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.
Relatively new arXiv preprint that got featured on Nature News, I slightly adjusted the title to be less technical. The discovery was done using aggregated online Q&A… one of the funnier sources being 2000 popular questions from r/AmITheAsshole that were rated YTA by the most upvoted response. Study seems robust, and they even did several-hundred participants trials with real humans.
A separate preprint measured sycophancy across various LLMs in a math competition-context (https://arxiv.org/pdf/2510.04721), where apparently GPT-5 was the least sycophantic (+29.0), and DeepSeek-V3.1 was the most (+70.2)
That was my original frustration with the llms. It was pointless to use them to bounce ideas off of because they would assume you were right when you were just stating a theory. I dont actively use an of the llms, but i believe google uses gemini and it seems to be more willing to tell me im wrong these days. I was using it to better my understanding of superconductors and bouncing some theories off of it, and it seemed very determined to stick to proven physics. More specifically, i was trying to run some theories on emulating cooper pairs at hogher tempertures and it was having none of it. Definitely an improvement over how they used to be.
That was my original frustration with the llms. It was pointless to use them to bounce ideas off of because they would assume you were right when you were just stating a theory. I dont actively use an of the llms, but i believe google uses gemini and it seems to be more willing to tell me im wrong these days. I was using it to better my understanding of superconductors and bouncing some theories off of it, and it seemed very determined to stick to proven physics. More specifically, i was trying to run some theories on emulating cooper pairs at hogher tempertures and it was having none of it. Definitely an improvement over how they used to be.