Paying for Information, Not for Clicks: A Block-Level Publisher Content Marketplace for AI Answers

Markus Mobius, Luca Braghieri, Sarah Eichmeyer, Daniel G. Goldstein, Nicole Immorlica, Oliver Kiss, Ro'ee Levy, Brendan Lucier, Ishan Mahajan, Jingyi Qiu, Tanya Rosenblat, David M. Rothschild, Andras Sari, Alexsandrs Slivkins and Colin Watson

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Abstract: We propose a publisher content marketplace that allocates payments for information used in AI-generated answers. The unit of attribution is a ``lego block:'' a distinct fact or piece of commentary that may appear in articles from multiple publishers. A simple news-reading model connects three payment rules: equal sharing among all publishers reporting on a block, rewarding the publisher who reported a block first, and rewarding indispensable contributions. Each can be represented within a Shapley-value framework. We evaluate the framework using answers to 1,974 search-engine queries about a heavily covered news episode. When attribution considers only the articles retrieved for an individual query, most cited blocks appear unique, and the rules produce nearly identical allocations. A global index covering the full news corpus reveals overlap across publishers, making the choice of payment rule consequential. Premium publishers, defined as established outlets with substantial newsgathering operations, receive 42% of allocated value under equal sharing, compared with 46-50% under a first-publication rule, depending on the treatment of publication timestamps. These findings highlight the importance of a shared global index that records which publishers provide each block and when they published it. Attribution restricted to the sources retrieved for an individual answer lacks the information needed to distinguish these payment policies.

Date: 2026-10
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