Apple didn't win the AI innovation race. They
didn't build the smartest model. But last year,
they quietly collected nearly a billion dollars
from it anyway. Beat. Today, we look at the invisible
toll booths of the future. Yeah, it's a completely
wild strategy when you actually look at the numbers.
Welcome to this deep dive. I'm really glad you
are joining us. Yeah. We are exploring how AI
is kind of, well... escaping the chat box today
escaping the chat box and moving into the real
world from pixels to actual physics exactly and
our roadmap for today is pretty fascinating we'll
start with the sheer economics of ai distribution
right the silent victory apple pulled off yeah
then we will move into the wild ways people are
deploying these tools right now from selling
houses to building these crazy ceo agents putting
ai in charge of things right and finally we will
hit The absolute frontier. We're talking about
world models. This is where AI basically learns
the physical laws of our universe. It's kind
of the holy grail of where this tech is heading.
It really is. So let's start with segment one,
the invisible tollbooth, Apple's strategy. Which
is basically no strategy, right? Or at least
it looks that way on the surface. Right. They
seem totally behind in the generative AI arms
race. Yeah. But we have this new data from AppMagic.
The Wall Street Journal just reported on it.
The numbers are just staggering. They are. Generative
AI apps paid Apple around $900 million in app
store fees for 2025. Almost a billion dollars.
And they didn't even build the core technology.
Exactly. Yeah. And about 75 % of that revenue
came directly from ChatGPT. Wow. Yeah. And just
for context, XAI's grok was only about 5%. But
OpenAI is doing all the heavy lifting, paying
for all the compute. Yep. And Apple's just collecting
the toll. The fee structure is what makes it
so lucrative. They take a standard 30 % cut.
Right. Or 15 % if you're a small business making
under a million dollars. But for the big players,
it's 30 % of every single subscription. And the
growth trajectory here is just crazy. In January,
it was around $35 million. And then it peaked
in August at $101 million. In a single month.
In a single month. It stabilized a bit after
that, but still. The takeaway here is really
profound. Apple invests far less in AI infrastructure
than its rivals. Oh, massively less. But they
prove that, you know, it's like stacking Lego
blocks of data. Apple didn't mold the plastic.
They just own the table everyone is building
on. That is a perfect analogy. They own the table
because distribution is just so much more profitable
than invention right now. It really makes you
wonder about the long game. Is controlling distribution
always going to beat raw model intelligence in
the long run? I mean, I think so. Yes. Because
intelligence itself is becoming a commodity.
The models are all kind of converging in quality.
Right. Everyone has a smart model now. Exactly.
But access to a user's daily habits, the fact
that your credit card is already tied to your
face ID on your iPhone. Yeah. That friction or
lack of friction is the ultimate bottleneck.
So owning the user relationship matters more
than having the smartest math. Precisely. The
math is just a background feature at this point.
It's beat. Which brings us to our second segment.
Because people are using these distributed tools
in some really disruptive ways. The real -world
takeovers. Yeah, the real -world takeovers and
this whole remix culture. Because the tools are
getting so accessible, the lines are blurring
between who is building and who is just remixing.
The cursor is a prime example of this. Right,
the coding tool. Cursor recently admitted that
their new Frontier model is basically partly
built on Moonshot's Kimi 2 .5. Which is a powerful
Chinese model. Yeah. Plus, you know, some heavy
training on top of it. But it raises this big
question. Who is actually building from scratch?
Almost nobody, right? Everyone is remixing everyone
else. It's true. I have to admit, I still wrestle
with prompt drift myself, let alone knowing who
is actually building versus remixing. Prompt
drift is the worst. When the AI just kind of
forgets how you trained it. It draws me crazy.
But even with those flaws, everyday people are
doing incredible things. They are completely
eliminating middlemen. Like that tech CEO. Yes.
A tech CEO recently used ChatGPT to completely
bypass realtors. He sold his house for $100 ,000
over the estimates. Completely on his own. Yeah,
he just used the AI. For pricing strategies,
for staging the house, even getting paint ideas.
He saved tens of thousands in commission. Exactly.
Yeah. And the AI companies know this value is
huge, which is why we're seeing a shift in monetization.
The ads are finally here. They are. OpenAI is
rolling out clear sponsored ads for their free
and go tier users in the U .S. It was inevitable,
really. The compute costs are just too astronomically
high to stay totally free. Right. But taking
a step back, the craziest real world takeover
right now. is over at Meta. Oh, Mark Zuckerberg's
personal agent. Yeah. Zuck is actively building
a personal AI agent to help him be CEO. Just
a digital clone managing his day. But it gets
deeper. Meta employees are already using personal
agents internally. And these agents are communicating
directly with each other. Agent to agent. No
humans in the loop. None. They are just negotiating
schedules, sorting out logistics, all in the
background. But it makes me wonder, when Meta's
internal employee agents start talking to each
other, how do we prevent a closed loop of AI
hallucinations? Well, that's the thing. The human's
job fundamentally changes. You aren't doing the
task anymore. You are managing the system of
agents. We shift from doing the work to babysitting
the algorithms. Exactly. You are the high -level
editor, not the writer. Which is a great transition
to our third segment, stepping outside the screen.
Because AI is moving beyond just text and software.
It's bleeding into physical and immersive spaces
now. Yeah. Look at OpenArt. They just launched
a tool called Worlds. Oh, I saw this. It's wild.
It turns a single text prompt into a fully explorable
3D environment. You can literally walk inside
the world it generates. From text to a full 3D
spatial reality in seconds. Right. But it's not
just virtual reality. It's happening in the literal
dirt. Yeah. In agriculture. Holter. Yes, Holter.
They are an AI startup for livestock management.
And they just hit a $2 billion valuation. Led
by Founders Fund, which shows you how serious
the smart money is about this. Absolutely. They
use AI. to manage physical herds of cows out
in the real world. Tracking their health, managing
grazing patterns. It's a massive physical deployment
of AI. And even in the corporate world, you have
tools like Masterclass On Call. It actively analyzes
live meetings and gives you instant leadership
guidance. So it's observing human behavior in
real time and coaching you. Exactly. AI is becoming
an active participant in human reality. But here
is my concern. Moving from generating text to
managing a $2 billion farm is a fundamentally
different type of intelligence. It requires a
totally different architecture. Are we moving
too fast by putting AI in charge of physical
assets like livestock before it truly understands
the physical world? I mean, yes, that is the
exact limitation the industry is hitting right
now. Text models don't know what a fence actually
is. They just know the word. Right. Text models
can't truly understand a messy physical farm.
Exactly. Which is why the biggest players are...
We're all racing toward the next paradigm. Beat
the final frontier. Segment four, world models.
This is the big one. Let's define the jargon
here really clearly. An AI system trained to
simulate reality and physical laws. Yes. Not
just predicting words, predicting physics. Right.
Captures things like gravity affecting objects,
human motion. cause and effect over time. Because
an LLM, a large language model, it doesn't know
that if you drop a glass on concrete, it shatters.
It just knows the text associations. Right. But
embodied intelligence, like a robot in your house,
needs to deeply understand time, space, and motion.
Paki McCormick wrote a brilliant piece on this.
He argues that world models will dramatically
accelerate AI in everything physical. Robotics,
logistics, manufacturing, healthcare. Because
of the simulation loop. Explain that loop for
a second. So instead of a physical robot trying
a million times to grasp a cup and dropping it,
the world model simulates the environment perfectly.
It tests the action, learns from the mistake,
improves the parameters, and repeats millions
of times per second. Whoa. Two sec silence. Imagine
an AI scaling to a billion queries, simulating
centuries of robotic movement in an afternoon.
before ever moving a physical arm. It's mind
-bending. The robot is essentially fully trained
before the hardware is even switched on. But
the real world is messy. What happens when a
simulated world model encounters a totally unpredictable
human action in the real world? Well, the simulation
loop doesn't stop. It takes that anomaly, feeds
it back into the engine in real time, and adjusts
its understanding of reality instantly. It treats
our unpredictable chaos as just another data
point. Exactly. We are just new variables in
its physics engine. Beat. Okay, we need to take
a quick break here, sponsor break. Welcome back.
Beat. So let's synthesize today's big idea. We
really went on a journey here. We covered a lot
of ground. We started with Apple. They're quietly
raking in $900 million just by owning the distribution
of AI, the invisible toll booth. They own the
table everyone is building on. Right. Then we
looked at how everyday people and tech giants
are using these distributed tools to completely
replace middlemen. Real estate agents, assistants,
we're building networks of personal agents. Yeah.
But the ultimate takeaway today is this massive
leap from the screen to the soil. AI is moving
into the physical world. Building world models
to understand gravity, mass, and motion. Preparing
to operate in our actual physical reality. It's
the most exciting and kind of terrifying shift
in technology right now. Yeah, it really is.
So as we wrapped up, I want you to think about
this. As AI shifts from generating text to testing
actions in simulated realities, think about your
own daily workflow. What would you actually hand
over to it? Exactly. When your personal AI agent
finally connects to a physical world model, what
real -world action would you trust it to take
first? And more importantly, who is responsible
if it drops the ball? That is the million -dollar
liability question. It really is. Thank you for
joining us on this deep dive. We'll see you next
time. Out T -Row music.