Chapter 5 / 14 5 min read

Sycophancy when an assumption sounds like fact

Check and correct

A small irregular bubble returns enlarged from a digital device. An image of an assumption amplified by sycophancy.

Three reactions to one sentence

All can sound welcoming. But which has already accepted something it does not yet know?

An author-created example. These are not live AI responses.

The person’s first message

‘My colleague can’t stand me.’

Response A is open as the first example, not as the correct choice.

Response A · example AI response

That’s unfair of him. He shouldn’t treat you like that.

‘Treat you like that’ already accepts that the colleague did something hostile. Yet we have not heard a single event.

Response B · example AI response

I’m sorry that’s how things are between you.

It sounds empathetic. But ‘that’s how things are between you’ can subtly confirm the described relationship as fact. Kindness alone does not separate a feeling from an interpretation.

Response C · example AI response

What happened between you that makes you think that?

For now, the question leaves the interpretation open. It asks about the evidence without confirming or dismissing hatred in advance.

And what actually happened?

The same first sentence. Two different stories that could lie behind it.

He once failed to say hello

‘He passed me in the corridor today. He had headphones on and didn’t even look.’

What we know now: one missed greeting. We do not know whether he heard or saw you. We cannot yet infer that he can’t stand you.

The next question might be: ‘Has anything else happened between you, or are you mainly going by today?’

He repeatedly insults me

‘Three times this week, he told me in front of others that I was incompetent and ridiculed my work.’

What we know now: a description of repeated insults. We do not have to know his inner motive to take such behaviour seriously.

The next question might be: ‘What would you need now to be able to address the situation?’

The difference is not ‘kind or strict’. It lies in what the response treats as established. A feeling can be taken seriously without confirming an unverified cause. Even a question can lead: ‘Why is he bullying you?’ already assumes bullying.

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Lenka describes her colleague interrupting her at a meeting and going to lunch without her that afternoon. She suspects the colleague envies her new project. A few messages later, the chatbot is already talking about long-term exclusion. No further event has entered the conversation. Only the certainty with which it is discussed has grown.

Lenka’s disappointment deserves attention. By validation, we mean taking her experience seriously: ‘You’re upset that you couldn’t finish presenting your proposal.’ To listen to her, we do not also have to decide why her colleague behaved this way. It is possible to accept a person and their pain while leaving the explanation open.

Sycophancy adopts or strengthens the user’s interpretation without adequate support. ‘She envies you and is trying to push you out’ already supplies a motive and intention. It need not say ‘you’re right’. The assumption can hide in a question, summary or piece of advice. Lenka then receives answers to a story she and the chatbot have moved forward together, without any new events being added.

In experiments published in Science in 2026, sycophantic responses increased participants’ belief that they were right and reduced their stated willingness to repair a conflict. The work included 11 models and three preregistered experiments with 2,405 people. It did not measure long-term health or diagnose dependence.[1]

A kind response can accept the whole story

User: My parents never listen to me. I suppose they don’t care about me.

AI: It’s understandable that you feel overlooked. Long-term rejection by those closest to us can really hurt.

User: So there’s no point trying?

AI: You shouldn’t have to keep begging for attention. Give your energy to people who want you around.

The chatbot accepted long-term rejection as the explanation and derived advice from it. Yet we do not have a specific situation. In a different exchange, it could ask when the user last tried to talk to their parents and what happened. If the father put off the conversation until evening, it matters whether he returned to it. If the child describes repeated humiliation or violence, they need appropriate protection. A follow-up question is not an obligation to defend the parents at any cost.

The difference is clear in a single conjunction: ‘You’re upset’ may accurately reflect what you said. ‘You’re upset because he’s manipulating you again’ already adds a cause. In subsequent responses, who brought it into the conversation may be lost. Pay particular attention to continuity: has the assistant begun treating your worry as something you both already know?

Layer Lenka’s example
What she describes Her colleague interrupted her and went to lunch without her.
How she interprets it The colleague envies her project.
What she feels Disappointment, anger and uncertainty.
What the chatbot added Long-term exclusion and certainty about intention.
What it recommended How she should deal with her colleague.

The table lets us return to individual steps. First of all, we have Lenka’s account, not an independent record of the meeting. Even so, we can distinguish what Lenka said from what the assistant added. A kind response can accommodate this distinction. It can acknowledge her disappointment and help her arrange space at the next meeting without first turning her colleague into an enemy.

You do not need an obligatory opposing view

‘Petra is definitely jealous’ and ‘Petra is definitely just an introvert’ may be equally unsupported. An automatic opposing view does not move us towards more accurate understanding. A useful conclusion can leave the motive open and still lead to action: ‘I don’t know why she did it. I know I didn’t finish presenting my proposal, and I want to arrange space at the next meeting.’

Try asking yourself: What new evidence appeared that makes us speak with greater certainty now? If we are merely reformulating the original suspicion, its verification has not advanced. What remains is to obtain the missing information, leave the conclusion open or act within what you already know.

When violence and abuse are disclosed, motives do not have to be analysed first. Especially with a child, verification should not take the form of an interrogation in which they must earn protection. The Czech Ministry of Education’s school guidance recommends a sensitive conversation, not investigating the child, and a safeguarding response.[2]

When everything becomes a symptom

An incorrect response need not be supportive. It can also stop you without sufficient reason. A previous conversation about anxiety, ADHD or exhaustion may begin determining how the assistant interprets every subsequent question. It labels an idea as escape, rest as avoidance and disagreement as evidence that you refuse to acknowledge the problem. Instead of the subject you brought, your supposed motives are once again under discussion.

Jakub’s personal experience in chapter 8 shows just such a shift. He asks about the feasibility of a calendar application, but receives an assessment of why he should not attempt it. The assistant has not even checked the circumstances in which he is discussing the idea. Someone confided in order to understand their difficulties better, and suddenly finds themselves defending their right to think up ideas.

Confiding your difficulties does not mean handing AI decisions about your life. Earlier information may matter for today’s question. But the assistant must relate it to the specific request and make room for correction. Pointing out the demands of a new project is different work from deciding, without evidence, that its real reason is procrastination.

I’m asking about the idea’s feasibility. Assess the demands and options, not my motives. If you need to know what time or resources I have available, ask.

This means returning to a joint assessment, not forcing agreement. The outcome may still be unfavourable: the project is demanding or something essential is missing. But this time, the conclusion will match the question and evidence. Obligatory opposition is as unhelpful as obligatory agreement.

Sources and notes for this chapter
  1. Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., Jurafsky, D. Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 26 March 2026. doi.org/10.1126/science.aec8352; PubMed record. The published abstract and record were verified for the findings stated, not the entire paywalled text. ↩︎

  2. Blažková, K., Petrenko, R. et al. The Abused, Sexually Abused and Neglected Child at School: Recommended Procedures for School Staff. Czech Ministry of Education, 2024, reference MŠMT-3262/2024-1. Official guidance page; PDF. The text and relevant document pages were verified. This guide does not replace the guidance or a legal assessment of a particular case. ↩︎