AI Finds a Fit in Nuclear Power Operations

AI Meets Nuclear Power With Caution

Big tech companies have been courting nuclear energy for steady, 24/7 electricity. Meta, Google and Microsoft have struck deals with reactor operators and startups to secure carbon‑free baseload power. The nuclear sector, in turn, is warming to artificial intelligence—not to hand over reactor controls, but to sharpen business and operational processes.

A clear example is Nuclearn, founded by Bradley Fox and Jerrold Vincent after work at Palo Verde Nuclear Generating Station. The pair began automating repetitive tasks with data science and later advanced models. Their startup now reports software in use at more than 65 reactors and recently closed a $10.5 million Series A led by Blue Bear Capital.

Nuclearn builds models trained on nuclear‑specific language and can deliver cloud or on‑site deployments to meet strict security rules. Typical outputs include routine documentation that staff review and sign, automation of repetitive reporting, and workflow triage when a model is unsure about an item.

Regulatory context matters. The U.S. Nuclear Regulatory Commission treats most AI in the industry as a tool—akin to engineering software or spreadsheets—so legal responsibility remains with people. Operators set thresholds for automation and require human sign‑off on critical decisions.

That human‑in‑the‑loop posture shows up in practice: if the model lacks confidence, it routes tasks back to designated staff. Customers are asked to think of AI deployments as a "junior employee" that drafts work for an experienced operator to verify.

• Automating routine documentation and regulatory submissions
• Speeding inspection reports and checklists while preserving human oversight
• Triage and workflow routing when anomalies appear
• Supporting procurement, inventory and maintenance planning through pattern detection

These use cases point to immediate business value: fewer staff hours spent on paperwork, faster regulatory reporting, and better allocation of engineering time to safety‑critical work. They also reinforce safety: no credible vendor is proposing that AI run a reactor autonomously.

But challenges remain. Security often demands on‑prem hardware and air‑gapped options. Model performance must be explainable and auditable for regulators. And operators need clear escalation paths so that uncertainty always lands back with qualified staff.

For utilities, the sensible path is iterative: pilot automation on low‑risk workflows, measure time and cost savings, validate with inspectors, then expand. That approach preserves safety while capturing efficiency gains that can improve grid resilience and lower operational costs.

QuarkyByte looks at this shift the way we approach any tech modernization: assess where AI reduces manual toil, design human‑centered workflows, and quantify the operational and regulatory impact. For power companies and regulators alike, the goal is pragmatic adoption—faster, leaner operations without trading off oversight.

Nuclear and AI are not a romance at the level of control systems, but they are partnering where it matters: making complex plants run more efficiently and making engineer time count. Expect more tailored, security‑first AI pilots as utilities chase both decarbonization and operational rigor.