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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →AI tasks are getting more energy-efficient, but total electricity use by data centres is still rising. The International Energy Agency (IEA) says data-centre electricity consumption grew 17% in 2025, while the AI-focused portion grew 50%. Those are facility-level figures—not a measurement of electricity used by an individual AI agent. The key distinction is that efficiency can reduce energy per task even as more people use AI and more demanding workloads increase the total.
Why an AI agent can use more energy than a simple query
A simple text request may involve one model response. An agentic workflow can break a goal into steps, make repeated model calls, use tools, and check or revise its work. Reasoning and video-generation tasks can also require substantially more computation than straightforward text generation.
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The IEA reports that energy use per AI task has fallen by at least an order of magnitude annually in recent years. At the same time, it says agentic, reasoning, and video-generation workloads can use hundreds or thousands of times more energy per query than simple text generation. These are broad comparisons between task categories, not a multiplier that applies to every agent or workflow. The number of steps, model, hardware, and data-centre conditions all matter. IEA, “Executive summary – Key Questions on Energy and AI”
Why efficiency does not settle the total
Lower energy use per task does not guarantee lower electricity use overall. If usage expands, or if people shift from simple prompts to more elaborate workflows, total demand can grow despite efficiency gains. The IEA’s figures cover data centres and AI-focused facilities; they do not isolate the global electricity consumption of AI agents.
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How much electricity data centres use
The IEA’s 2026 update estimates that data centres used 485 terawatt-hours (TWh) of electricity in 2025. It projects that total to reach about 950 TWh in 2030—roughly double and about 3% of global electricity demand in that year. The projection is for all data centres, not for AI agents alone, and it is a forecast rather than a measured future total. IEA, 16 April 2026
Individual-query estimates should not be treated as universal agent figures. A 2025 preprint estimated 0.43 watt-hours (Wh) for one short GPT-4o query and more than 33 Wh for some long-prompt cases under its own framework. Those estimates depend on the study’s assumptions and do not establish what a typical agent workflow consumes. Jegham et al., “How Hungry is AI?”, arXiv, 14 May 2025
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What “thirsty” means for water
Data centres can require water for cooling, but their water footprint also includes indirect use associated with electricity supply and chip production. Water consumption is the portion not returned to its original source; water withdrawal is the amount taken from sources, including water that may later be returned. The two measures are not interchangeable.
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The IEA estimates current data-centre water consumption at around 560 billion litres a year and projects around 1,200 billion litres a year in its 2030 base case. These are estimates for data centres, not water attributed to an individual AI query. The amount varies with cooling technology, local climate, and the source of electricity. IEA, “Energy demand from AI,” 10 April 2025
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For scale, the IEA estimates that a 100-megawatt US hyperscale data centre consumes around 2 million litres of water per day in total, with more than 60% of that use indirect. This is a modelled facility estimate, not a per-prompt measure. IEA, Energy and AI report (2025)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the forecast is uncertain—and what can constrain growth
The 2030 figure is an IEA projection, not a certainty. The IEA identifies efficiency, adoption, changing model capabilities, financial conditions, and physical bottlenecks as factors that could affect demand. It also points to grid connections, equipment supply, chips, and planning as constraints on data-centre expansion.
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- 【Smart Charging for Devices】Automatically cuts power once your device reaches the low-battery limit you set, preventing overcharging.
- 【Auto-Shutoff】Prevents electrical overload by automatically shutting off devices that use too much power.
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AI training and use can create large, rapid swings in power demand. Storage is one response under development, alongside efforts to secure additional electricity supply and make data-centre operations more flexible. Storage can help manage when power is needed; it does not make the underlying energy demand disappear.
IEA Executive Director Fatih Birol has described the relationship as two-sided: “while AI is still an energy taker, it is also becoming an energy maker” through solutions such as flexible data centres and long-duration energy storage. IEA, 16 April 2026
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