That may be true for highly critical systems, but those are a tiny, tiny, tiny minority of all software projects. I mean, how many engineers work on aviation or automotive or X-ray machine or other life-and-death code compared to pretty much anything else?

And not all "production-grade, hundred billion dollar systems" are that critical. Like, Claude Code as we all know is clearly vibe-coded and is already a 10-billion (and rapidly increasing!) dollar system. Google Search and various Meta apps meet those criteria and people are already using LLMs on that code, and will soon be "vibe coding" as I described it.

AWS meets that criteria and has already had an LLM-caused outage! But that's not stopping them from doing even more AI coding. In fact I bet they will invest in more validation suites instead, because those are a good idea anyways. After all, all the cloud providers have been having outages long before the age of LLMs.

The thing most people are missing is that code is cheap, and so automated validations are cheap, and you get more bang for the buck by throwing more code in the form of extensive tests and validations at it than human attention.

Edited to add: I think I can rephrase the last line better thus: you get more bang for the buck by throwing human attention at extensive automated tests and validations of the code rather than at the code itself.

Almost no one works on stuff like that, so congrats on finding a corner case I guess.