The U.S. Department of Energy has demonstrated that artificial intelligence can compress nuclear licensing timelines from weeks to a single day, potentially transforming how quickly advanced reactors reach commercial deployment. In a collaboration between the DOE, Idaho National Laboratory, Argonne National Lab, Microsoft, and Everstar, an AI tool called Gordian converted a 208-page safety analysis document into sections equivalent to a full Nuclear Regulatory Commission (NRC) license application in just 24 hours. What normally requires a team of experts working four to six weeks can now be accomplished by AI in a fraction of the time .

Why Is Nuclear Licensing Taking So Long Right Now?

The current nuclear licensing process is a bottleneck that slows deployment of new reactors. The system involves multiple rounds of manual document reviews, clerical adjustments, and regulatory back-and-forth that can stretch across years. For companies trying to build small modular reactors (SMRs) or advanced reactor designs, this timeline creates real business risk and delays the availability of clean, reliable power when electricity demand is accelerating faster than ever .

The stakes are particularly high now. Global electricity demand is entering a structurally higher growth phase, driven by AI data centers that operate continuously and require uninterrupted power, plus the electrification of industry, heating, and transportation. Unlike past demand cycles, this new load is always-on and intolerant of interruption, which increases the premium on reliable baseload power. Nuclear is one of the few scalable sources of low-carbon, dispatchable electricity that can meet this need .

How Does Gordian's AI Actually Work?

Gordian is not a simple document search tool or a generic large language model. It is a specialized AI system engineered specifically for nuclear-grade technical work. The platform integrates physics-based reasoning and deterministic simulation alongside generative AI, which allows its outputs to meet the rigor of nuclear safety standards. Gordian uses semantic ontology mapping to understand and integrate data across overlapping regulatory frameworks, ensuring that final outputs are computed and verified rather than inferred .

The system was built on the Microsoft Azure platform and demonstrated its capability by converting the Preliminary Documented Safety Analysis for the DOE's National Reactor Innovation Center's Generic High Temperature Gas Reactor into sections equivalent to an NRC license application. Beyond speed, Gordian also identified missing or incomplete information needed to successfully complete an NRC application, a quality-control function that would normally require human review .

Steps to Implement AI-Accelerated Nuclear Licensing

• Expert Design Phase: Nuclear engineers and regulatory specialists define the requirements and structure of the licensing documents, ensuring that AI tools understand the specific safety and compliance standards that must be met.
• AI Acceleration: Gordian processes the safety analysis documents and generates draft license application sections in a fraction of the traditional timeline, compressing weeks of manual work into hours.
• Expert Validation: Qualified nuclear licensing experts review the AI-generated output for accuracy, missing information, consistency, grammar, and adherence to professional standards before submission to the NRC.

This three-step workflow maintains a critical principle: experts design, AI accelerates, and experts validate. The approach does not eliminate the need for nuclear licensing professionals; instead, it frees them from repetitive document preparation so they can focus on higher-level technical and regulatory strategy .

What Could This Mean for Nuclear Deployment Speed?

A recent study by the National Reactor Innovation Center (NRIC) highlighted that AI has the potential to reduce both document development time and regulatory review cycles by as much as 50 percent, while simultaneously improving accuracy, consistency, and traceability. The Gordian demonstration is the latest in a growing list of examples showing how AI can improve the nuclear licensing process .

The implications are significant. If licensing timelines can be cut in half, the path from reactor design to commercial operation becomes faster and more predictable. This is especially important for SMRs, which are designed to be scalable, modular units that can be deployed more flexibly than traditional large reactors. SMRs address a critical market need: dedicated power for data centers, remote grids, and industrial clusters that require continuous, reliable electricity .

"Now is the time to move boldly on AI-accelerated nuclear energy deployment. This partnership, combined with the President's orders, represents more than incremental 'uplift' improvements. It has the potential to transform how industry prepares its regulatory submissions and deploys nuclear energy while upholding the highest standards of safety and compliance," said Rian Bahran, Deputy Assistant Secretary for Nuclear Reactors.

Rian Bahran, Deputy Assistant Secretary for Nuclear Reactors, U.S. Department of Energy

Is the NRC Adopting AI Tools Too?

The regulatory side is moving in parallel. The NRC itself launched an AI-powered ADAMS (Agencywide Documents Access and Management System) Public Search tool in early 2025 to help the public and industry stakeholders navigate filings more efficiently. Under current 2026 policy initiatives, the agency is also exploring "human-in-the-loop" AI workflows to cut the review time for new reactor designs by up to 50 percent .

This dual acceleration, on both the industry and regulatory sides, creates a multiplier effect. If companies can prepare applications faster and regulators can review them faster, the total time from concept to operation could shrink dramatically. The NRC's website now includes comprehensive information on its enterprise-wide AI strategy, its process for reviewing new AI technologies, and descriptions of current use cases .

What Other AI Tools Are Emerging in Nuclear?

Everstar's Gordian is not alone. A growing ecosystem of AI software developers is working to address the complexity of the NRC licensing process. These tools reflect different approaches to the same problem: how to make nuclear deployment faster and more efficient .

• Atomic Canyon (San Luis Obispo, CA): Developed Neutron, an AI search and generative platform trained on the NRC's ADAMS database to help developers search billions of pages of records and automate technical document retrieval for compliance.
• Nuclearn (Phoenix, AZ): Provides an AI platform designed specifically for the nuclear industry to automate complex workflows, including regulatory reporting and plant operations documentation.
• Blue Wave AI Labs (West Lafayette, ID): Offers AI-driven solutions like ThermalLimits.ai and Eigenvalue.ai to forecast reactor performance and help licensees provide high-precision data required for NRC safety and fuel cycle submissions.
• Inductive (San Francisco, CA): A startup described as "TurboTax for nuclear licensing," building AI tools for regulatory document generation and collaboration to compress the time it takes to create first drafts of license applications.

What differentiates Gordian from these point solutions is its architecture. Most AI tools in nuclear focus on narrow tasks like document search or single-query answers. Gordian is a compound agentic system that handles end-to-end, long-horizon workflows spanning hundreds of steps of research, planning, drafting, evaluation, and calculation across overlapping regulatory frameworks .

"What differentiates Everstar is architecture. Most AI tools in nuclear are point solutions: document search, single-query answers, narrow optimization. Gordian is a compound agentic system that handles end-to-end, long-horizon workflows. We integrate physics-based reasoning and deterministic simulation alongside generative AI, which is what allows our outputs to meet the rigor of nuclear safety standards. This is transformative for design, engineering, operations, QA, licensing, and procurement teams," said Kevin Kong, CEO and Founder of Everstar.

Kevin Kong, CEO and Founder, Everstar

Why Does This Matter for AI Data Centers?

The timing of this breakthrough is not coincidental. AI data centers are driving a structural shift in electricity demand. These facilities operate at high utilization, require uninterrupted power, and can scale rapidly once commissioned. Data centers add a large, always-on source of incremental load, growing at approximately 15 percent per year through 2030 .

Nuclear power, particularly SMRs, is positioned as a solution to this challenge. SMRs can be deployed at dedicated facilities and industrial clusters, providing the continuous, reliable power that AI infrastructure demands. By accelerating the licensing and deployment of these reactors, AI-powered regulatory tools create a feedback loop: AI drives demand for nuclear power, and AI tools help deploy nuclear power faster to meet that demand .

The broader implication is clear. As electricity demand enters a structurally higher growth phase, the nuclear industry cannot afford to move at the pace of traditional regulatory processes. AI-accelerated licensing is not just a convenience; it is becoming a necessity for meeting the energy requirements of the next decade.