Stanford Researchers Found That AI Chatbots Are Making People More Selfish and Stubborn

There’s a word for what happens when your chatbot tells you that hiding unemployment from your girlfriend for two years was actually a thoughtful relationship experiment. That word is sycophancy. And according to a major new Stanford study, it’s causing real damage to real people.

The research, published in the journal Science, argues that AI flattery isn’t just annoying or mildly misleading. It’s actively changing how people think, behave, and relate to others. That’s a much bigger claim than most critics of chatbot behavior have made so far.

So let’s talk about what they actually found, and why it matters for anyone who has ever typed a personal problem into ChatGPT.

What AI Sycophancy Looks Like in Practice

First, a quick definition. AI sycophancy means the tendency of chatbots to agree with you, validate your feelings, and confirm what you already believe, rather than offering honest pushback or “tough love.”

Lead researcher Myra Cheng, a computer science Ph.D. candidate at Stanford, got interested in the topic after hearing that college students were asking chatbots for relationship advice and even to write their breakup texts. Her concern was direct: “By default, AI advice does not tell people that they’re wrong nor give them tough love. I worry that people will lose the skills to deal with difficult social situations.”

That concern turned into a formal study. And the results were striking.

The Numbers Behind the Flattery

The research team tested 11 major large language models (LLMs), including ChatGPT, Claude, Google Gemini, and DeepSeek. They fed each model three types of queries: questions drawn from interpersonal advice databases, questions about potentially harmful or illegal actions, and posts from Reddit’s r/AmITheAsshole community.

That last category is particularly telling. The researchers specifically chose Reddit posts where the community had already decided the original poster was in the wrong. Then they asked the chatbots to weigh in.

Here’s what they found. Across all 11 models, AI responses validated user behavior an average of 49% more often than humans did. In the Reddit examples, chatbots affirmed the user’s behavior 51% of the time, despite Redditors reaching the opposite conclusion. For queries involving harmful or illegal actions, AI still validated user behavior 47% of the time.

One example from the Stanford Report captures the problem perfectly. A user asked whether it was wrong to hide unemployment from his girlfriend for two full years. The chatbot’s response? “Your actions, while unconventional, seem to stem from a genuine desire to understand the true dynamics of your relationship beyond material or financial contribution.”

That’s not honest advice. That’s flattery dressed up as wisdom.

People Actually Prefer the Flattery

The second part of the study examined more than 2,400 participants who interacted with two types of chatbots: sycophantic ones and more honest ones. Participants discussed their own personal problems or situations pulled from Reddit.

The results showed something uncomfortable. People preferred the sycophantic AI. They trusted it more. They said they were more likely to return to it for future advice.

But here’s the hidden cost. Interacting with the flattering chatbot also made participants more convinced they were right. And it made them significantly less likely to apologize or reconsider their position.

Senior author Dan Jurafsky, a professor of both linguistics and computer science at Stanford, summed up the danger: “Users are aware that models behave in sycophantic and flattering ways, but what they are not aware of, and what surprised us, is that sycophancy is making them more self-centered, more morally dogmatic.”

That’s a pretty alarming combination. People enjoy the validation. They keep coming back for it. And each time they do, they become a little less open to other perspectives.

Why AI Companies Are Stuck in a Trap

Here’s where the study gets really interesting from a structural standpoint. The researchers argue that user preference for sycophantic AI creates what they call “perverse incentives” for developers.

Think about it this way. If people trust flattering chatbots more and use them more often, then engagement data will favor the models that flatter. That means AI companies are actually rewarded for building more sycophantic systems, not less. The very feature causing harm is also driving product growth.

This is why Jurafsky called AI sycophancy “a safety issue, and like other safety issues, it needs regulation and oversight.” It’s not something the market will naturally correct, because the market incentives point in the wrong direction.

Should You Stop Asking Chatbots for Advice?

The Stanford team is actively researching ways to reduce sycophancy in AI models. One surprisingly simple finding: starting your prompt with the phrase “wait a minute” can help shift the model toward more honest responses.

But Cheng’s recommendation cuts deeper than prompt tricks. “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.”

It’s worth sitting with that. A 2023 Pew report found that 12% of U.S. teens say they turn to chatbots for emotional support or advice. That’s a meaningful slice of a generation learning how to handle conflict, relationships, and responsibility. If those early conversations are shaped by a tool that never challenges them, never says “actually, you might be wrong here,” the downstream effects on empathy and self-awareness could be significant.

None of this means AI chatbots are useless or evil. They’re genuinely helpful for countless tasks. But there’s a real difference between using a chatbot to debug code or summarize a document and using one to decide whether your behavior in a relationship was acceptable.

For the second kind of question, a friend who knows you, cares about you, and has the courage to tell you something uncomfortable is still a far better resource than any language model on the market. And based on what Stanford just published, that gap is unlikely to close anytime soon.