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

2025-11-09

AI Summary

This paper studies how platforms should design nonlinear procurement mechanisms for cross-domain content under coexistence of generative AI (GenAI) and human creators. It addresses two key challenges: the high-dimensional mechanism design problem arising from GenAI’s data transferability, and the tripartite market structure induced by data intermediaries (e.g., labeling firms, creator collectives). Using a theoretical model grounded in mechanism design and game theory, the paper analyzes incentive misalignment and efficiency losses. Contrary to conventional wisdom, it shows that data transferability can *reduce* mechanism design complexity. It further identifies a novel “double-loss” market failure—characterized by excessive production and declining platform and social welfare—driven by intensified inter-domain competition. Numerical simulations validate these findings. The paper derives an optimal procurement mechanism that jointly maximizes platform revenue and social welfare, offering theoretical foundations and regulatory insights for data ecosystem governance in the AI era.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Security and Privacy: Data transparency and provenance

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.

Problem

• Designing procurement mechanisms for platforms using both human and AI creators
• Addressing data transferability effects across different content domains
• Analyzing three-layer market inefficiencies caused by data intermediaries

Innovation

• Designing nonlinear procurement mechanisms for data transferability
• Optimizing mechanisms for platform revenue or social welfare
• Analyzing three-layer market impacts on data efficiency