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AI data centres draw electricity to run servers and cool buildings, use water directly in some cooling systems and indirectly through power generation and chip manufacturing, and occupy land along with the infrastructure needed to connect them. The clearest global electricity figures cover all data centres—not AI alone. Water and land impacts are harder to compare, and national totals can conceal pressure concentrated in particular grids and catchments.
What counts as an AI data centre?
An AI service runs on physical computing equipment housed in data centres: buildings filled with servers, storage, networking equipment and systems that supply power and remove heat. Some facilities are purpose-built for AI; others serve a mix of AI workloads, cloud computing, websites, business software and other digital services. A data-centre total therefore cannot be treated as a measurement of AI alone.
The distinction matters when interpreting headline numbers. In its 2025 report Energy and AI, the International Energy Agency (IEA) estimates electricity use across the global data-centre sector, not the electricity used only by AI models. The IEA identifies AI as the main driver of projected growth, but other digital services contribute too. A facility’s total also changes with its equipment, workload, cooling design and local conditions.
There are three different resource questions to keep separate: how much electricity a facility consumes, how much water is used directly on site and elsewhere in its supply chain, and how much land is occupied or affected by the infrastructure serving it. Each has a different boundary, so one headline measure cannot capture the whole footprint.
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How much electricity do data centres use?
The IEA estimates that data centres consumed about 415 terawatt-hours (TWh) of electricity worldwide in 2024, approximately 1.5% of global electricity consumption. Its 2025 base case projects about 945 TWh in 2030. That 2030 figure is a scenario projection, not a measurement or a guaranteed outcome. It depends on factors including AI adoption, hardware and software efficiency, infrastructure constraints and other assumptions.
The figures describe data centres as a sector, not AI workloads in isolation. They indicate the scale of the infrastructure behind digital services and the direction of the IEA’s base case; they do not tell a reader how much electricity a particular AI query, company or facility uses.
Where a facility’s electricity goes
Servers are typically the largest electricity-consuming component. The IEA estimates that servers account for about 60% on average of electricity demand in modern data centres. The rest supplies other information-technology equipment and facility systems, including cooling and power infrastructure.
Cooling’s share varies sharply by facility category. In the IEA’s estimates, it can be around 7% in efficient hyperscale data centres and more than 30% in less-efficient enterprise facilities. These are examples of variation across types of data centres, not a universal split that can be assigned to every AI site. A single cooling percentage would obscure differences in design and operating conditions.
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Electricity use is also a local grid question, not just a global energy statistic. The IEA notes that data-centre loads cluster geographically. A large new facility or group of facilities can create a concentrated demand for power in a particular area, where available grid capacity and generation may be more important than the sector’s global share. The effect depends on where and when the electricity is required and what local infrastructure can supply it.
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How do data centres use water?
Direct cooling is only one part of the water footprint. A 2025 report, Water use in AI and Data Centres, hosted by the UK government and listing Rich Kenny as lead author, identifies three principal pathways: cooling, electricity generation and semiconductor manufacturing. The report puts it this way: “Data centres, which are the foundational physical infrastructure for artificial intelligence, consume water through three primary mechanisms: cooling, electricity generation, and the manufacturing of semiconductor chips.”
| Water pathway | What it includes | Where it occurs |
|---|---|---|
| Direct operational use | Water used by cooling systems. Evaporative systems consume water as it evaporates. | At the data-centre site. |
| Electricity-related use | Water consumed or withdrawn when generating the electricity used by the facility. The amount depends in part on the electricity generation mix. | At power-generation sites, outside the facility boundary. |
| Upstream manufacturing | Water used to make semiconductor chips and related equipment. | Across manufacturing supply chains. |
The UK-hosted report repeats IEA estimates of more than 560 billion litres per year for data-centre-sector water use, with a possible increase to 1,200 billion litres by 2030. These are estimates presented for the sector in the report’s cited framing; they are not a direct metering census of AI-specific water use. The values should not be read as the amount used only for on-site cooling, or as a measure for a particular facility.
Withdrawal is not the same as consumption
Water withdrawal is water taken from a source; water consumption is the portion not returned to that source in the same way, often because it has evaporated or otherwise become unavailable locally. The terms are not interchangeable. A water figure is meaningful only when its measure and boundary are clear: for example, whether it counts direct site use or a broader supply-chain footprint, and whether it reports withdrawal or consumption.
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One operational measure is water usage effectiveness (WUE). The Australian Government’s September 2026 consultation paper, Getting it right: Building AI infrastructure that works for Australia, defines WUE as annual water use in litres divided by annual IT energy use in kilowatt-hours. WUE can help describe water use relative to IT energy, but it is shaped by facility design, workload, climate and water sources. The consultation notes that WUE is less widely reported than power usage effectiveness (PUE); a WUE value alone does not describe a facility’s catchment conditions or the water used to supply its electricity and equipment.
Why cooling choices involve trade-offs
Cooling technology influences both electricity demand and direct water use. Water-based cooling can reduce electricity demand while increasing the amount of water used on site. The balance varies with facility design, local climate, the water source and the conditions in which the data centre operates. There is no single cooling approach or water-per-computation figure that applies to all facilities.
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Cooling systems can include evaporative, air-based and liquid approaches. The Australian consultation describes closed-loop liquid cooling as an option that can significantly reduce consumption. That does not establish that every closed-loop design has the same result, or that a particular system eliminates all water use across the facility’s electricity and equipment supply chains. The relevant comparison is site-specific: direct water, electricity use, local water availability and the full boundary being counted.
When comparing two facilities, ask what the reported number covers and under what conditions it was measured. A direct cooling figure should not be mistaken for a full water footprint. A low on-site water figure, by itself, does not reveal water used in electricity generation or manufacturing.
Why local water impacts can matter despite small national shares
National averages can hide local pressure. In its September 2026 consultation paper, the Australian Government estimates that data-centre cooling used 5.5 gigalitres of water in Australia in 2025, around 0.04% of industrial water use. The paper also cautions that effects can be significant where facilities draw from a shared supply system or catchment. The national percentage is not a guarantee that every proposed site has a negligible local effect.
The same distinction applies to electricity: a modest global share can coincide with a substantial new load in one place. For water, the significance depends not only on annual volume but also on the source, local demand, drought conditions and what else relies on the same catchment or system.
The Australian paper states: “National Cabinet has agreed that large data centres cannot threaten scarce water resources, undermine water security or drive-up water bills.” This is policy framing in a consultation paper, not by itself a complete description of enacted standards. A reader assessing a proposed project should distinguish policy statements and consultation proposals from rules that are in force.
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What land does AI infrastructure require?
A data centre needs a site for buildings and equipment, but its physical footprint does not end at the property boundary. It also depends on electricity connections, generation and transmission infrastructure, networks, access and permitting. A land comparison should say whether it counts only the facility parcel or includes related infrastructure and indirect land impacts.
The United Nations University Institute for Water, Environment and Health’s 2026 report, Environmental Cost of Artificial Intelligence: Carbon, Water, and Land Footprints, describes carbon, water and land footprints connected to electricity generation and data-centre siting. It emphasizes that burdens can be concentrated around host communities even while benefits are distributed more widely. Electricity-related impacts also depend on where and how power is generated; renewable electricity does not automatically mean zero water or land impacts.
No comparable global acreage estimate or land-use-change series is established here. That means a defensible global total cannot be supplied, and campus examples should not be substituted for one. The available evidence supports describing the components and siting questions, not attaching a precise worldwide area to AI data centres.
A 2026 U.S. Geological Survey report summary describes spatial analysis of 771 AI data centres and more than 3,300 power plants across 11 western U.S. states and Alaska, including their proximity to federal public lands. Those counts refer to that study area, not a complete U.S. or global inventory. The full report page was not available in the cited material, so the numbers should not be used to infer a national land footprint.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess a claim about an AI data centre’s footprint
Before comparing a headline figure with another site or year, check whether the measurement has the same scope. These questions help prevent a sector estimate, site figure or scenario from being misrepresented:
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- What is being counted? Is the figure for AI workloads, one facility or the entire data-centre sector? The IEA’s 415 TWh estimate for 2024 is sector-wide.
- What period and status does it describe? Separate observed or estimated historical use from a future scenario. The IEA’s 945 TWh figure is its 2030 base-case projection.
- Where is the impact? Global or national totals do not identify pressure on a particular grid, water supply or catchment.
- What is the water boundary? Separate direct cooling from electricity-related water and manufacturing, and check whether the measure is withdrawal or consumption.
- What cooling system and conditions apply? Design, climate, workload and water source affect outcomes; one site’s values may not transfer to another.
- What does the land figure include? Distinguish the facility parcel from generation, transmission, roads and other associated infrastructure. If there is no comparable area measure, say so rather than imply precision.
These checks also clarify what a number cannot establish. A global electricity total does not assign electricity use to an individual AI task; an on-site cooling figure does not capture the full water footprint; and a building’s parcel is not a complete account of land associated with its power and network needs.
Why estimates differ and what they do not tell us
Data-centre resource estimates depend on definitions and system boundaries as well as on the facilities themselves. AI and other workloads may share buildings and equipment; cooling and power arrangements differ; and indirect water or land impacts occur beyond a data centre’s fence. As a result, figures from different sources are not automatically comparable just because they use the same units.
Forecasts add another layer of uncertainty. The IEA’s 2030 electricity figure is a base case that depends on assumptions about adoption, efficiency and infrastructure. Actual demand could differ if those conditions change. A projection should therefore be described with its year and scenario, not as a measured future total.
Water and land measures need similar care. The sector-wide water estimates cited above do not resolve how much water is used by AI alone, and the available land evidence does not support a global acreage total. The most useful account keeps those limits visible while examining where facilities are built, what resources they draw on and how those demands interact with local infrastructure.
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