一口气了解英伟达,芯片新王凭什么是他?
Summary
TLDRThe video traces Nvidia and CEO Jensen Huang's rise from graphics card company to AI powerhouse. It examines how Nvidia leveraged its GPUs for parallel computing, invested heavily in R&D, and developed CUDA software to make GPUs programmable. This enabled breakthroughs in AI. However, Nvidia owes much success to luck from the crypto mining boom. Now Nvidia dominates training of AI models. With AI's vast potential, tech giants aim to compete, but Nvidia's enduring monopoly may be hard to challenge.
Takeaways
- 😀 Nvidia started off by specializing in graphics processing chips and established itself as a leader in the gaming industry
- 🤓 Jensen Huang had the vision to make Nvidia's GPUs more versatile through the CUDA software platform
- 🔥 Cryptocurrency mining created huge demand for Nvidia's GPUs from 2018-2021
- 🧠 Nvidia GPUs proved enormously effective for AI model training after 2012, cementing Nvidia's dominance
- 💰 Nvidia's data center segment now generates over half its revenues due to AI demand
- 👷♂️ Chipmakers need to iterate very fast - Nvidia stays ahead through huge R&D investments
- 🏭 The chip industry has high barriers to entry and tends to concentrate power
- 🚘 Nvidia is expanding into self-driving car computers and other growth areas
- 🗺️ Major tech firms see Nvidia as a strategic asset and are wary of its dominance
- ❓ It remains to be seen if Nvidia can maintain its edge as the AI market evolves
Q & A
When was Nvidia founded and by whom?
-Nvidia was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem.
What breakthrough product established Nvidia as an industry leader in 1999?
-The GeForce256 graphics card established Nvidia as an industry leader in 1999. It was the first 'GPU' that was designed specifically for graphics processing.
How did the rise of Bitcoin mining benefit Nvidia?
-The rise in Bitcoin mining brought huge demand for mining hardware. Since graphics cards are very efficient for crypto mining, this greatly benefited Nvidia's graphics card business.
What is the significance of Nvidia's CUDA platform?
-CUDA made Nvidia GPUs programmable and expanded their use for general purpose computing beyond just graphics processing. This laid the foundation for using GPUs in AI.
Why did the AlexNet deep learning model represent an important breakthrough?
-The AlexNet model that won the 2012 ImageNet competition utilized GPUs for its neural network training. This showed the potential of GPU accelerated deep learning.
How has Nvidia tailored its GPU products for AI workloads?
-Nvidia has optimized its GPU architecture, created software frameworks like CUDA, and launched specialized products like the A100 GPU specifically for accelerating AI workloads.
What are some key moves Nvidia has made to expand beyond GPUs?
-Major moves include the acquisitions of Mellanox and ARM, investments in automotive tech, and initiatives in areas like Metaverse.
Why is it difficult to build an enduring competitive advantage in the chip industry?
-The rapid pace of innovation means advantages don't last long before new technologies emerge. Nvidia tries to stay ahead with heavy R&D spending and continuous new product introduction.
How are tech giants responding to Nvidia’s dominance in AI chips?
-Large tech firms like Google, Meta, and Microsoft are conducting their own AI chip research and development to reduce reliance on Nvidia GPUs.
What does Nvidia’s future success depend on in the face of rising competition?
-Nvidia's future hinges on sustaining technological leadership in GPUs via continuous R&D innovation, expanding into new high-growth markets like AI and automotive, and leveraging vertical integration synergies across hardware and software stack.
Outlines
😀 Early History of Nvidia and Jensen Huang
This paragraph provides background on Nvidia CEO Jensen Huang's early history, including being born in Taiwan, moving to the US at age 9, and working at semiconductor companies. It then discusses how Huang and two others started Nvidia in 1993 to focus on graphics processing chips, obtained venture capital funding, and struggled initially before finally finding success with the Riva 128 graphic card.
😲 The Graphics Card Duopoly - Nvidia vs AMD
This paragraph explains how through acquisitions and mergers, the graphics card market consolidated to just two players by the early 2000s - Nvidia and AMD. It discusses their continuing competition over the years, with Nvidia slowly dominating more market share and becoming the global GPU leader.
💰 Bitcoin Mining Drove Massive Demand for Nvidia GPUs
This paragraph highlights how the bitcoin/crypto mining boom created huge unexpected demand and revenue for Nvidia's GPUs due to their parallel computing capabilities being very suitable for the computations needed in mining. However, it notes mining was just a sideshow and not Nvidia's main business.
🤖 AI and Deep Learning Drove Next Wave of GPU Demand
This paragraph explains how Nvidia GPUs proved hugely beneficial for AI and deep learning due to their high parallel computation ability. It discusses Nvidia's strategic moves to optimize GPUs for AI and build an ecosystem around this, leading to dominance as the go-to provider of GPUs for AI industry leaders.
😎 Nvidia's Moat, Valuation, and Competition in the AI Chip Wars
This final paragraph analyzes Nvidia's moat, extremely high valuation, and competition from tech giants getting into AI chips. It discusses the high R&D investments needed to compete in rapidly iterating chip markets, and questions whether Nvidia can maintain its leadership in the AI industry battles ahead.
Mindmap
Keywords
💡GPU
💡CUDA
💡AI training
💡Moore's Law
💡R&D investment
💡Acquisitions
💡First mover advantage
💡Mining
💡Cloud
💡Competition
Highlights
Nvidia released their first quarter earnings, stock soared by 30% on the day, Market cap reached trillion of dollars, propelled Nvidia into becoming sixth-largest company in the world
Nvidia participated in almost all of global tech innovation, cloud computing, cryptocurrency, Metaverse, Artificial Intelligence, Nvidia is main player in all these
Nvidia A100 graphics cards, an indicator to measure a company’s computational power
Sounds arrogant right, But that’s the truth, With his foresight from over 20 years, and his unchanging outfit style, becomes the Godfather of AI
Nvidia is gradually, eating away AMD’s market share, from 60% in 2010, slowly expanded to , 80% in 2022., becoming global GPU hegemon
Thanks to NVIDIA’s graphics card, the theory of neural network has been able to realise, It made a sensation in the academic world
There is a consensus in the field of AI, If you want to do AI, then no doubt, you have to buy NVIDIA’s graphics card
This makes NVIDIA’s graphics card, in constant short supply for years, NVIDIA was very nice, they designed a GPU specifically for mining
AI, could be so popular in 2023 right, In fact, it is not AI that first makes the, accelerated computing capabilities of graphics cards, realise its commercial value.
Data center occupies 56% of their business, Gaming dropped to 33%, The earning report released on 24th May, their revenue dropped significantly due, to sluggish global demand in gaming
Your estimation is incorrect, Our revenue is $11 billion, 50% more
Cathode Wood, liquidated NVIDIA stocks, in January through her ETF , Lately NVIDIA stock price is rising, she was reviled in the, investment circle
There are some professional institutions, dare not to not invest in NVIDIA , because AI is the biggest wave, with biggest opportunity in the market,
Those who understand economy would know that, if you sell shovel during gold rush, could you be making a fortune? The marginal profit would quickly be eaten up,
Have you ever thought why?, We’ll get deep into the, feature of chip industry,
Transcripts
On May 24,
Nvidia released their first quarter earnings
Many investors are calling this an unprecedented
once in a lifetime release
Nvidia relied on
the dazzling data
giving Wall Street a slap in the face
The stock soared by 30% on the day
Market cap reached trillion of dollars
propelled Nvidia into becoming sixth-largest company in the world
surpassing Tesla
and approaching Amazon
Who would have thought that
a company selling graphics cards
would become the biggest winner
in the AI war in 2023
For the past few years
Nvidia participated in almost all of global tech innovation
cloud computing
cryptocurrency
Metaverse
Artificial Intelligence
Nvidia is main player in all these
Majority of the AI model you’ve heard of
are trained with Nvidia graphics cards.
Not only are they an industry leader
but they monopolised global AI training industry
by occupying 95% market share
Even the quantity of owning
Nvidia A100 graphics cards
an indicator to measure a company’s computational power
The founder, Jensen Huang said
Sounds arrogant right
But that’s the truth
With his foresight from over 20 years
and his unchanging outfit style
becomes the Godfather of AI
You must be wondering
what’s so great about Nvidia
How did they monopolise?
Why nobody can compete against them
Today let Lin take you down a trip
to the story of Nvidia
We’ll also talk about the secret behind
graphic card and chip industry
Let’s begin with the rise of
Jensen Huang and Nvidia
In 1963
Jensen Huang was born in Tainan, Taiwan
which means he is 60 years old this year
At the age of 9, he moved to US
after graduating from college
he worked in two semiconductor companies
focusing on chip design
one of them is AMD
a company that fought with Jensen
for half of their lifetime
until now
After finishing his Master at Stanford, Jensen turned 30
With two other tech guys who were
also in their 30s with big ambition
they planned to do something big
They believed that 3D graphics processing
has great potential in the future
So in 1993
they established NVIDIA
specialises in graphic processing chip
Jensen Huang is the CEO.
he still is today
With recommendation from his former boss
he obtained $20 million capital investment
from Sequoia Capital
After all gaming industry needs real-time rendering when playing
You can’t say there’s no 3D games
but definitely can’t play with normal computer
Many games are quite classic
but the graphic
is basically, well as long as you can see a figure there
because this kind of 3D image processing
is computationally intensive
It was difficult for the CPU at that time
Normally they need a specific chip
to process the graphics
and this chip is graphic card
In the early days, the graphics card was very simple
it was a 3D accelerator card at most
Now when you hear 3D accelerator
It sounds like a small workshop business
That's right
actually at that time 3D games and
3D rendering
was at seeding stage
Graphics card companies like Nvidia
are actually a lot
at least 50-60 companies
There was no uniform standard for both hardware and software
Whoever comes out with better research
can publish their own standard.
Often times
when you finally came up with a graphics card
but it actually is
not compatible with other people’s standard
Just one word
Chaotic
One of the most famous company at the time
is a company called 3dfx
It was established in 1994,
a year later than Nvidia
At that time, a graphics card called Voodoo
was all the rage
Many popular games during that time
relied on Voodoo graphic card
It wasn’t going so well with NVIDIA
Although they obtained capitals
and had very professional team
but their NV1 was not successful
NV2 was aborted
By 1997
NVIDIA was hanging on by a thread
9 more months till they run out of money
The company downsized its staff from 100
till there were about 30 people left
Jensen Huang took a gamble
Just when the company only had
6 months of operating capitals
They released Riva128 graphic card carrying NV3
With its good price/performance ratio,
they finally occupy a place in the market
and allow NVIDIA to survive
Actually Jensen and his team
are very strong in their R&D
After they figure out
the market direction
they managed to enter fast lane quickly
They reached a long-term strategic cooperation with TSMC
At the same time they cooperate closely with Microsoft
supported the Direct 3D display standard introduced by Microsoft
Finally they rose in the sea
of competition in graphics card industry
With RivaTNT
it helped
NVIDIA to become
industry leader in graphics card industry
In 1999, NVIDIA
successfully listed on NASDAQ
After listing
NVIDIA had more money and
in September 1999, they released
the epoch-making GeForce256
which made them the leading force among their competitors
I believe that gamers
should be familiar with
this GeForce series.
It has also become Nvidia’s
the flagship line of consumer graphics card
Jensen Huang named GeForce256
the world’s first GPU
The first truly dedicated graphics card
The claim is
basically accepted by everyone
Hence some people might generally claimed that
Nvidia invented the graphics card.
As for why this dedicated graphics card is so powerful,
we will talk about it later.
At that time, Microsoft happened to be working on Xbox
With GeForce 256
powerful performance, NVIDIA managed to
score $200 million worth of order
After building image processing hardware for Xbox
they then score another with Sony’s PS3
From 1999 to 2002,
Nvidia’s revenue
almost doubled every year to
$2 billion
becoming the only player in the market
They started to acquire
competitors in the same industry
one of them we mentioned earlier
the once popular 3dfx
Another major player in the market
ATi was acquired by AMD
And so in the early 2000s
after a series of merger and acquisition
happening in the market
There are only two players left in the market
Nvidia and AMD.
Until now, the dedicated graphics card market
has been dominated by these two companies.
I don’t know if you have heard of
the legendary N card and A card
actually refer to the graphics cards of these two companies.
Gamers started to argue
whether N card or A card is better
It doesn’t matter which one is better
after that there isn’t a
third company exists in the market
like B card of C card or X card
It’s a two-horse race
However NVIDIA is gradually
eating away AMD’s market share
from 60% in 2010
slowly expanded to
80% in 2022.
becoming global GPU hegemon
The speed of development of GPU technology itself
is also jaw-dropping
The rapid development of gaming industry
has supported Nvidia,
At the same time, NVIDIA’s graphics card development
has promoted the development of the gaming industry.
Look at the new games coming out every year
so much improvement in image quality
Even if you don’t understand game
you can see the speed of progress
In fact, the overall graphics card market
is in a tripartite state
Three biggest player – INTEL, NVIDIA, AMD
Intel occupied 71% of market share
Nvidia is 17% and AMD is 12%.
You must be wondering
why is there Intel
and their market share is way higher
Didn’t you just say that Nvidia is the biggest player?
Actually
this graphics card is not the same graphics card
Because graphics card
is divided into dedicated and integrated graphics card
If you were to compare both
then Intel indeed is the biggest player
but they sell mostly integrated graphics card
Integrated graphics card is placed together with CPU
they share memory
So Intel taking advantage of their position in CPU industry
monopolise the integrated graphics card market share
However the integrated graphics card
is quite weak, I won’t explain in details here
Comparing with NVIDIA’s dedicated graphics card
although they are both graphics card but they don’t belong in same market
Just from dedicated graphics market POV
NVIDIA occupied 80% of the market share
Some of you might start to get bored now
Alright we know
NVIDIA designs graphics card and chips
they are very good at it
I’ve been talking a lot about
3D rendering and gaming
How does it have anything to do with AI
Why are all these AI companies want to buy graphics card
and it has to be NVIDIA’s graphics card
Don’t worry, we’ll talk about graphics card characteristic
in a computer, Central Processing Unit
The purpose of its design is that
it can do everything
It’s sequential computing
and it can carry out very complex logical reasoning
However image processing
doesn’t care much about sequential computing
It’s more concerned with computational volume
For example, a 4k video
has 10 million pixels
Let’s say there’s 30 frames per second
then each pixel and frame
has to compute correspond colour
based on
shadow and action
This requires non-stop, very fast
and massive simple calculations
is Graphics Processing Unit
It’s especially designed to
do this kind of computation
The foundation of the chip design
is to optimise parallel computing
So for CPU
is 64 or 128 core at best
while GPU could have thousands of core
computing together at the same time
See this video is giving
a very good explanation
CPU is like a very precise
very strong gun firing one shot at a time
The shots are fired in clear order
but slow,
GPU on the other hand
is like having thousands of this gun
firing at the same time
Due to GPU special feature
Jensen Huang started to think about
how to maximise
its potential
It’s definitely as simple as 3D image processing
and rendering
Can they carry out
more of General Purpose Computing
General Purpose Computing
General Purpose Graphics Processing
But it’s not simple to use GPU
to do this kind of
general purpose computing
Because the purpose of its design is not for this
so the programming is very difficult
Not anyone can do this job
Jensen Huang was thinking that
if graphics card was to realise its greater potential
it needs to be programmable
By chance
he saw a project by a
PhD student in Stanford
Using C language programming
to let GPU do some computing
Jensen thought this idea is amazing
and offered this fella a job at NVIDIA
He appointed him with a very important job
and let him lead the team to carry out R&D
in making GPU programmable
Finally in 2006
NVIDIA officially released CUDA
making GPU programmable
In order to build this CUDA system,
NVIDIA invested a large and
unreasonable amount of
capitals and human resources into it
Originally those graphics card that
to support CUDA
Originally those graphics card that
specializes in 3D graphics processing
need many top engineers to
make it programmable
But now anyone can do it by
buying an NVIDIA graphics card
and use it with
CUDA library
Through CUDA
NVIDIA expanded the boundary of graphics card
from gaming and 3D image processing
to a whole realm of accelerated computing
Like aerospace, biopharmaceuticals
Weather forecasting, energy exploration and so on
are actually using large amount of NVIDIA graphics card
to carry out computation
Many have tried to create
software like CUDA
to challenge Nvidia’s position.
But Nvidia
has a monopoly on hardware itself.
They can try everything
to merge their hardware, graphics card
with the software, CUDA
and make them work really well with each other
Through hardware and software merger
they formed a very strong moat
Does this make you think of another company?
That’s Apple
They all build something
that’s had been bandied about in business world
an Ecosystem
Microsoft, Adobe
they all have their own strong ecosystem
The amount of capital Jensen invested in CUDA
might sound reasonable to you now
It makes sense
but if you look at short-term return
It is very unreasonable.
Wall Street is quite displeased with this thing
because although the computation performance of GPU
is outstanding
but their application is not much
For a long period of time
it can only focus on area
that requires massive computing
To put it bluntly, it's not profitable at all
Who would’ve thought that AI
could be so popular in 2023 right
In fact, it is not AI that first makes the
accelerated computing capabilities of graphics cards
realise its commercial value.
It was a coincidence
A trend that’s not related to AI at all
Something that even someone like Jensen
could not have expected
The explosion of Bitcoin
brought a huge demand for mining
Mining in essence is mindless computing
to encrypt and decrypt
In order to do mining
in faster way then you will need to use graphics card
and you have to use NVIDIA’s graphics card
I believe most of you
only know about using graphics card to do
computation from the mining part
The huge demand for mining
is like a godsend for NVIDIA
a big big gift
This makes NVIDIA’s graphics card
in constant short supply for years
NVIDIA was very nice
they designed a GPU specifically for mining
Of course many would say
Mining pollutes the environment,
meaningless computation
all sorts of problems
Graphics card capability
was seen by many
and NVIDIA did make lots of money because of it
According to analysts
Between 2018 to 2021
a period when bitcoin was very popular
NVIDIA could earn up to
$1 to $3 billion annually
NVIDIA’s market cap even
surpassed the giant
Intel
During the time when bitcoin was very popular
their market cap even approached trillion dollars
Although mining has made NVIDIA a lot of money
but it is after all not its main business
After the crash of crypto market
NVIDIA’s stock plunged by 46%
So far mining is only
a sideshow at best
We all know
what’s really been helping NVIDIA lately
Artificial Intelligence
We mentioned that graphics card
massive parallel computing capability
is very suitable for deep learning and machine learning
AI
has to keep learning
up to billions of times
So GPU parallel computing capability
is where it fits in
Jensen Huang showed that
to train a large language model
in comparison with CPU
GPU server
can complete it at 4% of the cost
and 1.2% of power.
Therefore, GPU CPU
are not on the same level
This is determined by its underlying structure.
Jensen Huang
just wanted to subtly tell you that
to train large language model
only a fool would use CPU
You have to use GPU
If you use GPU, you’d better use CUDA.
Then you have to buy the graphics card from NVIDIA
Actually a decade ago
no one knows graphics card can be used in AI
AI itself
is more theoretical than practical
The change occurred in 2012.
At that time, there’s a
very famous computer competition
called ImageNet.
Everyone was competing
whose algorithm could better
recognise the content of the image
From No.2 to No.4
their error rate
is about 26% to 29%.
A team called AlexNet
made it to 16.4%
Ten points ahead of the second place
and won the comepetition
they used neural networks to train their models
with NVIDIA’s graphics card
We’ve talked about this in ChatGPT episode
the theory of neural network has been around for a long time
but it had not been realised
the problem was with computational power
Thanks to NVIDIA’s graphics card
the theory of neural network has been able to realise
It made a sensation in the academic world
Jensen is also very serious about
GPU’s application in AI
They went all in after 2012
and let NVIDIA’s graphics card
do accelerated calculations easily and conveniently
Apart from investment focused on CUDA
there are also optimisation on AI for graphics card
including software support
platform support and etc
Later there is a consensus in the field of AI
If you want to do AI
then no doubt
you have to buy NVIDIA’s graphics card
Google, Amazon, Microsoft, Baidu
all used NVIDIA’s graphics card
to train the models
The most famous one in this wave is
NVIDIA’s A100
ChatGPT was trained with over 10,000 of
graphics cards.
A100 has also
become the standard for training large model
For major AI companies
if they couldn’t come out with significant result
but still want to catch the wave
What do they do
They fight in owning graphics card
They’ll tell you
how many NVIDIA’s
A100 graphics card they bought
ranging from thousands to tens of thousands
So this caused NVIDIA’s graphics card
to be in short supply for a long time
Their price went up to ten of thousands of dollars
Last year NVIDIA released
upgraded version of A100, H100
It has four to six times the performance of the A100
So don’t think that
NVIDIA is just in luck
just because they soar the highest
They spent a decade
and a lot of money and effort
to build their own wings
Just waiting for the right wind
Now that the wings are completed
the wind has come
NVIDIA definitely will want to
fly high
This is A100 system board released by
Jensen Huang 3 years ago
a big guy over 20kg
became the world’s largest GPU
Looks intimidating
This year he released another thing
DGX GH200 supercomputer.
The size is in 1:1 ratio with the actual thing
They use 240km of optic cable
is kilometre
that’s 240,000 metres
Its weigh is equal to 4 adult elephants
with internal memory of 144TB
This GPU is connected using
technology like NVLink, NVSwitch
And these 4 elephants
is actually a mega graphics card
it is specifically used for AI computation
It's expected to be ready by the end of the year
Google Cloud, Meta, Microsoft
are the first to access it
With such big first-mover advantage
NVIDIA is not limited to making
only graphics card and chip design
In 2019, they spent $6.9 billion
to acquire an Israeli chip company
called Mellanox
and came up with something called DPU
This thing is very powerful too
Jensen Huang said that
this acquisition is the most
successful strategic decision he has ever made
NVIDIA has started to merge
GPU, CPU and DPU together
and created
a server with incredible computing power
Supercomputer
is slowly creeping towards CPU market
and launched a variety of products
The eighth supercomputer in the world
DGX BasePOD
Accelerated superchip
GPU Grace Hopper
computing platform
BlueField-3 DPU and so on
Good names right
but you probably don’t understand
In short, Nvidia
covers everything
from chips to supercomputers
they’ve got it all covered
What if you can’t afford it
or don’t want to buy so many hardware?
It’s okay
NVIDIA can lease it to you
just get connected online
and use NVIDIA’s server
This is AI cloud business
It provides services to end user
as well as upstream companies
They came up with a software called cuLitho
to help TSMC, ASML
these upstream chip makers
Improve the performance of Inverse lithography technology by up to 40 times
By the way,
I have a Spanish friend who told me
that the name cuLitho
in Spanish sounds like
a booty
One word
Incredible
See, NVIDIA expanding across hardware,
software, services and etc
becoming the biggest winner in this AI wave
US sanctions towards China
bans the sale of A100 and H100
to China.
The impact on Nvidia is actually quite big.
China occupies a quarter of
NVIDIA market
They don’t want the cake
fly away just because of US government
When a reporter interviewed
Jensen Huang asking him
how big is the impact of this for NVIDIA
His answer is impermeable
First we will definitely work closely
with US government’s policy
Express their position first
At the same time we are trying
within the rules
to satisfy the demand
of Chinese consumers
He offends neither side
NVIDIA released
A800 graphics card
bypass a bunch of sensitive technology that has been sanctioned
This is made especially just for China
Alright
we understand the entire background of Nvidia,
it’s very clear now when you look at their earnings report
Currently they divided their business into 4 segments
Gaming, data centre
automotive, professional graphics processing
Previously NVIDIA’s ace business was gaming
and data center is about accelerated computing
lAI, Cloud service and etc
are all in this segment
These two segments are the main ones
In 2018,
Gaming occupied half of their business
Data center is a quarter
by 2022,
Data center occupies 56% of their business
Gaming dropped to 33%
The earning report released on 24th May
their revenue dropped significantly due
to sluggish global demand in gaming
This is actually what Wall Street expected.
However data center segment
is strong
18% growth from previous quarter
Most importantly the revenue in second quarter
blinded Wall Street
Wall Street predicted that
their revenue for Q2 would be $7.2 billion
but NVIDIA came out and said
Your estimation is incorrect
Our revenue is $11 billion, 50% more
For automotive segment
it is a segment with great potential
Not only have to do chip for cars
also need to create car system
Another big cake
But this is at initial stage
It’s difficult to say what’s going to happen in future
So we’ll not discuss it
Before this NVIDIA released Omniverse
to bet on Metaverse
Although we haven’t seen much return yet
but I’ve watched the promotional video
It really is quite cool
If Metaverse ushered in explosive period
Then NVIDIA will again be on of the biggest winner
It’s definite
Anyway, the general picture is that
gaming needs graphics card
mining needs graphics card
AI computing needs graphics card
Need graphics card look for NVIDIA
NVIDIA stock price
rose by over 1000 fold
from its initial IPO
becoming the sixth largest company in the world
And it looks like it's just getting started
From the stories earlier
you would’ve believe Jensen is almost godlike
Every step is so precise.
but he actually made a lot of mistakes along the way
For the continuity of the story
I ignored it
In the early 2000s
There was a series of graphics card failures
almost defeated by ATI
there were also insider trading committed by employees
SEC did a thorough investigation on them
There was also problem with over-marketing
They even tried to enter mobile phone chip market
but it was all a failure
Looking at their stock price
Although it has risen so much as a whole
but in 2002 it dropped by 90%
2008 dropped 80%
with more than 50% of retracement
This occurs every 3 to 5 years
Everyone knows
Jensen is great
but he is no fortune-teller
His obsession with leather jacket
I don’t know what else to say
Alright
Let’s talk a little bit about NVIDIA’s stock price
I’m worried that many would rush
to buy
NVIDIA’s stock after this video
I wouldn’t say you cannot buy
but just don’t go buy it
on a whim
The estimated value for NVIDIA
is too high from every perspectives
Their Price to Earning ratio is over 200
Price to Sales ratio is 38
For Apple, Microsoft and Google
their Price to Earning is less than 40
Tesla is only over 70
Compare them with those in same industry
Their revenue
is less than half of Intel’s
but market cap is 7 times more than Intel
revenue is similar to AMD
but market cap is 5 times more than AMD
One word
Expensive
Of course they are expensive for a reason
For a company in an industry like this
moreover it’s a monopolistic company
Its valuation is no longer one of the
main criteria for judging stock price
One of the main reason
do you know what it is?
There are some professional institutions
dare not to not invest in NVIDIA
because AI is the biggest wave
with biggest opportunity in the market
And NVIDIA is the biggest player
in this AI wave
When these funds invested in NVIDIA
even if the stock price fall
investors wouldn’t complain much
but if NVIDIA stock price keeps rising
and they didn’t invest because the price is expensive
then investors would really be pissed
Cathie Wood
liquidated NVIDIA stocks
in January through her ETF
Lately NVIDIA stock price is rising
she was reviled in the
investment circle
Choosing to not invest in this type of company
would risk fund their reputation
There’s a term in English
that best describe this behaviour
It is FoMO
Fear of Missing Out
They fear losing the potential rise
more than buying at expensive price
This is similar to Tesla two years ago
EV has great future
and Tesla was
the only option
even if its valuation
is astronomical compare to
other automotive companies
but funds still continued to buy
The behaviour of FoMo
in turn pushes the stock price
of these companies to an even higher position
You can say that
for these type of companies
they just have huge risk premium
but just because of this
you can’t say that their stock price is too high
That's not necessarily true
Because it they can sustain this momentum
and maintain this development trend
Then their stock price will continue to rise
There's a metaphor that many people often use
The wave we mentioned
is similar to gold rush
It’s hard to bet where the gold is
or who will find the gold
but there is a sure-fire deal
and that is you can sell shovels
Nvidia
is like selling a shovel at digital age
at AI age
This metaphor sounds right
and quite reasonable
But whenever I hear this analogy
it doesn’t seems right
Those who understand economy would know that
if you sell shovel during gold rush
could you be making a fortune?
The first one would perhaps earn a little
but if more people are selling it
and it’s easy to make shovel
then the marginal profit would quickly be eaten up
so in most industries
if you sell this so-called shovel
the upstream production tool
Due to its low entry barrier
the competition in the industry will be very fierce
so the profit margin will be low
However for NVIDIA
they can achieve a monopoly and a market value
of trillions by doing this so-called shovel
Its similar to TSMC
more basic than shovel, they make hammer
a tool to make shovel
And they can monopolise that
Have you ever thought why?
We’ll get deep into the
feature of chip industry
Have you heard of Moore’s Law?
On an integrated circuit
the number of transistors that can be accommodated
double up every 18 months
You can understand it as
The speed of chip can be faster every 18 months
Actually for the past few years
The speed of CPU evolution
is difficult to catch up with Moore’s law
However Jensen found that
the advancement of GPU dedicated graphics card
is faster than prediction of Moore’s Law
Performance increase more than triples every 2 years
This pattern even has
its own term
Huang's Law
No matter which law it is
This is a distinctive feature
in chip industry
The initial cost to invest is very high
and requires a lot of talents and equipment
The problem is that
the iteration rate is too fast
This is an industry that is constantly running
the iteration rate of this industry is too fast
From business perspective
this pose a tricky problem
it’s difficult for company to build their own moat
Moat is very important
to a company
For a traditional company
if they spent a lot of money to build a factory, a railroad
then they have large-scale advantage
and cost advantage
These advantages work as a moat
This moat will protect the company for a long time
The moat of internet company is stronger
Once a network effect is established
Wechat, Tiktok, Facebook
users are your strong moat.
For chip industry
there are Moore’s Law, Huang’s Law
No matter how good of a chip you release today
it’ll be outdated in two years
You won’t know who’ll make it next year
and take you down with his new technology
In the 90s
during the graphics card war
A company established less than two years
3DFX
quickly became the industry standard
with their Voodoo graphics card
5 years later they became dreary
and was acquired by NVIDIA
This is because their moat
hasn’t finished building and was destroyed
In this industry, it’s difficult to rely on a single product
or a single technology
to form a moat that lasts more than 2 years
Of course you can slowly accumulate many patents
to defend yourself
but the truth is
people will always find a way
to get around those patents
it doesn’t provide much protection
You have to keep running
and run faster than everyone
This running ability is your moat
In chip industry
the moat is R&D
You have to develop a complete set of
talents, facilities, organisation structure
These form the moat for chip company
This is why R&D is expensive
First is the initial cost is very high
requires a lot of talents
most importantly you have to continuously
keep the iteration and running speed high
Not a lot of people can stand it
Those tech giants are
actually very focused on technological innovation
Amazon, Google, Microsoft
their R&D investment is 10% to 15%
of their revenue
For NVIDIA
they have to keep invest around
25% of their revenue for R&D
The Huang’s Law
is not a natural phenomenon
It is the result of
a rat race he created
If we were to tell a story
we can say NVIDIA
has been monopolising graphics card market since 2006
end in just one sentence
However for many times
they overturned
their own framework and technology
They pushed themselves to release
new generation of chip every 6 months
For example their latest ray tracing RTX technology
completely overturned
the program accumulated before
They use deep learning method
through 1 pixel
they guess what the other 8 pixels surround it looks like
to speed up image processing.
This intense running speed
makes only those who run fastest
with largest pool of talent
and wealthiest companies
can make money.
The rest could only run behind
This is why we often see that
after a war in chip industry
it always ends in acquisition
It's not just the difficulty of developing chips
it’s mainly because it’s not economical
Even those tech giants
with plentiful talents
won't set foot in the chip industry
unless they are forced to
But things are different now
The AI field is one of the biggest possible
battleground for the tech giants
In this battle, graphics card is too important
after all it determines computing speed
Non of them wish to be
strangled by NVIDIA
during AI era right
Although currently they are
NVIDIA’s biggest customer
but at the same time they delve into
R&D and chip making in full swing
Google already developed a chip
specifically designed for AI training called TPU
According to them,
it is more efficient than Nvidia's graphics card.
In 2017, Meta already used
over 20,000 NVIDIA's graphics cards
for AI training
but now they are also
all in in researching chip, calling it MTIA
Actually from 2020
NVIDIA has been actively seeking
Masayoshi Son’s Softbank
to acquire their chip company ARM
at the price of $40 billion
But Qualcomm, Microsoft and Google
are all strongly against it
The deal fell through
From here you can see that those tech giants
are actually very afraid of Nvidia.
Emerging from the early days graphics card industry
is his ability
Having laid out CUDA since 20 years ago
is his vision
Continuous monopoly in graphics card industry
is his endurance
Meeting the demand for massive computing power in mining
is his luck
The unchanged fashion style
is his devotion
When long accumulation period
meets the AI wave
NVIDIA naturally obtain
first-mover advantage
Facing new vast potential market
and competitors looking to get in on the action
Can NVIDIA fight their
way out again in this
new wave of AI fight?
It’s hard to say
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