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Nobody has tested the Peter principle with LLM agents. Five search wordings found one data-free blog post. The ingredients all exist separately, though, and one published table already shows the precondition: an LLM's rank as a worker does not predict its rank as a manager.
The mechanism has two rival explanations, and an experiment has to separate them.
Agent-based models say the effect depends entirely on the competence-transfer assumption. Pluchino, Rapisarda & Garofalo 2010 (Ig Nobel 2010) ran a 160-seat, six-level pyramid under two assumptions: competence carries over on promotion ("common sense") or is redrawn independently ("Peter hypothesis"). Change in efficiency versus baseline:
| Promotion rule | Competence carries over | Competence redrawn |
|---|---|---|
| Promote the best | +9% | −10% |
| Promote the worst | −5% | +12% |
| Random | +2% | +1% |
Random promotion is the only rule that never loses. Their 2011 follow-up found random helps only when more than half of positions follow the redraw rule. Fetta et al. 2012 added social networks and found merit promotion was best and "no clear evidence" for the principle. Rajguru, Churchill & Graham 2025 is the best template for an LLM version: each agent has a four-skill vector, each level has a requirement profile, and performance is their dot product. The Peter effect is strong when requirements shift sharply between levels and weak when skills transfer.
Human field evidence is real but modest. Benson, Li & Shue 2019 (QJE, sales workers): doubling pre-promotion sales raises promotion odds about 15% and goes with roughly a 7.5% drop in each subordinate's sales under that manager. Collaboration experience predicted good managers but was not rewarded. They note firms may accept worse managers because promotion doubles as the incentive scheme, a factor LLM agents lack.
LLM evidence that worker and manager skill come apart:
Reusable machinery: DyLAN peer-rated agent importance scores (a ready-made performance review), GPTSwarm graph optimization (reorgs, not promotions), and cascades like FrugalGPT that escalate tasks rather than agents.
Design implication. The Peter effect only appears in a pool where role competences decorrelate. Measure an agent × role competence matrix first; its worker-manager correlation is the knob, and Pluchino's model then predicts the outcome before any promotion runs. Experiment X5 below is built on this.
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