The Vibe Coding Hangover Is Real: What Nobody Tells You About AI-Generated Code in Production

Source: Dev.to

Last month, a startup founder sent me their codebase for review. “Built in a weekend with Cursor,” he said proudly. “Ready for our Series A demo.”

I opened the repo. Thirty seconds later, I closed Slack and poured myself a drink.

The code worked. That wasn’t the problem. The problem was that it worked for now—held together by duct tape, prayers, and whatever hallucination Claude had that Tuesday afternoon.

This is the vibe‑coding hangover. And if you’ve shipped AI‑generated code to production this year, you might already be feeling it.

What Is Vibe Coding, Really?

The term exploded in early 2025 after Andrej Karpathy tweeted about “fully giving in to the vibes” and “forgetting that the code even exists.”

Collins Dictionary just named it their 2025 Word of the Year.

And now 84 % of developers are using AI coding tools daily.

But here’s what nobody talks about: there are actually two definitions of vibe coding floating around, and conflating them is causing chaos.

Most of the horror stories come from Definition 1. Most of the productivity gains come from Definition 2.

The problem? Everyone thinks they’re doing Definition 2.

The 5 Patterns I See in Every Vibe‑Coded Disaster

After reviewing 15 + vibe‑coded projects this year—from weekend hacks to funded startups—I’ve identified the recurring nightmares.

1. The “It Works On My Machine” Illusion

AI is exceptionally good at solving the happy path. Ask Claude to build a login form, and you’ll get a beautiful form that works perfectly when users enter valid emails and correct passwords.

Ask what happens when:

• Someone pastes 50 000 characters into the email field
• The database connection times out mid‑authentication
• A user double‑clicks the submit button
• The session expires between form load and submission

Crickets.

I reviewed a payment‑processing flow last month. The AI‑generated code handled successful payments flawlessly. Failed payments? The error handling was literally:

} catch (error) {
  console.log("Something went wrong");
}

That was it. In a payment system. Handling real money.

2. The Architectural Amnesia

Dirty secret: AI doesn’t remember your architecture.

Every prompt is a fresh start. So when you ask for Feature A on Monday, Feature B on Tuesday, and Feature C on Wednesday, you get three features that each make perfect sense in isolation—and absolute chaos when combined.

I’ve seen:

• Three different state‑management approaches in one React app
• Database queries that bypass the ORM you set up specifically for security
• Authentication logic duplicated (differently) in seven places
• API endpoints that return data in four different formats

The AI solves each problem optimally for that problem. It has no concept of “we decided to do it this way for a reason.”

3. The Security Time Bomb

This one keeps me up at night.

A Swedish vibe‑coding platform called Lovable had a security audit earlier this year. Out of 1 645 applications built with the tool, 170 had vulnerabilities that would expose user data to anyone who looked.

That’s a 10 % critical‑failure rate on apps founders thought were production‑ready.

Common patterns:

// AI‑generated “authentication”
const isAdmin = req.query.admin === 'true';

// AI‑generated “database query”
const user = await db.query(`SELECT * FROM users WHERE id = ${userId}`);

// AI‑generated “file upload”
const filename = req.file.originalname;
fs.writeFileSync(`./uploads/${filename}`, req.file.buffer);

Each snippet is a textbook security failure: SQL injection, path traversal, privilege escalation via query parameter.

They work and pass manual testing, so the demo looks great—until someone actually tries to exploit them.

4. The Debugging Nightmare

A developer who reviewed a vibe‑coded app told me:

“Debugging AI‑generated code is harder than writing it manually.”

Why? Because when you write code, you build a mental model of how it works. You know why a variable is named tempBuffer. You remember the edge case you handled on line 47.

With vibe‑coded projects, you’re debugging someone else’s code—except that “someone else” doesn’t exist. The AI that wrote it has no memory of the conversation.

I spent 6 hours hunting a bug in a vibe‑coded codebase. The issue? Two functions with nearly identical names (processUserData and processUserInfo) that did completely different things. One was called in production; the other was the one that actually worked correctly.

The AI had generated both at different times, solving the same problem differently, and the developer never noticed.

5. The Spaghetti Avalanche

Every AI coding tool has a different “style.” Some prefer functional approaches. Some love classes. Some generate comments; some don’t. Some use async/await; others use .then() chains.

When you vibe‑code over weeks or months, you end up with a codebase that looks like it was written by a team of 50 developers who never talked to each other—because that’s essentially what happened.

I reviewed a Next.js app where:

• Some API routes used the App Router pattern, others used the Pages Router pattern
• Some routes were raw Express middleware shoved into both routers
• Data fetching used SWR, React Query, and raw fetch in different components

Each piece worked. Together, they were unmaintainable.

The Numbers Don’t Lie

Let’s talk about what the research actually shows.

The Productivity Myth

Yes, AI can boost short‑term velocity, but the long‑term cost of technical debt, security incidents, and onboarding friction often outweighs the gains.

What to Do Instead

1. Treat AI as a pair programmer, not a magician.
2. Enforce a review checklist (security, error handling, style).
3. Lock down architectural decisions in a shared design doc that the AI can reference.
4. Automate linting, testing, and static analysis to catch the low‑hanging fruit the AI misses.
5. Allocate time for refactoring—don’t ship the first‑draft AI output as “done.”

TL;DR

Vibe coding isn’t inherently evil, but definition matters. If you’re only accepting whatever the AI spits out, you’ll end up with fragile, insecure, and unmaintainable systems. Use AI to accelerate boilerplate, still review everything, and keep the architecture consistent. Otherwise, you’ll keep paying the hangover price.

Assisted Coding: The Reality Check

Assisted coding can cut development time by 25‑50 % in controlled studies.
But Google’s DORA research found that, in real‑world enterprise settings, teams that rely heavily on AI assistants actually show slower delivery times once debugging and rework are factored in.

The Quality Gap

• Microsoft and IBM estimate that ~20‑30 % of their code is now AI‑generated.
• IBM’s CEO says that percentage will stay because the maintenance burden of AI code makes higher percentages unsustainable.

The $1.5 Trillion Problem

Industry analysts project $1.5 trillion in accumulated technical debt from AI‑generated code by 2027.
That’s not a typo—trillion, with a T.

So What Actually Works?

I’m not anti‑AI coding. I use Cursor and Claude Code daily, but I use them very differently than the “vibe coding” meme suggests.

The “AI Conductor” Approach

Think of yourself as an orchestra conductor, not a passenger.

What AI should do

• Generate boilerplate you’d write anyway.
• Suggest approaches you can evaluate.
• Write tests for logic you understand.
• Explain code you’re reviewing.
• Refactor working code to be cleaner.

What AI should NOT do

• Make architectural decisions.
• Handle security‑critical paths without review.
• Generate code you can’t explain.
• Write business logic you haven’t specified precisely.

The 30‑Second Rule

Before accepting any AI‑generated code, ask yourself:

“Could I explain what this does to a junior developer in 30 seconds?”

If the answer is no, don’t merge it. Period.

This single rule has saved me from probably 80 % of the bugs I would have shipped otherwise.

The Review Ritual

Every piece of AI‑generated code gets:

1. Read‑through – Do I understand every line?
2. Edge‑case check – What happens with null/undefined/empty/huge inputs?
3. Security glance – Is user input handled safely?
4. Architecture fit – Does this match how we do things in this codebase?

It takes 5 minutes and has saved me hundreds of hours of debugging.

The Real Talk About Junior Developers

The vibe‑coding explosion will hurt junior developers more than anyone.

• Senior developers use AI to outsource typing, not thinking. They already know what good code looks like and can evaluate AI output against years of pattern recognition.
• Junior developers use AI to outsource learning. They skip the part where they understand why things work, creating a skills gap that widens daily.

I’ve interviewed junior developers who could “build anything with AI” but couldn’t explain how a for loop works. They could prompt their way to a working app but couldn’t debug it when something broke.

My advice to new developers:
Learn the fundamentals first. Use AI as a tool, not a crutch. The developers who will thrive in 2026 and beyond are the ones who understand enough to know when the AI is wrong.

The Bottom Line

Vibe coding isn’t inherently good or bad. It’s a tool, and like all tools, the outcome depends on who’s wielding it.

• Experienced developers using AI to accelerate work they understand deeply → great. You’re probably more productive than ever.
• Shipping AI‑generated code you don’t fully understand → you’re building on sand. The hangover is coming. It might be next week, next year, but it’s coming.

The question isn’t whether AI changes how we code—it already has.
The question is whether you’re using it as a power tool or as an excuse to skip the hard parts.

Choose wisely.

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What’s been your experience with AI‑generated code in production?
Have you hit the vibe‑coding hangover yet? Drop your horror stories (or success stories) in the comments.