Think about the assumptions we make every single
day. Beat, we spent decades thinking AI was just
a chat bot. Yeah, just a simple digital box.
Right, you type a prompt, you get text back.
But what happens when that chat bot finally stands
up? It starts acting like a digital employee.
Exactly. or when it autonomously solves 80 -year
-old math problems. These are puzzles that baffled
the absolute brightest human geniuses. It completely
changes our relationship with this technology.
I mean, the ground is shifting right beneath
our feet. Welcome to this deep dive for you today.
We are looking at a stack of incredibly fresh
sources. Really exciting stuff. They map out
the next era of artificial intelligence. We are
going to trace the massive hardware shift happening
right now. We'll explore the mind bending billions
trading hands between top labs. We'll examine
the tools turning AI into proactive workers.
And finally, we'll look at a historic breakthrough
in autonomous reasoning. It is a fascinating
blueprint of what comes next. So to truly understand
how AI is getting smarter. We have to start physically.
Right at the foundation. Yeah. We have to look
at the underlying hardware. And we really need
to follow the money moving behind it. Right.
And the numbers here are absolutely staggering.
NVIDIA just posted another record -breaking financial
quarter. What are we looking at? They reported
$81 .6 billion in revenue. That is up 20 % from
the previous quarter. That is a massive leap
in just... Three months. Yeah, it really is.
Their data center business alone hit $75 .2 billion.
Wow. That shows infrastructure demand is still
incredibly strong. The physical foundation is
still being rapidly built. I want to pause on
the financial flexing here. They approved $80
billion in share buybacks. What does that signal
to the broader tech market? Well, it signals
massive internal confidence in their future growth.
They are telling the market they have plenty
of cash. Right. But what's even more interesting
is their venture capital strategy. They hold
$43 billion in private startup stakes right now.
Exactly. They bought $18 .5 billion this quarter
alone. That is wild. They aren't just selling
chips to random companies anymore. They are actively
funding the exact startups that buy their hardware.
It creates this massive self -sustaining financial
flywheel. Yeah, it does. That's brilliant, but
it can't accelerate forever, right? No, probably
not. Forecasts predict $91 billion for the next
quarter, but their overall growth rate may slow
to 12%. Plus, their China export revenue remains
highly uncertain right now. That brings us to
the biggest pivot in their strategy. Jensen Huang
just announced a new $200 billion market. This
is huge. It centers entirely around a new CPU
called Vera. This is a fundamental shift in computing
architecture. Vera is built specifically for
agentic AI workflows. Let's define that term
for everyone listening. Agentic AI is AI that
takes actions independently to achieve a goal.
Perfect definition. Beat. Think of this shift
like a busy corporate office. The GPUs are the
creative brainstormers in the room. Right. They
generate huge ideas and process heavy data. Exactly.
But CPUs like Vera are the actual project managers.
They're the ones executing the complex daily
tasks. That analogy is spot on. NVIDIA isn't
just selling GPUs to researchers anymore. They
want to power the full AI stack entirely. From
massive data centers down to proactive autonomous
agents. Huang says Vera has already generated
$20 billion in sales. That is just in this current
year alone. It proves the market demand. for
agentic hardware is real. Future AI agents will
deeply need these CPU -like systems. Why is that?
Because they need them to use external tools
efficiently. They have to run sequential tasks
without constant human intervention. How does
NVIDIA stepping into CPUs affect the rest of
the industry? Well, they aren't just selling
individual processing parts anymore. They want
to own the entire operational architecture. Right.
They are building the infrastructure for autonomous
digital workers. So they own the brains and the
hands now. Exactly. They are the foundation of
the new economy. But if they are supplying the
brains and the hands the energy required to power,
that must be astronomical. Oh, it is. Who is
actually footing that massive computing bill?
The operational costs are becoming almost incomprehensible
today. Let's look at Excel. for a very clear
example. Okay. They burned $6 .4 billion last
year. That was on just $3 .2 billion in revenue.
The math there is incredibly brutal to look at.
It is. And a recent SpaceX IPO filing revealed
something even wilder. What was it? It detailed
Grok's plan for a multiple trillion parameter
model. A trillion parameter model has a trillion
adjustable internal data connections. Yeah. That
ambitious plan will make the compute bill substantially
bigger. But here is the wildest twist in our
sources. I know what you're going to say. Anthropic
is reportedly paying XAI directly for compute
power. They are paying $1 .5 billion to Sex Silence
per month. Yeah. That is for access to Colossus
Compute through May 2029. Whoa. Imagine scaling
to a billion queries. The power required to run
an entire data center just for one model. That
is the reality of an industrial revolution scale.
You are paying for miles of servers and gigawatts
of power. It is a staggeringly expensive physical
footprint. Yet despite these massive infrastructure
costs, Anthropic expects its first profitable
quarter very soon. Right. Their revenue is...
jumping to around $10 .9 billion. That is a massive
milestone for the entire tech industry. It proves
the underlying business model can actually work.
It dropped right alongside rumors of an open
AI IPO. The market is maturing incredibly fast
right now. Extremely fast. How does a company
possibly survive burning billions a year? It's
a massive infrastructure land grab right now.
The sheer utility of the resulting models is
the price. Right. That utility will eventually
outweigh the massive upfront cost. We see this
clearly with Anthropic finally hitting profitability.
We must spend billions today to make trillions
tomorrow. Exactly. The initial capital expenditure
is the only way forward. So what incredible utility
justifies paying a billion dollars monthly? It
is the fundamental shift from generating text
to taking action. We are looking at the toolkit
for these new digital workers. The ecosystem
of agentic tools is exploding right now. And
it completely changes how we interact with software.
EXA just raised $250 million. That is at a $2
.2 billion valuation. Yeah. They are simply building
a search engine for AI agents. This is a crucial
piece of the puzzle. Agents don't browse websites
like human beings do. Right, they can't see the
screen. Exactly. They can't navigate flashy pop
-ups or read layout designs. They need raw...
perfectly structured data to operate effectively.
So EGZA provides that exact structured data layer
for them. Yeah. And developer demand for this
is just huge. Google is also making massive updates
to their tool ecosystem. Gemini Omni is generating
entire new video scenes now. It's amazing. It
uses mixed inputs to create high quality outputs.
You can combine images, audio, video, and text
seamlessly. Yeah. It is completely changing the
creative production pipeline forever. Google
AI Studio is taking this even further today.
It turns simple text prompts into native Android
apps. The traditional barrier to software coding
is rapidly disappearing. Then there's anti -gravity
2 .0 to discuss. It is turning AI into a real
developer workspace. This one is wild. They feature
parallel agents and background coding tasks seamlessly.
We should definitely explain what parallel agents
actually do. Parallel agents are multiple AI
programs working on tasks simultaneously. Beat,
I still wrestle with prompt drift myself. Oh,
same here. That's when AI slowly loses focus
on your original instructions. So the idea of
parallel agents managing whole workflows feels
wild. It is a lot for human beings to adapt to.
We're moving from prompting to managing a digital
staff. That is probably why Andrew Nehring released
a free course. It is a two and a half hour masterclass
on AI prompting. Very timely. It is designed
to help humans keep up with these tools. Prompting
is quickly becoming a necessary professional
management skill. We also have to talk about
proactive actions. Gemini Spark handles proactive
actions across Google products natively. Right.
It can manage tasks under your direction entirely
in the background. But having proactive agents
doing things. automatically raises massive concerns.
You need serious security frameworks before you
deploy them. Absolutely. OpenAI is adopting Google's
SynthID watermarking for generated images. They
are also building a public verification checker
very soon. We really need to know what's real
and what's AI. We are seeing movement on the
regulatory front, too. The European Commission
released draft guidance on high -risk AI. It
clarifies exactly which systems fall under stricter
rules. Now we have impending U .S. executive
orders coming soon. OpenAI and Anthropic are
discussing voluntary frameworks as well. They
are talking about model sharing directly with
the government. It's all moving so fast. So how
do we prevent chaos if agents are acting proactively?
We have to rely on strict verification systems.
The strict necessity of Google SynthEye is a
vital start. Yeah. We also need those incoming
EU and US regulatory frameworks. Trust requires
built -in digital watermarks and strict guardrails.
Right. And without those guardrails, the... enterprise
utility drops to zero. We've talked extensively
about the physical hardware and the money. We
explored the powerful new agentic tools being
built. But the ultimate test of this era is pure
reasoning. Can these machine learning models
actually think deeply? This is the most profound
question in the field today. OpenAI just announced
a truly historic mathematical breakthrough. GPT
-5 .5 autonomously created a completely original
geometric proof. It's incredible. It disproves
a famous geometry conjecture by Paul Erdos. He
proposed this specific riddle back in 1946. This
mathematical problem stood unsolved for nearly
80 years. Mathematicians strongly believed the
best solution would involve square grids. But
the AI model found an entirely new geom... construction.
Right. Crucially, this wasn't a specialized math
tool at all. It was a general purpose reasoning
model exploring the logic. It worked through
the complex logical steps completely on its own.
Wait, hold on. I have to push back a little bit.
Okay, go ahead. Didn't this exact same thing
happen late last year? OpenAI faced intense public
skepticism back then. They did. People found
the models were just surfacing existing human
work. They weren't actually solving brand new
geometric problems. Why should we believe this
time is any different? Yeah, and that skepticism
was completely justified back then. But this
is exactly why this specific moment matters.
How so? OpenAI came incredibly prepared for the
public pushback this time. They brought the receipts?
They absolutely did. They didn't just drop a
press release. They subjected the model's output.
to rigorous human peer review. They included
supporting remarks from highly respected mathematicians
today. People like Noga, Alon, and Melanie Wood
reviewed the work. And Thomas Bloom, who runs
the Erdin's Problems website itself. These experts
validated that the logical steps were genuinely
novel. This proves the model generated completely
new mathematical logic. Wow. It didn't just memorize
old data from the internet. Does this mean AI
is now better at math than humans? It's not really
about being quote unquote better than us. It
is about AI being capable of genuine logical
discovery. It works right alongside human experts
to find objective truth. AI is a peer now, not
just a calculator. Exactly. It fundamentally
changes how scientific discovery will happen.
Sponsor. Let's bring all these incredible pieces
together for you. The era of the simple chatbot
is truly over today. From NVIDIA's Verichips
to billion -dollar compute infrastructure deals,
the entire tech world is fundamentally re -architecting
for proactive agents. We are arming general -purpose
reasoning engines with massive hardware capabilities.
They are smart enough to solve 80 -year -old
math puzzles autonomously, and we are giving
them tools to operate natively inside workflows.
Right. They are quickly becoming our digital
colleagues and active project managers. It is
a profound shift in how humans interact with
machines. I want to leave you with a final provocative
thought today. If a general purpose AI model
can look at an 80 -year -old geometry problem
and find a completely new angle that human geniuses
missed for decades, what longstanding assumptions
in your own industry is an AI agent about to
completely rewrite? It is a thrilling, slightly
terrifying question to consider. Thank you for
taking this deep dive with us today. ODTRO Music.