The Fallback as Signal: Preserved Human Skill, Liability, and Competence Signaling in Credence-Good Markets under Improving AI

Abstract

Firms that deploy improving but imperfect AI must decide how much to keep human workers engaged. Engagement lowers current output yet builds the fallback skill the firm needs when AI fails. We ask what that fallback skill signals to two audiences at once: mobile workers, who sort across firms on the skill trajectory a job builds, and clients, who cannot observe skill in a credence-good market and must infer competence. We embed the engagement-skill dynamics of Singh, Ghosh and Dai (2026) in a signaling game and add a liability commitment. Because a more-skilled provider fails less often precisely in the states where AI is down, the expected cost of a liability pledge is decreasing in fallback skill. This restores Spence-Mirrlees single crossing on a type that is endogenous - built, not drawn - and yields a separating equilibrium in which liability certifies preserved human competence that no artifact can certify once AI writes as well as the expert. We characterize the least-cost separating pledge schedule, show that client stakes shift engagement toward or away from the least-skilled worker depending on the size of the pledge, and derive a stakes threshold above which building skill dominates free-riding on a rival's training - reversing the asymmetric-specialization result of the underlying labor model. Two boundaries close the market from both sides: small tickets cannot fund enforcement, and large tickets exceed the provider's solvency. An agent-based version of the market reproduces the analytical thresholds under noisy beliefs, learning-by-record and worker churn.

v2: Corrects the author first names in the Singh, Ghosh and Dai reference (verified against the arXiv listing). Typography: Latin Modern replaces bitmap-rendered T1 Computer Modern and all figures are re-exported without Type-3 fonts; no content changes, no number changes. 49 pages, 7 figures. Replication code and seeds in the ancillary files. Also SSRN 7198738. JEL: D82, D86, J24, K13, L15, O33