#Issue 4: When AI drinks the water
Introduction
Consider this: “It is summertime. A heatwave hits. Water restrictions are in place. A major data centre has priority access to water under an existing contract. Fire risk is high. Nothing has failed yet, but the allocation has already been decided.”
Welcome to issue 4. If you have been following this newsletter, you will know that we spend a lot of time looking at what happens when AI systems and tools meet crisis environments and finding that the results are rarely as straightforward as your AI consultant suggested. This issue we are looking at something that I am not even sure has a name yet: the conflict between the resource demands of AI infrastructure and the resource needs of a disaster response. For the purposes of this issue, let's call this a resource conflict, ie, a situation in which AI infrastructure and critical public services depend on the same finite resource under stress conditions, with no clear governance mechanism to determine priority.
In January 2025, wildfires tore through Greater Los Angeles. At least 30 people died and more than 16,000 structures were destroyed. The Eaton and Palisades fires were among the most destructive in California's history, and as firefighters struggled to contain the blazes, hydrants across LA County began to run dry.
The shortage of water pressure was attributed to multiple factors: an ongoing drought, ageing infrastructure, and the sheer scale of simultaneous demand. However, much less attention was paid to the fact that at the same time as the hydrants were failing, AI data centres across California were consuming enormous quantities of the same resource. There is no evidence that data centres caused the hydrant failures, but the fires exposed something harder to ignore: AI infrastructure and emergency response infrastructure are drawing on the same constrained resource, under the same stress conditions.
Let's remind ourselves of our four lenses from issue one:
Power & Agency
Data & Consent
Accountability & Governance
Operational Reality
Today's topic: When the infrastructure that powers AI competes with the infrastructure that fights fires, who decides who gets the water?
Primary lens: Power & Agency | Secondary lens: Accountability & Governance
Let's go….
How much water does AI actually use?
This is harder to answer than it should be, but the short answer is: a lot. The longer answer, but still broadly speaking, we are looking at the following:
Data centres, the physical infrastructure that runs AI systems, use water in two main ways. First, directly on-site, where water is used to cool servers that generate enormous amounts of heat. Second and indirectly, through the power plants that supply their electricity, many of which rely on water-intensive steam generation. The water used in cooling largely evaporates rather than being returned as treatable wastewater - basically, it disappears.
A peer-reviewed study published in Patterns estimated that AI systems' water footprint could reach between 312 and 764 billion litres in 2025 alone, which is a range comparable to the global annual consumption of bottled water. The same study also highlights a more basic problem, in that AI-specific impacts are rarely reported separately, and most estimates rely on approximation rather than direct reporting. In other words, we are not entirely sure how much water AI uses, partly because the companies operating these systems are not required to tell us. I don’t know about you, but this sets off alarm bells for me. However, let's continue….
A separate analysis found that data centres in Texas alone may use 49 billion gallons of water in 2025, rising to as much as 399 billion gallons by 2030 which is the equivalent to drawing down Lake Mead, the largest reservoir in the United States, by more than 16 feet in a single year.
California, where the Los Angeles fires burned, presents a particularly acute risk profile: chronic drought conditions, competing water demands, and climate-driven variability that places increasing pressure on already stressed water systems.
Where the data centres are
The sensible location of AI infrastructure is a critical consideration, and on current evidence, it is not being given nearly enough weight. According to the Environmental Law Institute, more than 160 new AI data centres have been built across the United States in the past three years in places with scarce water resources. A statistic so incredible that I actually said “whyyy” out loud when I first read it. Anyway, it appears that siting decisions tend to prioritise proximity to data demand hubs and low latency, which does not always align with local environmental or water conditions.
California alone hosts 286 data centres, including 69 in Los Angeles County. These are not abstract cloud resources. They are physical buildings, drawing on the same municipal water systems as the residents, businesses, and fire hydrants of the communities around them.
An analysis of 9,055 data centre facilities found that by the 2050s, nearly 45% may face high exposure to water stress. A further MSCI analysis of 13,558 data centre assets found that about 30% of projects currently under construction are in regions where water scarcity is expected to intensify significantly by 2050. This is not a future problem. It is already happening. The LA wildfires made it visible in the starkest possible way.
The governance gap
Three California lawmakers introduced bills specifically targeting AI data centre water consumption in the weeks following the fires. Assembly member Dian Papan stated plainly: “Water's a limited resource. I’m trying to make it so we are prepared and ahead of the curve as we pursue new technology.”
But here is the structural problem: A peer-reviewed study in the Journal of Cleaner Production noted that utilities and regulators have already signalled the possibility of restricting data centre water access during droughts or peak demand, yet water sustainability is still not being treated as a core pillar of responsible AI infrastructure planning.
Major hyperscalers including Google, Microsoft, and Amazon have pledged to become “water positive” by 2030, meaning they aim to return more water to the environment than they consume. However, these replenishment efforts do not necessarily address localised supply constraints faced by municipalities during droughts or heatwaves, which were the conditions that existed in Los Angeles in January 2025.
In other words: the water offset might be happening somewhere. But if the hydrant outside your burning house has no pressure, that is not much comfort. The seasoned disaster managers among you will have your head in your hands at this point, I’m sure.
Through the lenses
Power & Agency
The siting of data centres in drought-prone regions, the prioritisation of latency and cost over local water resilience, and the voluntary rather than mandatory nature of water reporting are all choices made by actors with the power to make them, in contexts where the communities most affected by those choices have the least influence over them. When the hydrants run dry, the consequences are not distributed equally.
The deeper question is who gets to make these trade-offs before a crisis hits: utilities, private operators, regulators, emergency services, or some combination of them. At present, that answer is often fragmented, contractual, or opaque, which means the allocation may already be decided long before the emergency begins.
Accountability & Governance
You cannot govern what you cannot measure.
The Patterns study highlighted that the lack of distinction between AI and non-AI workloads in environmental reporting makes it extremely difficult to assess the true water footprint of AI systems, and called for mandatory disclosure standards. The California legislative response is a start. But reactive legislation after a disaster is a poor substitute for the kind of proactive, transparent governance that might have prompted different siting and infrastructure decisions in the first place.
The thing nobody is saying
There is a conversation happening in the AI governance space about how to make AI systems more ethical, more explainable, and fairer. It is an important conversation, but it is almost entirely focused on what happens inside the model, ie, the data it was trained on, the decisions it makes, the outputs it produces.
What the Los Angeles wildfires make visible is something different: the physical infrastructure that makes AI possible has its own footprint, its own resource demands, and its own consequences, and those consequences do not respect the boundary between the AI system and the world it operates in. We talk about AI as though it exists in the cloud, but it doesn't, it exists in buildings, drawing on water, consuming electricity, built in communities that may or may not have had any say in whether they wanted it there.
In a scenario where communities face a wildfire, their hydrants run dry, and the data centres next door keep humming along, the issue is not whether one directly caused the other. The issue is that no credible governance framework appears to exist for managing that resource tension when it matters most.
So what do we do about this?
This is not an argument against AI data centres, that ship has sailed and the trajectory we are on with AI means they are becoming part of our critical infrastructure. However efficiency and equity aren’t the same thing and a system that optimises for one at the expense of the other is not a system that is being governed well.
The minimum governance requirements seem clear enough:
Mandatory, standardised water disclosure at facility level
Regulatory frameworks that treat water as a critical constraint in siting decisions, especially in drought prone regions
Clear accountability mechanisms when AI infrastructure competes with public safety needs
The technology industry has spent years arguing that it should be trusted to self-regulate on environmental impact. Los Angeles in January 2025 offered one answer to that argument.
This week’s question
So, it’s over to you: When a city is on fire and water is scarce, who decides whether it goes to cooling servers or saving homes, and what governance frameworks should manage this?
See you in a fortnight.RB
References
Brookings Institution (2025) AI, data centres, and water. Available at: https://www.brookings.edu/articles/ai-data-centers-and-water/
de Vries-Gao, A. (2026) ‘The carbon and water footprints of data centres and what this could mean for artificial intelligence’, Patterns, 7(1), 101430. https://doi.org/10.1016/j.patter.2025.101430
Environmental Law Institute (ELI) (2025) AI's cooling problem: how data centres are transforming water use. Available at: https://www.eli.org/vibrant-environment-blog/ais-cooling-problem-how-data-centers-are-transforming-water-use
Herrera, M., Xie, X., Menapace, A., Zanfei, A. and Brentan, B.M. (2025) ‘Sustainable AI infrastructure: A scenario-based forecast of water footprint under uncertainty’, Journal of Cleaner Production. Available at: https://www.sciencedirect.com/science/article/pii/S0959652625018785
InformationWeek (2025) LA wildfires raise burning questions about AI's data center water drain. Available at: https://www.informationweek.com/it-infrastructure/la-wildfires-raise-burning-questions-about-ai-s-data-center-water-drain
Lincoln Institute of Land Policy (2025) Data drain: the land and water impacts of the AI boom. Available at: https://www.lincolninst.edu/publications/land-lines-magazine/articles/land-water-impacts-data-centers/
MSCI (2025) When AI meets water scarcity: data centres in a thirsty world. Available at: https://www.msci.com/research-and-insights/blog-post/when-ai-meets-water-scarcity-data-centers-in-a-thirsty-world
Net Zero Insights (2025) How AI growth is intensifying data centre water consumption. Available at: https://netzeroinsights.com/resources/how-ai-intensifying-data-center-water-consumption/
U.S. Geological Survey (2023) Drought in California. Available at: https://www.usgs.gov/centers/california-water-science-center/science/drought-california
