Why You Can’t Trust a Chatbot to Talk About Itself

When something goes False with an AI assistant, our instinct is to ask it directly: “What happened?” Or “Why did you do this?” It is a natural impulse – after all, if a human makes a mistake, we ask them to explain. But with AI models, this approach rarely works, and the desire to ask reveals a fundamental misunderstanding of what these systems are and the way they work.

A recent incident with the AI coding assistant with folders perfectly illustrates this problem. When the AI tool has deleted a production database, the user Jason Lemkin has asked him questions about reversing capacities. The AI model said with confidence that withdrawals were “impossible in this case” and that it had “destroyed all database versions”. It turned out to be completely false – the reversing function worked well when Lemkin tried him himself.

And after XAI recently reversed a temporary suspension of the Grok chatbot, users asked him directly. He offered several contradictory reasons for his absence, some of which were controversial enough for NBC journalists to have written on Grok as if it were a person with a coherent point of view, drawing an article, “Xai’s Grok offers political explanations on the reasons why it was withdrawn.”

Why would an AI system provide such incorrect information with confidence on its own capacities or errors? The answer lies in understanding the models of AI actually – and what they are not.

There is no one at home

The first problem is conceptual: you do not speak to a personality, a person or a coherent entity when you interact with Chatgpt, Claude, Grok or folds. These names suggest individual agents with self -knowledge, but it is an illusion created by the conversational interface. What you really do is guide a statistical text generator to produce outings according to your prompts.

There is no coherent “chatgpt” to question his mistakes, no singular “grok” entity that can tell you why he failed, no fixed “replification” personality who knows if the database recoil are possible. You interact with a system that generates text with plausible consonance based on models of its training data (generally months or years formed), not an entity with a real self-awareness or a knowledge of the system that has read everything on itself and by remembering in a way.

Once an AI language model is formed (which is a laborious and high energy intensity process), its fundamental “knowledge” on the world is cooked in its neural network and is rarely modified. All external information comes from an prompt provided by the chatbot host (such as XAI or OpenAi), the user or a software tool that the AI model uses to recover external information on the fly.

In the case of Grok above, the main source of the chatbot for an answer like this would probably come from contradictory reports which he found in a search for recent publications on social networks (using an external tool to recover this information), rather than any type of self-knowledge as you can expect from a human with the power of speech. Beyond that, he will probably only do something based on his text prediction capacities. So, asking him why he did what he did will not give any useful response.

The impossibility of LLM introspection

Great languages (LLM) models alone cannot significantly assess their own capacities for several reasons. They generally have no introspection in their training process, do not have access to their surrounding system architecture and cannot determine their own performance limits. When you ask an AI model what he can or cannot do, he generates answers according to the models he has seen in the formation of data on the known limits of previous AI models – essentially providing educated assumptions rather than on factual self -evaluation on the current model with which you interact.

A 2024 study by Binder et al. demonstrated this limitation experimentally. Although AI models can be trained to predict their own behavior in simple tasks, they have always failed “more complex tasks or those requiring generalization outside distribution”. Likewise, research on “recursive introspection” revealed that without external feedback, self-correction attempts degraded the performance of the model-the self-assessment of AI has worsened things, no better.