Interesting observation.

I think AI is changing software bugs in two opposite ways at the same time:

It removes many simple bugs, but it also creates new classes of higher-level problems.

For example, AI is great at:

• syntax fixes
• boilerplate cleanup
• repetitive refactoring
• basic test generation

But once projects become larger, the difficult bugs shift toward:

• architecture inconsistencies
• hidden dependencies
• context drift across files
• incorrect assumptions generated confidently by AI
• long-session reasoning failures

After working on several AI-assisted projects, I noticed debugging becomes less about fixing one broken line and more about understanding system behavior across the entire repository.

In a strange way, AI reduces “small bugs” while increasing the importance of engineering judgment.