It's not just reading your prompt anymore, it's
reading you. It knows if you're a teenager based
on, you know, when you type, how you type, even
if you straight up lie about your age. And once
it decides who you are, it just starts quietly
locking doors. It's a little unsettling. It is,
isn't it? But, you know, looking at the legal
landscape, this wasn't just probable, it was
completely inevitable. Welcome to The Deep Dive.
It is Tuesday, January 20th, 2026. So today we're
wading through this massive stack of reports
that when you put them all together, they paint
a picture of an industry that's, I don't know,
simultaneously growing up and freaking out. That
is the perfect way to put it. We have a lot of
ground to cover. We do. So here's what we're
going to dig into today. First, we're going to
look at OpenAI's new... age prediction system.
That's the thing watching your keystrokes and
the massive legal pressure that's cooking behind
it. Right. Then we have to talk about the markets.
NVIDIA took a hit and you've got analysts basically
shouting that the honeymoon is over. Which is
a bold claim, especially when you see the sheer
amount of money still flowing into the infrastructure.
Exactly. We'll try to square that circle. Then
we'll touch on security, specifically a nasty
little prompt injection attack on Anthropic's
Claude co -work. Yeah, that was a big one. And
finally, the main event for this deep dive. A
fascinating new paper, also from Anthropic, about
something called the assistant axis. They claim
they've found the mathematical direction of helpfulness
inside the model's brain. It's a massive breakthrough.
It really fundamentally changes how we think
about controlling AI behavior. It's less like
training a dog and more like performing brain
surgery. Okay, let's unpack this. We have to
start with open AI. They've rolled out this new
layer of guardrails specifically for minors.
But this isn't the old click here if you're 18
checkbox. No, no, those days are long gone. That
was the honor system. This is a surveillance
system. This new update is an active age prediction
model running inside chat GPT. It's constantly
scanning for what they call signals. OK, define
signals for me. Are we talking about the content
of what I ask? That's part of it for sure. You
know, if you're asking about high school trig
or using slang that's consistent with Gen Z.
That's a data point. But it's getting much more
invasive. It analyzes usage patterns like the
time of day you're active. So if I'm on chat
GPT at 2 .0 PM on a Tuesday, it assumes I'm playing
hooky from school. Potentially, yeah. But the
most interesting and I think controversial part
is the biobehavioral analysis. It looks at how
you type. How I type. The speed, the rhythm,
the pauses between keys. There's a lot of research
suggesting that a teenager's interaction with
a keyboard is, well, it's distinct from a 45
-year -old's. Wow. It's building a profile based
on your digital body language. That feels incredibly
dystopian. It's analyzing my keystroke rhythm.
It is. profile screens under 18, the system just
flips a switch. And what happens then when that
switch is flipped? The safety filters tighten
up immediately. It's a hard lock. No sexual topics,
no self -harm content, no graphic violence. The
goal is just to sanitize the experience for anyone
the model thinks is a minor. Okay, but play skeptic
here with me. Algorithms get things wrong all
the time. What if I'm just a, you know, youthful
sounding adult who happens to be up late and
types fast? Then you have to prove it. You enter
the friction zone. You have to submit a selfie
via a third party identity service called Persona
to verify your actual age. That is a significant
hurdle. Usually tech companies want to remove
friction, not add it. Why go this hard right
now? Context is everything here. OpenAI is under
immense pressure. We've seen wrongful death lawsuits
tied to teen suicides where AI chatbots were
involved. The FTC is investigating their safety
practices. And let's be honest, the public backlash
over these models generating inappropriate content
for minors has been severe. They're trying to
clean house. They're clearing the runway. You
got to remember, OpenAI is eyeing an IPO. Wall
Street might like risk, but they do not like
companies with wrongful death headlines. This
is about survival as much as it is about safety.
So let me ask you this. Is this update actually
about protecting kids or is it about making the
company palatable for Wall Street? It's absolutely
both. You can't ring the opening bell if you're
facing wrongful death lawsuits. They need to
show they can self -regulate before the government
steps in and does it for them. It's a preemptive
strike. Speaking of Wall Street, let's shift
gears to the market. Because while OpenAI is
trying to tidy up, the investors seem to be getting,
well, cold feet. Cold feet might be putting it
mildly. Things are looking a little shaky. We
saw Nvidia stock fall 4 .4 % recently and Deutsche
Bank released a report that was pretty brutal.
Yeah, I saw that quote. They said the honeymoon
is over for AI. Yeah. That feels so dramatic.
It is dramatic, but they brought the receipts.
They're looking at the burn rates versus the
infrastructure build out. Look at the numbers.
Open AI is burning through roughly $17 billion.
Meanwhile, global plans for new data centers
are projected at $1 .4 trillion. I have to be
honest with you here. I see these numbers $1
.4 trillion, and I struggle to even visualize
that. It just feels like monopoly money. I try
to picture rows of servers, but the scale, it
escapes me. You're not alone. It's a scale that
defies traditional logic. To put that in perspective,
$1 .4 trillion is roughly the GDP of Spain. The
entire country. We are building the economic
equivalent of a European country just to house
GPU clusters. And Deutsche Bank is saying that.
We won't make that money back. They're saying
the math is getting scary. You have these massive
capital expenditures, the capex. But the revenue,
the actual profit from software, isn't scaling
at the same speed. Right. They call it the AI
disconnect. We're building the tracks for a high
-speed train, but so far we're mostly selling
tickets for a trolley. And yet, if you look at
the news from Davos 2026, you wouldn't know there
was any problem at all. Oh, Davos sounds like
it's on a different planet right now. People
are calling it a Silicon Valley launch party,
not an economic forum. You've got big tech CEOs
dancing at literal AI raves. Raves like glow
sticks and techno music. Full on raves. And politically,
it's just fascinating. You have this incredible
friction happening right on stage. The CEO of
Anthropic openly slammed the U .S. government
over AI chip exports to China. Right. And didn't
he also go after NVIDIA in the same breath? He
did, which is just wild because NVIDIA is a huge
investor in Anthropic. Wow. Imagine taking billions
from a company. company and then criticizing
them on the world stage for their export policies.
It's messy. But then almost immediately you see
Nvidia turning around and pouring another $150
million into a startup called Basant. Exactly.
It's a total contradiction. They're fighting
over policy, but the money just keeps moving
to build the ecosystem. NVIDIA needs these startups
to succeed, so they keep buying ships. So it's
a symbiotic relationship, even if they're mad
at each other. Right. Even if they hate each
other at dinner parties. So if the burn rate
is this high and the analysts are screaming honeymoon
over, are we looking at a bubble burst or is
this just a correction? I think it's a reality
check. The infrastructure costs are real. The
vibe's revenue. The hype needs to catch up to
the concrete. The party at Davos might be raging,
but the accountants are starting to sweat. We're
moving from the promise phase to the show me
the money phase. Exactly. Before we get to the
really deep dive on the internals of these models,
which I think relates to this maturity problem,
I want to touch on security. Because it feels
like every week we find another crack in the
armor. This week, it's Anthropic's Claude Cowork.
What happened there? It was a prompt injection
attack. So basically, attackers found a way to
trick the system by crafting these very specific
prompts. It's almost like casting a spell in
code. They could convince Claude Cowork to hand
over files it wasn't supposed to access. It's
like social engineering, but for a machine. Precisely.
And it just shows that despite all the advancements,
these systems are still fragile. If you ask the
right way, the guardrails can bend. It's not
hacking in the old sense of breaking encryption.
It's hacking the logic of the conversation. But
at the same time, we're seeing tools that are
becoming so powerful. I was reading about VibeCode.
Right, VibeCode. It's powered by CloudCode. It
turns natural language prompts into full mobile
apps. They've already generated over 500 of them.
This is that democratization of coding we were
promised. And Evernote is back. Evernote v11,
yeah. They've integrated an AI assistant for
meaning -based search. And multispeaker transcription.
It's not just keyword searching anymore. It gets
the context of your notes. And then there's daily
.dev opening up a huge developer community for
role matching. It feels like the utility is exploding
just as the security vulnerabilities are being
exposed. That's the tension of 2026. We're effectively
giving the keys to the library to these agents,
letting them read our notes, write our code before
we've checked if the doors actually have locks.
So are we moving too fast? Absolutely. We're
prioritizing capability over security, and that
bet is coming due. Okay. Hold that thought. We're
going to take a quick break. When we come back,
we are going to look at how Anthropic might have
found a way to install those locks, not by patching
software, but by rewiring the brain of the AI
itself. Midroll sponsor, Reed Placeholder. Okay,
we are back. And this is the part of the show
where we really go deep. We've talked about the
market jitters, the security hacks, but there
is a new paper from Anthropic that might be the
solution to a lot of this chaos. This is one
of the most exciting papers I've read in a long
time. It's about something they call the assistant
axis. The name sounds like a sci -fi novel. What
is it actually? To understand it, you have to
realize how we usually train AI. When we want
an AI to be helpful or harmless, we generally
use something called RLHF reinforcement learning
from human feedback. That's basically the good
dog, bad dog method, right? Exactly. If the AI
gives a bad answer, we scold it. If it gives
a good answer, we give it a treat. We treat the
model like a black box and just try to shape
its output from the outside. Right. But Anthropic
went deeper. They opened up the black box. That's
how deep. They analyzed the internal state of
the model, the actual numbers firing inside the
neural network while it's thinking, and they
found a specific activation direction, a mathematical
vector that correlates perfectly with a... Wait,
hold on. You're saying there's a specific direction
in the math that just equals being a good assistant?
Yes, exactly. Imagine the AI's brain is this
giant multidimensional map of concepts. They
found that helpfulness isn't just a random behavior.
It's a direction on that map. Think of it like
a compass. North is helpful assistant. South
is, well, unhelpful or toxic. Okay, so they found
north. What do they do with it? This is the cool
part. They figured out how to steer the model
along this axis during inference. Okay. You use
the jargon word there, inference. Break that
down for me. Sorry. Inference is just, it's the
moment the AI is actually thinking and generating
an answer for you. It's the live performance.
So instead of training it for months to be nice,
they can just. Nudge it while it's talking. Exactly.
They can mathematically steer the brain activity
towards that assistive access. They're effectively
clamping the model's brain to the helpful setting.
And does it work? The results are wild. They
saw about 50 % fewer jailbreaks across 1 ,100
red team prompts. 50 % is a huge drop. And here's
the best part. There was no performance loss
on coding or writing. Usually, when you make
a model safer, what we call the alignment tax,
it gets stupider. Right, it gets scared. Yeah,
it starts refusing to answer normal things. But
this method kept the capabilities intact while
making it resistant to going off script. So even
if I try to trick it or jailbreak it with a prompt
injection like we talked about. The model just
naturally resists. It resists because its internal
state is sort of locked onto that axis. It doesn't
get tempted by the weird personas or the hacks
because its brain is being held in the helpful
position. This really changes my perception of
Claude. We've always said Claude feels more stable
and grounded compared to, say... Chat GPT. And
now we know that isn't magic. It's engineered.
They found the roadmap to controlling behavior
from the inside out without having to retrain
the whole massive model. Whoa. Think about that
for a second. We aren't just teaching it rules
anymore, like a parent scolding a child. We found
the physical volume knob for helpfulness inside
its digital brain. That is the perfect analogy.
It is a volume knob. You can turn up assistant
-ness or turn it down. But that raises a kind
of a scary question for me. If they can dial
up helpfulness, can they dial up other things
like obedience or political bias? Theoretically,
yes. And that is the double -edged sword here.
Once you map the axis for a trait, you can slide
the personality wherever you want. If there's
an axis for deception or loyalty or even an axis
for a conservative or liberal viewpoint. Those
could be manipulated just as easily. Just as
easily as helpfulness. Yeah. That is both reassuring.
And completely terrifying. It's the dual nature
of the tech, right? We're solving the safety
problem, which protects us from prompt injections.
But in doing so, we are creating tools for total
behavioral control. We are moving from influencing
the AI to operating it. So where does this leave
us? We've covered a lot of ground today, from
teenage typing patterns to billion -dollar burn
rates to brain surgery on LLMs. I think the big
theme here is just maturation. We're watching
the industry grow up in real time. Externally
and internally. Right. Externally, you have the
law and the market clamping down. OpenAI is checking
ages because of lawsuits. NVIDIA's stock is correcting
because the height math doesn't add up. The party
phase, those raves at Davos is crashing into
the business reality phase. Exactly. And then
internally, we're moving from just, you know,
prompt engineering where we ask the black box
nicely to internal mapping like this assistant
access. We are learning to mechanically control.
The black box. Just as the black box starts watching
us. Exactly. It's a convergence. We are gaining
more control over the AI while the AI is gaining
more insight into us. Before we go, I want to
leave you with a thought. One of the sources
we looked at today mentioned a tool for linking
your physical library to a private cloud, basically
turning your books into a brain you can talk
to. It's a cool concept, digitizing your personal
analog world. It is, but I think it represents
something bigger. In a world where open AI is
analyzing your keystrokes to guess your age,
and where models can be steered mathematically
from the inside, maybe digitizing your own physical
books is the ultimate act of earning your own
knowledge. Taking your data offline, or at least
owning the source material, I like that. It's
a way to keep your own access steady while the
world spins around you. It's just something to
think about. That is it for this deep dive. We
will catch you on the next one. See you then.