GenAI vs. Human Creators: Procurement Mechanism Design in Two-/Three-Layer Markets

Rui Ai, David Simchi-Levi, Haifeng Xu

Abstract: With the rapid advancement of generative AI (GenAI), mechanism design adapted to its unique characteristics poses new theoretical and practical challenges. Unlike traditional goods, content from one domain can enhance the training and performance of GenAI models in other domains. For example, OpenAI’s video generation model Sora (Liu et al., 2024b) relies heavily on image data to improve video generation quality. In this work, we study nonlinear procurement mechanism design under data transferability, where online platforms employ both human creators and GenAI to satisfy cross-domain content demand. We propose optimal mechanisms that maximize either platform revenue or social welfare and identify the specific properties of GenAI that make such high-dimensional design problems tractable. Our analysis further reveals which domains face stronger competitive pressure and which tend to experience overproduction. Moreover, the growing role of data intermediaries, including labeling companies such as Scale AI and creator organizations such as The Wall Street Journal, introduces a third layer into the traditional platform–creator structure. We show that this three-layer market can result in a lose-lose outcome, reducing both platform revenue and social welfare, as large pre-signed contracts distort creators’ incentives and lead to inefficiencies in the data market. These findings suggest a need for government regulation of the GenAI data ecosystem, and our theoretical insights are further supported by numerical simulations.

Submission Type: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

We have revised the manuscript accordingly.

1. In Section 2, we added a discussion explaining why the high-dimensional setting is significantly more difficult. In particular, we explain that the high-dimensional setting requires an additional path-independence condition, under which the one-dimensional method is no longer applicable.
2. We added further discussion regarding the lose-lose effect. We conjecture that the lose-lose effect is general, because the three-layer market distorts both revenue and social welfare. We also explain why such distortions lead to the lose-lose effect.
3. Furthermore, we provided more details in the proofs concerning the IC and IR properties. We hope that these additions help address concerns about the level of economic expertise required to understand the paper.

We hope these revisions improve the clarity and completeness of the paper. Thank you!

Assigned Action Editor: ~Hongyang_R._Zhang1

Submission Number: 6450