Yes, broadly—but the headline needs important qualifications. The International Energy Agency (IEA) forecasts global data-center electricity consumption rising from about 485 terawatt-hours (TWh) in 2025 to 950 TWh in 2030, an increase of almost 96%. That is a forecast for annual global energy consumption, not a guarantee that every country’s data-center demand, local peak load, or installed capacity will double.
AI is the main growth driver, but conventional cloud computing, storage, streaming, search, enterprise software and other digital services also contribute. The practical investment and infrastructure question is less whether global consumption can double than whether utilities can deliver reliable power where new data centers are being built.
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What “double” actually means
Electricity statistics use different measurements, and confusing them can make a reasonable forecast sound much more certain than it is.
- Annual electricity consumption: measured in TWh. This is the basis for the IEA’s forecast of 485 TWh in 2025 and 950 TWh in 2030.
- Power demand or load: measured in gigawatts (GW), describing the rate at which facilities draw electricity. Gartner estimates worldwide data-center power demand at approximately 104 GW in 2025 and 132 GW in 2026.
- Installed capacity: the amount of data-center capacity built or planned. Planned megawatts may not be energized, occupied or operating at maximum utilization.
- Peak demand: the highest draw at a particular time. This determines many local grid requirements and does not equal annual energy consumption.
In simple terms, 950 TWh is not the same as a continuous 950 GW load. TWh measures energy used over a year; GW measures power at a point in time.
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The forecasts vary substantially
The IEA’s central forecast is not the only estimate. Gartner’s June 2026 forecast puts global data-center electricity consumption at 565 TWh in 2026, up from 447 TWh in 2025, and above 1,200 TWh by 2030. Gartner’s estimate is materially higher than the IEA’s, partly because the organizations use different definitions and assumptions.
| Source | Geography | Metric | Forecast |
|---|---|---|---|
| IEA | Global | Annual electricity | 485 TWh in 2025 to 950 TWh in 2030 |
| Gartner | Global | Annual electricity | 447 TWh in 2025 to 565 TWh in 2026; above 1,200 TWh in 2030 |
| LBNL | United States | Share of national electricity | Up to 11.8% by 2030 in its scenario analysis |
| EPRI | United States | Demand scenarios | Multiple cases through 2030; announced megawatts are a pipeline indicator |
| BloombergNEF | United States | Installed capacity | 118 GW in 2030 |
These numbers should not be combined as if they measured the same thing. A TWh energy forecast cannot be directly compared with a GW capacity forecast.
Why AI is accelerating electricity demand
AI systems require large clusters of specialized processors. Training can create intense bursts of computation, while inference—the process of responding to users and applications—can produce more persistent demand as AI becomes embedded in search, office software, customer service, devices, robotics and industrial systems.
McKinsey projects AI-training data-center demand rising from 31 GW in 2025 to 62 GW in 2030, while inference demand rises from 31 GW to 93 GW. The implication is important: inference could eventually be the larger AI power category even if training remains computationally intensive.
AI is not the whole data-center market. Cloud storage, databases, streaming, cybersecurity, enterprise applications and other conventional workloads continue to consume electricity. EPRI-cited estimates put AI workloads at roughly 15% to 25% of current data-center electricity use, with that share increasing.
Efficiency also has an uncertain effect. More efficient chips and better cooling reduce electricity per computation, but lower costs can encourage companies and consumers to run more computations. Efficiency may slow total growth without reversing it.
The United States is a concentrated case
U.S. estimates differ because they use different scenarios and measures. LBNL’s 2025 update estimates that data centers could account for 11.8% of total U.S. electricity consumption by 2030 under its modeled cases. The U.S. Department of Energy also cites an EPRI estimate that data centers could reach as much as 9% of U.S. electricity generation by 2030, compared with approximately 4% in 2023.
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The IEA estimates that U.S. data centers could account for nearly half of U.S. electricity-demand growth through 2030. That means nearly half of the additional demand—not half of all electricity consumption.
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BloombergNEF’s July 2026 outlook raises projected U.S. installed data-center capacity in 2030 to 118 GW. Again, this is capacity, not a direct forecast of annual TWh consumption or average load.
The grid bottleneck is local
A global doubling will not be spread evenly across countries or utility territories. Data centers cluster where developers can obtain land, fiber connectivity, cooling, tax advantages and—most importantly—power.
That concentration can overwhelm a transmission corridor, substation or regional generation market even when data centers remain a modest share of global electricity use. Northern Virginia, Texas and other fast-growing locations illustrate why local interconnection capacity can matter more than a global percentage.
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EPRI warns that announced nominal megawatts should be treated as a pipeline indicator, not a near-term peak-load forecast. Projects may be delayed, downsized, canceled or energized in stages. Utilization, cooling, backup systems, onsite generation and flexible workloads also affect the actual grid load.
How developers may obtain the needed power
Utility connections
Grid power is usually the foundation, but connection timing depends on available generation, transmission capacity, transformer supply and regulatory approvals. Developers and utilities increasingly need credible construction schedules and financial commitments rather than informal campus announcements.
Onsite natural-gas generation
Onsite gas generation can reduce dependence on a delayed grid connection, but it introduces fuel, emissions, permitting, noise and maintenance risks. The IEA estimates that reliable onsite gas supply for critical and variable data-center loads may require 30% to 70% more generation infrastructure than nominal demand because backup and flexibility requirements create overcapacity.
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Renewables and storage
Wind and solar can provide large quantities of annual energy, often through power-purchase agreements. But matching annual consumption with renewable generation is different from delivering electricity every hour. Batteries can provide backup, peak shaving and grid services, but storage duration, recharge availability and market rules determine how much firm power they can provide.
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Nuclear, geothermal and hybrid systems
Nuclear, geothermal, hydroelectric generation and hybrid microgrids may provide firm or lower-carbon power, but many projects have long development timelines. They cannot automatically satisfy most near-term demand. The appropriate mix depends on the site, reliability requirements, emissions rules, fuel availability and interconnection schedule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What could make the forecast too high?
- AI adoption grows more slowly than expected.
- Smaller or specialized models replace some large-model workloads.
- Chip supply remains constrained or power prices make some services uneconomic.
- Data-center projects are delayed, canceled or denied interconnection.
- Utilization rates remain below assumptions.
- Regulators restrict development in areas with water or grid constraints.
- Workloads shift to existing facilities instead of creating equivalent new global demand.
What could make it too low?
- AI inference becomes embedded in everyday software, devices, robotics and industrial systems.
- AI agents generate continuous workloads rather than occasional queries.
- More announced projects reach construction and operation.
- Chip utilization and data-center power density rise faster than efficiency improves.
- Video generation, autonomous systems, scientific computing and industrial simulation expand rapidly.
What this means for investors and households
For investors, the key distinction is between announced capacity and energized capacity. Interconnection status, permits, power-purchase agreements, equipment orders, construction milestones and the local utility’s ability to serve the load are more informative than a headline campus size.
The beneficiaries may extend beyond chip and server companies to manufacturers of transformers, switchgear, UPS systems, cooling equipment, batteries, generators, fuel cells, grid software and microgrid controls. However, supplier demand does not guarantee that every project or technology will be economical.
Utilities may need to invest in generation and transmission before a proposed data center reaches full operation. That creates a rate-design question: who pays if a project is delayed or canceled? Regulators and communities will also weigh water use, emissions, noise, tax incentives, roads and other infrastructure costs.
For households, the impact will depend on the local utility’s generation mix, transmission position, cost-allocation rules and ability to secure new supply. A global doubling does not automatically mean every household will see electricity prices double. In some regions, however, large new loads could increase pressure on rates or infrastructure spending.
The timing matters
As of 2026, most major forecasts cited here end in 2030. A literal “next five years” from August 2026 would end around August 2031. Therefore, the strongest evidence supports a doubling from 2025 to 2030, broadly consistent with rapid growth through the late 2020s and early 2030s—but it does not establish a precise electricity total for August 2031.
The most accurate version of the headline is: Global data-center annual electricity consumption is forecast to nearly double from 2025 to 2030, driven primarily by AI, with the largest grid effects concentrated in particular regions.
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