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Sprint projectJun 13, 2025London, UK

Algorithmic Governance for A Narrow Path

Josh Thorsteinson · Team Dream Team

Submitted to Red Teaming A Narrow Path: ControlAI Policy Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Algorithmic Governance for A Narrow Path

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We found that A Narrow Path has a major weakness: algorithmic improvements that make AI more efficient can bypass compute-based safety controls. We recommend expanding oversight to include algorithm development, restricting high-risk algorithms, requiring safety testing for efficient algorithms, and watermarking AI models to prevent unauthorized copying. These changes would strengthen A Narrow Path against dangerous AI development.

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Does the analysis realistically assess what government agencies, resources, and expertise would be needed to implement these policies? Are the identified implementation challenges specific and grounded in understanding of how similar policies have worked (or failed) in practice? Does the submission adequately consider bureaucratic, technical, and coordination complexities involved in enforcement? How well does the analysis account for real-world constraints like budget limitations, regulatory capture, and inter-agency coordination?

Does the analysis identify specific ways the policies could fail to prevent ASI development or be circumvented by determined actors? How thoroughly does the submission examine edge cases, loopholes, or unintended consequences that could undermine the 20-year goal? Does the assessment consider different threat models (state actors, rogue researchers, corporate actors) and how policies address each? Are the identified failure modes realistic and significant, or primarily theoretical edge cases?

Does the submission cite relevant historical examples of similar policies (nuclear non-proliferation, export controls, dual-use technology regulation) to support its arguments? Are claims backed by empirical data, documented case studies, or credible expert analysis rather than speculation? How well does the analysis draw lessons from comparable regulatory domains to assess likely outcomes? Does the submission avoid making unsupported assertions about what "would" or "could" happen without evidence?

  1. Overall, a focused and interesting critique. We agree that algorithmic speedups are indeed a concern and think your focused critiques here are interesting. Some of your solutions are particularly interesting but challenging -- for example, the list of dangerous algorithmic methods would be helpful for regulators, but also could inadvertently provide a roadmap for those defecting from the rules to not build such a thing.

    Still, some very thoughtful ideas here and a clear presentation of why algorithmic speedups are a risk.

  2. I liked the algorithmic efficiency forecasting and the policy suggestions.

Cite this project

@misc{thorsteinson2025algorithmic,
  title = {{Algorithmic Governance for A Narrow Path}},
  author = {Josh Thorsteinson},
  year = {2025},
  month = jun,
  note = {Submitted to Red Teaming A Narrow Path: ControlAI Policy Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/algorithmic-governance-for-a-narrow-path-06sv}},
  url = {https://apartresearch.com/sprints/projects/algorithmic-governance-for-a-narrow-path-06sv}
}

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