Imagine a single machine, just one. But this
machine draws more electrical power than the
entire city of San Francisco. Wow. And that's
not, you know, science fiction. As of right now,
January 19th, 2026, it is a physical reality
humming away on the grid. That is the kind of
stat that just stops you in your tracks. Yeah.
I mean, we are not talking about a server farm
anymore. We're talking about energy use on the
scale of a, well, a small country. Welcome to
the Deep Dive. Today we're navigating a world
of some pretty wild extremes. We've reached a
point where the infrastructure for intelligence
is becoming truly titanic. Literal city -sized
brain. Exactly. And yet at the exact same moment
people are what fleeing to the countryside to
knit sweaters. It's just we have the biggest
computers in history and also a massive spike
in yarn sales. It's a fascinating dichotomy.
It is. And we're going to unpack that tension.
We have a stack of reports today covering the
activation of XAI's Colossus 2, this strange
cultural thing called the analog backlash, and
some really interesting research from DeepMind
on how to keep these massive systems safe without
going broke. Plus, on a much more practical level,
we really need to talk about why copying and
pasting from an AI still completely ruins your
formatting. Yes. Let's start with the heavy metal,
the hardware. We're looking at these reports
on XAI, Elon Musk's AI company, and this new
cluster, Colossus 2. Cluster just feels like
such a small word for what this actually is.
I mean, to set the scene, XAI has just activated
a one gigawatt supercomputer. One gigawatt. Yeah.
And it's purpose built for one thing, training
the Grok series of models. And just for context,
that's roughly the output of a large nuclear
reactor, right? Exactly. They have built a machine
that effectively requires a nuclear reactor's
worth of juice just to turn on. And the timeline
is just while Colossus 1 took, what, about 122
days to bring online, Colossus 2 followed immediately
after. Just bang, bang. And looking at the specs
here, it's not even stopping at one gigawatt.
Oh, no, that's just the starting line. The reports
are saying it's already set to expand to one
and a half gigawatts by April and then hit two
gigawatts soon after that. The logistics of that
are, well, they're mind -bending. It has to sit
on dedicated grid lines. It has its own on -site
substations. And the heat, just imagine the thermodynamics
of, what, 555 ,000 GPUs running at full tilt.
Yeah, the cooling systems alone must be an engineering
marvel. It's liquid cooling on just a ridiculous
scale. And you mentioned the GPUs, 555 ,000 of
them. The procurement number is just eye -watering.
The cost for that hardware alone is estimated
at roughly $18 billion. $18 billion. That suddenly
explains why XAI blew right past their original
$15 billion funding goal. Yeah, if you want to
play at this table, the buy -in is astronomical.
They ended up raising a $20 billion Series E,
backed by, you know, all the heavy hitters, Fidelity,
Nvidia, Cisco. And the stated mission here, the
philosophy behind spending all this money, is
to understand the universe. They claim that to
do that, you need the world's biggest brain.
Right. And practically speaking, this machine
is what's going to power Grok 4, Grok Voice,
and the upcoming Grok 5. Technologically, though,
what stands out to me is that they've kind of
hit a ceiling, but not on hardware. It's a software
ceiling. Right. The reports say that the global
batch sizes for training are now limited by optimization,
not by compute. They effectively have enough
hardware to do continuous trillion parameter
training. It's just there. It's brute force on
a celestial scale, which I have to ask. Does
building a brain the size of a city actually
guarantee understanding? Or are we just building
the most expensive pattern matching machine in
human history? That is the multibillion dollar
question, isn't it? But I guess when you have
half a million GPUs, the strategy is pretty clear
that quantity might just have a quality all its
own. The hope is that understanding just kind
of emerges from the math if you make the math
big enough. OK, so while XAI is building the
digital equivalent of a Dyson sphere. the rest
of us, the human world, we seem to be reacting
in a very, very different way. Yeah. Welcome
to what some are calling the 2026 paradox. On
one side, the digital economy is absolutely on
fire. OpenAI has revealed they hit $20 billion
in annualized revenue for 2025. That's triple
the year before. The numbers are vertical. Totally.
And Gemini API traffic doubled in just five months.
But then you look at the cultural data. And you
get yarn. You get yarn. I'm not kidding. Sales
of knitting kits at Michaels have jumped 1 ,000.
200%. Wow. Searches for analog lifestyle have
just exploded. It's this weird vibe where for
a lot of people, 2026 feels a lot like 1996.
It feels like a retreat. Like while the algorithms
are eating the Internet, people are just craving
something tactile, something they can, you know,
control from start to finish without a prompt
window. Well, there's some real economic anxiety
under that, too. Economists are warning that
2026 is the year this all hits the political
radar. AI isn't necessarily taking everyone's
job overnight. What it is doing is deleting the
on -ramp. The entry -level positions. Exactly.
All the junior work is being done by the models
now. So it's getting harder and harder for beginners
to break into these industries. You have this
squeeze where the digital elite are generating
massive revenue, and a lot of other people are
feeling locked out. And they knit. They knit.
It's a way to feel competent, I think. When you
knit a scarf, you know exactly how it was made.
There are no hallucinations in wool. No hallucinations
in wool. I like that. But for those of us who
do have to work in this digital storm, we can't
just go knit all day. We have to use these tools.
And honestly, it's still kind of a mess. It really
is. I was reading through the source material
on perfectly formatted reports, and it just...
it hit home we talk about super intelligence
but most of us are just struggling to get a document
to look right you spend hours on a prompt you
get great text you paste it into google docs
and It just explodes. The formatting nightmare.
Giant bold text where a header should be. Bullet
points that turn into random dashes. It just
looks so amateur. I still wrestle with prompt
drift myself. You spend like three hours trying
to get the headers just right, and the model
just gives you bold text instead of proper H1
tags. It's infuriating. The friction is so real.
The whole promise of AI is speed. But if you
spend 20 minutes reformatting font sizes and
bullet points, you haven't actually saved any
time. So what's the fix? The sources mentioned
some new empowered tools. Well, Google has rolled
out this personal intelligence thing in Gemini.
It's supposed to act like a, quote, weirdly well
-informed best friend that remembers your context.
But for the formatting problem specifically,
the solution seems to be learning how to force
the output. What do you mean by force it? You
have to stop asking nicely. You need to be incredibly
specific about the underlying code. You have
to tell it, I'll put this in Markdown that maps
to the specific H1 and H2 tags. Or you use these
new bridge tools like Noodle Seed or Flow Genie
that structure the data before it even hits the
document. So we have to learn to speak the machine's
language just to get it to speak ours properly.
Pretty much. Until the AI can intuitively understand
your company's brand style guide, you have to
manually override its default chaos. It really
brings up a question of utility then. If we have
to fight the AI to format a simple page, are
we really more productive yet? Only if you master
those force formatting prompts. Otherwise, yeah,
it's often just a very messy copy paste job.
Now, speaking of looking under the hood, let's
pivot to something arguably more critical than
font sizes, safety. With models as big as Colossus
2, the question of control is, it's massive.
Yeah, and DeepMind just dropped a paper on this
that is... Honestly, a game changer. This is
the activation probes paper? Yes. And if you
care about AI safety, but you also care about,
you know, not lighting money on fire, this is
huge. The problem they're really solving here
is cost, right? Monitoring these huge models
for safety usually costs a fortune. An insane
amount. Traditionally, to check if an AI is being
toxic or dangerous, you have to run its output
through another AI to check it. It's like hiring
a supervisor to watch every single employee 24
-7. And that supervisor demands a big salary.
A huge one in compute power. So DeepMind's solution
is to stop watching the output and start watching
the brain itself. So as it's thinking. Precisely.
As the AI is processing a request, it's doing
all this internal matrix math. So DeepMind developed
what they call activation probes. And you can
think of them like tiny sensors or like a tiny
brain scan. They scan the model's internal activity
while it's thinking. So it's like reading the
mind before the mouth opens. That's the perfect
analogy. It's eavesdropping on the inner monologue.
And because it's just reading data that is already
being computed anyway, it's incredibly cheap.
How cheap are we talking? We're talking 10 ,000
times cheaper than running a full safety monitor
on top. 10 ,000 times. That effectively makes
safety free or close to it. Basically. And it
works using a few clever layers. They use these
multi -max probes to pick out signals and huge
prompts, up to a million tokens. And then something
called rolling mean attention to filter out all
the noise and spot, you know, the toxic thoughts.
And what if a probe isn't sure about something?
Then... And only then does it call in the supervisor.
It's a cascade system. If the probe sees a red
flag, it sends just that little snippet to a
lightweight model like Gemini Flash to get a
second opinion. So you cut your overall safety
costs by 50x or more without really losing reliability.
That's a brilliant piece of engineering. It's
moving safety from an external policing layer
to an internal awareness layer. We're essentially
installing a conscience into the AI that costs
almost nothing to run. A lightweight moral compass
embedded right there in the matrix math. I want
to take just a brief pause here. We've covered
city -sized computers, the return of knitting,
the struggle of formatting a simple document,
and now the internal conscience of the machine.
We'll be right back. And we are back. We've definitely
covered a lot of ground today. Let's try to synthesize
this for a moment, connect some of these dots.
Okay, the big picture. We've got this massive
convergence happening. On one side, you have
a company like XAI. They are building physical
infrastructure at a scale that challenges the
power grids of major American cities. 1 .5 gigawatts.
$18 billion. Pure brute force. And then on the
complete other end of the spectrum, you have
DeepMind. Right. They're looking at the microscopic
level. They're finding these tiny efficiencies,
inserting tiny, cheap probes into the math to
make these things safe without bankrupting the
companies that run them. So you have this macro
expansion and this micro optimization happening
at the exact same time. Exactly. And who is sandwiched
in the middle of all this silicon and code? Us.
The humans. Yeah, the humans who are generating
billions in revenue for open AI, but also the
humans who are feeling totally overwhelmed by
it all. The humans buying all that yarn. It's
a real tug of war. We're building these, you
know, gods in the cloud. But down here on Earth,
we just want a nice sweater and a document that
formats correctly. And honestly, the fact that
we can't get the document to format correctly
is probably why we're all knitting the sweater.
It's a need to feel competence. To feel like
we can actually finish a task. I think that's
going to be the theme in 2026. Competence in
an age of automation. Before we wrap up, for
anyone listening who is still stuck in that formatting
loop. Yes, please do yourself a favor. Look up
the formatting guides for these models. Learn
the force formatting prompts. I'm telling you,
it's a small skill, but it will change your daily
quality of life. And as you head out into your
week, I want to leave you with one final thought
on that. analog backlash. Is this surge in yarn
sales just a temporary fad, a little blip? Or
are we seeing the beginning of a permanent bifurcation,
a real split between a digital elite who live
entirely in the stream and a kind of analog resistance
that chooses to disconnect? That is something
to think about. Are we heading for a world where
true luxury just means being offline? I think
we might be. Thanks for diving in with us. See
you in the deep end.