Engineering teams adopting AI coding tools initially celebrate doubled velocity and faster feature shipping. But by month three, a troubling pattern emerges: the time needed to safely modify AI-generated code keeps climbing. While the generated code itself improves in quality, teams producing the most AI code increasingly request rewrites.

This paradox reveals a structural issue. AI-generated code tends to create monoliths where everything is coupled together, making every change require full comprehension. Function signatures don't document assumptions, and services call each other in specific orders without any explanation in the codebase. The result is that every modification demands deep review and complete understanding.

The solution lies in composability - building systems from components with well-defined boundaries, declared dependencies, and isolated testability. When AI generates code into a flat directory without structural constraints, it produces unstructured output regardless of model quality. Tools like Bit and Nx already provide structural feedback to human developers, and the next evolution is giving AI these same constraints during generation. The real productivity metric isn't how fast AI can generate code, but how quickly teams can ship to production and still maintain control over changes weeks later.