GPT 5.2 didn't just process data this week, it actually
conjectured a brand new formula for single minus gluon tree
amplitudes. Which, if you aren't a
theoretical physicist, is a problem that human
mathematicians generally describe as incredibly long and
painful. Yeah, we are talking about what
is typically 1/4 page of really dense messy algebra.
And the AI identified a hidden pattern that simplified all of
that into a single, elegant product structure.
And that is massively significant because this goes
way beyond a calculator doing arithmetic faster than a human
right. We are looking at a system
analyzing a mess of complexity and spotting A symmetry that
human experts had missed for decades, right?
It is the difference between simply computing a result and
actually understanding the architecture of the physics
problem itself. It proves we are building
genuine collaborators now. But while the science side is
having this massive Eureka moment, the way these AI
companies operate is shifting just as drastically.
We are moving entirely away from the era of chat bots where you
type a question and get an answer.
We are entering the era of agents and Co workers that
actually execute tasks. That distinction really matters.
A chat bot talks, an agent acts, and right now the industry has
split into two very distinct battles to make that happen.
There is one battle for raw speed and hardware
infrastructure, and another for deep autonomous execution.
Exactly. Which brings us to the core
question we are going to be exploring today.
In a week where AI is proving theorems and learning to drive,
our computers, are the safety guardrails crumbling under the
pressure to win massive government and enterprise
contracts? It is the defining tension of
everything happening right now. And the autonomous side of that
battle just got a huge injection of talent.
As of this morning, February 25th, 2026, Anthropic has
officially acquired Vercept. This is a major signal flare.
Vercept was a Seattle based startup and they were founded by
some real heavy hitters from the Allen Institute for AI.
They had built this desktop agent called V.
I've actually seen demos of V It is a creepy and impressive and
equal measure. It doesn't just read the
underlying text code, it physically sees the screen
elements. Right, the visual component is
the main hurdle everyone has been trying to clear.
Yeah, for a long time. If you wanted an AI system to
use a computer, you had to hook it up to an API.
That is essentially a special backdoor that lets the software
talk directly to other software. But the real world is incredibly
messy. Most software out there doesn't
have clean API's. Exactly.
So VAI interacts directly with the graphical user interface.
The actual pixels on the monitor.
It looks at the screen just like you do.
It identifies that a specific Gray rectangle is a submit
button and a specific white box is a text field.
Which sounds so simple to us. It does, but for a machine it is
incredibly difficult. It knows the grounding problem.
You have to perfectly map pixel coordinates to semantic actions.
And this acquisition perfectly explains the sudden jump in
Anthropics performance numbers. We saw the new benchmarks for
Claude Sonnet 4.6 this week, specifically looking at AUS
World. Yes.
For context, the Alice World benchmark is the standard test
for measuring how well an AI can navigate an operating system.
The test asks things like can it open a spreadsheet, copy a
specific cell, open a web browser, paste that date into a
form and hit enter. Real computer use.
Yes, and in late 2024, the best models in the world were scoring
under 15% on that benchmark. Which is functionally useless
for a user. You would spend more time
correcting its mistakes than the time it theoretically saves you.
A 15% success rate is basically a toy, but Sonnet 4.6, which is
clearly integrating this new Recept tech, is now hitting
72.5%. That crosses a massive threshold
at over 72%. You can actually walk away from
your desk and let the agent run. You can trust it with a multi
step workflow. That is entirely why Antropic
bought them. They aren't interested in
keeping the Vibe brand alive. They are integrating the team of
about 20 engineers to make Claude fully capable of direct
computer use. They want an AI that writes the
e-mail for you, opens your client, attaches the PDF and
click send. So Anthropic is heavily betting
on the smart autonomous agent that navigates a messy desktop
environment. Open AI, however, seems to be
betting on something else entirely.
This week they released GPT 5.3 codecs Spark.
Spark is the operative word there.
This new model is entirely obsessed with latency.
And they are achieving this incredible speed through
specialized hardware, right? This goes beyond standard
software optimization. It is absolutely a hardware
play. Spark is running on Cerebra's
Wafer Scale Engine 3. I really need you to visualize
this for a second because I saw a photograph of this chip
recently. Standard computer chips are
small. They are roughly the size of a
postage stamp. This Cerebra's chip is the size
of a dinner plate. Yeah, it's the size of a giant
pancake. It is an entire uncut silicon
wafer, usually in standard chip manufacturing where you take a
silicon wafer and cut it into hundreds of tiny individual
chips. So reverse just keeps the whole
thing intact as one massive processor.
Why does keeping it intact matter so much for speed?
Because in a traditional setup, you have your memory sitting on
one physical stick and the processor sitting on another.
Data has to physically travel back and forth through wires to
compute anything. Even if we get the speed of
light, that travel takes time and consumes a lot of energy.
Right on the wafer scale engine, the memory and the compute cores
are right next to each other on the exact same piece of silicon.
The data transfer delay is effectively eliminated.
It is nearly instant. And the practical result of that
architecture is over 1000 tokens per second.
Well over 1000. Wait, hold on, let's back up a
second. To put that in perspective, a
human being reads roughly 5 words a second, generating 1000
tokens. A second means a massive wall of
text appears instantly on your screen.
You can't even begin to read it as it generates.
You absolutely cannot. But for writing code, which is
exactly what Spark is designed for, it fundamentally changes
the texture of the work. It is essentially 15 times
faster at coding than their standard model.
They are marketing this as conversational coding.
Think about how you use a standard chat bot right now.
You type a prompt. You wait maybe 10 seconds.
The code appears block by block. You read it.
It feels very much like sending a letter and waiting for a reply
in the mail. A turn based interaction.
Right, with Spark the code generates so fast you can
interrupt at mid thought. You can see it going down the
wrong logical path in line three of a function and just stop it
instantly. You correct it on the fly.
So it feels more like jamming with a musician in a studio.
That is the perfect analogy. It creates a tight real time
feedback loop. But there is a serious trade off
here. You do not get that kind of
speed for free. The model is less rigorous.
Exactly. Spark is completely latency
first. If you look at the SWE Bench Pro
scores, which is the premier software engineering benchmark,
Spark scores significantly lower than the full GPT 5.3 codecs
model. And perhaps more worryingly,
Open AI explicitly states it is not rated for high capability
cybersecurity work. So we have a purposeful division
now. If you want the AI to discover
the gluon tree amplitude formula, you wait patiently for
the slow deep thinking model. If you want to hack together a
website infrastructure in 10 minutes, you use Spark for fast
execution. It is a deliberate split between
deep reasoning and velocity. Speaking of deep reasoning and
that physics discovery we mentioned at the start, we sort
of glossed over where that actually took place.
That discovery did not happen in a standard chat window on a
browser. It happened inside Prism.
Open AI Prism. Yeah, this is their brand new
workspace designed specifically for scientists, and it is
fascinating because it attacks the actual workflow of doing
science. It is a fully Latex native
writing environment. Latex is the complex typesetting
system that pretty much every physicist and mathematician uses
to write their formal papers. Prism integrates the AI directly
into that source mode. Plenty of standard text editors
have AI plugins these days, but Prism is completely different
because of the context window and the deep integration.
Prism reads the entire project structure simultaneously.
It sees your equations, your citations, your raw empirical
data files, and all of your figures.
So if I go in and tweak a variable in my core equation on
page 2, Prism automatically knows to update the resulting
graph in figure 3 on page 10. Yes, it validates if your
empirical results actually match your theoretical model.
It can check all your citations against the actual text of the
reference papers to ensure you are quoting them correctly.
It is doing the tedious grunt work of consistency that usually
drives researchers crazy. And I asked about the business
model earlier because I noticed they are offering this entirely
for free for personal accounts. That is the classic Silicon
Valley play. They want to become the default
infrastructure for scientific discovery.
Right now, a scientist might use Overly for collaborative
writing, Zotero to manage their citations, and Python for data
analysis. Prism is designed to replace all
of those fragmented tools. They want the next Nobel Prize
winning discovery to happen natively inside an open AI
interface. Discovery is an incredibly
valuable commodity. If you own the tool where the
science happens, you get to see where human knowledge is going
before anyone else does. Which brings us directly to the
money, because whether it is via navigating a messy desktop or
Prism writing a theoretical physics paper, this technology
has to be monetized and simply selling an API key to developers
isn't cutting it for these massive valuations anymore.
We are seeing the rise of the true AI Co worker and the
massive consulting army is required to install them.
Open AI refers to these as frontier alliances.
They have realized a harsh truth.
You cannot just hand a Fortune 500 company access to a super
intelligent model and expect them to magically become more
productive. The companies literally do not
know how to use it. They have no idea how to wire it
into their legacy systems, so Open AI has officially partnered
with McKenzie, BCG, Accenture, and Cap Gemini.
These are the massive consulting firms you traditionally hire
when you want to fire half your staff and completely restructure
the remaining half. Or, phrase more charitably,
they're the people you hire when you need to redesign your
organization's central nerve, the system.
These consulting firms are wiring the frontier platform
directly into massive corporate data warehouses and customer
relationship management systems. They are actively redesigning
organizational workflows to accommodate autonomous agents.
It is essentially organizational surgery.
And it is very expensive surgery.
Entropic is playing this enterprise game just as hard
right now. They are currently hitting a $14
billion revenue run rate, and to fuel that massive infrastructure
and enterprise expansion, they just closed their Series G
funding round. The number on that round was
staggering, $30 billion. $30 billion in cash.
That completely values Anthropic at $380 billion.
That is an immense, almost incomprehensible amount of
capital. But here is where the tension we
talked about earlier really surfaces.
We have $30 billion funding rounds, we have heavy military
interest, and we have these massive consulting armies
deploying agents. What happens to the original
safety mission? Enthropic was founded
specifically by people leaving Open AI to be the definitive
safety company. That core mission is severely
colliding with reality right now.
Just yesterday, on February 24th, Anthropic released version
3 Point O of their Responsible Scaling Policy, or RSPI.
Read through that document last night.
There is a very specific, very controversial change in the
language. In the previous versions of the
RSP, Anthropic had a hard written commitment.
They stated they would unilaterally pause all
development if they couldn't meet certain strict safety
measures. If a model was deemed too
dangerous or autonomous to contain, they would stop
training. Period.
And in version 3 point O that is gone.
That unilateral commitment is entirely gone.
They've replaced it with a clear bifurcation.
They now differentiate between industry recommendations and
company plans. So they're publicly recommending
that the entire AI industry should pause if things get
dangerous, but they are no longer promising that they will
pause if their competitors keep going.
They are framing it as a classic collective action problem.
Their argument, which is highly rational from a pure business
perspective, is that if Anthropic pauses to build
perfect safety guards, but open AI or a massive state backed lab
in China keeps scaling and throffic just loses market
share. They lose their influence over
the industry. Exactly.
They effectively cede the future to actors who might be far less
concerned with safety than they are.
It is the ultimate prisoner's dilemma.
If I play nice and you decide to play rough, I lose everything,
so I am forced to keep playing rough.
But there's another massive pressure point here that we
absolutely have to discuss the Pentagon.
The US Department of Defense has been getting incredibly loud
about AI integration over the last year.
They have. The Pentagon explicitly
communicated that a strict refusal by an AI company to work
on national security tasks was viewed as a supply chain risk.
Supply chain risk, that is highly specific bureaucrat speak
for we are not going to buy anything from you.
Exactly. It is all about reliability.
If the United States government is going to heavily integrate
your AI agents into their defense system, they need
absolute certainty that you aren't going to suddenly turn
the servers off because of an internal moral qualm about how
the tech is being used. So this policy shifted.
Anthropic isn't just about preserving enterprise market
share, it is about making themselves a viable long term
government contractor. This new policy is a calculated
pivot to survive in a world where the US government is
suddenly the biggest, most important customer in the room.
The entire concept of safety is being redefined in real time.
It is no longer about slowing down to ensure alignment, it is
about staying in the absolute lead so you have the power to
set the rules. And while they are fiercely
fighting for those lucrative government contracts, they're
also simultaneously writing massive checks to make their
foundational legal problems disappear.
We really have to talk about the data that powers all of this.
The cost of content, we finally have a concrete price tag on it.
The Barts V Anthropic Settlement. $1.5 billion.
That is a historic number for a copyright lawsuit.
It completely sets the precedent for the entire industry.
For a very long time, the standard defense from these AI
labs was fair use. The argument was always that an
AI learns exactly like a human learns.
It reads publicly available information and internalizes the
concepts. But the settlement strongly
suggests that the actual cost of doing business moving forward
involves paying out billion dollar class action settlements
to the authors and creators. And the details of this specific
case are crucial. This wasn't just a broad
philosophical debate about whether an AI reading a
purchased book is fair use. It was specifically focused on
the book's three data set. Right, the shadow libraries.
This was a massive data set that was largely composed of directly
pirated books, so the legal battle shifted away from a high
level debate about copyright theory to a very specific,
undeniable accusation. You downloaded this material
from a known pirate site and your engineers knew exactly what
they were doing. A $1.5 billion penalty
absolutely stings, but for a company that just raised $30
billion in cash a few days ago, it is highly affordable.
It is literally just a line item on a spreadsheet for them.
And that is the dark irony of this entire settlement.
A $1.5 billion penalty instantly destroys any new startup.
It completely bankrupts the university research lab trying
to build an open source model. But for Anthropic or Open AI or
Google, it is just the toll they have to pay to get on the
highway. It effectively entrenches the
giants. They're the only entities on
Earth who can actually afford to retroactively pay for the data
they already scraped from the Internet.
It completely clears the competitive field.
So tying all of this together, we have AI models right now that
are capable of genuine world changing brilliance.
They are discovering hidden physics formulas, you're coding
at the speed of thought, and we have autonomous agents that can
finally navigate the messy pixel based reality of our everyday
computers. But to actually get those agents
out of the lab and into the real world, these companies are
making a very specific set of compromises.
They're wiring themselves deep into the corporate structure
through massive consulting firms.
They're softening their founding safety pledges to seamlessly
aligned with military interests. And they are paying billion
dollar fines to retroactively legalize the aggressive data
gathering that made the model smart in the first place.
We spent years intensely debating whether artificial
intelligence would be safe or whether it would be perfectly
aligned with human values. But looking at the reality of
2026, safety isn't a philosophical stance anymore.
It is simply a clause in a massive enterprise Oregon
government contract. And it is being defined entirely
by whoever is signing the biggest check, whether that
happens to be the Pentagon or the venture capitalists.
If you're not subscribed yet, take a second and hit follow on
whatever app you're using. It helps us keep making this.
We appreciate you being here.