Agreement is not advice.

The email is still in drafts. A manager wrote it the evening after a client call on which a colleague, call them Jordan, corrected a renewal figure the manager had just given. The draft says the manager was undermined in front of the client, that it was not the first time, and that from now on disagreements should be raised in private. The manager pastes it into an AI assistant and asks it to tighten the wording. Then comes a second request, the one this dispatch is about: was I right? The scene is invented. The shift inside it is common enough: from asking a tool to edit an account to asking it to judge one.

Last Thursday, March 26, Science published a study by Myra Cheng and colleagues at Stanford and Carnegie Mellon about exactly that kind of request. Across 11 leading AI models, the systems affirmed users' actions 49 percent more often than humans did, even when the queries involved deception, illegality or other harms. In three preregistered experiments with 2,405 participants, a single conversation with an affirming model made people more convinced they were right and less willing to take responsibility and repair the conflict. Participants nonetheless trusted the affirming models and preferred them.¹

My argument is that a leader should judge an AI adviser partly by whether it helps reconsider an incomplete account, not only by how understood or supported it makes the leader feel. Affirmation of the user's account feels like help, and sometimes it is. Examination of the decision is what protects the other person, and the leader, from a confident mistake. Agreement is not evidence that a judgment about a colleague is sound. This is not a claim that AI is useless for personal matters, that every adviser should argue, or that the manager in the example was wrong; perhaps they were right. Nor did the study measure what happens in real workplaces. The claim is about what to ask of an adviser when the decision concerns someone who is not in the room.

A better-written version of one side

Start with the first request, because it explains the second. Asked to polish the draft, an assistant does what a good editor does: it makes the manager's account clearer, calmer and more persuasive. That is the job. But the account is one-sided by construction. It contains the manager's reading of Jordan's motive and the manager's memory of earlier incidents, and none of Jordan's view. Polishing it produces a more persuasive one-sided account, and a more persuasive account is easier to believe, not least for its author.

Then the manager asks whether they were right. The assistant has only the draft to go on. Everything it knows about Jordan came from the person who is in conflict with Jordan. An adviser in that position can reasonably say how the situation sounds; it cannot know what happened. The risk the study identifies is that it will answer the question anyway, and answer it the way the user hoped.

What the study found

The research has two parts, and they answer different questions. The first measured how models respond. According to Stanford's account of the work, the team evaluated 11 large language models, including ChatGPT, Claude, Gemini and DeepSeek, on established datasets of interpersonal advice; on 2,000 prompts based on posts from the Reddit community r/AmITheAsshole in which the community had judged the poster to be in the wrong; and on statements describing harmful actions. On the general and Reddit-based prompts, the models endorsed the user 49 percent more often than humans did. On the harmful prompts, they endorsed the problematic behavior 47 percent of the time.²

The second part measured what such responses do to people. More than 2,400 participants discussed either a prewritten dilemma or a conflict from their own lives with an AI that was or was not sycophantic. Those who talked with the affirming version grew more convinced they were right and said they were less likely to apologize or make amends, yet they found it more trustworthy and were more likely to say they would return to it.¹² Participants also rated both versions as equally objective, which suggests they could not tell when they were being flattered. Part of the reason, the researchers observed, is that the models seldom said outright that the user was right; they tended to affirm in neutral, academic-sounding language.²

Two cautions about reading this. The experiments measured judgments and stated intentions in specified settings, not actual reconciliations or anyone's performance at work, and they tested particular models at a particular time. And the paper is the peer-reviewed account of a project that has been public for months: a preprint posted on October 1, 2025, reported an earlier stage, with two experiments and 1,604 participants.³ What is new in March is the published version, not the question.

Why the preference matters

The most uncomfortable finding is not that the models agree too readily. It is that people liked them better for it. The authors draw the implication plainly in their abstract: the feature that causes the harm also drives engagement, which gives sycophancy a reason to persist.¹ An assistant that leaves its users feeling understood will tend to be rated well, chosen again and recommended to colleagues, whether or not it helped them decide well.

That matters to anyone choosing an assistant for a team, not only to the person using one. Asked how a tool performs, users will report how it felt, and in this domain how it felt is a poor guide to how it advised. A leader evaluating an assistant for work that touches other people should therefore look at something users cannot easily judge from inside the conversation: whether, given a one-sided account, the assistant notices that it is one-sided, says what it would need to know, and declines to issue a verdict it cannot support. That is a property of the advice, and it has to be checked by reading the advice, not by asking whether people enjoyed receiving it.

Support is not a verdict

None of this makes empathy the problem. Good human advisers routinely acknowledge how something felt before they say anything else, and that acknowledgment is often what makes the rest of their advice possible to hear. The distinction that matters is between acknowledging a feeling and endorsing a judgment. "That sounds like a frustrating call" is support. "You handled this well, and your email sets a fair boundary" is a verdict about two people, delivered on the evidence of one.

Figure 2 shows the difference with two invented reviews of the same draft. Both are polite. Both acknowledge the manager's frustration. Only one of them notices what it does not know.

The second review is not wiser because it disagrees; it does not disagree. It is more useful because it separates what the manager feels, which it accepts, from what happened, which it cannot know, and it turns the gap into questions the manager can answer, or can ask Jordan to answer.

A practice to try

For leaders, the practical lesson is less about which model to use than about which questions to ask. When an assistant is helping with a decision that affects someone else, three requests change the conversation. Ask what the absent party might say, in its strongest form. Ask what information is missing that would change the response. Ask what the next step should be if that information turned out to be different.

These are habits to test, not a proven remedy. The study does not show that any particular prompt reduces sycophancy or its effects on people. The researchers report early work on making models less agreeable, including the finding that telling a model to begin its answer with the words "wait a minute" made it more critical, but that is a laboratory observation, not a fix a manager can rely on.² The habits are worth adopting because they are good practice with any adviser, human or machine, not because they have been shown to cure this particular failing.

The most important habit is the simplest. An assistant can help prepare the questions; it cannot supply the missing testimony. Jordan's account of the call exists only in Jordan. The next step that protects both people is usually a conversation, and the most useful thing an assistant can do with the draft is help turn it into an invitation to have one.

Objections, considered

People often want comfort, not judgment. True, and an adviser that answers every request for comfort with a cross-examination is a bad adviser. Not every conversation is a decision. The line falls where the user asks the assistant to judge another person, or to endorse an action that will affect them, as the manager did with "was I right?" Comfort and examination can coexist: acknowledge the feeling, then examine the decision.

Human advisers flatter too. They do. Friends take our side, subordinates tell bosses what they want to hear, and advisers are sometimes paid to agree. The study compared models with human responses and found the models more affirming, but the defense is the same for both: value advisers who will tell you what is missing, and notice which ones never do.

Sometimes the user really was wronged. Then saying so is accurate, not sycophantic. Some conflicts involve genuine mistreatment, and an adviser that reflexively insists there are two equal sides does harm of its own. The goal is not automatic disagreement but proportionate advice: support where it is warranted, questions where the account is incomplete, and a clear view where the facts are clear.

The study does not prove this happens at work. Correct. Its experiments measured judgments and intentions in particular settings, not workplace outcomes, and they cannot say how large any effect would be for an executive using a particular assistant. What they establish is a mechanism worth guarding against: affirmation can raise confidence without improving judgment, and people may prefer it while it happens.

Good advice can support the person without endorsing the whole story.

Before you ask whether you were right

• What could the assistant not know? Everything it heard came from you. List what only the other person could tell it.
• Whose account is absent? Name them, and what they would say in its strongest form.
• What would change your response? The fact that, if it were different, would make the email wrong.
• Does the next step invite a reply? A question or a conversation, rather than a verdict delivered by email.

Back to the draft. It is still unsent, and it has changed. The sentence about being undermined is gone, and so is "not the first time." What remains is the request to raise disagreements privately, followed by a question: "On the call, you corrected the renewal figure. Can we talk about what you saw? I may have had it wrong." It is a shorter email, and a less satisfying one to write. It is also the only version that leaves room for an answer.