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The distinction matters: AI is a fast-growing part of data-centre demand, but total data-centre electricity is not an AI-only figure. Meanwhile, falling electricity use per task does not guarantee falling overall consumption. Adoption, more demanding AI capabilities, and the number and type of tasks all affect the total.
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What does “AI electricity use” mean?
Electricity use is the energy consumed over time, commonly reported in kilowatt-hours or terawatt-hours. One TWh is one billion kilowatt-hours. Power, by contrast, describes how much electricity a facility draws at a moment and is usually stated in megawatts (MW). A data centre’s MW rating tells you about its capacity or load; its TWh figure tells you how much electricity it uses over a period. The two measures answer different questions.
There is no single global total that cleanly counts every watt consumed by AI. AI workloads run in data centres alongside cloud services, storage, websites, business software, and other digital services. Data-centre electricity is the best available broad proxy for AI’s infrastructure footprint, but it includes those non-AI uses. Conversely, a figure for AI-focused data centres does not necessarily capture every AI computation performed on other infrastructure.
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The IEA’s 2025 estimate put data-centre consumption at 415 TWh in 2024, around 1.5% of world electricity. Its 2026 update reports 485 TWh in 2025. These are estimates for all data centres, not measurements of AI alone. The 2026 update also reports that total data-centre electricity demand grew 17% in 2025.
How much electricity do data centres use now, and what is forecast?
The latest central outlook in the IEA’s 2026 update projects total data-centre electricity consumption to reach 950 TWh in 2030, up from 485 TWh in 2025. That is close to twice the reported 2025 total. The IEA estimates the 2030 figure at about 3% of global electricity demand. It also projects electricity consumption at AI-focused data centres to triple between 2025 and 2030.
These figures describe different scopes. The 950 TWh projection is for all data centres; the tripling projection is for AI-focused data centres. Neither means that AI will account for all data-centre electricity or that global electricity demand itself will double. They are forecasts, not readings from a future meter, and the AI-focused figure is not an exact global tally of every AI workload.
The 2025 IEA analysis had estimated about 945 TWh of total data-centre use in 2030, close to the newer central estimate. The historical baseline and latest estimate should still be read with their dates attached: 415 TWh was the 2025 report’s estimate for 2024, while 485 TWh is the 2026 update’s reported total for 2025. Changes between estimates can reflect updated information as well as actual growth.
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Why can AI use less energy per task but more electricity overall?
Three forces shape demand: efficiency, adoption, and changing model capabilities. The IEA reports that energy use per AI task has fallen by at least an order of magnitude annually in recent years. That is a claim about the energy required for an individual task, not about the total electricity used by the sector.
If each task becomes cheaper in energy but people and organizations run many more tasks, aggregate use can still rise. The task mix matters too: a growing share of computationally demanding work can offset efficiency gains. In its 2026 executive summary, the IEA describes AI energy demand as the result of “improvements in efficiency, surging uptake, and changing model capabilities, which can unlock new and, in many instances, far more energy-intensive use cases.”
That is why efficiency statistics need a denominator. “Energy per task” is not the same measure as “electricity used by data centres.” The first can fall even as the second rises, if the volume of work or its average complexity increases enough.
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AI workloads vary substantially. A simple text-generation request is not a reliable stand-in for video generation, reasoning, or agentic tasks—systems that can carry out multi-step work. The IEA says video generation, reasoning, and agentic tasks can use hundreds or thousands of times more electricity per query than simple text generation.
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That comparison is about relative energy per query across task types; it is not a universal figure for how many kilowatt-hours a particular prompt uses. The cited IEA summary does not provide one globally applicable per-query consumption value. A specific answer would depend on the workload and the system running it, and should not be inferred from a facility’s total electricity bill or from a simple text-query estimate.
There is also no comprehensive worldwide accounting of how frequently people use AI or how deep those interactions are, according to the IEA’s 2026 report. Without a complete count of tasks and their types, per-query figures cannot simply be multiplied into a definitive global AI total.
What does a large data centre draw, and why does location matter?
Facility power illustrates the scale of infrastructure without telling you the energy cost of an individual AI task. The IEA’s topic page says conventional data centres typically draw 10–25 MW, while hyperscale AI centres can exceed 100 MW. Its comparison equates 100 MW to the annual electricity consumption of 100,000 households. This is an illustration of facility scale, not a per-query estimate or a claim that every AI centre draws more than 100 MW.
Large loads are not evenly spread across the electricity system. A global share can appear modest while a particular region faces a significant new demand concentrated in a few locations. The IEA’s 2025 executive summary reported that nearly half of US data-centre capacity was in five regional clusters. That is a US-specific concentration finding, not a description of every country or grid.
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Connecting a large facility requires more than enough electricity in the abstract. Local network capacity, transmission links, connection queues, and the availability of key equipment can affect whether and when a project can be served. The IEA warns that grid queues, long transmission build times, and shortages of key equipment can delay projects. These constraints are uneven: their effects depend on where capacity is proposed and the condition of the relevant grid.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is AI the main reason global electricity demand is growing?
Not in the IEA’s 2025 base case. Data-centre demand growth accounts for less than 10% of global electricity-demand growth over 2024–2030 in that analysis. Industry growth, electrification, electric vehicles, and air conditioning are larger contributors to global demand growth overall.
That global comparison does not make data-centre demand irrelevant. A smaller share of worldwide growth can still be difficult to accommodate in a region where facilities cluster and require substantial connections. Global percentages describe the scale relative to the whole electricity system; they do not show how much spare capacity is available at a specific substation or how quickly local infrastructure can be expanded.
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How uncertain are projections beyond 2030?
Forecasts depend on how quickly organizations adopt AI, how much electricity efficiency improvements save, which capabilities and uses become common, and whether energy infrastructure can keep pace. These are uncertain, interacting factors rather than fixed inputs. The IEA’s 2025 analysis presented a central estimate of about 945 TWh for data centres in 2030 and included sensitivity cases to show how the outcome could change.
For 2035, that analysis ranged from around 700 TWh in a “headwinds” case to more than 1,700 TWh in a “lift-off” case. Those endpoints are conditional scenarios, not equally likely forecasts or a guaranteed range. They illustrate how different combinations of adoption, efficiency, and energy-sector bottlenecks can change the outcome; they should not be read as two competing measurements of future consumption.
The 2026 update’s central projection—950 TWh for total data centres in 2030—offers the latest headline outlook in the cited IEA material. It remains a projection. The more distant the forecast, the more important it is to state which assumptions and scenario are being discussed rather than treating one number as certain.
How to read claims about AI’s electricity use
When two figures appear to disagree, check what each one counts before comparing them. One may cover AI-focused facilities and another all data centres; one may be a measured-year estimate and another a forecast. Per-task energy and total electricity are different quantities, as are a simple text request and a video or agentic workload.
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- Time: Is it a reported estimate for a past year or a projection for a future one?
- Metric: Does it describe electricity per task, facility power, annual energy use, or a share of global demand?
- Workload: Does the estimate concern simple text, video generation, reasoning, or agentic work?
- Geography: Is it a worldwide total, a national figure, or a local grid issue?
- Forecast status: Is it a central projection or a conditional scenario?
Keeping those distinctions intact gives the most accurate answer: total data-centre electricity is growing quickly, AI-focused consumption is growing faster, and no single number yet captures all AI electricity use worldwide. Per-task efficiency is improving, but adoption and more demanding uses can still raise total demand. The size of the global share and the pressure on an individual grid are related, but they are not interchangeable measures.
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