The Last Costly Signal: How Generative AI Collapses Competence Signaling and Why Liability Sustains Markets for Expert Services

Generative artificial intelligence has reduced the cost of producing convincing artifacts of expertise-reports, analyses, proposals-to nearly zero. Signaling theory predicts that signals whose informational content rests on production cost lose that content when production becomes cheap. We formalize this prediction for markets for expert services, a class of credence goods, by modeling generative AI as a compression of the discernible headroom between what machines produce at negligible cost and what buyers can distinguish at all. Below a critical headroom, no separating equilibrium in production-side signals exists; the market pools, high-competence providers earn no premium, and those with outside options exit-Akerlof's lemons dynamic. We show that an outcome-contingent signal-a warranty backed by damages D with ex-post verifiability phi-restores full separation for any level of AI capability whenever phi*D >= v, where v is the value of a solved problem. The expected cost of liability depends on whether the problem is solved, not on document production costs. A corollary shows that provenance certification (e.g., C2PA), whose cost is type-independent, cannot restore separation. Agent-based Monte-Carlo simulations illustrate the dynamics. Two further results endogenize contract institutions: civil procedure costs set a minimum ticket size v_min below which no credible enforcement threat exists; under liability insurance, separation depends on retained risk or risk-rated premiums. We state falsification conditions and propose a preregistered choice-based conjoint experiment with decision-makers in the German-speaking B2B expert-services market.