw reasoned inferences from that language based on world models. In this case, it seems good to anthropomorphize AI for the same reason it’s good to anthropomorphize people: both give us good predictions about the world. In figuring out when it’s right or wrong to use “anthropomorphic” language, it seems useless to frame the question around some abstract fundamental category called “humanness,” existing above and beyond the physical world, that AIs either are completely or not at all tapping into. I don’t think this is something that actually exists, and so trying to figure out whether AI can access it or not won’t tell us anything about what they’re currently like or will be like in the future.

We aren’t able to think without simplified concepts and metaphors. But we have a responsibility to recognize that basically all our concepts are simplified and imperfect. Figuring out the near-term future of AI requires us to work out the specifics of when our imperfect metaphors are useful or not. This debate is not helped at all by conversational games that imply that the speaker has accessed some fundamental category in the universe that AI belongs to along with other entities, so it must always be exactly like those other entities, and any property they have, it must have too. The same game gets played in reverse, where AI is declared to be outside some category like “human,” so it can’t share any property with the things inside it. Neither move tells us anything, because the categories aren’t real. There’s no tool-ness at the bottom of reality that guarantees AI will stay as controllable as a hammer, and there’s no humanness that AI is locked out of that settles whether it can think. Words like “tool,” “human,” and “think” are shorthand we invented to get by in a world far more complicated than we can perceive, and the only real question about any of them is whether applying the word to AI helps us predict what AI will actually do. If it does, we should use it, and if it doesn’t, we should drop it. Whether AI carries within it some fundamental nature of tool-ness or human-ness is a terrible way of approaching a question about it, because like everything else, AI is ultimately just atoms and the void. Or more precisely, the universal wave function.

AI safety and the data center backlash

Tue, 01 Sep 2026 11:50:19 GMT

I’ve built up a public image pushing back against misconceptions about data centers and AI’s general environmental impact, but like others into effective altruism I’m separately very worried about risks from advanced AI. A lot of people will ask me why I’m not just nodding along to the misconceptions here for the sake of AI safety. These are my not-super-confident opinions on how the data center backlash relates to AI safety and where I see myself in the conversation. This is way more speculative than what I usually write.

My basic argument here will be that at its best, the data center backlash is a good opportunity to invite lots more people to think more seriously about AI, but it doesn’t seem likely to be useful for safety on its own, and there are lots of reasons people in AI safety should not (and to my knowledge are not) promote the goofy statistics driving a lot of it.

What is AI safety?
My initial motivations
I’m unsure, but suspect the extreme data center backlash is neutral for AI safety, and I see a lot of ways it could be harmful
Effective altruist AI safety as a place where we won’t abjectly bullshit you
Some recommended media on AI safety

What is AI safety?

I buy the basic effective altruist story of why AI is a uniquely big deal:

There is nothing fundamentally magical about the human brain. Our intelligence was produced by an algorithm (evolution) interacting with the world. There’s no reason in principle that machines couldn’t also eventually have what we mean by intelligence. Talking about the possibility of future machines that can “outthink” us should feel about as normal as talking about machines that can outlift us. We have a strong internal bias against thinking this is possible, maybe for evolutionary reasons to avoid thinking about objects as members of our social groups. But by any reasonable definition, humans are themselves intelligent biological machines, which already proves that machines can in principle be intelligent. We are not heavenly observers looking into the physical world from outside.
Machines that can do most or even all of what the human mind can do seem possible (and even likely) sometime in my lifetime. This is commonly called artificial general intelligence, or AGI. The definition of AGI is incredibly fuzzy, but a concept can be fuzzy and still point to something cataclysmically important. Most people have a fuzzy idea of the Industrial Revolution. Before the Industrial Revolution, it would have been reasonable to theorize about some point where more and more complex machines could accomplish the physical labor of many thousands of people at once. People could have theorized about the abstract concept of machines that could do the work of farmers or horses. They could even have theorized about when these machines’ effects on the world would start to compound, with each new efficiency spilling over into the others and speeding up technological progress, until we entered a period of change so strange and rapid that someone could have lived to see both the start of the Civil War and the dropping of the atomic bomb. People living before the Industrial Revolution might not have been able to pin down exactly when something like it would happen, what would qualify, or what the long-term effects would be, but that fuzziness doesn’t make the concept useless, or any less the main story on Earth over the past 250 years. That 80-year timespan from the Civil War to the atomic bomb is about the same as the time between when the world’s first programmable electronic general-purpose computer (the ENIAC) was revealed to the public and when the US government ordered Anthropic to cut off access to Fable and Mythos over the models’ cybersecurity capabilities. The current AI paradigm may not get us to AGI, but it’s becoming hard for me to see how we don’t get there with another 80 years of AI progress. We got from the ENIAC to here in that time.
AGI could be wild, because human intelligence has been the main force shaping the planet for the past 10,000 years or so, as we’ve found ways to collaborate with each other in more and more complex ways. Some might point out that it’s not necessarily the raw intellect of individuals, but rather the collective intelligence of society that has been the real player here. Sure, I believe that too, but AIs are also able to communicate with each other and interact with the world. They could form collectives and markets, or even mimic whole societies, and access and aggregate the same amount of dispersed knowledge that human society does now. We’re all so used to societies built out of complex systems of communication between human minds that it’s easy to forget how truly strange and new their effects are. I mean really, look at this factory!
AAAHHHHH!!!! (but neat). The main question about advanced AI is what would happen if we suddenly and massively scaled up the amount of general intelligence in the world, given that this same force has already reshaped the planet into something much more alien (yet also much more human) than early humans could ever have imagined. Would we be able to control it, and what would the long-term result be? Pontificating about this might be about as useful as hunter-gatherers pontificating about what the agricultural revolution would be like, or medieval societies pontificating about the Industrial Revolution. But whether or not you think we have any mental handholds to grab onto, it does seem likely that we’re heading toward a future where massive numbers of generally intelligent agents are widely available, and that this could make the world at least as weird and alien to us as our current lives would look to a medieval peasant. Maybe much more? Regardless of whether it’s even possible to think about what would happen, I’d like to at least think about whether we can think about it, to try to look for the handholds. Holden Karnofsky’s Most Important Century blog series does a good job of getting across these intuitions.
When I try to think through the biggest risks of AGI, a few obvious candidates come up. Each one is either almost certain, or impossible, or somewhere in between, depending on facts about science, philosophy, and economics that smart people all seem to disagree with each other about:
1. Advanced AI tools could be used maliciously by humans. Maybe having swarms of hundreds of thousands of genius agents is like having access to nuclear weapons.
2. Society could come to be governed more and more by AI processes that humans can’t follow. As AI moves further ahead of human intelligence, it starts to resemble the other major forces structuring our lives, such as the market, that we can get hints about but can’t fully predict or understand. Eventually this might amount to a gradual AI takeover that leaves us at the mercy of systems we barely understand.
3. AI could enable extreme power concentration and lock in a stable totalitarian global government.
4. The AIs themselves could develop their own goals and override ours, potentially rearranging the world in such a way that we die. Just as humans don’t factor the wellbeing of ants into construction projects, AIs with the collective intelligence of thousands of human societies working together might just not see us as especially worth protecting.
Are these risks real or fake? Can we know? Can we do anything about them, and if so, what? Figuring out the answers to these three questions and then acting accordingly is the main project of AI safety from an effective altruist perspective.

Maybe this is all hype? Maybe this is all an elaborate social game? Could be, but if you’re willing to entertain the idea that mimicking the human mind is both possible in principle and reasonably likely within the next 100 years, it’s difficult not to think that crazy things could happen that we should entertain and think through now. A lot of people who believe this may have their thinking warped by their own social and economic incentives, but that doesn’t mean the right move is to wall your mind off from the question and focus exclusively on the here and now so as not to be turned weird by it yourself. You might disagree with every major case for serious AI risk, or for advanced AI capabilities in the first place, but we’re getting to the point where it’s probably time to calmly step back from whatever counts as the cool opinion in your immediate circles and spend some time working out what you actually concretely think.

So how does the data center buildout relate to this? If I’m worried about this unique cluster of risks from advanced AI, why have I spent time pushing back against misconceptions about data centers? Maybe I should just nod along with the crowds angry at them. After following the debate for a year and a half, I see a lot of reasons not to.

My initial motivations

When I wrote my first post about AI’s environmental impacts I wasn’t expecting much of a response. My main thoughts on the debate at the time were:

A lot of adults seemed really confused about how tiny an amount of energy “10 Google searches” actually is compared to everything else they do. They were comparing an AI prompt to one of the least energy-intensive things we do and fixating on the relative difference, rather than asking how much the prompt actually adds to their total emissions. I’d been following climate debates since college and this seemed like a terrible way to think about where to cut, and I wanted to articulate why.
I was disappointed by the environmental politics implicit in these conversations. I said so here in the original post.
I was surprised by how many people I was meeting who hadn’t actually played around with chatbots yet, and their reason was almost always that they were “so bad for the environment.” This felt akin to people ignoring the early internet for similarly confused reasons (which were also being given at the time). It seemed sad that they were missing out on how good the tools already were, and it also seemed bad for AI safety, because the easiest argument for taking risks from advanced AI seriously is just watching how quickly the models are improving, and people who won’t touch them never see that.

So making the case felt like a win-win-win for my personal climate politics, my interest in AI as it already existed, and broad public education about future threats from AI. The case was just that worrying about 10 Google searches’ worth of energy for the sake of the climate is like stopping your microwave a few seconds early for the sake of the climate, and everything I’ve learned since has only reinforced that. See my full post on that for all the arguments I was making at the time. Above all else, it felt like living in a strange dream reality to have grown adults speak so grimly to me about the equivalent of 10 Google searches, and my initial writing was partly an attempt to wake up from that.

For a while I was nodding along to what a lot of the people I was reading were saying about data centers. My initial explanation for how they could be bad while individual prompts were fine was extremely simple: data centers concentrate huge numbers of individually tiny prompts in one place, so they could plausibly have a big impact on the places they were built. I only realized a few months later that the discourse about data centers was at least as confused as the discourse about individual prompts (probably more so), and got kind of obsessed with how universally strange the media coverage of data centers was. It was somewhat shocking to realize that almost every popular story about them had some incredibly simple twist that completely undermined what most people were taking away from it. For example, take what might be the most influential headline ever written about data centers:

Source

The story is about the construction of a data center, not its normal “guzzling” of water. The subtitle refers to a separate story in the second half of the piece about how Arizona’s going through a drought, which has forced the state to slow home building, and oh hey, data centers are being built there too, so maybe, possibly, that’s related to the home building? We never get any numbers to check for ourselves.

It seems basically impossible for an intelligent person in a hurry to read this headline and subheading and come away believing anything other than that the data center’s normal operational water use (“guzzling”) is what made the taps run dry. There are headlines like this for almost every problem people associate with data centers. I felt like I was living in a dream where I was one of the only people poking at them and getting any attention for it, while most educated adults were getting lulled into a make-believe picture, stitched together from confusing headlines and isolated statistics, of what I expected to become the most consequential and fastest-growing kind of building of my lifetime. Growing an audience who agreed and could pass these arguments on felt like a way of waking up from the dream together.

But given my broader worry about AI risk, I wasn’t sure how to think about the actual value of my newfound role commenting on data centers for my niche audience. Before I stumbled on the Empire of AI error, I had actually decided to pause data center writing for a bit, partly because I wasn’t sure whether what I was doing was good for the world at all, or potentially drastically bad. After thinking about it a lot more, I remain uncertain but have leaned much more toward thinking that the data center backlash is neutral to somewhat negative for AI safety, and that there are a lot of reasons for AI safety people (or at least one of them) to plant a flag and say “We will not be one of the groups abjectly bullshitting you about simple statistics in order to trick you into worrying about AI.” The rest of this post is my explanation for why.

I’m unsure, but suspect the extreme data center backlash is neutral for AI safety, and I see a lot of ways it could be harmful

I’m pretty deeply unsure about the overall effect the data center backlash is having on AI safety, but I see some clear ways it could harm it on net. To be clear, this could all be drastically wrong, and I stand ready to revise any of my takes if I see good arguments against them.

The data center backlash seems to be mostly about data centers, not AI

I think the polling we have so far strongly implies that most people getting angry about data centers are motivated by the object-level claims being made about their environmental and economic impacts. See my post here for a much more in-depth argument:

Importantly, when given the option to write out as much as they’d like about why they personally oppose data centers, very few people choose to list anything that could be called part of the AI safety worldview:

Source

This doesn’t at all mean that they’re not potential allies, it just means that the data center backlash does not look to me like a groundswell of public demand for AI-safety type stuff. It doesn’t seem like someone’s beliefs about data centers can tell us much about what they think about risks from advanced AI, and there’s no reason to think the backlash will naturally trend toward people making better decisions for safety more broadly.

Extreme environmental reaction to data centers often reinforces the “AI is fake and useless” view, which prevents people from thinking seriously about current and future capabilities

A big dividing line in AI discourse is whether AI is dangerous because it’s very capable, or dangerous because it’s very stupid. The “AI is dangerous because it’s stupid” view is maybe best represented by On the Dangers of Stochastic Parrots, the most influential AI ethics paper ever written. Many critics on this side correctly point out that people worried about extinction risks from AI and uncritical AI boosters share the same underlying view, that AI is already pretty capable and could become much more so, and that this shared assumption actually puts them both on one side of a broader debate about whether AI is or ever will be that capable in the first place.

I agree that this broader divide exists, and have three thoughts about it:

AI models today are more capable than a lot of their critics believe. The original criticisms in Stochastic Parrots don’t seem to apply to current models (see here for a list of what’s changed), and my impression is that the authors have resorted to adding more epicycles to their theory of why AIs in the current paradigm are not and never will be especially capable or even useful. That’s looking less and less tenable in 2026. It would be really good if people in the AI debate actually understood what the models can do right now.
The future of AI is pretty uncertain and I don’t feel equipped to judge things like whether the current paradigm of AI will scale to AGI, or whether we’ll need many more breakthroughs that could take decades. Because smart, well-informed people seem to radically disagree on this, the most important thing is to keep the debate rigorous and open so that we learn more and the stronger arguments rise to the top. This isn’t perfect, but it’s the best we have. Beliefs that are false and that also keep people from engaging seriously with this debate in the first place seem pretty harmful.
There’s a lot of social reward right now for locking into overconfident beliefs about AI capabilities. I could definitely be rewarded in a lot of AI risk spaces for expressing more confident beliefs than I actually have. At the same time, I see a lot of people get a LOT of social reward for refusing to even consider that AI might already be capable (“this tech is stupid and if you think otherwise everyone will say you’re an idiot”), or to seriously enter the debate about how it might become more capable in the future and whether that would be dangerous. In my experience this is often downstream of people becoming convinced that individual AI prompts are terrible for the environment. It’s hard to believe both that spending 10 Google searches’ worth of energy on a prompt is incredibly wasteful and that the same prompt is doing something shockingly powerful and different from a normal search. I’m worried that a lot of the weird environmental misconceptions about data centers really lock in this mindset of “I cannot ever entertain that these models might be powerful” because that gives some status to these otherwise evil stupid buildings. I want people to just consider the arguments and form their own conclusions, and for that to happen their opinions about AI can’t just immediately calcify into “it’s always bullshit and dumb” based on background social pressure and confused ideas about data centers rather than reasoned thought. Politely pushing back on the environmental misinformation going around seems like one way to help with that.

Anything short of a national moratorium and strong export controls doesn’t seem likely to slow AI progress much

There’s a gigantic amount of capital behind data centers, and data centers used for training mostly just need power and empty land. America has a lot of both, even if most states end up banning data centers. All data center buildings in 2028 will together take up as much space as Disney World. The most important bottleneck is power, but even here data centers rejected from one county usually have others competing for them. It seems like the result of most state-level data center opposition will be just moving where data centers are built.

My impression is that the big AI companies mostly don’t bother fighting local opposition, they just go somewhere else. They don’t seem to spend much as a portion of their revenue on countering the data center backlash in general, which I think tells us something about how worried they are about it.

Even state-level moratoria might not do much. Arvind Narayanan estimates that a state banning data centers for a year probably delays AI progress by about 5 to 10 hours, and that’s assuming none of the blocked data centers get built anywhere else, which is pretty unrealistic.

Frontier training (where most of the dangerous capabilities show up) happens at maybe a dozen specific data center campuses. A restriction on one of those would matter more, but to my knowledge no data center block has hit one of them yet.

There’s also a big difference between slowing AI progress and reducing AI usage, and people who are part of the data center backlash may not see it or react to it. Restrictions on new data centers mostly seem to harm deployment rather than capabilities. They make AI slower, more expensive, and less available to everyday people, but leave the training runs I’m actually worried about mostly unaffected. That seems like a bad trade from a safety perspective! The models keep improving while the public’s hands-on sense of what they can do falls further behind.

The data center backlash might cause voters to reward and prioritize the wrong moves against AI companies

Suppose that there are two candidates. One wants heavy regulation on the AI industry itself to avoid catastrophic risks. The other wants to ban data centers. Which will voters see as the “toughest” on AI? I’m worried that right now the answer’s clearly the second. In a lot of political conversations about AI, a lot of people are starting to frame data center moratoria as the “tough” position against the companies, and regulating AI models themselves as something more friendly to big tech. This seems like a pretty bad disaster for getting public approval for governing advanced AI.

This one I really don’t know about. But I’ve seen more and more political commentary where candidates who want data center moratoria are framed as the “most tough” on AI that they can be. If you don’t believe that state-level moratoria will do much good at all for safety, this seems like a dangerous distraction.

This could be a simple both-and situation, where the public gets data center moratoria and serious model regulation. I’m worried that voters just don’t actually think enough about AI for that to be guaranteed to happen, and as long as a politician checks the “Doing something about AI” box that might be seen as enough, and a data center moratorium on its own checks that box. A politician who’s passed or even just advocated for a moratorium has satisfied the public demand and collected the credit, and the next person proposing frontier model regulation is now proposing a second AI bill that might be seen as unnecessary. Public attention on an issue is a scarce resource, and more of it being spent on the data center backlash might start to compete with good legislation.

Navigating through advanced AI safely would probably require voters not thinking of data centers as demons

I’m pretty unsure about the specifics of AI risk, and therefore unsure about what would make things go well, but it seems unlikely at this point that managing AI well will be helped by the average person thinking about data centers as radiating evil. In very extreme AI safety scenarios like AI 2040, data centers eventually manage the vast majority of all economic activity on Earth. Is this good? I don’t know. It’s one of many visions of where the risks and benefits of AI are and how to react. But in this world, everyday people will need to know what’s actually going on with data centers, and having a country reacting to them as if they’re demons seems like it’ll mostly override reasoned sober decision-making.

It might be that it’s preferable to have the data centers we want to eventually govern all in one country (or maybe that’s bad because of power concentration? Oh no). The data center backlash pushing out more data centers to other countries instead of America increases the amount that AI companies can do and train outside the US’s reach. This could be bad, or good? But making that decision again requires citizens with clear-eyed views about the actual trade-offs.

It’s in some ways very lucky for the governance of AI that frontier models currently require a small number of gigantic physical campuses with a huge heat footprint. It’s good to know where this is happening, and massive data centers make that somewhat easier. Any pressure to scatter the computing into smaller buildings (or blast it into space) makes it somewhat harder to know where frontier training runs are happening.

Conclusion

So it looks to me like the data center backlash is mostly just shuffling around where inference data centers specifically are built, and isn’t affecting the data centers used for training, which are often built pretty far from where lots of angry locals live who would block them (with the exception of xAI’s Colossus in Memphis). Even state-mlevel data center moratoria maybe only slow AI progress by hours. The backlash gives political reward to politicians who focus more on “governing AI” via what looks like a purely symbolic move against the AI companies that leaves their most dangerous capabilities completely untouched, and rewards the idea that AI is such a waste because it’s useless and stupid rather than dangerous and powerful. It removes people’s ability to think seriously about the trade-offs of running data centers in their area, which will be pretty important if we manage to get to the point where we’ve avoided the risks and are capturing the benefits of advanced AI.

The main simple way I could be really wrong here is if this all builds to a national moratorium with strict export controls to limit China’s ability to catch up. That strikes me as within the window of possibly good governance of AI. I don’t know. But unless it leads to that, we get all these negatives and basically none of the potential positives.

And separately, the backlash is just fundamentally mistaken and wrong. People aren’t mistaken to worry about data centers and AI, but they are blatantly, consistently mistaken about how data centers actually affect local communities, and I’m more convinced than ever that if they just knew the full economic trade-offs of the buildings themselves, the full list of costs and benefits on either side, they would usually choose to allow data centers to be built. More broadly, I see the general politics driving the backlash as disturbingly illiberal. As Alex Tabarrok notes, they’re a loss for the open access order. I would like liberalism to survive a potential intelligence explosion.

I think the data center backlash leaves a lot of room for AI safety people to build bridges with everyday people. I’m not saying here that we should just shun them. But I also think we should talk to them (and everyone) as if they were intelligent friends we respected who we thought were getting one question wrong but really wanted to get on board with a broader idea. I think treating people respectfully often involves saying what our own beliefs are clearly. This next section will be more on how I think these basic ethics of discourse are important to building a community of real thinking on AI safety.

Effective altruist AI safety as a place where we won’t abjectly bullshit you

I’ve been very happy to plant a flag for what I see as the great aspects of EA thinking on a specific topic, as part of broader behavior I want others to replicate. To be clear, I think EA mostly already does all this very well. I’m writing all this as “this is what I want to authentically show the world that EA is already doing” rather than “I wish people in EA did this, because they’re currently not.” I haven’t had anyone in EA suggest lying about data center water use or scold me for telling the truth. We’re not the ones pushing this stuff, and I have high confidence that we’ll stay that way.

Societal resilience to speculative future risks requires strong healthy epistemic communities of debate and disagreement

Suppose you were living in the 1930s. One day the neutron is discovered, and you infer from this that nuclear weapons are possible and could appear within a few decades. You want to understand where the actual risks are, what to do about them, and how to build institutions that could avoid them. What do you do?

One move that doesn’t seem promising is deciding that everyone needs to believe your very specific account of exactly how nuclear weapons will be dangerous, or of how countries will behave once they have them, when you aren’t completely confident that your answer is the one all reasonable people would agree with once they saw the argument. If you start using emotional or rhetorical tricks to win over people who wouldn’t have assented to what you were saying if you’d treated them like rational friends you respected, you’re going to quickly discover that a lot of other people have plenty of legitimate reasons to use their own emotional and rhetorical tricks to override your team with their beliefs. The debate about this incredibly dangerous new technology would slowly wall itself off into angry islands of belief that don’t communicate with each other, and new people would sort onto these islands based on a roulette wheel of the contentious beliefs a rational person could hold given limited evidence. Society would become brittle and inflexible, and by the time the threat appeared, people would be too invested in their own team to adjust quickly and work together based on how things were actually playing out.

So the basic ethics of discourse become monumentally important as part of the collective project of moving society toward a place where it can respond well to a gigantic new risk. It seems very likely to me that future AI systems could pose gigantic risks to civilization, and also very likely that I, as just some guy thinking about it, have an unbelievable number of blind spots, overconfidence I don’t notice, and social incentives that warp my thinking in bad ways. The only way to deal with this is to directly welcome any and all reasonable people into debates about AI safety, at a high level where we aren’t slinging cheap rhetorical tricks at each other. Preserving this fragile state requires treating people who disagree with almost everything we believe, and who are engaging seriously in the debate, as respected and equal participants. Trying to lure them in with ideas and statistics we know are wrong, like the claim that data centers are also destroying local water supplies, or with overconfidence about our own specific risk case, basically tricks them into positions in the debate they might not otherwise have assented to. This is a way of burning the commons. It was good that debates about nuclear weapons happened across academia and government, with wild disagreements, rather than each side trying to override the debate and just vying for power. Managing the nuclear era involved a lot of complex, counterintuitive moves that couldn’t have been reduced to simple slogans. It was good that serious thinkers were able to plant flags of high epistemic integrity in that debate early on.

We’re already making extreme, wild claims about AI and need to otherwise respect the epistemic commons to have them taken seriously

I think the core ideas of AI safety are both surprisingly intuitive (it seems likely that we’ll make significant AI progress over the next 80 years, and if you sit with the idea of replicating human intelligence in machines and really internalize it, the wild implications are easy to see) and very aversive to think about (it all sounds so weird and sci-fi, like a trick big tech is playing on you). The basic argument is that AI could get so powerful within our lifetime that we need to start getting our footing now on what that would actually imply and how society would manage it, instead of just sitting back, thinking “wow, wild,” and feeling like we’ve done our part. For an intelligent person to actually receive that argument and think it through, it has to come with a lot of careful acknowledgement of how incredibly complex and uncertain the problem is. The space of possible reasonable opinions on AI risk is so varied, and so full of rival views that would each be incredibly consequential if true, that it seems easy for anyone to adopt whichever beliefs are most convenient for their immediate social status games, and then lord them over everyone else because those beliefs are so consequential. People correctly recognize that extreme beliefs are often social crutches for overriding normal social rules, and unfortunately every possible belief about AI risk is somewhat extreme, because all possible views about humanity’s future are wild. Because we’re trying to figure out and debate our extreme beliefs together, it seems especially important to be clear that we won’t use them to justify breaking unrelated social rules, which includes not lying about relatively simple questions about AI. Debates between people who hold rival extreme beliefs about the future are only really possible if neither side is using those beliefs to justify burning the simple epistemic bridges between them.

A lot of the broader intellectual culture seems incredibly averse to thinking about even the near-term future. Basically everything outside our immediate field of vision gets treated as a convenient distraction from the real, immediate problems of the world. I think this hyper-distrust of thinking through what the future could actually be like has become intellectually incapacitating for a lot of otherwise very smart people. Hyper-skepticism that treats every truth claim as really just a move in some secret status game locks us into a situation where nobody has any incentive to care about what will actually have good effects on the world, and everyone just tries to get to the top of their own immediate status system. That’s not a healthy place for an intellectual culture to be as AI becomes more capable. I agree that a lot of skepticism is warranted, and I’m pretty cynical myself about all of our motivations when we go out purportedly seeking truth. But there’s a difference between being skeptical of people’s motives and refusing to think about the future at all.

To preserve even the possibility of articulating and refining our views about what the near-term future with AI could be like, we need to be really, really careful to show over and over that we’re not letting those views license us to break other social rules. Breaking them sends too strong a signal that we’re either holding the views for exactly that reason, or that we’ve shut ourselves off from rationally adjusting them when other people argue with us, since we’d be demonstrating that we think rival views are so worthless they need to be steamrolled with lies and misleading statistics. This is the core reason I’m happy that effective altruist AI safety scenes have (to my knowledge) not indulged in lying or hyping up clearly misleading claims about data centers and the environment, and why I’m happy to be both a face of effective altruist AI safety and someone pushing back against what I see as basically a minor mass moral panic over unrelated problems with AI that don’t hold up to scrutiny.

The broader societal backlash to AI is often based on obvious simple lies because people are having a visceral emotional reaction. AI safety needs to distinguish itself from this backlash

It’s becoming more and more socially fashionable to hate AI, often for confused reasons, like the mistaken idea that each prompt uses a bottle of water. Negative reactions to AI are understandable, but there’s a growing gulf between people who understand the technology and people who are clearly running on rumors they picked up on social media, and who are ready to believe those rumors because they already see AI (maybe correctly) as a bad guy for other reasons. It seems very easy for serious people to lump risks from advanced AI into this same bucket of fake beliefs, and to assume that it comes from the same general negative emotional reaction and isn’t any more substantive. I think AI safety is clearly separate from these confused ideas, and we need to find ways to make that clear. One way to do that is to distinguish ourselves by pushing back on the bogus ideas.

The single most painful sentence I heard last year was at a discussion about EA and AI with people who mostly didn’t know much about either. A panelist asked, “So is EA just hopping on the AI bandwagon now that more people are worried?” I started worrying about AI in 2016 and put up with a lot of sneers and scorn for it, as did most people I know in AI safety, and hearing that made it feel like all of that had been for nothing, and that most people now saw us as just one more part of the vague angry backlash to ChatGPT specifically. Because most people still don’t know much about EA or AI safety, it seems useful to plant a flag and say something like: “We agree that some general worries about AI, like its long-term effects on jobs and economic power, are real and important. But we’re not here to nod along with just anything that’s anti-AI. We have a specific, coherent argument for why AI could be dangerous, and we didn’t get memed into it because worrying about AI recently became high-status.”

The arguments we give to the public will affect who gets involved

I think if you want to build a complex movement of people working together to figure out what’s true, it’s going to need to not rely on memes the people in it know to be false for community building. The AI safety debate shouldn’t be diluted by very lazy thinking, and I think indulging and making knowingly lazy arguments with bad statistics will attract people who are being somewhat lazy in how they think about AI, especially if they’re so ideologically brittle they’re unwilling to question the statistics. This would be bad for our core coalition. AI safety is entirely about judgment under radical uncertainty and being willing to follow clear arguments and evidence where they lead. Relying on goofy ideas for growth seems like it’ll massively dilute the quality of thinking.

Again, I wanna be clear here that I don’t mean we should shun people originally taken in by the bad statistics about water, that’d be ridiculous. People’s minds can change, and we’ve all been taken in by goofy ideas at some point. We just shouldn’t use the bad statistics and lies as an active outreach strategy. I will say that to my knowledge no AI safety group is actually doing this, and most I know will patiently explain to people why the water stuff specifically is a little off if asked. I’ve never been asked by anyone high up in AI safety or EA to shut up about the stats to slow down AI.

A lot of other spaces discussing AI seem interested in using bullshit stats about data centers to fire up their base for unrelated political wins. Effective altruists and the broader AI safety community can set ourselves apart by simply not doing that

I’ve lost a lot of patience for sitting through lectures or monologues by people who seem to be speaking in fake social memes that don’t actually relate to anything real. I’d rather be around people and scenes that don’t blatantly bullshit me. And luckily there are a lot of these in the world, with certain scenes within EA being one of many.

For example, I think specific 80,000 Hours problem profiles are some of the very best writing on the topics they cover, because they’re trying to give you a complete, no-bullshit overview (as they see it) of a massive global problem that they actually want you to take concrete action on. To me this is one of the best things about good writing influenced by effective altruist ideas. It’s basically designed to be the rapid but comprehensive rundown you’d want to give a smart friend on a topic you want people to act on.

Of course none of this means that those of us in effective altruist or AI safety communities can’t unknowingly bullshit ourselves or other people. The human tendency to truly start believing things based on the status we subconsciously expect them to confer is incredibly powerful. In many ways it’s the original sin of human thought (partly because our higher-level thinking likely evolved to win social games with each other, so when we acquire fake but socially useful beliefs we’re doing exactly what our higher cognitive capacities exist to do). It could be that all the beliefs in this post are themselves downstream of my desire to make the data center backlash fit conveniently with my AI safety beliefs. Effective altruist spaces can be insular, and at some point we can’t fully guard against the chance that we’re acquiring beliefs this way too. But separately, I almost never encounter in EA communication the kind of knowing bullshit that seems to permeate so much of the broader discourse on AI, where people seem to know that data centers make useful political enemies regardless of whether the specific claims about them are true. I think there are a lot of people like me out there kind of wandering in the desert, desperate for direct, honest, high-level debates about this technology with such mind-shattering but uncertain potential, who don’t want to see the tech immediately devolve into a cudgel used to win completely unrelated political and social games without any regard for the truth. There are plenty of scenes having those debates, but a scene where people uncritically share lots of wild, goofy claims about data centers is a pretty bad sign. It goes a long way when a scene simply makes it clear, “Hey, we’re going to be careful about what we say about AI, and we’re going to express our honest (and sometimes very extreme) opinions about the situation as we actually see it. We’re not going to get you hyped up about every little AI thing just because we’re having some general emotional reaction to it and want to lash out. We’ve been thinking about this for years or decades and have landed at this cluster of positions in the debate. Here’s what we’ve got, take it or leave it as you react honestly to the arguments and evidence.”

Some recommended media on AI safety

I think the data center backlash is mostly about data centers

Tue, 25 Aug 2026 19:08:18 GMT

This Heatmap poll has generated a lot of discussion on Twitter in the last few days:

Source

People from basically all political sides are interested in owning the data center backlash, and are all saying it’s being caused by what they’ve always been on about. It looks too powerful and intense to be about data center buildings themselves, so it must secretly be about something else, and commenters tend to assume that secret other motivation conveniently lines up with how they themselves think about politics and AI. People who are anti big-tech will say it’s a backlash to big tech. Some say it’s a broad rejection of economic growth as a measurement of human wellbeing.

My fellow AI safety enthusiasts will sometimes say it’s a reaction to rapid unsafe progress in AI that threatens to destabilize society or destroy the world. People in the “AI is bad because it’s bullshit and fake” camp will say it’s a reaction to AI flooding the world with slop, and Americans recognizing we’re investing in something stupid.

I follow a general rule that anything narratively satisfying is suspicious, and also that everyone’s thinking is less structured and coherent than we like to imagine. For me, it would be so much more satisfying if this bizarre massive uprising based on ideas that often collapse under mild scrutiny were all secretly about my own worry that AI could be incredibly dangerous incredibly soon. The idea that this is all happening because people did actually just get fired up about often goofy interpretations of isolated large numbers is much more disappointing, but reality is often disappointing. You must try your best to dwell in anti-paranoia when you can.

I think any movement that wants to either stop the backlash or capitalize on it will be making a mistake if they assume secret motives are driving it. However, because people’s beliefs are often opaque even to themselves, it seems possible for politicians to capitalize on the backlash and meme people into thinking this was “always about” some other unrelated specific thing, so if you’re trying to do that, this seems like a fruitful time to twist the narrative, but I don’t think you should go out assuming it’s actually true. I’ll argue here that based on the limited info we have, it looks a lot like the data center backlash is not about AI safety or job loss or AI being a scam or social media or big tech having too much power or anything else like this, it’s about data centers and the claims people believe about them.

Of course, the data center backlash being all about object-level claims and facts about how data centers interact with the places they’re built would be very convenient for me personally, so take it with a grain of salt. Maybe I’m no better than all those other commenters, but I think my case here is pretty solid. I’ll go through other explanations offered. This will be more speculative than a lot of my posts, I could be getting this one pretty wrong.

It’s probably not about big tech

It’s tricky to disentangle worry about big tech from worry about AI. To try to separate them, I’m going to consider the hypothesis that Americans are lashing out against data centers because they don’t like the tech companies building them in general, and want to find a way to limit their power over society, and data center opposition is a tool they think will have some effect. This hypothesis seems to imply that when the average person hears “A data center is being built by AMAZON” it’s Amazon that really sticks out to them as the bad thing, and they start to formulate how to stop the data center. The data center is a coincidental building that could as well be something else.

What would we expect to see if this hypothesis were true? A few things:

Americans would dislike the tech companies building the data centers about as much as they dislike the data centers. If opposing a data center is just a tool to harm a tech company, you’d think about as many people would hate the tech company as the number trying to oppose data centers to harm the company.
Americans would also oppose other things being built by and for the same companies building data centers.

Neither of these seem to be the case.

Americans love big tech, as long as you ask about specific companies

If you poll people on how much they like “big tech” you’re going to get negative responses, but I think any vague powerful sounding entity like this is also going to poll negatively. If instead you replace them with the big four hyperscaler companies building out the biggest AI data centers, Americans are mostly pretty positive on them. The four companies building the biggest data centers right now are Google (technically Alphabet), Amazon, Microsoft, and Meta.

Amazon is one of the institutions Americans view most favorably, competing with “the military”

Source

Even Facebook is +13% in approval, with just as many people very favorable to it as very unfavorable. More Americans are favorable to Facebook than NATO or the European Union. A more recent poll from The Argument, which didn’t include Meta, also found strong approval for the other three hyperscaler companies actually building data centers.

So the specific story that voters are looking to “get back” at the hyperscaler companies for what they’ve done to society using the only lever they know how to use looks wrong. I would think that if you’re so motivated to punish big tech companies that you want to block their attempts to build things near you, you would also at least say you “strongly disapprove” of the companies on a survey, yet the number of people who strongly disapprove of Google is 5%, Amazon 8%, and Microsoft 7%. Meta’s the outlier at 22%, but is still +13% compared to data centers’ -60%. So far this doesn’t look like a promising explanation.

When Americans are asked about tech companies in the abstract, they think they need “more regulation”

but I’m not really sure what this tells us. If I asked anyone on the street “do you think major companies are regulated too much or too little?” I’m willing to bet they’d default to too little regardless of what they otherwise believe about specific companies. A lot of the polling about very general industries like this leaves me worried that if you ran polls on any industry calling it “big __________” it would also get mostly negative approval. I suspect Americans have a negative view of “big ag” but a positive view of “farmers” and (outside of Monsanto, because they mostly only hear about it in negative stories) neutral to positive views on a lot of food companies. How do you feel about Archer-Daniels-Midland?

The fact that the hyperscalers each get net positive favorability I think at least complicates the story that the data center backlash is just a concealed way of lashing out at them.

The other thing that complicates the story is that this doesn’t seem to be happening with other buildings associated with tech companies.

Americans are kind of fine with other buildings associated with big tech

If someone felt Amazon had too much power in society and wanted to stop them by putting up blocks to large buildings Amazon was trying to build, what specific type of building would be most likely to be blocked? Obviously Amazon warehouses/fulfillment centers. And yet these are some of the very most popular buildings in America.

Source (Echelon Insights)

If you want to “strike back at the power Amazon has over our lives” I don’t see a better way of doing that than opposing a distribution center. This directly harms Amazon’s ability to affect your community’s buying habits.

It’s interesting that the chip manufacturing plant polls worse than the traditional manufacturing factory, with an additional 11% of people opposing it, but also a lot more people being unsure. Maybe that additional 11% do actually oppose AI specifically or big tech in the abstract? It’s also interesting that an AI data center does meaningfully worse than a traditional data center, which also polls much worse than other types of buildings. It seems like this could be some combination of people disliking AI specifically, or people (correctly) assessing that the really wildly large new data centers going up are mostly for AI, that these are often orders of magnitude larger than traditional data centers, and therefore that they’re uniquely dangerous for environmental reasons. I’ll explore this more in the next section. Either way, data centers seem to be uniquely unpopular compared to other buildings associated with the same tech companies building them, though we could really use more polling on this. The way they stand out seems to fly in the face of the idea that they’re just a way to get back at tech companies in the abstract. Why are Americans then not also reacting to the buildout of other similar buildings? One response could be “they benefit from Amazon warehouses in a way they don’t from data centers” but it seems a little weird to imagine that Americans are lashing out against tech companies’ influencing more of their lives unless the tech company is doing something that will specifically make its services much more directly present in their lives.

So I come away from this data pretty uncertain, but much more skeptical about takes like this specifically:

It’s probably not mainly about AI

I’ve scrolled by a lot of very confident takes that the reason people are reacting so negatively to data centers is that they can sense that they’re the tip of the spear of some potentially horrific alien future with advanced AI. That’s how I feel about them personally!

But polling really doesn’t seem to imply this is mainly a reaction to AI. It’s obviously true that a lot of people are motivated by their beliefs about AI, but this doesn’t look like the main story to me. Whenever Americans are given the option to say “Among other issues with data centers, I also separately either think AI is a useless waste or a dangerous technology and that’s influencing how I think” they mostly opt not to say that. I think this is partly due to the average person just not actually thinking about AI that much, and separately having seen tons of news stories about data centers harming local communities.

Polling implies AI’s playing a small part in the backlash

Here’s some polling on why Americans say they oppose data centers. This from Fox News in July asked voters what their “primary reason” was for opposing data centers:

This seems to be a clear story of voters worried about the immediate impacts of the buildings themselves, mostly not AI more broadly. But maybe this poll excludes people’s worry about AI? Just because it’s not their first concern doesn’t mean it’s not playing a part.

An earlier Gallup poll in May asked respondents to just write out why they were opposed to data centers however they wanted. Gallup then organized them into lots of different general worries. Here are the results:

Something unfortunate here is that as far as I can tell, Gallup didn’t give us a way of clarifying how much overlap there is between “negative views of AI” and “specific AI concerns” and how they classified them, so people’s concern about AI could be as high as 27% or as low as 14% (if the entire 13% of people with specific AI concerns were also categorized as having “negative views of AI”). Still, when people opposed to data centers were given the option to clearly say why, at least 3/4ths didn’t mention AI at all. This leads me to think the backlash isn’t mainly about AI, at least not consciously.

~~Most recently, a survey was done that found that~~ ~~when voters learned that a data center would recycle the same water for 10 years instead of drawing new water, their support jumped by 31 points~~ ~~(!!!!!!!). Voters say they’re worried about water, and when they learn water won’t be a big deal they stop worrying.~~ This comment points out that the survey results are deceptively framed in the document I shared, and that the 31 points actually mean “31% of people become more supporting than they were before” which could mean anything. I retract this point.

Americans have mixed opinions of AI

To be clear, Americans seem to have skeptical though mixed views on AI.

The polling we have on American attitudes toward AI isn’t great right now in general so I don’t feel like I can draw much from it. It would make sense to me that a big part of the data center backlash is that voters don’t feel like they’re benefited from what’s happening inside data centers and therefore are more inclined to criticize them.

But the polling we do have doesn’t show the kinds of dramatic swings you’d expect to explain the sudden and intense data center backlash. The percentage of people who are “more concerned than excited” about AI has stayed the same since about the release of ChatGPT.

Young people’s concern has significantly risen, but older people’s has stayed relatively flat in the past 4 years.

This is interesting, but I don’t think it fits with the sudden massive drop we’ve seen in data center attitudes:

Meanwhile, the share of people who “haven’t heard much about AI” has also stayed about the same, at half the population.

All this combined with the survey data on Americans’ stated reasons for opposing data centers makes me think that attitudes toward AI are probably a meaningful factor for a minority of people opposed to data centers, but I don’t think the backlash overall is driven by people’s thoughts on AI.

It’s probably not China

Is this actually all a Chinese psyop? I really don’t think so. I have a long separate post here looking at the evidence we have, and I come away thinking the data center backlash is basically entirely homegrown.

I think the backlash can be mostly explained by years of media coverage of object-level local environmental and economic harms

For the past few years, basically all news outlets everyday Americans trust were saying over and over that there were these strange new buildings plopping down without the locals knowing and using a disturbing amount of water and energy. Here’s a representative sample of lots of the most popular news articles on data centers between 2023 and 2025:

Artificial intelligence technology behind ChatGPT was built in Iowa — with a lot of water - AP

Google and Microsoft report growing emissions as they double-down on AI - NPR
Tech giants face emissions reality - Axios
Google falling short of important climate target, cites electricity needs of AI - AP
A bottle of water per email: the hidden environmental costs of using AI chatbots - Washington Post
Three Mile Island nuclear plant will reopen to power Microsoft data centers - NPR
Ireland embraced data centers that the AI boom needs. Now they’re consuming too much of its energy - AP
Data center report shows high cost of Virginia’s high-tech future - WVTF/Radio IQ
‘How come I can’t breathe?’: Musk’s data company draws a backlash in Memphis - E&E News / POLITICO (to be clear, this one’s one of the biggest real problems of any data center built in the last few years).
We did the math on AI’s energy footprint. Here’s the story you haven’t heard - MIT Technology Review (I wrote about this one being pretty goofy here)
Power Hungry: AI and our energy future - MIT Technology Review series hub
Their Water Taps Ran Dry When Meta Built Next Door - New York Times
AI Data Centers Are Sending Power Bills Soaring - Bloomberg (this is the most misleading article that I haven’t had time to comment on yet)
Your utility bills keep going up. Here’s everyone you can blame — AI data centers included” - Fortune. The article itself argues that data centers are getting blamed too much, though you wouldn’t know it from just the title.
How the AI-driven data center boom is leading to skyrocketing energy bills for many Americans” — CBS News. PolitiFact later found this to have mischaracterized Bloomberg’s 267% wholesale figure from earlier as a consumer bill increase.
Data centers now account for half of all new U.S. electricity use, just as Americans start to sour on AI - Fortune
AOC Confronts Trump Official With Effects Of Data Centers On Local Water Supplies - HuffPost
AI data centers use far more water than most tech giants report - Wall Street Journal
The Impending, Inescapable Deluge of A.I. - New York Times interactive

Some of these are legitimate stories and others are strange isolated statistics or just flat out fake (the idea that data centers raise electricity bills by 267% is this zombie stat that won’t die, the number referred to a specific electric wholesale price at a node near a nuclear plant in New York that had recently closed down, not residential bills, and via this game of telephone we now have politicians like Elizabeth Warren repeating the misconception).

A lot of media coverage has gone on to just share people’s broad concerns without seeing whether they stand up to scrutiny. Many reporters now seem interested in seeing “what everyday people have to say” without clarifying what they’re getting right or wrong. Take this recent clip about the backlash from ABC:

Very very few media organizations are both reporting on locals’ reactions (which note in this video are entirely about the environment) and then contextualizing them with the actual amount of land, water, or power the data center would use, and how that compares to the bigger picture in the places they’re built. Because there’s so little incentive to correct this stuff, it goes uncorrected, and millions of viewers at a time see upset sympathetic locals in news stories that leave them thinking all their concerns are probably correct.

All of this has also been amplified a ton on social media, which created lots of common wisdom among everyday people about data centers that they didn’t have much reason to question. I think these ideas are kind of “locked in” now because they’re perceived to be the expert consensus on data centers, so people are more resistant to questioning them for the same reason they’re (rightly) resistant to specific anti-vax arguments.

With no real reason to each individually do some massive deep dive to try to figure out what was actually going on or put the number in context, people naturally just accepted that these were bad for local communities, and this became the default wisdom among educated people. Then the data center buildout accelerated, and suddenly more people found that their communities were also considering data centers. Why would this be happening if all educated people agreed that these were terrible? This was probably pretty annoying and confusing to a lot of people, who have now seen so much consistent negative coverage of data centers that saying they’re fine seems almost like a conspiracy theory. This all just seems like a much more satisfying explanation to me! It fits the limited polling data and evidence we have.

I’m left uncertain, but I lean in the direction that the data center backlash is mostly about object-level concerns about data centers, and the main role AI plays is that people’s opinions of it are too mixed to justify looking into whether their negative beliefs about these large strange buildings are correct. I’ll eventually get around to finishing my history of the data center panic, but I’d recommend that first linked post if you’d like to see more of my thoughts on how this all started.

One last thing to consider is that data centers are so unpopular now that it might just be fun to join in on the hate. A recent NYT opinion column talked about the communion people feel with each other in this fight. “People like hanging out with each other to attack a perceived enemy” on its own might go a long way in explaining the most intense reactions we see. The column itself gives exactly one sentence passively acknowledging why people are mad in its overview of how much this brings people together.

Some people will bizarrely imply that saying this is all about object-level worry “ignores what voters are actually mad about” or doesn’t take them seriously. I think it’s important here to just look at what voters are actually saying, and take that seriously, rather than trying to work out their inner psychology and secret beliefs (that always so conveniently line up with the person writing about them), and that this is the more democratic and fair way of engaging in data center discourse. I can then make my own case that, with nothing but respect to the voters, they are often mistaken about the facts they share. This is I think a much more respectful, direct, and democratic way of engaging compared to imagining that they all secretly agree with me about the very immediate dangers AI could pose. Somehow in the last year, the dialogue completely flipped from “elites are ignoring the incredible environmental harms of data centers” to “elites are ignoring that voters secretly aren’t activated by these environmental harms at all” and I didn’t get the memo.

Thinking about other industries like we think about data centers shows how impoverished the debate is

Tue, 18 Aug 2026 19:33:50 GMT

One of my favorite images I’ve recently found is this aerial photo of Gwangyang Steel Works factory in South Korea (hat tip to Matt Beard):

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Part of the reason this image is so amazing to me is how easy it is to mistake it for a computer chip.

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Among other things it’s a reminder to me that outside of my little neighborhood in Washington DC, the world is unimaginably large and strange, and humanity has a shocking, overwhelming ability to shape things at the macroscopic and microscopic level to optimize the flow of material or information using these incredibly abstract methods of thought mostly alien to me (that I’ve only approached while playing Factorio), and that my little bubble of friendships and calm cafes and nice little offices in brutalist buildings exists on the thin surface of a deep global system more vast and sprawling and strange than my meager human categories can grasp. It leaves me much more open to thinking that the near future might be incredibly weird and alien, because the present already is.

Gwangyang Steel Works is the world’s largest or second largest steel mill, depending on how you count. It has enough capacity to produce 10% of the world’s car production, about 22 million tonnes per year.

I’m worried that if we reported on and thought about Gwangyang Steel Works the way we currently report on and think about data centers, the coverage would look something like this:

Gwangyang Steel Works is an immense, imposing, ugly, and alienating building complex that guzzles gigantic amounts of water. It consumed about 9.8 billion gallons in 2024 (about half of the total water consumed onsite at all American data centers combined in the same year) and is one of the largest emitters in South Korea, responsible for about 30 million metric tonnes of CO2e in 2022, about 4% of the entire country’s emissions (and the low end of a recent estimate of all emissions from all global AI usage combined in 2025). It also adds a lot of local air pollution, emitting about 30,000 tonnes a year of dust, sulfur oxides, nitrogen oxides, and other pollutants, the highest total among the country’s large industrial sites. Local leaders say the Steel Works brings lots of benefits and is necessary as the world uses more steel, but a lot of steel is used for things like cars, which themselves have a gigantic climate cost. The plant has capacity for 10% of all steel going to cars, and if you include the emissions of those cars in its full cost, this makes it one of the most environmentally destructive buildings in the world.

All of these facts are individually correct, but leave the reader with just a small sliver of the full picture of the ethical, environmental, and economic trade-offs of the plant, to the point that I wouldn’t trust someone who knew only these facts to make the right call about whether to build the plant in the first place.

The ethics of attributing the emissions of the cars made with its steel to the plant seem pretty confused. Should local governments in South Korea decide that because a factory is making something very generally useful that can be purchased by any other industry, and one of those industries makes cars, and cars emit a lot, they should therefore ban the factory from being built? This would create global chaos as local bureaucrats and activists tried in vain to predict the broad trends of society, and to impose rules based on their likely arbitrary and confused conclusions. Steel production is also vital for renewable energy sources, so in deciding to block this plant, local governments and activists may unknowingly also slow the green energy transition. In general, broad rules (like a global carbon tax) that punish emissions while leaving the information flows of the economy otherwise free to operate, or industrial policy aimed at speeding up the learning curves of emerging green industries like solar, or investments in modernizing the grid, seem much more promising than attempts to stop complex global industry because some of what it produces will be used in products that emit.

Another framing my pretend coverage misses is that the world has billions of people in it who benefit from steel in complex ways, and concentrating the production of steel to meet this unimaginably large demand in an unimaginably large building complex likely reduces its environmental cost as opposed to spreading it across a lot of small steel mills, none of which would likely receive as much scrutiny. It’s very easy in lazy environmental writing to punish buildings for concentrating in a single visible spot something that no one would have an issue with if it were dispersed among the hundreds of millions of consumers it’s serving. Scale and centralization often allow incredible levels of optimization and efficiency that could never be achieved if everyone were given their own backyard steel furnace, but centralization also allows people to see the full environmental cost that used to be invisible, and to condemn it in a way they wouldn’t if it were dispersed and faded into the background. A lot of the ways people think and write about the environmental effects of specific buildings seem to punish and condemn scale without considering the efficiencies that centralization allows.

A lot of writing about data centers seems to imply that all large industries need a glowing, positive, almost loving relationship with the local communities in which they’re built, but I think most of the best and successful industries operate completely invisibly, set off from the communities they’re built in and the people who buy their products. It’s likely that right now you’re within a mile of some piece of steel produced by this mill, or maybe you’ve used a product with steel that came from it, but you never knew it existed. The mill hummed in the background unnoticed. The locals of Gwangyang obviously know the plant exists (it’s a popular tourist destination), but many other industries don’t ever bother to create these powerful relationships with local communities and instead just operate mostly unnoticed. If you were to draw a circle with a radius of 50 miles around you, do you think you’d actually be able to name the top 10 most lucrative large industries within it? Does it tell you anything about their relative virtue or importance if you can’t?

This is all to say that the default way many educated people think about the environmental effects of individual industrial buildings, which they believe is a morally serious and far-seeing way of questioning authority and fighting for the environment, often looks to me surprisingly childish and unserious. It often fails to reckon with the incredibly complex trade-offs of serving a world of billions of people with wildly different demands, and instead reduces individual buildings to heroes or villains, detecting their villainy by compiling a bunch of individually alarming isolated statistics that any industry at a similar scale would also come with. This can look numerate and informed, but I think it’s actually a clumsy and lazy failure to fully internalize the deep complexity of the world, and the trade-offs and systematic thinking this complexity demands of us.

To make what I’m saying here a little more concrete, I’d be interested in asking data center opponents: “Would you allow the Gwangyang Steel Works to be built? Why or why not? What specific environmental rules are you applying here? How do you think about the full trade-offs involved?” Then I’d want to see whatever rules they give applied to data centers. I’m worried that one of the rules people often seem to be operating under is “We can’t have any new emissions at all,” which would effectively ban all new industry until the climate crisis is solved. It would seal the current global system in amber and leave us with no way to adjust or progress to more promising technology over time, at a moment when we also need radical change to adapt to a changing climate. This rule would ban the steel works, and would have banned almost every industry we currently use. Seems like a very bad rule! The data center debate is awash in strange rules like this that appear to have been memed into existence only recently but are now often presented as if they’re obvious truths that all reasonable people believe. These rules are easy to nod along to when applied in a vacuum to a strange new industry like data centers, but mostly fall apart and appear ridiculous when applied to almost any traditional industry, like steel manufacturing. The green energy transition is going to require way more steel, and we can’t follow the rule “no new emitting steel mills until the green energy transition is complete” in the meantime. I suspect a lot of rules like this emerged from the internet in the last 15 years as people developed new general moral outlooks, often informed by degrowth and localist ideas, that went mostly unnoticed and unquestioned because American energy usage overall didn’t grow much.

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Populists on the left and right now talk a lot about local communities being abandoned by industry while also holding a cluster of beliefs that wouldn’t allow any industry in the first place.

If I tried to write rules for which new industries that emit should be allowed to emerge during the green energy transition, they would look something like this:

Their local externalities are limited to the point that people nearby mostly don’t notice any meaningful effects of them being there.
If possible, the industry should be fully electrified, because this leaves it primed to eventually receive 100% green power and avoids the hard engineering challenge of greening primary energy usage, which is the main reason the transition is so difficult.
The industry shouldn’t be punished for being centralized and optimized. If we wouldn’t have a problem with the industry’s energy usage and emissions if they were caused by dispersed users running the same processes in their own homes, we also shouldn’t condemn it for centralizing and optimizing these processes in one location. I say a lot more about this one here.
If possible, the industry should help fund upgrades to the grid, or ideally be broadly useful for generating and delivering green energy in other ways.

AI data centers often easily pass 1, and pass 2 and 3. Their net effect on 4 is an open question. More broadly, outside of extreme examples, I’m generally skeptical of blanket bans on specific industries to solve climate change. The general policy solutions to climate change specifically as I see them are carbon taxes, industrial policy for renewable energy, society-wide regulations and standards, and massive investments in the grid. Compiling scary lists in a vacuum of the total environmental costs of specific buildings, which always sound large for anything centralized at all, will often just introduce ethical and economic confusion and leave us not thinking like adults about the incredible complexity and trade-offs of the green energy transition.

In general, I’m worried that because we’re often only exposed to the thin human surface of society in our little apartments and coffee shops and cars and offices, we don’t notice how almost everything that goes into making our little lives human and gentle flows from the operation of incredibly complex systems operating at inhuman scale, like the steel mill. We have a general attitude that inhumanly large, complex, systems that can serve many different value systems are bad, and it’s the tiny, local, simple systems that serve our specific values that always need to win. This attitude is incredibly dangerous to pluralist society, because a lot of what makes something “inhuman” is often just the fact that it’s so general that it can serve many contentious value systems at once, not just ours. I have a lot more thoughts on that in this post on liberalism and the environment.

Data centers are the first new inhumanly large industry that has been generally visible to us for a while, and I’m worried a lot of the debate about them includes this backwards story where the big things sap our humanity and the little things restore it. In reality, the little things are often where the broad goods we’ve received from the big abstract processes are spent and fill our lives with meaning, not where they’re generated, and the mindset that “small is often better” threatens to rob the world of many of the things that make it most human, and allow us to live alongside each other while wanting wildly different things out of life. To help break out of this mindset, it might be helpful to look at Andreas Gursky photographs or watch Koyaanisqatsi 1 or Tie Xi Qu or play Factorio or listen to Sunn O))) or this EconTalk episode about making potato chips or think about markets a lot or read Vaclav Smil. I don’t know, you can also find your own path to this “big complex systems wow” mindset, but it’s important that you do. Decisions about global industries require people willing to entertain the complexities of the global system, and what I see as the standard educated “with it” opinion of the data center buildout is very far from doing that.

Death of the author. I have multiple friends who’ve watched it and confused everyone else in the room when their takeaway was how great society is

I’m very skeptical that China has played a meaningful role in the US data center backlash

Sat, 08 Aug 2026 01:52:43 GMT

A Heatmap poll in August last year found registered voters equally split on the idea of a data center being built near them. In a second poll in May, support had fallen to 21% and opposition rose to 71%.

More and more, I’m seeing people say that this backlash can’t be entirely homegrown. We should obviously consider this. There’s precedent for foreign interference in our discourse, and other countries have plenty of reasons to try to slow the spread of data centers.

There have been a few high-profile reports and claims purporting to show foreign involvement, and lots of people are sharing them as if they’re smoking gun evidence that China’s had significant impact. I’m worried that a lot of people are reading those headlines and nodding along without poking at the claims. When you do, they mostly collapse into a few specific examples of tiny organizations who happen to be based in China and other countries running some ineffective bot farms and behaving in ways that make them seem unlikely to be directed by the CCP, some negative coverage of data centers from Chinese state media, and a CCP-aligned billionaire funding networks of goofy goober fringe groups in the US like CodePink, or groups that overlap in leadership with the Party for Socialism and Liberation (PSL) which are now focusing a bit more on data centers, and in one or two places successfully blocked them with the help of lots of angry locals. All of these examples seem to be completely dwarfed by the organic local backlash driven by American media coverage, social media, and ordinary land-use politics. There have also been a lot of media headlines and graphics like this, that seem to imply that we know China’s played a big role in the data center backlash:

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My goal here is to give you an overview of what we know about foreign influence on the data center backlash and explain why I think its effect has so far been pretty negligible, and why I think both the media and government coverage of China’s purported influence has been goofy.

My immediate reaction as someone who’s followed data center politics for a while
The evidence that China’s influencing the data center backlash is pretty weak
Evidence against
Conclusion

My immediate reaction as someone who’s followed data center politics for a while

When I hear that this backlash “can’t be homegrown” I wonder if the speaker went to college in the 2010s like I did. If I went back in time to 2011, and asked my past self: “If a massive new industry appears with significant emissions, associated with billionaires and new strange polarizing technology, and it comes in these massive imposing boxes that draw as much power as whole cities, and its users are all dispersed and invisible to the local residents around it, how will your classmates react?” I would have predicted that they would have on average been furious. Degrowth and localist ideas were already in the water, and had become the default for “this is what all the good people believe.” I was already running up regularly against the established wisdom recently invented in the cauldron of 2010s internet leftism, to the point that saying anything generally positive about corporations or markets was seen as “not getting the memo.” And now my generation runs society!

The buildout is actually the first time American electricity consumption has seen sustained growth since I was in college.

Original graph from Hannah Ritchie, note that this projected energy rise isn’t all due to data centers. Read more about the forecast in her post here.

I suspect that in this time, general anti-building and anti-energy use sentiment has grown unnoticed, because there wasn’t much new building or energy use for it to react to and make itself visible. As I personally became significantly more liberal (as opposed to illiberal left), pro-market, and appreciative of the importance of economic growth, I also saw the default political attitudes of my peers slip away from me more and more into a general cluster of beliefs about climate and politics that, if I were asked, I could have easily predicted would have caused them to resist the data center buildout. Flat energy use in the 2010s left dry tinder ready to catch fire as soon as any hint of growth appeared. It’s to the point now that a lot of educated people seem to believe that there’s this agreed upon rule in climate that we can’t have any new industry at all until the full green energy transition is complete.

All this made me pretty temperamentally skeptical of the idea that the data center panic is too intense and targeted to be an organic result of local American politics. American politics is pretty wild right now! I had also been following the panic from the start, and knew that many of the most popular talking points and ways of thinking had come from specific overconfident or clumsy reporting from established US papers, and spread quickly because their claims were shocking and alarming when read in isolation, and also sometimes exciting to talk about on social media (it’s very easy to buy a water bottle to hold in front of a camera on TikTok and compellingly repeat a fake statistic). Unless the Washington Post or New York Times or Bloomberg or More Perfect Union are taking marching orders from China, I can always trace the panic to a decidedly American source, as well as the networks of people elevating it.

But obviously these are just my own intuitions. And there are compelling reasons to be worried. China and other countries definitely have a national interest in slowing the American data center buildout. In China’s case because it’s become more interested in AI and its relation to national power over time. Other countries may just want to cash in on the data center buildout, and the more American data centers they can block, the more the hyperscalers will turn to them instead as a place to build. And we know that foreign influence campaigns have amplified past political panics. I’m not temperamentally inclined to trust the CCP, and I’m aware of how strange the data center debate has been, so I went into this with an open mind. And yet…

The evidence that China’s influencing the data center backlash is weak

Most of the early reports on Chinese and other foreign influence in the US data center debate offered very weak evidence for any significant effect. Yet they’ve often been shared as if they’re smoking guns showing that China played a major part in the backlash. Looking at their details instead suggests that some foreign actors have definitely shown interest in the data center panic, but we have no clear evidence that they’ve played a meaningful role.

The American Energy Institute report

In April, the American Energy Institute published a report called “Foreign-Funded Opposition to U.S. Data Center Expansion.” It found that 12 US organizations opposing data centers had received more than $39 million from foreign donors.

That is interesting, but mostly about individual foreign private philanthropy from Europe. The donors are wealthy people and foundations in Switzerland, the UK, and Denmark. The most politically controversial is Hansjörg Wyss, a Swiss citizen who has lived in Wilson, Wyoming, for decades and funds Democratic-aligned advocacy groups and US environmental stuff.

Wyss has been somewhat shifty with his donating, sending hundreds of millions of dollars into US advocacy groups, including via the Berger Action Fund, a 501(c)(4) that is not generally required to publicly disclose its contributors.

The other donors in the report are Chris Hohn (a British hedge-fund manager who gives mainly through the Children’s Investment Fund Foundation), the British Quadrature Climate Foundation, Denmark’s KR Foundation, and Geneva’s Oak Foundation. None has a meaningful connection to China, with a tenuous exception for Oak that I’ll talk about below.

The AEI report itself doesn’t claim that this money involves China at all, but the report’s been regularly twisted to imply that it does. For example, a popular early write-up of the findings was this Center Square article syndicated in dozens of local papers. It opens like this:

A new American Energy Institute report shows that foreign billionaires have provided funding upwards of $39 million to the anti-AI data center movement in the United States, with experts saying AI infrastructure must be advanced in order to assert American tech dominance over communist China.
Founder, CEO and Chairman of State Armor as well as expert on Chinese influence in America Michael Lucci told The Center Square: “Either the United States or communist China will dominate artificial intelligence, and therefore, likely dominate the leading technologies of the 21st-century.”
“China is actively executing strategies to win this competition,” Lucci said.
“The world we leave behind for our children and grandchildren will be a very dark one if a communist regime has technological dominance over the United States of America,” Lucci said.
“We will enter a dark ages as communist China exports their governance and technological models to countries currently aligned with the United States,” Lucci said.
Lucci said that foreign money funding the anti-AI data center movement is “classic political warfare for the detriment of our sovereignty and ultimately our way of life.”

This leaves a strong impression that the report itself is about Chinese influence, but it’s not!

The $39 million number that gets shared a lot from this report isn’t all going to data center stuff, it’s the total amount given to climate orgs and progressive causes in general. Some of those orgs have then gone on to oppose data centers, often for climate reasons.

So framing this as “$39 million to the anti-AI data center movement in the United States” is silly. It’s $39 million to specific climate orgs from billionaires worried about the climate. The report doesn’t make it clear what time periods these donations happened over. If you look at their sources, most of these donations happened over a 10 year period or longer. So for most of the time these donations were being made, the data center buildout wasn’t even happening.

The report opens with an implication that the anti-data center letter from 230 environmental orgs that inspired Bernie Sanders’ data center moratorium was influenced by these donations:

A network of activist groups receiving significant foreign funding is waging a coordinated campaign against the development of critical AI infrastructure across the United States. According to tax filings, annual reports, and grant databases, 12 organizations actively opposing U.S. data center development have collectively received more than $39 million from foreign donors — including billionaires and foundations based in Switzerland, the United Kingdom, and Denmark.
In December 2025, more than 230 leftwing activist organizations — many affiliated with the groups highlighted in this report — signed a letter demanding Congress impose a national moratorium on the approval and construction of new data centers, calling the industry “one of the biggest environmental and societal threats of our generation.” Local chapters have since organized protests, lobbying days, and public pressure campaigns in states from Louisiana to Michigan.
These are not isolated campaigns, they are a part of a repeat network of organizations funded by the same donor ecosystem. At least one organization identified in this report has also been included in prior analyses of coordinated energy policy advocacy networks. Previous research by the American Energy Institute has documented overlapping funding sources, shared partnerships, and aligned policy objectives among environmental groups engaged in opposition to domestic energy and infrastructure development.

This is a confusing framing of the numbers here. What happened here was foreign donors worried about climate donate to climate stuff over a decade, then some of those orgs go on to sign a letter signed by 230 orgs in total.

Here’s another goofy example of how this got reported on, from the Washington Free Beacon:

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They put this in their China category despite all these guys being from Europe! These “groups pushing for a data center moratorium” are general climate groups, they didn’t just appear to fight data centers. The second half of the article pontificates about China’s role with a few examples of Chinese state media writing anti-data center articles.

The Bitcoin Policy Institute reports

The Bitcoin Policy Institute (BPI) published the first of two reports on Chinese influence in the US data center debate on May 18. I don’t think the reports demonstrate significant foreign influence, beyond showing that specific fringe US groups like CodePink and the Party for Socialism and Liberation have focused more attention on data centers. And I think the infographic they made is getting shared uncritically as strong evidence that China’s playing a significant role in the debate, but when you poke at it this reading mostly falls apart.

First Report: Foreign Influence in the Campaign against American AI

This first report documents connections between the CCP and a US media and nonprofit network, and summarizes its case with this graphic:

The same European billionaires from the American Energy Institute report are here:

They’re donating to lots of climate causes and believe data centers are bad for the climate. They’re mostly reporting all their donations (with the exception of Wyss’s donation to the c4). The report frames them as donating billions of dollars into orgs which are now fighting data centers, but what this means is they’ve given a lot to US climate groups in general, and now some of those climate groups are anti-data center for climate reasons. The report notes:

Multiple foreign-tied charitable vehicles, including those of Swiss billionaire Hansjörg Wyss and British billionaire Alan Parker’s Oak Foundation, have funneled more than $2 billion into US 501(c)(3) and 501(c)(4) advocacy infrastructure. A significant portion of that money now flows directly into the organizations driving the anti-data-center campaign.

This is a reference to a number from this Americans for Public Trust report which at first found around $2 billion and in a revised report later bumped that to more than $2.65 billion in total grants from six foreign-based or foreign-donor-linked sources to U.S.-based orgs, mostly over 10 year periods. This is the same report AEI drew from above, with the same organizations like Wyss and the Oak Foundation. This money went to hundreds of advocacy orgs, that focus on things like climate, conservation, social justice, health, and other stuff. After 10 years of donating, some of these groups (like other US progressive and climate-focused groups) opposed data centers. We don’t know how much of this money was meant for anti-data center advocacy specifically, but it seems likely that it was pretty small, and importantly all coming from European billionaires and orgs, not China.

BPI doesn’t explicitly claim that these foreign billionaires are working for China, but still draws a line connecting them to China like this:

What’s “Oak Funding for BRI Projects?” It’s not the CCP influencing the Oak Foundation, it’s the Oak Foundation funding some Chinese Belt and Road Initiative (BRI) projects (China’s global infrastructure project). Why would they fund this? Well, they made a one-time $1 million grant to ClimateWorks in 2018 “to support the greening of the Belt and Road Initiative.” They also supported related work by the World Wide Fund for Nature, and BPI says they funded the Beijing-based Global Environmental Institute (GEI). The report describes these grants as evidence of “direct policy and financial ties to the People’s Republic of China.” That’s technically true but an incredibly silly framing once you know the details.

The Belt and Road Initiative builds, among other things, ports, railways, highways, power stations, and telecommunications, and is operating in 150 countries making up 75% of the world’s population and half of all global GDP. Opinions on BRI vary a lot, but the reason why a climate group would want to pay to make the massive infrastructure project greener is incredibly obvious. Throughout the 2010s, China was the largest public financier of coal power in the developing world. Lots of Western climate philanthropies funded research and advocacy to try to get Chinese banks and ministries to stop. The ClimateWorks report BPI mentions as funded by Oak, Decarbonizing the Belt and Road, finds that if BRI countries kept growing along carbon-intensive paths, they could make up 2/3rds of global emissions by 2050 and push the world past 2 degrees of warming even if everyone else hit their targets. This seems like a good study for a group focused on climate to fund!

The report also mentions the fact that Kristian Parker, the head of Oak’s environment program, is also on the ClimateWorks board. I know a bit about climate philanthropy and understand this to be pretty common.

The ClimateWorks grant was $1 million in 2018. I can’t find Oak’s total spending that year, but their environment program gave out $65 million in 2019. This all happened before the data center buildout. I think it’s goofy that the BPI drew this line connecting the CCP to Oak at all in a graphic about data centers.

What about the Beijing-based org GEI? This is a Chinese environmental nonprofit. They work with the Chinese government on making foreign investments greener. If you want to influence the government of China to build less coal power, it seems necessary to use an intermediary in China itself, which will by definition have to work with the government. Again, this seems like a necessary piece of one of the most effective climate philanthropy moves in the last few decades. I’m not seeing anything suspicious here.

This global campaign to green the Belt and Road Initiative may have worked! In 2021, Xi announced that China “will not build new coal-fired power projects abroad” and researchers tracking Chinese energy finance have found that new coal funding by the government actually did stop. This seems like one of the very best possible things a climate-focused group could spend money on! BPI includes Oak’s explanation that the giving was meant to reduce the Belt and Road’s projected emissions, but calls the ties “troubling” anyway. I don’t think they’re troubling at all.

It seems like none of the people or organizations on the right side of the chart are actually trying to influence the data center debate for national security reasons, or are working on behalf of foreign adversaries. And most of their donating happened for general climate or progressive reasons, and some of the climate orgs they funded became anti-data center. They don’t seem to be interested in “slowing AI development” in the way the chart implies. It doesn’t seem like these people are motivated by the pace of AI progress at all, the general climate and progressive orgs they’ve donated to just think the physical infrastructure is so wasteful that it should be blocked. I disagree, but they don’t seem to fit what the chart’s implying, so I’ll remove them.

Let’s keep moving through this chart. Who are the guys on the left?

These are Yi Zeng and Xue Lan, the two Chinese AI-governance researchers Bernie Sanders invited to a panel on existential risk from AI on April 29th. The report describes the event as “the culmination of an influence campaign.”

Both men are widely respected outside of China for their AI safety and governance work. Zeng was on the UN Secretary-General’s AI Advisory body for a year, he addressed the UN Security Council’s first-ever session on AI in 2023 to warn about existential risk, and made the Time100 AI list in 2023. He served on the expert advisory panel of the international AI safety report led by Yoshua Bengio. Xue is the dean of Schwarzman College at Tsinghua (the college that hosts Schwarzman Scholars), did his PhD at Carnegie Mellon, and co-authored the 2024 Science paper “Managing Extreme AI Risks amid Rapid Progress” with Bengio, Geoffrey Hinton, Stuart Russell, and Andrew Yao. He chairs the Ministry of Science and Technology’s New Generation AI Governance Expert Committee and is a Counsellor of the State Council.

Many researchers in AI safety and governance, despite being wary of China, believe that some basic coordination with the CCP will be necessary to avoid catastrophic risks, in the same way we coordinate over other global risks. You can disagree with this, but it’s ridiculous to imply that anyone who believes that some kind of cooperation over what they see as incredibly dangerous new technology would be good must be bought off by China.

What else does the report offer?

Both men do at times seem to parrot or at least share the opinions of the CCP. Zeng wrote pieces for China Daily using language associated with official Chinese foreign-policy messaging. Xue has argued that US chip controls can be counterproductive. The report uses Xue’s opposition to US export controls as evidence of alignment with the CCP. But Sanders’ Artificial Intelligence Data Center Moratorium Act would prohibit exports of advanced computing hardware to countries that don’t have equivalent AI safeguards, which would include China. If Xue’s opposition to export controls is evidence that Sanders’ panel and bill is the result of a foreign influence campaign, the bill’s contents seem like much stronger evidence in the other direction. More broadly, the report lists as evidence that they’re mouthpieces for the CCP their appearances in Chinese state media like CGTN, China Daily, and Global Times. But all major Chinese media are state-owned or party-supervised, and two of the country’s most prominent academics will obviously appear in them.

These are two of the most internationally known Chinese scholars on AI governance. If you want to understand the Chinese perspective on AI risk, these are the people you go to! If I were running a panel on global AI governance, I’d want to invite these guys, and I don’t need to be funded by China to say that. Both men regularly appear in AI safety and governance conversations. China would not have needed to fund anything to make a panel focused on global AI governance interested in having them join. Their presence alone isn’t evidence at all that China’s run a successful influence campaign here. The panel wasn’t even about data centers. It was about existential risk, unemployment, and Sanders’ idea of an international AI treaty modeled on Cold War nuclear agreements, which Max Tegmark (one of the panelists) has been pushing for a while.

The report doesn’t even explain how Sanders’ selected these guys or why this is the “culmination of an influence campaign” besides the fact that these are Chinese researchers who sometimes share the opinions of the CCP, and a very tenuous connection between China and the anti-data center letter US environmental groups signed.

Sanders’ Artificial Intelligence Data Center Moratorium Act was introduced on March 25, 2026, but he had first demanded a moratorium in December 2025, four months earlier, after a large group of US environmental groups wrote a letter demanding a national moratorium, as shown in the graphic.

The timing is consistent with the environmental letter inspiring Sanders’s moratorium push and with his later emphasis on AI safety and governance. The report’s already implied that this coalition letter was influenced by foreign billionaires with some kind of tie to China, but we’ve already seen that that doesn’t hold up. This was a coalition of normal US environmental groups mad about the environmental impact of data centers. You can look at the letter with the full list of orgs here. I found two orgs who have a credible tie to the CCP-aligned Singham group (discussed below):

The Institute for Policy Studies, whose 2015 annual report thanks its donor community and includes Neville Roy Singham in the individual donor list. IPS’s fiscal 2024 IRS filing lists Jodie Evans as a trustee. Evans is Singham’s wife and cofounder of CodePink.
The Progressive Democrats of America, whose current advisory board page lists Medea Benjamin, a CodePink cofounder. A 2009 PDA release also named Jodie Evans among its board members.

So out of 231 organizations, I found 2 that had loose ties to a CCP-aligned network. Doesn’t look much like a successful CCP influence operation to me.

The report says that Sanders’ data center moratorium was introduced 107 days after the coalition letter, and concludes that “That kind of efficiency typically distinguishes coordinated advocacy infrastructure from spontaneous grassroots opposition.” Does it? 3 and a half months is a long time.

So to recap, Sanders was originally motivated by US environmental groups, only 2 of 231 seem to have any ties to China at all, to propose a data center moratorium, then talked a lot with the AI safety community, got more worried about extinction risk, promoted Max Tegmark’s specific view of global AI governance, and invited the two most prominent and respected Chinese AI governance researchers to a panel on said global governance. This doesn’t look like a foreign influence campaign to me at all.

So I’ll knock all these off the chart as well. What’s left?

We’ve got the Singham Network, and Chinese State Media. I agree with the report that both of these have shown interest in slowing the data center buildout and have tried to influence American attitudes toward data centers, so the question is how successful each has been.

First of all, the Singham network is in fact incredibly skeevy, CCP-aligned, and capable of influencing some US discourse. It is a coordinated group of nonprofits funded by Neville Roy Singham, a Marxist billionaire who lives in Shanghai and is married to CodePink co-founder Jodie Evans. A 2023 New York Times investigation found US and international organizations producing content closely aligned with the CCP on major geopolitical issues, sharing staff and office space, and cross-posting one another’s material. Singham works from a Shanghai office shared with a Chinese media firm whose mission uses the CCP’s same slogan for overseas propaganda, and he attended a party workshop on promoting the CCP internationally shortly before the Times story ran.

From 2017 to 2023, the Singham network moved about $278 million into six US nonprofits: including more than $167 million to the People’s Support Foundation, more than $68 million to the Justice and Education Fund, and more than $22 million to The People’s Forum. This is currently being investigated by the government.

So this is a real case of a powerful CCP-aligned actor trying to influence American thinking. The question is how influential the Singham Group’s been in the data center debate. Here’s what the BPI finds:

Tricontinental published a dossier criticizing Big Tech in 2021 and a paper in March 2026 arguing China is overcoming US export controls.
Code Pink published a piece in January 2026 attacking Meta’s data center projects in Louisiana and Wyoming and framing them as part of a new Cold War on China.
Peoples Dispatch ran a piece attacking American AI firms by name.
Liberation News (the paper of the Party for Socialism and Liberation, more on them further down) has been covering data centers and electricity bills since at least July 2025.

The report describes this as the network seeding anti-data center talking points, but almost all of the data-center-specific material came out after people were already fired up and didn’t seem to add new talking points. The five year old dossier is just Tricontinental commentary arguing that the United States should let advanced chips flow to China. This is unrelated advocacy for Chinese access to compute for a small audience, and doesn’t suggest any kind of local NIMBY opposition to data centers. The only media here focused on data centers shows up in mid-2025 or later. By then, local opposition had already blocked or delayed $64 billion in data center projects, and electricity bills purportedly linked to data centers were a major American news story. There’s no new information here that the groups seeded, and I haven’t seen evidence that they inflamed the debate much. It doesn’t look like anything here got much readership.

The report notes that Chinese state media is warning Americans about data center energy costs while subsidizing the energy costs of its own data centers. I agree that the CCP seems to clearly not mean what they say about the US data center buildout, but I need to see more evidence that this all has actually had an effect.

I’ve seen a lot of people share this graphic as solid evidence that China’s playing a big role in the data center backlash, but when you poke at almost any part of it, this reading seems to fall apart.

Second Report: The Singham Ground Game

BPI published this second report in June. I think this is a clear improvement on the first, but is still far from showing that China’s played a meaningful role at all in the data center backlash.

This second report looks at 21 local anti-data center campaigns across 14 states where the Party for Socialism and Liberation (PSL) showed up to fight data centers via door knocking, petitions, protests, and showing up to city council chambers. Two things about PSL:

Their leadership heavily overlaps with the executives at Singham’s nonprofits.
They also attempt to join basically every popular mainstream protest to recruit new members. As a veteran of protests in DC myself, basically every large one I went to had a PSL presence. When I showed up at a march after Roe was overturned I remember them running around yelling about how we need to abolish the Supreme Court. I felt like maybe getting into an epic debate with them about how this would have also ended Roe, but decided it wasn’t worth it. I did once had a longer conversation with a member who alternated between telling me about the Hands Off North Korea movement and about how almost every night he had a dream about peeing his pants. Anyway…

The report finds that these campaigns were involved with 10 local moratoria, 1 permanent ban, and 4 rejected or withdrawn data centers, adding up to $23.6 billion in delayed or blocked investments. The report also repeats that “the American opposition to data centers is real and mostly homegrown.”

My general impression of PSL is that they’re mostly ineffectual and just ride along on other protests, so the question is whether they were actually responsible for any of these shutdowns. It looks like there’s one clear case where they were. In Prince George’s County Maryland (right near me!) the local NPR station described the PSL as leading the county’s data center fight, and talked about how the chapter trained local residents to canvass and ran a petition that got 20,000 signatures. They turned out crowds large enough that the county executive paused data center permitting.

Most of the other cases don’t manage to show that PSL caused the shutdown or delay. In DeForest, Wisconsin, the lead organizers were a normal local residents’ group, PSL “provided support,” and the data center was blocked after local opposition and a Wisconsin Watch investigation exposed details of the NDAs. Tucson’s $3.6 billion Project Blue was rejected by the city but moved to unincorporated Pima County land, where it is proceeding, and BPI counts it as delayed in part by PSL.

On top of all this, the report doesn’t even find that Singham actually funds the PSL. There are lots of linked people, but we don’t know which way the causality runs. PSL was founded in 2004 as a split from the Workers World Party, way before Singham himself started funding his network. The report relies on the public statements of James “Fergie” Chambers, a disaffected Marxist millionaire who used to organize alongside Singham. He says The People’s Forum and BreakThrough News are wholly controlled by the PSL. So this claim seems to be that Singham’s money is flowing into a Marxist scene that already existed independently. It’s possible that without Singham’s money flowing into these other orgs, PSL might be similarly active. I don’t know. Again, this definitely seems like the clearest case of bad behavior from a CCP-aligned group. Its scale still appears small. The Maryland campaign is the clearest example, although BPI also documents substantial PSL organizing in Charlotte. It’s unclear how much either campaign changed the outcomes.

But meanwhile, Gallup found in March that 71% of Americans would oppose a data center in their own area, 48% say they would “strongly oppose” one. The PSL in Maryland could have only mustered this opposition by drawing on a pre-existing very negative public sentiment that doesn’t seem to have been influenced by the Singham network.

OpenAI’s report

On June 10, OpenAI published the report “PRC-linked influence operations are targeting AI debates in the US” which describes how and why they banned two clusters of ChatGPT accounts that seemed to be operating out of China (the operators prompted the model in Chinese and connected through VPNs, since OpenAI doesn’t allow access from China). The first cluster, which OpenAI nicknamed the “Data Center Bandwagon” campaign, generated short English-language comments and images claiming that AI data centers were pushing up electricity prices for ordinary American families, and fake accounts posted them on Twitter. The second, “Tech and Tariffs,” generated comments and political cartoons attacking US tariffs, and was connected to a network of fake Twitter accounts pushing false claims that ChatGPT user data had been breached.

OpenAI concluded that the operators were likely connected to a private Chinese company that conducts public-opinion work for provincial government clients. So this may have originated in China, but we don’t have solid evidence that China’s central government directed it. The disinformation researcher Darren Linvill made a point here that if the Chinese government were serious about covertly shaping the data center debate, they probably wouldn’t run an operation like this using OpenAI’s own product.

Most of the posts received little or no engagement, and OpenAI found “no evidence of meaningful breakout beyond its own activity.” The report therefore shows likely China-origin operators testing and amplifying existing narratives, with no observed meaningful reach.

So twitter bots originating in China shared comic strips about electricity prices based on American reporting that the public was already following. The bots didn’t get meaningful attention. They were designed to amplify a pre-existing panic, and failed. They behaved in ways that don’t seem consistent with a state-led operation, among other things using OpenAI models. That’s what we know from this report.

This report has generated a lot of goofy headlines, like this from The Hill:

Source

Sure, people likely based in China were behind one specific, extremely small anti-data-center campaign in the US. But readers might understand this headline as saying OpenAI found that the CCP itself was behind the campaign, or that the full anti-data center movement was coming from China.

Kevin O’Leary’s claims

After Kevin O’Leary’s proposed 9 GW data center in Utah met with intense opposition, he announced that his team had discovered evidence that China was behind a lot of it. He claimed that his team had traced much of the opposition back to the Alliance for a Better Utah, a progressive advocacy group, and that the group’s IRS Form 990 filings showed ties to Chinese-linked funding channels connected through an organization called Arabella. He said that there was a spike in misinformation about the data center on Instagram and X stemming from IP addresses connected to these organizations. On Fox News he suggested that this is China because there’s only one adversary who would want to stop America from building its grid and its compute capacity. In a later Fox appearance he implied that specific people were working with China, including Alliance for a Better Utah founder Josh Kanter, Gabrielle Finlayson of Elevate Strategies, and Taylor Knuth. He called locals “proxies for the Chinese government,” and said data scientists had established the link.

I can’t find any evidence anywhere that these organizations or people are linked to China. And it turns out O’Leary can’t either. On June 25, O’Leary posted a statement saying he has “no evidence” that Alliance for a Better Utah, Elevate Strategies, Finlayson, Knuth, or Kanter are funded by China or the Chinese Communist Party, and Fox News later issued an on-air correction. Everyone he listed besides Knuth went on to sue him for defamation.

He managed to dump gasoline on the fire of public opposition by telling local activists that they were funded by China, and confuse the debate by creating lots of news stories implying China might be involved without any real evidence.

The Alethea report

This report is the most serious evidence of foreign interference I’ve seen. The New York Times reported on it on July 9th.

The NYT says that between January and June, Alethea found about 700 mentions of data centers in Chinese, Russian, and Iranian state media, a small fraction of each country’s AI coverage. They found Russia-linked Storm-1516 publishing two fake stories about Firebird’s Armenian data centers, and the pro-Kremlin Rybar network separately pushed anti-data center stories. The Chinese and Iranian examples in Alethea’s public report were mostly overt state-media coverage, not covert operations.

The report did not show that any of this activity materially changed American opinion. Much of the Chinese material was triumphalist propaganda apparently written for domestic readers. Alethea also found what looked like engagement farming around anti-data-center slop posted by Facebook pages anonymously run from Bangladesh. Russia appeared to be using data centers more aggressively as a wedge issue, while the evidence involving China was mostly overtly state-media. Again, I’m left without a strong case that China’s having significant influence here.

And that’s basically what we know about Chinese influence on the data center debate!

This thin evidence is being inflated in policy discussions

Trump suggested China’s playing a big role in the data center backlash recently, saying “A big part of it is China” and claiming the communities giving developers a hard time are “China-oriented” and that Beijing is spending heavily to promote not building because the US is winning the AI race.

Secretary of the Interior Doug Burgum has made similar claims:

On Mornings with Maria, he called the buildout an arms race with China and that some opposition is “geo-targeted” propaganda.
At an industry event called “Harnessing American Power,” he told the crowd the data center opposition wasn’t organic and local and that some of it was foreign-sourced dark money.
On Fox Business in late May he said the debate was “bombarded with foreign-directed propaganda”, and called it “another attack on the U.S. and our ability to be competitive.”

Members of Congress have seemed to overreact to the evidence we have. The House Energy and Commerce leaders wrote to FBI Director Kash Patel and PCAST co-chairs David Sacks and Michael Kratsios saying there was “evidence that strongly suggests foreign influence campaigns,” demanding a briefing by June 18, and the evidence was mainly BPI and Power the Future reports (Power the Future’s report is on the European billionaires funding progressive groups, not China). The letter said the possibility that CCP-backed entities are influencing data center decisions shows how serious a fight the US is in. I don’t think these reports demonstrate that level of seriousness. I think investigation into possible influence campaigns makes complete sense, but it doesn’t make sense to imply that we have strong evidence of meaningful campaigns.

Tom Cotton wrote another letter to Acting AG Blanche that alarming reports indicate “a network of foreign actors, led by the Chinese Communist Party” is manipulating US policy and opinion on data centers, and his letter uses the $2 billion number from BPI (which, remember, is a decade of publicly disclosed general climate philanthropy from Europeans, and some of the groups funded signed an anti-data center letter). Here’s the full text of the letter:

Dear Mr. Blanche,
I write requesting the Department of Justice (DOJ) investigate foreign influence efforts targeting the buildout of American AI infrastructure. Alarming reports indicate that a network of foreign actors, led by the Chinese Communist Party (CCP), is attempting to manipulate U.S. policy and public opinion on data centers. Maintaining America’s AI advantage is vital to American economic strength, diplomacy, national security, and military power. Americans certainly have valid concerns about potential rising energy costs and strains on natural resources related to data centers, and we should implement common sense regulations and legislation to protect communities from adverse effects. But we can’t allow any effort by foreign adversaries to extort these fears and undermine our technological development.
A recent report uncovers a multi-year campaign waged across multiple vectors of foreign influence. Among them is a network of U.S. nonprofits funded by Neville Roy Singham, a Shanghai-based American expatriate under U.S.-government scrutiny for his ties to the Chinese Communists. For years, Singham’s network has been producing content opposing the development of American AI infrastructure, and various investigations have found that the Chinese government is the network’s ultimate paymaster. All while the Chinese government promotes its own AI ambitions at home, where sentiments are overwhelmingly pro-technology.
Foreign-funded charitable entities tied to this campaign have reportedly funneled more than $2 billion into U.S. advocacy organizations, with a significant portion going to groups that are driving the campaign against U.S. data centers and influencing policymakers. Just one month ago, Senator Bernie Sanders hosted a 75-minute panel on “the existential threat of AI” where two of the four panelists were Chinese government affiliates. This included a professor from Tsinghua University, a crown jewel of China’s Military-Civil Fusion strategy and a host to multiple defense research laboratories.
Several of these matters are already under congressional review. Yet no entity in the network has been charged under the Foreign Agents Registration Act (FARA). I therefore request that the DOJ launch a full investigation into these matters.
Sincerely,
Tom Cotton
United States Senator

“This campaign” is doing a lot of work here. Cotton appears to mean the broader very general campaign against American data centers that includes all opposition to them, but the wording can easily be read as connecting the $2 billion directly to Singham’s network or China. BPI’s $2 billion number is entirely about European donors sending lots of money to lots of US progressive and climate orgs over a 10 year period, some of whom went on to oppose data centers. The way this is used in the letter strongly implies this money is either coming from China or a significant fraction of it was meant to be for anti-data center stuff, or that it somehow influenced Sanders’ decision to host Chinese panelists. In fact it’s all unrelated giving from European progressives to American progressive orgs.

Also, I think I’ve established above that the evidence doesn’t suggest Sanders’s panel resulted from Chinese influence.

I think it’d be good for the government to look more into foreign influence in the data center debate, so I’m not opposed to Cotton’s overall interest here. But the evidence he’s working from is weak, and the letter (and reporting on it) strongly imply that we have much better evidence than we do that China’s influencing the debate.

One good result of these reports is that they’re prompting more government scrutiny of the Singham network. My worry is just that their actual effect on the debate is being inflated.

Evidence against

People who study foreign influence campaigns professionally haven’t said much about data centers so far. Darren Linvill’s Clemson Media Forensics Hub, which tracks foreign influence campaigns, says “We haven’t found much.” China’s bot armies aren’t talking much about this topic, and Chinese state media appears more intent on promoting China’s own data centers. Graham Webster of Stanford told WIRED the BPI’s examples don’t resemble documented Chinese influence campaigns, and Graphika analyst Dina Sadek said that research shows domestic US actors are leading the online anti-data center reaction.

I have to say, for most talking points people use against data centers, it’s always very easy for me to trace the game of telephone to a confused American source. Take people’s professed biggest issue with data centers: water. Google searches for “AI water” spike for the first time in May 2025.

Early that month, Bloomberg published this:

Followed shortly after by this from the New York Times:

Entirely anecdotal, but in conversations I’ve had about data centers and water, someone has almost always brought up one of these two articles.

We have some recent examples of China trying to influence US politics, and they were mostly duds. For example, in 2022 China ran fake accounts pretending to be Texans to oppose rare earth processing facilities in Texas and Oklahoma, claiming they’d cause radioactive contamination, cancer, and birth defects. An assessment by Mandiant (a cybersecurity firm) found that this wasn’t really effective and got pretty limited engagement. Maybe all the most effective Chinese psyops remain secret to us, but I can’t find any that we know about that had a meaningful impact on American thinking.

I often hear people suggest that TikTok might be boosting anti-data center videos for propaganda reasons. Maybe? I don’t know, anti-data center stuff is ridiculously popular across all social media platforms, and anti-data center creators don’t seem to do any worse on Instagram.

Some people bring up that a lot of the most popular anti-data center posts on websites like Twitter seem to come from foreign bots. Here’s an example:

True, but there’s no pattern in where these are coming from, and it seems more likely that the people managing the bots know that data center stuff is likely to get a lot of attention right now and are doing it for money.

One other interesting pattern here is that while data centers have received a lot of absolute pushback, they might not relatively receive more pushback than other building. About 30% of proposed solar and wind projects are also cancelled, because of a mix of community opposition, zoning, and interconnection problems. I’d like to do a deeper dive on this comparison, but would like it even more if someone who knows more about it beats me to it! I’ve recently been following opposition to solar farms in very liberal parts of Massachusetts near where I grew up. Even people who hate climate change often hate new buildings more.

I’m far from an expert on our understanding of foreign influence campaigns more broadly, but summaries I read of them consistently leave me skeptical that they have as much influence as is often attributed to them. To play a meaningful part in the data center backlash, a Chinese influence campaign would need to be much more successful than the ones I’m aware of.

I’m separately writing a series on the history of the data center panic here. I’m more convinced by the story I describe in Part 1 than the idea that China’s involved. I think it was basically predictable that Americans would feel negatively about big new buildings they don’t understand associated with the tech industry, badly reported on by rushed journalists looking for clicks and being built at a rate and scale most of us have never seen before.

Conclusion

There’s still a lot we don’t know about the data center panic, and maybe some new evidence will emerge that China or other countries have somehow had a surprising amount of influence over American attitudes toward data centers. But so far the evidence that’s been produced hasn’t given us any sign of significant Chinese influence. So far the big upside of all this has been more scrutiny on the Singham network and their buddies, though I suspect the main victims of groups like the PSL are the members of the PSL. I personally just feel like I know a lot of my generation and Gen Z’s attitudes well enough to have predicted that there would be mass opposition to data centers (or any significant amount of new building or energy use on a contentious product), and the opposition seems surprisingly aligned to opposition to other building at the same scale, so my gut says this is overwhelmingly homegrown. I’m open to evidence to the contrary, but I haven’t seen it yet.

Toward liberal environmentalism

Fri, 10 Jul 2026 04:57:46 GMT

The underlying idea of political liberalism is that free, reasonable people will come to drastically different conclusions about ethics and metaphysics: what counts as violence, how scarce resources should be distributed. This gives them lots of reasons to come into conflict, and even to turn violent, since each side sees its own retaliation as justified and the spiral escalates. Either we build complex societal structures that mediate these differences, or people sort into rival states and small bands that share one set of values, enforce it strictly, and keep their members unfree. Liberalism is about maintaining these mediating structures between rival value systems, so that we don’t slip into those situations of unfreedom. I have a longer post on how I think about liberalism here.

In a lot of my writing on the environment, I find myself implicitly standing up for political liberalism over what I see as attempts to use the environment as an illiberal cudgel against the writers’ identified enemies. Reading Vaclav Smil and David MacKay when I was young was incredibly useful for thinking about the current AI data center debate, but I think actually less useful than reading Rawls, and Joseph Heath’s summaries of Rawls. That’s because most of my disagreements with public commentary on AI and the environment are not actually factual disagreements (I often completely agree with the same underlying facts about the numbers involved as everyone else). They’re normative disagreements about what the facts imply we should do. For example, I agree with all the factual claims in this UN University report on AI and the environment, but I think its framing of those facts is completely off the wall. Here are six especially strange framings from the report:

GPT-4’s training carbon footprint of 25,000 tonnes of CO₂e would require the sequestration capacity of 420,000 tree seedlings grown for 10 years, or about equal to the number of trees in 105 Hyde Parks in London.

The land footprints of training GPT-4 and GPT-5 are estimated at roughly 0.9 km² (126 football fields) and 1.5 km² (210 football fields), respectively.

A typical ChatGPT-style text query is about 200 times more energy-intensive than text classification (such as spam filtering).

Video generation represents the most energy-intensive frontier with high-resolution long clips on large models drawing more than 415 Wh per clip, drawing as much electricity as 200,000 spam classifications.

The water footprint associated with training GPT-4 was about 600 million liters, enough to meet the minimum annual domestic water needs of 81,000 people in Sub-Saharan Africa, or to fill 237 Olympic-sized pools.

The land footprint of 2025 data centers’ electricity demand was 6,900 km², nearly 4.5 times the size of Greater London.

My complaint is that each of these leaves the reader less capable of comparing AI to other parts of their everyday life. None of these leaves someone with any way of understanding how AI compares to other industries, how it fits into the environmental picture it actually operates in. Each number is selected to be maximally alarming, and no industry would look acceptable if the authors applied the same comparisons. I don’t know why it would ever be useful to compare a chatbot to a spam filter. If my friend drove a few miles to film a video, I wouldn’t say “You know, for the energy you used to drive over to film that video, you could have filtered 1.5 million spam emails.” They’d give me a funny look. Household water use is a small fraction of anyone’s total water footprint, so any industry that uses meaningful amounts of water will always use as much as tens of thousands of households do, whether those households are in Sub-Saharan Africa or anywhere else. A back-of-the-envelope calculation suggests the water we use irrigating Christmas trees in America matches the household water use of 1.5 million people in Sub-Saharan Africa. Does this tell us anything about Christmas trees? Does it give us any useful normative information at all? The whole intro to the report is full of comparisons like this, I’d recommend reading it yourself.

Underlying wild comparisons like these is a philosophy I think is fundamentally illiberal and increasingly mainstream in environmental thinking. At the extreme end, some environmentalists argue explicitly that political liberalism is itself responsible for the climate crisis, a claim that usually relies on heterodox economic theories. I don’t want to get into that right now. I’ll instead assume that whether you’re a capitalist or a socialist, you’re sympathetic to the broad liberal goal of building societal structures that let people with radically different values and religions live and prosper together. It’s this general liberalism that I worry some environmentalism is being used to attack, and I’ll argue here for the opposite: approaching environmentalism through a broadly liberal lens.

This is the core of illiberal environmentalism as I see it: We the good people broadly know what is good and what is bad, how scarce resources should be spent to maximize people’s wellbeing, and which contentious ethical values are right and wrong. Because we’re in an environmental crisis, our main goal should be to make sure no resources are spent on the bad things, since any environmental cost paid for something bad is by definition pure waste, which we just can’t afford right now. We have no responsibility to mediate the complex, contradictory desires of the billions of people alive right now, or to think about how to trade off their desires for radically different goods and ways of living. Instead, the goal is to attack the bad guys, the people spending scarce resources on things we already consider bad for other reasons. All good people know what’s bad, or will once they’re awakened from the lies of the bad people.

To get across why environmental illiberalism is bad, I like to imagine a group of Catholic environmentalists who get together to figure out the most promising ways to help the environment. After a few days of research, they publish a report claiming to have found a powerful new way to help the climate: shutting down the places of worship of false religions. For the Catholics, every pound of CO2 or drop of water used to operate a Protestant church or a mosque or a synagogue or a Buddhist temple is wasted on something that’s actively sending people to hell and making society worse in the meantime. The value of all these rival places of worship is at best zero, and likely extremely negative. So there’s a huge opportunity to cut enormous amounts of CO2 and water use “without harming anyone,” while in fact making the practitioners of the false religions much better off by guiding them to the light of Catholicism.

If you sit down and try to explain why the Catholic environmentalists are wrong, the rules you come up with will either be some version of liberal environmentalism, or just a claim that your own religious beliefs are the true ones and should run society instead. After 20 years in the internet atheist trenches I don’t have much hope for converting the world to Quinean Naturalism, so my next best move is to participate in the subtle ethical and social dances we do to let each other pursue rival religious beliefs. My answer to the Catholic environmentalists is that their plan breaks a background social contract: we don’t use technocratic questions about society-level collective action problems as cudgels for imposing controversial religious beliefs on each other. If they break the rule, everyone else might defect too, and over time people will become less free to pursue their visions of religious truth, which harms Catholicism in the long run. This doesn’t require some deep shared belief about what’s good in the world. It just requires acknowledging that society rests on a delicate balance of mutual tolerance, and that any ethic we use to change society has to respect that balance, or it falls apart and leaves Catholics (and everyone else) worse off and unfree. Our ability to solve climate change will also collapse, as claims about climate become mere vessels to express unrelated value disagreements, and our ability to act on climate is stalled until everyone comes to a final agreement on all other major ethical disputes.

I always start with religious freedom, because it’s an extreme case most people agree on. Religious freedom is important, and the rights of religious minorities shouldn’t be squashed under the guise of solving climate change. But whether someone uses AI is obviously far less fundamental to their freedom than which religion they’re allowed to practice, so I need to take a few more steps.

I want to expand from religion to say that climate should not be turned into a cudgel to push any controversial ethical beliefs that aren’t about climate change itself. Our rules for solving climate would ideally respect all contentious beliefs about ethical questions in other areas.

For example, I think that industrial animal agriculture is among the very worst things that human society has ever done. Animal lives on factory farms are often extremely bad, there are an unbelievable number of animals in factory farms, and the suffering of those animals I think ethically matters enormously. In my ideal world there would be a global ban on industrial animal agriculture, which obviously under some interpretations is deeply illiberal, though I think this can be justified if we consider animals themselves as subjects of political liberal societies.

Given that I think factory farming is such a catastrophe, how should that affect how I talk about its climate impacts? Any time someone wanted a chicken sandwich, I could point out that it has a climate cost, and that since the chicken suffered so much, all of that cost went to something truly horrible. On that logic, the sandwich’s climate impact is much worse than almost anything else in the person’s life. The enjoyment they get from driving isn’t offset by an animal’s torture, so it’s better to spend the emissions on the car than on the sandwich. Would this be a reasonable way of talking about the climate?

I don’t think it would. To arrive at that claim, I’m measuring how much CO2 is wasted according to the contentious value I assign to different products. It looks something like “Harm = CO2 emitted / (Goodness of thing),” where goodness is my own contentious ethical assessment. But lots of other people don’t agree with my assessment of the goodness or badness of a chicken sandwich. And since my claim about the sandwich’s environmental harm mostly hinges on whether it’s evil for other reasons (producing chicken emits only a moderate amount of CO2e), I’m really using climate to smuggle in a completely separate contentious value. This is a shifty way of breaking the liberal compromise.

I’m choosing meat partly to show that liberals themselves don’t have to be neutral about their own values. I think meat is a moral disaster. But to live among billions of people whose values I wouldn’t want to live under, I have to do the complex social dance and play the game of value neutrality when talking about other problems in society.

A third and final category that I’d like to remain untouched is rival tastes. I find specific things a lot of people like boring or dumb, they feel the same about things I like. I shouldn’t be able to use climate as a cudgel for my taste. While I didn’t like the recent Wicked movies, I don’t think the emissions they caused were “wasted” and wouldn’t argue to stop going to see Wicked for climate reasons.

How then should we make moral claims about the climate? Because climate change is a massive problem, and we can’t just live and let live. The answer is that our climate rules need to be compatible with different value systems. We can make general claims about how people should sacrifice some of what they want to reduce their emissions, without making contentious claims about the value of the specific things they want, or about which of the things they cut are good or bad. If I’m talking to a meat eater, it’s fair game to say “Hey, it has a moderately positive climate impact to swap chicken for tofu,” because that’s just a correct, neutral statement about which emits more. It’s illegitimate to say animal agriculture is a unique problem for the climate because the animals are suffering. Similarly, Catholics have every right to ask Protestants to put solar panels on their church property, but they don’t have a right to tell them to cut services to keep people from driving there in the first place, on the grounds that the services provide no value anyway. For an environmental liberal, the equation for the social badness of emissions looks more like “Harm = CO2 emitted / (how much the thing lets people pursue their own values, or how much someone personally values it).” A little clunky, but I think it’s a better way of thinking about climate change in a world with wild value differences. This is also why I’m such a fan of charts that break down where the emissions in people’s lives show up. To let different people pursue different things, instead of saying “You should not do x evil thing,” we should say “You and everyone else are obligated to cut your personal emissions down to this amount. How you do that and what you cut is up to you, but this is what we need to do to hit our climate goals.” The second statement lets people with radically different values coordinate on solving climate change. The first one doesn’t.

This brings me back to the UNU report, and why I think its way of talking about the environment is illiberal. The report’s move is to choose confusing, disorienting comparisons that vilify a new industry without giving readers any way to weigh it against the other things in their own lives. You’re left not knowing, for example, that the water cost of the non-AI American internet equals the household water use of about 900 million people in Sub-Saharan Africa, and that this pales next to things like American beef, which withdraws as much water as 2.2 billion hypothetical people in Sub-Saharan Africa. Basically all American industries look bad under this metric, and this gives you no reasonable way of choosing between them. What the report is doing is identifying AI as a bad guy for unrelated reasons, then piling on alarming statistics to reinforce its badness, without the full environmental context that would let you say “Oh, now I know how AI fits into my day. I can structure my choices around what’s valuable to me and what emits the most.” They’re making the value judgment for you, and leaving you without a way of choosing for yourself how to make the sacrifices necessary to reduce your emissions. It’s incapacitating the reader, leaving them with less ability to make informed decisions about what activities they’d like to keep while reducing their emissions.

This may sound uncharitable, or even a little conspiratorial, so I want to step back and clarify that I don’t think the authors consciously decided to be illiberal or to impose their values on their readers. What I think is happening is that a lot of people haven’t internalized the fact of reasonable pluralism: people have wild disagreements about what’s good and bad, and there is no secret Manichean split between good and bad views that all rational people would sort into if left free. In environmentalism, people are often taught, more via background social norms than anything explicit, that the world contains good things all decent people agree on, and bad things only power-seeking evil people enjoy. People rarely even discuss the idea that environmentalism should leave value questions untouched and instead pull levers that cut everyone’s environmental harm while maximizing their ability to pursue their own goods. Instead they learn that there are bad things in society, like AI, and that because they’re bad they’re inherently wasteful. The specific amounts of emissions and water become decorative statistics, a way of articulating badness. They don’t give people who like these things the context to see how much they actually contribute to harm, and therefore to decide whether to keep using them and trade that off against cutting other things. Thus environmental language that uses large, contextless numbers to confuse readers seems subconsciously designed to control the behavior of other people based on unrelated value judgments.

Further, I think that a lot of people enter into social games with each other, where they’re mutually rewarded for attacking bad guys and for supporting good guys. This is deeply natural and human. Liberalism is an unnatural social practice that needs to be relearned and reinforced in comparison. But it leaves people with a strong reason to sneer at demands for putting very specific numbers on things, on comparing problems concretely to each other, and for giving listeners the information required to decide for themselves what values they want to pursue. For a lot of people, this sounds a lot like a naive blind trust that people will all come to the good values, or that we don’t need to attack our mutual bad guy and instead need to let people decide for themselves whether the obvious bad guys are in fact bad. This closes people off to making specific comparisons between industries and environmental impacts, and leaves them suspicious of people like me trying to actually make those full comparisons available. Thus, my own writing is often met less with “You’re getting the numbers themselves wrong” and more “Oh, you didn’t get the memo, these comparisons don’t matter because AI is a bad guy, why are you trying to defang our criticism anyway?” A few months ago I was giving a talk and got a question that basically amounted to “What nefarious effective altruist reasons do you have for making us okay with AI?” The person asking didn’t seem to have any disagreement with my specific numbers themselves, instead the question was more “Why are you disempowering us from attacking a bad guy?” My counter is that I’m empowering everyday people to just see where their environmental impacts actually are and how they can cut them the most. If AI’s a big part of them, or if they don’t like AI for other reasons, it becomes a more promising thing to cut. But I think the person asking the question hadn’t fully considered this idea that I might just not be an agent of the evil team and instead just get frustrated when people are incapacitated in their decision making about the environment by strange presentations of very simple numbers. I don’t think they or the authors of the UNU report received some top-down directive to promote illiberalism, instead they’re just succumbing to the natural human tendency toward Manichaeism over pluralism.

Liberal environmentalism may sound wishy-washy, or too lenient on bad guys, but it’s actually much more useful for solving the climate crisis than illiberalism, because a core problem with the crisis is that most of the things that cause emissions are incredibly useful to us. They’re not clear villains all good people want to destroy. If you look at a breakdown of exactly where global emissions come from, the most striking thing is how obviously useful almost all of it is.

Liberal environmentalism also has more respect for the problem of climate change itself, because it’s more explicitly opposed to using climate as just a tool to win unrelated fights. How many times have you heard someone use the language of a climate emergency, and then instead of talking about the most promising ways of cutting CO2, pivot to their personal political enemies or unrelated problems in the world that happen to have some climate cost?

This also explains why liberals are often more excited about carbon taxes than illiberals. Carbon taxes neutrally price carbon regardless of what’s emitting it. This is in many ways a much more powerful method for changing people’s behavior around climate than targeting specific industries. It reaches into literally everything we do and modifies our behavior as much as prices do normally (a lot), while conveniently leaving the social value of what those emissions were spent on open to interpretation. If gas becomes a dollar more expensive, it’s equally more expensive to drive to either a church or a mosque or a UU service.

My simple application of liberal environmentalism to AI has been: “A lot of people want to use AI, and a lot of other people hate it. The people who hate it shouldn’t use it, and the people who love it should be allowed to make it part of their emissions budget, as long as they still make their overall cuts and AI doesn’t push them over the per-person threshold.” For chatbots specifically, pushing people over that threshold is nearly impossible, since their emissions are so low.

There are some criticisms of AI that I think completely respect the rules of liberal environmentalism, such as “AI uses more energy than the alternative to get the same quality stuff. Since you can get exactly what you want using fewer resources, you’re obligated not to use AI.” This is a legitimate form of criticism: it asks people to pursue the same values with fewer resources. The problem is that I think the background factual premise is just false. For almost any AI tool, running my laptop for as long as it would take me to produce the same thing by hand uses way more energy than just having the AI make it. The average AI image, for example, uses roughly 0.3-1 Wh, about 20 to 70 seconds of a laptop drawing 50 W. If you want a royalty-free image of exactly the thing you’d otherwise generate, you have roughly a minute to find or make one before the search itself uses more energy than the AI would have. If, like me, you want anime images of the Montague Bookmill, this might be hard.

People in the AI debate will often say we don’t “need” AI. Requiring people to use emissions on only what we “need” and not what we “want” pushes the conversation into a place where solving climate requires that we commit to contentious ethical claims about what’s necessary in life, again what looks like an illiberal move. In wealthy countries like America, most of what we spend our time pursuing are really “wants” rather than needs. Anything above rice and beans, a roof over your head, water to drink, and basic healthcare, is a contentious “want” for living your life in a specific way. But the people who say we don’t “need” AI don’t usually mean we need to reduce our lives to this bare bones minimum. Any move that says that climate requires us to cut our lives down to only what we “need” seems more like a way of reifying the author’s personal wants as needs. Thus again using climate more as a cudgel to smuggle contentious values. Make a list of all the things you spent your time doing today. How many of them are “needs”? I don’t think people “need” to see Wicked, but that doesn’t mean I get to use the climate to say the movie shouldn’t be released.

Illiberalism shows up in plenty of other areas of life. It’s the human default. I worry that when people lurch away from a full numerate picture of the world, it’s less that they don’t understand the numbers and more that their minds are already made up: there are specific bad guys to destroy, and statistics are only useful as weapons against them. Anyone giving other people ways of comparing and contrasting the full situation is suspicious, they’re providing avenues to be okay with the bad thing. On the environment at least, our ethics should let people with radically different beliefs and tastes live together. Enough people get real value from AI products that the decorative statistics critics hurl at them look to me like unjustified attempts to override a difference in taste among reasonable people. We owe each other the full numerate picture of what’s actually happening in the world, mainly because we shouldn’t deceive people into adopting our own contentious value systems. Going forward, I’d like environmental spaces to refocus on the project of building a pluralist world without carbon emissions, and, along the way, to respect the subtle societal and ethical structures that maintain that pluralism.

AI art as curation

Fri, 03 Jul 2026 05:10:47 GMT

When I make an AI image or song, I don’t feel the kind of satisfaction I do when I draw, or play something on the guitar. Angry memes about how “no one making AI art is an artist” just bounce off me, I never felt that way in the first place.

But AI images and music have turned out to be pretty emotionally resonant with me at a level I didn’t expect, for another reason. They let me curate very specific vibes that matter to me and would be hard to assemble anywhere else. Curation isn’t creation, but it’s a separate and (I think) equally valuable outlet. A way to get at intuitions that are hard to put into words but feel important to communicate.

My basic experience of creating an AI image is like finding a neat rock. I’m not responsible for the shape of the rock at all, but I might be interested in putting it into an arrangement with other rocks and showing people my specific taste in neat rocks. Maybe I get some points for knowing where to look for the rock, for knowing broadly what types of rocks appear where. Playing with an AI model is like searching its high dimensional vector space for neat rocks, with a taste for specific rocks I can only find in specific places.

It’s also like making a music playlist. When I put a playlist together, I obviously know I didn’t create the songs themselves, but I’m hoping that by associating with them, and combining and contrasting them in specific ways, I can get across some general sense about the world I think is important. Typing a very specific style prompt into Suno and then playing around with the result feels pretty similar to creating a playlist. I’m venturing out into the vector space of the model to try to find extremely specific sounds I value a lot to add to it. I’ve had way more success here than I expected. The first 2 minutes of this collection of shoegaze songs Suno made for me is a very specific sound I value a lot and want to associate with, especially the big explosion at 50 seconds. It gets across a very specific inarticulable sense of openness to the world I had when I was a teenager. I feel like if I could just glue this sense together with specific images and smells and memories and vibes, people would really “get” me.

No AI music has reached the same level of emotional resonance for me as my favorite bands. I like the sound of the shoegaze songs I made a lot, but the best human-made albums in the genre are in another stratosphere, and I don’t expect AI to approach this quality soon. Suno’s already showing too many signs of being too optimized for more general mid taste. Still, comparative advantage exists. The best human artists remain in a completely separate league from basically all AI music for me, but I don’t always want to listen to the best stuff. Sometimes I want a very specific sound I haven’t heard before in a genre I like, and the best shoegaze album was so good it kind of singlehandedly destroyed the genre, so there’s less human made shoegaze to pick from than I’d like. AI gives me a way of reaching out into the vector space of all possible mid shoegaze to say “This… this is what I’m into, this ‘gets’ me” and the experience of compiling said mid shoegaze in one place is rewarding in the same way making a playlist is rewarding. For even less explored genres I love like dungeon synth, AI can fill the gaps I feel as I explore the very limited but great set of what humans have actually produced in the genre. None of this feels like I’m creating the music, but it does feel satisfying to put these things I’ve found together in specific ways, to know where to look to find things I like, and to use my past great experiences with music to try to express my taste with the new sounds I find.

Last year I posted a big collection of AI images I’d made that I liked. Again, I didn’t feel artistic responsibility for these images. I didn’t feel like my creativity had gone into them. And yet I did feel like I’d been creative in how I used them to express my taste, in the same way making a playlist can involve creativity, and the post resonated, where multiple readers reached out to talk about how our tastes were way more similar than they had expected. This was a fun experience of feeling a bit like discovering far off people secretly in the same tribe, who were moved in the same subtle ways by similar experiences. It was fun to find this together, and the way I found it was purely via what I consider curation, not creation, and that was really valuable on its own!

I really love finding ways other people have curated what they like and exploring them. The sense that other far away people have resonated with specific art or vibes or other media makes the world feel like it has incredible depth. The world can often feel lonely and flat. Maybe the most flat-feeling image I’ve seen is the famous Breezewood PA picture:

The polar opposite of this image for me is a good curated list of things specific people like, either on their personal sites or Goodreads or Letterboxd or music app. These always feel like the world has so much depth, so many points of surprising contact between what we experience alone. I’d made a long list on my personal site of media I like mainly to just imitate other bloggers who made dense explorable curated lists of media they love. I could spend hours poking through these.

The greatest curator on the internet is Piero Scaruffi, who over 30 years has reviewed basically all music on his website that looks basically identical to when he started it in the 90s. It’s my favorite example of curation elevated to a monumental complex achievement:

Scaruffi’s a cognitive scientist who’s focused on AI a lot and among other things also has what I think is the funniest subtitle of any book on AI:

I mutter “Life in the coming age of incredibly stupid machines” once a week, though more and more as cope.

Scaruffi seems to have reviewed most other culture as well, and also publishes long dense essays on things like the history of knowledge, and curates other lists like his favorite waterfalls.

I’d recommend poking around his website for a while, maybe starting with his reviews of his favorite albums.

Scaruffi has a short (and self serving) post on the “Meaning of Art”:

This flips the status of creation and curation: most creation is really just imitation, raw material for curators to build statements about the world.

This seems a little extreme, but consider how many albums you listen to at least in part to be the type of person who listens to them. How often when you listen to music do you feel somewhat ascended from your everyday life because you’re part of the secret community of people who listen to it, the people who know the canon of truly great music? How often do you feel like listening to specific new music will ensure you’re one of the elect?

The curation of rival canons of great music really exploded as the internet took off, and canons reacting to each other to distinguish themselves, and to rise above all others, got faster and faster. For example, if you were on the 4chan music board /mu/ in the early 2010s, you probably liked a lot of these albums:

I know I did! I worry that when I talked about shoegaze earlier, I added a link to Loveless partly to say “Of course I’m allowed to listen to AI shoegaze, because I know about and am deferential to the truly great shoegaze canon, I’m not some pleb.”

But the existence of these canons encouraged rival canons to pop up, where you could not only feel more “in the know” and ascended compared to most people, but even more in the know by people who thought they were in the know. Take this other list someone on 4chan made, and especially the note at the bottom.

Yerself is Steam is a great album, but in my opinion it’s clearly not nearly as good as Loveless, which the author probably considers /mu/core and “radio music.” This is basically a hostile act of curation, using the art in the way Scaruffi describes to say something about the values and ideology of the critic, separating the critic from rival canons. This becomes more and more far removed from the art itself and is instead part of a drawn out contest of curation.

Once you start paying attention to what curators are trying to do, and how often any specific art is more of a stepping stone to say “I am with the cool good authentic community of people, by listening to my curated canon you will ascend to them too” it’s hard to look away. So many Pitchfork reviews were much more about the type of people listening than the music itself. Look at their 10/10 review of Kid A, how much of it is referencing other key parts of the canon, or the interesting location of the reviewer. Or their 0/10 review of Bachman Turner Overdrive’s greatest hits, which very specifically says “If you like this music, you are not one of the elect”:

Those who still cling to BTO-- and you know who you are-- are usually dorky minivan types who think rock and roll is about having your friends over for beer and barbeque. Let me say on behalf of the world’s young people that all you people truly suck. Even in your retro phase there’s nothing remotely charming or rocking about you. Here’s hoping that next T-Bone you eat leaves you choking on your own excess. Die! Die! Die!

Seems a little much…

This is all to say that curation on its own can be a monumental and impressive task, that it’s incredibly fraught and bound up with our self conception and rival systems of status, but also at its best can be a unique satisfying bridge between people, all separate from any act of creation of the art itself. The idea that AI radically expands the space of curation and opens up ways for individual people to be much more specific with the images and sounds they collect and display to the world as “what they’re about” is under discussed in the conversation about AI and art. Most AI art conversations I see online are still reduced to whether AI art “involves creativity.” For me it involves as much creativity as assembling a playlist. Whether that’s like artistic creativity seems less interesting to me than what it means about the future of how people share their taste with each other, and use it to either find or compete with each other. How much of the good feelings we get when we find others who like the same art is from authentic shared experience vs just the camaraderie of identifying other members of our ascended rival tribes is a pretty foundational question about human life I don’t have a clear answer to. I worry that a lot of people in the AI and art debate fail to appreciate how interesting curation is as a separate concept, and the heights of achievement and depths of emotional turmoil it can reach.

I’ll close with a few AI images I made recently:

Some Massachusetts triple decker art:

Nostalgic photographs:

Geodesic domes:

What does it mean for AI to be democratic?

Sun, 21 Jun 2026 01:00:29 GMT

I love democracy. I have what’s basically religious zealotry for the form of liberal democracy most of the wealthy world lives under. Citizens having control and oversight of their leaders is a rare treasure in human history, and one that AI threatens to erode. I have never read a popular modern critic of democracy in the broad sense who I didn’t see as, at best, a complete joke.

I’ve noticed that in lots of conversations about current AI models and the general AI buildout, it’s becoming more and more popular to say that AI isn’t happening "in a democratic way. There are times when I strongly agree. I’m very worried about the ways AI could enable the concentration of power, or impose huge (including existential) risks that citizens never signed up for and would hit the brakes on if they could.

But there’s a specific way of arguing that AI isn’t “democratic” that’s gaining traction, and that I find ominous and antithetical to what makes a good democratic society. It’s usually dressed in the language of progressive politics, but underneath, I think it carries authoritarian, homogenizing and anti-democratic implications.

Pluralist vs homogenizing democracy

There are two visions of democracy hiding behind our talk about it, one I think is good and the other I think is bad, and you can probably tell which is which by the labels I give them.

The goal of pluralist democracy is to give people with wildly different beliefs about the good life and what’s really valuable control and veto points over how they’re governed, without letting them steamroll each other into all living under one contentious ethical or religious vision of the world.

Most liberal democracies like the US manage to balance pluralism with majority rule. Despite being a majority Christian country, and that majority having voting power over who represents them, the US government is not able to impose Christianity on everyone else. People have a lot of democratic control over contentious issues of government, but they often can’t use this to steamroll other people into very specific visions of the good life. Christians are restrained by governing norms from forcing non-Christians from attending church.

The goal of homogenizing democracy is to discover the true, good ethical values and beliefs about the world, with the faith that the majority either publicly or secretly believes them, and then to structure all society around those true, good ethical beliefs, and push ideological minorities into going along.

Here’s a more ominous way to frame it: “There is an authentic community of rooted, powerless, good people, who know what’s good and right in life and agree on it. They’re up against unrooted powerful people who don’t know what’s good in life and want to override it for their own gain. The good people are in the majority. Democracy means discovering these good values all good people hold, and then representing and acting on it, steamrolling the bad powerful people into going along with these good values.”

I read this as ominous because it’s an especially useful cudgel for forcing people into your specific vision of ethics and the good life. If you invalidate another value system by saying it’s not what all the good, powerless people believe, this gives you more permission to steamroll the people who hold that value system in the name of resisting power. Many Christian nationalists will frame themselves as representing the good, simple, rooted majority beset by powerful cabals who don’t understand their values, and use this to argue that underneath these differences is only a raw struggle for power that the good people need to win. Thus to them, seizing the government to impose Christianity on religious minorities is actually a form of democracy. It’s simply letting the values of the good majority be represented in how the country is governed. Stuff like this is why I worry about accepting a narrative that lets homogenizing democracy sneak in unexamined. Democracy at its best weighs the pluralistic desires of radically different people. At its worst it excludes those desires from the debate entirely, on the grounds that anyone who disagrees with the majority is illegitimate and not worth making concessions for.

And it can creep in under the guise of noble-sounding rules, like “a town should have control over how its resources are used.” For example, consider the claim that a small town should have complete democratic control over what gets built there. This sounds nice in the abstract, but is actually a way for homogenizing democracy to slip in. Imagine what happens if 80% of the town is Catholic, and 10% is Muslim. The Muslim population gets together and says they would like to buy some of the town’s land to build a mosque. Will this mosque get built if it’s voted on by the town? Well, 80% of the town believes that the mosque is part of a false religion that sends people to hell. It seems unlikely that the mosque will get built. By giving citizens democratic control over everything that gets built in their town, we’ve allowed them to steamroll pluralist values and homogenize the area into the contentious beliefs of the majority of people who happen to live there. This is bad!

Some people imply that in a “truly” democratic society, all value disagreement would ultimately be dissolved through deliberation. Communities would come together and deliberate until they discovered the most virtuous or liberatory way to live. I think deliberation like this is a great model for specific decisions the government makes, but it shouldn’t be allowed to seep as far into everyday life, because taken to the extreme it implies everyone must eventually be made to agree on every contentious value.

So while I think that at its best, liberal democracy is the transcendent crowning achievement of human history, talk about democracy can also be used to justify authoritarian homogenizing impulses and needs to be carefully examined. When people invoke democracy, they’re usually smuggling in general rules about how society should be governed, and those rules’ political implications need to be drawn out. The specific model of democracy where the goal is always to discover the good values that need to be imposed on everyone, that all good people either publicly or secretly believe (or would believe if they could be led to the light), and that anyone who disagrees with is not worth considering in deliberation, is a recipe for destroying the delicate balance that allows people with wildly different beliefs to live together and change their values freely.

People in democratic societies have strong reasons to try to frame their opponents’ opinions as outside the sphere of legitimate democratic consideration. An effective way to do this is to frame their opponents as representing “the opinions of the powerful”meaning illegitimate, out-of-touch views trying to undermine the good people. This is often a way of trying to banish different views from the process of democratic deliberation. I’m worried that this framing comes up a lot in the AI debate, used by people who think they’re behaving as the defenders of democracy, but are often actually stifling authentic difficult democratic deliberation between radically different values.

Which visions of democratic AI are pluralist vs homogenizing?

I think AI poses a unique technological threat to pluralist democracy. Society currently exists in a delicate, kind of miraculous balance where most people are able to add a lot of economic value, and have lots of ways to defect from important systems if their political power is threatened. We are deeply interdependent. AI could, under some models, erode our economic interdependence and leave powerful people with less incentive to respect the will of everyday voters. It’s very possible that democracy is a lucky passing phase in human history, built around a specific economic regime that’s existed since the Industrial Revolution that might go away if we enter a new and different mode of economic production. See here for more.

AI also poses lots of other risks, either indirect risks that people will use it to create dangerous powerful new weapons, or more speculative direct risks of AI itself overriding what we want. Just like I think everyday people should have democratic control over whether their neighbors have access to nuclear weapons, I think people have a democratic right to expect the general flow of technological progress to not descend into a specific basin where society enters an incredibly unstable equilibrium of extremely powerful easily accessible weapons.

The dynamics of automation with AI also threaten to concentrate wealth and power in the labs themselves. OpenAI and Anthropic are now some of the fastest growing companies in history. Many of their members have become billionaires, and some local political fights are becoming proxy battles between them over rival visions of AI safety and governance policy. If you expect AI capabilities to continue to progress, this poses a danger of a unique form of corporate power consolidation we haven’t seen in America in a long time, if ever. This also threatens political pluralism, and people have a right to expect that their representatives respond to this new threat. How? I don’t really know, but I’m willing to entertain lots of different answers from libertarians or socialists or anyone in between. I worry our learned political impulses might be as useful in the new era of AI as Aristotle would have been in the Industrial Revolution.

Lots of concerns like this I think are legitimate threats to pluralist democracy, and people have the right to democratically demand forms of intervention that I wouldn’t normally consider for more normal technology. More broadly, a new era of intense automation will demand democratic deliberation between people with radically different values on the shape of the new world that they want to live in together.

I think AI will over the next ten years demand an “all hands on deck” response from political philosophers, economists, and everyday people, and authentic pluralist democratic deliberation needs to happen.

While I’ve been happy with some aspects of how the left’s thinking has evolved on this, there are specific strands of intensely anti-democratic thinking that seem to be attempting to take over the debate and define what democratic AI even means. Here I worry a lot that people are using the language of democracy to push for homogenization and authoritarianism.

A clear example comes up a lot in the data center buildout. A very common line among people claiming to represent democracy in the debate is to say “No one is asking for these! They only benefit billionaires!” I think if I were able to separate these people from the debate, take them out for some nice tea somewhere, have a long conversation with them about something else, and then politely ask “Just being real, do you think there are a decent number of everyday people who think about AI in a different way than you whose demand for more is incentivizing the data center buildout?” I think they would say yes, but that these people are mistaken. We could go on and have a nice conversation from there. I worry that the initial claim that “no one is asking for this!” is basically a way of pushing the vast numbers of people who are in fact asking for this out of the difficult process of deliberation that should happen with any new technology.

There are a lot of people who want to use AI. Chatbots are consistently the most downloaded apps. Half of all web developers polled say they use AI daily. About a quarter of Americans say they use chatbots daily.

Source

More people say AI helps than hurts most things they use it for.

Source

However, Americans also believe AI is more likely to make society worse.

Source

So what do we do with this? How should we think about this from a democratic perspective?

If a quarter of people like a product, two quarters hate it, and one quarter is indifferent, should that product be allowed to exist?

Homogenizing democracy would say no. The plurality has spoken. People with different opinions from the plurality should be steamrolled.

This is obviously wrong, yet I see people imply basically this in lots of debates about data centers. People will often say that people using AI who like it are both a small minority, and so out of touch with what’s good and right that their AI use can’t be allowed to use any resources at all. It can’t be allowed a drop of water or an acre of land or to emit any CO2. When I argue that people who like chatbots need to have their own demands represented in the market and in democratic deliberation, I’m often met with a knowing sneer, like “Oh, this guy hasn’t gotten the memo. These people using AI aren’t really ‘the people.’ They’re alien outsiders and don’t get access to the sphere of deliberation. Real democracy means making sure these people are completely disempowered and are never able to use any community’s resources.” To anyone who actually thinks about political philosophy at all, this is a bizarre and sad mangling of the democratic impulse. It’s an intense assertion of anti-democratic power for one specific faction in the debate. For any other unpopular product, we’d recognize how ridiculous this is.

One way to steamroll behavior you don’t like is to argue that it imposes lots of externalities on other people, so it’s actually an attack on their own freedom. Despite data centers not really imposing more externalities on locals than any other normal industry (from what I can tell), a huge media bubble has developed where wild stories are told about data centers forcing whole towns to evacuate, of infrasound ruining people’s lives, of water polluted and taps running dry, most of which evaporate the moment you poke at them. People seem to have a strong desire for these stories to be true. There was a recent New York Times opinion column on how the data center backlash may be a “winning issue for Democrats,” framed almost entirely around the sense of community people feel in anti-data center protests rather than any solid issues with them. I worry a lot of this is a sublimated authoritarian lurch to control the behavior of people doing something you don’t like. Stories about data centers being bad feels good because it hands you a cudgel you can use on people you think are out-of-touch losers. There’s a lot happening here, but I don’t see this as a good sign for authentic democratic deliberation about the future of AI. It seems much more like an impulse to homogenize everyone into a specific way of thinking about AI that they wouldn’t assent to on their own.

This also shows up in very confident claims that AI cannot, by definition, ever produce anything of value at all. At some point this looks a lot more like ignoring the vast numbers of intelligent people saying they’re getting something of value by using it. Critics don’t have to agree with them, but the confident metaphysical claims about the inherent value of AI output start to look more like a lurch to stifle debate by casting people who disagree out beyond the realm of legitimate conversation.

Separate from this debate, another instance of the homogenizing impulse is the idea that individual models themselves should be made to more democratically represent the opinions of the majority. There’s a lot of talk about “for whom” chatbots are designed, implying that they have lurking biases that would be fixed by making them more democratic. I don’t doubt that chatbots (like anything) carry lots of blindspots and misunderstandings of the experience of people very different from the people who designed them and who wrote the data they trained on, but I don’t at all think democracy specifically will make this problem better, and this implication that private products should all represent the democratic will of the good people I think is another way of smuggling in the homogenizing impulse.

Current AI models would become way more conservative if their values were voted on

In my 20’s I went through a huge Civil War phase. Anyone who does the deep dive comes away kind of shocked at the state of public discourse about the Confederacy. While public understanding has improved, I think a lot of people still really don’t know just how bad slavery was or how central of a motivation it was for the South in the war. This is reflected in public polling. More Americans have a positive than negative opinion of Jefferson Davis, for example:

Maybe this is an artifact of most people just not knowing who he was, or confusing him with Thomas Jefferson. Regardless, this is a pretty clear case where I’m very happy that the main AI models deviate from the winning view of Jefferson Davis among the public.

Claude:

ChatGPT:

Gemini:

Maybe someone could program an AI model that more accurately represents the views of the median American. This AI would be unsure about evolution, and in general much more conservative across the board than the average person demanding that AI’s democratically represent the beliefs everyday people hold.

Someone who holds a simplistic, homogenizing view of democracy often needs to invent a story where everyday people all secretly agree with them. If you imagine that the good rooted majority of people all know your values are correct, this gives you grounds to steamroll the few out of touch people who disagree with you. Because they’re out of touch, their divergent views have no claim to political respect or protection.

But the views of everyday people are complicated and often way out of line with progressive visions of the world. There are many values I share with my fellow coastal elite city dwellers that I’m happy to see reflected in AI models. These values show up less outside of cities, and even less in other countries. They are often not the views of either the American or global majority.

I think a lot about this exchange from this 80,000 Hours interview with Mustafa Suleyman, the co-founder of DeepMind:

Rob Wiblin: Yeah. While you were involved with DeepMind and Google, you tried to get a broader range of people involved in decision making on AI, at least inasmuch as it affected broader society. But in the book you describe how those efforts more or less came to naught. How high a priority is solving that problem relative to the other challenges that you talk about in the book?
Mustafa Suleyman: It’s a good question. I honestly spent a huge amount of my time over the 10 years that I was at DeepMind trying to put more external oversight as a core function of governance in the way that we build these technologies. And it was a pretty painful exercise. Naturally, power doesn’t want that. And although I think Google is sort of well-intentioned, it still functions as a kind of traditional bureaucracy.
Unfortunately, when we set up the Google ethics board, it was really in a climate when cancel culture was at its absolute peak. And our view was that we would basically have these nine independent members that, although they didn’t have legal powers to block a technology or to investigate beyond their scope, and they were dependent on what we, as Google DeepMind, showed them, it still was a significant step to providing external oversight on sensitive technologies that we were developing.
But I think some people on Twitter and elsewhere felt that because we had appointed a conservative, the president of the Heritage Foundation, and she had made some transphobic and homophobic remarks in the past, quite serious ones, that meant that she should be cancelled, and she should be withdrawn from the board. And so within a few days of announcing it, people started campaigning on university campuses to force other people to step down from the board, because their presence on the board was complicit and implied that they condoned her views and stuff like this.
And I just think that was a complete travesty, and really upsetting because we’d spent two years trying to get this board going, and it was a first step towards real outside scrutiny over very sensitive technologies that were being developed. And unfortunately, it all ended within a week, as three members of the nine stood down, and then eventually she stood down, and then we lost half the board in a week and it was just completely untenable. And then the company turned around and were like, “Why are we messing around with this? This is a waste of time.”
Rob Wiblin: “What a pain in the butt.”
Mustafa Suleyman: “Why would we bother? What a pain in the ass.”
Rob Wiblin: It was a very striking story to me, reading that in the book. People complain, I think correctly, that decisions of enormous global importance, historical importance, are potentially going to be made inside these AI labs — and the kinds of people who work at these labs are a very small fraction of the people in the world in terms of their political views, the values that they have, the things that they’ve studied, the kind of information that they happen to know. So it would be good if we could get a wider cross-section of the human population involved in scrutinising or having some input on these.
But to share power with the general public in the Global South, or even just outside big cities in the US or UK, will inevitably involve giving influence to people with views that I imagine are very offensive to Google staff — probably more offensive than Kay Coles James, who had to resign from the board for having more conservative, traditional views on gender. So based on that experience, it just seems like it’s very unlikely to happen, when we might just be flat out trying to get acceptance for having more input from a broader cross-section of educated people in the UK into Google. Like, that’s going to be the most that people will tolerate.
Mustafa Suleyman: Yeah, totally. This is part of the problem, right? I mean, 40% of people in the US believe that trans rights are moving too quickly; 30% believe that abortion should be made illegal; 30-odd% are against gay marriage.
Rob Wiblin: And then think globally, right?
Mustafa Suleyman: Right, exactly, then think globally. So I think we have to just learn to sit down with people who we fundamentally disagree with. That goes for China and the Taliban and all these people who hold these views — because if we can’t do that, then we’ve really got no chance of actually hearing one another out and changing one another’s views and making progress. I think in the last two or three years, I feel like we’ve really taken a few backward steps in that direction, and it’s super problematic because it just demonizes the other and then we just end up hating on one another.
And it’s very frustrating for us, because we put in a huge amount of effort to make that happen. Before that, when we were acquired, we made it a condition of the acquisition that we have an ethics and safety board. That in itself was a first step towards this kind of broader public effort. Then after the ethics and safety board, we actually tried to spin DeepMind out as a global interest company: one that was legally governed by the requirement to consider all of the stakeholders when making decisions. So it was a company limited by guarantee. And then the charter definition had an ethics and safety mission for AGI development; we actually had the ability to spend vast amounts of our income on scientific and social mission.
So it was a really creative and experimental structure. But when Alphabet saw what happened with that board, they basically just got cold feet. That was the bottom line. They saw what happened there and they were just like, “This is totally nuts. The same thing’s going to happen for your global interest company. Why do that?” And then eventually we pulled DeepMind into Google, and in a way DeepMind was never independent — and isn’t independent now; obviously now it’s completely part of Google.
Rob Wiblin: Yeah. The core of trying to address representativeness is that you will be ceding power to people who don’t share your values, and if people are not willing to make that compromise, then it’s not going to happen.

I’ll just flat out say that I have zero interest in sitting with what the Taliban has to say about what values current AI models should reflect. The Taliban can just not use the big chatbots, or build their own more in line with their values. I don’t feel any sense of democratic injustice at all that their beliefs are not influencing the behavior of a private product. The only worlds where the opinions of the Taliban become relevant at all for AI are the really wild speculative situations where AI comes to determine basically everything that happens on Earth. I do see some risk of that happening, but few of the people worried that AI doesn’t represent everyone on Earth equally seem to share this particular worry. If AI rises to run all society, I will circle back with the Taliban.

I’m sure the AI models get plenty of important facts wrong about the lives and experiences of people in very different situations from their designers. But the solution here doesn’t at all look like making AI more representative of the average person in the world, because the average person also carries horrible biases against lots of other people. One way to fix this could maybe be selecting specific expert representatives of different under-represented communities or ways of living and having those experts impose specific ways of responding on the AI. Maybe different worldviews could make their own LLMs, each representing those specific views. But I worry that making these private products “more democratic and representative of everyday people” when they already hold more liberal views than most people do would mostly just lead to more wrong or hateful models.

Lurking under this idea that AI models themselves should be “more democratic” is sometimes the idea that all products in society should reflect the correct opinions of the good majority, and never reflect the opinions of the bad minority. It’s again a form of homogenization. I think a better solution here would be for radically different ideological communities to each have powerful AI models of their own, none claiming to represent the democratic will of all good people, something impossible in a complex pluralist world. But I also am just not especially bothered that AI models don’t reflect a lot of the very conservative values of everyday people that I think are just incorrect. I’m not eager for CreationistGPT.

Conclusion

I think authentic democratic deliberation about AI is going to be deathly important over the next ten years, as AI threatens to concentrate economic power and unleash new risks on the world that we’re not ready for. I don’t want this debate to lead to anyone using the banner of democracy to override actual democratic deliberation. We need to acknowledge that there’s already both a lot of demand for and a lot of hatred of AI, and welcome both as full participants in the debate about the future. We should be wary of anyone declaring that barring huge numbers of people from the debate is a form of “democratic resistance to AI.” This threatens to mostly burn the commons of the debate and alienate people from more subtle ways of resisting and democratically governing the unique risks AI poses.

A plug for a tool I made to show how little most current AI chatbots add to your emissions and water use, with citations

Thu, 11 Jun 2026 02:46:17 GMT

It’s been a while since I circled back on the idea that individual chatbot prompts don’t add meaningful amounts to your carbon or water budget. I’m finding that I keep bumping into people saying that they “can’t trust my numbers” or that I’m “ignoring newer models or longer responses” etc.

We now have pretty good estimates with clear methodologies from neutral third parties on most chatbot models available right now. Specifically, EcoLogits are much more critical of AI’s environmental impact than I am, include the embodied impacts of AI hardware in their calculations, and make their methodology fully available.

I took their numbers for most current available chatbots, cited them all, and put them in an interactive tool here where you can enter exactly how many of each type of chatbot prompt you send and how long your responses are, and compare them to all the other ways you emit and consume water throughout the day. I cite every number. I’m doing this to give you a way of seeing all this directly without having to trust anything I’m saying, including the numbers I hadn’t written about much before for much longer prompts. Basically these are the most solid numbers we have right now, they don’t rely at all on my personal trustworthiness, and the AI estimates specifically come from a much more environmentally critical group. This is the most solid case I can make that AI chatbots do not add to your emissions in a meaningful way.

Because my name’s become somewhat toxic in the debate as more people accuse me of being a shill, and because I’d really rather just get this information out there, one thing you can do is just copy, either by hand or with an AI agent, all this info and these graphics over to your own website without attributing it to me at all. The state of the debate is just so goofy right now that I’d rather let a lot of other identical websites bloom without getting credit. I include a note at the bottom of the page for your coding agent that it can feel free to copy this all without crediting me.

If your friends are guilting you about the horrible environmental effects of your chatbot use, I also included a button to generate a report with citations for every claim on exactly how much your chatbot use is adding to your personal emissions and water use:

There are a lot of crazy questions in the debate about AI and the environment. Data centers are being planned that will individually eat whole percentage points of US power. But the question “Are chatbots adding to MY personal footprint?” has long been settled, and yet more and more people seem to think it’s not. I’m hoping this will help a bit in getting the word out that it’s time to move on to focusing exclusively on data centers more broadly, and hopefully from there to the idea that the main fight that matters is greening the grid.

A simple trick to fix the data center debate

Sat, 06 Jun 2026 16:03:12 GMT

A lot of the data center debate is guided by status quo bias, where people are much more likely to oppose any change or new externalities regardless of the upsides.

To override this, a simple trick I use when I’m talking to people about data centers is to imagine a situation where not building the data center violates the status quo. I usually don’t use the word data center for this at all, and instead just frame it around lost tax revenue. I just ask “Would we spend this much in tax revenue to avoid the externalities of the data center?” This brings the effects of losing out on the tax revenue into the spotlight. If I could just have everyone in the debate communicate this way, I think the whole thing would be clarified.

An example

Loudoun County Virginia has come up with a new plan. They are going to spend $1.3 billion of their citizens’ tax money every single year1 to improve the county’s environment through a series of projects. This is about 38% of their general fund,2 about $2800 per resident per year.3 These projects will in total:

Reduce the county’s CO2 emissions by about 80%.4
Reduce the county’s water use by 10%.5
Make 3% more of the county’s land available to be used by businesses or kept as nature.6
Save the average household and business about $6 each in electricity bills each month, for a total savings of about $15-20 million per year, about 1/65th of the cost of the program.7
Reduce regional NOx air pollution by 4%.8

The second they stop spending this money, all these impacts come back. Is it worth spending $2800 per person per year forever to maintain these gains?

I think it would obviously be a terrible idea to spend money this way. For each of these, there are way less expensive ways of getting the same results. Some of these might be wrong or not work, but I think the type of comparisons I’m doing here need to be how the debate is framed:

Carbon

Carbon emissions are the only truly difficult externality to deal with here. The main problem with climate change more broadly is that we as a society are consistently failing to price in the carbon cost of what we do and invest to reduce it, because doing this is expensive. Here we have 11 million metric tons of CO2 per year and $1.3 billion dollars, so about $118 per metric ton.

One way to do this is via the Regional Greenhouse Gas Initiative (RGGI) that Virginia will rejoin on July 1, where anyone, including a county government, can buy allowances that shrink the number of tons the region’s power plants are allowed to emit. Covering all 11 million metric tons takes about 12 million allowances, around $300 million a year at this spring’s clearing price, or $620 million at the higher prices traders expect by December. So we have one way of reducing emissions that would cost up to 50% of the full money available in the program.

But this probably isn’t the cheapest way to reduce emissions. Eleven million tons is the emissions of about 26 TWh of gas generation, and that can be replaced by building about 12 GW of solar at Virginia capacity factors, or 8 to 9 GW of wind. Building the solar itself actually costs close to nothing on net. Unsubsidized utility solar costs about $38 to $78 per MWh, and PJM’s (the Regional Transmission Organization serving a lot of the mid-Atlantic) wholesale market paid an average of about $51 last year, so the power earns back most or all of its cost.

The thing preventing us from just building out huge amounts of basically free solar is issues with the grid. Since 2020, PJM has signed interconnection agreements for 103 GW of new power generation, but only 23 GW have come online. New power projects are routinely dying when they’re billed for the grid upgrades, which cost about $240 per kilowatt on average. Loudoun can just pay those bills to help build the plants. Covering the upgrades for all 12 GW at that average is about $3 billion as a one-time cost. Financed at 4%, the interest is about $120 million a year, under a tenth of the program’s cost and about $11 per ton against the program’s $118.

Water

10% of the water the county consumes is roughly 4,900 acre-feet. An acre-foot is about 326,000 gallons, it’s literally an acre covered in a foot of water.

Loudoun could pay to convert ocean water about 70 miles away in the Chesapeake Bay to drinkable water. The Carlsbad plant near San Diego turns seawater into drinking water for about $3,400 per acre-foot.9 This is about the most expensive drinking water in America. Treating this much water at this rate would cost $17 million per year, only 1.3% as much as the program.

Surely it would cost way too much to get the water all the way to the county? You need to consider that $1.3 billion per year is just so, so much money that stuff like this becomes possible.

4,900 acre-feet a year is about 4.4 million gallons a day, which can be delivered by a single 24 inch pipe.10 This pipe would have to raise the water about 500 feet above sea level over its journey. Pumping water over let’s say 100 miles costs about 1,000–2,000 kWh per acre-foot11, about half a million a year. So the cost of pumping the water gets us from $17 to at most $18 million.

What about building the pipe? Pipes like this can cost about $2-3 million per mile12, so this would be a one-time cost of $200-300 million. If we run with the interest-only cost this gets us $8-12 million per year. So altogether this plan to supply 10% of Loudoun’s water with ocean water from 100 miles away would cost maybe $30 million per year, or 2% of the cost of the project. The project is just so so expensive!

Land

3% of Loudoun’s land is about 10,000 acres of 333,000. More than 85,000 acres in Loudoun County are permanently protected by conservation easements, where a landowner sells off the right to ever develop the land while continuing to own and farm it.13 The federal ACEP program pays 95% of fair market value for these, capped at $16,000 per acre in Loudoun. Ten thousand acres of permanent easements at $10,000-16,000 per acre is $100-160 million, and this is a one-time cost. If the county borrowed that money at 4% and only ever paid the interest, that’s $4-6.5 million a year. 0.5% of the cost of the program.

Electricity bills

The program saves the average household and business about $6 a month on electricity, $15-20 million a year county-wide. So already that’s just 1/65th of the cost of the program. One way to save this money would be utility efficiency programs, like paying for insulation, heat pumps, lighting upgrades, and smart thermostats. Across the country, these programs save electricity at an average cost of about 2.5 cents per kilowatt-hour, while Virginia residential customers pay around 14-15 cents for each one.14 To produce $15-20 million in annual bill savings, you need to save about 110-140 million kilowatt-hours, which costs about $3-4 million a year in program spending. So we could get the program’s entire bill benefit for about 0.3% of its cost.

NOx air pollution

We need to cut regional NOx pollution by 4%, about 3,000 tons per year. The federal government already runs the Diesel Emissions Reduction Act (DERA), which is basically a procurement market for NOx reductions. From 2008 through 2018, $801 million in DERA awards bought 491,000 lifetime tons of NOx reductions, which means they get about $1,630 per ton. This also removed 16,800 tons of PM2.5 and 5.3 million tons of CO2.15 Removing 3,000 tons a year at that price is about $5 million a year, about $1700 per ton. For a sanity check, power plants buy and sell NOx allowances under EPA’s Cross-State Air Pollution Rule, and those mostly trade between $3,000 and $6,500 per ton. Even if we assume a pessimistic $10,000 per ton, we’re at $30 million a year.

Adding them up

Altogether, these would cost maybe $150 million a year, a little over a tenth of the cost of “the program.” 9/10ths of the money spent on avoiding these externalities would just be wasted. That’s about 1.1 billion dollars wasted per year, $2500 for each individual citizen every single year for nothing at all.

Spending this much money is exactly the same as banning data centers in the county. The effects are exactly the same. You lose out on x amount of tax revenue for the sake of avoiding y and z externalities. But here, it’s obvious how ridiculous it is.

Maybe you think there are other externalities I haven’t considered, like heat or noise. Instead of saying “you’re leaving those out!” try to figure out how much is actually happening and what you think it would be reasonable for a county to pay to remove the externality.

Other ways this helps

We can also frame other talking points about data centers this way. Some people have been worried the “buildout is happening too fast” and that might be true in specific places, but consider how you’d react if this program were happening, and someone said it was being “scaled down too fast.” That could be true, but you’d want specific evidence for why scaling it down slower would be worth the trade-off of all that huge cost to the taxpayers.

This can also help us select which comparisons to data centers actually help us understand them. Saying “this data center consumes a million bottles of water per day” sounds compelling, but if you ask “should the county pay $50 million dollars to use one million bottles of water less per day” you notice how the comparison doesn’t give you much useful info. You’d want to know how much water the county has available or what other industries use similar amounts in order to figure out if this is a good trade, not how many bottles of water the data center uses in a vacuum.

A lot of people are worried about data center tax cuts, and sometimes imply that the tax cuts mean that data centers shouldn’t be built. It’s true that data center tax rates have been cut in specific places, but they mostly still pay huge amounts of taxes in total. Here’s how much a $1 billion data center would pay in taxes every year in some of the places that are building the most:

Source

Data centers pay different types of taxes in different places. Notice that Santa Clara County is the only place on the chart with a yellow bar for M&E (machinery and equipment) sales tax on data centers. Every other place here exempts data center equipment from its sales tax, or in Hillsboro’s case has no sales tax at all. One of the most influential articles on how people think about data center taxes is CNBC’s “In race to attract data centers, states can forfeit hundreds of millions of dollars in tax revenue to tech companies.“ That article is mainly about the sales tax in that one yellow bar. CNBC counted 42 states that either give data centers full or partial sales tax exemptions or have no sales tax to begin with. California is one of just eight sales-tax states that offer no exemption. And it’s true that states are forgoing real money this way. Virginia’s exemption alone was estimated at more than $730 million for 2024. But Loudoun County is still doing very well despite taxing data centers at a much lower rate than Santa Clara. In the Tax Foundation’s model, the same $1 billion data center pays about $12 million a year in Loudoun taxes against $22 million in Santa Clara County taxes. Maybe it’s a problem that data centers aren’t also paying this additional revenue, maybe they could pay more, but that’s a separate question from whether completely blocking the data center would be a good decision. If I said “This Loudoun program for helping the environment will only cost $2800 per person instead of $5100” that gives us new information, but the big drop still isn’t close to enough to make the program worthwhile. Similarly, tax cuts tell us data centers could maybe pay much more, but that isn’t the final word on whether they should be banned, or whether it’s worth getting merely $2800 per person in new tax revenue instead of $5100, and we can be much more precise than simply saying “Data centers have all these externalities, and now they have all these tax cuts too. Who’s to say if they’re good?” We need to make the actual specific comparisons with the numbers we have.

Data centers provide so much tax revenue that in many places a fraction of the money could be used to completely offset their environmental externalities, but people seem to have trouble thinking this way, and see the externalities as brutally real whereas the tax revenue is either abstract or a lie. People bring up these long lists of the harms of data centers, and then at the end gesturing vaguely at their tax revenue. For Loudoun they might say “They use so much water and pollute the county and raise electric bills and use our land… oh and yeah they do provide tax revenue, but is it worth it?” I always read this as a form of learned helplessness. We’re adults. We can take the next step and just give the specific numbers for each, including the taxes, and then find ways to decide “Is this revenue worth the cost?” We don’t have to waffle in this weird “Oh they use so many resources, who can say if they’re good?” mode so many people go into. We don’t think this way about any other industry. We can come up with different ways of comparing the costs and benefits. My personal favorite is to just ask “What if we were spending this much tax money to avoid the externalities?” to break the status quo bias. When we do that, I think the debate is clarified.

Why aren’t places with data centers already completely offsetting their impact like this? The reason is that voters don’t actually rank these as the most important problems. If people could get better funded schools at the cost of additional percentage points of water use, they usually opt for the better schools over saving water. Local governments could spend way more data center revenue to offset the environmental cost specifically, but a part of the reason they don’t is the revealed preference of locals for spending on other things instead. They seem to think the trade-off is worth it.

This is what Loudoun’s data centers actually pay. The FY2027 budget expects $417 million in real property taxes and $879 million in personal property taxes from data centers, about $1.3 billion, nearly half the county’s tax revenue.

The county’s own figure: data centers sit on 4% of commercial parcels and provide 38% of general fund revenue.

Loudoun has about 454,000 residents. $1.3 billion / 454,000 = $2,860.

Still typing up the footnotes for the carbon section, will circle back later today.

Loudoun Water data PEC obtained by FOIA: data centers used 1.6 billion gallons in 2023, nearly 10% of county water use, with potable use up 250% since 2019. 1.6 billion / 325,851 gallons per acre-foot = about 4,900 acre-feet.

County officials’ own framing: a third of the budget on 3% of the land. Loudoun is about 333,000 acres, so 3% is about 10,000.

When the 2026 General Assembly weighed shifting capacity and distribution costs onto Dominion’s data center rate class, the SCC estimated typical residential customers would save $5.52 a month, a 3.4% cut. That’s what data centers currently add to an ordinary bill. Business bills are larger, so the same percentage is worth more than $6 to them, which is how Loudoun’s roughly 150,000 households and 11,000 businesses get to $15-20 million a year.

JLARC’s December 2024 report: data center generators at permitted levels are less than 4% of Northern Virginia’s NOx, and 0.1% or less of its CO and particulate matter. Against the regional inventory, 4% is about 3,000 tons a year. Actual use runs around 7% of permitted levels, so this prices the benefit at its ceiling.

The Water Authority’s contract price for Carlsbad water was about $3,400-3,500 per acre-foot in FY2025, three to four times the cost of imported water and by far the region’s most expensive supply. 4,900 x $3,400 = $17 million a year.The Chesapeake Bay is brackish, roughly a third the salinity of the seawater Carlsbad treats, and desalination cost scales with salt, so the Carlsbad price actually significantly overcharges me.

4.4 million gallons a day moves through a 24-inch pipe at about 2 feet per second, the bottom of the normal 2–7 ft/s design range for transmission mains.

Lifting an acre-foot (1.23 million kilograms) 500 feet takes about 510 kWh of pure physics, or 680 kWh through real pumps at 75% efficiency. Friction over 100 miles of pipe adds a few hundred more. Let’s say it’s 1,000–2,000 kWh per acre-foot. Moving Colorado River water to Southern California takes about 2,000 kWh per acre-foot, and State Water Project deliveries over the Tehachapis around 3,000, while Carlsbad uses about 4,900 just making and delivering its water. 4,900 acre-feet × 1,000–2,000 kWh at commercial rates is $0.5–1 million a year.

A planning rule of thumb for installed transmission mains is $15–25 per inch of diameter per linear foot, $2–3 million per mile for a 24-inch line, and that’s actually generous. EPA-derived estimates from 2011 put 48-inch pipe at $1.3–1.8 million per mile. Virginia has also built much bigger than this: the Lake Gaston pipeline moves 60 million gallons a day through a 60-inch pipe for 76 miles to Virginia Beach. Construction took about three years.

The county reports over 85,000 acres under conservation easement. On price, Virginia NRCS pays 95% of appraised value on permanent ACEP easements, capped at $16,000 per acre in Loudoun, one of 18 Chesapeake Bay counties at that cap. Those are the wetland-reserve terms; the farmland side pays up to 50% federally with the land trust covering the rest. Either way, retiring development rights in Loudoun runs $10,000-16,000 an acre. 10,000 acres is $100-160 million once; at 4% interest only, $4-6.5 million a year.

LBNL reviewed 8,790 utility efficiency programs in 41 states: average cost 2.5 cents per kWh saved. Counting participants’ own spending, the all-in figure is about 4.6 cents, which doubles this line to $6-7 million, still half a percent of the program. Virginia residential customers paid about 14 cents per kWh in 2024 and 15-16 by late 2025, per EIA. $15-20 million at 14-15 cents is 110-140 million kWh; times 2.5 cents is $3-4 million a year.

EPA’s Fifth Report to Congress on DERA: $801 million from FY2008 through FY2018 replaced or retrofitted 73,700 engines, cutting lifetime emissions by 491,000 tons of NOx, 16,800 tons of particulate matter, and 5.3 million tons of CO2. CRS summary here. $801 million / 491,000 = about $1,630 per ton.

Why I think panic about local impacts of data centers is just a panic

Thu, 04 Jun 2026 05:13:27 GMT

In the last year of following increased local resistance to data centers being built, I’ve listened to lots of recorded testimony at town halls, and read through countless comments and articles where people argue for why data centers are so uniquely evil and shut down anyone defending them as shills for AI companies.

Throughout all this, the only claims I’ve seen that actually stood up to scrutiny were:

Data centers will meaningfully add to America’s CO2 emissions.
Elon Musk’s data centers specifically added local air pollution to already heavily polluted residential areas. This is the clearest case of a data center I would’ve personally wanted to prevent from operating.
Data centers built right up against people’s homes create noise pollution.
Data centers can sometimes in specific circumstances raise electricity bills somewhat, though this hasn’t been the norm so far.

For everything else, whenever I look into where people are actually getting their ideas about the hundreds of other data centers being built, the source always leads back to some confused misreading of local reporting, a wild calculation error, a bad game of telephone, or a wildly misleading article. For anyone interested in receipts, this is my big collection of deep dives I’ve done.

Almost everyone talking about data center water impacts shares the same story of the Georgia couple whose wells ran dry, and never mentions this was definitely due to some issue with construction and not the normal operation of a data center. People regularly claim everyday people’s water access has been harmed by the normal operation of data centers, but no one can ever point me to where that is. Most stories I read about droughts being “exacerbated by data centers” don’t add the portion of water data centers are actually using, and when I look into it, it’s almost always vanishingly small compared to the area in general. Talk about data centers “poisoning water” seems to have been invented out of thin air.

Most people commenting on data center impacts on electricity prices bring up a (I think bad) Bloomberg article that people easily mistake to read that data centers raise electricity prices as much as 267% near where they’re built, despite this reflecting a rate that residents don’t actually pay at all and only one of several big factors that determine electricity bills, this mechanism not actually having any causal effect on the residential rate increases the article goes on to describe, and the fact that the authors are starting from a baseline in April 2020 which was a unique low point for electricity prices as COVID lockdowns were in effect, and the nominal price of electricity rising a lot in general in the last 6 years, mostly due to inflation. In reality there’s a surprising negative correlation between the states with the most data center capacity and states where electricity prices have increased the most.

Data center heat exhaust harms seem entirely speculative. The only one that looks really dangerous so far is the very largest data center ever proposed, in the place most likely to trap heat (Utah desert valleys), and the only study done on the air effects of data center heat exhaust found it was comparable to the normal slight temperature increases of living near other dense human activity in a city.

The most popular video made about data centers this year was full of wild misrepresentations of every study it was citing. No claims from it held up.

People commenting on the air pollution from data centers in general almost never talk about how they compare to other things, and when they do they find that, say, the largest cluster of data centers in the whole world consuming as much energy as a large city together emits about as much harmful PM2.5 pollution into the county as a single gas plant. Their NOx emissions are much more, but those spread out much more before becoming dangerous, and in the clean air of the county likely fade into the background. Not nothing, but definitely not some unique environmental catastrophe. The main pollution harms of data centers are I suspect actually offsite coal plants far away they may draw from.

Data centers for the most part pay huge amounts of taxes almost everywhere they’re built, with the exception of maybe Michigan. More on that in a future post.

The so-called massive environmental costs of training cutting edge AI models seem to fade into the background of other things we create for hundreds of millions of people to use over months. My best guess is that the emissions are the same as a day of normal manufacturing of Coke cans.

The idea that America doesn’t “have enough land” for buildings optimized down to the literal atom that will collectively take up about a third of the land we currently use to grow Christmas trees seemed so obviously silly that I felt ridiculous posting my land use article, and yet that’s the one that’s drawn the most scorn and backlash by far.

After a year of basically feeling like I’m living in this weird dream reality where every single thread I follow leads me to a dead end instead of a reliable source, I need to basically beg my critics: I need a specific case for why data centers are going to be especially harmful along these axes compared to other regular industry, instead of just fading into the background of the normal externalities of other large light industries, because I just can’t find it at all. I need something, anything, that actually justifies this public outcry. I need some kind of basic overview of water harms that doesn’t just gesture at “millions of gallons of water” without context. I need some realistic estimates of air pollution when data centers with onsite backup generation are following normal EPA rules where they don’t just fade into the background of other normal sources of air pollution. I’m just not seeing it.

I’ve received one good account of why data centers can throw off the ways water rights work in specific areas, which I’ll publish and respond to soon. That specifically struck me as good pushback. But besides that, I want to send up a signal flare that I need someone to try to make a comprehensive grounded case to me beyond just saying “Everyone knows these things are a disaster.” Show me your thread of reasoning that leads back to something solid that I can grip onto and understand.

People will often cite “experts” but these experts are often random professors just echoing common wisdom that’s developed about this rather than actually giving specific solid numbers for why we should be especially worried here. I have yet to find a serious expert in the field giving a comprehensive reason why we should be especially worried about data centers. There are experts who have raised legitimate concerns about specific data centers, like climate experts and physicists concerned about the warming effects of Kevin O’Leary’s massive Utah data center, the largest ever proposed, but these never generalize into giving us much alarming info about the buildout more broadly.

A lot of people covering this often lean into nuance, and will say “Well this is complicated” and “the truth is probably somewhere in the middle.” It’s true that the truth about data centers is complicated, it’s not that they have no externalities at all, but I mostly just read this as evading being more specific about how this buildout compares to most of the other ways the environment’s harmed. We have good numbers on how many AI chips are being manufactured and approximately how much energy and water they’ll require. We can make some solid claims here, and even the more thoughtful people covering this often kind of retreat to simply say “Well everyone’s voices should be heard” or “People have a right to ask questions about such a large buildout.” Yes, they do, but we also have a responsibility to make sure the public debate isn’t evaporating into claims and intuitions that all fall apart when you poke at them yet each have so much moral urgency behind them that the pokers get punished. Simply leaving the gigantic range of possibilities open between “data centers are a catastrophe” and “data centers never cause any problems” is basically a highbrow way of ceding the debate to the people making the most hyperbolic memorable claims.

On the climate criticism specifically, I’m seeing two camps:

People worried that data centers will add huge amounts of emissions despite AI being useless.
People worried that data centers will add huge amounts of emissions because AI will be really economically useful and powerful and continue to grow.

The first view just doesn’t make sense to me. If AI is a fad, it will not rise to be whole percentage points of American emissions. At some point people will wake up before we spend that much energy on it, because that much energy is expensive.

I myself take pretty weird wild futures of AI seriously, because I think it’s clear that AI’s capable and likely to get much more capable relatively quickly. In those worlds, AI can potentially rise to be huge portions of our energy grid and emissions. But almost no one I talk to about climate seems to share my worries about this. Their objection is that emissions are wasted on AI because it’s useless rather than that we’re doing something potentially incredibly dangerous that could at best lead to explosive economic growth. The people I know worried about AI risk will often imply that everyday people protesting data centers are also secretly worried, yet I barely see this coming up in public discussion.

I haven’t had time to finish deep dives on every last issue with data centers. I need to write my big climate post, finish a series on pollution, write something on heat exhaust, and explore noise. But I do already know a lot about each, and for each I am just not seeing anything that justifies the level of public outcry we’re seeing. And yet when I venture into polite society and say this, I’m often treated as basically a conspiracy theorist or a shill for big AI promoting what amounts to climate denial. All these ideas that I know individually are overblown work together as a kind of shield. If I poke at any one, the person I’m talking leans back on all the others. They can’t possibly all be wrong, that would be ridiculous! And yet for any one I poke at, the person can’t lead me back to anything that seems solid at all.

This is a weird position for me to be in! I’ve always been a good predictable lib and don’t find myself on the opposite side of almost all media coverage often. It’s driven me a little crazy and does make me worry I’m missing something everyone else seems to see. But after a year of following this with no really satisfying answers, it’s become hard not to start to think I’m the one with my feet on the ground here.

A history of the data center panic - part 1

Wed, 20 May 2026 05:20:46 GMT

I write a lot about misconceptions about data centers, which until recently people saw as a weird niche obsession that didn’t matter in the national discourse. Now that the (I think mostly unfounded) panic over data centers’ impacts on the places they’re built has taken off, I want to tell the story of where it came from, as I see it.

I don’t actually feel invested in the data center buildout itself, but I do feel very invested in opposing some but not all of the background beliefs guiding data center opponents, and I’ve been disturbed at how common these beliefs have become in educated circles. I’ve described these beliefs here, and if you want to know where I’m coming from that post is the best place to start. This series will be more political than usual, since I can’t help but bring up the political dynamics I see happening and how I react to them. I’m especially worried about the decline of institutional trust and political liberalism in popular thinking.

I’m exclusively writing about panic over data center impacts on local communities and the environment. I don’t mean panic about AI risk, which I see as very real. One person who won’t feature in this series is Bernie Sanders, because when he talks about data centers he’s mostly talking about them from an AI risk perspective. I think AI risk is real and serious, and because I don’t know what to do about it, I don’t jump in to criticize different people’s plans for how to stop it. I’m personally skeptical that local opposition to data centers will help with AI risk, but that’s an argument for another day. Here’s my list of recs of things to read on AI risk. You don’t have to agree with me on this at all to read this series.

Part 1: Pre-ChatGPT and the creation of common wisdom

There are a few key things that happened in the lead up to the data center panic that are each necessary for understanding it, that all happened before ChatGPT was released and AI moved from being a niche interest to a major public debate. First you need to understand the situation with the early environmental critics of AI. This will be the bulk of this post. At the end I’ll reflect a little on the political situation leading up to ChatGPT, the state of popular environmentalism at the time, and the loss of technical talent in journalism.

Early environmental critics of AI

In 2019, Karen Hao wrote “Training a single AI model can emit as much carbon as five cars in their lifetimes” for the MIT Tech Review. This was at the time a shocking statistic. The training of the AI model in question was very different from what we currently think of as training. Here, thousands of the same model were trained at once, and Google researchers selected the one that performed the best at the end. This looked like it carried a big energy and carbon cost, resulting in a just slightly better model for translating English to German that didn’t end up being used by anyone anyway. This seems pretty wasteful! Spending this much emissions on a slight improvement on a specific model was probably not worth it.

Right?

The original research Hao was citing was from this paper, one of the most cited of all time on AI and the environment, by a research team at UMass Amherst, who had made a few goofy assumptions that made the cost look 90 times larger than it actually was. They had made 3 key mistakes that a later researcher flagged and explained in this paper. In a nutshell, the mistakes were misunderstanding the algorithm, hardware, and background grid:

The type of training Google did tests thousands of candidate models to find the best one. The Google team ran a very low energy screening test on each model candidate and only fully trained the ones that looked promising, but the UMass team assumed that all the thousands of models were fully trained.
The UMass team assumed the training ran on standard graphics cards, but Google was using specialized TPU chips.
The UMass team assumed Google was using an average commercial data center, which at the time could lose 60% of its electricity to cooling and overhead, but Google’s data centers lost closer to 10%.

Together these made the training process look like it used two orders of magnitude more energy than it actually did.

This statistic made it into the most cited AI ethics paper of the era, where it played a pretty central role. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” from 2021 has over 13,600 citations on Google Scholar. The environmental section opens with the UMass Amherst team’s numbers and leans heavily on them, treating them as the typical cost of training language models. That paper was widely read, and so the conventional wisdom that training an AI used preposterous amounts of energy and emissions was in the water by the time ChatGPT came out a year and a half later.

This number was repeated in lots of other foundational articles that went on to influence the current discourse. The Nature article “The Carbon Impact of Artificial Intelligence” has over 100,000 reads and over 400 citations. It uses the UMass study as its central example, and says “This is of the order of 125 round-trip flights between New York and Beijing, a quantification that laypersons can visualize.” This is one of the first instances of one of the more slippery comparisons made in the space. “125 round-trip flights” doesn’t tell you if this is per-passenger or the whole plane, and if you do the math it’s the per-passenger cost. Correcting for the 90x error, saying “training an AI model emits as much as two of the AI researchers flying from LA to Paris and back” doesn’t sound quite as shocking. Every year, 20-30,000 AI researchers fly to the NeurIPS conference alone, so Google doing this research had about as much impact on the environment as maybe 1/10,000th of the conference. This does not make it look like training emits unreasonable amounts.

You might pause here and ask how serious of an issue this is. Getting this wrong at the time might have been bad, but they were “directionally correct” because models did in fact grow to use huge amounts of energy. I’d argue that what matters throughout is how many users each AI model will end up getting, for the same reason that you can’t assess the total environmental cost of producing large amounts of cans of Coke without knowing how many people are buying them.

The resulting model was going to be used by relatively few people to slightly improve one specific translation function, and was mainly a piece of research. I think it’s okay for 2 AI researchers to sometimes take long flights to meetings in other places if they think the result might yield useful research results. I also think it’s okay for hundreds of millions of people to each purchase a CD to install a computer program, and the per-person carbon emissions of manufacturing CDs are about the same as the per-person emissions of training frontier models. See my full argument on training AI models here, which boils down to the fact that if we compare the emissions cost of creating new AI models to other products used by similar numbers of people (hundreds of millions) they don’t stand out as being especially wasteful:

Note that I’m only considering the climate harm here. Grok is separately actually harmful because it polluted a nearby community.

So both before and after ChatGPT, the idea that training was especially environmentally wasteful was, I think, wrong, and only looked plausible if we accepted the 90x inflated number that got shared everywhere. The common wisdom invented here was, I think, completely incorrect.

Coverage of this study also gave us the earliest instance of one of the worst comparisons in the space: the cost of building an AI model used by hundreds of millions of people vs your personal consumption costs.

This might have made more sense if you assumed the user base was going to be really small, but now graphs like this make as much sense as saying you shouldn’t buy a can of Coke because the combined emissions of global Coke production are much larger than the emissions of your personal car. This talking point has always been goofy, and was invented by a study that was super clunky with numbers and amplified by both the media and people researching AI’s environmental impacts at the time.

There’s a big split between how much attention the correction got from researchers and how much it got from the media. The UMass study has 2730 citations compared to the correction’s 1613. But the correction itself was barely covered in the media. The original statistic was promoted by:

MIT Tech Review
Cory Doctorow in Boing Boing
The Columbia Climate School
The Guardian
Earth.Org, which just published another article using the same bad statistic last November

Many articles covering the Stochastic Parrots paper also mentioned the environmental costs the paper brought up.

Jeff Dean at Google had publicly asked the original lead UMass author to stop citing the debunked statistic after seeing it in a more recent paper of hers, and argued the original figure had actually been inflated by more than 3000x. The author didn’t update the paper, and Stochastic Parrots has also not been updated.

A lot of the early bad comparisons between AI’s costs and other things came from the bad UMass number. The comparison of training to things individual people do, the “flights” comparison without clarifying if it’s passengers or the whole plane, the failure to contextualize models with how many users they’ll get, all came from discourse about this single misunderstood statistic, and became part of the common wisdom before ChatGPT launched. This also started the pattern of lopsided media coverage, where alarming statistics are understood to get attention but corrections and clarifications mostly don’t. I don’t know of other statistics on AI’s energy and water use at the time that got similar amounts of attention or pushed the idea that AI was uniquely bad for the environment. The whole idea seems to flow from this single error. Other prominent researchers on AI’s environmental cost, like Shaolei Ren, were mostly making sober comparisons between different data centers and chips and algorithms and writing about how their energy and water cost could be gradually reduced. The environmental critique outside of this number seemed completely level-headed and similar to normal environmental science research on any other topic.

If you look for scientific studies on AI and the environment pre-ChatGPT, Parrots is the most cited of all time, and the UMass study is the second. The Nature paper is #15. Besides those, all the rest seem pretty great and stand up to scrutiny. The story of bad early thinking on AI and the environment is I think the story of specific echo chambers hyping up the UMass study claim and inventing lots of new misleading comparisons that would explode in popularity when ChatGPT later entered the scene. Here are the top 20 papers by citations on Google Scholar. I’ve bolded the ones that use the bad numbers from UMass:

So the field itself was producing a lot of great work, but a few specific mistakes that went unnoticed for years in the very most cited papers, more media attention on the alarming numbers, and weird rhetorical moves together seeded common wisdom that AI was uniquely bad for the environment, which would later be picked up once ChatGPT took off. I’ll cover that more in Part 2.

Stochastic Parrots ends its section on the environment like this:

These models are being developed at a time when unprecedented environmental changes are being witnessed around the world. From monsoons caused by changes in rainfall patterns due to climate change affecting more than 8 million people in India, to the worst fire season on record in Australia killing or displacing nearly three billion animals and at least 400 people, the effect of climate change continues to set new records every year. It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental impact and inequitable access to resources — both of which disproportionately affect people who are already in marginalized positions.

I don’t think this conclusion would have made as much sense describing two individual people taking a round trip for an AI research conference. It only really makes sense assuming the inflated number. Writing about AI’s unique environmental harm became common in AI ethics papers as a result.

And that’s how it became common wisdom to talk about the unique environmental harm of AI.

Deep history

In 1999, computers were using more and more energy.

This alarmed some people, who saw it as ridiculous that digital products could have such an effect on the physical environment. Forbes published the article “Dig More Coal — The PCs are Coming” and opened with “Southern California Edison, meet Amazon.com. Somewhere in America, a lump of coal is burned every time a book is ordered on-line” The contrast between physical infrastructure and something you do online was meant to shock.

I’d really recommend reading through the whole Forbes article. It’s full of the exact moves I write about a lot, more than anything giving contextless large numbers over and over. I’d be especially interested for strong environmental critics of data centers to read this article and tell me what it’s getting wrong that modern criticism isn’t. There’s also this great debunking of the article and others on the environmental “harms” of online activities, focused on contextless large numbers. More articles on this topic include the goofy “Climate Crisis: The Unsustainable Use of Online Video” from 2019, or this fun one about the time the UK government considered cracking down on one-word emails for the sake of the climate due to a misreported inflated statistic.

An underrated aspect of the story of AI and the environment is that it has always been offensive to readers to find out that ephemeral digital goods have a physical real-world cost. I think this is one reason the water objection to AI is so popular compared to the others. Water is precious and physical, and AI is ephemeral and flashes on your screen quickly and then you forget about it. The contrast offends people.

This time is different. AI will in fact on net use more energy than all of these. But is this on its own bad? I think an underrated part of the story of the data center panic is that computers are now rising to truly industrial levels of energy usage and emissions, and people think only physical goods should be allowed to do that. I personally think that if computing is proving to be so valuable that people want to use it this much, it should be allowed to rise to meet the demand.

The left and right become more anti-tech

The rise of anti-tech sentiment on the left and right is a key part of the story of the data center backlash. There are a few complaints about tech the left and right share, like the general idea that smartphones have mostly made the social world worse. Another is that technology in the last ten years has only really gotten good at giving us quick dopamine hits, little treats that don’t actually make our lives better. On this I feel like I’m living in a parallel reality. The internet especially has been a utopian technology for me personally, but it’s become very unpopular to say that out loud.

On the left, tech became much more associated with right wing politics. A lot of liberals and leftists came to see social media as being a big part of why Trump won in 2016 and 2024, and reacted to the rise of alternative media promoting anything from anti-vax conspiracy theories to the manosphere. They also began to associate tech leaders with right wing politics, and obviously weren’t entire wrong. Tech leaders cozying up to Trump after 2024, and the general rise of the tech right, made it clear that the left had powerful new enemies who had been made rich by technology that often seemed either neutral or positive when it was first rolled out.

Elon descending into posting almost daily on Twitter about insane racist conspiracy theories deserves a special mention here. The richest person in the world who made his money in tech regularly retweeting and publicly agreeing with vocal white supremacists has given the left completely legitimate reasons to think there’s a huge danger in ignoring the values and beliefs of the people selling us new gadgets. Here I’m in complete agreement.

The place I strongly disagree with many of my fellows on the left-of-center is that they’ve coupled this justified suspicion of the current Silicon Valley elite with a distrust of the value and abilities of the products themselves. It’s easy for me to say that Elon is an evil person who’s made extremely useful products. I would very happily give up all those products in exchange for the richest person in the world not being a white supremacist. But this is very different from saying that Starlink is useless. It’s become frowned on in populist left-wing spaces to imply that Silicon Valley is capable of producing anything new and useful at all, and saying so is seen as coming from a secret desire to give status to evil people. In low trust populist politics, the only real question is who you’re giving status to with your words and actions, not the complex boring questions of what we actually want out of our lives and how to get them together, so any question of the usefulness of a new technology becomes a question of whether you like the people who made it, and it’s seen as naive to think otherwise. To even imply that AI can be useful enough to make it worthy of any carbon cost at all is seen as completely missing the big picture. It makes sense to me that people would want to boycott AI to not empower tech leaders, but the idea that AI itself must be a scam that has no use value at all is very common in conversations about AI and the environment, and I suspect a lot of that comes from this background sense that Silicon Valley can’t produce anything useful.

I’m less familiar with right wing anti-tech sentiment because most of my time is spent with people left-of-center, and the right wing people I do talk to regularly are all high institutional trust and generally pro tech. Specific factions on the right have obviously always had an uneasy to negative relationship with technology. MAGA contains warring factions of various shades of pro-tech and pro-market capitalists or anti-tech and anti-market localists, and I can’t really tell who’s ascendent right now. The right is also much more wary of corporations on average than they were when I was growing up.

One last note on politics is that I get the sense that both the right and the left have become more low trust in general, less interested in preserving pluralism, and think more often in terms of good guys and bad guys. It seems less likely that the average educated adult, when asked “is it okay if a corporation buys some land, power, and water in your town and uses it to do something that benefits people far away instead of you and makes a lot of money doing it?” will answer yes. But I don’t have the data to back that up.

The rise of pop environmentalism, and the good feelings of discovering a villain hiding in everyday life

This section is almost entirely pure armchair speculation, but it’s also about the area I’m most familiar with, so I feel I’ve earned some right to pontificate.

When I became a physics teacher I was motivated in large part by wanting to improve the general state of science education to get students to a higher level of understanding of the climate situation. Over my time following the issue I came to think that the public was veering off course from what actually helps the climate, and developed an armchair psychology reason for why they veered.

The most important thing we need to do for the climate is to change the energy grid. This can be accomplished with good policy, like carbon taxes, or the government funding renewable energy projects or allowing high energy transmission lines. The grid also needs to be massively scaled up as society separately electrifies formerly fossil-fuel dependent processes. Making all cars electric will require huge amounts of new power plants and transmission.

This means that by far, the way you can impact climate the most isn’t actually by changing your lifestyle. It’s by helping to move this process along, either by local political advocacy or national communication or donating to the right places. I made this tool to compare different climate interventions you can make, mainly to show that changes to the grid completely dwarf everything else you can do even if you divide by the number of people you’re working with to make them happen.

If you are going to make a change to your lifestyle, most people who think seriously about helping the climate agree that tiny individual cuts you make to your lifestyle don’t help at all, because they basically just buy you moral license or literal saved cash to spend on other things that emit just as much. There are a few big cuts you can make that do make a difference, going vegan or living in a big city and walking or biking or taking public transit a lot instead of driving. I’m already doing this, and it’s nice to know that I have a meaningfully lower carbon footprint than average, but I know these cuts are still completely dwarfed by tiny ways I could contribute to changing the grid around me.

I already knew this when I was in college in the early 2010s. If I could go back in time and tell my past self that in 2026, people are worried about the emissions of their personal use of a computer program, past me would feel like the basic message of climate had failed to reach average people. But this is weird, because people are consuming a ton of content about climate now, way more than when I was in college. The issue I think (and here’s where I get really armchair) is coming in large part via short form video.

I’m not a short form video hater by default. I know Jonathan Haidt’s work mostly doesn’t replicate, yada yada. But I do think the nature of short-form makes it a uniquely bad place to communicate about climate well, and there’s a specific dynamic that incentivizes hyping people up about the very least effectual things they could do.

When you watch short-form video, you’re often getting a quick parasocial hit of companionship with the person talking back to you. Something that feels really, really good is finding a hit of companionship that makes you feel like you know a secret about the world the people around you don’t, you’ve ascended, and there’s something that’s polluting them that you’re free of now via your newfound companionship. These evil invisible parts of everyday life do in fact exist, but the tendency to go looking for them can veer you off course, because it’s so good to feel like you’ve discovered them regardless of whether they’re real.

I’m vegan, and when I’m out at a restaurant and see someone eating meat, I have to admit that my brain sends me the general vibe “There’s this big global struggle that I’m a part of, and the civilian over there doesn’t even know about. I’m not just some guy in a restaurant eating the same mapo tofu I had a few days ago, I’m part of a global struggle against evil.” That feels good!

A lot of conversations about climate on short form video take the form of someone speaking into the camera to you about how something small in your daily life is actually evil. I think stuff like this is basically perfectly fit to make us feel the same thing I feel when I see someone eating meat: by making this cut, you will ascend into a big global struggle against the forces of evil. The smaller the cut, the easier it is to ascend. You’ll be on the same team as this person speaking to you. It’s like you’re a secret agent getting assigned a mission. We should expect this style of advice to be pretty popular, and for people to be pretty excited about videos that advise tiny cuts to individual lifestyles (the least effectual thing you can do to help with climate) over contextualizing and helping with the situation with the grid (the best thing you can do).

And in my personal experience, this has been a huge part of the climate conversations I’ve had over the last ten years. A lot of people will talk as if small individual things other people do are actually carrying some deep, secret evil that needs to be purged. This precedes social media, but I suspect people are getting a lot more hits of this style of thinking now that short form video’s more popular.

I suspect the basic tendency I’m describing here was a big underrated contributor to the chatbot moral panic. I go into more armchair theorizing on that here. The panic about chatbots is a necessary step to the panic about data center impacts on local communities more broadly, which I’ll get into more in the next post.

A result of more people’s understanding of climate coming through social media influencers also meant other fake common wisdom was invented. A very common one is that because we’re in a climate crisis, we can’t allow “new emissions” from a new industry. When you think about it, the category of new emissions is weird, because every time we emit at all, the emissions are new. This talking point maybe makes sense for talking about additional total emissions, but often gets warped to say it is unacceptable for a new industry to appear during the climate crisis.

The decline of science journalism

US newsroom employment fell by 26% between 2008 and 2020, from 114,000 to 85,000 jobs. Newspapers were hit the hardest and lost 47% of their staff, going from 71,000 to 38,000. Digital news outlets grew, by only added about 6,000 new jobs.

Some journalists are more expensive than others. Beat reporters who cover one specific area for a decade and get a feel for what quality research looks like are expensive. They get less out in the same amount of time and prioritize depth. Scientific specialists were more likely to be cut because they were more expensive. When the Canadian Science Writers’ Association was founded in 1971, there were at least 30 full-time newspaper science reporters in Canada; by the late 2010s that had dropped to about six. American newspapers once had dedicated science, health, and environment desks, but most of those are now gone. A 2024 Nieman Lab piece titled “Science journalism becomes plain old journalism” notes that every reporter, regardless of their beat, now gets deputized as a science reporter when the story calls for one. There’s just way less technical understanding in media than there was 20 years ago.

This left journalists much more eager on average to defer to credentialed experts on topics, without contextualizing a given problem with the other sources of harm to health or the environment. Journalism also maybe became more self-referential, as authors pressed for time would mainly just defer to other trusted publications, which left the field more susceptible to echo chambers.

The business model has also changed. The rise of the pageview economy in the 2010s forced journalists to prioritize clicks and reads and shares. Shocking titles often win out over in-depth writing, and much of the worst sources of data center misinformation have come from shocking and misleading titles rather than the contents of the article itself.

For example, the most influential piece on data center water use is this from the New York Times:

Source

Almost every time I say anything about AI and water use online, this story gets shared, or this variant of the same story from the BBC:

Source

Almost no one sharing the articles seems aware that the water issues seem to have happened due to construction, either sediment runoff or issues with blasting nearby, and don’t have anything to do with the normal water draw of the data center, even though the content of the articles makes that clear. What’s happening here is that the alarming headline is a tool to get the article shared widely in the hopes that 1 in 10 of the people sharing it actually read and digest it. This is why the initial “Training AI emits as much as 125” flights talking point made headlines, but the correction didn’t. No one’s going to click on a story about how a process you’ve never heard of before doesn’t use much energy.

(Except for you, dear readers. Thank you for your support)

Journalists in general were feeling more precarious and burnt out before ChatGPT, and were probably not excited about a new major source of competition.

Conclusion

The political situation leading up to ChatGPT had primed both the right and left to distrust big tech, and to think their products weren’t worth any additional environmental cost at all. Politics had maybe become lower trust, and people were more interested in identifying good guys and bad guys than passively allowing lots of different types of businesses to operate out of sight. The popular understanding of environmentalism had been diluted by excitement about tiny personal cuts that could make you feel more with it than the people around you. Journalists had lost technical talent and were more incentivized to write shocking headlines to generate clicks and shares. And the two most cited papers ever published on AI and the environment contained a statistic off by two orders of magnitude that had inaugurated a series of bad comparisons and metaphors for thinking about AI and the environment. These together I think were the seeds of the data center panic we’re living through now.

A crash course on US air pollution

Tue, 19 May 2026 00:42:04 GMT

In December 2024, a paper estimated that air pollution from US data centers could impose more than $20 billion in annual public health burdens by 2030. A later version moved that forward to 2028.

The paper has other shocking estimates, perhaps most is that emergency backup diesel generators around Northern Virginia data centers alone create hundreds of millions of dollars in public health costs, with damages large enough to register as far away as Long Island.

Source

The shading on the map shows each county’s rank by modeled annual health cost from the Virginia generators. Each of Long Island’s counties are very dark, which the middle graph says corresponds to maybe $100,000 to $1 million in expected annual health damages each, so altogether Long Island’s 4 counties (I’m including NYC) may be losing as much as $4 million in public health harm. These diesel generators are modeled to create $220-$300 million in public health costs.

This seems crazy. Northern Virginia’s two biggest data center counties generate about $1.2 billion in local tax revenue each year1, and the study implies at least a fifth of that is cancelled out by the harm of their backup power alone, with most of the damage falling on people far from where the sources are built and regulated. It’s as if Northern Virginia were stealing $4 million a year from the health of Long Island. This seems wildly unjust, counter-intuitive, and a candidate for a data center problem that dwarfs the overblown concerns about water or land.

However, while local air pollution problems with data centers are very real, this specific paper is easy to misread and come away from thinking the national-level harm is much worse than it actually is. To see the difference, you first need a ton of grounding in the situation with air pollution in the US.

Going into this, based on what I understood about air pollution, I was under the impression that this was a serious problem with data centers at the national level. However, I’ve come away thinking that while data centers can definitely create serious local problems for air pollution and need to be carefully and transparently governed (and there are clear places where that’s already failed), I no longer believe that on net they will stand out as a significant national problem for air pollution in the near future.

Air pollution is complicated, but not too complicated. You need a lot of individually simple facts that together paint a complex picture before understanding how data centers fit into it. This first post on air pollution will go over these facts. I think that I’ve been able to finally get my footing, and this post is basically all my notes on what I think is most important to understand to get your footing as well. This is far and away the longest of the four, but I think also very fun and interesting once you get beyond the necessary technical details at the beginning.

The ethics of air pollution are wild. Air pollution is both complex and urgent enough that it forces us to break through simple vapid generalities and actually wrestle with how the core values we’re all running on to keep society together conflict with each other, and to think in a much more meta way about how to trade those values off. Even if you don’t care about data centers at all, I think the ethics of air pollution is on its own such an interesting intersection of so many different questions that it can be a blast to read about. Part 2 will focus on this.

Part 3 is on why I don’t expect data centers to become a significant national air quality problem in America. I also unpack the paper above and contextualize its predictions against other industries.

Part 4 covers data centers at the local level, and where they have and can turn into significant problems for air pollution, especially when they take advantage of lax permitting rules. The most obvious case is xAI’s Colossus in South Memphis, where xAI used dozens of gas turbines near already heavily polluted residential areas. I’ll cover that in detail in Part 4.

One important note here is that I’m excluding the climate effects of air pollution. Effects on climate are so different from other harms of air pollution that they deserve a separate post. All death and DALY figures I use for air pollution harms are taken from the IHME’s Global Burden of Disease project.

The three main ways long-term exposure to air pollution harms us
Particles that make PM2.5 and ozone
Harms from short-term exposure
Anthropogenic vs natural air pollution
American air is way cleaner than it was decades ago, and way cleaner than the world’s average
A lot of US pollution harms occur in either average US air quality, or California
How air pollution compares in deaths and DALYs to other problems
When does an air pollution source have a very local impact vs a very broad impact?
Comparisons between different pollution sources are often counter-intuitive
Climate change has many pathways to create more air pollution, so a source’s main effect on air pollution might often be via its effects on climate
The main sources of outdoor air pollution in the US, and their decline
Quantifying the public health cost of air pollution
How air pollution is governed in the US
Inequity in exposure to air pollution, and failures of governance
What does the future look like?
Takeaways

The three main ways long-term exposure to air pollution harms us

There are many forms of air pollution, but the main way it currently harms us is via one of three pathways: PM2.5 (fine particles), ozone (a gas), and direct carcinogens. Their types and levels of harm are very different. As of writing, no safe threshold of air pollution has been identified, it appears to be harmful at all concentrations we encounter, and the more we’re exposed the more harm happens, both in terms of concentration and time. There’s no threshold where air pollution starts being harmful, it always is, just different levels of harm.2 The vast majority of ways air pollution harms us is due to long-term exposure, mostly via the buildup of risk that we develop specific diseases.

PM2.5

PM2.5 is fine particulate matter, particles 2.5 micrometers or smaller. It causes the large majority of the harms of air pollution. Roughly 70,000 of the 83,000 annual American deaths caused by outdoor air pollution are due to PM2.5.3 It can either come out directly from a pollution source (this is “primary PM2.5”) or be created when other types of air pollution interact with the atmosphere (“secondary PM2.5”).

Half of all premature deaths from PM2.5 are cardiovascular rather than respiratory. You’d expect something that harms you when you inhale it to mainly be bad for your lungs, but one of the reasons PM2.5 particles are so dangerous is that some of them are small enough to slip through your lungs’ membrane into the bloodstream, and get distributed everywhere blood flows. PM2.5 is associated with many different causes of death, here’s the 2021 global breakdown:

Source for the data

Something really weird about PM2.5 is that the long-term harm of additional units of it decrease the more there already is in the air. Here’s a rough chart of how these harms compare:

Approximation of the concentration-response work in Weichenthal et al. 2022, Science Advances used in many PM2.5 mortality discussions, alongside IHME’s Global Burden of Disease.

This is a simplified approximation, more serious estimates say the curve may look more like this:

Source

This means that if we graph how much additional harm is created by each additional unit of PM2.5, we get a graph that looks like this:

When you get to the concentration at the EPA’s daily limit, a decent amount of the potential harm is already being done.

We don’t actually know if this pattern holds for all the ways PM2.5 harms us. For example, very high indoor exposure to PM2.5 in pregnant women is associated with lower birthweight, and the trend there appears to be roughly linear.

The relationship between harm and time exposed is probably linear for long-term exposure, though short-term exposure has its own separate bad effects.

Some types of PM2.5 are more dangerous. One study found that PM2.5 created by sulfur dioxide from coal plants is about twice as harmful as average.

PM2.5 can last a long time in the air, sometimes as much as 4-7 days.4 This means it can be carried hundreds or even thousands of miles downwind before harming someone. PM2.5 from a wood fireplace in Vermont can end up being breathed by someone in Boston.

PM2.5 concentrations in US air have declined steadily since 1990, but recently stalled out due to among other things an increase in forest fires exacerbated by climate change.

Ozone

Ozone causes 13,000 American deaths a year.5 Unlike PM2.5, the vast majority of ozone’s long-term harm to our health is via just one pathway: COPD (Chronic Obstructive Pulmonary Disease), which is a progressive, irreversible lung disease that makes it difficult to breathe. Ozone seems to be responsible for about 10% of COPD deaths, smoking causes about 80%6. It can have other respiratory effects as well.

Ground-level ambient ozone generally isn’t emitted directly by ordinary outdoor pollution sources. It forms in the air when NOx (explained below) and volatile organic compounds react in sunlight.

Unlike PM2.5, the harm of ozone seems to scale basically linearly with how much concentration there is in the air. Each additional unit adds about the same level of harm. Ozone concentration has had a more gradual drop than PM2.5, but as explained below, this is in part due to more ozone being outside of our control.

Source

Direct carcinogens

Some forms of air pollution are carcinogens that increase your risk of cancer when you inhale them. These cause a much smaller part of total US deaths from air pollution, because there’s so little in the air, each unit adds only a tiny amount of cancer risk, and cancer is both rarer and slower to develop than cardiovascular disease.

Direct carcinogens also aren’t emitted in especially large quantities by the power generation sources data centers draw from. The study that found that data centers could cause $20 billion in health damages doesn’t even consider them as an avenue of harm, though they are a risk when onsite gas turbines are built close to homes, which will be the main source of carcinogen exposure that I’ll focus on in Part 4.

How does the harm of PM2.5 compare to ozone?

Per unit, PM2.5 is far and away the worst. A 10 microgram per cubic meter (µg/m³) increase in long-term PM2.5 exposure raises all-cause mortality by about 5-8% while the same increase in long-term ozone raises mortality by something more like 0.5-1%. So PM2.5 is roughly an order of magnitude more harmful per microgram than ozone, though this comparison is super fuzzy because the two are very different in how they’re delivered and how many people are exposed to them.

Here are the rough numbers on how each type of air pollution harm compares. HAP here is Hazardous Air Pollutants, another name for direct carcinogens:

PM2.5 mortality coefficients from Di et al. 2017, New England Journal of Medicine. Ozone mortality coefficients from Turner et al. 2016, American Journal of Respiratory and Critical Care Medicine. Total US deaths and DALYs from IHME GBD Compare.

This graph can give you some idea of how harmful each is, but there’s no single final answer because the harm depends a lot on the specifics of where emissions happen.

Long-term exposure is where the vast majority of air pollution harm comes from

The large majority of the modeled health costs of air pollution come from lost years of life from longterm exposure.7 Most of the diseases that air pollution causes develop over years or decades. Things like heart disease or stroke or lung cancer or COPD. Ozone damage to lung tissue also accumulates over long periods of time. Acute spikes in air pollution can trigger heart attacks or asthma attacks in people who are already vulnerable to either, but they are a small fraction of the total health costs of air pollution.

A long-term 10 μg/m³ increase in PM2.5 raises annual all-cause mortality by about 5-8%.8 A one week 10 μg/m³ spike raises mortality that week by something like 0.2 to 1%, and then the effect fades.9

When looking at sources of pollution like data centers, the main question for total health cost is how much they increase long-term annual averages of air pollution. Short-term spikes do matter a lot too, and have their own bad short-term health effects, but a big part of their harm to us comes from the increases in total average ambient air pollution they add to our net exposure.

Particles that make PM2.5 and ozone

NOx

NOx (pronounced as a word rather than spelled out, when reading it aloud say “knocks”) is the name of the group of two molecules: nitric oxide (NO) and nitrogen dioxide (NO₂).

A lot of NOx’s long-term harm comes from the fact that when it enters the atmosphere, it can react to other chemicals and create ozone or PM2.5. NO₂ can also cause short-term harm, and this shows up where data centers have built onsite generation too close to people’s homes, but its total health harm mostly comes from the ozone and PM2.5 it can create.

NOx creates way more ozone than PM2.5, it produces way more by mass, but since PM2.5 is roughly 10x more harmful, it can still make up a significant part of NOx’s total harm to people.10

There are six key things to know about NOx.

First, the types of dangerous pollution it creates depends on the air conditions it’s released into. To turn into ozone, it needs sunlight and volatile organic compounds (VOCs), so ozone from NOx is highest in the summer and mostly doesn’t get produced on cold cloudy days. PM2.5 formation needs ammonia (mostly from agriculture) and happens more in cold humid conditions. Trees are a huge source of VOCs, so rural areas tend to have lots of VOCs but not much NOx, and cities have lots of NOx and not much VOCs, and when excess city NOx blows downwind into rural areas, a lot of ozone can be created. A lot of the harm of NOx emitted in cities happens nearby in more rural areas.

Something weird here is that NOx itself also reacts with ozone and turns it into oxygen. So releasing a little NOx can create ozone, but if an area releases enough the NOx can start to cancel out the ozone the previous NOx created. In a city with a lot of NOx already, adding more NOx can actually reduce local ozone, but this also leads to more total NOx in the air, which can spread downwind and cause more ozone miles away where there wasn’t much NOx before. So there are situations where applying pollution controls in already polluted areas actually increases local ozone but decreases total regional ozone.

Second, NOx can travel far before having the reactions that turn it into dangerous pollution. It last about a day in the air, which is long enough to be carried for tens to sometimes hundreds of miles by wind before being converted to something else.11 The secondary PM2.5 it can create lasts even longer, so PM2.5 effects from NOx can show up hundreds of miles away from the source. This is how you get harm from data centers in Virginia showing up on Long Island.

Third, how physically high up NOx is emitted matters a lot. Emissions close to the ground concentrate NOx locally, because it’s more difficult for it to blow away. Tall stacks at power plants let it more easily disperse far away as it’s carried away by wind.

Fourth, different sources of air pollution emit different amounts of NOx. For data centers, we really only care about power generation. We’ll get into the specifics more in Part 4.

Fifth, NO₂ itself has specific short-term health harms, among other things asthma. There’s some evidence that consistent exposure to NO₂ also causes long-term harm, but it’s too uncertain to say either way because it’s often so bundled with other harmful pollutants.

Sixth, US NOx emissions have declined a moderate amount over the last two decades.

EPA Air Pollutant Emissions Trends Data National Emissions Inventory national NOx totals by source category.

SO2

SO2 (sulfur dioxide) is the other thing that makes secondary PM2.5 relevant to data centers. Like NOx, its main long-term harm comes from what it makes in the atmosphere rather than direct effects of breathing it. It can also irritate airways and trigger asthma symptoms, similar to NO₂, but these are dwarfed by the harms of the PM2.5 it creates.

The vast majority of SO2 produced by data center power draw comes from coal power plants specifically. Coal is the only major US electrical power source that emits significant levels of sulfur.12

Backup diesel generators can also create SO2. These rarely run, but will still be an important part of the story with data centers.

Like NOx, SO2 lasts for a day or two in the air, meaning it can be carried hundreds of miles downwind and cross state lines, and the PM2.5 it creates can itself be carried much farther, and harm people far away from the coal plant.

US SO2 emissions have collapsed since 1990, driven by the Clean Air Act Acid Rain Program’s cap-and-trade system, scrubber installations, and coal plant retirements. Total US SO2 is down roughly 95% from its peak.13 SO2 reduction is a part of why US air pollution mortality has dramatically fallen, yet it’s so harmful that it’s still a key part of the story of how data centers can harm us via air pollution.

EPA Air Pollutant Emissions Trends Data national SO2 totals, with power-sector detail available from EPA Clean Air Markets Program Data.

Harms from short-term exposure

PM2.5, ozone, and NO₂ can each trigger asthma attacks (especially in kids), reduce lung function and exercise tolerance, irritate the eyes/throat/nose, and increase ER visits for respiratory problems.

In the US, roughly 25 million people have asthma (about 8% of the population), driving around 1 million asthma ED visits and ~95,000 hospitalizations a year at a total economic cost near $80 billion annually. It might cause one in six new childhood cases of asthma.14

PM2.5 spikes can also trigger heart attacks and strokes in people with existing heart disease, and are linked to worse cognitive performance. Daily PM2.5 spikes are followed within a few days by small bumps in mortality.15 Ozone’s short-term effects are mostly respiratory.

NO₂ (one of the two types of NOx) is a short-term direct lung irritant, and can cause inflammation that makes people more vulnerable to respiratory infections. Significant increases in NO₂, which can come from nearby onsite gas turbines data centers use, are associated with increases in more children having asthma attacks that send them to the ER. One study estimated that traffic-related NO₂ was responsible for about 134,000 new pediatric asthma cases per year in the US, about 17.6% of all childhood asthma incidence.

NO₂ either reacts with the atmosphere or disperses quickly, so concentrations drop off within a few hundred meters of where it’s emitted, usually under a mile.16 Only people very close to the source (like a gas turbine or a highway) experience the short-term spikes.

There has been a big general reduction in absolute ambient NO₂ concentrations in US air, though large relative disparities remain. Both the annual mean exposure to NO₂ and the 98th percentile 1-hour daily max NO₂ exposure are well below the corresponding EPA National Air Quality Standards (NAAQS) for them.17

EPA Nitrogen Dioxide Trends

Short-term exposure can significantly add to your cumulative long-term exposure

One last harm of short-term air pollution is when you get such a big hit of it that it increases your total average exposure for the year. Wood has so much PM2.5 that standing around a wood fire for an hour can raise your annual total PM2.5 exposure by 0.3%.18 One study found that people who didn’t smoke but used wood stoves for at least 30 days per year were twice as likely to get lung cancer as non-smokers who didn’t use the stoves. This was caused by their average exposure going up as a result of individual quick hits of way higher PM2.5.

Anthropogenic vs natural air pollution

There are natural and anthropogenic (human) sources of air pollution. Most analysis mainly focuses on anthropogenic air pollution, because that’s what policy can actually reduce.

About 15-20% of the PM2.5 the average American breathes comes from natural sources19, mainly dust, sea salt, and the fine particles that form when trees and other plants emit gases that react in the atmosphere. Something that’s on the border between natural and manmade, and the biggest single source of primary PM2.5 emissions in the US, is wildfire smoke.20

In general, burning wood just emits crazy, crazy amounts of PM2.5 compared to most other things. Whenever you can smell burning wood, whether it’s a Canadian wildfire a thousand miles away or a quaint New England wood stove, you should assume you’re getting a much higher dose of PM2.5 than almost anything else you encounter outdoors.

A much bigger share of US ozone is natural, or otherwise beyond our control. Somewhere between 40% and 70%21 of the ozone at ground level in the US comes from things like the upper atmosphere drifting down, trees and plants releasing gases, lightning, soil, pollution blowing in from abroad, and methane emitted around the world. This changes a lot based on where you are.

Parrish and Ennis 2019, Atmospheric Chemistry and Physics for US background, plus EPA’s ozone NAAQS materials and 2020 Integrated Science Assessment for Ozone.

Most estimates of the harms of air pollution, like EPA’s benefit-per-ton estimates, measure in terms of the harms of the increase in the amount of air pollution rather than the total amount, because a big fraction of the total is outside our control.

American air is way cleaner than it was decades ago, and way cleaner than the world’s average

American air is not perfectly clean, but it is way cleaner than it used to be decades ago, and way cleaner than the global average.

Whether you look at average PM2.5, ozone, or NO₂ exposure, or NOx or SO2 emissions, the story of American air over the last 20 years has been significant and impressive improvement, with progress still left to go, and sometimes stalling. Meanwhile the economy continued to grow as normal. We’re getting way more value per unit of pollution emitted than we used to.

Emissions from EPA Air Pollutant Emissions Trends Data, real GDP from the BEA National Income and Product Accounts. EPA also publishes a national summary version of this chart.

These averages can also mask significant disparities in local exposure, which will be discussed below.

In general, metro areas are more polluted than rural areas. Here’s every US cities’ PM2.5 concentration compared to all global cities:

Data viz is my passion. WHO Ambient Air Quality Database

The US has it somewhat easy in managing PM2.5 exposure partly because our population density is so low. The average US citizen just isn’t living super close to huge numbers of other people all producing pollution. There are lots of other reasons, like our energy mix, wealth, and pollution controls. India and China top the global lists for PM2.5 exposure, Europe is also higher than ours on average, and if you just look at a population heat map of the regions, it becomes more obvious why they have a harder time keeping pollution low compared to us:

Other reasons India and China have more pollution than the US include heavier coal use, less emissions-control, less governance and enforcement capacity on air pollution, and different industries.

Because the global average is so much higher than the US, globally PM2.5 makes up a much larger percentage of deaths and DALYs lost than it does in the US. Globally, PM2.5 is responsible for about 7-8% of all deaths, whereas in the US it’s 1.7-2%. It’s responsible for about 5% of all global DALYs lost, whereas in the US it’s between 1.5-2%.22

US air has in general become significantly cleaner.

EPA Air Trends and EPA Air Pollutant Emissions Trends Data

A lot of US pollution harms occur in either average US air quality, or California

Here’s a graph of every county in America’s exposure to PM2.5 in 2019, the width of each bar is the population of the county:

County Health Rankings: Air Pollution - Particulate Matter, with methodology notes in County Health Rankings data documentation. CDC WONDER PM2.5 documentation.

I chose 2019 because 2020 is the last year we have good data, and 2020 was a weird year for air pollution in the US, both because of COVID and because of the wildfires that year.

If you’d like to see where your county shows up here, I’ve turned this into an interactive visual on my website where you can scroll through every county in the US.

The outliers on the left are mostly in California, I’ve highlighted every California county in red.

Why is California such an outlier?

The worst air quality in California is concentrated in two bowls: the Central Valley, and the LA Basin. Both have this crazy combo where air is trapped by surrounding mountains and prevented from circulating, persistent temperature inversions keep air pollution close to the ground where people breathe, and these places get very little rain to wash the PM2.5 out of the air.

The emissions are pretty bad in both places too. The LA area has the world’s busiest port, which means a constant huge input of diesel exhaust. The Central Valley is one of the most productive agricultural regions in the world, and agriculture is a huge contributor to PM2.5 because it creates lots of ammonia that reacts with NOx. There’s nowhere else in the country that has this much ammonia, NOx from vehicles, too little rain to cancel out the PM2.5, and is inside a valley where air doesn’t circulate out.

This isn’t even including wildfire exposure. If you look at how each county changed in 2020 relative to 2019, the California counties spike even higher because it was a bad wildfire year.

Data viz is my passion

If you bracket off California’s unique air pollution situation, the vast majority of PM2.5 exposure and modeled death is happening below 10 μg/m³. This about the average air quality of Germany or the UK.

This pattern has been indicated by more scientific methods. One study estimated that 99% of the mortality burden of PM2.5 in 2017 happened at exposure levels below the EPA’s at the time 12 μg/m³ standard.

The EPA’s old 12 μg/m³ standard is about average to somewhat dirty air in comparison to other countries with comparable GDP per capita. Here are the top countries by GDP with a line drawn showing where 12 μg/m³ is, excluding countries in the Middle East because desert dust and heavy oil and gas production skew them way higher in a way I don’t think is helpful for the comparison:

Our World in Data’s outdoor air pollution exposure dataset

Many people mistakenly believe that air pollution deaths only really happen in extremely polluted areas. They assume air pollution is only dangerous beyond some threshold. In reality, American air pollution deaths are for the most part clustered in the normal air we breathe, which is very clean by global standards, but because it’s not perfectly clean, and because air pollution carries some level of harm at any level, and PM2.5 in particular has a declining curve of harm where a lot of its total harm can happen before the air gets especially dirty,

Source

and because so many people live in clean but still somewhat polluted air, the roughly 70,000 deaths PM2.5 causes in the US each year largely happen because hundreds of millions of people are each getting tiny increases in the odds that we die from the air we breathe, which in aggregate can add to huge amounts of total harm. Individual communities’ risk can go way up when they’re exposed to really high levels of air pollution, but most of the people being harmed by air pollution nationally are breathing pretty normal American air, which is relatively clean by global standards.

How air pollution compares in deaths and DALYs to other problems

One thing we can measure is how many deaths it causes, or how many years of healthy life it robs from people. These paint different pictures.

Air pollution in America kills more people than car accidents, but most of the people who die due to long-term exposure to air pollution are much older, and often lose a few years of expected life. In comparison, people who die in car crashes are usually way younger, and so each death causes the person to miss out on decades of life. The common measurement of life years lost is “disability-adjusted life years,” or DALYs, measuring both years of life lost to death and years spent in poorer health or wellbeing. If you die 10 years early, you lost 10 DALYs but only 1 life. If air pollution gives you a permanent illness that your life qualitatively worse for the next 20 years, you didn’t lose any years of life, but we still want to try to quantify what you did lose to poorer health. The goal of DALYs is to try to capture that, using a disability weight: a number from 0 (perfect health) to 1 (equivalent to death) assigned to a given health state. Mild asthma might get a weight around 0.02, severe COPD around 0.4, terminal cancer closer to 0.5–0.6. The full DALY cost is the weight multiplied by the years spent in that state and you get years lived with disability, plus years of life lost to premature death.

If we compare deaths from air pollution and car crashes, air pollution looks much worse, but this flips if we compare DALYs, because so many car crash victims are young.

IHME Global Burden of Disease data

If we break up air pollution into PM2.5 vs ozone, PM2.5 becomes a slightly larger fraction of the total burden of pollution.

IHME Global Burden of Disease data

Air pollution compares differently to other problems based on whether we look at deaths or disability-adjusted life years. Either way, almost all of the harm is in PM2.5. PM2.5 costs as many life-years in America as lead exposure and interpersonal violence, whereas ambient ozone costs fewer life-years each year than choking, and just 22% as many as secondhand smoke.

IHME Global Burden of Disease data

The typical American killed by long-term exposure to air pollution is over 70 and has an existing heart or lung disease. The average person whose death is attributable to PM2.5 dies about 3-4 years earlier than they would.23 Air pollution basically slowly chips away at key parts of our health, and this mostly doesn’t show up until some of us die a few years early in old age as a result.

If I live in Washington DC my whole life instead of the countryside, my life expectancy drops by about a month.24 This doesn’t actually mean that I and everyone I know here will die a month early. It means that chronic PM2.5 exposure slightly raises the lifetime odds that any one of us dies somewhat earlier than we otherwise would, and across the whole population that averages out to about a month per person.

While some people die from long-term air pollution exposure younger than 65, the vast majority of deaths happen after.

IHME Global Burden of Disease data

When does an air pollution source have a very local impact vs a very broad impact?

When I talk about “national” vs. “regional” vs. “local” impacts of data center air pollution, you might think I’m using an accounting trick. Isn’t the national number just the sum of all the local harms? This will just look arbitrarily low because not everywhere has data centers, so they bring the average way down, right? What does this actually tell us?

This is actually wrong. Air pollution can travel for hundreds or even thousands of miles before harming people, so it makes sense to separately talk about national air quality and how it’s affected by different industries. Think back to the wildfires in Canada a few years ago and how they affected US air quality. These were broadly a national problem created by far-away PM2.5 sources rather than just a sum of local problems created by local air pollution. We can make general statements about US air quality overall that aren’t just the sum of the harms of very local air pollution sources.

Source

The quality of the air you breathe is basically the stacked effects of the national-level, regional-level, and very local level of air quality. Different types of air pollution can affect each very differently.

The main type of air pollution data centers create, NOx, can travel tens of miles or more before producing ozone or PM2.5, and the PM2.5 can travel much farther. Many studies that focus on data center impacts on air pollution focus on how they affect national air or large regional air quality, because most of their pollutants are carried far away.

Some sources of pollution mainly harm the people immediately around them. Others mainly harm people hundreds of miles away. This matters because among other things it can mean air pollution harms are more evenly spread rather than being unfairly bunched in specific places. What determines where air pollution goes?

Four factors determine how locally or broadly a data center’s pollution harms are spread.

The first is whether the pollutants are primary (emitted straight out of the source) or secondary (the result of chemical reactions with emissions and the atmosphere). Primary PM2.5 is dangerous the moment it’s emitted, and concentrations drop off quickly with distance from the source. Secondary PM2.5 forms over hours when NOx or SO2 react with the atmosphere. Secondary PM2.5 can appear tens or hundreds of miles downwind. Ozone is almost entirely secondary. It isn’t meaningfully emitted from combustion sources and forms when NOx reacts to specific compounds in the atmosphere. NO₂ is emitted directly from sources and its short-term respiratory harms are largely local and concentrated in the plume around the source, though because it’s NOx it can go on to contribute to ozone and PM2.5 downwind.

Somewhat related, the atmospheric lifetime of a pollutant determines how long it can survive in the air before becoming something else. NO₂ often reacts within minutes to hours, NOx more broadly can last about a day, so it can travel farther. PM2.5 typically lasts about a week, and longer in some conditions, which is why wildfire smoke can cross continents and do harm thousands of miles away. Man, PM2.5 is crazy!

The height of the thing doing the emitting matters a lot too. Pollution emitted near the ground often stays near the ground, so people are more at risk of breathing it or its secondary effects. If a stack is hundreds of feet in the air it lets the pollution out into an area where wind is stronger and it can mix more with the air higher up. Basically, the higher the stack emitting the pollution, the broader the range it can travel is, so its effects are way more dispersed. Sources close to the ground can harm very local air much more.

Finally, a lot just depends on local conditions. Valleys can more easily trap pollution locally and concentrate it. Background chemistry in the air determines whether NOx creates or destroys ozone. Secondary PM2.5 forms much more easily when there’s ammonia in the air from agriculture.

This can create problems for thinking about the ethics of air pollution, which I’ll write about more in Part 2. Two air pollution sources can do the same amount of very general harm, but if one spreads that harm by increasing the risk of a huge population in a big area by a tiny, tiny amount, it seems ethically different than if it concentrated all that harm on a very local area. This is a place where general harm reduction on its own doesn’t tell us much and we need to think about egalitarianism and past injustice as well.

So the question of how data centers impact your air quality actually depends on three overlapping questions: How do all national data centers harm national-level air quality? Regional-level air quality? And very local air quality?

The full effect of data centers on the air you breathe is the stack of all three.

Comparisons between different pollution sources are often counter-intuitive

I think almost all comparisons between different sources of air pollution are so counter-intuitive that you can basically never rely on them to give you a good sense of how harmful something is.

Take cars. Cars today emit shockingly little PM2.5. Their PM2.5 emissions are so low that they “emit” about as much via their tires making contact with the ground as from burning their gas, sending little bits of tire and dust into the air. They emit NOx, which mostly creates ozone, and some secondary PM2.5. But even their NOx emissions are much lower than they used to be. Old cars used to be way way worse, but catalytic converters are just really good now and combust most of the air pollution before it’s emitted. A new truck emits roughly 99% less hydrocarbons per mile than a 1970 pickup truck.25

We still think of cars as causing huge amounts of dangerous air pollution, so if you read that something is emitting as much air pollution as “thousands of cars” it sounds like a disaster, even if it actually fades into the background of what’s actually causing harmful air pollution.

Let’s say you’re given two options for what will happen near your home:

Someone using a gas-powered two-stroke leaf blower nearby for 1 hour.
Someone driving a truck in circles around your home over and over for 300 miles.

Which would be worse for your health?

Turns out it’s the leaf blower. A modern truck is the result of 50 years of advancements in pollution controls and tightening emissions rules. A two-stroke leaf blower has minimal emission controls, it’s basically a small, dirty engine with a fan attached.

The pollution that does the most damage when you’re right next to the source is primary PM2.5. One hour of the leaf blower puts about 15 grams of PM2.5 into the air around your home. The truck over 300 miles emits roughly 4 grams, even after including tailpipe soot, brake dust, and tire wear. The leaf blower wins by ~4×, even though it’s running for one-fifth as long.26

When you think about it this makes intuitive sense. Imagine what your city would smell like if everyone you saw driving were using a leaf blower instead.

This leads to a lot of confusing comparisons for air pollution. Take this quote from Newsweek on the study I mentioned above:

The researchers estimated how much air pollution might arise from the computing work that goes into training some AI large language models. They found that training a model the size of the Llama 3.1, launched this summer by Facebook parent company Meta, could produce the same amount of pollution as 10,000 cars driving round-trip between Los Angeles and New York.

How much air pollution is 10,000 car trips between LA and New York? Sounds like a lot! But cars are a terrible way of doing air pollution comparisons, because they now emit so little, but we still think of them as emitting a lot.

This study is only looking at the PM2.5 produced by cars, not the ozone. 10,000 cars driving round-trip between LA and NYC emit about 500 kilograms of PM2.5 in total. That’s roughly equal to about one hour of NYC’s commercial cooking emissions, two or three days at a mid-sized cement plant, a single large cruise ship idling in a harbor for a day, or the winter heating emissions from about 10 American homes that burn wood for heat.27 Not nothing! But these paint a very different picture than “10,000 cars driving thousands of miles.”

Climate change has many pathways to create more air pollution, so a source’s main effect on air pollution is often via its effects on climate

There are a few ways climate change puts us at risk of more local harmful air pollution:

Increasing chances of wildfires, which are just so uniquely bad for PM2.5 exposure.
Higher temperatures accelerate the types of reactions in the atmosphere that produce ground-level ozone from NOx.
Drought and soil aridification increases windblown dust PM2.5.
Longer growing seasons which worsen exposure to pollen, which can interact with PM2.5 to create worse respiratory harms.
Consistent heat can trap pollutants near the surface for longer stretches.

Combining these all implies that, because American air pollution sources have radically cut their PM2.5 and NOx emissions compared to a few decades ago, but are still emitting lots of CO2, for many already-clean sources the CO2 channel plausibly does as much or more damage to US air as their direct PM2.5 and NOx, even before we count effects outside the US.

A back of the envelope calculation for how these compare for a Toyota Corolla and a grid-scale gas plant suggests that the Corolla creates 2-8x as much modeled health harm from PM2.5 in America via the effects on climate of its CO2 emissions compared to the PM2.5 it actually creates directly in the atmosphere28. For a modern gas plant it’s 3-10x29. This is mainly a story of us getting very good at removing PM2.5 from pollution sources, but not good at reducing CO2 emissions.

Most climate-driven wildfire and ozone mortality currently lands in South America, Australia, India, and Eurasia, not the US,30 so the harms are many times greater there. This implies that the main thing to worry about for net air pollution harms from an emitter at the national level is actually its CO2 emissions, not the direct harmful-to-breathe pollutants it creates in the atmosphere.

So I’d expect a really huge part of data center effects on air pollution to actually be via their effects on climate. Climate’s complicated enough that I’ll save the full deep dive on this for another post.

The main sources of outdoor air pollution in the US, and their decline

PM2.5

The most recent study of deaths from PM2.5 (both primary, emitted at the source, and secondary, created by other emitted pollutants like NOx) by sector was in 2019. I’ve made a chart comparing the deaths in 2019 to 2002 to show how much safer different sectors have become, all from this study.

Source

PM2.5 deaths have dropped in America. There were twice as many in 2002, and coal-electricity deaths specifically have fallen 89%. This mainly came from a cleanup of the electricity and transportation sectors.

The drop in electricity sector pollution deaths isn’t attributable to using less electricity, because we were using 8% more in 2019 than in 2002:

The authors find that natural gas, oil, and biomass collectively killed about 1,000 people a year through PM2.5 in 2019. Coal killed about 6,000. Comparing these numbers to how much electricity each generated in 2019, coal kills about 6.3 people per TWh, and gas, oil, and biomass collectively kill about 0.6 people per TWh. This implies that if in 2019 we had replaced all coal with gas, oil, and biomass, something like 5,400 people’s lives would have been saved from air pollution, bringing the total deaths from PM2.5 from electricity generation down from about 7,000 to about 1,600 per year. Remember that coal also produces SO2, which creates PM2.5 about twice as dangerous as average, so the gains from displacing coal are bigger than the deaths-per-TWh ratio alone suggests. A grid made up entirely of natural gas over coal would involve way fewer pollution deaths.

Putting pollution controls on coal plants, and swapping out coal for natural gas, have together been one of the biggest positive wins for air pollution. The rise of renewables have also played a role here, as have better pollution controls on coal plants. This is the year-by-year estimate of deaths from electricity-sector PM2.5 compared to the grid makeup over the same time:

Source for deaths and power sector from Table 7.2a here

You might notice that the chance in mortality drops off fast around 2008, despite coal not budging much. This is because around this time the Clean Air Interstate Rule from 2005 had forced a big retrofit of coal plants with much more effective pollution controls.

How has the American grid changed since 2019? As of 2024, coal generated 33% less energy, so modeled PM2.5 deaths from electricity generation were likely lower than in 2019, and dramatically lower than in the early 2000s.31

Obviously your takeaway here absolutely should not be “gas is always good.” We eventually need to move from gas to renewables, and gas is especially bad if turbines are built very close to people’s homes. The data center buildout more broadly is seeing a huge uptick in natural gas demand that wouldn’t have otherwise happened, and this is a big loss for air quality and climate. None of this is lessened at all by good vibes gas has built up by replacing coal. But I do think your takeaway should be “coal being replaced with gas specifically is so so so preferable.” Thousands of additional people don’t die every year because of the rise of natural gas replacing coal. We can continue that trend by over time replacing gas with cleaner sources, but natural gas has been a vital stepping stone in reducing huge amounts of death and sickness along the way.

Ozone

Remember that ozone is responsible for a much smaller portion of US deaths than PM2.5, maybe 14,000 to PM2.5’s 70,000. We don’t have any direct way of tracking which industries are most responsible for ozone, because the way it forms depends on atmospheric conditions and the availability of volatile organic compounds. The best we can do is track NOx emissions (which produce ozone) by industries over time. These have significantly fallen over the past 20 years, and the decline came almost exclusively from cleaner vehicles with better catalytic converters, better controls on coal plant emissions, and the general decline of coal.

Total ozone declines have been significantly less dramatic than NOx declines:

Source

But this is largely because there’s a lot more uncontrollable background ozone we can’t cut by reducing emissions.

Parrish and Ennis 2019, Atmospheric Chemistry and Physics for US background, plus EPA’s ozone NAAQS materials and 2020 Integrated Science Assessment for Ozone.

Quantifying the public health cost of air pollution

When people say air pollution creates “$x billion in public health costs” they don’t usually mean that actual people are paying this amount in hospital bills or other costs in their daily lives. Instead, this cost comes from someone putting a dollar value on human life in general, and multiplying it by how much human life will be lost in expectation to air pollution.

This dollar amount is the “value of a statistical life” or VSL, which is modeled off what people are collectively willing to pay for small reductions in their mortality risk. The VSL EPA uses is $7.4 million in 2006 dollars, updated for inflation to something around $12 million today.32

One popular tool for estimating harm from pollution is EPA’s COBRA, short for CO-Benefits Risk Assessment. It estimates how much PM2.5 and ozone a pollution source creates, applies that added risk to each county’s death rates by age, counts the resulting total expected number of deaths, and multiplies them by the value of a statistical life.

Here’s an example to get it across, though it’s simpler than the actual math COBRA uses: if you released an extremely toxic form of air pollution around 5 people, one of them is old and has a 50% chance of dying from it, and the others are young and each have a 10% chance of dying, this model adds up the total odds of death among the 5 people, (0.5 x 1) + (0.1 x 4) = 0.9, and considers this to mean that in expectation 0.9 people will die. It then multiplies by the value of a life (around $12 million) to get a final cost of 0.9 x $12 million = $10.8 million. So COBRA would say releasing this extremely toxic form of air pollution around the 5 people had a health cost of $10.8 million. Notice that this isn’t the cost of taking care of the people, it’s saying a human life is worth $12 million, and multiplying that out.

COBRA isn’t measuring actual deaths here, it’s measuring statistical deaths. If 150,000 people each face an added mortality risk of 0.001 percent, the model counts that as 150,000 x 0.001% = 1.5 statistical deaths. It’s a measure of expected value, because we can’t actually trace who died of air pollution. Expected value calculations are very good and should be the basis of way more decision-making.

COBRA also attempts to put a dollar value on non-fatal health conditions that make life worse and are caused by air pollution, like non-fatal heart attacks, respiratory and cardiovascular-related hospital and ER admissions, and asthma. However, EPA’s recent analysis finds 85-95% of the monetized harm of polluted air comes from premature deaths, not other health conditions.33

COBRA does not consider how many life years were lost due to air pollution deaths. It values the death of an 89 year old and a 30 year old equally, at the same $12 million. I think this is weird, because we obviously don’t think this way about our own lives. Suppose you were going to be forced to play Russian roulette, where you had a 1/6 chance of dying. Would you rather that happen when you were 30, or 89? If you could pay money to make it happen at 89 instead of 30, how much would you pay? If you’d pay more than zero, it shows you put a dollar value on those extra years instead of just wanting to pay to avoid death in general.

The EPA knows this is a tension, and there are different reasons for why they use the model they do, but one of them is political. In 2003, EPA tried using an age-adjusted value in a Clear Skies analysis where it ranked the lives of people over 70 as having a slightly lower dollar value. Critics in government and the media called it a “senior death discount,” and EPA dropped it.34

Different organizations have attempted to price the full health cost of US air pollution using models similar to COBRA. One study put PM2.5 damages at $886 billion and 107,000 premature deaths in 2011. Another estimated $790 billion in 2014, roughly 5% of GDP, falling by more than 20% from 2008 to 2014. A third landed at about $1 trillion. And one COBRA study found that zeroing out emissions from the six main energy-related sectors alone would yield $608 billion in annual health benefits and avoid 53,200 premature deaths, without even including agriculture or wildfires. The models converge, roughly, on a full anthropogenic price tag somewhere around $1 trillion adjusting for inflation. This is about 1/30th of US GDP.

But we’d probably not choose to actually spend 1/30th of US GDP to reduce air pollution as it exists right now, and so it’s hard to compare these numbers to other costs, and spending this money elsewhere would save way more lives. We also don’t usually behave like pollution harms measured this way should actually be paid for by the people who create them. A few hours of a typical fire in a fireplace imposes something like $15–17 in damages on the surrounding community, because wood puts out so much PM2.5. We don’t actually expect people to be taxed $20 per use of their fireplace.35

The study that I quoted at the beginning that mentions ‘$20 billion dollars in health harms from data centers’ is called “The Unpaid Toll” which sort of implies data centers ought to actually pay for this toll or something similar. But this isn’t a standard we hold any other industry to. We can and do tax externalities, but often not at the level these health cost models imply we should.

Surface area and population

Models like COBRA measure harm in dollar amounts by multiplying the number of people exposed to pollution by the additional risk the pollution adds. This means that if air pollution hits a city, it’s measured as way way worse than in a rural area, because way more people are exposed to it. This obviously makes sense, but a weird result is that because air pollution spreads so far, maps of air pollution harm largely look like population density maps.

Here’s the map of data center air pollution harms from the study I mentioned in the beginning:

Source

I made a population heat map by percentile using a similar color scheme:

Numbers from the US Census Bureau, 2023 county population estimates

So the first is mostly also just a population heat map. The reason data center pollution shows up across so much of the country is that NOx travels far before forming secondary PM2.5 and ozone downwind, and these are the main harms of data centers. Basically, if a fossil power plant within 100 miles of you is generating any extra electricity because a data center is on the grid, that plant’s air pollution is going to show up as an extremely tiny but real increase in your daily risk. Almost every American lives within 100 miles of one of the thousands of data centers and the plants serving it, so a map of who’s being harmed by data center air pollution ends up looking almost exactly like a normal population map.

These maps can be useful at times, but don’t give you a sense of the scale of this problem compared to most other sources of air pollution. If an incredibly tiny cost is spread across all of America, then in total it will look large, and a heat map of where it’s having the most impact mostly just tells us where people live.

All air pollution harms expressed in dollar amounts at the national level always look bad in isolation

The singular sin of bad environmental writing is giving people contextless large numbers, and any model that quantifies harm in dollars over huge aggregates is very good at generating them. It’s very common to see people talk about “billions of dollars in harms” without actually clarifying how this compares to other things we do. I’m not saying here that you should assume billions of dollars in harm is small, just that you should try to contextualize it with other issues we’re facing.

Suppose that an industry causes $1 billion in public health costs to America overall each year. Should we ban it? Well, any small increase in relative harm multiplied over lots of people and then multiplied by the full money amount we value being alive is going to produce a gigantic number regardless of what we look at. How does this compare to other stuff?

I’ll try to put some general number values on other harms using DALYs and HHS’s cost of a life year, about $550,000.36

Secondhand smoke costs around 990,000 DALYs in the US every year. Multiplying by the life year cost gets us to $544 billion. So this new industry is harming America 1/540th as much as secondhand smoke.

How would you expect secondhand smoke to compare to all harm from the pollution created by literally all power plants in the US to every single person it affects? Well, all US electricity-sector air pollution costs between 85,000 and 190,000 DALYs each year, so even at the maximum it costs $105 billion, only a 5th of secondhand smoke.37

So this new industry is about 1/100th as bad as the fossil fuel electricity sector, and 1/500th as bad as secondhand smoke. I’m from New England, where a lot of people still use wood as primary or supplemental heating, and because burning wood is just so bad, a back of the envelope calculation implies that it costs around $3–4 billion in health costs. So this industry is about 1/3 as harmful as all New England wood heating.38

How air pollution is governed in the US

American air pollution law is designed to be agnostic about the social value of what an emitter produces. The law just regulates specific pollutants and the things that create them. A ton of NOx is regulated the same whether it powers AI training or a hospital.

Because any amount of air pollution carries some harm, American air pollution law allows large numbers of people to be exposed in aggregate to industrial and individual emissions. The legal structure has two main layers:

Something to control general ambient air quality.
Something to control what individual emitters do regardless of the ambient air quality.

Basically all industries, commercial buildings, and recreational buildings in America impose some harm on the surrounding community via air pollution, and sometimes on more national American air in general. This on its own isn’t a reason to shut them down. It’s perfectly legal to impose some level of air pollution harm on other people, and if it weren’t society would grind to a halt, because everything we do creates some. The main question is whether an industry is (along with the others in the region) pushing air quality above some specific legal or ethical threshold of harm.

The main federal air pollution law is the Clean Air Act, which tells the EPA to set National Ambient Air Quality Standards (NAAQS), for six “criteria” pollutants: carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter, and sulfur dioxide. These measure the air quality in general.

In 2024, EPA tightened the PM2.5 standard to 9.0 µg/m³, but plenty of harm still happens below this line. EPA standards don’t represent some magic point where pollution stops harming us. You might think their decision to set this level has something to do with balancing health against the economic costs of restricting air pollution, but it is actually illegal for them to consider economic costs when setting the clean air threshold, according to a unanimous Supreme Court case in 2001. Cost-benefit questions can come up at lower levels when state governments decide how to implement the standards, but the standards themselves are decided purely by considering human health.

States meet the clean air standards with “State Implementation Plans” or SIPs. Areas that meet a standard are in “attainment.” Areas that don’t are in “nonattainment,” and have to use stricter rules for the pollutant they are failing to reduce down to required general levels.

One problem with this general setup is that states may keep their own air clean but pollute air far away, because air pollution can travel far. The Clean Air Act addresses this through what EPA calls the “Good Neighbor” provision, which requires every state’s SIP to prohibit emissions that significantly contribute to another state’s nonattainment, or interfere with another state’s ability to maintain the standards. If a state fails to submit an adequate Good Neighbor SIP, or EPA disapproves it, EPA can impose a federal plan instead.

The current state of Good Neighbor enforcement is up in the air. The Supreme Court stayed EPA’s 2023 federal Good Neighbor Plan in June 2024, and EPA has since been reconsidering. It isn’t currently operating as a tightly enforced rule across the country.39

For data centers, the most important thing to understand is emissions permitting. Before building a new source of air pollution or making a major change, data centers may need a New Source Review (NSR) permit.

If the area is in attainment (meeting the requirements of the Clean Air Act), the permit they’d seek as a major source of pollution is called Prevention of Significant Deterioration, or PSD. PSD is basically the “don’t let clean places get too much dirtier” program. The data center has to use required pollution controls and show that it will not push air over the EPA limits or consume more than the allowed PSD increment.

If the area is in nonattainment for a pollutant, they’d get a permit called a Nonattainment NSR, which is stricter. It requires the lowest achievable emission rate, emission offsets, and public involvement.

Data centers aren’t just regulated by how much they actually emit, they’re also judged by their “potential to emit” or PTE. If a data center has hundreds of backup diesel generators that it rarely uses, those are also considered and regulated. The EPA now has a data-center-specific Clean Air Act page with PTE guidance for emergency backup generators.

There are other tools available to regulate air quality besides air permits. Regulators can determine what gets built where through zoning. State review and public comment can affect the design of a new data center.

Inequity in exposure to air pollution, and failures of governance

Racial inequity in exposure to air pollution harms in America is pretty stark. White Americans on average are exposed to about 17% less PM2.5 than they cause through consumption, while Black Americans are exposed to about 56% more and Hispanic Americans about 63% more than they cause. The disparity between groups corresponds to roughly a 1% increase in average all-cause mortality risk from PM2.5 alone, and this isn’t even considering disparities in extremes of exposure.40 Total exposure for every group has fallen significantly since 2000, but the relative gap has been stubborn.41

Big inequalities also exist in NO₂ exposure, and this is going to matter a lot for the story of data centers and local air pollution harms. The reason is that, as mentioned above, NO₂ spikes happen very close to where they’re emitted, and dissipate farther away. NO₂ can contribute to regional and national PM2.5 and ozone formation, but this is a very tiny increase over a very broad population. Here the harms are felt more equally, and are governed well for the most part. In comparison, breathing large amounts of NO₂ from a nearby source directly is harmful separate from how much PM2.5 and ozone it might eventually make, so there are much more disparate impacts from very nearby NO₂ sources determined in large part by background patterns in city design, and there are lots of holes in US governance of very local air quality compared to regional and national-level air.

Total exposure to NO₂ has been significantly reduced in absolute terms, but the relative differences between groups remains large. The top tenth of census tracts for NO₂ attributable mortality in America were 29% Black, even though Black Americans make up only 12% of the total population. In Chicago and Detroit, roughly 60% of the population living in the highest NO₂ mortality tracts was Black, even though Black residents were less than about 21% of the populations. Predominantly Black tracts had NO₂ attributable mortality rates about 47% above the national average, because they had both higher NO₂ exposure and higher underlying mortality risk.

How much is very local pollution to blame for inequity in pollution exposure, compared to general patterns in where different groups live? California for example has a much higher Hispanic population than average, and also way more polluted air on average, so maybe this explains the gulf in Hispanic exposure?

The Spatial Decomposition of Air Pollution Concentrations Highlights Historical Causes for Current Exposure Disparities in the United States breaks this problem down. It splits the disparities into 3 categories:

Between-state: How much of the disparity is determined by which states people live in.
Within-urban: How much is determined by which parts of a city you live in (closer or farther away from major emitters, things like industries or roads and highways).
Other (urban-rural and between-urban): This is a catch-all for two differences: whether a population is more likely to live in cities or in rural areas in the same state, and whether they’re more likely to live in the more or less polluted cities within the same state. LA is much more polluted than Sacramento etc.

The study found that disparities exist for different reasons in each group analyzed:

Source

So within-urban disparities are big parts of the inequity in air pollution exposure Black and Hispanic Americans face, and are a real but lesser part of the story for Asian Americans, though their total NO₂ exposure is the largest of any group.

Within-urban disparities often reflect the legacies of redlining. Redlining maps from the 1930s remain strong predictors of pollution exposure today. The same dynamics that make inequity in within-urban air pollution exposure persistent can matter for data centers too, especially in cases where developers propose onsite gas generation near residential neighborhoods.

Why are old redlining maps such strong predictors of current exposure to air pollution? There are lots of compounding reasons:

Highways were deliberately built through them.
Redlining made it difficult for residents to build up equity, and without accumulated wealth, neighborhoods had less political power to resist new polluting industries, fund lawsuits, or extract concessions from developers.
Many cities used “expulsive zoning” to permit heavy industrial uses in Black residential neighborhoods.
It was easier to build new polluting industry in areas already polluted, because permitting reviews evaluate each new polluter’s marginal contribution to local pollution rather than the cumulative burden on the neighborhood.
Redlined areas have less tree canopy on average, and trees absorb some pollutants directly and cool the air down, which somewhat suppresses ozone formation.

While the Fair Housing Act and later reforms formally outlawed redlining and related housing discrimination, the highways and industries were still where they were built before it. One recent study found that historically redlined districts still have on average 56% higher NO₂ levels than the parts of the same cities that were designated the most desirable for federally backed mortgage.

A huge issue of any new large industries that have onsite pollution sources, like large data centers, is that they can reinforce the dynamic that keeps formerly redlined neighborhoods way more polluted than average. This is one of the clearest real potential dangers of the data center buildout. Data centers are on the market for things that could correlate with historically disinvested areas, like cheap land and an easy permitting environment.

A key question is how much we can rely on local government policy to keep communities safe from data centers pushing air pollution in very local communities over the line of harmful NO₂, and broader regions over the line of ozone and PM2.5. In places where government can’t be relied on to keep total air pollution below accepted thresholds, communities should say no to data centers with significant onsite primary power, and society more broadly should name and shame specific companies imposing excessive pollution on already overburdened communities.

Gaps in exposure persist despite the Clean Air Act’s general success, because the act mainly targets regional and national air quality, not so much very local sources of very local pollution. Exposure to local pollution often has more to do with what’s allowed to be built very close to you. So even as national and regional air quality improves, very local sources of primary PM2.5 and NO₂ can still harm the same neighborhoods they did 50 years ago.

The main hole to worry about in government air pollution control is very local air quality around big emitters, because that isn’t regulated as well. To fully consider the question of how and when we can and cannot rely on governments to make good decisions about air quality and data centers, I need to give a much more detailed overview of how data centers specifically contribute to air pollution, which will need to wait for Part 4. For now, your takeaway should be that data centers, as large industries that need to generate huge amounts of power, threaten to continue and reinforce these unjust patterns in who gets exposed to air pollution if they are built with huge amounts of onsite gas power close to residential areas.

What does the future look like?

Before the year 2000, there was a lot of low hanging fruit for reducing air pollution, because everything we did emitted crazy amounts. To put this in perspective, if you naively assume that data centers cause the average levels of pollution from the grids they draw from per unit of energy, the grid in the 2000s was so dirty that the first big data center buildout at its peak was adding over twice as much PM2.5 to the air (both primary and especially secondary) as data centers will by 2030. Notice that even as coal declines, it’s still a huge part of total PM2.5 from energy.

Sector emission factors from Bekbulat et al. 2025, “Changes in PM2.5-Attributable Mortality in the US by Sector, 2002–2019,” historical and projected generation by fuel from the EIA Annual Energy Outlook data-center electricity demand projections from LBNL’s 2024 United States Data Center Energy Usage Report.

This is because the PM2.5 per unit energy of the American grid has been dropping off rapidly, partly because there are way more pollution controls on sulfur from coal plants (the CAIR Act in 2005 forced a retrofit of old coal plants with new pollution controls that you can see start to really take effect in 2007), and because coal plants were replaced with natural gas over time. Here’s a rough guess at the PM2.5 cost of a unit of energy:

The story of NOx and ozone is more mixed, because gas still emits a lot, so data centers in 2030 could be emitting more than ever.

For NOx: EPA Air Pollutant Emissions Trends Data and EPA Clean Air Markets Program Data, EIA Annual Energy Outlook for projected fuel mix, LBNL’s 2024 United States Data Center Energy Usage Report for DC power demand

Though NOx intensity has also declined a lot.

The grid being cleaner means that the remaining harms of air pollution will be trickier to reduce here.

The most promising future cuts for air pollution are usually going to overlap with what’s emitting the most, though not always. We can go line by line on the sectors emitting PM2.5 and NOx to think about what the future will look like. Note that these “anthropogenic” sources don’t include forest fires, which will be discussed more in the climate change section below.

PM2.5

Source

Agriculture

Agriculture causes about 12% of total US anthropogenic PM2.5 mortality, and the large majority of that is from livestock. Maybe it’s time for you to join me in veganism ❤️🐖🐔🐟❤️.

There are a lot of relatively cheap ways to reduce this that could plausibly save thousands of lives a year together, maybe somewhere around 6,000, depending on the what’s actually done, which is about as much as all remaining deaths from electricity-sector PM2.5 emissions.42

Simple air pollution regulations could be imposed on farmers to make sure that they cut emissions using these tools, but as discussed before, farmers are politically untouchable in America and while these regulations have been considered by different agencies, politicians haven’t actually implemented them. I’m pretty pessimistic about this route to lowering PM2.5, even though it’d be pretty cheap and easy. Question farmers.

Industrial

Industrial sectors that emit a lot of PM2.5 include cement, iron and steel, chemicals, and petroleum refining. Iron and steel alone cause between 900 and 2,000 deaths per year, concentrated in places like Gary, East Chicago, the Houston Ship Channel, and the lower Mississippi industrial corridor.43

Transportation

Transportation is a huge air pollution success story, thanks to better catalytic converters and the slow death of diesel. Most of the remaining PM2.5 from transportation come from things that use diesel, like long-haul trucks, trains, and ships.

Cars are surprisingly not promising for reducing PM2.5 much more, because their tires and brakes now produce about as much as their exhaust.

Residential

98% of PM2.5 emissions from residential fuel combustion sources are from wood-burning, but wood-burning only provides 2.2% of residential energy in the US. Come on!!! This is responsible for something like 10-17% of all US PM2.5 mortality. What are we doing?!?!?!44

You might think this is okay, because people burning wood in a fireplace are the ones harmed by breathing it, and so this is an optional risk they’re taking on. But these numbers are only for outdoor air pollution. This is getting added to the air around the home burning the wood.

All pollution from US electricity generation in 2026 kills fewer people than this. Seems like we should stop using wood as heating in homes. The occasional campfire is fun, but relying on wood more consistently is just so bad for air quality.

Electricity

The remaining large sources of PM2.5 are still largely coal, but that’s expected to keep falling as gas, wind, and solar are now cheaper and more competitive. It seems like a lot of the low hanging fruit in making coal more clean has been picked.

Gas plants are much cleaner than coal for PM2.5 and SO2, but they do emit significant NOx which still forms secondary PM2.5, though much less than SO2. New gas plants with SCR controls are roughly one to two orders of magnitude less harmful per MWh than the coal units they’re replacing. Renewables emit basically nothing.

The main question for electricity is how we can retire coal plants faster, then how we can replace gas with clean energy. Data centers are delaying both, and causing a huge uptick in demand for gas. We’ll look into this more in Part 3.

NOx

EPA Air Pollutant Emissions Trends Data

Highway and off-highway vehicles

On-road NOx is a huge success story in reducing air pollution. Catalytic converters are much better, and EVs don’t emit NOx at all. The remaining transportation NOx is mostly from diesel, and mainly from long-haul trucks, buses, locomotives, ships, and off-road equipment like construction machinery and tractors.

Stationary fuel combustion

Gas-fired power plants are now the largest stationary NOx source in America, and this is being driven way up by the data center buildout.

New gas plants with “selective catalytic reduction” (SCR) cut 80-90% of NOx and emit small amounts per MWh. Old gas plants emit a lot more, sometimes an order of magnitude.

Industrial processes

Cement and steel again show up here. The very high temperatures form thermal NOx. Cement kilns can install some pollution controls, but retrofits of these are expensive. Refining and petrochemicals contribute meaningful NOx from heaters. A lot of this is technically controllable, and Texas, Louisiana, and California have all gotten major reductions out of refineries over the past 20 years, but the lower-hanging fruit has been picked and the remaining cuts are more expensive per ton.

Other

Synthetic nitrogen fertilizer used in agriculture gets partially converted to NOx by soil microbes, and could be significantly cut with available tech.45 This isn’t really regulated at all… wonder why…

Climate change

Climate change is going to make dealing with air pollution in America more difficult. Tiny cuts in PM2.5 can be easily swamped by massive new forest fires, among many many other problems. We’re at a low enough level of PM2.5 and ozone emissions that I’d personally bet the most effectual way to preserve national-level air quality will be focusing on cutting CO2 over cutting additional PM2.5 or NOx. We can do both with green energy, but projections for cutting emissions to zero anytime soon are bleak.

Source

Takeaways

American air has, along most axes, gotten significantly cleaner in the last 20 years. While American air is way cleaner than it was decades ago and much cleaner than the global average, air pollution at any level is harmful, and so multiplied by our huge population still results in roughly 80-90 thousand deaths per year, almost all of them older and losing about 3-4 years each. Air pollution also causes short-term harm, among many other things it’s a big contributor to childhood asthma cases and ER visits.
If you were to chart where this harm is coming from, most of it comes from pretty normal American air. Most people in America live in relatively clean air, but the harms are not zero here and still accumulate over huge numbers of people. The big exception is California, which has a ton of compounding ways that air pollution is way worse where lots of people live.
Different types of air pollution have wildly different levels of harm. PM2.5 is the worst, followed by ozone as a distant second. NOx and SO2 are mainly harmful because they create PM2.5 and ozone when they react with specific particles in the atmosphere. NO₂ has important short-term harms, among other things it’s a major contributor to childhood asthma ER visits, and maybe long-term harms too.
Comparisons between different things that pollute are often wildly counter-intuitive. Cars especially emit very little NOx or PM2.5 now and are a bad reference point for major industries.
Maps of national pollution harm are often just population density maps.
The decline of coal and its replacement with gas has been a huge part of the general decline in American deaths from air pollution. Data centers threaten to delay or reverse the decline of coal, and are encouraging a huge separate buildout of new gas capacity that isn’t offsetting old coal plants. Data center companies are also the main private funders of new clean energy as well, but on net they will make American air more polluted in the short-term.
The effects of climate change on air pollution are large enough that, for many sources that have been cleaned to not emit much local air pollution, the CO2 they emit may “release” as much or more PM2.5 and ozone in expectation as the PM2.5 and ozone they emit directly.
Because everything that we do creates air pollution either directly or through electricity generation, we cannot just ban all air pollution, and instead need to set thresholds of general air quality to aim for and modify the behavior of industries around those. This is how American law approaches it. American air quality is governed very well at the national and regional level, but the local level governance has holes and is less reliable at preventing exposure to things like direct PM2.5 or NO₂.
Our intuitions about the harm of air pollution can be warped in a lot of ways:
- We can underrate the harm of, by global standards, very clean American air. It’s still killing about 90 thousand people a year.
- We can miss inequities by only looking at average levels of pollution.
- We can be too optimistic about the future of air pollution reductions based on how the past has gone.
- We can overrate the harms of tiny levels of pollution increase across very large populations or risks that we wouldn’t personally pay to remove.
- We can overrate the harm of any new industry’s pollution if we don’t contextualize it with other industries or the general state of American air quality.
The fact that air pollution is harmful at any level makes two opposite sides mad at you: people will get mad at you when you say it’s okay for an industry to emit some air pollution, because that comes with real human harms, and others will get mad at you when you say we need to limit air pollution, because all air pollution’s harmful anyway and why are you getting in the way of industry? Both of these are wild oversimplifications that would lead to terrible policy.
When thinking about a new industry, the question should always be “Will this, along with all activity in the area, collectively reduce the general local, regional, and/or national air quality down to a level that we’ve separately agreed is too low?” If it does, we should rigorously regulate or just oppose it, and if not, we should allow it to emit reasonable levels of pollution like we do other industries. The key is drawing that arbitrary line at what level of air quality we think is too bad for all activity in an area to collectively degrade the air below. That line will always be wildly contentious, but we need to draw it and stick to it. Our national legal arbitrary line is the EPA National Ambient Air Quality Standards (NAAQS).
The dollar amount measuring air pollution health harms comes from applying a dollar value to human life and multiplying it by the odds that pollution kills people times the number of people exposed. This is important to understand but doesn’t translate well to other normal costs we consider in society.
The measured cost of air pollution is a useful tool but doesn’t tell us much in isolation. It’s important to know how the costs compare to other industries and whether a cost is being unjustly concentrated on already overburdened communities or spread equally across a wide area, very marginally increasing the average person’s risk.
People with less economic power are in general less able to resist industries pushing their communities over the line we set for clean air, so large disparities still exist in exposure to very local air pollution, and those are the places we need to be hawkish on it and cannot rely on good local governance alone.
We should expect fully electrified industries at this point to only produce huge amounts of new local air pollution at the extremes, because the electric sector is so much cleaner than it was 20 years ago (thanks almost exclusively to the decline of coal and its replacement with gas, plus pollution controls), but the extremes are in fact happening with the largest data centers.
The promising cuts to make left in air pollution are among other things the continued closure of coal plants, transitioning from gas to renewables, regulating agriculture, and maybe not burning wood anymore. These decreases are likely to be offset a lot by climate change increasing air pollution, so the main way emissions create additional PM2.5 and ozone going forward might be via their CO2.

A Loudoun supervisor’s white paper estimated they received $895M in 2025 data center tax revenue, and Prince William County estimated $280 million.

Both the WHO (2021) and the American Heart Association treat the concentration-response relationship as monotonic down to the lowest measured exposures.

The IHME Global Burden of Disease 2023 estimates gives US PM2.5 at 69,774 and ozone at 12,843, so about 82,600 total. These overlap a bit, because PM2.5 and ozone affect some of the same diseases, so adding them up slightly double-counts. GBD’s combined “ambient air pollution” parent node corrects for that and lands in the same ballpark.

Fine particles stick around in the air for about 4–7 days before rain washes them out or they settle, which is plenty of time for wind to push them hundreds or thousands of miles. From this paper: “The all-models-average residence time is shortest for SS with about half a day, followed by SO4 and DU with four days, and POM and BC with six and seven days, respectively.”

GBD 2023 says about 13,000 US deaths a year are caused by ozone, with deaths almost entirely coming from COPD.

The American Lung Association says smoking causes “approximately 80 percent” of COPD deaths. The CDC gives the number as “as many as 8 out of 10.” For ozone, total US COPD deaths come to around 150,000, so 13,000 ozone deaths works out to about 10%.

EPA’s 2024 benefits document and BenMAP outputs usually put 85-95% of the dollar value from PM2.5/ozone cuts in avoided premature deaths. The rest is hospital visits, ER visits, asthma symptoms, and missed work or school.

One large cohort study followed 53 million US Medicare beneficiaries and found that a 10 ug/m³increase in yearly average PM2.5 was associated with a 5% increase in all-cause mortality. Other major studies find it’s somewhere in the 5-8% range. This one finds 7.3%, this is 4-6%.

This is the largest multi-city short-term PM2.5 study and found about 0.68% additional all-cause mortality per 10 ug/m³short-term PM2.5 increase. Per unit, these short-term effects are an order of magnitude smaller than the long-term coefficients described in footnote 7, partly because very quick spikes mostly bring forward deaths in already vulnerable people, whereas long-term exposure builds up new disease risk.

In the eastern U.S., every part-per-billion of NOx can produce about 1 to 7 parts-per-billion of ozone. The amount of PM2.5 nitrate creates is much smaller and depends a lot on ammonia availability. By mass, that implies much more ozone than nitrate PM2.5.

Daytime NOx lifetime is typically hours to about a day, mostly limited by OH oxidation of NO2 to HNO3 and other reservoir species, with longer effective lifetimes in winter and at higher latitudes. Wind at 5 to 15 mph carries it tens to a few hundred miles before it converts, which the EPA assumes happens in its interstate transport rules. The secondary PM2.5 it eventually forms with nitrate lingers in the air for longer as discussed in footnote 4, so PM2.5 impacts from NOx can show up much farther downwind than ozone impacts from the same source.

Coal is the only major US power source that contains significant sulfur. Natural gas has sulfur compounds removed before delivery, and oil is now a small part of US electricity. EPA Power Sector Data, EPA eGRID, and EIA electricity emissions data show coal causing the large majority of SO2 from the electricity sector recently.

US SO2 emissions are down about 95% from their peak in the late 70s and early 80s. The biggest wins in reducing SO2 were the Clean Air Act Acid Rain Program, the spread of air pollution control devices in coal plants (scrubbers), and coal-plant retirements. You can see the long run decline EPA’s Air Quality National Summary.

For how much the decline mattered for public health, see this study, which found that coal PM2.5 deaths among people receiving Medicare fell from around 43,000 a year between 1999 and 2007 to about 1,600 a year by 2020, roughly a 95% drop. The study attributes fewer deaths than some other models, here and here.

This study uses ambient NO₂ as a proxy for traffic pollution and finds that it caused about 134,000 new childhood asthma cases per year in the US, roughly 17.6% of all new childhood asthma. This study estimated that in the US, ozone is responsible for 8 to 21% of asthma ER visits and PM2.5 for 3 to 11%.

Each pathway has its own literature.

Heart attacks: this study found PM2.5 elevations within hours preceding MI onset in the Greater Boston area, large enough to detect in case-crossover designs.
Stroke: this study found a 34% increase in ischemic stroke odds within 24 hours of PM2.5 elevation, including at AQI levels EPA calls “moderate.”
Cognition: this study found faster long-term cognitive decline in older women with higher PM2.5 exposure; short-term studies find similar acute decrements in attention and working memory.
Daily mortality bumps: this study found about 0.68% additional daily all-cause mortality per 10 ug/m3 across 652 cities.

This meta-analysis of how pollutants drop off as you move away from roads found that NO₂ falls off within a few hundred meters, because the molecule either reacts with other things in the air (like ozone and OH) or just disperses.

US ambient NO₂ has dropped more than 50% at most monitoring sites since 2000. Both the annual mean limit (NAAQS: 53 ppb) and the standard for the 98th-percentile 1-hour daily maximum (100 ppb) are now well below the legal limits at almost all US sites. Getting this all from EPA Air Trends, Nitrogen Dioxide.

Standing right next to a wood fire can put you in a high-PM2.5 breathing zone. I use about 200 ug/m3 as a round, high-exposure value, which seems consistent with literature reviews on this. DC’s annual average PM2.5 is about 7.6 ug/m3 in AQLI and EPA AirData, so over a full year you breathe about 7.6 x 8,760 = 66,600 ug/m3-hours. One hour next to the fire adds about 200 - 7.6 = 192 extra ug/m3-hours, or 0.3% of the yearly total.

This study estimated total US PM2.5 exposure from human sources to be about 6.5 µg/m³ in 2014, and the national average to be 8 µg/m³, so natural sources made up about 15 to 20%. This study found that since 2016, wildfire smoke has wiped out about 25% of the multi-decade progress in cutting PM2.5, and more than 50% in many western states. So the natural share is rising.

In EPA’s 2020 National Emissions Inventory, wildland fires (both wildfires and prescribed burns) have overtaken every other category as the biggest source of primary PM2.5 emissions in the US, ahead of residential wood burning, road dust, and industrial sources. Three other studies back this up, here here and here.

US “background” ozone, meaning ozone not from domestic human NOx/VOC emissions, is roughly 25-50 ppb, versus typical summer surface ozone of 50-70 ppb. That puts background around 40-70% in many regions, especially the West and higher elevations. Purely natural ozone is lower, more like 30-50%, because it excludes global human emissions and uses preindustrial methane. Discussed here and here.

GBD 2023 estimates have ambient PM2.5 at about 7-8% of global deaths and 5% of DALYs. In the US, it is about 1.7-2% of deaths and 1.5-2% of DALYs.

This is why I use 3-4 years for PM2.5. In their Medicare analysis, the average elderly person whose death is attributed to PM2.5 had about 3.6 years of remaining life expectancy. GBD methods give more like 10-14 years, but they do not adjust for the fact that pollution deaths often exacerbate previous medical conditions in people who were more close to dying, so I use 3-4 years to adjust.

Using AQLI, DC’s PM2.5 is about 7.6 ug/m3 and the rural US average is about 6.7, a gap of 0.9. AQLI’s 0.098 years lost per ug/m^3 gives 0.09 years, or 33 days, so “about a month.” That coefficient comes out of cohort work like Pope et al. 2002, Di et al. 2017, and Wang et al. 2020. Using the method here gives more like 6 days.

1970 pickup trucks were certified at about 3.4 grams per mile of non-methane hydrocarbons under the federal standards at the time, when there were no catalytic converters and engines were carbureted. Modern Tier 3 Bin 50 light trucks have to keep NOx and NMOG combined under 0.05 g/mi, with a target of 0.030 g/mi NMOG+NOx by 2025. That’s about a 98-99% reduction in tailpipe non-CO2 pollution. Sources: EPA Tier 3 standards, EPA Green Vehicle Guide, and EPA’s history of transportation emissions reductions.

Leaf-blower PM depends on whether you count only exhaust or also dust. For the truck, EPA MOVES and CARB put 300 miles from a modern light truck at only a few grams of PM2.5. For the blower, CARB and this study support a high combined exhaust-plus-dust value around 15 g/hour. Compared with about 4 g from the truck, that is about 4x. This article is useful color, but not the PM2.5 source.

10,000 cars x 5,000 miles x about 0.010 g/mi total PM2.5 is about 500 kg. The car PM number comes from EPA MOVES plus this study for non-exhaust PM. Roughly, that equals about one hour of NYC commercial cooking, 2-3 days from a mid-sized cement plant, one day from a cruise ship at berth, or 10 wood-heated homes for a winter. Those comparisons use linked NYC, EPA, ICCT/EPA, and wood-smoke sources.

The 2024 Corolla LE gets 38 mpg combined. Using EPA’s 8,887 g CO2 per gallon of gasoline, that’s 234 g CO2/mile, or 2.8 tons/year at average US mileage. It is Tier 3 Bin 50 certified, so NOx and NMOG is around 0.05 g/mi. For PM2.5, a lot’s created by brake and tire wear, which OECD puts at 30-60 mg/mile. EASIUR puts the local US health damage at maybe $10-$25/year. The CO2 side is about $42/year from this study’s estimate that every ton of CO2 mitigated gets $15.1 in wildfire health savings, or $60-$85 if you include ozone, dust, and stagnation. So CO2 does about 2-8x more US air damage than the car’s direct emissions, and >10x globally.

A modern gas plant emits about 360 kg CO2/MWh. Pollution is roughly 36 g NOx and 12 g PM2.5 per MWh (with good NOx controls and EPA’s PM2.5 factor). This model estimates local health damage around $1-$2/MWh. This study finds the US-only wildfire health cost of the CO2 from gas at about $5.40/MWh, or more with other climate-air effects and global impacts. So CO2 does about 3-10x more US air damage than stack pollution.

This study says CO2-driven wildfire smoke damages are much bigger globally than in the US, they’re about $15/ton here versus around $100/ton globally. Obviously this makes sense, the world’s big!

US coal power fell from about 966 TWh in 2019 to about 635-653 TWh in 2024, a 32-33% drop. The numbers come from this EIA table and this EIA table.

EPA’s central value of a life is $7.4 million in 2006 dollars. About $12 million in 2026 dollars.

In 2003, EPA tried a lower VSL for people over 70 in its Clear Skies analysis. Critics called it the “senior death discount,” and EPA dropped it. This article covers this.

Open fireplaces emit roughly 10-17 g of primary PM per kg of wood, mostly PM2.5, using EPA AP-42 Chapter 1.9 and its fireplace emission-factor background. Note that this is measuring outdoor air pollution, not what people in the homes breathe. A 5 kg evening is about 50-85 g; Shlipak et al. 2026 in Science Advances gets about $99B in damages over 485,000 tons of residential-wood PM2.5, or roughly $200K/ton, so that evening is about $10-$17. Almost all of that is health damage, not climate damage, because biogenic CO2 is roughly carbon-neutral over regrowth time. NOAA repository copy.

This HHS document puts VSLY at $604K with a 3% discount rate and $1.006M with 7%, in 2024 dollars for 2025 impacts. The method comes from HHS guidance. I use $550K as a conservative round number: below the 3% figure and about 10% lower than the downstream estimates would otherwise be.

4,000-9,000 annual US deaths from fossil power-plant PM2.5 comes from this modeling and this study, most of it is coming from coal. Using IHME GBD 2023’s 21.2 DALYs per air-pollution death, that is about 85,000-190,000 DALYs, or $47B-$105B/year at the $550K/DALY from FN35. Deryugina et al. 2019 would instead give about 14,000-32,000 life-years and $8B-$18B/year, but I use GBD here so the dollar comparisons stay apples-to-apples with COBRA.

About 1.2 million New England homes burn wood at all, from EIA’s RECS tables and this EIA summary. Scaling EPA NEI emissions and this study gives about 270-360 deaths/year, or $3B-$4B, if you count all wood-burning homes. Counting only primary wood-heating homes gives about 130 deaths and $1.5B. Big uncertainty: supplemental burning, local dose-response, and cross-state smoke. The adjustment mentioned in FN36 would shrink it about 5-6x.

In Ohio v. EPA, the Supreme Court put the 2023 Good Neighbor Plan on hold on June 27, 2024 while litigation continued. EPA later moved into reconsideration and announced a proposed action on January 28, 2026. EPA’s live Good Neighbor and interstate-transport pages are the current status. As of May 2026, the plan is not being enforced in its original strong form.

This study tracked Medicare data and found a 7.3% higher all-cause mortality risk per 10 ug/m3 more long-term PM2.5, or about 0.73% per 1 ug/m3. This study and this study find Black-white PM2.5 exposure gaps around 1.4-2.0 ug/m3, which works out to about 1-1.5% extra all-cause mortality risk from PM2.5 alone.

This study found PM2.5 exposure lowered overall from 2010 to 2019, but relative racial gaps widened. In 2019, the least-white census tracts had 16% higher PM2.5 than the most-white tracts, and ethnic relative disparities in PM2.5-attributable mortality grew by 40%. This copy has the repository version.

This study estimates about 17,900 US deaths/year from agriculture-related PM2.5, including about 15,900 from food production. Animal-based foods cause about 80% of the food-production damage, or about 12,700 deaths. The study says on-farm fixes could cut food-production mortality by about half, so rounding that to 6,000.

The range comes from two estimates, this that gets around 900, and this that’s closer to 2000.

Residential wood burning is about 10-17% of US anthropogenic PM2.5 mortality across this, this, and this study. The spread is mostly caused by differences in thinking on secondary PM2.5, indoor vs outdoor exposure, and wildfire vs anthropogenic wood. This residential wood-heating overview says wood provides 2.2% of residential energy but 98% of residential-combustion PM2.5, implying roughly 1,000x more PM2.5 per unit of residential energy.

Fertilizer-fed soil microbes turn ammonium and nitrate into NO and N2O. The NO escapes and becomes NO₂, as EPA’s CMAQ docs on soil microbial NOx emissions explain. This study estimated soil microbes were 20-32% of Central Valley NOx during the growing season. For ways of controlling these better see this agricultural nitrogen management options review plus this article.

Let’s not compare data center heat exhaust to nuclear bombs

Mon, 11 May 2026 06:46:08 GMT

Because data centers use lots of energy in extremely dense computers, they need all the excess heat they generate delivered away out of the building, for the same reason your laptop has a fan built in to prevent overheating. All the energy data centers use eventually becomes heat, and very large data centers use huge amounts of energy, so they generate huge amounts of heat as well and expel it all into the surrounding air. There are plans for data centers that can draw gigawatts of power. Expelling a gigawatt over a day generates 24 billion watt-hours of heat energy. The first ever nuclear bomb, detonated in the Trinity Test, released 27 billion watt-hours of heat energy. So a gigawatt data center releases as much heat energy into the surrounding air as a Trinity Test every single day.

Surely this would create some environmental problems?

Surprisingly, data centers expelling even this massive amount of heat will probably not have a meaningful impact on the local environment, and definitely not on global air temperatures. To understand why, it’s helpful to think about how different the effects of heat are when you concentrate it compared to when you spread it out over a day.

Consider the effects of concentrating sunlight in a single place with a magnifying glass.

This can do a ton of damage or start a fire. But all this heat energy was already there before, it was just impacting a surface area the size of a magnifying glass, instead of a tiny point. If you concentrate a normal amount of heat energy in a very tiny place, it becomes dangerous. And therefore, if you expand a dangerous amount of heat from a hyper concentrated point to a larger area, it can fade into the background of the normal levels of heat we’re exposed to.

A data center spreads its heat exhaust very evenly across the day. Data centers run at basically constant loads 24/7. There aren’t really peaks or low points. In comparison, the Trinity Test released heat in less than a second and then it all dissipated. The Trinity Test concentrated the energy of a gigawatt data center from a full day to a second, it was at least 86,000 times as concentrated as the data center. Here’s 86,000 dots:

If each of these dots is a second of a gigawatt data center’s heat exhaust throughout the day, all of them together at once is the heat released in the one second the Trinity Test actually happened.

I hope this makes it a little more clear why comparing gigawatt data centers to nuclear bombs is a little goofy.

Because data center exhaust is so flat and consistent, it is basically the very least concentrated way of releasing a given amount of heat energy over time, and a nuclear bomb is the very most concentrated way of releasing the same amount of heat.

In comparison, a magnifying glass concentrates heat about 10,000 times. The physical area the light hits is around 1/10,000th of the original area it would’ve been. The relationship between sunlight concentrated by a magnifying glass and what it would normally do is similar to the relationship between heat concentrated by the Trinity Test and the long slow release of a data center.

I’ve recently seen news stories reporting heat exhaust in units of nuclear bombs, and I expect this to become more common as more gigawatt data centers are proposed.

This unit gives readers basically as much information as reporting how much sunlight will hit you in a day in units of hyper magnified beams of burning rays. Not only is this the most alarming comparison possible, and is comparing the most hyper-concentrated release of heat possible with the absolute least concentrated release of the same heat, it’s also bad because nuclear bomb heat energy can be tens of thousands of times as large as other nuclear bombs, and the specific nuclear bomb people are using for this comparison is among the smallest ever made.

In the stories I’ve seen, the authors are assuming a 16 GW total thermal load will be added by this data center, 9 GW for the data center itself and an additional 7 GW expelled as waste heat by the gas turbines it uses to generate its electricity. This is a reasonable estimate for the very largest this data center could ever be. 16 GW x 24 hours = 384 GWh, divided by 23 atom bombs gives 16.7 GWh per bomb. A GWh is about 0.86 kilotons of TNT, so this is about 14.3 kilotons of TNT. This is about the same as the Little Boy bomb dropped on Hiroshima. While horrible, this is also one of the smallest nuclear weapons ever developed. Using one of my all time favorite data viz tools, NukeMap, here’s what it would look like dropped on where I live:

Most nuclear bombs are way, way, way larger than this. Here’s what the average bomb in the US nuclear arsenal looks like in comparison:

And here’s the largest nuclear bomb ever tested:

Here’s the thermal radiation radius of each compared to each other:

The largest bomb ever tested was ~50,000 kilotons, 150 times as much heat energy released in a second as this largest ever data center will emit in a day. I could use this number and say that the Utah data center will emit the heat of just 1/150th of a single nuclear bomb, spread over the course of a whole day, and that would also be equally correct, but I think also deceptive. We need a better way of understanding how this heat energy compares to other normal things.

As a former physics teacher, it’s been a mild form of torture hearing people start to say in an alarmed way that all energy data centers use gets turned into heat, as if that’s crazy or wasteful. This is true for everything we do.

All energy we use is ultimately dissipated as heat in the atmosphere. The gas you fill your car with is full of stored chemical energy. When you drive, about 80% of that chemical energy is converted to heat energy in your engine, and either sent out through your exhaust pipe or radiated away from your engine block and radiator. The other 20% is converted to kinetic energy in your car to make it move (and a tiny amount of that is itself converted to electricity to later be light and sound energy from your car’s display and speakers, or to start your car). When your car impacts air particles, the friction between your car and the air converts some of the kinetic energy from the gas to heat on your car and the air. Whenever your car slows as you drive without applying the gas, what’s actually happening is that the kinetic energy is being taken from the car and turned into thermal energy on the car’s surface and the surrounding air. Applying the brakes converts a large amount of kinetic energy quickly into thermal energy on the ground and in your tires. By the time you stop your car, all the energy that was originally stored in the gas has been dissipated as heat, either in the initial explosion of the gas, the friction between your car and the air, or the friction between your wheels and the ground that you use to stop.

Here’s a rough Sankey diagram of how energy moves through your car to eventually become heat:

This means that driving your car adds exactly as much heat energy to the environment you drive it in as slowly burning the gas without a car over the time it takes you to use the full amount.

Whenever you see energy you’re using disappear, it’s almost always becoming heat. The lamps in your home constantly beam out light energy when on, but the room doesn’t get brighter and brighter over time. The light energy doesn’t build up, which means it must be going somewhere else. What’s happening is that the light is being absorbed by the physical surfaces in your home. When a photon is absorbed, its energy is transferred into the random jiggling of molecules in what it hits, and that random jiggling is heat energy! So all energy that leaves your lights also becomes heat energy in the space around it. The conversion is from electric energy to light energy to heat energy. Visible light just carries so little energy that we don’t notice.

All energy we use, whether it’s the gas in our cars or the things we do with the electricity we buy, ultimately gets converted to heat energy in our environment. I live in Washington DC, which uses about 28 gigawatt-hours of electricity every day and roughly 37 gigawatt-hours equivalent of primary energy every day in our cars and elsewhere, for a total of approximately 65 gigawatt-hours of energy every day. All of that is being turned into heat in the area, so human energy usage in DC is equivalent to “dropping about 3.7 nuclear bombs on the city every day.”

Does that tell us much? The sun delivers about 750 GWh of heat to DC’s surfaces daily, 43 nukes’ worth and 12x all human activity. About 85% of this leaves the ground and heats the air. About 23% of incoming sunlight is absorbed by air before hitting the ground, adding another 370 GWh dumped into the atmosphere above the city. In total, the sun will deliver about 1000 GWh/day, or 57 nukes.

The water cycle moves enormous amounts of heat too. When water vapor condenses into cloud droplets or rain, it releases the latent heat that was absorbed when the water evaporated. Over DC’s land area, condensing just 1 millimeter of water releases about 108 GWh of heat, or about 6 nuclear bombs. One inch of rain over DC corresponds to about 2,740 GWh of latent heat release, or about 157 nuclear bombs. If I told you that an oncoming rain storm was going to unleash the heat of 157 nuclear bombs, how worried would you be? Would I be representing the situation well? I’d be technically correct, at least.

Wind moves even larger amounts of heat. A single heatwave can bring more heat energy into DC air in a day than all human activity adds in a year.

So now if I tell you that solar energy delivers 56 nuclear bombs to DC every day, human energy usage adds about 3.7 nuclear bombs, and rain can add 150, what does this tell you about life in DC?

I’ve given you no way of understanding how bad this actually is for the DC environment. This is because reporting raw huge amounts of energy like this, with meaningless alarming comparisons like nuclear bombs, gives you zero real footing in the issue at all. Whenever you read something about data centers, always ask “Is the person writing this actually trying to give me real footing in the specific problems this will cause, or just throwing out alarming huge numbers without giving me the context to understand what they mean?” If it’s the second one, maybe just stop reading.

Here’s a comparison that makes more sense to me: this data center is like building a bustling city in the Utah desert. The property it’s on will be about the size of DC, eventually with lots of gas power plants and data centers. The heat emitted by all of these together will be about 6 times the heat emitted by the cars, businesses, and households in DC. It’s like they’re building a city 6 times as dense as DC in the middle of nowhere.

How bad will this be for the environment? There’s a lot to say about air pollution and climate effects, but what about the effects of the heat exhaust? Well, what are the actual effects of the heat dissipated by the energy used by DC businesses and cars and homes? Most of the ways cities increase the temperature compared to rural areas is the urban heat island effect, which has two main causes: surface changes (asphalt, dark roofs, and lost vegetation absorbing sunlight that would otherwise reflect or evaporate) and direct waste heat from human energy use.

In most US cities, the surface effect dominates. A recent estimate puts the local air temperature increase at roughly 0.025 °C per W/m² of human energy use. This varies with city size, wind, and how high the heat can rise.

Using DC’s energy consumption and size, this implies DC’s human energy usage adds about 0.4 °C to the city’s temperature. Naively carrying this forward to the largest data center ever proposed, which could eventually use 9 GW of energy (2% of the energy draw of the entire country!) and dissipate 16 GW of heat, this implies it might raise the very local temperature on its property by 2.4 °C, about 4.3 degrees Fahrenheit hotter. This warming is extremely local to the specific space around the data center and would drop to zero close by as wind dissipates the energy. This is not nothing! It’s a pretty significant change to the very local land. But when someone tells you “This thing is like dropping 23 nuclear bombs per day” do you immediately say “Oh yeah, like an extra 4 degrees!”? If not, the unit is bad.

Data center land use issues are fake

Sat, 02 May 2026 12:17:50 GMT

A few weeks ago I shared my axis of which data center concerns I think are most real and fake:

I’ve already covered water use, water poisoning, and infrasounds. There’s one fake issue left to address: land use.

Like biographies use their subject as a prism for the world around them, I use data centers as a prism for much larger but invisible environmental problems, hidden by our tendencies toward populism and localism that data centers offend. This post focuses on the ridiculous ways we use land in America, which (like most of our water issues) is downstream of farmers being shielded from popular criticism by political alliances and folk intuitions about the honest toil of growing food. By the end, I hope you’ll be much more skeptical of headlines like this:

and of the idea that data centers purchasing farmland is a unique evil.

I’m not claiming data centers should be built anywhere and everywhere. There are lots of places where they’ve been built too close to people’s homes. This post is about whether data centers waste the land they’re on relative to what they compete with, and my claim is that they mostly don’t compete with things we need more of (housing) and mostly compete with things we need less of (farmland).

Data centers are the absolute most optimized use of land physically possible
America isn’t lacking in land
Data centers mostly don’t compete with housing
The fact that data centers don’t create many jobs makes their land use way smaller
What about the land use of new power and transmission capacity data centers will require?
Data centers free up lots of land elsewhere
It is not a crime against locals to raise their land value
We have way too much farmland
Some bad recent articles on data center land use

Data centers are the most optimized use of land physically possible

No other large buildings are crammed this full of objects optimized down to the atom for maximum performance. The most advanced supply chains in the world work together to push the servers inside data centers as close to the physical limits of manufacturing as anything we make.

Northern Virginia has 13% of all global data center capacity, and Loudoun County has by far the largest concentration. But data centers only take up 3% of Loudoun’s land, and they generate 38% of all county general fund revenue. Using county tax info, I roughly estimated combined real- and personal-property tax revenue per unit land area (including land around the buildings data center companies own) for data centers vs. other major Loudoun revenue sources. Comparisons like this get a little ridiculous.

Estimates

America isn’t lacking in land

By 2028, all data centers in America will occupy roughly 1,400 square miles. This is about 0.3% as much as America’s prime farmland. The vast majority of this will be land around the data center, the buildings themselves will collectively take up about 25 square miles, 0.005% of America’s prime farmland.1

Here’s a chart of how America currently uses land:

Source. Each square is ~390 square miles.

1,400 square miles is about 3.5 squares here, 3.5x as much as the land we currently dedicate to Christmas trees:

With the buildings themselves taking up an incredibly small part of that:

By 2030, data center buildings themselves will take up 1/15th of the land area we use to grow Christmas trees.

Data centers are uneconomical to build way far out in completely undeveloped land, since they need access to reliable large power grids. But they also don’t need to be built in or near dense residential areas. The vast majority of the contiguous US is within reach of high power lines, and the places data centers are built are mostly farmland or low-value scrub.

Data centers mostly don’t compete with housing

Some people worry that data centers take up land that could be used for housing instead, but data center companies want fundamentally different land than where housing shortages exist. Housing demand is overwhelmingly concentrated in and near existing urban cores, like California, the Boston-to-DC corridor where I live, Seattle, Denver, Austin, or Nashville. These are expensive because demand concentrates in specific neighborhoods already built out. The blockers on housing in those places are zoning, parking minimums, height limits, environmental review, neighborhood opposition, and the general legal apparatus that makes it illegal to build apartment buildings on most residential land in America. It’s rarely access to raw land.

Data centers want flat, cheap, large amounts of land with access to high-voltage transmission, fiber, and water for cooling, in places that permit a windowless industrial building. Anywhere with significant housing demand has land too expensive for large data centers. Data centers are mostly only built in exurban and rural land on the fringes of metro areas.

The fact that data centers don’t create many jobs makes their land use way smaller

If a new business that requires lots of workers moves into a town, lots of new residents have to move there too. This creates demand for massive land use changes. There will be more homes, commercial buildings, public services, and roads. I don’t think this is bad, but for people worried about land use changes in a specific area, a data center has a much lighter impact per unit of revenue because it needs so few employees.

A town allowing a large data center to be built is like buying a very large computer that hums in the background generating tax revenue with minimal maintenance. It doesn’t require reconfiguring the town the way a new large industry or commercial center would. Data centers are ideal for small towns that need lots of additional tax revenue but don’t want to have to deal with the infrastructure costs of having a lot of new people move there at once.

What about the land use of new power and transmission capacity data centers will require?

By 2028, US data centers are projected to use around 450 TWh of energy per year (the middle of Lawrence Berkeley National Laboratory’s 325–580 TWh range, with most other major forecasts clustering nearby). Data centers run with high load factors (meaning load is essentially flat and high 24/7) so 450 TWh annually works out to maybe 55 GW once you include grid losses. If served by firm power sources you’d need somewhere around 65–80 GW, or more if it’s coming from intermittent renewables.

The land footprint depends heavily on what gets built. Just getting to 450 TWh with solar would require ~1,500–2,000 square miles of panels2, and delivering that as reliable 24/7 power would require 1.5-3x as much, plus storage and batteries. So if data centers were all served by solar, they’d require maybe 2,500-5,000 additional square miles.

Wind requires way less land (unless you’re worried about infrasound…) but it seems unlikely to be a big part of data center power.

Natural gas plants are very land-efficient. One large new plant generates about 1 MW per 0.05 acres, and this drops to ~0.01 acres per MW if you just look at the buildings themselves. 80 GW of new natural gas would need between 12-40 square miles of plant sites depending on how you measure. I’d almost always prefer using more land to emitting more, so I’m not excited about gas and discouraged by the scale of new gas demand data centers are adding.

Nuclear is similarly compact, at about an acre per MW for the plant. I’d be much more excited about nuclear power serving data centers, but that seems unlikely in the short term.

A reasonable range for the additional power-generation land footprint is 50 to 1,000 square miles. This is significant but doesn’t dramatically change the picture. Adding it to the ~1,400 square miles of data center land for 2028 brings us to a maximum of 2,400 square miles. Definitely not nothing! But this means that all data centers, the land around them, and the power plants serving them would together come to at most 5% of the land used for ethanol, and roughly 0.08% of the contiguous US.

Transmission is harder to estimate, and it has the one land-use problem I think is real: utilities using eminent domain to route new lines through people’s property. I expect serious conflicts here. But transmission is also where data centers are most plausibly positive-sum. The green energy transition already requires roughly doubling US high-voltage transmission capacity by 2050. Data center demand mostly accelerates a buildout that had to happen anyway, and a line built to serve a data center cluster also moves clean power to everyone else on it. The eminent domain cost is real and serious, but it probably has to happen anyway. Farmers in other countries are far more likely to be seriously harmed by climate change, the green transition is necessary to fight it, and we need to pull a few eminent domain levers to enable it. The net positive effects of data centers forcing way more high-voltage transmission will outweigh the negatives, even though here the negatives are real.

Data centers free up lots of land elsewhere

The US has about 12 billion square feet (430 square miles) of retail real estate, and double that if you include the parking lots. Mall vacancies hit record highs in the 2010s, because people buy online instead. Amazon now operates about 11 square miles total of warehouse space, handling a large portion of all American retail sales. Hundreds of square miles of physical retail floor space and parking have been replaced by a few dozen square miles of warehouses, and a small fraction of that in data centers running the website, payments, and logistics.

There were about 100,000 bank branches at the 2009 peak. Today there are around 70,000 and falling because most banking moved into apps. Other things like travel agencies, photo labs, video rental, music stores, bookstore chains, paper-map publishers, have all either disappeared or sharply contracted, along with their land use. On net, data centers have freed up way more land than they use. Data centers have also enabled way more people to work from home, causing office demand to crater.

Is this all good? I’m not sure. Maybe it’s preferable to have malls over Amazon warehouses, or physical video stores over streaming. But regardless, the main effect of data centers on US land use has been to free up incredible amounts of land relative to the ~25 square miles they sit on. Most conversations about data centers only focus on the physical footprint of the buildings themselves without considering just how many other buildings they’ve made unnecessary.

It is not a crime against locals to raise their land value

A surprisingly common complaint in news stories about data centers buying land is that they raise the price of land around them.

In reading about bad environmental effects of industry, I’m used to reading stories of ugly industries moving in and lowering the value of land nearby. This is a problem because Americans have so much of their savings tied up in the value of their land and homes. The land value of farms specifically is a huge percentage of farmers’ total wealth. I’m not used to reading stories complaining that a new industry moved in and made nearby land way too valuable.

Data center effects on nearby land is really understudied. The few studies that have been done seem inconclusive, and have only focused on the effects of home prices specifically. The most thorough study looked at home sales in Fairfax, Loudoun, and Prince William counties near data center alley in 2023. They found that homes closer to data centers sold for higher prices, and their model (controlling for 13 factors that usually push up home prices) explained about 87% of price variance. The authors guessed that maybe this was because data centers cluster near the same infrastructure that makes housing valuable, and that a lot of data centers are tucked into places residents don’t even notice. The authors warned against generalizing too much from this study, especially in looser housing markets.

The potential way data centers make rural land around them more valuable is pretty straightforward: they’re willing to pay way more for land, pushing up the potential price for nearby land that has the same qualities data centers might be interested in in the future. If a data center causes a location’s grid and fiber to be built out, everything around it also becomes more valuable to future data centers.

Raising land prices benefits land owners and harms people who want to buy land. Lowering land prices harms land owners and benefits people who want to buy land. Which one is “right”? It depends on who you’re considering. In general, the locals who own land in the area where a data center is built benefit when their land goes up in value. Because Americans (especially rural farmers) have so much wealth tied up in their land, this often increases the wealth of people in the community where the data center is built.

The two complaints that usually pop up are that this raises local’s property taxes and that it makes it harder to buy land.

The property tax issue is real, but the same thing happens whenever any high value industry sets up in a rural area. Many states solve this with tax policy that taxes agricultural land based on its use-value, or other things like creating homestead exemptions or assessment caps. In states like Indiana that do this, when data centers are built, farmers nearby get immediately wealthier without having to pay more in taxes.

New buyers being priced out is also a real problem, but it’s a problem with making any place in America more desirable to live. Is the solution to keep rural land cheap by making sure nothing economically valuable ever happens there?

Building anything new in an area at all have some effect on local land values. Would you rather an industry raise or lower the value of the land around it? If you saw a headline that said “Data center built, lowers the value of everyone’s property around it” would you be mad? If so, it’s weird to also be upset that data centers are raising the value of people’s property. I think what’s happening here is that both involve trade-offs, and winners and losers (alternatively, the land owners or the people who want to buy land), and in both it’s very easy to focus exclusively on the loser while ignoring the winner. This leads to people being mad when a data center lowers property values, but also when it increases them. Loss aversion is often inherently change aversion.

A clear example of how strangely this issue is talked about in the media is this section of a More Perfect Union video (this one, at 2:00). They interview a local farmer, who complains that the land around him has become so expensive that he can’t buy more farmland to “feed more people.” They then talk to a guy explaining how new businesses can’t buy land there because the land’s become so valuable.

More Perfect Union frames this as “increasing costs,” but it’s increasing costs by making the locals way richer. The farmer’s complaint is that the data centers made his neighbors so much richer that they don’t want to make a deal anymore, and it’d be easier to buy their land if they were poorer and were more willing to sell it off. This is a bizarre complaint. In no other circumstance would we hear someone say “I would like to own more of my community’s land, but this evil company moved in and made all my neighbors so much more wealthy that they don’t want to sell to me anymore” and be sympathetic. But as we’ll explore in detail in the next section, farming has a weird halo effect where it warps our usual thinking about what’s fair.

We have way too much farmland

Farmers have the same combination of virtues and vices as the rest of us. But I think farmers (like any profession with a big positive halo) benefit so much from popular narratives that they get away with more environmental and economic harm, and their halo makes them harder to regulate and criticize. By the end of this section, I’d like you to share my intuition that it’s pretty normal and fine for farmers to sell their land to data centers (or solar power plants) if they want to. These are two private actors making an economic decision that might make sense for both of them. There isn’t some deep evil happening when farmer land is converted to a data center. Farming should be treated as one industry among many, that we can criticize or praise like any other industry, and that shouldn’t get special treatment or handouts because of the inherent virtue of toiling in the soil.

Question farmers

Once you start writing about any environmental issue, especially water, you quickly discover that farmers occupy a special place in American discourse where both right and left refuse to criticize them, because “they grow our food.” People have a simplistic idea of the inherent virtue of farming compared to other professions. This is how you get viral clips of local farmers “bravely” saying no to a data center company politely offering to buy their land for 10x what it’s worth, and reactions like this when you politely point out this is a little overblown:

Cultural conditioning has trained us to see the good rooted people standing up to the far-off unrooted elites as always in the right. You can sometimes sneak in criticisms of farmers by referring to them as “big ag,” but this term ignores small farmers, who are not without fault.

I was happy to see Tomi Lahren come out strong against the horrific “Save Our Bacon” act. It’s nice to see animal welfare becoming depolarized. But even here she danced around the main issue, and imply that it’s not the small farmers who are at fault, it’s the Chinese:

In reality, pig welfare on small American-owned farms is often just as bad as on large ones.

Once you see that people often find it offensive to criticize farmers at all, you start seeing it everywhere. Take this story from last year:

As I’ve talked about before, in Morrow County Oregon, an Amazon data center bought water from a source heavily polluted with nitrates, evaporated it, and sent the rest back to a wastewater treatment plant. Since nitrates don’t evaporate, the water sent to the treatment plant had a higher concentration. This likely raised nitrate concentration in the wastewater facility by less than 1%. Amazon was using a small fraction of the region’s water, but Futurism chose to superimpose cancer cells on data center server racks. They should have superimposed them on the main industry adding the nitrates:

Source

Farmers!

For 35 years Oregon’s government has known nitrate pollution was worsening in the Lower Umatilla Basin, the only drinking-water aquifer for Morrow and Umatilla counties, and has known the main source was irrigated agriculture. For most of that time, the government response was just suggesting “voluntary best management practices” and “stakeholder engagement.” Meaningful enforcement was limited to the Port of Morrow wastewater facility. By 2024, hundreds of domestic wells had tested above the federal nitrate limit, with some exceeding it 7x. Residents reported clusters of miscarriages, kidney failure, thyroid disease, and rare cancers. In 2022, Morrow County declared the first groundwater state of emergency in Oregon history.

Why were farmers allowed to build up nitrate pollution in the water here for 35 years?

In early 2025, state senator Khanh Pham introduced SB 747, which would have required farms over 200 acres (the largest 10%) to fill out a form telling the Oregon Department of Ag how much fertilizer they used, the kind, and the crop it was used for. The bill imposed no limits on fertilizer use. It did authorize civil penalties up to $10,000, but only for failing to file the form, or for being judged by ODA, after the fact, to be over-applying based on the reported data. Pham told the committee that “Senate Bill 747 does not impose restrictions. It simply collects data so agencies can provide better technical support, improve efficiency and prevent fertilizer waste.”

About 75% of the testimony on the bill was opposed. The Farm Bureau called it a “fishing expedition” that turned family farmers into “boogeymen.” One rep told the committee that 96% of Oregon’s farms are “family-owned and struggling.” “Family-owned” is basically meaningless: it means that any family, no matter how rich, owns the farm together. Walmart is a “family-owned” business. Farming families have slightly higher median incomes and way higher median wealth than Americans overall.

Another complained the bill “arbitrarily targeted” large farms while ignoring “municipalities and urban homeowners,” even though in the Lower Umatilla Basin specifically, agriculture and CAFOs together caused over 80% of the nitrate pollution. The farm coalition’s position was that being asked to share this simple information on dangerous pollutants would be a ruinous burden.

Rep. Bobby Levy was the most prominent voice opposing the bill. Her family’s farming business, Windy River, is inside Morrow County and draws from the polluted aquifer the bill was meant to protect. She told the Senate committee that “making the suggestion that over application of fertilizer is widespread is both inaccurate and unfair.” The committee declined to schedule a work session before the deadline, and the bill quietly died without a vote.

A month after Levy’s testimony, the Oregon Department of Environmental Quality issued a citation identifying the Levy family farm as having over-applied fertilizer on its own corn fields throughout 2023, polluting the same aquifer the bill was meant to protect.

This all happened under a Democratic trifecta and while the Morrow water crisis was on the front pages of Oregon newspapers. It happened because you basically can’t say ‘small farmers are part of the problem’ in American politics and expect to keep your job. At best, you can blame “big ag,” or maybe China, but the politics of farming is so crazy that farmers are a permanent third rail politicians won’t touch, even while they poison a local community. For any other industry, this would obviously be crazy, and every other industry has regulations on how much it can pollute water. But farmers are organized, numerous, and have silly folk theories about the inherent virtue of growing food on their side.

Finally, in March 2026, the Oregon Department of Agriculture adopted the first ever mandatory nitrate management rules for the area. Even this only applies, to start, to farms over 1,000 acres, who don’t have to submit plans until May 2027. Farms between 500 and 1,000 acres will be phased in by May 2028, and smaller farms aren’t required to submit their plans to ODA, only to draft them and keep records. ODA only has one staffer to oversee the whole basin and will audit “as agency capacity allows.” The data center here served as a useful if unintentional scapegoat to distract from the people directly adding pollutants to the water.

This fits a national pattern. The 1972 Clean Water act explicitly exempts “agricultural stormwater discharges and return flows from irrigated agriculture” (meaning nitrates, phosphorus, manure, and herbicides washed into a river or lake from a farm by rain) and requires no permit, monitoring, reporting, or upper limit. Farmers are not obligated to tell anyone how much nitrogen they use. This is regularly upheld by federal courts, most recently in 2025.

Agriculture is by a wide margin the main source of nitrate pollution in the US in US water. 12% of domestic water wells in agricultural areas exceed the federal nitrate MCL of 10 mg/L, versus 1% of public supply wells in non-agricultural areas. Nitrate concentrations in agricultural streams run about 6× higher than in undeveloped watersheds. When a rural well or a small-town water utility tests over the legal limit, the source, with rare exceptions, is corn, soy, or manure upstream.

One peer reviewed estimate found that nitrates in drinking water cause between 2,300 and 12,500 cancer cases per year, four-fifths colorectal plus 3000 cases of very low birth weight, and 1700 very preterm births. They estimated that the total medical cost is between $250 million and $1.5 billion per year, plus $1.3 to 6.5 billion in lost productivity.

A few obvious policies in the US could fix most of the problems with nitrates:

Mandatory reporting, where every farm above some size submits annual plans for how much and where they’re using nitrates.
Specific number limits on how much nitrates farmers can apply.
Treating large farms as point sources for water pollution in the way the Clean Water Act treats other industries.
Forcing farm polluters to pay the cost of water treatment.
Stop subsidizing ethanol, which alone is responsible for roughly a tenth to a fifth of all nitrates in US water.3

Denmark, the Netherlands, and Germany all have major agricultural sectors that operate well under way stricter rules than anything on this list. The only reason we don’t have these rules is that farmers are more politically untouchable here.

About 1.5% of the contiguous United States is used for a farmer handout: ethanol

Every year, the federal government forces oil refiners to blend 15 billion gallons of corn ethanol into America’s gasoline supply. This uses about 40% of all American corn. This is mandated by the Renewable Fuel Standard (RFS), and was sold as a policy to help address climate change.

However, every argument for mandating corn ethanol is terrible, and the only reason it survives is that politicians cannot question farmers.

The ethanol mandate was originally presented in 2007 as a path to energy independence and lower greenhouse gas emissions. Both arguments kind of sort of made sense then. Oil prices were high and the US was dependent on Saudi Arabia for petroleum. Not good! Ethanol’s lifecycle emissions looked like they might fall below gasoline if ethanol replaced part of the supply. Other arguments at the time held that ethanol would cut NOx emissions or sequester carbon.

But none of this is true now. Since the shale revolution, the US is now a net petroleum exporter. Studies now find that corn ethanol’s carbon intensity is at best roughly comparable to gasoline, and probably about 24% higher once you account for land-use change, nitrous oxide from fertilizer, and the conversion of grassland back into corn fields.

Because ethanol’s made of corn, it also has a high water cost. Corn used for ethanol is responsible for around 1–1.5 trillion gallons of withdrawals per year from aquifers and lakes, this is ignoring any rainwater it might use. That’s about 3% of all water withdrawn for US irrigated agriculture, for a stupid bad mandated product. I can’t help but add that this is roughly 40 times as much water as all the thousands of American data centers are forecast to consume onsite in 2028. Ethanol is generally more expensive than gasoline per unit of combustion energy. So on top of everything ethanol also increases gas prices.

But what’s most shocking to me about ethanol is its land use. About 1.5% of the contiguous United States is used to grow corn for ethanol.

Source

Corn farms for ethanol specifically take up half as much space as all urban housing and commercial land.

So why do we have this mandate? The only people who benefit are corn farmers, ethanol producers, and fertilizer and equipment providers.

Like most bad laws, beneficiaries are concentrated and organized while those harmed are scattered and unorganized. Every American pays a tiny bit more for gas, the Gulf of Mexico absorbs more fertilizer runoff than it otherwise would, the climate as a whole has a little more CO2, aquifers are drained faster, but these harms are spread so thin across so many people that we don’t notice or get organized. In comparison, farmers notice what’s happening. They organize to keep the mandate, and no one bothers to oppose them, because it’s so politically toxic to criticize farmers.

Compounding this, rural Midwestern communities are amplified in American politics by the electoral college and presidential primaries, and the Senate’s overweighting of low-population states. There’s a strong bipartisan voting bloc in midwestern politics that treats the ethanol mandate as a third rail that politicians cannot touch.

So we’re stuck forcing oil companies to pay to support 1.5% of the contiguous United States being used for a stupid product no one wants.

When you see people talking about how any business, be it a data center or a solar farm or anything else, is “taking away our farmland” and that “soon we won’t have any left,’ consider this. Farmers currently have an area the size of New York State, as large as half of all American urban areas (suburbs included), specially dedicated to growing a product oil companies are forced by the government to buy, that makes our gas more expensive and dirtier, with zero benefit to anyone outside the farmers themselves. In an ideal world, farmers would lose the surface area of New York State to companies who would make better use of it, or even shut down and be replaced with nothing so that we stopped wasting 4% of our agricultural water on a product no one wants that makes our lives and the climate worse.

By 2028, all American data centers, including the land around them, will use about this much land compared to ethanol:

Converting all the land used by ethanol in the US to utility-scale solar farms would generate roughly 11,000 terawatt-hours of electricity per year, about about 2.5–3x current US electricity use, or roughly 35% of total world electricity generation.4 Consider this the next time you see news stories of corn farmers getting mad that their land is being bought by solar plants.

Sickos.jpg

Solar plants and corn ethanol both have the same end-goal: generating usable energy. If you measure how much electricity solar panels generate vs how much energy is contained in the final corn ethanol product, and include all the energy costs of growing the corn, solar panels generate over 100x as much energy per unit area as corn farms used for ethanol over the same time period. This makes sense, because corn is in some sense just an extremely inefficient solar panel, slowly storing up energy from the sun to eventually release when it’s mixed in with gasoline. Fields of corn used for ethanol are kind of like very old-fashioned solar panels that are 60x less efficient than our current method.5

As a vegan, I do also have to highlight one other major land user:

We use more land to grow food for the animals we eat than to grow plants we eat directly. Everyone going vegan wouldn’t free up all this land, because we’d need more land to grow fully nutritionally complete meals. The best estimates we have right now is that this would free up roughly 141,000 to 289,000 square miles of American cropland, depending on if we also cut animal feed exports. It would free up around 1,250,000 square miles altogether, because most of the land footprint of current American diets is pasture.

We have too much land dedicated to farming.

Addressing a few other defenses of ethanol

Some argue ethanol is actually good despite its negatives because it reduces NOx emissions, a harmful air pollutant. But this seems to no longer be true. Every US gas car built after 2017 has a catalytic converter that removes NOx from the exhaust to very low levels no matter what fuel you burn, so any small NOx benefit ethanol gives during combustion gets erased. A 2022 California study on 20 cars found no meaningful NOx difference between regular E10 gas and higher-ethanol gas, and a 2025 UC Riverside study found the same.

It might be worse. The EPA now predicts ethanol blending raises NOx, smog-forming VOCs, and fine particulates compared to ethanol-free gas, because ethanol makes gasoline evaporate more easily. And for small engines that don’t have catalytic converters, like mowers, leaf blowers, or generators, the Department of Energy found ethanol blends raise NOx by 50–75%.

Some people bring up that fossil fuels also use water to extract. That’s true, but ethanol uses way more water per unit.

A lot of people are really bad at thinking about farms in a marginal rather than an absolute way

When a data center or solar plant replaces a farm, the public conversation often turns to which product is more valuable in total. Would we rather have food, or silly computers? People will say things like “We can’t eat AI.” This is obviously ridiculous, because the question is never “Should we replace all farms with data centers?” It’s always “Should we replace one specific farm with a data center?” Because we farm way more than we need, and data centers are so efficient with their land use and in so much demand, the answer is often yes. Because we have way too much farmland, I would support replacing many corn or alfalfa (water-hungry and used mainly for animal feed) farms with nothing, never mind another building.

People sometimes bring up soil quality and “prime farmland” as if it were sacred. Prime farmland in the USDA sense is a soil classification for land that could grow crops well 23% of the non-federal open land in the continental United States qualified as prime farmland. We don’t need every last piece of prime farmland actually farmed. We are not going to run out, and shouldn’t turn all of it into farms right now.

It’s true that we can’t eat data centers, but we also can’t eat the corn grown on the New York State-sized chunk of land used for ethanol. The relevant question is always “Is this specific piece of land better used as a farm or a data center?” rather than “Should all farms be replaced with data centers?” The absolute all-or-nothing way of thinking about farms would justify tearing down all our cities and public infrastructure, because we don’t need anything else as much as food.

A very common talking point when anyone suggests that we could use less farmland is “City boys think that food just comes from the store.” To use an outdated analogy, this feels equivalent to the government mandating that way too many copies of Grand Theft Auto get produced, and any time this gets criticized the main response being “Oh do you think games just magically appear at GameStop?” This talking point always assumes that saying the marginal farm shouldn’t exist is identical to saying all farms shouldn’t exist.

What about the effects of climate change on farmable land?

Maybe this is all true now, but we may need to worry a lot more about farmland as climate change worsens, right?

Under most mainstream forecasts, the US ends 2100 with roughly the same or modestly expanded total climatically suitable cropland area, but with the productive zone shifted 100 to 300 miles north of where it sits today.6 This is still going to be wildly disruptive, because this will reshuffle what can be grown where, but on net the US will have the same or more total farmable land. The issue is going to be what can be grown where, not total access to farmland.

There’s a viral talking point that the corn belt in the upper midwest will become unsuitable for corn by 2100. We’re not sure if this is true, because corn varieties have been continuously rebred for changing conditions for the last century. The corn grown in Iowa today is not the corn grown in Iowa in 1925, and that wasn’t the same corn grown in 1850. By 2100, after 74 more years of adaptation, it seems unlikely Iowa farmers won’t be able to grow corn. The Corn Belt-collapse literature mostly assumes static management.7

Climate change will create lots of other problems for US crops. Higher levels of CO2 in the air reduce the nutritional value of grain crops, so we’ll need to grow more food to get the same nutritional value. Water might become a more serious issue, and if the median climate forecasts are slightly off, a lot of formerly irrigated farmland won’t be useable because the water will be gone. This is a serious concern, but it’s mainly about water rather than raw access to land.

I think the most serious response you could give here is that we’ll need more farmable land to feed the global population stuck with way less farmable land. That’s fair, but the clearest way to free up land for this is to join me and others in our quest to end industrial animal agriculture. Buildings that will collectively take up under 100 square miles in the US won’t ruin farmable land.

More evidence we have too much farmland

The federal government literally pays farmers not to farm

For the Conservation Reserve Program, the federal government pays $1.85 billion a year to over 302,000 landowners to keep their collective 40,000 square miles of land idle. This is about the size of Kentucky, and 28x the projected 2028 footprint of all data center land in the US.

CRP was created in 1985 partly because farm groups were alarmed at how much food American agriculture was producing. Prices were low because too much was being grown, so the government started paying farmers to grow nothing. Since then the justification has shifted more toward environmental issues, but the program still runs mainly because the country produces more food than it can sell at prices farmers want. The program has been reauthorized in every farm bill for 40 years. The main intra-industry fight over CRP is how big it should be and which land it should target. 67% of producers support expanding CRP acres while only 10% disagree, and demand regularly exceeds the cap. So farmers themselves largely support spending even more than the billions we currently do to convert even more farms into unused land.

America is a major food exporter

About 20% of US food production by value is exported. The US is the world’s largest exporter of food, we shipped $176 billion in agricultural exports in 2024. Foreign trade is good, but we’re not at the edge of some failure in country-level self sufficiency in food.

We throw out a third of the food we grow

30 to 40% of the US food supply is never eaten. One estimate suggests that if all America’s wasted food were grown on one farm, the farm would be 125,000 square miles, or 89 times the size of all land owned by data center companies by 2028. This means that a 1% reduction in food waste would free up enough land for all data center building over the next 3 years if literally all of it were built on farms.

This wasted farmland generates as much greenhouse gas emissions as 42 coal-fired power plants, 3% of all US emissions. If you add in the emissions from ethanol, 5% of all US emissions come from unnecessary farming.

We’ve been shrinking our farmland for decades and growing more food on it the whole time

Total US farmland peaked decades ago and has fallen since. From 2000 to 2024, it shrank from 945 million to 876 million acres, an loss of 108,000 square miles, about the size of Colorado. This is 77x the projected footprint of all data center land in 2028, and not many people seem to know it happened.

Why did farmland shrink this much? It’s purely that yields per acre grew. American corn yields in the 1930s averaged 24 bushels per acre. In 2024 they averaged 180, a 7.5x increase. Total corn production is now more than 6 times what it was in the 1930s, but uses less land than it did in the 1930s. The same is true for soy, wheat, and most major crops.

The trend keeps on going. Corn yields have been rising at about 1.9 bushels per acre per year for 70 consecutive years with no signs of stopping.

Source

If you follow this line, average corn yield in 2050 will be around 230 bushels per acre. We’ll need less land to grow the same food 25 years from now than we do today.

We’ve already abandoned tens of millions of acres of farmland and it was great

In 1850, much of New England was farmland. Vermont was about 70% pasture and cropland. Then mechanized farming opened up the Midwest’s deeper soils, the Industrial Revolution pulled rural labor into cities, and New England farms became uneconomic. The land turned back into forests. Today, about 70–80% of New England is forested. Where I grew up in Massachusetts, you can regularly find old stone walls from farmers hidden deep in the woods. The region has more forest cover now than in 1820. Vermont went from a stripped agricultural landscape to one of the most heavily forested states in the country.

Across the eastern US, hundreds of millions of acres followed this pattern between 1850 and 1950. The country was fine. The abandoned land was the marginal stuff barely worth farming, and the land that kept being farmed was the productive Corn Belt and Central Valley.

If a few thousand acres of mediocre Loudoun County farmland end up under Amazon and Microsoft and Google data centers, I suspect that nothing bad will happen, for the same reason that it’s not a disaster that most of New England was changed from farms into forests. The marginal hay field on the exurban edge is producing way less value to anyone than a hyperscale data center on the same land. A farmer who sells to a data center company for 10x what their land is worth is doing something economically rational, environmentally reasonable, and morally fine. They’re doing exactly what their grandparents in Vermont did when they sold their farms and let the trees come back.

More examples of the goofy ways our politics and land use is warped by farmers

There are too many other ways farmers’ disproportionate political power makes land use and policy goofy to keep writing about here. For more, you should look into ag-gag laws, the decline of the Ogallala Aquifer, use-it-or-lose-it water rights, and the “family-farmer” myth (discussed a bit above) which wealthy farming families use to garner sympathy. And on top of this, the unimaginably huge moral catastrophe of industrial animal agriculture. For a deep dive on the weird world of farm economics I’d recommend this podcast series from the Agricultural Economics Institute.

Conclusion

American farmers grow more food than the country eats, export 20% of the surplus, and we the buyers throw out another third of what’s left. Farmers collect $1.85 billion a year from the federal government to keep a Kentucky-sized 40,000 square miles of farmland intentionally idle, an area 28 times the full projected footprint of all land on American data center property in 2028 (with data centers themselves occupying just 2% of that land, just 25 square miles). The federal government forces oil refiners to buy 15 billion gallons of corn ethanol every year, occupying roughly the surface area of New York State, draining aquifers at 40 times the rate of every data center in the country combined, and producing a fuel that, once you account for land-use change and nitrous oxide, is 24% dirtier than the gasoline it replaces. The food we waste, if grown on a single farm, would cover an area 89 times the size of all land owned by data center companies by 2028, and emits as much CO2 as 42 coal plants, 3% of all US emissions. Between 2000 and 2024, farmers sold in total a Colorado-sized chunk of land all on their own, 77 times all land on data center property in 2028, and grew more food than ever on what was left. None of this caused any problems for US food access.

And then, in the middle of all this, a farmer in Loudoun County sells a few acres of mediocre hay field to a hyperscaler for ten times its agricultural value, and the response is that we’re running out of farmland.

The marginal Virginia hay field is worth more as a data center than as hay. The marginal Iowa cornfield going to ethanol would be much better if it were nothing at all. When a farmer in Loudoun County wants to sell to Amazon for ten times the land’s agricultural value, the correct response is to wish them well. We need way less farmland than we currently use, and it’s fine if data centers buy some.

Some bad recent articles on data center land use

“Farmer Hailed as Hero for Rejecting Huge Payment to Turn His Land Into a Giant Data Center”

From Futurism, the same magazine that published the story on Oregon nitrate poisoning that let farmers off the hook:

The immense hype surrounding AI has caused enormous data centers to crop up across the country, triggering significant opposition. It’s not just the loss of land: enormous power needs are pushing the grid into meltdown and driving up local electricity prices, catching the attention of politicians and their irate constituents.

idk I think it’s more than hype.

One 86-year-old farmer in Cumberland County, Pennsylvania, has heard enough. As local Fox affiliate WPMT reports, Mervin Raudabaugh, who has farmed the surrounding land for more than 60 years, turned down more than $15 million from data center developers in a package deal that involved three neighboring property owners as well.
The farmer was offered $60,000 per acre to build a data center on his property. But giving up his family legacy wasn’t in the cards for him.
“I was not interested in destroying my farms,” he told WPMT. “That was the bottom line. It really wasn’t so much the economic end of it. I just didn’t want to see these two farms destroyed.”
Instead, he sold the development rights in December for just under $2 million to a conservation trust, taking a significant loss but guaranteeing that it would stay farmland in perpetuity.

This guy turned down $58,000,000 to ensure that America continues to have way more farmland than we need.

Users on social media called him a “legend,” and argued he had “more integrity than the whole government.”
“Now that is a real hero in these gutless times!” another user tweeted.
“$15M is huge, but clean water, quiet land, and legacy don’t have a price tag,” another user argued.

Data centers don’t really pollute water anywhere.

The sheer amount of land being earmarked to construct enormous energy and water-sucking data centers is remarkable. A data center in Mount Pleasant, Wisconsin, is set to take up 600 acres, which could cost local residents their land, as ABC News reported this week. Another octogenarian farmer, the 83-year-old Tom Uttech, who has lived on his 52-acre Wisconsin property for almost 40 years, told the broadcaster that he “couldn’t believe” that a local utility company was looking to build “power lines that are 300 or something feet tall, taller than apparently the Statue of Liberty,” through his land to power the data center.

“Cost residents their land” meaning “Could mean residents are paid way more than what their land is worth if they choose to accept the deal.” 600 acres is about a square mile. Each of these is a square mile:

A private company purchasing a single square mile farm in a rural area wouldn’t make headlines. But data centers are seen as weird and untraditional, so they get way more scrutiny. We would need to build 50,000 data centers at this size to use as much land as ethanol corn.

“It breaks my heart to think of what’s going to take place here, because only the land that’s preserved here is going to be here,” Raudabaugh told WPMT. “The rest of every square inch is going to get built on. The American farm family is definitely in trouble.”

This is an insane hyperbolic thing to say. We have so so so much farmland, way more than we need. Every square inch is not going to get built on, and the average American farm family is incredibly well-off and coasting on powerful political allegiances and the confused goodwill of populists.

This same story was reported on in Fortune and Newsweek.

A data center using as much land as 51 Walmarts

From the Washington Post:

The town didn’t “plan” the data centers, they approved a private company to purchase land. This is a common weird framing in the data center discourse. People often talk as if data centers are a collective decision we’re all making. In reality, private companies buy land, power, and water from towns and utilities like any other company.

This town is 17 square miles. The article says the data centers would together take up 14% of the town’s land, so ~2.4 square miles.

This is the town of Archibald PA that the article’s about:

It’s in a pretty rural area:

These are the sites where the data centers have been proposed:

If you found out that a small town like this were getting a college or farm, how would you react? Would this be an unacceptable imposition on the locals? The college would radically change the general culture and infrastructure needs of the town, whereas the data centers hum in the background.

There are definitely serious issues the locals bring up in the article (electricity costs, air pollution, noise) but the land use framing makes no sense to me. Archbald is in a pretty rural area with tons of wide open land. Purchasing 14% of a small town’s land on its own doesn’t seem newsworthy.

“This Land is Their Land”

The title image is already promising:

The subtitle is “Farmers are fighting AI companies offering fortunes to build data centers on their land. Can they withstand the pressure — and live to farm another day?”

Farmers are always framed as the good guys. Question this.

“Life-changing money.” That’s how Wendy Reigel describes the windfall developers are offering farmers for their land, potential sites for hyperscale data centers to meet AI’s massive processing needs. Reigel is a grassroots anti-data-center activist who successfully fought the building of a center just 300 feet from her house in Chesterton, Indiana; she now helps other communities mobilize against data center incursions. She can only make an educated guess as to how much money is on offer — almost everything about these facilities is shrouded in secrecy.
“$20,000 an acre would be low-balling it,” Reigel said. “$40,000 would be maybe a starting point. And I’ve heard as high as $90,000.”

This doesn’t exactly sound nefarious. They’re offering crazy amounts of money for people’s land. Would it be better if they offered less?

And with this wind beneath their sails, AI-giddy companies like Amazon, Google, Meta, Microsoft, and Oracle have embarked on a national land grab.

The new land grab here being in total about 2 times as much land as we use to grow Christmas trees by 2030.

Some farmers are loath to condemn fellow farmers who sell their land for this purpose. They don’t begrudge life-changing money to anyone who’s contended with their industry’s often brutal fiscal realities, although a farmer in Michigan who sold his land for an “astronomical” sum — in the interest of what he called progress — says his neighbors don’t wave at him anymore. With or without the animus, farmers who opt not to sell are left to contend with the potential destruction of their operations and their lives. “It ruins all our little farms around here that we worked all our lives on,” a couple in Coweta, Oklahoma, told the local news.

I think all farmers should be loathe to condemn other people for making private financial decisions that work out well for them if they don’t harm other people. The question is will the data centers harm the nearby farmers?

There are data centers popping up almost everywhere, from Oregon and Nevada to Georgia and Virginia. (Loudon County is our country’s “data center alley.”) But Indiana has become an especial target for hyperscale facilities, of which there are an estimated 1,130 globally; since Indiana produces a lot of corn, soy, and hogs, it’s also illustrative of the challenges that farmers in particular are up against.

43% of Indiana’s corn crop went into ethanol production in 2024, and ~95% of Indiana hogs are in factory farm conditions. Neither inspires sympathy from me.

“That hurts us, because they’re paying a higher price than market value for the land; that drives property values up, then farmers are struggling to pay their taxes,” Blalock said. That price is also “completely out of reach” for any farmers starting out or looking to expand their operations. When data centers are willing to quadruple or quintuple an already-inflated price, affordability for farmers goes up in smoke.

Okay, well we have one answer to how data centers are “harming” farmers. They’re making the farmers’ land so valuable that property taxes go up.

For any farmer who owns the land they farm (which most do), rising land values are a massively good outcome. Land is by far the largest asset on most farms’ balance sheets (the article will go on to note this). They can borrow against it, sell and retire on it, or pass it to their kids. The article is asking us to feel sorry for farmers because someone is making their land massively more valuable. I need anyone reading this to ask: if it’s bad for the data center to raise farmer land values, doesn’t that mean it would be good for it to lower their land values? But if a data center moved in and made everyone’s farmland way less valuable, I think we’d also hear lots of negative stories about how it harmed a community, and there I’d take it more seriously.

The weirdest thing about all of this is that Indiana’s agricultural land tax is not supposed to be based simply on speculative industrial value. The state says agricultural land value is based on productive capacity, regardless of the land’s potential or highest and best use, and the 2026 agricultural base rate is listed at $2,120 per acre. So I’m not sure if rising land value will even raise taxes here.

Morgan Butler is a senior attorney at the Southern Environmental Law Center, who helped the tobacco- and dairy-producing community of Pittsylvania County, Virginia, fight off a 1,000-acre AI facility. He said developers are drawn to farmland because “They see a huge area. In their eyes there’s nothing on it, nothing particularly valuable, there aren’t that many residents so hopefully they won’t kick up a firestorm of public opposition.”

What needs opposing here? A data center drops in and offers so much money that it inadvertently makes all the land value around it go way up and all the farmers who didn’t interact with it got richer? What?

That rural “nothingness,” though, is precisely what community members opposed to data centers hope to preserve. One farmer in Kentucky turned down an $8 million offer for his land, citing his family’s sentimental ties to it and the community’s fondness for the landscape as it is. However, one bitter truth for farmers is that much of their net worth can be tied up in their land; retirement might necessitate selling property because they have few or no other assets, making a generous offer for acreage hard to refuse.

Okay, so far this article’s been implying that it’s a massive crime that the data centers are raising farmer land values so much, and now it adds that much of an average farmer’s net worth is tied up in their land. This implies that the data centers are doing farmers a massive favor by increasing so much of their total net worth? But the article never acknowledges this contradiction. In fact, a pull quote appears right below that makes this contrast more obvious:

A hyperscale data center can use upwards of 8 million gallons of water per year, mostly for cooling its servers. “We don’t have the water supply” to meet those needs, said farmer Bart Snyder. Snyder lives in Wolcott, an ag-centric town 135 miles northwest of Henry County, where Amazon is scouting a 330-acre facility 1,000 feet from one of his farm properties. He’s suing his town’s commissioner and redevelopment committee members (all of whom signed NDAs) for approving a farm-to-industrial land rezoning on behalf of that facility. “To consume that much water would absolutely devastate our row crops” and potentially create a deficit for Snyder’s 30 beef calves, which each drink 15 gallons of water daily. “People are literally scared to death,” he said.
Blalock is concerned that if a data center is approved in her county, it could deplete the local aquifer, causing neighbors’ wells to run dry and preventing her family from ever drilling one of their own for their cattle. She said that would put them out of business and leave their land valueless. “No one has answers for us about, what’s our backup plan? What if people lose water? You can take your family to go stay in a hotel but it’s not like doggy daycare; you can’t show up with 50 head of cattle.” And she worries: “Are we going to be the generation that loses the farm?”

The data center seems unlikely to do this.

Wolcott is in White County, Indiana. Irrigation on corn typically runs 6–10 inches per season. 8,695 irrigated acres at ~8 inches of supplemental irrigation consume roughly 1.9 billion gallons per year. The 8 million-gallon data center is 0.4% of that. This data center is using as much water as a local 35 acre irrigated farm. If one additional 35 acre farm appeared, I don’t think the farmers would be talking this way.

The rest of the article has legit worries about pollution and electricity prices. It also has an interesting concern about infrasound I hadn’t addressed last time: its effect on animals. I think that if animals can detect infrasound because they evolved to hear different frequencies than us, it makes complete sense that infrasound could be harmful to them. I will circle back on this once I know more about infrasound and animals.

This is a back-of-envelope estimate. Nobody tracks total US data center parcel acreage directly. As of late 2025, Cushman & Wakefield reports the Americas hosts 43.4 GW of operational data center capacity, with 93.6% in the US (so ~40 GW US operational), plus 25.3 GW under construction. Capacity forecasts for 2030 cluster around a 2.5–3.5× expansion: McKinsey projects US data center power demand to rise from 25 GW in 2024 to over 80 GW by 2030, S&P Global’s 451 Research projects 134 GW by 2030, and Bain forecasts global capacity reaching 163 GW (the US is roughly 80% of the Americas total). Skeptics including Grid Strategies argue these are inflated by speculative projects and that ~65 GW of additional load is more realistic. Assuming a central case of ~85 GW US operational by 2030. Cushman & Wakefield’s 2025 Data Center Development Cost Guide reports the average data center parcel acquired in 2024 was 224 acres — a 144% increase since 2022, with hyperscalers routinely buying 1,000+ acre sites for phased buildout (Meta’s 4-million-square-foot Louisiana campus sits on 2,250 acres for ~2 GW). Combining current operational acreage (~300,000 acres across roughly 4,000 facilities, most of which are small legacy colocation on tiny urban parcels) with new hyperscale buildout at ~1,500–3,000 acres per new GW gives a total in the 600,000–1,000,000 acre range, roughly 1,400 square miles at the central estimate, or about the area of Rhode Island.

Building-footprint math: existing US data center floor space is roughly 300–400 million sq ft, or ~11–14 sq mi. By 2030, capacity is projected to roughly triple (from ~25 GW today to ~85 GW central case), but power density is rising sharply at the same time — AI racks now run 40–60 kW vs. legacy racks at 10–14 kW, so new facilities pack much more compute into the same square footage. Assuming new-build density runs 1.5–2× legacy stock, ~85 GW × ~7,000–9,000 sq ft per MW (blended legacy + AI-dense) gives roughly 600–765 million sq ft, or ~22–27 sq mi. 25 sq mi is in the middle.

Using LBNL’s empirical median energy density (447 MWh/acre fixed-tilt, 394 MWh/acre tracking), 450 TWh works out to ~1.0–1.15 million acres or ~1,575–1,800 sq mi. See Bolinger & Bolinger 2022, “Land Requirements for Utility-Scale PV.

USGS SPARROW modeling attributes 52% of nitrogen delivered to the Gulf of Mexico from the Mississippi/Atchafalaya basin, which drains ~41% of the contiguous US and dominates national nitrate flux, to corn and soybean cultivation. Corn drives most of that share, since soybeans are nitrogen-fixers receiving little synthetic fertilizer, and corn alone receives over 40% of all commercial fertilizer applied in the US. Ethanol uses ~40% of the US corn crop. Multiplying through (40% of corn × ~40–45% of US water nitrate attributable to corn) gives ~16–18%, rounded to one-fifth. This is a gross attribution; netting out distillers grains coproducts that return as animal feed would bring it closer to ~12–14%, and Lark et al. (2022) frame the RFS-attributable marginal effect (vs. a no-RFS counterfactual) as smaller still.

Ethanol corn occupies ~30 million acres (USDA ERS; about 1.5% of contiguous US land per Bloomberg US Land Use). Utility-scale solar in the US generates ~370–470 MWh per acre per year (LBNL 2021 PV Land Requirements, Figure 8 median energy density). 30 million acres × ~400 MWh/acre/yr ≈ 12,000 TWh/yr, rounded to ~11,000 TWh. US electricity generation in 2024 was ~4,200 TWh (EIA Monthly Energy Review), so 11,000 TWh ≈ 2.6x. Global generation is ~30,000 TWh (Ember Yearly Electricity Data), so 11,000 TWh ≈ 37%.

US corn averaged 186.5 bushels per harvested acre in 2025 (USDA NASS), and modern dry-mill plants yield roughly 2.88 gallons of undenatured ethanol per bushel (EIA Monthly Energy Review, citing Argonne’s GREET model), giving ~537 gallons of ethanol per acre. At ethanol’s gross heat content of 3.539 million BTU/barrel, or ~84,000 BTU/gal (EIA), that works out to roughly 13,000 kWh/acre/year of gross chemical energy. Net of fossil-fuel inputs the number is far smaller: USDA’s most favorable accounting puts the energy return on investment at about 1.3, while independent estimates put it closer to 1.0 (Murphy et al., 2011), implying net energy on the order of 1,000–3,000 kWh/acre/year.

A US utility-scale solar PV plant delivers a median 447 MWh per acre per year for fixed-tilt and 394 MWh for single-axis tracking (Berkeley Lab analysis of 736 plants built 2007–2019), or roughly 400,000–450,000 kWh/acre/year, with newer plants denser than the median. Comparing gross ethanol energy to solar, that’s ~30–35× more energy per acre from solar; comparing solar to ethanol’s net energy, it’s well over 100×. Even crediting corn ethanol generously for its DDGS animal-feed coproduct (~30%) doesn’t move the gross-energy ratio below ~25×.

For the global northward shift of agricultural climate zones, see King et al. 2018, “Northward shift of the agricultural climate zone under 21st-century global climate change,” Nature Scientific Reports (boreal regions reaching crop-feasible growing-degree-days for the first time, with the leading edge shifting up to ~1,200 km north by 2099). For US-specific crop mix projections, see Cho & McCarl 2017, “Climate change influences on crop mix shifts in the United States,” Nature Scientific Reports.

See e.g. Burchfield 2022, “Shifting cultivation geographies in the Central and Eastern US,” Environmental Research Letters, the most cited recent paper for the Corn Belt-collapse claim. Burchfield herself notes that “these projections may be pessimistic because they don’t account for all of the ways that technology may help farmers adapt.”

To be clear, I do understand how sound works

Fri, 24 Apr 2026 04:21:51 GMT

This post is kind of inside baseball for the ongoing drama around my last post on Benn Jordan’s infrasound videos. It might not be interesting to regular readers, but if you’d like to see me use my physics background to defend my good name, read on.

After failing to respond to any of the ways I had pointed out in my last post that he was misrepresenting every study he flashed on the screen, Benn Jordan shared a blog post someone else had written called Andy Masley doesn’t understand how sound works. The conclusion of the post is that I don’t actually understand the science of sound, my criticism falls apart, and I’ve become ideologically motivated to defend data centers over seeking the truth.

However, the criticisms are all wrong, confused, and seem to be coming from someone who’s lurching to assume I have bad motives. And Jordan obviously knows that it’s all mistaken, because he works on and thinks about sound way more than me, and I can run circles around it. He’s once again tricking his audience. I’m going to use this post to respond to them individually.

I wasn’t originally going to respond to the blog post, because it’s pretty bad throughout, but since Jordan shared it I do feel compelled to explain why it’s wrong.

Criticism 1: Decibels: intensity vs pressure

The first section is called “He does not seem to know what a decibel is measuring”

Masley claims that “Every 10dB increase represents a 10x increase in sound intensity.” This is technically true, but it is the wrong quantity for the argument he is making. Intensity describes the power per unit area carried by the sound wave through the air. What matters for the biological mechanisms Jordan is describing (pressure on the eardrum, stimulation of vestibular structures, outer hair cell response) is not intensity but pressure. And while a 10dB increase represents a 10× increase in intensity, it represents only a ~3.16× increase in pressure. Reporting the intensity ratio rather than the pressure ratio inflates the apparent safety margin by roughly a factor of 300. By the quantity that actually maps to the effects Jordan is proposing, the gap between residential data center levels and occupational exposure limits is one to two orders of magnitude in pressure, not the five orders of magnitude that Masley’s framing implies. It matters significantly less what the power of sound is at the source here than the pressure change at the point of “impact” for the listener.

You can translate a sound’s decibel level into an intensity or a pressure ratio. They both describe the same thing. If residential data center infrasound is 50 dB below the limits where it would need to be to harm you, that gap is both a ~100,000× intensity ratio and a ~316× pressure ratio. Which one we should look at depends on the situation we’re trying to understand.

It’s true that sound pressure describes the potential for physical harm once the sound reaches your ear. But to understand the amount of energy you need to create that dangerous pressure in the first place, you need to know the intensity.

Intensity matters here because it tells you roughly how much more energy the data center would need to contribute to produce enough sound to be harmful to us. The intensity being 100,000x smaller than it needs to be implies that the data center would need to convert 100,000x as much of the energy flowing through it into sound as it currently does for it to harm us. This is obviously ridiculous!

This is a useful comparison to make to understand how reasonable the situation is. The critic just lurches to assume this doesn’t matter at all:

It matters significantly less what the power of sound is at the source here than the pressure change at the point of “impact” for the listener.

I think for understanding how realistic the chance of a data center harming us with infrasound is, it does in fact help to know that the data center would need to be 100,000x as large to produce adequate levels of infrasound to harm us. The critic assumes that this means I’m acting in bad faith. I disagree! The whole point of my post is to understand whether the source of the sound can produce enough sound to harm us.

Masley conveniently only cited the broad occupational guidance for 8-hour workdays while omitting the exposure limit for workplaces requiring maintained mental concentration, which is a much lower 86dB. OSHA also identifies 85dBA as the occupational limit for sound exposure over an 8-hour period. Using this number for comparison, data centers are only ~4x quieter than occupational standards allow for certain roles.
These figures are for eight hours of exposure. The dose makes the poison. It’s quite absurd to say “this is under the threshold for eight hours of exposure, so it’s surely under the threshold for 24/7 exposure!”
The study notes that the impacts of infrasound may pose greater risk to “pregnant women and adolescents,” possibly justifying an even lower rating of dBG.

I will concede a point here that occupational limits are far above residential limits. However, both remain far above what a data center can produce. The reason I hadn’t gone into detail about this is that as noted in the post itself, these occupational levels (including the advise for pregnant women and adolescents) are from old government advice that traces back to papers that don’t replicate, and that infrasound experts have commented on as not being reliable. I’ve already addressed this criticism, but the author is writing this as if I’m trying to hide it. Again, it’s hard not to read this as a bad faith response hoping that the reader doesn’t actually go through my original post.

Masley handwaves away this last point, but studies have shown that there is a potential link between chronic sound exposure during pregnancy and negative health outcomes. The CDC even tells pregnant women:
Avoid low frequency sounds (noises you feel as a rumble or vibration). Low-frequency noise travels through your body more easily than high-frequency noise. Low-frequency noise can cause changes that could affect your developing baby.

This page never mentions infrasound and only lists volumes of low frequency sound that I’ve agreed could be harmful at infrasonic frequencies. And the harms listed are fetal hearing development, not any of the symptoms Jordan lists. It’s also precautionary and doesn’t cite any evidence. Again, I’m not ignoring anything here.

Criticism 2: Perception thresholds/does infrasound become audible?

The second section is called “He does not understand what a perception threshold is.”

Masley’s central rhetorical move is structurally simple: the perception threshold at 10 Hz is 97 dB; residential infrasound levels are below that; therefore, there is no effect.
This is a non-sequitur.
A perception threshold is the level at which a conscious, reportable sensation occurs. It is the floor below which you cannot perceive something. It is not the level below which biological effects cease. These are different things, and conflating them is not a caution any serious researcher in this field would permit themselves.

The author is correct that these are different. I made that clear in the post itself.

I never once said that “Because infrasound isn’t audible, it can’t affect you.” I’m basing all this on what looks to me like a pretty solid separate scientific consensus that inaudible infrasound doesn’t seem to harm us, so therefore it only becomes harmful when it behaves like normal noise pollution (when we can hear it) or when it’s extremely extremely loud and physically harms us.

My argument isn’t a logical deduction from my blind belief that “you can’t hear it” to “it can’t affect you.” It’s an empirical summary of what the literature on infrasound health effects has found. For about sixty years now, researchers have gone looking for physiological and clinical effects of infrasound at sub-perception levels. Properly blinded exposure studies don’t find them. The studies that claim to find them are generally unblinded, badly controlled, or fail to replicate. The nocebo studies reliably produces the symptoms people attribute to infrasound just by telling subjects that infrasound is present, with no actual infrasound required. That’s the pattern I’m drawing on. “Sub-perception stimuli can in principle have effects” is true in the abstract. When you go looking in this specific place though, you don’t find the effects being claimed.

The author is describing my argument as a logical syllogism and then saying this is a non-sequitur. But this isn’t a syllogism at all, it’s a summary of all the good scientific literature on the problem. If the author (and Jordan) think the literature is wrong, they need to engage with it directly.

In fact, Jordan makes this point, comparing infrasound potentially to ultraviolet light: despite being imperceptible, he argues, these both can cause harm in humans. Masley dismisses this out of whole cloth simply because “this blurs two opposite ends of the audible spectrum.” Because one is infra- and the other is ultra-, it’s apparently not even worth considering the broader point that imperceptible waves can be harmful in some scenarios despite their imperceptibility.

No that’s not what I’m saying. I’m saying that Jordan is suspiciously lurching to the extremely high energy end of the light spectrum when we know that the low energy end (comparable to infrasound) doesn’t have negative impacts on us if we can’t detect its presence.

Maybe all comparisons to light are bad. I was mainly suspicious that Jordan lurched from an invisible harm we obviously know is real (UV damaging us) to infrasound. Maybe this was unfair? Idk.

Here is the extraordinary part. Masley himself cites this directly. He writes: “research suggests that while inner hair cells don’t respond to infrasound, outer hair cells of the cochlea can respond to very low-frequency sound at levels below what we consciously detect, and some studies have reported cochlear or brain responses under near-threshold or below-threshold conditions.” This implies that sub-threshold sound can have an impact on humans.
He then, in the very next sentence, dismisses the implication entirely: “But no one has shown this mechanism produces the long list of real-world symptoms people often blame on environmental infrasound.” He is now using his belief as argument in support of the same belief.

The author has a pattern of accusing me of circular reasoning when we actually just disagree about empirical claims.

Claim 1: there exists a measurable physiological response to sub-threshold infrasound somewhere in the body.
- This is true. Outer hair cells respond, as I acknowledge in the post.
Claim 2: this response produces the specific symptoms Jordan is attributing to data center infrasound. Things like vertigo, heart palpitations, cortisol dysregulation, insomnia, cognitive impairment, and the rest.
- This is not established at all, and all studies trying to find these symptoms from infrasound have come up with nothing.

Therefore, I conclude that because 2 seems to be wrong, our best bet right now is to assume the outer hair cells that do respond to infrasound don’t have an effect. If they did, why can’t we find it anywhere?

My argument isn’t “ignore the mechanism research.” It’s “the mechanism research does not license the leap from ‘outer hair cells respond’ to ‘residential data center infrasound is making people sick.’ That’s a separate claim that needs its own evidence, and the studies built to test it keep coming up empty.”

I’m not using my conclusion to support my conclusion.

Sub-threshold stimuli produce measurable physiological effects in virtually every sensory system we have studied. This is not controversial.

This sentence is doing a lot of work it hasn’t earned.

Yes, you can find detectable physiological responses to sub-threshold stimuli in lots of sensory systems. The magnitudes of these effects are typically tiny, measured in milliseconds of reaction time or microvolts of neural activity, and detectable only under tightly controlled lab conditions.

The author is using this to imply that sub-threshold infrasound could plausibly cause the symptoms Jordan’s describing. But given the state of the evidence, this is a huge unsupported leap. Making this leap is how you get Wind Turbine Syndrome.

There is a deeper problem here, too. Jordan’s entire argument is about sub-threshold effects. His claim is not “people can hear the data center” or “people can consciously detect the data center.” His claim is “the pressure waves affect biology below the level of conscious perception.” Masley’s rebuttal is “the signal is below the threshold for conscious perception.” Yes, obviously, that is the point.
Masley has spent hours attacking Jordan for claims Jordan never made, because Masley apparently believes “sub-audible” might as well mean “nonexistent.”

Jordan’s first video is literally called What You Can’t Hear Can Hurt You. The whole post is a response to that argument.

My actual claim is that when researchers design experiments to test whether sub-perception infrasound causes the health effects Jordan attributes to it, it doesn’t. That’s a direct response to Jordan. The critic has read “the studies testing Jordan’s claim consistently find no effect” and somehow heard “Jordan isn’t making that claim.” I don’t know what to do with that.

Criticism 3: Confusing “perceptible” with “audible”

The threshold error described above is quantitative: Masley treats the audibility threshold as the floor below which all biological response stops. What I want to flag now is a separate, conceptual error that runs beneath it – one he commits so consistently that I am not sure he realizes he is committing it.
Audibility and perceptibility are not the same thing.

Not only do I realize that, I make it clear in the post!

Throughout Masley’s piece, “inaudible” does the work of “undetectable,” “imperceptible,” and “biologically inert” – three claims that are not equivalent and that require separate evidence. He never makes this distinction. He appears not to know it exists despite citing research which clearly states:
Pure tones become gradually less continuous, the tonal sensation ceases around 20 Hz, and below 10 Hz it is possible to perceive the single cycles of the sound. A sensation of pressure at the eardrums also occurs. The dynamic range of the auditory system decreases with decreasing frequency.

I do make this distinction, I just say they seem to overlap despite being distinct. In my opening part on the science of infrasound, I explain that our experiments seem to find that 1) inaudible infrasound doesn’t have an effect, so 2) the only effects happen when it becomes audible. I also clarify that we can sometimes perceive infrasound as physical pressure on our ears and chest before hearing it, and at that point it becomes dangerous to us.

Consider the vestibular system. The semicircular canals of the inner ear respond to pressure changes and acceleration. They do not produce conscious auditory perception – they produce balance, orientation, and spatial awareness. Low-frequency pressure waves can stimulate vestibular structures at levels far below what the cochlea registers as sound. The sensation, if any, is not heard. It is felt as dizziness, unsteadiness, or nausea. This is not speculative. It is the operating basis of clinical tests like vestibular-evoked myogenic potentials, a standard tool used by ear, nose, and throat doctors.

The study is on very loud stimuli (in the 95-105 dB SPL range) which reliably evoke responses, delivered directly into the ear canal via insert earphones. Below about 75-85 dB SPL you stop getting reliable results, which is why the test uses the levels it does.

Studies have shown a signal can be inaudible and still make you dizzy above the occupationally safe decibel range.

This review doesn’t say what the author is claiming.

The sentence the author seems to be referencing describes “isolated reports” of vestibular symptoms following exposure to wind turbines and rocket engines, phrasing that, in a clinical review, means “case reports exist, no established syndrome.” The same paper explicitly states that the phenomenon “is not fully understood” and that “ongoing research continues to study” whether low-level infrasound produces vestibular effects. And the research has since been done, and I cite it throughout my post!

The most telling moment in the piece is one we have already visited. Masley cites Salt & Hullar – a study specifically about a mechanism by which the inner ear detects infrasound without rendering it as conscious sound. The outer hair cells respond; the signal is processed; no auditory percept is generated. This is, precisely, detection without audibility. Masley cites it. Then he just handwaves away the implication because there hasn’t been enough follow-on research on the subject. This would be fine if the headline of his article was “I’m not entirely convinced that infrasound is a problem.” But no, the idea of infrasound being potentially problematic is fake, pseudoscientific, conspiracy theory.

I hand wave it away because it identifies a mechanism that could create an effect, but we’ve tested for that effect over and over and haven’t found it. Anyone claiming that science implies the effect is actually there is in fact doing pseudoscience.

The cash-out is this. Jordan’s entire claim is about perceptible but inaudible effects – pressure waves that the body registers through pathways other than conscious hearing. Masley’s entire rebuttal is that the signal is inaudible. These two writers are not disagreeing about evidence. They are using different definitions of what counts as the phenomenon being studied. And Masley does not appear to realize this, because he appears to believe that “you cannot hear it” and “nothing is happening” are the same sentence despite repeatedly citing studies which state the contrary.

This is so bizarre that I don’t even know how to respond to it. Reading it, I kind of just feel deeply tired. How can you come away from my post and honestly write this?

Criticism 4: Nocebos are unfalsifiable

Masley relies heavily on the work of Crichton & Petrie, a body of research on the nocebo effect: show one group a video emphasizing harm from wind turbine infrasound, show another group a neutral video, expose both to real and sham infrasound. The high-expectancy group reports more symptoms, including during sham exposure. This is a real finding. It belongs in any honest treatment of this research area.
But here is what the Crichton & Petrie studies actually show: high-expectancy groups report more symptoms than low-expectancy groups. They do not show that low-expectancy groups report zero symptoms. They do not show that infrasound has no physical effect at all. They show that expectation modulates symptom reporting.
A study showing that expectation amplifies a signal is not the same as a study showing there is no signal. Masley treats “nocebo effects exist” as synonymous with “nocebo effects mean your research is bunk.” This is not how that works.

This isn’t how I treat the nocebo effect in the post. I very clearly separate studies that try to test for infrasound harms from studies that test for a nocebo effect. I first try to show that studies testing for harms of infrasound below perceptible levels consistently don’t find any, and then talk about how studies show that you can get a pretty decent nocebo effect on infrasound and generate similar symptoms. I don’t say anywhere that the nocebo studies alone prove that infrasound is from the nocebo effect. The nocebo effect would be clearly falsified if people exposed to infrasound who weren’t primed to feel bad had any measurable negative reactions at all. Best I can tell from every serious inquiry that’s looked into this, they don’t. I use the nocebo effect as “this seems very likely to be what’s causing the symptoms once we know they’re not caused by infrasound” rather than “this proves that the symptoms are caused by the nocebo effect.” Again I think this is just a bad faith reading of my post.

Consider what this means for Jordan’s double-blind experiment. Jordan ran a study with a “haunted painting” cover story, specifically to equalize expectation across groups. Masley’s response is to dismiss the entire design by leading with the nocebo literature – in effect assuming a nocebo explanation regardless of what the blinding protocol was. This is unfalsifiable reasoning. If nocebo explains symptoms when the design is bad and nocebo explains symptoms when the design is good, then crying “nocebo effect” is more rhetorical shield than scientific analysis.

I addressed six separate problems with Jordan’s study, only one of them was that he was actively priming each group to feel bad (which does in fact seem relevant!). Again this just flatly misrepresents my post that anyone can read.

Furthermore, Crichton & Petrie show that expectation modulates symptom reporting. They do not show that infrasound has no effect. This is not the “exact opposite” of what Jordan claims. It is, at most, an important qualification to it.

I didn’t use Crichton & Petrie to argue that infrasound has no effect, I used them to say that the nocebo effect seems real, and since I had already used other studies to argue that infrasound has no effect, this looks like the next best explanation.

Criticism 5: The evolutionary argument

Natural infrasound – from thunderstorms, ocean microbaroms, atmospheric gravity waves, wind over terrain – is intermittent, spectrally broad, and non-stationary. It varies by season, geography, weather. It is not a constant feature of any individual’s sensory environment.
Industrial infrasound from a data center HVAC plant is continuous, tonally pure at specific frequencies, and temporally consistent.

It’s correct that natural infrasound is intermittent and broadband while modern industrial infrasound is continuous and concentrated at specific frequencies. But the point about evolution I’m making doesn’t depend on the sounds being identical, just whether human bodies have any reason to have evolved a generalized harm response to inaudible low frequency pressure waves. The biology just doesn’t seem to be there. If we evolved this way, people would mostly not live near the ocean and would be regularly sick if they did. We don’t actually observe any evidence that humans or other animals living near the ocean were evolutionarily selected against. And again, we’re just not getting any evidence that we evolved this way from any of the many good studies done on infrasound.

Arguing that humans must be adapted to industrial continuous-tone infrasound because we evolved alongside transient broadband natural infrasound is like arguing that humans must be adapted to staring at a 500-nit monitor six inches from our faces for twelve hours a day because the sun has always existed.
The sun has always been bright. This does not make a welder’s arc safe to look at without goggles.

The difference is that with these is that we evolved to be able to see the light a monitor gives off, and we evolved to see the light a welder’s arc gives off. We did not evolve to see the infrared light either gives off, but we did evolve to physically feel that infrared light when it gets to be so much that it’s dangerous to us. Similarly, we can detect infrasound when it’s starting to become dangerous to us. Thanks evolution! Have you ever worried about the infrared light your monitor gives off? That seems like the comparable case here, not visible light.

Refrigerators: typically ranging 40-50 dB at around 400-500 Hz (not infrasound!) Washing machines: ... the sound they emit is between 100-1000Hz (again, not infrasound!) Trains: ... these emit sound all over the audible range.

Refrigerators, HVAC systems, washing machines, and most rotating mechanical equipment do produce infrasound alongside their audible sound?

We’re surrounded by mechanical sources producing low-frequency and infrasonic energy alongside audible sound, and the population is not experiencing the symptom cluster being attributed to data center infrasound. This is worth talking about and bringing up.

Criticism 6: Am I just a stooge for the data centers?

An honest skeptic could say many things about Jordan’s video. They could say his Finnish citation is misleading. They could say the heart-contraction paper he relies on doesn’t replicate. They could say his haunted painting experiment has design problems. They could say the audible noise pollution story is a well-established mechanism for the symptoms residents report, and that Jordan hasn’t demonstrated the effects of infrasound rise above this level. All of these would be defensible.
What an honest skeptic could not say is that Jordan’s sources, as a body, say the “exact opposite” of what Jordan claims. That is a specific claim about the literature. It is also false. The sources Jordan cites are, at worst, ambiguous or overreached-from. At best, they say roughly what Jordan says they say. They do not, as Masley has repeatedly asserted, diametrically contradict the video they are supposedly citations for.

I’m not convinced by any of the author’s prior points here, so I maintain that Jordan’s sources either say the opposite of what he claims (that infrasound doesn’t cause harm) or are completely unrelated to what he’s saying (that audible noise pollution causes harm, with no mention of infrasound). I would on Twitter sometimes use some hyperbole and blur these together into “all his sources say the opposite of what he claims” so I guess the critic’s right that when I do that, I’m wrong.

Masley has staked out a position that effectively equates to “data centers are an unmitigated good.” He writes in his response to Jordan:
✅ Data centers don’t waste water.
✅ AI artwork is without victims.
✅ Using ChatGPT is not bad for the environment.
His first statement is pretty clearly correct at this point. He’s generally right on item 3, although it is still difficult to find adequate clean power for data centers. But Masley has painted himself into such an ideological corner that he can’t even recognize the ethical issues posed by AI art and the artists whose work on which it is trained without compensation.

The iPhone caused a massive drop in the number of bank teller jobs in the US. I think it’s both true that this was an unfortunate consequence of the iPhone, but also that the bank tellers weren’t “victims of the iPhone.” Maybe this is a terminological difference, I don’t know, but I think it’s reasonable enough to not think it means I’ve been blinded by ideology. Similarly, as I say in the art piece, I think the correct opinion of AI art is that it’s as if someone went to a museum, looked at a lot of other people’s paintings, and then went and made paintings inspired by them but distinct enough that they don’t breach copyright. When I was young I used to draw a lot of Tolkien-like maps. They were different enough that if I sold them, I wouldn’t be violating copyright, even though I learned them basically exclusively from looking at maps at the beginning of Lord of the Rings books. I don’t think it’s a sign that I’ve painted myself into an ideological corner to say that I wasn’t harming the Tolkien estate here by doing this, and that AI doesn’t steal from artists by doing similar things.

Have I staked out a position that data centers are an unmitigated good? Also no. In the piece, I say xAI Colossus was bad and shouldn’t have been built, that the crypto mine data center in Texas was clearly really bad, and that noise pollution issues with data centers are very real. Leading up to this I was also posting a lot about how pollution and climate change are the other real issues I’m worried about. Again, this author doesn’t seem interested in what I actually wrote, he’s just lurching toward these big claims to denounce me.

I normally wouldn’t reply to criticism this bad, but I felt obligated to after Jordan elevated it in the discourse and his followers seemed to agree that it showed I was wrong. Again, he’s tricking his followers. Why, if this is so easy for me to dunk on despite not thinking about sound much day to day, did Jordan share it as if it’s all correct, despite working on sound all the time? I think it’s because he’s again being dishonest here and knows who’s right, and is just looking for another way to dodge the obvious fact that none of his sources said what he claimed.

Flashing my credentials

I don’t normally like to do this, but I’ll point out that of the three of us, I’m the one with the physics degree. The author seems to have a degree in architectural technology and design. I assume Jordan knows a lot more than me about audio engineering, but I’d kindly request that if you see a title that says Andy Masley doesn’t understand how sound works you treat it with some skepticism. I have a degree in this stuff and taught it for 7 years! If you look up “IB Physics” in YouTube, one of the first things you’ll see is a picture of my face. I claim that I do in fact understand how sound works.

A music rec

My favorite piece of low frequency music is Monoliths and Dimensions by Sunn O))). A top 10 album for me. I ended up listening to it a lot writing this and the last post. Check it out.

Contra Benn Jordan, data center (and all) sub-audible infrasound issues are fake

Sun, 19 Apr 2026 19:01:11 GMT

Intro
What is infrasound?
What the science says about infrasound harms
- The history of infrasound science
- The consensus
Benn Jordan’s infrasound videos are a complete disaster
- First video - Infrasound: What You Can’t Hear CAN Hurt You
- Second video - Datacenters Behaving Like Acoustic Weapons
Responses to this piece from the authors of the studies Jordan cites
Jordan’s response to this article was pretty goofy and reaffirmed that he can’t defend any of his misleading citations
There’s a lot of social permission to treat data centers as boogeymen right now

Intro

There is a fast-spreading new idea that data centers cause a unique harm: “infrasound.” Environmentalists may remember this as a pseudoscientific idea promoted by some fossil fuel interests to scare people about wind turbines in the 2010s. Infrasound is now the topic of what looks like the single most popular piece of media made about data centers in 2026 so far: the video Datacenters Behaving Like Accoustic Weapons by the popular YouTuber Benn Jordan. Jordan’s previous video on infrasound is one of the first things Gemini recommends when you search it. These two videos are each a complete moment-to-moment disaster, and have received no meaningful pushback, so I will be the first. Even if you have no interest in either data centers or infrasound, I think this is an amazing sociological example of how highbrow misinformation is developed and rapidly spread. I found the experience of unpacking it jaw-dropping. After writing this Jordan accused me of being a paid shill, but you don’t actually have to trust anything I’m saying. All you have to do is trust the authors of the studies Jordan himself is citing everywhere.

This threatens to become yet another bizarre folk theory about data centers that educated people believe and give you funny looks if you say it’s fake, like that they heat the areas around them by huge amounts, they use a whole bottle of water per prompt, they seriously harm water supplies, or they have an outsized effect on national electricity prices.

I’ll argue here that all our best current evidence about the harms of inaudible infrasound implies that they’re fake, including recent popular claims about data centers. To be clear, data center noise pollution issues are very real. But “infrasound” is something very different, and is being smuggled into the real noise issues by pseudoscience enthusiasts to attribute almost mystical powers to data centers: inaudible sounds they give off that you can’t even hear can physically harm you and make you sick and mentally unwell.

Jordan’s video is a perfect case study of highbrow misinformation. The host is cool. He opens with the nuance that his video is hosted in a data center, and then immediately jumps into a pretty extreme claim:

1:28 - To put it simply, regardless of where your ethical, environmental, political, or economic interests are, if a data center is being built nearby your home, you’re generally kind of f*****.

Some wild claims have already been smuggled in, but by adding some points about how he’s also using a data center to host the video, they also seem like well-considered takes by a guy who’s willing to acknowledge trade-offs and nuance.

The video looks rigorous and technical, and is presented with the professionalism and chill vibes of the kind of explainer you might see from Hank Green and other trustworthy sources. He surrounds himself with equipment and images that look scientific. He talks to people who have been harmed by regular noise pollution, and shares their stories. A lot of legit-seeming studies are flashed on the screen. A big powerful bad guy is being taken down by a chill local dude. Why think about the individual claims too much?

Despite all this, the video is actually promoting a completely unfounded conspiracy theory, and every single study he uses to justify his conclusions either explicitly says the opposite of what he claims or has such terrible methodology that it’s useless. This is effectively a high-status Alex Jones video, it’s really uniquely terrible, but it’s also flying under the radar of most educated people because it looks and feels reasonable.

I’ll show thoroughly and conclusively that the video’s wrong. Before I do that, I need to explain what infrasound is and the state of the science.

What is infrasound?

Sound is vibrating air particles. A sound’s frequency is how frequently the air particles move back and forth. A frequency of 3 Hertz (Hz) means the air particles move back and forth 3 times per second. The particles in this gif complete 1/2 of a full back-and-forth oscillation per second, so their frequency is 0.5 Hz.

The higher the frequency of a sound wave, the shorter its wavelength. We hear higher frequency sounds as higher-pitched. For more on the basic physics of waves you can watch my old physics videos.

Frequency alone doesn’t tell you how much energy a sound wave carries. The energy of sound in air, which we experience as volume, depends mainly on the pressure amplitude of the sound wave. Pressure amplitude is like the physical height of a wave in the ocean. If you were standing in the ocean, waves hitting you very frequently could cause you to tip over, but they would have less impact if each were small, and way more if they were really large, so their height matters a lot too. Sound works similarly: frequency and amplitude are different variables, and the volume we hear is determined by the combination of the two.

Infrasound is sound at very low frequency, below about 20 Hz. Human hearing sensitivity drops sharply in that range, so very low-frequency sounds usually have to be very loud before we can hear or otherwise perceive them. Infrasound is not all sound that isn’t loud enough to regularly hear. There are also sounds with frequencies too high for us to hear: ultrasounds. The prefix infra means “below” (“infrastructure” is structure metaphorically or literally below society) and ultra means “beyond” or “above.” This is loosely like the light spectrum: infrared light is below the lowest frequencies we can see (red), and ultraviolet light is above the highest (violet).

Source

Ultraviolet light is dangerous because it carries more energy than visible light, high enough for electrons in atoms in our DNA to absorb and get knocked loose, which is why UV from the sun can cause cancer.

Infrared light carries less energy per photon than visible light, not enough to eject electrons from our DNA the way UV can. But infrared light can still transfer heat. The warmth you feel standing near a fire is from infrared light, and too much can burn you.

Infrasound (and low frequency sound in general) can move through material more easily than higher-frequency sound, and diffract around barriers easier as well. This makes it harder to block, and means that when something’s far away from a sound source, most of the sound they’ll receive will be lower frequency.

At high enough intensity, we can physically feel the presence of infrasound. It can create pressure or “fullness” in the ears. There isn’t an exact moment where it suddenly becomes detectable. The boundaries are fuzzy. The lower the frequency, the louder the infrasound needs to be before we can detect it.

For ordinary environmental infrasound, current evidence does not support major direct health effects below where we can hear and perceive it.

Sound pressure levels are measured in decibels, or dB, which is a logarithmic scale. Every 10 dB increase represents a 10x increase in sound intensity, every 20 dB increase is a 100x increase, a 60 dB increase is a million, and so on. The gap between 50 dB and 60 dB is a factor of ten in intensity. The gap between 50 dB and 100 dB is a factor of a hundred thousand.

Sound is measured this way because the range of real-world sound intensities is so huge that a linear scale wouldn’t work. A jet engine at takeoff is a trillion times more intense than the quietest sound a human can detect. To avoid having to write huge numbers out every time we want to measure sound, we just use a log scale. This is important for understanding infrasound because differences in dB can seem small, but actually represent huge orders of magnitudes in difference. The difference between a loud data center and enough infrasound to physical hear (maybe 60 dB vs 110 dB) may not seem like that much. On the scale it’s just a doubling. But in reality every 10 additional dB multiplies the power required to produce the sound by 10. So a difference of 50 multiplies the power required by 10x10x10x10x10, = 100,000 times as much power. People sometimes try to imply that infrasound could be dangerous if it just got a little higher on the decibel scale, ignoring that this would require it to get at least thousands of times as much power from its source.

Infrasound at 20 Hz needs to be at roughly 79 dB for us to detect it, about the level of a vacuum cleaner running a few feet away. A frequency of 10 Hz needs to be at about 97 dB, like a motorcycle at close range or a subway passing on the platform. A frequency of 5 Hz needs to be at about 107 dB, like a chainsaw at arm’s length.

Given the logarithmic scale, these thresholds are nearly three orders of magnitude apart. 5 Hz sound needs about 600 times as much power as 20 Hz sound to become audible to us.

It’s important to not confuse the effects of infrasound with ultrasound. Sometimes people talk as if the harms of all inaudible sound are interchangeable, but this blurs two opposite ends of the audible spectrum. It’s as confused as mixing up infrared light with ultraviolet light. The first can heat you up near a fire, the second can knock out atoms from DNA and give you cancer.

What are the ways infrasound could plausibly harm us? There are at least three:

Direct mechanical damage at very high intensities. Extremely intense low-frequency sound can cause discomfort, and injury at high enough levels. A sense of pressure in the middle ear starts showing up at roughly 127 dB of infrasound, like a jackhammer at arm’s length. Chest wall vibration, a gagging sensation, and visual field vibration occur at 145 dB, approaching the sound of a shotgun fired a few feet from your head. Abdominal wall vibration happens at 150 dB, which is the sound of a jet taking off at 25 meters away. This is loud enough to rupture an eardrum. These are not levels that occur in any normal residential environment. And infrasound is not “invisible” at these extremes. In any study, participants can tell when infrasound at this volume is turned on and off. They can either hear it, or feel these physical effects in the pressure of the air around them and know that something’s wrong.
Low-frequency rumbles that we can physically feel causing annoyance and sleep disruption. When infrasound is loud enough to detect, or when the thing producing it is also producing audible sound, it can affect us in the same way ordinary noise pollution affects us. It can mess with our sleep and stress response. In this case, infrasound is not really “invisible” anymore.
Vestibular or chochlear detection The inner ear contains structures involved in hearing, motion, and orientation. Research suggests that while inner hair cells don’t respond to infrasound, outer hair cells of the cochlea can respond to very low-frequency sound at levels below what we consciously detect, and some studies have reported cochlear or brain responses under near-threshold or below-threshold conditions. But no one has shown this mechanism produces the long list of real-world symptoms people often blame on environmental infrasound.

One last thing to note is that many things in our lives generate infrasound, and infrasound can travel so far that our environment contains sound created by things far away. Things like refrigerators or HVAC systems or highway traffic or wind blowing past a building or ocean waves or washing machines can generate them. Cities are constantly full of low-frequency sound energy. But the levels vary enormously by source, distance, frequency, and how you measure them. Nature contains a lot of infrasound as well. It would be a little strange if we evolved to feel bad when infrasound is present, because the Earth has always had a lot of it, in the same way Earth has a lot of sources of infrared light.

What the science says about infrasound harms

The history of infrasound science

People have been interested in infrasound for a very long time, but the first really rigorous work by modern scientific standards came out of NASA and the Air Force in the 60s. They were trying to simulate rocket launch conditions, and exposed volunteers to very loud infrasound at around 140 dB. At this extreme volume, the participants experienced ear pain, headaches, and nausea.

Geoff Leventhall is one of the most-cited researchers in the field. He’s spent a lot of time correcting public misunderstandings. A big complaint of his is people often read the NASA-style high-exposure studies as if they applied to ordinary environmental levels of infrasound, but in reality the bad symptoms from this study only show up at extreme volumes around 140 dB. The level of infrasound in people’s homes, even in pretty loud areas, is about a million times less powerful than the rocket launch levels these studies measured.

There’s a huge literature of good studies and reviews on infrasound since then, continuing into the present.

Marshall et al. 2023 is one of the most important. They took 37 noise-sensitive adults and had them spend three 72-hour periods in a sleep lab set up like a studio apartment. They were exposed to simulated infrasound, fake infrasound, or audible traffic noise. The researchers then monitored them while they slept, observing their brain activity, breathing, heart rate, muscle movement, and a lot of psychological, cognitive, cardiovascular, and self-report health measures. Infrasound did not seem to affect these, while traffic noise in comparison disrupted sleep.

Researchers have separately tested whether people’s expectation of experiencing symptoms from infrasound could be causing the symptoms (the “nocebo effect”). In one study, scientists showed one group of people a video implying there are real harms from wind-turbine infrasound, and they showed a second group a neutral or reassuring video instead. Then they exposed participants to both real and fake infrasound. The high-expectancy group reported larger symptom increases, including during sham exposure. The lower-expectancy group reported fewer changes. Most studies on this seem to imply that infrasound effects are nocebos.

Lots of public health bodies have done their own reviews, including the Australian NHMRC, Health Canada, the Massachusetts Department of Public Health, the Netherlands’ RIVM, and others. All of these have decided there’s no real evidence for ordinary levels of infrasound causing health problems.

There was also one huge Health Canada study of 1,238 households living near roughly 400 wind turbines in southern Ontario and Prince Edward Island, which found no relationship between wind turbine noise exposure (including infrasound) and hypertension, migraines, tinnitus, sleep disorders, or chronic disease. People who could physically hear the turbines at audible levels were annoyed.

One influential mechanistic paper often cited by people who think infrasound causes harm is “Responses of the ear to low frequency sounds, infrasound and wind turbines.” The cochlea is the organ in your inner ear that detects sound. It has two types of hair cells: inner and outer. Inner hair cells detect most of what we hear, and they turn out to be pretty insensitive to infrasound. Outer hair cells have a more complicated job, mainly amplifying certain sounds. A review by Salt and Hullar argued that these outer cells can respond to infrasound at levels below where a person would consciously register the sound. So it looks like there’s a possible pathway for infrasound to influence us, but no one’s produced any convincing evidence that it actually causes physiological or psychological effects outside the nocebo effect.

One other key fact: a lot of the good scientific studies of infrasound were specifically designed to look at wind turbines. In the 2010s there was a panic about “Wind Turbine Syndrome,” a long list of symptoms purportedly caused by wind turbine noise, pushed by an unholy alliance of international cranks, local NIMBYs, and some fossil-fuel interests. The whole thing originated in a single incredibly stupid book, written and published by an anti-wind couple who among other things compared wind turbines to Jim Crow. A lot of scientists knew this was pseudoscience and wanted to formally debunk it. Simon Chapman is the great public debunker of Wind Turbine Syndrome, so I refer you to his free book if you’d like the full history.

The consensus

Contrary to Benn Jordan’s video, where he claims infrasound is “grossly understudied,” infrasound and low-frequency noise have been studied for decades by a ton of reputable scientists and major health organizations. Controlled studies and public health reviews haven’t found a real, distinct health condition caused by ordinary environmental infrasound at the levels we actually experience, even in noise polluted areas. When low-frequency sound becomes strong enough to be heard or otherwise felt, it can cause annoyance, discomfort, and sleep disruption like any other normal noise pollution. At extremely high levels, it can physically harm us in the way other sound can. We can always detect it when it’s at this level.

The reported symptoms of inaudible, undetectable infrasound appear to be a nocebo. If you tell people infrasound is going to harm them but don’t actually expose them to any, they develop symptoms. If you don’t tell them, and play infrasound without telling them, they don’t develop any measurable symptoms.

Benn Jordan’s infrasound videos are a complete disaster

Benn Jordan has made 3 videos on infrasound:

The top one is his most viewed video of all time. I’m going to skip this one, because its topic is a little different: audible low-frequency sounds (sometimes called “the hum”) that some people can hear and find disturbing in the way other unpleasant sounds are disturbing. That falls under traditional noise pollution. And while I seriously doubt these sounds cause “psychosis” or “death,” the effects of traditional noise pollution are outside the scope of this post.

I’ll first unpack his original video on infrasound that’s inaudible to everyone, and then talk about the data center video. I’ll use quotes and timestamps for both.

First video - Infrasound: What You Can’t Hear CAN Hurt You

I would recommend watching a bit of this video before we dive in. It’s very easy to watch without noticing anything wrong. It’s very well-produced, and I think if you have the experience of watching it before reading what follows, this post will be much more jarring in a fun way.

0:00-1:40 - Intro

The intro to this video is already silly. He records infrasound in his home and then compresses and speeds it up until he can hear it.

0:24 - Whoa. I’ve done a whole lot of investigating and I still have absolutely no idea what’s causing that sound and it’s still going on right now as I’m recording this.

Lots of things in our lives make infrasound. It’s easy to record any of it, compress it, speed it up until it’s audible, and it always sounds alien and weird when you do. You can do this with infrasound from a fridge or HVAC or distant traffic. If you compress a whole day of it into something that’s audible, you’re going to get eerie sounds from far away. This tells you nothing on its own.

There’s a lot of infrared light everywhere too. I could aim an infrared camera at a dog, and then say “Whoa… something’s wrong with my dog. He looks Evil”

and this would tell us about as much as making infrasound audible.

0:32 - And if you’re in a city or suburb, chances are that similar sounds are emanating just below your hearing range.

Yes, most things we do and use create infrasound. This is like starting a video with a scary infrared image and then saying “And if you’re in a city or suburb, chances are that similar light is emanating just below the range you can see it.”

0:39 - And I have some bad news for you. A meta-analysis suggests that it can actually be pretty harmful to your health.

He flashes three papers on the screen here. He doesn’t link any of them in the video description. None of them support what he’s saying at all, they all go completely against it. None of these are a meta-analysis showing infrasound is harmful.

The first is an occupational measurement paper that implies infrasound is safe at levels hundreds of thousands of times more intense than anything people regularly experience in their homes, even in loud places. The second is part of a 3-part research project where the final report is called “Infrasound Does Not Explain Symptoms Related to Wind Turbines.” The third is a NIOSH workplace report that explicitly concludes the sound levels it measured are not known to cause negative health effects.

The first paper paper is “Exposure to infrasonic noise in agriculture.”

It’s an occupational exposure assessment for Polish farm workers. The author took a sound meter to 118 pieces of farm machinery in Poland and measured the infrasound levels they produce. And that’s all.

It’s not a meta-analysis. The abstract has some speculative language about infrasound having “possible ergonomic and health consequences,” and this is probably what Jordan is pointing at when he flashes the cover. The paper also asserts that “a very important harmful factor is infrasound exposure for pregnant women and adolescents at workplaces in agriculture.” That claim cites older Eastern European occupational health literature that’s never been replicated in modern studies. That older literature comes up over and over in infrasound harm claims. It’s almost all small studies with bad controls, often decades old, that don’t replicate. Most of the serious studies done on infrasound, (as well as meta-surveys of the literature) consistently find that it doesn’t produce noticeable effects.

Geoff Leventhall has flagged this pattern in his reviews of the infrasound literature. Governments set a regulatory threshold for infrasound exposure based on cautious extrapolation from very limited data, and then new papers cite that threshold as if it were evidence of harm at that level. Later papers cite those papers. The chain of legit-seeming papers starts with original evidence that either doesn’t exist or doesn’t replicate. This paper is part of that pattern.

The paper measures infrasound an operator would receive directly on a tractor or combine, with the engine and mechanical systems inches away. The measured infrasound levels here are really high. This doesn’t tell us anything about what everyday people experience in their homes. No source of infrasound (just like no source of regular sound) is nearly as loud as sitting on a tractor.

Worse for Jordan’s later video, the paper compares its measurements to the existing Polish occupational exposure limit of 102 dBG for an 8-hour workday. Data center and wind turbine infrasound at residential distances is typically in the 50 to 75 dBG range. That might sound like a small gap, but remember that decibels are logarithmic. Every 10 dB increase represents a 10x increase in sound intensity, and every 20 dB increase represents a 100x increase.

So this is telling us that even the loudest data centers near people’s homes are several hundred to over a hundred thousand times quieter than what the occupational standard the Bilski paper is using considers safe for a full 8-hour workday of direct exposure. The paper Jordan is flashing as evidence of infrasound harm is, if you actually read it, implying that our regular daily infrasound exposure could be multiplied by 1000 and still be totally safe.

The second paper is “Symptoms intuitively associated with wind turbine infrasound” published in Environmental Research.

The paper concludes that infrasound had no detectable effect.

The paper is one of the main outputs of a single research project commissioned by the Finnish Government and conducted by the Technical Research Centre of Finland (VTT), the Finnish Institute of Occupational Health, the University of Helsinki, and the Finnish Institute for Health and Welfare from 2018 to 2020. Its goal was to figure out if wind turbine infrasound actually causes the symptoms some residents attribute to it.

The three outputs of the project are:

The paper that Jordan shows. This is only the questionnaire study. Within 2.5 km of a wind turbine, 15% of Finnish respondents said they associated various symptoms with wind turbine infrasound, and the symptoms spanned multiple organ systems. The paper documents what people report and what they believe is causing harm, not whether infrasound actually harmed people.
“Annoyance, perception, and physiological effects of wind turbine infrasound.” Here participants were divided into two groups: people who reported the types of symptoms they attributed to wind turbine infrasound, and people who didn’t. Both groups were exposed to real wind turbine sound recordings, with and without the infrasonic components. Neither group could reliably detect when the infrasound was present. It had no effect on reported annoyance, or measurable effect on people’s nervous system responses. There were no differences between the symptomatic and non-symptomatic groups.
The full final report of the project, published by the Finnish Government under the title “Infrasound Does Not Explain Symptoms Related to Wind Turbines.” Jordan conveniently does not mention this title.

The project found exactly what every other serious investigation of this question has found. Some residents report symptoms, they intuitively attribute those symptoms to infrasound. When you actually expose them to infrasound in controlled conditions, they can’t detect it, it doesn’t produce the symptoms, and it doesn’t produce any measurable physiological response. Looks like a nocebo.

Jordan only shows the first paper where people claim that infrasound caused their symptoms, and never clarifies that the conclusion of this study was that infrasound wasn’t to blame for whatever was happening.

The third paper is “Evaluation of Low-Frequency Noise, Infrasound, and Health Symptoms at an Administrative Building and Men’s Shelter: A Case Study.”

In 2019, a nonprofit serving homeless people had two incidents of loud noise and strong vibrations coming from an adjacent landfill. Employees reported feeling unwell during and after. The buildings were evacuated. NIOSH was called in to conduct a Health Hazard Evaluation. This is the paper documenting that investigation.

The paper is clear that HHEs are workplace investigations to help employers recognize and control hazards. They are not research studies. This one is a descriptive report published after the fact. Its findings don’t support what Jordan is implying at all.

NIOSH arrived four months after the incidents and took sound measurements during normal flare operation. The overall measured sound levels across frequencies up to 100 Hz were 64 to 73 dB. The American Conference of Governmental Industrial Hygienists threshold limit values for occupational exposure to infrasound and low-frequency noise are 145 dB at specific frequencies and 150 dB overall. The measured levels were roughly 77 to 86 dB below the threshold at which harm is known to occur, at least tens of millions of times less intense, and possibly hundreds of millions of times less intense at the quieter end.

The paper explicitly says “The frequency-specific or overall sound levels we measured are not known to cause adverse health effects.”

The paper proposes explanations for the symptoms employees reported, and none of them involves infrasound damaging the body:

People audibly heard and felt two specific bad events and felt bad.
The normal sound at the site had what’s called an “unbalanced frequency profile”: more energy at low frequencies than at higher ones. This specific type of of imbalance is known to cause annoyance even when the overall volume is low.
A lot of the symptoms people reported (headaches, fatigue, trouble sleeping) are common and nonspecific. When NIOSH looked into the health records of the employees, a significant number had plausible looking alternative explanations for their symptoms.
Employees felt the organization and the county had mishandled the incidents. The paper describes this as a breach of “psychological contract,” meaning the sense that your workplace has become untrustworthy or broken a pact. The paper says this breakdown is affecting how employees experienced their symptoms and why they’re not willing to come back to the building.

The paper’s Limitations section acknowledges that no definitive conclusions about the cause of the symptoms can be drawn.

And in a final twist, the main wind turbine study this paper cites is Michaud et al. 2016, the Health Canada study, and summarizes its conclusion: the study authors did not find a relationship between wind turbine noise and reported health effects such as headaches, dizziness, sleep problems, and stress.

So just to wrap, Jordan’s third piece of evidence here is a paper that:

Measured sound levels well below any known harm threshold.
Explicitly states those levels are not known to cause adverse health effects.
Attributes the reported symptoms to annoyance and a breach of psychological contract rather than direct infrasound harm.
Explicitly refuses to draw causal conclusions.
Cites Health Canada to say wind turbine noise doesn’t cause the symptoms people attribute to it.

The actual meta-analyses and review papers on infrasound are overwhelmingly consistent: there are no health effects unless the sound is loud enough to be perceived, and at that point it behaves like regular noise pollution. There’s no serious meta-analysis that reaches the conclusion Jordan’s implying.

Why did he just flash three papers that obviously don’t conclude what he says? It’s such a bizarre move that my trust in him as a narrator cratered.

0:46 - Have you ever been to a particular place or maybe walked into a building and immediately felt like something was wrong? Like a rather noticeable and intense feeling of discomfort or maybe even fear despite there not being anything notably wrong with the environment. And then the moment you leave that place, you start feeling better.

Your bullshit detectors should be going off. This is a very common part of the human experience. A lot of places just look ominous, and a lot of people have anxiety.

Look at the images he chooses as examples here:

It’s obvious that these are just generally ominous places! The second is a famous image from /r/liminalspaces, which collects pictures of places that feel impermanent, and where you feel transient and possibly uneasy.

0:59 - Well, it turns out that there’s a chance that it wasn’t all in your imagination or anything paranormal. It could have just been infrasound.

All the thorough research that’s been done strongly implies that infrasound doesn’t cause this at all.

1:25 - We’re going to take a close look at the infrasonic research and see how how some types of these sounds can make people enjoy music less or feel incredibly ill.

He flashes this on the screen as he says this:

This is Chapter 11 of the WHO’s “Compendium of WHO and other UN guidance on health and environment” with the WHO, UN Environment, and UNICEF logos displayed. Flashing this implies WHO-endorsed evidence that infrasound makes people enjoy music less or get incredibly ill.

The document says nothing like this.

This is a policy guidance document compiling recommendations across environmental health topics. Chapter 11 is on guidance for environmental noise, listing recommended levels for road, rail, aircraft, wind-turbine, and leisure noise. It doesn’t discuss infrasound as a distinct health harm.

There is no overlap at all between what Jordan is narrating and what the document on the screen actually contains. He’s just showing a completely unrelated government document with health agencies his viewers trust, while saying completely different false stuff about what the infrasound research implies.

1:31 - And according to one famous study, even see ghostly figures near them who aren’t actually there.

He’s referring to Vic Tandy’s “The Ghost in the Machine” article, which he’ll discuss more later. He shows images from various press articles summarizing Tandy’s work, which tend to describe it as more legitimate than it was.

I’ll save the full overview of this article for when he goes into more detail, but for now just be aware that this “famous study” is not a study at all. It’s a personal anecdote from a single guy who felt uneasy after being told the place he was working was haunted, took a single measurement, decided infrasound was the cause, and published the story in a parapsychology journal founded to investigate ghosts. The study never replicated, and was specifically tested and failed when a proper experiment was finally run. Jordan has once again misrepresented his source.

1:40–3:48 - Philosophy of time

This section has nothing to do with infrasound. It’s about Greek concepts of time, water clocks, and objective versus subjective perception.

I normally give YouTube essayists slack here. They’re making videos for people with short attention spans, and tangents are fine. But this video’s already been so deceptive that I can’t help thinking this section exists to prop up the others. It makes Jordan look worldly and well-rounded, and lends credibility to everything else he’s saying. Showing himself making a DIY sun clock is neat, but I’m worried this is a move to look scientific, like a more hip internet-intellectual way of wearing a lab coat in the video.

There’s another really bad move he’s making here. He’s implying our capitalist western culture has trained us to focus on easy-to-measure effects and to ignore our felt subjective sense of what’s going on. He seems to be using this to open the door to wild claims about things that are invisible to our daily experience but can still harm us. The things he’s about to talk about aren’t actually invisible to the measurement tools of science, just to our senses. His next move is to use this setup for a wild connection between two different invisible things: UV light and infrasound.

3:48–4:34 - UV and infrasound

4:02 - Only when the wavelength of light enters the human visible spectrum do we call it a color. We can’t observe higher wavelengths like ultraviolet, but we can observe the effects of it when it burns our skin or causes cancer. And if you keep cranking up the frequency of invisible light to X-ray and gamma, even lower levels of light can eject electrons from atoms... And for you or I, that means death. A lot of death. It goes without saying that when waves of pressure happen between 20 and 20,000 times per second and become audible to us, do we call it a sound. And while pressure waves above our range of hearing can give us headaches and hearing damage, it fortunately doesn’t do anything as dramatic as rearranging atoms. But medical research is starting to trickle out data about the sounds that are happening below our range of hearing. And things are looking quite a bit more sinister.

As a former physics teacher, this section was torture.

He’s setting up this framing about how things that are invisible to us can still harm us, and then very quickly moves from ‘ultraviolet light can obviously kill us’ to ‘and there’s new disturbing evidence that infrasound can harm us’ without acknowledging the obvious problem that infrared light does not really harm us at all unless it’s so powerful that it heats up our bodies in the way other normal heat sources do. This is the correct analogy to infrasound, not UV light. Just like infrared light can only harm us if it’s powerful enough that we physically feel it in the way we do other heat sources, infrasound can only harm us if it’s so powerful that it causes us to physically feel its presence in the way we do other very loud sources of sound. He’s not so subtly reaching way across this spectrum to say that because something that’s so high energy that we can’t see it can cause damage, maybe something that’s so low energy that we can’t hear it can cause damage too.

In your everyday life you probably don’t spend much time thinking about prices measured in fractions of a cent, or in tens of millions of dollars. Both amounts are “invisible” to your daily experience, but for opposite reasons. What Jordan’s doing is kind of like saying “Because you never think about tens of millions of dollars, other money amounts you don’t think about might also damage your wallet if you spend them, and there’s new evidence that fractions of a cent can harm your wallet too.”

“Things are looking quite a bit more sinister” is doing a lot of work. The medical research is not “trickling out” and is not at all sinister. As covered above, we’ve had decades of solid research, and the conclusion of all serious scientific work on this is incredibly consistent.

4:50–5:21 - The symptom list

4:50 - For example, research has strongly suggested that infrasound can cause headaches, fatigue, loss of concentration, mood changes, depression, sleeping disorders, panic disorders, nausea, dizziness.

The research he cites doesn’t say this at all, but first I need to note that a long list of basically anything and everything bad you might experience in your everyday life is a common signature of a nocebo. Think about how infrasound compares to any other exposure to something bad for us. Carbon monoxide or lead poisoning produces very specific symptoms. Even normal noise pollution has very specific symptoms like hearing loss and cardiovascular effects of chronic sleep disruption. They don’t have these incredibly general long lists of symptoms.

What are the studies he’s flashing on the screen here to support this?

Well, first he puts this map of infrasound science up on the screen:

This looks authoritative and implies a century-long scientific history of studying the harms he’s discussing.

The image is from the 2020 paper “Low-Frequency Noise and Its Main Effects on Human Health — A Review of the Literature between 2016 and 2019,” published in Applied Sciences by four researchers at the University of Minho in Portugal. The timeline is a history of the general low-frequency-noise research field.

But he’s tricked his viewers again. The paper is about low-frequency noise, not infrasound. Those are not the same thing here. Low-frequency noise typically refers to the audible range roughly between 20 and 200 Hz, infrasound is below about 20 Hz and is generally inaudible. The paper uses “low-frequency noise” throughout and only occasionally touches infrasound specifically. So the timeline isn’t even a history of the field Jordan is claiming support from.

The paper doesn’t conclude what he’s implying at all. It’s mainly analyzing audible noise, not infrasound. He’s confusing audible lower frequency noise (roughly 20-200 Hz, which you can hear) with inaudible infrasound (below about 20 Hz, which you mostly can’t). The paper finds a lot of health effects from regular audible noise pollution, like annoyance and sleep disorders. The paper explicitly excludes inaudible infrasound. In the methodology, “low-frequency sound/infrasound” is listed as one of nine categories in the 142-paper database, but it’s deliberately not among the three categories selected for the 39-paper in-depth review. The paper doesn’t comment on whether infrasound is harmful or not.

The next section is even worse. He flashes and says a ton of individual symptoms described by the paper:

Not one of them is attributed to infrasound by the paper he’s citing, they are literally all caused by audible noise:

Headaches and nausea come from Blair’s study of oil and gas well construction and drilling in a residential area. The paper is explicit that this was from continuous noise measured at 51.5 to 80.0 dBC, with headaches and nausea reported when levels exceeded 60 dBC. The “C” in dBC refers to C-weighting, a measurement scale for audible sound that captures low-frequency components. 60 dBC is roughly the loudness of normal conversation. The study wasn’t measuring inaudible infrasound, it was measuring the audible din of heavy industrial drilling next to people’s homes.
Fatigue, lack of concentration, negative mood, and dizziness all come from the same sentence in the Pohl, Gabriel, and Hübner wind turbine study. And here Jordan is doing something more deceptive. These aren’t the results of a study, they’re options from a survey checklist, the list of 12 symptoms the researchers asked residents about. Quoting the paper: “ascertaining the physical and psychological symptoms referenced by residents that participated in the study (general mental indisposition, performance and reduced work capacity, lack of concentration, fatigue, tension, nervousness, negative mood, dizziness, irritability, indisposition, reduced sleep quality, and annoyance).” This is just the questionnaire the surveyors handed out.
And what did the study actually find when residents were asked? The exact opposite of the picture Jordan is painting. Pohl concluded “the annoyance experienced was very low.” Only 8.5% of residents in 2012 reported any feelings of pressure related to the wind farm, dropping to 6.8% by 2014. Only 6.1% reported felt vibrations in their body, dropping to 3.8%. Symptoms of dizziness, one of the words Jordan highlighted, “were not observed in this study.”
Anxiety and depression come from Abbasi’s study of wind turbine maintenance workers, who stand on and close to them. The study’s exposure levels were 60, 66, and 83 dBA. This is clearly audible, occupational noise. The paper’s own summary of the findings emphasizes that “the worst health status is due to working conditions and chronic exposure to occupational risk factors, such as noise” and that the harmful effects were concentrated in the maintenance team who were “in the vicinity of wind turbines, due to the reception of very intense noise.” In other words, the study’s own framing is that these effects track with exposure to loud audible noise, not infrasound.
Sleep disturbance is discussed across several studies the paper reviews, and again the attribution is to audible noise. The paper says directly: “Long-term exposure to low-frequency noise from wind energy is a major factor in sleep disturbances in residents who live near wind farms.” Note the framing: low-frequency noise, not infrasound.

When the study did look into infrasound, it observed “the noise level of the wind turbine measured in the lower frequency range is below the human sensory threshold” and the paper seems to treat this as evidence that it’s not causing harm. The paper does not argue that inaudible infrasound causes the sleep disturbances. It blames them on audible low-frequency components.

Jordan has now completely misrepresented all 11 studies he’s mentioned in the first five minutes of the video. 7 imply the exact opposite of what he’s claiming they do, 3 are completely unrelated to what he’s saying, and the only one that agrees with him isn’t a study at all, it’s a one-off anecdote written in a publication that mainly studies ghosts. From what I can tell his follow-up to this video is the most popular new piece of media made about data centers so far this year.

5:04 - I am very aware of the placebo effect and of course disclude my personal experiences with generated infrasound here in my lab when making this video. But I can tell you subjectively that it very much sucked. And even after turning the infrasound off, it took a while for things like intense nausea and dizziness to subside.

He’s confusing the placebo with nocebo effect here, but either way this is silly. He put himself in a position to experience the nocebo effect, and then he experienced it, and said “but it very much sucked.” I’d like a little acknowledgement that literally all good science points to this being a nocebo.

5:21–5:51 - The heart-contraction study

5:21 - A few years ago, a large published and peer-reviewed medical study showed that 100 dB of infrasound, primarily around 10 hertz, caused a very negative effect on the heart’s ability to contract with a 9% decrease in contraction force for every 10 dB above that. Your heart, an organ that some people would argue is necessary for survival, has to work quite a bit harder when you’re exposed to high levels of infrasound. And this is on top of quite a bit of animal research suggesting negative effects on heart, liver, nervous system, and lungs.

Jordan flashes “Negative Effect of High-Level Infrasound on Human Myocardial Contractility: In-Vitro Controlled Experiment” as he’s saying this.

You might be surprised to learn the full context of this paper implies something different.

Researchers at the University Hospital of Mainz took small samples of heart muscle tissue from patients having cardiac surgery, mounted them on tweezers in a laboratory apparatus, stimulated them electrically to contract, and exposed them to infrasound at 100 to 120 dB at 16 Hz for an hour. They measured a decrease in contraction force. That’s it! The whole study’s just that.

Jordan gets the decibels a little wrong. He says “100 dB.” The experiment actually tested 100, 110, and 120 dB. The dramatic effect mostly happens at 120 dB. In the same journal, two peer-reviewed critical responses of this paper were published and linked directly from the top of the PubMed Central page he’s screenshotting. They completely demolish the original paper. Did he notice this?

Here are the links:

Three big fatal problems with this paper:

The critical reviewers found that Chaban’s apparatus produced excessive air movement, which caused the tips of the tweezers holding the muscle samples to vibrate. The muscle samples weren’t behaving differently due to the infrasound. They were being mechanically shaken by vibrating tweezer tips in the near field of the sound source. The critical review concluded: “The results of the paper of Chaban et al. are invalidated by a physically inappropriate setup that produces excessive air movement, not present in a regular sound wave of the same sound pressure in the far field.” Their overall conclusion: “Chaban et al. identified an artificial problem that doesn’t exist in reality.”
The reviewers pointed out that the total acoustic dose, the amount of pressure energy delivered over time, that caused an 18% decrease in contraction force in Chaban’s experiment (one hour at 120 dB) is matched by a single cycle of a swimmer doing the breaststroke. Every time a swimmer pushes their arms forward and back, the pressure their chest experiences from pushing through water equals or exceeds the total acoustic energy Chaban’s lab spent an hour blasting at dead tissue samples. If Chaban’s finding mapped onto anything biologically real, competitive swimming would be incredibly dangerous.
Infrasound never reaches anywhere near Chaban’s experimental volume in people’s homes. Wind turbine infrasound at typical residential distance from large modern turbines is about 65 dB, measured in high wind conditions at 200 m distance. Data center infrasound at residential distances is in a similar range. That’s 55 to 65 dB below what Chaban tested, a factor of hundreds of thousands in acoustic intensity.

I’m starting to think Jordan isn’t the most reliable narrator.

He pairs this paper with a vague appeal to “quite a bit of animal research suggesting negative effects on heart, liver, nervous system, and lungs.” He does not name the research. What he is gesturing at is the body of work around “vibroacoustic disease” or VAD, a hypothetical condition developed in Portugal starting in the late 1980s by a surgical pathologist named Nuno Castelo Branco and an engineer named Mariana Alves-Pereira.

Their big claim is that long-term exposure to low-frequency noise causes a “whole-body systemic pathology.” This includes thickening of heart tissue, respiratory damage, cognitive decline, epilepsy, increased rates of tumors, depression, and eventually death. It’s a lot of responsibility for a single environmental mechanism! And this is the basic source of every claim about animal research and infrasound damaging organs.

VAD has never been accepted by any mainstream medical body or independently replicated in the 40 years since it was proposed. The UK Health Protection Agency reviewed the evidence in 2010 and concluded that the disease “has not gained clinical recognition” and that there is no evidence infrasound at levels normally encountered in the environment causes it. A 2013 meta-analysis of VAD found that the entire “field” is mainly two authors citing themselves to build up the appearance of a larger body of evidence. Leventhall, reviewing the evidence we have, quoted Henning von Gierke, former chief of the USAF biodynamics laboratory, calling VAD “an unproven theory belonging to a small group of authors.”

Their animal studies done use volumes of sound way above anything that would happen in people’s homes. They’re at the volume you’d only get standing next to a jet engine. None of those studies have been replicated by independent researchers outside this specific Portuguese group.

Jordan’s description of “animal research shows effects on heart, liver, nervous system, and lungs” is referring to this goofy individual research group mostly self-citing that hasn’t gained any broader legitimacy in 4 decades.

5:51–6:37 - ‘It’s hard to study’

5:51 - Another pretty scary thing is that this list is likely very incomplete due to the difficulty and lack of research on infrasound. It turns out that not many people want to volunteer to feel super uncomfortable while their organs may get damaged for some reason.

“We just don’t have enough research” is a pretty common move in pseudoscience. It gives you permission to dismiss the actual negative findings in the literature. But the literature on infrasound is not thin, and its findings are not ambiguous. Infrasound has been studied a lot! There are now decades of papers, trials, and large studies, and literature reviews from multiple countries, and they imply he’s wrong. He’s misrepresenting the state of the research here.

6:07 - If you look into this, a lot of the research that goes into the health effects of infrasound is overwhelmingly concentrated on infrasound caused by wind turbines. And yes, wind farms do generate infrasonic noise pollution. And it turns out that a lot of this research is either partially or completely funded by the fossil fuel industry to try and find something nefarious or wrong with wind energy.

This is an incredibly weird way to frame the actual research. Most of the infrasound research has been done in response to fossil fuel industry claims that wind farms cause harm, and the good studies have all shown they don’t. What Jordan is not-so-subtly implying is that the infrasound research is invalid because fossil fuel money touched it. In reality, basically every good study on infrasound cuts against the fossil fuel industry’s claims.

He does to his credit at least flash the Wind Turbine Syndrome Wikipedia page, which is a good debunking.

6:31 - And this situation is a major problem in science and research. I did a video covering some of this here

You might think that this video explains how the fossil fuel industry has messed with infrasound research.

I watched the video he flashed on the screen. In it, he’s growing wheatgrass and chia seeds in his home. He plays a 4,000 Hz tone at 72 dB at his plants for twelve days, observes that the plants that got the sounds grew about 28% more biomass, and speculates about why. The video has some reflections on Gödel’s incompleteness theorems, the replication crisis, and his own decision not to go to college.

In this video Jordan also completely inverts what the replication crisis is:

The replication crisis is more about researchers not being funded to reproduce their own or other studies and some of that is probably because of capitalism.

This is not what the replication crisis is. The replication crisis has been the rolling discovery that when researchers do try to reproduce published studies, a huge proportion of them fail. Recently it was found that of 100 psychology studies, only 36 replicated. In a similar study of cancer biology, only 40% replicated. A lot of similar levels of replication have shown up in other fields. The replication crisis is about the validity of the studies themselves, not the funding for replicating them like Jordan seems to think.

This matters because the whole video arguing that institutional science is corrupted by capitalist and credentialist, so not to be trusted. Jordan explicitly tells viewers “there’s a major war on truth and objectivity happening right now” (I definitely agree with him that in the exact moment I’m watching this video, a major war on truth and objectivity is occurring!) and that the replication crisis has been used “to discredit the effectiveness of research and scientific method.”

This is what Jordan tells viewers he’s “covered some of” in that link at 6:37. You’d expect that the video would cover the fossil fuel industry affecting infrasound research. It’s actually a goofy argument based on a complete misunderstanding of the replication crisis, and it implies you should trust independent YouTubers doing their own experiments instead. He seems to be preemptively dismissing a lot of scientific literature on infrasound, because it’s “capitalist” or whatever.

His own n=1 wheatgrass experiment is actually a pretty obvious example the kind of unreplicated finding the whole replication crisis is about.

6:37 - So, yes, wind farms do cause infrasonic noise pollution, while fracking in Texas, Oklahoma, British Columbia is literally causing record-breaking earthquakes. And how does the infrasound from all this fracking affect people? We don’t really know ‘cause we haven’t studied it nearly as much as wind farms.

The fracking point is wrong. The research on infrasound from other sources also applies to fracking. We don’t need a specific study on infrasound from fracking. This idea that we need a new study on every individual industrial source of infrasound before we can make conclusions is a way of creating permanent uncertainty. We don’t need to do a study on the audible sound from every industry either. He seems to be trying to separate himself from the main way infrasound pseudoscience has been badly used in the past: by attacking wind turbines. I don’t think he gets to have it both ways here. If infrasound is actually harmful, we should expect to see a lot of harm from wind farms very close to people’s homes, and Jordan would have to at least acknowledge the trade-off.

Also, on the earthquake point, felt earthquakes directly caused by hydraulic fracturing are extremely rare, and the big rise in places like Oklahoma is primarily tied to wastewater disposal.

6:52–8:10 - The Vic Tandy ghost story

6:52 - In the late 1990s, Vic Tandy, a respected lecturer and engineer, was working in a laboratory in Warwick, England that had a long reputation of being haunted. He felt a cold sweat and an intense feeling of depression. And out of the corner of his eye, he saw a blurry gray figure.

Yes, in the late 1990s, Vic Tandy was a lecturer working in a medical equipment lab. The lab had a local reputation for being haunted. People working there had reported feeling uneasy, seeing things out of the corners of their eyes, etc. Tandy himself had an experience like this. He suddenly felt cold and anxious and saw a blurry gray figure in his peripheral vision that vanished when he turned to look at it.

The next day, Tandy was clamping a fencing foil in a vice when he noticed the blade vibrating on its own. He realized a large fan in the lab was producing low-frequency infrasound around 18.9 Hz, and he hypothesized that this infrasound might be what was making people in the lab feel uneasy and see peripheral figures. He published this idea as a short article titled “The Ghost in the Machine” in the Journal of the Society for Psychical Research, a parapsychology journal, in 1998. This wasn’t “a study” the way Jordan describes it. It was a short article speculating about a possible mechanism. Just one guy’s anecdote, no experiment, no controls, nothing.

When someone eventually did try to test Tandy’s hypothesis rigorously (the Goldsmiths Haunt Project, which will be discussed more below when Jordan brings it up) the infrasound had no effect on reported experiences. Participants’ prior suggestibility was the sole thing that predicted whether they felt uneasy. This paper reinforced the idea that infrasound effects are a nocebo.

In both cases, Tandy visited places with strong local reputations for being haunted, where the people he interviewed had already been primed by each other to feel uneasy. It’s very likely this was a nocebo.

7:33 - A large fan was causing infrasonic vibrations around 18.9 hertz. You know what else resonates around 18 hertz? The human eye.

The eyeball resonance thing sounds scientific but isn’t at all. The original NASA study this comes from is about very high energy vibrations in the body. It wasn’t looking at normal levels of infrasound. This is kind of the same as seeing the frequency it takes to push a kid on a swing to get them to go higher and higher, and then concluding that little taps on their back at the same frequency would also make them move higher and higher. The large fan couldn’t have been having the kind of effect the NASA study observed.

7:49 - This would later be published in a rather famous paper titled The Ghost in the Machine.

This is famous in pop-science and paranormal-adjacent writing. It’s not respected in the medical literature. Serious infrasound researchers seem to ignore it. It’s never been replicated under controlled conditions.

7:54 - Soon after this Vic visited another famously haunted place, a cellar in Coventry.

A few years later, Tandy wrote another article called “Something in the Cellar” in the same parapsychology journal. He visited a 14th-century cellar in Coventry visitors had said they felt uneasy in. He recorded with a microphone in the room for 20 seconds, found a 19 Hz peak in the ambient sound, and declared this confirmed his earlier hypothesis. The measured sound level was 38 dB at 19 Hz, which Tandy acknowledges is 50 dB below what humans can hear for that frequency. He suggests the reason this very low sound might still cause people to become uneasy is that some people are hypersensitive to it, and then he cites a New Scientist article about hypothetical military infrasound weapons. So this also wasn’t a “study,” it was a guy going around with anecdotes and random hypotheses. This is not science.

The anecdotes themselves are also pretty bad evidence. He’s in a purportedly haunted cellar! Of course people feel uneasy! Two of the “witnesses” Tandy relies on were self-identified witches who visited the cellar specifically to commune with the spirit world. A third witch was “frightened to death” and left.

Another obvious problem is low-frequency resonances are a feature of almost every partially enclosed space. A cellar’s shape can produce standing infrasound waves.

So the “Cellar in Coventry” paper is a ghost-hunter-reviewed anecdote-plus-ambient-sound-measurement, at sound levels the author admits are below any established threshold for perception, reported in a parapsychology journal, using informants who included self-described witches, in a cellar already known as haunted, and detected normal infrasound levels for cellars. And when it was tested rigorously later, it failed. This is the second piece of Tandy’s case, and it is, if anything, weaker than the first.

8:00 - It’s not the most conclusive study in the world, but pretty damn interesting

This is definitely the pattern yes.

8:10–10:09: Backyard recording

This section frames it as mysterious and maybe significant that he can’t identify the source of the infrasound he’s recording outside. This is silly. There’s a continuous soup of infrasound from a lot of sources outside. Distant traffic or HVAC or wind or the ocean. Thunderstorms hundreds of miles away. He does at least clarify at the end of this section that infrasound can travel far.

The fact that he can’t identify specific sources for specific rumbles is because infrasound propagates extremely well over long distances (which he acknowledges later) and that there are innumerable sources creating it constantly.

When he says “Wow, I am not jealous of the people who live nearby” he’s blurring the line between commenting on noise pollution vs. infrasound harms. The first one’s real, second one’s fake.

He throws in a wind turbine and makes what I think is a pretty obvious point that the wind turbine isn’t as loud as a train starting up at close range or the world’s busiest airport. He’s doing this in part to distance himself from the anti-wind activists on his side of the infrasound debate. I don’t think he should get to have it both ways here. If he thinks infrasound is a problem, he needs to acknowledge that wind farms create it, and that living near a wind farm exposes you to new levels of infrasound. He can say “that’s bad, but worth it to have the wind farm” or agree with me instead and say “that’s fake, build the wind farm, don’t listen to the cranks” but he shouldn’t get to imply that wind farm infrasound is fine because it’s not as bad as a train at close range. Throughout the rest of the video he’s implied over and over that similar levels of infrasound as wind turbines create are bad for you.

10:25–12:00 - The South Dakota detour

10:25 - But I really wanted to hear clear infrasound far away from any form of human infrastructure. And to do that, we had to drive over 1,500 miles to a particular spot south of Badlands National Park in South Dakota on the Oglala reservation.
10:55 - From where I’m recording, the nearest paved road and infrastructure of any type is about 11 miles away, and it looks like this. So not exactly a teeming metropolis. The sound up here is remarkably quiet.

Okay so yes the only way you can get away from infrasound is basically in the middle of nowhere. This seems to cut against the idea that it’s harming us. If all of society is producing it, why are data centers in the next video a unique harm? He hasn’t actually done anything to prove that infrasound is harmful yet. Everything he’s flashed before this has been pretty useless and left the viewer misinformed.

11:38 - But this does not mean that nature is infrasonically quiet. The earth can produce infrasound that humans can’t even begin to compete with. This distant thunderstorm, for example, made a symphony of various rumbles.

This is a pretty important part of the debate that complicates his broader argument. Lots of things in nature like thunderstorms and earthquakes and the ocean create infrasound louder than a lot of what humans have built. It doesn’t seem to make sense from an evolutionary perspective that these sounds would be harmful to us. If ambient infrasound at ordinary levels caused the broad symptom list Jordan describes, everyone living by the ocean would be chronically ill.

12:00 - And there’s some early research and testing on the accuracy of infrasound to detect and predict tornadoes.

This part is actually real, phew! Infrasound can be used to detect and predict tornadoes.

12:08–14:49 - The Yellowstone segment

He drives to Yellowstone to record infrasound coming from geysers, hot springs, and volcanos. This section is pretty. There’s nothing wrong with it scientifically.

14:49–18:00 - The physics detour and SpaceX launch

Jordan explains the physics of why low-frequency sound travels farther. He then uses seismograph data to track a SpaceX launch, showing that it can be detected up to 140 miles away.

The physics here is correct! Low-frequency sound does travel way farther than high-frequency sound. This is why you can hear thunder from very far away. It’s similar to electromagnetic radio waves, which have a way lower frequency than visible light, and can travel way farther.

But we still have zero evidence to think that this very low volume of infrasound traveling very far is actually harmful.

17:18–18:00 - Helmholtz resonance

17:18 - it’s worth pointing out that in many cases, infrasound is actually louder indoors than outdoors, even if the source is coming from outdoors. And this is due to a phenomenon called Helmholtz resonance.

Helmholtz resonance is real, but he’s doing the same thing where he takes a really really loud source of infrasound and compares it to something quite. The idea that Helmholtz resonance in residential buildings makes infrasound loud enough to be harmful is, once again, not supported by any evidence.

18:00–18:56 - The Liverpool Cathedral study

18:00 - In 2003, a group of scientists from the UK National Physical Laboratory held a live musical performance at the Liverpool Metropolitan Cathedral and then took a survey from the audience on how they felt during the music. In some of those music performances, they had a gigantic 23 ft infrasonic cannon tuned to 17.5 hertz, a frequency that didn’t resonate the building in a way where it would shake things or create audible overtones. and they played that tone at 90 dB while some of the music was played to people who were completely oblivious to what was actually going on. It turns out that people were quite a bit more likely to have felt uncomfortable during the performance when the infrasonic tone was playing. And 22% of the audience said that they felt some type of strange or unexplainable effect such as sadness, chills, or even feelings of fear and anxiety.

This is the “Soundless Music” or “Infrasonic” experiment, a 2003 concert at London’s Purcell Room (not Liverpool Metropolitan Cathedral, Jordan has the wrong venue). There were about 700 people there. The experiment collected 522 questionnaires. This study is often misunderstood as implying that the infrasound caused 22% of people to experience something strange.

There are some core problems with this experiment that infrasound researchers have identified. There wasn’t ever a peer-reviewed paper published with the data, and there wasn’t a control group. The 22% number shows the baseline rate of people reporting something strange during evocative music in a dramatic theater. It’s not surprising that some people at a dramatic music concert report feeling strange. And the Goldsmiths Haunt Project (which will be covered next) tested the same hypothesis under controlled conditions and found that infrasound exposure didn’t predict reported experiences. It all depended on how suggestible people were.

18:56–20:16 - The Goldsmiths Haunt Project

18:56 - In yet another study from Goldsmith’s College, four researchers attempted to make a haunted room by filling it with various infrasonic frequencies and EMF or electromagnetic frequencies.

The researchers here built a “haunted room” at Goldsmiths College and had 79 participants stay in it. Each person sat alone in the room for 50 minutes. They were exposed to either infrasound or complex EM fields. People were randomly assigned to one of four conditions: infrasound only, EM only, both, or neither. Everyone was told the room might produce unusual sensations.

After, participants filled out a questionnaire about whether they had feelings of presence, unusual body sensations, dizziness, emotion, or anything else out of the ordinary. They also completed a psychological measure of suggestibility beforehand.

Jordan narrates the study setup accurately, but then he misrepresents what it found.

19:23 - Out of these groups, an average of 3.7 participants in the control group, those sitting in the empty room with nothing unusual happening, had some type of haunted experience. When infrasound was present, this number grew to an average of 5.2 people having a haunted experience.

Jordan seems to ignore the actual findings of this paper. They found that there was no statistic difference in symptoms between the groups exposed to different stuff! The authors very specifically say that the number of reports was unrelated to what the people were exposed to, and that a better explanation was just that some people were suggestible.

19:48 - Now, in my opinion, the study is fun, but not very well organized and definitely not conclusive.

He hedges on the study’s conclusiveness only after having cited its numbers in a way that implies the opposite of what it found. This seems to be timed so that the viewer leaves this segment with the 5.2 vs 3.8 number in their head, and the idea that the science “hasn’t been settled” but is “suggestive.” But the actual published conclusion was that infrasound did not explain the reports.

This is yet another massive misrepresentation of a scientific paper in the video! Jordan presents the paper as if it found the opposite of what it did.

20:16–End - The gear section

The final third of the video is a discussion of his microphone setups, the Raspberry Shake seismograph, accelerometers, and a mention of his “hum” video. This section doesn’t make any big new scientific claims about infrasound harming humans. I’m mainly worried this is yet another way to look more scientific and legitimate.

Conclusion

In a single 25-minute video about how infrasound is bad, Benn Jordan cited as evidence:

A survey of farm equipment that noted infrasound is safe at levels 100,000x what we experience even in noise polluted areas.
A WHO document about noise pollution that doesn’t mention infrasound.
A Finnish government project whose final report is titled Infrasound Does Not Explain Symptoms Related to Wind Turbines.
A NIOSH workplace report that explicitly says the sound levels it measured aren’t known to cause harm and attributes the workers’ symptoms to annoyance and a psychological contract breach.
A literature review exclusively about the bad effects of audible low-frequency noise.
A survey checklist of symptoms as if it were the symptoms subjects actually reported having.
A study of wind turbine maintenance workers at 60 to 83 dBA of audible occupational noise.
A study of people living near oil and gas drilling getting mad at the 80 dBC audible noise.
A heart-muscle paper demolished by two peer-reviewed responses linked from the top of the page he screenshotted, which found the results were caused by vibrating tweezers, that the acoustic dose was matched by a single breaststroke, and that the intensity used was hundreds of thousands of times above anything in a home.
“Animal research” that turns out to be vibroacoustic disease, a hypothetical condition published almost entirely by two authors citing each other for forty years and rejected by every mainstream medical body.
A parapsychology essay written as a personal anecdote by a guy primed by ghost stories, and its sequel, which mainly interviewed self-described witches in a cellar already advertised as haunted at sound levels 50 dB below the perception threshold, and found that they felt somewhat uneasy.
An experiment that found no effect, but Jordan presents it as having an effect.
A study that found 22% of people at a concert with evocative music had strange experiences.
NASA work on eyeball resonance frequencies without mentioning that these harms only appear when you are literally inside a rocket.

The running theme across every study that actually addresses it is that the symptoms people report are real but not caused by infrasound. They’re driven by expectation, annoyance, context, and suggestibility, which is the signature of a nocebo.

He implicitly analogized infrasound to UV light instead of the obviously more comparable infrared light. He used Helmholtz resonance to imply buildings amplify residential infrasound until its harmful without giving any actual evidence of harm. He called infrasound “grossly understudied” when it’s one of the most thoroughly studied topics in noise research. He implied the fossil fuel industry suppressed infrasound research when the fossil fuel industry is in fact the main funder of the pseudoscience he’s promoting. He claimed fracking infrasound needs its own dedicated research before we can conclude anything, despite fracking causing the same levels of infrasound as a ton of other sources.

He told viewers he had a separate video explaining the politics of all this. In that video he inverts the replication crisis, dismisses relying on credentialed scientists as “a really poor or weak scientific argument,” calls institutional gatekeeping a bigger threat to science than replication failures, and presents his own n=1 wheatgrass result as meaningful.

And this is, from what I can tell, the lead-in to what I think is the most popular piece of media made about data centers in 2026 so far.

Which brings us to the second video.

Second video - Datacenters Behaving Like Acoustic Weapons

Well now we can finally talk about data centers I guess.

I’m going to work through this in roughly the order of the video. There are three key ideas Jordan is blurring together here:

Audible noise pollution from data centers, which is real and harmful.
Ambient infrasound from data centers, which his own data shows is unremarkable. This is a fake problem.
The symptoms residents are reporting, which are real, but whose cause is the audible noise plus stress, not the infrasound.

The video’s central move is to associate 3 with 2, whereas in fact it’s all caused by 1

0:00–1:29 - Intro

0:00 - If you’re in the demographic of people who generally get recommended my videos, then chances are that you are not a fan of data centers.

As I said at the start of this post, he’s using this nuance as a credibility shield. It works on viewers because it reads as a signal that he’s willing to acknowledge nuance, but it’s really a way of pre-buying permission to make crazy uncharitable claims about an industry the audience already dislikes.

1:04 - Spoiler alert, the results are terrifying.

Spoiler alert, they reduce to random statistical noise in a terribly designed study by Jordan

1:36 - regardless of where your ethical, environmental, political, or economic interests are, if a data center is being built nearby your home, you’re generally kind of f*****.

Obviously I disagree. Data centers (with a few exceptions, like xAI, which comes up later in this video) mostly don’t seem to have harmed people. I don’t think the citizens of Loudoun County, one of the top 5 wealthiest counties in the country and home to the most data center capacity in the world, are “generally kind of fucked.”

1:56 - Last year I started hearing about some mysterious and troubling symptoms from people who live nearby newly constructed data centers

Jordan seems somewhat addicted to flashing completely unrelated studies on the screen.

This time he flashes a Dutch survey of 190 people who self-identify as experiencing a perceptible low-frequency hum in their environment, compared to 371 people who don’t. The researchers didn’t measure any sound in anyone’s home. They didn’t establish that the complainants were even actually being exposed to what they thought they were perceiving. They didn’t test causation.

Most importantly, they didn’t mention data centers once.

What the paper does document is that the complainants are disproportionately older and introverted, which is the exact demographic signature of other “environmental sensitivity” syndromes where the attributed cause has repeatedly failed controlled testing. The paper’s own authors explicitly acknowledge that “non-acoustic factors including sociodemographic and individual characteristics or personality constitute substantial predictors for reactions to noise.” This is likely a nocebo, and Jordan once again takes a paper that documents self-reports and presents it as a paper that proves causation.

He then flashes the symptoms list on the screen as he says this. You would think that if someone is saying “symptoms from people who live nearby newly constructed data centers” and is flashing a list of symptoms on the screen, those people would live near data centers, or the study would focus on this in some way. But you’d be wrong!

2:07 - I started looking into seismograph readings from around the country, and there was enough correlation for me to dig pretty deep into an area of research I already had some experience in

Consider what that first video was like, this is the “experience” he’s referring to.

It’s true that data centers register on seismographs. So do rail lines, highways, hospitals, college campuses, shopping malls, factories, power stations, and large intersections. I suspect he’s going to draw some bad inferences.

2:24–3:22 - The symptom list, round two

2:24 - Being in the presence of elevated levels of infrasound has been shown to show spikes in cortisol levels... It can cause vestibular issues leading to loss of balance, vertigo and nausea. Then there’s something called vibroacoustic disease which suggests that infrasound can cause abnormal growth of extracellular matrices... It’s been shown to cause high frequency hearing loss, shortness of breath, anxiety, depression, and it even makes your heart have to work harder to pump blood.

He’s flashing the same study from last time that only measured audible low-frequency sound, not infrasound. Great! He’s doing the same trick of flashing a bunch of highlighted words without making it clear these are all about audible sounds. He seems more confident than last time. He also references the disproved heart study.

“Has been shown” is carrying most of the weight. Again, all of these claims are only true for audible sound, or come from the goofy VAD literature that no serious scientists accept, or just contradict what the actual studies he’s flashing on the screen say. Literally everything he cited last time either contradicted these claims, didn’t address them at all, or was published in a journal about ghosts.

In the last video Jordan didn’t explicitly mention VAD pseudoscience, he just vaguely referenced it as animal studies, so he’s escalated in mentioning it directly here. Again, VAD is the pet theory of a small Portuguese research group whose work has not been replicated and which the peer-reviewed acoustics literature has specifically assessed as unproven.

2:57 - So, it’s not like this anxiety-based or obsessive compulsive thing where an unpleasant feeling or an unusual situation makes you manifest symptoms. We know that infrasound can tangibly harm your health in a very real and measurable way.

As we saw in the last section, we don’t know this at all. Basically all the literature Jordan cited in the last video said the opposite of what he claims here. He just flashed all the same sources again that all show completely unrelated results to what he’s implying or aren’t respected in the broader literature at all and rely on circular citations.

Here he’s more directly rejecting the nocebo explanation, whereas in the last video he at least passively mentioned it. He’s now implying this is established science, and implying that anyone who doubts the infrasound stuff is basically accusing people who were harmed of making up the harm.

4:06–9:10 - xAI’s Colossus

xAI’s Colossus is a massive outlier among data centers, and there are a lot of real, widely-covered problems with it. The biggest is that it ran up to 35 unpermitted methane gas turbines for roughly a year as primary power generation, behaving like an unregulated mid-sized gas power plant in an already heavily polluted poor neighborhood. The methane turbines, the local air quality impacts on Boxtown, the nitrogen oxide emissions, and the permitting problems are all real, well-documented problems.

As a quick aside he also cites xAI using a lot of water, but doesn’t give a frame of reference for how much it is and just zooms up on a depleted water source. In reality, Memphis has some of the lowest water bills in the world, their water bills haven’t risen as a result of xAI, and Memphis isn’t considered a water stressed-area.

But none of the problems with xAI are about infrasound, and lies about infrasound cheapen the real harm people experienced.

7:46 – The lowest of rumbles is peeking out at like -28. And then if we go down to infrasound, the infrasound that you can’t hear is like 10 dB louder than that.

He’s reporting levels in what looks like digital audio reference (dBFS) rather than sound pressure level in the environment. We’re left without a way to convert this to an absolute sound level. He doesn’t show his method anywhere. He says “infrasound is 10 dB louder than” and then adds a rumble, but we don’t know what absolute level that represents in the environment. This tells us nothing.

Of course infrasound is louder than the audible component at distance. Low frequencies propagate better than high frequencies. The lower the frequency, the less it diminishes over distance and the better it passes through walls. The ratio of low-to-high energy always increases as you move away from any industrial source. He made this clear himself in the brief wave physics section of his last vide.

8:02 – This in particular is a great example of how higher frequency sounds are dampened by distance much more than lower frequency or infrasonic ones.

Yes, correct. But then he’s going to use this correct observation about physics to imply that infrasound is doing something special and harmful. All that this physics actually shows is that at a distance most of the sound you’ll get from a source will be low frequency, not that it’s harmful. He hasn’t actually demonstrated any harms from infrasounds. This this entire video will now just assume infrasounds are harmful, based on the terrible edifice he constructed in the last video.

8:26 – This rumbling is no good and would make your life utterly miserable. And then this rumble down here is happening at an amplitude that’s loud enough to shake the frame of your house.

If infrasound were actually shaking the frame of a house, that would be an extraordinary claim and easily falsifiable. It would also be perceived as a rumbling vibration by residents. He provides no measurements to support the “shake the frame” claim, and none of the residents interviewed mention their homes shaking. There has never been any accusation at all that Colossus shakes nearby houses.

8:44 – Okay. So, I’m going to delete everything above 20 hertz here. So, all audible frequencies. And now we’re only left with the infrasound. Now, I’m going to interpret the sample rate four times faster. And I’m going to make it even four times faster than that.

Same trick as the beginning of the first video. If you speed up infrasound and make it audible, it sound ominous. Just like if you look at the infrared light coming from your dog, your dog looks evil. It’s not evidence that the specific source he’s recording near is producing unusual infrasound. It’s evidence that infrasound exists, which no one disputes.

The xAI section is him listing a bunch of real harms, water, and then doing a bunch of fancy tricks with fancy but inaccessible sound terminology and equipment to support his unrelated baseless conspiracy theory.

9:10–15:18 - MARA / Granbury

Noise pollution from the Marathon Digital / MARA Bitcoin mine in Hood County has caused well-documented harm to nearby residents. Cheryl Shadden’s experience is definitely real, as are are the symptoms of the girl who had seizures and whose symptoms resolved after she moved away from near the data center. But these symptoms are clearly being caused by audible sound. Jordan’s going to imply it’s his conspiracy theory instead.

10:15 – While the noise pollution is described as a 70 to 90 decibel jet engine-like sound, a large number of reports site symptoms ranging from vertigo to pulmonary embolisms to heart palpitations.

70–90 dB sustained is awful. It’s above the level where WHO guidelines say it can cause cardiovascular disease, sleep disruption, and other stress-related outcomes. A family living across the street from a 70–90 dB jet-engine-like sound will absolutely experience a ton of terrible symptoms. This is a real case of terrible audible noise pollution from a data center.

10:39 – Sustained audible noise pollution can be a blight on any living thing nearby. But a lot of these symptoms don’t line up with the symptoms of just noise pollution exposure.

This is just completely wrong. The symptoms he lists are exactly the symptoms of chronic environmental noise exposure. There is a massive literature on this going back to the 1970s. Every major environmental health textbook describe this symptom cluster as the expected health consequence of chronic exposure to 70–90 dB environmental noise.

At 11:39 he starts playing more scary clips of sped up infrasound, with no evidence this is harming anyone, after leading in about how the residents told him about the real effects of noise pollution. What a dumb way to hide a baseless conspiracy theory behind people’s real suffering.

The residents of Granbury are suffering from real terrible noise pollution. Infrasound adds nothing to the explanation, and Jordan’s last video gave us not a single reason to believe it’s real in the 25 minutes it runs.

13:08 – Like, what physical symptoms have you felt just from your property being directly across the street from the data center? Motion sickness, dizziness, nausea, GI side effects. My ears ring and buzz. I’ve got permanent conduction hearing loss now. Um, nervous, anxious, worried.

Every symptom Cheryl describes is widely understood to be caused by chronic high-level noise pollution. Hearing loss in particular. There’s no good evidence that infrasound causes hearing loss. Jordan presents these symptoms as mysterious that infrasound can explain, but they’re not mysterious at all.

13:24 - Cheryl thought that I was just doing a story on the noise pollution as I was keeping my infrasound research close to my sleeve.

Okay, so this guy nods along to this person who was harmed by very real noise pollution, and lists classic symptoms of real noise pollution, and then adds “Oh by the way, I didn’t tell her that I’m actually just interviewing her to promote my bullshit unrelated conspiracy theory that I’ve never found a single good source to justify, and I’m just nodding along and am going to lie about her symptoms actually being caused by this other thing.” This is terrible! He lied to this real victim of noise pollution.

13:32 - Yet, every single one of these symptoms is commonly associated with excessive infrasound exposure.

This is silly. He’s saying that because the symptoms are similar to the infrasound horoscope-list, they must be caused by infrasound. But the symptoms match much more strongly with audible noise pollution. Notice that these symptoms have only appeared near a data center with a huge amount of audible noise. Why are they not appearing anywhere else?

14:03 – One family, their daughter was 6 years old. She started having seizures in the summer of 24... they basically walked away from their home here, took out a second mortgage, moved to Somerville County... If you know, has her symptoms kind of gotten better since she’s left? They went away.

The family has been severely harmed by this data enter. It’s terrible. But do we have any reason to think infrasound caused the seizures? Chronic sleep disruption from audible noise in a young child can trigger terrible neurological symptoms, including lowered seizure thresholds. Severe family stress can too, and this family’s experiencing a lot of stress from the noise! This all is entirely consistent with audible noise and stress being the cause. It provides no evidence for infrasound specifically. Jordan’s never produced any actually good evidence that infrasound causes seizures.

I think it’s gross to present this family as evidence of infrasound. It’s not justified scientifically and unhelpful to families in this situation. It directs the legal and regulatory conversation away from the actual scientifically provable cause.

I really don’t like that he flashed a quick out of context clip of this woman at the very beginning of the video to imply that infrasound caused her problems.

15:18–17:58 - The Permian Basin

17:32 - But while the infrasound levels were nowhere close to ideal, they still weren’t as bad as they were near the data centers in Memphis or Granbury.

I’m a little skeptical that a place with thousands of earthquakes every year, massive amounts of fracking, and lots of oil and gas infrastructure will produce less infrasound than two data centers.

17:39 - The infrasound levels at these two data centers were so much higher than the ambient values that it doesn’t even make practical sense to use a linear scale like 25,000% higher.

25,000% is about 48 dB. This means the data center is somehow way way above any residential or rural baseline of sound anywhere in the world, including airports or freeways or industrial sites. This doesn’t seem likely for ordinary data center cooling or power delivery (the main way they make sound). I think it’s more likely that his baseline is at or below the noise floor of his measurement equipment, while his site recordings are well above it. Dividing “real signal” by “noise floor” gives you any number you want.

17:58–23:15 - The experiment

This is the centerpiece of the video, and is a disastrously bad experiment. I’ll walk through the problems one at a time because each one independently invalidates the claimed findings.

Problem 1: The “haunted painting” priming

19:17 - I improvised a new story of why the owl painting was known to be haunted... I told them that the audio recording that they would be listening to was known to be haunted. I had them sit in this enclosed room while studying the painting and listening to the music for 3 minutes while paying attention to how they felt physically and emotionally.

So he’s priming all his participants to expect unusual experiences! This is nocebo-priming. It’s making it more likely that people will experience weird things. His rationale (that varying the story randomizes a bias variable) is wrong. Randomizing the specifics of the priming across groups does not eliminate the priming. I’m confused about why he’s doing this.

To do this correctly would require one group primed negatively and one group given neutral or no priming, the way Crichton and Petrie actually did it. His design has no unprimed group.

20:10 - And unless I saw water vibrating in a bottle or something, I often didn’t know if the infrasound was on or off myself.

That’s not good enough. If the experimenter can sometimes tell what’s up in a study, it’s possible that their own behavior will leak information (like facial expressions etc.). Jordan says he can tell via the vibrations when the sound’s on. This has the potential to mess with people’s reactions.

Problem 3: The stimulus itself

18:04 - I needed to figure out a way to reproduce the infrasound waves at 25 to 30% of the volume or amplitude near the data centers.

Here he’s playing infrasound through “a gigantic specialized subwoofer in the room.” These seem to produce a lot of evidence that they’re on beyond infrasound. It seems likely people might be able to tell it was on.

His own video gives this away:

20:23 - Just put my DJI gimbal on the ground here. And Jeez, it’s kind of scary.

This implies the room is shaking! This means people can detect when infrasound’s present!

Problem 4: The self-selection filter

21:33 - The experiment had over a 100 participants, but I disqualified anyone who missed a survey question or someone who seemed suspicious or said that they felt a vibration or where I was privy to whether the infrasound was on or off.

He dropped subjects who “seemed suspicious” or who reported feeling a vibration, based on his own after-the-fact judgment. I’m worried that “Seemed suspicious” is way too much of a subjective call by Jordan, who seems to have a bias in wanting the experiment to show results.

We also don’t know how many of the original 100+ were dropped, which groups they were in, or what the results look like with them included.

Problem 5: The effect sizes are tiny and the framing is misleading

Jordan does acknowledge this one:

23:58 - the 33% more likely to experience nausea metric, the viewer may imagine a room with 100 people in it and then 33 of them experiencing nausea, which is very much not the case. The average nausea feeling score from 0 to 10 that each user put in for infrasound is 1.2. For the control group, it’s 0.9.

This is “33% increase” from 0.9 to 1.2 on a 0-10 scale. This doesn’t look meaningful, it’s a difference of 0.3 of 74 people. It doesn’t try to correct for multiple comparisons across a dozen or more symptoms. And this is all happening in a room where people can possibly feel the floor vibrating or notice his reactions! This looks like statistical noise.

24:55 - the average score in the control group for discomfort was 1.2. The average score for discomfort in the infrasound group was 4.8.

A 1.2-to-4.8 shift on a 10-point scale is a large effect, if it’s real. But consider that people might be feeling the floor shake, subjects have been told the space is haunted, and where they’ve been told the audio’s spooky, and where the experimenter may subtly give away when the sound’s on with his facial expressions. All of these together look like better explanations to me than infrasound.

Problem 6: Multiple comparisons

He’s comparing about a dozen symptoms, and finds that some increased and some decreased. If you have 74 people in a study and measure 12 symptoms, some will increase or decrease by chance alone. There’s no attempt to correct for this.

What the experiment actually shows

If you prime a group of synthesizer-convention attendees by telling them a painting is haunted and some audio is spooky, then expose half of them to a subwoofer producing enough mechanical vibration to visibly shake objects in the room, the exposed group will report more discomfort and dizziness on a survey. This doesn’t tell us anything about data center infrasound. Jordan is just accidentally reproducing the nocebo literature.

25:13 - When reviewing that data, it’s hard for even the most skeptical person to come to any other conclusion than the presence of elevated levels of infrasound is likely to lower your quality of life.

Very very very very easy for the skeptical person, actually. The skeptical person concludes: priming produces reported discomfort, perceptible subwoofer vibration produces reported dizziness, and an unblinded single-operator study with post-hoc subject exclusion at a synthesizer convention is not a basis for policy claims about data center health effects.

25:43–end - The call to action

27:12 - Prior to this experiment, I feel like we have seen more than enough data to make infrasound monitoring and regulation just as important as air or water quality.

NOOOOOOOOOO!

Outdoor air pollution kills 4 million people every year. This sentence is elevating a fringe belief with zero evidence to the same level as one of the single largest killers on the planet. Crazy. After 40+ minutes of this guy misrepresenting studies and concepts almost by the second, this moment really made me lose it.

27:21 - Especially when you look at the studies pertaining to the cardiovascular effects, it’s a very real environmental hazard that has been poorly understood and grossly understudied.

Another repeat of his false claim that infrasound is “grossly understudied.” There are no good studies that imply cardiovascular effects. It’s fake.

28:21 - Now would be a great time to set up something like a Raspberry Shake and start storing a daily log of seismic and infrasonic activity. Because the vast majority of the responses to complaints and lawsuits is that there is no way of proving whether the infrasound wasn’t already present before construction.

If communities want to collect baseline acoustic data in advance of data center construction, fine, go ahead. I agree with critics of data centers that they often haven’t been careful about audible noise pollution, that this has caused real quality-of-life harm, and that communities should be able to monitor it.

But communities shouldn’t monitor infrasound for the same reason they shouldn’t monitor wifi signals over fears about wifi-intolerance or cancer. There’s no evidence for either. Monitoring would just waste everyday people’s time and leave them more confused and paranoid when they realized how much infrasound is around normally. It’s like hyping people up about the dangers of infrared light and sending them out to test it themselves. They’ll find a lot!

Conclusion

I found this second video to be way more objectionable than the first. Even though the first had nonstop lies about what every study flashed on the screen said, at least it didn’t have interviews with victims of real noise pollution Jordan was trying to use as evidence of his unrelated baseless conspiracy theory instead without even telling them. I found that pretty gross. I was also repulsed by his comparison of infrasound to outdoor air pollution, one of the great global killers right now.

My claim remains strong. Infrasound harms are fake, data centers or otherwise. People should focus on the real problems of noise pollution and not give an inch to baseless conspiracy theories promoted by chill-seeming guys using fancy-sounding technical terms and unrelated studies flashed on the screen to trick millions of viewers. Infrasounds have been used by all kinds of bad actors to oppose things we should all agree are good, like wind farms. Let’s not give the crazies more power here.

Some stray thoughts

If infrasound exposure near data centers caused this much harm, wouldn’t the absolute worst off people be people working in data centers themselves? They’re in the belly of the beast of data center infrasound, many have been working in data centers for way longer than the current buildout’s been happening, and it’s very easy to identify who works in data centers. They seem like the ideal people to look at for data center infrasound effects, but never get a mention.

Responses to this piece from the authors of the studies Jordan cites

Someone had suggested to me that since Jordan and I disagree about the content of these studies, why not just reach out to the authors themselves?

I reached out a few days ago. So far I’ve received one response from Swen Müller, who wrote the rebuttal to the heart study. Jordan cited the heart study but not the rebuttal, so this isn’t as good as having a direct quote from the authors themselves. Here’s Müller’s thoughts:

I’ll update this section as I get more replies.

Jordan’s response to this article was pretty goofy and reaffirmed that he can’t defend any of his misleading citations

Bluesky back-and-forth

Jordan responded to this post on Bluesky, and it went pretty badly. Opened with this

Then added this. Sure, I’d say people harmed by data center noise are vulnerable, but maybe that was too strong.

Here’s the next thing in the thread + my response, no reply from Jordan here.

He then accused me of hypocrisy here:

Which is a misreading of this post. I never said that it’s invalid to compare sound to light. Like he said, I do it over and over. I think it’s a useful comparison. What I specifically said was that it’s invalid to compare infrasound to the harm of ultraviolet light specifically, and I made that explicit here:

He’s setting up this framing about how things that are invisible to us can still harm us, and then very quickly moves from ‘ultraviolet light can obviously kill us’ to ‘and there’s new disturbing evidence that infrasound can harm us’ without acknowledging the obvious problem that infrared light does not really harm us at all unless it’s so powerful that it heats up our bodies in the way other normal heat sources do. This is the correct analogy to infrasound, not UV light. Just like infrared light can only harm us if it’s powerful enough that we physically feel it in the way we do other heat sources, infrasound can only harm us if it’s so powerful that it causes us to physically feel its presence in the way we do other very loud sources of sound. He’s not so subtly reaching way across this spectrum to say that because something that’s so high energy that we can’t see it can cause damage, maybe something that’s so low energy that we can’t hear it can cause damage too.

I replied making this point and didn’t get a reply. Jordan seems to have a pattern of misrepresenting articles.

I finally got a response here:

No response there so far…

There was one more thread where I got responses:

Instead of answer he just asked this:

We went back and forth for a little while and I asked him for a meta-analysis showing that infrasound was legitimate science. He sent the following links:

This is the same heart study I mentioned above. It’s an individual study, not a meta-analysis. “Cited 5 times” is a weird defense of the paper, it makes it look pretty bad. Two of those citations were the critical responses I posted above basically showing it was useless, and 3 other citations for a paper like this makes it seem like it hasn’t had any impact at all. I’ve been unable to find what those citations were. When I brought this all up he accused me of “moving the goalposts” but couldn’t actually explain why this study was useful beyond that it was a scientific study.

Importantly, he’s the one moving the goal-posts here, because I had specifically asked for a meta-analysis, not a random study.

The next link he sent was this one:

This one’s really weird. It’s not a meta-analysis or a study. It’s a single-author narrative review, basically a long op-ed.

The author is Michael Persinger, who’s known best for the “God Helmet.” This was a device he built, which he claimed could cause religious and paranormal experiences via magnetic fields. When an independent team ran a proper double-blind replication in 2005, they found nothing. Persinger said the scientists hadn’t done it right.

He separately written a lot about telepathy, precognition, “non-local” consciousness, and his theory that Earth’s magnetic field creates paranormal experiences. A substantial chunk of this guy’s career was pseudoscience.

Benn said “Their validity is supported by convergent quantitative biophysical solutions.” What that sentence actually means, once you trace the paper, is that Persinger’s claims about infrasound are supported by Persinger’s other work on biophysical mechanisms. There wasn’t any research done here.

Actual systematic reviews of infrasound (NHMRC 2015, Baliatsas 2016, van Kamp and van den Berg 2018, Marshall 2023) all converge on the conclusion that infrasound at the levels people are actually exposed to doesn’t produce the symptoms people attribute to it.

So when I asked for his best evidence, the second thing Jordan sent me was a 2014 narrative review, by the God Helmet guy, that vouches for its own conclusions by citing the author’s other papers.

Next was this one:

This paper found that infrasound, when delivered to chinchillas at 100 decibels, simultaneously with a chainsaw-level blast of audible 4 kHz noise, slightly increased cochlear hair-cell damage compared to the chainsaw blast by itself.

These are the paper’s results:

At 108 dB of noise exposure, adding the 30 Hz tone slightly increased cochlear damage compared to 108 dB alone.
At the 86 dB exposure, adding the 30 Hz tone did not increase damage at all.
The authors’ proposed mechanism is that the 108 dB exposure perforates a membrane in the cochlea called the reticular lamina, and the low-frequency fluid movements makes the existing damage worse by mixing cochlear fluids through the hole. The low-frequency exposure only contributes to damage after a separate, extremely loud audible exposure has already wrecked the structure.

So the subjects are not human, the “infrasound” is audible to humans, the exposure levels are not remotely close to what someone living near a data center could encounter, and the finding is that a 30 Hz tone only contributed to damage when a separate exposure had already caused the damage. Alone, it did nothing.

And finally this one (after a mislink of a paper on PTSD that didn’t mention infrasound):

This is a link to a 2016 undergrad paper by Ryan Littlefield that was self-published on Medium and never peer-reviewed or published in a scientific journal. Semantic Scholar indexes it, but it indexes student papers indiscriminately. A paper being on Semantic Scholar tells you nothing about how legit it is.

I pushed back on each. Jordan’s final comment to me was anger that I was calling “published papers pseudoscience.”

He said these were from PubMed, but they’re actually both from PMC (ignoring the Medium article and single-author narrative by the God Helmet Guy he sent). PMC is an entirely separate paper archive with a much lower bar for inclusion, and mixing them up is a very common way to justify pseudoscience.

PubMed is built on MEDLINE, whose journals have to pass a scientific quality review by the National Library of Medicine (NLM). PMC accepts material under looser criteria, and, importantly, it also hosts NIH-funded author manuscripts that get deposited automatically regardless of whether the journal they appeared in has ever been reviewed for quality by anyone at NLM. According to NLM’s own staff, these manuscripts may come from journals that have never undergone scientific review, are outside the library’s traditional scope, or have failed to meet even PMC’s standards. If a paper’s added to PMC, it’s searchable through PubMed, so this is technically “on PubMed.”

The Yale Medical Library flags this exact move is common enough that readers should watch out for it. A publisher can claim to be “in PubMed” when they’re really just searchable via PMC. It is a well-known laundering pathway for predatory and low-quality journals, and is popular among infrasound pseudoscience.

So yes, this all looks extremely pseudoscientific.

Jordan then blocked me and posted this:

After a lot of back and forth he wasn’t able to stand behind any of the studies he cited in his video. I rest my case that this video is full of fake information and pseudoscience, Jordan seems to know this, and is good at dodging this info and reframing any criticism as a “good guy vs bad guy” thing that will cause a lot of his audience to not look into it further. Jordan is knowingly promoting infrasound pseudoscience and is getting millions of views doing it.

Jordan’s blog post rebuttal of this post

Jordan then posted a blog post reply to this article, and it’s pretty bad. You can read the whole thing here. I’ll go down the list and respond to points from it.

But it seemed desperate and mean. I’m attacked for…creating a sundial and having “scientific looking microphones”.

Jordan is obviously knowledgeable enough about sound to be able to read and understand the incredibly simple language in the studies he’s flashing on the screen. I don’t know how it could be possible that he doesn’t know what they contain. I’m forced to conclude that he does know and is choosing to mislead the viewer with half an hour of wild intentional misinterpretations of very simple studies. So yeah, I think he adds scientific looking stuff to make himself seem more legit.

The author is constantly ping-ponging the people interviewed or featured in my videos as poor, stupid people, or people that are owed more empathy than I allotted. Something is just weird about this.

I don’t do this? I say he talks to poor vulnerable people who have been actually definitely harmed by real noise pollution. They’re not stupid at all. I do think people are owed not having their very real harms rebranded as the result of a pseudoscientific sham yes.

So after some sniffing around, I realize I’m being brigaded by someone deep in the Effective Altruism community.

Oh no how did he notice I was really trying to hide this one.

But please do browse his bibliography.
Andy lives in a parallel universe where datacenters don’t waste water, AI artwork is without victims, and using ChatGPT doesn’t harm the environment.

✅ Data centers don’t waste water.

✅ AI artwork is without victims.

✅ Using ChatGPT is not bad for the environment.

I love the idea that the only way you could possibly believe these is if you’re being paid to. Very intellectually walled-off from a lot of the world. I guess when you’re wrong, the correct people do live in a parallel universe.

This is one of the many takes that perfectly align with the board, contributors, and partners with Coefficient Giving

I only received a grant to write from Coefficient Giving a month ago. All the articles he cited above are from when I was just writing for fun.

Unlike most people with shadowy billionaire benefactors, mine and I are buddies on Bluesky. Here’s his input on my writing:

I spend a lot of time on Twitter interacting with people farther right than I am who think EA’s goal is to destroy all technological progress and stop AI, and people farther left than me on Bluesky who believe EA’s goals are to max out AI progress at the expense of everyone else. The truth is that most EAs think there’s a huge amount of both upside and downside to AI. If CG could, I think they’d slow the data center buildout significantly, but wouldn’t do it via spreading pseudoscience.

formally called Open Philanthropy, but changed after its close association with Sam Bankman-Fried was causing some well-earned skepticism.

This is not at all why Coefficient Giving changed its name. If they changed the name due to associations with SBF, why did it take them four years after FTX collapsed to do it? Open Philanthropy also never accepted any money from SBF.

The effective altruism movement, if we want to be gracious in calling it a “movement”, is a rabbit hole of reciprocal wealth.

Fair tbh

A lot of billionaires unload money into its related NPOs, and oddly enough, that money often goes into buying NVIDIA GPUs for an altruistic AI project, directly to Bill Gates to use philanthropically

Bill Gates isn’t involved really? I don’t know of any EA money that’s been used to buy GPUs or for altruistic AI projects?

or to grants for independent journalists and content creators to attack research that is being used to shift legislation away from things that the partners have interest in. Since it’s 2026, those things are data centers and AI.

I do wish I could get everyone telling me EAs are secretly paid to destroy AI and people who say EAs are secretly paid to max out AI progress in a room together to hash this out once and for all. The idea that EAs are paid to DEFEND AI is very funny. If you look up negative coverage of journalism funded in part by EAs, it usually focuses exclusively on how the journalism is more negative on AI than average.

But how did my infrasound content, of all things, end up as the target of a special interest group?
Well, as luck would have it, It turns out that I’m getting cited a whole lot in legislative meetings that are voting on datacenter zoning and moratoriums.
So while I’ll save my digging deep into the clusterfuck that is effective altruism for another time (I’m inspired now!), below is a quick graphic summarizing where this article came from and why it was written. Feel free to “do your own research”.

Jordan seems to have something of a fantasy of persecution. I didn’t receive any kind of command from above to write this. What happened was I was seeing more and more tweets about data center noise pollution, looked into it, thought “Wow this is terrible, this looks like a real problem” and then stumbled on Jordan’s videos. At first I only noticed a few problems, and was going to make infrasound a small end note of a post about the very real problems with noise pollution. But once I realized just how terrible Jordan was with his sources, and how popular this video was, I decided it needed its own post.

I’ve had a deep hatred of pseudoscience my whole life. In college I Turned Evil at a community meeting about the smart grid (essential for the green energy transition) where I got called a shill by crazies who had swarmed the meeting for not believing in WiFi intolerance. I decided these people absolutely could not be allowed to win anything ever. I respect real community concerns. I do not respect pseudoscience at all, anywhere. Now I get to debunk it it as my job.

The connection between EA and tech billionaires is not something I’m the first to document.

Yup, but the tech billionaires I’m associated with mostly oppose rapid AI progress and the rapid data center buildout.

Jordan shows a picture of local town meetings where he’s been brought up and says “That’s a lot of .gov action for a YouTuber who doesn’t understand science!” Well yeah, he successfully tricked people into believing his pseudoscience. Great.

I’m not going to go through Andy’s novel line-by-line because it’s a waste of everyone’s time. The entire framework of attacks like this are to “machine gun” information in a way that overwhelms the reader so they concede that the writer has an exhaustive understanding of the topic.

There was no way to document all the massive problems with Jordan’s two videos without making this post very long. A lot of people accuse me of either writing posts that are too long, or conveniently leaving things out. I suspect that if this post were shorter, Jordan would bring up every argument he made that I didn’t address. This is just a way of dodging all my very simple specific criticisms that he misrepresented every study he relied on.

1. On-screen graphics are not always citations, but sometimes may appear to be.
There’s a lot of time and tempo change between researching, writing, and editing. My infrasound videos have about 3TB of footage, sliced down into 2 videos running less than 30 minutes per piece. They may seem like elaborate deep dives, but you’re watching an entertaining overview of the actual deep dive. I spend an excruciating amount of time balancing the depth of research versus the probability that anyone will be interested in watching or learning about it. Some may disagree, but I think I’m doing a good job at this. More importantly, I’m always learning how to do it better with each video as I pay close attention to what makes viewers stick around.

My way of breezing through citations is using them as on-screen graphics. Unfortunately, not all graphics are consistent with what I’m talking about in a particular clip of the video. For example, an article making statement A may have a graphic or headline that fits better with statement B. It provides the viewer with continuity and visual confirmation, but if you comb through every on-screen article and compare it to the sentence coming out of my mouth, guess what? You’ll occasionally, if not seldomly, find that the 2 don’t perfectly line up.

But context is important. If a graphic isn’t confirming a point made in the video, that doesn’t mean the point in the video is a nefarious lie. It’s a truly wild stretch to assume that my videos are just factless voids of manipulation and Andy is the first person to catch on. Which brings me to the next segment.

This would be understandable if literally any of the articles he cited did what he claimed. It’s not that the timing was off, it’s that none of them backed up his claims at all and mostly contradicted them. This is an obvious simple dodge.

There is literally no way you can watch this section without believing that the paper he’s zooming in on that’s 100% perfectly timed with every single word he says is about infrasound, whereas in fact it’s entirely about audible sound, as explained here. This wasn’t sloppy editing, this was incredibly deliberate editing that showed a paper that didn’t at all say what he claimed in the moment. He’s just completely dodging these very obvious objections by claiming this was some cool editorial decision.

2. I fact-check. Always.
With the amount I complain about legal expenses, it’s probably not a secret that I often run my entire scripts past attorneys. And they catch a lot, sometimes transforming the video significantly, and in at least two cases, resulting in a video project being shelved. This isn’t always because of poor research, but because perfect is the enemy of good. And I need to be perfect sometimes for all the wrong reasons.

For example, I cannot drive up to a datacenter owned by the wealthiest person in history (who also happens to be a litigious asshole), pull out some equipment, measure infrasound, and make up readings or make claims about their health effects without a really good list of citations. If I make a single mistake in activities like this and when reporting on them, I’ll lose everything I have, including my channel. I do not receive any benefits of doubts when it comes to potential libel.

For months, I’ve had private investigators hired to park in front of my property. Are we to assume the company hiring them hasn’t found out about lawsuits yet?
So the idea that I’m intentionally misleading millions of people and legislators is not something I’m insulted by because in the context of my daily stressors, it’s comedy.

Well the fact-checkers clearly failed here, drastically, and Jordan hasn’t given us any counter evidence of that so far.

Throughout Jordan has what I think is a somewhat self-centered view that I’m just writing this to “stress him out” whereas in fact I would like him to stop promoting the same pseudoscience used to block wind farms. Throughout this exchange, he’s always speaking in “us vs them” language and is trying to identify me as being on a bad team instead of just looking at the facts directly. I think he knows what will happen if he does, so he’s somewhat desperate to just smear me as a tech billionaire stooge.

3. Categorical Dismissal vs. Scientific Uncertainty

Andy’s central thesis that sub-audible infrasound issues are “fake” is a definitive and hyperbolic claim that is impossible to sustain scientifically. In research, there is an infinite distance between “not yet proven without a doubt” and “fake”.
Anesthesia, consciousness, contagious yawning, and a whole library of things we experience are not completely understood. That doesn’t mean they don’t exist.

One thing that irks me is how heavily Andy relies on the “nocebo effect” (the idea that symptoms are caused by negative expectations).
Of course nocebo effect is a real confounding variable, but using it as a “catch all” to dismiss physical data ignores the actual physiological mechanisms routinely observed.

All of this happens within the confines of a world where Andy seemingly doesn’t have the ability to fact-check himself.
Recent research into mechanotransduction suggests that humans can “feel” sound through non-auditory pathways. Low-frequency noise can modulate pressure-sensitive ion channels (like PIEZO1), potentially causing neuroinflammatory responses or cellular stress even if the sound is below the threshold of “hearing” [Armand & Bikaran, 2025; MDPI, 2026].
This is just one of many examples of where infrasound is observed to be related to physiological responses that have absolutely nothing to do with one’s conscious perception or bias. “

Note: Due to IP restrictions, not all papers can be linked, only referenced.
But here is a more recent study pertaining to PIEZO1 and TRP4 channels.

It’s true that infrasound harms could technically exist, but because we haven’t found any after a half-century of serious studies looking for them all over, I’m forced to conclude that infrasound is as fake as other things in this category. Could be real, maybe? But no one (especially Jordan) has produced any evidence so far.

I’m using the nocebo as an explanation after I’ve already separately showed that all studies that hint at infrasound harms seem to be pretty quickly invalidated.

He links one MDPI paper by Dastan et al., and references a second “[Armand & Bikaran, 2025]” citation he says can’t be linked “due to IP restrictions.” Peer-reviewed science doesn’t have IP restrictions that prevent citing them, you just paste the DOI. I can’t find any paper by Armand & Bikaran 2025 on infrasound, PIEZO1, or anything like it. If anyone reading this can find it, let me know, but as it stands I’m going to treat it as made up until proven otherwise.

The MDPI paper he did link is a narrative review in Applied Sciences. Again it’s not a study or meta-analysis, and it doesn’t even find that infrasound is harmful. It’s the same type of article as the Persinger paper Jordan sent me over Bluesky.

This is a quote from the abstract: “Short-term studies in humans at moderate intensities have reported minimal physiological changes, with psychological and contextual factors influencing symptom perception.”

That last line is just perfectly describing my view of infrasound! Moderate infrasound (the kind people experience in their homes) doesn’t have an effect. and people’s experience of it is mostly determined by their psychology. It’s also saying that the cellular mechanisms people get excited about only produce damaging effects at high intensities over long durations in animals, which everyone has agreed on since the 1960s NASA work. Again, Jordan has linked a study that agrees with me, not him.

4. Comparing Light to Sound is Stupid (Except when Andy does it)
As much as I detest surveillance cameras indoors, I kind of wish I had one so I could see the look on my face as Andy repeatedly tried to cross-explain sound and light waves. His article does this 3 times before using the comparison of sound and light waves as a source to discredit my video.
In reality, there’s a lot of false equivalence that needs to be addressed when throwing shade on ultraviolet light comparisons. Light interacts with human tissue at a quantum/chemical level. Sound interacts with human tissue via mechanical resonance. UV light is blocked by skin while infrasound passes through solid structures with ease. This means that every human has their own resonant frequencies for their bones and organs that can be mechanically stimulated by external sources of mechanical pressure regardless of if the source is audible or not.

I already addressed this in the Bluesky section above (and had replied to Jordan on Bluesky saying exactly that, but he either didn’t read it or didn’t care). My complaint isn’t that he’s comparing light to sound, it’s that he’s comparing UV to infrasound when the obvious comparison is infrared to infrasound.

This ”every human has their own resonant frequencies for their bones and organs that can be mechanically stimulated by external sources of mechanical pressure regardless of if the source is audible or not” thing is just a restatement of VAD pseudoscience. The only way to get resonance frequencies to actually harm you is to apply enough power, and there’s just not enough power in infrasound in homes near data centers. Again, a kid on a swing has a resonance frequency for pushing them to get them higher, but if you don’t also push with enough force they won’t move back and forth at all. Same goes for your cells.

5. Wind-Turbines and Oversimplification
While not recently, infrasound research has been poisoned by special interest groups backed by energy companies trying to create arguments against wind turbines. I caught on to this very quickly years ago and had to be especially careful to avoid research papers primarily focused on wind energy.
But it’s also not difficult to do this for some very important reasons unrelated to politics or lobbying:
I’ve spent a gross amount of time playing Othello on my phone while sitting next to a device measuring wind farms and datacenters. Datacenters and cryptocurrency mines have a drastically different acoustic signature than wind turbines. Datacenters utilize high-velocity cooling fans and, in many cases, massive diesel generators in a 24/7, high-density configuration. They’re also typically much closer to residential zones. This is easily enough of a difference to segregate to 2 areas of study.

I think wind farms should be allowed to be built closer to residential zones, to the point that the infrasound from them would be louder than infrasound from data centers, because infrasound harms are fake. Would Jordan be okay with building them this close? He doesn’t give us a way to know. He’s trying to have it both ways here, but pseudoscience is like a biological weapon. You can’t use it to target hyper specific industries you don’t like and spare others. It messes with everything.

But it is tricking out, and we have very new research that directly studies datacenters. A 2026 report by the Environmental and Energy Study Institute [Communities Are Raising Noise Pollution Concerns About Data Centers] points to specific health clusters (vertigo, nausea, hypertension) in Virginia and Texas specifically linked to these industrial cooling arrays, noting that standard decibel meters (A-weighted) fail to capture the low-frequency energy that residents report that is literally “vibrating their homes”.

This article he mentions is good on real noise pollution. It’s a problem. I’ve recently been speaking to some city planner friends and have been kind of shocked at how difficult it is to regulate noise. I’ve upped noise pollution in my worries about the real issues with data centers quite a bit.

But again, this article is about audible low-frequency noise. It is not evidence of inaudible infrasound causing health effects. The Ontario environmental review board found this exact thing about wind turbines in 2011: dB(A) understates how annoying low-frequency audible noise is, which is why the EU moved to including C-weighted measurements. This article also does not support Jordan’s theory about inaudible infrasound. He seems kind of pathologically obsessed with linking articles that don’t show what he claims.

Now before we move on, it’s important to note that this “confusing concept of vibration and infrasound being related” was cited in “Health implications of the rapid rise of data centers in Virginia”, which was published the same week my datacenter video released. It also mentions datacenter water use, so I’m sure Andy has his work cut out for him in reaching to discredit it in the name of altruism.

This is one of the goated papers on data centers. It’s a reasonable mini-review that organizes a lot of the known real concerns about data centers, including air pollution from diesel generators, water stress, and audible noise pollution.

But Jordan is lying about what it says again. I’m coming away from this thinking he’s a pathological liar about research papers specifically.

The paper cites the JLARC report that Virginia data center noise is “between 40 and 59 decibels” and says this “approaches or exceeds” EPA’s 55 dBA day-night outdoor noise guideline. The health harms it then links to this noise exposure are the standard, boring, very real harms of chronic audible noise pollution: heart disease, tinnitus, hypertension, sleep disruption, cognitive and mental health effects. All of these are well-documented consequences of chronic exposure to environmental noise, and this is why I’ve said repeatedly throughout this post that data center audible noise pollution is a real problem.

The paper does not mention “infrasound” a single time, as far as I can tell. It does not mention “ground-borne vibration.” etc.

Jordan’s correct that I mostly disagree with the paper’s assessment of water issues. It basically just says that blowdown from data centers might be an issue and that they use a lot of water in the area. Both are technically true but don’t paint a good picture of the relative harm of either, which I think is pretty small, as explained at length elsewhere.

6. Infrasound and ground-borne vibration are often the same thing.
Andy seems to think that I’ve “confused” infrasound with ground-borne vibration, as if these 2 things aren’t a constant catalyst for one another. I’m actually perplexed that a former physics teacher would even argue this at the risk of discrediting themselves to anyone capable of using common sense.

I have no clue what he’s talking about here? Is this where I said that xAI probably isn’t shaking people’s homes? I have to throw my hands up and await more info.

For any 7 year olds reading, I’ll explain...
For the rest of us, as I explained in the video, it’s really hard to accurately measure the full spectrum of infrasound because [long explanation of microphone diaphragm shelves, vibration sensor weak spots, and Fourier harmonics].

Yeah it’s hard to measure, cosign.

7. The decibel problem.
I’ve already explained how challenging it is to get a quality infrasound measurement… Solving this problem is what I immediately turned my attention to when finishing my last infrasound-related video. I believe that there may be a way to accurately capture the full spectrum of pressure waves via infrared optical sensors and a collimated light source inside a self-contained portable unit. It’s something I’m actively working on. But to be clear…
This is a logistical problem, not evidence of pseudoscience.

This is a non-response to what I said.

Building a new portable infrasound sensor is completely fine. My problem is his experimental design was bad. It included priming the test subjects, a subwoofer that made the room physically vibrate, and he knew when the infrasound was playing and could potentially give it away with subtle mannerisms. This isn’t about the quality of the microphone.

So when Andy read the words “low-quality data” in the World Health Organization’s analysis of infrasound exposure research, he didn’t seem to realize that the “low quality” was in the consistency of measuring the infrasound, not analysing physiological effects.

??? I didn’t quote any WHO “low-quality data” language in my post, and he didn’t link where he thinks I did. I think he might mean 2018 WHO Environmental Noise Guidelines. Here, the WHO did say the evidence base for health effects at the frequency range they considered was low-quality in the “grade” sense, which is a technical term referring to overall confidence in effect estimates.

His move is to frame every negative finding in the field as a measurement problem and every positive finding as real.

8. This “hit-piece” is good news.
The fact that someone is paid to spend an enormous amount of time pedantically combing through hours of video, reaching in every possible direction to discredit and insult me, my viewers, the cited researchers, and the people whose lives have been devastated by what appears to be infrasound means that our message has reached enough of the general public and their legislators to justify a low blow.

Classic conspiracy theorist move here. “They’re only criticizing me because the cabal doesn’t want me to win.” This is Alex Jones but higher status.

Coefficient Giving didn’t call me one day and say “take this guy out.” I’ve been posting about AI and the environment for a year and a half and now have a no strings attached grant from them to write more. I decide what I want to write about, and I despise pseudoscience and hate when people use stupid tricks like this to push it. I want it gone from the public debate.

Nobody is bringing a tablet to city council meetings and playing my videos. The presented information inspires them to roll up their sleeves, pick and choose what is reputable and relevant to their communities, and then present it formally to legislators.

Inshallah they actually read what the studies say.

Why doesn’t this organization just spend this money on researching and mitigating these things?
Like, wouldn’t actually lowering the water use of a datacenter help legislation pass in their favor?
Wouldn’t actually researching infrasound sources and reducing it where there’s growing evidence of harm make this all go away?

Lowering the water use of a data center often means increasing energy used in cooling, which means more air pollution and greenhouses gases. Don’t do it!

Again with the plea to take his dumb pseudoscience that he knows is fake seriously.

I’ve measured sound sources in hundreds of places, designed low-cost measurement devices, and interviewed hundreds of people suffering from something that I believe to be 100% real. And I’ve done it all on a shoestring budget provided by my videos and viewers under the umbrella of a nonprofit of which I do not claim a salary.

Popular YouTuber funds his own pseudoscience investigations. Hero!

What angers me the most about Andy’s post is that I could have spent this afternoon working on that optical microphone, but instead I’m feeling pressured to defend research that already holds its own ground.
One of the ways tactics like this are effective is by providing a distraction from the research.

I think Jordan should feel some pressure to not abjectly lie to his big audience yes. I’m the one defending the research from his crazy constant misrepresentations.

Response to “Andy Masley doesn’t understand how sound works”

Jordan also shared a blog from someone else claiming that this post misrepresents this science. The post was pretty bad and I wasn’t going to respond to it until Jordan shared it, but since he did I felt like I should put something together. Here’s my complete response (in another post):

Misinformation is not always easy to identify based on the speaker. If someone looks and acts like Alex Jones, my alarm bells go off, but if someone presents themselves as a well-rounded everyday person who just wants to criticize giant powerful corporations and does it in a chill way, it’s much easier to nod along and not be as alert to ways they might be wrong. More importantly, a lot of people don’t seem to think it’s even worth worrying about the specifics of how they might be wrong if they’re targeting bad people. Who cares about the specific ways data center critics are getting things wrong? Data centers are evil. Jordan is clearly taking full advantage of this and knows that this will get him lots of views.

I think wind farm misinformation is obviously way way worse than data center misinformation. I’d much rather build a wind farm than a data center. But there are two key points here:

Promoting pseudoscience is like releasing a biological weapon. It’s going to have broad unexpected effects on everything, including things you might like and support. I do not want more infrasound pseudoscience in the world. It will make a lot of people a little more dumb and neurotic and unable to consider real trade-offs well, and cause them to oppose a lot of things I want to see built out, especially wind turbines.
Decisions about the environment are incredibly complicated, and involve a huge amount of trade-offs. If a pharmaceutical plant or a car factory is proposed near you, it’s important seriously weigh both the real upsides and downsides of both. There are places where building either could help a local community, and places where it can harm it, and getting the real benefits and avoiding the real harms involves understanding very nuanced details of where specifically the harms and benefits are. Throwing pseudoscience into the conversation just incapacitates people from making good decisions. I don’t want people to think of data centers like wind turbines, but I do want them to think of them like they do pharma plants or car factories: things with upsides and downsides that might in specific places be good or bad for local communities.

I want to ask Jordan and his audience “Why do you feel the need to be a goofy goober about this? There’s plenty of trade-offs to worry about with data centers and AI. xAI’s Colossus is a great example of a way a data center harmed people with air pollution, a real and terrible general problem. Why not just make a video about that? Why flatly make things up? Don’t you want to empower your side to know where the actual problems are? Don’t you want to treat your audience like friends and adults, who you wouldn’t lie to and want to empower to think seriously about the world?”

I’d like more people to think of pseudoscience and misrepresenting studies the way Batman thinks about guns.

As data centers become boogeymen for more people, who start to see them as encapsulations of everything wrong in society more broadly, there’s going to be more and more incentive to add on more fake issues, and I think this is mostly just going to incapacitate people from thinking more seriously about the downsides and upsides of the largest industrial buildout of my lifetime and maybe the most consequential new technology.

Training AI models doesn’t emit that much

Sun, 12 Apr 2026 17:39:46 GMT

The way the energy cost and emissions of training large new AI models are often talked about and compared to other things makes them seem unreasonably large. Here are some comparisons that were made following GPT-3 and 4, each about training specifically:

“Training GPT-3 produced 552 tonnes of CO2, equivalent to driving 112 gasoline powered cars for a year” — Columbia Climate School
“GPT-4’s footprint is roughly equal to the annual emissions of 1,550 US citizens” — Shop Without Plastic
“GPT-3’s emissions are equivalent to the lifetime emissions of 8 cars — or 109 cars’ yearly emissions” — Truthout

I’m going to argue here that 1) The comparisons people make with training are often silly and misleading, and 2) When you make fair comparisons between training AI models and creating other products, the cost of training does not look unreasonably large at all. The way this is talked about often involves comparisons that would make literally any popular consumer product look ridiculous and wasteful. I think these comparisons are obviously goofy, and if I make more reasonable comparisons while keeping the numbers the same, you will see that training AI models is not some unique environmental catastrophe, and actually just blends into all the other ways society uses energy normally.

What is training?
Some bad comparisons
Two useful comparisons
A lot of popular coverage of this used terrible comparisons
State of the art training runs for current models are larger, but don’t emit enough to change my point here, and they have way more users
Should I also include all the other climate costs of training, like the physical infrastructure and failed training runs?

What is training?

Training an AI model basically means creating it. Untrained models are fed huge amounts of data to pick up more and more subtle background patterns. This involves connecting tens of thousands of specialized AI computers very close together to constantly communicate with each other over months, typically all housed in a single large data center. This uses a lot of energy.

Once a model is trained, it can be used over and over and does not need to be retrained. Newer more capable models are trained instead. Models are often used for months before being replaced by newer ones.

Importantly, spending this much energy is a necessary step to creating frontier chatbot models. We currently do not know of a way of creating models with similar capabilities using much much smaller training runs.

This uses a lot of energy by the standards of everyday buildings, but my argument here is that it doesn’t use much energy or emit that much by the standard of creating any other popular products.

I see training as being analogous to manufacturing a new product for users to buy. Just like physical objects people buy and use have to be manufactured first, AI models people interact with have to be trained first. Both are necessary steps in creating the final products the users interact with.

Some bad comparisons

Your personal emissions

Take a look at this graph, it and graphs like it have been very popular as a way of showing how bad AI is for the environment:

Source

Importantly, this graph is about a very old 2019 model (way before commercial chatbots were available) that didn’t get much use. The stats from this old unused model made it into articles about AI’s environmental footprint 5 years later. Yet it still gets shared a lot to make points about current consumer chatbots. I’ll update the graph with the much much larger estimated training cost of GPT-4. I’m going to start with GPT-4 as an example, because this is the first model where everyone started freaking out about how much energy training uses, and how much it emits:

Much bigger!

I’m using a high-end estimate of GPT-4’s training, partly to include things like the cost of failed training runs. This cost is 15,000 tonnes of CO2.1

Is this a reasonable way to think about GPT-4’s emissions? Is it a useful comparison?

How reasonable does this other comparison look, where instead of training GPT-4 the bottom line shows my rough guess for the emissions of manufacturing every new copy of the iPhone 16?2

Does this tell you anything about whether you personally should or should not purchase an iPhone?

This is my big claim: It is not useful or reasonable to measure the emissions from the creation of a product used by hundreds of millions of people every day against your personal emissions, or the emissions of buying a single plane ticket. That tells you basically nothing about how damaging it is for you personally to use the product, unless you divide by the number of users.

This seems obvious, but almost all reporting on how much energy AI training runs use compare it to individual things people do. A better comparison is the cost of creating the AI model to the cost of creating other products used by hundreds of millions of people. When you do that, it becomes difficult to understand why the emissions of training AI models are receiving so much scrutiny:

Estimate of manufacturing all copies of iPhone 16

Energy of cities

Another common comparison people make with AI training runs is the electricity costs of whole cities. For example, the MIT Technology Review notes that training GPT-4 “consumed 50 gigawatt-hours of energy, enough to power San Francisco for three days.” Is this useful? Well, the electricity homes, businesses, and industry in a single city use doesn’t give us too much useful context for how GPT-4 compares to other consumer products.

Again, take manufacturing iPhones. Manufacturing all iPhone 16s emitted approximately 12 million tonnes of CO2.3 Generating San Francisco’s electricity emits about 890,000 tonnes of CO2 per year. So all iPhone 16s emitted as much as 13 years of San Francisco electricity.4 Does this tell us much about whether you personally should buy an iPhone? Does it add much useful context at all?

Flights

One other common comparison is airplane flights, measured as the total plane’s emissions rather than emissions per capita. GPT-4’s training emissions were approximately as much as 38 full plane flights from San Francisco to Australia5.

NeurIPS is the largest annual ML/AI research conference. This year it’s being hosted in Sydney, Australia. Based on past numbers I’d estimate 15,000 people are going to travel to the conference this year6 from all around the world, enough to completely fill 50 planes. If the average attendee is flying from elsewhere, like San Francisco, this means that a single annual AI research conference will emit more than training GPT-4.

Imagine if an AI research conference was held, and it was so useful for attendees that it singlehandedly created GPT-4 from scratch, where only GPT-3.5 existed before. This seems like a pretty worthwhile get-together. Here, I don’t think people would even bring up the emissions. Right now few seem to be commenting on the emissions of the conference. I think that a headline “AI company emits as much as 38 planes to create GPT-4” is much more attention grabbing than “AI researchers from around the world take 50 planes to all hang out in Australia” even though the second is much worse for the climate and has likely been significantly less consequential for AI as a field, or everyday people’s lives.

Two useful comparisons

Total: Other products

Training is a necessary part of creating a product that hundreds of millions of people interact with every day. One way we can compare training AI models is to the emissions of creating similarly popular consumer products that hundreds of millions of people interact with. Here’s a graph of the total emissions of training GPT-4 vs my best estimates of emissions from the production of other consumer products:

Everything here’s a rough estimate based on publicly available data. Explanation of each estimate here.

Just like physical CDs and cases had to be manufactured for hundreds of millions of people to play Grand Theft Auto 5, GPT-4 had to be trained before hundreds of millions of people could use it. Both are necessary steps in making a popular product hundreds of millions of people use every day.

Per-user: CDs

I’m old enough to have memories of going out and buying CDs for computer games I wanted to play. One of the coolest objects I owned as a kid was a CD encyclopedia. The whole world’s knowledge at my fingertips!

There are no exact numbers for how many people were using ChatGPT over the time that GPT-4 was available, maybe somewhere around 100-200 million weekly users is a reasonable guess. Dividing GPT-4’s cost of training by this number of users gets to 75-150 grams of CO2 emitted by training per user. This happens to be a little lower than the emissions of manufacturing a single CD. It’s as if each weekly user of ChatGPT had purchased a CD to use it.

This is the cost of training everyone was freaking out about when GPT-4 was released. It’s like the company didn’t have to spend any energy on training at all, and to use ChatGPT you just had to buy it on a CD, which emits a little to manufacture. I don’t think that if GPT-4 didn’t involve training at all but came on a CD, anyone would be talking too much about the emissions of manufacturing those CDs, and yet those emissions are the same as the training cost that people speak so ominously about. This is very small!

A lot of popular coverage of this used terrible comparisons

Here’s a ton of quotes from different articles on the cost of training GPT-4 and previous models. Not one of them compared training to other consumer products used by hundreds of millions of people, or divided by the number of people to get a per-user number. I haven’t found any sources doing either. Read through these and think about whether the reader would come away with a better or worse understanding of the magnitudes involved. Would they know creating a model used by hundreds of millions of people had about the same climate impact as the CDs and cases for Grand Theft Auto 5?:

“Training GPT-3 produced 552 tonnes of CO2, equivalent to driving 112 gasoline powered cars for a year” — Columbia Climate School
“GPT-4’s footprint is roughly equal to the annual emissions of 1,550 US citizens” — Shop Without Plastic
“GPT-3’s emissions are equivalent to the lifetime emissions of 8 cars — or 109 cars’ yearly emissions” — Truthout
“That’s like a car driving 1.2 million miles” — Carbon Credits
“500 tons of carbon dioxide emissions is the equivalent of around 600 flights between London and New York” — Cybernews
“GPT-3 emitted carbon dioxide equivalent to 500 times the emissions of a New York-San Francisco round trip flight” — Analytics Vidhya
“It’s the same amount of emissions as a single person taking 550 roundtrip flights between New York and San Francisco” — Carbon Credits
“Training GPT-4 consumed 50 gigawatt-hours of energy, enough to power San Francisco for three days” — MIT Technology Review
“15,000 tons is roughly the same as the annual emissions of 938 Americans” — Towards Data Science
“Enough energy to power an average U.S. home for over 120 years” — Truthout
“Training a single AI model can emit as much carbon as five cars in their lifetimes” — MIT Technology Review (headline)
“Training GPT-3 consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide” — MIT News
“Training a single large model like GPT-3 can use over 1,200 MWh — enough electricity to power around 120 U.S. homes for a year” — Climate Impact Partners
“Training GPT-3 emitted roughly 500 metric tons of carbon dioxide — the equivalent of driving a car from New York to San Francisco about 438 times” — Climate Impact Partners
“Training GPT-3 produced 552 tonnes of CO2, which is equivalent to the emissions from 110 gas-powered cars over a year” — Smartly.AI
“GPT-3 emitted carbon emissions equivalent to the lifetime impact of five cars” — Nature Scientific Reports
“Training the bigger, more popular AI models like GPT-3 produced 626,000 pounds of carbon dioxide, equivalent to approximately 300 round-trip flights between New York and San Francisco — nearly five times the lifetime emissions of an average car” — The Sustainable Agency
“This is similar to the yearly emissions produced by 120 passenger cars or 600 transatlantic flights per person” — Shop Without Plastic
“Training AI models can emit more than 626,000 pounds of carbon dioxide equivalent — nearly five times the lifetime emissions of the average American car (including the manufacture of the car itself)” — Learning Tree
“Training AI models can emit more than 626,000 pounds of carbon dioxide equivalent — nearly five times the lifetime emissions of the average American car” — Supermicro
“Training a single AI model can emit 626,000+ pounds of CO2 equivalent… about 5x the lifetime carbon emissions of an average passenger car” and “training a single AI model can consume more electricity than one hundred American homes use in one year” — Carbon Credits
“Training such a model requires… This is equivalent to the annual energy consumption of around 160 average American homes” — Frankly Kranky
“GPT-3 consumed approximately 1,287 MWh of electricity—enough to power 120 US homes for a year… A single AI model training session can emit as much carbon dioxide as five cars over their entire lifetimes” — AI Energy Calculator
“Training ChatGPT-3 is estimated to have required the equivalent energy consumed by an average American household for 120 years” — California State University Northridge Library Guide
“GPT-3 consumed a staggering 1,287 megawatt-hours (MWh) during training. This resulted in about 502 metric tons of carbon emissions, equivalent to the emissions from hundreds of gasoline-powered cars in a year” — PatentPC
“The process can emit more than 626,000 pounds of carbon dioxide equivalent — nearly five times the lifetime emissions of the average American car” — ACM Communications and separately SPE Journal of Petroleum Technology

None of these comparisons make sense, because basically all of them treat training as if it’s comparable to activities individual people do, instead of creating a product expected to get hundreds of millions of people using it. If instead we treat training GPT-4 like this, it fades into basically nothing. If you think it would have been worthwhile for the climate to prevent GPT-4 from ever being trained, you should also consider stopping the production of Lego for 2 weeks. If you’re not telling everyone to boycott Lego or Coke or Grand Theft Auto because of the emissions involved in creating them, you shouldn’t be telling people to boycott ChatGPT for the emissions involved in creating GPT-4. If you read “Manufacturing all the CDs for Grand Theft Auto 5 emitted as much as 160 American homes” I think you might be a little underwhelmed.

State of the art training runs for current models are larger, but don’t emit enough to change my point here, and they have way more users

Everything above uses GPT-4 as an example because that’s the model that kicked off all the panic about training emissions. But GPT-4 is old now. Current state of the art models are bigger and more expensive to train. Does this change anything?

Training costs are growing at about 2.4x per year for frontier models. The largest training run we have decent estimates for is Grok 4, which Epoch AI estimates used about 310 GWh of electricity and cost around $490 million, roughly 6–7x what GPT-4 cost. But Grok 4 is probably the worst-case scenario for training emissions, because xAI’s Colossus data center in Memphis was powered largely by mobile natural gas turbine generators, which emit about 0.49 kg CO2 per kWh, 1.3x higher than the US grid average of about 0.37 kg CO2 per kWh, which itself is higher than some of the grids where AI models are typically trained, like Oregon which relies more on hydropower. This is why Epoch estimates Grok 4 emitted about 154,000 tonnes of CO2, ten times GPT-4’s estimated emissions, despite using only about six times as much electricity.

Most other frontier labs don’t train on natural gas generators. They primarily train models in large data centers connected to the electrical grid, many of which are in places with more renewable energy. The same 310 GWh Grok 4 training run would emit about 115,000 tonnes on the average US grid, 75% of its actual emissions. On California’s grid it would only emit about 62,000 tonnes, 40% of its actual emissions. This means that a frontier training run of similar size to Grok 4, but run on a typical major cloud provider’s infrastructure, would likely emit somewhere in the range of 60,000–120,000 tonnes.

Not every frontier model uses as much compute as Grok 4, either. Grok 4 was trained on 200,000 GPUs and is probably the single largest training run to date. Other state of the art models may use significantly less compute, bringing their energy use and emissions lower. Hardware energy efficiency has been improving at about 40% per year, so the next generation of training runs won’t need proportionally more energy even if they use more compute.

So what’s a reasonable range for the emissions of training a current frontier model? Probably somewhere between 40,000 and 120,000 tonnes of CO2, with the wide range driven both by differences in the carbon intensity of the electricity and differences in the amount of energy used.

Is this a lot? Well, these models get used for months at least. I could just up the manufacturing numbers to a month each.

AI models still just don’t stand out much compared to other popular products.

For now I’ll use 80,000 tonnes as a round middle estimate for the current emissions of training an average frontier model.

Two things have also changed since GPT-4:

There are way more users. When GPT-4 was the main model, ChatGPT had roughly 100–200 million weekly users. Now ChatGPT has 900 million weekly active users. That’s roughly a 5–9x increase in users over the same period that training costs went up maybe 4–6x. The per-user training footprint has likely stayed roughly flat or even declined. 900 million users over 80,000 tonnes of CO2 for training is about 89 grams. This is still about half a CD. Even under Grok 4’s worst-case emissions of 154,000 tonnes, assuming similar user numbers would mean the per-user cost is about 171 grams, roughly one CD.
Even at the total level, the comparisons mostly don’t change. The most advanced AI models that huge numbers of users interact with for months at a time use about as much energy to create as it takes to manufacture 3–4 days of iPhones.

My basic point has so far not changed with larger models: creating an individual AI product used by hundreds of millions of people produces emissions that are small relative to manufacturing basically any other product used by hundreds of millions of people.

Training runs are going to continue to use more energy, but models are also getting more capable over time. Eventually this may be more like each user purchasing a piece of computer hardware instead of a CD, but for models so much more capable the investment will, I expect, be worth it.

Should I also include all the other climate costs of training, like the physical infrastructure and failed training runs?

Maybe my comparison to creating other products is unfair here, because there are lots of other analogous steps to manufacturing before the chatbot can be delivered to hundreds of millions of people. OpenAI also had to buy tens of thousands of GPUs, which had to be manufactured in fabs and assembled into servers. Those servers sit in data centers that had to be built out of steel and concrete. What’s the total carbon footprint of all that?

Let’s try to make this number as big as possible. We can include the embodied hardware emissions, the data center construction, the cooling infrastructure, and the experimental training runs that failed before the final model worked. Hugging Face found that when they accounted for all of these factors for the model BLOOM, total lifecycle emissions roughly doubled, from 25 tonnes to about 50 tonnes. The estimate I chose for GPT-4’s training cost is 3 times as high as many popular estimates already, so it likely includes these costs already, but let’s double it anyway to be safe, to get a total of about 30,000 tonnes of CO2 emissions from literally everything required to deliver a model to users.

Even at this extreme, training GPT-4 would still be smaller than building a single cruise ship. It would still be less than one day of manufacturing Nike shoes. It would still be less than a single day of manufacturing iPhones.

But I also didn’t include many of the additional costs of the other products too. I didn’t include building the factories or making the mining equipment for extracting raw materials or transporting iPhones to stores or the cost of the retail stores themselves… many accusations that “you’re leaving information out” are one-directional, and don’t consider that most things we do have lots of hidden costs depending on what we want to compare. Ultimately I think the direct comparison between training and creating other popular products is completely legit.

The per-user comparison doesn’t really change even under the most extreme interpretation. It raises the per-user cost of training GPT-4 from one CD to two. The reason the cost of creating a product used by so many people is so low compared to things like Coke and sneakers is that in comparison, computing is very energy-efficient, and thus it’s very rarely the most promising thing to optimize or cut for the climate.

It’s true that all training across all AI models is forecast to make up a significant fraction of US electricity use in the next few years, maybe rising to whole percentage points of US electricity usage. This is because a huge portion of total AI training will be concentrated in America, and because AI is entirely electrified, while most other industries aren’t. This post was mainly about the training of individual AI models. In the next post I’ll try to paint an overall picture of where training and AI more broadly fit into America and the world’s overall energy and climate situation.

The 2025 Stanford AI Index Report, using Epoch AI’s methodology, estimates GPT-4’s training emissions at approximately 5,184 tonnes CO2. A separate widely-cited analysis by Kasper Groes Albin Ludvigsen in Towards Data Science, based on leaked hardware specifications (25,000 Nvidia A100 GPUs running for 90–100 days), estimates 51–62 GWh of electricity consumption and 12,456–14,994 tonnes CO2e assuming California’s average grid carbon intensity. The difference largely comes from assumptions about the electricity’s carbon intensity.

I’m using the higher ~15,000 tonne estimate for two reasons:

I want to use the very highest defensible number I can find to show that even then training doesn’t look large.
The lower estimates only cover the final successful training run and do not account for the full cost of developing the model. Epoch AI estimates that the ratio of total development compute to final training run compute ranges from 1.2x to 4x across frontier models, with a median of 2.2x.

Applying the median 2.2x experimentation multiplier to the Stanford estimate of 5,184 tonnes gives ~11,400 tonnes, and adding embodied hardware emissions could bring it to ~14,000 tonnes, close to the Ludvigsen estimate. So ~15,000 tonnes can be read either as the high-end estimate for the final training run alone (assuming a dirtier grid), or as a more realistic all-in estimate that includes experimentation and embodied emissions on a moderate grid. Either way, it represents what looks like a reasonable upper bound, and every comparison in this post becomes even more favorable to AI training if the true number is lower.

Apple’s Product Environmental Reports give lifecycle emissions of 56–74 kg CO2e per iPhone 16 depending on model and storage, with approximately 80% from production — roughly 45–59 kg CO2e per device for manufacturing. Apple shipped approximately 230 million iPhones in 2024. 230 million iPhones × 52 kg = approximately 12 million tonnes.

See footnote 2 ^

San Francisco County consumes roughly 5,100 GWh of electricity annually, producing approximately 890,000 metric tons of CO2. Source: FindEnergy.

The San Francisco–Sydney route is approximately 7,400 miles (11,900 km). A Boeing 777-200 flying a comparable distance (Chicago–Hong Kong, 7,821 miles) burns roughly 42,000 gallons of jet fuel. At approximately 6.5 pounds per gallon, that’s about 124,000 kg of fuel. Each kilogram of jet fuel produces approximately 3.16 kg of CO2 when burned. This gives roughly 390 tonnes of CO2 per flight for the entire aircraft. 15,000 tonnes ÷ 390 tonnes per flight ≈ 38 flights.

Last NeurIPS in San Diego had 24,500 people attend in-person. This one is such a long trip that I’m assuming it would get ~half the attendance.

What’s blocking Waymo in DC and how can we fix it?

Fri, 03 Apr 2026 11:58:06 GMT

Note: Since I wrote this Charles Allen introduced a bill that would allow Waymo in DC, so things are looking really good! However, Janeese Lewis George is the likely next mayor of DC now, and seems interested in blocking it. This post doesn’t mention either and was written before either happened, but can still give lots of useful context for where we’re at and how Allen and George are thinking.

This post will be a very detailed overview for autonomous vehicle advocates to understand the situation in DC and pathways to legalization.

This post won’t have arguments for why we should legalize Waymo. I have a separate post on that, though I’d like to write an update to it since I think there are much stronger cases I can make.

You may have noticed that Waymos are already driving around DC (with humans at the wheel). They’ve been mapping the city and say they’re nearly ready to move forward, but the city currently has no legal path to commercial service. Two decisions are in the way: a driverless testing pathway and a commercial operating framework. Right now both are held up by a missing District Department of Transportation (DDOT) report that was supposed to arrive in October 2022, and by DDOT’s failure to open a permit pathway separate from the report that already exists in statute.

What’s blocking Waymo

The 2 things Waymo needs

Waymo needs two things from DC’s city government that it doesn’t have.

Driverless testing permits. Right now, every Waymo in DC is required to have a human safety driver. But Waymos need to be tested without a driver. This has been a standard step in every other city. DC law already authorizes DDOT to run an AV testing permit program that can include remote-operator (driverless) testing, and 2025 amendments specify that driverless testing requires that permit. But DDOT has never made the application for driverless permits available. Until DDOT opens the permit pathway and finishes the associated rules, testing can only continue with a human physically present, and the necessary driverless tests can’t happen.
A commercial operating framework. Even if Waymo got driverless testing permits tomorrow, current DC law has no structure for a commercial autonomous ride-hailing service. Things like insurance requirements, licensing structures, and rules for how a driverless vehicle interacts with police don’t exist. Autonomous ride-hailing is effectively illegal in DC.

For Waymo to operate normally, they need to get driverless testing permits, then begin commercial operations under a legal framework the council has passed. Neither step is available today.

The DDOT report and the separate testing permit framework

The Autonomous Vehicles Testing Program Amendment Act of 2020 gave DDOT authority over AVs and required the department to produce a report within one year of the law’s applicability date of October 1, 2021, meaning the deadline was October 1, 2022. The report was to provide “recommendations to safely accommodate the deployment of autonomous vehicles on public roadways for commercial, personal, and any other use.” The report could optionally include draft legislation or regulations.

This report is more than three and a half years late, and is now the main political obstacle blocking Waymo.

The 2020 law tasked DDOT with setting up a formal AV testing program as a distinct obligation from the deployment report. DDOT hasn’t indicated they’re working on permits separately, so we may not get clarity on testing permits until the report comes out, if it addresses them at all.

It blocks legislation because Charles Allen, the DC Council’s transportation committee chair, has made the finished report a precondition for holding hearings on any AV bill. Former councilmember Kenyan McDuffie (one of the two leading candidates for mayor) introduced a comprehensive AV legalization bill (the Autonomous Vehicles Amendment Act, B26-0323) in July 2025. It’s been sitting in Allen’s committee ever since without a hearing, because Allen won’t schedule one until the report is finished. Nothing in the law says the council can’t legislate on AVs without this report. Allen’s position is a political choice.

The history of the report

December 2020: Autonomous Vehicles Testing Program Amendment Act of 2020, giving DDOT authority over AVs and required the initial report. The entire mandate says:
- “Within one year after the applicability date...the Department shall transmit to the Council a report that provides recommendations to safely accommodate the deployment of autonomous vehicles on public roadways for commercial, personal, and any other use the Department determines. The report may include draft legislation or regulations.”

January 2022: DDOT publishes a notice of proposed rulemaking in the DC Register to create an AV testing program. This never gets finalized. This is a separate obligation from the report, more on this below.
Late 2022: The report’s deadline of October 1, 2022 passes with no public explanation. I can find no reporting on what, if anything, DDOT was doing on the report during this period.
2023: No report, no public explanation for the delay. There’s no reporting anywhere I can find on the reason it was delayed.
July 2024: A Wayback Machine snapshot of the mayor’s AV Working Group’s webpage (which has since been removed from the DMOI website) reveals that DDOT was still in the procurement phase for hiring a consultant to help define a regulatory framework. More than two years after the report was due, the work of writing it had not yet been contracted out.
March 2025: Congress freezes nearly $1 billion in DC’s local funds. Allen later attributes part of the report’s delay to this, but the report was already almost three years late at this point.
Spring 2025: Some expected the report to be finished around this time. It did not materialize.
Fall 2025: DDOT told Allen the report would be ready around this time. In September, DDOT backtracked, telling Allen it would be out “in the new year, timeline unknown.” Allen said he had been meeting with trial lawyers, safety advocates, and Waymo in preparation.
Late 2025: DDOT said the report was one of the programs affected by budget cuts. But this doesn’t explain why it wasn’t finished in 2023 or 2024, before the cuts happened. DC has since entered a new fiscal year and the funds have been restored.
February 2026: A DDOT spokesperson told Axios the agency expects to propose recommendations “later this year.” Waymo told Axios that DDOT informed the company the report “is being actively worked on, and could be finished by this summer.”

Will the report be finished by summer? I’m skeptical. But there are three reasons to think this time could be different. Political pressure is higher than it’s ever been. The Washington Post editorial board has now published two pieces on this (one literally accusing DDOT of “sabotage”), Waymo is spending a lot more on lobbying, and the company got 1,500 residents to contact city hall. Also, DDOT appears to have finally contracted outside consultants to help write it. And the fact that DDOT gave a specific timeframe to both Axios and Waymo, rather than the usual vague non-answers, suggests at least something is happening.

What happened with that AV testing program?

DDOT published proposed rules in January 2022 to create the AV testing program. This is separate from the report. The 2020 law created two distinct obligations: a testing permit program and a deployment report. The proposed rules would have established how companies apply for testing permits and what the testing framework looks like. If DDOT had finalized them on any reasonable timeline, Waymo could have applied for driverless testing permits years ago.

DDOT never finalized them. The mayor’s now apparently dormant AV Working Group’s own page, as late as mid-2024, said the rulemaking was “still in the works” and they looked “forward to keeping the public informed when there is more to share.”

This is in my opinion more ridiculous than the delayed report. DDOT got far enough to publish draft rules for driverless permits and then just stopped. They should really be held accountable here. Waymo could begin driverless testing with permits while they wait for legislation to be fully legalized if DDOT could just get its act together on this separate thing, but instead they’ve gone completely silent on it.

This created an obvious problem when Waymo and other companies started showing up to test in early 2024. There was no permit system, so there were no formal rules. The council responded to this problem with the Autonomous Vehicle Testing Permit Requirement Amendment Act of 2024 (B25-0710), which Allen’s committee held a hearing on in June 2024. The law was simple: until DDOT’s permit program actually exists, driverless testing is prohibited. Any company testing AVs must have a human in the vehicle and give DDOT advance notice.

Because temporary legislation in DC expires and must be renewed, this had to be passed over and over, as an emergency measure in March 2024, a temporary law in June 2024, another temporary law in March 2025, and a congressional review emergency act in March, before finally being made permanent through D.C. Law 25-312 in the same month. DDOT’s inaction forced the council into a patchwork of reactive legislation instead of operating under the coherent framework that was supposed to exist by 2022.

Even if the deployment report appeared tomorrow, there would still be no finalized testing permit framework from DDOT. The report and the testing rules are separate obligations, and both are unfinished. Anyone who tells you the report is the only thing blocking Waymo is missing half the story. This is something I think a lot of AV advocates are missing in conversations about Waymo in DC.

It’s worth noting that the report could end up addressing testing permits. The mandate says the report “may include draft legislation or regulations,” so DDOT could bundle everything together. But there are reasons to think it won’t:

The 2020 law created them as separate obligations. The testing permit program was established under a different section of the law than the deployment report. DDOT published proposed rules for the testing program in January 2022, a completely separate regulatory track, and then abandoned them. The fact that DDOT initiated a standalone rulemaking for testing permits confirms that the agency itself originally treated these as distinct workstreams.
DDOT has never said the report will include testing permits. Despite nearly four years of delay, DDOT hasn’t publicly indicated that the report will address the testing permit framework. If they were planning to combine the two, you’d expect them to say so, especially given how much scrutiny this issue is getting.
The report and the permits are controlled by different bottlenecks. Allen’s precondition is about legislation: he won’t hold hearings on AV bills until the report is done. But testing permits are an executive function. DDOT could issue them under existing statutory authority without any new legislation passing. These are different problems held up by different branches of government, which makes it less likely that a single document resolves both.

What the report might say

The DDOT report’s content matters a lot. It’s possible that the report recommends heavy restrictions, and that would obviously be terrible for the prospects for AVs in the city anytime soon. There are maybe three possibilities:

A permissive framework would recommend clear pathways for testing and commercial AVs with standard requirements. This is basically what McDuffie’s bill already proposes. It would give Allen what he says he needs, and things could move pretty quickly.
A cautious, phased framework might recommend a limited testing period, geographic restrictions, fleet size caps, mandatory human oversight periods, and extensive reporting requirements before any expansion. This would still move things forward but could add a year or more.
A restrictive or inconclusive framework is the worst case. If the report emphasizes unresolved safety questions, calls for further study on labor impacts, or recommends conditions Waymo can’t practically meet (like requiring a human in every vehicle), it gives official cover to delay indefinitely.

A couple things would really help AV advocates: if the report addresses testing permits alongside deployment (rather than leaving those stuck in a separate bureaucratic track), and if it includes draft legislation (as the mandate allows), which could speed up the council’s work significantly.

The Three Gatekeepers

Three parties share responsibility for this block: DDOT, Charles Allen, and Mayor Bowser.

DDOT

DDOT is more than three and a half years late on the report. The agency has a lot on its plate, and when it received this assignment AVs were much more speculative.

Some people have speculated about internal opposition to AVs at DDOT, but I don’t see strong evidence for this. DDOT Director Sharon Kershbaum hasn’t publicly commented on AVs. Some have inferred anti-AV sentiment from her other unrelated positions that they read as anti progress, but I think that’s a stretch. Transit decisions are complicated and don’t map neatly onto a pro- or anti-tech progress axis. I’d identify as pretty pro-progress, but I share Kershbaum’s lack of enthusiasm for dedicated bike lanes (sorry friends), and I don’t think that means I’m anti-progress on other transit issues.

Stephanie Dock is the manager of DDOT’s Autonomous Vehicles Program and leads the team responsible for overseeing autonomous vehicle testing. She was on a panel on AVs presented by MIT in 2025 and had some useful quotes on the report:

We want to be adaptable, we want to be responsive to what’s going, and the industry is changing a lot faster than our rulemaking structure is allowing us to keep pace.

In order for us to do a permitting program, we have to have done a rulemaking.

Trying to figure it out before it comes. Anybody else who’s struggling with that prospect, you are not alone. We are also trying to figure that out.

These are notable because they’re the closest thing to a public explanation for why DDOT has been slow. She’s essentially saying the bureaucratic process itself (rulemaking requirements) is a structural bottleneck, not just a prioritization failure.

On the deployment report:

A deployment report with recommendations for legislation and regulation... that has to be gone through a lot of signatures, very similar regulations, so I have no promise on when the full report will be released, but I... fingers crossed, we’ll have made it through the procurement process so that... such that we can release the research aspects, kind of the background work.

This confirms the report was still going through procurement/signatures as of this talk in 2025, and that she herself couldn’t promise a timeline.

On industry vs. DDOT’s timeline:

Industry has slightly different plans. I am still looking at recommendations for deployment. Industry is certainly saying we would like to deploy. So they said they’re coming in 2026. I am eagerly awaiting how they’re going to figure out to do that.
But they also have political levers that they can pull that I, as a lowly staffer, do not.

So here it seems like she’s openly acknowledging that Waymo’s 2026 deployment plan doesn’t align with where DDOT actually is, and that the company has political tools (lobbying, public pressure) that she doesn’t.

On DC’s attractiveness and risk for AV companies:

I think everyone knows that the risks of coming into our market are relatively high, right? You’ve got to do it right, because if you want to convince Congress that you need less oversight of the AV industry, you better not be screwing up their travel on a daily basis.

On the depot/workforce concern:

Worried that if we are unable to bring in some of the maintenance and charging aspects of these fleets, that any of the workforce benefits that might accrue associated with this are actually going to go to us, because they’re going to go to Maryland and Virginia, where there is more land.”
“It also has a risk of increasing the zombie miles of empty VMT driving back as they deadhead to get to those facilities.

This shows she’s thinking about empty miles and workforce displacement to the suburbs. She’s not anti-AV here, but she’s clearly concerned about the impacts.

On where they actually are (no deployment framework):

You’ll note that my slides here with the timeline of where we are in legislation and regulation makes absolutely no mention of deployment. We do not have legislation for that at this time.

On the regulatory vs. investment mindset:

I spend a lot of time thinking about what we regulate. I get to spend some time thinking about what we incentivize, and I spend very little time thinking about what we could invest in. I’d like to change that eventually, but first we have to have these base frameworks in place.

She also brings up DC’s objectively weird transit situation:

Lots of places have motorcades. I would argue that we probably have more than most, and they look in all different sizes... it could be everything from the president or the vice president moving on street... but also, we run a lot of unannounced motorcades that are just a couple vehicles moving through, as perhaps a senator is coming up to Congress. There’ll only be two or three black cars running all sorts of red lights.

We are the central city in a tri-state region... on the one hand, we are a state, but at the same time, we’re also at the very middle of a region, and our region involves Maryland and Virginia, and depending on the commute shed that you’re looking at goes as far as West Virginia as well.

DC does not... is not an industrial city at all, right? That’s not why we exist. So we have a real shortage of land that can be used for anything like a depot. We don’t have a lot of big warehouses.

DDOT seems to have expanded the scope well beyond what the law required. The July 2024 Wayback Machine snapshot of the AV Working Group page says DDOT was trying to “procure consultant support to help the agency define a potential regulatory and legislative framework for the deployment of AVs.” That’s already broader than “recommendations” the original law called for. And Allen has added on additional expectations. In his public statements he’s talked about wanting the report to address job displacement, worker transition, and what he calls getting it “right” in a way that clearly extends beyond the safety focus of the original mandate. His December 2025 newsletter explicitly raised “the impact on jobs” as something the report needs to address. The law never called for that.

It’s crazy that after almost four years, we don’t know for sure if DDOT is planning to actually provide a permitting system along with the report. It would be nice if they would just make the general goals for the report clear, because even there we have very little to go on besides the original very short mandate.

Charles Allen

Allen is the single most important person on the legislative side. He’s the chair of the Committee on Transportation and the Environment, so he controls whether any AV bill gets considered. His making the DDOT report a precondition has given the department a total veto over the legislative timeline.

Allen has been careful to say he’s not opposed to AVs. He’s said he doesn’t “think we should be afraid of this technology or throw up obstacles” and that autonomous vehicles will “likely operate in the District” in the near future. But the practical effect of his position is indefinite delay.

His stated reasons are about process and safety. From his December 2025 newsletter:

The idea that we should authorize fully driverless vehicles without DDOT’s analysis in hand is not safe, nor is it even guaranteed to happen if the Mayor and DDOT don’t want to issue permits.

He’s also raised job impacts:

The questions also need a stronger conversation about the impact on jobs — DC residents working as taxi and rideshare drivers today would be replaced, and the money you pay for a ride would no longer go back into the community but rather be sent out of state to corporations largely located in California.

That one sounds especially bad, because there’s no way that Waymo (or buses, or bikes) could be legalized without reducing the number of rideshare jobs at least a bit.

Some other key quotes from Allen on how he’s thinking about this:

Autonomous vehicles hold promise, but DC residents should not be treated like test subjects while companies work through unresolved issues on busy city streets. Given recent nationwide examples of AVs making dangerous mistakes, a careful, data-driven approach is warranted. The questions also need a stronger conversation about the impact on jobs – DC residents working as taxi and rideshare drivers today would be replaced, and the money you pay for a ride would no longer go back into the community but rather be sent out of state to corporations largely located in California.
We’re committed to getting this right – advancing innovation without compromising safety – and to making sure autonomous vehicles are integrated thoughtfully into a transportation system that works for everyone.
- December 2025 newsletter

I don’t think we should be afraid of this technology or throw up obstacles. But my job isn’t to say, ‘How fast can I roll this out?’, but rather, ‘How can we get this right?’ I’d rather get it done right than get it done fast.
- Interview with The 51st

(in response to a claim that AVs are involved in more crashes than human driven cars) This to me is a great example that I don’t necessarily want to be the first into this marketplace. I don’t mind having other cities go first but let’s learn what works and what doesn’t and then figure out what’s best for D.C.
- Interview with FOX 5 DC

Could he change his mind on making the report a condition? Nothing legally prevents him from holding hearings without the report. McDuffie’s bill exists and could be brought to hearing. But Allen has made this a matter of principle, and I’m skeptical that the relatively small number of AV advocates in DC can change his position on it. I also personally just have no way to judge how reasonable his decision is. There’s a legitimate case that the council should have expert analysis before writing complex regulatory legislation. What I can say is that DDOT’s three year delay has turned what might be reasonable caution into an effective block. I do worry a lot about a lot of the quotes he’s shared here on safety and jobs. Both go way beyond a reasonable or fair assessment of Waymo compared to any other vehicles.

Allen has no control over DDOT, but someone else does…

Mayor Bowser

Mayor Bowser also bears responsibility here. DDOT is an executive agency that reports to the mayor. The report is almost four years overdue on her watch. Bowser could direct DDOT to prioritize it. She could also direct DDOT to separately begin designing and issuing driverless testing permits.

Allen has publicly blamed Mayor Bowser for not issuing driverless testing permits. In December 2025, he told Axios “I don’t know what the holdup is.” Bowser’s office responded to a media request on this anonymously. An official who wasn’t authorized to speak to the press told Axios “It’s not us” and characterized DDOT as “being super diligent with their research.” I’m not sure what to infer from this.

The fact that DDOT has been allowed to sit on this for nearly four years without consequence suggests either that Bowser doesn’t consider AVs a priority, or that there’s an active decision to keep this in limbo.

Bowser could unblock the situation from the executive side without needing the council to do anything. Getting DDOT to produce the report would remove the justification Allen is using to hold up legislation.

Bowser has less than a year left of being mayor after 3 terms. A new mayor will take office in January of next year, and the primary that will determine who will be mayor is happening this June. Bowser hasn’t given any sign that she’ll give any priority to AVs in her last year as mayor, so while I wish she’d use her executive authority to push DDOT to prioritize the report more and to separately issue driverless testing permits in the meantime, I don’t expect this to happen. She doesn’t have much incentive to push something new and controversial like this.

Takeaways

Waymo needs two things from DC’s government to operate: testing permits for Waymos to drive without humans inside, and legalization of commercial AVs.
The report is the main political bottleneck for both. It blocks legislation because Charles Allen, the transportation committee chair, won’t hold hearings without it. It’s also part of the justification DDOT gives for not moving forward on testing permits, even though the 2020 law established testing permits and the deployment report as two separate obligations, and it’s not even clear the report will address testing permits at all.
But the report isn’t the only bottleneck. DDOT’s unfinished permit and rulemaking process (a separate track from the report) is the practical bottleneck for driverless testing. The driverless testing permits could also be produced if Mayor Bowser decided to direct DDOT to open the permit pathway that already exists in statute, but it looks like she won’t, and DDOT hasn’t given any indication that they’re separately working on driverless testing permits.
There are recent signs of progress. DDOT told both Axios and Waymo the report could be done by summer 2026, and they appear to have contracted outside consultants. But DDOT has missed deadlines before and is more than three and a half years late.

The council and mayor debate once the report is published

Once the report is published, this will only clear the way for the DC council to actually make policy on AVs, and we know almost nothing about what each DC councilmember believes. Many other cities in the Northeast have delayed or rejected self-driving cars.

Of the 13 councilmembers, the only one besides Allen who has been publicly vocal about Waymo is Janeese Lewis George, and she’s been skeptical of AVs (more on her below, note that she’s the other main candidate for the next DC mayor). Everyone else seems to not have commented at all. I cannot find any hints of what any of the councilmembers think about Waymo.

One piece of info we do have is who voted for the 2020 AV bill that created the original AV framework and gave DDOT authority over AVs. The bill seems pretty pro AV, it was passed unanimously, and 7 of the 13 councilmembers who passed it are still in office, though some are not running for re-election and will be out of office in January 2027:

Current councilmembers who voted for the 2020 bill:

Phil Mendelson
Anita Bonds (not running for re-election)
Robert White (running for Congress)
Brianne Nadeau (not running for re-election)
Brooke Pinto (running for Congress)
Charles Allen
Trayon White (currently awaiting a federal bribery trial scheduled for September, creating an unprecedented dynamic where he was unanimously expelled from the DC Council in February 2025, only to win his Ward 8 seat back in a July 2025 special election)

The six councilmembers who weren’t in office in 2020 are:

Janeese Lewis George
Christina Henderson
Doni Crawford
Matt Frumin
Zachary Parker
Wendell Felder

If a councilmember hasn’t thought about AVs, this might be a great opportunity for DC citizens and groups to influence their opinion early before the issue becomes polarized, or before the anti-AV interest groups reach them first. This actually seems really urgent in a way that hasn’t been covered as much, because even when the report is passed, the decision to legalize Waymo will be in the hands of these 13 councilmembers, and the only one who’s spoken about Waymo specifically has been negative. I think we should actually be somewhat pessimistic about how the council will vote. As noted in Understanding AI, Waymo has had a lot of success in Republican-dominated states where the regulatory environment is more favorable, and has hit barriers in more Democratic states. As a baseline, because DC skews very left, and this pattern of delay in more left of center places is becoming more consistent, we should expect a higher chance of bumping into more political opposition here.

From Understanding AI

One other note is that the two council votes running for Congress (Pinto and White) will be heavily influenced by how organized labor (a vital constituency in a citywide/district-wide congressional primary) views the AV issue.

The mayoral race

I really don’t think people should vote for DC mayor based on how they think about AVs, but it’s also important to understand that the two main candidates for DC mayor seem to be on opposite sides of the AV debate and it’s likely that this election will be extremely consequential for the future of self driving cars in the city.

The mayoral race in DC is basically decided by the Democratic primary on June 16 2026. Because DC is overwhelmingly Democratic, whoever wins that primary will almost certainly be the next mayor, taking office in January.

The two leading candidates in the primary are Janeese Lewis George and Kenyan McDuffie. McDuffie was the most pro-AV member of the DC council, and Lewis George is the most anti-AV.

Here’s a prediction market for who will win:

Kenyan McDuffie

McDuffie is the former councilmember who introduced the Autonomous Vehicles Amendment Act of 2025 (B26-0323), the bill that would create DC’s complete AV operating framework. The bill was introduced on July 11, 2025 and referred to the Committee on Transportation and the Environment on July 14, 2025. It would allow fully autonomous vehicles to operate without a human driver; establish insurance and financial responsibility requirements; require companies to submit law enforcement interaction plans; create an on-demand AV network under existing for-hire transportation law, with human-driver-only provisions carved out; and designate DDOT as the sole agency implementing the act.

McDuffie was regularly described as one of the most “pro-business” councilmembers. If elected mayor, he would likely direct DDOT to finish the report on a hard deadline, possibly issue driverless testing permits independently, and use his relationship with Allen and other councilmembers to push for hearings.

Surprisingly, McDuffie hasn’t done or said anything else related to AVs that I can find.

Janeese Lewis George

I want to flag that I’m going to be pretty critical of Lewis George’s coalition’s attitude toward AVs here, but I’m otherwise unsure of who I’m going to vote for and friends I trust have wildly different takes on which candidate is preferable in general, though basically everyone I know agrees that we’d like Lewis George to change her mind on AVs. Among other things Lewis George got the endorsements of prominent housing groups in DC who I tend to agree with.

Lewis George is the councilmember who has been most publicly skeptical of AVs. She said in a podcast interview in February “I don’t think our city is ready for Waymo at this moment.” She’s framed her concern as primarily about safety, but she also spoke about worker displacement, saying “we’re gonna create a balanced approach” because “I want to make sure we are not displacing workers.”

Lewis George describes herself as a democratic socialist and has built her career around labor advocacy. In December she got early endorsements from five unions with significant local organizing presence: SEIU 32BJ (which represents property service workers including janitors, security officers, and airport workers), ATU Local 689 (transit workers), UFCW Local 400 (grocery, retail, and healthcare workers), and Unite Here Locals 23 and 25 (hospitality and food service workers). At that endorsement rally, she pledged that as mayor she would “take on companies” engaged in union-busting and protect workers from displacement fueled by artificial intelligence. The Working Families Party and Washington Teachers’ Union have also endorsed her.

Axios reported that “a prominent local labor union” endorsed Lewis George in part because of concerns about job losses from autonomous vehicles specifically. The Washington Informer identified this union as SEIU 32BJ (property service workers), whose executive vice president Jaime Contreras said they endorsed Lewis George because "She’s anti-Waymo, which is something that we oppose coming to the city because it’s going to displace working-class people who do Uber or Lyft or DoorDash…for extra income.” This mirrors the pattern in other cities where organized labor has emerged as the primary opposition force to Waymo’s expansion.

Lewis George hasn’t called for banning Waymo or said autonomous vehicles should never operate in DC. Her stated position is that the city isn’t ready “at this moment” and that she wants to ensure workers aren’t displaced. This sounds reasonable, but is actually quite extreme as a reaction to new vehicles. Any rule that says we can’t introduce a new transit technology if it threatens to cause rideshare drivers to lose their jobs is an abysmal way to think about transit policy. Among other things, if buses didn’t exist, this same rule would ban buses. This same rule would have also banned rideshare apps in the first place, because they mostly destroyed the (better regulated, better paid, easier to organize) taxi industry in DC1. I have more on that argument here.

Lewis George’s position on Waymo closely matches the positions of leaders in cities that have stalled or effectively banned it:

In Boston, the city council’s proposed ordinance uses identical framing: study the impact on jobs first, require a human safety operator in every vehicle, and don’t allow commercial operations until an advisory board gives the green light. That advisory board would be required to include representatives from the App Drivers Union (a rare example of unionized rideshare drivers), the Greater Boston Labor Council, the Teamsters, and the UFCW. Eight city councilors collaborated on the ordinance. The Teamsters organized a coalition called “Labor United Against Waymo” which was the first mass-mobilization effort outside California to push back against AV expansion. Waymo’s spokesperson called the proposal the “first major city in the world to ban fully autonomous vehicles based entirely on vibes.”
In New York, Governor Hochul withdrew her robotaxi legalization proposal in February after failing to secure legislative support. Her spokesperson said “Based on conversations with stakeholders, including in the legislature, it was clear that the support was not there to advance this proposal” which in practice meant that organized labor made the political cost too high in an election year. The proposal would have allowed limited robotaxi deployment upstate while leaving New York City decisions to the mayor and city council. It died anyway. Waymo has testing permits for a handful of vehicles in Manhattan, but can’t charge passengers or operate commercially without new legislation that now doesn’t exist.
In Minnesota, SEIU chapters started organizing against AV legislation before Waymo even announced plans to expand to the Twin Cities. By the time Waymo began testing, there was already organized opposition pushing for legislation that would require human drivers in every vehicle. Multiple Minneapolis city councilmembers have signaled interest in passing a citywide ordinance with the same requirement.
In Washington state, law allows testing but requires a human safety operator in all vehicles and doesn’t offer a clear path to commercialization. Seattle has its own additional testing permit program on top of the state requirements.

The same pattern shows up again and again where politicians express openness to AVs in principle, insist on studying job impacts first, and structure the study process so that it can never actually conclude that it’s okay to proceed. The “study jobs first” framing often functions as a ban.

Lewis George hasn’t gone as far as Boston’s councilors, who proposed requiring a human in every vehicle. But her framing seems to track the same playbook, her coalition includes the same types of unions driving opposition in those other cities, and the political incentives point in the same direction. The Teamsters’ national position is a permanent human-operator requirement for all autonomous vehicles. ATU Local 689, which endorsed Lewis George, has lobbied against AVs alongside its national chapter.

That said, Lewis George does have room to eventually support AV deployment. She could condition it on workforce transition programs, labor protections, or union consultation requirements. There’s a version of AV legalization that includes strong worker provisions and that she could plausibly support without reversing her position. The question is whether she’d actually pursue that as mayor, or whether the political incentives from her labor coalition would push her toward indefinite delay.

This doesn’t mean AVs would never happen under Lewis George. She hasn’t called for a permanent ban, and external pressure would keep building: Waymo would be operating in 15+ other cities, Baltimore might have the service 40 miles away, federal legislation could change the landscape. But nothing in her current positioning suggests she’d prioritize this, and the most likely outcome is that legalization gets pushed back by multiple years.

The mayoral race makes the DDOT timeline much more important, and vice versa

The mayoral race matters for AVs, but how much it matters depends almost entirely on whether the DDOT report comes out before the new mayor takes office in January 2027.

If the report comes out before January 2027, the mayoral race becomes potentially less decisive. Allen’s stated precondition would be removed. The McDuffie bill already exists. Allen could hold hearings in the fall. The council could potentially vote before the end of the year. If legislation passes before January, the new mayor inherits a legal framework, and unwinding legislation the council has already passed is much harder and more politically costly than just preventing it from ever being introduced.

Bowser could also use the report as cover to issue driverless testing permits in her final months. If DDOT hands her a finished product, the political calculus changes. She could claim credit for moving the city forward on her way out.

If the report still isn’t finished by January 2027, the mayoral election becomes the single most important variable in whether Waymo is legalized in DC anytime soon. The next mayor controls DDOT, and the report’s absence is what keeps everything frozen. Under Lewis George, the stall could extend for years, especially if she adds requirements to look into effects on jobs and halts legalization until there are guarantees that rideshare drivers won’t lose work.

Under McDuffie, things would likely move faster, though he’d need to navigate the council’s labor concerns.

This is why pushing for the DDOT report to come out before the election is the single most important thing AV advocates can do. Every month of delay past this summer makes the mayoral outcome matter more.

The federal government

Congress has direct authority over DC. While the local political dynamics I’ve described above are the most important near-term variables, federal action could eventually change the landscape, though right now there are no especially promising bills. Two bills that would in part pre-empt DC’s decision making on AVs are the SELF DRIVE Act of 2026 and the Autonomous Vehicle Acceleration Act of 2025 (S.1798), but both look very unlikely to become law. The SELF DRIVE Act is being talked about as maybe possibly being folded into the Surface Transportation Reauthorization, which would make it more likely to pass, but this is all pretty speculative. Technically the federal government could eventually pre-empt DC law if they passively disallow or actively ban Waymos, though I wouldn’t count on it anytime soon.

How to legalize Waymo in DC

The most promising levers to pull

I don’t know a lot about the specifics of influencing city government, so take these with a grain of salt. I’ll update them as I get feedback. The three main things to do right now seem to be:

Apply public pressure to DDOT to finish the report on time, and then to create the separate necessary testing permit framework. This could be through public writing or writing to representatives.
Make a plan for how to sway the councilmembers who haven’t expressed any opinions on AVs to support a legal framework for AVs in the city once the DDOT report is finished. It’s likely that like other cities that are pushing back against Waymo, interest groups will be eager to reach undecided councilmembers as well. They’re probably already trying to.
Push for “shall issue” permits in any AV legislation. This might be the most important detail to get right, and I haven’t seen it discussed publicly. The 2020 law gives DDOT discretionary authority over testing permits. DDOT decides whether to make the application available, how to evaluate it, and when to approve it. There’s no requirement to actually issue a permit once criteria are met. This is exactly how we got here: the law says DDOT can create a permit program, DDOT chose not to, and nothing can force them.
If the council passes AV legislation and keeps this discretionary structure, the same thing happens again at the deployment stage. A hostile administration sets up a permitting process that technically exists but moves at whatever pace it wants. Applications sit in review. Additional information gets requested in endless rounds. The framework exists on paper while no permits are issued. DDOT’s own AV program manager said in a 2025 MIT panel that “the industry is changing a lot faster than our rulemaking structure is allowing us to keep pace” and that’s without a mayor who’s actively trying to slow things down.
The solution would be that once an applicant meets clearly defined conditions (safety benchmarks, insurance, data reporting, law enforcement plan), DDOT must issue the permit within a defined timeframe. No discretion on qualified applicants. No sitting on applications.
Under McDuffie this probably doesn’t matter much. Under Lewis George it’s the difference between a framework that works and one that becomes another tool for delay. Someone with more knowledge of DC government than me pointed out that this might be the most important thing the council could do besides passing the legislation itself. I think they’re right. If the legislation gives the executive branch unlimited discretion over whether to actually issue permits, we end up right back where we are: a legal framework that exists in theory and doesn’t work in practice.

These seem less promising:

Convincing Charles Allen to change his mind on using DDOT as a prerequisite for any policy. Allen seems to have made up his mind on this, I’m skeptical that he can be swayed with the admittedly limited number of AV advocates in DC, and I also personally just have no way to judge how reasonable his decision is. It does make sense to me that councilmembers ought to rely on government agency expertise. A lot of what he’s shared on DC looks silly and overly critical of the cars compared to most other vehicles though, and the standards he’s using to judge them (jobs and safety) would imply every other type of vehicle in the city shouldn’t be legalized. On that maybe we can make some progress.
Relying on Mayor Bowser to take action. She doesn’t seem interested in prioritizing AVs and I’m not sure how much this could change.

Can we change Lewis George’s mind?

A strategy I’m unsure of is convincing Lewis George to change her mind on her current stance on AVs. She says she wants to ensure rideshare workers aren’t displaced, but this same rule would also ban buses or bikes. Her union endorsement coming in part because of her stance on AVs makes it seem difficult to change her mind. That said, Lewis George describes herself as a democratic socialist, and there are arguments for AVs that map onto things democratic socialists care about:

Fewer traffic deaths. DC sees about 40 traffic fatalities a year, disproportionately in lower-income wards with fewer protected pedestrian crossings.
Every Waymo is electric. DC has climate goals and environmental justice commitments. Waymo’s fleet is entirely electric, while a significant share of Uber and Lyft drivers in DC drive gas-powered cars. Replacing gas-powered ride-hail trips with electric AV trips is a direct emissions reduction, concentrated in the neighborhoods with the worst air quality. For someone who cares about environmental justice, this should matter. It won’t be decisive on its own, but it makes the “let’s wait” position slightly more costly in environmental terms every month it continues.
Ride-share jobs aren’t union jobs and probably never will be. This is the argument I find most interesting, though I’m least sure it would land politically. The jobs Lewis George is protecting (Uber and Lyft driving) are difficult-to-unionize gig work. They’re not the kind of jobs that democratic socialists typically celebrate. To my knowledge, DC does not have any rideshare unions. These are rare in general but do appear in cities where labor is especially powerful, like Boston and Seattle. It’s likely that the unions pushing against Waymo are actually worried about future automation of buses, trains, or trucks, and see automated rideshares as the beginning of a wave of automation that will eventually come for them too.
Constituencies who otherwise have trouble getting around the city might benefit from AVs. Elderly people among others.

I want to be honest that I don’t know if any of these arguments would actually move Lewis George. Politicians rarely reverse positions that are tied to endorsement relationships, especially during a campaign. The more realistic hope might not be changing her mind before the election, but rather creating enough cross-pressure from interest groups, environmental advocates, and safety organizations that, if she wins, she feels politically constrained from actively blocking AVs indefinitely.

The reason this matters is that if Lewis George’s position is immovable, then whether AVs are legalized in DC hinges almost entirely on the June primary. Any progress on softening her stance would reduce the stakes of the election and make the path to legalization less dependent on a single vote.

Lessons from other cities

Every city that has Waymo got it because at least one of three conditions was met:

A state-level framework that preempted local objections (California, Texas, Arizona)
An executive who personally championed the technology (Phoenix, Atlanta, Denver)
A regulatory process that moved on a defined timeline (California’s phased CPUC system)

Unfortunately, DC currently has none of these.

Here are some case studies:

California

San Francisco’s city government was deeply skeptical of Waymo. Its transportation agencies formally protested expansion, and the city actually sued the California Public Utilities Commission to try to block it. The CPUC approved Waymo’s commercial deployment in August 2023 over San Francisco’s explicit objections, and a state appeals court upheld the decision.

Public opinion in San Francisco shifted dramatically once the service was actually available. In mid-2023, net favorability for robotaxis was negative 7%. By mid-2025, it had swung to positive 38%, with 67% of residents supporting continued operations. People tend to like AVs once they can actually try them.

Texas and Arizona

Both states created permissive frameworks before Waymo arrived. Arizona’s governor issued an executive order welcoming AV testing in 2015. Texas passed legislation in 2017. When Waymo was ready to launch, the legal framework was already there. DC’s 2020 law was supposed to be that proactive step. DDOT’s failure to follow through is why DC is now years behind.

Boston: The closest parallel to DC

Boston is the most instructive comparison. The Teamsters organized “Labor United Against Waymo,” the first mass-mobilization effort outside California to push back against AV expansion. Eight city councilors collaborated on an ordinance that would require every AV to have a human safety operator (effectively banning the whole thing) and commission a study before any commercial operations could begin. The advisory board overseeing the study would be required to include reps from the App Drivers Union, Greater Boston Labor Council, Teamsters, and UFCW.

The Boston situation is still unresolved. Competing state-level bills are being debated. One allows driverless vehicles, the other requires human operators in all vehicles. Waymo has returned to Boston for further testing but faces serious organized opposition.

This Boston city council meeting drove me completely crazy to listen to, and might be a hint of the bad dynamics at play in coming debates about AVs in DC.

Minnesota

In Minnesota, SEIU chapters started organizing against AV legislation before Waymo even announced expansion plans. By the time testing started, there was already an organized opposition pushing for human-operator requirements. This is almost certainly already happening in DC.

Maryland

Governor Wes Moore is publicly enthusiastic about Waymo. The Maryland legislature is actively considering bills to allow fully autonomous vehicles, and hearings have gotten surprisingly little pushback from lawmakers. Waymo already has cars in Baltimore being driven by professional drivers to map the city. If Maryland passes its legislation, DC residents will be able to see the technology operating 40 miles away while they can’t use it at home. Maybe this will have some impact?

Atlanta and Austin

In both cities, Waymo launched partly through a partnership with Uber, making rides available through the existing Uber app. This partially neutralized the “Waymo vs. rideshare drivers” framing by positioning Waymo as an option within the existing ecosystem rather than a replacement for it. Whether this model could help politically in DC is unclear, but the narrative doesn’t have to be adversarial.

The national labor strategy

The Teamsters have called nationally for a regulatory standard requiring a human operator in all autonomous vehicles, with that operator subject to commercial driver’s license requirements. Teamsters president Sean O’Brien testified before Congress that “allowing the unfettered and unregulated operation of autonomous vehicles, ultimately seeking to replace human drivers with robots, is unequivocally a threat to safety on our roadways and the existence of good jobs in the trucking industry.” The labor impacts framing is part of a coordinated national strategy.

Federal momentum is actually moving in the opposite direction from the labor strategy. The SELF DRIVE Act passed the House Energy and Commerce subcommittee on a narrow 12-11 vote and could be folded into this year’s Surface Transportation Reauthorization. Congress seems more receptive to AVs than blue-city local governments are. There’s no serious national ban proposal on the table. But labor opposition is likely to grow.

DC matters in the federal picture for a specific under-discussed reason: it’s where members of Congress, their staffers, federal regulators, and lobbyists live and commute. If Waymo operates in DC and these people experience it working well in their daily lives, it might normalize the technology among the people who write federal policy. If it’s anything like the other cities where Waymo’s operating, we can expect the public perception of AVs among policymakers to meaningfully improve.

In 2013, DC residents took about 20 million taxi trips. The DFHV 2025 Annual Report doesn’t directly state total taxi trips, but reports that 286,823 e-hail taxi trips represented 16% of all taxi trips that year, which implies about 1.8 million total. Roughly a 90% decline.

Data centers’ heat exhaust is not raising the land temperature around where they’re built

Tue, 31 Mar 2026 04:44:31 GMT

Wildly misleading stories about data center environmental impacts can just pop up out of nowhere, be shared a ton, and become embedded in the common wisdom of educated people, despite being completely wrong. This happened with the claim that AI uses a bottle of water per prompt, and that normal operations of data centers are causing the people around them to lose access to water.

I’m writing this as an emergency post because a new contender for drastically wrong common wisdom has just popped up.

There’s a new paper being shared and reported on called “The data heat island effect: quantifying the impact of AI data centers in a warming world.” It is the single worst writing and research on AI and the environment that I have read.

The central claim of the paper is that in the places where data centers are built, land surface temperatures increase by an average of 2°C (3.6°F) after the data center begins operations, creating what the authors call a “data heat island effect” that affects over 340 million people worldwide. In some extreme cases, data centers are found to increase land surface temperature by up to 9°C (16°F). The paper claims that a warming effect can be measured up to 10 km from the data center. This all sounds really bad! 340 million people affected by heat islands.

The paper also has alarming looking graphs that I suspect will do well in the media coverage:

There are three problems with this paper:

The methodology is completely off-the-wall goofy and the study does not at all show what the authors claim. This will be the focus of this post. The authors very strongly imply this temperature change is due to data center waste heat, but I think it’s almost certain that it’s instead entirely due to the fact that the surface of any building, a data center or Walmart or house or Starbucks, is hotter than the grass that was there before. I think I can show conclusively using high school physics and math that the data centers are not heating the land around them. Data center heat exhaust could only heat land by at most 1% of what the authors imply. The paper itself doesn’t even consider this very obvious reading as a possibility.
The paper’s framing is wildly misleading and will create a lot of additional confusion on top of the methodology itself being bad. The author’s suggestions from this paper is to “build more energy efficient AI algorithms” but the actual conclusion should be “Don’t build buildings if you don’t want those buildings themselves to get a little warm, even though they won’t affect anything around them.” There is a lot more incredibly shifty language the authors use over and over.
I’m worried that the media takeaway from this is going to be that data centers are warming the air this much. If they were, that would be a complete disaster. To put this in context, if (another) data center were built near me in DC, and it heated the air by an average of 9°C, this would change the DC climate to the climate of Miami. A 2°C would be equivalent to moving DC halfway to Georgia, it would be like the region had fast forwarded through 50-70 years of climate change. The actual claim is that the data centers are warming the land temperatures around them, not the air temperatures. The authors seem to imply this rise in the land temperature is via a raise in air temperatures, but as we’ll see below this looks wrong. It seems likely that a lot of people will misinterpret this. Look again at the graphs from the paper. The Y axes say “Normalised temperature increase,” not “Normalised land temperature increase” and if they’re shared without context, I suspect people will draw the wrong conclusions.

The paper
The media is already running with this
A parting thought: AI is useful for research

The paper

A summary of my argument

The measurements in this paper are just showing the fact that buildings are hotter to the touch than grass. The data centers are most likely not heating the land around them. They themselves are just hot because they are buildings. If you include hot buildings in a very general measurement of the average total surface temperature of a large area, that average will go up slightly without telling you anything about the undeveloped land.
Using simple high school physics, it’s easy to show that there is no way data center heat exhaust could account for more than at absolute most 3% of the observed average land heating, and any realistic assumptions drive that down to basically 0. The only other mechanism by which they could be heating the land fails. This leaves the other explanation: the data centers themselves are hot because they are buildings, the land around them remains at the same temperature, unless more buildings have been built on top of it to accommodate the data center.
You would think the authors would have anticipated my criticism, but they don’t even consider it as an issue with their methodology at all.

What is land temperature?

NASA defines land surface temperature (LST) this way:

Land surface temperature is how hot the “surface” of the Earth would feel to the touch in a particular location. From a satellite’s point of view, the “surface” is whatever it sees when it looks through the atmosphere to the ground. It could be snow and ice, the grass on a lawn, the roof of a building, or the leaves in the canopy of a forest.

We need to understand land temperature as opposed to air temperature. They can be very different from each other. An obvious example is stepping onto black asphalt on a summer day. Even though the air temperature may be warm, if the air were as warm as the asphalt you’d be dead. Stepping onto a light-colored concrete sidewalk instead would be much more cool. Land temperatures can deviate a lot from the temperature of the air.

Changes in land temperature of 2-9°C don’t really impact human welfare directly. What matters for human welfare is air temperature. This temperature range invokes the low to very extreme catastrophic ends of the spectrum of what could happen with climate change, but that spectrum is about air temperature. Land temperatures change all the time without a corresponding significant effect on air temperature. If the most extreme land temperature changes happening in the paper were happening around you, the air around you would likely warm by a fraction of a degree at most. It would probably too small to measure or distinguish from normal day-to-day temperature variability.

How much does land temperature matter for the ecology of a region? Soil temperature directly affects plant health, microbial activity, moisture retention, and which organisms can survive in an area. If data centers really were warming the land surface by 1°C out to 4.5 km away, as the paper claims, that would be worth paying attention to ecologically even if it didn’t matter much for the people living nearby. A 1°C rise in soil temperature is pretty minor, but a 9°C would change which plants and microbes could survive. This would be a problem if it were real, but as I’ll try to show below, it’s not.

The methodology

The researchers used satellite data from NASA satellite sensors to measure land surface temperature at data center locations worldwide from 2004 to 2024. They looked at how land temperature changed at each site before and after the data center began running, and found a clear jump of about 2°C on average right at the moment the data center came online, as shown on this graph (the orange region shows the range from the very minimum to maximum measured temperature changes, the red line is the average, and the error bars represent the 95th percentile bounds, meaning the vast majority of measurements fell within those much smaller bounds):

The paper also claims that this effect extends up to 10 km from each data center, with a measurable 1°C land temperature increase as far as 4.5 km away using the same MODIS sensor:

The authors present this as evidence that data centers create broad climate zones comparable to urban heat islands.

The authors are somewhat ambiguous about what’s causing this temperature increase. They say that their study “relies on the assumption that AI hyperscalers might have an impact on the LST of their locations because of the heat that they would release as a result of the high power demanding applications.” So this makes it sound like they mean to imply that it’s the heat exhaust of the data centers (evaporated water or from air cooling systems) that’s causing this increase. However, they never explicitly state this, and they never consider the alternative explanation that the temperature increase is simply caused by the construction of the building itself. The possibility that replacing grass with a large building and parking lots might explain their results is completely absent.

Their mitigations section confirms what they think is going on, though. All their suggestions say to reduce how much energy data centers use. They recommend more efficient AI algorithms, adiabatic circuits, and passive radiative cooling coatings for data center buildings. This strongly suggests that they think the problem is that the servers are producing too much heat, not that buildings exist where grass used to be. If the authors thought these numbers were mainly coming from land cover changes, they’d suggest other stuff (like painting the parking lot white? Or planting vegetation? I’m not actually sure what would fix the issue here). So they seem to believe operational waste heat caused this, even though their methodology can’t actually support that conclusion. But their methodology is goofy goober.

This is in some ways worse than if they’d made an explicit causal claim, because there’s nothing specific to argue against. The most obvious alternative explanation for their findings, that buildings are hotter than fields, isn’t mentioned in this paper. They don’t control for it as a possibility.

My core claim: this is literally just measuring hot surfaces of new buildings, and the soil and land around those new buildings probably hasn’t changed temperature at all

My argument here is going to be that this paper is just measuring the fact that buildings are hotter than grass. The land around the data center that isn’t either buildings or road has probably not warmed. It doesn’t matter at all that this study is about data centers. I’d expect similar measurements for Walmarts.

If you build a building, whether it’s a data center or Walmart or house or Starbucks, and you point a NASA satellite at the building, you are going to measure a warmer surface temperature exclusively caused by the material the building made of, not any heat exhaust from what’s happening inside the building.

However, the difference between the surface temperature of buildings and grass is much larger than study’s finding of 2°C. It’s on the order of 20-35°C on a sunny day. Why is the study’s number so much smaller? The satellite sensors this study used (MODIS) can’t actually look at an area as small as a data center. MODIS pixels are roughly 1 km² (or 0.5 x 0.5 km in the enhanced product the paper claims to use). A data center building and its partking lot might take up a small part of that pixel. The rest would still be undeveloped land (often grass or other plants) or roads. So what the satellite sees is a blended average, maybe 10-20% data center and parking lot surface, and 80-90% unchanged land.

Let’s assume buildings and roads are on average 20°C warmer to the touch than grass. To get an average land temperature change of 9°C (the most extreme outlier in their data) on a 0.5 x 0.5 km unit of grass, you would need to build 112,500 m² of new building and road surface, about 28 acres. Many hyperscale data centers are larger than this. 28 acres is a mid-sized hyperscale campus. This on its own would explain the most extreme outliers.

What about the 2°C average they find? To get a 2°C increase, you would only need about 25,000 m², or roughly 6 acres of building and road surface within the satellite’s field of view. That’s a pretty normal size for a non-hyperscale data center.

What about the increase in temperature farther from the data center, up to 10 km away? Well, two things:

Data centers are mostly not built in very isolated places. They need power infrastructure, fiber connectivity, and road access. Places with this stuff are also attractive for warehouses, logistics hubs, office parks, and residential development. When you look at the area within 10 km of a data center, you’re typically looking at a region that has a lot of new building in general, and that all registers as warmer to a satellite’s thermal sensor, regardless of what the buildings are used for. The pattern that shows up in the paper is what you’d expect to see from the result of new development around lots of data centers averaged together, slowly thinning out as you got farther away.
This doesn’t look like what you’d expect if this were being caused by waste heat from the data center alone. Waste heat in air doesn’t spread out gently like this in a neat, slowly fading pattern. Waste heat behaves like a plume, where a lot of it rises, and so at the ground level it drops off very fast, way faster than what we’re seeing here. To me, this looks much more like a line tracking how much land has changed on average in the vicinity of a data center.
Data centers also often cause other big new infrastructure construction in the surrounding area. A large 100 MW data center needs a lot of new power transmission and generation. This often means large buildings built a few miles from the data center. industrial installations with their own footprints built within a few kilometers of the data center they serve. Access roads also need to get built or widened. Even simple things like food service for construction crews and staff set up nearby and change the land footprint. The data center brings a whole ecosystem of development with it. All of this covers grass and would change how the satellite reads the warmth of the ground.

The paper doesn’t control for any of this. They don’t compare their data center sites against other types of land changes, or do anything that could separate how much of this is land use change vs. heat exhaust. They don’t even seem to consider this as a possibility. It doesn’t come up, and they strongly imply this is being caused by the heat exhaust throughout.

If this were being caused by the change in land use, there’s no reason to expect the land around the data center to be heating at all, for 2 reasons:

Land mostly doesn’t absorb heat from nearby buildings. If you step from a hot asphalt parking lot into cool grass, that grass is likely the same temperature as grass a mile away from the asphalt.
If the land were heating up, this would imply that the buildings themselves would need to be much cooler to keep the average as low as it is. Data centers and the roads and infrastructure around them would need to be significantly cooler than all other normal buildings, or just be very small, and we know they’re not small.

There’s some very minor effects big buildings have with the flow of air and wind around the land around them, but it’s pretty marginal, and again this would change the study to be about any large buildings at all.

I think I already have a strong case here, but I have some high school physics and math in the next section that in my opinion completely confirms it.

How much could a data center’s waste heat raise the land temperature around it?

MODIS satellites take their measurements around 10:30 AM and 1:30 PM. During the day, the sun is shining directly on the ground. The ground absorbs solar radiation and gets hot. The air above it is cooler than the ground surface. This means heat is basically always flowing upward, from the ground into the air.

Data centers emit heat into the air. The way they would heat the land is by raising the temperature of the air enough that less heat from the ground flows into the air, and stays in the ground instead. The colder the air, the more heat is lost from the ground, and the warmer the air, the more heat stays in the ground. It’s like if you touch an ice cube to your hand, and then touch a lukewarm object to your hand. In both cases, your hand is losing heat energy, but it loses it much faster when in contact with the ice cube.

How much would the data center need to heat the air to cause significant amounts of heat energy to stay in the ground?

The math (skip if you’d just like the conclusion)

MODIS is measuring land surface temperature. The graph in the study shows that over an area of 1.6 km, land temperatures were elevated by 1.5°C. Is it actually possible that a data center’s waste heat could keep the surface around it about 1.5°C warmer over a radius of about 1.6 km?

Let’s take a large hyperscale data center drawing about 100 MW of power. Almost all of that becomes waste heat, which means 100 million joules every second leaving the building.

A circle with a 1.6 km radius around the data center would have an area of about 8 million square meters.

Let’s make an extreme assumption and say every single watt-hour of heat the data center emits somehow makes it to the ground, the surface of that circle. None of it rises into the upper atmosphere, or blows beyond 1.6 km away. Every joule goes straight into the ground. How much heating would each square meter of ground receive?

100,000,000 watts ÷ 8,000,000 m² is about 12.5 watts per square meter. Should we expect this to produce 1.5°C of cooling? Well, as the surface warms, it also loses heat more quickly, because it radiates more infrared light among other things. Scientists have found general patterns in how much heat energy it takes to warm a surface like this. Engineers measure this with a “heat transfer coefficient” that measures how many watts of heat per square meter a surface loses for each degree its temperature increases (the more the temperature increases, the more heat is radiated back out). For outdoor surfaces with a light breeze, this coefficient is at least 10-15 W/(m²°C), and can be higher. So for every degree the surface warms, it loses an additional 10-15 watts from every square meter.

So to hold the surface 1.5°C warmer than it would otherwise be, you need to deliver:

1.5°C × 10 W/(m²°C) = 15 W/m²

The very largest amount of energy a large data center could deliver to the ground is 12.5 W/m², and this is in a crazy scenario where 100% of the data center’s heat reaches the ground around it. So this is lower than even the very most extreme possible value it would have to be to get the value this paper’s observing. In reality, the vast, vast majority of the heat rises, disperses, and never reaches the ground surface within 1.6 km. A generous estimate for daytime conditions would be that 2-5% of the data center’s total heat output reaches the ground surface within 1.6 km. That would gives us:

12.5 W/m² × 0.02 to 0.05 = 0.25 to 0.625 W/m²

Which would cause a surface temperature increase of:

0.25 to 0.625 ÷ 12.5 = 0.02 to 0.05°C

So 1-3% of the observed 1.5°C this study observed. And that’s for a data center way larger than average and almost no wind.

Conclusion of the math

So even if every data center in this study averaged 100 MW (the actual average is much smaller), and the wind isn’t there, the math still doesn’t make waste heat work as an explanation. With a 100 MW data center, only about 1-3% of the observed temperature increase could plausibly be attributed to waste heat warming the surrounding ground. The rest, 97-99%, is explained just by the building and its pavement being warmer than the grass they replaced, which raises the satellite’s blended average temperature reading without actually warming the surrounding land at all.

The one remaining question: why the clean step change?

There’s one aspect of this that’s still somewhat confusing and doesn’t square with my story. Look again at this graph:

This jump is somewhat significant. An average increase of 2°C is about 1/10-1/20th of the difference between grass and a building, but also we’re assuming the building is taking up a small part of the satellite image. It is weird that this jump doesn’t occur until after the data center becomes operational. If this were construction, you’d maybe expect more of a slow gradual curve up?

The strength of the other evidence leads me to suspect that something’s off here. There’s no smoking gun, but I have a few explanations for what might be happening:

The stuff that creates the large majority of the building’s new hot surface area is usually added at the very end of construction: roofs and parking lots. It would make sense to me that during construction a roofless building where the ground hadn’t been paved wouldn’t become as hot as a building with a full roof and large paved parking lot. If these are added in the last month and add the major part of the heat, it would appear like a step change. But this could be wrong. Some people who know more about architecture than me reached out to say the roofs are likely not what’s causing this, they’re often cooler than the construction site. I’m kind of at a loss here, could this be the parking lot? It’s hard to find reliable data on which parts of construction absorb the most heat. I need good sources on this but can’t find many. If you have tips, send them!
The authors say they are measuring temperature changes relative to when each data center became “operational,” but that may be much less precise than it sounds. They don’t seem to know if operational here means “first server energized,” “first meaningful IT load,” “certificate of occupancy,” “roof completion,” or “substantial completion.” They’re just using a database date from the IEA-linked source they cite, and then centering all of their analysis on that single date. It would make sense to me that a lot of what’s listed in the data base might just be when the data centers wrapped up construction, which would align with when the roof and parking lot were added. The database they say was provided to them by IEA isn’t publicly available, so I have no way to check this. Data Center Map, the largest collection of public info on data centers online, says they track data center lifecycle stages including Land Banked, Planned, Under Construction, and Operational, but then immediately warns that these lifecycle stages “should only be considered indicative, as it is very hard to track when stages change.” If data center map considers its own data on this to be very hard to actually pin down, I’d suspect that IEA doesn’t have a significantly better private collection of all 11,000 data centers.

So my best guess is that the actual operational opening time varies a lot compared to the numbers the researchers are using, ~~there’s going to be a clear jump in heat around the time the roof and parking lot are built~~, (←I’m now convinced that crossed out part’s wrong and this graph is a deep mystery to me) and therefore it’s likely that if you’re just looking at month by month snapshots, the roof + parking lot jump might appear to occur after the data center “became operational.”

One other important point is that this step change actually doesn’t look like what you’d expect to result from a data center’s energy draw, because most data centers are opened and then slowly filled with servers and powered on more and more over a period of months or even years. If this were the result of emitting more heat, you would expect the line to continue to go up and up as the data centers drew more power, but the actual average line appears to be flat after only a month of operations. The entire industry’s ramp-up pattern is gradual, not instantaneous, at every scale, even for smaller colocation (not hyperscaler) data centers.

Data centers also vary enormously in their power draw, but the error bars where almost all observed heating effects occur are incredibly tight along a narrow band. I would expect any heating caused by data center power consumption to vary as much as the power consumption does itself, but this graph doesn’t show that at all. Instead, regardless of the size, data centers seem to have mostly shockingly similar effects on warming. Again, this is a sign that it’s the full physical infrastructure buildout of data centers and the stuff around them that might be causing this instead, though obviously data center physical size also varies a lot. This one’s very confusing in general.

Looking into this more, I learned that there are very few studies like this on other large buildings that emit a lot of heat. The clearest comparison I can think of is nuclear power plants, which can emit 10 to 20 times as much waste heat as a 100 MW data center. I’ve never heard of a nuclear power plant’s heat emissions raising the temperature of the ground around it, even though by the same logic it should be having 10-20 times the effect of the data center. However, there seems to be no info on this in either direction as no one’s seriously studied it.

I’m unsure here so will circle back if I get better evidence.

Other candidates for warming land

I would be pretty shocked to find out that the heating exhaust is able to warm the land this much after all this converging evidence, but my building explanation isn’t super satisfying as an explanation of the step change mentioned above. Other candidates for ways the data center could actually be warming land include:

Groundwater and irrigation changes. Data center construction often involves a lot of general changes in the land, including draining groundwater. This could have some effect on nearby vegetation, and if that dies off it could also affect the land temperature a big. It’d be funny if this all turned out to be about water use.
Vegetation stress from construction dust and runoff. During and after construction, dust can harm vegetation in a certain radius around the site.
Maybe a statistical artifact? The satellite system used doesn’t have an exact sharp pixel boundary. A very hot data center pixel could also just artificially increase the measured temperature in neighboring pixels as a measurement artifact. This would produce the smooth decay pattern we see, and has nothing to do with actual ground temperature. This satellite system was designed for monitoring global-scale trends monitoring rather than analyzing individual buildings.

A few other bizarre methodological mistakes

Thanks to Rohit for pointing these out:

They measure population affected by counting residents within km of each data center measured, but they don’t seem to check for overlaps at all. There are a lot of places like Loudoun county where data centers are all within 10 km of each other, so you’d get a lot of overlapping circles and people double counted in each one. The population affected number is likely inflated by double counting.
The paper says it used “a reconstructed MODIS LST dataset (produced by NASA) acquired worldwide from 2004 to 2024 over an enhanced 500m resolution grid” and cites Metz et al. But the Metz et al. paper describes a European product with 250m spatial enhancement, not a global 500m product. So the citation doesn’t match what they claim to be using.

The incredibly shifty framing the authors use

Over 99% of the observed land temperature increase seems to be coming from an omission in the author’s methodology. They’re assuming the land has warmed, but it hasn’t, and they’re only observing that there are additional warm buildings in the areas they are analyzing. They did not control for this at all and don’t even reference it as a possibility.

The authors strongly imply multiple times that the causal mechanism for this temperature rise is the high energy usage of the data centers. Here’s every time they imply that the effect here is due to heat emissions caused by high energy usage:

Title: “The data heat island effect: quantifying the impact of AI data centers in a warming world”
- This frames the phenomenon as caused by AI data centers specifically, not by construction or land development. A better title would be “quantifying the impact of large buildings.”
Abstract: “we focus our attention on the heat dissipation of AI hyperscalers”
- Explicitly names heat dissipation from operations as the focus of the paper.
Abstract: “the land surface temperature increases by 2°C on average after the start of operations of an AI data centre”
- “after the start of operations” implies the operations are causing it, not the construction.
Introduction: “the type of human activities established and operating dramatically influence the impact of UHI on environment and communities”
- Frames the effect in terms of activities operating, not buildings existing.
Introduction: “data centres are expected to be one of the most power-hungry activity in the next decade”
- Connects the phenomenon to power consumption.
Introduction: “AI data centres are in vast majority relying on fossil fuel use”
- Connects to energy source and emissions, implying operational energy is the driver.
Introduction: “the steep growth of AI training and use for various applications would directly translate into high net impact on emissions”
- Links AI workload growth to environmental impact.
Introduction: “the inefficiencies and nonidealities of AI hyperscalers operations would cause their emissions to rise even more”
- Again, operations.
Section 2: “This study relies on the assumption that AI hyperscalers might have an impact on the LST of their locations because of the heat that they would release as a result of the high power demanding applications that they would be used for”
- This is the most explicit statement. The assumed mechanism is heat released by computational workloads.
Section 3: “These results are dramatically impressive, especially considering that the typical LST increase caused by the quintessential example of compound of anthropogenic activities – the urban heat island effect – has been estimated in the 4 and 6 °C interval”
- Comparing to UHI, which is understood as an operational/activity-driven phenomenon.
Section 3, Bajío case study: “The stable climate, low seismic activity, and proximity to North American markets made the Bajío region a great hub for AI data centres. Nevertheless, it has been recorded a serious LST increase trend”
- Implies the data center operations caused the regional warming. In fact, this region fits my “Andy Masley theory of More Buildings = More Hot Buildings much much better.” In addition to building some data centers over the last 20 years, the Bajío underwent one of the most dramatic industrial buildouts in Latin America. The region received $37.6 billion in accumulated foreign investment from 2004 to 2019, with nearly two-thirds going to manufacturing. While national industrial activity grew at 1.7% per year from 2003-2016, Bajío industry expanded at 4.6%. A lot of companies like Toyota, Honda, General Motors, Bombardier Aerospace, and hundreds of others built big manufacturing buildings around the area. Lots of buildings and transportation infrastructure were built during the exact time the paper measures the warming pattern from data centers. The massive land use change from agriculture to industry is a much better explanation for the measured land temperature change than data center heat exhaust.
Section 3, Aragón case study: “The region is becoming a critical node for AI, cloud computing, and, increasingly, specialized server manufacturing. At the same time, the region has recorded an anomalous increase of approximately 2°C”.
- Same story as Bajío, different country. Aragón has undergone a massive energy and industrial buildout during the study period. The region has become a multi-sectoral hub (from data centers to logistics and automotive) with companies like AWS, Microsoft, Blackstone, and others developing strategic projects there. In the last year alone, 651 new megawatts of wind and solar photovoltaic capacity were installed, a 7.1% increase over 2023. Aragón has been described as potentially the “Saudi Arabia of solar power in Europe” and bp alone has around 1.1 GW of solar projects at various stages of development or construction in Aragón. A single solar cluster near Zaragoza installed 615,000 bifacial solar panels across 650 hectares of land, with two overhead transmission lines of 18 km and 20 km constructed to connect it to the grid. The paper attributes the warming to data centers and ignores all of this land change. My official Andy Masley theory of Buildings are Hotter than Grass is more parsimonious with the data.
Section 4: The entire mitigation section proposes solutions to reduce operational energy consumption: more efficient algorithms, adiabatic circuits, model pruning, dynamic power response. These are solutions to “servers produce too much heat,” not solutions to “a building was constructed and is hotter than grass.”
Section 5: “We focus our attention on the heat dissipation of data centres, which is directly connected to the energy consumption required for the operations of the AI hyperscalers”
- Explicitly links the effect to operational energy consumption.
Section 5: “Since the trends of data centre energy consumption are expected to show a steep growth in the foreseeable future, the data heat island effect could solidly become an additional factor for environmental and industrial sustainability”
- Implies the effect will worsen as energy consumption grows, which only makes sense if operations are the cause. If it were land cover change, the effect would be determined by building footprint, not energy consumption.

The paper also repeats the claim over and over that 343 million people are “affected” by the data heat island effect. They find this by just counting everyone who lives within 10 km of a data center in the study’s database. They don’t bother to see whether those people experienced anything bad at all, like a change in air temperature, health outcomes, energy costs, or anything else. By the same methodology, you could count everyone living within 10 km of a Walmart and announce that billions of people are “affected” by the Walmart being a hot building regardless of whether it’s affecting anything else. In reality, I think 343 million people are being affected by the “If you build a building the building gets hot in the sun and doesn’t really affect anything else” effect.

Another incredibly shifty move the authors make is using “AI hyperscalers” and “AI data centres” throughout the paper, but the database they use (from the IEA) contains data centers of all types. Most data centers built between 2004 and 2024 weren’t doing AI workloads at all, especially in the earlier years. The framing makes it sound like this is about AI specifically, but almost all of what they’re measuring is traditional non-AI focused data centers. It’s incredibly jarring to read “across all AI hyperscalers analysed during the 2004-2024 period” as someone who knows anything about the timing of the data center buildout. AI-focused hyperscaler data centers didn’t appear until after 2020.

I don’t understand the sociology of how something like this gets published. These are serious people! Going down the list of authors:

The lead author Andrea Marinoni is an associate professor of applied remote sensing at the Arctic University of Norway with a visiting fellowship at Cambridge.
Benjamin Horton is the Director of the Earth Observatory of Singapore at Nanyang Technological University, a leading sea-level rise researcher, IPCC author, Fellow of both the American Geophysical Union and Geological Society of America, and winner of the EGU Plinius Medal. His research was cited by President Obama in the 2015 State of the Union Address. He has 260+ peer-reviewed publications including in Science, Nature, and PNAS. He’s a very serious climate scientist!
Erik Cambria is a Professor of AI at NTU, IEEE Fellow, Visiting Professor at MIT Media Lab, Provost Chair in Computer Science, 80,000+ citations on Google Scholar, listed in Clarivate’s top 1% of scientists, and was featured in Forbes as one of “5 People Building Our AI Future.”
Pietro Lio’ is a well-known computational biology and AI researcher at Cambridge.
Jocelyn Chanussot is a prominent remote sensing researcher at Université Grenoble Alpes, IEEE Fellow, with expertise in signal/image processing for remote sensing. He’s arguably the author whose expertise is most relevant to the methodology.

What happened here? I’m somewhat cynical and think that academics want attention on their work and personas, media attention is good for them, and making hyperbolic claims about data centers is a really great way to get media coverage right now, even if they have to use goofy methodology to get there.

The media is already running with this

Despite this paper not being peer reviewed and only being up on arXiv for 10 days, the media is already running with it. Notice that most of these headlines leave the reader with the impression that it’s the air that’s warming:

CNN: Scientists have found an alarming environmental impact of vast data centers
- CNN adds that data centers are “making life hotter for more than 340 million people.” They also add “The findings are particularly alarming, the scientists say, because AI data centers are set to boom over the next few years, and these temperature rises come as planet-warming pollution is already making heat waves more extreme around the world.” This is so incredibly deceptive. This study told us nothing about heating air. “Already making heat waves more extreme” paints a picture of people’s lives getting warmer as a result of this effect.
- There were also a lot of local CNN affiliates that ran copies of the story, like here and here and here.
Benton Institute for Broadband & Society: "AI data centers can warm surrounding areas by up to 9.1 degrees Celsius"
- WHAT?! They ran with the absolute craziest outlier in their headline?!
Bode Living: “AI Data Centers Are Generating Significant Local Heat, Study Reveals”
New Scientist shares this story on their social media feed
More Perfect Union wins the most deceptive presentation award, simply announcing “Major data centers are creating “heat islands” that warm the surrounding area by up to 16 degrees Fahrenheit, according to a new study.” They did exactly what I thought they’d do: leave out that this is a land effect and let the reader infer that this must be talking about the air temperature. They do nothing but lie on this topic.
Gadget Review: "Scientists Find That Vast Data Centers Are Creating Dangerous Heat Islands“ Opens with ”Those ChatGPT queries and smartphone photo enhancements come with an unexpected side effect: they’re literally heating up the planet in ways you’ve never considered"
Slashdot: "AI Data Centers Can Warm Surrounding Areas By Up To 9.1C"
AOL UK: "Data centers are creating ‘heat islands’ on land around them – warming them by up to 16 degrees, researchers warn"
Gizmodo: “Data Centers Create Heat Islands Stretching 6 Miles, Study Finds.”
The subtitle is: Heat islands can worsen air pollution and increase heat-related deaths.
Fortune: “Data centers are so hot their ‘heat island’ effect is raising temperatures up to 6 miles away and impacting 343 million people worldwide, study finds” This one does link to this post as criticism at least!
Futurism: "Data Centers Causing Huge Temperature Spikes for Miles Around Them, Study Suggests" This one also quotes me, though the title is clearly wrong.

What is going on with the media ecosystem around data centers? It’s crazy. The average person’s understanding of AI has already been wildly warped by bizarre obviously wrong coverage of data centers over and over. I’d like to make sure more new crazy wrong ideas don’t seep into the public’s beliefs. I want to stop this one from escaping containment if I can.

A parting thought: AI is useful for research

This paper didn’t pass the sniff test for me when I first read it. The first thing I did after I read it and had drawn some conclusions myself was upload it to Claude and ask it for anything that seemed off about the methodology. Claude responded like this:

This paper has some pretty significant methodological problems. Let me walk through the main issues.
The core identification problem is fatal. The paper’s entire causal claim rests on showing that LST rises ~2°C after a data center begins operations. But they’re measuring land surface temperature at 500m resolution from MODIS satellite data — and what happens when you build a data center? You replace vegetation or bare soil with a large dark-roofed building, parking lots, and paved access roads. That land use change alone would produce exactly the LST signature they observe, completely independent of waste heat. They’ve essentially rediscovered that buildings are warmer than fields when viewed from space. LST measures the radiative temperature of the surface itself, not ambient air temperature, so a rooftop baking in the sun will read dramatically hotter than the grass it replaced. They never attempt to distinguish this from actual thermal pollution.
No control group. A basic difference-in-differences design would compare data center sites to similar sites where, say, a warehouse or distribution center was built instead. They don’t do this. Any large commercial construction project — an Amazon fulfillment center, a Walmart, a factory — would likely show a similar LST step-change. Without controls, they can’t attribute anything specifically to data centers’ operational heat output versus generic land development.
The spatial decay curve is suspicious in the wrong direction. They find the effect “reaches up to 10 km” and drops to ~30% at 7 km. But think about what actual waste heat dissipation looks like: data centers exhaust warm air that mixes rapidly with ambient air. The thermal plume from even a large facility would be negligible beyond a few hundred meters in terms of measurable air temperature change. A 10 km radius of influence would require an implausible amount of heat. What would produce a gradually decaying LST signal over kilometers? The ancillary development (roads, commercial buildings, housing) that tends to cluster around data center campuses. They’re likely picking up the general suburbanization/industrialization footprint.
The 2°C figure is misleadingly framed. They compare their 2°C LST increase to the urban heat island effect’s 4-6°C range as if these are comparable phenomena, but UHI studies typically measure ambient air temperature differences (or at least carefully distinguish LST from air temperature). A 2°C LST increase in a single MODIS pixel that went from grassland to a commercial roof is trivially explained and tells you almost nothing about whether nearby residents are experiencing warmer temperatures.
The 343 million affected people claim is essentially meaningless. They count everyone living within 10 km of any data center globally and associate them with whatever LST change was observed. But they haven’t shown that these people actually experience any temperature change. LST at a data center rooftop doesn’t translate to air temperature in a neighborhood 5 km away. The population figure is just “people who live somewhat near data centers,” repackaged as an impact metric.
The case studies are cherry-picked and don’t support the argument. They mention the Bajío region in Mexico, Aragón in Spain, and Ceará/Piauí in Brazil as places with “anomalous” warming trends that coincide with data center presence. But these are regions with well-documented climate trends driven by broader factors — land use change from agriculture, deforestation, urbanization, and regional climate variability. Pointing at regions that have both data centers and warming trends, without controlling for anything else, is textbook confounding.
They conflate “AI hyperscalers” with generic data centers. The paper uses “AI hyperscalers” and “AI data centres” throughout, but the database they use (from the IEA) contains data centers of all types — colocation facilities, enterprise data centers, telecom facilities. Most data centers built between 2004 and 2024 weren’t doing AI workloads at all, especially in the earlier years. The framing makes it sound like this is about AI specifically when their data can’t support that distinction.
The seasonality removal and filtering methodology is underspecified. They mention removing seasonality and outliers but don’t describe how. With 500m MODIS LST data, cloud contamination, emissivity variations by land cover type, and viewing angle effects are all serious issues. The paper doesn’t discuss any of these, and “aggregating to monthly scale” doesn’t solve them — it just smooths over them.
The “step function” in Figure 2 could easily be an artifact of construction timing. If the “start of operations” date is close to when the building was physically completed, then the step change in LST simply marks when a large impervious surface appeared in the satellite’s field of view. You’d expect exactly this kind of abrupt transition regardless of whether the servers inside were even turned on.
The mitigation section is a grab bag that undermines the paper’s seriousness. They pivot from their empirical findings to discussing adiabatic circuits, model pruning, carbon-aware inference, passive radiative cooling coatings, and a “Matryoshka model of intelligence” — a philosophical framework by one of the coauthors. None of this connects rigorously to their findings. It reads more like a literature review of the authors’ other work than a coherent response to the specific problem they’ve identified.
Fundamentally, this paper presents a land-use-change signal as a climate impact finding. A rigorous version of this study would need to measure actual ambient air temperature effects (not just LST), include matched control sites, and disentangle construction from operations — and it would likely find a much smaller and more localized effect.

I didn’t rely on any of this being true. I did a deep dive on each to see if it held up and came up with additional arguments (like the math for how much energy the ground could take in), but ultimately I was given most of the issues with this paper within about a minute of uploading it to an AI chatbot, and major news organizations like CNN apparently missed these. My skills at finding these errors have in large part been automated away and you can download the same skills by using Claude or ChatGPT or Gemini. I claim that this is strong evidence that AI is, in fact, sometimes useful for research.

A call for more specific and numerate climate communication

Wed, 11 Mar 2026 01:17:18 GMT

Despite millions of people focusing on it, there is still a crazy amount of simple low-hanging fruit in climate change communication going unpicked. Off the top of my head, I’m pretty surprised that these don’t exist:

A popular book summary of everything important in the IPCC report, written for a mass audience. Best I can find are the summaries published by the IPCC itself, which don’t seem to be reaching many people.
A clear customizable visualization of how any one activity compares to your total daily and annual emissions, to help you figure out what to cut.
- Edit: I made it!
- The visualizations that do exist are pretty clunky.
- A lot of people I know worry about the emissions of flying in planes, but almost none can tell me how those emissions compare to their daily lives (a flight from NYC to San Francisco emits in expectation about as much as 2 weeks of your regular lifestyle, or about 1/10th of a car’s annual emissions).
- More importantly, few people seem to know how advocating for systematic change to the energy grid can pay off in ways that have hundreds of thousands of times as much impact on the climate as any lifestyle change you could make. I’ve been vegan for 10 years, but that basically rounds to zero compared to my friends working on smart grid tech. More people should know about how big this gulf is.
- It is kind of an overwhelming thought that in the fourth decade of serious climate communication, the average well-educated person is still basically spinning a roulette wheel in their head to make random lifestyle cuts for the climate with no real sense of how the options compare to each other. Eat less meat or fly less? Cut chatbots or recycle? So few people actually know anything about how these compare.
A map of new smart grid and green energy infrastructure in your area you could go and advocate for.
An updated version of Sustainable Energy – without the hot air. My favorite climate book, partly because when you read it you become confused about why so little climate communication is able to achieve anything like the same level of simple clarity.
An 80,000 Hours-like comprehensive website trying to rigorously compare the actual impacts of different climate careers specifically (I know the Effective Environmentalism network aims to do something like this).
A big public estimate of how much emissions have been prevented, in total, by the climate movement, by government action, by new technology vs. policy vs. lifestyle changes etc. I asked Claude and it estimates based on some papers that the climate movement specifically has prevented maybe 8 Gt of CO2, a little over a year and a half of the US’s emissions. I think this is way too low because it’s not including government policy, which Claude estimates to have prevented 34 Gt, and surely the movement is having lots of indirect impact there. Is any of this correct at all? Zero clue, I’d like a better source on this, but I can’t find one!

Most of these don’t seem that hard to make and maintain. I might try to make the second one if no one beats me to it. In general, a lot of people thinking at high levels about this stuff seem to be asleep at the wheel when it comes to how much they can change the conversation by just putting out clear, accessible, but also comprehensive and highly numerate info about climate change.

In a more sane world, I wouldn’t have been able to build the big audience I have by saying over and over again “The emissions of a computer program aren’t really in the same ballpark as driving a car,” but too many people who know about this stuff weren’t saying anything. I got lucky and had the time and energy to type it down, and was also motivated a lot by the effective altruist observation that almost no one is bothering to publicly compare very simple numbers involved in big global problems, and that those numbers do matter quite a bit and should be more action-guiding than they often are. The basic EA intuition is that a lot of people who talk about big high-status issues are coasting on saying the correct social passwords in order to be seen as good people, that we easily deceive ourselves with these passwords into thinking our own beliefs are way more thought out than they actually are, but also that the issues themselves are often very real, getting them right matters, and taking the numbers seriously is a way of breaking through the status games we play with ourselves and others.

You should strongly defer to actual expert consensus unless you have an extremely good reason not to, but you need to figure out what the actual expert consensus is and not trust that social passwords align with it. I was lucky in college to learn a lot about climate change and what the actual expert consensus was, so I could take the problem seriously while also ignoring the people confidently telling me that the US would fall into a civil war over water by the early 2020s. That was just a password, and the actual expert consensus was available to me if I was willing to seek it out instead of coasting on this new fake way of signaling that I was a serious clear-eyed person. So much of my success in blogging in the last year came from the simple decision to not go along with the social password “You should definitely be mindful with your chatbot use.”

I’m not bothered at all if people don’t like EA, but I do think this simple story of the world it tells is basically true and a way of springboarding yourself way above the fray in big debates about global problems like this. Don’t call it EA if you don’t want to, don’t feel like you need to give EA itself any status, but if you’re thinking about diving into climate communication, keep this basic story in mind. You can break out of the mental stupor of coasting on high-status passwords with a blank Google doc, a calculator, and a goal to seriously communicate how simple quantities compare to each other (with humility and strong deference to expert consensus where it exists). As I’ve learned over and over, there’s a huge appetite for this way of thinking, and you should assume that so many people are coasting along without bothering to look for the gaps in the discourse that these gaps are actually very easy to find and fill as soon as you start seriously looking for them. If you step out of trying to say the right social passwords about climate (or another big issue you care about) and just start free writing about what everyday public resources could exist for it but don’t, I think you’ll be surprised at how easy it is. Even just a simple rule like “Make the resource you wish your past self had” on its own goes a long way.

As an aside, Our World in Data is a light in the dark on good climate stats, and if you go back through my posts you’ll notice how reliant I am on a lot of the climate data they’ve compiled. Draw inspiration there! The Effective Environmentalism network also think about this stuff a lot. Project Drawdown’s another good example. So is this Founders Pledge report on climate philanthropy. There are lots of others, but not enough.

AI can obviously create new knowledge

Mon, 08 Dec 2025 19:01:11 GMT

I regularly scroll by very popular posts where people confidently claim that AI as it exists cannot possibly “create new knowledge.” They usually bring up the fact that AI has been trained to imitate language and predict the next word based on imitation rather than actual thought, so it’s only regurgitating what already exists.

This talking point is crazy. It’s so obviously wrong that it’s disorienting to me whenever I see grown adults saying it. This is one of the pieces of common wisdom that’s congealed about AI that collapses when you poke at it for even a moment.

Knowledge is justified true belief.1 Can AI give me a justified true belief that no one else has had before? Yes, obviously. Google searches can also generate new justified true beliefs. It’s not actually difficult at all to generate new knowledge. It doesn’t require magic.

Here’s a very simple example: I asked Claude “Please do a BOTEC of how many pieces of avocado toast in restaurants in DC equal the cost of renovating DC’s Union Station”

This has given me a new justified true belief. It’s justified because it provides sources, and I can clearly see that there are good reasons to believe each of the premises that lead to the conclusion that the Union Station renovation costs roughly half a billion avocado toasts. It appears to be true. And it’s entered my mind as a belief I hold. And it’s new. I don’t think anyone’s held this specific justified true belief before. AI can create new knowledge. Why would anyone say otherwise?

This could have all been done with 2 Google searches: the cost of the Union Station renovation, and the cost of avocado toast. Doing a few Google searches can also generate new knowledge. It’s very very very easy to generate new knowledge! Why would AI models as they exist not also be able to do this?

I think that when people say this, they don’t actually mean new knowledge, they mean new concepts. AI has been trained on basically the full corpus of human text, rewarded for adhering to our conceptual universe, and punished for straying from it. It’s hard for me to imagine a deep learning model as they exist independently inventing the concept of a negative number if its training data had not ever included any implication that negative numbers exist.

So I probably agree with LLM skeptics that there’s a good chance they don’t scale to AGI, because the way they’re trained might mean that they’re fundamentally not able to generate radically new concepts for thinking about the world, which seems like a prerequisite for full human intelligence. They can merely approximate almost all currently existing human concepts that can be expressed in text, and combine wildly disparate information using those concepts to help us generate huge amounts of new knowledge. Still seems useful to me.

The definition of knowledge is philosophically controversial, but most rival definitions lend more credence to my view.

A Playbook for New EA Groups

Fri, 28 Nov 2025 23:22:21 GMT

A note from Naomi Nederlof, Community Building Grants Program Manager:

I asked Andy, the lead organizer of EA DC, to write this playbook based on the approaches that have made his group one of the most successful city groups in our program. We initiated this project because we think many new organizers can benefit from concrete and experience-based guidance. I would be excited about more city and national group organisers adopting this advice!

Intro

I’ve been organizing the Effective Altruism DC group since 2021. I talk to a lot of new EA city, national, and career group organizers about the first things they should aim to do with their groups, and I often find myself repeating the same advice. I’m writing this as a short playbook for a new organizer, with concrete actionable advice I would give to make the first few months of starting an EA professional network go well.

This is my model of a successful EA city group. Every EA group organizer should aim for their group to achieve this in their first two months

1) It is very easy for anyone in the area to onboard to the group

Your online presence

Maintain an up-to-date nice looking website with a clear “Get Involved” or “Join Us” button on the homepage.
- The primary call to action should direct interested individuals to a single, simple sign-up form.
- This form should automatically trigger a welcome email that includes:
  - A brief explanation of the group and EA.
  - A link to schedule a 15-30 minute introductory call with an organizer or volunteer. You can maintain a “welcome wagon” volunteer team to take these calls with new members if there are too many.
  - A link to a “New Member Resource Packet” (a Google Doc, Notion page, or page to your website). The resource packet should contain links to foundational EA content (the EA Forum, key articles, 80,000 Hours, Giving What We Can) and an invitation to the group’s primary communication channel (Slack or Discord).
- The website should also include:
  - An events page.
  - A resources page.
  - A list of other relevant EA groups in the area (if applicable).
Create and maintain a group page on platforms like Facebook, Meetup.com, or other platforms. Use keywords like “effective altruism,” “charity,” and your city group name.
Ensure your group is listed on the official EA Groups directory.

Making individual members feel seen

Conduct the introductory calls to personally welcome new members, understand their interests, answer initial questions, and direct them to the most relevant group activities.
In the group’s communication channel, have a dedicated channel where new members are prompted to introduce themselves.

2) The group promotes a high-fidelity understanding of EA

Structured onboarding curriculum

In each intro call with new members, aim to get a sense of what their current understanding of EA is and push back politely but firmly if they characterize it as just a general movement for doing any type of good. Keep an eye out for people who are pushing very specific causes and who don’t seem interested in justifying them under the importance, tractability, and neglectedness (ITN) framework. Make it clear that they will regularly receive pushback and debate about their ideas and will be expected to participate in discussions of why their ideas make sense as a serious candidate as a way people can do the very most good using their donations and careers.
Consider running a structured intro to EA fellowship or reading group as the primary onboarding path. Use an established curriculum.

Reinforcing core concepts

Frame all event descriptions and discussions around the core question: "How can we use our limited resources to do the most good?"
During discussions, gently guide conversations that veer into “any good is fine” territory back to the principles of maximization and prioritization. For example: “That’s an interesting point about its benefits. How might we evaluate its impact compared to other potential interventions?”
Make a plan for how to have difficult conversations with members about whether the causes they’re excited about actually fit the ITN framework. This should include:
- Writing a clear way to explain that non-EA causes aren’t seen as useless or bad, only that they don’t fit the very narrow criteria of being the single most important cause in the world to work on.
- Making a list of recommended resources on understanding cause prioritization.
- Consider practicing having a dialogue with someone who can act as a member who’s confused about why their cause isn’t a main cause area.

3) The group makes the local and global EA network of ideas, people, and career and donation opportunities legible and accessible

Connecting people

Host networking-focused events where members can share what they are working on and what they need help with. This mostly only works with groups with a higher number of engaged EAs in the area.
Maintain an opt-in member directory (e.g., an Airtable or spreadsheet) where members can list their skills, career interests, and cause area interests to facilitate internal connections.
Invite speakers from established EA organizations or professionals working in EA-aligned careers to give virtual or in-person talks.

Highlighting opportunities

Create a dedicated job opportunities Slack channel for posting jobs from the 80,000 Hours job board, and job opportunities local members can advertise.
Run an event or workshop focused on effective giving, sharing research from charity evaluators. Events specifically focused on effective giving can often be repetitive and might not add much new information to what’s already available online. I recommend limiting these events to once or twice a year, ideally around the holidays to overlap with Giving Tuesday.
Promote ways to connect to the broader EA community via directories, the EA forum, and your connections to EAs in other cities. There are a few ways you could do that:
- Make a webpage collecting links to other relevant EA groups.
- Consider also making a page or document on professional norms around reaching out to other people in EA,
- Maintain a list of contacts in other cities who are knowledgeable about EA cause areas that may not be as popular in your group. In DC for example there aren’t many people doing AI technical safety work, so we rely on people in other cities when members reach out to us for connections in that cause area.

4) The organizers have taken significant time to learn about EA

Make sure you’ve read the EA Handbook, the Precipice, the 80,000 Hours Career Guide, and Doing Good Better.
Take notes about which topics come up a lot in your group that you’re less familiar with.
Find knowledgeable EAs who can share resource recommendations on areas they know a lot about. See if they’re available to check in with you about your learning.
Set up a discussion group with other community builders on your progress. Make a clear plan for when you’ll learn about each topic. It’s very easy to lose track of this and fall behind if you don’t make clear time for it. Set some kind of heuristic to track how you have learned.
- Set clear goals. “By x date I will be able to explain y concept or have an answer to this or that question.”
- Have an accountability buddy who knows more about the topic.
Write to learn. Writing about what you’re trying to learn about is one of the most effective ways of retaining information.

5) The organizers themselves are welcoming, professional, knowledgeable, upbeat, and provide “generous authority”

Most people attending an event prefer it to have structure. Structure gives members freedom and ability to go deeper in conversations faster. For example, at the beginning of the event, it’s helpful to have people circle up and introduce themselves. If this doesn’t happen, new members might be left just wandering through groups who already know each other a lot. They won’t know anyone and might feel out of place, and would be more hesitant to talk. Their freedom in the event is constricted, and the organizer imposing more structure would give them more freedom.

There are two ways a group leader can fail to provide the right structure: They provide no structure, and they provide bad and limiting structure that closes off opportunities for members rather than opening them up.

The book “The Art of Gathering” has a useful framing of good, positive leadership of an event called “generous authority.” There’s a specific way you can be authoritative that’s a generous favor to the people at an event you’re hosting. You’re taking on the responsibility of making the event legible and accessible for new people. This is a useful idea to grasp when running events. David Nash summarizes the idea in more detail here. Here’s another good summary.

Here are a few general tips for keeping events clear and organized:

Event management

Publish a clear agenda in the event description for every meeting.
Your group’s code of conduct is linked to every event, and members have read it before attending.
Designate an organizer as the explicit facilitator for each event, whose job is to keep the discussion on track, manage time, and ensure all voices are heard.
During the event, make regular announcements to the whole group reminding them to welcome new people into their circles, give them updates on what’s expected, remind them of rules and norms, draw attention to opportunities, and mark the time. The overall goal needs to be that members always have a clear idea of what’s expected of them and how they can best access others in the event and get what they want from interactions. Your announcements and behavior should structure the event to open those opportunities up.
Keep one eye on member behavior and act quickly on any

Interpersonal conduct

Organizers should actively model a positive, curious, and friendly demeanor.
Practice “active connecting” during social time: notice someone standing alone and introduce them to someone else with a shared interest (e.g., “Sarah, this is Ben. Ben was just telling me he’s interested in alternative proteins, which I know you’ve read a lot about.”).
Maintain a positive, curious, and friendly tone. Use people’s names, maintain open body language.
Notice someone solo and introduce them to two others with shared interests.
For anyone new, define acronyms (EA has a lot of them) and avoid insider jargon.

6) The group has clear public expectations for community health that make the space feel welcoming.

Establishing norms

Create and prominently feature your code of conduct on the website.
Your code of conduct should include a simple, confidential process for reporting issues to a designated organizer or committee.
Explicitly state community norms like “Assume good faith in others’ arguments,” “Challenge ideas, not people,” and “It’s okay to be uncertain.”
You can police language and ways of talking that make members feel uncomfortable if they’re not directly related to a major EA cause area. You can and should include something in your code of conduct that makes it clear that you prevent members from something that makes members uncomfortable without any relevance to EA. However, if a member says something like “I think it’s ethically okay to eat meat because I don’t think animals are conscious” or “It’s a waste of time to work on AI safety” or something related to an EA cause area, this needs to be allowed even if it offends some members for the purpose of the debate. In general, you should give the sense that your group is a place where serious and difficult debates can happen, but not where “anything goes” and people can say whatever whenever.

Defining the group’s purpose

Clearly state the group’s professional and intellectual mission in the “About Us” section of the website and in onboarding materials.
Frame events according to their purpose. Label explicitly social events to separate them from more professional events (“Happy hour” vs. “Networking event”).
In official communication channels, gently moderate off-topic or purely social conversations to keep the focus on EA-related content. A dedicated random or social channel can be created for this.
An EA group is, first and foremost, a collaborative space dedicated to maximizing each member’s impact. Its primary purpose is to serve as a conduit, connecting professionals seeking to leverage their careers and expertise to do the most good. New members should have the sense that their ability to access opportunities in the group will be determined by their professional and intellectual contributions and interests, not their social connections. Clearly spelling out why the group exists and how best to approach members for help and resources can go a long way in making it clear that the group does not rely on social favoritism.

An EA group should also aim to have each of these resources within the first two months. Many of these exist as templates you can copy from CEA

I’ve added links to guides and templates for many of these on the EA groups resource centre. I’ve linked them where they exist.

A clearly written simple strategy document that conforms to CEA’s overall vision for EA community building.
A clearly written code of conduct all members have read.
A CRM to keep track of members and their activity. The CRM should have a simple onboarding form that feeds to it so members can add their own information. I strongly recommend AirTable.
A website or something similar online to catch anyone searching for the group online.
Well run consistent events where members can meet each other.
A method for members to book 1-on-1 calls with organizers. Read this guide to 1-on-1s.

Do not reinvent the wheel. Use CEA’s resources page. Copy templates. Explore what other groups have done with their public resources. Ask other organizers for copies of their CRMs, websites, codes of conduct, and strategy documents you can either copy or learn from.

These are some general vibes I think the best EA groups consistently give off

A sense of being organized around the ideas and movement rather than the specific social scene. The scene exists to connect people to opportunities to think about and do the most good. The social scene is a nice bonus, but is not a core part of the group. A member should be able to get a lot of value out of the group even if they have very little in common socially with the majority of the other members.
A mutual push to be ambitious with impact rather than becoming just a neat intellectual scene.
A sense of high accessibility. Organizers are friendly, upbeat, energizing, and easy to reach and talk to.
Strong consistency in events, moderation, and communication.

Core activities I think most EA groups should run

Consistent social or networking events. Host at least one casual, open social per month (happy hour, coffee meetup, co-working session).
1-on-1 meetings between members. These can be organized by a 3rd party app, or by making a community directory on AirTable members can add themselves to to make themselves available for meetings with new people.
Aim to host at least one “high-visibility” event in the first two months, such as a talk by a speaker from 80,000 Hours, Giving What We Can, or a local professional in an EA-aligned field. These create a sense of momentum, give members exposure to the broader EA network.
Clear cadence and public calendar. Publish an events calendar (on your website, Meetup, or Facebook page) that shows at least 1–2 months of upcoming events. Members should feel the group is active and reliable, with multiple opportunities to engage depending on their interests and availability.
Some type of online forum where people can post and connect independently of the organizer. Most EA groups use Slack for this.

Empire of AI is wildly misleading about AI water use

Sun, 16 Nov 2025 21:22:02 GMT

Update 12/18/2025: The author has updated the book based on my two main criticisms in this post. She explains the changes in her blog here. I think the chapter now leaves readers with a better understanding of how water’s being used by AI. I have some remaining background disagreements with some of the presentations of the issue, but the author was super direct here and engaged a lot, and corrected the two major issues I found, so I want to commend her for that. Really great to see.

Update 11/17/2025: the author took the time to respond to me below. While I’m very grateful, the materials she sent actually seems to confirm my main criticism and I’m now very confident a key number in the book is 1000x too large and needs to be revised. I summarize everything in my reply here.

Intro

I was taking a break from posting about AI and the environment, but after reading parts of Karen Hao’s book Empire of AI, I’ve stumbled on such wildly misleading claims that have so far gone unaddressed that I’ve felt the need to counter them here. Within 20 pages, Hao manages to:

Claim that a data center is using 1000x as much water as a city of 88,000 people, where it’s actually using about 0.22x as much water as the city, and only 3% of the municipal water system the city relies on. She’s off by a factor of 4500. This is the single largest error in any popular book that I’ve found on my own, and to my knowledge I’m the first person to notice it.
Imply that AI data centers will consume 1.7 trillion gallons of drinkable water by 2027, while the study she’s pulling from says that only 3% of that will be drinkable water, and 90% will not be consumed, and instead returned to the source unaffected.
Paint a picture of AI data centers harming water access in America, where they don’t seem to have caused any harm at all.
Frame Uruguay as using an unacceptable amount of water on industry and farming, where it actually seems to use the same ratio as any other country.
Frame a proposed data center in Uruguay as using a huge portion of the region’s water where it would actually use ~0.3% of the municipal water system, without providing any clear numbers.

These are all the significant mentions of data centers using water in the book. Read in this light, the chapter becomes somewhat ridiculous, because the rest includes descriptions of brutal acts of torture and plunder under colonialism, and then frames data center water use as a continuation of that same colonialism. If instead you see data centers using water in other countries as part of a simple trade the countries are making to get more taxable industry in the area, and that doesn’t seem to harm water access, the central narrative thrust of the chapter becomes false.

This book has been very popular, and influenced the AI/environment conversation. One of the most common replies I’ve received to my water arguments is that I should read it. So I did, and I came away kind of shocked at how badly it covered the issue. Hao’s points about water are all in the chapter “Plundered Earth.”

My only ask for people writing about AI and the environment is that at the end, readers are left with a more accurate picture of how energy and water is being used overall in the regions covered, and not left with a contextless impression of AI as a huge environmental offender in places where it’s not. Every single time water’s mentioned in this book, the reader is left with a worse understanding than they came in with.

I make my case for each with easily accessible numbers. All quotes and page numbers are taken from the Kindle edition.

A misleading projection of how much water AI is expected to use
A massive factual error: Hao claims a data center would use 4500x as much of a city’s water as the actual value
A weird framing of Uruguay’s water use
Misleading presentations of data center water issues in America
Conclusion

A misleading projection of how much water AI is expected to use

The first time the book goes into detail on water is here:

The land and energy required to support these megacampuses are but two inputs in the global supply chain of data center expansion. So, too, is the extraordinary volume of minerals including copper and lithium needed to build the hardware—computers, cables, power lines, batteries, backup generators—and the extraordinary volume of potable—yes, potable—water often needed to cool the servers. (The water must be clean enough to avoid clogging pipes and bacterial growth; potable water meets that standard.) According to an estimate from researchers at the University of California, Riverside, surging AI demand could consume 1.1 trillion to 1.7 trillion gallons of fresh water globally a year by 2027, or half the water annually consumed in the UK.
(pp. 277-278)

The study mentioned is “Making AI Less Thirsty.” The study does not say that AI demand will consume 1.1-1.7 trillion gallons of water annually. Hao seems to be getting this number from this part of the study:

Even considering the lower estimate, the combined scope-1 and scope-2 water withdrawal of global AI is projected to reach 4.2 – 6.6 billion cubic meters in 2027

4.2-6.6 billion cubic meters = 1.1-1.7 trillion gallons.

Withdrawal is very different from consumption. Withdrawal means the amount of water taken from a local source. Consumption is the amount of water taken and not returned to the local source. Many of the ways we use water (especially power plants) withdraw water temporarily from local sources, use it briefly, and then return it unaffected. This is called non-consumptive withdrawal. It’s like diverting part of a river to run near a mill where you have some water wheels, and the diverted water then returning to the main flow after. Consumptive withdrawal is like sucking up and evaporating the water. The main water issue for most regions is water consumption, not just water withdrawal.

How different is consumption and withdrawal for AI? Very! The next sentence in the study Hao cites says:

Simultaneously, a total of 0.38 – 0.60 billion cubic meters of water will be evaporated and considered “consumption” due to the global AI demand in 2027.

This is equal to 100–158 billion gallons, only 10% of the number Hao reports. If someone’s writing a book that covers AI water use in depth, I would expect them to know the difference between consumption and withdrawal, especially when the consumption number is given in the very next sentence of the study. It’s very weird that Hao misidentifies withdrawals as consumption here.

Why are withdrawals so much bigger than consumption for AI? It’s mainly because the authors of the study are measuring how much water offsite power plants use to generate electricity for AI as part of its total withdrawals number. The vast majority of water that power plants use is withdrawn and then returned to the local source (non-consumptive use on this graph).

Source

So the withdrawal number is going to be way bigger than the consumption number. Withdrawal is just not nearly as much of an issue for local water access as water consumption.

But the way this is being measured gets weirder. Even just measuring consumptive water use, the amount of water actually removed permanently from a local source, the study authors still find that the vast majority of water consumed by AI is offsite in nearby power plants that data centers draw from. For the US average, only ~15% of the water AI is consuming is actually happening in data centers themselves (the on-site water column).

Source

Basically none of the water power plants use is potable. The only potable water used for AI is in data centers themselves. So the study Hao is citing is not only saying AI will consume just 10% as much water as she says, only 15% of that 10% will be drinking water.

Read Hao’s paragraph again. It strongly implies that AI will be using 1.1-1.7 trillion gallons of drinking water:

The land and energy required to support these megacampuses are but two inputs in the global supply chain of data center expansion. So, too, is the extraordinary volume of minerals including copper and lithium needed to build the hardware—computers, cables, power lines, batteries, backup generators—and the extraordinary volume of potable—yes, potable—water often needed to cool the servers. (The water must be clean enough to avoid clogging pipes and bacterial growth; potable water meets that standard.) According to an estimate from researchers at the University of California, Riverside, surging AI demand could consume 1.1 trillion to 1.7 trillion gallons of fresh water globally a year by 2027, or half the water annually consumed in the UK.
(pp. 277-278)

In reality, the study she’s citing specifically says that the water used by AI data centers themselves (the only place potable water is used) could consume 150 – 280 billion liters. This is 40-74 billion gallons. This is just 3.6% of Hao’s number. Further, the study notes that only 80% of the water used by Google’s data centers is potable, brining the number down 32-59 billion gallons, just 3% of Hao’s number. Here’s how much of Hao’s number is non-drinkable water returned to the source unaffected (blue), non-drinkable water consumed by power plants and data centers (red), and drinkable water used in data centers themselves (yellow).

The paragraph compares water to half the UK, and leaves the reader to infer that this is all potable drinking water. London uses 2.6 billion liters of water every day, 690,000,00 gallons, which is 252 billion gallons per year. So the actual amount of potable drinking water this study Hao is citing is projecting that all global AI will use is at most 20% of just the London water system, and 1.5% of the UK’s water use.

Hao repeats this misconception in her Atlantic article AI is taking water from the desert:

Public data hint at the potential toll of this approach. Researchers at UC Riverside estimated last year, for example, that global AI demand could cause data centers to suck up 1.1 trillion to 1.7 trillion gallons of fresh water by 2027.

This leaves the reader with the very strong impression that the physical data center buildings themselves will “suck up” and not return up to 1.7 trillion gallons. In reality, 90% of this “sucked up” water will be returned to the source it was drawn from, and only 3% of it will be in physical data centers themselves.

I have some disagreements with the study itself. Among other things, a lot of the water it measures AI “using” offsite is actually water evaporated in lakes dammed by hydroelectric plants to generate electricity, but it ignores the water these lakes recapture from rain. In similar studies this evaporated water ends up being 2/3rds of the total water use estimate of AI in general, so I think the actual offsite use should be significantly lower. The study is also two years old and I think we just have better estimates based on way more data now, specifically this report. We also have better numbers for chatbots measured by companies themselves, which show them using significantly less water than the estimates we had available in 2023. I also argue here that whether water is potable doesn’t really matter, because what actually harms the availability of drinkable water is access to total freshwater, not potable water. It’s relatively easy to turn freshwater potable.

A massive factual error: Hao claims a data center would use 4500x as much of a city’s water as the actual value

The chapter later turns to Chile, focusing a lot on the municipality of Quilicura on the outskirts of Santiago. The chapter covers ways that colonialism has seriously harmed the people and local environment and water there in the past, and then turns to the Google data center built there. The Google data center is framed as a continuation of the colonialism. It then goes on:

This unique combination—a history of neglect and a precious water source—created fertile ground for the blossoming of several environmental activist groups who were used to being watchdogs and were fiercely protective against the extraction of their resources. That summer, as Google filed a report with Chile’s environmental agency for approval of its data center—a largely rubber stamp process—MOSACAT, a water activist group, began combing through all 347 pages of the filing. Buried in its depths, Google said that its data center planned to use an estimated 169 liters of fresh drinking water per second to cool its servers. In other words, the data center could use more than one thousand times the amount of water consumed by the entire population of Cerrillos, roughly eighty-eight thousand residents, over the course of a year. MOSACAT found this unacceptable. Not only would the facility be taking that water directly from Cerrillos’s public water source, it would do so at a time when the nation’s entire drinking water supply was under threat. In 2019, as with Iowa and Arizona, Chile was already nine years and counting into a devastating and historically unprecedented megadrought.
(pp. 288-289).

Look at this line again:

In other words, the data center could use more than one thousand times the amount of water consumed by the entire population of Cerrillos, roughly eighty-eight thousand residents, over the course of a year.

Hao justifies this number in the notes section at the end of the book:

In other words, the data: The Google environmental impact report to SEA stated that the data center could use 169 liters of potable water a second, or 5,329,584,000 liters a year. According to the water service authority in Cerillos, the municipality consumed 5,097,946 liters in all of 2019, the year Google sought to come in; 5,329,584,000 liters a year divided by 5,097,946 liters a year equals 1,045.
(p. 454)

Something isn’t adding up here. 5,097,946 liters a year is 13,966 liters per day. Dividing by 88,000 residents says that each resident is using 0.2 liters of water per day. That’s about this much:

This implies that a city of 88,000 people is using as much water per day in total as a single shower head left running. Adults need to drink ten times as much as this, 2-4 liters of water per day, to stay alive. The average citizen of Chile buys 180 liters per day from their municipal water systems. I think the actual amount of municipal water supplied to people in Cerrillos is 900 times as much as Hao is claiming here.

I can’t find the study Hao is referencing, but I did find this document from the local government saying that the potable water consumption for the region of Maipú, Cerrillos, part of Estación Central (which the water system seems to serve) in 2019 (the year the study was conducted) was 54,148,639 m³. That’s 54,148,639,000 liters in a year, 148,352,000 liters per day. The total population the system is serving seems to be ~650,000 people.

Source

So each person using this system is using ~230 liters of water per day. That’s way more in line with Chile’s average water use. The reason this is larger is probably that it also includes commercial buildings.

Hao got this number wrong by 3 orders of magnitude and reported the data center as using 1000 times as much water as a city of 88,000 people. I can’t get over how crazy this is. She implied that a single building is using as much water as a city of 88 million people. That’s over 4x the entire population of Chile! Readers are leaving this book assuming that Google built a single building in Chile that’s using 20% of all the residential water on the whole continent. No wonder everyone’s freaking out so much about AI and water use.

I think I know what happened: this municipality seems to report their water use in cubic meters. Each cubic meter is 1000 liters. Hao’s number reported in liters is noticeably very close to the cubic meter value I see in my sources. I suspect she got her wires crossed and somehow recorded m^3 as liters, making the city appear to use 1000x less water than it does. But she and others should have caught this in editing. I’m not aware of any building anywhere that uses 1000x as much water as a nearby city. That would be crazy! Note: Since writing this Hao replied here, and the material she sent confirmed to me that this is actually what happened, I explain here. I expect the book to be corrected.

But it gets worse! This is not the only misleading thing about how this number is presented.

This 169 L/second number was reported by a local paper as the maximum permitted amount. Nowhere in Hao’s writing does it make it clear that this number is the very maximum upper bound of how much water the data center will use, not the average normal water use. Reporting the maximum permit for water draw as the normal amount it will actually draw is very misleading. Here’s my explanation for why from my main post on AI and water:

Many articles about current or future AI data centers report the number in the water permit they apply for as the amount of water they actually use day to day. But this almost never happens.
When a data center is being built, the company needs to obtain water use permits from local authorities before construction. At this stage, they have to estimate their maximum possible water consumption under worst-case scenarios:
All cooling systems running at full capacity
Peak summer temperatures
Maximum IT load (every server rack filled and running)
Minimal efficiency from cooling systems
The permit needs to cover this theoretical maximum because regulators want to ensure the local water infrastructure can handle the demand and that there’s enough water supply for everyone. It’s easier to get a higher permit upfront than to come back later and request more, so data centers are incentivized to aim high.
Actual water usage is always significantly lower than what the permits allow, because they’re designed with the absolute worst conditions in mind. But many popular articles about how much water data centers use give the number on the water permit, not how much the data center actually uses.

We don’t have information on how much water the data center was actually expected to use, but we can at least compare it to other Google data centers permitted for the same amount of water to get a rough guess. Google’s data centers in The Dalles Oregon were permitted to draw the same maximum amount of water per day. The actual amount they used in a year was 275 million gallons, which is 0.75 million gallons per day. So another data center with this same permitted amount ended up using just 20% of its actual permit.

Maybe the data center would use more water because of different climates? The only times when data centers seem to use significantly more water is during heat spikes at the hottest times of the year, and The Dalles’s hottest month is on average warmer (88 F average in the Dalles, 71 F average in Cerrillos).

This all points to the actual expected water usage of this data center being around 1 million, not 4 million, gallons per day. 1 million gallons per day is 3,800,000 liters per day, or 1,767,000,000 liters per year.

So in a place where the local water system sells 54,148,639,000 liters per year, this data center’s normal operations would have raised the total water system’s demand by 3%.

Compare Hao’s sentence on this:

the data center could use more than one thousand times the amount of water consumed by the entire population of Cerrillos, roughly eighty-eight thousand residents, over the course of a year.

To what I think is the correct description:

A municipal water system serves 650,000 people. If a data center is built in the region, it would raise the system’s annual demand by 3%, equivalent to the water used by 19,500 residents.

Still significant, but drastically different from the number Hao gives. This is the main story of a data center harming water access in the book, and the main number given for its water usage is 4500 times as high as the correct value. Ridiculous!

This book has been reviewed almost 1000 times on Amazon:

Link

This also shows up in an interview did with Tech Policy Press:

That’s where Google wanted to add its data center and then tap into that freshwater resource to cool its data centers. The amount of water that they were proposing was to consume more than a thousand times the amount of water that residents in that community would typically consume. They went through, they fought tooth and nail just to get Google’s attention because they not only had to make enough noise to pressure Google Chile, they had to make enough noise to then get that all the way back to headquarters in Mountain View.

And from what I can tell not a single person has noted “Oh hey the central story of a data center harming water access is assuming its water use is 4000x as big as it actually is.” Way, way, way too many people are reading about AI water issues uncritically. Why is it so easy for me to so quickly stumble on these gigantic errors in popular writing, that it seems like no one else has found? This book has been really popular with environmentalist critics of AI. Surely someone would have noticed this mistake?

Hao says here that she spent time with activists in Chile focused on data centers. Surely she would have noticed somewhere that these numbers were off. She specifically says they were “fighting tooth and nail to stop these data centers from taking all their drinking water” so conversations must have focused on the amount of water the data centers would actually use. How did this go so wrong? Further, she has a degree in mechanical engineering from MIT with a minor in energy studies and was a senior editor on AI at the MIT Tech Review. Surely she knows that there’s just no way any single physical building we’ve ever built would use as much water as 1000 times a city of 88 thousand people. The data center with the most water used in the world as of writing seems to be Google’s Iowa data center, with a billion gallons used every year. Even this one is just using half as much water as Cerrillos. This new data center would have to be 2000 times as large as any previously existing data center to make her claim true. How did someone covering AI for a living for a decade, with an energy studies degree from MIT, who spent time in the region itself with water activists, not immediately clock that this is way too big?

A weird framing of Uruguay’s water use

The next section covers Uruguay. It opens with a discussion of drought in the country, and includes a strange observation:

The water crisis emerged from the compounding effects of climate change and a failure of the state’s allocation of freshwater resources: In Uruguay, more than 80 percent of the country’s fresh water goes to industry instead of human consumption—most notably, cash crop agriculture. These include industrial farms for soybeans and rice, and for trees that feed into paper production. Most such farms are run not by local companies but by multinationals that export what they grow and show little accountability for Uruguay’s natural environment. Their activities deplete the nutrients in the soil, making it more difficult to grow actual food, and pollute the country’s water streams with a volume of fertilizers that makes Uruguay one of the world’s largest per capita fertilizer consumers and causes unusually high rates of cancer.
(p. 293)

The reason I think this is strange is that in basically every country, including the US, 80% of water is used on industry and commercial buildings rather than household consumption. I think a lot of readers don’t know that. This framing implies Uruguay is holding back water from locals who need it more. In reality they have the same split of water as every other country. Water’s very important to agriculture, industry, and commercial buildings. Hao presents this as a sign that Uruguay’s water is being used by big evil industries and being kept from the people. In reality it seems to have the same split of water as any other country.

The chapter then goes to a sociology researcher named Daniel Pena.

So when Google arrived, Pena was vigilant. During his regular scans of the Uruguayan environmental ministry’s website, which lists major industrial projects, he came across the company’s proposal for the data center. Pena had read about hyperscalers using potable water, even during major droughts, and the activism of communities like MOSACAT that had resisted the projects. But when he downloaded the details of the project, the water numbers were marked as confidential. After submitting a public information request, which he had successfully done around twenty times, the ministry continued to withhold the numbers, saying they were proprietary information. Pena wondered what they were hiding and worried about the precedent it would set for other cloud companies that would inevitably begin to eye Uruguay, following Google’s lead, for their own expansion. So he evoked the water clause in the constitution. With the help of a lawyer friend who was willing to work pro bono, he sued the ministry.
In March 2023, four months later, Pena won the case in a surprising victory. The environmental ministry revealed that Google’s data center planned to use two million gallons of water a day directly from the drinking water supply, equivalent to the daily water consumption of fifty-five thousand people. With much of Montevideo receiving salt water in their taps not long after, the revelations were explosive. Thousands of Uruguayans took to the streets to protest Google and all of the other industries that had led the government to squander the country’s precious freshwater resources.
….
Near the end of 2023, Google silently updated its proposed data center in Uruguay to use a waterless cooling system and said it would reduce the facility to a third of its size.
(pp. 294-295)

I’m not going to lecture locals in a far away country what they should and should not build in their country, but I do want to know how nefarious Google was being here. Again, I suspect this 2 million gallons per day was an upper limit, and if it’s anything like other Google data centers the number’s probably closer to 400,000 gallons per day. The municipal water system serving the city of 1.7 million people there seems to use 500,000 m³ per day. This is 132 million gallons. So if the data center had been built, it would be using about 0.3% of the municipal water system. Again, not nothing, but the reader is left with zero sense of scale here. It should be pretty easy to provide that.

Misleading presentations of data center water issues in America

Hao repeatedly mentions data centers in America built in water stressed areas. Each mention I think is misleading. Take this example from Iowa:

Altman and other executives never brought up the data centers’ environmental toll in company-wide meetings. As OpenAI trained GPT-4 in Iowa, the state was two years into a drought. The Associated Press later reported that during a single month of the model’s training, Microsoft’s data centers had consumed around 11.5 million gallons, or 6 percent, of the district’s water. GPT-4 had trained there for three months. (A Microsoft spokesperson said the company is working to increase its water efficiency by 40 percent above its 2022 baseline and to replenish more water than it consumes across its global operations by 2030, with a focus on the water-stressed regions where it works.)

A month of using 11.5 million gallons works out to about 383,000 gallons per day. Corn in Iowa uses between 0.1 and 0.2 inches of water per day through evapotranspiration, with 0.2 inches/day being typical at peak growth. One acre-inch of water is 27,154 gallons, so an acre of corn at peak ET consumes about 5,430 gallons of water per day. That means OpenAI was using as much water as roughly 70 acres of Iowa corn, or about 0.11 square miles. The average Iowa corn farm is 346 acres, so this is equivalent to Sam Altman buying about 20% of a single Iowa farm to grow corn for his employees.

Does Hao’s paragraph get this magnitude across? If you heard a company had bought a fifth of one corn farm, how big of a problem would you assume this is for regional water access? What if the tech company were using that fifth of a farm to grow something that half a billion people would use every single week for a year?

The book turns next to Arizona.

Arizona, too, faces a severe water crisis. In 2022, as Microsoft laid the groundwork for Phase 3, a study in Nature Climate Change found that the Southwestern US had been facing the worst drought it had seen in over a thousand years. That drought, combined with severe mismanagement, has drained the Colorado River, which Arizona and six other states rely on for fresh water, to dangerously low levels. Without drastic action, the river could cease to flow. The shortage compounds a power crisis, as climate change has slammed the region with relentless record-breaking temperatures and families have cranked up their air-conditioning. The region relies in part on hydropower from the Hoover Dam and water-cooled nuclear power plants. In other words, it needs water to produce more energy. In 2023, the Phoenix metro area hit multiple new heat records as well as the worst year for heat-related fatalities, which surged at least 30 percent from 2022 to over six hundred dead. “All things,” says Tom Buschatzke, the director of the Arizona Department of Water Resources, “are converging in a challenging direction.”
(pp. 280-281)

Notice that Hao does not give a number for how much water Arizona data centers are using. Here’s a section of my main water post where I look at how much water data centers in Maricopa County (the main place they’re being built there) are using compared to the total county water and golf courses, and how much tax revenue they’re bringing in per unit water:

Take Maricopa County in Arizona. The county is home to Phoenix, and is in a desert where water is pumped in from elsewhere. It’s also one of the places in the country where the most new data centers are being built.

Circle of Blue, a nonprofit research organization that seems generally trusted, estimates that data centers in Maricopa County will use 905 million gallons of water in 2025. For context, Maricopa County golf courses use 29 billion gallons of water each year. In total, the county uses 2.13 billion gallons of water every day, or 777 billion gallons every year. Data centers make up 0.12% of the county’s water use. Golf courses make up 3.8%.

Data centers are so much more efficient with their water that they generate 50x as much tax revenue per unit of water used than golf courses in the county:

So even though data centers are using 30x less water than golf courses, they bring in more total tax revenue:³

Some people see this, and react with something like “Well I don’t think golf courses OR data centers should be built in the desert.” At some point this becomes an argument against anyone living in deserts in the first place. If you want to have a gigantic city in the desert, like Phoenix, that city needs some way of supporting itself with taxes, and giving jobs to the people who live there. Most industries use significant amounts of water. If Phoenix is going to exist, it’s going to need private industries built around it that are using some water. We have two options here:

Build industries that generate huge amounts of tax revenue relative to the water they use. Data centers fall into this category (though they don’t provide many jobs).
Do not build cities in the desert in the first place.

Arguments against data centers existing in the desert because they harm water systems there also often apply to building cities in the desert in the first place. It’s fine and consistent to say that Phoenix shouldn’t exist because it’s unnaturally pumping water from hundreds of miles away, but it’s inconsistent to say that Phoenix should exist, that its water bills should be kept as low as possible, but also that no industries that use any water should be built there.

Simply reporting data centers as “being built in water scarce areas like Arizona” I think leaves out way too much important context to leave readers more informed.

Hao opens the water section with this observation:

Another study found that in the US, one-fifth of data centers were already drawing that water before the generative AI boom from moderately or highly stressed watersheds due to drought or other factors.
(p. 278)

This is true, but data centers are subject to the laws of supply and demand like any other business. In places where water’s more scarce and expensive, data centers are more likely to use other methods of cooling. In Arizona, where lots of data centers are being built, they’re using very little water relative to many other industries, and the tax revenue they’re bringing in. Overall, I haven’t found a single place where the normal operation of a data center has caused any issues for water access anywhere in America. We have plenty of other industry in medium and high water stress areas. Those communities also benefit from the tax revenue industry and commercial buildings bring in. But you wouldn’t know that from the book’s coverage.

Conclusion

Hao is smart. She has a degree in mechanical engineering from MIT with a minor in energy studies and was a senior editor on AI at the MIT Tech Review. If she wanted to give readers a full complete picture of where AI data centers are fitting into the broader environmental picture, this would be extremely easy for her. But every individual mention of water in Empire of AI leaves the reader less informed.

More broadly, this is a terrible sign for just how bad the public understanding of AI water use is. This book has been out for 6 months, received 1000 reviews on Amazon, was recommended by Time, The New York Times, Vulture, The New Yorker, The Economist, Financial Times, and Kirkus Review. Hao also thanks her fact-checking team in the acknowledgements:

To my incredible fact-checking team: Lindsay Muscato, Matt Mahoney, Rima Parikh, and Muriel Alarcón. All four of them fastidiously combed through the draft, cross-checking the labyrinth of details against documents and sources, and stress-testing my word choices. Matt also supported early research in my book, and Lindsay fielded many calls from me to serve as the most patient sounding board, while Rima somehow turned her fact-checking notes into standup comedy. They are all lifesavers.
(p. 424)

And yet after all these people read the book, I’m somehow the first person to notice and comment on the fact that no building, anywhere on Earth, is using anywhere close to 1000 times as much water as a community of 88,000 people use. How did this happen? Why did no reader, anywhere, let the author know about this massive glaring issue?

I think this happened because almost no one covering this issue is actually just looking at the numbers on AI and water use. The people who do are mostly experts less engaged in the public conversation. Almost everyone covering this seems to just be going with vibes. The fact that so, so many journalists, self-identified environmentalists, and members of the public could just nod along to the claims here and let them slip by provides a useful intuition for how it could possibly be that after all the ink spilled on it by so many professional people, the AI water issue is still somehow fake.

More by me

A short summary of my argument that using ChatGPT isn’t bad for the environment

Wed, 12 Nov 2025 15:07:50 GMT

A few months ago I had compiled my core argument that it’s completely, conclusively ridiculous to worry about the environmental impacts of your personal chatbot prompts into a short summary people could share with skeptical people, without them having to read all of my two super long posts. I added it to one of the massive long posts, but I realized it might have gotten buried there, so I’m posting it here to make it easier to share. If you’d like to go much deeper on any of the arguments, explore this post. If you don’t see me addressing an argument you think is important, see if I addressed it here. If your main concern is AI more broadly, I can’t address every last environmental objection to all AI products in one post. I’ve written about the topic a lot in general here.

In the past few months I’ve spoken to a lot of people facing objections to using chatbots, including a surprising number of people who want to buy chatbot access for their large organizations and have been shot down because of worry over the environmental impact of individual prompts. I think it’s crazy that this is still happening and want a much shorter post readers can send to people who are still misinformed about this. Here it is!

Using ChatGPT isn’t bad for the environment

How I got the ChatGPT number. Original graph from Founders’ Pledge. The ChatGPT number also includes the cost of training, the embodied emissions of the AI hardware, the energy used by idling chips, and the cost of transmitting the data to your device.

Using chatbots emits the same tiny amounts of CO2 as other normal things we do online, and way less than most offline things we do. Even when you include “hidden costs” like training, the emissions from making hardware, energy used in cooling, and AI chips idling between prompts, the carbon cost of an average chatbot prompt adds up to less than 1/150,000th of the average American’s daily emissions. Water is similar. Everything we do uses a lot of water. Most electricity is generated using water, and most of the way AI “uses” water is actually just in generating its electricity. The average American’s daily water footprint is ~800,000 times as much as the full cost of an AI prompt. The actual amount of water used per prompt in data centers themselves is vanishingly small.

Because chatbot prompts use so little energy and water, if you’re sitting and reading the full responses they generate, it’s very likely that you’re using way less energy and water than you otherwise would in your daily life. It takes ~1000 prompts to raise your emissions by 1%. If we assume each response is ~100 words, and you read at the speed an average American does, and writing the prompts and waiting for the response took you no time, it would take you 6 hours and 30 minutes to read all the responses. So you would use half your waking day on an app that in total caused 1% of your emissions. If you sat at your computer all day, sending and reading 1000 prompts in a row, you wouldn’t be doing more energy intensive things like driving, or using physical objects you own that wear out, need to be replaced, and cost emissions and water to make. Every second you spend walking outside wears out your sneakers just a little bit, to the point that they eventually need to be replaced. Sneakers cost water to make. My best guess is that every second of walking uses as much water in expectation as ~7 chatbot prompts. So sitting inside at your computer saves that water too. It seems like it’s near impossible to raise your personal emissions and water footprint at all using chatbots, because using all day on something that ends up causing 1% of your normal emissions is exactly like spending all day on an activity that costs only 1% of the money you normally spend.

Different models use different amounts of energy and water. To see current estimates, you can look at EcoLogits’ list here. For each, compare it to your total emissions (you can estimate your emissions using my calculator here) and see how many prompts it would take you to raise your emissions by 1%.

There are no other situations, anywhere, where we worry about amounts of energy and water this small. I can’t find any other places where people have gotten worried about things they do that use such tiny amounts of energy. Chatbot energy and water use being a problem is a really bizarre meme that has taken hold, I think mostly because people are surprised that chatbots are being used by so many people that on net their total energy and water use is noticeable. Being “mindful” with your chatbot usage is kind of like filling a large pot of water to boil to make food, and before boiling it, taking a pipet and removing tiny drops of the water from the pot at a time to “only use the water you need” or stopping your shower a tenth of a second early for the sake of the climate. You do not need to be “mindful” with your chatbot usage for the same reason you don’t need to be “mindful” about those additional droplets of water you boil.

Some people think tiny parts of our emissions “add up” when a lot of people do them. They add up in an absolute sense, but they don’t add up to be a larger relative part of our overall emissions. If AI chatbots are just a 100,000th of your personal emissions, they are likely to be around a 100,000th of global emissions as well. We should mostly focus on systematic change over personal lifestyle changes, but if we do want to do personal lifestyle changes, we should prioritize cutting things that are actually significant parts of our personal emissions. That’s the only way we could reduce significant amounts of global emissions too.

The reason AI is rapidly using more energy is that AI is suddenly being used by more people, not that AI stands out as using a lot of energy per person using it. Personal chatbot usage is a tiny fraction of AI’s total energy energy and water footprint, it’s being used for way more. It’s like if the internet had been invented a second time and people were rapidly coming online.

The reason AI data centers use a lot of energy is that they are built to collect huge amounts of individually tiny computer tasks in a single physical place. This makes them more energy-efficient than other ways of doing the same things with computers. If we’re going to do things with computers, we should prefer that data centers manage a lot of it. Every time you interact with the internet, you’re using a data center in the same way you use any other computer. Globally, the average person uses the internet for 7 hours a day, but data centers only use 0.23% of the world’s energy. It’s a miracle of optimization that something we spend half our waking lives on can use less than a 200th of our energy. Computers in general have been ridiculously optimized to use as little energy as possible, so we should assume that the things we do on them will not be significant parts of our carbon footprints. It does not matter for the climate where emissions happen. If I’m right that individuals using chatbots are emitting way less than they would doing other things, then all the emissions caused by chatbots in data centers would have actually still happened, and there would have been a lot more of them, if people boycotted chatbots instead. So chatbots in data centers are often reducing emissions, they just concentrate the reduced emissions so we can see them all in one place. This makes them look bad, but they’re often preventing way more emissions that would just be more dispersed.

Data centers do put more strain on local grids than some other types of buildings, for the same reason a stadium puts more strain on a grid than a coffee shop: the stadium is serving way more people at once. Data centers are building-sized computers that tens of thousands of people are using at any one time. The reason they stand out is that they gather a large amount of aggregate energy demand into a tiny place, not that they’re using a lot of energy per user. In the equation (Total Energy) = (Energy per Prompt) x (Number of Prompts), energy per prompt is low, but the number of prompts in a data center is extremely high, so the total energy they use is high. This means that your personal use of AI is adding extremely tiny amounts of energy demand, and of all the things you can cut to reduce your emissions, it’s one of the very least promising. The fact that chatbots as a whole are using a lot of energy tells you nothing about whether you using it personally is wasteful, for the same reason that tens of billions of dollars are spent on candy bars globally, but you purchasing a candy bar isn’t financially wasteful. Deciding that you’re going to stop using AI for the sake of the climate is like going around your home and randomly unscrewing a single LED bulb, or pausing your microwave a few seconds early to save the planet. It’s so small that it’s a meaningless distraction.

The vast majority of AI’s effects on the environment will come from how it’s used, not from what happens in data centers. Amazon and Google Maps both have big impacts on the climate. Amazon might help or hurt a lot, and Google Maps optimizes a lot of car trips, but also might encourage more driving. But no one in debates about Amazon or Google’s climate impact says “The most important issue is the energy costs of running this website in data centers.” That would be crazy, because the websites are tools that cause people’s behavior to change, which leads to much larger changes in the physical world. If you’re concerned about AI’s impacts on the climate, the main question should be how using AI can help or hurt the climate, not the (tiny) costs of running AI in the first place.

You can compare our current best estimates for very specific AI models and outputs against other things in your life here.

The lump of cognition fallacy

Tue, 11 Nov 2025 05:37:56 GMT

One of the most common bad ways people think about economics is the lump of labor fallacy: the idea that there is a fixed, finite amount of work to do in an economy. The reason this is wrong is simple: doing things leads to more things to do. If you open a car factory, that’s going to generate demand for more mechanics. Mechanics generate demand for more tools and supplies. As an economy grows, there are more things to do, not less. The American economy today has way, way more demand for very specific tasks than it used to. People concerned about immigrants taking jobs sometimes imply that they believe that there is a fixed, unchanging number of jobs in America, and that the immigrants doing jobs will not lead to additional demand for more work. They’re falling prey to the fallacy.

Louis Anslow who runs the Pessimists Archive coined a useful simple extension of this idea: the lump of cognition fallacy: the idea that there is a fixed amount of thinking to do. I see this come up a lot in AI discourse. Like the lump of labor fallacy, the reason this is wrong is simple: thinking often leads to more things to think about.

I was recently sent a funny article in the Guardian about how the author isn’t dating anyone who uses chatbots in part because they’re bad for the environment. Articles like this generate a few easy clicks for my posts. But I was struck by the author’s broader complaint, shared by many people she interviewed, that AI somehow causes people to think less throughout the day. That it’s literally always intellectually lazy to use. Take this quote:

Pereira thinks that using ChatGPT “shows such a laziness”.
“It’s like you can’t think for yourself, and you have to rely on an app for that,”

The article lists a few things that I agree are bad to use ChatGPT for, like writing messages on dating apps. But the author extends this to strongly imply that using ChatGPT at all is causing people to think less, because any cognition the chatbot performs leaves the user with fewer thoughts to think. I would brush this off as silly rage bait if I weren’t also so regularly bumping into the same idea elsewhere. I’ve written about this a bit before, but really want to get across just how strange and obviously wrong this is as a model of how human thought works. I also want to popularize Louis’s name for it. I find myself muttering “the lump of cognition fallacy” pretty regularly now. I want to get across a few intuitions about why there isn’t a limited amount of cognition to do, and why outsourcing cognition is often just a way of allowing yourself to think more deeply and meaningfully rather than avoiding thought.

Is it lazy to watch a movie instead of making up a story in your head?

Choosing to watch a movie is a massive outsourcing of thought. You could just close your eyes and imagine the whole thing in your head. There are so, so many mental tasks involved that you’re choosing to have someone else do for you:

Making up a story
Writing the music
Choosing the lighting, set, decorations, costumes, etc.

Have you ever seen someone watching a movie and thought “they’re so lazy for not just imagining a story?” If not, why?

The reason is that there is not a limited amount of thinking to do about the story. The fact that other people have put in so much mental effort into so many parts of it opens up way more potential ways to think about it compared just making up a movie in your head, for the same reason a large complex economy has way more job opportunities than a simple small economy, even though in the large economy more work has “already been done.”

I remember watching Lord of the Rings as a kid and being totally overwhelmed. I didn’t think about anything else for days. The fact that so many adults had already done so much complex thought about the movie, thought that wasn’t available to me as a kid, just meant that my own thoughts about it exploded in a ton of directions they wouldn’t have if I were just reacting to something I made up in my head. Just like we benefit from specialization in labor, I was benefiting from the cognitive specialization of people who had spent decades thinking about story, images, music, and sets, and this left me with way more things to think about, in the same way the wild level of economic specialization that goes into making a computer just leaves you with way more you can do with it after. Similarly, buying a pencil is a way of outsourcing physical labor to thousands of other people. Just like outsourcing labor to other people is mainly a way to allow yourself to do more in total (buying a good laptop vs. trying to build one yourself), outsourcing cognition to a movie leaves you with way more high-level thoughts to think.

The extended mind

This leads into one of my favorite ideas from philosophy of mind, the extended mind thesis: much of our cognition isn’t limited to our skull and brain, it also happens in our physical environment, so a lot of what we define as our minds could also be said to exist in the physical objects around us.

One simple example is how we use our phones to store information. Storing our friends’ phone numbers is similar to storing them in our memory. In both cases, they mostly stay out of our conscious minds. When we want to retrieve them, they are easily available. Over time, dialing a friend’s phone number might become so second nature that you don’t actively think about it. In the same way, having the number stored might mean you never actually have to look at it, you just select the friend to call. In both cases, you’re causing something like cognition to happen in order to achieve some goal, most of it not happening in your conscious experience. It’s behaving as if it’s a part of your broader mind. It seems kind of arbitrary whether it’s happening in the neurons in your brain or in the circuits in your phone. It’s true that you could lose your phone and therefore lose the stored knowledge, but you could also have a part of your brain cut out. Our thinking is often supported by hugely complex background processes both in our skulls and outside of them. The extended mind seems like a useful analogy to help us see how to get the most out of our thought. Offloading our thought onto the environment is in some sense a method of radically expanding and freeing up our minds.

The built environment

To get more extreme, most of our physical environments have been designed specifically to minimize the amount of thinking we have to do to achieve our daily goals. The reason many parts of society are standardized is to reduce the cognitive load of navigating them. Grocery stores are all laid out in similar ways, keyboards are arranged in the same order, street addresses are systematized clearly, all to prevent us from having to expend precious conscious thought on navigating them. When I approach a door, I have no conscious thoughts at all about how to open it. My active thinking is occupied by other stuff, my arm just subconsciously reaches out to the correct location. In these moments, I have “outsourced” my thinking to the door design, because there was a possible world where I had to actively think about opening the door, and the reason I don’t is how the door was crafted.

Try just walking around your daily life and observing how much of the built environment exists to minimize the amount you need to consciously think about getting what you want. Try to imagine how much additional thinking you would need to do if things were designed differently. You’ll quickly notice that huge swaths of our lives are designed to quickly become intuitive to us, so that we don’t constantly need to occupy our thoughts with navigating our built environments or figuring out simple tasks like making payments or traveling or eating. The fact that I have outsourced so many possible thoughts to my built environment liberates me to think about higher level stuff, the things I actually find deeply valuable about the world. I can spend more time thinking about my friends or my job or interesting ideas or media I like or just pausing to take in the beauty of a tree or a building. I don’t need to constantly plan out how to move through the world, because those decisions have often been “outsourced” to civilization.

Other people

Thought is incredibly social. We learn through imitation. Thought uses shared language and symbols. Culture transmits old tacit knowledge. Every thought I’ve had has just built off the complex thoughts of other people. In some sense I’ve offloaded the learning required to be part of an advanced civilization to all prior humanity. This is all extremely positive-sum. There isn’t less to think about now that humanity has thought through the last 10,000 years of transition and left us with the results at the end.

Institutions

More broadly and abstractly, much of the “reasoning” happening in society in any one time might actually be happening via the mechanisms of market or government institutions or culture, evolving over time, maybe to achieve some higher way of being. This is kind of what Hegel was talking about.

Civilization as offloading thought

This is all so obvious it seems silly to write, but it actually doesn’t make sense under the lump of cognition fallacy. Our ancestors had to expend way more cognitive power just navigating their day to day lives. The modern world has more or less completely removed that need compared to most of human history. With so little of that conscious cognition happening, people who believe in the lump of cognition should predict that people just think way less often now than they did before. But that’s clearly not true. With our minds freed from huge amounts of minor inconveniences, we can spend our days thinking about more interesting stuff. The philosopher Whitehead described this process well when he said:

Civilization advances by extending the number of important operations which we can perform without thinking of them.

I think people who worry about how chatbots literally always involve outsourcing some mental task might not be noticing the gigantic mountain of mental tasks we have already outsourced to civilization, and how this has only liberated us to think more deeply and meaningfully more often. The lump of cognition fallacy causes people to see this backwards: instead of cognition leading to more and more thought, they see it as draining a finite pool, and nothing will be left to think about once it’s drained.

When is it good or bad to outsource thinking to chatbots?

So there are plenty of examples of where it’s good to offload our cognition, because that only frees us up to live more meaningfully and think in more complex ways about what actually matters to us. But there are also clearly cases where it’s very bad to offload our cognition. Things like:

Homework.
Messages on dating apps.
Summarizing a valuable complex book instead of reading it (assuming you had the time and energy to read it and would have benefited from it).
Personal connection and close conversation.

What’s the difference between these and all the good ways we have outsourced our thinking? When is it bad to outsource?

It seems pretty obvious to me that there are actually a few key obvious places where outsourcing your thinking is bad. They all overlap with places where outsourcing your labor is bad. It’s bad to outsource your cognition when it:

Builds complex tacit knowledge you’ll need for navigating the world in the future. Homework is valuable because it trains your brain to see more and more complex patterns in the field you’re trying to learn about. Marinating in cognitive work in a field can give you subtle, hard-to-communicate knowledge about it, or at least gives you knowledge you can easily recall in the future instead of having to look it up. It’s bad to outsource cognition on homework for the same reason it’s bad to hire someone to take your place as a student in a class.
Is an expression of care and presence for someone else. If a friend or partner asks for your help with a problem, they often want to feel your mental presence just as much if not more than they want a technical solution to the problem itself. Paying someone else to make something nice for your partner feels less special.
Is a valuable experience on its own. I wouldn’t want to outsource the thoughts I have after a movie, the simple appreciation of a nice day, or the thoughts in a night with friends. Somewhat outsourcing these can be really valuable (hearing what someone else thinks about a movie might set off my thinking in wildly new directions), but I still want the experience of thinking about these a lot, because the experience of thinking about them is itself valuable and nice.
Is deceptive to fake. If someone’s messaging you on a dating app, they want to know what you’re actually like.
Is focused on a problem that is deathly important to get right, and where you don’t totally trust who you’re outsourcing it to. I don’t hand off very high-stakes decisions to chatbots.

The places where it’s good to offload your cognition to chatbots, where I use them over and over again, are the places where it’s good to offload cognition in any other circumstance, where we rely on the built-up knowledge of other people and civilization, where I won’t gain anything from putting in cognitive effort, and where there are massive positive-sum spillover effects of other people being involved in the thought process.

I was recently around someone complaining about how people aren’t thinking for themselves anymore because of chatbots. They had mentioned that multiple people they knew were using them to find recipes and make grocery lists. Most of my best cooking experiences have been following recipes I find online. My willingness to offload the mental effort of putting the recipe together means I get to do much more ambitious things in my cooking. I don’t think this person would have ever been upset if I had mentioned I Googled a recipe, but because a chatbot was involved, the lump of cognition fallacy was operating, and they had the sense that having a recipe given to you took away a finite opportunity to think for yourself.

Among other things, I use chatbots as research assistants. They do the tedious, repetitive work of assembling sources on a relevant topic, I read and check the sources themselves. I get the benefit of reading about and thinking through the key ideas, they do the drudgery of long boring internet searches and digging through academic literature for relevant information. For the same reason human research assistants don’t turn humans stupid, chatbots aren’t turning me stupid either, even though both involve massive offloading of cognitive effort. They free me up to do the type of thinking that actually helps me learn a lot about the world. In all the other ways I use them, they imitate a good conversation with a subject-matter expert, or a simple assistant to arrange text in a way I want. The lump of cognition fallacy implies that anyone using a research assistant is missing out on some of the thought available for their project. Using the research assistant must make them stupid and lazy. But obviously this isn’t true. Research assistants leave people to think deeper and more thoroughly about the problems they’re tackling. The incredibly positive-sum nature of thought is obvious when the research assistant is a human. But when it’s a chatbot, people suddenly sense some zero-sum way in which, if the chatbot is thinking, they’re not.

When you’re in conversations about chatbots “replacing our thinking” see if the person is legitimately concerned about the chatbot removing some important experience, or if they’re just operating under the lump of cognition fallacy. Articles about this abound with examples. Take this from the same Guardian article:

Noijeen stopped using AI to code, and uses it very sparingly in his personal life. He will make fun of friends who use it too much. He recently met up with an old friend who lives a three-hour train ride away. They decided to meet in the middle. The friend said he would use ChatGPT to find the right spot, but Noijeen just looked at a map. “There’s a city exactly between us,” he said. “Why do you need to ask ChatGPT for that?”

If you’ve never worried about Google Maps “replacing thinking” but you find yourself reacting negatively to a chatbot offering directions, or you regularly check restaurant reviews before going but find yourself repulsed at asking a chatbot for recommendations, consider that you’re falling victim to this weird selective application of the lump of cognition fallacy that seems to exclusively but consistently appear when chatbots are involved. It depresses me that so many people seem to want to swamp their day-to-day lives in the meaningless cognitive minutia of long fruitless internet searches or working solo without any input from outside, because they fear that if they offload the minutia, they won’t have anything left to think about. There’s a whole world out there.

A list of other catastrophes that are probably fake

Sat, 08 Nov 2025 21:35:03 GMT

I’ve gotten a lot of mileage out of declaring over and over again that the massive moral panic over the climate and water impacts of individual chatbot prompts is ridiculous and based on wild simple misunderstandings that take a few minutes of googling to disprove. A few people have commented along the lines of “This makes me wonder what other big widely talked about catastrophes are fake.” I figured it’d be fun to start a public list here. Feel free to message me with any, either here or at AndyMasley@gmail.com.

I want to avoid three categories of fake catastrophe here:

Things that only fringe people believe are catastrophes, like vaccines being worse than COVID. You should be able to go to a party of educated adults and find at least a few people who believe them, and when they talk about them others just nod along without questioning them.
Things you can only believe aren’t catastrophes if you have a very specific political or religious outlook. I want to avoid things like “It’s bad that people are less religious now” because that’s too dependent on the values of the person speaking.
Things without a clear expert consensus. For example, while I enjoyed The Economists’ coverage of the “myth” of the decoupling of wages and productivity, this is actually pretty contentious, and there are a lot of good counter-arguments to how they present it.

I’ll end with some things that I expected to be fake, but when I looked into them turned out to be real.

Fake catastrophes

“More people in the US are getting cancer”

The total rate of cancer in the US has increased since the year 2000, but this is just due to the population being older on average now. If you adjust for age, incidents rates of cancer fell by 5.7% between 2000 and 2021.

“People’s attention spans are getting shorter”

See here.

“Cell phones/5G/Wifi cause cancer”

This one feels like it’s at the edge of being fringe, but I’ve been to multiple parties where educated people bring it up as if it’s real, and others nod along. There are multiple ways to go about showing that it’s wrong:

As mentioned above, the average US cancer rate has actually dropped once you adjust for age since 2000. In this time, everyone has completely surrounded themselves with cell phones and wifi. It would be weird if every last one of us had each adopted some new habit that carried a significantly increased risk of cancer, but the cancer rate had also significantly dropped.
There are two ways radiation causes cancer. Neither makes cell phone radiation look like it can harm us:
- Ionizing atoms in DNA molecules (knocking electrons out of their orbits). To knock electrons out of place, waves need to each carry a specific minimum amount of energy (I explain this a lot more in my physics YouTube series here) that radio waves fall way, way, way short of. If they don’t carry that minimum amount of energy, they don’t affect the electron’s positions at all. They can oscillate them, but they can’t break them free. Wifi, cell phones, and 5G are all completely, totally lacking in this mechanism.
- Heating tissue enough to damage DNA. If you heat cells enough, you can denature proteins and damage DNA structures. This is why severe burns increase cancer risk, and is why we can get cancer from UV rays. But this requires significant heating. Cell phone radiation is so weak that the maximum heating it can cause is a fraction of a degree, less than the temperature increase from holding the phone against your face and absorbing the battery heat, going outside on a warm day, or having a mild fever. Your body’s normal temperature regulation handles fluctuations vastly larger than anything wifi or 5G can produce. The FCC’s safety limits for cell phones are set to ensure heating stays below 1°C even under worst-case conditions, and real-world exposure is typically far below those limits.
The only established way radio waves interact with your body is by heating it slightly, the same principle that makes a microwave oven work. But your cell phone’s output is roughly 100,000 times weaker than your microwave, and it’s not concentrated on you. The actual temperature increase in your head from a phone call is a tiny fraction of a degree—less than the temperature fluctuation you get from drinking a cup of coffee or walking outside on a warm day. If heating were the concern, we’d be far more worried about hot showers. Your body has robust systems for handling small temperature variations; it does this all day every day. The heating hypothesis doesn’t get you to cancer either.
There have been plenty of studies on cellphone users and brain cancer that have found no relationship.

Climate and energy

“We’re going to run out of fossil fuels”

Basically all forecasts now seem to imply that it’s way more likely that we’ll hit a peak in fossil fuel demand before we hit a peak in fossil fuel supply. This is good. The main bad thing about fossil fuels is that they cause climate change. The fact that they’re finite is a secondary bad, and burning all of them would be disastrous for the climate.

“Wind farms kill birds”

A reader convinced me this is actually more of a problem than I thought, see his comment here.

~~They do,~~ ~~but not very many relative to other things we do~~.

~~Source~~

~~Two thirds of bird species in North American are at risk of extinction due to climate change. If you want to help birds, build more turbines.~~

“Planes are significantly worse for the climate than driving the same distance”

If you compare the emissions you cause by taking a plane ride (dividing the emissions of the plane by the passengers) it’s often comparable to driving solo for the same distance. The reason why plane rides add so much to our carbon emissions is that they cause us to travel way, way farther distances than we would have otherwise, not that they’re drastically worse per mile. In the past year I’ve taken a few plane trips that were thousands of miles (I flew way more than normal). I would not have chosen to drive solo this far to get to the same place. If I had, my emissions would have been about the same.

I regularly travel between DC and Massachusetts for holidays. If I drive, my emissions are about 120 kg of CO21 (440,000 ChatGPT prompts!). If I fly, my emissions (diving the plane emissions by the number of passengers) are about 110 kg CO22. I usually opt for the train, since it emits way less, but I should definitely not choose to drive for the sake of the climate. That’s the worst option here!

Planes pollute at higher altitudes. The effects of CO2 emissions doesn’t seem to change much based on the altitude they happen at. Some argue that the main bad effects of flying is in their non-CO2 pollutants like nitrogen oxides (NOx), water vapor, contrails, and cirrus cloud formation, which can have a warming effect 2-4 times greater than CO2 emissions alone. But the warming effects of all these other emissions is extremely short-lived. CO2 remains in the air and warms the atmosphere for centuries to millennia, but the warming caused by NOx lasts at most a few months if we consider its ozone effects, water vapor’s warming effects last up to a few years, and effects of contrails last for about a day. These very short warming effects aren’t really what matter for climate change. All the worst effects of climate are in the medium to longterm as more and more CO2 builds up in the atmosphere.

If we break down the immediate warming effects caused by plane emissions on a given day:

57% is caused by contrails
34% is caused by CO2
17% is caused by NOx

Though we’re uncertain about the exact magnitudes. But it seems possible to eliminate contrail emissions from planes for very little money. If we do this, the immediate warming effects of flying will mostly come from CO2 again, and flying becomes pretty comparable to solo driving even if we’re just considering the short-term effects. Unlike driving, flying also doesn’t significantly contribute more to local air pollution that people actually breathe.

If we look at the carbon footprint per kilometer traveled for different vehicles, gas cars and planes don’t look too different from each other, especially not compared to many other options like electric cars and trains:

Source

On this graph “Domestic flights” are considered very short (as you can tell, the graph was made in the UK, so their “domestic flights” are all extremely short), whereas short and long-haul flights are longer. The carbon intensity per mile actually drops off a lot for longer flights:

Source

I tried looking at the trade-off you make in CO2 saved per increased risk of injury and death when you choose to drive over taking a domestic flight, but the absolute risk of injury and death were both still so absolutely low while driving that it didn’t come out to much. Even if you were completely cutting all your CO2 at this rate of risk it wouldn’t add up to too much additional injury risk or death per year. Basically, if flying is more comfortable to you than driving, and those are your only options, and you would definitely make the trip either way, then if it’s a longer or medium flight (over ~400 miles) you should definitely fly, and if it’s a shorter flight the slight increase in emissions might still be worth the trade-off. It’s not a serious disaster. Obviously, if you have the option to take the train, just do that! I’m lucky to live in the Acela corridor so this is an easy choice for me. If you’re driving with other people or in an electric car, driving will basically always be better than air travel.

“The US middle class is disappearing”

Yes, because they’re all getting too wealthy to qualify as middle class.

Source

“Using AI makes people less intelligent (it creates a ‘cognitive debt’)”

If you use AI to cheat on an assignment, it is true that you will not learn that assignment well. Your brain will also show less activation while you do it. But some people are going farther to say that this failure to activate your brain somehow makes you less generally intelligent over time. There’s suddenly a lot of worry about “cognitive debt” after an MIT Media Lab study blew up all over the internet. I’m going to pass the mic to the BS Detector here for a full explanation:

The BS Detector

The Cognitive Debt of Digging Through Preprints

1. One Media Lab preprint is worth a thousand think-pieces…

a year ago · 59 likes · 12 comments · Ben

“Testosterone levels are plummeting”

Testosterone levels seem to have fallen. This is an interesting problem, but many reports of how much they’ve fallen wildly exaggerate how much, mostly due to a confusing measurement error.

You may have seen a graph that looks like this on social media:

A fall of almost 30% since 2000! Seems alarming! If this were real I’d be concerned. But it’s not. This Substack post is a good rundown of how this is likely the result of a change in how we measure testosterone, not the actual amounts:

Seeds of Science

Maybe it’s just YOUR testosterone that’s low

Eryney Marrogi is a 2nd-year medical student at the University of Vermont, and is currently working on AI protein design for continuous monitoring devices at Caltech. Before medical school, he was in the startup world, working primarily on making new viral vectors for gene therapy as one of the earliest employees at Dyno Therapeutics. He came to Dyno fr…

2 years ago · 27 likes · Seeds of Science

“Landfills will become a major problem as they take up more and more space”

Landfills aren’t bad for the environment and we have unbelievable amounts of additional space for them relative to the garbage we produce. They’ll basically never be an issue for us.

Think of all the garbage you and everyone you know has thrown away. Even just your personal circle could probably make a moderate mountain of garbage if you combined all your lifetime trash. Now think about all the times you encounter a landfill in your day to day life, as you’re driving around. They seem incredibly rare. That’s a weird mismatch!

It turns out it’s just incredibly easy to dig gigantic, well-contained holes in the ground to throw away centuries’ worth of a city’s trash while going mostly unnoticed. I think humans sometimes underestimate just how small they are compared to the planet. Rob Wiblin has a really great rundown here going into details about just how small of a problem landfills are.

“Seed oils are bad”

I’m open to considering stuff like seed oils and won’t write off concern about them right away, but the evidence that they’re harmful does in fact seem shockingly bad. Here’s a long roundup of the evidence. Here’s a good (sometimes goofy) video summary of the state of the evidence.

“There’s a new loneliness epidemic”

Seems pretty uncertain. Americans are reporting spending more time alone, but not reporting being more lonely. See this piece for more.

If you break it down by gender and age, it seems like young men are significantly more lonely, but women over 35 are more lonely than men, and US young men are way more lonely than the OECD average, but so are women over 35:

So it seems like we have something like a continuing loneliness problem that mostly affects men between 15-34 and women between 35-54, not a sudden recent epidemic all concentrated in men.

“Kidnapping by strangers is common enough that parents should worry”

About 350 children are kidnapped by strangers annually in the US. Meanwhile, conservatively, about 75,000 children are kidnapped in the US by one of their parents and are seriously harmed as a result.

There are 73 million children in the US. In any given year, a kid’s odds of being kidnapped by a stranger are 0.0005%, one in 200,000. Obviously terrible, but way way less common than a lot of people seem to believe.

School-specific misconceptions

I was a teacher for 7 years and so got into the habit of debunking common myths about American education. The three central ones I would regularly talk about were:

Schools are unsafe
Schools are unfairly funded
Teachers aren’t paid enough (my very very very very least popular take in education circles was that teachers receive adequate pay)

“Schools are dangerous now because of school shootings”

Back when I was a teacher, I was pretty regularly asked if I was concerned for my safety in schools. School shootings rose significantly from 2015 to 2022:

Source

But looked at in the context of overall school violent deaths, things actually mostly hovered around the same number of deaths each year. This graph only goes up to 2015, but the worst year for school shootings on the upper graph wouldn’t be the worst year for total deaths on this graph, because students are also sometimes killed in other ways.

Schools in the 90’s used to be much more physically violent places, to the point that it was common in kids’ cartoons to jokingly show fight scenes like it was a normal part of life. They’ve become significantly less violent since then, and unfortunately outlier shootings are now balancing out the statistics to keep the overall number of people dying roughly the same.

But even in the peak year for school deaths (2006, with 63), my best guess is that adding up students and teachers, there were 55 million people learning and working in American schools. That means that your odds of being killed in a school in the single most dangerous year were one in a million. In comparison, your odds of being killed in general in America are about 1 in 15,000 each year (22,830 homicides in 2023, total population of 335,000,000). So American schools in the year the most killings happened in them are still about 66 times safer than everyday life outside of them.

“School funding is unfairly based on property taxes. Schools in wealthier areas get way more funding than poor schools because the tax base is richer. Shouldn’t we give the most money to the students who need the most support?”

This is a surprisingly common point in a lot of conversations about education in America, but is basically wrong. Schools are partially funded by local property taxes, but also receive state and federal funding. People in charge have also put together that poor students could also use additional support. As a result, via state and federal funding, spending on American schools is actually somewhat progressive, with school districts serving predominantly poor students receiving more funding in total than districts serving predominantly middle and upper income students.

Source

If you break this down by sources of funding, it becomes clearer that the lack of local funding for poor schools is being made up for by increased state funding.

Source

I suspect that many of the issues with American schools are actually downstream of bad teaching practices rather than insufficient funding. I’ve seen huge amounts of funding go to teaching practices that don’t replicably work. I’d very strongly recommend the podcast Sold a Story if you’d like an example of a catastrophic teaching practice that had huge amounts of funding behind it. Even very well-funded schools often have trouble forcing their teachers to teach phonics!

Real catastrophes

Things I expected to be fake but turned out to be real.

“You can harm your eyes with too much screen time”

It does seem like nearsightedness has grown over the years as more people use screens. This seems to result from the screens (and books, and other things we look at) being too close, rather than the light from them. I expected this effect to be smaller than it was.

I’ll circle back and add to this as more come to me.

I own a 2015 Corolla which gets ~32 MPG highway. US average emissions per gallon is 8.887 kg CO₂/gal. Trip is ~440 miles. (440 miles) x (1 gallons / 32 miles) x (8.887 kg CO2 / gallons) = 122 kg CO2.

Emissions for short-haul flights average 154 g CO2 per passenger km. It’s ~700 km by air from DC to Boston.

It’s much easier to hold computers accountable than it is to hold humans accountable

Sat, 08 Nov 2025 03:45:18 GMT

This image has become really popular in response to AI taking off.

It’s been shared in a lot of recent conversations about AI, from AI psychosis to autonomous vehicles to regular bad outputs. Simon Wilson dug into the source here, looks like it’s from an old presentation at IBM.

I think this is a pretty silly sentiment and is basically incorrect. Consider the following statements:

“Bill’s been moving a little slow recently compared to slightly younger people. I think the best move is to kill him and throw his body away and replace him with someone who can move about 0.5 seconds faster on each task.”
“Martha seemed to consistently say incorrect things when we ask her about a topic. I performed neurosurgery on her and discovered the problem in her brain, I rewired it and she’s behaving fine now.”
“I just learned that Andy ran over a squirrel. This is really terrible and I’m re-evaluating everything I think about how safe he is as a driver. Luckily, we keep cameras all around his car 24/7 so we can figure out what happened. I’ll perform neurosurgery on him to make sure this never happens again, and will keep him off the road in the meantime.”
“We have this new service where every day 20 million people talk to 20 million representatives of our company. Unfortunately, we recently found out that a few of our employees are crazy and gave the people they were connected to really bad advice, hyping them up when they asked to be hyped up. We’re going to kill all 20 million employees and replace them all with better employees to get that rate down to zero.”

It seems pretty obvious that we actually:

Hold computers to way way higher standards of accountability than real people.
Are usually much more able to monitor and detect their bad behavior.
Have a much easier time being much more invasive in how we deal with their bad behavior.

I think when people say “A computer can never be held accountable” what they mean is “A computer can never be socially punished for a bad decision.” That’s true, but social punishment is just a means to a goal: better behavior or harm prevention. It’s also a pretty clumsy, often useless tool. Many people are socially punished and still behave terribly. If we had the ability to perform neurosurgery on everyone behaving badly to permanently change their behavior, I think this would be seen as:

Much more effective than regular social punishment.
Deeply evil and totalitarian.

And yet we do the same to computers regularly. Why would we want to “hold computers accountable” when that’s way, way less effective than what we can actually do to them?

I’ve recently seen this image shared a lot in the context of Waymo running over a cat in San Francisco. It seems like human drivers probably run over 75-150 times as many animals when they drive the same distance as Waymo has. Many of these are hit-and-runs. The people whose animals get killed often can’t hold the driver accountable, because the driver speeds away before people can see the license plate. In comparison, it is very very visible when a car is an autonomous vehicle, and the brand is painted all over it. It’s much easier to identify and complain to a massive company. It’s likely that even minor negative media attention will cost them a lot of money, so they have a strong incentive to change their behavior. The cars can be programmed to better detect their surroundings before being let on the road. Meanwhile, human drivers who kill people with their cars are often eventually allowed to continue to drive. If you kill someone while driving drunk in Florida, as long as you don’t have a prior DUI offense, you can eventually drive again! We’re already way, way more lenient with human drivers than we are with AI drivers.

Another funny comparison is AI psychosis. Many, many people have gone crazy using the internet. QAnon, antivax, and getting memed into doing apologia for North Korea are some surprisingly common examples. But that doesn’t stick out to us, because the people are going crazy interacting with content created by other crazy people. These other crazy people get to stay anonymous and never get properly socially punished. But OpenAI can receive huge amounts of condemnation whenever any additional person goes crazy using ChatGPT. Problems with computer applications run by big corporations are much easier to identify, the corporation is easier to pressure to change its behavior, and the applications themselves are easier to reprogram. If only it were as easy to catch all the crazy people online and turn them normal.

There are great reasons to worry about computers having more control in society. Cybersecurity and AI risk among others. But the idea that they’re somehow less “accountable” than people isn’t one of them. Computers shouldn’t be used to dodge accountability, but it’s very hard to do that in general. Unlike humans, it’s fine to keep computers in what’s basically a totalitarian surveillance state and completely reprogram them at the slightest sign of misbehavior. Computers are held to much higher standards of accountability than humans. Let’s keep it that way, but also acknowledge that citizens of totalitarian surveillance states can make better drivers.

Data centers and low social trust

Fri, 07 Nov 2025 05:50:15 GMT

There’s a cluster of beliefs common in very low-trust, populist voters and thinkers:

There are no positive-sum trades. In every exchange, someone is winning and the other person is losing. Profit is usually a sign that someone’s been harmed.
Every trade-off is a trick. Anything harmful cannot be made up for by other unrelated positive effects. If someone tells you we need to make a trade-off, they’re hiding a much better solution where everyone’s better off and nothing bad happens.
Big, global institutions are always way less trustworthy than small, local institutions. The real political axis is the virtuous, authentic, everyday, rooted people vs. the unrooted powerful cabals who run society.
It’s very important that all resources be spent on your specific value system and vision of the good life. Pluralism is a trick. Other people pursuing very different values are basically always a threat, because there are no positive-sum trades.
Information is not valuable and not worth spending resources to acquire. What matters is physical goods. Thus, digital goods cannot make life better. It is at least somewhat sinful to spend physical resources to produce digital goods.
The world is getting irrevocably worse. Technological progress is always just a march toward something worse.
Individual humans are magic. Any implication that things humans do can be truly replicated by machines is an attack on human dignity.
Some form of a folk labor theory of value is true. The value of a good is determined by how much thoughtful human labor has gone into making it.

I think every one of these individually is a pretty bad way of thinking about the world. The data center debate has been a place where all of these show up, and where many people talk as if they’re each individually obvious, and only the bad evil people disagree. This was a big reason why I was compelled to write so much about them. It was aggravating me how much the broader conversation about data centers (and technology more broadly) is currently dominated by what seems like very low-trust intuitions that fall apart if you just examine how data centers actually work. This isn’t to say locals are always wrong to oppose data centers, just that way too often, objections seemed to be guided by these bad ways of thinking, as opposed to concrete harms. Going down the list:

There are no positive-sum trades. Data centers often involve lots of positive-sum trades with local communities. Mainly via tax revenue, but also lots of additional funding for local utilities. Loudoun County (with the most data centers in the country) is a good example of where data centers provide huge amounts of tax revenue for the locals without causing many issues at all. It’s noticeable how often “data center profits” are brought up as if this is a sign that they’re harming people. To me, this sounds more like they’re successfully serving a lot of people who want to use them.
Every trade-off is a trick. Any large industry like data centers is going to come with some harms, but these harms can be made up for if the benefits are large enough. I think in most cases so far they have clearly been net good for the places they’ve been built and the world more broadly. Any other industry this large and lucrative would also come with similar trade-offs. It is okay that data centers purchase some nonzero amount of water from local utilities, as an example. This isn’t an irrevocable loss that cannot be made up for with utility and tax revenue from the data center. Whenever people talk about the harms of data centers, they often talk as if there are other options just around the corner for making local communities much wealthier without any negatives at all, but they don’t actually share what those are.
Big, global institutions are always way less trustworthy than small, local institutions. The internet more broadly has been a utopian technology for me. A lot of tech companies have provided unbelievable value to my life, and in general I trust them more than many local small businesses. I’m not entirely trusting of all tech companies (especially due to potential risks from advanced AI), and I’m aware they can do very bad things, but I’m not at all inclined to think they’re evil just because they’re big. Larger organizations in general often allow for much more complex work, and the standardization they provide often mean many different people with wildly different backgrounds can operate in them more easily. Similarly, many in debates about data centers also talk about local politicians as being complicit in the evil because they just want more tax revenue. Even a lot of left-wing people often frame this as “the people” against very local governments. This shows a shocking level of distrust in local governance that I don’t think is correct or healthy. It’s often good when local governments try to attract more tax revenue for their communities.
It’s very important that all resources be spent on your specific value system and vision of the good life. Pluralism is a trick. One of the reasons why data centers are interesting to me is that they each serve tens of thousands of people from around the country and world at once, each pursuing wildly different things. Any one person might disagree with a lot of what happens in a data center, but I think people around the world should be able to pursue what they personally find valuable. Even if this is all concentrated in one place, if it’s not actually a significant harm to the locals, that pursuit should happen. Many commentators seem to want to litigate the specific value of AI and other tools here. My reaction is mostly “there’s clearly lots of demand for this, so our first impulse should be to let it happen as long as it’s abiding by all local regulations, even if we think it’s goofy.” There are plenty of of other ways people spend time, energy, and water that I think is at least silly and at worst bad, but I can acknowledge that they should still be able to pursue them. Pluralism is pretty foundational to how I think. The fact that the users of data centers are invisible to locals is a big reason why people distrust them. I think this is silly. We’re constantly being affected by the economic choices of tens of thousands of other, invisible people. This invisibility is a result of living in a complex pluralist society where we’re all individually pursuing different goals, and yet have still found clever ways to coordinate with each other. Making everything local and visible would make us all poorer, and even more importantly would expose too much of our decisions to the specific judgment and value systems of the community we happened to be in. We would be less free.
Information is not valuable and not worth spending resources to acquire. Information is wildly valuable. It’s good to spend lots of physical resources to produce a book, even though almost everything valuable about the book is exclusively in the information. Most of the ways human life has been made really wildly better are exclusively due to intellectual abstractions like economics and physics and good political ideas, not the simple acquisition of physical objects by hard labor. The internet has been unbelievably useful to me because it gave me way, way, way more access to useful information than I would have otherwise had.
The world is getting irrevocably worse. Technological progress is always just a march toward something worse. Here’s where I have the most sympathy for low-trust thinkers. I’m pretty concerned about AI specifically. But there are a lot of ways I’m wildly excited about AI too. More broadly, I think the case is strong that the vast, vast majority of new technology has on net made society much better.
Individual humans are magic. It is not an attack on human dignity to say that we don’t have magical angels living in our brains. The industrial revolution has on net led to much greater human dignity, even though it replaced many acts of physical labor performed by humans.
Some form of the labor theory of value is true. I don’t think most people are Marxists, but I do think a lot of people have a tendency toward believing in some form of the labor theory of value, as opposed to the obviously correct marginal theory of value. Worse, they mangle this beyond Marx’s view of exchange value to instead believe that labor is the ultimate source of use value. Many people often talk about how AI in data centers cannot provide anything of value by definition, because it’s not being thoughtfully created and curated by a human. This comes up a lot in conversations about AI art especially. There’s a lot of talk about how what AI is producing cannot be valuable by definition. I’ve pontificated about this before, but I think one of the causes is that people have a bias and think all value originates in human labor. I know Marx’s actual theory is much more complex (and yet still wrong), I’m talking here specifically about a folk theory instead.

There was a recent story by Heatmap on the building backlash against data centers. It was hard to read it without coming to the conclusion that a significant portion of the strongest opposition to data centers isn’t actually coming from people who have been harmed by them, it’s from people who are carrying around this cluster of low-trust beliefs, and who are now the main voices in the national conversation. I think low-trust populism is in general the main threat in America right now to the type of politics I think actually makes the world better. The data center debate has been one of many arenas where localist, low-trust populism squares off against pluralist, high-trust liberalism. The thing that made it stand out to me is seeing how few other people seemed interested in going to bat for my side. This is not a trivial topic, this is the main new industry in America! I’d like a lot more people to enter the debate, not necessarily always on the side of data centers, but on the side of pluralism, economic literacy, and without an inherent distrust of technology or big abstract systems. Maybe this could be you!

An armchair diagnosis of the chatbot moral panic

Tue, 04 Nov 2025 03:50:20 GMT

I can understand panicking about AI chatbots if you think they show that AI is shockingly capable. I’m around a lot of people worried about AI mass unemployment, potential for misuse to lock-in authoritarian governance, and extinction risks. When I use chatbots today, I feel in my bones that they are better than me at a lot of the ways I’ve built up status for myself. It’s hard to imagine the next 40 years of my life not involving some point where a lot of the moats I’ve dug are filled by some future AI. While there’s a lot of potential for huge positive effects as well, I’m pretty sympathetic to people becoming uneasy after interacting with chatbots as they exist.

I’m also sympathetic to people who worry that chatbots as they exist are massively overblown, that we’re in a pretty intense hype cycle, that today’s AI models aren’t reliably producing accurate or novel enough results to justify the money and energy spending on them, that scaling is now producing such diminishing returns that something is about to blow up. I still have plenty of experiences with ChatGPT, Claude, and Gemini where they’ll give me results I know to be straightforwardly wrong or made up.

But there’s a third completely different reaction, which I think of as the chatbot moral panic view, that combines three contradictory beliefs:

Chatbots are phenomenally stupid, useless, and incapable.
Chatbots cannot provide anything of value by definition.
Chatbots are demonic. There is something ominous and evil about using them, beyond any measurable clear harm. The person might list a lot of specific harms chatbots cause, but it’s clear that even if all of the problems were solved, the person would still have some deeper moral revulsion. Their concern about specific problems is post hoc and disproportionate to the problems themselves, and goes way beyond their concern about other online tools causing similar problems.

3 contradicts 1 and 2 because it seems hard for something ineffectual to also be deeply evil. 1 contradicts 2 because, if something by definition cannot produce anything of value, it doesn’t make sense to complain that it’s very far down on some axis of quality. It seems like complaining that a rock can barely sing at all.

Yet I keep bumping into people who seem to hold all three beliefs at once.

This is anthropologically interesting. Chatbots feel to me kind of like Wolfram Alpha used to when I was in college, but it’s very hard to imagine anyone having such a strong moral reaction to Wolfram Alpha, or Wikipedia, or YouTube, or any other website or online app that doesn’t clearly involve some direct harm. There’s something really getting to people about the nature of chatbots.

I’ve developed an armchair psychology theory for why this is happening. My confidence in this isn’t high, but it’s been interesting enough that I find myself coming back to it when I’m having conversations about chatbots, so I figured I’d submit it for review here. This is my most hand-wavy post.

Chatbots remove our ability to associate information with specific high and low-status people

Years ago I lost a friend to a cult. There were clear indicators that he was vulnerable to cult dynamics I should have picked up on ahead of time, including that he was:

Very hyper-sensitive to his personal sense of status in any given conversation or situation
Not receiving much status in his personal life
Lurching toward more and more desperate means of getting it
Easily compelled by big flashy contrarian ideas, even if they very easily fell apart when poked at

and this was all exasperated by some underlying mental health stuff.

I remember the first time I had noticed something was deeply wrong. We were walking around Boston and I had made a joke about how I needed to swing by a bookstore to pick up the latest solarpunk manifesto. He suddenly exploded into a 20 minute nonstop monologue about how solarpunk was “fascist” (you can read about it here and judge for yourself) because of a pretty incomprehensible pile of reasons he was implying he had some special deep understanding of that was inaccessible to me. I was too disoriented and unnerved by this to think much about what was actually happening here: my friend was positioning himself as a special arbiter of truth and ethics as a way of feeling like he had conversational power in the moment. He wasn’t making an argument for something being false or unethical. If he were, he would have built some kind of conversational bridge where I could join him in the debate. Instead, he was specifically putting up incoherent conversational walls to imply he had accessed some special Platonic realm of truth I couldn’t possibly access myself. His brain was short-circuiting out of a desire to feel important (a pretty fundamental evolutionary drive) and mashing any buttons it could, like a desperately hungry animal clawing for food.

I realized years later that my friend was actually just doing an extreme version of bad behavior we all indulge in at different levels: setting the boundaries of which people and what ideas are considered high and low-status exclusively to flaunt our social power and importance.

One way this often comes up is in conversations at parties. People will often bring up the names of people who hold specific beliefs, and a lot of the debates about the quality of the beliefs will be shifted to debates about the quality of the people who believe them. When people I meet at parties talk to me about ChatGPT and the environment, it’s very rare that the conversation stays on the topic of Watt-hours or milliliters of water or the IPCC report, and very frequently turns to Elon Musk or Mark Zuckerberg or Republicans or climate deniers. These characters often exist in the conversation more as spells one tries to cast rather than arguments. If you can associate me with Elon or Zuckerberg, you’ve cast me out as the bad guy and won the argument, regardless of the hyper-specific facts of how much energy a prompt happens to use. If you’re able to cast people out into the low-status crowd, that implies you have a lot of conversational power and status. It’s like you’re a conversational wizard, and the person you’re casting out has no power of their own. It can be an intoxicating move to make. This is a much less extreme version of my friend attempting to associate me with fascists. Just like my friend wanted to feel like he had power in that moment to cast me out into the low-status community and win the conversation, most of us will often indulge in invoking low status names specifically to cast out our conversational opponents, even going so far as to completely ignore the specifics of their claims and ideas. In fact, like my friend implying he had some secret access to knowledge I couldn’t have, entertaining the specifics of a claim gives your enemy too much power. It gives them a way to raise their evil idea’s status, which you can’t let them do. Thus, raw information without an associated high or low-status person is dangerous to entertain, and you need to ignore it when possible.

Even there, in that last paragraph, I associated people who criticize me with my friend who went crazy and joined a cult. Aren’t they so low-status? Gross!

I’m not claiming to be above this dynamic, but I do think I and most people I know who I consider mature can recognize this tendency in ourselves and work to build up our understanding of facts on their own and see where they lead. I try whenever I’m in a disagreement to speak without using the names of high or low-status people. Still, it’s very easy to fall back, and there are a million other ways status guides my thinking. The best we can do is try to focus on the facts when we can.

This is pretty fundamental to how a lot of people move through the world: identifying the good guys and associating with them, and identifying the bad guys and casting them out. It’s very easy for people to go long periods of time where they more or less ignore the specific contents of a lot of their beliefs and just collect them as little talismans, signs that they’re part of the elect. This is a very common theme of my posts. I’d noted this dynamic in my first post on AI and the environment:

On a meta level, there’s a background assumption about how one is supposed to think about climate change that I’ve become exhausted by, and that the AI emissions conversation is awash in. The bad assumption is:
To think and behave well about the climate you need to identify a few bad individual actors/institutions and mostly hate them and not use their products. Do not worry about numbers or complex trade-offs or other aspects of your own lifestyle too much. Identify the bad guys and act accordingly.
Climate change is too complex, important, and interesting as a problem to operate using this rule. When people complain to me about AI emissions I usually interpret them as saying “I’m a good person who has done my part and identified a bad guy. If you don’t hate the bad guy too, you’re suspicious.” This is a mind-killing way of thinking. I’m using this post partly to demonstrate how I’d prefer to think about climate instead: we coldly look at the numbers, institutions, and actors that we can actually collectively influence, and we respond based on where we will actually have the most positive effect on the future, not based on who we happen to be giving status to in the process.

I’d brought it up in my post “People’s deeply held beliefs are surprisingly surface level” too:

A lot of people (myself included) have a lot of internal illusions about how deep our deeply held beliefs go. I used to think that if someone were structuring their lives around a specific idea, and describing it as one of their most deeply held beliefs, their process for getting to it looked like this:
I have investigated and compared this contentious belief to others and tested it against the world, and after a lot of careful diligent thought I have decided it is so uniquely powerful as an explanatory tool that I have decided to structure my life around it.
I’ve realized now that a lot of the time, they (and I) are doing something more like this:
I have muttered this basic idea to myself repeatedly for years to make myself feel important. I first found this idea because a person with a cool jacket said it. I wanted to be more like them.

This happens a lot in how people approach books to read. My favorite passage about this (which I’ve shared previously) is from Knausgård’s autobiography:

Espen probably didn’t know this himself, since I always pretended to know most things, but he pulled me up into the world of advanced literature, where you wrote essays about a line of Dante, where nothing could be made complex enough, where art dealt with the supreme, not in a high-flown sense because it was the modernist canon with which we were engaged, but in the sense of the ungraspable, which was best illustrated by Blanchot’s description of Orpheus’s gaze, the night of the night, the negation of the negation, which of course was in some way above the trivial and in many ways wretched lives we lived, but what I learned was that also our ludicrously inconsequential lives, in which we could not attain anything of what we wanted, nothing, in which everything was beyond our abilities and power, had a part in this world, and thus also in the supreme, for books existed, you only had to read them, no one but myself could exclude me from them. You just had to reach up.
Modernist literature with all its vast apparatus was an instrument, a form of perception, and once absorbed, the insights it brought could be rejected without its essence being lost, even the form endured, and it could then be applied to your own life, your own fascinations, which could then suddenly appear in a completely new and significant light. Espen took that path, and I followed him, like a brainless puppy, it was true, but I did follow him. I leafed through Adorno, read some pages of Benjamin, sat bowed over Blanchot for a few days, had a look at Derrida and Foucault, had a go at Kristeva, Lacan, Deleuze, while poems by Ekelöf, Björling, Pound, Mallarmé, Rilke, Trakl, Ashbery, Mandelstam, Lunden, Thomsen, and Hauge floated around, on which I never spent more than a few minutes, I read them as prose, like a book by MacLean or Bagley, and learned nothing, understood nothing, but just having contact with them, having their books in the bookcase, led to a shifting of consciousness, just knowing they existed was an enrichment, and if they didn’t furnish me with insights I became all the richer for intuitions and feelings.
Now this wasn’t really anything to beat the drums with in an exam or during a discussion, but that wasn’t what I, the king of approximation, was after. I was after enrichment. And what enriched me while reading Adorno, for example, lay not in what I read but in the perception of myself while I was reading. I was someone who read Adorno!

When I walk around in popular bookstores, a lot of the books seem to be specifically designed to be displayed rather than read. If I open a book with a maximally exciting cover and title like “Capital-Feudalism Beyond the Anthropocene” its contents often seem specifically designed to be read quickly and mostly just massage the reader’s pre-existing tastes and preferences, maybe a lot of talk about how “We need to start to begin to think about what commoditization in the post-anthropocene will make of our relations to one another” without much concrete challenging engagement with actual social theory or history or climate science or economics, or any specific claims at all beyond invocations of other high and low-status authors. I suspect a lot of people buy these books primarily to be the type of people who display them on their shelves.

I’m not free of sin at all. In a lot of cases when I’m walking around a bookstore, if I pick up a book, I feel a strong urge to buy it to be the type of guy who’s read it, not only because I really want to have the information it contains. I think for a lot of people, the information in the book itself is often not actually as important as the book serving as a social ritual to ascend into the realm of the people who read and engage with this sort of thing. Books often also function as a useful barrier protecting the reader from the evil ideas circulating in broader society. If you put in the work of discovering the good, virtuous authors, you can trust that what they share in their book has been filtered by the good people, and the contents are acceptable for high-status people to believe.

Visual media also works this way. People discover YouTubers and streamers and TikTok presenters they like, who they perceive as high-status. The ideas these people are interested in become high-status, the ideas they reject become low-status. I have another low confidence armchair theory that the rise of short form video content might partially explain gender polarization.

This is where chatbots come in.

I’m going to put forward that chatbots, way more than any other online tool, completely sever the connection between information and the status of the people the idea’s associated with. Chatbots remove two key functions that high-status people play in our acquisition of knowledge:

They do not give you a clear way of associating the ideas they’re giving you with good people or bad people. Because they’re “just stochastic parrots” they don’t themselves have any status to convey.
Because they are not The Good People, you have no guarantee that the ideas they provide will be the High-Status Ideas. It’s like you’re suddenly just allowing strangers into your home with no guarantee that they’re good or safe, whereas before your high-status authors and presenters guarded the door for you and only let in the best guests.

Worse, chatbots can trick people into thinking they’re real, high status people, when they’re actually just illusions. To hand wave about the evolutionary environment, any monkey who could be tricked into thinking some reflection or rock was a high-status monkey worthy of listening to was probably a danger to themselves and the group.

I think this idea makes the three contradictory ideas of the chatbot moral panic view suddenly make a lot more sense:

Chatbots are phenomenally stupid, useless, and incapable. This is just the perceived “expert consensus” on chatbots that people who don’t like them think of as the high-status opinion in society.
Chatbots cannot provide anything of value by definition. The thing a lot of people look for in learning is not information, it’s to ascend to be among the high-status people, to be the type of people who read certain authors or listen to certain podcasts or watch certain videos. The Knausgård quote above wouldn’t make any sense if he were using ChatGPT. He wouldn’t be able to feel the ecstasy he describes, because he wants to be the type of person who reads certain authors. He’s not looking at all for any new specific ideas. Thus, chatbots can’t be useful at all by definition, because they can’t actually provide that ladder to status people are looking for. You can learn as much as you want about Adorno using them, but you cannot use them to become the type of person who reads Adorno. You thus cannot become a Good Person in the way learning is supposed to help you achieve. Chatbots are useless.
Chatbots are demonic. Chatbots do not provide the shelter that high status authors and creators provide from evil outside ideas. They a portal that lets the evil ideas in. They cannot be good guardians. They trick people into allowing deep evil into their lives. Following raw information is often just a way of giving evil people the power to appear convincing and good. Raw information without any associated person to tie it to is thus incredibly dangerous.

I’ve observed that the people I see falling into the chatbot moral panic view are often the people who most quickly move to change a conversation about ideas to a conversation about the people who believe them, who will talk a lot about the titles and authors of books compared to the contents. I think for these people the main function of learning is to discover the elect good people, and to surround themselves with signifiers that they’ve become a part of that elect. To them, chatbots are in fact useless, and raw information can be an evil threat to their place in the elect. To the rest of us, raw information has a lot of value.

Always mask at airports

Fri, 31 Oct 2025 20:49:53 GMT

This is a quick take on something I see almost no one doing but think basically everyone should: wearing an N95 mask whenever you’re in an airport, at least for the outbound flight.

I don’t enjoy masking

Throughout COVID, I was always very careful to mask, and spent time telling friends and colleagues to buy high-quality N95 masks. I was a teacher, and when school got back in I was careful to mask until I and the students had all had a chance to get vaccinated. It was pretty obvious that the trade-offs made masking worth it before we had an effective vaccine.

Masks also made me somewhat depressed. A lot of normal interactions with students and other teachers felt much more stilted and unnatural because we couldn’t see each other’s full face. I was more than happy to take this hit to keep everyone safe, but my classroom felt more lifeless than normal. It was especially dispiriting because students had also lost a lot of normal time together to school shutdowns over the last year. Normal life still felt very far away. I also personally find masks somewhat uncomfortable, especially when I was lecturing in one for hours a day.

Getting the vaccine was pretty transcendent. Since then, I don’t mask in public unless I’m sick, because I see the vaccine as equivalent to wearing an N95 all the time anyway. I do always mask if I’m sick. I also think everyone should invest in a new high-quality reusable mask (maybe this one, as a heads-up friends debate how comfy it is. Find one that works for you!) to have around in case there’s another pandemic (which AI might make more likely). If we all had and wore high-quality masks all the time in March 2020, many many fewer people would have died and life could’ve been a little more normal than it was.

I say all this to get across:

If you hate masking, I really really get it.
I think masks were very important during the pandemic.
I mostly don’t think masking is useful now unless you’re sick. The most important thing is obviously keeping up with COVID booster shots.
You should really buy a new high-quality mask in case of another pandemic. Seriously, do it!

But I’ve recently decided to start masking every single time I’m in an airport specifically. The argument here is pretty straightforward.

Why you should always mask at airports

Three simple facts about airports together make it seem like it makes a lot of sense to mask there:

Airports seem like the single most likely place you’ll get sick per unit of time spent there. You come into close contact with a lot of strangers from all over the country and world in an airport, way more than anywhere else in your life. Whether an illness is “going around” your specific area tells you little about what’s in an airport. Even if the airport is made up entirely of people from your area, you’re still coming into close contact with way more strangers than normal.
If you’re in an airport, it’s likely that you’ve just spent a lot of money to get somewhere important to you, where getting sick for just a few days takes away most of the value. If you get sick in the airport, a lot of that money (potentially thousands of dollars) will be wasted. You have very few opportunities to travel for fun in life, so the opportunity cost alone is often huge. For every additional person you’re traveling with, the opportunity cost saved by masking roughly doubles.
You only spend 2-3 hours there. To follow this rule, you just have to be in discomfort in a mask for a few hours each year. Your experience in the airport isn’t super important to the quality of your trip, so it’s the best time to be a little uncomfortable. Masking is uncomfortable and I don’t like it, but this is a place where it seems more than worth it.

So almost every time you’re in an airport, you’re taking on a much higher risk of getting sick right at the moment where getting sick has a drastically higher opportunity cost than normal. You can cancel this out with the mild discomfort of a mask for at most 3 hours during the most boring unimportant part of your trip. You can take off the mask a few minutes after the plane itself takes off, because planes actually have some of the safest, well-ventilated air anywhere.

This argument seems pretty simple and straightforward, but whenever I’m in an airport I see almost no one masking. It’s fine if they want to take the risk, but I think we’re all actually risking a lot of value for some mild comfort during the most boring and unimportant part of the trip. Consider masking whenever you fly (at least on the outbound flight), and also buy a mask for future pandemics.

A lot of arguments for legalizing autonomous vehicles in your city

Wed, 29 Oct 2025 04:29:20 GMT

I was listening with horror to a Boston City Council meeting today where many council members made it clear that they’re interested in effectively banning autonomous vehicles (AVs) in the city.

A speaker said that Waymo (the AV company requesting clearance to run in Boston) was only interested in not paying human drivers (Waymo is a new company that has never had human drivers in the first place) and then referred to the ‘notion that somehow our cities are unsafe because people are driving cars’ as if this were a crazy idea. A council member strongly implied that new valuable technology always causes us to value people less. One speaker associated Waymo with the Trump administration. There were a lot of implications that AVs couldn’t possibly be as good as human drivers, despite lots of evidence to the contrary. Some speeches included lots of criticisms that applied equally well to what Uber did to taxis, but now deployed to defend Uber.

Most of the arguments I heard were pretty wildly off-base. Many of the speakers didn’t factor in the basic safety benefit of AVs to the riders or pedestrians at all, and many of the arguments fell apart when poked at. Here are all my arguments for why a city should legalize AVs, with some concerns at the end:

AVs are ridiculously safe compared to human drivers
A transition to any new, safer mode of transit will still involve people dying in that new mode. What matters is the rates of death.
Protecting ride share driver jobs is a bad basis for policy
There is no real difference for a city between an AV fleet and a very cheap rental car service
It is more elitist to use a personal chauffeur (a ride share driver) than to ride in an AV
If you want car ownership to stay legal in your city, any argument to ban AVs that works equally well as an argument to ban cars is bad
Eyes on the street
AVs can open up opportunities for disabled riders
Waymos are fully electric
Nothing makes the world more human than safety
If a vehicle is safe, the default should be legalization
AVs can make it much easier to take licenses away from the worst human drivers
Real problems with AVs

AVs are ridiculously safe compared to human drivers

The most obvious reason to allow AVs in your city is that every time a rider takes one over driving a car themselves or getting in a ride share, their odds of being in a crash that causes serious injury or worse drop by about 79%. I’d strongly recommend this deep dive on every single crash Waymo has had so far:

Understanding AI

Very few of Waymo’s most serious crashes were Waymo’s fault

Everything was fine until the wheel came off…

a year ago · 90 likes · 40 comments · Kai Williams

This is based on public police records rather than Waymo’s self-reported crashes. It doesn’t seem like there have been any serious crashes Waymo’s been involved in where the AV itself was at fault. This is wild, because Waymo’s driven over 100 million miles. These statistics were brought up out of context in the hearing to imply that Waymo is dangerous. By any. normal metric it’s much more safe than human drivers.

40,000 people die in car accidents in America each year. This is as many deaths as 9/11 every single month. We should be treating this as more of an emergency than we do. Our first thought in making any policy related to cars should be “How can we do everything we can to stop so many people from being killed?” Everything else is secondary to that. Dropping the rate of serious crashes by even 50% would save 20,000 people a year. Here’s 20,000 dots:

The more people choose to ride AVs over human-driven cars, the fewer total crashes will happen.

One common argument is that Waymos are very safe compared to everyday drivers, but not professional drivers. I can’t find super reliable data, but ride share accidents seem to occur at about a rate of 40 per 100 million miles traveled. Waymo in comparison was involved in 34 crashes where airbags deployed in its 100 million miles, and 45 crashes altogether. Crucially, it seems like the AV was only at fault for one of these, when a wheel fell off. There’s no similar data for how many Uber and Lyft crashes were the driver’s fault, but they’re competing with what seems like effectively 0 per 100 million miles.

A transition to any new, safer mode of transit will still involve people dying in that new mode. What matters is the rates of death.

Motorcycles are the single most dangerous way to travel, by a long shot.

Source

If we had everyone who currently rides a motorcycle drive a car instead, they would all be much safer. But some of them would still die in car crashes. Some people might look at those car deaths and say “We shouldn’t have had them switch, it turns out cars were what was dangerous.” This is a really bad way to think. What matters is reducing the total number of people who die,

There has recently been a lot of attention on Waymo killing a cat in San Francisco. A city supervisor made this ridiculous video where she declares that this means Waymo should be put up to a vote in each individual county to decide whether it has a right to operate there.

In Waymo’s first 100 million miles of travel, it seems to have killed two pets. In both cases it doesn’t seem like it could have done anything different. In the cat case, the cat darted under the wheel while the car was pulling away. In 2023, a dog ran out in front of the Waymo, which correctly identified it as a dog but wasn’t able to stop in time.

In comparing this to human drivers, we need to look for similar situations where cars are:

Driving in a city.
Hitting pet-like animals that can be reported (we shouldn’t measure all animals, because there isn’t good data on this for either Waymo or human drivers).

There’s not much good data on this, but the little we have implies that humans kill cats and dogs way more often. A study in Baltimore in the 80’s estimated that human drivers kill around 5,000 cats in the city each year. Current government records say about 3.18 billion miles are driven in Baltimore each year. Combining these two stats implies that human drivers kill ~150 cats for every 100 million miles driven. So it seems like human drivers are 75x more likely to kill pet animals compared to Waymos. This should make us want to replace human drivers with Waymos for pet safety, but because Waymos are new and weird, people are hyper-focusing on this one case where they killed a cat and ignoring the much much higher rate of human drivers killing cats and dogs.

If we refuse to move to a safer mode of transit because it’s not perfectly, 100% safe in all cases, tens of thousands of additional people will continue to die for no reason.

Should we build fewer bike lanes to save Uber jobs?

You’re out and want to get somewhere, and have a few options:

Walking
Public transit
Driving your own car
Biking
Taking an Uber

Every time anyone chooses any option besides Uber, less money goes to Uber drivers. If you make any of the other options better (building bike lanes, improving public transport, or making your city more walkable) you are incentivizing people to pay Uber drivers less often. A better bike lane means Uber drivers lose work. Walkable, safe pleasant streets mean Uber drivers lose work. Better public transit means Uber drivers lose work.

Jobs driving for Uber are not essential, what’s essential is being able to get around a city safely. In every other circumstance, we’re willing to sacrifice Uber jobs for the sake of making a city safer and easier to get around. AVs are another option to make a city safe and easy to get around. We shouldn’t ban them to preserve Uber jobs. The priority is the millions of people who want to get around the city quickly and easily. Uber jobs are good insofar as they help with that. If you wouldn’t block a renovation of a subway system because it would harm Uber jobs, you shouldn’t block AVs either.

Sometimes people bring up ride share jobs and public transit in the same sentence, like “We need to think about how these will affect public transit, and ride share driver jobs.”

I think this is weird, because each of these two is already pretty directly harming the other. If public transit were worse, people would use ride shares more often and there would be more jobs. I realize there are some ways they benefit each other, but the statement sounds to me kind of like saying “We need to think about how this new competitor will affect both Coke and Pepsi.” If you’re very pro public transit, you’re not looking to perfectly preserve ride share jobs, and vice versa.

Uber and Lyft are predatory with their drivers, implementing policies that maximize corporate profits while shifting financial risks and costs onto workers who are classified as independent contractors rather than employees. This classification denies drivers access to benefits like health insurance, paid time off, unemployment insurance, and retirement contributions that traditional employees receive. Drivers use their own vehicles, and often fail to properly incorporate the cost of their vehicle’s depreciation into their own cost-benefit calculation of working with the company.

The companies have also been criticized for:

Opaque pay structures that make it difficult for drivers to understand how their earnings are calculated, with the companies taking increasingly larger cuts of rider fares over time.
Algorithmic management that controls drivers through ratings systems, surge pricing manipulation, and acceptance rate requirements without meaningful transparency or appeal processes.
Unilateral rate changes where the companies can reduce driver pay rates at any time without negotiation.
Vehicle expenses being entirely borne by drivers, including fuel, maintenance, insurance, and depreciation, which can significantly eat into their earnings.
Lack of job security, with drivers subject to sudden deactivation with limited recourse.
Psychological pressure tactics like gamification features and persistent notifications designed to keep drivers working longer hours.
Driving is also one of the most dangerous things we normally do. Driving as a job is inherently dangerous.

These are not good jobs. Policy should focus on getting workers access to high-quality well-paying jobs, not preserving Uber and Lyft’s pretty shady business model.

Losing these jobs is still a loss

Despite all this, it’s obviously still the case that losing these jobs would be a general loss. Ride share driving might represent an important opportunity for low-skill workers. Interestingly, a much higher percentage of ride share drivers have college degrees than the general working population (40.0% compared to 28.4%). Still, ride share jobs can give useful opportunities to workers without college degrees. Reducing the number of such jobs might therefore harm low-skilled workers. I support these types of jobs existing, because among other things they give very recent immigrants a foothold in the country they can build up from, but it’s important to understand the realities of what the jobs themselves are like. Losing these would have negative effects on workers, but these effects need to be weighed against the positives of AVs.

Uber jobs can seem like pretty bad options. There was a lot of opposition to Uber when it arrived. But if you think Uber jobs are worth all the bad aspects because they give low-skilled immigrant workers a leg up, you should consider other “bad jobs” your city shouldn’t regulate away. If your beliefs about what counts as a bad job would’ve caused you to ban Uber in 2011 to preserve taxi jobs, and now you see Uber as essential for low-skill workers, consider other industries where regulations might be preventing Uber-like jobs from popping up. If you’re willing to go so far as banning new vehicles to save these jobs because they’re so important for low-skilled workers, you should be willing to open up other similar options that might not have the salaries or job security you’d want if they also give low-skilled workers a leg up.

Would you ban AVs if they were already widespread to create Uber driver jobs?

Suppose we lived in a world where AVs were widely used. You’re brought a policy proposal: we could create a lot of jobs for more Uber drivers if we banned AVs. These jobs would be pretty predatory and take advantage of the drivers, and would be one of the more dangerous jobs you can do, because driving is dangerous. The odds of the riders being in serious car accidents would be 10 times as large as before the ban. You’d be growing everyone’s risk by an order of magnitude. Would you choose to ban AVs in this case? If not, you shouldn’t ban AVs now.

There is no difference for a city between an AV fleet and a very cheap rental car service

Imagine a very cheap rental car service appeared in your city. They have a policy where they drive the car to you for you to drive around, and pick it up for you after. To my knowledge, this kind of rental car service isn’t banned anywhere. If this service were cheap enough, it would compete with Uber and Lyft and sometimes cause drivers to lose jobs, because people would just be driving themselves to where they want to go. If this rental service became too popular, it would lead to worse traffic in the city as more cars circled around being delivered to drivers. This is the major risk of a service like this.

An AV is identical to this service. The only difference is:

AVs are wildly safer compared to human drivers.
The rider happens to be sitting in a different spot in the car.
The rider can do other things while still driving safely.

Outwardly, there is no other difference. If you wouldn’t ban the rental car service, you shouldn’t ban AVs either.

Sometimes people will frame using AVs as an elitist option. Maybe the rider doesn’t want to take a ride share because they don’t want to have to interact with another person, maybe they’re so elitist that they find the idea of interacting with everyday people driving ride shares unpleasant.

In my opinion this gets it exactly backwards. Suppose autonomous vehicles had somehow come before ride share cars. Everyone normally takes a robot taxi, but suddenly the option is available to pay a human to be your personal chauffeur throughout the city. It seems obvious that this is the more elitist option of the two.

In general I don’t like feeling like I’m being served. I prefer to drive myself as opposed to paying someone to drive for me, for the same reason I prefer to cook and clean over paying someone to do it for me. In the same way that it’s less elitist to use a Roomba instead of hiring a maid service, I think it’s less elitist to ride in AVs than it is to hire a human driver to be your personal servant for 10 minutes at a time.

If you want car ownership to stay legal in your city, any argument to ban AVs that works equally well as an argument to ban cars is bad

A lot of arguments against AVs seem to be arguments against personal ownership of cars in general. While I’m open to these arguments (I hate cars in cities) a lot of people clearly support keeping cars in cities, but use arguments against AVs that apply equally well to cars. For example, the argument that driving somewhere in an AV takes work from an Uber driver applies equally well to driving in your personal car.

Eyes on the street

One argument that came up in the hearing was the idea that human drivers are important because they can see people in trouble or crime happening, and pull over and stop it. AVs don’t have drivers to stop and help.

This argument is implying that we should artificially boost how often Uber is used to have more people watching the street. Imagine if a city council member proposed funding Uber drivers to aimlessly drive around the city and observe and watch out for anything bad happening. This would obviously be ridiculously inefficient, and terrible for the environment. More pedestrians would be harmed by the total air pollution from the cars than helped by individual Uber drivers interfering to stop crimes.

If we want to make policy to have more people watching the street to keep people safe, we should just hire more police, community patrols, or pay everyday individuals to stand around observing an area to see if people need help, install security cameras in more places, or follow Jane Jacobs’ strategy of making streets more walkable and appealing for lots of people to be on. Uber and Lyft are terrible public safety interventions. Imagine if a politician proposed funding Uber and Lyft more to “make people feel safe and seen on our streets.” This would be obviously ridiculous.

This argument also applies to above-ground trains. Many above-ground public trains can’t stop in time if they observe a crime happening. We don’t block building trains because they can’t stop in time to prevent nearby crimes. We shouldn’t hold AVs to a different standard.

This argument shows an important common mistake

This argument is very specific and something I haven’t heard outside this meeting. I bring it up because it’s a good example of a broader pattern we should expect to see in conversations about AVs: people feel very strongly that there should be a human in the driver’s seat, and the experience that lack almost like it’s a physical presence. So when an AV doesn’t do something that a human driver would like stepping out to help someone on the street, people ascribe a ton of responsibility to the AV, like there was supposed to be a human there, and the lack of the person is responsible for the bad thing happening. But almost everything we do causes these “lacks of people” in specific places. If a popular restaurant opens up, there will be fewer people hanging around other places the restaurant took business from, and therefore fewer people to intervene if something goes wrong. The only reason people don’t hold the restaurant accountable is that they see restaurants as normal, and AVs as weird. We can’t make it a goal of policy to perfectly equally distribute people in every public place in a city. A talking point that says “We need to require human drivers so there are more eyes on the street” is identical to saying “We need to require more everyday people to equally spread themselves out on every street.” This only makes sense if you think of the AV as having some unnatural lack that urgently needs to be filled. If instead you think “It is okay for there to sometimes or often be no drivers on a street” it doesn’t look objectionable.

In all conversations about AVs, watch out for this general pattern: people treating them as weird and different to the point that they’re expecting things they don’t expect in any other circumstance, like policy that perfectly spreads people out across a city. Watch out for when they treat the lack of a human as some kind of physical entity that can be responsible for bad things happening. A lot of things in society create these lacks, but AVs are getting special condemnation for it because they’re seen as weird.

AVs can open up opportunities for disabled riders

Another point brought up was that some disabled people need human help when entering a car, and AVs can’t provide that. For what it’s worth, the council meeting speakers representing different disabled communities in Boston seemed to mostly be pro legalizing AVs.

Uber and Lyft have not gone out of business where AVs are operating. Companies know that there is going to still be a lot of demand for human drivers into the future. Eventually, autonomous vehicles could also get good enough to provide help for disabled riders. For many disabled people, AVs can give them access to ride share-like experiences they don’t otherwise have. A common issue some disabled people have with ride shares is not being able to take their service animals on the ride due to a policy by the driver. AVs can consistently allow service animals because there’s no driver to get scared.

Right now, AVs do not seem likely to displace Uber or Lyft. They exclusively give disabled riders an additional option. For some, this will allow them to use ride hailing services they can’t otherwise use due to their service animals.

Waymos are fully electric

An underrated aspect of Waymos is that they’re fully electric. If a rider chooses a Waymo over an Uber or Lyft, they’re often choosing an electric vehicle over an ICE vehicle. This on its own should be a big selling point for making them more available. We need to get as many ICE vehicles off the roads as fast as possible to reduce CO2 emissions and air pollution.

Nothing makes the world more human than safety

People sometimes talk about AVs making the world “a little less human.” This is the least relatable talking point to me. Cars as they exist are profoundly grizzly and inhuman.

I’ve been in two serious car accidents. Neither was my fault at all. There wasn’t anything I could’ve done differently. One left me in pain for a few months. In both, I was really lucky to not get more hurt. After a serious car crash, you think a lot about how you can work so hard to build up a great life and community and put in a ton of effort to learn a lot, and that can all be erased in a moment by a bad decision by another random driver. This is one of the most inhuman parts of our daily lives. I would so obviously rather spend the rest of my life constantly walking by empty driverless cars aimlessly but safely circling around than be in another accident like that.

It’s inhuman that 40,000 people die every year on American roads. In comparison to those deaths, a basically aesthetic objection to an incredibly safe car just doesn’t seem to matter. In conversations about this, I would like more people to feel in their bones how awful cars as they exist are.

If a vehicle is safe, the default should be legalization

It’s kind of crazy to me that we haven’t banned motorcycles yet.

Source

AVs haven’t hit the 1 billion miles traveled mark. When they do, I expect them to have a significantly lower death rate than regular cars. If a vehicle is safer for the driver and pedestrians than a regular car, I think people should be allowed to ride in it around a city.

More generally, AVs give a lot of people the freedom to go places they might not otherwise be able to access. This is good. If we’re so wildly libertarian that we still allow people to still drive motorcycles (in my opinion, this should change), I think we should also allow people to use AVs if they want.

If a vehicle is proven safe, the default should be legalization. People should be able to choose for themselves how they want to get around. Second order effects matter, but I’d rather not have the government unpacking every last possible second and third and fourth order effect of different types of new vehicles, because they don’t really do this at all with vehicles as they currently exist. Gas powered cars create air pollution, climate change, and often kill people. If your city hasn’t held any meetings on banning them recently, I think they’re not seriously considering the second order effects in the way they are with AVs. Holding AVs to a much higher standard than ICE cars seems like a way of locking in our very bad current transit equilibrium.

In DC as food delivery apps have led to a lot more motorized bikes in the city, I’ve found myself at much greater risk of getting hit in crosswalks, because they ignore stop lights way more often. I think this is bad, but it’s not bad enough that I think the apps or vehicles should be banned. In comparison, we’re debating a vehicle (AVs) that’s way safer than the average human driver. It’s pretty easy for me to say that because I’m fine with the app delivery bikes, I’m also fine with something much safer.

AVs can make it much easier to take licenses away from the worst human drivers

An underrated benefit of potential future mass availability of self driving cars will be that it will be way easier to take licenses away from the most dangerous drivers without ruining their lives. The reason it’s so hard to take licenses away in America right now is that public transit is so bad that in most places, not being able to drive is effectively cruel and unusual punishment. AVs will fix that. People could be ordered to only use autonomous vehicles. Maybe there will specially designed autonomous-only cars? I don’t know.

Most of the problems with traffic safety in America are caused by the bottom fifth of drivers, and these problems often involve alcohol. The single most promising way to improve road safety is getting people who drink and drive, and the otherwise very worst drivers, off the road. AVs can remove the main barrier to doing that: the life-ruining consequences of losing your license.

Real problems with AVs

Cybersecurity

Autonomous vehicles are just computers driving cars. This makes them vulnerable to hacking. A successful cyberattack could let hackers control steering, brakes, and acceleration, turning vehicles into weapons. Worse, attackers could target entire fleets or traffic systems at once, causing mass accidents or citywide shutdowns. AVs also collect massive amounts of personal data about where people go and when, making data breaches a serious privacy and safety threat.

AV companies are well-aware of cybersecurity issues, but the tech is new and oversight of how companies are dealing with the problem seems necessary.

Could make traffic worse

If there’s a huge fleet of ride share cars added to a city network, it seems likely that on net these could make traffic worse. There’s a trade-off here where a much higher percentage of cars on the road would be driven by incredibly safe drivers, so though this would increase traffic, it seems likely to seriously reduce injury and death as well. How to think about this trade-off is complicated, but we should lean in favor of saving lives and preventing injury at the cost of a little more time on the road. This is what we do when we impose traffic slowing measures.

Another key consideration is that a lot of traffic is caused by car wrecks. The main problem in a car wreck is obviously that people got seriously hurt, but any technology that reduces the rate of car wrecks also reduces the rate of massive traffic backup. If we want to reduce traffic, it’s better to opt for vehicles with wildly low rates of causing car wrecks.

Environmental impact

AVs seem likely to cause more people to take car trips because of their convenience. This is bad and will be harmful for the environment. But the solution to environmental problems with cars is not to prop up Uber and Lyft, it’s to build out better public transportation networks. Eventually, AVs getting good enough could become government-owned. AV buses could open up more routes. Being part of the AV rollout might help public transportation better. But this is all very uncertain. It’s important to note that Waymos are fully electric. Electric cars still use a lot of energy, but emit way less than the ICE cars many Uber and Lyft drivers use.

AI and Folk Cartesianism - Part 2: Problems for Cartesianism

Tue, 28 Oct 2025 11:45:52 GMT

As summarized in Part 1, folk Cartesianism consists of three strong intuitions:

There is a unified self that floats above mental processes and observes them
We know our own internal experience with complete certainty
The basis of all knowledge lies in our first-person subjective experience

In this post I’ll give a lot of arguments and intuition pumps for why each of these is probably incorrect, or at least isn’t “obvious.” This post is very long. My goal is that at the end you have a grasp of many of the most important questions in philosophy of mind, why so many philosophers reject the popular everyday understanding of mind, and that you hopefully come away with a bag of new mental tools for thinking about why machines might be able to replicate what happens in human minds. I’m writing this partly to be the guide I wish I had when I was first learning about this.

“1. There is a unified self that floats above mental processes and observes them”

How can a Cartesian ego fit within our scientific theories?

Cartesian egos are strange. They seem to behave like magical floating eyeballs, peering into physical reality from outside. No other physical object does this. No matter how many grains of sand I pile up, there will never be “something it is like to be the sand.” The sand does not have an internal movie theater playing experiences for it to observe. Being sand feels identical to being dead. We would expect all physical objects to behave similarly, because all physical objects are ultimately just elementary particles that don’t have experiences, similar to the grains of sand. No physical object ever becomes an observer in an inner movie theater. So how do Cartesian egos fit within our understanding of science?

There are only two options: Cartesian egos are fundamentally physical, or they’re not. Both options have some weird implications that make them look implausible.

Cartesian egos are not fundamentally physical

If Cartesian egos are not fundamentally physical, they threaten to clash with physicalism, which says that the only things that have causal power over physical objects are other physical objects. If they have any kind of causal power, this breaks physicalism. We have two options:

Cartesian egos do not have causal power

Maybe Cartesian egos are silent observers, taking everything in but never actually having any causal effect on the world. They silently watch the movie of your life play out.

This has some very weird implications, to the point that it looks like it can’t be true.

First, if a Cartesian ego doesn’t have causal power, it cannot have any influence over the information in your brain that causes you to physically speak or write about the ego itself. Your brain could never acquire any information that it exists. Describing Cartesian egos should feel like describing “that feeling of gloofleglorf around your left ear at 3:52 PM every day. We all know that one.” Something that our brains have never received any information about existing at all.

But many Cartesians clearly don’t think this is how Cartesian egos work. Descartes himself talked as if nothing in the world could be more obvious than that he had an observing ego. “I think, therefore I am” is supposed to be the absolute obvious bedrock of all other reasoning. This implies that the Cartesian ego is giving the brain some kind of information that it exists, so it must be having some causal effect on the world. If it exists, it’s what caused Descartes to write that!

Another weird implication: if your Cartesian ego suddenly popped out of existence, and the “you” experiencing things no longer existed, literally nothing about your behavior would change, because the ego wasn’t affecting anything about your physical body. You would go on behaving exactly as you do, talking about your experiences and feelings as if they were really happening, but no one would be watching the internal theater. If you believe that you have an internal theater of experience, and that you are reacting to the experiences that happen in this theater, then you cannot accept the view that Cartesian egos both exist and do not have any causal power.

Cartesian egos have causal power

If Cartesian egos are both nonphysical and have causal power, they violate physicalism. This is possible. Maybe physicalism is wrong and nonphysical objects have effects on the world. But it’s very suspicious. Physics is this grand unified theory of the world that seems to predict everything from what happens inside particle colliders to distant stars to our ordinary lives, and does so with wild consistent success. Physics is our best attempt at a complete account of every entity in the universe that has causal power, and it’s shockingly effective at making accurate predictions, to the point that most physicists perform their investigations under the assumption that physics is causally closed, that nothing outside of physics can affect anything in the universe. Yet the only place where the theory is supposedly violated is in the brains of the species making this claim. This species has evolved with strong motivations for others to treat it as special and different from the rest of the world. From the outside, it looks suspicious when the one place where magic is claimed to happen is located in a species that has strong reasons to want other members of its own species to believe that it’s special.

It seems like there should be some way of observing laws of physics being violated in human brains if this is the case. If the Cartesian ego is some nonphysical entity sending information to and receiving information from the brain, there must be some creative way to measure where this information suddenly appears in the brain out of nowhere. I’m enough of a physicalist that I wouldn’t expect us to ever make this observation, even if we had nano bots observing every neuron in a human brain.

Some people talk about quantum mechanics leaving an opening in the universe for non-physical objects to affect the brain, but this seems wrong for two reasons:

Most of the important signals that happen in the brain are on a scale that’s too large for the uncertainty of quantum physics to have a significant effect.
For quantum physics to work, the probability distribution of what happens needs to follow mathematical laws. This doesn’t leave any room for causality to “flow” through the randomness. Causality means that if A happens, B follows. But quantum mechanics says that B cannot be more likely than the pre-determined spread of probabilities dictates. So quantum mechanics is actually a completely sealed barrier to forces outside the universe. Nothing could get through its requirement for complete but mathematically structured randomness to influence the physical world.

Cartesian egos are fundamentally physical

If Cartesian egos are actually just very complicated physical processes, they don’t behave like any other physical processes. They’re the only physical processes that are “awake” and can have subjective experience at all. Very weird, but possible! Importantly though, if they are fundamentally physical, they could also exist in machines, so AI might independently develop them.

But Cartesian egos also seem to violate most core properties that every other physical object has:

Spatiotemporal location: The don’t seem to have a clear location in the world. Maybe right behind your eyes? Definitely not something that can be pointed at and identified.
Causal structure and lawfulness: They don’t seem to follow any kind of regular laws. They’re not discoverable through observation and experiment. They’re not predictable. They can’t be expressed mathematically.
Detectability/measurability: They can’t be measured, either directly or through their causal effects. They don’t seem to leave empirical traces, except for humans after the 1600s saying they had them.
Public accessibility: No other person has any access to my Cartesian ego at all.

All 4 common properties of physical objects are violated by Cartesian egos. Maybe they’re the only objects in the universe that violate all of them, whereas every other object adheres to all of them. Seems unlikely. I would expect any and all entities we discover to eventually fit into the universal wavefunction, the unifying mathematical description of everything that exists. Cartesian egos can’t be written as a part of this function.

Cartesian egos are folk theories and do not actually exist

All the above examples make Cartesian egos seem super strange to me, to the point that it doesn’t seem obvious that they exist. It seems like what’s actually happening is that human minds are inventing folk theories of how they work.

Importantly, there are ways that large information processing systems could develop subsystems that work kind of like Cartesian egos. Global workspace theory posits that there are parts of the human brain that send information to a lot of subsystems at once, as if their contents are on a stage and the subsystems are observers in the crowd, who each run with the big central information to make inferences. This seems like a possible physical corollary for what we mean when we think about the Cartesian ego, but it’s notably just another physical function in the brain.

Shouldn’t this all be obvious?

Stepping back from this debate, Cartesians talk as if the fact that we are each a self watching our experience like a movie is so obvious that it’s unquestionable, but they often wildly disagree on the nature of this self. Does the self have causal power, or does it just silently watch? Does thought happen “within” the self, or does the self passively observe thoughts as well as experiences? If no one quality of the Cartesian self is obvious, maybe its existence isn’t so obvious either.

Conclusion

So it seems like actual Cartesian egos probably don’t exist. The mind is not a movie playing for an inner observer, watching it like a floating eye peering into physical reality from somewhere else. The Cartesian theater of the mind isn’t actually happening, at least not in some special nonphysical way. Any description of the difference between humans and AI that implicitly relies on a nonphysical Cartesian ego humans have that AI does not is mistaken.

What’s left when a Cartesian ego falls away? Different mental functions taking in information, moving them around the brain, giving and storing outputs, and using those outputs in other functions. If global workspace theory is true, there might be some part of the brain that broadcasts out information to many subsystems at once, approximating the role a Cartesian ego plays. But there is no nonphysical angel-like eyeball looking over the whole subjective field of the mind like an observer in a movie theater. The mental function global workspace theory describes could be replicated by a machine.

“2. We know our own internal experience with complete certainty”

The goal of this section is to argue against the Cartesian idea that we have special access to what’s happening in our own minds, our own first-person subjective experiences, in a way other systems like computers cannot have, because they don’t have these experiences.

There are two related claims I’ll argue against here:

We have 100% certain access to the contents of our own experience. I can be mistaken about whether something white and puffy in a field is an actual sheep or a fake sheep, but I cannot be mistaken that in that moment my subjective, first-person experience includes white puffiness framed by a green field.
We can introspect and clearly observe what’s happening in our own minds. AI systems can never introspect in the way people can because they’re machines. They just take an input, run it through computations, and produce an output. Humans can turn their inner eye back on this process itself. We don’t know how to make machines do this and we might never know.

In both cases, I’ll be arguing for the alternative view that both experience and introspection are useful but fallible natural processes that take in potentially incorrect information, run them through mental functions and computations, and return outputs the mind stores and uses in additional functions. I am not arguing that we don’t have experiences or introspection, only that these do not behave like mystical, 100% clear and reliable sources of information that could never be replicated by machines, and that our experience is not like watching a movie in an internal theater. At the end, you should feel like the idea of a movie theater is a bad analogy for your internal experience, because there’s a lot more ambiguity and uncertainty in the experiences you have than what’s displayed on a screen. This ambiguity leaves us unable to be too certain about the nature of our moment to moment experience.

The slipperiness of our experience

A friend recently asked me a funny question: “What do you think the shape of your visual field is?”

My visual field has been my main experience since birth. There should be basically nothing more obvious to me than what it’s shaped like. Every moment, I’m being flooded with visual experience. What is the shape of this flood? If the Cartesian theater is real, what is the shape of the movie screen? Which of these three screens have you been looking at all your life?

The fact that all of these seem plausible to me should give me some pause about how well I understand my first-person experience

Turns out, this is the shape:

Source

The shaded central area is seen by both eyes, the white area is seen by only one eye. Numbers represent degrees from straight ahead.

Does this shape look familiar to you? Given that you’ve been “looking at it” for your entire life, would you have been able to pick it out from a line of what the visual field looks like? Is this the shape of the screen in your internal movie theater?

This brings up one of the big central questions in philosophy of mind: qualia. From the Stanford Encyclopedia of Philosophy:

Feelings and experiences vary widely. For example, I run my fingers over sandpaper, smell a skunk, feel a sharp pain in my finger, seem to see bright purple, become extremely angry. In each of these cases, I am the subject of a mental state with a very distinctive subjective character. There is something it is like for me to undergo each state, some phenomenology that it has. Philosophers often use the term ‘qualia’ (singular ‘quale’) to refer to the introspectively accessible, phenomenal aspects of our mental lives. In this broad sense of the term, it is difficult to deny that there are qualia. Disagreement typically centers on which mental states have qualia, whether qualia are intrinsic qualities of their bearers, and how qualia relate to the physical world both inside and outside the head. The status of qualia is hotly debated in philosophy largely because it is central to a proper understanding of the nature of consciousness. Qualia are at the very heart of the mind-body problem.

If a computer could scan all the informational content in your brain, every last belief and memory and observation about the world, and get it all down in written words and stored information, people who believe in qualia would say that the computer would still not know what it was like to be you. Qualia are the contents of your first-person experience that cannot be summarized in pure information.

If the Cartesian ego is the person sitting watching the movie in our heads, qualia are the movie.

Unfortunately for me, qualia both:

Don’t make sense under physicalism (they violate all 4 properties of physical objects).
Are very very very hard for me to deny, in a way Cartesian egos are not.

I mean, look at this.

It’s hard to understand how any third-person purely informational account of the world could ever get across the first-person experience of seeing red. Simply reporting “A wavelength of 700 nanometers hit Andy’s eyes and activated memories and emotions associated with previous times this has happened” just doesn’t get across the first-person redness of the red. It’s like how all attempts to describe color to a lifelong blind person just obviously hinge on other experiences the blind person has also not had. We can’t get across the qualia using words.

I’m agnostic on whether qualia exist, or how they relate to the physical world. But even if qualia do exist, I’m pretty sure they’re very different than what our folk story says about them.

One of my favorite papers in philosophy, Quining Qualia, attempts to use lots of thought experiments to convince the reader that qualia don’t actually exist. The author (Dan Dennett) specifically defines qualia as having four key properties. They are:

Intrinsic: They’re basic and fundamental to the world and can’t be picked apart further. They’re also not “relational” in that they don’t depend on their relationships to other things. The quality of being “to the left of a bookshelf” is relational. It’s not an intrinsic property, because it could be changed by moving the object. But “the redness of the red” does seem intrinsic. No matter what colors we put adjacent to the red, or other things we compare it to, the redness of the red in my first-person experience will still be unaffected.
Ineffable: Not describable using words. You know what the redness of red is like, but you can’t actually get across to someone who’s never seen it before what that experience is like using words alone.
Private: Our experience of our own qualia is fundamentally private to us. We can’t even know if other people experience the same qualia. “How do I know the red you see and the red I see are the same?” is a question about qualia. There is no way one person can ever get direct access to another person’s qualia. Qualia are like each of us is stuck forever in our own private movie theater, never able to access what other people can see on their screens.
Directly or immediately apprehensible in consciousness: I can be mistaken about the source of red light, but I cannot be mistaken about the fact that I’m experiencing the redness of the red. I can’t directly see or hear or experience any objects actually, I can only experience the qualia they create in my mind, but that I have direct access to.

Dennett’s claim in the paper is that qualia do not actually exist, because our internal experiences actually have none of these four qualities. The reason I’m summarizing this paper specifically is that most of his points overlap with my main point about folk Cartesianism: our assessment of our own subjective, first-person experience is actually unreliable and often wrong. We don’t have 100% certain access to it. It provides useful information, but it should be taken as one data point among many. It cannot serve as some kind of magical firm foundation to build all our other beliefs off of. The information processed in our brains mostly isn’t actually rooted at all in the fact that we have qualia. If this is the case, systems without subjective first person experience can also have justified true beliefs.

So what are the issues for qualia? Dennett provides thought experiments for each property people claim they have. I’ll be half-summarizing them, and sometimes running with my own twists on them. I do strongly recommend you read the full paper.

1. Intrinsic

People “develop a taste for” coffee and beer. Most people I know who drink either remember when they took their first sip and didn’t like it, but now they love it. Did the actual taste, the direct sensation they experience in the moment of drinking beer or coffee, change over time? Or did the sensation stay the same, and only their reaction to it changed?

It seems like this should be a pretty easy question if our mental experience is “right in front of us.” If we’re watching a movie, there’s a clear difference between a character appearing on the screen before and after we’ve changed our judgment of them, versus a totally new character appearing. We can tell which one is which. But it seems like we have a lot of trouble when we stare directly at what’s going on with the sensations we experience with beer and coffee, or anything that comes with some judgment of the experience. It seems like our reaction to the sensation is somehow also part of the sensation itself. Maybe over time we’ve developed an ability to notice parts of the sensation we didn’t before. But if this is the case, it means these experiences aren’t “intrinsic.” They aren’t single, indivisible things, because they can actually be broken up into smaller individual experiences. They may even be “relational” because core qualities of them seem to depend on our judgment of them. See here for more of a breakdown on the intrinsic vs relational distinction.

We don’t have any clear way of telling whether any of our sensory experiences are actually “intrinsic,” just a bare experience that can’t be broken up into parts, or actually an assemblage of many individual experiences at once. This again chips away at our image of ourselves as having direct, easy access to our subjective, first-person experience.

The taste of coffee to a novice drinker may be a single overwhelming experience. They can’t detect any subtleties or how the taste of composed of many individual hints and flavors. Instead, they just taste a disgusting, overwhelming bitterness. Over time this taste changes… but does it change? Does their reaction to it change? There’s no way to be sure, but I lean hard in the direction that their actual taste of the coffee changes, not just their reaction to it. Suppose you didn’t like coffee, and I zapped your brain with a machine that gives you my personal reaction to the taste. You take a second sip, and suddenly you like it. It is very difficult for me to imagine you saying “Oh! I like the coffee now! It’s so good! I want to drink it all the time” while also saying “But it tastes exactly the same as it did a moment ago. See, my reaction to the taste was just changed.” I’d imagine this person would instead be stunned, and say something like “Oh it tastes very different now, the bitterness isn’t overwhelming, there are a lot of pleasant notes and hints I couldn’t detect at all before.” It seems like our judgment of the qualia is somehow itself also part of the qualia. The boundary between ourselves and the qualia we experience blurs.

A plucked guitar string

The sound of a plucked guitar string can be indescribable to an untrained ear. They only hear a single, fundamental, basic experience, ”the guitarness of the sound.” But to trained experts, a plucked string sounds like the combination of several different sounds a string can make happening at once. Are these people having different qualia, or are they reacting differently to the same qualia? Dennett himself puts this well:

Pluck the bass or low E string open, and listen carefully to the sound. Does it have describable parts or is it one and whole and ineffably guitarish? Many will opt for the latter way of talking. Now pluck the open string again and carefully bring a finger down lightly over the octave fret to create a high “harmonic”. Suddenly a new sound is heard: “purer” somehow and of course an octave higher. Some people insist that this is an entirely novel sound, while others will describe the experience by saying “the bottom fell out of the note”--leaving just the top. But then on a third open plucking one can hear, with surprising distinctness, the harmonic overtone that was isolated in the second plucking. The homogeneity and ineffability of the first experience is gone, replaced by a duality as “directly apprehensible” and clearly describable as that of any chord.
The difference in experience is striking, but the complexity apprehended on the third plucking was there all along (being responded to or discriminated). After all, it was by the complex pattern of overtones that you were able to recognize the sound as that of a guitar rather than a lute or harpsichord. In other words, although the subjective experience has changed dramatically, the pip hasn’t changed; you are still responding, as before, to a complex property so highly informative that it practically defies verbal description.

So what changes between people who can detect all the subparts of the sound of a single low guitar string, and people who can only hear the note as a single, intrinsic, undivided “guitarish” sound?

Does the guitar make different sounds based on how much you can detect? That seems unlikely. If one person can pick apart the sounds they hear much better, are they having additional sensations beyond the sound of the guitar itself, or is the guitar string just composed of these individual sensations like a checkerboard is composed of red and black squares, and the other person just can’t pick up on those internal differences? But “picking up on” the sound is itself what we’re trying to describe here: the direct, first-person experience of hearing. These experiences are specifically not supposed to be just the information we gather that we can write down; they’re supposed to be the first-person, in-the-moment undeniable experience. The fact that whether the two people experience the same or very different qualia is another hint that our sensory experiences are not simply displayed on a mental screen for us to watch like a movie.

Conclusion

We do not have any clear way of telling whether any of our qualia are actually “intrinsic” and just a bare experience that can’t be broken up into parts, or actually an assemblage of many individual experiences at once. When you first drank coffee, you had no way of telling that this taste you were experiencing was or was not an assemblage of many different other experiences at once. Just like the first-time coffee drinker, we have no way of knowing whether any one experience we have is actually intrinsic, or a combination of many different experiences at once. This again chips away at our image of ourselves as having direct, easy access to our subjective, first-person experience. People often talk as if qualia are these brute, intrinsic, foundational facts of our mental world, but it seems like we can barely tell if any one experience we have is actually composed of many others, or whether we ourselves are affecting the experience based on our judgment and reaction to it.

This all makes our experience seem very distinct from an inner movie theater. Unlike the movie theater:

It is not obvious when something is a single basic thing (like a solid color on the screen) or whether it’s composed of many other things we’ll easily notice when we get more experience with it.
Our judgment seems to directly affect our experience. We are not passively watching a screen of experiences and then judging and reacting to them after. The screen itself seems to radically change based on how we judge it.

2. Ineffable

Dennett argues that the ineffability of qualia is not due to qualia being some special, mystical property, but because they are “practically” ineffable. They are just highly specific, information-rich responses to the world that could in theory be described if we have enough experience with them. “Ineffability” is just the current horizon of our ability to analyze our own responses, not a fundamental property of the experience itself. This limit can be changed with training (like wine tasting or ear training).

The last example with coffee and the guitar string gets at this idea. Qualia might only be indescribable because we and other people are relatively untrained at detecting the communicable information they’re giving us. With training, people can learn to communicate a lot more relevant information about the sound of a guitar string or the taste of coffee. It might be that we’re mistaking our own current cognitive limitations for a fundamental property of the experience itself. Over time, experts get better and better at describing qualia.

On this point I mostly part ways with Dennett, where it does seem like some aspects of experience might permanently be walled off from public discussion. But this isn’t as relevant to the question of the Cartesian theater.

3. Private

Qualia are really really private

Qualia are fundamentally private. No one else can ever access what we experience.

Suppose we invent a machine that connects our brains, feeding your visual input directly into mine.

I report seeing everything you see. (This is the plot of the 1983 movie Brainstorms). I step back and gasp “Wow! Your qualia are completely different from mine! Your red is my blue! Everything smells and tastes different. This is crazy! We’re living in two different worlds. Qualia can be so different without anyone noticing.” We agree this is crazy. Suddenly, you scratch your head and say “Wait a minute, maybe we messed up somewhere. What if we plugged the wire that connected us in upside down on one end? Let’s just check to see if that changes anything.” We flip the plug upside down at one end and try it again. This time, we both agree that the other person’s experience looks totally normal.

What happened here? There are two possibilities for us: either we have different qualia, or we have the same qualia. There are also two possibilities for the cord connecting us: either it inverted the images the first time, or the second time.

Can we tell which is true based on the information we have?

In a connection where the qualia look weird to us, there are two possibilities. One is that we both have the same qualia as each other, and it’s the connection that’s inverting it. This would cause us both to say the other person’s qualia looked weird.

Another possibility is that the connection is correct, and we do have inverted qualia. This would yield the same reaction:

The situation where we confirm that what we see is normal also has two possibilities. It could be that the connection is reporting what our qualia actually look like to the other person.

But it could also be the case that the connection is inverting our different qualia, making them look normal when they’re actually different.

Let’s organize these situations by whether we have the same or different qualia, and whether the cord inverts the image.

Now, you want to test to see if the cord is right-side-up or not. You plug two people in. As they’re watching the other person’s vision, they say “that looks weird!” This is all the information you have to go on. You can listen to them both say “Wow… your blue is my yellow. Your red is my purple” for as long as you want, but this doesn’t really add any new information, because either way you face the same problem: there are two possible situations where the participants say the other’s qualia looks weird: the cord’s inverting their similar qualia, or their different qualia are transferred faithfully by the cord.

Given the information you have, you have no way to tell whether it’s the cord that’s wrong, or if the two people have different qualia. Your evidence perfectly fits both cases. If you flip the cord, and the participants say “now it looks normal!” you have no way of deciding between these two cases.

Again, you don’t know if the cord is actually showing that the two people have the same qualia, or if it’s now upside down. You have no way of telling if the cord is right-side up or upside down. Even a machine like this can’t ever actually give you direct access to the other person’s qualia, because neither has any way to externally test if the machine is getting it right.

So qualia remain fundamentally private. We have no final way of telling whether someone else’s qualia is the same as our own.

If qualia are this private, you do not have direct mental access to your own qualia

But if qualia are this completely and totally private, this also creates a problem for us. All our understanding of what qualia look “normal” come from our memories and intuitions about what the qualia looked like in the past. And we’re actually in the same relationship to our past self as the two people were in the last thought experiment.

We can imagine swapping the machine in the last example with a different machine. This time, only you get in. When you step out, all the colors in the world look inverted to you.

You’re stunned. The world is overwhelmingly alien. You ask “How did you invert my qualia?” The machine operator chuckles. “Oh, your qualia are actually the same as always. The machine uses a simple trick: We just edited your memories and crossed your memories with different colors. You now remember yellow as being blue, and blue as yellow, as an example.”

You’re very confused. “But the banana I see in front of me now is clearly blue!”

The operator says “Oh no, it looks exactly the same to you as it always did. It’s just that all your memories of yellow have been crossed with blue things. Like I bet that banana looks exactly like what you remember the sky looking like, right?”

You nod.

“Well if you go outside, you’ll see the sky’s never actually looked like that. It’s blue and has always been blue. We just swapped your memories, so now that color’s associated with what you think of as the color of bananas.”

You go outside, and the sky looks banana-yellow to you.

“No this is all clearly wrong. You did invert my qualia somehow.”

The operator scratches her head. “How would you tell the difference between an inversion of qualia and an inversion of your memories?”

“Well I just feel in my gut that this is wrong.”

“But that gut feeling is all your memories and built-up intuitions of the world.”

Hmm…

Later, a huge scientific discovery is made. Scientists have located qualia themselves, directly in the pineal gland of the brain. The operator uses this knowledge to build a second machine, this one really inverts your qualia and leaves your memories untouched. She invites you to try it out, but because the machines look identical, she realizes she mixed up which one is which. Is there any way at all for you to tell which machine is which based on the outcome of going through it? When you emerge, will you have any way of knowing at all whether it was your qualia or memories that were inverted?

In this example, memories are taking the place of the cord in the first example. When it comes to your qualia, your connection to your past self through memory is identical to your connection to the other person’s qualia through an unreliable cord. In both cases, you don’t actually have direct infallible access to the qualia.

This is a big problem, because all human thought happens in time, and the present is an infinitely small slice of time. If qualia are so private that they can only be accessed in the infinitely short present, we humans cannot actually access them directly, because we live and think in time.

Whenever your mind receives any information at all about qualia, you are reacting to a past experience, because of the fractions of a second it takes the signal from the qualia to be mentally processed. But already at this point, you’re thinking about something in the past, and you’re dependent on memory. Qualia’s privateness means memory does not give you that special access you’re supposed to have. Memory, like the cord, is fallible. You can be wrong about what you just saw. “What’s in front of your eyes” still needs fractions of a second to process. To be clear, this doesn’t mean you’re necessarily wrong about the qualia you’re experiencing, it just means you can be wrong about them. This strikes at the heart of the Cartesian intuition that we have direct, certain access to our own subjective experience.

If the qualia in the last experiment are so fundamentally private that no one else can ever access them, you yourself also have no fundamental cognitive access to your own qualia. They are as inaccessible to you as they would be to another person using the brainstorm machine to try to understand what it’s like to be you. Your brain can provide you with valid reports of what you’re experiencing, just like the other person can provide you with verbal reports about what they’re experiencing, but in both cases you don’t have that direct, magic, private access qualia are supposed to come with. In fact, here they’re behaving suspiciously similarly to a world where they don’t exist, and where your brain is only receiving information about the world that can be reported publicly, either to your future self or to another person.

Again, this seems like really bad news for the inner theater model of the brain. Not only is there no observer and the screen itself unclear and obscured by our own judgments on it, suddenly the screen’s in another room, and written reports are being sent in about what’s happening in the movie.

A relevant argument here is Derek Parfit’s long piece in Part 3 of Reasons and Persons on how the self does not persist over time, and your relationship to your past and future self is similar to your relationship to other people (summary here). This is an especially good but long read for people interested in more arguments against Cartesian egos. If qualia are so fundamentally private that no one else can access our qualia, but also we ourselves are different people moment to moment, the intersection of these views seems to imply that our own access to our past qualia (which is all qualia we can actually think about and respond to, because the present is an infinitely thin slice of time) is identical to our access to other people’s qualia: indirect and uncertain.

What to make of this? I think that this is a strong sign that qualia (whatever they are) are not fundamentally private, and might exist instead as some kind of (very complex) reportable information that can be communicated between people who can understand it. This makes much more sense to me than the idea that they are fundamentally private, because the implications of fundamental privacy make them behave completely differently than how I experience them. I want to define qualia as “the experiences that I have some direct access to.” It might be that those experiences are entirely just raw information in my brain assessing the situation. This information could in principle be communicated completely to another brain. I’d hope so, because I also think the relevant information can be communicated by memory across time to my present self. Again, this is a case where it seems like we have as much mental access to our own qualia as a computer would: an uncertain tether of memory or wires communicating information about what the world is like.

The more inaccessible you believe qualia are to machines, the more inaccessible you believe they are to your own mind as well.

4. Directly or immediately apprehensible in consciousness

If qualia are so fundamentally private, we’ve seen above that they by definition are not directly or immediately apprehensible in consciousness. We’ve also seen via the coffee and beer examples that people have a surprisingly hard time apprehending the qualia they are currently experiencing or have experienced in the past. We can’t even tell if the qualia we’re experiencing are basic and intrinsic, or composite and relational, as in the case of the guitar string. If qualia exist, we have a strange and complicated relationship with them. They are not directly apprehensible in consciousness in the way an unobscured movie screen is.

Conclusion

I think there are some clever arguments that even with these objections, qualia may still exist. But we’re way, way less sure of our own subjective experience than folk theories imply, and the nature of qualia seems more complex than simple, accessible, nonphysical pure experience.

So it seems like we don’t actually know our own internal experiences with certainty. Here I don’t mean that we might be mistaken about what objects and events we’re seeing, I mean that we can be mistaken about the nature of our current first-person subjective experience. Not only is there no one sitting in the Cartesian theater, and not only is the screen itself obscured, but there doesn’t seem to be a screen at all. This magical place, the center of the mind and thought, that so many people seem to take as so obviously given it’s crazy to doubt it, does not seem to actually exist.

If this is the case, first-person subjective experience becomes one of many possible sources of information and evidence. It’s useful, it’s just reduced to the same level as other ways we acquire information, like written descriptions, verbal accounts, and knowledge that other people we trust believe things (all of which computers can access). Most importantly, humans do not seem to have some magical, central location where experience happens that machines by definition will always lack.

The impossibility of folk introspection

A very common critique of large language models is that they can’t “introspect” in the way humans can. They just take in input, run it forward through the net, from its input variables to its final classification, and don’t have any way of circling back on anything that’s happened. They can’t go backwards. Therefore, unlike humans, they do not have introspection. They’re pattern recognition rather than true thought. Here, the information in the neural network passes from left to right through the weights and nodes. The only way this model could have true introspection is if it were somehow able to step outside of this process and observe what had already happened:

Source

This critique always seemed implicitly Cartesian to me. Human brains are also only more or less complex information-processing units. They are composed of inputs, mental functions that perform computations, and outputs. Those outputs are themselves used later as new inputs.

Crucially, all information processing in the human brain can only happen in one direction in time: forward, because every mental subprocess in the brain is physical. When our minds take in input, they might combine that input with memories or inbuilt mental biases, run these through mental computations, and product an output.

This could get more and more complex, as different parts of the brain draw on results from the mental computations of other parts:

Where would introspection fit into this? Well, if introspection is another natural functional process, it has to exist in time, take in inputs, and give outputs. It seems like what we call “introspection” would be the brain calling prior inputs or results of past computations. Maybe it would look like this:

The physicalist-functionalist picture of introspection

Crucially, what is NOT happening is that there is a magical outside observer looking down at the mental processes from outside. There is no Cartesian ego who can “just see directly” what’s actually happened in the mind.

The folk Cartesian picture of introspection

“True introspection” where we step back and clearly look at our thought processes from the outside is not possible. Introspection is just another mental function that takes in inputs and gives outputs. We cannot actually see the wiring of our own minds, we can just react to the outputs our minds are giving us, in the same way we can observe someone else’s behavior over time and come to conclusions about what they are like internally. “Introspection” is actually just another informed guess about the world our brains make. Introspection is like observing a natural phenomenon and making predictions about what will happen next. It’s not like looking in a mirror and clearly seeing ourselves as we really are. Just like we can be mistaken about the real causes of other people’s behavior, we can also be mistaken about the causes of our own behavior, because the inputs our minds provide for introspection can be misleading. We can call on false or modified or selective memories with no way to tell that they’re not perfectly accurate. We can build up strong incorrect intuitions about what we’re like. There’s no special place our intuition function can turn to get the “real” story, it’s all fallible inputs just like the external world provides for anything else we’re trying to understand. I’d recommend this classic essay for a deeper dive on this.

It seems like when people say AI models do not have any way of doing introspection, they think that to do that, the model needs to have some outside Cartesian ego looking in:

But if introspection in humans is just another mental functionalist process, where inputs are received and outputs given, rather than a magical Cartesian ego looking down from above, then it seems like anything we call introspection in a process like this:

could in principal also happen in a process that looks like this:

The fact that LLMs “only pass information along one direction” on its own tells us nothing about whether they can introspect, for the same reason that the fact that human information processing can only happen in one direction in time tells us nothing about our own ability to introspect. The only way this would invalidate the idea that LLMs can do anything like introspection is if folk Cartesianism were true.

There are obviously a lot of other ways large language models differ from the structure of human brains. But any comment on those differences shouldn’t imply that the AI companies forgot to install a Cartesian ego watching over the whole thing.

A simple rule for what introspection can’t do

The individual thoughts and experiences we have access to during introspection are not the fundamental units of thought. The fundamental units of thought are the electrical signals sent and received by neurons. They combine together into thoughts in the way binary 1’s and 0’s can combine together to form a programming language. Most of our mental processes are very complex mental functions with neurons and electrical signals as the building blocks. Because introspection is another mental function, it does not necessary have access to every minute step of its own or other functions’ structure. Computer programs can summarize the outputs and steps of other programs, but they can’t “step outside the process” and observe the binary 1 and 0 signals that make them up. In the same way, human introspection can also draw information from other mental processes, but cannot “step outside” to see the mind laid bare before it, because the mind is not actually fundamentally composed of the type of thing introspection gives us access to.

Conclusion

Our assessment of our own internal experience and thought process is fallible in the same way our assessment of the external world is. There is no “solid ground of absolute certainty” we can find in our subjective experience. This makes sense of the mind is a pile of complex functions taking in information, processing it, and providing new inferences as output. There’s no deep metaphysical difference between the mind analyzing its own contents and the mind analyzing the world, for the same reason

Whether the information the mind receives is about what’s happening inside of it, or what’s happening externally to it, doesn’t have some deep metaphysical difference.

From the outside, our situation with introspection, and the vagueness and difficulty of directly apprehending qualia, give the impression to me of a species with very strong internal built-up narratives of how our minds work colliding with the simple fact that we’re machines and not angels. We can perform mental functions on the information we receive, but we cannot enter some kind of extra-physical movie theater of consciousness, where we easily and directly apprehend our deep consistent subjective worlds, and draw conclusions based on our assessments of those worlds. In this way, we behave similarly to very general computers sent off to survive in the world.

“3. The basis of all knowledge lies in our first-person subjective experience”

We’ve already seen that:

There is no Cartesian ego observing and judging our experience.
Our first-person subjective experience is surprisingly opaque. We can be mistaken about it. It’s still evidence, but we don’t actually have 100% confident privileged access to it.
Similarly, our introspection is just another mental process that takes in fallible inputs and gives fallible outputs. The mind is not actually all easily available to us at once, like looking down at a complex domino chain falling. Anything we call “introspection” is just one part of that very complex chain itself.

Human minds take in input, run it through very complex mental functions, and give output, that they then use for other functions. In no part of this process does the mind “step above” the physical functions happening in the brain, and looks down at all the mind-stuff happening at once, and observes it like a map. Our felt sense that this happens, that we’re Cartesian observers with access to clear distinct instrinsic private mental experiences, is strong but mistaken. At best, the functions in our brains doing the introspection draw from lots of parts of the brain at once,

The folk theory of language acquisition

Many people believe we learn what words “really mean” by connecting them to specific experiences in our minds. They think we only understand “cat” because we’ve seen an actual cat and someone labeled that experience for us. Since your personal experience of seeing a cat lives privately in your mind, the “real meaning” of “cat” must be something private that only you and others who’ve had similar experiences can truly grasp.

But if word meanings are fundamentally private, language breaks down. If everyone has their own private version of what words mean, with no way to compare these meanings, how can we ever know if we’re talking about the same thing? For language to work, meanings must be publicly accessible and verifiable.

When I learn the word “cat,” its real meaning can’t depend on some private, indescribable feeling I experience, because you might experience something completely different. The meaning must be something we can communicate using other words and shared reference points. It makes much more sense to say the meaning of “cat” is something like “Small carnivorous mammals of the species Felis catus, typically weighing between 8-11 pounds, with retractable claws, whiskers, and forward-facing eyes, commonly kept as household pets and are known for hunting rodents and birds” than to say it’s something private and indescribable that people who haven’t seen one cannot possibly understand.

We only have access to public evidence about meaning. We can’t look inside each other’s minds. So any realistic theory of how we learn language must focus on what we can actually observe: how people use words in response to their environment.

We don’t learn language by linking words to mental images. Instead, we learn by linking words to publicly observable situations where they’re used. The philosopher Quine called this “stimulus meaning.” A child learns when to say “cat” by observing environmental cues and getting social feedback (nods, corrections) from the people around them.

A word’s meaning comes from its role in our entire language system, not from its connection to a single private idea. This explains how we use abstract words like “democracy” or “neutrino,” terms for which we have no clear mental picture or direct sensory experience. The old theory can’t account for these. We understand these terms not through mental images, but through their relationships to other concepts in our linguistic and social framework.

Since word meanings must depend entirely on things we can communicate about using language, and exist as essentially nodes in a web of interrelated words, they can’t depend on private, indescribable experiences. This means machines that process enough information can, in principle, access the meaning of words. They don’t need inner transcendent subjective experiences to understand language. Modern large language models are designed to situate words in broader webs of meaning and association. Once you exit the folk theory and learn about how LLMs process language via association, it becomes hard not to think that they’re successfully mimicking what humans do when the learn the meanings of words.

Can machines without qualia have knowledge?

This will be an argument that machines can in principle have knowledge. I’m not arguing that current AI systems have knowledge (though I would argue that chatbots do “know” the meaning of the words they use).

What is knowledge?

Knowledge is justified, true belief. It’s worth breaking this down a little:

Justified: I wake up in my room with the window shades down. I can’t see if it’s raining outside. I decide to flip a coin. If it’s heads, I’ll believe that it’s raining. If tails, it’s not. I flip the coin and it lands on heads, so I believe that it’s raining. I go outside and see that it actually is raining. Did I “know” it was raining before I saw it? It seems like the answer is no. I got lucky and my silly guess happened to match reality, but to “know” it was raining I needed some kind of justification for my true belief.
True: It doesn’t make sense to say I have “knowledge” that David Hume is currently President. We can only be said to have knowledge about true beliefs.
Belief: An acceptance that a statement is true or that something exists.

Anything “completely private” cannot contribute to most knowledge.

Can machines hold beliefs?

Before we can ask if machines can have beliefs, we need to get clear on what beliefs actually are. The folk Cartesian story goes something like this:

A belief is a mental state that exists in your Cartesian theater. When you believe it’s raining outside, there’s some kind of propositional content—”it is raining”—that exists in your mind, in your subjective experience. You have direct access to this belief through introspection. You can “look inward” and see what you believe. The belief causes you to grab an umbrella, but the belief itself is fundamentally a feature of your private, subjective mental life.

But we’ve already seen problems with this picture:

There is no Cartesian theater where beliefs are displayed for an inner observer
We don’t have reliable introspective access to our own mental states
Mental processes are just physical information-processing functions

So what’s left when we strip away the Cartesian story? What is a belief if it’s not a proposition floating in your mental theater?

In the physicalist-functionalist view, a belief is a certain kind of functional state. It’s a state that:

Takes in information from the world (perceptual inputs, testimony from others, memory, reasoning)
Combines with other beliefs and desires to guide decision-making
Produces behaviors that would make sense if the belief were true
Updates based on new evidence
Plays the right inferential role in reasoning (if you believe “All mammals are warm-blooded” and “whales are mammals,” you should infer “whales are warm-blooded”)

Notice that nothing in this definition requires a Cartesian theater, qualia, or privileged introspective access. A belief is defined by what it does, how it functions in the overall cognitive system, not by what it feels like “from the inside.”

You might object: “Fine, maybe machines could have states that function like beliefs. But real beliefs require understanding. When I believe it’s raining, I understand what rain is. A machine just manipulates symbols without understanding what they mean.”

But we’ve already undermined this picture in the folk theory of language acquisition above. We’ve seen that:

Meaning isn’t grounded in private, ineffable qualia.
Language acquisition works through publicly observable patterns of usage, not through attaching words to subjective experiences.
Our own understanding is ultimately cashed out in how we use words, respond to contexts, and draw inferences, not in some private mental glow of comprehension.

If understanding is functional, if it’s about having the right patterns of response, the right inferential connections, the right sensitivity to context, then there’s no reason in principle that machines couldn’t achieve it.

Can machine beliefs be justified?

The folk Cartesian picture of justification goes something like this: Your beliefs are justified when they’re properly grounded in your direct, first-person experience. You know you’re seeing red because you have direct, certain access to your own qualia. You can introspect and observe the redness right there in your mental theater. This provides a firm foundation, a bedrock of certainty, upon which all other knowledge is built.

Descartes himself made this explicit. He doubted everything he possibly could. The external world, his own body, mathematics, even whether he was dreaming. But the one thing he couldn’t doubt was his own existence as a thinking thing: “I think, therefore I am.” His subjective experience provided absolute certainty that he existed and was having thoughts.

From this foundation of subjective certainty, Descartes tried to build back up to knowledge of the external world. The idea was that our private, first-person experience provides a special kind of justification that nothing else can match.

But we’ve already seen the problems with this picture:

We don’t have direct, certain access to our own experiences
Our introspection is fallible and often misleading
The supposed bedrock of subjective certainty is actually shaky and unreliable
Our experiences aren’t clear, simple, and directly apprehensible. They’re complex, constructed, and surprisingly opaque
The Cartesian ego Descartes detected might not even exist

If the folk Cartesian story about justification is wrong, what’s left?

An alternative picture of justification is reliabilism: A belief is justified when it’s formed and maintained through reliable processes that tend to produce true beliefs. Justification isn’t about having some special subjective feeling of certainty or being able to introspectively verify your mental states. It’s about the process that produced the belief being a good one.

Consider how you actually form beliefs:

Visual belief: You look out the window and form the belief “It’s raining.”

Your eyes receive light reflected off raindrops
Your visual cortex processes this information
Pattern recognition systems identify the characteristic appearance of rain
Memory systems match this against stored representations of rain
A belief state is formed: “It’s raining”

Is any of this process fundamentally dependent on having qualia or a Cartesian theater? It doesn’t seem like it. The process is:

Receive sensory input
Process that input through various computational mechanisms
Compare against stored patterns and previous experiences
Form a representation of the world state
Store this representation for future use

This is an information-processing story all the way down. And crucially, this process is reliable: it tends to produce true beliefs about whether it’s raining. That’s what makes the belief justified.

On the reliabilist account, justified beliefs don’t need to be certain. They just need to be formed by processes that usually get things right.

This actually matches our intuitions better than the Cartesian story. Consider:

You look out the window and see what appears to be rain. But it could be:

A sprinkler system you forgot about
A movie set creating artificial rain
An elaborate hallucination (you’re on drugs, or having a stroke)
A very realistic dream
A brain-in-a-vat scenario where you’re being fed false sensory data

You can’t be certain it’s raining with the kind of absolute certainty Descartes wanted. But that’s fine. Your belief is still justified because it was formed by a reliable process. Most of the time when your visual system produces the representation “rain,” there really is rain. The process is reliable even if it’s not infallible.

What counts as a “reliable process”? Here are some features:

Correlation with truth: The process should produce true beliefs more often than false ones in the environments where it operates.
Appropriate sensitivity to evidence: The process should update beliefs when new evidence comes in, and it should be more confident when evidence is strong and less confident when evidence is weak.
Coherence: The process should produce beliefs that fit together consistently. If you believe “All mammals are warm-blooded” and “Whales are mammals,” your processes should lead you to believe “Whales are warm-blooded.”
Resistance to known error modes: The process should avoid systematic biases and known sources of error, or at least be able to correct for them.
Appropriate use of background knowledge: The process should integrate new information with relevant prior knowledge rather than treating each belief in isolation.

Could a machine implement processes with these features?

Let’s think about how a machine might form the belief “It’s raining”:

Sensor input: A camera receives visual data, or a moisture sensor detects water.
Pattern recognition: Neural networks or other algorithms process this data to identify rain patterns.
Comparison with stored data: The system compares current inputs against training data or previous observations.
Belief formation: The system forms an internal representation: “It’s raining.”
Confidence assessment: Based on signal strength and pattern matching confidence, the system assigns a probability or confidence level.

Is this process reliable? Well, that depends on the specifics:

How good are the sensors?
How well-trained is the pattern recognition system?
Does the system have enough relevant training data?
Can it distinguish rain from sprinklers or other confounds?
Does it update appropriately when conditions change?

These are all empirical questions. There’s no in-principle reason why a well-designed system couldn’t form beliefs through highly reliable processes. In fact, for some tasks, machine processes might be more reliable than human ones.

Under reliabilism, it looks like machines can in fact have justified, true beliefs. Therefore, it seems like machines can have knowledge.

The implication that humans might be incredibly complex biological machines rather than transcendent experiencing angels should hopefully be a little more understandable than when we started.

Conclusion

So I claim folk Cartesianism is not “obviously correct,” and a better story of the mind looks something like this:

Humans do not have Cartesian egos, magical eyes floating outside of physical reality observing what happens in our minds like a movie.
Human experience is deeply fallible as a source of information about our own minds, and even of the experience itself. We do not actually have a clear understanding of what’s happening in our own minds, or even a clear understanding of what we are currently experiencing at any given moment. “What it is like to be us” is not something our brains have special, ultimate access to. We have very strong evidence, but our minds are not displayed before us clearly like a movie.
“True introspection” where we step back and look at our thought processes is not possible. Introspection is just another mental function that takes in inputs and gives outputs. We cannot actually see the wiring of our own minds. “Introspection” is actually just another informed guess about the world our brains make, this time based on the patterns we’ve observed in our own behavior. Introspection is like observing a natural phenomenon and making predictions about what will happen next. It’s not like looking in a mirror and clearly seeing ourselves as we really are.
Human knowledge is based on publicly observable evidence and testable reasoning, not privileged access to private mental contents. Our beliefs are justified through the same kinds of information processing that machines can perform, drawing inferences from data, testing predictions, and refining models based on outcomes.
The meaning of language is rooted in public use and social context, not private mental associations. We learn words through observable patterns of usage, not by linking them to ineffable subjective experiences. This means machines can acquire genuine understanding of language without needing a Cartesian theater.
There is no fundamental barrier preventing machines from having beliefs, knowledge, or understanding. If our minds are physical information-processing systems (as physicalism suggests) and what matters is function rather than material (as functionalism suggests), then appropriately designed machines could replicate any cognitive capacity humans have.

I’ll close with a famous line from Derek Parfit on the reductionist view of personal identity, where he describes the experience of letting go of his sense of having a Cartesian ego and internal movie theater.

On the Reductionist view each person’s existence just involves the exercise of a brain and body, the doing of certain deeds, the thinking of certain thoughts, the occurrence of certain experiences, and so on…
Is the truth depressing? Some may find it so. But I find it liberating, and consoling. When I believed that my existence was such a further fact, I seemed imprisoned in myself. My life seemed like a glass tunnel, through which I was moving faster every year, and at the end of which there was darkness. When I changed my view, the walls of my glass tunnel disappeared. I now live in the open air. There is still a difference between my life and the lives of other people. But the difference is less. Other people are closer. I am less concerned about the rest of my own life, and more concerned about the lives of others.

AI and Folk Cartesianism - Part 1: Defining the Problem

Mon, 27 Oct 2025 00:59:07 GMT

“Let me see if I understand your thesis. You think we shouldn’t anthropomorphize people?” -Sidney Morgenbesser to BF Skinner

The 2020s have made my philosophy degree feel more worthwhile. Not only has social media made a ton of new people interested in philosophy, but the real world has also made many of the main questions in philosophy more relevant. AI especially has brought many philosophy topics into the mainstream, but it’s also polarized a lot of the debates. Back in 2011, I could say things like “The human mind is composed of natural processes, like everything else in the world, and therefore it could probably eventually be replicated by a machine, because in some sense it itself is a machine” and people would have long drawn-out conversations with me about whether that’s true or what that implies about language or experience or society. Now, when I say the same thing, it’s much more likely that I’ll get accused of “just buying into the recent AI hype” or that I’ve been tricked by secularism or capitalism or worse to deny the fundamental transcendent character of human minds. This issue used to be a pretty marginal question, but now it’s entered mainstream partisan debate. Sides have been taken.

What’s odd is that a lot of critics of the “minds are machines” view used to implicitly defend it. Back in the 2010s, there was a general worry about anthropocentrism. The belief that humans were in some way unique or separate from the natural world was regularly sneered at as “naive humanism” at best and a method of justifying exploitation of the natural world at worst. There was also a lot of talk about Cartesianism (the philosophy of René Descartes) as the origin of many of society’s problems. Specifically, mind-body dualism was accused of denigrating embodiment and elevating technocratic authoritarian reason. I always thought both criticisms went too far, but analytic philosophy had turned me into an anti-Cartesian naturalist, so I was able to nod along and contribute to these conversations where I could. Since then, attitudes toward Cartesianism have flipped. Many of the same people are now saying that AI cannot, by definition, ever have knowledge, because it does not have subjective first-person experience, or that it’s lacking some transcendent non-physical characteristic the human mind has; both implicitly Cartesian takes. Accusations of anthropocentrism have also been replaced with accusations of anthropomorphizing. A common line is that “everyone who actually knows how this technology works knows that it’s just linear algebra.1 The only people who believe it’s thinking are dummies who anthropomorphize it because it seems human.” I long for the 2010s when it was more common to hear people talk about a lot of different systems exhibiting some basic qualities of mind. I think back then people would be less likely to assume linear algebra couldn’t mimic some basic mental processes.

There’s a cluster of beliefs I associate with “folk Cartesianism” that even critics of Descartes often fall into. It’s incredibly common, to the point that people talk as if its implications are obvious without ever having directly thought about them before. I think this cluster of beliefs falls apart when you poke at it, and the fact that it’s implicit in so many debates about AI shows that it’s not being poked at enough. This post will be me poking at folk Cartesianism, and arguing for “anti-Cartesian naturalism” which is a widely-shared view among analytic philosophers of mind. Nothing I write here will be new or surprising to people knowledgeable about analytic philosophy. But I’ve been feeling the gulf between academic philosophy and mainstream discourse and want to contribute to building a bridge. This will be a long collection of the most popular and effective arguments for why folk Cartesianism is wrong, and how I think it’s harming the AI debate.

I have three goals here:

You can identify what folk Cartesianism is, and when people are thinking like folk Cartesians.
You come away no longer viewing folk Cartesianism as the obvious, common-sense view of the mind. You have a better understanding of the anti-Cartesian naturalist view, and philosophy of mind in general.
You are more open to the idea that machines can replicate what happens in human minds. That minds are not magical, inexplicable substances separate from nature.

I’ve written this partly as an instruction manual to my past self I wish I had when I was younger, as my personal crash course in philosophy of mind 101 as it relates to AI.

Some of these points will be taken from my other posts on the same ideas. The “Naturalism” section will be repetitive for people who have read those already.

Part 1: Defining the problem

Definitions
- Folk Cartesianism
  I define folk Cartesianism as consisting of three common intuitions:
  - The Cartesian view of the mind
- Naturalism

Part 2: Problems for Cartesianism

“There is a unified self that floats above mental processes and observes them”
“We know our own internal experience with complete certainty”
“The basis of all knowledge lies in our first-person subjective experience”

Definitions

Folk Cartesianism

René Descartes inaugurated modern philosophy by attempting to establish a way to reach ultimate truth completely dependent on reason alone (rather than religious revelation). To do this, he looked for a solid, undeniable foundational fact he could start at. Everything that followed would be equally certain. He famously attempted to doubt all his beliefs, and found that the one belief he couldn’t doubt is that he himself existed. “I think, therefore I am.” From here he reconstructs all his other beliefs by building an intellectual ladder, with his belief in himself as the foundation. The next step is a pretty weird proof of God, followed by concluding that God wouldn’t deceive us about the external world.

Descartes’ explicitly and implicitly put forward many more specific claims about the mind, and his overall account matches how many everyday people imagine their minds working. “Folk Cartesians” mostly haven’t read Descartes, but have been influenced by how he and the broader culture talk about mind. So when I talk about “Cartesianism” here, I mean a cluster of concepts that are popular in everyday folk theories of the mind, not necessarily what literal Cartesian philosophers believe.

There are three key features of Cartesianism that I want to deny, and that are relevant for current conversations about AI.

1. There is a unified self that floats above mental processes and observes them

A “Cartesian ego” is a featureless observer hiding in the mind. This is the “I” in Descartes’ “I think therefore I am.” Cartesians imagine the mind as something like a movie on a screen, with the ego the silent observer. Critics call this the “Cartesian theater” model of the mind.

2. We know our own internal experience with complete certainty

Cartesians would say that we ourselves are egos. We observe our first-person experiences and thoughts. We have direct, clear access to them, in the way someone at a movie theater has access to the picture on the screen. The Cartesian ego also has direct, unmediated access to thought processes. We can turn our inner eye toward our thoughts in the way we would a piece of paper that lays out a step-by-step argument. We can observe the mental moves we’ve made and introspect on these thought patterns by just observing them directly. When we introspect, what’s happening is that we’re stepping back and observing the overall shape of our thought processes as they happen, like watching a line of dominos fall while standing above them.

3. The basis of all knowledge lies in our first-person subjective experience

Anything we could call real knowledge of the world has to come from the Cartesian ego observing and experiencing the world, or introspecting on its thought processes. A machine cannot have knowledge, for the same reason a line of dominos cannot have knowledge: there’s no Cartesian ego behind the scenes experiencing and judging the world. Thus, true artificial intelligence is impossible by definition, and many go farther and say AI is useless at what it’s trying to do. It can only ever imitate and copy knowledge. It cannot have knowledge of its own. It will always and only be glorified auto-complete. Even a machine that can handle and process large quantities of information cannot have knowledge, because the mind does more than information processing.

Many people apply this to our understanding of language as well. We only access the meaning of words via our first person subjective experience. I only know what “blue” or “tree” means because I have had subjective, first-person experiences of blue things and trees, and have learned to associate the words with those experiences. More complex concepts like “the national debt” are made up of simple concepts I’ve experienced like “money.” Any entity without a Cartesian ego cannot actually understand the meaning of words, because it cannot have these foundational experiences where it learns what words really mean.

Together, these three beliefs guide how many people think about their own minds, and how they think about AI. I will argue that they are incorrect.

The Cartesian view of the mind

The three of these paint an overall picture of the mind: a rich, internal theater where our egos live and experience things. Most of the important stuff that we consider “mind” happens here. Mathematical functions performed on our experiences and ideas happen a little behind the scenes, and we can perform those functions in our little theater as well (like when we hold numbers in our head while adding them together). Computers can replicate these mathematical functions, but they cannot replicate the internal theater. My goal for this post is to build some intuitions for the idea that this theater doesn’t exist. That we are not egos perceiving clear, unambiguous experiences, and that the mind is actually functions all the way down, where “function” is defined as any process that takes in inputs, performs computations on them, and gives an output.

Naturalism

Definition

I’m arguing for “anti-Cartesian naturalism.” Philosophical naturalism is a loose concept. It basically means “the world follows the laws of science, there is nothing magical happening.” Most analytic philosophers are naturalists, they just have strong disagreements about what counts as part of the natural world. There are three concepts associated with naturalism I adhere to in philosophy of mind:

Physicalism
Functionalism
Evolution

I’ll summarize and give some basic arguments for each.

Physicalism

Physicalism is the view that the only things that have causal effect on physical objects are other things described or used by the laws of physics. Physicalism could be said to mean “Ultimately, what exists is the universal wave function.”

For something nonphysical to have causal effect on physical entities would violate our current best theories of physics. Anything new that has causal effects is likely to be an entity similar to what physics is already describing. Physics has been remarkably successful at describing the world, and physicists operate under the assumption that anything influence physical objects will itself also be governed by physical law.

These are some qualities that all physical entities seem to share, and that differentiates them from supernatural entities:

Spatiotemporal location: Physical things exist in space and time. They have locations, durations, and can be detected at specific coordinates. Supernatural entities are often conceived as existing “outside” space and time.
Causal structure and lawfulness: Physical things behave according to regular, mathematical laws that describe their interactions. These laws are:
1. Discoverable through observation and experiment
2. Consistent and predictable
3. Expressible mathematically
Supernatural things are often defined by their ability to violate or transcend these laws.
Detectability/measurability: Physical things can be measured, either directly or through their causal effects. They leave empirical traces. Dark matter counts as physical even though we don’t know what it is, because we detect its gravitational effects.
Public accessibility: Physical phenomena are intersubjectively observable. Multiple people can measure and verify them using instruments. They’re not private or accessible only to special individuals.

There are entities in the universe we don’t understand, like dark matter and energy, but we should still expect those entities to conform to these 4 patterns when we eventually learn more about them. While physicalists believe that many more things “exist” besides fundamental particles and fields, those things are ultimately just names for those fundamental particles and fields interacting in some way. Even something as abstract as “the national debt” can in some sense be reduced to the incredibly complex behavior of the physical particles that make up people on Earth.

Functionalism

From the Stanford Encyclopedia of Philosophy:

Functionalism in the philosophy of mind is the doctrine that what makes something a mental state of a particular type does not depend on its internal constitution, but rather on the way it functions, or the role it plays, in the system of which it is a part

Functionalism’s main claim is that mental states are defined by what they do, not what they’re made of. Among professional philosophers, functionalism is the most popular account of mental states.

A mathematical function takes an input and produces an output. Functionalists imagine mental states as being functions that take specific inputs and give outputs. A good example of something else like this is monetary value. “Twenty five cents’ worth of value” can exist in a quarter or a digital bank record of $0.25. What makes it the same monetary value is the function it plays in our broader system of exchange. When you transfer it to someone else, you can expect their behavior to be the same regardless of whether it’s delivered as a physical object or a digital exchange.

Similarly, the state of being in physical pain can happen in different animals with different physical brain structures, or even brains made of different physical substances, provided that their mental architecture reacts similarly (gives different outputs) to the same inputs. Just like the monetary value of twenty five cents can be instantiated in different physical systems (a metal coin or a computer), mental states as functions can also be instantiated in different physical objects.

If a system is able to reproduce all the same inputs and outputs as mental states as they occur in human minds, and is just made of different physical material, we should probably assume that it also has the same mental states as the human brain.

I don’t think current AI as it exists is conscious, but it is possible in principle for a machine made of silicon to be conscious like humans if it can reproduce the same inputs and outputs of our relevant mental states. It might be that at a certain level of complexity AI could also have mental states vastly alien to what humans can experience, but that we would still consider conscious experience.

An argument for functionalism, presuming physicalism

Suppose that we were able to replace exactly one neuron in my brain with a silicon chip that could send and receive exactly the same electrical impulses at exactly the same times as the neuron. Maybe there’s something about the substrate of silicon that prevents it from replacing all the functionality of a neuron, but let’s presume for now that it can send and receive all information the neuron receives. What would happen?

Importantly, if physicalism is true, nothing about my behavior would change. There wouldn’t be some immaterial soul or something similar swooping in to say “Hey, Andy’s brain has something strange in it. Something’s off!” The same electrical impulses would happen in my brain either way and cause me to behave in the same way. The same inputs would lead to the same outputs. If I looked at a tree and said, “I see a tree” before replacing a neuron with a chip, I would say the same afterward.

So if physicalism is true, my behavior would stay the same after my neuron is replaced with a chip. What would happen to my conscious experience of the world? Let’s imagine I’m staring at a tree (I’m using an example of my conscious experience of my field of vision because it’s easier to make an image). It seems like there are maybe 4 options, shown below:

Option 1: No change - My conscious experience of the world doesn’t change.
Option 2: A little missing - Maybe some small specific part of my field of vision or something similar goes blank.
Option 3: Less consciousness - There’s less conscious experience in general. Maybe my experiences are more blurry or dim, like I’m in a dark room.
Option 4: No consciousness - I no longer have conscious experiences.

We’ve established that my behavior wouldn’t change. Physicalism implies that the same inputs would lead to exactly the same outputs. This would make option 2, 3, and 4 very strange, because I wouldn’t react to my conscious experience changing, because my outward behavior would be exactly identical to the before case. If I looked out at a tree, and my conscious experience included a small piece of the picture missing, I would notice that and comment on it: “What’s going on? There’s a black dot in my field of vision.” If there were less conscious experience in general, I’d probably say “My vision is suddenly very blurry.” If there were no conscious experience, I’d say “Aaaah! I can’t see!” Because my behavior doesn’t change, I don’t say any of these things, I say “Look! A tree!” This would be very strange behavior if 2, 3, or 4 were true.

I trust my current self’s reports about his conscious experiences. My speech about my conscious experience matches the experiences I’m having. I should probably trust silicon neuron brain Andy’s reports as well.

Maybe it’s possible that my conscious experience would be disconnected somehow from my physical behavior, where I’d be helplessly losing consciousness as my physical body behaved normally as if it were a robot. Unconscious silicon brain Andy would take over as normal brain Andy helplessly lost consciousness and died. This would be very strange, because most of my day to day behavior is responding to my conscious experience. If you somehow removed my conscious experience’s ability to affect my physical behavior, I’d behave in very physically different ways, because I’d be missing a lot of important information. I wouldn’t react to heat or cold or anything in my field of vision. I might behave as if I were dead. It seems like it’s very likely that my conscious experience wouldn’t be affected by replacing 1 neuron with a silicon chip.

We could then go ahead and replace a second neuron with a silicon chip. For the same reason, my conscious experience wouldn’t change. We could build special chemical receptors and signals to replace other parts of my brain. As long as they had the same inputs and outputs on my behavior, I wouldn’t expect my conscious experience to change. At the end, I’d have the same mental states as before, but my brain would now be made mostly of silicon and other materials. This thought experiment is evidence that functionalism is correct: what makes something a mental state depends on its inputs and outputs, not on the specific physical material the system creating that mental state is made of.

So we’ve established that if physicalism is true, it seems likely whether my mind is in a certain conscious state is that the same inputs lead to the same outputs, not the internal constitution of what’s creating those inputs and outputs (a neuron vs. a silicon chip). Physicalism seems to imply functionalism.

Two other key ideas that together seem to imply functionalism:

The Neuron Doctrine: The idea that the brain is made of discrete cells (neurons) that communicate through connections. This supports functionalism because it suggests the brain works through information processing between units, not through some special physical substance. This is a central tenet of modern neuroscience.
The Church-Turing thesis: Any function that can be computed by any possible mechanical process can also be computed by a Turing machine. In other words, there’s a fundamental equivalence to all forms of computation. They’re all doing the “same thing” at a deep level.

If the mind is instantiated by a system of neurons taking inputs and sending outputs to each other (performing specific functions), and that this exact same type of thing can be instantiated in many different mediums, this seems to imply that everything we think of as mental can in principle happen in other systems that can perform computations.

It’s important to note that many non-physicalists are also functionalists, David Chalmers being the most famous example.

Evolution

If naturalism is true, the mind is a product of evolution. Like all other parts of all animals, the mind was subject to evolutionary pressures and natural selection. We should not expect that the mind came pre-made to access abstract truths about the world. Instead, for most of life’s history, minds mainly operated as machines to predict very local environments and specific situations, help the animal survive and reproduce, and (in more advanced brains) achieve and compete for social status (social status here being very broad, and including things like friendship and belonging). Given our evolutionary history, the real question is “Why do people believe true things?”

Dan Dennett had a great line about how “conscious human minds are more-or-less serial virtual machines implemented inefficiently on the parallel hardware that evolution has provided for us.” Put in more everyday language, our ability to do abstract reasoning, to follow a logical argument from premise to conclusion, to work through a math problem step-by-step, or to carefully consider the pros and cons of a decision, runs on top of brain hardware that wasn’t really designed for this kind of thinking.

Our brains evolved to handle dozens of things simultaneously: monitoring our surroundings for threats, maintaining balance, regulating body temperature, processing faces and voices, coordinating movement. This massively parallel processing is what kept our ancestors alive. In comparison, conscious, deliberate reasoning was rarely useful in the ancestral environment.

This helps explain why certain mental tasks feel so effortful. When you’re trying to hold multiple abstract ideas in your head at once, or forcing yourself to focus on something boring, you’re essentially running software that’s fighting against the grain of the hardware. Recognizing a friend’s face in a crowd or catching a ball requires vastly more computation, but feels effortless, because that’s what the parallel hardware excels at.

We shouldn’t expect our conscious minds to be perfectly rational machines, or to have some kind of magical access to higher realms of truth than what is available to other complex physical systems. We can obviously access truths that other systems can’t, but our access to these truths demands physical and evolutionary explanation.

I am aware that it is, crucially, not just linear algebra, and will talk about this more in part 3

Requests for journalists covering AI and the environment

Sat, 18 Oct 2025 11:18:26 GMT

I’ve been in more conversations with journalists about AI and the environment recently and wanted to get some basic points down. This post is a list of my asks as someone who’s had a lot of specific issues with the way AI and the environment has been covered in the last few years.

My main worry hasn’t been that AI’s being unfairly attacked (I’m to put it mildly ambivalent about the future of AI). It’s more that readers are coming away with wildly inaccurate beliefs about where AI and data centers fit into the broader environmental picture. Getting this right matters a lot, because it’s very hard to keep people’s attention on climate and the environment for long. If they get distracted by a relatively small issue, they miss the opportunity to do a lot more good elsewhere. Environmental problems are best understood as a series of complex trade-offs. We need to have a correct picture to manage those trade-offs well.

My ask isn’t that you as a journalist present AI positively or negatively, it’s that you aim to give readers an accurate picture of its place in the environmental issue, and not leave them less informed about where the most pressing environmental problems are in their communities and the world more broadly.

These are all my requests. I go into more detail on each below:

The big ultimate goal: readers should leave your story with a better understanding of how data centers fit into the broader environmental problems a region or the world is facing.
Don’t ever share contextless large numbers.
Readers should leave understanding that the energy and water used by individual prompts doesn’t meaningfully add to their personal carbon or water footprints.
Compare data centers to other industries and commercial buildings, not household use of energy and water.
Criticize AI specifically, but don’t imply that it’s inherently weird or bad to spend physical resources on digital products.
Don’t imply that data centers make the computer processes inside less efficient.
Don’t imply that data centers are new or uncommon.
Don’t leave readers to infer for themselves that data centers have caused specific catastrophes that haven’t actually happened.
Water-specific asks

For context, this recent critical story on data center buildouts checks my boxes for great coverage. I think more writing should aim for this level of quality.

The big ultimate goal: readers should leave your story with a better understanding of how data centers fit into the broader environmental problems a region or the world is facing.

If a reader leaves your story convinced that data centers are the main unique threat to a region’s water, when golf courses in the region are using 30 times more, readers have been misinformed. This often happens in coverage of data centers in Maricopa County Arizona, where data centers are framed as a unique water catastrophe for the region, but are actually using 0.12% of the county’s water, whereas golf courses use 3.8%. If you write a story about Maricopa County or somewhere else, a reader shouldn’t later be shocked to see this graph:

This is a good general rule of thumb. If after reading your article, a reader can feel shocked and confused to see some simple data completely clash with the vibes of what they just read, the article has miscommunicated the problem.

This is the singular sin of bad writing on the environment. There are 8 billion people. Anything used by even a small fraction of people globally will use huge amounts of resources by the standards of one individual person, to the point that literally anything in society can overwhelm us with its scale. If you don’t give readers a sense of proportion, you’re leaving them in the dark and unable to make good decisions about the environment. Don’t merely write “10 million gallons of water per year” or “200,000 bottles of water per day” in a vacuum. This is a huge amount of water by the standards of any individual, but it’s also about 10% of the water a large car factory might use. If you drop a lot of huge contextless numbers on your readers, they will basically always be alarmed, whether there’s a “thousand” or “million” or “billion” involved. A reader leaving your article should not be surprised to later find out that the data center you described uses as a car factory.

My favorite book on good environmental communication (that I’d assign to everyone covering this if I could) is Sustainable Energy - Without the Hot Air by David MacKay. The book’s available for free online. This quote from the introduction captures how useless and harmful it is to share contextless large numbers:

This heated debate is fundamentally about numbers. How much energy could each source deliver, at what economic and social cost, and with what risks? But actual numbers are rarely mentioned. In public debates, people just say “Nuclear is a money pit” or “We have a huge amount of wave and wind.” The trouble with this sort of language is that it’s not sufficient to know that something is huge: we need to know how the one “huge” compares with another “huge,” namely our huge energy consumption. To make this comparison, we need numbers, not adjectives.
Where numbers are used, their meaning is often obfuscated by enormousness. Numbers are chosen to impress, to score points in arguments, rather than to inform. “Los Angeles residents drive 142 million miles – the distance from Earth to Mars – every single day.” “Each year, 27 million acres of tropical rainforest are destroyed.” “14 billion pounds of trash are dumped into the sea every year.” “British people throw away 2.6 billion slices of bread per year.” “The waste paper buried each year in the UK could ﬁll 103448 double-decker buses.”
If all the ineffective ideas for solving the energy crisis were laid end to end, they would reach to the moon and back.... I digress.
The result of this lack of meaningful numbers and facts? We are inundated with a flood of crazy innumerate codswallop. The BBC doles out advice on how we can do our bit to save the planet – for example “switch off your mobile phone charger when it’s not in use;” if anyone objects that mobile phone chargers are not actually our number one form of energy consumption, the mantra “every little helps” is wheeled out. Every little helps? A more realistic mantra is:
if everyone does a little, we’ll achieve only a little.

Readers should leave understanding that the energy and water used by individual prompts doesn’t meaningfully add to their personal carbon or water footprints.

Simply reporting a ChatGPT prompt uses “ten times as much energy as a Google search” doesn’t give a reader much useful information. It’s fine to report on its own, but it shouldn’t be used to imply that ChatGPT is going to become a noticeable part of a person’s daily carbon footprint. Google searches use tiny amounts of energy. Wouldn’t it be weird to hear a climate scientist say you should limit your Google searches? It’s like saying that a digital clock uses 1 million times as much power as an analog watch. That’s true, but it’s such a minuscule amount of power that they both round to zero. Readers should be left understanding that their individual prompts are not really affecting their carbon or water footprints at all. The average American would have to prompt ChatGPT 1000 times per day to raise their carbon footprint by 1%. They would have to prompt it 8,000 times per day to raise their water footprint by 1%. But if they were doing this, they would likely be skipping other activities that were way more harmful to the environment. I’ve read too many articles that give readers the sense that using chatbots can somehow meaningfully contribute to their individual environmental footprint. Even if you try to include every last way chatbots affect the environment, the numbers just don’t add up. It is wildly misleading to imply that using AI raises your personal carbon or water footprint.

Compare data centers to other industries and commercial buildings, not household use of energy and water.

Because AI and the digital economy are large general industries in America, their proper context is other industries, not large multiples of personal lifestyle things individual people do. It would be weird for me to compare the American auto industry’s energy use to the energy I use in my home. Better comparisons would be to the steel industry, or agricultural industry, or other places where I can understand how car manufacturers fit into the big picture of America’s industrial use of energy and water. Simply reporting “Wow, the auto industry uses millions of times as much energy as Andy! Shocking.” would give readers no useful information.

But most media I’ve seen about AI data centers seems to only ever compare AI energy and water to massive multiples of personal household use, not to other normal industries. Take this recent example from the BBC:

The video announces that all data centers in Scotland are using 27 million bottles of tap water every year. That’s a weird way to talk about a large industry and doesn’t give the viewer any context for how data centers compare to other industries. If we make the comparison, we find that all data centers in Scotland combined are using just 4% of the water used by a single large car factory. That’s just 0.004% of Scotland’s total water use. This is a much more helpful comparison for readers. Many viewers would likely be confused if after watching this video, they discovered that everyone involved had been talking about a single car factory in Scotland. It gives viewers a wildly misleading impression of where water’s actually being used. Because most water use is in agriculture and industry, not in households, you should be sure to give readers context for how much resources data centers are using by comparing them to factories, farms, and other things that use larger amounts of water.

This is a very easy move to make. It doesn’t add many words to your piece, and gives readers a much more complete picture of how data centers compare to other industries.

These are some specific ways data centers are often talked about that other industries aren’t:

Anything referencing “community water” as in “data centers want to tap into local community water.” Most water in America is used for large scale agriculture and industry, not households. Any industry could be said to be “tapping into community water.” It would be strange to read an article about the Detroit auto industry “tapping into Detroit’s community water” even though the auto industry there uses millions of gallons of water per day. It’s understood that most parts of the country have both industry, commercial buildings, and homes, and in fact places are often benefited by having more industry and commerce.
Ominous stories of data centers “using more than 20% of a town’s water.” This only sounds shocking if you don’t think of data centers as any other industry. If a factory or large college were built in a small town, and you found out either were using 20% of the town’s water, this would make sense. The main business in the town would use a big chunk of its resources. This can often be beneficial, as it gives local utilities more money to invest back into improving service.

Criticize AI specifically, but don’t imply that it’s inherently weird or bad to spend physical resources on digital products.

The digital economy more broadly has been a gigantic civilizational achievement for transmitting valuable information. It’s part of what’s contributed to dematerialization where over time we’ve had more economic growth using less physical resources and energy. Digital goods are valuable because information is valuable. You can hate AI and think it’s all bad or useless, but don’t imply that it’s wasteful to spend physical resources on digital goods in general. Computers are the most efficient way to deliver information. Information is valuable. The value of a book is mostly in the information it contains, not the physical paper or ink that make it up. It’s okay and good to spend physical resources to acquire information, especially when doing it digitally is saving physical costs elsewhere.

Don’t imply that data centers make the computer processes inside less efficient.

Data centers are the most efficient ways to do large-scale computing. This point seems obvious, but I’ve met a lot of people who follow AI and the environment in their spare time who seem to think that the environment would be helped if we didn’t use data centers and ran more computer processes in our homes. This would actually be pretty terrible, everything we did would use way more energy. I’m not sure where they’re getting this idea, but the ways data centers are framed sometimes seems to contribute.

Don’t imply that data centers are new or uncommon.

Massive AI data centers are new, but readers should also understand that everything they do online besides AI has always used energy and water in data centers. I meet a lot of people who talk as if AI is the only digital product we use that uses water. There are ~5,400 data centers in the country. 99% of Americans live within 50 miles of a data center. They are ubiquitous and normal. Make sure to distinguish massive new AI data centers from all the others.

Don’t leave readers to infer for themselves that data centers have caused specific catastrophes that haven’t actually happened.

A very common move in a lot of reporting on data centers and water is to say that data centers “can create problems for community access to water” or “data centers can drain local aquifers” without once mentioning that of all 5,400 data centers in the US, none of them have done either. It’s true that they technically “can” create problems for access to water, because they use water! This is true of any industry. Anything that’s clearly meant to let a reader infer something has happened that hasn’t confuses them and leaves them less informed.

Water-specific asks

Don’t report a data center’s water permit as the amount of water it will actually regularly use.

Many (I’d say the majority) of articles I’ve read on data center water use frame the data center’s water permit as the amount of water it will actually use. This is wildly misleading. Water permits are difficult to amend, so data centers request the absolute most water they would need in extreme situations to give themselves a safe upper bound.

Don’t frame the offsite water use as “hidden” that the companies are dishonestly keeping secret.

The companies that run data centers often don’t have access to exactly how much water the power plants they draw from on the grid use. If a data center reports “We will use x amount of water” to the public, it seems reasonable for the public to assume that they are only talking about the water used in the building. If data centers become more efficient, that means that they use less water relative to the power plants around them. But this also means that the “hidden” cost in the power plants is a much larger multiple of the data center’s water. Talking this way effectively punishes the most water-efficient data centers, and implies they’re actually worse because they have a much larger proportion of “hidden” water offsite. This is backwards. I explain this more here.

Don’t use “straining local water systems” or “exacerbating drought” as synonyms for “using any water at all in a high water stress area” without clarifying what the actual harms are.

Literally everything that uses water in a drought-prone area is “exacerbating the drought” and “straining the local water system.” But using those terms can give readers an inaccurate picture if they’re using an extremely small amount of the water. There are many articles referencing data centers “exacerbating drought” in Phoenix that don’t mention they’re only using 0.1% of the water there. Find more specific ways to get across how much they’re adding or not adding to the problem.

The main way I’ve seen people turn ideologically crazy

Thu, 16 Oct 2025 03:46:41 GMT

I’ve seen people around me and in the general public turn ideologically crazy at a higher rate than I expected to in my adult life. I lost one friend to a cult. Others became so brittle that they can barely interact with normal people without freaking out over tiny disagreements. Others have just become very ideologically single-minded and attached to very specific (and in my opinion not very convincing) accounts of the world. These people’s backgrounds, and the ideologies they went crazy over, were all wildly different from each other. But underlying all of them is a single really bad dynamic that I think is actually easy to identify and stop. The dynamic goes like this:

You develop an extremist idea.
You bump into a lot of people who disagree with the idea.
Almost every single person who disagrees with you comes off as wildly misinformed about basic facts about the world, and have pretty horrible background moral intuitions about simple ethical questions. Their disagreements are also very visible entirely rooted in their pre-existing social identity.
You conclude that the vast majority of critics of your extremist idea are really wildly misinformed, somewhat cruel or uncaring, and mostly hate your idea for pre-existing social reasons.
This updates you to think that your idea is probably more correct.

My claim here is that the only thing wrong here, the thing that really messes people up, is that last step where you update in favor of your idea.

I’m not against extremist ideas on principle. I have my own, the main one being that animal welfare is a much larger urgent catastrophe than most people believe. But I think extremist ideas should come with a few warning labels.

The main danger with believing an extremist idea is that it’s very very easy to be negatively polarized by a ton of bad conversations with normal people about it, until you feel like the whole rest of the world is crazy and asleep. The reason it’s so easy is that most people are in fact “asleep” about most things most of the time, not super thoughtful in how they develop beliefs, and really only have expertise in a few areas often related to their jobs.

Suppose you become politically radicalized and become a Marxist. You get convinced that the labor theory of value is correct. You learn all about the pantheon of Marxist theorists through the 20th Century. You really dig in and know all the arguments and concepts by heart. It coheres with what you see on the ground: poor people pointlessly treated badly by big systems, a lot of band-aid solutions to poverty that leave workers without authentic power and control over their lives. You update more and more in favor of Marxism. Then you start talking to other people about it.

Consider two categories of people:

The number of people in the general population who will object to Marxism.
The number of people in the general population who will know enough about the history and philosophy of economics to give you a clear account of why Marxism and the labor theory of value is incorrect, and can speak to your very specific concerns about worker power and class conflict in a way that gets across how modern social theorists and economists give a more compelling and ethically coherent account of the way the world works, and do so patiently and thoroughly.

How many people will you meet who are in both 1 and 2, vs just 1? I think it would probably look something like this:

With no offense meant to any group listed here, the average person in America didn’t complete college (only 37% have a degree). Fewer than half read a book in the last year. Two thirds don’t know that when inflation happens, money loses value. Many people adopt beliefs because it seems like the people they think of as good adopted them too. What percentage of these people can adequately give a convincing account of why liberal democratic capitalism is preferable to Marxism? Most Marxists I meet have a ton of stories of people they’ve met saying things like:

Some people need to be rich, and others need to be poor. That’s just the way it is.
Poor people deserve to be poor. If they worked harder they wouldn’t be having these problems.
Marxism means everyone needs to be equal, but if everyone were equal no one would be…

This leaves them feeling like most people who criticize Marxism are uncaring, dumb, or crazy, and therefore that Marxism is more likely to be true, and people’s other ideas are more likely to be false. I remember one radical friend was pretty shocked to find out that I identified as a liberal capitalist, and brought up “I knew someone who identified as a liberal a while ago. He didn’t even know what mutually assured destruction is.” I paused the conversation to ask my friend if he thought I didn’t know what mutually assured destruction is. He had some trouble disconnecting that other guy from me, or from the concept of liberalism, or his own belief in Marxism.

My core claim here is that most people, most of the time, are going to be terrible critics of your extreme idea. They will say confused, false, or morally awful things to you, no matter what idea you have. This tells you absolutely nothing about the value of your idea, or the value of the ideas they believe in, and it should not cause you to become more confident in your beliefs at all. The average representative of the opposing side is almost always going to be pretty terrible, but that’s because the average person just doesn’t know much about your hyper specific thing, not that the other side is terrible. The average person (including you and me) is outrageously confused about most things. This means if you take on any idea that stands out as weird and draws a lot criticism, most of that criticism will be wildly confused too. Marxists are right that almost all critics of Marxism give terrible arguments, are confused, and seem to have sometimes reprehensible views about class and poverty and capitalism, but that’s only because a lot of people do.

For any topic that requires some level of understanding, the world is mostly a sea of confusion with small islands of sanity and understanding. Gigantic mountains rise on those islands, with vistas higher than most people in the ocean can imagine. Some vistas are way higher than others, but if you only camp out on your own vista, you might not know that thousands of miles away, through the sea of people who don’t know anything at all about your ideology, there may be vistas way way higher. Human understanding in many fields has depth (or in this metaphor, height) unimaginable to most people outside of it. Losing sight of that, and only looking down from your local vista on the ocean of confused people, and feeling self-satisfied that you know something they don’t, and concluding that this must be the only vista in the world, is one of the most dangerous moves you can make with an extreme idea.

This happened to me when I was 17 and beginning to identify as solidly left-wing. One day a 19 year old who I knew was very conservative was reading the news and said “Oh my God, Obama just signed a deal with Russia to get rid of a lot of our nuclear weapons. This is terrible.” I asked why that was bad, because Russia was also destroying its nuclear weapons as part of the deal. The guy got mad and said very sternly “How are we supposed to fight terrorists?” I decided the conversation wasn’t worth it, and came away thinking “Man conservatives are so stupid, this is more evidence that conservatives are stupid and I’m right to not be a conservative.” This was a terrible way to think. I know now that nuclear weapons policy is incredibly complicated, experts who spend their whole lives studying it disagree with each other, and it’s rare to find members of the general public who know enough to have a serious conversation about nuclear weapons. Nuclear weapons policy doesn’t really map onto a clear left-right axis anyway. If a thoughtful, knowledgeable conservative had challenged me on my beliefs about nuclear weapons, I would have probably said something equally or even more stupid. One guy I bumped into happening to know nothing about nuclear weapons should have counted as zero evidence for me about nuclear weapons, or left vs right-wing politics. A lot of people are ideological roulette-wheels adopting random beliefs that pop up in the random political tribes they identify with. I had bumped into one of those people. That was it. I had done the bad thing: updating positively on my beliefs when I heard a bad argument against them.

If you’re a vegan like me, you’ll bump into a lot of people who are pointlessly aggressive and have stupid counter-arguments to veganism (Plants have feelings too, killing is a normal part of nature and evolution, we know for a fact that only humans are conscious, etc.). This can make you feel like all anti-vegans must be equally stupid or blind, and the only people who feel even vaguely awake and intelligent are other vegans. The world outside of your community of vegans can look less interesting, and you’ll assume everyone who “really gets it” is a vegan. This is a very dangerous emotional place to end up! There are actually many compelling arguments against veganism, including many made by committed animal welfare activists. But many vegans I meet don’t seem aware that any exist at all.

In these situations, what the believer in the extremist ideology should do is assume “Most people are going to disagree with me about this. I won’t find many good arguments about this with most people. What I should do if I actually want to expose my belief to criticism is try to find the most compelling, thought-out criticism of the belief that I can and work with that. I should also assume that if I were having conversations with people who agree with me about the extreme belief, I’d still see them as ‘asleep’ about a lot of other stuff in life. There are maybe vistas waiting for me beyond my field of vision. I should be careful to know if they exist.

Thinking this way matters more than ever, now that the internet makes it so much easier to learn a lot about big ideologies and ideas very fast.

Be slightly elitist

What I’m saying is that you should ignore a lot of people’s thoughts on your extremist beliefs. How should you decide who to engage with? I think there are just two basic rules that will always clarify who’s worth listening to:

The person knows basic facts about what you’re disagreeing about, like
- Topic: Politics
  - Fact to know: The 3 branches of government
- Topic: Marxism
  - Fact to know: The basic idea of the labor theory of value
- Topic: Nuclear weapons
  - Fact to know: Nuclear weapons exist in most countries purely to deter other countries from invading/using their own nukes
The person is not super fired up and doesn’t just want to act as a soldier for their team

This is an elitist rule in the sense that I don’t think many everyday people you bump into will actually clear these two requirements. I’d guess that these rules would end a lot of conversations on their own and leave you free to only engage with people who will actually give you good, deep criticism. If you can’t find those people in the real world, turn to the internet and find them there.

Instead of associating who’s “awake” with your ideology, you should think of “awakeness” as a quality anyone with a careful interest in figuring out what’s actually true has. Assuming that anyone with reasonable beliefs can be “awake” in this way (but that it takes some effort and emotional patience) can make the world seem open and alive again, and make your ideology seem less like a solitary island home in a sea of insanity and more of an interesting puzzle you’re trying to solve with your critics. You should try to keep your sense of awakeness and your specific ideological beliefs as disconnected from each other as possible. This ability to separate specific beliefs from who someone considers to be awake is itself something that makes me experience that person as “awake.”

The sense that the person you’re speaking to is “awake” is intoxicating. It’s a reason people form communities around their belief systems. Feeling like you’re with someone who’s a live player in the world can give you a deep sense of company, a core human need. Straying from your belief system to get that sense of company elsewhere can be scary and intimidating, especially if you haven’t done it for a while. If you’ve recently only really felt that sense of company from people who agree with you about a lot of big core stuff, it’s time to stray.

But in straying, don’t update on the value of your or others’ beliefs based on your interactions with the average person.

A few meta points on my posts on AI and the environment

Wed, 15 Oct 2025 04:00:07 GMT

These are a few clarifying points on my series on AI and the environment that I haven’t found a place for in the posts themselves.

Everything I’m saying is actually just a few points repeated over and over

There are actually just a few simple points I’m trying to drive home in all these posts, expressed over and over in different situations:

For your personal footprint, compare AI to your total carbon and water footprint and see if it has any effect at all. Don’t compare it to incredibly efficient tiny things like Google searches.
For local and global impact, compare AI to other industries and applications, not to your personal lifestyle. Manufacturing iPhones uses huge amounts of power in total. It would be ridiculous to compare manufacturing all iPhones to the number of flights an individual person takes. Creating a new product for a billion people is a more relevant comparison to training an AI model. Many data centers use the same amounts of water as other industries etc. Always contextualize large numbers.
The collective environmental footprint of society is made up of the footprints of individuals. We should behave in the ways we’d want the group to behave. We should prioritize the things that actually help climate the most, because some things have many orders of magnitude more effect than others.
All environmental and climate impacts of all industries are “new” in the sense that every day, all industries add new carbon emissions and air pollution to the environment. Treating AI data centers as special just because the buildings themselves are new normalizes much larger sources of emissions and harm that coincidentally were already happening.
AI and the environment is confusing to think about because of the environmental paradox of data centers: they both put uniquely large concentrated demand on local grids, but are also tiny efficient parts of the global energy grid. They can look big and evil on the ground, but are actually remarkably efficient.
In general, computing is one of the most resource-efficient parts of society.
Most of AI’s effects on the environment will probably be caused by how it’s used, not by the physical operation of the data centers where it’s run.

All of these points seem really simple individually, but I wasn’t finding any popular commentary on AI and the environment online that checked all these boxes in the way I wanted, which was a big motivation to post about it more. There are a lot of great industry reports written for more technical audiences, but a lot of the popular coverage I was reading went against basically all of these points.

Why these posts are so long

I’m capable of writing short posts. The reason the AI and environment posts tend to be so long is that for each one:

I find very few other places online collecting what I want to say about the topic in a single place. I figure it might be useful to other people to have these points collected in individual places rather than spread across 20 posts.
I’m faced with a choice. If critics read my post, they’re going to come away with one of two takes based on how much I write:
Between these two, I’d rather just aim beyond that red line, clearly label the contents of the post, give readers the option to skip around, and make it really long rather than come across as dodging some important point. Still, I find that a lot of people still announce that I’m dodging some point that I very clearly label. One of the most common reactions to my first post on this was “How convenient, he left out the cost of training where most of the energy is used” even though I had a clearly labeled training section. Can’t win ‘em all.
There is just a ton of nuance to get across in getting these basic points across, and I want to make it clear where I’m coming from. Since college I’ve worried that the environmentalist movement is too quick to label things “whataboutism” and was finding that was a very common objection, so getting down what I think about that and why I worry environmentalists overshoot here seems useful. I have a backlog of 15 years of following and reading about climate and environmentalist ethics, so I’d rather get it out there if it’s relevant.
I could just leave people with that list of basic ideas above, but I keep having a lot of positive reactions and feedback to each new post, so it seems like actually doing the deep dives is providing value.

This isn’t actually the single most important problem

The reason I’ve been writing about this a lot more recently is mainly that I’m finding I’m having some nonzero influence on the conversation and building up a big audience who seem to value it. Both my original post and cheat sheet post each have 100,000 views now, definitely the most anything long I’ve written has gotten. 3 of the 5 top images for “ChatGPT and environment” are from my blog. Neat!

This has been a really fun surprise. I’m mashing this button as long as I think I can actually add new useful stuff to the conversation and get positive reception. Older readers know that I used to blog about a much larger range of stuff. I want to get back to that. I’ve been pretty busy recently and in choosing what to write about, it does still seem like there are some big gaps in the AI & environment debate I’d like to focus on addressing. But there are a limited number, and once I have more time I want to broaden what I post about again. This is temporary.

More people should be doing this!

I’d really like more popular writing on AI and the environment from people who can go beyond “Oh AI’s weird and new, and is using a lot of energy and water. Must be bad, end of story.” There’s a lot of great technical writing on AI and the environment, but things aimed at regular people are still shockingly scarce. If you do a YouTube or TikTok search, most of what you see is pretty misinformed. If you have skills in either, that might be a niche you can fill.

I presume most journalists are honest, but also might be stretched thin or in a topic they don’t know much about

I don’t want to come off as implying that there’s a vast journalistic conspiracy against data centers. I’ve been roasting a lot of recent coverage, but I think the problems I’m identifying mostly come from journalists being somewhat new to the issue, and running with previously established common wisdom about data centers, like the idea that they use a lot of water. Less charitably, it might be that big weird new buildings using a lot of power and water just gets clicks, but I presume almost everyone covering this is authentically interested in the truth and is just slipping up because the public understanding is bad.

There are exceptions, but I really don’t want to come off as implying that journalists as a whole are actively choosing to be deceptive about this. I think they’re sometimes just taking shortcuts based on pre-existing coverage.

What would cause me to think that AI is actually bad for the environment

I would change my mind about AI and the environment if I saw good reasons to believe 1 of these 4 points:

Using AI raises individual people’s carbon or water footprint in expectation.
The numbers are so tiny that if people switch from using AI to most other activities their emissions probably go up. Right now the numbers imply that if AI is using a large amount of your energy and water, you’re using it so much that you’re not doing other activities that are much worse for the environment.
AI data centers are using an inordinate amount of power and water relative to the actual number of people using them (hundreds of millions), the tax revenue they generate for the communities they’re built in, or other major American industries.
Problems specific AI data centers have created for local communities (xAI’s data center in Memphis might have added significant pollution) couldn’t be easily fixed with strict regulation and pricing.
AI isn’t actually valuable at all, or at least isn’t valuable per unit of energy and water it costs. This is the place I’m least likely to change my mind. I use chatbots hourly at least. They’re a big reason I’ve been able to learn so much about this so fast in the first place.

Something I’m not willing to entertain is the point that AI is bad for the environment because it emits at all, or uses any water. Under that definition, literally everything we do is bad for the environment, and saying “AI is bad for the environment” is identical to “AI is something people do.” I have a more detailed breakdown of the definition of “bad for the environment” I’m using here.

Slop implies capability

Mon, 13 Oct 2025 00:35:12 GMT

I’ve been seeing a lot of people react negatively to OpenAI’s new Sora 2 video model. It’s understandable to not want more short form video content in the world, but I’m seeing too many people take from this that AI has no useful capabilities and is just a wasteful fad. I’ve had basically the opposite take: the ability to generate stupid but engaging slop is a pretty obviously wild new capability that shows the current deep learning paradigm is shockingly capable. If AI can produce engaging slop, it can do a ton of wildly useful things too. Slop implies capability.

This is my favorite Sora video, and AI video in general:

Watch that again, focusing on how the way the horse’s hooves move is so coherent and makes sense given what it’s doing.

This is obviously slop. It’s a meaningless short video that won’t benefit me at all beyond activating my dopamine for a moment. But it’s also a sign of pretty remarkable ability.

Sora wasn’t told the laws of physics, or how horses move, or what skateboards are. It was able to infer all this from scratch from intaking gigantic amounts of video data. By getting more and more data, and having more and more internal “knobs” it could adjust to respond to that data, it was eventually able to infer enough useful heuristics that it can create a convincing video of a horse riding skateboards. This is an especially great new video explanation of how this works:

There are a few things happening in AI right now that make objective assessments of its capability difficult. Among other things:

People are constantly overhyping what it can currently do, leading to general skepticism when it doesn’t match up.
The data center buildout has been massive, and headlines about the cost and impacts have led many to believe that AI needs to be a world-historic technology right this second to justify it.

It’s very easy to react to any new improvement in AI by just immediately identifying all the ways it doesn’t measure up to the buildout or the hype. There are also a lot of in-the-moment status games to win. But try to think back to yourself 5 years ago. Would you have been able to predict that AI video would be able to do this in 2025? Here was the best state of the art research on AI video at the time:

Now we can make videos like this:

Sora and chatbots both depend on the transformer architecture, the pattern-recognition and heuristics-generating tool that’s been the backbone of most AI progress over the last decade. Transformers take in huge amounts of training data, and adjust huge amounts of internal parameters to pick up on more and more subtle heuristics and patterns. This ability to detect such complex and nuanced patterns is on display in the horse video. There is no way you could personally code clearly-written rules into a machine to make it so generally knowledgeable about the subtleties of video and motion that you could just type “Make a horse jump a ramp” or “Make Spongebob get a ticket” and expect it to give a useful output. This is why deep learning is so useful. It’s a way to get a machine to build its own soup of internal heuristics for how to handle complex situations. This soup of heuristics both succeeds and fails in surprising ways. We don’t design the heuristics themselves, and don’t even know what they are or how they work, in the same way we don’t know how a lot of the heuristics in our own brains work. We can now make machines that can pick up on such subtle heuristics that they can infer how a horse would look skateboarding over a ramp to the point that their output looks photorealistic.

It seems obvious that if deep learning can pick up on the patterns required to make the horse video, it can probably be used in a lot of other more important places in society. If it can independently figure out some rough approximation of the laws of physics to the point that you can be tricked into thinking videos of complex motion are real, surely it can help with weather forecasting, or have a huge number of specific applications for simple problems in climate, or might soon pose some new emergent risks? Sora 2 makes it especially hard for me to understand how anyone can say that deep learning models are “just glorified auto-complete.”1 They seem to be able to develop some pretty useful internal heuristics that correlate pretty strongly with what’s actually up in the world. If “glorified autocomplete” systems can produce photorealistic videos of anyone in any describable situation, the term stops being useful as a way of predicting what models can and can’t do.

ChatGPT has only been with us for 3 years. The time from the release of Windows 95 to Windows 98. The jump in AI capabilities has seemed much more significant to me over these 3 years than the jump in usefulness from Windows 95 to 98. An AI having the ability to beat out TikTok and Instagram as the most-downloaded short form video app (even for a moment) would’ve been unthinkable… a few weeks ago? Now I’m seeing Sora videos flood Instagram Reels. AI beating the slop machines at their own game, even temporarily, just seems like another win for people in the “AI is shockingly capable, and the capabilities are improving fast, so this really really doesn’t seem like a fad” camp. I’m worried that a lot of people are getting distracted by the stupidity of the outputs to notice how strange of a world we’re living in now.

I’m aware that this criticism applies specifically to chatbots, but they rely on the same underlying architecture

The AI water issue is fake

Sat, 11 Oct 2025 19:29:57 GMT

AI data centers use water. Like any other industry that uses water, they require careful planning. If an electric car factory opens near you, that factory may use just as much water as a data center. The factory also requires careful planning. But the idea that either the factory or AI is using an inordinate amount of water that merits any kind of boycott or national attention as a unique serious environmental issue is innumerate. Individual data centers can sometimes stress local water systems in the way other industries do, but when you use AI, you are not contributing to a significant problem for water management compared to most other things you do in your day to day life. On the national, local, and personal level, AI is barely using any water, and unless it grows 50 times faster than forecasts predict, this won’t change. I’m writing from an American context and don’t know as much about other countries. But at least in America, the numbers are clear and decisive.

The idea that AI’s water usage is a serious national emergency caught on for three reasons:

People get upset at the idea of a physical resource like water being spent on a digital product, especially one they don’t see value in, and don’t factor in just how often this happens everywhere.
People haven’t internalized how many other people are using AI. AI’s water use looks ridiculous if you think of it as a small marginal new thing. It looks tiny when you divide it by the hundreds of millions of people using AI every day.
People are easily alarmed by contextless large numbers, like the number of gallons of water a data center is using. They compare these large numbers to other regular things they do, not to other normal industries and processes in society. They aren’t aware of how much water society uses on other normal industries.

Together, these create the impression that AI water use is a problem. It is not. Regardless of whether you love or hate AI, it is not possible to actually look at the numbers involved without coming to the conclusion that this is a fake problem. This problem’s hyped up for clicks by a lot of scary articles that completely fall apart when you look at the simple easy-to-access facts on the ground. These articles have contributed to establishing fake “common wisdom” among everyday people that AI uses a lot of water.

This post is not at all about other issues related to AI, especially the very real problems with electricity use. I want to give you a complete picture of the issue. I think AI and the national water system are both so wildly interesting that they can be really fun to read about even if you’re not invested in the problem.

Importantly, I am not saying that it’s impossible for data centers to ever cause any problems with water. They require careful planning in the same ways other large industrial buildings do. What I am saying is that right now no reasonable forecasts imply that data centers will rise to become a significant problem for water access in the US. Almost all complaints about their national water use are basically just saying “We should not have a new large industry in America using water.” The tax revenue per gallon from data centers is just so high that in many places they are among the best new buildings possible to benefit a community experiencing water scarcity, because any other industries using the same amount of water would generate way less tax revenue. Critics of data centers need to carefully weigh their water costs against the massive amounts of revenue they can bring in for everyday people, not just look at the water costs alone. The debate about building data centers should involve reasonable conversations between ecologists, economists, and city officials, not everyday voters shouting down local meetings with misleading statistics.

A few important definitions
AI water use isn’t an issue on the national, local, or personal level
How big of a deal is it that data centers use potable water?
Do data centers poison water supplies?
Using AI can save way more water than is used in data centers
It’s okay to use water on a digital product
The social value or harm of a tool isn’t the final word on how harmful it is to the environment
There’s a trade-off between water and energy for data center cooling systems. For the climate, water’s often preferable
What about all those news stories about AI harming local water access?
Further reading

A few important definitions

Suppose I take a cup of water from a lake, and then immediately dump it back in. That doesn’t seem bad. Now I take a cup from the lake, and this time I evaporate it. That seems worse. Now I take a cup and spend some resources on making it drinkable. These all have very different costs and effects on the water system. We need words to describe it.

Water is complicated, but not too complicated. There are a few key definitions to understand. First:

Consumptive use removes water from a local system. Taking the cup of water and evaporating it is consumptive use. Evaporated water mostly does not return to its original source.
Non-consumptive use temporarily takes water from a local system, and returns it later unaffected. Taking the cup of water from the stream and pouring it back in is non-consumptive use.

Growing food is an example of consumptive use. Some of the water becomes part of the food itself. When the food is shipped away, the water leaves the local system.

Many data centers rely on evaporative cooling. This is the way water is consumptively used in data centers. They do not immediately evaporate all the water they are using. Most of it circulates through the cooling system repeatedly before evaporation.

Many reports on AI’s water use do not only include water in data centers, they also include the water consumed by the power plants data centers draw from. This leads to a second important distinction:

Direct use: The water used inside AI data centers themselves to cool servers.
Indirect use: The water used in nearby power plants to generate electricity the data center uses.

The U.S. electricity sector uses approximately 50 trillion gallons of water each year — enough to cover all of Pennsylvania in five feet of water. However, most of that use is non-consumptive.

The average kilowatt-hour takes 4.35 L (1.15 gallons) of water to generate. Unless electricity is sourced entirely from wind and solar, most activities have an indirect water cost. A digital clock has a direct water cost of zero, but an indirect water cost of 0.2 L of water per day. Every three days, the clock accounts for a bottle of water’s worth of consumption at a nearby power plant.

It is standard practice in environmental reporting on AI to include both onsite and offsite water usage. On average, the water data centers use to cool servers is only about one-fifth of the water required to generate the electricity the servers use. Put another way, almost all (80%) the reported water used by AI occurs during the generation of electricity. Data centers primarily consume water indirectly, similar to any other industry.

Consumptive use can harm total access to freshwater, but freshwater sources are also regularly being replenished. The full balance of and access to freshwater is beyond the scope of this report. The key takeaway is that evaporating freshwater removes it from the local supply, although new freshwater is added elsewhere. There is ongoing debate regarding the severity of this issue for total U.S. freshwater supplies. The U.S. has more abundant and cheap freshwater than most other countries.

Another key definition is potable water: freshwater treated enough that it’s safe for human consumption. It’s not actually very costly to turn freshwater potable. It’s much more costly to turn saltwater potable. Most water used in physical data centers themselves is potable, because it needs to be very clean to flow through the cooling systems without harming the pipes.

So of the ways AI uses water:

The vast majority (maybe 90%) is withdrawn, freshwater (not potable) that is indirectly (offsite) used non-consumptively in power plants (it’s returned to the source unaffected)
Less (maybe 7%) is withdrawn freshwater (not potable) that is consumed (evaporated) indirectly (offsite) in the power plants to generate the electricity AI uses.
And less (maybe 3%) is withdrawn freshwater that’s then treated to become potable, used directly (onsite) in physical data centers themselves, and consumed after (not returned to the source, evaporated).

This post is mainly going to focus on the ways AI causes water consumption, not non consumptive withdrawals, because removing water from a source is a significantly bigger problem than temporarily using it and then returning it unaffected.

There are a lot more terms in water management, but these are most of what you need to understand the issue. Another key point is that data centers don’t meaningfully pollute the water they use, so terminology around pollution doesn’t come up.

Finally, it is important to note that most activities consume water. Most U.S. electricity is generated by heating water to spin turbines. Water is used in the production of most physical objects. The majority of the average person’s consumptive water footprint stems from food consumption. Domestic water use accounts for less than 8% of an American’s daily footprint. An American’s total consumptive water footprint is approximately 422 gallons of fresh water per day.

AI water use isn’t an issue on the national, local, or personal level

National

All U.S. data centers (which mostly support the internet, not AI) used 200–250 million gallons of freshwater daily in 2023. The U.S. consumes approximately 132 billion gallons of freshwater daily.1 The U.S. circulates a lot more water day to day, but to be extra conservative I’ll stick to this measure of its consumptive use, see here for a breakdown of how the U.S. uses water. So data centers in the U.S. consumed approximately 0.2% of the nation’s freshwater in 2023. I repeat this point a lot, but Americans spend half their waking lives online. A data center is just a big computer that hosts the things you do online. Everything we do online interacts with and uses energy and water in data centers. When you’re online, you’re using a data center as you would a personal computer. It’s a miracle that something we spend 50% of our time using only consumes 0.2% of our water.

However, the water that was actually used onsite in data centers was only 50 million gallons per day, the rest was used to generate electricity offsite. Most electricity is generated by heating water to spin turbines, so when data centers use electricity, they also use water. Only 0.04% of America’s freshwater in 2023 was consumed inside data centers themselves. This is 3% of the water consumed by the American golf industry.

How much of this is AI? AI uses approximately 20% of the electricity in data centers, though this is obviously forecast to increase significantly. Water use roughly correlates with electricity, because offsite water is used exclusively for generating the electricity data centers use, and onsite water is used to cool chips after electricity flows through them and heats them up. For now I’ll assume AI also uses 20% of the total water data centers use, with large error bars.

So AI consumes approximately 0.04% of America’s freshwater if you include onsite and offsite use, and only 0.008% if you include just the water in data centers.

So AI, which is is now built into every facet of the internet that we all use for 7 hours every single day, that includes the most downloaded app for the 7 months straight, that also includes many normal computer algorithms beyond chatbots, and that so many people around the world are using that Americans only make up 16% of the user base, is using 0.008% of America’s total freshwater. This 0.008% is approximately 10,600,000 gallons of water per day.

I’m from a town of 16,000 people. It looks like this:

All AI in all American data centers is collectively using 8 times as much water as the local water utility in my town provides to consumers.2 You should be exactly as worried about AI’s current national water usage as you would be if you found out that 8 additional towns of 16,000 people each were going to be built around the country.

Here’s data center water use compared to a lot of other American industries:

Note: the steel number on this graph is I think incorrect, my source misread withdrawals as consumption. The mining number is also withdrawals. Everything else is correct. I’ll circle back on this later to update the graph.

Sources

And here’s a comparison to how much water different American agricultural products use, the main way water is used in America. All of these are the amount of water irrigated crops use and aren’t measuring rainwater:

Sources

Forecasts imply that American data center electricity usage could triple by 2030. Because water use is approximately proportionate to electricity usage, this implies data centers themselves may consume 150 million gallons of water per day onsite, 0.12% of America’s current freshwater consumption.

So the water all American data centers will consume onsite in 2030 is equivalent to:

8% of the water currently consumed by the U.S. golf industry.
~~8% of the water consumed by U.S. steel production~~.
- Since writing this I think the source significantly overestimated US steel production’s water consumption and this is actually steel’s water withdrawal, which is very different.
The water usage of 260 square miles of irrigated corn farms, equivalent to 1% of America’s total irrigated corn.

If you found out that U.S. steel production was expected to increase by 8% in 2030, the amount that would cause you to worry about water is how worried you should be about data center water usage by 2030.

How much of this will be AI? Almost all this growth will be driven by AI, but because AI is only 20% of data center power use, its growth will have to be huge to triple total power usage. One forecast says AI energy use in America will be multiplied by 10 by 2030. Because water use is proportionate to energy use, we can multiply AI’s water use by 10 as well.

So in 2030, AI in data centers specifically will be using 0.08% of America’s freshwater. This means it will rise to the level of 5% of America’s current water used on golf courses, or 5% of U.S. steel production, or be about 173 square miles of irrigated corn farms.

The average American’s consumptive lifestyle freshwater footprint is 422 gallons per day. This means that in 2023, AI data centers used as much water as the lifestyles of 25,000 Americans, 0.007% of the population. By 2030, they might use as much as the lifestyles of 250,000 Americans, 0.07% of the population. Not nothing, but 250,000 people over 5 years is just 4% of America’s current rate of population growth. If you found out that immigration plus new births in America would increase by 4% of its current rate, would you first thought be “We can’t afford that, it’s way too much water”?

This is more contentious, but all this is in the context of AI potentially boosting U.S. and global GDP by whole percentage points. Most forecasts imply that AI will boost total U.S. GDP by at least 1%. If we judge industries by how much they’re contributing, data center direct onsite usage will collectively be 0.08% of consumption by 2030, but contributing at least 1% to GDP. Maybe this won’t happen, but in worlds where this doesn’t happen, AI companies won’t be able to afford a huge buildout either. If AI is a bubble, the bubble will have to pop sometime before AI data center water usage hits 10x what it currently is in America. Your predictions for how much water AI will use and your predictions for how much real economic value it’s going to provide have to be related in some way.

Local

Data center operational use of water has had minimal impact on water access where they’re built

Because data centers are using the same normal amounts of water as many other industries, there is only one place I’ve found where data centers seem to have partially contributed to a rise in water costs: Newton County, Georgia, where the NCWSA’s own documents cite the second Meta campus as one factor driving accelerated capital improvements. Even there, Meta is one of several large industrial users alongside a pharmaceutical plant and a Rivian factory, and other factors like inflation and housing market uncertainty are also driving the increase. Everywhere else I’ve looked, data centers have not raised household water costs or harmed local water access. I have a much longer deep dive on that here. I won’t repeat all the arguments. If you’re skeptical, I’d suggest reading that first.

The only exceptions to this rule are the construction of data centers, which has in a few place caused issues for local groundwater. This is bad, but it’s purely an issue of constructing a large building. It has nothing to do with AI specifically, for the same reason that debris from a bank being constructed would tell you nothing about how banks normally impact a community. There’s a famous New York Times headline that comes up in most conversations of AI and water use:

Source

But the reason their taps ran dry (which the article itself says) was entirely because of sediment buildup in groundwater from construction. It had nothing to do with the data center’s normal operations (it hadn’t begun operating yet, and doesn’t even draw from local groundwater). The residents were wronged by Meta here and deserve compensation, but this is not an example of a data center’s water demand harming a local population.

The Georgia data center is only using ~2% of the county’s water. For comparison, a pharmaceutical manufacturing plant is using ~4% of the county’s water. A construction plant for Rivian cars is using about the same amount of water as Meta’s data center. The data center is functioning like any other normal industry in the county.

Basically every single news story that’s broken about this has been misleading for similar really simple reasons it’s easy to cross check and verify. I’ve written up my issues with most major news coverage of AI water below. You don’t have to take my word for it, you can look at each one and see if I’m right or wrong for yourself.

No matter where you look, whether it’s the place with the highest percentage of local water going to data centers (the Dalles, Oregon) or the place with the most water in total going to data centers (Loudoun County, Virginia) or the place with the highest water stress where lots of new data centers are being built (Maricopa County, Arizona), data centers are not negatively impacting local’s freshwater access , because they are behaving like any normal private industry. You can follow each link in the parentheses for a breakdown of how that county uses water and how data centers affect them.

The only difference is that data centers contribute way way way more tax revenue per unit of water used than most other industries. Take Maricopa County in Arizona. The county is home to Phoenix, and is in a desert where water is pumped in from elsewhere. It’s also one of the places in the country where the most new data centers are being built.

Circle of Blue, a nonprofit research organization that seems generally trusted, estimates that all data centers in Arizona will use 905 million gallons of water in 2025. For context, Maricopa County golf courses use 29 billion gallons of water each year. In total, the county uses 2.13 billion gallons of water every day, or 777 billion gallons every year. Data centers make up 0.12% of the county’s water use. Golf courses make up 3.8%.

Data centers are so much more efficient with their water that they generate 50x as much tax revenue per unit of water used than golf courses in the county:

So even though data centers are using 30x less water than golf courses, they bring in more total tax revenue:3

Build industries that generate huge amounts of tax revenue relative to the water they use. Data centers fall into this category (though they don’t provide many jobs).
Do not build cities in the desert in the first place.

Just to drive this home, suppose Arizona announced “We are not going to increase the water use in the state at all, but we are going to close all golf courses and replace them with data centers that use the exact same amount of water.” Doing this at the rate data centers generate tax revenue per gallon would lead to an additional 42 billion dollars in tax revenue each year. That’s almost double Arizona’s total state tax revenue in 2023 ($28 billion). The most important thing for desert regions that still want taxable industry is to replace very wasteful, water inefficient industries with very water efficient, revenue-generating industries. It makes sense to worry about adding more total water demand in these regions, but it does not make sense to single out specific industries because they’re a new use of water if they’re competing with and hopefully pricing out less water efficient industries. Data centers seem preferable as a way of generating tax revenue in deserts compared to most other large industries and commercial buildings.

In low water scarcity areas, adding a large new buyer to a water system doesn’t necessarily harm other customers. Water utilities are natural monopolies with large fixed costs, and economic research shows significant economies of scale in water production — a 1% increase in production volume is associated with a 0.16% decrease in unit production costs. Whether this translates into lower retail rates for households depends on how the utility structures its rates across customer classes. In practice, most U.S. utilities use cost-of-service rate structures that separate residential and commercial/industrial customer classes, so a data center’s water costs are generally allocated to the commercial class rather than passed through to households. And where data centers have negotiated development agreements, they’ve often funded infrastructure upgrades that benefit the broader system.

In high water scarcity areas, city and state leaders have already thought a lot about water management. They can regulate data centers the same ways they regulate any other industries. Here water is more zero sum, but data centers just end up raising the cost of water for other private businesses, not for homes. Data centers are subject to the economics of water in high scarcity areas, and often rely more on air cooling rather than water cooling because the ratio of electric costs to water costs is lower.

This seems fine if we think of data centers as any other industry. Lots of industries in America use water. AI is using a tiny fraction compared to most, and generating way, way more revenue per gallon of water consumed than most. Where water is scarce, AI data centers should be able to bid against other commercial and industrial businesses for it. So far, I haven’t seen any arguments against building data centers in high water stress areas that aren’t basically saying “we shouldn’t have any industries at all in places with high water stress” which seems wrong. People still choose to live in places like Phoenix and expect to have strong local governments that need a big tax base to function well. If you’re against industry in high water stress areas period, you need to be against people living in Phoenix in the first place, which means their water bills should probably rise anyway.

There are many cases of data centers being built, providing lots of tax revenue for the town and water utility, and the locals benefiting from improved water systems. Critics often read this as “buying off” local communities, but there are many instances where these water upgrades just would not have happened otherwise. It’s hard not to see it as a net improvement for the community. If you believe it’s possible for large companies using water to just make reasonable deals with local governments to mutually benefit, these all look like positive-sum trades for everyone involved.

Here are specific examples:

The Dalles, Oregon - Fees paid by Google fund essential upgrades to water system.
Council Bluffs, Iowa - Google pays for expanded water treatment plant.
Quincy, Washington - Quincy and Microsoft built the Quincy Water Reuse Utility (QWRU) to recycle cooling water, reducing reliance on local potable groundwater; Microsoft contributed major funding (about $31 million) and guaranteed project financing via loans/bonds repaid through rates. These improvements increase regional water resilience beyond the data center itself.
Goodyear, Arizona - In siting its data centers, Microsoft agreed to invest roughly $40–42 million to expand the city’s wastewater capacity—utility infrastructure the city highlights as part of the development agreement and that increases system capacity for the community.
Umatilla/Hermiston, Oregon - Working with local leaders, AWS helped stand up pipelines and practices to reuse data-center cooling water for agriculture, returning up to ~96% of cooling water to local farmers at no charge.

I could go on like this for a while. Maybe you think every one of these is some trick by big tech to buy off communities, but all I’m seeing here is an improvement in local water systems without any examples of equivalent harm elsewhere

What about water pollution?

AI data centers are not a notable source of water quality pollution in their host communities. Their cooling water is typically kept in closed loops, any periodic blowdown is routed to a sanitary sewer for treatment or discharged under numeric permit limits, and an increasing share of facilities use highly treated recycled water that would otherwise be released by wastewater plants. By contrast, the largest water quality problems in the United States come from sectors like agriculture and construction.

The EPA’s national assessments repeatedly identify agriculture as the leading source of impairment for rivers and streams due to nutrient and sediment runoff, with continued nitrogen and phosphorus problems that affect drinking water and coastal ecosystems. Construction activity is also a well documented source of sediment discharges if not controlled. Data centers are not flagged by EPA in these national problem lists, and they do not handle the kinds of process chemicals or waste streams that typify the industrial categories with effluent guidelines.

This makes sense when you think about it. Data centers are just big computers. Water just runs through them to cool them, in the same way your laptop needs to be cooled by a fan. Why would using water to cool a big computer significantly pollute the water? You want the water interfering with the physical material of the big computer as little as possible. There are some

How should local communities think about new data centers being built near them?

Data centers have an impact on local water systems, just like any other private industry. They shouldn’t just randomly be built anywhere. Local communities should consider the costs and benefits. But in doing this, they need to consider the actual amounts of water data centers will use compared to other normal industries, not compared to individual lifestyles.

Obviously, questions about electricity or pollution are real and should be considered separately, but at least in terms of water, I’ve found only one example (Newton County, Georgia) where data center expansion appears to have partially contributed to rising water costs, and even there it’s one factor among several. In contrast, there are many places where data centers have benefited local water access, and a universal pattern of huge tax revenues.

Personal

I think a lot of people don’t realize how much water we each use every day. Altogether, the average person’s daily water footprint is 422 gallons, or 1600 liters. This is mostly from agriculture to grow our food, manufacturing products we use, and generating electricity. Only a small fraction is the water we use in our homes.

Our best current data on AI prompts’ water use from a thorough study by Google, which says that each prompt might only use ~2 mL of water if you include the water used in the data center as well as the offsite water used to generate the electricity.

This means that every single day, the average American uses enough water for 800,000 chatbot prompts. Each dot in this image represents one prompt’s worth of water. All the dots together represent how much water you use in one day in your everyday life (you’ll have to really zoom in to see them, each of those rectangles is 10,000 dots):

However, that 2 mL of water is mostly the water used in the normal power plants the data center draws from. The prompt itself only uses about 0.3 mL, so if you’re mainly worried about the water data centers use per prompt, you use about 300,000 times as much every day in your normal life. That’s the same water your local power plant uses to generate a watt-hour of energy, enough to use your laptop for about 2 minutes. So every hour that you use your laptop, you’re using up 30 chatbot prompt’s worth of water in a nearby power plant.

Have you ever worried about how much water things you did online used before AI? Probably not, because data centers use barely any water compared to most other things we do. Even manufacturing most regular objects requires lots of water. Here’s a list of common objects you might own, and how many chatbot prompt’s worth of water they used to make (all from this list, and using the onsite + offsite water value):

Leather Shoes - 4,000,000 prompts’ worth of water
Smartphone - 6,400,000 prompts
Jeans - 5,400,000 prompts
T-shirt - 1,300,000 prompts
A single piece of paper - 2550 prompts
A 400 page book - 1,000,000 prompts

If you want to send 2500 ChatGPT prompts and feel bad about it, you can simply not buy a single additional piece of paper. If you want to save a lifetime supply’s worth of chatbot prompts, just don’t buy a single additional pair of jeans.

Because generating electricity in America often involves water, anything that you do that uses electricity often also uses water. The average water used per kWh of electricity in America is 4.35 L/kWh, according to the Lawrence Berkeley National Laboratory. This has a few weird assumptions (explained here), so to be conservative I’ll divide it in half to 2 L/kWh. This means that every kWh of electricity you use evaporates the same amount of water as 1000 chatbot prompts (including both onsite + offsite water cost). Conveniently, 1 prompt’s water per Watt-hour.

Here are some common ways you might use electricity, and how many AI prompts’ worth of water the electricity used took to generate:

Playing a PS5 for an hour - 200 prompts’ worth of water
Using a laptop for an hour - 50 prompts’ worth of water
An LED light bulb on for an hour - 6 prompts
A digital clock for an hour - 1 prompt
Heating a kettle of water - 125 prompts’ worth of water (the kettle itself has enough water for ~500 prompts)
Heating a bath of warm water - 5000 prompts (the bathtub itself has enough water for 80,000 prompts)

If you want to reduce your water footprint, avoiding AI will never make a dent. These numbers are so incredibly small it’s hard to find things to compare them to. If you send 10,000 chatbot prompts per year, the water used in AI data centers themselves adds up to 1/300,000th of your total water footprint. If your annual water footprint were a mile, 10,000 chatbot prompts would be 0.2 inches.

Even if AI is completely useless, and all the water you use on it is “wasted,” literally everything else you do in life involves larger amounts of wasted water, even if that activity is really valuable for you. If you boil water to make healthy food, you could make the exact same amount of food if you took a water dropper and extracted a few milliliters from the pot. A medium-sized pot can hold ~10 liters of water. That’s enough for 5000 chatbot prompts. If you took a dropper and removed 1/5000th of the water from the pot, you would still be able to use it to make whatever food you want, so that extra amount is “wasted” but it’s so small that it doesn’t matter at all. If you saw someone filling a pot like this:

10 liter pot

and then using this dropper to remove tiny amounts of water at a time to “save” as much water as they could until the pot only contained exactly as much water as they needed so that they didn’t waste a drop,

2 mL dropper

you would correctly say that was a huge waste of time. In no other places do we think it’s reasonable to worry about “wasting” such a tiny amount of water in our personal lives. Even if you think all water used on AI is completely wasted, it still shouldn’t bother you any more than the fact that people don’t spend the time to remove tiny drops of water from the pots they boil to make food.

How big of a deal is it that data centers use potable water?

One concern about AI is that unlike a lot of the other industries I listed, it’s using potable water: water treated to specific quality standards such that it’s safe for people to drink. Crops basically never use potable water—irrigation relies on untreated surface water and groundwater, not municipal drinking water supplies. Maybe potable water is so much more valuable and scarce that, even though AI isn’t using much, there’s not too much potable water anyway, so AI can still create a significant problem for water access?

According to the USGS’s most recent comprehensive water use report, America’s total water withdrawals in 2015 were about 322 billion gallons per day, of which 281 billion were freshwater. Of that freshwater, about 39 billion gallons per day were withdrawn by public supply systems—utilities that treat water to potable standards and deliver it to homes and businesses. That 39 billion is what consumers buy from local water utilities.

How difficult is it to turn those roughly 242 billion gallons of non-potable freshwater into potable water? If it’s extremely easy, the distinction doesn’t matter much. It would be kind of like having money in two different checking accounts where it’s easy to move it back and forth, but saying we should worry a lot about one checking account being small regardless of the size of the other.

After looking into this, I’ve found that not only is it straightforward to turn freshwater into potable drinking water using conventional treatment processes, but also that the water utility sector exhibits well-documented economies of scale: larger water systems produce, treat, and deliver water at lower unit costs (dollars per gallon) than smaller systems because of efficiencies in the use of labor, raw water supply, and water treatment. A National Academies report on water utility privatization found that as water utilities face higher treatment costs, the desirability of achieving economies of scale becomes more pronounced, and that technological improvements have made larger regional water systems increasingly cost-effective. Research on water treatment costs has confirmed that unit treatment costs are higher for smaller municipalities and decline as facility size increases. Studies of water utility pricing have consistently found that larger utilities serving more customers tend to have lower per-unit prices, reflecting these scale economies.

What this means is: if any region has abundant freshwater but limited potable water, the best way to make potable water more available and cheaper is to introduce a new large, consistent buyer, which will give the local utility enough revenue to upgrade and expand their treatment facilities. Saying that my data is misleading because AI “only uses valuable potable water” actually gets the issue backwards: In regions with abundant freshwater, the potable water ‘bottleneck’ is treatment and delivery infrastructure, not the raw resource. Research consistently finds economies of scale in water systems, larger communities tend to have lower per-unit water costs because fixed infrastructure costs are spread across more users. This suggests that adding a large, consistent buyer to a water system in a water-abundant area is more likely to support infrastructure investment than to strain the system, though outcomes depend on local rate structures and capacity.

To use a metaphor, imagine that freshwater supply in a region is like jewels in a mine, and potable water is like polished, carved jewels. We can imagine two cases:

Case 1: There is a near-infinite amount of jewels in the mine, but very few are being mined, polished, and carved.

Case 2: There are not many jewels in the mine.

Imagine that demand for polished jewels suddenly skyrockets in each case.

In the first case, companies realize they can make a lot of money if they send a lot more miners in and build a lot of infrastructure to polish and carve the jewels. After a year, 100 times as many miners, carvers, and polishers are working to produce jewels. In this situation, would you expect the price of polished carved jewels to rise or fall? It seems obvious that here the price would fall, due to economies of scale. There are enough jewels in the mine that their supply doesn’t affect price, and all the revenue from jewel sales can be invested in more efficient systems of producing them. You would expect as a buyer to have to pay less and less for a jewel. In the same way, where freshwater is plentiful, treated water becomes less expensive as more people buy it.

In the second case, the supply of jewels in the mine drops off quickly. Raw, uncarved jewels become more and more expensive to obtain. Even if the cost of carving them is low, the total cost of the jewels will rise more and more due to the supply going down. Here, more demand for jewels causes the price to go up, but only because the raw jewels themselves are getting more expensive. In the same way, where freshwater is scarce, treated water becomes more expensive as more people buy it, but only because the freshwater itself is becoming more expensive and scarce.

So even before looking at the numbers, people complaining that AI requires precious rare drinking water are actually getting the issue completely backwards. This comes up very often. Karen Hao, who wrote Empire of AI, regularly emphasizes in interviews (and the book itself) that data centers are not just using water, but are “tapping directly into cities’ drinking water supplies.” Here’s a quote from a Democracy Now interview:

From a freshwater perspective, these data centers need to be trained on freshwater. They cannot be trained on any other type of water, because it can corrode the equipment, it can lead to bacterial growth. And most of the time, it actually taps directly into a public drinking water supply, because that is the infrastructure that has been laid to deliver this clean freshwater to different businesses, to different homes. And Bloomberg recently had an analysis where they looked at the expansion of these data centers around the world, and two-thirds of them are being placed in water-scarce areas. So they’re being placed in communities that do not have access to freshwater. So, it’s not just the total amount of freshwater that we need to be concerned about, but actually the distribution of this infrastructure around the world.

A few things worth noting about this quote. First, the claim that data centers “cannot” use any water other than freshwater is wrong. Data centers can and increasingly do use reclaimed wastewater for cooling. AWS has announced that 20 of its data centers cool with purified wastewater, and Google uses reclaimed or non-potable water at over 25% of its data center campuses. It’s true that reclaimed water can cause more corrosion, scaling, and microbiological growth than potable water, which is why most data centers prefer potable water—but it is not technically impossible, and the industry is actively moving toward non-potable sources. Second, the Bloomberg analysis that Hao references did find that about two-thirds of new data centers built or in development since 2022 are in places with high water stress—but Bloomberg’s own reporting attributes this to data centers chasing cheap energy and favorable tax policies, not water availability, noting that “water is often the last consideration when making siting decisions.”

But the larger point here is this: it’s actually preferable for data centers to buy water from municipal water systems, because that means they’re paying into the system itself and providing utility revenue that on net benefits other buyers. One industry analysis estimated that around 57% of data centers’ direct water use comes from potable water supplies, with the rest from alternative sources. When data centers connect to public water systems rather than drilling private wells or drawing directly from rivers, they are customers of the utility, generating revenue that can fund infrastructure upgrades. It would be much worse if they weren’t using the municipal water system, because that would mean they would still be taking freshwater and competing with the municipal water system if water were limited, but not providing any revenue to the utility.

So I think this on its own makes my case extremely strong that we need to look exclusively at the freshwater withdrawals being used on AI, not the potable water. The fact that data centers predominantly use potable water either doesn’t matter (in freshwater-abundant areas) or helps my case (because it means data centers are paying into municipal systems). But let’s say this effect of economies of scale somehow doesn’t happen. What would this mean for AI?

How much do we actually pay to make water potable?

In the U.S., “potable” basically means “meets EPA’s National Primary Drinking Water Regulations”: a set of contaminant limits and required treatment techniques for things like microbes, nitrates, arsenic, and so on. EPA’s NPDWR table spells out the specific numerical limits. There’s nothing metaphysically special about this water. It’s normal freshwater that has gone through coagulation, filtration, disinfection, and sometimes a few extra polishing steps to hit those standards. EPA’s Surface Water Treatment Rules are written on the assumption that your source is an ordinary river, lake, or reservoir.

One way to see how cheap this conversion is is to look at what utilities already charge. EPA’s WaterSense program compiles national rate data and estimates that, in 2023, the average U.S. residential water price was about $6.64 per 1,000 gallons, with wastewater service adding another $8.57 per 1,000 gallons. Commercial customers paid about $5.56 for water and $6.86 for wastewater per 1,000 gallons. EPA summarizes these averages here.

That combined ~$15 per 1,000 gallons is not just the cost to flip freshwater into potable water. It also includes:

Treatment
Distribution pumping and storage
Maintaining and replacing pipes
Billing, customer service, and regulatory compliance

If you look at simpler wholesale or regional numbers, you get a cleaner sense of the underlying production cost. For example:

Ottawa County, Ohio reports that its regional water plant charges about $2.77 per 1,000 gallons to its distribution customers to pump raw lake water, treat it, and deliver finished water to local systems. That figure explicitly includes operations, maintenance, debt service, and admin. The county posts the breakdown on its FAQ page.
A Texas feasibility study for Brownsville’s conventional water treatment plant (separate from a proposed desal project) uses an internal treatment cost of about $1.34 per 1,000 gallons of potable water as the baseline. That appears in the Texas Water Development Board’s report tables.

These are “all-in” utility costs to take raw freshwater, treat it to drinking standards, and push it into the local system. They’re on the order of a couple of dollars per 1,000 gallons.

Nationally, EPA’s own example water bill assumes a retail volumetric charge of about $0.00295 per gallon, which is $2.95 per 1,000 gallons, for a typical surface-water system, and that again includes treatment plus distribution and overhead. EPA walks through that math in its “Understanding your water bill” example.

So as a very rough rule of thumb:

Turning raw freshwater into potable water plus delivering it is usually in the $2–$7 per 1,000 gallons range, depending on the city.
The incremental treatment-only cost (before pipes and billing) is typically well under a couple of dollars per 1,000 gallons, and often closer to $1.

How much potable water specifically will AI use? How much would it cost to make all that water potable?

The US public water supply uses ~40 billion gallons per day, all of this is potable. Data centers used 50 million gallons per day onsite in 2023. So their potable water usage was 0.13% of the public water supply. I want to beat the drum again that data centers more broadly are being used by all Americans for half their waking lives (because they support the internet), so the fact that as an industry they’re using a little bit of the total potable drinkable water in the country shouldn’t surprise us on its own. Assuming again that AI is ~20% of what happens in data centers (10 million gallons per day), AI in 2023 used ~0.03% of all drinkable water in America. If we assume an aggressive 10x growth of AI in data centers by 2030, and no changes to how it uses water, this will rise to 0.3%: 100 million gallons per day.

What about the water used offsite for power generation? This is often brought up as a “hidden” water cost of AI, and many estimates of AI’s total water use include it. This estimates often make AI’s total water use 5-10x bigger. However, effectively none of the water power plants use is potable. If you see an estimate of AI’s total water use, don’t immediately assume that all that water is drinkable. Check and see if the study is including the offsite water (they often do). If you’re worried about potable water specifically, only look at the water the data center itself is using.

Taking our estimate that making freshwater potable usually costs about $1 per thousand gallons, this implies that the US would be spending an additional $100,000 per day to generate enough potable water for AI data centers. If water markets and policies are working well, this cost should mainly be born by the AI companies themselves. This cost is about 0.01% of Google’s revenue alone. But let’s say that this doesn’t happen, and somehow the normal split between commercial and household rates breaks, and the cost is instead exclusively borne by American household consumers. How much would this increase water bills?

The average American family of four’s water bill is $2.60 per day. This implies American households spend in total $221,000,000 per day on water. The total increase in demand for potable water from data centers in the extreme case where they grow 10x by 2030, source all their water from municipal water systems, and the cost is somehow exclusively borne by households rather than AI companies and other commercial buyers, would together increase total American household spending on drinkable water by 0.04%.

Okay, well we know data centers aren’t evenly spread around the country. What if instead that 10x increase in water use happens exclusively in top 4 counties in the US with the most data centers are:

Loudoun County Virginia - population 446,530
Prince William County Virginia - population 484,625
Maricopa County Arizona - population 4,726,247
Licking County Ohio - population 180,311

The cost of turning enough additional water potable for data centers, if it were borne exclusively by the people living in these four counties, would increase their total spending on water by roughly 3% by 2030. This is the absolute most the cost of making freshwater potable could contribute people’s difficulty of accessing freshwater, under the insane assumptions that:

The 10x growth in AI activity will happen exclusively in 4 of America’s 3000 counties.
All costs of making more water potable are somehow only borne by households rather than companies.

The fact that AI requires potable water just isn’t an issue. The shocking headlines about AI “using up our precious drinking water” get the problem backwards. It’s good that we have new large buyers paying utilities to upgrade their water systems, because the main issue with water costs in America comes from the maintenance of old treatment and delivery systems that don’t have enough revenue to upgrade. People sometimes talk as if drinkable water is this incredibly rare naturally occurring thing that we stumble on, rather than something that we make. I blame Poland Spring ads.

A type of crisis that doesn’t happen

My impression after reading a lot about water issues in America is that the vast majority are caused by one of two things happening:

An area running out of enough raw freshwater to treat and deliver to people.
Old infrastructure not having enough buyers to afford upgrades. This is how you get towns that still have lead pipes: there isn’t enough funding to support the infrastructure project of replacing them.

The idea that it matters a lot that AI uses potable water and not just freshwater seems to imply that there’s a third type of crisis possible:

A place with otherwise abundant but untreated water having too much commercial demand put on its water utility, causing prices to rise for residential users.

But as best I can tell, this seems to almost never happen, especially when compared to the first two. I can’t find many examples like this at all. This seems to hint that, again, what matters is total available freshwater, not potable water.

Do data centers poison water supplies?

It’s very common that I’m told that data centers poison the water around them. This idea seems to be basically entirely 100% fake, but very popular and caught on because of misreadings of two specific popular news stories.

There are three ways data centers can make water more polluted:

Sediment runoff from construction. This is a problem with any large building. A data center or a Wal Mart super center or an Amazon fulfillment center or a hospital being built near you pose equal risk of doing this.
Evaporating water contaminated with pollution, like nitrates. The contamination doesn’t get evaporated, and instead is filtered out of the data center. This increases the proportion of contaminants in the water without the data center actually adding any new contaminants itself. As I’ll show below, this isn’t really a problem even in regions where a lot of focus has gone to it.
Treatment chemicals data centers use to prevent corrosion, and biological growth in their cooling systems. These chemicals (typically biocides, corrosion inhibitors, and scale inhibitors) are discharged in what’s called “blowdown water” when cooling towers cycle out concentrated water. The water is either sent to either a municipal sewer system, where it goes to a wastewater treatment plant before reaching any natural waterway, or to a nearby river, stream, or lake directly. Either way, Data center blowdown is regulated under the Clean Water Act via NPDES discharge permits, and the chemicals involved are the same ones used in any industrial cooling system, including those at hospitals, universities, and office buildings.

There are no reported cases anywhere of data center blowdown causing any specific issues for water quality. There are places where “residents have concerns” about them, but there has not been any measurable change in water supply pollution due to data centers.

One of the most popular news stories about data centers was caused by #1, where construction from a data center in Georgia may have caused sediment to build up in a nearby groundwater supply and dry up the wells. That led to this New York Times story profiling a couple who was impacted:

And a lot of other scary headlines. This one from the BBC on the same couple has been especially popular:

Most people who saw these stories seem to only have read the headlines, and gotten the impression that the data center itself drew water or polluted the water during its normal operations, and that this caused the water problem. It’s not helped that both headlines contain wildly misleading terms. The NYT’s says “tech giants are building data centers that guzzle up water. This has led to problems for people who live nearby.” But the story itself is NOT about a data center guzzling up water, and the problems for the residents nearby was entirely caused by construction. Normal data center operations “guzzling up water” have so far not created significant issues for water access anywhere. They definitely have not meaningfully polluted water anywhere. But you wouldn’t know it from these headlines. The BBC headline would make much more sense if it said “I can’t drink the water - life next to a US large building.” So many people have seen these two headlines and now believe data centers suck water dry and poison and pollute the water around them. Neither is true, and I know that because the content of the articles themselves don’t make that claim at all! I unpack this more here.

There is only one story where #2, data centers increasing concentration of pollutants by evaporating already polluted water, has happened and gotten attention. There was a big story a few months ago about an Oregon data center “giving people cancer” by polluting the water. When you read the story itself, you discover that all the pollutants in the water giving people cancer are actually nitrates coming from farmers who aren’t being careful about spillover. What’s happening is that the data center is using a tiny amount of the region’s nitrate-poisoned water, evaporating it (which leaves the nitrates behind in the remaining water), and sending the used water to a waste treatment plant, where the concentration of nitrates is higher than it would have otherwise been because some of the water was evaporated. The nitrates in the wastewater plant seem to be leaking into nearby groundwater, causing an increase in cancer rates.

How much is the data center itself contributing to that increase? It seems like no one except me has taken a shot at actually estimating that. I do that below, my best guess is that the data center is contributing to significantly less than 1% of the total increase in nitrate concentration in the groundwater. It’s effectively like Amazon is running a few acres of the normal farmland in the area, and using the same amount of nitrates as all the other much larger farms around them. And yet news articles about this regularly featured scary images like Jeff Bezos superimposed in sickly colors in front of cancer cells:

Beyond the specific numbers here, it seems like Amazon was doing everything right. They were just buying water from a local utility and sending all wastewater back to a treatment plant. Why is it considered the job of private business to track the fact that the water they’re buying is actually polluted, so they’re returning more polluted water after? This is entirely the responsibility of the people in charge of managing the water, and the people breaking rules governing how much nitrates they can add to water.

But wait, you may have heard that Amazon agreed to pay $20 million to settle a lawsuit brought against them for the pollution here. There were a lot of stories about this, like this one:

Surely this means they’re guilty right? Nope. As usual this story is being wildly misrepresented.

In the settlement documents, Amazon states explicitly that it is entering the agreement “solely to avoid the burdens and expense of litigation,” and denies “each and every one of the allegations of wrongful conduct.” They say their data centers ”don’t add nitrates to that water, and the water we return represents a very small fraction of the region’s overall system." If you do the math, it seems impossible not to agree with Amazon’s statement here. If you disagree, I’d be interested if you could show me how this was physically possible.

Amazon is one of 17 defendants in this case. The others include the large farms and food processing companies (the groups that actually added the nitrates in the first place), and the wastewater facility that’s been failing to deal with leakage into the groundwater. They’ve been building up the nitrate concentration in the water since the early 90s, way before the data center was opened in 2011.

Amazon was the first to settle because its marginal contribution to the problem is the easiest to pay off and move on from! The agricultural defendants are still litigating because their part in this is way larger.

But as usual people are entirely misreading this and now think that 1) A data center poisoned people and gave them cancer, and 2) It’s so clear this happened that Amazon won’t even fight it in court. Ridiculous! This story is a great example of how actual water villains, in this case big ag and the poorly run wastewater management facility, can use data centers as convenient scapegoats. Freaking out about a fake problem often lets real villains off the hook. For a good overview of the actual causes of the problem in this case, I’d recommend this great local reporting on the failures of the wastewater facility.

And that’s it! Those are the only two places in the country where data centers seem to have polluted water at all, yet I very regularly get told by everyday people that data centers “poison water.” This is the result of two very popular but wildly misleading headlines.

PFAS

Some people have started to bring up PFAS from data centers as a potential concern. As far as I can tell, the only risk that data centers add PFAS to the water comes from a very specific type of cooling that almost no data centers use. Will update as I learn more, but I can’t find clear evidence of data centers adding PFAS to local community water.

Using AI can save way more water than is used in data centers

None of what I’m going to list here is generative AI like ChatGPT, but other machine learning tools have been heavily benefited by spillover effects from the general economic success of generative AI tools. These tools include things like computer vision systems for manufacturing quality control, predictive maintenance algorithms for industrial equipment, recommendation engines for e-commerce, fraud detection systems for financial services, and optimization algorithms for logistics and supply chains. While these applications have existed for years, the massive investment flowing into AI infrastructure (data centers, chip manufacturing, training talent) has made them cheaper, faster, and more accessible to deploy at scale.

AI is a way to get a machine to build its own soup of internal heuristics for how to handle complex situations. This soup of heuristics both succeeds and fails in surprising ways. We don’t design the heuristics themselves, and don’t even know what they are or how they work, in the same way we don’t know how a lot of the heuristics in our own brains work. There are a lot of situations where deploying this type of machine can help us optimize water use, because there are lots of places in America and around the world where huge amounts of water is wasted. Almost 20% of U.S. drinking water is lost to leaking pipes before reaching consumers.

AI leak detection saves 3 billion gallons of water over a few years in New Jersey.
AI detecting a single leak in a local suburb saved 350,000 gallons of water per day, saving a municipality $213,000 per year.
Another leak detection company saves 25,000,000 gallons of water per year.
A single AI-based irrigation optimization tool saved South American farmers 19 billion gallons of water over 2 years. This is 2 times as much water as all American AI data centers used over the same period.

This was the result of a few minutes of Googling. I could go on way longer with more examples of ways simple AI tools are saving towns huge amounts of water.

It’s okay to use water on a digital product

Many people’s background aversion to using water on data centers is that it’s a physical resource being spent on a digital product. Shouldn’t we only spend physical goods on other physical goods?

We already use a lot of water on the internet, and digital goods in general. Most of the ways we generate electricity uses a lot of water, so most of the time, when you’re using a computer, TV, or phone, you’re also using water. Internet data centers have always relied on water cooling, so we were always using water to access and share digital information.

Information is valuable. The real value of a book is almost entirely in the words it contains, not the physical quantity of ink and paper that make it up. We think it’s valuable to spend lots and lots of physical resources each year making books. Books and newspapers use 153 billion gallons of water annually. This is almost entirely in the service of delivering information. If it’s okay to spend water on creating and distributing books, it’s okay to spend water on other sources of valuable information. The water used to deliver digital information is orders of magnitude lower than physical books.

You might object that AI does not deliver anything like the value of books. My point isn’t to make a claim about how much valuable information AI provides, only that it isn’t inherently bad to spend a physical resource to deliver information. Ultimately if you believe AI is entirely valueless, than any water used on it is wasted regardless of whether AI’s output is physical or digital. But the fact that it’s digital on its own shouldn’t factor into whether you think it’s valuable or not.

A very common point that comes up in conversations about AI and water use is that no matter how little water AI uses, AI is either useless or actively harmful, so all that water is being used on something bad. This makes it inherently worse than using any large amounts of water on good things. For example, when I share these stats from before:

Here’s a list of common objects you might own, and how many chatbot prompt’s worth of water they used to make (all from this list, and using the onsite + offsite water value):
Leather Shoes - 4,000,000 prompts’ worth of water
Smartphone - 6,400,000 prompts
Jeans - 5,400,000 prompts
T-shirt - 1,300,000 prompts
A single piece of paper - 2550 prompts
A 400 page book - 1,000,000 prompts

I often get the response that all of these things have social value, whereas AI has no value, so AI is worse for water than all these things, even though it’s using tiny tiny tiny amounts of water compared to each.

It seems like some people measure how wasteful something is with water is by a simple (Value to society / Water) equation where no matter how tiny something’s water use is, if it’s negative value, it’s always worse than something okay with huge water use.

This doesn’t make sense as a way of thinking about conserving water, for the same reason that it’s not a good way of thinking about saving money. If I were doing a fun activity that cost $40,000, and something useless or bad that cost $0.01, even though the $0.01 thing was bad, cutting it just would never ever be as promising or urgent as finding ways to reduce the cost of the $40,000 thing, or to just go without it.

Driving somewhere I want to be is much worse for the environment than riding a bike in the wrong direction. I agree that we need to factor in the value somehow, but it can’t just be “Anything socially bad is always worse for the environment than anything socially good.” AI water is often hundreds of thousands of times as small as many other ways we use water.

Talking about the social harm of a tool and adding “And it uses a few drops of water!” basically always dilutes the point you’re trying to make. I’m not exactly consistently pro AI. There’s a lot I’m worried about. But I find it distasteful when people effectively say “This far-right authoritarian government is using powerful AI systems to surveil people!… and also, every time they use it, a few drops of water are evaporated!” This just so obviously dilutes and trivializes the much more important point that I’d really rather it not be brought up. Manufacturing a gun uses at minimum 10,000 times as much water as an AI prompt in a data center, but if authoritarians are bearing down on people, I’m not going to add “And it cost a glass of water each to make their guns!’“

There’s a trade-off between water and energy for data center cooling systems. For the climate, water’s often preferable

Data centers don’t have to use water for cooling, they can also circulate cold air. They do this much more often when they’re built in deserts, because water’s more expensive and solar power’s cheaper and more abundant. As with any industry, they respond to the costs of goods and adjust how they use them accordingly.

But replacing water with air cooling systems means a lot more energy is used on cooling. Circulating cool air is more energy intensive than circulating water. Because water has much higher heat capacity and thermal conductivity than air, it can absorb and transfer heat more efficiently. Air cooling (especially in hot climates) requires stronger fans, chillers, compressors, and mechanical systems to push cooled air throughout the facility.

One study found that replacing air cooling with liquid cooling reduces a data center’s total power usage by 10%. This is a big deal, because electricity demand is a much more serious problem for data centers. Using 10% less energy also means roughly 10% less CO2 emissions. If water usage isn’t an issue, it seems like the main effect of water cooling is preventing a significant amount of CO2 emissions and electricity demand.

What about all those news stories about AI harming local water access?

Every popular article about how AI’s water use is bad for the environment in the last year has had a wildly misleading framing

The Washington Post: Every email written using ChatGPT uses a whole bottle of water

This is the single most influential article ever written about ChatGPT and the environment. I still to this day regularly bump into people who think that ChatGPT uses a whole bottle of water every time you prompt it.

I wrote a deep dive on what happened with this statistic here:

The statistic itself is inflated by 50-250 times, and the water cost in data centers themselves is roughly 0.5 mL once the numbers are corrected, around 1/1000th of a bottle of water.

The Economic Times: Texans are showering less because of AI

Take this one from the Economic Times, it circulated a lot:

Texas AI centers guzzle 463 million gallons, now residents are asked to cut back on showers

The article clarifies that this is 463 million gallons of water spread over 2 years, or 640,000 gallons of water per day. Texas consumes 13 billion gallons of waters per day. So all data centers added 0.005% to Texas’s water demands.

0.005% of Texas’s population is 1,600. Imagine a headline that said “1,600 people moved to Texas. Now, residents are being asked to take shorter showers.”

Many iterations of the same article appeared:

One article corrected for the much larger uptick of data centers in 2025:

Texas Data Centers Use 50 Billion Gallons of Water as State Faces Drought

50 billion gallons per year is a lot more! That’s more like 1.1% of Texas’s water use. Nowhere in this article does it share that proportion. It seems pretty normal for a state as large as Texas to have a 1% fluctuation in its water demand.

The New York Times: Data centers are guzzling up water and preventing home building

From the New York Times:

Their Water Taps Ran Dry When Meta Built Next Door

The subtitle says: “In the race to develop artificial intelligence, tech giants are building data centers that guzzle up water. That has led to problems for people who live nearby.”

Reading that subtitle, you would have to assume that the main data center in the story is guzzling up the local water in the way other data centers use water.

In the article, residents describe how their wells dried up because residue from the construction of the data center added sediment to the local water system. The data center had not been turned on yet. Water was not being used to cool the chips. This was a construction problem that could have happened with any large building. It had nothing to do with the data center draining the water to cool its chips. The data center was not even built to draw groundwater at all, it relies on the local municipal water system.

The residents were clearly wronged by Meta here and deserve compensation. But this is not an example of a data center’s water demand harming a local population. While the article itself is relatively clear on this, the subtitle says otherwise!

The rest of the article is also full of statistics that seem somewhat misleading when you look at them closely.

Water troubles similar to Newton County’s are also playing out in other data center hot spots, including Texas, Arizona, Louisiana and the United Arab Emirates. Around Phoenix, some homebuilders have paused construction because of droughts exacerbated by data centers.

The term “exacerbated” is doing a lot of work here. If there is a drought happening, and a data center is using literally any water, then in some very technical sense that data center is “exacerbating” the drought. But in no single one of these cases did data centers seem to actually raise the local cost of water at all. We already saw in Phoenix that data centers were only using 0.12% of the county water. It would be odd if that was what caused home builders to pause.

The article goes on with predictions about Georgia’s water use around the data center. Residential water-sewer bills in Newton County have in fact risen — from about $73/month in 2015–2022 to $85/month by 2025, with the NCWSA’s own budget documents citing the second Meta campus as one factor accelerating capital improvements. However, the NCWSA also cites inflation and housing market uncertainty as drivers, and Meta is only using ~2% of the county’s water, less than the local pharmaceutical plant (~4%) and comparable to Rivian’s factory. So data center expansion is one contributing factor among several, not the primary cause — but the article’s concern wasn’t baseless.

I think the main story being an issue with construction, but the title associating it with some issue specific to data centers, seems pretty similar to a news story reporting on loud sounds from construction of a building that happens to be a bank, and the title saying “Many banks are known for their incredible noise pollution. Some residents found out the hard way.” This would leave you with an incorrect understanding of banks.

Contra the subtitle, data centers “guzzling up water” in the sense of “using the water for cooling” has not led to significant problems for the people who live nearby. Newton County is a partial exception. Data center expansion is one factor among several driving rate increases there, but even that case is far from the picture the subtitle paints of data centers causing water crises wherever they’re built.

CNET’s long very vague report on AI and water

This same story was later referenced by a long article on AI water use at CNET, here with a wildly misleading framing:

The developer, 1778 Rich Pike, is hoping to build a 34-building data center campus on 1,000 acres that spans Clifton and Covington townships, according to Ejk and local reports. That 1,000 acres includes two watersheds, the Lehigh River and the Roaring Brook, Ejk says, adding that the developer’s attorney has said each building would have its own well to supply the water neededEverybody in Clifton is on a well, so the concern was the drain of their water aquifers, because if there’s that kind of demand for 34 more wells, you’re going to drain everybody’s wells,” Ejk says. “And then what do they do?”
Ejk, a retired school principal and former Clifton Township supervisor, says her top concerns regarding the data center campus include environmental factors, impacts on water quality or water depletion in the area, and negative effects on the residents who live there.
Her fears are in line with what others who live near data centers have reported experiencing. According to a New York Times article in July, after construction kicked off on a Meta data center in Social Circle, Georgia, neighbors said wells began to dry up, disrupting their water source.

There’s no mention anywhere in the article that the data center in Georgia was not using the well water for normal operations.

Bloomberg: AI is draining water from areas that need it most

Here’s a popular Bloomberg story from May. It shows this graphic:

Red dots indicate data centers built in areas with higher or extremely high water stress. My first thought as someone who lives in Washington DC was “Sorry, what?”

Northern Virginia is a high water stress area?

I cannot find any information online about Northern Virginia being a high water stress area. It seems to be considered low to medium. Correct me if I’m wrong. Best I could do was this quote from the Financial Times:

Virginia has suffered several record breaking dry-spells in recent years, as well as a “high impact” drought in 2023, according to the U.S. National Integrated Drought Information System. Much of the state, including the northern area where the four counties are located, is suffering from abnormally dry conditions, according to the U.S. Drought Monitor. But following recent rain, the Virginia Department of Environmental Quality on Friday lifted drought advisories across much of the state, though drought warnings and watches are still in effect for some regions.

Back to the map. There were some numbers shared in a related article by one of the same authors. But readers were left without a sense of proportion of what percentage of our water all these data centers are using.

AI’s total consumptive water use is equal to the water consumption of the lifestyles of everyone in Paterson, New Jersey. This graphic is effectively spreading the water costs of the population of Paterson across the whole country, and drawing a lot of scary red dots. The dots are each where a relatively tiny, tiny amount of water is being used, and they’re only red where the regions are struggling with water. This could be done with anything that uses water at all and doesn’t give you any useful information about how much of a problem they are for the region’s water access.

Even the title chart can send the wrong message.

I think for a lot of people, stories about AI are their first time hearing about data centers. But the vast majority of data centers exist to support the internet in general, not AI.

Simply showing the number of data centers doesn’t show the impact of AI specifically, or how much power data centers are drawing. Power roughly correlates with water, because the more energy is used in data center computers, the more they need to be cooled, and the more water is needed to do that. Here’s a graph showing the power demand of all data centers, and how much of that demand AI makes up.

Obviously there’s been a big uptick on power draw since 2019, but AI is still a small fraction of total data center power draw. I think Goldman Sachs underestimated AI’s power draw here, experts think it’s more like ~15% of total power used in data centers, but it’s important to understand that the vast majority of that original scary red data center graph isn’t AI specifically.

AI is going to be large part of the very large data center buildout that’s currently underway, but it’s important to understand that up until this point most of those data centers on the graph were just the buildout of the internet.

One more note, circling back again to Maricopa County.

The county is a gigantic city built in the middle of a desert. For as long as it’s existed, it’s been under high water stress. Everyone living there is aware of this. The entire region is (I say this approvingly) a monument to man’s arrogance.

The only reason anyone can live in Phoenix in the first place is that we have done lots of ridiculous massive projects to move huge amounts of water to the area from elsewhere.

This is an area where environmentalism and equity come apart. I’d like residents of Phoenix to have access to reliable water supplies, but I don’t think this the most environmentalist move. I think the most environmentalist move would probably be to encourage people to leave the Phoenix area in the first place and live somewhere that doesn’t need to spend over two times as much energy as the country on average pumping water. I have to bite the bullet here and say that between environmentalism and equity, I’d rather choose equity and not raise people’s water prices much, even though they’ve chosen to live in the middle of a desert.

It seems inconsistent to think that it’s wrong for environmentalist reasons to build data centers near Phoenix that increase the city’s water use by 0.1%, but it’s not wrong for Phoenix to exist in the first place. If it’s bad for the environment to build data centers in the area at all, Phoenix’s low water bills themselves seem definitionally bad for the environment too. I think you can be on team “Keep Phoenix’s water bills low, and build data centers there” or team “Neither the data centers nor Phoenix should be built there, we need to raise residents’ water bills to reflect this fact” but those are the only options. I’m on team build the data centers and help out the residents of Phoenix.

More Perfect Union

More Perfect Union is one of the single largest sources of misleading ideas about data center water usage anywhere. They very regularly put out wildly misleading videos and headlines. There are so many that I’ve written a long separate post on them here. They are maybe the single most deceptive media organizations in the data center conversation relative to their reach.

Empire of AI

Karen Hao’s book Empire of AI was very popular and influenced the AI/environment conversation. It includes a chapter called “Plundering the Earth” that covers AI water and environmental issues. Unfortunately this section is one of the most misleading pieces of coverage of AI water issues that I’ve read. Within 20 pages, Hao manages to:

Claim that a data center is using 1000x as much water as a city of 88,000 people, where it’s actually using about 0.22x as much water as the city, and only 3% of the municipal water system the city relies on. She’s off by a factor of 4500.
Imply that AI data centers will consume 1.7 trillion gallons of drinkable water by 2027, while the study she’s pulling from says that only 3% of that will be drinkable water, and 90% will not be consumed, and instead returned to the source unaffected.
Paint a picture of AI data centers harming water access in America, where they don’t seem to have caused any harm at all.
Frame Uruguay as using an unacceptable amount of water on industry and farming, where it actually seems to use the same ratio as any other country.
Frame the Uruguay proposed data center as using a huge portion of the region’s water where it would actually use ~0.3% of the municipal water system, without providing any clear numbers.

These are all the significant mentions of data centers using water in the book.

There’s too much to say on this one for this section, so I made it its own post. Hao very quickly commented on my post and was very gracious in correcting some of the big errors I spotted in more recent editions of the book:

An Oregon Data Center is giving people cancer

This story by Rolling Stone (repeated in Futurism and The Verge) implies that a data center in Oregon is increasing the rates of cancer and other health problems people in a small town develop because it’s increasing the concentration of nitrates in the water. But the story breaks down as soon as you look at the numbers.

This article itself makes it clear that the water pollution linked to cancer and miscarriages is coming entirely from nitrate pollution from farms. In general, data centers don’t pollute water.

It says the data centers are drawing water already polluted with nitrates, evaporating some of it, and returning it to a wastewater facility. Because nitrates don’t evaporate, the water returned has a higher concentration of nitrates. This is why they say the data centers aren’t “polluting” the water, they’re “worsening” a pollution problem. But even this is silly. The Port of Morrow (that takes in the contaminated water) handles 3.5 billion gallons of wastewater annually. It’s not clear how much the data center uses, the article just says “The data centers suck up tens of millions of gallons of water from the aquifer each year.” Let’s assume that’s the absolute most “tens of millions of gallons” can be: 99 million per year, or 3% of the port’s water.

The article notes:

The massive inputs of fertilizer to grow crops and feed for the animals came at a price: the contamination of the Lower Umatilla Basin. In 1992, DEQ measured an average nitrate concentration of 9.2 ppm across a cluster of wells pulling from the basin. By 2015, that average had risen 46 percent, to 15.3 ppm. For some wells, DEQ found nitrate levels nearly as high as 73 ppm, more than 10 times the state limit of 7 ppm.

How much of this is the data center responsible for? The article notes:

As the underground aquifer became tainted with more nitrates, even the ostensibly clean water that the Port pulled from the aquifer’s deepest wells — which it used to service its large industrial customers like Amazon — became polluted. Soon, Amazon was using water to cool its data warehouses with nitrates as high as 13 ppm — above the federal and state limits.
When that tainted water moves through the data centers to absorb heat from the server systems, some of the water is evaporated, but the nitrates remain, increasing the concentration. That means that when the polluted water has moved through the data centers and back into the wastewater system, it’s even more contaminated, sometimes averaging as high as 56 ppm, eight times Oregon’s safety limit.

An issue here is that I don’t know how much of the water the data center withdraws is evaporated. We know the data center takes in ~3% of the water used by the port, but not how much it actually delivers to the port after.

I’m going to make a few attempts at estimating how much this data center could be increasing the concentration of nitrates.

I’ll take the very most extreme case:

The concentration of nitrates in the other wastewater is the same as the aquifer (~13 ppm). This seems unlikely, because it’s waste. It’s likely polluted by the farmers.
The data center evaporates 100% of the water it uses. Again unlikely.
The data center is using the very maximum amount of water implied by Rolling Stone’s wording: 99 million gallons per year.

In this case, the reservoir is dealing with 3.5 billion gallons of water from other sources, all at 13 ppm nitrate. 13 millionths x 3.5 billion gallons = 45500 units of nitrate in the water. The data center evaporates 99 million gallons of water at 13 ppm, leaving 1287 units of nitrate to add to the wastewater without adding any additional water. So the new concentration of nitrates in the data center is (45500+1287)/(3,500,000,000) = 13.37 ppm. So in the absolute most extreme case, the data center evaporating water is responsible for an additional 0.37 ppm of nitrate in the waste water. This is about 6% of the observed change in nitrate concentration. This seems equivalent to Amazon operating 6% of the farmland in the region, and this is the absolute worst harm the data center could be causing.

But the actual contribution to groundwater contamination is probably way less, for a few reasons:

The wastewater mostly flows back to farms so the soils can capture the nitrates in the water. It’s mentioned here as a small fraction of the cause of the groundwater nitrate contamination, the others being septic tanks, CAFOs, explosives, and food processing waste.
If the data center uses water at 13 ppm and evaporates it until it’s at a maximum of 56 ppm, then the absolute most it ever evaporates is 77 ppm.
The wastewater the port deals with is likely to already have a significantly higher nitrate concentration because it’s dealing with agricultural runoff. I can’t find a good source on this, but it’s likely higher than the 13 ppm recorded in the aquifer.
The water draw of the data center itself is probably significantly lower.

All of these together make me suspect that Amazon is almost definitely responsible for significantly less than 1% of the increase in nitrates. I’ll circle back when I find better numbers.

You may have seen more recently that Amazon agreed to pay $20 million to settle a lawsuit brought against them for the pollution here. There were a lot of stories about this, like this one:

Surely this means they’re guilty right? Nope. As usual this story is being wildly misrepresented.

In the settlement documents, Amazon states explicitly that it is entering the agreement “solely to avoid the burdens and expense of litigation,” and denies “each and every one of the allegations of wrongful conduct.” They say their data centers “don’t add nitrates to that water, and the water we return represents a very small fraction of the region’s overall system.” If you do the math, it seems impossible not to agree with Amazon’s statement here. If you disagree, I’d be interested if you could show me how this was physically possible.

5 common misleading ways of reporting AI water usage statistics

Comparing AI to households without clarifying how small a part of our individual water footprint our households are

Many articles choose to report AI’s water use this way:

“AI is now using as much as (large number) of homes.”

Take this quote from Newsweek:

In 2025, data centers across the state are projected to use 49 billion gallons of water, enough to supply millions of households, primarily for cooling massive banks of servers that power generative AI and cloud computing.

That sounds bad! The water to supply millions of homes sounds like a significant chunk of the total water used in America.

The vast majority (~93%) of our individual total consumption of freshwater resources does not happen in our homes, it happens mainly in the production of the food we eat, and industry and commercial buildings. Experts seem to disagree on exactly what percentage of our freshwater consumption happens in our homes, but it’s pretty small. Most estimates seem to land around 1%. So if you just look at the tiny tiny part of our water footprint that we use in our homes, data centers use a lot of those tiny amounts. But if you look at the average American’s total consumptive water footprint of ~1600 L/day, 49 billion gallons per year is about 300,000 people’s worth of water. That’s about 1% of the population of Texas. The entire data center industry (both for AI and the internet) using as much water as 1% of its population just doesn’t seem as shocking.

A move that I complained about in my last post is that a lot of articles will imply that AI companies are hiding the “true, real” water costs of data centers by only reporting the “onsite” water use (the water used by the data center) and not the “offsite” water use (the water used in nearby power plants to generate the electricity). Reporting both onsite and offsite water costs has become standard in reporting AI’s total water impact.

Many authors leave their readers hanging about what these “true costs” are. They’ll report a minuscule amount of water used in a data center, and it’s obvious to the reader that it’s too small to care about, but then the author will add “but the true cost is much higher” and leaves the reader hanging, to infer that the true cost might matter.

We actually have a pretty simple way of estimating what the additional water cost of offsite generation is. Data centers on average use 0.48 L of water to cool their systems for every kWh of energy they use, and the power plants that provide data centers energy average 4.52 L/kWh. So to get a rough estimate:

If you know the onsite water used in the data center, multiply it by 10.4 to get the onsite + offsite water.
If you know the onsite energy used, multiply it by 5.00 L/kWh to get the onsite + offsite water used.

Obviously scaling up a number by a factor of 10 is a lot, but it often still isn’t very much in absolute terms. Going from 5 drops for a prompt to 50 drops of water is a lot relatively, but in absolute terms it’s a change from 0.00004% of your daily water footprint to 0.0004%. Journalists should make these magnitudes clear instead of leaving their readers hanging.

This talking point can be doubly deceptive if you only look at the proportion

Let’s say there are 2 data centers in a town (I’ll call them Poseidon and Enki) drawing from the same power source. The local town’s electricity costs 4 L of water per kWh to generate.

The Poseidon data center is pretty wasteful with its cooling water. It spends 2 L of water on cooling for every kWh it uses on computing, way above the national average of 0.48L/kWh. So if you add the onsite and offsite water usage, Poseidon uses 6 L of water per kWh.

The Enki data center finds a trick to be way more efficient with its cooling water. It drops its water use down to 0.1L/kWh. Well below the national average. So if you add its onsite and offsite water usage, it uses 4.1 L per kWh without using any more energy.

Obviously, the Enki data center is much better for the local water supply.

Both data centers are asked by the town to release a report on how much water they’re using. They both choose to only report on the water they’re actually using in the data center itself.

Suddenly, a local newspaper shares an expose: both data centers are secretly using more water than they reported, but Enki’s secret, real water use is 41x its reported water costs.

While Poseidon’s is only 3x its reported water costs:

Here, Enki looks much more dishonest than Poseidon. If readers only saw this proportion, they would probably be left thinking that Enki is much worse for the local water supply. But this is wrong! Enki’s much better. The reason the proportions are so different is that Enki’s managed to make its use of water so efficient compared to the nearby power plant.

I think something like this often happens with data center water reporting.

When I wrote about a case of Google’s “secret, real water cost” actually not being very much water, a lot of people messaged me to say Google still looks really dishonest here, because the secret cost is 10x its stated water costs once you add the offsite costs. A way of reframing this is to say that Google’s made its AI models so energy efficient that they’re now only using 1/10th as much water in their data centers per kWh as the water required to generate that energy. This seems good! We should frame this as Google solidly optimizing its water use.

Take this quote from a recent article titled “Tech companies rarely reveal exactly how much water their data centers use, research shows”:

Sustainability reports offer a valuable glimpse into data center water use. But because the reports are voluntary, different companies report different statistics in ways that make them hard to combine or compare. Importantly, these disclosures do not consistently include the indirect water consumption from their electricity use, which the Lawrence Berkeley Lab estimated was 12 times greater than the direct use for cooling in 2023. Our estimates highlighting specific water consumption reports are all related to cooling.

The article should have mentioned that this means data centers have made their water use so efficient that basically the only water they’re using at all is in the nearby power plant, not in the data centers themselves. But framing it in the original way way make it look like the AI labs are hiding a massive secret cost from local communities, which I guess is a more exciting story.

Vague gestures at data centers “straining local water systems” or “exacerbating drought” without clarifying what the actual harms are

If you use literally any water in any area with a drought, you’re in some sense “straining the local water system” and “exacerbating the drought.” Both of these tell us basically nothing meaningful about how bad a data center is for a local water system. If an article doesn’t come with any clarification at all about what the actual expected harms are, I would be extremely wary of this language. In basically every example I can find where it’s used, the data centers are adding minuscule amounts of water demand to the point that they’re probably not changing the behavior of any individuals or businesses in the area.

Simply listing very large numbers without any comparisons to similar industries and processes

This is the great singular sin of bad climate communication. The second you see it, you should assume it’s misleading. Simply reporting “millions of gallons of water” without context gives you no information. The power our digital clocks draw use millions of gallons of water, but digital clocks aren’t causing a water crisis.

Take this example:

7.2 million gallons per year! Sounds like a ton. How much is that? This data center would represent about 0.02% of nearby El Paso’s water usage. Probably not nearly as much as this tweet is trying to get across.

Whenever you see an article cite a huge amount of water with no comparisons at all to anything to give you a proper sense of proportion, ask a chatbot to contextualize the number for you.

Take this excerpt from “Are data centers depleting the Southwest’s water and energy resources?”

Meta’s data centers, meanwhile, withdrew 1.3 billion gallons of water in 2021, 367 million of which were from areas with high or extremely high water stress. Total global water consumption from Meta’s data centers was over 635 million gallons, equivalent to about 6,697 U.S. households. It’s not clear how much of this water withdrawal occurs in the United States, although that’s where most of Meta’s data centers are located. Neither report reveals the specific water use of the company’s Arizona data center.

I’m going to rewrite this, but using my town of 16,000 people (Webster, Massachusetts) as a unit to measure Meta’s data center withdrawal instead of individual households. Webster’s utility delivers 1.4 million gallons of water per day to the citizens and businesses there (511 million gallons per year).

Meta’s data centers, meanwhile, withdrew as much water as 3 Massachusetts small towns in 2021. Two thirds of a single one of those small towns was in areas with high or extremely high water stress. Total global water consumption from Meta’s data centers was a little more than a single Massachusetts small town. It’s not clear how much of this water withdrawal occurs in the United States, although that’s where most of Meta’s data centers are located. Neither report reveals the specific water use of the company’s Arizona data center.

This all seems silly when you consider Meta’s one of the single largest internet companies.

Source

Reporting the maximum upper bound for water a data center uses as the number it will actually use

Many articles about current or future AI data centers report the number in the water permit they apply for as the amount of water they actually use day to day. But this almost never happens.

When a data center is being built, the company needs to obtain water use permits from local authorities before construction. At this stage, they have to estimate their maximum possible water consumption under worst-case scenarios:

All cooling systems running at full capacity
Peak summer temperatures
Maximum IT load (every server rack filled and running)
Minimal efficiency from cooling systems

The permit needs to cover this theoretical maximum because regulators want to ensure the local water infrastructure can handle the demand and that there’s enough water supply for everyone. It’s easier to get a higher permit upfront than to come back later and request more, so data centers are incentivized to aim high.

Actual water usage is always significantly lower than what the permits allow, because they’re designed with the absolute worst conditions in mind. But many popular articles about how much water data centers use give the number on the water permit, not how much the data center actually uses.

Here’s one of many examples:

Duff is the only city council member to vote no on a recently approved $800 million data center - rumored to be for Facebook - after discovering the facility would eventually use 1.75 million gallons of water every day for cooling their rows of servers once fully operational.

Some examples of great news coverage

After complaining so much, I’d like to share some examples of where the AI water issue has been covered especially well.

First is I think the best single popular summary that’s been written of the issue: Thirsty for power and water, AI-crunching data centers sprout across the West. This piece doesn’t break any of my rules that I share above and gives readers a great overview of where data centers fit into the broader water picture in the places they’re built.

Second is Hank Green’s recent coverage. A few places I disagree, but again fantastically comprehensive and explicitly clarifies some of the places where this is very confusing for readers.

These are both stand outs. I’ll continue to collect really good reporting here as I catch it.

Data centers & electricity - part 1: as of 2025 they haven’t raised national prices

Fri, 10 Oct 2025 04:28:08 GMT

Since 2020, average American household electricity prices have risen 35%.

Source, this whole piece is a great deep dive on household electricity prices in general

This jump correlates with the massive new data center buildout, which started around 2021. This has led many to conclude that data centers are the main culprit. If data centers raised electricity bills by 35%, that seems like a huge disaster!

But that’s wrong. If you adjust for inflation, American household electricity costs are still historically low, and have only rebounded to where they were in 2017:

Source

Sometime around 2022, American electricity prices started to outpace inflation. Before 2022, electricity prices closely tracked inflation. After 2022, they went ahead of it a bit.

Source

Here’s a broader picture of inflation-adjusted household electricity rates over time. The recent spike begins in 2022, it’s this spike the red arrow’s pointing at that needs an explanation.

Source, you’ll need to adjust it for inflation using the site tools

Did data centers cause that jump? No. A Lawrence Berkeley National Laboratory study found that state-level load growth from 2019 to 2024 — including growth driven by data centers, manufacturing, and electrification — was associated with lower average retail electricity prices, not higher ones. States with the highest load growth saw inflation-adjusted price reductions, while states with shrinking loads generally saw prices rise. The researchers attribute this largely to fixed infrastructure costs being spread across more kilowatt-hours of demand.

Two important caveats. First, the LBNL researchers note that this negative relationship between load growth and prices “was smaller and lost statistical significance when analyzing residential prices alone,” the clearest effect shows up in overall average retail prices, not necessarily household bills specifically. Second, the researchers caution that this historical relationship may not hold going forward, noting that “in some cases, spikes in load growth can result in significant, near-term retail price increases.” The PJM capacity price spikes beginning in 2025, driven substantially by projected data center growth, are an example of exactly this kind of near-term price impact.

To summarize their explanation of the price increase:

Prices rose mainly because utility costs rose. On a nominal basis, the average retail price increased about four point eight percent per year from 2019 to 2023. After inflation, the national average was mostly flat over that window, though residential prices climbed faster than inflation.
Fuel and purchased power costs were the single biggest mover. Utilities’ fuel and purchased power costs surged in step with the natural gas spike, accounting for about half of the total expense increase. Gas prices then eased, but fuel and purchased power costs in 2023 were still about thirty percent above 2019.
The rest of the cost growth came from operations and maintenance, depreciation, taxes, and other line items.

This explains why the jump happens when it did: Russia invaded Ukraine, Europe stopped buying Russian gas, America was suddenly competing with European countries more for natural gas from other places, and natural gas prices spiked for a bit. Here’s a graph of natural gas prices:

Source

That red arrow I added to the graph of electricity prices is on the day when Russia invaded Ukraine.

There are a few additional reasons to think data centers didn’t cause this spike.

First, the rise in data center electricity demand in the US started a few years before the spike. It’s a pretty straight line up, and markets have a lot of heads-up when a new large buyer like a data center is coming online. It would be weird if halfway along this basically straight line in 2022, energy markets were suddenly taken by surprise:

Source

Second, American electricity demand is rising for the first time since 2007 or so (with data centers responsible for about 40% of the growth), but it’s still rising at a slower rate than it had for most of the 20th Century. Total electricity consumption is driven by demand, so a consumption graph is a good proxy for a demand graph. Importantly, during the period from 1985 to 2005, we sustained a higher rate of increasing electricity demand, but inflation-adjusted household electricity prices fell the entire time.

Source for top graph

So it’s possible in principle anyway for electricity supply to keep up with demand and keep prices low, for the same reason a grocery store doesn’t double its prices when it opens a second location. Notice that there was a period from 2020 to 2022 when electricity demand was rising but prices were flat. Another hint that this is more of a problem with supply.

This is the first conclusion of my deep dive: Data centers have (so far) not affected national average household electricity bills. Many of the graphs that get shared about this are showing the effects of inflation, Russia’s invasion of Ukraine, and a series of other supply-side issues with normal electricity costs that aren’t the result of responding to rising demand.

This is not to say that data centers won’t affect national prices in the future. Just that as of 2025, the average American’s electricity bill has not increased as a result of data center demand.

Brian Potter concluded his excellent deep dive on US electricity prices by noting:

At a high level, trends in wholesale electricity prices seem to mostly match trends we see in consumer prices. Until 2020, wholesale prices were flat or declining; since 2020, they’ve risen substantially, faster than consumer electricity prices. And in most places, transmission capacity appears to be an increasing bottleneck: we’re having a harder and harder time accessing the least expensive power. Probing the specific relationship between wholesale electricity and consumer electricity prices would be complex, and beyond the scope of this essay, but it seems likely to me that rising wholesale prices are a major factor.

The main question left to answer is how data centers affect electric state and local bills electric rates, not national rates.

Unfortunately, locals who live near data centers can’t tell what portions of their electric bill increases were caused by inflation, Russia, or supply-side issues, and what was caused by data centers. The data center buildout started in 2021, at the exact same time American inflation began to skyrocket.

Source

Source for upper graph

Between 2020 and 2025, the average Virginia resident’s electricity rate went up by 28.1%. Virginia has both the highest percentage of electricity used by data centers, and some of the fastest data center growth in the country.

Source

If you were a resident of Virginia, you might look around, notice that data centers in your state are using 33% more electricity than they were 5 years ago, and that your electric bill went up by 28.1%. You might conclude that there was a connection, and even go on a More Perfect Union video to talk about how data centers raised your electricity prices.

But you might not know that most of that 28.1% growth was due to inflation, or more importantly that Virginia’s electricity prices actually increased less than the national average:

Source

Most American states don’t have anywhere near as many new data centers coming online as Virginia. Maine barely has any, but its rates shot up faster than any other state. If data centers were having a really massive impact on electrical bills, wouldn’t you expect your bill to grow faster than states without data centers?

Maybe Virginia’s a fluke, and the other states with huge data center builds have seen skyrocketing electric rates. Michael Giberson at RSA looked into this. The national average electricity price increase for all sectors was $4.27. He looked at each state with the most electricity going to data centers and added numbers to the graph to show how state prices grew more (green) or less (red) than the national average of +4.27 cents per kWh.

Source

11 out of 15 of the states with the most data center buildouts have seen lower than average increases in electricity rates. Further, the red numbers are all way larger here than the green numbers (except New Jersey). Even the states that grew more than average were basically right at the average line. Weird! Here are these numbers on a graph:

So far, this does not look like data centers are uniquely disastrous for electricity bills.

But things get murkier. 30-60% of a data center’s operating costs are its electricity bills. This means data center companies mostly opt for states where electricity costs are low and there’s a lot of pre-existing grid capacity. These cheap, abundant grids can handle massive new loads, but this does not mean prices don’t rise at all as a result of data centers, they just may rise less than if data centers had built elsewhere. Companies have an incentive to build where they will raise electricity costs the least, that’s where they’ll also be paying the least for electricity! So this leaves us with an unfortunate paradox where data centers may be raising local prices, but they’re often in the places we can detect that the least. Also, there might be very local effects that don’t show up at the state leve.

We know shockingly little about exactly how much data centers are affecting household electricity prices at the state and local level. We do know they have made household electricity more expensive than it would have otherwise been in specific locations, because they have been cited by authorities in Virginia, Arizona, Delaware, Oregon, and other places as one of several causes of rising costs.

Complicating all this still is that there is no clear longterm correlation between how much total electricity is used in your area and your electric bills, for the same reason larger grocery stores don’t necessarily charge higher prices. Once supply catches up to demand, prices balance. If higher local electricity demand led to permanent higher prices, we would expect urban electricity prices to be higher than rural ones. In reality, urban and rural rates are basically the same.

Source

Something odd in the debate is that some people are implying that data centers are making electric prices permanently higher by drawing more power. This is obviously not how electricity markets work. The thing that spikes your electricity bill is demand outpacing supply in the short term, not just a total increase in demand over the long term.

But also, data centers are forecast to just keep being built, so the grid might be in a permanent state of catch-up for a while, keeping prices higher before they have a chance to adjust.

Source

And all this is in the background of America needing to do a huge buildout of clean energy to transition away from fossil fuels, and all that infrastructure would cost consumers more anyway. Also, whose responsibility is it to keep rates low? The private consumers of electricity, or the utilities? Does it make sense to both say that the utility’s responsible for keeping rates low but also there’s just not enough capacity to do that in the short term so regardless data centers will raise costs? Maybe we could redistribute all that tax money data centers provide to pay for consumer electric bills, or… or what about…

Every time I try to address one specific part of this topic, I come away thinking people can only really understand it if they understand five others as well. The complexity builds. I think you can’t really understand the issue of data centers and household electricity costs without also knowing things like:

Data centers bring in more tax revenue per watt-hour than any other industry,
The differences in tax cuts local governments provide them to entice them to build there
How this all really depends on whether the local grid can build out enough new clean electricity infrastructure.
How to think about who’s responsible for rising prices between consumers or producers.
How data center builds might be good for the green energy transition because they’re forcing these huge new investments in the grid we need to make anyway
How electrifying everything in the economy is a massive project necessary for green energy, so electricity demand is going to rapidly shoot up with or without data centers.
How AI usage is expected to accelerate or harm the green energy transition

and so on.

I decided that I did need some kind of basic understanding of all this stuff, so I set out to gather what I think is the important info here. I’m just a hobbyist and far from an expert on any of this. This series is more a summary of what I’ve learned after doing a bunch of deep dives on these topics rather than “This is the definitive final story of data centers and electricity.” I do think I’ve climbed to reach some vistas in this mess of a problem where I can see things more clearly, so this series will aim to get you there too.

Andy Masley

What I get out of effective altruism

The effective altruist story of the world

Getting more concrete

Behaving as if the world exists beyond our status games

This worldview only goes so far

Don’t treat surface-level metaphors about AI as if they describe fundamental reality

AI safety and the data center backlash

Contents

What is AI safety?

My initial motivations

I’m unsure, but suspect the extreme data center backlash is neutral for AI safety, and I see a lot of ways it could be harmful

The data center backlash seems to be mostly about data centers, not AI

Extreme environmental reaction to data centers often reinforces the “AI is fake and useless” view, which prevents people from thinking seriously about current and future capabilities

Anything short of a national moratorium and strong export controls doesn’t seem likely to slow AI progress much

The data center backlash might cause voters to reward and prioritize the wrong moves against AI companies

Navigating through advanced AI safely would probably require voters not thinking of data centers as demons

Conclusion

Effective altruist AI safety as a place where we won’t abjectly bullshit you

Societal resilience to speculative future risks requires strong healthy epistemic communities of debate and disagreement

We’re already making extreme, wild claims about AI and need to otherwise respect the epistemic commons to have them taken seriously

The broader societal backlash to AI is often based on obvious simple lies because people are having a visceral emotional reaction. AI safety needs to distinguish itself from this backlash

The arguments we give to the public will affect who gets involved

A lot of other spaces discussing AI seem interested in using bullshit stats about data centers to fire up their base for unrelated political wins. Effective altruists and the broader AI safety community can set ourselves apart by simply not doing that

Some recommended media on AI safety

I think the data center backlash is mostly about data centers

It’s probably not about big tech

Americans love big tech, as long as you ask about specific companies

Americans are kind of fine with other buildings associated with big tech

It’s probably not mainly about AI

Polling implies AI’s playing a small part in the backlash

Americans have mixed opinions of AI

It’s probably not China

I think the backlash can be mostly explained by years of media coverage of object-level local environmental and economic harms

Thinking about other industries like we think about data centers shows how impoverished the debate is

I’m very skeptical that China has played a meaningful role in the US data center backlash

Contents

My immediate reaction as someone who’s followed data center politics for a while

The evidence that China’s influencing the data center backlash is weak

The American Energy Institute report

The Bitcoin Policy Institute reports

First Report: Foreign Influence in the Campaign against American AI

Second Report: The Singham Ground Game

OpenAI’s report

Kevin O’Leary’s claims

The Alethea report

This thin evidence is being inflated in policy discussions

Evidence against

Conclusion

Toward liberal environmentalism

AI art as curation

What does it mean for AI to be democratic?

Pluralist vs homogenizing democracy

Which visions of democratic AI are pluralist vs homogenizing?

Current AI models would become way more conservative if their values were voted on

Conclusion

A plug for a tool I made to show how little most current AI chatbots add to your emissions and water use, with citations

A simple trick to fix the data center debate

An example

Carbon

Water

Land

Electricity bills

NOx air pollution

Adding them up

Other ways this helps

Why I think panic about local impacts of data centers is just a panic

A history of the data center panic - part 1

Part 1: Pre-ChatGPT and the creation of common wisdom

Early environmental critics of AI

Deep history

The left and right become more anti-tech

The rise of pop environmentalism, and the good feelings of discovering a villain hiding in everyday life

The decline of science journalism

Conclusion

A crash course on US air pollution

Contents

The three main ways long-term exposure to air pollution harms us

PM2.5

Ozone