When these massive AI models are running, whether
you're training the next big thing or just serving
billions of queries a day, there's this really
simple question that every company has to ask
itself. Are we trying to be the fastest? Or are
we just trying to be the cheapest? Because right
now, Google is... very clearly choosing to be
the cheapest. And that whole strategy is their
direct shot at Nvidia's dominance. And welcome
to the deep dive. Today, we're digging into the
latest intelligence on these new AI infrastructure
wars. And it's... It's a really fascinating fight
because the goal isn't just about winning on
performance benchmarks anymore. It's about making
AI compute so cheap that it just becomes the
default. Exactly. So first, we're going to unpack
that whole cost per token battle. It's Google's
very specialized TPUs versus NVIDIA's GPUs that
are everywhere. It's a fight for the very foundation
of AI. Yeah, and after we break down that chip
war, we've got a really fast -paced segment,
some critical security alerts, a look at some
surprising new consumer tech. I'm talking translation
glasses. And we'll explore some new interactive
learning tools that are coming online. And then
finally, we're jumping into a huge medical breakthrough.
This one is out of Harvard Medical School, a
new AI tool called PopEVE. And this system is
solving these really complex genetic mysteries
that, you know, even the big one, Alpha Missense,
couldn't quite get right. We'll look at the data,
and it's pretty clear this is a huge step forward.
So let's start right there with the hardware.
Looking at Google versus NVIDIA, I mean, the
sources we saw make it really clear. Google has
completely given up on winning the benchmark
race. They're going all in on cost supremacy.
That's the pivot. That is the entire strategy.
Google's goal is to make AI compute so cheap,
so accessible, that other companies almost have
no choice but to use Google Cloud. They want
to win on price. And the key to their advantage,
they own the whole stack. Right, the entire vertical
stack, from the chip design itself, the TPUs,
all the way to the data centers they run in,
and then the software that sits on top of all
of it. And that control just gives them this
incredible power over pricing. Precisely. I mean,
if you look at Nvidia's market right now, a huge
chunk of their revenue comes from the margins
on their hardware. We hear estimates of, what,
70 % markups on those high -end GPUs? Meanwhile,
Google can basically sell its own custom TPUs
through Google Cloud at cost, or maybe even below
cost. And they can do that because they just
make the money back on all the other services
that are tied into that cloud ecosystem, or just
on pure volume. And this is a total game -changer
when you look at where the real spending is happening
now. Absolutely. The market has shifted so dramatically,
90%, maybe even more, of all the AI spending
today. It's not for training the models. That's
a one -time cost, basically. Most of the money
is for inference. It's running the model every
single time a user asks a question. So the main
thing that these big cloud customers care about
is no longer raw speed. It's not FLOPS, floating
point operations per second, which is what training
always focused on. Correct. The metric that truly
matters now is the lowest possible cost per token,
but at a massive scale. And Google has designed
the TPU specifically for that one job. Drive
the cost of inference down as low as it can possibly
go. And this cost advantage is, well, it's causing
real anxiety for NVIDIA. The buzz we're hearing
is that huge players, think meta, anthropic.
they might shift billions of dollars in compute
spend over to Google's TPUs. Even if they only
move a little bit of that, it could shave, what,
10 % off NVIDIA's AI revenue. That's an earthquake.
It is. Now, NVIDIA's defense is strong, but it's
entirely about the platform. They immediately
come out and say TPUs are locked in, narrow purpose,
inflexible. And what's so fascinating is how
they use CUA, their software ecosystem, as their
shield. CUA is the bedrock of their whole argument.
It is. The combination of their GPUs and CUDA.
Well, it works with almost any model. It handles
training and inference, no problem. And it runs
everywhere, any cloud or even on -prem. So NVIDIA
is fighting a cost war with the platform argument.
They're basically saying, we're the default.
We're the flexible ecosystem you can bet your
whole company on. So if that lock -in argument
is so powerful, you know, the fear of getting
stuck with one vendor like Google, what's stopping
these big players, the metas and anthropics,
from... Just sticking with NVIDIA's flexibility
for the long term. The massive cost savings.
It just incentivizes adopting specialized hardware,
even with those lock -in fears. Dollar stock.
Especially when you're dealing with billions
of user queries every day. It's a tough trade
-off. Okay, switching gears. Let's hit some rapid
-fire updates from the world of AI. Starting
with security and trust, which this just keeps
coming up in all the sources we see. Yeah, we
saw a pretty big security alert about chat GPT
user data. And it wasn't OpenAI's main platform
that got breached. It was a third -party partner
with a sloppy configuration that exposed user
data, emails, things like that. And this just
brings up such a critical point about the whole
AI ecosystem. As we weave these tools deeper
into our businesses, our security isn't just
about the main company anymore. It's tied to
the weakest link in the chain, the partners,
the APIs, all the extensions they use. It's a
distributed risk. Exactly. Even if you have perfect
internal controls, that one API gateway on a
partner system can be the point of failure. And
honestly, it's something I still wrestle with,
you know, trying to manage security across, what,
a dozen different APIs and platforms. It's just...
It's incredibly difficult to keep a consistent
security posture when you rely on that many outside
integrations. That's a really important point.
Meanwhile, this whole idea of trust is being
tested by the tech itself. We all saw those Thanksgiving
photos of Elon Musk and Mark Zuckerberg that
just went everywhere. Oh, they were so convincing.
But verified later as totally fake, made by a
tool called Nano Banana Pro. It just goes to
show you how easy it's becoming to create really
high quality, convincing misinformation. Deep
fakes are here. On a more positive note, the
tools for learning are getting way more interactive.
I'm really excited about this. Gem and I just
rolled out these new interactive diagrams. And
this is where the tech goes beyond just being
like an audio textbook. If you're looking at
a complex system, like a diagram of a cell or
the digestive system, you can now tap on any
specific part of it. And you instantly get a
definition, a deep explanation, all the context
just for that one little piece. It's like having
a dynamic tutor for any complex image you see.
And we're also seeing AI pop up in some surprising
consumer hardware. Alibaba just dropped some
AI glasses that are surprisingly cheap. They
look pretty much like normal glasses, but they
can scan prices and translate speech in real
time as you're walking around. A practical use
case. Finally. And then on the pure ambition
side of things, IBM is launching a $500 million.
And it's focused specifically on AI and quantum
breakthroughs. Their goal is not small. They're
aiming for a fault -tolerant quantum computer
by 2029. Wait, by 2029? Whoa. I mean, imagine
scaling that. In just four years. Fault -tolerant
compute power at that level. That changes, well,
it changes everything. Every industry from material
science to finance. It changes what's even possible.
Before we move on to genetics, we did see some
really useful career advice from an ex -meta
director about how to break into the AI field.
So beyond the usual talk about getting a PhD,
what was the most valuable takeaway you saw from
those tips? Practical experience. Solving real
-world problems is way more critical than just
having advanced degrees. That makes sense. Okay,
so moving from compute and careers, let's jump
into a genuine breakthrough in medicine. We're
talking genetics. And the huge challenge of finding
that one critical disease signal inside all of
the harmless genetic noise. Right. So DeepMind's
alpha missants made huge headlines for flagging
potentially harmful DNA mutations, but just flagging
a mutation. That's only the first step. The really
hard part is figuring out which of those mutations
actually causes a disease and which ones are
just, you know, common variations that don't
do anything, the background noise. And that difference,
the signal versus the noise, that's exactly where
this new AI from Harvard Medical School, Poppy
VE, is just proving to be incredible. PopBVE's
accuracy really comes down to its method. It
doesn't just look at human DNA. First, it analyzes
mutation patterns across hundreds of thousands
of different species. That gives it this massive
evolutionary context. It's like checking a variant
against the master blueprint for all of life,
not just the latest human version. So it's basically
asking, how important has this gene been over
millions of years? If it's essential for a frog
and a fish, it's probably pretty important for
a human too. Precisely. And then it calibrates
those evolutionary predictions against these
huge databases of healthy human genomes. Just
to double check if a variant is actually common
in people who don't have the disease, the result
is a much, much more reliable ranking system
for doctors. And the numbers here are just dramatic.
Pop EV cuts false positives by over 75 % compared
to alpha -miscence. That is a huge improvement
in clarity for a diagnosis. Think about what
that means for a patient. Alpha -miscence flagged
44 % of healthy people as having harmful genetic
variants. That creates so much unnecessary anxiety
and confusion. Pop EVD, after it gets rid of
all those false alarms, only flags 11%. That
reduction in noise is life -changing. And this
is where the real -world impact is just stunning.
Researchers... ran PopEvie on the data from 31
,000 undiagnosed children, all with severe developmental
issues. These were cases that had stumped doctors
for years. The results were immediate. I mean,
just incredible. Poppy solved one out of every
three cases that had previously been unexplained.
And it wasn't just confirming things we already
knew. It flagged over 120 new genes that had
never been linked to a human disease before.
And get this, at least 24 of those have already
been independently verified by other research
teams. That's massive validation. It proves this
AI is moving from just being an identifier to
a real diagnostic discovery tool. It's actually
accelerating research. So given how successful
PPE has been with these rare developmental disorders,
how quickly do you think this kind of method
could be adopted for screening more common hereditary
conditions? The high accuracy is going to rapidly
accelerate its adoption for broad population
screening and for personalized medicine. Wow.
This has been a really comprehensive deep dive.
We've covered the infrastructure battles, the
security landscape, and now the frontier of medicine.
Can we quickly recap the big ideas we hit today?
The big strategy battle in AI compute, it's not
about raw speed anymore. It's about owning the
stack and winning on cost. Google is making a
massive, very deliberate play for inference supremacy.
And as these AI tools get deeper into our lives,
our personal security is critically dependent
on the reliability of all those third -party
partners and APIs, not just the main company.
And finally, AI, like POPEE, is moving beyond
simple prediction. It's becoming a genuine, verifiable
tool for diagnostic discovery. It's drastically
cutting down that painful, crucial diagnostic
time for families who are dealing with these
severe, unexplained illnesses. Which really brings
us to our final thought for you to think about.
If Harvard's AI can solve one in three previously
unexplained genetic mysteries today, What percentage
of major human illnesses, the common ones we
all deal with, will be fully explained by these
AI models in, say, the next five years? That's
the question. And that's what's driving all this
innovation forward. Thank you for sharing your
sources with us for this deep dive. We really
encourage you to explore these ideas further.
We will catch you on the next deep dive.