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

Research from arXiv indicates that the use of Large Language Model (LLM)-based agents, which increasingly act on behalf of individuals for various tasks, may reduce the distinctiveness and diversity of people's choices. This phenomenon, termed 'The Basic B*** Effect', suggests that delegating identity-defining choices to AI could lead to less unique interpersonal choices and a narrower breadth of intrapersonal choices over time. Initial findings are derived from a field study analyzing social media behavior.

Why it matters

This research highlights a potential societal impact of pervasive AI agent adoption on individual expression and choice behavior. Organizations deploying or integrating AI agents must consider the broader implications for user autonomy and the preservation of diverse decision-making patterns, which could influence market dynamics and cultural trends. Understanding these effects is critical for strategic planning in technology development and ethical AI governance.

Key insights

• LLM-based agents are increasingly being used to perform tasks on behalf of individuals, including writing emails, buying groceries, and booking restaurants.
• The delegation of human decision-making to AI raises fundamental questions about its impact on personal identity and choices.
• The study investigates two identity-relevant outcomes: interpersonal distinctiveness (uniqueness of choices relative to others) and intrapersonal diversity (breadth of choices over time).
• Preliminary findings suggest that the use of LLM-based agents reduces both interpersonal distinctiveness and intrapersonal diversity.
• The research utilized data from 110,000 real choices from 1,000 U.S. social media users to compare generic and personalized agents against human choices.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2509.02910