The rapid adoption of AI-assisted tools like GitHub Copilot has rekindled interest in engineering productivity. However, a holistic understanding of these tools' real-world impact on engineering productivity remains caught between marketing claims of substantial gains and initial studies focused on task-level improvements. While the marketing narrative is challenged by emerging real-world evidence, the community still lacks deep, non-anecdotal studies that not only observe but also explain the disconnect. In this article, we aim to do just that. We introduce and investigate the efficiency-throughput gap, a phenomenon where individual developer efficiency gains fail to translate into measurable organizational throughput. Grounded in the Organizational Ohm's Law (OOL) framework, our real-world field study provides a methodology to both empirically document this gap and offer a data-driven explanation.

Research motivation and principles. The motivation of this work is to understand the impact of GitHub Copilot on engineering productivity to inform the strategic integration of generative AI into existing product development. Specifically, we aim to understand how engineers use GitHub Copilot, and whether its adoption yields measurable engineering productivity gains. Furthermore, this research seeks to identify the requirements for effective measurement of engineering productivity in corporate settings. To facilitate practical business decisions, our field study was designed to reduce disruption to established engineering practices and to reflect authentic real-world conditions rather than artificial laboratory settings. Our experimental design adheres to three guiding principles: first, to generate actionable business insights while satisfying academic curiosity; second, to minimize the burden on participating engineers and avoid introducing unnecessary research-only processes; and third, to measure relevant data and behaviors as accurately as possible to reflect real-life operational contexts.

Organizational Ohm's Law. The study's experimental design and methodology are informed by the OOL framework, a concept we previously developed to analyze engineering productivity. The framework draws an analogy between electrical circuits and organizational systems, stating that outcome current, and hence organizational productivity, is proportional to outcome-output efficiency and organizational potential, and inversely proportional to organizational resistance. By approximating organizational resistance through quantifiable time allocations, the law shows that engineering productivity is positively correlated with the percentage time spent on core software engineering activities (i.e., coding and testing), the relative average engineer motivation, and the relative average engineer skill levels. Leveraging this framework, we measured engineer time allocation before and after GitHub Copilot adoption across various areas, including coding, testing, communication, waiting for decisions and dependencies, and documentation.