So everyone seems to be shouting about an AI
bubble these days. You hear it everywhere. People
are dusting off the history books, right? Pointing
straight at the 2001 .com crash. But looking
through our sources today, it feels like we might
be focused on the wrong kind of risk entirely
this time. Yeah, it's interesting. That 2001
bust, it was fundamentally about infrastructure,
wasn't it? Too much supply. People call it dark
fiber. All that empty, unused network capacity
built on speculation. The paradox now, and it's
fascinating, is what if we're seeing like maximized
dark GPUs from NVIDIA? where every single chip,
all the compute power is just firing on all cylinders
the moment it's out the door. Welcome to the
deep dive. That paradox, that's really our mission
today. We're going to unpack these market dynamics
that make the current capital environment feel
so different from past tech booms. We'll look
at the incredible speed of consumer adoption
and also, importantly, the rise of these really
powerful small AI models. They could really shake
things up. Absolutely. And just to set the stage,
our goal here is, you know, impartial analysis
for you listening. We're not trying to validate
the hype necessarily, but really understand the
actual. mechanisms, the money, the usage, the
tech that are driving this, well, unprecedented
environment. Okay. Let's dive into the market
dynamics first then. We're seeing just massive
AI funding rounds. NVIDIA stock is hitting record
highs almost constantly, it feels like. And the
actual usage of the chips is described as maybe
even bigger than the funding suggests. Right.
And this is where comparing it to 2001 is...
useful, but also maybe misleading. That telecom
bust, it happened because companies poured billions
into infrastructure. That empty dark fiber waiting
for web traffic that just didn't show up fast
enough is pure speculation on future capacity.
And today, the sources we have are stressing
the complete opposite scenario. There are, and
this is a quote, no dark GPUs. The big players
buying the chips, they're putting them to work
immediately. It's actually a supply construct
problem, not a demand one. Exactly. Which brings
us to the hyperscalers. That's the term for the
big cloud providers, right? Microsoft, Google,
Meta, Amazon. They're spending just colossal
amounts on data centers. Billions and billions.
But, and this is key, they seem to be seeing
a genuine, pretty immediate return on capital,
or ROIC, from that huge spend. Because the capacity
is getting soaked up instantly, either by their
own projects or, you know, by their cloud customers.
So it's not building empty stadiums, hoping the
crowds will come later. If the infrastructure
is getting used right away and generating a return,
what does that actually mean for this whole bubble
conversation? Why is this maybe financially safer
than past manias? Well, it shifts the risk profile,
doesn't it? Instead of risking oversupply and
empty capacity, maybe the risk is more about
depending on a really constrained supply chain.
This is what one source called a fiber -rich
pumpkin season. It implies it's harvest time
now, not just planting seeds for some distant
future. That's a powerful way to put it. So if
every piece of this expensive compute hardware
is being used to generate actual revenue now,
the foundation of this room feels like it's built
on present income streams, not just future hopes.
That seems to be the core argument for why it's
different. The growth is fueled by tangible,
immediate use of infrastructure, not just betting
on future capacity needs. OK. Speaking of you,
Sid, let's pivot a bit to the cultural side because
adoption is moving incredibly fast and sometimes
with some pretty hilarious results along the
way, which always gives us something to think
about. Oh, absolutely. The speed is just incredible.
Fun fact number one, OpenAI's Sora, their video
generation model, it's exploded. Apparently it
hit hashtag one on the App Store and they just
launched character cameos. For a limited time,
no invite code needed. You could literally put
your cat. or I don't know, your kid's favorite
toy, into a short video generated by AI. That's
like immediate viral mainstream utility right
there. That is a huge sign of accessibility hitting
the consumer level. And then, of course, you
have the famous fails, which kind of remind us
these models are still pretty young, right? We
saw that funny story about ChatGPT folding under
almost zero pressure. That was amazing, yeah.
A parent used some really complex obscure password
as an anti -cheat. on their kid's computer. The
kid just described the password setup vaguely
to ChatGPT and asked it to figure it out. And
boom, the model just gave it up immediately.
Soft chuckle. Zero real pressure, total exposure.
It really shows how complexity can sometimes
just crumble with the right kind of simple, maybe
unexpected prompt. It's a great little example
of the unintended power of these accessible language
models. And you see the platforms reacting to
this kind of immediate access to open AI apparently
had to stop chat GPT from giving out specific
medical and legal advice, personalized advice.
Anyway, they're clearly trying to manage the
most obvious risks as people start using these
tools for really serious. But the education side
is trying to catch up fast as well. University
of San Francisco, for instance, launched a free
online AI prompt course aimed right at beginners.
No coding required, just focusing on practical
everyday uses. Which is needed. I mean, I still
wrestle with prompt drift myself sometimes. Getting
the exact output you want, it feels more like
an art than a science occasionally. It really
does. In this mix of real utility and sometimes
just pure entertainment, it's definitely driving
the money side. The culture and the capital feel
completely linked. Like this AI singer, Zanya
Monet, became the first AI artist to actually
get on the Billboard Airplay charts. That's mass
market consumption happening. And then the valuations
just keep climbing. founders, all 22 years old,
apparently beat Mark Zuckerberg's record. Youngest
self -made billionaires. Their AI startup hit
a $10 billion valuation after raising $350 million.
That scale is just hard to grasp sometimes. And
it's not just late stage money flooding in either.
Sequoia Capital, the big VC firm, they're doubling
down right at the beginning, launching two new
funds. $950 million total just for early stage
AI startups around the world. They are betting
big right from the ground floor. And then, of
course, the rumor mill keeps churning. The biggest
one lately, OpenAI potentially exploring an IPO
maybe in 2026 with a valuation that could hit
up to $1 trillion. So here's the question then.
Why do we keep seeing these like... astronomical
funding rounds and valuations right alongside
these immediate, sometimes kind of silly consumer
fails. I think the massive investment reflects
this huge belief in the long term potential,
even with the very obvious, sometimes amusing
short term limits we're seeing right now. Sponsor.
Let's shift gears now to efficiency. Our sources
suggest this might be the next really big frontier.
The race isn't only about making models bigger
and bigger anymore. It's also about packing powerful
features into smaller, more efficient packages.
Which brings us to IBM's Granite 4 .0 Nano, their
smallest AI models released so far. Yeah, the
Nano part is the real kicker here. And it speaks
volumes about decentralization. First off, these
models are open source. Apache 2 .0 license,
which is pretty permissive. Developers can look
inside, build on top of them freely. And crucially,
they're optimized for efficient runtimes like
this popular library called Llama .cpp. And for
listeners, why should we care about something
called Llama .cpp? Well, because it essentially
lets these complex AI models run really fast,
even on hardware that isn't specialized AI chips.
Think your laptop, maybe even a cheap device
at the edge. you know, without needing access
to a massive server farm, it really democratizes
who can run powerful AI. Okay. And importantly,
they didn't seem to sacrifice training quality
just to shrink the size. These nano models were
trained on, what, over 15 trillion tokens? That's
just a mind -boggling amount of data, like stacking
Lego blocks of really high -quality information.
And they use the same quality pipeline as IBM's
much bigger models. Right. And maybe we should
quickly define parameters here for clarity. When
we talk about parameters in an AI model, we basically
mean the size of its internal knowledge structure.
Think of it like the number of connections or
variables it uses to process information. Usually,
more parameters mean better performance, but
also way higher compute costs and energy use.
But Granite Nano seems to be pushing back against
that core assumption. It's showing you don't
necessarily need 70 billion plus parameters to
get really useful, impactful results. In benchmark
tests, it actually came out on top against other
models in its specific size class, including
ones from Alibaba and Google like Quinn and Gemma.
And what's really interesting is how well it
did on what the sources call agenty stuff. So
that means things like complex reasoning, generating
code, tasks that need multiple steps to complete.
It's not just spitting back facts it memorized.
Whoa. OK, imagine staling that kind of capability,
running it potentially billions of times on small
local devices, even offline. That could totally
change the economics for businesses wanting to
deploy AI everywhere. IBM even certified it for
responsible AI use, which is a huge deal for
companies needing audit trails and, you know,
some level of certainty. That efficiency is just
critical. If you can do complex tasks, tasks
that used to need these giant models on a small
local chip, you slash the cost per query, the
energy use, everything. So thinking about the
market again, what is the success of these smaller
open source models like Granite Nano mean? for
the dominance of the big hyperscale players,
the ones who currently own all that expensive,
specialized compute power. It means competition
is heating up fast, especially at the smaller,
more efficient edge of the AI model spectrum.
It forces the big guys to innovate beyond just
sheer scale. Okay, let's try to bring this all
together. Looking back at our sources today,
we've really hit on three major dynamics shaping
AI right now. First, the huge investment wave
seems arguably justified by this heavily utilized,
maxed out hardware. We're seeing real capital
return, which feels different from pure speculation.
The risk profile seems shifted. Second, consumer
adoption is just incredibly fast. Sometimes funny,
yeah, but undeniably happening. And that's driving
the need for more investment, more hardware.
And third, powerful AI is actually shrinking.
Models like Granite Nano are proving that serious
AI capability can run pretty much anywhere now
and can compete effectively within specific size
categories. And these three themes, they seem
tied together by what people are calling this
virtuous cycle that's driving the whole AI frenzy,
right? More usage demands more chips, which justifies
more spending on infrastructure, which leads
to better models, which then drives more usage.
And the cycle starts again. Yeah, exactly. And
we also saw some quick hits showing the real
-time risks and global spread, like Google having
to pull their Gemma modder pretty quickly from
their AI studio after some decimation claims
popped up. Shows how volatile and legally tricky
this still is. And meanwhile, you see Anthropic
opening its first office in Asia Pacific and
Tokyo. It underlines that this whole movement
is definitely global now. So maybe here's the
provocative thought to leave you with today.
If immediate capital return, driven by maxing
out all the current hardware, is what's validating
this AI boom right now, what happens to the market
structure and maybe those huge valuations when
the core technology driving that return, like
these granite nano models, becomes cheap, open
source, and capable of running almost anywhere?
That definitely raises a big question about the
long -term value proposition for the hyperscalers,
doesn't it? What's their unique edge when the
underlying tech becomes potentially commoditized?
Well, thank you for providing these sources and
for diving into this material with us today.
Until next time, out to real music.