Speaker 1: What if.
Speaker 2: What if the most analyzed, the most debated tech magnate
in the world isn't actually competing in the industries we
think he is, right, I mean, think about it, while
everyone else, you know, Microsoft, Google, Amazon, they are all
aggressively playing chess on this incredibly crowded board, fighting over
the exact same scarce resources.
Speaker 3: The same chips, the same power grid.
Speaker 1: Exactly, And meanwhile Elon Musk is just off in the
corner building an entirely new board from scratch.
Speaker 3: It really fundamentally changes how you look at the whole
tech landscape. I mean, he isn't trying to out maneuver
the Titans by you know, writing slightly more efficient code.
Speaker 1: Right, or getting a fractionally better enterprise contract.
Speaker 3: Yeah, exactly. He is creating a completely separate gain where
the existing rules of scarcity, software integration, all of that,
it just simply does not apply.
Speaker 1: Well, welcome to thrilling Threads. And if you are joining
us today, we know exactly who you are.
Speaker 3: We do.
Speaker 1: You're the kind of listener who doesn't just want the
morning headlines, right, You want to understand the actual underlying
architecture of where the future is heading.
Speaker 3: So whether you are catching up on the absolute latest
tech trends for a massive strategy meeting, or you're just
insanely curious about how all these seemingly disconnected pieces of
the modern world actually fit together. Consider this your shortcut
to a massive aha moment and.
Speaker 1: The analysis where unpacking today is. Honestly, it's truly sweeping.
It connects dots that most financial analysts and even tech
reporters they don't even realize are on the same piece
of paper.
Speaker 3: Yeah, they're looking at the trees and missing the forest.
So our mission today is to thoroughly unpack this fascinating
comprehensive framework we've.
Speaker 1: Received, and the core thesis is wild, It really is.
Speaker 3: The thesis is that Elon Musk's ventures right Tesla, SpaceX
XAI optimists they are not a portfolio of separate, siloed.
Speaker 1: Companies, which is how everyone treats them, right, but.
Speaker 3: They are actually a single, unified, vertically integrated physical AI system. Yeah,
and we are going to explore every single layer of
this sprawling ecosystem today, from orbiting solar powered data centers
in space to cars parked in your driveway that moonlight
as office workers. It sounds like sci fi but it's happening.
Speaker 1: We want to see exactly how each piece feeds all
the other pieces. So where do we even begin with
an architecture that is this massive?
Speaker 3: Well, I think we have to start with the fundamental
paradigm shift in how artificial intelligence actually interacts with the world. Okay,
the overarching idea here is changing the playing field entirely, right,
And the most vivid illustration of this is something that
was announced fairly recently back on March eleventh.
Speaker 1: Oh right, the joint project.
Speaker 3: Yeah, between Tesla and Xai. Internally, they refer to it
as Macrohard or sometimes Digital Optimists Macrohard.
Speaker 1: I mean, I do love that. It's a very cheeky
literal inversion of Microsoft.
Speaker 3: Very on brand.
Speaker 1: Yeah, totally. So what exactly is this macrohard initiative attempting
to do?
Speaker 3: So It's an AIA agent designed to essentially do the
work of an entire software company.
Speaker 1: Wow.
Speaker 3: But to understand why this is a completely different game,
we have to look at how the rest of the industry,
you know, Microsoft's at Google's anthropic, how they are currently
building their AI agents. When you use say Microsoft Copilot
or Claude to automated task how is that AI actually
talking to the software.
Speaker 1: Well, it's communicating through the back end. It uses APIs
application programming interfaces exactly. It's basically reading the underlying code
of the software to figure out what data is there,
and then it injects its own code to make actions happen.
So the developers of the AI and the developers of
the software have to explicitly build those bridges so the
two systems can even talk to each other.
Speaker 3: And building those bridges is incredibly labor intensive. I mean,
you have to write custom integration code for every single
application you want the AI to interact with.
Speaker 1: It's exhausting.
Speaker 3: Yeah, and if an app updates its UI or you know,
changes its API structure, the bridge breaks. The developers have
to scramble to fix it.
Speaker 1: It's just brittle, very fragile.
Speaker 3: Musk's approach with digital optimists throws all of that out
the window. Instead of building an AI that reads back
end code, he is building an AI that functions exactly
like a human being does.
Speaker 1: Okay, So it's the difference between say, hiring a team
of interpreters to translate every single local dialect in the
world for you, versus just taking a magic pill that
lets you universally understand human intent.
Speaker 3: That's a great way to put it.
Speaker 1: If an API based AI is a train, it's stuck
on predetermined tracks, right yep. It can only go where
the custom integrations and the specific code have already been
laid down fast on its specific route. Sure, but completely
useless if you want to go off course. Exactly so,
how does digital optimists go off road?
Speaker 3: By using pure vision? Digitally speaking, it sees what you see, okay.
It continuously processes the last five seconds of screen video,
taking in the visual layout of a desktop, a web site,
or you know, a proprietary enterprise app, and it executes
actions using a digital mouse and a digital keyboard, exactly
like a human operator would. No custom integrations, no back
end APIs.
Speaker 1: I have to admit I struggle with this a bit
oh so well. I understand the elegance of the idea,
but navigating a chaotic computer desktop purely by looking at
it seems incredibly difficult.
Speaker 3: For a machine it is.
Speaker 1: I know, Ashock Elswami, Tesla's VP of AI, He's quoted
saying it's so obvious you can solve this with cameras.
It's an AI problem. But isn't analyzing live high resolution
video of a computer screen vastly more compute intensive, Oh absolutely,
and frankly more prone to hallucination than just reading a
clean text feed from a structured API. Like, why take
the harder road.
Speaker 3: Because if you solve the harder road, you achieve absolute universality,
meaning what exactly think about it? The digital world, our
computer interfaces, our software websites, it was all built for
qu eyes in human hands. We didn't build graphical user
interfaces for machines to read in the background. We built
them with buttons drop down. Men use visual cues that
are meant for biological processing.
Speaker 1: That makes sense.
Speaker 3: So if you TACAI to look at a screen and understand, ah,
that red rectangles is a submit button and that blinking
line is the text field, then that AI can suddenly
use every single piece of software ever created, past, present
and future.
Speaker 1: Without writing a single line of custom integration code. So
if a company updates their app tomorrow and moves the
button to the left side of the screen, the AI
just sees it moved and clicks it anyway, just like
a human would.
Speaker 3: Precisely you bypass the entire API bottleneck. Now regarding your
point about compute intensity, you are entirely correct. Yeah, videos
have Analyzing live video is immensely heavy, and this is
where the vertical integration really starts to show its teeth.
Speaker 1: Okay, wait on me.
Speaker 3: Digital Optimus isn't running on some generic cloud server. It
runs on Tesla's in house AI four hardware, which they
may facture themselves for only about six hundred and fifty
dollars per unit.
Speaker 1: Wow that cheap.
Speaker 3: Yeah. It processes the visual input locally right there on
the chip and only calls out to Xai's massive GROC
cloud when it needs heavy strategic.
Speaker 1: Reasoning what they call system two thinking exactly, So, the
local chip handles the reflexes like where the mouse is,
what the icon looks like, and GROC handles the logic
of what the task actually is.
Speaker 3: You got it.
Speaker 1: But the hardware aspect leads to an extrapolation that honestly,
it sounds like science fiction. There is a stated vision
that your personal Tesla, while it is just sitting parked,
can actually do this office work for you.
Speaker 3: Right if you follow the logic of the hardware, it
makes perfect sense. How so, every single Tesla equipped with
this AI four computer is essentially a highly capable localized
inference node. Okay, when you aren't actively driving it, that
computer is just sitting idle in your garage or parking
lot doing nothing, doing absolutely nothing. So the vision is
to turn those millions of parked cars into a massive
distributed compute network.
Speaker 1: So wait, while I'm sleeping, the computer inside my car
boots up digital optimists and just starts churning through digital
office tasks.
Speaker 3: Yes, and think about the contrast here. Everyone else in
the tech industry right now is fighting tooth and nail
to build massive centralized server farms for cloud compute.
Speaker 1: Right data centers.
Speaker 3: They are begging local governments for power permits. Meanwhile, Tesla
already has millions of these highly capable compute nodes deployed
all over the world. Yeah, and the best part, they
were paid for by the consumer.
Speaker 1: Oh wow, that is wild.
Speaker 3: And to power them for heavy compute tasks, they are
deploying millions of dedicated units at supercharger stations. Those stations
represent roughly seven gigawatts of available deployed power.
Speaker 1: Seven gigawats.
Speaker 3: Yeah.
Speaker 1: To put that in perspective for you listening, one gigawatt
can power a mid sized city that is a staggering
amount of energy infrastructure just sitting there.
Speaker 3: It really is.
Speaker 1: So instead of building a multi billion dollar data center
from scratch in the middle of a desert somewhere, he
just taps into the computers that people have already bought,
which are plugged into a power network he already owned.
Speaker 3: It's a completely decentralized supercomputer.
Speaker 2: Yeah.
Speaker 3: But we have to ask the critical question here, which is,
how does a car company get so absurdly good at
computer vision that they can confidently apply it to complex
office software?
Speaker 1: Right, Because navigating a cluttered spreadsheet is hard, but navigating
a cluttered intersection in Mumbai is a nightmare exactly. And
that's the secret, isn't it. They've been training this visual
intelligence on the chaotic physical streets for years.
Speaker 3: Which brings us to the foundation of this entire ecosystem.
We have to completely reframe how we view Tesla as
a company.
Speaker 1: Yeah. I think people still get this wrong.
Speaker 3: Most financial analysts still value Tesla primarily as an automotive
company that happens to have some cool AI features tacked
onto the dashboard hardware company, right, But the reality is
the exact inverse. Tesla is an AI company that happens
to sell cars to fund its data collection.
Speaker 1: So if they are an AI company, what is their
proprietary data?
Speaker 3: Video?
Speaker 1: Video? Right, every single tesla on the road is equipped
with eight cameras capturing continuous high definition video.
Speaker 3: Of the real world, every single one, every.
Speaker 1: Time someone drives to the grocery store, those cameras are
feeding the largest real world AI training data set on Earth, and.
Speaker 3: The scale of this data set is frankly difficult to comprehend.
The full self driving or FSD fleet has logged over
eight point four billion cumulative miles.
Speaker 1: Eight point four billion miles, but that's sa gift. I
mean the Earth is roughly twenty four thousand miles in circumference.
We are talking about driving around the entire planet hundreds
of thousands of times. It's not just highway driving in
sunny California either. It's snowstorms in Minnesota, deer jumping out
in rural Texas, chaotic pedestrian traffic in Manhattan.
Speaker 3: And the growth rate is purely exponential. They went from
six million miles in twenty twenty one to four point
twenty five billion by the start of twenty twenty five. Okay,
then in just the first fifty days of twenty twenty
six alone, they added another one billion miles.
Speaker 1: A billion miles in fifty days. Yeah, that means they
are capturing every conceivable edge case out there. Think about
the weirdest things you've seen while driving.
Speaker 3: Oh, there's always something like.
Speaker 1: A person walking a dog while riding a unicycle in
a chicken suit. A Tesla camera has almost certainly seen it,
categorized it, and trained the neural network on how to
react to it.
Speaker 3: That volume of edge case data is the ultimate mote.
Achieving safe unsupervised self driving at scale requires an estimated
ten billion mile benchmark just to iron out those statistical anomalies.
At their current pace, they will likely cross that threshold
this year now.
Speaker 1: Waimo cruz zekes these companies are also trying to build
self driving cars. They are, but they are playing a
fundamentally different game, right. They aren't trying to solve the
vision problem the way Tesla is not at all.
Speaker 3: They are relying on a completely different architecture. Yeah, the
competitors use expensive light OAR sensors which shoot thousands of
lasers out to create exact three D maps of the environment.
Speaker 1: So they use lasers, not just cameras, right, and.
Speaker 3: They combine those lasers with heavily pre mapped environments and
rigid geofence zones. They are solving autonomous driving city by city,
map by map, which.
Speaker 1: Goes back to the train analogy from earlier exactly a
WAYML car is a train on highly specific tracks. As
long as the car stays within the perfectly mapped, perfectly
laser scan zone of downtown Phoenix or San Francisco, it operates.
Speaker 3: Beautifully, flawlessly usually, But.
Speaker 1: If a construction crew alters an intersection overnight, or if
you ask it to drive on a dirt road in
Montana that hasn't been pre mapped, the system encounters the scenario,
it doesn't understand, and it just stops.
Speaker 3: It's a brittle system. Tesla conversely, relies on pure vision,
no expensive laser sensors, no pre map geofences, just cameras
and a neural network.
Speaker 1: So WEAIMO is trying to solve the specific task of driving, yes,
but Tesla is trying to solve the universal challenge of
vision and general spatial intelligence.
Speaker 3: Exactly because if you saw real world navigation using only cameras,
you haven't just built a chauffeur. What have you built?
You've built an artificial intelligence that inherently understands physics, depth,
object permanence, and human behavior. Wow, it knows that a
ball rolling into the street means a child might be
chasing it.
Speaker 1: But the industry debate around this is fierce, very fierce.
And I'll play devil's advocate here because the criticism of
pure vision is heavy.
Speaker 3: Go for it.
Speaker 1: Ladar provides exact mathematical distance. It shoots a laser, it
bounces back, The computer calculates the time of flight, and
it knows exactly to the millimeter how far away that
pedestrian is.
Speaker 3: That's true.
Speaker 1: Pure vision, however, relies on inference. The computer has to
guess the distance based on flat two D pixels. It
has to use parallax and temporal memory to infer three
D depth. When human lives are at stake at seventy
miles per hour, isn't relying on inference incredibly risky compared
to the certainty of a laser.
Speaker 3: It's the central tension of the autonomous vehicle race. The
defense of pure vision anchors back to biological equivalents. Biological equivalent, Yes,
the physical world was built for humans, and humans navigate
it without lasers shooting out of our foreheads.
Speaker 1: That's a good point.
Speaker 3: We drive using two optical sensors, our eyes and a
biological neural network behind them, our brain. We infer depth, speed,
and trajectory purely from visual data, and we do it
remarkably well.
Speaker 1: We do most of the time anyway, right.
Speaker 3: So the argument is that if a biological neural network
can do it safely, a digital neural network trained on
ten billion miles of diverse global data can do it
even safer and without.
Speaker 1: The crazy hardware.
Speaker 3: Costs, exactly without the crushing hardware costs and geofencing limitations
of light ar.
Speaker 1: It's the ultimate bet on software over hardware. And if
that bet on purevision pays off, the economic implicate cations
are just wild. They're staggering, because if you have millions
of data collecting AI robots on wheels rolling around the
world and suddenly you prove they don't need a human
driver at all, the entire economic model of car ownership
flips completely upside down.
Speaker 3: Which brings us to the concept of transportation as a service. Yeah,
and what is widely misunderstood about the robotaxi model.
Speaker 1: Yeah, let's talk about robotaxi. Whenever the media covers it,
the framing is almost always Tesla versus Uber. Always it's
portrayed as a battle of ride hailing apps competing for
market share. But that framing completely misses the forest for
the trees, doesn't it.
Speaker 3: It really does. It ignores the fundamental inefficiency of the
modern automobile. Cars as they exist today are arguably the
most inefficient assets in our society.
Speaker 1: Inefficient how well the average.
Speaker 3: Car sits idle for roughly twenty two hours a day.
Speaker 1: Twenty two hours.
Speaker 3: Yeah, it is a rapidly depreciating piece of heavy machinery
that just sits in a driveway or a parking garage,
bleeding money in the form of insurance, maintenance, and loan
payments every single month.
Speaker 1: It's a very expensive two ton metal brick taking a
valuable real estate ninety percent of the time.
Speaker 3: Precisely, the Robotaxi plan aims to take the seven million
tesla's already on the road and transform them from depreciating
liabilities into revenue generating assets. Okay, how when you aren't
using your vehicle, you tap a button on your phone
and the car wakes up, unplugs itself, and drives off
to provide rise to other people on the network.
Speaker 1: The proposed economics of this are fascinating. They are the
owner of the car keeps seventy five percent of the
revenue generated, while the network Tesla takes twenty five percent
to cover the routing, software, network management, and insurance. The
estimates suggests an owner could earn anywhere from ten thousand
dollars to fifty thousand dollars a year, depending on their
lotation and how often they let the car work.
Speaker 3: Consider the financial relief that offers the average consumer. If
your car is generating even ten thousand dollars a year
while you are asleep or sitting at your office desk,
the vehicle is literally paying for its own fire, financing,
and insurance.
Speaker 1: It transitions from an expense to an income stream exactly.
Speaker 3: And from a corporate perspective, the scale is an absolute
cheat code, a cheat code. Think about it. Even if
only ten percent of current Tesla owners opt into this network,
just a ten percent adoption rate that immediately yields a
fleet of seven hundred thousand autonomous vehicles operating globally.
Speaker 1: And what is the capital expenditure to acquire those seven
hundred thousand vehicles zero zero. The consumer already bought the
hardware Uber and weimo have to go out raise capital,
purchase expensive specialized vehicles, outfit them with tens of thousands
of dollars of light our equipment, and deploy them city
by city in a highly capital intensive rollout. It's painfully slow,
but Tesla simply pushes an over the air software update
to cars that are already sitting in people's driveways. It
is an instantaneous, distributed deployment.
Speaker 3: The Airbnb model taken to its absolute extreme.
Speaker 1: Oh totally, Airbnb let you monetize your spare bedroom, but
you still had to watch the sheets and you know,
interact with the guests. With this, your spare bedroom unplugs itself,
leaves your house, generates cash flow all night, and parks
itself back in your driveway before you even wake up.
Speaker 3: This completely alters the trajectory of personal transportation. It's not
just about undercutting ubers pricing, though. The forthcoming Cybercab is
designed to do exactly that.
Speaker 1: Oh the Cybercab.
Speaker 3: Yeah, the Cybercab, which is a purpose built robotaxi entering
production next month it will cost under thirty thousand dollars
to manufacture. It won't even have a steering wheel or pedals.
Speaker 1: Wait, no pedals at all, none, and.
Speaker 3: Operating costs are projected it under twenty cents per mile.
Speaker 1: Under twenty cents a mile, That is up to ninety
percent cheaper than a current uber or lyft ride. If
point to point transportation becomes that radically cheap and instantly accessible,
why would anyone living in a suburb or a city
ever buy a personal car?
Speaker 3: Again, that is the ultimate disruption. Transportation as a service
makes personal car ownership optional, even financially irrational for many people. Absolutely,
you'll limit the need for personal auto insurance, routine maintenance,
and home parking spaces. Yeah, the ripple effects through urban
planning alone, like turning massive concrete parking lots into housing
or green spaces, are staggering.
Speaker 1: It's a total re architecting of modern society. It is,
and yet incredibly solving the autonomous car is only the
beginning of the vision.
Speaker 3: Just the beginning.
Speaker 1: Because if you have successfully developed an AI brain that
is sophisticated enough to navigate the unpredictable chaos of a
busy city street and versatile enough to manage a digital
computer desktop. What happens when you take that exact same
generalized intelligence and give it arms and legs.
Speaker 3: You get Optimists and the true manifestation of a unified
physical AI ecosystem.
Speaker 1: When most people think of humanoid robots right their minds
immediately go to Boston Dynamics. Sure, we've all seen the
viral videos of the Atlas robot doing backflips, parkour and
dancing to pop songs. It's brilliant engineering, very impressive hardware.
But comparing Optimists to Boston Dynamics is fundamentally flawed.
Speaker 3: Isn't it entirely Yeah, Boston Dynamics build standalone, highly specialized
robotic products. They are incredible pieces of megatronics, but they
are bespoke machines built for specific physical tasks. Optimism is
not a separate product, and the traditional sense, it is
quite literally the physical manifestation of the car's AI.
Speaker 1: So the car and the robot are sharing a brain.
Speaker 3: They were running the exact same end to end neural network.
They use a single neural world simulator for training.
Speaker 1: Oh wow.
Speaker 3: The underlying architecture that takes visual input from a camera
on a car's bumper and translates it into steering and
breaking commands. Is simply adapted to take visual input from
the cameras in the robot's head and translate it into arm, hand,
and leg movements.
Speaker 1: That is a staggering concept. That means the eight point
four billion miles of driving data we discussed earlier that
massive mode of data isn't just making the cars better
at driving. It's actively making the humanoid robots it's smarter.
Speaker 3: Yes, because the fundamental challenge of both driving and robotics
isn't the specific task itself. The challenge is understanding the
physical world. Right by navigating billions of miles of roads,
the AI has learned the universal rules of physics. It
understands gravity, momentum, depth of perception, how solid objects interact,
and how humans move unpredictably through space.
Speaker 1: I understand the theory, but help me bridge the practical
gap here. If the AI learns how to successfully identify
and stop for a pedestrian at a busy crosswalk in Chicago,
how does that specific automotive knowledge translate to helping an
optimist robot fold laundry in my living room or assemble
a battery pack in a factory in Texas.
Speaker 3: It comes down to generalize spatial awareness. When the car
learns to stop for a pedestrian, it isn't just learning
a rigid rule that says stop at white lines, okay.
It is learning complex spatial reasoning. It is learning to
identify a three D object moving through a three D environment,
predict its physical trajectory, and manipulate its own physical form
the car to interact safely with that object. All Right
said that underlying spatial reasoning is identical to the reasoning
required for a robot hand to reach out graph the
delicate piece of fabric, understand the fabric's physical properties, and
fold it without tearing it. It's all just manipulating physical
matter in three dimensional space based on visual input.
Speaker 1: And because they are sharing this unified neural brain, it
creates an unstoppable flywheel effect.
Speaker 3: The flywheel is the core driver of evaluation model. Here,
let's trace the loop.
Speaker 1: Let's do it.
Speaker 3: You have millions of cars on the road collecting edge
case data. More cars equals more very data. More data
trains a vastly smarter generalized AI. Because they share a brain,
that smarter AI instantly creates smarter more capable optimist robots.
Those smarter robots are then deployed directly into tessel zone
factories to manufacture things cheaper, safer, and faster.
Speaker 1: And what are those robots manufacts uring. They are building
more cars and more robots exactly, which then deploy into
the world to collect even more data, spinning the flywheel
faster and faster, driving costs down at every revolution.
Speaker 3: The scale of this robotic deployment is already underway. Over
one thousand Gen three optimist robots have already been deployed internally.
Speaker 1: Wow.
Speaker 3: Furthermore, they are currently converting the Fremont factories Model S
and X lines to produce one million robots per year
one million, and with the massive gigatexis facility, they are
targeting a staggering ten million robots per year with the
upcoming Gen four.
Speaker 1: Ten million humanoid robots a year. It's almost impossible to
visualize that level of manufacturing. It is, but there's a
hard physics limit to this robotic flywheel, isn't there. You
can build ten million humanoids, you can deploy millions of
autonomous cars, and you can run digital optimists on millions
of parked computers, but all of that artificial intelligence requires
a terrifying, almost incomprehensible amount of computational power. Yes, and
you can't just plug all of that into the wall.
The electrical grid is already tapped out. This brings us
to a massive bottleneck that threatens this entire vision the
global energy crisis.
Speaker 3: If you look at the macrotns and technology right now,
energy is the only true currency. Everyone is building colossal
AI data centers to train their models, and they are
utterly draining regional power grids.
Speaker 1: How bad is it?
Speaker 3: US data centers are currently drawing forty one gigawants of power.
That represents one hundred and fifty percent increase in just
the last five.
Speaker 1: Years to put forty one gigawatts. In perspective, that rivals
the output of all the nuclear power plants in the
United States combined.
Speaker 3: Just for data centers.
Speaker 1: It's for data centers, and the trajectory is completely unsustainable. PJM,
which is the largest grid operator in the country serving
over sixty five million people across multiple states, projects they
will be six gigawats short of basic reliability requirements by
twenty twenty seven, that is next year. That is next year.
Speaker 3: We are facing the very real potential for blackouts simply
because server farms are consuming too much electricity.
Speaker 1: What's fascinating about the grid crisis is how it completely
crosses traditional political divides.
Speaker 3: It really does.
Speaker 1: You have politicians from completely different sides of the aisle,
like Senator Bernie Sanders and Governor Ron DeSantis both pointing
out the exact same issue. And we are just looking
at the math here right, not endorsing any political stance.
But when both sides are publicly raising alarms about the
sheer strain these data centers are putting on local power grids,
you know it's serious.
Speaker 3: Absolutely.
Speaker 1: When massive tech companies siphon off gigawatts of power, it
restricts supply, which drives up utility bills for everyday consumers.
The math doesn't care about politics. A gigawatch shortfall is
a gigawatch shortfall, and constituents are getting angry.
Speaker 3: With grid power becoming this incredibly scarce, politically volatile resource.
The major tech titans.
Speaker 1: Are panicking, they really are.
Speaker 3: Google, Microsoft and Amazon are desperately fighting each other for
whatever grid power is left. They're locking up long term
nuclear contracts. Microsoft is famously involved in a massive effort
to restart the decommissioned three Mile Island nuclear facility. They're
completely reliant on and effectively begging traditional utility companies for
permission to grow.
Speaker 1: But Musk is playing a different game here too. He
has a three prong strategy to completely bypass this earthly
grid bottleneck.
Speaker 3: We of number one Tesla Energy.
Speaker 1: Let's talk about it.
Speaker 3: While Microsoft and Google are begging for power, Tesla is
actually selling the power infrastructure. Last year alone, they deployed
forty seven gigawatt hours of commercial energy storage, generating over
twelve billion dollars in revenue. Wow, and they rely on
their own hardware. Xai's massive Memphis supercomputer named Colossus isn't
just subject to the whims of the local Tennessee grid.
Speaker 1: What do they use?
Speaker 3: It is powered and stabilized by one hundred and sixty
eight Tesla megapas, representing hundreds of millions of dollars of
proprietary energy storage, acting as a massive buffer.
Speaker 1: So they insulate themselves from grid instability. But storing power
still requires generating power, which brings us to move number
two solar generation right.
Speaker 3: The stated target is to build out one hundred gigawatts
per year of US solar manufacturing.
Speaker 1: Capacity one hundred gigawatts a year. That is more than
double what the entire US data center industry is currently consuming.
Speaker 3: The goal is complete energy autonomy. By pairing that massive
solar generation with the megapex storage, they can create self
sufficient mini power plants for their data centers and factories,
cutting energy costs by up to forty percent compared to
traditional fossil fuel reliance.
Speaker 1: It's like everyone in the tech industry is crammed into
a tiny kitchen viciously fighting over the very last slice
of pie, and Elon just walks outside and builds a
massive solar powered bakery in the backyard. Great analogy, but
eventually even the backyard isn't big enough because move number
three is where the architecture leaves the atmosphere entirely. Let's
talk about the space play.
Speaker 3: This is perhaps the most audacious paradigm shift in the
entire thesis has filed with the FCC for a constellation
of up to one million low Earth orbit satellites. A million,
but these are not for providing starlink internet to remote areas.
They're specifically designed to be solar powered orbital AI data centers.
Speaker 1: Orbital data centers. They want to put the heavy compute
in space. I hear that, and my initial reaction is
why go through the immense difficulty of launching servers into
the vacuum of space?
Speaker 3: Because in lower th orbit there are no grid constraints.
There are no local zoning boards to fight, no land
use battles with municipalities, and no politicians complaining about consumer
electricity prices.
Speaker 1: That's true.
Speaker 3: Most importantly, if you position them in specific sun syncredous orbits,
the sun never sets. You have continuous, unfiltered solar energy
bombarding the solar panels twenty four hours a day, seven
days a week. You never need a battery backup.
Speaker 1: The proposed math is staggering. SpaceX estimates that launching one
million tons of these satellites annually, with each ton generating
one hundred kilowatts of compute power, could add one hundred
gigawatts of AI compute capacity every single year, every single year,
free energy beamed directly from the Sun into silicon processors
floating in zero gravity. But I'm sorry I have to
push back on this. Sure it sounds like a billionaire's
sci fi vanity project. The sheer cost of rocketry aside.
What about the laws of physics? What about latency?
Speaker 3: H latency?
Speaker 1: Yeah, If I am sitting in my parked car and
digital optimist is trying to click a button on a
spreadsheet and it has to beam that request up to
a satellite, process it and beam it back down, the
lag would make the system completely unusable for real time applications.
Speaker 3: If they were relying on orbit for real time inference,
you'd be absolutely right. The round trip latency would be
a deal breaker for reflexes. But that's exactly why the
vertical integration saves. The concept has the local AI four
hardware inside the cars, and the robots handles the split
second real time decisions the fast reflexes. The orbital compute
layer would be strictly reserved for the heavy asynchronous training
of the massive foundation models.
Speaker 1: Oh, I get it right.
Speaker 3: When you are training a neural network on billions of
miles of video data over weeks or months, it doesn't
matter if it takes a few extra milliseconds for the
data packet to retorbit.
Speaker 1: Ah. So the heavy lifting is done in the sky
and the quick reflexes happen on the ground.
Speaker 3: Exactly and regarding your point on the sheer cost of rocketry.
That is exactly why SpaceX has spent the last decade
relentlessly developing fully reusable, massive payload rockets like Starship. Right,
the entire economic purpose of Starship is to drive the
cost orbit down to a level where launching a million
tons of server racks becomes financially viable.
Speaker 1: It all connects, It's a closed loop. SpaceX exists to
launch the data centers which provide the infinite compute to
train the AI which runs the robots which build the
cars and the rocket. It is dizzying, it really is.
But to pull off orbital compute, millions of robots and
autonomous cars at this global scale, you can't just rely
on outside suppliers for your basic building blocks. You have
to build the machine that builds the machine. Yes, which
brings us to the crucial concept of owning the stack.
Speaker 3: Vertical integration is the invisible connective tissue holding this entire
ecosystem together. Yeah, Apple, Microsoft, Google, they are all tech titans,
but none of them own the entire stack.
Speaker 1: That's true.
Speaker 3: Apple doesn't own its own power grid, Microsoft doesn't manufacture
its own vehicles, Google doesn't fabricate its own silicon or
launch its own space data centers. Musk's architecture seeks to
control the entire vertical, the energy, the chips, the factories,
the hardware, and the software models.
Speaker 1: Let's start with chips, because right now silicon is the
single biggest bottleneck in AI outside of energy. It is
if you are a tech company and you want to
train in AI, you are desperately waiting in line at
one specific company TSMC and Taiwan. You weigh in line,
you pay whatever in Nvidia charges for their GPUs, and
you hope geopolitical tensions don't disrupt the supply chain.
Speaker 3: But Musk is bypassing that line entirely. They are building
terrafact out, a massive twenty to twenty five billion dollar
chip fabrication facility in Texas, targeting a formal launch around
March twenty first. They are aiming for a cutting edge
two nanometer process technology, projecting a manufacturing capacity of one
million wafers per month by twenty thirty.
Speaker 1: Let's break down what a two minimeter process actually means,
because it sounds like jargon to a lot of people. Sure,
when we talk about nanometers in ships, imagine the transistor
as a microscopic drawbridge for electricity. The smaller you make
those drawbridges down to just a few atoms wide, the
more of them you can pack onto a single piece
of silicon.
Speaker 3: Exactly, more transistors mean exponentially more computing power. But crucially,
because the electrical current has less distance to travel, smaller
transistors require far less electricity and generate significantly less heat,
which is key absolutely when you are trying to solve
a global energy bottleneck and put computers inside humanoid robots.
Highly efficient microscopic chips are just as vital as massive
solar farms, and the.
Speaker 1: Hardware they are baking in Texas is custom designed for
their exact neural network architecture. The upcoming AI five chip
is projected to have forty to fifty times more compute
power and nine times more memory than the current AI
four generation. Usually, by designing and fabricating their own custom silicon,
they completely eliminate their dependency on nvidious pricing and TSMC's
geographic vulnerabilities.
Speaker 3: It guarantees total hardware sovereignty. And speaking of hardware, we
have to look at how they actually assemble these physical products.
The manufacturing process itself is a proprietary.
Speaker 1: Advantage right unboxed manufacturing.
Speaker 3: With the upcoming cybercab, they're introducing a manufacturing revolution. They
call it the unboxed process.
Speaker 1: To really appreciate unboxed manufacturing, you have to look at
the physics of a traditional auto assembly line. For over
a century since Henry Ford, automakers have built cars sequentially.
You start with a metal frame and it moves slowly
down a massive mile long conveyor belt. A worker puts
on a door or the line moves. Another worker drops
in an engine, the line moves. It's entirely linear, like.
Speaker 3: Stringing pearls on a necklace, one piece at a time
in a straight line. It's slow, it requires massive amounts
of factory floor space, and if one station breaks down,
the entire line stops.
Speaker 1: It's a bottleneck.
Speaker 3: The unboxed process shatters that linear constraint. It is modular
parallel assembly.
Speaker 1: Think of it more like a group of people building
a complex lego set. Instead of passing one single block around,
different teams are building different sections simultaneously exactly. One group
builds the front end, another builds the rear casting, another
builds the interior seats. All these complex subassemblies are completed independently,
and then they are brought together at the very end
and snapped together in one fluid automated motion.
Speaker 3: The efficiency gains are incredible. This parallel modular approach cuts
the necessary factory footprint by forty percent, it reduces labor
costs by thirty percent, and it cuts total assembly time
by fifty percent.
Speaker 2: Wow.
Speaker 3: Their target cycle time T is under ten seconds per vehicle.
That is in traditional automotive manufacturing anymore. That is the
speed and precision of consumer electronics. That is how you
assemble iPhones, applied to two ton autonomous vehicles, and.
Speaker 1: This manufacturing prowess is applied universally across the ecosystem. The
factories themselves are just nodes in the network. The Fremont
factory converts to building robots. Texas bills the custom chips
and the cybercabs. Houston builds the Megapax to power the supercomputers.
Speaker 3: They own the factories that build.
Speaker 1: The machines, and finally, governing all of this physical hardware
is the top layer of the stack, the software mind.
We touched on Xai's GROC earlier, but it is much
more than just a consumer chatbot generating text right.
Speaker 3: The consumer side of GROC is projected to generate roughly
one point two billion dollars in subscription revenue, which is nice,
But the real strategic value isn't a few dollars a
month from Internet users now, the real value is having
an in house foundation model. GROC is the system to
reasoning layer.
Speaker 1: Okay, unpack that.
Speaker 3: While a local AI on the car or a robot
handles the immediate real time spatial execution, rock ACTS is
the deep strategic thinker. It provides the contextual logical understanding
required for digital optimists to navigate a complex enterprise software suite,
or for a humanoid robot to plan out a multi
step physical manufacturing task.
Speaker 1: So it's the brains of the operation exactly.
Speaker 3: Having an in house competitor to open AI's GPT or
Google's Gemini that is natively seamlessly integrated into your own
proprietary silicon in your own physical robots is the ultimate
master stroke of vertical integration.
Speaker 1: Every single layer, from the solar panel to the silicon
ship to the software model makes all the other layers cheaper, faster,
and smarter.
Speaker 3: That's the ecosystem.
Speaker 1: Okay, we have just painted a picture of an absolute
technological juggernaut. It sounds invincible. It sounds like a closed
loop empire that cannot possibly fail. But what happens when
this grand vision collides with reality? We need to ground this.
Let's look at the immense risks and what financial analysts
call the unpriced optionality.
Speaker 3: We absolutely must address the execution risks because a plan
of this magnitude carries existential threats. First and foremost are
the timelines. Oh yeah, Elon Musk is notoriously, perhaps pathologically
optimistic about when these bleeding edge technologies will actually be finished.
Speaker 1: To put it mildly, the Robotaxi network was supposed to
be driving US around years ago. Optimists was supposed to
be a household helper by now right. Terafab is a
twenty five billion dollar construction project that will take years
to fully ramp up, assuming there are no supply chain disasters,
and the orbital data.
Speaker 3: Center still theoretical.
Speaker 1: Yeah, the technology to process heavy AI compute in the
radiation of space barely even exists yet, and launching a
million tons of hardware certainly doesn't have regulatory or FCC approval.
Speaker 3: And then there's the massive capital expenditure required to keep
the flywheel spinning while these technologies mature. Tesla's projected CAPEX
in twenty twenty six alone needs twenty billion dollars.
Speaker 1: That's a lot of money.
Speaker 3: That is an astronomical amount of cash to burn. If
these long term bets hit unexpected roadblocks, If the Rootaxi
network gets bogged down in endless municipal regulatory hearings, or
if the Optimist robot proves too clumsy to work safely
outside of a highly controlled factory environment, tens of billions
of dollars in capital could be completely scranded.
Speaker 1: Not to mention, the existing tech titans aren't just taking
a nap. Weimo is actively expanding their operational footprint right now,
mapping new cities. Google and Anthropic are releasing vastly smarter
AI models every few months. Nvidia is entrenching its GPU
ecosystem deeper into every enterprise on Earth. Just because a
new custom stack is being built doesn't automatically mean the
existing massive industry stack just vanishes.
Speaker 3: Those are very real existential risks, but this provides a
vital framework for you, the listener. As you evaluate this ecosystem,
you have to step back and look at the underlying
macroeconomic trends.
Speaker 1: Okay, let's walk through them.
Speaker 3: Ask yourself, over the next decade, are we going to
need more AI compute globally or less?
Speaker 1: Definitely more?
Speaker 3: Is energy generation and grid capacity going to be the
ultimate bottleneck for that compute?
Speaker 1: The data strongly suggests yes.
Speaker 3: Is pure vertical integration fundamentally more defensible and agile than
being totally dependent on third party international suppliers for your
chips and software?
Speaker 1: Almost always yes?
Speaker 3: And is real world, messy, chaotic visual data more valuable
for training physical AI than clean, simulated laboratory data. So
the final synthesis of this framework is this. The risk
is that this totally different game fails because the timelines
are simply too long, or the capital requirements are too heavy,
or the physics are just too hard to overcome. Right,
But the opportunity is that no one else is even
playing this specific game. If the timeline for the Robotaxi
or optimists slips by two years or even five years,
the underlying vertically integrated architecture being built remains entirely without peer.
Speaker 1: It's like judging an Olympic decaflee only by their score.
In the pole vault. If you look at Tesla and say, well,
their automotive profit margins dipped by two percent this quarter,
you are entirely missing the big picture of what they're
actually training for, completely missing it, and that brings us
to the concept of unpriced optionality.
Speaker 3: This is crucial for anyone trying to understand the financial
reality of this system. Currently, the broader market still largely
prices Tesla as strictly as a traditional car company.
Speaker 1: Yeah, they treat it like Ford or GM exactly.
Speaker 3: Analysts look at traditional automotive multiples, like how many metal
boxes they sold, what the margin on the metal was,
and they price the stock accordingly, perhaps sprinkling in a
slight premium for the AI hype.
Speaker 1: But if this vertically integrated thesis holds true, that pricing
model is completely disconnected from reality.
Speaker 3: Entirely disconnected. Because a scaled ROBOTAXI network wiping out personal
car ownership that's not priced in, enterprise grade digital optimists
functionally doing the back office work of entire software companies
not priced in a twenty five billion dollar terrafab churning
out proprietary hyper efficient AI chips not priced in, and
orbital compute data centers floating in space harvesting infinite solar energy.
That's not even on a market's radar yet.
Speaker 1: So for the learners out there who want to watch
this play out in real time over the next few years,
what are the key metrics we should track to see
if this grand vision is actually coming together or if
it's falling apart.
Speaker 3: There are three main indicators to watch closely. First, watch
the cybercab production ramps starting next month. If they can
truly utilize that modular, unboxed manufacturing process at scale to
turn out cheap vehicles, it validates their entire hardware and
factory thesis. Okay, that's one second. Closely, watch the commercial
energy storage deployment numbers. Every megapac they sell or install
is another brick in their independent decentralized power infrastructure. Got it?
Speaker 1: And the third.
Speaker 3: Third, we have to watch that ten billion mile FSD benchmark. Yeah,
when they cross that massive data line, which is imminent,
we need to see if it actually unlocks the generalized
unsupervised autonomy they've been promising, proving that pure vision can
conquer the physical world.
Speaker 1: If those three metrics hold strong, the flywheel is spinning
exactly as design exactly. This has been an absolutely staggering
journey through a truly mind bending framework. But before we
wrap up today, I want to leave you with one final,
deeply provocative thought, Tom all Over, something that builds on
all this but takes it one step further into the future.
Speaker 3: Okay, I'm ready.
Speaker 1: We've talked extensively today about leveraging vertical integration to drive
the cost of compute, transportation and manufacturing into the ground.
But if you extrapolate this ecosystem to its logical conclusion, right,
if energy eventually trends towards zero cost because of infinite
solar arrays in orbit, and if physical labor trends towards
zero cost because of millions of tireless humanoid robots, So
what actually happens to the fundamental nature of human economics?
Speaker 3: That is the million dollar question, or I guess the
trillion dollar question. Our entire global economic system, capitalism itself,
is based entirely on the management of scarcity.
Speaker 1: Exactly, the scarcity of human labor, the scarcity of physical resources,
the scarcity of energy.
Speaker 3: If this unified physical AI system successfully replaces scarcity with
absolute abundance, the rules of the game. Don't just change
for tech companies, they change for humanity. If energy and
labor are essentially free, what becomes the new most valuable
resource in our society? Is it pure human creativity? Is
it authentic emotional connection? We might be watching the infrastructure
of a post scarcity world being built right in front
of us, cleverly disguised as a car company.
Speaker 1: That is a thought that is going to keep me
up tonight. So we've laid out the pieces of this massive,
complex puzzle. Now we want to know where you stand.
Do you think this grand vertically integrated everything engine will
actually succeed and fundamentally reshape our economic reality? Or do
you think it is just a multi billion dollar house
of cards waiting for a stiff breeze of regulatory or
engineering reality to knock it down. Drop a comment, share
your thoughts, and let's keep this conversation going.
Speaker 3: Thank you so much for joining us as we unpack
this incredible vision. It's always a pleasure to explore these
paradigm shifts with you.
Speaker 1: Absolutely until next time, keep questioning the board you're playing on,
and thanks for joining us on this edition of Thrilling Threads.