But the determinism isn't the real distinction. LLM inference can be made deterministic (temparaute zero, etc.). And multi-threaded compiler can be non-dterministic.

And human developers are also non-deterministic. There is always the nondeterminism even before AI.

I think the real difference is how leaky the abstraction is. If you just need to understand language instead of compiler internal to write correct code, the compiler is non-leaky abstrction. If you rely on compiler to do auto SIMD optimization, and have to do hacks to trigger the optimization, compiler is leaky abstraction.

For AI, if you don't ever need to read the code and AI writes perfectly bug-free program that perfectly follows instruction, then AI is a non-leaky abstraction, but that's not the case for now. To make code maintainable and debuggable you still need to read the code if the project is complex enough.

Model outputs are deterministic. Your API provider may be injecting untunable amounts of randomness, but if you're not at the whim of an opaque cloud service, your model can be as deterministic as you please.

The unease that you describe isn't about models not being deterministic (because they are), it's that generating outputs from LLMs is an inherently inductive process (operating backwards by extrapolating from observations and examples) rather than a deductive process (operating forwards from careful logical reasoning founded on axiomatic principles). Deductive processes are more reliable, predictable, and analyzable than inductive processes, but they're also much harder to produce for many classes of problems.

"Non-determinism" (i.e. the "N" in NP-Complete) does not mean "random", it means "following all paths at once" (there's a really elegant way to understand the List monad as representing non-deterministic computing). It's such a fundamental (and delightful) concept in computer science that refusing to understand what it means is a tragedy related to the loss of technical language itself.

But, even if we just sub "non-deterministic" for "inconsistent" or "random" or "stochastic" or whatever better term you come up with. Statements like the following just seem to not understand the basic architecture of LLMs:

> Given the same input prompt, a model's output is not guaranteed to be identical on every run, at least under normal running conditions.

"Normal running conditions"? The reproducibility of the output is 100% a property of how you choose to sample from an LLM. There are virtually endless ways you can define your sampler and whether or not the same result is reproducible is a fairly minor aspect of all the other choices you make when sampling.

At the end of the day, each step of the LLM gives you a distribution over next tokens, and you can do with that what you like.

In the end the article almost becomes a tautology: the output of an LLM represents a probability distribution over all the outputs it believes are valid with some variation around an expected output. Yes, that's literally what an LLM is.

This tracks well with my experiences integrating AI into a software dev pipeline. Sure the output of an LLM is not deterministic in the same sense as a compiler (at least when the LLM has the typical configuration). However, that just means the LLM should not be solely responsible for the final product of your pipeline.