The Limitless Podcast

Ejaaz:
All right josh the ai nerds are

Ejaaz:
fighting again this past weekend there was

Ejaaz:
a very prestigious competition called the international math olympiad which

Ejaaz:
hosts some of the brightest smartest mathematicians of our time and they're

Ejaaz:
typically high schoolers and basically they come together and they take a really

Ejaaz:
hard math test this is like four to five hours and those that score the highest, get medals.

Ejaaz:
You can get bronze, silver, and the highest scorers get gold medals.

Ejaaz:
So what's this going to do with AI?

Ejaaz:
Well, recently, over the last couple of years, the organizers of this International

Ejaaz:
Math Olympiad decided to start inviting AI models to participate as contestants.

Ejaaz:
And they did terribly. Like, no one's come even near the human geniuses.

Ejaaz:
Except this year, Josh, where they came to play and not one,

Ejaaz:
but two AI models achieved not silver, but gold medals, which is just an insane thing, right?

Ejaaz:
So it should be all fun and games, right? What a fairytale story.

Ejaaz:
Well, unfortunately, OpenAI and Google got into an online spat where they started

Ejaaz:
accusing each other of cheating.

Ejaaz:
Now, remember, these are trillion dollar companies. So essentially,

Ejaaz:
Josh, I was teleported this weekend back to my high school days where I felt

Ejaaz:
like the teacher had to come in, separate the kids from arguing over some kind

Ejaaz:
of random homework problem and get them to chill out.

Josh:
We will look back at this episode and laugh at it like it's a joke because these

Josh:
AIs, they're competing against high schoolers. That's so lame.

Josh:
Only high schoolers? Like, come on, and you're just barely getting gold.

Ejaaz:
Well, in their defense, Josh, these are some pretty smart high schoolers,

Ejaaz:
man. Like I was looking at some of these math problems.

Ejaaz:
I don't know if you can see my screen here. I'm sharing the official site.

Ejaaz:
And if you look at some of these problems, here we go.

Ejaaz:
And then like, okay, so they have basically, they host this competition in a

Ejaaz:
different country each year.

Ejaaz:
And you can kind of like download the test yourselves after the fact to see

Ejaaz:
how well you could do it. I had a look at this one, Josh from the Afrikaans.

Ejaaz:
I basically don't understand anything. One second. All right,

Ejaaz:
take a look at that. Take a look at this.

Josh:
That looks like quite a bit of squiggly lines on a page.

Ejaaz:
You know what? That could be mistaken for a piece of art in a gallery if you

Ejaaz:
didn't peer too closely at it. This looks insane.

Josh:
Okay, so I take it back. So the high schoolers are probably pretty smart then.

Josh:
And I guess the AI performing as well as the high schoolers is probably a pretty big deal, right?

Josh:
Because that looks like very complicated math problems that I'm assuming most

Josh:
of the smartest people in the world cannot solve.

Ejaaz:
Exactly. Yeah. This is like something that is technically set for high schoolers

Ejaaz:
and sometimes college kids, but is meant to demonstrate prowess in the field.

Ejaaz:
So there's a lot of university academics, which obviously do math degrees and

Ejaaz:
they do PhDs, but those are in very specific problems. So you kind of like in

Ejaaz:
science, you just need to kind of pick and choose your lane and then dedicate your life to it.

Ejaaz:
High schoolers is kind of college kids are kind of like the last point before

Ejaaz:
you jump into your specialization.

Ejaaz:
So really, if you're the best at generalized maths, you're going to compete in this competition.

Ejaaz:
And what's so interesting is typically AI models haven't been able to perform

Ejaaz:
very well because they needed a lot of context beforehand about the problem, Josh.

Ejaaz:
So they needed to know that, you know, there was certain, you know,

Ejaaz:
X equals something and Y equals something.

Ejaaz:
And they had to have defined parameters to kind of figure out the problem.

Ejaaz:
But this was the first time that AI models basically were just given a blank

Ejaaz:
sheet of paper or not a blank sheet of paper.

Ejaaz:
But they stared at the problem just as we just looked at it just now and had

Ejaaz:
to read the words, read the characters, interpret what that meant in the context

Ejaaz:
of that situation and the way that the question was framed and then figure it out themselves.

Ejaaz:
So it's as if the AI models had a camera that looked at a paper,

Ejaaz:
similar way that we look at test papers as kids through our eyes and figure it out themselves.

Josh:
So what changed? What happened in the last year that made it so much better?

Josh:
Because it went from, what, basically zero of six to now six or five of six questions answered.

Josh:
Now it's a gold medalist. So what happened?

Ejaaz:
So listen, I'm not going to try and explain it, but maybe you and I can decipher

Ejaaz:
it through the legends themselves that built these models, right?

Ejaaz:
Okay, so let me paint the scene for you, Josh.

Ejaaz:
It is Saturday evening.

Ejaaz:
You know, normal people are usually out and about. They're having fun.

Ejaaz:
They're probably having dinner, catching up with friends or chilling at home, watching a movie.

Ejaaz:
And this guy called Alexander Wei, who is OpenAI's head of reasoning.

Ejaaz:
Reasoning is basically this new fancy technique that AI models have typically

Ejaaz:
demonstrated, which has brought them up to like the frontier level of AI models.

Ejaaz:
Basically, if your model can do reasoning, it's typically a pretty smart model, right?

Ejaaz:
And he posts this tweet saying, I'm excited to share that our latest OpenAI

Ejaaz:
Experimental Reasoning LLM has achieved a longstanding grand challenge in AI,

Ejaaz:
a gold medal level performance on the world's most prestigious math competition,

Ejaaz:
the International Math Olympiad.

Ejaaz:
And he goes on to describe, you know, how the model basically took on each problem

Ejaaz:
in its own regard and solved it and how this is a massive success and win for

Ejaaz:
AI models and how, most importantly.

Ejaaz:
OpenAI was the first ever model to complete this.

Ejaaz:
And not too long after he posts that tweet, Josh, Sam Altman jumps in here, right?

Ejaaz:
And he goes, again, he kind of echoes similar thoughts. We achieved gold medal

Ejaaz:
level performance on the 2025 IMO competition with general purpose reasoning.

Ejaaz:
And then he kind of like shells GPT-5 at the end. Basically,

Ejaaz:
it's like a promotive thing for OpenAI.

Ejaaz:
And I will say that this is really cool because what they've achieved is something

Ejaaz:
that hasn't been done before, right? So very impressive feat.

Ejaaz:
And in terms of how this works specifically, Cheryl Su here gives a really good breakdown.

Ejaaz:
She says, the model solves these problems without tools like coding or Lean,

Ejaaz:
which is another coding tool.

Ejaaz:
It just uses natural language. So as I said earlier, It kind of reads the paper

Ejaaz:
and just kind of interprets what it thinks it means.

Ejaaz:
And it also has the same amount of time to do the test as other kits, so 4.5 hours.

Ejaaz:
And she says, we see the model reason at a very high level, trying out different

Ejaaz:
strategies, making observations from examples, and testing different hypotheses out.

Ejaaz:
And she says, it's crazy how we've gone from 12% on the AIME test,

Ejaaz:
which is what GPT-4O, which is OpenAI's early model, got to IMO gold,

Ejaaz:
International Math Olympiad gold medal in 15 months.

Ejaaz:
So just to set that in context, Josh, that is a crazy leap in 15 months.

Ejaaz:
Imagine going from eighth grade level math to the best.

Ejaaz:
Mathematician in the world in 15 months. It's a pretty insane thing.

Ejaaz:
Yeah, I'd say so. So essentially the breakthrough that Cheryl is highlighting

Ejaaz:
here is number one, the model didn't need any context.

Ejaaz:
Number two, it used really high level reasoning to figure out the problems from first principles.

Ejaaz:
And number three, it was able to test out multiple hypotheses at the same time

Ejaaz:
instead of trying to one shot the problem.

Ejaaz:
Typically in the past when AI models have been given a prompt or a problem,

Ejaaz:
it tries to just like give it its best shot and give you one solution, Josh.

Ejaaz:
Whereas what these models, these reasoning models do really well is they are

Ejaaz:
able to hypothetically entertain many different scenarios and then pick the

Ejaaz:
best one of which it thought it was an answer.

Ejaaz:
And it ended up with the gold medal, which is insane, right?

Ejaaz:
But it wasn't entirely without a few glitches here and there, Josh.

Ejaaz:
So if you look at this post from Jasper, he read through the entire kind of

Ejaaz:
like problem set that OpenAI's model went through. and he points out that some weird anomalies.

Ejaaz:
So he kind of like talks about like how it kind of like analyzed and a bunch of things.

Ejaaz:
And he goes, however, the write-up is kind of messy. He goes,

Ejaaz:
it overuses shorthand and sentence fragments.

Ejaaz:
It introduces new terms without definitions, for example, forbidden and sunny partners.

Ejaaz:
I have no idea what either of those terms could mean, but it was just apparently

Ejaaz:
just interspersing these phrases during its analysis.

Ejaaz:
And so as a reviewer, or as an examiner, they were reading this,

Ejaaz:
they were like, sorry, wait, what is it talking about?

Ejaaz:
It got to the right answer, but what is it talking about, right?

Ejaaz:
The other key point from this post is it was unable to solve one problem, problem six.

Ejaaz:
And I'm not even gonna try and get into why it failed on that problem,

Ejaaz:
but it was just particularly hard for it to figure out.

Ejaaz:
But it still scored a high enough percentage that it got a gold medal.

Ejaaz:
So it's basically a win for OpenAI, but that's when the drama starts unfolding.

Ejaaz:
So I've got this post up from Mikhail Samin, which kind of like sparks this entire fight, Josh.

Ejaaz:
He goes, according to a friend, the IMO, which is the International Math Olympiad.

Ejaaz:
Asked AI companies not to steal the spotlight from kids and to wait a week after

Ejaaz:
the closing ceremony to announce the results.

Ejaaz:
OpenAI instead announced the results before the closing ceremony. Yeah.

Ejaaz:
And then he goes on to basically say how this is essentially like some kind

Ejaaz:
of clout chasing move from OpenAI.

Ejaaz:
And OK, I tried to evaluate this, Josh, from OpenAI's kind of perspective,

Ejaaz:
which is they basically want to steal the limelight,

Ejaaz:
but also say that they were the first AI model to ever achieve gold on this

Ejaaz:
competition, which puts them in a good light and makes users want to choose

Ejaaz:
OpenAI and solidify the branding that OpenAI is the best. right?

Ejaaz:
But on the other side, you know, they're kind of like stealing the spotlight

Ejaaz:
from the kids, as this post says. But that's not actually the main trope.

Ejaaz:
The main trope here, Josh, is OpenAI wasn't the only model to achieve a goal, right?

Ejaaz:
At the same time, during the same testing period, you had Google achieving the exact same score.

Ejaaz:
So then the question becomes, okay, well, it was whoever was ethical about announcing their own result.

Ejaaz:
This post from Demis Hassabis, which is Google's head of AI,

Ejaaz:
basically posts, and I'll note two days later, Official results are in.

Ejaaz:
Gemini, which is their flagship model, achieved gold medal level in the International Math Olympiad.

Ejaaz:
An advanced version was able to solve five out of six problems.

Ejaaz:
So same as OpenAI, same thing, struggled on the sixth problem.

Ejaaz:
Incredible progress. Huge congrats to the team.

Ejaaz:
And a tweet here says that Google

Ejaaz:
basically had to wait for marketing to approve the tweet until Monday.

Ejaaz:
But OpenAI shared theirs first at 1 a.m.

Ejaaz:
On Saturday and stole the spotlight.

Ejaaz:
And we see the screenshot from Demis Hassabis, which, you know,

Ejaaz:
he further clarifies this, basically saying,

Ejaaz:
by the way, as an aside, we didn't announce on Friday because we respected the

Ejaaz:
IMO's board's original request that all AI labs share the results only after

Ejaaz:
the official results have been verified.

Ejaaz:
Now that we've been given permission to share, blah, blah, blah,

Ejaaz:
he shares. So Demis is playing the like good Samaritan here.

Ejaaz:
He's like, ah, you know, we also have the good model, but we,

Ejaaz:
you know, we have some pride and some manners about how we deal with these things.

Ejaaz:
That's where it starts to get a little uglier, Josh, because we have OpenAI

Ejaaz:
chiming in to this tweet, which basically says, and this is some random commenting

Ejaaz:
on OpenAI and this entire situation.

Ejaaz:
So OpenAI basically has zero advantages except the size of the team,

Ejaaz:
aka the OpenAI team was claimed to be smaller than Google Gemini's team.

Ejaaz:
So what he's inferring here is there's no real difference between OpenAI's models

Ejaaz:
and Google Gemini's models. You can pretty much use either or.

Ejaaz:
OpenAI maybe has a smaller team to build that model, but who the hell cares?

Ejaaz:
And then one of the AI model researchers at OpenAI basically comes in and says,

Ejaaz:
well, I think it's also interesting that they they

Ejaaz:
being google curated and provided useful context

Ejaaz:
to the model which we did not feels like

Ejaaz:
taking your tutor's cheat sheet with you into the exam so shots basically being

Ejaaz:
fired from open ai saying hey um you cheated you gave context to your model

Ejaaz:
and that was why it was able to achieve gold we open ai didn't provide any of

Ejaaz:
that context and it was able to reason from first principles, there you have it.

Ejaaz:
But then directly beneath it, Vinay Rameshes, who is a Google DeepMind AI researcher, responds,

Ejaaz:
it's worth noting actually that a deep think system, which is Google's AI system

Ejaaz:
with no access to this corpus, so no context, also got gold.

Ejaaz:
Again, according to the official graders, and he puts this in brackets because

Ejaaz:
OpenAI didn't wait for the official graders to mark their score,

Ejaaz:
with exactly the same score.

Ejaaz:
So basically, this is like a pissing contest between two of the top AI model providers.

Ejaaz:
Here's my take, Josh. And then I really want to kind of lean into what you think

Ejaaz:
about this whole debacle.

Ejaaz:
Number one, this seems so childish to me.

Ejaaz:
Like, eventually, AI models were eventually going to get smarter or smart enough

Ejaaz:
to solve these mathematical problems.

Ejaaz:
And I think you said this earlier on.

Ejaaz:
This is something that they're going to probably laugh about 10 years from now,

Ejaaz:
right? that they were able to solve whatever, the most complex mathematic problems

Ejaaz:
for humans, mere humans.

Ejaaz:
And now AI is off creating wonderful scientific discoveries for us that we would

Ejaaz:
have never comprehended or figured out ourselves, right?

Ejaaz:
So firstly, you're arguing over something that's so silly.

Ejaaz:
But number two, this kind of seems desperate on the open AI side.

Ejaaz:
And maybe I'm being biased, but I'm just going to give you my take.

Ejaaz:
Open AI has kind of had a series of stumbles recently.

Ejaaz:
They claimed that they were going to release gpt5 which

Ejaaz:
is their brand new frontier model but they've delayed it many months

Ejaaz:
now um they got outperformed by

Ejaaz:
grok 4 from xai uh so now

Ejaaz:
they have a new benchmark that they need to beat a new model that they basically

Ejaaz:
need to outcompete uh they claimed that they were going to release a new open

Ejaaz:
source model and then delayed it after a chinese open source model was released

Ejaaz:
and had one trillion parameters and outperformed not just their model,

Ejaaz:
but any other open source model out there.

Ejaaz:
And so I feel like they're looking

Ejaaz:
for a win, right? They released their agent this week or last week.

Ejaaz:
And so, you know, that had mixed review, mixed feedback.

Ejaaz:
So I feel like Sam is desperate for a win.

Ejaaz:
People are criticizing consistently their moat, asking what has OpenAI got?

Ejaaz:
They've lost a ton of researchers to Meta and other companies.

Ejaaz:
I feel like their back's against the wall.

Ejaaz:
Sam's scared and he basically needs to grab any kind of win.

Ejaaz:
So it reeks of desperation.

Ejaaz:
What's your take, Josh?

Josh:
I do empathize with the team. They've been coming under fire from every single angle.

Josh:
I mean, you have Zuck poaching all of their talent, and then all of the other

Josh:
open-source AI models are beating them at their own game.

Josh:
And they're just kind of, they're really getting beat up now.

Josh:
And I think that they're looking to get some footing. I'm sure this probably plays a role in it.

Josh:
But I'm sure behind the scenes, they're really trying to fight hard to put their

Josh:
feet back on stable ground, to get GPT-5 out the door, to build Project Stargate

Josh:
and make this big infrastructure network.

Josh:
They need some wins. So sure, this was probably an attempt to get ahead,

Josh:
make them look good, win over some more hearts and minds.

Josh:
But I think the most interesting part of the whole story is less the drama and

Josh:
more the fact that these models were able to accomplish a really impressive

Josh:
feat over such a short period of time.

Josh:
From what I understand, previously when they attempted to solve these problems,

Josh:
they used a custom training data set.

Josh:
They used custom tool sets. It was mostly a model trained on solving mathematical problems.

Josh:
And with this version, both the OpenAI version and the Gemini models,

Josh:
they were both general purpose models.

Josh:
They were not trained specifically with the intention of solving mathematical problems.

Josh:
These are the general models that people day to day are using.

Josh:
They're just now able to solve these math problems using this new general intelligence.

Josh:
So it's a really interesting breakthrough that I think we get from reinforcement

Josh:
learning that now there is not so much of an advantage to training a model specific

Josh:
to one's skill set when you could just make it great at everything.

Josh:
There was one thing that I noticed that some people call it cheating, other people don't.

Josh:
But so with the mathematical, with the actual test that high school was had

Josh:
to take, they're not allowed to use tools and they have a limited amount of

Josh:
time per question to answer.

Josh:
The models that, the OpenAI model and the Gemini model, they had infinite amount

Josh:
of time to answer and they were allowed to use tools.

Josh:
So there still are small differences in these.

Ejaaz:
Were they allowed to like use the internet?

Josh:
I don't know the specifics. I would imagine at least calculators,

Josh:
at most probably the full repertoire of what we have currently available to

Josh:
us, which is full internet search, code writing abilities. They could do their

Josh:
own mathematical checks.

Josh:
So I would just assume the minimum amount of constraints possible.

Josh:
So there was much less constraints on the models, But they did solve the questions.

Josh:
And I think that's super impressive. They got five out of six right.

Josh:
Which was gold and better than almost every student, if I'm not mistaken.

Josh:
Only a few students got the six out of six completely correct.

Josh:
It's just cool to see the rate of progress of these models getting better.

Josh:
That over the course of the last 15 months or so, they went from horrible and

Josh:
narrowly trained to incredible and generally trained.

Josh:
And as long as that trend keeps going, I think the drama matters less than the

Josh:
output, which is models are getting really good at solving really hard math problems.

Josh:
And original ones too, that the world has never seen before.

Ejaaz:
Yeah, well, that last point is actually the main takeaway that I had,

Ejaaz:
Josh, which is it's original, never-before-seen problems.

Ejaaz:
Typically, these AI models are trained on things that they've seen before, as you said, right?

Ejaaz:
They're trained on data sets. So they've already seen the problem,

Ejaaz:
and then they have to work out, they know the answer, and they have to work

Ejaaz:
out how to get there, right? So they kind of have a leading factor.

Ejaaz:
Here, it's just kind of like completely unknown.

Ejaaz:
The other thing is, this is kind of like the culmination of a trend,

Ejaaz:
Josh, which is these AI models are really good at doing kind of binary tasks.

Ejaaz:
And I don't want to reduce mathematics to binary tasks, but technically it's

Ejaaz:
numbers, sequential formulas, that kind of stuff, right?

Ejaaz:
So if you can run enough compute at a thing, and if you can get that AI model

Ejaaz:
to consider all different decision parts, It's going to eventually get to the answer, right?

Ejaaz:
But it's always a specific answer at the end of that, right?

Ejaaz:
Whereas when it comes to more subjective things, more human experiential things,

Ejaaz:
AI has typically struggled to...

Ejaaz:
Improve at the same rate that it has for like all these different scientific

Ejaaz:
and math problems so i'm glad that we've reached this pinnacle feat i think

Ejaaz:
ai models have are really good at one thing and not so great at other things

Ejaaz:
and i'm excited to see how like they kind of like try to start leapfrogging

Ejaaz:
each other over the next couple of years.

Josh:
Yeah it's it's that directional progress that we like

Josh:
math is clearly the first because you can write down

Josh:
proofs and you could check your work and there is an actual verifiable solution

Josh:
and i think that's why we're seeing a lot of the progress start early

Josh:
in math and then hopefully go on to these other places but

Josh:
what we are seeing is these first signs of

Josh:
new knowledge breakthroughs where it's solving a

Josh:
new and novel problem that hasn't been

Josh:
released before based on its previous data set

Josh:
so it's not just pattern matching like you mentioned earlier where it has

Josh:
this data set of questions it's kind of finding the right examples and

Josh:
then applying that logic to the question it's actually

Josh:
reasoning and it's it's reasoning in many instances and

Josh:
then it's comparing its work and it's it's coming to a conclusion

Josh:
and we saw this with the grok heavy model last week too when

Josh:
it released um where i think the the new

Josh:
meta is many instances solving hard

Josh:
problems and then comparing so you lower that error rate more

Josh:
and more and more each time and what we're seeing is great progress so

Josh:
i mean although open ai and google are fighting again they're both they're both

Josh:
fighting over over exciting progress and sure maybe one tried to sweep in and

Josh:
steal the valor but they both did an excellent job in actually completing these

Josh:
problems and placing gold in a test that was previously not possible to do from an ai model you

Ejaaz:
Know who the real winners are here out of this josh.

Josh:
Who's that high school kids

Ejaaz:
Who now have an AI model that can do all their math homework for them.

Josh:
Isn't that incredible? Like, man, think about it.

Ejaaz:
I wish I had that.

Josh:
You have an AI model that is as smart as the smartest people on planet Earth

Josh:
in high school. If it could solve those math problems, it could solve anything.

Ejaaz:
It sounds human as well, Josh. So, like, your teacher is going to struggle unless

Ejaaz:
they use AI themselves to figure out whether you just did that yourself or completely

Ejaaz:
just ran that through GPT, your mom's GPT subscription.

Josh:
It really forces you to re-evaluate the school model, right?

Josh:
Because now that this information is so readily accessible, it's so easy to solve these problems.

Josh:
Is that the actual thing worth learning? Or is it how to use these tools that's

Josh:
more important to get to the answer?

Josh:
And there's this there's this dual pronged approach and we see we see

Josh:
developers and programmers talk about this a lot where as soon

Josh:
as they start to rely too heavily on the tools they start

Josh:
to lose their touch they start to lose their ability to to deeply

Josh:
understand how it reaches conclusions um but

Josh:
is that worth it in exchange for getting to the answer much quicker and then

Josh:
being able to seek many more answers i don't know it's weird dynamic if i was

Josh:
a teacher i'd be worried because i mean similar to what we saw with the calculator

Josh:
it just replace the thinking process and just yield you an answer and

Ejaaz:
The thing with the calculator is like you you're

Ejaaz:
using the calculator so it figures out the answer for you but you kind of

Ejaaz:
loosely understand how it is working right you

Ejaaz:
know what numbers it's crunching to get to that answer and then typically you

Ejaaz:
do a few things on a calculator and then you get to your eventual answer for

Ejaaz:
whatever the original question was the issue with or the concern that you're

Ejaaz:
highlighting here with AI is it's doing really complex problems,

Ejaaz:
which kids don't even need to understand in the first place just to get an answer,

Ejaaz:
which they can then give to their teacher, get a grade and then go to university.

Ejaaz:
But the kids don't actually learn actively in that process.

Ejaaz:
And it's going to be a concerning trend if we see kids just trying to go from

Ejaaz:
zero to 100% without understanding anything in between.

Ejaaz:
A trend to watch.

Josh:
This is our episode from a few weeks ago. Is AI making you dumber?

Josh:
Yes. And I think that's just going to continue to be the question.

Josh:
Oh, God. And I think the answer is it's all dependent on how you choose to use

Josh:
the tools that you're given.

Josh:
And if you use these tools as further leverage. So I'm sure these math olympiads

Josh:
who can actually complete the problems would love to have this model to check

Josh:
the problems and to work through the problems and to figure out shortcuts on

Josh:
solving these problems.

Josh:
Where if you deeply understand it, then this becomes an amazing tool to check

Josh:
your work, to generate new questions for you.

Josh:
It's a great study, buddy. or if you are not an olympiad and you still want

Josh:
to get to the answer well you just kind of cheat your way through and you just

Josh:
ask it for exactly what you want so it's that it's that split again and it's

Josh:
up to the person to take their own agency solve their own problems and try to

Josh:
use these for for tools of leverage instead of just problem solving machines that

Ejaaz:
Actually reminds me of this tweet i saw yesterday josh um so what you're looking

Ejaaz:
at here is a tweet from dave white dave White is a very prestigious investment

Ejaaz:
slash research advisor at this fund called Paradigm,

Ejaaz:
which basically it's a crypto fund, but it is one of the wealthiest funds out there.

Ejaaz:
So a lot of the investments they made were massive wins. And a lot of the reasoning

Ejaaz:
of those wins was from Dave White's analysis.

Ejaaz:
He is a deeply thoughtful mathematician at his core, and he is famed for doing

Ejaaz:
a lot of analyses on companies, mathematical analyses that have ended up, you know.

Ejaaz:
Determining whether a fund puts $100 million in a company or zero, right?

Ejaaz:
So a very important job worth hundreds of millions of dollars, right?

Ejaaz:
And what he says here, basically, is him having an identity crisis,

Ejaaz:
because he has looked up to the IMO, the International Math Olympiad.

Ejaaz:
And he goes on to say in this tweet that subconsciously, whenever he's met a

Ejaaz:
gold medalist IMO champion, he's always subconsciously thought that they were

Ejaaz:
smarter than him, that he is more respecting of them.

Ejaaz:
And now with this news that AI models basically can do his job for him,

Ejaaz:
can reason better than him at some of these math problems, he now has an identity crisis.

Ejaaz:
He doesn't know kind of where to go from this. And if people like Dave White

Ejaaz:
is having this kind of like disillusioned sentiment from how smart AI is,

Ejaaz:
you can imagine how this is going to happen for everyone else in all of the

Ejaaz:
other sectors, Josh, right?

Ejaaz:
It doesn't matter if you're a mathematician or an investment research advisor,

Ejaaz:
you could be a technician in some kind of engineering industrial role,

Ejaaz:
or you could be a teacher, or you could be a kid or a high schooler.

Ejaaz:
I think this disillusionment is going to spread. And I think it's super important

Ejaaz:
for people to kind of like evolve their thinking, like you said,

Ejaaz:
Josh, and learn how to leverage these tools versus just consume.

Josh:
Yeah, this is, I mean, this is crazy. There's a lot of people that are going

Josh:
to have to adapt to this new world order of intelligence, where if you build

Josh:
up your entire identity around being intelligent, well, perhaps you're going to have to alter the way

Josh:
present yourself as intelligent because the meaning of intelligence is becoming

Josh:
commoditized among these tools that are now reduced down to a single chat box.

Ejaaz:
Yep. Benchmarks are going to have to reset themselves completely.

Ejaaz:
But folks, that is the end of this episode. Thank you so much for tuning in again.

Ejaaz:
Josh and I are going hammer and tong at Limitless.

Ejaaz:
Our goal is to get you the hottest and trending topics and news fresh out the

Ejaaz:
door, give you our commentary, our thoughts, and hopefully some useful insights for you.

Ejaaz:
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Ejaaz:
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Ejaaz:
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Ejaaz:
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Ejaaz:
we'll see you on the next one.