Ban the Use of AI in the Nuclear-Energy Permitting Process

In addition to rolling back long-established safety thresholds, the government is concerningly using AI technologies to expedite regulatory processes, such as nuclear licensing and commissioning for civil and defense nuclear facilities. Nuclear licensing is a well-established process that requires nuclear operators to demonstrate that the risks arising from their lifetime operations will be adequately controlled, and to take responsibility for controlling and addressing these risks.1 Introducing generative AI to “streamline” nuclear licensing increases the likelihood that mistakes will arise in the process. Research has consistently demonstrated the lack of accuracy of generative AI and LLMs, including a high rate of inaccurate results,2 high hallucination rates,3 and a demonstrated bias in widely used LLMs toward overgeneralizing scientific conclusions.4 Even minute errors can compromise nuclear safety thresholds and lead to catastrophic consequences, including widespread radiation exposure.5 The federal government must ban the use of generative AI systems in the nuclear-energy permitting process, where safety is at stake.

  1. Guerra and Khlaaf, Fission for Algorithms, 22–23. ↩︎
  2. Ibid., 27; Jason Wei et al.,“Measuring Short-Form Factuality in Large Language Models,” arXiv, November 7, 2024,
    https://doi.org/10.48550/arXiv.2411.04368. ↩︎
  3. Jeremy Hsu, “AI Hallucinations Are Getting Worse – and They’re Here to Stay,” New Scientist, May 9, 2025,
    https://www.newscientist.com/article/2479545-ai-hallucinations-are-getting-worse-and-theyre-here-to-stay; Maxwell Zeff, “OpenAI’s New Reasoning AI Models Hallucinate More,” TechCrunch, April 18, 2025,
    https://techcrunch.com/2025/04/18/openais-new-reasoning-ai-models-hallucinate-more; Gyana Swain, “OpenAI Admits AI Hallucinations Are Mathematically Inevitable, Not Just Engineering Flaws,” ComputerWorld, September 18, 2025, 
    https://www.computerworld.com/article/4059383/openai-admits-ai-hallucinations-are-mathematically-inevitable-not-just-engineering-flaws.html; Guerra and Khlaaf, Fission for Algorithms, 27. ↩︎
  4. Uwe Peters and Benjamin Chin-Yee, “Generalization Bias in Large Language Model Summarization of Scientific Research,” Royal Society Open
    Science 12 (March 2025): 241776, https://royalsocietypublishing.org/doi/epdf/10.1098/rsos.241776; Guerra and Khlaaf, Fission for Algorithms, 27. ↩︎
  5. Ibid., 23. ↩︎