Lenny's Podcast: Product | Career | Growth
00:24:13 · Lenny Rachitsky, Elizabeth Stone
[00:24:13] S0: And when you say specialists, are you thinking front end, I'm a front end engineer versus a backend or are there other versions of that? [00:24:18] S1: Yeah. Or it could be a domain set of knowledge of I'm a deep- [00:24:22] S0: Like a payments expert? [00:24:23] S1: I'm a payments expert. I'm an ads marketplace design expert. I'm an expert in this very specific tooling that studio productions use. So, their specialist and subject matter expertise is an advantage, provided that person is willing to grow and extend into, is this really still the right tool or the right way to think about the problem? So, I think it's the layers of the stack from an engineering perspective that there's less specialty. And then tools that are unlikely to be static or to have a lot of inertia around them. I would think we would want people who are able to innovate and imagine what's the future version of this. And so, we want more talent like that. [00:25:09] S0: Awesome. So, coming back to the systems thinking piece, people hearing this are like, "Okay, I got to work on my systems thinking skillset." How do people develop the skill? Is it just do it for a long time, work at a lot of complex projects? I think of this book that everyone always references with the slinky on the front, Thinking in Systems. Yeah. How do people learn this?
00:25:31 · Elizabeth Stone
[00:25:31] S1: Small trick. Each problem you're trying to solve, step out one click to the like, what am I assuming is true about the broader space in solving this problem? So, I was given a task to build some new feature for the Netflix member experience. Let me take one beat and think about, what is the bigger consumer problem we're trying to solve here? What's the type of content that this feature is going to be able to support? Do I think that the way I was planning to build this is going to make sense in a way that scales across multiple content types? Or it could be something that's a capability that then is contributed to a platform set of offerings from multiple areas. Is the consumer problem that I'm solving with this feature going to be one of the most important consumer problems that Netflix is going to need to solve, as we have an expanding world of entertainment and we want to make it more personalized and immersive? Those are all questions that you don't have to boil the whole ocean. You don't have to solve for Netflix's overall strategy and who are we relative to competition. But you take the thing you're responsible for and you just do one zoom out of the problem you're solving and question that. I wouldn't spend too long in the questioning state, because then you're stuck, then you're not making forward progress. But I think that helps people to think in terms of systems, and question that, are we solving the right problem in the right way that matters for the end consumer?
00:27:07 · Lenny Rachitsky, Elizabeth Stone
[00:27:07] S0: As you describe it, another way I'm thinking about it is think... if you were your manager, what's their broader perspective across not just your one team and problem and KPI, but the larger picture? [00:27:18] S1: I've got advice over years that is similar to that, which is are there ways that I can do my job that helps my manager do their job? And so, if I thought about all the things I'm directly responsible for, but I though about it from the perspective of my manager, so not just product and tech, but finance and content and other parts of the business. I would naturally zoom out and think about how all these component pieces need to come together, and how the whole could be greater than the sum of the parts. I think that's useful thinking. And for engineers to think about, how do I leave a better version of these systems? How do I think about the thing that's going to be high quality and scale for others? There's both a, how do I help my manager? And there's, how do I help my colleagues? Which is a core part of some of our engineering principles of do the thing that is right for the broader organization, instead of just what's right for you locally. That's systems thinking as well. So, it's not just seniority, but it's breadth of the way I solve this problem and I build this, is it going to be useful to my colleagues? And am I going to leave a stronger version of things for the future set of innovations that we want to make? [00:28:24] S0: That is awesome tactical advice. Making your manager's life easier is always a good tactic career-wise.
00:28:24 · Lenny Rachitsky, Elizabeth Stone
[00:28:24] S0: That is awesome tactical advice. Making your manager's life easier is always a good tactic career-wise. [00:28:31] S1: Several reasons. Yeah. [00:28:34] S0: Following the thread a little bit, I know you all added career ladders and levels. Recently, it was a new thing. You used to not have these things. So, on that thread, what have you added to the career ladders within this AI world, if anything, that you find you want people to lean into more, you're looking to more or not? Did you not change your career ladders and performance criteria?
00:28:58 · Elizabeth Stone
[00:28:58] S1: So, the way we've approached this so far is, instead of trying to articulate at each level exactly how AI changes those expectations, to instead put an overlay across all of the talent at Netflix, people on the team and those who are hiring, to talk about an aspiration for AI fluency. And what that looks like is going to vary by function. It's going to vary based on where you are in your career. That could be what level you're in or what type of role or persona work you're doing. But the aspiration for AI fluency, which is a tough thing to define. So, does it mean that I have an experimentation mindset? Does it mean that I know where AI is useful and not useful? Does it mean that I've actually built things using AI? I feel like the way that has shown up in career ladders and how we talk about it evolves almost by the quarter, if not month or day, because the tech itself is advancing so much. So, the most useful thing is not to make it level specific or role specific, but to encourage everyone towards the expectation on AI fluency, which doesn't mean use it as a tech for the sake of tech. It's tech where it's useful, to have good judgment about that and to have the mindset to be open-minded to explore and try new things, that's the non-negotiable for all roles. And that's true at the senior most levels of Netflix, where we talk about we too need to have deep fluency in AI, even if we're not writing code as part of our day jobs. So, that's changed. And then that's showing up in our hiring practices as well, getting comfortable within interviews, exploring how are people thinking about AI or technology. What are they using in their day-to-day or their current job? How comfortable are they with change and exploration? And even for things like coding interviews, allowing candidates, of course, to use AI tools, because that's going to be part of what the work requires now. So, those have been shifts that we've made, but I doubt it's a shift that's done versus we're right in the middle of it.
00:30:58 · Lenny Rachitsky
[00:30:58] S0: I'm just going to keep following this thread. Obviously, AI is transformative for coding. It's a big unlock for prototyping. Are there other use cases of AI at Netflix that have been really impactful, that people may not think about or not realize?