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Show Notes
“How much productivity is AI actually giving your engineering teams?” is the wrong question.
In this episode of the HangarDX podcast, Ankit Jain, co-founder and CEO of Aviator, talks with Yegor Denisov-Blanch, researcher at Stanford University, about how engineering productivity is actually measured—and what the data says about AI’s impact on software teams.
Yegor shares insights from large-scale studies on developer output, why early AI productivity claims were overstated, how high-performing teams compound their gains, why some teams see no benefit at all, and what engineering leaders should (and shouldn’t) measure when rolling out AI across the software development lifecycle.
00:00 Introduction to Developer Productivity Research
06:12 Research Methodology and Expert Evaluations
10:35 Impact of AI on Developer Productivity
18:56 Invisible Contributions and Team Dynamics
24:59 Navigating Speed in Startups vs. Enterprises
26:34 The Role of AI in Productivity Gains
28:24 Measuring AI Usage and Results
30:14 Experimentation and Adaptation in AI
33:40 Understanding Ghost Engineers
38:07 Remote Work and Performance Dynamics
About Yegor Denisov-Blanch
Yegor helps software engineering teams make better decisions with data.
Currently he is a researcher at Stanford University. Previously, Yegor led digital transformation at DHL, and was a national champion Olympic weightlifter.
About Hangar DX (https://dx.community/)
The Hangar is a community of senior DevOps and senior software engineers focused on developer experience. This is a space where vetted, experienced professionals can exchange ideas, share hard-earned wisdom, troubleshoot issues, and ultimately help each other in their projects and careers.
We invite developers who work in DX and platform teams at their respective companies, or who are interested in developer productivity.