Engineering Enablement by Abi Noda

DX
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This is a weekly podcast focused on developer productivity and the teams and leaders dedicated to improving it. Topics include in-depth interviews with Platform and DevEx teams, as well as the latest research and approaches on measuring developer productivity. The EE podcast is hosted by Abi Noda, the founder and CEO of DX (getdx.com) and published researcher focused on developing measurement methods to help organizations improve developer experience and productivity.

  1. The evolving role of DevProd teams in the AI era

    SEP 26

    The evolving role of DevProd teams in the AI era

    CEO Abi Noda is joined by DX CTO Laura Tacho to discuss the evolving role of Platform and DevProd teams in the AI era. Together, they unpack how AI is reshaping platform responsibilities, from evaluation and rollout to measurement, tool standardization, and guardrails. They explore why fundamentals like documentation and feedback loops matter more than ever for both developers and AI agents. They also share insights on reducing tool sprawl, hardening systems for higher throughput, and leveraging AI to tackle tech debt, modernize legacy code, and improve workflows across the SDLC. Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda   • Substack: ​​https://substack.com/@abinoda   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: Why platform teams need to evolve (02:34) The challenge of defining platform teams and how AI is changing expectations (04:44) Why evaluating and rolling out AI tools is becoming a core platform responsibility (07:14) Why platform teams need solid measurement frameworks to evaluate AI tools (08:56) Why platform leaders should champion education and advocacy on measurement (11:20) How AI code stresses pipelines and why platform teams must harden systems (12:24) Why platform teams must go beyond training to standardize tools and create workflows (14:31) How platform teams control tool sprawl (16:22) Why platform teams need strong guardrails and safety checks (18:41) The importance of standardizing tools and knowledge (19:44) The opportunity for platform teams to apply AI at scale across the organization (23:40) Quick recap of the key points so far (24:33) How AI helps modernize legacy code and handle migrations (25:45) Why focusing on fundamentals benefits both developers and AI agents (27:42) Identifying SDLC bottlenecks beyond AI code generation (30:08) Techniques for optimizing legacy code bases  (32:47) How AI helps tackle tech debt and large-scale code migrations (35:40) Tools across the SDLC Referenced: DX Core 4 Productivity FrameworkMeasuring AI code assistants and agentsAbi Noda's LinkedIn postMeasuring AI code assistants and agents with the AI Measurement FrameworkThe SPACE framework: A comprehensive guide to developer productivityCommon workflows - AnthropicEnterprise Tech Leadership Summit Las Vegas 2025Driving enterprise-wide AI tool adoption with Bruno PassosAccelerating Large-Scale Test Migration with LLMs | by Charles Covey-Brandt | The Airbnb Tech Blog | MediumJustin Reock - DX | LinkedInA New Tool Saved Morgan Stanley More Than 280,000 Hours This Year - Business Insider

    37 min
  2. Lessons from Twilio’s multi-year platform consolidation

    SEP 12

    Lessons from Twilio’s multi-year platform consolidation

    In this episode, host Laura Tacho speaks with Jesse Adametz, Senior Engineering Leader on the Developer Platform at Twilio. Jesse is leading Twilio’s multi-year platform consolidation, unifying tech stacks across large acquisitions and driving migrations at enterprise scale. He discusses platform adoption, the limits of Kubernetes, and how Twilio balances modernization with pragmatism. The conversation also explores treating developer experience as a product, offering “change as a service,” and Twilio’s evolving approach to AI adoption and platform support. 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 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 (01:30) Jesse’s background and how he ended up at Twilio (04:00) What SRE teaches leaders and ICs (06:06) Where Twilio started the post-acquisition integration (08:22) Why platform migrations can’t follow a straight-line plan (10:05) How Twilio balances multiple strategies for migrations (12:30) The human side of change: advocacy, training, and alignment (17:46) Treating developer experience as a first-class product (21:40) What “change as a service” looks like in practice (24:57) A mandateless approach: creating voluntary adoption through value (28:50) How Twilio demonstrates value with metrics and reviews (30:41) Why Kubernetes wasn’t the right fit for all Twilio workloads  (36:12) How Twilio decides when to expose complexity (38:23) Lessons from Kubernetes hype and how AI demands more experimentation (44:48) Where AI fits into Twilio’s platform strategy (49:45) How guilds fill needs the platform team hasn’t yet met (51:17) The future of platform in centralizing knowledge and standards (54:32) How Twilio evaluates tools for fit, pricing, and reliability  (57:53) Where Twilio applies AI in reliability, and where Jesse is skeptical (59:26) Laura’s vibe-coded side project built on Twilio (1:01:11) How external lessons shape Twilio’s approach to platform support and docs Referenced: The AI Measurement FrameworkExperianTransact-SQL - WikipediaTwilioKubernetesCopilotClaude CodeWindsurfCursorBedrock

    1h 6m
  3. Driving enterprise-wide AI tool adoption

    SEP 5

    Driving enterprise-wide AI tool adoption

    In this episode of Engineering Enablement, host Laura Tacho talks with Bruno Passos, Product Lead for Developer Experience at Booking.com, about how the company is rolling out AI tools across a 3,000-person engineering team. Bruno shares how Booking.com set ambitious innovation goals, why cultural change mattered as much as technology, and the education practices that turned hesitant developers into daily users. He also reflects on the early barriers, from low adoption and knowledge gaps to procurement hurdles, and explains the interventions that worked, including learning paths, hackathon-style workshops, Slack communities, and centralized procurement. The result is that Booking.com now sits in the top 25 percent of companies for AI adoption. Where to find Bruno Passos: • LinkedIn: https://www.linkedin.com/in/brpassos/ • X: https://x.com/brunopassos 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 (01:09) Bruno’s role at Booking.com and an overview of the business  (02:19) Booking.com’s goals when introducing AI tooling (03:26) Why Booking.com made such an ambitious innovation ratio goal  (06:46) The beginning of Booking.com’s journey with AI (08:54) Why the initial adoption of Cody was low (13:17) How education and enablement fueled adoption (15:48) The importance of a top-down cultural change for AI adoption (17:38) The ongoing journey of determining the right metrics (21:44) Measuring the longer-term impact of AI  (27:04) How Booking.com solved internal bottlenecks to testing new tools (32:10) Booking.com’s framework for evaluating new tools (35:50) The state of adoption at Booking.com and efforts to expand AI use (37:07) What’s still undetermined about AI’s impact on PR/MR quality (39:48) How Booking.com is addressing lagging adoption and monitoring churn (43:24) How Booking.com’s Slack community lowers friction for questions and support (44:35) Closing thoughts on what’s next for Booking.com’s AI plan Referenced: Measuring AI code assistants and agentsDX Core 4 FrameworkBooking.comSourcegraph SearchCody | AI coding assistant from SourcegraphGreyson Junggren - DX | LinkedIn

    47 min
  4. Measuring AI code assistants and agents with the AI Measurement Framework

    AUG 15

    Measuring AI code assistants and agents with the AI Measurement Framework

    In this episode of Engineering Enablement, DX CTO Laura Tacho and CEO Abi Noda break down how to measure developer productivity in the age of AI using DX’s AI Measurement Framework. Drawing on research with industry leaders, vendors, and hundreds of organizations, they explain how to move beyond vendor hype and headlines to make data-driven decisions about AI adoption. They cover why some fundamentals of productivity measurement remain constant, the pitfalls of over-relying on flawed metrics like acceptance rate, and how to track AI’s real impact across utilization, quality, and cost. The conversation also explores measuring agentic workflows, expanding the definition of “developer” to include new AI-enabled contributors, and avoiding second-order effects like technical debt and slowed PR throughput. Whether you’re rolling out AI coding tools, experimenting with autonomous agents, or just trying to separate signal from noise, this episode offers a practical roadmap for understanding AI’s role in your organization—and ensuring it delivers sustainable, long-term gains. Where to find Laura Tacho: • X: https://x.com/rhein_wein • LinkedIn: https://www.linkedin.com/in/lauratacho/ • Website: https://lauratacho.com/ Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  In this episode, we cover: (00:00) Intro (01:26) The challenge of measuring developer productivity in the AI age (04:17) Measuring productivity in the AI era — what stays the same and what changes (07:25) How to use DX’s AI Measurement Framework  (13:10) Measuring AI’s true impact from adoption rates to long-term quality and maintainability (16:31) Why acceptance rate is flawed — and DX’s approach to tracking AI-authored code (18:25) Three ways to gather measurement data (21:55) How Google measures time savings and why self-reported data is misleading (24:25) How to measure agentic workflows and a case for expanding the definition of developer (28:50) A case for not overemphasizing AI’s role (30:31) Measuring second-order effects  (32:26) Audience Q&A: applying metrics in practice (36:45) Wrap up: best practices for rollout and communication  Referenced: DX Core 4 Productivity FrameworkMeasuring AI code assistants and agentsAI is making Google engineers 10% more productive, says Sundar Pichai - Business Insider

    41 min
  5. How to cut through the hype and measure AI’s real impact (Live from LeadDev London)

    AUG 8

    How to cut through the hype and measure AI’s real impact (Live from LeadDev London)

    In this special episode of the Engineering Enablement podcast, recorded live at LeadDev London, DX CTO Laura Tacho explores the growing gap between AI headlines and the reality inside engineering teams—and what leaders can do to close it. Laura shares data from nearly 39,000 developers across 184 companies, highlights the Core 4 and introduces the AI Measurement Framework, and offers a practical playbook for using data to improve developer experience, measure AI’s true impact, and build better software without compromising long-term performance. Where to find Laura Tacho: • X: https://x.com/rhein_wein • LinkedIn: https://www.linkedin.com/in/lauratacho/ • Website: https://lauratacho.com/ In this episode, we cover: (00:00) Intro: Laura’s keynote from LDX3 (01:44) The problem with asking how much faster can we go with AI? (03:02) How the disappointment gap creates barriers to AI adoption (06:20) What AI adoption looks like at top-performing organizations (07:53) What leaders must do to turn AI into meaningful impact (10:50) Why building better software with AI still depends on fundamentals (12:03) An overview of the DX Core 4 Framework (13:22) Why developer experience is the biggest performance lever (15:12) How Block used Core 4 and DXI to identify 500,000 hours in time savings (16:08) How to get started with Core 4 (17:32) Measuring AI with the AI Measurement Framework (21:45) Final takeaways and how to get started with confidence Referenced: LDX3 by LeadDev | The Festival of Software Engineering Leadership | LondonSoftware engineering with LLMs in 2025: reality checkSPACE framework, PRs per engineer, AI researchThe AI adoption playbook: Lessons from Microsoft's internal strategyDX Core 4 Productivity FrameworkNicole ForsgrenMargaret-Anne StoreyDropbox.comEtsyPfizerDrew Houston - Dropbox | LinkedInBlockCursorDora.devSourcegraphBooking.com

    23 min
  6. Unpacking METR’s findings: Does AI slow developers down?

    AUG 1

    Unpacking METR’s findings: Does AI slow developers down?

    In this episode of the Engineering Enablement podcast, host Abi Noda is joined by Quentin Anthony, Head of Model Training at Zyphra and a contributor at EleutherAI. Quentin participated in METR’s recent study on AI coding tools, which revealed that developers often slowed down when using AI—despite feeling more productive. He and Abi unpack the unexpected results of the study, which tasks AI tools actually help with, and how engineering teams can adopt them more effectively by focusing on task-level fit and developing better digital hygiene. Where to find Quentin Anthony:  • LinkedIn: https://www.linkedin.com/in/quentin-anthony/ • X: https://x.com/QuentinAnthon15 Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda  In this episode, we cover: (00:00) Intro (01:32) A brief overview of Quentin’s background and current work (02:05) An explanation of METR and the study Quentin participated in  (11:02) Surprising results of the METR study  (12:47) Quentin’s takeaways from the study’s results  (16:30) How developers can avoid bloated code bases through self-reflection (19:31) Signs that you’re not making progress with a model  (21:25) What is “context rot”? (23:04) Advice for combating context rot (25:34) How to make the most of your idle time as a developer (28:13) Developer hygiene: the case for selectively using AI tools (33:28) How to interact effectively with new models (35:28) Why organizations should focus on tasks that AI handles well (38:01) Where AI fits in the software development lifecycle (39:40) How to approach testing with models (40:31) What makes models different  (42:05) Quentin’s thoughts on agents  Referenced: DX Core 4 Productivity FrameworkZyphraEleutherAIMETRCursorClaudeLibreChatGoogle GeminiIntroducing OpenAI o3 and o4-miniMETR’s study on how AI affects developer productivityQuentin Anthony on X: "I was one of the 16 devs in this study."Context rot from Hacker NewsTracing the thoughts of a large language modelKimiGrok 4 | xAI

    44 min
  7. CarGurus’ journey building a developer portal and increasing AI adoption

    JUL 11

    CarGurus’ journey building a developer portal and increasing AI adoption

    In this episode, Abi Noda talks with Frank Fodera, Director of Engineering for Developer Experience at CarGurus. Frank shares the story behind CarGurus’ transition from a monolithic architecture to microservices, and how that journey led to the creation of their internal developer portal, Showroom. He outlines the five pillars of the IDP, how it integrates with infrastructure, and why they chose to build rather than buy. The conversation also explores how CarGurus is approaching AI tool adoption across the engineering team, from experiments and metrics to culture change and leadership buy-in. Where to find Frank Fodera :  • LinkedIn: https://www.linkedin.com/in/frankfodera/ Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda  In this episode, we cover: (00:00) Intro: IDPs (Internal Developer Portals) and AI  (02:07) The IDP journey at CarGurus (05:53) A breakdown of the people responsible for building the IDP (07:05) The five pillars of the Showroom IDP (09:12) How DevX worked with infrastructure (11:13) The business impact of Showroom (13:57) The transition from monolith to microservices and struggles along the way (15:54) The benefits of building a custom IDP (19:10) How CarGurus drives AI coding tool adoption  (28:48) Getting started with an AI initiative (31:50) Metrics to track  (34:06) Tips for driving AI adoption Referenced: DX Core 4 Productivity Framework Internal Developer Portals: Use Cases and Key ComponentsStrangler Fig Pattern - Azure Architecture Center | Microsoft LearnSpotify for BackstageThe AI adoption playbook: Lessons from Microsoft's internal strategy

    39 min
  8. Snowflake’s playbook for operational excellence

    JUN 20

    Snowflake’s playbook for operational excellence

    In this episode, Abi Noda speaks with Gilad Turbahn, Head of Developer Productivity, and Amy Yuan, Director of Engineering at Snowflake, about how their team builds and sustains operational excellence. They break down the practices and principles that guide their work—from creating two-way communication channels to treating engineers as customers. The conversation explores how Snowflake fosters trust, uses feedback loops to shape priorities, and maintains alignment through thoughtful planning. You’ll also hear how they engage with teams across the org, convert detractors, and use Customer Advisory Boards to bring voices from across the company into the decision-making process. Where to find Amy Yuan:  • LinkedIn: https://www.linkedin.com/in/amy-yuan-a8ba783/ Where to find Gilad Turbahn: • LinkedIn: https://www.linkedin.com/in/giladturbahn/ Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda  In this episode, we cover: (00:00) Intro: an overview of operational excellence (04:13) Obstacles to executing with operational excellence (05:51) An overview of the Snowflake playbook for operational excellence (08:25) Who does the work of reaching out to customers (09:06) The importance of customer engagement (10:19) How Snowflake does customer engagement  (14:13) The types of feedback received and the two camps (supporters and detractors) (16:55) How to influence detractors and how detractors actually help  (18:27) Using insiders as messengers (22:48) An overview of Snowflake’s customer advisory board (26:10) The importance of meeting in person (learnings from Warsaw and Berlin office visits) (28:08) Managing up (30:07) How planning is done at Snowflake (36:25) Setting targets for OKRs, and Snowflake’s philosophy on metrics  (39:22) The annual plan and how it’s shared  Referenced: CTO buy-in, measuring sentiment, and customer focusSnowflakeBenoit Dageville - Snowflake Computing | LinkedInThierry Cruanes - Snowflake Computing | LinkedIn

    45 min
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This is a weekly podcast focused on developer productivity and the teams and leaders dedicated to improving it. Topics include in-depth interviews with Platform and DevEx teams, as well as the latest research and approaches on measuring developer productivity. The EE podcast is hosted by Abi Noda, the founder and CEO of DX (getdx.com) and published researcher focused on developing measurement methods to help organizations improve developer experience and productivity.

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