Is it safe for nuclear power plants to use AI?

The key lies in which layer it is applied to. Current practical applications focus on administrative and engineering support tasks such as document processing, classification, and information retrieval, without touching core unit control. Safety-critical decisions remain human-driven; the AI's role is to organize and present data to human operators. This clear boundary is what makes regulatory approval possible in the nuclear industry.

Why must industrial AI incorporate physical models?

Pure data-driven models can generate statistically plausible yet physically impossible recommendations, such as operating parameters that violate mass balance. Once engineers see this kind of output, they lose trust in the system entirely. By adding physical constraints, AI recommendations at least fall within physically feasible boundaries—a watershed moment for whether the technology gets adopted, rather than just a technical bell and whistle.

What makes deploying AI difficult for petrochemical plants in Taiwan?

The biggest bottleneck is usually data, not technology: whether historical control system data has been completely preserved, whether labeling is consistent, and whether data silos can be broken down across facilities. Most legacy plants get stuck right at this stage. It is recommended to conduct a comprehensive data inventory before selecting a model; otherwise, you'll end up with a system you can't even feed.

With Taiwan's nuclear units being decommissioned, do these tools still matter?

Absolutely. Decommissioning engineering, nuclear waste management, and compliance documentation still generate massive workloads. Furthermore, the methodology behind these tools—transforming vast amounts of unstructured technical documents into searchable, auto-classifiable assets—holds direct reference value for Taipower, CPC Corporation, and the facility management of semiconductor plants.

Even Nuclear Power Plants Are Using AI: Three Deployment Pathways in the Energy Sector and the Homework Ahead for Taiwan

Over 70 nuclear facilities in North America and the UK are using AI to screen regulatory reports, and oil refineries won't adopt AI predictions until they pass mass conservation checks. This article breaks down the three pathways for integrating AI into energy and utilities, explaining why whether engineers dare to follow its advice matters far more than how large the model is.

A process engineer who spent twenty years at Taiwan’s Mailiao Formosa Plastics plant once told me something I’ve never forgotten: "Our plant is bursting with sensors, but the ones we actually use to make decisions? Probably just that handful of gauges."

A British startup later turned that sentiment into a hard number. Applied Computing noted that oil refineries and petrochemical plants use less than 8% of available data when making operational decisions.

Where does the remaining 92% go? It sits in data historians, with nobody having the time to look at it.

Why the Energy Sector is Uniquely Difficult

Implementing AI in energy and utilities is in an entirely different league compared to general enterprises. There are three reasons for this.

Extremely strict regulation. Every document in a nuclear power plant is subject to audit, and every decision must be traceable. A "the model says so" reasoning simply won't fly.

Sky-high costs of failure. If an e-commerce site recommends the wrong product, the loss is a single order; if the operating parameters in a refinery are wrong, it can lead to a shutdown or a safety incident.

Engineers do not trust black boxes. This is the most crucial point, yet it is rarely discussed. If you present a senior engineer with a UI showing an "AI suggestion to increase temperature by 5 degrees," their very first question will be "Why?" If you can't answer, that system becomes a piece of expensive decoration.

Precisely because it is so difficult, AI deployment case studies in the energy sector are especially worth watching—passing the review here is equivalent to passing the strictest stress test possible.

Path One: Start with Documents, Touch No Controls

The most pragmatic entry point is tackling work that is "important but won't cause explosions."

Nuclearn is a representative of this path. It handles document and workflow automation for nuclear power plants—such as automatic classification and screening of condition reports, equipment and parts data queries, and engineering document retrieval and QA. Its product lines are split by actual departments: Equipment AI, Parts AI, Engineering AI, Performance Improvement AI, and a nuclear-specific chatbot called AtomAssist.

Its pitch is humble: "Faster operations, lower backlogs, less risk." The company serves over 70 nuclear facilities across North America and the UK, and its founding team hails from institutions like the Palo Verde Generating Station.

Notice what it deliberately avoids touching: Unit controls and safety-related decisions are completely off-limits. All AI outputs are delivered directly to humans, who make the final call. This self-limitation isn't conservative; it's the exact reason why it gets adopted.

Nuclear power plants are among the document-heaviest facilities in the world. Every anomaly requires a condition report, and every report needs manual classification and screening. This kind of work—"high volume, clear rules, but consuming massive amounts of engineering hours"—is the absolute sweet point for AI.

Path Two: Use Physics as the Gatekeeper

The second path is more technical and much smarter.

Applied Computing's product, Orbital, stacks three models together. A time-series model connects to control systems and data historians to predict trends and detect anomalies; a language model trained on chemical engineering knowledge interprets plant documents and generates action recommendations.

The middle layer is the game-changer: A physics model acts as the gatekeeper, ensuring that predictions adhere to mass balance, conservation of energy, and reaction kinetics.

This design directly solves the trust issue mentioned earlier. A purely data-driven model might output statistical suggestions that look reasonable on paper, but are physically impossible. Once an engineer sees that happen even once, the system is dead to them.

By adding physical constraints, AI suggestions are restricted to physically feasible ranges, allowing engineers to verify whether "this recommendation makes sense." This is the watershed moment for whether a system gets adopted.

Headquartered in London with a remote-first approach, the company was founded by Imperial College alumni. Dan Jeavons, former AI General Manager at Shell, serves as President, and the company has successfully closed a £9 million seed round. Having Shell’s practical background serves as an instant pass in this industry.

Path Three: Solving the "Can't Build It" Problem

The third path takes an even bigger leap.

Everstar focuses not on the operations of existing power plants, but on solving the crisis of building new ones. Its platform "Gordian" is an AI platform for nuclear operations covering engineering, regulatory licensing, certification, and personnel training; "Titan" is a scalable manufacturing system, with the company setting a goal to "build 400 GW in the US by 2050."

Its scope targets six major links: engineering and regulatory licensing, vendor qualification, workforce development, manufacturing quality control, construction efficiency, and autonomous plant operation. The company officially positions itself as "moving nuclear from paper to steel."

Let's be clear about one thing: AI can accelerate document preparation, data retrieval, and consistency checks—tasks that consume massive man-hours—but regulatory reviews by authorities still take time. A realistic understanding is that it compresses the "waiting for submission" phase, not the "regulatory review" process.

Nevertheless, the company's very existence highlights one hard truth: The true bottleneck of the nuclear renaissance is execution, not technology. It’s not that we don't know how to build them; it’s that the processes, supply chains, and talent pipelines have broken down.

As a side note, Overstory belongs to a similar broad category—it uses satellite imagery to analyze vegetation risks near power lines, serving six of the top ten power utilities in North America, shifting utility trimming budgets from a "cyclical" approach to a "risk-based" one.

Market Impact Analysis

For Taiwanese Users

The average person won't interact with these tools directly, but the impact is indirect yet real: fewer blackouts, more stable electricity pricing, and fewer industrial safety accidents. Overstory’s vegetation risk management addresses the exact same challenge Taiwan faces with tree-fall outages during every typhoon season.

For Taiwanese Enterprises

This section is the core takeaway. Taiwan's petrochemical sectors (CPC Corporation, Formosa Plastics Group), power generation (Taipower), and semiconductor facility management are textbook scenarios for these types of technologies—they feature aging equipment, massive amounts of data, experiential knowledge locked inside the heads of senior engineers, and a wave of retirements hitting that exact generation.

However, I will be brutally honest: Most Taiwanese manufacturers will get stuck on data, not models. Are historical control system data properly preserved? Are naming formats consistent across different plants? Can cross-system integration be achieved? If you can't answer these three questions, buying any system is useless.

The recommended sequence is: First, conduct a data inventory → Second, pick a small-scale document automation pilot → Third, build internal trust → Finally, move on to process optimization. Do not reverse this order.

For Developers

If you are building industrial AI, Applied Computing’s architecture is worth copying: Don’t just make predictions; make verifiable predictions. Turn domain physics or business rules into a constraint layer, allowing users to check for themselves whether the outputs make sense. This is worth more than any massive LLM.

Furthermore, "vertical depth" is a genuine moat in this industry. Nuclearn emphasizes that it isn't just wrapping a UI around a general-purpose LLM, but tailoring its solution specifically for nuclear environments—this distinction determines who wins contracts in heavily regulated industries.

Future Development Trends

1. AI electricity demand will in turn drive the AI transformation of the energy sector. Data center power consumption is altering operational pressures on the utility sector, and one of the tools to cope with this pressure happens to be AI. This flywheel will spin faster and faster. For related discussions in Taiwan, refer to the Taiwan Data Center Power Review.

2. "Auditability" will become a standard feature of industrial AI. Heavily regulated industries will demand that every AI output can be traced back to its sources and rationale. This will force the entire industrial AI market toward explainability.

3. The retirement wave is the ultimate catalyst. A massive wave of senior process engineers in Taiwan's manufacturing industries is retiring, and the knowledge trapped inside human brains is vanishing. The urgency of turning technical documents into searchable, queryable assets will only escalate.

TheAI Academy Summary & Review

My deepest takeaway from this field is this: AI adoption in the energy sector actually teaches us "how to make people willing to trust AI."

It’s not about building bigger models; it’s about drawing clear boundaries (what AI does versus what humans do), making outputs verifiable (physical constraints, source traceability), and starting deployments where the stakes of failure are lowest (documents rather than controls).

These three principles apply to any industry—it’s just that other industries haven't been forced into a corner where they have to do it.

Review: Even the most heavily regulated and conservative nuclear industry is using AI to handle documents. Excuses from other industries claiming "we are too unique to use it" are holding less and less ground.

Concrete advice for Taiwanese readers: If you work in IT or engineering departments within process manufacturing, power generation, or any heavy industry, do one thing this month—audit where your technical documents are stored, how many there are, and what formats they are in. I bet most companies can't answer. And that answer is the real variable determining whether you can deploy AI three years from now, not your budget.

Further Reading: The AI Predictive Maintenance Guide covers an introductory angle of the same context; for building internal enterprise tools, refer to the AI Internal Tools Guide; find more manufacturing and industrial AI tools in the Tools Overview.

Sources

Compiled from public information; official sources prevail. This article does not constitute any investment or engineering decision-making advice; please consult professional consultants before actual deployment.