I'm Buying These 3 Stocks to Get Rich (Without Getting Lucky)

Summary

The video explores the growing energy demands caused by AI, particularly with tools like ChatGPT, which consumes significantly more power than traditional Google searches. It highlights companies like Verve, Broadcom, and Marvell that are creating solutions to manage the energy consumption and cooling challenges in data centers. The AI boom is projected to increase power demand by 160% by 2030, making energy-efficient infrastructure crucial. The video also touches on custom AI chips and how investors can capitalize on these trends through innovations in cooling, chip design, and data center optimization.

Takeaways

• 🔋 **AI Power Consumption**: A single Chat GPT query consumes up to 10 times the electricity of a Google Search.
• 📈 **Growing Power Demand**: Power demand from data centers has doubled in the last 5 years and is predicted to grow by another 160% by 2030.
• 🌐 **Global AI Market Growth**: The global AI market is expected to grow almost 12 times over the next 8 years with a CAGR of 36.8%.
• 💧 **Cooling Solutions**: Verve Holdings provides power and thermal management solutions, including liquid cooling systems for data centers.
• 💻 **Custom AI Chips**: Broadcom designs custom power-efficient AI chips for tech giants like Google and Meta Platforms.
• 🔌 **Efficient AI Chips and Switches**: Marvel makes efficient AI chips and switches for data centers, aiming to reduce power consumption.
• 📊 **AI Training Energy**: Training large AI models, especially trillion-parameter models, is very energy-intensive.
• 🏭 **Data Center Infrastructure**: Many data centers need to make infrastructure changes to support more efficient cooling and AI workloads.
• 💹 **Investment Opportunities**: The Fundrise Innovation Fund offers a way to invest in top private pre-IPO companies in the AI and data infrastructure space.
• 🚀 **Broadcom's Dominance**: Broadcom leads the ASIC market with a 55-60% share and is a key player in AI and data center infrastructure.

Q & A

How much electricity does a single Chat GPT query consume compared to a Google Search?

A single Chat GPT query consumes up to 10 times the electricity of a Google Search. Specifically, a Chat GPT prompt costs 2.9 watt-hours, while a Google search uses around 0.3 watt-hours.

What is Goldman Sachs' prediction for power demand from data centers by 2030?

Goldman Sachs predicts that power demand from data centers will grow by another 160% by 2030.

What is the significance of Verve's liquid cooling systems in the context of AI power consumption?

Verve's liquid cooling systems are significant because they provide power and thermal management solutions for data centers, which are crucial for handling the high power consumption of AI applications. These systems are designed for high-density deployments and can integrate with existing data center infrastructures.

What is Broadcom's role in the AI chip market?

Broadcom is a leader in the ASIC market with a dominant 55 to 60% share and a major focus on AI and data center infrastructure. They co-designed the last six generations of Google TPUs and are involved in designing custom power-efficient AI chips for tech giants like Google and Meta Platforms.

How does Marvel's involvement in the ASIC market compare to Broadcom?

Marvel is the second-largest company in the ASIC market with around a 14% share. They have partnerships with hyperscale customers and are expected to double their share of the overall data center accelerator market in the coming years.

What is the projected growth rate of the global artificial intelligence market over the next 8 years?

The global artificial intelligence market is expected to almost 12x over the next 8 years, which is a compound annual growth rate of 36.8%.

How does the power consumption of AI models during training compare to inference?

Training and retraining large AI models is very energy-intensive, especially when we're talking about trillion parameter models. For example, GPT-4 took over 50 gigawatt-hours to train, which is about 0.2% of the electricity generated by the entire State of California over a year.

What is the projected growth rate for the liquid cooling market for data centers by 2030?

The liquid cooling market for data centers is expected to more than quadruple by 2030, which would be a compound annual growth rate of 27.6% for the next 6 years.

How does the age of the US power grid impact the power consumption of AI?

The average age of the US power grid is around 40 years old, with over a quarter of the grid being 50 years old or older. This aging infrastructure can struggle to meet the increased power demands of AI, which can exacerbate power consumption issues.

What is the significance of the Fundrise Innovation Fund in relation to AI investments?

The Fundrise Innovation Fund provides regular investors access to some of the top private pre-IPO companies in the AI and data infrastructure sectors, allowing them to invest in the next generation of AI applications before they go public.

Outlines

00:00

🔌 The Power Struggle of AI Growth

This paragraph discusses the significant energy consumption of artificial intelligence, particularly highlighting that a single AI query can consume up to 10 times the electricity of a Google search. It outlines the rapid growth in power demand from data centers, which has doubled in the last five years and is predicted to increase by another 160% by 2030. The speaker plans to discuss the energy consumption of AI, the impact on data centers, and companies like Verve, Broadcom, and Marvel that are addressing this issue by providing power and thermal management solutions, custom AI chips, and efficient AI chips and switches. The energy cost of training AI models, such as GPT-4, is also emphasized, along with the importance of factors like data center design, power grid age, and the increasing power efficiency of GPUs.

05:00

🌐 Investing in the AI Boom: Opportunities and Challenges

The second paragraph focuses on the exponential growth of the AI market, which is expected to increase almost 12 times over the next 8 years. It discusses the trend of companies delaying their IPOs, potentially causing investors to miss out on significant returns. The paragraph introduces the Fundrise Innovation fund as a way for regular investors to access top private pre-IPO companies. It also covers the shift towards direct-to-chip liquid cooling in data centers, which is predicted to quadruple by 2030, and Verve's expansion plans to meet this demand. Additionally, it touches on the custom chips used by tech giants like Amazon, Microsoft, and Google, and the competition in the ASIC market, particularly between Broadcom and Marvel.

10:01

💡 Broadcom and Marvel: Powering AI with ASICs

This paragraph delves into the roles of Broadcom and Marvel in the ASIC market, with Broadcom leading with a 55-60% share and a significant focus on AI and data center infrastructure. It mentions Broadcom's involvement in designing Google's TPUs and its potential to expand into custom chips for other companies. The paragraph also discusses Marvel's position as the second-largest ASIC company, its partnership with hyperscale customers, and its focus on data center products. The growth of Broadcom's networking revenue and the potential for the global AI ASIC market to grow significantly by 2033 is also highlighted.

15:03

🚀 The Future of Data Center Power Demand and Efficiency

The final paragraph addresses the increasing power demand of data centers due to AI, which is expected to rise by 15-20% annually through 2030, potentially reaching 16% of total US power consumption. It emphasizes the need for more than just efficient cooling and ASICs to meet this demand, suggesting that the companies mentioned earlier will play crucial roles. The speaker encourages understanding the science behind these stocks for investment purposes and signs off with a reminder that the best investment is in oneself.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is central to the discussion about the increasing power consumption due to data centers and the need for efficient power and thermal management solutions. The script mentions AI applications like chat GPT and generative AI, which consume significant amounts of electricity.

💡Data Centers

Data Centers are facilities used to house servers, storage systems, and networking equipment that organizations use to store, process, and manage data. The video highlights the growing power demand from data centers due to the rise of AI, which is a concern because these centers already consume a large amount of electricity, and this demand is expected to increase further.

💡Power Consumption

Power Consumption refers to the amount of electrical energy used by electronic devices over a period. The video emphasizes the high power consumption of AI applications, comparing it to a Google search and highlighting how much more electricity is used, especially when training large AI models like GPT-4.

💡Thermal Management

Thermal Management is the process of controlling and removing heat generated by electronic devices to maintain optimal operating temperatures. The video discusses Verve Holdings, a company that provides thermal management solutions for data centers, which is crucial as AI applications generate a lot of heat, increasing the need for efficient cooling systems.

💡ASIC (Application-Specific Integrated Circuit)

ASIC refers to custom-designed integrated circuits created for a specific use rather than general-purpose use. The video mentions Broadcom and Marvel as leaders in the ASIC market, designing chips for AI applications that are more energy-efficient than general processors like GPUs and CPUs.

💡GPU (Graphics Processing Unit)

A GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. The video discusses how GPUs, especially Nvidia's Hopper GPUs, consume more power but are also more efficient in AI training and inference tasks.

💡Efficiency

Efficiency in the context of the video refers to the ability of a system or device to perform its intended function with minimal waste of energy or resources. The video explores companies like Verve, Broadcom, and Marvel that are developing solutions to improve the efficiency of AI applications in data centers.

💡Verve Holdings

Verve Holdings is a company mentioned in the video that provides power and thermal management solutions for data centers. It is positioned as a company that can benefit from the growing demand for energy-efficient solutions in data centers due to the power-hungry nature of AI applications.

💡Broadcom

Broadcom is a technology company discussed in the video that designs custom power-efficient AI chips and ASICs for tech giants. It has a significant share in the ASIC market and is highlighted as a company that can profit from the growing need for efficient AI hardware.

💡Marvel

Marvel is another company mentioned in the video that makes efficient AI chips and switches for data centers. It is highlighted as a competitor to Broadcom in the ASIC market, with a focus on creating energy-efficient solutions for AI applications.

💡Investment

Investment in the context of the video refers to the act of allocating resources, such as money, with the expectation of earning a return. The video suggests that investing in companies like Verve, Broadcom, and Marvel, which are tackling the power consumption problem of AI, can be a lucrative opportunity.

Highlights

AI's growing power consumption is a major concern, with ChatGPT queries using 10 times the electricity of a Google search.

Goldman Sachs predicts power demand from data centers will grow by 160% by 2030, driven by AI workloads.

Verve Holdings (VRT) provides power and thermal management solutions, including liquid cooling systems, to help data centers handle high-density AI applications.

AI model training is energy-intensive; for instance, GPT-4 used over 50 gigawatt-hours of electricity, equivalent to 0.2% of California's annual power.

Nvidia’s latest Hopper GPUs consume 700 watts each, nearly double the power of the previous generation, contributing to rising power demands.

Direct-to-chip liquid cooling, which is 3,000 times more effective than air cooling, is expected to grow rapidly, quadrupling by 2030.

Broadcom holds a 55-60% market share in the custom chip (ASIC) market, co-designing Google's TPUs and meta's training and inference accelerators.

Broadcom’s custom AI chip program is expected to generate $8 billion in revenue in 2024 and $10 billion in 2025, mostly from Google’s TPUs.

Verve’s production capacity for liquid cooling systems is projected to expand by 45 times in 2024, with a $7 billion backlog in orders.

Broadcom’s networking products, including Tomahawk Ethernet switches, saw a 44% year-over-year revenue growth in a single quarter.

Nvidia dominates the data center GPU market, with estimates of 92-98% market share, but faces rising competition from custom AI chips.

Companies like Amazon, Google, and Microsoft are developing their own AI chips (ASICs) to reduce reliance on Nvidia GPUs.

AI’s rapid growth is expected to drive data center power consumption to 16% of total U.S. electricity by 2030.

The liquid cooling market is expected to grow at a compound annual rate of 27.6% until 2030, driven by high-density AI computing needs.

Marvell Technology, the second-largest player in the ASIC market, is growing its data center business with custom AI chip programs, partnering with major tech giants.

Transcripts

artificial intelligence has a huge

problem a single chat GPT query can take

up to 10 times the electricity of a

Google Search and while power demand

from data centers has already doubled

Over The Last 5 Years Goldman Sachs

predicts that it'll grow by another 160%

by 2030 so in this episode I'll

highlight a few companies that are

tackling this exact problem positioning

them to win big no matter which AI

companies come out on top making them a

great way to get rich without getting

lucky your time is valuable so let's get

right into it first things first I'm not

here to waste your time so here's

exactly what I'm going to cover I'll go

over the big Power problem that AI is

causing right now I'll talk about Verve

which is a company that provides power

and thermal Management Solutions for

data centers broadcom which designs

custom power efficient AI chips for Tech

giants like Google and meta platforms

and Marvel a company that also makes

efficient AI chips and switches for data

centers it's important to understand

just how much power AI is projected to

consume over the next few years so let

me break that down first a single chat

GPT prompt costs 2.9 wat hours that's

like keeping a 5w LED bulb on for a

little over half an hour while using AI

to generate a single image can cost as

much electricity as charging your phone

compare that to a Google search which

also takes in a query and returns text

and images but only uses around 0 .3 wat

hours in the process a couple of wat

hours may not seem like much but there

are roughly 9 billion Google searches

every day and if we move them all to

generative AI it would take on the order

of 10 tratt hours more to serve all of

those requests that's enough electricity

to power almost a million homes for an

entire year but this is actually a bad

comparison because people don't use

generative AI tools the same way they

use Google for example chat GPT tends to

be more of a dialogue between the user

and an AI model that can really rack up

the number of queries compared to a

Google Search and that's just the

inference side of the story training and

retraining large AI models is very

energy intensive too especially when

we're talking about trillion parameter

models for example GPT 4 took over 50

gwatt hours to train or about .2% of the

electricity generated by the entire

State of California over a year as an

investor this worries me for three

reasons first the amount of compute

needed to train AI models has been

doubling roughly every 6 months talk

about exponential growth second that

gets multiplied by the number of

foundation models being trained which is

also growing exponentially and third

even though you can use chat GPT almost

anywhere in the world it consumes energy

only at the server's location energy

accounts for up to 70% of a data

Center's total cost cost of operations

so the hardware and the racks how the

data center facility is designed and

weighed out and even the age of its

local power grid all really matter and

by the way the average age of the US

power grid is around 40 years old with

over a quarter of the grid being 50

years old or older and AI only makes

this problem worse for example nvidia's

previous generation a100 gpus use about

400 watts but the current generation of

Hopper gpus run at 700 Watts that's

almost twice the power and four or five

times the power of CPU based servers

it's worth noting that the h100 gpus are

up to nine times faster at AI training

and 30 times faster for inference over

the A1 100s so the power efficiency of

nvidia's gpus is going way up with every

generation but Power demand is going up

faster so it takes more than just high

performance gpus to solve this problem

let's start with cooling since that

accounts for up to 40% of a data

Center's energy us which means 28% of

the total cost of operations ver of

Holdings ticker symbol VRT provides

power and thermal Management Solutions

for data centers like their libert

liquid cooling systems these systems are

built specifically for highdensity

deployments like the ones that power

intense AI applications providing cold

plates and directed chip Cooling in a

way that integrates with existing data

center infrastructures which is a big

deal because around 90% of all server

racks are air cooled today

in fact most data centers even run their

h100 chips at low enough power so they

can be air cooled so a lot of them will

need to make massive infrastructure

changes to support direct to chip liquid

cooling for nvidia's upcoming Blackwell

systems if they want to run those chips

at Peak Performance that includes

hyperscalers like Amazon Google and

Microsoft all of which need to support

power hungry AI workloads for thousands

of other businesses speaking of which

according to Market us the Global

artificial intelligence Market is

expected to almost 12x over the next 8

years which is a compound annual growth

rate of

36.8% but many of the companies building

the next generation of AI applications

are not publicly traded think about the

9s and early 2000s companies like Amazon

and Google went public very early in

their growth cycle but today companies

are waiting an average of 10 years or

longer to go public that means investors

like us can miss out on most of the

returns from the next Amazon the next

Google the next Nvidia so I spent a lot

of time digging into this and The

fundrise Innovation fund is a great way

to invest in some of the best tech

companies before they go public venture

capital is usually only for the ultra

wealthy but fund Rises Innovation fund

gives regular investors access to some

of the top private pre-ipo companies on

Earth without breaking the bank The

fundrise Innovation fund also has an

impressive track record already

investing over $100 million into some of

the largest most inem demand Ai and data

infrastructure companies so if you want

access to some of the best weight stage

AI companies before they IPO check out

the fundrise Innovation fund with my

link below today all right so 90% of all

server racks are air cooled today but

industry estimates suggest that up to

80% of data center cooling will become

direct to chip liquid cooling over time

direct to chip liquid cooling is where a

heat conductive copper plate sits on top

of a chip chip just like a normal heat

sink but instead of being air cooled by

a fan the plate is connected to two

pipes one pipe brings in Cool Water to

absorb the heat from the plate and the

other pipe moves hot water away direct

to chip liquid cooling is up to 3,000

times more effective than air Cooling

and better cooling means that servers

can be stacked closer together without

overheating every data center has a

fixed amount of space so they need to

optimize their cooling if they want to

squeeze the most compute out of their

entire facility as a result the liquid

cooling market for data centers is

expected to more than quadruple by 2030

which would be a compound annual growth

rate of

27.6% for the next 6 years and Verve

definitely knows that according to their

quarter2 earnings call they're on track

to expand their liquid cooling

production capacity by a whopping 45x

over the course of 2024 Verve also

doubled their production capacity for

power management products over the last

3 years and they plan to double It Again

by the end of 2025 all of these

expansions should lead directly to more

Revenue since Verve is currently limited

by Supply not demand Verve had a$7

billion backlog of orders at the end of

quarter 2 which was up 11% quarter over

quarter and 47% year over-year Verve

stock is already up around 120% year to

dat and I definitely think they have

plenty of room to run as the AI boom

continues compute and connectivity are

also energy intensive so let's talk

about them next today Nvidia has a

massive share of the data center GPU

Market with estimates ranging from 92%

all the way to 98% market share but gpus

are not the only way to process intense

AI workloads over the last 3 years I've

spent a lot of time covering the custom

chips used by Amazon web services

Microsoft Azure and Google Cloud these

custom chips are called as6 application

specific integrated circuits and they do

exactly what their name implies their

design is tailored to a specific

application which simplifies the Chip's

architecture the result is a chip that

can run a narrow set of workloads

extremely efficiently at the cost of

supporting fewer kinds of workloads than

more General processors like gpus and

CPUs so as Amazon Microsoft Google and

their Cloud clients need more support

for a specific kind of workload like

running large language models

synthesizing speech from text or

generating images they could make a chip

for that workload and free up their more

expensive Nvidia infrastructure for

other tasks all three hyperscalers are

making big investments into their own

semiconductor Supply chains to reduce

their overall Reliance on Nvidia over

time Amazon has their inferentia and

tranium chips for AI inference and

training respectively Microsoft has

their Azure Maya accelerators and of

course Google has their tensor

processing units or tpus the demand for

A6 is so high that even an Nvidia is

building a new business unit focused on

making custom chips for other companies

which could help extend their Cuda

ecosystem to new kinds of chips but

Nvidia will have some serious

competition in this space from rival

companies like Marvel technology and

broadcom so let's talk about them next

broadcom is the leader of the Asic

Market with a dominant 55 to 60% share

and a major focus on AI and data center

infrastructure broadcom co-designed the

last six generations of Google tpus and

that partnership got extended to the

next generation of tpus as well which

shows just how sticky these chip design

relationships can be once they're up and

running Google claims that their sixth

generation Trillium tpus are 67% more

energy efficient than their current

generation with 4.7 times more Peak

compute performance and JP Morgan

analysts estimate that broadcom's TPU

program will generate $8 billion in

Revenue in 2024 and another 10 billion

in 2025 and that's just from Google's

tpus broadcom is also behind every

generation of the MTI chips which are

meta's training and inference

accelerators and broadcom's Ambitions

for custom AI chips don't stop with

Google or meta in July broadcom was

rumored to be in talks with open AI to

design as6 for them as well but who

knows what'll happen now that open ai's

Chief technical officer Mira Madi is

leaving and open AI is becoming a

for-profit company let me know if you

want a separate Deep dive on all of that

and the resulting drama but broadcom

makes more than just custom chips for

Tech Giants according to broadcom CEO

Hawk tan more than

99.5% of all internet traffic Touches at

least one broadcom chip Jim I got to

tell you in

99.5% of every bit of data that flows in

the internet will cross at least one or

more broadcom chip Brom has several

lines of network switches that also use

as6 designed to optimize Network traffic

by choosing the best path for data

packets based on the Network's layout

and its current conditions broadcom's

Tomahawk series is tailored specifically

for ethernet switches and their Jericho

line is for more complex networks that

need core and Edge Computing this past

quarter broadcom's networking Revenue

grew by 44% year-over-year after they

doubled the number of switches that they

sold and broadcom stock is it's up

nearly 60% year-to date broadcom may not

be another Nvidia but honestly that's a

good thing too because it's exposed to

all the growth that AI has to offer in a

different way than Nvidia ethernet

versus infiniband and as6 versus gpus

that's why holding broadcom and Nvidia

is a great way to have your cake and eat

it too by the way the global market for

AI as6 is expected to roughly 10x in

size by 2033 which is a compound annual

growth rate of around 30 % for the next

10 years as6 currently represent 16% of

the total data center accelerator market

and I expect that to meaningfully

increase over time as AI applications

get more diverse and power consumption

becomes an even bigger issue than it is

today Marvel is the second largest

company in the Asic Market only behind

broadcom with around a 14% share so

holding both broadcom and Marvel stock

means owning roughly 75% of the entire

Asic Market according to a recent call

with Wall Street analysts Marvel

currently has around a 10% share of the

overall data center accelerator market

and expects to double that over the next

few years back in May Marvel confirmed

that they partnered with at least three

hyperscale customers which analysts

currently believe are Amazon web

services tranium and inferentia chips

Microsoft azure's Maya accelerators and

Google's arm-based Axion data center

CPUs and going back to our original

Power problem Google claims their Axion

CPU is 60% more power efficient than

comparable x86 chips that are produced

by AMD and Intel but compute is just one

side of the story moving data over a

massive network is also extremely energy

intensive picking the right Network

switches can reduce power consumption by

around 30% Marvel dominates the space

with a range of products like Optical

and copper transceivers high performance

Optical interconnects for data centers

and a broad range of ethernet switches

in March Marvel extended their

longstanding partnership with tsmc to

develop their next generation of AI

infrastructure products using tsmc's 2

nanometer process technology it's worth

noting that Marvel stock hasn't done

nearly as well as broadcom since

Marvel's total revenues have been in

Decline but under the hood Marvel's

Revenue mix is Shifting substantially

with data centers now accounting for

almost 70% of their total revenue last

quarter up from around 33% at the start

of 2023 kind of like how Nvidia saw a

sharp decline in their revenue when they

pivoted from gaming gpus to Data Center

chips a few years ago case in point

Marvel's Data Center business grew 7%

quarter over quarter and a whopping 87%

year over-year and for this quarter

Marvel forecast data center revenues to

accelerate even faster thanks to their

custom AI chip programs which are

beginning to ramp right now the AI boom

is expected to increase data center

Demand by 15 to 20% every year through

2030 at which point they could reach a

whopping 16% of total us power

consumption that's the same demand as

about 2/3 of all the homes in the United

States and while every generation of

nvidia's gpus is more efficient than the

last AI has caused the overall demand

for data center power to far exceed

Supply it will take much more than

efficient cooling solutions from

companies like veriff or the as6 for AI

Computing designed by broadcom and

Marvell but in my opinion all three of

these companies will will end up being

important pieces of the puzzle and

that's why it's so important to

understand the science behind the stocks

and if you want to see what else I'm

investing in to get rich without getting

lucky check out this video next either

way thanks for watching and until next

time this is ticker simple you my name

is Alex reminding you that the best

investment you can make is in you