omfg | This changes EVERYTHING in AI [NVDA / Groq]

This changes absolutely everything for

Nvidia. This is remarkable, especially

with Nvidia trading for just a $1.67

peg, suggesting that Nvidia could be

worth over $300 per share. Now, in this

video, I'm going to break down and

reconcile why would it make sense not to

hold Nvidia, what are the big downsides,

especially after this massive $20

billion acquisition. But why did Nvidia

actually make this acquisition? What's

the play here? What are they getting at?

And importantly, what does it mean for

companies like Coreweave, Nbby, XAI?

What does it mean for these other

companies that have been hoarding Nvidia

chips? And better yet, did Nvidia

hint that this was exactly what was

going to happen by themselves getting

out of the data center rental or leasing

business. Ah, a lot to break down in

this video. Let's get into it. A quick

note, obviously, I'm not in my studio.

Uh, I saw the snow in Tahoe and I'm

like, Jack, let's go skiing. I asked

Max, too, but he didn't want to come.

But anyway, we're gonna be skiing this

weekend again. [laughter] All right, so

let's get into it. So, obviously, Grock,

some people pronounce it grog, some

people say Grock. I'm just going to

pronounce it Grock. That's not to

confuse you with X AI's Gro or Elon

Musk's Grock, the chatbot. This is a

separate company also called Grock

spelled GR OQ

that is a chip designer and more

importantly a software designer for

those chips. Keep that in mind. So this

is a $20 billion deal. I wrote down some

notes to keep this smooth for us. They

were valued at $6.9 billion in

September. Now they have a $20 billion

essential buyout deal going on. It's not

a buyout. It's a licensing deal. They're

doing that to skirt and like regul play

regulatory arbitrage and get around uh

merger and acquisition uh antirust uh

laws and blockades and two-year approval

processes. So Nvidia is just basically

like, nah, you guys keep doing your data

centers. We're just going to take your

top talent and your intellectual

property and we'll give the company $20

billion. So that's good for the

shareholders of obviously Grock, but it

gives Nvidia exactly what they want. And

you'll see in just a moment why this is

so important. I'm I'm personally very

excited about this. I I when we had our

course member live stream this morning,

I'm like, this is bullish Nvidia. I

don't know how long it's going to take

for the market to sort of digest it, but

this is overall bullish for Nvidia. That

was part of our alpha report this

morning. If you're not a member of that

yet, go to mekevin.com. You can see

that, see that analysis. and of course

get my top 10 stocks to buy for the next

10 years. Now, Grock chips. Now, here's

the key. A lot of people are going to

say this is a play around memory that

Nvidia's GPUs

utilize high bandwidth memory, which is

really just stacked up DRAM memory,

which that's kind of redundant because

the M and D RAM is memory, but anyway,

just to make it simple to understand.

Um, Grock chips use a different style of

memory. It's more expensive and you

actually need substantially less

megabytes compared to gigabytes of

memory to process what Grock does well.

Now, before I really get into the

technical weeds, I think it's worth

understanding what Jensen said they're

planning on doing and what the CEO of

Grock, the founder of the company, said

their intentions are. So, Jonathan Ross,

the founder of Grock, who worked on TPUs

at Google, was a contributor to the

invention of TPUs at Google in 2020 in

2016 and then moved on. He argues uh

that these chips are really designed to

be the low margin inference computers

for the artificial intelligence wave.

And he made this pitch and this argument

possibly in a way not to piss off Nvidia

to say, "Oh, we actually help Nvidia."

And there are many interviews of him

saying we help Nvidia because Nvidia can

sell their high margin GPUs and we'll

just take the inference market while

they take training and inference is the

low margin business. Now I'm going to

get to why this was a perfect setup

right now. See the CEO of Grock never

said anything bad about Nvidia. instead

sold his product as something that could

actually help Nvidia. And I think that

is exactly what set up the acquisition.

Now, why is this acquisition so

brilliant by Nvidia? And then we can get

into some more of the technical weeds.

Here's a simple way to compare the two

chips. This is the simple part. We're

going to leave some of the detailed weed

stuff for a little bit later in the

segment. So the detailed weeds separate

right now. Overview GPUs great at

training models. Think uh all like

memorizing the entire internet and

trying to generate some kind of world

view that we can then ask questions

about and generate new answers or new

ideas or new images from. Genai. TPUs

from Google have been an attempt to do

something similar at a cheaper cost.

Basically, cutting out Nvidia's margins.

That's why Google likes their own TPUs.

That's why Amazon is making their own

chips. There's an incentive for

companies to make their own chips to

really cut out those 75% gross margins

that Nvidia has. Now Gro is really or

Grock is really interesting because

Grock doesn't prop to say hey we're

going to help you train this worldview

model instead Grock comes in and says

hey we're going to help you where people

actually utilize artificial intelligence

and that's in communicating with AI bots

customer service agents live

translations through like metagasses

and what you'll find today is people

meme AI chat bots with how slow they

are. If you've ever gone on Tik Tok and

you've seen people joke with the the

sort of like uh talking to GPT in real

life, people be like, "Oh, uh, hey GPT.

Uh, can you let me know where the

closest pizza restaurant is?"

Ah, cool. You want to know where the

closest pizza restaurant is? Are you

looking for Chicago style or more of an

Italian style? You know, it's it's you

know, the latency is the awkward part

and people joke about that. Pilots have

made jokes about having AI as a co-pilot

and you'll get something like

GPT gear downoop.

Ah, I see. Uh, so you're requesting that

I put the gear down. Uh, is there a

reason you want to put the gear down now

compared to later? That just put the

damn gear down. You know, the captain

then freaks out, right? That's this sort

of joke. But that joke really helps us

understand where the problem in AI sits

today. Now, I'm going to inject my

opinion here for a moment. I've been

providing in my opinion facts and data

and kind of trying to build this story

up because it's a lot. It's a lot to

digest and so we're going to take this

elephant in small bites together. That's

how I like to teach. Uh so I want you to

think for a moment of uh overall when

you ask a chatbot a question uh you're

looking for a rapid response but you

don't really mind waiting for a moment

because it's a chatbot it's in your app.

But what happens when you're on the

phone and you're selling customer

service that seems humanlike,

but it has a massive delay. And that's

where that human effect from that humor

really highlights the problem with

delays. And that gets to my opinion and

it has to do with the commoditization of

AI. See, watch this.

I don't believe that generative uh AI or

any of the AI we have today is going to

lead to artificial general intelligence.

So to understand my conclusion here, you

have to understand that my bias is that

AGI is bull crap. It is this moonshot

idea that helps people raise money for

their startups, but artificial general

intelligence is not where we're going.

That's not where the money is going to

be made. So you have to understand that

first. Two, not only do I not believe

we're going to have artificial general

intelligence, but I also believe, and

I've been saying this for years, that

language learning models are going to be

commodities. There's nothing going to be

in the future. Nothing will be unique

about using Gemini or using uh uh

Twitter's Grock, you know, Elon's Grock

or using Llama uh or Anthropics Claude,

whatever. None of that will matter.

They'll all give you probably within one

percentage point the same answer. In

which case, if they commoditize, then we

already have a risk that we've overbuilt

our GPU capacity. Right? This is why I

have the opinion that in the long term

Oracle, Coreweave, NBIS, these companies

are in the long term untouchable because

they're bag holders of GPUs that Nvidia

wants to sell and not hold. This is

partly why I think Nvidia got out of the

chip holding business.

Now, they've made some creative deals

with Coree to lease some of those back

and that creates some circular

questions, but ignore that for a moment.

So, no artificial general intelligence,

commoditized chat bots. Got it? So, then

really the companies that win are

companies that utilize the chat bots

along with their own custom MLS the

best. Those could be aggregators. Those

could be aggregators like UIP path for

healthcare and finance. They could be

aggregators like uh you've got the

ontological setup where you kind of

structure data over at Palunteer and

then aggregate which LLMs were going to

use to create or decipher conclusions

from this big data helping basically big

corporations get into big data. Right?

Palunteer where UiPath might appeal to

sort of everyone. Palanteer sort of

applies or appeals I should say to the

richest companies and the rich

government. Okay. So they've really

niched into the expensive customer

cohort whereas UiPath is maybe more of

the discount version player, right? The

we're here for everybody to try to set

up these AI agents for everybody if you

will. Salesforce is in that boat as

well. Okay. Uh so so then you have

aggregators. Okay. So artificial general

intelligence don't see it happen. Uh

LLMs are just commoditized

encyclopedias. Think about it like

Wikipedia in your eyeballs, okay? You've

got Wiki right in front of your face all

the time. Uh and then on the flip side,

we have aggregators that can make money

or people who figure out how to actually

make the AI technology we have today

productive for mass industry. This is

where obviously I'm biased, but I think

it's a great example. I like that we are

creating real estate AI that does not

rely on artificial general intelligence.

It actually relies on our own machine

learning training and tuning partly in

combination with LLMs which are

commoditized. We love that they're

commoditized because they reduce the

expense of using LLMs. But our ML is

what gives us proprietary data and skill

and technology at House Hack and

Reinvest, right? you know that already.

We have a fundra for that closing at the

end of the year. If you want to buy the

AI product, which releases next week,

you can do that at reinvest.co or

househack.com. It's the same company.

This video is not a solicitation. Yes,

we are ending the 5% yield uh starting

December 31st. Now, if you get in before

that, you still get the 5% yield through

conversion. Obviously, it's a real

estate backed company and we're

launching our AI based on the skills

that we've been building and the MLS

that we've been building since 2018.

Before the AI boom, we have been working

on our MLS and so we're so excited to

bring these to mass market for agents

and otherwise. So, you have to

understand this baseline. Now, where

does Grock come into all of this? Where

Grock comes into all of this is speed.

They eliminate the latency of live AI

translations for language. This is one

of the reasons why I think Dualingo has

kind of been tanking a little bit

because yes, you can learn a language in

a fantastic manner with Dualingo. But

why do you need to learn a language if

you could just put on AI glasses or use

your phone or use AirPods like what

Apple is doing now? Say these are, you

know, simple. Put them in, two people,

put them in, what do you got? Live

translations. any form of latency kills

the functionality of that. Gro Gro's

product solves that and they solve that

by using SRAMM on the actual chips. Now,

that does end up leading to a larger

footprint for each chip, which isn't

ideal, but it solves that problem by

embedding the memory where the compute

is occurring. The reason they can do

this and the reason they can do this

with so much of a smaller set of memory.

Yes, SRAMM is much more expensive than

DRAM. It's like replacing, you know,

copper with gold. It's very expensive,

but you need so much less of it. Why?

Software. And this is the 4D chess move

from Nvidia. What I've just built out

for you are my baseline opinions, the

deal acquisition that happened and how

Grock's product is faster even though

the actual underlying memory SRAMM is

more expensive. This is a faster

product. But why is Nvidia doing this?

What is the 4D chess move? Well, this is

where I wrote down that I believe this

is a brilliant move by Jensen. Even

though people talk about this being a

memory hedge to hedge against rising

DRAM prices, to me, this is actually a

hedge against artificial general

intelligence, as in AGI never happening.

Because if AGI doesn't happen and LLMs

get commoditized, then GPUs aren't

actually that valuable. And guess who's

left holding the bag? Not Nvidia. No,

Oracle, Nbby, Iron, and the other uh

plays like frankly XAI uh or any GPU

hoarder. They're the bag holders. See,

AGI would need a lot of GPU and compute.

But commoditized LMS do not.

Commoditized LMS, in my opinion, need

speed. If we could do a deep research

and sit around for 45 minutes waiting

for the deep research to run or we could

get it instantaneously,

that's where the money is. Now, one of

the reasons practically that Grock is

able to pull this off is they utilize a

software infrastructure to predetermine

how the chips are going to operate. Now,

I'm not going to get into the deep weeds

of how the transistors and the

capacitors and the refresh rates and all

that stuff works inside of the actual

chips. We're going to keep this a little

bit more basic. But what I am going to

say is that this speed is critical and

the speed is created by software. Now,

if you remember what has Nvidia's moat

always been, it's not necessarily that

they have TSMC supply chains on lock.

That is very true. It's not necessarily

that uh they have uh SKH Highex's supply

chains unlocked or Micron or Samsung

supply chains unlocked to make sure

Nvidia gets what Nvidia needs.

And it's not necessarily to say that

Nvidia's GPUs won't be valuable, but it

is to say that what has made Nvidia's

GPUs even more valuable and has enabled

them to get a lock on the industry is

their CUDA software. But their CUDA

software today doesn't actually

integrate the same software that Grock

uses to speed up inference output.

That's what this is a play about. If you

read between the lines with what Jensen

shared in a leaked email, he said,

quote, "We plan to integrate Grock's low

latency processes into the NVIDIA AI

factory architecture, extending the

platform to serve an even broader range

of AI inference and realtime workloads."

Ah, realtime workloads. in other words

haven't actually been able to output

realtime workloads for the AI factories

the digital twins that Jensen always

pitches because they don't have as fast

of an inference technology because even

their Reuben chip which was supposed to

be faster at inference still relies so

heavily on GPUs that because the GPUs

themselves the way they are architected

out have built-in traffic cops they are

slower. There is underutilization in the

GPUs and you're just not going to get

answers as quickly. Now, this is where

we could finally put the pieces of the

puzzle together to understand the 4D

chess move here. And I want to be clear,

I'm not trying to shill Nvidia. Uh like

I think there are big risks with Nvidia.

We're going to talk about that here. But

this is the 4D chess move. Nvidia buys

Gro's IP. They integrate the software

technology of using software to be the

traffic cop rather than the chip to

organize in a deterministic way how the

chips are going to operate. In English,

make it damn fast through software.

They're going to integrate that software

intellectual property into CUDA. Now

CUDA becomes from behemoth to mega

behemoth of the future. See, I think we

go from a future of AGI, which ain't

happening anytime soon, so nowhere,

right? I think we go from this world

view language model or world model

generation through GPUs to just give us

answers fast and real-time answers fast.

And that will be useful in piloting, in

factories, in machinery, in healthcare,

and in surgeries, anywhere you need

realtime

solutions. Now, Nvidia builds that

software into CUDA, making CUDA even

more of a behemoth for the next

generation of artificial intelligence.

And ironically, the next generation of

artificial intelligence isn't actually

any different than what we use today.

The only difference is faster. It's the

same crap delivered to you faster.

Nvidia moes it into CUDA. When Nvidia

moes it into CUDA, guess who

[clears throat]

is going to be building the chips for

these inference purposes that then the

next bag holders can buy from Nvidia at

no not low margins but at high Nvidia

margins. Well, the people who are going

to buy it are going to be the next

generation of data centers. Whether

they're data centers in space, probably

not, or just NBIS 2.0, Coreweave 2.0,

Oracle 2.0, whatever. Those new bag

holders are then going to buy these

SRAMM centric chips that speed up our

artificial intelligence inference

output. So, the answers that we get from

AI. So, the next generation of bag

holders will buy those chips. The first

generation again, Oracle, Enbis,

Cororeweave, uh, whatever, XAI, they'll

suffer. Gen two will then meme up until

the next innovation comes and then those

become the next bag holders. But guess

who wins every time? Every generation of

bag holders continuously the company

that wins is the one selling the product

at the highest possible margin because

they've locked the industry down with

CUDA for worldview training and now CUDA

for inference output. It's Nvidia.

It's brilliant. Let me take a look at

some of my other notes because that was

all kind of from the top of my head

there. But I've been I've just been

absorbing so much of this. I love this.

This I think is very exciting. I I I

think Jensen is not only the most

brilliant salesman in the world uh for

selling his stock and his company and

his product

next to maybe what you see at Palanteer.

Uh but but he actually puts out like

these margins are incredible. So let's

take a look at some uh some of these

other notes here. So uh let's see. We

spend okay there's this thesis that you

spend money on training and you make

money on inference. So I kind of agree

with that. I think people don't really

care that you're training a model. They

want the output. It's kind of like us

with my little AI startup disrupting

real estate, right? We're trying to

democratize wedge deals across the

entire country. Nobody knows wedge deals

more than me. We created the wedge deal.

Basically, it's like buying a Warren

Buffett real estate with a margin of

safety. And if we can put that into an

app and sell it and help people make

tens to hundreds of thousands of

dollars, knock on wood, hopefully the

value of that app will be massive. And

the point there is not again to pitch

house hack. It's to simply say that when

you can utilize AI in places that hasn't

been used before, like this old school

industry like real estate that most tech

bros know nothing about today. That is

where money, actual money can be made.

So I I couldn't be more excited about

that. So um the founder of Grock also

talks about how much latency matters. I

love this. This was from a 2024

interview. He says if you show a human A

or B and say which is faster, they won't

know. It's imperceptible these days to

tell you which is 100 milliseconds

faster A or B. But if let's say B is 100

milliseconds faster, conversion with

that 100 millisecond improvement will

increase by 8% on desktop and 30% on

mobile. In other words, it matters.

Speed matters. That's what he's trying

to say. But we we already know this. I

mean, do you want a humanoid robot

that's walking around likeoop?

No. [laughter]

Even Jack knows he doesn't want that.

He's laughing about it over there

because nobody wants aoop

robot. We want fast answers, baby. So,

this is very exciting. Now, um, Grock

LPUs, language processing units. This is

just sort of the fancy name they have

for it. Great for voice AI. Instant

text. Relies on static. A random access

memory. That's SRAMM. It's fast. It's

expensive. It's low capacity. The

bottleneck is how much capacity you

could have, but you could string

hundreds of these together if you want,

which is exactly what Nvidia loves to

hear. Like, think about that. Nvidia

hearing what what somebody wants to

string hundreds of these together. We'd

love to sell that product. It's kind of

brilliant. Uh, okay. uh they uh kill

logic control on the actual chip which

lets the chip just do what it wants to

do which is focus on speed. Uh the

analogy that I came up with this uh with

on this is think about the GPU kind of

like a semi-truck. Massive volume,

efficiency of volume, not very nimble,

unstoppable once it's going like it can

move a lot of product across the United

States really well. But if you're like,

"Bro, I just need this letter from

Kevin's house to the post office fast."

You take the Tesla Roadster baby, you

take the roof off, you put the jetpack

on the back, you get airborne, and you

go deliver that piece of mail. That's

the difference between Gro's product and

a high bandwidth memory GPU semitr. Uh

so uh you know some people say Nvidia is

just hedging against a DRAM uh you know

supply chain collapse or some kind of

inefficiency coming in the future. Uh

maybe maybe I think that's like uh

that's like basic level analysis like

okay sure. Uh I think you can also say

that you know Gro's had a lot of deals

with the Saudis and uh and has raised

money from the Saudis and uh has has

made deals with the Saudis. I think

there could be an opportunity there for

Nvidia also to sort of get deeper into

the door of the Middle East because for

Nvidia to really keep growing. They kind

of need to start selling to like rich

countries like Singapore or which they

already do to some extent but more to

the Middle East, the oil rich countries,

right? uh Nvidia gets the brains and the

AP. They're leaving or the the IP and

they're leaving the cloud business

intact again because Nvidia doesn't want

that. They want to sell to the cloud

business. If anything, Grock ends up the

leftover business ends up being a buyer

of these Nvidia chips, right? Uh these

now Nvidia Grock chips. It's the

ultimate irony. It's like you bought the

IP of the software and the technology

and the chip from Grock and then turn

around and sell to what's left of Grock.

Now Nvidia branded Grock chips. It's so

brilliant. This to me is offense on

commoditization, defense on

commoditization. It's you're basically

making a bet that everything's going to

commoditize and so you're going on

offense on that, but then you're also

defending against the decline of revenue

that's probably going to happen on GPUs,

which is bad for AMD, right?

Uh then uh and AMD will catch up. You

know, I'm not trying to be bearish AMD.

Uh let's see. Oh, yeah. Okay. And then I

did want to say that like, you know,

does yes, I I've got a $300 price target

for Nvidia, but what what gives me the

biggest concern for Nvidia? Okay.

Biggest concern for Nvidia,

Sam Alman. Uh and this actually reduces

some of that concern. I've continuously

believed that Sam Alman with his $ 1.4

$4 trillion of spend is a ridiculous

ludicrous joke and it's going to fail.

It's it's not a bet I want to make on.

So, I've been escaping Nvidia because of

that. This actually flips that because

over the next not necessarily short

term, but over the next two to four

years, this actually makes Nvidia a

crazy powerhouse. Now, that doesn't mean

there can't be a correction in the short

term, especially if you know we actually

do end up going into the commoditization

direction and Sam Alman falters. Who

knows, maybe Open AAI goes bankrupt.

They stop getting funding. Private

credit creates problems. Labor market

stress leads the market to fall.

Whatever. If the market falls, then

spending amongst the upper 40 to 60% of

American spenders falls. That leads the

economy into a recession. There'll be a

better buying opportunity to buy Nvidia.

And then that $300 price target will

come true in maybe a decade, right? But,

uh, if if Samman stays up and running,

then hey, maybe it'll happen sooner. But

those are like that's my concern with

Nvidia. So I can't say like oh my gosh

yolo calls on Nvidia because there are

road bumps on the way or in in the path.

But if you are a very long-term hodler

here, this is a a moat builder. They

just deepen their mode against Nvidia,

against Google, uh it doesn't matter.

They're they're deepening their mode

against anyone and they're staying away

from the bag holders which are buying

the chips from Nvidia at 75% gross

margins, 55% net margins. This is

absolutely brilliant. 4D chess. Like,

man, I think it's so freaking awesome.

Uh, this this is the kind of stuff that

like I want to emulate after with with

my startup house. Sorry, I always go

back to that, but it's my baby, you

know? I want to do this kind of stuff

over the next 60 years. I think this is

this is so cool. Knock on wood, maybe

even longer. But, um, yeah, very

excited. So, if you want to learn more,

remember, none of this this video can't

be a solicitation. Just go to

househack.com, reinvest.co, learn more.

If your browser for some reason doesn't

work, there's one little IP sector

somewhere in the country uh for I think

it's Spectrum. Uh use your cell phone

and then the website should work. But

like 99.9% of you have access uh with no

issues at all. And uh if you want to

actually buy the AI product, you can do

that. We'll be raising the price on

December 29th and we'll have a big

coupon expiration because the product

will be releasing and the coupon code is

release the app. [laughter] Anyway,

thanks so much for watching. We'll see

you in the next one. Goodbye and good

luck out there.