OP's entire argument rests on this presumption, and while it certainly sounds like the industry is headed in this direction, it's definitely way too early to equate the success of building proofs of concept with success at maintaining mission-critical production systems across industry verticals, as OP attempts to:
> The prototype is working software. And the improvement and testing of that prototype is further enabled by more improvement loops with the AI. It gets better with more testing and verification, not through human code review, but through usage and testing.
Who drives usage and testing today? Who takes user feedback from the "usage and testing" and translate it into something that The Machine can use for improvements? Humans do. There is no agentic harness for managing at the level of the product itself, and I'm not convinced that there ever will be, because it's a fundamentally political concern. And not the low-stakes intra-team kind like tabs vs. spaces - the high-stakes, do-we-close-the-deal-or-not kind. Even if agents hypothetically could handle that level of stakes - they simply lack the context to do so, and will continue to lack the context to do so, at least until we get AGI in a humanoid robotic form factor.
Software engineering isn't dead. As a separate field with a dedicated job title, it's arguably dying in a world where it becomes a table-stakes skillset for Product roles. It is simply cheaper to employ 2x Product Engineers at $300k/year each, armed with $200k/year each in tokens, than it is to staff out a team of eight Software Engineers at $150k/year each. And this is before a hypothetical crash in API token pricing, or agility benefits from aligning fewer humans.
It's happening slowly in smaller companies, and hasn't happened yet at scale because, while you can teach Product skills to most Software Engineers and can't teach Software skills to most Product folk, most big-cap executives haven't gotten this memo yet. But the economic pressures are there.