OK, picture this. You have successfully gathered
the smartest minds in the history of the universe into one
single room. You have Einstein, you have
Curie, you have Turing, you have Hawking.
Millions of them. Actually, in the context of the
current tech landscape, these are your GPU's.
They are the silicon brains that are ready to solve cancer,
crack, fusion energy, and, I don't know, write the next Great
American novel all at the same time.
A room full of potential. Right, but there is a catch.
There is always a catch. And it's a massive 1.
The room is the size of a football stadium, and it is
deafeningly loud. Everyone is shouting.
And because sound travels relatively slowly, by the time
Einstein hears what Turing shouted from the other side of
the room, the man has passed. The calculation is stale.
So instead of a super intelligence, you just have a
very expensive, very loud cafeteria.
That is a terrifyingly accurate metaphor for the single biggest
bottleneck in artificial intelligence right now.
We are, you know, we're collectively obsessed with the
brains, the chips, the NVIDIA H1, hundreds, the black whales.
The shiny objects. Exactly the shiny objects, but
we have completely neglected the ears and the mouths, the parts
that let them all talk to each other.
Exactly. And that is where we are
starting today. We are ignoring the processors
for a moment to look at the hidden nervous system of the AI
boom. We are going to talk about a
piece of hardware that acts as the universal translator in that
room of geniuses. And this little piece of
hardware is called an optical transceiver.
It sounds like a prop from Star Trek, but it is actually the
specific component that might determine whether AI scales up
to the level of AGI or stalls out because of well.
Physics. It really is that critical.
It's the plumbing. And when the plumbing fails, it
doesn't matter how smart the people in the building are.
To guide us through this, we are pulling from a fascinating
TechCrunch article. It's dated February 17th, 2026.
The title is SpaceX Vets Raise $50 Layer Series A for Data
Centrelinks and it's written by Tim Fernholtz.
This story really has everything.
I mean, it has rocket scientists coming back to Earth to solve a
terrestrial problem. It has a $50 million bet from a
major VC firm, Drive Capital. And it has a manufacturing
challenge that touches on everything from global
geopolitics to the future of American industry.
It's a huge story packed into one little startup.
The protagonists here are a startup called Mesh Optical
Technologies, and as the headline suggests, they aren't
your typical Silicon Valley software kids.
No, not at all. They are former SpaceX engineers
who cut their teeth working on Starlink.
Which is crucial context, right? Right.
They spent years figuring out how to make satellites talk to
each other in the vacuum of space using lasers.
I mean, think about that environment.
You can't send a repairman. It has to work, and it has to
work perfectly for years. So they're trying to apply that
same rigor, that same kind of, you know, bulletproof
engineering to the data center. Exactly.
So here is our mission for this deep dive.
We are going to explain what an optical transceiver actually is,
using analogies that even a business executive who has never
touched a soldering iron can understand.
And we'll breakdown why the founders background at SpaceX
makes them uniquely qualified for this specific challenge.
And then we are going to explore the massive, and I mean massive
challenge of bringing lights out automated manufacturing back to
the US. It is a story about light speed
and the sheer difficulty of building physical things in a
digital age. It's atoms versus bits.
I love it. Let's jump right in Part 1, the
tech because optical transceiver sounds complicated.
It sounds intimidating. It does, but the concept is
actually very elegant if we strip away the jargon, I
promise. OK, so let's do that for the
business executive listening, who knows strategy, who knows
panel statements, but maybe skipped electrical engineering
in college. Help us visualize this.
Let's go back to our room of geniuses, right?
The loud cafeteria inside a computer chip.
So inside that geniuses brain information travels as
electricity. OK, it's electrons moving
through tiny copper or traces, and that works great for very,
very short distances, like millimeters inside a chip or
maybe centimeters on a motherboard.
It's super fast and efficient for that microscopic scale.
OK, so electrons are the local dialect.
They speak electricity. That's the internal monologue of
the genius. That is a perfect way to put it.
It's their internal thought process.
But electricity has a physical limitation as soon as you try to
push it fast over to long distance, and in modern
computing, long distance can just be across a server rack
barely a few feet. Just a few feet.
Yeah, we're not talking miles. As soon as you do that, it meets
resistance. The copper wire literally fights
back. It fights back it.
Gets hot, the signal degrades. It becomes noisy.
It's like trying to shout across that football field.
You lose your voice and the person on the other end only
hears mumbling. The message gets garbled.
So the local dialect doesn't travel well.
You can't shout electricity across the room and expect
anyone to understand you. Precisely.
You lose both speed and clarity, but there is a universal
language that travels perfectly over long distances with almost
no loss. Light.
Light photons. This stuff of fiber optics.
Right. Light is incredibly fast.
I mean, it's the speed limit of the universe.
It generates almost no heat compared to electricity during
transmission, and it can carry a massive amount of data without
losing integrity. You can send a beam of light
thousands of miles under the ocean and the message arrives
perfectly clear. OK, so now we have a problem.
The chips speak electricity, but the best way to connect them is
with the light. They're speaking two different
languages. Exactly.
So you need a translator. And that's the transceiver.
That is a transceiver. It is a tiny, incredibly
sophisticated little box that sits at the edge of the
computer. It takes the electrical signal
from the GPU, the local dialect translates it into a pulse of
laser light, zips it across the fiber optic cable to another
computer, and then another transceiver at the other end
catches that light pulse and translates it back into
electricity so the receiving chip can understand it.
So it's literally a translator box.
Electricity in, light out, and on the other side, light in,
electricity out. That is exactly what it does,
and it has to do this billions, even trillions of times per
second with near perfect accuracy.
It's an amazing piece of engineering.
For a long time we didn't really need this for short distances,
right? We used something else, right?
For a long time, we relied on radio frequencies, or RF, to
move data over copper cables for these shorter distances.
Think about your Ethernet cable at home.
But Travis Brashears, the CEO of Mesh Optical, put it really well
in the source material he said. I'm quoting here.
The world has primarily focused on radio frequencies for a long
time. We want to be at the precipice
of transition from RF to photonics.
Precipice of transition? That sounds dramatic.
It is, though. He's talking about a fundamental
shift in the physics of how we build computers, right?
We're hitting the wall with copper and RF.
You just can't push electrons any faster or denser without the
the components. So the future is moving from
pushing electrons through copper to shooting photons through
glass. Yes, and not just for the long
haul cables under the ocean, which we've done for decades,
but for the short, incredibly fast connections between
computers in the same room. The connections that form the
brain of an AI. So if I'm that executive, I'm
thinking, OK, I understand the box, it translates, but why does
this matter now? Why is this suddenly a crisis?
Because the sheer volume of conversation has exploded.
It's not one genius talking to another anymore.
It's a million geniuses all trying to talk to each other at
the exact same time. The vision is interconnecting
everything, right? I think Brashears mentions that.
He does. He explicitly says we want to
interconnect everything, and not just computers.
But that's where we're starting. This is step one in a much
larger vision. OK, so that's the what?
It's a high speed translator for the internet's internal traffic.
Now let's talk about the why. Because usually plumbing is
boring. You only notice it when it
breaks. Why is Thrive Capital dumping
$50 million into this right now? Because the plumbing is about to
burst. In fact, it's already leaking.
That sounds ominous. It's the scale of AI.
We have to wrap our heads around the numbers here because they
are just staggering. When we talk about scaling a web
app, maybe you add a few more servers to handle more users.
Easy enough. Yeah, When you scale an AI
model, you are building a supercomputer that acts as one
cohesive brain. All the parts have to work in
perfect concert. The source material specifically
mentions a 1,000,000 GPU cluster, which just a few years
ago would have sounded like science fiction.
Completely. And here is the key stat from
the article that blew my mind. Breshear says someone will brag
about a million GPU cluster. You have to multiply by 4 to 5
for the number of transceivers in that cluster.
Wait, hold on, let's do that math. 1,000,000 GPU's?
You're telling me you need 4 to 5 million optical transceivers?
Correct. 4 to 5 million of these high tech translator boxes for
one single installation. Why?
That seems counterintuitive? Why does one chip need 5
translators? Because of how deep learning
works, these models, like the ones powering the chat bots we
all use, aren't trained on a single computer.
They're too big. They are trained by slicing the
brain into thousands of pieces and spreading it across
thousands of chips. But those chips have to
constantly talk to each other. It's a constant, furious
conversation. Hey, I learned this pattern.
What did you see? I updated this weight.
You need to adjust that. So the chatter is constant.
It's not just one chip sending another a file to download, it's
active real time collaboration. It's relentless.
It's a many to many conversation.
Every GPU needs a high speed link to many other GPU's.
If those links are slow, if the translators can't keep up, the
whole system stalls. You have a million Ferraris
stuck in traffic. And at that point, you spent
billions on GPU's that are just sitting there waiting.
Exactly. You're just burning electricity.
That's why the market is exploding.
Companies are desperate for faster, more efficient plumbing.
And it is a massive market. The article brings up a really
concrete example. It mentions A supplier called
AOI. Yes, Applied Optoelectronics, an
established US supplier. A big player.
And they won a contract just last year worth $4 billion with
AB, with AB $4 billion. And that was just for AWS data
centers. 1 customer, one contract.
Wow, that gives you a sense of the pot of gold at the end of
this rainbow. We are talking about a
foundational component for the entire AI industry.
OK, so the money is there, the demand is there, everyone needs
this. But what is Mesh Optical doing
differently? Why not just buy from AOI or the
other existing guys? There must be a catch.
There is. The existing solutions are
hitting a wall in efficiency. This is where we get into the
weeds of the engineering a bit, but stay with me, it's
important. Let's do it.
Mesh is core innovation. Their secret sauce is a new
design that removes a specific, commonly used component from the
transceiver. The source calls it a power
hungry component. Right, they're referring to
something called a DSP, a digital signal processor or
similar components called retimers.
Think of these as little amplifiers and error checkers
for the signal. They cleaned up the mumbling I
mentioned earlier. OK, that sounds useful.
Why would you want to remove it? Because it uses a ton of power
and generates a ton of heat, it's a necessary evil in older
designs. Mesh believes their architecture
is so clean and efficient that they don't need it, or at least
they can use a much simpler, lower power version.
So by stripping that out, what's the impact?
They claim they can reduce the power usage of a GPU cluster by
3% to 5%. You know, I can hear the
listener saying 3%. That's it.
I tip more than that for coffee. It sounds tiny.
I know, right? In the consumer world, 3% is a
rounding error. You wouldn't even notice it on
your phone's battery life. Exactly.
But in the world of hyperscalers, Amazon, Google,
Microsoft, 3% is a fortune. An absolute fortune.
Because these data centers use as much power as a a small city.
A medium sized city in some cases.
We were talking about gigawatts of electricity.
A 3% of 5% reduction across a massive cluster saves 10s of
millions of dollars a year in electricity bills.
But honestly, the money is almost the secondary benefit.
What's the primary one? Heat.
Heat is the enemy. Heat is the ultimate enemy of
computing. Every single Watt of power you
use generates heat. If you generate heat, you have
to run massive air conditioning systems to cool it down, which
takes more power. It's this vicious cycle I see.
If mesh can reduce the heat generated by the plumbing by 5%,
that means you can pack the GPU's closer together, or you
can run them harder and faster without the melting.
It's a win for density and a win for performance.
Philip Clark, the partner at Thrive Capital.
He pointed this out too in the article.
He did. He said mesh is solving the
immediate term need for better interconnects to keep scaling
AI. This isn't a science project.
For 10 years from now. The hyper scalers have Rd. maps,
they know the power and heat limits that are about to hit
next year and they are desperate for solutions.
They need this hardware yesterday, literally.
So we have a massive demand, a bottleneck in physics, and a
solution that saves critical power and heat.
Now let's look at the team, because this isn't just three
grads from a coding boot camp. This is the SpaceX DNA.
This is one of the most compelling parts of the story
for me. The founders Travis Brashears,
the CEO, Chairman Ramos, the President, and Serena Grown
Heberly, the VP of product. They all work together at
SpaceX, not just at the same company, but on the same
projects. Yes, specifically they worked on
optical communications links for Starlink.
Starlink is the satellite Internet constellation, right?
The one with thousands of satellites.
That's the one. Thousands of satellites orbiting
Earth beaming Internet down to us.
But crucially, those satellites also have to talk to each other
in space. How do they do that?
With lasers, that is an optical link.
They are shooting beams of light from one satellite to another
across hundreds of miles of empty space to relay data around
the globe. So they were building laser
plumbing for space. Precisely.
And let's just think about space for a moment.
It is the harshest engineering environment imaginable.
You have extreme temperature swings, boiling hot in the sun,
freezing cold in the earths shadow.
You have constant radiation bombarding the electronics.
And most importantly, you cannot send a repair technician to low
Earth orbit. If the Wi-Fi breaks in space,
nobody can come and reboot the router.
Exact. You can't just jiggle the cable.
So the engineering culture at SpaceX is all about extreme
reliability, vertical integration, and aggressive
scaling. You don't just build 1 perfect
satellite, you build thousands of them cheaply and quickly.
And that mindset is what they're bringing to this new problem.
The article mentions a specific aha moment for them.
It wasn't just hey let's go do a startup.
It came out of a problem they were trying to solve at SpaceX.
It did. This is a classic scratch your
own itch story. They were designing a new
generation of space F satellites that were very compute hungry.
They needed to move a lot of data inside the satellite itself
between different processors. So naturally they did what any
engineer would do. They look at the existing market
for optical transceivers to see if they could just buy the parts
off the shelf, and they saw limitations.
That's the very polite engineering way of saying this
stuff isn't good enough. It's too slow.
It uses too much power and it's not reliable enough for what we
need. I love that it's the ultimate
engineer's move. We checked the store, didn't
like the merchandise, so we decided to build our own
factory. It's classic innovation by
necessity. They couldn't find what they
needed for the rigor of space, so they decided to build it
themselves. And then they had the bigger
realization. Wait a minute, the data centers
down on Earth have the exact same problem, just at a much,
much bigger scale. They are taking that space grade
mindset where failure is not an option and efficiency is
everything, and applying it to the data center floor.
And that SpaceX DNA informs their entire strategy.
It's not just about the design of the product, it's about how
they plan to build it. Which brings us to the biggest
hurdle in this entire story, the real crux of the challenge.
The manufacturing. The manufacturing this.
Is Part 4 the challenge? And frankly, this is the part
that makes me nervous for them, because designing a cool gadget
on a computer is one thing. Building millions of them is a
whole other ball game. And building millions of them in
the United States is a whole other level of difficulty on top
of that. It's like playing the game on
ultra hard mode. Let's look at their goals from
the article. They want to be manufacturing
1000 units per day within the year.
Is an absolute Sprint. And then the real goal is to
qualify for bulk orders in 2027 and 2028.
That means ramping up to 10s of thousands a day to feed the
hyperscalers. And the founders say the main
challenge, the thing that keeps them up at night, is executing
lights out automated manufacturing techniques.
Let's unpack that term. Lights out.
It's a very evocative phrase. It is.
Ideally it means you can literally turn the lights off in
the factory and leave because there are no humans working on
the assembly line. It's just robots building
things, 2047. Exactly.
It implies total automation, high precision, high speed, and
theoretically 0 human error. And here is the brutal reality.
The United States, as an industrial base, has largely
forgotten how to do this for optoelectronics.
The source is pretty blunt about this.
It says this expertise is heavily concentrated in China.
It is the China problem, as it's often called over the last 20-30
years. The entire supply chain, the
engineering talent, the specific institutional know how for mass
producing these tiny complex optical components.
It all moved to Asia and primarily to China.
There is an anecdote in the article that really, really
drives this home for me. It's about a European equipment
supplier. The German form story.
This stuck with me too. It's so telling.
Tell us about that, because it's a perfect illustration of the
problem. O Mesh Otical is in the process
of buying the complex machinery they need to set U their factory
in Los Angeles. They go to a German firm that
makes the best in class manufacturing gear.
The robots that build the transceivers.
The standard intake form for that German company, the one you
have to fill out to become a customer, asks for a Chinese
company registration number. Wow, not just company
registration number, specifically a Chinese one.
Just think about the baked in assumption there.
The form assumes by default that if you are a company buying this
highly specialized equipment, you must be a Chinese company,
because who else in the world would be buying it at scale?
That is a sobering detail. It shows just how entrenched
that global supply chain dominance is.
It's not just a talking point. It's built into the paperwork.
It really is. It's not just that China has the
factories. The entire global ecosystem of
suppliers and experts assumes China is the only place this
kind of manufacturing happens. But Mesh is trying to change
that. And this is where the
geopolitical angle comes roaring in Thrive Capital.
Philip Clark again. He didn't mince words about the
national security aspect of this.
No, he didn't. And just to be clear for our
listeners, this is the perspective of the investor in
the source text he wrote to TechCrunch, and I'm
paraphrasing, but the gist was if AI is the most important
technology of our generation and they believe it is, then having
the critical CapEx parts, the foundational hardware running
through misaligned competitive countries is a massive strategic
problem. Misaligned countries that is VC
speak for. We don't want our entire AI
backbone dependent on China. That's exactly what it means.
It's a supply chain risk. The article notes that trade
restrictions haven't hit this specific market yet, but
everyone sees the writing on the wall.
If geopolitical tensions rise, you don't want your access to
optical transceivers suddenly cut off.
That would strangle your domestic AI progress overnight.
So Mesh sees a strategic advantage in building a supply
chain outside of China. They're positioning themselves
as the secure domestic alternative.
They do. They are trying to get ahead of
the dilemma to offer a solution before it becomes a full blown
crisis for the US tech industry. And they are doing it by Co
locating their design and production.
Right. Their design team is in Los
Angeles. Their production line is being
built in Los Angeles. They're in the same building.
Why does that matter in a world of Zoom and global logistics?
Why not design in LA and manufacture in, I don't know,
Vietnam or Mexico? Because when you are trying to
invent a completely new manufacturing process, this
lights out automation. You need the design engineer
standing next to the robots on the factory floor.
If there is a tiny 3% efficiency gain to be found, you find it by
watching the robot, quinking the code and iterating instantly.
You don't find it by emailing a factory halfway across the world
and waiting two weeks for a new prototype to ship back.
The feedback loop has to be instantaneous.
So it's about the speed of iteration, the speed of learning
it's. Speed.
And they believe ultimately it's about cost.
They're making a bet that if they can crack the code on
automation, they can actually produce these things cheaper in
the US than they could by outsourcing, simply because they
eliminate the shipping, the delays, the tariffs, and the
communication overhead. That is the Holy Grail of re
shoring, isn't it? Bringing manufacturing back not
out of some sense of patriotism, but because it's actually Better
Business it. It the only way it works long
term. Patriotism is great for a press
release, but unit economics rule the world.
I want to pause on the lights out aspect for a second.
We say robots building robots, but what does that actually look
like for something this small and delicate?
That's a great question because we aren't talking about the
giant robotic arms you see welding car frames in a Tesla
factory. These transceivers are small,
maybe the size of a stick of gum.
The components inside are microscopic.
So we're talking about incredible precision.
Extreme precision. Remember, one of the key steps
is to align a tiny laser with a fiber optic cable.
The core of that fiber optic cable is about the width of a
human hair. You have to shoot a beam of
light perfectly into that hair width target, and you have to do
it while everything is hot and vibrating slightly, and then you
have to lock it in place with a microscopic dab of epoxy.
And doing that by hand is slow and.
Difficult. It's incredibly slow, and it's
prone to air. If a human technician has a
shaky hand after their morning coffee, the laser misses the
target and the part is garbage. Automation allows for what's
called active alignment. It's where the robot holds the
laser. A sensor measures the light
coming out the other end of the fiber, and the robot software
uses that feedback to adjust the lasers position by nanometers
until the signal is perfect, then it locks it in.
And it does this in a fraction of a second.
Right, to hit that 1000 units a day target and eventually 10s of
thousands, those robots have to be a blur of motion performing
these microscopic surgeries over and over again.
Florida State. So when they say lights out,
it's not just about saving money on electricity for the room
lights, it's about creating a process that humans physically
cannot perform at the required speed and quality.
Exactly. Humans become the bottom neck in
the manufacturing process. Just like copper is the
bottleneck in the data transmission process.
Mesh is trying to remove the human element from the assembly
line to reach the necessary scale and precision.
It's removing friction everywhere.
Friction in the wire, Friction in the factory.
That is the theme of this whole story, reducing friction to
increase speed. Let's synthesize this.
We are moving into part 5 here, connecting all these dots.
Let's look at the narrative arc we've uncovered, because it's a
really powerful one. We started with a fundamental
bottleneck in physics, the AI geniuses.
The GPU's are so fast that the current plumbing can't keep up.
Right, The copper wires and the old radio frequency tech are
hitting a wall. The conversation in the loud
cafeteria is breaking down. Enter the solution, a team of
engineers from SpaceX who spent years solving this exact
problem, just in the much harsher environment of the
vacuum of space. They bring a mindset of extreme
reliability and a clever new design that saves critical power
and reduces HEAT, which is the number one enemy of performance.
But they run head first into a wall, a geopolitical and
industrial wall. The manufacturing capability to
build this thing at scale doesn't really exist in the West
anymore. So they have to build that too.
They have to reinvent the factory floor while they
reinvent the component that's built on it.
It's a double challenge. It's product innovation plus
process innovation happening at the same time.
Which is incredibly risky. Most startups would only tackle
one of those, but if it works, the payoff is absolutely
enormous. You don't just own a product,
you own the entire means of production.
Let's go back to that quote from Travis Brochures one more time.
We want to interconnect everything.
That is the bigger picture here. We are fixated on AI right now
because that is the immediate term demand that's driving the
$50 million investment. But think about what happens
when this kind of optical communication becomes cheap,
power efficient, and ubiquitous. It's not just about faster chat
bots or better image generators. No, it's about a fundamental
shift in how all machines communicate.
We are moving from a world where computers talk to each other via
electricity to a world where they talk to each other via.
Light. Light is the new standard for
internal communication, not just for long distance.
Exactly. Imagine self driving cars,
factory robots, airplanes, even your home appliances.
If they can all start communicating with each other
with the speed and efficiency of light, the latency, the delay
all but disappears. The power consumption drops
dramatically. The bandwidth becomes
effectively infinite for most applications.
It's like upgrading the nervous system of the entire planet from
copper wires to a fiber optics. That is a great way to put it.
It's a planetary scale nervous system upgrade.
So for our listeners, specifically the learners out
there who love to stay ahead of the curve, what is the key take
away from this deep dive? The take away is this.
Don't just look at the smarts and don't just focus on the
software or the LMS. When you read about the next big
AI breakthrough or the next super chip from NVIDIA, ask
yourself the boring question how is the data moving?
Look for the plumbing. Look for the plumbing.
Look at the optical links. That is where the physical
constraints are, and in technology the biggest
constraints are always where the biggest opportunities hide.
That's where the next billion dollar companies are being
built. That is fascinating.
It's a reminder that even in our increasingly digital world,
atoms still matter. You still have to build the
physical thing that moves the photons from A to B.
Absolutely. The cloud is not a cloud.
The cloud is made of metal, silicon and glass running in a
giant power hungry building. I want to challenge one part of
this before we go. We talked a lot about the SpaceX
DNA being a huge asset, but is there a potential downside to
that culture? That's a fair question.
What's the risk? SpaceX is famous for its move
fast and break things ethos. They literally have a highlight
reel of their rockets blowing up.
Rapid unscheduled disassembly. Right.
They test a failure, they build a rocket, launch it, watch it
explode, learn why, and then build another one better in a
few weeks. Can you apply that culture to a
supply chain for Amazon or Google?
If you ship a million bad transceivers to AWS, you don't
get a second chance. Yo, you get sued and your
company dies. Yeah, you're absolutely right.
A rocket blowing up on a remote test stand is valuable data.
A data center in Ohio going dark because your transceivers failed
is an economic catastrophe for your customer.
So that is the tension they have to manage.
They have to keep the innovation, speed and the
agility of SpaceX, but combine it with the Six Sigma 0 defect
reliability of a traditional boring industrial manufacturer.
And that transition from scrappy startup mode to trusted
industrial supplier mode is where so many promising hardware
companies fail. It's known as the Valley of
Death. Scaling from the first working
prototype to the first million units off the assembly line,
that is where the wheels usually fall off.
It is So the $50 million from Thrive Capital, that's not just
for R&D, that's basically the fuel to build the bridge to get
them across that valley before the money runs out.
High stakes. Extremely high stakes, but as we
said, the rewards on the other side are astronomical.
OK, before we sign off, I want to leave the listener with a
final thought, a bit of a provocation to chew on.
Go for it. We talked about this lights out
manufacturing. The idea of a fully automated
factory running in the dark mesh is betting their whole company
that they can pull this off in the USA.
Very bold bet. If they succeed, if these three
engineers from SpaceX can actually figure out how to mass
roduce these incredibly complex recision electronics in Los
Angeles and do it cheaper than the established layers in China,
what does that mean? Does that prove that the whole
narrative about US manufacturing being dead is wrong?
Exactly, Is this the start of a renaissance, a blueprint for how
to bring high tech manufacturing back?
Or is it just a special exception?
A1 off? That's only possible because the
product is so strategically valuable for AI that you can
justify the massive initial investment.
That is the billion dollar question, isn't it?
If they crack the code, maybe we see lights out factories for
other complex electronics. Maybe some consumer electronics
manufacturing comes back. But if they fail.
That it just reinforces the dominant idea that complex
hardware belongs in Asia and software and design belong in
the US. Which is a very dangerous
dichotomy if you care about supply chain security and
national resilience. It absolutely is.
And consider this as a final, final thought.
We are moving toward a world where the speed of light isn't
just a constant in physics textbooks, it is rapidly
becoming the standard speed for internal machine thought.
When the speed of thought literally equals the speed of
light, things are going to get very interesting very quickly.
Indeed they are. And on that note, we are going
to wrap it up for today. It's been a pleasure digging
into this one. Thanks for diving deep with us.
This has been the deep dive. We'll see you on the next one.
Stay curious.