Artificial Intelligence and Nuclear Weapons Proliferation: The Technological Arms Race for (In)visibility
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Abstract (arXiv)
A robust nonproliferation regime has contained the spread of nuclear weapons to just nine states. Yet, emerging and disruptive technologies are reshaping the landscape of nuclear risks, presenting a critical juncture for decision makers. This article lays out the contours of an overlooked but intensifying technological arms race for nuclear (in)visibility, driven by the interplay between proliferation-enabling technologies (PETs) and detection-enhancing technologies (DETs). We argue that the strategic pattern of proliferation will be increasingly shaped by the innovation pace in these domains. Artificial intelligence (AI) introduces unprecedented complexity to this equation, as its rapid scaling and knowledge substitution capabilities accelerate PET development and challenge traditional monitoring and verification methods. To analyze this dynamic, we develop a formal model centered on a Relative Advantage Index (RAI), quantifying the shifting balance between PETs and DETs. Our model explores how asymmetric technological advancement, particularly logistic AI-driven PET growth versus stepwise DET improvements, expands the band of uncertainty surrounding proliferation detectability. Through replicable scenario-based simulations, we evaluate the impact of varying PET growth rates and DET investment strategies on cumulative nuclear breakout risk. We identify a strategic fork ahead, where detection may no longer suffice without broader PET governance. Governments and international organizations should accordingly invest in policies and tools agile enough to keep pace with tomorrow's technology.
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7 of 91 references.
References · 7
- 2023Mathematical Capabilities of ChatGPTcited by 268
- 2022Will we run out of data? Limits of LLM scaling based on human-generated datacited by 195
- 2023Confidence-Building Measures for Artificial Intelligence: Workshop Proceedingscited by 9
- 2025What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structurecited by 6
- 2024SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Modelscited by 5
- 2025Out of the Loop Again: How Dangerous is Weaponizing Automated Nuclear Systems?cited by 2
- 2025Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Researchcited by 2
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