The Verification Bottleneck

Abstract

This paper studies how generative AI, by lowering the cost of research production, affects scientific verification in a general-equilibrium model of peer review. Privately informed researchers choose observable output, an editor optimally assigns review depth, and reviewers supply time according to its expected scientific contribution. Costly output helps screen research quality, and cheaper production lets weaker researchers imitate stronger ones more readily. Beyond a technological threshold, reviewer capacity and verified knowledge weakly decline even under optimal editorial decisions, and strictly when a screening constraint has positive shadow value. Reviewer withdrawal can amplify the loss. With observable quality, the same progress weakly increases both.