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AI data centers use electricity to run computing equipment and the systems that keep it powered, connected, and cool. Their water footprint includes both on-site cooling and water used upstream to generate electricity and manufacture chips; their land footprint can mean either the facility site or land associated with its electricity supply. Those are different measures, and none has one universal value: workload, facility design, energy source, and location all matter.
What does an AI data center use electricity for?
Electricity powers more than the processors running an AI workload. Servers perform computation and storage, while networking and storage equipment move and retain data. Cooling and environmental controls manage the facility, and power systems such as uninterruptible power supplies, backup generators, lighting, and other site infrastructure also contribute to its electricity demand.
The proportions vary by facility. The International Energy Agency (IEA) says servers account for around 60% of electricity demand in modern data centers on average, but the share varies considerably by facility type. Cooling can account for about 7% in efficient hyperscale facilities and more than 30% in less-efficient enterprise facilities, according to the IEA. These are facility-level ranges, not a fixed breakdown for every AI site.
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How much electricity do AI data centers use?
There is no single global electricity total for AI data centers in the figures available here. The IEA reports total data-center demand, including non-AI uses, while noting that electricity use by AI-focused data centers is growing faster than total data-center consumption.
| Measure | Reported figure | What it describes |
|---|---|---|
| Global data-center electricity consumption in 2024 | About 415 TWh, roughly 1.5% of global electricity consumption | IEA estimate for all data centers, not AI alone; the IEA said demand had grown at about 12% annually over the previous five years. |
| Global data-center electricity consumption in 2025 | About 485 TWh | IEA 2026 outlook projection for all data centers. |
| Global data-center electricity consumption in 2030 | About 950 TWh, around 3% of projected global electricity demand | IEA 2026 outlook projection for all data centers; it is not an AI-only forecast. |
The 2025 and 2030 figures are projections in the IEA’s 2026 outlook, not measured outcomes. The outlook says future demand depends on deployment as well as supply-chain and market constraints, so the forecast carries uncertainty. Its figures should not be treated as a guaranteed trajectory.
The IEA’s Energy and AI report, published 10 April 2025, puts the dependence plainly: “There is no AI without energy – specifically electricity for data centres.” The point is not that every data-center kilowatt-hour serves AI; it is that the computing infrastructure on which AI depends requires electricity.
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How do data centers use water?
Water enters the footprint through more than the facility’s cooling system. Some sites use water directly for cooling. Additional water may be used to generate the electricity the site consumes and to manufacture semiconductors and microchips. The balance among these sources depends on cooling technology, climate, and the electricity supply.
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Direct use at the facility
On-site cooling can involve water, but a direct cooling figure does not describe all water associated with a data center. Nor can one site’s rate be applied to another without accounting for its design and location. In particular, an on-site disclosure and a broader estimate that includes power generation and chip manufacturing have different boundaries.
Indirect use in electricity and hardware supply
The IEA’s 2025 report gives an illustrative estimate of around 2 million litres of total water consumption per day for a 100 MW US hyperscale data center. In that estimate, more than 60% is indirect, and the report equates the total to the consumption of about 6,500 households. This is an example tied to a particular facility scale and geography, not a universal daily rate for data centers.
At the global level, the IEA estimated data-center water consumption at around 560 billion litres per year at the time of its 2025 report, potentially rising to around 1,200 billion litres per year in its 2030 base case. For 2023, it attributed about two-thirds of consumption to primary energy supply and electricity generation, around one-quarter to direct cooling, and the remainder to semiconductor and microchip manufacturing. Because this accounting includes upstream uses, it cannot be compared directly with a cooling-only disclosure.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhat is the difference between water withdrawal and consumption?
A withdrawal is water taken from a surface-water or groundwater source. Consumption is the portion not returned to the original source, for example water lost through evaporation. A facility can withdraw a substantial amount without consuming all of it, so the two terms are not interchangeable when reading a footprint estimate.
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What is the land footprint of data centers?
“Land footprint” can refer to at least two different things: land physically occupied by data-center buildings and sites, or land associated with the electricity used by data centers, including electricity generation and supply chains. A figure for the second category is not acreage directly occupied by data-center buildings.
The United Nations University Institute for Water, Environment and Health (UNU-INWEH) estimated the land footprint associated with data-center electricity demand at 6,900 km² in 2025 and said it could exceed 14,500 km² by 2030. These are electricity-related footprint estimates, not direct measurements of facility sites. In the same report, UNU-INWEH estimated that global data centers used 448 TWh of electricity in 2025 and projected 945 TWh in 2030. The figures are the report’s estimates and projections, not a universal land-per-data-center ratio.
A separate, geographically limited view comes from the US Geological Survey’s (USGS) 2026 spatial synthesis. It identifies 771 AI data centers and more than 3,300 power plants across 11 western US states and Alaska, and examines proximity to transmission, power plants, and Bureau of Land Management lands. This is a regional siting analysis; the report does not provide a global count or a full environmental or regulatory impact assessment. It should not be mistaken for a measure of the land physically occupied by all AI data centers.
Why do location and cooling choices change the footprint?
A data center’s footprint reflects connected choices, not a single efficiency score. Workload affects computing demand; cooling design affects direct water use and electricity use; the local electricity mix influences upstream water and land impacts; and local climate and water conditions affect what a given cooling approach means in practice.
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The IEA describes a water-versus-energy efficiency trade-off: cooling systems that minimize water use can consume more energy, and systems that use less energy can use more water. The direction and size of a trade-off depend on the site and technology; there is no universally best choice established by the figures here.
Electricity sources also shift impacts among categories. UNU-INWEH stresses that a change that reduces carbon impact does not necessarily reduce water or land impact. Dr. Miriam Aczel, a UNU-INWEH researcher and lead author, said in the organization’s 3 June 2026 report announcement: “What surprised us most is how often the choices that look greenest from a carbon perspective end up worse for water or for land.” The practical implication is to compare the relevant resource measures together rather than treating low carbon as a complete account of environmental impact.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare two data centers or footprint claims?
Start by checking what each number counts. A comparison is useful only if its boundaries and units are visible: site electricity is not the same as global demand, direct cooling is not total water consumption, and electricity-related land is not facility acreage.
- Identify the scope. Check whether the figure covers one facility, a company’s portfolio, a national total, or global data centers. Confirm whether it refers to AI specifically or all data-center activity.
- Separate direct and indirect water. Ask whether cooling withdrawals, cooling consumption, electricity-generation water, and chip-manufacturing water are included. Do not compare a cooling-only figure with a total that includes upstream use as if they measured the same thing.
- Check the land definition. Determine whether a figure means the physical site area or land associated with electricity generation and supply chains. Keep those measures separate.
- Match the time and status. Distinguish a historical estimate from a forecast, and retain the report year and forecast year. The IEA’s 2025 and 2030 electricity numbers, for example, are projections in its 2026 outlook.
- Consider local conditions and the power supply. Compare cooling design, climate, local water conditions, grid capacity, electricity sources, and access to generation and transmission infrastructure where those details are available.
- Report multiple impacts together. Include electricity, water, and land measures rather than declaring a site or technology “cleanest” on the basis of just one. State when a comparable metric is not available instead of filling the gap with an assumption.
These checks also explain why a single figure per AI query is not established by the cited facility- and system-level estimates. A per-query value would require a task-specific method and explicit assumptions; it cannot be inferred from a global data-center total alone.
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What can current figures tell us—and what can’t they?
The estimates establish that data centers use material amounts of electricity and that their water and land implications extend beyond the buildings themselves. They also show why reporting boundaries matter: direct cooling is only one part of water use, while electricity supply can have its own water and land footprint.
They do not establish one agreed global figure for land physically occupied by AI data-center buildings alone, or a universal electricity, water, or land figure for each AI query. The IEA, UNU-INWEH, and USGS measures differ in date, geography, and scope. A sound comparison preserves those qualifications rather than treating unlike figures as interchangeable.
Frequently Asked Questions
Why can two reports give different water figures for data centers?
They may count different parts of the water footprint: one may report on-site cooling, while another includes water used for electricity generation or chip manufacturing. Check the accounting boundary and whether the figure measures withdrawals or consumption before comparing the numbers.
Can I estimate water or electricity use for one AI prompt from these totals?
Not reliably. The cited figures are facility-level or system-level estimates, and they do not specify a universal per-prompt method; a prompt-level estimate would need task-specific assumptions about the computation and its supporting infrastructure.
Does locating a data center near a power plant mean its electricity has a lower footprint?
Proximity is a siting characteristic, not a complete measure of electricity’s environmental effects. The USGS 2026 synthesis examines proximity to power plants and transmission in 11 western states and Alaska, but does not provide a full environmental or regulatory impact assessment.
Do data-center water estimates count water used to make chips?
Some broader estimates do. The IEA’s 2025 global accounting includes semiconductor and microchip manufacturing, whereas an on-site cooling disclosure may not; the scope needs to be checked for each number.
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