The title is provocative but there's truth to it. The distinction between "vibing" with AI tools and actually controlling the output is crucial for production code.

I've seen this with code generation tools - developers who treat AI suggestions as magic often struggle when the output doesn't work or introduces subtle bugs. The professionals who succeed are those who understand what the AI is doing, validate the output rigorously, and maintain clear mental models of their system.

This becomes especially important for code quality and technical debt. If you're just accepting AI-generated code without understanding architectural implications, you're building a maintenance nightmare. Control means being able to reason about tradeoffs, not just getting something that "works" in the moment.