Lenny's Podcast: Product | Career | Growth
01:15:39 · Lenny Rachitsky, Caitlin Kalinowski
[01:15:39] S1: Along those lines, I have to ask you, recently you became famous, on Twitter at least, for quitting OpenAI. You tweeted that you're leaving with your brief explanation and got seven million views, 50, I don't know, 8,000 likes. What happened? Why'd you leave OpenAI? What happened? [01:16:01] S0: Yeah. So what I said in my tweet was that I have a lot of friends in the executive side of OpenAI that I care a lot about, I think are really good people. And I feel that what happened with the decision making, the speed of the decision making, the governance, and the lack of defined guardrails around the announcement of the Department of War deal is not how I thought it should have been done. And both of those things can be true. And so my hope, Lenny, was that there's a third path. You see a lot of people just going along with what their company's doing and then you see some people that are kind of scorched earth about it. In this case, that didn't make sense for me. I didn't feel that way about the company. OpenAI is an amazing company and I was able to help build a robotics program there and attract some of that top talent in robotics, I think in the world. And so I have a lot of, I don't know, this is a group of people I care a lot about. And you can also disagree with friends and feel like what they did isn't good and isn't right. And that's where I ended up and that's what I tweeted about. It was going to get reported on, so I tweeted before that happened.
01:16:01 · Caitlin Kalinowski, Lenny Rachitsky
[01:16:01] S0: Yeah. So what I said in my tweet was that I have a lot of friends in the executive side of OpenAI that I care a lot about, I think are really good people. And I feel that what happened with the decision making, the speed of the decision making, the governance, and the lack of defined guardrails around the announcement of the Department of War deal is not how I thought it should have been done. And both of those things can be true. And so my hope, Lenny, was that there's a third path. You see a lot of people just going along with what their company's doing and then you see some people that are kind of scorched earth about it. In this case, that didn't make sense for me. I didn't feel that way about the company. OpenAI is an amazing company and I was able to help build a robotics program there and attract some of that top talent in robotics, I think in the world. And so I have a lot of, I don't know, this is a group of people I care a lot about. And you can also disagree with friends and feel like what they did isn't good and isn't right. And that's where I ended up and that's what I tweeted about. It was going to get reported on, so I tweeted before that happened. [01:17:24] S1: This is a great opportunity to just whisper to me, what OpenAI is working on? What is this robotics device [inaudible 01:17:30], just like just between you and me?
01:17:24 · Lenny Rachitsky, Caitlin Kalinowski
[01:17:24] S1: This is a great opportunity to just whisper to me, what OpenAI is working on? What is this robotics device [inaudible 01:17:30], just like just between you and me? [01:17:32] S0: I wish I could say. You know, Lenny, part of the fun of our job is we get to see things before everybody else does, but part of the flip side of that is we can't talk about anything internal or any IP. What I can say is the team's really strong and I was really, really grateful for the opportunity to help. But I also thought that after what happened happened, it was time for me to ... I couldn't continue to work there because you don't know what's going to happen next time. And my hope was that my decision made it easier for other folks to talk about what their boundaries were and hold them and we'll see what happens there. [01:18:09] S1: So speaking of team building, this is something I definitely wanted to ask you about. So as I said, I asked a bunch of people what to talk to you about and someone that, I think it was maybe a colleague, former colleague, Marianna [inaudible 01:18:20]. Did you work with her? [01:18:09] S0: Yeah. [01:18:21] S1: Okay. [01:18:21] S0: She's a friend. Yeah. [01:18:22] S1: Okay. She's a friend. So she told me that, here's what she said about you, that, "Your brilliance as a leader lies in hiring exceptional teams. I'd be curious about the kinds of people that she finds indispensable in an era where everyone is concerned about their jobs." So talk about what you've learned about just what you look for when you're hiring folks for your team.
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.