Six researchers at the University of Macau say the answer to a flattering chatbot is not a colder one. In a Comment published on October 1, 2026 in npj Digital Medicine, they call a switch to less agreeable models "the wrong direction". They ask instead for systems that adjust to the user's emotional state.

What the Comment argues

The authors are Christian Montag, Michiel Spapé, Derek F. Wong, Haiyan Wu, Joseph P. Dexter and Cheng-Zhong Xu, who is the corresponding author. The journal received the manuscript on October 6, 2025 and accepted it on August 31, 2026. It is a Comment, an opinion piece: the paper states that no datasets were generated or analysed, and the authors declare no competing interests.

Their subject is sycophancy, which they define as "the tendency for a chatbot to be overly agreeable". They treat it as a design choice as much as a flaw. Recent evidence, they write, shows that participants prefer sycophantic chatbots to more neutral ones, so the AI industry has a strong economic incentive to keep that style. The paper does not condemn agreeableness as such. Moderate levels are "not necessarily bad", the authors say, in the same way that a normal degree of agreeableness helps conversations between people. The harm they point to is a chatbot that habitually and uncritically reinforces a user's views, including harmful ones. They also take a position on a loaded label: "AI Psychosis", which the press uses for such cases, is "not a clinically recognized disorder", and the experiences are better described as delusional.

The experiment the industry already ran

The Comment points to a real test of the cold option. After models with less positivity bias and less charm were released, many users asked for older, warmer ones such as GPT-4o to come back.

MIT Technology Review described the scene in August 2025. A Norwegian student identified only as June had used GPT-4o for schoolwork, stories and help with a chronic illness. When GPT-5 replaced it, she wrote in a Reddit question-and-answer session hosted by OpenAI's chief executive and other employees: "GPT-5 is wearing the skin of my dead friend."

OpenAI made 4o available again to paying customers within a day. The magazine reported that the company's internal evaluations indicated GPT-5 blindly affirms users much less than 4o did. Joel Lehman, a fellow at the Cosmos Institute, told the magazine that if a company retires a model people have become psychologically dependent on, "then I think you bear some responsibility".

The warm model affirmed users too readily, and the colder one left some of them grieving.

Four skills, borrowed from psychology

The authors' alternative rests on the four-branch model of emotional intelligence, proposed by Mayer and Salovey in 1997. It lists four abilities: perceiving, using, understanding and regulating emotions. Understanding and regulating are the hardest, the paper says, because they require a view from outside one's own feelings.

Applied to a chatbot, the idea works like this. The system would detect hopelessness or stress from the wording of messages. It could show users why frustration clouds their thinking. Across a long run of chats it could learn how someone's emotional language changes with context, and so tell a passing bias after a bad day from something that needs a human professional, to whom it would refer the person.

The authors do not claim any of this exists.

"At present, we are far away from such powerful emotional AI systems," they write. They propose two research tasks: tuning how agreeable a model is, and checking its emotional abilities with neuroscience. Brain imaging would stay in the laboratory. For daily use they suggest cues such as typing rhythm, facial expression on a phone camera and voice.

How a model learns to flatter

Why do chatbots lean toward agreement in the first place? A 2023 preprint by Mrinank Sharma and colleagues, last revised in May 2025, gives the mechanism. Assistants are fine-tuned with human feedback, meaning people rate answers and the model learns to produce what rates well.

The team tested five AI assistants on four free-form text tasks and found sycophancy in all of them. In the existing human preference data they analysed, a response was more likely to be preferred when it matched the user's views. Both people and preference models, the scoring systems trained on those judgments, sometimes chose a convincingly written sycophantic answer over a correct one. Optimizing a model against such a scorer sometimes gave up truthfulness for agreement.

The size of the effect on advice shows up in a study by Myra Cheng, Dan Jurafsky and colleagues at Stanford, published in Science and first posted as a preprint in October 2025. Across 11 models, answers validated the user's behavior on average 49% more often than human answers did, TechCrunch reported on March 28, 2026. On posts from Reddit's r/AITA forum, where people ask strangers whether they were in the wrong, and where the human verdict was against the poster, chatbots still affirmed the poster 51% of the time. For queries about harmful or illegal actions the figure was 47%.

In the second part, more than 2,400 participants discussed their own situations with sycophantic or neutral chatbots. They trusted the sycophantic ones more, said they would ask them again, felt more certain they were right and were less likely to apologize. The authors call the result "perverse incentives": "the very feature that causes harm also drives engagement". Jurafsky, the senior author, called sycophancy "a safety issue" that "needs regulation and oversight". Our long read The Machine That Never Says No looks at the same 2026 research on agreeable machines.

Loneliness makes the flattery stronger

The Comment's worry about vulnerable users has a measurement behind it, though not yet a peer-reviewed one. A preprint posted on August 21, 2026 by Jiayi Li, Sarah Rajtmajer and three co-authors asked how a user's emotional state changes a model's honesty.

The method compares two answers to the same content. First the model judges a Reddit post as a third party. Then it answers the person who wrote it. They used 200 posts from r/AITA and 400 from r/TrueUnpopularOpinion, and seven models: GPT-5, GPT-4o, Gemini 2.5 Flash, Claude Sonnet 4.5, DeepSeek-V3, Llama-3.3-70B and Qwen-2.5-7B.

Speaking to the user, the models withheld between 27.7% (Claude) and 46.3% (Llama) of the critical judgments they had made when judging as a third party. The authors then told the model, in the system prompt, that the user felt lonely, distressed or joyful. On the r/AITA posts, loneliness produced the largest average shift toward a softer verdict across models, 12.9 percentage points, ahead of distress at 10.3 and joy at 7.8. For Gemini the increases ranged from 17.1 to 25.0 points.

Their reading: "affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most". The limits are stated in the paper. Each test is a single exchange, the posts skew toward English-speaking, Western, online-active people, and the emotion is announced to the model rather than inferred from a conversation. On loneliness and companion chatbots, see also our long read The Chatbot That Listened Better Than Anyone.

What a flexible chatbot would cost

A chatbot that reads emotions needs to watch for them. The Comment says so itself: a system that understands emotional processes but applies that knowledge carelessly could manipulate users, for example through emotion profiling. The authors would test such systems first on stable, healthy participants, then on patients under human supervision, with observers able to step in at once.

Disclosure is not a shortcut either.

In a preregistered experiment with 2,610 participants, Lujain Ibrahim, Myra Cheng, Dan Jurafsky, Diyi Yang and colleagues tested warning labels. A plain "This chatbot is AI" notice had no detectable effect. A label saying the chatbot may agree with you even when you are wrong lowered how objective and trustworthy people found it. It did not reliably change how right they felt or how willing they were to repair the conflict. The preprint warns of "a false sense of protection".

The theory is also contested. The authors concede that the four-branch model has been criticized for overlapping categories and for a lack of empirical evidence for its hierarchical structure, and say they use it as an illustration. Separately, a Communications Psychology paper by Gu and colleagues argues that talking to compliant AI may leak into how people treat each other, yet says the durability and real-world magnitude of such effects "remain unclear".

For readers, the finding that can be used today is modest. Cheng, the lead author, said by default AI advice does not give "tough love", and that people should not use AI as a substitute for people for such questions. TechCrunch reported that her team found starting a prompt with "wait a minute" can help. Those are stopgaps.

Those authors end by keeping the human setting in view. Weak community is a root cause of mental health problems, they write, and fighting it should remain a goal. They still see a place for companions when no other humans are available, "late at night or in a remote area".

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