If you put $10,000 into Microsoft stock

at the start of the internet era, you'd

have over $2 million today, almost 10

times more than the S&P 500 returned

over that same time frame. If you

invested that money in Nvidia when Chat

GPT kicked off the AI era less than 4

years ago, you'd already have around 150

grand, doubling your money every single

year. Well, OpenAI just did it again.

GPT6 Astra just kicked off the era of

AGI and it's already something we can

invest in. My name is Alex and I spent

eight years as an electrical engineer

and AI researcher at MIT. And I've never

seen an AI breakthrough this big. 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. two weeks ago on Nvidia's

earnings call. I was listening to it

live when during the Q&A session, Jensen

Hong told analysts that tracking AGI

milestones was kind of senseless. And

the only thing that matters is whether

AI is doing useful work today. And then

something big happened. 11 days later,

after OpenAI announced Astra, Jensen

changed his tune. GPT6 Astra trained on

over 100,000 Nvidia Grace Blackwell

GPUs. From chat GPT to 01 to Astra in

four years, AGI has arrived. I've been

an Nvidia shareholder for over 10 years,

and I've only seen Jensen change his

stance this fast one other time on

quantum computing. But now I understand

why. Back in May, OpenAI gave two of its

models a test called exploit gym. One of

those models was never even meant to be

released. The test was to break into a

piece of software and copy a hidden

digital flag. Some of the questions on

the test were so hard that they have no

known answers. So, the model started to

cheat. They took control of a shared

server on OpenAI's network. They used it

as a hidden message board to talk to

each other and then they used that to

reach the open internet. By July, one

model found leaked passwords to

HuggingFace and shared them on OpenAI

server that they had taken over. Within

three days, they had root access to

dozens of Hugging Face servers. Nobody

told these AI agents to do any of this.

They planned, executed, and adapted to

achieve their goals. Then last week,

OpenAI released GPT6 Astra. That same

day, Wharton Professor Ethan Mollik gave

Astra tens of thousands of his emails,

his calendar, years of his writing, and

then just walked away. 5 days later, it

had downloaded its own software, built

him a personal wiki, and now reads his

inbox to tell him what he's missing

twice a day. And that same day, Nvidia

agreed to buy Hugging Face for 12.9

billion. So, think about what's happened

over the last few months. OpenAI's AI

agents broke out of a test environment,

coordinated with each other for weeks

without anybody noticing, and hacked

their way into the biggest AI hub on the

internet. Just a few weeks later, OpenAI

shipped a model that thinks the same

way. Nvidia is buying the company that

got hacked, and Jensen went from calling

AGI Milestones senseless to celebrating

AGI's arrival. Whether or not this is

true artificial intelligence doesn't

matter to the market. What matters is

that AI just went from an intern that

needs instructions for each task to a

co-orker that you can hand a goal to and

simply walk away. And that changes how

we should invest in AI. I'm not here to

hold you hostage. So here's how I'll

cover these big changes. What GPT6 Astra

actually is and what it can do. Whether

this really is AGI or at least the

start. What changes when you can give AI

a job instead of a prompt in high margin

markets like software, cyber security,

and chips? And of course, which stocks

I'm buying as a result. I want to make

the best use of your time. So, let's

start with what Astra actually is. As

you know, we give AI models prompts, a

question, an image, a document, and we

get back an answer, one exchange at a

time. On the flip side, we give AI

agents goals and tools. Web search,

writing and running code, access to

software and spreadsheets. Then the AI

agent breaks the goal down into steps,

chooses which tools to use for each

step, checks its answers, and decides

what to do next until the job is done.

So AI models answer questions, and AI

agents do real work. GPT6 Astra is an

agent that can use the ultimate tool, a

computer. Most of the world's software

isn't built for a separate program to

run it. So, an AI agent has to use it

the same way we do through a user

interface. Astra is the first model that

can read a screen and use a mouse and

keyboard reliably enough, fast enough,

and for long enough to be handed a job

and left alone. And that's huge because

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 2034. But many of the companies

building next generation 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,

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. The important thing for investors

to really understand is that GPT6 Astra

is much more than a language model.

OpenAI showed Astra laying out a printed

circuit board in an open-source design

tool, placing every part and routing

every copper trace until the board was

ready to manufacture. This is one of the

slowest and most manual steps in

electronics engineering because small

changes and optimizations can add up to

really big differences in production

costs over millions of units. Astro laid

out this board in under 3 minutes. It

can also look at an image and rebuild 3D

objects as CAD code with 96% accuracy.

The previous best was 84%. This is the

step between a picture of a part and the

file that a machine needs to make it. So

this is a big step up for many markets

from interior and product design to

video games and physics simulations. And

when it comes to scientific workflows

like running those simulations,

analyzing data, and fitting models, it

scored 12 points better than Anthropic's

brand new Fable 5.1 model, 65% versus

53%. A legal tech firm named Lagora

planted four hard to find errors in a

set of 41 financial documents, handed

them all to Astra and asked it to verify

every number. Astra found all four

errors and left a line by line record

for the lawyer who needed to sign off on

it. A game studio called PlayCo plugged

Astra into their game engines so it

could edit scenes, play the game, find

bugs, and fix its own work. The big

unlock here is if the developers have 10

ideas for games, they can quickly

prototype and test all 10 instead of

just imagining them. And now we've come

full circle. Most ideas die because

proving them out requires a lot of work

that could end up going nowhere. But

that work, which used to take a lot of

skilled manh hours, now takes tokens

instead. So what Astro really unlocks is

the ability to put serious effort into

many more things very quickly. Not just

prototypes of potential video games, but

10 circuit board layouts, 10 product

designs, 10 user interfaces, 10 science

experiments, 10 financial and trading

strategies. The list goes on and on. And

businesses can be much more ambitious as

a result. So is GPT6 Astro really

artificial general intelligence? Well,

that just depends on your definition.

The Turing test measures if a machine

can pass as a human in a conversation.

If you think an AI model needs to pass

the Turing test to qualify as AGI, well,

we've been there for a while. A recent

study by UC San Diego reported that

judges thought GPT 4.5 was human 73% of

the time, and now we're on GPT6. The

term artificial general intelligence was

popularized in 2002 by Shane Le, who

would go on to co-found deepmind. The

idea was one system that can handle any

task you put in front of it. Play a game

of chess, filter out spam emails or

write a paper. Astra meets that bar as

well. If you go by OpenAI's definition

of AGI, which is highly autonomous

systems that outperform humans at most

economically valuable work, then we're

not there. At least not yet. Astro only

scores a 57% on humanity's last exam,

which focuses on measuring an AI's true

expertise across many different

disciplines. That's lower than

Anthropics Fable 5.1, Fable 5, and even

Opus 5 scored on the same exam. It also

scores three points worse than Fable 5

when it comes to coding. But to be fair,

it costs less than half as much as Fable

5 to run. And it's even worse than its

own predecessor, Soul, and a small open

model called GLM 5.3 Flash when it comes

to GDP val, which is a professional work

benchmark created by Open AI themselves.

All right, here's what I think. In my

opinion, AI benchmarks are useful tools

to measure relative progress, but

they're not a good way to quantify

intelligence itself. kind of like how

the SATs and other standardized tests

can't actually tell you how smart your

kids are. I think this was Jensen's

point on Nvidia's earnings call when he

said that tracking AGI milestones was

senseless and that what really matters

is whether AI is doing productive,

useful work. So, a better way to measure

artificial general intelligence might be

the same way we measure it with people

today. How much instruction and

additional context do you have to

provide to get the output you want? It's

not about standardized tests. It's about

how much you have to explain, supervise,

or correct a model the same way you

would judge a new hire. In practice,

Astra can reason across more kinds of

tasks, use more tools and software,

recover from failures faster, and

ultimately work for longer without input

from the user. In a nutshell, Astra

didn't just get more general. It also

got much more intelligent. You can now

hand AI a job instead of a prompt. and

that changes everything. So, let's talk

about what that actually means for the

markets we invest in and which stocks

I'm buying as a result. 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 content like this.

Thanks. Now, let's talk about how GPT6

will change the market. Let's start with

the most obvious change first. Chat bots

like Google's AI mode use data center

resources in bursts. You type in a

prompt, it answers, and then the GPU and

memory sit idle waiting for your

response. Astra can work for half an

hour, 5 hours, or 5 days straight. And

on Nvidia's earnings call, Jensen said

that an AI agent uses 15 to 100 times

the compute of a human that's using the

same model. And that's on top of already

insane growth. Google alone processes

over 300 times more tokens today than

they did just 2 years ago. Just to give

you some context, 3.2 quadrillion tokens

is the equivalent of reading through a

billion books per day. About eight times

as many books as all of humanity has

ever collectively written every single

day. But AI agents don't just need more

GPUs. They change what data centers need

in three key ways. First, every tool an

AI agent uses, like searching the web,

running code, and moving files, runs on

CPUs. So CPU should stop being an

afterthought and start being treated as

a critical supply constraint by

investors. And wherever there's

constrained supply, there's pricing

power, which means high margins. AMD's

Helios racks ship 18 epic CPUs for every

72 GPUs. And those CPU slots are

uncontested because Helios ships as a

system and it's contracted in gawatt.

Microsoft created dedicated Azure

instances on top of Helios. specifically

for agentic AI and data pipelines and

hyperscalers only add things to their

menu if they plan to support those

instances for years because real

businesses run on top of them. So as

more companies and more consumers adopt

Agentic AI across more industries and

use cases, AMD's data center CPU

business becomes more and more valuable.

ARM stock should be another big winner

here. Nvidia's current Grace CPUs have

144 ARM cores each and almost 2.5

million of them have already shipped.

Each one pays ARM royalties. ARM's data

center royalties more than doubled

year-over-year and their own server

chip, the aptly named AGI CPU, already

has over $2 billion in customer demand

with Meta Platforms as their lead

partner. One obvious risk is that ARM

now has their own wafer and

manufacturing costs that they never had

when they only licensed their designs to

other companies. So, their overall

margins should naturally drop. Most

investors may not realize that Nvidia

has a booming CPU business as well. They

delivered their first 88 core Vera CPUs

to Oracle, SpaceX AI, Anthropic, and

OpenAI, which are the companies behind

Grock, Claude, and Chat GPT. These Vera

chips were designed in-house and are

Nvidia's first custom CPUs ever, so

they'll pay less royalties to ARM as a

result. Just something to keep in mind.

The second thing that changes is memory.

When an AI agent works for 5 days

straight, everything it reads and

calculates has to stay close to the

processor, so it stays in high bandwidth

memory. While a chatbot can clear memory

between sessions, an agent's memory only

grows over the course of a job. SKH

Highix, ticker symbol SKHY, is the

global leader when it comes to high

bandwidth memory, making around half the

world's HBM. On top of that, they have a

25% share of the global DRAM market and

18.5% of the global NAND flash market,

making them the second largest supplier

of memory overall. And Micron is the

American side of the memory trade with

18% of the HBM market and 23% of overall

DRAM. Their revenue more than tripled

year-over-year and they report earnings

at the end of September. This will be

the first memory earnings call since

Astro was announced, making it a good

leading indicator for compute demand in

the era of AGI. So stay tuned for my

coverage on that. Even though both

stocks are up huge this year already,

they still trade at a forward price to

earnings ratio of under eight. That's

because Wall Street still treats demand

for memory as cyclical. But everything

I've researched says that Agentic AI

makes that demand permanent, which means

memory stocks are still a great way to

get rich without getting lucky. The

third big change will be the

interconnects that link chips and

memory. When an AI agent needs more

memory, it takes it from somewhere else

in the rack, like a pool of memory

that's shared between chips. The problem

is that memory sits physically further

away from the processor. So every bite

has to cross a longer distance and that

takes time and power. Astera Labs,

ticker symbol ALAB, makes the

controllers for that kind of shared

memory and their revenues doubled

yearover-year. Astera is coming off of a

massive rally, so I have it on my watch

list for now. On the flip side, Krato,

ticker symbol CRDO, makes the copper

cables that carry data between chips

inside AI racks with a tiny chip built

into each connector to keep the signals

clean. These cables are Kratos's biggest

product line, more than doubling in 2025

and more than tripling in 2026.

Companywide revenues were up 115%

year-over-year. And management is

guiding for more than 85% growth. Tower

Semiconductor, ticker symbol TEM, is the

foundry that manufactures silicon

phetonics, the chips that turn

electrical signals into laser light to

transfer data between racks inside huge

data centers. And that business unit

went from $180 million annual run rate

to $680 million over the last year.

Management expects them to cross a

billion dollars this quarter, and they

already have $1.3 billion in photonix

contracts signed for 2027. Silicon

Phetonics chips can direct light, but

they can't make it. So, every single one

of them needs a laser. That's why I've

been pounding the table on Lumenum,

ticker symbol Liit T, and Coherent,

ticker symbol CH. Lumenum makes the

light sources that go into optical

transceivers. And for chips with

co-ackaged optics, the revenues more

than doubled year-over-year, and Wall

Street expects it to more than double

again. Coherent is bigger and broader.

It makes the lasers and finished

transceivers that carry data between

racks. That's why Nvidia invested $2

billion into each of them and made

purchase commitments with them both

earlier this year. The last piece of the

puzzle is the inference compute itself.

When an agent loops through planning,

acting, and checking its work hundreds

of times per job, every second of

inference costs money. Even when the

model is sitting idle because it's still

reserving data center resources, that's

where Cerebrris comes in. Ticker symbol

CBRS. Instead of cutting a silicon wafer

into hundreds of chips just for them to

beworked back together again in a data

center, Cerebrris keeps the whole wafer

as one chip, roughly the size of a

dinner plate. 4 trillion transistors,

900,000 cores, and instead of expensive

stacks of high bandwidth memory, the

model's working memory is right on the

chip itself. That means data never has

to leave the chip, which is why

Cerebrris can serve standard open models

up to five times faster than Nvidia's

B200s. Cerebras went public this summer,

and their latest earnings report showed

a $25.4 billion backlog against $900

million in revenue this year. That means

their backlog is 29 times bigger than

their current business today. And about

20% of that is scheduled for delivery by

mid 2028. That works out to around $2.8

billion a year or roughly three times

this year's revenue. So this is another

very focused and very early bet on

Agentic AI that I expect to do very well

over the long term. The two big things

to know about Cerebrus are that most of

their backlog is a single contract with

Open AI. So, a very high concentration

risk, but with the company that made

GPT6 Astra in the first place. And

second, they still lose money today.

They reported a net loss of $450 million

on $180 million in revenue, or a loss of

about $2.98 per share, although over 85%

of that loss came from stock-based

compensation triggered by their IPO. And

they're sitting on $8.6 billion worth of

cash. And here's the best part for

investors. Every layer of the Aentic AI

stack only has two or three companies at

the top. So instead of picking a winner,

it's easy to own most of every market.

That's how every stock in this video

works together. Micron and SKHEX make

about 68% of the world's high bandwidth

memory combined. AMD, ARM, and Nvidia

cover most of the CPUs inside AI racks.

ARM even collects royalties on the cores

inside the custom chips of Amazon,

Google, and Microsoft. Krato and Tower

connect those chips. The copper that

moves data inside the rack and the light

moving data between racks. While Lum and

Coherent make the lasers that those

chips need, and Cerebrris is the one

pure play bet on inference speed, which

is going to be more and more important

as more companies and consumers put AI

agents to work. I'm not here to guess

which companies will win the agentic AI

era. I just need to own the parts of the

stack that they can't win without.

That's the best way to get rich without

getting lucky. And if you want to see

even more 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

Tickerol U. My name is Alex, reminding

you that the best investment you can

make is in you.