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While major powers such as the United States, China, and Russia are rapidly advancing military AI, with initiatives like NATO’s AI Strategy shaping a new competitive landscape, this study examines a critical yet underexplored question: How do third-party states acquire strategic autonomy in the military AI era, and how their behavior reshapes strategic stability? Using a qualitative case study approach, this research employs a two-dimensional analytical framework characterized by technological capability foundation and strategic positioning choice to analyze strategic decisions across multiple third-party states. The study finds that four traits of military AI reshape their strategic options: its general-purpose nature lowers entry barriers across domains; dual-use ambiguity expands acquisition pathways; low marginal cost enables rapid capability diffusion; and data-dependency turns local datasets into critical absorptive assets. Together, these properties erode traditional great-power technology monopolies. Based on these features, five patterns of third-party behavior are emerging. The US allies, such as Japan and Australia, build interdependence through co-development and data sharing. Transfer recipients like Pakistan and the UAE localize AI by exploiting its general-purpose character. Hedgers such as Singapore and India diversify through flexible procurement of software-layer services. Chokepoint controllers, including the Netherlands and South Korea, gain influence via critical supply-chain roles. Autonomous developers like France pursue strategic independence. Overall, regarding strategic stability, third-party behavior creates dual effects: power decentralization enhances constraint mechanisms through supply chain interdependence, yet simultaneously introduces destabilizing forces such as compressed decision-making timelines. Military AI diffusion is facilitating a technology-driven, quasi-multipolar security order.
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Cai et al. (2025) studied this question.
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