“Vibe Coding”: The Illusion & The Reality

At a recent AI coding event, I watched a non-technical founder proudly say: “I built my entire startup with Cursor. I don’t even know coding.” Five minutes later, I overheard a senior engineer mutter: “Great. Now any guy can claim to be a founder and ship an MVP.”

Two groups in the same room, looking at the same future: one ecstatic, the other quietly bitter.
The way I look at it: AI is lowering the floor, but it’s raising the ceiling even faster.

And the people who understand what AI can replace, and what it absolutely cannot, will define the next decade of engineering.

What “Vibe Coding” Actually Replaces

Let’s give credit where it’s due: AI genuinely empowers non-technical founders. It can now:

• Generate a UI

• Create CRUD backends

• Spin up auth

• Integrate with APIs

• Produce demo-level prototypes

• Build an MVP without knowing what a pointer is

That used to require weeks of engineering time.
Today it’s a prompt away. So yes: the barrier to starting is drastically lower.
We should celebrate that.

More people experimenting → more ideas → more innovation.

But the hype stops right here.

What AI Will Never Replace

The moment a product gets real and innovative:
traffic, load, dependencies, users, edge cases, failures
vibe coding collapses instantly.

AI cannot:

• design scalable architectures

• understand invariants or failure domains

• reason about production constraints

• foresee cache misses, GC pressure, or lock contention

• prevent consistency violations in distributed systems

• evaluate long-term maintainability

• understand product tradeoffs or organizational constraints

AI can explain what’s already built.
AI cannot design something that will hold up six months later.

This is not speculation.
This is physics, systems engineering, and experience.

The Truth: Coding Is Easy Now. Engineering Is Not.

A founder with no background can now generate thousands of lines of code.
But they cannot answer the questions that make or break real software:

• Will this scale?

• Will this explode under concurrency?

• Will this break invariants?

• Will this violate SLOs?

• Will this create a nightmare in on-call rotation?

• Will this be maintainable by a real team?

LLMs can produce possibilities.
Engineers produce reality.

Why Technical People Shouldn’t Get Bitter

If you’re a senior engineer watching AI collapse the barrier to writing code, resist the urge to feel threatened. Because here’s the twist:

The more code AI generates, the more valuable deep engineering judgment becomes.

AI increases:

• complexity

• velocity

• entropy

• architectural drift

• risk

And with that comes massive demand for engineers who understand:

• design

• constraints

• systems behavior

• scaling

• correctness

• simplicity

AI replaces typing.
It does not replace thinking.

And the people who can think better than AI can hallucinate will eat.

What Technical People Should Focus On Now

a. Master the parts AI is bad at

• Systems design

• Distributed systems

• Scalability

• Performance engineering

• Failure analysis

• Operational risk

• Architecture tradeoffs

• Simplicity

b. Stop competing with AI on “writing code”

Code is the byproduct.
Understanding is the asset.

c. Become an orchestrator of humans + agents

Future teams are hybrid teams.
Humans + models + automations.
The senior engineer becomes the conductor.

d. Lean into judgment

AI cannot distinguish:

• elegant vs overengineered

• scalable vs fragile

• pragmatic vs performative

• maintainable vs meltdown-in-12-months

You can. That is your moat.

Conclusion

AI will let anyone build software.
But only engineers will keep it alive.

The future doesn’t belong to people who can “vibe code.”
It belongs to the people who can see the failure paths AI can’t, design the systems AI won’t, and make the decisions AI doesn’t understand.

AI lowers the floor.
Engineering raises the ceiling.
And the people who know the difference will define the next era of technology.