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Why AI depends on electricity
Training develops or updates a model; inference uses a trained model to produce an output. Both rely on computing hardware in data centres, including servers equipped with specialized accelerators such as GPUs. The International Energy Agency (IEA) puts it plainly in its 2025 report Energy and AI: “There is no AI without energy – specifically electricity for data centres.”
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A data centre is more than its servers. Its electricity also supports storage, networking, cooling and environmental controls, uninterruptible power supplies, and other facility systems. The mix varies by site and equipment.
Where data-centre electricity goes
IEA figures describe averages and examples across facility types, not a fixed breakdown that applies to every data centre.
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- Servers: around 60% of electricity use in modern data centres on average.
- Storage: around 5%.
- Networking: up to 5%.
- Cooling: around 7% in efficient hyperscale data centres, compared with over 30% in less-efficient enterprise facilities.
These shares illustrate why the facility matters: the energy cost of delivering computation includes both the equipment doing the work and the infrastructure that keeps it operating.
Data-centre electricity is not the same as AI electricity
The IEA estimates that data centres used about 415 TWh of electricity in 2024, roughly 1.5% of global electricity. That is a total for data centres, not an estimate of electricity used by AI alone. Data centres support other digital services too, and the cited figure does not isolate AI’s share.
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In its 2025 Base Case, the IEA projects data-centre electricity use of about 945 TWh in 2030. This is a modelled scenario, not an observed figure or a certainty. The IEA also presents 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. Neither scenario should be read as a guaranteed outcome.
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Why some AI tasks use much more energy than others
“An AI query” is not a single, consistent unit of work. Energy use depends on the model, the task, the amount of computation required, and the system boundary being counted. A simple text request is not equivalent to generating video or handling a multi-step task.
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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. That comparison describes substantial differences between workload types; it does not establish one universal energy figure for a chatbot request or a ranking that applies to every model.
Comparisons are most meaningful when they hold the useful output and task quality reasonably constant, specify what counts as the task, and clarify whether they measure accelerator or server energy alone or total data-centre electricity including cooling and supporting systems. A number without those boundaries can give a misleading impression of what a task costs.
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- INSTALLS IN CIRCUIT PANEL of most homes with clamp-on sensors. Supports Single phase, Single-split phase, and 2-wire systems. 3-wire systems; 3-phase, 4-wire Wye systems with earthed (TN or TT) neutral (no-Delta) are supported with an additional 200A sensor (sold separately).
- 24/7 ENERGY MANAGEMENT AND MONITORING: Automate, manage and control your home's real power anywhere, anytime to prevent costly repairs, conserve energy, and save costs. Monitor solar / net metering. PROTECTED BY A 1-YEAR WARRANTY.
- LOWER YOUR ELECTRIC BILL: Configure settings in the Emporia Energy App to automate energy management for time of use, peak demand, excess solar, and rewards programs. You can even see live reporting and invaluable savings opportunities instantly. Gauge real-time spending and get actionable notifications and automated energy management to help you reduce costs.
- REAL-TIME ENERGY DATA: REQUIRES 2.4 GHz WIFI WITH AN INTERNET CONNECTION to monitor energy use with iPhone / Android / Web app. Vue sensors collect energy data and are accurate from ±2%. The Vue is UL and CE Listed for your safety. 1 second data is only available in the app (when actively open) and retained 3 hours. Minute and hour data are retained in the cloud. 1 minute data is retained 7 days, 1 hour data is retained indefinitely. Export cloud data whenever you want in the app.
How efficiency can improve while total demand rises
Efficiency measures how much energy is needed for a defined amount of computation or useful output. Total electricity use also depends on how many tasks are run and how demanding they are. If each task takes less energy but people and organizations use AI more often, or shift toward more intensive tasks, overall demand can still rise.
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Efficiency can come from better hardware, software and models, or facility improvements. The IEA’s High Efficiency case illustrates how stronger gains across these areas could change projected demand; it is a scenario, not evidence that any particular product or optimization will deliver a specified saving.
What to keep in mind when reading energy claims
- Check the scope: a data-centre total is not an AI-only total.
- Check the date and status: distinguish measured consumption from a forecast, and identify the scenario behind a projection.
- Check the workload: simple text, video generation, reasoning and agentic tasks can have very different energy profiles.
- Check the system boundary: server-only figures exclude some facility overhead; data-centre electricity includes supporting infrastructure.
- Check what “efficient” means: energy per task and total electricity demand answer different questions.
The IEA’s 2025 and 2026 publications provide global data-centre estimates and scenario analysis, but they do not establish one representative energy cost for every AI query. Treating workload, infrastructure and adoption as separate parts of the question gives a clearer picture than any single headline number.
Sources: IEA, Energy demand from AI – Energy and AI (2025); IEA, Executive summary – Key Questions on Energy and AI (2026); IEA, Executive summary – Energy and AI (2025).
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