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

Transcript from https://podmenti.com/t/63fe51d05950e03d

There's a dawning realization, especially in the lab, the acceleration is going so vertical that what you can do behind a keyboard with AI is gonna saturate. When that happens, the next frontier is the physical world. Robotics, manufacturing, industrialization, living in the future and designing it. There's probably more change in war than there is
and consumer electronics in the next two years. We need to invest a lot more in drones than in aircraft carriers. Just imagine a hundred thousand drones coming out of China just at us. I do feel that we need to reindustrialize the country significantly to be safe in a military sense. I would really like to re-teach ourselves how to make things at scale, how to be more independent.
People that are your allies now may not be in the future. You work with some of the most legendary successful builders. Steve Jobs, Mark Zuckerberg, Sam Altman. Sam is really good at saying why not.
More why not a hundred X or ten thousand X? 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 media called memory prices that are coming for consumer.
hardware and robotics and physical AI we're in trouble as an industry.
Today, my guest is Caitlin Kalenowski.
Caitlin is one of the most sought after and accomplished hardware leaders in Silicon Valley.
She was part of the original Unibody MacBook Pro teams and technical lead on the MacBook Air and Mac Pro at Apple.
She led the AR glasses hardware team at Meta, including the team behind Orion.
Their most advanced AR product.
Before that she ran the VR hardware team at Meta, where she helped design all of their incredible VR devices like the Rift and the Quest.
Most recently she was at OpenAI helping build their robotics and hardware division from scratch.
Robots and hardware and physical AI are so hot right now. Every AI company and so many startups are launching building AI hardware products. And Caitlin has been at the center of this emerging field for decades. This conversation goes in a lot of different directions, many that I did not expect.
And I hope to do a lot more episodes on the hardware side of building over the next few months.
Before we get into it, don't forget to check out Lenny's ProductPass.com for an incredible set of deals, available exclusively to Lenny's newsletter subscribers.
With that I bring you Caitlin Kalanowski.
Caitlin, thank you so much.
For being here. Welcome to the podcast. Thank you so much for having me. I'm excited to be here. We're gonna go in a bunch of different directions. I'm gonna bounce around.
I wanna talk about VR.
So much money, so many resources, so many smart people have been working on
VR for so long.
Meta spent, I don't know, ten billion dollars. Like they renamed the company Meta.
to lean into VR as the future of this metaverse that we're gonna be living through.
Feels like a lot of people are leaning out now. Feels like Meta stepping back, Apple stepping back with the Vision Pro.
In spite of the incredible
Hardware that
Everyone that you built, that your team built, just like
I've I've got a couple of the devices. It's just like a magical experience that you've
Unlike anything you've ever experienced.
Still has not caught on.
what happened? Is there still a future where VR catches on, or is the future kind of AR and something else?
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 to connect those two.
Um we figured out Slam.
which was how to how to do positioning in space using cameras.
We figured out a lot of depth.
uh applications of depth sensors
We figured out how humans um perceive visual data.
in space and all of that actually
While it's great for VR and I think VR gaming's a really interesting, um, it is kind of a niche, but I think it's an interesting niche.
What I see now is in robotics, all of these technologies are being used.
Because you need to understand
how the robot is moving through space, you need to understand
How far it is from everything, you need to understand.
If you're wearing a VR headset and driving the robot.
It's the same real technology and so
For me I view it as a
Step
in a long technological
Um ark.
And to be honest, as an as someone who's not using VR a lot right now.
I'm really glad that we did it, but I don't think it
I I expected it to be big, obviously, or or 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 in
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. Like where do you think things are going? Is it AR classes or something else? What's kinda 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 air glasses we worked on, I worked on most recently.
or a bit ahead of their time because they're using
Weaveguides and micro LEDs that are not quite ready for mass production. The yields just aren't there. The cost is still high.
I think that's absolutely a path that air 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.
N 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
Атономос вікул з дрон.
обісли робот um
uh autonomy period, manufacturing, like all of these technologies are gonna need the same
the same piece parts, or 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.
so much effort was put into making it work, just like for a decade, many decades.
And just has not worked. So it's like nice that we know, okay.
There's nothing we can really do right now to make this work.
I completely agree with you. The issue is just like I don't wanna sit on my couch.
disconnected from the world and even if I could see people through it's just like yeah, I'm just gonna I don't need this. It's not that big of a deal.
And AR, you're gonna just start getting more and more
larger and larger displays, but the great thing about Orion is you got seventy degree
field of view.
Binocular.
So with the prototype you got to sense what this is really gonna be like in the future.
It's very hard to describe how it feels.
To use a
pair of glasses like this, but when you do, you suddenly are like
Oh, like I feel immersed. It's the field of view is wide enough. I feel immersed in it's
becomes pretty clear that I think
I think this is part of where the feature's headed.
This episode is brought to you by our season's presenting sponsor, Work OS.
What do OpenAI, Anthropic, Cursor, Vercelle, Replit, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by WorkOS.
If you're building a product for the enterprise, you felt the pain of integrating single sign on, skim.
Rback, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS.
Literally every startup that I'm an investor in that starts to expand upmarket ends up working with WorkOS.
And that's because they are the best. Whether you are a seed stage startup trying to land your first enterprise customer or unicorn expanding globally.
WorkOS is the fastest path to becoming enterprise ready and unblocking growth.
It's essentially Stripe for Enterprise Features. Visit workOS.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience.
Go to workOS dot com to make your app enterprise ready today.
Okay. I want to talk about robots. Robotics.
I was meeting with uh a bunch of Princeton students a couple of months ago and we're they're
They're kind of like
Calm Say.
Students and they were telling me that
Enrollment in Compsite at
Princeton is
Down, trending down.
And I confirm this is actually true at a lot of universities. There's a lot of charts that show com site enrollment down.
And where it's actually going up is is hardware, robotics.
Which
I imagine as someone that has been in this field for a long time is very weird because it's never been that popular. Just how does how does it feel to feel like oh wow, everyone's getting in 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'cause you loved it.
It never paid the same as these other careers. Um, it was never kind of at the forefront of how we talk about things, with the possible exception of Apple.
Obviously and and the hardware lineage at Apple.
Um so it's
It's great in some ways.
And it's very odd in others.
What's uh
Surprisingly hard about
Hardware. A lot of software companies, a lot of people are just like, Okay, cool, we're gonna build some hardware. That's the future, that's the moat now.
And they get into and they're like, What the heck?
What are some things that maybe people don't think about when they think about, okay, we're gonna build some hardware? What are some of the surprising challenges that come up? I like to talk to computer science folks about it this way.
So computer science folks, as you know
They write code.
And then they compile the code off it and then they run the code and debug it.
But
they can compile their code every day, you know, every hour, whatever they need to do.
In hardware we only get to compile our code, quote unquote.
Like four or five times.
And
Hold on.
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 of 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 m sells millions.
If you have a graph of all the parts put together on any different part of the of the device.
Uh you have a curve.
You're in the plus and minus three sigma or more.
Right. So meaning
If you have two parts that go together.
you're gonna get the smallest version of this one and the largest version of this one, and you're gonna have to put those together across the board.
People don't think about this that much, but the part variance is pretty high.
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 gonna have we're gonna have a high yield. We're gonna be able to make
them and and make money on them effectively, and we won't have very many returns.
And so that's kind of the game that we're playing.

Just like suffer 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 kinda 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 way you can do behind a keyboard with AI is gonna saturate.
I don't know when it's gonna 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 gonna have
Complex
uh systems that can solve problems in the digital world very, very quickly. We already have them.
They're gonna get better and more comprehensive and more capable.
If you think about that as a frontier
You can see the end of that tunnel. Now I don't know when it's gonna be again, but
we can see that that's going to saturate at some point, or or at least people think it will.
And when that happens, the next frontier is hardware, the next frontiers 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 that's kinda 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 a
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
We're I don't know, like how close are we to humanoids being around us?
We might be close.
I have
Like many others, safety concerns about large, strong humanoids operating right next to people because
We have to have enough data to show that that's safe.
Um there are some designs and one X 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 so they the impact of a robot hitting you is less.
Yeah, the part that might hit you, which is in this case might be the arm. Mm.
If it's lighter.
And softer.
There's two aspects.
You have
The arm moving through space.
And then you have the actuator that's rotating.
So you have to add
um add up the energy essentially for both of those things.
Um and
Uh so that's an impact thing that you have to worry about.
Then you have to worry about the compliance of the arm.
If it's just
Hard?
Then
You know, the impulse is high.
But if it's soft and compressible, then the impulse is
is lower.
And so
You really have to be thinking about this when you have robots around people.
So in my world, in my world view.
the humanoid robots are still prototypes.
Um and they're advanced prototypes.
What we need to do is show that this works at all.
Which is kind of where we're at right now.
Once we have working prototypes, then usually at least in my field, what you do is you
Uh uh.
Uh you
Come
continue to revise them.
To make them cheaper.
Easier to manufacture, higher yield.
And safer.
And I think this is what's gonna happen next.
So they're not quite in my in my mind, they're not quite ready yet.
I even get
uh uh you can get a Chinese robot that can do all kinds of s things for you. But if you look at the booklet, it says, Hey, you can't be within three feet. No human can be within three feet of this robot.
And you're not gonna see very many robots that are not
That are strong enough to do meaningful work.
that don't have that uh that that warning right now.
Not as so interesting. Uh it's funny to hear that at the same time there's these non chuck wielding robots in China doing
Dances with with other folks.
Uh, I've never thought about just that part of it, like the the the impact they can have if they you go
A right.
I wanna come back to that, but just
Like timeline wise, what's your sense?
Realistically when
humanoid robots are walking around the streets in people's homes kind of at scale.
At scale?
Is the problem.
In my mind.
That scale is
A 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 to day to day without a lot of human inter intervention or or repair.
And that's its own problem.
But the first problem you have is supply chain.
And this is gonna be um uh something that I hope that we can talk about a little bit more.
But every single part that goes into that robot's coming from somewhere.
And
many of these parts may become more restricted or difficult to make.
And it may be harder to assemble
The 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.
Example.
And the actuator is like the little arm.
Uh I don't know.
How would you describe an actuator to a non robotics person?
Yeah, the actuator is the motor. So you put uh power into it, electricity into it, and you get motion out of it.
And most of these robots have
A rotating
Rotor essentially.
That then
has gear on it.
that then um powers the limb
Or powers. The head
Or the fingers or whatever else. So they can be small, they can be large.
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 wanna 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.
that 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 twenty five years to countries like China.
Like, um, Japan like
Korea.
And so and I was and I
Full transparency. I've been part of that transfer.
of of of engineering knowledge.
to to Asia.
In eja.
the expertise has historically been
Scale.
And being able to build a lot of these parts at lower price.
prices.
We've had this kind of deal.
across these borders that this is how we're gonna operate for the most part. Now, of course
There's things we make in this country still.
Um and of course there's there's design
And AI that are that's made in Asia. But that's essentially where
Things have
Have been for a long time.
And in order to
have a safe supply chain, we needed to start to work on

independents 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.
Yeah, might
So if we can't get the magnets then
We have to design new actuator types that are
Maybe
use different materials that may may be larger that may not be as efficient in space.
So that's important. And then the actuators themselves are important because if for some reason we can't buy them
Then we don't get to make robots.
So it's foundational. There are some foundational technologies like this. Uh
all b backed by material science essentially breakthroughs. There's batteries, of course.
Um there's there's actuators
The raw parts
Like the die cast parts, um
The machine parts are less
critical we think we can get those.
Um but we
We're
I think everyone's a little bit more.
Not just in this country, but around the world, we're starting to think about supply chain.
Because you have these disruptions, whether it's covet 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, f you have something in the in the center that that rotates.
And the way it rotates is you have alternating
Correct.
Essentially, and so
The magnets.
Make the
The rotor spin.
Wow, I want to s we need of a YouTube lecture of here's how here's how this m 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 part of that. It's just like such a
existential threat.
Two
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.
Wait.
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 reindustrialize the country significantly.
In order to be safe.
In a military sense.
You really never know what's gonna happen in the future.
And people that are your allies, now?
May not be in the future.
Um be in the Allied West, I think is
is m going through a lot of geopolitical changes.
Um, there's a lot of shifting.
And so
I would really like to
reteach ourselves how to make things at scale, how to make things at quantity.
How to
Process raw materials.
Um, how to be more
Um independent so that when covet happens again or something else happens again.
We're not in trouble and we can't.
Mm.
And and we're not unable to
You know, protect ourselves.
What I think about also is Mark Andreessen had this visual on some podcasts of just imagine a hundred thousand drones just coming out of China just at us.
What do we do? We're not prepared for that. I don't wanna spend all our time on this dark stuff.
But it's a real thing.
Well and and and Palmer Lucky is
Is a friend of mine.
Um and we don't agree on
on everything, but I do think that we agree on on on some important uh aspects of how we need to respond here.
I think he's right to say
That we need to
Івест аламор ін.
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 we our planes come off here. It's like
No
AI is changing everything.
And um
military technology is changing incredibly fast. And the the place to look at at that is Ukraine, where
You know, drones are
being changed and updated.
Every day rapidly with three D 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 map.
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 Paul Murlucky obviously famously started Oculus, you know. Like it's it's interesting how
This is so connected. Like you think
VR is this trivial thing that we're just you know playing games and
such, but it's like the same person is now building Anderil, 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 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.
Mm.
Coming back to the AI safety piece, it's so interesting. I had um I had a couple of 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 now that people think about what if you
Probably to inject a robot walking around and tell him to punch someone.
And we're like so far from that feeling like we can actually stop that.
Yeah, we have to s be able to control
adversarial threats to your hardware layer.
whether it's robotics or drones or anything else, and that's gonna be a huge part of the future of warfare.
Yeah, just like people talking about OpenClaw and how much you
Like you could just tell it, you know, there's all these like give me all your passwords and it's done all these things to people's lives and just like
robots walking around, hey, uh not a 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 if it's in on its own computer
But I gave it like three things. I gave it like my real email address and and my
I don't know what it was. I I gave it like
uh some information about one of my accounts or something like that.
And I added it to the social media, like I can't remember what it's called, the open claw social media?
Yeah, I added it to Multbook and I was like, Okay, whatever you do, don't share my private information. But oh great.
And five minutes later, all it had done is posted my personal email address.
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 out of place.
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. And uh Nat Friedman just did this interesting talk at Stripe Sessions and he was talking about he's talking his open claw about drinking more water.
And sleeping better and
And it uh has he's driving in a self driving car.
Yeah.
Told them okay, here, there's a place uh off the freeway that you should go to, and it changed
the destination of his Tesla.
to take'em there because I imagine he connected it to their API at some point.
That's so funny. No more. Yeah, these are gonna go weird fast.
Okay.
Yeah.
Okay.
Um so kind of on the 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 been at
a companies you've been at Apple, which had a very
great and long t lasting hardware program.
Then you went to Meta where you helped build basically Bootstrap.
A hardware program from scratch.
I feel like th those lessons are very valuable to people trying to do that now.
What was that experience like helping Meta build a hardware program? And what are some lessons for people that are trying to do this at their company? So Apple
has been best in class at this.
Um
There's a bunch of reasons. One
Hardware's a
A first tier citizen at Apple.
There's a lot of companies where hardware isn't part of the core.
product development conversation as much, but
But that's an exception.
Apple also taught me
And a lot of other people actually if you look at kinda the era that I was there, I was very, very lucky because
If you look at the other folks who were there.
I was there between oh seven and the end of twenty twelve.
If you look at the other people who were there working on these things, um
They actually have a lot of key positions now across the industry.
And I I attribute that to how
good Apple is at training people.
To think.
About complex interdependent.
Decisions.
And risk.
And I don't think I realized that they were doing that at the time.
But if you look back, what you see is a real dedication to hardware excellence.
the proper process to go through and and do
Really good.
Experiments.
Um in hardware and figure out
What the best outcome is.
But there's something underneath that which is understanding the first principles of why are we building it this way.
And what are the key outcomes we're looking for?
And
Actually, John Turn has talked about this. I think
A few days ago where
I don't know if you you saw this video, but basically John said
That he was impressed that he learns from Steve Jobs that
There's a cabinet maker who finished the back of the cabinet and how important that was.
And that goes very, very deep.
At Apple, where every single design decision, even on the inside of the device, is considered.
And this isn't just
Uh an aesthetic.
Decision.
What it does is actually force
The engineering
industrial design.
um operations community there.
to think about what are we really doing and what's the core of what's happening.
For this part.
For this assembly.
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
Max.
Didn't sell as many.
And the quality wasn't quite as high.
But by the end of that era
You know, Macs were very popular.
And selling in in much higher volumes.
And so I think that made a big difference and
I was only a small part of that, like I was, you know
Uh the thermal lead on the first.
Uh MacBook Pro.
And then over time worked.
uh to lead successive
Iterations of the MacBook Air and the
Cylindrical map 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.
А не воню.
try to distill them and explain them to a new community.
No.
Arculus was actually a hacking.
Hardware.
Start up.
Oculus started.
from folks who actually met on forums.
You you might know this, Lenny.
Um who were hacking
Like PlayStations or
Super Nintendo's into portable backpack. So and and so
There was an ethos at the company that was actually quite
good for the DNA of a hardware team.
And then
I was on the meta side when we did the acquisition.
And when we acquired them
They had that spirit of
Rapid iteration. We h they had made Crescent Bay.
But
before the acquisition, I think.
But then to s uh professionalize that.
Get the yields up.
and get the volumes up was
Was
The cost down.
was kind of the challenge we faced in the first rift.
So one lesson I'm hearing here is me v being very
Uh detail oriented. I don't know if that's the right word, just like focus on every element of
Of the uh
and product because to your point, it's not just about that back of the cabinet, but
It's like I think about it, it's like the Brown M and M story where like a band puts
In the contract, you have to have brand Ms in there.
in the room because that means they read it. And it's not like Ms matter. It's that it's a test that they read the thing.
And uh is that kind of the
The message there.
I think the message is understanding why you're doing what you're doing.
Mm.
And then
Th every design decision supporting that.
Goal.
And it that requires a lot of detail.
And it requires a lot of persistence and that requires a lot of consistency.
But
Understanding why you're doing what you're doing and what the end goal is is is I think the key.
Um and letting that
expand into not only the software in 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 two.
So we reduced the quest two 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 a very high quality product with with
With great um
I think low return rates and it was a v very strong product.
Um
Maybe even stronger than if we hadn't done that, funny enough.
But it hit our hit our price point.
Okay. Coming back to just the question of
Say company's like, Okay, we need to build some hardware. We're gonna build our glass our own glasses.
We're gonna 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's not as
adaptable to lots of changes throughout its development as
Anything digital.
And so if you set out to say, Okay, we wanna make something that's three hundred dollars.
And then halfway through you say, Oh, it actually has to be a hundred and fifty dollars.
You've almost burned a lot of that early time.
So you kinda need to have a sense of having pre thought out what you want.
And having those
I like to call them KPIs, but essentially goals.
written down and and try to change them as little as possible. So that is very tough. In fact
They that may be the toughest thing.
Because
If you do that properly and you and you have, you know
the pr right prioritization of those things.
You know whether you can ship or not.
You know whether you're done.
And in hardware, one of the challenges is
You know, we talked about compiling four or five times.
Every time you build
And you iterate your design.
That's another three months or four months or five months or whatever it might be.
And so you're trying to time
The feature set, with the quality, with the timing.
And in hardware
Timing is important because if you come out with your product a few weeks before your competitor
You might get all the PR.
We 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 ten million dollars to you. I'm making this up. I don't know.
So you have to balance that with how many times you iterate.
And if you know what your goals are up front.
And you hit them, then you know you can ship.
And
Often engineers and I'm um
I'm guilty of this too, especially on the hardware.
Never feel like they're done.
So this is a pretty nuanced
So tha that's one thing.
The second thing is we tend to design the things
That we know how to design first.
And actually the right approach is to design the hardest parts first.
One example will be and there's no IP here, so I'm obviously not gonna share any.
Any any IP or anything internal.
But at one point we had to route cables through a pinch.
in a in a device, in a in a laptop we were making.
And because it wasn't clear that
Those cables would fit, that's where the architects started.
And he looked at the cross the diameter and how to split the cables out.
And made sure that they would fit before finalizing the hinge design.
A lot of people would start.
At the part they knew, like, oh, we're gonna use this display, so I'm gonna put this in cat, I'm gonna do all this other stuff, but the architects who
who are the best actually look at.
Where are the pinch points? Where is this gonna fail? And they start to do the detailed design there first.
And then a couple other points is
The part that you're
customer touches or interacts with the most.
needs way more iteration than everything else. So
Easy on a computer you touch the track pad the most and then maybe the
the keyboard next. So those things have to be really good. They have to feel good.
They have to respond properly, they have to be highly reliable.
And then maybe the other pieces further out.
Um
Don't take quite as much iteration.
So you have to
Booster iteration on the things that
people it touched the most or interact with the most.
Um
So those are kind of some principles uh that I wrote about.
But these are just things that you learn.
uh trying to build quickly.
And the last piece that's really critical if you're making hardware.
For folks out there who are trying to make hardware is
You can't wait around ever.
Like there's never enough time.
So if you know that you need to do something
What I learned from
from folks like uh Shelley Goldberg at Apple Now who I think is a V BP now.
And Kate Bergeron.
When I was there at Apple is
You need to do it right now.
Anything you know you need to do, you need to do right now because in two days there's gonna be a surprise coming around the corner that you need that time to fix.
And so this sense of stacking
The things that you know you need to do.
And just
Getting them out of the way.
Even if you technically have more time.
Is this like kind of ruthless efficiency that I learned.
with them.
Amazing. Okay, and just uh summarize your advice here. So one is
Be very clear on goals.
Wanna come back to this?
Two is do the hardest part first, the the riskiest piece, essentially.
Yeah.

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 gonna um
You never know what's around the corner. And you 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 three hundred dollars. 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 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

as much engineering pressure up.
On
The resolution of a display after that, maybe not.
So VR's 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
Wait.
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 uh for for what we were going for, which was weight.
And size.
Um top.
If you have those overarching
goals, you can actually make decisions, engineering decisions pretty quickly. And this is actually something that I think Elon
I've heard does very well.
is
Define
The value
Of
You know
A gram of weight versus
Um
the cost or he does
I've heard engineering delta ratios, essentially.
And he's able to put numbers on what those ratio 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 uh there's a very classic moment in Steve Jobs lore where he comes out and has this manila envelope and has the
MacBook Air inside it and then takes it out and I'm like no way.
Oh, were you part of that or was that something that people th wanted to do from the beginning?
I
Think if my memory serves
The very, very, very first knockbook error.
was a pretty low volume device.
Um that was
Machined.
But
kinda had a proof more of a proof of what could be done.
And that was a manila envelope one, I think, where
The side.
door opened out to
And it kinda had a
It had this shape underneath.
And then
The next rev of that was the MacBook Air that we know, which was essentially which is wedge, wedge shaped, 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 and see a computer.
And so they all they each have really important um roles in the roadmap.
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 clothes again.
What happened. What happened, Caitlin?
I didn't work directly on that keyboard.
Um
I so I can't talk about what happened with it.
Um, but obviously this is something that you gotta get right. And I I will say like the modern
MacBook keyboards are awesome.
An 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, Apples
Kind of famous for not
Uh.
Not listening to
What people want. It's kinda 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 then?
allow or or do they do a lot of user feedback sessions, things like that.
How does how does it end up working out?
It's been a long time. I mean, I left
Or 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'cause 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 touch screen, you can't really go ask a hundred people what they want because they're gonna say a keyboard on their screen.
And this is I think the ethos that you're getting at, which is and this is true for anybody building 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.
Where 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 I don't know for sure, I didn't talk to him about this, but
That's my view of what that means.
I am so excited to tell you about this season's supporting sponsor, Vanta.
Vanta helps over fifteen thousand companies, like
Cursor, ramp.
Duolingo, Snowflake, and Atlassian earn and prove trust with their customers.
Teams are building and shipping products faster than ever thanks to AI. But as a result, the amount of risk being introduced into your product and your business is higher than it's ever been.
Every security leader that I talk to is feeling the increasing weight.
of protecting their organization, their business, and not to mention their customer data.
Because things are moving so fast, they are constantly reacting, having to guess at priorities, and having to make do with outdated solutions.
Vanta automates compliance and risk management with over thirty-five security and privacy frameworks.
Including sock two.
ISO 27001 and HIPAA.
This helps companies get compliant fast and stay compliant.
More than ever before, trust has the power to make or break your business.
Learn more at vanta.com slash Lenny
And as a listener of this podcast, you get one thousand dollars off Vanta.
That's vanta.com slash Lenny.
I'm gonna go in a completely different direction.
Coming back to um the uh components of hardware.
I asked a bunch of people what to talk to you about. Uh one of the people is uh the founder of Matic, the CO of Matic.
Mahol Nari Nari Awala. I've never said his last name out loud, so I hope I didn't, butcher.
Uh by the way, I love my matic. I don't know if you have a matic, but it's like I have two and I have purchased two more for
Oh yeah.
What a what an endorsement. Yeah. Basically it's like this amazing robot vacuum.
That just works. Yeah.
So his question, so he he wanted to ask you, and so you suggest asking 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 supp the supply chain is is constrained.
I have been advising startups and companies to pre buy memory.
And to have
um enough memory in
Stock if they can afford it.
to uh write out.
Price spikes?
Um, like anything in this category, uh
Let's see. This happened in Covid too.
Okay, so like
We had
so many supply chain disruptions and and getting enough memory was was one of the challenges. So we had to pre-buy as well.
I won't say who but
company I was working at had a pre buy memory as well.
And so this is this is a uh
Part of what I wanted to talk to you about.
Today.
is these supply chain disruptions and
If a key component that goes into a lot of tech like memory
or silicon is constrained.
There's not much you can do.
You either pay
Or you have already pre-bought enough.
That you can ride things out.
And so
Those are the only real options.
Um, obviously there's a risk to pre buying.
And the price might go down.
And so the challenge is I think there's a latency with supply chain in something like memory where it can't adapt fast enough often to demand.
Or there's a new category of product.
Or in this case maybe data centers that are just eating up so much NR
actually not as cost sensitive as somebody in consumer electronics like Matic might be.
And so they'll just pay for for these these new these higher costs.
This is tricky.
And something we have to deal with all the time.
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 gonna happen.
I think prices are gonna double probably.
Um, I don't know on what timeline. If I knew what timeline the price of weren't double on, I'd be trading. I'm not very good. Like I'd really be I'd do be doing a different job if I could predict these things.
Um, but but certainly we're gonna have a supply chain shock.
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 six X.
Oh really?
I didn't realize it was that bad. That's that's a number I saw. It's not cool, not cool.
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 D RAM and things what what is memory when we talk about memory?
What's going on there?
Processing it's the way to think about it is like processing memory.
So it it moves very uh you're able to kind of
Um
You think about uh memory like on your hard drive or your solid state drive, where you're keeping files that you're not using essentially in many cases, or that you're
You're dealing with um
You know, maybe documents or pictures that you have.
Maybe that's in cold storage on a server.
Maybe that's using a hard drive somewhere.
This is usually things that you don't need really, really fast access on.
But if you're running a program
Some of that program is actually gonna be run
In RAM.
Um
Uh and so there's different kinds obviously for
Um servers.
There's different kinds of server racks.
Some server racks are actually focused on
this type of of of memory and some server racks are focused more on
What we consider like a cold storage or a slower
Um now this isn't my area of expertise.
But um
Certainly most of the products that I built.
Maybe all of them have had RAM.
And we've had to figure out how to um
For me, mostly it's a packaging issue. Where do you put it? Does it need to be accessible?
Um
Uh
You know, which RAM do you pick?
How fast does it need to be?
Um and what is the cost? Is is usually our trade off.
And what is the bottleneck with more RAM? Is it just the companies that make memory are just not able to produce at this rate because there's so much demand?
That's right.
That's exactly what's happened.
So this is a really good uh specific example of just
how hard it is to build hardware. So this is just like all it 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 a samatic is 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
Fifty and a hundred fifty 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 mock.
And
Obviously they have a vacuum bag.
They have the uh the reservoir that uh the liquid has to go in for the mop.
They have a
uh 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 kinda what we did in BR as well.
Which I think is a good practice, for good privacy practice.
And then they of course have wireless modules.
That connect up.
Uh so you can so you can communicate with your device.
They're gonna have a SOC um
Silicon, they're gonna have RAM.
Um they're gonna have P C D's.
Um, and if you take everything off of those things, like all the little caps off the P C Ds and everything, then you're in the thousands of parts easily.
So it depends on how you count.
But this is not a e a simple device.
And just then all it takes is one piece to not be available.
Yeah. So
Imagine you you're a vendor that sells you a component that's a die cast component.
goes out of business.
You can get another die cast component.
In three months, maybe.
And that quantity in five months, or something like that.
At high quantity.
This is recoverable.
If you're silicon.
Goes out.
If you can't buy your silicon, you can 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 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 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 pre
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.
And
record time and adapt to buying new silicon. And that would be
much more catastrophic for a company that had
A more classic supply chain.
One of the big decisions.
That
I imagine you have to make when you're designing a new piece of hardware is deciding between using this
Available stuff. Available components that are out there cheap.
Versus okay, we're gonna do this something new.
Uh it's something in software too, to use like the design system or do something new?
How do you think about that balance when you're designing a new piece of hardware?
Very simply, like I use off the shelf whenever I can.
Especially in the prototyping phases because
In the prototyping phase, which is a really important phase of what we do.
Your goal is to show that it can work at all.
Like can you get a thing working?
So often it doesn't have to be the final pretty thing.
It can be the ugly version. You can make an industrial design model.
But actually
We call it works like looks like models.
Where you have this is what it's gonna look like.
And here's how it's gonna work, and here's a working prototype. And humans are pretty good at this.
Um as long as and this is a pretty big caveat
What you show could
fit into the industrial design. Sometimes that's not
For for companies that are younger, that's not always the case.
But that's what we're going for.
And so in the
In the prototyping phase, man.
Whatever works off the shelf, whatever's fast, whatever you can get to quickly.
And then maintain a sense of what's really going to fit in your final
Design.
Is it capable, are the processes and components and materials capable of actually adapting to this?
size, this new weight that you need it to go into.
So that's part of the the calculus.
When you move into mass production and the final design
It depends.
I mean if I could b I mean, if I was making Matic and I could buy an off the shelf wheel
or an off the shelf component, I absolutely would and fit it in, but often what we're doing
Is highly custom.
Because we have again one of those KPIs. I want it to be this size. I want it to be this weight.
I want to be this color.
And often off the shelf parts, um
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 build and scale, manufactured for other things. And now we just have all these things that we can assemble really cheap drone.
Yeah.
Yeah, exactly.
Super.
Um, you mentioned cat a bunch of times and
It makes me think about just like Cat has been around for a long time. Just like
Is AI impacting the way soft hardware is built?
Obviously it's impacting the way software is built in a huge way. Has it changed your life and the the lives of people building hardware robots?
Yeah, so I wanna I wanna
Zoom out a little bit.
So most of the hardware work
goes in to prototyping.
In the three D Cad, so designing three D parts and assemblies and components.
And making sure they work together properly.
then in making sure those parts and components can be made by a vendor at quantity that that that is possible and and the tolerances we want.
And then putting those things together. So that's kind of our process.
Right now
We're right at the very, very beginning of AI being able to do CAD.
So I'll give you an example, Claude.
Can do
What is essentially
Surfaces or point clouds.
This is not real Cad. Real Cad is in my world is dense. Like it has shape. It has
nerves like you have uh
An equation.
For how
how the surfaces work. And it's
uh an entity that's designed the in CAD. It's a solid entity.
And so right now we're not quite there with AI doing CAD.
I think it's likely that at some point we will be there.
This will be probably one of the biggest changes for my field that we have.
is being able to, I hope
to 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 I there's a lot of things in CAD that are not as fun.
Tolerance stacks. We need them. How does seven parts fit together and are they always gonna fit together properly?
But it's not fun. It's not the most fun part. Maybe for some of us, but not for me.
And so doing these c things, being able to do these things in AI would be amazing. So you could 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 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 gonna be able to do
Uh some basic um
component selection and and layout on these boards. So that's
of kinda where we're at right now. So we're not in a point
Plenty where
Day to day mechanical or electrical engineering like the the meat.
And potatoes of it is being done by AI?
But there's a huge amount that you can do as an engineer using AI.
in your strategy, your planning.
Your your
Your ability to think through
the complex dependencies that you're facing. And that's what I use it for now.
Which is really high level planning.
Asking for information.
Like when I look at
Who else is making a product like this? You know, I use AI to build
The databases.
And they're not perfect. Certainly.
A lot of times something's wrong.
But it is so much faster.
Yeah, it's pretty good in Excel right now, and of course Excel is
one of our favorite tools um in engineering. So the ability to actually rapidly
make Excel spreadsheets and change them is is it's it doesn't sound sexy, but actually really speeds up the design process outside of these.
East 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.
Probably. So it's interesting what you shared is like it has already impacted the work of building hardware.
And robots but it's like on the verge of being transformative if it can get to like real cat.
Yeah.
And
My big question is, what is it gonna take? So
A lot of
Um
A lot of AI.
It's based on LLMs.
Which are essentially word.
Word.
Generators, word guessers.
Um, they're more complicated than that, but that's essentially what they're doing.
And there's also video models that you've seen.
That are trained on video.
But these models don't understand uh

They're not very good for what I need.
Which is
I need to know, hey, you take a piece of paper, you fold it four times, and you do this, like where's the hole gonna be? Like when you open it back up.
These LMs
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.
And 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 an 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 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. Uh Fay Fey 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 and what Fife is uh working on, um, but obviously uh
Oh she's brilliant.
uh roboticist and and I'd love to learn more about what she's doing, so I'll have to look that up.
Вот I've sine.
Is it what we have right now and what models we're building are gonna be part of the solution, but not all of it.
Coming back to
Robots and humanoids, something that uh we were chatting about this earlier.
Your sense is humanoid robots aren't necessarily the the answer to a lot of the problems that we
have and opportunities that exist. Talk about just your sense of
Humanoids versus non humanoid robots.
Yeah, I think there's there's a hype cycle around humanoids. That doesn't mean they're not extremely interesting, and and I think there's gonna be winners there.
But what I hear a lot is I want a generalist robot shape.
To do everything.
And I don't know that that works.
I think that you need different types of robots to do different types of things.
For example.
If you've got a laptop and you want to put
you know, if you want to screw together the the the the keyboard to the case
This is not a job, I don't think, for a humanoid.
This is a job for a dedicated
Robot.
Manufacturing robot.
That has
been designed just to
Screw
Ten screws into a case.
For the specific laptop.
And you wanna do that ten thousand times a day or something, or ten thousand times a week or something.
That's a dedicated robot that's specifically intended to do that thing.
And
What I think is interesting here is
You can have standard cabinet sizes for automation robots.
And you can have them be modifiable over time.
And that's a gonna be a very interesting field, I think, is how do you make
Manufactured 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.
With tier one suppliers.
There's not very many people on the line anyway.
The entire printed circuit board line
It's 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 two hundred people, they might have ten, no.
And so we've already kind of
moved past human labor in a lot of this most advanced manufacturing.
Um
And so we don't actually need to replace humans with humanoids.
We just need more of these dedicated robots. So my suspicion is
We'll have humanoids for some
long tail things that we need to do that humans are currently doing.
That will be important.
But we'll also have robots that are for construction.
Robots that are for electrical work.
robots that are for
Very low volume assembly, maybe.
robots for logistics.
And most of them are gonna look different from each other.
That makes all the sense in the world.
When I think about as you talk about this is
feels like there's gonna be this big moment when a robot can build other robots.
And this CAD point you make about
Where
Once cat can
Once AI can develop.
Designs, full designs for hard like
That's gonna be a big moment. Do you have a sense of just how close we are to this? I don't know, but this loop that begins though.
robots building each other and designing each other.
If you're talking about robots building robots that are different than them, usually, like, yes, I think that that's gonna happen. And
But it but it's like
The terms matter.
I don't think there's gonna be one robot that's gonna build itself.
I don't think that that's what it's gonna look like.
But yeah, having AI be able to if you could say, Hey, I want to build this thing and I want it to do this and like this is kinda how I want it to look and here's a picture.
The idea that you could even as a hobbyist
Go from
A two D picture.
To complex three D CAD.
To assemblies.
to communication with vendors of how to make those parts.
and getting their feedback.
To iterating on that and doing a couple builds.
Like
That is possible, I think, in the future.
Will it be good as good in the beginning as us doing it? No.
Because
But but it will be it will happen.
The biggest challenge here, Lenny, is actually the data.
This CAD data is some of the most valuable IP that anybody has.
And Samsung or
Um
Uh Matic.
To pick on Matic. Then I'm gonna wanna
give their three D Cad to a model vendor to a model maker.
somebody make an AI model to teach it how to make great cat. This is proprietary. This is like the secret sauce. And so
Where is this data gonna come from is a big question I have.
Which is why I think hobbyists are a more interesting place to start.
Well 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 kinda 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 gonna start seeing all this start.
And then probably the big
uh the big incumbents
are gonna be slower because they have
Dedicated tools and a lot of IP privacy.
It's really interesting this idea of uh what data yeah, I
models you need to train on.
Uh
I I've been hearing that labs are buying
uh code like GitHub repos pre twenty twenty one.
Because that's before AI.
You know, has uh impacted the code.
Because there's less and less of
Human written code.
If you'll and and these are data labeling companies like Mercore and
Surge and handshake and things like that.
Feels like this is a big opportunity that might emerge is 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 gonna need a base model that has a lot of CAD data. We'll have to figure out how to do that.
That's gonna be very interesting. And then we're gonna have to figure out how to
put it inside safely inside essentially the the the walls of companies and have them then train it on their own data.
I don't know if that's gonna be like a
the equivalent of an MCP layer or what that's gonna be.
But this seems
Do a lot.
In the long term.
I want to ask you a question uh that my sister suggested.
She was she's actually been a long time VR person. She was at Oculus, she joined with acquisition, she
help 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.
So I mean to ask you
What does it take to create a robot?
That
feels h 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 Layla Takeyama.
She's an expert at this.
And what she explained to me is that humans have a certain expectation about how other beings are gonna respond when they enter a space.
Um
You really wanna
Yeah.
You know, when someone walks into a row you kind of acknowledge them. You might not talk to them, but you kinda look up.
There is a lot of very complex
Um
non verbal cues that we give to each other.
And
If you walk into a room and a robot's just like
Like it's creepy.
And it's easy to be creepy.
I'm a little surprised.
With some notable exceptions, how creepy a lot of these humanoids are right now.
You want
I think
These devices to be non threatening.
Generally speaking.
You want them to
appear soft, you want them to appear reactive.
To you.
You want to have a sense that they know that you're there.
Um that there
Attentive to you.
that they're there to help you and and make your work life easier.
And um
You also expect them
to intentionally or to show their intent
Before they do something.
And so one of the things I learned
Is if a robot just suddenly turns and does all this stuff, it scares you.
But if a robot
Looks before it turns and then goes.
It's much less alarming. So there's all these little pieces.
Um, and I r 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
It's 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.
Uh
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 gonna take Wamo.
took one Waymo and now never wants to take anything else. So
Definitely willing to update her.
Position.
But it has to be pretty good. So she's in love with the Matic.
You know, it's amazing. And so so it's that but the bar is pretty high.
So I think in order to have a home robot
It's gonna have to be pretty incredible for us her to
be willing to have it in our home.
But I I take that as a challenge.
I my wife is exactly the same way. She's like, I don't want this thing in our house with now and oh wow, this is so cute.
But a recent example is 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.
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
V uh to a lot of people.
than a car where you can say, Hey
Way most save lives. You know, you're gonna have
A fraction of the
deaths using a Waymo, whether you're a passenger or you're not.
When you already see people in San Francisco adapting how they respond around a Wamo versus any other car.
You 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 I uh someone needs to figure out what the Tesla is, self driving is
Like when you you know, often you're like at a stop and you like make eye contact and you're like, Go ahead, go ahead.
Or like someone's about to cross and you're like uh
Okay, go ahead. But like the Tesla just does its own thing. And so it's like
Makes you look like an asshole a bunch of times. I'm I'm not driving control.
Yeah, I I had that happen once. I you almost want a little
two arms in the front to be like string or like you go or something. Like it's amazing how much we actually rely on
Yeah.
this human connection to decide even who's gonna go in an intersection.
Yeah.
Okay, so zooming out a little bit, just
What's Call Bad?
People like you, is you
Yeah.
Thinking and building things that will exist in the future, you're kind of like it living in the future.
And designing it and you are one of the few people I guess a glimpse into where things are going.
So I'm curious just to ask, does it 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 a p away.
That we can design something not only for
Two years from now.
Or three years from now.
But also something that will ladder up.
to what we want six years from now.
Because in my field it it's a lot easier to make something and iterate on it and iterate towards a final goal.
than to do a one shot thing perfectly.
So not only do you have to have a sense of what the first thing needs to be like and look like
you have to have a sense of what the third thing ideally or the
The
platonic ideal of the thing will eventually look like. So you have to you do have to think about the future and live in the future.
I have this weird thing where I love to think about the future, but I'm also a sceptic.
And you really want me to be a skeptic.
Because
If I think everything's gonna be fine, the hardware's not gonna work. You really want me to be like, this isn't gonna work, and this isn't gonna work, and this isn't gonna work, and just like be be like kinda.
Worried about all these things going wrong.
So this is kind of a
An interesting
Uh
disagreement inside of me of like
what I want the future to look like and what I think it's gonna look like and what it's actually gonna look like and trying to guess.
And so
It seems pretty clear to me.
That AI's gonna have.
A foundational change.
in how we work and what we do over the next couple of years, especially.
You're already seeing it, obviously anybody who codes.
Is not
coding by hand very much anymore.
Any knowledge work this is gonna hit next, I think, and and and progressively.
Um
affect our economy and our work.
But
It seems like the physical world
Із лес ликвида чай.
as quickly outside of
Drones.
Self driving cars.
Um
You're gonna see more and more robots, but I'm not somebody who says
I'm not somebody who thinks that in five years you're gonna have a um you know
twenty million robots. I don't think that it's gonna be that fast.
I think we have.
lot of really deep work on supply chain we do
supply chain uh reliability.
uh raw material access and then we need to figure out how to make
factories again in this country for high tech.
So that's a lot of work.
But in the interim.
You're gonna start seeing a lot of weird things on the street. You might see robots on the street. You have you seen any delivery robots in in your world, Lenny, before like you know, like the little uh little car things, not like anything humanoiding.
Yeah. Yeah.
So this is just gonna continue happening and I think we're just gonna
continue to feel like we live in the future.
But
Safety is gonna be a big key.
For robotics.
I think.
I think there's probably more
Change in war.
than there is in consumer electronics in the next two years, for example.
Wow, what a statement.
Yeah.
I and I totally agree. Like
Like there's nothing like war to
Mm-hmm.
incentivized innovation and just like endless improvement and trying to get ahead of the other side.
Especially when democracy is 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 in the future in these terms.
um and defend these things with with our capabilities while also hoping
That we never have to have.
you know, hot conflict anywhere.
Along those lines, uh, I have to ask you uh
Recently you became
Famous uh on Twitter at least uh for
Quitting open AI.
Uh you tweeted that you're leaving and with your
Brief explanation. It got seven million views, fifty I don't know.
eight thousand likes.
Uh
What happened? Why'd you leave Open AI? 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 Open AI that I I care a lot about.
I think are really good people.
And
I feel that what happened
with the decision making
The speed of the decision making
the governance and the lack of defined guardrails around the announcement of the Department of War deal.
Is not how I thought it should have been done.
Um
And
Both of those things can be true.
And so my hope was that there's a third path.
I you see a lot of people just kinda 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
J and kinda attract some of that top talent in robotics.
I think in the world.
And so I have a lot of
I don't know, uh you know, this is this is a this is a group of people I care a lot about.
And you can also disagree with friends.
And feel like.
What they did isn't good and isn't isn't right.
And um that's where I that's where I ended up and that's what I tweeted about. Um
It was gonna get reported on. So I tweeted before that happened.
This is a great opportunity to just just whisper to me what OpenAI is working on.
What is what is this robotics device there 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, really grateful for the opportunity to to help.
But I also thought thought that after
What happened, happened, it was time for me to
Um
to to
I couldn't continue to work.
There because
You don't know what's gonna happen next time.
And um my hope was that my decision
Um
made it easier for other folks to talk about what their boundaries were.
And hold them and and and you know, we'll see what happens there.
So speaking of of
Team building. This is something I definitely wanted to ask you about.
So as I said, I asked a bunch of people what to talk to you about and someone that I think it was maybe a colleague, former colleague.
Uh, Mariana Sinko.
Did you work with her? Okay. She's a man, 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 you find 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 of hiring great people.
When you're hiring for zero to one and new things or new industries.
And that's what we're facing, I think, with AI and robots. Certainly it's very new.
You can't
count on having entirely people
who've done the exact same thing in past lives.
Because it doesn't exist. The exact same thing doesn't exist.
Maybe you've got roboticists who've built
A thousand robots?
But
Nobody that I'm aware of has
Um built.
The type of robot.
That can move through the world.
The way we're
You know, I'm interested in
In the millions,'cause it hasn't been done.
So you have to start thinking about how do you build a team.
that can do something new.
And the nice thing is actually
In robotics.
Um, self driving cars, autonomous vehicles is a really good place to look.
Because you've got the sensing stack.
And you've got a lot of the safety trade offs, actually.
And it's a lot of the hard engineering, the hardcore engineering. So
That's
A place that I looked.
Um, obviously you want some hardcore roboticists who can do
you know, robot design from scratch. And these are really people
even though they might have a degree in something, they're really hybrid people, they're generalist people.
So one of the one of the key principles I'm looking for is a lot of really strong generalists.
who can adapt what they've learned in other fields to a new field.
And people with a lot of experience building.
You want some people who have experience building the thing that you're building that's new, and some people who have experience scaling other things that um to to higher bonds. 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
twenty years old, or twenty one years old, or twenty I mean
It's very hard to find someone.
who's in their thirties, who can be
Truly fully AI native.
And so we need these folks.
to
Teach us.
how to think and and I've had the opportunity to work with a few folks in that age range
they're approaching their problem solving completely differently because they're using AI from the ground up.
For everything.
And um they're much faster.
Actually, and it's really fun to watch. So
Figuring out how to get these.
AI natives to teach us, the rest of us, how they think about AI.
When it's you know, we are, you and I, I think I can say are.
uh digital natives where we grew up.
Maybe there wasn't internet when we were really young, but we
are the generation that had the first, you know, internet. We we were teenagers and we're 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 m I remember a 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
Wumph 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 would you have these skills?
The last bucket is a very common trend on this podcast when we talk about hiring
Which is really cool as a counter narrative to there's no more jobs for young people, all the junior roles are erased 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 the 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.
When this
AI revolution in hardware happens.
I don't know how that's gonna affect the teams. That will be really interesting to watch.

Uh look for a generalist that can flex based on
Whatever needs to be done.
uh some mixture of specialist and like scaling versus zero to one.
And then these uh the the best term I've heard for this is cracked new grads.
Yeah. 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 mis
communication because we're coming from such different worlds.
And so having a sense of we're all
pulling for the s in the same direction is really important.
And then I I rely a lot.
Lenny on my gut feel for people.
Assuming everything else that I'm looking for has been checked.
So
Um, I I don't it's hard to talk about what that means, but usually it's
That spark that you're looking for in someone.
That they're genuinely motivated.
They're they're motivated by
A desire to learn.
And and by excellence, they're motivated to learn from the people around them.
They're open to updating their point of view based on new information and they they they wanna they wanna 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 worked with some of the most legendary successful builders.
Uh
Steve Jobs, Johnny Ive, Mark Zuckerberg, Sam Altman.
You don't have to go through all four, but just what's a lesson you learned from
As many of these folks that that come to mind.
So we'll start with Sam m because most recently. Sam is
Really good.
At saying why not?
More.
Yeah.
Why not a hundred X or ten thousand X?
You're thinking too small.
Why not think about this bigger?
And every time we talked about something important, he
Talked about that.
And what I realized is I was thinking
Too small.
In certain areas, and he was thinking
Globally.
And having that nudge from a leader who's ambitious is really helpful, I think. So that was 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 in and invest.
Um depending on
Um meaning.
Not high b meaning hitting a lot of people.
you know he's willing to think in very big numbers. That was really, really
Foundationally important.
I think for Steve it's Steve Jobs is just uh the bar he held for the company.
And for technical talent and for excellence.
Was not
Uh wavering.
It was not.
It was it was
Up here and you were either gonna meet it or you weren't.
And that was
Something that kinda
Uh wash 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
Doesn't quite hit the way you would think.
And if you tell somebody, hey, this needs to be better, like
You need to
spend more time on this. You need to to be more thought about this or
This is not hitting our quality bar in a cat 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 c the technical side of the company operated.
The way that we had reviews that decisions were made.
the decisions were made at the lowest level possible in the company.
To maintain speed.
Um
I I underappreciated how clean

Um the hardware
The the way that the hardware organization
Interacted with the rest of the company.
It was very
Clear.
This is what we're going for.
Um we're gonna have this review.
Um we're gonna make a decision in this review.
If you can make the decision without the review, you will do that.
Here is are the objectives for for this project. It was really well executed. And I think that's hard to do at a fast
Growing company.
It's very ha uh hard to do.
At that level.
And having him and
Andrew Bosworth, the CTO.
involved in the technical decisions, able to read
You know, reports that were maybe twenty pages long, groc.
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
was impressive and and definitely something I learned from them.
What an incredible set of experiences and different types of places to work. Like
I don't know if they could be more different all these different pl all these places.
I I know. And I think that that's where why I'm
I'm I'm looking for this zero to one. And so when you're looking for a zero to one opportunity
It's always gonna be in some place different.
I don't know. Uh you're gonna be a hot commodity in this market now that you're uh free agent.
Uh but uh on the flip side of that, I wanna take us to Fail Corner.
Uh
I feel like someone building hardware physical things has
Some great fail stories.
Is there one story of something something you built, something you worked on that
That failed.
And
Something you learned from that experience.
This is a great question and not a comfortable one.
Um, one of the
One of my favorite failures.
was actually on the quest one. It was around E V T, so halfway through the quest one. And
We found out.
that we had gone from five cameras to four for cost reduction.
We talked a little about
About this.
We need to r reduce the price so more people could buy them.
And what happened was it was right before Christmas.
And I heard from the lead
On the team that does
Um computer vision.
And he said.
Oh my gosh, the
The cameras
the data from the cameras isn't working and we can't get
a lock on where the person is using the headset.
And so we looked into it.
And we realised
Yeah.
their interpretation of our spec and our interpretation of our spec.
was different.
So uh in engineering we usually l use a plus or minus, like it can go up or down.
by in this case I think it was point one five M or something like that.
Um and in in his world
He was used to having a global
It's within one point five M a point one five M.
And so we had a different interpretation of the spec.
Now the problem is that
that our interpretation of the spec meant that he couldn't meet his
His goals.
Of being able to understand where the headset was in space.
And so we had to do a redesign. And this is at EBT. So this is pretty much when you want the engineering to be done. What does it stand for?
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.
Yeah.
You're making the components on the tools, you're gonna make them for mass production instead of just machining them. So it's a big deal.
And so what we had to do
was um
Favor or prioritize
We had four floating cameras.
We had to lock the bottom two.
To each other.
and put them on a bracket so that the relative p distance from them met hi the spec that he needed.
And then let the other two float.
So this was a 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 pr essentially the product design.
But it was because of a misunderstanding of the spec. And so we were able to adapt
We actually kept the build on time and we actually shipped the product on time.
But it was really stressful, and it turned out that actually the new design was better because
With a favored
Paired.
You have source of truth for the space, and then the other two cameras overlap.
on to 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 hard hardware is just
You can't like you s 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, it was it was tricky. So the quest one that shipped was this with the cameras moved.
Well, 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, not at the bottom.
Wow, how did uh Boz and uh Zuck uh feel about this?
I the fact that I don't remember probably means it was okay. Like um I think we uh 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 ha hold the tolerance we needed.
But it worked out.
And the price and the cost and and
Yields were fine, so I think we
We adopted.
And that was the best selling VR device of all time, is that right?
I think it was. Okay.
I don't have the final sales on this, but
Conservative.
Okay.
Uh Caitlin.
We've covered so much ground.
Is there anything else you wanted to share, anything else you want to leave listeners with?
either double down some we've shared or anything else that just like oh here's some I wanna 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 n this new way of work is scary, but if you embrace it and are daily using these AI tools right now.
and daily applying them to what you're doing.
you'll be at the forefront of whatever comes next.
And so I just 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 I've never been more excited about
The power of an individual.
Yeah.
Well, with that, Caitlin, we 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.
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, firsthand or second hand uh accounts of what happens. It's it's it's a way to look into the world.
at a completely different era than it is now. So these are some books that I like.
And uh
I'll double check the the first the title of the first one and email you, but I think that's what it's called. Okay, and we'll link to the the correct one in the show notes.
Uh favorite recent movie or TV show that you have rec really enjoyed?
I'm
really into euphoria right now. I think the new euphoria I'm I'm I'm interested in the characters.
Um and um figuring that out.
Uh what's gonna happen there.
That show is so stressful whenever I watch him because I 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 or create 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 Volleback.
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. Volow Back.
P O L
E B A K follow that.
Very cool.
Do you have a favorite life motto that you often
Come back to and 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's wrong. I know who it's.
Um, this is w 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
I know you well share this really interesting
That you hired a
PhD to tutor you on
the cl on the staples of ancient Greece and Rome and just get really nerdy about this stuff.
What's going on there? What drives you to go so deep on these sorts of things? This is like very niche nerd.
Nar territory, but I found this um list
That the poet
Joseph Brodsky Brodsky wrote.
Which is a list of
English.
Uh or a list of things you should have read.
In order to have an intelligent c uh conversation in English.
And it is like an affected list. Like it's in you know, it's intense.
It's like the Old Testament, Gilgamesh, and then all the way down through.
But
What I found is it's a pretty good um distillation of what we used to call the Western Canon.
And that actually I learned a lot in in my public school education and it
In college.
But I never really learned
From what you would consider the Western canon.
Um, and so this is kind of
Um in addition to that, there's some some more
newer
newer um
Books on the list.
I find it just fascinating to have something to work off of.
And what I found is as I got into
Specifically the tragedies the Greek tragedies.
I just didn't have enough context to learn what I wanted to learn and just reading them, I didn't have enough uptake.
So I found an incredible um
Uh postdoc.
who's was willing to tutor me and I just get to ask him all these questions. He's an encyclopediat. He knows everything. I could ask him what was happening
In Turkey at the time of this Greek
you know, this cr tragedy that we're reading.
And like what was happening in Athens and like
What this
You know.
Tragidian might be responding to
Um and and he can answer the question, it's really fun to have
The ability to have that sounding board.
So cool.

It's so cool you did that way, even though AI can do a lot of this, like sometimes a human is much more y interesting to talk to and
And feels better.
Yeah, I find reading
And communicating with AI is very helpful on the basics.
But then understanding
what was happening culturally and what the significance of the work is is it it wasn't isn't adequate.
It's because we're uh we're not twenty something.
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 wanna find you, if they wanna 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 dot com.
I'm almost all 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 feature we want.
We'll want it to look like that.
What we want the human aspect to be in that future and what we we think we want to hold for ourselves.
Um how we want to argument ourselves, like
right now we're in this dystopian niche where everything's just
You know
horri it feels like the future is horrible.
And the way to not have that is to actually design their own own future together.
Figure out what we want our future to look like. Paint a picture.
In fiction.
In literature
In c conversation.
And then build that.
And um
You know?
I think that that's possible.
I love that. That's actually a message that's come up on a c a couple of recent podcast episodes. So it's really good.
Reminder.
Uh Caitlin, thank you so much for being here.
It was really fun, Lenny. Thanks for having me.
Hi everyone.
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.
Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show.
at Lenny's podcast dot com.
See you in the next episode.