Okay, so just imagine this for a second. You're
running a pharma startup, right? And you're using
one of these next -gen AI models, let's say,
I don't know, something way past GPT -5. Right.
And this thing, it's not just, you know, drafting
your emails. It's running millions of simulations.
It hypothesizes a new chemical compound, and
it basically discovers a cure for a rare disease.
Wow. You stand to make billions, but then...
You get this tap on the shoulder. The AI company
doesn't want your $20 a month subscription fee.
They want a percentage. A cut of the cure. Cut
of the cure. They want royalties. Because in
their view. Without them, you're just a scientist
with a hunch. And that's the paradigm shift we're
really waking up to this week. It creates such
a wild precedent, doesn't it? I mean, we are
moving from AI as a tool, like a really, really
smart calculator, to AI as a partner. A stakeholder.
A stakeholder. And if your stakeholders are digital,
the economics of everything are about to get
very weird. Welcome to the Deep Dive. It is Sunday,
January 25th, 2026. Today, we're unpacking a
stack of sources that suggest the rules of the
game are changing and fast. We're not just talking
about better chatbots anymore. Not at all. We
are looking at a fundamental shift in business
models, a huge leap in how AI perceives physical
reality, and honestly, a bit of a crisis when
it comes to trusting what these things even tell
us. We have a lot to get through. We're going
to start with that open AI profit sharing news,
which I think is probably the biggest economic
story in tech so far this year. Yeah, for sure.
Then we'll look at what people are calling the
GPT -2 moment for world models, basically AI
that can build physics aware realities from scratch.
Which is just mind blowing tech. It is. But we
also have to look at the messy side of it. We've
got reports of Google's AI prioritizing YouTube
over, say, the Mayo Clinic for medical advice.
Yikes. And some internal drugs. between deep
mind and open AI that's starting to spill out
into public. And then finally, we'll ground all
of it with some practical tools. Google have
a new breakdown of prompt engineering that's
actually useful, and Apple is, believe it or
not, finally giving serious. So let's start there.
Let's unpack this open AI news. We're seeing
reports surface about a major strategy shift,
specifically coming from some comments by Sarah
Fryer, the COO. What exactly is being proposed
here? So the gist is that the flat fee model,
the whole pay 20 bucks a month and use the bot,
is becoming kind of outdated for high level enterprise
use. Open AI is exploring a licensing model.
And the argument, their argument, is that modern
models, especially the ones they have in the
lab right now, they aren't just summarizing PDFs
anymore. They're acting like an AI researcher
that never sleeps. That phrase, that really stood
out to me in the notes, an AI researcher that
never sleeps. It implies a level of agency that
we haven't really given to software before. Exactly.
I mean, think about what these models are doing
in 2026. They're running millions of hypothesis
tests across decades of research papers. They're
stitching together data from simulations, from
real world experiments, and they're proposing
experiments that actually work. We're seeing
this in biotech, in energy, materials, science.
So the logic is, if the AI is doing the real
heavy lifting of the invention, the creator of
that AI deserves a slice of the pie. Right. If
you hire a world -class scientist and she invents
a new type of battery, she usually gets a bonus.
Maybe some stock options, right? Sure. OpenAI
is basically saying, we provided the digital
scientist. We want in on the downstream success.
It's treating the model as a collaborator, not
just a utility you rent. But it changes the definition
of collaboration, doesn't it? I mean, usually
collaboration implies two humans sharing risk,
sharing effort. Here, one party is a software
vendor. But I suppose if the software is providing
genuine cognitive labor. Cognitive labor. That's
the key word. If a model suggests a financial
strategy that makes a hedge fund an extra $100
million, OpenAI looks at that and says, that
wasn't just a tool like Excel. That was insight.
But the complexities here seem massive. Well,
they are. Who owns the IP? How do you even track
value? That's what I'm wondering. If I use, say,
five different AI tools, one for coding, one
for biology, one for data analysis, and I have
a breakthrough, who gets the check? Do they all
split it? It sounds like a legal nightmare. Oh,
absolutely. It is incredibly messy. But the signal
is what's important here, and it's crystal clear.
OpenAI sees itself as a stakeholder in your breakthroughs.
They are betting their models will be so integral
to the next wave of scientific discovery, and
they don't want to leave that value on the table
for $20 a month. It's a bold move. It speaks
to a level of confidence in their tech that's...
Well, it's almost arrogant, but maybe it's justified
if the results are there. It really is. It's
them saying, we know we're going to help you
win, so we want our cut. So here's the question
then. Does taking a cut of the win incentivize
OpenAI to build drastically better models for
us? Or does it just create this massive barrier
where companies stop using them to protect their
own innovation? It aligns incentives, but might
scare off companies protecting their IP. Okay,
let's pivot from the business side to the tech
itself. While the business guys are figuring
out how to charge for this intelligence, the
engineers are figuring out how to make that intelligence
understand the physical world. Yes. We're hearing
a lot about world models. This is the fun stuff.
We're seeing what some experts are calling the
GPT -2 moment for world models. Okay. So for
listeners who might not remember 2019 all that
clearly, just remind us why GPT -2 is the benchmark
here. Right. So GPT -2 wasn't the model that
changed the world that was really GPT -3 and
4. But GPT -2 was the first time that text generation
felt stable enough to build on. It wasn't perfect,
but it worked. We've just hit that same milestone.
But for generating video in 3D worlds? And we
have two specific players mentioned here driving
this, Odyssey and World Labs. Right. Let's start
with Odyssey 2 Pro. This is a new API that generates
interactive, physics -aware video. And I really
want to stress physics -aware. Okay. You aren't
just generating a video file that plays from
start to finish. You're generating a simulation.
So what happens if I type in, say, a laughing
baby? Okay, so it generates the scene almost
instantly. We're talking 720p at about 22 frames
per second. But here's the kicker. The model
is actively predicting the next frame like a
physics engine. Wow. It understands gravity,
light. how things move. And you can interact
with it in real time. It's not a movie. It's
a little sandbox. That distinction feels crucial.
It's moving from just capturing reality to actively
simulating it. And World Labs is doing something
similar, but in 3D space. Exactly. World Labs
just dropped their marble model. You can upload
a single photo or even just describe a scene.
And in about five minutes, it gives you a fully
navigable 3D world. Five minutes? Five minutes.
It figures out the layout, the lighting, the
depth. It uses this tech called Gaussian Splats
to... render it all gaussian splats it sounds
like a messy painting technique but i know it's
a big deal technically it's surprisingly elegant
the simple version is it's a way of representing
3d scenes as these sort of fuzzy blobs the splats
that all blend together to look solid right and
it's way faster to render than traditional polygons
so you go from a flat 2d picture of a living
room to literally Walking around inside that
living room in five minutes. That just feels
like a massive leap for industries like gaming
or architecture. You don't need to code the wall.
You just dream the wall. Whoa. And imagine scaling
that. Imagine just saying, I want a city that
looks like 1920s Paris, but with neon lights.
and then having a walkable simulation in minutes.
We're moving from generating a picture to stepping
into the simulation. It's a holodeck. It's a
holodeck just on your screen. It's incredible.
But it also blurs the line between creation and
hallucination. If we can generate physics -aware
reality on the fly, what happens to the whole
concept of filming a movie or coding a game?
Traditional production dies. We move from capturing
reality to hallucinating consistent realities.
That idea, hallucinating realities, it brings
us to a more grounding and maybe more concerning
topic. We have to talk about trust because while
these models are busy building worlds, they're
also answering our questions about our health,
about history. And the latest reports are mixed,
to say the least. Mixed is a very polite way
to put it. Yeah. We've got a bit of a crisis
of trust brewing here. There's a new study out
about Google's AI overviews, you know, the summaries
at the top of a search. Right. It turns out.
When it comes to health information, the AI is
citing YouTube videos more often than actual
medical sites. Yeah. More than the Mayo Clinic.
That is deeply unsettling. I mean, YouTube is
fantastic if you need to fix a leaky sink. But
for medical advice, it's a complete minefield
of anecdotes and unverified claims. Right. And
the studies show that even reputable medical
sources were ranking lower than video content.
It looks like the algorithm is prioritizing engagement.
what people click on over actual medical accuracy.
So it's the Internet's popularity contest problem,
but now it's being presented as an authoritative
answer by an AI. Precisely. And this isn't just
a Google problem. We're seeing tension between
the big labs, too. DeepMind's CEO publicly called
out OpenAI recently. Yeah, that was spicy. What
happened there? He basically questioned them,
rushing ads into ChatGPT. His point was, you
know, how can you claim to be a trusted assistant
if you're also trying to sell me something? It's
a conflict of interest. If I ask my assistant
for the best running shoe, I want the best shoe,
not the one that paid for a placement. That makes
sense. And then there's this issue of models
learning from other models. We saw that with
GPT -5 .2, right? Oh, the Grokopedia incident.
Yes. So reportedly, GPT -5 .2 started referencing
Elon Musk's Grokopedia, which is the data source
for his Grok model. Okay. And the problem with
that is? The problem is Grok has a very specific,
let's call it an edgy worldview. So all of a
sudden, GPT -5 .2 starts giving these weird controversial
takes on really sensitive topics like AIDS or
slavery. It's like a contagion effect. One model
hallucinates or carries a bias, and the next
model comes along, scrapes that output, and treats
it as fact. Exactly. It's a huge data contamination
problem. If the internet is just flooding with
AI -generated content, then new models are just
training on old models' outputs. It's like making
a photocopy of a photocopy. The image just...
degrades over time. So if major models are citing
YouTube and each other's hallucinations, are
we just entering a feedback loop of misinformation?
Yes. It's a digital echo chamber degrading the
quality of truth. We're going to take a brief
pause here. All right. We've talked about the
high level economics, the futuristic tech. Let's
try to bring this back down to earth a bit. What
can people listening actually use this week?
We've got some updates from Google, Apple and
a few interesting startups. OK, let's start with
learning. Google has this six -hour prompt engineering
course, which I know sounds exhausting. Yeah,
that's a commitment. But a newsletter author
we follow distilled it down beautifully. And
the core value isn't just a list of magic words.
It's about understanding these five key principles
that help you start to think like the model thinks.
Thinking like the model. That really does seem
to be the critical skill for 2026. It's not about
memorizing commands. It's about understanding
the logic flow. Exactly. It's about context,
constraints, iteration. And speaking of iteration,
I have to make a vulnerable admission here. Go
for it. I still wrestle with prompt drift myself.
You know, you get a great result and then you
change one tiny word and suddenly the AI just
goes completely off the rails. Oh, it happens
to everyone. You think you finally mastered it
and then the model decides that concise means
be rude. Precisely. So these courses are actually
pretty vital. But for people who just want the
tech to work better, Apple is finally catching
up. We're hearing that the Apple intelligence
updates coming in February are pretty significant.
Siri will finally be able to read on -screen
content. That seems like such a basic feature.
It's amazing that's been missing. It's huge for
context. If you're looking at an email and you
say, remind me about this, Siri will finally
know what this is. And there's a bigger, more
chatbot -style update planned for June. We also
saw a big acquisition in the business world.
Yelp bought a startup called Hatch for, what,
$270 million? Yeah, this is a smart move. Hatch
is an AI for service businesses. So think plumbers,
movers, that sort of thing. It helps them auto
-reply to customers. Yelp is basically saying,
we don't just want to list your business. We
want to handle your frontline customer service.
It's automation for the less glamorous but essential
parts of the economy. Totally. And any smaller
tools that caught your eye this week? Two, really
quickly. One is called Pliable. It's an AI -native
analytics platform. It helps you see the forest
for the trees with your data, but without needing
a data science degree. The other one is for creators.
Thumbfy .est. Thumbfast. Yeah. You drop in a
face, describe a vision, and it generates a YouTube
thumbnail instantly. You can grab inspiration
from thumbnails that are already out there and
just iterate until it's perfect. It's pure, simple
utility. It's fascinating. We have A .I. negotiating
million dollar drug discoveries on one end and
A .I. making YouTube thumbnails on the other.
The range is just staggering. That's the ecosystem
right now. Yeah. From the sublime. all the way
to the clickbait. With tools automating everything
from thumbnails to customer service, what is
the remaining role for human intuition? Curating
the output and defining the creative vision.
Let's try to pull all of this together. We've
covered a lot of ground today. We have. And I
think there really is a through line here. We're
seeing the industry mature, maybe. Maybe it's
losing some of its innocence. That's a good way
to put it. Economically, we're moving from simple
subscriptions to this complex idea of value sharing.
Open AI wanting royalties is a sign they believe
their product isn't just software anymore, it's
a co -founder. And technologically, we're graduating
from static images to these physics -aware worlds.
That GPT -2 moment for world models means we
are right on the cusp of generated reality being
standard. But culturally, we're... are really
struggling with this messy middle. The trust
issues at Google, the data contamination with
Grokopedia. It shows that as these models get
more powerful, they also get more dangerous if
we aren't careful about what we feed them. Right.
The models are becoming less like tools and more
like partners. Like any partner, sometimes they're
brilliant -like when they discover a new material,
and sometimes they're just completely unreliable
citing a random YouTuber for medical advice.
It forces us to be sharper, I think. We can't
just passively consume the output anymore, we
have to audit it. Absolutely. The human in the
loop is more important than ever, even if that
loop is getting faster and more automated. If
you want to dive a little deeper on any of this,
I'd really recommend checking out that distillation
of the Google prompt course. It's a low stakes
way to just sharpen your skills. Yeah. And if
you have access, play around with Odyssey or
World Labs. Seeing a static image turn into a
3D world is it's something you really have to
experience to understand. It's a trip. Highly
recommended. I want to leave you with one final
thought today. We talked about OpenAI wanting
a cut of your profits because their AI helped
you think. It's an interesting argument, but
it raises the flip side of that coin. If OpenAI
wants a share of the win because their model
was a partner, does that mean they're also liable
if their AI helps you fail? If the researcher
that never sleeps hallucinates a strategy that
bankrupts your company. Do they share in that
loss? That is the billion dollar question, isn't
it? Thanks for listening. We'll see you in the
deep end next time. Bye for now.