Nuclear power startups have been pitching themselves as the antidote to what ails AI data centers: power that’s always available. Bill Gates-founded TerraPower is one of the latest to throw its hat that into that ring, with Bloomberg reporting that the startup plans to announce its first data center project this year. TerraPower did not say who the customer will be, though in January, it announced that Meta had agreed to buy eight of its Natrium power plants.
The data center project, expected to break ground in 2027, would be the company’s second power plant, with its first already under construction in Wyoming. Not every nuclear reactor is suited to data center duty, but TerraPower possesses one key advantage — energy storage — that promises to give it an edge over competitors. And it’s all thanks to renewable power sources like wind and solar.
Nuclear reactors, TerraPower’s included, work best when they’re running at full tilt. Of all the different types of power plants, nuclear reactors have the highest capacity factor — 92.5% of the time, they generate at maximum power in the U.S. But in a way, they need to be.
Existing reactors are slow to ramp up and down, capable of increasing or decreasing only about 5% of their total rated output per minute, according to the National Laboratory of the Rockies. New small modular reactors (SMRs), which many startups are pursuing, can react faster, about 10% of their rated output per minute, per NRL. But running at reduced capacity isn’t ideal — it’s hard to make money when you’re not generating electrons.
That’s true of any power plant, but it’s especially true of nuclear, which has the highest capital expenditures of any generating technology. Startups are hoping that mass manufacturing of SMRs will bring capex down, but that has yet to be proven. And if it does work, it could take a decade or more to reap the benefits.
Every startup acknowledges that its early power plants will be expensive, so it makes sense to operate it them at peak capacity as often as possible. For data centers, especially those that rely on behind-the-meter power, that poses a challenge. Their loads, especially when training AI or responding to prompts, can sink and soar quickly as GPUs respond to the tasks.
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