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.
Definition 1: The Meme
"I don't read the code. I just accept what the AI gives me and move on."
Definition 2: The Reality
"I use AI to generate boilerplate faster, but I still review everything and understand what's happening."
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 login 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
Here's the dirty secret: AI doesn't remember your architecture.
Every prompt is a fresh start. So when you ask for Feature A on Monday, then Feature B on Tuesday, then 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 7 places
- API endpoints that return data in 4 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 that founders thought were production-ready.
The patterns I see constantly:
// 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 of these is a security 101 failure. SQL injection. Path traversal. Privilege escalation via query parameter.
But they work! They pass manual testing! 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 that variable is named tempBuffer. You remember that 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. There's no one to ask, "why did you do it this way?" The AI that wrote it has no memory of the conversation.
I spent 6 hours last month 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, some 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 patterns
- Some used Pages Router patterns
- Some were raw Express middleware somehow shoved into both
- The 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-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 using AI assistants heavily actually showed slower delivery times once you factor in debugging and rework.
The Quality Gap:
Microsoft and IBM both estimate that ~20-30% of their code is now AI-generated. But IBM's CEO specifically said that's where the percentage will stay because the maintenance burden of AI code makes higher percentages unsustainable.
The $1.5 Trillion Problem:
Industry analysts are projecting $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, I ask myself: "Could I explain what this does to a junior developer in 30 seconds?"
If the answer is no, I 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:
- A read-through - Do I understand every line?
- An edge case check - What happens with null/undefined/empty/huge inputs?
- A security glance - Is this handling user input safely?
- An architecture fit - Does this match how we do things in this codebase?
It takes 5 minutes. It's saved me hundreds of hours of debugging.
The Real Talk About Junior Developers
This is where I get controversial.
The vibe coding explosion is going to hurt junior developers more than anyone. Here's why:
When senior developers use AI, they're outsourcing typing, not thinking. They know what good code looks like. They can evaluate AI output against years of pattern recognition.
When junior developers use AI, they're outsourcing learning. They're skipping the part where you understand why things work. And that creates a skills gap that widens every day.
I've interviewed junior developers this year 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.
That's not preparation for a career. That's preparation for obsolescence.
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.
If you're an experienced developer using AI to accelerate work you understand deeply...great. You're probably more productive than ever.
If you're shipping AI-generated code you don't fully understand to production, you're building on sand. The hangover is coming. It might be next week. It might be 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.
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.
Top comments (4)
In my honest (and probably unpopular) opinion, we should never use generated code in production without thoroughly reviewing it first. Vibe coding is only any good for the rapid construction of throw-away prototypes.
I don’t think that’s an unpopular opinion at all, honestly. I mostly agree with you. I do think there is a middle ground. For experienced developers, AI can be useful beyond throwaway prototypes, but only when it is treated as a fast assistant rather than an authority. The moment you cannot clearly explain the code you are shipping, it likely does not belong in a live production system.
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