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Sprint projectFeb 2, 2026Seville, Spain

No One Thanks You for Disasters That Never Happened: Pricing AI Risk While Making AI Safety Investable

Ana Belen Barbero Castejon, Stan Threepwood · Team No One Thanks You for Disasters That Never Happened

Submitted to The Technical AI Governance Challenge. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: No One Thanks You for Disasters That Never Happened: Pricing AI Risk While Making AI Safety Investable

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This paper examines whether risk quantifi cation and pricing can function as a practical mechanism of AI governance. We present a prototype framework for pricing AI risk under deep uncertainty, using a scenario-based frequency–severity decomposition with dependency-aware propagation and aggregation with catastrophe-style tail modeling. Loss distributions are evaluated using standard actuarial metrics such as Expected Loss (EL), Value at Risk (VaR), and Tail Value at Risk (TVaR), enabling premium estimation in data-scarce environments.

These findings suggest a potential pathway for AI insurance and reinsurance to function as coordination infrastructure, aligning incentives across companies, developers, insurers, and regulators while enabling scalable investment in AI safety, security and operations.

Application available here: https://pricing-ai-risks.netlify.app

Code available here: https://github.com/stanthreepwood/ai-risk-pricing

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. The problem is real but the proposed solution doesn't capture the whole complexity I think and hard to validate.

Cite this project

@misc{castejon2026no,
  title = {{No One Thanks You for Disasters That Never Happened: Pricing AI Risk While Making AI Safety Investable}},
  author = {Ana Belen Barbero Castejon and Stan Threepwood},
  year = {2026},
  month = feb,
  note = {Submitted to The Technical AI Governance Challenge, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/no-one-thanks-you-for-disasters-that-never-happened-pricing-ai-risk-while-making-ai-safety-investable-gjwu}},
  url = {https://apartresearch.com/sprints/projects/no-one-thanks-you-for-disasters-that-never-happened-pricing-ai-risk-while-making-ai-safety-investable-gjwu}
}

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