Lenny's Podcast: Product | Career | Growth
01:18:39 · Caitlin Kalinowski
[01:18:39] S0: Yeah. I'm lucky that I've had a lot of time, a lot of reps basically on hiring people. And so I have a strategy of hiring great people. When you're hiring for zero to one and new things or new industries, and that's what we're facing, I think with AI and robots, certainly it's very new, you can't count on having entirely people who've done the exact same thing in past lives because it doesn't exist. The exact same thing doesn't exist. Maybe you've got roboticists who've built a thousand robots, but nobody that I'm aware of has built the type of robot that can move through the world the way I'm interested in, in the millions, because it hasn't been done. So you have to start thinking about how do you build a team that can do something new? And the nice thing is actually in robotics, self-driving cars, autonomous vehicles is a really good place to look because you've got the sensing stack and you've got a lot of the safety trade-offs actually. And it's a lot of the hard engineering, the hardcore engineering. So that's a place that I looked. Obviously, you want some hardcore roboticists who can do robot design from scratch. And these are really people, even though they might have a degree in something, they're really hybrid people. They're generalist people. So one of the key principles I'm looking for is a lot of really strong generalists who can adapt what they've learned in other fields to a new field and people with a lot of experience building. You want some people who have experience building the thing that you're building that's new and some people who have experience scaling other things to [inaudible 01:20:14]. So you need to look at that. And then with young people, this is where it gets really fun, Lenny, is the only AI native people essentially who use AI so natively that it's baked into their engineering process are 20 years old or 21 years old or 28. I mean, it's very hard to find someone who's in their 30s who can be truly fully AI native. And so we need these folks to teach us how to think. And I've had the opportunity to work with a few folks in that age range. They're approaching their problem solving completely differently because they're using AI from the ground up for everything and they're much faster actually and it's really fun to watch.
01:20:52 · Caitlin Kalinowski, Lenny Rachitsky
So figuring out how to get these AI natives to teach us, the rest of us, how they think about AI when it's, you know, we are, you and I, I think I can say are digital natives where we grew up, maybe there wasn't internet when we were really young, but we are the generation that had the first internet. We were teenagers. And we are the generation that had the first cell phones really in scale. We're an important generation because we had the first, I remember freshman year at Stanford we had the first databases that you could access and you could share movies on, I think is what we did, and music on or whatever it was, but this was new. And so we were native in these things and that gave us a lot of oomph in creating new technologies for it. But we have to accept that we're not native in these new technologies and you really want some folks who are hungry and excited and want to learn who do have these skills. [01:21:46] S1: That last bucket is a very common trend on this podcast when we talk about hiring, which is really cool as a counter narrative to there's no more jobs for young people, that junior roles are erased because of AI.
01:21:46 · Lenny Rachitsky, Caitlin Kalinowski
[01:21:46] S1: That last bucket is a very common trend on this podcast when we talk about hiring, which is really cool as a counter narrative to there's no more jobs for young people, that junior roles are erased because of AI. [01:21:57] S0: Yeah, I don't see it that way. I think we need them. I also think that we need to build new technologists. There's the obvious question of what happens if we don't have teams that have senior and junior people, but I think what you find when you actually build these teams is you have to have both. You must have both. The team size just might be a little bit smaller than it used to be. When this AI revolution in hardware happens, I don't know how that's going to affect the teams. That will be really interesting to watch. [01:22:26] S1: So what I heard here is just look for a generalist that can flex based on whatever needs to be done, some mixture of specialists in scaling versus zero to one. And then the best term I've heard for this is cracked new grads that are AI native essentially, that are just doing everything AI first.
01:22:26 · Lenny Rachitsky, Caitlin Kalinowski
[01:22:26] S1: So what I heard here is just look for a generalist that can flex based on whatever needs to be done, some mixture of specialists in scaling versus zero to one. And then the best term I've heard for this is cracked new grads that are AI native essentially, that are just doing everything AI first. [01:22:45] S0: Yep. And then what we didn't talk about, of course, is mission alignment, which actually unifies a team. So if everyone coming in is aligned to the mission, that helps a lot because especially in the world of AI researchers and hardware folks, there's a lot of miscommunication because we're coming from such different worlds. And so having a sense of we're all pulling in the same direction is really important. And then I rely a lot, Lenny, on my gut feel for people, assuming everything else that I'm looking for has been checked. It's hard to talk about what that means, but usually it's that spark that you're looking for in someone, that they're genuinely motivated. They're motivated by a desire to learn and by excellence, they're motivated to learn from the people around them. They're open to updating their point of view based on new information and they want to win. I mean, these are the things that really matter when you're building a team.
01:22:45 · Caitlin Kalinowski, Lenny Rachitsky
[01:22:45] S0: Yep. And then what we didn't talk about, of course, is mission alignment, which actually unifies a team. So if everyone coming in is aligned to the mission, that helps a lot because especially in the world of AI researchers and hardware folks, there's a lot of miscommunication because we're coming from such different worlds. And so having a sense of we're all pulling in the same direction is really important. And then I rely a lot, Lenny, on my gut feel for people, assuming everything else that I'm looking for has been checked. It's hard to talk about what that means, but usually it's that spark that you're looking for in someone, that they're genuinely motivated. They're motivated by a desire to learn and by excellence, they're motivated to learn from the people around them. They're open to updating their point of view based on new information and they want to win. I mean, these are the things that really matter when you're building a team. [01:23:43] S1: Awesome. Okay. Just a couple more questions. Something I've been wanting to ask for a long time is you worked with some of the most legendary, successful builders, Steve Jobs, Jony Ive, Mark Zuckerberg, Sam Altman. You don't have to go through all four, but just what's a lesson you learned from as many of these folks that come to mind?
01:24:01 · Caitlin Kalinowski
[01:24:01] S0: So I'll start with Sam, because most recently, Sam is really good at saying, "Why not more? Why not 100X or 10,000X? You are thinking too small. Why not think about this bigger?" And every time we talked about something important, he talked about that. And what I realized is I was thinking too small in certain areas and he was thinking globally, and having that nudge from a leader who's ambitious is really helpful, I think. So that was a big thing that I learned from him about he's willing to go for it at high volume and invest depending on ... Meaning hitting a lot of people. He's willing to think in very big numbers. That was really, really foundationally important. I think for Steve Jobs it's just the bar he held for the company and for technical talent and for excellence was not wavering. It was up here, and you were either going to meet it or you weren't. And that was something that kind of washed through the whole company. When you are a young, ambitious person and you hear that something's not good enough, that can be extremely motivating actually. And it doesn't quite hit the way you would think. And if you tell somebody, "Hey, this needs to be better. You need to spend more time on this. You need to be more thoughtful about this," or, "This is not hitting our quality bar in [inaudible 01:25:35]," or something, that's impactful. And I think you never want to hear that again. So it's very, very motivating. And then Mark Zuckerberg, I think that he ... I have to say he ran a company very, very well. So the way that the technical side of the company operated, the way that we had reviews, that decisions were made, the decisions were made to the lowest level possible in the company to maintain speed. I underappreciated how clean and well run ... The way that the hardware organization interacted with the rest of the company, it was very clear, "This is what we're going for. We're going to have this review. We're going to make a decision in this review. If you can make the decision without the review, you will do that. Here's the objectives for this project." It was really well executed. And I think that's hard to do at a fast growing company. It's very hard to do at that level.