The scale of modern A .I. is it's almost impossible
to really grasp. I mean, right now, the cost
of building the current A .I. infrastructure
is already more than the Manhattan Project and
the Apollo space program combined. That is a
staggering comparison. It truly is. We're talking
about physical structures that need the same
amount of land as, what, 450 soccer fields? And
they require enough electricity to light up a
million homes. This isn't just, you know, scaling
up tech. This is a whole new standard for global
infrastructure. Welcome to the Deep Dive. We've
gone through the latest research to bring you
the most crucial insights into this, this massive
shift. Our mission today is pretty clear. We're
exploring two huge frontiers in AI that are happening
at the same time. First, there's the conceptual
one, why LLMs have sort of hit a wall, and what
this idea of spatial intelligence actually means
for what's next. And second, we're going to dive
into the physical reality of it all, the just
eye -watering cost and the unbelievable scale
of these new ultra -mega data centers that have
to run these models. That includes the, frankly,
astonishing $32 billion Stargate project. So
let's unpack this. We have to start with Fei
-Fei Li. For anyone who doesn't know, she's the
Stanford professor behind ImageNet, which is,
I mean, it's essentially the foundation for the
whole deep learning revolution we're living through.
Right. And she just put out what is basically
a manifesto. It's a really strong declaration
that LLMs, large language models, have pretty
much reached their ceiling. She argues that the
future of AI isn't just about better language.
It requires something she calls spatial intelligence.
So what does that actually mean for an AI, I
mean? What is spatial intelligence when you take
it out of the human context? It's the kind of
thing we do every single day without even thinking
about it. Yeah. It's the ability to understand
and navigate and interact with three -dimensional
space. Yeah. Like think about catching a set
of keys, someone tosses you in a dark room. Or
a firefighter who has to instantly read a chaotic
smoke -filled space to find the safest way out.
It's about applying the basic rules of physics
and prediction to the world you can actually
touch. Exactly. Yeah. Light's big critique is
that LLMs, for all their amazing chat abilities,
are completely blind to reality. They don't know
that if you drop something, it falls down. Always.
They have zero concept of gravity or mass unless
we spell it out for them in text. So to fix that,
she's proposing that AI needs what she calls
world models. And she says these models have
to have three core capabilities to bridge that
gap between just language and actual physics.
Okay, so the first one is that they have to be
generative. The models have to be able to create
these really complex 3D environments that, and
this is key, strictly obey the rules of real
world physics. No weird floating teacups. Second,
they have to be completely multimodal. So they
need to process everything at once. Text. images,
video, depth maps, even data from real world
sensors. It's a full sensory understanding. Right.
And finally, they have to be interactive. They
need to predict with really high accuracy what's
going to happen when a user does something inside
that simulation. That's how you get real cause
and effect learning. And this isn't just a theory,
right? Her team at World Labs is already shipping
a tool called Marble, which takes a simple text
prompt and turns it into a 3D scene you can actually
walk around in. Yeah, and what's so fascinating
is how they're doing it. They're probably using
things like neural lamberis, neural radiance
fields, which are these models that can build
a 3D scene from 2D images. It's moving from AI
describing the world. to AI building it. I still
kind of wrestle with trying to figure out how
to bridge what I type into an LLM and what the
real world does. It just seems like such a massive
leap. That's a totally fair struggle. It really
gets to the heart of the challenge. But the need
for agents that can operate in the real world,
you know, robots, self -driving cars, it means
the industry is being forced to push for these
hybrid spatial models right now. Since language
is so, so foundational to how we think. How quickly
is this shift from pure language to these spatial
models actually going to impact the AI tools
that we're all using every day? The need for
real -world interaction is already pushing the
immediate deployment of these new... hybrid models.
So moving from the theoretical, let's look at
what's happening on the ground right now. Even
as we're looking towards spatial AI, we're still
grappling with some really basic challenges and
seeing some fascinating new applications. Yeah,
on the challenge side, there's still that fundamental
problem of why AI struggles so much to tell the
difference between a fact and a subjective belief.
One report called it the missing piece, which
is really about causal modeling. The AI often
gets the what, but it totally misses the why
behind the data. And at the same time, agents
are getting so much smarter. I love this example
of the Minecraft AI agent named Steve. Oh, it's
brilliant. You can give Steve just one high -level
command. Something like, mine some iron or build
me a castle. And Steve doesn't just do it. It
actually spawns a bunch of other little agents
that coordinate and work together like a real
team to get it done. OK, let's talk about friction,
because that is definitely heating up, especially
around data. We saw a huge example of this with
Wikipedia recently. Right. Wikipedia, which is,
let's be honest, the bedrock for so much AI training
data, is basically telling AI companies to please
stop scraping its entire site. They want everyone
to use their paid API instead. Why is that such
a big deal? Well, it's about fairness. Yeah.
You know. Giving credit for all that human labor
and making sure the data quality stays high.
They even made a pointed reference to Grokopedia,
which was a pretty clear jab at Grok and XAI
for allegedly relying so heavily on scraped content.
So it sounds like quality training data, the
fuel for all of this, is going to be heavily
monetized from now on. And that ties directly
into the money, into financing. Crusoe, which
is an AI energy and infrastructure company, just
secured a huge investment. 1 .3. eight billion
dollars it puts their valuation at 10 billion
that is a massive vote of confidence what makes
them so special crusoe's all about tackling the
energy problem They capture wasted energy, like
natural gas that would just be flared off at
a site, and they use it to power computing centers
right there. The fact that NVIDIA is a major
investor just shows how critical that link between
energy and compute has become. We're also seeing
friction in creative ethics. The showrunner for
Amazon's House of David called using over 350
AI -generated shots magical filmmaking. But a
lot of critics just immediately called it cheap,
a way to replace human artists. And that tension
is not gone. going away. It's a real philosophical
split between efficiency and human artistry.
Let's end this section on a positive note though.
Privacy. Google launched something called Private
AI Compute. It lets you use the full cloud power
of Gemini, but it ensures that no one, not even
Google, can see the data you're processing. Yeah,
they do it using secure enclaves, which are like
these hardware -level black boxes. It allows
for really sensitive data to be computed privately,
which is a huge deal for a lot of applications.
So what does Wikipedia demanding payment mean
for the future long -term availability of truly
open training data? It suggests quality training
data, the fuel of AI, will be heavily monetized
moving forward. Okay, let's shift now to the
physical frontier. the sheer infrastructure you
need for all this. An Epoch AI report said that
spending on these specialized AI data centers
is on track to pass $300 billion by the end of
2025. You have to put that number in perspective.
$300 billion is almost 1 % of the entire U .S.
GDP. It's more than the Apollo program and the
Manhattan Project combined. This isn't just an
investment. It's a state -level commitment to
a single piece of technology. And the headline
example, the one everyone's talking about, is
OpenAI's proposed Stargate Abilene project. The
numbers are just. They're hard to believe. We're
talking a $32 billion price tag. It needs those
450 soccer fields of land. And the critical part,
it will draw one gigawatt of power. That is enough
electricity for about a million homes, all for
one site. And it will have 250 times the compute
capacity of GPT -4. But here's the thing that
really changes the paradigm, the engineering
implication of this. It's the paradox of latency.
It used to be that you had to build data centers
near users to be fast. Now, latency just doesn't
matter as much. The reason is that the time it
takes the model to actually think and generate
an answer, the inference time, is about 100 times
longer than it takes to send data all the way
around the globe. Whoa. So imagine having so
much computing power that you could literally
bounce data off the moon and you would still
be bottlenecked by the model itself, not by how
fast you could send the signal. That changes
everything. It really does. The era of needing
to be geographically close for speed is just
over. Yeah. The entire physical constraint has
shifted. Now, data centers get built wherever
power is cheapest and most available, not where
the users are. Which brings us right back to
that power challenge. Only a few countries can
realistically handle multiple sites that each
need over a gigawatt of power constantly. The
solutions seem to be starting with natural gas,
then layering on solar and wind through big grid
interconnects. The race isn't for faster chips
anymore. It's a race for massive, reliable energy
sources. So if the competition has completely
shifted from speed to just sheer scale and access
to power, does that fundamentally change? who's
going to lead the next phase of AI. Absolutely.
The future is being shaped by the very few entities
that can build, finance, and secure these colossal
power sources faster than anyone else on the
planet. So if we synthesize everything from our
deep dive today, we really covered three major
themes. First, we're redefining the core objective
of AI. We're moving away from just pure language
and towards these complex spatial world models.
Second, at the exact same time, we're battling
this intense data and ethical friction in the
applications we have now. You know, everything
from creative tension in Hollywood to who owns
and gets to use Wikipedia's data. And finally,
all of this innovation, both the big ideas and
the stuff happening today, is being built on
an infrastructure of just completely unprecedented
scale. It's far bigger than any historical megaproject
in its cost and its power demand. So you now
have a pretty clear structure for understanding
how AI is going to evolve over the next decade.
You can see the conceptual limits, the immediate
frictions, and the staggering physical cost of
making it all happen. And here's something to
think about. If one gigawatt of power becomes
the minimum price to play in advanced AI, what
happens to the innovative coder in a garage?
What happens to that decentralized future we
all used to imagine? Something to mull on. Thank
you for joining us for this deep dive into the
dual frontiers of artificial intelligence. We
really encourage you to keep exploring these
topics.