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Sprint projectSep 14, 2026Vancouver, BC

A playbook for the next warning shot

Peti Setabandhu, Pera Kasemsripitak · Team Asit

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

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A Playbook for the Next Warning Shot helps AI safety communicators respond to suspected misalignment and failures of control while evidence is still emerging. It connects incident triage to response guidance, prepared statements, shared vocabulary, and audience-tailored explanations. Testing on hypothetical scenarios informed revisions, including how to distinguish stopped activity from contained effects and avoid unsupported causal claims. The aim is to turn timely attention into informed understanding while making uncertainty and corrections clear.

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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.
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  1. The idea of taking an emerging incident and decomposing how to communicate this to different audiences and at different points in term has value. However, more clearly defining use cases where this would have a meaningful impact, and tailoring for different audiences, would help sharpen criteria against which to assess your approach against. I was also unclear about who a 'AI safety communicator' would be in practice - a representative from a frontier developer reporting the incident to the EU office? A journalist reporting on AI safety? A concerned person reporting to their network? Each of these would have differing needs.

    For example, if the aim is to elicit detail that different governments need (whether from a regulator or an impacted jurisdiction), accuracy and timeliness could be priorities over clarity of terminology to a non-technical audience. If the aim was to communicate the severity of the incident to a non-technical audience, then different communication requirements would govern this. Adapted to a narrower audience, this could be useful for informing reporting requirements to a body such as the EU AI Office, but it would need some adaption.

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  2. I appreciate the author's attempts to categorize and characterize AI incidents. I could see a project like this partnering with existing AI incident databases as a means to quickly share information via various media.

Cite this project

@misc{setabandhu2026playbook,
  title = {{A playbook for the next warning shot}},
  author = {Peti Setabandhu and Pera Kasemsripitak},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/a-playbook-for-the-next-warning-shot-q5yp}},
  url = {https://apartresearch.com/sprints/projects/a-playbook-for-the-next-warning-shot-q5yp}
}

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