After 3 years, we’re coming out of retirement, because something fundamental broke open in the last few months—something that changes everything about how we work.

We Don’t Know How to Learn This Yet

AI coding tools promise a 10X—maybe 100X—productivity boost. But here’s what we’re seeing: most engineers don’t know how to learn these tools. The old playbook—read the docs, practice, master—doesn’t work when the tools are fundamentally stochastic and changing weekly. Even worse, there’s nowhere to go for real instruction. Documentation tells you what features exist, not how to think differently about your work.

Deep Conversations with Practitioners

We’re rebooting the Staff Engineer podcast with a specific focus: practitioners using AI to deliver valuable outcomes with specific examples.

The Future Isn’t Evenly Distributed

Right now, there are deep pockets of breakthrough AI usage everywhere. From engineers, to philosophers. Small teams moving at speeds large organizations can’t match..

These practices exist, but they’re isolated.

What We’re Looking For

Practitioners over theorists. We’re not interested in abstract conversations about what AI might do. If you’re using AI to deliver outcomes and have specific examples of what worked and what didn’t, we want to talk.

Details over declarations. “AI made me 10X more productive” is a headline. “I rewrote my entire workflow around X pattern, which failed until I realized Y, and now I’m shipping features in days that used to take weeks” is the conversation we want.

Diverse domains, unified question. We’re starting with staff engineers because that’s our foundation—engineers expected to show great judgment at scale. But we’ll talk to anyone whose work sheds light on our core question: What does good engineering judgment look like when the tools are stochastic, the landscape changes monthly, and the bottleneck shifts from implementation to direction?

Our Thesis

A fundamental shift is happening in how we work. The engineers authoring this future—not just experiencing it—will have massive advantages. We choose authorship.

But we don’t know what that looks like yet. We don’t have the playbook. That’s what we’re building.

How to Participate

We’re setting this up in two ways:

1. Join Our Listening Sessions

Before we start recording episodes, we want to hear from you. We’re organizing Zoom sessions to discuss:

• What you’re running into with AI in your work
• The problems you’re facing
• The surprising wins
• The bureaucratic barriers
• The things you wish someone would talk about

Sign up to join a session

2. Suggest Guests (Including Yourself)

Know someone doing interesting work with AI? Are you doing interesting work with AI?

We’re looking for:

• Practitioners (not people selling AI tools)
• People delivering outcomes, not just observing
• Specific examples of what worked and what didn’t
• Willingness to go deep on the details

Fill out form with your suggestions

This episode is a celebration of the journey we have been on as this podcast comes to a close. We have had such a great time bringing you these interviews and we are excited about a new chapter, taking the lessons we have learned forward into different spaces. It's been a lot of work putting this show together, but it has also been such a pleasure doing it. And, as we all know, nothing good lasts forever! So to close the circle in a sense, we decided to host a conversation between the two of us where we interview each other as we have with our guests in the past, talking about mentorship, resources, coding as a leader, and much more! We also get into some of our thoughts on continuous delivery, prioritizing work, our backgrounds in engineering, and how to handle disagreements. As we enter new phases in our lives, we want to thank everyone for tuning in and supporting us and we hope to reconnect with you all in the future!

Links

• David Noël-Romas on Twitter
• Alex Kessinger on Twitter
• Stitch Fix
• Stripe
• JavaScript: The Good Parts
• Douglas Crockford
• Monkeybrains
• Kill It With Fire
• Trillion Dollar Coach
• Martha Acosta
• Etsy Debriefing Facilitation Guide
• High Output Management
• How to Win Friends & Influence People
• Influence

The structures of an organization can often be self-reinforcing, and in a changing environment, this becomes a recipe for future vulnerabilities. That is why senior ICs need to play a slightly discordant role at times by alerting teams to issues conventionally outside of their bubble of concern. Peter Stout is a Technical Director at Netflix where he has a cross-functional role at the juncture of business and technology. He joins us on the show today to share some of the finer details around what inhabiting this position in the above manner looks like. We start by hearing Peter describe himself as a generalist, and share how this played out in the broad focus of his college degree as well as in his career pivot from Chemistry into Software Engineering. We discuss the rapid growth of the engineering team at Netflix, how this has led to less tightly-defined roles for junior and senior engineers, and how this factors into the way Peter approaches his place in the organization. Peter talks about the shift he made from technician to technical director and how much of the skills he learned from the former position he brings into the latter. He talks about his tendency to seek out the blank spots in the organization and how he tries to focus on a long-term vision, using that to guide him as he connects the dots between teams and influences decision making. Here Peter considers his role as a disruptor and how he gauges how much pressure to apply while still staying largely in sync. We also have a great conversation about Peter’s approach to mentorship and his philosophy around how he grew into the leadership position he occupies. Tune in today!

Links

• Peter Stout on LinkedIn
• Netflix
• Range
• The Leadership Pipeline