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.

翻译：本文提出一个统一的计算框架，用以研究生成式人工智能（GenAI）如何重塑内容平台经济中的福利、不平等性与多样性。通过将福利经济学与基于主体的模拟相结合，我们在一个具有多维质量异质性的双边市场中，建模了人工智能生成者、人类创作者与消费者之间的协同演化动态。与静态模型不同，我们的框架将人工智能学习内生化，将其视为人类数据合成的函数，并将人类适应行为建模为技能向创意利基市场的战略性重新配置。结果表明，尽管生成式人工智能通过技术质量提升和价格抑制显著提高了消费者剩余，但它也引发了针对人类从业者的技能偏向性替代，并加剧了市场集中度。通过对六种治理模式的评估，我们揭示了一个根本性的“政策三难困境”，即平台必须在配置效率、分配公平与生态系统可持续性之间进行非平凡的权衡。我们的研究结果强调，算法多样性和支持创作者的佣金结构是维持生成式人工智能时代长尾参与和包容性社会福利的关键经济机制。