By Amit Katwala
In Machines of Loving Grace, his utopian vision of a world transformed, Anthropic founder Dario Amodei promises that AI could soon deliver “a country of geniuses in a data center.”
That could come with a country-sized impact on the environment. By 2030, if current projections for data center build-outs prove correct, they will consume as much energy as Japan, and as much water as needed to supply everyone in sub-Saharan Africa. As the GPUs inside burn out and get replaced, data centers could generate as much electronic waste as Denmark, Norway or Austria, and strain the world’s supplies of critical minerals.
Right now, the environmental impact of AI is relatively small — all data centers account for just 0.5% of global CO2 emissions — but as frontier AI companies push for the scale they hope will usher in transformative AI, the real-world footprint of the technology is likely to get bigger. Data centers are already a political issue, with vocal protests from local residents against planned construction because of fears over their water and energy use. Moratoriums have been put forward by American lawmakers in some states and one has been signed into law in New York.
Even if AI keeps scaling at the rate many predict, its impact on the global environment is likely to remain relatively small compared to sectors like transportation and manufacturing. But it is at the local level — far from the prompt-hackers and token-maxxers — that AI’s true environmental impact is likely to be felt.
The question of compute
Data centers are not a new phenomenon. There are more than 12,000 commercial facilities worldwide, doing everyday things like storing email attachments or delivering the latest streaming show to smartphones. But the rise of AI has created a perfect storm that has parallels in the recent past.
Alex de Vries-Gao, a researcher at VU Amsterdam’s Institute for Environmental Studies, works on the sustainability of emerging technologies. He’s spent most of the last decade studying cryptocurrencies, which have been widely criticized for the amount of energy used to mine coins. In 2021, de Vries-Gao noted that incentives for crypto miners were directly opposed to environmental goals — the way miners are rewarded with coins for solving complex equations means that the more computing power and electricity they used, the more money they made.
After ChatGPT launched in 2022, de Vries-Gao realized AI shared the same pattern: increasing the scale of large language models has tracked with their performance, becoming a goal in itself for companies like OpenAI, Anthropic and Google. Model sizes have increased from billions to trillions of parameters, and it now takes a lot more power and a lot more chips to train the latest models. “It incentivizes the use of more resources through a ‘bigger is better’ dynamic,” de Vries-Gao says.
There are thousands of new data center projects planned globally, representing trillions of dollars in investment. According to research institute Epoch AI (which is funded by Coefficient Giving, Transformer’s primary funder), global AI computing capacity has grown 3.3 times a year since 2022. That’s expected to slow down because of constraints on chip supply and power, but the International Energy Agency (IEA) still projects that data centers will account for around 3% of global electricity demand by 2030, and de Vries-Gao predicts that AI will be responsible for around half of that. (Many of the figures reported in the literature on this topic are for data centers as a whole, rather than AI data centers specifically, as information about the split is not generally made public by frontier AI companies or data center providers). At that point, data center carbon emissions would be comparable to half the global aviation industry, according to de Vries-Gao. The speed of the build-out exacerbates the issue, and means that AI’s carbon footprint may scale faster, particularly in the short term. Projections about future data center emissions have tended to assume that they will use the average power mix of whatever grid they plug into — a combination of fossil fuels, nuclear and renewables. But the existing electricity grid can’t keep up with the demand for power from new data centers — in Virginia, the world’s largest data center market, there’s a seven-year wait for new connections to the grid for areas serviced by Dominion Energy.
Instead, as they race for scale, data center developers are bypassing the grid. “A new source of demand is not going to get the average power mix,” de Vries-Gao explains. “It’s probably for a big part getting the marginal mix, which is going to be mostly fossil fuel-based.” New data centers are disproportionately likely to use more carbon-intensive sources of energy, such as on-site gas turbines. New data from Global Energy Monitor released last month found that in the US, the pipeline for gas projects for data centers jumped from 97GW at the end of 2025 to 189GW by the end of June. So the IEA’s figure may still be an underestimate. As well as contributing to global warming through CO2 emissions, these sites can also increase air pollution in the local area. In May, the NAACP filed for an injunction against xAI’s power plant in Southaven, Mississippi, which it claims could emit thousands of tons of nitrogen oxides per year, as well as formaldehyde and other harmful chemicals. Additionally, a preprint by Shaolei Ren and colleagues at UC Riverside estimates that by 2030, pollution from data centers could cause an additional 1,300 deaths in the United States, and impose around $20.9b in public health costs, each year.
One of the AI industry’s arguments is that the environmental impact of data centers will slow down over time as each new generation of chips does more with less power. So far, that doesn’t seem to have made much difference — and there’s a reason.
The efficiency paradox
GPUs are certainly becoming more energy efficient all the time. So why isn’t this translating into a smaller environmental footprint? Sasha Luccioni, a former AI researcher at Hugging Face and co-founder of Sustainable AI Group, points to the Jevons paradox — which observes that making a technology more efficient increases demand to such an extent that it can outweigh any efficiency savings.
Better GPUs will be used to train bigger models more efficiently, which will make those models cheaper to use, resulting in more people using them — and so the total environmental impact will keep increasing.
The micro trend of improving GPU efficiency and a falling energy cost per token is being completely overwhelmed by the macro trend: AI in everything, all the time. And leaps forward in performance seem to come with a leap in power demands. Luccioni was part of a consortium of researchers, including representatives from Cohere and Meta, who developed the AI Energy Score, which gives a star rating to models based on their energy efficiency during inference. They found that, on average, reasoning models — which work through prompts step by step in a process that aims to mimic human thought — use 30 times more energy than models with no reasoning, or with reasoning switched off.
The environmental impact of AI would persist even if the frontier AI companies stopped training new models today, because the bulk of the footprint comes not from training new models, but the inference costs incurred when people query them. There hasn’t been a lot of concrete data on this in recent years, but in 2022 a Google paper revealed that about 60% of its machine learning footprint was inference, not training. It’s safe to assume that the balance has shifted even more toward inference, de Vries-Gao says, given the explosion in popularity of AI chatbots among the general public since the launch of ChatGPT later that year. The UN University now estimates inference at 80% to 90% of AI’s total energy consumption.
A lot of that energy comes back out as heat. So it’s cooling, rather than compute, that has come to dominate the debate on AI’s environmental impact. Reshaping the entire global economy practically overnight will be thirsty work, it seems.
Cooling off
In 2023, US data centers used approximately 17b gallons of water to cool down GPUs by carrying excess heat away. It sounds like a lot, until you consider that US golf courses use approximately 425b gallons a year.
A bit about how data center cooling works first, though. Although some data centers use evaporative cooling, for which the water has to be constantly replenished, many either use closed-loop systems that don’t need to be refilled, or immersion cooling, in which GPUs sit in a dielectric fluid that carries heat away. (That has its own environmental impact — 3M recently stopped making its Novec and Fluorinert fluids because of concerns over PFAS — the so-called “forever chemicals” that have been linked to an increased risk of cancer and other conditions).
“Collectively, the data center industry used significantly less water than other essential industries in 2025, including the food and beverage and semiconductor sectors,” says Dan Diorio, who works on state policy and government affairs at the Data Center Coalition, a lobbying group backed by several big tech companies. Although you might argue that for most of us, data centers aren’t quite as essential as food.
And that’s only part of the story, because the bulk of AI’s water use is not in cooling chips, it’s in generating the electricity to run them. de Vries-Gao’s research suggests that 90% of a data center’s total water footprint is from indirect use, from the power stations that supply it. Data published by Meta last year found that its indirect water use from purchased electricity was 23 times higher than the water consumed inside its data centers.
If you count both direct water use and indirect water use, de Vries-Gao estimates that AI used between 312b and 765b liters of water in 2025 — roughly the same as all of the bottled water drunk worldwide in a year. By 2030, the IEA predicts that data center water usage will hit 1.2t liters. A report from the UN University uses a different calculation method, and predicts that the water footprint of data center power generation will hit 9.3t liters — enough to fulfill the domestic needs of 1.3b people in sub-Saharan Africa (or about four days of global beef production, according to one estimate).
In the same way that data centers have become a lightning rod for people’s frustrations about AI, because they’re the most visible face of it, water has become the most prominent environmental issue — perhaps because it’s the one that feels most tangible. It’s hard for people to picture the positive benefits of data centers, but everyone can imagine what it’s like to have their taps run dry.
The problem is again one of incentives. New technologies like direct-to-chip liquid cooling can cut the amount of water used and AI companies are rushing to implement them as opposition to data centers rises. But if they use more power to run, they could end up increasing the overall amount of water used, just moving it to a different, less salient, place in the chain.
“You can’t say you’re solving a problem without looking at the complete picture,” says de Vries-Gao. “You’re solving a tiny part of the problem, while this might be making the full impact worse.”
A matter of materials
While protesters and politicians have aired their concerns about data center-related pollution and water use, less attention has been paid to the raw minerals that will go into the data center build-out — or what researcher Sophia Falk has called the “materiality of AI.”
In the absence of data from chipmakers, Falk, a PhD researcher at the University of Bonn’s Sustainable AI Lab, and her colleagues have been putting GPUs into industrial blenders to figure out their material composition. Dismantling an Nvidia A100 GPU in this way revealed that there were 32 separate elements inside — including iron, silicon, nickel and tin — but that 91% of it was copper.
GPUs may need to be replaced every three years, and in 2024, a paper published in the journal Nature Computational Science estimated that the cumulative e-waste generated by AI would hit 5m tonnes by 2030. But de Vries-Gao’s latest work, published in February this year, suggests that may be an overestimate — the high value of GPUs and the scarcity of new chips means they are likely to be reused rather than scrapped. He puts the figure much lower — at between 131,000 and 225,000 tonnes. But that’s still a significant amount of potentially toxic scrap metal — on par with Denmark, Norway or Austria’s annual output of e-waste.
The material impact extends far beyond the chips themselves. A research team led from the Colorado School of Mines has made projections about the amount of copper and other materials the data center build-out will need in the future — not just inside the buildings themselves, but also in the wiring that will connect them to the grid. In 2025, data centers accounted for about 576,000 tonnes of copper, about 2% of the global refined output. By 2030, that’s expected to hit 1.88m tonnes — 6.5% of the global output, and roughly equivalent to the entire annual refined copper consumption of the United States. With ore grades for copper falling to less than 1%, that could mean a billion tonnes of rock being mined every year, mostly from open pits in countries such as Chile and Peru, where mines compete with towns for scarce water resources.
There will also be competition for copper from other sources — wind and solar plants need grid connections, electric vehicles need batteries. “These results establish AI data centers as a significant new competitor for the same constrained materials and processing capacity required for grid expansion and the energy transition,” write the paper’s authors. “The speed and scale of the digital revolution may be structurally misaligned with the geological and investment cycles of the physical world.”
When will this be a problem?
According to the IEA’s “lift off” scenario, by the 2030s, data centers will peak at around 500m tonnes of CO2 emissions per year before falling as new nuclear and renewable energy generation comes online. This would represent about 0.4% of the world’s total carbon budget — the maximum level of CO2 emissions that will keep global warming under 1.5 degrees celsius. Put another way, each year that global AI runs at the level predicted by the IEA for 2030 wipes out almost a decade’s worth of progress on cutting CO2 emissions in the European Union.
Amodei and other AI proponents have argued that the technology will lead to scientific breakthroughs that bring down carbon emissions — that it will reduce more emissions than it causes. The IEA report agrees that widespread adoption of AI tools in industries like oil and gas and transportation could reduce CO2 emissions by 1.4b tonnes by 2035, but says that “there is currently no momentum that could ensure the widespread adoption of these AI applications.” de Vries-Gao argues that the type of AI that will have a positive impact on climate change is likely to be simpler machine learning algorithms — not LLMs or video generators.
Mostly, whether AI is a problem will depend on where you are in the world. Climate change will impact vulnerable communities hardest, and there will also be pronounced local effects in the areas around data centers: air pollution, heat islands (one study suggests these can increase air temperatures downwind of data centers by 2 degrees celsius), the constant hum of ventilation systems. “The costs are very much affecting very specific communities, while the benefits are probably more widespread,” de Vries-Gao says.
The local effect is especially pronounced for water use. By 2030, US data centers are projected to account for between 0.6% and 1.1% of public water withdrawals — less than is lost to leaking pipes. But at a local level, a single facility can quickly swallow up capacity, leaving local residents facing a drop in pressure or wells running dry.
It’s a similar story for materials. Copper mines in Chile and Peru, nickel being smelted in Indonesia, cobalt extraction in the Democratic Republic of Congo — none of these places are likely to host data centers, and few of the people living near the mines will benefit from better large language models.
The impact of AI on the global environment is likely to be relatively small when compared to the biggest emitters of carbon or consumers of water, even if it continues to scale at the rate many predict. But every transformative technology has incurred costs that don’t affect its end users, and AI will be no different. Amodei’s country of geniuses in a data center will need a country’s worth of power, water and mineral extraction. Some humans will have to live in it too.
Amit Katwala is a senior fellow at MIT Tech Review, supported by the Tarbell Center for AI Journalism. He is a former features editor for Wired Magazine.
“ In 2023, US data centers used approximately 17b gallons of water to cool down GPUs by carrying excess heat away. It sounds like a lot, until you consider that US golf courses use approximately 425byper-scaled data centers in 2023 versus gallons a year. “.
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As it stands, this could be read as boosterism under the guys of analysis. This paragraph itself needs verified citations - verified # of hyper-scaled data centers in 2023 vs those in development & planned for 2026; 27, 28, 29, 30; identified geo-locations; environmental conditions public resources needed to sustain data centers. Water is not the only impact there is air pollution, sound pollution, light pollution, heat pollution natural resource extraction. When we speak of the repercussions from hyper scaled data centers, we need to look at them through a cumulative lens, not merely through a happy face analogy of a golf course which, in themselves, have generated a fair amount of criticism.
The EU already made part of this measurable. Commission Delegated Regulation (EU) 2024/1364, adopted under the Energy Efficiency Directive, requires operators of data centres with an installed information technology power demand of at least 500 kW to report information and key performance indicators to the European database on data centres, using a common measurement and calculation methodology.
The threshold is the interesting part. It captures the facility, not the model. A modest site running inference for a large customer reports; the company buying that inference does not. Aggregate footprints stay hard to attribute as long as the reporting unit is the building.