States are integrating AI into military and diplomatic functions, with consequences for deterrence, crisis stability, and agreement-making that remain underanalyzed. This project produces analytical papers and policy briefs on two questions: under what conditions delegation to AI systems destabilizes crises, and whether machine-verifiable commitments could improve the verifiability of future agreements.
About the project
The project has two subprojects.
The first concerns delegation and stability: how the delegation of military and diplomatic functions to AI systems changes crisis dynamics. Work here combines a structured synthesis of the wargaming, escalation, and automation literatures with historical cases of delegation and pre-delegation of authority, including nuclear command-and-control arrangements and their false-alarm record (the Petrov incident in 1983, the NORAD training-tape incidents), the Soviet Perimeter system, and financial flash crashes as a deployed-agent analogy. Theoretical anchors include Schelling on commitment (The Strategy of Conflict) and Fearon on rationalist explanations for war (Fearon 1995, https://web.stanford.edu/group/fearon-research/cgi-bin/wordpress/wp-content/uploads/2013/10/Rationalist-Explanations-for-War.pdf). The intended output is a framework paper on the conditions under which delegation destabilizes crises.
The second concerns machine-verifiable commitments and arms control. Some commitment mechanisms from game theory (program equilibrium, mutual transparency) become implementable when negotiating parties are inspectable software, with implications for verification, deterrence, and treaty design. This subproject maps arms-control verification precedents (managed access, national technical means, the intrusiveness-secrecy tradeoff) onto agent transparency mechanisms. The intended outputs are an analytical paper and a short brief for arms-control audiences.
Theory of change
Self-escalation and coordination failure between military AI systems already appear as named risk categories in international dialogues, but there is little analytical groundwork for the deployment norms or agreement designs that would preserve stability as these functions are delegated. The framework paper provides part of that groundwork. The commitments subproject examines a mechanism by which AI-era agreements could be more verifiable than their nuclear-era predecessors; verification has historically been a central point of failure in arms control. Results are intended to inform both policy research and the international dialogues now forming around military AI.
Your role
Mentees own research modules: a literature domain (wargaming and escalation, command-and-control history, verification regimes) or a historical case, delivered as structured syntheses and drafted sections that feed into co-authored outputs. We meet weekly to set direction, and I provide detailed asynchronous feedback on writing. The project is part of a broader research program on strategic interaction under AI delegation, so this work continues past the round.
Prerequisites
Strong analytical writing. Working familiarity with current AI capabilities and deployment patterns. Preferably background in international relations, strategic studies, or security policy, through formal study or serious self-study, including familiarity with the deterrence and escalation literature (for example Schelling, Fearon, and the counterforce debates). Arms-control knowledge is helpful for the commitments subproject.
Location preference
No geographical preference; weekly meetings between 11:00 and 18:00 UTC.
Application question(s)
- Link to a writing sample, ideally from a research context (required).
- (20 min) Pick one historical case where a state pre-delegated use-of-force or escalation-relevant authority to an automated or standing system. What made it stabilizing or destabilizing, and what feature of AI delegation breaks the analogy? (250 words)
- Optional: name one way in which arms-control verification precedent does NOT transfer to verifying properties of AI agents. (150 words)
About the mentor
Amritanshu Prasad is an AI safety researcher working on strategic interaction under AI delegation: how bargaining, crises, and agreements change when AI agents act on behalf of states and companies. He is a member of the working group on international AI governance at Uppsala University's Alva Myrdal Centre for Nuclear Disarmament and studies the strategic parameters of historical arms-control agreements in collaboration with the Oxford Martin AI Governance Initiative.
Previously, he was a fellow at Pivotal Research, where he developed coordinated vulnerability disclosure frameworks for AI systems with Robert Trager; co-authored a scheming-propensity evaluation paper at LASR Labs under the supervision of David Lindner (Google DeepMind); contributed to UK AISI's ControlArena; and worked on METR's HCAST benchmark.