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arXiv · 2608.18357

Capability-Based Planning for AI Crisis Preparedness

Isaak Mengesha·Charlie Collins·Juan Felipe Cerón Uribe·Salvatore d'Ambrosio·Vickie Ellis
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Abstract

Capability-based planning drives preparedness in defense and homeland security, but has yet to be applied seriously to AI. Government AI preparations follow a predict-then-act paradigm: rank risks by likelihood and impact, then prepare for the highest expected harm. AI resists prediction: expert timelines disagree by orders of magnitude, and official reviews concede that likelihood-based risk assessment fails for exactly this class of risk. Drawing on principles of decision making under deep uncertainty, we propose a methodological framework in three parts: a scenario library sampled systematically across declared axes; a rating procedure that assesses each government capability against each scenario on coarse, gated criteria; and a prioritization step that maps the resulting matrix onto decision rules a government might adopt. Through a pilot across the four most severe AI-enabled threat classes, we illustrate the kind of insight the instrument yields and provide a proof of concept for capability-based planning as a practical tool for AI crisis preparedness.

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Isaak Mengesha, Charlie Collins, Juan Felipe Cerón Uribe, Salvatore d'Ambrosio, Vickie Ellis. 2026-08-18. Capability-Based Planning for AI Crisis Preparedness. https://arxiv.org/abs/2608.18357

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