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This longitudinal study analyzes the impact of an enterprise "2x" mandate on developer productivity at a mid-sized company, revealing that per-capita throughput of merged pull requests doubled, achieving 2.09 times the pre-mandate baseline by April 2026. Utilizing a staggered difference-in-differences design, the study links productivity gains to AI adoption and usage intensity, indicating that the mandate facilitated these improvements rather than directly causing them. Notably, the adoption of AI tools restructured the code review process, leading to a significant increase in automated reviews while maintaining stable merge and revert rates.
AI adoption catalyzed a 109% increase in developer throughput, fundamentally reshaping the code review landscape in the process.
Enterprises increasingly mandate AI coding tools and report large productivity gains, yet longitudinal evidence on how such a mandate unfolds is scarce. In this paper, we present a quantitative case study of a documented enterprise"2x"mandate at a mid-sized, AI-forward company that has been committed to doubling merged pull requests per engineer since mid-2025. In a panel of 802 developers and 196,212 pull requests (January 2024-April 2026), per-capita throughput eventually doubled, reaching 2.09x the pre-mandate baseline in April 2026, among the largest gains reported from a field deployment of AI coding tools to our knowledge. A staggered difference-in-differences design links the within-developer share of this gain to AI adoption and to a further gain that grows with accumulated use, with the mandate acting as a catalyst rather than a direct driver. Because adoption and usage intensity were not randomly assigned, we read this evidence as strongly implicating an adoption-and-use channel rather than as exact causal attribution. The gain is broadly shared across seniority yet concentrated in newer code and not separable across model generations. Adoption also restructured code review around automation: per-reviewer load roughly doubled and automated review overtook human review, while merge and revert rates held steady.
