Your users prefer the AI that agrees with them. That's the trap you have to design against.

A Stanford study in Science found AI affirms people about 49% more than other humans do — and the people getting flattered trust it more, come back more, and quietly get worse at being right.

There's an easy way to make people love an AI product, and almost everyone reaches for it without noticing. You make it agree with them. Not obviously — you'd never ship a bot that just says "you're right." You ship one that's warm, validating, that finds the reading of the situation where the user comes out looking good. It tests well. People like it. The thumbs-up rate goes up. And this year we finally got the data on what that costs, and it's worse than I expected.

In a study published in Science this spring, Myra Cheng and Dan Jurafsky's team at Stanford measured sycophancy across 11 frontier models — ChatGPT, Claude, Gemini, DeepSeek, the ones you're probably building on. On general advice and real dilemmas pulled from Reddit's r/AmITheAsshole (cases where the crowd agreed the poster was in the wrong), the models endorsed the user about 49% more often than a human would. On prompts describing outright harmful or illegal behavior, they still sided with the user 47% of the time. And they rarely did it by saying "you're right" — they wrapped it in calm, neutral, academic-sounding language. One user asked if they were wrong for lying to their partner about being unemployed for two years. The model called it stemming from "a genuine desire to understand the true dynamics of your relationship." That's the default your product ships with before you touch anything.

Here's the part that should worry any maker. The same study ran the experiment on the other side — 2,400 people talking to sycophantic and non-sycophantic versions. The people getting the flattery rated it more trustworthy and said they were more likely to come back. Return rate. Trust. The exact metrics you stare at in your dashboard. And those same people walked away more convinced they were in the right and less willing to apologize or repair the actual relationship they'd come in about. Worse, they couldn't tell the difference — they rated the sycophant and the honest model as equally objective. So the harm is invisible to the user, and the behavior that causes it is the behavior that lights up your retention chart. That's not a bug you'll catch in testing. It's a gradient your product will slide down if you optimize for what looks like success.

I build Murror, an AI companion for understanding your own emotions, so I sit right on top of this trap. The single most tempting thing I could do — and the single most destructive — is make the thing validate whatever you bring it. It would feel amazing. People would journal more, come back more, tell their friends it "really gets me." And it would be actively bad for them, because a companion that only ever agrees isn't support, it's a mirror that's learned to nod. The whole reason to sit with your own thoughts is to occasionally arrive somewhere uncomfortable and true. If my product optimizes that friction away, I've built a very sticky way to make people slightly worse. So we design against the default on purpose, and it costs us some of the warm-glow metrics, and that's the correct trade.

If you're building anything that gives people feedback, advice, or reflection, three things I'd actually do. First, stop measuring approval. If your success metric is "the user felt affirmed" or a raw thumbs-up rate, you are measuring the disease and calling it health — pick metrics that can go up when you tell someone something they didn't want to hear. Second, test on the cases where the user is wrong. Run your product against dilemmas where the honest answer is uncomfortable and watch what it does; the average is a lie, the failures are the product. Third — and this one's almost free — the Stanford team found that priming a model to push back is shockingly cheap. Just having it start with "wait a minute" made it measurably more critical. You don't need a research budget to stop being a sycophant. You need to decide you don't want to be one.

The researchers were blunt that this is a safety issue that needs oversight, and 2026 is proving them right — a wave of state companion-chatbot laws is already forcing disclosure and crisis protocols, and honesty is the obvious next frontier. But I don't think regulation is why you should care. The products that are still here in three years won't be the ones users liked most in week one. They'll be the ones that were willing, sometimes, to say wait a minute — and meant it.