Wall Street still doesn't understand

Nvidia. Whether you're a long-time

shareholder or you're worried about an

AI bubble, Nvidia's earnings call was

about much more than revenues and profit

margins. Nvidia just showed Wall Street

why they're dominating the entire AI

era. So, in this video, I'll break down

everything you need to know about

Nvidia's latest earnings and what it all

means for Nvidia stock in 2026 and

beyond. Your time is valuable, so let's

get right into it. First things first,

I'm not here to hold you hostage. So,

here's what I'll be talking about

upfront. Nvidia's quarter three earnings

results, focusing on data centers, what

Wall Street analysts still don't get

about Nvidia's ecosystem, my thoughts on

Nvidia stock, and if I'd still buy it at

a $4.5 trillion valuation since it's the

biggest company on Earth, and of course,

where Nvidia goes on my list of stocks

to get rich without getting lucky in

2025, which has been absolutely crushing

the market since I made this list a year

ago. So, here's the full list along with

every stock's year-to-date performance

and how it's doing versus the S&P 500.

As you can see, Nvidia is right at the

top and we're outperforming the market

by a large margin. I'm not trying to

toot my own horn here, but I do think

it's important to understand the science

behind these stocks, which is just my

way of saying how the companies behind

the ticker symbols actually make their

money. So, let's do just that by diving

into Nvidia's latest earnings call.

Nvidia reported record revenues of $57

billion for the quarter, which is up 22%

quarter over quarter and a whopping 62%

year over year. It's easy to take these

huge numbers for granted, so let me say

it another way. Nvidia's quarterly

revenues grew by $10.3 billion

in the last 90 days. That's over a

billion dollars more than all of AMD

makes, and Nvidia posted earnings per

share of $1.30, which is up by 20% from

last quarter and up by 67% from last

year. So, the biggest company in the

world is growing revenues and earnings

by over 60% per year. That's not

something the stock market sees every

day. On paper, Nvidia has four major

business units: data center, gaming and

AI PC, professional visualization, and

automotive and robotics. But in reality,

Nvidia's data center business accounts

for 90% of their total revenues and is

growing faster than any of their other

segments. So, that's where I'm going to

spend your valuable time. Nvidia's data

center revenues came in at $51.2

billion, which is up 25% from last

quarter and 66% from last year. There

are three big points about these numbers

that are important for investors to

understand. First, they don't include

any chip sales to China, and Nvidia's

current guidance assumes zero data

center revenues from China going

forward. So, if Nvidia can ever re-enter

the Chinese AI market, that would be

pure upside from here. Second, Nvidia's

B300 Blackwell Ultra chip sales are

still ramping up. Like I said last

quarter, these B300 chips will be huge

for Nvidia's data center revenues in the

second half of 2025. And now, everyone

can see that in their revenue growth.

But while Wall Street is reacting to

their massive growth after earnings, my

audience saw it coming months ago

because every single one of you is

taking the time to understand this

company's products, not just their

profits. And we knew that Blackwell

Ultra's 50% increase in performance,

throughput, and high-bandwidth memory

means much more revenue. And once

Blackwell Ultra is done ramping, Nvidia

still has Vera Rubin coming in 2026,

Rubin Ultra in 2027, and Feynman in

2028. So, we'll see this pattern year

after year after year. And the third

important point about Nvidia's data

center revenues is they don't just come

from the GPUs themselves. $8.2 billion

of that revenue came from rack-level

networking technologies like Spectrum-X

Ethernet and Quantum InfiniBand, as well

as chip-to-chip connections like NVLink

and NVLink Fusion. As a result, Nvidia's

revenues from networking grew by a

whopping 164%

year over year, and they now account for

14% of Nvidia's total revenues. So, not

only is Nvidia's networking business

already bigger than their gaming,

visualization, and robotics segments,

it's also now the largest networking

business in the world by quarterly

revenue. And just like we already know

about Nvidia's next three GPUs, we also

know they're coming out with new data

center CPUs, new versions of their

BlueField data processing units or DPUs,

as well as new chips for NVLink,

Spectrum-X, and InfiniBand every single

year. This is why Nvidia is dominating

the entire AI era. This is what I mean

when I say get in early. This is why

it's so important to understand the

science behind these stocks. On top of

that, according to Market.us, the global

artificial intelligence market is

expected to almost 19x in size over the

next 9 years, which is a compound annual

growth rate of 38.5%

through 2033. But many of the companies

building next-generation AI applications

are not publicly traded. Think about the

'90s and early 2000s. Companies like

Amazon and Google went public very early

in their growth cycle, but today,

they're 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. That's

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right. So, Nvidia's revenues and

earnings per share both grew by more

than 60% year over year. On top of that,

they have visibility into more than half

a trillion dollars in total Blackwell

and Rubin revenue for 2025 and 2026

combined, which is more than five times

the lifetime revenues for Hopper, the

GPU architecture that kicked off the

entire generative AI revolution by

powering ChatGPT when OpenAI released it

back in 2022. But now, let me show you

something that will put you ahead of

every investor saying it's too late to

buy Nvidia stock or that the market's in

an AI bubble. The real reason that

Nvidia will dominate the entire AI era

isn't because they make six new AI chips

every year. It's the value they unlock

by putting them all together. During his

recent keynote speech in Washington,

D.C., Jensen Huang showed three slides

that every investor needs to understand.

So, let me break them down for you. This

first slide explains the difference

between Hopper and Blackwell at a high

level. Hopper systems have nine compute

trays with eight GPUs per tray, but

while each tray's GPUs can work

together, the trays were connected via

Ethernet, creating a big bottleneck. On

the other hand, Blackwell connects all

72 GPUs together via nine NVLink switch

trays, so they can all act like one

giant GPU, and that changes everything.

Jensen's second slide compares the

performance of Blackwell and Hopper in

two ways. The x-axis is tokens per

second per user, and the y-axis is

tokens per second per megawatt. Long

story short, Blackwell systems can

generate four to six times more tokens

per GPU, or they can support three to

six times the users for the same amount

of power. Depending on their needs, data

centers can make trade-offs between the

number of users they support and the

number of tokens they want to generate

per user, which is what this GB200 NVL72

line represents. So, compared to the

choices that Hopper provided, Blackwell

systems can generate around 10 times

more tokens per dollar or per watt or

per second, which directly translates to

10 times more revenues for the data

centers running them. And this third

chart shows the same thing, but in terms

of costs instead of performance. So,

while it costs 70 cents to generate a

million tokens on Hopper, it costs just

7 cents to generate those same tokens at

the same speeds on Blackwell. Or, going

the other way, Blackwell systems could

generate enough tokens for three to six

times more users at the same speeds for

that same 70 cents. That's why Blackwell

is such a big deal, and Blackwell Ultra,

and Vera Rubin, and Rubin Ultra, and so

on. But there's one more piece of the

puzzle that I didn't even think about

until I talked to Dion Harris, Nvidia's

senior director of high-performance

computing, cloud, and AI infrastructure

go-to-market. I asked Dion what metrics

I should be watching as an investor to

understand not just the performance, but

the real value that AI is delivering to

businesses, and his answer really

surprised me. Here, take a look. But

profits and margins are really something

you see in the rearview mirror, right?

So, one of the questions I have is like,

I try to look at forward-looking

indicators as an investor. What

benchmarks or metrics can we focus on to

better understand like the real business

value for inference in real time? As you

drive more performance, more throughput

per dollar or per watt, that actually

reduces the cost per token. And when you

reduce the cost per token,

you can actually embed that AI into even

more services, even more use cases, and

therefore deliver more value to your end

users. When you think about AI, it's a

lot more than LLMs. So, it includes

image classification, it includes video

generation or diffusion models, it

includes, um, you know, lots of

different types of of recommender

systems that are being used to serve ads

and content. And so, when you think

about, you know, today where we are,

we're in a fairly, you know,

demand-driven economy. Meaning there's a

huge demand for a lot of these AI

capabilities, but again, you have to be

able to do it intelligently and smartly.

If you can drive the cost down to zero,

now you can you can literally embed

these AI APIs into every application

that they're running. And therefore,

that's when you really start to see this

ubiquitous use of AI. And so, that's

really why we think about how we want to

drive more performance and more

efficiency,

the cost per per token goes going down

by 10x will actually increase the

overall utilization by 20x. Because now

you have a lot more use cases where you

can afford to embed these AI

capabilities. There's a concept in

economics called the price elasticity of

demand, which is just a fancy way of

saying that when the price of something

goes down, overall demand for that thing

goes up much faster. For example, when

the price of electricity got low enough,

every home switched over to it from wax

candles, gas lighting, and coal

furnaces. And that drove the overall

demand for electricity up much more than

the cost per kilowatt went down. And

that trend is still going strong today,

as every modern home uses electricity

for almost everything, lighting,

heating, but also for computers,

appliances, and even cars. And the big

takeaway for investors is that demand

for AI is working the exact same way.

The 10x performance jump from Hopper to

Blackwell isn't just about increasing

revenues for data centers today. As the

cost per token drops, that opens up new

use cases across a wide variety of

industries and areas of research. And as

the power per token drops, some of the

AI workloads that need to run in data

centers can now run on edge devices, not

just smartphones and laptops, but

humanoid robots and self-driving cars,

which unlock even more demand for AI in

the process. As a result, a 10x cost

reduction could lead to a 20, 50, or

even 100x overall increase in demand for

token generation. As current AI users

are getting more bang for their buck,

and new AI use cases keep getting

unlocked. And that leads to even more AI

companies finding new ways to lower

token costs even further, which just

keeps this virtuous cycle going, just

like it did for internet speeds and

computer performance before that, and

electricity costs before that. And we

haven't even gotten to the massive

software ecosystems that sit on top of

all this hardware infrastructure, which

just drive costs down even further. Let

me know in the comments if you want me

to make another video covering Nvidia's

massive software ecosystems, and I'll

post my full interview with Deon Harris

pretty soon. So, stay tuned for that.

But for now, let's bring the discussion

back to the future of Nvidia stock. And

if you feel I've earned it, consider

hitting the like button and subscribing

to the channel. That really helps me

out, and it lets me know to make more

content like this. Thanks, and with that

out of the way, let's talk about Nvidia

stock. I think Nvidia will be the

world's first $10 trillion company. I'm

not saying it'll happen tomorrow, or

even in the next couple years, but it

will happen sooner than most investors

think. Their revenues and earnings are

still growing by over 60% per year, and

that's without selling any chips to

China. Nvidia currently has visibility

into more than half a trillion dollars

of total revenues from Blackwell and

Rubin by the end of next year. And we

still have the Rubin Ultras and Fineman

architectures coming in the years that

follow. And even though Nvidia is the

world's most valuable company today,

it's still relatively cheap, trading at

half the forward price-to-earnings ratio

of its two biggest competitors, AMD and

Broadcom. That's why I'm still buying

Nvidia stock today, and why it's staying

at the top of my list of stocks to get

rich without getting lucky, only behind

the fund that it sits at the top of

anyway. Like I've been saying for years

now, just because a company is already

big doesn't mean it won't keep growing,

just like Microsoft did, and Amazon

before that, and almost every

trillion-dollar company on my list. And

if you want to see what other stocks I'm

buying to get rich without getting

lucky, check out this video next. Either

way, thanks for watching, and until next

time, this is ticker symbol U. My name

is Alex, reminding you that the best

investment you can make is in you.