Beyond the AI Productivity Bump: Building Metrics That Last

Most engineering teams have adopted AI coding tools. The productivity gains are real. Research across 2,172 developer-weeks shows roughly 25% year-over-year improvement for regular AI users, along with meaningful gains in test coverage and review efficiency.

But adoption was the easy part. As AI becomes embedded in daily workflows, new questions are surfacing: code churn is climbing faster than output, duplication is expanding, and the metrics most teams rely on weren't designed to capture what's changing underneath.

This session digs into what the data actually shows about AI-assisted development, where the gains are durable, where they're fragile, and what engineering leaders should be paying attention to as AI goes from experiment to everyday.

Discussion based on findings from the AI Multiplier Effect report: https://www.gitkraken.com/reports/ai-multiplier-effect