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AI data centers are not running out of electricity everywhere. The problem is that new, power-hungry campuses are often being built faster than local grids, connections and electrical equipment can be upgraded. That can delay when AI servers—and the chips inside them—are installed and switched on. It is separate from shortages of high-bandwidth memory and advanced chip packaging, which constrain some AI-server production directly.
Why are AI data centers running short of power?
The shortage is mainly about where electricity is available and when a new facility can get connected, not whether the world has exhausted its total supply. Data centers tend to cluster in particular areas. A country may have enough electricity in aggregate while a specific utility territory, substation or transmission corridor cannot serve a new campus on its requested schedule.
Grid operators must assess a proposed facility’s effect on the system, approve a connection and, where needed, build or upgrade substations and transmission lines. Those steps take longer than developing a data-center project. The International Energy Agency (IEA) says grid-connection waits can reach five to ten years in many jurisdictions.
The scale of the forecast growth is substantial, though it remains a projection rather than a settled outcome. In its 2026 Key Questions on Energy and AI analysis, the IEA estimates global data-center electricity consumption at 485 terawatt-hours (TWh) in 2025 and projects 950 TWh in 2030—close to 3% of global electricity demand. It projects that electricity use by AI-focused data centers will triple between 2025 and 2030.
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The IEA also reports that data-center electricity demand grew 17% in 2025, compared with 3% growth in global electricity demand; consumption by AI-focused data centers grew 50% that year. Efficiency improvements can reduce electricity needed for a given task, but increased use and newer services—including video generation, reasoning and agentic tasks—can raise total demand.
Why AI makes the power problem harder
AI accelerators are increasingly assembled into dense, networked racks. More computing power in a compact space means greater demands on electrical delivery and cooling. A facility must be designed not just for its expected annual energy use, but also for the amount and variability of power its equipment draws at a given moment.
The IEA estimates that AI-server power density increased elevenfold from 2020 to 2025 and could rise fourfold again by 2027. It compares the peak demand of a future advanced rack with the peak power demand of 65 households. That is a comparison of peak power—not annual energy consumption.
Workloads also affect the shape of demand. GPUs working together can cause fast changes in power use as they perform calculations and exchange data. These swings can occur over very short intervals or minutes, making power management and storage relevant alongside the amount of generation available.
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What is holding up new power connections?
Several constraints can overlap, and fixing one does not necessarily remove the others. New generation may help supply the wider system, for example, but it will not by itself resolve a local transmission bottleneck or an unfinished grid connection.
- Connection queues and grid upgrades: A large load may need a new or upgraded connection, substation or transmission capacity. In an ERCOT example, the IEA says the large-load connection queue grew from about 63 gigawatts (GW) in December 2024 to more than 230 GW by January 2026. Data centers made up around three-quarters of that queue, but queued projects are not all guaranteed to be built.
- Long-lead electrical equipment: The IEA’s 2026 report cites Wood Mackenzie (2025) estimates of average transformer lead times of two to three years and gas-turbine delivery times of about five years. These are reported industry estimates, not guaranteed schedules for every project.
- Generation and transmission take different paths: Building generation can add electricity to a region, but the necessary wires and local connection capacity may still be missing. A campus also needs power delivered at its own location and at the times it needs it.
For a developer, the practical result is that having a project site and server orders does not ensure that the facility can receive grid power on schedule. For a utility customer, the significance depends on local conditions: the effect of a large new load is not identical in every region.
How much electricity might data centers use?
Forecasts should be compared with their geography and publication date in view. The IEA’s 2026 global central case is the newer outlook; the U.S. estimate below is from a separate 2024 report and should not be read as a direct comparison with that global forecast.
| Scope and source | Historical estimate | Projection | How to read it |
|---|---|---|---|
| Global data centers, IEA Key Questions on Energy and AI (2026) | 485 TWh in 2025 | 950 TWh in 2030 | IEA central outlook; close to 3% of global electricity demand in 2030. AI-focused data-center consumption is projected to triple from 2025 to 2030. |
| United States, LBNL report summarized by the U.S. Department of Energy (2024) | 176 TWh in 2023, about 4.4% of U.S. electricity | 325–580 TWh in 2028, or 6.7–12% of U.S. electricity | U.S.-specific estimate and range from the 2024 report, not a global forecast. |
The IEA’s 2025 Energy and AI report offered an earlier base case: global electricity generation to supply data centers rising from 460 TWh in 2024 to more than 1,000 TWh in 2030 and 1,300 TWh in 2035. The newer 2026 outlook should be used for the current central projection; the earlier figures show how the forecast has been framed in a prior report, not an additional current estimate.
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Electricity sources also differ by region. In the IEA’s 2025 report-era base case, natural gas supplied more than 40% of U.S. data-center electricity, renewables 24%, nuclear around 20%, and coal around 15%; in China, coal was close to 70%. These are estimates in that report, not real-time measurements, and the report projects changes in the mix over time.
How does the power shortage affect AI chip supply?
Power limits can delay the deployment of AI computing. If a data center cannot connect or operate on schedule, the related AI-server installation and the demand for chips to equip that facility may be pushed back. The sources reviewed do not quantify a direct reduction in semiconductor-fab output caused by electricity constraints at data centers.
Chip production has separate constraints. The IEA identifies high-end packaging capacity as a constraint on high-end chips in 2025. It says high-bandwidth memory (HBM) became a binding limit on AI-server production from the second half of 2025 into early 2026. Citing IDC (2025), the IEA says the HBM shortage could last at least until late 2027.
The IEA’s analysis, based on cited industry sources, estimates that existing HBM production could support around 25 GW of AI-ready servers per year through 2027. That is an estimate of supported server capacity, not a guaranteed shipment figure or a measure of total chip supply.
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| Constraint | What it restricts | What a delay can mean |
|---|---|---|
| Electricity supply, grid connections and delivery equipment | Where and when a data center can be connected and run | AI-server deployment, and the timing of demand for chips that would go into those servers |
| Advanced packaging and HBM capacity | Production of some high-end chips and AI servers | The ability to manufacture and assemble certain AI systems, even where a data center can get power |
The distinction matters: more available electricity does not manufacture HBM or add packaging capacity, and more chips do not solve a delayed grid connection. Both can limit how quickly AI computing capacity comes online, but at different points in the chain.
What could relieve the constraints?
No single fix works everywhere. Grid capacity, generation, connection processes, equipment supply and facility operations address different parts of the problem. The IEA describes several approaches, each with timing and location trade-offs.
- Build grid and generation capacity: Transmission upgrades, local grid investment and new generation can help, but must be coordinated with the location of new loads. Queue reforms—including stronger tests of whether projects are ready and non-firm connection offers—may help operators manage requests and available capacity.
- Make better use of existing power systems: Flexible operations can shift some electricity use to times when the system has more capacity. Whether that is practical depends on the workload and the local grid; it does not substitute for a needed physical connection or upgrade.
- Add storage: Batteries can help manage rapid changes in demand and support reliability. The IEA estimates that 20–25 GW of battery storage could be installed at data centers globally by 2030, conditional on incentives. This is a conditional estimate, not a forecast of installations that are certain to happen.
- Use onsite generation selectively: Developers may pursue onsite gas power where grid connections are slow, but a project still needs equipment, permits, fuel infrastructure and reliability provisions. The IEA’s 2026 analysis says reliable onsite generation may require 30–70% more capacity than the data-center load. It also cautions that turbine backlogs and other requirements can erase an expected schedule advantage.
In its 2025 base case, the IEA projected renewables would meet nearly half of added data-center electricity demand over the following five years, followed by natural gas and coal, with nuclear becoming more important toward and beyond the end of the decade. That is a scenario and the expected mix differs across regions; it is not a promise that a specific campus will use a particular source.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Will data centers drive up electricity prices?
Not automatically, and not uniformly. The IEA says rapid data-center growth may put upward pressure on prices where supply is tight or investment does not match actual load. New demand can also improve the use of existing power assets where supply is ample. The outcome depends on local generation, grid constraints, investment and how much of the proposed data-center pipeline is actually built.
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For households, a national data-center forecast alone cannot establish whether a local utility bill will rise or by how much. The relevant question is whether new local costs—such as generation or grid investment—are needed and how they are allocated. The IEA frames price pressure as a conditional risk, not a universal result.
Why the outlook is uncertain
The IEA’s central projection is useful for understanding the scale of possible demand, but it is not a certainty. Some proposed facilities may never proceed; financing and expected returns affect construction. At the same time, more efficient AI can reduce electricity use per task while broader uptake and energy-intensive services can increase total use. Those forces pull in different directions, and their balance will affect how much new power and infrastructure is needed.
The IEA describes AI as having “the potential to be an important tool to enhance energy security and sustainability” in Key Questions on Energy and AI. That is an institutional statement about potential, not a guarantee that AI will reduce energy demand or resolve the near-term connection and equipment constraints.
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