The ever-expanding role of artificial intelligence in critical infrastructure is reaching an inflection point, and few sectors illustrate this convergence as dramatically as nuclear energy. The United States Idaho National Laboratory (INL), in collaboration with Microsoft Corporation, is spearheading an ambitious effort to streamline the complex and often arduous process of nuclear power plant licensing using Azure cloud and next-generation AI solutions. This multifaceted initiative may mark a turning point for how nuclear technology innovators get approval to build and operate the reactors poised to power an energy-hungry digital future.
Securing a license to build and operate a nuclear power plant (NPP) in the United States represents one of the most detailed and scrutinized engineering undertakings imaginable. The process, managed primarily through the Nuclear Regulatory Commission (NRC) and supported by the Department of Energy (DOE), involves preparing and submitting mammoth volumes of technical documentation. These documents not only describe the reactor design, site characteristics, safety assessments, fuels, and cooling methods, but also provide highly structured safety analysis, risk mitigation plans, historical evidence, and engineering justifications. For reactor developers—both those working on incremental upgrades to established Light Water Reactors (LWRs) and those pioneering altogether new classes of advanced reactors—generating these reports consumes time, manpower, and money on an extraordinary scale.
A single licensing application for a new nuclear facility can stretch across thousands of pages, pulling data and evidence from myriad scientific, engineering, and regulatory archives. The need for rigorous verification keeps the process slow, often adding months or years to deployment timelines. As the world’s thirst for reliable, carbon-free energy continues to grow—and as emerging technologies such as AI data centers drive demand further—unlocking efficiency in this process could have major implications for both the energy sector and climate action at large.
This technology does not perform the technical or safety analysis itself; rather, it turns the laborious report-construction phase into a predominantly automated workflow. By ingesting and analyzing vast stores of pre-existing nuclear engineering and safety documentation, the Azure AI system can rapidly generate high-quality drafts required by regulatory authorities such as the NRC and DOE. Crucially, the process remains subject to strict human verification and oversight, ensuring that no machine-generated error finds its way into the critical path of nuclear safety compliance.
Jess Gehin, Associate Laboratory Director for Nuclear Science & Technology at INL, underscored the transformative potential of this AI-driven efficiency. “Introducing AI technologies will enhance efficiency and accelerate the deployment of advanced nuclear technologies,” Gehin explained, pointing to both time savings and enhanced throughput for innovative designs that find themselves hamstrung by traditional licensing bottlenecks.
Notably, the tool is designed with flexibility and future-proofing in mind. It can adapt to the requirements of both new LWR projects and upgrades to existing fleets—an important distinction given the ongoing life extension programs for America’s aging reactors. But more significantly, it’s positioned to handle the unprecedented diversity of advanced reactors—systems with novel fuel forms, coolants, and operating regimes that routinely challenge the boundaries of current licensing paradigms.
INL and Microsoft’s collaboration is not a first foray into digital innovation in nuclear. The lab, together with Idaho State University (ISU), already used Azure last year to create the world’s first nuclear reactor digital twin—a virtual, data-rich replica of ISU’s AGN-201 training reactor. Digital twins create live, continuously updated models that mirror the real-world condition and operation of a physical plant. They unlock powerful new capabilities in monitoring, predictive maintenance, and operator training. When paired with AI-driven document workflow, the nuclear sector moves closer to a future of continuous, data-informed improvement from initial concept through operations.
This aligns with global trends. Countries including the UK, Canada, and South Korea are also investigating how AI and digital technology can reduce regulatory resistance while preserving—and ideally, improving—safety standards. However, the United States, with its world-leading nuclear regulatory system and unmatched base of legacy infrastructure, is uniquely positioned to set a new benchmark for responsible, technology-driven change.
Furthermore, no amount of automation changes the importance of actual technical analysis, design safety, or operational readiness. The Azure AI solution, as currently described, does not evaluate the underlying science or risk profiles of the reactors themselves. It brings efficiency to the paperwork but cannot substitute for engineering rigor or experienced judgment. Therefore, its greatest value will be in supplementing—but never replacing—the talent and expertise of the U.S. nuclear workforce.
There are also practical hurdles to widespread adoption. The diverse community of reactor developers spans established utilities, government laboratories, and nimble startups—many of whom lack the IT infrastructure or experience to capitalize on cloud-based AI at immediate scale. Early research is needed, as INL’s Ritter acknowledges, to fully map which licensing workloads benefit most from this approach and to validate the reliability and adaptability of AI-generated documents across a wide range of reactor types and site environments.
According to recent coverage by World Nuclear News and The Wall Street Journal, generative AI and cloud automation are among the top-ranked digital tools being evaluated for streamlining nuclear licensing not just in the US but worldwide. However, experts caution that the last 10% of regulatory review—often the most detail-intensive phase—will remain firmly in the hands of highly trained professionals for the foreseeable future. Peer countries’ early experiments with similar digital solutions have shown promising gains in throughput and speed, but they also highlight persistent issues with data inconsistency, legacy system integration, and change management within highly-regulated sectors.
In user forums and community roundtables sponsored by international reactor organizations, participants widely agree that documentation automation could deliver significant time savings—especially for reactors with unconventional fuels or safety regimes, which are likely to flood U.S. licensing channels as advanced nuclear development accelerates. Nevertheless, the consensus is plain: human oversight and transparency must remain the guiding principles, with AI treated as a “copilot” rather than an “autopilot” in regulatory compliance.
For INL and the wider nuclear community, partnerships like this open the door to a world where software and computation—traditionally considered secondary to hard engineering—become core pillars of plant design, licensing, and ultimately, deployment. By proactively engaging with digital transformation now, the nuclear establishment increases its odds of staying relevant, efficient, and agile during a time of rapid technological change and intense global competition.
INL’s early outreach to validate the applicability of generative AI in this context is a prudent first step. The next crucial phase rests in robust pilot deployments, sober evaluation of both the strengths and blind spots of the technology, and transparent sharing of outcomes with the broader industry and the public.
The partnership signals a future where the barriers to deploying safe, reliable nuclear energy are lowered not by cutting corners, but by leveraging the best of human ingenuity and AI-enabled automation. As the world races toward a more electrified, decarbonized, and information-driven society, these collaborations may well determine how fast we can build—and trust—the clean energy systems that power tomorrow.
Source: Nuclear Engineering International INL and Microsoft to streamline nuclear licensing - Nuclear Engineering International
The Legacy and Challenge of Nuclear Licensing
Securing a license to build and operate a nuclear power plant (NPP) in the United States represents one of the most detailed and scrutinized engineering undertakings imaginable. The process, managed primarily through the Nuclear Regulatory Commission (NRC) and supported by the Department of Energy (DOE), involves preparing and submitting mammoth volumes of technical documentation. These documents not only describe the reactor design, site characteristics, safety assessments, fuels, and cooling methods, but also provide highly structured safety analysis, risk mitigation plans, historical evidence, and engineering justifications. For reactor developers—both those working on incremental upgrades to established Light Water Reactors (LWRs) and those pioneering altogether new classes of advanced reactors—generating these reports consumes time, manpower, and money on an extraordinary scale.A single licensing application for a new nuclear facility can stretch across thousands of pages, pulling data and evidence from myriad scientific, engineering, and regulatory archives. The need for rigorous verification keeps the process slow, often adding months or years to deployment timelines. As the world’s thirst for reliable, carbon-free energy continues to grow—and as emerging technologies such as AI data centers drive demand further—unlocking efficiency in this process could have major implications for both the energy sector and climate action at large.
Enter Azure AI: A New Approach to Document Generation
The fundamental innovation emerging from the INL-Microsoft partnership is a generative AI-powered solution built on Microsoft’s Azure cloud platform. Through this collaboration, and with DOE support via the National Reactor Innovation Centre (NRIC), INL aims to automate the generation of complex engineering and safety analysis reports—a core component of nuclear licensing submissions.This technology does not perform the technical or safety analysis itself; rather, it turns the laborious report-construction phase into a predominantly automated workflow. By ingesting and analyzing vast stores of pre-existing nuclear engineering and safety documentation, the Azure AI system can rapidly generate high-quality drafts required by regulatory authorities such as the NRC and DOE. Crucially, the process remains subject to strict human verification and oversight, ensuring that no machine-generated error finds its way into the critical path of nuclear safety compliance.
Jess Gehin, Associate Laboratory Director for Nuclear Science & Technology at INL, underscored the transformative potential of this AI-driven efficiency. “Introducing AI technologies will enhance efficiency and accelerate the deployment of advanced nuclear technologies,” Gehin explained, pointing to both time savings and enhanced throughput for innovative designs that find themselves hamstrung by traditional licensing bottlenecks.
Breaking Down the Technology: What Azure AI Delivers
At the technical level, the Azure-based tool developed for INL is equipped to:- Consume nuclear engineering and safety documents from a variety of trusted sources, applying machine learning models to parse, organize, and understand the content.
- Automatically construct licensing documents, mapping requirements from the NRC and DOE to the content extracted from source material.
- Save developers weeks—potentially months—by eliminating the need for manual compilation and repetitive document editing.
- Guarantee that every AI-generated document serves as a draft for human experts to review, edit, and approve, maintaining the gold standard for nuclear safety and regulatory compliance.
Notably, the tool is designed with flexibility and future-proofing in mind. It can adapt to the requirements of both new LWR projects and upgrades to existing fleets—an important distinction given the ongoing life extension programs for America’s aging reactors. But more significantly, it’s positioned to handle the unprecedented diversity of advanced reactors—systems with novel fuel forms, coolants, and operating regimes that routinely challenge the boundaries of current licensing paradigms.
Broader Applicability, Early Research, and the Promise of Digital Twins
What makes this initiative especially relevant in today’s nuclear innovation landscape is its broad potential applicability. As Chris Ritter, INL’s Division Director of Scientific Computing & AI, stated, “AI holds significant potential to accelerate the process to design, license, and deploy new nuclear energy for the nation’s increasing energy needs.” This comment does not reflect mere hype. With numerous advanced reactor startups aiming to deliver new concepts to the grid within the decade, any edge in regulatory navigation translates directly into first-mover advantage.INL and Microsoft’s collaboration is not a first foray into digital innovation in nuclear. The lab, together with Idaho State University (ISU), already used Azure last year to create the world’s first nuclear reactor digital twin—a virtual, data-rich replica of ISU’s AGN-201 training reactor. Digital twins create live, continuously updated models that mirror the real-world condition and operation of a physical plant. They unlock powerful new capabilities in monitoring, predictive maintenance, and operator training. When paired with AI-driven document workflow, the nuclear sector moves closer to a future of continuous, data-informed improvement from initial concept through operations.
Regulatory Context: Policy Winds and Demand Shifts
It’s impossible to divorce the rapid acceleration of nuclear licensing innovation from its policy context. Recent executive orders signed by the president are explicitly pushing for a streamlined approach to NPP licensing, recognizing that surging data center build-out driven by AI and cloud computing will dramatically ramp up electricity demand in coming years. The stakes are high: slow licensing processes could hamstring both carbon mitigation strategies and the digital economy’s continued expansion.This aligns with global trends. Countries including the UK, Canada, and South Korea are also investigating how AI and digital technology can reduce regulatory resistance while preserving—and ideally, improving—safety standards. However, the United States, with its world-leading nuclear regulatory system and unmatched base of legacy infrastructure, is uniquely positioned to set a new benchmark for responsible, technology-driven change.
Risks and What Still Needs to Be Proven
While the benefits of this AI-enabled approach are clear, they are not without risks and critical caveats. Automating parts of the licensing process, even with robust human oversight, requires unprecedented transparency into both the data sources being used and the decision-making logic of the AI models themselves. Any ‘black box’ aspect could undermine trust with the NRC, other regulatory authorities, or the public. Historically, the nuclear energy sector has had to work hard to earn acceptance—and even the smallest error or oversight in application documentation can have lasting impacts on public perception and licensing momentum.Furthermore, no amount of automation changes the importance of actual technical analysis, design safety, or operational readiness. The Azure AI solution, as currently described, does not evaluate the underlying science or risk profiles of the reactors themselves. It brings efficiency to the paperwork but cannot substitute for engineering rigor or experienced judgment. Therefore, its greatest value will be in supplementing—but never replacing—the talent and expertise of the U.S. nuclear workforce.
There are also practical hurdles to widespread adoption. The diverse community of reactor developers spans established utilities, government laboratories, and nimble startups—many of whom lack the IT infrastructure or experience to capitalize on cloud-based AI at immediate scale. Early research is needed, as INL’s Ritter acknowledges, to fully map which licensing workloads benefit most from this approach and to validate the reliability and adaptability of AI-generated documents across a wide range of reactor types and site environments.
Cross-Checking the AI Leap: Independent Perspectives
To ensure the claims of breakthrough efficiency and broad applicability withstand scrutiny, it is essential to cross-reference the INL-Microsoft collaboration with commentary from both nuclear industry analysts and regulatory observers.According to recent coverage by World Nuclear News and The Wall Street Journal, generative AI and cloud automation are among the top-ranked digital tools being evaluated for streamlining nuclear licensing not just in the US but worldwide. However, experts caution that the last 10% of regulatory review—often the most detail-intensive phase—will remain firmly in the hands of highly trained professionals for the foreseeable future. Peer countries’ early experiments with similar digital solutions have shown promising gains in throughput and speed, but they also highlight persistent issues with data inconsistency, legacy system integration, and change management within highly-regulated sectors.
In user forums and community roundtables sponsored by international reactor organizations, participants widely agree that documentation automation could deliver significant time savings—especially for reactors with unconventional fuels or safety regimes, which are likely to flood U.S. licensing channels as advanced nuclear development accelerates. Nevertheless, the consensus is plain: human oversight and transparency must remain the guiding principles, with AI treated as a “copilot” rather than an “autopilot” in regulatory compliance.
Strategic Implications: Fueling the Next Nuclear Era
Zooming out, the INL-Microsoft partnership highlights the emerging strategic alignment between national laboratories, big tech companies, and the energy sector. Microsoft and its cloud competitors are motivated not just by new business verticals, but by the existential need to secure reliable, emission-free electricity that can power AI research, digital commerce, and a rapidly electrifying industrial base.For INL and the wider nuclear community, partnerships like this open the door to a world where software and computation—traditionally considered secondary to hard engineering—become core pillars of plant design, licensing, and ultimately, deployment. By proactively engaging with digital transformation now, the nuclear establishment increases its odds of staying relevant, efficient, and agile during a time of rapid technological change and intense global competition.
Looking Ahead: What Success Could Mean
Success for the Azure AI-driven licensing solution would represent a rare win-win at the nexus of safety, speed, and innovation. Reduced regulatory turnaround for new reactor designs could enable gigawatts of carbon-free energy to reach U.S. grids in time to offset both aging fossil infrastructure and the ballooning needs of data-powered industries. Lessons learned in America would almost certainly inform digital licensing reforms everywhere new nuclear is being considered.INL’s early outreach to validate the applicability of generative AI in this context is a prudent first step. The next crucial phase rests in robust pilot deployments, sober evaluation of both the strengths and blind spots of the technology, and transparent sharing of outcomes with the broader industry and the public.
The partnership signals a future where the barriers to deploying safe, reliable nuclear energy are lowered not by cutting corners, but by leveraging the best of human ingenuity and AI-enabled automation. As the world races toward a more electrified, decarbonized, and information-driven society, these collaborations may well determine how fast we can build—and trust—the clean energy systems that power tomorrow.
Source: Nuclear Engineering International INL and Microsoft to streamline nuclear licensing - Nuclear Engineering International
