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 content rests on production cost lose it when production becomes cheap. We formalize this for expert services, a class of credence goods, by modeling AI as a compression of the discernible headroom between what machines produce at negligible cost and what buyers can distinguish. 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. An outcome-contingent signal-a warranty backed by damages D with ex-post verifiability phi-restores full separation at any level of AI capability whenever phi*D>= v, the value of a solved problem, under four preconditions stated explicitly and priced in turn: no seller-side private information beyond type; verifiable collectible retention behind the promise; no buyer influence on outcome or claim; negligible enforcement deadweight. Expected liability cost depends on whether the problem is solved, not on production costs. A further proposition shows that provenance certification priced as a type-independent stamp (e.g., C2PA) cannot restore full separation, while a verified commitment to forgo the AI frontier re-imposes the pre-AI artifact cost function. Two results endogenize contract institutions: civil-procedure costs set a minimum ticket size v_min below which the modeled court-enforced warranty cannot sustain separation; under liability insurance the signal-effective quantity is the retained, collectible exposure. We state falsification conditions and propose a preregistered conjoint experiment with German-speaking B2B decision-makers; the estimand is willingness to pay in excess of the promise's actuarial value.