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

Abstract

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 <= min{Lbar, mv}, yielding the survival condition xi*kappabar <= 1 - 1/(M*theta_0) with M = min{Lbar/v, m} and a regime switch at the ticket size v* = Lbar/m: small engagements are constrained by the penalty doctrine, large ones by capital. Calibrated to six engagement types across four jurisdictions, the generative-AI insurance exclusions effective January 2026 shift eight of twenty-four cells from doctrine-bound to solvency-bound and cut the cells surviving xi=0.4 from twelve to four. The insurance withdrawal is a civil-law event; the common-law cells were already at the boundary.