Lenny's Podcast: Product | Career | Growth
00:13:43 · Elizabeth Stone
[00:13:43] S1: I'm not sure that it matches exactly to functions, but I can tell you what we're seeing more of, we need more of. We need more systems thinkers in a world with AI. That looks a little bit different across functions, but I could play out a couple examples. So, in our core infrastructure team at Netflix in central engineering, a lot of what made Netflix successful over time was that local teams with specific business problems could move fast to deliver. They very often were not feeling like they needed to be on a central paved path. They built the stack that they needed to solve the problem and have the impact. In a world of AI with agents operating across multiple systems, wanting source of truth data, the importance of having preferred paved paths that get the most of the benefits and produce some guardrails so we can make sure we're doing good work. Common infrastructure, common paved paths, solving problems once with a core set of capabilities becomes more important. So, we are hiring more people who can look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI. So, that's one of the lenses, but also just with a lens of what got Netflix here doesn't get Netflix there. And we're going to have to have a stronger set of infrastructure to move quickly in this future. So, that means that engineering profiles are more distributed systems, more infrastructure, more of that system thinking mindset than a local business expertise. Though, of course, we still have people who are deep in personalization and advertising and content delivery. So, it's more something additive for us to have that core infrastructure and systems thinking. If I take another example, like design, it's extremely important that our experienced design team is developing templates and again, systems thinking for what does great user design look like at Netflix, so that they can enable lots of people, including those who are not designers by training, to develop products that are coherent, that fit into the end-to-end member experience. I get really nervous about having different design languages or different types of user interactions and shipping Frankensteins, basically. So, designers need to then be the people we're hiring again for design systems thinking.
00:16:14 · Elizabeth Stone, Lenny Rachitsky
How do we think about templates and expression of the brand? And what a good user experience looks like. And what is Netflix and the Netflix differentiated special sauce? So, there's more people on our design team that have to think that way now than, could I help to design a specific feature for a specific product? So, there's this stepping back to look at the big picture that I think is happening in every single function. And that requires some reorientation of skills among the existing team and also hiring people who've got that type of expertise. And across all of it, it's a mindset shift. So, we are not hiring people who are not excited to explore, try new things, understand lots is changing and feel comfortable with that ambiguity, be comfortable that there's a blurring of how we work and how we partner. That's true for people who are already at Netflix and people who we are adding to the team, that that curiosity innovation mindset, it's not been more important, at least in the time that I've been working in this field. [00:17:22] S0: On the systems thinking piece, is the reason this is becoming more important, that people are moving so fast that you need to invest in platforms and frameworks and design language, and basically teach people to fish so they can not be blocked or are there other reasons?
00:17:38 · Elizabeth Stone
[00:17:38] S1: I think it's probably velocity. So, platforms do have a benefit of leverage. So, in general, that's an opportunity with or without AI, for a platform to get most teams 80% of the way there. And then they don't have to reinvent those building blocks. We have more bets that we're making across the business, more things we're trying to build. So, platform mindsets are good, and it's something that is relatively more recent for Netflix to think about that being a real critical enabler. There is also the sense of a scaffolding in a world of AI. So, not just the higher velocity, but you have more people doing more types of work that are different or new, like we were talking about. And there's risk that comes with, how do you think about access and identity in that situation? How do you think about security in that situation? How do you think about shipping high quality code and design and user experiences? And so, I don't think it scales well to have each person who's building something have to go figure out, could you remind me what good looks like here? And what are the bumpers or guardrails I should keep in mind? I think we need to encode that in our paved paths and our ways of working. And for a data science or analytical field to encode, here's the source of truth data, here's how to interpret it, here's how to access it, here's what to do with it or not to do with it and to be careful with certain types of data. An organization that has thousands of people can no longer rely on tribal knowledge or I'm going to find the one person who knows this. So, this was a challenge that was there before AI. It's probably a more urgent challenge with AI. And I like the idea of using AI or any new tech to motivate... We knew this is work we needed to do. No time like the present to invest in that more heavily across the team.
00:19:27 · Lenny Rachitsky, Elizabeth Stone
[00:19:27] S0: I wonder if another reason for this becoming more valuable is because agents are now doing a lot of work. And giving them the context, giving them the scaffolding, giving them the design language, just speeds all that up? [00:19:38] S1: Yeah. And one of the visions we have at Netflix is we will have so many agents that are contributing to doing work that you need to be able to reason and rationalize throughout that. The humans are the ones guiding what's the problem we need to solve. Do I feel like what we're producing is impactful and high quality output? But the work will be done by both humans and agents. And that creates velocity and benefits and it creates risks. And I think that's important, especially from an engineering perspective, that we figure out how to manage that in a way that lets people move quickly, but doesn't create undue downside or risks for the company. [00:20:20] S0: This connects so directly with... Jenny Wen was on the podcast. She was Head of Design for Claude Code and Cowork and had this whole design process is dead kind of thesis. And the pitch there is just there's no time for design, the design process. And instead, as a designer, you're just kind of steering people and pointing them in the direction and adjusting and also thinking big pictures when you have the time. And it feels like that's what you're describing here is create the platform for people to move fast and then there's no time for design process of a specific new feature.
00:20:51 · Elizabeth Stone, Lenny Rachitsky
[00:20:51] S1: I have mixed feelings about that, because we do want to enable with infrastructure and systems thinking, more people to do great work with strong design as part of it. Why not take that opportunity that the new tech provides? But for our most important priorities, design is critical to solve things in the right way. So, we do still make time for important design work. It can move faster. The designers themselves have more tools in their toolkit, so they can do incredible work at a faster velocity, show more options, learn, iterate, test more quickly. But I think it would be a mistake to say design and deep design expertise and thinking gets squeezed out, just because we can write code faster, we can do data analysis faster. At least for a large scale consumer product like Netflix, I feel like we would lose one of the things that makes Netflix great, which is the product technology and design makes a lot of complexity invisible and makes for a seamless customer experience. That's a design mindset that has to be core to it. So, the work itself might look different, but I don't think we lose the mindset. [00:22:06] S0: That's an awesome counterpoint. So, what I'm hearing is trending up skills, attributes you look for, systems thinking. And this mindset of being comfortable, and excited about change, and what's coming, and not being stuck in your own ways. What are you finding is trending down? What are you less looking for that you used to value more highly?
00:22:28 · Elizabeth Stone
[00:22:28] S1: The days of very narrow, deep specialization feel more limited to me. I can come up with examples where we still need it, because there's an industry or technology expertise where there's only a few people in the world who really know how things work. We have examples of that on the team for encoding or how our playback systems work, and things that have been incredibly innovative and novel for Netflix. I still believe we need specialized practitioners in those spaces. But as a general rule, compared to five or 10 years ago, I would believe we have fewer specialists and more people who are generalists or adaptable in multiple directions. And that could be adaptable across functional expertise. It could be adaptable across flavors of engineering. So, can I navigate both backend and front end systems? Can I hook into infrastructure with a lot of expertise? I think the mindset now needs to be I can learn that quickly. And that goes back to the systems thinking. So, I think specialists can learn to have a broader array of tools more easily than was true in the past. So, we need fewer of them perhaps, because talent's able to grow in that direction. And there's something about sticking to a narrow specialty that maybe triggers for me a concern about, what about the mindset of growing in different directions and exploring? And I don't want to be too narrow even in my own assessment of that, but it's important that people who are specialists still have that sense of, I want to try a new way of solving these problems versus the way we have in the past.