Automatic Coding

If you're learning to code in 2025, you've probably heard some version of: "Why bother? AI can just write code for you now." I get why this might make you question whether it's worth investing time in learning programming fundamentals. But here's what I've discovered after two years of coding alongside AI: this is actually the best time in history to become a programmer.

AI hasn't made programming obsolete - it's made it accessible to more people and opened up possibilities that didn't exist before. But you still need to learn the fundamentals. In fact, understanding code is more valuable now than ever, because AI amplifies what you know rather than replacing what you don't.

Why learning to code manually makes you better at coding automatically

Think of AI coding tools like having a incredibly skilled junior developer working with you. They can write code fast and handle routine tasks, but they need direction. They need someone who understands what good code looks like, knows when something's going wrong, and can make the big architectural decisions.

That someone is you.

When I sit down with Claude to build something, I'm not just typing prompts and hoping for the best. I'm having a technical conversation. I can look at the code it generates and immediately spot issues: "This function doesn't handle edge cases properly" or "This database design won't scale" or "We need to add error handling here."

But I can only do that because I spent years learning how code works. The AI makes me faster and more productive, but my foundational knowledge is what makes the collaboration effective.

I've watched people try to use AI coding tools without understanding programming, and they hit walls fast. They can't debug when things break. They can't tell good code from bad code. They can't break complex problems down into manageable pieces. The AI becomes a frustrating black box instead of a powerful collaborator.

The opportunity in front of you

Here's what's exciting: we're living through a massive expansion in what's possible to build. When development gets faster and cheaper, more ideas become worth pursuing. More companies need custom software. More problems become solvable with code.

I'm working on projects now that would have been too expensive or time-consuming to attempt just a few years ago. Solo developers are building applications that used to require entire teams. Startups are exploring ideas that would have needed massive upfront engineering investments.

This isn't theoretical - I'm seeing it everywhere. Small businesses that never considered custom software are now getting applications built for their specific needs. Creative people are building interactive art projects that blend code and design in ways that weren't feasible before. Researchers are prototyping complex simulations in days instead of months.

The constraint was never ideas or demand for software. It was the human effort required to build it. AI is removing that constraint, which means vastly more opportunities for people who understand how to build software.

What this means for you as a learner

If you're starting your programming journey now, you have advantages that previous generations didn't have:

You can learn faster: When you're stuck on a concept, AI can explain it in different ways until something clicks. It can generate examples, help you debug your code, and walk you through complex topics step by step. I wish I'd had this when I was learning.

You can experiment more boldly: Want to try building a web app but don't know how to set up a server? AI can help you get the infrastructure running while you focus on learning the core concepts. You can explore ideas without getting bogged down in setup and configuration.

You get immediate feedback: Instead of wondering if your code is good, you can ask AI to review it and explain what could be improved. It's like having a patient mentor available 24/7.

You can build real things sooner: Rather than spending months on toy problems, you can work on projects that actually interest you. The AI handles the boilerplate while you focus on the logic and problem-solving.

But here's the key: these advantages only work if you're actively learning the fundamentals. The AI enhances your understanding - it doesn't replace the need for understanding.

The learning path that works

The most effective approach I've seen is learning programming concepts manually, then immediately applying them with AI assistance on real projects. Learn how variables and functions work by writing simple programs by hand. Then use AI to help you build something more complex that uses those concepts.

Study data structures and algorithms the traditional way so you understand why they matter. Then let AI help you implement them in real applications while you focus on choosing the right approach for your specific problem.

Learn debugging by working through problems step by step on your own. Then use AI to help you debug more complex issues while applying those same systematic thinking skills.

The pattern is: learn the concept, understand why it matters, then use AI to scale up your application of that knowledge.

The mindset shift

Instead of thinking "AI will replace me," try thinking "AI will amplify me." Instead of worrying that coding fundamentals are becoming irrelevant, realize they're becoming more important as differentiators.

The developers who are thriving with AI tools aren't the ones trying to avoid learning programming - they're the ones who learned programming well and then figured out how to use AI to do more ambitious things faster.

There's never been a better time to start this journey. The tools are better, the learning resources are more accessible, and the opportunities are expanding rapidly. But you still need to put in the work to understand how software actually works.

Your competitive advantage

Here's something that might surprise you: many experienced developers are struggling to adapt to AI-assisted development. They're set in their ways, skeptical of the tools, or trying to use AI as a replacement for thinking rather than an aid to thinking.

As someone learning now, you can develop the right habits from the start. You can learn to collaborate effectively with AI while building strong fundamentals. You can become fluent in both manual coding and AI-assisted development in a way that many senior developers haven't yet achieved.

This gives you a unique advantage. Companies need developers who can work effectively with AI tools, but they also need developers who understand software deeply enough to make good decisions about what to build and how to build it.

The future belongs to programmers who combine strong technical fundamentals with fluency in AI-assisted development. That could be you.

Getting started

If you're convinced that learning to code is worth it (and I hope you are), here's my advice: start with the fundamentals, but don't be afraid to use AI tools as learning aids from day one. Learn Python or JavaScript the traditional way, but ask AI to explain concepts when you're confused. Build simple projects by hand, then use AI to help you tackle more ambitious ones.

Most importantly, remember that programming is fundamentally about problem-solving. AI can help you implement solutions faster, but you still need to develop the ability to break problems down, think systematically, and make good technical decisions.

The combination of strong fundamentals plus AI fluency is incredibly powerful. And right now, while the tools are new and many developers haven't figured out how to use them effectively, there's a window of opportunity for people who get this right from the beginning.

The best time to plant a tree was 20 years ago. The second-best time is now. The same applies to learning programming in the age of AI.