US Data Center Power Demand Outstrips Supply by 10x, Threatening AI Boom Halt by 2030
Report indicates US data center power demand grows far faster than grid capacity, forcing companies to turn to gas turbines and nuclear power.
According to TechRadar, a new report by insurance broker Howden states that US data center power demand currently outstrips supply capacity by ten times. If new power sources are not brought online, this bottleneck could bring the AI boom to a grinding halt by 2030.
Data shows that since 2000, US data center power demand has increased by 19% year on year, while actual electricity grid expansion has only grown by 2% annually. In 2025, data centers accounted for 5% of total grid capacity, up from just 0.1% in 2000. To continue expansion without increasing grid usage, up to 58% of power demand will need to be sourced from alternatives.
Major AI companies are taking various measures to address this gap. Anthropic recently revealed it is investing $3.4 billion in onsite gas turbine generators to supply power for new sites. However, this makes the company heavily reliant on natural gas supplies, and the noise and emissions from these turbines have raised health concerns among local residents.
Other companies are turning to nuclear power. The Palisades Nuclear Plant in Michigan, Crane Clean Energy Center in Pennsylvania, and Duane Arnold Energy Center in Iowa are set to receive a total of $4.4 billion in federal grants to be recommissioned, with Microsoft and Google first in line to receive energy from these sites. Additionally, Amazon plans to purchase over 960 megawatts of energy from small modular reactors (SMRs) in Iowa at a cost likely exceeding $1 billion, though these sites are yet to be approved and may not be operational until the mid-2030s.
While alternative solutions are being pursued, grid expansion is typically funded through ratepayers, leading experts to predict that power costs in some states could rise by as much as 50% by 2030. Meanwhile, building new nuclear plants is expensive and time-consuming, requiring significant investment to meet safety standards, meaning continued AI infrastructure expansion still requires substantial capital and time buffers.