GOTO - The Brightest Minds in Tech

State of the Art of DORA Metrics & AI Integration • Nathen Harvey & Charles Humble

Jan 30, 2026
Guest
Nathen Harvey
Nathen Harvey, leader of DORA research at Google Cloud and product manager focused on software delivery, joins to discuss AI's real effects on software development. He explains why early AI adoption can hurt stability and throughput. He outlines DORA's five delivery metrics and a seven-capability AI model. He explores AI as an amplifier, documentation gains, trust challenges, and practical next steps for teams.
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INSIGHT

DORA's Purpose And Dual Role

  • DORA measures software delivery performance to show how teams ship value faster and safer.
  • The metrics reveal leading indicators for outcomes and lagging indicators for team capabilities.
ADVICE

Turn Metrics Into Experiments

  • Use DORA metrics for a baseline and run capability assessments to find weak spots.
  • Form hypotheses (e.g., improve CI) and run experiments to validate improvements in your context.
INSIGHT

AI Can Worsen System Throughput

  • Early AI adoption correlated with faster code generation but worse throughput and stability.
  • Generated code exposed downstream bottlenecks because writing code often wasn't the system constraint.
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