AI Reaches the Nuclear Control Room: 94 U.S. Reactors Now Wired In

The full U.S. commercial nuclear fleet of 94 operating reactors received offers this year to integrate artificial intelligence into daily work, and most operators accepted. The scale makes nuclear power the first U.S. heavy-industry sector to commit to AI at fleet-wide penetration, according to industry executives and project documents reviewed for this report.

The adoption cuts against the industry's reputation for extreme caution. A nuclear reactor is the kind of environment where error margins are measured in centimeters, not percentages. The same regulatory conservatism that defined decades of U.S. nuclear licensing now governs every AI deployment inside the sector.

Why is the nuclear industry suddenly adopting AI?

Two pressures, both measurable. Electricity demand from hyperscale data centers, facilities that house more than 100,000 graphics processors for AI training, is pulling reactor restarts and new builds back into the U.S. energy mix. The nuclear workforce, by contrast, has shrunk faster than new engineers can replace it.

Jerrold Vincent, chief financial officer and cofounder of the AI-for-nuclear startup Nuclearn, named the bottleneck directly. "There's this huge growth in demand from advanced-reactor developers and in new nuclear," Vincent said. "But the existing workforce has been spread really thin. So most of the time when we're coming into a customer, [they're saying,] 'I've had jobs open and I can't fill these positions. We need to do the things required by regulation. How the heck are we going to do it?'"

Nuclearn, founded in California, says its products are deployed at 65 U.S. partners. The list includes traditional operating plants and NuScale Power Corp., a developer of small modular reactors. Microsoft and Nvidia have separately teamed to apply AI across "the entire life cycle of a nuclear plant, spanning site permitting, design, construction, and continuous operations," according to a joint project announcement.

What does Atomic Canyon's NIVA actually do?

It searches decades of regulatory filings, maintenance logs, and operating-experience reports. NIVA, the Nuclear Industry Virtual Assistant, launched in August from San Luis Obispo, Calif.-based Atomic Canyon. The product indexes archives across the U.S. nuclear sector for retrieval by plant engineers.

The NIVA project started when Joseph Dominguez, chairman of Constellation Energy, the country's largest nuclear operator, challenged three nonprofit industry data repositories in 2024 to make their archives queryable in the manner of a consumer large language model. Constellation declined to be interviewed. The task fell to Atomic Canyon, whose prior work applied AI to health care, another conservative industry.

Rob Austin, the Electric Power Research Institute's (EPRI, a nonprofit utility research consortium) project lead for NIVA, described the target dataset. "The goal was to make decades of operating experience, hundreds of thousands literally of operating experience reports, tens of thousands of technical documents, and a whole lot of information available for people to find," Austin said. EPRI, the Institute of Nuclear Power Operations (INPO, an industry peer-review body), and the Nuclear Energy Institute (NEI, the sector's Washington policy trade group) jointly consolidated their archives in 2025 to feed the system.

Atomic Canyon trained the model on 53 million pages of publicly available Nuclear Regulatory Commission filings. The company then partnered with Oak Ridge National Laboratory, a U.S. Department of Energy research facility in Tennessee, to build the underlying architecture.

Trey Lauderdale, Atomic Canyon's chief executive, explained the technical choice. "Technically, they're sentence-embedding models," Lauderdale said. "Fancy way of saying, we taught AI how to seek the nuclear language. Nuclear is almost like its own vernacular."

Can AI actually run a reactor?

No. Not now, and not for years, by the executives' own account. Current deployments remain advisory. They help engineers file compliance documents and recall historical precedents. They do not touch reactor controls.

Vincent drew the line plainly. "Every engineering decision has to be evaluated, independently verified, and checked," Vincent said. "If you think of it as ChatGPT for nuclear, it's actually not a bad place to start."

Lauderdale pushed the operational horizon further out. "I cannot see, in the short or the long term, an environment where AI is making decisions around the operation of the plant, because of the regulatory nature and also because of the risk nature," Lauderdale said. "You don't want machines making decisions that could impact the operations. So, I believe we're going to have humans in the loop for a very, very long time."

The narrowest pathway toward operational AI runs through quality assurance. EPRI is testing machine-vision systems, software algorithms that analyze images to detect defects, against welds inside reactor containment buildings, each currently inspected by a human. The pilot keeps a human reviewer on every AI recommendation. No plant permits AI to sign off on its own inspections.

What workforce problem is AI actually solving?

A pipeline collapse, two decades in the making. The U.S. construction halt that began after the 1979 Three Mile Island accident thinned the cohort of trained nuclear engineers. Average workforce age has climbed steadily. Replacement hiring has not kept pace with retirements, and no U.S. university now produces nuclear-trained graduates in the numbers the existing fleet requires.

Lauderdale framed the choice bluntly. "There is absolutely no way we as an industry will be able to have this nuclear resurgence or nuclear renaissance without augmenting the capacity of this knowledge-based workforce," Lauderdale said.

The bottleneck is documentation. A single plant generates paperwork for every engineering change, every inspection, every compliance check. Searching that archive historically required reading. AI compresses the search.

Austin described the daily case. Engineers troubleshooting a house-size vertical pump, a 1,000-horsepower motor that circulates cooling water through the reactor, draw on EPRI maintenance guides and tens of thousands of INPO incident reports. AI's task is to synthesize them. The output cites every source so a human can verify each recommendation.

"I want to know all the other nuclear power plants that have that same part," Lauderdale said. "Have any of them seen a similar issue? How did they fix it? What's the troubleshooting look like?"

What limits remain on nuclear AI deployment?

Data security, primarily. Nuclear operational records are largely proprietary or protected under federal law. Both NIVA and Nuclearn operate inside a single plant's network.

Nuclearn delivers single-location deployments only. A plant's data never leaves its site, Vincent said. NIVA searches only databases that already required federal background clearances for human access. The same rules cover AI queries against those archives.

Technology cannot erase the human firewall. It does, however, create fresh pressure to share data more openly between operators, Austin said.

What comes next for AI in U.S. reactors

The combined demand from hyperscale data centers and a contracting nuclear workforce has produced a constraint that no amount of capital alone can resolve. AI is being deployed first at the documentation layer, where the bandwidth crisis is sharpest and the safety risk lowest. Fleet-wide adoption across all 94 U.S. reactors puts the American nuclear sector roughly two to three years ahead of comparable AI rollouts in European grids, where deployment data remains unpublished. The next measurable milestone will be the first regulator-approved AI-assisted inspection at a U.S. plant, a step EPRI's weld-monitoring pilot could reach within 18 months if its human-in-the-loop reviews continue to clear safety thresholds set by the Nuclear Regulatory Commission.