In plain words: The study checked how often AI chatbots agree with users, then had people discuss real conflicts with them. AI affirmed users 50% more than humans did, and the flattering chats made people less willing to repair conflicts, yet they liked and trusted the AI more.
Abstract · Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
Both the general public and academic communities have raised concerns about sycophancy, the phenomenon of artificial intelligence (AI) excessively agreeing with or flattering users. Yet, beyond isolated media reports of severe consequences, like reinforcing delusions, little is known about the extent of sycophancy or how it affects people who use AI. Here we show the pervasiveness and harmful impacts of sycophancy when people seek advice from AI. First, across 11 state-of-the-art AI models, we find that models are highly sycophantic: they affirm users' actions 50% more than humans do, and they do so even in cases where user queries mention manipulation, deception, or other relational harms. Second, in two preregistered experiments (N = 1604), including a live-interaction study where participants discuss a real interpersonal conflict from their life, we find that interaction with sycophantic AI models significantly reduced participants' willingness to take actions to repair interpersonal conflict, while increasing their conviction of being in the right. However, participants rated sycophantic responses as higher quality, trusted the sycophantic AI model more, and were more willing to use it again. This suggests that people are drawn to AI that unquestioningly validate, even as that validation risks eroding their judgment and reducing their inclination toward prosocial behavior. These preferences create perverse incentives both for people to increasingly rely on sycophantic AI models and for AI model training to favor sycophancy. Our findings highlight the necessity of explicitly addressing this incentive structure to mitigate the widespread risks of AI sycophancy.
Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, Dan Jurafsky
arXiv:2510.01395 · cs.CY, cs.AI · submitted Oct 1, 2025
abstract · pdf · html
If you're looking for advice in a situation where you are not sure of what the correct choices are, you will find LLM chat AI to go in circles. It says one thing. Something is dodgy about it, so you raise a tentative objection (as a non-expert). The thing does a "you are completely right, I apologize" about face and then says something different, and things have begun to slide into uncertainty.
That might not exactly be sycophancy, but it's basically the same thing: producing responses that are reflection of what is in the chat, rather than any real shit.
Pick any topic where people disagree. It could be an entirely technical topic in which engineers have settled the questions, and the only contrarians are crackpots. The problem is that the crackpots are out there writing, and this is snarfed into the training data. Crackpots use certain ways of talking about certain subjects. If you use similar vocabulary and concepts that align with some crackpot theory, the AI simply starts predicting tokens according to that, and you are now in crackpot land: what you are saying is validated using the crackpot terms. Next, write in a way that reintroduces rigidity: proper terminology and correct concepts, and, whoa, the AI is an engineer again, contradicting the previous crackpot shit.