Still Your Car. Still Your Wrench.

I bought into the hype.
Or maybe I just misread it.

I saw AI coding tools spin up full-stack apps from prompts. Static sites. Hosting and CI/CD included.
It looked like magic — like I could stop coding and start prompting.

“This is it,” I thought. “This is the future. No more IDEs. No more debugging.”

Wrong.

The Tools Got Smarter — But the Work Didn't Disappear

What I’ve learned since then?

AI tools aren’t replacing the work. They’re just moving it around.

They’re brilliant as pair programmers. They scaffold things fast.
But they don’t understand context. They don’t follow your standards.
And they definitely don’t clean up their own mess.

You still need to:

• Know your tech stack
• Debug broken flows
• Manage architecture
• Own the integration points

You still have to code. Maybe not every line — but certainly every failure.

It Feels Like Inheriting Someone Else’s Codebase

Even when you write the prompts, it doesn’t feel like your code.

It feels like picking up a 10-year-old project from a dev who vanished:

• The folder structure doesn’t make sense
• Logic is buried in places you didn’t expect
• The naming conventions change halfway through
• You’re constantly asking: “Why is this even here?”

When something breaks — and it will — you’re not debugging your code.
You’re reverse-engineering something the AI stitched together.

The Mechanic Metaphor

Imagine this:

You ask a robot to build you a car.
You even used another robot to write the spec.
The car shows up. It runs. You’re impressed.

But then the check engine light comes on.

Now what?

You still have to open the hood.
You still need to know what subsystem failed.
You still need the wiring diagram.

Because when it’s broken, it’s your problem — not the AI’s.

What These Tools Actually Are

They’re not auto-pilots.
They’re not no-code platforms.
They’re accelerators — if you already know where you’re going.

Used well, they:

• Speed up your workflow
• Help you explore options
• Save you from boilerplate

Used naively, they:

• Create brittle foundations
• Hide bugs behind confident scaffolding
• Cost you time in rework and refactoring

How to Stay in Control

1. Scaffold, Then Own
   Let AI do the boring part. Then rework it until you actually understand it.
2. Set Standards Early
   Don’t let the AI guess. Guide it with structure and naming conventions from the start.
3. Stay in the Stack
   Know the framework. Know the CLI. Be ready to dive into the code when it fails.
4. Think Like a Tech Lead, Not a Prompt Engineer
   Treat the AI like a junior dev: helpful, fast, but not accountable. You’re still the architect.

What I See Coming Next

We’re already seeing the rise of a second market.

A new class of “vibe coders” — people who deeply understand how to work with AI tools — are packaging and selling their skills.
Not just the code, but the prompt patterns, workflows, and mental models needed to make the AI generate reliable, production-grade output.

In other words:

“Don’t prompt it yourself — I’ll do it for you. Or better yet, buy my pre-trained prompt stack that already works.”

This is already happening. And it makes sense.

Because the value isn’t just in writing code anymore. It’s in knowing how to get good code from the machine, and how to fix it when it goes sideways.

Expect to see more of this:

• Prebuilt prompt libraries
• AI-tuned boilerplate kits
• “Prompt Engineers for Hire” who are just devs with great taste and clean structure

It’s not no-code.
It’s not low-code.
It’s just new-code — and the people who thrive will be the ones who treat the AI as part of the stack, not a silver bullet.

Bottom Line

AI coding tools are here.
They’re powerful.
They’re fun.
They’re making me faster.

But they haven’t replaced the responsibility.

When the check engine light comes on, it’s still my job to open the hood.

And if you’re building anything that needs to last, scale, or run in production?
That’s your job too.