From Seller to Data Supplier: How Creator Compensation Enables AI Displacement

Lijuan Luo, Emaad Manzoor, Nathan Yang, Fan Yao

Working paper.Preprint1 min read

Abstract

Creative platforms use creators’ content to train generative AI systems that compete with the same creators. This paper studies how compensation for training data changes equilibrium pricing, participation, and demand. In the model, compensation can enable AI to displace creators’ direct sales while retaining them as paid data suppliers. Without compensation, creator exit and the resulting loss of data constrain displacement. Under the baseline assumptions, compensation increases creator rents and total surplus while reducing consumer surplus, illustrating why displacement and harm to creators need not coincide.
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This is a working paper with Lijuan Luo, Emaad Manzoor, and Nathan Yang. The full draft is not publicly available. You can ask questions about the paper through an AI assistant grounded in the author-supplied draft.

Generative AI Creator Compensation Platform Design
Fan Yao
Authors
Fan Yao
Assistant Professor
I am an assistant professor in the Department of Statistics and Operations Research (STOR) at UNC Chapel Hill. I received my PhD in Computer Science from the University of Virginia, advised by Haifeng Xu and Hongning Wang. Prior to obtaining my PhD, I spent two years at the University of Chicago and received both my BS and MS in Computational Mathematics from Peking University.