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AI Coding Tools Speed Up Development but Risk Creating Review Bottlenecks

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The growing adoption of AI-assisted coding tools is significantly reducing the time developers need to implement tasks, allowing more work to be completed within the same timeframe. However, this productivity gain is creating a downstream challenge: code review and integration stages struggle to keep pace with the faster output, leading to a rise in work-in-progress and pull request backlogs. Experts suggest the issue lies not with AI itself but with development workflows that have not adapted to match the increased generation speed. One recommended approach, highlighted by DORA research, is working in small batches — breaking implementations into smaller, independently reviewable changes rather than large pull requests. Smaller pull requests require less context from reviewers, make regressions easier to spot, and allow architectural feedback to surface earlier in the process.

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