We are living in the future. Beat. But it is
a remarkably messy one. Yeah, that's kind of
the best way to put it. We build machines to
find truth. Sometimes they just dream instead.
They absolutely do. Consider a hallucinating
AI trying to verify a war zone video. Instead
of finding facts, it generates a fake image.
Right. It literally fabricates the scene. And
then it offers that hallucination as... actual
proof. Which completely shatters our baseline
for reality. The technology is sprinting right
now. Our guardrails are Barely jogging. It's
a profound tension. Welcome to the Deep Dive.
We're really glad you're here with us. We are
pulling from a fascinating stack of sources today.
It's a heavy stack, too. We've got major tectonic
shifts happening across the entire industry.
Okay, let's unpack this. We're looking at the
new consumer AI rankings today. We're also exploring
a massive graveyard of 5 ,700 failed startups.
It's a literal graveyard of ideas. Exactly. After
that, we'll examine a billion -dollar push for
reasoning AI. And finally, Jensen Huang's trillion
-dollar theory of our physical future. That last
one is a massive five -layer cake. It explains
literally everything happening in the hardware
space right now. It really does. Beep. Let's
begin with the new global order. Andresen Horowitz
just released their latest consumer AI report.
Right. The A16's top 100. This is their sixth
edition. And this is the list everyone in the
industry actually watches. Because it filters
out the empty hype. It measures raw, verifiable
user traffic. It shows us what you are actually
using daily. And the landscape is heavily consolidated
at the top. ChatGPT still completely dominates
the space. The scale is just staggering. They
crossed 900 million weekly active users. Whoa,
imagine scaling to a billion queries. The server
load alone is mind -bending. It's a massive portion
of humanity. Nearly a billion people talking
to a machine every week. Two sec silence. But
the willingness to pay is also shifting. It is.
Claude and Gemini are seeing massive financial
traction. Their paid subscriptions skyrocketed
over 200 % last year. People only pay for subscriptions
when a tool actively saves them time. Exactly.
It has to solve a tangible, painful problem.
It's graduating from a novelty toy to core enterprise
infrastructure. But the report highlights a much
bigger structural shift. Traditional legacy apps
are suddenly dominating the list. This is a critical
pivot. We're talking about platforms like Canva,
CapCut, Notion, and Grammarly. Right. They aren't
new AI startups. No, they are established. giants,
but they're surviving because they're adopting
embedded AI. They're weaving the intelligence
directly into their existing workflows. We should
clarify that term. Embedded AI simply means intelligence
built directly into software you already use.
It's the intelligence coming to your cursor.
It's a brilliant defensive strategy. I mean,
why would you open a separate tab for ChatGPT
to write a memo when Notion has the exact same
capability baked right into the blank page? The
friction is just gone. Completely gone. It neutralizes
the threat of new standalone tools. But this
report also maps out a hard geographical fracture.
The global AI market is splitting apart. What's
fascinating here is the geopolitical reality
of that split. We're seeing three highly distinct
isolated ecosystems emerge globally. The West
has its well -known sandbox, but the Chinese
apps are aggressively capturing global market
share. They really are. Apps like Manos hit number
44 on the global list. Ginsbark fought its way
to number 47. They aren't just serving domestic
users anymore. No, they're competing on the world
stage. Meanwhile, the Russian ecosystem is isolating
almost entirely. They're building a walled digital
garden. Yeah. OpenClaw is notably absent from
the rankings, though to be fair, that's largely
due to the report's specific timeframe. It's
essentially a new digital Cold War. Every superpower
realizes something profound. Whoever controls
the foundational models controls the cultural
narrative. Foundational models being the massive
general purpose engines. powering most AI applications.
Right. They dictate how a society processes information.
Beat. Does embedded AI ultimately kill the standalone
app? Standalone innovates. Embedded scales. We
absolutely need both to push boundaries. That
brings up a natural question of execution. Let's
examine today's triumphs, fails, and strange
hallucinations. The daily reality of working
with these models is a total roller coaster.
There's a fascinating project tracking dead AI
startups. It's a literal graveyard listing over
5 ,700 failed companies. It sounds depressing,
but it's actually an incredible strategic playbook.
How so? It's a map of early adopter demand. People
clearly wanted these tools to exist. The timing
was just completely wrong. Think about the early
virtual assistants from around 2012 or 2015.
Exactly. Back then, founders had to manually
code every possible user intent. If a user asked
a question slightly differently, the whole system
broke. Right. It was brutally expensive and technically
impossible to scale. The unit economics were
completely broken. But today, a large language
model handles that out of the box. It understands
the intent naturally. And it does it for fractions
of a penny. It's like finding an abandoned gold
mine. You go back after the invention of modern
drilling. Suddenly, what was impossible is now
highly profitable. The raw materials haven't
changed. Our extraction tools have evolved. Exactly.
Speaking of execution, OpenAI just shipped a
quiet revolution for education. Oh, this is huge.
They added highly interactive math and science
tools directly into ChatGPT. It covers over 70
different complex concepts. You can tweak a physics
equation and watch the corresponding graph change
instantly. It feels exactly like stacking Lego
blocks of data. It makes abstract theoretical
math incredibly tactile. It transforms a static
answer into a dynamic playground. But as our
tools get sharper, our shared reality gets much
blurrier. Yeah, the information ecosystem is
getting dangerously murky right now. Fake AI
-generated visuals are spreading rapidly on X.
These are highly realistic images specifically
depicting the war in Iran. And this is where
the guardrails completely failed. Grok. The platform's
native AI attempted to step in. A video of a
missile strike circulated online. Grok analyzed
the video to verify its authenticity. But instead
of flagging it, Grok hallucinated. It actually
generated its own fake AI image to serve as proof
that the event happened. It manufactured alternative
evidence to support a completely fabricated reality.
It is a profound systemic vulnerability. The
mechanism designed to protect truth became the
primary engine of deception. Beat. How do we
trust AI to verify information? We can't yet.
AI checking AI still creates massive blind spots.
That vulnerability requires incredible computing
power to fix. Which brings us to our third segment.
the trillion -dollar infrastructure race. The
physical world is desperately trying to catch
up to the software. We are seeing a massive geographic
footprint expanding. In Mississippi, XAI just
secured approval for a giant new data center.
And the sheer scale of this facility is difficult
to overstate. It's causing immense friction.
Local residents are raising serious environmental
concerns about potential pollution and water
usage. Right. Meanwhile, regional officials are
pointing to the massive economic investment.
physical dispute over a digital footprint. You
just can't run the digital future without aggressively
terraforming the physical present. It requires
a staggering amount of raw energy. Look at the
new venture from OpenAI's co -founder. It's called
the Thinking Machines Lab. They just signed a
massive infrastructure deal with Nvidia. They
plan to run one full gigawatt of AI compute starting
in 2027. We should define that. Compute is the
processing power needed to run heavy software.
And a gigawatt is literally the power required
to run a medium -sized city. It's the output
of a nuclear reactor dedicated entirely to thinking
machines. Nvidia isn't just supplying them. They
are actively investing in the lab. Meanwhile,
Meta is making very strange, aggressive acquisitions.
They just bought a viral platform called Moldbook.
It's essentially a social network built entirely
for AI agents. It's a bizarre concept at first
glance. I mean, why do software programs need
a social network? Why do they? Because future
agents need an environment to negotiate. Imagine
your AI scheduling assistant needs to bargain
with my AI travel agent. They need a shared digital
protocol to exchange data and trade favors. Exactly.
The Moldbook founders are now joining Meta's
core superintelligence team. Beat, here's where
it gets really interesting. Yann LeCun's new
startup, AMI, just raised massive capital. They
pulled in $1 .03 billion. Their valuation is
hitting at $3 .5 billion. For a brand new company,
that's wild. The money is huge, but the architectural
philosophy is what actually matters. Lacoon wants
to abandon how we currently build AI. He is actively
rejecting the architecture behind ChatGPT and
Claude. Today's models are fundamentally prediction
engines. They've read the entire internet. Right.
They are incredibly good at mimicking logic by
simply guessing the most statistically probable
next word. Right. And they sound brilliant. Yeah.
But they don't actually possess a mental model
of physics. They don't truly understand cause
and effect. They just string plausible words
together. Lacoon argues that predicting words
is a dead end. AMI is trying to build a system
that can genuinely reason. It needs to plan 10
steps ahead in the physical world. It has to
hold a persistent worldview. If it drops a virtual
glass, it needs to know it shatters. Without
just quoting a Wikipedia article about gravity.
Two sec silence. Are prediction -based models
finally hitting a wall? Yes, predicting the next
word isn't the same as actual real -world planning.
We're going to take a quick second, but when
we come back, we're cutting into a five -layer
cake. Sponsor. Welcome back to the Deep Dive.
Let's move into our fourth segment. We are looking
at rapid -fire empowered tools. These aren't
theoretical models. These are agentic workflows
you can deploy right now. Exactly. First up is
a tool called Vozo. It's designed for global
video distribution. It translates the spoken
text in your videos. But the killer feature is
that it preserves the original visual timing.
Right. If you have a massive corporate slide
deck, you record it once, and Vozo seamlessly
dubs it into 20 languages without breaking the
visual sync. It completely removes the friction
of global communication. Then we have Chronicle
2 .0. This acts as an autonomous AI design partner.
It takes your chaotic, raw notes and instantly
turns them into polished, highly visual slides.
It automatically and strictly adheres to your
specific brand guidelines. Next on the stack
is Sitefire .ai. This one shifts from design
into aggressive distribution. It deploys specialized
agents to crawl the web. They analyze exactly
what type of content actually drives high -quality
citations. Then those agents write brand -aware
articles tailored to that data. They even generate
custom outreach emails to secure the backlinks.
It automates the entire SEO lifecycle. Finally,
there's Fish Audio S2. This one is incredible,
but admittedly a little terrifying. It generates
highly realistic synthetic voices. It captures
deep emotional nuance, subtle breathing, and
natural rhythm. And it accomplishes all of that
using only 10 seconds of reference audio. 10
seconds to permanently clone a human voice. Beat.
But managing these tools is inherently chaotic.
I still wrestle with prompt drift myself. Oh,
absolutely. Everyone does. Prompt shift is when
an AI slowly forgets your original instructions.
You start a session and the output is flawless.
20 prompts later, the model has completely lost
the plot. It starts wandering off topic. You
have to constantly wrangle it back to the original
constraints. It requires exhausting vigilance.
That's the core problem these new tools solve.
They handle the complex prompting invisibly.
The user just pushes a button. The agent handles
the drift, the context window, and the error
correction behind the scenes. What connects all
these new tools? They all turn complex, multi
-step tasks into simple, one -click agent workflows.
That transition from chatting to delegating is
everything. Beat. Which leads us perfectly into
our final segment, Jensen Huang's five -layer
AI cake. If you want to understand the economics
of the next decade, this is The Master Thesis.
Jensen Huang is the CEO of Nvidia. He recently
published a rare, deeply analytical blog post.
He argued that the hundreds of billions we've
spent so far, that's just the warm up. He stated
plainly that the build out will require trillions
more. Trillions with a T. He believes AI is no
longer a software category. It is core infrastructure.
It is exactly as vital as the digital economy's
power grid. If we connect this to the bigger
picture. This perfectly explains the gigawatt
deals and those fierce data center controversies
in Mississippi. It's all connected through his
five -layer stack model. Let's break it down.
Layer one, the foundation, is energy. This means
literal power plants, nuclear reactors, solar
farms, and the raw electricity grid. Layer two
is chips. These are the GPUs. GPUs are specialized
chips designed to process multiple tasks simultaneously.
This is NVIDIA's absolute dominance. Layer three
is infrastructure. The massive, sprawling data
centers, the advanced cooling systems, and thousands
of miles of networking cables. Layer four is
models. This is the software brain. The foundational
models like ChatGPT, Claude, and LeCun's new
reasoning engine. And finally, layer five, the
peak, is applications. The specific tools we
just discussed. Vozo, Sitefire, Copilot, and
the chatbots. Kwong describes a massive, inescapable
economic gravity. Demand sits purely at Layer
5. You and I use an application to write an email.
That application pulls computing power from the
Layer 4 model. To run that model, it aggressively
pulls on the Layer 3 data center infrastructure.
Which forces those data centers to buy millions
of Layer 2 computer chips. And those millions
of chips put a catastrophic strain on the layer
one raw electricity grid. Demand at the top strains
every single physical layer below it. It's a
violent chain reaction. It drives relentless
physical expansion and intense hiring across
every single industry in the stack. Is the energy
layer the ultimate bottleneck? Absolutely. Without
power plants, there are no chatbots. Energy is
the ceiling. That is a sobering physical limitation
to sex silence. So what does this all mean? It
means we have to stop viewing AI as just an app
on our phones. We started today discussing 900
million weekly chat GPT users. That massive number
sits at the very top of the layer cake. And from
the user's perspective, the application just
feels like weightless magic. But that digital
magic is anchored to a brutal, heavy physical
reality. It connects directly to gigawatt data
centers. It demands nuclear level energy. It
triggers complex community disputes over local
resources and land use. The big idea here is
a total paradigm shift. AI is no longer a software
boom. It is a physical transformation of our
planet. It is arguably the most aggressive physical
infrastructure build out of the 21st century.
We are pouring millions of tons of concrete.
We are laying massive high voltage power lines
across continents. We are literally restructuring
the physical world to support a digital mind.
And we're doing it at breakneck speed. It's a
profound moment in our history. Beat, we want
to leave you with one final slightly unsettling
thought. Yeah, this is a heavy one. Consider
this vast, highly interdependent, five -layer
physical stack. If AI truly becomes our core
societal infrastructure, exactly like electricity
or the internet, what happens to humanity the
day we experience our first global AI blackout?
That is a genuinely terrifying scenario to picture.
It's something to seriously mull over as you
watch these data centers go up. Thank you for
joining us on this deep dive. We always appreciate
your time and your curiosity. Thanks for exploring
it with us. Take care of yourselves out there.