一口气了解英伟达，芯片新王凭什么是他？

Summary

The 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

00:00

😀 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.

05:02

😲 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.

10:03

💰 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.

15:08

🤖 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.

20:10

😎 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.

Keywords

💡GPU

GPU stands for Graphics Processing Unit. It is a specialized electronic circuit designed to rapidly process and alter memory to accelerate the creation of images in a frame buffer for output to a display. GPUs are used extensively in deep learning and AI training because they can handle the massive parallel computation required. The video explains how Nvidia focused on GPU technology and innovation to position itself as a leader in AI.

💡CUDA

CUDA is a parallel computing platform and API model created by Nvidia that allows software developers to use GPUs for general purpose processing. The video highlights how Nvidia invested heavily in developing CUDA over many years to make GPUs more flexible, programmable, and useful beyond just graphics rendering. This opened up new commercial applications and was pivotal in enabling GPU-accelerated AI training.

💡AI training

AI training refers to the compute-intensive process of developing, optimizing and enhancing artificial intelligence models, usually based on neural networks. The emergence of AI and growth in model sizes created massive demand for parallel processing power that only GPUs could provide cost-effectively. Nvidia captured this opportunity early which fueled its rise.

💡Moore's Law

Moore's Law predicts that the number of transistors on an integrated circuit doubles about every 18-24 months. This drives exponentially increasing computing power over time. However, the video explains that GPU performance has advanced even faster than Moore's Law, dubbed Huang's Law. This rapid innovation cycle creates huge incumbency advantages.

💡R&D investment

The video emphasizes that continuous, heavy R&D spending is crucial in the chip industry to keep pace with the rate of innovation. While tech giants invest 10-15% in R&D, Nvidia spends about 25% to maintain its leadership. This intense focus on running faster than rivals forms Nvidia's core moat.

💡Acquisitions

Acquisitions, like Nvidia's takeover of 3dfx and Mellanox, allow rapid absorption of outside technology and talent. This is key to sustaining momentum in the fast-changing chip industry. The failed bid to acquire ARM also shows how wary rivals are of moves that expand Nvidia's ecosystem.

💡First mover advantage

By recognizing the potential of GPU computing early, investing in CUDA for years without immediate payoff, and building an ecosystem spanning hardware, software and platforms, Nvidia established first mover advantage just as AI began accelerating. This perfect timing and long runway explains its explosive growth.

💡Mining

Cryptocurrency mining offered an unexpected revenue boost for Nvidia between 2018-2021, earning up to $3 billion annually from surging graphics card demand. However, the video notes this is ultimately a sideshow, with the surge in AI workloads being the primary tailwind.

💡Cloud

Nvidia is expanding into offering GPU-based cloud computing services, allowing customers to rent hardware time rather than own it outright. This cloud business targets both end users and partners, further strengthening Nvidia's end-to-end ecosystem.

💡Competition

While dominant currently, the video warns that tech giants like Google, Meta and Microsoft are wary of reliance on Nvidia and are now racing to design their own AI chips. Emerging competitors could eventually erode Nvidia's lead, spurring its push into new areas like cloud, cars, metaverse, etc to diversify.

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