Reliance on generative AI can reduce cultural variance and diversity, especially in creative work. This reduction in variance has already led to problems in model performance, including model collapse and hallucination. In this paper, we examine the long-term consequences of AI use for human cultural evolution and the conditions under which widespread AI use may lead to "cultural collapse", a process in which reliance on AI-generated content reduces human variation and innovation and slows cumulative cultural evolution. Using an agent-based model and evolutionary game theory, we compare two types of AI use: complement and substitute. AI-complement users seek suggestions and guidance while remaining the main producers of the final output, whereas AI-substitute users provide minimal input, and rely on AI to produce most of the output. We then study how these use strategies compete and spread under evolutionary dynamics. We find that AI-substitute users prevail under individual-level selection despite the stronger reduction in cultural variance. By contrast, AI-complement users can benefit their groups by maintaining the variance needed for exploration, and can therefore be favored under cultural group selection when group boundaries are strong. Overall, our findings shed light on the long-term, population-level effects of AI adoption and inform policy and organizational strategies to mitigate these risks.

翻译：对生成式人工智能的依赖可能降低文化变异与多样性，尤其在创造性工作中。这种变异度的减少已引发模型性能问题，包括模型崩溃与幻觉现象。本文探讨人工智能应用对人类文化演化的长期影响，并分析在何种条件下广泛的人工智能使用可能导致"文化崩溃"——即依赖人工智能生成内容会减少人类文化变异与创新，并延缓累积性文化演化的过程。通过基于主体的建模与演化博弈论方法，我们比较两种人工智能使用模式：互补型与替代型。人工智能互补型用户寻求建议与指导，同时保持作为最终产出的主要生产者；而人工智能替代型用户仅提供最低限度输入，依赖人工智能完成大部分产出。我们进而研究这些使用策略在演化动力学下的竞争与传播机制。研究发现：尽管人工智能替代型用户会显著削弱文化变异，但在个体层面选择压力下仍占据优势。相比之下，人工智能互补型用户能通过维持探索所需的变异度使群体获益，因此在群体边界较强时可通过文化群体选择机制获得优势。总体而言，本研究揭示了人工智能采纳在种群层面的长期效应，并为制定缓解相关风险的政策与组织策略提供理论依据。