AI coding tools like GitHub Copilot and others have dramatically accelerated the code writing phase. However, many teams are experiencing a paradox: despite faster code generation, the overall delivery pipeline is not speeding up proportionally. This article explores why. The bottleneck has shifted from writing code to clarifying requirements, ensuring quality, and coordinating across roles. Engineers are now juggling multiple AI-generated code streams, leading to increased cognitive load and context switching. The author argues that teams need to rethink their delivery processes to fully leverage AI, focusing on better requirement specification, automated testing, and streamlined review workflows. This is a timely signal for engineering leaders evaluating AI adoption.
Analysis of how AI coding tools shift bottlenecks from coding to requirement clarification, quality assurance, and coordination.