"I'm EXPOSING this NO MATTER what..." - Chamath Palihapitiya On Nvidia Stock

Summary

The video delves into Nvidia's staggering $247 billion single-day market cap gain, sparking discussions on the sustainability of its growth trajectory and potential competitors vying for a slice of the lucrative AI market. Experts analyze Nvidia's unique positioning, the complexity of its chips, and the driving forces behind major tech companies' aggressive investments. However, concerns arise over the absence of revenue-generating AI applications to justify the current spending spree, raising questions about the terminal value of Nvidia's dominance. As the AI revolution unfolds, the industry eagerly awaits the emergence of groundbreaking applications that could reshape the landscape.

Takeaways

• 😮 Nvidia experienced a record-breaking $247 billion single-day gain in market cap, following its exceptional quarterly results and projections.
• 🤖 The surge in demand for Nvidia's AI chips is being driven mainly by big tech companies like Amazon, Google, and Microsoft, as they race to build infrastructure for upcoming AI applications.
• 🏭 However, most of the current AI applications are still proofs of concept and demos, rather than fully-fledged production systems.
• ❓ There are questions about the sustainability of Nvidia's growth and whether new competitors will eventually emerge to compete away its profits, as typically happens in capitalism.
• 💰 The accounting treatment of these AI chip purchases as capital expenditures, rather than operating expenses, has incentivized big tech's massive spending on Nvidia's products.
• 🌐 The internet's history suggests that if the infrastructure is built, innovative applications will eventually follow to utilize it, driving long-term demand.
• 👨‍💼 Enterprise adoption of AI, in addition to consumer applications, is expected to be a significant driver of demand for Nvidia's products.
• 💥 While Nvidia's valuation has skyrocketed, some analysts question whether it can sustain a market cap comparable to the size of the economy its products may enable.
• 🚀 There is a belief that AI is still in its early stages, with a decade-long wave of new applications and innovations yet to come, further fueling demand for Nvidia's offerings.
• ⚠️ Concerns remain about the potential emergence of cheaper, alternative solutions that could disrupt Nvidia's dominance in the long run.

Q & A

What triggered Nvidia's significant market cap gain mentioned in the script?

Nvidia's significant market cap gain was triggered by their overwhelming success and earnings in the AI and computing sector, leading to a $247 billion increase in market cap.

Why are most AI applications today considered 'toy apps' according to the script?

Most AI applications are considered 'toy apps' because they are primarily proofs of concept and demos run in a sandbox environment, not production-level code or applications integrated into critical systems.

How did Meta's focus shift and layoff strategy affect its market cap earlier in the year?

Meta's focus shift and decision to lay off 20,000 people, after its exploration into Reality Labs, positively affected its market cap, adding $196 billion.

What does the script suggest about the sustainability of Nvidia's profits from competitors?

The script suggests that Nvidia's substantial profits may eventually be competed away, as in capitalism, over-earning attracts competitors who aim to capture a portion of those earnings, particularly in the absence of a monopoly.

What was Freeberg's perspective on the possibility of Nvidia becoming a 10 trillion dollar company?

Freeberg discussed the accelerated compute buildout in data centers and the potential for Nvidia, considering whether the initial infrastructure investment will generate equivalent or greater value in the application layer.

Why is Nvidia's current success seen as a potentially temporary phenomenon?

Nvidia's current success is seen as potentially temporary due to the risk of new competitors entering the market with lower cost solutions or innovations that could erode Nvidia's market share and profits.

What does the script mention about the importance of the application layer for future profits?

The script highlights that the long-term value and profitability in the tech industry may eventually shift towards the application layer, as infrastructure buildouts reach completion and new applications generate revenue.

How did the script compare Nvidia's situation to historical examples like Cisco and Oracle?

The script compared Nvidia's situation to Cisco and Oracle, noting how early dominance in hardware and infrastructure did not guarantee long-term market control as cheaper, more efficient solutions emerged.

What accounting advantage do big tech companies have when investing in infrastructure like Nvidia's chips?

Big tech companies can account for large purchases of Nvidia's chips as capital expenditures, allowing them to spread the cost over several years on the balance sheet, rather than taking an immediate hit to the income statement.

How does the script suggest Nvidia's future market position will be determined?

Nvidia's future market position is suggested to be determined by the total addressable market (TAM) for GPUs, Nvidia's sustained market share, and the balance between infrastructure buildout and the creation of new, profitable applications.

Outlines

00:00

🤯 Nvidia's Astronomical Market Cap Gain

The video discusses Nvidia's unprecedented single-day gain of $247 billion in market capitalization, attributed to the growing demand for their AI chips from major tech companies. It highlights the current state of AI applications as primarily proofs of concept and demos, rather than full-scale production systems. The discussion also touches on the uncertainty surrounding Nvidia's ability to sustain such growth and maintain their competitive edge in the face of potential competitors aiming to capture a share of the lucrative AI market.

05:00

🧐 The Real Value Proposition Behind Nvidia's Growth

The discussion delves into the driving factors behind Nvidia's revenue surge, primarily the substantial spending by major cloud service providers and tech giants on Nvidia's AI hardware. However, the real question raised is whether the current demand and revenue will translate into tangible long-term value creation. The panelists debate the potential for new competitors to emerge and compete away Nvidia's profits, drawing parallels to historical examples like Cisco and the early days of the internet build-out. The crux of the matter is whether the current spending is a precursor to sustained revenue generation from viable AI applications or merely a temporary buildup phase.

10:01

📈 The Accounting Drivers Behind Big Tech's AI Spending Spree

This part of the discussion delves into the accounting mechanisms and motivations behind the massive AI infrastructure spending by major tech companies. It highlights how these companies can capitalize their AI hardware purchases, allowing them to spread the costs over several years while utilizing their substantial cash reserves. The panelists discuss the implications of this accounting treatment, suggesting that it incentivizes cloud service providers to invest heavily in building out the next generation of AI infrastructure, even if the immediate revenue generation from AI applications is uncertain. The discussion underscores the complex interplay between accounting practices, cash reserves, antitrust concerns, and the drive for technological dominance in the AI domain.

15:02

⚖️ Assessing Nvidia's Long-Term Market Value and Competitive Landscape

The conversation shifts towards evaluating Nvidia's long-term market value and the competitive dynamics that may shape the AI hardware market. The panelists discuss the potential for Nvidia's market share to decline as new competitors emerge, while acknowledging the challenges associated with developing and manufacturing advanced AI chips. The discussion also explores the distinction between one-time infrastructure buildout and sustainable, recurring revenue streams, raising questions about the terminal value of Nvidia's AI business. Historical analogies, such as the dotcom bubble and the subsequent emergence of new applications and services, are drawn to illustrate the potential for AI applications to drive sustained demand for AI hardware over time.

20:03

🔮 The Future of AI Applications and Infrastructure Demand

The final part of the discussion focuses on the future potential of AI applications and the resulting demand for AI infrastructure. The panelists acknowledge that while the current demand is driven by major tech companies building out cloud data centers, the true value creation will come from the development of innovative AI applications across both consumer and enterprise domains. Drawing parallels with the history of the internet and the eventual adoption of broadband and streaming services, the discussion suggests that the AI infrastructure buildout today may pave the way for a wave of new AI-enabled applications and services in the coming decade. The conversation highlights the potential for sustained demand for AI hardware, provided that the applications and use cases continue to evolve and drive technological advancement.

Keywords

💡Market Cap

Market capitalization refers to the total value of a company's outstanding shares of stock. In the context of the video, it discusses significant single-day gains in market cap for certain companies, emphasizing the impact of technological advancements and strategic business decisions on their valuations. For example, Nvidia and Meta are mentioned as companies that added billions to their market cap, reflecting investor optimism and the potential growth driven by AI and strategic refocusing.

💡AI Applications

AI applications refer to the various uses of artificial intelligence technology in solving problems or enhancing capabilities across different sectors. The video notes that most AI applications today are seen as 'toy apps', primarily used for demonstration or proof of concept rather than full-scale production. This highlights the early stage of AI integration into practical, everyday use and the potential for more impactful applications in the future.

💡Production Code

Production code refers to software that is deployed for actual use in a live environment, as opposed to being in a development, testing, or demo phase. The video points out that the overwhelming majority of AI applications are not yet production code, indicating that while there is a lot of experimentation and development happening, few AI technologies have been fully implemented into operational systems.

💡Competition

Competition, in the context of the video, refers to the dynamic where companies attempt to outperform each other by offering better products, services, or prices. It mentions that in capitalism, high earnings attract competitors who seek to 'compete away' those earnings. The discussion includes how Nvidia's success and high profits in AI and GPU markets are likely to attract competitors aiming to capture some of their market share.

💡Monopoly

A monopoly occurs when a single company or entity has exclusive control over a particular market or product, allowing it to dominate sales and set prices without competition. The video uses Google as an example of a company that has been able to 'over earn' for decades due to its near-monopolistic control over search, contrasting this with markets where competition is more intense and monopolies are less established.

💡Cloud Infrastructure

Cloud infrastructure refers to the hardware and software components, such as servers, storage, and networking, that make up the cloud computing environment. The video discusses accelerated compute buildouts in data centers, emphasizing the role of Nvidia's technology in enabling this infrastructure. This infrastructure is foundational for developing AI applications and services, indicating a shift towards more scalable and efficient computing solutions.

💡Data Centers

Data centers are facilities that house computer systems and associated components, such as telecommunications and storage systems. They play a critical role in cloud computing and internet services. The video mentions significant revenue Nvidia is generating from its data center segment, highlighting the demand for GPUs in powering the infrastructure behind AI and cloud services.

💡Capital Expenditure

Capital expenditure, or CapEx, refers to funds used by a company to acquire, upgrade, and maintain physical assets such as property, industrial buildings, or equipment. The video explains how investments in Nvidia's GPUs by big tech companies are categorized as capital expenditures, which allows these companies to build out next-generation cloud infrastructure without immediate impacts on their profit and loss statements.

💡Terminal Value

Terminal value refers to the future value of an asset, investment, or company at the end of a forecasted period, assuming a certain rate of growth into perpetuity. The video debates Nvidia's terminal value in the context of its current market dominance and potential competition, pondering the sustainability of its growth and market share in the face of evolving technological landscapes and emerging competitors.

💡Revenue Recognition

Revenue recognition is an accounting principle that determines the specific conditions under which revenue is recognized or accounted for. The video touches on this concept while discussing how big tech companies account for their investments in Nvidia's technology, noting that these investments are not immediately recorded as expenses but are capitalized and depreciated over time. This practice affects how companies report their financial performance and invest in infrastructure.

Highlights

The overwhelming majority of the apps that we're seeing in AI today are toy apps that are run as proofs of concept and demos and run in a sandbox it is not production code.

Nvidia added $247 billion in market cap in a single day, one of the largest single day gains in market cap.

The real question is who will step up to try to compete away Nvidia's profits, as competitors will eventually try to compete away over-earnings in the absence of a monopoly.

The question is whether the initial cost of the infrastructure will exceed the ultimate value that's going to be realized on the application layer.

The infrastructure buildout is the first phase, and the real question is whether the applications and tools built on top of that infrastructure will justify the cost.

GPUs are much more complex and harder to commoditize than the networking equipment Cisco was selling, which made it easier for competitors to eventually erode Cisco's dominance.

Big tech companies are buying Nvidia's products because they have cash sitting idle, and they can capitalize the expenditure on their balance sheets instead of taking a hit on their income statements.

The accounting environment is motivating big cloud providers to build out the next generation of infrastructure, as they can't grow through M&A due to antitrust concerns.

There's a question of how much of Nvidia's revenue is sustainable ongoing revenue versus a one-time buildout.

Nvidia's market share is expected to decline from the current 91%, but analysts still expect them to have a 60% market share in 5 years.

The key question is what the total addressable market (TAM) for GPUs will be, and what Nvidia's market share within that TAM will be.

There is a framework for evaluating Nvidia's potential market cap by comparing the expected economy it will enable to the market caps of companies like Intel and Microsoft that enabled previous technological economies.

The history of the internet shows that if the infrastructure is built, applications will eventually be developed to use that capacity.

AI applications are expected to be developed for both consumer and enterprise use cases, utilizing the cloud data centers that are buying Nvidia's GPUs.

We are likely at the beginning of a decade-long wave of new AI applications and creativity being enabled by the current infrastructure buildout.

Transcripts

this is the largest single day gain in

market cap most of the apps the

overwhelming majority of the apps that

we're seeing in AI today are toy apps

that are run as proofs of concept and

demos and run in a sandbox it is not

production code this is not we've

rebuilt the

entire autopilot system for the Boeing

and it's now run with agents and B and

all of this training that's not what's

happening so

$247 billion added in market cap

previously meta did something similar

earlier this year remember everybody was

down on that stock because they were

doing all the crazy stuff with reality

labs and then they got focused and laid

off 20,000 people they added $196

billion shth your general thoughts here

on something I don't think anybody saw

coming except for you and your

investment in Gro maybe and a couple of

others I think what I would tell you is

that the bigger principle and we've

talked about this a lot Jason is that in

capitalism when you over earn for enough

of a time what happens is competitors

decide to try to compete away your

earnings in the absence of a monopoly

the amount of time that you have tends

to be small and it shrinks so in the

case of a monopoly for example take

Google you can over earn for decades and

it takes a very very long time for

somebody to try to displace you we're

just starting to see the beginnings of

that with things like perplexity and

other services that are chipping away at

the Google Monopoly but at some point in

time all of these excess profits are

competed away in the case of Nvidia what

you're now starting to see is them over

earn in a very massive way so the real

question is who will step up to try to

compete away away those

profits the old Bezos quote right your

margin is my opportunity and I think

we're starting to see and you've

mentioned grock who had a super viral

moment I think this week but you're

starting to see the emergence of a more

detailed understanding of what this

Market actually means and as a

result who will compete away the

inference Market who will compete away

the training market and the economics of

that are just becoming known to now more

and more people freeberg your thoughts

we were talking I think

was last week or the week before about

possibility of Nvidia being a 10

trillion dollar company the largest

company in the world what are your

thoughts on the spectacular results and

then sh's point Everybody is watching

this going maybe I can get a slice of

that pie and maybe I can create a more

competitive offering obviously we saw

Sam hman rumored to be raising 7

trillion which feels like a fake number

feels like that's maybe the market size

or something but your thoughts here I

don't think anything's changed on the

Nvidia front there's this accelerated

compute buildout underway in data

centers everyone's building

infrastructure and then everyone's

trying to build applications and tools

and services on top of that

infrastructure the infrastructure

buildout is kind of the first phase the

real question ultimately will be does

the initial cost of the infrastructure

exceed the ultimate value that's going

to be realized on the application layer

in the early days of the internet a lot

of people were buying Oracle servers

they were like 3,000 bucks a server

and they were running these Oracle

servers out of an Internet connected

Data Center and it you know took a

couple of years before folks realized

that

for large scale distributed compute

applications you're better off using

cheaper Hardware you know cheaper server

racks cheaper hard drives cheaper buses

and assuming a shorter lifespan on those

servers and you could cycle them in and

out and you didn't need the redundancy

you didn't need the certainty you didn't

need the the runtime guarantees and so

you could use a lower cost higher

failure rate but much much net lower

cost kind of approach to building out a

data center for internet serving and so

the Oracle servers didn't really take

the market and early on everyone thought

that they would so I think Chamas point

is right now Nvidia has been at this for

a very long time and the real question

is how much of an advantage do they have

particularly that there is this need to

use Fabs to build replacement technology

so over time will there be better

solutions that use Hardware that's not

as good but the software figures out and

they build new architecture for running

on that Hardware in a way that kind of

mimics what we saw in the early days of

the build out of the internet so um TBD

right the same is true in in switches

right so in networking a lot of the

high-end high quality networking

companies got beaten up when lower cost

Solutions came to Market later and so

they looked like they were going to be

the biggest business ever I mean you

could look at Cisco during the early

days of the internet buildout and

everyone thought Cisco was the picks and

shovels of the internet they were going

to make all the all the values going to

agud to Cisco so we're kind of in that

same phase right now with Nvidia the

real question is is this going to be a

much harder Hill to compete on than

we've ever seen given the development

cycle on chips and the requirement to

use these Fabs to build chips it may be

a harder Hill to kind of get up SE so

we'll see your thoughts you think um

we're getting to the point where maybe

we'll have bought too many of these uh

built out too much infrastructure and it

will take time for the application layer

as freeberg was alluding to to monetize

it well I think the question everyone's

asking right now is are are these

results sustainable can Nvidia keep

growing at these astounding rates you

know will the buildout continue and the

comparison everyone's making is to Cisco

and there's this chart that's been going

around overlaying the Nvidia stock price

on The Cisco stock price and you can see

here the orange line is NVIDIA and the

blue line is Cisco and it's almost like

a a perfect match now what happened is

that at a similar point in the original

buildout of the internet of the Doom era

you had the market crash at the end of

March of uh 2000 and Cisco never really

recovered from that Peak valuation um

but I think there's a lot of reasons to

believe Nvidia is different one is that

if you look at nvidia's multiples

they're nowhere near where Cisco were

back then so the market in 1999 and

early 2000 was way more bubbly than it

is now so nvidia's valuation is much

more grounded in real Revenue real

margins real

profit second you have the issue of

competitive mode Cisco was selling

servers and networking equipment

fundamentally that equipment was much

easier to copy and commoditize than gpus

these GPU chips are really complicated I

think Jensen made the point that their

Hopper 100 product he said you know

don't even think of it just like a chip

there's actually 355,000 components in

this product and it weighs 70 pounds

this is more like a Mainframe computer

or something that's dedicated to

processing yeah it's somewhere between a

rack server and the entire rack yeah

it's Giant and it's heavy and it's

complex it does say something here

chamath I think about

how well positioned big Tech is in terms

of seeing an opportunity and quick

mobilizing to capture that opportunity

these servers are being bought

by you know people like Amazon I'm sure

Apple obviously Facebook meta I don't

know if Google's buying them as well I

would assume so Tesla so everybody's

buying these things and they had tons of

cash sitting around it is pretty amazing

how Nimble the industry is and this

opportunity feels like everybody is

looking at it like mobile and Cloud I

have to get mobilized quickly to not get

disrupted you're bringing up an

excellent point and I I would like to

tie it together with friberg's point so

at some point all of this spend has to

make money right otherwise you're you're

going to look really foolish for having

spent 20 and 30 and 440 billion so Nick

if you just go back to the to the

revenue slide of Nvidia I can try to

give you a framing of this at least the

way that I think about it so if if you

look at this like what you're talking

about is look who is going to spend 22.1

billion well you said it Jason it's all

a big Tech why because they have that

money on the balance sheet sitting idle

but when you spend $22 billion their

investors are going to demand a rate of

return on that and so if you think about

what a reasonable rate of return is call

it 30 40 50% and then you factor in and

that's profit and then you factor in all

of the other things that need to support

that that $22 billion of spend needs to

generate probably $45 billion of Revenue

m and so Jason the question to your

point and to Freed br's point the

$664,000 question is who in this last

quarter is going to make 45 billion on

that 22 billion of spend and again what

I would tell you to be really honest

about this is that what you're seeing is

more about big companies musling people

around with their balance sheet and

being able to go to Nvidia and say I

will give you committed pre purchases

over the next three or four quarters

and less about here is a product that

I'm shipping that actually makes money

which I need enormous more compute

resources for it's not the latter most

of the apps the overwhelming majority of

the apps that we're seeing in AI today

are toy apps that are run as proofs of

concept and demos and run in a sandbox

it is not production code this is not

we've rebuilt the

entire autopilot system for the Boeing

and it's now run with agents and Bots

and all of this training that's not

what's happening so it is a really

important question today the demand is

clear it's the big guys with huge gobs

of money and by the way Nvidia is super

smart to take it because they can now

forecast demand for the next two or

three

quarters I think we still need to see

the next big thing and if you look in

the past what the past has showed you

it's the big guys don't really invent

the new things that make a ton of money

it's the new guys who because they don't

have a lot of money and they have to be

a little bit more industrious come up

with something really authentic and new

yeah constraint makes for great art yeah

we haven't seen that yet so I think the

revenue scale will continue for like the

next two or three years probably for

NVIDIA but the real question is what is

the terminal value and it's the same

thing that Sach showed in that Cisco

slide people ultimately realized that

the value was going to go

to other parts of the stack the

application layer and as more and more

money was acred at the application layer

of the internet less and less Revenue

multiple and credit was given to Cisco

and that's nothing against Cisco because

their revenue continued to compound a

really funny story that happened

recently McDonald's in China if you

order a McFlurry they ask you if you

want a cybertruck toy with it and it

only sells for $20

but the problem is they only made that

available to less than 50,000 customers

so their cybertruck toy is already sold

out and it's right now in the retail

market and sells for hundreds of dollars

maybe and Elon commented on this and he

said that he had no idea that this was

happening and added in that case I will

definitely have some just for you to

know the first link in description click

on it if you want to buy this cybertruck

toy I don't know if this is a

collaboration but Tesla in China has

posted about this and also McDonald's in

China posted about it and Elon is the

only guy that doesn't seem to know

anything about this but anyways in the

next couple of years this product might

even sell for thousands of dollars we

don't get that many chances to buy rary

Collectibles like this right and they

did an incredible job but the valuation

got cut so freeberg if we're looking at

this chart the winner of Netflix the

winner of The Cisco chart might in fact

be somebody like Netflix they actually

got you know hundreds of millions of

consumers to give them Cash go Facebook

and then you have Google and Facebook as

well generating all that traffic and

then YouTube of course who do you see

the winner here as in terms of the

application layer who are the billion

customers here who are going to spend 20

bucks a month five bucks a month

whatever it is so here well I mean let

me just start with this important point

if you look at where that revenue is

coming from to chamat point it's coming

from big cloud service providers so

Google and others are building out

clouds that other application developers

can build their AI tools and

applications on top of so a lot of the

buildout is in these cloud data centers

that are owned and operated by these big

tech companies the 18 billion of data

center Revenue that Nvidia realized is

revenue to them but it's not an

operating expense to the companies that

are building out so this is an important

point on why this is happening at such

an accelerated Pace when a big company

buys these chips from Nvidia they don't

have to from an accounting basis Market

as an expense in their income statement

it actually gets booked as a capital

expenditure in the cash flow statement

it gets put on the balance sheet and

they depreciate it over time and so they

can spend $20 billion of cash because

Google and others have 100 billion of

cash sitting on the balance sheet and

they've been struggling to find ways to

grow their business through Acquisitions

one of the reasons is they there aren't

enough companies out there that they can

buy at a good multiple that can give

them a good increase in profit the other

one is that anti trust authorities are

blocking all of their Acquisitions and

so what do you do with all that cash

well you can build out the next gen of

cloud infrastructure and you don't have

to take the hit on your p&l by doing it

so it ends up in the balance sheet and

then you depreciate it over typically

four to seven years so that money gets

paid out on the on the income statement

at these big companies over a seven-year

period so there's a really great

accounting and m&a environment driver

here that's causing the big cloud data

center provid to step in and say this is

a great time for us to build out the

next generation of infrastructure that

could generate profits for us in the

future because we've got all this cash

sitting around we don't have to take a

p&l hit we don't have to acquire a cash

burning business and you know frankly

we're not going to be able to grow

through m&a because of antitrust right

now anyway so there's a lot of other

motivating factors that are causing this

near-term acceleration as they're trying

to find ways to grow yeah and all this I

know I know that was an accounting point

but I think it's a really important

valid one if you if 100 billion gets

spent this year divided by four 25

billion in Revenue would have to come

from that or something in that range

yeah and so sax any guesses you have to

just keep in mind I think freeberg what

you said is very true for gcp spend but

not necessarily for Google spend it's

true for AWS spend but not necessarily

for Amazon spend and it's true for Azure

spend not true for Microsoft spend and

it's largely not true for Tesla and

Facebook because they don't have clouds

so I think the question to your point

that and for obvious reason Nvidia

doesn't disclose it is what is the

percentage of that 21 billion that just

went to those Cloud providers that

they'll then Expos to to to everybody

else versus what was just absorbed

because at Facebook Mark had that video

about how many h100s that's all for him

right but it is still it is still

capitalized as my point so they don't

have to book that as an expense it sits

on the balance sheet yeah and they earn

it down over time you're helping to

explain why these big cloud service

providers are spending so much on the

data CER because they're very profitable

and there's nowhere else to put the

money right well so that would seem to

indicate that this is more in the

category of onetime buildout than

sustainable ongoing Revenue I think the

the big question is the one that jamath

asked which is what's the terminal value

of Nvidia I think like a simple

framework for thinking about that is

what is the total addressable Market or

Tam related to gpus and then what is

their market share going to be right now

their market share is something like 91%

that's clearly going to come down but

the remote appears to be substantial the

Wall Street analysts I've been listening

to think that in 5 years they're still

going to have 60s something percent

market share so they're going to have a

substantial percentage of this Market or

this Tam then the question is I think

with respect to Tam is what is onetime

buildout versus steady state now I think

that clearly there's a lot of buildout

happening now that's almost like a

backfill of capacity that people people

are realizing they need but even the

numbers you're seeing this quarter kind

of understate it because first of all

Nvidia was Supply constrained they could

not produce enough chips to satisfy all

the demand their revenue would would

have been even higher if they had more

capacity what's hotter than Tesla stock

this year it's something that would be

worth thousands of dollars in the near

future the rare limited edition cyber

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second you just look at their forecast

so the fiscal year that just ended they

did around 60 billion of Revenue they're

forecasting 110 billion for the fiscal

year that just started so they're

already projecting to almost double

based on the demand that they clearly

have visibility into already so it's

very hard to know exactly what the

terminal or steady state value of this

Market's going to be even once the cloud

service providers do this big buildout

presumably there's always going to be a

need to stay up to date with the latest

chips right here's a framework for you

sax tell me if this makes

sense intel was the basically the mother

of all of modern compute up until today

right I think the CPU was

the the most fundamental Workhorse that

enabled local PCS it

enabled networking it enabled the

internet and so when you look at the

market cap of it as an example that's

about10 odd billion dollars

today the economy that it created that

it supports is probably measured call it

in a trillion or two trillion dollars

maybe five trillion let's just be really

generous right and so you you can see

that there's this ratio of the enabler

of an economy and the size of the

economy and those things tend to be

relatively fixed and they recur

repeatedly over and over and over if you

look at Microsoft it's Market cap

relative to the economy that it enables

so the question for NVIDIA in my mind

would be not that it is it not going to

go up in the next 18 to 24 months it

probably is for exactly the reason you

said it is super set up to have a very

good meet and beat guidance for the

street which they'll eat up and all of

the algorithms that trade the press

releases will drive the price higher and

all of this stuff will just create a

trend

upward I think the bigger question is if

it's a four or five trillion dollar

market cap in the next two or three

years will it support a hundred trillion

dollar economy get a free mini cyber

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limited time offer because that's what

you would need to believe for those

ratios to hold otherwise everything is

just broken on the internet yeah I mean

so the history of the internet is that

if you build it they will come meaning

that if you make the investment in the

capital assets necessary to power the

next generation of applications those

applications have always eventually

gotten written even though it was hard

to predict them at the time so in the

late 90s when we had the whole.com

bubble and then bust you had this

tremendous buildout not just of kind of

servers and all the networking equipment

but there was a huge fiber buildout y by

all the telecom companies and the

telecom companies had a Cisco like you

know uh Peak it was worse you know wcom

and them they went bankrupt a lot of

them yeah well the problem there was

that a lot of the that happened with

debt and so when you had the dot crash

and all the valuations came down to

earth that's why a lot of them went

under yeah Cisco wasn't in that position

but anyway my point is in the early

2000s when the dotom crash happened

everyone thought that these telecom

companies had over invested in fiber as

it turns out all that fiber eventually

got used the internet went from you know

dial up to broadband we started doing

seeing streaming social networking all

these applications started eating up

that band with so I think that the

history of these things is that the

applications eventually get written they

get developed if you build the

infrastructure to power them and I think

with AI the thing that's exciting to me

as someone who's really more of an

application investor is that we're just

at the beginning I think of a huge wave

of a lot of new creativity and

applications that's going to be written

and it's not just B Toc it's going to be

B2B as well you guys haven't really

mentioned that it's not just consumers

and consumer applications are going to

use these cloud data centers that are

buying up all these gpus it's it's enemy

Enterprises too I mean these Enterprises

are using Azure they're using Google

cloud and so forth so there's a lot I

think that's still to come I mean we're

just at the beginning of a wave that's

probably going to last at least a decade

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