Engineering Enablement by DX

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 Framework
• Experian
• Transact-SQL - Wikipedia
• Twilio
• Kubernetes
• Copilot
• Claude Code
• Windsurf
• Cursor
• Bedrock

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 agents
• DX Core 4 Framework
• Booking.com
• Sourcegraph Search
• Cody | AI coding assistant from Sourcegraph
• Greyson Junggren - DX | LinkedIn

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 Framework
• Measuring AI code assistants and agents
• AI is making Google engineers 10% more productive, says Sundar Pichai - Business Insider

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 | London
• Software engineering with LLMs in 2025: reality check
• SPACE framework, PRs per engineer, AI research
• The AI adoption playbook: Lessons from Microsoft's internal strategy
• DX Core 4 Productivity Framework
• Nicole Forsgren
• Margaret-Anne Storey
• Dropbox.com
• Etsy
• Pfizer
• Drew Houston - Dropbox | LinkedIn
• Block
• Cursor
• Dora.dev
• Sourcegraph
• Booking.com

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 Framework
• Zyphra
• EleutherAI
• METR
• Cursor
• Claude
• LibreChat
• Google Gemini
• Introducing OpenAI o3 and o4-mini
• METR’s study on how AI affects developer productivity
• Quentin Anthony on X: "I was one of the 16 devs in this study."
• Context rot from Hacker News
• Tracing the thoughts of a large language model
• Kimi
• Grok 4 | xAI

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 Components
• Strangler Fig Pattern - Azure Architecture Center | Microsoft Learn
• Spotify for Backstage
• The AI adoption playbook: Lessons from Microsoft's internal strategy

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 focus
• Snowflake
• Benoit Dageville - Snowflake Computing | LinkedIn
• Thierry Cruanes - Snowflake Computing | LinkedIn

In this episode, Abi Noda speaks with DX CTO Laura Tacho about the real obstacles holding back AI adoption in engineering teams. They discuss why technical challenges are rarely the blocker, and how fear, unclear expectations, and inflated hype can stall progress. Laura shares practical strategies for driving adoption, including how to model usage from the top down, build momentum through champions and training programs, and measure impact effectively—starting with establishing a baseline before introducing AI tools.

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: The full spectrum of AI adoption

(03:02) The hype of AI

(04:46) Some statistics around the current state of AI coding tool adoption

(07:27) The real barriers to AI adoption

(09:31) How to drive AI adoption 

(15:47) Measuring AI’s impact 

(19:49) More strategies for driving AI adoption 

(23:54) The Methods companies are actually using to drive impact

(29:15) Questions from the chat 

(39:48) Wrapping up

Referenced:

• DX Core 4 Productivity Framework
• The AI adoption playbook: Lessons from Microsoft's internal strategy
• Microsoft CEO says up to 30% of the company's code was written by AI | TechCrunch
• Viral Shopify CEO Manifesto Says AI Now Mandatory For All Employees
• DORA | Impact of Generative AI in Software Development
• Guide to AI assisted engineering
• Justin Reock - DX | LinkedIn

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