We Built What?

Everyone is shipping faster. But speed alone isn't what separates the teams winning right now. Wei-Wei Wu, CEO at Momentic, has a clear take on what does: a tighter feedback loop and the discipline to verify what you're building against what you actually intended to build.

In this episode, Wei Wei and host Emma Webb get into why experimentation is the only way to find out what's possible, why the spec needs to be your source of truth and not just a design artifact, and why more code output is not the same as more value delivered.

They also cover what 2 billion test steps have taught him about how teams are actually shipping software, why ambiguity is your biggest enemy when working with AI agents, and why the QA engineer skill set turns out to be exactly what the agentic era needs.

Wei-Wei's take: the price you pay for not experimenting is you don't actually know what's possible. But you still need to know what you were trying to build in the first place.

If you lead an engineering team thinking about quality, velocity, and what it means to ship with confidence, 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.

Chris Kelly has had a front row seat to every major shift in software engineering, from GitHub to New Relic to Salesforce to Fire Hydrant. His take on where we are now: this one is different. Not faster horses. A completely different kind of work.

In this episode, Chris and host Emma Webb get into why the job of a software engineer has fundamentally changed from working the factory line to building the factory itself, what that means for the engineers who loved the craft of writing code, and why good software design matters more than ever in an agentic world.

They also get into why the IDE is fading, how Cosmos came to be, what it looks like to build an entire SDLC around agents rather than bolt AI onto an existing one, and why engineering leaders are being asked to do the hardest thing possible: technical change, organizational change, and keep the lights on, all at the same time.

If you're trying to wrap your head around what AI native engineering actually looks like 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.

Gregor has built a community of over 200,000 engineering leaders through his Engineering Leadership Newsletter. He talks to teams across the globe every day so he has a grounded take on what's actually working right now and what's just noise.

In this episode, Gregor and host Emma Webb get into why the best engineering leaders aren't chasing 20 agents and five new tools. They're focused on what's always mattered: solving the right problems, serving the business, and taking care of their people.

They also cover the identity shift that's making this moment emotionally hard for a lot of engineers, why the skills of a great engineering manager turn out to be exactly the skills you need to work well with AI, why Gregor thinks we're at peak token maxing, and why personal brand building is no longer optional for engineering leaders who want a seat at the table.

Gregor's closing take: you can't replace a great culture with any AI tool. Great culture will always be the biggest productivity hack.

If you lead an engineering team and you're trying to stay grounded while everything moves fast, 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.

Writing code isn't the bottleneck anymore. So what is? Pranay Prakash, engineering leader at Vercel, has a clear answer: taste.

In this episode, Pranay and host Emma Webb get into what it means to ship tasteful software in an era where you can ship almost anything, how Vercel maintains its famously high bar for developer experience at nearly 800 people, and why the answer to scaling taste across an org isn't more process. It's obsession.

They also get into durable agents and what Vercel Workflows unlocks for teams building long running agentic systems, what a taste linter actually looks like in practice, and Pranay's surprisingly calming take on the pace of change: the fundamentals haven't changed. Go to Nicaragua for a year. You'll be fine.

If you're an engineering leader thinking about quality, taste, and what it means to build well in the agentic era, 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.

Rob Zuber has been CTO at CircleCI for nearly 12 years, watching software engineering evolve through the SaaS era, the mobile era, and now the agentic era. This time, he says, the rate of change is different. And so is what it means to lead.

In this episode, Rob and host Emma Webb get into what happens to an engineering leader when their expertise is suddenly irrelevant, why Rob's answer was to get hands-on and start building again, and why being AI native isn't a destination you ever actually reach.

They also cover the CI debate nobody can agree on, the token cost tension every engineering leader is quietly navigating, and how CircleCI is hiring differently in the agentic era.

Rob's closing thought: we're not going to plan the new way of working. We're going to live it.

If you lead an engineering org trying to figure out how your role and your team need to evolve, 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.

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.