---
id: ai-enfeeblement-and-the-peter-principle/07-gaps-and-caveats
title: 'AI enfeeblement, idle supervisors and the Peter principle in agents: 7. Gaps
  and caveats'
report: https://ai.hiina.space/reports/ai-enfeeblement-and-the-peter-principle.md
section: 7
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# AI enfeeblement, idle supervisors and the Peter principle in agents: 7. Gaps and caveats

- Section 7 of 9 of the report "AI enfeeblement, idle supervisors and the Peter principle in agents" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/ai-enfeeblement-and-the-peter-principle.md
- Previous: https://ai.hiina.space/reports/ai-enfeeblement-and-the-peter-principle/06-candidate-experiments.md
- Next: https://ai.hiina.space/reports/ai-enfeeblement-and-the-peter-principle/08-conclusion.md

## 7. Gaps and caveats

Five gaps look real after several search wordings each; the rest are places I didn't search exhaustively.

| Gap | Status | Nearest work |
| --- | --- | --- |
| Same model, tool switched off, tracked over tool-RL training | Real gap (\~8 wordings) | Toolformer (one point, no harm), TRICE (SFT ablation collapses) |
| Closed-book recall after search RL | Real gap (3 wordings) | OTC-PO and R1-Searcher++ report call counts only |
| Free behavior of a supervisor generating tokens while a stronger delegate works | Real gap | Vend 2 anecdote, Fukui 2026 private-monologue measure, async systems that prescribe the wait |
| Peter principle tested with LLM agents | Real gap (5 wordings) | MoA role reversal, Guo 2024 leader reassignment, Rajguru 2025 scripted ABM |
| Dark room or active inference applied to human-AI delegation | Real gap as far as found | 2026 cognitive-atrophy papers that don't mention it |
| Multi-week human RCT of LLM access with a washout test | Likely gap | Single-session RCTs (Bastani, Shen & Tamkin, Liu, Maier) |
| Weak untrained orchestrator over strong workers, strength swept | Not found, not exhaustive | W4S trains a weak workflow designer; Minions is the inverse |

**Caveats.**

- Many sources are 2026 preprints. A separate verification pass opened 30 of the less familiar citations and confirmed 28 as stated; the two mismatches were corrected above. I still have not read most of them past the abstract or key table.
- Several human results rest on small samples (GPS n=13, Casner n=16, the MIT EEG crossover n=18) or self-report. The Google effect should be treated as not replicated.
- I read the two dark room commentaries only through Sun & Firestone's summary, and could not open primary texts for the McGill isolation studies or Hembree & Dessart.
- Agent behaviors such as the bliss attractor and side projects are model-version specific and will go stale; any agent-sociology result built on them should be dated to the model versions tested.
- Cost figures in section 6 are order-of-magnitude estimates, not quotes. Check current API and GPU rental prices before budgeting.

---

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space