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.