No-Regret Preparedness: A Framework for Investments That Mitigate Both Advanced-AI and Conventional Catastrophic Risk

A persistent obstacle to catastrophic-AI preparedness is political economy: governments facing immediate threats rarely fund speculative-seeming AI risk work. But a subset of investments have a valuable property - they materially improve resilience against advanced-AI catastrophe AND against near-term conventional or hybrid threats at the same time. For example, national biosurveillance and domestic vaccine capacity guard against both AI-enabled engineered pathogens and hostile state bio-programs. Hardened, distributed communications survive both an AGI-level cyberattack and conventional electronic warfare. Analysts trained to detect AI-generated influence operations are equally effective against ordinary state disinformation. Where this convergence holds, the decision matrix strongly favors preparation: you are better off in every scenario.

The problem is that this argument is currently asserted more than tested. Some claimed co-benefits are real; others are motivated reasoning that lets people relabel spending they already wanted as AI-safety-relevant. A rigorous framework needs to separate the two.

Core question: Which national-resilience investments genuinely mitigate both advanced-AI and conventional catastrophic risk - and how strong is the co-benefit in each case?

1. Candidate mapping. Mentees enumerate candidate investment classes (biosurveillance and medical countermeasures; distributed/hardened comms; AI-influence-operation detection; critical-infrastructure resilience; red-team and incident-response capacity; secure compute) and specify, for each, the AI-catastrophe threat vector and the conventional threat vector it addresses.

2. Co-benefit assessment. For each class, evaluate how strong the dual-use claim actually is, using a transparent rubric: does the AI-relevant capability genuinely transfer, or only superficially? Where does the analogy break? The goal is an honest scorecard, including cases where convergence fails.

3. The framework. Produce a decision framework a government or funder could apply to any proposed investment to test its no-regret status, plus a scored assessment of the main investment classes, with a short case application to a specific country's resilience portfolio.

Milestones: weeks 1–4, candidate mapping and rubric design; weeks 5–8, completed co-benefit assessments (midterm report); weeks 9–12, framework and scorecard (final report / Demo Day). My own work developing the convergent-resilience concept will be shared as a starting point; the mentees' job is to test and extend it, including identifying where it breaks down.

The bottleneck for much catastrophic-AI preparedness - especially AI-enabled biosecurity and infrastructure resilience - is not knowing what to build but getting it funded against competing priorities. A rigorous co-benefit framework changes the political economy: it lets advocates make an argument that survives scrutiny (“this protects you against threats you already take seriously, and against AI catastrophe”) instead of one easily dismissed as speculative. By honestly scoring where the co-benefit is weak, it avoids laundering ordinary defense spending as safety work. The payoff is more real resources flowing to the specific infrastructure that mitigates the most severe AI-enabled catastrophe channels.

Mentees are the core research team. Each takes ownership of one or more investment classes and is responsible for both the threat mapping and the honest co-benefit assessment - including arguing, where the evidence points that way, that a “dual-use” claim doesn't hold up. We agree on the scoring rubric together early on. The framework-design and final report stages are collaborative. I particularly want mentees who will resist the temptation to make every investment look convergent; the intellectual value depends on the scorecard being credible.

Strong reading and synthesis skills across policy, legal, and institutional documents - this is a research-and-writing project, not a technical one.

A background in public policy, law, international relations, political science, economics, security studies or a related field, OR demonstrated equivalent research experience.

Basic understanding of catastrophic AI risk pathways (especially AI-enabled bio and cyber). No ML or programming background required.

No geographic requirement. A weekly meeting slot spanning European and North American time zones will be agreed with the team.