What Is Vibe Coding and Why It Breaks

Vibe coding is the practice of building software by describing what you want to an AI coding assistant — Cursor, Copilot, Windsurf, or similar — and accepting the generated code without deep review. While it gets you to an MVP fast, it creates technical debt that explodes later.

You built a working app in a weekend using AI. It demos well. Your users are signing up. Then Monday hits and you realize you have no idea how half the code works. You can't fix a bug without creating three more. You're scared to touch anything.

That's the vibe coding trap. And you're not alone.

What Is Vibe Coding?

Vibe coding is the practice of building software by describing what you want to an AI coding assistant — Cursor, Copilot, Windsurf, or similar — and accepting the generated code without deep review. The term was coined by Andrej Karpathy in early 2025, and it described a legitimate new workflow: let the AI handle the boilerplate, the plumbing, the tedious parts, while you steer the product direction.

The appeal is obvious. A solo founder with a product vision but limited backend experience can now ship a working API in a day. A startup can prototype ten features in the time it used to take to build two. The barrier to creating software dropped to nearly zero.

But there's a difference between generating code and engineering software. Vibe coding optimizes for the first and completely ignores the second.

Why Teams Keep Doing It Anyway

Let's be honest about the trade-off. Traditional software development is slow. Writing tests, documenting decisions, architecting modules, code reviewing — all of it takes time. When you're a solo founder or a two-person startup competing against well-funded teams, speed is survival.

Vibe coding gives you that speed. You can go from idea to deployed product in days. The AI handles the implementation details. You focus on what the product should do, not how the code should be structured.

This works. Until it doesn't.

The problem isn't that AI writes bad code. The problem is that AI writes code with no long-term intent. It solves the immediate prompt. It doesn't think about the module you'll need to add next quarter. It doesn't anticipate the edge case that will take down production at 2 AM. It doesn't care about the next developer who reads this file — which might be you, six months from now, when you've forgotten what everything does.

The 5 Failure Modes of Vibe-Coded Projects

After working with dozens of teams cleaning up AI-generated codebases, we see the same patterns repeating. Here are the five most common ways vibe-coded projects break.

Failure Mode 1: No Tests

AI coding assistants don't generate tests by default. They'll write tests if you ask, but the default workflow is: describe feature, get code, move on. This means you accumulate thousands of lines of untested code.

Without tests, every change is a gamble. You don't know what you broke until a user tells you. Refactoring becomes terrifying because you have no safety net. The codebase becomes fragile glass that everyone is afraid to touch.

We've seen projects with 20,000+ lines of code and zero automated tests. The founders didn't skip testing on purpose. They just never got around to it because the AI made it so easy to keep building.

Failure Mode 2: No Documentation

AI-generated code rarely includes meaningful comments or documentation. The code itself is the documentation — except nobody reads 40 files of auto-generated TypeScript to understand how authentication flows through the system.

When you need to onboard a new developer, they're lost. When you come back to a module after three months, you're lost. When something breaks and you need to understand the architecture to fix it, you're guessing.

Documentation isn't just nice-to-have. It's the difference between fixing a bug in 20 minutes and spending two days just understanding what the code is supposed to do before you can even start fixing it.

Failure Mode 3: Spaghetti Architecture

AI tools optimize for the current prompt. They don't maintain a coherent mental model of your system's architecture. The result is code that works but has no structure.

We see this constantly: business logic tangled into API handlers, database queries scattered across UI components, five different ways to do the same thing because each AI session started fresh. There's no separation of concerns, no consistent patterns, no clear module boundaries.

This "spaghetti architecture" makes every change risky. You can't modify one feature without potentially breaking another. Adding new features means copying and modifying existing code rather than extending clean abstractions. The codebase becomes a tangled web where every file depends on every other file.

Failure Mode 4: Hidden Dependencies

AI tools introduce dependencies freely. Need to parse a date? There's a library for that. Need to validate an email? There's a library for that. Need to generate a UUID? Believe it or not, there's a library for that.

A vibe-coded project can easily accumulate 200+ npm dependencies, many of which overlap in functionality, some of which are abandoned, and a few of which have known vulnerabilities. The AI doesn't check whether a dependency is well-maintained. It doesn't notice when two libraries do the same thing. It just reaches for whatever solves the immediate problem.

These hidden dependencies create maintenance nightmares. Security patches need to be tracked. Breaking changes in upstream packages can cascade through your system. Bundle sizes balloon. Build times increase. And when something breaks in a dependency, debugging is nearly impossible because you didn't choose these libraries — the AI did.

Failure Mode 5: Security Holes

This is the one that can actually hurt your users. AI-generated code frequently introduces security vulnerabilities:

• Hardcoded API keys and secrets in source files
• SQL injection vulnerabilities from string concatenation instead of parameterized queries
• Missing input validation on API endpoints
• Improper authentication checks or missing authorization guards
• Insecure session handling or token storage
• CORS misconfigurations that expose your API to cross-origin attacks

The AI generates code that works, but "works" and "is secure" are different things. Without security review, these vulnerabilities sit in production, potentially exposing user data.

Real Examples of What Breaks

Let's make this concrete. Here's what we see in real projects:

The Authentication Nightmare: A startup built their auth flow with three different AI sessions. Session one set up JWT tokens. Session two added OAuth. Session three layered session cookies on top. The result: three overlapping auth mechanisms, none fully secure, some users logged in via different paths with different token types. Fixing it required rebuilding the entire auth system. The Database Migration Disaster: An AI assistant generated database schema changes across multiple files without using a migration tool. The schema in the code didn't match production. New fields were referenced in code but didn't exist in the database. Users got 500 errors on every other feature. The Dependency Hell Project: A Next.js app accumulated 340 dependencies. Three had known CVEs. Two were abandoned. The build took 8 minutes. The node_modules folder was 1.2 GB. Nobody could explain why any of these dependencies were needed. The Silent Data Loss: An AI-generated data transformation function silently dropped records that didn't match the expected format instead of raising an error. Users lost data for weeks before anyone noticed.

Why "Just Add Tests Later" Doesn't Work

The most common advice for vibe-coded projects is simple: just add tests later. This sounds reasonable but it almost never works in practice.

Here's why. Tests are easiest to write when the code is designed to be testable. Vibe-coded code is not. It has tight coupling, unclear interfaces, side effects scattered everywhere, and no dependency injection. Writing tests for this code is like trying to put seatbelts in a car after it's already been built — theoretically possible, practically miserable.

More importantly, the teams generating vibe code are usually focused on shipping features. "Add tests later" becomes "add tests never" because there's always a more urgent feature to build. The technical debt compounds silently until the day the system falls over.

The Real Cost of Vibe Coding

Vibe coding doesn't save time. It defers time. You save hours in the beginning and pay for them with interest later.

We've seen the math play out repeatedly:

• A feature that takes 4 hours to vibe-code takes 40 hours to fix when it breaks in production
• A module that takes 2 days to generate takes 2 weeks to refactor when you need to extend it
• A project that takes 1 week to launch takes 3 months to stabilize

The compounding effect is brutal. Every new feature adds to the mess. Every fix creates new bugs. Every developer who joins the team takes longer to onboard. The project that was "almost done" six months ago is still "almost done" today.

What To Do Instead

This isn't an argument against AI coding tools. They're incredibly powerful. The argument is against undisciplined use.

You can use vibe coding effectively by treating it as one tool in your workflow rather than the entire workflow. Generate code with AI, then review it. Add tests as you go. Maintain documentation. Enforce architectural patterns. Review dependencies. Audit security.

Or, if your codebase is already a mess, get it cleaned up before it gets worse.

Learn about how to refactor AI-generated code safely and compare which AI coding tools produce the best output.

FAQ

Is vibe coding bad?

Vibe coding isn't inherently bad. It's a fast way to generate working code. The problem is when it's the only workflow — no tests, no reviews, no architecture decisions. Used alongside engineering discipline, AI code generation is a powerful accelerator.

Who is most at risk from vibe coding?

Solo founders, non-technical founders, and small teams without senior engineering oversight. These groups are most likely to ship vibe-coded code to production without the safety nets that prevent the failure modes described above.

How do I know if my codebase is in trouble?

Common warning signs: you're afraid to deploy changes, adding a new feature takes longer than it should, you can't explain your own architecture, you have more than 100 dependencies, or you have no automated tests. If three or more of these sound familiar, your codebase needs attention.

Can't I just use AI to fix AI-generated code?

Sometimes, but it's a gamble. AI fixing AI-generated code often introduces new patterns that are inconsistent with the existing code. Without human oversight and engineering judgment, you can end up with a worse mess. It's like using a bandaid to fix a broken bone.

When should I stop vibe coding and start engineering?

The moment your code goes to production and real users depend on it. Prototype with vibe coding all you want. But the transition from prototype to product requires engineering discipline: testing, documentation, security review, and architecture.

---

Your codebase doesn't have to stay a mess. Get a free vibe-code assessment from Mitrix and find out exactly what needs to happen to stabilize your project without breaking what's already working.