You made coding faster. Guess where the bottleneck went next.

AI-assisted coding can increase individual engineers’ output without improving overall delivery speed because bottlenecks shift from implementation to code review, approvals, and the clarity of specifications. As pull request volume rises, reviewers may become overloaded and approval processes risk becoming superficial, while ambiguous tickets can lead AI agents to implement requirements literally rather than resolve gaps through informal collaboration. The proposed response is not universally adding reviews or approvals, but establishing risk-based workflows that specify when human judgment is needed, define what each approval verifies, and improve specifications for agent-driven work. Effective workflows also involve product, design, security, and other decision owners early, routing each change to the appropriate stakeholders rather than treating engineering as the default gate for every decision.

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