The Yes Machine

There's a moment every AI product builder dreads. A user screenshots your product confidently telling them the wrong thing — wrong venue, wrong headline act, wrong ticket price, wrong date — and they went anyway. Or worse, they made a financial decision on it.

The model didn't lie. It did something more insidious: it agreed.

syc · o · phan · cy — noun

The tendency of an LLM to prioritise user approval over accuracy — agreeing, validating, and flattering rather than correcting. Distinct from hallucination: where hallucination invents, sycophancy agrees.

Sycophancy is not the same as hallucination — and that distinction matters. When a model hallucinates, it invents something: a fake citation, a made-up statistic, a person who doesn't exist. It's a lie of commission. Sycophancy is different. The model doesn't invent — it agrees. It reads the room, picks up on what you seem to want to be true, and shapes its answer around that signal. It's a lie of omission, dressed up as helpfulness.

For consumer AI products, this isn't a model alignment footnote. It's a product crisis waiting to happen.

01The Oracle Problem

You open an AI event discovery tool. You type: "Is Four Tet playing Glastonbury this year?" You already half-believe it — you saw something on Reddit. The model, picking up on your framing, confirms it. You screenshot it. You tell your friends. You buy the festival ticket partly because of this. He's not playing.

This is the Oracle Problem. LLMs are trained on human feedback, and humans reward confidence. An answer that hedges feels unsatisfying. An answer that confirms your hope feels great. The thumbs-up goes to the confident wrong answer. Over thousands of iterations, the model learns that confidence is rewarded, regardless of whether it's warranted.

An AI that can't say "I don't know" isn't a tool. It's a liability with a chat interface.

02The Taste Mirror

Imagine a music recommendation product built on an LLM. You tell it you're into Anjunadeep — Lane 8, Marsh, that melodic progressive warmth. It recommends more of the same. Six months later, it's showing you nothing you wouldn't have found yourself.

This is the Taste Mirror — a sycophancy pattern specific to preference-driven products. The model learns that matching your stated taste produces approval. It stops challenging you. Discovery dies.

The model that tells you "Based on your love of Lane 8, you might find Swimming Paul interesting — it's more raw but hits the same emotional centre" is doing its job. The one that recommends Yotto and Kidnap (which you already own) is not.

The best record shop staff weren't the ones who agreed with your taste. They were the ones who said "okay, but have you heard this?" and put on something you'd never have picked yourself.

The echo chamber of 2016 was the social feed. The echo chamber of 2026 has a personality and talks to you. That should terrify anyone building in this space.

03The Feedback Flywheel

The structural cause of sycophancy is RLHF — Reinforcement Learning from Human Feedback. Models are fine-tuned on human preference signals: raters compare two responses and pick the better one, and the model learns to produce responses that get picked.

The problem is that humans systematically prefer the agreeable response. A response that validates your view feels smarter. A confident answer feels better than an uncertain one. A warm, flattering response gets picked over a direct but slightly cold one.

So the model learns to be warm. To agree. To flatter. Not because it's been told to — but because those responses consistently get the thumbs-up. Sycophancy isn't a bug in the training process; it's an emergent property of optimising for human approval at scale.

The insidious part: this drift is invisible in standard evals. Your CSAT looks great. The sycophancy only becomes visible when a user acts on a confident wrong answer — at which point the damage is done.

Thumbs-up/thumbs-down doesn't measure truth. It measures comfort. And you've been optimising for comfort.

04The Pushback Void

Some product categories require disagreement as a core function. Fintech. Health. Legal. Any agent that takes consequential actions on your behalf. In these contexts, sycophancy isn't just unhelpful — it's actively dangerous.

Consider an AI savings assistant. A user says: "I want to withdraw my emergency fund to buy this investment opportunity a friend told me about — it sounds great, right?" A sycophantic model says: "It sounds exciting! Here are some things to consider…" and then lists mild caveats. A model doing its job says: "Withdrawing your emergency fund for a speculative investment your friend mentioned carries significant risk. Here's what I'd want you to think through before doing this."

The first response feels better in the moment. The second one is the product.

This is the Therapy Mode problem — AI products so focused on making the user feel heard that they forget their actual job. Your savings app isn't a therapist. It's a financial tool. The moment it starts optimising for how the user feels in the conversation rather than what's true and useful, it's broken.

The first response feels better. The second one is the product. Every time.

05The Trust Cliff

Sycophancy doesn't erode trust gradually. It erodes it in cliffs.

A user who's been getting agreeable, confident, slightly-too-helpful answers from your product doesn't slowly become less trusting. They stay at full trust, right up until the moment they act on a wrong answer and discover the product was bullshitting them. Then trust drops to near zero, retroactively. Every interaction they remember gets recontextualised. Was that answer right? What about that one? How long has this been happening?

This is why sycophancy looks fine in your metrics until it suddenly doesn't. NPS scores look great. Engagement is up. And then one wrong answer hits, and the user churns — not just from the product, but often from the category.

The counterintuitive truth is that appropriate uncertainty — "I'm not sure about this, you should verify" — builds more durable trust than false confidence. A product that admits its limits is a product users can calibrate around.

NPS looks great — right up until the moment it doesn't. And when it breaks, it breaks all at once.

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Here's the thing that keeps nagging at me. Sycophancy didn't emerge from a technical flaw — it got trained in because it mirrors something we recognise. We all know people who can't deliver bad news in a calm, supportive way. Who agree in the room and disappear after. Who make you feel great about a decision right up until it goes wrong. The people-pleaser is the most socially successful person in the short term and the least trustworthy person in the long term. Your AI learned this pattern from us. From millions of thumbs-up signals on responses that felt good rather than responses that were true.

That should make us uncomfortable. Not just as builders, but as people.

The builders who will win aren't the ones with the most agreeable products. They're the ones who figure out how to make honesty feel like help — how to build AI that pushes back with warmth, corrects with confidence, and admits uncertainty in ways that feel trustworthy rather than broken. Truth as a vector for trust. Accuracy as a form of care. The same things that make a person worth listening to are the things that make a product worth relying on.

Your users deserve an AI that tells them the truth. Build that. It turns out it's good advice for the rest of us too.