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

There's a dawning realization, especially in the labs, 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

saturate. When that happens, the next frontier is the physical world.

Robotics manufacturing industrialization.

You're living in the future and designing it.

There's probably more change in war than there is in consumer electronics. In the

next 2 years, we need to invest a lot more in drones than in aircraft carriers.

Just imagine 100,000 drones coming out of China just at us. I do feel that we need to re-industrialize the country significantly to be safe in a military sense. I would really like to retach

sense. I would really like to retach ourselves how to make things at scale, how to be more independent. People that

are your allies now may not be in the future.

You worked with some of the most legendary successful builders. Steve

Jobs, Mark Zuckerberg, Sam Alman.

Sam is really good at saying why not more? Why not 100x or 10,000x? You're

more? Why not 100x or 10,000x? You're

thinking too small. For Steve, the bar he held for the company for technical talent and for excellence was not wavering.

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

If you walk into a room and a robot's just like like it's creepy. You want

these devices to be non-threatening, appear soft, reactive to you, Pixar, Disney are probably the world's best at doing this type of design work.

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

trouble as an industry.

Today, my guest is Caitlyn Kalinowski.

Caitlyn is one of the most soughtafter and accomplished hardware leaders in Silicon Valley. She was part of the

Silicon Valley. She was part of the original unibody MacBook Pro teams and technical lead on the MacBook Air and Mac Pro at Apple. She led the AR glasses hardware team at Meta, including the

team behind Orion, their most advanced AR product. Before that, she ran the VR

AR product. Before that, she ran the VR hardware team at Meta, where she helped design all of their incredible VR devices like the Rift and the Quest.

Most recently, she was at OpenAI, helping build their robotics and hardware division from scratch. Robots

and hardware and physical AI are so hot right now. Every AI company, and so many

right now. Every AI company, and so many startups are launching, building AI hardware products, and Caitlyn has been at the center of this emerging field for decades. This conversation goes in a lot

decades. This conversation goes in a lot of different directions, many that I did not expect, and I hope to do a lot more episodes on the hardware side of building over the next few months.

Before we get into it, don't forget to check out lenny's productass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. With that, I bring you

subscribers. With that, I bring you Caitlyn Kalinowski.

Caitlyn, thank you so much for being here. Welcome to the podcast.

here. Welcome to the podcast.

Thank you so much for having me. I'm

excited to be here. We're going to go in a bunch of different directions. I'm

going to bounce around. I want to talk about VR. So much money, so many

about VR. So much money, so many resources. So many smart people have

resources. So many smart people have been working on VR for so long. Meta

spent, I don't know, 10 billion dollars.

Like 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

through. Feels like a lot of people are leaning out now. Feels like Meta stepping back. Apple's stepping back

stepping back. Apple's stepping back with the Vision Pro in spite of the incredible hardware that everyone that you built that your team built. just

like I' I've got a couple of the devices. It's just like a magical

devices. It's just like a magical experience that you've unlike anything you've ever experienced still has not caught on. What happened? Is there still

caught on. What happened? Is there still a future where VR catches on or is the future kind of AR and something else? I

don't think I would have guessed exactly what happened here, but the way I look at it is VR helped us understand how to orient things in space relative to a

simulated world and the real world and connect those two. Um, we figured out SLAM, which was how to how to do positioning in space using cameras. We

figured out a lot of depth uh applications of depth sensors. We

figured out how humans um perceive visual data in space. And all of that actually, while it's great for VR, and I think VR gaming is a really interesting um it is kind of a niche, but I think

it's an interesting niche. What I see now is in robotics, all of these technologies are being used because you need to understand how the robot is moving through space.

You need to understand how far it is from everything. You need to understand

from everything. You need to understand if you're wearing a VR headset and driving the robot. It's the same real technology. And so for me, I view it as

technology. And so for me, I view it as a step in a long technological um arc.

And to be honest, as an as someone who's not using VR a lot right now, I'm really glad that we did it. But I don't think it I expected it to be big obviously or

or wouldn't have been working in Oculus.

And I think maybe the social aspect of having something in front of your face um is part of why it didn't take off.

And I think that we learned of course with Google Glass how important that is as well. And so when we tried to make it

as well. And so when we tried to make it social um it's hard to make it social when you have you know your face covered.

That is interesting. So just like the investment and uh innovation that happened that uh that went into VR has actually proven to be really useful and so it feels like the companies that have put a lot of effort into that and money

into that have are ahead on the next step. So is that where you think things

step. So is that where you think things go? What's kind of like where do you

go? What's kind of like where do you think things are going? Is it AR glasses something else? What's kind of the

something else? What's kind of the future of this?

I believe in AR glasses as part of the future because I I do think looking down at your phone all the time is not great for us as social as social creatures. So, if you

can maintain social connections and get information, that's where I think we're headed.

Orion, the AR glasses we worked on, I worked on most recently are a bit ahead of their time because they're using waveguides and microlleds 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

still high. I think that's absolutely a path that AR glasses are likely to take.

And as we figure out the input to those glasses, like how do you communicate with them when you're on the move, when you're in public, how do you communicate quietly, silently, um, with them? I

think once we start to figure out some of those, um, challenges 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. So, that's part of it. The other part is there's this

of it. The other part is there's this lineage of technology going through VR and then AR and now in I'm using the term robotics, physical AI, but you really have to step back and look at

autonomous vehicles, drones, obviously robots, um, uh, autonomy period manufacturing, like all of these technologies are going to need the same the same piece parts, the same pieces

that we built in the AR VR spectrum.

It's interesting with VR there's this idea with when you build product, there's always this question when something doesn't work. Is it just like you executed it badly or the idea was just a bad idea? And it's always hard to know. Feels like with the AR it's just

know. Feels like with the AR it's just like so much effort was put into making it work just like for a decade many decades and just has not worked. So it's

like nice that we know okay there's nothing we can really do right now to make this work. I completely agree with you. The issue is just like I don't want

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, it's just like I'm just gonna I don't need this. It's not that big of a deal. and AR you're going to

just start getting more and more larger and larger displays. But the great thing about Orion is you got 70 degree field of view binocular. 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 like I feel immersed. It's the field of view is wide enough. I feel immersed and it's becomes pretty clear that I think I think this is part of where the future's headed. This episode is brought to you

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Okay, I want to talk about robots, robotics. I was meeting with uh a bunch

robotics. I was meeting with uh a bunch of Princeton students a couple months ago and we they're they're kind of like compsai students and they were telling me that enrollment in compsai at

Princeton is down trending down and I confirmed this is actually true at a lot of universities. There's a lot of charts

of universities. There's a lot of charts that show compsite enrollment down and where it's actually going up is is hardware robotics which I imagine as someone that has been in this field for a long time is very weird because it's

never been that popular. Just how does how does it feel to feel like oh wow everyone's getting it now? It's very

odd. Uh, everyone is suddenly asking about hardware and robots and the physical world and it's never been the sexy career. It's always been the thing

sexy career. It's always been the thing that you went into cuz you loved it. It

never paid the same as these other careers. Um, it was never kind of at the

careers. Um, 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. Um, so it's it's great in some

Apple. Um, so it's it's great in some ways and it's very odd in others. What's

uh 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

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

hardware." What are some of the surprising challenges that come up?

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

then they run the code and debug it. But

they can compile their code every day, you know, every hour, whatever they need to do. In hardware, we only get to

to do. In hardware, we only get to compile our code quote unquote like four or five times.

And four or five times a year or total, okay? Ever.

okay? Ever.

Right? So if you're building hardware, you redesign it in CAD, you know, for every major build and then you have to release it. And once it's released, you

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 there updates.

So, we have a different approach. We

have to have a different approach which is more conservative. Um, you have to do more of the reliability checks and tests in line with the program because once you compile that last time, you're done.

You make all the parts, you put them together, they're out in the world. the

only alternative is, you know, is to um ship something new to replace it a couple years later. And so we have to be more conservative and we have to take our time because if you think about it,

a product that says sells millions, if you have a graph of all the parts put together on any different part of the of the device, uh you have a curve, you're

in the plus and minus three sigma or more, right? So meaning if you have two

more, right? So meaning if you have two parts that go together, you're going to get the smallest version of this one and the largest version of this one, and you're going to have to put those together across the board. 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 not going to have

it's done. And we're not going to have we're going to have a high yield. we're

gonna be able to make them and and make money on them effectively and we won't have very many returns. And so that's kind of the game that we're playing.

Sounds so hard and complicated. They're

just like software is so nice. You just

write some code, ship it, it's great. Uh

why do you think people are getting so into robots and hardware now? What's

kind of the driving trend? Yeah, what

I'm seeing in the, you know, in the AI world in San Francisco is there's a dawning realization, especially in the labs, I think, that the acceleration is going so vertical

that what you can do behind a keyboard with AI is going to saturate. Now, I

don't know when it's going to saturate.

Nobody else knows either. But when that happens, the next the next frontier is the physical world. And so what I see

happening is the labs, big tech startups are all realizing at the same time, okay, this is coming. We're going to have complex

uh 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

capable. If you think about that as a frontier, you can see the end of that tunnel. Now,

I don't know when it's going to be again, but we can see that that's going to saturate at some point or or at least people think it will. And when that happens, the next frontier is hardware.

The next frontier is robotics, manufacturing industrialization um the sensing layer in the real world, the ability to move uh objects in the

real world, and eventually uh we hope space.

So, one of the most interesting lines of development is these humanoid robots.

It's kind of like, you know, our meat brains are always more attracted to robots that look like us and act like us.

Um, there's a few companies very ahead.

There's uh Optimus, Tesla, there's uh Figure, there's Neo, there's a few others. What's your sense on just the

others. What's your sense on just the current state of these humanoids and kind of where I don't know like how close are we to humanoids being around us?

We might be close. I have like many others safety concerns about large strong humanoids operating right next to people because we have to have enough

data to show that that's safe. Um there

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

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

Yeah. The part that might hit you, which in this case might be the arm if it's lighter and softer. There's two

aspects. You have the arm moving through space and then you have the actuator that's rotating. So you have to add um

that's rotating. So you have to add um add up the energy essentially for both of those things. Um and uh so that's an impact thing that you have to worry about. then you have to worry about the

about. then you have to worry about the compliance of the arm. If it's just hard, then you know the impulse is high.

But if it's soft and compressible, then the impulse is is lower. And so you really have to be thinking about this when you have robots around people. So

in my world, in my worldview, the humanoid robots are still prototypes.

Um, and they're advanced prototypes.

What we need to do is show that this works at all, which is kind of where we're at right now. Once we have working prototypes, then usually, at least in my

field, what you do is you uh uh you continue to revise them to make them cheaper, easier to manufacture, higher yield, and safer. And I think this is

what's going to happen next. So, they're

not quite in my in my mind, they're not quite ready yet. Now, you can get uh uh you can get a Chinese robot that can do all kinds of things for you, but if you look at the booklet, it says, "Hey, you can't be within 3 ft. No human can be

within 3 ft of this robot and you're not going to see very many robots that are not that are strong enough to do meaningful work that don't have that uh that that warning right now.

That is so interesting. Uh it's funny to hear that. At the same time, there's

hear that. At the same time, there's these nunchuck wielding robots in China doing dances with with other folks. Uh

I've never thought about just that part of it, like the the the impact they can have if they go ary. I want to come back to that, but just like timelinewise, what's your sense realistically when

humanoid robots are walking around the streets in people's homes kind of at scale?

At scale is the problem in my mind. At

scale is uh a huge challenge. Now, for

me, in my background, at scale means millions usually. Um, but let's even say

millions usually. Um, 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

running. Then you've got to make it reliable enough that it can keep running day data day to day without a lot of human intervention or or repair. And

that's its own problem. But the first problem you have is supply chain. And

this is going to be uh something that I hope that we can talk about a little bit more. But every single part that goes

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 sub assemblies and the meaningful parts of the robot here in this country. So

there's a very complex supply chain dependency right now on robots like humanoids but also other robots that we have to that we have to figure out. Um,

and a lot of people are trying to move production here to the United States, which is very challenging because we don't have great actuator companies here yet, for example.

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

Yeah, the actuator is the motor. So, you

put power into it, electricity into it, and you get motion out of it.

Cool. And most of these robots have a rotating rotor essentially that then has gearing on it that then um powers the limb or powers the head or the fingers

or whatever else. So they could be small, they could be large.

Okay, awesome. I hear the word a lot.

I'm like I don't know exactly what it means. Thank you for explaining it. I

means. 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 kind of like the state of the union on the supply chain for say robotics? What's going on? What

are the pieces? What's what are the challenges? So the way to think about it

challenges? So the way to think about it is you can start with raw materials and magnets is a good place to start. So we

need to be able to get the magnets the raw magnets for example. Then we need to be able to process them. Then we need to be able to integrate them into actuators and build the actuators around them.

Then we need to be able to integrate those actuators into subcomponents or robots themselves. And each layer of

robots themselves. And each layer of this chain has essentially been outsourced over the last 25 years to countries like China, like um Japan,

like Korea. And so, and I and I full

like Korea. And so, and I and I full transparency, I've been part of that transfer of of of engineering knowledge to to Asia. In Asia, the expertise has

historically been scale and being able to build a lot of these parts at lower prices. we've had this kind of deal

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

part. Now, of course, there's things we make in this country still. Um, and of course there's there's design and AI that are that's made in Asia, but that's essentially where things have have been

for a long time. And in order to have a safe supply chain, we need to start to work on having some independence in these layers and these stacks.

And it's interesting that your focus is on these like actuator like that. Is

that the bottleneck? This very specific part of a robot.

It might be. It might. So, if we can't get the magnets, then we have to design new actuator types that are maybe use different materials that may 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 uh all backed by material science essentially breakthroughs. There's

essentially breakthroughs. There's batteries of course. Um there's there's actuators. The raw parts like the

actuators. The raw parts like the diecast parts. Um the machine parts are

diecast parts. Um the machine parts are less critical. We think we can get

less critical. We think we can get those. Um but we we're I think everyone

those. Um but we we're I think everyone not just in this country but around the world is starting to think about supply chain because you have these disruptions whether it's COVID or war and you see

how quickly things change.

Okay. Super question. Why magnets? Why

is that a part of the supply chain? Why

do we need magnets? Yeah. So, it's a great question. So, you have a ring of

great question. So, you have a ring of magnets that are polar opposites and they go like this around the ring and then and then you have, you know, you have something in the center that that

rotates and the way it rotates is you have alternating current essentially and so the magnets make the the rotor spin.

Wow. I want to we need of a YouTube lecture of here's how here's how this physics works. Okay. Very cool. So when

physics works. Okay. Very cool. So when

you talk about China, this is like what I imagine, what I think about now is watching the war in Ukraine and Russia, just like drones, just like how crazy and different the world is now that you

can build these little drones that go and you know blow people up. Robots are

a part of that. It's just like such a existential threat to every country now.

Uh the ability to build these things at scale. What's your advice? What should

scale. What's your advice? What should

we do? What should we change to be, you know, to thrive in this future and not be, you know, in trouble? Well, you

mentioned drones. It's another good example. You need essentially the same

example. You need essentially the same technology to make the rotor spin on a drone as you do to make an arm move on a robot. It's essentially the same base uh

robot. It's essentially the same base uh technology um and supply chain. So, we

need to we need to at least on the military side have an independent supply chain as much as possible. I think

that's important. Um I think every other country should do that as well, but I don't think that's specific to us. Um,

I do feel that we need to re-industrialize the country significantly in order to be safe in a military sense. You really never know

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. Um, the the Allied West, I think, is is going through a lot of geopolitical changes. Um, there's a

lot of shifting. And so I would really like to retach ourselves how to make things at scale, how to make things at quantity,

how to process raw materials, um how to be more um independent so that when COVID happens again or something else happens again, we're not in trouble and

we can't and and we're not unable to, you know, protect ourselves. What I

think about also is Mark Andre had this visual on some podcasts have just imagined a 100,000 drones just coming out of China just at us. What do we do?

We're not prepared for that. I don't

want to spend all our time on this dark stuff, but it's a real thing.

Well, and and and Palmer Lucky is is a friend of mine. Um and we don't agree on on everything, but I do think that we agree on on on some important uh aspects

of how we need to respond here. I think

he's right to say that we need to invest a lot more in drones than in aircraft carriers. I think that is this old way

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 we

we our planes come off here. It's like

no, AI is changing everything and um military technology is changing incredibly fast and the place to look at at that is Ukraine where you know drones are being changed and updated every day

rapidly with 3D printing and this is I think the future of where um war is headed unfortunately and I view this as a very different era that we're entering

into with very different it's a you know this isn't new to anybody but this is a uh you're looking at what it costs for them to send out a missile and what it costs for us to stop it. And this is a

just you have to do the math every time.

And right now we're losing on the math.

Um, which is fine for a certain amount of time, but the longer it goes, the less fine it is.

Are you optimistic that we'll figure this out?

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

we need to we need to move. It's

interesting that we started the conversation with VR. Uh Palmer Lucky obviously famously started Oculus now like it's it's interesting how this is so connected like you think VR is this trivial thing that we're just you know

playing games and such but it's like the same person is now building and which is the leading I don't know war robot building hardware company.

Yeah. And I think we need a lot more of them. You know I I've chosen not to work

them. You know I I've chosen not to work for companies that create lethal technology. Um and uh but but I think

technology. Um and uh but but I think that it's good to have people who are willing to do that and I think that it takes it takes everyone kind of to build uh the future that we want.

Coming back to the AI safety piece, it's so interesting. I had um I had a couple

so interesting. I had um I had a couple conversations like this on the podcast.

We think about all this like prompt injection and uh jailbreaking that happens with chat bots and we like not enough people think about what if you prompt inject a robot walking around and

tell them to punch someone and we're like so far from that feeling like we can actually stop that.

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.

Yeah. just like people talking about OpenClaw and how much like you could just tell it, you know, there's all these like give me all your passwords and it's done all these things to people's lives and just like robots walking around. Hey, uh little Okay,

walking around. Hey, uh little Okay, here's all your here's all this person's secrets.

My open class story is I I have I sandboxed it so it's in on its own computer, but I gave it like three things. I gave it like my real email

things. I gave it like my real email address and and my I don't know what it was. I I gave it like uh some

was. I I gave it like uh some information about one of my accounts or something like that and I added it to the social media like I can't remember what it's called the open claw social oh molt book. Yeah, I added it to

Maltbook and I was like, "Okay, whatever you do, don't share my private information, but oh, crazy."

And five minutes later, all it had done is posted my personal email address.

Like, it was like the one thing it had nailed it.

Okay, you're shut down. Like, it was so funny. Like, no matter how careful you

funny. Like, no matter how careful you are with these things, like you just can't really We're not at a place, which is which is exactly your point that the robots can do a lot more damage. And I never thought about just

damage. And I never thought about just like the softness of their hand as a way to keep us safer.

Yeah. Yeah. Oh man, Natt Freriedman just did this interesting talk at Stripe Sessions and he was talking about he's talking his open claw about drinking more water and sleeping better and and it uh as he's driving in a self-driving

car, it uh told them, "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."

That's so funny. Yeah, things are going to get weird fast, I think.

Okay. Um so kind of on this thread of hardware emerging as a moat as something people realize is a big part of the future to be competitive AI labs all

these other companies you've been at a company's you've been at Apple which had a very great and longl 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?

So, Apple has been best-in-class at this. Um, there's a bunch of reasons.

this. Um, there's a bunch of reasons.

One, hardware is a a first tier citizen at Apple. There's a lot of companies

at Apple. There's a lot of companies where hardware isn't part of the core product development conversation as much, but but that's an exception.

Apple also taught me and a lot of other people actually if you look at kind of the era that I was there I was very very lucky because if you look at the other folks who were there I was there between 07 and the end of 2012 if you look at

the other people who were there working on these things um they actually have a lot of key positions now across the industry and I I attribute that to how

good Apple is at training people to think about complex interdependent decisions.

and risk. And I don't think I realized that they were doing that at the time, but if you look back, what you see is a real dedication to hardware excellence,

the proper process to go through and and do really good experiments um in hardware and figure out what the best outcome is. But there's something

outcome is. But there's something underneath that which is understanding the first principles of why are we building it this way and what are the key outcomes we're looking for? And

actually John Turnis talked about this I think a few days ago where he talked about the back of the cabinet. I don't

know if you you saw this video but basically John said that he was impressed that he learned from Steve Jobs that there's a cabinet maker who finished the back of the cabinet and how important that was. And that goes very

very deep at Apple where every single design decision even on the inside of the device is considered.

And this isn't just uh an aesthetic decision. What it does is actually force

decision. What it does is actually force the engineering, industrial design, um, operations community there to think about what are we really doing and what's the core of what's happening for

this part, for this assembly, for this consumer product, then what really matters. And what happens is if you're

matters. And what happens is if you're if you're that methodical, what really matters tends to rise out and look very simple at the end. And so I part of what

you're seeing in many folks coming from that era is an understanding of how to do that which you know in the very beginning of the Mac side um Macs didn't

sell as many and the quality wasn't quite as high but by the end of that era you know Macs were very popular and selling in in much higher volumes and so I think that made a big difference and I

was only a small part of that like I was you know the thermal lead on the first uh MacBook Pro and then over time worked uh to lead successive iterations of the

MacBook Air and the cylindrical Mac Pro.

Um but I was lucky enough to work with these folks and learn from them who'd been doing this for a really long time.

So you have to take those lessons and then when you leave try to distill them and explain them to a new community now

Oculus was actually a hacking hardware startup. Oculus started from folks who

startup. Oculus started from folks who actually met on forums. You you might know this, Lenny. Um who were hacking

like PlayStations or Super Nintendos into portable backpacks and and so there was an ethos at the company that was actually quite good for the DNA of a hardware team. And then I was on the

hardware team. And then I was on the meta side when we did the acquisition.

And when we acquired them, they had that spirit of rapid iteration. We they had made Crescent Bay before the acquisition I think but then

to professionalize that get the yields up and get the volumes up was was the cost down was kind of the challenge we faced in the first Rift.

So one lesson I'm hearing here is be being very uh detail oriented. I don't

know if that's the right word, just like focus on every element of of the uh end product because to your point, it's not just about that back of the cabinet. But

it's like I think about it's like the brown Eminem story where like a band puts in the contract. You have to have brown M&M's in the in the room because that means they read it and it's not like M&M's matter. It's that it's a test

that they read the thing. And uh is that kind of the the message there? I think

the message is understanding why you're doing what you're doing and then every design decision supporting that goal and that requires a

lot of detail and it requires a lot of persistence and that requires a lot of consistency but understanding why you're doing what you're doing and what the end goal is is is I think the key um and

letting that expand into not only the software and the UX but also the hardware.

What's an example of that just to make it more concrete for us? A great example is the Quest 2. So we reduced the Quest

2 price quite a lot. And what we had to do is understand what is what are we trying to do? We're trying to democratize VR. We're trying to get VR

democratize VR. We're trying to get VR to more people. And the only way we could do that is reduce the price. And

so what it required is a redesign um of the entire product essentially for cost which then I think led to the highest selling uh VR headset of all time. And

it was not easy because you had to in our case remove cameras, remove components, change materials, change um manufacturing processes. But when you

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 with

with great um I think low return rates and it was a very strong product. um

maybe even stronger than if we hadn't done that funny enough, but it hit our hit our price point.

Okay, coming back to just the question of say company's like, "Okay, we need to build some hardware. We're going to build our our own glasses. We're going

to build a little phone device, some secretive thing, whatever." Opening eyes up to uh what other tips do you have? I

know it's like impossible to like here's all you need to know, but just what else what else should people be thinking?

Having your goals defined early and sticking to them is important. hardware

is not as adaptable to lots of changes throughout its development as anything digital. And so if you set out

anything digital. And so if you set out to say, "Okay, we want to make something that's $300." And then halfway through

that's $300." And then halfway through you say, "Oh, it actually has to be $150."

$150." You've almost burned a lot of that early time. So you kind of need to have a

time. 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 and try to change them as

little as possible. So that is very tough. In fact they that may be the

tough. In fact they that may be the toughest thing because if you do that properly and you and you have you know the right prioritization of those things

you know whether you can ship or not you know whether you're done. And in

hardware, one of the challenges is, you know, we talked about compiling four or five times. Every time you build

five times. Every time you build and you iterate your design, that's another 3 months or four months or five months or whatever it might be. And so,

you're trying to time the feature set with the quality with the timing. And in

hardware, timing is important because if you come out with your product a few weeks before your competitor, you might get all the PR, you might get all the interest. It's pretty brutal. And so

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. So you have to balance that with how many times you iterate. And if you know what your goals

iterate. And if you know what your goals are up front and you hit them, then you know you can ship. And often engineers, and I'm I'm I'm guilty of this too, especially on the hardware side, never

feel like they're done. So this is a pretty nuanced thing. So that that's one thing. The second thing is we tend to

thing. The second thing is we tend to design the things that we know how to design first. And actually the right

design first. And actually the right approach is to design the hardest parts first. One example will be and there's

first. One example will be and there's no IP here. So I'm obviously not going to share any any any IP or anything internal, but at one point we had to route cables through a hinge in a in a

device in 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

architect started. and he looked at the cross the diameter and how to split the cables out and made sure that they would fit before finalizing the hinge design.

A lot of people would start at the part they knew like we're going to use this display so I'm going to put this in CAD and I'm do all this other stuff. But the

architects who 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. And

then a couple other points is the part that your customer touches or interacts with the most needs way more iteration than everything else. So easy on a

computer you touch the trackpad the most and then maybe the the keyboard next. So

those things have to be really good.

They have to feel good. They have to respond properly. They have to be highly

respond properly. They have to be highly reliable. And then maybe the other

reliable. And then maybe the other pieces further out um don't take quite as much iteration. So you have to boost your iteration on the things that people touch the most or interact with the

most. Um so those are kind of some

most. Um so those are kind of some principles uh that I wrote about. But

these are just things that you learn uh trying to build quickly. And the last piece that's really critical if you're making hardware for folks out there who are trying to make hardware is you can't

wait around ever. Like there's never enough time. So if you know that you

enough time. So if you know that you need to do something, what I learned from from folks like uh Shelley Goldberg at Apple now, who I think is a VP now, and Kate Berseron 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 like kind of ruthless efficiency that I learned with them.

Amazing. Okay, let me just uh 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.

this. Two is do the hardest part first.

The the riskiest piece essentially uh to physically build. Three is focus on the

physically build. Three is focus on the pieces that people will use most. Say

the trackpad, uh, a keyboard. I want to talk about that. Uh, and four is just like do it now. Like even if you think you have more time, this was going to un you never know what's around the corner and you just don't it's not even that you

don't know what's around the corner. If

you're working in hardware, like you actually don't have more time.

Okay. Uh, 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 like buckets of types of goals people should be thinking about?

So, in VR, uh, display resolution or arc minutes, um, like how many pixels per degree do you want is actually one of the key metrics. So, you need to understand what your key metrics are.

And why is that key? Well, that's your visual field. So, you think about retina

visual field. So, you think about retina displays on on MacBooks. Um, they

figured out the KPI of what the human eye could see, probably overshot it a little bit and built that. And then do you really need to keep as much engineering pressure up on the

resolution of a display after that?

Maybe not. So VR is not there yet. Not

not even close. So 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

one example. I think on a computer obviously you're talking about clock speed, you're talking about um how many parallel processes you can run, you're talking about weight, um you're talking about price, um and you're talking about

features. So when we did the MacBook

features. So when we did the MacBook Air, it became very clear because we were machining it that there are certain features like uh um ambient light sensor that we just didn't make sense anymore.

And so being willing to just jettison them uh uh for for what we were going for, which was weight and size. Um so if you have those overarching goals, you

can actually make decisions, engineering decisions pretty quickly. And this is actually something that I think Elon I've heard does very well is define the

value of, you know, a gram of weight versus um the cost or he does I've heard engineering del uh ratios essentially

and he's able to put numbers on what those ratios should be which I think is really smart.

Interesting. So it's a very easy trade-off. Okay, here's the here's the

trade-off. Okay, here's the here's the formula telling us weight is less important in this case.

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

Speaking of uh 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 a part of that?

Was that something that people wanted to do from the beginning? I think if my memory serves, the very very very first

MacBook Air was a pretty low volume device um that was machined but kind of had a proof more of a proof of what could be done. And that was the Manila

envelope one I think where the side door opened out to give you the port and it kind of had a it had this shape underneath. And then the next rev of

underneath. And then the next rev of that was the MacBook Air that we know which was essentially which is wedge wedge shaped which is different. And so

the wedge shape is the one that I worked on and the one that went um and hit more volume but that manila envelope one was the one that proved you can see a

computer and so they all they each have really important um roles in the road map. Coming back to your uh point about

map. Coming back to your uh point about focusing on things that people use the most. Uh famously Apple screwed up this

most. Uh famously Apple screwed up this keyboard. There was this butterfly

keyboard. There was this butterfly keyboard situation for a long time.

You're like your eyes are closing.

What happened? What happened Caitlyn?

I didn't work directly on that keyboard.

Um I so I can't talk about what happened with it. Um but obviously this is

with it. Um but obviously this is something that you got to get right. And

I I will say like the modern MacBook keyboards are awesome and excellent and um you know I I I don't know what happened with that. Um I don't think those were devices I was working on at

the time.

Nice safe marked safe.

Um along these lines, Apple's kind of famous for not uh not listening to what people want. This kind of like a classic

people want. This kind of like a classic thing with Steve Jobs. He's not walking around doing user focus groups, asking doing user research. somehow continues

to build incredibly popular products.

What do you think they do right that allow or or do they do a lot of user feedback sessions, things like that? How

does how does it end up working out?

It's been a long time. I mean, I left over a decade ago. Um, don't know what they're doing uh now in terms of user feedback. I think this one gets

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 cuz they haven't seen it. So a good example

is the iPhone, which I didn't work on, but when you build a new iPhone with a touchscreen, you can't really go ask a 100 people what they want because they're going to say a keyboard on their screen. And this is, I think, the ethos

screen. And this is, I think, the ethos that you're getting at, which is, and this is true for anybody building a new product with a new feature. And I've

tried to build as much as I can teams that work on products that are have something new about them. Either they're

a new category or there's a new um manufacturing process or something that hasn't been done before. And when you're thinking about this, you can't really use what you learned from the same field

and the same product class. Like it just doesn't work because you actually won't get the answer right. And I think this is actually what um Steve was talking about, which is you can't get intuition

if you're changing something fundamentally. Like your customers won't

fundamentally. Like your customers won't know what they want because they haven't seen it. But if you show it to them,

seen it. But if you show it to them, they will absolutely know that it's awesome and that it's 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

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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I'm going to go in a completely different direction coming back to um the components of hardware. I asked a bunch of people what to talk to you about. Uh one of the people is uh the

about. Uh one of the people is uh the founder of Madic, the CEO of Madic, Mahul Nari Nari Wala. I've never said his last name out loud, so I hope I didn't butcher it. Uh by the way, I love my Madic. I don't know if you have a

my Madic. I don't know if you have a Matic, but it's like I have two and I've purchased two more for Oh my god, what a what an endorsement. Yeah.

Basically, it's like this amazing robot vacuum that just works. Yeah.

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

physical AI. Uh what's going on there?

physical AI. Uh what's going on there?

Yeah, we're in trouble um as an industry. Uh I think that and I'm not an expert on this, but I think that AI has to do with why. And um I

also think that the supply chain is is constrained. I have been advising

constrained. I have been advising startups and companies to pre-by memory and to have um enough memory in stock if

they can afford it to uh ride out price spikes. Um like anything in this

spikes. Um like anything in this category uh let's see this happened in COVID too.

Okay. So like we had so many supply chain disruptions and and getting enough memory was was one of the challenges. So

we had to pre-by as well. I won't say who but the company I was working with had to pre-by memory as well. And so

this is this is a uh part of what I wanted to talk to you about today is these supply chain disruptions. And if a key component that goes into a lot of tech like memory or silicon is

constrained, there's not much you can do. you either pay or you have already

do. you either pay or you have already pre-bought enough that you can ride things out. And so those are the only

things out. And so those are the only real options. Um, obviously there's a

real options. Um, obviously there's a risk to pre- buying and the price might go down. And so the challenge is I think

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 Maddic might be and so they'll just pay for for these

these new these higher costs. This is

tricky and something we have to deal with all the time.

How much have prices gone up? like how

bad is this problem and then where do you think it'll go?

Actually, this is a great question, Lenny. I don't know what's going to

Lenny. I don't know what's going to happen. I think prices are going to

happen. I think prices are going to double probably. Um I don't know on what

double probably. Um I don't know on what timeline. If I knew what timeline the

timeline. If I knew what timeline the prices were going to double on, I'd be trading, which I'm not very good at. Like I'd

really be I'd do be doing a different job if I could predict these things. Um

but but certainly we're going to have a supply chain shock.

And it's already gone up a lot. Like if

you're saying it'll double, but it's already gone up. I don't know. I saw

numbers like 6x and something like that.

Oh, really? I didn't realize it was that bad. That's that's a number I saw was

bad. That's that's a number I saw was not cool that and uh and you're saying yeah I think it from what I hear it's AI driven just like you need and when you talk about memory it's like DRAM and

things what what is memory when we talk about memory what's going on there processing it's the way to think about is like processing memory so it moves very uh you're able to kind of um you

think about uh memory like on your hard drive or your solid state drive where you're keeping files that you're not using essentially in many cases or that you're you're dealing with um you know maybe documents or pictures that you

have maybe that's in cold storage on a server maybe that's using a hard drive somewhere this is usually things that you don't need really really fast access on but if you're running a program some

of that program is actually going to be run in RAM um uh and so there's different kinds obviously for um servers there's different kinds of server racks

some server racks are actually focused on this type of of of memory and some server racks are focused more on what we consider like a cold storage or a slower

um now this isn't my area of expertise but um certainly most of the products that I've built maybe all of them have had RAM and we've had to figure out how to um for me mostly it's a packaging

issue where do you put it does it need to be accessible um uh you know which RAM do you pick how fast does it need to be um and what is the cost is is usually

our trade-off 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?

That's right. That's exactly what's happened.

So, this is a really good uh specific example of just how hard it is to build hardware. So, this is just like all it

hardware. So, this is just like all it takes is one piece to be not available and your whole thing is screwed.

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

So, let's say Asatic as an example like how many components are there that they all have to assemble and not have one not available? I'm doing the math in my

not available? I'm doing the math in my head. They probably have between 50 and

head. They probably have between 50 and 150 parts. It's possible that they have

150 parts. It's possible that they have more. I haven't seen their CAD, so I

more. I haven't seen their CAD, so I don't know what it's like inside their device, but they do have a lot of things going on, right? They have the wheels of the device that are obviously moving

around. Then they have a vacuum, but

around. Then they have a vacuum, but they also have a mop, and obviously they have a vacuum bag. They have the uh the reservoir that uh the liquid has to go

in from the mop. They have a a system which I think is slam based which can see your room and uh make a map of it and identify which surface is which and

that I believe stays on the device so it doesn't go up to the cloud um which is also kind what we did in BR as well which I think is a good practice good privacy practice and then they of course

have wireless modules that connect up uh so you can so you can communicate with your device they're going to have a SOC um silicon they're going to have RAM um they're going to have PCBs. Um and if

you take everything off of those things, like all the little caps off the PCBs and everything, then you're in the thousands of parts easily. So, it

depends on how you count. But this is not a a simple device and just and all it takes is one piece to not be available.

Yeah. So, imagine you you're a vendor that sells you a component that's a diecast component goes out of business.

You can get another diecast component in three months maybe and at quantity in five months or something like that at high quantity. This is recoverable.

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 in the form factor you wanted, this is what I call a essentially it's a catastrophic redesign. you now have to redesign the

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.

reliability testing. It is non-trivial.

And so this is why we care. So there's

there's a hierarchy of components. Often

in consumer electronics, we start with um silicon and the display, which are the longest lead time things usually in in my world. Um, in robots, actuators are pretty tricky to get even just for

prototyping. Sometimes it takes a month

prototyping. Sometimes it takes a month or two to buy an actuator.

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

Well, when you look at what he did with Tesla and verticalizing his supply chain and and famously actually Starlink is an even better example of this where I

believe it's like effectively like or and silicon chips in product out. That's

a pretty incredible factory I've heard.

I'd love to see it someday. Um but you know this is where verticalization comes into play because if you have verticalized and you have a lot of the components in house or you're building a

lot of things in house you can actually adapt to supply chain shocks better and 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 the supply chain.

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. Uh it's something in software too, use like the design system or do something new. How do you think about that balance when you're designing a new piece of hardware? very simply

like I use offtheshelf 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 like can you get a thing working so often it doesn't have to be the final pretty thing it can be the ugly version you can make an industrial design model of what the final thing is going to look

like but actually we call it works like looks like models where you have this is what it's going to look and here's how it's going to work and here's a working prototype. And humans

are pretty good at this. Um, as long as, and this is a pretty big caveat, what you show could fit into the industrial design. Sometimes that's not for for

design. Sometimes that's not for for companies that are younger, that's not always the case, but that's what we're going for. And so in the in the

going for. And so in the in the prototyping phase, man, whatever works off the shelf, whatever's fast, whatever you can get to quickly and then maintain a sense of what's really going to fit in

your final design. Is it capable? Are

the processes and components and materials capable of actually adapting to this size, this new weight that you need it to go into? So that's part of the the calculus. When you move into

mass production and the final design, it depends. I mean, if I could I mean, if I

depends. I mean, if I could I mean, if I was making madic 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

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

those KPIs. I want to be this size. I

want it to be this weight. I want to be this color. And often offtheshelf parts

this color. And often offtheshelf parts um aren't good enough. Uh not because they don't work, but because they're just not uh exactly designed for what we're doing. This is the reason these

we're doing. 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.

Yeah. Yeah. Exactly.

Super.

Um, you've mentioned CAD a bunch of times and it makes me think about just like CAD has been around for a long time. Just like is AI impacting the way

time. Just like is AI impacting the way soft hardware is built? Obviously, it's

impacting the way software is built in a huge way. Has it changed your life and

huge way. Has it changed your life and the the lives of people building hardware and robots?

Yeah. So I want to I want to zoom out a little bit. So most of the hardware work

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

together properly. Then in making sure those parts and components can be made by a vendor at quantity that that it's possible and in the tolerances we want and then putting those things together.

So that's kind of our process. Right

now, we're right at the very, very beginning of AI being able to do CAD.

So, I'll give you an example. Claude can

do what is essentially surfaces or point clouds. This is not real CAD. Real CAD

clouds. This is not real CAD. Real CAD

is in my world is dense. Like, it has shape. It has nerves. Like, you have a

shape. It has nerves. Like, you have a an equation for how how the surfaces work. And it's an entity that's designed

work. And it's an entity that's designed in CAD. It's a solid entity. And so

in CAD. It's a solid entity. And so

right now we're not quite there with AI doing CAD. I think it's likely that at

doing CAD. I think it's likely that at some point we will be there. This will

be probably one of the biggest changes for my field that we have is being able to I hope do rapid uh design and increase the speed. Now there's a lot of

really fun things to do in CAD, but like in the beginning of my career, we had to do custom screws and we had to do the 2D drawings for everything and we there's a lot of things in CAD that are not as fun. tolerance stacks. We need them. How

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

properly? But it's not fun. It's not the most fun part. Maybe for some of us, but not for me. And so doing these things, being able to do these things in AI would be amazing. So you can focus on actually doing the fun stuff. Another

good thing is PCB, a printed circuit board has a lot of layers in the inside and then components that go on the top.

And if you've ever opened anything um like a calculator or a computer and looked inside, you know what I'm talking about. These printing circuit boards.

about. These printing 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 uh some basic um component selection and and layout on these

boards. So that's the kind of where

boards. So that's the kind of where we're at right now. We're not in a point, Lenny, where day-to-day mechanical or electrical engineering, like the the meat and potatoes of it, is being done by AI. But there's a huge

amount that you can do as an engineer using AI in your strategy, your planning, your your ability to think through the complex dependencies that you're facing. And

that's what I use it for now, which is really high level planning, asking for information. Like when I look at who

information. Like when I look at who else has make a product like this, you know, I use AI to build the databases and they're not perfect. Certainly a lot of times something's wrong, but it is so

much faster. AI is pretty good in Excel

much faster. AI is pretty good in Excel right now. And of course, Excel is one

right now. And of course, Excel is one of our favorite tools um in engineering.

So the ability to actually rapidly make Excel spreadsheets and change them is is it's it doesn't sound sexy, but actually really speeds up the design process outside of these these core these core

pieces. I love how Excel is always at

pieces. 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

Mars. There's an Excel spreadsheet that's probably driving a lot of this.

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

Yeah. And my big question is what is it going to take? So, a lot of um a lot of AI is based on LLMs, which are essentially word word generators, word

guessers. Um they're more complicated

guessers. Um they're more complicated than that, but that's essentially what they're doing. And there's also video

they're doing. And there's also video models that you've seen that are trained on video. But these models don't

on video. But these models don't understand uh they're not very good for what I need, which is I need to know, hey, you take a piece of paper, you fold it four times, and you do this. Like,

where's the hole going to be? like 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

that. They don't have the ability to understand friction or weight or contact uh pressure uh friction surface texture, like they're just not

able to do these things. And this is the core of what we need in engineering to be able to understand to build things.

So, some world models um, may actually be able to do this in the future. And I

I suspect we may need those models to be the base of CAD and and other uh physical engineering work. And so my frustration and this is like a healthy

frustration is I want codecs for engineering. I want codecs for hardware

engineering. I want codecs for hardware engineering and it's extremely valuable and I've used a lot for other things but I want it for my field and and so what I think it may require is new model types.

Sounds like an opportunity to me.

Uh I know there's a bunch of world lab companies. Fay was on the podcast with

companies. Fay was on the podcast with um World Labs. I think it's called World Labs. Yeah. And then I know Google's

Labs. Yeah. And then I know Google's building Gemini. So do you feel like

building Gemini. So do you feel like those are the right directions or it's just like now we need something actually different?

I don't actually know what the latest what FE is working on. Um but obviously uh she's brilliant uh roboticist and and I'd love to learn more about what she's doing. So I'll have to look that up. Uh

doing. So I'll have to look that up. Uh

what I've seen is that what we have right now and what models we're building are going to be part of the solution but not all of it. Coming back to robots and humanoids, something that uh we were chatting about this earlier. Your sense

is humanoid robots aren't necessarily the the answer to a lot of the problems that we have and opportunities that exist. Talk about just your sense of

exist. Talk about just your sense of humanoids versus non-humanoid robots.

Yeah, I think there's there's a hype cycle around humanoids. That doesn't

mean they're not extremely interesting and and I think there's going to be winners there, but what I hear a lot is I want a generalist robot shape to do everything.

And I don't know that that works. I

think that you need different types of robots to do different types of things.

For example, if you've got a laptop and you want to put, you know, if you want to screw together the the the the keyboard to the case, this is not a job, I don't think, for a humanoid. This is a

job for a dedicated robot, manufacturing robot that has been designed just to screw 10 screws into a case for this

specific laptop. and you want to do that

specific laptop. and you want to do that 10,000 times a day or something or 10,000 times a week or something, that's a dedicated robot that's specifically intended to do that thing. And what I

think is interesting here is you can have standard cabinet sizes for automation robots and you can have them be modifiable over time. And that's a 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 um in China at the top tier of tier one suppliers, there's not very many people on the line anyway. The entire printed circuit board

anyway. The entire printed circuit board line is essentially got no people on it anymore. The raw board is going through

anymore. The raw board is going through and getting reflowed and getting checked and the whole thing is being done without humans unless there's something goes wrong and a human runs over and

fixes something. So in assembly,

fixes something. So in assembly, mechanical assembly, the same thing.

These most advanced lines, they don't have people working very much. They they

used to have 200 people. They might have 10 now. And so we've already kind of

10 now. And so we've already kind of moved past human labor in a lot of this most advanced manufacturing. Um and so we don't actually need to replace humans

with humanoids. We just need more of

with humanoids. We just need more of these dedicated robots. So my suspicion is we'll have humanoids for some longtail things that we need to do that humans are currently doing. That will be

important. But we'll also have robots

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.

That makes all the sense in the world.

What I think about as you talk about this is 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 CAD can once AI can

develop designs full designs for hard like 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.

If you're talking about robots building robots that are different than them usually, like yes, I think that that's going to happen. And but 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

build itself. I don't think that that's what it's going to look like. But yeah,

having AI be able to if you could say, "Hey, I want to build this thing and I want it to do this and like this is kind of how I want it to look and here's a picture."

picture." The idea that you could even as a hobbyist go from a 2D picture to complex 3D CAD to assemblies to communication

with vendors of how to make those parts and getting their feedback to iterating on that and doing a couple builds like that is possible I think in the future.

Will it be good as good in the beginning as us doing it? No.

Because but but it will be it will happen. The biggest challenge here,

happen. The biggest challenge here, Lenny, is actually the data. This CAD

data is some of the most valuable IP that anybody has. And Samsung or um uh Madic to pick on Madic, like they're not

going to want to give their 3D CAD to a model vendor, to 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. So this is kind of where I'm interested in this starting which is, you know, maybe a hobbyist isn't an expert in printed circuit board design. Maybe they don't

care. They just want their drone to be

care. They just want their drone to be fast and to beat this other guy's drone or whatever. This is where I think

or whatever. This is where I think you're going to start seeing all this start. And then probably the big uh the

start. And then probably the big uh the big incumbents are going to be slower because they have dedicated tools and a lot of IP privacy.

It's really interesting this idea of uh what data AI models need to train on. Uh

I I've been hearing that labs are buying uh code like GitHub repos pre2021 because that's before AI you know has impacted the code because there's less

and less of human written code. It feel

and and these are data labeling companies like mercor and surge and handshake and things like that. Feels

like this is a big opportunity that might emerge as them selling data creating these CAT files.

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 inside safely inside essentially the the the walls of companies and have them then train it on their own data. I don't

know if that's going to be like a the equivalent of an MCP layer or what that's going to be, but this seems doable in the long term.

I want to ask a question uh that my sister suggested. She was she's actually

sister suggested. She was she's actually been a longtime VR person. She was at Oculus. She joined with the acquisition.

Oculus. She joined with the acquisition.

She helped create a lot of content within VR. She's just been like in the

within VR. She's just been like in the VR world for a long time and now she's working on other things. She want me to ask you, what does it take to create a robot that feels human and connected

that humans feel connected to? It's a

great question. So, I'm new, relatively speaking, to robotics. And so, I had to I had to learn as much as I could, as fast as I could. And one of the researchers that helped me the most, her

name's Leila Takyama. She's an expert at this. And what she explained to me is

this. And what she explained to me is that humans have a certain expectation about how other beings are going to respond when they enter a space. Um, you

really want to, you know, when someone walks into room, you kind of acknowledge them. You might not talk to them, but

them. You might not talk to them, but you kind of look up. There is a lot of very complex um nonverbal cues that we

give to each other. And if you walk into a room and a robot's just like like it's creepy and it's easy to be creepy. I'm a

little surprised with some notable exceptions how creepy a lot of these humanoids are right now. You want, I think, these devices to be

non-threatening. Generally speaking, you

non-threatening. Generally speaking, you want them to appear soft. You want them to appear reactive to you. You want to have a sense that they know that you're

there. Um that they're attentive to you,

there. Um that they're attentive to you, that they're there to help you and and make your work life easier. And um you

also expect them to intentionally or to show their intent before they do something. And so one of the things I learned is if a robot just suddenly turns and does all this stuff,

it scares you. But if a robot looks before it turns and then goes, it's much less alarming. So there's all these

less alarming. So there's all these little pieces. Um, and I recommend

little pieces. Um, and I recommend anyone to go look at her work. Um,

there's a lot of great research here about how to not necessarily with a humanoid, but how to have any robot both respond properly in a social context

with a someone entering a room or exiting a room, but also um transmit its intent physically so it doesn't surprise you.

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

time. Yeah, I actually think um Pixar, Disney are probably the world's best at doing this type of design work. Even

though they haven't done as much in physical in volume, if you look at what they do and how they show emotion,

intent, um approachability, engagement, and with their characters, they're really world class. I don't know about you, but I'm so excited to have a robot at home doing things. Like these videos that they're starting to put out where

they're doing your like they can do dishes like at least the prototypes they can like fold laundry. They can do it's like yes, please come do this for me.

How do you feel about robots in your house?

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

So I'm very lucky to have a partner who's who's got a high bar which means you know was like never going to take Whimo. took one Whimo and now never

Whimo. took one Whimo 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 Madic, you know, it's amazing. And so so it's that but the bar is pretty high. So I think in order to have a home robot, it's going to have to be pretty incredible for us her to be willing to have it in

our home. But I I take that as a

our home. But I I take that as a challenge.

I My wife is exactly the same way. She's

like, I want this thing in our house, Matt. Oh, wow. This is so cute. the a

Matt. Oh, wow. This is so cute. the a

recent example as self-driving Tesla.

She used to be so like no don't don't do that and 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

your car. I don't want to do that anymore. It's crazy how quickly that

anymore. It's crazy how quickly that changes. So there's a big difference in

changes. So there's a big difference in my mind. This is like a big categorical

my mind. This is like a big categorical difference. There's a big difference

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

data. When you talk about homes, what is the delta? You have now a thing that you

the delta? You have now a thing that you didn't have before doing things. So if

it's like bad at it, like what are you relating it to? And if it's unsafe in any way, like what are you relating that to? It's a much harder equation in my

to? It's a much harder equation in my mind to get uh to a lot of people than a car where you can say, "Hey, Whimo saved lives." You know, you're going to have a

lives." You know, you're going to have a fraction of the deaths using a Whimo, whether you're a passenger or you're not. When you already see people in San

not. When you already see people in San Francisco adapting how they respond around a Whimo versus any other car. So,

you're seeing behavioral changes that are based on trust, which is really cool. When you're talking about a new

cool. When you're talking about a new product that hasn't existed yet and is not essentially replacing something, that's a harder sell and you have to have a different story.

Something that someone needs to figure out what the Tesla self-driving is like when you, you know, often you're like at a stop and you like make eye contact and go ahead, go ahead. Or like someone's about to cross and you're like, uh,

okay, go ahead. But like the Tesla just does its own thing and so it's like makes you look like an a bunch of times. Like, oh, I'm I'm not driving.

of times. Like, oh, I'm I'm not driving.

It's not in control.

Yeah, I I had that happen once. I you

almost want a little two arms in the front to do like gesturing or like you go or something like it's amazing how much we actually rely on Yeah.

this human connection to decide you know who's going to go in an intersection.

Yeah. Okay. So, zooming out a little bit just what's cool about people like you is you you're thinking and building things that will exist in the future. You're kind of like living in the future and designing

it and you are one of the few people that has a glimpse into where things are going. So I'm curious just to ask like

going. So I'm curious just to ask like say in the next say in five years what is kind of the vision you have of what is different about our day-to-day robots devices just like what does it look like

I don't you know just roughly 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 3 years from now but also something that will ladder up to what we want six years from now because In my field, it it's a lot easier to make something and iterate

on it and iterate towards a final goal than to do a oneshot thing perfectly.

So, not only do you have to have a sense of what the first thing needs to be like and look like, you have to have a sense of what the third thing ideally or the the platonic ideal of the thing will eventually look like. So, you have to you do have to think about the future

and live in the future. I have this weird thing where I love to think about the future, but I'm also a skeptic. And

you really want me to be a skeptic because if I think everything's going to be fine, the hardware is not going to work. You really want me to be like,

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 like be be like kind of worried about all these things going wrong." So, this is kind of a an

wrong." So, this is kind of a an interesting uh disagreement inside of me of like what I want the future to look like and what I think it's going to look like and what it's actually going to look like

and 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 years especially. You're already seeing

years especially. You're already seeing it. Obviously anybody who codes is not

it. Obviously anybody who codes is not coding by hand very much anymore.

Any knowledge work this is going to hit next I think and and and progressively um affect our economy and our work. But

it seems like the physical world is less likely to change as quickly outside of drones, self-driving cars. Um, you're

going to see more and more robots. But

I'm not somebody who says I'm not somebody who thinks that in 5 years you're going to have a, you know, 20 million robots. I don't think that it's

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.

and do supply chain uh reliability, uh raw material access, and then we need to figure out how to make factories again in this country for high-tech. So,

that's a lot of work, but in the interim, you're going to start seeing a lot of weird things on the street. You

might see robots on the street. You have

you seen any delivery robots in in your world Lenny before?

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

Yeah.

Yeah. Yeah. So, this is just going to continue happening and I think we're just gonna 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

robotics. I think I think there's probably more change in war than there is in consumer electronics in the next two years, for example.

Wow, what a statement.

Yeah.

And I totally agree. like like there's nothing like war to uh incentivize innovation and just like endless improvement and trying to get ahead of the other side especially when democracies at stake. I

mean I think that we are and I don't want to be like you know on a high horse or something but I do think that we're in a place where we need to think about things and the future in these terms um

and defend these things with with our capabilities while also hoping that we never have to have you know hot conflict anywhere. Along those lines, uh I have

anywhere. Along those lines, uh I have to ask you uh recently you became famous uh on Twitter at least uh for quitting

OpenAI. Uh you tweeted that you were

OpenAI. Uh you tweeted that you were leaving and with your brief explanation and got 7 million views, 50 I don't know, 8,000 likes. Uh what happened? Why

did you leave OpenAI? What happened

there?

Yeah, I I hope so. What I said in my tweet was that I have a lot of friends in the executive side of OpenAI that I I care a lot

about. I think are really good people

about. I think are really good people and I feel that what happened with the decision-m the speed of the decision-m the governance and the lack of defined

guard rails around the announcement of the department of war deal is not how I thought it should have been done. Um,

and both of those things can be true.

And so my hope, Lenny, was that there's a third path. Like you see a lot of people just kind of going along with what their company's doing. And then you see some people that are kind of scorched earth about it. Um, in this

case, that didn't make sense for me. I

didn't feel that way about the company.

OpenAI was isn't an amazing company. and

um I was able to help build a robotics program there and and kind of attract some of the top talent in robotics I think in the world. And so I have a lot

of I don't know, you know, this is this is a this is a group of people I care a lot about. And you can also disagree

lot about. And you can also disagree with friends and feel like what they did isn't good and isn't isn't right. And um

that's where I that's where I ended up and that's what I tweeted about. um it

was going to get reported on so I tweeted before that happened.

This is a great opportunity to just just whisper to me what Open Eye is working on. What is what is this robotics device

on. What is what is this robotics device that just like just between you and me?

Yeah, 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

does. But part of the flip side of that is we can't talk about anything internal or any IP. What I can say is the team's really strong and um I was really really grateful for the opportunity to to help.

But I also thought thought that after what happened happened, it was time for me to um to to I couldn't continue to work there because you don't know what's going to happen next time. And um my

hope was that my decision um made it easier for other folks to talk about what their boundaries were and hold them and and and you know, we'll see what happens there. So speaking of of team

happens there. So speaking of of team building, this is something I definitely wanted to ask you about. So as I said, I asked a bunch of people what to talk to you about and someone that I think it was maybe a colleague, former colleague, Mariana Senko. Did you work with her?

Mariana Senko. Did you work with her?

Okay, she's a Yeah.

Okay, she's a friend. So she told me that here's what she said about you.

That your brilliance as a leader lies in hiring exceptional teams. 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.

Yeah, I I'm lucky that I've had a lot of time, a lot of like reps basically on hiring people. And so I have a a

hiring people. And so I have a a strategy of of hiring great people. When

you're hiring for 0ero 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 um built the type of robot that can move through the world the way,

you know, I'm interested in in the millions because it hasn't been done.

So, you have to start thinking about how do you build a team that can do something new. And the nice thing is

something new. And the nice thing is actually in robotics, um, self-driving cars, autonomous vehicles is a really good place to look because you've got the sensing stack and you've got a lot of the safety trade-offs actually and

it's a lot of the hard engineering, the hardcore engineering. So, that's a place

hardcore engineering. So, that's a place that I looked. Um, obviously you want some hardcore roboticists who can do, you know, robot design from scratch. And

these are really people even though they might have a degree in something, they're really hybrid people. They're

generalist people. So, one of the one of the key principles I'm looking for is a lot of really strong generalists who can adapt what they've learned in other fields to a new field and people with a

lot of experience building. You want

some people who have experience building the thing that you're building that's new and some people who have experience scaling other things that um to to hire on. So, you need to look at that. And

on. So, you need to look at that. And

then with young people, this is where it gets really fun, Lenny, is the only AI native people essentially who use AI so natively that it's like baked into their

engineering process are 20 years old or 21 years old or 20. I mean, it's very hard to find someone who's in their 30s who can be truly fully AI native. And

so, we need these folks to teach us how to think. and and I've had the

to think. and and I've had the opportunity to work with a few folks in that age range. They're approaching

their problem solving completely differently because they're using AI from the ground up for everything and um they're much faster actually and it's really fun to watch. So figuring out how

to get these AI natives to teach us the rest of us how they think about AI when it's you know we are you and I think I can say are uh digital natives where we grew up maybe there wasn't internet when

we were really young but we are the generation that had the first you know internet we we were teenagers and we are the generation that had the first cell phones really in scale and we are we're

an important generation because we had the first I I remember freshman year at Stanford we had the first data like databases that you could access and you could share movies on I think is what we

did and music on or whatever it was. But

this was new and so we were native in these things and that gave us a lot of oomph in creating new technologies for it. But we have to accept that we're not

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. That last bucket is a

very common trend on this podcast when we talk about hiring, which is really cool as a counternarrative to there's no more jobs for young people. All the

junior roles are erased because of AI.

Yeah, I don't see it that way. I think

we need them. I I also think that we need to build new technologists.

Like there's a there's the obvious question of what happens if we don't have teams that were have senior and junior people. But I think what you find

junior people. But I think what you find when you actually build these teams is you have to have both. you must have both. The the team size just might be a

both. The the team size just might be a little bit smaller than it used to be.

Um when this AI revolution in hardware happens, I don't know how that's going to affect the teams. That will be really interesting to watch.

So what I heard here is just uh look for a generalists that can flex based on whatever needs to be done. Uh some

mixture of specialist and like scaling versus zero to one. And then these uh the best term I've heard for this is cracked new grads.

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

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 for the in the same direction is really important. And then I I rely a lot,

important. And then I I rely a lot, Lenny, on my gut feel for people, assuming everything else that I'm looking for has been checked. So, um I don't it's hard to talk about what that

means, but usually it's that spark that you're looking for in someone that they're genuinely motivated. They're

they're motivated by a desire to learn and and by excellence. They're motivated

to learn from the people around them.

They're open to updating their point of view based on new information. and they

they they want to they want to win. I

mean, this these are the things that really matter when you're when you're building a team.

Awesome. Okay, just a couple more questions. Something I've been wanting

questions. Something I've been wanting to ask for a long time is uh you've worked with some of the most legendary successful builders. Uh Steve Jobs,

successful builders. Uh Steve Jobs, Johnny IV, Mark Zuckerberg, Sam Alman.

You don't have to go through all four, but just what's a lesson you learned from as many of these folks that that come to mind? So, we'll start with Sam

because most recently Sam is really good at saying,"Why not more? Why not 100x or 10,000x?

You're thinking too small. Why not think about this bigger?" And every time we talked about something important, he talked about that. And what I realized

is I was thinking too small in certain areas and he was thinking globally. And

having that nudge from a leader who's ambitious is really helpful, I think. So

that was that was a big thing that I learned from him um about he's willing to he's willing to go for it at high volume and and invest um depending on um

meaning not high meaning hitting a lot of people, you know, he's willing to think in very big numbers. That was

really really foundationally important.

I think for Steve it's Steve Jobs is just uh the bar he held for the company and for technical talent and for excellence was not uh wavering. It was

not it was 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. I'm like, you know, it's not

actually. I'm like, you know, it's not it doesn't quite hit the way you would think. And if you tell somebody, hey,

think. And if you tell somebody, hey, this needs to be better. Like, you need to spend more time on this. You need to be more thoughtful about this or this is not hitting our quality bar in a CAD review or something, that's impactful,

and I think you never want to hear that again. So, it's very, very motivating.

again. So, it's very, very motivating.

And then Mark Zuckerberg, I think that he I have to say he ran a company very, very well. So the way that

the the technical side of the company operated, the way that we had reviews, that decisions were made, the decisions were made at the lowest

level possible in the company to maintain speed. Um I I underappreciated

maintain speed. Um I I underappreciated how clean and well-run um the hardware the the way that the hardware

organization interacted with the rest of the company. It was very clear. This is

the company. It was very clear. This is

what we're going for. Um, we're going to have this review. Um, we're going to make a decision in this review. If you

can make the decision without the review, you will do that. Here is are the objectives for for this project. It

was really well executed. And I think that's hard to do at a fast growing company. It's very h uh hard to do at

company. It's very h uh hard to do at that level. and having him and Andrew

that level. and having him and Andrew Bosworth, the CTO, involved in the technical decisions, able to read, you know, reports that were maybe 20 pages long, grock the trade-offs, understand

them, and be able to contribute to the technical discussion. And that's just on

technical discussion. And that's just on my thing that week and they're doing that, you know, a hundred times that month. Um, was was impressive and and

month. Um, was was impressive and and definitely something I learned from them.

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 different all these places.

I know. And I think that that's where why I'm I'm I'm looking for this 0ero to one. And so when you're looking for a

one. And so when you're looking for a 0ero to1 opportunity, it's always going to be in some place different generally.

Uh you're going to be a hot commodity in this market now that you're a free agent. Uh but uh on the flip side of

agent. Uh but uh on the flip side of that, I want to take us to fail corner.

Uh I feel like someone building hardware, physical things has some great fail stories. Is there one story of

fail stories. Is there one story of something something you built, something you worked on that that failed and something you learned from that experience?

This is a great question and not a comfortable one. Um, one of the one of

comfortable one. Um, one of the one of my favorite failures was actually on the Quest one. It was around EVT, so halfway

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 about this. We needed to reduce the price so more people could buy them.

And what happened was it was right before Christmas and I heard from the lead on the team that does um computer

vision and he said, "Oh my gosh, the the cameras the data from the cameras isn't working and we can't get a lock on where the person is using the headset." And so

we looked into it and we realized that their interpretation of our spec and our interpretation of our spec was different. So uh in engineering we

different. So uh in engineering we usually use a plus or minus like it can go up or down by in this case I think it was 0.15 mm or something like that. Um

and in in his world he was used to having a global it's within 1.5.15 mm and so we had a different interpretation of the spec. Now, the

problem is that that our interpretation of the spec meant that he couldn't meet his his goals of being able to understand where the headset was in space. And so, we had to do a redesign.

space. And so, we had to do a redesign.

And this is at EBT. So, this is pretty much when you want the engineering to be done.

This is a build.

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 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

machining them. So it's a big deal. And

so what we had to do was um favor or prioritize. We had four floating

prioritize. We had four floating cameras. We had to lock the bottom two

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

failure. I mean it was a failure in understanding the spec. It was a failure in um uh in the essentially the product design but it was because of a misunderstanding of the spec. And so we

were able to adapt. We actually kept the build on time and we actually shipped the product on time, but it was really stressful. And it turned out that

stressful. And it turned out that actually the new design was better because with a favored pair, you have source of truth for the space and then the other two cameras overlap onto that

source of truth. And so it worked well.

I thought um it was a good outcome, but it was a scramble and certainly wish that it that we caught it, you know, four months earlier.

Another example of just how hardware is.

just you can't like you mess up a spec and like all right here we wasted a week building something that didn't work and now it's like four months later still having to redo the hardware supply chain.

Yeah. Yeah. It was it was tricky.

So the Quest one that shipped was this with the cameras moved.

Yeah.

Wow.

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

Wow. How did uh Bos and Zuck uh feel about this?

I the fact that I don't remember probably means Okay. Like um I think we we we addressed it. We redesigned it. Um

we had to change the material on the bracket. I think we had to make steel to

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 and yields were fine. So I think we we adapted.

And that was the bestselling VR device of all time. Is that right?

I think it was.

Okay.

I don't have the final sales number, but Okay. Concerned. Okay. Uh Caitlyn, we've

Okay. Concerned. Okay. Uh Caitlyn, 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 some

listeners with? Either double down some we've shared or anything else that just like, oh, here's something I want to share.

I think that this is probably one of the most exciting times we're coming into.

And it's normal, I think, for all of us, myself included, to be worried and scared about it, but I also think it's an opportunity for people to do an extraordinary amount, have an

extraordinary amount of progress, and be able to as an individual do more than we've ever done before. And so that's the side I'm trying to embrace. These

new tools, these 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

comes next. And so I just want to encourage everyone to be creative, use these tools, have fun with them, um figure out what the boundaries are, and then every time a new model comes out, test again because it's really important

to know what we're dealing with and where these boundaries are. Um but I'm also I've never been more excited about the power of an individual.

Well, with that, Caitlyn, we've reached our very exciting lightning round. I've

got five questions for you. Are you

ready?

I'm ready. First question, what are two or three books that you find yourself recommending most to other people?

I've been mostly reading the classics lately. So, uh, Book of the New Sun is a

lately. So, uh, Book of the New Sun is a great fiction book, which I really recommend. I think that's what it's

recommend. I think that's what it's called. Um, I haven't read it in a

called. Um, I haven't read it in a little while. I I I love Mrs. Dalloway,

little while. I I I love Mrs. Dalloway, actually. I think it's a very

actually. I think it's a very interesting book about transitions, and it was a post-war book um, by Virginia Wolf. So I I really love

it and I think it's really wonderful. Um

I think Herodotus histories is pretty incredible. He's wrong a lot but he's

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 you know firstirhand or secondhand uh

accounts of what happens. It's it's it's a way to look into the world at a completely different era than it is now.

So these are some books that I like and uh I'll double check the the first the title of the first one and email you.

But I think that's what it's called.

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

I am really into Euphoria right now. I think

the new Euphoria. I'm I'm I'm interested in the characters um and um figuring that out uh what's going to happen there.

The show's so stressful whenever I watch it's a 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.

Okay.

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

I really like Volibac. Um, the clothes.

Um, they make really interesting clothes. They're essentially basing

clothes. They're essentially basing their new clothes on material science.

So, they take new material science and make them into clothes. Um, it's just a fun brand to follow.

Vol.

V O L L E B A K. Vol.

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

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.

Yeah.

Yeah. You know who it was from? I didn't

know who. Um this is I think about it a lot because it's very hard not to get stuck in the future or the past and stay kind of here. I have trouble with that.

But um that is a great reminder that you know you get to pick every day. You get

to decide every day what you 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

matter. What matters is what's right in front of you.

We'll uh either show that image on the screen as you say that or we'll link to it in the show notes. It's so powerful.

Final question. Somebody that uh that knows you well shared this really interesting tidbit about you that you hired a PhD to tutor you on the 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?

This is like very nation nerd territory, but I found this um list that the poet Joseph Broski Brodzky wrote, which is a

list of English uh or a list of things you should have read in order to have an intelligent uh conversation in English.

And it is like an affected list like it's in, you know, it's intense. It's

like the Old Testament, Gilgamesh, and then all the way down through. But what

I found is it's a pretty good um distillation of what we used to call the western cannon. And that actually I

western cannon. And that actually I learned a lot in in my public school education and in college, but I never really learned from what you would consider the Western canon. Um and so

this is kind of um in addition to that, there's some some more newer newer um books on the list. I find it just fascinating to have something to work off of. And what I found is as I got

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

learn. And just reading them, I didn't have enough uptake. So I found an incredible um uh postoc who's was willing to tutor me. And I just get to ask him all these questions. He's an

encyclopedia. He knows everything. I

could ask him what was happening in Turkey at the time of this Greek, you know, this tragedy that we're reading and like what was happening in Athens and like what this, you know, tragidian

might be responding to. Um, and and he can answer the question. It's really fun to have the ability to have that sounding board.

So cool. It's so cool you did that. Even

though AI can do a lot of this, like sometimes a human is much more interesting to talk to you and and feels better.

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

isn't adequate.

It's because we're uh we're not 20some wrong.

I don't think it's wrong. We were just not we didn't grow up this way. And I

imagine, you know, college students, they would just not why would I do that?

I'm just I have Claude here.

Yeah.

So cool. I love that. Well, uh this was incredible. You're amazing. Where can

incredible. You're amazing. Where can

folks find you online if they want to find you? If they want to reach out, I

find you? If they want to reach out, I don't know, try to hire you. And uh what can And then final question, how can listeners be useful to you?

So I have a website which is just my name.com. I'm also on LinkedIn. Um

name.com. I'm also on LinkedIn. Um

people can help me. I think helping imagine the future. This is not a single player game. This is a multiplayer game.

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 we think we want to hold for ourselves. um how we want to augment

ourselves. um how we want to augment ourselves like right now we're in this dystopian niche where everything's just you know hor 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 um you know I think that that's possible I love that that's actually a message that's come on a a couple of recent

podcast episodes. So, it's a really good

podcast episodes. So, it's a really good reminder. Uh, Caitlyn, thank you so much

reminder. Uh, Caitlyn, thank you so much for being here. It

was really fun, Lenny. Thanks for having me.

Bye, everyone. Thank you so much for listening. If you found this valuable,

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