LLMs are extrapolation machines. They have some amount of hardcoded knowledge, and they weave a narrative around this knowledgebase while extrapolating claims that are likely given the memorized training data. This extrapolation can be in the form of logical entailment, high probability guesses or just wild guessing. The training regime doesn't distinguish between different kinds of prediction so it never learns to heavily weigh logical entailment and suppress wild guessing. It turns out that much of the text we produce is highly amenable to extrapolation so LLMs learn to be highly effective at bullshitting.