AI has transformed code review by shifting the burden of proof onto developers. While AI can generate code quickly, it can't ensure that it works as intended. Developers need to provide evidence of functionality through manual tests and automated verification. By 2026, over 30% of senior developers expect to ship mostly AI-generated code. However, AI struggles with logic, security, and edge cases, leading to a 75% increase in logic errors. Solo developers often work at "inference speed," relying on automated tests to catch issues, while teams emphasize human oversight for quality, security, and maintainability. Solo developers treat AI as a powerful intern, shipping features rapidly while conducting minimal reviews. They prioritize strong testing practices, aiming for over 70% coverage, and often use AI to create and run tests. Still, they engage in manual testing and critical reasoning to ensure the final product works. In contrast, teams face increased complexity with AI-generated code, as the volume of pull requests rises. Team members use AI review bots for initial checks but require human approval to address context and compliance issues. The risk of shipping unreviewed code becomes significant when AI-generated changes flood the review process. Security remains a critical area where human oversight is essential. AI-generated code has a high rate of security flaws, with 45% containing vulnerabilities and logic errors occurring at 1.75 times the rate of human-written code. Developers must treat AI as a fast intern for security-sensitive code and ensure thorough human review. Code review also serves as a knowledge transfer mechanism within teams. If developers submit AI-generated code they don’t fully understand, it undermines resilience, making debugging difficult for others. Managing the volume of AI-generated code is crucial to avoid bottlenecks in the review process. Effective use of AI review tools requires thoughtful configuration to maximize their value. While some teams report catching over 95% of bugs, others find AI comments unhelpful. Smaller, incremental pull requests are encouraged, allowing for clearer communication and easier review. Ultimately, a human must take responsibility for the code, regardless of AI involvement. The emerging best practice, known as the PR Contract, emphasizes providing clear intent and proof of functionality through tests to maintain accountability in the review process.