AI might promise speed and productivity. But it comes with serious issues.
The other day I found a senior coder quitting AI coding after finding out that:
- AI was like a sloppy coder who bypassed tests and wrote bad code
- AI had stolen all the joy of solving problems from him
After experimenting with AI, I realized I was becoming lazier than usual. Since then, I've set simple rules to avoid losing my skills.
But those aren't the biggest problems.
A failed experiment revealed a more serious problem
Recently I found out about the experiment of a team of coders that revealed a deeper problem. This one,
After trying to write a feature at work only by prompting, they realized:
[Even when AI is capable of writing all of our code], a huge issue remains: I lose my mental model of the codebase.
...
Until I can trust the AI completely, I need to keep my own mental model alive. Otherwise, every time I need to do something myself feels like joining a new company.
It takes time to get familiar with a complex codebase.
At past jobs, it took me about a year to feel confident. Of course, your mileage may vary.
But when that happens, you feel like driving through familiar streets with only one hand on the wheel:
- You know the architecture, the folder structure, and even a rough sketch of code blocks.
- You know how modules connect and what to touch or avoid.
- You can even remember file and function names.
You've built the mental models and gained all the context. Without them, you feel like walking into a dark room.
When AI writes our code, it's stealing the context and the feeling of knowing a codebase like the palm of your hand.
Use AI if you want, but be the one dictating what to do.
Draw the boundaries of the solution, then let AI fill in the details.
Be the pilot and let AI be your copilot.
When AI can handle syntax, it's time to work on skills it can't, like collaboration, clear communication, and problem-solving. That's why I wrote Street-Smart Coding, the roadmap I wish I had on my journey from junior to senior.