NVIDIA: Jensen Huang. From near collapse to becoming the world’s biggest company

Transcript from https://podmenti.com/t/327530f5449e2b53

We've sort of have the outlines of
all of the troughs and there are a lot of troughs. Yeah. And and I'm trying to even imagine like quarterly earning call in two thousand seven when your stock price is in the toilet and you're pretty much a little bit more than a little bit. And I gotta tell you, it's embarrassing.
І вас і берисін, і вас хуліатін.
Your employees are probably embarrassed for you?
In fact, you know, right now it is really quite hard for me to resurface those feelings.
And the reason for that is but I spend all of my time
All of my
Life trying to forget yesterday.
What do they teach athletes? Forget the last point. Yeah.
It's about forgetting.
Welcome to How I Built This, a show about innovators, entrepreneurs, idealists, and the stories behind the movements.
They built.
Guy Raz and on the show today, how Jensen Wong went from making graphics chips for gamers
to powering the AI revolution.
And building the biggest company.
In the world.
Yeah.
If NVIDIA were a country, it would be one of the five richest in the world.
just behind the US and China.
NVIDIA's value is now more than the entire economic output of Japan, or the UK, or France. That's how big this company is.
And it's also probably the single most important company in the world right now. It is the biggest player powering the AI revolution.
But NVIDIA didn't start out that way. The company actually began by selling graphics processors for video games, and for a good 20 years,
Gamers were NVIDIA's main customers. But back in the 2000s, the company's co-founder, Jensen Wang, made a pretty important bet.
A bet that those graphics processors known as GPUs

Power video games.
He believed these processors could be the cornerstone for the future of supercomputing.
This was a very bold and very expensive. Now, bear with me for a moment here because if you're not super familiar with the technical terms around AI and computing, I will do my best to explain.
So
Think of a computer like a kitchen.
The CPU or central processing unit
Is like a master chef. It's really smart, it can do almost anything, but it can only cook a small number of dishes at a time.
The GPU or graphics processing unit
is like a kitchen with thousands of line cooks.
Each one isn't as talented as the chef, but together, these guys can crank out thousands of simple tasks.
All at once.
Now to make that kitchen do even more.
Jensen poured billions of dollars into developing a platform and software layer.
called Cuda.
Which is basically the instruction manual that lets you use all those line cooks in entirely new ways.
So what Kuda basically did is turn the GPU from a video game tool
Into a general purpose supercomputer.
The problem with it, though, is that it was way ahead of its time.
Only a tiny number of users, mostly university researchers, had any need for it.
And so every single NVIDIA processor sold to a teenager playing video games was actually very sophisticated, and yet that teenager had no need for it.
And so for nearly a decade, NVIDIA

The stock price stayed flat. Sometimes it actually fell.
And at one point there were rumors of a hostile takeover bid.
Many investors were losing faith, a lot of them were dumping the stock.
And people outside the company were questioning Jensen's obsession with CUDA.
But he kept going.
He withstood massive pressure to move away from it because he really believed in the power of this platform.
Which may be the most remarkable thing about the story, because a decade after NVIDIA started this experiment,
The bat.
started to pay off.
And when it did.
It was like every slot machine in the casino hit at once.
Nvidia's chips found a massive new market in the emerging world of artificial intelligence.
And today, NVIDIA is at the center of it.
Their chips
dominate the computing power needed to power AI.
So how did Jensen Wang see it coming?
What did he believe that others didn't?
And what does he think about where all this is going?
That's what I wanted to find out when I sat down with him at NVIDIA headquarters in Santa Clara, California.

Jin Sin Wang was born in Taiwan in the early 1960s.
He spent some of his childhood in Thailand, and when he was about nine years old,
His parents started to get worried about political unrest in Thailand.
and decided to move to the US.
They sent Jensen and one of his brothers ahead of the rest of the family.
to what they thought was a normal boarding school.
And there weren't that many boarding schools in America that would take international students, but
Uh somehow Onita Baptist Institute.
In Kentucky, Clay County, Kentucky.
Little tiny town.
No stoplight.
And there was a school.
On a small
Small mound.
You know, kind of a small hill.
But the probably the the most important feature was that it was a boarding school and it was incredibly affordable.
Yeah.
You know, didn't have much money and
And so we went there and every kid
that live there.
had to work and so they had no custodians.
And so we were, you know.
We're the we uh took care of ourselves and
My job is to clean the bathrooms and
Apparently I was the youngest kid that ever went there.
I still am, I think.
'Cause you were ten. I was nine when I went there, yeah.
Uh I've got great memories of it.
I love the place.
It was a tough school.
Because they invited kids from all walks of life. Yeah. And so
You know, I was nine and my roommate was sixteen. And and so
None of the closet had doors and none of the drawers because everything had to be
out in the open. You just don't know what the kids are gonna have. Yeah. So
Welcome to America. Yeah, right. But I but I loved it. It was incredible.
I was on the swim team and the soccer team and then afterwards
The the coach would take you out to uh give you a treat.
And um
I remember recording
We used to record a tape.
And we send it back to our parents.
Yeah, we didn't long distance phone calls cost too much money and so we're we never spoke to them live for
Until they came to United States.
Almost two years later.
And so we would record
You know, what happened this last month.
And uh I remember I remember telling them that after the swim meet
The coach took us to
This incredible restaurant and
lights everywhere is it's like from outer space and
All the
Food was in boxes and it was McDonald's. It's McDonald's. Yeah. Which was magical. Yeah. Incredible. Yeah. Yeah.
Um
You eventually reunite with your family in Poland, which is where your dad
Settles and you grew up there.
And you went on to this is
The early eight, late seventies, early eight, you go to Oregon State.
And this is like the beginning of what would become
the computer evolution.
Electrical engineering,'cause I don't think they had a computer science program there at the time. And that's also where you would meet the person who would become your wife.
Yeah, so
When I was in high school
I wasn't very outgoing and so I didn't have that many friends.
All of my best friends were in two clubs.
One club was the uh math club and then the other club was the computer club. Nice. It was the same you know, four kids.
And my best friend.
In high school I I asked him, you know, where he was going to college and he said Oregon State.
And he asked me where I was going to college and
Never crossed my mind to go anywhere out of town for you know

Going to a great university it never crossed my mind.
And so
He said he was going to Oregon State. I said, Yeah, that sounds great. I'll go to Oregon State. And so we were roommates.
And uh
There, uh we both like enrolled in electrical engineering.
And um our lab.
The class had two hundred must have two hundred fifty kids. It was a big big class.
And there were three girls.
You know, and so
I noticed Lori is is a super pretty girl and um I was probably the youngest kid in class then as well.
I skipped two grades in school, so
Uh we were in lab together and I found a way to
Arrange myself into the same
Lab as her, same group as her.
Until we be became lab partners and here we are. Um both of you after you graduate moved to California. This is like nineteen eighty four ish, I think.
Which is the center of the revolution, right? I mean your you your first job was with A M D.
Yeah.
And I think she also had a job. Silicon Graphics. Silicon Graphics, yeah.
Well what did it look like to design?
computer chips in nineteen eighty four. I mean
Was it CAD? Was it what did that look what did that actually physically
Yeah. Well
At the time I was the
The last generation of chip designers that did it by hand.
And the first generation of chip designers that use software.
To design ships.
that ran into computers that ran software. And so
It was an incredible time.
You know, it's kinda the print perspective.
At the time.
The chip I was working on had a few thousand
Transistors.
And now uh we design ships that are two hundred billion, a trillion transistors.
And so.
Yeah.
So
the scale of the problems that that we work on now.
Compared to where we started is incredible.
But anyhow.
My office mate went off to work for a startup company called LSI Logic.
And she called me and said, Hey, you know, um
This is really quite a special company, you ought to come take a look.
And so I went to take a look at LSI Logic and it turned out this is an extraordinary company and completely revolutionary in what they were doing, really in a lot of ways
invented the modern way of doing designing.
The way of designing ships.
And so
I joined them.
I guess I was, you know, probably employee number one one fifty or one seventy or something like that. And you were like twenty two probably at the time. Yeah. I was just a kid. I was just a kid, yeah.
And from what I like from what I've read, I mean, they gave you a lot of responsibility. I mean they they I mean, you were eventually you would be in charge of tooling, right? Like that was sort of
Yeah.
Uh the way LSI Logic's business worked.
is they had all the technology the tools and they made the chips.
But they would make it for somebody else who created computers. And so some microsystems and silicon graphics and
incredible companies at the time in Silicon Valley who were
systems companies, but they needed a chip company to help them build the chips and so
So the CEO of LSI Logic, you know, recognized my talents and put me in front of all these companies to help them.
Mm.
And uh I met some incredible people.
I mean you were
This was sort of like the P C boom, the clone wars. I mean, there was a lot of demand for what LSI was producing, right? I mean it was an exciting
Time.
To be here, to be young and to watch.
Yeah, probably that next
Well LSI Logic was was an extraordinary company because
It happened at precisely the time, exactly as you're saying, Guy, that
Mini computers, many supercomputers, supercomputers
All of those types of giant systems were being created by companies like Digital or and then the PC revolution started.
And so I was really in the right place at the right time, and I saw
uh new industries being created. Saw a lot of great startups and
I was able to see.
See uh first hand, you know, companies being built and
Great technology, bad strategy.
Moderate technology, excellent strategy. I saw it all.
It was a quite an incredible thing. And then and of course I met Kristen Curtis.
Those people.
Curtis.
Supreme and Chris Mal.
Uh we're at sun.
These the story is that these guys wanted to create at some micro systems a
A chip for
Specifically. Computer graphics for for games and
were rebuffed. And so as often as the case they decided to start their own company. Yeah. And they approached you and they wanted you to work with them. And this is around nineteen ninety two, ninety three, and
From what I understand, I mean you had a great job.
You were well paid, you were well on the path to upper management, like it
This could have been a great
stable career, you already had uh, I think, at least one kid.
At the time. Yeah, Madison, yeah. And your wife
was looking after the kids, so everyone depended on your salary.
And I think we had a whopping.
Thirty thousand dollars in a bank.
Um
When they initially approached you, I mean
Did you think that I'm not gonna take that risk? I'm not gonna leave this amazing job.
Yeah, I mean it they um
But they they didn't give up asking me.
'Cause they wanted you to run there.
This business.
That they had an idea for.
But I I think
In the end.
What we got excited about.
was probably just a piece of revolution in the end. That
Here's it the first time a computer was going to be
general purpose and
you're gonna use it for all kinds of different applications and
If it had computer graphics.
like what people were seeing with Jurassic Park and Silicon Graphics and
If we could figure out a way to make it affordable and architect something that would fit into a a personal computer architecture.
You know, maybe there's a company here.
But I mean at the time there were like sixty companies trying to do this. Right. And so was there any part of you, do you remember any part of you thinking, God, what if this doesn't work out?
No.
I should have.

It didn't it didn't really.
You were you were prepared for whatever happened would happen. Yeah.
Maybe that's what's called vision. Maybe maybe that's you know, determination come from but
And manifested in my mind that
And an industry manifested in my mind that that was so crystal clear.
Never once did it.
Cross my mind it wouldn't work out.
So NVIDIA is the company, which I I guess comes from the Latin word envy. NVIDIA and um
You guys start working on the first product.
Which is gonna be a revolutionary
Graphics.
Processing.
That is going to make it.
We were the first three D graphics company in the world that started with the idea that we bring three D graphics to consumers. Yeah.
And so nineteen ninety three, it's founded and you you start to work on this product.
And you guys raise a little bit of money to do it. And
This is gonna be a game changer, right? I mean, this is gonna be
Like the coolest thing.
Yeah, we thought so.
The technology
The we created
Allowed us to generate images
But using
using a lot less electronics, a lot fewer chips and a lot more affordable.
than these giant supercomputers that Silicon Graphics was making at the time.
you know, at the time the computer that generated the images for
For uh Jurassic Park would
cost a million dollars at a time.
We needed to get something to fit into about three hundred bucks. And so
And so that
Gap was so large that
uh we had to reinvent the algorithms all together.
People got very excited about it. This is the N V one.
A chip. Yeah, we stole two hundred fifty thousand.
I've now
You know, I I believe you could sell two hundred fifty thousand of almost anything.
But the algorithm didn't really work.
And so we we received two hundred fifty thousand back. I mean it it all came back.
It was not just our lesson, it was a disaster.
Yeah, it was a
probably a technology disaster as as great of a technology disaster ever seen, you know. I think the
The right architecture, the right algorithm is inverse texture mapping. Mm-hmm. Ours was called forward texture mapping.
At this point.
There were probably twenty, thirty.
three D graphics companies and they were all doing it the right way.
And we're the only one doing it this weird way.
And uh Microsoft has
about to announce Windows ninety five.
And Windows ninety five has a
API called direct X.
And DirectX does it.
The right way.
And we were incompatible with direct text?
And so anyways.
We chose some approaches that were just fundamentally wrong.
I've read that that
understandably create a lot of tension.
with you and your co founders because
It was two and a half years of work and
And I from what I also understand is the all the architecture that you'd created for the next N V two and N V three was based
On M V one.
I imagine I mean do you remember
The three of you.
Just going at it.
Yeah. Well,
The arguments the argument
That was pretty stressful because we were um
We had a contract with a company called Sega.
The video game.

And they contracted us for twelve million dollars in nineteen ninety five, nineteen ninety four. And they were guaranteed to buy
Yeah, to use it for our game console.
And so we were contracted to do that already and we had invested two and a half years
And so the question is how do we deal with this contract?
If we cancel the contract, how does the company stay alive?
And if we don't cancel the contract.
You know, the company doesn't go out of business.
And so there's the argument of let's not cancel the contract.
Let's keep on going.
Then
Of course that there's the argument.
The contract is based on an architecture.
That is fundamentally flawed.
And so why finish something?
The wrong way.
And uh it would have burned another two years.
And in two years time, thirty other companies were in so far ahead of us, doing it the right way, we'll never catch up.
And so this was the the law.
And um
If we decide to change the architecture
We've got to go cancel the contract.
Right.
And so
I went to Japan.
And um I contacted the CEO.
Sega. Sega. His name is I Major.
And I told him.
My recommendation for you.
is that Sega finds another partner.
to build the three D graphic system and their next game console.
I have a request, however.
That even though we're not finishing the contract.
I still needed the money.

Because if y if we didn't have the money, the company would be out of business.
And
I really believe the company deserves to succeed.
And so
I asked him if he would convert.
the rest of the contract to an investment in our company.

And he says, But Jensen, you know
your company is like has thirty competitors, you're most likely gonna go out of business. I don't even know what your business plan is.
And I was like, I tell you, I'm not sure what my business plan is, but my first job as a CEO is to make sure we don't go out of business and we've got to get
the technology on the right track.
And if you could help me with this.
I think it's a good thing.
We'll figure out a way and I think it's gonna be a great investment.
Anyways.
He talked to his board.
and they turn the rest of the five million dollars of investment or contract into our investment in our company.
I took that five million dollars when we came back.
Yeah, uh I was incredibly grateful. Came back and here we are.
The company was a little bit too big.
I had to cut it back in half. You were like a hundred two hundred and fifty people or something. Like uh almost, yeah.
And so I cut it back to Because you hired all these people anticipating N B one, N B two, N B three.
And we're gonna get all these Sega games, all these King Castle games into the PC industry. So anyways
I laid off two thirds of the company.
And um
It was incredibly hard to do.
And also you you now had to focus on making the N V three, which which was a new chip that would actually work.
Uh, and I mean I mean to be clear, you you you needed to do this not only to compete with all the other companies out there, but basically
to save yourselves, right? To save NVIDIA.
Yeah, well it it's scary.
It's scary even now.
You're asking me.
You know, these are traumatic experiences thirty three years ago.
And um
I so so we decided, okay, first
Let's just decide to do
Things the right way.
But
We don't actually know how to do it the right way.
And then the next problem I have.
Is that
We have five million dollars.
But
By the time that
We were done.
Designing the chip.
uh the company would have ran on money before the chip comes back.
And it's usually being made in.

Or do you start working with C S M C in Taiwan because initially
Your chips are made in Europe, I think. That's right. Right. But the capacity wasn't.
There. So you had to move it. So this is like night around ninety seven, you start to shift production to Taiwan. That's right. And you would run out of money before they were
able to produce them.
That's right.
And so the problem was
Back in the old days.
You would design the chip.
The chip comes back.
You will write the software for the chip.
Fix whatever bugs you.
Found and then you were
Make the trip again.
And that would go around a couple of times. Yeah.
And back in the old days.
it would take about a year and a half to two years to design a chip and get it to work and ship into production.
Well, we didn't have a year and a half.
We had about
Six months.
We were on fumes.
Which means you couldn't do all of the processes. We couldn't iterate.
Yeah. Yeah, I couldn't prototype and how did you know it was gonna work?
And so we heard about this company.
Call Icos was building an emulator.
And this incredible machine z giant machine.
It would pretend to be your chip.
And so you would take all of your software and put it into this machine.
And his machine.
would pretend to be the chip and you would plug this machine
into a PC and run the software. So you could see how it so you could see how it would work.
Oh, and it was super tight accurate.
Well, supposedly.
And so I called the company and I said, Hey, listen.
Uh I heard you guys have a this machine called emulator.
I would like to buy one.
And they said, Well that's terrific, but unfortunately
We had no customers that we're going out of business.
But
We had this
One that's left over.
If you want, you could buy it out of you know the creditor who
Now owns it.
And so we did. We bought this leftover piece of machinery from this company that went
ultimately went out of business.
And um
I took half of our company's money, so we we were already running on fumes.
Yeah, so I
Cut that life short.
Even further.

bought this machine that
Nobody else wanted to buy.
Brought to the company, I said, Here we are.
We've got to put
Nvidia's chip.
M V three, we have a one twenty eight.
Into this machine.
Did it anybody say this is crazy.
I mean the this isn't you're taking half the money.
I mean you're the CEO. We don't even have that was the money we had was already not enough. Yeah. And we took half of that and spent it. But these engineers who are used to a process, right? And used to a way of doing things.
You're basically saying we're gonna
put it in the emulator and that's gonna be it. And then we're going to fabrication. I mean did anybody say this is not a good idea?
It was the only idea we had.
I gotta tell I don't
I didn't remember anybody
Objecting to it.
But
I also didn't remember
Asking too many people f whether they objected to it.
You know, I think smart engineers reason about things.
I guess we always knew it was existential, we always knew we were going out of business.
But I just remember us
Being super calm.
Super focused.
And we just step by step by step.
Reason that it was the only chance for us to be that had to work. It had to be.
Your this is
I don't know if it's apocryphal or but I think it's true.
you are known to say at that time our company's thirty days away from.
Going out of business. Yeah.
a meme and a mantra for a while but it was true амі ю сор
This thing had to work.
And we
I'm not revealing any secret. It did work.
It did actually work.
Mostly.
But enough to save the company. Yeah.
Good enough to save the company.
Well we come back in just a moment.
Jensen identifies a whole new market for NVIDIA.
A market
That does not yet exist.
Stay with us, I'm Guy Raz, and you're listening to How I Built This.
Hey, welcome back to How I Built This. I'm Guy Raz.
So it's the late nineteen nineties and NVIDIA has escaped extinction.
by using an unproven machine to test its latest chip. And as it turns out, that new chip
The Reva 128.
Is a hit.
Which means the company can now write its next chapter.
We're a hundred percent focused on the gaming industry.
We were the first computer graphics company ever created to focus on one
Application industry.
Fully.
Which was video games. Yeah.
So the way we saw the world.
That the chip is important.
But ultimately what
What makes people happy and what really creates industries.
It's the applications that were on top of the chips that make
New things possible.
recognizing the importance of application developers or game developers so that we can help them
Realize
uh the full potential of our chip.
While we make their application as wonderful as possible.
Um
I know I wanna just pivot for a second because I
You know, here you are, you're still a young guy.
you know, in your late thirties, mid-late thirties, running a company. It's the first time you are the CEO of a company.
It's not like you have any management training or you can go to business school, which is very normal, right?
And you kind of had to teach yourself.
Because you have
And had a reputation for being like
A hard.
yelling at people, demanding
Excellent.
Tearing into people when they're
when you feel like they didn't do great work. But I I read that you
stumbled upon this book by Christian Claytonson. Mm-hmm.
Uh Claire Cristenson, for forgive me, he's no longer alive. Harvard School Professor called the innovator syllable. Yeah.
Very important book and it had a huge impact on you. Like you read this book, because he has this example of like Honda. They were making motor scooters for kids and no one was paying attention to them. And so they when they started making cars.
they had this advantage'cause they could really scale quickly and get out there.
And you read this book and you
were inspired by it. What do you remember about that book that you thought
That's it.
This is the thing that I need to think about.
Uh the single most important thing about technology is
The moment it becomes good enough.
When you overserved the market.
You're ready for disruption.
In fact, at the NVIDIA journey every single step of the way.
We were always the disruptor.
So I think the the large lesson of Clay Christiansen is really about disruption and and how technology emerges.
out of thin air that apparently looks like toys.
But went off to disrupt large markets.
If you look at Nvidia Sriber one twenty eight,
The quality of it was okay.
But it was good enough.
that it disrupted an entire market.
Um if you looked at
NVIDIA's first uh
GPU that was designed for high performance computing. It was
Not perfect, but it was good enough. And so
Example after example after example.
uh the way that that we went off to revolutionize large industries.
Initially it looks a little toyish.
But the outcome has always been the same.
Ай, се, а ванна jump ahead uh a little because in the late nineties.
NVIDIA developed a new technology called parallel computing.
In this
basically gave your chips the ability to perform multiple
calculations at the same time, multiple tasks, right?
But this was another gamble, because I think a lot of other companies had tried and failed to produce parallel computing chips, right?
Um
Uh, during that time there were all kinds of different processors being created.
And um
People were trying to
Come up with new ways of doing computation.
And then we realized that computer graphics if it was just beautiful, but the world was static.
It was hard to create beautiful and immersive worlds. And so you really need to find a way to bring
Physics into
that virtual world.
So that you know, waters would flow and
Yeah.
Leaves would blow in the wind and
Explosions would look like explosions and and so we would
We would try to
Use the processor.
which was incredibly parallel to express, you know, the types of algorithms that represent
Real time physics today.
And so
That was really the beginning of our journey.
down that world of general purpose programmability. Meanwhile,
Uh scientists around the world noticed that
Nvidia's processors were super powerful.
Lots of multiple things. I mean'cause you were thinking, okay, this could be used by game designers, maybe in the film industry, which it eventually would be.
But still it was like
You were thinking beyond people playing video games on their consoles or
We would use it for fluid dynamics and image processing and particle physics and one of the areas that really caught my eye was the whole field of inverse physics or imaging.
And two doctors.
At Mass General were using
Are graphics chips.
To do CT reconstruction.
And it was during that time.
um other types of techniques for general purpose computing was coming along.
Which ultimately led
Two.
What we now call Cuda.
Alright, so let's jump into this because you launched this project called CUDA or C U D A.
in roughly two thousand six. And to put this in in very basic terms,
Cuda is this platform that makes your graphics chips a lot more versatile, right? And originally you thought this would be great for scientists and researchers who had to process tons of data.
But meantime, your customers were gamers. And and so they were getting these
really sophisticated chips that they
didn't really need or use, right?
Yeah, and so
You just go back to the inspiration that we had at the time that that it could be used for a whole bunch of other types of general purpose.
computation, parallel computing nature computation.
And image processing could be one of them, you know, there's a whole bunch of them in early experimentation, but none of that.
Pay the bills.
Most of those applications were in universities and
uh the researchers, you know, didn't buy too many of them to justify. Yeah. And so the only thing that really paid the bills was video games.
You understood that the GPU could do something, lots of things.
But that we just had had not imagined those things yet.
Yeah, we we knew of some things and during that time.
I was constantly looking for.
algorithms that required parallel computing. I was always looking for algorithms that
somehow only ran on supercomputers, but if we could just figure out a way to bring it into
A personal computer, its exposure or its ability to reach more people could be incredible. Constantly looking for things like that.
I'm trying to
understand'cause basically
From two thousand six to twenty twelve.

either collapsing at times or wasn't moving.
Uh you had a refund.
hundreds of millions of dollars to people who didn't like the processors.
And still there was this conviction in continuing down this path, this this cuda path.
Because
You and the people around you believe that there was
something there that you couldn't really know exactly what it was.
But it was going to be something
So let's keep
Investing in this.
get into just the space of how you were dealing with, you know
You're a publicly traded company, your stock price was
terrible. You probably had a lot of pressure from investors.
What made you and the people that you worked with say
We're gonna keep our heads down and keep going.'Cause it was a long time. We're talking
Six, seven, eight years.
on this thing that nobody understood and was and had zero cur not zero but very little commercial success.
Well that's that's when CEOs have to be CEOs.
We believed on first principles. This should be quite useful.
And
I had to believe that
It's quite useful.
Now the question is what's the strategy?
for creating this new architecture for computing.
that everybody would be able to enjoy.
And
The problem with computer architectures is
This chicken or the egg problem.
Let's say you create a brand new architecture is incredible. It's the most amazing thing in the world.
But computers are built.
To run software. Mm-hmm.
And if your install base
Is not large enough.
It doesn't attract software developers, because developers want to program on large install-based computers like the iPhone and PC and
And so the problem is
Even though we believed that this architecture was going to be incredible, that Cuda was going to be everywhere.
Or could be everywhere. How do you get it everywhere?
And if you don't get it everywhere, how do you attract a developer? And if you had no developers, who would write the killer app? And if there's no killer app, then why would people buy it?
And so the answer was very simple.
It was literally sitting in front of us.
And
І що реквієр са крифа.
The answer was
Let's use G Force, which is the GPU that is now everywhere in the world.
Used for playing video games. Yeah.
And let's have G force.
Carry on its back.
Cooler.
To every single computer in the world.
Now, of course, by doing so, our gross margins would go from
Bad to horrible.
Uh okay, well let's get past that.
Let's not worry about the fact that our years.
I know.
Two.
You never think that it's
Nobody ever thinks it's that long. And I thought it was gonna be next year is gonna be okay, and then next year it's gonna be okay, and next year it's gonna be okay.
But non the less
The cost was incredible.
In Karen Kuda?
And we couldn't charge anybody for it.
And so we gave it away.
Meanwhile
We started up programs.

Every single university
We evangelized Cuda, we flee around the world.
to pitch cuda whenever you can. Meanwhile
G Forces was taking Cuda out to the world.
Yeah, and so the hope was that some day
Some software company.
Or some researcher, or hopefully a lot of them, eventually
wouldn't know how to use CUDA and they would take advantage of it because it's sitting right there on G Force.
Was there ever
Any thought in your mind that maybe we're too early.
We we might be the ones who actually make this revolutionary
Technology but
We might not be the ones to bring it to the promised land.
Yeah, all the time.
But that's you know.
Then there was nine hundred other smart strategies and
Yeah.
The list of
New ideas that
We came up with to keep the company alive and
You know, successful for just another few more days and
It was countless.
Hm.
And so you're solving the problem both in making sure that you stay alive long enough to proliferate this technology everywhere, looking for every possible way. You're making it easier and easier for people to use this technology. You're teaching people to do it. You're talking to software developers, and you're saying, hey,
You know that imaging software that you had?
Maybe Photoshop for example or
Some video imaging system for broadcast. Can we m modify that so that it runs on CUDA? And we're live I mean, but were enough people adopting it to
Make it seem viable.
G Force kept the lights on.
And just didn't do it very well.
You know, we were always under pressure because
Unlike other comp our competitors, they didn't have to carry CUDA on its back. G Force carried a CUDA on its back.
For literally twenty five years now. And people were using this product and not even knowing what it could do. Not one day.
Yeah.
It was just all sitting there, except for the scientists.
Except for researchers in universities that that heard about CUDA and said, Oh
All you have to do is go buy G Force?
But then at this point, I mean everything was about to change.
for NVIDIA.
Because in the early two thousand tens, a group of researchers in Toronto were
But
Two NVIDIA gaming GPUs, right, or G Forces.
Uh they plug them into a computer in a bedroom and then they trained a neural network to recognize images
way better than any computer had ever done before. And this was essentially the beginning of the AI boom, right? So so when you found out about that,
What do you remember? I mean, do you remember thinking
This is what we've been waiting for for fifteen years.
Well we
At that time.
Uh several different groups reached out to us to ask us for help.
On using Cuda.
To accelerate deep learning.
And the reason for that was because there was a contest coming up for computer vision called Image Net.
And they they were all developing similar techniques. Nidikta wanted to use Kuda.
Instead of using CPUs, which would have taken thousands of CPUs.
CPU is one task at a time. One task at a time. And they could use our GPUs with just a few GPUs, maybe a couple of them, running simultaneously on CUDA. Maybe they could train these deep learning models a lot faster.
Yeah, a lot more cost effectively. And so they reached out all at the same time. And um
the leap over previous generation algorithms were so significant
uh it really caught my eye. And so we asked ourselves, you know, what is this thing about?
And where can it go from here?
Uh, what is the implication to our chips? What's the implication to computers? And so like we do with everything, you know, reasonable.
Uh the so what's?
Which uh led to a lot of other good decisions.
It was a weekend, I guess in twenty thirteen, where you sent a note out and essentially said, Well now.
an AI company.
Like
Friday you were focused on the gaming. I mean,'cause the majority of your business was still gaming.
Maybe um people who were doing three D animation and doing graphic for films, that was a pretty significant source of
your customer base. But now you are saying, Well, we are
becoming a different company.
Yeah, and the process kinda goes like
Yeah, what I was explaining earlier.
As I do with everything, I break things down to first principles and and you ask yourself, what did I learn?
Why is it impactful?
What are the foundations of this technology that made it effective?
Can it
Do more than this?
Uh how far can this algorithm go?
what is the implication to our computer industry. So you just gotta go through all of that. You know, it's no different than than somebody writing a business plan about something, except, you know, this is kind of how my brain's wired for almost everything.
I'm good at connecting dots, good at system thinking.
And uh
You know, I came to the conclusion that this this could be a r a really significant future direction for the company.
Yeah.
Long before I would send out an email that declares something
I had already through tens
of engagements with different groups.
Each one of the groups have already been brought along. So by the time that I sent an email to the company integrating everything, everybody's already been brought along. And so it just kinda tends to be my
My management style, but at some point
I will.
You know, set a direction for the company.
I'm curious.
Again about
stress management and conviction, like
I wonder
How you
personally dealt with with that. I mean there were periods of time where
You and obviously other people believe in the potential of this thing, like the capacity is gonna be enormous.
But we just have to stay the course and nothing is really
Moving for a while. I mean it's gonna
take a long time, but you don't know that. You don't have a crystal ball.
I mean
Are you just
comfortable with that level of stress? Or I mean do you
Feel
'Cause you don't strike me as somebody who was saying, We're gonna be the biggest company we're gonna be the masters of
Not going out of business is always high on my list.
Yeah.
Yeah.
Showing up in life.
Matters a lot.
Showing up every single day matters a lot.
Guy, I g I guess um
To me it's not that complicated.
There's a lot of unknowns.
And being a CO you're dealing with mostly unknowns and
I'm very comfortable with uncept.
But the first thing you have to do is to the best of your ability reason about
You know, what it is that we're doing.
And what do we believe in?
I deeply believed, and because I deeply believe it.
Uh I help everybody else believe it, and I really believe they believed it.
And then of course
We don't want to dedicate our lives to go work on something that the world's already already have.
I don't want
our company working on things to capture share from somebody.
They built a market. We want to take their share. And so let's go and fight.

And when your share point goes up by a point, you celebrate with joy and
I mean I I just don't find any joy in any.
Going in a completely different direction. Creating something that's really, really hard. And I believe if it works, it would make such an extraordinary impact.
It was not hard to stay the course.
they would have been harder to give up.
You know, I I think that because you believe so deeply in something, it's already manifest manifested fully in my head. I imagine everybody using it. GForce has carried it to everybody's computer, everybody has CUDA inside their PC.
And so the question is not to me.
Whether we would succeed.
The only question is when and who is going to be the first application.

Well we come back in just a moment.
NVIDIA becomes a major player on the world stage.
And Jensen reflects on the future of AI.
and on his own reluctance to reflect on himself.
Stay with us.
I'm Guy Raz and you're listening to How I Built This.
Hey, welcome back to How I Built This. I'm Guy Roz. So it's 2013, and NVIDIA is now pivoting away from making chips for gaming to making chips for AI. And as the technology takes off, NVIDIA starts to soar.
right along with it.
And I'm gonna jump ahead to the part of the story that's familiar to all of us because we've all lived it. By 2022, ChatGPT and Claude start to transform the workplace in our daily lives.
Massive data centers go up around the world.
And most of them are full of NVIDIA chips.
NVIDIA becomes the biggest company in the world and has as much influence on world affairs as some countries, maybe most countries.
But
Just to say the obvious here.
A lot of that?
Can feel.
Kind of disconcerting.
You know you're well aware of lots of people who have a lot of worry about this. I mean Jeffrey Hinton, one of the earliest users of your GPUs to train these networks.
They've signed letter saying, you know, uh there's a famous letter came out May of twenty twenty three.
Saying, look, to quote it, mitigating the risk of extinction from AI should be a global priority alongside other societal scale risks, such as pandemics, nuclear war.
Signed by Bill Gates and Sam Alpen and Darry O'Ma and many, many others.
You didn't sign this letter and you have
been really clear that you are not
worried about it. You think a lot of this is alarmism. A lot of the
conversation around AI and w how it's going to take over our jobs and
can manipulate you know, all kinds of things.
Um
Why do you think
the people who are making those warnings wrong.
Yeah.
I think that first thing that
Everybody should do.
Is to take
the science fiction and the Hollywood versions.
Of
Artificial intelligence.
And set that aside.
And come back to
a sensible understanding of what a computer is.
It's
Computers, it's software.
It is not conscious.
Having said all that
I think we all everybody wants
the same thing that the technology evolves
In a safe way.
I am worried about it.
And so the question is how do you channel your worry?
What are you worried about?
Well, we have to build the technology safely.
And so
Uh, could you imagine?
If the auto industry every single day
Told you.
That the car's gonna kill lots and lots of people.
It's their job.
To make
The car safer.
Yeah. Channel their worry towards making the car safer.
I don't think it's helpful.
For the
Airline industry to tell you.
That every single day if the plane fails, it we crash out of the sky.
I think all of those
Narratives are not helpful.
people who are making some of this technology.
I I guess I've got
a different perspective on why it is that they're doing that. Yeah.
Uh maybe it's because they want the world to to realise
The technology they invented was so powerful.
Um it's a celebration of their own achievements.
Maybe they are genuinely scared themselves.
And that they believe that
Only they?
Can create a technology and keep it safe.
Ignoring the fact that
That
There are millions of innovators around the world.
We're thinking about it.
AI safety.
AI security and building technology for AI safety and AI security.
Th there were even attitudes that that we ought to slow down.
AI innovation.
Which I believe is exactly the opposite. We got to speed up AI innovation because through innovation we can make the technology safer, more secure.
And obviously it's proven to be true.
The fact of the matter is the AI technology of today
is more grounded on truth, it hallucinates less, it lies less, and all of that technology
Um needed to be invented.
And so I my single greatest worry is that United States doesn't take advantage of the technology.
Because we scared everybody.
W what do you mean?
Uh, right now the sentiment for AI in the United States is lower.
Than most countries.
Because the message is it's gonna take your job.
It's gonna take your job, it's gonna be
existential threat is going to go into a singularity and it'll be the end of the world.
Here or by West? Far, by far. I travel the world by far.
in the West is uh really out of control.
And I think we're doing ourselves the service. Let me give you an example.
So a decade ago.
Somebody said listen.
The first profession that's going to be wiped out.
Is radiology. Yeah.
And the reason for that is because the first model that was invented was used for computer vision. Computer vision is used would be trained to read radiology scans better than
Any humans can.
And therefore no radiologists will be necessary. Well it turns out they were absolutely right. A hundred percent right.
AI has now completely revolutionized radiology.
Every single radiologist uses AI to study the scans. It does it incredibly fast, incredibly accurately, better than a human can.
However.
Radiologists demand has gone up.
Yeah.
The number of

The number of scans has now grown.
Because it's cheaper. Because it's cheaper and faster, and therefore you want to do more scans so that you can do a better job diagnosing disease.
What is fundamentally missed is that a job
has a purpose and a task.
The task to study the radiology the scans.
But the purpose
is to diagnose disease.
Turns out.
Hospitals.
Arnell?
Seeing more patients.
Generating more revenues.
Which enables them to support more patients, and they need to hire more radiologists. Now where's the harm?
the harm could be.
That by the way.
Well known experts.
declaring the end of an entire profession.
Who's gonna go into radiology?
So young people who
wanted to be in radiology, decided
that this profession is completely obsolete and therefore don't come in.
What happens? The world doesn't have enough radiologists.
As a result, we've brought harm.
So we you're saying we the conversation we're having here is very binary in the United States. Like we're saying
It's gonna be this or this, and so.
the argument you're making is that w there's a lack of imagination. So for example, you know, I've got a son who's gonna go to college next year, right? And so
A lot of the conversations, what are these kids gonna do in in five years? What are the jobs gonna be? I mean, if law firms are using AI for discovery, if
consulting firms are using AI for, you know, w what would an entry level job would be of finance firms, et cetera, et cetera.
Would you argue that that's a lack of imagination that we actually
Are reaching those conclusions because we can't think beyond.
That scenario?
Partly.
I believe the opportunities the potential
uh for a new college grad in computer science or computer engineering or software engineering or chip design today.
Is far, far greater.
Then
the opportunities and potential when I came out of school. The tools they have to work with.
Is a billion times more capable.
It's
Super highly automated already today, and ya
They're busier than ever. And the reason for that is because
We have ideas of the things that we want to build that we didn't conceive of at the time without the tools that we have.
AI is going to help
This next generation of new college grads achieve greater things, build greater things.
not take their jobs.
For us to scare them into
Not even want to go to college.
Okay, not even wanted to be a computer scientist.
Is a disservice to society. You're not saving anybody. You're talking a whole bunch of people that
out of professions that we need in the future.
And that conversation's not happening in China.
Um they're not buying it.
They're just not buying it.
For me.
The most harmful thing we could do to our society, our closest families, is to scare everybody and
So that we don't benefit.
in the next several decades.
The other
countries will.
We can't allow that to happen.
Do you think that there is a possibility that in ten years from now, NVIDIA will have an order of magnitude more employees?
Before AI or after AI, I don't think it would make any difference.
The difference is that
With AI.
Along with my employees.
We're gonna have hundreds, thousands of AI assistants helping them doing amazing things.
And so our expectations of our company will be different. Yeah. Things that take t ten years, I think it'll take one year.
And so so is that good or bad? It's just it's thrilling. It's exhilarating, I think.
Yeah.
You know there's
There's just so much.
Of the universe that we don't understand.
For the first time in history.
We have the technology to do so.
We will do in the next several decades what it took humanity several hundred years to achieve.
Well, as a dad of two boys, I hope you're right.
Yeah, I expect to be.
I know that you don't like
Uh

But I I read a
An excerpt from a new book that's coming out. It's called Defending Taiwan by a writer named Ake Freeman.
And he paints a scenario of a possible
Conflict. Hot conflict.
Uh where
You know.
All of a sudden.
Taiwan is somehow
Invaded or occupied, whatever you want to call it, right?
And we've seen what
You know.
We've got some challenges geopolitically great.
I mean what part of that
Let's just say that scenario's not realistic. That it would be
destructive for China as well,'cause their economy really depends on
on the US market.
Um
Does any part of the fact that so such a high percentage of advanced chips, not just NVIDIA, but advanced chips are produced in Taiwan?
Is any
Part of that should people be concerned about that.
At all.
I we should always have
A resilient supply chain.
That's part of building a strong company.
And a resilient supply chain has diversity and redundancy.
But
Uh sometimes you don't have
the benefit of diversity and redundancy. So you make the best you can.
Alright.
I think the there are a lot of conflated questions.
That are put into that concern.
Yeah.
You know if you ask me, am I concerned about a
A hot conflict.
I'm less concerned than most.
But depending on
The actions of the leaders.
We may cause
The hot conflict.
And so
I am of the opinion that we all must be
More long term minded.
If we can.
We ought to uh have a more balanced and nuanced
Set of policies.
Not all or nothing. Don't push your adversaries, don't don't push your
competitors to the wall where they have no choice
Or where there's no cost.
to a strategic alternative.
And so I think that
You know, in
In your question, do I believe it?
Um the answer is I don't.
Because
I believe.
Can it happen? Of course it can happen and everywhere, obviously. However
I believe in the the wisdom of the people involved.
What have you had to unlearn?
As a leader.
The bigger
this company has gotten and the more experienced you've gotten, I mean you
You are
You know, known for being hard
Demanding, hard charging, you
Demand the best out of people, you have
Yelled at people, all those things.
Would you say those things are still how you
manage or have you unlearned some of those things or have you
Change some of those things in your style?
I don't think people care about style.
Mm.
I think people care about
Your values and what you care about.
So long as you're tough on the same things.
Every time it comes up.
So long as
The moment that it's over, it's over.
And they know that
They're safe.
You love'em.
You want them to succeed.
This is their life's work and you want it to be as good as it can be.
Everybody knows.
That
I have enough of everything.
Whatever it is that anybody thinks they need more of, I've got plenty of it.
And yet I work harder than ever.
And the reason for that is because I want
Everything that I have, they have.
I want them to see to realize.
Their dreams.
Um
So long as you're pure and high integrity and
completely there for them.
you could be quite challenging to people and they know
But you're on their side.
You you have been on the record as basically saying you've been asked would you do this again?
Would you do this all over again? And then I've I'm sort of paraphrasing what you said a version of
No.
So uh can you explain that I mean
We've sort of
Have the outlines of
All of the troughs and there were a lot of troughs. Yeah. And and I'm trying to even imagine like quarterly early. I I'm trying to imagine quarterly earning calls in two thousand seven when your stock price is in the toilet and you're putting it in.
I'm trying to figure out how you can do that.
is a publicly traded company withstand the pressure and
And as you say, the embarrassment.
It was embarrassing, it was humiliating.
You're the only face that everybody hates.
Your employees are probably embarrassed for you.
Your question about doing it again.
You know, most people
I just think they're being dishonest.
Yeah.
So let me just tell you why.
When somebody asked me would I do this again?
If your question is
Knowing how NVIDIA turned out.
Yeah.
Knowing the contribution we've made to the world.
Knowing the consequence of
The company today.
H how it impacts so many different industries.
All of the benefits that we
Have accrued as a result of our success.
Do I love those things?
The answer is yes.
But that wasn't the question.
You know?
The question is suppose I knew everything.
Then
That I now know.
How hard it is.
And
All of the pain and suffering and all the embarrassment and humiliation and all the setbacks and
You compress all of that.
And you told that thirty year old kid.
Listen.
Um, this is gonna take a lot longer than you think.
However fast you thought your company was going to be successful, it's not gonna be anything like that.
And
You're going to be the person who delivers most of the most horrific.
Financial.
Return news that anybody's ever explained.
You know, so on so forth. You'll be going out of business. You you'll be uh you have to lay people off.
Would you start again?
The answer absolutely not.
And so I think a lot of people forget.
That
The pain and suffering necessary to
The endurance necessary to do something great.
is because you're always looking forward and forgetting the past. Yeah. I spend all my time forgetting yesterday. Yeah. In fact, you know, right now a as we're talking,
It is really quite hard for me to
Resurface.
Mm-hmm.
Memories and resurface those feelings.
And the reason for that is but I spend all of my time.
All of my
Life trying to forget yesterday.
So that you can get back on that horse.
That's
In fact
What did they teach athletes? Forget the last point. Yeah.
It's about forgetting.
Yeah, I understand. I mean the the sacrifice, right? I mean you've got a family, like you probably
missed a lot of things. You probably did twelve hour days, seven days a week for years.
And after you sort of really began to focus on AI, it doubled again. I mean it was a it was a all those things.
All true.
Oh true.
And during the time when the kids were still young, I was I was uh finishing my master's at Stanford.
And so I was busy on multiple levels and
Yeah, I missed all their karate tournaments.
I
I I missed a lot.
I missed a lot.
I don't know that I've ever asked them.
what it was like on the receiving side of it.
But
If you're gonna build a company, you you need somebody strong
And you need somebody amazing like my wife.
And uh she took care of every
Everything Lori took care of everything.
And uh I don't remember complaining even one time.
One nanosecond or even one instance.
That's the gift that that she gave me.
And
Both kids love NVIDIA to their
to their core.
And both work here.
Both work here.
Um, they both went off to in their own careers for a while and
We were lucky to attract them back and
Both kids.
Have read
every shareholder letter, gone to every shareholder meeting.
Been to every single conference that
That I've ever done.
Uh I've missed most of theirs. They've been to all of mine.
And
Spencer's thirty five, Madison's thirty four, and the company's thirty three years old, and that kinda puts it in perspective. I mean they
They've had, you know, MV in their lives the whole time. And uh
Despite the fact that it was gone a lot.
You know, they found a way to
Always the company and and uh I'm I'm really lucky.
Your story to me is just
It's a story of
Pressure.
Right. And now it's it's like
You know, you
You did not intend or anticipate that this was going to be
The company it is.
It's importance is
Global.

Responsibility.
Yeah.
And humility is helpful.
Yeah. Being grounded is awful.
Not doing this job from a yacht is helpful.

No.

Yeah.
The fact that the company was built stone by stone.
You know, with the people that are here.
Everybody's still grounded.
I think it's helpful.
You also have to realize
That the company.
Also
Evolved over thirty years.
You know it recently.
But is it
One of the oldest technology companies in the world.
Um
I
I know that um
We have talked a lot about you and your life.
But the uh the guy who wrote a biography of you in NVIDIA, Stephen Witt, said you were one of the most challenging
Great reservation in doing so, yes. And I wonder is that because you just
Find it to be
self aggrandizing or I don't know, just you find it to be you know being self reflective. I mean going back to what you said about forgetting everything that happened and just moving forward
Is that part of it? Is it that you don't um I notice that when you talk about your kids or
Even your parents and the struggle sacrif sacrifice they make you do
you know, get a little bit emotional and I wonder whether
Part of not liking to talk about yourself is maybe some of that.
Uh yeah, even that that's a hard question. Why don't I like talking about myself?
I I didn't do anything by myself.
I was fortunate to have two amazing amazing friends, Chris and Curtis.
I'm surrounded by amazing computer scientists here.
Um
I made a lot of good decisions.
I've made a large number of lesser good decisions.
I just don't find any of that to be very special.
Or that interesting.
It's just not

Bill.
You know, the way I'm built.
As you put me in crisis.
Alright, the perfect day.
Is we are in trouble.
And you just create the worst possible condition, and Jensen, you need we need you to come and help us.
Things through this.
That's my perfect.
That's my vibe.

Battlefield. Yeah. I'm a battlefield CEO.
And so I I think I'm just uncomfortable.
Talking about it.
Maybe.
I I I don't know why, Guy.
I don't know why.
Maybe I need therapy.
You know, that help helped me figure it out. Have you had it before? No.
No, this is as close as I come. You don't strike me as as I mean, you talked about your dad's as close as it comes. Your dad's eighty eight, right? And he's retired and and so you strike me as um
Yeah, yeah.
It sort of m move forward and and maybe your dad also like
You know.
Isn't sort of looking back on his life wistfully, he's Yeah.
I'm sure proud of the UN.
And maybe some of that comes from there. Do you would you say that
All of those things you mentioned, the people you met and
the decisions that were made, the good decisions and the bad ones and the
grind and the result now.

I mean it uh you hear it a million times and it's probably still crazy to hear it the most valuable
company in the world, right? I mean they
the value this company is bigger than
Most countries.
By far.
Um
Do you think that
That happened because of all of those elements, all the people, the hard work, the grind.
Or do you think that
Luck played a bigger role.
Um

Several hundred forks in the road.
In a lifetime.
I've largely taken the right one.
Combination of
Good judgment.
Good values.
Good friends, informed decisions.
Critical thinking.
And some dose of luck.
Led me to
Choosing the best of
Several options.

When the company didn't.
Choose the best options.
We created a system that allowed it to
Self reflect.
To not be so hard on
ourselves for making a bad decision.
That we can quickly.
And not let
the bad decision be fatal.
But I I think that in the end
The secret to it all.
You know, the only thing I've really learned about it all.
Is that the next fork in the road is
About to come.
And
the people that are gonna help me.
Make the best decisions.
likely will be different than the ones that helped me make the last last decisions.
And
I've always felt safe making strategic decisions. I've always felt safe.
Taking risks.
Mm-hmm.
Maybe it's because
I know that when I get home, Lori's still there.
You know?
Maybe it's because
When I get home I know the kids are there. Maybe I know
Got
All of our employees are gonna be here.
Maybe that's the
That's as complicated as it is. I
I wish I could be more
Technical.
In explaining
How companies
are built and run?
But I built this one.
From the first day.
And
It's much more personal.
I frankly believe.
You know, because people say it's not business, it's personal.
It's always personal.
That's Jensen Wong.
Co founder and CEO
of NVIDIA.
And yes, during our interview, he was wearing his signature black leather jacket.
Black t-shirt, black jeans, black belt, and blaes.
It's what he wears daily, he says it makes his life simpler.
It's one less decision he has to think about.
each day.
Hey, thanks so much for listening to the show this week. Please make sure to click the follow button on your podcast app so you never miss a new episode of the show. And if you're interested in insights, ideas, and lessons from some of the world's greatest entrepreneurs, please do sign up for my newsletter at guyros.com or on Substack.
This episode was researched and produced by Alex Chung with music composed by Ramteen Arablui.
It was edited by Niva Grant.
Our engineers are Patrick Murray and Robert Rodriguez. Our production staff also includes Casey Herman, Chris Massini, JC Howard, Catherine Cypher, Carrie Thompson, Carla Esteves, Sam Paulson, John Isabella, and Elaine Coates.
I'm Guy Raz, and you've been listening to how I built this.