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Engineering Enablement by DX

DX

The show focused on developer productivity and the teams and leaders dedicated to improving it. Each episode features in-depth interviews with Platform and DevEx teams, along with the latest research and approaches for measuring developer productivity. Presented by DX (getdx.com), the developer intelligence platform designed by researchers.

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  • 26 episodes
  • fortnightly
  • Avg 39 min
  • English

Episodes 26

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  • June 8 · 26 min

    The current impact of AI on engineering velocity: What 400 companies are seeing (Abi Noda & Brian Houck)

    Recorded live at DX Annual, Abi Noda, co-founder and CEO of DX, joins Brian Houck of Microsoft to share an early look at DX’s new research on AI’s impact on engineering velocity. Drawing on data from a sample of DX customers, they discuss what companies are actually seeing as AI adoption matures. Most organizations in the study saw pull request throughput increase by 10 to 15 percent—far more modest than the 10x gains often promised in industry headlines. They explore why coding remains only a small part of developer work, where time saved by AI may be going, and the unintended consequences of moving faster, from shifting bottlenecks to “false velocity.” Abi also shares how engineering leaders are applying AI beyond coding and how DX is evolving its measurement framework to account for both human and agent productivity. Where to find Brian Houck: • LinkedIn: https://www.linkedin.com/in/brianhouck/ Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover: (00:00) Intro (00:53) What motivated DX’s research into AI’s impact on engineering velocity (02:36) How DX designed the study and selected companies (04:54) What DX’s data reveals about AI’s impact on engineering throughput (06:31) Why PR throughput was the most practical metric to publish (08:21) Why AI productivity gains are lower than many leaders expected (10:24) How an all-in culture can amplify AI productivity gains (12:35) Why it’s hard to track where AI-generated time savings are going (15:04) Unintended consequences of AI-driven productivity gains (17:12) Why leaders should look beyond coding to the rest of the SDLC (19:43) Cognitive debt and the human costs of AI-assisted development (21:33) How DX’s AI measurement framework is evolving (24:42) How to make agents more effective Referenced: • DX Core 4 Productivity Framework • DORA, SPACE, and DevEx: Which framework should you use? • Time Warp: The Gap Between Developers’ Ideal vs Actual Workweeks in an AI-Driven Era - Microsoft • Research • How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt • Measuring AI code assistants and agents

  • June 8 · 33 min

    Mapping the new SDLC at BNY: Codifying AI into every step of the delivery lifecycle (Jason Valentino)

    Jason Valentino is Head of Software Engineering Strategy at BNY, where he oversees developer tooling, DevEx, platform workflows, and software delivery governance across more than 8,000 engineers. In this session from DX Annual, Jason shares how BNY moved beyond AI coding assistants to rethink the entire software delivery lifecycle. He explains how his team identified bottlenecks across the SDLC, prioritized automation opportunities, and applied AI to planning, peer review, testing, change management, and compliance workflows. Jason also discusses what it takes to scale AI inside a highly regulated enterprise, including rewriting policies, partnering closely with risk and audit teams, and building a culture that encourages experimentation and rapid sharing of ideas. Where to find Jason Valentino: • LinkedIn: https://www.linkedin.com/in/jasonvalentino In this episode, we cover: (00:00) Intro (01:20) Early results from AI coding tools at BNY (04:08) The 3X stress test: What breaks if engineering throughput triples? (06:56) Three ways to apply AI across the SDLC: IDE and CLI tools (08:07) Using autonomous AI agents for repetitive engineering tasks (09:16) Embedding AI directly into SDLC workflows (12:27) Why leaders should encourage experimentation and “start saying yes” (15:00) Q&A: How platform and productivity teams are evolving to support AI (16:33) Q&A: Rewriting policies and controls for AI-assisted software delivery (17:52) Q&A: How AI is affecting software quality and test ownership (19:00) Q&A: What Jason is most proud of: Practical examples of AI across the SDLC (20:30) Q&A: How BNY handles duplicated work across AI initiatives (22:30) Q&A: How BNY uses AI to support regulatory and compliance work (23:30) Q&A: Automating code reviews and change tickets (25:55) Q&A: How increased AI-driven throughput is affecting on-call and reliability (27:11) Q&A: How BNY works with risk and audit partners to move quickly with AI (29:01) Q&A: How BNY scales successful AI use cases across the organization (30:42) Q&A: What Jason is most proud of after BNY’s busiest year with AI Referenced: • AI-assisted engineering: Q4 impact report • Measuring AI code assistants and agents • Measuring developer productivity with the DX Core 4 • Windsurf • Claude Code by Anthropic | AI Coding Agent, Terminal, IDE • Codex | AI Coding Agent

  • June 8 · 43 min

    Designing the AI‑native engineering organization with 1Password, Microsoft and Atlassian

    Abi Noda is joined live at DX Annual by three engineering leaders shaping AI adoption at scale: Tim Bozarth, Corporate Vice President in Microsoft’s CoreAI division; Nancy Wang, CTO of 1Password; and Taroon Mandhana, CTO of AI and Teamwork at Atlassian. Together, they discuss how AI is changing engineering organizations, from team structures and planning cycles to hiring, governance, and measurement. The panel explores how the profile of a great engineer is evolving, why smaller cross-functional teams are becoming more effective, and what happens when product managers, designers, and customer support teams start contributing code. They also share why they are encouraging AI adoption through enablement, training, and local champions rather than mandates, and how AI is shifting more of the software development lifecycle toward planning and validation. Finally, they discuss where human judgment remains essential, how to measure adoption and manage token usage, and how to connect AI investments to business outcomes while preserving room for experimentation and learning. Where to find Nancy Wang: • LinkedIn: https://www.linkedin.com/in/wangnancy Where to find Taroon Mandhana: • LinkedIn: https://www.linkedin.com/in/taroonm Where to find Tim Bozarth: • LinkedIn: https://www.linkedin.com/in/tbozarth Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover: (00:00) Intro (01:08) Introducing the panelists (02:16) AI’s impact on engineering team structures and planning cycles (05:00) How the role of the engineer is changing and what makes a great engineer (10:11) The opportunities and challenges of non-engineers writing code (15:26) Encouraging AI adoption without mandating it (21:25) What an AI-native SDLC looks like and why human judgment still matters (30:56) Measuring AI adoption, token usage, and ROI (37:06) How to tie AI investments to business outcomes Referenced: • DX Core 4 Productivity Framework • Microsoft • 1Password • Atlassian • Jira • Confluence • Loom • Rovo • Amazon Operating Cadence - Working Backwards

  • April 10 · 50 min

    Assumptions as code: SiriusXM’s approach to platform prioritization

    Eleanor Millman, Senior Staff Product Manager, and Mina Tawadrous, Associate Director of Platform Engineering at SiriusXM, join host Justin Reock to discuss how platform teams can scale prioritization without relying on revenue. They share how SiriusXM moved beyond RICE to build a custom framework for internal platforms, using weighted factors like developer speed, reliability, cost, and trust to guide decisions across teams. The episode also explores their concept of “assumptions as code,” in which teams store and reuse assumptions in a central repository to reduce misalignment and improve decision-making, with AI helping to surface and validate those assumptions. They close with how this system is shaping SiriusXM’s 2026 prioritization approach and what it signals about a broader shift toward builder-driven product development. Where to find Eleanor Millman: • LinkedIn: https://www.linkedin.com/in/eleanor-millman-98b10350 Where to find Mina Tawadrous: • LinkedIn: https://www.linkedin.com/in/mina-tawadrous Where to find Justin Reock: • LinkedIn: https://www.linkedin.com/in/justinreock In this episode, we cover: (00:00) Intro (01:17) Mina’s role and path into platform engineering (02:03) Eleanor’s background and shift into product (03:15) Scaling prioritization across platform engineering teams (05:41) Aligning platform priorities with stakeholders (09:08) Evolving RICE into a platform-specific prioritization framework (11:33) Iterating on the prioritization framework over time (16:57) How the framework, data, and conversations drive alignment (19:06) Storing assumptions as code in a central repository (26:47) Resolving assumption conflicts with user interviews (30:47) How stored assumptions integrate with AI workflows (35:30) Standard mode and different user personas (37:20) The industry shift towards builders (41:04) The challenges of platform engineering (43:36) How SiriusXM is prioritizing in 2026 Referenced: • Measuring AI code assistants and agents • SiriusXM • VMware • How SiriusXM revamped their platform and developer experience • RICE Scoring Model | Prioritization Method Overview • The evaporating cloud: A tool for resolving workplace conflict

  • April 3 · 38 min

    Measuring AI impact, assessing readiness, and new data trends

    In this episode of Engineering Enablement, Jesse Adametz joins Abi Noda, this time to host. Together, they explore how AI is showing up across the SDLC, not just in code generation, and how it is shifting bottlenecks across the development process. They unpack what “AI readiness” actually means in practice, and why it often comes down to developer experience fundamentals like documentation, environments, and feedback loops. They also discuss why enablement matters more than tool choice, how teams are thinking about measuring ROI, and what changes as background agents become more common. Finally, they explore how the role of the engineer may evolve, the open questions teams are still grappling with, and the challenges of non-engineers contributing to codebases. 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 Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover: (00:00) Intro (02:12) Where AI is showing up across the SDLC (05:53) AI readiness and its link to developer experience (08:23) Why enablement, education, and experimentation matter more than tool choice (13:05) The case for a dedicated enablement team (14:50) Measuring AI ROI: challenges and tradeoffs (19:46) Background agents and token spend (24:12) Measuring agent output with PR throughput (26:58) How the engineer role might change (31:01) Specs and documentation in the age of AI (33:11) Non-engineers writing code (35:30) What’s changing in the SDLC and open questions Referenced: • Measuring AI code assistants and agents • Lessons from Twilio’s multi-year platform consolidation • The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win • How Claude remembers your project - Claude Code Docs • specIsJustCode : r/ProgrammerHumor

  • February 6 · 39 min

    Scaling developer experience across 1,000 engineers at Dropbox

    Developer productivity is often framed as a tooling initiative or a morale issue. At scale, it’s a more complex socio-technical systems challenge that spans engineering foundations, leadership alignment, organizational structure, and culture. In this episode, Laura Tacho sits down with Uma Namasivayam, Senior Director, Engineering Productivity at Dropbox, to discuss how the company approaches developer experience across an organization of nearly 1,000 engineers. Uma explains why productivity must be treated as a business problem, how executive alignment enables sustained progress, and what it means to run developer experience like a product. The conversation also explores the intersection of AI and developer experience. Uma shares how Dropbox prepared its engineering systems to support AI adoption, why daily AI use depends more on habits than access, and how the company evaluates build-versus-buy decisions as AI tools struggle to scale in large environments. The episode concludes with a candid discussion of the open questions facing engineering leaders today: how to understand where AI-driven capacity actually goes, and how to connect improvements in developer experience to meaningful business outcomes in 2026. Where to find Uma Namasivayam: • LinkedIn: https://www.linkedin.com/in/unamasivayam Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/ • X: https://x.com/rhein_wein • Website: https://lauratacho.com/ • Laura’s course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-course In this episode, we cover: (00:00) Intro (00:45) Dropbox’s engineering org (01:59) Why developer productivity is a business problem (04:08) The role of executive sponsorship in developer productivity (06:02) How DX’s Core Four framework created a shared language (08:13) Treating developer experience as a product (11:30) How Dropbox prioritizes developer experience work (14:20) The challenge of tying developer experience to business outcomes (16:38) How AI and developer experience intersect at Dropbox (18:35) The prerequisites for AI adoption to accelerate work (20:26) How Dropbox encourages daily AI use (23:12) AI use beyond code completion (25:00) Managing AI tool demand at scale (27:56) Early results from Dropbox’s AI efforts (30:05) Progress on developer experience at Dropbox (32:55) Advice for organizations investing in developer experience (34:25) Capacity tradeoffs for developer experience (35:59) The unanswered questions around AI and capacity in 2026 Referenced: • DX Core 4 Productivity Framework • Dropbox.com

Showing 21–26 of 26 episodes