Lenny's Podcast: Product | Career | Growth
Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO)
This episode appears to contain dynamically inserted ads. Transcript timestamps may be approximate.
Everyone can be everything now. PMs can ship code, designers can write PRDs, engineers can product. And there's this confusion and frustration of what is my job anymore. Any time a new technology comes along, you go through a storming phase before you go through the forming phase of things. We are in the middle of that right now. I don't think that means we should put AI back into the box and say let's not use it. If we all become builders, will we still need separate functions? I still see a craft excellence that's really important, that I don't think is going away anytime soon. I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce. If you look at the early culture deck of Netflix, high agency, autonomy, paying top of market, this is what I hear constantly now from how the top AI labs operate. Netflix's culture has always been excellent as an operating system. It's a resistance to do the thing that a lot of bigger companies would do and to feel comfortable in that discomfort very often. What are the ingredients to make this happen? Talent density is the non-negotiable, being very comfortable with risk-taking. In cases where things are not going well, not assume that process is going to fix it. What have you added to the career ladders within this AI world? We need more systems thinkers. People who can look across all the business domains and abstract that to, here's the building blocks we're going to need. How do people learn this? Small trick. Each problem you're trying to solve, step out one click to the, "What am I assuming is true?" about the broader space. Today, my guest is Elizabeth Stone, Product and Technology Officer at Netflix. This is Elizabeth's second visit to the podcast. Her first visit when she was just a CTO was, for the longest time, one of the most popular episodes of this podcast, you'll soon see why. This is such a killer conversation because when we chatted two and a half years ago, AI was only starting to emerge. And as a long time head of engineering and product and data science, Elizabeth has such a unique perspective on where things are heading and what's worth paying attention to. The part in Netflix, Elizabeth was VP of Science at Lyft, Chief Operating Officer at Nuna, an economist at the Analysis Group, and a trader at Merrill Lynch. Before we get into it, don't forget to check out lenniesproductcast.com for an entire year free of the hottest and best crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you Elizabeth Stone. Elizabeth, thank you so much for being here and welcome back to the podcast. Thank you. I'm honored to be here once and now twice. That's right. That's a rare, a rare treat for me. I don't know if you know this, but your first visit to the podcast, your episode ended up being my second most popular episode. You're right behind Brian Chesky for the longest time. Wow. I'm pleasantly surprised and also mildly competitive of how do I get to the first spot, but I'll set that aside for now. This is her. This is her shot. Brian's amazing. So I'll let that one go. Yeah, he is. And then there's just like all these fancy AI people that are just coming in and coming in high. So it's been two and a half years at this point. A lot's changed. Obviously AI, something AI is allowing people to do is everyone can kind of be everything now. This idea of PMs can ship code, designers can write PRDs and engineers can product and everyone's everything. There's a bunch of elements of this conversation. One is that I've heard from people that there's also this kind of confusion and frustration of like, what is my job anymore? Like, what am I responsible for as a PM as a designer? Is that something you've experienced? I hear it within Netflix, for sure. I think anytime a new technology comes along, especially one that's as transformative as JNI, you go through a storming phase before you go through the forming phase of things. And I think we are in the middle of that right now. I don't think that means we should put AI back into the box and say, let's not use it,
Transcription truncated.