Even if you work at an AI company, being truly AI native is harder than it sounds. Vinay Perneti, VP of Engineering at Augment Code, knows this firsthand.

In this episode, Vinay and host Emma Webb share Augment's own AI native journey. They get into what triggered the realization that the team wasn't AI native enough, how the definition of AI native keeps moving, and what it actually looks like to build an engineering org around agents rather than just alongside them.

They also cover what Augment learned from surveying 219 engineering leaders about the excitement and fear they're holding at the same time, why 54% of leaders are worried about losing shared understanding of the codebase and how Augment is solving it, and why the model you're using today is the worst model you'll use for the rest of the year.

Survey can be found here: augmentcode.com/resources/state-of-ai-native-engineering-2026

This one is a bit different from our other episodes. It's less outside perspective and more inside look at what it takes to walk the walk. If you're an engineering leader trying to figure out what AI native actually means in practice, this one's for you. Have someone you want us to interview or a topic you want us to cover? Let us know on X or LinkedIn.

Everyone says they want to be AI native. Far fewer organizations actually know what that means or how to get there.

Andrew Lau, CEO at Jellyfish, works with engineering teams every day who are trying to close that gap. In this episode, he and host Emma Webb dig into what truly AI native engineering orgs are doing differently. They get into why legacy codebases and established customer bases create a transformation challenge that fresh-start companies simply don't face.

They also cover the token budget problem hitting finance teams everywhere, what the adoption, productivity, outcomes framework looks like in practice, and why becoming AI native forces teams to rethink how product and engineering work together.

Andrew's take: this is an inventor's time. A maker's time. There's never been a better moment to get your hands back in the work.

If you lead an engineering org trying to figure out what AI native really means for your team, this one's for you. Have someone you want us to interview or a topic you want us to cover? Let us know on X or LinkedIn.

Engineering leaders are drowning in AI hype. Everyone's hearing promises of 5x, 10x productivity gains, and very few are actually seeing them. Justin Reock, CTO at DX (recently acquired by Atlassian), has the data to cut through the noise.

Host Emma Webb and Justin get into what top-performing teams are doing differently, why AI coding tools are only attacking 16% of the problem, and what "AI readiness" really means in practice.

They also cover rubric-based self-validation for agentic code, token cost optimization, and the danger of Goodhart's Law in engineering metrics. Plus a hot take: if developer experience is the leading indicator of developer productivity, agent experience is going to be the leading indicator of agent productivity.

If you lead an engineering team and you're trying to figure out where AI fits and whether it's working, this one's for you.

Have someone you want us to interview or a topic you want us to cover? Let us know on X or LinkedIn.