We’re all using AI copilots like crazy. The news is full of tools measuring LOC written, velocity, and ‘AI ROI.’ But honestly, are we just moving complexity somewhere else?
When you merge that clean-looking, 200-line solution generated in 5 minutes, how many hours will it cost the next engineer to debug, refactor, or secure the subtle architectural debt the AI introduced? Is the productivity paradox real faster today, slower for the rest of the quarter? Curious about what your PRs are actually showing.
It’s the garbage-in, garbage-out of the new era. My PR review comments are now 70% ‘The AI gave you the happy path, now think about the edge cases it forgot.’ We’re swapping junior devs for junior AI. Pay me to refactor, I guess.
Welcome to the new metrics game. Managers need to justify the Copilot license, so they optimize for LOC/day. When the bill comes due in 18 months in the form of an unmaintainable monolith, the current team will be gone, and the cycle will repeat. Classic short-term incentive hell.
It’s forcing engineers to think higher-level. If I spend less time on syntax and more time on architecture and security reviews, the net quality goes up. The tool is just a force multiplier. If your team is using it for ‘vibe coding,’ that’s a people problem, not an AI problem
Been through the framework hype cycle, the microservices hype cycle, now this. AI is a great macro tool like boilerplate, tests, or migration scripts. But you still need a human to define the boundary conditions. If you’re relying on it for your core business logic, you don’t understand your core business.