Executive Summary
- Despite the explosion in AI coding tool adoption, engineering VPs are failing to measure actual productivity outcomes, leading to a costly disconnect between usage and value.
Strategic Deep-Dive
AI coding assistants like those from OpenAI, Anthropic, and various startups are seeing massive adoption. However, a critical blind spot has emerged in technical management: the failure to distinguish between usage and outcomes. VPs of Engineering are often enamored by high adoption rates—how many developers are using the tool daily—without questioning if it actually improves code quality or speed to market.
This reliance on “vanity metrics” masks the economic reality that AI-generated code often requires more rigorous review and can lead to technical debt if not managed correctly. As the novelty wears off, the focus must shift from how much AI is used to the tangible impact on software delivery performance.
