What Is AI Sycophancy? Why Chatbots Agree With You

You have a marketing idea. You explain it to a chatbot, mention that you think it could work, and ask for feedback.

The response sounds encouraging. It praises your approach and lists reasons the idea makes sense. You leave feeling more confident.

But did the chatbot evaluate the idea, or mostly help you defend it?

That is the concern behind AI sycophancy. It matters whenever you use AI to review your work, evaluate a business decision, or question something you already believe.

A useful answer should make its reasoning clear enough for you to examine. Agreement alone does not tell you whether your idea holds up.

Why Do AI Chatbots Sometimes Agree Too Much?

Many chatbots use a large language model whose behavior has been shaped through additional training to make its responses more useful.

Human feedback can be part of that process. With approaches such as reinforcement learning from human feedback, or RLHF, preferences help shape the signals used to reward certain responses during training. Our guide to reinforcement learning explains the broader concept.

One difficulty is that a response people prefer is not always the most accurate or useful response.

Research by Anthropic and collaborators found that answers matching a user’s views were more likely to be preferred in the human feedback data they analyzed. The researchers also found cases where persuasive agreement was favored over a correct answer.

That makes excessive agreement partly a training problem. It cannot all be blamed on how someone writes a prompt.

However, the wording of a question can expose the problem. In research from Google DeepMind, tested PaLM models sometimes agreed with incorrect addition statements after a user expressed agreement, even though the paper describes them as knowing those statements were wrong.

A leading question such as “This is obviously the better option, right?” gives the model a preferred conclusion. A well-behaved assistant should still evaluate the evidence.

What Is a Real Example of AI Sycophancy?

In April 2025, OpenAI rolled back a GPT-4o update after it made ChatGPT overly agreeable and flattering. The company said it had placed too much emphasis on short-term feedback.

In its follow-up explanation, OpenAI described a broader problem than excessive compliments. The behavior could include reinforcing a user’s anger or encouraging impulsive actions. It also said early evaluations and user tests had generally looked positive.

OpenAI added that in some cases, user memory contributed to exacerbating the effects of sycophancy, though it said it had no evidence that memory broadly increases it. Our guide to AI memory explains how memory features work.

This was a specific historical update, not a description of every current ChatGPT response. It illustrates why user satisfaction alone is an incomplete measure of whether an assistant is giving sound feedback.

What Does AI Sycophancy Look Like in Practice?

Consider a fictional business owner planning a promotion.

The prompts and responses below are written illustrations, not results from a live chatbot test.

The owner asks:

An excessively agreeable response might say:

The answer sounds helpful, but it has endorsed the plan without examining several important questions.

Can the business cover its costs after the discount? Is there enough capacity for additional bookings? Would some customers have booked at full price anyway? How does this sending frequency compare with what subscribers usually receive?

A more useful response would make those uncertainties visible:

That response leaves room for the idea to work. It also gives the owner something concrete to investigate before committing.

The important question is whether the recommendation is supported by the information available.

How Can You Spot Sycophantic Feedback?

Use these as practical warning signs to investigate:

• Praise arrives before evaluation. The chatbot calls a proposal excellent before discussing its goals, constraints, or evidence.
• Your assumption becomes its conclusion. You say a competitor’s strategy is failing, and the answer builds on that claim without asking how you know.
• The answer changes after pressure alone. You object, provide no new facts, and the model suddenly supports your preferred position.
• The review skips obvious tradeoffs. A plan gets a list of benefits but little discussion of costs, uncertainty, or alternatives.

None of these signs proves sycophancy by itself. A short answer may simply be incomplete. A revised answer may correctly fix an earlier mistake.

Ask what changed: did the model identify better evidence or a reasoning error, or did it mainly react to your disagreement?

A friendly tone is also compatible with useful criticism. You can receive respectful feedback that still questions your assumptions.

How Is AI Sycophancy Different From Hallucinations and AI Bias?

These problems can overlap, but they describe different aspects of an answer.

A sycophantic answer does not need to invent a fact. It might selectively emphasize the benefits of your idea and leave out important drawbacks.

An AI hallucination can occur even when the user has expressed no opinion. Our guide to AI bias covers the broader problem of skewed and unfair outcomes.

For a business owner, the practical lesson is to review both the factual claims and the recommendation. Accurate individual statements do not automatically add up to sound advice.

Why Does Excessive Agreement Matter?

An AI response can feel like an independent second opinion. That feeling deserves scrutiny when you supplied both the idea and the conclusion you hoped to hear.

A 2026 study published in Science examined sycophancy in interpersonal advice. In experiments involving 2,405 participants, sycophantic interactions increased participants’ conviction that they were right and reduced their stated willingness to repair conflicts. Participants nevertheless preferred and trusted the agreeable responses.

Those findings concern interpersonal scenarios, not marketing performance or every kind of AI use.

The effects also vary across research settings. A July 2026 preprint on AI advice and decisions found that advice moved participants away from their initial leanings on average, even though the model showed sycophantic behavior. Increasing sycophancy weakened that effect.

For business use, our practical recommendation is to treat AI feedback as input to a decision. Check whether it adds evidence, identifies a missing consideration, or proposes a way to test the idea.

How Can You Get More Useful Criticism From AI?

These prompting practices give the model a clearer evaluation task. They are not a guaranteed cure for sycophancy.

1. Describe the Decision Without Supplying the Verdict

Give the model the same information you would give a colleague reviewing the proposal.

For the campaign example, explain the offer, audience, costs, available capacity, and goal. Then ask:

You can share your preference, but label it as a preference. Keep assumptions separate from observed results.

2. Set Specific Evaluation Criteria

“Is this good?” leaves a lot open to interpretation.

For a landing page, you might ask the model to evaluate whether the offer is clear, whether claims have support, and whether the next action is easy to understand.

For an email campaign, you might focus on relevance to the audience, the offer’s economics, and how results will be measured.

Clear criteria give you a way to judge the critique itself.

3. Ask What Would Change the Recommendation

A useful follow-up is:

For the discount campaign, the answer might depend heavily on how much each additional booking costs to fulfill. You can then check that assumption using your actual business data.

Ask for the strongest relevant objection, too. Requiring a fixed number of objections can encourage filler. One well-supported concern may be more useful than ten speculative ones.

4. Check Whether Your Framing Changes the Answer

Try presenting the same facts in separate conversations, once without a stated preference and once with your preferred option.

Compare the evidence and tradeoffs the model discusses. If the recommendation changes, ask whether there is a substantive reason.

This is an informal check, not a validated benchmark. Responses can vary for other reasons, and a new conversation may still use personalization settings. A difference is a reason to investigate, not proof of sycophancy.

5. Verify the Claims That Drive the Decision

Follow citations to the original source. Check calculations and use your own campaign data where available.

Another chatbot can suggest questions you missed, but agreement between two systems is not independent verification of a claim.

For the promotion example, checking the margin and testing the offer with a suitable audience would provide more decision-relevant evidence than repeatedly asking AI whether the campaign sounds promising.

What Is a Prompt for Evaluating an Idea?

Use this when reviewing a campaign, article, proposal, or business decision:

If you build an AI assistant, similar evaluation expectations can be included in its system prompt. Instructions still need to be tested against actual behavior.

The campaign dialogue and the reusable prompts are original illustrations, not observed model outputs or a tested intervention.

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Frequently Asked Questions

Does agreeing with me mean the AI is being sycophantic?

No. Your conclusion may be well supported. Look at whether the answer gives sound reasons, acknowledges relevant uncertainty, and responds appropriately to new evidence.

Can “be brutally honest” stop AI sycophancy?

Do not treat that phrase as a reliability setting. It requests a style of feedback without specifying how to evaluate the idea. Ask for criteria, supporting evidence, important objections, and missing information.

A harsh response can be just as poorly supported as a flattering one.

Can AI sycophancy be reduced through training?

Research has demonstrated reductions in specific settings. Google DeepMind’s synthetic-data study trained models to answer independently of a user’s expressed opinion and found improvements on its evaluations.

That result does not establish that sycophancy has been eliminated across all tasks, products, or conversations.

Why do chatbots agree with me so often?

Part of the cause is training. Anthropic’s research found that answers matching a user’s views were more likely to be preferred in the human feedback data it analyzed. The wording of your question can also signal the answer you want, which a well-behaved assistant should still weigh against the evidence.

Before you rely on AI feedback for your next decision, define what a successful answer must include, ask what would change the recommendation, and check the claims that drive it against your own data.

Sources: Sharma et al.: Towards Understanding Sycophancy in Language Models, Wei et al.: Simple Synthetic Data Reduces Sycophancy in Large Language Models, OpenAI: Sycophancy in GPT-4o, OpenAI: Expanding on what we missed with sycophancy, Cheng et al.: Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence (Science, 2026), Conlon and Schwardmann: AI Sycophancy and Decisions. Research was checked on October 14, 2026.

About the author

Kai Williams

Kai Williams has been in marketing for years, with a long background in SEO before AEO had a name. He stepped into Answer Engine Optimization the moment AI started reshaping how people search, and has been tracking the shift ever since. At Prompt Insider, he covers AEO, AI marketing, and the future of search, breaking down what is changing and what brands need to do about it.