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AI is getting more efficient per task, but data-centre electricity use is still rising. The International Energy Agency (IEA) estimates that global data-centre electricity consumption grew 17% in 2025, while the AI-focused portion grew 50%. Those are facility-wide figures, not a meter reading for an individual AI agent. They show why lower energy use per query does not necessarily mean lower total demand.

Why an AI agent can use more electricity than a simple query

A simple text request may require one model response. An agentic task can involve multiple steps: interpreting a goal, planning, using tools, checking results, and asking a model for further decisions. The amount of work therefore depends on the task and its implementation, not just on the label “AI agent.”

The IEA reports that energy use per AI task has fallen by at least an order of magnitude annually in recent years. It also says reasoning, agentic, 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 fixed multiplier for every agent or workflow. IEA executive summary, 2026

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For a particular workflow, energy use can vary with the model, prompt length, number of model calls or agent steps, hardware, utilization, and data-centre overhead. A 2025 preprint estimated 0.43 watt-hours for one short GPT-4o query and more than 33 watt-hours for some long-prompt cases within its own framework. Those estimates are not universal measurements of agent queries. Jegham et al., arXiv, 14 May 2025

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What the latest data-centre figures show

The IEA’s 2026 update estimates that data centres used 485 terawatt-hours (TWh) of electricity worldwide in 2025. It projects total consumption to reach about 950 TWh in 2030—roughly double the 2025 level and around 3% of projected global electricity demand that year. This is a forecast for data centres overall, not a projection for AI agents alone; the IEA’s outlook is subject to uncertainty. IEA, 16 April 2026

The difference between per-task efficiency and total consumption is central. If each task takes less electricity but the number of tasks grows, or more tasks use energy-intensive reasoning, agentic, or video workloads, overall demand can still increase. The IEA’s figures describe data-centre growth; they do not isolate the amount attributable to agents.

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What “thirsty” means for water

Data-centre water use includes water used directly for cooling and indirect water use associated with generating electricity and producing chips. Water withdrawal is the amount taken from a source; water consumption is the portion not returned to its original source. The two measures should not be treated as interchangeable.

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In its 2025 analysis, the IEA estimated current data-centre water consumption at around 560 billion litres per year and projected around 1,200 billion litres per year in 2030 in its base case. Those estimates cover data centres, not individual AI prompts, and water intensity varies with cooling technology, local climate, and electricity supply. IEA, Energy and AI, 10 April 2025

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As an illustration of facility scale, the IEA modelled a 100-megawatt US hyperscale data centre as consuming 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 universal figure or a per-query water footprint. IEA, Energy and AI report, 2025

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Why growing demand is an infrastructure issue

More electricity demand does not translate automatically into more electricity being available where and when a data centre needs it. The IEA identifies grid connections, equipment supply, chips, and planning as constraints. It also highlights large, rapid power swings associated with AI training and use, which can complicate operation of the power system.

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Storage is one response being developed to help manage changing demand, alongside additional generation and more flexible data-centre operations. It can help balance timing, but it does not eliminate the energy required to run AI workloads. The IEA also points to efficiency, adoption, changing model capabilities, and financial conditions as factors that affect how demand develops. IEA, 16 April 2026

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The practical takeaway is not that every AI agent is wasteful or that efficiency gains do not matter. It is that the energy impact depends on both the electricity used for each workload and how widely—and how intensively—those workloads are deployed.

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