A new report from the IEA assesses how the relationship between energy and artificial intelligence (AI) is evolving rapidly, drawing on the latest data and analysis and close tracking of technological and economic developments in the AI sector.
Building on the IEA's landmark Energy and AI report from April 2025, the new analysis published today finds that the field has continued to develop at speed. Driven by data centre investments, the capital expenditure of five large technology companies surged to more than $400 billion in 2025 and is set to increase by a further 75% in 2026. Electricity demand from data centres soared by 17% in 2025, and that of AI-focused data centres climbed even faster - well outpacing growth in global electricity demand of 3%.
According to the report - Key Questions on Energy and AI - power consumption per AI task is declining rapidly, with efficiency improving at a rate unprecedented in energy history. However, more people are using AI, and energy-intensive uses - such as AI agents - are on the rise. As a result, electricity consumption from data centres is set to double by 2030, and power use from those focused on AI is poised to triple.
At the same time, AI deployment is increasingly coming up against a range of physical bottlenecks, limiting the rate at which data centres can expand in the near term. Supply chains for energy technologies such as gas turbines and transformers, as well as for advanced chips and IT components, have tightened over the past year - and the swelling pipeline of data centre projects is straining planning and regulatory systems, holding up grid connections and other necessary approvals.
To solve the energy challenges at hand, the tech sector is adopting new approaches. It accounted for around 40% of all corporate power purchase agreements for renewables signed in 2025, and is also now a major source of momentum for the nuclear and advanced geothermal industries. The pipeline of conditional offtake agreements between data centre operators and small modular reactor (SMR) nuclear projects has grown from 25 gigawatts at the end of 2024 to 45 gigawatts today, indicating that the momentum behind AI could accelerate the commercialisation of new energy technologies.
Constrained by slow grid connections, data centre developers are also advancing a large number of projects with onsite natural gas-based power generation, largely in the United States. First-of-its-kind IEA data from satellite-based tracking shows that many of these projects remain in their early stages, highlighting the technical and financial hurdles that need to be overcome. One of the key challenges is that AI data centres have rapid and large swings in demand, and meeting their power needs reliably can stretch the technical capabilities of onsite gas plants. For this reason, onsite battery storage is becoming a critical technology for the next generation of AI data centres, which could make them an asset to grids with the right incentives.
"The IEA was early in recognising that there is no AI without energy - and that countries that provide secure, affordable and rapid access to electricity will be one step ahead," said IEA Executive Director Fatih Birol. "Now, we see that while AI is still an energy taker, it is also becoming an energy maker - driving forward innovative solutions like next-generation nuclear reactors, flexible data centres and long-duration energy storage. To help countries that seize on this opportunity to modernise their energy systems, and to tackle bottlenecks and other concerns associated with AI's rapid growth, collaboration between policymakers and the energy and tech sectors remains crucial."
He continued: "To enable greater dialogue, I am pleased to announce that we will soon launch a new platform for government and industry to regularly discuss energy and AI issues. In addition to providing the latest data, the IEA will continue to facilitate the collaboration needed to maximise AI's energy benefits and overcome key challenges."
The report shows that AI may well be critical for global industrial innovation and competitiveness. It finds that proven applications of AI could help firms in energy-intensive industries reduce their energy costs by 3 to 10 percentage points. However, the energy sector as a whole is not yet taking full advantage of AI's potential, according to the report, with lack of sufficient digital skills and data availability emerging as key barriers to adoption.
Over the past year, social concerns around AI have also grown, with data centres serving as a highly visible flashpoint for concerns around energy prices and the environment. The report finds that if the right mix of policies and infrastructure investment are in place, increases in electricity demand do not necessarily raise prices. However, data centres can create special challenges for electricity affordability, since they have large, concentrated power loads and scale up rapidly, often triggering the need for new generation assets and grid investment. Even so, the report finds that policymakers have tools on hand to manage affordability issues, including those that encourage the smart integration of data centres into grids and incentivise data centres to operate more flexibly.
