The resurgence of nuclear energy in the United States is no longer a theoretical discussion. It is a practical response to a complex reality: the country needs reliable, scalable power, and it needs it now. Nuclear energy, with its carbon-free baseload capacity, is an essential part of that equation. Yet for all its technical sophistication, the nuclear industry remains burdened by an extraordinary administrative load that slows progress and consumes valuable human expertise.
Nowhere is this more evident than in the licensing and regulatory processes overseen by the Nuclear Regulatory Commission. Decades of operation have generated billions of records. Every maintenance action, engineering change, inspection, and regulatory decision must be documented, preserved, and retrievable. This is not busywork. It is the foundation of nuclear safety. But it is also a profound drain on time and talent when engineers and operators spend thousands of hours each year simply searching for information that already exists.
The experience at Diablo Canyon, California’s only operating nuclear power plant, clearly illustrates the challenge. When state leaders chose to extend the plant’s life to 2030, the decision triggered an urgent, complex licensing effort spanning thousands of pages of documentation. Staff were forced to comb through decades of records under intense time pressure. In one recent case, a single-component issue led to a six-month investigation, diverting specialists from their core technical responsibilities.
This is precisely the kind of problem artificial intelligence is well-suited to address, provided it is implemented correctly. The collaboration between Atomic Canyon, Diablo Canyon, and Oak Ridge National Laboratory demonstrates what happens when AI is built for a specific mission rather than retrofitted from consumer tools. Nuclear operations demand precision, repeatability, and trust. A system that guesses, improvises, or hallucinates is not merely unhelpful; it is unacceptable.
By training nuclear-specific AI models from the ground up, using the Frontier exascale supercomputer at Oak Ridge, this project has taken a fundamentally different approach. Instead of focusing on text generation, the emphasis is on accurate retrieval: finding the correct document, in the proper context, every time. Training these models on the NRC’s ADAMS database, which documents the full regulatory history of the U.S. nuclear fleet, ensures that the system understands the language, structure, and logic of nuclear regulation.
The early results are telling. Staff at Diablo Canyon are already reporting meaningful productivity gains. Engineers can spend more time solving technical problems and less time navigating opaque databases that require years of institutional knowledge to use effectively. The return on investment is measured not only in hours saved, but in reduced operational risk and improved organizational focus.
What makes this effort especially significant is its open-source philosophy. By making the foundational models available to the broader industry, Atomic Canyon and its partners are signaling that this is not a competitive advantage to be hoarded, but an infrastructure improvement that benefits the entire nuclear ecosystem. In an industry where safety and reliability are collective responsibilities, that matters.
As nuclear power reclaims its role in the national energy mix, innovation must extend beyond reactors and fuels. The systems governing licensing, compliance, and knowledge management are equally critical. Properly applied, AI can help modernize those systems without compromising rigor or safety.
This project offers a glimpse of what responsible, domain-specific AI can achieve when paired with world-class computing resources and deep institutional expertise. It is not a shortcut around regulation. It is a more innovative way to meet it.
