LLMs Are Lowering Coding Cost — But They May Be Increasing Debugging Complexity

Short summary

AI-assisted development enabled rapid implementation of an iOS app's encryption and iCloud sync features, but multiple AI code reviewers approved code that was syntactically correct yet systemically broken. The core insight: AI excels at code correctness but struggles with runtime behavior at OS boundaries—as AI-generated code scales, debugging complexity rises, making system-level understanding increasingly valuable.

• •AI code reviewers can miss system-level bugs masked by clean logs and successful API calls
• •Code correctness ≠ system correctness, especially at OS/framework boundaries
• •Debugging costs rise as development velocity increases with AI—system verification becomes the bottleneck

Generated with AI, which can make mistakes.