The world is waking up to a hidden crisis: the data centers that power our AI systems are draining freshwater resources at an alarming rate. Every time you ask ChatGPT to write a 100-word email, it consumes around 519 milliliters of water, according to a 2025 study by Li and Ren. This might not seem like much, but when you scale it up to the millions of queries made daily, the numbers become staggering. By 2027, global AI infrastructure is projected to consume between 4.2 and 6.6 billion cubic meters of water annually, nearly half of the United Kingdom's total annual water withdrawal. And that's just the direct water usage; it doesn't account for the indirect water required to generate the electricity that powers these data centers.
What makes this crisis even more concerning is the location of these data centers. Many are being built in regions already facing water stress. Microsoft, for instance, acknowledged in its 2023 sustainability report that around 42% of its water consumption came from areas classified as water-stressed under the World Resources Institute's rating system. Google reported that 15% of its freshwater withdrawals in 2023 came from areas facing high water scarcity.
The consequences of this water usage are already being felt. In Chile, Google paused a planned $200 million data center near Santiago after an environmental court ruled that the company had not adequately assessed the impact on the Central Santiago Aquifer. The country had been facing drought for 15 years and had begun rationing residential water in 2022. In Querétaro, Mexico, where 32 new data centers are planned, the state experienced its worst drought in a century in 2024.
The issue is not just the volume of water being used, but also the lack of transparency. The available figures are based only on what companies have chosen to disclose. There are three major disclosure gaps: the difference between water withdrawal and water consumption, the difference between direct cooling water and indirect water used for electricity generation, and the absence of facility-level data. The UC Riverside paper is important because it uses publicly available information to estimate these hidden gaps.
As independent researchers produce credible estimates, technology companies may face growing pressure to disclose more detailed water data. The big question for AI is whether it can deliver its promised benefits faster than its own water consumption grows. While AI has the potential to help solve climate and water problems through better climate modeling, improved irrigation systems, and more accurate drought prediction, the current trend suggests that its water footprint is expanding faster than its potential solutions.
This crisis highlights the need for a more sustainable approach to AI development and deployment. As we embrace the power of artificial intelligence, we must also address its environmental impact. The future of AI depends on our ability to balance innovation with responsibility, ensuring that its benefits are not offset by its environmental costs.
