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Shadow AI: The New Trend Nuclear Cannot Afford

Posted on by sondra

Executive Summary

A new pattern is emerging in enterprise AI adoption: Shadow AI. Employees, frustrated with slow-moving official systems, are turning to consumer-grade AI tools like ChatGPT to get work done. MIT’s State of AI in Business 2025 study reports that over 90% of knowledge workers now use unsanctioned AI for drafting, research, and analysis.

In many industries, this trend raises governance and security concerns. In nuclear, it introduces regulatory, safety, and compliance risks that cannot be tolerated.

This paper examines Shadow AI as an enterprise trend, analyzes why it is incompatible with nuclear operations, and outlines how regulator-ready, domain-specific AI addresses the gap.

1. Shadow AI: A Growing Trend

MIT’s research identifies Shadow AI as one of the fastest-growing dynamics in AI adoption:

  • Broad worker adoption. Employees bypass enterprise systems and use consumer AI tools directly.

  • Enterprise lag. Corporate IT and compliance groups struggle to deploy secure alternatives at the same pace.

  • Risk exposure. Sensitive information is copied into cloud-based tools with little oversight.

In industries like retail or marketing, the risks are financial. In nuclear, they are regulatory and existential.

2. Why Nuclear Cannot Tolerate Shadow AI

Nuclear operations rely on strict compliance regimes that consumer AI tools cannot meet:

  • Part 810 Regulations. Export control prohibits the uncontrolled transfer of nuclear technical data. Shadow AI platforms, typically hosted on global cloud infrastructure, are non-compliant by default.

  • Licensing-Basis Sensitivity. Technical Specifications, FSARs, and design-basis documents cannot be exposed to uncontrolled platforms. Even summaries must be regulator-ready.

  • Audit Requirements. NRC oversight requires every evaluation and document to be traceable and verifiable. Shadow AI outputs are not.

Simply put: Shadow AI creates compliance gaps that nuclear regulators and operators cannot accept.

3. Case Study: Licensing Research

Observed Behavior:
Frustrated by slow search systems, engineers tested consumer AI tools to summarize licensing requirements. The AI produced text that appeared helpful but lacked citations and omitted key references.

Outcome:
Outputs could not be defended in NRC-facing documentation. The practice created risk, not efficiency.

Domain-Specific Alternative:
Nuclearn’s Gamma 2 model retrieves licensing basis documents from secure, on-premise repositories. Outputs include full citations, reasoning steps, and maintain alignment with IV&V. Engineers remain in control, but repetitive search is automated.

Result: regulator-ready documentation, compliant with Part 810, without Shadow AI risk.

4. Shadow AI vs. Secure AI

The Shadow AI trend reflects a workforce reality: employees want faster, more usable tools. Restrictive policies alone will not stop Shadow AI. Without secure alternatives, adoption will continue underground.

The solution is not prohibition, but replacement. Nuclear operators must provide systems that are:

  • As usable as consumer AI. Engineers will only adopt what improves their daily work.

  • As secure as required. On-premise, Part 810 compliant, and regulator-ready.

  • Domain-specific. Trained on nuclear acronyms, licensing structures, and workflows.

5. Implications for Nuclear Operators

  • Shadow AI is not theoretical. It is already happening across industries. Nuclear cannot assume immunity.

  • Regulatory exposure is immediate. Even one instance of sensitive data entered into a consumer AI platform may trigger compliance investigations.

  • Workforce demand must be addressed. Engineers will seek usable AI. If utilities don’t provide compliant systems, Shadow AI will fill the gap.

Conclusion

Shadow AI is the new trend shaping enterprise AI adoption. In nuclear, it is untenable. The compliance, regulatory, and safety demands of the industry mean that consumer-grade AI tools cannot be tolerated inside the plant.

The solution is not banning AI use — it is providing secure, domain-specific alternatives that meet the same standard as the industry itself: safety, compliance, and reviewability.

Nuclearn demonstrates that when AI is designed for nuclear — Part 810 compliant, regulator-ready, and embedded in real workflows — it delivers measurable value while eliminating the risks of Shadow AI.

From Pilots to Production: Why Nuclear AI Must Cross the Divide

Posted on by sondra

Executive Summary

Enterprise adoption of generative AI is widespread, but measurable impact remains rare. The MIT State of AI in Business 2025 report found that only 5% of enterprise AI pilots advance into production. The remainder stall due to integration challenges, lack of compliance alignment, and outputs that do not withstand scrutiny.

In nuclear energy, this failure rate cannot be tolerated. Pilots that never scale waste engineering hours, introduce compliance risk, and erode workforce trust. This paper examines why most AI efforts fail to transition, why nuclear’s regulatory environment magnifies the risk, and what design principles are required for AI systems to succeed in production.

1. The Pilot Trap

Across industries, the “pilot trap” is common. Demos and small-scale trials show potential but collapse when scaled. Three recurring factors are identified in MIT’s research:

  1. Workflow Misalignment – Pilots address isolated tasks but fail when integrated into enterprise systems.

  2. Compliance Blind Spots – Outputs lack the transparency needed for audit or regulatory review.

  3. Cultural Resistance – After repeated failures, workforces lose trust in AI initiatives.

For most industries, these failures represent opportunity costs. In nuclear, the consequences are higher. Every pilot requires engineering time, often from senior staff. If the pilot fails, scarce expertise has been diverted from safety and operational priorities.

2. Why Nuclear Is Different

Nuclear operations impose requirements that generic AI tools rarely meet:

  • Independent Verification and Validation (IV&V): All calculations, evaluations, and analyses must be reviewable. Outputs that cannot be traced to source data are unusable.

  • Part 810 Compliance: U.S. export control regulations prohibit uncontrolled data transfer. Cloud-hosted consumer AI platforms cannot meet this requirement.

  • Licensing Basis Alignment: Documentation associated with plant licensing must withstand regulatory audit. Outputs that lack defensibility introduce unacceptable risk.

These conditions mean that nuclear cannot rely on general-purpose AI. Tools must be designed specifically for regulated, documentation-heavy workflows.

3. Case Study: Condition Report Screening

Nuclear plants generate thousands of Condition Reports annually. Each requires screening for safety significance, categorization, and assignment. Historically, this workload demands dedicated teams of experienced staff.

Pilot attempts with generic AI:

  • Demonstrated short-term gains in categorization speed.

  • Failed to provide traceable reasoning or regulatory-suitable documentation.

  • Stalled at the pilot stage due to lack of reviewability.

Production deployment with nuclear-specific AI:

  • Automated initial screening with embedded reasoning steps and citations.

  • Retained IV&V by keeping engineers in the review loop.

  • Scaled to full fleet use, saving tens of thousands of engineering hours annually.

This example illustrates the critical distinction: pilots demonstrate potential; production requires compliance-ready outputs.

4. Case Study: 50.59 Evaluations

The 50.59 process determines whether plant modifications require NRC approval. Evaluations typically require 8–40 hours of engineering time and extensive document research.

Pilot attempts with generic AI:

  • Produced draft summaries of licensing documents.

  • Lacked sufficient traceability for NRC acceptance.

  • Failed to progress beyond trial use.

Production deployment with nuclear-specific AI:

  • Retrieved relevant licensing basis documents with citations.

  • Assembled draft evaluations in ~30 minutes.

  • Enabled engineers to complete reviews in ~2 hours, maintaining full compliance.

The ability to produce regulator-ready outputs was the determining factor in moving from pilot to fleet deployment.

5. Lessons from MIT Applied to Nuclear

MIT’s research identifies three conditions for bridging the gap between pilots and production:

  1. Domain Specificity: Tools must be trained on industry-specific data sets.

  2. Workflow Integration: Systems must embed within existing processes rather than operate in isolation.

  3. Adaptive Learning: AI must improve with use and align with regulatory context.

Applied to nuclear, these principles translate to:

  • Training models on NRC filings, license renewals, and utility documents.

  • Embedding tools into CAP, 50.59, and outage workflows.

  • Designing outputs for traceability, citation, and regulatory review.

Without these conditions, AI pilots in nuclear will remain demonstrations with no lasting impact.

6. Implications for Nuclear Operators

The findings have clear implications:

  • Evaluate vendors beyond demos. Demand evidence of regulator-ready outputs, not just functional prototypes.

  • Prioritize compliance from the start. Systems must be Part 810 compliant and built for IV&V.

  • Focus on critical workflows. Target documentation-heavy processes where measurable impact can be achieved without compromising safety.

  • Guard against cultural fatigue. Each failed pilot increases resistance. Operators should commit only to systems designed for production.

Conclusion

The majority of enterprise AI pilots fail to transition into production. In nuclear, this failure rate is not sustainable. Documentation is safety-critical, compliance is non-negotiable, and workforce trust is essential.

To bridge the gap from pilot to production, AI systems must be domain-specific, workflow-integrated, and regulator-ready. Evidence from early deployments shows that when these conditions are met, nuclear plants can save thousands of engineering hours annually while maintaining safety and compliance.

The lesson is clear: nuclear must move beyond pilots. Production-ready AI, designed for nuclear, is not optional — it is required.

The GenAI Divide — Why Generic AI Fails in Nuclear

Posted on by sondra

Introduction

Across industries, generative AI is being tested in pilots, proof-of-concepts, and trials. The promise is simple: automate routine work, generate documentation faster, and let knowledge workers focus on higher-value tasks.

But the data tell a different story. In its State of AI in Business 2025 report, MIT found that 95% of enterprise GenAI pilots fail to deliver measurable value. Most never move beyond a demonstration. They stall because they don’t integrate into workflows, they forget context, or they produce outputs that can’t be trusted in regulated environments.

For nuclear, this failure rate isn’t just disappointing — it’s unacceptable. Documentation in nuclear isn’t optional; it is the backbone of safety, compliance, and regulatory oversight. If an AI tool cannot produce outputs that are traceable, reviewable, and regulator-ready, it has no place inside the plant.

This is the GenAI Divide. Most industries are struggling to cross it. Nuclear requires a different approach.

What MIT Found

MIT researchers analyzed more than 300 AI initiatives and interviewed senior leaders across industries. Their conclusions highlight why adoption is high but impact is low:

  • High pilot activity, low production: More than 80% of organizations have tested tools like ChatGPT or Copilot. Fewer than 5% of custom AI solutions made it to production.

  • Generic adoption, limited disruption: Consumer tools help with quick drafting, but enterprise-grade deployments stall.

  • The learning gap: Most tools don’t retain context, adapt to workflows, or improve over time. This brittleness means they can’t handle complex processes.

In short, pilots succeed at showing potential. They fail at delivering operational transformation.

Why Nuclear Can’t Afford the Divide

In many industries, failed pilots mean lost time or missed efficiency. In nuclear, they can undermine safety and compliance.

  1. Documentation is not peripheral.
    Every Condition Report, Corrective Action Program entry, or 50.59 evaluation is required by regulation. These aren’t internal notes; they are part of the permanent regulatory record.

  2. Traceability is essential.
    Every calculation, every engineering judgment, every modification review must be linked back to source material. If outputs cannot be cited and verified, they cannot be used.

  3. Workforce turnover magnifies the need.
    With a quarter of the nuclear workforce set to retire within five years, plants need tools that help new engineers become productive quickly. AI that generates unreviewable or inaccurate documentation wastes scarce expertise instead of preserving it.

The conclusion is clear: nuclear cannot tolerate the 95% failure rate seen in other industries. AI must meet the same standards as the industry itself — safety, transparency, and compliance.

Nuclearn’s Approach

Nuclearn was founded by nuclear professionals who saw these challenges firsthand at Palo Verde. Our approach is fundamentally different from generic AI deployments:

  • Nuclear-specific data sets: Our Gamma 2 model is trained on NRC filings, license renewals, technical specifications, and utility-provided documentation. It understands the acronyms, licensing basis requirements, and processes unique to nuclear.

  • Reviewable outputs: Every output includes citations back to source material and exposes the AI’s reasoning steps. Engineers can perform independent verification and validation (IV&V) just as they would for junior engineer work.

  • Workflow integration: Nuclearn doesn’t sit on the side as a chatbot. It is embedded into CAP screening, 50.59 evaluations, outage planning, and licensing research — the real processes that consume plant resources.

  • On-premise, secure deployment: Data never leaves plant control. Our systems are Part 810 compliant and designed to meet U.S. export control regulations.

Case Example: CAP Screening

At a typical reactor, thousands of Condition Reports are filed every year. By regulation, every CR must be screened and categorized: is it adverse to quality? Does it require corrective action? Which group is responsible?

Historically, this requires full-time teams of experienced staff. It is repetitive, manual, and essential.

With Nuclearn:

  • AI automates the screening and categorization process.

  • Experienced engineers remain in the loop, reviewing and verifying.

  • Plants save tens of thousands of hours annually, freeing highly skilled staff for higher-value work.

The process is faster and more consistent — but still compliant with regulatory expectations for reviewability.

Case Example: 50.59 Evaluations

The 50.59 process requires engineers to determine whether a proposed modification changes the plant’s licensing basis and whether NRC notification is required. It is one of the most documentation-intensive processes in the industry.

Traditionally:

  • Each evaluation takes between 8 and 40 hours.

  • Engineers must search thousands of pages of licensing documents.

  • Work often involves multiple layers of review and verification.

With Nuclearn’s agent-based workflows:

  • Relevant licensing basis documents are retrieved automatically.

  • Key requirements and citations are assembled.

  • Engineers receive a draft evaluation in about 30 minutes.

The final review still takes human expertise, but the process now takes ~2 hours instead of several days. Outputs remain fully traceable, with citations back to source material for regulatory confidence.

Aligning with Industry Findings

Where most AI pilots fail, Nuclearn succeeds because our approach directly addresses the barriers highlighted by the industry reports:

  • Process-specific customization: We don’t try to solve everything. We focus on CAP, 50.59, outage planning, and licensing.

  • Workflow integration: Our tools are embedded in actual plant processes, not running in isolation.

  • Learning and adaptation: Our models are trained on nuclear-specific data and tuned for each utility.

  • Compliance and traceability: Outputs are regulator-ready, built for IV&V.

This is exactly what MIT identifies as the path across the GenAI Divide: adaptive, embedded, domain-specific systems

Closing Thought

The MIT study is a warning. Most enterprises will spend money and time on AI tools that never scale. They will produce demos, not durable solutions.

Nuclear does not have that luxury. Our industry requires AI that can withstand NRC oversight, peer review, and decades of operational scrutiny. That is what Nuclearn delivers: solutions that are reviewable, verifiable, and regulator-ready.

If AI can meet nuclear’s bar, it can meet any bar.

The Top 5 Misconceptions About AI in Nuclear

Posted on by sondra

Artificial Intelligence is gaining momentum across every sector of the energy industry, and nuclear is no exception. Yet, despite real-world deployments and growing acceptance among engineers and regulators, outdated assumptions about AI persist—slowing adoption, blocking innovation, and ultimately costing facilities time, money, and human capital.

At Nuclearn, we’ve seen firsthand how AI can help nuclear teams solve critical challenges—from accelerating 50.59 evaluations to digitizing field work execution. But to unlock its full potential, we must first confront the myths.

This post breaks down the five most persistent misconceptions we encounter when discussing AI with utilities, engineers, regulators, and the public—and why each one is due for retirement.

1. AI Replaces People

The most common misconception—and often the most emotional—is that AI is here to replace engineers, planners, or operations staff. This could not be further from the truth.

Every Nuclearn solution is built on a “human-in-the-loop” foundation. Our AI is designed to assist professionals by automating repetitive or time-consuming tasks, allowing them to spend more time applying their critical thinking, experience, and judgment.

For example, Engineering AI can scan through thousands of condition reports (CRs) in seconds to surface relevant issues. But the decision about whether a 50.59 threshold has been met? That still belongs to the engineer.

“We don’t take engineers out of the loop—we give them better tools inside it.”

2. Regulators Won’t Allow AI

It’s true that nuclear is one of the most heavily regulated industries on Earth. And that’s a good thing. But assuming that regulation and AI are incompatible misses an important shift happening in the industry.

Regulators are not opposing AI—they are increasingly participating in conversations about how it can support safer, more auditable operations.

Nuclearn’s AI systems are designed to enhance compliance. Every recommendation is traceable. Every interaction can be logged. Outputs are consistent, reviewable, and auditable.

In fact, our human-in-the-loop workflows often provide more accountability than existing paper-driven systems.

We work within the bounds of 10 CFR 50.59, 50.72, Appendix B, and Part 810. AI isn’t a loophole—it’s a tool to execute regulatory responsibilities with greater efficiency and rigor.

3. Generic AI Tools Work Just Fine

You may have heard that ChatGPT or a plug-in LLM can “handle nuclear documentation.” We’ve even heard teams ask if they could simply drop their procedures into a public chatbot and get answers back.

Let’s be clear: generic AI tools are not built for nuclear.

They do not understand:

  • Plant-specific licensing basis and design basis rules
  • Condition reporting systems
  • Corrective action protocols
  • Engineering workflows governed by QA and safety compliance

By contrast, Nuclearn’s tools are trained with nuclear-specific language, rulesets, and domain experience. Our platforms know the difference between a maintenance rule failure and a licensing threshold. That context matters.

“You wouldn’t use a kitchen timer to run a reactor. Don’t use generic AI to run your plant.”

4. AI Can’t Be Deployed Securely

Security is not an afterthought. It’s a starting point.

All Nuclearn solutions are deployable on-premise, behind your firewall, and air-gapped if necessary. We are fully Part 810–compliant, meaning none of your data is ever exposed to public models or cloud APIs.

In secure deployments, we:

  • Run local inference on utility-controlled infrastructure
  • Support role-based access and authentication
  • Provide complete audit trails for every AI decision
  • Operate within closed systems with full IT visibility

Our customers run these systems in the most critical environments in the U.S. nuclear fleet. And they trust our infrastructure to meet their cyber and export control needs without compromise.

5. AI is Only for Advanced Utilities

Some leaders assume their plant is too small, too traditional, or too legacy to benefit from AI. That’s another myth.

In reality, the tools that make the biggest difference are often the ones that address universally painful problems—like documentation backlog, mod review time, or CR screening.

That’s why we’ve built our solutions to be modular, phased, and adaptable. Whether you’re a one-unit site with a lean staff or a multi-site operator with enterprise systems, AI can work for you.

In fact, some of our most successful deployments have been at facilities that viewed digital transformation as a necessity—not a luxury.

Conclusion: Moving Beyond Myths

The nuclear industry is evolving. Energy demand is rising. The workforce is retiring. And the expectations on plant performance, compliance, and efficiency have never been higher.

AI isn’t a futuristic concept. It’s a present-day enabler. But only if we let go of the myths that have held us back.

At Nuclearn, we’re ready to work with you to explore what secure, explainable, human-centered AI looks like in your plant. Let’s move beyond misconceptions—and get to work.

Nuclearn Joins Texas Nuclear Alliance to Help Power the Future of Clean Energy

Posted on by sondra

Nuclearn was proudly welcomed as a founding member of the Texas Nuclear Alliance (TNA)—a milestone that reflects both our commitment to the future of nuclear energy and the importance of Texas in leading that future.

As a company built by nuclear engineers for nuclear engineers, Nuclearn has long recognized that modernizing nuclear operations requires more than just better hardware—it requires smarter software. That’s where our AI-powered, nuclear-specific solutions come in, and that’s why joining the Texas Nuclear Alliance is so meaningful to us.

“In our rapidly growing AI economy, Nuclearn is meeting the moment by modernizing nuclear operations and bringing new levels of efficiency to safe, reliable energy,” said TNA President Reed Clay. “TNA is proud to partner with the bright minds at Nuclearn and looks forward to working together to unlock the full potential of nuclear energy right here in Texas.”

Why This Partnership Matters

Formed in the aftermath of Winter Storm Uri in 2022, the Texas Nuclear Alliance is the only organization in the state dedicated solely to the advancement of nuclear technology. TNA’s mission is bold and clear: to make Texas the Nuclear Capital of the World.

That’s a mission we fully align with.

Texas has long been a leader in energy, with a strong track record in oil, gas, wind, and solar. But as the demand for secure, reliable, low-carbon energy accelerates, nuclear is increasingly recognized as the only always-on clean energy source that can scale fast enough to meet the moment.

Nuclearn’s decision to join TNA as a founding member is a reflection of our commitment to ensuring nuclear energy remains at the core of Texas’s energy strategy—and of the global clean energy transition.

“Texas’s leadership in energy innovation, combined with Nuclearn’s nuclear AI expertise, further reinforces that the future of nuclear energy is secure, efficient, and scalable,” said Brad Fox, CEO and Co-Founder of Nuclearn.

What Nuclearn Brings to the Table

Our mission has always been grounded in one simple idea: Nuclear deserves better tools.

Nuclearn creates AI-powered software designed specifically for nuclear operations—solutions built by engineers who understand the complexity of the field. Our platform is trusted by more than 60 nuclear reactors worldwide, helping teams enhance safety, streamline operations, and reduce time spent on repetitive, manual tasks.

Our software capabilities include:

  • Outage planning and scheduling automation

  • AI-assisted documentation and reporting

  • CAP and QA process acceleration

  • On-premise deployment for maximum security and compliance

  • Pre-trained, nuclear-specific large language models (LLMs)

“We see incredible opportunity in aligning with the Texas Nuclear Alliance to accelerate next-generation nuclear deployment,” said Jerrold Vincent, CFO and Co-Founder of Nuclearn. “We’re eager to support the mission of making Texas the global leader in clean, reliable nuclear energy.”

Shared Vision, Shared Impact

By joining the Texas Nuclear Alliance, we’re adding our voice—and our capabilities—to a powerful coalition that includes policymakers, industry leaders, utilities, reactor developers, and community stakeholders. Together, we are focused on four key goals:

  1. Accelerating Deployment: From advanced reactors to SMRs, our tools can help streamline regulatory approvals, documentation, and training—reducing friction in the deployment process.

  2. Workforce Modernization: AI isn’t replacing people—it’s enabling them to focus on high-value work. We’re building solutions that support knowledge transfer, training, and field productivity.

  3. Operational Excellence: From CAP and QA to maintenance and planning, our platform enables faster, safer decision-making at every level of plant operations.

  4. Trust and Transparency: Our explainable AI models are designed to meet nuclear’s high standards for compliance and traceability.

With this founding membership, we’re not just talking about innovation—we’re actively investing in it. Together with the TNA, we’re committed to creating real momentum for nuclear’s growth in Texas and beyond.

Texas as a Launchpad for the Future

Texas is uniquely positioned to lead the next generation of nuclear innovation. With its expansive energy infrastructure, technology-forward business climate, and a growing need for dispatchable clean energy, it provides the perfect proving ground for what’s possible when policy, technology, and purpose align.

The Texas Nuclear Alliance provides a platform to accelerate this alignment. By bringing industry and innovation together, TNA is setting the stage for long-term, scalable progress.

For Nuclearn, being part of this founding group means we’re not only helping shape the future of nuclear in Texas—we’re also gaining valuable insights and partnerships that will inform our broader mission across the country and around the world.

Looking Ahead

We’re honored to work alongside TNA and its members to advance nuclear energy through AI, innovation, and purpose-driven collaboration.

As a company, we’ll continue building software that enables nuclear teams to do their best work—whether that’s preparing for an outage, training a new generation of operators, or planning the next phase of reactor development.

The opportunity is enormous. The need is urgent. And the time is now.

We’re ready.

Beyond the Buzz: How GenAI Is Delivering Real Results in Nuclear and Utility Operations

Posted on by sondra

The rise of Generative AI (GenAI) has changed how the world thinks about automation—but in nuclear and utility operations, the conversation has already shifted to what AI is doing in the field. Across operations, planning, safety, and engineering, GenAI is now part of how real work gets done.

At Nuclearn, we don’t build for hype. We build for the realities of secure, highly regulated environments. Our AI platform is designed specifically for nuclear and utility teams and is deployed in the field, supporting work at over 48 facilities in the U.S., Canada, and the U.K.

In this post, we’re breaking down what GenAI is currently doing on the ground, where it’s having a measurable impact, and why success depends on aligning AI with real operational workflows, not theoretical possibilities.

From Concept to Capability

For many organizations, the GenAI conversation started with curiosity. Could it make processes more efficient? Could it help with documentation? Could it reduce repetitive manual work?

Today, those questions are being answered by field teams using Nuclearn.

The most successful sites didn’t ask AI to transform their world overnight. They started with well-defined use cases, aligned their internal teams, and focused on delivering outcomes with clear value and traceability.

Here are the areas where GenAI is already embedded in day-to-day nuclear work.

Current Use Cases for GenAI in Nuclear and Utility Operations

✅ FSAR and Tech Spec Research

Engineers often spend time manually searching across large, version-controlled documents to find design references or validate assumptions. With GenAI, they can enter a natural-language question and receive a detailed response with specific citations from source materials.

Validation Path:

  • Every output is sourced and linked to exact regulatory documentation.

  • All citation paths are transparent for engineering review.

  • Sites control the source data.

✅ Procedure Cross-Referencing

Procedure writers and reviewers use GenAI to identify where one change might impact other connected procedures or protocols. This is especially useful when dealing with cascading effects across systems or plant conditions.

Validation Path:

  • AI flags linked procedures but does not finalize changes.

  • Suggested impacts are provided with excerpts from each document for human review.

  • Peer reviewers use the tool as a checklist enhancer, not a replacement.

✅ Safety Observation Summarization

Frontline staff and supervisors use Nuclearn’s AI to turn field notes and observations into structured summaries. These summaries are then reviewed and integrated into corrective action programs.

Validation Path:

  • The platform does not “decide” root causes—rather, it surfaces consistent language based on previous entries.

  • Users are prompted to review and confirm summaries before submission.

  • All content generated can be traced to original user input.

Why These Use Cases Succeed

One of the reasons Nuclearn’s AI delivers value where others fall short is because our approach is focused on augmentation, not automation. AI isn’t replacing engineers or operators—it’s giving them a faster, more informed starting point.

What makes that possible?

  • Security-First Design: Deployed on-premise or in government-approved environments.

  • Explainable Outputs: All responses are documented with reasoning and source path.

  • Persona-Based Logic: AI behavior adjusts based on whether the user is a procedure writer, planner, or safety engineer.

  • Custom Knowledge Bases: Data belongs to the site, not to a public model or shared server.

We’ve found that success with GenAI depends on three things:

  1. Contextual accuracy

  2. Security integration

  3. Staff involvement in adoption

What Field Teams Are Saying

Across facilities, we’ve heard similar feedback:

  • Engineers want faster access to structured references, not more data dumps.

  • Planners want a second set of eyes on tagging logic, not a black box.

  • Safety teams want cleaner summaries, not templated outputs.

When GenAI is introduced with those needs in mind, it’s quickly seen not as a threat, but as a support system.

Avoiding Common Pitfalls

Not all AI models are ready for nuclear. Some platforms are built for commercial use or are too generalized to handle regulatory nuances. Here are a few flags that suggest a solution may not be fit for this environment:

  • No citation support: If it doesn’t show its sources, it can’t be trusted.

  • One-size-fits-all logic: Nuclear doesn’t operate like finance or retail, and neither should its AI.

  • Cloud-only deployment: Sites need control over data—public cloud models may not meet that need.

  • No understanding of standards: If it doesn’t align with NQA-1 or 10 CFR principles, it shouldn’t be in your stack.

At Nuclearn, every deployment is supported by onboarding, site-specific configuration, and training aligned to real workflows.

Final Thoughts

GenAI has moved beyond buzzwords in the nuclear sector. It’s in the field, in the workflow, and in the hands of professionals who are validating its value daily.

By focusing on purpose-built design, explainability, and secure deployment, Nuclearn is showing what it looks like to implement GenAI the right way—without shortcuts, compromises, or gimmicks.

This is not future-state talk. This is now.
And it’s only just beginning.

Why New Entrants Validate What We’ve Already Built

Posted on by sondra

The energy industry is experiencing a long-overdue shift—one where AI is no longer a novelty but a necessity. And in the nuclear and utility sectors, we’re beginning to see something we welcome: more vendors entering the space.

It’s a positive sign. The growing interest from startups, enterprise AI labs, and newly formed nuclear-focused technology companies is a clear signal that the market is ready to modernize. Everyone—from operators to regulators—is looking for smarter, faster, and more secure ways to manage highly regulated, high-impact work.

But here’s the truth: not all solutions are created equal.

Some new entrants are offering early-stage beta tools. Others are repackaging general-purpose AI under the banner of “nuclear transformation.” What many still lack is what we’ve spent the last four years building at Nuclearn—deep operational understanding, embedded security architecture, and proven use cases deployed at scale.

Validation of the Mission

We’re not threatened by more players in the space. We welcome them. Every new entrant, every investor conversation, every “nuclear AI” LinkedIn post is validating what we’ve already proven: AI is no longer optional in this industry—it’s essential.

Since 2021, we’ve been supporting real-world operations across 48+ nuclear and utility sites in the U.S., Canada, and the U.K. We’ve worked inside secure environments, with live operational data, building tools that move the needle on efficiency, accuracy, and safety.

In other words, we’re not experimenting. We’re executing.

How Nuclearn Sets the Standard

Nuclearn wasn’t adapted for nuclear—it was built for it. Our team of nuclear engineers, planners, and outage veterans knew that generic AI couldn’t meet regulatory, compliance, or cultural requirements. So we designed a platform that could.

Here’s what differentiates Nuclearn in an increasingly noisy space:

  • Field-Proven Deployment: Our tools are actively in use at commercial nuclear sites—not in simulation, not in “pilot purgatory.”
  • Part 810 Compliant: Our system architecture was designed with export control, cyber resilience, and data sovereignty in mind from day one.
  • On-Prem & GovCloud Options: We know what IT, security, and operations teams need—and we offer deployment flexibility to match.
  • Designed for Real Workflows: Procedure updates, FSAR crosswalks, outage readiness, tagging validation, safety documentation—these aren’t buzzwords. They’re everyday challenges we solve.

While others are still preparing for the work, Nuclearn is already helping teams:

  • Cut hours from outage document prep
  • Reduce the review burden on procedure writers
  • Accelerate tagging accuracy during planning
  • Analyze safety observations and generate reports in real-time
  • Our platform doesn’t just talk about nuclear. It speaks fluently.

Why Competition Matters

Yes, we’re leading this category—but we don’t want to be alone in it. Innovation benefits from pressure and perspective. When more companies try to build for this space, we all learn what works, what doesn’t, and what’s required to earn trust in high-stakes environments.

Healthy competition pushes everyone to do better—for customers, for industry standards, and the future of nuclear.

But let’s be clear: this isn’t an industry that has time for AI that “might” work. This is a mission-critical environment. There is no room for hallucinated citations, opaque black boxes, or half-secure integrations.

So while we’re glad the space is growing, we’ll continue focusing on the things that matter most:
Security. Compliance. Trust. And results.

Customers Aren’t Looking for Options—They’re Looking for Outcomes

What we’re hearing in the field is that buyers aren’t overwhelmed—they’re skeptical. Leaders at plants, utilities, and national labs are asking:

  • Is it secure?
  • Is it proven?
  • Does it integrate with our workflow?
  • Can we deploy it without adding risk?

This is where Nuclearn continues to stand apart. Because our answers are:
✅ Yes.
✅ Yes.
✅ Yes.
✅ And yes.

We’ve never been interested in tech for tech’s sake. We’re here to build solutions that reduce friction, reclaim hours, and elevate the work of nuclear professionals.

The Bar Is High—And That’s a Good Thing

We’ve helped raise expectations. And we’re proud of that. Because when we hold ourselves—and our peers—to a higher standard, the entire industry benefits.

We want a world where:

  • AI-powered documentation becomes the norm, not the exception
  • Safety data is reviewed with contextual intelligence
  • Engineers are free to engineer, not just fill out forms

That’s not science fiction. That’s what our users are doing with Nuclearn—right now.

Final Word

The rise of new entrants into the nuclear and utility AI space is exciting. It means this sector is finally getting the innovation attention it deserves.

But we’re not racing to catch up. We’re defining the pace.
We’re already supporting operations, delivering value, and earning the trust of nuclear’s most security-conscious customers.

We’re not the future of nuclear AI.
We’re its present.

The Second Nuclear Renaissance Is Here—Now Let’s Get to Work

Posted on by sondra

The White House’s May 2025 Executive Orders mark the clearest national endorsement of nuclear energy in decades. As CRO of Nuclearn and a longtime nuclear professional, I see this as both validation and a call to action.

The Executive Orders direct immediate action: regulatory modernization, advanced reactor deployment, domestic fuel production, and streamlining of NRC processes. It’s a big win for the industry, but it only matters if we deliver.

To move from intent to impact, we need more than new builds. We need modern systems. The nuclear renaissance must be powered not just by concrete and steel, but by intelligent, secure platforms that enable safer, faster, and more collaborative operations.

That’s exactly what we’ve built at Nuclearn. Our AI-driven platform supports:

  • FSAR and tech spec cross-referencing

  • Automated documentation workflows

  • Safety observation analysis

  • Real-time tagging validation

  • On-prem and compliant deployments

We’re not theorizing. We’re in the field, helping over 48 plants in the U.S., Canada, and the U.K. digitize the most critical parts of their operations.

This renaissance is also a global competition. Nations are racing to scale nuclear while proving they can do it safely, affordably, and with workforce agility. The U.S. can lead—but only if it embraces digital infrastructure alongside physical.

Let’s make sure we build this next chapter with tools that are:

  • Built for compliance

  • Proven in practice

  • Designed by nuclear professionals

At Nuclearn, we’re ready. We’re honored to be the platform many are already choosing to help meet the moment.

Let’s lead with confidence, with integrity, and with smarter systems.

Evolving the Brand. Honoring the Mission.

Posted on by sondra

At Nuclearn, we believe that how we show up visually should reflect how we operate technically: with clarity, confidence, and purpose.

That’s why this summer, we’re rolling out a new logo and brand identity that reflects our momentum, growth, and deepening role across the nuclear and utility sectors. This isn’t change for change’s sake. This is evolution with intention.

From our earliest days, we’ve been committed to solving real problems in regulated industries. Our founding team includes nuclear engineers and operational professionals who lived the pain points of documentation, compliance, outage planning, and legacy systems. Nuclearn was built to do what other platforms couldn’t: deliver secure, purpose-built AI designed for the work that matters most.

Today, with over 48 leading nuclear and utility sites using Nuclearn across the U.S., Canada, and the U.K., our visual identity is catching up to our impact.

Why We Made the Change

The energy and utility sectors are evolving rapidly. New technology. New policies. New expectations. But too many vendors are still offering generic AI dressed up in industry lingo.

We don’t retrofit. We build specifically for this world.

The updated Nuclearn logo reflects the strength, precision, and intelligence behind our platform. It’s modern and grounded—just like our software. Our updated color palette retains our signature green but with sharper contrast and improved accessibility. And our typography and layout updates signal clarity, security, and purpose.

What Isn’t Changing

While the look is evolving, our mission remains the same:

  • We build secure, compliant AI solutions for regulated industries.

  • We empower engineers, operators, and analysts to spend less time on admin and more time solving problems.

  • We honor the integrity and safety culture of the nuclear industry in every deployment.

We’re still the team that shows up onsite, listens closely, and iterates fast. We’re still offering on-prem and government cloud deployments. We’re still committed to Part 810 compliance, data sovereignty, and customer ownership of information.

What It Means for You

If you’re already a Nuclearn customer, this change will start to show up in your interface, documents, and support materials beginning in July. Your functionality, security, and data remain unchanged.

If you’re exploring Nuclearn, this new look reflects the confidence our clients already have in our products. When they choose Nuclearn, they’re choosing a platform that doesn’t just promise compliance and speed—it delivers both.

Brand Built for a Movement

The nuclear and utility sectors are entering a new era. More investment. More scrutiny. More urgency. This requires not just innovation, but identity.

We believe a brand should express what a company values. For us, that means:

  • Trust through transparency

  • Intelligence through usability

  • Innovation grounded in real-world performance

So while this update may be visual, the reasoning behind it runs deep. We’re preparing for the next decade of growth, and we’re proud to do so with a sharper, stronger identity.

Because when you’re building the future of nuclear, you should look like it.

Iberian Peninsula Blackout: How the April 28, 2025 Outage Unfolded and Lessons for Grid Resilience

Posted on by sondra

Overview of the Iberian Blackout

On April 28, 2025, a massive power outage swept across the Iberian Peninsula, plunging most of Spain and all of Portugal into darkness. The blackout struck suddenly around 12:33 p.m. local time, bringing daily life to a standstill. Planes were grounded, metros halted mid-journey, and hospitals scrambled to switch to backup generators, Reuters.com. Spain’s Interior Ministry declared a national emergency as millions coped without electricity in one of the largest European power failures on record, ecfr.eu. By the next morning, power had been restored to nearly all affected areas, but the outage, one of the biggest in Europe’s history, left serious questions in its wake.orgecfr.eu.

A Cascading Failure: How the Outage Unfolded

Grid operators indicate that this was no ordinary outage, but a cascading failure that unfolded in a matter of seconds. According to Red Eléctrica de España (REE), Spain’s transmission operator, an initial disturbance occurred shortly after 12:30 p.m., akin to the sudden loss of a large power plant. The system’s safeguards kicked in, and the grid almost stabilized – but 1.5 seconds later, a second event struck, overwhelming the system. In those critical few seconds, Spain suffered a loss of 15 GW of generation – about 60% of national demand – in a cascading trip of power sources.. This precipitous drop in supply sent frequencies and voltages plummeting outside safe bounds. European grids are engineered to handle the unexpected loss of a big plant or power line (an “N-1” event), but here multiple failures hit in quick succession. The chain reaction exceeded what European systems are designed to manage, causing the Iberian grid to buckle under the stress.

As the events cascaded, Spain’s grid became electrically isolated from its neighbors. The disturbance apparently began in Spain’s network and rippled outward – REE has pointed to a sudden disconnection from the French grid as the likely trigger, which in turn severed the Iberian Peninsula from continental Europe. Once Spain and Portugal were cut off (“islanded”) together, they had to balance themselves with no outside help. With such a massive generation deficit, the entire Iberian system collapsed into a total blackout. Even parts of southern France and microstates like Andorra experienced brief outages as the shockwaves spread.

In short, the outage unfolded as a rapid domino effect: an initial fault or disruption led to protective shutdowns, which triggered further losses of generation and grid connections in a vicious circle. Power plants and substations tripped offline to protect themselves, but in doing so, they magnified the imbalance. The result was a continent-scale grid fragmentation, with Iberia going dark in an instant.

Probing the Root Causes

In the aftermath, investigators are working to pin down the exact root cause of this unprecedented failure. At this stage, no definitive cause has been confirmed, and officials caution that the analysis will take time. Spanish Prime Minister Pedro Sánchez announced that all hypotheses remain on the table as experts analyze data from the grid disturbance. However, some early theories have already been ruled out by grid operators. REE stated “preliminarily” that no cyberattack, human error, or extreme weather phenomenon was to blame for the blackout. This aligns with reports that weather conditions were fair at the time, and so far, there’s no evidence of malicious activity in control systems.

Notably, a rather exotic explanation made headlines initially: a “rare atmospheric phenomenon” called induced atmospheric vibration was cited in some media reports as a possible trigger (carbonbrief.org). This theory suggested that sudden temperature changes in the upper atmosphere caused oscillations in high-voltage lines, disrupting the grid’s synchronization. However, the Portuguese grid operator REN quickly clarified that this was misattributed to them and is not a commonly recognized cause of blackouts, carbonbrief.org. Experts have also expressed skepticism – such dramatic weather-induced oscillations are extremely rare, and conditions in Spain were calm that day. While the concept of atmospheric waves affecting power lines isn’t entirely implausible, it remains an unconfirmed hypothesis and is likely not the main culprit.

What, then, do investigators suspect? Attention has focused on the electrical link between Spain and France, a critical interconnection that was undergoing maintenance on one circuit and carrying unusually high flows on the remaining lines. A fault or overload on this interconnector could have caused it to trip offline – essentially cutting Iberia off from the rest of Europe in an instant. REE indicated that a failure at the French connection precipitated the knock-on effects that led to the collapse. If the tie-line to France went down while Spain was exporting or importing large amounts of power, the sudden imbalance would cause the frequency to swing violently. With only a few gigawatts of interconnection capacity, Iberia is almost an “electrical island” under such conditions. That Monday, Spain may have been exporting energy (thanks to strong midday renewables generation), meaning the loss of the French link abruptly left a surplus of power with nowhere to go, followed by an even larger deficit as generators tripped – a one-two punch for stability.

Grid experts also observed unusual frequency oscillations across Europe just before the blackout, suggesting a continent-wide resonance might have been developing. The fact that fluctuations were recorded as far away as Latvia in the same moments hints at a complex inter-area disturbance in the synchronous European grid. This raises the possibility that the Iberian event was not entirely isolated, but related to broader oscillatory behavior on the European network carbonbrief.org. Investigators from the European Network of Transmission System Operators (ENTSO-E) are surely examining whether a far-reaching oscillation or control malfunction precipitated the Iberian collapse.

In summary, the root cause appears to be a confluence of factors: a critical interconnector trip, rapid cascading failures in generation and load, and the inherent vulnerabilities of a modern grid running with razor-thin margins for error. It was the speed and scale of the collapse that stunned grid operators – an event beyond worst-case designs. As Eduardo Prieto of REE noted, “the extent of the loss of power was beyond what European systems are designed to handle,” Reuters.com. This has prompted urgent reflection on how to bolster the grid against such extreme events.

Renewables Integration and Grid Stability

Iberia’s blackout has also spurred debate about the role of renewable energy in grid stability. Spain and Portugal have rapidly expanded solar and wind generation in recent years as part of the clean energy transition. Just days before the outage, Spain’s grid ran 100% on renewables for the first time (on April 16) – a point noted by many observers (carbonbrief.org). At the time of the blackout (late morning on April 28), solar farms were producing a significant share of electricity, supplemented by wind and hydro, while conventional plants (gas, coal) were at lower output. This means the grid was relying heavily on inverter-based resources (solar panels and wind turbines) at that moment.

Importantly, a power system dominated by renewables behaves differently than one anchored by large fossil or nuclear plants. One key challenge is lower inertia. Traditional power plants with big spinning turbines (like coal, gas, and nuclear units) naturally resist frequency swings, acting as a stabilizing ballast. Most renewables, by contrast, connect via power electronics and don’t inherently provide that rotational inertia. During the Iberian event, system inertia was likely on the low side – it was a sunny midday with high renewable output and some transmission elements out of service. As a result, when the disturbance hit, the grid’s frequency plummeted faster than protective systems could respond. As one engineer put it, today’s grid frequency “plunges more quickly than protections can act” in a high-renewables scenario when a big disruption occurs. In other words, low inertia contributed to the speed and severity of the cascade.

It’s critical to note that renewables themselves did not cause the blackout, but the incident does highlight the integration challenges of a cleaner grid. Some commentators were quick to blame renewables or climate policies, but experts have pushed back on that narrative. The system had operated with a similar renewables mix on other days without incident; a specific technical fault set off this chain reaction, not simply the presence of solar farms. However, the high renewables share likely influenced how the event unfolded, by reducing the available inertia and perhaps by the behavior of inverter controls during the frequency swings. Grid operators have implemented grid code requirements for wind and solar plants to ride through disturbances and even provide synthetic inertia (mimicking the stabilizing effect of turbines. But despite these measures, a fast, large upset can still be hard to arrest in a system with many inverter-based resources. The blackout is a stark reminder that as we transition to cleaner energy, grid stability measures must evolve in parallel.

In Spain’s case, the renewable energy transition is well underway – renewables supplied 56% of the country’s electricity in 2024 on an annual basis. This is a fantastic achievement for sustainability, yet it stresses a grid built decades ago around conventional generation. Much of Europe’s transmission infrastructure (transformers, lines, safeguards) is aging – about 40% of the EU’s grid is over 40 years old ecfr.eu. Upgrading this hardware and the associated software controls is vital to accommodate a more variable, decentralized supply mix. The Iberian blackout exposed these growing pains: an advanced grid that needs a new toolkit to handle the dynamics of the 21st-century energy mix. Solutions like energy storage, faster-reacting reserve power, and grid-forming inverter technology can help renewables-rich grids self-stabilize after shocks. Spain has considerable hydropower and some battery projects that can offer quick-balancing capabilities, but on April 28, the disturbance was too great for the existing safeguards to contain.

Insights from Experts on Preventing Future Blackouts

The scope of the Iberian outage has prompted power system experts worldwide to scrutinize what happened and how to prevent a repeat. On May 6, the Electric Power Research Institute (EPRI) convened a special webinar analyzing the event. In this session, Daniel Brooks (EPRI’s Senior VP for Energy Delivery and Customer Solutions), along with grid specialists Sean McGuinness and Eamonn Lannoye, discussed initial findings and lessons for grid resiliency. They placed the Iberian blackout in context, noting that cascading outages, while rare, are not unheard of, and we can learn from past incidents. (For instance, a 2021 European grid disturbance also originated on the Spanish-French border, though its impacts were contained.)

Early insights from the EPRI analysis underline several resilience lessons:

  • Strengthen Grid Infrastructure: Europe must modernize and expand its transmission networks, especially cross-border interconnectors. In an integrated grid, robust interconnections act as shock absorbers, allowing neighboring regions to share support during a crisis. Currently, bottlenecks in inter-country links can hinder rapid support, as seen when Iberia’s tie-line to France failed, leaving no path for aid. Improving and adding interconnectors (e.g., between Spain and France) would make it easier to contain disturbances by spreading out the impact ecfr.eu. In short, a more connected grid is a more resilient grid, provided those links are reliable.

  • Deploy Advanced Stabilization Technologies: With renewable penetration rising, grid operators need new tools to maintain balance and frequency stability. One priority is investing in energy storage and fast-ramping resources. Grid-scale batteries, pumped hydro storage, and emerging solutions like hydrogen energy storage can act as buffers – absorbing excess energy or injecting power on a split-second notice ecfr.eu. These resources provide a kind of insurance, helping to arrest frequency drops or fill sudden supply gaps. Additionally, “grid-forming” inverter technology in wind and solar farms can allow renewables to emulate many of the grid-supporting characteristics of traditional plants (providing virtual inertia and voltage support). Enhancing inertial response – whether through synchronous condensers, advanced inverters, or simply keeping some conventional units online – is critical so that future grids can ride through shocks without cascading.

  • Improve System Monitoring and Coordination: The Iberian event highlighted how quickly a local fault can escalate in a complex network. Better real-time awareness and automated controls are essential. Experts recommend accelerating the adoption of smart grids and AI-based forecasting/controls to give grid operators a clearer picture of grid stress in real time ecfr.eu. For example, wide-area monitoring systems can detect abnormal frequency oscillations and trigger corrective actions (like controlled load shedding or re-dispatching generation) before the situation becomes unrecoverable. Digitalization of the grid – including smart meters, sensors, and predictive analytics – will enable a faster and more precise response to anomalies, whether caused by equipment failure, weather extremes, or cyber threats ecfr.eu. In the Iberian case, automated defense schemes did activate (such as under-frequency load shedding that cut power to some customers to rebalance frequency), but future systems may need to act even quicker and more intelligently across regions.

  • Plan for Extreme “N-k” Contingencies: Grid planning criteria may need revision in light of this event. Traditionally, systems are designed to withstand the loss of any single element (N-1). Operators are now considering how to prepare for multiple simultaneous failures (N-2 or N-k scenarios) that, while very unlikely, can have catastrophic impacts. This could mean building in more redundancy, adjusting protection settings to be less “all-or-nothing,” and conducting regular stress tests of the grid’s response to extreme events. EPRI’s experts emphasized that resilience isn’t just about preventing outages, but limiting their scope and duration. Indeed, the fact that Iberia was blacked out for only ~15 hours owes to effective restoration planning, including black-start capabilities and cross-border assistance once systems were ready to reconnect. Continuous improvement in restoration strategies (like sectionalizing the grid and restarting in phases) is another lesson to carry forward.

Ultimately, the consensus from the webinar and other expert analyses is that the Iberian blackout was a wake-up call. It underscores the need to invest in a more resilient grid to support the clean energy transition ecfr.euecfr.eu. Europe, and the world, must shore up grid reliability even as we welcome more renewable power. As EU Energy Commissioner Kadri Simson summarized after the event, “our electricity systems need to be prepared for a new reality – this cannot be reduced to a specific source of energy”carbonbrief.org. In other words, rather than pointing fingers at renewables, the focus should be on building a stronger system that can handle the new energy landscape.

Dependable Baseload: Nuclear’s Stabilizing Role

One critical element of grid resilience is maintaining a balanced mix of energy sources, including stable baseload generators. In this context, nuclear power provides unique advantages for grid stability. Spain’s nuclear fleet – 7 reactors totaling about 7 GW – supplied roughly 19% of the country’s electricity in 2024, making nuclear the second-largest generation source after wind power. These nuclear plants operate at steady output and are not affected by daily weather or seasonal variability. During periods of grid stress, a running nuclear unit is a rock of stability: its output doesn’t suddenly drop due to a lack of sun or wind, and it typically isn’t tripped off by minor disturbances. Nuclear reactors also come with large spinning turbo-generators, which inherently contribute strong rotational inertia and voltage support to the grid. In essence, they act like giant gyroscopes, damping rapid frequency changes and helping to keep the voltage steady.

Had there been more baseload units online in Iberia at the time of the April 28 event, the initial frequency dip might have been less severe, potentially giving grid protections more time to react. (For instance, France’s grid, which has a high share of nuclear, has historically seen fewer large frequency deviations, partly thanks to the inertia of its nuclear fleet.) Of course, nuclear plants are not very flexible in ramping output quickly, so they cannot single-handedly cover a sudden shortfall. But their presence means the grid has a reliable floor of generation that can anchor the system. During the Iberian blackout, once the grid collapsed, all generators – including nuclear stations – had to shut down for safety. However, nuclear stations are designed with robust safety systems to handle grid loss and can assist in recovery once the grid is stable enough to accept power. Their value is most felt in preventing outages to begin with: by reducing reliance on intermittent imports and weather-driven sources, nuclear energy can mitigate the risk factors that lead to crises.

Moreover, nuclear plants often have long refueling cycles and high availability rates, meaning they’re online and providing power the vast majority of the time. This high reliability complements renewables: when the wind isn’t blowing or the sun isn’t shining, nuclear is there to carry the load steadily. In a scenario like Iberia’s, if some other plants or interconnectors go down unexpectedly, having sufficient nuclear (and other firm generation) capacity online creates a buffer that the grid can lean on. It’s telling that even as Spain pushes toward 100% renewable electricity, there is a growing appreciation that eliminating firm, inertia-rich sources could pose reliability challenges carbonbrief.org. A diversified mix, with nuclear as a key component, offers a hedge against blackouts.

Nuclearn’s Mission for a Resilient Energy Future

At Nuclearn, our mission is to ensure that nuclear power can play its fullest role in a resilient, clean energy grid. We support nuclear operators by providing advanced analytics and operational efficiency tools that help keep reactors running safely, flexibly, and cost-effectively. In light of events like the Iberian blackout, Nuclearn’s work is more relevant than ever. Our technology solutions empower plant operators with real-time insights into equipment performance, grid conditions, and predictive maintenance needs.

In conclusion, the April 28 Iberian blackout offers important lessons for all of us in the energy industry. It highlighted both the vulnerabilities of a changing power system and the incredible resilience of operators who restored an entire nation’s power in hours. At Nuclearn, we approach these challenges with a spirit of optimism and innovation. We are confident that with smart planning, technology, and a balanced mix that includes dependable nuclear energy, the grid of the future will be cleaner and stronger. Our commitment is to help make that future a reality, working hand-in-hand with the nuclear community to bolster grid reliability and prevent outages – so that events like the Iberian blackout remain exceedingly rare.

Citations:

  • Emma Pinedo et al., “Power begins to return after huge outage hits Spain and Portugal,” Reuters, April 29, 2025. reuters.comreuters.comreuters.comreuters.com

  • Carbon Brief (Molly Lempriere et al.), “Q&A: What we do – and do not – know about the blackout in Spain and Portugal,” April 30, 2025. carbonbrief.orgcarbonbrief.orgcarbonbrief.orgcarbonbrief.orgcarbonbrief.org

  • Science Media Centre, expert comments by Prof. Jianzhong Wu, Prof. Keith Bell, et al., “Expert reaction to power outages across Spain and Portugal,” April 28, 2025. sciencemediacentre.org

  • EPRI (Electric Power Research Institute), “EPRI Webcast of Initial Findings from April 28, 2025 Iberia Blackout” – LinkedIn post by EPRI, May 6, 2025. linkedin.comlinkedin.com

  • James Cupps, “Technical Analysis of Spain’s Power Grid and the April 28, 2025 Outage,” LinkedIn, May 2025. linkedin.comlinkedin.comlinkedin.comlinkedin.comlinkedin.com

  • Euronews, Aleksandar Brezar & Clea Skopeliti, “Spain, Portugal and parts of France hit by massive power outage,” April 28, 2025. euronews.com

  • Szymon Kardaś, “Lights out: Why Iberia’s power cut is a warning for EU energy security,” ECFR Policy Alert, May 7, 2025. ecfr.euecfr.euecfr.euecfr.euecfr.eu

  • VigoHoy (Spanish news site), “¿Cómo es posible que se haya caído la luz en toda España?” (in Spanish), April 28, 2025 – quoted in LinkedIn analysis.