Why are you reviewing AI code in detail? Do you also review the assembly output of GCC line by line?
In the case of writing, it’s like hiring someone to write a book for you vs. hiring someone to translate a book you wrote into another language. In the first case, you didn’t really define the message for readers, whereas in the second case you did, and the translator is converting that same message for another audience to consume.
In the case of LLMs, the behavior is non-deterministic and inconsistent. If I don’t explain how handle an edge case or give a performance constraint, the LLM will still produce code and may do so in different ways, handling edge cases differently and with different performance characteristics. I can’t reason about how the LLM will fill in those gaps, it’s “random.”
Maybe you don’t care about how the LLM handles those edge cases or handles performance, but that’s different than a deterministic abstraction whose implementation details you don’t care about, but whose logic and performance is deterministic and consistent
The closest thing we have to vetting LLMs is “whoa look, it escaped this sandbox, that’s prolly not great but it’s so cool!”
I wouldn't use it for flight control software yet, at least not without careful review, but most software isn't exactly critical. At the same time, I wouldn't trust flight control software that was only reviewed by humans, since AI is so much better at debugging.
We'll probably need humans in the loop for safety critical software for at least a year or two, before AI fully outpaces humans at generating correct code.
So I assume you don’t fly? Or is it only software created after 2025 which must be reviewed by the All Knowing Entity?
And, AI is rapidly getting better than people at both code review and authorship, so a human deeply involved is turning into nothing but a slowdown. The main purpose people have is testing that the specs were, in fact, implemented properly.
The vast majority of properly written software was already plumbing well over a decade ago. The software engineering is making high level decisions based on experience with respect to the existing tools and the needs of the business. If you're not already using LLMs that way, you would have been a similarly bad manager of human devs writing similar inadequate slop. Less code has always been better code.
The line in the sand for these arguments really ought to be whether you think LLMs are better than humans who actually know what they're doing.
If you think LLMs are better, or could get better while continuing to use statistical methods, you automatically lose the argument (delusional/ignorant) and any hope of regaining credibility. That's not dogma. That's the science.
twitch
Gcc makes maybe 1 mistake ever 2 billion emissions. LLMs make 1 mistake ever 3rd emission.