Where Trust in Automated Review Actually Comes From

In a recent analysis, developers are cautioned against the common practice of using multiple AI models to validate code reviews. The author argues that because models are often trained on similar datasets, they share the same blind spots, making a 'second opinion' from another AI redundant. Instead of relying solely on diff analysis—which can be misleading or easily fooled by plausible-looking code—the article suggests focusing on objective facts. Key metrics such as database migrations, lockfile changes, test coverage, and historical risk in specific code areas provide a more reliable assessment of a pull request's safety. While AI models can be useful when configured with independent evidence sources, the author emphasizes that true risk intelligence comes from looking beyond the code diff to understand the broader context of the changes being implemented.