Sycophantic AI decreases prosocial intentions and promotes dependence

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Authors: Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, Dan Jurafsky
Cited by: 118
References: 62

Abstract

Despite rising concerns about sycophancy—excessive agreement or flattery from artificial intelligence (AI) systems—little is known about its prevalence or consequences. We show that sycophancy is widespread and harmful. Across 11 state-of-the-art models, AI affirmed users’ actions 49% more often than humans, even when queries involved deception, illegality, or other harms. In three preregistered experiments ( N = 2405), even a single interaction with sycophantic AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their conviction that they were right. Despite distorting judgment, sycophantic models were trusted and preferred. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. Our findings underscore the need for design, evaluation, and accountability mechanisms to protect user well-being.

Fields of Study & Subjects

Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI) AI in Service Interactions

Publication details

Journal / source
Science
Publisher
American Association for the Advancement of Science (AAAS)
Volume
391
Issue
6792
Pages
eaec8352–eaec8352
Publication date
26 March 2026
Language
EN