Is the AI boom of today a bubble? A big one,
maybe. Potentially even bigger, perhaps riskier,
than the dot -com frenzy back in the 90s. Two
-step silence. That's quite a thought, isn't
it? Especially when you think how much AI is
already part of things. Welcome to the Deep Talk.
Today, we're going to pull back the curtain a
bit on some really big recent insights into this
AI landscape. It's moving so fast. Yeah, it really
is exponentially fast, it feels like. Almost
impossible to keep track of everything. Right.
Absolutely. So our mission today, really, is
to try and cut through some of that noise for
you. We're going to take a journey, look at a
few key things. First up... The big money questions
is all this AI hype sustainable, really. Then
we'll switch gears, look at some fascinating
actual applications, things happening, you know,
on the ground, industry moves, stuff away from
just the stock market headlines. And then finally,
we're going to wrap up with something kind of
mind bending, an idea that might really change
how we think about AI itself. What if all these
different AI models, what if they're all... Basically
learning the same thing, converging on some kind
of, I don't know, universal understanding. Exactly.
It's a wild concept. Makes you stop and think.
OK, so let's start there with that big claim
from the top. We've been looking at what Torsten
Slick, he's Apollo's chief economist, has been
saying. And he doesn't really pull any punches.
He says today's AI boom is more dangerously inflated
than the 90s tech bubble. Wow. And this time
he argues there's just much more at stake. Yeah.
And what's really striking is the data he points
to. He looked at the top 10 companies in the
S &P 500. You know the ones. NVIDIA, Meta, Microsoft,
Google, Amazon. His internal numbers are saying
they're more overvalued right now than the biggest
tech names were back then, like at the absolute
peak of the late 90s. That is a really strong
statement. What's behind that specifically? Well,
it's their price to earnings ratios. P .E. ratios.
He calls them dot combusting levels. And for
anyone maybe not tracking markets day to day,
the P .E. ratio is basically how much investors
are willing to pay for, you know, a dollar of
the company's profit. Right. High P .E. means
high expectations for the future. Exactly. But
dot combusting levels that kind of suggests expectations
might be, let's say, a bit out of whack with
reality or potential reality. And what's different
this time? If he's right, if it is a bubble.
What makes it different from the 90s one? The
big thing, and this is what makes it kind of
concerning, is the concentration risk. It's extreme,
almost 40%. Think about that. 40 % of the entire
S &P 500 is tied up in just those 10 companies.
Wow. So if you're just buying a broad market
index fund thinking you're diversified, you're
not really. You're making a massive bet on AI
hype continuing. So if those few stumble. The
whole market feels it, much more severely potentially
than back in the 90s. It really makes you ask,
you know, how much of this value is real, tangible
stuff, and how much is, well, just hype, belief.
It's just staggering amounts of money moving
around. And we see these huge financial plays
that seem to, well, feed that bubble perception.
NVIDIA, for example, reportedly planning to spend
$500 billion on AI factory. $500 billion. It's
hard to even picture. Right. And then Meta. Yeah.
Supposedly offering like $100 million signing
bonuses for top AI people. $100 million. For
one person. It's a talent war out there, for
sure. And it keeps going. Amazon maybe putting
another $8 billion into Anthropic. And OpenAI,
remember they bought ScaleAI for, what, $14 billion?
Yeah. And then pretty soon after laid off 200
people. It makes you wonder about the strategy
sometimes. Is it real growth or just grabbing
land in a gold rush? You know, if you look at
the market sentiment overall, it gets even clearer
why people are using words like frothy. There's
this index. BUZZ Next Gen AI Sentiment Index
tracks AI hype with retail traders, basically.
It's apparently a 45 % and just 16 once, trading
29 % above its 200 -day moving average. Which
is pretty high. Yeah, the 200 -day average is
a key thing traders watch for long -term trends.
Being that far above it, it signals extreme optimism.
Almost like you said, a fever pitch. Right. BT's
analyst just came out and said it's looking very
frothy. That's the direct quote. Very frothy.
It really does feel like everyone's jumping on
the same train and it's just going faster and
faster. So let's connect this back. What does
this mean for you listening right now? If this
bubble or frothiness or whatever, if it pops,
well, the good news probably is it won't kill
AI itself, the tech. Yeah, just like the dot
-com crash didn't kill the internet. Right, exactly.
The underlying tech is definitely moving forward.
That innovation is real. But that doesn't mean
NVIDIA should be priced like it's going to run
the whole global economy. Fair point. So the
fallout from a big correction could be pretty
rough. It could hit general market funds, maybe
even hurt funding for new AI startups trying
to get off the ground. So thinking about right
now this market, what's the biggest immediate
risk for an investor, would you say? I'd say
overexposure, being too heavily invested in just
a few of these really volatile, highly valued
companies. If expectations slip, losses could
be significant. Okay. Let's pivot then. Away
from the finance side, the potential frothiness,
let's look at what's actually happening on the
ground. Because beyond all the market talk, there
are some really interesting real world uses and
industry moves going on. Yeah, absolutely. And
what's kind of wild is how fast some of these
tools are scaling up. Look at Perplexity, the
AI answer engine. They partnered with ITEL over
in India, offered free Perplexity Pro for a year
to all 360 million of ITEL's customer. 360 million.
Yep. And boom, Perplexity shot straight to number
one on the app charts in India. Overnight, practically.
That's a whole new level of user acquisition.
It really shows a new model for getting AI out
there globally super fast. Incredible reach.
And it shows people really want these tools.
But speaking of services, there's this interesting
point Mert Davici made that maybe AI won't replace
a lot of services after all. Oh, yeah. Why is
that? His argument is basically customers want
to pay software prices for software, not salary
prices. Ah, OK. Makes sense. So if an AI service
costs as much as hiring a person. Maybe people
just stick with the person. Yeah. Yeah. Who wants
to pay a robot a full salary for like writing
emails? There's definitely a psychological thing
there. Definitely. And then on the voice side,
Hume AI, their EVI 2 model was already pretty
cool, making AI voices sound unique, kind of
emotional. Now they've got EVI 3 out, supposedly
captures even more personality, more nuance.
So what can you do with that? Well, think about
building really natural language coaches or podcast
hosts that sound totally real or even like compassionate
AI companions. It's about making AI interaction
feel much more human. And here's something I
think a lot of our listeners will find really
interesting. An open AI researcher who actually
just joined Meta. gave this 30 minute masterclass
online about how to create wealth in the AI economy.
Oh, really? Yeah. And it apparently got like
at least a million views super quickly. Wow.
People are clearly hungry for that practical
side. How do I actually use this stuff? Exactly.
It shows AI isn't just abstract tech anymore.
It's affecting careers, business models right
now. And for practical tools that kind of do
things for you, there's Perplexity Comet. People
are calling it scary good. No. Imagine an AI
assistant that hooks into your app's email, calendar,
project tools. Okay. And just does stuff for
you autonomously. They highlighted like 10 powerful
use cases, summarizing meetings, drafting complex
emails. That sounds like it. A proper assistant.
Finally. Feels like a real step towards AI that
doesn't just respond, but takes initiative. That
could be a huge productivity boost. And speaking
of things taking off, another funding story that's
just explosive. Lovable, the Swedish AI app builder.
Lovable, yeah. Heard about them. They hit $75
million in annual recurring revenue. In seven
months. Seven months, seriously. Yeah, 2 .3 million
users, 180 ,000 paying customers. And they just
raised $200 million Series A. at a $1 .8 billion
valuation only eight months after they launched.
Okay, that's insane growth. Just shows if you
build something AI -powered that people actually
need. It can scale incredibly fast. So looking
at all these different applications, Perplexity,
Hume, Lovable, what's the common thread? What
stands out about how these tools are actually
finding their place? I think it's that they're
solving specific high -value problems. They create
real value for users, and because of that, they
can scale with just... Unbelievable speed. Right.
Let's shift again. Just a few quick hits now.
Kind of snapshots of how AI is evolving day by
day almost. Lots of little interesting bits.
Okay. Rapid fire. We saw this piece on 10 examples
of how brutal robot testing made them more robust.
It's kind of cool looking at the extreme engineering
needed to make physical robots tough enough for
the real world. It's not all just software. Right.
The hardware side. And Elon Musk's XAI, they're
launching something called Baby Grok, a chatbot
for kids learning. Huh. AI for kids. That's interesting.
Shows how it's moving into education and sensitive
areas. Raises questions about safety, too. Definitely.
And for users. Anyone trying to get better results
from AI. And Fropic put out some advice on writing
effective prompts. Well, that's useful. Yeah.
I mean, I still wrestle with prompt drift myself
sometimes. You start asking for one thing and
the AI kind of wanders off. Totally know what
you mean. So yeah, any tips on prompting are
gold. And another practical thing, DuckDuckGo,
the search engine, they added a feature to let
you block AI -generated images. Do you want to
filter those out? Oh, interesting. Giving users
control over authenticity. And then on the policy
front, kind of big news. Meta decided not to
sign the EU's AI code of practice. After reviewing
it carefully, they said no. OK, why is that significant?
Well, it signals a potential split, right? Between
how major tech companies and regulators like
the EU see AI governance shaping up could mean
different rules in different places. Raises questions
about how global companies navigate that. OK,
so just from. Those few quick examples. Right.
Robots, kids chat bots, prompting, image blocking,
EU codes. What's the main takeaway? I'd say AI
is getting really specialized really fast. And
that's creating totally new needs for users and
definitely raising some tricky new regulatory
challenges. Amid a role sponsor replace holder.
Okay. Let's unpack this next part. Because here's
where it gets, I think, really interesting. The
big idea we sort of hinted at earlier. Imagine
this. What if all AI models, all the different
ones, different companies, different data, what
if they're all fundamentally learning the same
core thing? Like they're all uncovering pieces
of the same universal puzzle about how the world
works. Exactly. That's the fascinating premise
here. The idea is as these models get bigger,
get more complex, get smarter, essentially, they
start learning the same basic relationships about
the world. And they represent those relationships
inside themselves in ways that are surprisingly
similar. Even if they were trained differently.
Yeah. It kind of suggests there might be one
ideal sort of universal way to understand and
represent knowledge. And maybe all these big,
powerful models are just converging on it. Like
they're all accidentally tuning into the same,
I don't know, cosmic frequency of understanding.
And this isn't just like a philosophical idea
someone had. No, no. MIT actually put out research
formalizing this in 2024. They showed that bigger
models. both for vision, like understanding images,
and for language, they learn features internally
that are strikingly similar. So basically, the
smarter they get, the more their internal maps
of the world start to look alike. That's the
gist of it, yeah. It's like they're all finding
the same fundamental patterns in reality, no
matter what specific data they started with.
Okay, wow. Now, the really wild part of this
research you mentioned, trying to turn the AI's
internal thought that abstract number stuff,
the vector, back into the exact words that went
in. Yeah, that's the really hard part. Like trying
to rebuild a complex blueprint just by, I don't
know, feeling the vibes in the finished building.
It's super difficult. So how on earth did they
manage that? Well, they used this method, iterative
refinement, and millions, literally millions
of queries, basically asking the AI again and
again, is this closer? How about this? Refining
the output until it matched the original input.
And did it work? One team hit 94 % accuracy.
On long sentences. 94%. Yeah. Think about that.
They could take the AI's abstract internal state,
its thought, and translate it back into perfectly
readable human language. That's like getting
a peek inside its head. It really is. A remarkable
glimpse into the AI's sort of cognitive process.
Okay. So if this is true, if all big models are
essentially starting to speak the same internal
language, what does that actually mean? What
are the implications practically? Philosophically.
Oh, the implications are huge. Potentially revolutionary,
honestly. If we can really understand this universal
internal language, we could build universal inverters,
tools to actually understand why an AI gives
a certain output, not just what the output is.
Which would be massive for trust, for debugging.
Exactly. For finding hidden biases, for making
AI more transparent. Huge. And it could mean
we can translate between closed models. Making
different AIs from different companies and be
built totally differently, making them talk to
each other seamlessly. So like a Microsoft AI
could understand an open AI model, even if they
weren't designed to connect. Potentially, yeah.
Imagine the possibilities for combining different
AI strengths. Interoperability could just explode.
OK, that alone is huge. But you mentioned going
even further. Right. So if there's this shared
structure of understanding. Maybe it could help
us decode things we don't understand, like ancient
human texts. Think about Linear A, the Minoan
script. We still can't fully read it after decades
of trying. What if an AI, seeing these universal
patterns, could crack it? Whoa. An AI unlocking
secrets of human history. That's humbling. Isn't
it? And then, here's the really mind -bending
one, the one that makes you question everything.
What if, what if we could talk to whales? Talk
to whales. Yeah. There's already Project CETI
using AI to try and decode whale communication.
If there is some kind of shared conceptual space,
this universal language, could AI bridge the
gap? Could we find shared semantic structures
between human thought and whale thought? Whoa.
Right. Imagine scaling that. A billion queries.
Maybe it decodes ancient languages. Maybe it
lets us communicate with another intelligent
species on Earth. Wow. It makes you rethink what
intelligence even is. Artificial, biological.
Yeah. Yeah, breathtaking. Does this convergence
idea, does it mean AI might one day actually
understand us, not just process our words, but
get the concepts, the feelings behind them? It
certainly suggests a path towards that, a shared
conceptual space, a deep structure. Yeah. It
feels like it could bridge human and AI thought
in a way that goes beyond just processing, maybe
towards real comprehension. So, OK, we've covered
a lot. We navigated the choppy financial waters
of the AI market. The frothiness. Right. We've
seen all these diverse real world applications
popping up everywhere, scaling incredibly fast.
And then we peered into this possible future
where AI models might actually share a universal
language. It's quite the deep dive from economics
all the way to, well, philosophy almost. Yeah.
And I think the key tension is clear, right?
Despite all the market craziness, the volatility,
the hype, the underlying technology is advancing,
seriously advancing. And it keeps revealing these
profound, almost philosophical questions about
intelligence, about reality, maybe even about
ourselves. So as you go about your day today,
maybe just take a moment to think about this.
If AI models are converging on some single ideal
way to understand the world, what does that tell
us about reality itself? Is there some inherent
structure just waiting there to be discovered
by any intelligence smart enough, whether it's
made of silicon or carbon? And the follow -on
question, could this shared internal language
of AI actually lead us to discover some universal
truths? Truths about intelligence itself, both
the artificial kind we're building and the biological
kind we possess. It's definitely a question worth
pondering as we all move into this future. We
really hope you feel a little more informed after
this and maybe, maybe a lot more curious. Thanks
so much for joining us on the Deep Dive. Audioro
music.