I think the biggest shift AI is forcing isn't from coding to prompting it's from implementation thinking to systems thinking.

As code generation becomes cheaper, the bottleneck moves upstream: defining boundaries, failure modes, observability, security, and operational guarantees. Those decisions determine whether the next million lines of AI-generated code become an asset or technical debt.

In a few years, I wouldn't be surprised if engineering maturity is measured less by "how much code your team writes" and more by how well your architecture constrains the code AI is allowed to generate. The best engineering organizations won't produce more code they'll produce systems where even imperfect code can't cause disproportionate damage. That's a much stronger definition of production-ready than simply "it works."