Imagine a billion dollars, not for the latest
shiny AI chatbot or some big social network,
but for, well, for organizing data. What's that
about? Sounds kind of wild, right? Yeah. It's
absolutely crucial. It's really the silent force
behind how AI truly learns and gets better. Welcome
to the Deep Dive. Today, we're going to follow
the money. uh take a journey into the world of
artificial intelligence just looking at july
2025 alone billions, literally billions of dollars
flowed into companies that are shaping AI's future.
Yeah. And we're going to unpack where all that
financial energy is really going inside the AI
ecosystem. Think of it like mapping out the nervous
system of this whole thing as it grows so fast.
We'll look at everything from the textbooks AI
learns from, to its muscles, the actual computing
power, the agents that do tasks for us, and even
the stuff you don't see. the infrastructure making
it all work. Yeah. Our mission for you, the listener,
is to get past just the huge numbers. We want
to understand the big ideas behind them. What's
this flood of cash telling us about where AI
is actually heading and what might it mean for
us? Let's start exploring. Okay, so our first
stop is right at the core of AI. When we think
about artificial intelligence, yeah, our minds
often jump straight to those huge language models.
But what about the raw materials, the fundamental
building blocks that actually make them smart?
Exactly. We're seeing these massive investments
in what we could call AI's textbooks, you know,
the data and its muscles, the specialized computer
power. It's like like funding the library in
the gym for this new intelligence. And here's
where it gets really interesting, maybe even
a bit counterintuitive. Surge AI landed a staggering
billion dollars, a billion for data labeling.
That feels like a huge amount for something that
sounds so meticulous. Yeah. What exactly is data
labeling and why is it worth so much right now?
Right. So data labeling is basically this super
careful process of organizing and tagging raw
data. Like imagine feeding an AI thousands of
pictures. And for every single one, a person
marks it. This is a cat or that's a stop sign.
You're giving the AI organized examples so it
can learn patterns. OK. It's not just about having
lots of data. It's about having really good,
high quality data. This human curated stuff is
like. The bedrock for AI models to get nuance,
avoid bias, and actually work reliably out in
the real world. A billion dollar bet here kind
of tells you that getting that data quality right
with humans involved is still a major bottleneck.
It's a really high value piece of the puzzle,
even as AI gets smarter. So it's the quiet, really
precise work that truly feeds and refines an
AI's intelligence. Eat. Fascinating. Then, switching
gears a bit, you have Grok. They snag $600 million
for AI computer chips. But these aren't your
standard chips. They're what we call inference
chips. Inference chips. Okay. And for our listener,
what does inference mean in this context? What
are these chips doing? So inference is when an
AI model, one that's already been trained, uses
what it learned to actually make a prediction
or a decision. And it needs to do that really
fast. Inference chips are basically specialized
computer hardware built just for running those
AI programs quickly. It's different from the
huge computer clusters used for training the
AI, which is like the teaching phase. Inference
chips are for the doing phase, running what the
AI knows super fast and often using way less
energy. Grok getting that much money shows there's
huge demand for putting AI to work efficiently
in real time, everywhere from your phone to massive
data centers. It's all about speed and sustainability,
too. These seem like quite different things,
carefully labeling data versus building super
fast chips. What's the common thread? What connects
these foundational investments? They're both
core infrastructure. Really, they represent the
foundational layers that let AI grow and function
effectively. That makes a lot of sense. OK, moving
beyond that foundation, we're seeing AI shift
from just being a tool to becoming more of a
true agent, something capable of doing complex
tasks on its own, sometimes with a surprising
amount of autonomy. Yeah, totally. It's like
upgrading from, you know. basic calculator to
having a digital assistant that doesn't just
give suggestions, but actually does stuff for
you. It anticipates things. So Kuvi .ai got $700
million for something called agentic finance.
What does that actually look like? How would
that work for, say, a person or a business? Well,
think of it less like your current financial
advisor app that gives you tips and more like
an AI financial manager. It could potentially
execute trades or rebalance your portfolio or
manage company expenses sort of automatically
based on patterns it learns and goals you set.
Agentic finance basically means AI making smart
financial decisions independently. It signals
a shift. You know, from AI just assisting us
passively to becoming an active participant,
especially in finance. That sounds incredibly
powerful, definitely useful. But I can also imagine
some businesses maybe feeling a bit nervous about
privacy or maybe losing control if an AI is managing
spending all by itself. How do companies like
RAMP, which uses AI for spend management, how
do they handle those worries? Yeah, that's a
really important point. Companies like RAMP usually
tackle this by focusing hard on transparency.
and giving users lots of control. The AI isn't
meant to be some rogue agent. It's more like
an intelligent layer on top of existing processes.
It spots patterns, flags things that look weird
in spending, maybe auto -approves small things
within rules you set up. But the human oversight
is still there, crucially. The AI just takes
away a ton of the manual grunt work, letting
people focus on the bigger picture, the strategy,
not just typing numbers. Right. That makes sense.
And then in healthcare, you mentioned Ambien's
healthcare, using AI to automatically write...
medical notes from doctor -patient talks. That
sounds like it could be a massive help for doctors
drowning in paperwork. Absolutely. It saves doctors
so much time, lets them actually focus more on
the patient in front of them instead of constantly
thinking about the documentation. It's a really
tangible quality of life improvement for them.
And Motive, applying AI in tracking vehicles,
managing driver safety, that seems critical for
logistics. Definitely. And then there's NAN.
which helps different software apps talk to each
other and work together automatically. Think
of it like connecting Lego blocks of data, but
intelligently automating these really complex
workflows between systems that normally don't
cooperate. These examples really show AI moving
into more proactive roles. But beyond just automating
tasks, how do they fundamentally show AI taking
initiative? They highlight AI managing complex,
you know, multi -step tasks independently across
different industries. Okay, let's shift focus
again. How is AI directly touching our lives
in more visible, maybe more personal ways? Thinking
about things like creativity or even critical
public services. Yeah, these are the applications
where you really start to feel the immediate,
tangible effects of this technology, sometimes
in pretty profound ways. For example, Falves
is working deep in generative AI for media. They
use something called diffusion models. Diffusion
models? Yeah, diffusion models are basically
AI systems that can create totally new media,
like images, video, even music, just from a simple
text description you give them. It's radically
changing creative fields, artists, designers.
They can quickly try out ideas or generate completely
new content at a scale that just wasn't possible
before. And Carbon, that's fascinating. Using
AI for emergency response, helping 911 dispatchers
find callers faster, maybe sharing live video
from a scene. That sounds like it could literally
save lives. It really underscores the potential
scale and impact, doesn't it? I mean, just thinking
about AI speeding up emergency response by even
a few seconds, but across millions and millions
of calls, you're talking about fundamentally
saving lives. Whoa. Imagine scaling that to a
billion queries or just the sheer number of lives
potentially saved through faster response. That's
kind of a moment of true wonder, right? A really
profound example of AI's potential positive impact.
Wow. Yeah. And then Slingshot AI is looking at
Using AI for mental health coaching, like a personal
AI guide for well -being accessible almost anywhere.
That's another very personal application. For
sure. And then Ultramix uses AI to analyze heart
scans. looking for early signs of heart disease.
That's incredibly vital. Catching things earlier
means better outcomes for patients. These are
intensely personal uses. How does AI really impact
our daily lives through these kinds of innovations
and creative work and critical services? From
how we create art to how we manage our health,
AI is deeply transforming personal experiences.
Okay, finally, let's explore the areas that maybe
aren't as flashy, but are just as crucial. The
funding pouring into the behind the scenes infrastructure
and the vital security that holds everything
together. Yeah. This is the essential scaffolding,
you know, the unseen guardians for the whole
AI world. Without this stuff working reliably
and securely, none of the cool applications would
function at scale. Right. So Observe helps companies
manage and actually understand the massive amounts
of data they have stored in the cloud. making
sense of all that digital noise. And 1KOMMA5Degrees,
that's a clean energy company using technology,
often involving AI, to make homes smarter, more
energy efficient. Kind of a quiet revolution,
but really powerful for sustainability. Anaconda
provides open source tools for building AI applications.
That sounds like it makes complex AI development
more accessible for everyone, right? Not just
big companies. Exactly. It democratizes it a
bit. Inheritives provide security for older software
legacy systems that maybe aren't supported anymore
but are still critical. Keeps companies safe
from hackers. It's like a digital bodyguard for
aging tech infrastructure. And Oxide Computer
Company, interesting, they actually build the
servers for cloud computing from the ground up,
not just assemble parts, designing the hardware
itself. Then there's Noma Security, building
what they call an autonomous security platform.
And autonomous here means it works by itself.
Pretty much, yeah. Autonomous security means
AI protecting computer systems automatically,
without needing constant human oversight. It
learns what normal looks like for a system, spots
weird stuff, anomalies, and can actually shut
down threats before they cause damage. All on
its own. 2047. That seems like a massive change
in how we approach cybersecurity, moving from
just reacting to threats to actually predicting
and stopping them proactively. Huge shift. Absolutely.
And then SAFE gives businesses sort of a cybersecurity
credit score, helps them understand their specific
risks in a clear, quantifiable way. Looking at
their vulnerabilities, I have to admit, I still
wrestle with prompt drift myself sometimes, trying
to get an AI to consistently do what I want.
So understanding vulnerabilities and managing
risk proactively is just key in this complex
world. And GUPS focuses on conversational AI,
powering those chatbots we're all interacting
with more and more for customer service and things
like that. Right. And Ashby helps companies hire
better with an all -in -one recruiting platform,
often using AI, you know, to sift through applications
and find the best potential candidates much faster.
So we've got data management, clean energy tech.
open source tools, security for old and new systems,
even recruiting. What's the common thread tying
these diverse companies together in this sort
of unseen pillar category? They really represent
the essential, often invisible systems that enable
both AI itself and just modern business operations
in general. Sponsor. So pulling this all together,
what does it all mean? This deep dive into where
the AI money's been flowing recently. It paints
a pretty clear picture, doesn't it? The cash
isn't just going into the brains, those big language
models everyone talks about. Right, exactly.
It's flowing into the entire ecosystem, like
every single layer. From the textbooks that meticulously
label data, teaching the AI to the muscles, those
super fast, efficient computer chips. the agents
that are starting to perform complex tasks for
us kind of on their own. We've seen these huge
investments covering everything from the absolute
bedrock foundations of AI, like preparing data
and building specialized hardware. all the way
through to these really advanced applications
that directly touch our daily health, our safety,
even our creativity. It's a truly distributed
effort. It feels like a systemic re -architecture
happening across pretty much every industry.
This isn't just some tech bubble, you know. It
feels more like a fundamental rethinking of how
businesses run and how we interact with the world.
It's pretty profound when you look at it that
way. This explosion of funding seems to tell
us that AI isn't just a passing trend. It feels
like a profound, undeniable shift that's already
well underway. It truly is. We're basically watching
the building blocks of an AI first future get
put into place right now, sometimes quietly,
but very, very rapidly. So for you, the listener,
maybe here's a provocative thought to leave you
with. As this huge amount of capital keeps pouring
in and reshaping the landscape. How will this
AI -driven future really change the fabric of
our society over the next, say, five, ten years?
What completely new applications, things we maybe
can't even properly imagine yet, might emerge
from these investments and fundamentally alter
our daily lives? Keep your eyes open, because
the implications are definitely far -reaching.
And honestly, the journey feels like it's just
getting started. Thank you for joining us on
this deep dive into the latest AI funding landscape.
We hope you feel a bit more informed and maybe
just a little more curious about the world that's
being built all around us. Out Hero Music.