The Impact of Generative AI on Content Platforms: A Two-Sided Market Analysis with Multi-Dimensional Quality Heterogeneity

Abstract

This paper presents a unified computational framework to examine how generative AI (GenAI) reshapes welfare, inequality, and diversity in content platform economies. By integrating welfare economics with agent-based simulations, we model the co-evolutionary dynamics among AI generators, human creators, and consumers within a two-sided market characterized by multi-dimensional quality heterogeneity. Unlike static models, our framework endogenizes AI learning as a function of human data synthesis and models human adaptation as a strategic reallocation of skills toward creative niches. The results reveal that while GenAI significantly enhances consumer surplus through technical quality gains and price depression, it triggers a skill-biased displacement of human incumbents and intensifies market concentration. Through the evaluation of six governance regimes, we identify a fundamental ``Policy Trilemma'' where platforms must navigate non-trivial trade-offs between allocative efficiency, distributional equity, and ecosystem sustainability. Our findings highlight that algorithmic diversity and pro-creative commission structures function as essential economic mechanisms for sustaining long-tail participation and inclusive social welfare in the generative AI era.