AI Coding: Efficiency Over Experience

We've reached a point where AI coding tools can generate a thousand lines of boilerplate in seconds, yet the actual satisfaction of solving a hard problem is vanishing. It's a strange paradox: our productivity is skyrocketing, but the "craft" of coding feels like it's being replaced by a cycle of prompt-and-review.

When I use tools like Claude Code or Cursor, the velocity is undeniable. I can scaffold a full feature without touching a documentation page. But there's a risk of falling into a "decadent" workflow where we stop thinking about architecture because the LLM just makes it "work." We get the result (the pleasure of a working feature) without the intellectual struggle (the pleasure of the process).

To avoid this, I've started forcing a "manual-first" constraint for core logic. I'll map out the state machine or the data flow on a whiteboard before letting an agent touch the codebase. This keeps the AI as a high-speed implementation tool rather than the primary architect.

The goal should be using an AI workflow to remove the drudgery, not the thinking. If we just lean on the generation without the deep dive, we aren't actually becoming better developers; we're just becoming better editors of AI-generated code.