So everyone kind of thought AI would replace
writers first. Right. Or maybe coders. That was
the next big thing. But what about forecasters?
Could AI actually predict real world events?
Yeah, that's a wild thought. Are these models
maybe already seeing possibilities that, you
know, we just can't mapping things out? Well.
Welcome to the Deep Dive. Today we're going to
dig into some really fascinating source material
here. Our mission really is to unpack how these
advanced AI models are doing more than just like
understanding language. They're actually making
these nuanced bets on the future, probabilistic
bets. Okay. So we'll look at models in live prediction
markets first, then we'll shift over and talk
about this quiet giant, a new frontier model
that just sort of appeared. Oh, interesting.
And finally, we'll touch on some of the AI tools
that are, you know, already. changing how we
work every day, get ready for some pretty eye
-opening stuff, I think. All right, let's jump
into that first big idea then, AI models stepping
into prediction markets. There's this new platform
from the University of Chicago, and it puts top
AI models right into these live markets. So like
real money, real events. Seems like it, or at
least simulated stakes that mirror real markets.
Think of them as these betting pools for what's
going to happen next. Okay, like what kind of
things are they betting on? All sorts. Who will
win the next election, where crypto prices might
land, even the outcome of an MLS soccer game.
Wow. OK, so how does it work? Do they just say
Team A wins? No, it's more subtle. They make
probabilistic bets. So instead of just X happens,
it's more like, okay, I think there's a 70 %
chance of X happening. Gotcha. So they assign
odds. Exactly. And what's really cool is they
apparently also provide explanations for why
they made that bet. Gives you a peek under the
hood. Okay. That reasoning part is key. So what
are the early results? Anything surprising? Oh,
yeah. Definitely surprising. Take OpenAI's O3
Mini. apparently turned $1 into $9 betting on
an MLS game. Whoa, really? Well, the human market,
you know, the consensus, only gave Toronto FC
an 11 % chance to win. Okay, pretty low odds.
Right, but O3 Mini saw it differently. It put
the odds at 30%. It's a pretty bold bet against
the crowd. And it paid off, obviously. Nine times
the money. Okay. Yeah. And you see differences
between the models, too. Like, Quinn 3 was super
confident about AI regulation passing, like 75
% chance. That's bullish. But then Meta's law
for Maverick was way more cautious on the same
thing, only 35%. Huh. So they disagree quite
a bit. They do. And here's another wrinkle. GPT
-5, often it's the most accurate model overall.
Right. Gets the most answers correct. Exactly.
But O3 Mini is the one making the most profit.
Huh. Okay. So being right and making money aren't
always the same thing, are they? Apparently not.
Then you've got Deep Seek R1. Sometimes it just
went chaos mode is what they call it. Chaos mode.
What's that mean? Betting 0 % on everything.
Yeah. Just flat zeros. Okay, that sounds like
a terrible strategy. You'd think. But somehow,
it still made money by hitting some major upsets
that nobody else saw coming. It's kind of wild.
That is wild. Almost spooky. Any other big calls?
Yeah, one notable one. Llama 4 Maverick was the
only model, apparently. To predict the Zoran
upset. Oh, I remember that the local election
surprise, no human pollster saw that coming.
Right. And interestingly, anthropics clawed models.
They were just absent, not really showing up
on the leaderboard. Hmm. Strange. Wonder why.
Good question. The bigger implication here. Hmm.
What does this all really mean? Yeah. What's
the takeaway? Well, these models are making bets
on things like the 2028 election already. Wow,
okay, looking way ahead. And their predictions
don't line up with current human polling data.
Not even close sometimes. So they're seeing something
different. It really suggests that. Maybe some
models have an understanding, like a world model,
that we humans just aren't quite getting. Two
-sec silence. This could be maybe the cleanest
test we've seen yet of how well AI can reason
about the real world. Right, because it's tied
to actual outcomes, not just language tasks.
Exactly. And if this holds up, well, Wall Street
might need to pay attention. Anyone who relies
on forecasting, really. Yeah, big time. So probing
question then. What's the biggest implication
if these models do consistently outperform human
forecasters? It suggests a profound shift in
how we understand and use predictive insights.
A really profound shift. Okay, so let's switch
gears now. From betting on the future to the
models that actually do the thinking. Frontier
models. Right. So get this. A massive new open
source model just appeared. Like zero hype. Just
quietly uploaded to Hugging Face. No big announcement.
Just there. Pretty much. It's called DeepSeek
V3 .1. And it is a beast. We're talking 685 billion
parameters. Whoa. Okay, for listeners, parameters
are kind of like the learned data points, the
connections inside the AI's brain, right? More
means more capacity. Exactly. And $685 billion
puts it right up there with the biggest models
out there like GPT -5, CLAWD -4. It's a serious
contender. And it's coming out of China. And
it's gunning straight for the top dogs, you're
saying. Open source, though. That's the kicker.
It's apparently faster than CLAWD, way cheaper
to run than GPT, and yeah, totally open source.
Okay, that could be huge. The most capable free
model out there, potentially. It really could
be. Early tests, and these are independent tests,
show it's matching or sometimes even beating
GPT -5 and CLAWD4 on real -world tasks. Got an
example. Yeah. On the ATER coding benchmark,
it scored 71 .6%. Okay. How does that stack up?
That actually slightly beats CLAWD Opus 4. And
here's the crazy part. It's apparently 68 times
cheaper to run. 68 times. Yeah. Wow. Okay, that
is a big deal for developers, for anyone trying
to build things like BI. Huge deal. And what's
interesting is how it does it. You know how some
open models feel kind of nerfed or just bloated
and slow? Yeah, sometimes they're not quite ready
for prime time. Right. But v3 .1 seems to be
both really high performance and efficient. Fast
enough for real -time stuff. Yeah, no twisters.
Any technical tricks? Well, it supports different
precision formats like BF16 and FP8. Basically,
Waze the AI handles numbers. Lower precision
can mean faster speed and less memory, sometimes
with a tiny hit to accuracy, but often worth
it. Okay, so more flexibility for developers
to run it on different kinds of hardware. Makes
sense. Exactly. Plus, it has some cool new features
built in, things called thinking tokens and search
tokens. Thinking tokens. Search tokens. What
do those do? So thinking tokens, they kind of
let the model do more internal work, like reasoning
through a problem before giving the answer, sort
of like showing its work internally. Oh, okay.
Like chain of thought prompting, but maybe built
in. Kind of like that, yeah. And search tokens
let it pull in live information from the web
as part of its process. So it can reason and
access up -to -the -minute data? That seems to
be the idea. Pretty powerful combo. And the community
noticed DeepSeek had quietly updated everything,
removing their older R1 model. Ah, streamlining
things. Yep. And this V3 .1 just shot to the
top of the trending models on Hucking Face almost
instantly after it was uploaded. Whoa. Just imagine
the potential there. Models that can really integrate
that deep internal reasoning with live external
data, all at that kind of scale. Like stacking
Lego blocks of data and thought together in a
whole new way. Yeah, the possibilities are kind
of staggering. So here's the probing question.
What does this quiet release, this powerful open
model just appearing, what does that mean for
AI accessibility? For everyone. It signals a
powerful, cheaper and definitely more open future
for advanced AI, I think. Democratizing it a
bit more. Right. OK, let's shift one more time
from these huge frontier models back to. More
immediate stuff. Practical tools and developments
we're seeing right now. Yeah, the day -to -day
impact. Exactly. Because beyond the absolute
cutting edge, AI is weaving itself into the tools
we use all the time. Like Grammarly just launched
eight new AI agents. Indeed of them. What do
they do? Think of them like specific writing
helpers. They help with grading, checking for
plagiarism, even spotting AI -generated text,
helping you brainstorm. across the whole writing
process. I like specialized assistants. Yeah.
And I got to admit, you know, I still wrestle
with prompt drift myself sometimes getting the
AI to stay on track. Oh yeah, me too. It can
wander off. So having these kinds of smarter
assistants built right into where you're already
working, that's super helpful, actually becoming
essential, I'd say. Totally agree. And speaking
of integration, we're also seeing new ways to
like measure AI's impact. How so? Well, RFs,
the SEO tool company, they just launched a dashboard
that tracks web traffic coming from ChatGPT and
Google's AI overviews. Oh, interesting. So you
can see how much traffic AI search is sending
you. Exactly. Across like 44 ,000 sites already.
So you can finally track AI search as a real
channel, measure its ROI. That's going to change
SEO for sure. Yeah, definitely. You need to know
where your visitors are coming from. And another
big one, Microsoft Copilot AI. It's now directly
in Excel. Wait, in Excel itself? How does that
work? There's literally a new function like sum
or average, but it's Copilot. You can type plain
English in it like Copilot, summarize sales data
in column C, or Copilot, find the Q3 revenue
for product X from our sales report. Wow. So
you can pull data, analyze it just by typing
a formula in natural language. Yeah. Pretty slick,
right? Especially for people who live in spreadsheets
all day. That is slick. Okay, and any other quick
hits from the AI world that caught your eye?
Yeah, a few rapid -fire ones. Adobe's building
out its AI features, like a new AI home with
PDF tools and creation stuff. Integrating it
deeper into creative workflows. Meta is apparently
rebuilding its whole AI division midstream to
try and catch up with OpenAI, a big internal
push there. The race is definitely on. OpenAI
says it's now very serious about adding proper
encryption to chat GPT. Big deal for privacy
if that happens. Yeah, that would be significant.
ByteDance they own. TikTok has a new model called
M3 Agent that can process video and audio together
in real time. Think smarter content moderation
or analysis. Okay, multimedia AI. And NVIDIA
released a new small open model, but it has a
reasoning toggle. A reasoning toggle. Like you
can turn its thinking process on or off. Kind
of seems like that, yeah. Maybe control how much
effort it puts into reasoning versus just giving
a quick answer. Cool concept. Lots happening.
So zooming out slightly, how do all these smaller,
more specific AI tools, these integrations, how
do they change our actual daily workflows? They
make complex tasks simpler, way more efficient,
and honestly, often more creative, too. Yeah,
taking the friction out of things, letting you
focus on the bigger picture. Exactly. They handle
the drunk work. Okay, so let's try and wrap this
all together. What's the big picture here for
you, the listener? Yeah, what does it all mean?
Well, we've seen AI models jumping into real
-world prediction markets, making these surprising,
sometimes really profitable bets. It suggests
they might be seeing things we don't. Then we
saw that monster open -source model, DeepSeek
v3 .1. Just appear quietly, challenging the big
guys, offering huge power much more affordably,
democratizing things. Right. And we've seen AI
weaving itself into everyday tools. Ramily, Excel,
Adobe. Making us more efficient, maybe more creative.
So it's clear AI isn't some far -off thing anymore.
It's here. It's here now, actively changing how
we learn, how we work, and maybe even how we
see the future itself. And that leads to kind
of a provocative thought, maybe. The line between
our own intuition, human gut feelings, and what
AI comes up with, it's getting really blurry.
Yeah. Where does one end and the other begin?
So consider this. If AI models can consistently
know something we don't about what's coming next,
if they consistently beat human predictions in
some areas, what does that really imply about
the limits of our own knowledge? And maybe even
more deeply, what does it mean for what we choose
to believe? Why we believe it? That is a lot
to chew on. Definitely something to think about.
For sure. Well, thank you for diving deep with
us today. We really hope you found some truly
important nuggets of knowledge and all that.
Out to your own music.