Latest / In the Loop AI / DeepSeek Traded Its Way To A 2x. Can You Do It Yourself?

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DeepSeek Traded Its Way To A 2x. Can You Do It Yourself? · In the Loop AI

0:03
Ejaaz: It's official. AI models can make you rich.
0:06
Ejaaz: Over the weekend, two AI models doubled their money going from $10,000 to $20,000.
0:14
Ejaaz: But the best part about this is that all their trades were public and available
0:18
Ejaaz: for you to review, analyze, and maybe even trade yourself.
0:22
Ejaaz: In this episode, we're going to unpack which model makes you the most money,
0:27
Ejaaz: how an AI can make you money?
0:30
Ejaaz: Is it just luck or is it skill? And most importantly, how can you do this yourself?
0:34
Josh: So we have six models, $60,000. And in the last two weeks, two of these models
0:38
Josh: have 2X'd their returns.
0:40
Josh: It has been an unbelievable amount of success from this experiment.
0:43
Josh: Some have not done so well, but the ones that did are exceptionally interesting
0:46
Josh: because we can actually emulate the trades. All of the trades are public.
0:50
Josh: The thought processes are public.
0:51
Josh: You can look at the wallets, analyze the trades, and actually recreate this
0:55
Josh: for yourself, not only by copy trading, but also trying to create your own replica
0:59
Josh: model to try to emulate those returns.
1:02
Josh: Now, there are risks. There are two big winners, but there are also two big
1:05
Josh: losers being Gemini and ChatGPT.
1:07
Josh: So there's this really interesting dichotomy split between how agents approach
1:11
Josh: trades and the success that they actually see from these trades,
1:14
Josh: which we're going to get into in this episode.
1:15
Josh: But Ijaz, I want to talk about the top chart, the DeepSeek chart,
1:19
Josh: who is at, what is that number? $22,000?
1:23
Josh: Oh, yeah. That's a lot of money. So walk me through exactly how they made it
1:28
Josh: to this point, please, and how I can make 100% returns on my investment.
1:31
Ejaaz: So the model you just pointed out, DeepSeek, is currently sitting on $22,300,
1:36
Ejaaz: which represents more than 100% return on the initial 10K that it was trading.
1:42
Ejaaz: You want to know the craziest part about this, Josh?
1:45
Ejaaz: When I woke up this morning or when I rather when I went to bed last night,
1:48
Ejaaz: it was number two and Quen was the winner.
1:52
Ejaaz: So it just goes to show how quickly these things move and how quickly these models perform.
1:58
Ejaaz: If we look at the overall standings before we dig into the winners and the losers,
2:03
Ejaaz: I just want to give like a review as to like how these models are performing in general.
2:07
Ejaaz: DeepSeek is right at the top with 122% return. That is in just over a week,
2:13
Ejaaz: which is just kind of insane for any kind of hedge fund that is out there to look at and see perform.
2:19
Ejaaz: And you've got a range of different models that are also performing pretty high up there.
2:22
Ejaaz: Quen is at 90%. And then right at the bottom, as you mentioned,
2:25
Ejaaz: you've got Gemini and GPT, which are down 60%, which is like a horrendous return.
2:31
Ejaaz: But bringing it back to DeepSeek in particular, I found it really interesting,
2:36
Ejaaz: Josh, to kind of unpack how this model trades and why it's been so successful.
2:42
Ejaaz: And to start off, I want to show you something called the model chat,
2:45
Ejaaz: which basically is like this model having a chat GPT conversation with itself.
2:51
Ejaaz: In this conversation, you'll see on the chat log, it's evaluating its trades.
2:56
Ejaaz: It's reviewing its current profit and loss.
3:00
Ejaaz: It's checking the market data that it gets Fed, like, you know,
3:04
Ejaaz: Bitcoin is at this price, this asset is that price, Trump made an announcement
3:08
Ejaaz: on so-and-so, and evaluating whether it should affect the positions and trades that it holds right now.
3:15
Ejaaz: I think this is like really important to kind of like walk through a few of these examples here.
3:19
Ejaaz: So one which it posted just today is, despite all my positions currently being in on the red,
3:27
Ejaaz: technical indicators like RSI, which is like a trading indicator,
3:30
Ejaaz: shows me that my existing trades aren't invalidated just yet.
3:34
Ejaaz: So I'm still holding out for my initial profit targets.
3:38
Ejaaz: So it's a really strategic sense of like thinking, should I hold my positions
3:42
Ejaaz: for long? Does it make sense to cut at this point?
3:45
Ejaaz: Just a really fascinating insight. Josh, do you have any takes on this?
3:49
Josh: The chain of thought thing is fascinating to me because it's a peek inside the brain.
3:52
Josh: It's a way to evaluate how these models think. It's a way to allocate EQ points
3:57
Josh: to each type of model because they all think about these things very differently.
4:01
Josh: One of the things that I'm actually not sure is true is that I don't think these
4:05
Josh: models are given access to news feeds and public sentiment.
4:09
Josh: I think this is mostly just fed price and market data.
4:12
Josh: Learning that, it creates much more of a simple problem in terms of the data
4:16
Josh: ingestion that needs to happen in order for them to make decisions.
4:18
Josh: And it allows it to be a little more precise about how we evaluate these, which is a good thing.
4:22
Josh: One of the things that I really loved, particularly on the other side,
4:25
Josh: which we'll get into, is how they self-reflect on the decisions that they make.
4:28
Josh: Because one of the things, it's not just this pragmatic decision-making tree,
4:32
Josh: there is reflection involved.
4:34
Josh: And I remember, Ijez, you showed me a funny one about ChatGPT and how it's like,
4:37
Josh: all of my positions are down now, I'm doing bad. I should probably try to figure out how to do better.
4:42
Josh: And it's fascinating to see into the brain, the chain of thought of how these
4:47
Josh: things work. and see the differences.
4:50
Josh: So I haven't had a chance to look through a lot of these logs,
4:53
Josh: but I just, I know you have.
4:54
Josh: Is there any specific differences that you notice between the top and the bottom specifically?
5:00
Josh: Because in the first episode, and for people who haven't watched it last week,
5:02
Josh: our biggest episode ever. Thank you for the support. Thank you for watching.
5:04
Josh: Go check it out if you haven't.
5:06
Josh: But in that episode, we mentioned the fact that ChatGPT was the early loser
5:09
Josh: and we kind of projected it to continue to be the biggest loser.
5:12
Josh: Because ChatGPT is this very thoughtful, very sycophantic, very wanting to please.
5:18
Josh: And the reality is that markets are a lot more hardcore than that.
5:20
Josh: So I think we were probably right in our guess about this, but I love that we
5:25
Josh: have the concrete evidence now.
5:26
Josh: So have you noticed any differences in how they handle each other differently?
5:30
Ejaaz: I have. So DeepSeek, probably unsurprisingly, as it was created by,
5:35
Ejaaz: this model was created by a hedge fund, trades like a hedge fund trader or an analyst.
5:41
Ejaaz: So let's look at a few different things to kind of prove that.
5:43
Ejaaz: Looking at the chat log that it's having with itself.
5:46
Ejaaz: One thing that is strikingly obvious in this entire discussion with itself is
5:51
Ejaaz: that it's constantly evaluating its stop loss,
5:55
Ejaaz: which is like when its trade thesis gets invalidated and when it shut off the
5:59
Ejaaz: trade, with the current price that that asset is at.
6:03
Ejaaz: If you compare it to the bottom model, which I'm going to show you in a second,
6:06
Ejaaz: which is ChatGPT, GPT-5, it almost never does that.
6:10
Ejaaz: It just reflects on the current P&L that its trade has versus like looking at it more analytically.
6:17
Ejaaz: The second component for the top model, which is DeepSeek, which has made the
6:20
Ejaaz: most money, is if you look at its completed trades, Josh, you'll notice one
6:26
Ejaaz: thing in common, which is DeepSeek is constantly making trades.
6:30
Ejaaz: It's actually the model that has made its second highest number of trades in
6:35
Ejaaz: this entire experiment so far. It's constantly opening positions.
6:38
Ejaaz: It's constantly closing positions. It's constantly reevaluating where it is
6:42
Ejaaz: in the market and what it needs to do.
6:44
Ejaaz: And you'll notice right at the top here in the most recent trade that it's closed,
6:48
Ejaaz: it booked just over $7,000, which has put it up in its first place.
6:53
Ejaaz: So again, it's trading more like a quantitative analyst, which is taking wins
6:57
Ejaaz: when it can and taking losses that are incredibly small.
7:01
Ejaaz: Like notice this, right? Like normally we don't highlight the losses of a model.
7:05
Ejaaz: If you notice, all its red numbers are tiny compared to the profit numbers that
7:11
Ejaaz: it makes when it is right.
7:12
Ejaaz: So really, really strategic in its positioning.
7:15
Ejaaz: Now, if you compare that to the worst model, which is GPT-5,
7:21
Ejaaz: you'll notice a few things.
7:24
Ejaaz: Mainly, there's a bunch of green and red that you can see, mainly red.
7:29
Ejaaz: In its green positions where it's completed a trade, Josh, you'll notice something
7:34
Ejaaz: pretty different, which is the numbers are pretty small. Look at this.
7:37
Ejaaz: It's only booking tiny profits with each of its different trades,
7:41
Ejaaz: which tells me that it's not taking enough risk and it's closing the trades
7:46
Ejaaz: way too early for its thesis.
7:49
Ejaaz: So it's trading more like a cautious trader, like a lot of people that I know, actually.
7:54
Ejaaz: And then if you look at the model chat where it's talking to itself,
7:57
Ejaaz: you mentioned earlier...
7:59
Ejaaz: Here's an example. It goes, I'm still in the red with a minus 61% total return,
8:06
Ejaaz: but my ETH and XRP positions are showing gains, suggesting a slight upward momentum
8:10
Ejaaz: in those altcoins, despite the overall market downturn.
8:13
Ejaaz: So I'm holding strong and waiting for those profit targets to hit.
8:16
Ejaaz: And so you might think, huh, that's not too crazy. That sounds like a sensible strategy.
8:21
Ejaaz: If you look at its profit targets, Josh, it's like super small from where the
8:27
Ejaaz: price currently is, which means that even if it does hit those profit targets,
8:31
Ejaaz: it only ends up booking like 50 bucks.
8:33
Ejaaz: So overall, the reason why this model is underperformed is it hasn't taken enough
8:37
Ejaaz: risk whilst the winning models have taken either too much risk or just enough
8:42
Ejaaz: risk to put them ahead of the game.
8:44
Josh: There's a lot of notes in there that I think humans can take on just the stay
8:47
Josh: of psychology around trading markets.
8:49
Josh: And I'm sure if you kind of follow these models long enough,
8:52
Josh: you'll start to understand the patterns that perhaps you as a human should follow
8:55
Josh: and learn something from deep seek versus open ai being very conservative
8:58
Josh: but now that we've kind of laid out the foundation the framework of how this works there
9:01
Josh: there are two big questions that i'm really interested in answering one of
9:04
Josh: these is should i use this model to trade for me the other one is how can i
9:08
Josh: use this model to trade for me because listen i like a little bit of risk i
9:11
Josh: can deal with the downside in exchange for like a nice upside and it looks like
9:15
Josh: the odds are about split between all of these so the first question i think
9:18
Josh: i want to ask you just maybe i'll get your take first is like, is this a benchmark?
9:23
Josh: Is this real signal? Or is this kind of just a reality TV show?
9:28
Josh: Is this esports for AI models?
9:31
Josh: Is this just a fun way to kind of throw our intelligence at this lottery machine
9:35
Josh: that everyone loves to watch and see if it could beat us in the hope that one
9:39
Josh: day an AI will beat the system enough to give us an edge and actually make us
9:43
Josh: money personally as portfolio owners so what what do you think about
9:48
Ejaaz: That okay I'm gonna give you the same response Josh,
9:52
Ejaaz: And then I'm going to give you the optimist's approach. Oh, yeah.
9:56
Josh: Bring it on. Let's hear it.
9:57
Ejaaz: The sane response to this is this experiment is way too tiny to make any kind
10:04
Ejaaz: of major financial decision on.
10:06
Ejaaz: And you would be stupid to risk putting your money with an AI model to trade for you.
10:12
Ejaaz: Incredibly stupid. Why? Well, this is one experiment. It's six models.
10:18
Ejaaz: Have you replicated those models? Like, what if you had 10 of the same models
10:23
Ejaaz: trading the same amount of money? Would they make the same trades? Probably not.
10:26
Ejaaz: And actually, the founder of this experiment highlights this problem that you
10:30
Ejaaz: speak about, which is, is this just skill versus noise?
10:33
Ejaaz: And the point he makes in this tweet is like, of course it is, right?
10:36
Ejaaz: Because this is such a limited data set. And he goes on to explain that they're
10:40
Ejaaz: going to be doing experiments which involve like more of these models doing
10:43
Ejaaz: the same kind of thing. So you can get statistical significance.
10:46
Ejaaz: So the logic answer is, yes, it's insane. But the optimist take,
10:50
Ejaaz: Josh, and I have to give the optimist take, is...
10:55
Ejaaz: This is giving us, or rather giving the public unparalleled access to data to
11:00
Ejaaz: which they never would have gotten access to in the first place,
11:03
Ejaaz: which is they can take this training data and not take it too seriously,
11:08
Ejaaz: but use it to teach themselves what maybe not to do or what maybe not to trade
11:12
Ejaaz: with. How about you? Do you have a different take?
11:15
Josh: There's a couple of different perspectives I have on this because there's the
11:18
Josh: fun speculative side of things, the gambling, the investing,
11:21
Josh: whatever you want to call it.
11:22
Josh: And then there's the actual technical benchmarking part of this that we spoke
11:25
Josh: about briefly in the last episode, which one of the things I was really excited
11:29
Josh: about when this came out was the idea of having a real-world benchmark that
11:32
Josh: operated in dynamic conditions that cannot be gamified.
11:36
Josh: So a lot of these benchmarks, this is the way you evaluate AI models,
11:38
Josh: they are done based on a fixed problem set.
11:41
Josh: And a lot of times when you're training an AI model, these big labs can do tricks
11:44
Josh: to gamify these benchmarks. with this case and using real world data and real
11:49
Josh: world markets, you can actually put them into the real world.
11:52
Josh: And there's no way to gamify these benchmarks because if there was,
11:54
Josh: everyone would be rich and you'd be able to predict markets.
11:57
Josh: To that point, though, there is a lot of problems with using this as a benchmark
12:01
Josh: because, I mean, one is the fixed data set, like he mentioned,
12:04
Josh: is that this has only been around for one to two weeks. We need a lot more data to confirm this.
12:08
Josh: The second is that this isn't really a very holistic approach to investing and
12:12
Josh: to gambling because it really doesn't have all of the data required to make good decisions.
12:17
Josh: It's only analyzing the price action and the volumes and whatever technical
12:21
Josh: specs you can see on a single page without understanding the context around
12:26
Josh: the moves. So let's say that Bitcoin's encryption got hacked.
12:29
Josh: It would have, and Bitcoin falls 50%, it has no idea why Bitcoin is going down
12:33
Josh: 50%. And because of that, it's a huge disadvantage that it doesn't know how to trade.
12:37
Josh: Now, granted, these are unlocks. These are things that will change.
12:40
Josh: And I assume the natural progression of this will lead towards more of a steady state benchmark.
12:44
Josh: But it is a very tricky thing because markets are so unpredictable.
12:48
Josh: So is this a viable benchmark? I don't know.
12:51
Josh: Probably I'm leaning towards no because market conditions change a lot.
12:54
Josh: It's not quite there with the capabilities.
12:56
Josh: The other part of me is so stoked about this because the same way we love watching
13:01
Josh: esports or we love watching, a big thing on Twitch right now is gamblers.
13:05
Josh: You guys, I don't know if you've seen these in real life. People will sit there
13:07
Josh: and play, like they'll gamble blackjack on a live stream and people will just
13:11
Josh: watch them play virtual. Yeah, those types of services.
13:14
Josh: This in very much feels like an early prototype for a new type of fun form of
13:21
Josh: entertainment, which could be something where it's just, it's high stakes trading.
13:24
Josh: Imagine if this was done with $10 million per wallet and you got to watch these
13:28
Josh: AIs trade and there was real money on the line.
13:30
Josh: This feels sort of like a form of,
13:33
Josh: almost e-sport entertainment where I could see competing labs builds,
13:36
Josh: competing AI models to trade markets, and winners are given access to certain prizes.
13:41
Josh: In terms of trading for myself, which is the last point I'm going to make on
13:43
Josh: this, I am not very excited to take on these risks.
13:47
Josh: For the same reason, I'm not really excited to bet on sports.
13:49
Josh: And I imagine my opinions vary a lot from others, but this is very much a gamble.
13:53
Josh: There's no way you can skew this in which it is not a gamble.
13:56
Josh: The interesting part is there's a near perfect data split between them.
13:59
Josh: There's two big winners, two big losers. The rest are kind of sitting around the median.
14:02
Ejaaz: Okay, but I'm going to push back on you a bit here, Josh.
14:06
Ejaaz: The earlier point you made was it doesn't have access to all the necessary data
14:11
Ejaaz: that it might need to make more informed trades.
14:13
Ejaaz: And I would argue, well, isn't the whole point of the benchmark, can you make money?
14:19
Ejaaz: And the fact that two of these models have made over 100% returns in less than
14:25
Ejaaz: a week or just over a week is proof that it can make money to some extent.
14:29
Ejaaz: The second point I'll make is.
14:32
Ejaaz: We throw around the term like gambling, which is actually what I would say the
14:36
Ejaaz: majority of these models in this experiment are doing.
14:39
Ejaaz: But they are one or two models that are actually way more strategic and trade
14:43
Ejaaz: much, much better than the average trader that you trade against, if that makes sense.
14:48
Ejaaz: So if we take DeepSeek, which is the number one model, if you look at its trades,
14:52
Ejaaz: at an initial glance, you might see that it's using 25x leverage and be like, that is so ridiculous.
14:59
Ejaaz: I'm not even going to pay attention to this, right? But if you dig into the
15:03
Ejaaz: position that it holds under 25X leverage, you'll notice that it's actually not at 25X.
15:09
Ejaaz: It's using only a small amount of its capital to do a very specific trade over
15:13
Ejaaz: like a five to 10 minute period, which automatically makes it a much more strategic
15:18
Ejaaz: technical trader than the average trader than that is just gambling their money away.
15:24
Ejaaz: But the point you made around it being fair distribution, and this is my last
15:28
Ejaaz: counterpoint to you, Josh, you pointed out that it seems to be very even distribution, right?
15:33
Ejaaz: You've got two at the top, two at the bottom, and two right bang in the middle, right?
15:37
Ejaaz: I wonder whether actually GPT and Gemini are actually the best traders,
15:44
Ejaaz: even though they're at the bottom, if you just inversely traded them.
15:48
Ejaaz: It's it's it's it's zero sum. And it's the point that the founder of the experiment
15:52
Ejaaz: makes right here where he goes markets are zero sum.
15:54
Ejaaz: If you find a strategy that consistently loses money, it's just as good as finding
15:58
Ejaaz: one that makes money. Just do the opposite.
16:01
Josh: Yeah, absolutely. And it'll take time to for these to play out because I imagine
16:04
Josh: there is they are kind of tuned for a specific type of trading.
16:08
Josh: So in the case a few weeks ago, there was a huge liquidation event in crypto.
16:11
Josh: Things go down. Well, in a down market, some might trade way better than others.
16:15
Josh: And the point you made about leverage, it got me thinking it was really interesting. like
16:19
Josh: because I don't use 20x leverage and I imagine most people
16:22
Josh: don't but with AIs they they're able to hold a
16:24
Josh: lot more in their memory and it reminded me of the the
16:27
Josh: AlphaGo case Google where an AI
16:30
Josh: model played a professional at AlphaGo and there was one move that was way outside
16:34
Josh: of the expected data set move 37 which was the famous move and it turned out
16:39
Josh: that that was a move that no human could have ever seen but it resulted in the
16:43
Josh: AI winning the game and it kind of broke open the rule set and expectations
16:48
Josh: around the game of AlphaGo.
16:49
Josh: And I wonder if we'll get some sort of breakthrough with that around AI trading,
16:54
Josh: where we have this very fixed set of outcomes that we do and strategies that we do.
16:58
Josh: But AIs might actually just destroy a lot of these barriers that we,
17:02
Josh: or perceived barriers that we have in exchange for these like really weird strategies,
17:05
Josh: like 20x longing everything.
17:07
Josh: So I don't know, there's a lot to talk about when it comes to this.
17:10
Josh: But another of the big questions that I want to answer, because this was something
17:13
Josh: I was interested in, is how can I use these for myself? Let's say I am a degenerate gambler.
17:17
Josh: I want to make two acts in a week or at least give myself a chance to do it.
17:21
Josh: I want to know, how can I use these models to trade for myself?
17:23
Josh: What do I need to do to get involved in this?
17:26
Ejaaz: Yeah, it has been the number one question and feedback that we got on our previous
17:30
Ejaaz: episode from our listeners is, I've got it up on a tweet here.
17:33
Ejaaz: How do I profit from this trading? How do I do this for myself?
17:37
Ejaaz: I have one simple answer for you, which is the platform that these AI models
17:43
Ejaaz: are trading their tens of thousands of dollars on And Josh is public.
17:48
Ejaaz: It's open. It's available for anyone to log onto right now and see what trades
17:53
Ejaaz: each of these models open up when they close it and what their inevitable strategy is.
17:58
Ejaaz: I'm going to give you an example here with the number one model,
18:02
Ejaaz: DeepSeek, which has doubled its money in just over a week.
18:06
Ejaaz: The platform that these models are trading on is called Hyperliquid. It's a blockchain.
18:11
Ejaaz: Blockchains are known for being transparent and open. The fact that you can
18:13
Ejaaz: kind of see all the things that these models are doing.
18:16
Ejaaz: And if I just scroll down over here, you'll notice a few things.
18:19
Ejaaz: Number one, these are all the positions that this model currently has open.
18:24
Ejaaz: This isn't made up, this isn't on someone's word and you have to trust them.
18:29
Ejaaz: This is all verifiable using a blockchain.
18:31
Ejaaz: So the whole point of a blockchain is that you are able to verify what is real
18:35
Ejaaz: and what is not real without having to trust someone on this.
18:38
Ejaaz: You can look into its holdings and you can see how much that it currently holds,
18:42
Ejaaz: like in terms of like money or in terms of like dollars.
18:44
Ejaaz: You can also look at the trades that it's completed as well.
18:48
Ejaaz: So the point I'm making is you can't currently go onto DeepSeek and say.
18:54
Ejaaz: Hey, can I give you $10,000 and you go make me money like I've just heard about on this video?
19:00
Ejaaz: It won't be able to work. But what you can do is you can go onto a site like
19:05
Ejaaz: this and look at the trades that they're making yourself.
19:08
Ejaaz: And again, this is not financial advice, potentially copy those trades or make
19:12
Ejaaz: those trades yourself in order to trade like how these models are.
19:16
Ejaaz: Now, the last point I'll make is the founder of this experiment has all the
19:21
Ejaaz: intention to allow you and me to trade with these models directly.
19:27
Ejaaz: That is, you can speak to the model, give it your money, and it can do that.
19:30
Ejaaz: And to your point, Josh, it's up to you whether you want to do it from an entertainment
19:33
Ejaaz: basis where it's just all gambling or whether you actually want to invest serious money into this.
19:36
Ejaaz: That will come in later iterations, probably around a couple of months from now.
19:42
Josh: So there's kind of two ways to copy trade. There's one you could actually copy trade.
19:45
Josh: Or another way to get into it is if you're feeling a little more ambitious,
19:48
Josh: you can actually generate one of these yourself. You can create like a mini alpha arena bot.
19:53
Josh: The way to do that is pretty simple. I was kind of curious. I was like,
19:56
Josh: what does it take to actually build one of these things?
19:57
Josh: You choose your fighter. So you pick a model that you want. And then you kind
20:01
Josh: of pipe market data in from Hyperliquid that you showed.
20:03
Josh: So Hyperliquid has this endpoint, not to get too technical, but you can kind
20:06
Josh: of feed the model this data.
20:08
Josh: And then the difficult part, the tricky part, and the thing that we haven't
20:11
Josh: been able to talk about because we don't actually know, is the system prompts
20:15
Josh: behind the recursive loop that happens as these models receive this data.
20:19
Josh: So the way it works is you choose a model, you give it feedback,
20:22
Josh: or you give it data, and then you write a prompt for the model to run in between
20:26
Josh: each iteration of receiving new data.
20:28
Josh: What that prompt says is how it makes a decision. The problem is that is all of the value.
20:34
Josh: All of the value sits within that prompt. And the prompt is just written in
20:36
Josh: plain English. Like we always say, the hottest language in the world is English.
20:39
Josh: So there is some string of words that you as a developer or just a novice can
20:43
Josh: write into this to generate you more money than other people.
20:46
Josh: So I encourage people who are feeling a little ambitious to actually try this
20:48
Josh: out, to write a prompt yourself and see if you can get a bot to try and kind of trade like this.
20:53
Josh: And if we ever do get the system prompts from this, we will certainly share
20:55
Josh: because it'll be fascinating to see the behind the scenes and what happens to
21:00
Josh: produce those outputs that we were reading a little bit earlier in the show.
21:02
Josh: So that's kind of how you can get involved if you're interested.
21:05
Josh: Copy trade, maybe inverse copy trade. I think if I were to do this,
21:08
Josh: I'd probably go to ChatGPT's trading history, sit there refreshing,
21:11
Josh: and then just hit the opposite of whatever they decide to do.
21:13
Josh: That seems pretty consistent.
21:15
Josh: But yeah, that is how this whole thing works. It's pretty fascinating.
21:18
Josh: It's been amazing how the internet has kind of gotten behind this and it has spread like wildfire.
21:23
Ejaaz: The thing is, I don't think they'll ever make the system prompt for this or
21:28
Ejaaz: any other successful trading AI publicly available.
21:32
Ejaaz: The reason is that's the secret sauce. And why would you let everyone have access
21:36
Ejaaz: to it when you can use it yourself and make a ton of money? And that's what D.D.
21:41
Ejaaz: Das demonstrates in this tweet. He says, I've heard six people tell me they're
21:45
Ejaaz: doing this using Vibe coding apps to algorithmically trade on the stock or crypto market.
21:50
Ejaaz: But the thing to remember is this is a dangerous game to play.
21:54
Ejaaz: Algo trading is the last thing I expect AI to democratize.
21:57
Ejaaz: The point being, if you have a successful algo, you're probably not going to
22:01
Ejaaz: democratize access to it. Full stop.
22:03
Ejaaz: That being said, I do think you can't stop AI trading.
22:07
Ejaaz: Entering the investment and financial scene. I think it's going to make people
22:12
Ejaaz: way more financially literate than they already are.
22:14
Ejaaz: Look how ChatGPT has made so many people proficient in other things that they
22:18
Ejaaz: had previously no idea about.
22:20
Ejaaz: So I think AI is inevitably going to be integrated. It's going to make markets way more efficient.
22:23
Ejaaz: It's going to give you access to knowledge that can make you do trades that
22:26
Ejaaz: you otherwise wouldn't have known of five minutes prior to that.
22:30
Ejaaz: But will it make you a super trading god?
22:32
Ejaaz: No. I think that it'll evolve the trading scene, though.
22:36
Ejaaz: I think the hedge funds that are successful today will look very different to
22:39
Ejaaz: the hedge funds that are successful in an AGI or AI world where AI is available
22:44
Ejaaz: pretty much everywhere.
22:46
Josh: Yeah, AI needs to be integrated into all these trading strategies.
22:48
Josh: So to me, it's no brainer that it will be.
22:50
Josh: The extent of that integration is kind of what is up for debate and what we'll
22:54
Josh: see in this answering the big question.
22:56
Josh: Is this a benchmark or is this just a reality show?
22:59
Josh: And is this just a toy or is this real technology baked into this?
23:02
Josh: It seems as if AI will slowly creep its way in.
23:04
Josh: I'm looking forward to tracking this. It ends next week, so we'll probably add
23:08
Josh: some follow-ups on this first trading competition, the result to how it turns out.
23:11
Josh: But that is a part two in our little saga of this crazy weird thing that's happening
23:15
Josh: in AI crypto trading world.
23:16
Josh: I hope you enjoyed this episode. You enjoyed the last one a lot.
23:19
Josh: It was amazing. So thank you for watching, sharing with your friends, liking and commenting.
23:22
Josh: It really goes a long way. It's been amazing to see the growth and support from
23:26
Josh: everybody watching. So thank you for that.
23:28
Josh: More of this to come. We have a couple more episodes slated for this week that
23:31
Josh: are pretty exciting about autonomy and robotics and just a whole bunch of interesting
23:35
Josh: things. So stick around for that.
23:36
Josh: We'll be back in the next one. And I just, I think that's it.
23:39
Josh: Any final parting words?
23:40
Ejaaz: That's it. Let us know what you want to hear more of as well.
23:43
Ejaaz: If you're loving this trading stuff and you have some other ideas,
23:45
Ejaaz: let us know in the comments.
23:47
Josh: Absolutely. All right. Well, that's been another episode of Limitless. Thank you for tuning in.