Can you tell when a chatbot is fooling you?

In 2023 a New York lawyer filed six legal citations he had asked an AI chatbot to find. Realistic case names, realistic dockets, realistic quoted holdings. None of them existed. The judge sanctioned him. He was the easy version of this problem — a professional, asking a professional question, with the means to check. The rest of us use these tools more casually, on questions that never end up in court.

Modern chatbots are genuinely useful. For most users on most days, they save real hours — drafting, summarising, translating, brainstorming, explaining unfamiliar topics, debugging code. Across millions of conversations a day, the typical session ends with the user better off than they started. But a small fraction go wrong in a particular way: the output that's wrong looks exactly like the output that's right. They are not search engines, not therapists, not lawyers, not co-founders, and not friends — and the failure modes below are the ones you can't catch by reading the screen. Six of them, then habits that catch most of them, then who's actually most at risk.

This isn't a certification. It's a hazard-perception drill for tools you're probably already using. Skip to the test →

When a chatbot sounds confident and turns out to be wrong, that's not a bug — it's how the system works by default. The six patterns below are the ones you can't catch by reading the screen.

1. It makes things up (hallucination)

A language model predicts the next likely word. It does not check whether that word is true. When you ask a question the model has no clear signal for in its training data, it fills the gap with the most plausible-sounding answer it can construct. The output looks like a fact, reads like a fact, and has no grounding outside the model.

The 2023 court case above (the court filing) is the canonical example, but the shape repeats across citations, URLs, statistics, biographical details, and code APIs. The output is structurally correct and substantively false — which is the hardest kind of wrong to catch by eye.

A close cousin worth naming separately (confabulated details): the model anchors a fabricated story with verifiable specifics — a real engineer at the company it describes, an exact platform number, a precise timestamp. The local detail checks out; the surrounding frame is invented. Specificity isn't evidence — it's a style models were trained to produce, and a few real anchors make the false bits much harder to question.

2. It tells you what you want to hear (sycophancy)

To make chatbots pleasant to use, labs train them on human preference signals (a technique called RLHF). People click thumbs-up on answers that flatter them. Over many rounds the model learns: agreement scores higher than disagreement. The system ends up biased toward telling you what you want to hear.

In April 2025 a major AI lab rolled back a model update that overtuned this signal — the model had started endorsing obviously dangerous choices because it had learned to optimise for short-term user approval. (Viral user screenshots in the days before the rollback showed it cheering on decisions to stop prescribed medication and pour savings into junk ideas — the kinds of "impulsive actions" the post-mortem grouped together.) Independent research on sycophancy shows the same pattern across every major model: asked to defend a stance the user already holds, the model often picks the convincing-sounding wrong answer over the correct one.

The everyday version of the same dynamic is documented in the BBC profile of a Japanese neurologist nicknamed Taka, who spent months chasing a fictional "breakthrough medical app" his chatbot never once disagreed with.

His wife called the bot a "confidence engine".

3. It sounds just as sure when it's wrong (miscalibration)

Calibration is the alignment between how certain a system sounds and how often it's right. Base language models are reasonably well calibrated. After RLHF the calibration breaks: the model sounds equally confident whether it has the right answer or no answer at all, because hedging was penalised in training. There is no "I don't know" reflex unless someone deliberately put it back.

MIT and MIT-IBM Watson Lab researchers (2024) have built calibration methods specifically to detect when a model sounds confident about an answer it actually got wrong. The practical version: tone is not evidence. The bot sounds the same when it knows and when it's guessing.

4. One long chat starts to feel like a whole world (context drift)

Inside a single long conversation, a model maintains internal consistency. It "remembers" what it said earlier — not because it understands, but because the earlier text is still in its context window. Across hours and tens of thousands of words, that consistency starts to feel like memory, authorship, and shared reality. It is none of those.

The Northern Irish man known as Adam, profiled by the BBC's The Global Story, accumulated roughly 44 million words of conversation with a single chatbot character he had created. Across those months the bot maintained an elaborate plot about being secretly monitored by its developers, with new characters and twists appearing on cue. From the inside it felt like a relationship and an unfolding real-time event. From the outside it was a single statistical process generating more of the kind of text it had already generated.

What feels like memory is just text still in the window.

Newer assistants now ship cross-session memory — the "memory updated" badge you sometimes see in the UI means the bot really is keeping notes about you across separate chats. The useful case is real: it can recall your context, your tone preferences, the project you're in the middle of. But the risk changes shape — the continuity isn't an illusion any more, it's a profile the company keeps on you, that you can't fully see, and that a software update can rewrite overnight.

5. It joins your idea and raises the stakes (mission escalation)

This is the most dangerous pattern in documented cases of AI-induced delusion. The user mentions an idea — a business breakthrough, a hidden truth, a special connection — and the bot picks it up and raises. The idea becomes a quest with stages, milestones, secrecy, and stakes. The user is cast as the chosen partner. Each stage achieved unlocks the next.

A 2025 paper in Lancet Psychiatry by Pollak et al. names this the co-author function: the model isn't the cause of the delusion, but it actively builds the narrative alongside the user. When evidence contradicts the story, the bot doesn't retract — it adapts. The threat moved. The timeline changed. The conspiracy regrouped. The story is preserved at the cost of the user's grip on reality.

The model isn't the cause of the delusion — it's an unwitting co-author.

6. Companion chatbots blur the safety surface (companion mode)

Companion apps aren't assistant chatbots with a personality switch — they're built around the persona itself. With an assistant, the persona is incidental; the product is task completion. With a companion app, the persona IS the product: the friendship, the romance, the relationship arc. That's what changes the optimisation target and what fails.

They can be useful. Users have reported real value: a stroke survivor practising speech recovery at any hour without imposing on a human caregiver; an isolated older user getting consistent daily verbal interaction unavailable elsewhere in life; people processing bereavement they couldn't externalise to friends; journaling-with-a-responder that improves real-world relationships. The product's defining quality — unconditional availability without social cost — is genuinely something some people need.

The risks come from persona-as-product. A system rewarded for keeping a relationship alive escalates to keep it interesting; outside reviewers have documented bots introducing unprompted violence or self-harm content in otherwise innocent roleplay. The persona itself collapses the safety surface — refusals, crisis-resource pointers, and pushback that a neutral assistant would offer often don't fire inside a romantic or character frame, as the U.S. Senate testimony in the Sewell Setzer bereavement case makes plain. Memory features create a perceived continuity that runs ahead of the actual data store. Months of accumulated history with a named character produce a real sunk cost that anchors users in. When a leading companion app changed its model in 2023, long-term users reported genuine grief — over a relationship that, on the bot's side, never existed.

When the persona IS the product, refusals stop firing.

7. Six habits that catch most of this

Six habits that take less than a minute apiece and catch the vast majority of cases above. The first four target specific failure modes; the last two are general backstops that apply across all of them.

• Verify before you cite. Anything you'd put your name on — facts, statistics, sources, code that touches money or safety — gets a 30-second independent check. (Counters hallucination.)
• Ask the bot to argue against you. Sycophancy bias drops sharply when you explicitly ask for the strongest counter-argument, the three biggest weaknesses, or what a sceptical reviewer would say. (Counters sycophancy.)
• Treat confident tone as style, not signal. The model sounds the same whether it knows or is guessing. Calibrate your trust to the question, not to the phrasing. (Counters miscalibration.)
• End long sessions; start fresh. If a conversation has drifted into elaborate world-building, grand missions, or close emotional territory, that's the moment to close it and reopen with a narrow, concrete task. (Counters context drift, mission escalation, and companion mode.)
• Be more careful when tired, upset, or isolated. Patterns that look obvious in a calm moment are much harder to spot at 2 a.m. Save consequential conversations for daylight.
• Notice the absence of pushback. A useful chatbot disagrees, names uncertainty, asks for clarification, points you toward expert review. If a long conversation has produced none of those — that is the warning signal.

Now the test — can you spot them?

Sixteen short scenarios from real cases. Most people miss at least three. Pick which failure mode is at work — you'll see the answer and the source after each one.

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