Rajeev Rajan (CTO @ Atlassian) shares the leadership playbook he used to transform Atlassian’s engineering culture, and how that cultural foundation directly powered the build and launch of Rovo (Atlassian’s new AI powered app). We cover how they reduced ship time from 120 days to zero, why “developer joy” is the metric that matters, and how to create a community of developer productivity champions to scale DevEx transformation. Rajeev also breaks down his principles for systematizing autonomy and empowerment, including frameworks for giving direct reports more ownership. Plus, a look at the future of Atlassian’s “Systems of Work”!

This is a special episode, highlighting a session from ELC Annual 2025! The true promise of AI isn’t in replicating human intelligence. It’s in developing entirely new forms of non-human intelligence that perceive and understand the world in fundamentally different ways. Jamie Lien (Co-Founder and Chief Scientist @ Archetype AI) and Rashi Agarwal (Head of AI Engineering @ GoodLeap) explore the emergence of "Physical AI" - machines that sense the world through modalities beyond human biology to form internal representations free from our biases and then translate that understanding back to us in human terms.

Ruslan Belkin (Head of Platform Engineering @ Inflection AI) joins us to deconstruct fundamental shifts in engineering leadership. We explore the future of user interfaces, his “sci-fi” approach to establish & test product vision, & how to leverage “investor decks” for better decision-making and project validation. Ruslan also dives into the complexities of building emotional intelligence into AI systems, cultivating an outcome-oriented engineering culture & avoiding process traps. Plus, we discuss how to keep up with the velocity of change (including when new research necessitates a major pivot), synthetic data & the future of data as a defensibility strategy, & why agent reliability is the massive opportunity ahead. 

At ELC 2025, what began as a discussion on defining outcome-oriented metrics for AI features turned into something more revealing: a shared struggle among engineering leaders to measure the real impact of AI-assisted work. This piece explores why teams often mistake productivity spikes for progress, the missing link between activity and outcome, and how to bridge that gap with metrics that actually matter.