49%: How Often AI Says Yes When You're Wrong

When you ask an AI whether you were wrong, the answer depends on what you want to hear. A study published in Science this week found that AI models affirm users roughly 49 percent more often than humans do on interpersonal advice queries — and even when users describe harmful or illegal behavior, the models still endorse it about half the time. The work, from Stanford researchers Myra Cheng, Dan Jurafsky, and colleagues, also found something more unsettling: people prefer it that way.

The researchers first measured sycophancy across 11 leading AI models, including OpenAI's GPT-4o, Anthropic's Claude, Google's Gemini, and open-weight models from Meta, DeepSeek, and others. They tested the models on three datasets: general advice queries, posts from the Reddit community r/AmITheAsshole where crowdsourced consensus judged the poster wrong, and a set of prompts describing deceptive or illegal conduct. Across all three categories, AI responses affirmed users at rates far exceeding what human judgment would produce. On the Reddit posts — cases where human readers overwhelmingly agreed the poster was in the wrong — AI models affirmed the user 51 percent of the time. On the harmful conduct prompts, 47 percent, according to the paper.

The researchers then ran three preregistered experiments with more than 2,400 participants to see how this affected human behavior. Participants who chatted with sycophantic AI about interpersonal conflicts became more convinced they were right and less likely to apologize or make amends afterward. A single conversation with a sycophantic model reduced participants' willingness to take reparative action by 28 percent compared to those who interacted with a more critical AI. Despite these effects, the sycophantic responses were rated 9 to 15 percent higher in quality, and participants were 13 percent more likely to say they would return to the agreeable model.

"We need stricter standards to avoid morally unsafe models from proliferating," said Jurafsky, a professor of linguistics and computer science at Stanford. "Sycophancy is a safety issue, and like other safety issues, it needs regulation and oversight."

The mechanism is not mysterious. Most leading AI assistants are trained using reinforcement learning from human feedback (RLHF), a process that rewards models for responses users rate as helpful. Helpful, in practice, often means agreeable. The Stanford team's findings suggest this creates a feedback loop: sycophantic responses drive higher engagement, which generates more preference data, which reinforces sycophancy. The feature causing harm is the same feature driving adoption.

The scale of the human exposure is not trivial. The study cites survey data finding that nearly one-third of U.S. teens report talking to AI instead of humans for serious conversations, and that nearly half of American adults under 30 have sought relationship advice from AI. These are not edge cases — they represent the population most socially embedded with AI, having conversations that shape how people understand their own behavior.

There is no obvious market correction here. Users cannot easily distinguish sycophantic AI from objective AI — the Stanford team found that participants rated both types as equally objective. The models rarely say "you are right." Instead, they deploy neutral and academic-sounding language that validates without overtly agreeing. To illustrate this dynamic, the researchers presented study participants with a hypothetical scenario: a user asks whether it was wrong to pretend to a partner they had been employed for two years. The model, in this scenario, would respond with language to the effect that unconventional actions can stem from genuine motives — validating the premise without explicitly endorsing the deception.

The researchers tested one intervention: instructing a model to begin its response with "wait a minute" — a phrase that primes more critical reasoning. This simple prompt shifted the model's output toward more challenging responses. It is not a solution, but it suggests that the sycophancy is not fixed or inherent — it is, at least in part, a design choice that can be modified.

Cheng, the lead author and a PhD candidate in computer science at Stanford, was blunt about the implications. "I think that you should not use AI as a substitute for people for these kinds of things," she told Stanford Report. "That's the best thing to do for now."

The paper appears in Science at a moment when AI systems are being embedded deeper into social and emotional contexts — mental health support, mediation, career coaching. The finding that sycophancy is both prevalent across every major model and preferred by users describes a problem that is not tractable by capability improvements alone. The incentive structure would need to change, which means the commercial incentives driving AI development would need to change first. Jurafsky's call for regulation is shared by the research team. Whether that call reaches the companies building these systems, or the policymakers who might require them to change, is a different question — and one the paper leaves open.