The Verification Gap Behind Every AI-Generated Release

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Code creation has never been faster, but the machinery around it has not sped up in step. AI coding tools can now produce more lines in a morning than a team used to ship in a sprint, while testing pipelines, review processes and device coverage still move at the pace they did two years ago. The result is a widening gap between how much software is being written and how much of it anyone can actually verify — and that gap is where production incidents live. Mike Vizard sits down with Prince Kohli, CEO of Sauce Labs, to work through what that imbalance is doing to release quality. Kohli’s framing is that leaders keep mistaking code velocity for product velocity. Generating an application in an afternoon is not the same thing as shipping one. Reviews still need to happen, tests still need to be authored, device coverage still needs to be real, user journeys still need production-like validation. Skip those layers and the AI advantage evaporates the first time a subtle defect makes it to customers. Kohli lays out a specific failure pattern engineering leaders should look for: using the same AI model to write the code and verify the code, which he compares to letting a student grade their own homework. Mature teams are moving to independent verification systems that evaluate application intent, author their own tests, run them in the right environments and troubleshoot the failures they surface. Human reviewers also carry a heavier cognitive load in this world, because developers now read a lot more code than they write and often lack the context behind the AI-generated …

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