Larger training runs and widespread deployment of future artificial intelligence (AI) systems may demand a rapid scale-up of computational resources (compute) that require unprecedented amounts of power. We find that globally, AI data centers could need ten gigawatts (GW) of additional power capacity in 2025 alone, which is more than the total power capacity of the state of Utah. If exponential growth in chip supply continues, AI data centers will need 68 GW in total by 2027—almost a doubling of global data center power requirements from 2022 and close to California’s 2022 total power capacity of 86 GW. Given recent training compute growth, data centers hosting large training runs pose a particular challenge. Training could demand up to 1 GW in a single location by 2028 and 8 GW—equivalent to eight nuclear reactors—by 2030, assuming that current training compute scaling trends persist. The United States currently leads the world in data centers and AI compute, but unprecedented demand leaves the industry struggling to find the power capacity needed for rapidly building new data centers. Failure to address current bottlenecks may compel U.S. companies to relocate AI infrastructure abroad, potentially compromising the U.S. competitive advantage in compute and AI and increasing the risk of intellectual property theft. More research is needed to assess bottlenecks for U.S. data center build-out and identify solutions, which may include simplifying permitting for power generation, transmission infrastructure, and data center construction.