The AI Productivity Paradox: How Developer Throughput Can Stall
devops.com, Wednesday, February 25th, 2026
Software engineering leaders have invested heavily in generative AI coding assistants for over two years-and for good reason. For many teams, the productivity gains appear significant. I hear the same story in conversations with leadership at dozens of enterprises: thanks to AI, developers complete tasks faster, write more code, and spend less time on boilerplate activities.
But I am also seeing a potentially dangerous paradox emerging; perhaps a new kind of technical debt, and a danger that you will experience at an accelerated pace (acceleration being the true hallmark of everything AI). While individual productivity is soaring, overall throughput, meaning the rate at which secure, stable, production-ready code is deployed, is stagnating or even declining in many cases.
The reason is a simple but costly equation: a significant percentage of AI-generated code contains vulnerabilities. AI-assisted code always looks correct on the surface (a hallmark of generative AI, which is trained first and foremost to appear correct, whether or not it is correct). But hard-to-detect vulnerabilities in AI-assisted code create bottlenecks in the CI/CD pipeline where the time saved in writing the code is lost or even outweighed by the ensuing security-driven regressions, failed builds, and remediation cycles.