Should humans still review all your code?

The rapid adoption of AI-generated code is challenging traditional code review processes, which were initially established to ensure knowledge sharing, ownership, consistent development practices, and quality control. While AI tools can expedite code production, they also introduce complexities that human reviewers struggle to manage, such as increased batch sizes and higher defect rates in AI-coauthored pull requests. Organizations need to reconsider what aspects of code reviews are still effective and explore alternative approaches, including automated tools and layered verification models, to maintain code quality and accountability in this new landscape. The focus should be on differentiating between types of code changes, leveraging AI for defect detection, and balancing the benefits of AI-generated code with the risks of accumulating technical debt. Ultimately, engineering teams must adapt their processes to align with the evolving capabilities of AI while ensuring that the code they deliver is both functional and maintainable.