Intro music. So let's talk about a specific week,
September 11th to 18th, 2025. Right. Because
something really shifted in AI funding then.
It wasn't just about bits and bytes anymore.
It got physical. Yeah, exactly. That was the
week we saw that huge headline. Figure AI, you
know, the robotics company, they pulled in a
Series C round of over a billion dollars. A billion
dollars for a humanoid robot. I mean, that's
such a clear signal, isn't it? It really is.
It says the next big step for AI, it isn't just
about like thinking faster inside a computer.
It's about doing things, actually interacting
with the real world. Welcome to the deep dive.
We've basically spent time digging through what,
like 20 major funding announcements just from
that one week. Yeah, quite a stack. And we're
trying to see, you know, where's the smart money
actually going? What are the real trends here
for you? So our plan for this deep dive, we've
grouped these these huge investments into three
main buckets, three themes that seem to define
what's coming next. First, we're looking at AI
literally taking physical shape, you know, robotics,
advanced manufacturing. Right. They're moving
to the physical. Then second, we've got to look
at the infrastructure needed for all this. We're
talking specialized chips, security layers, the
stuff that makes it all run. Exactly. And third,
how AI is starting to automate really specialized
high stakes work. Think science, complex coding.
Got it. So let's start with that billion dollar
bet, the physical world stuff. Yeah, let's dive
in. The biggest signal has to be that capital
flowing into AI that can, you know, actually
touch and manipulate things. Figure AI's deal
on September 16th was, well, it was the headline.
It really was. And they're building general purpose
humanoid robots. So basically robots shaped like
us. Two arms, two legs, a torso. The idea being
the sort of ultimate general purpose machine.
Right. Something that can work alongside us.
Yeah. And critically, operate in our world. Use
the tools we use. Navigate spaces designed for
humans. That's the key. Which, you know, directly
hits things like labor shortages, automating
jobs that are maybe dangerous or just hard to
fill. Logistics comes to mind immediately. For
sure. But OK, let's think about that billion
dollars. That's a massive amount for a robot
that's, let's be honest, still mostly in development,
right? Yeah. What are the risks investors are
taking on there besides just the tech not working
out? That's a good point. I think maybe the biggest
hurdle isn't always the tech itself. It's regulation.
It's public acceptance. This kind of money, it's
not just funding lab work. It's pushing to scale
up manufacturing, deploy with partners, like
now. It feels like investors are betting the
regulatory side will sort itself out quickly,
or maybe they intend to push it. Okay, that makes
sense. It's a bet on deployment happening soon,
not just maybe someday. And it connects to other
things happening that week, right? It's not just
the robot. Exactly. We also saw Divergent Technologies
raise $290 million. They're building something
called DAPS, the Digital Adaptive Production
System. This is like manufacturing 2 .0. Yeah,
DAPS. It mixes AI design, 3D printing, and then
robotic assembly. It's a completely different
way of thinking about production lines, moving
away from those rigid old school factories. Totally.
Instead of a factory built for just one specific
part, DAPS is modular. It's defined by software.
That means you can make complex things, maybe
aerospace parts, custom car components, way faster
and cheaper. Oh. Okay, just imagine scaling that
DAPS tech. You could rapidly spin up production
for custom parts for almost anything. Cars, planes,
spacecraft. That software -defined flexibility
is... That speed. It really is. And then there's
passive logic. They pulled in 74 million in the
same space. They call what they do generative
autonomy. Generative autonomy. Their system stands
a whole building like a commercial office building,
creates this super detailed digital twin. And
then the AI generates the entire control system
for it. HVAC, lighting, everything. It just designs
and optimizes it automatically, replacing a ton
of manual engineering work. So it's complex physical
system control, but done by software. Based on
a scan. Yeah. It's pretty advanced. So back to
figure AI for a second. If that billion dollars
means investors expect mass production and deployment
soon. Yeah. What does that signal about their
confidence in these other pieces like DPS and
pass logic, the things needed to support those
robots? It tells me they think the support infrastructure
problem is basically. Solve or solvable very
quickly. They believe we can make the custom
parts the robots need and manage the complex
environments they'll work in. OK, so that covers
the what's AI getting physical. But that kind
of ambition, it needs some serious power behind
it. Right. Which brings us to the infrastructure
investments. Exactly. Theme number two. Yeah.
The infrastructure, speed, security, making sure
AI can be everywhere. And the big name here was
Groke. G -R -O -Q. They raise a huge seven hundred
and fifty million dollars. Right, Groke? They
build specialized chips for AI inference. They
call them LPUs, language processing units. Maybe
just quickly clarify inference for everyone.
Sure. Inference is basically just the AI using
its training, taking its learned model and applying
it to give you an answer or perform an action
in real time. LPUs are specifically designed
to run those big language models very, very fast.
Less lag. So they're tackling the latency problem,
the spinning wheel of death when you're waiting
for the AI. Pretty much, yeah. Because slow AI
is just a bad experience, especially if you're
trying to use it for real work. Groke's tech
aims for that smooth, real -time interaction
you need for widespread adoption. It makes you
wonder, though, if Groke really nails this instant
response time for powerful AI, what does that
do to the established players like the big GPU
makers? Does the whole competition shift from
training speed to, like... user experience speed.
I think it suggests exactly that. The market
seems to be saying, OK, the models are smart
enough for now. Let's make them fast and cheap
enough to actually use everywhere. The bottleneck
has definitely moved. Yeah, that tracks. And
we also saw money going into the plumbing, the
system infrastructure. Upscale AI raised 100
million. They're building an open standard AI
networking platform. Open standard being key
there. It stops companies getting locked into
one vendor. It's like creating a common language.
So AI systems running in the cloud or on a company's
own servers or even on tiny devices out in the
field can all talk to each other easily. Interoperability.
Super important for big companies. And if you're
connecting all these systems, you absolutely
need security. We saw two big plays there. ARIA
raised $100 million for general enterprise AI
security. Kind of the essential tools for managing
access, checking for threats across lots of different
AI models. The basic blocking and tackling. Irregular
raised 80 million, and they focus specifically
on securing the frontier AI models. Right. The
really big cutting edge ones from places like
OpenAI or Google. Exactly. And they're worried
about. New kinds of attacks, things like data
poisoning, where someone messes up the training
data. Right. Or prompt injection, which is basically
tricking the AI into ignoring its safety rules
and doing something it shouldn't. Yeah. I still
wrestle with understanding prompt injection vulnerabilities
myself sometimes. It's genuinely hard to make
these flexible models consistently stable and
safe. So, yeah, I appreciate companies focusing
just on that defense layer. It feels really necessary.
So when you add it up, Groke's speed focus. plus
the security funding from area and irregular,
that's over a billion dollars just on inference
and safety in one week. Does that suggest that
the main thing holding back wider AI adoption
isn't that the models aren't smart enough, but
that they're perceived as too risky or too slow?
I think, yes, it feels like the intelligence
is largely there, at least for many tasks. The
money is now flooding in to make that intelligence
fast, reliable and secure enough for businesses
and people to actually trust it everywhere. That's
the foundation for the physical stuff we talked
about first. OK, which leads us nicely into the
third major trend. automating the experts. Right.
Using AI not just for general tasks, but to speed
up discovery and efficiency in really complex,
specialized fields. And the standout here, maybe
the biggest surprise for some, was Leela Sciences.
They raised a huge $235 million in a Series A.
That's massive for an A round. It really is.
And what they're building is, well, they call
them self -driving labs, AI -powered autonomous
research labs. Self -driving labs. Okay, break
that down. It's like a closed loop. The AI designs
an experiment. Robots physically carry it out.
Then the AI analyzes the results and uses that
to design the next experiment. It just runs continuously,
247. Wow. So it takes the human scientists out
of the loop for the day -to -day bench work.
Essentially, yeah. It lets discovery happen at
machine speed. You can see the potential impact
on things like discovering new drugs or materials.
It's potentially profound. But building a fully
automated lab sounds incredibly expensive and
complicated. What's the immediate hurdle they
have to jump over to make that huge Series A
make sense? Beyond just the pure engineering
challenge, I think it's standardization. Real
world labs are often messy, full of custom equipment.
To make a self -driving lab work at scale, you
need to standardize everything, the hardware,
the processes. That's a massive task. But the
payoff, if they can do it, removing that human
bottleneck from discovery. Yeah, I see why investors
are interested. And sticking with health, we
saw conceivable life sciences get 50 million.
They're automating the IVF lab using AI and robotics.
Okay, so taking those complex manual steps and
fertility treatments and automating them. Right.
The aim is better consistency, maybe higher success
rates, and making these treatments more accessible
because automation could lower costs. Makes sense.
And then in a totally different vertical, software
development, which AI has been targeting for
a while, CodeRabbit raised $60 million. Yeah,
an AI code review platform. So the AI reads new
code developers right. finds errors, suggests
fixes, even summarizes what changed. Exactly.
It speeds up the whole development process, improves
the quality of the code, and hopefully frees
up senior engineers to think about bigger picture
problems instead of just line -by -line reviews.
And there were others too, right? Like Apex raising
$200 million for satellite platforms, Greenlight
getting almost $50 million for construction permits.
Yeah, lots of vertical -specific plays getting
serious backing. Looking at this whole category,
automating science labs, IVF procedures, code
reviews, these are things we used to think needed
deep human expertise and judgment for every step.
Is this story here just replacing those experts?
Or is it changing what the expert does? I think
it's fundamentally changing the role. It's freeing
up the experts, the scientists, the doctors,
the top developers, letting them focus on strategy,
on their really hard problems, on creativity.
While the AI handles the more routine execution
parts, it lets the humans move up the value chain.
Mid -role sponsor, Reed. All right, so let's
try and pull this all together. Looking back
at that week in September 2025, what did these
big funding rounds really tell us? I think the
biggest message is that AI is moving out of the
purely digital realm. It's stepping into the
physical world in a serious way. Yeah. First,
you saw that huge push towards commercializing
physical AI, that billion for figures robots,
plus the money for advanced manufacturing like
DPS. It signals readiness for actual deployment.
Second, you can't have physical AI without the
right engine. So a massive commitment to the
infrastructure, the need for speed like Grok's
chips, and the absolute necessity of security
and stability from companies like Irregular and
Aria, making AI trustworthy at scale. And third,
the money flowing into what you call closed -loop
automation, systems that can run entire complex
workflows themselves, whether it's discovering
drugs or reviewing code, supercharging specialized
fields. Yeah, the big picture takeaway feels
like a clear pivot. We're moving from AI that
just thinks or predicts to AI systems that operate,
that build, that manage things in the tangible
physical world. So that phrase from the source
material about the figure AI robot, calling it
the ultimate general purpose machine, if they
actually achieve that, if it gets real dexterity,
real autonomy, what's the final implication?
Well, think about it. If that machine can truly
operate anywhere a human can, What space that
we've designed, a factory floor, a building site,
even just a regular kitchen, won't eventually
be designed with autonomous machines in mind
or be occupied by them. That's a pretty profound
thought. That feels like the core message behind
that billion dollar signal. That's the future
these massive investments are betting on a deeply
physical, increasingly automated world. Thanks
for joining us for this deep dive. Yeah, I hope
this gives you a clearer picture of where things
are heading in this incredibly fast moving space.
Outro music.