AI is changing how developers handle code review, emphasizing the need for evidence that code works before it’s merged. By early 2026, over 30% of senior developers reported shipping mostly AI-generated code. However, while AI can draft features quickly, it struggles with logic and security, leading to a significant increase in errors—up to 75% more common in logic. For solo developers, the key is to rely on AI as a powerful assistant while maintaining strong testing practices and manual checks. They tend to work at "inference speed," trusting AI-generated outputs but ensuring robust verification systems are in place. In team environments, AI tools are used to streamline the review process, but human oversight remains essential. As AI adoption rises, the volume of pull requests has increased, leading to larger PRs and a higher incidence of issues—up 24% in incidents per PR. This creates a bottleneck as the speed of code generation outpaces human capacity to review. Teams are encouraged to adopt incremental changes, ensuring that human sign-off focuses on aspects AI cannot assess, like alignment with project goals and context. Security is a major concern, with AI-generated code exhibiting a higher frequency of vulnerabilities, particularly in logic and XSS attacks. Developers must treat AI as a supplementary tool while enforcing strict security checks, especially in sensitive areas like authentication and payment systems. The article points out that knowledge transfer within teams suffers when developers submit AI-generated code they do not understand, complicating debugging and maintenance. Effective use of AI in code review hinges on thoughtful configuration of tools to catch genuine issues while avoiding unnecessary noise. Accountability remains with humans, ensuring that AI's contributions do not absolve developers of their responsibility.