Revealing Elon’s Secret AI Trading Bot: Is It Worth It?

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.