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AI uses electricity mainly because training and running models require computation in data centres—and those facilities also need power for storage, networking, cooling, and electrical systems. More efficient hardware and software can reduce energy per task, but total electricity use can still rise if AI is used more often or for more demanding tasks.
Where AI’s electricity goes
AI training and inference—the use of a trained model to produce an output—run on servers, often with specialized accelerators such as GPUs. Those servers sit inside data centres, which also draw power for storage, networking, cooling and environmental controls, uninterruptible power supplies, and other supporting equipment. The balance varies by facility.
The International Energy Agency (IEA) estimates that servers use around 60% of electricity in modern data centres on average. Storage accounts for around 5%, while networking can use up to 5%. Cooling’s share differs substantially by facility type: about 7% in efficient hyperscale data centres, compared with more than 30% in less-efficient enterprise facilities. These are contextual estimates, not fixed shares for every data centre. IEA, Energy demand from AI (2025)
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallAs the IEA put it in its 2025 report, “There is no AI without energy – specifically electricity for data centres.” That statement describes the infrastructure dependency; it does not mean all data-centre electricity is used by AI. IEA, Energy and AI (2025)
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How much electricity do data centres use?
The IEA estimates that data centres worldwide used about 415 terawatt-hours (TWh) of electricity in 2024, roughly 1.5% of global electricity use. This is a total for data centres, not an estimate of AI-only consumption. The distinction matters because data centres support many digital services, and the published total does not assign all their electricity to AI.
For 2030, the IEA’s 2025 Base Case projects data-centre electricity use of about 945 TWh. That is a modelled scenario, not an observed figure or a guaranteed outcome. The IEA also describes a High Efficiency case in which stronger progress in hardware, software, and infrastructure efficiency allows more services to be delivered with less electricity than in the Base Case. IEA, Energy demand from AI (2025)
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Why one AI task can use far more energy than another
“An AI query” is not a single, consistent unit of energy use. The workload, model, output, and system boundary all matter. A simple text response is not directly comparable with generating video, performing extended reasoning, or completing a multi-step task through an agentic system.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The IEA’s 2026 update says video generation, reasoning, and agentic tasks can use hundreds or thousands of times more energy per query than simple text generation. This describes differences among task types; it is not a universal measurement for every model or product. The update does not establish one representative energy figure for a chatbot query or a blanket comparison with a conventional web search. IEA, Key Questions on Energy and AI, executive summary (2026)
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Why efficiency does not guarantee lower total electricity use
Efficiency can mean less electricity to complete a defined task, or more useful output from the same electricity. But a reduction in energy per task does not necessarily reduce overall consumption. If lower costs and new capabilities lead to more AI use—or if users shift toward more demanding workloads—total demand can increase even as individual tasks become more efficient.
The IEA’s 2026 update reports at least an order-of-magnitude annual decline in energy use per AI task in recent years, while also reporting that AI-focused data-centre electricity consumption grew 50% in 2025. The first figure is a statement about a recent trend, not a guaranteed future rate; the second concerns AI-focused data centres, not all data centres. Taken together, they illustrate why per-task efficiency and total electricity demand should be treated as different measures. IEA, Key Questions on Energy and AI, executive summary (2026)
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Efficiency improvements can come from better hardware, more efficient models and software, or improvements to data-centre infrastructure. Their effect depends on what is being measured: accelerator or server energy alone is not the same boundary as total facility electricity, and a fair comparison also needs to hold the task and useful output reasonably consistent.
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How to interpret AI energy claims
- Check what is being counted. A figure for a server or accelerator is narrower than data-centre electricity, which includes supporting equipment.
- Check the workload. Energy per task varies substantially between simple text generation and tasks such as video generation, reasoning, and agentic work.
- Separate measured use from projections. The IEA’s 415 TWh figure is its estimate of total data-centre electricity use in 2024; 945 TWh is the IEA’s projected 2030 Base Case.
- Look for the scenario assumptions. The IEA’s High Efficiency case is a conditional outlook based on stronger efficiency progress, not proof of a specific saving from a particular model or product.
- Distinguish per-task efficiency from total demand. Lower energy per task can coexist with rising electricity use as adoption and workload intensity grow.
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