Yesterday, I saw it again — another tweet, another thread, another startup founder proudly sharing their “web app built in 10 minutes.”
They used v0, Bolt, Lovable, or one of the new wave of AI-powered builders.
And yes — it looks clean. It works. It validates an idea. It’s fast.
But then I scroll through the interface…
Blank space everywhere.
Layouts that feel like placeholders.
Features so minimal they barely qualify as “MVP.”
And I can’t help but think:
We’re confusing speed with substance.
The Illusion of Instant Engineering
Let’s be honest — these AI tools are incredible at taking you from 0 to 1.
You’re a non-technical founder? Great. You can now prototype something that looks like a product.
You’re a junior dev? Awesome. You can generate boilerplate, scaffold components, and skip Googling syntax.
But here’s the truth:
AI doesn’t replace software engineering. It amplifies it.
It’s like giving someone a power drill and saying, “Now you’re a carpenter.”
Well — you can make holes. Maybe even assemble a shelf.
But if you don’t know wood, joints, load-bearing walls — you’re not building a house.
The Myth of the No-Code Dream
I’ve seen founders get excited. They build a landing page with AI, connect a Stripe button, and call it a SaaS.
Then they hit a wall:
- “How do I handle user roles?”
- “Can I customize the auth flow?”
- “Why does the UI break on mobile?”
- “How do I scale this to 10k users?”
Suddenly, the magic vanishes.
The blank spaces aren’t just visual — they’re architectural.
These tools are optimized for idea validation, not product longevity.
They’re great for testing demand. Horrible for building systems.
And for non-technical people?
It’s not empowerment — it’s frustration disguised as freedom.
You’re handed the keys to a car with no engine.
AI as a Co-Pilot, Not the Pilot
Now, let’s talk about us — the engineers.
Tools like Cursor, Winsurf, GitHub Copilot — they’re changing how we work.
I use them every day.
They write my unit tests. Generate API routes. Suggest refactorings.
But here’s what they don’t do:
- They don’t understand your domain logic.
- They don’t debug race conditions in distributed systems.
- They don’t design scalable architectures.
- They don’t negotiate trade-offs between latency, cost, and maintainability.
Instead, they accelerate the fluent.
If you know your fundamentals — data structures, system design, testing principles — AI makes you blazing fast.
If you don’t? You’ll generate broken code faster than ever.
The Real Bottleneck Was Never Code
We used to think the bottleneck was writing code.
Now we know: it’s understanding context.
A few years ago, jumping into a new framework meant hours of tutorials, docs, trial and error.
Now? I drop into a Rust project, ask my AI, “How does ownership work here?” and get a concise explanation in context.
It’s not about typing less.
It’s about thinking faster.
But that only works if you’ve built the mental models.
AI can’t teach you when to use a cache, only how to implement one.
So What’s the Future?
The future isn’t AI replacing engineers.
It’s engineers who use AI outcompeting those who don’t.
The bar to start is lower than ever.
The bar to scale is higher than ever.
If you’re building something real — something that lasts, grows, evolves — you’ll hit the limits of “vibe coding” fast.
And when you do, you’ll need:
- Deep technical understanding
- Systemic thinking
- The patience to refactor, re-architect, re-think
No AI is going to do that for you.
Final Thought
AI didn’t make coding obsolete.
It made thinking more important.
The tools are getting smarter.
But the best engineers won’t be the ones who type the least.
They’ll be the ones who ask the best questions.
And that’s not something you can automate.