There's this UK analyst claiming the AI financial
boom right now. It's 17 times worse than the
dot -com bubble. 17 times that number. Well,
it really makes you stop and think, doesn't it?
Deserves a proper look. It really does. Yeah.
And thanks for sharing those sources with us.
It's clear we're dealing with this massive contradiction,
right? You've got these sky high AI valuations
and then you look for the actual profitable uses
right now. And it's well, it's a different picture.
Exactly. So today, that's what we're going to
do. We'll unpack that bubble claim, see where
AI is actually delivering ROI and also touch
on this really intense work culture popping up
again in Silicon Valley to try and meet. these
huge expectations. OK, let's dig into that big
number first. The 17 times bubble. This analyst,
Julian Guerin, he's not mincing words. 17 times
the dotcom era. Four times the 2008 crash. Beep.
That's a heavy statement. Mm hmm. And. It's crucial
to get why he's saying that the dotcom thing
that was built on maybe shaky revenue projections.
Right. But this A .I. boom, he argues it's built
more on just sheer market cap momentum. It's
exploded way, way past any kind of near term
profit reality. Yeah. You've got what, 10. major
AI startups, together they've added something
like a trillion dollars in market value. A trillion.
But you look closer and almost all of them are
still, you know, deeply unprofitable. It's this
profitability paradox. It really is. So you got
to ask, who is making money here? And right now
the answer seems pretty clear. It's mostly NVIDIA
selling the hardware, the shovels for the gold
rush. Basically, they're getting consistent returns.
Pretty much everyone else developing the core
AI models. They're basically just, well, bleeding
cash, funding operations based on what they hope
will happen down the line. And all that cash
burn. Yeah. It's making the traditional money
people, the VCs, a bit nervous. You're seeing
them pull back now. They look at the valuations
and just think they're, well, kind of absurd.
Right. So the whole ecosystem is increasingly
propped up by just a few huge players. The mega
backers, as Garen calls them. Yeah, not your
usual VCs. We're talking SoftBank, sovereign
wealth funds. Even NVIDIA itself is investing
heavily back into the ecosystem. They're the
capital life support right now. And the bet seems
to be if they just keep pouring money in, eventually
one of these companies will crack artificial
general intelligence or AGI. So Garen lays out
two possible futures, basically. Option one.
Yeah. The money keeps flowing, but it never really
delivers that massive value everyone expects.
It's just this capital treadmill or option two.
Someone actually does achieve true AGI, a genuine
breakthrough. And he's betting on option one,
isn't he? He seems to think the market hype has
just gotten way out ahead of the actual tech
capabilities right now. I'm going to say just
trying to understand the tech itself can be tough.
Honestly, sometimes I wrestle with prompt drift
myself, you know, getting consistent results.
So when you talk about these billion dollar market
shifts. Yeah. Yeah. It's a lot to wrap your head
around. So given all that cash burn right across
the board. What's the single biggest factor keeping
these valuations so incredibly high? It really
seems to boil down to those mega backers. Them
plus this persistent belief, this hope that true
AGI is just around the corner. OK, let's shift
gears a bit away from the financials and towards
some more practical stuff. If you're trying to
get a better handle on the tech itself, Stanford
actually has this really good four part lecture
series out. It's about five and a half hours
total. Gives you a pretty solid kind of medium
depth understanding of LLMs, you know, large
language models, the AIs that generate human
like text. And we are seeing some pretty cool
jumps in what these things can do. There's a
new tool, DeepSeq OCR, for reading documents.
Apparently it's really impressing people. It
managed to accurately read some properly messy
handwritten. written letters, which is not easy.
Yeah, that's huge. Imagine for archives or just
extracting data. But there's always this ethical
tension simmering underneath, isn't there? Like
Microsoft's AI chief coming out and publicly
criticizing ChatGPT's ability to generate erotica.
That feels kind of awkward given Microsoft put,
what, $13 billion into open AI? It does. And
you also mentioned the Australian government.
suing Microsoft over confusion about AI pricing
changes. It just shows how regulators are scrambling
to keep up, trying to figure out how to handle
the ambiguity of these new AI systems. And here's
something that feels genuinely concerning in
the sources you shared. This trend of extreme
overwork making a comeback in Silicon Valley.
AI startups are apparently bringing back China's
infamous 996 grind. That's 9 a .m. to 9 p .m.
six days a week. Wow. Yeah, and there's even
that dark joke going around among researchers
about the 002 schedule, zero sleep, zero days
off, just two hours maybe. The pressure to deliver,
to hit those short -term milestones, maybe justify
those crazy valuations we talked about, it's
pushing people really, really hard. It seems
like a dangerous game. That kind of pressure
cooker environment might get quick results, but...
Burning out your best people. Yeah, and it introduces
a huge risk of mistakes, right? Errors creeping
into the very foundations of the models they're
building. Not good long term. Meanwhile, the
big players are still pushing into new areas.
OpenAI has apparently got a secret project, building
a generative music tool. Ah, okay. Like SunoAI
and those existing tools. Turning text prompts
or even audio snippets into full songs. Always
looking for that next big creative market to
disrupt. So if this 996 work culture, this intense
grind, is becoming widespread again, how does
that really impact AI innovation in the long
run? Well, you might get some rapid gains, sure,
short bursts of progress. But the risk of burnout,
costly errors, and ultimately maybe less thoughtful,
lower quality innovation seems pretty high. Okay,
let's focus now on some specific tools and maybe
more importantly, some really impactful applications.
We saw Google talking about vibe coding. in their
AI studio. Sounds like they're trying to streamline
how engineers work, make development faster,
more intuitive. But the applications that really
jumped out, I think, were in biodefense and health.
There's this company, Valfos, backed by OpenAI
folks. They launched with $30 million specifically
to build AI biodefense systems. Yes, systems
designed to detect and figure out how to counter
new pathogens. In just hours. Yeah. Hours. I
mean, think about that. That's a massive leap
in how quickly we could respond to, say, a new
pandemic threat. It really is. And then there
was that other study from Utah showing AI could
spot parasites in human stool samples really
quickly and accurately. Pause. Whoa. I mean,
just imagine scaling that kind of detection speed,
applying it globally to public health surveillance.
That's tangible value. That justifies serious
investment, you know? Absolutely. And the support
system for all this is growing, too. Amazon,
OpenAI, NVIDIA, they're teaming up to turn Cal
State into a big AI training center, recognizing
we need more people who actually know how to
build and use this stuff. There's even a paper
mapping out like 90 different AI coding tools
available right now. So thinking about all those
tools and applications. Beyond the big names,
what was the most surprising practical use case
we came across this time? For me, it was that
health stuff. The AI detecting pathogens and
parasites so quickly and accurately that speed
just feels like a potential game changer for
public health. Mineral sponsor read, insert sponsor
read here. All right. So we've talked about the
bubble fears, the potential, the pressures. Let's
look at some hard data now, specifically from
industries already using this tech. We're pulling
this from the Artificial Analysis 2025 State
of Generative Media report, based on input from
about 300 developers and creators using these
tools right now. The data really highlights Google's
position, especially in creative AI, for making
images. Google Gemini is number one, 74 % usage
among those surveyed. OpenAI's model, GPT image,
is next at 64%. So, strong competition there.
Yeah, and that lead carries over into video generation
too. Google VO. Leading the pack with 69 % usage
in this group, competition's clearly driving
things forward. And what's driving the choice
for these creators? What makes them pick one
model over another? Overwhelmingly, it's quality.
That's the number one factor, cited by like 76
% to 82%, depending on the task. Makes sense.
Cost comes second, yeah. But the message is clear.
If the output isn't good enough, isn't high quality,
then people just aren't going to use it regardless
of price. OK, so let's connect this back to that
bubble debate we started with. This feels really
important. Sixty five percent, almost two thirds
of the organizations surveyed said they're already
seeing a return on their investment in generative
AI or they expect to see it within the next year.
Right now. OK, that's likely self -reported data.
You got to take it with maybe a small grain of
salt. But still. 65%. That's pretty strong validation,
isn't it? It suggests that investment in these
specific high quality focused applications like
generative media is paying off for many right
now. Yeah, it kind of counterbalances the hype
around the purely research focused arms that
are still burning cash. There's real measurable
value being created in certain sectors. If quality
is the absolute top priority, does that automatically
mean the big expensive proprietary models from,
say, Google and OpenAI will just always dominate
the open source alternatives? Well, quality is
definitely driving adoption now. But the report
suggests that the ability to fine tune models
for specific needs and dealing with IP and licensing
stuff, those are becoming really important secondary
factors pretty quickly. So bringing it all together.
What does this mean for you listening? We've
really grappled with the central tension today,
haven't we? On one hand, you've got this immense
financial speculation that potential 17X bubble
Garen warned about. Huge risk. But on the other
hand, you have clear, demonstrable ROI already
happening in fields like generative media and
these incredible potential breakthroughs in areas
like biodefense. Yeah, it's a paradox, like we
said. Massive financial risk running alongside
absolutely essential functional progress. The
debate isn't really if AI is valuable anymore,
is it? It seems more about where the sustainable
value is actually landing. Is it with the companies
selling the infrastructure like NVIDIA and Google
through its cloud? Or is it eventually going
to come from those research arms chasing AGI
even though they're burning cash now? And maybe
here's a final thought to chew on. If 55 % of
organizations say they're already getting ROI
from AI, What do the other 35 % need to figure
out? What needs to change in their strategy or
maybe the specific way they're applying AI so
they don't get left behind? That's the challenge
of applying this knowledge in real time, isn't
it? Absolutely. Well, thanks again for sharing
the sources and letting us dive deep into all
this with you today. Definitely check out those
Stanford lectures if you're interested and maybe
take a look at that generative media report data.
Lots to think about. Out to your own music.