How Much Coding Do You Actually Need in the AI Era?

I keep coming back to this question: is it still worth learning to code when you can get so much done without writing everything yourself?

You don't need to spend months memorizing Python syntax before building something. Pick a project, start working on it, and learn what you need along the way.

But there's a catch. You need to understand enough to know when something isn't right.

For example, imagine you're building an API that returns products from a database. Everything works fine with a few records, but once the database grows, the endpoint becomes painfully slow.

If you understand SQL queries, indexing, and pagination, you have somewhere to start. Otherwise, you might spend hours changing code without ever finding the actual problem.

That's why I wouldn't skip the fundamentals.

Learn how variables, functions, loops, data structures, APIs, and errors work. Get comfortable reading code, running tests, and figuring out why something broke.

You don't have to know everything before starting a project. In fact, working on real projects is probably one of the best ways to learn.

And there's no reason to stop at the basics if you're building more complex systems. The more you understand about databases, architecture, and how different parts of an application fit together, the better decisions you can make.

So, how much coding knowledge is enough?

Enough to understand what you're building, recognize when something is wrong, and figure out what to do next.

You can always learn more when you need it.

What matters is knowing more than just what to ask for. You need to understand what you're getting back.

Where would you draw the line? Are the fundamentals enough, or is deeper programming knowledge still essential?