AI’s water and electricity use soars in 2025

AI created as much carbon pollution this year as New York City and guzzled up as much H20 as people consume globally in water bottles, according to new estimates.

The study paints what’s likely a pretty conservative picture of AI’s environmental impact since it’s based on the relatively limited amount of data that’s currently available to the public. A lack of transparency from tech companies makes it harder to see the potential environmental toll of AI becoming a part of everyday tasks, argues the author of the study who’s been tracking the electricity consumption of data centers used for AI and crypto mining over the years.

“There’s no way to put an extremely accurate number on this, but it’s going to be really big regardless… In the end, everyone is paying the price for this,” says Alex de Vries-Gao, a PhD candidate at the VU Amsterdam Institute for Environmental Studies who published his paper today in the journal Patterns.

To crunch these numbers, de Vries-Gao built on earlier research that found that power demand for AI globally could reach 23GW this year — surpassing the amount of electricity used for Bitcoin mining in 2024. While many tech companies divulge total numbers for their carbon emissions and direct water use in annual sustainability reports, they don’t typically break those numbers down to show how many resources AI consumes. De Vries-Gao found a work-around by using analyst estimates, companies’ earnings calls, and other publicly available information to gauge hardware production for AI and how much energy that hardware likely uses.

Once he figured out how much electricity these AI systems would likely consume, he could use that to forecast the amount of planet-heating pollution that would likely create. That came out to between 32.6 and 79.7 million tons annually. For comparison, New York City emits around 50 million tons of carbon dioxide annually.

Data centers can also be big water guzzlers, an issue that’s similarly tied to their electricity use. Water is used in cooling systems for data centers to keep servers from overheating. Power plants also demand significant amounts of water needed to cool equipment and turn turbines using steam, which makes up a majority of a data center’s water footprint. The push to build new data centers for generative AI has also fueled plans to build more power plants, which in turn use more water and (and create more greenhouse gas pollution if they burn fossil fuels).

AI could use between 312.5 and 764.6 billion liters of water this year, according to de Vries-Gao. That reaches even higher than a previous study conducted in 2023 that estimates that water use could be as much as 600 billion liters in 2027.

“I think that’s the biggest surprise,” says Shaolei Ren, one of the authors of that 2023 study and an associate professor of electrical and computer engineering at the University of California, Riverside. “[de Vries-Gao’s] paper is really timely… especially as we are seeing increasingly polarized views about AI and water,” Ren adds.

Across the US, which has more of these facilities than any other country in the world, there’s been a surge in local opposition to new data center projects that is often driven by concerns about water and power usage.

Even with the higher projection for water use, Ren says de Vries-Gao’s analysis is “really conservative” because it only captures the environmental effects of operating AI equipment — excluding the additional effects that accumulate along the supply chain and at the end of a device’s life.

There’s a pretty wide range of outcomes because companies are failing to disclose more accurate data. De Vries-Gao gathered whatever information he could from sustainability reports, but found that they often exclude key details, like their indirect water consumption from electricity demand and how much is used for AI specifically. Emissions and water consumption can vary depending on where a data center is located and how dirty the local power grid is in there, so being more forthcoming about where they operate or plan to build new data centers would also shine a greater light on AI’s growing environmental impact.

“We can really ask ourselves, is this how we want it to be?
Is this fair?” de Vries-Gao says. “We really need to have that transparency, so we can start having that discussion.”