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Why is demand for data-centre infrastructure growing?
AI is adding to demand for computing, but it is only one part of data-centre electricity use. Facilities also support conventional computing and other digital services, so a data-centre total must not be read as an AI-only figure.
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The International Energy Agency (IEA) estimated that data centres used about 415 terawatt-hours (TWh) of electricity in 2024, roughly 1.5% of global electricity consumption. That estimate covers data centres generally, not AI alone (IEA, Energy and AI, 2025).
The IEA’s 2026 update estimates that total data-centre electricity demand grew 17% in 2025 to about 485 TWh, while electricity use at AI-focused data centres grew 50%. These are global estimates; they do not describe the growth rate or workload mix at every facility or in every region. The update projects AI-focused data-centre electricity use to triple between 2025 and 2030.
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How large could the electricity requirement become?
Forecasts are scenario-based, not promises about what will be built or consumed. The 2025 and 2026 IEA outlooks put 2030 demand near 950 TWh, but use different report years and estimates. The 2026 projection is an update, not a second measurement of 2030 demand.
| IEA estimate or scenario | Data-centre electricity demand | Scope and qualification |
|---|---|---|
| 2024 estimate, published 2025 | About 415 TWh; around 1.5% of global electricity consumption | All data centres; not AI alone |
| 2025 estimate, published 2026 | About 485 TWh | All data centres; the IEA estimates demand grew 17% from 2024 |
| 2030 Base Case, published 2025 | About 945 TWh | IEA scenario projection |
| 2030 outlook, published 2026 | About 950 TWh | IEA updated global projection |
| 2035 Headwinds case, published 2025 | About 700 TWh | Lower-demand IEA scenario |
| 2035 High Efficiency case, published 2025 | About 970 TWh | IEA scenario assuming stronger efficiency progress |
| 2035 Lift-Off case, published 2025 | More than 1,700 TWh | Higher-demand IEA scenario |
The spread between the IEA’s 2035 cases reflects uncertainty in AI uptake, efficiency improvements and energy-system bottlenecks. Its 2026 update says constrained grid connections and equipment supply make more aggressive near-term scenarios less likely, while energy-intensive AI uses could raise demand later.
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Where can efficiency reduce the load?
Efficiency needs to be treated as a portfolio across software, hardware and facility infrastructure. The IEA’s 2025 Base Case breaks down the net increase in data-centre electricity demand from 2024 to 2030—not the share of total electricity each component consumes—as follows:
| Source of net electricity growth | Share of net increase, 2024–2030 |
|---|---|
| Accelerated servers | Almost half |
| Conventional servers | About one fifth |
| Cooling and other infrastructure | About one fifth |
| Other IT equipment | About one tenth |
Those shares point to where planning attention matters: the type of computing being deployed, the efficiency of its software and servers, and the energy required to support it. A facility-level improvement should be assessed against the workload it serves and the electricity boundary being measured. Saving electricity per unit of computing is not the same as reducing total electricity if the amount of computing grows.
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- Software and workload: Assess which workloads require accelerated computing and where conventional compute is appropriate. Software efficiency is part of the IEA’s High Efficiency scenario assumptions.
- Servers and other IT: Compare hardware using performance and energy measures relevant to the workload, rather than assuming that a newer or more powerful server automatically improves overall efficiency.
- Cooling and facility infrastructure: Include cooling and other supporting systems in the energy boundary. The IEA attributes around one fifth of the Base Case’s net demand growth to cooling and other infrastructure.
- Measure comparable work: IEA 4E’s Total Energy Model 4.0 uses computational-capacity and efficiency metrics, including standardised SERT measurements, and segments data-centre types and workloads. Comparisons are more useful when workload and measurement boundaries are explicit.
In the IEA’s 2025 High Efficiency case, stronger progress in software, hardware and infrastructure produces more than 15% energy savings relative to the comparable outlook. This is a modelled scenario outcome, not a guaranteed saving for an individual project.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why do local power constraints matter more than the global share suggests?
A global percentage can make demand look modest while obscuring where it is concentrated. The IEA’s 2024 estimate put the United States at 45% of data-centre electricity consumption, China at 25%, and Europe at 15%. The IEA also notes that US capacity is concentrated in five regional clusters. Those regional totals and clusters matter because a grid must serve loads at specific places, not at a global average.
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The IEA’s 2026 update identifies slow grid connections, constrained supplies of transformers and power electronics, and rapid swings in AI loads as practical challenges. Consequently, site feasibility depends on local interconnection timing, equipment availability, regional electricity supply and the shape of the facility’s demand—not just on national or global electricity totals.
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Use a site-and-load decision check
- Workload mix: Establish the expected balance of AI/accelerated and conventional computing; these affect demand differently.
- Efficiency boundary: Set out whether the comparison covers software, servers, cooling and other infrastructure, and distinguish energy per unit of work from total facility electricity.
- Reliability and flexibility: Account for rapid AI load swings. Assess whether storage or flexible operations could support reliable supply and provide grid value in the specific context.
- Grid and equipment: Check interconnection constraints, connection timing and availability of transformers and power electronics for the proposed location.
- Measurement: Use workload-appropriate, comparable measures, and state what is inside the energy boundary before comparing projects.
No single efficiency measure resolves every constraint: improving a server metric does not secure a grid connection, and a feasible connection does not establish that a facility uses electricity efficiently. Planning needs to test both sides together.
What do data-centre emissions figures include?
The IEA estimated around 180 million tonnes (Mt) of indirect CO2 emissions in 2024 from electricity used by all data centres, excluding backup generation (IEA, Energy and AI, 2025). This is not an AI-only estimate. It represents emissions associated with electricity consumption, so it should not be treated as a facility-specific figure or as a count of every source of data-centre emissions.
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