The Future of U.S. Nuclear Power Is Really a Data & AI Problem

By: Steven K. Mauss, CEO of Knowledge Relay, Inc.

U.S. electricity generation quietly hit a new record in 2025: 4,430 TWh, up 2.8% from 2024.

That growth wasn’t driven by a sudden surge in households buying toasters; it came from commercial and industrial load, including data centers and AI workloads that want power 24/7, not just when the sun is shining.

In parallel, the U.S. Department of Energy (DOE) has put a bold nuclear roadmap on the table: add roughly 35 GW of new nuclear by 2035, reach a sustained pace of around 15 GW per year by 2040, and deliver more than 200 GW of additional capacity by 2050, effectively tripling today’s fleet.

On paper, this is exactly what the grid and AI’s appetite needs. In practice, it will not happen without a modern data and AI foundation inside nuclear operators.

The roadmap is clear. The data isn’t.

Tripling nuclear is not just about technology choices (PWR vs. SMR vs. advanced). It’s about whether the industry can make decisions fast enough, with enough confidence, across hundreds of overlapping projects.

DOE’s framework assumes three parallel lanes:

• Keep and extend the current fleet.
• Restart and uprate where it’s safe and economic.
• Deploy new large and advanced reactors at an industrial scale.

Each of those lanes is fundamentally a data and AI problem:

• Life extensions depend on long-term performance, equipment, and risk data that live across dozens of systems and decades of history.
• Restarts (like Palisades and other candidates) depend on reconciling legacy plant data with modern sensors, designs, and regulatory expectations.
• New builds and advanced reactors depend on realistic, data driven project controls, not wishful schedules in disconnected spreadsheets.
• If the data isn’t unified, governed, and visible, every reactor project is a one off bet instead of a repeatable program.

Four constraints will decide whether nuclear meets AI era demand.

When you talk to operators, four constraints come up repeatedly: fuel, licensing, supply chain, and workforce.

Here’s what they look like through a data and AI lens:

• Fuel → You need scenario modeling across multiple fuel types and suppliers, with risk and cost analytics that update as geopolitics and HALEU availability change.
• Licensing → You need structured metrics, traceable data, and explainable AI that support applications, inspections, and ongoing reporting, not PDFs and tribal knowledge.
• Supply chain → You need cross project performance data, leading indicators, and schedule risk predictions for critical components and vendors.
• Workforce → You need automation and AI copilots that let lean project controls and operations teams manage more work without lowering the safety bar. If you can’t see these constraints in the data, you will always be responding to them too late.

Where Knowledge Relay fits: an AI ready nuclear data layer

At Knowledge Relay, we’ve spent years helping nuclear and energy organizations get out of spreadsheet and silo jail and into governed, AI ready data environments.

For this next wave of nuclear, three capabilities matter most:

1. KR Data Lakehouse®
   A single, governed source of truth for plant, outage, and project data, built on a KR Data Lakehouse architecture that you control inside your own secure environment.
   1. Historian, project controls, finance, and work management data in one model
   2. Lineage and governance so you can defend every metric in front of regulators and boards
2. KR AI – in plant machine learning and copilots
   AI and ML tuned for nuclear realities: schedule risk prediction, cost and contingency forecasting, and predictive maintenance models that run on your data, inside your perimeter.
   1. AI that highlights risk on critical path activities before you burn float
   2. Copilots that assemble regulatory metrics and narratives from governed data instead of manual cut and paste
3. Automated metrics, dashboards, and integration
   KR Data Scheduler and KR Metrics automate the plumbing and the storytelling.
   1. Industrial ETL moving data reliably between historians, Primavera, SAP/Maximo, and BI tools
   2. Standardized scorecards for executives, regulators, and plant staff that show the same numbers, not conflicting versions of the truth

The point is not that “AI will save nuclear.” The point is that without a modern data and AI layer, the tripling roadmap is just a slide.

A layered strategy for nuclear in the AI era

If you’re responsible for nuclear operations, project controls, or fleet strategy, here’s a pragmatic way to think about the next decade:

Today: Protect and optimize the existing fleet

• Build your governed Data Lakehouse on top of what you have.
• Automate your most painful metrics, reports, and outage dashboards first.

Next: De risk restarts and uprates with AI

• Use ML on legacy plus current data to identify technical and schedule risk early.
• Let AI support the business case and licensing narrative with traceable evidence.

Then: Launch advanced reactors with an AI native stack from day one

• Start projects with a shared data model, real time metrics, and AI assisted project controls baked into contracts and governance.
• Treat every reactor as part of a learning fleet, not a standalone exception.

My take:

The U.S. nuclear buildout is no longer limited by physics or policy intent. It’s limited by whether we can trust, govern, and act on our own data fast enough to make hundreds of good decisions in parallel.

If your nuclear energy organization is serious about restarts, uprates, or advanced reactors and yet you’re wrestling with data silos, manual reporting, or AI pilots that can’t get out of the lab, I’d be happy to compare notes on what we’re seeing with Knowledge Relay’s clients.

What would you need to see in your data to be confident in committing to the next 5–10 GW?