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.