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Explore three data-driven strategies to address AI-generated code quality challenges in this 27-minute conference talk from LeadDev New York 2025. Examine research findings from over 250 million lines of code across major repositories including Google, Microsoft, Meta, and 1,000+ other organizations to understand how AI-authored code differs from human-written code in terms of maintainability and technical debt accumulation. Learn about the unprecedented scale of code quality changes occurring as AI reduces human-guided development, discover consistent patterns in how AI can compromise code quality, and understand the hidden costs of increased development velocity. Gain insights into how traditional best practices like DRY (Don't Repeat Yourself) principles are being affected by AI code generation, and develop practical policies to mitigate the long-term risks while maintaining the benefits of AI-assisted development. Access comprehensive data analysis combining five years of code operation trends with newly-released AI usage APIs to build informed engineering leadership strategies for managing AI adoption in software development teams.
