Artificial Intelligence

Artificial intelligence (AI) is emerging as one of the most consequential technologies of our time. In recent years, the capabilities of AI systems have grown quickly due to improved computing power, a boom in data availability and breakthroughs in the design of AI models, leading to rapid adoption by both businesses and individuals. 

Though significant uncertainties remain, AI has the potential to transform the energy sector in the coming decade. It is set to drive a surge in electricity demand from data centres around the world while also unlocking significant opportunities to cut costs, enhance competitiveness and reduce emissions.

To better understand the growing connections between energy and AI, the IEA launched a major new initiative in 2024: Energy for AI, and AI for Energy. As part of this work, the IEA organised the Global Conference on Energy and AI, a first-of-its-kind platform for dialogue among governments, the energy industry, the tech sector, researchers and civil society. In April 2025, the IEA published Energy and AI, a groundbreaking report that provides the most comprehensive, data-driven global analysis on the energy-AI nexus to date.

As the tech sector and energy industry become more intertwined than ever before, the IEA will continue to provide data and robust analysis to inform decision makers. It will also facilitate ongoing dialogue and collaboration among stakeholders, which is essential to maximising benefits and reducing risks.

The training and deployment of AI models mainly occurs in data centres. While traditional data centres use between 10 and 25 megawatts (MW) of power, demand by hyperscale AI centres can exceed 100 MW – equivalent to the annual electricity consumption of 100,000 households. The largest data centre announced is set to consume as much electricity as 5 million households.

In 2024, data centres accounted for 1.5% of worldwide electricity demand. By 2030, this share is set to rise to about 3% in the IEA’s base case, with electricity demand from data centres worldwide more than doubling to around 945 terawatt-hours (TWh). That is slightly more than the entire electricity consumption of Japan today. While this is still a relatively small portion of the global total, the effects are poised to be particularly strong in some countries. For example, in the United States, data centres are on course to account for almost half of the growth in electricity demand to 2030; in Japan, more than half; and in Malaysia, as much as one-fifth.

A diverse range of energy sources will be tapped to meet data centres’ rising electricity needs globally – though renewables and natural gas are currently set to take the lead due to their cost-competitiveness and availability in key markets.

Uncertainty around current and future data centre consumption calls for a scenario-based approach to guide energy sector decisions. The IEA has developed a number of sensitivity cases that consider variations in how quickly AI is adopted, how fast efficiency improvements occur, and the emergence of persistent energy sector bottlenecks. The IEA has developed a number of sensitivity cases that consider variations in how quickly AI is adopted, how fast efficiency improvements occur, and the potential emergence of persistent energy sector bottlenecks.

The energy system is becoming more electrified, digitalised and decentralised. AI solutions can help meet the challenges arising from these shifts – optimising operations, reducing costs, improving efficiency and cutting emissions. While the broader sectoral impact of AI is hard to quantify, the IEA estimates that if existing AI applications are widely adopted by the electricity sector, for example, they could save up to $110 billion annually and unlock 175 gigawatts (GW) of transmission capacity. 

Additionally, AI is emerging as a powerful tool for scientific discovery, helping researchers to find, test and commercialise innovations faster. Innovation lead times for new energy technologies often span decades. Reducing this period will be key to achieving energy sector goals such as greater security, competitiveness and sustainability.

Only 2% of the equity raised by energy start-ups has gone to companies with an AI-related value proposition. That said, many energy innovation challenges are characterised by the kinds of problems AI is good at solving: those involving highly complex design, the need to balance performance trade-offs for optimal outcomes, and rich datasets.

Increasing digitalisation creates new vulnerabilities for the energy system, as well as solutions. Cyberattacks on energy utilities have tripled in the past four years and become more sophisticated because of AI. At the same time, AI is becoming a critical tool for energy companies to defend against such attacks. And there are other benefits: AI applications such as predictive maintenance and automated monitoring of critical energy sites can improve the management of physical energy infrastructure. They can also help to balance electricity networks that are growing more complex and decentralised.

At the same time, the rapid expansion of electricity systems, including for data centres, has intensified pressure on the supply chains for key grid components like transformers. The security of complex and globalised supply chains for critical minerals and materials used in data centres – such as copper and gallium – is also coming into focus. 

The rise of AI has sparked concerns that its energy use could worsen climate change, while also raising hopes that AI solutions can help reduce emissions. In the IEA’s base case, emissions from electricity use by data centres grow to 300 million tonnes by 2035 from 180 million tonnes today. While this is less than 1.5% of total energy sector emissions during this period, data centres are among the fastest-growing sources of emissions.

The widespread adoption of existing AI applications could lead to emissions reductions that are far larger than emissions from data centres. However, various barriers to AI adoption would need to be overcome to unlock these gains. And rebound effects – for example, shifts away from public transport to autonomous cars – could undercut some of these benefits.  

The IEA has long been at the forefront of understanding the links between the energy sector and digitalisation. As the only global agency tracking all fuels, technologies, sectors and geographies, it is uniquely placed to analyse the connections between AI and energy. 

Building on the Global Conference on Energy and AI, the IEA's key contributions to the AI Action Summit chaired by France and India in February 2025, and the publication of the Energy and AI report, the Agency will soon launch a new Observatory on Energy and AI, which will gather the most comprehensive and recent data worldwide on AI’s electricity needs, in addition to tracking cutting-edge AI applications across the energy sector.