AI-Generated Code Is Already Competing With Human Code — Daksh Gupta, Greptile

This talk provides empirical evidence on the quality of AI-generated code in enterprise codebases, analyzing over a million pull requests. It demonstrates that AI-generated code (from models like Codex, Claude Code, and Devin) performs comparably to human-written code across metrics like revert rates, issue severity (P0/P1/P2), and review rounds. Notably, humans were more likely to introduce P0 bugs. The talk also details specific failure modes for different AI models (e.g., SQL injection for Claude, auth bypass for Devin, N+1 queries for Cursor) and discusses the implications for code review at scale, proposing a new validation framework based on user contract adherence, future violation likelihood, and author intent, implemented using blast-radius analysis and sandboxed browser agents.