Jensen Huang, Founder and CEO of NVIDIA
Summary
TLDRIn this inspiring interview, Jensen Huang, CEO of NVIDIA, shares his remarkable journey of transforming a startup into a pioneering tech giant. He reveals the core principles that have guided his leadership, emphasizing the importance of empowering employees, fostering a culture of innovation, and staying true to one's beliefs. Huang reflects on overcoming challenging times, such as the 80% market cap loss during the financial crisis, and highlights the role of 'early indicators of future success' in driving the company forward. He discusses the disruptive potential of generative AI and encourages a first-principles approach to harnessing its power responsibly. Throughout the interview, Huang's wisdom and passion for pushing the boundaries of computing shine through, inspiring viewers to redefine their own tomorrows.
Takeaways
- 😀 Have a strong core belief and vision, but be willing to adapt and reinvent along the way based on new information and opportunities.
- 🤖 Aggressively pursue new technologies and applications that can solve problems current general computing cannot address, even if the market doesn't exist yet.
- 📚 Continuously learn and study from first principles - treat challenges as opportunities to gain new knowledge and insights.
- 🏆 Seek out 'early indicators of future success' rather than getting fixated on short-term financial metrics.
- 🧠 Empower employees by being transparent, informing them of the broader context, and showing how you reason through problems.
- 🌳 Design the organizational structure from first principles based on what you are trying to build, not legacy hierarchical models.
- 🔭 Look backwards from the future you envision for your company and work backwards to get there.
- 🚀 Embrace generative AI as a paradigm shift that will fundamentally change computing and disrupts industries.
- ⚖️ Support tailored regulation for AI products/services by existing bodies, not blanket regulation across domains.
- 👥 Surround yourself with people you love and take them on the journey with your core beliefs as the guiding force.
Q & A
What motivated Jensen Huang to leave LSI Logic and become a founder?
-Jensen was motivated by the opportunity to build a company focused on solving problems that normal computers powered by general-purpose computing couldn't, as suggested by his colleagues Chris and Curtis.
How did Nvidia choose its first killer app and what was it?
-Nvidia's first killer app was decided to be 3D graphics, targeting the application of video games. This decision was based on the intersection of the challenge of making 3D graphics affordable and the non-existent video game market for PCs at the time.
What was Nvidia's approach to overcoming the challenge of their technology becoming incompatible with Microsoft's direct 3D?
-Nvidia reset the company by adopting the OpenGL standard for computer graphics after their initial technology became incompatible with Microsoft's direct 3D, allowing them to innovate and create something the world had never seen before.
How did Jensen Huang ensure Nvidia stayed on track during challenging times, such as when its market cap dropped 80%?
-Jensen focused on core beliefs and the fundamentals of the business, maintaining the same approach to work and leadership, and ensuring the company remained focused on its mission despite external market pressures.
What role does generative AI play in Nvidia's vision for the future?
-Generative AI is seen as a revolutionary technology that will change the nature of computing, enabling software to understand and generate new content, which will have broad implications for various applications and industries.
What were the key principles behind the creation of Nvidia's organizational structure?
-Nvidia's organizational structure is designed to be flat, prioritizing empowerment and information accessibility across all levels to ensure every employee can contribute effectively to the company's mission.
How does Jensen Huang view the potential challenges and regulations surrounding AI?
-While acknowledging the need for technological advancements to address safety and ethical considerations, Jensen emphasizes the importance of context-specific regulations across different industries, rather than overarching regulations that might stifle innovation.
What was Nvidia's strategy for entering and growing the gaming market for PCs?
-Nvidia focused on creating technology and markets simultaneously, recognizing the need to both innovate in 3D graphics technology and actively develop the gaming market for PCs to ensure the company's success.
How did Nvidia adapt when realizing their initial vision wasn't working as expected?
-Nvidia was flexible in its approach, shifting focus from their initial vision to explore and capitalize on the emerging market of 3D graphics for gaming, demonstrating the company's ability to pivot based on market demands and technological possibilities.
What does Jensen Huang consider as the most important aspect of his leadership role at Nvidia?
-Jensen Huang believes his most important role is to create the conditions that allow his employees to do their life's work, focusing on empowerment, information sharing, and fostering an environment where everyone can contribute to Nvidia's mission.
Outlines
👨💼 Founding Nvidia and Securing Initial Funding
Jensen describes how he and his co-founders Curtis Priem and Chris Malachowsky decided to start Nvidia during the microprocessor revolution in the early 1990s. Their goal was to build a company that could solve problems that normal computers couldn't handle. Jensen shares the story of how he secured funding from Don Valentine, a renowned Silicon Valley investor, with the help of his former boss at LSI Logic, Wilfred Corrigan.
🎮 Creating the 3D Gaming Market for PCs
Jensen explains how Nvidia's initial focus was on creating the technology and market for 3D graphics in video games on PCs. This involved developing affordable hardware and software solutions to enable 3D graphics, which were previously only available on expensive workstations. Nvidia had to create both the technology and the market, working with companies like Electronic Arts to drive adoption.
💡 Pivoting and Implementing the OpenGL Pipeline
Jensen discusses how Nvidia had to pivot and reset the company when their initial 3D graphics technology became incompatible with Microsoft's Direct3D standard. They implemented the OpenGL pipeline by studying a textbook on how Silicon Graphics did computer graphics. This pivotal moment gave the company confidence that they could succeed by learning and reinventing from first principles.
📈 Expanding into New Markets and Applications
Jensen talks about Nvidia's efforts to expand into new markets and applications beyond 3D graphics, such as computational drug design, weather simulation, and artificial intelligence. He highlights the importance of identifying 'early indicators of future success' to guide the company's investments in emerging technologies, even when the markets don't yet exist.
🤖 The Impact of Deep Learning and AI
Jensen discusses how Nvidia recognized the potential of deep learning early on and worked with researchers to develop tools and languages for expressing neural network algorithms on their processors. He emphasizes the importance of reasoning from first principles to understand the implications of new technologies like AI and how they could fundamentally change computing and software development.
💼 Leadership During Challenging Times
Jensen shares his approach to leading Nvidia through challenging times, such as when the company lost 80% of its market cap during the financial crisis. He emphasizes the importance of gut-checking core beliefs, focusing on the company's mission, and being transparent with employees to keep them motivated and empowered.
🗺️ Creating a Flat and Empowered Organization
Jensen explains his philosophy behind creating a flat and highly empowered organization at Nvidia. He believes in minimizing layers of information flow and trusting employees with important information to enable them to make smart decisions. Jensen's goal is to create conditions where employees can do their life's work and contribute to the company's mission.
🔮 The Future of Generative AI and Computing
Jensen discusses the implications of generative AI and its ability to understand and generate content across different modalities (text, images, speech, etc.). He explains how this technology could fundamentally change the way we process information, write software, and approach computing. Jensen encourages reasoning from first principles to understand how different industries and applications might be disrupted by generative AI.
🌍 Redefining Tomorrow and Nvidia's Contribution
When asked about redefining tomorrow, Jensen emphasizes the importance of making a unique contribution to advancing the future of computing, which he believes is the most important instrument for humanity. He envisions Nvidia being remembered as a company that changed everything through its work in creating new technologies and enabling new markets.
🤖 Addressing Concerns About AI Development Pace and Regulation
Jensen acknowledges the concerns about the pace of AI development and the need for regulation. He emphasizes the importance of advancing technologies for grounding, safety, and security to mitigate risks. Jensen advocates for product and service-specific regulations rather than an overarching 'super regulation' and highlights the need for social implications of AI to be addressed.
🤵 Rapid-Fire Questions and Advice
Jensen responds to a series of rapid-fire questions, including recalling his fondest memory from working at Denny's, what he would wear if there were a shortage of black leather jackets, and what he would title a hypothetical textbook. As a parting piece of advice, he encourages having a core belief, gut-checking it every day, pursuing it with all one's might, surrounding oneself with loved ones, and taking them on that journey.
🙏 Closing Remarks
The session concludes with Jensen expressing gratitude for the opportunity to share his insights and experiences.
Mindmap
Keywords
💡Founder
💡Accelerated Computing
💡3D Graphics
💡Microprocessor Revolution
💡Entrepreneurship
💡Computational Drug Design
💡AI (Artificial Intelligence)
💡Business Plan
💡Innovation
💡Ecosystem
Highlights
Jensen Huang explains his motivation for co-founding NVIDIA, driven by the belief that they could create a computer that solves problems regular CPUs couldn't handle and the aspiration to work on impactful projects that would make the world better.
Huang emphasizes the importance of finding early indicators of future success when investing in a long-term vision, even when the market doesn't exist yet. He cites examples like the first fuzzy cat image from deep learning research as an early sign of its potential impact.
During challenging times like NVIDIA's 80% market cap drop, Huang advocates for going back to core beliefs, gut-checking assumptions, and continuing the work if those foundational principles remain unchanged.
Huang attributes NVIDIA's success to creating a highly empowered organization where employees understand the context and can make smart decisions by being transparent with information and trusting them to handle complex situations.
Huang explains how generative AI represents a fundamental shift in computing, moving from retrieval-based models to generation-based models, and the implications this will have on industries, applications, and how we think about computing overall.
Huang advocates for going back to first principles and reasoning through the core implications of new technologies like generative AI, rather than being constrained by existing organizational structures or ways of thinking.
Huang believes that regulations for AI should be focused on products and services, leveraging existing regulatory bodies like the FDA and FAA, rather than creating overarching, cross-cutting regulations that may impede progress.
Huang advises having a core belief, gut-checking it every day, pursuing it with all your might for a very long time, and surrounding yourself with people you love on that journey.
Huang emphasizes the importance of creating the conditions for employees to do their life's work, which involves empowering them with information, transparency, and trust, rather than limiting information flow.
Huang highlights the value of learning from textbooks and archive papers, going back to first principles, and reinventing solutions based on current conditions and motivations, rather than incrementally building on past approaches.
Huang discusses the early decision to focus on 3D graphics and video games as the initial killer app for NVIDIA, despite the technology being expensive and the market being non-existent at the time, driven by the belief in the importance of the work.
Huang emphasizes the importance of making a unique contribution that advances the field of computing, which he sees as the most important instrument of humanity, and being remembered for doing something insanely hard that changed everything.
Huang advises against being constrained by existing organizational structures or hierarchies, and instead designing organizations based on first principles, considering the inputs, outputs, and environmental conditions the company operates in.
Huang shares his approach of looking forward in time and reasoning backwards, imagining the impact NVIDIA will have made and working backwards to achieve that desired end result, rather than looking forward from the present.
Huang highlights the importance of surrounding himself with genius-level talent across all domains, acknowledging that NVIDIA has the deepest technology management team the world has ever seen.
Transcripts
[Music]
Jensen this is such an honor thank you
for being here I'm delighted to be here
thank you in honor of your return to
Stanford I decided we'd start talking
about the time when you first left you
joined LSI logic and that was one of the
most exciting companies at the time
you're building a phenomenal reputation
with some of the biggest names in Tech
and yet you decide to leave to become a
Founder what motivated
you uh uh Chris and Curtis Chris and
Curtis uh uh I was an engineer at LS
logic and Chris and Curtis were at Sun
and I was working with with uh some of
the brightest Minds in computer science
at the time of all time uh including
andyto shim and others uh building
building workstations and Graphics
workstations and so on so forth and uh
Chris and Curtis uh uh said one day that
they like to leave some son and they
like uh me to go figure out what they're
going to go leave
four and and um I had a great
job but they they insisted that I uh
figure out you know with them how to how
to build a company and so so we hung out
at Denny when whenever they Dro by and
and uh uh which was which is by the way
my alma marter my my first
company uh you know my first job before
for before CEO was a was a dishwasher
and so and and I did that very
well and and so anyways uh we got
together and and we we DEC and it was
during the the microprocessor Revolution
this is 1993 and and 1992 when we were
getting together the PC Revolution was
just getting going you you know that
Windows 95 obviously which is the
Revolutionary version of Windows uh
didn't even come to the market yet and
Pentium wasn't even announced yet and so
and this is this is all before the right
before the PC Revolution and it was it
was pretty clear that that uh the
microprocessor was going to be very
important and we we thought you know why
don't we build a company uh to go solve
problems that a normal computer that is
powered by general purpose Computing
can't and and so that that became the
company's Mission uh to go to go build a
computer uh the type of computers and
solve problems that normal computers
can't and to this day uh we're focusing
on that and if you look at all the the
problems that that um and the markets
that we opened up as a result uh it's
you know things like uh computational
drug design um uh weather simulation
materials design these are all things
that we're really really proud of uh
robotics uh self-driving cars uh
autonomous autonomous uh software we
call artificial intelligence and then
all you know of course uh we uh we drove
the the uh U the techn techology so hard
that that eventually the computational
cost uh uh went to approximately zero
and then enabled enabled a whole new way
of developing software where the
computer wrote the software itself
artificial intelligence as we know it
today and so so I that was that was it
that was the journey yeah thank you all
for
[Laughter]
coming well these applications are on
all of our minds today but back then the
CEO of LSI logic
convinced his biggest investor Don
Valentine to meet with you he is
obviously the founder of seoa yeah now I
can see a lot of Founders here edging
forward in anticipation but how did you
convince the most sought-after investor
in Silicon Valley to invest in a team of
firsttime Founders building a new
product for a market that doesn't even
exist I I didn't know how to write a
business
plan and and uh uh so I went to a went
to a book
bookstore and back then there were
bookstores and and and um in the
business book section there was this
book and it was written by somebody I
knew Gordon Bell and this book I should
go find it again but it's a very large
book and the book says how to write a
business
plan and and that was you know a highly
specific title for a very niche market
and it seems like he wrote it for like
you know 14 people and I was one of them
and and so I I bought the book I I
should have known right away that that
it was a bad idea because that you know
Gordon is super super smart and super
smart people have a lot to say and and
they wanted you know and I I'm pretty
sure Gordon wants to teach me how to
write a business plan uh completely and
so I I picked up this book it's like 450
pages long well I never got through it
not even close I I flipped through it a
few pages and I go you know what by the
time I'm done reading this this thing
I'll be out of business I'll be out of
money and and uh Lori and I only had
about 6 months uh in the bank and we had
already Spencer Madison and and uh and a
dog and so the five of us had to live
off of you know uh whatever money we had
in the bank and and so I didn't have
much time uh and so instead of writing
the business plan uh I just went to talk
to to W Coran he turn he called me one
day and said hey you know you left the
company you didn't even tell me what you
were doing I want you to come back and
explain it to me and so I went back and
I explained it to Wi and wi wi at the
end of it he he said I have no idea what
you
said and and um that's one of the worst
elevator pitches I've ever
heard um and then he picked up the phone
and he called Don Valentine and he he
called Don and he says Don I want you to
give I'm going to send a kid over I want
you to give him
money he's one of the best employees l
logic ever ever had and um I and and so
the thing I learned is is
uh uh you you can make up a great
interview you could even have a bad
interview but you can't run away from
your past and so have a good past you
know try to have a good past and and and
in a lot of ways I was serious when I
said I was a good dishwasher I was
probably Denny's best
dishwasher um I I planned my work I was
organized you know I was Misan plus and
then I washed The Living Daylights out
of the dishes and then and then you know
they promoted me to bus I was certain
I'm the best bus boy Denny's ever had
you know I was I never left a station
with empty-handed I never came back
empty-handed I was very efficient and
then they and so anyways eventually I
became you know a CEO I'm working I'm
still working on being being a good CEO
but you talk about being the bad you
needed to be the best among 89 other
companies that were funded after you to
build the same thing and then with 6 to9
months of Runway left you realized that
the initial Vision was just not going to
work MH how did you decide what to do
next to save the company when the cards
were so stacked against
you well we started uh this company
called for Accelerated Computing and the
question is what is it for what's the
killer app and and uh that was that that
came our first great
decision um and this is what sequa
funded the first great decision was the
first killer app was going to be 3D
graphics and the the the technology was
going to be 3D graphics and the
application was going to be video games
at the Time 3D Graphics was impossible
to make cheap it was Million
dooll image generators from Silicon
graphics and the video and so it was a
million dollars and and it's hard to
make cheap um and the video game Market
was0 billion doar so you have this
incredible technology that's hard to uh
commoditize and commercialize and then
you have this Market that doesn't exist
that was that intersection was the
founding of our company and and I still
remember uh when when Don at the end of
my
presentation uh you know Don was still
kind of he he said you know know one of
the things he said to me which made a
lot of sense back then makes a lot of
sense today he says startups don't
invest in startups or startups don't
partner with startups and his point is
that in order for NVIDIA to succeed we
needed another startup to succeed and
that other startup was Electronic
Arts and and then on the way out he he
reminded me that electronic arts's
CTO is 14 years old and had to be driven
to work by his
mom and he just wanted to remind me that
that's who I'm relying on that that and
then and uh and then after that he said
if you lose my money I'll kill you and
that that was that was kind of my
memories of that first
meeting uh but nonetheless uh we created
we created something uh we went on uh
the next several years to go create the
market to create the gaming market for
PCs and it took a long time to do so
we're still doing it today uh we realize
that not only do you have to create the
technology and uh invent a new way of
doing computer Graphics so that what was
a million dollars is now you know three
400 $500 um that fits in the computer
and you have to go create this new
market so we have to create technology
create markets the idea that a company
would create technology create markets
defines Nvidia today almost everything
we do we create technology we create
markets that's that's the reason why
people say we have a you know people
call it a stack an ecosystem words like
that um but that's basically it at the
core for 30 years what Nvidia realized
we had to do is in order to uh create
the conditions by which somebody could
buy our products we had to go invent
this new market and uh it's the reason
why we were early in autonomous driving
it was the reason why we're early in
deep learning it was the reason why
we're early and just about all these
things including uh computational drug
disc drug design and and Discovery um
all these different areas we're trying
to create the market while we're
creating the technology and so that
that's um uh okay and then we got we got
going and and then and then um Microsoft
introduced uh a standard called direct
3D and that spawned off hundreds of
companies and we found ourselves a
couple years later competing with just
about everybody and and the thing that
that we invented the company the
technology we invented uh 3D graphics
with the consumerized 3D with turns out
to be incompatible with direct 3D so we
started this company we had this 3D
Graphics thing we million-dollar thing
we're trying to make it consumerized and
so we invented all this technology and
then shortly after it became
incompatible and um uh so we had to
reset the company uh or go out of
business but we didn't know how to we
didn't know how to build it the way that
Microsoft had defined it and um and I
remember I remember a meeting at at you
know on a weekend and the conversation
was you know we now have 89
competitors uh I understand that the way
we do it is not not right but we don't
know how to do it the right
way and and um thankfully there was
another bookstore and
um and the bookstore is called fries
Fries electronics I don't think I don't
know if it's still here um and so I had
I had I had um I I I think I drove madis
and my daughter on a weekend to fries
and and it was sitting right
there the openg manual uh which would
defined uh how silicon Graphics did
computer graphics and so it was it was
right there it was like $68 a book and
so I had a couple hundred dollar I
bought three books I took it back to the
office and I said guys I found it our
future and I handed out I had three
versions of it handed out had a big nice
centerfold you know the centerfold is
the opengl pipeline which is the
computer Graphics Pipeline and um uh and
I handed it to uh the same Geniuses that
I founded the company with and we
implemented the openg pipeline like
nobody had ever implemented the opengl
pipeline and we built something the
world never seen and so uh a lot of
lessons are right there that moment in
time for our company
uh gave us so much confidence and the
reason for that is you can
succeed in doing something inventing a
future even if you were not informed
about it at all and is kind of the my
attitude about everything now you know
when somebody tells me about something
and I've never heard of it before or if
I've heard of it never don't understand
how it works at all my first thought is
always you know how hard can it be
and it's probably just a textbook away
you know you're probably one archive
paper away from figuring this out and so
I spent a lot of time reading archive
papers and um and it it's true it's true
you can you can um now of course you
can't learn how somebody else does
something and do it exactly the same way
and hope to have a different outcome but
you could learn how something can be
done and then go back to First
principles and ask yourself um giving
the conditions today given my motivation
given the instruments the tools um given
you know how things have changed how
would I redo
this how would I reinvent this whole
thing how would I design a how would I
build a car today would I build it
incrementally from 1950s and 1900s how
would I build a computer today how would
I write software today does that make
sense and so I go back to First
principles all the time uh even in the
company today and just reset ourselves
you know because the world has changed
and U the way we wrote software in the
past was monolithic and it's designed
for supercomputers but now it's
disaggregated it's you know so on so
forth and how we think about software
today how we think about computers today
how we think just always cause your
company always cause yourself to go back
to first first principles and it creates
lots and lots of opportunities yeah the
way you applied this technology turns to
be revolutionary you get all the
momentum that you need to IPO and then
some more because you grow your Revenue
nine times in the next four years but in
the middle of all of this success you
decide to Pivot a little bit the focus
of innovation happening at Nvidia based
on a phone call you have with this
chemistry professor can you tell us
about that phone call and how you
connected the dots from what you heard
to where you
went uh remember at the core the company
was uh pioneering a new way of doing
Computing computer Graphics was the
first application uh but we already
always knew that there would be other
applications and so image processing
came particle physics came fluids came
so on so forth all kinds of interesting
things that we wanted to do uh we made
the processor more programmable so that
we could express more algorithms if you
will and then one day we invented um uh
programable shaders which made all forms
of Imaging and computer Graphics
programmable that was a great
breakthrough so we invented Ed that on
top of that we invented uh we we tried
to look for ways to express um uh more
comp more sophisticated algorithms uh
that could be computation that could be
computed on our processor which is very
different than a CPU and so we we
created this thing called CG this I
think it was 2003 or so C for
gpus it predated Cuda by about three
years um the same person who wrote The
textbook that saved the company Mark
Hilgard wrote that
textbook and um I and so CG was was
super cool we wrote textbooks about it
we started teaching people how to use it
we developed tools and such um and then
several several researchers discovered
it uh many of the researchers here
students here at Stanford was using it
um many of the the engineers that that
then became uh engineers at Nvidia were
were uh playing with it uh
uh a doctor a couple of doctors at at
Mass General picked it up and used it
for uh CT reconstruction so I flew out
and saw them and said you know what are
you guys doing with this thing and uh
they told me about that and then and
then uh a um uh a
computational a Quantum chemist uh used
it to um uh Express his his algorithms
and so I I realized that that there's
there's some evidence that people might
want to use this
uh and and it gave it gave us gave us
you know incrementally more more
confidence that that we ought to go do
this that that this field this form of
computing could solve problems that
normal computers really can't and and um
reinforced our belief and and kept us
going every time you heard something new
you really savored that surprise and
that seems to be a theme throughout your
leadership at Nvidia U it feels like you
make the these bets so far in advance of
Technology inflections that when the
Apple finally falls from the tree you're
standing right there in your black
leather jacket waiting to catch
it how do you find the conv always seems
like a diving catch oh it does seem like
a diving catch you do things based on
core beliefs you know we we uh we we
deeply believe that that we uh we
could create a computer that solves
problems Norm processing can't do that
there are limits to what a CPU can do
there are limits to what general purpose
Computing can do and then there are
interesting problems uh that we can go
solve the question the question is
always are those in interesting problems
only or are they can they also be
interesting markets because if they're
not interesting markets it's not
sustainable and Nvidia went through
about a decade where we were investing
in this future and the markets didn't
exist there was only One Market at the
time was computer Graphics uh for 10 15
years the markets that fuels Nvidia
today just didn't exist and so so how do
you
continue um uh with all of the people
around you you know our company and you
know nvidia's management team and all of
the amazing Engineers that they're
creating this future with me um all of
your shareholders your board of
directors all your partners you're
you're taking everybody with you and
there's no evidence uh of a market that
is really really challenging you know
the fact that the technology can solve
problems and the fact that you have
research papers that that are used that
that are made possible because of it are
interesting but you're always looking
for that market but nonetheless before a
market exists you still need early
indicators of future
success you know we we have this phrase
in the company is is you know there's a
phrase called key performance indicators
unfortunately kpis are hard to
understand I find kpis hard to
understand what's a good
kpi you know a lot of people you know
when when we look for kpis we go gross
margins that's not a kpi that's a
result you know you're looking for
something that's an early indicators of
future positive results okay and as
early as possible and the reason for
that is because you want early indic
that early sign that you're going in the
right direction
and so we have this phrase is called EO
ifs FS you know early indicators e FS
early indicators of future success and
and um and it helps people uh uh because
I was using it all the time to give the
company hope that hey look we solved
this problem we solved that problem we
solved this problem the markets didn't
exist but there were important problems
and that's what the company's about to
solve these problems uh we want to be
sustainable
and therefore the markets have to exist
at some point but you you want you want
to decouple the result from um uh from
evidence that you're doing the right
thing okay and so so so that's how you
that's how you kind of solve this
problem of investing into something
that's very very far away um and having
the the conviction uh to stay on the
road is to find as early as possible the
indicators that you're doing the right
things and so uh start with a core
belief unless something you know changes
your mind you continue to believe in it
and um look for early indicators of
future success what are some of those
early indicators that have been used by
product teams at
Nvidia uh all kinds
um uh uh I saw I saw I saw a uh a paper
uh long before I saw the paper I met
some people that needed my help on on um
uh on this thing called Deep learning at
a time I didn't even know what deep
learning Le was and um and they needed
us to create a domain specific language
so that um all of their algorithms could
be expressed easily on our on our
processors and we created this thing
called
cdnn and it's essentially the SQL um uh
SQL is in in storage Computing this is
um neuron network computing and uh we
created a a language if you will domain
specific language for that you know kind
of like the openg GL of of uh deep
learning and so we we uh they needed us
to do that so that they they could
express their mathematics and uh they
didn't understand Cuda but they
understood their deep learning and so we
created this thing in the middle for
them uh and the reason why we did it was
because uh even though there were zero I
mean this you know these researchers had
no money uh and and this is kind of one
of the the great skills of our company
that that you're willing to do something
even though the financial returns are
complet completely non-existent or maybe
very very far out even if you believed
in it uh we we ask ourselves you know is
this worthy work to do um does this
Advance a field of science somewhere
that matters notice this is something
that I I've been talking about you know
since the very beginning of time uh we
ex we we find inspiration uh not from
the size of a market from but from the
importance of the
work uh because the importance of the
work is the early indicators of a future
Market
and nobody has to write a nobody has to
do a a um a business case on it nobody
has to show me a a pnl uh nobody has to
show me a financial forecast the only
question is is this important work and
if we didn't do it uh would it happen
without us now if we didn't do something
and something could happen without us it
gives me tremendous Joy actually and the
reason for that is could you imagine the
world got better you didn't have to lift
a finger that's the definition of you
know of of uh ultimate laziness and and
and in a lot of ways in a lot of ways
you want that habit and the reason for
that is
this uh you want the company to be lazy
about doing things that other people
always do can do if somebody else can do
it let them do it we should go select
the things that if we didn't do it the
world the world would fall apart you
have to convince yourself of that that
if I don't do this it won't get
done that is Inc and and if that work is
hard and that work is impactful and
important then it gives you a sense of
purpose does that make sense and so our
company has been selecting these
projects deep learning was just one of
them and the first indicator of of the
success of that was this you know fuzzy
cat that that Andrew an came up with and
um then Alex
kvki uh detected cats um you know not
all the time but you know successfully
enough that it was you know this might
take us somewhere and then we reasoned
about the structure of deep learning and
you know we're computer scientists and
we understand how things work and and so
we we uh we convinced ourselves this
could change everything and and um and
anyhow that but that's an that's an
example so these selections that you've
made they've paid huge dividends both
literally and figuratively um but you've
had to steer the company through some
very challenging times like when it lost
80% of its market cap amid the financial
crisis cuz what Wall Street didn't
believe in your bet on ML um in times
like these how do you steer the company
and keep the employees motivated at the
task at hand uh it's this is the my
reaction during that time is the same
reaction I had about this week uh
earlier today you asked me about this
week my pulse was exactly the
same this week is no different than last
week or the week before that um and so
the opposite of that you know when you
drop it
80% um it don't get me
wrong when when your share price drops
80% it's a little embarrassing okay and
and um you just want to you just want to
wear a t-shirt that says wasn't my
fault
um but even more than that you just you
just don't want to you you don't want to
get out of your bed you don't want to
leave the house um all of that is true
all of that is true um but then you go
back to go back to just doing your job I
woke up at the same time I prioritize my
day in the same way uh I go back to what
do I believe uh you got to gut check
always gut check back to the court you
know what do you believe uh what are the
most important things uh and uh just
check them off you know sometimes
sometimes it's helpful to you know
family loves me okay check um you know
double you know right and so you just
got to check it off and and you go back
to your core um and then go back to work
and and then every conversations go back
to the core uh keep the company focused
back on the core do you believe in it
did something change the stock price
changed but did something else change
the physics change the gravity
change did did all of the things that
that that we assumed uh that we believed
that led to our decision did any of
those things change because if those
things change you got to change
everything but if none of those things
change you change nothing you keep on
going yeah yeah that's how you do
it in speaking with your employees they
say that you try to avoid the
public in speaking with your employees
they've said that your leadership
including the employees I'm just
kidding no le lead leaders have to be
seen unfortunately that's the hard
that's the hard part you know I I I was
I was I was at I was I was an electrical
engineering student and I was quite
Young when I went to school um when I
went to went to College I was I was
still 16 years old and so I was I was
young when I did everything and and so I
was a bit of an introvert kind of you
know I'm shy I don't enjoy public
speaking I'm delighted to be here I'm
not suggesting um but but it's it's not
something that I do naturally and and um
I and so so when when things are
challenging um uh it's not easy to be in
front of precisely the people that you
care most about
you know and the reason for that is
because could you imagine a company
meeting we just our stock prices dropped
by
80% and the most important thing I have
to do as the CEO is this to come and
face you explain it and partly you're
not sure why
partly you're not sure how long uh how
bad yeah you just don't know these
things and and but you still got to
explain it face face all these people
and you know what they're thinking you
know you you know some of them are
probably thinking we're doomed uh some
people are probably thinking you're an
idiot and some people are probably
thinking you know something else and so
I um there are a lot of things that
people are thinking and you know that
they're thinking those things but you
still have to get in front of them and
and and deal you know do the hard work
they may be thinking of those things but
yet not a single person of your
leadership team left during times like
this and in fact
unemployable
that's what I keep reminding them I'm
just kidding I'm surrounded by Geniuses
I'm surrounded by Geniuses yeah other
Geniuses un un unbelievable uh Nvidia is
well known to have singularly the best
management team on the planet this is
the deepest technology management team
the world's ever seen I'm surrounded by
a whole bunch of them and they're just
genius business teams marketing teams
sales teams just incredible and
engineering teams my research teams
unbelievable yeah your employees say
that your leadership style is very
engaged you have 50 direct reports you
encourage people across all parts of the
organization to send you the top five
things on their mind and you constantly
remind people that no task is beneath
you can you tell us why you've
purposefully designed such a flat
organization and how should we be
thinking about our organizations that we
designed in the
future uh no task is is to me no task is
beneath me because remember I used to be
a dishwasher and I and I mean that I
used to clean toilets I mean you know I
cleaned a lot of toilets I've cleaned
more toilets than all of you
combined and and some of them just can't
[Laughter]
unsee I don't know I I don't know what
to tell you you know that's life and and
so so uh uh you can't show me and you
can't show me a task that is that's
beneath me um now I'm not doing it I'm
not doing it uh only because because of
uh you know whether it's beneath me or
not beneath me U if you send me
something and you want my input on it
and I can be of service to you and in my
in my review of IT share with you how I
reason through it uh I've made a
contribution to you I've made I've made
it possible for you to see how I reason
through something and and by reasoning
as you know how someone reasons through
something empowers you you go oh my gosh
that's how you reason through something
like this it's not as complicated as it
seems this is how you reason through
something that's super ambiguous this is
how you reason through something that's
incalculable this is how you reason
through something that you know seems to
be very scary this is how you seem do
you understand and so I show people how
to reason through things all the
time strategy things you know how to
forecast something how to break a
problem down uh and you're just you're
empowering people all over the place and
so that's how I see it if you send me
something you want me to help review it
uh I'll do my best and I'll show you how
I would do it um I in the process of
doing that of course I learned a lot
from you is that right you gave me a
seat of a lot of information I learned a
lot and so I I feel rewarded by the
process um it does take a lot of energy
sometimes because you know you got in
order to add value to somebody and
they're incredibly smart as a starting
point and I'm surrounded by incredibly
smart people you have to at least get to
their plane you know you have to get
into their head space and that's really
hard that's really hard um and that
takes just an enormous amount of
emotional and intellectual energy and so
I feel exhausted after after I I work on
things like that um I'm surrounded by by
a lot of great people a CEO should have
the most direct report rep s um uh by
definition because the people that
reports to the CEO requires the least
amount of
management it makes no sense to me that
CEOs have so few people reporting to
them except for one fact that I know to
be true the the knowledge the
information of a CEO is supposedly so so
valuable so secretive you can only share
with two other people or
three and their information is so
invaluable so incredibly secretive that
they can only share with a couple
more well
um I don't believe in in in a culture an
environment where the information that
you possess is the reason why you have
power I would like us all to to to
contribute to the company and our
position in the company should have
something to do with our ability to
reason through complicated things lead
other people to um achieve greatness um
Inspire Empower other people um support
other people those are the reasons why
the the management team exists in
service of all of the other people that
work in the company to create the
conditions by which all of the all of
these amazing people who volunteer to
come work for you instead of all the
other amazing high-tech companies around
the world they elected they volunteer to
work for you and so you should create
the conditions by which they could do
their life's work which is
Mission you know you probably heard it
i' I've said that you know pretty
clearly and I and I believe that what my
job is is very simply to create the
conditions by which you could do your
life's work and so how do I do that what
does that condition look like well that
condition should um result in great deal
of empowerment you should you can only
be empowered if you understand the
circumstance isn't it right you have to
understand the cont you have to
understand the context of the situation
you're in in order for you to come up
with great ideas
and so I have to create a circumstance
where you understand the context which
means you have to be
informed and the best way to be informed
is for there to be as little layers
of information
mutilation right between us and so
that's the reason why it's very often
that I'm reasoning through things like
in an audience like this I say first of
all this is the beginning facts these
are the data that we have um this is how
I would reason through it these are some
of the assumptions these are some of the
unknowns these are some of the
knowns and so you reason through it and
now you've created an organization
that's highly empowered nvidia's 30,000
people we're the smallest large company
in the world we're tiny little company
but every employee is so empowered and
they're making smart decisions on my
behalf every single day and the reason
for that is because you know they
understand that they understand my
condition
they understand my condition I'm very
transparent with people um and uh and I
believe that that I can trust you with
the information often times the
information is hard to hear and uh the
the situations are complicated uh but I
trust that you can handle it you're you
know a lot of people hear me say you
know these you're adults here you can
handle this sometimes they're not really
adults they just
graduated I'm just kidding I know that
when I first graduated was barely an
adult and um I but I was I was fortunate
that I was trusted with with uh with uh
important information so I want to do
that I want to create the conditions for
people to do
that I do want to now address the topic
that is on everybody's mind AI last week
you said that generative Ai and
accelerated Computing have hit the
Tipping Point so as this technology
becomes more mainstream what are the
applications that you personally are
most excited about
well you have to go back to First
principles and ask yourself what is
generative AI what happened um what
happened was we have a we now have the
ability to have software that can
understand something they they can
understand why you know what is first of
all we digitized everything that was you
know like for example Gene sequencing
you digitized genes but what does it
mean that sequence of genes what does it
mean we've digitized amino acids um but
what does it mean uh and so we now have
the ability we dig digitize words we
digitize sounds uh we digitize images
and videos we digitize a lot of things
but what does it mean we now have the
ability through um a lot of study a lot
of Da data and from their patterns and
relationships we We Now understand what
they mean not only do we understand what
they mean we we can translate between
them because we learned about the
meaning of these things in the same
world we didn't learn about them
separately so we we learned about speech
and and words and and paragraphs and
vocabulary in the same context and so we
found correlations between them and
they're all you know registered if you
will registered to each other and so now
we uh not only do we understand uh the
modality the meaning of each modality we
can understand how to translate between
them and so uh for obvious things you
could caption video to text that's
captioning uh text to uh images M
Journey uh text to text chat GPT I
amazing things and so so we now we now
know that uh we understand meaning and
we can translate uh the translation of
something is generation of information
and and um uh and all of a sudden you
you have to take your you take a step
back and ask yourself um uh what is the
implication in every single layer of
everything that we do and so I'm
exercising in front of you I'm reasoning
in front of you uh the same thing I did
a quarter uh 15 years ago when I first
saw um uh alexnet some 13 14 years ago I
guess um I how I reasoned through it uh
what did I
see how interesting what can it
do very cool but then most importantly
what does it mean what does it mean what
does it mean to every single layer of
computing because you know we're in the
world of computing and so what it means
is that that the way that we um process
information fundamentally will be
different in the future that's what
Nvidia builds you know chips and system
the way we write software will be
fundamentally different in the future
the type of software we'll be able to
write write in the future will be
different new applications and then ALS
also the processing of those
applications will be different what was
historically a retrieval based model
where uh in uh information was pre
pre-recorded if you will almost you know
we wrote the text pre-recorded and we
retrieved it based on uh some
recommender system algorithm in the
future uh some seed of information will
be will be uh the starting point we call
them prompts you as you guys know and
then we generate the rest of it and so
the future of computing will be highly
generated well let me give you an
example of what's
happening for example uh we're having a
conversation right now very little of
the information I'm trans I'm conveying
to you is Retreat most of it is
generated it's called intelligence
and so in the future we're going to have
a lot more generative our computers will
will perform in that way it's going to
be highly generative instead of Highly
retrieval based you go back and you got
to ask yourself you know now for for you
know entrepreneurs you got to ask
yourself uh what industries will be
disrupted therefore will we think about
networking the same way will we think
about storage the same way will we think
about would we be as abusive of internet
traffic as we are today probably not
notice we're having a conversation right
now and and I to get in my car every
every
question so we don't have to be as
abusive of of transformation information
transporting as we used to um uh what's
going to be more what's going to be less
uh what kind of applications you know
etc etc so you can go through the entire
industrial spread and ask yourself
what's going to get disrupted what's
going to get be different what's going
to get NED you know so on so forth and
and that reasoning starts from what is
happening what is generative
AI Foundation Al what is happening go
back to First principles with all things
there was something I was going to tell
you about organization you asked the
question and I forgot to answer it the
way you create an organization by the
way someday um don't worry about how
other companies or charts look you start
from first principles remember what an
organization is designed to
do the organizations of the past where
there's a king you know
CE and then then you have all all these
you know the Royal subjects you know the
Royal Court and then eaff and then you
keep working your way down eventually
they're employees well the reason why it
was designed that way is because they
they wanted the employees to have as low
information as possible because their
fundamental purpose of the soldiers is
to die in the field of
battle to die without asking questions
you guys know
this I don't I only have 30,000
employees I would like them none of them
to die
I would like them to question everything
does that make sense and so the way you
organize in the past and the way you
organize today is very different to
Second the question is what is nid what
does Nvidia build an organization is
designed so that we could build what it
whatever it is we build
better and so if we all build different
things why why are we organized the same
way why would why would this
organizational Machinery be exactly the
same irrespective of what you build it
doesn't make make any sense you build
computers you organize this way you
build healthare Services you build
exactly the same way it makes no sense
whatsoever and so you had to go back to
First principles just ask yourself what
kind of Machinery what what is the input
what is the output what are the
properties of this environment you know
what what is the what is the what is the
forest that this animal has to live in
what is this characteristics is it
stable most of the time you're trying to
squeeze out the last drop of water or is
it changing all the time being attacked
by everybody and so you got to
understand you know you're the CEO your
job is to architect this company that's
my first job to create the conditions by
which you can do your life's work and
the architecture has to be right and so
you have to go back to First principles
and think about those things and I was
fortunate that that when I was 29 years
old you know I had the benefit of of of
taking a step back and asking myself you
know how would I build this company for
the future and what would it look like
and you know what's the operating system
which is called culture what do we what
kind of behavior do we en encourage
enhance and what what do we discourage
and not enhance you know so on so forth
and anyways I want to save time for
audience questions but um this year's
theme for view from the top is
redefining tomorrow and one question
we've asked all of our guests is Jensen
as the co-founder and CEO of Nvidia if
you were to close your eyes and
magically change one thing about
tomorrow what would it
be
were we supposed to think about this in
advance I I'm going to give you a
horrible answer
um I I don't know that it's one thing
look there are a lot of things we don't
control you know there are a lot of
things we don't control um your job is
to make a unique contribution live a
life of
purpose to do something that nobody else
in the world would do or can do to make
a unique contribution so that in the
event that after you done
um everybody says you know the world was
better because you were
here and so I think that that to me um I
live I live my life kind of like this I
go forward in time and I Look Backwards
so you asked me a question that's
exactly from a from a computer vision
pose perspective exactly the opposite of
how I think I never look forward from
where I am I go forward in time and look
backwards and the reason for that is
it's
easier I would look backwards and kind
of read my
history we did this and we did that way
and we broke that prom down does that
make sense and so it's a little bit like
um how you guys solve problems you
figure figure out what is the end result
that you're looking for and you work
backwards to achieve it and so I imagine
Nvidia uh making a unique contribution
to advancing the the future of of uh of
computing which is the single most
important instrument of all
Humanity now it's not about our self
self-importance but this is just what
we're good at and it's incredibly hard
to do and we believe we can make an
absolute unique contribution it's taken
US 31 years to be here and we're still
just beginning our journey and so this
is insanely hard to
do and uh uh When I Look Backwards I
believe that we made I believe that that
we're going to be remembered as a
company that kind of changed everything
not because we went out and changed
everything through all the things that
we said but because we did this one
thing that was insanely hard to do that
we're incredibly good at doing that we
loved doing we did for a long time I'm
part of the GSP lead I graduated in 2023
so my question is how do you see see
your company in the next decade as what
challenges do you see your company would
face and how you are positioned for that
first of all can I just tell you what
was going on through my head as you say
what
challenges the list that flew by my
head was so so large uh that that I was
trying to figure out what to
select um now the honest truth is is
that when you ask that question
most of the challenges that showed up
for me were technical
challenges and the reason for that is
because that was my
morning if you were to you know chosen
yesterday um it might have been Market
creation
challenges there are some markets that I
gosh I just desperately would love to
create I just can't we just do it
already you know but we can't do it
alone Nvidia is a technology platform
company we're here in service of a whole
bunch of other the companies so that
they could realize if you will our hopes
and dreams through
them and and so some of the things that
I would love I would love for the world
of biology to to be at a point where
it's kind of like the world of Chip
design 40 years ago computer AED and
design um Eda that entire industry
really made possible for us today and I
believe we're going to make possible for
them tomorrow
computer AED drug design because we're
able to now represent genes and proteins
and even cells now very very close to be
able to represent and understand the
meaning of a cell a combination of a
whole bunch of genes um what is a cell
mean it's kind of like what does that
paragraph mean well if we could
understand a a cell like we can
understand a paragraph imagine what we
could do and so uh so so I'm I'm anxious
for that to happen you know I'm kind of
excited about that uh there's some that
I'm just excited about that I know we
around the corner on for example uh
humanoid
robotics very very close around the
corner and the reason for that is
because if you can tokenize and
understand speech why can't you tokenize
and understand uh
manipulation and so so these kind of
computer science techniques you once you
figure something out you ask yourself
well if got do that why can't I do that
and so I'm excited about those kind of
things um and so that challenge is kind
of a happy
challenge uh some of the some of the
other challenges some of the other
challenges of course are industrial and
geopolitical and they're social and and
but you've heard all that stuff before
these are all true you know the social
issues in in the world uh the
geopolitical issues in the world uh why
can't we just get along uh things in the
world why do I have to say those kind of
things in the world um why do we have to
say those things and then amplify them
in the world uh why do we have to judge
people so much in the world uh you you
know all those things you guys all know
that I don't have to say those things
over again my name is Jose I'm a class
of the 2023 uh from the GSB my question
is uh are you worried at all about the
pace at which we're developing AI um and
do you believe that any sort of
Regulation might be needed thank you uh
yeah that's uh the answer is yes and no
um we need uh you you know that the the
the greatest breakthrough in uh modern
AI of course deep learning and it
enabled great progress but another
incredible breakthrough is something
that that humans know and we practice
all the time uh and we just invented it
for uh for language models called uh
grounding reinforcement learning human
feedback um I provide reinforcement
learning human feedback every day that's
my job um and their for their parents in
the room uh you're providing
reinforcement learning human feedback
all the time okay now we just figured
out how to do that um at a system
systematic level for artificial
intelligence there are a whole bunch of
other technology necessary to uh
guardrail uh
fine-tune ground for example how do I
generate um how do I generate uh uh uh
tokens that obey the laws of physics you
know right now things are floating in
space and doing things and they don't
they don't obey the laws of physics um
how do that requires technology Guard
railing requires technology fine-tuning
requires technology alignment requires
technology safety requires technology
the reason why planes are so safe is
because you know all of the autopilot
systems are are surrounded by diversity
and redundancy and all kinds of
different functional safety and active
safety systems that were
invented I need all of that to be
invented much much faster uh you also
know that that the border between
security and artificial intelligence
cyber security and artificial
intelligence is going to become blurry
and blurry we need technology to advance
very very quickly in the area of cyber
security in in order to protect us from
artificial intelligence and so so in a
lot of ways we need technology to go
faster a lot faster okay uh regulation
there's two types of Regulation uh
there's social regulation I don't know
what to do about that but there's
product and services regulation know
exactly what to do about that okay so um
the fa the FAA the FDA the uh Nitsa you
name it all the the fs and all the NS
and all the you know fcc's the they all
have regulations for products and
services that are have particular use
cases uh um uh bar exams and doctors and
you know so on so forth um you all have
uh qual qualification exams you all have
standards that you have to reach you all
have to uh continuously be certified uh
accountants and so on so forth whether
it's a product or a service there are
lots and lots of
regulations please do not add a super
regulation that cuts across of it the
regulator who is regulating accounting
should not be the regulator that
regulates a
doctor you know I love accountants um
but I I just you know if I ever need an
open heart surgery the fact that they
can close books is interesting but not
sufficient and so and so I I would like
I would like um all of those all of
those fields that already have products
and services um to also enhance their
regulation in context of in the context
of AI okay but I left out this one very
big one which is this the social
implication of AI and how do you how do
you deal with that I don't have great
answers for that um but you know enough
people are talking about it but it's
important to subdivide all of this into
chunks does that make sense so that we
don't we don't become super hyperfocused
on this one thing at the expense of a
whole bunch of routine things that we
could have done and as a result people
are getting killed by cars and planes
and you know it doesn't make any sense
we should make sure that we we do the
right things there okay very practical
things may I take one more question well
we have some rapid fire questions for
you as view from the tradition
okay I was trying to avoid
that okay all right far away far away
well your first job was at Denny's they
now have a booth dedicated to you what
was your fondest memory of working my
second job was AMD by the
way is there Booth dedicated to me there
I'm just
kidding um I'm I love my job there I did
I love there it's a great company yeah
yeah um if there were a worldwide
shortage of black leather jackets what
would we be see you
wearing oh no I've I've got a large
reservoir of black
jackets I'm the I'll be the only person
who is who is not
concerned um you spoke a lot about
textbooks if you had to write one what
would it be
called I wouldn't write
one you're asking me a hypothetical
question that has no possibility of of
of uh that's fair and finally if you
could share one parting piece of advice
to broadcast across Stanford what would
it
be uh it's not a word but but um I you
know have a core belief
um gut check it every
day I pursue it with all your
might pursue it for a very long
time surround yourself with people you
love and take them on that right so
that's the story of Nvidia Jensen this
last hour has been a treat thank you for
spending thank you very
much
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تصفح المزيد من مقاطع الفيديو ذات الصلة
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NVIDIA CEO Jensen Huang Reveals AI Future: "NIMS" Digital Humans, World Simulations & AI Factories.
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Sam Altman CEO of OpenAI | Podcast | In Good Company | Norges Bank Investment Management
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How To Make $100,000/Month Doing What You Love
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NVIDIA CEO Jensen Huang's 2024 EPIC Graduation Speech at Caltech
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Your HERO Started With LESS Than What You Have NOW - What's Your EXCUSE?!
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A Conversation with the Jensen Huang of Nvidia: Who Will Shape the Future of AI? (Full Interview)
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