Quoting Andrew Kelley

As artificial intelligence continues to reshape software development, a critical misconception persists: that distinguishing between human-written and AI-generated code is impossible. According to open-source maintainers and developers, this belief is demonstrably false. The reality is that Large Language Models (LLMs) produce characteristic errors that differ fundamentally from typical human mistakes, making AI-assisted submissions identifiable through careful code review.

While no detection method catches 100% of AI-assisted pull requests, experienced developers have learned to recognize telltale hallucination patterns. LLMs tend to generate plausible-sounding but incorrect code, inventing non-existent functions, misremembering API specifications, and creating logic that appears sound on the surface but fails under scrutiny. Human errors, by contrast, typically stem from incomplete understanding, typos, or logical oversights—fundamentally different failure modes that leave distinct fingerprints in code reviews.

The distinction becomes apparent when examining specific issues:

• LLMs confidently implement solutions that reference libraries or functions that don't exist
• Human errors usually involve genuine misunderstandings rather than fabricated technical details
• AI-generated code often contains subtle logical inconsistencies that humans would catch during testing
• Pattern recognition in commit messages and code structure reveals LLM-typical behaviors
• Human developers make mistakes tied to their specific knowledge gaps and experience level

This ability to distinguish AI assistance from human contributions holds significant implications for software quality and community trust. Open-source maintainers can implement appropriate review standards without outright banning AI tools. Instead, they can establish expectations for human oversight and accountability in code submissions.

Understanding these fundamental differences between human and AI errors enables projects to harness LLM assistance while maintaining code integrity. Rather than creating an unverifiable landscape, developers have developed practical methodologies for identifying when code requires additional scrutiny. This distinction supports the evolution of responsible AI integration in collaborative development environments, ensuring that automation enhances rather than compromises the quality standards that open-source communities depend upon.