We often talk about A .I. in terms of, you know,
the algorithms, the models themselves. But maybe
the real story, the one that's kind of mind blowing
right now is the physical scale. Yeah. The infrastructure.
Oh, absolutely. It's staggering. And there's
this one stat about open A .I. that just puts
everything into perspective. They're apparently
aiming to build something they call an infrastructure
factory. An infrastructure factory. Yeah. Designed
to produce one gigawatt of A .I. compute every
single. One gigawatt weekly. I mean, it's hard
to even picture that kind of pace. It really
is. It means they're operating on a completely
different level. If you try to contextualize
that, you're basically looking at combining like...
All the big NVIDIA deployments, plus the Tesla
Gigafactory, plus major AWS data centers, all
running at once. And they plan to just keep doing
that. Yeah. Week after week. That seems to be
the plan. Yeah. Just to handle the training and
deployment needs for whatever comes next. It
forces a total rethink of, well, compute economics.
Welcome to the Deep Dive. Today, we're going
to dive into the latest industry sources. cut
through some of the noise and pull out the key
things you probably need to know. We'll be looking
at some big corporate shifts, some very real
practical threats that are emerging, and also
this rising global competition, especially around
the cost of performance. Right. So roadmap wise,
first up, we'll unpack the new structure at OpenAI,
what that means for their speed. Then we'll get
into some immediate disruptions, things like
fake AI receipts, workforce impacts, new tools.
And finally, we absolutely have to talk about
these super competitive Chinese models. They're
beating Western models pretty badly on cost.
Let's jump in. Segment one, OpenAI's new reality.
OK, so focusing first on the, let's say, the
strategic and financial side. The big news is
OpenAI has finally finished this really complex
legal reshuffle. They are now officially a for
-profit company. That's right. The pivot is complete.
But that ownership is still intentionally messy.
You know, it's not like a standard corporate
setup, which keeps people guessing. Yeah. We
learned the original nonprofit foundation, it
still owns 26 % of the new thing, the OpenAI
group. Right. And meanwhile, Microsoft, they
are dug in deep. They've got about 27 % of this
new group. That stake is valued at, what, around
$135 billion now? Wow. But the real power might
be the exclusive model rights they hold. That
runs until 2032. 2032. That's a long time. That
gives Microsoft huge control over where this
tech goes commercially, doesn't it? For the next
decade, pretty much. Yeah. And speaking of valuation
frenzy, here's a kind of interesting side note.
Elon Musk apparently tried to buy them not long
ago for $97 billion. $97 billion. Obviously,
that didn't happen. Maybe it wasn't even enough.
Who knows? But the why behind this whole for
profit shift is key. They basically have to remove
all the legal friction. The whole structure made
it hard to raise the truly astronomical amounts
of money they need for that physical infrastructure
we talked about. So open the floodgates for investment.
Scale up faster. Exactly. Which brings us back
to that infrastructure goal. The infrastructure
factory. One gigawatt per week. Whoa. I mean,
just imagine trying to scale to handle say a
billion queries consistently. That kind of investment,
that output. It's removed all the hardware bottlenecks
they've ever had, right? It unlocks speed in
a way we haven't seen. And you see that urgency
reflected internally, too. Oh, so? Well, their
chief scientist, Jakub Pachocki, recently suggested
superintelligence might be less than 10 years
away. Less than 10 years. Yeah, and that doesn't
sound like a casual prediction. It sounds like
it's based on the speed they're seeing right
now internally. So the implication there is we
could see big jumps in model performance, maybe
even in the next six months. Before we even get
to GPT -6. That seems to be the hint, yes. The
training runs are moving faster than maybe even
they predicted. Okay, so here's a question then.
With all this money pouring in, this infrastructure,
this speed, does that guarantee open AI market
dominance long term? Well, the capital flow is
huge, definitely. But those proprietary rights,
the control aspects, they really complicate the
market picture. Right. So the money's there,
but control rights make it complicated. Exactly.
OK, let's shift gears from the big money story
to more immediate practical things hitting people
segment two. And first up is something that's
honestly a bit scary. The realism of these models
is creating a well. a crisis in digital trust.
We need to talk about fake AI receipts. Yeah,
this is becoming a million dollar problem. Literally.
We're not talking about clumsy Photoshop anymore.
These are like professional level fakes. Good
enough to fool corporate expense systems. So
the takeaway is you just can't trust your eyes
anymore. Not with digital documents. Pretty much.
Visual evidence alone isn't enough. Assume verification
is needed. Digital trust is eroding fast. Okay.
At the same time, though, powerful creative tools
are getting easier to access, right? Adobe just
put AI assistance into Express and Photoshop.
That's huge, yeah. Tools that used to need serious
design skills, now usable with simple text prompts.
It makes creativity more accessible, sure. But
it also probably fuels that forgery problem we
just mentioned. Exactly. Double -edged sword.
And look at commerce integration. OpenAI and
PayPal are partnering up. For instant checkout
within ChatGPT. Right. So you could be chatting,
the AI suggests something, and boom, you buy
it right there. Conversation at checkout. Seamless.
And speaking of access, look at India. They're
getting 12 months of free ChatGPT Go access for
a limited time. That's a big strategic play for
market share. And this tech leap is causing weird...
contrasts in the corporate world, too, like Amazon
laying off thousands. Right. Thousands of people
gone. But it's not because Amazon's struggling
financially. No, they're doing it while massively
increasing their spending on AI development.
It's a really stark restructuring of the workforce
happening right now. It really shows the tradeoff
companies are making, swapping human capital
for compute capital. And sometimes, you know,
that compute capital doesn't quite behave. Yeah.
Yeah. Slight juggle. The community reactions
are always quick when models stumble. I saw that
post about ChatGPT Atlas being dragged into the
bin after everyone initially called it top one.
Oh yeah, that went viral. You know, that kind
of hit home for me. I mean, I still wrestle with
prompt drift myself sometimes. You figure something
out one day, the perfect prompt, and the next
day, poof. It just doesn't work the same. It's
frustrating. It really is. It's just the nature
of working on the bleeding edge, I guess. Unstable.
Meanwhile, governments are trying to react to
all this. The EU just launched a $5 billion fund.
Right, for AI, quantum computing, semiconductors.
Trying to catch up. Explicitly, yeah. Trying
to match the scale of investment coming out of
the U .S. and Asia. Okay, so thinking about those
fake receipts again. Yeah. Given that risk, how
much should we actually rely on digital confirmation
for, say, financial stuff? I think we have to
assume verification is needed outside the document
itself. You can't just trust the digital file
anymore. Right. Digital trust is eroding. Always
cross verify the source. OK, let's move to our
final segment, the cost frontier. This is where
models from China are really shaking things up.
Yeah, this is a potentially huge market shift
coming from the east. We need to talk about a
model called Minimax M2. OK, Minimax M2. What's
the goal there? The goal is basically GPT -4
level performance, but without the crazy high
price tag you get with the big U .S. models.
And the cost difference is big. Staggering. M2
apparently runs at just 8 % of the cost of running
something comparable like Cloud Sonnet. 8 %?
Wow. That changes everything, doesn't it? How
do they do that? Well, the key is the tech underneath.
M2 is what's called a sparse mixture of experts
model and Moe. Okay, jargon alert. Mixture of
experts. Can you break that down simply? Sure.
Think of it like this. Instead of using its entire
massive brain for every single question, a MoE
model only activates a small, specialized part,
like an expert team, that's best suited for that
specific task. Ah, okay, so it's not firing up
the whole engine every time. Exactly. Maybe it
only uses, say, 10 billion parameters out of
a much larger total for one query. That makes
it way lighter, much faster, and radically more
efficient than running a huge, dense model constantly.
And the practical result of that efficiency?
It's fast. uses less memory, and crucially, you
don't need those giant, super expensive GPU clusters
to run it well. That just lowers the barrier
to entry massively for innovators everywhere.
Okay, and how does it actually perform? You said
GPT -4 level. The benchmarks was really strong.
It's reportedly outperforming older Cloud 4 models
in some areas, and it's nearly matching the newer
Cloud 4 .5. So the big picture here is Chinese
labs, Minimax, DeepSeq, Moonshot. They're putting
out high quality open models, models that compete
performance wise with the West. But crush them
on operational costs. Yes. These are good for
what kinds of applications? They're ideal if
you're building, say, agent workflows or custom
code co -pilots, retrieval bots, specialized
tool chains, anything where cost efficiency matters,
especially if you want to self -host outside
the big cloud platforms. Right. So probing question
then. If the running cost drops by like 90 percent.
What does that cost advantage really mean for
companies trying to build new agents, maybe outside
the orbit of AWS or Google Cloud? Well, fundamentally,
it democratizes access, right? It shifts innovation
power. Yeah. Away from just the giant labs, potentially
towards smaller, more agile builders anywhere
in the world. So cheaper models mean faster innovation
and wider access for startups. Exactly. Mid -roll
sponsor, Reed Placeholder. Okay, so wrapping
up this deep dive, the main threads seem to be
this incredible race for physical infrastructure,
this hyper -financed build -out. You know, OpenAI's
one gigawatt goal really symbolizes that. And
that massive spending really sets the stage for
the competitive shift we're seeing. These cost
-effective open models, particularly from the
East, like MiniMax M2, they're completely changing
the definition of high performance for developers
because now it's actually affordable. It feels
like AI is becoming more distributed, moving
beyond just a few huge labs. I think that's right.
And connecting back to the physical buildout,
the infrastructure, we talked about the scale,
the 1GW factory, the Amazon layoffs linked to
AI spending. There's one more piece here related
to secrecy. Yeah, there are reports suggesting
that nondisclosure agreements, NDAs, are being
used quite aggressively, specifically to keep
details about these new AI data centers, the
tech, the operations hidden from the public,
particularly in the U .S. Given the sheer scale
and speed of all this building the equivalent
of a massive data center complex every single
week, what exactly are we not being told about
the physical foundation supporting arguably the
most powerful technology we've ever built? That's
the question to maybe keep mulling over. What's
behind the curtain of all this infrastructure?
Indeed. Something to think about. Thanks for
joining us for this deep dive, El Trio Music.