Better models don't solve a judgment bottleneck

AI coding agents have greatly increased code production, but research tracking over 100,000 GitHub developers suggests that gains diminish as work moves from writing code to delivering software, with autonomous agents associated with a 180% increase in coding activity but only 30% more releases. Because production delivery still depends on human coordination, architectural context, testing, and accountability, code review has become a central bottleneck and a decision-making process rather than merely a check of implementation quality. The text argues that teams should triage pull requests according to their value, risk, dependencies, readiness, and required expertise, allowing routine changes to use automation and focused review while high-consequence changes receive deeper scrutiny. It also recommends converting expert judgment into reusable standards, testing expectations, risk classifications, and independent verification processes so that organizational knowledge can guide work produced by many changing AI models. The durable advantage, it concludes, lies not in any single coding model but in the systems that preserve context, evaluate evidence, govern merges, and monitor software after release.

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