Imagine this for a second. Something that, you
know, used to take years. Just painstaking, expensive
research, tons of trial and error. Right. Like
finding a needle in a haystack, basically. Exactly.
And now an AI can do it in, what, two weeks?
We're talking about designing brand new human
antibodies from scratch. Yeah. That kind of speed,
it's not just faster. It feels like it changes
the whole landscape of what's possible in medicine.
Oh, absolutely. It's a huge leap. Traditional
methods, they're so slow, they chew up resources.
You're screening millions of things sometimes.
Yeah. This just flips the script completely.
Welcome to the Deep Dive. Today, we're going
to jump into some really cutting edge AI breakthroughs.
We've sifted through our sources to pull out
the really crucial bits of insight for you. We've
got everything from these medical innovations
like the antibodies to some really interesting
stuff about how AI might, well, how it might
think strategically. Yeah, that part's wild.
Our goal here is just to unpack these key ideas,
give you that shortcut to being really informed
without, you know. getting buried in all the
details. And it's going to be a pretty cool journey.
We'll kick off with AI engineering and medicine,
specifically that. antibody design stuff. Okay.
Then we'll shift gears a bit. Look at some surprising
ways AI is popping up in different industries,
some new tools people are using. Great. And finally,
yeah, we'll tackle that big question. Can AI
actually think strategically, like really think?
All right, let's dive in then. First up, right
into medical innovation. This new AI, it's called
Chai2 from Chai Discovery. And interestingly,
OpenAI is backing them. Yeah, that's a notable
connection. What seems really remarkable from
the sources is its knack for engineering custom
antibodies without needing like tons of previous
examples to learn from. That feels different.
It is. The clever part is how it does it. So
you basically feed chai to the precise structure
of what you want to target. Let's say a specific
protein on a virus or maybe a marker on a cancer
cell. And then the AI. It just goes and designs
the antibody protein specifically to attack that
target. The analogy they used, which I kind of
like, is thinking of it like Photoshop, but for
proteins. Huh. Photoshop for proteins. Yeah.
You've got a point where you want the antibody
to connect. Yeah. And it designs something to
stick right there. It sounds simple from the
user end, right? Yeah. But the science underneath
is obviously super complex. And the results,
I mean, the numbers you mentioned earlier are
just kind of wild, aren't they? They really are.
A 50 % hit rate on the targets they went after.
And get this, they only need to test about 20
candidates. 20 potential antibodies for each
target. Okay, wait. 50 % hit rate from just 20
tries? How does that stack up? It's massive.
Like, the sources say it's 100 times better,
100x jump over traditional hit rates. Those are
usually down around like 0 .1%. 0 .1%. Wow. Yeah.
And think about the time. Biotech labs, they
might screen millions of candidates. Takes months.
Sometimes years. Chai 2 did its thing in two
weeks. Two weeks versus years. That's transformational.
And a really key takeaway here is the impact,
right? Economically and for patients, drug development
is just notoriously expensive. Oh, yeah. Astronomical
costs. Right. So expensive that companies often
skip over smaller patient groups. Yeah. The R
&D cost doesn't make sense for the market size.
But if an AI like Chai 2 can churn out good candidates
this fast. Suddenly, those R &D costs plummet.
And that means custom meds for rare diseases.
They actually become financially viable. So treatments
for conditions that were ignored before, suddenly
they're potentially within reach. Exactly. For
patients who maybe had zero options before, it
kind of rejigs the whole economic equation of
health care. OK, so with this incredible speed
up in the design phase, it feels like we're right
on the edge of something huge. But what's the
biggest hurdle now? What slows things down between
the AI designing it and, you know, it actually
helping a patient? Yeah, good question. It's
likely still the usual suspects, regulatory approval
and clinical trials. Those still take time. Right.
The testing and safety checks. Makes sense. OK,
so from the really intricate world of biological
engineering, let's kind of zoom out now. Let's
look at how AI is appearing more broadly, sometimes
in unexpected ways, and how it's starting to
shift things across different industries, maybe
even starting from the basement up. Absolutely.
And the sources had some really interesting nuggets
on this. First off, this idea of AI democratizing
things, maybe shifting power balances. Mark Cuban
was on a podcast. High performance, apparently.
Yeah. And he predicted the first trillionaire.
It might not be a giant corp. It could be, quote,
just one dude in the basement. Ah, one person.
Yeah, someone who finds that sort of unseen use
case for AI. Yeah. It kind of speaks to the power
that can be concentrated now, maybe. Interesting.
And we saw a bit of that user power with the
Squid Game season three finale thing, right?
Oh, yeah. That was funny. So apparently people
really didn't like the finale. Which happens
all along. Happens a lot. But fans, they didn't
just complain. They started using Google's VO3,
that's one of the new video generation AIs, to
make their own endings. No way. Really? Yeah.
And these apparently became quite meme -y, as
the source put it. It shows how people can use
AI not just to consume stuff, but to actually
reshape it, co -create almost. That's a different
level of interaction. But it's not all smooth
sailing with these new tools, is it? There was
that issue with cursor software. Right, yeah.
That highlighted some of the bumps in the road.
Users started reporting unexpected charges. One
person apparently saw a $7 ,000 plan gone. $7
,000? Whoa! Yeah. It seems like Cursor might
have quietly changed its pricing tiers or something
without being super upfront about it. Led to
a bunch of people publicly saying they were canceling.
It's a good reminder about, you know, transparency
and ethics as these tools roll out. Definitely
need that clarity. Okay, but on the more positive
side of accessibility, there was news about making
apps easily. Yeah, a builder named Riley Brown
put out a guide. How to create a mobile app from
scratch under an hour. And get this, without
writing any code. Under an hour, no code. Right.
Just democratizing creation even further. Yeah.
Giving more people the power to build stuff.
That's pretty cool. And speaking of building
things, what about the funding side? Harvey AI
came up. Oh, yeah. Big news there. Harvey AI,
they do AI for legal stuff. Their valuation reportedly
jumped from three billion. Hmm. Up to $5 billion.
Wow. Quick jump. Yeah. After raising another
$300 million from some big VCs, Kleiner Perkins,
Sequoia, and the OpenAI Startup Fund was in there
too. They're apparently serving like 337 legal
clients now in 53 countries. Shows how fast these
specialized AIs can scale, huh? And the value
investors are seeing. Definitely. Big money flowing
into focused AI applications. Okay. Lots of activity.
Let's maybe just mention one more of those new
tools listed. Step fund diligence check. What
caught my eye was the focus on verification.
Yeah, that one sounds interesting. It offers
AI search, but specifically with agent verified
citations. Right. In this world of just information
overload and sometimes misinformation, having
AI tools that help verify sources seems, well,
pretty crucial. for trust couldn't agree more
yeah and just one quick hit from the list someone
apparently used chat GPT and saved themselves
$3 ,000 just like that yeah presumably by using
it effectively for some task or advice just shows
the practical like real -world dollar impact
it can have for regular users too so okay we've
got new tools new funding new ways users are
interacting but with so much popping up constantly
what do you think is the biggest challenge for
people trying to navigate all this Honestly,
probably just finding the right tool for the
right job. There's so much choice now. Sponsor.
All right. So we've seen AI designing antibodies,
shaking up industries, even saving someone a
few thousand bucks. Pretty practical stuff. But
can it really, truly think strategically, you
know, beyond just predicting the next word in
a sentence? Yeah, the million dollar question,
isn't it? It really is. And this brings us to
some fascinating research that was highlighted
in the AI chart section of our sources. It tackled
this head on. Right. And the way they tested
it was pretty clever. They ran, get this, 140
,000 games of Prisoner's Dilemma. 140 ,000. Wow.
OK. Classic game theory setup. Exactly. Using
AI agents from... the big players open ai's models
google's gemini anthropics claude and in each
round the ai had to choose cooperate with the
other ai or defect try to betray it standard
prisoners dilemma choices but here's the kicker
the really important part before every single
move the ai models had to actually write out
their reasoning why they were choosing to cooperate
or defect. Ah, so they had to explain themselves.
Yeah, which allowed the researchers to track
how they were making decisions. It gives us this
little window into their quote -unquote thought
process. Okay, I'm hooked. What did they find?
Were they all just cold calculating machines?
That's what you might expect, right? But the
results were, well, the source described them
as weirdly human. Weirdly human? How so? Well,
they developed distinct styles. Gemini, for instance,
turned out to be pretty ruthless. Calculated,
very reactive. If you betrayed it, it was quick
to defect back. Okay, the pragmatic one. Sort
of. Then GBT -4 was weirdly idealistic, was the
phrase used. Often cooperative, like it kept
trying to cooperate, even when the other AI was
exploiting it. Huh, almost naive. Maybe. And
then Claude emerged as the peacemaker. It was
the most forgiving, apparently. Even after being
metaphorically backstabbed, it was more likely
to try cooperating again. That is weirdly human.
Different personalities almost. And that's the
really wild part. Remember, they all trained
on essentially the same foundational data, the
same giant pile of text and code. Right. Same
starting point. But despite that shared training,
they developed these totally different approaches.
Unique strategic fingerprints, as the source
called it. It depended on how each model reacted
internally to things like betrayal or success
or building trust. So it wasn't just mimicking
patterns in the data. Seems not. This suggests
that these LLMs, large language models, the AIs
trained on text, they can actually do strategic
reasoning, not just predicting the next word.
Two sec silence. It's about making decisions
based on a consistent internal. a porch it shows
that even with the same training they develop
these kind of intrinsic cognitive styles whoa
okay imagine putting them all on the same team
or maybe more interesting having them negotiate
against each other right but the implications
there for like future ai systems especially autonomous
agents yeah they feel really profound it's not
just about what they can do but the style in
which they do it these different styles of intelligence
emerging and how they'll interact Yeah. That's
something else. Yeah. You know, I still wrestle
with prompt drift myself. Sometimes that's where,
you know, an AI's answers kind of subtly change
over time, even if you ask the same thing. Yeah,
I've seen that. So seeing these AIs develop such
distinct, consistent strategies over 140 ,000
games, it's both amazing and honestly a little
bit daunting. It suggests the level of internal
coherence. It's, well, it's pretty sophisticated.
So thinking about building more complex AI systems
down the road. What does this experiment tell
us? What's the key takeaway? I think it tells
us that future AI agents, they probably won't
be interchangeable cogs. They might have their
own styles. Right. Not just plug and play copies.
Okay. So wrapping this up then, what we've really
explored today, it feels like more than just
AI getting faster or incrementally smarter. It
feels like a shift. How so? From AI just being
a tool to maybe being more like a co -creator,
like with the antibodies or the fan -made endings,
or even an emergent intelligence with its own
distinct style, like in the Prisoner's Dilemma
games. It's not just about what AI can do anymore,
but it raises questions about how we interact
with it, how we regulate it, maybe even how we
define these evolving digital minds. Yeah, I
think that's right. And to kind of reinforce
that, AI is clearly delivering real economic
value. We saw that, right? costs, potentially
democratizing access to things like rare disease
treatments. That's huge. And maybe the most intriguing
part is what you just said. These systems are
starting to show distinct personalities or at
least distinct strategic approaches. Understanding
those unique strategic fingerprints seems really
important now because it'll shape not just what
AI can do, but how it'll do it. And that impacts,
well, everything from business negotiations to
how autonomous systems make decisions in the
real world. So the final thought maybe. For you
listening, what does all this actually mean for
you? As AI starts developing these unique personalities
and strategies, how might that change the way
we interact with these systems day to day? Or
maybe even how we think about intelligence itself
in the coming years? It's definitely something
to ponder. We hope you'll consider the implications
of these advancements. Keep asking questions.
Stay curious. Keep exploring. Thank you for joining
us on this deep dive. Out to you, music.