What if I told you that the most

powerful AI company on Earth isn't

Nvidia or Tesla or Palantir? And what if

it just posted its best quarter of the

entire AI era, and the stock dropped

anyway? My name is Alex, and I spent 8

years as an electrical engineer and AI

researcher at MIT, and I've never seen

one company dominate so many high-growth

markets at the same time. So, let me

show you what just happened and how I'm

investing in it. Your time is valuable,

so let's get right into it. I want to

start this video in an interesting

place, your web browser. You type a

question into the search bar, hit enter,

and within a single second, you have

your answer. Not just a bunch of links

or copy-pasted quotes, but three or four

paragraphs of organized information

drawn from dozens of sources and

reassembled into something just for you.

An answer that didn't exist anywhere on

the internet until you asked for it. I

find that pretty amazing, but what's

even more amazing is what happens in

that 1 second. The four layers of insane

technology that your question had to go

through and how much it all costs. The

first layer is the AI model itself. As

fancy and as complicated as everybody

tries to make it sound, all AI models

really do is predict the next step.

Large language models predict the next

word, video generation models predict

the next frame, and so on. And all AI

models have three stages to their life

cycle. The first stage is training,

where you show a model a huge amount of

data. Every website on the internet,

almost every digital book, all the

publicly available code, and then you

give it a brand new sentence and ask it

to guess the next word. When it guesses

wrong, you have it try again. The more

training data you give the model, the

more chips you let it use to train, and

the longer you let it train on those

chips, the better its predictions

become. Then, once you're happy with the

model's overall performance, you lock in

the parameters, billions of numbers that

describe everything the model learned

along the way. When somebody says a

model has 400 billion parameters,

they're referring to these numbers.

Training on more data takes longer, so

it costs more money. Using more chips

means more electricity, which costs more

money. And giving the model more

parameters also costs more money. Model

makers like OpenAI and Anthropic focus

on this stage, which is why they end up

burning so much money. Stage two is

fine-tuning. Most people forget about

this step, but it's the one where a lot

of the magic happens. A freshly trained

model has no idea what a helpful answer

looks like, so you have to give it

feedback over time. Thumbs up and thumbs

down, follow-up prompts with more

context and instructions, custom

guardrails, all until the model learns

to give the right answer in the right

format for that specific use case. This

is also where models learn to break

complex prompts into steps, use

chain-of-thought reasoning to solve each

step, put everything back together

correctly, and return one coherent

answer. Fine-tuning is also where

smaller, faster, and cheaper models even

come from. Once you have a huge,

high-performing AI model that you spent

millions of dollars to train, you can

make it teach much smaller models. The

main model works out millions of

answers, and the smaller model copies

its homework. Not just the answer, but

also how it got there. That's called

distillation, and it's how all these AI

labs have so many different models for

different kinds of tasks. Fine-tuning

costs a fraction of what it cost to

train a model, which is why most

companies can afford to at least try

this step themselves. When you hear

about a business building its own AI on

top of an open-source model like Llama

or DeepSeek or Chimney K3, this is what

they're doing. So, training happens

once, and fine-tuning happens every few

months to keep the model up to date for

its specific uses. But this final stage

happens every time you hit enter. Stage

three is called inference. This is the

step where everything comes together and

it happens in that single second between

you hitting enter and reading your

answer. The model reads your brand new

and let's be honest, probably poorly

written question and has to create a

step-by-step plan to tackle it. It has

to execute that plan, organize its

answer and return it all to you. It

decides how much to think and reason

about each step, what tools to call and

what code to write along the way, what

information to go look up and read, what

other models it can talk to and it can

even spin up little copies of itself,

which are called sub-agents, to do all

these things in parallel. Here's the

most important thing that investors need

to understand. For stages one and two,

training and fine-tuning, the company

decides how much to spend, how much data

to train on, how many parameters to use,

how many chips to use and for how long.

But customers decide the cost of

inference based on the length and

complexity of their prompts, how often

they prompt and what they want their

outputs to look like. So, the more

successful an AI company becomes and the

more of the market that it owns, the

more expensive inference becomes for

them. This is why we keep seeing

headlines where the biggest AI companies

are spending more and more on AI

infrastructure. But the headlines you

don't see can still move the markets and

that's where Ground News comes in.

Ground News analyzes over 60,000

articles a day and rates each news

source for political bias and

factuality. For example, check out this

story about Elon Musk using AI to make a

historically accurate version of the

Odyssey with almost three times as much

coverage from the left versus the right.

Only half the sources have a high

factuality rating and there's some

serious bias here. Headlines on the left

say that Elon Musk got mocked and melted

down, while headlines on the right focus

on the project itself. And their blind

spot feed shows me which stories are

being ignored by one side or the other

because knowing what isn't being talked

about is just as important as what is.

These features help me keep my facts

straight and save me a ton of time. And

right now, Ground News is giving my

audience 40% off their Vantage plan.

That's their biggest discount yet. So,

go to ground.news/TSY

or click my link in the description to

get unlimited access to every Ground

News feature for just $5 a month. That's

a no-brainer for any serious investor.

All right. So, training, fine-tuning,

and running AI models are very different

jobs, which means they require very

different kinds of data center

infrastructure. Training and fine-tuning

are all about networking thousands of

powerful chips to work together and

making sure they never sit around

waiting for each other. While inference

is all about pulling billions of

parameters out of memory and generating

individual answers as cheap as possible

billions of times per day. This is why

different companies focus on different

layers of the AI stack. OpenAI and

Anthropic build and run the models.

Nvidia, AMD, and Broadcom design the

chips, while TSMC, Intel, Samsung, SK

Hynix, and Micron actually build them.

CoreWeave, Nebius, and Airen build, run,

and rent out the data centers, while

Arista, Coherent, and Lumentum build the

networks. The list goes on and on. So,

when you type a question into your

browser, hit enter, and get your answer

within a single second, it's because

hundreds of hardware and software

companies and vendors built a massive

ecosystem to make it happen. The models,

the chips, the data centers, and the

networks connecting it all together.

But, there's exactly one company that

owns every single layer in its entire

stack, even the web browser this whole

story started with. And that company is

Google. Google owns the Chrome browser

and the Gemini AI models. They have

their own custom AI chips called Tensor

Processing Units, or TPUs for short,

which they've been running in their own

data centers since 2015, 7 years before

ChatGPT even existed. Google also runs

on its own fiber optic networks,

including private undersea cables that

they paid to lay across the ocean floor.

They even own YouTube, so their AI is

what put this video in front of you

right now. Of course, other companies

have some of these things, too. For

example, Microsoft has their own custom

chips called Maia, their own data

centers with Microsoft Azure, the

Windows and Office ecosystems, and I'm

pretty sure at least six people still

use Microsoft Bing. Good job, little

buddy. But Microsoft still uses OpenAI's

models for their heaviest workloads, and

Amazon uses Anthropic's. Google is the

only company on Earth that owns every

single part of the stack that we just

walked through. So, the big question for

investors is this: What does all this

vertical integration actually get them?

And is it worth it? There are two ways

to answer this question, so let me walk

you through both. First, here's the

technology answer. When you own every

layer of your stack, you can change

anything you want to work with

everything else. Google's Gemini models

are optimized to run on their TPU chips.

Their networks and their servers are

configured based on their own workloads.

They also have access to Google Search

and YouTube to help find answers during

inference. If any one layer costs too

much, that's what the company focuses on

next. And if someone else makes a

breakthrough in any layer, a better

model, a faster chip, or cheaper

cooling, Google can pull it straight

into their own stack without having to

ask a supplier for permission. No other

company can do that. On July 3rd, Google

published an investor presentation

explaining exactly why this matters.

This presentation actually marks 10

years since Google launched their TPUs.

Remember how training and inference are

completely different jobs? well, Google

split their eighth generation TPUs into

two separate chips, the 8T for training

and the 8I for inference, which they

could only do because they own that

layer of their stack. Optimizations like

these reduce the serving costs for

Gemini by 78%

last year alone, and the costs of core

AI responses fell by another 30% since

the launch of Gemini 3 last November.

But as you know, when the costs for

something drop, overall demand and

spending rise even faster, since more

people will use it more often for more

use cases. That's called Jevons paradox,

and this is the ultimate example. Gemini

now powers 13 different products and

services with over a billion users each.

AI overviews reaches over 2.5 billion

people every month. AI mode passed a

billion monthly active users a year

after it launched, and the Gemini app

crossed 900 million monthly active

users, more than doubling

year-over-year, while over 8.5 million

developers are building on top of

Google's models every month. Oh yeah,

and Gmail's security systems block 10

million spam emails every single minute.

As a result, the total amount of tokens

that Google processes each month grew by

more than 300x over the last 2 years,

and they now process over 3.2

quadrillion tokens a month. To put that

in perspective, that's the equivalent of

about 800 full-length HD movies every

single second 24/7. So even though their

model costs fell by more than 75%, the

total costs of running it are still

rising fast. That's the technology

answer. Now, let's talk about the money

answer, and if you feel I've earned it,

consider hitting the like button and

subscribing to the channel. That really

helps, and it lets me know to make more

deep dives like this. Thanks, and now

let's talk about Google's earnings.

Alphabet reported earnings on July 22nd,

7 weeks after that investor

presentation, and almost every number I

just covered grew faster than I

expected. The Gemini app went from 900

million monthly active users to 950

million. They went from processing 19

billion model API tokens per minute to

22 billion. Google Cloud's backlog grew

from $462 billion

to $514 billion. That's not a mistake.

Their backlog is over half a trillion

dollars. And revenues came in at $120

billion for the quarter, up 24%

year-over-year. That's Google's 12th

straight quarter of double-digit growth.

But this headline number was hiding

something big. Their net income jumped

nearly 300% to $112 billion.

But 99 billion of that came from gains

on stocks that the company owns, mostly

Anthropic and SpaceX. Google invested

about $900 million

into SpaceX back in 2015, and that stake

is worth $94 billion after the IPO. But

here's the catch. They can't sell a

single share. That 94 billion is what

the stake was worth on June 30th, the

last day of the quarter. But $80 billion

of that is still in SpaceX's lockup

period, which releases in stages between

now and December 8th. And the other $14

billion stays frozen until quarter three

of next year. But SpaceX is down by

about 25% in the last month alone. So

Google's share is worth significantly

less today. So if we strip out those

paper gains out of their earnings, they

actually had about $2.85 in earnings per

share, which was actually below Wall

Street's expectations. And their

operating income grew by 30%, not the

300% reported by the headlines. But even

with all that, I think Wall Street is

making a big mistake on this stock

because everything we just walked

through, the models, the chips, the data

centers, and the network shows up on one

line of this earnings report, Google

Cloud. Google Cloud revenue came in at

$24.8 billion for the quarter, which is

up 82% year-over-year. Most analysts

think that a business of this size has

to have slower growth because it's

already so big, but Google Cloud is

actually accelerating. Revenues grew by

48%, then 63%, and now 82%

year-over-year. And their operating

income still tripled from $2.8 billion a

year ago to $8.8 billion today. That

means their operating margins went from

about 21% to 36% in a single year. So,

they're making much more profit while

almost doubling in size. But, the

biggest thing in their earnings report

for me was a single sentence that I've

never seen before. Google Cloud

generates product revenues primarily

from the sale of TPU systems. This is

the first quarter that Alphabet

recognized revenues from selling their

TPUs, meaning Google is directly selling

their custom chips. Neither Microsoft

nor Amazon do that today. For a decade,

TPUs were something you could only rent

through Google Cloud, but today they're

being installed directly in other

companies' data centers. And I expect

these product revenues to ramp up

significantly over the next few

quarters, especially with their $514

billion backlog. But, all this growth

comes at a serious cost. Google spent

$45 billion in the second quarter alone

on land, buildings, chips, and cooling.

That's literally double what they spent

this time last year. On top of that,

Google increased their already

astronomical capex budget from $185

billion at the midpoint to $200 billion

for 2026. That's their second spending

increase so far this year, and

management already said that spending in

2027 will be much higher than that. In

fact, Alphabet signed a whopping 811

billion dollars in future purchase

agreements, chips, equipment, data

centers, and electricity that they're

contractually obligated to buy. 200

billion dollars of this is due in the

next year, and 3 months earlier, the

whole number was 332 billion dollars.

Fun fact, some of this money is for

energy contracts that run until 2054,

which means Google signed electricity

bills that are due 28 years from now for

data centers that don't even exist yet.

That's how serious this AI race is

becoming, and it also had a serious

impact on their free cash flows, which

came in at negative 5.9 billion dollars

for the quarter. Two quarters ago, that

number was 24.6 billion dollars, and

last quarter, it was still above 10

billion. Today, they're burning money,

issuing new shares, selling long-term

bonds, and they've stopped buying back

shares for the first time since 2017.

Their long-term debt more than doubled

in the last 6 months, and their interest

bill basically 5X. No wonder the stock

fell 7% the day after earnings. All

right, so after everything we covered in

this video, here's my personal opinion

on Google stock. Google is spending a

lot of money, like an absolutely insane

amount of money. But, this is a company

that serves billions of people every

single month across every single kind of

device and online service. Google Cloud

is growing faster than Microsoft Azure

and Amazon Web Services put together.

They have a backlog worth more than half

a trillion dollars, and they just

started selling physical chips to other

companies. So, while analysts see a

company that went from being one of Wall

Street's safest picks to one of the the

biggest burners of cash, I see the only

company on Earth that can actually

justify this level of spending, exactly

because they own every single layer of

their AI stack. The model, the chips,

the data centers, the networks, and even

the browser window that this video

started with. Google isn't burning

money. They're investing in themselves,

and to me, that's the best way to get

rich without getting lucky. 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.