The single biggest argument against AI

stocks just died. Wall Street analysts

have been saying the same thing for

years now. AI is a bubble and the entire

tech sector will crash as soon as

spending slows down even a little. But

three major developments just put an end

to that argument and kicked off what

could be the next phase of the AI era.

My name is Alex and I spent 8 years as

an electrical engineer and AI researcher

at MIT. And I've never seen chip

companies move this fast. Let me show

you what's going on and how I'm

investing in it. Your time is valuable,

so let's get right into it. The biggest

case against AI has always been that

spending will eventually slow down, and

when it does, every stock that lives and

dies by that spending will go down with

it. It's only a matter of time.

Hyperscalers like Google, Amazon,

Microsoft, and Meta Platforms report

their capex budgets every quarter, and

every quarter, it's been growing like

crazy. In fact, their AI spending has

been compounding at over 60% per year

since 2023, and it's actually expected

to speed up. These four companies are

currently on course to spend over $700

billion on data centers this year alone,

compared to the $375 billion they spent

in 2025. That's a 90% increase

year-over-year. And it's coming at a

huge cost. Amazon's free cash flow went

from $18 billion to negative7.6 billion

over the last 12 months. But just a few

weeks ago, Amazon increased their capex

budget for 2026 from $200 billion to

$220 billion due to the price of memory

chips. That same week, Alphabet added

$15 billion to its capex budget while

reporting that their free cash flow

turned negative for the first time since

Google went public in 2004. and Meta's

free cash flows are down by 91%. You'd

think that spending would slow down once

the biggest companies driving it burn

through all their cash flows and then

some, but instead they're borrowing

money and issuing new shares. Amazon

sold $37 billion worth of bonds in

quarter 1. Alphabet issued over $65

billion worth of bonds so far this year

and announced an $85 billion equity

raise in June, the biggest raise in

American corporate history. debt covered

9% of hyperscaler capex in 2024. Today,

it covers 32%. It's important for

investors to understand why spending

isn't slowing down and why these

companies keep investing in AI at all

costs. Data center infrastructure is a

zero- sum game. There's only so much

land and grid connected power in the

first place, and every acre and gawatt

you don't get is one that your

competition does. A big new data center

can wait anywhere from 2 to 6 years for

a grid connection. And even when it's

all bought up, spending will just shift

to the next big bottleneck. Cooling,

compute density, network, and memory

speeds, all of which are zero sum games,

too. Since every single part of the AI

stack is currently supply constrained.

So the question isn't whether AI

spending will slow down, but where it

will shift to next. And three of the

biggest AI chip companies on Earth,

Nvidia, Cerabus, and AMD, all have very

different answers. Let's start with AMD.

AMD's answer is memory. An AI model is

made up of billions of numbers called

parameters. And the actual value of each

parameter comes from the patterns that

it learned during training. Those values

are called weights. And each weight

takes up about 16 bits or two bytes of

memory. So, a model with 400 billion

parameters needs around 800 GB of

memory. A single Nvidia H200 GPU has 141

GB of memory. So, you'd need about six

of them just to ask the model a single

question. Today, those weights live in

memory that sit outside of the

processor, and the processor spends most

of its time waiting for data to arrive.

That wait time is the bottleneck. On

August 6th, AMD announced plans to

acquire a company that fixes this by

etching the model directly into the

chip. The company is called Talis, a

chip startup founded in 2023, and it

etches AI model weights directly into

the metal layers of a chip, so they

don't have to be loaded from memory. The

benefits here are huge. Total throughput

goes way up and latency goes way down

when the processor stops having to wait

for data to come from memory. But the

trade-off is pretty huge, too. Each chip

is permanently dedicated to a single

specific AI model, exchanging

generalpurpose programmability for

maximum efficiency. If the acquisition

does go through, AMD will combine these

specialized chips with their own

instinct GPUs and rack scale setups like

Helios so that the GPUs can process

prompts and manage dynamic workloads.

While these new fixedweight chips handle

the memoryheavy decode phase of

inference, kind of like Nvidia's recent

partnership with Grock. But where

Grock's language processing units or

LPUs have 500 megabytes of ultraast

onchip memory, Talis's HC1 chips turn

the model directly into hardware. A

weight of one is a physically connected

wire and a zero is a broken path. So the

math is automatically executed as data

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Talis's chip completely destroys the

memory wall, which is the big bottleneck

of moving data back and forth between a

processor and separate memory. Here's

what that means in practice. A typical

cloud AI provider can serve a model like

Llama 3.18B at around 140 tokens per

second per user. That same model can hit

17,000 tokens per second on Batalis

chip, making it around 120 times faster

than a traditional GPU. responses are

generated so fast that entire pages of

text appear instantly rather than

streaming in line by line. But here's

the big problem. If the chip is directly

tied to a single model and chips take

years to design, doesn't that mean that

the chip will be obsolete by the time it

leaves the production line? Well,

actually, Talus found a way to get

around that problem with a pretty clever

design. Instead of redesigning the whole

chip for every new model, Talus uses a

tiered manufacturing process that breaks

the chip into more than a 100 physical

layers of silicon and metal stacked on

top of each other. Everything underneath

the top two layers, the transistors, the

power distribution, the math units, and

the memory never has to change. Only the

two metal layers at the very top

translate a model specific weights into

a physical chip. And since 98% of the

chip stays the same between models, they

can be made ahead of time and stored

until a model is chosen for that chip.

Then once the two layers are designed

specifically for a new model, TSMC can

pull the base wafer off a shelf, add

those two layers, and ship the chip

within 60 days. This two-month

turnaround completely shifts the

economics around data centers. Instead

of buying generalpurpose GPUs or

spending many years and many more

billions of dollars designing custom

AS6, they can just buy a cluster of

chips hardwired for specific models, run

those chips for a year, and then

literally swap them out when the next

model drops. And not only is it up to

120 times faster than a traditional GPU,

it's also roughly 20 times cheaper since

it's made on older 6nanometer

technology. It has no high bandwidth

memory and it uses much simpler chip

packaging. Talis also says that their

chips cost less than a penny per million

tokens to run versus about 3.8 for an

Nvidia Blackwell running the same model.

So, when spending shifts from acquiring

land, buildings, and power, AMD is

betting that it'll shift into

hyperoptimized, lowcost chips that can

be easily swapped out as models keep

improving. But while AMD is cutting

costs, Cerebrris is cutting cables. A

modern AI cluster has thousands of

separate chips stitched together with

network cables and switches. Every time

data moves from one chip to another, it

costs time and power. And just like with

memory, chips can spend more time

waiting for the data to arrive over a

network than they spend processing it.

Think about what this actually means.

Chip makers spend tens of thousands of

dollars turning a big silicon wafer into

dozens of individual chips. Then they

spend billions of dollars connecting

those chips back together inside data

centers. Cerebras is betting their

entire company that this approach is

wrong. So they never cut the wafer in

the first place. Instead, they turn it

into one massive chip called the wafer

scale engine, or WSE for short. Their

current generation is the WSE3, and each

side is about 8 in. That's around 30

times more area than Nvidia's Blackwell

B200 chips. If Nvidia's chips are the

size of postage stamps, then Cerebras'

are the size of dinner plates. But, as

you know, size doesn't matter. It's all

about how you use it. Cerebras' chips

have 4 trillion transistors. That's 19

times more than Nvidia's B200's. But

since the chip is around 30 times

bigger, that means that Nvidia actually

packs over 50% more transistors into the

same area because their chips are made

on a more advanced process node by TSMC.

The wafer scale engine also has a

whopping 900,000 cores, four times more

than Blackwell, and also comes with a

whopping 44 GB of SRAMM, which is the

same kind of high-speed memory used in

the Gro LPUs, except, you know, 88 times

more of it. That memory can move data at

21 pabytes per second, which is about

2600 times the memory bandwidth of

Nvidia's Blackwell B200's. There are

over 18,000 titles on Netflix and their

uncompressed master archive is about

four pabytes. That means this Cerebras

chip can move Netflix's entire library

between cores five times every second.

That's a huge deal for AI inference

performance. And it's all because Nvidia

has to move their data between chips,

across cables, and through switches. All

of which add extra time to every

transfer. While Cerebrris simply moves

data across one massive chip. No hops,

no cables, only compute. Cerebras just

reported earnings for the first time

since going public and the numbers speak

for themselves. The remaining

performance obligations hit $25.4

billion. That's basically their backlog.

Cerebras is guiding for $880 to $890

million in revenue this year. So that

backlog is worth about 29 times

everything they expect to sell in 2026.

They expect to deliver on about 22% of

that backlog in the next 2 years and

another 43% in the 2 years after that.

That first chunk works out to about $5.6

billion by the middle of 2028 or roughly

$2.8 billion per year, which means their

revenue should roughly triple. Revenue

from hardware came in at $82 million,

which is up 17% from last year. On the

official books, hardware sales look like

they fell by 23%. But that's only

because of a $28 million charge for

stock warrants that Cerebras handed to

Open AAI. If you remove that charge,

their hardware sales grew. This is

exactly why I ignore headlines and dig

into the numbers myself. All right,

hardware sales are up, but only by 17%.

On the other hand, revenues from cloud

and services hit $127 million, which is

up $287%

year-over-year. So, the real money for

Cerebras is in selling access to their

machines, not selling the machines

themselves. And their gross margins

fell, but mostly because they can't

build capacity fast enough to serve all

the demand for their chips. So, they

actually end up renting their own

systems back from the cloud companies

that bought them to fill the gap. So,

while AMD and Talus are betting on model

specific chips, Cerebrris is betting on

wafer sized ones. But we can't talk

about the future of AI chips without

talking about Nvidia. 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 Nvidia.

The question isn't whether AI spending

will slow down, it's where it will shift

to next. Nvidia's answer is to the chips

themselves as investable assets. Think

about how you'd build a skyscraper.

Nobody is paying cash. A developer

borrows most of the money because the

building holds its value. And the lender

knows that it can be sold if something

goes sideways. That's all an investable

asset is something you can borrow

against because everyone agrees it'll

still be worth something later. AI data

centers mostly get paid for out of a

company's free cash flows, which as I

just said earlier is starting to run

out. So Nvidia is trying to change where

that money comes from altogether. On

August 10th, Nvidia announced deals with

Apollo, Black Rockck, Blackstone,

Brookfield, Goldman Sachs, and KKR to

build financing platforms for over half

a trillion dollars worth of AI

infrastructure. None of that money is

Nvidia's. It's pension funds, sovereign

wealth funds, insurers, the slowest and

most conservative money on the planet.

Jensen Hong's big idea is that a GPU

makes good collateral because someone

else will always want it. And Nvidia's

software updates extend its useful

lifespan over time. For example, Nvidia

came out with an open- source software

package called Tensor RT LLM, which

doubled the inference performance of

large language models running on H100s.

That means every H100 already sitting in

a data center got roughly twice as good

at running large language models

overnight for free. And that software

also works on Nvidia's older Ampear and

Ada Loveace chips, too. And all the way

up through Blackwell. The part that I'm

not so sure about is what these older

chips will actually sell for on the

secondhand market. Cars lose a lot of

value based on their age and how they

compare to newer models, not just on how

fast they drive or their miles per

gallon. AI chips might depreciate the

same way. We just don't know yet. There

are also three big catches to Nvidia's

deals. First, none of them are binding,

at least not yet. Nobody has actually

committed a single dollar. Second,

Nvidia could quietly be on the hook for

the difference. An Nvidia blog post

published the next day said that Nvidia

may cover up to 25% of the losses if the

equipment ends up being worth less than

a loan assumed. And third, the loans can

last longer than the contracts to pay

them off. The same day as the

announcement, Coreeave closed a $2.6

billion loan against its GPUs. The loan

runs for about 5 years, while the

customer contracts renting those same

GPUs only last for three. So, if those

GPUs don't get rented again 3 years from

now, even though they'll be much older,

that could cause some serious issues

with paying back the 5-year loan. So,

the big question the market needs to

answer is what is the actual useful

lifespan of a GPU? Is it 3 years? Is it

five? That determines whether they're

worth investing in as an asset class at

all. And we might get an answer on

August 26th when Nvidia reports their

earnings. Either way, the biggest case

against AI has always been that spending

will eventually slow down. And when it

does, every stock that lives or dies by

that spending will go down with it. But

Amazon and Google both went free cash

flow negative and raised capital to

spend even more. So the question isn't

whether AI spending will slow down, but

where it shifts to next. And three of

the biggest AI chip companies all have

different answers. AMD is betting it'll

shift to hyperoptimized lowcost chips

that can easily be swapped out as models

keep improving. Cerebras is betting on

waferiz chips that cut out rack level

network cables and switches altogether.

And Nvidia is betting that GPUs will

turn into an asset class of their own

because somebody will always want to

rent them. Either way, I expect AI

spending to keep speeding up until the

world runs out of land and power for

data centers. And after that, spending

will just shift to filling them. Money

is no longer the constraint here. That's

why I think investing in AI is still a

great 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.