Programming has never been particularly easy.
You write something, run it, and it breaks.
You fix it, and something else breaks.
You spend twenty minutes looking at the same line of code before realizing that you forgot a single character.
It can be frustrating.
But that frustration is also part of learning.
Now, AI can remove a lot of it.
And I'm not sure that's always a good thing.
The easiest way to write code
Since ChatGPT became popular, programming has changed considerably.
If you don't remember how to use a library, you can ask.
If you don't understand an error, you can paste it into a chatbot.
If you don't know how to implement something, you can describe what you want and get a working example.
For programmers, this is obviously useful.
For programming students, it can be even more useful.
When I started university, ChatGPT was already part of the programming experience. It was an incredibly convenient tool. Whenever I got stuck, I could ask for help instead of spending a long time searching through documentation or trying different solutions.
And eventually, there was a period where I relied on it too much.
A lot of the code I was writing was generated with AI. It made everything faster, but I started noticing something that I didn't expect.
I was becoming less comfortable with small problems.
Things like syntax errors, for example, became more annoying than they should have been. When you constantly have an AI writing or correcting your code, you don't necessarily get the same practice of making mistakes yourself.
The code was getting better.
My ability to write it from scratch wasn't always improving at the same speed.
That made me realize something: making programming easier can sometimes make learning programming harder.
The struggle actually matters
When you're learning to program, mistakes are not just obstacles.
They are feedback.
You forget the syntax of something, make a mistake, see the error, and eventually remember it.
You don't understand why a loop behaves differently than expected, so you investigate it.
You try three approaches that don't work before finding one that does.
Those experiences stay with you.
AI can skip many of those steps.
Instead of struggling with an error, you can ask AI to explain it.
Instead of trying to design an algorithm, you can ask AI to create one.
Instead of remembering syntax, you can generate the code and move on.
That's fantastic when the goal is productivity.
But learning and productivity are not always the same thing.
Sometimes the part we want to eliminate is exactly the part that teaches us.
This doesn't mean AI is bad for programmers
It would be unfair to blame AI for this.
Programmers have always looked for ways to make their work easier.
We use libraries instead of writing everything from scratch.
We use frameworks.
We use autocomplete.
We search documentation and Stack Overflow.
We reuse code all the time.
The goal of programming has never been to suffer as much as possible while writing code.
The difference is that AI can now do something much closer to the thinking process itself.
And that's where things become interesting.
A library saves you from writing something yourself.
AI can potentially save you from figuring out how to write it at all.
There is a big difference between those two things.
The next step could be even bigger
And we're probably still at the beginning.
With the next generation of AI models, such as GPT Astra 6, programming assistance could move much further.
Instead of asking an AI to write a function, you might give it a feature and let it work through the entire process.
It could understand the codebase, modify several files, run tests, find problems, fix them, and try again.
At that point, typing code might become one of the least important parts of software development.
That could be incredibly productive.
But it also raises an uncomfortable question:
If AI does more of the programming process, how do we make sure programmers still understand programming?
The fundamentals become more important, not less
It might seem like AI makes learning the fundamentals unnecessary.
I think the opposite is true.
The better AI becomes at writing code, the more important it becomes to understand what that code is doing.
Otherwise, you can end up in a strange situation where your AI-generated program works, but you don't really know why.
And that becomes a problem the moment something goes wrong.
You can't properly debug code you don't understand.
You can't judge whether an implementation is unnecessarily complicated if you don't understand the alternatives.
And you can't tell whether an AI solution is good just because it compiles.
This is probably why the advice to "learn the fundamentals" isn't going away anytime soon.
The fundamentals may actually become the thing that separates someone who uses AI to program from someone who simply asks AI for programs.
Maybe we don't need to write everything anymore
I don't think the answer is to stop using AI.
That wouldn't make much sense.
If a tool can save programmers hours of repetitive work, we should probably use it.
The important part is knowing what we are giving up in exchange for that convenience.
If I'm working on a real project and AI can generate some boilerplate code in seconds, great.
If I'm learning a new concept and AI immediately solves the entire problem for me, maybe I should stop and try it myself first.
The difference isn't really between using AI and not using AI.
It's between using AI to make yourself more capable and using AI so that you don't have to become capable in the first place.
And I think that distinction is going to become increasingly important.
Programming was never just about writing code
Maybe that's the biggest lesson here.
Programming isn't valuable simply because you can type code into a computer.
It's valuable because you can understand a problem, break it down, reason about possible solutions, recognize when something is wrong, and decide what should happen next.
AI can help with all of that.
But if we let it do everything, we might slowly lose the opportunity to practice those skills ourselves.
When I look back at how I used AI when I first started programming, I don't regret using it.
It helped me a lot.
But I also wouldn't want to go back to a point where I couldn't solve a problem without asking it first.
Because sometimes, the struggle isn't getting in the way of learning.
The struggle is the learning.
And as AI gets better, knowing when to let it help — and when to put it aside — might become one of the most important skills a programmer can have.
And do you think coding has become easier🤔?
Top comments (1)
You've touched on a pivotal issue regarding the balance between productivity and learning in programming, especially with the rise of AI tools. The notion that AI can streamline the coding process while potentially hindering the troubleshooting skills that come from hands-on experience is an important conversation for the community. It might be beneficial to integrate structured learning paths that utilize AI for assistance, while still emphasizing problem-solving and debugging exercises that reinforce foundational skills. If you're exploring ways to enhance this balance in your project, I’d be open to discussing a paid collaboration to help develop those educational tools. What are your thoughts on incorporating such a framework?