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Sprint projectSep 14, 2026New York, NY

Warning Shots Eliminate Models, Not Uncertainty

Ayan Sivaram

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Warning shots are said to fail because they do not travel. I argue they travel and move the wrong quantity. An incident raises the estimated rate of safety-case-falsifying events and separately forecloses layers of the safety case that cannot accommodate it. Under unanimity among veto players with heterogeneous priors, action tracks not the average estimate but the optimistic bound: the most reassuring layer still standing for the least persuaded player. Two bodies of evidence inducing the same distribution of posteriors can therefore differ in whether they produce action, and severity moves neither channel. In the July–September 2026 agent-containment record, state enforcement followed the victim’s forensic reconstruction rather than the breach, and every response arrived through the lowest-veto-count channel available. Reporting duties should attach to events falsifying providers’ published capability claims: a trigger fixed ex ante that scales with capability, where outcome-magnitude thresholds do not.

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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?

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  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?

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  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.
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  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

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  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.
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  1. ai generated. i cannot follow

  2. I like the governance/decision-theory angle a lot!

    I found the writing tone a bit too conceptual for my taste, but overall it provides a clear-eyed view on AI policy and governance while also formalizing a lot of those ideas in a mathematical footprint.

Cite this project

@misc{sivaram2026warning,
  title = {{Warning Shots Eliminate Models, Not Uncertainty}},
  author = {Ayan Sivaram},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/warning-shots-eliminate-models-not-uncertainty-4j3i}},
  url = {https://apartresearch.com/sprints/projects/warning-shots-eliminate-models-not-uncertainty-4j3i}
}

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