Engineering Enablement by DX

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.codesWhere to find Eirini Kalliamvakou: • LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865• X: https://x.com/irina_kAlWhere to find Brian Houck: • LinkedIn: https://www.linkedin.com/in/brianhouckWhere 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-97720378In 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 challengeReferenced:• Etsy• GitHub• Microsoft• Twilio• Google • Stewart Reichling• What is the SPACE framework and when should you use it?

In this session from DX Annual, Uma Namasivayam, Senior Director of Engineering Productivity at Dropbox, shares how the company's developer productivity efforts evolved from improving developer experience to preparing for the agentic era.He explains how Dropbox approached AI adoption across its engineering organization, the impact it had on developer productivity, and why faster code generation is creating new bottlenecks in areas such as code review, validation, and CI/CD. He also discusses Dropbox's efforts to rethink engineering systems, measurement, and workflows, including the development of agentic tooling and new metrics designed to move beyond PR throughput and toward product velocity.Where to find Uma Namasivayam:• LinkedIn: https://www.linkedin.com/in/unamasivayIn this episode, we cover:(00:00) Intro(00:57) The beginning of Dropbox’s DX journey(02:34) AI adoption at Dropbox: what made it work  (04:46) The results of Dropbox's AI adoption efforts(05:39) What the results mean for the business (06:55) The phases of AI adoption and where they are now(08:00) The new bottlenecks(09:16) Three challenges Dropbox faces moving into agentic engineering(10:05) How Dropbox is redesigning the SDLC for agentic engineering(15:46) The new metrics that matter (19:16) Final takeawaysReferenced:• Dropbox • Developer Experience Index (DXI) | DX • DX Core 4 Productivity Framework• Cursor• Claude Code | Anthropic's agentic coding system• JetBrains • Visual Studio Code• Jira | Project Management for the AI Era | Atlassian• GitHub 

Indeed increased AI coding tool adoption from roughly 25% to 97% across its engineering organization, but getting engineers to use the tools was only part of the challenge.In this session from DX Annual, Michael Redding, Principal Product Manager, and Jeff Davis, VP of Core Infrastructure at Indeed, explain how the company used structured training, leadership support, and ongoing community engagement to help more than 2,000 engineers build practical AI skills. They share why an early train-the-trainer model fell short, how they redesigned their approach around hands-on learning, and what they learned about balancing adoption, measurement, and psychological safety.They also discuss the impact of the program on coding time, the role of continuous enablement after formal training ended, and how Indeed is preparing for the next phase of AI adoption, including agentic workflows and AI-powered coaching.Where to find Jeff Davis: • LinkedIn: https://www.linkedin.com/in/utjeffd Where to find Michael Redding:• LinkedIn: https://www.linkedin.com/in/reddingsetgoIn this episode, we cover:(00:00) Intro(01:05) Indeed's DX survey from January 2025(02:30) The two-part strategy to double engineering productivity(04:21) How Indeed increased AI adoption from 25% to 97%(15:40) Results from Indeed's AI training program(18:33) How Indeed sustains AI adoption and learning(23:06) What's next for AI enablement at Indeed(24:41) Q&A: How coding time was calculated(25:25) Q&A: How Indeed uses AI playbooks(26:40) Q&A: Balancing asynchronous and live AI training(28:22) Q&A: Psychological safety during AI adoption(31:44) Q&A: Why AI adoption spikes after the holidays(33:20) Q&A: The metrics Indeed tracked (35:22) Q&A: Where the time savings are going (36:54) Q&A: Reaching engineers who skipped the training(38:08) Closing thoughtsReferenced:• Indeed• Claude Code | Anthropic's agentic coding system• Cursor• Windsurf• Amp Code• The Complete Guide to Building Skills for Claude | Anthropic• Measuring developer productivity with the DX Core 4