Lenny's Podcast: Product | Career | Growth
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?
00:56:26 · Lenny Rachitsky, Elizabeth Stone
[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? [00:56:42] S1: I think there's a difference between being able to write lines of code in a particular language, like Python or C++ and understanding how code, computer systems, products work. And I don't think the latter is going away, because if we trusted agents to know all the languages and write all the code, we're not going to know why is something... is it a good product? Is it a bad product? Is it working as we expected when it doesn't? Like I mentioned earlier, we take a lot of risk. We fail fast, we recover fast. That requires an understanding of how are these systems working. I might use an agent to help me understand those things, help me detect an anomaly or something that's broken faster and triage it, but I still need to have a fluency of what is this thing that we're building and how does it work, so I know if it's good and I know how to fix it. I don't know. I hope that doesn't go away, because that's like a, how do we make the world a better place through the stuff that we're building? I think requires some understanding of what we've built. [00:57:46] S0: What I'm hearing, which it makes sense, is you may not have to write the code, but you have to understand it and what's happening. But it's so much harder to just... as a person not writing it to actually have that instilled in you.
00:57:46 · Lenny Rachitsky, Elizabeth Stone
[00:57:46] S0: What I'm hearing, which it makes sense, is you may not have to write the code, but you have to understand it and what's happening. But it's so much harder to just... as a person not writing it to actually have that instilled in you. [00:57:59] S1: I think that's one of the things that the learning curve is very steep on right now. So, looking at some of the code that some of these models or agents are writing, they're very hard to follow. It's like, I know I'm getting better performance from this, but I have no idea why. And if this thing breaks, I'm going to have no idea how to fix it. That makes me uncomfortable. Maybe that's because I'm still on that learning curve of, how do we operate in that world? What's the set of tests or rationalization and understanding that we need to have to get comfortable with it? But at first glance, it looks very unfamiliar and very unsettling. So, I think engineering over time will evolve to be comfortable with that, and have fluency in it, and know how to guide new tech, and agents, and new capabilities to make sure that we feel really good about what the output is. [00:58:48] S0: I wonder what the metaphor is for this where this... I continue to be astounded by how much engineering has transformed in two years. It's like a completely different dropdown. We used to sit there in NID and write code, and now you're just talking to agents and reviewing code and shipping 100 PRs a day.
00:58:48 · Lenny Rachitsky, Elizabeth Stone
[00:58:48] S0: I wonder what the metaphor is for this where this... I continue to be astounded by how much engineering has transformed in two years. It's like a completely different dropdown. We used to sit there in NID and write code, and now you're just talking to agents and reviewing code and shipping 100 PRs a day. [00:59:05] S1: It feels like it's an acceleration of how much engineering has changed. But if you looked over the last 10 years or 20 years, you would say the same thing. So, there's just something that's moving faster. And it's hard to wrap our heads around how quickly it's moved in the past couple of years. But it's not totally unfamiliar that engineering or data science or product would have these big shifts, just like how filmmaking works. If you look over the last 100 years, it's unbelievably different because of technology and new tools that we've brought to it. Just feels like the cycle is speeding up. [00:59:46] S0: Okay. I want to talk about entertainment for a brief moment. I'm curious just how entertainment will change over time in the next, I don't know, five, 10 years. Just today, we open up Netflix, check out some shows, watch some videos, it hasn't changed in a while. Just that idea of like, oh, I'm going to watch The Pitt and just watch it all. I'm going to watch a movie. I got TikTok, I got Instagram, feeds of stuff. How much different do you think this will be in, I don't know, five years, the way we entertain ourselves?