A few years ago, the roadmap felt clear.
Learn programming.
Build projects.
Practice algorithms.
Get hired.
Build a stable career.
That promise brought an entire generation into tech.
People stayed up until 2:00 a.m. debugging errors they barely understood. They watched the same tutorial three times because something just refused to click. They spent weekends building portfolio projects nobody asked for, hoping one day somebody would finally notice.
And honestly? For a while, the promise felt real.
Software engineering became one of the most recommended careers on the internet. Every platform repeated the same message:
“Learn to code. Your future self will thank you.”
So people listened.
They got computer science degrees.
They joined bootcamps.
They solved hundreds of LeetCode problems after work or school.
They sent hundreds of resumes into application portals that never responded.
And now...
The same people are opening LinkedIn every morning to another headline about AI replacing engineers, companies freezing hiring, or thousands of developers getting laid off.
At some point, almost every developer has quietly asked themselves the same question:
Was all of this even worth it?
The Fear Around AI Feels Very Real
We should stop pretending people are overreacting.
The anxiety in the tech industry right now is real.
You see someone open an AI coding assistant, describe an app in plain English, and suddenly a working prototype appears in minutes.
A few years ago, building that same thing might have taken days.
That changes how people think about software engineering.
It especially hits beginners hard.
Because when you see AI generating code instantly, it becomes easy to wonder whether all those years spent learning syntax, debugging, architecture, and frameworks are slowly becoming irrelevant.
And honestly, I understand why so many people feel discouraged.
The industry itself isn’t helping.
Every week, another company announces “AI-first restructuring” like it’s some futuristic badge of honor. Investors applaud. Executives write optimistic posts about productivity.
But behind those announcements are real developers trying to figure out what happened to the career path they were told was safe.
And here’s the part nobody says loudly enough:
A lot of these layoffs are not purely caused by AI.
Many companies massively overhired during the pandemic. Money was cheap, growth expectations were unrealistic, and engineering teams expanded faster than they probably should have.
Now the market changed.
So instead of saying:
“We made bad hiring decisions.”
…it sounds much better to say:
“We are restructuring around AI innovation.”
AI became part strategy, part narrative, and part shield for decisions companies were already heading toward.
That doesn’t make the fear less painful for developers. But it does change the conversation.
The Problem With “Vibe Coding”
There’s another topic that keeps coming up lately: vibe coding.
And to be fair, some of it is genuinely impressive.
People with little technical experience can now build surprisingly useful things using tools like AI coding assistants, no-code platforms, and prompt-based workflows.
That speed is real.
But there’s also something dangerous hidden underneath the excitement.
When someone doesn’t truly understand the code they generated, they also don’t understand when the code is failing.
And software rarely breaks at the perfect moment.
It breaks at 2:13 a.m. in production.
It breaks when users are losing data.
It breaks when systems behave differently under real traffic.
It breaks when edge cases appear that nobody thought about during the demo.
That’s where experience matters.
Because the hardest part of engineering was never just typing code into a file. The hard part is understanding systems deeply enough to debug them when reality stops matching expectations.
AI can accelerate development.
But acceleration without understanding creates a different kind of problem.
And eventually, companies will run into that reality.
Companies Might Be Creating a Bigger Problem
One thing that genuinely worries me is how many companies are slowing down junior hiring.
Every senior engineer people admire today was once a confused beginner.
They made mistakes in low-risk environments.
They asked bad questions.
They broke things.
They got mentored.
They slowly learned how real systems work.
That process takes years.
You cannot skip it with prompts.
If companies stop investing in junior developers because AI looks cheaper in the short term, they may create a massive experience gap later.
Because senior engineers don’t magically appear out of nowhere.
The industry still needs people who understand infrastructure, debugging, scalability, architecture, reliability, security, and long-term system design.
Those skills are built through experience, not generated instantly.
And I think some companies are going to realize that much later than they should.
So… Should You Still Keep Coding?
I think the answer depends on why you started in the first place.
If coding was only about chasing salaries, then yes, this moment probably feels terrifying.
But for a lot of people, that wasn’t the real reason.
Most developers remember a specific moment when programming suddenly became exciting.
Maybe it was a tiny Python script that finally worked.
Maybe it was a personal website you proudly showed your family.
Maybe it was automating something annoying and realizing:
“Wait… I can actually build things.”
That feeling matters more than people admit.
Because programming changes the way you think.
You learn how to approach overwhelming problems calmly.
You learn how to debug confusion instead of panicking inside it.
You learn how to break impossible-looking systems into smaller solvable pieces.
Those skills do not disappear because AI exists.
In fact, they become even more valuable.
Because the people who will thrive in the AI era are probably not the people who memorize syntax the fastest.
They’re the people who understand systems, context, tradeoffs, and problem-solving deeply enough to guide the tools correctly.
AI changes the workflow.
It does not eliminate the need for thinking.
The Future of Software Engineering Probably Looks Different
I do think software engineering is changing permanently.
Junior roles may evolve.
Interview expectations may shift.
The way we build products is already changing rapidly.
But I don’t think this means coding is dead.
I think it means shallow knowledge is becoming less valuable while deep understanding becomes more important.
The developers who survive long-term probably won’t be the ones competing with AI.
They’ll be the ones learning how to work with it while still understanding what’s happening underneath.
And honestly?
That has always been true in tech.
Every major shift changed the tools.
The internet changed development.
Cloud platforms changed development.
Open source changed development.
Frameworks changed development.
Now AI is changing development too.
But the people who kept learning usually adapted.
Maybe This Is the Real Skill
Maybe programming was never really about memorizing languages.
Maybe the real skill was learning how to stay curious when things stop making sense.
Learning how to sit with frustration long enough to solve something difficult.
Learning how to think clearly when systems become messy.
That mindset still matters.
Probably more than ever.
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Top comments (20)
"Should you still learn coding?" - of course you should, it's one of the foundational skills as a developer, even when you let AI generate most of the code - how can you judge whether the code AI generated is any good, if you've never written any yourself?
I understand and recognize that people's focus will shift somewhat, but "don't spend time learn coding anymore" (but only learn prompting, and 'architecture', etc) - that doesn't make any sense to me (but, that's also not what you said - you said "coding isn't dead" :-)
Yeah, I agree with you.
Even if AI writes a lot of the code, you still need that foundation to actually understand, evaluate, and trust what it produces; otherwise, you’re just guessing.
The shift feels more like how we code, not whether we should learn it at all.
You can also look at it this way. AI filters out weak engineers from the get go. They think AI will take their jobs. They will be scared off and that means more for us. The engineers that keep on upskilling, adapting, and learning deep will be rewarded. Patience and discipline is what's needed for this generation. Sadly, those two values are scarce nowadays (thanks to social media).
I totally agree with you about upskilling and adapting. That’s definitely what will matter long-term.
I also think deep problem-solving still matters more than ever.
Agree, but how do you acquire problem solving skills if you never start writing any code - by solving Sudoku puzzles, or playing chess? I agree that understanding architecture and so on is important, and will become even more important - but I think that learning how to code (and doing it) is still an important skill, which also helps to develop the other skills ...
Yeah, I agree with that.
As @leob said, you don’t really build problem-solving skills in a vacuum. Coding itself is still one of the best ways to develop them. Even with AI tools, actually writing and struggling through code is what helps you understand how systems work and how to think through problems properly.
So it’s not really coding vs problem-solving, coding is still a core way you develop problem-solving in the first place.
Spot on: "you don’t really build problem-solving skills in a vacuum. Coding itself is still one of the best ways to develop them" ...
I like the memes, gifs and your style of presenting!
Glad you liked it 😄
I try to keep it real (and a bit fun) so it doesn’t feel like another “serious post”.
Appreciate you reading it!
Most of my work is now a) specify what I want, b) AI makes a plan, c) I make modifications to the plan, d) AI implements, e) I make small changes and optimizations and review.
Most of the times it works well. 10% of the times I have to step in and do things myself. Those 10% are the most important
That’s a really accurate way to describe how a lot of workflows are evolving.
The interesting part is exactly what you said; that 10% where things break or need real judgment is often where the most value (and learning) sits. That’s usually where understanding the system still matters more than anything AI can generate.
Precisely.
Imagine letting AI handle everything related to production bugs in payment or banking systems. Hell yeah, then thousands of people could lose money or suddenly have incorrect balances in their bank accounts. 😃
Yeah 😅 that’s exactly where the “just let AI handle it” mindset starts hitting reality very quickly.
In high-stakes systems like payments or banking, understanding what the code is actually doing still matters a lot. One small mistake there can turn into a very expensive problem fast.
The systems of today were built to help people make fewer of the mistakes they used to make when using simpler, less constrained systems. Think C++ vs C, or JS/React vs JS/jQuery. AI doesn't make the same mistakes we do, so it doesn't need all those guardrails. It actually works better without them.
It takes us years to gain the skills to work with these systems; years that won't be available in a world where AI does the coding. Future coders will never have coded a React product from scratch, so they stand little chance of understanding the code AI produces using React in all its glory, or what's wrong when it breaks.
Modern systems are hugely complex and have inconsistencies that require work-arounds, such as needing virtual environments to avoid version incompatibilities. And too often the documentation for a library never got updated when the next version was released. Stack Overflow is full of answers that were once correct but are now completely invalid. We then accuse AI of hallucinating instead of asking why are we asking it to work with these systems when it would produce better results from something far simpler.
Maybe someone will eventually figure out a form of language that makes it both easier for AI to create with and people to validate. Is it too much to ask that we speak the same language?
I took a JS/React proof-of-concept mockup and got Claude Code to translate it into something based on vanilla JS and some home-made tools. No hallucinations, code that's readable by both man and machine and a product that does what was asked. So don't tell me all these advanced frameworks and techniques are needed by AI coders; in most cases they aren't. Worse than that, they actually get in the way of delivering good products.
Yeah that's interesting, I do see your point ...
the vibe coding section hit differently. shipped an AI generated RAG pipeline for a client last year — worked great in testing, fell apart under real query load because the chunking strategy the LLM picked was optimizing for demo scenarios, not production retrieval.
nobody on the team could debug it because nobody understood the retrieval logic. just "it worked when we prompted it."
the 10% Lars mentioned in the comments is exactly where i live now — that's where you either know the system or you're guessing at 2am.
curious if you think the experience gap compounds: does each generation of AI first devs have less capacity to teach the next?
Could't agree more, senior developers didn't come instantly, they were build by deep understanding of trial-error cycle and that is what AI did, bypassing repetitive trial-error