Competition and Diversity in Generative AI

TL;DR

This paper investigates how competition influences content diversity in generative AI, revealing that competitive markets promote diversity and mitigate homogenization, contrasting prior evidence of reduced diversity.

Contribution

It introduces a game-theoretic model showing competition encourages diverse AI models and validates findings through empirical experiments with language models in a word game.

Findings

Competitive markets promote content diversity.

Models performing well in isolation may underperform in competition.

Competition drives diversification in AI model development.

Abstract

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