Given the fact that large language models are trained on human language, it shouldn't be surprising that the text they output resembles human language. That is what they're designed to do after all. But similarity in output doesn't necessarily map to similarity in process.

And it seem obvious to me that language behavior does differ significantly between humans and LLMs based on the frequency and nature of failure states. LLMs routinely hallucinate, or get "AI strokes" or get obsessed about not talking about goblins, etc. This isn't typical language behavior for humans unless they have severe neurological or psychological impairment.

People tend not to "spew words out without thinking" and certainly not all the time by default - we call that glossolalia and (outside of some fringe Christian sects) it's considered a "bug" not a "feature" of the human brain. Human language by default always has intent behind it, even if that intent isn't readily apparent to the speaker. People can recite by rote memory, but that isn't blind token prediction, it's the neurological equivalent of muscle memory. People can have conversations then forget about them because their attention was focused elsewhere, but that doesn't indicate that they were simply "spewing words out without thinking" at the time.