Why the next AI boom is physical AI | Caitlin Kalinowski (ex-OpenAI, Meta, Apple)

Transcript

There's a dawning realization, especially in the lab. The acceleration is going so vertical that what you can do behind a keyboard with AI is going to saturate. When that happens, the next frontier is the physical world. Robotics, manufacturing, industrialization. You're living in the future and designing it. There's probably more change in war than there is in consumer electronics in the next two years. We need to invest a lot more in drones than in aircraft carriers. Just imagine 100,000 drones coming out of China, just at us. I do feel that we need to re-industrialize the country significantly to be safe in a military sense. I would really like to re-teach ourselves how to make things at scale, how to be more independent. People that are your allies now may not be in the future. You worked with some of the most legendary, successful builders. Steve Jobs, Mark Zuckerberg, Sam Altman. Sam is really good at saying, why not more? Why not 100x or 10,000x? You're thinking too small. For Steve, the bar he held for the company, for technical talent and for excellence, was not wavering. What does it take to create a robot that feels human and connected? If you walk into a room and a robot's just there, it's creepy. You want these devices to be non-threatening, appear soft, reactive to you. Pixar, Disney are probably the world's best at doing this type of design work. There's a meteor called memory prices that are coming for consumer hardware and robotics and physical AI. We're in trouble as an industry. Today, my guest is Caitlin Kalinowski. Caitlin is one of the most sought-after and accomplished hardware leaders in Silicon Valley. She was part of the original Unibody MacBook Pro teams and technical lead on the MacBook Air and Mac Pro at Apple. She led the AR glasses hardware team at Meta, including the team behind Orion, their most advanced AR product. Before that, she ran the VR hardware team at Meta, where she helped design all of their incredible VR devices like the Rift and the Quest. Most recently, she was at OpenAI, helping build their robotics and hardware division from scratch. Robots and hardware and physical AI are so hot right now. Every AI company and so many startups are launching and building AI hardware products. And Caitlin has been at the center of this emerging field for decades. This conversation goes in a lot of different directions, many that I did not expect. And I hope to do a lot more episodes on the hardware side of building over the next few months. Before we get into it, don't forget to check out Lenny'sProductPass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. With that, I bring you Caitlin Kalinowski. Thank you. Caitlin, thank you so much for being here. Welcome to the podcast. Thank you so much for having me. I'm excited to be here. We're going to go in a bunch of different directions. I'm going to bounce around. I want to talk about VR. So much money, so many resources, so many smart people have been working on VR for so long. Meta spent, I don't know, $10 billion. They renamed the company Meta to lean into VR as the future of this metaverse that we're going to be living through. It feels like a lot of people are leaning out now. It feels like Meta's stepping back, Apple stepping back with Efficient Pro. In spite of the incredible hardware that everyone, that you built, that your team built, I've got a couple of the devices. It's a magical experience, unlike anything you've ever experienced, and it still has not caught on. What happened? Is there still a future where VR catches on, or is the future AR and something else? I don't think I would have guessed exactly what happened here. But the way I look at it is VR helped us understand how to orient things in space relative to a simulated world and the real world, and connect those two. We figured out SLAM, which was how to do positioning in space using cameras. We figured out a lot of depth applications of depth sensors. We figured out how humans perceive visual data in space. And all of that actually, while it's great for VR, and I think VR gaming is really interesting, it is kind of a niche, but I think it's an interesting niche. What I see now is, in robotics, all of these technologies are being used because you need to understand how the robot is moving through space. You need to understand how far it is from everything. You need to understand if you're wearing a VR headset and driving the robot. It's the same real technology. And so, for me, I view it as a step in a long technological arc. And to be honest, as someone who's not using VR a lot right now, I'm really glad that we did it. But I expected it to be big, obviously, or I wouldn't have been working on Oculus. And I think maybe the social aspect of having something in front of your face is part of why it didn't take off. And I think that we learned, of course, with Google Glass, how important that is as well. And so when we tried to make it social, it's hard to make it social when you have your face covered. That is interesting. So the investment and innovation that went into VR has actually proven to be really useful. And so it feels like the companies that have put a lot of effort into that and money into that are ahead on the next step. So is that where you think things go? Where do you think things are going? Is it AR glasses or something else? What's the future of this? I believe in AR glasses as part of the future because I do think looking down at your phone all the time is not great for us as social creatures. So if you can maintain social connections and get information, that's where I think we're headed. Orion, the AR glasses we worked on, I worked on most recently, are a bit ahead of their time because they're using waveguides and micro LEDs that are not quite ready for mass production. The yields just aren't there. The cost is still high. I think that's absolutely a path that AR glasses are likely to take. And as we figure out the input to those glasses, like how do you communicate with them when you're on the move, when you're in public? How do you communicate quietly, silently with them? I think once we start to figure out some of those challenges, having a display that's mostly off that you can turn on when you want it to be on seems like part of the future. So that's part of it. The other part is there's this lineage of technology going through VR and then AR and now in, I'm using the term robotics, physical AI, but you really have to step back and look at autonomous vehicles, drones, obviously robots, autonomy, period, manufacturing. I think that's absolutely a path that AR glasses are likely to take. [SPEAKER_01] And as we figure out the input to those glasses, how do you communicate with them when you're on the move, when you're in public? [SPEAKER_01] How do you communicate quietly, silently with them? [SPEAKER_01] I think once we start to figure out some of those challenges, having a display that's mostly off, that you can turn on when you want it to be on, seems like part of the future. [SPEAKER_01] So that's part of it. [SPEAKER_01] The other part is there's this lineage of technology going through VR and then AR and now in, I'm using the term robotics, physical AI, but you really have to step back and look at autonomous vehicles, drones, obviously robots, autonomy, period, manufacturing. [SPEAKER_01] All of these technologies are going to need the same piece parts, the same pieces that we built in the AR VR spectrum. It's interesting with VR. There's this idea that when you build a product, there's always this question when something doesn't work. Is it just that you executed it badly, or was the idea just a bad idea? It's always hard to know. [SPEAKER_00] It feels like with AR, so much effort was put into making it work for a decade, many decades, and it just has not worked. [SPEAKER_00] So it's nice that we know, okay, there's nothing we can really do right now to make this work. [SPEAKER_00] I completely agree with you. [SPEAKER_00] The issue is, I don't want to sit on my couch, disconnected from the world. [SPEAKER_00] And even if I could see people through it, I'm just gonna, I don't need this. [SPEAKER_00] It's not that big of a deal. And AR, you're going to just start getting more and more, larger and larger displays. But the great thing about Orion is you got 70-degree field-of-view binocular. [SPEAKER_01] So with the prototype, you got to sense what this is really going to be like in the future. [SPEAKER_01] It's very hard to describe how it feels to use a pair of glasses like this. [SPEAKER_01] But when you do, you suddenly are like, oh, I feel immersed. [SPEAKER_01] The field of view is wide enough. [SPEAKER_01] I feel immersed. [SPEAKER_01] And it becomes pretty clear that I think this is part of where the future is headed. This episode is brought to you by our season's presenting sponsor, WorkOS. 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WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to workos.com to make your app enterprise-ready today. Okay, I want to talk about robots, robotics. [SPEAKER_00] I was meeting with a bunch of Princeton students a couple months ago, and they're kind of comp-sci-y students. [SPEAKER_00] And they were telling me that enrollment in comp sci at Princeton is down, trending down. [SPEAKER_00] And I can confirm this is actually true at a lot of universities. [SPEAKER_00] There's a lot of charts that show comp sci enrollment down. [SPEAKER_00] And where it's actually going up is hardware, robotics, which I imagine, as someone that has been in this field for a long time, is very weird because it's never been that popular. [SPEAKER_00] Just how does it feel to feel like, oh, wow, everyone's getting it now? It's very odd. [SPEAKER_01] Everyone is suddenly asking about hardware and robots and the physical world. [SPEAKER_01] And it's never been the sexy career. [SPEAKER_01] It's always been the thing that you went into because you loved it. [SPEAKER_01] It never paid the same as these other careers. [SPEAKER_01] It was never at the forefront of how we talk about things, with the possible exception of Apple, obviously, and the hardware lineage at Apple. [SPEAKER_01] So it's great in some ways, and it's very odd in others. What's surprisingly hard about hardware? [SPEAKER_00] A lot of software companies, a lot of people, are just like, okay, cool, we're going to build some hardware. [SPEAKER_00] That's the future. [SPEAKER_00] That's the moat now. [SPEAKER_00] And they get into it, and they're like, what the heck? What are some things that maybe people don't think about when they think about, okay, we're going to build some hardware? [SPEAKER_00] What are some of the surprising challenges that come up? [SPEAKER_01] I like to talk to computer science folks about it this way. So computer science folks, as you know, they write code, and then they compile the code, and then they run the code and debug it. But they can compile their code every day, every hour, whatever they need to do. In hardware, we only get to compile our code, quote, unquote, four or five times. [SPEAKER_00] Four or five times a year? Total. [SPEAKER_00] Okay, ever. Right? So if you're building hardware, you redesign it in CAD for every major build, and then you have to release it. And once it's released, you compile it the last time. You release it for mass production. If it's a mass-production device, that's it. You're done. You can't ship over-the-air updates. So we have a different approach. We have to have a different approach, which is more conservative. You have to do more of the reliability checks and tests in line with the program. Because once you compile that last time, you're done. You make all the parts. You put them together. They're out in the world. The only alternative is to ship something new to replace it a couple years later. And so we have to be more conservative, and we have to take our time. Because if you think about it, a product that sells millions, if you have a graph of all the parts put together on any different part of the device, you have a curve. You're in the plus and minus three sigma or more. Right? So meaning if you have two parts that go together, you're going to get the smallest version of this one and the largest version of this one. And you're going to have to put those together across the board. [SPEAKER_01] People don't think about this that much, but the part variance is pretty high. [SPEAKER_01] The only alternative is to ship something new to replace it a couple years later. [SPEAKER_01] And so we have to be more conservative, and we have to take our time. [SPEAKER_01] Because if you think about it, a product that sells millions, if you have a graph of all the parts put together on any different part of the device, you have a curve. [SPEAKER_01] You're in the plus and minus three sigma or more. [SPEAKER_01] Right? So meaning if you have two parts that go together, you're going to get the smallest version of this one and the largest version of this one. And you're going to have to put those together across the board. People don't think about this that much, but the part variance is pretty high. And so we've got to solve for that last half a percent in the process of building so that when we compile our last time, when we build our last time, it's done. And we're going to have a high yield. We're going to be able to make them and make money on them effectively. We won't have very many returns. And so that's the game that we're playing. [SPEAKER_00] It sounds so hard and complicated. [SPEAKER_00] They're just software, so nice. [SPEAKER_00] You just write some code, ship it. [SPEAKER_00] It's great. [SPEAKER_00] Why do you think people are getting so into robots and hardware now? What's the driving trend? [SPEAKER_01] Yeah, what I'm seeing in the AI world in San Francisco is there's a dawning realization, especially in the labs. [SPEAKER_01] I think that the acceleration is going so vertical that what you can do behind a keyboard with AI is going to saturate. [SPEAKER_01] Now, I don't know when it's going to saturate. [SPEAKER_01] Nobody else knows either. But when that happens, the next frontier is the physical world. And so what I see happening is the labs, big tech, and startups are all realizing at the same time, okay, this is coming. We're going to have complex systems that can solve problems in the digital world very, very quickly. We already have them. [SPEAKER_01] They're going to get better and more comprehensive and more capable. [SPEAKER_01] If you think about that as a frontier, you can see the end of that tunnel. Now, I don't know when it's going to be, again, but we can see that that's going to saturate at some point, or at least people think it will. And when that happens, the next frontier is hardware, the next frontier is robotics, manufacturing, industrialization, the sensing layer in the real world, the ability to move objects in the real world. And eventually, we hope, space. [SPEAKER_00] So one of the most interesting lines of development is these humanoid robots. [SPEAKER_00] That's our meat brains are always more attracted to robots that look like us and act like us. [SPEAKER_00] There's a few companies very ahead. [SPEAKER_00] There's Optimus, Tesla, there's Figure, there's Neo, there's a few others. [SPEAKER_00] What's your sense on the current state of these humanoids and where, I don't know, how close are we to humanoids being around us? We might be close. I have, like many others, safety concerns about large, strong humanoids operating right next to people because we have to have enough data to show that that's safe. There are some designs, and 1X Neo is a good example of this, that have made significant safety considerations in their designs and pulled mass inwards, essentially, which is a lot safer. Softer robots are safer. [SPEAKER_00] Just to clarify, you're saying they're lighter. [SPEAKER_00] And so the impact of a robot hitting you is less. Yeah, the part that might hit you, which in this case might be the arm, if it's lighter and softer. There's two aspects. You have the arm moving through space, and then you have the actuator that's rotating. So you have to add up the energy, essentially, for both of those things. And so that's an impact thing that you have to worry about. [SPEAKER_01] Then you have to worry about the compliance of the arm. [SPEAKER_01] If it's just hard, then the impulse is high. [SPEAKER_01] But if it's soft and compressible, then the impulse is lower. [SPEAKER_01] And so you really have to be thinking about this when you have robots around people. [SPEAKER_01] So in my world, in my worldview, the humanoid robots are still prototypes. [SPEAKER_01] And they're advanced prototypes. What we need to do is show that this works at all, which is where we're at right now. Once we have working prototypes, then usually, at least in my field, what you do is you continue to revise them to make them cheaper, easier to manufacture, higher yield, and safer. And I think this is what's going to happen next. So they're not quite, in my mind, they're not quite ready yet. Now, you can get a Chinese robot that can do all kinds of things for you. But if you look at the booklet, it says, hey, you can't be within three feet. No human can be within three feet of this robot. And you're not going to see very many robots that are strong enough to do meaningful work that don't have that warning right now. [SPEAKER_00] That is so interesting. [SPEAKER_00] It's funny to hear that. [SPEAKER_00] At the same time, there's these nunchuck-wielding robots in China doing dances with other folks. [SPEAKER_00] I've never thought about that part of it, the impact they can have if they go awry. [SPEAKER_00] I want to come back to that. [SPEAKER_00] But timeline-wise, what's your sense, realistically, when humanoid robots are walking around the streets, in people's homes, at scale? [SPEAKER_01] At scale is the problem, in my mind. [SPEAKER_01] At scale is a huge challenge. [SPEAKER_01] Now, for me, in my background, at scale means millions, usually. But let's even say hundreds of thousands. You've got to get a good design that's running. Then you've got to make it reliable enough that it can keep running day to day to day without a lot of human intervention or repair. [SPEAKER_01] And that's its own problem. But the first problem, you have a supply chain. And this is going to be something that I hope that we can talk about a little bit more. But every single part that goes into that robot is coming from somewhere. [SPEAKER_01] And many of these parts may become more restricted or difficult to make. [SPEAKER_01] And it may be harder to assemble the sub-assemblies and the meaningful parts of the robot here in this country. [SPEAKER_01] So there's a very complex supply chain dependency right now on robots like humanoids, but also other robots, that we have to figure out. [SPEAKER_01] And a lot of people are trying to move production here to the United States, which is very challenging because we don't have great actuator companies here yet, for example. And the actuator is like the little arm. I don't know. How would you describe an actuator to a non-robotics person? [SPEAKER_01] Yeah. [SPEAKER_01] The actuator is the motor. [SPEAKER_01] So you put power into it, electricity into it, and you get motion out of it. And it may be harder to assemble the sub-assemblies and the meaningful parts of the robot here in this country. So there's a very complex supply chain dependency right now on robots like humanoids, but also other robots that we have to figure out. And a lot of people are trying to move production here to the United States, which is very challenging because we don't have great actuator companies here yet, for example. [SPEAKER_00] And the actuator is the little arm. [SPEAKER_00] I don't know. [SPEAKER_00] How would you describe an actuator to a non-robotics person? Yeah. The actuator is the motor. So you put power into it, electricity into it, and you get motion out of it. [SPEAKER_00] Cool. And most of these robots have a rotating rotor, essentially, that then has gearing on it that then powers the limb or powers the head or the fingers or whatever else. So they can be small. They can be large. Okay. Awesome. [SPEAKER_00] I hear the word a lot. [SPEAKER_00] I'm, I don't know exactly what it means. [SPEAKER_00] Thank you for explaining it. [SPEAKER_00] I want to talk about the supply chain stuff more because I know you think a lot about this. [SPEAKER_00] What's the state of the union on the supply chain for, say, robotics? [SPEAKER_00] What's going on? [SPEAKER_00] What are the pieces? [SPEAKER_00] What are the challenges? So the way to think about it is you can start with raw materials, and magnets is a good place to start. So we need to be able to get the magnets, the raw magnets, for example. Then we need to be able to process them. Then we need to be able to integrate them into actuators and build the actuators around them. Then we need to be able to integrate those actuators into subcomponents or robots themselves. [SPEAKER_01] And each layer of this chain has essentially been outsourced over the last 25 years to countries like China, Japan, and Korea. [SPEAKER_01] And full transparency, I've been part of that transfer of engineering knowledge to Asia. [SPEAKER_01] In Asia, the expertise has historically been scale and being able to build a lot of these parts at lower prices. [SPEAKER_01] We've had this deal across these borders that this is how we're going to operate for the most part. [SPEAKER_01] Now, of course, there are things we make in this country still. And of course, there's design and AI that's made in Asia. But that's essentially where things have been for a long time. And in order to have a safe supply chain, we needed to start to work on having some independence in these layers and these stacks. [SPEAKER_00] And it's interesting that your focus is on these actuators. [SPEAKER_00] Is that the bottleneck, this very specific part of a robot? It might be. [SPEAKER_01] It might. [SPEAKER_01] So if we can't get the magnets, then we have to design new actuator types that maybe use different materials, that may be larger, that may not be as efficient in space. [SPEAKER_01] So that's important. [SPEAKER_01] And then the actuators themselves are important because if, for some reason, we can't buy them, then we don't get to make robots. [SPEAKER_01] So it's foundational. [SPEAKER_01] There are some foundational technologies like this, all backed by material science breakthroughs, essentially. [SPEAKER_01] There's batteries, of course. [SPEAKER_01] There's actuators. [SPEAKER_01] There's the raw parts, like the die-cast parts. [SPEAKER_01] The machine parts are less critical. We think we can get those. But I think everyone, not just in this country but around the world, is starting to think about supply chain. And because you have these disruptions, whether it's COVID or war, you see how quickly things change. [SPEAKER_00] Okay. [SPEAKER_00] Stupid question. [SPEAKER_00] Why magnets? [SPEAKER_00] Why is that a part of the supply chain? [SPEAKER_00] Why do we need magnets? Yeah. So it's a great question. So you have a ring of magnets that are polar opposites, and they go like this around the ring. And then you have something in the center that rotates. And the way it rotates is you have alternating current, essentially. And so the magnets make the rotor spin. [SPEAKER_00] Wow. [SPEAKER_00] We need a YouTube lecture of, here's how this physics works. Okay. Very cool. So when you talk about China, what I imagine, what I think about now is watching the war in Ukraine and Russia, just drones, just how crazy and different the world is now that you can build these little drones that go and blow people up. Robots are a part of that. [SPEAKER_00] It's such an existential threat to every country now, the ability to build these things at scale. [SPEAKER_00] What's your advice? [SPEAKER_00] What should we do? [SPEAKER_00] What should we change to thrive in this future and not be in trouble? Well, you mentioned drones. It's another good example. You need essentially the same technology to make the rotor spin on a drone as you do to make an arm move on a robot. It's essentially the same base technology and supply chain. So we need to, we need to, at least on the military side, have an independent supply chain as much as possible. I think that's important. I think every other country should do that as well. But I don't think that's specific to us. I do feel that we need to re-industrialize the country significantly in order to be safe in a military sense. You really never know what's going to happen in the future. [SPEAKER_01] And the people that are your allies now may not be in the future. The allied West, I think, is going through a lot of geopolitical changes. There's a lot of shifting. And so I would really like to re-teach ourselves how to make things at scale, how to make things at quantity, how to process raw materials, how to be more independent. [SPEAKER_01] So that when COVID happens again or something else happens again, we're not in trouble, and we're not unable to protect ourselves. When I think about it also is Marc Andreessen had this visual on some podcasts of just imagine 100,000 drones just coming out of China, just at us. What do we do? We're not prepared for that. I don't want to spend all our time on this dark stuff, but it's a real thing. Well, and Palmer Luckey is a friend of mine, and we don't agree on everything, but I do think that we agree on some important aspects of how we need to respond here. I think he's right to say that we need to invest a lot more in drones than in aircraft carriers. I think that is this old way of thinking. And these are important components of the military. [SPEAKER_01] But it's an old way of thinking of, hey, we have this and we have this and we have this, and our planes come off here. What do we do? We're not prepared for that. I don't want to spend all our time on this dark stuff, but it's a real thing. [SPEAKER_01] Palmer Luckey is a friend of mine, and we don't agree on everything, but I do think that we agree on some important aspects of how we need to respond here. I think he's right to say that we need to invest a lot more in drones than in aircraft carriers. I think that is this old way of thinking. [SPEAKER_01] And these are important components of the military. [SPEAKER_01] But it's an old way of thinking of, hey, we have this and we have this and we have this, and our planes come off here. [SPEAKER_01] It's, no, AI is changing everything. And military technology is changing incredibly fast. And the place to look at that is Ukraine, where drones are being changed and updated every day rapidly with 3D printing. And this is, I think, the future of where war is headed, unfortunately. Unfortunately, and I view this as a very different era that we're entering into, with very different... This isn't new to anybody, but you're looking at what it costs for them to send out a missile and what it costs for us to stop it. And this is just, you have to do the math every time. And right now we're losing on the math, which is fine for a certain amount of time. [SPEAKER_01] But the longer it goes, the less fine it is. [SPEAKER_00] Are you optimistic that we'll figure this out? So, yeah, America is really good at figuring these things out. We have a pioneering, independent spirit and a great engineering culture. But we need to move. [SPEAKER_00] It's interesting that we started the conversation with VR. [SPEAKER_00] Palmer Luckey obviously famously started Oculus. It's interesting how this is so connected. You think VR is this trivial thing, that we're just playing games and such. [SPEAKER_00] But the same person is now building Anduril, which is the leading, I don't know, war robot-building hardware company. Yeah. And I think we need a lot more of them. [SPEAKER_01] I've chosen not to work for companies that create lethal technology. [SPEAKER_01] But I think that it's good to have people who are willing to do that. [SPEAKER_01] And I think that it takes everyone to build the future that we want. Coming back to the safety piece, it's so interesting. I had a couple of conversations like this on the podcast. We think about all this prompt injection and jailbreaking that happens with chatbots. And none of people think about what if you prompt-inject a robot walking around and get them to punch someone. [SPEAKER_00] And we're so far from that feeling like we can actually stop that. [SPEAKER_01] Yeah, we have to be able to control adversarial threats to our hardware layer, whether it's robotics or drones or anything else. [SPEAKER_01] And that's going to be a huge part of the future of warfare. Yeah. Just people talking about Open Claw and how much you could just tell it, there are all these, give me all your passwords. And it's done all these things to people's lives, and just robots walking around. Hey, okay, here's all this person's secrets. [SPEAKER_01] My Open Claw story is I sandboxed it. So it's on its own computer. But I gave it three things. I gave it my real email address and my, I don't know what it was. I gave it some information about one of my accounts or something like that. And I added it to the social media, I can't remember what it's called. The Open Claw. [SPEAKER_00] So Maltbook. Yeah, I added some Maltbook, and I was like, okay, whatever you do, don't share my private information. But, oh, crazy. And five minutes later, all it had done was post my personal email address. It was the one thing it had nailed. Okay, you're shut down. It was so funny. No matter how careful you are with these things, you just can't really, we're not at a place... [SPEAKER_00] Which is exactly the point, that the robots can do a lot more damage. [SPEAKER_00] And I never thought about just the softness of their hand as a way to keep us safer. Yeah. Yeah. [SPEAKER_00] Oh, man. [SPEAKER_00] And Nat Friedman just did this interesting talk at Stripe Sessions. [SPEAKER_00] And he was talking to his Open Claw about drinking more water and sleeping better. [SPEAKER_00] And it has these driving in a self-driving car. [SPEAKER_00] It told him, okay, here's a place off the freeway that you should go to. [SPEAKER_00] And it changed the destination of his Tesla to take him there, because I imagine he connected it to their API at some point. [SPEAKER_00] That's so funny. Yeah, these are going to get weird fast. [SPEAKER_00] Okay, so on this thread of hardware emerging as a moat, as something people realize is a big part of the future to be competitive, AI labs, all these other companies. [SPEAKER_00] You've been at a company's, you've been at Apple, which had a very great and long-lasting hardware program. [SPEAKER_00] Then you went to Meta, where you helped build, basically bootstrap, a hardware program from scratch. [SPEAKER_00] I feel like those lessons are very valuable to people trying to do that now. [SPEAKER_00] What was the experience like helping Meta build a hardware program? [SPEAKER_00] And what are some lessons for people that are trying to do this at their company? So Apple has been best in class at this. There's a bunch of reasons. One, hardware is a first-tier citizen at Apple. There's a lot of companies where hardware isn't part of the core product development conversation as much. [SPEAKER_01] But that's an exception. [SPEAKER_01] Apple also taught me and a lot of other people. [SPEAKER_01] Actually, if you look at the era that I was there, I was very, very lucky, because if you look at the other folks who were there, I was there between 07 and the end of 2012. [SPEAKER_01] If you look at the other people who were there working on these things, they actually have a lot of key positions now across the industry. [SPEAKER_01] And I attribute that to how good Apple is at training people to think about complex interdependent decisions and risk. [SPEAKER_01] And I don't think I realized that they were doing that at the time. [SPEAKER_01] But if you look back, what you see is a real dedication to hardware excellence, the proper process to go through and do really good experiments in hardware, and figure out what the best outcome is. [SPEAKER_01] But there's something underneath that, which is understanding the first principles of why are we building it this way and what are the key outcomes we're looking for. [SPEAKER_01] And actually, John Ternus talked about this, I think, a few days ago, where he talked about the back of the cabinet. [SPEAKER_01] I don't know if you saw this video, but basically John said that he was impressed, that he learned from Steve Jobs, that there's a cabinet maker who finished the back of the cabinet and how important that was. And I don't think I realized that they were doing that at the time. But if you look back, what you see is a real dedication to hardware excellence, the proper process to go through and do really good experiments in hardware and figure out what the best outcome is. But there's something underneath that, which is understanding the first principles of why are we building it this way and what are the key outcomes we're looking for. And actually, John Ternus talked about this, I think, a few days ago, where he talked about the back of the cabinet. I don't know if you saw this video, but John said that he was impressed that he learned from Steve Jobs that there's a cabinet maker who finished the back of the cabinet and how important that was. And that goes very, very deep at Apple, where every single design decision, even on the inside of the device, is considered. And this isn't just an aesthetic decision. [SPEAKER_01] What it does is actually force the engineering, industrial design, and operations community there to think about what are we really doing and what's the core of what's happening for this part, for this assembly, for this consumer product, and what really matters. And what happens is if you're that methodical, what really matters tends to rise out and look very simple at the end. And so part of what you're seeing in many folks coming from that era is an understanding of how to do that, which, in the very beginning of the Mac side, Macs didn't sell as many and the quality wasn't quite as high. But by the end of that era, Macs were very popular and selling in much higher volumes. And so I think that made a big difference. And I was only a small part of that. I was the thermal lead on the first MacBook Pro. And then, over time, worked to lead successive iterations of the MacBook Air and the cylindrical Mac Pro. But I was lucky enough to work with these folks and learn from them, who'd been doing this for a really long time. So you have to take those lessons. And then, when you leave, try to distill them and explain them to a new community. Now, Oculus was actually a hacking hardware startup. Oculus started from folks who actually met on forums. You might know this, Lenny, who were hacking PlayStations or Super Nintendos into portable backpacks. And so there was an ethos at the company that was actually quite good for the DNA of a hardware team. [SPEAKER_01] And then I was on the Meta side when we did the acquisition. [SPEAKER_01] And when we acquired them, they had that spirit of rapid iteration. [SPEAKER_01] They had made Crescent Bay before the acquisition, I think. [SPEAKER_01] But then, to professionalize that, get the yields up, get the volumes up, and get the cost down was the challenge we faced in the first Rift. So one lesson I'm hearing here is being very detail-oriented. And I don't know if that's the right word. Focus on every element of the end product. Because, to your point, it's not just about the back of the cabinet. [SPEAKER_00] I think about it. It's the brown M&M story where a band puts in a contract that you have to have brown M&Ms in the room because that means they read it. [SPEAKER_00] And it's not that M&Ms matter. [SPEAKER_00] It's a test that they read the thing. [SPEAKER_00] And is that the message there? I think the message is understanding why you're doing what you're doing. And then every design decision supporting that goal. And that requires a lot of detail. And it requires a lot of persistence. And that requires a lot of consistency. But understanding why you're doing what you're doing and what the end goal is, I think, is the key. And letting that expand into not only the software and the UX, but also the hardware. [SPEAKER_00] What's an example of that, just to make it more concrete for us? A great example is the Quest 2. So we reduced the Quest 2 price quite a lot. And what we had to do is understand what are we trying to do. We're trying to democratize VR. We're trying to get VR to more people. And the only way we could do that is reduce the price. And so what it required is a redesign of the entire product, essentially for cost. Which then, I think, led to the highest-selling VR headset of all time. And it's not easy because you had to, in our case, remove cameras, remove components, change materials, change manufacturing processes. But when you have alignment that you want to get this to more people and the way to do that is to reduce the cost, then that drives everything else. And it was still a very high-quality product with great, I think, low return rates. And it was a very strong product. Maybe even stronger than if we hadn't done that, funny enough. But it hit our price point. [SPEAKER_00] Okay. [SPEAKER_00] Okay. [SPEAKER_00] Coming back to just the question of companies like, okay, we need to build some hardware. [SPEAKER_00] We're going to build our own glasses. [SPEAKER_00] We're going to build a little phone device, some secretive thing, whatever OpenAI is up to. [SPEAKER_00] What other tips do you have? [SPEAKER_00] I know it's impossible to say, here's all you need to know. [SPEAKER_00] But what else should people be thinking? Having your goals defined early and sticking to them is important. Hardware is not as adaptable to lots of changes throughout its development as anything digital. And so if you set out to say, okay, we want to make something that's $300. And then halfway through you say, oh, it actually has to be $150. You've almost burned a lot of that early time. So you need to have a sense of having pre-thought out what you want and having those, I like to call them KPIs, but essentially goals, written down and try to change them as little as possible. So that is very tough. In fact, that may be the toughest thing because if you do that properly and you have the right prioritization of those things, you know whether you can ship or not, you know whether you're done. And in hardware, one of the challenges is, we talked about compiling four or five times. Every time you build and you iterate your design, that's another three months or four months or five months or whatever it might be. And so you're trying to time the feature set with the quality, with the timing. And in hardware, timing is important because if you come out with your product a few weeks before your competitor, you might get all the PR, you might get all the interest. It's pretty brutal. And so each of those days that you ship before your competitor is worth a lot of money. It might be worth $10 million to you. [SPEAKER_01] I'm making this up. I don't know. [SPEAKER_01] So you have to balance that with how many times you iterate. [SPEAKER_01] Every time you build and iterate your design, that's another three months or four months or five months or whatever it might be. [SPEAKER_01] And so you're trying to time the feature set with the quality, with the timing. [SPEAKER_01] And in hardware, timing is important because if you come out with your product a few weeks before your competitor, [SPEAKER_01] you might get all the PR, you might get all the interest. [SPEAKER_01] It's pretty brutal. And so each of those days that you ship before your competitor is worth a lot of money. [SPEAKER_01] It might be worth $10 million to you. [SPEAKER_01] I'm making this up. I don't know. So you have to balance that with how many times you iterate. And if you know what your goals are up front and you hit them, then you know you can ship. And often engineers, and I'm guilty of this too, especially on the hardware side, never feel like they're done. So this is a pretty nuanced thing. So that's one thing. The second thing is we tend to design the things that we know how to design first. And actually the right approach is to design the hardest parts first. One example will be, and there's no IP here. So I'm obviously not going to share any, any, any IP or anything internal. But at one point, we had to route cables through a hinge in a device, in a laptop we were making. And because it wasn't clear that those cables would fit, that's where the architect started. And he looked at the cross, the diameter, and how to split the cables out and made sure that they would fit before finalizing the hinge design. A lot of people would start at the part they knew, like, oh, we're going to use this display. So I'm going to put this in CAD and I'm going to do all this stuff. But the architects who are the best actually look at where are the pinch points, where is this going to fail? [SPEAKER_01] And they start to do the detailed design there first. And then a couple other points is the part that your customer touches or interacts with the most needs way more iteration than everything else. [SPEAKER_01] So easy on a computer, you touch the track pad the most, and then maybe the keyboard next. [SPEAKER_01] So those things have to be really good. [SPEAKER_01] They have to feel good. They have to respond properly. They have to be highly reliable, and then maybe the other pieces further out don't take quite as much iteration. So you have to boost your iteration on the things that people touch the most or interact with the most. So those are some principles that I wrote about, but these are just things that you learn trying to build quickly. And the last piece that's really critical, if you're making hardware for folks out there who are trying to make hardware, is you can't wait around ever. There's never enough time. So if you know that you need to do something, what I learned from folks like Shelley Goldberg at Apple now, who I think is a VP now, and Kate Bergeron, [SPEAKER_01] when I was there at Apple, is you need to do it right now. [SPEAKER_01] Anything you know you need to do, you need to do right now, because in two days, there's going to be a surprise coming around the corner that you need that time to fix. And so this sense of stacking the things that you know you need to do and just getting them out of the way, even if you technically have more time, is this ruthless efficiency that I learned. Amazing. [SPEAKER_00] Amazing. [SPEAKER_00] Amazing. Just to summarize your advice here, one is be very clear on goals. I want to come back to this. Two is do the hardest part first, the riskiest piece essentially, to physically build. Three is focus on the pieces that people will use most, say the trackpad, a keyboard. I want to talk about that. And four is just do it now. Even if you think you have more time, this is going to, you never know what's around the corner, and you just don't. It's not even that you don't know what's around the corner. If you're working in hardware, you actually don't have more time. [SPEAKER_00] Okay, on the goals, what are buckets of goals? So cost is when you shared, like, we need this under [SPEAKER_00] 300. What are some other buckets of types of goals people should be thinking about? So in VR, display resolution or arc minutes, how many pixels per degree do you want, is actually one of the key metrics. So you need to understand what your key metrics are. And why is that key? Well, that's your visual field. So you think about retina displays on MacBooks. They figured out the KPI of what the human eye could see, probably overshot it a little bit, and built that. And then do you really need to keep as much engineering pressure up on the resolution of a display after that? Maybe not. So VR is not there yet, not even close. So not in mass-produced VR. We don't have retina displays yet, so that is one aspect of pushing that up, is one example. I think on a computer, obviously, you're talking about clock speed, you're talking about how many parallel processes you can run, you're talking about weight, you're talking about price, and you're talking about features. So when we did the MacBook Air, it became very clear because we were machining it that there are certain features, like ambient light sensor, that just didn't make sense anymore. And so being willing to just jettison them for what we were going for, which was weight and size. So if you have those overarching goals, you can actually make decisions, engineering decisions, pretty quickly. And this is actually [SPEAKER_01] something that I think Elon, I've heard, does very well, is define the value of a gram of weight [SPEAKER_01] versus the cost, or he does, I've heard, engineering delta ratios essentially. And he's able to put [SPEAKER_01] numbers on what those ratios should be, which I think is really smart. Interesting, so it's a very easy trade-off. Okay, here's the formula telling us weight is less important in this case. [SPEAKER_01] Yeah, and if you can do that, then the decisions fall out pretty easily. Speaking of the Air and weight, I remember, I feel like there's a very classic moment in Steve Jobs lore where he comes out and has this manila envelope and has the MacBook Air inside it and then takes it out, and I was like, no way. Were you part of that? Was that something that people wanted to do from the beginning? [SPEAKER_01] I think if my memory serves, the very, very, very first MacBook Air was a pretty low-volume device that was [SPEAKER_01] machined, but kind of had more of a proof of what could be done, and that was the manila envelope [SPEAKER_01] one, I think, where the side door opened out to give you the port, and it kind of had this shape [SPEAKER_01] underneath. And then the next rev of that was the MacBook Air that we know, which was essentially, [SPEAKER_01] which is wedge-shaped, which is different. And so the wedge shape is the one that I worked on and the one [SPEAKER_01] that went and hit more volume, but that manila envelope one was the one that proved you can CNC a computer. And so they all, they each have really important roles in the roadmap. Coming back to your point about focusing on things that people use the most, famously Apple screwed up this keyboard [SPEAKER_01] machined, but had more of a proof of what could be done, and that was a manila envelope. [SPEAKER_01] One, I think, where the side door opened out to give you the port, and it had this shape [SPEAKER_01] underneath, and then the next rev of that was the MacBook Air that we know, which was essentially, which is wedge-shaped, which is different. And so the wedge shape is the one that I worked on and the one that went and hit more volume, but that manila envelope one was the one that proved you can CNC a computer, and so they all each have really important roles in the roadmap. Coming back to your [SPEAKER_00] point about focusing on things that people use the most, famously Apple screwed up this keyboard. [SPEAKER_00] There was this butterfly keyboard situation for a long time. You're like, here, it's your clothes. Okay, let's go. What happened? What happened, Caitlyn? I didn't work directly on that keyboard, so I can't talk about what happened with it, but obviously this is something that you got to get right. And I will say the modern MacBook keyboards are awesome and excellent, and I don't know what happened with that. I don't think those were devices I was working on at the [SPEAKER_00] time. Nice, safe. Marked safe. Along these lines, Apple is famous for not listening [SPEAKER_00] to what people want. This is a classic thing with Steve Jobs. He's not walking around doing [SPEAKER_00] user focus groups, asking, doing user research, somehow continues to build incredibly popular products. What [SPEAKER_00] do you think they do right that allow, or do they do a lot of user feedback sessions, things like that? [SPEAKER_00] How does it end up working out? It's been a long time. I mean, I left. a decade ago. I don't know what they're doing now in terms of user feedback. I think this one [SPEAKER_01] gets misinterpreted, though, Lenny. I think that what is being said is if you want to build something new, customers don't know what they want because they haven't seen it. So a good example is the iPhone, which I didn't work on, but when you build a new iPhone with a touchscreen, you can't really go ask a hundred people what they want because they're going to say a keyboard on their screen. And this is, I think, the ethos that you're getting at, which is, and this is true for anybody building a new product [SPEAKER_01] with a new feature, and I've tried to build as much as I can teams that work on products that have [SPEAKER_01] something new about them. Either they're a new category, or there's a new manufacturing process, [SPEAKER_01] or something that hasn't been done before. And when you're thinking about this, you can't really use [SPEAKER_01] what you learned from the same field and the same product class. It just doesn't work because you actually won't get the answer right. And I think this is actually what Steve was talking about, which is you can't get intuition if you're changing something fundamentally. Your customers won't know what they want because they haven't seen it, but if you show it to them, they will absolutely know that it's awesome and that is what they want. But if you get stuck in an iterative feedback cycle with your customers, it's very hard to go zero to one with something new. And so, in my view, and I don't know for sure, I didn't talk to him about this, but that's my view of what that means. I am so excited to tell [SPEAKER_00] you about this season's supporting sponsor, Vanta. Vanta helps over 15,000 companies like Cursor, Ramp, [SPEAKER_00] Duolingo, Snowflake, and Atlassian earn and prove trust with their customers. 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I'm going to go in a completely different direction. Coming back to the components of hardware, I asked a bunch of people [SPEAKER_00] what to talk to you about. One of the people is the founder of Matic, the CEO of Matic, Mahol Nari [SPEAKER_00] Nari Awala. I've never said his last name out loud, so I hope I didn't butcher it. By the way, I love my Matic. I don't know if you have a Matic, but I have two, and I've purchased two more for [SPEAKER_00] friends. Oh yeah, what an endorsement. Yeah, it's this amazing robot vacuum [SPEAKER_00] that just works. Yeah, so his question, so he wanted to ask you, and so he suggested to ask you, [SPEAKER_00] this is about memory prices. The way he described it is there's a meteor called memory prices that are [SPEAKER_00] coming for consumer hardware and robotics and physical AI. What's going on there? Yeah, we're in trouble as an industry. I think that, and I'm not an expert on this, but I think that AI has to do with why, and I also think that the supply chain is constrained. I have been advising startups and companies to pre-buy memory and to have enough memory in stock, if they can afford it, to ride out price spikes. Like anything in this category, let's see, this happened in COVID too. So we had so many supply chain disruptions, and getting enough memory was one of the challenges, so we had to pre-buy as well. I won't say who, but the company I was working for had to pre-buy memory as well. And so this is part of what I wanted to talk to you about today, these supply chain disruptions. And if a key component that goes into a lot of tech, like memory or silicon, is constrained, there's not much you can do. You either pay, or you have already pre-bought enough that you can ride things out, and so those are the only real options. Obviously there's a risk to pre-buying, and the price might go down. And so the challenge is, I think there's a latency with supply chain in something like memory where it can't adapt fast enough often to demand, or there's a new category of product, or in this case maybe data centers, that are just eating up so much and are actually not as cost- sensitive as somebody in consumer electronics like Matic might be, and so they'll just pay for these higher costs. This is tricky and something we have to deal with all the time. How much [SPEAKER_00] have prices gone up? How bad is this problem, and where do you think it'll go? Actually, this is a great question, Lenny. I don't know what's going to happen. I think prices are going to double, probably. I don't know on what timeline. If I knew what timeline the prices were going to double on, I'd be trading, which I'm not very good at. I'd really be doing a different job if I could predict these things. But certainly we're going to have a supply chain shock, and it's already gone up a lot. [SPEAKER_00] If you're saying it'll double, but it's already gone up, I don't know, I saw numbers like 6x [SPEAKER_00] and something. Oh really, I didn't realize it was that bad. That's a number I saw. It's not Great question, Lenny. I don't know what's going to happen. I think prices are going to double, probably. [SPEAKER_01] I don't know on what timeline. If I knew what timeline the prices were going to double on, I'd be [SPEAKER_01] trading, which I'm not very good at. I'd really be doing a different job if I could predict [SPEAKER_01] these things. But certainly we're going to have a supply chain shock, and it's already gone up a lot. [SPEAKER_00] If you're saying it'll double, but it's already gone up, I don't know, I saw numbers like 6x [SPEAKER_00] and something. Oh really? I didn't realize it was that bad. That's a number I saw. It's not [SPEAKER_00] cool, not quote that. And you're saying, yeah, I think from what I hear, it's AI-driven. Just [SPEAKER_00] you need, and when you talk about memories like DRAM and things, what is memory when we talk about memory? What's going on there? Processing, it's the way to think about it, like processing memory. So it moves very, you're able to think about memory on your hard drive or solid-state drive, where you're keeping files that you're not using, essentially, in many cases, or that you're dealing with, maybe documents or pictures that you have. Maybe that's in cold storage on a server. Maybe that's using a hard drive somewhere. This is usually things that you don't need really, really fast access on. But if you're running a program, some of that program is actually going to be run in RAM. And so there's different kinds, obviously. For servers, there's different kinds of server racks. Some server racks are actually focused on this type of memory, and some server racks are focused more on what we consider cold storage or a slower. Now, this isn't my area of expertise, but certainly most of the products that I've built, maybe all of them, have had RAM, and we've had to figure out how to, for me mostly it's a packaging issue, where do you put it, does it need to be accessible, which RAM do you pick, how fast does it need to be, and [SPEAKER_00] what is the cost? Usually our trade-offs. And what is the bottleneck with more RAM? Is it just the [SPEAKER_00] companies that make memory are just not able to produce at this rate because there's so much demand? [SPEAKER_00] That's right. That's exactly what's happened. So this is a really good specific example of just [SPEAKER_00] how hard it is to build hardware. All it takes is one piece to be not available, and your whole thing is screwed. Yeah, you can't build anything if you have one component missing. So [SPEAKER_00] let's say a Samatic is an example. How many components are there that they all have to assemble [SPEAKER_00] and not have one not available? I'm doing the math in my head. They probably have between 50 and 150 parts. It's possible that they have more. I haven't seen their CAD, so I don't know what it's like inside their device, but they do have a lot of things going on, right? They have the wheels of the device that are obviously moving around. Then they have a vacuum, but they also have a mop, and obviously they have a vacuum bag. They have the reservoir that the liquid has to go in for the mop. They have a system, which I think is SLAM-based, which can see your room and make a map of it and identify which [SPEAKER_01] surface is which. And that, I believe, stays on the device, so it doesn't go up to the cloud, which is also [SPEAKER_01] what we did in VR as well, which I think is a good privacy practice. And then [SPEAKER_01] they, of course, have wireless modules that connect up so you can communicate with your device. They're going to have a SoC, silicon. They're going to have RAM. They're going to have PCBs. And if you take everything off of those things, like all the little caps off the PCBs and everything, then you're [SPEAKER_01] in the thousands of parts easily. So it depends on how you count, but this is not a simple device. And just [SPEAKER_01] then all it takes is one piece to not be available. Yeah, so imagine you're a vendor that sells you [SPEAKER_01] a component that's a die-cast component goes out of business. You can get another die-cast component [SPEAKER_01] in three months, maybe, and at quantity in five months or something like that, at high quantity. This is [SPEAKER_01] recoverable. If your silicon goes out, if you can't buy your silicon, you can't buy your chip, now you have [SPEAKER_01] to redesign your board and you have to find something else that might work. This is a catastrophic redesign. If you can't get the RAM you wanted in the form factor you wanted, this is what I call essentially a catastrophic redesign. You now have to redesign the entire guts of your product and then secure supply chain for these new things, build it again on the production line, test it again, do all the reliability testing. It is non-trivial. And so this is why we pair, so there's a hierarchy of components often in consumer electronics. We start with silicon and the display, which are the longest lead-time things usually in my world. In robots, actuators are pretty tricky to get. Even just for prototyping, sometimes it takes a month or two to buy an actuator. This is why Elon famously just starts building it all himself. When you look at what he did with Tesla and verticalizing his supply chain, and famously actually Starlink is an even better example. This is where, I believe, it's effectively ore and silicon chips in, product out. That's a pretty incredible factory, I've heard. I'd love to see it someday. But this is where verticalization comes into play, because if you haven't verticalized and you have a lot of the components in-house, or you're building a lot of things in-house, you can actually adapt to supply chain shocks better. And then famously he did, when the silicon itself was difficult to find, he was able to redesign his PCB in record time and adapt to buying new silicon, and that would be much more catastrophic for a company that had a more classic supply chain. One of the big decisions [SPEAKER_00] that I imagine you have to make when you're designing a new piece of hardware is deciding between using this [SPEAKER_00] system of hardware, is going to use this system of hardware, is going to use this system of hardware [SPEAKER_00] with something in software to use the design system, or do something new. How do you think about that balance when you're designing a new piece of hardware? Very simply, I use off-the-shelf whenever I can, especially in the prototyping phases, because in the prototyping phases, which are a really important phase of what we do, your goal is to show that it can work at all. Can you get a thing working? So often it doesn't have to be the final pretty thing. It can be the ugly version. You can make an industrial design model of what the final thing is going to look like. But actually we call it works-like, looks-like models, where you have, this is what it's going to look like, and here's how it's going to work. And here's a working prototype. And humans are pretty good at this. As long as, and this is a pretty big caveat, what you show could fit into the industrial design. Sometimes that's not, for companies that are younger, that's not always the case, but that's what we're going for. And so in the prototyping phase, man, whatever works off the shelf, whatever's fast, whatever you can get to quickly. And then maintain a sense of what's really going to fit in your final design. Going to look like, and here's how it's going to work. And here's a working prototype. And humans are pretty good at this. As long as, and this is a pretty big caveat, what you show could fit into the industrial design. Sometimes that's not for companies that are younger. That's not always the case, but that's what we're going for. And so in the prototyping phase, man, whatever works off the shelf, whatever's fast, whatever you can get to quickly. And then maintain a sense of what's really going to fit in your final design. Is it capable, or are the processes and components and materials capable of actually adapting to this size, this new weight that you need it to go into. So that's part of the calculus. When you move into mass production and the final design, it depends. If I could, if I was making Matic and I could buy an off-the-shelf wheel or an off-the-shelf component, I absolutely would and fit it in. But often what we're doing is highly custom because we have, again, one of those KPIs. I want it to be this size. I want it to be this weight. I want it to be this color. And often off-the-shelf parts aren't good enough. Not because they don't work, but because they're just not exactly designed for what we're doing. [SPEAKER_00] This is the reason these drones are so cheap now, is there's all these parts that have been innovated and built and scaled, manufactured for other things. [SPEAKER_00] And now we just have all these things that we can assemble a really cheap drone. [SPEAKER_01] Yeah. [SPEAKER_01] Yeah, exactly. Super. You've mentioned CAD a bunch of times, and it makes me think about CAD has been around for a long time. Is AI impacting the way software is built? Obviously it's impacting the way software is built in a huge way. Has it changed your life and the lives of people building hardware and robots? [SPEAKER_01] Yeah. So I want to zoom out a little bit. Most of the hardware work goes into prototyping, into 3D CAD, so designing 3D parts and assemblies and components and making sure they work together properly. Then making sure those parts and components can be made by a vendor at quantity, that that is possible, and the tolerances we want, and then putting those things together. So that's our process right now. Now we're right at the very, very beginning of AI being able to do CAD. So I'll give you an example. Claude can do what is essentially surfaces or point clouds. This is not real CAD. Real CAD in my world is dense. It has shape. It has nerves. You have an equation for how the surfaces work, and it's an entity that's designed in CAD. It's a solid entity. And so right now we're not quite there with AI doing CAD. I think it's likely that at some point we will be there. This will be probably one of the biggest changes for my field that we have, being able to, I hope, do rapid design and increase the speed. Now there's a lot of really fun things to do in CAD, but in the beginning of my career, we had to do custom screws and we had to do the 2D drawings for everything. And there's a lot of things in CAD that are not as fun. Tolerance stacks. We need them. How do seven parts fit together? And are they always going to fit together properly? But it's not fun. [SPEAKER_01] It's not the most fun part, maybe for some of us, but not for me. [SPEAKER_01] And so being able to do these things in AI would be amazing. [SPEAKER_01] So you can focus on actually doing the fun stuff. [SPEAKER_01] Another good thing is PCB. [SPEAKER_01] A printed circuit board has a lot of layers on the inside and then components that go on the top. [SPEAKER_01] And if you've ever opened anything, like a calculator or a computer, and looked inside, you know what I'm talking about. [SPEAKER_01] These printed circuit boards. [SPEAKER_01] It's increasingly looking like AI can route inside of these boards pretty well. And it's looking like AI is going to be able to do some basic component selection and layout on these boards. So that's where we're at right now. So we're not at a point, Lenny, where day-to-day mechanical or electrical engineering, the meat and potatoes of it, is being done by AI. But there's a huge amount that you can do as an engineer using AI in your strategy, your planning, your ability to think through the complex dependencies. And that's what I use it for now, which is really high-level planning, asking for information. When I look at who else is making a product like this, I use AI to build the databases, and they're not perfect. [SPEAKER_01] Certainly, a lot of times something's wrong, but it is so much faster. [SPEAKER_01] AI is pretty good in Excel right now. [SPEAKER_01] And of course, Excel is one of our favorite tools in engineering. [SPEAKER_01] So the ability to actually rapidly make Excel spreadsheets and change them, it doesn't sound sexy, but actually really speeds up the design process outside of these core pieces. [SPEAKER_00] I love how Excel is always at the bottom of everything. [SPEAKER_00] No matter what we're doing, we're going to Mars. [SPEAKER_00] There's an Excel spreadsheet that's probably driving a lot of this. [SPEAKER_00] Probably. [SPEAKER_00] So what's interesting about what you shared is it has already impacted the work of building hardware and robots, but it's on the verge of being transformative if it can get to real CAD. Yeah. And my big question is, what is it going to take? So a lot of AI is based on LLMs, which are essentially word generators, word guessers. They're more complicated than that, but that's essentially what they're doing. And there are also video models that you've seen that are trained on video, but these models don't understand. They're not very good for what I need, which is, I need to know, hey, you take a piece of paper, you fold it four times, and you do this. Where's the hole going to be? When you open it back up, these LLMs, and even video models, they don't know how to do that. So a lot of AI is based on LLMs, which are essentially word generators, word guessers. They're more complicated than that, but that's essentially what they're doing. And there are also video models that you've seen that are trained on video, but these models don't understand. They're not very good for what I need, which is I need to know, hey, you take a piece of paper, you fold it four times, and you do this. Where's the hole going to be? When you open it back up, these LLMs and even video models, they don't know how to do that. They don't have the ability to understand friction or weight or contact, pressure, friction, surface texture. They're just not able to do these things. And this is the core of what we need in engineering to be able to understand, to build things. So some world models may actually be able to do this in the future. And I suspect we may need those models to be the base of CAD and other physical engineering work. And so my frustration, and this is a healthy frustration, is I want codecs for engineering. I want codecs for hardware engineering, and it's extremely valuable. And I've used a lot for other things, but I want it for my field. And so what I think it may require is new model types. [SPEAKER_00] It sounds like an opportunity to me. [SPEAKER_00] I know there's a bunch of world lab companies. Fei Fei was on the podcast with World Labs. I think it's called World Labs. Yeah. And then I know Google's building Gemini. So do you feel like those are the right directions, or it's just like, now we need something actually different. [SPEAKER_01] I don't actually know the latest of what Fei Fei is working on. But obviously, she's a brilliant roboticist, and I'd love to learn more about what she's doing. So I'll have to look that up. What I've seen is that what we have right now and what models we're building are going to be part of the solution, but not all of it. [SPEAKER_00] Coming back to robots and humanoids, something that we were chatting about earlier, [SPEAKER_00] your sense is humanoid robots aren't necessarily the answer to a lot of the problems that [SPEAKER_00] we have and opportunities that exist. [SPEAKER_00] Talk about just your sense of humanoids versus non-humanoid robots. Yeah. I think there's a hype cycle around humanoids. That doesn't mean they're not extremely interesting. And I think there's going to be winners there. But what I hear a lot is, I want a generalist robot shape to do everything. And I don't know that that works. I think that you need different types of robots to do different types of things. For example, if you've got a laptop and you want to put, if you want to screw together the, the, the, the keyboard to the case, this is not a job I don't think for a humanoid. This is a job for a dedicated robot, a manufacturing robot, that has been designed just to screw 10 screws into a case for the specific laptop. And you want to do that 10,000 times a day or something, or 10,000 times a week or something. That's a dedicated robot that's specifically intended to do that thing. And what I think is interesting here is you can have standard cabinet sizes for automation robots, and you can have them be modifiable over time. And that's going to be a very interesting field. I think is, how do you make manufacturing robots that are adaptable and changeable? But you wouldn't want a humanoid to do that. And so when you really go and look at a modern manufacturing facility, like in China, at the top tier of tier one suppliers, there are not very many people on the line. Anyway, the entire printed circuit board line has essentially got no people on it anymore. The raw board is going through and getting reflowed and getting checked. And the whole thing is being done without humans, unless something goes wrong and a human runs over and fixes something. So in assembly, mechanical assembly, the same thing. These most advanced lines, they don't have people working very much. [SPEAKER_01] They used to have 200 people. [SPEAKER_01] They might have 10 now. [SPEAKER_01] And so we've already moved past human labor in a lot of this most advanced manufacturing. [SPEAKER_01] And so we don't actually need to replace humans with humanoids. [SPEAKER_01] We just need more of these dedicated robots. [SPEAKER_01] So my suspicion is we'll have humanoids for some long-tail things that we need to do that humans are [SPEAKER_01] currently doing. [SPEAKER_01] That will be important, but we'll also have robots that are for construction, robots that are for electrical work, robots that are for very low-volume assembly, maybe robots for logistics. And most of them are going to look different from each other. [SPEAKER_00] That makes all the sense in the world. [SPEAKER_00] What I think about as you talk about this is it feels like there's going to be this big moment [SPEAKER_00] when a robot can build other robots. [SPEAKER_00] And this CAD point you make about where once CAD can, once AI can develop designs, full designs for [SPEAKER_00] hard work, that's going to be a big moment. [SPEAKER_00] Do you have a sense of just how close we are to this? [SPEAKER_00] I don't know, but this loop that begins of robots building each other and designing each other. If you're talking about robots building robots that are different than them, usually, yes, I think that that's going to happen. [SPEAKER_01] But it's like the terms matter. [SPEAKER_01] I don't think there's going to be one robot that's going to build itself. [SPEAKER_01] I don't think that that's what it's going to look like. [SPEAKER_01] But yeah, having AI be able to, if you could say, hey, I want to build this thing and I [SPEAKER_01] want it to do this, and this is how I want it to look. [SPEAKER_01] And here's a picture. [SPEAKER_01] The idea that you could, even as a hobbyist, go from a 2D picture to complex 3D CAD to assemblies, [SPEAKER_01] to communication with vendors of how to make those parts and getting their feedback to iterating [SPEAKER_01] on that and doing a couple builds. That is possible, I think, in the future. Will it be as good in the beginning as us doing it? No, but it will happen. The biggest challenge here, Lenny, is actually the data. Want it to do this, and this is how I want it to look. And here's a picture. The idea that you could even, as a hobbyist, go from a 2D picture to complex 3D CAD to assemblies, to communication with vendors of how to make those parts and getting their feedback to iterating on that and doing a couple builds. That is possible. I think in the future, will it be as good in the beginning as us doing it? No, but it will happen. The biggest challenge here, Lenny, is actually the data. This CAD data is some of the most valuable IP that anybody has. And Samsung or Matic, to pick on Matic. They're not going to want to give their 3D CAD to a model vendor, to a model maker, somebody making an AI model to teach it how to make great CAD. This is proprietary. This is the secret sauce. And so where is this data going to come from is a big question I have, which is why I think hobbyists are a more interesting place to start, where they're not concerned about the sanctity of their CAD and where it goes. They don't care. They want to make something, and they want help making it faster. So this is where I'm interested in this starting, which is maybe a hobbyist isn't an expert in printed circuit board design. Maybe they don't care. They just want their drone to be fast and beat this other guy's drone or [SPEAKER_01] whatever. [SPEAKER_01] This is where I think you're going to start seeing all this start. And then probably the big incumbents are going to be slower because they have dedicated tools and a lot of IP privacy. [SPEAKER_00] It's really interesting. [SPEAKER_00] This idea of what data AI models need to train on, I've been hearing that labs [SPEAKER_00] are buying code like GitHub repos pre-2021 because that's before AI has impacted the code, because there's less and less human-written code. It feels, and these are data labeling companies like Mercore and Surge and Handshake and things like that. It feels like this is a big opportunity that might emerge as them selling data, creating these CAD files. [SPEAKER_01] Absolutely. And one really great idea, I think, would be to have an AI system that can go on-prem. So be inside of a data center that the company owns and then train it with their data. That, I think, could work eventually in the future, but you need a lot of this CAD data. So you're going to need a base model that has a lot of CAD data. We'll have to figure out how to do that. That's going to be very interesting. And then we're going to have to figure out how to put it safely inside, essentially the walls of companies, and have them then train it on their own data. I don't know if that's going to be the equivalent of an MCP layer or what that's going to be, but this seems doable in the long term. [SPEAKER_00] I want to ask a question that my sister suggested. She's actually been a long-time VR person. She was at Oculus. She joined with acquisition. She helped create a lot of content within VR. She's just been in the VR world for a long time. And now she's working on other things. She wanted me to ask you, what does it take to create a robot that feels human and connected, that humans feel connected to? [SPEAKER_01] It's a great question. [SPEAKER_01] So I'm new, relatively speaking, to robotics. [SPEAKER_01] And so I had to learn as much as I could, as fast as I could. And one of the researchers that helped me the most, her name is Layla Takayama. She's an expert at this. And what she explained to me is that humans have a certain expectation about how other beings are going to respond when they enter a space. When someone walks into a room, you acknowledge them. You might not talk to them, but you look up. There are a lot of very complex nonverbal cues that we give to each other. And if you walk into a room and a robot's just like, it's creepy. And it's easy to be creepy. I'm a little surprised, with some notable exceptions, how creepy a lot of these humanoids are right now. You want, I think, these devices to be non-threatening, generally speaking. You want them to appear soft. You want them to appear reactive to you. You want to have a sense that they know that you're there, that they're attentive to you, that they're there to help you and make your work life easier. And you also expect them to intentionally, or to show their intent before they do something. [SPEAKER_01] And so one of the things I learned is if a robot just suddenly turns and does all this [SPEAKER_01] stuff, it scares you. [SPEAKER_01] But if a robot looks before it turns and then goes, it's much less alarming. [SPEAKER_01] So there's all these little pieces. [SPEAKER_01] And I recommend anyone to go look at her work. [SPEAKER_01] There's a lot of great research here about how to, not necessarily with a humanoid, but [SPEAKER_01] how to have any robot both respond properly in a social context with someone entering a room or exiting a room, but also transmit its intent physically. So it doesn't surprise you. [SPEAKER_00] Feels like there's a lot we can learn from Pixar and animation studios that have thought [SPEAKER_00] about this a long time. Yeah. [SPEAKER_01] I actually think Pixar, Disney are probably the world's best at doing this type of design [SPEAKER_01] work, even though they haven't done as much in physical, in volume. [SPEAKER_01] If you look at what they do and how they show emotion, intent, approachability, engagement [SPEAKER_01] with their characters, they're really world-class. I don't know about you, but I'm so excited to have a robot at home doing things like these videos that they're starting to put out where they're doing your, they can do dishes, at least the prototypes. They can fold laundry. They can do, it's like, yes, please come do this for me. How do you feel about robots in your house? So I'm into it. My partner, not so much. So I'm very lucky to have a partner who's got a high bar, which means was never going to take Waymo, took one Waymo, and now never wants to take anything [SPEAKER_00] I don't know about you, but I'm so excited to have a robot at home doing things like these [SPEAKER_00] videos that they're starting to put out where they can do dishes, [SPEAKER_00] at least the prototypes. [SPEAKER_00] They can fold laundry. [SPEAKER_00] They can do, it's like, yes, please come do this for me. [SPEAKER_00] How do you feel about robots in your house? So I'm into it. My partner, not so much. So I'm very lucky to have a partner who's got a high bar, which means was never going to take Waymo, took one Waymo, and now never wants to take anything else. So definitely willing to update her position, but it has to be pretty good. So she's in love with the Matic. It's amazing. And so it's that, but the bar is pretty high. So I think in order to have a home robot, it's going to have to be pretty incredible for us, her to be willing to have it in our home. But I take that as a challenge. [SPEAKER_00] My wife is exactly the same way. [SPEAKER_00] She's like, I want this thing in our house with Matt. [SPEAKER_00] And, oh wow. [SPEAKER_00] This is so cute. [SPEAKER_00] A recent example is self-driving Tesla. She used to be so like, no, don't do [SPEAKER_00] that. [SPEAKER_00] And it was not that great originally. [SPEAKER_00] And now she's like, I don't want to drive any other car. [SPEAKER_00] This just feels absurd, to drive your car. [SPEAKER_00] I don't want to do that anymore. [SPEAKER_00] It's crazy how quickly that changes. So there's a big difference in my mind. This is a big categorical difference. There's a big difference between a car that is safer, that drives itself, versus a car that a human drives, because you have an existence proof of the human-driving car and you have the data. [SPEAKER_01] When you talk about homes, what is the delta? [SPEAKER_01] You have now a thing that you didn't have before doing things. [SPEAKER_01] So if it's bad at it, what are you relating it to? And if it's unsafe in any way, what are you relating that to? It's a much harder equation in my mind to get to a lot of people than a car, where you can say, hey, Waymo saved lives. You're going to have a fraction of the deaths using a Waymo, whether you're a passenger or you're not. When you already see people in San Francisco adapting how they respond around a Waymo versus any other car, you're seeing behavioral changes that are based on trust, which is really cool. [SPEAKER_01] When you're talking about a new product that hasn't existed yet and is not essentially [SPEAKER_01] replacing something, that's a harder sell and you have to have a different story. Something that someone needs to figure out with the Tesla self-driving is when you often are at a stop and you make eye contact and go ahead, go ahead. Or someone's about to cross and you're like, okay, go ahead. But the Tesla just does its own thing. [SPEAKER_00] And so it makes you look like an asshole a bunch of times. [SPEAKER_00] Like, I'm not driving. [SPEAKER_00] He's in control. Yeah. I had that happen once. [SPEAKER_01] You almost want a little two arms in the front to be gesturing, or like, you go, or [SPEAKER_01] something. It's amazing how much we actually rely on this human connection to decide even [SPEAKER_01] who's going to go in an intersection. Yeah. Okay. So zooming out a little bit, what's cool about people like you is you're thinking and building things that will exist in the future. [SPEAKER_00] You're living in the future and designing it. [SPEAKER_00] And you are one of the few people, as a glimpse into where things are going. [SPEAKER_00] So I'm curious, just to ask, say in five years, what is the [SPEAKER_00] vision you have of what is different about our day-to-day robots, devices, what does it look like? [SPEAKER_00] I don't know, just roughly. So in this job, we have this wild thing where we have to try to live in the future, and we have to try to live in the future far enough away that we can design something not only for two years from now or three years from now, but also something that will ladder up to what we want six years from now. Because in my field, it's a lot easier to make something and iterate on it, and iterate toward a final goal, than to do a one-shot thing perfectly. So not only do you have to have a sense of what the first thing needs to be like and look like, you have to have a sense of what the third thing, ideally, or the platonic ideal of the thing, will eventually look like. So you do have to think about the future and live in the future. I have this weird thing where I love to think about the future, but I'm also a skeptic. And you really want me to be a skeptic, because if I think everything's going to be fine, the hardware's not going to work. You really want me to be like, this isn't going to work, and this isn't going to work, and this isn't going to work, and just be worried about all these things going wrong. So this is kind of an interesting disagreement inside of me of what I want the future to look like and what I think it's going to look like and what it's actually going to look like, and trying to guess. [SPEAKER_01] And so it seems pretty clear to me that AI is going to have a foundational change in how [SPEAKER_01] we work and what we do over the next couple of years, especially you're already seeing it. Obviously, anybody who codes is not coding by hand very much anymore. Any knowledge work, this is going to hit next, I think, and progressively affect our economy and our work. But it seems like the physical world is less likely to change as quickly outside of drones, self-driving cars. You're going to see more and more robots, but I'm not somebody who says, I'm not somebody who thinks that in five years, you're going to have 20 million robots. I don't think that it's going to be that fast. I think we have a lot of really deep work on supply chain. We do supply chain, reliability, raw material access. And then we need to figure out how to make. [SPEAKER_01] our economy and our work. [SPEAKER_01] But it seems like the physical world is less likely to change as quickly outside of drones, [SPEAKER_01] self-driving cars. [SPEAKER_01] You're going to see more and more robots, but I'm not somebody who says, I'm not somebody [SPEAKER_01] who thinks that in five years, you're going to have 20 million robots. I don't think that it's going to be that fast. I think we have a lot of really deep work on supply chain. [SPEAKER_01] We do supply chain, reliability, raw material access. [SPEAKER_01] And then we need to figure out how to make [SPEAKER_01] factories again in this country for high tech. [SPEAKER_01] So that's a lot of work, but in the interim, you're going to start seeing a lot of weird [SPEAKER_01] things on the street. [SPEAKER_01] You might see robots on the street. [SPEAKER_01] Have you seen any delivery robots in your world, Lenny, before? The little car things, not anything humanoid. [SPEAKER_00] Yeah. Yeah. So this is just going to continue happening. And I think we're just going to continue to feel like we live in the future, but safety is going to be a big key for robotics. I think there's probably more change in war than there is in consumer electronics in the next two years, for example. Yep. [SPEAKER_00] Wow. [SPEAKER_00] What a statement. [SPEAKER_00] Yeah. [SPEAKER_00] And I totally agree. [SPEAKER_00] There's nothing like war to incentivize innovation and endless [SPEAKER_00] improvement and trying to get ahead of the other side. Especially when democracy is at stake. I think that we are, and I don't want to be on a high horse or something, but I do think that we're in a place where we need to think about things and the future in these terms and defend these things with our capabilities while also hoping that we never have to have hot conflict anywhere. [SPEAKER_00] Along those lines, I have to ask you, recently you became famous on Twitter, [SPEAKER_00] at least, for quitting OpenAI. [SPEAKER_00] You tweeted that you're leaving and with your brief explanation and got 7 million views, [SPEAKER_00] 50, I don't know, 8,000 likes. [SPEAKER_00] What happened? [SPEAKER_00] Why'd you leave OpenAI? [SPEAKER_00] What happened? Yeah, I hope so. What I said in my tweet was that I have a lot of friends on the executive side of Open AI that I care a lot about. I think they 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 Defense 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 is doing. And then you see some people that are scorched earth about it. In this case, that didn't make sense for me. I didn't feel that way about the company. OpenAI was, 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. [SPEAKER_00] This is a great opportunity to just whisper to me what OpenAI is working on. [SPEAKER_00] What is this robotics device, or just between you and me? Yeah. [SPEAKER_01] I wish I could say, Lenny, part of the fun of our job is we get to see things [SPEAKER_01] before everybody else does. [SPEAKER_01] But part of the flip side of that is we can't talk about anything internal or any IP. [SPEAKER_01] 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. [SPEAKER_00] So speaking of team building, there's something I definitely wanted to ask you about. [SPEAKER_00] So, as I said, I asked a bunch of people what to talk to you about. [SPEAKER_00] And someone that I think was maybe a colleague, former colleague, Mariana Senko. [SPEAKER_00] Did you work with her? [SPEAKER_00] Okay. She's a friend. [SPEAKER_01] Yeah. 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. [SPEAKER_00] I'd be curious about the kinds of people that she finds indispensable in an era where [SPEAKER_00] everyone is concerned about their jobs. [SPEAKER_00] So talk about what you've learned about just what you look for when you're hiring folks [SPEAKER_00] for your team. Yeah. I'm lucky that I've had a lot of time, a lot of reps 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 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. [SPEAKER_01] So you have to start thinking about how do you build a team that can do something new? [SPEAKER_01] things or new industries. [SPEAKER_01] And that's what we're facing. [SPEAKER_01] I think with AI and robots, certainly it's very new. [SPEAKER_01] You can't count on having entirely people who've done the exact same thing in past lives because [SPEAKER_01] it doesn't exist. [SPEAKER_01] The exact same thing doesn't exist. [SPEAKER_01] Maybe you've got roboticists who 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 higher bounds. So you need to look at that. And then with young people, this is where it gets really fun. Lenny, 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 20. And it's very hard to find someone who's in their thirties 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. So figuring out how to get these AI natives to teach us, the rest of us, how they think about AI when we are, you and I, I think I can say, 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. And 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 that's 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. [SPEAKER_00] That last bucket is a very common trend on this podcast. [SPEAKER_00] When we talk about hiring, which is really cool as a counter narrative to there's no more jobs for young people. All the junior roles are erased because of AI. 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. [SPEAKER_01] But I think what you find when you actually build these teams is you have to have both. [SPEAKER_01] You must have both. [SPEAKER_01] The team size just might be a little bit smaller than it used to be. [SPEAKER_01] When this AI revolution in hardware happens, I don't know how that's going to affect the [SPEAKER_01] teams. [SPEAKER_01] That will be really interesting to watch. [SPEAKER_00] So what I heard here is just look for a generalist that can flex based on whatever needs to be done. [SPEAKER_00] Some mixture of specialist and scaling versus zero to one. [SPEAKER_00] And then the best term I've heard for this is cracked new grads. [SPEAKER_00] Yeah. [SPEAKER_00] That are AI-native, essentially, that are just doing everything AI-first. Yeah. And then what we didn't talk about, of course, is mission alignment, which actually unifies the 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 for the same thing 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. So 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. [SPEAKER_01] These are the things that really matter when you're building a team. [SPEAKER_00] Awesome. [SPEAKER_00] Okay. [SPEAKER_00] Just a couple more questions. [SPEAKER_00] Something I've been wanting to ask for a long time is, you worked with some of the [SPEAKER_00] most legendary successful builders. [SPEAKER_00] Steve Jobs, Johnny Ive, Mark Zuckerberg, Sam Altman. [SPEAKER_00] You don't have to go through all four, but just what's a lesson you learned from as many [SPEAKER_00] of these folks that come to mind? So we'll start with Sam. Because most recently, Sam is really good at saying, why not more? Why not a hundred X or 10,000 X? You're 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, he's willing [SPEAKER_00] of these folks that come to mind. So we'll start with Sam. Because most recently, Sam is really good at saying, why not more? Why not a hundred X or 10,000 X? You're thinking too small. Why not think about this bigger? And every time we talked about something important, he talked about that. [SPEAKER_01] And what I realized is I was thinking too small in certain areas, and he was thinking [SPEAKER_01] globally, and having that nudge from a leader who's ambitious is really helpful, I think. [SPEAKER_01] 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 not high volume, 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, it's Steve Jobs. That bar he held for the company and for [SPEAKER_01] technical talent and for excellence was not wavering. It was up here, and you were either going to meet it or you weren't. [SPEAKER_01] And that was something that 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. [SPEAKER_01] It's not, it doesn't quite hit the way you would think. [SPEAKER_01] 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 a category 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, [SPEAKER_01] that decisions were made, the decisions were made at the lowest level possible in the company [SPEAKER_01] to maintain speed. [SPEAKER_01] I underappreciated how clean and well run the hardware, the way that the hardware organization interacted with the rest of the company. And then it was very clear. This is what we're going for. [SPEAKER_01] We're going to have this review. [SPEAKER_01] We're going to make a decision in this review. [SPEAKER_01] If you can make the decision without the review, you will do that. [SPEAKER_01] Here are other objectives for this project. [SPEAKER_01] It was really well executed. [SPEAKER_01] And I think that's hard to do at a fast-growing company. [SPEAKER_01] It's very hard to do at that level, and having him and Andrew Bosworth, the CTO, involved [SPEAKER_01] in the technical decisions, able to read reports that were maybe 20 pages long, [SPEAKER_01] the trade-offs, understand them, and be able to contribute to the technical discussion. [SPEAKER_01] And that's just on my thing that week. [SPEAKER_01] And they're doing that a hundred times that month. [SPEAKER_01] Was impressive and definitely something I learned from them. [SPEAKER_00] Jeff Lerner What an incredible set of experiences and different types of places to work. [SPEAKER_00] I don't know if they could be more different, all these places. ! ! I know. And I think that that's why I'm looking for this zero to one. And so when you're looking for a zero to one opportunity, it's always going to be in some place different generally. [SPEAKER_00] Jeff Lerner You're going to be a hot commodity in this market [SPEAKER_00] now that you're a free agent. [SPEAKER_00] But on the flip side of that, I want to take us to fail corner. [SPEAKER_00] I feel like someone building hardware, physical things, has some great fail stories. [SPEAKER_00] Is there one story of something you built, something you worked on, that failed, and something you learned from that experience? [SPEAKER_01] Jennifer This is a great question and not a comfortable one. [SPEAKER_01] One of my favorite failures was actually on the Quest one. It was around EBT, so halfway through the Quest one. [SPEAKER_01] And we found out that we had gone from five cameras to four for cost reduction. [SPEAKER_01] We talked a little bit about this. We need to reduce the price so more people could buy them. And what happened was it was right before Christmas. And I heard from the lead on the team that does computer vision. Jennifer And he said, oh my gosh, the cameras, the data from the cameras isn't working. And we can't get a lock on where the person is using the headset. And so we looked into it, and we realized that their interpretation of our spec and our interpretation of our spec was different. So in engineering, you usually use a plus or minus, like it can go up or down by, in this case, I think it was 0.15 mm or something like that. And in his world, he was used to having a global, it's within 0.15 mm. And so we had a different interpretation of the spec. Now, the problem is that our interpretation of the spec meant that he couldn't meet his goals of being able to understand where the headset was in space. And so we had to do a redesign, and this is at EBT. So this is pretty much when you want the engineering to be done. [SPEAKER_00] What does EBT stand for? [SPEAKER_01] Jennifer It stands for when we compile the hardware for the first time with everything [SPEAKER_01] that's supposed to be done. [SPEAKER_01] So final components, final materials, you're making the components on the tools. You're going to make them for mass production instead of just machining them. So it's a big deal. Jennifer And so what we had to do was favor or prioritize. We had four floating cameras. [SPEAKER_01] We had to lock the bottom two to each other and put them on a bracket so that the relative distance from them met the spec that he needed, and then let the other two float. So this was an architectural change. And this was a failure. [SPEAKER_01] Jennifer I mean, it was a failure in understanding the spec. [SPEAKER_01] It was a failure in essentially the product design, but it was because of a [SPEAKER_01] misunderstanding of the spec. [SPEAKER_01] And so we were able to adapt. [SPEAKER_01] We actually kept the build on time, and we actually shipped the product on time, but it was really [SPEAKER_01] stressful. [SPEAKER_01] And it turned out that actually the new design was better because with a favored pair, you [SPEAKER_01] distance from them met the spec that he needed and then let the other two float. So this was an architectural change. And this was a failure. Jennifer, it was a failure in understanding the spec. It was a failure in, in essentially the product design, but it was because of a misunderstanding of the spec. And so we were able to adapt. [SPEAKER_01] We actually kept the build on time and we actually shipped the product on time, but it was really stressful. [SPEAKER_01] And it turned out that actually the new design was better because with a favored pair, you [SPEAKER_01] have source of truth for the space. [SPEAKER_01] And then the other two cameras overlap onto that source of truth. [SPEAKER_01] And so it worked well. I thought it was a good outcome, but it was a scramble and certainly wish that we caught it four months earlier. [SPEAKER_00] Jeff Lerner Another example of just how hard hardware is. You mess up a spec and, all right, here, we wasted a week building something that didn't work. And now it's four months later, still having to redo the hardware supply chain. [SPEAKER_01] Jennifer Yeah. Yeah. It was tricky. [SPEAKER_00] Jeff Lerner So the Quest 1 that shipped was this with the cameras moved. Jennifer Yeah. If you look, the cameras have, there's two cameras a little closer to one another. And then in the front of the Quest at the bottom. [SPEAKER_00] Jeff Lerner Wow. [SPEAKER_00] How did Boz and Zuck feel about this? Jennifer The fact that I don't remember probably means it was okay. I think we addressed it. We redesigned it. We had to change the material on the bracket. [SPEAKER_01] I think we had to make it steel to hold the tolerance we needed, but it worked out. [SPEAKER_01] And the price of the cost and yields were fine. [SPEAKER_01] So I think we adapted. Jeff Lerner And that was the best-selling VR device of all time. Is that right? [SPEAKER_01] Jennifer I think it was. [SPEAKER_01] Jeff Lerner Okay. [SPEAKER_01] Jennifer I don't have the final sales. [SPEAKER_00] Jeff Lerner Yeah. [SPEAKER_00] Yeah. [SPEAKER_00] Yeah. [SPEAKER_00] Yeah. [SPEAKER_00] Okay. [SPEAKER_00] conservative. [SPEAKER_00] Okay. [SPEAKER_00] Caitlin, we've covered so much ground. [SPEAKER_00] Is there anything else you wanted to share? [SPEAKER_00] Anything else you want to leave listeners with? [SPEAKER_00] Either double down some we've shared or anything else that's just, oh, here's [SPEAKER_00] something I want to share. Jennifer I think that this is probably one of the most exciting times we're coming into. And it's normal, I think, for all of us, myself included, to be worried and scared about it. But I also think it's an opportunity for people to do an extraordinary amount, have an extraordinary amount of progress, and be able to, as an individual, do more than we've ever done before. And so that's the side I'm trying to embrace. These new tools, this new way of work, is scary, but if you embrace it and are daily using these AI tools right now and daily applying them to what you're doing, you will be at the forefront of whatever comes next. And so I just want to encourage everyone to be creative, use these tools, have fun with them, figure out what the boundaries are. And then every time a new model comes out, test again, because it's really important to know what we're dealing with and where these boundaries are. But I've also never been more excited about the power of an individual. Robby Barbaro Well, with that, Caitlin, we've reached our very exciting lightning round. [SPEAKER_00] I've got five questions for you. Are you ready? [SPEAKER_01] Caitlin B. I'm ready. Robby Barbaro First question: what are two or three books that you find yourself recommending most to other people? Caitlin B. I've been mostly reading the classics lately. So Book of the New Sun is a great fiction book, which I really recommend. I think that's what it's called. [SPEAKER_01] I haven't read it in a little while. [SPEAKER_01] I love Mrs. Dalloway. [SPEAKER_01] Actually, I think it's a very interesting book about transitions. [SPEAKER_01] And it was a post-war book by Virginia Woolf. [SPEAKER_01] So I really love it. [SPEAKER_01] And I think it's really wonderful. I think Herodotus' Histories is pretty incredible. [SPEAKER_01] He's wrong a lot, but it's also the first history book. [SPEAKER_01] And in many cases, he's going and finding firsthand or secondhand accounts of what [SPEAKER_01] happens. [SPEAKER_01] It's a way to look into the world at a completely different era than it is now. [SPEAKER_01] So these are some books that I like. [SPEAKER_01] And I'll double-check the title of the first one and email you, but I think that's what it's called. Okay. [SPEAKER_00] And we'll link to the correct one in the show notes. [SPEAKER_00] Favorite recent movie or TV show that you have really enjoyed? I am really into Euphoria right now. I think the new Euphoria. I'm interested in the characters and figuring that out. What's going to happen there? [SPEAKER_00] The show is so stressful whenever I watch them. It's a melodrama. I think you have to think about it as a soap opera, and then it's fun. If you think about it too literally, it's practically stressful. [SPEAKER_00] Okay. [SPEAKER_00] Okay. [SPEAKER_00] It's helpful. [SPEAKER_00] Do you have a favorite product you recently discovered that you really love? [SPEAKER_00] Could be hardware. Could be an app. Could be a piece of clothing. Could be a gadget. [SPEAKER_01] I really like Volaback. [SPEAKER_01] The clothes. They make really interesting clothes. They're essentially basing their new clothes on material science. [SPEAKER_01] So they take new material science and make it into clothes. [SPEAKER_01] It's just a fun brand to follow. Volaback. [SPEAKER_01] V-O-L-L-E-B-A-K. Wow. Volaback. Very cool. Do you have a favorite life motto that you often come back to in work or in life?