What Nobody Tells You About AI Coding

Nobody tells you that AI-augmented coding makes implementation skills abundant and strategic thinking priceless.

Last weekend, I experimented with a simple chatbot for my blog. I got it working in thirty minutes. Even just a few months ago, this was a nights and weekends endeavor, at best.

Well, mostly working. RAG pipeline, embeddings, and the whole thing connected and responding to questions about my posts—but with the occasional hallucination I hadn’t fixed yet.

Something felt different. Where was the part where I spent three hours debugging why the vector database wouldn’t talk to the frontend? Where was the documentation rabbit hole? The Stack Overflow shame spiral?

None of that happened.

It got me thinking about where we are today as engineers compared to a few months ago. A shift at the core of our practice is happening in real time.

I used to think good developers were fast typists who memorized syntax. Now they’re people who know what to build and recognize when it’s built right. The muscle memory that took years to develop? Less relevant every month.

I have a friend who writes everything from scratch and is proud to understand every line. Meanwhile, others ship releases in the time it takes him to set up authentication. In a way, we’re both right. My friend understands his codebase perfectly. I understand my users’ problems perfectly. Different skills for a different game.

I find I’m doing much more creative work now as well. When you’re not burned out from wrestling with dependencies and import statements, you have brain space for the interesting questions. What should this actually do? How should it feel to use? What problem are we solving? Does it matter?

Even yesterday, a newer developer asked if AI would make them obsolete. Wrong question. The right question is: what kind of developer do you want to be? The kind who can implement anything, or the kind who knows what’s worth implementing?

Both matter. But only one is getting scarcer.