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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAI data centers use electricity to run computing equipment and the systems that keep it operating: servers and accelerators, storage, networking, cooling, power conditioning and backup equipment. The International Energy Agency’s 2026 central projection puts global data-center electricity use at 485 terawatt-hours (TWh) in 2025 and 950 TWh in 2030—roughly twice as much, and around 3% of global electricity demand by 2030. That is a forecast, not a measured 2030 result.
What electricity does a data center need?
A data center is more than a room of computers. Its servers process information; storage systems retain it; networking equipment moves it; and facility systems supply stable power and maintain conditions in which the hardware can run. AI uses the same broad infrastructure, but relies heavily on specialized processors called accelerators for many workloads. Those devices change both the mix of equipment inside a facility and the amount of power needed in a given amount of space.
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Electricity consumption is therefore not the same as the power rating of a single server or accelerator. It is the sum of equipment operating across the facility, plus the energy used to support that equipment. A facility’s total varies with its size, workload, hardware, efficiency and cooling design. A figure for all data centers worldwide also says little by itself about the load on one local grid: facilities are geographically concentrated, while global totals average across many places.
Servers are the largest component, but not the only one
The IEA’s 2025 analysis estimates that servers use about 60% of electricity in modern data centers on average. Storage accounts for about 5%, and networking can account for up to 5%. Cooling and environmental controls keep equipment within suitable temperature and humidity ranges, but their share varies widely: around 7% in efficient hyperscale facilities and more than 30% in less-efficient enterprise data centers. These are indicative shares, not a fixed recipe for every facility; equipment and facility design matter.
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Power conditioning also matters. Data centers use uninterruptible power supply (UPS) batteries and backup generators to support reliability. Backup generators are rarely used, but their presence is part of the infrastructure needed by facilities that require dependable service. The categories should not be confused: the servers perform computation, while cooling, power systems and other auxiliary equipment make sustained operation possible.
How AI changes data-center electricity use
AI increases demand chiefly by adding accelerated computing capacity and by raising the power density of that capacity. Accelerators are designed for workloads such as AI, and adding them to a data center changes its server mix. More computing demand can mean more servers, but AI’s electricity implications are also shaped by how much power each installation draws and how much computing it delivers per unit of energy.
In its 2025 base case, the IEA projected electricity use by accelerated servers, mainly driven by AI, to grow 30% annually, compared with 9% for conventional servers. Accelerated servers accounted for almost half of the projected net increase in data-center electricity consumption to 2030 in that analysis. This is a projection about server categories in the report, not a claim that every AI application or data center has the same energy use.
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The IEA’s 2026 update reports that AI-server power density rose elevenfold between 2020 and 2025 and projected a further fourfold increase by 2027. Power density refers to the power required in a given physical area, not to the total electricity used by every facility. Higher density can make power delivery and heat removal more demanding at a site even before considering how many sites are built.
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How much electricity do data centers use?
Forecast numbers need their report edition and baseline year attached. The IEA’s 2026 update is the latest central projection in the material available here. Its 2025 report used a different estimate and baseline. They should be read as separate report vintages, not stitched together as though they were one continuous measurement series.
| IEA report | Consumption figure | What it means |
|---|---|---|
| 2026 update | 485 TWh in 2025, projected to reach 950 TWh in 2030 | The updated central projection; the 2030 level is around 3% of global electricity demand. |
| 2025 report | 415 TWh in 2024, or around 1.5% of global electricity consumption | The earlier estimate, based on a different report edition and baseline year. That report said use had grown around 12% annually over the preceding five years. |
For a sense of scale, 1 TWh is one billion kilowatt-hours. If annual electricity use were spread evenly through every hour of a year, 485 TWh would correspond to an average load of about 55 gigawatts (GW), and 950 TWh to about 108 GW. These are arithmetic conversions of the annual figures, not a description of the actual load at any particular hour; real demand varies over time and across facilities.
The 2026 update also projects AI-focused data-center electricity consumption to triple between 2025 and 2030. That refers to AI-focused facilities or consumption in the IEA’s analysis, not to all data-center electricity use: conventional computing and other digital services remain part of the total.
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More AI computing is being deployed
The IEA identifies AI as the most important driver of projected data-center electricity growth, alongside rising demand for other digital services. Increased use of AI can encourage companies to deploy more accelerator-equipped servers. How quickly that happens depends in the near term on accelerator adoption and server shipments, and over a longer period on whether demand for energy-intensive AI uses expands.
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Hardware and software efficiency can change the outcome
Efficiency complicates any simple extrapolation from today’s demand. Improvements in hardware and software can reduce the electricity needed for a given amount of computing, while growth in the number or intensity of workloads can increase total use. The relevant question is not only whether each device becomes more efficient, but also how much computing is deployed and how it is used. That is one reason the IEA presents scenarios rather than a guaranteed path.
Building electricity infrastructure takes time
A data center can become operational in two to three years, according to the IEA’s 2025 analysis, while power infrastructure generally has longer planning and construction lead times. If facility development moves faster than the grid can add connections and supply, a project may have difficulty obtaining power where and when it needs it. The timing mismatch can shape where data centers are built and how quickly planned capacity becomes usable.
The 2026 update points to near-term bottlenecks in chips and energy equipment, which lower the likelihood of more aggressive near-term scenarios. It also identifies possible upside after 2030 if those constraints ease and energy-intensive AI uses grow. A high forecast is therefore not an inevitable outcome: deployment depends on both demand and the ability to supply the hardware, energy equipment and electricity infrastructure.
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Data centers’ share of global electricity growth is relatively modest in the IEA’s 2025 analysis, but that does not mean their effects are modest everywhere. Facilities cluster in particular regions, so a large new load can compete for limited grid connections even when the global share appears small. The location of demand matters as much as the worldwide total for understanding local consequences.
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The IEA’s 2025 summary attributed around one-tenth of global electricity-demand growth to data centers through 2030, and more than one-fifth of demand growth in advanced economies. In the same report’s outlook, the United States, China and Europe were the largest markets; the United States and China together accounted for nearly 80% of projected global growth to 2030.
For 2024, the IEA attributed 45% of data-center electricity use to the United States, 25% to China and 15% to Europe. Those shares describe the regional distribution in that report, not the share of each region’s own electricity use. They help explain why the global average cannot tell a community whether a nearby power system is under pressure.
Grid constraints can delay projects
The IEA’s 2025 executive summary estimated that around 20% of planned data-center projects could face delays if grid risks were not addressed. It also said transmission lines in advanced economies could take four to eight years to build, and that wait times for key components such as transformers and cables had doubled in the three years leading up to that report. These are dated conditions reported in 2025, not permanent lead-time guarantees for every project.
The 2026 update notes that developers in the United States are advancing onsite natural-gas projects amid slow grid connections. Onsite generation can change how a project meets some of its power needs, but it does not remove the broader need to address grid constraints. The physical effects still depend on where facilities are located, what supply is available and how power infrastructure develops.
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Where the electricity comes from
The electricity mix depends on the location of each facility. The IEA’s 2025 estimate of the global physical mix consumed by data centers included coal, renewables, natural gas and nuclear power in substantial shares.
| Source in the IEA’s 2025 estimate | Share of physical electricity mix consumed |
|---|---|
| Coal | About 30% |
| Renewables | About 27% |
| Natural gas | About 26% |
| Nuclear | About 15% |
This is an estimate of the physical electricity mix serving data centers, including onsite generation and the local grid mix. It is not the same as an operator’s contractual procurement claims. For example, a power purchase agreement is a contract to buy electricity or support generation; it does not establish that the same source physically serves a particular facility at every hour. Keeping physical supply distinct from procurement claims avoids overstating what a contract says about the power flowing to a site.
For additional demand through 2030, the IEA’s 2025 base case projected renewables would meet nearly half of growth, while coal and natural gas together would meet over 40% of the additional demand. The balance varies by region: the report described natural gas as the largest current source in the United States and coal as dominant in China. The IEA expected nuclear to become more important after 2030, but planned generation and technologies are future additions, not current supply.
How uncertain are the forecasts?
Data-center electricity projections depend on variables that can move in different directions: AI uptake, efficiency gains, supply-chain capacity, energy-equipment availability and grid connections. The IEA’s 2025 sensitivity cases varied AI adoption, efficiency and energy-system bottlenecks. In that report, the 2035 range ran from about 700 TWh in the Headwinds case to more than 1,700 TWh in the Lift-Off case. Those are scenario outcomes, not a forecast range that should be detached from its assumptions.
The 2026 update keeps the central projection close to the previous trajectory while emphasizing near-term bottlenecks and the possibility of longer-term upside. The two editions should not be blended into a single figure without preserving their dates, baselines and scenario labels. For readers, the central message is that demand is rising rapidly in the outlook, but the eventual scale depends on how quickly computing is deployed and how effectively energy and grid infrastructure can keep pace.
What the scale comparison with households does—and does not—tell you
The IEA’s 2025 executive summary says: “A typical AI-focused data centre consumes as much electricity as 100 000 households, but the largest ones under construction today will consume 20 times as much.” This is the IEA’s comparison, and its wording distinguishes a typical AI-focused facility from the largest facilities under construction at the time. It is a way to convey the scale of a large site, not a universal conversion factor for all AI data centers or a substitute for a facility’s measured electricity consumption.
Household comparisons can make a large load easier to picture, but they obscure differences in household consumption, facility size, workload and location. For questions about climate or grid impact, the more useful follow-up is what generates the electricity, when it is needed, and whether the grid serving the site has capacity. The same annual consumption can have different local implications depending on where demand is added and what power sources are available.
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