Is the current boom in artificial intelligence
like a financial bubble just waiting to pop,
wiping out trillions like back in 2001? Or is
it something really fundamentally different,
almost like a self -perpetuating infinite money
glitch? It's kind of wild to think about. Two
-sec silence, yeah. Today, we're diving deep.
We've got 12 essential lessons from 28 intense
months right there in the AI trenches. We're
going to unpack why the underlying tech is absolutely
transformative, where the actual money is being
made right now. And that critical skill you really
need to survive the coming labor disruption.
It's serious stuff. Welcome back, everyone. Yeah,
welcome back to the deep dive. And look, this
isn't just theory, right? We're looking at patterns
drawn from people with actual skin in the game.
Right, moving past those. you know academic debates
you usually hear exactly okay so let's unpack
this we've got a roadmap three major areas to
explore today first the financial reality why
this feels more like a vc bubble maybe not a
tech bubble and how nvidia ended up being this
this central indispensable Okay. Then second,
we'll shift to the technical change. We're going
to look at why smaller, more focused models using
things like reinforcement learning, why they're
actually taking the lead now over those huge
generic ones we saw dominate the last couple
of years. And finally, the human cost. The very
real job displacement we see coming and this
kind of counterintuitive idea, the return to
code. Let's jump in. Where do we start? The money.
Let's start with the money. The big question.
bubble or not right the bubble debate it's constant
you know there's this solid heuristic this rule
of thumb if everyone is screaming bubble it probably
isn't going to be that sudden crash they're imagining
yeah it's different from say 2008 or the crypto
thing in 2021 where there wasn't as much skepticism
beforehand exactly but i mean if revenue growth
is genuinely explosive You mentioned anthropic
reporting, like 10x year -over -year growth,
NVIDIA smashing expectations quarter after quarter.
Why are we even still calling this a bubble?
Isn't the VC side just scrambling to catch up
to something totally new? That's the tricky part,
isn't it? It is. And honestly, the people calling
bubble, they do have some strong short -term
points. The investment levels are... Well, they're
insane. Like 17 million seed rounds. Billions
for companies with no actual product yet. Yeah.
Billions for just talent, like getting Andrej
Karpathy or Elias Asitskiver on board. It's nuts.
Right. Hard to justify that unless the underlying
tech was really, really real. Precisely. And
it is. The core tech is delivering real value
now. It's writing code, analyzing data, making
people more productive. That's why it's not a
technology bubble you see. OK. But the overfunded,
often unprofitable startups built on top of it.
Yeah, a lot of those are going to collapse. It's
more like a VC bubble riding in a tech revolution.
So like a correction is coming for those weak
players. Definitely likely. Maybe a 10, even
30 percent market correction when those companies
fail. But the core tech. It sticks. Okay. That
makes sense. And this brings us to that weird
infinite money glitch thing, the loop between
NVIDIA and the big AI labs. Ah, yes. Yeah. Strange
loop. It's where the structural friction really
lies. It's like the ultimate capital recycling
machine. How does it work? Exactly. So NVIDIA
acts like a VC, right? They bore huge capital,
let's say $100 billion just as an example, into
a lab like OpenAI. Okay. But OpenAI has to spend
that money on compute. Which means... NVIDIA's
GPUs. They're really high margin graphics cards.
Exactly. The money flows straight back to NVIDIA's
revenue. Their stock price goes up. Which lets
them make more investments and the loop just
keeps going. It's brilliant. A self -feeding
system. Genius, really. But it exposes this tough
truth, doesn't it? Pretty much everyone else
in the AI game, the labs, the startups, they're
losing money. Burning millions, billions on training
costs. Absolutely. Jensen Huang and NVIDIA. They're
selling the picks and shovels in this gold rush.
They've got the guaranteed massive profit margins.
Everyone else is just spending. And that must
be why Google's pushing their TPUs so hard. Amazon's
building custom silicon. Meta's designing their
own chips. They have to. It's desperation. They
need to break this dependency on NVIDIA to control
their own destiny, frankly, just to survive long
term. Okay, so if owning the hardware, the compute
is the main battleground. What's the single biggest
constraint holding back AI growth and revenue
right now? If labs could just get more GPUs,
could they make more money? Compute. It's the
ultimate physical choke point. It limits how
fast labs can expand and even meet the demand
they already have. Labs like Anthropic and OpenAI
could likely 2x or 3x their revenue today if
they just have more GPUs. So compute is the constraint.
Got it. The bottleneck limiting revenue and expansion.
Mineral sponsor, Reed Placeholder. All right,
we're back. So if hardware is the choke point,
how are companies fighting back? It sounds like
they're changing the definition of what good
AI even means. That's right. Which brings us
neatly into the technical shifts we're seeing.
Yeah, the whole era of just building the biggest
possible model. That's kind of ending, you know,
like a lot of analysts found some recent releases
disappointing. But they were intentionally smaller,
weren't they? Smaller than previous versions.
Exactly. It wasn't about ideology. It was driven
by that cost barrier we talked about. Yeah. Size
just started giving diminishing returns on performance
compared to the crazy jump in compute cost. So
efficiency and speed are the new game. That's
the real frontier now, efficiency and speed.
And the breakthrough that's really keeping the
momentum going is reinforcement learning, or
RL. OK, define RL for us quickly. Sure. It's
basically when the AI learns by doing a task,
like actually trying to write code or play a
game, and then it gets rewards or penalties based
on how well it did. So it's moving beyond just
reading like all the text on the Internet. Right.
It's learning from action and consequence. And
what's fascinating is how fast the AIs are getting
better at coding and math specifically. Why those
deterministic tasks? tasks with clear right or
wrong answers? Why are they improving so much
faster than, say, creative writing? Because the
feedback is clean. If the code doesn't compile,
boom, wrong path. If the math equation fails,
nope, try again. The AI immediately knows to
discard that way of thinking. Ah, I see. So it
can generate tons of attempts. Exactly. Like,
try a solution 500 different ways, find the one
successful reasoning trace, the one path that
worked, and then train itself just on that optimal
path. It's an amazing synthetic data advantage.
Wow. It's pretty humbling, actually, seeing how
fast those areas are moving. I still wrestle
with prompt drift myself. What's prompt drift
exactly? For listeners who haven't hit that wall
yet. Oh, it's maddening. It's basically when
you put in the exact same prompt, but the model
gives you wildly different answers over time.
Its internal behavior just changes. Makes reliable
work kind of impossible, right? Totally. And
that's much harder to fix for subjective things
like writing compared to deterministic stuff
like code. It's a real challenge. Okay, so that
brings us to this flow zone problem. The focus
now is on smaller, faster models because speed
is just critical for actually getting work done,
right? Like waiting five minutes for a code suggestion
just breaks your focus. Absolutely breaks your
flow state. Look at Anthropic's Haiku 4 .5 model.
People find it super useful, mainly because it's
three times cheaper and more than twice as fast
as their bigger Sonnet model. So good enough
but quick is way better than perfect but slow.
Vastly superior for most practical uses. Yeah.
And there's this new strategy reflecting that
idea, test time compute. Right. So instead of
blowing the whole compute budget on training
the absolute biggest model possible. What do
they do? They train a, let's say, medium sized
model. Then they save some of that compute budget
to use during inference, like when you're actually
using the model. They let the model think longer
and reason more deeply before it gives you the
answer. That's a great way to put it, like giving
a student extra time on the final exam to double
check the hard questions, even if they only studied.
a medium amount. Exactly like that. And this
efficiency, it's unlocking this exponential growth
in how long AI agents can work autonomously.
How long are we talking? Well, six months ago,
maybe 20 minutes was a good run for an autonomous
agent. Today, we're seeing sustained work sessions
like two to seven hours long. GPT -5 codecs apparently
had some seven hour autonomous sessions. Whoa.
Imagine scaling that. That unlocks really complex
stuff, right? Like huge code -based refactors
or deep research projects. Totally. The agent
can analyze, plan, run searches, synthesize the
findings, and just repeat that whole loop for
hours without a human stepping in. If that trajectory
keeps going, we could have agents working reliably
for days soon, automating tasks that take junior
engineers weeks. It's heading that way. It really
seems possible. This changes the game for complex
automation. Okay, how does this new strategy
using test time compute actually improve the
results if the model itself isn't bigger? Because
the smaller model gets dedicated time to reason
during inference right when it's needed. It leads
to better, more thoughtful answers than just
a fast, reflexive response from a giant model.
More thoughtful answers from smaller models.
Got it. Efficiency is the new size. The technical
shift is clear. Efficiency over brute force.
Now let's talk about data. What really separates
the winners long term? You mentioned XAI earlier.
Yeah, let's validate that prediction about XAI.
People kind of dismissed Elon Musk, but he always
had foundational advantages that meant he was
going to catch up. Unique data, capital, manufacturing
skill. Right. XAI gets exclusive access to X,
formerly Twitter, that real -time global conversation.
That gives their AI, Grok, this unique edge on
current events and slang that others just can't
legally scrape, right? Correct. That's a powerful
data mode. But the ultimate mode, it's physical.
It's Tesla and the Optimus robots. How so? Tesla
cars provide this constant stream of real -world
video footage, amazing for training world models
that understand physics and navigation. Yeah.
But Optimus. Optimus will generate something
totally unique. Physical embodied data. Three
-dimensional data from interacting with the world.
Embodying the data? What does that physical experience
give an AI that just reading text misses? Everything.
Seriously. Think about a human child. A toddler
is infinitely smarter than GPT -5 in some really
fundamental ways. How? Because the child learns
from physical feedback that isn't scraped from
the Internet. They learn about gravity by dropping
things, force by pushing things, object permanence,
social cues, all by doing things in the real
world. Ah, that embodied experience. That's what
current AIs are fundamentally missing. Totally.
It's a huge gap. Okay, but here's where things
get really interesting, maybe counterintuitive
again. Open source models seem to be catching
up incredibly fast, even matching or beating
the big closed systems. Yeah, this is blowing
up quietly. Take a model like GLM 4 .6 from Zippo
AI. It's open source and it's performing better
than Anthropic's closed source Claude Sonnet
on a lot of important coding benchmarks. So the
open source community is moving at lightning
speed. Undeniably fast, yeah. But why isn't this
like front page news? The incumbents, the big
labs like OpenAI and Anthropic, they kind of
ignore it, right? To protect their perceived
advantage, their moat. But there's also a practical
issue. What's that? Cloud infrastructure, AWS,
Google Cloud, Azure. They're optimized to run
the popular models efficiently. The GPT -series
cloud. So these powerful new open source models
seem slower just because the pipes aren't optimized
for them yet. Exactly. It creates friction. Yeah.
But the underlying quality of the models proves
how fast the open source community is innovating.
You know, Anthropix valued maybe $200 billion,
but a lab like Zippo is hitting comparable performance.
It's validation. Wild. OK, now for the more sobering
part, job displacement. Yeah, got to talk about
it. Look, the goal here isn't just disrupting
the, what, $300 billion software market. The
aim is replacing big chunks of the $15 trillion
global labor market. $15 trillion, wow. So the
trend we're likely going to see accelerate by
2026 is single job replacement agents. Meaning?
Think customers, court reps, sales development
reps, setting appointments, executive assistants.
These roles are prime targets for automation
by specialized AI agents. Which means huge. efficiency
gains for companies massive games but also significant
social unrest i mean widespread protests related
to ai job losses are genuinely predicted for
2026. that's heavy which leads us maybe surprisingly
to your prediction the return to code learning
computer science is going to be sexy again why
because it gives you asymmetrical leverage Massive
leverage. Explain that. Okay. A non -technical
person using some AI assistance. Maybe they get
2x, 3x faster at their job. That's nice. Useful.
Right. But a skilled programmer, someone who
really understands systems, logic, architecture,
they don't just get 3x faster. They become 100
times more powerful. 100 times? How? Because
you stop being just the coder writing lines.
You become the architect. You start managing
teams of AI agents. You leverage your deep technical
know -how to orchestrate these tools at a scale
nobody else can. You have to get to the cutting
edge to really crush the competition. So you
go from using AI to, like, help write an email.
To using AI to refactor an entire million lines.
code base in an afternoon by coordinating five
different specialized agent teams simultaneously.
That's the 100x leverage. Wow. So what does this
all mean for the average career then? If you're
listening right now, what's the takeaway? The
greatest advantage, the biggest upside, goes
to those with the technical competence to truly
leverage these new AI tools at a deep level.
Technical competence is key. Okay. Hashtag, hashtag
occlusion and final thought. All right. Let's
try to summarize the big ideas here. Three core
lessons, maybe. First, AI is real transformation,
not just a bubble, and it's backed by actual
explosive revenue growth, even if the VC funding
is bubbly. Right. And second, compute is that
critical physical constraint, the bottleneck,
which is fueling that kind of bizarre self -feeding
money loop between the AI labs and NVIDIA. And
third, the game has totally changed. That era
of just copycat startups reselling API tokens
at a loss, that's ending. It's over. The future
really belongs to efficiency, specialized models,
and that deep technical competence we just talked
about. And it feels like the conversation has
shifted too, right? Less focus on, you know,
abstract AI doomerism, the paperclip maximizers.
Yeah, thankfully. The focus has rightly shifted
to the very real world problems. Bias in the
models, market consolidation, and crucially,
labor disruption. These are tangible issues we
need to solve now. We've seen just how much capital,
how much energy is pouring into building the
physical infrastructure for AI, the data centers,
the silicon foundries, the power grid upgrades.
It's staggering. So maybe the final provocative
thought for you, the listener, is this. It's
about where you fit. You need to ask yourself,
are your skills foundational? Are they essential,
like the picks and shovels needed to build this
new AI infrastructure? Or... Are your skills
part of that repetitive lower leverage labor
that is now, frankly, being systematically replaced?
A heavy question, but a really necessary one
to ponder. Where does your unique leverage lie
in this new world? Yeah. Thank you for taking
this deep dive with us today. Lots to think about.
Always is. Talk soon.