AI’s power challenge is not just whether enough electricity can be generated. Data centres can grow faster than grids can connect them, and the resulting pressure is concentrated in particular places. Meeting demand will require better forecasts, coordinated investment, fairer cost allocation and more flexible data-centre operations—while also using digital tools to get more from existing grid infrastructure.
How much electricity do AI data centres use?
The International Energy Agency (IEA) says global data-centre electricity demand grew 17% in 2025, while electricity consumption at AI-focused data centres rose 50% that year. In the IEA’s 2026 central projection, worldwide data-centre use rises from 485 TWh in 2025 to about 950 TWh in 2030—around 3% of global electricity demand by then.
That global share can sound modest, but it does not describe where the load lands. A large new data centre or cluster can create an acute connection and investment challenge for its local grid even if data centres remain a small fraction of electricity use worldwide. National totals and local capacity are different measures, and planning has to account for both.
Why the forecast is uncertain
Energy use per task can fall as hardware, models and data-centre operations become more efficient. At the same time, the number and scale of AI tasks can grow, and applications such as video generation, complex reasoning and agentic workflows may consume much more energy per query than simple text generation. Efficiency improvements therefore do not, by themselves, establish that total demand will fall.
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The IEA’s projections are scenarios, not a guarantee of an inevitable energy crisis. It calls for frequent updates to forecasts, stronger disclosure and cooperation between data-centre developers and electricity-system operators so that investment decisions respond to observed demand rather than opaque or stale projections.
Why is supplying that power difficult?
Electricity generation is only one part of the problem. Data centres need connections, substations and other grid equipment, while projects also depend on permitting, construction capacity and supply chains. The IEA describes a timing mismatch: data-centre projects can move quickly, but electricity infrastructure typically requires longer planning and investment lead times.
Connections and project pipelines
When many projects seek capacity in the same area, a connection queue can become a bottleneck. The IEA recommends more proactive management of data-centre project pipelines and electricity-sector investment, including better management of connection queues and permitting. System operators need usable information about which proposed projects are credible, when they might start operating and how much power they expect to draw.
Equipment and concentrated loads
The scale and density of computing equipment matter as well as the number of facilities. The IEA reports that AI-server power density grew 11-fold from 2020 to 2025 and projects a further fourfold rise by 2027. Those figures describe server power density, not total electricity demand, but they point to increasing demands on the power systems serving AI equipment.
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Investment is also moving quickly: the IEA reports that five large technology companies spent more than USD 400 billion in capital expenditure in 2025 and estimates a further 75% increase in 2026. That is a measure of those companies’ capital expenditure, not a direct estimate of grid spending or of data-centre electricity costs.
What should the new playbook do?
1. Make demand forecasts more useful
Developers should share timely, decision-relevant information with system operators, and planners should update forecasts as projects, efficiency and AI use change. A forecast that distinguishes likely projects from speculative proposals can help avoid both underbuilding and investment based on demand that never arrives. The IEA’s call for stronger disclosure reflects the uncertainty around uptake, efficiency gains and the changing energy intensity of AI applications.
2. Coordinate projects with power investment
Data-centre project pipelines, connection decisions and electricity-sector investment need to be planned together. Queue management and permitting reforms can help identify which projects are ready to proceed and where network upgrades will be needed. Coordination cannot erase construction lead times, but it can make the sequence of projects and investment clearer.
3. Make cost responsibility explicit
Tariffs and other policy tools can determine how the cost of grid upgrades and new generation is shared. The right outcome depends on local system conditions: a new load may require investment where supply is tight, while predictable demand may improve the use of capacity that is already available. It is not accurate to assume that data centres always raise other customers’ bills—or that they automatically lower prices.
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Cost rules should make clear which upgrades are driven by a particular project, which investments benefit a wider group of users and how those costs are recovered. The IEA identifies fair allocation as a planning issue; the evidence here does not establish a single tariff design or comparable project-level costs.
4. Pay for flexibility, not just maximum demand
Some data-centre demand may be shifted, curtailed or managed around grid conditions, depending on the workload and the facility’s technical requirements. The IEA points to non-firm connections, demand response and grid-interactive onsite assets such as batteries and gas generators as potential flexibility tools. Their value depends on system design, incentives and demonstrated performance; flexibility should not be presumed simply because a facility has backup equipment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which power options can help data centres get reliable electricity?
No single supply option fits every location or operating requirement. The IEA says grid supply remains preferred by most data centres, while slow connections have led some U.S. developers to pursue onsite gas. Batteries can help manage fast load swings, and a flexible connection or demand response can sometimes support earlier access to the grid. Each option brings different timing, reliability and system trade-offs.
| Option | Potential role | Constraints and qualifications |
|---|---|---|
| Grid connection and expansion | Preferred supply route for most data centres, according to the IEA; network expansion can serve loads as part of the wider electricity system. | Connection queues, permitting, equipment supply and longer infrastructure lead times can delay capacity. Project-level costs and timelines are not stated in the IEA material summarized here. |
| Onsite natural-gas generation | Can provide onsite power; the IEA identifies it as an option pursued by some U.S. developers facing slow grid connections. | The IEA estimates a possible 15–27 GW of onsite natural-gas power serving data centres by 2030, an uncertain range mostly in the United States. For reliable service to critical, variable loads, onsite gas may require 30–70% more generation capacity than demand; turbine constraints mean it is not necessarily a faster route at scale. |
| Battery storage | Can help manage fast load swings and may provide a grid-interactive resource when connected and operated to do so. | The IEA estimates that around 20–25 GW of battery storage could be installed at data centres globally by 2030 if incentives and deployment align. This is conditional potential, not a guaranteed buildout or a claim that batteries can supply a facility indefinitely. |
| Flexible connections and demand response | Can allow some demand to respond to grid conditions and may help make a data centre a more flexible grid participant. | Availability depends on workload flexibility, technical performance, system rules and incentives; no general capacity or cost value is stated. |
The figures in the table are not comparable project cost or reliability measurements. They describe different roles and conditions, and the IEA material does not identify a universal best mix. Decisions also involve emissions and sustainability implications, fuel and equipment availability, operating costs and who bears the investment cost.
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Can AI help the grid as well as increase demand?
Yes, potentially—but the two effects should not be conflated. The data centres behind AI are a fast-growing source of electricity demand. Separately, AI and other digital tools can support grid optimisation, forecasting, situational awareness, resilience and risk management. In its September 2026 grid report, the IEA presents digital tools as a way to make better use of existing networks alongside network expansion, storage and demand-side flexibility.
Those are potential operational benefits, not proof of net energy savings. The IEA also reports that proven AI applications could reduce energy costs for firms in energy-intensive industries by 3–10 percentage points. That reported potential applies to firms using proven applications; it is not a guaranteed result for every business, and it does not establish that AI’s wider electricity demand will be offset.
What does reliable power for abundant AI ultimately depend on?
Reliable supply depends on coordinating data-centre growth with the infrastructure that serves it. Better project disclosure and updated forecasts help planners see demand; coordinated queues and investment address the timing gap; clear tariffs determine who pays; and flexibility can make some loads and onsite assets more useful to the grid. Digital tools may improve how networks are operated, but they complement rather than replace physical infrastructure.
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