The Cost of Saying 'Yes' in Software Development Has Shifted
Cost analysis for small changes now lies in decision-making rather than coding itself.
1Shifting Cost Dynamics
In traditional software development, the primary cost of a small feature request was coding. Now, the biggest resource drain comes from debates about whether to implement it. Engineers' 'is this really necessary' reflex loses meaning as AI tools reduce code generation costs. For instance, a simple data field addition can now be prototyped in 30 minutes rather than debated for two days.
2The Power of Prototyping
The first code version generated by AI now serves not as final product but as a 'discovery tool'. This approach transforms abstract scope discussions into concrete software artifacts: Which files does it touch? Is it testable? Does it preserve existing abstractions? A user activity timestamp request that yields a clean 4-line diff with passing tests can be shipped immediately.
3Ownership Cost
Cheaper code generation doesn't eliminate ownership costs. A technically functioning 1000-line change that no one wants to maintain remains expensive. Critical filters may carry high risk even with clean diffs. The key skill in the AI era becomes quickly answering 'can a human confidently review and own this?' rather than 'can we generate this code?'
4Revolution in Scope Discipline
Scope control traditionally happened before implementation because coding was expensive. Now some of this discipline shifts to review phase. For small changes, teams can request constrained attempts: behind existing feature flags, without altering public contracts, with added tests. This converts abstract debates into concrete evidence about actual implementation complexity.
5New Engineering Skills
In an AI-assisted world, the most valuable engineers won't be those who say yes to everything or reflexively say no, but those who can rapidly price uncertainty. Is this a product decision in disguise? Will review cost more than writing? For the right tasks, the fastest responsible answer becomes 'try it and see' using bounded AI-generated prototypes.
6The Core Transformation
A paradigm shift is occurring: the 'no because expensive' argument weakens as code generation costs drop. However, this doesn't mean scope creep disappears. Engineers' new role involves less guessing and more evaluating concrete outputs. AI's opportunity lies in making human judgment faster and better-informed, not replacing it.
Original article: GitHub Engineering — Yazılım Geliştirmede 'Evet' Demenin Maliyeti Değişti