Picture a rusted out industrial site in western
Pennsylvania, a relic, a defunct coal plant that's
been sitting silent for years. But now you see
construction crews all over it and they're not
tearing it down. They're bringing it back. They're
bringing it back to life, but not for coal. They're
turning it into this massive gas plant. And here's
the thing that just stops you in your tracks.
All that power. Every single megawatt isn't for
homes. It's not for a town. It's being resurrected
to feed a single data center campus. Yeah. It
is such a stark imagery. We think of this AI
revolution as this clean digital thing that lives
up in the cloud. Right. Ethereal. Exactly. But
then you see that plant or you see the pipelines
going down in Texas and you realize the cloud
is actually heavy. It's hot and it's hungry.
And right now it's demanding we dig up the industrial
pass to fuel our future. Welcome back to the
Deep Dive. I'm really glad you're here. Today
we're going to step back and just look at that
friction. The friction between our digital progress
and, well, physical reality. We've got a stack
of sources here that paint a very complex picture.
It's not just about the code anymore. It's about
physics. Right. And we're going to navigate this
in four parts. First, that gas boom. There's
a massive environmental cost to all this compute
and the numbers are, frankly, staggering. Then
we'll shift to the business landscape, which
is just chaotic right now. We're talking corporate
espionage, a literal space race. Actual servers
in orbit. Servers in orbit. We'll get there.
Then we'll look at the toolkit new software that's
changing how we work, plus a pretty serious security
warning. And finally, a big technical breakthrough
in how robots actually see the world. OK, let's
unpack this. We started with that plant in Pennsylvania.
That's just one data point from a much bigger
report we have here from Global Energy Monitor.
Yeah, but the core tension here is just simple
physics. Yeah. AI models are getting exponentially
larger. And as they get bigger, their appetite
for power just grows with them. You're talking
about data centers with thousands of GPUs. These
aren't like your laptop that goes to sleep. No.
These things need always -on power. They cannot
afford any downtime. But hold on a second. I
look at the sustainability reports from Microsoft,
Google, Amazon. They all have these really aggressive
net zero pledges for 2030. If they're pivoting
to gas, doesn't that just kill those goals? It
certainly puts them in serious jeopardy. But
here's the engineering reality they're up against.
Renewables are intermittent. Right. The sun doesn't
always shine. The wind doesn't always blow. To
run a GPU cluster at 100%, you need what's called
baseload power, consistent flatline energy. And
batteries. I mean, we always hear that battery
storage is the answer to that. Batteries are
great for smoothing out a few hours, but right
now, utility -scale battery storage, it just
hasn't scaled enough or gotten cheap enough to
bridge those long gaps for something this big.
We're talking gigawatts. So the industry is pivoting.
They're pivoting hard back to natural gas because
it provides that instant, reliable baseload.
The data from Global Energy Monitor shows the
U .S. is leading what they call a massive gas
boom, specifically for AI's energy problem. I
was looking at the emission projections in the
report. They are difficult to wrap your head
around. They're huge. If all the proposed U .S.
projects go forward, we're looking at potential
emissions of 12 .1 billion tons of CO2. Can we
put that in context? 12 .1 billion tons. To put
that in context, that is roughly double the current
annual emissions of the entire United States.
Double, just from the infrastructure to support
this one tech sector. That's the projection if
the full build -out happens. And if you look
globally, it could hit 53 .2 billion tons. It's
a complete reshaping of the energy landscape.
In the U .S., electricity demand could surge
60 % by 2050, and the main driver they cite is
AI. And this isn't happening evenly across the
country, is it? It seems really concentrated.
Texas is the epicenter. As of 2025, they had
57 .9 gigawatts of gas projects underway just
in Texas. But it's elsewhere, too. Meta is building
a $1 .5 billion data center in El Paso. And that
facility, gas -powered. It feels like we're training
the grid to depend on fossil fuels again at the
exact moment we were supposed to be weaning ourselves
off them. It's a pragmatic choice, I guess, but
a controversial one. They need the juice. There's
a political angle in here, too. The report mentions
comments from Donald Trump about this. That's
right. Trump has promised that companies like
Microsoft will, and I'm quoting, pay their own
way for this stuff. So no public funding. Right.
The idea is that tech giants shouldn't rely on
rate payers to build out these huge power draws.
But the report does note that the actual details
on how you'd enforce that are pretty scarce right
now. So we're trying to invent the future AI
autonomous agents. But to get there, we are doubling
down on energy sources from the 20th century.
We're burning the past to build the future. Yeah.
So I have to ask, does the utility of AI justify
doubling our carbon output? Immediate gains versus
long -term climate debt. Long -term climate debt.
That is a heavy bill. It really complicates the
story of AI as this clean future. But speaking
of complications, the business side is just as
messy. Let's look at the corporate battlefield.
It is absolutely chaotic. You're seeing this
mix of incredible profit, fierce rivalry, and
some like straight up spy novel stuff. Okay,
let's start there. The spy novel stuff. This
story about the Google engineer. Yeah, this is
just wild. A former engineer at Google was convicted
for stealing over 2 ,000 files, AI trade secrets.
But it wasn't just, you know, code snippets.
He was stealing model weights, architecture diagrams.
And he was pitching investors while he was still
there. While he was still employed. He was downloading
files, pitching investors in China to build a
rival startup, and then he tried to flee. It
just highlights how valuable this IP is right
now. Governments are treating these model weights
like state secrets. Because if you have the weights,
you don't need to spend the $100 million to train
the model. You can just run it. Exactly. You
skip the whole R &D cost and go right to deployment.
And speaking of money, we saw Microsoft's earnings.
The numbers are just eye -watering. Microsoft
pulled in $7 .6 billion in profit from OpenAI
in a single quarter. A single quarter. Yeah.
But here's the stat that really got me. Forty
five percent of their six hundred twenty five
billion in total deals came just from that partnership.
OK, that explains why they're so intertwined.
This isn't a side project for them. It's a massive
pillar of their whole valuation. Right. And the
market is shifting all over. We see QAI raising
one point five billion and now they're teaming
up with Apple. That's a huge strategic move.
Yeah. But while some are rising, others are pivoting.
OpenAI is actually retiring models next month,
including GPT -4 .0. I saw that, and the reason
was that barely anyone uses it. Which is funny
because I know for a fact some power users absolutely
loved that specific iteration. But in this field,
if a model isn't hitting critical mass, the maintenance
cost, the inference cost, it's just too high.
They cut it loose. Okay, so we talked about the
energy limits on Earth. Maybe that explains this
next story, which feels like it's pulled from
science fiction. China announced plans to launch
AI data centers into space. This is where it
gets really interesting. China's laid out a roadmap
to have this going by 2030. It's being framed
as a direct competition, Beijing versus Elon
Musk's SpaceX. The goal is to process AI data
off Earth. I'm assuming the logic is cooling,
right? I mean, space is cold. You solve the heat
problem instantly. Well, that's actually a common
misconception. Is it? Yeah. I mean, space is
cold, sure, but it's a vacuum. On Earth, we cool
things with air or water. That's convection.
The air carries heat away. In a vacuum, there's
no air. So there's nowhere for the heat to go.
Thermodynamically, getting rid of heat in space
is an absolute nightmare. So you can't just open
a window. Exactly. You would literally roast
your servers. You have to use these massive radiators
to radiate the heat away as infrared light. So
the engineering challenge isn't just launching
them. It's keeping them from melting down without
an atmosphere. Whoa. Imagine scaling that. managing
a billion queries in orbit while trying to radiate
heat into the void. It's the ultimate high ground.
But the physics are brutal. Still, they pull
it off. They bypass all the land and energy constraints
we just talked about on Earth. So looking at
this whole dynamic, is the rush for dominance
compromising security? Speed and scale are outpacing
safety protocols. All right, let's bring it back
down to Earth, to our laptops. The newsletter
highlighted a toolkit section, and I see two
categories here. tools for bureaucracy, and tools
that manipulate perception. But first, that security
warning. Claudebot. Right, Claudebot. So this
is an open source AI agent. It looks super powerful
because it can execute code on your local machine
to solve tasks for you. I have to admit, I still
wrestle with that urge to try new code like this
on my main machine. You see the demo, you get
excited, you just want to install it. I totally
get it. The curiosity takes over. But the source
material is very, very clear here. This is experimental
code. Do not install this on your main laptop.
If there's a vulnerability or if the agent just
hallucinates a command, it has access to your
entire file system. So use a burner laptop, basically.
Or use the alternative they mentioned, Cloudflare's
Moldworker. It's pretty much the same capability,
but it's all cloud -hosted. It's safer because
it's sandboxed away from your personal data.
Okay, let's look at what I'm calling the bureaucracy
killers, Pandata and StoryCV. These are fascinating
because they signal the end of the junior analyst
grind. Pandata takes messy input like CSVs, PDFs,
even photos of whiteboards, and it turns them
into these McKinsey -level reports. It structures
the data, writes the narrative for you. And StoryCV
does the same thing for resumes. Right. It just
interviews you. You talk to it about your work
history, and it writes the resume. It removes
that writer's block. But think about the implication.
We're automating the entry -level tasks that
used to teach people how to think strategically.
That's a really deep point. If the AI does the
grunt work, how do juniors ever learn the nuance?
OK, then there's the second category, the prompting
company. This sounds like reverse SEO. That is
the perfect way to describe it. In the old days,
you'd optimize your website so Google search
would find you. The prompting company finds what
questions people are asking chatbots, chat GPT,
Claude, and it optimizes your content. So those
AIs will cite your product as the answer. That
is incredibly clever. And also. A little dystopian.
We're optimizing our content so machines will
read it and then recommend us to humans. It's
the new marketing frontier. If the training data
doesn't know you exist, you're invisible. And
then highlight GPT. Simple browser extension.
You just highlight text, get an explanation.
It just reduces the friction of learning. So
looking at all these tools together, are we optimizing
for algorithms or humans? We're tailoring our
reality for machine consumption. Okay. For our
final segment, we're going to look at how robots
see the world. Or maybe how they struggle to
see it. Yeah, this is about a breakthrough from
China's ant group called Lingbot Depth. The problem
they identify is broken depth. Can you explain
what that is? Sure. When a robot looks at the
world with, say, a LiDAR or a camera, the data
isn't perfect. You have surfaces like glass or
shiny metal or just bad lighting, and they create
these holes in the data. So the robot literally
thinks there's a hole in the table. Or it just
can't tell how far away the cup is. Right. It
makes them clumsy. Lingbot Depth is a model designed
to fix this. It takes that broken depth map and
the color image, and it basically reconstructs
the missing 3D information. It fills in the blanks.
But the way they trained it is what's so brilliant.
They call it masked training. How does that work?
Okay, think of it like learning to read a sentence
where someone has taken a black marker and crossed
out every third word. Okay. At first, it's just
nonsense. But if you practice enough... You start
to understand the context. You know that after
the cat sat on the word, the next word is probably
Matt. You don't even need to see the word to
know it's there. So they blinded the AI on purpose
during training. Exactly. They forced it to learn
the context of the physical world. So now when
it sees broken data in the real world, it has
the intuition to fill it in accurately. And the
result is a robot that can handle like transparent
objects and messy environments. It creates a
really tight 2D to 3D understanding. And this
is crucial for what they call embodied AI, getting
robots out of the lab and into the real world.
So the question here, does this bridge the gap
between software and the physical world? Yes,
it gives machines intuition for physical space.
We're going to take a quick beat. We'll be right
back. And we're back. So let's try to tie all
this together. We started with a coal plant being
resurrected as a gas plant. We went to space
with servers that need massive radiators. We
looked at tools that write our resumes and robots
that learn to see by reading between the lines.
There is such a clear through line here. We are
witnessing a massive physical build out just
to support a digital revolution. We love to focus
on the software, the magic of the chat bot, right?
But the reality is steel and concrete and gas
and thermodynamics. It feels like a moment of
friction. We want the magic of Lingbot. through
broken pixels. We want the convenience of Pandata.
But the cost is being paid in old world fossil
fuels and really intense geopolitical rivalry.
Exactly. We're building tools that are effectively
giving machines intuition and creativity. But
to power them? We're reverting to these very
traditional, heavy industrial methods. The digital
future has a very, very heavy physical footprint.
Before we go, I want to leave you with a thought
on that prompting company tool. It really stuck
with me. If we start creating content primarily
to please AI chatbots, so they'll recommend us,
are we entering an era where humans no longer
write for humans, but for the training data of
the future? Are we just feeding the machine that
feeds us? That is a thought that will keep me
up tonight. Something to mull over. If you enjoyed
this deep dive, make sure you're subscribed.
We've got some great topics coming up. Thanks
for listening, everyone. See you in the next
deep dive.