Three Ceilings: Model Monoculture, Solvency, and the Penalty Doctrine in Markets for Expert Services

When generative AI drives the marginal cost of a persuasive expert artefact toward zero, production-cost signals of competence collapse and outcome-contingent liability commitments take their place. Such commitments look robust to better AI: a positive failure rate always leaves a residual to price. We show that this robustness rests on an unstated homogeneity assumption, and that a liability commitment faces three ceilings, not one. AI error decomposes into idiosyncratic and common components; capability growth eliminates the idiosyncratic part faster (Kim et al., 2025), so the residual becomes progressively common - and common error is precisely what a verifier drawn from the same foundation model cannot observe: judge scores correlate with judge-generator model similarity at an average r=0.84 (Goel et al., 2025). Provability is therefore state-dependent, theta_eff = theta_0(1 - xi*kappa). Separation requires the commitment that must be posted to fall below what can be posted, v/theta_eff