A developer opens a pull request. The feature works. The tests are green. The implementation looks reasonably clean. Then they realize the PR changes 17 files for what should have been a relatively small feature.
There is a new abstraction nobody on the team has seen before. A dependency has been added for functionality the project already had. Two similar utilities now exist in different parts of the codebase. The tests are extensive, but nobody is completely sure what assumptions they are actually testing.
And the author didn't write most of it manually. AI did. This is one of the less obvious problems created by AI coding tools. The problem isn't necessarily that AI generates bad code.
Sometimes it generates perfectly valid code. The problem is that it can generate code faster than a team can develop an understanding of it. That changes the bottleneck in software engineering.
The Cost of Writing Code Has Changed
