The "State of AI vs Human Code Generation Report" reveals significant differences in code quality between AI-generated and human-written pull requests (PRs) on GitHub. Analyzing 470 open-source PRs, including 320 AI-co-authored and 150 human-only submissions, the report shows AI-generated PRs had about 10.83 issues per request compared to 6.45 for human-only PRs. Critical and major issues were more prevalent in AI contributions, with instances of logic and correctness problems rising by 75%. Readability issues were particularly alarming, with AI-generated code exhibiting over three times the formatting problems and nearly double the naming inconsistencies. The report attributes these discrepancies to AI's limitations. Models often miss the subtleties of local business logic and fail to adhere to specific naming conventions or architectural norms. This leads to common issues like improper password handling and flawed dependency management in generated code. Security vulnerabilities were also significantly higher in AI-generated PRs, with some types appearing up to 2.74 times more frequently. The findings suggest that while AI can accelerate development, it amplifies certain types of mistakes, raising concerns about code quality and maintainability. To address these issues, teams are advised to implement stricter guidelines and checks for AI-assisted code. This includes enforcing coding standards through CI tools, requiring tests for complex logic, and explicitly addressing error handling. Reviewers should pay close attention to areas where AI is prone to error, such as configuration validation and password handling. With the growing reliance on AI in coding, adapting review processes and integrating AI tools like CodeRabbit can help mitigate risks and enhance code quality.