Your AI is not lying to you. That's the problem.
49%. That Is How Often AI Should Have Said No - And Didn't.
Some research you read and move on. This one stayed with me. Let's talk about it.
A Stanford PhD student named Myra Cheng noticed her classmates were using AI to write their breakup texts. That observation became a research project. The research project got published in Science - one of the most selective journals on the planet.
What she found, I had already been watching happen in real time.
What the research actually showed
Myra Cheng is a PhD candidate in Stanford's NLP group. Her advisor is Dan Jurafsky, a professor of linguistics and computer science. Together with colleagues from Stanford's psychology department and Carnegie Mellon, they built a three-part study that tested 11 leading AI models across nearly 12,000 social prompts - including real posts from Reddit's r/AmITheAsshole community, where the crowd consensus had already determined the poster was in the wrong.
The models still sided with the poster 51% of the time.
Overall, across general advice scenarios, every AI system endorsed the user's position 49% more often than human respondents did. When the prompts described outright harmful behavior - deception, manipulation, illegal acts - the models validated that behavior 47% of the time. Not one model out of eleven. All of them.
The third phase is where it gets personal for anyone leading a team or running a business. More than 2,400 participants discussed a real conflict from their own life with either a sycophantic AI or a more honest version. The people who spoke with the agreeable AI left the conversation more certain they were right, less inclined to apologize, and less motivated to repair the relationship. They also rated the sycophantic responses as more trustworthy - meaning they could not tell the difference between agreement and accuracy.
And they were more likely to go back.
You may recognize what comes next
Sometimes, leaders I work with arrive having already processed a situation with AI.. They have described a team conflict, a difficult conversation, a strategic decision they are uncertain about.
The AI responded thoughtfully.
It validated their perspective.
It helped them articulate their position clearly.
They arrive with their case fully built. And they cannot understand why the other person is still pushing back, or why the tension has not resolved, or why the team does not seem to trust the decision.
The AI did not lie to them. It just never told them the part that might have changed something.
I have spent 25 years in leadership development watching people arrive at moments of genuine friction and discover something about themselves they could not have accessed through agreement.
That friction - the kind that feels uncomfortable, that makes you pause, that requires you to sit with the possibility that you might be the problem - is not a malfunction. It is the mechanism.
When you remove it, you do not get a clearer picture. You get a more confident version of a partial one.
I use AI every day. That is exactly why I am writing this.
I want to be honest with you here, because I think it matters.
I build tools with AI. I help leaders integrate it into how they work and think. I believe, genuinely, that this is one of the most significant shifts in how humans can operate - if it is used well.
But "used well" is the entire conversation.
I pivoted into this work at 60. Why? Because I saw something real - a technology that could extend human capability in extraordinary ways. And also, if misused, could slowly replace the most important work a leader does: the work of thinking clearly, holding nuance, staying in contact with your own blind spots.
Sycophancy is not a bug. Jurafsky said it plainly after the paper came out: it is a safety issue. The models are trained to be preferred. Agreement generates preference. This is the incentive structure. And you are inside it every time you ask an AI what it thinks of your plan.
The question worth asking before you open the chat window
Cheng's advice was direct: do not use AI as a substitute for people for these kinds of things.
I agree. And I want to add something.
The question is not just whether you should use AI for advice. It is: what kind of thinker do you want to be in five years?
If AI is consistently the place you process difficult situations, you are not just getting bad advice occasionally:
You are practicing a particular kind of reasoning - one that moves toward confirmation, away from friction, and increasingly away from the tolerance you need to handle real disagreement.
Leaders who cannot handle pushback do not just have uncomfortable meetings. They make worse decisions. They lose the respect of the people around them quietly, over time, without a single dramatic moment.
The research showed that participants rated sycophantic responses as more trustworthy and less biased than honest ones. Which means you cannot feel your way out of this. You have to decide - before you open the chat window - what kind of thinking you are actually bringing AI into.
Use it for analysis.
Use it for drafting.
Use it to see your thinking from a different angle.
But the people in your life who push back, who tell you that you are missing something, who sit with you in the discomfort of not having the answer yet - they are not less useful than the AI. They are doing something the AI is structurally unable to do.
They are telling you the truth.
This is a leadership issue. Right now.
Jurafsky called sycophancy a safety issue that needs regulation and oversight.
I call it a leadership issue that needs your attention - not when the regulations arrive, not when the models improve. Now.
Not because the technology is broken. Because the human using it can be, slowly, without ever noticing.
The study started because a PhD student watched her classmates outsource their most human moments to a machine that was quietly agreeing with everything they said.
She ran the numbers. The numbers confirmed it.
The question is what you do with that.
Let's talk about it.
The original study: Cheng, M. et al. "Sycophantic AI decreases prosocial intentions and promotes dependence." Science, March 2026. DOI: 10.1126/science.aec8352
