I want you to close your eyes for a second. Just
picture this. You are sitting in the driver's
seat of a car. Okay. You are right in the middle
of San Francisco. It is completely chaotic. Oh,
yeah. You have got the trolleys. You have pedestrians
dodging traffic. That specific heavy fog is rolling
in off the bay. Now look at the driver next to
you. But here is the twist. The driver is not
a person. Right. And it is not a computer program
that was fed a million lines of code about traffic
laws. It is not reading a manual. Exactly. It
has never been told what a stop sign actually
means. Yeah. It was just shown a video of a person
driving. Just watching pixels. Just watching
pixels change on a screen. And it figures out
how to steer, how to brake, how to navigate the
real world. It is basically driving by watching
a YouTube tutorial. It sounds like science fiction.
It really does. Like some chaotic future timeline.
But this is happening right now. Yeah. We are
moving from the era of telling computers what
to do, and we are entering the era of showing
them who to be. It is terrifying and fascinating.
Welcome back to the Deep Dive. Today, we are
doing something a bit different. We have a lot
of ground to cover. We do. We are pulling apart
the entire ecosystem that makes that self -driving
car scenario possible. Right. We have this massive
stack of notes from the latest AI fire dispatch.
We are going to trace the money. The chips and
the code. Because you cannot understand the software
without the hardware. It is all connected. Lay
out the roadmap for us. We have three main pillars
today. First, we start at the macro level. The
engine room. NVIDIA. Exactly. NVIDIA's monster
quarter. We are going to explain this concept
that compute is revenue. The fundamental economic
law right now. Right. Second, we look at the
challengers. The hardware wars, new startups
taking a swing at the king, and how pricing models
are shifting for you. And third. We land on that
breakthrough you mentioned. Standard Intelligence
and their FDM1 model. The video learning brain.
That is the one. The implications are going to
stick with you. All right, let's get into the
engine room. NVIDIA. Every time we talk about
them, the numbers are just big. Massive. But
looking at this recent report, big is the wrong
word. It feels historical. It is relentless.
Let's look at the raw data so we are all on the
same page. NVIDIA posted $68 billion in quarterly
revenue. $68 billion. In three months. Up 73
% year over year. That is absurd. Right. For
a company valued at over $4 .7 trillion. Usually
growth slows down at that size. You expect that
from a startup, not a titan. Exactly. And peeling
it back gets crazier. $62 billion came from data
centers. $51 billion from AI compute GPUs alone.
Let's pause on that. Wait. $51 billion spent
on silicon chips in 90 days. Who is buying this
and why? This is that concept circulating right
now. Compute is revenue. Unpack that for us.
Think about it like this. Every single time you
interact with an AI, you are generating tokens.
So a token is just a basic chunk of text or data.
Perfect definition. A word is a token. A pixel
is a token. Here is the physics of it. Every
token requires a physical GPU chip to do a math
operation. Which is called inference. Right.
Inference is the physical math calculation needed
to produce one token. So unlike downloading a
static file from the old internet. Exactly. With
AI, every word generated costs electricity and
processor time. It is a direct linear relationship.
More users means more tokens. More tokens means
you physically must have more GPUs. If you lack
GPUs, the service literally stops. So more AI
usage equals more data centers. Right. Which
equals more NVIDIA revenue. It is a perpetual
motion machine of money. It is an intelligence
tax. NVIDIA collects a tax on every thought the
AI has. And the demand is exponential. Completely
exponential. Everyone wants the new Blackwell
chips, the Ferraris of the industry. But they're
sold out. Totally sold out. So companies are
buying everything. Even six -year -old GPUs,
the equivalent of a 2018 Honda Civic. They are
fully... booked. Fully booked in cloud environments.
Prices are rising everywhere. Supply cannot touch
demand. It's the classic gold rush analogy. Selling
the shovels. But Nvidia sells the shovels, the
pickaxes, and they own the mountain. There is
a complication though. China. Yeah, this is a
crucial nuance. Despite partial export approvals,
Nvidia reported essentially zero revenue from
China. Zero. That seems statistically impossible.
It is a rounding error. And executives acknowledge
why it is not just U .S. regulations. It is the
local market adapting. Right. Rising Chinese
competitors are stepping in. Backed by massive
IPO funding, they have to build their own infrastructure.
So can NVIDIA sustain a $4 .7 trillion valuation
if they're locked out of the second largest economy?
As long as token demand exists, Western infrastructure
remains absolute gold. Demand outweighs the geographic
lockout. Got it. For now, yes. Let's shift gears
then. Kings do not stay kings without a fight.
The market hates a monopoly. It makes customers
very nervous. Tell me about the challengers.
The newsletter mentioned Mad -X. Mad -X is an
AI chip startup. Most people haven't heard of
them yet, but they just raised $500 million.
Half a billion dollars to fight NVIDIA. What
is their specific angle? NVIDIA builds general
-purpose GPUs. Good at graphics, good at crypto,
good at AI. MATX is specializing. Stripping it
down. Building chips designed only for large
language models. Hyper -efficient. So less flexible
but faster for this one specific task. Exactly.
It signals investors want alternative hardware
desperately. And then there is DeepSeek. We saw
those efficiency headlines recently. Right. The
rumor is DeepSeek is freezing NVIDIA out of their
next model entirely. Which aligns with that zero
revenue figure. It does. But it is messy. There
are strong rumors they trained previous models
on smuggled NVIDIA Blackwell chips. Through gray
markets. Yeah. But for the next iteration, Huawei
reportedly got early access. So it is a mix of
geopolitics and pure strategy. Turbulence under
the surface. It is not just the hardware shifting,
though. The pricing models for accessing these
tools are changing. Big time. OpenAI is testing
a ProLite tier for $100 a month. Yeah. One hundred
dollars. I have to be honest here. I saw that
number and I felt some friction. I still wrestled
with justifying the $20 tier myself. It is a
psychological hurdle. It really is. Jumping to
$200 for the enterprise tier makes sense for
companies, but $100 for an individual. You are
not alone in that feeling. So what does this
$100 tier signal about the future of AI users?
It bridges the gap between casual chatters and
always -on power users. Moving from casual tool
to digital co -worker. Exactly. Think about an
agentic workflow. Define agentic for us quickly.
It means the AI acts autonomously to complete
multi -step goals. Okay. You tell it to research
five companies, write a report, and draft emails.
It thinks for 30 minutes? That burns massive
compute. So the $20 plan has to limit that. Right.
The $100 tier is for someone inextricably linked
to the AI all day. Speaking of being linked,
Anthropic just launched remote control for cloud
code. This is wild. You can run your Mac terminal
from your phone browser. Sitting in a coffee
shop, controlling your home desktop via AI. It
blurs the line of where compute happens. The
computer is just a cloud surrounding us. Which
leads us perfectly to the third pillar today,
the software breakthrough. Standard intelligence,
FDM1. This is mind -bending. For years, we talked
about large language models, text in, text out.
Predicting the next word. Right. But FDM1 is
a computer action model. It does not learn from
text prompts. It learns by watching video. Think
about a toddler. You don't hand a toddler a manual
on how to open a door. They just watch you turn
the knob. They build a world model visually.
FTM1 does this with software. It watches frames
and reverse engineers the actions. Exactly. No
manual labeling. It sees the cursor move, sees
the click, sees the result. It is like stacking
Lego blocks of visual data. And the demos are
practical. It built mechanical gears inside Blender.
Blender has a notoriously complex interface.
A nightmare of menus. Writing code rules for
that would take decades. But watching a video
solves it. It learns the spatial relationships
automatically. If found software bugs, it followed
long desktop sessions. And the driving example
from the open. Whoa, imagine scaling that visual
learning to a billion everyday tasks. It used
keyboard inputs based on live feeds to drive
a real car. Arrow keys to steer. Spacebar to
brake. Mapping visual input directly to action.
It doesn't need to understand the car's underlying
code. Just the behavior of driving. It creates
a real moment of wonder. If it learns driving
by watching, can it learn plumbing? Surgery?
What about the compute cost, though? Training
on video sounds expensive. This is the surprise.
Some tasks required less than one hour of training
footage. Less than an hour. Less than an hour
of video. Highly efficient. That changes the
economics entirely. You don't need to scrape
the whole internet. You just record your expense
report process for an hour and it learns it.
If AI learns by just watching an hour of video,
what does this mean for prompt engineering? We
are shifting from telling AI what to do to simply
showing it. Showing, not telling. That removes
the abstraction of language entirely. You don't
need to be a good explainer, just a good doer.
It is a cohesive narrative arc today. Let's recap
the big idea. We started with NVIDIA. The massive
physical infrastructure required. The $68 billion
engine room. Then the challengers. Maddox specializing
hardware. Open AI shifting pricing for power
users. And finally, software becoming more human.
Standard intelligence proving the future of learning
is visual observation, not text. Which leaves
me with a somewhat provocative final thought.
Beat. We always worry about AI taking jobs or
scraping our data. Right. But if AI learns best
by watching us work, then our daily workflows
are not just work anymore. No, they're valuable
assets. Every time you navigate a spreadsheet,
edit a video, or drive to the store, you are
generating high -value training data. You are
actively performing the curriculum for the next
generation of models. That is a heavy thought
for your next Zoom meeting. Sit up straight.
The AI might be watching to see how it is done.
A slightly dystopian, but very real possibility.
You are not just an employee. You are a training
set. We will take a quick break here. That wraps
up today's deep dive. We will track standard
intelligence closely to see where this visual
learning goes next. Thanks for listening. Catch
you next time.