Every beginner is quietly asking the same thing in 2026: why learn to code when the AI model can write it? The fear is fair, the entry path really ...
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I strongly agree. 😸
I think it's true that we don't need to spend much time manually typing every line of code anymore. AI can do that much faster, and in many cases, doing it all by hand feels like a waste of time.
But we still need to understand the code, and more importantly, be able to explain it.
Without solid fundamentals, we can't answer those questions or judge whether AI-generated code is actually correct.
In that sense, I feel like AI is stripping away the mechanical part of coding and making the core engineering skills understanding, reasoning, and judgment even more important.
Fully agree! Thanks for reading.
Reading about AI‑generated code taking down a payment service at 3 a.m. immediately made me want to leave a proper comment. Hahaha 😂. I’ve written quite a few articles about AI failures, and late‑night outages are by far the most common scenario, hahaha 😂.
This is an excellent, in‑depth piece. I’ve always believed AI cannot fully replace humans, because it truly has no soul. That is where we humans will always have the upper hand. Feelings arise from our soul, and self‑judgment grows from those feelings. No matter how human‑like AI appears, it is only mechanical imitation. Especially with today’s AI training, models tend to pander heavily to whoever asks the questions. It’s easy to get misled if we are not careful. A dashboard full of green checkmarks looks great, yet all‑green metrics often signal the most dangerous moments.
For newcomers getting into IT these days: AI makes a great teacher, but you must build your own judgment and understanding. A tool remains just a tool — we use tools, rather than letting tools use us.
My thoughts are a little scattered 😂. English is not my native language either, so I hope the AI translation preserves my original meaning.🤣
The all-green dashboard line is a good one 😄 Nothing alerts on a check that passed because it never ran. And your English sounds brilliant, especially to me, as I’m not a native speaker either. 🤣🤣🤣
Haha, credit goes to AI translation — that’s where I think AI shines the most.🤣
Reality is, the day AI code can be trusted, we're all on the chopping block. Till that day, code review and process understanding is the important skill. Writing, knowing syntax, etc. All the things a linter and compiler throw errors for... Those are worthless. No seriously, not even worth the time it takes to open the page of the book, let alone read it. You take 2 min to write a loop, AI does it in 20 seconds. The difference is the AI's harness mandates it has proper error handling, you dont.
That's the reality of the AI-enhanced workspace. You either fit in, or you prep your cv for a different occupation.
So what is worth learning? Patterns... Shockingly, the thing AI is literally designed for, is it's worst performing skill. It knows what a good pattern looks like, writes the loop, but it does so in isolation, it doesnt recognize that its loop is unsafe in the broader scheme of things. So your job is to recognize failure patterns before the AI writes the tests to 'prove' it's good. That way you dont waste hours of your colleagues' time finding the bug that never surfaces.
Second skill, performance benchmarking granularity. Often times with AI code, the best way to find a bug, is to look at latencies that dont make sense... Eg. a rule check executing sub nanosecond, not quite possible is it? So flag it, it's outlier data that doesnt make sense, so you should investigate. It also lets you spot areas that take too long... Eg. a db call that takes 20 seconds, probably means it's unindexed and you can make it better. These are things AI 'might' pick up on, but it often slips through, because it doesnt have the context that numbers that fast are impossible with current hardware.
Third skill, reasoning with AI. You need to be able to explain to it, why it's wrong and direct it towards fixing it. Lets say you're in unchartered waters, like writing a LLM harness and a custom quantization method (it happens 🙄), but here's the thing, if you dont know what to look for, how do you spot the error? You look at outputs and you explain what it does, vs what is expected and theorize what's a stepping stone in the right direction. So even if you have no clue what you're doing in context, you can shrink the problem down to primitives you do understand and can direct it. That's how you 'wing it', or in older days 'paste and pray' from stackoverflow.
The rest, is honestly just procedural. If it wasnt so expensive, all the rest would be in your CI tests in git on every push.
That about sums up what I've experienced are the core skills of a developer in 2026. It's not over-reliance on AI, it's understanding where your place is, when your tools are smarter than you are. Your only bargaining chips are context, vision and expectations, now turn those into an actionable plan in a few prompts. That's what's expected of you today, because most employers trust Claude more than they trust you.
Agreed. I write small apps, and I handle null safety by instantiating at declaration because any modern calculator has more memory than my app would ever need.
but AI will write null safety into instantiated members anyway.
knowing how to tell it not to, and knowing how to ask it for review for null safety without bloating your code, is the valuable tool.
Prompt writing, code review, Agentic tooling...along with enough knowledge of programming to actually know what you're looking at.
I just want to add a little tip for beginners, if there are any here who are reading this. AI can write the code but understanding it is your real advantage! And it is true: Never accept code you cannot explain, because you'll have to take responsibility for this code, and it's better to understand it 😁
I once heard someone on our team say that if you're writing code and don't understand what you're writing, you'd probably be better off being replaced by an AI assistant. 😅
I like the vending machine vs tutor comparison. If you just ask for code and paste whatever comes back, you can probably build a lot without actually learning much. But if you ask questions, and really try to figure out why something works, and actually debug things when they go wrong, AI can be a really good learning tool.
I still think syntax and being comfortable with a language are important though, just in a different way now. Memorizing every little thing matters a lot less, but being familiar enough with a language to read code quickly, recognize patterns, and notice when something looks weird is still really valuable.
But yeah, overall I completely agree. Learning to code still matters. What knowing how to code means is just changing.
So looks like that maybe we need to learn how to use one model against another. Maybe management skills will be more important to select proper solution ?
Feels like it's heading there. Where it actually lands, no clue 😄
the typing/verification split maps well onto what's happening in code review. as AI output volume goes up, the people who read generated code critically become the bottleneck, not the people who produce it. reading critically requires the same underlying model of how software works that you'd build by learning to write from scratch.
so the argument for learning to code shifts from 'you'll write more' to 'you need to understand what you're approving.' whether that shows up as a market premium before the junior market recovers is the part I'm less certain about.
what's your read on timelines — is this a brief blip or a structural permanent narrowing of the entry tier?
Fully agree. I'm not much beyond beginner level coding, but even that little bit has made using AI tools much better than if I knew nothing. Null safety, modularity, inheritance....AI will do what you tell it, and if you don't know what to tell it it will bloat and misform a lot of things.
I had an application that used lists heavily. It wanted to write code for every single array. If I didn't know what I know, creating a reusable list container that does add/remove/get on any supplied list wouldn't have even occurred to me, and I'd have about 10,000 more lines of code right now.