Probably the biggest plot twist in AI this year,
Apple. You know, the company famous for total
control is paying its biggest rival, Google,
a billion dollars. Yeah. It's really the ultimate
sign, isn't it? That trying to keep everything
proprietary, building your own intelligence completely
in -house, it's just too slow right now. The
pace is incredible. Welcome to the Deep Dive.
We're unpacking a really fascinating stack of
sources this week. We're looking at who is truly
building, or maybe more accurately, who is buying
AI power right now. Exactly. We're going to dive
into the, frankly, shocking details of that Apple
-Gemini partnership. We'll also look at some
dramatic breakups and bailouts shaking up the
big AI giants. And then we'll examine a pretty
crucial technical paper that's basically democratizing
access to those huge trillion parameter models,
making them more accessible. Okay, right. So
let's start with that core conflict. Apple, you
know, the champion of the closed garden, the
controlled ecosystem, they're licensing Google's
Gemini model for their main Apple intelligence
stuff. And that price tag, a reported $1 billion
a year. It just shows the scale of this compromise,
doesn't it? It's definitely more than just irony.
It feels like a temporary necessity. I mean,
our sources point out what a lot of users already
feel. Siri has been, and this is a quote, embarrassingly
bad for years, especially when you compare it
to modern AIs. Right. Apple is racing to build
its own massive model, like a one trillion parameter
one. But the timeline seems to be. Well, maybe
late 2026 at the earliest. And they just couldn't
wait. So we should clarify, Gemini isn't handling
the actual voice part of Siri. No, exactly. It's
powering the really heavy lifting underneath,
the complex stuff, specifically the summarizer
tool and those sophisticated planner features
in the new OS updates. They needed that advanced
reasoning like yesterday. Because their internal
models weren't cutting it. Apparently not. That
need became pretty urgent. because their internal
efforts were struggling. We heard Apple recently
lost, what, four to seven key researchers, people
working on those foundational models. Precisely
because the in -house solutions weren't performing
well enough. So they kind of had to go shopping.
And what's really fascinating here, especially
given Apple's reputation, is how they've set
up the security. Google gets the billion dollars,
sure, but the AI itself runs entirely on Apple's
own system, the private cloud compute architecture.
Right. which means Google doesn't touch the user
data. That lets Apple keep that critical control
layer they value so much, even if the core brain
is, well, outsourced. Beat. And it wasn't like
Google was the only option they looked at. No,
definitely not. We know they actively tested
competitors. OpenAI's models were in the mix.
Anthropix clawed. But Gemini won out. Why? Seems
like it demonstrated superior instruction following.
so it handles multi -step commands better, and
it apparently has a longer context memory, which
is super important for planning complex tasks
or summarizing long documents. OK, so it was
really about the technical fit for the specific
job. Makes sense. Yeah. But the complexity just
keeps growing, doesn't it? Because Google is
banned in mainland China. Apple's now having
to cut separate deals, local deals with giants
like Alibaba and Baidu just for the version of
Siri that runs there. Which creates this sort
of fragmented intelligence stack, doesn't it?
Yeah. Different brains, depending on where you
are. We're thinking about what this means for
Apple long term. OK, let me just ask directly
then. Beyond the huge price tag, what specific
technical capability really made Gemini the unavoidable
winner for Apple's needs, especially for those
planning tools? It showed better instruction
following and could maintain context over longer
interactions. Simple as that. Okay, now shifting
gears a bit, let's talk about the corporate tectonic
plates. Because we're seeing some major realignments
happening. There's this big shift, maybe even
a split, happening between Microsoft and OpenAI.
Microsoft is now officially breaking away, setting
up its own separate superintelligence team. Their
goal, build AGI independently. Wow. So they're
still partners, but strategically. Strategically,
the priority seems to be shifting towards Microsoft
having its own proprietary AGI development effort.
Still working with OpenAI, but... Also hedging
their bets, maybe building their own thing. And
this probably all ties back to the sheer cost,
right? We saw that little drama where OpenAI's
CFO hinted they might need a, what was it, a
$1 .4 trillion chip bailout? Yeah. A number so
big it sounds like a typo. Sam Altman immediately
denied it, of course. Right. But whether that
specific number is real or not, it definitely
highlights the absolutely staggering amount of
capital needed to seriously chase AGI. Like nation
state level spending. Only a tiny handful of
entities on the planet can even think about playing
at that level. It's mind boggling. But, you know,
looking at the other side of that investment
coin, it's not all just about buying more chips.
The OpenAI Foundation is also investing heavily,
about $25 billion into things like health care
applications and AI resilience. And they're partnering
strongly with Microsoft and SoftBank on that.
So it seems like a kind of two pronged strategy.
Chase AGI, you know, at almost any cost, but
also fund these critical sector applications.
Meanwhile. We also saw a pretty vulnerable admission
that really illustrates the maybe the ethical
debt the scaling race can create. You're talking
about Meta. Yeah. Sources revealed that Meta,
to help fund its massive AI expansion, was bankrolled
by roughly $16 billion. And part of that came
from deliberately allowing scam ads, ads targeting
users daily. It's pretty stark. They apparently.
tolerated these profitable scams because, well,
they generated too much revenue to just shut
them down easily. Ouch. And that pursuit of short
-term profit, it creates a long -term problem,
doesn't it? If your revenue relies on bad actors,
it inevitably poisons the data you're using to
train your models. And it just fundamentally
damages user trust. Yeah, that tension between
integrity and just sheer scale. It's not just
social media either. Look at Google. They've
aggressively pushed into becoming what sources
call a full -on financial AI researcher. They're
rolling out features, answering complex market
questions, providing live earnings transcripts
for traders in real time. They're pushing into
every potentially profitable sector they can
find with AI. It's an aggressive expansion. So
thinking about that meta -admission specifically.
What does that pursuit of high revenue, even
when it comes from bad actors, tell us about
the maybe the foundational integrity of the AI
models being built on that data? High profit
from bad actors risks data quality and user trust,
creating ethical debt. OK. Let's dive into the
tech side now, because this is where it gets
really interesting for me. We need to talk about
the physical barriers, the hardware barriers
that have mostly kept these super advanced AI
models in the hands of just a few giants like
Google or Nvidia. Right. We're talking about
the huge models, the ones with like. a trillion
parameters they usually need specialized incredibly
expensive hardware setups exactly and critically
most ai teams have kind of avoided using standard
cloud infrastructure like aws for these really
massive models and there's a specific reason
why which is aws's networking tech called efa
lacks a key feature something called gpu direct
async okay hold on gpu direct async we need to
break that down what does that missing feature
actually mean in Okay, think of it like this.
Imagine your giant AI model is spread across,
say, 10 different computers, each packed with
GPUs. Got it. Those 10 machines need to talk
to each other constantly, sharing data back and
forth, like... instantaneously for the model
to work. GPU direct async lets the GPUs on different
machines talk directly to each other super fast.
It cuts out the middleman, which is usually the
main computer brain, the CPU. Without it, the
GPUs have to kind of wait for the CPU on each
machine to manage all that data traffic. It creates
bottlenecks. The communication slows down so
much that these giant complex models often just...
crash or fail. Yeah. That's been the big hurdle
on standard cloud setups like AWS. Right. The
communication highway wasn't fast enough between
the different GPU workers. Exactly. But here's
the breakthrough. It comes from perplexity. They
published some really groundbreaking research
showing how you can actually run these trillion
parameter Mogi models, models like Kimi K2 and
DeepSeek V3 on regular off -the -shelf AWS cloud
machines. How? If the hardware feature is missing.
They basically found a clever software workaround.
Instead of needing new expensive hardware, they
built an intelligent system using software. The
CPU still helps coordinate, but the key is how
they move the data. Okay. They pack and shuttle
the data really smartly using a different technology
called RDMA. RDMA. Remote Direct Memory Access.
That's basically a way for computers to swap
data directly between their memories super fast,
right? Precisely. It's like... Imagine stacking
Lego blocks of data between the machines incredibly
fast and doing lots of stacks at the same time
concurrently. They figured out how to orchestrate
this data flow efficiently. using software and
rdma even without that specific gpu direct async
hardware feature so they built a software pipeline
yeah essentially a high -speed software pipeline
that makes the standard cloud function almost
like one of those specialized super expensive
supercomputers at least for this kind of workload
whoa okay just pause there for a second imagine
scaling your ai to handle like a billion queries
without needing custom -built proprietary hardware
that costs millions and millions This is like
figuring out how to run a Formula One car and
run it well on regular city streets using standard
infrastructure. That feels really profound, a
huge shift in accessibility. It absolutely is.
That's the core impact, democratization. Suddenly,
any development team, any startup with the decent
cloud budget and the necessary brainpower. they
can potentially join the Trillium Parameter League.
It kind of dissolves that hardware barrier. It
proves that really smart software engineering,
efficient code, can actually beat massive capital
spending. at least in some cases. So for the
developers, for the companies out there listening,
what's the key technical lesson from Perplexity's
work here about getting around these hardware
constraints? Smart software orchestration can
often bypass assumed limits of commodity cloud
hardware. Clever software beats brute force hardware.
Love it. Okay, just a quick detour to highlight
a couple of interesting new tools we're seeing
pop up. There's one called Maya One, which is
generating highly expressive speech. We're talking
over 20 different emotions. So moving way beyond
that typical kind of flat robotic AI voice, much
more natural sound. That's cool. And the other
one? Llama .cpp. This is really interesting for
developers and tinkerers. Okay, define Llama
.cpp for us simply. It's basically a lean framework
letting you run large open source AI models directly
on your own computer, even a decent laptop sometimes.
And the significance of that, you know, it really
shouldn't be understated. It means you don't
have to send your sensitive data off to Google
or OpenAI or whoever to get really good AI performance.
Right. It ties back to that control theme we
started with. Apple's struggle. Exactly. It potentially
gives sovereignty back to the user or the small
developer. You can run powerful models locally.
And sticking with actually using these models,
what about advanced prompting? Any new tricks?
Well, sources continue to highlight the effectiveness
of persona prompting. You know, telling the model
to ultra -think like Steve Jobs. or act as an
expert physicist explaining quantum entanglement.
Does that really work? It tends to get much sharper,
more focused answers from models like Claude
or GPT -4. It works, I think, because you're
essentially forcing the model into a specific
defined set of constraints, a particular style
or knowledge base, rather than letting it give
a generic average response. Yeah, I have to admit,
I still wrestle with prompt drift myself sometimes.
Getting a model to consistently sound like a
specific leader or maintain a complex persona
over a long conversation, it takes constant tweaking
and refinement. It's never quite as simple as
those quick online guides make it seem. It's
definitely an iterative process. Lots of back
and forth. For sure. It's an art as much as a
science right now. Okay. So if we pull way back
now, look at everything I've discussed. Yeah.
The big idea emerging today seems pretty clear.
Control. That old model of total proprietary
control is rapidly becoming obsolete, or at least
incredibly difficult to maintain. You have Apple,
the absolute champion of proprietary power, forced
to essentially rent its core intelligence from
its biggest rival, Google. And at the exact same
time, you have this grassroots technical brilliance,
like what Perplexity demonstrated, that's dissolving
the hardware barriers that used to enforce that
control. It sends a powerful message, doesn't
it? That really efficient code, smart software
can fundamentally challenge the financial might
of the world's biggest tech companies when it
comes to AI development. The material we explore
today really shows that the whole balance of
power in this AI race. It's unstable. It's constantly
shifting based on a new partnership deal one
week or a new algorithmic breakthrough the next.
Which leads to maybe a provocative thought to
leave folks with. If the world's most secretive,
most control -obsessed company, Apple, finds
itself forced to outsource core AI intelligence,
how much longer can any major company realistically
maintain true proprietary control over the most
advanced forms of AGI when they emerge? Is that
even the right model anymore? That's a great
question to ponder. And for you, the learner
listening today, if you want to dig deeper into
the tech enabling some of this shift, we really
encourage you to look into the concept of RDMA.
That's remote direct memory access. It's kind
of the invisible engine fueling this new era
of cloud efficiency for AI and understanding
it might be key to understanding where a large
scale AI is heading next. Thank you for joining
us on this deep dive. Outro music.