The International Energy Agency’s special report on Energy and AI has laid out the most detailed picture yet of how AI is reshaping global energy systems. The central message: AI is both a new demand centre and a new optimisation tool, and we need to prepare for both roles at once.
According to the IEA, electricity consumption by data centres is set to more than double by 2030 — and AI-focused facilities will quadruple their usage. In the US, energy demands from these clusters could soon exceed those of legacy heavy industries like steel and cement. Globally, data centre electricity consumption is expected to rise from 415 terawatt-hours in 2024 to nearly 1,200 terawatt-hours by 2035.
Despite those dramatic growth curves, the IEA is careful to put things in perspective. Even at 2030 levels, data centres are likely to account for around 3% of global electricity demand. That’s still modest compared to sectors like transport or manufacturing. And much of the concern comes from where these data centres are built and how quickly grids can adapt to accommodate them — not just how much they use.
But the more interesting part of the IEA’s analysis is about AI’s positive energy impact. The report highlights how AI tools can increase efficiency across electricity networks, industrial processes, and even the built environment. The agency estimates that full deployment of today’s AI optimisation tools could cut energy-related emissions by up to 5% by 2035 — more than offsetting the emissions caused by the data centres themselves in most scenarios.
Still, long-term solutions will require breakthroughs in clean power generation. And that’s where nuclear fusion enters the conversation.
China has an increasing lead in the race to commercial fusion. Its “artificial sun” — the EAST reactor — has set endurance records for plasma generation, moving the country closer to practical fusion-based electricity. Meanwhile, US startups like Commonwealth Fusion Systems (with its MIT-linked SPARC reactor) and Helion are promising to deliver net energy fusion in the next few years but face steep challenges in scaling up and securing funding.
If fusion does arrive, the implications for AI — and energy as a whole — are huge. A virtually limitless, emissions-free energy source would decouple AI progress from today’s trade-offs in grid pressure, fossil generation, and water usage. And more broadly, it could reduce the carbon intensity of other industrial processes — including those involved in chip fabrication, steel production, and even synthetic fuel development.
Takeaways: The numbers tell an interesting story about AI’s energy footprint. While data centres supporting AI systems are creating new demand at an unprecedented rate, this consumption remains a small fraction of global energy use compared to transport or traditional industry. The IEA’s analysis suggests AI could actually be climate-positive if its efficiency tools see widespread adoption across energy systems, industrial processes, and transport networks. However, the long-term picture gets complicated. For AI growth to stay compatible with climate goals beyond 2030, we’ll need substantial clean energy breakthroughs. Nuclear fusion, once dismissed as perpetually 30 years away, is now attracting serious investment and showing early technical promise. The race is on between AI’s growing appetite for power and our ability to generate that power cleanly.