The easiest number to remember from the International Energy Agency’s 2025 report is 945 TWh. That is its Base Case estimate for global datacenter electricity consumption in 2030, up from about 415 TWh in 2024[1]. The share rises from 1.5% to just under 3% of world electricity demand. Read alone, 3% sounds either reassuringly small or alarmingly large. Both reactions miss the report’s more useful conclusion. Electricity is a global statistic, but datacenters connect to local grids.
AI capacity arrives as a concentrated load. Nearly half of US datacenter capacity is located in five regional clusters. The United States and China together account for almost 80% of projected global datacenter demand growth through 2030. A single large site can draw power like an industrial plant, then request a connection in a market where several similar projects are already queued. The relevant question is therefore not only whether the world can generate another 530 TWh. It is whether a particular substation, transmission corridor and equipment supply chain can deliver hundreds of megawatts on the promised date.
945 TWh is a base case, not a measurement
The IEA builds its Base Case from industry expectations for server shipments, installed IT capacity, utilization, hardware efficiency and power usage effectiveness (PUE). Accelerated-server capacity grows almost fivefold by 2030, compared with 1.8× for conventional servers. Around 70% of the increase in server electricity use from 2025 to 2030 comes from accelerated systems. At the same time, average PUE improves from 1.41 to 1.29, avoiding about 90 TWh of demand and reducing cooling energy per unit of IT load by roughly 30%.
That combination explains why the forecast cannot be extrapolated from accelerator nameplate power alone. The stock and wattage of servers push demand upward. Better cooling, lower idle power and more efficient hardware pull it down. Utilization matters in both directions: an expensive AI server may perform more useful work per joule, yet operate for more hours at a high load.
The uncertainty band is wide. For 2030, the Lift-Off Case exceeds 1,260 TWh, the High Efficiency Case reaches about 800 TWh, and Headwinds reaches about 670 TWh. By 2035, the four cases span 700 to 1,720 TWh, while the Base Case is around 1,200 TWh. Lift-Off combines stronger AI adoption with fewer local energy constraints. High Efficiency keeps service demand on the base trajectory but improves hardware, software and facility efficiency. Headwinds assumes slower adoption and more infrastructure bottlenecks. These are conditional pathways, not confidence intervals.
A small global share can dominate local growth
Of the worldwide increase in electricity consumption through 2030, datacenters supply under one-tenth in the Base Case. The same load looks different when the denominator changes. In advanced economies, where demand has been nearly flat for years, datacenters contribute more than 20% of growth. In the United States they contribute nearly half. By 2030, US datacenters are projected to use more electricity than domestic production of aluminum, steel, cement, chemicals and other energy-intensive goods combined.
Existing concentrations already show the local effect. Ireland’s datacenters consume around 20% of metered electricity. Six US states are above 10%, and Virginia is around 25%. These shares are not a preview of the global average. They are evidence that an apparently modest worldwide load can become the defining customer of a regional power system.
The IEA estimates that grid constraints could delay about 20% of global datacenter capacity planned through 2030. An advanced-economy transmission build commonly lasts between four and eight years. The corresponding range in emerging economies is two to four years. Reported datacenter connection queues range from one to three years in the United States overall to as much as seven years in Northern Virginia and Germany, five to seven years in the United Kingdom, and up to ten years in the Netherlands. The volume of outstanding transformer orders expanded over 30% during 2024, while the transformer price index had risen 1.5× since 2020.
These figures describe schedule risk rather than an absolute shortage of global energy. Generation, grids and datacenters have different construction clocks. A campus can order servers against an 18-month plan while a transmission project remains in permitting. Duplicate or speculative connection requests further obscure which gigawatts are real. Capacity exists on a spreadsheet long before it can pass through the needed wires.
More electricity does not mean one supply source
Renewables provide about half of the additional electricity used by datacenters through 2035 in the Base Case, rising by roughly 450 TWh from today’s level. Natural gas generation serving datacenters increases by about 175 TWh over the same period. Coal contributes in some regions, while nuclear additions become more important after 2030. The IEA expects almost 20 GW of new nuclear capacity for datacenter supply between 2030 and 2035, mainly in the United States and China.
This mix also changes the emissions result. Electricity-related datacenter emissions peak around 320 Mt CO2 near 2030 in the Base Case and then ease as lower-emissions supply grows. That is less than 1% of global combustion emissions, but its regional effect is larger. Under Lift-Off, the curve stays near 475 Mt CO2 during the 2030s because more near-term demand is met with fossil generation. Annual renewable contracts do not by themselves prove that every hour of a facility’s load is served by a matching clean resource.
Flexibility helps only when the workload permits it
The report identifies location as the first flexibility lever. Training and some batch inference can tolerate distance from users, so a new cluster may be placed where generation, transmission and land are available. Interactive inference, data-sovereignty rules and access to skilled labor narrow that freedom. Existing network and talent hubs also reinforce themselves, which is why announced capacity continues to cluster even after power becomes scarce.
Time is the second lever. Some jobs can pause, shift to a low-price hour or move between regions. Real-time services cannot. Even flexible training has a cost because idle accelerators are expensive and distributed jobs may lose progress or efficiency when interrupted. Batteries, backup generation and thermal storage can shape the facility’s grid draw, but their duration and emissions vary. Flexibility should therefore be reported as a workload-specific operating range, not a claim that an AI campus can simply switch off.
The IEA forecast has limits. Server shipment expectations come from an industry that changes quickly, and public data on utilization, idle power and facility operations remain sparse. The 700 to 1,720 TWh range makes that uncertainty visible. Its practical value lies less in predicting the exact 2035 total than in aligning timescales. Chips improve in years, datacenters rise in a few years, and transmission can take most of a decade. AI infrastructure planning fails when those clocks are treated as one.
Source and attribution
This article is an editorial analysis prepared by Silicon & Systems from the IEA’s 2025 Energy and AI report and dataset. The prose and both figures were created for this article from reported values; no source figure or table is reproduced. The report is available under the Creative Commons Attribution 4.0 International license. Copyright (c) OECD/IEA 2025. The full report, methodology and download links are available from the IEA report page.