Why Vibe Coding Fails

The Call Always Sounds the Same

Someone built an application using AI. They aren't an engineer. They're a founder, a product person, a domain expert. They used ChatGPT, Claude, Copilot, or some combination, and they built something that works.

Then they call us because it stopped working. Or because it works unpredictably. Or because every attempt to add a feature breaks something else. Or because they got their first real traffic and the whole thing fell over.

The details vary. The pattern doesn't.

This is vibe coding: building software by prompting AI to generate code that you can't evaluate, debug, or reason about. It produces output that looks like software the way a stage set looks like a building. From the front, convincing. From any other angle, hollow. And just like a stage set, the moment someone tries to move in, the whole thing collapses.

Why It Feels Like It Works

Vibe coding is seductive because it delivers immediate, visible results. You describe what you want. Code appears. You run it. Something happens on screen. The feedback loop is tight, the dopamine is real, and the progress feels extraordinary.

This feeling is genuine. It's also the feeling of driving downhill with no brakes. You're covering ground fast. You just haven't hit the curve yet.

What you've built isn't a product. It's a demonstration. The distinction matters because demonstrations need to work once, under controlled conditions, for a sympathetic audience. Products need to work continuously, under adversarial conditions, for customers who'll find every edge case you didn't consider.

The gap between these two things isn't a gap of polish. It's a gap of engineering. And it's the gap that vibe coding can't cross.

The Anatomy of the Collapse

We've seen enough of these failures to map the progression with uncomfortable precision. We call it the vibe coding death spiral, and it follows the same three phases every time.

Phase one is euphoria. Features materialize from prompts. The founder ships a demo, shows investors, maybe even onboards early users. Everything works because the surface area is small and the usage is gentle. This phase produces a dangerous conviction: we don't need engineers.

Phase two is friction. New features start conflicting with old ones. Changes in one area produce unexpected behavior in another. The AI's suggestions become less reliable because the codebase has grown beyond what a single prompt can reason about. The founder responds by prompting harder, longer, more specifically. This sometimes works. It always makes the underlying problem worse. Every prompt that patches a symptom adds weight to the structure that's already buckling. The stage set now has load-bearing walls made of cardboard.

Phase three is crisis. Something breaks that can't be fixed by prompting. Data gets corrupted. A payment processes incorrectly. The application crashes under load. The founder opens the codebase and confronts a system they don't understand, built from layers of AI-generated code that even the AI can no longer reason about.

This progression isn't occasional. It's nearly universal. The variance is only in how long each phase lasts.

The Root Cause Isn't the AI

Blaming AI for vibe coding failures is like blaming a power saw for a collapsed deck. The tool cut exactly where it was told. The problem is that nobody with structural engineering knowledge was directing it.

AI generates code that satisfies the literal request. If you ask for a login page, you get a login page. What you don't get -- because you didn't ask -- is rate limiting, session management, secure password storage, injection protection, or graceful failure handling. The invisible requirements that separate a login page from a secure authentication system outnumber the visible ones ten to one.

The expertise gap isn't about knowing the answers. It's about knowing which questions to ask. Senior engineers spend their careers building a mental model of everything that can go wrong. Vibe coders don't know what they don't know. AI faithfully builds exactly what they asked for, which is never enough.

What We Find When We Look Inside

The codebases we inherit from vibe coding projects share consistent characteristics. We've started calling the pattern the layer cake: each prompt generates a self-contained layer of code, and none of the layers were designed to work together.

No separation of concerns. Business logic, data access, presentation, and configuration are interleaved in ways that make any individual change unpredictable. Each prompt generated a complete solution. Nobody ensured those solutions composed into a coherent system.

No error handling. The code assumes every operation succeeds. Database queries return results. API calls respond. Network connections hold. In production, none of these assumptions are reliable. The result is silent failures: data that disappears, operations that half-complete, states that should be impossible but aren't.

No security model. Authentication exists as a UI feature but not as a system guarantee. The login form is there. The server-side enforcement isn't. This isn't a minor omission. It's a liability waiting to become a headline.

No observability. When something goes wrong, there's no way to determine what happened. No logging, no metrics, no tracing. At 3am, with customers locked out of their accounts, guessing isn't a strategy.

No tests. "It worked when I tried it" is the entirety of the QA process. Every change is a gamble, every deployment is a prayer, and every release is an invitation for regression.

These aren't edge cases. They're the default outcome of building software without engineering judgment.

The Alternative Isn't Slower

The most persistent myth about vibe coding is that it's fast and proper engineering is slow. This is backwards.

Vibe coding is fast the way building on sand is fast. The foundation goes down easy. The structure doesn't stay up.

Proper engineering with AI assistance is genuinely fast because every piece of work builds on a coherent foundation. The second feature is easier than the first because the patterns are established. The tenth feature is easier than the second because the conventions are mature. The architecture supports the work instead of fighting it.

And critically, you build once. You don't build, discover it doesn't work, then rebuild. The rewrite that vibe coding guarantees is the most expensive line item in startup engineering, and it's entirely preventable.

What Actually Works

The answer isn't to avoid AI. It's to ensure that AI is directed by someone who understands what production software requires.

Fractional senior technical leadership gives you this expertise without the overhead of a full-time executive hire. An experienced CTO sets the architecture, establishes the standards, and reviews the output -- whether it comes from AI, junior developers, or contractors.

AI then becomes what it should be: a force multiplier for expertise, not a substitute for it. Senior judgment plus AI throughput is genuinely powerful, and it delivers results that vibe coding can't match.

There are no shortcuts in engineering. There's doing it right, and there's paying to do it twice.

Dealing with the aftermath of vibe coding? Or building something new and want to avoid it entirely? Let's talk about the right way to build software with AI.

For a deeper look at how we pair expertise with AI tools, read about our approach to AI-amplified engineering.