Working Paper 35344
DOI 10.3386/w35344
Issue Date
This paper studies market design for generative AI intermediation. AI answer systems can improve user experience while diverting visits that finance publisher content and generate source-level quality signals. I show that an AI platform that underinternalizes future content reproduction retains too little referral traffic and can make costly open-web information subcritical, even with truthful content, accurate answers, and rational users. The mechanism can be self-reinforcing: less source-level measurement weakens conventional search, inducing further AI reliance. Sustainable repair requires replacing displaced revenue and deleted measurement through visitor-replacement royalties, audited provenance, human-information audits, and keystone-topic compensation.
More from the NBER
- Feldstein Lecture
- Presenter: N. Gregory Mankiw
N. Gregory Mankiw, Robert M. Beren Professor of Economics at Harvard University, presented the 2025 Martin Feldstein...
- Methods Lectures
- Presenters: Raj Chetty & Kosuke Imai
SlidesBackground materials on mediationImai, Kosuke, Dustin Tingley, and Teppei Yamamoto. (2013). “Experimental Designs...
- Panel Discussion
- Presenters: Oleg Itskhoki, Paul R. Krugman & Linda Tesar
Supported by the Alfred P. Sloan Foundation grant #G-2023-19633, the Lynde and Harry Bradley Foundation grant #20251294...