The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices

Large language models (LLMs) increasingly act on people's behalf: they write emails, buy groceries, and book restaurants. While the outsourcing of human decision-making to AI can be convenient, it raises a fundamental question: how does delegating identity-defining choices to AI shape who people become? Across a large field study and a controlled experiment, we study the impact of agentic LLMs on two identity-relevant outcomes: interpersonal distinctiveness - how unique a person's choices are relative to others - and intrapersonal diversity - the breadth of a single person's choices over time. Study 1 uses 110,000 real choices drawn from social media behavior of 1,000 U.S. users to compare generic and personalized agents to a human baseline. Both agents shift people's choices toward more popular options, reducing the distinctiveness of their preferences. While the use of personalized agents tempers this homogenization (compared to generic agents), it also more strongly compresses the diversity of people's preference portfolios by narrowing their exploration across topics and psychological affinities. Study 2 replicates these patterns in an online experiment which mimics common real-world scenarios (e.g., choosing movies) and allows us to directly compare the AI agent's choices to those made by 348 participants (12,097 human choices). The findings also suggest that the flattening effects of AI agents are amplified when choices are made sequentially (vs. batch), and when agents rely on domain-specific user information for personalization. Understanding how AI agents compress human experience (and the trade-offs involved) is critical for designing systems that augment human agency and safeguard diversity in thought, taste, and expression.