AI’s Growing Power Needs Could Be Met by Nuclear Energy?

AI’s Growing Power Needs

Arificial intelligence is incredibly demanding when it comes to energy. Running tasks like training large language models such as ChatGPT, enabling real-time voice recognition, or powering autonomous systems depends on sprawling data centres packed with high-performance computing (HPC) gear, especially GPUs. AI’s growing power needs draw a tremendous amount of electricity and generate substantial heat, which in turn requires even more energy for cooling systems.

As AI becomes more deeply integrated into industries ranging from finance and healthcare to agriculture, the number and scale of data centres are only expected to grow. This surge raises serious questions about where all the needed power will come from.

• Renewables like solar and wind are crucial to a sustainable energy future, but they have a well-known limitation: they’re intermittent. Solar panels don’t work at night, and the wind doesn’t blow all the time. Battery storage can help, but today’s solutions still can’t fully support the uninterrupted, high-power needs of AI systems, especially at the hyperscale level. Data centres and training clusters running AI workloads can't afford power outages or even minor drops in energy supply.
• Nuclear energy is highly suited to meet AI’s growing power needs, generating enough to power data centres due to its continuous 24/7 operation, independent of weather conditions. It has a leading capacity factor of over 92.5%, far exceeding that of natural gas (56%), wind (35%), and solar (25%), ensuring consistent output. Its high energy density means a small amount of fuel generates a large amount of power, reducing the need for storage and transport. A single reactor can produce over 800 megawatts of electricity enough to support even the largest data centres. Additionally, nuclear power emits virtually no greenhouse gases during operation and requires significantly less land than other energy sources, offering both environmental and spatial efficiency.
• That’s where nuclear energy could play a bigger role in fulfilling AI’s growing power needs, but it's not without challenges. Reports from Gartner and Deloitte point to common issues like delays and budget overruns in nuclear plant construction, regulatory hurdles, and the need for a new generation of workers, since about 60% of the current workforce is between 30 and 54 years old, and 17% are already over 55. On top of that, small modular reactors (SMRs), while promising, are still in early stages and may not be commercially viable for another 8 to 10 years.