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The widely repeated “1% of the world’s energy” claim is based on a real peer-reviewed study—but it is easy to misstate. The 2020 study estimated that data centers consumed about 205 terawatt-hours (TWh) of electricity in 2018, or roughly 1% of global electricity use. It did not measure all forms of energy, and the figure was not a current 2026 statistic.
More recent International Energy Agency estimates put data-center electricity consumption at about 415 TWh, or 1.5% of global electricity demand, in 2024. In the IEA’s base case, that could rise to approximately 945 TWh—nearly 3% of global electricity—by 2030.
The study behind the 1% figure
The headline comes from “Recalibrating global data center energy-use estimates,” published in Science in February 2020. The authors—Eric R. Masanet, Arman Shehabi, Nuoa Lei, Sarah Josephine Smith and Jonathan G. Koomey—estimated global data-center electricity consumption for 2018.
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The study’s central estimate was approximately 205 TWh, equivalent to about 1% of worldwide electricity consumption at the time. The paper also estimated that data centers represented roughly 1% of global electricity use in 2010.
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The original study is available through Lawrence Berkeley National Laboratory. Its DOI is 10.1126/science.aba3758.
Why computing grew faster than electricity use
The result was lower than some widely circulated forecasts because the researchers used a bottom-up model. Instead of assuming that more data or computing automatically translated into proportionally more electricity, they estimated demand from the equipment and facilities actually providing the computing.
The analysis included servers, storage, networking equipment, cooling, power-delivery systems, utilization rates and the changing mix of enterprise, colocation, cloud and hyperscale facilities.
According to reporting on the study, the amount of computing performed in data centers increased by more than five times between 2010 and 2018, while data-center electricity consumption rose by only about 6%. These were modeled global estimates, not a complete set of direct meter readings from every facility.
Several changes helped contain electricity growth:
- More efficient servers: Newer hardware performed more work per watt.
- Better utilization: Cloud operators could consolidate workloads and keep equipment busier than many privately operated server rooms.
- Improved cooling and power systems: More efficient facility infrastructure reduced the electricity needed outside the computing equipment itself.
- Migration to cloud infrastructure: The study reported that smaller traditional data centers hosted about 79% of compute instances in 2010, while cloud data centers hosted about 89% by 2018. These figures describe the study’s modeled market shift, not a universal direct measurement of every workload.
Data centers use more than servers
A data center’s electricity demand is not limited to the processors doing the computing. Power conversion, cooling, pumps, fans, storage and networking all contribute to the facility load.
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The study estimated that IT equipment—including servers, storage and networking—used about 130 TWh in 2018, compared with approximately 92 TWh in 2010. Efficiency improvements in cooling and power infrastructure offset much of the increase in IT electricity.
That distinction also explains why different estimates can disagree. Studies may use different boundaries for data centers, include or exclude cryptocurrency mining and edge facilities, treat cooling differently, or estimate electricity from installed capacity rather than measured consumption.
What the 1% claim did—and did not—mean
The most accurate version of the original claim is: a 2020 Science study estimated that data centers consumed about 1% of global electricity in 2018.
Calling this “1% of the world’s energy” is a simplification. Electricity is one form of energy, and the estimate did not represent the entire energy footprint of digital services. It also did not necessarily include the energy used to manufacture chips and buildings, fuel backup generators, operate telecommunications networks, or power end-user devices.
Nor was 1% a local-impact measure. A global average can conceal large electricity loads in particular regions, along with transmission constraints, water demand, backup generation and competition for new grid connections.
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The post-2020 picture: 1.5% in 2024
The IEA estimates that data centers consumed about 415 TWh of electricity in 2024, or approximately 1.5% of worldwide electricity demand. That makes the old 1% figure a useful historical reference, not the best current estimate.
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The IEA’s base case projects data-center electricity consumption reaching about 945 TWh by 2030, just under 3% of global electricity demand. This is a scenario, not a guaranteed outcome. It depends on AI adoption, hardware supply, model efficiency, utilization, facility construction, grid access and the availability of new generation.
See the IEA’s estimates and assumptions in its reports on energy demand from AI and the executive summary.
Why AI is changing the demand curve
The 2020 study described a period in which efficiency and cloud consolidation largely restrained electricity growth. Generative AI and other high-performance workloads are creating a different phase.
The IEA expects electricity use by accelerated servers—hardware designed for AI and other intensive workloads—to grow by about 30% annually in its base case. Conventional-server consumption is projected to grow more slowly, at roughly 9% annually. Accelerated servers could account for nearly half of the net increase in global data-center electricity demand through 2030, while AI-optimized data-center demand could more than quadruple.
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More efficient AI chips do not automatically reduce total electricity use. They can make each computation cheaper while encouraging more computation, larger models and wider deployment. This is a version of the rebound effect: efficiency lowers the cost of using a resource, which can increase overall use.
A small global share can be a major local load
Data-center electricity demand is highly concentrated. The IEA estimates that the United States accounted for about 45% of global data-center electricity consumption in 2024, China about 25% and Europe about 15%. Nearly half of U.S. data-center capacity is concentrated in five regional clusters.
Conventional data centers commonly use roughly 10–25 megawatts (MW), while hyperscale AI facilities can exceed 100 MW, according to the IEA. These figures describe facility scales and should not be confused with constant average consumption. A facility’s nameplate capacity, connected load, peak demand, IT load and average operational demand are different measures.
In a constrained region, a new facility can require new transmission lines, generation capacity and substations even if data centers remain a modest percentage of global electricity. Local effects can include grid congestion, higher system costs, water-use conflicts and emissions from backup or supplemental generation.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsElectricity use is not the same as emissions
Two facilities consuming the same amount of electricity can have very different operational emissions. The result depends on the electricity mix, which may include coal, natural gas, nuclear power, hydropower, wind or solar.
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The IEA estimates that emissions from data centers’ electricity use could rise from approximately 180 million metric tons today to about 300 million metric tons by 2035 in its base case. Its higher “Lift-Off” case reaches about 500 million metric tons.
Those figures concern emissions associated with electricity use. They should not be casually compared with another industry unless the accounting boundaries match. Full life-cycle emissions would also consider chip manufacturing, construction materials, transmission, backup fuel and equipment replacement.
What will power future data centers?
The IEA expects renewables to meet nearly half of the additional electricity demand from data centers through 2030. Natural gas is also expected to expand, while nuclear power becomes increasingly important in some markets toward the end of the decade and beyond. More detail is available in the IEA’s analysis of energy supply for AI.
A company’s renewable-energy contract does not necessarily mean its facility is physically powered by renewable electricity every hour. Power-purchase agreements and renewable-energy certificates can support renewable generation financially, while the facility remains connected to a grid with a changing mix of sources. That distinction matters when discussing contractual emissions, physical electricity supply and local grid impacts.
Could the 2030 forecast be too high or too low?
The IEA’s projection is best treated as a reasoned base case rather than a fixed destination. Demand could be lower if AI models become substantially more efficient, hardware utilization improves, planned facilities are delayed, grid connections become difficult or adoption grows more slowly than expected.
It could be higher if AI services expand rapidly, inference workloads become widespread, organizations deploy more powerful systems or cheaper computation stimulates additional demand. Efficiency gains can slow electricity growth, but they are not guaranteed to offset workload growth indefinitely.
Bottom line
The original 1% claim was not fabricated, but it was compressed into wording that is broader and more timeless than the evidence supports. A 2020 Science study estimated that data centers used about 1% of global electricity in 2018, after efficiency gains and cloud consolidation kept demand nearly flat relative to explosive growth in computing.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The newer picture is different: the IEA estimates a 1.5% global electricity share in 2024 and projects nearly 3% by 2030 in its base case. Data centers remain a minority share globally, but AI is making them a rapidly growing and locally consequential electricity load.
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