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

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 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 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 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 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 original unibody MacBook Pro teams and technical lead on

the MacBook Air and Mac Pro at Apple. She led the AR glasses hardware team at Meta, including the team behind Orion, their most advanced AR product.

Before that, she ran the VR hardware team at Meta, where she helped design all of their incredible

VR devices like the Rift and the Quest. Most recently, she was at OpenAI, helping build their robotics and hardware division from scratch. Robots and

hardware and physical AI are so hot right now. Every AI company, and so many startups are launching, building AI hardware products, and Caitlyn has

been at the center of this emerging field for decades. This conversation goes in a lot of different

directions, many that I did not expect, and I hope to do a lot more episodes on the hardware side of building over the next few months. Before we get

into it, don't forget to check out lenny's productass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. With

that, I bring you Caitlyn Kalinowski. Caitlyn, thank you so much for being here. Welcome to the

podcast. >> Thank you so much for having me. I'm excited to be here. We're going to go in a bunch of different directions. I'm going to bounce around.

I want to talk about VR. So much money, so many resources. So many smart people have been working on VR for so long. Meta spent, I don't know, 10

billion 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 leaning out now. Feels like Meta 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 experience that you've unlike anything you've ever experienced still has not

caught on. What happened? Is there still a future where VR catches on or is the future kind of AR and something else? 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 if you're wearing a

VR headset and driving the robot. It's the same real technology. And so for me, I view it as a step in a long technological um arc. And to be honest,

as an as someone who's not using VR a lot right now, I'm really glad that we did it. But I don't

think it I expected it to be big obviously 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 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 go? What's kind of like where do you think things are going? Is it AR glasses 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 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 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 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 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 by our

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about robots, 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 that show compsite enrollment down and where it's actually going up is

hardware robotics which I imagine as someone that has been in this field for a long time is very weird

because it's never been that popular. 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 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

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

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 people don't think about when they think about, "Okay, we're going to build some

hardware." What are some of the surprising challenges that come up? >> 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 compile our code

quote unquote like four or five times. And >> four or five times a year or >> total, >> 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 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 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 we're going to have a high yield. we're gonna be able to make them and make money on them effectively and we won't have very many

returns. And so that's kind of the game that we're playing. >> 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 quickly. We

already have them. They're going to get better and more comprehensive and more capable. If you think about that as a frontier, you can see the end of

that tunnel. Now, I don't know when it's going to be again, but we can see that's going to saturate at some point or at least people think it will.

And when that happens, the next frontier is hardware. The next frontier is robotics, manufacturing,

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

about the compliance of the arm. If it's just hard, then you know the impulse is high. But if it's

soft and compressible, then the impulse is 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 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 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 warning right now. >> That is so interesting. Uh it's funny to hear that. At the same time, there's these

nunchuck wielding robots in China doing dances with other folks. Uh I've never thought about just that part

of it, like 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

hundreds of thousands. You've got to get a good design that's running. Then you've got to make it reliable enough that it can keep running day data

day to day without a lot of human intervention 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 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 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 is you can start with raw materials and magnets is a good place to start. So we need to

be able to get the magnets the raw magnets for example. Then we need to be able to process them. Then we

need to be able to integrate them into actuators and build the actuators around them. Then we need to be able to integrate those actuators into

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

um Japan, like Korea. And so, and I and I full transparency, I've been part of that transfer of of

engineering knowledge to Asia. In Asia, the expertise has historically been scale and being able to build a lot of these parts at lower prices. we've

had this kind of deal across these borders that this is how we're going to operate for the most part. Now, of course, there's things we make in this

country still. 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 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 batteries of course. Um there's there's actuators. The raw parts like the diecast parts. Um the machine parts are less

critical. We think we can get those. Um but 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 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 rotates and the way it rotates is you have alternating current essentially and so the magnets make

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 you talk about

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

different the world is now that you can build these little drones that go and you know blow people up.

Robots are a part of that. It's just like such a existential threat to every country now. Uh the ability to build these things at scale. What's your

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

drones. It's another good example. You need essentially the same technology to make the rotor spin on a

drone as you do to make an arm move on a robot. It's essentially the same base 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 what's going to happen in the future. And people that are your

allies now may not be in the future. Um, the Allied West, I think, 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 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 Palmer Lucky is a friend of mine. Um

and we don't agree on everything, but I do think that we agree 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 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 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 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 for companies that create lethal technology. Um and uh 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 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, 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 address and my I don't know what it was. I I gave it like uh some

information about one of my accounts or something like that and I added it to the social media like I can't remember what it's called the 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 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 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 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. One, hardware is a a first tier citizen at Apple.

There's a lot of companies where hardware isn't part of the core product development conversation as much, but 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

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

do really good experiments um in hardware and figure out what the best outcome is. But there's something underneath that which is understanding the

first principles of why are we building it this way and what are the key outcomes we're looking for?

And actually John 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 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 deep at Apple where every single design decision even on the

inside of the device is considered. And this isn't just uh an aesthetic decision. What it does is actually force the engineering, industrial design,

um, operations community there to think about what are we really doing and what's the core of what's happening for this part, for this assembly, for

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

methodical, what really matters tends to rise out and look very simple at the end. And so I 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 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 actually met on forums. You you might know this, Lenny. Um who were hacking like PlayStations or Super Nintendos into portable

backpacks and so there was an ethos at the company that was actually quite good for the DNA of a

hardware team. 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 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 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 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 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 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 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 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 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 to say, "Okay, we want to make

something that's $300." And then halfway through you say, "Oh, it actually has to be $150." You've almost burned a lot of that early time. So you kind

of need to have a sense of having pre-thought out what you want and having those I like to call them

KPIs but essentially goals written down and try to change them as little as possible. So that is very tough. In fact 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 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 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 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's one thing. The second thing is we tend to

design the things that we know how to design first. And actually the right approach is to design the hardest parts first. One example will be and

there's no IP here. So I'm obviously not going to share any any IP or anything internal, but at one

point we had to route cables through a hinge in a in a device in a laptop we were making. And because it wasn't clear that those cables would fit,

that's where the architect started. and he looked at the cross the diameter and how to split the cables out and made sure that they would fit before

finalizing the hinge design. A lot of people would start at the part they knew like 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 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 keyboard next. So those things have to be really good. They have to feel good. They have

to respond properly. They have to be highly reliable. And then maybe the other pieces further out um don't take quite as much iteration. So you have

to boost your iteration on the things that people touch the most or interact with the most. Um so

those are kind of some principles uh that I wrote about. But these are just things that you learn uh trying to build quickly. And the last piece

that's really critical if you're making hardware for folks out there who are trying to make hardware is you can't wait around ever. Like there's never

enough time. So if you know that you need to do something, what I learned from 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. The the riskiest piece essentially

uh to physically build. Three is focus on the pieces that people will use most. Say the trackpad, uh,

a keyboard. I 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 displays 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 obviously you're talking about clock speed, you're talking about um how many parallel processes

you can run, you're talking about weight, um you're talking about price, um and you're talking about features. So when we did the MacBook Air, it

became very clear because we were machining it that there are certain features like uh um ambient light

sensor that we just didn't make sense anymore. And so being willing to just jettison them uh 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 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 first MacBook Air was a pretty low volume device um

that was machined but kind of had a proof more of a proof of what could be done. And that was the Manila envelope one I think where the side door

opened out to give you the port and it kind of had a it had this shape underneath. And then the next rev of that was the MacBook Air that we know

which was essentially which is wedge 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 focusing on things that people use the most. Uh famously

Apple screwed up this 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 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 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 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 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 that you're getting at, which is, and this is true for

anybody building a new product with a new feature. And I've tried to build as much as I can teams that

work on products that are have something new about them. Either they're a new category or there's a new um manufacturing process or something that

hasn't been done before. And when you're thinking about this, you can't really use what you learned from the same field and the same product class.

Like it just doesn't work because you actually won't get the answer right. And I think this is actually

what um Steve was talking about, which is you can't get intuition if you're changing something fundamentally. Like your customers won't know what they

want because they haven't seen it. But if you show it to them, they will absolutely know that it's awesome and that 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 don't know for sure, I didn't talk to him about this, but that's my view of what that means. >> I am so

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That's vanta.com/lenny. 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 founder of Madic, the CEO of Madic, Mahul 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 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 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? >> 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 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 category uh let's see this happened in COVID too. Okay. So like we had so many supply chain disruptions and getting enough memory 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

pre-bought enough that you can ride things out. And so those are the only real options. Um, obviously there's a risk to pre- buying and the price

might go down. And so the challenge is I think there's a latency with supply chain in something like memory

where it can't adapt fast enough often to demand or there's a new category of product or in this case maybe data centers that are just eating up so

much and are actually not as cost-sensitive as somebody in consumer electronics like Maddic might be and so they'll just pay for 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

happen. I think prices are going to double probably. Um I don't know on what timeline. If I knew what timeline the prices were going to double on, I'd

be trading, which I'm not very good at. Like I'd really be I'd do be doing a different job if I could

predict these things. Um 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 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 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 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 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 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 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 head. They

probably have between 50 and 150 parts. It's possible that they have more. I haven't seen their CAD, so I don't know what it's like inside their

device, but they do have a lot of things going on, right? They have the wheels of the device that are obviously moving around. Then they have a

vacuum, but they also have a mop, and obviously they have a vacuum bag. They have the uh the reservoir that uh

the liquid has to go in 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'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. 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 entire guts of your

product and then secure supply chain for these new things, build it again on the production line, test it

again, do all the reliability testing. It is non-trivial. And so this is why we care. So there's there's a hierarchy of components. Often in consumer

electronics, we start with um silicon and the display, which are the longest lead time things usually in my world. Um, in robots, actuators are pretty

tricky to get even just for prototyping. Sometimes it takes a month or two to buy an actuator. >>

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

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

whatever works off the shelf, whatever's fast, whatever you can get to quickly and then maintain a sense of what's really going to fit in your final

design. Is it capable? Are the processes and components and materials capable of actually adapting to

this size, this new weight that you need it to go into? So that's part of the calculus. When you move into mass production and the final design, it

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 custom because we have again one of those KPIs. I

want to be this size. I want it to be this weight. I want to be this color. And often 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 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 soft hardware is built? Obviously, it's

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

hardware and robots? >> Yeah. So I want to I want to zoom out a little bit. So most of the hardware work goes into prototyping into 3D CAD. So

designing 3D parts and assemblies and components and making sure they work together properly. Then in making sure those parts and components can be

made by a vendor at quantity that 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 is in my world is dense. Like, it has shape. It has nerves.

Like, you have a an equation for how the surfaces work. And it's an entity that's designed in CAD.

It's a solid entity. And so right now we're not quite there with AI doing CAD. I think it's likely that at some point we will be there. This will be

probably one of the biggest changes for my field that we have 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 does

seven parts fit together and are they always going to fit together properly? But it's not fun. It's not the most fun part. Maybe for some of us, but

not for me. And so 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. 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 layout on these 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 meat and potatoes of it, is being done by AI.

But there's a huge amount that you can do as an engineer using AI in your strategy, your planning,

your ability to think through the complex dependencies that you're facing. And that's what I use it for now, which is really high level planning,

asking for information. 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 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 it's it doesn't sound sexy, but actually really speeds up the design process outside of these core these core 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 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 generators, word

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

trained on video. But these models don't understand uh 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 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 other uh physical engineering work. And so my frustration and this is like

a healthy frustration is I want codecs for engineering. I want codecs for hardware engineering and it's extremely valuable and I've used a lot for

other things but I want it for my field 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 um World Labs. I think it's called World

Labs. Yeah. And then I know Google's building Gemini. So do you feel like those are the right directions or it's just like 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 I'd love to learn more about what she's doing. So I'll have to look that up. Uh what I've seen is that what we have right

now and what models we're building are 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 answer to a

lot of the problems that we have and opportunities that exist. Talk about just your sense of humanoids versus non-humanoid robots. >> Yeah, I think

there's there's a hype cycle around humanoids. That doesn't mean they're not extremely interesting and I think there's going to be winners there, but

what I hear a lot is I want a generalist robot shape to do everything. And I don't know that 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 keyboard to the case, this is not a job, I don't think, for a humanoid. This is a job for a dedicated robot,

manufacturing robot that has been designed just to screw 10 screws into a case for this specific laptop.

and you want to do that 10,000 times a day or something or 10,000 times a week or something, that's a dedicated robot that's specifically intended to

do that thing. 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 line is essentially got no people on it anymore. The raw board is going through and getting

reflowed and getting checked and the whole thing is being done without humans unless there's something goes wrong and a human runs over and fixes

something. So in assembly, mechanical assembly, the same thing. These most advanced lines, they don't have people working very much. They they used to

have 200 people. They might have 10 now. And so we've already kind of moved past human labor in a lot

of this most advanced manufacturing. Um and so we don't actually need to replace humans with humanoids. We just need more of these dedicated robots.

So my suspicion is we'll have humanoids for some longtail things that we need to do that humans are currently doing. That will be important. But we'll

also have robots that are for construction, robots that are for electrical work, robots that are for

very low volume assembly, maybe robots for logistics, and most of them are going to look different from each other. >> 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's going to

happen. And 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'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." 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 it will be it will happen. The biggest challenge here, Lenny, is actually the data. This CAD data is some of

the most valuable IP that anybody has. And Samsung or um uh 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 fast and to

beat this other guy's drone or whatever. This is where I think you're going to start seeing all this

start. And then probably the big 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 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 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 been a longtime VR person. She was at Oculus. She joined with the acquisition.

She helped create a lot of content within VR. She's just been like in the VR world for a long time and

now she's working on other things. She 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 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 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 it's creepy and it's easy to be creepy. I'm a little surprised with some notable exceptions how creepy a lot of these

humanoids are right now. You want, I think, these devices to be non-threatening. Generally speaking, you want them to appear soft. You want them to

appear reactive to you. You want to have a sense that they know that you're there. Um that they're attentive

to you, that they're there to help you and make your work life easier. And um you also expect them to intentionally or to show their intent before

they do something. And so one of the things I learned is if a robot just suddenly turns and does all this stuff, it scares you. But if a robot looks

before it turns and then goes, it's much less alarming. So there's all these little pieces. Um, and I

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, 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 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 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 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 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 anymore. It's crazy how quickly that changes. So there's a big difference in my mind. This is like a big

categorical difference. There's a big difference between a car that is safer that drives itself versus a car that a human drives because you have an

existence proof of the human driving car and you have the data. When you talk about homes, what is the

delta? You have now a thing that you didn't have before doing things. So if it's like bad at it, like what are you relating it to? And if it's unsafe

in any way, like what are you relating that to? It's a much harder equation in my mind to get 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 fraction of the deaths using a Whimo, whether

you're a passenger or you're 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 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. 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'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 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'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 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, "This isn't going to work and this isn't going to work and this isn't going to work and just like be like kind of

worried about all these things going wrong." So, this is kind of a an interesting uh disagreement inside of

me of like what I want the future to look like and what I think it's 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 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 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 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 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 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 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 our

capabilities while also hoping that we never have to have you know hot conflict 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 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 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 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 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 whisper to me what Open

Eye is working 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

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 grateful for the opportunity to

help. But I also thought that after what happened, it was time for me to um 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 you know, we'll see what happens there. So speaking of team building, this is something I

definitely wanted to ask you about. So as I said, I asked a bunch of people what to talk to you about and

someone that I think it was maybe a colleague, former colleague, 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 strategy 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 the millions because it hasn't been done. So, you have to

start thinking about how do you build a team that can do something new. And the nice thing is actually in robotics, 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 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 hire 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 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 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 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 when you

actually build these teams is you have to have both. you must have both. The the team size just might be a

little bit smaller than it used to be. Um when this AI revolution in hardware happens, I don't know how that's 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, 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 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 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 to ask for a long time is uh you've worked with some of the most legendary 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 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 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 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 it doesn't quite hit the

way you would think. And if you tell somebody, hey, this needs to be better. Like, you need to spend

more time on this. You need to 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. And then Mark Zuckerberg, I think that he I have to say he ran a

company very, very well. So the way that the technical side of the company operated, the way that we had

reviews, that decisions were made, the decisions were made at the lowest level possible in the company to maintain speed. Um I I underappreciated how

clean and well-run um the hardware the way that the hardware organization interacted with the rest of the company. It was very clear. This is what

we're going for. Um, we're 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 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 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 my thing that week and they're doing that,

you know, a hundred times that month. Um, was impressive 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's where why I'm I'm I'm looking for this 0ero to 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 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 something you built, something you worked on 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 my favorite failures was

actually on the Quest one. It was around EVT, so halfway through the Quest one. And we found out that we

had gone from five cameras to four for cost reduction. We talked a little about this. We needed to reduce the price so more people could buy them. 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

cameras the data from the cameras isn't working and we can't get a lock on where the person is using the

headset." And so we looked into it and we realized that their interpretation of our spec and our interpretation of our spec was different. So uh in

engineering we usually 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 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 our interpretation of the spec meant that he couldn't meet his goals of being able to understand where the headset was

in space. And so, we had to do a redesign. And this is at EBT. So, this is pretty much when you want the engineering to be done. >> 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 so what we had to do was um favor or prioritize. We had four floating cameras. We had to lock

the bottom two to each other and put them on a bracket so that the relative distance from them met the

spec that he needed. and then let the other two float. So this was an architectural change and this was a failure. I mean it was a failure in

understanding the spec. It was a failure in 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 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 addressed it. We redesigned it. Um we had to change the material on the bracket. I think we

had to make steel to hold the tolerance we needed, but it worked out and the price and the cost and

yields were fine. So I think we adapted. >> 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 covered so much ground. Is there anything else you wanted to share?

Anything else you want to leave 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 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 great fiction book, which I really recommend. I think that's what it's called. Um, I haven't read it in a little while. I

I I love Mrs. Dalloway, actually. I think it's a very interesting book about transitions, and it was

a post-war book um, by Virginia 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 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 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 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 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 learned a lot 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 more newer um books on the list. I find it just fascinating to have

something to work off of. And what I found is as I got into specifically the tragedies, the Greek

tragedies, I just didn't have enough context to learn what I wanted to learn. And just reading them, I didn't have enough uptake. So I found an

incredible um uh 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 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 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 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 folks find you online if they want to find you?

If they want to reach out, I don't know, try to hire you. And 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 people can help me. I think helping

imagine the future. This is not a single player game. This is a multiplayer game. Figuring out what future we want, what we want it to look like, what

we want the human aspect to be in that future, and what we think we want to hold for ourselves. 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's possible >> I love that's actually a message that's come on a

a couple of recent podcast episodes. So, it's a really good 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,

you can subscribe to the show on Apple Podcast, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as

that really helps other listeners find the podcast. You can find all past episodes or learn more

about the show at lennispodcast.com. See you in the next episode.