AI adoption: why a code generator can slow development down
SimpleOne has described a new kind of technical debt: AI helps juniors and mid-level developers close tasks faster, but it overloads seniors with review and rework. The article advises looking beyond sprint velocity to review time, the number of iterations, incidents and team feedback. The proposed fix is a single backlog, a link between ITSM and SDLC, and a shift in focus from code generation to building competencies.
AI-processed from Habr AI; edited by Hamidun News
Adopting AI in development has a paradoxical effect: AI code generation can speed up junior developers but slow down the team's overall delivery.
Can AI slow down a team's development?
Yes. If developers use code generation without digging into the project context, every pull request goes through a long cycle of comments, and the load shifts to seniors, QA and support, who spend more time on review and fixes than was saved on the initial development.
Why does adopting AI in development require quality control?
Generated code often ignores the project's architecture and business logic. This creates technical debt that later demands heavy senior effort on review and refactoring.
Why can adopting AI slow down development?
When developers paste generated code without understanding the architecture and business logic, the burden of fixes falls on seniors. Every pull request needs extra review and rework, which takes more time than was spent on generation.
Key Questions Answered
Can AI slow down a team's development?
Yes. If developers use code generation without digging into the project context, every pull request goes through a long cycle of comments, and the load shifts to seniors, QA and support, who spend more time on review and fixes than was saved on the initial development.
Why does adopting AI in development require quality control?
Generated code often ignores the project's architecture and business logic. This creates technical debt that later demands heavy senior effort on review and refactoring.
Why can adopting AI slow down development?
When developers paste generated code without understanding the architecture and business logic, the burden of fixes falls on seniors. Every pull request needs extra review and rework, which takes more time than was spent on generation.
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