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AI uses electricity and water because it runs on data-center infrastructure—but most published data-center totals include far more than AI, and no single global figure cleanly captures AI’s full water footprint. The clearest evidence combines broad, scenario-based estimates of data-center demand with a small number of provider-specific measurements, each tied to a particular workload and accounting boundary.
Where AI’s energy and water use come from
AI training and inference run on data-center infrastructure: servers and accelerators, storage, networking, power equipment, backup systems, and cooling. Those facilities also support non-AI services, so a data center’s total electricity use cannot automatically be attributed to AI.
In its 2025 analysis, the International Energy Agency estimated that servers account for about 60% of electricity demand in modern data centers. Cooling accounts for about 7% at efficient hyperscale facilities but can exceed 30% at less-efficient enterprise facilities; the shares vary by facility type. These figures describe data centers generally, not a universal breakdown for AI workloads. IEA, Energy demand from AI (2025).
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Training develops a model; inference is the computation involved in responding when a deployed model is used. Training a large generative model can require tens of thousands of processors for months, according to the U.S. Government Accountability Office. Once deployed, repeated inference requests also use data-center resources. The available evidence does not establish one training-versus-inference split that applies across AI systems. GAO, Generative AI’s Environmental and Human Effects (2025).
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How much electricity do AI and data centers use?
The strongest headline figures are for all data centers, not AI alone. The estimates below differ in geography, publication date, and scenario, so they should not be read as a like-for-like series or as measurements of AI-only consumption.
| Source and scope | Reported figure | What it means |
|---|---|---|
| IEA, global data centers, 2024 | About 415 TWh, or about 1.5% of global electricity consumption | Estimated electricity use by data centers overall, not an AI-only total. IEA (2025). |
| IEA, global data centers, 2030 base case published in 2025 | About 945 TWh, just under 3% of global electricity consumption | A projection for all data centers. The IEA said accelerated servers, driven mainly by AI adoption, were expected to grow faster than conventional servers. IEA (2025). |
| IEA, global data centers, updated central projection for 2030 | About 950 TWh, roughly 3% of global electricity demand | The IEA’s 2026 update reports that global data-center electricity demand grew 17% in 2025 and AI-focused data-center demand grew 50% that year. It projects AI-focused data-center use to triple from 2025 to 2030; that is not a complete AI-only global electricity total. IEA, Key Questions on Energy and AI (2026). |
| LBNL, U.S. data centers, 2030 | 649 TWh in the reference case; 521–843 TWh across the compounded-uncertainty range | The report gives these all-data-center estimates as 11.8% of U.S. electricity in the reference case and 9.5–15.3% across scenarios. They do not isolate AI. Lawrence Berkeley National Laboratory (published June 2026). |
The IEA’s 2026 executive summary captures why a projection is not a fixed outcome: “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.” International Energy Agency (2026).
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How much electricity does one AI query use?
There is no defensible universal electricity figure for an AI query: the result depends on the model and workload, the hardware and its utilization, and whether the estimate includes data-center overhead. A figure from one provider or prompt type should not be applied to other assistants or tasks.
One provider-specific example is a 2025 paper by Google-affiliated researchers measuring production workloads: the median Gemini Apps text prompt used 0.24 watt-hours of electricity and 0.26 milliliters of water under the paper’s full-stack method. This is a measurement for that provider’s specified workload and method, not a benchmark for all AI prompts. Cooper Elsworth et al., Measuring the environmental impact of delivering AI at Google Scale (2025).
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Why AI water-use estimates vary
Water accounting has more than one boundary. Site water use covers water consumed at the data center, including for cooling. Source water includes water used to produce the electricity that the facility consumes. The latter is complex to calculate and depends strongly on the electricity source, as the GAO explains. A cooling-water figure that reports only onsite use is not a complete account of water associated with electricity generation. GAO (2025).
Water intensity also varies with local climate, cooling design, facility operations, and workload. The sources cited here do not establish a single, comparable, comprehensive global water total for AI alone. A water estimate is most useful when it identifies whether it covers onsite use or source water, which workload it represents, and where and when it was measured.
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Energy use, water use, and emissions are different measures
Electricity consumption is not the same as greenhouse-gas emissions. The generation mix and location of the electricity shape emissions associated with its use; equipment and construction can also contribute embodied emissions. Water use is a separate measure with its own boundaries. A claim about one of these impacts should not be treated as a proxy for the others. GAO (2025); Elsworth et al. (2025).
How to assess an AI footprint claim
When comparing two figures, check whether they describe the same kind of workload and use the same accounting boundaries. Useful details include:
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- Workload: which model or service, and whether the figure concerns training or inference.
- Measurement scope: whether the number covers AI alone or all data-center activity, and whether facility overhead is included.
- Technical conditions: hardware, utilization, and the measurement method.
- Location and electricity: where the workload ran and, for emissions or source-water accounting, what electricity supply was considered.
- Water boundary: onsite water use, source water, or both.
- Time and uncertainty: the measurement year or projection year, geography, and scenario assumptions.
These distinctions matter especially when comparing a measured prompt with a national or global projection: one describes a particular workload under a defined method, while the other estimates aggregate demand across many facilities and uses.
What individual users can—and cannot—measure
A person using an AI service cannot directly meter the remote data center’s electricity or water use for a prompt. A household energy monitor measures local devices, not the service’s remote computing and facility overhead. For a more informative view, look for providers to report workload-specific measurements, their system boundaries, and how they account for electricity and water. For any public estimate, apply the same checks above before comparing it with another figure.
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