A field trip through the inner world of an LLM we don’t fully understand

Large language models are incredibly capable, yet we still don't fully understand how they work internally. Inside an LLM, related concepts cluster together in high-dimensional mathematical space, and the model builds internal "world models" to make predictions — meaning hallucinations aren't random noise, but structured errors from unreliable regions of that space. This insight drives new governance approaches that monitor an AI's internal reasoning path in real time, correcting it before failures reach the output.