Global electricity demand is growing at an unprecedented pace, with projections indicating an increase of over 10,000 terawatt-hours by 2035, roughly equivalent to the current total consumption of all advanced economies. A major driver of this surge is artificial intelligence (AI), which relies heavily on data centers. A medium-sized data center consumes as much electricity as 100,000 households, and data center energy demand rose more than 75 percent between 2023 and 2024. By 2030, data centers are expected to account for over 20 percent of electricity demand growth in advanced economies.
In the United States, where many leading AI companies are based, the electricity required for AI-driven data processing is projected to exceed the combined consumption of aluminium, steel, cement, and chemical production by the end of the decade. AI applications are expanding across nearly every sector, including healthcare, public administration, transportation, agriculture, logistics, and education. Training advanced AI models alone requires tens of thousands of CPUs running continuously for weeks or months, creating immense and constant power needs.
Industry experts emphasize that clean, stable, round-the-clock electricity is essential to support AI. Manuel Greisinger, a senior manager at Google, notes that wind and solar alone cannot meet this demand, making nuclear energy a core component of the future energy infrastructure. IAEA Director General Manuel Grossi echoed this view, highlighting nuclear power’s ability to deliver low-carbon, reliable, high-density, scalable energy while stabilizing the grid.
The nuclear industry is currently in an expansion phase, with 71 reactors under construction globally, adding to the 441 already operating. The United States plans ten new reactors, while countries such as France, the UK, Poland, Russia, China, Japan, and the UAE are investing heavily in nuclear energy to meet rising energy and AI-related demands. Tech giants like Microsoft have committed to long-term nuclear power agreements, including restarting existing plants, to secure reliable electricity for data centers.
Small modular reactors (SMRs) are emerging as a promising solution for AI-driven energy needs. Unlike traditional large reactors, SMRs have a smaller footprint, upgraded safety systems, and can be deployed near industrial zones or data center campuses, avoiding grid constraints and transmission losses. The IAEA is actively supporting the development and regulatory approval of SMRs, and companies like Google have signed agreements to purchase nuclear energy from these reactors, with potential operational deployment by 2030.
Beyond terrestrial solutions, tech companies are exploring space-based solar energy to power large-scale machine learning in orbit. Prototype satellites are set to launch in 2027 to test data processing and radiation tolerance, showcasing innovation in energy generation and AI integration.
Whether through space-based solar, restarting old reactors, building new large-scale plants, or deploying SMRs, global efforts are converging on creating an energy system heavily reliant on nuclear power to meet the growing demands of AI and future civilization. Nuclear energy is positioned as the key enabler of a sustainable, high-capacity electricity grid capable of supporting the AI revolution.
