Cheap, Fallible Cognition and the Political Economy of Expertise

Abstract

The question of whether artificial intelligence will "destroy jobs" is too coarse to guide economic analysis or institutional design. A job is not an indivisible object, and machine cognition is not a uniform substitute for human labor. This paper develops a task-based and institutionally grounded framework for analyzing generative AI as cheap, scalable, and fallible cognition. The relevant margins are exposure, adoption, verification, question selection, workflow redesign, demand elasticity, apprenticeship, and rent allocation. We distinguish the technical reach of large language models from equilibrium labor-market displacement by introducing a task vulnerability index and an adoption condition that makes verification, liability, trust, and governance explicit. We then model occupations as governance bundles rather than task lists, firms as architectures of distributed intelligence, and labor-market effects as a balance among task compression, scale expansion, new human work, and institutional bargaining. A further implication is that when answer generation becomes abundant, the scarce human capital shifts upstream and downstream: toward asking economically meaningful questions, framing problems, generating hypotheses, interpreting results, and bearing responsibility for consequential use. The central dynamic concern is expertise formation: Junior tasks jointly produce current output, question sense, and future judgment, so their automation can raise short-run productivity while weakening the pipeline into accountable expertise unless AI is designed to teach rather than merely bypass. The paper concludes that AI's labor-market destiny is neither mechanical unemployment nor automatic abundance. It is an institutional equilibrium shaped by workflow design, apprenticeship systems, liability rules, competition policy, worker voice, and the distribution of rents from cheap cognition.