AI Assisted Coding: Quicker Code Doesn't Mean Higher Velocity (medium.com)

Engineers report writing code 3–5x faster with AI assistants, but many teams see no increase in sprint velocity because the bottleneck has simply moved downstream. Modern AI coding tools (Copilot, Claude Code, Cursor and others) produce context-aware functions, classes and tests, shifting the developer’s role from author to reviewer-editor. That change forces engineers to reverse-engineer AI-generated logic, re-run local tests, and manually patch or re-prompt for maintainability — tasks that can take as long or longer than writing the code themselves. The result is a “knowledge transfer” problem: both author and reviewer must learn the same new code, doubling cognitive load. Practically, this means faster generation alone won’t deliver productivity gains unless teams change workflows and tooling. Review time scales nonlinearly with change size, so teams should prompt for smaller, reviewable chunks, treat AI output like external contributions, and adopt pair programming patterns to catch issues earlier. Investment in review tooling that splits, annotates and groups AI-generated changes (for example, open-source projects like Armchr that support multiple models) is becoming as crucial as the generators themselves. Until AI tools prioritize reviewability and understandability, quick code will continue to create slow reviews and stagnant velocity.