> The moment you run LLM generated code, any hallucinated methods will be instantly obvious: you’ll get an error. You can fix that yourself or you can feed the error back into the LLM and watch it correct itself.<p>Interestingly though, this only works if there <i>is</i> an error. There are cases where you will not get an error; consider a loosely typed programming language like JS or Python, or simply any programming language when some of the API interface is unstructured, like using stringly-typed information (e.g. Go struct tags.) In some cases, this will just silently do nothing. In other cases, it might blow up at runtime, but that does still require you to hit the code path to trigger it, and maybe you don't have 100% test coverage.<p>So I'd argue hallucinations are not always safe, either. The scariest thing about LLMs in my mind is just the fact that they have completely different failure modes from humans, making it much harder to reason about exactly how "competent" they are: even humans are extremely difficult to compare with regards to competency, but when you throw in the alien behavior of LLMs, there's just no sense of it.<p>And btw, it is not true that feeding an error into an LLM will always result in it correcting the error. I've been using LLMs experimentally and even trying to guide it towards solving problems I know how to solve, sometimes it simply can't, and will just make a bigger and bigger mess. Due to the way LLMs confidently pretend to know the exact answer ahead of time, presumably due to the way they're trained, they will confidently do things that would make more sense to try and then undo when they don't work, like trying to mess with the linker order or add dependencies to a target to fix undefined reference errors (which are actually caused by e.g. ABI issues.) I still think LLMs are a useful programming tool, but we could use a bit more reality. If LLMs were as good as people sometimes imply, I'd expect an explosion in quality software to show up. (There are exceptions of course. I believe the first versions of Stirling PDF were GPT-generated so long ago.) I mean, machine-generated illustrations have flooded the Internet despite their shortcomings, but programming with AI assistance remains tricky and not yet the force multiplier it is often made out to be. I do not believe AI-assisted coding has hit its Stable Diffusion moment, if you will.<p>Now whether it will or not, is another story. Seems like the odds aren't that bad, but I do question if the architectures we have today are really the ones that'll take us there. Either way, if it happens, I'll see you all at the unemployment line.