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AI-generated capacity: Where does the time go? (US/EMEA)

DX brought together R&D leaders who have been examining where AI-generated capacity is going within their organizations. They shared where they’ve seen that capacity go, how they’ve identified opportunities to reinvest it, and what they’ve changed when the results weren’t what they expected. The session will give attendees questions to ask in their own organizations and examples of how other leaders are turning AI-generated time savings into greater impact.

This discussion includes Distinguished Scientist Eirini Kalliamvakou (DX) and Nathan Hunt (Snowflake).

The session covers the following:

Where the time saved from AI goes: DX data shows that the time developers save with AI has roughly doubled over the past year, from approximately 3 hours to 6 hours per week, while the share of time spent on new work (the innovation ratio) has remained relatively unchanged.
How AI is reshaping engineering workflows: How Snowflake is managing the increase in AI-generated code, including a 20%+ month-over-month increase in pull requests, and why code review, verification, and infrastructure are becoming new bottlenecks.
How to turn AI-generated capacity into impact: Lessons from Snowflake on improving engineering workflows, encouraging experimentation, and redirecting capacity toward higher-value work.

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Speakers

Nathan Hunt

Principal Software Engineer II/Architect, Snowflake
Eirini Kalliamvakou

Distinguished Scientist, DX

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