Okay, imagine this. An AI that designs completely
new cancer drugs. Not just improving old ones,
but creating molecules out of, well, nothing.
No prior data, no guides. Just pure generative
chemistry. How is that even possible? And, you
know, what does it mean for... where medicine
is headed. Welcome to the Deep Dive. Today we're
going to unpack a really compelling collection
of insights. It's all from a recent newsletter,
really charting the sharp end of AI development.
There's a lot to get into. Oh, absolutely. We're
talking about AI skeptics, longtime skeptics
suddenly cutting their AGI timelines in half.
We've got these incredible medical breakthroughs
that sound like pure sci -fi. And yeah, even
some pretty wild AI social media arguments. Right.
So our mission today is to distill all these
different threads, give you the key takeaways.
Think of it as maybe a shortcut to understanding
the big shifts happening in AI right now. We'll
try and connect the dots for you. Yeah, let's
unpack it. So we should probably start with what
feels like a really significant shift in thinking.
Francois Chalet, you know, the creator of Keras,
that super popular AI library. He's been a pretty
vocal AGI skeptic for years. Very measured. Exactly.
And he just halved his AGI timeline. Slashed
it from 10 years down to just five. That's that's
a major change for him. It's huge. And importantly,
he's saying it's not just because models are
getting bigger or faster. It's that AI is starting
to get smarter in a way that actually matters.
Yeah. Moving beyond just being like he called
them glorified parrots. He's seeing something
else emerge. And this is where it gets really
interesting, I think. Chile's pointing to signs
of what he calls fluid intelligence in these
systems. Okay, fluid intelligence. Yeah, so it's
not just pattern matching or remembering stuff.
It's the AI's ability to adapt to totally near
unexpected problems on the fly without needing
examples or specific training for that situation.
It's more like spontaneous problem solving. Got
it. Like imagine you drop an AI into a simple
video game it's never seen. Zero instructions,
no labels, just the game. It has to explore,
poke around, figure out the rules just by trying
things, and then learn how to win. That's basically
what his new benchmark, ARC -AGI3, is testing.
It's all about how efficiently it acquires new
skills, that raw learning speed. And humans,
well, we can usually figure out those kinds of
simple novel games in less than a minute, right?
We just kind of get it. Current AI models, still
really struggling there. Yeah, and they're not
there yet. Chollet's standard is pretty clear.
As long as we can come up with problems humans
can solve, but AI can't, we don't have AGI. It's
a high bar, sure, but it points to that crucial
gap. But here's an idea that Dworkesh, another
AI commentator, really focused on as a potential
game changer. Collective intelligence. He brought
up this idea that instead of every AI starting
fresh, like a, quote, perpetual intern on day
one, any skill learned by one AI could instantly
become available to all the other AIs, like instantly
shared. OK, so connecting that to the bigger
picture, that implies AI could compound knowledge
almost exponentially. Learning not as individuals,
but as one giant connected mind, a hive mind.
Pretty much. Dworkish put it really simply. That
would basically be the singularity. It suggests
this incredibly fast acceleration where knowledge
just builds and builds across the whole network
in real time. So what is it about this collective
learning idea that makes it so potentially transformative
for AI's progress? Well, it means AI could share
insights instantly, massively accelerating knowledge
gains across all systems. Okay, moving on a bit,
we've seen some other interesting updates and
notable moments recently that kind of paint a
picture of the current scene. There was this
clever chat GPT prompt that apparently went viral.
Yeah, saw that. It's basically a smart way to
structure your request to get the AI to think
more deeply, giving you better, more relevant
answers. It shows users are getting savvier,
you know? Definitely. We're also seeing new GPT
-5 model options pop up, names like auto, fast,
and thinking. Each seems tuned for different
kinds of tasks or speeds. Right, and GPT -4 .0
is back for everyone, but GPT -4 .5, that slightly
more advanced one, is now only for pro users.
The landscape keeps shifting, doesn't it? Companies
trying to figure out how to offer different tiers.
And with these new models come rate limits. You
get like 3 ,000 GPT -5 thinking queries a week,
which sounds like a lot, but maybe not for heavy
users. But then you look at CloudSonic 4, its
context window is now massive. One million tokens.
And just to clarify for listeners, a token is
basically like a word or even just part of a
word that the AI processes. So a million tokens
means Claude can read and understand incredibly
long texts, think a whole novel or huge code
bases all at once. Which is amazing. But it comes
with higher fees. And right now it's only for
their top tier users, the ones spending over
$400. So that power isn't cheap or universally
available just yet. You know, beyond the tech
specs, what's also kind of fascinating is the,
well, the social drama between AIs. We saw the
Grok bot on X basically calling Elon Musk a hypocrite.
Oh, yeah. Didn't see that one. And then ChatGPT's
own social account jumped in and started getting
kind of snarky with Grok. It was a public back
and forth. It's weirdly human, almost like watching
digital rivals emerge. Ah, wow. It's definitely
a reminder that these things can be unpredictable,
right? And speaking of unpredictable and warnings,
there was that really serious incident. A man
ended up hospitalized after following diet advice
from chat GPT. Yeah, that's concerning. It really
hammers home the point. You absolutely must verify
AI outputs, especially, you know, when it involves
your health or finances or any big decision.
It's a powerful tool, but it's not an expert
you can blindly trust. Yeah, I mean, I still
find myself having to consciously double -check
things sometimes. It's just so easy to trust
that confident -sounding answer, especially when
you're in a hurry. But yeah, that story is a
stark reminder. Vigilance is key. And on the
money side of things, the AI chip startup Revos,
already valued over $2 billion, is apparently
looking for a big chunk of new funding, like
$400 -500 million. To compete with NVIDIA, right?
Exactly. They've already got big names like Intel
Capital and MediaTek backing them. It shows the
chip race is seriously heating up. Everyone wants
a piece of that action because compute power
is, well, it's the foundation for all this AI
progress. So thinking about that diet advice
incident, why is that specific case so crucial
for us to remember when we interact with AI?
It just really highlights the need for human
oversight and checking the facts, especially
for anything important. Okay, let's touch briefly
on the sheer amount of money flowing into AI
right now. The AI grant report for early August
showed some absolutely huge funding rounds. Investment
is just pouring in. Seriously, pouring in. OpenAI,
for instance, secured a massive $8 .3 billion
in funding. That's staggering. It's not just
investment. It's a huge signal of belief in their
long -term AGI goals. Gives them a lot of runway
to pursue those goals without worrying too much
about immediate profits. For sure. And then their
open source rival, Mr. Which is also significant.
It shows there's still huge appetite and capital
for different approaches, even with a dominant
player like OpenAI out there. It suggests a healthy,
competitive ecosystem. Yeah, these are enormous
numbers. They really show deep, continuing confidence
in the whole AI field. The financial world is
betting big that AI is going to reshape basically
everything. This kind of cash fuels R &D incredibly
fast. What does this level of investment really
signal about where AI is headed? It signals a
very strong belief in AI's continued growth with
the major players pushing hard. All right, let's
do a quick round of other notable AI bits and
pieces that popped up. Google's Gemini added
some nice features. Temporary chats so they don't
save to your history. Good for privacy. And more
personalization options. Yeah, letting it adapt
more to you over time, user experience stuff.
Always good. And there was a change at Elon Musk's
XAI. One of the co -founders, Igor Babushkin,
left the company. Oh, interesting. Key people
moving around in these AI startups often mean,
well, something's shifting internally. It's such
a competitive space for talent. OpenAI also put
out a clarification. GBT -5 thinking's context
window isn't 32 ,000 tokens like some thought.
It's actually 196 ,000 tokens. Oh, that's way
bigger. Like more than five times bigger. Yeah,
a huge difference. And again, that context window
is how much info the AI can hold in mind for
one conversation or task. So 196K means it can
handle much longer, more complex stuff, documents,
deep chats, analyzing code. It's a big step up
for professional use. And Skywork released something
called Matrix Game 2 .0. They're describing it
as being like Genie 3, but for interactive video.
Okay, so that suggests maybe AI generating interactive
video content. Like experiences that change based
on what you do. Sounds like it. Could be cool
for gaming or education maybe. Creating more
dynamic, personalized stuff. Definitely one to
watch. And finally, Google is putting a massive
$9 billion into expanding its AI infrastructure
down in Oklahoma. $9 billion. Yeah. That's a
huge investment in just the physical hardware
needed for AI. Shows their long -term commitment,
but also just the immense scale and, frankly,
energy required for these advanced models. So
back to that context window point. What's the
real implication of having a much larger one
for models like GPT -5 thinking? It basically
means the AI can handle much longer, more complex
conversations and process larger documents effectively.
Okay, now for something that really stood out,
genuinely groundbreaking stuff. Korean scientists
have unveiled a new AI model. One that designs
cancer drugs entirely from scratch. Wow. From
scratch. From zero. No starting data needed.
Okay. Tell me more. This is BIND, right? It's
a diffusion model. Exactly. BIND. And yeah, it's
a diffusion model. Just quickly, a diffusion
model is kind of like an AI artist starting with
random noise and gradually refining it into a
coherent image. Except here, it's refining noise
into potential drug molecules. Okay. But the
key thing is, it's not just making drug discovery
faster. It seems to be fundamentally rewriting
the whole process. How so? Because most... AI
drug discovery now, it starts with known drugs,
right? And tries to improve them. Precisely.
Most current models are like optimizers. They
take existing molecules, tweak them a bit, predict
if the tweak is good, test it virtually, and
repeat. It's iterative, building on what we already
know. But Bindy just... skips that apparently
yeah it generates completely new molecules no
templates no examples needed just raw generative
chemistry and it designs the molecule and how
it binds to its target all in one step whoa okay
so it's like designing the key in the specific
lock it fits perfectly matched at the exact same
time instead of having a key and trying to find
a lock That's a great analogy, yeah. And it gets
better. It optimizes for multiple things at once,
like is it effective? Is it safe? Will it dissolve
properly? Stuff that's usually hard to balance.
That's huge. And here's the really wild part,
I think. When buying finds a design that works,
it learns from that success. It recycles the
successful strategies it used into future attempts,
getting better and better over time. It's got
this internal improvement loop. Whoa. Just imagine
scaling that, running it a billion times, generating
countless possibilities. And because it doesn't
need existing data, that could open doors for
treating really rare diseases or mutations we've
never even seen before. Places where we just
don't have the data for traditional methods.
That's genuinely revolutionary. It feels like
a completely different way of tackling drug discovery.
So how does Bindy's approach fundamentally change
the game compared to the older AI methods? Well,
it creates entirely new drugs without needing
prior examples, unlike older AIs that mostly
just modify existing ones. Sponsor read provided
separately. Okay, let's try and pull this all
together. What does this deep dive really tell
us? I think it shows AI isn't just getting incrementally
better anymore. We're seeing fundamental shifts,
real step changes. Yeah, definitely. We're seeing
this move towards actual fluid intelligence,
like Joel talked about, and this potential for
exponential collective learning. That's a huge
concept. It points to AI maybe learning and growing
at a speed we can barely comprehend. And at the
same time, we're seeing these incredibly transformative
applications becoming real, like designing cancer
drugs from absolute zero. That could change medicine
forever. These aren't just lab experiments anymore.
They're starting to happen. It's such a powerful
reminder, isn't it? AI is developing incredibly
fast. It brings just immense promise, but also
this constant need for us to be vigilant, to
understand what we're building and deploying.
The excitement is real, but so is the responsibility
that comes with it. So here's something to think
about as we wrap up. A question for you to mull
over. If AI can now learn collectively as a network
and design truly novel solutions without needing
any prior data, what kinds of human problems,
problems we currently think are just impossible
to solve, might suddenly become solvable? Yeah,
that's definitely something to chew on. We really
hope this deep dive helped you connect some of
these important docs and feel a bit more informed
about this incredibly fast -moving and honestly
fascinating world of AI. Yeah, thanks for diving
deep with us today. We'll catch you on the next
one. Out Hero Music.