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: competition. 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 competition 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 competitive 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：近期的实验与实证证据均表明，生成式人工智能的使用降低了所产生内容的多样性。采用相同或相似的人工智能模型似乎会导致行为趋于同质化。我们的研究始于一个相反方向的驱动力观察：竞争机制。当生产者相互竞争（例如争夺客户或关注度）时，他们会被激励去创造新颖或独特的内容。我们探究了竞争对内容多样性及整体社会福利的影响。通过形式化博弈论模型，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。进一步研究表明，在孤立环境下（即根据基准测试）表现良好的生成式AI模型，在竞争性市场中可能无法提供价值。我们的结果凸显了评估生成式AI模型时需关注其输出分布广度的重要性，尤其是当这些模型将被部署于竞争性环境时。我们通过使用语言模型进行"Scattergories"单词游戏（玩家因正确且独特的答案获得奖励）进行了实证验证。总体而言，我们的研究表明生成式AI导致的内容同质化在竞争性市场中难以持续，相反，下游市场的竞争可能推动AI模型开发的多样化。