Lenny's Podcast: Product | Career | Growth
00:09:30 · Elizabeth Stone
And I find even in my own work day-to-day, instead of sending an email that disrupts someone of like, remind me what research did we do in what year and what was the question and what was the test we ran? I can find that almost instantly. Then I can form my own, here's what I find interesting about this. And I've now skipped a couple steps towards, is there something actionable here? So, that's data analysis, it's modeling, it's distillation of information. And I'm seeing more people do that, to your original question. So, instead of that needing to be only the experts who were here for 20 years and saw every experiment or know where to find it, we're now able to do that faster within product and tech across all functions. And a big unlock for us is our business stakeholders, sitting in finance and content and advertising, can do that as well. And then bring back an initial hypothesis where they want to work more deeply with the data scientist and engineer and so on. So, there's something there about the hypothesis generation, prototyping, thinking deeply about problems, that feels like it's accelerating and that functions are able to do that in a more fluid way. But I still see comparative strengths. So, data scientists are still going to be experts at, can we trust this data? Are we interpreting it the right way? What's the data versus judgment that we should be applying here? A product manager is still going to be exceptional at saying, have we really framed the what of this, the problem we're solving in the right way? An engineer still has a craft around the how. How does this scale? What does high quality look like? What problems is this going to create for us based on how we build and deploy something? So, I still see the nuggets of that comparative advantage. It's just that we're able to move more fluidly in a lot of steps that normally we would have blockers on.
00:11:55 · Lenny Rachitsky
[00:11:55] S0: There's so much interesting stuff here. One is this last point you made, something I've been thinking about. If we all become builders, will we still need separate functions? There's this member of technical staff trend that is happening across AI, where it's like, all right, we don't have a title. You could be anything. You don't have to be in a bucket. What you're saying here is you believe we will continue to have specialties, product person, engineer, data science, designer. While they do more of other functions, there's still a lot of value, and tell me if I'm hearing you correct, in having this specific discipline and skill and background.
00:12:26 · Elizabeth Stone, Lenny Rachitsky
[00:12:26] S1: I still see a craft excellence that's really important in the disciplines that I don't think is going away anytime soon, even if there's fluidity or blurring of the work across the functional lines. It goes back to what I mentioned earlier of you still have humans who have to make sure that what we're doing makes sense. We're solving the right problems in a way that is best for Netflix members or business stakeholders. And that if I talk to an engineer, a data scientist, a designer, yes, they speak more languages now than they used to because they have the benefit of these AI tools, but there's still something that is not replaceable when I think about the craft and how they think about what good looks like. And that feels true across all levels. And I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce. So, yes, some things are easier, but that hasn't dissolved in my mind. [00:13:27] S0: Are there functions that you are finding you are hiring more of, like the pie chart pie expanding, say for engineering or PM or design or something, and then functions you're need less of with AI tooling and LLMs rising?
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.