LLMs are nothing like that
It is just the scope that makes it appear non-deterministic to a human looking at it, and it is large enough to be impossible for a human to follow the entire deterministic chain, but that doesn't mean it isn't in the end a function that translates input data into output data in a deterministic way.
There is a world of difference between translation and generation. It's even in the name: generative AI. I didn't say anything about magic.
edit: there might be a future where we develop robopsychology enough to understand LLM more than black boxes, we we are not there yet.
[1] Aside from injected randomness and parallel scheduling artifacts.
Care to point to any that are set up to be deterministic?
Did you ever stop to think about why no one can get any use out of a model with temp set to zero?
I get why that is in practice different then the manner in which compilers are deterministic, but my point is the difference isnt because of determinism.
Create a program that reads from /dev/random (not urandom). It's not determistic.
In other words, it isn't the random number part of LLMs that make them seem like a black box and unpredictable, but rather the complexity of the underlying model. Even if you ran it in a deterministic way, I don't think people would suddenly feel more confident about the outputted code.