I see people hallucinate on HN all the time. We tolerate it. Why should we? We should if the overall inclusion of unreliable things (humans) provide value. The error rate for LLMs doesn't matter. The net value does. So if the value is great enough to tolerate the error rate, we do. We don’t categorically dismiss the technology because it can fail really poorly. We design things all the time which can fail catastrophically. Seriously. So LLMs will appear anywhere where the net value is positive. Maybe you’re taking a more nuanced stance, but I see a lot of “if it can hallucinate even once we can’t use it” rhetoric here. And that’s simply irrational. Even “we can’t use it for important things” is wrong. Doctors are using LLMs today to help collate observed data and suggest diagnoses. Trained professional in the loop mitigates the “terrible failure”. So no I don’t even agree that LLMs shall be relegated to non-important things.
I also think categorically dismissing LLMs is a mistake.
However, an LLM for automated code generation (the context of the thread as I understand it) is basically a dubious-code-copy-paster on steroids. That was already the wrong way to develop code to begin with, automating and accelerating it is not an improvement.
There has never been a single case where I took code from Stack Overflow, which is already a relatively high quality source of such snippets, and didn't have to adapt it in at least some way to work with the code I already had. Heck, I often find rewriting the snippet entirely is better than copying and pasting it. Of course, I also give attribution, both for credit and for referring back to the original in case I made a mistake, the best solution changes in the future, there's context I didn't cover, etc. And in between the problems I solve with other people's help is a whole lot of code I write entirely on my own.
There are many cases of code in the wild being bad, not just from a "readability" or "performance" standpoint, but from a security standpoint. LLMs regurgitate bad code despite also having good code, and even the blog posts explaining what's good and what's bad, in their training corpus! And an LLM never gives attribution, partly because it was designed not to care, and partly because the end result is a synthesis of multiple sources rather than a pure regurgitation. Moreover, LLMs don't have much continuity, so they mix metaphors and naming conventions, they tie things together in absurd ways, etc. The end result is an unmaintainable mess, even if it happens to work.
So no, an LLM is not like a compiler, even though compilers often have their own special brand of crazy magic that isn't necessarily good. Nor is it going to deliver a robust way to turn abstract human thoughts into concrete code. It is still a useful tool, but it's not going to be an automated part of developing quality code. And this is going to be true for any non-coding scenario that requires at least the same level of reliability.
