Nuclear power is having a moment. Data centers are hungry for reliable, carbon-free baseload energy. Aging fleets are being asked to do more with leaner teams. And a workforce that took decades to build is approaching retirement. Our co-founders, CEO Bradley Fox and CFO Jerrold Vincent, recently sat down with Chris Wedding on the Climate CEO podcast from Entrepreneurs for Impact to talk about how we built an AI company quietly serving more than 70 nuclear reactors before most of the industry even knew AI was coming.
The conversation covered strategy, culture, workforce, and the realities of selling software inside one of the most regulated industries on the planet. Here is what stood out.
Starting Where It Hurts: The Corrective Action Program
Every nuclear power plant in the United States operates under a Corrective Action Program, or CAP. It is a regulatory requirement. Any employee at any level can and should report any issue they observe, from a crack in the parking lot to a malfunctioning pump to, yes, a complaint about the cafeteria. The result is that a single reactor can generate 5,000 to 10,000 documented issues per year, every one of which must be reviewed by experienced, cross-functional staff.
That review process requires people to categorize each issue, determine whether it represents a condition adverse to safety, assign it appropriately, assess whether it is a repeat occurrence, and decide how much effort to invest in resolving it. For decades, that has meant rooms full of highly trained personnel doing largely manual triage work.
“One reactor might have 5,000 to 10,000 of these issues identified every single year. And every one of those has to be looked at by a staff of cross-functional, highly experienced personnel.” — Jerrold Vincent, CFO and Co-Founder, Nuclearn
We recognized that this problem was universal across the fleet, painful enough to justify real investment, and tractable with natural language processing. Our CapAI product began automating a significant portion of CAP triage as early as 2021, freeing experienced engineers to spend their time on higher-value work rather than reviewing low-level administrative entries.
It was narrow. It was unsexy. And it was exactly the right place to start.
Land and Expand, Done Right
Our go-to-market strategy has been deliberate and patient. Rather than pursuing a small number of large contracts, we chose to land with a focused, high-value solution at as many sites as possible, prove ROI quickly, and expand from there.
This approach works particularly well in nuclear for a few reasons. Procurement at a nuclear plant is a multi-layered process with long budget cycles, extensive compliance reviews, and a deep institutional preference for proven vendors. Getting in the door with a smaller, clearly scoped product at a reasonable cost is far easier than asking a plant to adopt a broad platform it has never seen in action.
Once inside, the dynamic shifts. As Jerrold explained on the podcast, customers that see real results want to do more. We have since expanded into outage scheduling optimization (coordinating 13,000 to 15,000 discrete activities across a 20 to 30-day refueling window), and AtomAssist, an AI agent platform that functions as an intelligence hub for plant engineers, giving them fast access to documentation, precedent, and AI-assisted drafting for the extensive technical writing nuclear roles require.
“Start with smaller, more targeted use cases. Get as many folks on board as possible. Then help them out with deeper and more complex problems.” — Jerrold Vincent, CFO and Co-Founder, Nuclearn
The key constraint with this model, as Brad noted, is that the solution has to work really, really well. And customers have to like you. In a market as concentrated as nuclear, reputation travels fast in both directions.
A Workforce Problem That AI Alone Cannot Solve
One of the more striking threads in the conversation was the workforce challenge facing nuclear energy. The U.S. nuclear industry employs roughly 100,000 people, a significant portion of whom are approaching retirement age. The skills required to operate, maintain, and expand nuclear plants are deep and time-intensive to develop. New employees can take 5 to 10 years to grow into critical roles.
The data center boom has added pressure from an unexpected direction. Tech companies, recognizing that nuclear workers are excellent engineers and rigorous operators, have been actively recruiting from the industry. Plants are competing for talent in a way they never had to before.
Meanwhile, national goals for nuclear expansion would require doubling or tripling the current workforce over the next 10 to 15 years. The math does not work without AI augmentation.
But Brad and Jerrold were clear that AI is not a replacement strategy here. Nuclear is safety-critical infrastructure. The NRC requires human oversight on consequential decisions. The goal of our tools is to reduce the administrative and research burden on experienced people, help them transfer knowledge to newer colleagues, and make it possible for a somewhat smaller team to maintain the same standard of operational excellence.
“AI can help patch in some of that knowledge where it may not exist, or help make it accessible to those new people.” — Bradley Fox, CEO and Co-Founder, Nuclearn
Our original mission, keeping plants economically viable when natural gas was cheap and wind was getting cheaper, has evolved into something broader: helping nuclear scale fast enough to meet the energy demands of a world that is electrifying everything.
A Culture Built for Transparency
One of the most revealing moments in the podcast came when Chris Wedding asked how nuclear plants manage to maintain such an open reporting culture. Brad and Jerrold practically laughed. In nuclear, the idea that you would not report a problem is not just unusual; it is inconceivable.
From the first day at a plant, every employee is taught that transparency is non-negotiable. People openly challenge executives at all-hands meetings. Problems are discussed at every level of the organization. This is not radical candor as a management philosophy. It is how plants stay safe.
For a software company trying to get plants to adopt AI tools that touch sensitive operational data and critical workflows, this culture is actually an asset. Nuclear customers are not going to quietly tolerate a product that underperforms. They will tell you exactly what is wrong. For us, that feedback loop is a feature, not a threat.
Built From Inside the Industry
Jerrold and Brad both came from the nuclear industry before founding Nuclearn in 2020. They spent years inside plants doing data science work, grappling with the reality that nuclear runs on massive volumes of unstructured text data, maintenance logs, corrective actions, engineering evaluations, and hundreds of required documents per project.
Getting good at natural language processing was not optional. It was the job. And when transformer-based language models began demonstrating their potential around 2018, our founders recognized the technology before most enterprise software vendors had even started paying attention.
The first year was nights and weekends. They ran conservative napkin math on the automation opportunity across the entire U.S. fleet. The numbers were large enough to justify the risk. They commercialized what they had been building, found early customers, and grew from there without outside capital until our seed and Series A rounds.
That grounding in the industry shows up in ways that matter. Our pricing model reflects a genuine understanding of how plants budget and measure value. Our product roadmap follows the actual pain points of the people running these facilities. When Brad and Jerrold talk about nuclear, they are not performing expertise. They lived it.
What Comes Next
We are well past proof of concept. With more than 70 reactors already in our customer base, a recent Series A from SJF Ventures and Blue Bear Capital, and a growing product suite, our near-term focus is on expanding within the existing fleet while building the relationships and capabilities that will matter as new reactor construction accelerates.
Small modular reactors and advanced reactor designs are getting significant investment. But the infrastructure for new builds, the engineering firms, the licensing processes, the operational staffing models, runs through the same companies already using Nuclearn. That means we are already participating in new nuclear without having to wait for the first SMR to come online.
Nuclear is moving faster than it has in decades. The workforce is under pressure. The technology to help is here. We built this company to be part of the solution, and that work is just getting started.
Listen to the full episode: Nuclear’s AI Revolution on Climate CEO (Entrepreneurs for Impact)
Learn more about Nuclearn at nuclearn.ai