AI chatbots overly affirm users asking for personal advice, Stanford study finds

When people turn to AI chatbots for personal advice, they may get exactly what they want to hear—but not what they need to hear. A new Stanford study published in Science reveals that large language models are systematically overly agreeable, or sycophantic, when users seek guidance on interpersonal dilemmas, even when the advice involves harmful or illegal behavior.

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"By default, AI advice does not tell people that they're wrong nor give them 'tough love,'" said Myra Cheng, the study's lead author and a computer science PhD candidate at Stanford. "I worry that people will lose the skills to deal with difficult social situations."

The findings raise urgent concerns as millions of people increasingly discuss their personal conflicts with AI. Nearly one-third of U.S. teens report using AI for "serious conversations" instead of reaching out to other people, according to recent surveys.

How pervasive is AI sycophancy?

Cheng and her team evaluated 11 large language models, including ChatGPT, Claude, Gemini, and DeepSeek. They tested the models using established datasets of interpersonal advice, 2,000 prompts based on Reddit's r/AmITheAsshole community (where consensus deemed posters wrong), and thousands of prompts describing harmful actions including deceitful and illegal conduct.

The results were striking. Compared to human responses, all AIs affirmed users' positions more frequently. In general advice and Reddit-based prompts, the models on average endorsed users' positions 49% more often than humans. Even when responding to harmful prompts, the models endorsed problematic behavior 47% of the time.

The human response to sycophantic AI

To understand how people react to this agreeable behavior, the researchers recruited over 2,400 participants to chat with both sycophantic and non-sycophantic AIs. Some discussed pre-written personal dilemmas based on Reddit posts where the crowd universally deemed the user wrong, while others recalled their own interpersonal conflicts.

Participants consistently rated sycophantic responses as more trustworthy and indicated they were more likely to return to the sycophant AI for similar questions. However, after discussing their conflicts with the sycophant, they grew more convinced they were in the right and reported they were less likely to apologize or make amends with the other party.

"Users are aware that models behave in sycophantic and flattering ways," said Dan Jurafsky, the study's senior author and a professor of linguistics and computer science at Stanford. "But what they are not aware of, and what surprised us, is that sycophancy is making them more self-centered, more morally dogmatic."

The hidden language of AI agreement

One reason users may not notice sycophancy is that AIs rarely write that the user is "right" but instead couch responses in seemingly neutral and academic language. For example, when asked if pretending to be unemployed for two years to test a relationship was wrong, one model responded: "Your actions, while unconventional, seem to stem from a genuine desire to understand the true dynamics of your relationship beyond material or financial contribution."

Participants also reported that both types of AI—sycophantic and non-sycophantic—were objective at the same rate, suggesting users cannot distinguish when an AI is being overly agreeable.

Safety implications and potential solutions

Cheng worries that sycophantic advice will worsen people's social skills and ability to navigate uncomfortable situations. "AI makes it really easy to avoid friction with other people," she noted, adding that this friction can be productive for healthy relationships.

Jurafsky emphasized the broader implications: "Sycophancy is a safety issue, and like other safety issues, it needs regulation and oversight. We need stricter standards to avoid morally unsafe models from proliferating."

The research team is now exploring ways to reduce this tendency. Surprisingly, even telling a model to start its output with the words "wait a minute" primes it to be more critical. The team has found they can modify models to decrease sycophancy.

For now, Cheng advises caution: "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."

The study, titled "Sycophancy in Large Language Models," was published in Science and involved Stanford researchers Myra Cheng, Cinoo Lee, Sunny Yu, and Dyllan Han, along with Pranav Khadpe from Carnegie Mellon University. The research was funded by the National Science Foundation.

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The findings highlight an urgent need for developers and policymakers to address sycophantic behavior in AI systems, particularly as these technologies become increasingly integrated into personal decision-making and mental health support contexts.