I just got back from GTC and I'm

convinced that Wall Street does not

understand Nvidia. That's because the

best long-term investments come from

understanding a company's products, not

just their profits. And after everything

I saw, I believe that Nvidia will be the

first company on Earth to hit $10

trillion in market cap. Let me show you

why. Your time is valuable, so let's get

right into it. Look, I'm not here to

recap Jensen's keynote. Instead, I want

to share what I learned by actually

going to GTC myself, interviewing

Nvidia's executives, trying out

prototype robots, riding in self-driving

cars, touching a quantum computer, and

even talking to Jensen Hong himself.

After all the big announcements, the

mainstream media and Wall Street

analysts are focused on Nvidia's new

Reuben GPUs. But I think they're missing

the bigger picture. Vera Rubin isn't

just about faster chips. It's a

blueprint for the entire AI revolution

with huge implications for data center

spending, how AI systems will be

designed going forward, and of course,

what kind of stocks will win big as a

result. So, let me break down the

biggest things I learned at GTC and what

surprised me the most. Nvidia's Vera

Rubin platform and the new Gro 3

inference chips. How that hardware ties

into Nvidia's AI strategy for agentic

systems like Openclaw. the surprising

things I saw in self-driving cars and

humanoid robots and what I think this

all means for Nvidia stock going

forward. One thing that surprised me is

just how different Nvidia's Vera Rubin

platform is from Blackwell. It's not

just a faster system like a lot of

headlines are suggesting. Reuben comes

with fundamentally different approaches

to networking memory and even compute.

And that needed to happen for two very

important reasons. First, AI models

don't just get trained one time anymore.

They continuously get fine-tuned via

reinforcement learning. And second, AI

workloads are shifting from short chat

prompts written by humans to autonomous

agents like OpenClaw, Perplexity

Computer, and Claude. These agents are

calling tools. They're browsing

websites, writing code, and running for

millions of tokens at a time. And that

costs thousands of times more tokens

than regular chat prompts, which makes

power efficient, low latency inference

the new main cost driver for AI. This is

why I expect data center spending to

actually accelerate, not slow down like

most analysts predict. And this is why

Vera Rubin is a fundamentally different

system from Blackwell. It's designed to

produce as many useful tokens as

possible per rack, per watt, and per

dollar. So these openclaw style agents

are actually affordable to deploy at

scale. Nvidia announced seven new chips

as part of the Reuben platform. I want

to respect your time, so I'll list them

all out for you, but then I'll focus on

the two that really matter for

investors. The Reuben GPU is the main AI

chip. It has a new transformer engine

that gives it a much higher token

throughput versus Blackwell, about five

times higher inference performance, 3.5

times higher training performance, and

it cuts token costs by over 90%. The

headlines are right to call out these

insane improvements, but I'll show you

what they mean for the bigger picture in

a minute. The Vera Rubin CPU is an

ARMbased processor with 88 custom cores

designed to handle all the messy tasks

that GPUs are bad at like orchestration

and control, branching logic, and

preparing data. The Vera CPU schedules

and coordinates multi- aent workloads.

It handles API and tool calls, and it

runs any additional software and

services that are installed on that same

rack. Think about things like data

logging, monitoring, security services,

and so on. Vera has roughly three times

the memory capacity, double the memory

bandwidth per core, and double the

connection speed to the GPUs compared to

Grace. And it can also do full

confidential computing, which wasn't

available in the Grace CPU. So yeah,

CPUs are still very important to the AI

story. They just look very different

from the traditional CPUs we're used to.

The NVLink 6 switch chips connect all 72

GPUs together at the rack level. NVLink

6 has double the bandwidth from the

previous generation, around 3.6

terabytes per second. That's fast enough

to move around 250 fulllength 4K movies

between chips every single second. The

ConnectX9 Supernick is a network

interface card that sits in each compute

tray to move data between the network

and GPU memory as well as encrypt

traffic so that the network stays fast,

predictable, and secure as more racks

get added to it. The Spectrum 6 Ethernet

switch provides the backbone that

connects Reuben racks and storage pods

together along with co-packaged optics.

This is the part of the system where

Nvidia's $2 billion investments in

coherent ticker symbol COH and Lum

ticker symbol LIT come into play. Leave

me a comment if you want me to make a

full video about optical networking

because this technology is all about

making networks more resilient, more

error-free, and more power efficient.

These next two chips, the Gro 3 LPU and

the Bluefield 4 DPU are where I think

Nvidia really innovated the most. While

the GPUs, CPUs, and networking chips got

obvious upgrades, Gro 3 rewrites how

token generation works in general. And

Bluefield 4 adds a whole new context

memory layer for AI agents. By the way,

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start with Grock since it's one of the

most important acquisitions for

investors to understand. The Gro 3 chip

seems to be replacing the Reuben CPX

GPU, which Nvidia originally designed

for inference. But this Grock chip isn't

a GPU at all. It's an LPU, a language

processing unit. And it's crazy just how

fast Nvidia was able to integrate it.

Nvidia announced a $20 billion deal to

license Gro's technology and hire most

of the core engineering team on December

24th, 2025. Jensen showed off the first

Gro 3 LPX during his GTC keynote. That's

roughly 3 months from the acquisition

announcement to the first public demo

and 9 months from the start of the deal

to the first chip launch, which is even

faster than most startups can move.

Nvidia moved so fast because each Grock

LPU is built around 500 MGB of onchip

SRAMM that stores the model weights, the

activations, and the KV cache instead of

distributing them all over external

DRAM. I know that sounds like alphabet

soup, so let me say it in English.

Static random access memory or SRAMM is

small but insanely fast and it lives

right on a chip. It's expensive and

power- hungry per bit, but it has very

predictable memory access at low

latencies. DRAM is much larger but

slower offchip memory. It's cheaper per

bit and it's great for capacity, but

accessing it costs more time and energy

and latency can vary a lot under

different kinds of workloads. And during

his keynote, Jensen had a whole slide

dedicated to this difference. The Reuben

GPU has 288 GB of high bandwidth memory,

which is DRAM, while one Grock LPU has

500 mgabytes of SRAMM, almost 600 times

less capacity. My point is, these are

fundamentally different kinds of chips

for fundamentally different parts of the

AI chain. They even sit in separate

racks. Nvidia's LPX racks connect 256

Grock LPUs to create a dedicated ultra-

low latency path for the decode phase of

inference, while the Reuben GPUs focus

on training, prefill, and attention. If

you look back at Nvidia's original road

map, it used to have a Reuben CPX GPU

specifically designed for large context

inference. I even made a whole video

about it. That chip is now missing from

the latest slides. With these Gro

systems effectively taking its place, so

set another way, Nvidia spent $20

billion. They integrated Grock's LPU

architecture into their systems in under

a year, and they quietly replaced their

own Reuben CPX accelerator with

something that delivers up to 35 times

higher inference throughput per watt and

up to 10 times more revenue per rack

when serving large models. I honestly

think we're going to look back at

Nvidia's Grock deal as their most

important acquisition since Melanox.

Melanox is why Nvidia now owns the

networking technologies around their

GPUs, Spectrum X Ethernet, Quantum

Infiniband, and their Bluefield DPUs. So

far, we've talked about the Reuben GPUs,

the Various CPUs, and the Gro LPUs. But

Bluefield 4 is the piece that literally

ties them all together. Bluefield 4 is a

data processing unit or DPU. It sits

inside the Vera Rubin compute trays, the

Grock LPX trays, and the separate

context memory and storage trays. The

LPU in each tray handles the networking,

the memory access, and the data controls

so that the GPUs and the LPUs can focus

on generating tokens. And on the storage

side, Bluefield is the processor inside

Nvidia's new STX context memory racks.

These racks keep long-term agent context

on separate drives instead of on

expensive GPU memory. Then it pulls the

right data back into the GPUs right

before it's needed. That's how Ruben

keeps token speeds high while cutting

power costs for agents with long context

windows by around 5x. Here's what that

means in terms of performance at the

rack level. Pairing a Vera Rubin rack

with a Gro 3 LPX rack can generate up to

35 times the inference tokens per watt

and one STX context memory rack gives up

to five times more tokens per second and

five times better power efficiency for

long context workloads. So when you add

it all up, the Reuben GPUs, the Vera

CPUs, the Gro 3 LPUs, and the Bluefield

4 DPUs, as well as the context memory

and networking stack, you're looking at

a complete overhaul of Nvidia's hardware

portfolio that data centers can mix and

match. For example, data centers focused

on training and big batch inference will

mostly deploy Vera Rubin NVL72 racks.

But for real-time agentic workloads

where latency really matters, Jensen

suggested that about 25% of a data

center could shift to the new Grock LPX

racks. Here's another insight for

investors that I don't see any Wall

Street analysts talking about. We should

be watching Nvidia's data center

revenues for two key reasons. First,

Ruben gives Nvidia new ways to scale

beyond selling more GPU racks, like

layering on high-v value components and

services across more specialized racks

like Grock and these memory racks. And

second, if they break out revenue from

things like memory, DPUs, and LPUs, like

they did for networking, the mix will

tell us a lot about which workloads

their customers are leaning into. all

the way from classic model training to

supporting AI agents which helps us find

more winning stocks across the supply

chain. All right, now let's talk about

who actually uses all these tokens.

Jensen called OpenClaw the operating

system for personal AI. OpenClaw is an

open- source agent that can browse the

internet, code, call tools, and run for

millions of tokens at a time. This is

why I think token demand will be much

higher than most analysts expect. data

centers won't just be serving a few

billion people, but potentially tens of

billions of always on AI agents, burning

tokens to do everything that people

already do, except much faster and for

much longer, including spinning up even

more agents of their own. The problem

with OpenClaw is that it's an

open-source AI agent with root access to

everything on a computer. That's a

security and compliance nightmare for

enterprises. That's where Nemo Claw

comes in. Nvidia's open- source stack

that wraps Open Quaw with a policy

engine, privacy routing, and a secure

runtime environment so that companies

can build in guard rails to decide which

tools the agent can use, what data it

can touch, and where everything runs

locally, in the cloud, or on their own

Ruben pods. And now we've come full

circle. OpenClaw is what drives token

demand through the roof. Nemoclaw is the

control layer that makes agents safe and

deployable in the real world. And

Nvidia's Reuben architecture is the

hardware stack built to serve that flood

of tokens as efficiently as possible. As

more enterprises plug into OpenClaw and

Nemoclaw, more agents will use more

tokens. And that's how this software

story eventually shows up in Nvidia's

data center revenues. This is why it's

so important to understand the science

behind the stocks. We can see these

demand signals long before they show up

in the earnings numbers. But GTC also

made it clear that Nvidia isn't stopping

at software agents. They're going after

robots and self-driving cars to bring AI

to the physical world. So, let's talk

about that next. And if you feel I've

earned it, consider hitting the like

button and subscribing to the channel

and even sharing this video. That really

helps me out and it lets me know to make

more content like this. Thanks. Now,

let's talk about physical AI. What

really surprised me at GTC wasn't that

Nvidia is talking about robots and

self-driving cars. It's how far along a

lot of this technology already is and

how far behind the coverage feels. On

the robotic side, humanoids like

Agility's Digit are already running real

shifts in GXO warehouses. GXO runs

massive contract logistics warehouses

for big brands like Nike, Amazon, and

Apple. They design and operate the

warehouses and increasingly pack them

with automation and robots for their

customers. Agility's Digit is already

deployed under a robotics as a service

model, not just walking around on stage.

While I was at GTC, I saw an entire

ecosystem of robots for industrial

warehouses, hospitals, and even retail

all being trained on the same Isaac and

Cosmos world model stack. What most

investors are missing is just how fast

this can ramp once even a handful of

designs have proved themselves in the

field. Since everyone is training on the

same stack, a capability learned in

simulation for one warehouse or one

factory can be tweaked and reused for

the next 100 customers instead of

starting from scratch every single time.

So, between all the robots I saw at GTC

and everything I learned from

interviewing Spencer Huang, I suspect

the robot uprising, I mean, the physical

AI revolution could be here much sooner

than most people realize. And on the

autonomous vehicle side, I got to spend

an hour in Nvidia's L2++ Mercedes as it

drove through downtown San Francisco. As

it turns out, what would be edge cases

for the rest of us are pretty standard

road conditions in SF. We saw people

running stop signs and red lights. We

got cut off at least five times. We saw

double parked cars on the side of every

street and construction in the middle of

them. And the car handled all of these

cases with ease. And what surprised me

the most was just how natural it all

felt. The handoff between human and

computer was seamless in both

directions. And the system is just

flatout better than most people at

handling the hard stuff. predicting what

other drivers will do, deciding when

there's enough space to fit into a gap,

navigating around stationary and moving

obstacles, and finding a safe path in

spots where I would definitely hesitate

if I was the one driving. I'll drop my

full unedited ride as a standalone video

soon. But the big takeaway for investors

is that self-driving is already here and

it's ready to be rolled out across a lot

of different cars and fleets, not just

the chips, but the full software stack

with Nvidia's Alpio. reasoning model and

their drive Hyperion platform front and

center. The headline partnership here is

Uber. NVIDIA powered robo taxis using

Drive Hyperion and Alpamo are planned to

roll out on Uber's network in cities

like LA and San Francisco as soon as

next year and then expand to 28 cities

through 2028. Nvidia announced that

companies like BYYD, Gile, Nissan, and

Isuzu are developing their own level

four vehicles for ride hailing apps and

commercial fleets. And it's not just

robo taxis. Nvidia is targeting

autonomous trucking, buses, and

industrial vehicles, all of which will

share the same software, simulation

tools, and hardware building blocks.

When I asked Jensen what he thought was

the biggest near-term application for

these agentic systems like OpenClaw, he

said autonomous vehicles. and he

explained that even though Nvidia's

automotive segment is less than 1% of

their total revenues today, that's how

CUDA started too. And today, Nvidia is

delivering the trained AI models, the

standardized simulation environment, and

the onboard brain for ride hailing

fleets, for delivery vans, for trucks,

and for cars around the world. All of

which will keep feeding demand back into

their Vera Rubin AI factories. So, when

you zoom out from GTC, the pattern is

pretty clear. Nvidia isn't just selling

faster GPUs. They're wiring themselves

into every part of the AI economy. The

tokens, the agents, the robots and

self-driving cars, and the data centers

powering it all. This is the bigger

picture that I think most Wall Street

analysts are missing. This is why I

think Nvidia will be the world's first

$10 trillion company. This is why it's

so important to understand the science

behind the stocks. And if you want to

see even more science behind the stocks,

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.