AI Coding Agents Generate More Code But Not More Delivered Software

New research covered by Ars Technica finds that AI coding agents significantly increase raw code output but do not proportionally increase the amount of working, shipped software — pointing to a gap between code generation velocity and actual software delivery. The findings suggest that bottlenecks like code review, integration testing, debugging hallucinated logic, and architectural decision-making remain human-bound even when code writing is accelerated. For engineering teams adopting AI coding tools, this is a calibration signal: productivity gains may be overstated if measured only by lines of code or commit frequency rather than deployed features or bug resolution rates. The research reinforces the importance of measuring AI coding assistant ROI at the workflow level, not the line-of-code level. Developers and engineering managers should use this as a prompt to audit where their actual delivery bottlenecks sit before investing further in agent-based coding workflows.