AI coding tools are incredible. They write code 10x faster than humans. But speed without quality creates technical debt that compounds daily. We rescue 2-3 AI-broken projects every month. Here is what goes wrong and how to fix it.
The Pattern We See
A founder uses ChatGPT or Cursor to build an MVP. It works! The demo is impressive. They show it to investors. They get funding. Then they try to add features, fix bugs, and scale — and everything falls apart.
The code works for the demo but was never built to be maintained. There are no tests. No error handling. Hardcoded values everywhere. Functions that are 500 lines long. Security vulnerabilities that would make a security auditor cry.
The Five Most Common AI Code Problems
1. No error handling. AI generates the happy path. It does not think about what happens when the API is down, the database times out, or the user enters unexpected input. In production, errors are not edge cases — they are Tuesday.
2. Security vulnerabilities. SQL injection, XSS, exposed API keys, missing authentication checks. AI does not think adversarially. It generates code that works, not code that is safe.
3. No separation of concerns. AI tends to put everything in one file or one function. When you need to change the database, you end up rewriting the entire authentication system because they are tangled together.
4. Inconsistent patterns. If you prompt AI multiple times, each response uses slightly different patterns, naming conventions, and approaches. The resulting codebase feels like it was written by 10 different people who never talked to each other.
5. Missing tests. AI rarely writes tests unless you explicitly ask. And even when it does, the tests often test implementation details rather than behavior, making them brittle and useless.
How We Fix It
Our code rescue process is systematic. First, we audit: read every file, document every issue, prioritize by severity. Second, we fix critical issues: security vulnerabilities, data loss risks, crash-causing bugs. Third, we refactor: clean architecture, proper error handling, test coverage. Fourth, we document: so the next developer (or AI) can actually understand what is happening.
Prevention Is Cheaper Than Rescue
If you are using AI to write code, pair it with a senior developer who reviews every output. The developer does not need to write the code — they need to review it, test it, and refactor it. This costs 30% more upfront but saves 300% in rescue costs later.
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