January 23, 2025 • 44 mins
In the past few years, NVIDIA has become one of the most valuable and important companies in the world by making GPUs, the chips powering the AI boom. But where did the company come from, and why are NVIDIA chips the ones that dominate AI?
Tae Kim is the author of a new book called The Nvidia Way. In his book, he tells the story of how NVIDIA’s founder and CEO, Jensen Huang, set NVIDIA on the path to becoming what it is today.
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Speaker 1 (00:15):
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Artificial intelligence feels very abstract, feels very ephemeral, feels more
(00:53):
like an idea than a thing. But there is in
fact a thing there. AI is rooted in the physical world.
The thing or things are these little pieces of silicon
and metal called graphics processing units GPUs. These GPUs are expensive.
They cost thousands of dollars each. If you want to
(01:15):
build a state of the art AI model, you have
to buy tens of thousands of these GPUs, and most
of the GPUs come from a single company, in Vidia,
which is why in just the past few years Nvidia
has become one of the most valuable, most important companies.
Speaker 1 (01:34):
In the world.
Speaker 2 (01:40):
I'm Jacob Goldstein, and this is what's your problem. My
guest today is take him. He's a staff writer at
Baron's and the author of a new book about in Nvidia.
The books called The Nvidia Way. The book, of course,
points up being a lot about Jensen Wong, who co
founded the company back in nineteen ninety three and who
is still the CEO and who is really a wild
(02:04):
kind of terrifying, brilliant figure. And Tay and I talk
a lot about Jensen later in the interview, but we
started with this moment in two thousand and one that
led from Nvidia being a company that made graphics cards
that let people play video games on computers to becoming
this key company at the center of the AI revolution today.
(02:30):
So you write about this moment in two thousand and
one when a researcher, right, an academic at the University
of North Carolina, realizes he can basically like hack these
graphics cards, right, this hardware that's just made to make
pretty graphics on the computer to help in his research,
which is like not at all about graphics, right, It's
like modeling the weather or something. And it seems like
(02:54):
this moment is a big winds up being a big
turning point in the history of technology. Really tell me
about that moment.
Speaker 1 (03:03):
The key moment was with Mark Harris, where he was
a researcher at the University of North Carolina, and him
and a lot a bunch of other academics realized there's
all this computing power that you can hack the algorithm
to use that computing power to model thermodynamics of fluids
inside clouds, which was his thesis.
Speaker 2 (03:23):
And he realized that worked better than the CPU than
just sort of doing it the traditional way through the computes.
Speaker 1 (03:28):
Yeah, and a GPU has all these different cores that
run in parallel, and the CPU only typically has four
to eight cores, and GPUs have hundreds of thousands of cores.
Speaker 2 (03:39):
What's a CPU better at than a GPU? Like, why
would you have four to eight when you could have thousands?
Speaker 1 (03:46):
Well, it could run more complicated pieces of instructions and software,
and GPUs typically break down into much simpler tasks. But
the difference is you could run all those tasks across
a thousands So for certain workloads like AI, it's so
much faster than a CPU, which has to do things
(04:06):
kind of one after another serially. And Mark Harris and
all these academics started hacking into the graphics algorithms and
using that power to do all this scientific high performance computing.
And Mark Harris started a website and congregated all these
researchers and everyone's sharing the knowledge, sharing the tricks that
(04:26):
actually hack into this. So Nvidia sees this, they're seeing
people use this to do model stock options, the model
weather and saying, wow, this is actually really interesting.
Speaker 2 (04:37):
Yeah, that's like your dream if you're a company. Right,
It's like, oh, there's all these other things you could
do with this thing we're already.
Speaker 1 (04:43):
Making exactly so, and Jensen kind of had the foresight
to see this, Wow, this is a really big deal.
We should invest in this. So they wind up hiring
Mark Harris and look at all the things that all
these researchers are doing. And this is the beginning of
development Kuda, which is a programming platform for general purpose
(05:04):
GPU computing.
Speaker 2 (05:05):
And just to be clear, Kuda is like, it's a
programming language, but in this case they're programming what used
to be just the thing for graphics, but it turns
out to be good for these other things. And that
language it's in video's own language. Right, This is I
feel like, going to be important in the sort of
business story for why and video is so dominant today, right,
Like they write this language, they own the language, and
(05:27):
it's written just to work with their chips, is that right?
Speaker 1 (05:30):
Yes, no one else can use this. It's extensions on
a programming language that makes parallel computing easier for programmers
to make. So Jensen just invests in this, and he
actually thinks longer term than the average CEO. So most
CEOs looking out maybe the next quarter, maybe a year
or two. Jensen thinks in five, ten, fifteen year increments.
(05:54):
So he's always looking out, what's the next big computing shift,
what's the next big technology phase. So he saw KUDA
as the main thing where eventually his GPUs, you have
all this latent, enormous computing power will be able to
be used for science research, all these other simulation things.
And he didn't give up even when Wall Street wents
(06:16):
down on his next day, Why you wasting all this
die space is crushing your gross profit margins? He said, no,
this is the future computing. I'm going to invest in
it and it will work out someday.
Speaker 2 (06:28):
And when you say wasting all this die space, like
de space is like space on the chip, right, Like
he's making a physical thing and to commit to Kuda
to have this dedicated programming language, you actually have to
give up space on the chip. So that's it's costly, right,
Their margins go down. They're not optimizing for profits at
that moment.
Speaker 1 (06:48):
It's literal circuits called Kuda cores. Their hardware circuits are
optimized to run the Kuda language. So even internally executives
are like, why are we spending all this money allocating
dye space for something that people really aren't using that much,
that isn't generating revenue. But Jensen saw this as a future.
I actually kind of bring up the analogy of read
(07:10):
Hastings and Netflix. So when he started Netflix, you know,
the technology wasn't ready, consumers didn't really have broadband, but
he had this intuitive sense that someday video will be
streamed over the Internet and that was the future, and
it makes it's so obvious now, but back then it wasn't.
So Hastings positioned Netflix. He made money with DVD rentals
(07:32):
for a while and just stayed on top of the technology,
kept on investing in investing, and when the technology was
good enough, that's when he really pivoted Netflix to dominate
Internet stream Jensen did this multiple times with three D graphics,
video game graphics. He knew that someday PC video games
would be a big market programmable GPUs, Kuda and later
(07:56):
with AI on these full stack data center AI servers.
He just sees the future and is willing to keep
investing even if it's five ten years out. And Kuda
started in two thousand and six. I mean, things got
incrementally better for the next ten years, but it didn't
really take off till twenty twenty two.
Speaker 2 (08:16):
Till chat GPT. Basically twenty twenty two is chatchipt That's
when in video goes totally bonker.
Speaker 1 (08:21):
And that was the power of the large language model
of AI and all that stuff. And he's investing throughout
this whole thing for ten fifteen years.
Speaker 2 (08:29):
Yeah, so there is a moment. There's a moment in
the middle there. I do obviously want to get to
the chat GPT moment and the present, but there is
one more moment I feel like in the in the middle,
that is where in video is in the center of it.
And that's twenty twelve, right, So you have early you know,
two thousand and one, people realize, oh, you can pack
the GPU to do other things. Andybody's like, oh, that's interesting,
(08:49):
let's build a whole programming language. So people didn't do that,
And then in twenty twelve, you have this moment that
really seems like the birth of modern AI. Right, this
moment when when this AI model called alex net sort
of emerges into the world and everybody in the AI
(09:09):
world is like, oh my god, AI is here, right,
tell me about that moment.
Speaker 1 (09:16):
So alex Nett was a program that was created by
two researchers at the University of Toronto and they competed
in this competition called image net, which basically fed images
into the model, and they were able to recognize and
category rise image much more effectively than any other model
in the past. And the breakthrough was they used GPUs
(09:38):
for the first time and.
Speaker 2 (09:40):
It was like just a few, right, Like they were
grad students and they got their hands on like a
few in video GPUs and had them like running on
servers in the hallway or something.
Speaker 1 (09:48):
Yes, right, and these are video game GPUs. To be clear,
this isn't some enterprise complicated. They literally went to a
store and bought a bunch of video game GPUs and
it turned out to be very effective where they could
do the power and effectiveness of two thousand CPUs on
only twelve GPUs.
Speaker 2 (10:08):
And to be clear or that kind of vision AI,
it's machine learning. It's the same basic technology as is
used in large language models. Right, it's it's modern AI.
And so that moment is this moment when it's like, oh,
holy shit, GPUs are one hundred x better than CPUs
(10:29):
for AI. Yes, that's interesting, that's useful to know.
Speaker 1 (10:33):
So it was a combination of GPUs data and algorithms
that kind of combusted at that perfect moment. And then
Jensen saw this and it's like this is a big
deal and someday AI is going to be huge and
we need to invest big in this. So on top
of KUDA, he invests in all these AI libraries that
(10:54):
effectively managed the GPUs to do AI workloads the most
in the most effective manner. And he invested in libraries,
invested in software, he invested in researchers, and allocated a
lot of employees to this project starting in around twenty thirteen.
Speaker 2 (11:11):
And so that's that's more of the like it's not
just the chip, right, it's like the chip that is
optimized to be not only efficient on the hardware level.
But like there's all this software. So if you are
if you are building AI, if you're if you're writing
code for AI, like they make it really easy for
(11:31):
you to do it on in video GPUs.
Speaker 1 (11:34):
So it's all the math libraries, it's all the AI
frameworks work the best on in video GPUs. And they
added these things called tensor cores similar to krudercres. They
actually added hardware circuitry inside the GPUs that are optimized
to run AI training and workloads and all the all
the software that you're running. And this is over again,
(11:56):
this is over another nine eight years where they're building
all the software and hardware circuitry to run deep learning
AI the best.
Speaker 2 (12:06):
Okay, it's time. It's twot twenty two. Twenty twenty two,
chat GPT comes out. What's that mean for in video?
Speaker 1 (12:17):
So actually check chip takes off and it's it takes
about two quarters for the whole world to realize this
is a big deal. This, this model of natural language
processing where the computer actually understands what you're asking, and
that ability to draw insights and be effective with all
(12:40):
that natural language stuff just just blows people away, and
companies and startups realize we have a new AI boom here,
because it really unleashes a wave of capabilities that wasn't
wasn't doable before. So about six months later, that's when
the big bang I call it for Nvidia happens when
(13:01):
they say, oh my gosh, we're gonna we're gonna beat
our numbers that the street expects by four billion up.
And the stock went up like one hundred and seventy
billion dollars in value because the world realized.
Speaker 2 (13:15):
In like a day. Yes, right, there was one day,
and it was spring of twenty twenty three. Way talking
about and.
Speaker 1 (13:21):
This is three, this is three weeks after I had
the meeting with the book publisher who asked me if
I could do a book on video. So literally I started.
Speaker 2 (13:30):
Also good timing for you, good timing for a video,
Good timing for you, And and right, no, I remember
that day. And and there was a question then of like,
oh my god, is this a one off? Are they
going to keep growing this way?
Speaker 1 (13:44):
Right?
Speaker 2 (13:44):
Basically what they said was we sold way way more
GPUs than we thought we were gonna. Right, that's that's
what That's the basic thing they reported in their earnings
report and the subtext is this is the world and
in particular, like big tech companies with lots of money realizing, oh,
we've got to get into this AI game more than
(14:05):
we have been and to do that, we got to
buy a lot of really expensive GPUs from video.
Speaker 1 (14:11):
And Jensen is really smart at selling this stuff. Like,
he's very smart because every company, every company CEO, every startup,
they saw this risk as the extential for them because
if your competitor comes out with the AI featured offering
that's much better than what you have today, that that
(14:34):
isn't advantaged by the AI models, Yeah, that competitor could
could drive you out of business. So Jensen was very
smart as at selling to people that you need to
get on board.
Speaker 2 (14:47):
At making everybody scared of losing. Yes, well, and in particular, right,
I mean there is a small number of very large,
very rich tech companies that are a very big part
of in videos revenue. Right, it's like whatever the ones
you quld think of, Google, Meta, Microsoft, Amazon, Yes, I mean,
(15:09):
I guess open ai sort of is kind of connected
with Like those companies are buying a huge share of
these GPUs, right, They account for a big, big chunk
of in videos revenue. Yes, and those are good companies
they have as customers because they're super rich, right, they
can afford to pay you tens of billions of dollars
for your GPUs.
Speaker 1 (15:27):
But that's being said, they're reselling that GPU power to
companies and startups, right, so startups are buying that GPU
competing capacity from these.
Speaker 2 (15:38):
GNT tech companies. Yeah, that's a good point, right. So
they're in a way not the end customer. They're the
sort of intermediate kind of service providers.
Speaker 1 (15:45):
But they also use that for their own internal systems too,
like Meta uses a ton of GP power to make
their advertising algorithms more effective and to pick the videos
like tech talk does.
Speaker 2 (15:58):
And obviously Google Search is now becoming more and more
AI driven, and there's Gemini, which is right, I mean,
there's there's more direct use of AI by all of
the big tech companies as well. So there's another piece
of this which is interesting. I mean, because there's one
universe wherever it's like, oh, we got to get it
on AI, and they all buy the GPUs and then
(16:19):
that's kind of it. It's like a step function where
like there's this momentary rush where everybody buys the GPUs
and then whatever. They just upgrade every couple of years,
and it's more like a regular tech hardware business, which
is like good but not amazing. But there's another piece
of it that has been a huge deal for end video,
and that's the scaling law or the scaling hypothesis. Let's
(16:40):
talk about that. What's the what's the scaling hypothesis.
Speaker 1 (16:42):
So the scaling hypothesis, similar to what I said before,
is the combination of computing power. The more computing power
you add, the more data you increase, and the better
ways you figure out software algorithms for each of these
three buckets. If you increase it, the AI model becomes
more capable and more effective.
Speaker 2 (17:02):
And like, just to be clear, like even if the
algorithms aren't getting that much better, right, Like my understanding is,
and this is kind of a new idea. It's like, oh,
if we can just have more data and more computing
power will get better results.
Speaker 1 (17:18):
Yes, so it's great if you have all three doing
you know, basically showing up. But for this time period,
the last few years, the scaling law has really taken
off and companies are literally ten xing their compute power,
and these AI clusters going from sixteen thousand GPUs now
(17:40):
to one hundred thousand gps and now people are saying
they're going to build one million GPU clusters in the
next two three years.
Speaker 2 (17:47):
And in videos selling most of those GPUs.
Speaker 1 (17:51):
Yes, So if you go from sixteen thousand to one
hundred thousand GPUs in a couple of.
Speaker 2 (17:55):
Years, that's a good business to do.
Speaker 1 (17:57):
If you're increasing the scale of the hardware by ten
ten x every couple of years. And the exciting thing
for Nvidia is that, like what I argue is that
this thing is going to this trend is going to
continue in the next few years because you have the
scaling laws. Then you have this thing called multimodal where
they're using this GPUs not just for texts like in
(18:19):
chat TPT, you're using it for video and images generating
those things. And now there's these two other two other
waves of demand that are happening. There's this thing called
AI agents where these AI models can do multi step
tasks for you.
Speaker 2 (18:37):
Right, you could be like book me a ticket to
San Diego some weekend in June whenever it's the best deal, yes,
and then it does that.
Speaker 1 (18:45):
So that's going to happen in the next twelve months. Yes,
these AI agents will eventually, in the next year or
two be able to do all that tedious work automatically
and probably with less errors than an actual human being.
So AI agents multimodal. And now there's this thing called
test time compute, where the AI models, instead of just
(19:07):
spinning back an instant apply, can actually think about what's
the best way to respond to your question and spend
more time thinking about it and then give you a
higher quality answer. So there's all these things that all
of these test time compute, AI agents, multi modal. These
are all things that need more computing power, they need
(19:29):
more GPUs, and these are all things that are going
to drive in video's revenue in the next couple of years.
Speaker 2 (19:37):
We'll be back in a minute and we will talk,
among other things, about Jensen Wong, one of the most
successful entrepreneurs of the twenty first century and a man
who once told the colleague that he wakes up every morning,
looks in the mirror and says to himself, you suck.
(19:59):
I want to talk about Jensen a little bit. I
mean your book. You know, he is obviously the main
character in the history of Nvidio is the main character
in your book. He's been the CEO of n Video
for more than thirty years, which is like longer than
Bill Gates with the CEO of Microsoft, longer than any
you know, almost anybody in the s and p. Five
hundred has been a CEO at this point. His childhood
(20:20):
is quite interesting, right, Like, tell me just a little
bit about his childhood.
Speaker 1 (20:25):
He was born in Taiwan, and they moved around a
little bit to Thailand, and his father came to training
in New York and fell in love with America. This
is like the great American dream story. And the mother
and father started teaching Jensen's brother English ten words a day,
(20:47):
and they sent him to his aunt and uncle when
he was about age eight or nine, and the aunt
and uncle and the families. The funny thing is they
sent him to a reform school in Kentucky by mistake,
thinking that he will get a great education at this
boarding school in Kentucky, which.
Speaker 2 (21:04):
He just thought it was a boarding school, yes.
Speaker 1 (21:06):
But it turned out to be a school for troubled.
Speaker 2 (21:08):
Kids, huh, which Jensen was not. He was just like
a smart kid with ambitious for him parents.
Speaker 1 (21:15):
Yes, But he talks about how his time at Oneita
Baptist and student Kentucky was formational for him.
Speaker 2 (21:21):
What was it like for him?
Speaker 1 (21:23):
It was hard at the beginning, but he started befriending people.
He started playing chess with the janitor, and he just
learned how to deal with other people much better. And
he talks about how he learned his street fighter.
Speaker 2 (21:38):
Mentality because he was literally fighting.
Speaker 1 (21:42):
Yes, I mean there were bullies and all that stuff,
but he just learned how to deal with people and
the rough and tumble of kids back then. Eventually his
parents came from abroad and they settled in Oregon, and
he learned his work ethic from working at Denny's. He
(22:03):
talks about how he washed more dishes and cleaned more
bathrooms than any CEO in the history of CEOs. He
says he Denny's helped them in terms of social skills
and dealing with time pressure and dealing with customers. But
it's the work ethic that really sets him apart. So
even at the beginning, he was working from nine am
(22:26):
to midnight, and he just set a culture where Nvidia
employees work really hard.
Speaker 2 (22:32):
And I feel like at the beginning that's common, right,
Like that is the classic startup story. But the classic
startup stories you do that for five years and then
either you get giant and you hire a grown up
CEO and you you know, go start your blimp company
or whatever, or you sell or whatever. He's still doing that,
right He's sixty years old and like wildly rich and
(22:55):
and he's still working that much.
Speaker 1 (22:57):
He's working all weekend. He's working Saturday Sunday. When he
talks about when he goes to a movie, he doesn't
remember the movie because he's thinking about work. He finds
work relaxing and fund This is what drives him. He
loves working.
Speaker 2 (23:12):
There's a moment, you know, you don't you don't write
about his personal life at all. Once he sort of
grows up and starts in video. You know, reasonably because
the book's about in video, and so I just assumed
he just worked all the time and didn't have a family.
And the I think the only time in the book
that his family comes up is there's this scene where
he's on vacation and he's talking to some senior manager
(23:35):
at a video on the phone and they're like, what
are you doing. He's like, I'm sitting here on the
balcony watching my kids play in the sand and writing emails.
And it's like, like, if it's a movie, the kids
are never on stage, right, you just hear them off stage.
At that moment, You're like, oh, he has kids, Like
that was that was a weird moment to me reading
that line.
Speaker 1 (23:54):
So both executives told me they hate it when Jensen
so called goes on vacation because he winds up giving
them more directives and orders and more stuff to do
when he's not on vacation because he's emailing in them
do this, do that, and they they actually yell at him,
So play with your kids. He's like, no, I could
(24:15):
get real work done when I'm on vacation by doing
his emails. I mean, so it shows you his obsessions.
He's constantly thinking, he's constantly worried about what's happening at video.
Speaker 2 (24:28):
I mean it's interesting, right, Like he's not a balanced person, like,
which is why to some extent he has built the
thing that he has built.
Speaker 1 (24:40):
Right, He's completely obsessed with winning and he's extremely competitive.
Speaker 2 (24:46):
Yeah, there's a few other specific moments that really stood
out to me. There's one where I think this, I
think a salesperson told you this where they had just
had a great quarter they sold a ton of GPUs,
and the sales guy's talking to Jensen about how great
they're doing, and Jensen says to the sales guy about Jensen,
(25:08):
about himself. Jensen says, I look in the mirror every
morning and say you suck.
Speaker 1 (25:14):
So he he's almost like a self psychologist. He knows
if you start thinking that you're you're the best and
your your hot stuff, you might get complacent, you start
resting on your laurels, you might not work as hard enough.
So he he sees that, oh, we just had a
blowout quarter. What's the risk here? The risk is me
gain complacent. So I'm gonna look myself in the mirror
(25:36):
and say you suck and psych myself out.
Speaker 2 (25:38):
I mean that's that's that's one reading of it. That's
the like ten dimensional chest reading of it. I mean
there's another reading of it, which is like he's messed
up in a way that makes him super driven and successful.
Speaker 1 (25:51):
But it works.
Speaker 2 (25:52):
He actually definitely works. We can agree that it works.
Speaker 1 (25:56):
Like he does the opposite too, So when things are
really intimidating and he feels like, oh my gosh, how
am I going to do this, he tells himself, how
hard can it be? How hard can it be? So
he does it on both both ends in the spectrum
where he cites himself out when he feels intimidated, and
when he feels like he's on top of the world,
(26:16):
he tries to bring himself down back the earth.
Speaker 2 (26:19):
So the one other big piece of the Jensen Wong
experience that we haven't talked about is the way he
treats the people who work for him. Right, I want
to talk about a particular scene because I think it
puts a finer point on it. A scene from the
book where it's like a company wide meeting and it's
on like zoom or whatever, and he's yelling at a
(26:40):
guy in the meeting. He's yelling at him, and then
on top of that, he keeps telling the person filming
the meeting to zoom in on the guy he's yelling at.
Speaker 1 (26:49):
I had multiple sources tell me that it was the
most humiliating thing they've ever seen.
Speaker 2 (26:53):
I mean, it's that seems like bullying, like why zoom
in on the guy? Like what lesson is added by that?
Speaker 1 (27:01):
So he keeps on saying to mister Rayfield, you got
to get this chip back on track. The chip that
heels in charge was behind schedule, and he kept us
pointing to him, zooming in his face and telling him,
You've got to get this. This is not how you
run a business. You need to get this chip back
on track. And this kind of like high standard demanding
(27:23):
attitude I think is effective. It drives people. If you
get dressed down by Jensen, the next time, you're going
to work ten times more to make sure that you
do a better job.
Speaker 2 (27:36):
Maybe I don't want the world to be that way,
but it is that way, you know what I mean, Like,
maybe I want to believe that, like there's a kinder
world where people could do equally good work. But maybe
I'm wrong.
Speaker 1 (27:49):
And Steve Jobs was the same way. Yeah, I mean
it wasn't all sure, It wasn't all sunshine and rainbows.
Speaker 2 (27:55):
No, no, I mean the Isaacson book was really clear, right,
the Isaacson biography of Steve Jobs. He did not seem
like a good person in that book. He seemed like
really good at building amazing products, but like not like
a good human being by the sort of standard way
we think of what makes a good person.
Speaker 1 (28:12):
But I think the key point that we also have
to think about is people at Nvidia stay.
Speaker 2 (28:17):
Yeah, that's really interesting.
Speaker 1 (28:19):
The turnover is like one of the lowest in the
industry at only three percent compared to the average of
thirteen to fifteen percent.
Speaker 2 (28:24):
Yeah, certainly in the last few years. People stay because
you get rich if you stay, and you lose your
equity if you leave. But is that number true if
you go back to when the stock was flat.
Speaker 1 (28:34):
I don't have the numbers, but a lot of the
senior executives have been there more so than any other company. Yeah,
fifteen twenty twenty five years.
Speaker 2 (28:42):
Yeah, that's it.
Speaker 1 (28:43):
You don't really hear other than one instance of people
leaving to become a CEO of a different company. Uh huh.
And I think part of it is people realize that
Nvidia or the Gensen way of doing things is super effective,
so people want to be on the winning team. So
in one sense, he draws people hard, he dresses them down.
(29:05):
He's very blunt and direct. But he compensates people. No
one ever, hardly anyone complains about compensation. If you're effective
and you do a good job, he will like double
your stock compensation on the spot. So there's this meritocracy
that people really adore. And if you see an effective
(29:25):
leadership a culture that's based on meritocracy, if you're a
top engineer, you stay at that company.
Speaker 2 (29:33):
Yeah. Yeah, So you interviewed Jenson as as you were
working on the book. Was it scary to interview him?
Speaker 1 (29:42):
He was intimidating in the meeting, he he at he
didn't yell at me, but a couple of times he's saying,
you don't get nvidio in this line of questioning, and
I it takes a while to get used to it,
but you appreciate it because you see where the other
person is. Like when someone is blunt and direct like Jensen,
(30:02):
you know where that where he is at all times,
and then you could work at improving yourself. So he
talks about how what are you optimizing for? Are you
optimizing for a person's feelings or optimizing for what's good
for the company. So that's why he doesn't do one
on one meetings or career coaching. Typically at a large company,
(30:23):
when someone's doing poorly, the CEO will take them aside
and say, Bob, you need to do this.
Speaker 2 (30:28):
You need if it's a person's.
Speaker 1 (30:30):
Yeah, Jensen's like, why is Bob the only one that
gets a learning here? If I am blunt and direct
and show Bob what he's doing incorrectly, like that person
at that meeting, everyone in the room can learn. All
the employees up and down the ladder can learn. So
that's his philosophy is everyone should learn from their mistakes
(30:53):
and everyone should know where they are at all times.
Speaker 2 (30:56):
So I mean, so you write in the book that
in video really is like an extension of Jensen, right.
I think he used the metaphor of like the formula
one card that's like optimized for him as the driver.
So and he's in his sixties, which is not old
(31:20):
old but definitely not young. Like, is he gonna run
in video for another twenty years? What's going to happen?
Like there's no obvious successor, Like where does that go?
Speaker 1 (31:31):
I think there's there's no one else at in video.
I think that can run in video as effectively as Jensen.
And he loves the company so much. He loves what
he's doing. They're having enormous impact with this AI wave,
and he's so excited about the potential for curing cancer
and digital biology, the potential for robots, the potential for
(31:54):
AI to kind of disrupt education and help kids learn better.
That I don't see him leaving anytime soon. He loves
his job so much and he can't argue that he's
being ineffective. So I don't think for the next few
years there's anyone that's going to take over for him.
Someday Nvidia is going to have to come up with
(32:15):
a new CEO or a successor to Jensen, and that's
going to be a big question mark. It's that next
person going to be as effective as Jensen in terms
of being able to have the technical skill and the
compency to steer in video in the right direction. Well,
they have his business genius of coming up with all
(32:37):
these new strategies that he does time and time again.
That's going to be a huge question Marke.
Speaker 2 (32:44):
I mean people also talk about limits to scaling right
in AI. We were talking earlier about how part of
in video's wild growth over the last couple of years
has come from this fact that that you can just
add more GPUs basically and get better results. More GPUs,
(33:07):
more data, and you know, people talk about a running
out of data because like they're basically, as I understand it,
training on the whole Internet right now, which is like, Okay,
it's a lot of data. I mean, is that it
seems like nobody knows, right. It seems like they are
smart people who say, no, no, we'll have synthetic data
(33:29):
blah blah blah, we won't hit a sort of scaling wall.
And then there are people who make the other argument.
I certainly don't know, but does that seem right, Like
is that an open question right now? And is that
a meaningful question for nvideo?
Speaker 1 (33:40):
I think it's an open question. But like I said,
there's the multimodal stuff, there's AI agents, and then there's
proprietary data inside companies.
Speaker 2 (33:51):
Uh huh.
Speaker 1 (33:52):
So all these corporations have data going back decades. Companies
are going to use the power of these AI computing
systems to go through all their data internally, all their
proprietary data, and have all that knowledge at employees fingertips.
So an employee can ask what's the best way to
(34:15):
do this, and the AI computer is going to be
able to go back thirty years of data and figure
out the best piece of insight to help that employee.
And that's not really being done today.
Speaker 2 (34:26):
So Nvidia designs their chips their gips, but they don't
actually manufacture them, right Is it right that they're made
in Taiwan, where most cutting its chips are made now?
Speaker 1 (34:36):
Yes, So TESMC actually makes and manufacturers in videos chips
ever since the late nineteen nineties, and they're the best
at doing this all. A lot of the fabulist chip
designers in California use TSMC, and Video uses them too.
Speaker 2 (34:53):
And so I mean it is really interesting to me
to a lot of people that Taiwan is this incredibly
fraught geopolitical place. Right, China says Taiwan is part of China.
Taiwan says no, we are not part of China, and
that Taiwan is the only place in the world that
(35:13):
makes like the most important physical thing in the world
of technology today. Right, it makes like that's wild, Like
what do you make of that? And how does that
fit with the in video story?
Speaker 1 (35:25):
I think eventually Nvidia chips will be made in the
US TSMC. I mean, that's all part of this Chips
Act that the Biden administration has posed.
Speaker 2 (35:35):
So this this plant that TSMC is building in Arizona, Like,
is that advanced enough to make like frontier in Nvidia chips?
Speaker 1 (35:45):
So the CSMC factories in the US are always going
to be one or two generations behind the factories in Taiwan.
But that doesn't mean Nvidia can't use the factories in
the US if they're one or two generations behind for
their older chips. So I think that will eventually happen.
Speaker 2 (36:04):
It seems wild to me, Like, I mean, it seems
very possible that China will try and make Taiwan be
part of China, right, and and like we have all
these export controls to try and keep China from getting
cutting edge chips. Like it's a really interesting, complicated dynamic.
Speaker 1 (36:21):
I think people make a big issue at this, But
I think if it happens, like in Vidio, chips are
not going to be the main issue. It will cost
a global calamity where you know, we won't be able
to upgrade our cars, at laptops, nearly every computing device,
(36:41):
every appliance will not work if we don't have access
to Taiwan chips.
Speaker 2 (36:47):
I mean, presumably we could do some like sub Manhattan
Project scale Manhattan project to get a state of the
art TSMC factory somewhere that is not Taiwan, right, Like.
Speaker 1 (37:00):
We're doing some of that now, but it's not going
to be able to it's going to be ten twenty
of the capacity we need.
Speaker 2 (37:08):
Yeah, the capacity, right, it takes a long time, Like
the fabs are wildly complicated to build, and they cost
billions of dollars and they take years, so you couldn't
just do it.
Speaker 1 (37:18):
They cost ten to twenty billion dollars to build and
three to four years over three to four years, So
it's not something that can happen overnight. And if something happens,
like it's just gonna be so terrible and it'll be
like a depression. So I just hopefully it doesn't happen,
and you know it's not gonna The question isn't gonna
be about in videotips. The question is gonna be about
(37:38):
the global economy of that.
Speaker 2 (37:40):
So what do you think is the biggest risk for
video in the like five year timeframe?
Speaker 1 (37:48):
It's really tough to say.
Speaker 2 (37:49):
Now.
Speaker 1 (37:49):
I mean I see the next few years with all
the different AI innovations and all the progress. But again,
just like every big computing shift, what's the next big thing?
Could it be quantum computing? It can be like who knows, five, ten,
fifteen years from now? And every from the pcach with
Microsoft and Intel it's called Wintel. And then it went
(38:12):
to smartphones where Apple dominated with the iPhone, and now
in Vidia is dominating. In terms of the AI computing shift,
There's gonna be another shift, and right now we're at
the early stages of the AI computing movement. In five
ten years, there might be the next big thing that
(38:32):
I can't can't even foresee right now. And is Nvidia
gonna be able to see that coming? If Jensen's around,
I think he will. But if Jensen's not there, you know,
then just like every other computer, major computer company in history,
say IBM, it's very easy to get disrupted in the
(38:53):
technology industry.
Speaker 2 (38:58):
We'll be back in a minute with the Lightning round.
I want to finish with the Lightning round, which is
going to be considerably more random and digressive than the
conversation to this point. I want to talk about video
(39:20):
games and your experience with video games. What was the
first video game you ever loved?
Speaker 1 (39:30):
I think it's the first VAM I've ever tried, which
was Combat for the Datar twenty six hundred.
Speaker 2 (39:35):
I love that.
Speaker 1 (39:36):
I mean it's literally I remember coming home and my
dad buying the Autari twenty six hundred connected to a
black and white TV and being to use that joystick
to control the little tank going around the screen. That
was an amazing moment. It was an incredible moment that
this was possible. At the home, I had.
Speaker 2 (39:56):
An Atari twenty six hundred. I remember the joystick. I
remember Demon Attack, so kind of second tier game, but
I got really into it. What's the game you spent
the most hours on?
Speaker 1 (40:11):
Probably this game called Lemmings. I don't know if you
it's it's a puzzle strategy game where you control lemmings
and you guide them across a maze to get.
Speaker 2 (40:23):
The little creatures that are famed for jumping off cliffs,
which maybe they don't actually do.
Speaker 1 (40:27):
Yes, if you guide them the wrong way, they'll fall
off a cliff and to their death, and they make
this cute sound like oh no, so so. I played
so many hours of.
Speaker 2 (40:38):
That game, one thousand hours.
Speaker 1 (40:43):
Probably hundreds of hours. I want to say a thousand.
Speaker 2 (40:45):
Yeah. What's your most proud video game accomplishment?
Speaker 1 (40:52):
I still remember playing the Legend of Zelda, the first
one for the Nintendo Entertainment System and beating that game
and just like pumping my fists in the air. I
was over young man. Yeah.
Speaker 2 (41:07):
What is the most exciting video game innovation that's going
to happen in the next few years?
Speaker 1 (41:15):
Well, this is a little technical, but I think DLSS
from the video is going to get even more powerful,
which is this upscaling technology the gents actually invented in
a meeting where they fill in the details using AI,
so the frame rates are able to go much faster
(41:35):
and better.
Speaker 2 (41:36):
So it's basically like interpolation. It's sort of doing what
AI AI always does, yes, fort.
Speaker 1 (41:41):
But that's really effective. People can't tell the difference between
the real thing and the interpolated AI graphics, so that
will allow as this technology get better, that will allow
the graphics to be more for the realistic, and the
physics and the rate tracing better than ever before.
Speaker 2 (42:01):
What game are you most excited to play in twenty
twenty five?
Speaker 1 (42:05):
I mean Grand Theft Auto six if it comes out,
but it might get delayed.
Speaker 2 (42:08):
Next here, Okay, let's do a few non video game questions.
What's your favorite tech book besides the one you wrote.
Speaker 1 (42:22):
I guess it's still a tech book, The Innovator's Dilemma,
which is actually one of Jens's here books. It talks
about how companies get disrupted by startups and people underneath them.
It really goes into in depth about how the distrived industry.
Every successive generation that distribes there was a new market
leader because they couldn't disrupt themselves to the new or
(42:45):
smaller format.
Speaker 2 (42:46):
I mean, to me, the really interesting insight of that
book is the innovator is actually making a crappier product, right,
Like that's the surprise. It's not exactly like they come
along and do something better. They go to the crappy
end of the market and they make a cheap product
and it's not better than the thing the market leaders
(43:09):
and the market lader' is like, oh that, who cares
about that? That's just some low market, some low margin thing,
and the innovator comes up from below. Like that to
me is the really key insight of that book.
Speaker 1 (43:22):
And they scale the volumes, and once you have the volume,
it pretty much becomes game over for the incumbent because
they can't match that scale and the economies of scale
that comes with that.
Speaker 2 (43:36):
Defend pineapple on pizza.
Speaker 1 (43:39):
It just tastes good. I love it. I don't know
if you saw my tweets on it. I actually like
pineapple on pizza.
Speaker 2 (43:46):
Yes, I think it was Instagram. I think it was Instagram.
What's the best deal you ever got at Costco?
Speaker 1 (43:55):
It's still the hot dog and you can't beat the dollar.
Speaker 2 (43:57):
Fishbee Take Him is the author of the Nvidia Way.
Today's show was produced by Gabriel Hunter Chang. It was
edited by Lyddy Jean Kott and engineered by Sarah Bruguer.
You can email us at problem at Pushkin dot FM.
(44:20):
I'm Jacob Goldstein and we'll be back next week with
another episode of What's Your Problem.
Pushkin.
Speaker 2 (00:20):
Just a quick note at the start of the show.
We are very eager to hear what you think of
the show and how we can make it better, how
we can make a show that you like more than
the show we're making.
Speaker 1 (00:30):
Now.
Speaker 2 (00:30):
Tell us what we should do, more of what we
should do, less of what we should do, wildly differently.
Email us at problem at pushkin dot fm. Again problem
at pushkin dot fm. I'm looking forward to reading every email.
Artificial intelligence feels very abstract, feels very ephemeral, feels more
(00:53):
like an idea than a thing. But there is in
fact a thing there. AI is rooted in the physical world.
The thing or things are these little pieces of silicon
and metal called graphics processing units GPUs. These GPUs are expensive.
They cost thousands of dollars each. If you want to
(01:15):
build a state of the art AI model, you have
to buy tens of thousands of these GPUs, and most
of the GPUs come from a single company, in Vidia,
which is why in just the past few years Nvidia
has become one of the most valuable, most important companies.
Speaker 1 (01:34):
In the world.
Speaker 2 (01:40):
I'm Jacob Goldstein, and this is what's your problem. My
guest today is take him. He's a staff writer at
Baron's and the author of a new book about in Nvidia.
The books called The Nvidia Way. The book, of course,
points up being a lot about Jensen Wong, who co
founded the company back in nineteen ninety three and who
is still the CEO and who is really a wild
(02:04):
kind of terrifying, brilliant figure. And Tay and I talk
a lot about Jensen later in the interview, but we
started with this moment in two thousand and one that
led from Nvidia being a company that made graphics cards
that let people play video games on computers to becoming
this key company at the center of the AI revolution today.
(02:30):
So you write about this moment in two thousand and
one when a researcher, right, an academic at the University
of North Carolina, realizes he can basically like hack these
graphics cards, right, this hardware that's just made to make
pretty graphics on the computer to help in his research,
which is like not at all about graphics, right, It's
like modeling the weather or something. And it seems like
(02:54):
this moment is a big winds up being a big
turning point in the history of technology. Really tell me
about that moment.
Speaker 1 (03:03):
The key moment was with Mark Harris, where he was
a researcher at the University of North Carolina, and him
and a lot a bunch of other academics realized there's
all this computing power that you can hack the algorithm
to use that computing power to model thermodynamics of fluids
inside clouds, which was his thesis.
Speaker 2 (03:23):
And he realized that worked better than the CPU than
just sort of doing it the traditional way through the computes.
Speaker 1 (03:28):
Yeah, and a GPU has all these different cores that
run in parallel, and the CPU only typically has four
to eight cores, and GPUs have hundreds of thousands of cores.
Speaker 2 (03:39):
What's a CPU better at than a GPU? Like, why
would you have four to eight when you could have thousands?
Speaker 1 (03:46):
Well, it could run more complicated pieces of instructions and software,
and GPUs typically break down into much simpler tasks. But
the difference is you could run all those tasks across
a thousands So for certain workloads like AI, it's so
much faster than a CPU, which has to do things
(04:06):
kind of one after another serially. And Mark Harris and
all these academics started hacking into the graphics algorithms and
using that power to do all this scientific high performance computing.
And Mark Harris started a website and congregated all these
researchers and everyone's sharing the knowledge, sharing the tricks that
(04:26):
actually hack into this. So Nvidia sees this, they're seeing
people use this to do model stock options, the model
weather and saying, wow, this is actually really interesting.
Speaker 2 (04:37):
Yeah, that's like your dream if you're a company. Right,
It's like, oh, there's all these other things you could
do with this thing we're already.
Speaker 1 (04:43):
Making exactly so, and Jensen kind of had the foresight
to see this, Wow, this is a really big deal.
We should invest in this. So they wind up hiring
Mark Harris and look at all the things that all
these researchers are doing. And this is the beginning of
development Kuda, which is a programming platform for general purpose
(05:04):
GPU computing.
Speaker 2 (05:05):
And just to be clear, Kuda is like, it's a
programming language, but in this case they're programming what used
to be just the thing for graphics, but it turns
out to be good for these other things. And that
language it's in video's own language. Right, This is I
feel like, going to be important in the sort of
business story for why and video is so dominant today, right,
Like they write this language, they own the language, and
(05:27):
it's written just to work with their chips, is that right?
Speaker 1 (05:30):
Yes, no one else can use this. It's extensions on
a programming language that makes parallel computing easier for programmers
to make. So Jensen just invests in this, and he
actually thinks longer term than the average CEO. So most
CEOs looking out maybe the next quarter, maybe a year
or two. Jensen thinks in five, ten, fifteen year increments.
(05:54):
So he's always looking out, what's the next big computing shift,
what's the next big technology phase. So he saw KUDA
as the main thing where eventually his GPUs, you have
all this latent, enormous computing power will be able to
be used for science research, all these other simulation things.
And he didn't give up even when Wall Street wents
(06:16):
down on his next day, Why you wasting all this
die space is crushing your gross profit margins? He said, no,
this is the future computing. I'm going to invest in
it and it will work out someday.
Speaker 2 (06:28):
And when you say wasting all this die space, like
de space is like space on the chip, right, Like
he's making a physical thing and to commit to Kuda
to have this dedicated programming language, you actually have to
give up space on the chip. So that's it's costly, right,
Their margins go down. They're not optimizing for profits at
that moment.
Speaker 1 (06:48):
It's literal circuits called Kuda cores. Their hardware circuits are
optimized to run the Kuda language. So even internally executives
are like, why are we spending all this money allocating
dye space for something that people really aren't using that much,
that isn't generating revenue. But Jensen saw this as a future.
I actually kind of bring up the analogy of read
(07:10):
Hastings and Netflix. So when he started Netflix, you know,
the technology wasn't ready, consumers didn't really have broadband, but
he had this intuitive sense that someday video will be
streamed over the Internet and that was the future, and
it makes it's so obvious now, but back then it wasn't.
So Hastings positioned Netflix. He made money with DVD rentals
(07:32):
for a while and just stayed on top of the technology,
kept on investing in investing, and when the technology was
good enough, that's when he really pivoted Netflix to dominate
Internet stream Jensen did this multiple times with three D graphics,
video game graphics. He knew that someday PC video games
would be a big market programmable GPUs, Kuda and later
(07:56):
with AI on these full stack data center AI servers.
He just sees the future and is willing to keep
investing even if it's five ten years out. And Kuda
started in two thousand and six. I mean, things got
incrementally better for the next ten years, but it didn't
really take off till twenty twenty two.
Speaker 2 (08:16):
Till chat GPT. Basically twenty twenty two is chatchipt That's
when in video goes totally bonker.
Speaker 1 (08:21):
And that was the power of the large language model
of AI and all that stuff. And he's investing throughout
this whole thing for ten fifteen years.
Speaker 2 (08:29):
Yeah, so there is a moment. There's a moment in
the middle there. I do obviously want to get to
the chat GPT moment and the present, but there is
one more moment I feel like in the in the middle,
that is where in video is in the center of it.
And that's twenty twelve, right, So you have early you know,
two thousand and one, people realize, oh, you can pack
the GPU to do other things. Andybody's like, oh, that's interesting,
(08:49):
let's build a whole programming language. So people didn't do that,
And then in twenty twelve, you have this moment that
really seems like the birth of modern AI. Right, this
moment when when this AI model called alex net sort
of emerges into the world and everybody in the AI
(09:09):
world is like, oh my god, AI is here, right,
tell me about that moment.
Speaker 1 (09:16):
So alex Nett was a program that was created by
two researchers at the University of Toronto and they competed
in this competition called image net, which basically fed images
into the model, and they were able to recognize and
category rise image much more effectively than any other model
in the past. And the breakthrough was they used GPUs
(09:38):
for the first time and.
Speaker 2 (09:40):
It was like just a few, right, Like they were
grad students and they got their hands on like a
few in video GPUs and had them like running on
servers in the hallway or something.
Speaker 1 (09:48):
Yes, right, and these are video game GPUs. To be clear,
this isn't some enterprise complicated. They literally went to a
store and bought a bunch of video game GPUs and
it turned out to be very effective where they could
do the power and effectiveness of two thousand CPUs on
only twelve GPUs.
Speaker 2 (10:08):
And to be clear or that kind of vision AI,
it's machine learning. It's the same basic technology as is
used in large language models. Right, it's it's modern AI.
And so that moment is this moment when it's like, oh,
holy shit, GPUs are one hundred x better than CPUs
(10:29):
for AI. Yes, that's interesting, that's useful to know.
Speaker 1 (10:33):
So it was a combination of GPUs data and algorithms
that kind of combusted at that perfect moment. And then
Jensen saw this and it's like this is a big
deal and someday AI is going to be huge and
we need to invest big in this. So on top
of KUDA, he invests in all these AI libraries that
(10:54):
effectively managed the GPUs to do AI workloads the most
in the most effective manner. And he invested in libraries,
invested in software, he invested in researchers, and allocated a
lot of employees to this project starting in around twenty thirteen.
Speaker 2 (11:11):
And so that's that's more of the like it's not
just the chip, right, it's like the chip that is
optimized to be not only efficient on the hardware level.
But like there's all this software. So if you are
if you are building AI, if you're if you're writing
code for AI, like they make it really easy for
(11:31):
you to do it on in video GPUs.
Speaker 1 (11:34):
So it's all the math libraries, it's all the AI
frameworks work the best on in video GPUs. And they
added these things called tensor cores similar to krudercres. They
actually added hardware circuitry inside the GPUs that are optimized
to run AI training and workloads and all the all
the software that you're running. And this is over again,
(11:56):
this is over another nine eight years where they're building
all the software and hardware circuitry to run deep learning
AI the best.
Speaker 2 (12:06):
Okay, it's time. It's twot twenty two. Twenty twenty two,
chat GPT comes out. What's that mean for in video?
Speaker 1 (12:17):
So actually check chip takes off and it's it takes
about two quarters for the whole world to realize this
is a big deal. This, this model of natural language
processing where the computer actually understands what you're asking, and
that ability to draw insights and be effective with all
(12:40):
that natural language stuff just just blows people away, and
companies and startups realize we have a new AI boom here,
because it really unleashes a wave of capabilities that wasn't
wasn't doable before. So about six months later, that's when
the big bang I call it for Nvidia happens when
(13:01):
they say, oh my gosh, we're gonna we're gonna beat
our numbers that the street expects by four billion up.
And the stock went up like one hundred and seventy
billion dollars in value because the world realized.
Speaker 2 (13:15):
In like a day. Yes, right, there was one day,
and it was spring of twenty twenty three. Way talking
about and.
Speaker 1 (13:21):
This is three, this is three weeks after I had
the meeting with the book publisher who asked me if
I could do a book on video. So literally I started.
Speaker 2 (13:30):
Also good timing for you, good timing for a video,
Good timing for you, And and right, no, I remember
that day. And and there was a question then of like,
oh my god, is this a one off? Are they
going to keep growing this way?
Speaker 1 (13:44):
Right?
Speaker 2 (13:44):
Basically what they said was we sold way way more
GPUs than we thought we were gonna. Right, that's that's
what That's the basic thing they reported in their earnings
report and the subtext is this is the world and
in particular, like big tech companies with lots of money realizing, oh,
we've got to get into this AI game more than
(14:05):
we have been and to do that, we got to
buy a lot of really expensive GPUs from video.
Speaker 1 (14:11):
And Jensen is really smart at selling this stuff. Like,
he's very smart because every company, every company CEO, every startup,
they saw this risk as the extential for them because
if your competitor comes out with the AI featured offering
that's much better than what you have today, that that
(14:34):
isn't advantaged by the AI models, Yeah, that competitor could
could drive you out of business. So Jensen was very
smart as at selling to people that you need to
get on board.
Speaker 2 (14:47):
At making everybody scared of losing. Yes, well, and in particular, right,
I mean there is a small number of very large,
very rich tech companies that are a very big part
of in videos revenue. Right, it's like whatever the ones
you quld think of, Google, Meta, Microsoft, Amazon, Yes, I mean,
(15:09):
I guess open ai sort of is kind of connected
with Like those companies are buying a huge share of
these GPUs, right, They account for a big, big chunk
of in videos revenue. Yes, and those are good companies
they have as customers because they're super rich, right, they
can afford to pay you tens of billions of dollars
for your GPUs.
Speaker 1 (15:27):
But that's being said, they're reselling that GPU power to
companies and startups, right, so startups are buying that GPU
competing capacity from these.
Speaker 2 (15:38):
GNT tech companies. Yeah, that's a good point, right. So
they're in a way not the end customer. They're the
sort of intermediate kind of service providers.
Speaker 1 (15:45):
But they also use that for their own internal systems too,
like Meta uses a ton of GP power to make
their advertising algorithms more effective and to pick the videos
like tech talk does.
Speaker 2 (15:58):
And obviously Google Search is now becoming more and more
AI driven, and there's Gemini, which is right, I mean,
there's there's more direct use of AI by all of
the big tech companies as well. So there's another piece
of this which is interesting. I mean, because there's one
universe wherever it's like, oh, we got to get it
on AI, and they all buy the GPUs and then
(16:19):
that's kind of it. It's like a step function where
like there's this momentary rush where everybody buys the GPUs
and then whatever. They just upgrade every couple of years,
and it's more like a regular tech hardware business, which
is like good but not amazing. But there's another piece
of it that has been a huge deal for end video,
and that's the scaling law or the scaling hypothesis. Let's
(16:40):
talk about that. What's the what's the scaling hypothesis.
Speaker 1 (16:42):
So the scaling hypothesis, similar to what I said before,
is the combination of computing power. The more computing power
you add, the more data you increase, and the better
ways you figure out software algorithms for each of these
three buckets. If you increase it, the AI model becomes
more capable and more effective.
Speaker 2 (17:02):
And like, just to be clear, like even if the
algorithms aren't getting that much better, right, Like my understanding is,
and this is kind of a new idea. It's like, oh,
if we can just have more data and more computing
power will get better results.
Speaker 1 (17:18):
Yes, so it's great if you have all three doing
you know, basically showing up. But for this time period,
the last few years, the scaling law has really taken
off and companies are literally ten xing their compute power,
and these AI clusters going from sixteen thousand GPUs now
(17:40):
to one hundred thousand gps and now people are saying
they're going to build one million GPU clusters in the
next two three years.
Speaker 2 (17:47):
And in videos selling most of those GPUs.
Speaker 1 (17:51):
Yes, So if you go from sixteen thousand to one
hundred thousand GPUs in a couple of.
Speaker 2 (17:55):
Years, that's a good business to do.
Speaker 1 (17:57):
If you're increasing the scale of the hardware by ten
ten x every couple of years. And the exciting thing
for Nvidia is that, like what I argue is that
this thing is going to this trend is going to
continue in the next few years because you have the
scaling laws. Then you have this thing called multimodal where
they're using this GPUs not just for texts like in
(18:19):
chat TPT, you're using it for video and images generating
those things. And now there's these two other two other
waves of demand that are happening. There's this thing called
AI agents where these AI models can do multi step
tasks for you.
Speaker 2 (18:37):
Right, you could be like book me a ticket to
San Diego some weekend in June whenever it's the best deal, yes,
and then it does that.
Speaker 1 (18:45):
So that's going to happen in the next twelve months. Yes,
these AI agents will eventually, in the next year or
two be able to do all that tedious work automatically
and probably with less errors than an actual human being.
So AI agents multimodal. And now there's this thing called
test time compute, where the AI models, instead of just
(19:07):
spinning back an instant apply, can actually think about what's
the best way to respond to your question and spend
more time thinking about it and then give you a
higher quality answer. So there's all these things that all
of these test time compute, AI agents, multi modal. These
are all things that need more computing power, they need
(19:29):
more GPUs, and these are all things that are going
to drive in video's revenue in the next couple of years.
Speaker 2 (19:37):
We'll be back in a minute and we will talk,
among other things, about Jensen Wong, one of the most
successful entrepreneurs of the twenty first century and a man
who once told the colleague that he wakes up every morning,
looks in the mirror and says to himself, you suck.
(19:59):
I want to talk about Jensen a little bit. I
mean your book. You know, he is obviously the main
character in the history of Nvidio is the main character
in your book. He's been the CEO of n Video
for more than thirty years, which is like longer than
Bill Gates with the CEO of Microsoft, longer than any
you know, almost anybody in the s and p. Five
hundred has been a CEO at this point. His childhood
(20:20):
is quite interesting, right, Like, tell me just a little
bit about his childhood.
Speaker 1 (20:25):
He was born in Taiwan, and they moved around a
little bit to Thailand, and his father came to training
in New York and fell in love with America. This
is like the great American dream story. And the mother
and father started teaching Jensen's brother English ten words a day,
(20:47):
and they sent him to his aunt and uncle when
he was about age eight or nine, and the aunt
and uncle and the families. The funny thing is they
sent him to a reform school in Kentucky by mistake,
thinking that he will get a great education at this
boarding school in Kentucky, which.
Speaker 2 (21:04):
He just thought it was a boarding school, yes.
Speaker 1 (21:06):
But it turned out to be a school for troubled.
Speaker 2 (21:08):
Kids, huh, which Jensen was not. He was just like
a smart kid with ambitious for him parents.
Speaker 1 (21:15):
Yes, But he talks about how his time at Oneita
Baptist and student Kentucky was formational for him.
Speaker 2 (21:21):
What was it like for him?
Speaker 1 (21:23):
It was hard at the beginning, but he started befriending people.
He started playing chess with the janitor, and he just
learned how to deal with other people much better. And
he talks about how he learned his street fighter.
Speaker 2 (21:38):
Mentality because he was literally fighting.
Speaker 1 (21:42):
Yes, I mean there were bullies and all that stuff,
but he just learned how to deal with people and
the rough and tumble of kids back then. Eventually his
parents came from abroad and they settled in Oregon, and
he learned his work ethic from working at Denny's. He
(22:03):
talks about how he washed more dishes and cleaned more
bathrooms than any CEO in the history of CEOs. He
says he Denny's helped them in terms of social skills
and dealing with time pressure and dealing with customers. But
it's the work ethic that really sets him apart. So
even at the beginning, he was working from nine am
(22:26):
to midnight, and he just set a culture where Nvidia
employees work really hard.
Speaker 2 (22:32):
And I feel like at the beginning that's common, right,
Like that is the classic startup story. But the classic
startup stories you do that for five years and then
either you get giant and you hire a grown up
CEO and you you know, go start your blimp company
or whatever, or you sell or whatever. He's still doing that,
right He's sixty years old and like wildly rich and
(22:55):
and he's still working that much.
Speaker 1 (22:57):
He's working all weekend. He's working Saturday Sunday. When he
talks about when he goes to a movie, he doesn't
remember the movie because he's thinking about work. He finds
work relaxing and fund This is what drives him. He
loves working.
Speaker 2 (23:12):
There's a moment, you know, you don't you don't write
about his personal life at all. Once he sort of
grows up and starts in video. You know, reasonably because
the book's about in video, and so I just assumed
he just worked all the time and didn't have a family.
And the I think the only time in the book
that his family comes up is there's this scene where
he's on vacation and he's talking to some senior manager
(23:35):
at a video on the phone and they're like, what
are you doing. He's like, I'm sitting here on the
balcony watching my kids play in the sand and writing emails.
And it's like, like, if it's a movie, the kids
are never on stage, right, you just hear them off stage.
At that moment, You're like, oh, he has kids, Like
that was that was a weird moment to me reading
that line.
Speaker 1 (23:54):
So both executives told me they hate it when Jensen
so called goes on vacation because he winds up giving
them more directives and orders and more stuff to do
when he's not on vacation because he's emailing in them
do this, do that, and they they actually yell at him,
So play with your kids. He's like, no, I could
(24:15):
get real work done when I'm on vacation by doing
his emails. I mean, so it shows you his obsessions.
He's constantly thinking, he's constantly worried about what's happening at video.
Speaker 2 (24:28):
I mean it's interesting, right, Like he's not a balanced person, like,
which is why to some extent he has built the
thing that he has built.
Speaker 1 (24:40):
Right, He's completely obsessed with winning and he's extremely competitive.
Speaker 2 (24:46):
Yeah, there's a few other specific moments that really stood
out to me. There's one where I think this, I
think a salesperson told you this where they had just
had a great quarter they sold a ton of GPUs,
and the sales guy's talking to Jensen about how great
they're doing, and Jensen says to the sales guy about Jensen,
(25:08):
about himself. Jensen says, I look in the mirror every
morning and say you suck.
Speaker 1 (25:14):
So he he's almost like a self psychologist. He knows
if you start thinking that you're you're the best and
your your hot stuff, you might get complacent, you start
resting on your laurels, you might not work as hard enough.
So he he sees that, oh, we just had a
blowout quarter. What's the risk here? The risk is me
gain complacent. So I'm gonna look myself in the mirror
(25:36):
and say you suck and psych myself out.
Speaker 2 (25:38):
I mean that's that's that's one reading of it. That's
the like ten dimensional chest reading of it. I mean
there's another reading of it, which is like he's messed
up in a way that makes him super driven and successful.
Speaker 1 (25:51):
But it works.
Speaker 2 (25:52):
He actually definitely works. We can agree that it works.
Speaker 1 (25:56):
Like he does the opposite too, So when things are
really intimidating and he feels like, oh my gosh, how
am I going to do this, he tells himself, how
hard can it be? How hard can it be? So
he does it on both both ends in the spectrum
where he cites himself out when he feels intimidated, and
when he feels like he's on top of the world,
(26:16):
he tries to bring himself down back the earth.
Speaker 2 (26:19):
So the one other big piece of the Jensen Wong
experience that we haven't talked about is the way he
treats the people who work for him. Right, I want
to talk about a particular scene because I think it
puts a finer point on it. A scene from the
book where it's like a company wide meeting and it's
on like zoom or whatever, and he's yelling at a
(26:40):
guy in the meeting. He's yelling at him, and then
on top of that, he keeps telling the person filming
the meeting to zoom in on the guy he's yelling at.
Speaker 1 (26:49):
I had multiple sources tell me that it was the
most humiliating thing they've ever seen.
Speaker 2 (26:53):
I mean, it's that seems like bullying, like why zoom
in on the guy? Like what lesson is added by that?
Speaker 1 (27:01):
So he keeps on saying to mister Rayfield, you got
to get this chip back on track. The chip that
heels in charge was behind schedule, and he kept us
pointing to him, zooming in his face and telling him,
You've got to get this. This is not how you
run a business. You need to get this chip back
on track. And this kind of like high standard demanding
(27:23):
attitude I think is effective. It drives people. If you
get dressed down by Jensen, the next time, you're going
to work ten times more to make sure that you
do a better job.
Speaker 2 (27:36):
Maybe I don't want the world to be that way,
but it is that way, you know what I mean, Like,
maybe I want to believe that, like there's a kinder
world where people could do equally good work. But maybe
I'm wrong.
Speaker 1 (27:49):
And Steve Jobs was the same way. Yeah, I mean
it wasn't all sure, It wasn't all sunshine and rainbows.
Speaker 2 (27:55):
No, no, I mean the Isaacson book was really clear, right,
the Isaacson biography of Steve Jobs. He did not seem
like a good person in that book. He seemed like
really good at building amazing products, but like not like
a good human being by the sort of standard way
we think of what makes a good person.
Speaker 1 (28:12):
But I think the key point that we also have
to think about is people at Nvidia stay.
Speaker 2 (28:17):
Yeah, that's really interesting.
Speaker 1 (28:19):
The turnover is like one of the lowest in the
industry at only three percent compared to the average of
thirteen to fifteen percent.
Speaker 2 (28:24):
Yeah, certainly in the last few years. People stay because
you get rich if you stay, and you lose your
equity if you leave. But is that number true if
you go back to when the stock was flat.
Speaker 1 (28:34):
I don't have the numbers, but a lot of the
senior executives have been there more so than any other company. Yeah,
fifteen twenty twenty five years.
Speaker 2 (28:42):
Yeah, that's it.
Speaker 1 (28:43):
You don't really hear other than one instance of people
leaving to become a CEO of a different company. Uh huh.
And I think part of it is people realize that
Nvidia or the Gensen way of doing things is super effective,
so people want to be on the winning team. So
in one sense, he draws people hard, he dresses them down.
(29:05):
He's very blunt and direct. But he compensates people. No
one ever, hardly anyone complains about compensation. If you're effective
and you do a good job, he will like double
your stock compensation on the spot. So there's this meritocracy
that people really adore. And if you see an effective
(29:25):
leadership a culture that's based on meritocracy, if you're a
top engineer, you stay at that company.
Speaker 2 (29:33):
Yeah. Yeah, So you interviewed Jenson as as you were
working on the book. Was it scary to interview him?
Speaker 1 (29:42):
He was intimidating in the meeting, he he at he
didn't yell at me, but a couple of times he's saying,
you don't get nvidio in this line of questioning, and
I it takes a while to get used to it,
but you appreciate it because you see where the other
person is. Like when someone is blunt and direct like Jensen,
(30:02):
you know where that where he is at all times,
and then you could work at improving yourself. So he
talks about how what are you optimizing for? Are you
optimizing for a person's feelings or optimizing for what's good
for the company. So that's why he doesn't do one
on one meetings or career coaching. Typically at a large company,
(30:23):
when someone's doing poorly, the CEO will take them aside
and say, Bob, you need to do this.
Speaker 2 (30:28):
You need if it's a person's.
Speaker 1 (30:30):
Yeah, Jensen's like, why is Bob the only one that
gets a learning here? If I am blunt and direct
and show Bob what he's doing incorrectly, like that person
at that meeting, everyone in the room can learn. All
the employees up and down the ladder can learn. So
that's his philosophy is everyone should learn from their mistakes
(30:53):
and everyone should know where they are at all times.
Speaker 2 (30:56):
So I mean, so you write in the book that
in video really is like an extension of Jensen, right.
I think he used the metaphor of like the formula
one card that's like optimized for him as the driver.
So and he's in his sixties, which is not old
(31:20):
old but definitely not young. Like, is he gonna run
in video for another twenty years? What's going to happen?
Like there's no obvious successor, Like where does that go?
Speaker 1 (31:31):
I think there's there's no one else at in video.
I think that can run in video as effectively as Jensen.
And he loves the company so much. He loves what
he's doing. They're having enormous impact with this AI wave,
and he's so excited about the potential for curing cancer
and digital biology, the potential for robots, the potential for
(31:54):
AI to kind of disrupt education and help kids learn better.
That I don't see him leaving anytime soon. He loves
his job so much and he can't argue that he's
being ineffective. So I don't think for the next few
years there's anyone that's going to take over for him.
Someday Nvidia is going to have to come up with
(32:15):
a new CEO or a successor to Jensen, and that's
going to be a big question mark. It's that next
person going to be as effective as Jensen in terms
of being able to have the technical skill and the
compency to steer in video in the right direction. Well,
they have his business genius of coming up with all
(32:37):
these new strategies that he does time and time again.
That's going to be a huge question Marke.
Speaker 2 (32:44):
I mean people also talk about limits to scaling right
in AI. We were talking earlier about how part of
in video's wild growth over the last couple of years
has come from this fact that that you can just
add more GPUs basically and get better results. More GPUs,
(33:07):
more data, and you know, people talk about a running
out of data because like they're basically, as I understand it,
training on the whole Internet right now, which is like, Okay,
it's a lot of data. I mean, is that it
seems like nobody knows, right. It seems like they are
smart people who say, no, no, we'll have synthetic data
(33:29):
blah blah blah, we won't hit a sort of scaling wall.
And then there are people who make the other argument.
I certainly don't know, but does that seem right, Like
is that an open question right now? And is that
a meaningful question for nvideo?
Speaker 1 (33:40):
I think it's an open question. But like I said,
there's the multimodal stuff, there's AI agents, and then there's
proprietary data inside companies.
Speaker 2 (33:51):
Uh huh.
Speaker 1 (33:52):
So all these corporations have data going back decades. Companies
are going to use the power of these AI computing
systems to go through all their data internally, all their
proprietary data, and have all that knowledge at employees fingertips.
So an employee can ask what's the best way to
(34:15):
do this, and the AI computer is going to be
able to go back thirty years of data and figure
out the best piece of insight to help that employee.
And that's not really being done today.
Speaker 2 (34:26):
So Nvidia designs their chips their gips, but they don't
actually manufacture them, right Is it right that they're made
in Taiwan, where most cutting its chips are made now?
Speaker 1 (34:36):
Yes, So TESMC actually makes and manufacturers in videos chips
ever since the late nineteen nineties, and they're the best
at doing this all. A lot of the fabulist chip
designers in California use TSMC, and Video uses them too.
Speaker 2 (34:53):
And so I mean it is really interesting to me
to a lot of people that Taiwan is this incredibly
fraught geopolitical place. Right, China says Taiwan is part of China.
Taiwan says no, we are not part of China, and
that Taiwan is the only place in the world that
(35:13):
makes like the most important physical thing in the world
of technology today. Right, it makes like that's wild, Like
what do you make of that? And how does that
fit with the in video story?
Speaker 1 (35:25):
I think eventually Nvidia chips will be made in the
US TSMC. I mean, that's all part of this Chips
Act that the Biden administration has posed.
Speaker 2 (35:35):
So this this plant that TSMC is building in Arizona, Like,
is that advanced enough to make like frontier in Nvidia chips?
Speaker 1 (35:45):
So the CSMC factories in the US are always going
to be one or two generations behind the factories in Taiwan.
But that doesn't mean Nvidia can't use the factories in
the US if they're one or two generations behind for
their older chips. So I think that will eventually happen.
Speaker 2 (36:04):
It seems wild to me, Like, I mean, it seems
very possible that China will try and make Taiwan be
part of China, right, and and like we have all
these export controls to try and keep China from getting
cutting edge chips. Like it's a really interesting, complicated dynamic.
Speaker 1 (36:21):
I think people make a big issue at this, But
I think if it happens, like in Vidio, chips are
not going to be the main issue. It will cost
a global calamity where you know, we won't be able
to upgrade our cars, at laptops, nearly every computing device,
(36:41):
every appliance will not work if we don't have access
to Taiwan chips.
Speaker 2 (36:47):
I mean, presumably we could do some like sub Manhattan
Project scale Manhattan project to get a state of the
art TSMC factory somewhere that is not Taiwan, right, Like.
Speaker 1 (37:00):
We're doing some of that now, but it's not going
to be able to it's going to be ten twenty
of the capacity we need.
Speaker 2 (37:08):
Yeah, the capacity, right, it takes a long time, Like
the fabs are wildly complicated to build, and they cost
billions of dollars and they take years, so you couldn't
just do it.
Speaker 1 (37:18):
They cost ten to twenty billion dollars to build and
three to four years over three to four years, So
it's not something that can happen overnight. And if something happens,
like it's just gonna be so terrible and it'll be
like a depression. So I just hopefully it doesn't happen,
and you know it's not gonna The question isn't gonna
be about in videotips. The question is gonna be about
(37:38):
the global economy of that.
Speaker 2 (37:40):
So what do you think is the biggest risk for
video in the like five year timeframe?
Speaker 1 (37:48):
It's really tough to say.
Speaker 2 (37:49):
Now.
Speaker 1 (37:49):
I mean I see the next few years with all
the different AI innovations and all the progress. But again,
just like every big computing shift, what's the next big thing?
Could it be quantum computing? It can be like who knows, five, ten,
fifteen years from now? And every from the pcach with
Microsoft and Intel it's called Wintel. And then it went
(38:12):
to smartphones where Apple dominated with the iPhone, and now
in Vidia is dominating. In terms of the AI computing shift,
There's gonna be another shift, and right now we're at
the early stages of the AI computing movement. In five
ten years, there might be the next big thing that
(38:32):
I can't can't even foresee right now. And is Nvidia
gonna be able to see that coming? If Jensen's around,
I think he will. But if Jensen's not there, you know,
then just like every other computer, major computer company in history,
say IBM, it's very easy to get disrupted in the
(38:53):
technology industry.
Speaker 2 (38:58):
We'll be back in a minute with the Lightning round.
I want to finish with the Lightning round, which is
going to be considerably more random and digressive than the
conversation to this point. I want to talk about video
(39:20):
games and your experience with video games. What was the
first video game you ever loved?
Speaker 1 (39:30):
I think it's the first VAM I've ever tried, which
was Combat for the Datar twenty six hundred.
Speaker 2 (39:35):
I love that.
Speaker 1 (39:36):
I mean it's literally I remember coming home and my
dad buying the Autari twenty six hundred connected to a
black and white TV and being to use that joystick
to control the little tank going around the screen. That
was an amazing moment. It was an incredible moment that
this was possible. At the home, I had.
Speaker 2 (39:56):
An Atari twenty six hundred. I remember the joystick. I
remember Demon Attack, so kind of second tier game, but
I got really into it. What's the game you spent
the most hours on?
Speaker 1 (40:11):
Probably this game called Lemmings. I don't know if you
it's it's a puzzle strategy game where you control lemmings
and you guide them across a maze to get.
Speaker 2 (40:23):
The little creatures that are famed for jumping off cliffs,
which maybe they don't actually do.
Speaker 1 (40:27):
Yes, if you guide them the wrong way, they'll fall
off a cliff and to their death, and they make
this cute sound like oh no, so so. I played
so many hours of.
Speaker 2 (40:38):
That game, one thousand hours.
Speaker 1 (40:43):
Probably hundreds of hours. I want to say a thousand.
Speaker 2 (40:45):
Yeah. What's your most proud video game accomplishment?
Speaker 1 (40:52):
I still remember playing the Legend of Zelda, the first
one for the Nintendo Entertainment System and beating that game
and just like pumping my fists in the air. I
was over young man. Yeah.
Speaker 2 (41:07):
What is the most exciting video game innovation that's going
to happen in the next few years?
Speaker 1 (41:15):
Well, this is a little technical, but I think DLSS
from the video is going to get even more powerful,
which is this upscaling technology the gents actually invented in
a meeting where they fill in the details using AI,
so the frame rates are able to go much faster
(41:35):
and better.
Speaker 2 (41:36):
So it's basically like interpolation. It's sort of doing what
AI AI always does, yes, fort.
Speaker 1 (41:41):
But that's really effective. People can't tell the difference between
the real thing and the interpolated AI graphics, so that
will allow as this technology get better, that will allow
the graphics to be more for the realistic, and the
physics and the rate tracing better than ever before.
Speaker 2 (42:01):
What game are you most excited to play in twenty
twenty five?
Speaker 1 (42:05):
I mean Grand Theft Auto six if it comes out,
but it might get delayed.
Speaker 2 (42:08):
Next here, Okay, let's do a few non video game questions.
What's your favorite tech book besides the one you wrote.
Speaker 1 (42:22):
I guess it's still a tech book, The Innovator's Dilemma,
which is actually one of Jens's here books. It talks
about how companies get disrupted by startups and people underneath them.
It really goes into in depth about how the distrived industry.
Every successive generation that distribes there was a new market
leader because they couldn't disrupt themselves to the new or
(42:45):
smaller format.
Speaker 2 (42:46):
I mean, to me, the really interesting insight of that
book is the innovator is actually making a crappier product, right,
Like that's the surprise. It's not exactly like they come
along and do something better. They go to the crappy
end of the market and they make a cheap product
and it's not better than the thing the market leaders
(43:09):
and the market lader' is like, oh that, who cares
about that? That's just some low market, some low margin thing,
and the innovator comes up from below. Like that to
me is the really key insight of that book.
Speaker 1 (43:22):
And they scale the volumes, and once you have the volume,
it pretty much becomes game over for the incumbent because
they can't match that scale and the economies of scale
that comes with that.
Speaker 2 (43:36):
Defend pineapple on pizza.
Speaker 1 (43:39):
It just tastes good. I love it. I don't know
if you saw my tweets on it. I actually like
pineapple on pizza.
Speaker 2 (43:46):
Yes, I think it was Instagram. I think it was Instagram.
What's the best deal you ever got at Costco?
Speaker 1 (43:55):
It's still the hot dog and you can't beat the dollar.
Speaker 2 (43:57):
Fishbee Take Him is the author of the Nvidia Way.
Today's show was produced by Gabriel Hunter Chang. It was
edited by Lyddy Jean Kott and engineered by Sarah Bruguer.
You can email us at problem at Pushkin dot FM.
(44:20):
I'm Jacob Goldstein and we'll be back next week with
another episode of What's Your Problem.
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