What Is Vibe Coding? Why Your AI-Generated Code Is Probably Broken

Three weeks before my last launch, I ran a code review on the product I had been building with Cursor for two months.

I was not worried. The app worked. I had tested every flow. Users could sign up, connect their account, run the core feature. Everything behaved exactly as I intended.

The review found a critical authentication bypass on line 47. Anyone who knew the pattern could access any user account without a password. The AI had written it cleanly, confidently, and completely wrong.

That was the moment I understood what vibe coding actually is.

What is vibe coding?

Vibe coding is building software by describing what you want and letting an AI write the code. You focus on the product. The AI handles the implementation. Tools like Cursor, Copilot, Bolt, and Lovable have made this possible for millions of people who could not have built software any other way.

It is genuinely remarkable. A non-technical founder can ship a working SaaS product in a weekend. A developer can build in days what used to take weeks. The barrier to entry for software has collapsed.

But there is a gap nobody talks about at the beginning.

Why does AI-generated code break in production?

The AI is not making mistakes in the way a tired developer makes mistakes. It is producing code that matches the pattern of correct code without necessarily having the properties of correct code.

Think about how these models are trained. They learn from millions of code examples. The vast majority of those examples were written to demonstrate a concept, ship a tutorial, or solve a problem quickly. Not to survive a production environment with real users, real edge cases, and real attackers.

So the AI writes code that looks right, passes your tests, and works in your demo. And then it fails in the specific scenario nobody tested.

"The AI had written it cleanly, confidently, and completely wrong. That was the moment I understood what vibe coding actually is."

What are the most common failures in AI-generated code?

Silent failures. Your app calls an external API. The API goes down at 2am. The AI-generated code continues as if the call succeeded. No error. No log. No alert. Your users see broken data and you have no idea why.

Authentication errors. Auth flows are the most commonly AI-generated security feature and the most commonly flawed. The check that should happen on the server happens on the client. The token validation looks at the wrong field. The password reset flow has a bypass nobody noticed.

Calculation drift. You described your pricing logic in natural language. The AI modelled it from general patterns. Your actual business rules are subtly different. Every transaction is off by a small amount that compounds into a real problem over time.

N+1 database queries. The AI wrote a loop. Inside the loop it queries the database. For ten users this works. For ten thousand, your database buckles and your application stops responding.

Does this mean you should stop using AI coding tools?

No. The velocity is real. The capability is real. Going back to writing every line manually is not the answer.

The answer is adding the quality layer that catches what the AI misses. Not instead of AI. After it. Before production.

A 30-second review before you ship is the difference between finding the authentication bypass yourself and having a user find it for you.

Run a free review on your code before your next launch.