The Invisible Threat: AI and Nuclear Proliferation

by

Author: Denis Avetisyan

Rapid advances in artificial intelligence are fueling a new arms race, shifting the dynamics of nuclear weapons proliferation and demanding a reassessment of global safeguards.

This review analyzes the interplay between AI-enabled proliferation risks and detection technologies, proposing a framework for evaluating the evolving balance of power.

Despite decades of nonproliferation efforts, the emergence of disruptive technologies presents a critical juncture in managing nuclear risk. This paper, ‘Artificial Intelligence and Nuclear Weapons Proliferation: The Technological Arms Race for (In)visibility’, analyzes how advances in artificial intelligence are intensifying a technological competition between tools enabling proliferation and those designed for detection. We demonstrate that asymmetric technological progress-particularly rapid AI-driven advances in proliferation technologies-is expanding the uncertainty surrounding detectability and potentially eroding safeguards. Will proactive governance of these technologies prove essential to maintaining a stable international security environment?

The Evolving Threat: Proliferation in the 21st Century

Despite decades of concerted international effort dedicated to curbing the spread of nuclear weapons, the challenge of proliferation persists as a fundamental threat to global security. Initial optimism following the establishment of frameworks like the Treaty on the Non-Proliferation of Nuclear Weapons has gradually given way to a recognition of the enduring complexities involved. Several states continue to pursue or maintain nuclear arsenals outside of established agreements, while the risk of nuclear materials falling into the hands of non-state actors remains a serious concern. This ongoing challenge isn’t simply a continuation of Cold War dynamics; rather, it reflects evolving geopolitical landscapes, regional conflicts, and the increasing accessibility of technologies that blur the lines between peaceful nuclear programs and weapons development, demanding continuous vigilance and adaptive strategies.

The established architecture of nuclear non-proliferation faces unprecedented strain from technological advancements. While treaties like the NPT and the oversight of the IAEA have long served as cornerstones of global security, their effectiveness is being eroded by the proliferation of dual-use technologies. These innovations, readily available on the open market, blur the lines between peaceful applications and weapons development, complicating verification efforts and creating loopholes in existing safeguards. Traditional monitoring techniques, designed for large-scale, state-sponsored programs, struggle to detect and attribute activities involving smaller, more dispersed, and technologically sophisticated means of pursuing nuclear capabilities. Consequently, the international community now confronts a landscape where the risk of proliferation is not simply a matter of states openly flouting international norms, but of technologically adept actors exploiting the ambiguities created by rapid innovation.

Proliferation-Enabling Technologies (PETs) represent a fundamental shift in the landscape of nuclear weapons development, diminishing the traditional hurdles to acquiring sensitive materials and expertise. These aren’t necessarily the weapons themselves, but rather advancements in fields like computing, materials science, and biotechnology – all commercially available – that collectively streamline the process. High-performance computing, for example, allows for increasingly sophisticated weapons simulations, reducing the need for costly and detectable physical testing. Similarly, advancements in centrifuge technology and additive manufacturing – 3D printing – simplify uranium enrichment and component production, potentially circumventing established monitoring efforts. The accessibility of such dual-use technologies means that states and even non-state actors can make significant progress towards weapons capabilities with reduced investment, increased stealth, and a lowered technological threshold – creating a more complex and precarious global security environment.

The swift evolution of technology is fundamentally reshaping the landscape of nuclear proliferation, necessitating a critical reevaluation of current risk assessments and monitoring protocols. Advances in areas like artificial intelligence, quantum computing, and advanced materials are not only accelerating weapons development but also creating new avenues for circumvention of traditional safeguards. Existing monitoring strategies, largely predicated on tracking fissile materials and large-scale facilities, are increasingly challenged by the potential for clandestine programs leveraging readily available technologies and distributed networks. This demands a shift towards more dynamic, adaptable, and technologically sophisticated monitoring approaches, including enhanced data analytics, open-source intelligence gathering, and the development of new verification technologies capable of detecting subtle indicators of proliferation activity. A proactive and forward-looking reassessment is vital to ensure that non-proliferation efforts remain effective in this rapidly changing technological environment.

The Proliferation Landscape: Dual-Use Technologies and PET Growth

Proliferation risks are no longer solely confined to conventional weapons materials and infrastructure. Increasingly, Proliferation Event Triggers (PETs) include commercially available, “dual-use” technologies – items with legitimate civilian applications that can also contribute to weapons development. Notable examples are Artificial Intelligence (AI), particularly Large Language Models, and Additive Manufacturing (3D printing). These technologies lower the barriers to entry for actors seeking to develop or acquire weapons capabilities, as AI can accelerate design and analysis processes while 3D printing decentralizes production, reducing reliance on specialized facilities and established supply chains. This expansion of PETs beyond traditional materials necessitates a broadened scope for non-proliferation efforts and monitoring activities.

Large Language Models (LLMs) significantly reduce the time and expertise required for weapon design and analysis by automating tasks such as materials research, simulations, and code generation for complex systems. This acceleration bypasses traditional barriers to entry, lowering the technical skill threshold for potential proliferators. Concurrently, Additive Manufacturing, or 3D printing, enables the decentralized production of weapon components and even entire systems, diminishing the need for access to large, specialized manufacturing facilities and established supply chains. This combination of accessible design tools and distributed manufacturing capabilities fundamentally alters proliferation dynamics by increasing the number of actors capable of developing and producing potentially dangerous technologies.

Proliferation of Emerging Technologies (PET) exhibits a predictable growth pattern modeled by the Logistic Growth Curve. This curve initially demonstrates a period of slow expansion as technologies are developed and initial adoption occurs. However, as enabling infrastructure matures and costs decrease, the rate of proliferation accelerates. Current analysis indicates an annual growth rate of 1.19 for PETs under a ‘Transformative AI’ scenario. This rate signifies a compounding increase, meaning the absolute number of proliferating technologies grows exponentially. Understanding this projected growth is critical for forecasting future proliferation risks and allocating resources for effective mitigation strategies, as the curve suggests an eventual saturation point will be reached, though the timing of this saturation remains uncertain.

Predicting the trajectory of Proliferation-Enabling Technologies (PETs) is vital for effective risk management due to their adherence to a Logistic Growth Curve. This curve indicates initial slow adoption, followed by a period of accelerating expansion, and eventual stabilization. Current modeling, particularly in a ‘Transformative AI’ scenario, projects an annual growth rate of $1.19$ for PETs. Accurate forecasting allows for proactive identification of emerging proliferation pathways, enabling security measures and resource allocation to be prioritized before capabilities become widespread. Failing to anticipate this growth risks reactive, and potentially insufficient, countermeasures against rapidly evolving threats.

Countering the Threat: Detection-Enhancing Technologies

Detection-Enhancing Technologies (DETs), which encompass National Technical Means (NTM) such as satellite-based sensors and specialized monitoring equipment, are fundamental to the verification regimes underpinning international non-proliferation commitments. These technologies provide the means to monitor declared nuclear facilities, detect undeclared activities, and confirm the absence of diversion of nuclear materials. Specifically, DETs are employed to verify compliance with treaties like the Treaty on the Non-Proliferation of Nuclear Weapons (NPT) and safeguards agreements with the International Atomic Energy Agency (IAEA). Their capabilities include monitoring for nuclear explosions, detecting the production of fissile materials, and tracking the movement of sensitive nuclear technologies, thus providing essential assurance against the unauthorized development or acquisition of nuclear weapons.

Detection-Enhancing Technology (DET) development diverges from the incremental progression typical of Proliferation-Enabling Technology (PET) advancement. Instead, DET capability improves through distinct, non-linear ‘Stepwise Improvements’. Historical data indicates these improvements aren’t continuous; rather, they occur as discrete jumps in performance, termed ‘Moonshot’ upgrades. Quantitatively, observed Moonshot upgrades have yielded capability increases of +12 units, followed by two subsequent upgrades of +10 units each, demonstrating a pattern of substantial, punctuated advancement rather than steady, gradual growth.

Detection capabilities directly influence the overall Hazard Rate, which represents the probability of a successful nuclear breakout attempt. A higher efficacy in detecting diverted materials or undeclared activities reduces the likelihood of a clandestine program reaching completion, thereby lowering the Hazard Rate. Conversely, diminished detection capabilities increase the probability of undetected diversion and enrichment, raising the Hazard Rate. The Hazard Rate is not solely determined by detection effectiveness; it is a function of both the probability of detection and the consequences of non-detection, but improved detection technologies consistently contribute to a lower overall probability of proliferation. Quantitatively, increases in detection capability correlate with reductions in the estimated Hazard Rate, influencing strategic assessments of proliferation risk.

The success of a clandestine nuclear weapons program is inversely proportional to the advantage held by detection technologies over proliferation evasion techniques. A significant lead for detection systems – where the probability of detecting diverted material or undeclared facilities substantially exceeds the probability of successfully concealing them – increases the risk of program failure and discourages initiation. Conversely, if proliferation evasion techniques consistently outpace detection capabilities, the risk of undetected proliferation increases, and the feasibility of a clandestine program improves. This dynamic establishes a critical balance: a narrowing advantage for detection technologies necessitates increased monitoring intensity and potentially more intrusive verification measures to maintain an acceptable hazard rate, while a widening advantage reduces the need for such measures.

Quantifying Risk: The Relative Advantage Index

The Relative Advantage Index (RAI) is a quantitative metric designed to assess the balance between Proliferation Enabling Technologies (PETs) and Detection and Enforcement Technologies (DETs). It functions as a dynamic indicator of proliferation risk by continuously comparing the advancements and capabilities within these two technological spheres. The RAI is not a static value; rather, it reflects the evolving relationship between PET and DET development, allowing for real-time monitoring of potential vulnerabilities in the international non-proliferation regime. A calculated RAI value provides a basis for evaluating the effectiveness of counter-proliferation efforts and identifying areas where investment in DETs may be critical to maintaining international security.

The Relative Advantage Index (RAI) serves as a quantitative indicator of the balance between Procurement-related Events (PETs) and Detection and Enforcement Technologies (DETs). An increasing RAI value signifies that PETs – activities associated with acquiring materials or knowledge for a nuclear program – are occurring at a greater rate than advancements or deployments in DETs. This imbalance directly correlates with an elevated probability of a successful clandestine nuclear weapons program, as the capacity to procure necessary components and information exceeds the ability to detect and interdict those activities. A sufficiently high RAI is considered a key indicator potentially preceding an Opportunistic Breakout – a scenario where a state, possessing some nuclear capability, rapidly expands its arsenal due to a perceived security threat or lack of effective international oversight.

Under a ‘Disruptive AI’ scenario, modeling indicates a baseline cumulative probability of 0.26 for undetected nuclear proliferation over a ten-year period. However, substantial advancements in Detection and Evaluation Technologies (DET), specifically those categorized as ‘Moonshot’ improvements, demonstrate the potential to reduce this risk by approximately 37.5%. This equates to a reduction in the cumulative probability to approximately 0.163. The modeled reduction assumes consistent application of improved detection capabilities throughout the ten-year timeframe and highlights the significant impact of investment in advanced detection technologies on mitigating proliferation risks.

Combining export control implementation with ongoing Relative Advantage Index (RAI) monitoring and enhancements to detection capabilities is crucial for managing proliferation risk. Analysis indicates that a measured increase in detection sensitivity – specifically, a shift in slope from 0.4 to 0.6 – correlates with a quantifiable reduction in undetected proliferation over a ten-year period. This proactive approach allows for adaptive security measures, responding to shifts in the balance between Proliferation Enabling Technologies (PETs) and Detection and Enforcement Technologies (DETs). Consistent evaluation of the RAI, coupled with responsive export controls, enables a dynamic security posture designed to maintain international stability and reduce the likelihood of opportunistic breakout scenarios.

The pursuit of increasingly sophisticated artificial intelligence within the context of nuclear safeguards echoes a fundamental tenet of mathematical rigor. As the article details concerning the dynamic between proliferation-enabling technologies and detection capabilities, the assessment of relative advantage demands a provable, deterministic framework – not simply systems that appear functional. Kolmogorov aptly stated, “The most important thing in science is not to be afraid of big numbers.” This resonates with the complex calculations necessary to evaluate the shifting balance of power in this technological arms race, where even small advantages, quantified and verified through rigorous analysis, can have enormous consequences. The article’s emphasis on verification aligns perfectly with this principle; the reliability of any safeguard system hinges on its demonstrable, reproducible accuracy.

The Horizon Beckons

The analysis presented here, concerning the interplay of artificial intelligence and nuclear safeguards, ultimately reduces to a question of invariants. Let N approach infinity – what remains invariant? Not the specific algorithms employed, certainly, nor the sophistication of any given detection system. These are transient, destined for obsolescence with each iterative cycle of innovation. Instead, the enduring challenge lies in the fundamental asymmetry of advantage. A single successful evasion, a single undetected breach, negates a thousand successful detections. The proposed Relative Advantage Index provides a useful metric, but its long-term utility depends on accurately modeling a system perpetually striving for undetectable proliferation – a task bordering on the paradoxical.

Future work must move beyond empirical observation of technological capabilities and delve deeper into the game-theoretic underpinnings of this arms race. What constitutes a stable, albeit uneasy, equilibrium? Can verification regimes be designed that are robust not to the most likely threat, but to the least detectable? The focus should shift from chasing ever-more-complex detection algorithms to understanding the inherent limitations of any system reliant on imperfect information.

Ultimately, the pursuit of ‘invisibility’ through artificial intelligence isn’t a technological problem; it’s a mathematical one. The question isn’t whether a system can be detected, but whether the cost of detection exceeds the benefit of concealment. A truly elegant solution will not be found in clever algorithms, but in a provably stable framework for minimizing that cost differential – a framework, perhaps, predicated on radical transparency rather than increasingly sophisticated obfuscation.

Original article: https://arxiv.org/pdf/2512.07487.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-10 05:54