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The best current global proxy is electricity used by data centres, not a meter that counts AI alone. Data centres run AI workloads alongside many other digital services. The International Energy Agency (IEA) reports that all data centres used 485 terawatt-hours (TWh) of electricity in 2025 and projects 950 TWh in 2030, about 3% of global electricity demand. AI-focused data-centre consumption grew faster than the total in 2025 and is projected to triple from 2025 to 2030. Those figures describe different scopes: one is all data centres; the other is AI-focused facilities.
How much electricity does AI use?
There is no single comprehensive global figure for AI electricity use. The most useful available measure is data-centre electricity consumption, because data centres train and run AI models. But that total also includes electricity for non-AI digital services, so it should not be presented as an AI-only count. Nor is it a per-prompt estimate: it aggregates electricity at a much larger scale.
The IEA’s 2026 update reports that total data-centre electricity consumption reached 485 TWh in 2025. It projects 950 TWh in 2030, equivalent to around 3% of global electricity demand that year. These are respectively a reported figure for 2025 and a forecast for 2030—not two measured readings, and not the amount attributable exclusively to AI.
| Measure | Figure | What it describes |
|---|---|---|
| Data-centre electricity, 2024 | 415 TWh, about 1.5% of world electricity | IEA estimate published in 2025; all data centres, not AI alone. |
| Data-centre electricity, 2025 | 485 TWh | IEA figure reported in its 2026 update; all data centres. |
| Total data-centre electricity, 2030 | 950 TWh, around 3% of global electricity demand | IEA projection published in 2026; all data centres. |
| AI-focused data-centre electricity, 2025 | Consumption grew 50% during the year | IEA 2026 update; growth rate for AI-focused facilities, not their share of the total. |
| Total data-centre electricity, 2025 | Demand grew 17% during the year | IEA 2026 update; growth rate for all data centres. |
| AI-focused data-centre electricity, 2025–2030 | Projected to triple | IEA 2026 outlook; a forecast, not a measured result. |
The comparison with 2024 shows the scale of the increase in the all-data-centre measure: the IEA’s 2025 report estimated 415 TWh, while its 2026 update reports 485 TWh for 2025. The percentages also show that AI-focused facilities grew more quickly than data centres overall in 2025. They do not, by themselves, reveal how many TWh AI used: a growth rate is not an absolute consumption figure or a fraction of the total.
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Why isn’t there a precise AI-only total?
Data-centre electricity is a practical proxy, but it combines workloads with different purposes. A facility may run AI alongside other digital services, while the amount of AI activity itself varies by task and changes over time. The IEA says there are no comprehensive worldwide statistics quantifying how frequently and deeply AI is used. Without that information, a global AI-only total cannot be read directly from the all-data-centre figure.
It is also important to keep four kinds of claims separate:
- All data centres versus AI-focused facilities: the 485 TWh figure covers all data centres. The IEA separately reports growth and a projection for AI-focused data-centre consumption, but the cited update does not provide an AI-only total in TWh.
- Total electricity versus electricity per task: total consumption adds up activity across facilities and workloads. Per-task energy measures the electricity associated with a particular kind of task. One can decline while the other rises.
- Measured or reported year versus forecast year: 2025 is a reported figure in the IEA’s 2026 update; 2030 is a projection. Forecasts are conditional on how the sector develops.
- Global share versus local impact: a modest proportion of worldwide electricity demand can still be significant where data centres are clustered.
For the same reason, a claim about electricity per prompt does not answer how much electricity AI uses globally. It may refer to one model, one task, or one set of operating conditions; it says little about the number of tasks performed across all users and facilities. The IEA’s cited summary gives no universal per-query electricity value.
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What drives AI’s electricity demand?
The IEA describes three interacting forces: efficiency improvements, surging uptake, and changing model capabilities. In its 2026 executive summary, it writes: “The energy demand of AI is therefore the result of three rapidly evolving and uncertain trends: improvements in efficiency, surging uptake, and changing model capabilities, which can unlock new and, in many instances, far more energy-intensive use cases.” The statement is attributed to the IEA’s Key Questions on Energy and AI; no individual speaker is named.
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Efficiency: less electricity for a task does not guarantee a lower total
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 energy per task—not a claim that the sector’s total electricity use fell by the same amount, or fell at all. If each task takes less energy but the number of tasks grows, aggregate demand can still increase. The total can also rise if people shift toward more demanding tasks.
This distinction helps explain how rapid technical improvement can coexist with rising data-centre electricity consumption. Efficiency describes what it takes to do a given task; total use also depends on how many tasks are run and what those tasks involve. A lower energy cost per activity may enable more use, while the overall mix of applications changes. The IEA’s reported 2025 growth figures are consistent with the need to consider both efficiency and uptake rather than treating either one as the whole story.
Adoption: more AI activity adds up across users and services
As more people and businesses use AI, the number of computing tasks can increase. Each task may be relatively small or demanding, but electricity use accumulates across repeated activity and the infrastructure that runs it. That is why a striking per-task improvement does not settle the question of future demand: adoption affects the volume of work being performed.
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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 errorsThe IEA’s 2026 update reports 17% growth in total data-centre electricity demand in 2025 and 50% growth in electricity consumption from AI-focused data centres that year. Those figures indicate faster growth in the AI-focused category, but they are growth rates, not proof that AI accounted for 50% of all data-centre consumption or 50% of its increase.
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Changing capabilities: not every AI task has the same energy profile
A simple text-generation query is not a sound stand-in for every AI workload. According to the IEA, video generation, reasoning, and agentic tasks can use hundreds or thousands of times more electricity per query than simple text generation. The exact comparison depends on the task; the key point is that different capabilities can demand very different amounts of computation.
As models gain capabilities that support more complex uses, the mix of work may change as well as its volume. A forecast based on a fixed idea of what an “AI query” means can therefore miss an important driver. Any per-query figure needs context about the workload it measures, and it should not be generalized from text generation to video, reasoning, or agentic activity.
How large is AI’s demand compared with the electricity system?
The global total and the local grid effect answer different questions. In its 2025 base case, the IEA attributed less than 10% of global electricity-demand growth from 2024 to 2030 to data centres. Industry growth, electrification, electric vehicles, and air conditioning were larger contributors to worldwide demand growth in that analysis. This is a comparison of growth over a period, not a claim that data centres use less than 10% of the world’s electricity.
At the same time, data centres are concentrated in particular places. A sizeable new load in a cluster can create a connection or grid-integration challenge even when the worldwide share remains limited. The IEA’s 2025 executive summary reported that nearly half of US data-centre capacity was located in five regional clusters. It warned that grid queues, transmission build times, and shortages of key equipment can delay projects. That finding describes concentration in the United States; it does not mean every region faces the same conditions.
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Facility-scale comparisons make the concentrated-load issue easier to picture, but they do not estimate the electricity used by a query. The IEA’s topic page says conventional data centres typically draw 10–25 megawatts (MW), while hyperscale AI centres can exceed 100 MW. It equates 100 MW to the annual electricity consumption of 100,000 households. This is a facility-scale comparison, not a claim that every AI centre draws more than 100 MW or that one prompt uses household-scale electricity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do the 2030 and 2035 forecasts mean?
The latest central outlook in the cited material is the IEA’s 2026 projection: all data-centre electricity consumption rising from 485 TWh in 2025 to 950 TWh in 2030. The same update projects AI-focused data-centre consumption to triple between 2025 and 2030. These forecasts should not be merged into a single AI-only TWh estimate: the 950 TWh total includes non-AI data-centre services, and the tripling figure describes AI-focused consumption without stating its absolute total here.
The IEA’s 2025 analysis gave a similar central estimate of about 945 TWh for total data-centre electricity in 2030. The closeness of those two central estimates does not remove uncertainty about the pathway or about what happens later. In the 2025 analysis, scenario outcomes varied with adoption, efficiency gains, and energy-sector bottlenecks. For 2035, a headwinds case was around 700 TWh, while a lift-off case was above 1,700 TWh.
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Those 2035 endpoints are conditional scenarios, not equally likely predictions or a guaranteed range that will contain the outcome. A headwinds case and a lift-off case represent different assumptions about how the sector and its constraints evolve. The useful lesson is not to select the high or low endpoint as a certainty, but to recognize that adoption, technical efficiency, changing uses, and infrastructure limits can move the result substantially.
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How to evaluate a claim about AI electricity use
When two estimates appear to disagree, check what each one actually measures before comparing the numbers. A per-query result and an annual global total are not competing estimates of the same thing; neither are a forecast and a measured-year figure.
- Check the scope. Does the number cover AI-focused facilities or all data centres? The IEA’s 485 TWh figure for 2025 is the latter.
- Check the unit and time basis. Is the claim about electricity per task, a facility’s power draw in MW, or total electricity over a year in TWh? These describe different quantities.
- Check the workload. A simple text query is not interchangeable with video generation, reasoning, or agentic work; the IEA says the latter types can use hundreds or thousands of times more electricity per query.
- Check whether it is an observation or a projection. The IEA reports 485 TWh for all data centres in 2025 and projects 950 TWh in 2030.
- Check the geographic scale. A worldwide percentage will not show whether a particular grid can accommodate a concentrated new load.
- Check the scenario assumptions. Forecasts can depend on adoption, efficiency, model capabilities, and electricity-system bottlenecks; conditional scenarios should not be quoted as certain outcomes.
These checks prevent common category errors: calling all data-centre electricity “AI electricity,” reading a growth rate as a market share, or assuming that declining energy per task necessarily means falling total demand. They also explain why a confident universal number for electricity per AI query is not available from the cited IEA summary.
What the available figures support—and what they do not
The available evidence supports a clear conclusion about direction and scale, with important boundaries. Data-centre electricity use is growing, AI-focused facilities grew faster than the total in 2025, and the IEA expects both total data-centre use and AI-focused consumption to rise through 2030. But the figures do not establish one comprehensive global AI-only electricity total, a universal energy cost for a prompt, or a uniform grid effect across all regions.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteFor readers trying to understand AI’s electricity footprint, the most useful mental model is therefore not “one query equals one fixed amount.” Demand depends on the number of tasks, their energy intensity, and how efficiently they are run. At the system level, location matters too: a global share can look modest while a cluster of large facilities creates local connection pressures. Keeping those levels distinct makes the headline figures easier to interpret without overstating what they measure.
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