Engineering Enablement by DX

Meta researcher Moritz Beller joins Brian Houck to discuss how Meta measures developer productivity. Moritz explains diff authoring time and how Meta uses it to evaluate tools and guide engineering decisions. They explore how AI is changing engineers’ work and revealing gaps in traditional metrics. They also revisit Moritz’s “Mind the Gap” study and discuss the promise and risks of AI agents for testing.

Where to find Moritz Beller:
• LinkedIn: https://www.linkedin.com/in/inventitech 
• X: https://x.com/Inventitech 
• GitHub: https://github.com/Inventitech 
• Website: https://inventitech.com

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

In this episode, we cover:
(00:00) Intro
(02:10) Moritz’s role at Meta
(04:01) Measuring diff authoring time
(08:20) Measuring A/B experiments
(12:11) What diff authoring time reveals
(14:45) Planning and leadership reporting
(18:36) Why Meta measures teams, not individuals
(22:33) Where developer time goes with AI
(26:06) AI’s impact on junior and senior developers
(27:18) Capturing invisible work with AI
(29:32) The “Mind the Gap” study
(33:40) Revisiting “Mind the Gap” in 2026
(38:32) AI agents for software testing
(41:49) What remains hard to measure

Referenced:
• State of AI Impact in Engineering Q2 Report 2026
• GitHub Copilot and Developer Productivity: An Observational Dose-Response Analysis
• From Technical Debt to Cognitive and Intent Debt 
• Mind the Gap: On the Relationship Between Automatically Measured and Self-Reported Productivity
• The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
• When, how, and why developers (do not) test in their IDEs | Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering
• DORA, SPACE, and DevEx: Which framework should you use
• A Tale of Two Cities: Software Developers Working from Home During the COVID-19 Pandemic

In this episode of Engineering Enablement, Robert Lucero, Chief Architect at Okta, joins host Brian Houck to discuss how agentic AI is changing identity and access management. They explore how organizations should define agent identities, why authentication and authorization remain foundational, and how sandboxing, fine-grained permissions, and just-in-time access can enable agents to operate autonomously without creating unacceptable risk.
Robert shares what Okta has learned from driving AI adoption among security-minded engineers, what makes a repository ready for AI agents, and why strong testing, CI, and review processes matter even more as AI generates more code. They also discuss AI’s role in software validation, the need for human judgment, and whether AI will ultimately give the advantage to security teams or attackers.

Where to find Robert Lucero:
• LinkedIn: https://www.linkedin.com/in/rlucero

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

In this episode, we cover:
(00:00) Intro
(02:16) Meet Robert Lucero
(02:56) Why identity becomes critical as AI agents access more systems
(06:07) Governing automated access for non-deterministic agents
(08:01) Defining and managing agent identities
(10:32) Authentication and authorization for AI agents
(13:33) Determining how much autonomy to give AI agents
(19:44) Identity systems as the control plane for AI agents
(22:40) Driving AI adoption among security-minded engineers
(25:48) Why AI’s impact on engineering extends beyond coding
(28:44) The three components of AI readiness for repositories
(30:55) AI’s role in software testing and validation
(34:26) Whether AI gives defenders or attackers the advantage
(36:01) Where to find the best food in the country

Referenced:
• State of AI Impact in Engineering Q2 Report 2026
• Okta
• The Hugging Face incident and the road ahead | OpenAI
• GitHub Copilot · Your AI pair programmer
• Claude Code by Anthropic | AI Coding Agent, Terminal, IDE
• Eight Myths on Software Engineering and GenAI | Queue
• Adriana Corona

Max Kanat-Alexander is an Executive Distinguished Engineer at Capital One, where he helps lead developer experience and AI-assisted software development.
In this episode of Engineering Enablement, Max joins Brian Houck to discuss how AI is changing software development and why strong engineering fundamentals matter more than ever. They explore how engineering skills are evolving, why AI can amplify weaknesses in the development lifecycle, and how teams should rethink code review, quality, and testing.
Max also shares how leaders can assess whether their organizations are ready for more advanced AI workflows and why developing the next generation of senior engineers remains one of the biggest open questions in software engineering.

Where to find Max Kanat-Alexander:
• LinkedIn: https://www.linkedin.com/in/mkanat 
• X: https://x.com/mkanat 
• Blog: https://www.codesimplicity.com 

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

In this episode, we cover:
(00:00) Intro
(01:54) Max’s role at Capital One
(02:52) Where to invest in engineering organizations  
(06:36) The new entry-level engineering skills to pay attention to 
(10:39) Why deepening your understanding still matters 
(12:29) The bottlenecks around code review 
(19:40) Why human code reviews still have value 
(25:20) Why not all PRs need human review
(26:11) The vicious cycle of AI-driven development
(30:13) Using LLMs for refactoring 
(33:58) AI readiness and why fixing engineering systems is so hard
(38:42) Why research is needed on creating good senior engineers
(41:00) Why AI will increase the need for engineers

Referenced:
• DX Core 4 Productivity Framework
• Capital One 
• Code Review Guidelines at Google 
• Code Simplicity by Max Kanat-Alexander
• How Microsoft sees engineering bottlenecks changing with AI

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