In this episode, Abi Noda speaks with Derek DeBellis, lead researcher at Google’s DORA team, about their latest report on generative AI’s impact on software productivity.

They dive into how the survey was built, what it reveals about developer time and “flow,” and the surprising gap between individual and team outcomes. Derek also shares practical advice for leaders on measuring AI impact and aligning metrics with organizational goals.

Where to find Derek DeBellis: 

• LinkedIn: https://www.linkedin.com/in/derekdebellis/

Where to find Abi Noda:

• LinkedIn: https://www.linkedin.com/in/abinoda 

In this episode, we cover:

(00:00) Intro: DORA’s new Impact of Gen AI report

(03:24) The methodology used to put together the surveys DORA used for the report 

(06:44) An example of how a single word can throw off a question 

(07:59) How DORA measures flow 

(10:38) The two ways time was measured in the recent survey

(14:30) An overview of experiential surveying 

(16:14) Why DORA asks about time 

(19:50) Why Derek calls survey results ‘observational data’ 

(21:49) Interesting findings from the report 

(24:17) DORA’s definition of productivity 

(26:22) Why a 2.1% increase in individual productivity is significant 

(30:00) The report’s findings on decreased team delivery throughput and stability 

(32:40) Tips for measuring AI’s impact on productivity 

(38:20) Wrap up: understanding the data 

Referenced:

• DORA | Impact of Generative AI in Software Development
• The science behind DORA
• Yale Professor Divulges Strategies for a Happy Life 
• Incredible! Listening to ‘When I’m 64’ makes you forget your age
• Slow Productivity: The Lost Art of Accomplishment without Burnout
• DORA, SPACE, and DevEx: Which framework should you use?
• SPACE framework, PRs per engineer, AI research