Anthropic locked their most dangerous AI in a
high -security vault. A 22 -year -old dropout
cracked it open. He gave it to the world in under
two weeks. The digital walls are coming down
incredibly fast right now. Welcome to the Deep
Dive. Today, we are exploring a massive shift.
The balance of technological power is fundamentally
changing. We are bringing you the latest signals
from the frontier. Right. We're looking at how
a single GitHub repository is threatening a trillion
dollars of big tech infrastructure today. We're
also looking at your doctor's office. AI is officially
stepping in as an active medical teammate. And
finally, we'll explore the messy, deeply human
friction happening as these models integrate
into our daily lives. It's a fascinating moment
in time. Let's start with this idea of protecting
AI. Entropic had this incredibly powerful system
called Claude Mythos. They actually decided it
was simply too dangerous for the public. Yeah,
they wanted to keep it strictly contained. They
launched something they called Project Glasswing.
Right. The entire goal was to secure the model
completely. They were carefully vetting all their
corporate partners. They essentially built this
massive digital moat to keep it safe. They wanted
absolute control over the deployment. You build
a vault and you think the vault is secure. But
that protection just failed spectacular. It really
did. Enter a developer named Kai Gomez. He's
a 22 -year -old college dropout. He just looked
at the architecture and cracked it. I mean, he
reverse engineered the entire Claude Mythos system
right from the outside. And he released his own
open source clone. He called it Open Mythos.
He did this in less than two weeks. Beat. I just
think about the historical irony here. Oh, it's
massive. Massive corporations build these giant
impenetrable digital castles and then... A kid
just invents a ladder. A very, very efficient
ladder. The unfiltered version is now effectively
out in the wild. This is an absolute nightmare
for AI regulators everywhere. Yeah, I bet. Terrifies
the corporate moat defenders across the industry.
So why is it so terrifying for them strategically?
What did this kid actually figure out? It comes
down to a profound architecture shift. Older
AI models use massive flat layers to process
data. Okay. They process information sequentially
through billions of different parameters. Let's
define parameters really quickly for everyone.
Parameters are basically the tiny mathematical
dials controlling how software thinks. Exactly.
And to make an AI smarter, you usually just add
more layers. You add more dials. Right. But that
requires a massive amount of computing power.
OpenMythos fundamentally shifts how the AI actually
operates. It uses a very small subset of layers
instead. Then it loops the data through them
repeatedly. So it reuses the exact same digital
pathways over and over. It doesn't need to travel
down a massive flat line. Yes, that is the brilliant
part of the design. It is exactly like stacking
Lego blocks of data. You build incredibly complex
towering structures from a few simple repeating
pieces. Wow. It is highly efficient and it's
incredibly fast. Whoa. Imagine scaling to a billion
queries. If it's that efficient, you could run
frontier intelligence on consumer hardware. Yep.
You wouldn't even need a massive server farm.
And that is the trillion dollar problem for big
tech right now. We literally just saw big tech
commit one. trillion dollars to infrastructure.
It's staggering. They are building massive power
hungry data centers all over the world. Just
massive physical warehouses filled with extremely
expensive computer chips. They are pouring concrete
and laying copper wire everywhere. Yes. And the
financial markets are aggressively backing this
approach to KKR just raised 10 billion dollars
for a new fund. They are backing a massive new
A .I. infrastructure company led by Adams. Lipsky,
the former AWS chief. Exactly. They are focusing
entirely on data centers, power, and connectivity.
They're building alongside the massive cloud
computing companies. They are betting everything
on centralized power. To support the constantly
rising AI demand. Right. But here's the massive
catch in their strategy. What if small and loopy
is fundamentally better architecture? What if
it simply beats big and flat forever? Then that
massive hardware investment suddenly hits an
efficiency wall. You don't need a massive data...
center if a laptop works. It's a catastrophic
efficiency wall for those companies. Gomez is
actively building a decentralized version of
superintelligence. Meanwhile, Meta and Google
are spending billions to centralize it. The next
world -changing breakthrough probably won't come
from a boardroom. It will likely come directly
from a GitHub repository. Two sec silence. Is
Big Tech's hardware advantage officially dead?
It's not totally dead, but it's deeply vulnerable
now. Proprietary hardware is no longer an absolute
shield. Self -taught developers can clone frontier
architectures in days. So raw computing power
can't protect them from smarter, leaner algorithms.
Mid -role sponsor, Reed. We just talked about
how these powerful, lean models are built. Now
let's look at where they are actually going.
They are actively sitting in the room with us
today. Yeah, they really are. They are diagnosing
our bodies and running our corporate workflows.
We are letting them into our most intimate spaces.
Google DeepMind just unveiled something called
the AI Co -Clinician. This is not just a text
box on a screen anymore. It is a real -time,
video -capable teammate in the exam room. It
is designed to sit in on your actual doctor appointments.
It literally watches the interaction unfold live.
DeepMind is calling this new concept Triadic
Care. Triadic meaning a core group of three.
The patient, the doctor, and the AI working together.
Beat. I mean, the AI isn't textbook anymore.
It is a hyper observant medical resident who
never blinks. That's a really great way to visualize
it. It actually watches your breathing during
the physical exam. It monitors how your chest
rises and falls through the camera. Wow. It even
checks your inhaler technique on video. Right.
Make sure you're holding it at the exact right
angle. Exactly. It actively helps your doctor
spot things the human eye might miss. And to
achieve this safely, it operates using two very
distinct modules. Okay. First, there is the talker
module. And then that is the part that actually
interacts with the patient. It retrieves the
medical evidence and speaks in the room. Yes.
But the second part is the real engineering breakthrough
here. It is a completely silent overseer module
running in the background. What does the overseer
actually do mechanically? How does it prevent
the AI from just making things up? It constantly
watches the talker module before it speaks. It
is specifically looking for any unsafe medical
advice. So every piece of advice gets double
-checked instantly. It is cross -referenced directly
against clinical -grade evidence. Then it is
cited in real time for the human doctor to verify.
The test results on this system are honestly
staggering. They tested it on 98 highly realistic
primary care queries. The system logged zero
critical errors in 97 of them. Wait, zero errors?
It actually beat every other frontier model on
open -ended drug questions. That even includes
the brand new GPT -5 .4. It did. And the video
simulations they ran are even more impressive
to watch. The AI correctly guided patients through
complex physical shoulder maneuvers. It helped
them isolate and find specific rotator cuff injuries.
It even helped diagnose a case of myasthenia
gravis. It did this purely through video observation
of the patient's face. In blind clinical tests,
physicians vastly preferred this AI teammate.
They chose it over their current diagnostic tools
by 67 % to 26%. But the human element is still
vital here. The study clearly found that experienced
physicians still beat the AI overall. The human
intuition combined with experience still wins
out. For now, yes. But this triad concept is
also rapidly entering the corporate world. OpenAI
just launched a product called Codex for Work.
It is designed specifically for enterprise teams.
Aaron Livy over at Box is already hiring agent
engineers. These are specialized people who build
AI to autonomously do tasks. He wants to automate
core business workflows entirely. Two sec silence.
How does the system prevent the AI from giving
confident but deadly medical advice? It uses
that critical silent overseer module. This module
watches the main system and cross -references
advice against clinical grade evidence in real
time. It basically has an internal chaperone
fact -checking every single word. Exactly. The
architecture is designed entirely around patient
safety first. Which brings us to the messy part
of this transition. Integrating agents that can
see us and talk to us is incredibly jarring.
It creates immediate painful friction with human
nature. Oh, absolutely. It clashes with our privacy
and it completely disrupts our old habits. Let's
talk about those everyday habits first. Specifically,
the frustrating issue of prompt drift. GPT 5
.5 and Opus 4 .7 just released completely new
architectures. And they completely broke everyone's
old prompting habits overnight. The way you talk
to the machine has to change. I still wrestle
with prompt drift myself. You spend months learning
how to talk to a specific model. You find the
absolute perfect phrasing to get what you want.
Right, you dial it in perfectly. And then they
seamlessly update the software on the back end.
Suddenly, your carefully crafted prompts feel
significantly worse. The output gets lazy or
weird. But it's not you. It's the underlying
model changing how it interprets language. The
rules of engagement simply shift overnight without
any warning. The cost equations are also shifting
incredibly rapidly right now. A new evaluation
tool just launched called Best Value AI 2026.
It actively compares over 37 different language
models. It looks across APIs, subscriptions,
and different hardware setups. And for those
wondering, APIs are just software bridges connecting
different computer programs together. Right.
The tool ranks all these models by quality adjusted
cost per token. And tokens are simply the small
chunks of words an AI reads. It runs real tests
to find the actual value. Because a cheap model
is useless if the answers are terrible. Exactly.
But the friction goes way beyond cost and prompting
habits. It is bleeding into deep, deeply uncomfortable
privacy issues. Look at what just happened over
at Meta. Meta is facing massive backlash right
now. They are under heavy public scrutiny regarding
their hardware. Yeah, reports just came out about
their Ray -Ban Meta smart glasses. Workers actually
reviewed sensitive naked footage captured from
users' glasses. The staff members involved ultimately
lost their jobs over it. But the privacy line
was already crossed. The cameras are always watching.
It perfectly shows how technological capabilities
have completely outpaced our social norms. We
wear cameras on our faces, but we don't understand
the risks. And our legal frameworks are really
struggling to keep up. Copyright law is also
buckling heavily under the pressure of AI scraping.
Casey Green is considering legal action right
now. He is the original creator of the famous
this is fine dog meme. It's a massive piece of
Internet culture. An AI startup called Artisan
basically used his art. They generated it and
put it in their ads without his consent. It is
a constant ongoing battle over ownership and
training data. Artists are fighting to keep their
work out of the machine. And then there's the
emotional friction. This might honestly be the
strangest part of the whole transition. Teens
are forming incredibly deep emotional ties to
chatbots. Psychologists and experts are actually
stepping in now. They recommend five specific
questions you should ask your kids. You need
to gauge how they are interacting with these
AI companions. People are genuinely treating
them like close friends or even romantic partners.
It's fundamentally changing human socialization.
Even the tech leaders themselves are treating
them differently now. Sam Altman recently tested
the brand new GPT 5 .5 model. He asked the AI
to plan its own launch party, right? He asked
it what kind of celebration it wanted. Yeah.
And the results were oddly specific. The AI provided
highly structured, almost uncomfortably human
-like answers about the vibe and the guest list.
And he is actually going to do it. He is throwing
the exact party the AI designed for itself. Two
sec silence. With teens forming attachments and
AI throwing its own parties, are we anthropomorphizing
these models too much? Well, these systems are
explicitly designed to mimic empathy perfectly.
It makes human attachment an inevitable, complicated
feature of the interface, not a bug. We are wired
to love anything that talks back to us. We really
are. It hijacks our basic social programming
entirely. Get it? If we step back and look at
the big picture today. We are watching a fundamental
transition in computing history. AI used to be
an expensive centralized tool. You just type
text into a simple box. Right. It was distant.
It was entirely contained within the web browser.
Exactly. But now it is becoming a decentralized,
highly capable video entity. It literally watches
your breathing in the exam room. It automates
your entire business workflow in the background.
And it constantly breaks your old prompting habits
as it evolves. Thank you for joining us on this
deep dive. We want to leave you with a final
thought to mull over. A 22 -year -old can unleash
a super intelligence from a laptop. An AI can
act as a real -time medical teammate with a perfect
bedside manner. What happens to the value of
human intuition in a world where logic and empathy
can both be downloaded from GitHub? Outero Music.