I want you to visualize two very different things.
On one side, you have this plan for a gigawatt
scale supercomputer in India. They're calling
it Stargate. It's massive. Or city level power
consumption. Exactly. And then on the other side,
you just have a tweet, a warning from Andrew
Yang about the, quote, disemboweling of white
collar jobs. It feels like two completely separate
worlds. It does. But they're not. They're actually
the same story. That huge piece of infrastructure
is being built to automate the exact kind of
cognitive work that keeps our offices running.
Welcome to the Deep Dive. It is Wednesday, February
18th, 2026. If you're listening, you're probably
what we call the learner, someone who wants to
see the mechanics underneath the machine, not
just the headlines. And the headlines are just...
They're deafening right now. I was looking at
our sources for today, and it really feels like
the tectonic plates are shifting. We're talking
about physical AI infrastructure in India, a
total change in how software thinks, and then,
well, the economic fallout when all that hits
the job market. Yeah, it's a chain reaction.
So here's our plan. We're going to start in Delhi
with this whole Stargate thing and what sovereign
AI even means. Yeah, then we'll hit the agentic
shift when tools start doing things on their
own. And the cost of that intelligence, which
is... Honestly, it's pretty staggering. Yeah.
Then we'll land on that warning from Adrienne
about the end of the middle manager. It's a lot,
but you can draw a straight line through all
of it. Okay, so let's start with the hardware.
OpenAI for India, we hear these big user numbers
all the time, but what's the actual scale we're
talking about here? The scale is kind of hard
to wrap your head around. It's 100 million weekly
chat GPT users. Just in India? 100 million. Yeah.
I mean, that's like a third of the entire U .S.
population. But that's not even the big news.
The real story is the partnership between OpenAI
and the Tata Group. Which is basically the industrial
backbone of India. Right. Specifically, Tata
Consultancy Services, TCS, they've announced
the Stargate Initiative. And this isn't just
a cool name. They are literally building the
infrastructure. OpenAI is the first customer
for their new HyperVault data center. And they
are starting at 100 megawatts of capacity. For
anyone listening who's not an engineer, 100 megawatts.
Is that a lot? It's huge for a starting point.
Your typical big data center might be, I don't
know, 20 or 30 megs. But the plan here is to
scale to one gigawatt. A gigawatt. That's a nuclear
reactor. It is. It's the kind of power that lights
up a major city. And all of that energy is going
to be dedicated to one thing. Thinking. Processing
Tokens Which brings us to the key concept here,
sovereign AI. I keep seeing that term, sovereign
AI. It sounds very official, very diplomatic.
What does it actually mean, though? Think of
it like a national border for digital thought.
For years, if you used a big AI model, your data
was probably being sent to a server in Virginia
or Dublin. Right. But if you're the Indian government
or a huge bank there, you just can't have your
sensitive data leaving the country. That's a
non -starter. So sovereign AI means the whole
stack, the computers, the storage, the model.
It all lives inside India's borders. Exactly.
And that suddenly unlocks all these use cases
that were impossible before. Mission critical
government work, for example. You couldn't do
that before because of security. Right. Now you
can. So you see Tata rolling out chat GPT enterprise
to hundreds of thousands of their own people.
A's New Delhi is using it for medical education.
The data stays local. The intelligence is world
class. So it's about nations controlling their
own what? cognitive infrastructure? That's the
perfect phrase for it. Yeah. Just like you need
rows and bridges, nations now feel they need
their own sovereign AI capacity. Otherwise, you're
just renting intelligence from somebody else.
Okay. So you have this massive engine being built
in the East. Let's pivot to the software that
runs on these things. The big news right now
is what's being called the agentic shift. We
saw XAI drop Grok 4 .20 into public beta. And,
you know, Aside from the classic Elon Musk version
number, what's actually different under the hood?
The whole workflow is different. I mean, until
now, we've treated AI like a very smart encyclopedia.
You ask a question, it gives you an answer. It's
one to one. Grok 4 .2 changes that. When you
give it a complex task, it doesn't just answer.
It actually spins up four separate AI agents
that work in parallel. So like one agent is doing
research while another one is reasoning through
the logic. Exactly. It's like a little committee.
One researches, one reasons, one might draft
some code and another one reviews it. It's a
shift from just chatting to actually collaborating.
That makes sense. But my first thought is cost.
If I ask one question and it kicks off four different
processes, haven't I just like quadrupled my
compute cost? You absolutely have. And that is
the elephant in the room. You know, we saw Apple
integrating ChatGPT and Claude into CarPlay,
which is great. Tap to ask is way better than
yelling at your phone. Sure. But the ubiquity
of it all hides the incredible expense. I was
just reading the projections for Anthropic. The
numbers, they're just eye -watering. They're
heavy. Anthropic is projected to pay over $80
billion. That's billion with a B. $80 billion.
To Amazon, Google, and Microsoft by 2029. And
that is just the cloud computing bill. That's
the rent they pay just to exist. $80 billion
just to keep the lights on. And look, their sales
are exploding. Projected to hit like $6 .4 billion.
That's amazing growth. But just... Do the math.
The cost of generating this intelligence is astronomical.
That explains moves like Mistral acquiring Coyab.
Right. The French AI lab. They're trying to build
their own infrastructure muscle because renting
compute forever is just not sustainable. This
really isn't a software bubble then. It's an
infrastructure arms race. Totally. Okay. So we
have these huge expensive models. Yeah. How is
that actually trickling down into the tools that
you and I use every day? It's trickling down
fast, and it's changing what the tools do. Look
at something like WordPress. For 20 years, it
was a blank page. You had to do the work. Now
they've launched a built -in AI assistant. It
can rewrite your text, change the layout, all
without you leaving the dashboard. So the tool
is no longer passive. No. Or look at Flixier
for video editing. It automates trimming and
cutting. Or Figma. which is using Claude code
to turn a UI design directly into editable code
layers, the tool is becoming an active partner.
I have to. I have to make a bit of a vulnerable
admission here. I honestly still wrestle with
prompt drift myself. Oh, yeah. As these models
get smarter, I find myself kind of struggling.
Like, I get used to prompting Claude 4 .5, I
learn his little quirks, and then 4 .6 drops,
which it just did, and all my old prompts, my
spells, they don't work the same. The model just
thinks differently now. It's so different from
any other software, right? When Microsoft updates
Excel, SUM still means SUM. But when an AI model
updates, its whole reasoning path can shift.
It's like your colleague got a brain transplant
over the weekend. Yeah, you have to relearn how
to talk to them. And that leads us right to the
heaviest part of this. We've got hardware scaling
to gigawatts. We have software becoming agentic.
And that brings us to Andrew Yang. Yeah. That
piece he wrote on X about the end of the office,
it went absolutely viral. And the language he
uses, it's visceral. He calls it the great disemboweling
of white collar jobs. Disemboweling. He's not
pulling any punches. No. He's trying to shake
people up. His whole argument is that we are
witnessing the extinction of a very specific
kind of worker. The middle manager. The coordinator.
Exactly. But why the middle? For a decade, we
heard that robots were coming for the blue collar
jobs, for truck drivers and factory workers.
Why is the target now the person in middle management?
It all comes down to what he calls cost logic.
Replacing a truck driver is actually really,
really hard. You need robotics, sensors, insurance.
It's a mess. Right. Physical world problems.
But a middle manager, what is their primary output?
It's not a physical thing. In theory, it's strategy
and guidance and practice. It's a lot of emails.
It's summarizing reports from one team to send
up to the next level. It's high cost coordination.
It's routing information. And Yang's point is
that the AI agents we were just talking about,
they are infinitely cheaper and faster at routing
information than a human who is earning 150 grand
a year. The math is just, it's brutal. It's a
spreadsheet decision. He says if five managers
can be replaced by one operator with a fleet
of AI agents, the market will force that to happen.
You know, this gets to what I call the CEO's
dilemma. Think about this a lot. Let's say you're
a CEO and you want to do the right thing. You
don't want to fire people, but your main competitor
just automated their entire middle layer and
their profit margins just jumped 20%. Suddenly
your investors are on the phone screaming at
you. They don't care about your morals. They
care about the returns. You're trapped. You have
to make the same cuts just to survive. That's
the trap. It becomes a race to the bottom on
labor costs. And the ripple effects, they're
frightening. If you hollow out that whole middle
layer, what happens to entry -level jobs? There's
no next step. You hire a junior analyst to eventually
become a manager. But if that manager role doesn't
exist, why hire the junior in the first place?
And what does that do to the value of a degree?
Yang makes the point that if knowledge is basically
free and reasoning is cheap, does a generic business
degree even hold its value? We could see some
colleges, the weaker ones, just shut down. It's
possible. We're moving from an economy of credentialism
to an economy of capability. It's not about what
you know. It's about whether you can direct the
machine. It's a heavy thought. It really shakes
the foundation of what we've all been told is
a safe career. It does. It means the safe middle
is now the most dangerous place to be. And that's
the thing that keeps me up at night. OK, let's
try to tie all this together. We started with
gigawatts of power in India and we ended with
the deaths of the middle manager. How do those
two things connect? They are the hardware and
the software of the exact same phenomenon. That
Stargate initiative, that massive amount of compute.
That is the engine. Right. It's being built specifically
to run the kind of heavy reasoning models that
can perform that high -level coordination work.
So TATA and OpenAI are literally laying the physical
groundwork. For the exact economic shift Andrew
Yang is warning us about, it's a move from AI
as an assistant, something that helps you work
to AI as an operator, something that does the
work. The infrastructure is being built to support
the replacement of cognitive labor. That's the
big idea. That's it. And for you, the learner
listening to this, it's not about panic. It's
about recognizing that the middle is not a safe
place to be anymore. You either need to be building
the machine or directing the machine. Which leads
to the final thought I want to leave you with.
If that middle part of the corporate ladder just
disappears, what does a career path even look
like in 2030? Do you just go from intern straight
to executive? or do we need a completely new
structure for how people contribute value? That
might be the question of the decade. Maybe it's
not a ladder anymore. Maybe it's more like a
network of specialists. But the days of just
climbing the rungs, those are probably over.
So I want you to think about your own job this
week. Look at your calendar. How much of what
you do is coordination, just moving information
around, and how much is actual creation or direction?
Because the coordination part is being solved.
That's the work. Thank you for diving in with
us. It's a complicated new world, but at least
we can try to parse it together. See you in the
deep end. See you next time.