I think that actually reinforces the distinction being made. An LLM’s nondeterminism is in the generation process: given the same prompt and model state, sampling can produce different outputs. That doesn’t mean the underlying fact itself becomes nondeterministic.

A human who knows 1+1=2 can still say “3” because they misread the question, misspoke, were distracted, or made some other cognitive error. Likewise, an LLM can output “3” because the generation process selected an incorrect continuation. Those are both errors in producing an answer, not evidence that 1+1 somehow has multiple answers.

So yes, human mistakes and LLM sampling are mechanistically different. If your argument is that LLMs and humans can both make mistakes, then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.

> If your argument is that LLMs and humans can both make mistakes

It's not, I'm just pointing out that LLMs won't make that mistake.

You could ask an LLM what 1+1 is, and the number of times it says "3" is so small that it makes no sense to worry about it. It will phrase the response differently each time; that's the nondeterminism. But it won't say "3".

> then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.

Yes, if we ignore everything else, that seems like a reasonable question. But let's not ignore everything else, like the fact that LLMs are much more productive than humans and likely already make fewer mistakes than the average programmer.