LLM is indeed just computer function that does stats. And our brains are just electro-chemistry that does stats. This is why stylometric analysis of human writing is a thing.

My previous experience with things such as you have linked to, is they used to be quite poor. I assume they're better since then, but then again so are the models.

[0] https://news.ycombinator.com/item?id=47778171

Yes, but "better" means different things for each of these.

Detectors are trying to get better at distinguishing human from LLM-generated text.

LLMs are being improved to generate more useful (and benchmark maxxing) outputs, not to attempt to avoid detection.

LLMs are in fact explicitly trained to be as predictable as possible. The training goal is to minimize continuation prediction errors, which means they are in effect being trained to generate output where each word can be predicted by what came before it (which we can contrast to a human who tries to spice it up and keep it interesting by not being too predictable!).

RL post-training, which is especially used for computer code and math, is going to change this word-by-word predictability (detectability) a bit since the focus is now on a longer term goal rather than next word, but to some extent you could also view it as just steering/narrowing the output of the model towards that goal, not totally overriding the next-word statistics.

I don't know if there are AI detectors specifically trained to detect AI code rather than prose, but I'd expect that is more difficult to do, both because of the RL factor, and because computer code is so predictable in the first place - adhering to rigid syntax etc.