When AI Goes Wrong: Horror Stories from the Trenches

Building four ventures with AI in nine months sounds impressive. What I don't talk about enough: the disasters, the failures, the moments where AI went completely off the rails.

Here are the horror stories. Because if you're building with AI, you need to know what can go wrong—and how to recover.

The Infinite Loop of Doom

What happened:

I asked AI to refactor a React component. Simple request. Clean up the code, make it more maintainable.

It created a circular dependency. Component A imported Component B. Component B imported Component A. The app crashed on startup.

"No problem," I thought. "I'll ask AI to fix it."

AI tried to fix it by creating more circular dependencies. Each "fix" made it worse. The dependency graph looked like a plate of spaghetti.

Four hours later, I realized I needed to stop. Roll back. Start over.

What I learned:

Test immediately after each change. Don't let AI run for hours without verification. "It compiles" means absolutely nothing. And know when to stop digging and just roll back.

Now I commit before every major refactor. Test after every AI change. Set a 30-minute timer—if it's not working by then, I roll back and try a different approach.

The Database Migration That Ate Production Data

What happened:

AI generated a database migration. I reviewed it. Looked good. Clean SQL. Proper syntax.

I ran it in production.

It dropped a column that was still in use. Lost data. Two hours of downtime while I restored from backups.

What went wrong:

I didn't test the migration in staging first. AI didn't know about all the places that column was used—even with context, it can't see everything. And I trusted AI's confidence too much.

What I learned:

NEVER run AI-generated migrations without testing in staging. AI doesn't know your entire codebase. Always have backups. And staging environments exist for exactly this reason.

Now every migration gets tested in staging. Every database change gets extra scrutiny. And I use migration tools that can roll back.

The Security Hole

What happened:

AI built an API endpoint. Clean code. Worked perfectly. I shipped it.

A week later, I realized: no authentication check. The endpoint was wide open to the internet. Anyone could access it.

I caught it before anyone exploited it. Lucky.

What went wrong:

I didn't explicitly tell AI to add authentication. I assumed it would know. I didn't have a security checklist.

What I learned:

AI doesn't assume security by default. You must explicitly specify security requirements. Every endpoint needs a security review. Checklists save lives—and data.

Now I have a security checklist. Every endpoint gets reviewed for auth and authorization. I explicitly state security requirements in every prompt. And I use automated security scanning.

The Hallucinated API

What happened:

I asked AI to integrate with a third-party service. It wrote beautiful code. Clean, well-structured, properly typed.

It used API methods that don't exist.

I spent an hour debugging before I realized: AI had hallucinated the entire API. The methods looked plausible. The code was confident. But none of it was real.

I had to rewrite from scratch using actual documentation.

What went wrong:

I didn't verify the API methods existed. AI confidently used non-existent functions. I trusted AI's knowledge of external APIs.

What I learned:

AI hallucinates APIs and methods. Always verify against actual documentation. If it seems too easy, check the docs. AI's confidence means nothing.

Now I provide actual API documentation to AI. I verify method signatures before implementing. I test integrations immediately. And I never trust AI's memory of external APIs.

The Over-Engineered Monster

What happened:

I asked AI to build a simple feature. "Add a way for users to save their preferences."

It created an elaborate system with abstractions, factories, strategy patterns, and dependency injection. Ten times more complex than needed.

What should have been 50 lines was 500. It took me days to understand and simplify.

What went wrong:

I didn't specify "keep it simple." AI defaulted to over-engineering. I didn't catch it early enough.

What I learned:

AI loves to over-engineer. Simplicity must be explicitly requested. Review code for complexity, not just correctness. Simpler is almost always better.

Now I always specify "keep it simple" in my prompts. I review for unnecessary complexity. I ask "could this be simpler?" And I reject over-engineered solutions early.

The Inconsistent Codebase

What happened:

Multiple AI sessions over weeks. Each session used slightly different patterns.

The codebase became inconsistent. Three different ways to handle errors. Four different state management approaches. Two different naming conventions.

Maintenance nightmare.

What went wrong:

No style guide for AI. Each session was independent. I didn't enforce consistency.

What I learned:

AI needs a style guide. That's why we created SCOPE.md—a file that tells AI the patterns, conventions, and constraints for each project.

Now every project has a SCOPE.md. I enforce patterns and conventions. I do regular consistency reviews. And I refactor when I see divergence.

The Performance Disaster

What happened:

AI built a feature that worked perfectly. In development. With 10 test records.

In production with 10,000 records: 30-second load times. The classic N+1 query problem. One query to get the list, then one query for each item in the list.

I had to rewrite the entire data layer.

What went wrong:

I didn't test with realistic data volumes. AI optimized for correctness, not performance. I didn't review the database queries.

What I learned:

AI doesn't optimize for performance by default. Test with production-scale data. Review database queries carefully. Performance requirements must be explicit.

Now I test with realistic data volumes. I review all database queries. I specify performance requirements. And I use query analysis tools.

The Pattern

All these horror stories have something in common:

  1. Lack of context - AI didn't know the full picture
  2. Insufficient testing - I shipped without proper verification
  3. Over-trust - I believed AI's confidence
  4. Missing constraints - I didn't specify what NOT to do
  5. No safety nets - No rollback plan, no backups

Every disaster was preventable. Every one taught me something.

The Recovery Playbook

When AI goes wrong:

  1. Stop immediately - Don't let it dig deeper
  2. Roll back - Git is your friend
  3. Analyze - What actually went wrong?
  4. Add constraints - Update SCOPE.md to prevent recurrence
  5. Try again - With better context and constraints

Prevention:

  1. Commit often - Before every major change
  2. Test immediately - After every AI change
  3. Review carefully - Especially security and data operations
  4. Provide context - Give AI the full picture
  5. Specify constraints - Tell AI what NOT to do
  6. Use checklists - Security, performance, consistency

The Honest Truth

AI will mess up. You will ship bugs. Things will break. Data will be at risk. You'll waste time on hallucinated solutions.

But.

You'll still be 10x faster than without AI. The wins outweigh the losses. You learn to spot problems faster. The disasters become less frequent. The recovery becomes faster.

I've had AI delete production data, create security holes, hallucinate APIs, and over-engineer simple features. I've lost hours to circular dependencies and days to performance disasters.

And I'd still never go back to coding without AI.

What This Means for You

Don't fear AI failures—expect them. Build safety nets: git, backups, staging environments. Test everything. Trust but verify. Learn from each disaster.

And share your horror stories. We all have them. That's how we learn.

The horror stories are real. But so is the 10x productivity. The key is learning to spot problems fast, recover faster, and prevent them from happening again.

These disasters taught me more than the successes. Now I'm sharing them so you can learn without the pain.

What's your AI horror story? I guarantee you're not alone.

How This Was Created: This post was written by Mike, architected by Mike, and drafted with AI assistance. The ideas, experiences, and insights are 100% human. The execution is AI-augmented. Just like everything we build at Wizewerx.