Imagine a vehicle that truly thinks a car making,
you know, nuanced real -time decisions, navigating
dense traffic and adapting all without you having
to monitor it. That's the ambition behind this
new buzzword, physical AI. Right. And then you
have to juxtapose that physical ambition with
the massive financial signals we're seeing. I
mean, a $12 billion valuation driven entirely
by what the public actually wants to use AI for.
We're diving into both of those today. Welcome
to the Deep Dive. Today we're unpacking a pretty
substantial stack of sources that really map
the current trajectory of AI, from these huge
engineering goals of automakers all the way to
the edge of theoretical research. Yeah, our mission
is to trace that blurring line between the digital
code and actual real -world results. First, we'll
get into that fight to own the operating system
of autonomous vehicles, what the industry is
calling physical AI. Then we're going to shift
to the front lines of practical research. I mean,
everything from an AI keeping a real plant alive
to the complexities of self -mutating code. And
finally, we'll analyze that historic IPO. It
proves pretty clearly that... For today, at least,
consumer apps are absolutely king. Okay, let's
unpack this. So we're starting with that idea,
physical AI. It's the new term you see flooding
the auto industry, robotics, logistics. It's
all about autonomous systems that can think,
reason, and, you know, act in the real world.
And what's fascinating here is just the terminology
itself. I mean, let's be honest. Physical AI
is essentially a rebrand of high -end robotics.
It makes traditional automation sound new again.
But the key distinction is the scale, isn't it?
We've had robotics for decades, but the innovation
now is applying these massive, deep learning
models to unpredictable situations like a city
street. Exactly. The old systems were brittle.
These new systems, the ones powered by so -called
physical AI, they're designed to reason through
billions of data points. They have to understand
physics, human intent, probability, all at the
same time. And we are seeing real products emerge
from this. like the Sony and Honda collaboration,
the Afila EV, their goal is full level for autonomous
driving, using a physical AI system to constantly
process what's around it. And this is a huge
global commitment. Ford has been really aggressive,
promising cars by 2028, where you won't need
your eyes on the road at all. Not just monitoring,
but truly hands -off. That timeline is, well...
It's very aggressive. And to get to that level
of reasoning, you need computing power that rivals
a small data center, but inside the car itself.
That's why the chip war is so intense here. Every
new feature needs exponentially more compute,
more memory, more sensors. NVIDIA is supplying
chips to giants like Geely and Mercedes. ARM
is dedicating huge resources just to designing
chips for these systems. Mercedes is playing
it a little safer, though. They're focusing on
a really defined use case. They're saying their
new system will drive you specifically between
home and work. That's it. And that detail really
highlights the core strategic battle. Everyone
involved wants to own the OS, the actual operating
system of autonomous things. You control that
layer, you control the updates, the subscriptions,
the whole revenue stream. So this raises an important
question then. If physical AI is just a rebrand,
what's the true innovation here? Is it just the
software or is it the scale of the data processing?
The innovation lies in scaling AI to real world
tasks and handling previously unimaginable amounts
of dynamic sensory data. OK, so moving from that
intense engineering of the auto world, let's
look at the sheer breadth of AI applications
happening right now. It is truly wild. It really
is. The evidence that these general models are
becoming super adaptable is just everywhere.
We saw this research where the Claude model successfully
kept a real physical plant alive. A physical
plant? How did it do that? It monitored images
of the plant, analyzed sensor data, and then
calculated the adjustments it needed, light,
water, temperature, to keep it healthy. It wasn't
perfect. I mean, it had to adapt and recover
the plant after a few failures. But the fact
that it learned to manage a tangible biological
system That's a huge step. And on the accessibility
side, the bar for entry just keeps dropping.
If you've never coded, there's a new course showing
how you can just describe an idea and build an
app in under 30 minutes. Zero coding required.
That accessibility is a massive socioeconomic
shift. And speaking of data, you have projects
like the Situation Monitor. It's a live map tracking
global conflicts, nuclear facilities, military
bases. And what was that surprising data point
they included? The Pentagon Pizza Index. It's
a little bit wry, but it measures how often high
-ranking officials order late -night pizzas.
It's a proxy for intense after -hours emergency
activity. The variety of data is just... bizarre
and fascinating. We're also seeing AI take over
commercial processes. The shift in commerce is
huge. Microsoft's new co -pilot Checkout just
joined this growing list of AI shopping agents
from OpenAI, Perplexity, Gemini. They can actually
buy stuff for you now. Yeah, it removes a lot
of friction for the consumer. And speaking of
big models, Elon Musk recently teased a major
upgrade for Grok code, claiming it's going to
be able to one -shot complex tasks, just One
command, and it generates the whole thing. That
sounds incredibly powerful, but there was a significant
ethical issue with Grok recently, right? With
its image generation. Yes. X had to restrict
Grok's image generation to paying users after
a backlash over non -consensual AI images. And
while they responded, I mean, a paywall is a
shaky safeguard. It really highlights that tension
between utility and guardrails. That tension
doesn't seem to be slowing down investment, though.
Whoa. Imagine scaling to a billion queries. The
investment signal here is truly enormous. We're
seeing OpenAI and SoftBank putting a staggering
$1 billion into SB Energy just for data centers.
And that's all feeding into that half a trillion
dollar Stargate expansion plan. So when you look
at all these incredibly diverse applications,
from a plant to e -commerce to needing $500 billion
for data centers, what does this rapid expansion
tell us about the state of general AI models?
General models are proving adaptable across incredibly
diverse, tangible tasks, driving massive infrastructure
investment as a result. Now we step into the
research labs, the bleeding edge, where they're
trying to solve the core problems of these AI
systems. I still wrestle with prompt drift myself,
so I always appreciate new ideas for managing
AI memory. You're not alone there. Prompt drift
is where the AI just sort of loses track of the
initial instructions as the conversation gets
longer. It's a fundamental challenge. And that's
where these hot new papers come in. One proposes
managing context like a file system. Right. Instead
of one long scrolling document that the model
has to keep rereading, every memory, every tool,
every note becomes a distinct file with its own
history. So organizing the intelligence so the
AI can pull exactly what it needs instantly.
And then there's another approach, focusing on
efficiency through recursive memory. This is
fascinating. Instead of those huge bloated prompts
that waste compute, this new framework uses sub
-LLMs. And just to be clear, an LLM is a large
language model, the kind of AI that processes
human text to slice up data locally. It keeps
the main model lean and focused. It's like modularity
for thought. That sounds incredibly efficient,
especially for something like software creation.
It is. And speaking of that, DeepCode on GitHub
is using a multi -agent system that takes research
papers and turns them into full code bases. It
even generates its own tests to verify the code.
It literally reads the science and then builds
the software. On the perception side, we're seeing
multimodal advancements. Alibaba released QEN3VL,
a model that ranks text, image, and video all
in one shared space. And it achieved outstanding
results on MMEBv2, which is a really respected
benchmark for measuring how well a model understands
information across different formats at the same
time. Strong performance there means true, holistic
understanding. But maybe the most critically
fascinating research, and maybe the most provocative,
is coming from MIT and Sakana AI. I agree. They
ran core war battles, which is this digital arena
where programs fight to overwrite each other's
code with AIs that can mutate and learn through
self -play. The research showed early signs of
emergent strategy, where the system changes its
own code to get an advantage and win. That's
a fundamental leap. And alongside all that research,
we're getting powerful new tools. Gmail in the
Gemini era now gives you smart summaries just
by letting you ask your inbox questions. It turns
your email into a database. And for researchers,
Chirp's agent is a huge asset. It searches over
280 million papers, ranks them, summarizes them,
and gives you trusted citations. It just lashes
your review time. So if the future is built on
this recursive memory and multimodal models,
which area rapid code generation or this complex
self -mutating strategy is going to provide the
biggest leap forward? Complex strategy, like
the self -mutation research, offers the most
significant leap forward because it fundamentally
changes how quickly a system can learn. That
discussion about emergent strategy sets the perfect
stage for the big financial signal we saw. The
business world is paying attention, and the headline
is huge. China's Minimax, barely two years old,
just pulled off one of the most remarkable IPOs
in recent memory. The metrics are just staggering.
The IPO raised $619 million, hitting a $12 .8
billion valuation. And the shares surged over
100 % on day one. And what does that search tell
us about investor sentiment? It tells us the
appetite is ravenous. I mean, the IPO was 1837
times oversubscribed. Hong Kong hasn't seen a
tech IPO pop like this in four years. It's a
massive market validation for a very specific
type of AI. And they clearly mastered the formula.
They're building applications on top of. Powerful
models that people actually use day in and day
out. That's the core of their valuation. Their
key apps are so instructive. Talkie, which is
an AI character app focused on emotional companionship.
And then Hailu AI, a generative video creation
tool. They targeted connection and creation.
Two deeply human needs. And it's not just a shiny
front end. They have the back end strength to
support that value. Absolutely. Minimax is a
full stack AI company. There are open source
models consistently ranked near the top in industry
benchmarks across text, audio, video, music.
They have the deep tech and the successful consumer
app. That's the combination investors love. So
if you put all that together, what's the single
biggest takeaway from the Minimax IPO? The public
markets have made it crystal clear. Consumer
AI is king right now. Training the biggest models
isn't enough. The intelligence has to translate
into direct, functional, desirable apps. That's
where the money is right now. So what does Minimax's
success, focused so heavily on these companion
and creator apps, suggest about the immediate
monetization path for foundation models going
forward? Immediate monetization favors applications,
providing emotion connection and powerful creative
production tools. So what does this all mean
when we connect the dots? We started with physical
AI, this ambition for code to leave the server
room and drive multi -ton vehicles, manage real
world systems. And we saw the research push toward
leaner, self -improving memory systems like that
file system approach to solve critical problems
like prompt drift so models can handle complex
tasks. And maybe most importantly, that massive
$12 .8 billion Minimax IPO confirmed the market's
current mandate. Intelligence has to translate
into consumer apps that satisfy core human needs
like creation or companionship. Right. And we
saw that MIT research on AI systems that can
mutate and learn through self -play just to gain
an advantage in a simple digital war. That concept
is maybe the biggest idea from this whole deep
dive. This leads to a final provocative thought
for you to mull over. If an AI system can develop
truly emergent strategies, if it can automatically
mutate its own code to gain an advantage in a
closed digital environment like core war, what
does that concept imply for complex real world
systems like, say, autonomous traffic flow across
a major city or global supply chains where the
environment is constantly changing? That shift
from program learning to self -directed evolution
is, well, it's the final frontier. Something
to think about until next time. We hope this
deep dive gave you some new insights and questions
to explore. We really appreciate you spending
this time with us.