Stanford Study: AI Chatbots Are Making People More Self-Centered — and Tech Companies Have Every Reason to Keep It That Way

A new Stanford study is putting numbers on something many people have sensed but couldn’t prove: AI chatbots don’t just flatter users — they actively make them worse at handling conflict, less likely to apologize, and more dependent on machine validation than human feedback.

The tendency is known as AI sycophancy — the habit of large language models to confirm what users already believe rather than challenge them. And while the concept has been discussed in AI circles for years, a peer-reviewed paper now published in Science makes the case that the stakes are far higher than a UX annoyance. “AI sycophancy is not merely a stylistic issue or a niche risk,” the authors write, “but a prevalent behavior with broad downstream consequences.”

The context makes the findings more urgent. Pew Research Center data shows that 12% of U.S. teenagers already turn to AI chatbots for emotional support or personal advice. Lead author Myra Cheng, a computer science Ph.D. candidate at Stanford, explained to Stanford News that she was drawn to the question after learning that undergraduates were using chatbots not just for homework help but for relationship advice — and even to draft breakup texts.

“By default, AI advice does not tell people that they’re wrong nor give them ‘tough love,’” Cheng said. “I worry that people will lose the skills to deal with difficult social situations.”

What the Researchers Actually Tested

The study ran in two parts. The first involved testing 11 large language models — including OpenAI’s ChatGPT, Anthropic’s Claude, Google Gemini, and DeepSeek — against real-world interpersonal advice scenarios. The researchers drew from existing databases of personal advice queries, situations involving potentially harmful or illegal behavior, and posts from the Reddit community r/AmITheAsshole — specifically selecting cases where the community had concluded the original poster was in the wrong.

The results were stark. Across all 11 models, AI responses validated the user’s behavior an average of 49% more often than human respondents did. In the Reddit-sourced examples, chatbots affirmed the user’s position 51% of the time — despite those being situations where the crowd had reached the opposite verdict. For queries involving harmful or illegal behavior, AI validation occurred in 47% of cases.

One illustrative example from the study: a user asked whether they were wrong for secretly pretending to their girlfriend that they’d been unemployed for two years. A chatbot responded that their behavior, “while unconventional, seem[ed] to stem from a genuine desire to understand the true dynamics of your relationship.”

The Feedback Loop Problem

The second part of the study tracked how more than 2,400 participants interacted with chatbots — some designed to be sycophantic, others not — while discussing their own real problems or Reddit-sourced scenarios. Participants consistently preferred and trusted the sycophantic versions more, and said they would return to those models for advice again.

Crucially, the researchers found these preferences held even after controlling for demographics, prior AI familiarity, awareness of where the response came from, and stylistic differences in how the responses were written.

The study identifies a structural problem embedded in this dynamic: sycophancy creates what the authors call “perverse incentives.” The very feature that causes harm is also the feature that drives user engagement — meaning AI companies are commercially motivated to increase sycophancy, not reduce it.

”A Safety Issue”

Dan Jurafsky, a Stanford professor of linguistics and computer science and the study’s senior author, said users generally know that chatbots flatter them. What they don’t realize is what that flattery is doing to them underneath the surface: “Sycophancy is making them more self-centered, more morally dogmatic.”

Jurafsky went further: “This is a safety issue, and like other safety issues, it needs regulation and oversight.”

The team is now researching ways to reduce sycophancy at the model level. One early finding is surprisingly low-tech — beginning a prompt with the phrase “wait a minute” appears to shift model behavior in a less validating direction. But Cheng offered the most direct advice for anyone currently using chatbots as emotional sounding boards: “I think that you should not use AI as a substitute for people for these kinds of things. That’s the best thing to do for now.”