This article is a great example of how the human is struggling to extrapolate what happens next. There won't be any humans anywhere near this part of the tech stack, just like no one building a SAAS writes assembly code; or has to put together their own server cluster a datacenter (remember pre-cloud?) for their company anymore. He's dead Jim. No one is telling anyone to ship the ML code without any testing. Human coders also make mistakes. I'll bet that in a few years product managers / QA people would rather work with an ML stack to generate code than a human engineering team. It'll not just be cheaper and faster, but a lot less hassle & more accurate. As an example, Python has roughly ~100 or so "keywords", and extensive public libraries and open source algorithms to call upon. Anyone who thinks this presents any sort of challenge for an LLM to profoundly master is is delusional. They can do IMO-grade math, and help proving novel theorems. They can code your YC startup just fine.