Perhaps the biggest misconception about AI chatbots is the assumption that they understand the world, know facts, or possess consciousness. They don’t.

At their core, so-called Large Language Models (LLMs) are gigantic statistical tools. They have been trained on vast amounts of text from the internet and have learned to recognize patterns in language. When you ask a question, the AI ​​doesn’t “think” about the answer. Instead, it calculates which word is statistically most likely to follow your input and the words it has generated so far.

Example: If you ask “The capital of France is…”, the AI ​​has seen the sentence “The capital of France is Paris” trillions of times in its training data. The probability that “Paris” will be the next word is extremely high.

The problem: The system is optimized to generate a fluent, plausible, and grammatically correct answer—not necessarily a true answer. If a false piece of information sounds plausible and has occurred often enough in the training data, the AI ​​will confidently reproduce it.

This statistical approach leads directly to the most well-known problem with AI chatbots: so-called “hallucinations.” This refers to the fabrication of facts, sources, quotes, or events that sound convincing but lack any basis.

If the AI ​​cannot find an exact answer in its data patterns or has gaps in its knowledge, it simply fills them with the most probable word combinations. It then invents:

• Sources: It lists scientific studies or articles that never existed.
• Quotes: It attributes words to historical figures that they never uttered.
• Facts: It invents details about events or biographies.

The insidious thing about this is that the AI ​​presents these fabrications with the same authority and confident tone as correct facts. She almost never signals that she is unsure or that she is only offering advice.

An AI model is only ever as good as the data it was trained on. However, the internet is not a repository of pure, verified facts. It is full of opinions, biases, outdated information, and deliberate misinformation.

Outdated Knowledge: Most large models have a “knowledge stop”—a date up to which their training data extends. If you ask them about events that occurred after that date, they either can’t answer or (worse) they try to guess and hallucinate.

Bias: AI learns from texts written by humans—including all our implicit and explicit biases. If certain groups are over- or underrepresented in the training data, or are subject to stereotypes, the AI ​​adopts these patterns.

Faulty data: If a piece of misinformation is repeated often enough on the internet (e.g., a popular conspiracy theory or a historical myth), the AI ​​learns this pattern as “likely” and presents it as fact.

A human understands the context of a question. We understand irony, subtext, and the physical world around us. An AI doesn’t. It only processes text.

If an input is ambiguous, the AI ​​can easily misunderstand the intended context. It lacks genuine “world knowledge” or common sense to assess whether a generated answer is even logically or physically possible. It can tell you how to boil an egg, but it doesn’t “know” what an egg is, what heat is, or why you need water.

AI chatbots are impressive tools for creativity, summarizing, and pattern recognition. However, they are not infallible knowledge bases. Their errors are not “accidents” but inherent to the system. They stem from their statistical nature, their lack of genuine understanding, and the unavoidable flaws in their training data.

For users, this means one thing above all: Be wary of blind trust. Every answer from an AI chatbot, especially when it comes to facts, figures, medical advice, or historical data, must be critically examined and verified against reliable sources.