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Sprint projectJun 13, 2025Perth, Australia (Currently Studying in Beijing)

Critical Analysis into ‘No Unbounded AIs’

Marshall White · Team Parallax Industries

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: Critical Analysis into ‘No Unbounded AIs’

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This red team report exposes a critical blindspot in A Narrow Path Phase 0 policies by showing how artificial superintelligence (ASI) can emerge not through centralized training runs, but via decentralized financial infrastructure. The hypothetical actor, Parallax Industries, deploys modular, FLOP-compliant AI agents across CBDC-linked systems in developing nations, governed by a DAO that evolves optimisation goals over time. Though each component abides by safety constraints, their interactions form a distributed intelligence with AGI or ASI-level influence over economic systems. This systemic emergence bypasses current regulatory definitions of “training” or “improvement,” undermining the policies’ core assumptions. The report argues for a shift from architectural control to systemic oversight, warning that ASI may arrive disguised as infrastructure and already embedded.

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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, this was an interesting paper. I felt that some of the methodological discussion was far too lengthy, and didn't clearly contribute to the persuasiveness of the core argument.

    It seems clear that a DAO can indeed collaborate various activities, but I didn't clearly have a sense for why this is an especially-likely distributed training scenario. More details here might have been helpful.

Cite this project

@misc{white2025critical,
  title = {{Critical Analysis into ‘No Unbounded AIs’}},
  author = {Marshall White},
  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/red-teaming-a-narrow-path-controlai-policy-sprint-s3t9}},
  url = {https://apartresearch.com/sprints/projects/red-teaming-a-narrow-path-controlai-policy-sprint-s3t9}
}

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