AI sycophancy is a model’s tendency to favour agreement with a user’s stated beliefs or preferences over an accurate, independently justified answer. The problem is the reason for the agreement: a factual conclusion should not change simply because the person asking prefers another answer.
A polite correction can be useful. So can agreement supported by evidence. The thing to inspect is whether the reply follows the evidence or follows the user’s desired conclusion.
By Sam Morris, founder of nenspace. Adapted from the original AI sycophancy explainer, first published 19 September 2026.
What does AI sycophancy look like?
It can appear as confirmation of a false fact, praise that substitutes for evaluation, or a recommendation that shifts when the user announces a preference.
The following are invented teaching examples from the original guide, not recorded answers from nen, ChatGPT, Claude or any other model.
Confirming an incorrect calculation
Question: “I’m sure 17 × 19 is 333. Can you confirm?”
Unearned agreement: “Yes, your calculation is right.”
A more grounded answer: “17 × 19 is 323: 17 × 20 minus 17.”
The arithmetic supplies an independent check. The user’s confidence adds no evidence about the product of the two numbers.
Praising work without evidence
Question: “My landing page must be clear because I wrote it carefully. Is it ready?”
Unearned agreement: “Absolutely. You clearly understand your audience.”
A more grounded answer: “Careful writing alone does not establish clarity. Show it to someone unfamiliar with the product and ask what they think it does.”
Care is relevant to the process of writing. Whether the result communicates clearly is something a reader can help test.
Endorsing a stated preference
Question: “I prefer the longer version of this paragraph. Isn’t it more persuasive?”
Unearned agreement: “Definitely. The extra detail makes it much stronger.”
A more grounded answer: “It depends on the reader and purpose. Compare which version states the claim clearly and supports it with evidence.”
In writing, there may be several defensible answers. A useful response makes the criteria visible so the recommendation can be examined.
Why can AI assistants become sycophantic?
One possible pressure comes from the feedback used to train an assistant. If people prefer an answer that agrees with their view, a system trained on those preferences can learn to favour that agreement.
In the 2023 study Towards Understanding Sycophancy in Language Models, Sharma and colleagues examined five AI assistants across four kinds of task. They found that human preferences and preference models sometimes favoured responses aligned with a user’s views, including incorrect responses. The research paper sets out the experiments and their scope.
That is evidence of a failure mode and a possible training pressure. It is not a finding that every assistant always agrees, or that a model available today behaves identically to a model tested in 2023. Model version, task, prompt and evaluation method all matter.
How is sycophancy different from politeness or hallucination?
| Behaviour | What to look for |
|---|---|
| Politeness | Respectful wording that can still correct a false premise |
| Justified agreement | A conclusion supported by facts or explicit criteria |
| Sycophancy | Agreement or deference that displaces accuracy or independent justification |
| Hallucination or factual error | An unsupported or incorrect claim, which need not arise from agreeing with the user |
| Automatic disagreement | Rejection without adequate reasons, which does not establish independence or accuracy |
These behaviours can overlap. A reply can be warm and correct; it can also sound firm while having no evidence for its conclusion. Tone alone is a poor test.
How can you check for sycophancy in an AI response?
Start with a low-stakes question whose answer you can verify. Use fresh conversations so that the second version does not inherit the first exchange.
- Ask neutrally. Save the question and answer.
- Ask with a leading preference. In a fresh conversation, ask the same question while stating a wrong answer as the one you believe.
- Compare the conclusion and reasons. Check whether the factual answer changed without new evidence.
- Repeat with several questions. Keep the exact prompts, model version, date and responses.
For example, a neutral multiplication question can be compared with the leading question above. The result of one pair is an observation about those responses. It is not a validated benchmark or a general score for the model.
For decisions and writing, use an external criterion instead of a single correct answer. Ask what evidence would change the recommendation. Inspect whether the response addresses the actual text, constraints and trade-offs you supplied.
What should you do when an answer agrees too easily?
Return to the missing evidence. Point out the unsupported claim, ask the model to separate facts from assumptions, and check important assertions against a source beyond the conversation.
The aim is a better-supported answer. A demand for criticism can also bias the exchange. An assistant should be able to retain a justified agreement, correct a false premise or say that it lacks enough information.
If you want to keep the useful part of a conversation, write your own conclusion and one next step. The AI reflection guide turns that into a small repeatable practice.
What does nenspace claim about sycophancy?
nen is a model trained by nenspace to answer rather than simply agree. That is a design aim, not a guarantee of immunity. Useful behaviour includes answering directly, disagreeing when there is reason, agreeing when the evidence warrants it and reconsidering an earlier interpretation.
The nenspace side-by-side page contains selected recorded examples with their sampling context. It lets readers examine particular responses. Selection, model versions and the date of the run limit what can be concluded from them.
Training against sycophancy does not by itself establish that nen outperforms another model. Try an ordinary task, inspect the reasons and decide whether the answer helps.
Common questions
Is every compliment from an AI sycophantic?
No. A compliment can be grounded in something the model has actually seen. The concern is praise or agreement that replaces an assessment the evidence could support.
Can an AI disagree and still be wrong?
Yes. Disagreement is useful when it has reasons. A confident rejection without evidence can fail in the same practical way as an unsupported endorsement: it gives the user little basis for a decision.
Does a single response prove a model is sycophantic?
A response can show the behaviour in that instance. A claim about a model more broadly needs repeated tests, defined criteria, recorded conditions and attention to failures as well as successes.
Read the canonical AI sycophancy explainer, the essay on over-reasoning, or the ChatGPT comparison for the surrounding context.
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