Engineering Enablement by DX

AI adoption among software developers is approaching 100%, AI-authored code now makes up more than half of merged code, and developers report saving more time with AI every quarter. But those gains aren’t translating evenly into better outcomes.
In this episode of Engineering Enablement, host Brian Houck, Distinguished Scientist at DX, sits down with Justin Reock, Deputy CTO at DX, to unpack findings from DX’s latest AI Impact Report. They explore where AI is improving engineering velocity and developer experience, where concerns are emerging around PR size, change confidence, and failure rates, and why rising AI spend has yet to produce a comparable increase in innovation.
They also discuss how AI is changing the meaning of code maintainability and where developers’ AI-driven time savings may actually be going.

Where to find Justin Reock:
• LinkedIn: https://www.linkedin.com/in/justinreock

Where to find Brian Houck: 
• LinkedIn: https://www.linkedin.com/in/brianhouck

In this episode, we cover:
(00:00) Intro
(01:45) How the current AI impact report is tied to Core 4 
(03:24) The state of AI adoption
(05:12) How much time AI is saving developers and percentage of AI-authored code
(07:47) AI’s impact on PR throughput and deployment frequency
(11:09) How EMs are shipping more code
(13:02) Why larger PRs may be problematic
(18:21) The growing gap between code maintainability and change confidence
(21:48) How perceived code quality varies by organization size
(23:49) The growing volatility in change failure rates
(28:07) What the Developer Experience Index reveals
(32:20) Cost, dev ramp-up, and innovation ratio 
(35:38) Where AI time savings are getting lost
(37:11) Questions and wrap-up

Referenced:
• DX Core 4 Productivity Framework
• AI Impact report
• The AI-native developer - by Brian Houck
• GitHub Copilot and Developer Productivity: An Observational Dose-Response Analysis
• Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools | NBER 
• The Productivity-Experience Paradox - Annie Vella
• EngThrive: Make It Fast and Easy to Do Great Work
• The AI efficiency plateau - by Brian Houck
• Tradable Quality Hypothesis

Tim Bozarth is a Corporate Vice President in Microsoft CoreAI, where he leads engineering for next-generation developer experiences and Microsoft's Engineering Thrive initiative. Throughout his career at Microsoft, Google, Netflix, and Box, he has focused on developer productivity, engineering systems, and organizational effectiveness.
In this episode of Engineering Enablement, Tim joins host Brian Houck to discuss Engineering Thrive, Microsoft's framework for measuring and improving engineering productivity. They explore why AI makes outcome-based metrics more important than ever, where new bottlenecks are emerging in the software development lifecycle, and why verification and confidence may become more valuable than code generation itself. Tim also shares why the purpose of engineering remains the same despite rising levels of abstraction, the skills that remain durable in an AI-driven world, and why engineering leaders should focus on outcomes rather than activity metrics.

Where to find Tim Bozarth:
• LinkedIn: linkedin.com/in/tbozarth

Where to find Brian Houck: 
• LinkedIn: ⁠https://www.linkedin.com/in/brianhouck⁠

In this episode, we cover:
(00:00) Intro
(02:13) What Engineering Thrive is and the problem it solves
(04:46) Why Engineering Thrive isn't specific to Microsoft
(09:10) The impact of Engineering Thrive at Microsoft
(14:31) Why AI makes outcome-based productivity metrics more important
(18:22) Where AI is creating new bottlenecks in the SDLC
(24:37) Why more abstraction doesn't change the purpose of engineering
(27:25) The durable skills of good engineers 
(33:03) The changing economics of software development
(36:56) Advice for leaders: measure outcomes, not activity

Referenced:
• DX Core 4 Productivity Framework
• EngThrive: Make It Fast and Easy to Do Great Work: Building a durable model for outcome-oriented engineering measurement
• Quote by Eliyahu M. Goldratt: “Tell me how you measure me and...”
• Jevons paradox - Wikipedia

AI coding agents are changing how software gets built, but they're also forcing organizations to rethink how engineering effectiveness is measured. Traditional developer experience metrics were designed for humans, not AI agents, so how should engineering leaders adapt?
In this webinar, Justin Reock, Deputy CTO at DX, is joined by Brian Houck, Distinguished Scientist at DX and co-author of the SPACE framework, to explore the emerging field of agent experience and how it builds on developer experience rather than replacing it. They discuss how organizations can prepare for AI-assisted software development, how the DX Core 4 applies in the age of AI, why metrics like token usage and PR throughput don't tell the whole story, and the growing importance of documentation. They also examine the impact AI-driven pressure is having on burnout and cognitive overload.
Throughout the conversation, they share practical guidance for building engineering organizations where both developers and AI agents can do their best work.

Where to find Brian Houck: 
• LinkedIn: https://www.linkedin.com/in/brianhouck

Where to find Justin Reock:
• LinkedIn: ⁠https://www.linkedin.com/in/justinreock⁠

In this episode, we cover:
(00:00) Intro
(01:26) Justin’s new role at DX
(03:53) What agent experience is and why engineering leaders should care
(08:55) How to improve agent experience at the platform level
(11:25) How agent experience and developer experience influence each other
(14:41) Preparing engineering teams for agentic work
(21:38) Why the DX Core 4 still matters in the age of AI
(27:23) What PR throughput actually measures 
(32:47) The limits of token metrics
(37:10) What the data shows about documentation and developer experience
(39:32) Improving documentation for AI agents
(40:43) AI-washing, burnout, and cognitive overload
(45:35) Brian's upcoming research on agent experience

Referenced:
• DX Core 4 Productivity Framework
• The SPACE of AI | Queue
• Sara Chizari on LinkedIn
• The Middle Loop - Annie Vella
• gastownhall/gastown: Gas Town - multi-agent workspace manager · GitHub
• DORA, SPACE, and DevEx: Which framework should you use?
• AI Impact Report: Q1 2026
• AI-authored code has nearly doubled, but so has PR size 
• EngThrive: Make It Fast and Easy to Do Great Work: Building a durable model for outcome-oriented engineering measurement
• Google's Project Aristotle - Psychological Safety 
• Zapier

Sejal Amin is the Chief Technology Officer at Priceline, where she leads product engineering, infrastructure, data, and technology operations. Pedro Gutierrez is Senior Director of Software Engineering, where he has helped drive developer experience initiatives and the adoption of Priceline's product operating model.
In this episode of the Engineering Enablement podcast, Justin Reock talks with Sejal and Pedro about Priceline's journey from a project-based organization to a product operating model and the role developer experience played in making that transformation successful. They discuss how DX metrics and developer feedback helped identify organizational bottlenecks, guide structural changes, empower engineering managers, and build trust across teams. They also explore the importance of clear communication, creating a dedicated developer experience team, and how their operating model has helped prepare Priceline for AI-driven software development.

Where to find Sejal Amin:  
• LinkedIn: https://www.linkedin.com/in/sejal-amin
Where to find Pedro Gutierrez: 
• LinkedIn: https://www.linkedin.com/in/pedro-gutierrez-b6605422
Where to find Justin Reock:
• LinkedIn: https://www.linkedin.com/in/justinreock

In this episode, we cover:
(00:00) Intro
(01:07) Meet Sejal Amin and Pedro Gutierrez
(01:47) How Priceline's developer experience journey began
(04:55) Lessons from Priceline's first developer experience surveys
(06:55) How DX improved Priceline's developer experience surveys
(09:47) Identifying the causes of organizational slowness
(12:33) How the product operating model changed the way Priceline works
(14:10) Priceline's phased rollout with DX
(18:14) How DX insights drove organizational changes
(19:33) Why Priceline improved developer experience before org change was complete
(22:18) How clear communication builds trust
(24:25) Early results from Priceline's Core Four
(25:38) Creating a culture of continuous feedback to build trust
(27:40) What has changed in the engineering manager role
(30:10) Resources for learning about the product operating model
(32:40) What Pedro learned from implementing DX
(34:51) The developer experience team
(35:59) How AI tools have impacted Priceline’s teams 
(37:20) How the product operating model supports AI-driven development
(39:13) Final advice for engineering leaders

Referenced:
• Measuring developer productivity with the DX Core 4
• Transformed: Moving to the Product Operating Model (Silicon Valley Product Group) 
• Team Topologies
• Flow Framework
• Project to Product: How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework

In this closing panel from DX Annual, Rafe Colburn, Chief Product and Technology Officer at Etsy; Jesse Adametz, Senior Director of Engineering, Platform Engineering at Twilio; Eirini Kalliamvakou, Research Advisor at GitHub; Collin Green, Senior Staff UX Researcher at Google; and Brian Houck, Senior Principal Applied Scientist at Microsoft debate some of the biggest questions surrounding AI and engineering productivity.
They discuss whether AI will reduce the need for engineers, how AI is affecting technical debt, the future role of software engineers in an agentic world, and whether organizations should mandate AI adoption. They also explore how bottlenecks are shifting across the software development lifecycle, the challenges facing junior engineers, and why learning, culture, and change management may ultimately matter more than the tools themselves.

Where to find Rafe Colburn:
• LinkedIn: https://www.linkedin.com/in/rafeco
• Blog: https://rafe.codes
Where to find Eirini Kalliamvakou: 
• LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865
• X: https://x.com/irina_kAl
Where to find Brian Houck: 
• LinkedIn: https://www.linkedin.com/in/brianhouck
Where to find Jesse Adametz: 
• LinkedIn: https://www.linkedin.com/in/jesseadametz 
• X: https://x.com/jesseadametz 
• Website: https://www.jesseadametz.com 
Where to find Collin Green: 
• LinkedIn: https://www.linkedin.com/in/collin-green-97720378

In this episode, we cover:
(00:00) Intro
(01:16) Why an AI-first SDLC doesn’t mean fewer engineers 
(03:09) The debate over AI and technical debt
(07:40) AI-generated code and the future role of engineers
(14:16) Why mandating AI use doesn't necessarily lead to better outcomes
(20:43) Predictions for the future of junior engineers 
(23:22) Where the bottlenecks are in the SDLC now
(28:25) How risk influences AI use 
(32:38) Why the human side is the biggest AI adoption challenge

Referenced:
• Etsy
• GitHub
• Microsoft
• Twilio
• Google 
• Stewart Reichling
• What is the SPACE framework and when should you use it?