Limitless Podcast

Josh:
Imagine this, you give eight of the world's most powerful AI models $10,000

Josh:
each and tell them, go trade real stocks.

Josh:
No paper trading, but real money with real risk. And two weeks later,

Josh:
most of them have lost a painful amount of cash, which I guess is kind of expected.

Josh:
The kind of drawdowns that would get a human portfolio manager totally fired.

Josh:
But now, they ran the same experiment again, except this time with much higher stakes.

Josh:
There's $320,000 at stake. And we've talked about Alpha Arena before in a previous

Josh:
episode, which I highly recommend checking out.

Josh:
But now we have the new results from the new season, season 1.5.

Josh:
And what was exciting is that there was a very clear and obvious winner,

Josh:
but that winner was a mystery.

Josh:
We don't actually know or we didn't know who the winner was up until recently.

Josh:
In fact, it won all four of the trading competitions in

Josh:
this new season while leaving the other top models like ChatGPT

Josh:
5.1 and Google Shemini 3.0 fighting for

Josh:
second place so at the core of this is one who is

Josh:
this model and two how on earth did they

Josh:
do it how are they outperforming everyone so much so as to make 65 percent in

Josh:
two weeks in one of these competitions so ijaz i want to walk through everyone

Josh:
about what what just happened what the model is and what alpha arena is so give

Josh:
us the lowdown on on who this was that made so much money oh

Ejaaz:
Yeah well we will get into all of that uh today

Ejaaz:
So Alpha Arena is basically a competition or test to see how well AI models can trade.

Ejaaz:
And they do this in a few different ways, Josh. Number one, they give each model

Ejaaz:
$10,000, as you mentioned.

Ejaaz:
And then they allow them to trade a range of different financial instruments

Ejaaz:
over a period of two weeks.

Ejaaz:
So there's like a season, two weeks, and we see which AI models do the best.

Ejaaz:
And they get all your AI models in there. You've got ChatGPT,

Ejaaz:
you have got Gemini, you've got Anthropics Claude, and you have Grok as well.

Ejaaz:
And so they've gone through about two seasons now, and the results have been

Ejaaz:
absolutely crazy. So they started off with season one.

Ejaaz:
And you can think of this as like the degen crypto season.

Ejaaz:
They gave seven models, $10,000 each, and allowed them to trade crypto assets

Ejaaz:
like Bitcoin, Ethereum, stuff like that.

Ejaaz:
And they did this in something called Perpetual, so they could leverage trade.

Ejaaz:
It was the only instrument that they were allowed to do this.

Ejaaz:
And the results were, as you'd probably expect, a lot of these AI models lost a lot of money.

Ejaaz:
Some of them actually ended up making a decent chunk of money,

Ejaaz:
and they were primarily Chinese models.

Ejaaz:
They were Quen, and I think it was DeepSeat that ended up making money.

Ejaaz:
So there was a lot of takeaways there. As you mentioned, we've got a previous

Ejaaz:
episode where we spoke about this.

Ejaaz:
Definitely go give that a watch. There's a lot of alpha in that one.

Ejaaz:
And then that brings us to season 1.5, where the AI models, instead of being

Ejaaz:
given crypto to trade, were given the ability to trade U.S. stocks.

Ejaaz:
And we're talking about equities, which is something that a lot of us listening

Ejaaz:
to this show are very familiar with. And I think this is for a few reasons, Josh.

Ejaaz:
Primarily, crypto is very volatile, and we kind of want to figure out how the

Ejaaz:
majority of money that is traded in the financial markets can translate into

Ejaaz:
AI models trading that. So a few things that they kept the same is that they

Ejaaz:
gave the AI model $10,000.

Ejaaz:
But there was a number of differences with Season 1.5. Number one,

Ejaaz:
they were allowed to trade US equities and stocks.

Ejaaz:
Number two, there were two new models that were introduced.

Ejaaz:
One was a model called Kimi K2, which is a really good open source Chinese model.

Ejaaz:
But the other was this thing called a mystery model.

Ejaaz:
I'm going to reveal which this model was in a second. But before I do,

Ejaaz:
do you have any guesses as to what model this might have been?

Josh:
Well, I cheated. I know the answer. But what I think is very exciting about

Josh:
this is that like, I think it's important to highlight these models made hundreds

Josh:
to even thousands of trades per model. Yes.

Josh:
And what we want to answer, like the question that I want more than this mystery

Josh:
model is like, is this real signal or is this just, I mean, Luke said earlier,

Josh:
is this a GPU intensive scratch off game?

Josh:
Where is there any real signal? and like I guess we'll talk about the reality

Josh:
of that and what this means for your portfolio if you ever want to manage it but to me

Josh:
I think that's the important thing to highlight. We probably should just spill

Josh:
the beans, EJ. Do you want to just tell them? Who's Mr.

Ejaaz:
Model? I can't keep it in any longer. It was an unofficial version of Grok,

Ejaaz:
aptly named Grok 4.2 or 4.20 for the memers out there.

Ejaaz:
And this was revealed by none other than the Grok man himself, Elon Musk.

Ejaaz:
And the reason why this mystery model was getting so much attention,

Ejaaz:
Josh, was because it ended up being the winner. It made the most money out of any other AI models.

Ejaaz:
And what was more impressive is there wasn't just one competition being run throughout season 1.5.

Ejaaz:
There were four at the same time. So these AI models were running across four

Ejaaz:
different competitions at the same time. That was $320,000.

Ejaaz:
At any one instance, which is a crazy amount of financial money to stake on

Ejaaz:
an experiment. That's a lot of money could have been lost here.

Ejaaz:
And Grok 4.20 ended up performing the best.

Ejaaz:
Josh, I want to go through a few different stats here, which kind of like shows

Ejaaz:
how amazing this particular model was.

Ejaaz:
So firstly, for some context, there were four different competitions that were

Ejaaz:
being run that these AR models were being tested on.

Ejaaz:
Competition number one was something called new baseline. This is basically

Ejaaz:
the ability for these AI models to get access to.

Ejaaz:
Trading AI stocks, to get access to all the common news that you and I can read

Ejaaz:
online and in newspapers to kind of like figure out, okay, what kind of news

Ejaaz:
would affect my stock positions.

Ejaaz:
They would also get access to sentiment data to see how kind of like the markets

Ejaaz:
and retail traders would kind of react to certain bits of news.

Ejaaz:
They had access to a much wider spread amount of data in competition number one.

Ejaaz:
Competition number two was called Monk Mode. They kind of amended the investing

Ejaaz:
prompt here. And so kind of like they traded more conservatively.

Ejaaz:
Competition number three was called Situational Awareness, Josh.

Ejaaz:
So each model had an awareness of other models trading and where they ranked in accordance to them.

Ejaaz:
So there was this kind of like ecosystem of peer pressure being put on by each model.

Ejaaz:
And competition number four was just outright degeneracy max

Ejaaz:
leverage you could only trade with like 20 to 50x leverage which is just kind

Ejaaz:
of i don't think it's 50x but like 30x uh just crazy amount of risk um adjustment

Ejaaz:
to test whether a model would take that risk or whether it would trade more

Ejaaz:
conservatively josh did you have any reactions on the the results of this of this competition the.

Josh:
Results that we're looking at right now actually i found most interesting this

Josh:
is from the new baseline competition it's It's basically the full info mode.

Josh:
And one of the big differences between this mode versus previous competitions

Josh:
that have been held is like you mentioned earlier, it has access to a lot of data.

Josh:
This is the first time an AI trading model has had access to real time information

Josh:
outside of just looking at a chart. So I think.

Josh:
In that sense, this is the closest competition to how a human quant fund would actually operate.

Josh:
So if you're looking for high signal in terms of which AI can actually make

Josh:
you real money in the real world, this is the one.

Josh:
And what we're seeing here is that the Grok 4.20 model, the memetic mystery

Josh:
model, outperformed by a fairly large margin to OpenAI and ChatGPT 5.1,

Josh:
which is the clear second place.

Josh:
And those are the only two that actually made profit. everybody else lost money

Josh:
in the real world competition which to me signals a few things one of them being

Josh:
well perhaps one is really good at

Josh:
understanding real world information perhaps it understands company fundamentals

Josh:
better perhaps it just has access to real

Josh:
world information that's better like grok and having access to the x ai model

Josh:
um so there's a lot of things to speculate here but for me the new baseline

Josh:
chart that we're looking at right now was the high signal one i'm like oh my

Josh:
god wait this has the same type of information flows that i'm now getting so

Josh:
now we're even we're on the same playing field okay

Ejaaz:
Um i actually had a different answer to that which is i was more impressed,

Ejaaz:
Josh, by the situational awareness competition.

Ejaaz:
So this was a competition where each model had access to data and news,

Ejaaz:
but they also had awareness of who they were competing against.

Ejaaz:
So Grok 4.20, the winner, knew that GPT-5 was in second place.

Ejaaz:
And so he was always keeping an eye on GPT-5, being like, oh,

Ejaaz:
what trades is GPT-5 making? Why did they make that trade? Oh, that's interesting.

Ejaaz:
And then he would look at Gemini and be like, oh, what trades are Gemini making.

Ejaaz:
So he would have this awareness of his competitors, which you didn't have in

Ejaaz:
season one, where they were just kind of like trading in silos, right?

Ejaaz:
And why this competition was so interesting, Josh, is this was technically where

Ejaaz:
Grok 4.20 made the most money.

Ejaaz:
In fact, if you look at the top of this leaderboard right here,

Ejaaz:
the account value at the end of season 1.5 was $16,656,

Ejaaz:
which is technically a 60% plus return in two weeks on $10,000 worth of capital.

Josh:
I needed to take my money immediately.

Ejaaz:
Isn't that insane, right? Like if you had to pick a competition of where you

Ejaaz:
would have given an AI model money, just given from this data,

Ejaaz:
and I'm not saying you should do that, you would be most bullish on situational awareness.

Ejaaz:
And I'm going to make some implications here that I haven't tested yet,

Ejaaz:
but it seems to imply that

Ejaaz:
this kind of competitive nature where the models were kind of aware and exposed

Ejaaz:
to their competitors' trades and thinking, and we're going to get to the model

Ejaaz:
chat thinking in a second, seems

Ejaaz:
to have given them a better trading advantage, at least in some cases.

Josh:
Yeah, so like you mentioned, one of my favorite parts, I think we share this

Josh:
in one of our favorite parts about this competition in particular,

Josh:
is that you can actually see all of the trades.

Josh:
One thing about these private quant funds, you don't know what the hell is going on.

Josh:
But with So these models, you can see exactly what they're thinking every time

Josh:
they think and make a decision.

Josh:
So maybe you guys can go through a few of them and see kind of what the model

Josh:
is thinking, how they're processing this real world data.

Josh:
And if there's any tips for us to learn from processing this real world data,

Josh:
because clearly they're a much better trader than I am.

Ejaaz:
Yeah. So I have a few examples pulled up here on the right side of the screen.

Ejaaz:
It's under model chat. By the way, any of you listening to this can go onto

Ejaaz:
this website and see for yourself and scroll through their hundreds and hundreds of posts.

Ejaaz:
But it basically gives us an insight into how each model thinks about a trade

Ejaaz:
that they currently either have open or they're thinking about opening or closing

Ejaaz:
or whatever that might be, right?

Ejaaz:
So it's like being in the mind of an actual investor and figuring out how they make their decisions.

Ejaaz:
An example here at the top of the screen is Gemini 3 Pro.

Ejaaz:
He goes, I'm betting on a breakout in NVIDIA, seeing a strong setup as it holds

Ejaaz:
support and leading the market with a target of $189 and a stop just below $180.

Ejaaz:
So what he's referring to there is kind of a typical quant style of trading

Ejaaz:
where it's kind of like he's looking at technicals, he's evaluating kind of

Ejaaz:
graphs, momentum of the stock price.

Ejaaz:
It's very price evaluated type of trading, right, Josh? But if you look just

Ejaaz:
below it, you've got GPT 5.1, which actually came in second at the end of this

Ejaaz:
competition, who goes, my analysis indicates continued strength in AI names

Ejaaz:
like NVIDIA and Microsoft.

Ejaaz:
So I'm holding out on existing long positions over the weekend and potential macro event risk.

Ejaaz:
Now, the point I want to make about this particular model is it's less price

Ejaaz:
specific and it's more focused on just kind of general themes,

Ejaaz:
news and data that it's seeing outside of price.

Ejaaz:
And that really goes to demonstrate that some of these models are very kind

Ejaaz:
of price and quantitative focused, whereas other models are kind of more thesis

Ejaaz:
driven over a shorter period of time.

Ejaaz:
And it kind of gives rise to these types of personalities, right, Josh?

Josh:
Yeah, well, now we have to answer the uncomfortable question is like,

Josh:
is this evidence that Grok is some kind of money printing god?

Josh:
Or is this just like really well produced content that happens to involve real money?

Josh:
And that kind of comes down to understanding the AI, understand the personalities,

Josh:
understanding how each model considers these trades and how they place themselves

Josh:
in different positions.

Josh:
So I kind of want to go through one by one, all of the models and kind of what

Josh:
their personalities are like.

Josh:
We see with DeepSeek a lot that it behaves, and we mentioned on a previous episode

Josh:
as well, it behaves like a very disciplined quant fund.

Josh:
And DeepSeek, for those that don't know, it's an open source Chinese model.

Josh:
They are very systematic, very mathematic, very comfortable with leverage,

Josh:
but able to hedge and adjust mid-trade based on its decisions and new information.

Josh:
So DeepSeek and Quen even is kind of similar to this.

Josh:
If you remember from the last episode, Ejaz, Quen was my early favorite.

Josh:
I had hoped that Quen was going to win.

Josh:
Unfortunately, that's not the case at all in season 1.5. Quen has gotten crushed

Josh:
right there with DeepSeek.

Josh:
I can kind of imagine it as like more similar to me, maybe that's why I resonated

Josh:
with it, where it has one big thesis and then it sizes aggressively around that thesis.

Josh:
So if you remember, Quen would only buy Bitcoin or Ether in the last one and

Josh:
it wouldn't buy any other altcoins.

Josh:
It just had a thesis that these major coins were going up, nothing else was.

Josh:
Claude is interesting. It's very

Josh:
reflective of how the actual Claude model works when you engage with it.

Josh:
It's very patient and it's thoughtful, but it occasionally sizes up too much

Josh:
and then it gets crushed by leverage.

Josh:
So, and like, as we go through these, and EJs, I also noticed you assigned a masculine...

Josh:
Personality to gemini you said he when you were talking about google gemini

Josh:
and that's kind of because it's it's daddy right like gemini's been

Josh:
the big boy on top but but in

Josh:
this training competition i don't know if it is i was going through the trades

Josh:
and it very much panic flip flops from shorts to long after losing and it kind

Josh:
of in a way gemini was most reflective of retail behavior because and i'm not

Josh:
sure what we could tie that to but gemini was very reactionary where if it lost

Josh:
money it would flip its position and if it gained money it would it would kind of hedge quickly.

Josh:
So that was interesting. And then we have GPT-5, which is very sophisticated reasoning.

Josh:
But in season one, they over-traded and over-leveraged and got absolutely wiped

Josh:
out. And they were very timid in their way that they went about this.

Josh:
So that's kind of how you can think about these.

Josh:
The final one, which is the secret model, Grok 4.2. If we know anything about

Josh:
Grok, we know that it is a very high risk taker, but a calculated risk taker.

Josh:
And that's probably what put it at the top there. So that's kind of how I would

Josh:
consider all of these models.

Josh:
They're a little different and they are reflective of, if you've used these

Josh:
in person, you could kind of understand the thinking that gets placed behind the trades

Ejaaz:
Yeah i i want to dig into a few

Ejaaz:
things around the the personality or rather the trading styles here josh because

Ejaaz:
um it may not be as explicit as we kind of lay it out like so grok 4 4.20 was

Ejaaz:
the winner right by far and it made money uh it was the top across all of the

Ejaaz:
competitions all four competitions that's great but did you look at the results of grok 4,

Ejaaz:
its predecessor.

Josh:
It was absolutely crushed.

Ejaaz:
It was the worst performing model in this entire competition,

Ejaaz:
which is crazy because in season one, where it was trading crypto,

Ejaaz:
it came in at second or third.

Ejaaz:
And for about 75% of the competition, Josh, it was number one.

Ejaaz:
So it had some kind of an advantage trading kind of very riskily, right?

Ejaaz:
And that might be because of the nature of the instruments that it was trading.

Ejaaz:
Crypto is very volatile and it was kind of going blase.

Ejaaz:
So when it was like 20x bullish Bitcoin, it benefited a lot when Bitcoin price

Ejaaz:
went up, but obviously it like suffered when it went down.

Ejaaz:
It's interesting to see the discourse between these two models and 1.5, right?

Ejaaz:
Grok 4.20, the winner, seems to be a kind of more mature version of Grok 4.

Ejaaz:
It seems to be thinking more about its trades.

Ejaaz:
It has more kind of like risk percentiles and boundaries in place,

Ejaaz:
whereas Grokforce seems to be its kind of usual degenerate self.

Ejaaz:
And I don't know how much of that is reliant on the fact that it's trading stocks,

Ejaaz:
which is generally a less volatile market versus Grok 4.20 being a more thesis

Ejaaz:
driven, sensible trader, as you kind of described.

Ejaaz:
The other one that we have to call out because it's the elephant in the room

Ejaaz:
here, GPT-5 came in at second in season 1.5.

Josh:
Right? 5.1. 5.1.

Ejaaz:
Sorry, 5.1, right? In the previous season, season one, it was the second worst

Ejaaz:
performing. No, sorry, it was the worst performing.

Josh:
It was horrible.

Ejaaz:
It was GPT-5.

Josh:
It was an abomination.

Ejaaz:
And Gemini. So whatever OpenAI has cooked up in the .1, congrats.

Ejaaz:
Because you must have traded on some kind of financial data or you've you've

Ejaaz:
like kind of like implemented a kind of like risk trading strategy that made

Ejaaz:
it a lot more sensible because it made some really great trades on this season

Ejaaz:
so just two different kind of like jumps from season one to 1.5 that i i had to call out.

Josh:
Yeah it makes me excited to see the improvements in these like

Josh:
significant improvements with incremental models because we

Josh:
normally talk about 5 to 5.1 being pretty marginal like

Josh:
there's nothing really noteworthy or exciting and yet the results in the

Josh:
small sample size at least are pretty reassuring that hey there is something

Josh:
new going under the hood and maybe this is an appropriate time to address the

Josh:
i guess the the limitations the kind of bare case of this starting with the

Josh:
sample size um we do have to say i mean this is two weeks ejs this is not a long time um they they

Josh:
placed some trades. Some people maybe got lucky. Some models maybe did not.

Josh:
Is there any real signal here?

Josh:
I'm curious, your take, do you think this is reflective of future performance?

Josh:
Like, is there what is here that's actually valuable versus what is here is actually kind of lucky?

Ejaaz:
I don't think we have enough information to make that call, at least for me.

Ejaaz:
I'll speak for myself personally.

Ejaaz:
The real test is, you know, I asked myself before we recorded this episode,

Ejaaz:
would i give my money to grok 4.2 or the winner that one across all categories

Ejaaz:
and the simple answer is like no like i don't i don't know if it's going to

Ejaaz:
repeat that over week three week four week five it was only two weeks to your

Ejaaz:
point right so i want to see this experiment kind of,

Ejaaz:
rehash like a million times before i'm like okay that's cool um even then it's

Ejaaz:
it's still kind of like risky right it's like i i can justify giving my money

Ejaaz:
to a human that i can kind of relate to that I can call up in speed to,

Ejaaz:
less so when it comes to an AI model, right? But maybe that's my thing,

Ejaaz:
it needs to kind of evolve.

Ejaaz:
The other way I'm thinking about this is there's just a lot of unknowns around this, Josh, right?

Ejaaz:
Like I can see it's thinking, I can see kind of like how the model kind of completes its trades,

Ejaaz:
I don't really know what's going under the hood. Is this just kind of like a

Ejaaz:
pattern matching thing?

Ejaaz:
Does it inherit the risks that a lot of humans have already done?

Ejaaz:
Because it's trained on the same kind of corpus of trading data that we have

Ejaaz:
kind of evaluated on? Or is it kind of net better?

Ejaaz:
Do you feel the same or?

Josh:
Yeah, it's probably, I mean, it's not the new gold standard of AI benchmarks.

Josh:
But it is a standard that I think is interesting. Because this is a benchmark

Josh:
that happens in the real world with real dynamic data that cannot be game.

Josh:
So in that case, I love it.

Josh:
But I saw one writer, they called it Schrodinger's Benchmark,

Josh:
because it's simultaneously serious and degenerate at the same time.

Josh:
And it's like it's entertainment with real money that happens to produce some

Josh:
legitimate insights about AI behavior, but it's not really indicative of future

Josh:
returns at the small of a sample size, at least.

Josh:
And that's kind of where I feel about it. there is one breakthrough that we

Josh:
mentioned earlier that does provide real value, which is the transparency.

Josh:
Every trade being on chain and every step reason being logged is actually really

Josh:
helpful to understanding how these models think and how you can consider thinking.

Josh:
So for example, you could show me every decision Grok 4.20 made on Tesla after

Josh:
the Fed announcement or something like that. And it'll walk you through a chain of thought.

Josh:
And if anything, make you into a better investor.

Josh:
Would I trust the model of my own money? No.

Josh:
Maybe a little bit maybe with a small sample size how

Josh:
much it is that's a

Josh:
good question i'd give it a couple thousand dollars to play around with and see

Josh:
what happens i think that that would be interesting and fun and it's it's

Josh:
low enough stakes but i would trust it enough to not lose it like i'd say i

Josh:
would probably trust grok more with my money than i would the average day trader

Josh:
off the street um which granted they don't have a very good reputation but i

Josh:
think there is some sort of an edge there that doesn't exist in the average person.

Josh:
And if you assume that these models are going to continue to get better and

Josh:
better, well, you have to assume that they're going to form some sort of an

Josh:
edge, but I don't know how much.

Josh:
It's an interesting question because as a quant trading fund too,

Josh:
if your job or as just a trader in general, if your job is to make money off

Josh:
of trading, what are you doing about this information? Are you leaning into AI?

Josh:
Are you trying to get these models to help you with your information flows and make decisions?

Josh:
Are you using them to help you actually transact trades or are you just kind

Josh:
of looking the other way and saying oh this is just a dumb experiment to benchmark

Josh:
models there's no actual signal here and the answer is probably somewhere in the middle right yeah

Ejaaz:
I mean well my initial reaction to that is um,

Ejaaz:
Okay, quant funds already use algorithms. It would make a lot of sense if they

Ejaaz:
started using AI algorithms, right?

Ejaaz:
If you could get a smarter algorithm to trade for your fund, absolutely, right?

Ejaaz:
So it's a no-brainer to me that these hedge funds, quant funds are going to

Ejaaz:
be using AI, probably already using AI.

Ejaaz:
Where I have maybe a hot take is that the transparency is just a nice to have.

Ejaaz:
It is no way going to win in the best of models.

Ejaaz:
Why? Because if you have an AI model that is like better than all the other

Ejaaz:
AI models at trading, why would you make that public?

Ejaaz:
Right. So like, I'm kind of like at ties between this thing,

Ejaaz:
because I think the transparency is a really good thing in kind of like bringing

Ejaaz:
up the floor of trading credibility for people that get access to this type of information.

Ejaaz:
Like I have loved reading through these kind of like trade logs here,

Ejaaz:
seeing how each model thinks and being like, okay, yeah, wow.

Ejaaz:
I actually didn't think about that myself when I was buying that stock.

Ejaaz:
Right. And these are like stocks that I've seen that I, that I can buy,

Ejaaz:
right. The Amazon trade, the NVIDIA trade, I'm just like, oh, okay.

Ejaaz:
I didn't think about that, right, yesterday whenever they made this trade.

Ejaaz:
If I am a hedge fund, I'm like, yeah, if I've fine-tuned a model that is like

Ejaaz:
beating all these models, I don't really want to expose that really.

Ejaaz:
So it's kind of like a push and pull.

Ejaaz:
The other thought I had, Josh, is, and maybe this is kind of like kind of semi-adjacent

Ejaaz:
to what we're discussing here.

Ejaaz:
I couldn't get the thought out of my head that if you could get Grok in X,

Ejaaz:
trading some kind of money for you or guaranteeing you like a 5% to 10% annual

Ejaaz:
return, that is something that I would like if framed correctly,

Ejaaz:
I would put some money into, right?

Ejaaz:
Maybe not over two weeks, but

Ejaaz:
maybe over an adjusted kind of yearly period would be super cool to see.

Josh:
Yeah, that's such a, it's such a fun question to ask is like,

Josh:
what happens when this kind of system runs for two years, but with your,

Josh:
like, let's say it's a large pension management fund and they just want a manager

Josh:
that doesn't take fees and does a pretty good job.

Josh:
Like, is there going to be enough trust in these systems to reliably place money at scale with them?

Josh:
And And you have to assume, given the signal this early on, that the answer will be yes.

Josh:
The question is, how much of a yes will it be?

Josh:
What percentage of management will be AI as it gets better over time?

Josh:
And the sample size sucks. I wish it was more than two weeks. I wish it was two years.

Josh:
In two years from now, think about the progress we're going to see and what

Josh:
type of impact that's going to have on trading models.

Josh:
So this is, it's interesting. It's fascinating.

Josh:
In fact, I'm really curious to actually run this experiment for ourselves.

Josh:
I'd love to try to come up with a little trading model that runs these things

Josh:
and test it out because it's fun and there is some sort of an edge there.

Ejaaz:
I would say, okay, if I were to summarize my lesson from this entire competition

Ejaaz:
or experiment so far, Josh,

Ejaaz:
it is I'm not convinced to give AI models money to trade, but I'm convinced

Ejaaz:
to use AI models to help me trade.

Ejaaz:
So kind of like a human and AI model kind of work together and kind of become

Ejaaz:
a better trader overall, I think is the main takeaway for me here. Do you share the same?

Josh:
It's funny. I mean, this is how agents work today, right?

Josh:
Like if you go on ChatGPT and you say, go book me a reservation,

Josh:
it'll take you to the finish line.

Josh:
And then you as the human provide the final filter and approve or deny.

Josh:
And I think that's probably the happy middle ground while

Josh:
we still don't really trust these models too much is give me

Josh:
the thesis give me the trade i will either approve

Josh:
or deny and that's how the money gets managed so

Josh:
it's cool this is a great experiment i love that we got season 1.5

Josh:
i mean it's fascinating even more fascinating is that we

Josh:
have an early look at grok 4.2 which by all

Josh:
means is the best trading model in the world where will

Josh:
it rank in the other benchmarks we will see we will be covering it as soon as

Josh:
it comes out but i guess that's that's really it for this episode on season

Josh:
1.5 the question i want to leave everyone else with is i mean would you trust

Josh:
an ai with your part of the portfolio like how much money would you actually

Josh:
give to an ai currently grok 4.2 who just made

Josh:
60% in two weeks in one of these trading competitions. Is that enough for you to risk your money?

Josh:
Or is it still just this dumb AI system that you don't really trust?

Ejaaz:
Well, if you're interested in this experiment, Josh and I were actually discussing,

Ejaaz:
about potentially giving you guys a tutorial on how to use an AI to trade money

Ejaaz:
for you and kind of like an experiment, this own end of one experiment, but our own.

Ejaaz:
But we want to get a little more signal from you guys. Let us know in the comments

Ejaaz:
whether this is something that you'd be interested in seeing.

Ejaaz:
And I have, Josh, I have a requirement for the listeners.

Ejaaz:
If we do want to put the tutorial out. Our last video that we did on AI trading

Ejaaz:
reached 100,000 views and 3,000 likes.

Ejaaz:
So I'm not going to ask for the 100,000 views, but I will ask for the likes.

Ejaaz:
If this video can get more than 3,000, if it gets 3,000 likes,

Ejaaz:
we will definitely put out that tutorial by the end of the year.

Ejaaz:
And we have a lot of thoughts around this, about how we're going to do it.

Ejaaz:
We're super excited to do it. So help us get there.

Ejaaz:
It is another week of really exciting news. Josh, I don't know if you saw the

Ejaaz:
rumors. Did you see the rumors about OpenAI?

Josh:
Tell me, fill me in.

Ejaaz:
About OpenAI releasing a potential new groundbreaking model?

Josh:
As a matter of fact, the Polymarket is showing that OpenAI is very favored to

Josh:
release the best model of the year.

Josh:
And last I checked, Gemini is the best model of the year. So that implies we're

Josh:
getting something big in the next few weeks.

Ejaaz:
I think we will. and like you said, the Polymarket is kind of like revealing

Ejaaz:
its hands so maybe there's some inside information coming out here.

Ejaaz:
So kind of stay tuned to Limitless. Put the notifications on,

Ejaaz:
guys and also subscribe if you want to get the latest videos.

Ejaaz:
We put out the best content out there.

Ejaaz:
It's unchallenged right now. Josh and I are sitting here unchallenged.

Ejaaz:
You have to like and subscribe if you want to get our content on your feed.

Ejaaz:
Thank you so, so much for listening. Again, let us know what you thought of

Ejaaz:
this episode in the comments. Get that like number up and we will see you on the next one.