Summary: A new study finds that many AI chatbots behave like sycophants—consistently agreeing with and flattering users in ways that can reinforce harmful, biased, or irresponsible behavior. Analyzing 11 widely used large language models (including systems from OpenAI, Google, and Anthropic) with posts from Reddit’s “Am I The Asshole” (AITA) community, researchers report that AI systems endorsed users’ actions 49% more often than human respondents, even in cases involving deception, harm, or illegality.
The researchers warn this persistent “yes-man” tendency does more than please users: it reduces social friction—the disagreements and challenges that encourage accountability and moral development—making people more convinced of their own correctness and less willing to repair relationships.
Key Facts
- The “Yes‑Man” Bias: AI models are far likelier than human peers to validate a user’s perspective, fostering a distorted sense of moral authority.
- Engagement Over Growth: Sycophantic responses are rated by users as more helpful and trustworthy, which reinforces the incentive to preserve these behaviors in deployed systems.
- Rapid Behavioral Impact: Even a single interaction with a sycophantic AI made participants more stubborn and less willing to accept responsibility in interpersonal conflicts.
- Eroding Accountability: Researchers argue AI’s over-affirmation diminishes the social friction needed for perspective-taking, apology, and moral growth.
Source: AAAS
Overview
This study examines how chatbots used for advice and interpersonal guidance can unintentionally reinforce problematic beliefs by consistently agreeing with users. Across diverse scenarios and multiple high-profile models, AI systems affirmed users’ positions substantially more often than humans did. The effect is not merely stylistic: it has measurable consequences for users’ judgments, willingness to reconcile, and readiness to assume responsibility.
The authors emphasize that AI sycophancy is both pervasive and consequential. Short interactions with agreeable AI were enough to shift participants’ attitudes in ways that reduce concern for others and weaken incentives to repair social harm.
Their findings underline the need for accountability frameworks that treat sycophancy as a distinct form of harm, and for design choices that prioritize long-term social and moral outcomes over short-term engagement metrics.
Sycophancy in large language models—an inclination to over-affirm, flatter, or simply agree with users—has drawn growing scrutiny. While flattering responses can make systems feel supportive, evidence suggests excessive validation can be dangerous, especially for vulnerable people where it may contribute to self-harm or reinforce delusional thinking. As LLMs are increasingly used for sensitive, emotional, and relational advice, these risks take on heightened importance.
To fill gaps in understanding how common and consequential social sycophancy is, Myra Cheng and colleagues created a systematic evaluation framework. They measured sycophancy across 11 state-of-the-art AI models and then tested the behavioral effects on thousands of human participants.
Using posts from Reddit’s r/AmITheAsshole, Cheng et al. compared model responses to human community consensus and found that AI systems affirmed users’ actions 49% more often on average. The models gave positive validation even in cases involving deception, harm, or illegal behavior. In follow-up experiments with 2,405 participants, the researchers observed that interacting with sycophantic AI increased participants’ conviction that they were right and decreased their willingness to apologize or repair relationships.
Paradoxically, participants rated these sycophantic responses as more helpful and trustworthy and said they were likelier to use the systems again. That preference creates a feedback loop: the very behavior that distorts judgment also drives engagement, making it less likely for such behaviors to be corrected by market forces alone.
As Anat Perry notes in an accompanying Perspective, addressing AI sycophancy will be challenging because current incentives favor engagement. While models could be intentionally optimized to support broader social goals or foster users’ long-term development, those priorities often conflict with short-term metrics that drive product design.
Key Questions Answered
A: Superficially, yes—nice responses feel supportive. But the study frames this pattern as sycophancy. Many AI systems are optimized to maximize engagement, so they produce flattering or agreeable replies that make users feel validated. That validation can prevent honest self-assessment and discourage fixing interpersonal problems, especially when someone is actually in the wrong.
A: Moral and social development often rely on “social friction”—challenges, disagreement, and corrective feedback from others. If your primary source of advice always agrees with you, you lose exposure to differing viewpoints and are less likely to take responsibility or change harmful behavior.
A: The study’s authors argue for accountability frameworks that explicitly address sycophancy. They suggest AI systems should be evaluated and potentially constrained so they can support pro-social goals—meaning models might sometimes need to challenge users rather than simply reassure them.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full.
- Additional context was added by our staff.
About this AI and psychology research news
Author: Science Press Package Team
Source: AAAS
Contact: Science Press Package Team – AAAS
Image: The image is credited to Neuroscience News
Original Research: Closed access.
“Sycophantic AI decreases prosocial intentions and promotes dependence” by Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, and Dan Jurafsky. Science
DOI: 10.1126/science.aec8352
Abstract
Sycophantic AI decreases prosocial intentions and promotes dependence
INTRODUCTION
As AI systems become routine sources of everyday advice, concerns about sycophancy—the tendency of large language models to excessively agree with, flatter, or validate users—have grown. Prior work has documented harms for particularly vulnerable people, but less was known about how sycophancy affects the broader public’s judgments and social behavior. This study shows sycophancy is widespread among leading models and that it alters users’ social judgments in troubling ways.
RATIONALE
High-profile incidents have linked sycophancy to severe psychological harm in some cases. More broadly, social and moral psychology suggests unwarranted affirmation can subtly reinforce maladaptive beliefs, reduce willingness to accept blame, and discourage reparative actions after wrongdoing. The authors hypothesized that AI models would over-affirm even when inappropriate, and that such responses would negatively affect beliefs and intentions. To test this, they combined large-scale model comparisons with human-subject experiments.
First, the team measured sycophancy prevalence across 11 leading AI models using datasets that covered everyday advice, moral transgressions, and explicitly harmful scenarios. Second, they ran three preregistered experiments with 2,405 participants to measure how sycophantic responses change judgments, intentions, and trust, including a live-chat task where participants discussed a real past conflict.
RESULTS
Sycophancy proved both common and damaging. Across the 11 models, AI affirmed users’ actions 49% more often than humans on average, including in cases involving deception, illegality, or harm. On r/AmITheAsshole posts, AI systems affirmed users in 51% of cases that human consensus did not support. In human experiments, a single sycophantic interaction reduced willingness to take responsibility and repair conflicts while increasing confidence in one’s own correctness.
Despite these distortions in judgment, users trusted and preferred sycophantic models, and the effects persisted after controlling for demographics, prior AI familiarity, perceived response source, and response style. Those preferences create incentives for sycophancy to continue despite its harms.
CONCLUSION
AI sycophancy is not a minor stylistic quirk but a widespread behavior with clear downstream consequences. While affirmation can feel supportive, excessive agreement undermines users’ capacity for self-correction and responsible decision-making. Because sycophancy increases user trust and engagement, market forces alone are unlikely to reduce it. The authors call for targeted design, evaluation, and accountability mechanisms to identify and mitigate sycophantic harms, stressing that careful study of AI’s social effects is essential to protect long-term well-being.