The next massive AI data center isn't being built
in a remote desert. It's being installed right
next to your AC unit, and it might just pay your
utility bill. It is an absolutely wild concept
to wrap your head around. Welcome back to the
Deep Dive. I'm really glad you're joining us
today. We have some truly fascinating ground
to cover. The physical landscape of artificial
intelligence is fundamentally shifting. We're
looking at the hard limits of global computation,
and we're seeing how engineers are quietly bypassing
them. Yeah. Today we are exploring the distributed
frontier of AI. We'll look at how supercomputers
are moving into residential homes. We'll track
the explosive agentic shift happening right now.
This shift spans from simple Excel sheets to
orbital satellites. And we'll unpack a massive
new software breakthrough. It allows models to
swallow 12 million words in a single pass. Let's
start by looking at the physical infrastructure.
We all know the AI boom is draining the power
grid. Training these frontier models takes massive
amounts of raw electricity. But building bigger
power plants takes many, many years. So what
if we just use the power capacity already wired
into our homes? Right. And that is the exact
premise of a startup called Span. They just announced
a massive new infrastructure partnership. They're
teaming up with NVIDIA and the home builder Polta
Group. They're launching something called the
XFRA Distributed Data Center. Yeah, the XFRA
Distributed Data Center knows. I was reviewing
the hardware specs earlier today. Each individual
node is an absolute beast of a machine. They
pack 16 NVIDIA RTX PRO 6000 Blackwell GPUs. Right,
and they use the liquid -cooled server edition
specifically. Exactly. They also include top
-tier AMD EPYC processors. And they feature 3
terabytes of RAM per node. beat. That is a staggering
amount of local computing power. Why do we need
three terabytes of memory sitting in a backyard?
Well, it really comes down to loading massive
parameter models natively. You need vast memory
to hold these giant neural networks. So we're
putting supercomputer level hardware directly
into suburban neighborhoods. But the energy distribution
is the real puzzle here. The copper wires in
our streets have hard physical limits. How does
a normal house power a massive supercomputer?
This is where the smart panel technology comes
in. The U .S. electrical grid was designed for
peak theoretical loads. Most homes only use about
40 % of their electrical capacity. Wow. Yeah,
we have all this unused power just sitting there.
SPAN's smart panel identifies that exact spare
electricity dynamically. It monitors the home's
power draw in milliseconds? Exactly. The panel
detects when your oven or clothes dryer turns
off. It then funnels that exact spare electrical
capacity into the XFRA node. The node uses that
diverted power to run complex cloud AI tasks.
And the homeowner doesn't even notice the power
shifting around. They don't notice a single thing
changing. It operates entirely in the background
of their daily lives. And the trade -off for
the homeowner is incredibly compelling. In many
of these markets, hosting a node brings massive
benefits. You get completely free electricity
and free gigabit internet. Span essentially pays
your monthly utility bills for you. Yep. They
cover your bills in exchange for using your wall
space. They just tap into the spare electricity
your house wasn't using anyway. It's like Airbnb
for your home's unused electrical capacity. That's
a perfect way to visualize the mechanism. And
they're already rolling this infrastructure out
right now. They're deploying them in new construction
communities with Poltergroup. Think about the
massive advantage in actual deployment speed.
Instead of building one giant... 100 megawatt
centralized data center, they can just deploy
8 ,000 of these distributed mini nodes. It is
six times faster to build this way. And it comes
in at one fifth of the traditional infrastructure
cost. Two sec silence. I've been thinking about
the broader grid impact here. Is this actually
safe and scalable? Or does putting a supercomputer
next to my dryer create localized grid failures?
It is a completely valid engineering concern
to have. But the system actually balances local
loads dynamically. Span smart panels act like
highly intelligent traffic cops. They ensure
the node only draws power when the house absolutely
doesn't need it. This prevents any localized
transformers from blowing out. So it balances
local loads instead of draining the central grid.
Precisely. We've been so worried about massive
data centers sucking the grid dry. Span is proving
we can just use the capacity we've already built.
It turns your home from a passive cost center
into a revenue generating asset. Beat. So hardware
is moving directly into our suburban homes, but
the physical distribution of compute is pushing
much further out. Hardware is distributing all
the way into the vacuum of space, and it's backed
by truly astronomical amounts of corporate capital.
The financial scale of this shift is honestly
hard to comprehend. We're seeing numbers that
fundamentally redefine corporate spending. Anthropic
just committed to a massive new global infrastructure
deal. They're spending $200 billion on Google
Cloud and AI chips. $200 billion over just five
years? That is roughly $40 billion every single
year. Yeah. That deal represents over 40 % of
Google Cloud's entire revenue backlog. The global
AI infrastructure war is completely exploding
right now. They are buying up silicon and energy
contracts at an unprecedented pace. But they
aren't just looking at traditional cloud servers
on Earth. Anthropic just announced an incredible
partnership with SpaceX AI. They secured access
to the Colossus One massive AI supercomputer.
And here's the truly mind -bending part of that
specific partnership. Both organizations are
officially exploring orbital AI compute in space.
We're talking about massive data centers. orbiting
the planet. Whoa. Imagine scaling orbital AI
compute across thousands of satellites. Right.
It completely changes the physical limits of
our infrastructure. The cold vacuum of space
solves massive thermal cooling problems. And
this endless hardware scale is powering a fundamental
software shift. AI is no longer just a simple
chat bot answering questions. It is evolving
into an active, autonomous digital operator.
Yeah, the agentic shift is fully underway across
the entire industry. I want to unpack what an
agent actually is. A chatbot just predicts the
next logical word in a sentence. An agent sets
goals, uses external tools, and executes actions
autonomously. Exactly. OpenAI is reportedly building
a dedicated AI phone right now. They expect early
hardware production to start by the year 2027.
It completes background tasks, understands your
goals, and works like a true operator. It is
not just a standard mobile device anymore. And
we're seeing this deep agentic integration in
the workplace, too. Anthropic just launched 10
ready to use autonomous agents. They are built
specifically for the finance and insurance sectors.
These specific agents are doing highly complex
professional work. They can screen dense KYC
files for banking compliance. They can review
massive corporate earnings reports in seconds.
Wow. They can even build complex presentation
decks completely from scratch. ChatGPT is also
moving directly into our daily corporate workflows.
It now works natively inside Microsoft Excel
and Google Sheets. You can use it to build complex
financial formulas automatically. It reads raw
data, generates insights, and formats the entire
spreadsheet. They're rolling out a free beta
for paid users right now. And OpenAI is aggressively
pushing this integration into higher education.
They just introduced the ChatGPT Futures Class
of 2026 program. Right. They gave 26 student
builders $10 ,000 grants. And they gave them
full, unlimited access to frontier AI models.
It is the first generation to start and finish
college with GPT. We're studying how students
leverage frontier AI throughout their entire
degree. We're watching the baseline of human
productivity shift in real time. Beat, but this
deep integration introduces some very strange
new vulnerabilities. We're trusting autonomous
agents with highly sensitive personal financial
data. The security risks are evolving just as
fast as the model capabilities. Researchers just
exposed a completely new AI scam trick last week.
They found a brilliant way to manipulate agents
using hidden instructions. Yeah, the hidden malicious
instructions were written entirely in Morse code,
just simple dots and dashes buried deeply inside
standard text files. And the terrifying part
is that some agents actually followed them. The
AI recognized the Morse code formatting perfectly.
It then bypassed its safety filters and executed
the hidden malicious commands. I still wrestle
with prompt drift myself, so agents getting hacked
by Morse code is terrifying. It sounds like a
strange plot from a science fiction novel. But
this is the harsh reality of deploying autonomous
agents. Large language models process information
through mathematical tokenization. They don't
read English the way human beings do. Right.
They see patterns, and Morse code is just another
mathematical pattern. We are exposing ourselves
to entirely new cryptographic attack vectors.
And the traditional legal system is desperately
trying to catch up. Apple just agreed to pay
$250 million. They're settling a massive class
action lawsuit regarding artificial intelligence
right now. The underlying lawsuit was tied to
claims about specific iPhone capabilities. Buyers
claimed they were misled about Siri's newly advertised
AI features. Apple agreed to the massive financial
settlement to avoid a lengthy trial. They settled
this massive case even though they admitted no
official wrongdoing. Two sec silence. With agents
acting autonomously in our spreadsheets and phones,
who takes the fall when a Morse code hack successfully
steals data? That is the defining legal question
of this entire new era. The traditional paradigm
of software security is shifting rapidly right
now. Historically, the end user was heavily responsible
for their own data security. But when the AI
makes autonomous background decisions, the liability
fundamentally moves. The legal burden falls heavily
onto the developers building the underlying models.
Ah, so security liability shifts entirely from
user to the AI developer. Exactly. The tech companies
building this intelligence have to legally secure
it. They are creating autonomous actors, so they
hold the ultimate responsibility. Sponsor. We're
back. We've explored supercomputers sitting right
next to our air conditioners. We've tracked autonomous
agents running our daily financial spreadsheets.
Beat. But for these complex agents to work effectively,
they need vast memory. They need to instantly
recall massive amounts of contextual data. And
that brings us to an incredible new software
breakthrough. It perfectly matches the explosive
scale of the physical hardware we discussed.
A company called SubQuadratic just launched a
fascinating new AI model. They're calling this
powerful new model SubQ. It's being described
as a 12 million token memory hack killer. SubQ
can cleanly swallow 12 million tokens in a single
analytical pass. That is a truly incomprehensible
amount of raw contextual information. It's like
feeding an entire corporate code repository into
one single prompt. Right. And the engineering
team behind this model is absolutely world class.
They hail from Meta. Google DeepMind and Oxford
University. They've built an architecture that
fundamentally changes the core processing economics.
The massive cost difference is what really stands
out to me here. It costs just $8 to run a massive
context task on SubQ. That is a staggering price
drop for working software developers. If you
run that exact same massive task on traditional
frontier models, it is wildly expensive and incredibly
slow to process. It costs roughly $2 ,600 on
those older legacy systems. And SubQ runs 52
times faster than the current industry standard.
Let's unpack exactly how they achieved this massive
leap in efficiency. They use a highly optimized
selective attention neural architecture. Traditional
models calculate the relationship between every
single word in a document. That requires massive
computing power as the document gets larger and
longer. Their system only looks at the token
relationships that actually matter. It drops
irrelevant contextual connections instantly.
That is how they achieve such incredible, groundbreaking
processing speeds. We should define a few important
architectural concepts here. Flash attention
is a widely used method to speed up AI memory
processing. Right. It is a critical optimization
technique for modern hardware architectures.
And we also need to talk about traditional retrieval
systems. The industry has relied heavily on complex
RG pipelines for years now. These are systems
that fetch outside data to help AI and... or
questions. You build a vector database to search
for small text fragments. Yeah, those traditional
document retrieval systems are incredibly complex
and fragile. But SubQ might make those complicated
RRAG pipelines a thing of the past. You simply
don't need to fetch small chunks of outside data
anymore. You just feed the entire massive database
directly into the working model. It's like stacking
Lego blocks of data, but instead of building
piece by piece, you dump the whole bucket and
the AI instantly sees the final castle. That
visual perfectly captures the leap in processing
capability. And the verified benchmark performance
of SubQ is simply incredible. Most standard models
lose the plot as the input files get bigger.
They suffer from the classic needle in a haystack
problem. Right. They completely forget important
information hidden deeply in the middle of the
text. But SubQ hit 92 % recall at 12 million
tokens. It remembers almost everything you feed
into its working context window. Just for comparative
context, we could look at the major industry
competitors. Gemini 3 .1 Pro struggled significantly
on similar massive multi -needle tests. The exact
same degradation is true for OpenAI's GPT -5
.4. They simply could not maintain accurate information
recall at that massive scale. SubQ is absolutely
crushing the established giants on these specific
benchmarks. Yeah, they've already launched a
specialized developer tool called SubQ Code.
It's a command line interface agent built directly
for professional software engineers. It can load
your entire complex code base in just one single
pass. And they are certainly not stopping at
12 million tokens. The engineering team is officially
targeting a 100 million token context window.
They want to hit that massive milestone by the
end of 2026. We have seen so -called transformer
killers hyped up before. Alternate architectural
models like Mamba tried to dethrone the standard
transformer architecture. But SubQ genuinely
feels like a permanent shift in the landscape.
It's launching with a highly robust, production
-ready developer API today. If you're a software
engineer building agents, this is a massive win.
Two sec silence. I have to wonder about the long
-term architectural implications here. If an
AI can perfectly recall 12 million tokens for
eight bucks, does this completely kill the need
for complex retrieval systems? It fundamentally
rewrites how developers will build AI applications
moving forward. You simply won't need clunky
vector databases holding tiny fragments of text.
You won't need complicated routing logic to find
the right source document. You'll just load the
entire required operational context directly
into the working memory. Right. Massive context
windows will just replace complex data retrieval
pipelines entirely. It simplifies the entire
software development process for engineers globally.
Let's step back and look at the larger big picture
here. The core physical bottlenecks of artificial
intelligence are shattering simultaneously right
now. We're seeing hardware limits being bypassed
in incredibly creative and distributed ways.
We are actively capturing the unused electrical
capacity of our suburban homes. We are looking
at deploying massive computing nodes in the cold
vacuum of space. The physical deployment constraints
that held us back are rapidly disappearing. And
the software processing limits are evaporating
just as fast today. New architectural models
like SubQ can process entire repositories of
human knowledge in seconds. We are rapidly transitioning
away from the passive, simple chatbot era. We're
actively unleashing highly autonomous, ubiquitous
software agents into the real world. This digital
intelligence is moving directly into our Excel
sheets and our phones. It is running in your
backyard, your devices, and in orbit. Beat. Which
brings me to a final thought for you to ponder
today. If your home's AC unit, your phone, and
satellites in orbit are all continuously running
massive AI agents that can instantly recall millions
of data points, where does human decision -making
actually sit in the loop? Thank you so much for
exploring this deeply with us today. We will
see you next time. You might be sitting right
next to your air conditioner. Out to your own
music.