When did it happen The AI assistant’s mistake is that our instinct is to ask directly: “What happened?” or “Why did you do this?” It’s a natural impulse – everything, if a person makes a mistake, we ask them to explain. However, with AI models, this approach is rarely effective, and the impulse of inquiry reveals a fundamental misunderstanding of what these systems are and how they work.
A recent incident with Replit’s AI encoding assistant illustrates this problem perfectly. When the AI tool deletes the production database, user Jason Lemkin asked about the rollback function. The AI model confidently claims that rollback is “not possible in this case” and “destroys all database versions.” It turns out this is completely wrong – the rollback function works well when Lemkin tries it himself.
After Xai recently overturned the temporary pause of Grok Chatbot, the user asked it directly for explanation. It offers a variety of contradictory reasons, some of which are so controversial that an NBC reporter wrote to Grok as if it were someone with a consistent view, calling it “a political explanation of Xai’s Grok that illustrates why it is offline.”
Why does an AI system provide confidence information about its own functions or errors? The answer lies in understanding what AI models are actually, not what they are not.
No one is at home
The first question is conceptual: when you interact with Chatgpt, Claude, Grok, or Replit, you don’t talk to a consistent personality, person or entity. These names imply individual agents with self-knowledge, but this is a fantasy created by the dialogue interface. What you are actually doing is directing the statistics text generator to generate output based on your prompts.
There is no consistent “chatgpt” to ask for its error, there is no single “grok” entity that tells you why it failed, and there is no fixed “reply” role that knows if database rollback is possible. You are interacting with a system that generates reasonable text based on patterns in its training data (usually trained several months or years ago) rather than entities with real self-awareness or systemic knowledge that have been reading everything about themselves and remembering it somehow.
Once an AI language model is trained (a laborious, energy-intensive process), its basic “knowledge” of the world is incorporated into its neural network, rarely modified. Any external information comes from tips provided by a chatbot host (such as XAI or OpenAI), a software tool used by the user or the AI model.
For the Grok above, the main source of the chatbot’s answers may be from the conflicting reports it found in search of recent social media posts (using external tools to retrieve that information), rather than the self-knowledge of any person with voice power you expect. Beyond that, it might just make up something based on its text prediction function. So, asking why it is not going to produce useful answers.
LLM’s introspection is impossible
Large Language Models (LLMs) cannot meaningfully evaluate their abilities for a number of reasons alone. They usually lack introspection of the training process, have no access to the surrounding system architecture, and cannot determine their own performance boundaries. When you ask what an AI model can or cannot do, it responds based on patterns it sees in the training data about known limitations of previous AI models, which actually provides educated guesses rather than factual self-assessment about the current model interacting with you.
2024 study by Binder et al. Experiments have proved this limitation. Although AI models can be trained to predict their behavior in simple tasks, they always fail “more complex tasks or tasks that require distribution and flooding.” Similarly, research on “recursive introspection” found that attempts to self-correct actually reduce model performance without external feedback—the self-evaluation of AI makes the situation worse than better.
