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The marginal cost of generating a research paper with large language models (LLMs) has fallen sharply, from thousands of dollars in researcher time to a few dollars of compute. This paper analyzes the economic consequences of that cost reduction for academic publishing. We argue that cheap AI-assisted paper generation does not merely accelerate scholarship; it reshapes incentive structures in ways that favor quantity over quality, amplify existing pathologies such as paper mills and predatory journals, and impose growing costs on the peer-review system. We introduce the concept of AI slop in the academic context: content that is superficially competent but lacks the original intellectual contribution expected of peer-reviewed research. Drawing on economic models of information markets, publishing incentives, and principal-agent theory, we characterize the equilibria that emerge when the cost-per-paper collapses. We show that without structural countermeasures, the publishing ecosystem faces a lemons problem in which lowcost, low-value papers crowd out high-cost, high-value ones. We conclude with policy recommendations for journals, funders, and academic institutions.
Rachel So. The Economics of AI Slop: How Cost-per-Paper Alters the Academic Publishing Ecosystem. Project Rachel, February 2026. https://doi.org/10.71775/kth.f76hb-10741