A.I. Still Consumes A Lot Of Water – It Is Possible To Fix It – JP

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As weeks go by, the scale of the appalling devastation caused by the wildfires in Los Angeles becomes clearer. The fires have not been completely extinguished yet, but the search for answers to explain their origin is underway. The shortages fueled partisan finger-pointing over blame. No matter how this search ends, it is a fact that water became scarce when it was most needed. Southern California, often the location of wildfires, is also a hub for the A.I. boom. The region has seen a surge in A.I. data centers energy consumption, resulting in immense strain on the state’s resources¹. So, is it possible to blame, even partially, the A.I. industry for the lack of water supply to fight the fire’? Let’s see.

Big tech companies have indeed built or leased a lot of data centers – the engine rooms for AI. They have poured an estimated $105 billion during 2023 into these vast, power-hungry facilities. That spending spree increased the electricity demand and raised environmental concerns because those data centers are power-hungry: a query to ChatGPT requires nearly 10 times as much electricity as a regular Google search, according to a recent estimate²– it has been stated that ChatGPT responds to 195 million requests per day³. Thus, as the A.I. revolution “gathers steam”, Goldman Sachs estimates that data center power demand will grow 160% by 2030⁴, It is interesting to mention that by 2028 A.I. could represent about 19% of that data center power demand ⁵. Consequently, energy consumption by data centers worldwide will at least double over the next few years. In the USA, after some years of a decrease in the electricity demand until 2023, the expansion of the data center sector is expected to account for more than one-third of additional demand through 2026⁶.

Thus, the impact of the data center’s growing expansion upon the increase of the electricity demand can not be overlooked, “this surge in data center electricity demand, however, should be understood in the context of the much larger electricity demand that is expected to occur over the next few decades from a combination of electric vehicle adoption, onshoring of manufacturing, hydrogen utilization, and the electrification of industry and buildings”⁷. However, tracking water usage for data centers sometimes is

challenging due to insufficient reporting and transparency. AI model cards include information about the carbon footprint related to energy consumption during model training but generally provide little to no information about water usage⁸.

All and all, A.I. benefits are huge. However, its overuse could lead to energy overspending – nothing new, it is like keeping all of your home´s lights turned on when you leave it. Moreover, there are many water-saving techniques data centers are deploying, including immersion cooling (submerging servers in liquid), free cooling (using outside air in colder climates), direct-to-chip cooling, and more⁹.

Moreover, there are some ways data centers can limit water consumptionas well, as collecting and analyzing water usage data to reveal water use, as big companies already do; finding ways to reuse water; experimenting with new water management techniques; building new facilities in colder climates – it may particularly apply to warm, windy and dry Southern California weather; replacing legacy systems. In addition, hardware efficiency improvements, and innovations in model architectures and algorithms could help to mitigate or even reduce AI-related electricity consumption in the long term.

Conclusion: A.I. tools can not be blamed for the fire disaster. They neither originated it nor were responsible for the eventual lack of water. Firefighters ran out of water because the system wasn’t built to pump out that much water over a sustained period – not because it was misappropriated by data centers¹⁰. There is no evidence to come to a different conclusion. But the appalling drama has called attention to AI’s environmental impact. Let’s address it aiming to advance AI’s environmental sustainability and ensure its positive net contribution to mitigating climate change ¹¹

1. “Tech vs. Nature: The Complex Role of AI in Wildfire Control” by Ali Azhar, see https://www.aiwire.net/2025/01/14/tech-vs-nature-the-complex-role-of-ai-in-wildfire-control/
2. “Will A.I. Ruin the Planet or Save the Planet?” By Steve Lohr, see https://www.nytimes.com/2024/08/26/climate/ai-planet-climate-change.html?searchResultPosition=6
3. “The growing energy footprint of artificial intelligence”, by Alex de Vrie, see https://www.cell.com/joule/fulltext/S2542-4351(23)00365-3
4. Goldman Sachs “AI is poised to drive 160% increase in data center power demand”, see https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand
5. Goldman Sachs “AI is poised to drive 160% increase in data center power demand”, see https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand
6. International Energy Agency, 2024 “Executive summary”, see: https://www.iea.org/reports/electricity-2024/executive-summary
7. “2024 United States Data Center Energy Usage Report”, US Department of Energy, Berkeley Lab, Energy Analysis & Environmental Impacts Division- LBNL-2001637.
8. The University of Illinois, Urbana Champaign, The Grainger College of Engineering Civil & Environmental Engineering, “AI’s Challenging Waters” by Ana Pinheiro Privette, see https://cee.illinois.edu/news/AIs-Challenging-Waters#:~:text=In%20contrast%2C%20smaller%20data%20centers,to%20that%20of%204200%20persons.
9. “LA Wildfires Raise Burning Questions About AI’s Data Center Water Drain”, by Shane Snider, see https://www.informationweek.com/it-infrastructure/la-wildfires-raise-burning-questions-about-ai-s-data-center-water-drain
10. “ChatGPT isn’t responsible for the Los Angeles fires, but it does use a crazy amount of water”, by Cecily Mauran. See https://mashable.com/article/chatgpt-water-los-angeles-fires
11. Harvard Business review, “The Uneven Distribution of AI’s Environmental Impacts” by Shaolei Ren and Adam Wierman. See:https://hbr.org/2024/07/the-uneven-distribution-of-ais-environmental-impacts