The figure for a single email comes from a 2025 peer-reviewed paper in Communications of the ACM by Pengfei Li, Shaolei Ren, and colleagues at the University of California, Riverside. The paper, titled “Making AI Less Thirsty,” sets out the methodology by which the per-query water footprint of large language models can be estimated. The figure for the 100-word email is approximately 519 millilitres, which is close enough to the volume of a standard bottle of water for the bottle to be the practical comparison. The number includes both the direct water used to cool the data centre’s servers and the indirect water used to generate the electricity those servers consume.

The 519 millilitre figure assumes a single response. Most users do not send a single response. They have conversations.

The same research group estimates that a single sustained conversation with a chatbot, defined as somewhere between ten and fifty exchanges, consumes approximately the same 500-millilitre order of magnitude. The figure scales by a factor of one each time the conversation extends.

Why AI needs water at all

Data centres generate heat. The servers processing AI queries are essentially small radiators running at high intensity for as long as the workload continues. The chips at the heart of contemporary AI training and inference, the high-end graphics processing units manufactured primarily by Nvidia, dissipate between 300 and 700 watts each, depending on the model. A single training run for a large language model uses tens of thousands of these chips simultaneously, for weeks or months at a time. The heat has to go somewhere.

The most common method for moving that heat out of a data centre is evaporative cooling. Water is pumped through pipes that run alongside or directly across the heat-producing equipment, absorbs the heat, and is then exposed to the air. A portion of the water evaporates, carrying the heat into the atmosphere as water vapour. The remaining water cycles back through the system. Approximately 80 per cent of the water drawn into an evaporative cooling system is lost to evaporation. The rest returns to local water systems, sometimes at higher temperatures and with chemical residues from the cooling process.

The newer generation of data centres built specifically for AI workloads are larger, more dense, and more thermally intense than the data centres built for general cloud computing in the 2010s. A single large hyperscale AI campus can now consume more water in a day than a town of ten thousand people uses for everything: drinking, washing, cooking, sanitation, agriculture, and irrigation combined.

This video explains exactly what big tech promised and how AI is doing the opposite of that.

How much, in actual numbers

Google’s most recent Environmental Report, covering the 2024 financial year, sets out the water consumption of the company’s global operations in detail. The combined figure for 2024 was approximately 8.1 billion gallons, of which approximately 95 per cent was used at data centres. The 2024 figure was an 8 per cent increase on 2023. The 2023 figure had been a 17 per cent increase on 2022. The 2022 figure had been a 20 per cent increase on 2021. The cumulative result is that Google’s water consumption nearly doubled between 2021 and 2024, with the company itself naming AI workload growth as the primary driver in successive environmental reports.

Microsoft’s figures are similar in shape, smaller in absolute scale. The company reported water consumption of approximately 1.7 billion gallons in 2022, a 34 per cent year-on-year increase. The growth has continued. The independent investigative reporting on Microsoft’s data centre cluster in West Des Moines, Iowa, where the GPT-4 training runs were conducted in 2022, has documented that a single training run consumed 11.5 million gallons of water in July 2022 and another 13.4 million gallons in August. The same cluster has, in subsequent years, expanded to five separate facilities collectively drawing 68.5 million gallons annually from the West Des Moines municipal water system, more than any other industrial user in the metropolitan area.

Meta consumed approximately 813 million gallons globally in 2023, with 95 per cent of that volume used at data centres. Amazon, which operates the largest cloud infrastructure in the world, does not publish aggregate water consumption figures.

The Lawrence Berkeley National Laboratory’s 2024 Data Center Energy Usage Report, prepared for the United States Department of Energy under the Energy Act of 2020, estimated that data centres in the United States consumed approximately 17.4 billion gallons of water directly through cooling in 2023. The same report estimated that an additional 211 billion gallons of water were consumed indirectly through the electricity required to power the same data centres. The indirect figure is approximately twelve times larger than the direct figure. The report projects that the direct figure could double or quadruple by 2028. The indirect figure scales in the same proportion.

Where the water comes from

The Li and Ren paper projects that global AI demand will require somewhere between 4.2 and 6.6 billion cubic metres of water withdrawal annually by 2027. The lower estimate is approximately the total annual water withdrawal of four Denmarks. The higher estimate approaches half the total annual water withdrawal of the entire United Kingdom. Both estimates assume current trajectories of AI workload growth and current water-efficiency practices. Neither estimate accounts for the possibility that AI demand continues to grow faster than the modelled trajectory.

The water has to come from somewhere. In Microsoft’s 2023 sustainability report, the company acknowledged that approximately 42 per cent of its water consumption that year came from regions classified as “water-stressed” under the World Resources Institute’s standard rating system. Google’s equivalent figure for 2023 was 15 per cent of freshwater withdrawals from regions of “high water scarcity.” Both figures, on the trajectory of the past three years, are likely to increase rather than decrease.

The concrete consequences of those abstract percentages are now visible in specific locations. In September 2024, Google announced it was pausing its planned 200-million-dollar data centre in Cerrillos, near Santiago, Chile, after a Chilean environmental court partially reversed the project’s original 2020 permit. The court ruled that the company had not adequately accounted for the impact on the Central Santiago Aquifer in a country that had been in a continuous drought for fifteen years and had begun rationing residential water in 2022. The project is now under revision.

In Querétaro, Mexico, where 32 new data centres are currently planned, the state suffered its worst drought in a century in 2024, with seventeen of eighteen municipalities affected and the drinking water supply for thousands of families at risk. Microsoft has secured rights to approximately 25 million litres of water annually from a local aquifer that is currently running a 60-million-litre annual deficit. In Uruguay, currently experiencing its worst drought in 70 years, Google’s proposed data centre in Canelones would, in its first operational phase, consume approximately 7.6 million litres of water per day, equivalent to the daily residential water needs of 55,000 people. In Goodyear and Buckeye, Arizona, a 14-billion-dollar data centre project was withdrawn in 2024 after local resident organisations successfully pressed elected officials to deny the necessary rezoning. In Aragón, Spain, multiple data centre projects are advancing in regions where agricultural water rights are already contested.

The pattern, on the available evidence, is that the cooling infrastructure for global AI is being built preferentially in regions where freshwater is cheap, regulatory oversight is loose, and the local population is least positioned to negotiate.

What companies don’t disclose

The figures cited above are the figures the companies have made public. The full water footprint of the AI industry is, by every available assessment, larger than the figures voluntarily disclosed in sustainability reports.

Three specific gaps recur across the disclosure landscape. The first is the gap between water withdrawal, which is the volume drawn from local sources, and water consumption, which is the volume permanently lost to evaporation. Most corporate reports name only one of these figures, and the choice between them can shift the apparent footprint by a factor of three or more depending on which is reported. The second is the gap between direct cooling water and indirect electricity-generation water. Almost no corporate report includes the indirect figure, despite the Lawrence Berkeley estimate that the indirect figure is approximately twelve times the direct one. The third is the gap between aggregate global figures and facility-level figures. A company-wide annual total tells a stakeholder nothing about whether the company’s data centre in a drought-stressed Arizona town is straining the local aquifer.

The reasons for the disclosure gaps are several. Some are methodological: the per-facility water footprint of a data centre depends on cooling technology, local climate, electricity-grid mix, and seasonal demand variation, none of which the company necessarily measures with precision. Some are competitive: detailed facility-level water disclosure could give competitors useful intelligence about a company’s infrastructure plans. Some are reputational: a company that discloses its full water footprint and is then criticised for the size of it is exposed to public-relations risk in a way that a company reporting only aggregate figures is not.

The Li and Ren paper’s contribution to the literature is, in significant part, that it produces credible estimates of the gaps. The figures that the AI industry has not been willing to publish are figures that academic researchers, using publicly available proxies for cooling efficiency and electricity-grid water intensity, are now able to estimate within reasonable bounds.

What is at stake

The global infrastructure for processing AI queries is being built faster than any new technology infrastructure in modern history, on a financing trajectory that McKinsey has projected at approximately 5.2 trillion US dollars by 2030. The physical buildings the trillion-dollar investment is producing are, in their fundamental operational requirements, large industrial-scale evaporative cooling systems with computing equipment inside them.

Each query is small. The aggregate is not.

Half the United Kingdom’s annual water withdrawal, evaporating into the atmosphere from cooling towers across the world’s data centres by 2027, is not a marginal correction to a global water balance that is otherwise stable. Global freshwater scarcity is increasing on every measured trajectory. Approximately one-quarter of the world’s population, by United Nations projections, will face severe water stress by 2030. The water the AI industry is now drawing from aquifers, rivers, and reservoirs, increasingly in the regions least able to spare it, is competing directly with that population.

The technologies the AI industry is developing have, by any reasonable analysis, the potential to contribute to solving some of the same water-management problems they are now exacerbating, through better climate modelling, more efficient irrigation, more accurate weather prediction, and more sophisticated drought response. Whether the contribution arrives at scale faster than the consumption does is the open question that determines whether the trade-off, on the long view, is worth it.

On the present trajectory, the answer is unclear.

What the trajectory will look like by 2027 depends on decisions being made, in board rooms and government offices and local zoning meetings, now.

The post Writing a single 100-word email with ChatGPT consumes approximately the volume of a standard bottle of water, the global infrastructure processing AI queries is projected to use the equivalent of half the United Kingdom’s annual water withdrawal by 2027, and much of that water is being drawn from regions already experiencing severe drought. appeared first on Space Daily.

According to predictions from weather organizations across the planet, a super El Niño weather event is likely to begin over the next few weeks. It’s predicted to drive up temperatures across the globe, bringing increased chances of heatwaves, drought, wildfires, and even famines – plus, this year’s El Niño is likely to be stronger than usual, making those effects worse. Let’s take a look.

Scientists say melting sea ice may have pushed the Arctic Ocean past a tipping point, triggering changes that could reshape marine life for decades. Scientists have identified what appears to be a major and potentially irreversible change in the Arctic Ocean. According to a new study, climate-driven sea ice loss has altered the region’s chemistry [...]

According to predictions from weather organizations across the planet, a super El Niño weather event is likely to begin over the next few weeks. It’s predicted to drive up temperatures across the globe, bringing increased chances of heatwaves, drought, wildfires, and even famines – plus, this year’s El Niño is likely to be stronger than usual, making those effects worse. Let’s take a look.

The years 2023 and 2024 were the warmest on record, coinciding with a powerful Pacific climate event known as El Niño. El Niño is the warm phase of a natural climate cycle in which surface waters in the eastern Pacific are unusually warm, bringing record-breaking heatwaves in the Amazon and heavy rainfall in the southern USA. Its counterpart, La Niña, is the cool phase that brings wetter conditions to the Northern USA. 

In a typical El Niño, warm water in the eastern Pacific weakens the winds blowing westward across the tropical Pacific, known as trade winds, allowing more warm water to flow eastward – a self-reinforcing cycle that amplifies the event. However, the 2023 El Niño differed because the ocean warmed intensely, but the trade winds remained strong. Researchers from the Scripps Institution of Oceanography, led by Qihua Peng and Shang-Ping Xie, recently investigated how and why this unusual event occurred.

First, the researchers tracked how air pressures changed across the Pacific during the event using a metric calculated by NOAA, known as the Southern Oscillation Index. When the eastern Pacific warms during an El Niño, the difference in air pressure across the Pacific typically decreases. In 2023, they found that temperatures in the eastern Pacific rose to more than 3°F (2°C) above normal, yet the drop in air pressure was only about 31% as strong as they expected. They also calculated that changes in wind speed and direction could only account for about 30% of the warming. So why was the 2023 El Niño so strong?

To answer this question, the research team then looked beyond the Pacific, analyzing sea surface temperatures from NOAA satellite data. They found that the North Atlantic and Indian Oceans also experienced record-breaking heat in 2023, with temperatures in the North Atlantic exceeding 2°F (1°C) above normal – the warmest in recent history. This suggested that El Niño events can develop in response to ocean conditions worldwide, not just those in the Pacific.

Next, the team used a computer program that simulates how the atmosphere responds to ocean temperatures, called the Community Atmosphere Model, to examine how heat from other oceans affects the Pacific. They found that heat in the North Atlantic and Indian Oceans generated large columns of hot air rising over those regions. This air cooled at high altitudes and then sank over the central Pacific, strengthening a large-scale loop of rising and sinking air that drives trade winds westward. Strengthening this circulation worked against El Niño by keeping trade winds blowing westward about 30% more strongly than Pacific warming alone would have. If the trade winds remained strong, why was the eastern Pacific so warm in 2023?  

To answer this question, the researchers studied 3 consecutive La Niña years between 2020 and 2023, analyzing ocean temperature and sea level data from NOAA’s Global Ocean Data System. During those years, strengthened trade winds transported heat into the western Pacific. As the seawater got warmer, it expanded, a process known as thermal expansion. Over those 3 years, thermal expansion and constant wind created a “pile” of warm water in the western Pacific, which reached its highest level of stored heat since 1982. When the trade winds eventually relaxed as La Niña faded, this piled-up warm water surged eastward, setting the stage for the El Niño event.

To test whether this stored heat alone could drive an El Niño, the team used a computer program that models oceanic and atmospheric interactions, called a coupled general circulation model. They input observed ocean temperatures from April 1, 2023, when La Niña ended, but removed all wind changes after that date. Their model successfully reproduced 87% of the warming observed between June and December 2023, which suggested that trade winds contributed just 13%. Stored heat was carried eastward by massive underwater waves traveling along the equator. As these waves reached the Eastern Pacific, they pushed cold water deeper, allowing surface water to warm. The researchers concluded that this oceanic process drove the 2023 El Niño to develop without the usual wind-driven feedback.

The team suggested that in a warming world, large heat reservoirs in the western Pacific will likely become more common, leading to more frequent strong El Niños. However, because their analysis focused on a single event, it remains unclear how often El Niños develop through oceanic processes alone. Ultimately, their study showed that the ocean can be more than a passive partner in El Niño – it can be the driving force.

The post Why the 2023 El Niño broke records appeared first on Sciworthy.