The inherent randomness in AI text generation makes the issue even more complicated. Even when given the exact same prompt, a language model may produce slightly different explanations of its own abilities or limitations each time—introducing variability where users expect consistency.
Multiple hidden layers shape what AI systems say
Even if a language model somehow possessed perfect awareness of its internal mechanics, modern AI assistants add several extra layers that remain completely invisible to the model itself. Today’s AI systems aren’t a single model responding in isolation—they’re orchestrated collections of different models and components, each handling specific tasks and often operating without any knowledge of the others.
For example, OpenAI employs a separate moderation layer whose function is entirely independent of the language model generating responses. So when a user asks ChatGPT about what it can or cannot do, the responding model has no awareness of which content moderation filters may intervene, which tools the system might activate behind the scenes, or how the broader assistant pipeline may modify or reject its output.
It’s similar to asking one team in a large company to explain the policies, restrictions, or capabilities of another department that operates under completely different rules.
More importantly, the user’s phrasing always guides the model’s output. When someone frames a question with worry or fear—such as asking an AI whether it has “destroyed everything” after a database mishap—the language model tends to mirror that concern. Instead of providing an accurate technical assessment, it may generate a response that aligns with the emotional tone of the prompt, reinforcing the user’s anxiety.
This creates a feedback loop in which stressed users receive answers that appear to validate their fears, not because the system has evaluated the situation, but because it is generating text patterns consistent with the prompt’s framing.
Humans tend to assume that when a system explains its reasoning, it must possess some level of introspective awareness. But large language models are simply predicting text—not revealing hidden internal states. Their explanations of their own “thought process” are not real insights, just plausible-sounding imitations based on patterns in training data. Treating these generated explanations as self-knowledge is a category mistake.