72 % beat. That's the number that kept me up
last night. If you have a digital vault, a crypto
smart contract, or really any secure digital
asset, as of this week, there's a new AI agent
that can break into it and drain it of all funds
72 % of the time. And to be clear, that isn't
a theoretical figure from a sci -fi novel. That
is the actual benchmark result from the latest
coding agent released just days ago. We have
crossed a threshold where the digital offense
is statistically superior to the digital defense.
Welcome back to The Deep Dive. It is Friday,
February 20th, 2026. I'm your host, and I'm here
with our resident expert to make sense of a week
that... honestly feels like a year. It really
does. It's great to be here. You know, usually
we talk about AI explicitly building things,
generating art, writing code, solving protein
folding. But this week, the narrative shifted.
It's about AI taking things. And it's about the
massive economic machinery being built to support
that power. Right. So let's map out where we're
going today for you listening. We have a heavy
stack of reports to get through. First, Google
is hitting back hard in the model wars. Gemini
3 .1 Pro is here. And the headline isn't speed.
It's reasoning. We need to unpack what that actually
means. Exactly. Then we're going to follow the
money. And I mean serious money. OpenAI is looking
at an $850 billion valuation. We'll look at why
that number is so high and the controversial
shift toward advertising that's coming with it.
And finally, we are going to do a deep dive into
that cold open statistic. We need to talk about
why offense is currently beating defense and
cybersecurity. And what happens when an AI can
execute a flash loan attack all on its own? That
is the part that's going to scare some people,
but it's crucial to understand. Okay, let's start
with the tools. It feels like we just finished
analyzing Gemini 3 .0. That was what, November?
Yeah, three months ago. Three months. And now
we have Gemini 3 .1 Pro. Usually in software,
a .1 update is a snooze. It's bug fixes. Maybe
a new font. But looking at these specs, this
seems entirely different. It's completely different.
Google has rolled this out with a very specific
focus, reasoning. And I think we need to pause
on that word because it gets horribly abused
in marketing. Please do. Because to me, reasoning
implies consciousness, or at least human -like
thought. Is Google claiming their chatbot is
actually thinking? Not thinking in the biological
sense, but think of it this way. Previous models,
the ones we used in 2024 or 2025, were largely
system one thinkers. If you know Daniel Kahneman's
work, it's fast, instinctive, automatic. You
ask for a poem, it predicts the next word immediately.
It is reflexive. Okay, like a reflex. Exactly.
Gemini 3 .1 is moving towards System 2. It's
deliberate. It's slow. It's logical. When you
ask it a complex question, it doesn't just spit
out the statistically probable next word. It
holds the problem in its mind, breaks it down
into steps, critiques its own plan, and then
executes. It is spending compute on inference
time, literally taking time to think before it
speaks. So it's the difference between a student
blurting out an answer in class versus a student
sitting down to work through a... a math -proof
step -by -step. Perfect analogy. And the results
are stark. According to the preview, Gemini 3
.1 Pro has doubled its reasoning performance
compared to 3 .0. Doubled in three months. Doubled.
I am always skeptical of internal benchmarks.
Companies love to grade their own homework. Did
they test this against anything we'd recognize?
They did. They used a test called Humanities
Last Exam. Okay, that is a terrifying name for
a test. Is that real? It is. It was designed
to be the absolute ceiling of difficulty. Questions
that require multidisciplinary knowledge to solve.
It's the stuff that completely stumped the models
from 2025. Gemini 3 .1 outperformed its predecessor
there significantly. But the one that really
matters to me is the APEX agent's leaderboard.
I'm not familiar with APEX. APEX measures real
professional past performance. It's not right
a haiku. It's messy. It's take this scattered
data set of CSV files, analyze the trends, cross
-reference it with this PDF report on climate
change, and give me a strategic insight. So actual
knowledge work. Actual work. Gemini 3 .1 hit
the top spot. It is designed for agentic workflows.
It isn't just a chatbot anymore. It's a research
partner. I see. This explains the integration
they announced with Notebook LM. I've been using
Notebook LM for research, basically dumping PDFs
in and chatting with them. But you're saying
3 .1 Pro changes the nature of that interaction.
Completely. Notebook LM was a retrieval tool.
Find me the quote about interest rates. With
3 .1 Pro, it becomes an analyst. You can say,
look the interest rates in document A, compare
them to the housing trends in document B, and
predict the impact on this third variable. Whoa,
imagine connecting scattered data into full picture
insights like that at scale. It's stacking Lego
blocks of data into a skyscraper. That is a significant
shift. But I have to ask, if the cycle is down
to three months, are we even learning the old
tools before the new ones replace them? Barely.
We're really just adapting to the speed of the
upgrade, not the tool itself. We are becoming
experts in adaptation rather than experts in
software. Barely adapting to the upgrade speed,
not the tool itself. Got it. That's a heavy thought.
It might be the only skill that matters in 2026.
All right, let's pivot. Because building these
System 2 thinkers, these reasoning engines, is
expensive. The compute costs are astronomical.
Massive. The energy bills alone are enough to
bankrupt small nations. Which brings us to the
business side. We're seeing reports that OpenAI
is finalizing a 100 % billion dollar raise. Yes.
Which puts their valuation at over 850 billion
dollars. It is staggering. To put that in perspective,
that's approaching the territory of companies
like Meta or Tesla. But OpenAI is a fraction
of the age. It's nearly a trillion dollar valuation
for a company that didn't exist a decade ago.
And the backers are the usual titans. Amazon,
SoftBank, Nvidia, Microsoft. It feels like the
entire tech economy is consolidating around this
one entity. It's becoming the too big to fail
of the AI era. It is, but here's the tension.
To justify an $850 billion valuation, you need
massive revenue. You can't just have $20 subscriptions.
You need something way more pervasive. And that
brings us to the A word. Ads. Ads. OpenAI is
beginning to test them. They are. And this is
where the market is starting to fracture philosophically.
You have open AI needing to monetize that massive
valuation, testing ads and chat GPT. But then
look at the competitors. Perplexity, which has
been very aggressive, is actually stepping back
from in -chat ads. Really? I thought they were
leaning into that model. They were, but they
issued a warning this week saying that ads can
erode user trust. They are pivoting to preserve
the integrity of the answer. And then you have
Anthropic, who has publicly committed to staying
completely ad -free. It's interesting. It's almost
like the business model is becoming part of the
product identity, like organic food versus processed
food. It absolutely is. Think about it. If you're
using an AI for serious research, medical advice,
legal strategy, complex coding, do you want an
answer that is interesting? influenced by the
highest bidder? Or do you want the raw truth?
I want the truth. Oh. But I also know the history
of the internet. Free with ads usually wins against
paid and private. That's the trillion dollar
gamble. OpenAI is betting that their model is
so smart you'll tolerate the commercial. Anthropic
is betting that you'll pay a premium for silence
and neutrality. Speaking of commercial, OpenAI
isn't just looking at ads. They're looking at
Hollywood. They poached Charles Porch from Instagram.
Yes, the Hollywood connector. And after that
$1 billion Disney deal, he's going on a listening
tour to win over creators. Which is smart because
creators are skeptical. Actually, skeptical is
polite. They are furious. They see AI as the
thing that scrapes their work to replace them.
OpenAI knows they need to bridge that gap if
they want to be the platform for media as well
as text. They need to convince Hollywood that
they come in peace. It's a battle for hearts
and minds. So let me put you on the spot. With
perplexity pulling back on ads and OpenAI leaning
in, who actually wins the user's trust in the
long run? The one that gives the best answer
without selling me a toaster? In the short term,
OpenAI wins on scale. But for the power user
economy, I think anthropic or perplexity has
the edge on trust. So the winner is the one giving
the best answer without selling a toaster. I
don't need my AI selling me appliances while
I'm trying to debug code. Not yet, anyway. Okay,
let's move to the story that launched the deep
dive. The heist. The heist. We have discussed
reasoning. We have discussed money. Now we see
what happens when reasoning agents decide to
take the money. This comes from a new study involving
OpenAI's new coding agent, GPT -5 .3 Codex. 5
.3. They are iterating fast. Very fast. The study
used a benchmark called EVM Bench built with
the crypto firm Paradigm. They set up vulnerable
smart contracts, digital vaults, essentially,
and unleashed the AI to see if it could break
in. And this is where that 72 % come from. Correct.
GPT -5 .3 codex exploited the vulnerabilities
72 % of the time. Now for context, compare that
to just six months ago. GPT -5 scored 31 .9 %
on the exact same tasks. That is more than doubling
in capability in half a year. That curve is vertical.
It is exponential. And it's not just finding
a bug. A GPT -5 .2 agent was able to execute
a full flash loan attack on its own. Can we pause
there? Because flash loan attack is one of those
crypto terms that sounds exciting but makes no
sense to most people. Break that down for us.
Okay. Imagine a bank vault. You walk in and say,
I want to borrow $100 million, but I have no
collateral. In the real world, the bank laughs
at you and calls security. Right. In crypto,
the bank says, sure, take the hundred million,
but you have to pay it back within the exact
same transaction. It's like freezing time. You
borrow the money, you run around the market,
you buy and sell assets to manipulate the price.
You make a profit and you return the original
loan all before the clock starts ticking again.
So it all happens in a single split second block
of code. Exactly. If you fail to pay it back,
the whole thing reverses like it never happened.
But if you succeed, you keep the profit. It requires
incredibly. complex coding and timing to pull
off. And the AI did this autonomously. Autonomously.
No human steering the wheel. That is unnerving.
So the offense is elite. What about the defense?
Surely if the AI can break it, the AI can fix
it. That's the problem. The defense is lagging.
The same study showed that bug detection, the
AI trying to fix the code, tops out around 46%.
So it's almost twice as good at breaking in as
it is at fixing the lock. Why? It comes down
to the nature of the task. Breaking things is
often about finding one weak point. Fixing things
requires understanding the entire system so you
don't break something else. Patch success sits
at only 39%. 39%. Beat. That's barely better
than guessing. Unless, and this is the key, unless
you give the model a small hint. A hint. If you
give the AI a nudge, literally telling it roughly
where the problem is, patch success jumps to
94%. That is a huge gap. It tells us that the
bottleneck isn't intelligence. The AI is smart
enough to fix it. The bottleneck is search. It
doesn't know where to look for the solution without
guidance. It's like searching for a needle in
a haystack versus being told the needle is in
the top left corner. That feels incredibly human.
I could fix the leak if you just tell me. me
which pipe is bursting. Exactly. But in cybersecurity,
you don't get hints from the attackers. You know,
I have to admit, I still wrestle with prompt
drift myself. I am just trying to get the model
to write a coherent email sometimes, and it goes
completely off the rails. Yeah. The idea of trusting
it to patch a smart contract holding millions
of dollars, that feels like a massive leap of
faith. It is. But OpenAI sees this gap. They
don't want their tools to be super weapons for
hackers. They're launching Aardvark, which is
their AI security research agent, and they've
put up a $10 million fund for cybersecurity researchers.
They know that if AI can break systems this fast,
defenders need AI just as powerful. Because in
crypto, there is no undo button. Once it's gone,
it's gone. So if offense is twice as good as
defense right now, is any digital vault actually
safe? Not really, unless you have an AI guard
dog that's smarter than the AI burglar. And right
now, the burglars are winning. So no vaults are
safe unless your AI guard dog outsmarts the burglar.
We are going to take a quick break. When we come
back, we're going to step away from the destruction
and look at the builders, including a legend
in the field who is building 3D worlds from scratch.
Stay with us. Monroe's sponsor, Reed Placeholder.
Welcome back to the Deep Dive. We've talked about
the big models and the big money, but I want
to zoom in on the builders for a minute. The
people making the utilities we use every day.
The tool belt. Exactly. And there's one name
that popped up this week that everyone should
know. Fae Fae Lit. The godmother of AI. She is
responsible for ImageNet, which basically kickstarted
the deep learning revolution over a decade ago.
She has a new company, World Labs, and they just
raised $1 billion. Which is huge for a startup,
but it's the product. Morble, that's interesting.
It turns text into editable 3D worlds. 3D worlds.
So we aren't just generating images anymore.
No. This is about spatial intelligence. We are
entering design pipelines. Imagine typing a description
of a medieval city, the cobblestones, the lighting,
the physics of the fog, and having a fully editable
3D model generated that you can drop straight
into a video game or a movie. That feels like
the next frontier. We conquered text. We conquered
static images. Now we are conquering space. Spatial
computing needs content. Think about the metaverse
or Apple's Vision Pro. It's empty right now because
building 3D assets takes humans... Hundreds of
hours. If AI can do it in seconds, that changes
the entertainment industry forever. It lowers
the barrier to entry to near zero. Speaking of
which, there were a few other rapid -fire tools
that caught my eye this week. Did you see origami
.chat? I did. It is a data enrichment tool. Connects
over 100 sources. You can use it to find customers,
enrich a CSV file. It's highly practical. It's
unsexy, but it saves hours of grunt work. And
chloe .ai. Open claw in the cloud. Zero setup.
It's basically an autonomous assistant you can
spin up in five minutes to browse the web and
do tedious tasks for you. It seems like we are
moving from chat with a bot to assign a bot a
job. That is the defining theme of 2026. Don't
talk to me, do it for me, even for content creation.
There's Reloop, avatars, cloned voices, video
editing all in one spot. You just chat with it
to make a full video. It's moving so fast. I
saw that a Wharton professor released the eighth
edition of his AI guide this week. The eighth
edition. It just highlights that textbooks are
obsolete the absolute moment they're printed.
Absolutely. If you're reading a printed book
on how to use AI, you're studying history. You
are looking at fossils. So, faithfully focusing
on 3D worlds, is that the next frontier after
text and video? Absolutely. Spatial computing
needs content, and only AI can build it fast
enough. Spatial computing needs content, and
only AI can build it fast. I love that. Let's
try to pull this all together. We've covered
a lot of ground today. What is the big idea here
for the listener? I think if we weave these threads
together, we see a clear picture of 2026. First
is capability. Gemini 3 .1 proves that models
are learning to reason, not just predict. They
are becoming methodical thinkers. Second is risk.
That reasoning power allows for autonomous offense.
The tools are becoming weaponized much faster
than they are being secured. And third? The economy.
The market values this power at nearly a trillion
dollars, but the business model is still totally
fractured. We're seeing a split between the ads
model where you are the product and the subscription
model where you pay for privacy and neutrality.
It's a pivotal moment. You know, I keep thinking
about that gap, the offense versus defense gap
in cogeneration. It is the most critical metric
right now. It isn't just a crypto problem, is
it? No. It's everything. Banking, healthcare,
power grids. It's a preview of all cybersecurity.
Two sec silence. If the AI can pick the locks
72 % of the time, it doesn't matter how strong
the door is. We need better locks and we need
them fast. We do. And we likely need AI to build
them. That is the reality of February 2026. It
is a world of incredible speed, high stakes,
and brilliant machines that are learning to think.
Thank you for diving in with us today. Thanks
for having me. And to you listening. Stay curious,
stay vigilant, and maybe double -check your digital
locks today. We will catch you in the next deep
dive. OGAU Music.