The competence–control trade-off in military AI innovation: Autonomous weapons systems and shifting modes of state control over private experts

Developments in military AI highlight that maintaining state control over military innovation that is driven by private corporate experts is challenging. Even the leading military AI power, the United States, has struggled to meet this challenge, while trying different modes of control over time. What explains these struggles and the shifting modes of state control over private military innovation? Bringing together the ‘competence–control theory’ of indirect governance and research on technology-driven transformations in the making of national security, we propose a novel theory of state control over military innovation. We argue that states face a trade-off between (fostering) the competence of private corporate experts and (enhancing) state control over military innovation. This trade-off is shaped by technological change and geopolitical competition. Depending on the relative strength of these drivers, varying prioritisations of competence or control lead to different hierarchical or non-hierarchical, capacities- or rules-based modes of control. Tracing the evolution of the US national security state and its relations to private corporate experts in the subfield of autonomous weapons systems, we demonstrate that our theoretical argument explains otherwise puzzling intertemporal variation in control modes. Our findings have important policy implications for the institutional design of military innovation.