Code Rot at Machine Speed: Preventing AI-Induced Technical Debt
summary
AI-generated code often arrives fast but fragile — a Copilot example adds ~20 lines in 40 seconds and shows deep nesting, missing exception handling and resource leaks. A measured approach uses a code health metric (green code yields ~10x faster changes and ~15x fewer defects), benchmarking on 5,000 Python files shows refactoring LLMs break code more when health is low (hallucination ~30%), and a deterministic MCP server plus IDE checks successfully gates and guides safer AI edits.
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chapters (8)
- 0:00AI-driven code rot
- 4:36Rebound effect in AI code
- 11:00AI code quality risks
- 17:37Code health for maintainability
- 25:25AI understanding human-readable code
- 31:44AI readiness and risk mitigation
- 33:28Future of AI-assisted coding
- 34:36Q&A on AI code health
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