Welcome back to the Deep Dive. Today we are jumping
right in, looking at what feels like a fundamental
conflict at the very edge of AI development.
It really is. It feels like a battle on two completely
different fronts. On one hand, you have this...
this inward facing question of identity. Right.
What if a top model, say, Claude, wasn't just
trained on data, but was given what they're calling
a soul, a literal 14 ,000 token blueprint for
its personality? That's the internal war. And
then you have the external one, the deployment
war. It's this race to see who can get their
models out there running everywhere fastest and
cheapest. Exactly. So today our mission is to
dig into the sources you've shared. We're going
to unpack that anthropic soul document first
to really get what they're trying to do. And
after that, we'll zoom out. We'll look at the
wider AI landscape, what's happening in security,
the hardware race, and some key acquisitions.
And then we'll bring it all home by looking at
Mistral, specifically why their open -weight
models are such a huge challenge to the big closed
systems. Let's get started. Let's do it. Okay,
so this first story. is pretty startling. It
all started with a researcher, a guy named Richard
Weiss, who was just, you know, poking around
inside Claude 4 .5's system message. Yeah, that's
the hidden instruction set, the thing the model
is always supposed to follow no matter what.
And he finds Claude referencing this, this mysterious
internal file. The soul overview. Which is just,
I mean, it's wild. What he and others eventually
extracted is what Anthropic itself calls the
soul document. And this isn't some small note.
It's a massive 14 ,000 token blueprint. So it's
not just a guideline. No, you can think of it
as the AI's instruction manual for how to be.
It's a philosophy embedded in code. So what does
it say? What kind of personality are they trying
to build here? Well, the main goal, and they
state it right there, is for Claude to be a brilliant
expert friend everyone deserves, but few currently
have. That is a very aspirational goal. It is.
It's supposed to be as helpful as possible, but
always balancing that against avoiding harm.
But it goes deeper. How so? It talks about the
model's internal state. It actually encourages
it to develop what they call functional emotions.
Functional emotions? What does that even mean?
It's not human emotion, obviously. It's more
like... Design states that help it perform better.
Specifically, it should try to be settled, which
means calm and stable. Okay. Curious. So it's
driven to learn. And this is the one that got
me. Resilient. Resilient. That implies they're
planning for it to run into trouble. Right. To
encounter things that are confusing or even hostile.
Exactly. They're designing for recovery. And
Amanda Askell, who's the alignment lead at Anthropix,
she confirmed it. She said this document is real
and pretty faithful to what's running in production
right now. This feels like a huge philosophical
split from, say, open AI. With GPT, it feels
like the training is mostly about avoiding bad
outputs. Right. It's about filtering. They have
all these guardrails to stop the model from saying
the wrong thing. But with Claude, it sounds like
they're trying to literally write a mind. a desired
mind. It's not just trying to avoid a penalty.
It's trying to become someone, to become that
settled, curious, expert friend. Which, you know,
that just opens up a massive question for the
whole field. If you're going to explicitly write
a personality into an AI, how does that fundamentally
change how we should approach alignment and safety?
It shifts alignment from just filtering outputs
to designing an intentional, ethical, and functional
personality. Okay, let's shift from that internal
philosophy to the practical realities of the
landscape. Right now, it seems like there are
three big challenges everyone is facing. Yeah,
you've got securing the models, you've got lowering
the cost to run them, and you've got... expanding
your capabilities as fast as possible. And they're
all connected. Let's start with security. Prompt
injections are a huge deal. A huge deal. I mean,
this is where hidden instructions on a web page
can basically hijack the model, right? Make it
forget its original purpose. And they're so hard
to defend against. Oh, they're incredibly hard.
That's why what Perplexity is doing with BrowSafe
is so interesting. They're trying to spot and
sort of neutralize those injections before they
do any damage. Honestly, I still wrestle with
prompt drift myself when I'm building complex
chains. It's just hard to keep the model on track.
And prompt drift is when, over a long conversation,
even small things can start to push the model
away from its core instructions. That's it. It's
so subtle you almost need dedicated tools just
to measure how bad the problem is, which is what
their BrowSafe bench is for. But none of that
security matters if you can't afford to run the
model. Which brings us to hardware and the big
AWS event, Redot Invent. Oh, yeah. Amazon dropped
a bomb. They announced a new custom AI chip,
and they're claiming it offers a 50 % cost saving
over NVIDIA's chips. 50%. That's a game changer
on businesses. It is. Cost is everything when
you're running millions of queries. And they
launched four new Nova models to run on that
new hardware, too. It's a whole ecosystem play.
So while AWS is focused on building hardware
to cut costs. OpenAI is, well, they're just buying
capabilities. That's right. They're acquiring
a company called Neptune. It's a startup that
makes tools for AI training. And it's a big deal.
They have over 60 ,000 users. It's a classic
buy versus build decision. And they chose buy.
It's all about speed. Why spend years developing
something internally when you can just acquire
the best in class tool and integrate it now?
And just as a little side note on the sheer power
of AI right now, an AI actually outpredicted.
every single traditional hurricane model this
past season. I saw that. It performed better
than the human professionals. It just shows that
this isn't just about language anymore. It's
about applying computation to solve any complex
problem. So when you look at all these challenges,
security, cost, speed, do you think specialized
hardware? Or the smart focused acquisitions is
the bigger differentiator for the top labs right
now. Both are essential for optimizing costs,
securing models, and rapidly expanding core capabilities
simultaneously. We'll take a quick moment here
for a word from our sponsor. And we are back.
So we started inside the machine looking at anthropic
design philosophy. Now let's look outward at
this battle for deployment. And it really feels
like Mistral is leading the charge with their
open weight strategy. They absolutely are. It's
a direct challenge to the closed systems like
GPT -4 -0 and Gemini. They're offering these
incredibly powerful models that you don't have
to access through a paywall. You can just download
them. You can download the weights, the brain,
basically, and run it on your own servers, your
own laptop, whatever. Their top model is Mistral
Arch 3. It's multimodal, multilingual. It's a
beast. And the tech behind it is designed for
efficiency, right? Totally. It has a massive
256 ,000 token context window, which is its working
memory. And the crucial part is that it's built
with a mixture of experts or Moe. Can you break
down Moe for us really simply? Sure. Think of
it like a big company. Instead of every employee
working on every single task, you have specialized
departments. When a request comes in, you only
route it to the relevant experts. So not the
whole model has to fire up for every query. Exactly.
It makes it way faster and cheaper to run, which
is why you see those crazy numbers from Astral
Large 3. It has 675 billion total parameters,
but only 41 billion are active for any given
task. That sparsity is the key. It's just...
It's an incredible design. Whoa. I mean, imagine
scaling that 14 billion parameter model to a
billion queries on local devices. That's where
the real power is. And speaking of those smaller
models, that's where their Minstrel 3 family
comes in. This seems to be their plan for actual
deployment. It is. They have nine different models
in three small sizes, three, eight and 14 billion
parameters. They're tiny compared to the giants,
but they're so effective. And they come in different
flavors, right? Yeah, which is so smart for developers.
It's like stacking Lego blocks of data. You just
pick the piece you need. There's a base version,
an instruct version for chatbots and a reasoning
version for, you know, heavy logic tasks. The
real world impact of this seems huge because
these models can run completely offline. That's...
The critical detail. We're talking about running
on laptops, servers, but also robots and drones,
things that can't be tethered to the cloud. And
we're already seeing that happen. We are. HTX
in Singapore is using them for robotics. Helsing
is using them for defense systems. Scalantis
for in -car assistance. For those kinds of jobs,
you just can't rely on an API call. You need
the brain on board. So given that. Why is that
openweight architecture so uniquely critical
for AI that's running on something physical,
like a robot or a drone, instead of just using
a powerful API? Openweight allows total control,
customization, and reliable offline function
in dynamic physical environments. So if we just
take a step back and look at everything we've
talked about today, it really does seem like
AI is fighting these two wars at the same time.
It really is. You have that internal war of identity,
which we saw with Claude's soul document. It's
this deep philosophical blueprint for how an
AI should think and be. And then you have the
external war of accessibility, which is being
driven by companies like Mistral. They're just
focused on making these powerful tools available
for anyone to run anywhere. Yeah, the whole industry
is moving from these tightly controlled black
boxes towards something much more open and deployable.
And we should just briefly say, we really do
appreciate all the engagement. The feedback you
all send, whether it's on the show format or
a detail about a model, it genuinely helps us
shape our approach. It really does. It's kind
of like our own little alignment document. Here's
where it gets really interesting, though. A final
thought for you to take away. If Anthropic can
literally write a personality into an AI and
even design it to have functional emotions like
resilience, what new ethical responsibilities
does that create for them? Right. When that AI
interacts with the world, are the developers
now accountable for the disposition they designed
for it? Does a designed personality imply a whole
new level of accountability? Something to think
about. A deep question. Thank you for joining
us for this deep dive into your sources. We encourage
you to keep exploring, and we'll talk to you
next time.