Should You Learn to Code in 2026?

There is a debate happening online right now about whether people should bother learning to code. AI writes code. AI debugs code. AI deploys code. So why would anyone spend months learning something a model can do in seconds?

I think the answer is yes. But not for the reasons most people argue.

You don't learn to code so you can compete with AI at writing functions. You learn to code so you can direct AI effectively, understand where it falls short, and know when the thing it just built is going to fall over in production. You learn system design so you can make architectural decisions an LLM will never make for you. You learn test driven development and QA loops so you can actually verify what you ship. You build the mental model that turns you from someone who prompts into someone who builds.

That mental model doesn't come from watching. It comes from doing. And I know because I tried both.

What didn't work

I'm self taught. No CS degree, no bootcamp. Four years ago I had never written a line of code.

I started where everyone starts. YouTube tutorials. Watched someone build a todo app, followed along, typed what they typed. It felt productive in the moment. But I wasn't learning. I was copying. The second I closed the video and tried to build something on my own, I had nothing. I couldn't translate what I'd watched into anything original because I'd never actually thought through the problems myself.

Static tutorials have this fundamental issue. They give you the answer before you've struggled with the question. You type along, it works, and you move on with the illusion that you understood it. A week later you can't remember any of it.

What actually stuck

The first thing that worked for me was Angela Yu's Web Development Bootcamp on Udemy. What made it different was that she actually made you build things. Not just follow along. The exercises forced you to solve problems before showing you the solution, and that tiny shift made all the difference.

But the real learning started when I built something for real.

I built RocketTags into an actual business. Real users, real payments, real production errors at inconvenient hours. That is where I learned to code. Not from a curriculum. From running into a bug that broke checkout for paying customers and having to figure out what went wrong with nothing but stack traces and determination.

Every error I hit forced me to understand something I'd been skating over. Every debugging session built the kind of intuition that no tutorial could give me. I learned more in my first month of real production issues than I did in six months of courses.

That pattern held. The biggest gains always came from building with real stakes. Courses gave me vocabulary. Building gave me understanding.

How I learn now

Today I ship production Go services and run multiple Claude Code agents in parallel to build software. AI tools made that possible. But I still learn every day, and the process looks different than it used to.

I still build things. That never stopped being the best way to learn. But I supplement with books that go deep on the stuff I encounter in practice. Designing Data-Intensive Applications by Martin Kleppmann gave me the systems thinking I was missing. The Go Programming Language by Donovan and Kernighan grounded my understanding of the language I work in every day. I still take Udemy courses when I want structured exposure to something new.

But the newest piece is something I built for myself.

When you're building with AI agents, code shows up fast. Entire files, new patterns, architectural decisions. It's easy to accept it all and keep moving. But I noticed myself doing exactly what I warn others about: shipping code I didn't fully understand. The same comprehension debt I write about was accumulating in my own work.

So I built Pear. It sits in my terminal, watches the code being produced, and teaches me the concepts behind what just happened. It acts as a circuit breaker. When I'm about to accept something I don't fully understand, Pear flags the pattern and explains it in context. It forces me to intentionally engage with the code being produced by agents instead of blindly accepting it.

The real answer

Should you learn to code in 2026? Yes. But the way you learn has to change.

Passive tutorials won't cut it. Copying code from a video won't build the mental model you need. What works is building real things, running into real problems, and engaging with the code your tools produce rather than just accepting it.

AI didn't make coding knowledge less valuable. It made it more valuable. Because now you're not just writing code. You're directing agents that write code. And the difference between someone who can do that well and someone who can't is exactly the understanding that comes from learning to code in the first place.

The tools changed. The learning didn't.