AI adoption in software engineering has reached near-saturation, shifting the industry focus from simple usage to the sophistication of AI integration. While PR throughput and developer time savings continue to climb, these gains are accompanied by a concerning increase in PR size and volatility, which threatens code maintainability and long-term quality. Engineering managers are increasingly contributing code, potentially fostering a deeper understanding of systemic developer friction. However, the massive surge in AI-related operational costs has not yet translated into a proportional increase in innovation or value delivery. Current data indicates that AI acts as an amplifier for existing organizational practices, meaning that without structured policies and a focus on reducing non-AI bottlenecks—such as meeting overhead and build times—the promised productivity gains remain elusive and potentially offset by increased technical debt and token consumption.
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