The AI Native Dev - from Copilot today to AI Native Software Development tomorrow Why Tracking AI Usage Drives Better Results | Justin Reock & DX
19 snips
Aug 5, 2025 
Justin Reock, CTO at DX and an expert in measuring AI’s impact on software engineering, joins the conversation to explore the intricacies of AI integration in development. He highlights the difference between meaningful adoption and simply following trends. Topics include the dual levers of velocity—quality and maintainability—how proper measurement can drive real results, and the significant boost AI brings to developer productivity across the software development lifecycle. Insights into effective metrics for AI usage also take center stage.
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Balancing AI Velocity and Quality
- AI improves developer velocity but risks technical debt affecting maintainability.
- Measuring both short-term speed and long-term quality is crucial for true productivity impact.
Why Measure AI Usage?
- Measure AI adoption by tracking who uses it and how within your organization.
- Also track overall impact on code quality, delivery speed, and investment cost for data-driven decisions.
Begin with Utilization Metrics
- Start AI measurement with utilization metrics like active users and AI-assisted commits.
- Progress to impact and cost metrics for a comprehensive view of AI benefits.
