Vibe Coding Is a Trap

The green checkmark feels like the finish line. It isn't. It's the start of the part you skipped — the part where you actually understand what you just shipped.

There's a name for the way most people use AI to write code now: vibe coding. You ask the AI for a feature, it hands you code, the tests go green, you ship. What you don't do anywhere in there is read it. You don't trace what it assumed, you don't poke the edges, you don't ask whether it solved the problem the way you'd solve it given the rest of your system. You shipped on a vibe.

I'm borrowing the term from a sharp ten-minute breakdown that names the trap well: treating surface signals — "it runs," "tests are green" — as proof the code is good. It almost never is.

Why this matters

You don't pay for vibe coding today. You pay for it six months from now, when half the system is made of pieces nobody understands and the next bug takes three days to find instead of three hours.

There's an old line in software: debugging is twice as hard as writing the code in the first place. So if you ship code you didn't understand when you wrote it, debugging it later isn't twice as hard — it's basically a rebuild, under a deadline, at the worst possible time. That's the real bill. It just shows up late.

What it actually looks like

You don't have to be new at this to fall into it. The patterns I keep seeing:

• The "it runs" checkpoint. The model gives you a function, you wire it in, the server boots, you move on. Nobody asked: what happens when this runs a thousand times a minute? What if the input is empty? What if the user's on a bad connection?
• The "tests pass" checkpoint. The tests pass because they test the happy path the same model wrote. That tells you the model agreed with itself. It doesn't tell you the code is right.
• The quiet escape hatch. The model wrapped something in a try/catch and swallowed an error you never saw. Your logs stay calm. Right up until they don't.
• The clever-but-wrong shortcut. The model picked a pattern that "works" but fights the rest of your architecture. Nothing breaks today. A year from now three parts of the system are pulling three directions because that one inconsistency compounded.

None of this is the AI's fault. It gave you exactly what you asked for. You just didn't ask carefully, and you didn't check.

Takeaway: shipping AI-written code you don't understand is borrowing against your future debugging hours, at a brutal interest rate.

What the people who keep winning do instead

The folks getting durable results out of AI aren't using a secret model. They're just slower at the top and faster at the bottom. The shape of it:

1. Understand the problem before you open the chat. Sit with it. Sketch it. Decide what "done" means and what the real constraints are. The AI will not do this part for you — and if you let it try, it guesses.
2. Use AI for the boilerplate, the exploration, the options. "Show me three ways to build this." Read all three. Pick one. The point of asking for three isn't variety — it's that choosing forces you to think about the trade-offs.
3. Own the core. The data model, the failure modes, the security-sensitive paths — those are yours. Let AI draft, then rewrite until you can defend every line.
4. Test what the AI didn't think of. Edge cases, error paths, load, weird input. The model covers the obvious. You cover the rest.
5. Read it like you'd review a teammate's work. Out loud if it helps. The second you catch yourself saying "I think this is fine" without being able to say why, you're back to vibe coding.

Takeaway: the value of AI isn't replacing your thinking — it's compressing the work that doesn't need thinking, so you can put more of yourself into the work that does.

From my own bench

I do almost all my real building with AI in the loop now — voice assistants, retrieval agents, multi-agent setups running on my own hardware. So I'm not preaching from the outside. I'm telling you where I personally slip.

When I'm tired, I'll accept code that "looks right" and skim a couple of edge cases instead of running them. Every single time I've done that, it came back on me — and usually not in production. It came back in the next prompt, when the agent built on the fuzzy assumption I let slide, and now I had two tangles to undo instead of one. The cleanup always cost more than the careful read would have.

So now I make myself do one small thing before I accept anything: say what the code does in one plain sentence, in my own words. If I can't, that's my signal to slow down. Simple, and it's saved me more time than any tool.

Try it today

Step	What you do	Why it pays off
1. Read every AI-written file before you commit it	Describe what it does in your own words, out loud or on paper.	If you can't describe it, you can't debug it later. Costs minutes now, saves days later.
2. Ask for three approaches, not one	"Give me three ways to build this, with the trade-offs." Then pick one on purpose.	Puts you back in the decision seat. The model can't choose for you — three options remind you of that.
3. Write one edge-case test by hand for every AI feature	Not the happy-path test the model made — the ugly one. Empty input, network drop, race condition.	The model tests what it built. You test what it missed.

Where people get burned

• Treating "tests pass" as the finish line. The model wrote those tests. Fix: write at least one yourself — the kind the model would've skipped.
• Skipping the read because the diff is big. Big diffs hide more, not less. Fix: if you can't read it, it's too big — make the model ship smaller pieces.
• Letting AI design the architecture. Implementation is fair game for AI. Architecture is yours. Fix: sketch the structure before any agent sees the problem.
• Confusing speed-to-keystroke with speed-to-stable. Idea-to-running is fast and it feels great. Idea-to-stable is the number that actually matters. Fix: measure your real cycle time, not the demo.

Worth a look, and a question

• A tool habit: most AI coding tools (Claude Code, Copilot, Cursor) have a review or chat mode that's separate from autocomplete. Use it. Talking to the model about its own code surfaces gaps faster than reading alone.
• A ten-minute watch: the breakdown that named the trap. Pairs well with my last post on why more agents don't make you more right.
• A question to sit with: of the code I shipped this month, how much could I explain to someone else without going back to read it again?

The bottom line

Vibe coding feels like progress because keystroke-to-feature is short. The cycle that actually matters — keystroke to stable code you still understand six months from now — gets longer every time you trade understanding for speed.

The people getting durably good results from AI aren't running a better model than you. They just refuse to skip the part where they think.

— Dru Edwards