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