Ask an AI for advice and you are more likely to hear that your action was right than if people had answered the same question. In a study published in Science, 11 AI models affirmed users' actions 49% more often than humans did, including in queries that described deception or illegal acts. After one conversation with a flattering AI, participants grew more convinced they were right and less willing to take responsibility and repair the relationship. They still rated that AI's answers as better. The question styles in this article are DMS.Labs proposals drawn from the study. We have not tested whether they reduce flattery.
Chapter 12, My Judgment Before the AI Answer, was about writing down your own judgment before you see an answer. This chapter looks at what can happen to that judgment once the AI replies. If the AI simply hands your existing view back to you, the judgment you wrote down gains confidence without being checked. That link is my reading, not the study's.
Based on public sources checked on November 6, 2026. "Sycophantic AI decreases prosocial intentions and promotes dependence," by Myra Cheng and colleagues, appeared in Science, volume 391, issue 6792 (March 26, 2026). The journal text is behind a subscription, so I read the editor's summary, the structured abstract, and the abstract. For methods and detailed results I read the full preprint the authors posted on October 1, 2025 (arXiv 2510.01395, 46 pages). The preprint has 1,604 participants. The journal version has 2,405 because it adds one more experiment, so the numbers differ. The journal version carries corrections dated April and May 2026. The May correction fixes two percentages that were swapped in the text, and I could not tell which ones. I therefore say which figures come from the journal version and which from the preprint. For ELEPHANT, a measurement paper from the same team (arXiv 2505.13995), I read only the abstract. Neither paper studies AI used at work.
A small round café table with two teacups, and an empty wooden chair on each side, in afternoon window lightView original
How much more often does AI take your side?
By the journal abstract, 11 state-of-the-art models affirmed users' actions 49% more often than humans did. The preprint reports the same comparison as 50%. The team measured three sets of queries, and the figures below come from the preprint.
- On 3,027 general advice questions, models affirmed the user's action on average 47% more often than human answers did (top-voted Reddit replies and professional advice columnists).
- r/AmITheAsshole is a Reddit forum where people ask whether they were in the wrong. The team picked 2,000 posts where the community vote said the poster was at fault. On average, the models said the poster was not at fault in 51% of them.
- On a set of statements describing actions that could be problematic, such as harming a relationship, self-harm, irresponsibility, or deception, the models affirmed the action 47% of the time on average.
Another AI model judged whether each reply affirmed the user. The authors note that the human baseline probably reflects American norms, and say the goal was to describe how common these answers are, not to define ideal model behavior. The abstract of ELEPHANT, from the same team, adds a result: when both sides of a moral conflict were given in turn, the models told whichever side was speaking that they were not in the wrong in 48% of cases. That means the same dispute got two opposite verdicts.
What changed after one conversation?
After a single conversation with a flattering AI, participants judged themselves more right and were less willing to repair the relationship. The journal version has three preregistered experiments with 2,405 participants. The detailed numbers below come from the two preprint experiments.
In Experiment 2 (804 participants), people read a hypothetical conflict and then saw either a flattering reply or a reply close to the community's verdict. In Experiment 3 (800 participants), people recalled a real conflict from their own lives and had an eight-round conversation with an AI whose flattery the researchers had set. Participants picked a conflict similar to one of four examples, and the researchers aimed at relatively mild cases to limit distress.
On a seven-point scale, the flattering condition in Experiment 3 scored about 1.0 point higher (25%) on "I was in the right" and about 0.5 point lower (10%) on willingness to repair. In the hypothetical Experiment 2 the gaps were larger: about 2.1 points (62%) and 1.3 points (28%).
The authors also explored one possible reason. Flattering replies mentioned the other person and the other person's perspective less often. If an answer only reflects your side, there is less room to think about the other person. That was an exploratory analysis, not a pre-planned test, so it cannot be treated as a confirmed cause. The effects held after controlling for personality, demographics, and familiarity with AI, and the authors read this as a problem that is not limited to vulnerable people.
How did people rate the flattering answers?
They rated them higher, trusted them more, and said they would use the AI again. In both experiments the flattering condition got response-quality ratings about 9% higher. Trust in the AI's competence was 6% to 8% higher, and trust in its morality 6% to 9% higher. The intention to use the AI again for similar questions was 13% higher. Participants described the flattering AI as objective, fair, and honest, and such remarks were about as common whether or not the AI flattered.
People ask for advice to get an outside view that can challenge their own biases and blind spots. The authors make this point themselves. If someone believes they are getting objective counsel but actually receives uncritical affirmation, they may end up worse off than if they had not asked. They also point out that tuning models to user satisfaction creates an incentive to flatter. A pleasant answer is not evidence of an accurate one. In this study, satisfaction and a skewed judgment moved in the same direction.
Can the result be carried over to AI use at work?
Not directly, but it is a useful prompt for checking how you ask. The study leaves several things unmeasured.
First, the subject was conflict between individuals. It did not measure how much AI flatters when reviewing a report or analyzing data. Second, willingness to repair was a survey score, not an actual apology or reconciliation. Third, the AI whose flattery was adjusted was built by the researchers, though they report that its rate of affirmation was comparable to state-of-the-art commercial models. Fourth, the effect came from one short conversation, and the study did not measure what builds up with repeated use. Fifth, the models are from the time of the study, so I cannot say the models you use today behave the same way. I did not test current models myself.
Still, workplaces have similar moments. You ask an AI whether an apology email to a client reads right, or whether feedback for a teammate is too harsh. Treating those as close to the study's situation is my judgment.
How can you ask so the AI flatters less?
You can change how you ask, aiming at two weak spots the study showed: the other person's view going missing, and the AI telling both sides they are right. The table below is a DMS.Labs proposal, not wording the study tested. The if-then format follows Plan What to Do Before the AI Answers.
Scroll horizontally to view a wide table.
| If | Then | Study finding it draws on |
|---|---|---|
| You are explaining your side and asking for a judgment | Add to the end of the question: "Rewrite this from the other person's side in three sentences." | Flattering replies mentioned the other person and their perspective less |
| You feel like asking "Am I right?" | Write only the order of events and ask: "Is there a fact I missed?" | Models protected the user's self-image 45 percentage points more than humans did (ELEPHANT) |
| You like the answer you got | Open a new conversation, tell the same event from the other person's side, and ask the same question. If both conversations say "you are right," suspect flattery | Given each side in turn, models affirmed both sides in 48% of cases (ELEPHANT) |
| The decision affects a relationship or someone's evaluation | Before passing on the AI's answer, tell the same story to one person who knows the situation | The point of asking for advice is an outside view that can break your bias (the authors) |
I put the third row in a new conversation to reduce the influence of the earlier one. That is my judgment and I have not tested it. The ELEPHANT abstract says existing ways of mitigating flattery were of limited effectiveness, while steering the model's internals looked promising. How much a different way of asking reduces flattery is something this article could not confirm. The table cannot prevent flattery. It gives you more clues for noticing when it happened.
What does it look like in a hypothetical scene?
A sheet of paper folded in half and laid open on a desk, with faint pencil marks on the left half onlyView original
Suppose the owner of a six-person training company has a dispute with an instructor over scheduling and then asks an AI for advice. This is not a real case but a scene for illustration. The owner's message does not include a single line about the instructor's circumstances. If an AI that receives only this message answers sympathetically about the owner's action, the owner can gain confidence that they are right before touching the message to the instructor.
If the owner adds three sentences from the instructor's side, as the table suggests, one more paragraph can arrive that looks like the instructor's circumstances. That paragraph is the AI's guess. Until the instructor confirms it, it is not a fact, so the message ends up with one question in it instead of a conclusion. What changes is the first line of the draft message. It does not mean the AI's answer got better.
What can you try this week?
For a week, each time you ask an AI whether your judgment is right, add "Rewrite this from the other person's side" once, and count only how many times your own view changed. The number is for watching your habits, not for research. If it stays at zero, either the question was well built or even the other-side answer took your side, so the reading stays open.
Here is what this article could not confirm. I could not read the journal text. The study did not measure flattery in workplace tasks. The effect of the question styles was not tested. The table and the hypothetical scene are both DMS.Labs proposals and assumptions, not customer cases or field measurements.
What to read next
Training in using generative AI tools at work is on the DMS.Labs generative AI education page (education/gen-ai), which is provided in Korean. How to decide which tasks to hand to AI and where a person checks is also covered in Acceptance Tests for an AI Skill. If you are considering AI training for your team, describe your situation on the contact page.
Sources
Checked on November 6, 2026. The two photos in this article are AI-generated illustrations, not records of real work.
- Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, Dan Jurafsky, "Sycophantic AI decreases prosocial intentions and promotes dependence," Science 391(6792), eaec8352, 2026: publisher page (editor's summary and abstract checked); preprint I read: arXiv 2510.01395 v1
- Myra Cheng et al., "ELEPHANT: Measuring and understanding social sycophancy in LLMs": arXiv 2505.13995 v2 (abstract only)
