Speed metrics mask technical debt accumulating faster

Software teams are now measuring AI adoption the way their predecessors measured automated testing and code generation tools.

Lines of code per developer per sprint are climbing. Deployment velocity is accelerating.

I watched this exact pattern unfold with Java frameworks in the early 2000s. Companies switched to increasingly automated build systems and dependency management, and the metrics showed productivity gains almost immediately. Deployment cycles collapsed from months to weeks. Two years later, those same companies found themselves maintaining thousands of implicit dependencies no one fully understood, with build times that had somehow grown longer. Junior developers who had never learned to diagnose a broken classpath.

You're measuring velocity without measuring fragility. The tools that accelerate output don't always accelerate comprehension. When a developer uses an AI assistant to generate a function, the deployment metrics tick up and the code review happens faster now because there's more of it and reviewers stop looking as carefully. Six months later, the code is working but nobody knows why it's working. Or what assumptions it's built on — or why changing it breaks something unrelated.

The metric that mattered was wrong all along. It just took time to notice. We've watched this story happen before. We're still measuring the same things anyway — still incentivizing speed over stability, bulk over clarity, velocity over understanding. The only variable that's actually changed is that we're doing it with AI. Means it's happening faster and harder to track.