I’ve been watching the Vibe Coding hype cycle play out for over a year now, and honestly? I’m relieved it’s fading.
When it first dropped, my feed was flooded with “AI coding is the new era” posts. People were building full websites without writing a single line of code. Non-programmers shipped apps in two hours. It felt like magic.
But then people actually tried to work with it. And reality hit hard.
Why does the same bug break three times in a row? Why does fixing one thing break two others? Why does a 200-line project look beautiful, but a 2,000-line project turn into spaghetti within days? Why does the demo take 15 minutes, but shipping it takes two more weeks?
We’ve all been there. The honeymoon phase is officially over.
And I think that’s a great thing.
Because now the novelty seekers are moving on, and the people still here are the ones asking the harder questions: How does AI actually fit into real software engineering?
The Real Shift Happens After the Shiny Feeling Wears Off
A year ago, you’d tell an AI, “Build me a system,” and minutes later, you’d see a working UI, a database, clickable buttons. What used to take a developer a day now took 20 minutes.
But after a few months of using it on real projects, I realized something.
The same thing that makes Vibe Coding powerful is also what makes it dangerous: It makes building way too easy.
In the old world, you had to plan your data model, your module boundaries, your API design — because you were writing every line yourself. Mistakes were painful and expensive, so you thought ahead.
Now? You can just say, “Build me a system,” and the model starts typing. It’s so fast that you can skip the thinking part.
The problem shows up later — usually around the 70-80% mark. Suddenly, you can’t move forward. You don’t fully understand the code the AI wrote three weeks ago. You ask it to fix Bug A, it works, but Bug B pops up. You ask it to fix B, and it tweaks C to make it work, breaking something else in the process.
It feels like playing whack-a-mole with an enthusiastic intern who never sleeps.
This Isn’t New — Chip Design Has Been Here for Decades
I work in the semiconductor industry. We’ve been doing “Vibe Coding” for years — we just call it EDA (Electronic Design Automation).
Today’s chips have tens of billions of transistors. No engineer draws those by hand. We use synthesis tools to turn RTL into gate-level circuits. We use place-and-route tools to lay out logic on silicon. We use timing tools to check if signals arrive on time.
So from that perspective, the chip industry started automating decades ago.
But here’s the thing: we still need chip engineers.
Why? Because tools solve efficiency, not design. The hard problems haven’t gone anywhere. How do you partition the system? What performance targets do you set? How do you balance power and area? When something breaks, is it an architecture problem, a design flaw, or a manufacturing issue?
You still need a human to make those calls.
I see Vibe Coding the same way. The tools are getting better, but the fundamental engineering challenges remain.
From Toy to Tool — That’s Where We Are Right Now
The first few times you generate an app with AI, it feels magical. That’s the toy phase.
But a tool? A tool is boring. You don’t think about it. You just use it. A calculator is a tool. Nobody picks up a calculator and says, “Wow, this is amazing!” — but they’d never go back to doing math by hand.
AI coding will get there. Right now, people still say, “I used Claude to build this” or “Codex fixed that bug.” A few years from now, no one will say that. It’ll just be how you code.
What I’ve Learned After Using It for Real Work
Here’s where I’ve landed after a year of using Vibe Coding on actual projects:
Vibe Coding is incredibly good at taking you from 0 to 1. That first working version? It’s faster than anything I’ve ever used.
But 1 to N is still a serious engineering problem. Long-term maintainability, testing, deployment, and debugging — none of that disappears just because AI wrote the code.
And honestly, AI can make those problems worse because it generates code so fast. You end up with a massive codebase you don’t fully understand, and every fix introduces a new issue somewhere else.
That’s why the most effective way to use AI, in my experience, is to treat it like a junior engineer on your team:
- Start with the architecture.
- Break the task down into small, testable modules.
- Review every line of code the AI generates.
- Write tests. Run them.
- Document key decisions.
At that point, you’re not doing “Vibe Coding” anymore. You’re just doing software engineering — with a faster assistant.
The Irony: Beginners Get Excited, Experts Stay Cautious
One pattern I’ve noticed: people who don’t code get the most excited about Vibe Coding. Experienced engineers tend to be more cautious and deliberate.
But three years from now, who’s going to use it most effectively?
I’d bet on the engineers who already understand the fundamentals.
Because AI generates answers — but you still need to know what questions to ask. More importantly, you need to know if the answer is actually correct.
Someone who’s never touched a database can ask AI to build one. For a small project, that’s fine. But when you have 100,000 users, how do you handle backups? Access control? Concurrency? Disaster recovery?
Those problems don’t go away just because AI wrote the code. In fact, they can explode faster because the code was written faster.
Vibe Coding lowers the barrier to writing code. But it does not lower the barrier to engineering a system. That’s a crucial distinction.
What the Next Phase Looks Like
I think we’re moving toward a world where AI handles more and more of the implementation work. Engineers will spend less time writing code and more time on:
- Design
- Judgment
- Testing
- System thinking
That’s a good thing. Engineering should be about deciding how to build the building, not hauling every brick yourself.
So when people ask me if the Vibe Coding hype is dead, my honest answer is:
No — it’s just becoming normal. And that’s the real sign of success.
A Quick Word for Younger Engineers
I keep hearing students say, “If AI writes code, why should I learn data structures? Or operating systems? Or computer architecture?”
I think that’s backwards.
The better AI gets at executing, the more you need to get better at judgment. If a junior engineer’s main value used to be “I can write this function,” that’s no longer enough. AI can write it.
Your value now is: can you tell the AI what to write? Can you spot the problem in its output? Can you figure out why the system went down at 3 a.m.?
That’s where fundamentals matter more than ever.
This is exactly like what’s happening in chip design. Sure, AI will write RTL, generate verification code, and run timing optimizations. But someone who doesn’t understand digital circuits, computer architecture, or process limitations won’t become a chip architect just by using AI.
The real gap won’t be who uses AI and who doesn’t. It’ll be how much your existing expertise gets multiplied by the AI.
The Takeaway
So yes — the novelty is fading.
And no — I don’t think that’s a problem.
The real value of Vibe Coding isn’t in the demos or the hype. It’s in the boring, everyday work of building systems that last. That’s where we’re heading.
Next time you hear someone say Vibe Coding is dying, you can tell them this: the hype is dying. The adoption is just getting started.
