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