Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced. The use of the same or similar AI models appears to lead to more homogeneous behavior. Our work begins with the observation that there is a force pushing in the opposite direction: compe- tition. When producers compete with one another (e.g., for customers or attention), they are incentivized to create novel or unique content. We explore the impact com- petition has on both content diversity and overall social welfare. Through a formal game-theoretic model, we show that competitive markets select for diverse AI models, mitigating monoculture. We further show that a generative AI model that performs well in isolation (i.e., according to a benchmark) may fail to provide value in a compet- itive market. Our results highlight the importance of evaluating generative AI models across the breadth of their output distributions, particularly when they will be deployed in competitive environments. We validate our results empirically by using language models to play Scattergories, a word game in which players are rewarded for answers that are both correct and unique. Overall, our results suggest that homogenization due to generative AI is unlikely to persist in competitive markets, and instead, competition in downstream markets may drive diversification in AI model development

翻译：近期在实验室和实际应用中的证据表明，生成式人工智能的使用降低了所产生内容的多样性。使用相同或相似的人工智能模型似乎会导致更趋同的行为。我们的研究始于一个相反的观察：竞争。当生产者相互竞争（例如，争夺客户或关注度）时，他们有动机创造新颖或独特的内容。我们探讨了竞争对内容多样性和整体社会福利的影响。通过一个形式化的博弈论模型，我们证明竞争性市场会选择多样化的AI模型，从而缓解单一文化现象。我们进一步表明，在孤立环境中表现良好（即根据基准测试）的生成式AI模型，在竞争性市场中可能无法提供价值。我们的结果强调了评估生成式AI模型在其输出分布广度上的重要性，尤其是在它们将被部署于竞争性环境时。我们通过使用语言模型玩Scattergories（一种奖励既正确又独特答案的词汇游戏）来实证验证我们的结果。总体而言，我们的研究表明，由生成式AI导致的同质化在竞争性市场中不太可能持续存在，相反，下游市场的竞争可能会推动AI模型开发的多样化。