THE NUCLEAR INDUSTRY has a reputation for two things: generating enormous quantities of carbon-free electricity and generating even more enormous quantities of paperwork. On Tuesday at CERAWeek in Houston, Microsoft Vice Chair Brad Smith announced an "AI for nuclear" collaboration with Nvidia, aimed squarely at the second problem. The initiative will deploy generative AI, digital twins, and high-fidelity simulation tools across the full lifecycle of nuclear plant development — from permitting through operations — in an effort to compress timelines that have, for decades, stretched well beyond a decade.
Big Tech has contracted for at least 13 gigawatts of nuclear energy, a figure that has roughly doubled in the past year as Amazon, Google, Meta, and Microsoft scramble to secure around-the-clock, carbon-free power for their data centers. Microsoft alone has committed to a 20-year, $16 billion deal to restart a reactor at Three Mile Island, targeting 835 megawatts by 2028. Meta recently inked contracts covering up to 6.6 gigawatts. The appetite is real — but so is the bottleneck. The NRC licensing process alone can take more than a decade to complete, and construction adds years more. Traditional nuclear projects have been notorious for cost overruns measured in the billions.
The Microsoft-Nvidia collaboration attacks the problem from the documentation side. AI tools will identify inconsistencies across tens of thousands of pages of engineering and safety reports, unify project data into auditable digital records, and run 4D and 5D simulations — incorporating time scheduling and cost tracking — so that developers can, as Microsoft's blog post put it, "virtually construct the plant before shovels hit the dirt." The early proof point: Aalo Atomics, an Austin-based startup building modular microreactors for data centers, claims to have reduced its permitting process by 92% using Microsoft's generative AI tools, saving an estimated $80 million annually.
Fission impossible
But permitting, however painful, is only one node in a far longer chain. TerraPower's Natrium reactor in Wyoming — the first commercial non-light-water reactor approved by the NRC in over 40 years — received its construction permit in early 2026, five months ahead of the NRC's original timeline. The overall project, however, remains delayed, with completion now targeted for 2030, three years beyond its original DOE target. The lag did not come from slow document review. It came from supply chain complications, fuel availability constraints, and the irreducible complexity of building something that splits atoms for a living.
The nuclear industry's constraints are structural, not merely clerical. The United States has not completed a new reactor build on time and on budget in modern memory; Vogtle Units 3 and 4 in Georgia came in roughly seven years late and $17 billion over budget. The workforce has atrophied after decades of stagnation — the NRC itself has been under pressure from the Trump administration's Executive Order 14300, which mandates fixed deadlines including no more than 18 months for a final decision on an application to construct and operate a new reactor. Whether the commission can meet such deadlines without sacrificing the rigor that public trust in nuclear demands remains, to put it mildly, an open question.
None of this diminishes the potential of what Microsoft and Nvidia are building. If generative AI can turn a bespoke, artisanal permitting process into something repeatable and auditable — a kind of regulatory assembly line — that genuinely matters. Aalo Atomics, founded only in 2023, has already raised over $136 million, completed a full-scale non-nuclear prototype, and is targeting criticality for its Aalo-X reactor by mid-2026. That pace would be extraordinary for the nuclear sector. The company's CTO, Yasir Arafat, previously led the design of the MARVEL microreactor at Idaho National Laboratory, and the startup's approach — factory-assembled modules, sodium-cooled cores, no external water dependency — is explicitly designed to sidestep the bespoke engineering trap.
Still, the AI-for-nuclear thesis contains a familiar Silicon Valley assumption: that the hardest problems are information problems. In nuclear, many of the hardest problems are atoms-and-concrete problems — metallurgy, seismic engineering, spent fuel logistics, community consent. AI can make the paperwork faster. Whether it can make the physics faster is a different question entirely. The companies that crack nuclear's next chapter will need both the digital twins and the actual welders. Microsoft and Nvidia have bet on the former. The industry is still desperately short on the latter. ■
For more, join 75,000 subscribers getting tech's favorite brief here
