AI Writes Faster Than Humans Can Review: A Longitudinal Study of an Enterprise 2x Mandate

2026-07-02

AI Summary

This study addresses the lack of empirical evidence on the long-term impacts of mandatory AI programming tool adoption in enterprise settings, particularly regarding development efficiency and the mediating role of code review mechanisms. Leveraging longitudinal data from a mid-sized, AI-first firm between January 2024 and April 2026—encompassing 802 developers and 196,000 pull requests—the authors employ a staggered difference-in-differences design combined with panel data analysis to uncover the cumulative productivity effects across distinct “adoption” and “usage” phases. By April 2026, the average developer merged 2.09 times more pull requests than at baseline. Automated reviews accounted for over 50% of all reviews, doubling reviewer workload while maintaining stable merge and rollback rates, suggesting that AI has fundamentally reshaped the code review ecosystem.

Abstract

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.

Problem

• AI coding tools
• productivity gains
• longitudinal study
• code review
• enterprise mandate

Innovation

• AI coding tools
• longitudinal study
• difference-in-differences
• code review automation
• developer productivity