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

In this episode, Abi Noda is joined by Laura Tacho, CTO at DX, engineering leadership coach, and creator of the Core 4 framework. They explore how engineering organizations can avoid common pitfalls when adopting metrics frameworks like SPACE, DORA, and Core 4.

Laura shares a practical guide to getting started with Core 4—beginning with controllable input metrics that teams can actually influence. The conversation touches on Goodhart’s Law, why focusing too much on output metrics can lead to data distortion, and how leaders can build a culture of continuous improvement rooted in meaningful measurement.

Where to find Laura Tacho: 

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

• Website: https://lauratacho.com/

Where to find Abi Noda:

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

In this episode, we cover:

(00:00) Intro: Improving systems, not distorting data

(02:20) Goal setting with the new Core 4 framework

(08:01) A quick primer on Goodhart’s law

(10:02) Input vs. output metrics—and why targeting outputs is problematic

(13:38) A health analogy demonstrating input vs. output

(17:03) A look at how the key input metrics in Core 4 drive output metrics 

(24:08) How to counteract gamification 

(28:24) How to get developer buy-in

(30:48) The number of metrics to focus on 

(32:44) Helping leadership and teams connect the dots to how input goals drive output

(35:20) Demonstrating business impact 

(38:10) Best practices for goal setting

Referenced:

• DX Core 4 Productivity Framework
• Engineering Enablement Podcast
• DORA’s software delivery metrics: the four keys
• The SPACE of Developer Productivity: There’s more to it than you think
• DevEx: What Actually Drives Productivity
• DORA, SPACE, and DevEx: Which framework should you use?
• Goodhart's law 
• Nicole Forsgren - Microsoft | LinkedIn
• Campbell's law 
• Introducing Core 4: The best way to measure and improve your product velocity
• DX Core 4: Framework overview, key design principles, and practical applications
• DX Core 4: 2024 benchmarks - by Abi Noda

Brian Houck from Microsoft returns to discuss effective strategies for driving AI adoption among software development teams. Brian shares his insights into why the immense hype around AI often serves as a barrier rather than a facilitator for adoption, citing skepticism and inflated expectations among developers. He highlights the most effective approaches, including leadership advocacy, structured training, and cultivating local champions within teams to demonstrate practical use cases. 

Brian emphasizes the importance of honest communication about AI's capabilities, avoiding over-promises, and ensuring that teams clearly understand what AI tools are best suited for. Additionally, he discusses common pitfalls, such as placing excessive pressure on individuals through leaderboards and unrealistic mandates, and stresses the importance of framing AI as an assistant rather than a replacement for developer skills. Finally, Brian explores the role of data and metrics in adoption efforts, offering practical advice on how to measure usage effectively and sustainably.

Where to find Brian Houck: 

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

• Website: https://www.microsoft.com/en-us/research/people/bhouck/ 

Where to find Abi Noda:

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

In this episode, we cover:

(00:00) Intro: Why AI hype can hinder adoption among teams

(01:47) Key strategies companies use to successfully implement AI

(04:47) Understanding why adopting AI tools is uniquely challenging

(07:09) How clear and consistent leadership communication boosts AI adoption

(10:46) The value of team leaders ("local champions") demonstrating practical AI use

(14:26) Practical advice for identifying and empowering team champions

(16:31) Common mistakes companies make when encouraging AI adoption

(19:21) Simple technical reminders and nudges that encourage AI use

(20:24) Effective ways to track and measure AI usage through dashboards

(23:18) Working with team leaders and infrastructure teams to promote AI tools

(24:20) Understanding when to shift from adoption efforts to sustained use

(25:59) Insights into the real-world productivity impact of AI

(27:52) Discussing how AI affects long-term code maintenance

(29:02) Updates on ongoing research linking sleep quality to productivity

Referenced:

• DX Core 4 Productivity Framework
• Engineering Enablement Podcast
• DORA Metrics
• Dropbox Engineering Blog
• Etsy Engineering Blog
• Pfizer Digital Innovation
• Brown Bag Sessions – A Guide
• IDE Integration and AI Tools
• Developer Productivity Dashboard Examples

In this episode, we’re joined by author and researcher Gene Kim for a wide-ranging conversation on the evolution of DevOps, developer experience, and the systems thinking behind organizational performance. Gene shares insights from his latest work on socio-technical systems, the role of developer platforms, and how AI is reshaping the shape of engineering teams. We also explore the coordination challenges facing modern organizations, the limits of tooling, and the deeper principles that unite DevOps, lean, and platform engineering.

Mentions and links:

• Phoenix Project
• Decoding the DNA of the Toyota Production System
• Wiring the Winning Organization
• ETLS Vegas
• Find Gene on LinkedIn

Discussion points:

• (0:00) Introduction
• (2:12) The evolving landscape of developer experience
• (10:34) Option Value theory, and how GenAI helps developers
• (13:45) The aim of developer experience work
• (19:59) The significance of layer three changes
• (23:23) Framing developer experience
• (32:12) GenAI’s part in ‘the death of the stubborn developer”
• (36:05) GenAI’s implications on the workforce
• (38:05) Where Gene’s work is heading

In this episode, Airbnb Developer Productivity leader Anna Sulkina shares the story of how her team transformed itself and became more impactful within the organization. She starts by describing how the team previously operated, where teams were delivering but felt they needed more clarity and alignment across teams. Then, the conversation digs into the key changes they made, including reorganizing the team, clarifying team roles, defining strategy, and improving their measurement systems. 

Mentions and links

• Follow Anna on LinkedIn
• For A deeper look into how our Engineers and Data Scientists build a world of belonging, check out The Airbnb Tech Blog

Discussion points:

• (0:00) Intro
• (1:40) Skills that make a great developer productivity leader
• (4:36) Challenges in how the team operated previously
• (10:49) Changing the platform org’s focus and structure
• (16:04) Clarifying roles for EM’s, PM’s, and tech leads
• (20:22) How Airbnb defined its infrastructure org’s strategy
• (28:23) Improvements they’ve seen to developer experience satisfaction
• (32:13) The evolution of Airbnb’s developer experience survey

Many teams struggle to use developer productivity data effectively because they don’t know how to use it to decide what to do next. We know that data is here to help us improve, but how do you know where to look? And even then, what do you actually do to put the wheels of change in motion? Listen to this conversation with  Abi Noda and Laura Tacho (CEO and CTO at DX) about data-driven management and how to take a structured, analytical approach to using data for improvement.

Mentions and Links:

• Measuring developer productivity with the DX Core 4
• Laura’s developer productivity metrics course

Discussion points:

• (0:00) Intro
• (2:07) The challenge we’re seeing
• (6:53) Overview on using data
• (8:58) Use cases for data-engineering organizations
• (15:57) Use cases for data - engineering systems teams
• (21:38) Two types of metrics - Diagnostics and Improvement
• (38:09) Summary

In this episode, David Betts, leader of Twilio’s developer platform team, shares how Twilio leverages developer sentiment data to drive platform engineering initiatives, optimize Kubernetes adoption, and demonstrate ROI for leadership. David details Twilio’s journey from traditional metrics to sentiment-driven insights, the innovative tools his teams have built to streamline CI/CD workflows, and the strategies they use to align platform investments with organizational goals.

Mentions and links:

• Find David on LinkedIn
• Measuring developer productivity with the DX Core 4
• Ask Your Developer by Jeff Lawson, former CEO of Twilio

Discussion points:

• (0:00) Introduction
• (0:49) Twilio's developer platform team
• (2:03) Twilio's approach to release engineering and CD
• (4:10) How they use sentiment data and telemetry metrics
• (7:27) Comparing sentiment data and telemetry metrics
• (10:25) How to take action on sentiment data
• (13:16) What resonates with execs
• (15:44) Proving DX value: sentiment, efficiency, and ROI
• (19:15) Balancing quarterly and real-time developer feedback

Chris Chandler is a Senior Member of the Technical Staff for Developer Productivity at T-Mobile. Chris has led several major initiatives to improve developer experience including their internal developer portal, Starter Kits (a patented developer platform that predates Backstage), and Workforce Transformation Bootcamps for onboarding developers faster.

Mentions and links:

• Follow Chris on LinkedIn
• Measuring developer productivity with the DX Core 4
• Listen to Decoder with Nilay Patel.

Discussion points:

• (0:47) From developer experience to developer productivity
• (7:03) Getting executive buy-in for developer productivity initiatives
• (13:54) What Chris’s team is responsible for
• (17:02) How they’ve built relationships with other teams
• (20:57) How they built and got funding for Dev Console and Starter Kits
• (27:23) Homegrown solution vs Backstage

In this episode, Abi and Laura dive into the 2024 DX Core 4 benchmarks, sharing insights across data from 500+ companies. They discuss what these benchmarks mean for engineering leaders, how to interpret key metrics like the Developer Experience Index, and offer advice on how to best use benchmarking data in your organization.

Mentions and Links:

• DX core 4 benchmarks
• Measuring developer productivity with the DX Core 4
• Developer experience index (DXI)
• Will Larson’s article on the Core 4 and power of benchmarking data

Discussion points:

• (0:42) What benchmarks are for
• (3:44) Overview of the DX Core 4 benchmarks
• (6:07) PR throughput data 
• (11:05) Key insights related to startups and mobile teams 
• (14:54) Change fail rate data 
• (19:42) How to best use benchmarking data

In this episode, Abi and Laura introduce the DX Core 4, a new framework designed to simplify how organizations measure developer productivity. They discuss the evolution of productivity metrics, comparing Core 4 with frameworks like DORA, SPACE, and DevEx, and emphasize its focus on speed, effectiveness, quality, and impact. They explore why each metric was chosen, the importance of balancing productivity measures with developer experience, and how Core 4 can help engineering leaders align productivity goals with broader business objectives.   

Mentions and Links:

• Measuring developer productivity with the DX Core 4
• Laura’s developer productivity metrics course

Discussion Points:

• (2:42) Introduction to the DX Core 4
• (3:42) Identifying the Core 4's target audience and key stakeholders
• (4:38) Origins and purpose
• (9:20) Building executive alignment
• (14:15) Tying metrics to business value through output-oriented measures
• (24:45) Defining impact
• (32:42) Choosing between DORA, SPACE, and Core 4 frameworks