Hello and welcome to another episode of OddLotss podcast. I'm Jill Weisenthal

and I'm Tracy Aloway.

Tracy, I've I've always had this idea for the podcast or a thing that I've wanted to do. Okay. conceptually with podcasts is schedule every guest for two interviews.

Also the the audience complains. I wish you would asked that and then fill in all those gaps that had been inspired by the previous conversation.

I don't think it's a bad idea. I think it would double the number of episodes that we put out. But sure, there are topics that come up, usually things that we're just kind of new to and we're trying to learn about specifically technical things and one of those has to be AI, right?

AI and also, you know, I really had a great time. I guess last month we were in Chicago. We talked to a bunch of different, it was like a trading related trip. We uh interviewed Don Wilson. We interviewed the head of the CME. We had some other chats and they're all about the world of trading.

When it comes to trading, it's like, you know, we talked to long-term investors, portfolio managers, endowments. [clears throat] We talked to some people in the hedge fund space who like maybe have a holding period of several weeks or whatever. I actually really want to learn more about the trading like these people who have like a holding time of 1 second or something like that because that's where a lot of the tech and a lot of the actual like action is and how that world makes money and how they actually deploy technology is very interesting but still something I don't have my handle on.

Well, the practical application, right? And also the culture of AI on Wall Street. I find that really interesting because I remember I guess it was like more than a decade ago. But remember Lloyd Blankfine saying that Goldman Sachs is a technology company.

Yeah. And all these bank CEOs saying we're going to install ping pong tables to get all the coders. And now I see ads at trading firms and it's like we have a data center full of B200s or we have a data center full of G300s. Come work for us.

The only thing besides all their tech that I know is like every time you read a profile of any trading company, they're like and they love to play back gammon there. They love to play like all the article the chess boards are out and they could be seen playing chess over lunch etc. I get it. Okay, they like odds, they like games, they like whatever. Let's move the ball forward.

Well, there's also the underlying theme of is this all hype? Right. Because you do get the sense sometimes that companies are putting out press releases where they just mention AI to tick a box to be seen to be doing something and hope that their stock actually goes up.

And because so much of this is proprietary and people kind of have an excuse not to go into detail about it,

sometimes you do get the feeling that people are just talking about it and not actually using it.

Cynics, and I'm not saying this myself.

I know you're not a cynic. Speaking of trading and technology, Cynics would say that CME's deal with Google to build a cloud to you put trading on the cloud was hyped. That was a press release. People have said that people have made that charge and they don't understand why. You don't have to comment. You don't have to say anything further on that.

I do have a comment, but I'll hold it for our guest.

I'm just saying there is this world where people do press releases and cynics go, I don't really understand the point. Anyway, there's a very long wind up. Let's learn more about the world of trading. Let's learn more about AI and tech specifically. What does it even mean to apply AI within the realm of trading?

We are going to be speaking with Ian Dunning. He is the head of AI at Hudson River Trading. He was previously at Deep Mind. So, his uh trading and AI bonafides are about as good as it gets with

you've established them.

We've established that really the perfect guest to answer all our questions. So, Ian, thank you so much for uh coming on the podcast.

Yeah, I'm really happy to be here. I I I agree is the mystic the mystique factor is kind of overblown even if it's understandable why people embrace it sometimes.

We're going to blow past the mystique. Let's start with some like really just like rudimentary questions. Just the first one is like Hudson River Trading as a company.

Yeah.

How does it make money?

Yeah. So we are a sort of quantitative automated proprietary trading firm which is a lot of words but I guess the way I see it is we are a service provider to markets.

Okay.

The most clear example is market making. There is like a sort of utility to the world of being ready to buy or sell any product anytime anywhere.

And for us that means stocks, futures, options, crypto, bonds. And if you could say build a magical machine to quote a price to buy or sell at any instrument and you would want to be like the best possible price, like the tightest price, people would trade with you.

Yeah.

they would be happy because there's a counterparty for their trade and they get a kind of good price like a low spread and we're happy because we essentially pick up a penny in front of a steamroller.

Like we are making sort of money from that spread and we can pick up the pennies in front of a steamroller if we have a really magical device which tells us how everything should be worth the steamroller is coming.

Yeah, it tells us when the steamroller is coming. And so I I think that's kind of the very very sophisticated sort of middleman in some sense in the same way that Amazon is. Amazon doesn't make stuff but is a very valuable profitable company. Provides a service people get value of. Same thing. We're moving stock spawns through time and space between different counterparties. And yeah,

we will ask you about the steamroller in a few minutes. But before we do that, how does AI or the way you're using AI actually differ from the algorithmic or quant trading of old? Because I guess that one of the questions is is this, you know, a sort of evolutionary change, you know, maybe a marginal improvement on what already exists or is this something seismic and a step change the big shift in the way trading actually works.

Yeah, I mean I don't want to over overstate ourselves in some sense because in the space as you mentioned before it's very like opaque what sort of different firms of this class are doing. I can certainly speak to our own experience which is we've been doing this type of trading for 20 plus years.

and much like everyone who was doing this the way it kind of worked was you handcraft features that sort of based on human intuition oh I don't know the order book looks imbalanced there's more people wanting to buy than sell the price is going to go up soon or something like that.

and maybe you you get a bunch of very smart people and they think very hard like it's almost like making a very fancy watch you kind of artistically craft all these pieces and then maybe you use relatively simple meth mathematical techniques like linear regression to combine those predictors.

and I've been going to conferences and things and recruiting for a long time and even if today just go on the internet you'll people say things like oh that's all you can do in finance for some reason they'll say this they'll say something like oh it's too noisy or markets are too non-stationary or things like this and so that's all you can do.

and I guess that belief isn't really backed up by anything in my my opinion and like lived experience I guess and so we sort of viewed it horror for a long time is well basing in the world and ideally you would put this into kind of like a machine that does not have any human biases.

I don't know how to trade stocks myself like I I buy broad market ETFs. What do I know? And so we but if you could put all the m data into a box and it kind of could turn all that data it would find things that you would never be able to do this handcrafted thing.

And we started doing that very early relatively in 2014 2013 period. And over time over the last sort of decade or so much like in other contexts that are not finance there has been sort of a hockey stick and you can measure it by the size of the models the compute deployed.

And over time that way of modeling the markets initially was not like a hybrid with the traditional way kind of just like overtook it entirely. And so now our trading is entirely driven by this magical machine that consumes all our data.

I kind of keep saying this magical machine that consumes our data for a reason which is that this is how chat GPT is trained. It consumes all the data all the internet. It's kind of scraped and connected into one place. You train a model that kind of takes it all and something emergent comes from it. And that's why I'm kind of

a bit leading but that's why I'm talking about in the sense and I think that is materially different from the like I'm using my intuition of the markets to kind of construct a predictive model.

So just to be clear how much of the usefulness of AI here is about execution and the fact that you can crunch a lot of data really quickly with hundreds or thousands of GPUs versus spotting sophisticated patterns or discrepancies that you can exploit.

I think it's both. I think one of the things that people sort of missed with the whole like do a linear regression type thing is when you really think about how much data there is in financial markets generated and when I

say data I think it's important to think of it as every event that happens in markets not the sort of time series of prices but like the actual low-level substrate people are quoting trading retracting quotes that like low-level stuff is internet scale data set sizes.

and one of our sort of bitter lessony type things of AI was like You shouldn't think too hard about how to feature engineer this and pre-process it. You should kind of throw it all in just something a form of computation that can kind of make use of internet scale data.

In the 2010s, it was like computer vision. People used to make detectors for edges of images and things and they would combine them. And same thing, it's like that was a good approach, but you know, it's completely dominated by the idea of getting a very large number of GPUs and a kind of a pretty generic neural network form and powering through it.

As for like the how is it finding things that other methods could not, it's very hard to say. Our models are not very interpretable. And I think that's fine because as Joe mentioned, our sort of trading style and holding times are better thought of as like minutes, hours, maybe like a low single digit days for the most part.

And I guess in my mind it's unreasonable to expect them to be interpretable because I I don't know if I looked at the orderbook data for Tesla or something. Am I really going to be able to tell you better than random what the price of Tesla will be in a minute's time? And so I kind of think it like that.

If you have something that's clearly superhuman already,

what level of interpretability could you expect? Like that's very different, right, to normal AI, right? This is gets into some areas that I'm very interested, but just to like establish what we're talking about. Yeah. You're trading a stock like a Tesla, Nvidia, etc.

With your magic machine,

magic machine.

Now, we had another episode where we Well, that was the money box

as a magic box.

That's a different

That's a different one with this AI machine that is sort of arguably grown, right? It's sort of grown in a lab more than it is programmed. Much like a chatbot. I know it's a very different technology. like what is the price of Nvidia going to be tomorrow or what is the price of Nvidia going to be this afternoon? What you're saying is with your technology you have a better a better chance of getting that right that you actually might be able to make an informed prediction about the future in a way that you couldn't have done say 10 years ago.

Yes.

And that people who talked about this they would come up with reasons oh the stock market it's not like chess or go and therefore you can't really do predictions the same way. But what you're saying is that with these models which are different than LLMs, there is some at least on a short time scale predictive capacity.

Yes. I think I find this still to this day a little bit hard to believe. I think you get this kind of efficient market hypothesis stuff jumped into your head. It seems like someone's saying they can predict like the price of a stock in an hour. Your instinctual reaction is incredulously like just sounds like you're kind of bluffing or making it up and but no, these models can predict this.

I think it's the way to kind of reconcile the like really man like kind of instinct is that the predictions are very bad in some sense. We don't normally talk about like accuracy, but I think the way to think about it is like the accuracy is like 50.1% type thing. Like they're only a little bit better than random.

But I suppose an extra 1% like blows up your profits if if you're doing it at scale.

Doing doing it at scale doing it enough times and over time you kind of realize the biased coin flip. And as for why it might be possible to do this without kind of invoking magic, it's like

markets are very beautiful interaction of like many different parties, all the different kind of utilities, risk preferences and things

and the only way you really see what people are doing is by like the actions they take in markets and you kind of

it's sucking up all that like signal micro signal and extrapolating.

The cynicism or the skepticism about the possibility of machines that could predict the price of stocks is a little strange, right? Because machines ingest data then whatever maybe they see a pattern more likely than not this constellation of data means tomorrow will be green. Humans do this all the time. What else do we have besides data? Right?

You have an analyst and they put out a Tesla or whatever Nvidia is going to go to $500 a share.

How dare you insinuate I'm not smarter than a computer.

We we're like all humans have this data and much less data and yet humans are making predictions all the time. There's a whole industry of it. So the idea that therefore a was for some reason a computer couldn't do this with much more data than analysts ever have this I understand why the cynicism is comes off as a little strange.

I think some of the doubt stems from this idea that a lot of these models tend to be backward-looking right and some of them occasionally are pretty bad at spotting or reacting to big regime breaks.

And I guess the thinking again sometimes is that maybe humans are more flexible, maybe more adaptive in their thinking and they can kind of spot these big cultural shifts. How do you actually I guess prepare for those big pattern changes?

Yeah, I was at HIT for CO and I thought that was kind of like the most

that was a big pattern.

That was a big pattern break and things went totally fine. Actually, it was more of an engineering crisis in some ways. stock market volumes exploded and every system was just like screaming trying to keep up with the volume of activity.

But in terms of the predictions, they stayed quite good and I had to like reconcile this in my head as well. I guess this it is a matter of like horizon and like how far in the future are we talking intraday I think a lot of the price movement is driven by just observing like the flows.

It's hard for us as humans to observe, but it's like the relative patterns of buyers and sellers in the markets and it's like yes during COVID the volatility was massive and prices were moving up and down a lot but they were going up and down

during say March 2020 and so these models it was sort of out of domain for a human but I don't think in out of domain in some sense for the models

but I guess I also don't know how you would apply this thinking if you were trying to make sort of month ahead predictions. I often get like people being like, "Oh, everyone knows hedge funds, which we're not a hedge fund, is like a they're like flipping coins and it's some survivor bias thing."

And you know, I genuinely don't know about months out prediction stuff. That is not a data rich environment.

I mean, just by definition, there have been more days than months, right? And so therefore, prediction on a day basis, you're offered a lot more data. Is that what you're saying?

That rule of thumb is basically very useful. It is a and it extends all the way down to seconds.

Yeah. And we see that empirically all the time. And so yeah, I guess all the things I'm saying do have this caveat that it does rely on being a certain level of signal to noise. I definitely cannot make reasonable claims about the price of things in like a month using the same kind of like AI hammer.

I guess also to be specific, I'm talking a lot about using market data to make these predictions. And that's because on the sort of intraday time scale, that is the most important thing. It's all about flows and things back and forth.

If you're thinking about things in a month's time scale, I think that's fundamentals. And can AI be used for that? I don't know to be honest. And it's definitely outside my wheelhouse. And I guess people have various opinions about that. And maybe some people very much would like to claim that they can.

And you know, others maybe don't, but it's definitely outside of my area of expertise. And I I don't know. Wait, talk to us about the data that you're using or talk more because this is another area where people tend to talk in PR speak.

Sometimes we [clears throat] have access to all this data, unusual data, alternative data, and that's going to enable us to use AI better. What are you actually looking at? And what have you found, I guess, most useful?

Well, I think the thing that I found most counterintuitive when I started was that when you're thinking about predicting the prices of anything a minute, an hour out, by far the most useful thing is just market data.

This is the market data feeds you can buy from the exchanges for a pretty reasonable price. People often think this is some sort of like competitive moat. the data fees for these exchanges are not particularly high and in crypto you know where it's like a wild west but everyone can collect these feeds.

and so that is the most useful raw ingredient that is the most true expression of everyone's intents right they're going to the market they're quoting the buying selling that is the primary ingredient people get kind of caught up on the whole like oh no do you have a Twitter feed type of thing and Bloomberg sells a Twitter feed through a state of products and

buy that

buy that so it's every now and then obviously something happens news happens during market hours that moves the price dislocates the price. But if you really coldly rationalize that, that is a relatively infrequent thing compared to the overall massive markets.

So for thinking intraday, think these market data feeds. It's literally like a little events someone quoted at this price and this size. It's all anonymous. Market data feeds are anonymous. And so that is the raw stuff and it is vast.

There are just millions and millions of events per day per stock per future. When you get to the day days time scale, that's where the alternative data quote unquote kind of really comes in.

As in alternative to market data, the SEC filings, the news feeds, balance sheets, broker reports, things like this. That's where that comes in. And there's a vast sea of data offerings that people try and sell that.

I think in that kind of situation, you it's a very low sharp environment you start getting into. And it's it can be hard to attribute the extra sharp each of these things. But in some sense, it's also very democratized.

There are maybe people collecting very secret data sets, but my inbox, and I'm not even the person in charge of buying these alternative data sets, is often full of people trying to sell me the latest alternative data set. And I think a lot of them don't necessarily have much predictive value,

but clearly there's there's a market for it.

What's the craziest one you've seen?

Huh?

Can you remember?

I mean, people have definitely reacted very strongly to the Wall Street Bets era and tried to kind of bunch of Reddity extracted thing and go beyond just like raw captures of Reddit and trying to distill it into something.

But, you know, I just even just thinking about it, the meme stock thing is kind of almost talked about more after it happens than it happens before. And so, like I I don't know.

This sort of a sideways question. You mentioned interpretability and this got me something I've been wondering about AI for a while. Not even in the finance realm specifically. You're a deep mind which of course produced a great go player better than the greatest grandmaster in the world.

I played chess. We know that chess engines are much better than any human. On the other hand,

as far as I could tell, there is no good AI chess tutor. So, in other words, the chess crush, but like I've never been able to like get a thing where it's okay, you did this move, but you know what, you're closing this rook file and down the line because his like it doesn't do that.

The chess.com human talk is very rudimentary, etc. Can you talk a little bit about why there are these problems where some version of AI or machine learning or whatever can do fantastically well, but then the actual explanation of what it's doing, which I think is kind of what interpretability is, can't articulate in a plain English why it's able to do what it does.

I think it's just because these neuronet networks are some sense just like a big old blob of numbers and what we're aiming to do when we're training these models is to almost like free ourselves from almost all structure and they might learn things in a way that is nothing at all like how we learn things.

And so my my my best guess for like why it's hard is because they might be reasoning in some sense internally. And people use these words like reasoning. It kind of makes me wse. I've seen imagination and things used about neural networks. My god, I don't know.

It's like kind of anthropomorphization of them is kind of dangerous because they are essentially processing things internally in this way that I think is inherently

not like how we do and that is my best sort of guess. There are some interesting counter examples. One of my favorite sort of things in the past couple years was Golden Gate Claude which was anthropic made the model.

basically get very interested in the Golden Gate Bridge and every question they asked would come back to the Golden Gate Bridge. And so they're not completely impenetrable.

Yeah.

But it's clear that like it gets hard beyond a point to kind of map this back to how anyway like we think and it's it's very tempting to and exciting to and especially for like AI safety applications which aren't really relevant to me so much but I think it's very tempting to try.

Yeah.

No, it strikes me is that if you could solve that many jobs would you could actually make a lot of productivity gains. But I do think that's an important hurdle when you're training your models. So your models are different than large language models etc.

But what they have in common is this incredible amount of data, incredible amount of compute demand. How applicable if someone had worked on LLMs would your training process be to them? How interpret how could they move from that environment to yours? Are there enough similarities in the basic notions and compute and requirements to train a model such as yours versus what people are doing at the major labs?

I would say now in 2025 absolutely.

Okay.

But I would not have said that in 2020. And this is something that kind of just caught me by surprise having done this for a while now is that our problems are kind of defined by long sequential strings of information in some sense and extrapolating from that.

If I think back to the past of AI, it was like is this a is this is this like a hot dog or not? It's kind of like the like image classifier, you know, test.

Then uh there was some stuff with audio and things that was a little bit more familiar. robotics. Eh, but when we got to this sort of LLM era, it got very interesting because suddenly the problems were very similar in that you have you want to think back over like long histories, long context.

Okay, that sounds good. You've got a lot of data and you want to turn through it in as efficient way as possible. You also have to serve this model. This model has to run in like a relatively reasonable speed.

uh especially for the LM places there are a million people typing into chatgbt.com and they want to hear a response in a relatively prompt manner of course for us also the models have to make their predictions in a prompt manner of ways the predictions aren't useful.

so all these things mean that our sort of way of thinking about it has become very similar to the frontier LLM things it's just a very different modality we're operating on I guess primarily text and we're operating on this fileless interpretable but still a sequential stream of tokens

except our tokens are market events. And so it's a lot of fun because you know in terms of like the research that is still published you can kind of look at it for inspiration and draw comparisons but it's also it's very much its own problem which is kind of keeps me interested every day because it's like its own unique thing but it's different.

I want to go back to the point you made about data and I guess democratizing finance in many ways and maybe this is a weird question but I'm thinking back to the 2010s and we used to talk about the big investment banks as flow monsters. They see all these orders. They get all these orders. They see all the flow and that allows them to optimize on funding costs and other expenses.

is the idea that data and AI can kind of replicate that advantage so that everyone or not everyone but Hudson at least becomes its own little flow monster.

Yeah, I think there's still some trends and markets that worry me a little bit in terms of I guess our platonic ideal market structure is probably like everyone trades on exchange in a centralized place, but that is not really how things seem to be going.

and there's a huge amount of like off exchange dark quasi dark volume and I think there's still a lot of corners of the trading world where like being in the room is kind of like this big advantage and this is a very much anti-AII play in some sense. data is hidden. The data the flow data is hidden and it's not something that you can feed into a machine because there's very sparse amounts of it.

And so that's kind of a an interesting trend. A lot of this data gets reported in a centralized place later, but it's not prompt enough to be useful. And so to extend that AI thrives on data, this is in some sense like an issue for the long run, you need to kind of be in the rooms where the sort of trading is happening.

I'm glad you brought that up because that's specifically what I'm curious about from the sort of physical infrastructure side. Like if I have a query at a chat GPT,

I don't care if the model is like trained in like Ebene, Texas or yeah, wherever it gets back to me, I don't whatever. But I know that for high frequency trading at least on the execution side there are certain parts that you want to be literally coll-located and you want to have the shortest possible wire and however short it is ideally you'd like it to be shorter.

Can you talk about the differences and similarities between essentially your physical hardware stack versus what would be required at a large language model frontier lab.

Yeah like that. I think at a bulk level there's actually some pretty similar things. So there's often think about it as like latency and throughput. Latency being the time to react and then throughput kind of like how much thinking you can do in a certain period of time.

And so you're right that like this space demands like low latency.

Early in the 2010s it was a sort of flash boys book and perception where it was like really kind of about arbitrageing latency. I'm happy to report that in some sense all the latency has been arbitrageed for the most part. [laughter]

There's no more edge in shortening the wire.

There's there's probably like a little bit, but it's it's relatively small. And like I think if you look at the big quant trading firms, the the need to like really make the wires as short as they possibly can is done or no longer relevant, which is great cuz I find that stuff pretty boring. Personally,

I think about it more as like for a given kind of like speed of response, you should be the smartest person. So there's like this curve. If you're going to take a second to come up with your trading decision, better be a really really good decision. And then it doesn't kind of matter that it took a second.

And if you're going to take a microscond, well, a you probably can't do too much in a microcond, but you know, it better still be the best response in a microcond. And so,

but you could be a little worse.

You could be a little worse.

And then the second the second for sure. And so essentially for our training, we use the cloud. We have our own training data centers that we've built ourselves. That is basically the same although much much smaller scale. the scale of Googles and things. I don't know. It blows my mind for spending on on on stuff like this.

We are I think big if you're not comparing us to Google or Meta, but not that's not like bajillions of dollars. So training is is kind of the same inference. You we need to put the devices close to the exchanges

and we need to think very hard about the power usage and the latency, but we have hardware teams. We make our own FPGAAS. We make our own chips and we use off-the-shelf GPUs.

And what we try and do is we try and make sure that for any given sort of speed of response, we're making the smallest possible decision we can. So you can kind of

field programmable gate array.

Oh, there you go. Sorry. FPGA.

FPGA. Yeah.

Basically, all these different devices have different latencies and throughputs. GPUs have very high throughput. They are that's what they're useful for, right?

And so, but the problem with markets is they're kind of like narrow. The amount of traffic flowing into these like LLMs from everyone typing into their web browsers is just massive. and they do all sorts of clever things to kind of batch up requests and process and things. We don't really have that luxury really like the markets are going to happen at the speed they happen. We can't kind of like duck out for a while and catch up. We kind of need to stay in the game.

So, we have all these sort of interesting design challenges around how do we use GPUs which are relatively high latency. They take a while to get back a result, but they can process the whole stock market on one GPU type of thing versus the fast response.

And so, we have whole teams dedicated to thinking about, okay, I've got this like intelligent blob. How do I get answers out of it in different ways at different speeds? And that I think is where a lot of us smarts are going in this world these days rather than the like how do I make sure my microwave towers are like slightly better aligned somewhere in like rural Pennsylvania like which is a cool challenge in its own right but it's done.

I think I think people have found the straightest line from New Jersey to Chicago.

Joe brought up some of the cynicism around CME's cloud deal with Google and this came up speaking of a specific cynic who went on the record in one of our episodes. Don Wilson basically made the argument that matching on a cloud doesn't necessarily make sense because you might put in two orders and you're not really sure which order gets filled first.

I guess you're kind of back in that blackbox environment or maybe it's a latency issue. I don't know. Is that a problem that you're seeing?

It's something that I I worry about. Our general philosophy is market should be very like transparent and as fair as possible. So like equalizing access is a good thing in terms of participants shouldn't be able to like basically pull weird tricks to be faster.

On the other hand, I think you want reliability. So like this concept of like orders arriving at different times and being filled in different orders just doesn't seem like a very sensible way to run a market. It's something that requires a lot of effort to engineer around and it's just a good market design to have.

It is a very widespread though in existing exchanges across the world. We trade in like a vast number of countries and some of the exchanges have such amazing hardware that like if two orders are sent within like a nancond of each other, this exchange will never process them in the wrong order.

Even if there's a 100 different network ports and they're all connected, they have this amazing timestamping stuff. On the other hand, you might have like a crypto exchange where it kind of feels like a kid learned JavaScript and and ran set up a website and you're kind of like you send an order and you may or may not be confirmed that they even received it and then you kind of have to refresh your like account balance page like 5 minutes later to see if there's money in it or not.

And we kind of we'll take we'll deal with it as it is. But certainly we have a preference for kind of equalized access but sort of predictable outcomes. And I think that kind of leads to like people spending effort. I think it's not a necessarily a very great thing for society for people to be like stressing very hard about YLM.

Yeah. No, probably. I'm glad I'm glad that you report that we've uh moved on a little bit since then. Where are your constraints? You know, when you talk to LLM people, there's debates about right is it electricity? Is that the big constraint? Is it there just aren't enough GPUs? Is it talent? Is it whatever?

when you think about where you are now versus the optimal version of where or is it I mean data is the other big one because there's all this concern that LLMs are going to run out of training data etc where is the big constraint for you that you feel like you're solving for right now

I think in terms of like really the long-term strategic planning electricity is like quite clearly a very binding consideration when we think about spinning up new like GPU based training data centers

it really feels like is there electricity like finding pie piece of land to put a building in. There's a lot of land.

Yeah.

The electricity negotiations

and that's an issue at HRT that thinking about

even for us, you know, because we have a sort of hybrid mix of using cloud providers and building our own data centers and yeah, the negotiations and thinking about power constraints. We have an existing data center in a very cold place and we want to make it bigger and the data center people are fantastic to work with but they're saying like well we need to go talk to like the power grid and negotiate this next trunch and so on and it's just it often feels like that is the bottleneck.

and on the terms of a GPU availability it definitely was a crunch uh at some point in the past but I don't feel like that is

can you say a little bit more the entire stock market is riding on say a little bit more about how you perceive the GPU

I think I think if we ask for GPUs we will get them delivered in a prompt manner not necessarily like next day but I don't feel like that is the thing that we have a long pole and spinning up more

when was the when was the worst of the crunch

I guess 2023 late 2023 felt pretty bad I was I guess that was like the Nvidia hopper generation and I saw some number in Bloomberg yesterday that I think it was like Nvidia conference yesterday and they said something like it was like 1 million hopper class GPUs have been made, but already like 4 million Blackwell class GPUs have been made.

So, I think there's been a ramp up of supply, but I don't think they're also sitting on unsold inventory either. I think it is being consumed, but yeah, in terms of like what is the hard thing? I think electricity and I'm it's insane.

I as a very millennial person, I guess climate change was a big thing growing up in college. a lot of discussion about climate change. And to see people spinning up data centers very fast by basically buying as many gas turbines as they can and putting them outside. I'm like,

whoa. Like what are we doing? It's wild. But that's like the only way to get electricity promptly. You just have to throw gas turbines outside the building and turn them on. I It's pretty radical stuff. And I don't know how all the numbers that people are talking about for future data center expansion kind of math out because you just back of the envelope that power usage and things and

I I know that the Sam Almans of the world have thought about this talked about this. Oh, we need to be generating this much new power generation per unit time but there there's such daunting numbers. I just don't know how that is all going to work out.

But yeah, even for us in the grand scheme of things like a much smaller player in terms of power consumption. We think in terms of like tens of megawatts and not gigawatts which is more in most towns and cities and things but still

and but we find it like a challenge to find electricity at a reasonable price. Wait, on this note, can you talk to us a little bit more about where competitive advantage actually comes from in this space? Because if the GPU crunch is somewhat solved and if latency isn't as big an issue as it used to be, where are people actually getting their edge from?

Right. I mean, people talent was one of your other things as a constraint. Yeah, it is it is a very competitive people market. We're essentially asking for people to know a lot of things, be both good researchers and good engineers because I don't know in this AI era of a distinction is pretty blurry.

It's not something you can just whiteboard and then the coding is a little bit afterwards. Any kind of research idea you have is intimately connected to how you implement it. So that's already like a tough ask. So people are constrained people that we like I want to find and we pay well for those people as a result and it is competitive.

But I think the more subtle edge is almost like putting [snorts] it all together. Do you have people who can like an engineering team that can collect all the data, record it, make it available to the GPU training data center? This is like many I guess it's pabyte scale data set of sets and just storing that much data streaming it from wherever it's stored to wherever in the world the training data center is reliably.

These training runs are very expensive. And then once you've got that model serving it, so it kind of sounds do everything. And maybe that's kind of like a lame answer, but it really is. It's I think you need to be just optimizing the whole stack.

And so like my team is like the AI team. And so and that what that really means in practice is we're focused on training the models which is an important but not sufficient part of a whole stack because we would be kind of dead in the water without the teams at HRT who who think about how to like actually kind of get the data and and things to these systems and then the decisions out to the markets and keep up when things get busy all these things.

So when I think about our competitors I think there is a benefit to to scale. I can't imagine how you would start a new company like HRT in the year 2025 because of the huge initial lift to kind of build enough engineering scale to to achieve this sort of thing.

And so I think our sort of peer companies also have invested very heavily in engineering and will continue to do so. And there was an article in the FT like a little like a week or two ago about how firms like HIT are kind of extending themselves more into slower trading and there are firms that are kind of you know those slower firms are trying to kind of go faster and

yeah I was just going to ask about just like on the prediction standpoint. Okay, maybe you could predict what's with some reasonable confidence what's going to happen in the next hour or sometimes if you're lucky maybe a day. Like maybe a month is just ridiculous. But do you in your work is that horizon has it broadened?

It is. Yeah. I think one of the things for people who are aware of HRT even at all I think is still a perception. It's sort of a pre2020 perception of like we are purely high frequency trading firm. But we would say we are both high frequency and medium frequency trading firm and it's like a big part of our business.

One way to think about it I think is that by if I really have a view on what a stock should be in like five days time. Let's say I want to buy that stock. I'm going to acquire that stock overtime and maybe it's what's the best time to buy that stock over the 5day period.

Well, I have a model that tells me that the best pricing in an hour. So maybe the shorter term model should inform the longer term trade and cascading all the way down when you're doing this sort of slightly longer term or slightly slower frequency trading.

Is the fundamental job still the same which is you're in the liquidity provision service business just over longer you want to hold that warehousing or does it some because when I think of a fund when I think of a hedge fund I certainly don't think of

maybe to some extent some of their strategies might be sort of liquidity provision but it's more directional is it still that or is the fundamental reason why you make money the service you provide does it change by definition change over that horizon

I think the market making service provision does break down I think It stretches the analogy too far. I think you have to think of it as like liquidity taking which somehow seems more like aggressive or something

but the we're trading against orders resting on the books. Someone was like I want to sell this stock and we're like we will buy it from you because we think that in the long run it'll be worth doing it and so we do cross the spread and we do pay this transaction cost sometimes.

You know you can also kind of acquire position by market making but with a tilt. So really at the longer horizons I think the sort of marketmaking service analogy does break down but in some sense there's always a counterparty and they wanted to trade for a reason.

and I think a mental model that

I don't know you tell me if this sounds like too too wishy-washy but

I love a mental model. Uh you mentioned go and chess, right? So the thing about those is that they're they're zero sum games is only one winner. It's truly like a no like someone someone's unhappy, someone wins and maybe equally unhappy plus one minus one.

I think the reason that trading works is because it is in some sense positive sum you know money is conserved and I guess a little fee goes to the exchange. So in some sense money is at that moment of a trade is actually negative a little but utility people's general happiness I don't know my paycheck goes into my 401k provider and it buys some ETFs.

I'm relatively like insensitive to how exactly that happens I just I'm not going to look at it for another 40 years right um don't lie my I try not to look at it especially lately but uh yeah like the utility my utility is a very long horizon and so if someone sells it to me like at 1 cent different I don't really care so but like the person who made the scents happy and I'm happy because I got good liquidity, didn't cross a huge spread.

So that is kind of why I think it it all kind of makes sense and why people are trading together. But it's also why like thinking about markets like an alpha go sense doesn't make sense because it's kind of doesn't really apply.

If you thought of markets as HRT and and all our competitors all kind of in some sort of like death match, who's the smartest? Who's trying to pick each other off? Then well markets would be kind of like this giant standoff where no one would be trading. Everyone would be kind of be like waiting.

But obviously markets are very vibrant. I think it's because even when we're crossing the spread, it's because we're crossing the spread against someone who wanted to sell for whatever reason.

If we were right, I guess in 5 days time they might be like less happy. But maybe they weren't actually. Maybe they were just like hedging a position. They don't care what the stock's price was in 5 days. They just wanted to like hedge their position and we traded with them.

So that's the way I tell reconcilers in my head. It could still be like a sort of service provision. We make money only because someone else wants to trade. If no one was trading, we wouldn't exist,

right? And different market participants with different motivations and goals and aims. I want to go back to the talent question for a second. And I get the sense that engineers like open source and they like contributing to the research ecosystem on AI.

And then I get the sense that trading firms probably do not like open source and they're much more into protecting their proprietary models or data or whatever. How does a company like HRT how do you actually balance that tension?

Yeah, I mean this is also like a sort of really honest answer and that many years ago this was a relative comparative disadvantage for us for recruiting. Some we would often have conversations with maybe especially PhDs who are graduating and they would say like well I can go to Google and I can still publish my research and that kind of gives me optionality. People will know who I am.

If I go into an HRT or HR like firm, I essentially go behind this veil and I never emerge and people just have to kind of take it on faith I did smart things for many years and I would have basically no strong counter argument apart from the fact that actually writing papers is kind of overrated.

I've been there, done that. As when you get older, you will not care. Now though, there's this interesting situation where this golden era maybe of like being able to be work at a big tech company, be paid for publishing research is very much over.

the papers that do come out of the big AI labs are essentially kind of either very stale or not important and if you're working on the most important cutting edge things you can't share what you're doing and it's very secretive so in some sense the problem solved itself a little bit for me.

and people now recognize that IP should be protected I've even seen some of the sort of AI lab people think out loud about non-competes in public thinking tweeting about non-competes and things which is like an amazing turn of events because I feel like

that was very anothetical Right. I mean, they're like literally effectively banned in the state of California. And I think people were almost like proud of this fact and would also kind of hold it against the New York sort of trading world being like, "Oh, look at these people with their non-confers."

And a lot of that money is being paid for talent, but it's also in some sense paying for intellectual property.

Yeah. And like those people know how the soup is made and they are not writing it down and not committing any like explicit sort of IP theft. But if you hire five people who've been making the soup,

they process knowledge.

They know they know a lot of process knowledge. And you might suddenly feel a little differently about protecting that we spend a lot of time training our employees. Takes a long time for them to be productive. In some sense, it would be a shame if people could just take that knowledge and immediately leave.

And so, yeah,

just going back to the steamroller, I promised I promised we would when I hear AI in trading or I know people are very excited about agentbased AI nowadays. Part of me thinks back to one of the more amusing events in financial history, which is Joe, I'm sure you remember the time that one of Knight Capitals ALOS went.

Many people would not find that to be an amusing event at all. the worst nightmare possible by using from

for them

the peanut gallery. Yes. Right. Shod and Freud. So this Algo went rogue and bought like 7 billion to the whole company.

Yeah. Exactly.

What are the guardrails that you put in place to avoid the destiny of night capital.

So every training cycle we have a talk about the nightmare with a K and we have multiple ex the night employees at HRT as you might expect just from a lineage of a successful trading firm that ended in a kind of an unhappy way and we have many people who are at night.

The story is crazy. A successful training firm that ended in about 15 minutes.

Yeah. Yeah.

So, it's fair to say that that stuff haunts us and we try and take as many lessons away from that as possible. Defense and layers. So, I think one of the things that I like to emphasize with the AI stuff in particular is that it is not like there's some neural network directly sending orders to NY.

It is in some sense providing a plan and then traditional human heavily audited risk checked layers take the actions and that's just kind of how it has to be. And so for us, we are kind of on a on an operational day-to-day basis, it's just many many layers of sanity checking throughout the day.

And then at a sort of high level, it's very careful process, including processes to specifically avoid the the KCG type scenario of how are you even releasing new versions and what pre-release checks do you run and audits and we even during the day we have some I don't know I guess you call them like sanity checks of the neural networks to make sure that they are producing the values that we expected they would be producing and and

those sort of checking processes are kind of a little bit behind because they can't keep up with the like flow but they were enough to kind of Just again like every of a numeric stability of the model sane and things it's not it's not about losing money or making money in these are not like oh like risk in the kind of financial sense it's like operational risk.

but paranoia is deep and that's probably something that's still very different I think from this market from the sort of other AI world which I guess anything goes and like failure rates are kind of just priced in.

Yeah. But yeah, you could you could imagine just ruining everything. And I I guess we worry about losing money, but I think we worry more about taking action that a regulator would not want us to do because if you lose that trust of regulators, you lose it for a very long time.

and we trade in a lot of markets and we pay very close attention and have deep respect for the regulators and their decisions in all those markets. And this the rules are sometimes very complex. And man, do we watch that stuff like a hawk because you don't want to be kicked out of a country

for making an operational error. And this is a very low tolerance culture from regulators in terms of making mistakes. So we stress it a lot and I think we should because it's it's you know like the profit you make in 10 years by still being in the game versus move fast and break things.

It's not move fast and break things, but you still want to move fast.

I have like a million more questions. I ju but for the sake of time I'll just ask one more and I don't know even know whether it's something you're in position great position to answer about it's something I actually want to do an entire episode about at some point but as you would characterize it what happens in the second after a jobs report is released?

and the what I'm talking about specifically is numbers either flash on a screen or a piece of a text appears on a website and markets move around a lot all that and there's people then suddenly unless actually the jobs report was good and if you actually look at the wage number and then the stocks but in that instant in that first microcond after the release markets are already moving certainly before any human has had a chance to read the thing or form of use so what I assume is that there's training on here is the text and here are the things and whatever but to as you would put it or from the perspective of HRT what happens in the millisecond after an event.

so what I assume is that there's training on here is the text and here are the things and whatever but to as you would put it or from the perspective of HRT what happens in the millisecond after an event.

Yeah. So

could be an earnings report.

Yeah. I mean, so we have like a Bloomberg headlines feed that is like pretty low latency and if it's like a important article has like a star in the feed, things like this, right? You can do everything from having kind of a handcrafted logic to look for keywords through to putting it through like an AI model.

One of the things that I like still can't kind of wrap my head around is I guess without saying specific company names, there are options trading firms that have thousands of people that are essentially cyborg trading options. They have maybe 10 people trading like the options for a single big stock like Nvidia say

and they are humans staring at the feeds for these things and clicking buttons and they have user interfaces that are set up for them to hit the green button or the red button essentially very fast

that job.

It's weird. We actually once a monkey on a computer for a hackathon. We we got a PlayStation controller and kind of gave people a chance to try and practice reacting to events very fast. It's really tough but it's a learnable skill.

I I think in an efficient market sense this should be AIable.

Yeah,

it is challenging though because if you imagine kind of plumbing it into chat GBT, it would be too slow. Like the latency would probably be sufficiently high. I mean

it it's not that fast, right? It's fast for any normal day-to-day thing, but for markets it's kind of slow.

Also, and this is like a very interesting research challenge is like you can't literally use chat GBC to back test anything. It knows every Jerome Pal speech and knows what happened afterwards because it's trained on the whole internet. So how do you really get confidence that for the next Federal Reserve speech it's going to do the right thing?

Traditionally in finance you back test things to see how it done in the past.

But if in this case it's all kind of in sample like it's seen it all before

and I've seen academic finance papers where they try and like grapple with this and they say it still works and they try and account for this but I know just this stuff is really that smart. Yeah, that whole kind of thesis is that it's memorized everything it's been trained on. So why would it be reliable?

And so whenever you see someone's being like, "Oh, I ran every Federal Reserve speech through J GBT and it got it right like nine out of 10 times." It's like

only nine out of 10 times. Like why not 100%. So I do find that I do think it is interesting there how many humans are still involved on relatively high-speed trading. There are a lot of people still doing this in in sort of niche products and it's presumably because it's very hard to integrate all the information

this AGI 20 I don't know 2028 2030 I don't know there's still a lot of humans trading stocks and options and so like I don't know how to reconcile that but I think about that when I read

Ian Dunning that was uh fantastic there really are like hours more of conversations are we going to have you back next week looking forward to next week's episode but no that was uh great for me really appreciate Yeah, a pleasure. Thank you.

Tracy, I thought that was uh really great. I like this idea, the sort of anti-ynicism because you do hear a lot of people say, "Oh, no, like AI could solve things like chess or whatever, but the stock market is fundamentally different." And I've never been totally satisfied with some of the theories for why.

And like I get stocks are not like necessarily like a solvable problem in quite the same way, but humans make money on the market by matching patterns. Why can't smart silicon brains do the same thing?

Well, there's also history now. We have many years of HFT trading and algorithmically driven trading where people have made a lot of money. So, it seems to be working. The light bulb moment for me was where Ian talked about the time frame and the importance of the time frame. And I think that's really the key in many ways.

It's adapting what you're doing with AI to the data that's available. And the data on markets, most of it is going to be very short term and more seconds than minutes, more minutes than days, etc., etc.

And a lot of the data is also biased to immediacy versus past analysis which he spoke about as well.

It is always funny in finance people like oh 17 out of 19 times there's been this death cross of the S&P 500 stocks went down. It's like any serious data scientist would spit at that sample.

[laughter] It's like beyond a joke level to talk about a sample size of 19 as being all the time. death cross in a headline is so tempting.

That's true. You all you cannot advice to journalists never pass up a chance to put a death cross. I was glad to hear I thought a few things were interesting. One is I was glad to hear that the wirelength problem is no longer a thing. It's not just this race to get closer to the extreme.

That was kind of boring when people were talking about the cold war and HFT and all of that.

It's interesting that the GPU market is eased versus where it may have been a couple years ago. And it's interesting that even at a scale like a trading shop that electricity is proving to be a main constraint which does raise questions about are we just going to hit up against a wall given some of the AI plans that so many people are banking on for the chat bots.

Yeah, I thought also I guess the cultural shift in some of the labs was really interesting. this idea that they've become more proprietary and perhaps more uh

mysterious in some ways rather than the trading firms becoming more open.

Yeah, lots of great conversation. Answer some questions. Plenty more plenty more to go.

That was helpful. And uh I'm sure we'll talk to him again. Maybe not next week, but soonish.

Maybe next year.

All right. Shall we leave it there?

Let's leave it there.

This has been another episode of the Odd Thoughts podcast. I'm Tracy Aloway. You can follow me at Tracy. [music]

And I'm Joe Weisenthal. can follow me at the stalwart. Follow our guest Ian Dunning. He's Ian Dunning. Follow our producers Carmen Rodriguez at Carmen Arman. Dash-o Bennett at Dashbot and Kell Brooks at Kellbrooks. [music]

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