The gap between "AI is a 90% solution" and "100% required for production" is enormous. In my bubble, AI-generated code is maybe 70% useful, often less. The remaining 30% isn't minor polish—it's:

Understanding system architecture constraints Handling edge cases AI doesn't know exist Debugging when AI-generated code breaks in production Knowing when AI's "solution" creates more problems than it solves

That last 30% is what separates engineers from prompt writers. And it's not getting smaller—if anything, it's growing as systems get more complex.