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How much electricity do data centres use, and how much is from AI?
The IEA’s April 16, 2026 report, Key Questions on Energy and AI, estimates data-centre electricity use at roughly 485 TWh in 2025 and projects about 950 TWh in 2030. The 2030 figure would be around 3% of global electricity demand. These are modeled data-centre estimates and projections—not audited measurements of AI’s standalone electricity use.
Data-centre demand includes electricity for servers and the equipment that supports them, such as cooling. The IEA says servers account for around 60% of electricity demand in modern data centres on average, with the share varying by facility type. AI is an important and growing part of the load, but it is not the only one.
How the outlook has changed between reports
| IEA report and date | Global data-centre electricity estimate or projection | What the figure means |
|---|---|---|
| Energy and AI, 2025 | About 415 TWh in 2024, or roughly 1.5% of global electricity consumption; about 945 TWh in 2030 in the Base Case | The 2024 figure is an estimate; 2030 is a scenario projection. |
| Key Questions on Energy and AI, April 16, 2026 | About 485 TWh in 2025; about 950 TWh in 2030 | The newer global outlook; the 2030 figure is a projection, not an AI-only total. |
The two IEA reports’ 2030 projections are close, but they use different report vintages and the 2025 figure is explicitly labeled as the Base Case. Forecasts depend on assumptions about AI adoption, efficiency, hardware deployment and other constraints. A forecast is therefore best read with its date, scenario and geography attached, rather than treated as a fixed outcome.
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In the IEA’s 2025 analysis, accelerated servers—driven mainly by AI adoption—account for almost half of the net increase in global data-centre electricity use from 2024 to 2030. That is a share of the increase, not a claim that AI accounts for half of all data-centre consumption.
How much electricity does one ChatGPT prompt use?
No single prompt-energy figure describes all AI services. The result depends on the model, the length and type of task, the hardware and facility, how heavily the machines are used, and which parts of the infrastructure are counted. The available figure below is for Google’s Gemini Apps, not ChatGPT or the AI industry as a whole.
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Google’s measured Gemini Apps example
| Google-reported measure | Value for a median Gemini Apps text prompt in May 2025 | Boundary and qualification |
|---|---|---|
| Electricity, comprehensive method | 0.24 watt-hours (Wh) | Google’s 2025 study includes accelerator power, host CPU and DRAM, provisioned idle machine capacity, and data-centre overhead. |
| Electricity, narrower method | 0.10 Wh | Google’s 2025 study uses a narrower boundary that excludes some items included in its comprehensive method. |
| Emissions, comprehensive method | 0.03 grams of carbon-dioxide equivalent (gCO₂e) | Google’s company-reported estimate for the same median prompt and period. |
| Water, comprehensive method | 0.26 millilitres (mL) | Google’s company-reported estimate for the same median prompt and period. |
For that Gemini Apps prompt, the comprehensive method produces an energy estimate 2.4 times the narrower estimate. This illustrates why a prompt number without its measurement boundary can be misleading. Google also reports a 33-fold reduction in energy per median Gemini Apps text prompt between May 2024 and May 2025; that is a company-reported finding for its service and methodology, not evidence that every AI service saw the same change.
How much electricity do AI data centres use in the United States?
U.S. estimates cover a different geography from the IEA’s global totals and should not be added to them. They also come from different report years and modeling exercises.
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| Source and report vintage | U.S. data-centre electricity figure | Scope |
|---|---|---|
| U.S. Department of Energy announcement summarizing the 2024 Lawrence Berkeley National Laboratory report, December 20, 2024 | 176 TWh in 2023, about 4.4% of U.S. electricity; projected 325–580 TWh by 2028, about 6.7–12% | The announcement identifies AI applications among several contributors to data-centre demand. |
| Lawrence Berkeley National Laboratory, 2025 update | 521–843 TWh in 2030 | A range across compounded uncertainty scenarios for U.S. data-centre electricity consumption. |
In LBNL’s 2025 Reference Case, AI servers account for 55% of total data-centre energy use in 2030 and 84% of server energy use. Those percentages are model results for that U.S. scenario, not shares of global electricity. LBNL says AI-server deployment and power draw drive demand growth even as efficiency per computation improves; the laboratory also notes significant uncertainty and data gaps in its estimates.
Why can data centres matter so much to a local electricity grid?
A global percentage can conceal concentrated local demand. The IEA’s 2025 analysis describes data centres as spatially concentrated and cites them as accounting for around 20% of metered electricity supply in Ireland and 25% of electricity supply in Virginia. These are local figures reported in the IEA analysis—not global shares, and not a statement that the same proportion applies across either country or state.
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Electricity use and electricity generation are also different measures. In its 2025 Base Case, the IEA estimates that data-centre-related electricity generation was 460 TWh in 2024 and projects more than 1,000 TWh by 2030. It says renewables meet nearly half of the increase in generation through 2030, while fossil generation also grows to meet near-term demand. Generation figures describe the electricity produced to serve demand; they should not be substituted for consumption figures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is known—and not known—about AI’s wider footprint?
The available figures establish neither a universally comparable global AI-only electricity total nor a harmonized global total for AI-related water use. Google’s prompt-level energy, emissions and water figures are useful because the company describes their scope, but they are service- and method-specific rather than cross-provider benchmarks.
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Water and electricity impacts vary with location and infrastructure. A single company’s water estimate per prompt cannot be scaled into a reliable global AI-water total using the information available here. Likewise, data-centre electricity projections show the scale of infrastructure demand, but they do not isolate how much electricity belongs to AI as opposed to other workloads.
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