Lenny's Podcast: Product | Career | Growth
00:48:53 · Lenny Rachitsky, Elizabeth Stone
[00:48:53] S0: Awesome. I guess just to explain to people what this is so they don't have to go listen to a whole other podcast, I'll try to briefly explain it. The idea here, a part of the Netflix culture is that when you have people reporting to you, you should always be thinking, would I hire this person today knowing what I know about them? And if not, then I should probably let them go. And the idea there is to keep the high bar, to not ever just settle. Okay, this person, they're here, I guess we'll keep them around. Is that roughly the way to understand it? [00:49:21] S1: Yeah. And the way it can... it's sort of a corollary to that. If that person came to me today to say they were leaving, would I fight to keep them or not? Or would I say, if my sense is relief of, oh, yeah, it probably would be better to have someone else in this role, I should've taken action and having that conversation sooner. [00:49:41] S0: As you said, it's so many uncomfortable things you have to do to maintain this culture.
00:49:41 · Lenny Rachitsky, Elizabeth Stone
[00:49:41] S0: As you said, it's so many uncomfortable things you have to do to maintain this culture. [00:49:47] S1: Yeah. Well, the keeper test is one, maintaining talent density, context, not control among leaders. We talk about being highly aligned, but loosely coupled, which is where light process, the minimum to make sure we're clear on the priorities and we can execute them is what we're solving for. All of these things are not things that human beings or organizations at scale tend to do. So, it's constant diligence to try to maintain the thing that's made Netflix a special place. Because in the end, it's the work and the culture that attracts people and retains people. And we need that to be a successful business. [00:50:22] S0: That's exactly where I was going to go. So, to make this work, you need to attract the best people. It's always been very hard to attract the best people. It feels insanely hard these days with the amount of dollars flying around, the fancy AI labs, so much competition. It's crazy. What have you found to be effective in convincing the top people to still come to Netflix and join versus all the other fancy places they can go?
00:50:49 · Elizabeth Stone
[00:50:49] S1: Yeah. We've always had a lot of competition for talent. It might feel more pronounced right now, but we have great talent on the team. Maybe that goes without saying, but I feel like I should say it out loud because I believe it. We have incredible talent at Netflix, recent hires, long tenured people. I'm always impressed by the work that the team is doing. So, I don't feel like we've suffered or other companies are vacuuming up all the good people, because so many of them I do think sit at Netflix. It does feel like we have to be more explicit about the types of people and talent that tend to thrive at Netflix versus other companies, like some of the Frontier Labs. So, people at Netflix have to be passionate about the application of technology and the application or building products to solve a certain set of problems. You have to love entertainment. You have to love consumer products at scale. You have to love the global nature of that. There are a lot of incredibly talented people who love that sweet spot, I am one of them, between tech and product and entertainment. And how do you make those things come together in a way that's remarkable? And you use AI to do it. You use other technologies and products to do it, but that has to be something that drives you to be really excited about a lot of the roles at Netflix. If instead you're inspired by some of the foundational work that the frontier model companies are doing, which is exciting in its own way, it's a different persona. It's a different... Here's the problem space that I want to work in. But I don't think there's a shortage of people who get really excited about the applications of the technology and see the connection to that, to things that they love and use every day, like Netflix. And so, that gets me up in the morning and I think it gets a lot of the team members up. And we have this conversation about that's something special that only talent at Netflix can do, or fill in the blank for another industry that's deep in the application of it. I think that's inspiring.
00:52:54 · Lenny Rachitsky
[00:52:54] S0: I want to touch on a couple things that I've been thinking about in this world of AI that we're approaching. One is junior people. It feels like everyone's just like... This is a good example. You're hiring a lot of awesome senior people that have proven they're awesome and high talent, density, high bars. Also, just AI makes it so easy to do stuff that people may not be learning how to do anything. Like junior engineers, I'm thinking, or junior PMs, junior designers. How do new people become these awesome senior people? Is there anything you think about? Are you hiring junior people? How do you think about this? What happens with junior people not necessarily learning or having a path to learn to become the senior person?
00:53:39 · Elizabeth Stone
[00:53:39] S1: We are still hiring junior people and they're really important to our talent strategy. So, we still have an intern program, we still have a new grad program, which was new for us as of a few years ago. So, prior to a few years ago, we were only hiring more experienced talent across all the functions. Now we do hire people straight from undergrad and graduate programs and we'll continue to do that. So, even in a world of AI where some things are easier, we were talking earlier about mindset, AI fluency. From my experience, younger folks are more open-minded. They tend to be more native in some of these new ways of working. For a company like Netflix, they're also very fluent in how entertainment is changing, how consumer behaviors are changing, how product and tech is influencing that in the products that they're using. That's really important to have on our team. So, there's the part of the persona, which is who are you as a new grad who's an engineer? But there's also, who are you as someone who's in their early twenties and has a perspective on the world that is highly valuable and a comfort with the way the world is changing? So, that's why I say it's a critical part of our talent strategy. To the, okay, so you step into the role and you have AI tools that didn't exist five or 10 years ago. I would say mastery of the craft is still very important. So, going back to, as the team member, I'm responsible for the quality of code that I am submitting for production. I'm responsible for the quality of products that I'm building, how they are designed, what that user-consumer experience is. None of that is going away. So, if I think about more junior or earlier career talent on the teams, we need to be investing just as much in the mentorship of this is what good looks like. This is how you use these tools, but you still take accountability for what the outcomes are, what the quality of the output is. And I think I mentioned this earlier, I find that mastery and that craft excellence scarce still. So, we want to make sure we're teaching that. I think it's a valid concern of, how do I get that if I'm not as hands-on as I would've had to be? But you still carry responsibility for reviewing code, testing code, being able to diagnose problems, knowing what a good product looks like.
00:55:57 · Elizabeth Stone, Lenny Rachitsky
I think that's a very scarce skill to say, this is excellence in a product that solves a problem that matters and in how it's designed. So, I don't think that craft mastery, the importance of it is going away. Probably the way we train and grow talent has to change, because they're going to use different tools. And I can guarantee you that earlier career talent is going to be teaching older folks like me many new things, too. So, I think it goes in both directions. [00:56:26] S0: Where do you think engineering goes in, I don't know, five, 10 years? Do you think people need to still understand code? Or do you think there's this abstraction layer that sits on top where you don't even have to learn C++, Java, Python, whatever?