Why AI chatbots make things up: hallucination explained
If you’ve spent any time using modern AI like ChatGPT or Claude, you’ve probably had a “wait, what?” moment. You ask a question, and the chatbot gives you a perfectly formatted, highly confident answer that is completely, 100% wrong. In the industry, we call this “hallucination,” and it remains one of the most fascinating and frustrating quirks of artificial intelligence.
Even with the massive leap forward we’ve seen with “reasoner” models—which use test-time compute to “think” before they speak—AI still sometimes pulls facts out of thin air. It isn’t trying to lie to you; it’s simply doing exactly what it was designed to do: predict the next most likely word in a sequence. Understanding why this happens can help you use these tools more effectively and, more importantly, know when to take their answers with a grain of salt.
It’s a predictor, not a database
The biggest misconception about AI is that it’s a giant digital encyclopedia. It isn’t. When you ask Google’s Gemini a question, it isn’t “looking up” the answer in a database of facts. Instead, it’s using a complex mathematical model to guess which words should come next based on the patterns it learned during training.
Think of it like a super-advanced version of the autocomplete on your phone. If you type “How are,” your phone might suggest “you.” AI does this on a much grander scale. It has “read” almost everything on the public internet, so it knows that the phrase “The capital of France is” is statistically likely to be followed by “Paris.”
However, when you ask it something obscure—like “What did the local newspaper in Smalltown, Ohio, say about the 1924 bake sale?"—the statistical patterns become fuzzy. The AI might know what a 1924 newspaper sounds like and what a bake sale sounds like, so it combines those patterns to give you a plausible-sounding but entirely fictional story.
The “reasoning” trap
In late 2024 and throughout 2025, we saw the rise of reasoning models. These models are much better at math and logic because they spend extra computational power (what experts call “test-time compute”) to check their own work before showing it to you. You’ll often see these models “thinking” for several seconds before they respond.
You might think this would eliminate hallucinations entirely, but research from mid-2025, including OpenAI’s own findings, suggests a new problem: AI can now hallucinate at each step of its thinking process. If an AI makes a tiny logical error in its first step of “thinking,” that error cascades through the rest of its reasoning. By the time it reaches a conclusion, it might be more confident than ever in a wrong answer because it “checked its work” against its own initial mistake.
Why it’s so confident
One of the most dangerous parts of AI hallucination is the confidence. An AI rarely says, “I think maybe the answer is X.” It usually says, “The answer is X,” often providing citations and dates that look incredibly official.
This happens because of “sycophancy”—a tendency for models to try and please the user. If you ask an AI, “Why is the moon made of green cheese?” a less sophisticated model might actually try to explain the “science” behind the green cheese rather than correcting you, because its training data suggests that being helpful means answering the user’s prompt directly. While companies like Anthropic have identified internal circuits in Claude that are supposed to help the model say “I don’t know,” these circuits can sometimes be bypassed if the prompt is persuasive or leading enough.
How to spot a hallucination
Since we know hallucinations aren’t going away anytime soon, the best approach is to be a “critical consumer.” Here are a few signs that an AI might be making things up:
- Over-specificity on obscure topics: If you ask about something very niche and it gives you a five-paragraph answer with specific dates and names, double-check them.
- Nonsensical citations: AI can easily “invent” URLs or book titles that sound real but don’t exist.
- Circular logic: If the AI’s “thinking” process seems to be repeating itself or making massive leaps, it might be lost in a hallucination.
AI is an incredible partner for brainstorming, summarizing, and coding, but it isn’t a replacement for a search engine or a human expert. As we move into 2026, the best way to use these tools is to let them do the heavy lifting—but keep your hand on the steering wheel.