AI Coding Tools Made Shipping Faster But Testing Harder
The productivity argument for AI coding tools was always going to be easy to make. Developers ship more code in less time, sprints move faster, and the metrics that most engineering organizations track look better than they have in years. What took longer to surface was the other side of that equation, the part where the code that ships faster also breaks in ways that take longer to find, longer to fix, and longer to explain to the people who approved the tooling in the first place.
The problem is not that AI-generated code is bad. It is that the quality infrastructure most teams have was built for a world where developers were the bottleneckin the delivery pipeline, and in that world the testing process had time to keep up. AI coding tools removed that bottleneck and moved it somewhere else, and most teams discovered where by running into...
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