Everyone has an opinion on AI and coding right now. Some say it will replace developers. Others say it's just autocomplete on steroids. Here's mine — built from actually using it, not just talking about it.
The trap nobody warns you about
When you start using AI to code, the first feeling is power. You describe something, it generates it, you paste it in, it works. Incredible.
Then three days later you're debugging something you don't understand, in a file you didn't fully read, built on logic you never learned.
Vibe coding — describing what you want and accepting whatever comes out — is not development. It's outsourcing your thinking to a tool that doesn't understand your project.
The code runs. But you don't own it. And the moment something breaks, you have no idea where to start.
What AI actually is
An AI coding assistant is not a developer. It's a very fast, very well-read collaborator that has no context about your project, your constraints, or your goals — unless you give it that context explicitly.
It doesn't know:
- why you chose this architecture
- what that function is supposed to do in three months
- what tradeoffs you already decided against
You do. That's the difference. And that difference is everything.
The developer who uses AI well is the one who brings the thinking. The AI just helps execute it faster.
The minimum you need before prompting
Before you ask AI to help you build something, you need three things:
Know what you're building
Not just "a dashboard" — what data does it show, who reads it, what actions does it enable? The more precise your mental model, the better the output.
Know the concepts involved
If you're building an auth flow, you should understand sessions, tokens, and middleware — even at a surface level. AI can implement it, but you need to recognize when the implementation is wrong.
Know your stack
AI will confidently generate code for the wrong version of your framework, use a deprecated API, or suggest a library that conflicts with what you have. If you don't know your stack, you can't catch these mistakes.
A good rule: if you couldn't explain what the generated code does in plain English, don't commit it.
How I actually use it
Not as a generator. As an accelerator.
For understanding — when I hit a concept I don't fully grasp, I ask AI to explain it three different ways. Not to get the answer, but to build intuition faster than a documentation read alone.
For boilerplate — repetitive setup code, config files, type definitions. Stuff that's mechanical and well-defined. I review it, I don't just copy it.
For debugging — I describe the behavior, share the relevant code, and ask for hypotheses. I treat every answer as a lead to investigate, not a solution to apply blindly.
For refactoring — "here's this function, here's what it does, what are cleaner ways to write it?" Then I choose the one I understand best, not the most clever one.
What I refuse to delegate
Some things stay mine:
- Architecture decisions — how the app is structured, where data lives, how components communicate. AI has no stake in your codebase's long-term health. You do.
- Understanding a new concept — I'll use AI to accelerate learning, not skip it. There's a difference between "explain this to me" and "do this for me."
- Reading the output — every single line AI generates, I read. Not skim. Read. This is non-negotiable.
The bigger picture
The fear that AI will replace developers misses the point.
AI replaces the mechanical parts of the job — boilerplate, lookups, repetitive patterns. It doesn't replace judgment, architecture, debugging intuition, or the ability to understand a problem before solving it.
The developers most at risk are not the seniors who know too much. They're the juniors who use AI to avoid learning in the first place.
Use it to learn faster. Use it to move faster. But never use it to avoid thinking.
That part is still yours.