How is AI advancing nuclear R&D today?
My conversation with Kevin Kong of Everstar
If you follow nuclear energy through headlines, it feels like we’re living in the future already. SMRs seem like they’re right around the corner, fusion is always announcing another breakthrough, and big tech keeps signing new nuclear power deals for their data centers.
But if you step back and look at the grid, and the reality is that the U.S. has barely added net-new nuclear capacity in decades.
This week I sat down with Kevin Kong of Everstar to understand how companies like his are using AI to quietly change the slow, painful parts of nuclear development to help SMRs, advanced fission, and eventually fusion get deployed to our grid faster.
What are the main ways AI can help?
The biggest misconception about AI in nuclear is that it’s here to “design reactors.”
That’s not where the leverage is.
Nuclear already has solid physics, proven designs, and decades of operational experience. What it doesn’t have is speed. And most of that slowness has nothing to do with engineering itself.
Permitting is the obvious one. Nuclear projects live and die by regulatory approvals, and the work required to get there is enormous. Teams spend months digging through prior filings, regulatory guidance, environmental reviews, and historical precedent just to justify why a specific design or site should be allowed. AI doesn’t replace regulators or human judgment, but it can dramatically reduce the time spent searching, synthesizing, and drafting the first pass of compliant documentation.
“Nothing happens without approvals. You can have sites ready, designs ready, money ready, but it all comes down to getting green lights.” -Kevin Kong
Simulation and engineering workflows are another major area. Nuclear engineers already rely on physics-based models, but running those models and iterating on designs is slow and expensive. AI is increasingly being used as a way to accelerate iteration, explore more design scenarios, and reduce the overhead around setting up and validating each run.
Siting is another challenge where AI is useful. Choosing where to put a reactor requires decades of weather data, seismic risk, soil behavior, water access, environmental protections, local and tribal regulations, and emergency planning. That’s a massive research and synthesis problem. AI is well suited to pull together fragmented information and surface what actually matters for a given site.
That brings us to how AI can also help the industry deal with legacy information. Nuclear operators are constantly bouncing between outdated documents, poorly searchable government portals, and internal systems that were never designed to talk to each other. AI helps translate that mess into something usable, searchable, and consistent, which sounds boring until you realize how much time it saves.
“People are jumping between documents from a file cabinet in room C from the 1970s, government portals with terrible search, Google just to find the right document, and then back into Word redrafting things that have already been rewritten dozens of times.” - Kevin Kong
In short, AI is helping nuclear today by removing friction from everything around the reactor, while simulation and physics-based AI are also helping engineers get closer to nuclear fusion. Firms like NVIDIA working alongside university research labs on the problem (NVIDIA).
How is it being used today?
One of the clearest early use cases is in permitting and licensing, the part of nuclear development that most people outside the industry never think about but that eats years off every project. Nuclear labs and tech giants are already experimenting with this. In late 2025, Idaho National Laboratory announced a collaboration with Microsoft to lean on Azure cloud and AI tools specifically to help streamline the licensing process for new nuclear technologies (INL), speeding up regulatory research and documentation that traditionally takes huge amounts of time.
Permitting isn’t the only place AI is being embedded. On the operational side, utilities and vendors are beginning to use AI-driven scheduling and workflow tools to tighten up the mess of tasks that go into plant upgrades and construction plans. A recent report on collaborations between Aalo Atomics and Microsoft highlighted how generative AI and “AI agents” are being used to organize and optimize workflows around permitting and project planning (Nuclear Newswire) for a demonstration reactor, cutting cost and time by identifying the most impactful tasks to tackle first.
AI is also creeping into simulation and engineering, although it’s less flashy than the fusion headlines you see but arguably just as meaningful. Researchers and labs are feeding advanced algorithms with physics-based models to reduce iteration time and explore design spaces more quickly. These hybrid approaches, where AI augments deterministic models, are part of broader industry efforts to advance reactor design and safety analysis that organizations like the International Atomic Energy Agency have been talking about for years (NUCNET).
Even outside direct plant design or permitting, AI is being adopted in adjacent areas that matter down the road. Legacy reactors and academic facilities are starting to experiment with AI for remote monitoring, cybersecurity, and predictive maintenance, which future commercial designs will depend on for safety and reliability. These efforts don’t make big headlines yet, but they are essential groundwork for any grid-scale deployment.
Where are the gaps?
Despite the progress, there are still major problems AI hasn’t solved yet.
Construction and manufacturing remain huge challenges. The cost gap between building reactors in the U.S. versus countries like China is massive.
“Gigawatt-class reactors in the U.S. cost around ten billion dollars. In China, it’s closer to two and a half.” - Kevin Kong
Regulatory acceptance is another open question. AI can assist with analysis and drafting, but regulators still need to trust the outputs, understand how conclusions were reached, and feel confident that safety isn’t being compromised.
Fusion has its own suite of unsolved problems. AI can help with materials discovery and simulation, but the industry is still far from physics break-even, let alone commercial viability. Many fusion announcements are more about fundraising than deployment, and AI doesn’t change the underlying physics constraints. It can accelerate progress, but it can’t skip steps.
“There’s a big difference between physics break even and commercial break even, and we’re nowhere near commercial today.” - Kevin Kong
There’s also the human factor. Nuclear is a deeply conservative industry for good reason. Introducing new tools, even helpful ones, requires training, cultural buy-in, and clear accountability. AI that isn’t trusted or understood won’t get used, no matter how powerful it is.
Why does this matter?
The reason that the industry is so excited about nuclear is that it can potentially help our world reach a state of energy abundance.
“As energy costs come down, you start unlocking things that were previously too expensive like clean hydrogen, new transportation systems, more compute.” - Kevin Kong
The idea of energy abundance should be exciting to everyone and the main takeaway for me after this conversation was that AI will help make the nuclear industry move at a pace closer to the rest of the modern economy and thus help us reach abundance faster.
Obrigadão irmão ♥️
Hi William, following you now.