It creates this fascinating tension, doesn't
it? On one side, you have this massive grassroots
movement triggered by a $25 million donation.
People are actually canceling subscriptions in
protest. trying to stop the machine. And at the
exact same time. At literally the same time,
on the other side of the country, you have a
$10 billion construction project breaking ground.
Right. It's the ultimate contrast. You have the
human layer trying to pull the emergency brake,
and then you have the industrial layer hitting
the gas harder than we have ever seen. Hello
and welcome back to the Deep Dive. I've been
sitting with this stack of sources you sent over
for a few hours now, and I have to be honest,
the signal -to -noise ratio in the AI space this
week has been overwhelming. It feels that way.
It feels like the industry is splitting in two
directions at once. We have a cultural reckoning
happening in the software and a physical explosion
happening in the hardware. It really is an inflection
point. I mean, usually when we sit down, we're
obsessing over the models. Is it smarter? Is
it faster? But today's sources paint a much,
much messier picture. To really get what's happening
this week, we have to look at three specific
pillars. Okay, walk us through the roadmap. First,
we have to talk about the cultural backlash.
We're seeing the rise of this quit GPT movement.
This is activism finally colliding with algorithms.
Second, the infrastructure explosion. We aren't
talking about code here. We're talking concrete,
chips, and power grids, the physical reality
of the cloud. And third, despite all that noise,
the capability leap is still happening. We have
new tools from Alibaba and OpenAI that fundamentally
change how we actually work. Let's start with
the friction, the cultural backlash. For a long
time, the public sentiment around AI was just
pure awe. Look what this thing can do. But reading
these notes on QuitGPT, this feels different.
This isn't just people being afraid of the Terminator.
This is organized economic pressure. What sparked
this? It is different because it's political.
The catalyst here wasn't a software bug or a
bad answer. It was a $25 million donation to
Magia Inc. by Greg Brockman, the president of
OpenAI, and his wife. Okay, let's pause on that.
So we have a direct financial link from the leadership
of the leading AI company to a very polarizing
political campaign. Exactly. And that donation
basically lit the match. But if you look at the
source material, it's not just that one check.
It's a compounding effect. You have the donation,
but then you also have this new partnership between.
OpenAI and IC immigration and customs enforcement.
They're using ChatGPT4 for hiring and for administrative
processing. Which for a significant portion of
the tech workforce and that early adopter user
base represents a massive red line. A huge red
line. Remember, a lot of the people who pay for
ChatGPT Plus are in that creative tech adjacent
demographic. They feel a sense of ownership over
the tool. So when they see the leadership funding
one political side. and then the technology being
deployed by ICE, it just feels like a betrayal
of that original open and beneficial mission.
And Scott Galloway is involved now. I saw his
name in the notes. Yeah, the NYU professor. He's
very good at capturing the zeitgeist of these
things. He went viral urging people to resist
and unsubscribe. Right. He's framing it not just
as a boycott, but as a resistance against big
tech tools fueling specific political agendas.
And it's resonating. The sources say there are
about 17 ,000 pledges on the QuitGPT site so
far. Okay, 17 ,000. Let's look at the numbers.
On a human level, that's... That's a stadium
full of angry people. That's significant. It
is. But ChatGPT has something like 900 million
weekly users. Does 17 ,000 cancellations actually
matter to a company like OpenAI? Or is this just
screaming into the void? Financially, no. The
math is brutal. Only about 5 or 6 % of those
900 million users are even paid subscribers.
So even if they lost 100 ,000 subscribers, the
revenue hit is just a rounding error. It won't
stop the lights from coming on. OpenAI is burning
cash on compute, not relying on $20 subscriptions
to survive. So if it doesn't hurt the wallet,
is it just performative? I wouldn't call it performative.
I'd call it a signal. A cultural signal. OK.
You have to remember, OpenAI isn't just fighting
for customers. They are fighting for talent.
The engineers who build these models are often
very politically active, very ethics focused.
If the brand becomes toxic. If working for OpenAI
becomes synonymous with funding the bad guys
in the eyes of their peers, that hurts them way
more than losing some subscription revenue. That's
a great point. The talent war is the real war.
Exactly. If the smartest 25 year old researcher
refuses to sign the offer letter because of the
brand reputation, that's the actual cost. Precisely.
And it's not just consumers versus companies
anymore. The sources highlight something fascinating.
The companies are starting to fight each other
politically, too. It's a full -on war of the
pigs. I saw that bit about Anthropic. They frame
themselves as the safety -first, constitutional
AI alternative. What are they doing? They just
funded a $20 million super piece. $80 million?
Specifically to push for AI regulation. That
is not a small amount of change for a policy
push. No. And it's directly opposing the political
interests backed by open eyes. Fifty million
dollar push. So you have these two. massive entities
who are competitors in code now becoming competitors
in the political lobby. They're literally spending
tens of millions of dollars to shape the laws
that will govern the other. It really highlights
that AI isn't just a tool anymore. It's a political
battlefield. You can't just build it and they
will come. You have to build it, you have to
lobby for it, and you have to defend it from
your own user base. That's the reality of 2026.
So just to cap this segment off, does this consumer
pressure actually work in an age of enterprise
dominance? It creates a morale crisis, which
creates a tenant crisis, which eventually creates
a product crisis. So, yes, it matters. Interesting.
So while the culture is fighting over donations
and hiring policies and the lobbyists are fighting
over regulation, the actual physical machine
is just getting bigger. Oh, yeah. I want to pivot
to the infrastructure piece because the numbers
here are staggering. The hardware reality. This
is the stuff you can kick. I was looking at this
note about meta in Indiana. $10 billion for a
data center. It's monumental. Meta just broke
ground on this. And data center feels like too
small a word. Right. It's a campus. 13 buildings,
4 ,000 construction jobs. This is a physical
manifestation of their next -gen ambitions. When
Zuckerberg says they are building the future,
they are literally pouring the concrete for it
in Indiana. It changes how you visualize the
cloud, doesn't it? We tend to think of the cloud
as this ethereal thing floating above us. Right.
But it's actually 13 massive buildings in Indiana
sucking up electricity. And that electricity
part is becoming the friction point. The power
demands are so high that these companies are
having to play good neighbor in a very, very
expensive way. I saw that in the notes. Anthropic,
Microsoft, and OpenAI. are all promising to pay
the extra power bills. They have to. Imagine
you live in a town, a data center moves in, and
suddenly your residential electricity rate goes
up 20 % because demand spiked. That's a political
nightmare. It is. So they are preemptively saying,
we will cover the difference. It shows you how
desperate they are to keep these things running
without local backlash. They're essentially paying
rent on the grid itself. It's almost like a pollution
tax, but for electron consumption. Exactly. And
speaking of desperate need for speed, we have
to talk about this OpenAI and Cerebras deal.
This connects the hardware to the actual user
experience. This is Codex Spark. Right. OpenAI
launched Codex Spark. It's a lighter version
of GPT -5 .3 designed specifically for real -time
coding. But the moment of wonder here isn't the
software. It's the chip. It's the chip powering
it. The Cerebras chip. I saw the Spex 4T transistors.
Four trillion. Can we contextualize that? Because
trillion is one of those numbers that human brains
aren't good at visualizing. What does a four
trillion transistor chip actually allow you to
do that a normal chip doesn't? Speed. Specifically,
inference speed. So training is teaching the
AI. That takes months. Inference is when you
ask it a question and it answers. Right. That's
the daily usage. Usually there's a lag. You type,
the bubbles spin, the text streams out. With
a chip this size, the memory bandwidth is so
high that the latency basically vanishes. So
it moves from email correspondence to instant
messaging. It moves to thought speed. If you're
a coder using Codex Spark. The AI isn't pausing
to think. It's typing with you. It predicts your
next logic block before you even finish the syntax.
Wow. It changes the psychological relationship
with the tool. It feels less like a tool and
more like an extension of your own fingers. That's
incredible. And this ties into that valuation
for Modal Labs, right? Exactly. Modal Labs is
raising at a $2 .5 billion valuation. They focus
entirely on inference infrastructure, basically
the plumbing that allows these massive models
to run quickly and cheaply. The market is betting
heavily that this is the year we stop just training
models and start running them at massive scale.
So are we underestimating the physical footprint,
the concrete and electricity required for digital
intelligence? Absolutely. Software is now driving
the largest industrial construction boom in recent
history. Which brings us to the tools themselves.
We built the data centers. We fought the political
battles. Yeah. What can we actually do with this
stuff today that we couldn't do yesterday? This
is the fun part. The capabilities are shifting
from just chatting to doing. And the big story
here is Alibaba. Quinn Image 2 .0. Catchy name,
right? Rolls right off the tongue. The note here
says it's a PowerPoint native model. What does
that mean exactly? Okay, think about how most
image generators work right now. Mid -journey,
daily. You ask for a picture, you get a picture.
It's art. But if you try to put text on it, it
looks like alien hieroglyphics. If you try to
make it a specific resolution for a slide deck,
you have to crop it. Right. It's creative. But
it's not productive. Exactly. Quen changes that.
It merges generation and editing in one shot.
It handles 2048 by 2048 resolution. So no more
stitching tiles together. And crucially, it has
high text fidelity. So you can actually read
it. You can tell it. Make me a slide about Q3
earnings with a bar graph and the title growth
vector in Arial font. And it just does it. So
it's an AI competitor to Canva. not just an AI
computer to an artist. That's the promise. It's
moving the workflow. You aren't just generating
assets that you then have to take into Photoshop
to fix. You are producing a final asset right
in the chat. That is a significant shift for
enterprise users. I know I struggle with that,
constantly getting the image right, but having
the text look ridiculous. Right. And while Alibaba
is fixing images, Anthropic is coming for your
Office suite. They just unlocked file tools for
free users. Excel, PowerPoint, Word, no sh**.
Previously, you had to pay to have Claude analyze
these. Now it's open to everyone. That is a big
democratization move. Suddenly, having an AI
analyst go through your spreadsheets isn't a
premium feature. It's a baseline expectation.
Right. Exactly. But, and there is always a but
in this industry, with these new capabilities
comes a darker side. We have to talk about what
happened to Google's Gemini. The stealth attack.
I read this twice because I wasn't sure I understood
it. This wasn't a hack in the traditional sense,
right? They didn't break a password. No, they
didn't break in. They invited themselves in.
It's called a model copying campaign or model
distillation. Walk us through how that works.
So Google's Gemini is a trillion dollar brain.
It's huge, expensive, and smart. A competitor
or a bad actor wants that intelligence but doesn't
have the money to train it. Okay. So they hit
Gemini with over 100 ,000 very specific complex
prompts. Explain quantum physics. Write a legal
brief. Debug this code. And they just record
all the answers? Record the answers. Then they
take those high -quality answers and feed them
into their own much smaller, cheaper model. They
are teaching their cheap students using Google's
expensive textbook. Wow. Effectively, they are
siphoning off the intelligence of the master
model to create a clone. That sounds incredibly
difficult to police. It is. Google is calling
it theft. They are warning startups that if it
can happen to Gemini, it can happen to anyone.
But the legal framework here is nonexistent.
Is asking a question theft? Is learning from
an answer copyright infringement? It raises this
massive question about what intellectual property
even looks like when you can steal it just by
asking questions. If I can distill your secret
sauce just by talking to your bot, do you even
have a moat? That is the billion -dollar question.
And it connects back to that feeling of vulnerability.
We are building these massive systems, but the
edges are porous. Speaking of vulnerability,
reading through these sources made me think about
my own usage. I have to admit, I still wrestle
with what they call confident guessing. Oh, hallucinations.
Yeah, but specifically in high -stakes stuff,
like I'll use AI for a contract review or a compliance
check, and it sounds so sure of itself. But there's
that nagging fear. Is it making this up? The
source material mentioned a safety prompt rule
to help with this. Yes, this is a crucial takeaway
for you listening. The source mentioned a specific
framework for stopping that confident guessing.
Essentially, when you are doing something high
stakes, checking a legal doc, looking for citations,
you have to explicitly instruct the model on
the negative constraint. Meaning what? You tell
it. If you do not find the specific clause in
the text provided, state that you cannot find
it, do not infer, do not guess. It sounds so
simple. But we forget to do it. Yeah. We trust
the chat interface too much. We treat it like
a conversation where it's rude to be that blunt.
But you have to. We need to force it to prove
its claim. Absolutely. We have to move from being
passive users, hey, tell me about this, to active
supervisors. Analyze this. Improve your work.
So with things like Quinn merging editing and
generation, what does this actually mean for
creative workflows? We are moving from generating
ideas to producing final assets without leaving
the chat interface. It's a brave new world. It
certainly is. We're going to take a very quick
break to thank our partners who help keep this
deep dive running. And when we come back, we're
going to try to synthesize all this, the politics,
the concrete, and the code into one big picture.
Stay with us. Okay, we're back. We've covered
a lot of ground today. From the cancellation
parties of QuitGPT to the massive construction
sites in Indiana to the model thieves stealing
intelligence prompt by prompt. Yeah. When you
look at this entire stack of stories, what is
the through line for you? I think we are seeing
a massive divergence. That's the word that keeps
coming to mind. Avergence between what and what?
Between the human layer and the machine layer.
On one hand, you have the human layer pushing
back. You have the quit GPT movement. You have
the political maneuvering, the ethical concerns
about ICE and funding. It's messy. It's emotional.
It's loud. Right. It's people saying, wait, stop.
Does this align with our values? And on the other
hand. On the other hand, the machine layer is
just accelerating with total indifference to
that noise. Ten billion dollar chips, massive
data centers, models that can clone each other.
Yeah. The technology is no longer just software
on a screen. It is becoming a physical industrial
force and a political lightning rod simultaneously.
It's almost like the technology has gained enough
momentum that it's. It's decoupling from the
public sentiment. The boycott is happening, but
the cement trucks are still pouring. Exactly.
The train has left the station, and now we are
just arguing about who gets to sit in the conductor's
chair. That is a sobering thought. The scale
of the investment, the sheer physical weight
of it, makes it very hard to stop or even steer.
It does. And if I can leave you with one final
thing that's been sticking in my brain, it's
that story about the model copying. The Gemini
theft. Yeah. Think about it. If a trillion -dollar
company like Google, with all its defenses, can
have its intelligence stolen via 100 ,000 text
prompts, what does intellectual property even
mean in the age of fluid intelligence? If knowledge
can be decanted from one machine to another just
by asking questions, the economics of this whole
industry might be more fragile than we think.
That is a question I think we're going to be
wrestling with for the next decade. I think so,
too. If you want to protect your own workflows,
especially if you're using these tools for contracts
or anything legal, I highly recommend you check
out that safety prompt rule we mentioned. We'll
have the details and the specific phrasing in
the show notes. It's a small step, but it might
save you from a very confident, very wrong answer.
Worth a click. Thanks for diving Dean with us
today. It's a complex world out there, but hey,
at least we're figuring it out together. See
you next time. Take care, everyone.