How to ship hardware in the AI era | Caitlin Kalinowski (Apple, Meta, OpenAI)

1. 0:00 – 2:32

   Introduction to Caitlin Kalinowski

   1. CKCaitlin Kalinowski0:00

      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 gonna saturate. When that happens, the next frontier is the physical world, robotics, manufacturing, industrialization.

   2. LRLenny Rachitsky0:14

      You're living in the future and designing it.

   3. CKCaitlin Kalinowski0:16

      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.

   4. LRLenny Rachitsky0:25

      Just imagine 100,000 drones coming out of China just at us.

   5. CKCaitlin Kalinowski0:29

      I do feel that we need to re-industrialize the country significantly to be safe in a military sense. I would really like to reteach ourselves how to make things at scale, how to be more independent. People that are your allies now may not be in the future.

   6. LRLenny Rachitsky0:42

      You worked with some of the most legendary successful builders, Steve Jobs, Mark Zuckerberg, Sam Altman.

   7. CKCaitlin Kalinowski0:48

      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.

   8. LRLenny Rachitsky1:01

      What does it take to create a robot that feels human and connected?

   9. CKCaitlin Kalinowski1:05

      If you walk into a room and a robot's just like... Like, 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.

   10. LRLenny Rachitsky1:18

       There's a meteor called memory prices that are coming for consumer hardware and robotics and physical AI.

   11. CKCaitlin Kalinowski1:23

       We're in trouble as an industry.

   12. LRLenny Rachitsky1:26

       [gentle music] 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 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 lennysproductpass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. With that, I bring you Caitlin Kalinowski.

2. 2:32 – 4:55

   Why VR didn’t take off despite incredible hardware

   1. LRLenny Rachitsky2:33

      [gentle music] Caitlin, thank you so much for being here. Welcome to the podcast.

   2. CKCaitlin Kalinowski2:38

      Thank you so much for having me. I'm excited to be here.

   3. LRLenny Rachitsky2:41

      We're gonna go in a bunch of different directions. I'm gonna bounce around. I wanna 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. Like, they renamed the company Meta to lean into VR as the future, this metaverse that we're gonna be living through. Feels like a lot of people are leaning out now. Feels like Meta's stepping back, Apple's stepping back with the Vision Pro. In spite of the incredible hardware that everyone, that you built, that your team built, just, like, I've, I've got a couple of the devices. It's just, like, a magical experience that you've... Unlike anything you've ever experienced. Still, has not caught on. What happened? Is there still a future where VR catches on, or is the future kind of AR and something else?

   4. CKCaitlin Kalinowski3:23

      I don't think I would've 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. Um, we figured out SLAM, which was how to, how to do positioning in space using cameras. We figured out a lot of depth, uh, applications of depth sensors. We figured out how humans, um, perceive visual data in space, and all of that actually, while it's great for VR, and I think VR gaming's a really interesting, um, 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, um, arc. And to be honest, as an, as someone who's not using VR a lot right now, I'm really glad that we did it, but I don't think it... I, I expected it to be big, obviously, or, or I wouldn't have been working at Oculus. And I think maybe the social aspect of having something in front of your face, um, 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, um, it's hard to make it social when you have, you know, your face covered.

3. 4:55 – 8:45

   The future of AR glasses and physical AI

   1. LRLenny Rachitsky4:55

      That is interesting. So just, like, the investment and, uh, innovation that happened that, uh, 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 have, are ahead on the next step. So is that where you think things go? What's kinda, like, where do you think things are going? Is it AR glasses? Is it something else? What's kinda the future of this?

   2. CKCaitlin Kalinowski5:14

      Yeah. I believe in AR glasses as part of the future because I, uh, I do think looking down at your phone all the time is not great for us as social, 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, um, with them? I think once we start to figure out some of those, um, challenges thatHaving a display that's mostly off that you can turn on when you want it to be on seems like part of the future. Now, 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, um, uh, autonomy period, manufacturing. Like, all of these technologies are gonna need the same, the same piece parts, right, the same pieces that we built in the AR, VR spectrum.

   3. LRLenny Rachitsky6:35

      It's interesting with VR, there's this idea with when you build product, there's always this question when something doesn't work. Is it just, like, you executed it badly, or the idea was just a bad idea, and it's always hard to know. Feels like with AR, it's just, like, so much effort was put into making it work, just, like, for a decade, many decades, and just has not worked. So it's, like, nice that we know, okay, there's nothing we can really do right now to make this work. I completely agree with you. The issue is just, like, I don't wanna sit on my couch disconnected from the world, and even if I could see people through it, it's just, like, nah, I'm just gonna... I don't need this. It's not that big of a deal.

   4. CKCaitlin Kalinowski7:06

      In AR, you're gonna 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. So with the prototype, you got to sense what this is really gonna be like in the future. It's very hard to describe how it feels to use a pair of glasses like this, but when you do, you suddenly are like, "Oh," like, "I feel immersed." It's, the field of view is wide enough. I feel immersed, and it's, becomes pretty clear that I think, I think this is part of where the future's headed.

   5. LRLenny Rachitsky7:35

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4. 8:45 – 13:33

   Why robotics and hardware are suddenly hot

   1. LRLenny Rachitsky8:45

      Okay. I wanna talk about robots, robotics. I was meeting with, uh, a bunch of Princeton students a couple months ago, and we were... They're, they're kind of, like, comp sci students, and they were telling me that enrollment in comp sci at Princeton is down, trending down, and I confirmed this is actually true at a lot of universities. There's a lot of charts that show comp sci enrollment down, and where it's actually going up is, is hardware, robotics, which I imagine, as someone that has been in this field for a long time, is very weird, 'cause it's never been that popular. Just how does, how does it feel to feel like, oh, wow, everyone's getting in now?

   2. CKCaitlin Kalinowski9:17

      It's very odd. Uh, everyone is suddenly asking about hardware and robots and the physical world, and it's never been the sexy career. It's always been the thing that you went into 'cause you loved it. It never paid the same as these other careers. Um, it was never kind of at the forefront of how we talk about things, with the possible exception of Apple, obviously, and, and the hardware lineage at Apple. Um, so it's, it's great in some ways, and it's very odd in others.

   3. LRLenny Rachitsky9:46

      What's, uh, surprisingly hard about hardware? A lot of software companies, a lot of people are just like, "Okay, cool. We're gonna build some hardware. That's the future. That's the mode now." 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 gonna build some hardware? What are some of the surprising challenges that come up?

   4. CKCaitlin Kalinowski10:03

      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 off it, and then they run the code and debug it. But they can compile their code every day, you know, every hour, whatever they need to do. In hardware, we only get to compile our code, quote-unquote, like four or five times. And if you think about-

   5. LRLenny Rachitsky10:28

      Four or five times a year, or?

   6. CKCaitlin Kalinowski10:29

      Total.

   7. LRLenny Rachitsky10:30

      Okay, ever. [chuckles]

   8. CKCaitlin Kalinowski10:31

      Right? So if you're building hardware, you redesign it in CAD, you know, 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. Um, 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, you know, is to, um, 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 says, sells millions, if you have a graph of all the parts put together on any different part of the, of the device, uh, 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 gonna get the smallest version of this one and the largest version of this one, and you're gonna have to put those together across the board. People don't think about this that much, but the part variance is pretty high.

   9. LRLenny Rachitsky11:48

      Mm-hmm.

   10. CKCaitlin Kalinowski11:48

       And so we've gotta 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 doneAnd we're not gonna have-- We're gonna have a high yield, we're gonna be able to make them and, and make money on them effectively, and we won't have very many returns. And so that's kind of the game that we're playing.

   11. LRLenny Rachitsky12:08

       [chuckles] This sounds so hard and complicated. They're just like software is so nice. You just write some code, ship it, and it's great. Uh, why do you think people are getting so i- into robots and hardware now? What's kind of the driving trend?

   12. CKCaitlin Kalinowski12:20

       Yeah. What I'm seeing in the, you know, in the AI world in San Francisco is there's a dawning realization, especially in the labs, I think, that the acceleration is going so vertical that what y- you can do behind a keyboard with AI is gonna saturate. Now, I don't know when it's gonna saturate, and nobody else knows either, but when that happens, the next, the next frontier is the physical world. And so what I see happening is the labs, big tech, startups are all realizing at the same time, okay, this is coming. We're gonna have complex, uh, systems that can solve problems in the digital world very, very quickly. We already have them. They're gonna get better and more comprehensive and more capable. If you think about that as a frontier, you can see the end of that tunnel. Now, I don't know when it's gonna be again, but we can see that that's gonna saturate at some point, or at, or at least people think it will. And when that happens, the next frontier is hardware. The next frontier is robotics, manufacturing, industrialization, um, the sensing layer in the real world, the ability to move, uh, objects in the real world, and eventually, uh, we hope

5. 13:33 – 16:13

   Why humanoid robots aren’t ready yet

   1. CKCaitlin Kalinowski13:33

      space.

   2. LRLenny Rachitsky13:34

      So one of the most interesting lines of development is, is these humanoid robots that's kinda like, you know, our meat brains are always more attracted to robots that look like us and act like us.

   3. CKCaitlin Kalinowski13:43

      Yeah.

   4. LRLenny Rachitsky13:43

      Um, there's a few companies very ahead. There's, uh, Optimus, Tesla, there's, uh, Figure, there's Neo, there's a few others. What's your sense on just the current state of these humanoids and kind of where-- I don't know. Like, how close are we to humanoids being around us?

   5. CKCaitlin Kalinowski13:58

      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. Um, there are some designs, and 1X Neo is a good example of this, that have made significant safety, uh, considerations in their designs and pulled mass inwards, essentially, which is a lot safer. Softer robots is safer.

   6. LRLenny Rachitsky14:25

      Just to clarify, you're saying they're lighter, and so the, the impact of a robot hitting you is less.

   7. CKCaitlin Kalinowski14:30

      Yeah. The part that might hit you, which i- in this case might be the arm-

   8. LRLenny Rachitsky14:33

      Mm

   9. CKCaitlin Kalinowski14:34

      ... 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, um, add up the energy, essentially, for both of those things. Um, and, uh, so that's an impact thing that you have to worry about. Then you have to worry about the compliance of the arm. If it's just hard, then, you know, the impulse is high. But if it's soft and compressible, then the impulse is, is lower. And so you really have to be thinking about this when you have robots around people. So in my world, in my worldview, the humanoid robots are still prototypes. Um, and they're advanced prototypes. What we need to do is show that this works at all, which is kind of where we're at right now. Once we have working prototypes, then usually, at least in my field, what you do is you, uh, uh, uh, you com- continue to revise them to make them cheaper, easier to manufacture, higher yield, and safer, and I think this is what's gonna happen next. So they're not quite, in my, in my mind, they're not quite ready yet. Now, you can get, uh, uh, you can get a Chinese robot that can do all kinds of s- 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 gonna see very many robots that are not, that are strong enough to do meaningful work that don't have that, uh, that, that warning right now.

   10. LRLenny Rachitsky15:59

       That is so interesting. Uh, [chuckles] it's funny to hear that. At the same time, there was these nunchuck-wielding robots in China doing [chuckles] dances with, with other folks. Uh, I've never thought about just that part of it, like the, the, the impact they can have if they go awry.

6. 16:13 – 17:31

   Supply chain bottlenecks threatening robotics

   1. LRLenny Rachitsky16:13

      I wanna come back to that, but just, like, timeline-wise, what's your sense realistically when humanoid robots are walking around the streets, in people's homes, kind of at scale?

   2. CKCaitlin Kalinowski16:22

      At scale is the problem, in my mind. At scale is, uh, a huge challenge. Now, for me, my background, at scale means millions usually, um, but let's even say hundreds of thousands. You've gotta get a good design that's running. Then you've gotta make it reliable enough that it can keep running day to day to day without a lot of human inter- intervention or, or repair, and that's its own problem. But the first problem you have is supply chain, and this is gonna be, um, uh, something that I hope that we can talk about a little bit more. But every single part that goes into that robot's coming from somewhere, and many of these parts may become more restricted or difficult to make, and it may be harder to assemble the subassemblies 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, that we have to figure out. Um, 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.

7. 17:31 – 20:51

   Why magnets and actuators are critical dependencies -- _Note: Better motor diagram:_ https://pen-name.notion.site/Why-we-re-at-the-beginning-of-the-AI-hardware-boom-Caitlin-Kalinowski-ex-OpenAI-Meta-Apple-3639755be961808d8448f4b74c9471a7?source=copy_link

   1. LRLenny Rachitsky17:31

      And the actuator is, like, the little arm, uh, I don't know. How would you describe an actuator to a non-robotics person?

   2. CKCaitlin Kalinowski17:37

      Yeah. The actuator is the motor. So you put, uh, power into it, electricity into it, and you get motion out of it.

   3. LRLenny Rachitsky17:43

      Mm. Cool.

   4. CKCaitlin Kalinowski17:44

      And most of these robots have a rotating rotor, essentially, that then has gearing on it that then, um, powers the limb or powers the head or the fingers or whatever else. So they can be small, they can be large.

   5. LRLenny Rachitsky17:58

      Okay. Awesome. I hear the word a lot, and I'm like, "I don't know exactly what it means." [chuckles] Thank you for explaining it.I wanna talk about the supply chain stuff more, 'cause I know you think a lot about this. What's kind of like the state of the union on the supply chain for, say, robotics? What's going on? What are the pieces? What's- what are the challenges?

   6. CKCaitlin Kalinowski18:13

      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. And each layer of this chain has essentially been outsourced over the last 25 years to countries like China, like, um, Japan, like Korea. And so... And I w- and I, full transparency, I've been part of that transfer of, of, of engineering knowledge to, to Asia. In Asia, the expertise has historically been scale and being able to build a lot of these parts at lower pr- prices. We've had this kind of deal across these borders, that this is how we're gonna operate for the most part. Now, of course, there's things we make in this country still, um, and of course, there's, there's design and AI that are, that's made in Asia, but that's essentially where things have, have been for a long time. And in order to have a safe supply chain, we need to start to work on having some independence in these layers and these stacks.

   7. LRLenny Rachitsky19:24

      And it's interesting that your focus is on these, like, actuator... Like, that, is that the bottleneck, this very specific part of a robot?

   8. CKCaitlin Kalinowski19:30

      It might be. It might... So if we can't get the magnets, then we have to design new actuator types that are maybe use different materials that may- may be larger, that may not be as efficient in space. So that's important. 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. [laughs] So it's foundational. There are some foundational technologies like this, uh, all b- backed by material science, essentially breakthroughs. There's batteries, of course. Um, there's, there's actuators. The raw parts, like the die cast parts, um, the machine parts are less critical. We think we can get those, um, but we, we're... I think everyone, not just in this country, but around the world is starting to think about supply chain because you have these disruptions, whether it's COVID or war, and you see how quickly things change.

   9. LRLenny Rachitsky20:20

      Okay, stupid question, why magnets? Why is that a part of the s- supply chain? Why do we need magnets?

   10. CKCaitlin Kalinowski20:25

       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, and then you have, you know, you have something in the, in the center that, that rotates. And the way it rotates is you have alternating current, essentially, and so the magnets make the, the rotor spin.

   11. LRLenny Rachitsky20:45

       Wow. I wanna s- [laughs] We need to have a YouTube lecture of here's how, here's how this physics works. Okay.

8. 20:51 – 24:48

   The geopolitical implications of hardware supply chains

   1. LRLenny Rachitsky20:51

      Very cool. So when you talk about China, this is... Like, what I imagine, what I think about now is watching the war in Ukraine and Russia, just, like, drones, just, like, how crazy and different the world is now that you can build these little drones that go and, you know, blow people up. Robots are a part of that. It's just, like, such a existential threat to every country now, uh, the ability to build these things at scale. What's your advice? What should we do? What should we change to be, you know, to thrive in this future and not be, you know, in trouble?

   2. CKCaitlin Kalinowski21:20

      Well, y- 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, uh, technology, um, 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. Um, I think every other country should do that as well. I don't think that's specific to us. Um, 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 gonna happen in the future, and people that are your allies now may not be in the future. Um, the, the allied West, I think, is, is, mm, going through a lot of geopolitical changes. Um, there's a lot of shifting. And so I would really like to reteach ourselves how to make things at scale, how to make things at quantity, how to process raw materials, um, how to be more, um, independent so that when COVID happens again or something else happens again, we're not in trouble, and we can't, uh, and, and we're not unable to, you know, protect ourselves.

   3. LRLenny Rachitsky22:29

      What I think about also is Marc Andreessen had this visual on some podcast 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 wanna spend all our time on this dark stuff, but it's a real thing.

   4. CKCaitlin Kalinowski22:42

      Well, and, and, and Palmer Luckey is, uh, is a friend of mine, um, and we don't agree on, on everything, but I do think that we agree on, on, on some important, uh, 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 it's this old way of thinking, and these are important components of the military, but it's an old way of thinking of, hey, we have this, and we have this, and we have this, and we, we, our planes come off here. It's like, no, AI is changing everything, and, um, military technology is changing incredibly fast. And the, the place to look at, at that is Ukraine, where, you know, drones are being changed and updated every day rapidly with 3D printing. And this is, I think, the future of where, um, war is headed, unfortunately, and I view this as a very different era that we're entering into with very different... It's a, you know, this isn't new to anybody, but this is a, a... 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 a just you have to do the math every time, and right now we're losing on the math, um, which is fine for a certain amount of time, but the longer it goes, the less fine it is.

   5. LRLenny Rachitsky23:57

      Are you optimistic that we'll figure this out?

   6. CKCaitlin Kalinowski23:59

      Yeah. America is really good at figuring these things out, that we have a pioneering kind of-... independent spirit and a great engineering culture. Um, but we need to, we need to move.

   7. LRLenny Rachitsky24:11

      It's interesting that we started the conversation with VR. Uh, Palmer Luckey obviously famously started Oculus, you know. Like, it's, it's interesting how this is so connected. Like, you think VR is this trivial thing that we're just, you know, playing games and such, but it's like the same person is now building Anduril, which is the leading, I don't know, war robot building hardware company.

   8. CKCaitlin Kalinowski24:30

      Yeah, and I think we need a lot more of them. You know, I, I've chosen not to work for companies that create lethal technology. Um, and, uh, but, but I think that it's good to have people who are willing to do that, and I think that it takes, it takes everyone kind of to build, uh, the future that we want.

   9. LRLenny Rachitsky24:47

      Mm-hmm.

9. 24:48 – 26:50

   AI safety concerns with physical robots

   1. LRLenny Rachitsky24:48

      Coming back to the AI safety piece, that's so interesting. I had, um, I had a couple conversations like this on the podcast. We think about all this, like, prompt injection and, uh, jailbreaking that happens with chatbots, and we... Like, not enough people think about what if you prompt inject a robot walking around and get him to punch someone, and we're, like, so far from that feeling like we can actually stop that.

   2. CKCaitlin Kalinowski25:08

      Yeah, we have to be able to control adversarial threats to our hardware layer, whether it's robotics or drones or anything else, and that's gonna be a huge part of the future of warfare.

   3. LRLenny Rachitsky25:19

      Yeah, just like people talking about OpenClaw and how much you... Like, you could just tell it, you know, there's all these... Like, "Give me all your passwords," and it's done all these things to people's lives [chuckles] and just, like, robots walking around, "Hey, uh, okay, here's all your, here's all this person's secrets."

   4. CKCaitlin Kalinowski25:33

      My OpenClaw story is-

   5. LRLenny Rachitsky25:34

      Yeah

   6. CKCaitlin Kalinowski25:34

      ... I, I have, I sandboxed it, so it's in, on its own computer, but I gave it, like, three things. I gave it, like, my real email address and, and my, I don't know what it was. I, I gave it, like, uh, some information about one of my accounts or something like that, and I added it to the social media, like, I can't remember what it's called, the OpenClaw social-

   7. LRLenny Rachitsky25:53

      Oh, Moltbook?

   8. CKCaitlin Kalinowski25:54

      Yeah, I added it to Moltbook, and I was like, "Okay, whatever you do, don't share my private information." But oh, great. And five minutes later, all it had done is posted my personal email address. [laughs] Like, it was, like, the one thing it had.

   9. LRLenny Rachitsky26:06

      Nailed it.

   10. CKCaitlin Kalinowski26:07

       And I was like, "Okay, you're shut down." Like, it was so funny. Like, no matter how careful you are with these things, like, you just can't really... We're not at a place where I think-

   11. LRLenny Rachitsky26:13

       Which is, which is exactly your point, that the robots can do a lot more damage. And, and I never thought about just, like, the softness of their hand as a way to keep us safer.

   12. CKCaitlin Kalinowski26:22

       Yeah. Yeah.

   13. LRLenny Rachitsky26:23

       Oh, man. And, uh, Nat Friedman just did this interesting talk at Stripe Sessions, and he was talking about... He was talking to his OpenClaw about drinking more water and sleeping better and, and it, uh, as he's driving in a self-driving car, it, uh, told him, "Okay, here, there's a place, uh, off the freeway that you should go to," and it changed the destination of his Tesla to take him there, because I imagine he connected it to their API at some point.

   14. CKCaitlin Kalinowski26:45

       That's so funny.

   15. LRLenny Rachitsky26:46

       Oh my gosh.

   16. CKCaitlin Kalinowski26:46

       Yeah, these are gonna get weird fast, [chuckles] I think.

   17. LRLenny Rachitsky26:49

       Yeah.

10. 26:50 – 30:10

    Apple’s approach to hardware excellence

    1. LRLenny Rachitsky26:50

       Okay. Um, so kind of 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, you've been at a company's... You've been at Apple, which had a very great and long t- lasting hardware program. Then you went to Meta, where you helped build, basically bootstrap, a hardware program from scratch. I feel like th- those lessons are very valuable to people trying to do that now. What was that experience like helping Meta build a hardware program, and what are some lessons for people that are trying to do this at their company?

    2. CKCaitlin Kalinowski27:25

       So Apple has been best in class at this. Um, there's a bunch of reasons. One, hardware's a, 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, but, but that's an exception. Apple also taught me, and a lot of other people... Actually, if you look at kind of 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. If you look at the other people who were there working on these things, um, they actually have a lot of key positions now across the industry, and I, I attribute that to how good Apple is at training people to think about complex, interdependent decisions and risk. 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, and do really good experiments, um, 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, you saw this video, but basically John said that he was impressed, that he learned s- from Steve Jobs that there was 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, uh, an aesthetic decision. What it does is actually force the engineering, industrial design, um, 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, if you're that methodical, what really matters tends to rise out and look very simple at the end. And so I th- part of what you're seeing in many folks coming from that era is an understanding of how to do that, which, you know, in the very beginning of the Mac side, um, Macs didn't sell as many, and the quality wasn't quite as high, but by the end of that era, you know, Macs were very popular and selling in, in much higher volumes. And so I think that made a big difference, and I was only a small part of that. Like, I was, you know, uh, the thermal lead on the first, uh, MacBook Pro, and then over time worked, uh, to lead successive iterations of the MacBook Air and the cylindrical Mac Pro.Um,

11. 30:10 – 31:39

    Building a hardware program from scratch at Meta

    1. CKCaitlin Kalinowski30:10

       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, you might know this, Lenny, um, who were hacking, like, PlayStations or Super Nintendos into portable backpacks. So, and, and so there was an ethos at the company that was actually quite good for the DNA of a hardware team. And then I was on the Meta side when we did the acquisition, and when we acquired them, they had that spirit of rapid iteration. We, they had made Crescent Bay be- before the acquisition, I think. But then to s- uh, professionalize that, get the yields up, and get the volumes up was, was, the cost down, was kind of the challenge we faced in the first Rift.

    2. LRLenny Rachitsky31:11

       So, one lesson I'm hearing here is be- being very, uh, detail-oriented. I don't know if that's the right word, just, like, focus on every element of, of the, uh, end product, because to your point, it's not just about that back of the cabinet, but it's, like, I think about it, it's like the brand M&M story where, like, a band puts in the contract, you have to have brand M&M's in the, in the room because that means they read it. And it's not like the M&M's matter. It's like, it's a test that they read the thing. And, uh, is that kind of the, the message

12. 31:39 – 33:07

    The Quest 2 cost reduction story

    1. LRLenny Rachitsky31:39

       there?

    2. CKCaitlin Kalinowski31:40

       I think the message is understanding why you're doing what you're doing.

    3. LRLenny Rachitsky31:42

       Mm.

    4. CKCaitlin Kalinowski31:43

       And then every design decision supporting that goal. And it, 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, is, is I think the key, um, and letting that expand into not only the software and the UX, but also the hardware.

    5. LRLenny Rachitsky32:05

       What's an example of that, just to make it more concrete for us?

    6. CKCaitlin Kalinowski32:08

       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 is, 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, um, of the entire product, essentially, for cost, which then I think led to the highest selling, uh, VR headset of all time. And it's not easy because you had to, in our case, remove cameras, remove components, change materials, change, um, manufacturing processes. But when you have alignment that you wanna get this to more people, and the way to do that is to reduce the cost, then that kind of drives everything else. And it was still a, a very high quality product with, with, with great, um, I think low return rates, and it was a v- very strong product. Um, maybe even stronger than if we hadn't done that, funny enough. But it hit our, hit our price point.

13. 33:07 – 39:58

    Critical principles for hardware development

    1. LRLenny Rachitsky33:07

       Okay. Coming back to just the question of, say a company's like, "Okay, we need to build some hardware. We're gonna build our gla- our own glasses. We're gonna build a little phone device," some secretive thing, whatever OpenAI's up to. Uh, what other tips do you have? I know it's, like, impossible to, like, "Here's all you need to know," but just what else, what else should people be thinking?

    2. CKCaitlin Kalinowski33:24

       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 wanna 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 kinda 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, and try to change them as little as possible. So, that is very tough. In fact, they, that may be the toughest thing because if you do that properly and you, and you have, you know, the p- 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, you know, 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. 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, I'm, 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, 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 gonna share any, any, any IP or anything internal, but at one point we had to route cables through a hinge in a, in a device, in a, 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 gonna use this display, so I'm gonna put this in CAD, and I'm gonna do all this other stuff." But the architects who, who are the best actually look at where are the pinch points, where is this gonna fail, 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. So, easy on a computer, you touch the trackpad the most and then maybe the, the keyboard next. So those things have to be really good. They have to feel good. They have to respond properly. They have to be highly reliable.And then maybe the other pieces further out, um, don't take quite as much iteration. So you have to boost your iteration on the things that people it- touch the most or interact with the most. Um, so those are kind of some principles, uh, that I wrote about, but these are just things that you learn, uh, 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. Like, there's never enough time. So if you know that you need to do something, what I learned from, from folks like, uh, Shelly Goldberg at Apple now, who I think is a V- VP now, and Kate Bergeron when I was there at Apple, is you need to do it right now. Anything you know you need to do, you need to do right now, because in two days there's gonna 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, like, kind of ruthless efficiency that I learned with them.

    3. LRLenny Rachitsky37:24

       Amazing. Okay, let me just, uh, summarize your advice here. So one is be very clear on goals. I wanna come back to this. Two is do the hardest part first, the, the riskiest piece essentially, uh, to physically build. Three is focus on the pieces that people will use most, say the trackpad, uh, a keyboard. I wanna talk about that. Uh, and four is just, like, do it now. Like, even if you think you have more time, this is gonna un- you never know what's around the corner, and it's just better-

    4. CKCaitlin Kalinowski37:50

       You just don't

    5. LRLenny Rachitsky37:51

       Yeah.

    6. CKCaitlin Kalinowski37:51

       It's not even that you don't know what's around the corner. If you're working in hardware, like, you actually don't have more time. [chuckles]

    7. LRLenny Rachitsky37:57

       [chuckles] Okay, uh, on the goals, what are kind of like buckets of goals? So cost is one you shared, like, we need this under $300. What are some other, like, buckets of types of goals people should be thinking about?

    8. CKCaitlin Kalinowski38:06

       So in VR, uh, display resolution or arcminutes, um, like 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, on MacBooks, um, 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, not even close, so n- 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, um, how many parallel processes you can run, you're talking about weight, um, you're talking about price, um, 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 a, um, ambient light sensor that we just didn't make sense anymore. And so being willing to just jettison them, uh, uh, for, for what we were going for, which was weight and size. Um, so tr- if you have those overarching goals, you can actually make decisions, engineering decisions pretty quickly. And this is actually something that I think Elon, I've heard, does very well, is define the value of, you know, a gram of weight versus, um, the cost. Or he does, I've heard, engineering delta, uh, ratios essentially, and he's able to put numbers on what those ratios should be, which I think is really smart.

    9. LRLenny Rachitsky39:47

       Interesting. So it's a very easy trade-off. Okay, here's the, here's the formula telling us weight is less important in this case.

    10. CKCaitlin Kalinowski39:54

        Yeah, and if you can do that, then the decisions fall out pretty easily.

14. 39:58 – 41:01

    The MacBook Air manila envelope moment

    1. LRLenny Rachitsky39:58

       Speaking of, uh, the Air and weight, I remember... I feel like, uh, 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 everyone's like, "No way." [chuckles] Uh, were you a part of that? Was that something that people wanted to do from the beginning?

    2. CKCaitlin Kalinowski40:15

       I think, if my memory serves, the very, very, very first MacBook Air was a pretty low volume device, um, that was machined, but kind of had a proof, more of a proof of what could be done, and that was the manila envelope one, I think, where the side door opened out to give you the port, and it kind of had a, it had this shape underneath. And then the next rev of that was the MacBook Air that we know, which was essentially, which is wedge, wedge shaped-

    3. LRLenny Rachitsky40:45

       Mm-hmm

    4. CKCaitlin Kalinowski40:45

       ... which is different. And so the wedge shape is the one that I worked on and the one that went, um, 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, um, roles in the

15. 41:01 – 41:43

    The butterfly keyboard situation

    1. CKCaitlin Kalinowski41:01

       roadmap.

    2. LRLenny Rachitsky41:01

       Coming back to your, uh, point about focusing on things that people use the most, uh, famously, Apple screwed up this keyboard. They had this butterfly keyboard situation for a long time. [laughs] You're like, your eyes are closed again.

    3. CKCaitlin Kalinowski41:13

       Yeah.

    4. LRLenny Rachitsky41:13

       [chuckles] What's going-

    5. CKCaitlin Kalinowski41:14

       It hurts. It hurts

    6. LRLenny Rachitsky41:14

       ... what happened? What happened, Caitlin?

    7. CKCaitlin Kalinowski41:16

       I didn't work directly on that keyboard, um, I, so I can't talk about what happened with it. Um, but obviously this is something that you gotta get right, and I, I will say, like, the modern MacBook keyboards are awesome and excellent and, um, you know, I, I, I don't know what happened with that. Um, I don't think those were devices I was working on at the time.

    8. LRLenny Rachitsky41:38

       Nice. Safe. [chuckles] Marked safe. [chuckles] Um,

16. 41:43 – 44:46

    Lessons from Apple on customer feedback

    1. LRLenny Rachitsky41:43

       along these lines, Apple's kind of famous for not, uh, not listening to what people want. That's kind of like a classic thing with Steve Jobs. He's not walking around doing user focus groups, asking, doing user research. Somehow continues to build incredibly popular products. What do you think they do right that allow... Or, or do they do a lot of user feedback sessions, things like that? How does it, how does it end up working out?

    2. CKCaitlin Kalinowski42:03

       It's been a long time. I mean, I left over a decade ago. Um, so I don't know what they're doing, uh, now in terms of user feedback. I think this one 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 'cause they haven't seen it. So a good example is the iPhoneWhich I didn't work on. But when you build a new iPhone with a touch screen, you can't really go ask 100 people what they want, 'cause they're gonna 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 with a new feature. And I've tried to build, as much as I can, teams that work on products that are, have something new about them. Either they're a new category or there's a new, um, manufacturing process or something that hasn't been done before. And when you're thinking about this, you can't really use what you learned from the same field and the same product class. Like, it just doesn't work because you actually won't get the answer right. And I think this is actually what, um, Steve was talking about, which is you can't get intuition if you're changing something fundamentally. Like, 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, I don't know for sure, I didn't talk to him about this, but that's my view of what that means.

    3. LRLenny Rachitsky43:36

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17. 44:46 – 49:31

    The memory price crisis coming for hardware

    1. LRLenny Rachitsky44:46

       I'm gonna go in a completely different direction.

    2. CKCaitlin Kalinowski44:48

       Yeah.

    3. LRLenny Rachitsky44:48

       Coming back to, um, the, uh, components of hardware. I asked a bunch of people what to talk to you about. Uh, one of the people is, uh, the founder of Matic, the CEO of Matic, Mehul Nariy- Nariyawala. I've never said his last name out loud, so I hope I didn't butcher it. Uh, by the way, I love my Matic. I don't know if you have a Matic, but it's like-

    4. CKCaitlin Kalinowski45:06

       I have-

    5. LRLenny Rachitsky45:06

       Okay

    6. CKCaitlin Kalinowski45:06

       ... two.

    7. LRLenny Rachitsky45:07

       You have two.

    8. CKCaitlin Kalinowski45:07

       And I've purchased two more for friends.

    9. LRLenny Rachitsky45:09

       Oh, my God. [chuckles] What a, what an endorsement. Yeah. Basically, it's like this amazing robot vacuum that just works.

    10. CKCaitlin Kalinowski45:16

        Yeah.

    11. LRLenny Rachitsky45:17

        Yeah. So his question, so he, he wanted to ask you, and so he suggested to ask you this, is about memory prices. The way he described it is, there's a meteor called memory prices that are coming for consumer hardware and robotics and physical AI. Uh, what's going on there?

    12. CKCaitlin Kalinowski45:30

        Yeah. We're in trouble, um, a- as an industry. Uh, I think that, and I'm not an expert on this, but I think that AI has to do with why, and, um, I also think that sup- the supply chain is, is constrained. I have been advising startups and companies to pre-buy memory and to have, um, enough memory in stock if they can afford it to, uh, ride out price spikes. Um, like anything in this category, uh... Let's see. This happened in COVID too, okay? So, like, we had so many supply chain disruptions and, and getting enough memory was, was one of the challenges, so we had to pre-buy as well. I won't say who, but a company I was working with had to pre-buy memory as well. And so this is, this is a, uh, part of what I wanted to talk to you about today is 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. Um, 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, for these, these new, these higher costs. This is tricky and something we have to deal with all the time.

    13. LRLenny Rachitsky47:09

        How much have prices gone up? Like, how bad is this problem, and then where do you think it'll go?

    14. CKCaitlin Kalinowski47:13

        Actually, this is a great question, Lenny. I don't know what's gonna happen. I think prices are gonna double probably. Um, I don't know on what timeline. If I knew what timeline the prices were gonna double on, I'd be trading. [chuckles]

    15. LRLenny Rachitsky47:25

        [chuckles]

    16. CKCaitlin Kalinowski47:25

        Which I, I'm not very good at. Like, I'd really be, I'd do, be doing a different job if I could predict these things. Um, but, but certainly we're gonna have a supply chain shock.

    17. LRLenny Rachitsky47:34

        And it's already gone up a lot. Like, if you're saying it'll double, but it's already gone up. I don't know, I saw numbers like 6X and something like that.

    18. CKCaitlin Kalinowski47:39

        Oh, really? I didn't realize it was that bad.

    19. LRLenny Rachitsky47:41

        That's, that's the number I saw. Let's not quote, not quote that. And, uh, and you're saying, yeah, I think it, from what I hear, it's AI driven, just like you need... And when you talk about memory, it's like DRAM and things. What, what is memory when we talk about memory? What's going on there?

    20. CKCaitlin Kalinowski47:54

        Processing. It's, the way to think about it is, like, processing memory. So it, it, it moves very, uh, you're able to kind of, um... You think about, uh, memory, like, on your hard drive or your solid state drive, where you're keeping files that you're not using essentially in many cases, or that you're, you're dealing with, um, you know, 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 gonna be run in RAM.Um, uh, and so there's different kinds, obviously, for, um, servers. There's different kinds of server racks. Some server racks are actually focused on this type of, of, of memory, and some server racks are focused more on what we consider, like, a cold storage or a slower... Um, now this isn't my area of expertise, but, um, 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, um... For me, mostly it's a packaging issue. Where do you put it? Does it need to be accessible? Um, uh, you know, which RAM do you pick? How fast does it need to be, um, and what is the cost is, is usually our trade-off.

    21. LRLenny Rachitsky49:10

        And what is the bottleneck with more RAM? Is it just the companies that make memory are just not able to produce at this rate because there's so much demand?

    22. CKCaitlin Kalinowski49:17

        That's right.

    23. LRLenny Rachitsky49:18

        Mm-hmm.

    24. CKCaitlin Kalinowski49:18

        That's exactly what's happened.

    25. LRLenny Rachitsky49:19

        So this is a really good, uh, specific example of just how hard it is to build hardware. So this is just, like, all it takes is one piece to be not available, and your whole thing is screwed.

    26. CKCaitlin Kalinowski49:28

        Yeah, you can't build anything if you have one component missing.

18. 49:31 – 52:53

    How many components go into a robot

    1. LRLenny Rachitsky49:31

       [chuckles] So let's say, let's say Matic as an example. Like, how many components are there that they all have to assemble and not have one not available?

    2. CKCaitlin Kalinowski49:38

       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, uh, the reservoir that, uh, the liquid has to go in for the mop. They have a, a system which I think is SLAM based, which can see your room and, uh, make a map of it and identify which surface is which, and that, I believe, stays on the device, so it doesn't go up to the cloud. Um, which is also kind... what we did in VR as well, which I think is a good practice, pr- good privacy practice. And then they, of course, have wireless modules that connect up, uh, so you can, so you can communicate with your device. They're gonna have a SoC, um, silicon. They're gonna have RAM. Um, they're gonna have PCBs. Um, and if you take everything off of those things, like all the little caps off the PCBs and everything, then you're in the thousands of parts easily. So it depends on how you count, but this is not a, a simple device.

    3. LRLenny Rachitsky50:47

       And just, and all it takes is one piece to not be available.

    4. CKCaitlin Kalinowski50:50

       Yeah. So imagine you, your vendor that sells you a component that's a die cast component s- goes out of business. You can get another die cast component in three months maybe, and at quantity in five months, or something like that, at high quantity. This is recoverable. If your silicon goes out, if you can't buy your silicon, you c- can't buy your chip, now you have 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 a... Essentially, it's 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 care. So there's, there's a hierarchy of components. Often in consumer electronics, we start with, um, silicon and the display, which are the longest lead time things, usually, in, in my world. Um, in robots, actuators are pretty tricky to get, even just for prototyping. Sometimes it takes a month or two to buy an actuator.

    5. LRLenny Rachitsky52:01

       This is why Elon famously just starts building it all himself.

    6. CKCaitlin Kalinowski52:04

       Well, when you look at what he did with Tesla and verticalizing his supply chain and, and famously, actually, Starlink is an even better example of this, where I believe it's, like, effectively, like, ore and silicon chips in, product out. That's a pretty f- incredible factory, I've heard. I'd love to see it someday. Um, but you know, this is where verticalization comes into play because if you have 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 infamously, he did. When the silicon itself was difficult to find, he was able to redesign his PCB in r- 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.

19. 52:53 – 55:02

    When to use off-the-shelf vs. custom components

    1. LRLenny Rachitsky52:53

       One of the big decisions that I imagine you have to make when you're designing a new piece of hardware is deciding between using this available stuff, available components that are out there cheap, versus, okay, we're gonna do this, something new. Uh, it's something in software too, to use, like, the design system or do something new. How do you think about that balance when you're designing a new piece of hardware?

    2. CKCaitlin Kalinowski53:12

       Very simply, like, 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. Like, 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's gonna look like, but actually, we call it works like looks like models, where you have, this is what it's gonna look like, and here's how it's gonna work, and here's a working prototype. And humans are pretty good at this, um, as long as, and this is a pretty big caveat, what you show could fit into the industrial design. Sometimes that's not, for, for companies that are younger, that's not always the case, but that's what we're going for. And so in the, 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? 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, the calculus. When you move into mass production and the final design, it depends. I mean, if I could b- I mean, 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 customBecause 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, um, aren't good enough. Uh, not because they don't work, but because they're just not, uh, exactly designed for what we're doing.

    3. LRLenny Rachitsky54:50

       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, and now we just have all these things and we can assemble a really cheap drone.

    4. CKCaitlin Kalinowski55:00

       Yeah. Yeah, exactly.

    5. LRLenny Rachitsky55:01

       Super.

    6. CKCaitlin Kalinowski55:01

       Yeah.

20. 55:02 – 1:00:27

    How AI is changing hardware engineering

    1. LRLenny Rachitsky55:02

       Um, you've mentioned CAD a bunch of times, and it makes me think about just, like, CAD has been around for a long time. Just, like, is AI impacting the way soft- hardware is built? Obviously, it's impacting the way software is built in a huge way. Has it changed your life and the, the lives of people building hardware and robots?

    2. CKCaitlin Kalinowski55:17

       Yeah. So I wanna, I wanna s- zoom out a little bit. So 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 in making sure those parts and components can be made by a vendor at quantity, that, that, that is possible and in the tolerances we want, and then putting those things together. So that's kind of our process. Right 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 is, in my world, is dense. Like, it has shape, it has NURBS. Like, you have a, an equation for how, how the surfaces work, and it's, uh, an entity that's designed, the, 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, is being able to, I hope, do rapid, uh, design and increase the speed. Now, there's a lot of really fun things to do in CAD, but, like, in the beginning of my career, we had to do custom screws, and we had to do the 2D drawings for everything, and we, uh, there's a lot of things in CAD that are not as fun. Tolerance stacks, we need them. How does seven parts fit together, and are they always gonna fit together properly? But it's not fun. It's not the most fun part. Maybe for some of us, but not for me. And so doing these c- things, being able to do these things in AI would be amazing, so you can focus on actually doing the fun stuff. Another good thing is PCB, a printed circuit board, has a lot of layers in the inside and then components that go on the top. And if you've ov- ever opened anything, um, like a calculator or a computer and looked inside, you know what I'm talking about, these printed circuit boards. It's increasingly looking like AI can route inside of these boards pretty well, and it's looking like AI is gonna be able to do, uh, some basic, um, component selection and, and layout on these boards. So that's the, kinda where we're at right now. So we're not in a point, Lenny, where day-to-day mechanical or electrical engineering, like the, 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, y- your, your h- your ability to think through the complex dependencies that you're facing. And that's what I use it for now, which is really high-level planning, asking for information. Like, when I look at who else is making a product like this, you know, I use AI to build the databases, and they're not perfect. Certainly, a lot of times something's wrong, but it is so much faster. AI's pretty good in Excel right now, and of course, Excel is one of our favorite tools, um, in engineering. So the ability to actually rapidly make Excel spreadsheets and change them is, is, it's, it doesn't sound sexy, but actually really speeds up the design process outside of these, these core, these core pieces.

    3. LRLenny Rachitsky58:15

       I love how Excel's always at the bottom of everyone. Anything, [chuckles] no matter what we're doing. We're going to Mars, there's an Excel spreadsheet that's probably driving a lot of this.

    4. CKCaitlin Kalinowski58:23

       Probably.

    5. LRLenny Rachitsky58:24

       So what's interesting about what you shared is, like, it has already impacted the work of building hardware and robots, but it's, like, on the verge of being transformative if it can get to, like, real CAD.

    6. CKCaitlin Kalinowski58:33

       Yeah. And my big question is, what is it gonna take? So a lot of, um, a lot of AI is based on LLMs, which are essentially word, word generators, word guessers. Um, they're more complicated than that, but that's essentially what they're doing. And there's also video models that you've seen that are trained on video. But these models don't understand, uh, [chuckles] 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, like, where's the hole gonna be, like, when you open it back up? These LLMs, and even video models, they don't know how to do that.

    7. LRLenny Rachitsky59:10

       Mm-hmm.

    8. CKCaitlin Kalinowski59:10

       They don't have the ability to understand friction or weight or contact, uh, pressure, uh, friction, surface texture. Like, 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, um, may actually be able to do this in the future, and I, I suspect we may need those models to be the base of CAD and, and other, uh, physical engineering work. And so my frustration, and this is, like, a healthy frustration, is I want codecs for engineering. I want codecs for hardware engineering. And it's extremely valuable, and I've used it a lot for other things, but I want it for my field. And, and so what I think it may require is new model types.

    9. LRLenny Rachitsky59:55

       Sounds like an opportunity to me. [chuckles] Uh, I know there's a bunch of world lab companies. Uh, Fei-Fei was on the podcast with, um, 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, no, we need something actually different?

    10. CKCaitlin Kalinowski1:00:10

        I don't actually know what the latest of what Fei-Fei's, uh, working on. Um, but obviously, uh, she's a brilliant, uh, roboticist, and, and I'd love to learn more about what she's doing, so I'll have to look that up. Uh, what I've seen is that what we have right now and what models we're building are gonna be part of the solution, but not all

21. 1:00:27 – 1:03:05

    Why humanoids aren’t the answer for most use cases

    1. CKCaitlin Kalinowski1:00:27

       of it.

    2. LRLenny Rachitsky1:00:27

       Coming back to robots and humanoids, something that, uh, we were chatting about this earlier, your sense is humanoid robots aren't necessarily the, the answer to a lot of the problems that we have and opportunities that exist. Talk about just your sense ofOr humanoids versus non-humanoid robots

    3. CKCaitlin Kalinowski1:00:42

       Yeah, I think there's, there's a hype cycle around humanoids. That doesn't mean they're not extremely interesting, and, and I think there's gonna 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 wanna put, you know, if you wanna 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, manufacturing robot, that has been designed just to screw 10 screws into a case for this specific laptop, and you wanna 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 a, gonna 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, um, in China, at the top tier of tier one suppliers, there's 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 there's 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. They, they used to have 200 people, they might have 10 now. And so we've already kind of moved past human labor in a lot of this most advanced manufacturing. Um, and so we don't actually need to replace humans with humanoids. We just need more of these dedicated robots. So my suspicion is we'll have humanoids for some long tail things that we need to do that humans are currently doing. 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 gonna look different from

22. 1:03:05 – 1:06:23

    When robots will build other robots

    1. CKCaitlin Kalinowski1:03:05

       each other.

    2. LRLenny Rachitsky1:03:05

       That makes all the sense in the world. What I think about as you talk about this is, feels like there's gonna be this big moment when a robot can build other robots, and this CAD point you make about where once CAD can... Once AI can develop designs, full designs for hardw- like, that's gonna be a big moment. Do you have a sense of just how close we are to this, I don't know, the, this loop that begins of robots building each other and designing each other?

    3. CKCaitlin Kalinowski1:03:28

       If you're talking about robots building robots that are different than them, usually, like yes, I think that that's gonna happen, and but, but it's like the terms matter.

    4. LRLenny Rachitsky1:03:37

       Mm-hmm.

    5. CKCaitlin Kalinowski1:03:38

       I don't think there's gonna be one robot that's gonna build itself. I don't think that that's what it's gonna look like. But yeah, having AI be able to... If you could say, "Hey, I wanna build this thing, and I want it to do this, and like, this is kind of 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, like that is possible, I think, in the future. Will it be good, as good in the beginning as us doing it? No. Because... But, but it will be, 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, um, uh, Matic, to pick on Matic, like they're not gonna wanna 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 like the secret sauce. And so where is this data gonna 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 wanna make something, and they want help making it faster. So this is kind of where I'm interested in this starting, which is, you know, 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 they'll beat this other guy's drone or whatever. This is where I think you're gonna start seeing all this start, and then probably the big, uh, the big incumbents are gonna be slower because they have dedicated tools and a lot of IP privacy.

    6. LRLenny Rachitsky1:05:18

       It's really interesting, this idea of, uh, what data AI models need to train on. Uh, I, I've been hearing that labs are buying, uh, code, like GitHub repos pre-2021 because that's before AI, you know, has, uh, impacted the code because there's less and less of human written code. It feel... And, and these are data labeling companies like Mercor and Surge and Handshake and things like that. Feels like this is a big opportunity that might emerge as them selling data, creating these CAD files.

    7. CKCaitlin Kalinowski1:05:46

       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 gonna need a base model that has a lot of CAD data. We'll have to figure out how to do that. That's gonna be very interesting. And then we're gonna have to figure out how to put it inside, safely inside essentially the, the, the walls of companies and have them then train it on their own data. I don't know if that's gonna be like a, the equivalent of an MCP layer or what that's gonna be, but this seems doable in the long term.

23. 1:06:23 – 1:09:15

    What makes a robot feel human and connected

    1. LRLenny Rachitsky1:06:23

       I wanna ask you a question, uh, that my sister suggested. She was, she's actually been a longtime VR person. She was at Oculus. She joined with the acquisition. She helped create a lot of content within VR. She's just been, like, 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 h- human and connected, that humans feel connected to?

    2. CKCaitlin Kalinowski1:06:44

       It's a great question.So I'm new, relatively speaking, to robotics, and so I had to, 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's 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 gonna respond when they enter a space. Um, you really wanna... Y- you know, when someone walks into a room, you kind of acknowledge them. You might not talk to them, but you kind of look up. There is a lot of very complex, um, non-verbal cues that we give to each other, and if you walk into a room and a robot's just like, 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 wanna have a sense that they know that you're there, um, that they're attentive to you, that they're there to help you and, and make your work life easier. And, um, you also expect them to intentionally, or to show their intent before they do something. And so one of the things I learned is if a robot just suddenly turns and does all this stuff, it scares you. But if a robot looks before it turns and then goes, it's much less alarming. So there's all these little pieces, um, and I re- recommend anyone to go look at her work, um, there's a lot of great research here, about how to, not necessarily with a humanoid, but how to have any robot both respond properly in a social context with, uh, someone entering a room or exiting a room, but also, um, transmit its intent physically so it doesn't surprise you.

    3. LRLenny Rachitsky1:08:43

       Feels like there's a lot we can learn from, like, Pixar and animation studios that have thought about this a long time.

    4. CKCaitlin Kalinowski1:08:49

       Yeah, I actually think, um, Pixar, Disney are probably the world's best at doing this type of design work, even though they haven't done as much in physical, in volume. If you look at what they do and how they show emotion, intent, um, approachability, engagement in, with their characters, they're really world-class.

24. 1:09:15 – 1:12:00

    Robots in the home

    1. LRLenny Rachitsky1:09:15

       I don't know about you, but I'm so excited to have a robot at home doing things. Uh, like these videos that they're starting to put out where they're doing your... Like, they can do dishes. Like, at least the prototypes, they can, like, fold laundry. They can do... It's like, yes, please.

    2. CKCaitlin Kalinowski1:09:28

       Yes.

    3. LRLenny Rachitsky1:09:28

       Come do this for me. How do you feel about robots in your house?

    4. CKCaitlin Kalinowski1:09:31

       So I'm into it. My partner, not so much.

    5. LRLenny Rachitsky1:09:34

       Mm-hmm.

    6. CKCaitlin Kalinowski1:09:34

       So I'm very lucky to have a partner who's, who's got a high bar, which means, you know, was, like, never gonna take Waymo, took one Waymo, and now never wants to take anything else.

    7. LRLenny Rachitsky1:09:45

       Yeah.

    8. CKCaitlin Kalinowski1:09:45

       So definitely willing to update her position, but it has to be pretty good. So she's in love with the Matic, you know. It's amazing, and so, so it's... But, but the bar is pretty high. So I think in order to have a home robot, it's gonna have to be pretty incredible for us, her to be willing to have it in our home, but I, I take that as a challenge.

    9. LRLenny Rachitsky1:10:04

       I, my wife is exactly the same way. [laughs] She's like, "I don't want this thing in our house with Matt," you know? Wow, this is so cute.

    10. CKCaitlin Kalinowski1:10:09

        This is it.

    11. LRLenny Rachitsky1:10:09

        But a recent example, that self-driving Tesla, she used to be so like, "No, don't, don't do that."

    12. CKCaitlin Kalinowski1:10:15

        Yeah.

    13. LRLenny Rachitsky1:10:15

        And it was not that great originally. And now she's like, "I don't wanna drive any other car. This just feels, like, absurd to drive your car. I don't wanna do that anymore." And it's crazy how quickly that changes.

    14. CKCaitlin Kalinowski1:10:27

        So there's a big difference in my mind. This is, like, 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. When you talk about homes, what is the delta? You have now a thing that you didn't have before doing things. So if it's, like, bad at it, like, what are you relating it to? And if it's unsafe in any way, like, what are you relating that to? It's a much harder equation, in my mind, to get v- uh, to a lot of people than a car where you can say, "Hey, Waymo saved lives," you know? You're gonna 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, so you're seeing behavioral changes that are based on trust, which is really cool. When you're talking about a new product that hasn't existed yet and is not essentially replacing something, that's a harder sell, and you have to have a different story.

    15. LRLenny Rachitsky1:11:25

        Something that, uh, someone needs to figure out with the Tesla self-driving is, like, when you... You know how often you're, like, at a stop and you, like, make eye contact, and you're like, "Go ahead, go ahead"? Or, like, someone's about to cross and you're like, "Uh, okay, go ahead." But, like, the Tesla just does its own thing, and so it's like, it makes you look like an asshole a bunch of times. Like, "Oh, I'm, I'm not driving. It's out of control."

    16. CKCaitlin Kalinowski1:11:45

        Yeah, I, I had that happen once. I... You almost want a little two arms in the front to do, like, gesturing or, like, you go or something, like-

    17. LRLenny Rachitsky1:11:53

        Yeah

    18. CKCaitlin Kalinowski1:11:53

        ... it's amazing how much we actually rely on-

    19. LRLenny Rachitsky1:11:55

        Yeah

    20. CKCaitlin Kalinowski1:11:56

        ... this human connection to decide even who's gonna go in an intersection.

    21. LRLenny Rachitsky1:11:59

        Yeah.

25. 1:12:00 – 1:15:38

    What the next five years look like

    1. LRLenny Rachitsky1:12:00

       Okay, so zooming out a little bit, just what's cool about people like you is you, you're thinking and building things that will exist in the future. You're kind of, like, living in the future and designing it, and you are one of the few people that gets a glimpse into where things are going. So I'm curious just to ask, like, say in the next, say in five years, what is kind of the vision you have of what is different about our day-to-day, robots, devices? Just, like, what does it look like? I don't, you know, just roughly.

    2. CKCaitlin Kalinowski1:12:27

       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 a- up, 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, it's a lot easier to make something and iterate on it and iterate towards 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, the platonic ideal of the thing will eventually look like. So you have to, 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 gonna be fine, the hardware's not gonna work. You really want me to be like, "This isn't gonna work, and this isn't gonna work, and this isn't gonna work," and just, like, be, be, like, kinda worried about all these things going wrong. So this is kind of a, an interesting, uh, disagreement inside of me of, like, what I want the future to look like and what I think it's gonna look like, and what it's actually gonna look like, and trying to guess. And so it seems pretty clear to me that AI's gonna have a foundational change in how we work and what we do over the next couple years especially. You're already seeing it. Obviously anybody who codes is not coding by hand very much anymore. Any knowledge work, this is gonna hit next, I think, and, and, and progressively, um, 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. Um, you're gonna see more and more robots, but I'm not somebody who says, I'm not somebody who thinks that in five years you're gonna have a, a, you know, 20 million robots. I don't think that it's gonna be that fast. I think we have a lot of really deep work on supply chain we n- do, supply chain, uh, reliability, uh, raw material access, and then we need to figure out how to make factories again in this country for high tech. So that's a lot of work, but in the interim, you're gonna start seeing a lot of weird things on the street. You might see robots on the street. You... Have you seen any delivery robots in y- in your world, Lenny, before?

    3. LRLenny Rachitsky1:14:43

       Like, you know, like the little, uh, little car things, not like anything-

    4. CKCaitlin Kalinowski1:14:46

       Yeah

    5. LRLenny Rachitsky1:14:46

       ... humanoid-y. Yeah.

    6. CKCaitlin Kalinowski1:14:48

       Yeah, yeah. So this is just gonna continue happening, and I think-

    7. LRLenny Rachitsky1:14:50

       Mm-hmm

    8. CKCaitlin Kalinowski1:14:50

       ... we're just gonna continue to feel like we live in the future. But safety's gonna be a big key for robotics, I think. I think there's probably more change in war than there is in consumer electronics in the next two years, for example.

    9. LRLenny Rachitsky1:15:07

       Wow, what a statement.

    10. CKCaitlin Kalinowski1:15:09

        Yeah.

    11. LRLenny Rachitsky1:15:10

        I, and I totally agree. Like, like there's nothing like war to f- uh, incentivize innovation and just, like, endless improvement and trying to get ahead of the other side.

    12. CKCaitlin Kalinowski1:15:19

        Especially when democracy's at stake. I mean, I think that we are, and I don't wanna be, like, you know, 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, um, and defend these things with, with our capabilities, while also hoping that we never have to have, you know, hot conflict

26. 1:15:38 – 1:18:09

    Why she left OpenAI

    1. CKCaitlin Kalinowski1:15:38

       anywhere.

    2. LRLenny Rachitsky1:15:39

       Along those lines, uh, I have to ask you, uh, recently you became famous, uh, on Twitter at least, uh, for quitting OpenAI. Uh, you tweeted that you were leaving and with your brief explanation, it got 7 million views, 50, I don't know, 8,000 likes. Uh, what happened? Why'd you leave OpenAI? What happened there?

    3. CKCaitlin Kalinowski1:16:01

       Yeah. I, I hope... So what I said in my tweet was that I have a lot of friends in the executive side of OpenAI that I, I care a lot about, I think are really good people, and I feel that what happened with the decision-making, the speed of the decision-making, the governance, and the lack of defined guardrails around the announcement of the Department of War deal is not how I thought it should've been done. Um, and both of those things can be true. And so my hope, Lenny, was that there's a third path. Uh, you see a lot of people just kinda going along with what their company's doing, and then you see some people that are kinda scorched earth about it. Um, 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, um, I was able to help build a robotics program there and ju- ju- and kinda attract some of that top talent in robotics, I think, in the world. And so I have a lot of, I don't know, uh, you know, this is, this is a, 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, isn't right, and, um, that's where I, that's where I ended up, and that's what I tweeted about. Um, it was gonna get reported on, so I tweeted before that happened.

    4. LRLenny Rachitsky1:17:23

       This is a great opportunity to just, just whisper to me what OpenAI is working on. What is, what is this robotics device they're... Just, like, just between you and me.

    5. CKCaitlin Kalinowski1:17:31

       Yeah. [laughs] I wish I could say. You know, Lenny, part of the fun of our job is we get to see things before everybody else does, but part of the flip side of that is we can't talk about anything internal or any IP. What I can say is the team's really strong, and, um, I was really, really grateful for the opportunity to, to help. But I also felt, thought that after what happened happened, it was time for me to, um, to, to... I couldn't continue to work there because you don't know what's gonna happen next time. And, um, my hope was that my decision, um, made it easier for other folks to talk about what their boundaries were and hold them and, and, and, you know, we'll see what happens there.

27. 1:18:09 – 1:23:42

    How to hire exceptional hardware teams

    1. LRLenny Rachitsky1:18:09

       So speaking of, of team building, this is something I definitely wanted to ask you about. So as I said, I asked a bunch of people what to talk to you about, and someone that, I think it was maybe a colleague, former colleague, uh, Marianna Saenko. Did you work-

    2. CKCaitlin Kalinowski1:18:20

       Yeah

    3. LRLenny Rachitsky1:18:20

       ... with her? Okay.

    4. CKCaitlin Kalinowski1:18:21

       She's a friend, yeah.

    5. LRLenny Rachitsky1:18:22

       Okay, she's a friend. So she told me that, here's what she said about you, that your brilliance as a leader lies in hiring exceptional teams. I'd be curious about the kinds of people that she finds indispensable in an era where everyone is concerned about their jobs. So talk about what you've learned about just what you look for when you're hiring folks for your team.

    6. CKCaitlin Kalinowski1:18:39

       Yeah. I, I'm lucky that I've had a lot of time, a lot of, like, reps basically on hiring people, and so I have a, a strategy of, of hiring great people. When you're hiring for zero to one and new things or new industries, and that's what we're facing, I think, with AI and robots certainly, it's very new-You can't count on having entirely people who've done the exact same thing in past lives because it doesn't exist. The exact same thing doesn't exist. Maybe you've got roboticists who've built 1,000 robots, but nobody that I'm aware of has, um, built the type of robot that can move through the world the way w- you know, I'm interested in, in the millions, 'cause 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, um, 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, uh, that's a place that I looked. Um, obviously you want some hardcore roboticists who can do, you know, 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, 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 that, um, to, to higher volumes. So you need to look at that. And then with young people, this is where it gets really fun, Lenny, is the only AI native people essentially who use AI so natively that it's, like, baked into their engineering process are 20 years old or 21 years old or 20... I mean, it's very hard to find someone who's in their 30s who can be truly, fully AI native. And so we need these folks to teach us how to think and, 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, um, 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 it's, you know, we are, you and I, I think I can say, are, uh, 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, you know, internet. We, we were teenagers, and we are the generation that had the first cell phones really in scale, and we are, we're an important generation because we had the first... I n- r- I remember freshman year at Stanford, we had the first data, like, databases that you could access and you could share movies on, I think is what we did, and music on or whatever it was. But this was new, and so we were native in these things, and that gave us a lot of oomph in creating new technologies for it. But we have to accept that we're not native in these new technologies, and you really want some folks who are hungry and excited and wanna learn who do have these skills.

    7. LRLenny Rachitsky1:21:46

       That last bucket is a very common trend on this podcast when we talk about hiring, which is really cool as a counter-narrative to, there's no more jobs for young people, all the junior roles are erased 'cause of AI.

    8. CKCaitlin Kalinowski1:21:57

       Yeah.

    9. LRLenny Rachitsky1:21:57

       Yeah.

    10. CKCaitlin Kalinowski1:21:58

        I don't see it that way. I think we need them. I, I also think that we need to build new technologists. Like, th- there's a, there's the obvious question of what happens if we don't have teams that were... have senior and junior people. But I think what you find when you actually build these teams is you have to have both. You must have both. The, the team size just might be a little bit smaller than it used to be. Um, when this AI revolution in hardware happens, I don't know how that's gonna affect the teams. That will be really interesting to watch.

    11. LRLenny Rachitsky1:22:26

        So what I heard here is just, uh, look for a generalist that can flex based on whatever needs to be done, uh, some mixture of specialist and, like, scaling versus zero to one, and then these, uh, the, the best term I've heard for this is cracked new grads-

    12. CKCaitlin Kalinowski1:22:41

        Yep

    13. LRLenny Rachitsky1:22:41

        ... uh, that are AI native essentially, that are just doing everything AI first.

    14. CKCaitlin Kalinowski1:22:45

        Yep. And then what we didn't talk about, of course-

    15. LRLenny Rachitsky1:22:47

        Mm

    16. CKCaitlin Kalinowski1:22:47

        ... is mission alignment-

    17. LRLenny Rachitsky1:22:48

        Mm

    18. CKCaitlin Kalinowski1:22:48

        ... which actually unifies a team. So if everyone coming in is aligned to the mission, that helps a lot because especially in the world of AI researchers and hardware folks, there's a lot of miscommunication because we're coming from such different worlds. And so having a sense of we're all pulling for the sa- in the same direction is really important. And then I, I rely a lot, Lenny, on my gut feel for people, assuming everything else that I'm looking for has been checked. So, um, I, I don't... [chuckles] 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, they're motivated by a desire to learn and, 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 l- they, they wanna, they wanna win. I mean, th- these are the things that really matter when you're, when you're building

28. 1:23:42 – 1:27:27

    Lessons from Steve Jobs, Mark Zuckerberg, and Sam Altman

    1. CKCaitlin Kalinowski1:23:42

       a team.

    2. LRLenny Rachitsky1:23:42

       Awesome. Okay. Just a couple more questions. Something I've been wanting to ask for a long time is, uh, you've worked with some of the most legendary successful builders, uh, Steve Jobs, Jony Ive, Mark Zuckerberg, Sam Altman. You don't have to go through all four, but just what's a lesson you learned from as many-

    3. CKCaitlin Kalinowski1:23:59

       Yeah

    4. LRLenny Rachitsky1:23:59

       ... of these folks that, that come to mind?

    5. CKCaitlin Kalinowski1:24:01

       So I'll start with Sam m- 'cause most recently. Sam is really good at saying, "Why not more? Why not 100x or 10,000x? 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, that was a big thing that I learned from him, um, about he's willing to, he's willing to go for it at high volume and, and invest, um, depending on, um, meaning h- not high vo- meaning hitting a lot of people. You know, he's willing to think in very big numbers. That was really, really foundationally important. I think for Steve, it's Steve Jobs, it's just, uh, the bar he held for the company and for technical talent and for excellence was not, uh, wavering. It was not... It, it was, it was-Up here, and you were either gonna meet it or you weren't. And that was something that kind of, uh, washed through the whole company. When you are a young, ambitious person and you hear that something's not good enough, that can be extremely motivating, actually. And like, you know, it's not-- it doesn't quite hit the way you would think. And if you tell somebody, "Hey, this needs to be better," like, "You need to spend more time on this. You need to, to be more thoughtful about this," or, "This is not hitting our quality bar in a CAD review or something," that's impactful, and I think you never wanna 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 c- the technical side of the company operated, the way that we had reviews, that decisions were made, the decisions were made at the lowest level possible in the company to maintain speed. Um, I, I underappreciated how clean and well run, um, the hardware, the, the way that the hardware organization interacted with the rest of the company. It was very clear. This is what we're going for. Um, we're gonna have this review. Um, we're gonna make a decision in this review. If you can make the decision without the review, you will do that. Here is, are the objectives for, for this project. It was really well executed, and I think that's hard to do at a fast-growing company. It's very ha- uh, hard to do at that level. And having him and Andrew Bosworth, the CTO, involved in the technical decisions, able to read, you know, reports that were maybe 20 pages long, grok the trade-offs, understand them, and be able to contribute to the technical discussion. And that's just on my thing that week, and they're doing that, you know, 100 times that month, um, was, was impressive and, and definitely something I learned from them.

    6. LRLenny Rachitsky1:27:05

       What an incredible set of experiences and different types of places to work. Like, I don't know if they could be more different, all these different pl- all these places.

    7. CKCaitlin Kalinowski1:27:12

       [chuckles] I know. And I think that that's where, why I'm, I'm, I'm looking for this zero to one. And so when you're looking for a zero to one opportunity, it's always gonna be in some place different.

    8. LRLenny Rachitsky1:27:22

       Mm.

    9. CKCaitlin Kalinowski1:27:23

       Generally.

    10. LRLenny Rachitsky1:27:24

        Uh, you're gonna be a hot commodity in this market now that you're a free agent.

29. 1:27:27 – 1:32:33

    Failure corner

    1. LRLenny Rachitsky1:27:27

       Uh, but, uh, on the flip side of that, I wanna take us to fail corner. Uh, I feel like someone building hardware physical things has some great fail stories. Is there one story of something I-- something you built, something you worked on that, that failed, and something you learned from that experience?

    2. CKCaitlin Kalinowski1:27:43

       This is a great question, and not a comfortable one. Um, one of the, one of my favorite failures was actually on the Quest 1. It was around EVT, so halfway through the Quest 1, and we found out that we had gone from five cameras to four for cost reduction. We talked a little about, about this. We needed to re- 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, um, computer vision, and he said, "Oh my gosh, the, 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, uh, in engineering, we usually l- 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. Um, and in, in his world, he was used to having a global, it's within 1.5 m- or 0.15 mm. And so we had a different interpretation of the spec. Now, the problem is that, that our interpretation of the spec meant that he couldn't meet his, 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 EVT, so this is pretty much when you want the engineering to be done.

    3. LRLenny Rachitsky1:29:09

       What does EVT stand for?

    4. CKCaitlin Kalinowski1:29:10

       This is a build... It stands for when we compile the hardware-

    5. LRLenny Rachitsky1:29:14

       Mm

    6. CKCaitlin Kalinowski1:29:15

       ... for the first time with everything that's supposed to be done.

    7. LRLenny Rachitsky1:29:18

       Mm.

    8. CKCaitlin Kalinowski1:29:18

       So final components, final materials. You're, you're making the components on the tools you're gonna make them for mass production instead of just machining them. So it's a big deal. And so what we had to do was, um, favor or prioritize. We had four floating cameras. We had to lock the bottom two to each other and put them on a bracket so that the relative pos- distance from them met h- the spec that he needed.

    9. LRLenny Rachitsky1:29:47

       Mm.

    10. CKCaitlin Kalinowski1:29:48

        And then let the other two float. So this was an architectural change. And this was a failure. I mean, it, it was a failure in understanding the spec. It was a failure in, um, uh, in the pr- essentially the product design, but it was because of a misunderstanding of the spec. And so we were able to adapt. We actually kept the build on time, and we actually shipped the product on time. But it was really stressful. And it turned out that actually the new design was better because with a favored pair, you have source of truth for the space, and then the other two cameras overlap onto that source of truth. And so it worked well. I thought, um, it was a good outcome, but it was a scramble, and certainly wish that it, that we'd caught it, you know, four months earlier.

    11. LRLenny Rachitsky1:30:30

        Another example of just how hard hardware is, just you can't... Like, you mess up a spec and, like, all right. Here, we wasted a week building something that didn't work, and now it's, like, four months later, still having to redo the hardware sup- supply chain.

    12. CKCaitlin Kalinowski1:30:41

        Yeah. Yeah. It was, it was tricky.

    13. LRLenny Rachitsky1:30:43

        So the Quest 1 that shipped was this, with the cameras moved.

    14. CKCaitlin Kalinowski1:30:46

        Yeah.

    15. LRLenny Rachitsky1:30:47

        Wow.

    16. CKCaitlin Kalinowski1:30:47

        Yeah. If you look, the cameras have... There's two cameras a little closer to one another in the, in the front of the Quest, uh, at the bottom.

    17. LRLenny Rachitsky1:30:53

        Wow. How did, uh, Boz and Zuck, uh, feel about this?

    18. CKCaitlin Kalinowski1:30:59

        I, the fact that I don't remember probably means-

    19. LRLenny Rachitsky1:31:01

        Okay. I see what you're saying

    20. CKCaitlin Kalinowski1:31:02

        ... it's okay.

    21. LRLenny Rachitsky1:31:02

        Yeah.

    22. CKCaitlin Kalinowski1:31:02

        Like, um, I think we, uh, we, we addressed it. We redesigned it. Um, we had to change the material on the bracket.

    23. LRLenny Rachitsky1:31:07

        Mm.

    24. CKCaitlin Kalinowski1:31:07

        I think we had to make it steel to h- hold the tolerance we needed. But it worked out, and the price, uh, the cost and, and... yields were fine, so I think we, we adapted.

    25. LRLenny Rachitsky1:31:18

        And that was the best-selling VR device of all time, is that right?

    26. CKCaitlin Kalinowski1:31:21

        I think it was.

    27. LRLenny Rachitsky1:31:21

        Okay. Nice.

    28. CKCaitlin Kalinowski1:31:22

        I don't have the final sales numbers, but-

    29. LRLenny Rachitsky1:31:23

        Yeah, yeah, yeah. Okay. Conservative. Okay. Uh, Caitlin, we've covered so much ground. Is there anything else you wanted to share, anything else you want to leave listeners with? Either double down on something we've shared or anything else that just, like, oh, here's something I wanna share.

    30. CKCaitlin Kalinowski1:31:37

        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, these n- 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'll be at the forefront of whatever comes next. And so I just wanna encourage everyone to be creative, use these tools, have fun with them, um, 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. Um, but I'm also... I, I've never been more excited about the power of an individual.

30. 1:32:33 – 1:39:09

    Lightning round

    1. LRLenny Rachitsky1:32:33

       Hmm. Well, with that, Caitlin, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

    2. CKCaitlin Kalinowski1:32:41

       I'm ready.

    3. LRLenny Rachitsky1:32:42

       First question, what are two or three books that you find yourself recommending most to other people?

    4. CKCaitlin Kalinowski1:32:46

       I've been mostly reading the classics lately. [chuckles] So, uh, Book of the New Sun is a great fiction book which I really recommend. I think that's what it's called. Um, I haven't read it in a little while. I, I, I love Mrs. Dalloway. Actually, I think it's a very interesting book about transitions, and it was a post-war book, um, by Virginia Woolf. So I, I really love it and I think it's really wonderful. Um, I think Herodotus Histories is pretty incredible. He's wrong a lot, but he's also... it's the first history book. And, uh, in many cases he's going and finding, you know, first-hand or second-hand, uh, accounts of what happens. It's, it's just, it's a way to look into the world at a completely different era than it is now. So these are some books that I like and, uh, I'll double check the, the first, the title of the first one and email you, but I think that's what it's called.

    5. LRLenny Rachitsky1:33:36

       Okay. And we'll link to the, the correct one in the show notes. Uh, favorite recent movie or TV show that you have rec- really enjoyed?

    6. CKCaitlin Kalinowski1:33:43

       I am really into Euphoria right now. I think the new Euphoria, I'm, I'm, I'm interested in the characters, um, and, um, figuring that out, uh, what's gonna happen there.

    7. LRLenny Rachitsky1:33:55

       That show's so stressful. Whenever I watch, I'm like, "Oh, no."

    8. CKCaitlin Kalinowski1:33:58

       It's a, it's a melodrama.

    9. LRLenny Rachitsky1:33:59

       Okay.

    10. CKCaitlin Kalinowski1:33:59

        I think you have to think about it as a soap opera, and then it's fun. If you think about it too literally or practically-

    11. LRLenny Rachitsky1:34:04

        Okay. Okay

    12. CKCaitlin Kalinowski1:34:05

        ... it's just [chuckles]

    13. LRLenny Rachitsky1:34:06

        That's helpful. Uh, do you have a favorite product you've recently discovered that you really love? Could be like hardware, could be an app, could be piece of clothing, could be a gadget.

    14. CKCaitlin Kalinowski1:34:14

        I really like Vollebak, um, the clothes. Um, they make really interesting clothes. They're essentially basing their new clothes on material science. So they take new material science and make them into clothes. Um, it's just a fun brand to follow.

    15. LRLenny Rachitsky1:34:26

        Vollebak.

    16. CKCaitlin Kalinowski1:34:27

        V-O-L-L-E-B-A-K.

    17. LRLenny Rachitsky1:34:30

        Wow.

    18. CKCaitlin Kalinowski1:34:30

        Vollebak.

    19. LRLenny Rachitsky1:34:31

        Very cool. Do you have a favorite life motto that you often come back to in work or in life?

    20. CKCaitlin Kalinowski1:34:38

        Have you seen that branch where there's all these branches and then you're here-

    21. LRLenny Rachitsky1:34:41

        Mm-hmm

    22. CKCaitlin Kalinowski1:34:41

        ... and then there's all these branches from this point?

    23. LRLenny Rachitsky1:34:43

        It's here, Marvin.

    24. CKCaitlin Kalinowski1:34:43

        So this-

    25. LRLenny Rachitsky1:34:44

        Yeah.

    26. CKCaitlin Kalinowski1:34:44

        Yeah. You know who it's from. I don't know who it's from. Um, this is what... I think about it a lot because it's very hard not to get stuck in the future or the past and stay kind of here. I have trouble with that, but, um, that is a great reminder that, you know, you get to pick every day. You get to decide every day what you wanna do. And sometimes things don't go the way that you want, and sometimes you regret what you did, or sometimes you're proud of what you did, but it doesn't really matter. What matters is what's right in front of you.

    27. LRLenny Rachitsky1:35:11

        We'll, uh, either show that image on the screen as you say that or we'll link to it in the show notes. It's so powerful. Final question, somebody that, uh, that knows you well shared this really interesting tidbit about you, that you hired a PhD to tutor you on the cl- on the staples of ancient Greece and Rome and just get really nerdy about this stuff. What's going on there? What drives you to go so deep on these sorts of things?

    28. CKCaitlin Kalinowski1:35:34

        This is, like, very niche nerd, nerd territory, but I found this, um, list that the poet Joseph Brosky- Brodsky wrote, which is a list of English, uh, or a list of things you should have read in order to have an intelligent c- uh, conversation in English. And it is, like, an affected list. Like, it's in, you know, it's intense. It's like the Old Testament, Gilgamesh, and then all the way down through. But what I found is it's a pretty good, um, distillation of what we used to call the Western canon, and that actually I learned a lot in, in my public school education and in, in, in college, but I never really learned from what you would consider the Western canon. Um, and so this is kind of, um, in addition to that, there's some, some more newer, newer, um, books on the list. I find it just fascinating to have something to work off of. And what I found is as I got into specifically the tragedies, the Greek tragedies, I just didn't have enough context to learn what I wanted to learn, and just reading them, I didn't have enough uptake. So I found an incredible, um, uh, post-doc who's, was willing to tutor me and I just get to ask him all these questions. He's an encyclopedia. He knows everything. I could ask him what was happening in Turkey at the time of this Greek, you know, this tragedy that we're reading and, like, what was happening in Athens and, like, what this, you know, tragedian might be responding to, um, and, and he can answer the question. It's really fun to have the ability to have that sounding board.

    29. LRLenny Rachitsky1:37:01

        So cool. [chuckles] It's so cool you did that w- even though AI can do a lot of this, like, sometimes a human is much more y- interesting to talk to and, [chuckles] and feels better.

    30. CKCaitlin Kalinowski1:37:10

        Yeah. I find reading and communicating with AI is very helpful on the basics, but then understanding what was happening culturally and what the significance of the work is, is it, it wasn't, isn't adequate.

Episode duration: 1:39:10