Why we’re at the beginning of the AI hardware boom | Caitlin Kalinowski (ex–OpenAI, Meta, Apple)

**Caitlin Kalinowski** (00:00:00):
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 going to saturate. When that happens, the next frontier is the physical world. Robotics, manufacturing, industrialization.

**Lenny Rachitsky** (00:00:15):
Living in the future and designing it.

**Caitlin Kalinowski** (00:00:17):
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.

**Lenny Rachitsky** (00:00:26):
Just imagine 100,000 drones coming out of China just at us.

**Caitlin Kalinowski** (00:00:29):
I do feel that we need to reindustrialize 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.

**Lenny Rachitsky** (00:00:42):
You work with some of the most legendary successful builders, Steve Jobs, Mark Zuckerberg, Sam Altman.

**Caitlin Kalinowski** (00:00:48):
Sam is really good at saying, "Why not more? Why not 100X or 10,000 X? You are thinking too small." For Steve, the bar he held for the company for technical talent and for excellence was not wavering.

**Lenny Rachitsky** (00:01:02):
What does it take to create a robot that feels human and connected?

**Caitlin Kalinowski** (00:01:05):
If you walk into a room and are robots just 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.

**Lenny Rachitsky** (00:01:18):
There's a meteor called memory prices that are coming for consumer hardware and robotics and physical AI.

**Caitlin Kalinowski** (00:01:23):
We're in trouble as an industry.

**Lenny Rachitsky** (00:01:27):
**Caitlin Kalinowski** (00:02:39):
Thank you so much for having me. I'm excited to be here.

**Lenny Rachitsky** (00:02:41):
We're going to go in a bunch of different directions. I'm going to bounce around. I want to talk about VR. So much money, so many resources, so many smart people have been working on VR for so long. Meta spent, I don't know, $10 billion. They renamed the company, Meta, to lean into VR as the future of this metaverse that we're going to be living through. Feels like a lot of people are leaning out now. It 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 ... I've got a couple of the devices, it's just like a magical experience that you've ... Unlike anything you've ever experienced. It 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?

**Caitlin Kalinowski** (00:03:23):
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. We figured out SLAM, which was how to do positioning in space using cameras. We figured out a lot of depth applications of depth sensors. We figured out how humans perceive visual data in space and all of that actually while it's great for VR and I think VR gaming is really interesting, it is kind of a niche, but I think it's an interesting niche. What I see now is in robotics, all of these technologies are being used because you need to understand how the robot is moving through space. You need to understand how far it is from everything. You need to understand if you're wearing a VR headset and driving the robot, it's the same real technology.

**Caitlin Kalinowski** (00:04:16):
And so for me, I view it as a step in a long technological arc. And to be honest, as someone who's not using VR a lot right now, I'm really glad that we did it, but I don't think it ... I expected it to be big, obviously, or wouldn't have been working at Oculus. And I think maybe the social aspect of having something in front of your face is part of why it didn't take off. And I think that we learned of course with Google Glass how important that is as well. And so when we tried to make it social, it's hard to make it social when you have your face covered.

**Lenny Rachitsky** (00:04:55):
That is interesting. So just like the investment and innovation that happened that went into VR has actually proven to be really useful. And so it feels like the companies that have put a lot of effort into that and money into that are ahead on the next step. So is that where you think things go? Where do you think things are going? Is it AR glasses? Is something else? What's the future of this?

**Caitlin Kalinowski** (00:05:15):
I believe in AR glasses as part of the future because I do think looking down at your phone all the time is not great for us as social creatures. So if you can maintain social connections and get information, that's where I think we're headed. Orion, the AR glasses we worked on, I worked on most recently are a bit ahead of their time because they're using waveguides and MicroLED 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, how do you communicate with them when you're on the move, when you're in public? How do you communicate quietly, silently with them? I think once we start to figure out some of those challenges that having a display that's mostly off that you can turn on when you want it to be on seems like part of the future.

**Caitlin Kalinowski** (00:06:10):
So that's part of it. The other part is there's this lineage of technology going through VR and then AR and now in, I'm using the term robotics, physical AI, but you really have to step back and look at autonomous vehicles, drones, obviously robots, autonomy period, manufacturing. All of these technologies are going to need the same piece parts, the same pieces that we built in the AR/VR spectrum.

**Lenny Rachitsky** (00:06:36):
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 you executed it badly or the idea was just a bad idea and it's always hard to know. It feels like with the AR, it's just like so much effort was put into making it work just for a decade, many decades, and just has not worked. So it's 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 want to sit on my couch disconnected from the world." And even if I could see people through it's just like, "Eh, I'm just going to ... I don't need this. It's not that big of a deal."

**Caitlin Kalinowski** (00:07:08):
In AR, you're going to just start getting more and more larger and larger displays. But the great thing about Orion is you've got a 70 degree field of view binocular. So with the prototype, you got to sense what this is really going to 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, I feel immersed. The field of view is wide enough. I feel immersed." And it becomes pretty clear that I think this is part of where the future's headed.

**Lenny Rachitsky** (00:07:36):
**Caitlin Kalinowski** (00:09:18):
It's very odd. Everyone is suddenly asking about hardware and robots and the physical world and it's never been a sexy career. It's always been the thing that you went into because you loved it. It never paid the same as these other careers. It was never kind of at the forefront of how we talk about things with the possible exception of Apple, obviously, and the hardware lineage at Apple. So it's great in some ways and it's very odd in others.

**Lenny Rachitsky** (00:09:46):
What's surprisingly hard about hardware? A lot of software companies, a lot of people are just like, "Okay, cool. We're going to build some hardware." That's the future, that's the moat 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 going to build some hardware." What are some of the surprising challenges that come up?

**Caitlin Kalinowski** (00:10:03):
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 often and they run the code and debug it, but they can compile their code every day, every hour, whatever they need to do. In hardware, we only get to "compile our code" four or five times.

**Lenny Rachitsky** (00:10:28):
Four, five times a year or?

**Caitlin Kalinowski** (00:10:30):
Total.

**Lenny Rachitsky** (00:10:30):
Okay, ever.

**Caitlin Kalinowski** (00:10:32):
Right. So if you're building hardware, you redesign it in CAD for every major build and then you have to release it. And once it's released, you compile it the last time you release it for mass production. If it's a mass production device, that's it. You're done. You can't ship over their updates.

**Caitlin Kalinowski** (00:10:52):
So we have a different approach. We have to have a different approach, which is more conservative. You have to do more of the reliability checks and tests in line with the program because once you compile that last time, you're done. You make all the parts, you put them together, they're out in the world. The only alternative is to ship something new to replace it a couple years later. And so we have to be more conservative and we have to take our time because if you think about it, a product that sells millions, if you have a graph of all the parts put together on any different part of the device, you have a curve, you're in the plus and minus three sigma or more. So meaning if you have two parts that go together, you're going to get the smallest version of this one and the largest version of this one and you're going to have to put those together across the board.

**Caitlin Kalinowski** (00:11:45):
People don't think about this that much, but the part variance is pretty high. And so we've got to solve for that last half a percent in the process of building so that when we compile our last time, when we build our last time, it's done and we're going to have a high yield, we're going to be able to make them and make money on them effectively and we won't have very many returns. And so that's kind of the game that we're playing.

**Lenny Rachitsky** (00:12:08):
It sounds so hard and complicated. They're just like, "Software is so nice, you just write some code, ship it. It's great." Why do you think people are getting so into robots and hardware now? What's the driving trend?

**Caitlin Kalinowski** (00:12:20):
Yeah. What I'm seeing 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 you can do behind a keyboard with AI is going to saturate. And I don't know when it's going to saturate and nobody else knows either, but when that happens, the next frontier is the physical world.

**Caitlin Kalinowski** (00:12:45):
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 going to have complex systems that can solve problems in the digital world very, very quickly. We already have them. They're going to get better and more comprehensive and more capable." If you think about that as a frontier, you could see the end of that tunnel. Now, I don't know when it's going to be again, but we can see that that's going to saturate at some point, or at least people think it will. And when that happens, the next frontier is hardware. The next frontier is robotics, manufacturing, industrialization, the sensing layer in the real world, the ability to move objects in the real world and eventually we hope space.

**Lenny Rachitsky** (00:13:34):
So one of the most interesting lines of development is these humanoid robots, that's kind of like our meat brains are always more attracted to robots that look like us and act like us. There's a few companies very ahead. There's Optimus, Tesla, there's 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, how close are we to humanoids being around us?

**Caitlin Kalinowski** (00:13:58):
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. There are some designs and 1X NEO is a good example of this that have made significant safety considerations in their designs and pulled mass inwards essentially, which is a lot safer. Softer robots is safer.

**Lenny Rachitsky** (00:14:25):
Just to clarify, you're saying they're lighter and so the impact of a robot hitting you is less.

**Caitlin Kalinowski** (00:14:30):
Yeah. The part that might hit you, which in this case might be the arm, if it's lighter and softer ... There's two aspects, you have the arm moving through space and then you have the actuator that's rotating so you have to add up the energy essentially for both of those things. And so that's an impact thing that you have to worry about. Then you have to worry about the compliance of the arm. If it's just hard, then the impulse is high, but if it's soft and compressible, then the impulse is lower. And so you really have to be thinking about this when you have robots around people.

**Caitlin Kalinowski** (00:15:09):
So in my world, in my worldview, the humanoid robots are still prototypes 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 continue to revise them to make them cheaper, easier to manufacture, higher yield and safer. And I think this is what's going to happen next. In my mind, they're not quite ready yet. Now you can get a Chinese robot that can do all kinds of things for you, but if you look at the booklet, it says, "Hey, no human can be within three feet of this robot." And you're not going to see very many robots that are strong enough to do meaningful work that don't have that warning right now.

**Lenny Rachitsky** (00:15:59):
That is so interesting. It's funny to hear that at the same time there's these nunchuck wielding robots in China doing dances with other folks. I've never thought about just that part of it, the impact they can have if they go awry. I want to 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 at scale?

**Caitlin Kalinowski** (00:16:22):
At scale is the problem in my mind. At scale is a huge challenge. Now for me, my background at scale means millions usually, but let's even say hundreds of thousands. You've got to get a good design that's running. Then you've got to make it reliable enough that it can keep running day to day to day without a lot of human intervention or repair and that's its own problem.

**Caitlin Kalinowski** (00:16:47):
But the first problem you have is supply chain and this is going to be something that I hope that we can talk about a little bit more, but every single part that goes into that robot'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 figure out. And a lot of people are trying to move production here to the United States, which is very challenging because we don't have great actuator companies here yet, for example.

**Lenny Rachitsky** (00:17:32):
And the actuator is the little arm, I don't know ... How would you describe an actuator to a non-robotics person?

**Caitlin Kalinowski** (00:17:37):
Yeah, the actuator is the motor. So you put power into it, electricity into it, and you get motion out of it.

**Lenny Rachitsky** (00:17:38):
Cool.

**Caitlin Kalinowski** (00:17:44):
And most of these robots have a rotating rotor essentially that then has gearing on it that then powers the limb or powers the head or the fingers or whatever else. So they could be small, they could be large.

**Lenny Rachitsky** (00:17:58):
Okay, awesome. I hear the word a lot and I'm like, " I don't know exactly what it means." So thank you for explaining it. I want to talk about the supply chain stuff more because I know you think a lot about this,- what's the state of the union on the supply chain for say robotics? What's going on? What are the pieces? What are the challenges?

**Caitlin Kalinowski** (00:18:13):
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 Japan, like Korea.

**Caitlin Kalinowski** (00:18:44):
And full transparency, I've been part of that transfer of engineering knowledge to Asia. In Asia, the expertise has historically been scale and being able to build a lot of these parts at lower prices. We've had this kind of deal across these borders that this is how we're going to operate for the most part. Now, of course, there's things we make in this country still and of course there's design and AI that's made in Asia, but that's essentially where things have been for a long time. And in order to have a safe supply chain, we need to start to work on having some independence in these layers and these stacks.

**Lenny Rachitsky** (00:19:24):
And it's interesting that your focus is on these actuators. Is that the bottleneck, this very specific part of a robot?

**Caitlin Kalinowski** (00:19:30):
It might be. So if we can't get the magnets, then we have to design new actuator types that maybe use different materials that may be larger that may not be as efficient in space. 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. So it's foundational. There are some foundational technologies like this all backed by material science, essentially breakthroughs. There's batteries of course. There's actuators. The raw parts, the die-cast parts, the machine parts are less critical. We think we can get those, but I think everyone, not just in this country, but around the world is starting to think about supply chain because you have these disruptions, whether it's COVID or war and you see how quickly things change.

**Lenny Rachitsky** (00:20:20):
Okay, stupid question. Why magnets? Why is that a part of the supply chain? Why do we need magnets?

**Caitlin Kalinowski** (00:20:25):
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 you have something in the center that rotates. And the way it rotates is you have alternating current essentially, and so the magnets make the rotor spin.

**Lenny Rachitsky** (00:20:45):
Wow. We need a YouTube lecture of here's how physics works. Okay, very cool. So when you talk about China, this is ... 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 blow people up. Robots are a part of that. It's just like such a existential threat to every country now. The ability to build these things at scale. What's your advice? What should we do? What should we change to thrive in this future and not be in trouble?

**Caitlin Kalinowski** (00:21:21):
Well, you mentioned drones. It's another good example. You need essentially the same technology to make the rotor spin on a drone as you do to make an arm move on a robot. It's essentially the same base technology and supply chain. So we need to, at least on the military side, have an independent supply chain as much as possible. I think that's important. I think every other country should do that as well, but I don't think that's specific to us.

**Caitlin Kalinowski** (00:21:46):
I do feel that we need to reindustrialize the country significantly in order to be safe in a military sense. You really never know what's going to happen in the future and people that are your allies now may not be in the future. The allied West I think is going through a lot of geopolitical changes. There's a lot of shifting. And so I would really like to reteach ourselves how to make things at scale, how to make things at quantity, how to process raw materials, how to be more independent so that when COVID happens again or something else happens again, we're not in trouble and we're not unable to protect ourselves.

**Lenny Rachitsky** (00:22:30):
When I think about it also, is Marc Andreessen had this visual on some podcast of, just imagine 100,000 drums just coming out of China just at us, what do we do? We're not prepared for that. I don't want to spend all our time on this dark stuff, but it's this real thing.

**Caitlin Kalinowski** (00:22:43):
Well, and Palmer Luckey is a friend of mine and we don't agree on everything, but I do think that we agree on some important aspects of how we need to respond here. I think he's right to say that we need to invest a lot more in drones than in aircraft carriers. I think that is this old way of thinking, and these are important components of the military, but it's an old way of thinking of, "Hey, we have this and we have this and we have this and our planes come off here." It's like, no, AI is changing everything and military technology is changing incredibly fast. And the place to look at that is Ukraine where drones are being changed and updated every day rapidly with 3D printing. And this is, I think, the future of where war is headed, unfortunately.

**Caitlin Kalinowski** (00:23:34):
And I view this as a very different era that we're entering into with very different ... This isn't new to anybody, but you're looking at what it costs for them to send out a missile and what it costs for us to stop it. And this is just you have to do the math every time. And right now we're losing on the math, which is fine for a certain amount of time, but the longer it goes, the less fine it is.

**Lenny Rachitsky** (00:23:57):
Are you optimistic that we'll figure this out?

**Caitlin Kalinowski** (00:23:59):
Yeah, America is really good at figuring these things out. We have a pioneering kind of independent spirit and a great engineering culture, but we need to move.

**Lenny Rachitsky** (00:24:11):
It's interesting that we started the conversation with VR. Palmer Luckey obviously famously started Oculus now. It's interesting how this is so connected. You think VR is this trivial thing that we're just 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.

**Caitlin Kalinowski** (00:24:31):
Yeah. And I think we need a lot more of them. I've chosen not to work for companies that create lethal technology, but I think that it's good to have people who are willing to do that. And I think that it takes everyone to build the future that we want.

**Lenny Rachitsky** (00:24:49):
Coming back to the AI safety piece, it's so interesting. I had a couple conversations like this on the podcast. We think about all this prompt injection and jailbreaking that happens with chatbots and none of people think about what if you propped inject a robot walking ...

**Lenny Rachitsky** (00:25:00):
Like not enough people think about, what if you prompt inject a robot walking around and get them to punch someone? And we're so far from that feeling like we can actually stop that.

**Caitlin Kalinowski** (00:25:09):
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 going to be a huge part of the future of warfare.

**Lenny Rachitsky** (00:25:19):
Yeah. Just people talking about OpenClaw, and how much you ... Like you could just tell it, "Give me all your passwords," and it's done all these things to people's lives, and just like robots walking around, "Hey, okay, here's all this person's secrets."

**Caitlin Kalinowski** (00:25:33):
My OpenClaw story is I sandboxed it, so it's on its own computer, but I gave it three things. I gave it my real email address and my ... I don't know what it was. I gave it some information about one of my accounts or something like that. And I added it to the social media, I can't remember what it's called, the OpenClaw social-

**Lenny Rachitsky** (00:25:52):
Oh, Moltbook?

**Caitlin Kalinowski** (00:25:54):
Yeah. I added it to Moltbook and I was like, "Okay, whatever you do, don't share my private information, but go crazy." And five minutes later, all it had done is posted my personal email address. It was like the one thing it had.

**Lenny Rachitsky** (00:25:54):
Nailed it.

**Caitlin Kalinowski** (00:26:07):
And [inaudible 00:26:07] "Okay, you're shut down." It was so funny, no matter how careful you are with these things, you just can't really ... We're not at a place [inaudible 00:26:15]-

**Lenny Rachitsky** (00:26:14):
Which is exactly your point, that the robots can do a lot more damage. And they never thought about just the softness of their hand as a way to keep us safer.

**Caitlin Kalinowski** (00:26:23):
Yeah.

**Lenny Rachitsky** (00:26:23):
Oh man. Nat Friedman just did this interesting talk at Stripe Sessions, and he's talking to his OpenClaw about drinking more water and sleeping better. And as he's driving in a self-driving car, it told him, "Okay, here, there's a place 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.

**Caitlin Kalinowski** (00:26:45):
That's so funny. These are going to get weird fast, I think.

**Lenny Rachitsky** (00:26:51):
Okay. 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-lasting hardware program. Then you went to Meta where you helped build, basically bootstrap a hardware program from scratch. I feel like 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?

**Caitlin Kalinowski** (00:27:25):
So Apple has been best-in-class at this. There's a bunch of reasons. One, hardware is a first-tier citizen at Apple. There's a lot of companies where hardware isn't part of the core product development conversation as much, but that's an exception. Apple also taught me and a lot of other people actually, if you look at the era that I was there, I was very, very lucky, because if you look at the other folks who were there ... I was there between '07 and the end of 2012. If you look at the other people who were there working on these things, they actually have a lot of key positions now across the industry.

**Caitlin Kalinowski** (00:28:01):
And 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 do really good experiments in hardware, and figure out what the best outcome is.

**Caitlin Kalinowski** (00:28:32):
But there's something underneath that, which is understanding the first principles of why are we building it this way, and what are the key outcomes we're looking for? And actually, John Ternus talked about this, I think, a few days ago, where he talked about the back of the cabinet. I don't know if you saw this video, but basically John said that he was impressed, that he learns from Steve Jobs that there's a cabinet maker who finished the back of the cabinet, and how important that was.

**Caitlin Kalinowski** (00:28:57):
And that goes very, very deep at Apple, where every single design decision, even on the inside of the device, is considered. And this isn't just an aesthetic decision. What it does is actually force the engineering, industrial design 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?"

**Caitlin Kalinowski** (00:29:24):
And what happens is if you're that methodical, what really matters tends to rise out, and look very simple at the end. And so part of what you're seeing in many folks coming from that era is an understanding of how to do that, which in the very beginning of the Mac side, Macs didn't sell as many, and the quality wasn't quite as high, but by the end of that era, Macs were very popular and selling in much higher volumes.

**Caitlin Kalinowski** (00:29:55):
And so I think that made a big difference. And I was only a small part of that. I was the thermal lead on the first MacBook Pro, and then over time worked to lead successive iterations of the MacBook Air, and the cylindrical Mac Pro. But I was lucky enough to work with these folks and learn from them, who'd been doing this for a really long time.

**Caitlin Kalinowski** (00:30:15):
So you have to take those lessons, and then when you leave, try to distill them and explain them to a new community. Now, Oculus was actually a hacking hardware startup. Oculus started from folks who actually met on forums, you might know this, Lenny, who were hacking PlayStations or Super Nintendos into portable backpacks.

**Caitlin Kalinowski** (00:30:41):
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. They had made Crescent Bay before the acquisition, I think, but then to professionalize that, get the yields up, and get the volumes up was ... the cost down, was kind of the challenge we faced in the first Rift.

**Lenny Rachitsky** (00:31:11):
So on lesson I'm hearing here is being very detail-oriented. I don't know if that's the right word, just like, focus on every element of the end product. Because to your point, it's not just about that back of the cabinet, but I think about, it's like the brown M&M story, where like a band puts in the contract, you have to have brand M&Ms in the room, because that means they read it and it's not like the M&Ms matter, it's a test that they read the thing. And is that kind of the message there?

**Caitlin Kalinowski** (00:31:40):
I think the message is understanding why you're doing what you're doing, and then every design decision supporting that goal. And that requires a lot of detail, and it requires a lot of persistence, and that requires a lot of consistency, but understanding why you're doing what you're doing and what the end goal is is, I think, the key, and letting that expand into not only the software and the UX, but also the hardware.

**Lenny Rachitsky** (00:32:05):
What's an example of that, just to make it more concrete for us?

**Caitlin Kalinowski** (00:32:09):
A great example is the Quest 2. So we reduced the Quest 2 price quite a lot, and what we had to do is understand, what are we trying to do? We're trying to democratize VR, we're trying to get VR to more people. And the only way we could do that is reduce the price. And so what it required is a redesign of the entire product, essentially, for cost, which then, I think, led to the highest-selling VR headset of all time.

**Caitlin Kalinowski** (00:32:37):
And it's not easy, because you had to, in our case, remove cameras, remove components, change materials, change manufacturing processes. But when you have alignment that you want to get this to more people and the way to do that is to reduce the cost, then that kind of drives everything else. And it was still a very high-quality product with great, I think, low return rates, and it was a very strong product. Maybe even stronger than if we hadn't done that, funny enough, but it hit our price point.

**Lenny Rachitsky** (00:33:07):
Okay. Coming back to just the question of, say a company's like, "Okay, we need to build some hardware. We're going to build our own glasses. We're going to build a little phone device and secretive thing," whatever OpenAI is up to. What other tips do you have? I know it's impossible to like, "Here's all you need to know," but just what else should people be thinking?

**Caitlin Kalinowski** (00:33:24):
Having your goals defined early and sticking to them is important. Hardware's not as adaptable to lots of changes throughout its development as anything digital. And so if you set out to say, "Okay, we want to make something that's $300," and then halfway through you say, "Oh, it actually has to be $150," you've almost burned a lot of that early time.

**Caitlin Kalinowski** (00:33:49):
So you kind of need to have a sense of having pre-thought out what you want, and having those, I like to call them KPIs, but essentially goals written down, and try to change them as little as possible. So that is very tough. In fact, that may be the toughest thing. Because if you do that properly, and you have the right prioritization of those things, you know whether you can ship or not. You know whether you're done.

**Caitlin Kalinowski** (00:34:16):
And in hardware, one of the challenges is, we talked about compiling four or five times. Every time you build and you iterate your design, that's another three months or four months or five months or whatever it might be. And so you're trying to time the feature set, with the quality, with the timing.

**Caitlin Kalinowski** (00:34:34):
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 upfront and you hit them, then you know you can ship. And often engineers, and I'm guilty of this too, especially on the hardware side, never feel like they're done.

**Caitlin Kalinowski** (00:35:06):
So this is a pretty nuanced thing. So that's one thing. The second thing is we tend to design the things that we know how to design first, and actually the right approach is to design the hardest parts first. One example will be, and there's no IP here, so I'm obviously not going to share any IP or anything internal, but at one point we had to route cables through a hinge in a device, in a laptop we were making. And because it wasn't clear that those cables would fit, that's where the architect started. And he looked at the diameter, and how to split the cables out, and made sure that they would fit before finalizing the hinge design.

**Caitlin Kalinowski** (00:35:45):
A lot of people would start at the part they knew, like, "Oh, we're going to use this display, so I'm going to put this in CAD, and I'm going to do all this other stuff." But the architects who are the best actually look at where are the pinch points, where is this going to fail? And they start to do the detailed design there first.

**Caitlin Kalinowski** (00:35:58):
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 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 don't take quite as much iteration. So you have to boost your iteration on the things that people touch the most or interact with the most. So those are kind of some principles that I wrote about, but these are just things that you learn trying to build quickly. And the last piece that's really critical if you're making hardware for folks out there who are trying to make hardware is, you can't wait around, ever.

**Caitlin Kalinowski** (00:36:48):
There's never enough time. So if you know that you need to do something, what I learned from folks like Shelly Goldberg at Apple now, who I think is a 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 going to be a surprise coming around the corner that you need that time to fix. And so this sense of stacking the things that you know you need to do and just getting them out of the way, even if you technically have more time, is this kind of ruthless efficiency that I learned with them.

**Lenny Rachitsky** (00:37:24):
Amazing. Okay. Let me just summarize your advice here. So one is be very clear on goals, I want to come back to this. Two is do the hardest part first, the riskiest piece, essentially, to physically build. Three is focus on the pieces that people will use most, say the trackpad, keyboard. I want to talk about that. And four is just like, do it now. Even if you think you have more time, you never know what's around the corner, and it's better-

**Caitlin Kalinowski** (00:37:51):
You just don't. It's not even that you don't know what's around the corner. If you're working in hardware, you actually don't have more time.

**Lenny Rachitsky** (00:37:57):
Okay. On the goals, what are kind of like buckets of goals? So cost is when you shared like, "We need this under $300." What are some other buckets of types of goals people should be thinking about?

**Caitlin Kalinowski** (00:38:06):
So in VR display resolution or arc minutes, 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 MacBooks. They figured out the KPI of what the human eye could see, probably overshot it a little bit, and built that. And then do you really need to keep as much engineering pressure up on the resolution of a display after that? Maybe not.

**Caitlin Kalinowski** (00:38:38):
So VR's not there yet, not even close. Not in mass-produced VR, we don't have Retina displays yet. So that is one aspect of pushing that up is one example. I think on a computer, obviously you're talking about clock speed, you're talking about how many parallel processes you can run, you're talking about weight, you're talking about price, and you're talking about features. So when we did the MacBook Air, it became very clear, because we were machining it, that there are certain features, like ambient light sensor, that we just ... didn't make sense anymore. And so being willing to just jettison them for what we were going for, which was weight and size. So if you have those overarching goals, you can actually make decisions, engineering decisions pretty quickly.

**Caitlin Kalinowski** (00:39:24):
And this is actually something that I think Elon, I've heard, does very well, is define the value of a gram of weight versus the cost, or he does, I've heard, engineering ratios, essentially and he's able to put numbers on what those ratios should be, which I think is really smart.

**Lenny Rachitsky** (00:39:47):
Interesting. So it's a very easy trade-off. "Okay, here's the formula telling us weight is less important in this case."

**Caitlin Kalinowski** (00:39:54):
Yeah. And if you can do that, then the decisions fall out pretty easily.

**Lenny Rachitsky** (00:39:58):
Speaking of the Air and weight, I remember, I feel like there's a very classic moment in Steve Jobs lore where he comes out and has this manila envelope, and has the MacBook Air inside it, and then takes it out and everyone's like, "No way." Were you part of that? Was that something that people wanted to do from the beginning?

**Caitlin Kalinowski** (00:40:15):
I think, if my memory serves, the very, very, very first MacBook Air was a pretty low volume device that was machined, but kind of had 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 had this shape underneath.

**Caitlin Kalinowski** (00:40:39):
And then the next rev of that was the MacBook Air that we know, essentially which is wedge shaped, which is different. And so the wedge shape is the one that I worked on, and the one that went and hit more volume. But that manila envelope one was the one that proved you can CNC a computer. And so they each have really important roles in the roadmap.

**Lenny Rachitsky** (00:41:01):
Coming back to your point about focusing on things that people use the most, famously, Apple screwed up this keyboard. There was this butterfly keyboard situation for a long time. You're like, your eyes are closed [inaudible 00:41:16]. What happened? What happened, Caitlin?

**Caitlin Kalinowski** (00:41:17):
I didn't work directly on that keyboard, so I can't talk about what happened with it, but obviously this is something that you got to get right. And I will say, the modern MacBook keyboards are awesome and excellent, and I don't know what happened with that. I don't think those were devices I was working on at the time.

**Lenny Rachitsky** (00:41:38):
Nice. Safe. Marked safe. Along these lines, Apple's kind of famous for not listening to what people want. It'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, or do they do a lot of user feedback sessions, things like that? How does it end up working out?

**Caitlin Kalinowski** (00:42:04):
I mean, I left over a decade ago, so I don't know what they're doing 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, because they haven't seen it.

**Caitlin Kalinowski** (00:42:23):
So a good example is the iPhone, which I didn't work on, but when you build a new iPhone with a touchscreen, you can't really go ask a hundred people what they want, because they're going to say a keyboard on their screen. And this is, I think, the ethos that you're getting at, which is, and this is true for anybody building a new product with a new feature. And I've tried to build, as much as I can, teams that work on products that have something new about them.

**Caitlin Kalinowski** (00:42:47):
Either they're a new category, or there's a new 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. It just doesn't work, because you actually won't get the answer right.

**Caitlin Kalinowski** (00:43:05):
And I think this is actually what Steve was talking about, which is, you can't get intuition if you're changing something fundamentally. Your customers won't know what they want because they haven't seen it. But if you show it to them, they will absolutely know that it's awesome and that is what they want. But if you get stuck in an iterative feedback cycle with your customers, it's very hard to go zero to one with something new. And so in my view, and I don't know for sure, I didn't talk to him about this, but that's my view of what that means.

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**Lenny Rachitsky** (00:44:46):
I'm going to go in a completely different direction. Coming back to components of hardware, I asked a bunch of people what to I talk to you about. One of the people is the founder of Matic, the CEO of Matic, Mehul Nariyawala. I've never said his last name out loud, so I hope I didn't butcher it. By the way, I love my Matic. I don't know if you have a Matic, but it's like-

**Caitlin Kalinowski** (00:45:06):
I have two, and I've purchased two more for friends.

**Lenny Rachitsky** (00:45:10):
Oh, yeah. What an endorsement. Yeah. Basically, it's like this amazing robot vacuum that just works. So his question, so 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. What's going on there?

**Caitlin Kalinowski** (00:45:30):
Yeah, we are in trouble as an industry. And I'm not an expert on this, but I think that AI has to do with why. And I also think that the supply chain is constrained. I have been advising startups and companies to pre-buy memory, and to have enough memory in stock, if they can afford it, to ride out price spikes.

**Caitlin Kalinowski** (00:45:58):
Like anything in this category, let's see, this happened in COVID too. So we had so many supply chain disruptions, and getting enough memory was one of the challenges. So we had to pre-buy as well. I won't say who, but the company I was working with had to pre-buy memory as well. And so this is 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.

**Caitlin Kalinowski** (00:46:32):
You either pay, or you have already pre-bought enough that you can ride things out. And so those are the only real options. Obviously there's a risk to pre-buying, and the price might go down. And so the challenge is, I think, there's a latency with supply chain in something like memory, where it can't adapt fast enough, often, to demand, or there's a new category of product, or, in this case, maybe data centers, that are just eating up so much and are actually not as cost-sensitive as somebody in consumer electronics like Matic might be. And so they'll just pay for these higher costs. This is tricky and something we have to deal with all the time.

**Lenny Rachitsky** (00:47:09):
How much have prices gone up? How bad is this problem, and where do you think it'll go?

**Caitlin Kalinowski** (00:47:14):
Actually, this is a great question, Lenny. I don't know what's going to happen. I think prices are going to double, probably. I don't know on what timeline. If I knew what timeline the prices were going to double on, I'd be trading, which I'm not very good at. I'd really be, I'd be doing a different job if I could predict these things, but certainly we're going to have a supply chain shock.

**Lenny Rachitsky** (00:47:34):
And it's already gone up a lot. If you're saying it'll double, but it's already gone up. I don't know. I saw numbers like 6x and some like that.

**Caitlin Kalinowski** (00:47:39):
Oh, really? I didn't realize it was that bad.

**Lenny Rachitsky** (00:47:41):
That's a number I saw, let's not quote that. And you're saying, yeah, 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 is memory, when we talk about memory? What's going on there?

**Caitlin Kalinowski** (00:47:55):
Processing. So the way to think about it is processing memory. You think about memory 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 dealing with maybe documents or pictures that you have. Maybe that's in cold storage on a server, maybe that's using a hard drive somewhere.

**Caitlin Kalinowski** (00:48:18):
This is usually things that you don't need really, really fast access on. But if you're running a program, some of that program is actually going to be run in RAM. And so there's different kinds, obviously, for servers. There's different kinds of server racks. Some server racks are actually focused on this type of memory, and some server racks are focused more on what we consider like a cold storage or a slower ... Now, this isn't my area of expertise, but certainly most of the products that I built, maybe all of them have had RAM, and we've had to figure out how to ... For me, mostly it's a packaging issue. Where do you put it? Does it need to be accessible? Which RAM do you pick? How fast does it need to be, and what is the cost? Is usually our trade-offs.

**Lenny Rachitsky** (00:49:10):
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?

**Caitlin Kalinowski** (00:49:17):
That's right. That's exactly what's happened.

**Lenny Rachitsky** (00:49:19):
So this is a really good specific example of just how hard it is to build hardware. All it takes is one piece to be not available, and your whole thing's screwed.

**Caitlin Kalinowski** (00:49:29):
Yeah, you can't build anything if you have one component missing.

**Lenny Rachitsky** (00:49:32):
So let's say Matic is an example. How many components are there that they all have to assemble and not have one not available?

**Caitlin Kalinowski** (00:49:38):
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. They have the wheels of the device that are obviously moving around. Then they have a vacuum, but they also have a mop, and obviously they have a vacuum bag. They have the reservoir that-

**Caitlin Kalinowski** (00:50:00):
They have a vacuum bag. They have the reservoir that the liquid has to go in for the mop. They have a system which I think is SLAM-based, which can see your room and make a map of it and identify which surface is which, and that, I believe, stays on the device, so it doesn't go up to the cloud, which is also what we did in VR as well, which I think is a good privacy practice. And then, they, of course, have wireless modules that connect up so you can communicate with your device. They're going to have a SoC silicon, they're going to have RAM, they're going to have PCBs. 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 simple device.

**Lenny Rachitsky** (00:50:48):
And all it takes is one piece to not be available.

**Caitlin Kalinowski** (00:50:51):
Yeah. So imagine you're a vendor that sells you a component that's a die-cast component, 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 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 and the form factory 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.

**Caitlin Kalinowski** (00:51:41):
So there's a hierarchy of components. Often in consumer electronics, we start with silicon and the display, which are the longest lead time things usually in my world. In robots, actuators are pretty tricky to get, even just for prototyping. Sometimes it takes a month or two to buy an actuator.

**Lenny Rachitsky** (00:52:01):
This is why Elon famously just starts building it all himself.

**Caitlin Kalinowski** (00:52:04):
Well, when you look at what he did with Tesla and verticalizing his supply chain, and famously, actually, Starlink is an even better example of this where I believe it's effectively like or and silicon chips in, product out. That's a pretty incredible factory I've heard. I'd love to see it someday. But this is where verticalization comes into play because if you 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 then famously he did. When the silicon itself was difficult to find, he was able to redesign his PCB in record time and adapt to buying new silicon. And that would be much more catastrophic for a company that had a more classic supply chain.

**Lenny Rachitsky** (00:52:53):
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 going to do this something new." It's something in software too, to use the design system or do something new. How do you think about that balance when you're designing a new piece of hardware?

**Caitlin Kalinowski** (00:53:12):
Very simply, I use off the shelf whenever I can, especially in the prototyping phases because in the prototyping phases, which are really important phase of what we do, your goal is to show that it can work at all. Can you get a thing working? So often, it doesn't have to be the final pretty thing. It can be the ugly version. You can make an industrial design model of what the final thing's going to look like, but actually, we call it works like looks like models where you have, "This is what it's going to look like and here's how it's going to work and here's a working prototype." Humans are pretty good at this as long as, and this is a pretty big caveat, what you show could fit into the industrial design. For companies that are younger, that's not always the case, but that's what we're going for.

**Caitlin Kalinowski** (00:53:59):
And so, in the prototyping phase, man, whatever works, off the shelf, whatever's fast, whatever you can get to quickly, and then maintain a sense of what's really going to fit in your final design. Is it capable? Are the processes and components and materials capable of actually adapting to this size, this new weight that you need it to go into? So that's part of the calculus. When you move into mass production and the final design, it depends. 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 custom because we have, again, one of those KPIs, "I want to be this size. I want it to be this weight. I want to be this color." And often, off-the-shelf parts aren't good enough, not because they don't work, but because they're just not exactly designed for what we're doing.

**Lenny Rachitsky** (00:54:50):
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.

**Caitlin Kalinowski** (00:55:00):
Yeah. Yeah, exactly.

**Lenny Rachitsky** (00:55:01):
Super. 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 hardware is built? Obviously, it's impacting the way software is built in a huge way. Has it changed your life and the lives of people building hardware and robots?

**Caitlin Kalinowski** (00:55:17):
Yeah. So I want to 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 is possible and 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. It has shape. It has nerves. You have an equation for how the surfaces work, and it's an entity that's designed in CAD. It's a solid entity. And so, right now, we're not quite there with AI doing CAD.

**Caitlin Kalinowski** (00:56:13):
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 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. 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 going to 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, being able to do these things in AI would be amazing so you can focus on actually doing the fun stuff.

**Caitlin Kalinowski** (00:56:53):
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. If you've ever opened anything 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 going to be able to do some basic component selection and layout on these boards. So that's kind of where we're at right now. So we're not in a point, Lenny, where day-to-day mechanical or electrical engineering, like the meat and potatoes of it is being done by AI, but there's a huge amount that you can do as an engineer using AI in your strategy, your planning, your ability to think through the complex dependencies that you're facing. And that's what I use it for now, which is really high-level planning, asking for information.

**Caitlin Kalinowski** (00:57:48):
When I look at who else is making a product like this, 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 is pretty good in Excel right now, and of course, Excel is one of our favorite tools in engineering. So the ability to actually rapidly make Excel spreadsheets and change them is... It doesn't sound sexy, but actually really speeds up the design process outside of these core pieces.

**Lenny Rachitsky** (00:58:16):
I love how Excel is always at the bottom of everyone. Anything, no matter what we're doing, we're going to Mars, there's an Excel spreadsheet that's probably driving a lot of this.

**Caitlin Kalinowski** (00:58:23):
Probably.

**Lenny Rachitsky** (00:58:24):
So what's interesting about what you shared is 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 real CAD.

**Caitlin Kalinowski** (00:58:33):
Yeah. And my big question is what is it going to take? So a lot of AI is based on LLMs, which are essentially word generators, word guessers. They're more complicated than that, but that's essentially what they're doing. There's also video models that you've seen that are trained on video, but these models don't understand... They're not very good for what I need, which is I need to know, "Hey, you take a piece of paper, you fold it four times, and you do this. Where's the hole going to be when you open it back up?" These LLMs and even video models, they don't know how to do that. They don't have the ability to understand friction or weight or contact, pressure, friction, surface texture. They're just not able to do these things, and this is the core of what we need in engineering to be able to understand to build things.

**Caitlin Kalinowski** (00:59:26):
So some world models may actually be able to do this in the future, and I suspect we may need those models to be the base of CAD and other physical engineering work. And so, my frustration, and this is like a healthy frustration, is I want Codex for engineering. I want Codex for hardware engineering, and it's extremely valuable. I've used it a lot for other things, but I want it for my field. And so, what I think it may require is new model types.

**Lenny Rachitsky** (00:59:55):
Sounds like an opportunity to me. I know there's a bunch of World Lab companies. Fei-Fei was on the podcast with World Labs. I think it's called World Labs. Yeah. And then, I know Google's building Gemini. So do you feel like those are the right directions or it's just like, now, we need something actually different?

**Caitlin Kalinowski** (01:00:10):
I don't actually know what the latest of what Fei-Fei is working on, but obviously, she's a brilliant roboticist, and I'd love to learn more about what she's doing. So I'll have to look that up. What I've seen is that what we have right now and what models we're building are going to be part of the solution, but not all of it.

**Lenny Rachitsky** (01:00:27):
Coming back to robots and humanoids, something that we were chatting about this earlier, your sense is humanoid robots aren't necessarily the answer to a lot of the problems that we have and opportunities that exist. Talk about just your sense of humanoids versus non-humanoid robots.

**Caitlin Kalinowski** (01:00:42):
Yeah. I think there's a hype cycle around humanoids. That doesn't mean they're not extremely interesting, and I think there's going to be winners there. But what I hear a lot is, "I want a generalist robot shape to do everything." 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 if you want to screw together 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 want to do that 10,000 times a day or something or 10,000 times a week or something. That's a dedicated robot that's specifically intended to do that thing.

**Caitlin Kalinowski** (01:01:34):
What I think is interesting here is you can have standard cabinet sizes for automation robots and you can have them be modifiable over time, and that's going to be a very interesting field, I think, is how do you make manufacturing robots that are adaptable and changeable, but you wouldn't want a humanoid to do that. And so, when you really go and look at a modern manufacturing facility like in China, at the top tier of tier one suppliers, there's not very many people on the line anyway. The entire printed circuit board line is 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.

**Caitlin Kalinowski** (01:02:21):
So in mechanical assembly, the same thing. These most advanced lines, they don't have people working very much. 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. 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 going to look different from each other.

**Lenny Rachitsky** (01:03:05):
That makes all the sense in the world. What I think about as you talk about this is it feels like there's going to be this big moment when a robot can build other robots, and this CAD point you make about where once AI can develop full designs for hardware, that's going to be a big moment. Do you have a sense of just how close we are to this, I don't know, this loop that begins of robots building each other and designing each other?

**Caitlin Kalinowski** (01:03:29):
If you're talking about robots building robots that are different than them usually, yes, I think that that's going to happen, but it's like the terms matter. I don't think there's going to be one robot that's going to build itself. I don't think that that's what it's going to look like. If you could say, "Hey, I want to build this thing and I want it to do this and 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, that is possible, I think, in the future. Will it be good as good in the beginning as us doing it? No, but it will happen.

**Caitlin Kalinowski** (01:04:17):
The biggest challenge here, Lenny, is actually the data. This CAD data is some of the most valuable IP that anybody has, and Samsung or Matic, to pick on Matic, they're not going to want to give their 3D CAD to a model vendor, to a model maker, somebody making an AI model to teach it how to make great CAD. This is proprietary. This is like the secret sauce. And so, where is this data going to come from is a big question I have, which is why I think hobbyists are a more interesting place to start where they're not concerned about the sanctity of their CAD and where it goes. They don't care. They want to make something and they want help making it faster.

**Caitlin Kalinowski** (01:04:55):
So this is kind of where I'm interested in this starting, which is maybe a hobbyist isn't an expert in printed circuit board design. Maybe they don't care. They just want their drone to be fast and to beat this other guy's drone or whatever. This is where I think you're going to start seeing all this start, and then probably, the big incumbents are going to be slower because they have dedicated tools and a lot of IP privacy.

**Lenny Rachitsky** (01:05:18):
It's really interesting, this idea of what data AI models need to train on. I've been hearing that labs are buying code like GitHub repos pre 2021 because that's before AI has impacted the code because there's less and less of human-written code. And these are data labeling companies like Mercor and Surge and Handshake and things like that. It feels like this is a big opportunity that might emerge as them selling data, creating these CAD files.

**Caitlin Kalinowski** (01:05:46):
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 going to need a base model that has a lot of CAD data. We'll have to figure out how to do that. That's going to be very interesting. And then, we're going to have to figure out how to put it safely inside essentially the walls of companies and have them then train it on their own data. I don't know if that's going to be the equivalent of an MCP layer or what that's going to be, but this seems doable in the long term.

**Lenny Rachitsky** (01:06:23):
I want to ask you a question that my sister suggested. 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 in the VR world for a long time, and now, she's working on other things. She wanted me to ask you, what does it take to create a robot that feels human and that humans feel connected to?

**Caitlin Kalinowski** (01:06:45):
It's a great question. I'm new relatively speaking to robotics. And so, I had to learn as much as I could as fast as I could. One of the researchers that helped me the most, her name's Leila Takayama. She's an expert at this. What she explained to me is that humans have a certain expectation about how other beings are going to respond when they enter a space. When someone walks into a room, you kind of acknowledge them. You might not talk to them, but you kind of look up. There is a lot of very complex nonverbal cues that we give to each other. And if you walk into a room and a robot's just like, it's creepy, and it's easy to be creepy. I'm a little surprised with some notable exceptions how creepy a lot of these humanoids are right now. You want, I think, these devices to be non-threatening, generally speaking. You want them to appear soft, you want them to appear reactive to you.

**Caitlin Kalinowski** (01:07:47):
You want to have a sense that they know that you're there, that they're attentive to you, that they're there to help you and make your work life easier, and you also expect them to intentionally or to show their intent before they do something. 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, and I recommend anyone to go look at her work. 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 someone entering a room or exiting a room, but also transmit its intent physically so it doesn't surprise you.

**Lenny Rachitsky** (01:08:43):
It feels like there's a lot we can learn from Pixar and animation studios that have thought about this a long time.

**Caitlin Kalinowski** (01:08:49):
Yeah. I actually think 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, approachability, engagement with their characters, they're really world-class.

**Lenny Rachitsky** (01:09:16):
I don't know about you, but I'm so excited to have a robot at home doing things. These videos that they're starting to put out where they're doing your... They can do dishes, at least the prototypes, they can fold laundry, they can do... It's like, yes, please come do this for me. How do you feel about robots in your house?

**Caitlin Kalinowski** (01:09:32):
So I'm into it. My partner, not so much. So I'm very lucky to have a partner who's got a high bar, which means was like never going to take Waymo, took one Waymo, and now, never wants to take anything else. So definitely willing to update her position, but it has to be pretty good. So she's in love with the Matic. It's amazing, but the bar is pretty high. So I think in order to have a home robot, it's going to have to be pretty incredible for her to be willing to have it in our home. But I take that as a challenge.

**Lenny Rachitsky** (01:10:04):
My wife is exactly the same way. She's like, "I don't want this thing in our house with Matt. Oh, wow, this is so cute." A recent example, a self-driving Tesla. She used to be so like, "No, don't do that." It was not that great originally, and now, she's like, "I don't want to drive any other car. This just feels like absurd to drive your car. I don't want to do that anymore." It's crazy how quickly that changes.

**Caitlin Kalinowski** (01:10:27):
So there's a big difference in my mind. This is a big categorical difference. There's a big difference between a car that is safer that drives itself versus a car that a human drives because you have an existence proof of the human driving car and you have the data. 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 bad at it, what are you relating it to? And if it's unsafe in any way, what are you relating that to? It's a much harder equation in my mind to get to a lot of people than a car where you can say, "Hey, Waymo save lives." You're going to have a fraction of the deaths using a Waymo, whether you're a passenger or you're not. When you already see people in San Francisco adapting how they respond around a Waymo versus any other car, so you're seeing behavioral changes that are based on trust, which is really cool.

**Caitlin Kalinowski** (01:11:18):
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.

**Lenny Rachitsky** (01:11:26):
Something that someone needs to figure out with the Tesla self-driving is often, you're at a stop and you make eye contact and then, "Go ahead. Go ahead," or someone's about to cross and you're like, "Okay, go ahead." But the Tesla just does its own thing, and so it makes you look like an asshole a bunch of times like, "Oh, I'm not driving this and in control."

**Caitlin Kalinowski** (01:11:45):
Yeah. I had that happen once. You almost want a little two arms in the front to be gesturing or you go or something. It's amazing how much we actually rely on this human connection to decide even who's going to go in an intersection.

**Lenny Rachitsky** (01:11:59):
Yeah. Okay. So zooming out a little bit, just what's cool about people like you is you're thinking and building things that will exist in the future. You're kind of living in the future and designing it, and you are one of the few people who has a glimpse into where things are going. So I'm curious just to ask, say in five years, what is the vision you have of what is different about our day-to-day robots, devices? Just what does it look like just roughly?

**Caitlin Kalinowski** (01:12:27):
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 away that we can design something not only for two years from now or three years from now, but also something that will ladder up to what we want six years from now because in my field, it's a lot easier to make something and iterate on it and iterate 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 platonic ideal of the thing will eventually look like. So you do have to think about the future and live in the future.

**Caitlin Kalinowski** (01:13:06):
I have this weird thing where I love to think about the future, but I'm also a skeptic, and you really want me to be a skeptic because if I think everything's going to be fine, the hardware's not going to work. You really want me to be like, "This isn't going to work and this isn't going to work and this isn't going to work," and just be worried about all these things going wrong. So this is kind of an interesting disagreement inside of me of what I want the future to look like and what I think it's going to look like and what it's actually going to look like. I'm trying to guess. And so, it seems pretty clear to me that AI is going to have a foundational change in how we work and what we do over the next couple of years, especially. You're already seeing it. Obviously, anybody who codes is not coding by hand very much anymore.

**Caitlin Kalinowski** (01:13:52):
Any knowledge work, this is going to hit next, I think, and progressively affect our economy and our work, but it seems like the physical world is less likely to change as quickly outside of drones, self-driving cars. You're going to see more and more robots, but I'm not somebody who thinks that in five years, you're going to have 20 million robots. I don't think that it's going to be that fast. I think we have a lot of really deep work on supply chain when you do supply chain reliability, 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 going to start seeing a lot of weird things on the street. You might see robots on the street. Have you seen any delivery robots in your world, Lenny, before?

**Lenny Rachitsky** (01:14:44):
Like the little car things, not like anything humanoidy. Yeah.

**Caitlin Kalinowski** (01:14:48):
Yeah, yeah. So this is just going to continue happening, and I think we're just going to continue to feel like we live in the future, but safety is going to be a big key for robotics, I think. I think there's probably more change in war-

**Caitlin Kalinowski** (01:15:00):
I think there's probably more change in war than there is in consumer electronics in the next two years, for example.

**Lenny Rachitsky** (01:15:07):
Wow. What a statement.

**Caitlin Kalinowski** (01:15:09):
Yeah.

**Lenny Rachitsky** (01:15:10):
And I totally agree. There's nothing like war to incentivize innovation and just endless improvement and trying to get ahead of the other side.

**Caitlin Kalinowski** (01:15:20):
Especially when democracy is at stake. I mean, I think that we are, and I don't want to be on a high horse or something, but I do think that we're in a place where we need to think about things in the future in these terms and defend these things with our capabilities while also hoping that we never have to have hot conflict anywhere.

**Lenny Rachitsky** (01:15:39):
Along those lines, I have to ask you, recently you became famous, on Twitter at least, for quitting OpenAI. You tweeted that you're leaving with your brief explanation and got seven million views, 50, I don't know, 8,000 likes. What happened? Why'd you leave OpenAI? What happened?

**Caitlin Kalinowski** (01:16:01):
Yeah. So what I said in my tweet was that I have a lot of friends in the executive side of OpenAI that I care a lot about, I think are really good people. And I feel that what happened with the decision making, the speed of the decision making, the governance, and the lack of defined guardrails around the announcement of the Department of War deal is not how I thought it should have been done. And both of those things can be true.

**Caitlin Kalinowski** (01:16:35):
And so my hope, Lenny, was that there's a third path. You see a lot of people just going along with what their company's doing and then you see some people that are kind of scorched earth about it. In this case, that didn't make sense for me. I didn't feel that way about the company. OpenAI is an amazing company and I was able to help build a robotics program there and attract some of that top talent in robotics, I think in the world.

**Caitlin Kalinowski** (01:17:02):
And so I have a lot of, I don't know, this is a group of people I care a lot about. And you can also disagree with friends and feel like what they did isn't good and isn't right. And that's where I ended up and that's what I tweeted about. It was going to get reported on, so I tweeted before that happened.

**Lenny Rachitsky** (01:17:24):
This is a great opportunity to just whisper to me, what OpenAI is working on? What is this robotics device [inaudible 01:17:30], just like just between you and me?

**Caitlin Kalinowski** (01:17:32):
I wish I could say. You know, Lenny, part of the fun of our job is we get to see things before everybody else does, but part of the flip side of that is we can't talk about anything internal or any IP. What I can say is the team's really strong and I was really, really grateful for the opportunity to help.

**Caitlin Kalinowski** (01:17:49):
But I also thought that after what happened happened, it was time for me to ... I couldn't continue to work there because you don't know what's going to happen next time. And my hope was that my decision made it easier for other folks to talk about what their boundaries were and hold them and we'll see what happens there.

**Lenny Rachitsky** (01:18:09):
So speaking of team building, this is something I definitely wanted to ask you about. So as I said, I asked a bunch of people what to talk to you about and someone that, I think it was maybe a colleague, former colleague, Marianna [inaudible 01:18:20]. Did you work with her?

**Caitlin Kalinowski** (01:18:09):
Yeah.

**Lenny Rachitsky** (01:18:21):
Okay.

**Caitlin Kalinowski** (01:18:21):
She's a friend. Yeah.

**Lenny Rachitsky** (01:18:22):
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.

**Caitlin Kalinowski** (01:18:39):
Yeah. I'm lucky that I've had a lot of time, a lot of reps basically on hiring people. And so I have a strategy of hiring great people. When you're hiring for zero to one and new things or new industries, and that's what we're facing, I think with AI and robots, certainly it's very new, you can't count on having entirely people who've done the exact same thing in past lives because it doesn't exist. The exact same thing doesn't exist. Maybe you've got roboticists who've built a thousand robots, but nobody that I'm aware of has built the type of robot that can move through the world the way I'm interested in, in the millions, because it hasn't been done.

**Caitlin Kalinowski** (01:19:23):
So you have to start thinking about how do you build a team that can do something new? And the nice thing is actually in robotics, self-driving cars, autonomous vehicles is a really good place to look because you've got the sensing stack and you've got a lot of the safety trade-offs actually. And it's a lot of the hard engineering, the hardcore engineering. So that's a place that I looked. Obviously, you want some hardcore roboticists who can do robot design from scratch. And these are really people, even though they might have a degree in something, they're really hybrid people. They're generalist people.

So one of the key principles I'm looking for is a lot of really strong generalists who can adapt what they've learned in other fields to a new field and people with a lot of experience building. You want some people who have experience building the thing that you're building that's new and some people who have experience scaling other things to [inaudible 01:20:14]. So you need to look at that.

**Caitlin Kalinowski** (01:20:15):
And then with young people, this is where it gets really fun, Lenny, is the only AI native people essentially who use AI so natively that it's baked into their engineering process are 20 years old or 21 years old or 28. I mean, it's very hard to find someone who's in their 30s who can be truly fully AI native. And so we need these folks to teach us how to think. And I've had the opportunity to work with a few folks in that age range. They're approaching their problem solving completely differently because they're using AI from the ground up for everything and they're much faster actually and it's really fun to watch.

**Caitlin Kalinowski** (01:20:54):
So figuring out how to get these AI natives to teach us, the rest of us, how they think about AI when it's, you know, we are, you and I, I think I can say are digital natives where we grew up, maybe there wasn't internet when we were really young, but we are the generation that had the first internet. We were teenagers. And we are the generation that had the first cell phones really in scale.

**Caitlin Kalinowski** (01:21:16):
We're an important generation because we had the first, I remember freshman year at Stanford we had the first databases that you could access and you could share movies on, I think is what we did, and music on or whatever it was, but this was new. And so we were native in these things and that gave us a lot of oomph in creating new technologies for it. But we have to accept that we're not native in these new technologies and you really want some folks who are hungry and excited and want to learn who do have these skills.

**Lenny Rachitsky** (01:21:46):
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, that junior roles are erased because of AI.

**Caitlin Kalinowski** (01:21:57):
Yeah, I don't see it that way. I think we need them. I also think that we need to build new technologists. There's the obvious question of what happens if we don't have teams that have senior and junior people, but I think what you find when you actually build these teams is you have to have both. You must have both. The team size just might be a little bit smaller than it used to be. When this AI revolution in hardware happens, I don't know how that's going to affect the teams. That will be really interesting to watch.

**Lenny Rachitsky** (01:22:26):
So what I heard here is just look for a generalist that can flex based on whatever needs to be done, some mixture of specialists in scaling versus zero to one. And then the best term I've heard for this is cracked new grads that are AI native essentially, that are just doing everything AI first.

**Caitlin Kalinowski** (01:22:45):
Yep. And then what we didn't talk about, of course, is mission alignment, which actually unifies a team. So if everyone coming in is aligned to the mission, that helps a lot because especially in the world of AI researchers and hardware folks, there's a lot of miscommunication because we're coming from such different worlds. And so having a sense of we're all pulling in the same direction is really important.

**Caitlin Kalinowski** (01:23:08):
And then I rely a lot, Lenny, on my gut feel for people, assuming everything else that I'm looking for has been checked. It's hard to talk about what that means, but usually it's that spark that you're looking for in someone, that they're genuinely motivated. They're motivated by a desire to learn and by excellence, they're motivated to learn from the people around them. They're open to updating their point of view based on new information and they want to win. I mean, these are the things that really matter when you're building a team.

**Lenny Rachitsky** (01:23:43):
Awesome. Okay. Just a couple more questions. Something I've been wanting to ask for a long time is you worked with some of the most legendary, successful builders, Steve Jobs, Jony Ive, Mark Zuckerberg, Sam Altman. You don't have to go through all four, but just what's a lesson you learned from as many of these folks that come to mind?

**Caitlin Kalinowski** (01:24:01):
So I'll start with Sam, because most recently, Sam is really good at saying, "Why not more? Why not 100X or 10,000X? You are thinking too small. Why not think about this bigger?" And every time we talked about something important, he talked about that. And what I realized is I was thinking too small in certain areas and he was thinking globally, and having that nudge from a leader who's ambitious is really helpful, I think. So that was a big thing that I learned from him about he's willing to go for it at high volume and invest depending on ... Meaning hitting a lot of people. He's willing to think in very big numbers. That was really, really foundationally important.

I think for Steve Jobs it's just the bar he held for the company and for technical talent and for excellence was not wavering. It was up here, and you were either going to meet it or you weren't. And that was something that kind of washed through the whole company. When you are a young, ambitious person and you hear that something's not good enough, that can be extremely motivating actually. And it doesn't quite hit the way you would think. And if you tell somebody, "Hey, this needs to be better. You need to spend more time on this. You need to be more thoughtful about this," or, "This is not hitting our quality bar in [inaudible 01:25:35]," or something, that's impactful. And I think you never want to hear that again. So it's very, very motivating.

**Caitlin Kalinowski** (01:25:41):
And then Mark Zuckerberg, I think that he ... I have to say he ran a company very, very well. So the way that the technical side of the company operated, the way that we had reviews, that decisions were made, the decisions were made to the lowest level possible in the company to maintain speed. I underappreciated how clean and well run ... The way that the hardware organization interacted with the rest of the company, it was very clear, "This is what we're going for. We're going to have this review. We're going to make a decision in this review. If you can make the decision without the review, you will do that. Here's the objectives for this project."

It was really well executed. And I think that's hard to do at a fast growing company. It's very hard to do at that level. And having him and Andrew Bosworth, the CTO, involved in the technical decisions, able to read reports that were maybe 20 pages long, [inaudible 01:26:52] 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 a hundred times that month, was impressive and definitely something I learned from them.

**Lenny Rachitsky** (01:27:05):
What an incredible set of experiences and different types of places to work. I don't know if they could be more different, all these places.

**Caitlin Kalinowski** (01:27:13):
I know. And I think that that's why I'm looking for this zero to one. And so when you're looking for a zero to one opportunity, it's always going to be in some place different generally.

**Lenny Rachitsky** (01:27:24):
You're going to be a hot commodity in this market now that you're a free agent, but on the flip side of that, I want to take us to Fail Corner. I feel like someone building hardware physical things has some great fail stories. Is there one story of something you built, something you worked on that failed and something you learned from that experience?

**Caitlin Kalinowski** (01:27:44):
This is a great question and not a comfortable one. One of my favorite failures was actually on the Quest One. It was around EVT, so halfway through the Quest One, and we found out that we had gone from five cameras to four for cost reduction. We talked a little about this. We needed to reduce the price so more people could buy them.

**Caitlin Kalinowski** (01:28:06):
And what happened was it was right before Christmas and I heard from the lead on the team that does computer vision and he said, "Oh my gosh, the data from the cameras isn't working and we can't get a lock on where the person is using the headset." And so we looked into it and we realized that their interpretation of our spec and our interpretation of our spec was different. So in engineering, we usually use a plus or minus, like it can go up or down by, in this case I think it was 0.15mm or something like that. And in his world, he was used to having a global, it's within 0.15mm. And so we had a different interpretation of the spec.

**Caitlin Kalinowski** (01:28:54):
Now the problem is that our interpretation of the spec meant that he couldn't meet his goals of being able to understand where the headset was in space. And so we had to do a redesign and this is at EVT. So this is pretty much when you want the engineering to be done.

**Lenny Rachitsky** (01:29:09):
What does EVT stand for?

**Caitlin Kalinowski** (01:29:11):
It stands for when we compile the hardware for the first time with everything that's supposed to be done. So final components, final materials, you're making the components on the tools, you're going to make them for mass production instead of just machining them. So it's a big deal. And so what we had to do was 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 distance from them met the spec that he needed and then let the other two float. So this was an architectural change. And this was a failure. I mean, it was a failure in understanding the spec. It was a failure in 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.

**Caitlin Kalinowski** (01:30:10):
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 it was a good outcome, but it was a scramble and certainly wish that we caught it four months earlier.

**Lenny Rachitsky** (01:30:30):
Another example of just how hard hardware is, just 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 supply chain.

**Caitlin Kalinowski** (01:30:41):
Yeah. It was tricky.

**Lenny Rachitsky** (01:30:43):
So the Quest One that shipped was this with the cameras moved.

**Caitlin Kalinowski** (01:30:47):
Yeah. If you look, the cameras have, there's two cameras a little closer to one another in the front of the Quest than at the bottom.

**Lenny Rachitsky** (01:30:53):
Wow. How did Bos and Zuck feel about this?

**Caitlin Kalinowski** (01:30:59):
The fact that I don't remember probably means it was okay. I think we addressed it, we redesigned it. We had to change the material and the bracket. I think we had to make steel to hold the tolerance we needed. But it worked out and the price and the cost and yields were fine. So I think we adapted.

**Lenny Rachitsky** (01:31:18):
And that was the bestselling VR device of all time. Is that right?

**Caitlin Kalinowski** (01:31:21):
I think it was.

**Lenny Rachitsky** (01:31:21):
Okay.

**Caitlin Kalinowski** (01:31:22):
I don't have the final sales numbers, but yeah.

**Lenny Rachitsky** (01:31:24):
Okay [inaudible 01:31:25]. Okay. 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 something we've shared or anything else that's just like, "Oh, here's something I want to share."

**Caitlin Kalinowski** (01:31:38):
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 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.

**Caitlin Kalinowski** (01:32:01):
These new tools, this new way of work is scary, but if you embrace it and are daily using these AI tools right now and daily applying them to what you're doing, you'll be at the forefront of whatever comes next. And so I just want to encourage everyone to be creative, use these tools, have fun with them, figure out what the boundaries are, and then every time a new model comes out, test again because it's really important to know what we're dealing with and where these boundaries are. But I've never been more excited about the power of an individual.

**Lenny Rachitsky** (01:32:35):
Well, with that, Caitlin, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

**Caitlin Kalinowski** (01:32:41):
I'm ready.

**Lenny Rachitsky** (01:32:42):
First question, what are two or three books that you find yourself recommending most to other people?

**Caitlin Kalinowski** (01:32:46):
I've been mostly reading the classics lately. Book of the New Sun is a great fiction book, which I really recommend. I think that's what it's called. I haven't read it in a little while. I love Mrs. Dalloway. Actually, I think it's a very interesting book about transitions, and it was a post-war book by Virginia Woolf. So I really love it and I think it's really wonderful.

**Caitlin Kalinowski** (01:33:12):
I think Herodotus Histories is pretty incredible. He's wrong a lot, but he's also, it's the first history book and in many cases he's going and finding firsthand or secondhand accounts of what happens. 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 I'll double check the title of the first one and email you, but I think that's what it's called.

**Lenny Rachitsky** (01:33:36):
Okay. And we'll link to the correct one in the show notes. Favorite recent movie or TV show that you have really enjoyed?

**Caitlin Kalinowski** (01:33:43):
I'm really into Euphoria right now. I think the new Euphoria, I'm interested in the characters and figuring that out, what's going to happen there.

**Lenny Rachitsky** (01:33:56):
That show's so stressful whenever I watch.

**Caitlin Kalinowski** (01:33:59):
It's a melodrama. I think you have to think about it as a soap opera and then it's fun. If you think about it too literally or practically-

**Lenny Rachitsky** (01:34:05):
Okay. That's helpful. Do you have a favorite product you've recently discovered that you really love? Could be hardware, could be an app, could be a piece of clothing, could be a gadget.

**Caitlin Kalinowski** (01:34:14):
I really like Vollebak, the clothes. 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. It's just a fun brand to follow.

**Lenny Rachitsky** (01:34:26):
Vollebak.

**Caitlin Kalinowski** (01:34:27):
V-O-L-L-E-B- A-K.

**Lenny Rachitsky** (01:34:31):
Vollebak. Very cool. Do you have a favorite life motto that you often come back to in work or in life?

**Caitlin Kalinowski** (01:34:38):
Have you seen that branch where there's all these branches and then you're here and then there's all these branches from this point?

**Lenny Rachitsky** (01:34:43):
[inaudible 01:34:43], yeah.

**Caitlin Kalinowski** (01:34:45):
Yeah. You know who it's from. I didn't know who it was from. 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 that is a great reminder that you get to pick every day. You get to decide every day what you want to 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.

**Lenny Rachitsky** (01:35:11):
We'll 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 knows you well shared this really interesting tidbit about you, that you hired a PhD to tutor you 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?

**Caitlin Kalinowski** (01:35:34):
This is like very [inaudible 01:35:35] territory, but I found this list that the poet Joseph Brodsky wrote, which is a list of things you should have read in order to have an intelligent conversation in English. And it is like an affected list. 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 distillation of what we used to call the Western canon and that actually I learned a lot in my public school education and in college, but I never really learned from what you would consider the Western canon.

**Caitlin Kalinowski** (01:36:14):
And so this is kind of in addition to that, there's some more, newer 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 postdoc who was willing to tutor me and I just get to ask him all these questions. He's an encyclopedia. He knows everything.

**Caitlin Kalinowski** (01:36:44):
I could ask him what was happening in Turkey at the time of this Greek, this tragedy that we're reading and what was happening in Athens and what this tragedian might be responding to. And he can answer the question. It's really fun to have the ability to have that sounding board.

**Lenny Rachitsky** (01:37:01):
So cool. It's so cool you did that. Even though AI can do a lot of this, sometimes a human is much more interesting to talk to and feels better.

**Caitlin Kalinowski** (01:37:10):
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, it isn't adequate.

**Lenny Rachitsky** (01:37:21):
It's because we're not 20 something. We're used to humans.

**Caitlin Kalinowski** (01:37:25):
[inaudible 01:37:25] wrong.

**Lenny Rachitsky** (01:37:26):
I don't think it's wrong, but we didn't grow up this way. And I imagine college students, they would just not, "Why would I do that? I have Claude here."

**Caitlin Kalinowski** (01:37:34):
Yeah.

**Lenny Rachitsky** (01:37:34):
So cool. I love that. Well, this was incredible. You're amazing. Where can folks find you online if they want to find you, if they want to reach out, I don't know, try to hire you? And then final question, how can listeners be useful to you?

**Caitlin Kalinowski** (01:37:47):
So I have a website, which is just my name .com. I'm also on LinkedIn. People can help me, I think helping imagine the future. This is not a single player game. This is a multiplayer game. Figuring out what future we want, what we want it to look like, what we want the human aspect to be in that future and what we think we want to hold for ourselves, how we want to augment ourselves.

**Caitlin Kalinowski** (01:38:09):
So right now we're in this dystopian niche where everything's just ... It feels like the future is horrible. And the way to not have that is to actually design our own future together, figure out what we want our future to look like. Paint a picture in fiction, in literature, in conversation, and then build that. And I think that that's possible.

**Lenny Rachitsky** (01:38:34):
I love that. That's actually a message that's come up on a couple of recent podcast episodes. It's a really good reminder. Caitlin, thank you so much for being here.

**Caitlin Kalinowski** (01:38:44):
This was really fun, Lenny. Thanks for having me.

**Lenny Rachitsky** (01:38:46):
Bye, everyone.

**Lenny Rachitsky** (01:38:47):
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.