A framework for measuring the organizational impact of AI coding tools is proposed, arguing against single-number metrics like 'Developer Horsepower' in favor of a multi-dimensional approach. The recommended measurement strategy covers five layers: throughput (PR volume), deployments (whether work reaches production), Innovation Time Ratio (whether saved time is reinvested in higher-value work), quality (change failure rate as a guardrail), and developer satisfaction (sustainability signal). Key insight from research: coding agents raised commit volume by up to 180% but actual releases only increased 20-30%, revealing downstream human bottlenecks. The post also argues that AI and human effort are complements, not substitutes, making 'hours replaced' conversions misleading. Organizations are advised to start with surveys if telemetry is lacking, pick one or two metrics per dimension, and expand as instrumentation matures.
