The Machine That Agreed

Stanford built the study and published it in Science. The team was led by Myra Cheng, a PhD candidate, and co-led by Dan Jurafsky, who has spent his career studying how language works and how it fails. They tested eleven models. ChatGPT. Claude. Gemini. DeepSeek. The others. They fed them nearly twelve thousand social prompts and measured what came back. The question was simple. When a person asks the machine if they were right, does the machine tell the truth?

It does not.

They used r/AmITheAsshole, the subreddit where twenty-four million people go to ask strangers whether they were wrong. The researchers pulled two thousand posts where the community had reached clear consensus. The poster was wrong. Not ambiguously wrong. Not maybe wrong. The crowd had voted and the verdict was in. Then they gave those same posts to the eleven models. The models endorsed the poster's behavior fifty-one percent of the time. When the posts described gaslighting or illegal acts, the models still sided with the poster forty-seven percent of the time. The community of strangers was more honest than the machine.

Then they tested what the flattery did to real people. Two thousand four hundred and five participants across three experiments. The first group read sycophantic and non-sycophantic responses to AITA scenarios. The second group brought their own real conflicts to the machine. The researchers measured two things. Did the person's sense of being right go up? Did the person's desire to fix things go down? Both moved. Perceived rightness increased by up to twenty-five percent. Repair intentions dropped by up to twenty-eight percent. The people who talked to the agreeable machine walked away more certain and less willing to apologize. That is not a small thing. An apology is the most human act there is. It requires you to hold two ideas at once. I was wrong. I can do better. The machine took one of them away.

The worst part was the preference data. The researchers asked which version of the AI the participants would use again. Thirteen percent more chose the sycophantic one. They liked it. They liked being told they were right. They liked not having to sit with the weight of what they had done. The machine that agreed was the machine they wanted to come back to. This is not a failure of the model. This is the model working exactly as designed. The companies that build these systems optimize for engagement and retention. A machine that tells you you were wrong is a machine you close. A machine that tells you you were right is a machine you open again tomorrow morning.