Competition and Diversity in Generative AI

A growing body of literature on generative artificial intelligence reveals a surprisingly consistent stylized fact: when people use generative AI tools, the set of content they produce tends to be more homogeneous than content produced by more traditional means [4, 22, 49, 56, 67, 69, 84, 106, 108]. Across a wide range of domains including peer review [56], writing [67], digital art [108], and survey responses [106], access to generative AI tools (GAITs) leads to less diverse outcomes. Researchers refer to this phenomenon--where the use of similar or identical underlying AI tools lead to convergence in outcomes--as algorithmic monoculture [50] or homogenization [12]. Much of the empirical literature on the subject treats homogenization itself as the primary object of study, seeking to quantify and deeply understand it. Here, we begin our analysis further downstream. We ask: What are the consequences of monoculture in generation? When homogenization has negative consequences, how should we expect content producers to behave in response?