A large language model is trained on an enormous body of text with one objective: predict what comes next. Doing that well turns out to require learning facts, syntax, reasoning patterns and domain conventions, because all of them are needed to continue text accurately.
The mechanism explains the characteristic failure. The model optimises for plausible continuation rather than truth, which is why a confident, fluent, wrong answer is exactly what the training objective produces.