Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots Podcast.
Speaker 3: I'm Jill Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, I've always had this idea for the podcast, or
Speaker 2: a thing that I've wanted to do. Okay, conceptually with
Speaker 2: podcasts is schedule every guest for two interviewers. So you
Speaker 2: have the opening interview and you ask a bunch of
Speaker 2: questions and then it's, oh God, I really wish I
Speaker 2: had followed up on that. I had more. I was
Speaker 2: just starting to sort of get my head around this thing.
Speaker 1: Now.
Speaker 2: I could have asked the good questions and then like,
Speaker 2: have the person come back next week. Also, the audience complains,
Speaker 2: I wish it as that and then fill in all
Speaker 2: those gaps that had been inspired by the previous conversation.
Speaker 3: I don't think it's a bad idea. I think it
Speaker 3: would double the number of episodes that we put out.
Speaker 3: But sure there are topics that come up, usually things
Speaker 3: that were just kind of new to and we're trying
Speaker 3: to learn about specifically technical things, and one of those
Speaker 3: has to.
Speaker 2: Be AI, right, Ai? And also, you know, I really
Speaker 2: had a great time. I guess last month we were
Speaker 2: in Chicago. Yeah, we talked to a bunch of different
Speaker 2: it was like got trading related trip. We interviewed Don Wilson,
Speaker 2: we interviewed the head of the CMME. We had some
Speaker 2: other chats. So they're all about the world of trading.
Speaker 2: When it comes to trading, it's like, you know, we
Speaker 2: talked to long term investors, portfolio managers and daomas. We
Speaker 2: talked to some people in the hedge fund space who
Speaker 2: like maybe have a holding period of several weeks or whatever.
Speaker 2: I actually really want to learn more about the trading
Speaker 2: like these people who have like a holding time of
Speaker 2: one second or something like that, because that's where a
Speaker 2: lot of the tech and a lot of the actual
Speaker 2: like action is and how that world makes money and
Speaker 2: how they actually deployed technology is very interesting, but still
Speaker 2: something I don't have my handle.
Speaker 3: On, well, the practical application, right, and also the culture
Speaker 3: of AI on Wall Street. I find that really interesting
Speaker 3: because I remember, I guess it was like more than
Speaker 3: a decade ago, but remember Lloyd Blank find saying that
Speaker 3: Golden Sachs is a technology. Yeah, and all these bank
Speaker 3: CEOs saying we're going to install pingpong tables to get
Speaker 3: all the coders, and now I see ads at trading
Speaker 3: firms and it's like, we have a data center full
Speaker 3: of B two hundreds, or we have a data center
Speaker 3: full of G three hundreds. Come work for us.
Speaker 2: The only thing besides all their tech that I know
Speaker 2: is like every time you read a profile of any
Speaker 2: trading company, like and they love to play backcam and
Speaker 2: they love to play all the article the chessboards are out,
Speaker 2: they could be seen playing chess over launch, et cetera.
Speaker 2: I get it, Okay, they like us, they like games,
Speaker 2: they like whatever, let's move the ball for Well.
Speaker 3: There's also the underlying theme of is this all hype, right?
Speaker 3: Because you two get the sense sometimes that companies are
Speaker 3: putting out press releases where they just mention AI to
Speaker 3: tick a box, to be seen to be doing something
Speaker 3: and hope that their stock actually goes up. And because
Speaker 3: so much of this is proprietary and people kind of
Speaker 3: have an excuse not to go into detail about it,
Speaker 3: sometimes you do get the feeling that people are just
Speaker 3: talking about it and not actually using it.
Speaker 2: Cynics and I'm not saying.
Speaker 3: This myself, I know you're not a cynic.
Speaker 2: Speaking of trading and technology. Cinics would say that comes
Speaker 2: deal with Google to both of clouds, to you put
Speaker 2: trading on the cloud with hype, that that was a
Speaker 2: press release. People have said that people have made that
Speaker 2: charge and they don't understand why. You don't have to comment.
Speaker 2: You don't have to say anything further on that.
Speaker 3: I do have a comment, but I'll hold it for
Speaker 3: our guess.
Speaker 2: I'm just there is this world where people do press
Speaker 2: releases and cynics go. I don't really understand the point anyway.
Speaker 2: There's a very long lind up. Let's learn more about
Speaker 2: the world of trading. Let's learn more about AI and
Speaker 2: tech specifically, what does it even mean to apply AI
Speaker 2: within the realm of trading. We're going to be speaking
Speaker 2: with Ian Dunning. He is the head of AI at
Speaker 2: Hudson Rivert Trading. He's previously at deep Mind, so his
Speaker 2: trading and AI bonafides are about as good.
Speaker 4: As it gets.
Speaker 3: With me, you've established them, we've established that.
Speaker 2: Really the perfect guest answer all our questions. So I
Speaker 2: thank you so much for coming on the podcast.
Speaker 4: Yeah, I'm really happy to be here.
Speaker 5: I agree you as the mystique factor is kind of overblown,
Speaker 5: even if it's understandable white people embrace it.
Speaker 2: Sometimes we're gonna blow past the mystiq. Let's start with
Speaker 2: some like really just like rhudimentary questions, Like just the
Speaker 2: first one is like Huns the River trading as a company,
Speaker 2: how does it make money?
Speaker 5: Yeah, so we are a sort of quantitative automated proprietary
Speaker 5: trading firm. Which is a lot of words, but I
Speaker 5: guess the way I see it is we are a
Speaker 5: service provider to markets. Okay, the most clear example is
Speaker 5: market making. There is like a sort of utility to
Speaker 5: the world of being really just buy yourself any product, anytime, anywhere,
Speaker 5: and for us that means stocks, futures, options, crypto bonds.
Speaker 5: And if you could say, build a magical machine to
Speaker 5: quote a price to buy it or sell at any instrument,
Speaker 5: and you would want to be like the best possible price,
Speaker 5: like the tightest price.
Speaker 4: People would trade with you.
Speaker 5: They would be happy because there's a count for their
Speaker 5: trade and they get a kind of good price, like
Speaker 5: a low spread.
Speaker 4: And we're happy because.
Speaker 5: We essentially pick up a penny in front of a steamroller,
Speaker 5: like we are making sort of money from that spread,
Speaker 5: and we can pick up the pennies in front of
Speaker 5: a steamroller if we have a really magical device which
Speaker 5: tells us how what everything should be.
Speaker 2: When the steamroller is coming.
Speaker 4: Yeah, it tells us.
Speaker 5: When the steam roll is coming. And so I think
Speaker 5: that's kind of the very very sophisticated sort of middleman
Speaker 5: in some sense. And the same we've had Amazon is
Speaker 5: Amazon doesn't make stuff, but it's a very valuable, profitable
Speaker 5: company provides a service. People get value of same thing.
Speaker 5: We're moving stock spons through time and space between different counterpartties.
Speaker 2: And yeah, we will.
Speaker 3: Ask you about the steamroller in a few minutes. But
Speaker 3: before we do that, how does AI or the way
Speaker 3: you're using AI actually differ from the algorithmic or quant
Speaker 3: trading of olds, Because I guess that one of the
Speaker 3: questions is, is this, you know, a sort of evolutionary change,
Speaker 3: you know, maybe a marginal improvement on what already exists,
Speaker 3: or is this something seismic and a step change the
Speaker 3: big ship in the way trading actually works.
Speaker 5: Yeah, I mean I don't want to overstate ourselves in
Speaker 5: some sense, because into space, as you mentioned before, it's
Speaker 5: very like opaque what sort of different firms of this
Speaker 5: class are doing. I can siddenly speak to our own experience,
Speaker 5: which is we've been doing this type of trading for
Speaker 5: twenty plus years, and much like everyone who was doing this,
Speaker 5: the way it kind of worked was you handcraft features.
Speaker 5: It's sort of based on human intuition.
Speaker 4: Oh, I don't know.
Speaker 5: If the order book looks imbalanced, there is more people
Speaker 5: wanting to buy than sell, the price is going to
Speaker 5: go up soon, or something like that. And maybe you
Speaker 5: get a bunch of very smart people and they think
Speaker 5: very hard, like it's almost like making a very fancy
Speaker 5: watch kind of artistanally craft all these pieces, and then
Speaker 5: maybe you use relatively simple mathematical techniques like linear regression
Speaker 5: to combine those predictors. And I've been going to conferences
Speaker 5: and things and recruiting for a long time, and even
Speaker 5: if today's going the internet, you'll people say things like, oh,
Speaker 5: that's all you can do in finance. For some reason,
Speaker 5: they'll say this. They'll say something like, oh it's too noisy,
Speaker 5: or markets are too nonstationary or things like this, and
Speaker 5: so that's all you can do. And I guess that
Speaker 5: belief isn't really backed up by anything in my opinion,
Speaker 5: and like lived experience, I guess, And so we sort
Speaker 5: of viewed it more for a long time as well,
Speaker 5: everything that's happening in the world, and ideally you would
Speaker 5: put this into kind of like a machine that does
Speaker 5: not have any human biases.
Speaker 4: I don't know how to.
Speaker 5: Trade stocks myself, like I buy broad market ets, what
Speaker 5: do I know? And so we but if you could
Speaker 5: put all the data into a box and it kind
Speaker 5: of could jurn all about data, it would find things
Speaker 5: that you would never be able to do, this handcrafted thing.
Speaker 5: And we started doing that very early, relatively in se
Speaker 5: twenty fourteen twenty thirteen period, and over time, over less
Speaker 5: a decade or so, much like in other contexts that
Speaker 5: are not finance, there has been sort of a hockey
Speaker 5: stick and you can measure by the sizes of the
Speaker 5: models the compute deployed and over time that way of
Speaker 5: modeling the markets initially was not like a hybrid with
Speaker 5: the traditional way.
Speaker 4: Bially kind of just like overtook it.
Speaker 5: Entirely and so now our trading is entirely driven by
Speaker 5: this magical machine consumes of a data. I kind of
Speaker 5: keep saying this magical machine. It consumes of a data
Speaker 5: for a reason, which is that this is how chat
Speaker 5: GPT is trained, it consumes all the data all the Internet.
Speaker 5: It's kind of scraped and connected into one place. When
Speaker 5: you train a model, that kind of takes it all
Speaker 5: and something emergent comes from it. And that's why I'm
Speaker 5: kind of a bit of leading. But that's why I'm
Speaker 5: talking about in the sense, and I think that is
Speaker 5: materially different from the like I'm using my intuition of
Speaker 5: the markets to kind of construct a predictive model.
Speaker 3: So just to be clear, how much of usefulness of
Speaker 3: AI here is about execution and the fact that you
Speaker 3: can crunch a lot of data really quickly with hundreds
Speaker 3: or thousands of GPUs versus spotting sophisticated patterns or discrepancies
Speaker 3: that you can exploit.
Speaker 4: I think it's both.
Speaker 5: I think one of the things that people sort of
Speaker 5: missed with a whole like do a linear regression type
Speaker 5: thing is when you really think about how much data
Speaker 5: there is in financial markets generated. And when I say data,
Speaker 5: I think it's important to think of it as every
Speaker 5: event that happens in market. It's not the sort of
Speaker 5: time serious of prices, but like the actual low level
Speaker 5: substrate people are quoting trading retracting quotes that like low
Speaker 5: level stuff is internet scale data set sizes, and one
Speaker 5: of us sort of bitter lesson ye type things of
Speaker 5: AI was like, you know, you shouldn't think too hard
Speaker 5: about how to feature engineer this in pre process, that
Speaker 5: you should kind of throttle in to something a form
Speaker 5: of computation that can kind of make use of internet
Speaker 5: scale data. In the twenty tens, it was like computer
Speaker 5: vision people used to make detectors for edges of images
Speaker 5: and things and they would combine them and same thing.
Speaker 5: It's like that was a good approach, but you know,
Speaker 5: it's completely dominated by the idea of getting very large
Speaker 5: umber of GPUs and a kind of a pretty generic
Speaker 5: neural network form empowering through it. As for like the
Speaker 5: how is it finding things that other methods could not.
Speaker 5: It's very hard to say our models are not very interpretable.
Speaker 5: And I think that's fine because, as Joe mentioned, our
Speaker 5: sort of trading style and holding times, a bit of
Speaker 5: it is like minutes, hours, maybe like a low single
Speaker 5: digit days for the most part, And I guess in
Speaker 5: my mind it's unreasonable to expect them to be interpretable
Speaker 5: because I don't know if I looked at the autobook
Speaker 5: data for Tesla or something. Am I really going to
Speaker 5: be able to tell you better than random with the
Speaker 5: price of Tesla will be in a minute's time? And
Speaker 5: so I kind of think it like that, if you
Speaker 5: have something that's clearly superhuman already, what level of interpretability
Speaker 5: because you expect like this is very different right to
Speaker 5: normal AI.
Speaker 2: Right, this is gets into some areas that I'm very interested.
Speaker 2: But just to like establish what we're talking about, you're
Speaker 2: trading a stock like a Tesla in video, et cetera
Speaker 2: with your magic machine machine. We had another episode where
Speaker 2: that was the money box.
Speaker 3: That's a magic bo different, that's a different one.
Speaker 2: With this AI machine, it is sort of arguably grown, right,
Speaker 2: and it's sort of grown in a lab more than
Speaker 2: it is programmed, much like a chatbot. I know, it's
Speaker 2: very different technology, like what is the price of in
Speaker 2: video going to be tomorrow? Or what is the price
Speaker 2: of ennvideo going to be this afternoon? What you're saying
Speaker 2: is with your technology, you have a better chance of
Speaker 2: getting that right, that you actually might be able to
Speaker 2: make an informed prediction about the future in a way
Speaker 2: that you couldn't have done, say ten years ago. Yes,
Speaker 2: and that people who talked about this they would come
Speaker 2: up with reasons. Oh, the stock market. It's not like
Speaker 2: chess or go, and therefore you can't really do predictions
Speaker 2: the same way. But what you're saying is that with
Speaker 2: these models, which are different than lllums, there is some,
Speaker 2: at least on a short time scale, predictive capacity.
Speaker 5: Yes, I think I find this still to mistake a
Speaker 5: little bit hard to believe. I think you get this
Speaker 5: kind of efficient market hypothesis stuff jumped into your head.
Speaker 5: It seems if someone is saying they can predict like
Speaker 5: the price of a stock in an hour, your instinctual
Speaker 5: reaction is incredulity, Like just sounds like you're kind of
Speaker 5: bluffing or making it up. But no, these models can
Speaker 5: predict this, And I think it's the way to kind
Speaker 5: of reconcile the like really man like kind of distinction
Speaker 5: is that the predictions are very bad in some sense.
Speaker 5: We don't no way to talk about like accuracy. But I
Speaker 5: think the way to think about it is like the
Speaker 5: accuracy is like fifty point one percent type thing, like
Speaker 5: they're only a little bit better than random.
Speaker 3: But I suppose an extra one percent like blows up
Speaker 3: your profits if you're doing it.
Speaker 5: Doing it scale doing it enough times and over time
Speaker 5: you kind of realize the biased coin flip. And as
Speaker 5: for why it might be possible to do this without
Speaker 5: kind of invoking magic, It's like markets are very beautiful
Speaker 5: interaction of like many different potties, all the different kind
Speaker 5: of utilities and risk preferences and things, and the only
Speaker 5: way you really see what people are doing is by
Speaker 5: like the actions they take in markets, and you kind
Speaker 5: of it's sucking up all that like signal, that micro
Speaker 5: signal and extrapolating.
Speaker 2: The synicism or the skepticism about the possibility of machines
Speaker 2: that could predict the price of stocks is a little strange, right,
Speaker 2: because machines ingest data then whatever, maybe they see a
Speaker 2: pattern more likely than not, this consolation of data means
Speaker 2: tomorrow will be green. Humans do this all the time.
Speaker 2: What else do we have besides data?
Speaker 4: Right?
Speaker 2: You have an analyst and they put out of Tesla
Speaker 2: or whatever in video is going to go to five
Speaker 2: hundred dollars a show?
Speaker 3: How dare you insinuate I'm not smarter than a computer show?
Speaker 2: We were like all humans have this data and much
Speaker 2: less data, and yet humans are making predictions all the time.
Speaker 2: They have a whole industry of it, so the idea
Speaker 2: that therefore was for some reason a computer couldn't do
Speaker 2: this with much more data analysts ever, have I understand
Speaker 2: why the cynicism comes off as a little strength.
Speaker 3: I think some of the doubt stems from this idea
Speaker 3: that a lot of these models tend to be backward looking, right,
Speaker 3: and some of them occasionally are pretty bad at spotting
Speaker 3: or reacting to big regime breaks. And I guess the
Speaker 3: thinking again sometimes is that maybe humans are more flexible,
Speaker 3: maybe more adaptive in their thinking, and they can kind
Speaker 3: of spot these big cultural shifts. How do you actually,
Speaker 3: I guess, prepare for those big pattern changes.
Speaker 5: Yeah, I was at HIT for COVID, and I thought
Speaker 5: that was kind of like the most that was a pattern,
Speaker 5: that was a big pattern break, and things went totally fine. Actually,
Speaker 5: it was more of an engineering crisis in some ways.
Speaker 5: Stock market volumes exploded and every system was just like
Speaker 5: screaming trying to keep up with the volume of activity.
Speaker 5: But in terms of the predictions they stayed quite good.
Speaker 5: And I had like Rickoncilus in my head as well.
Speaker 5: I guess it is a matter of like horizon and
Speaker 5: like how far in the future are we talking intra day.
Speaker 5: I think a lot of the price movement is driven
Speaker 5: by just observing, like the flows. It's hard for us
Speaker 5: as humans to observe, but it's like the relative patterns
Speaker 5: of buyers and cells in the markets. And it's like, yes,
Speaker 5: during COVID, volatility was massive and prices were moving up
Speaker 5: and down a lot, but they were going up and
Speaker 5: down during say March twenty twenty, and so these models
Speaker 5: it was sort of out of domain for a human.
Speaker 4: But I don't think out of domain in some sense
Speaker 4: for the models.
Speaker 5: But I guess I also don't know how you would
Speaker 5: apply this thinking if you were trying to make it a.
Speaker 4: Month ahead predictions.
Speaker 5: I often get like people being like, oh, everyone knows
Speaker 5: hedge funds, which were not a hedge fund is like
Speaker 5: like flipping coins, and it's some survivor bias thing. And
Speaker 5: you know, I genuinely don't know about months out prediction
Speaker 5: stuff that is not a data rich environment.
Speaker 2: Just by definition, there have been more days than months,
Speaker 2: so therefore prediction on a day basis you're offered a
Speaker 2: lot more data that.
Speaker 5: The rule of thumb is basically very useful and it
Speaker 5: extends all the way down to seconds, and we see
Speaker 5: that empirically all the time. And so yeah, I guess
Speaker 5: all the things I'm saying, do you have this heavy
Speaker 5: out that it does rely on sudden of it being
Speaker 5: a certain level of signals and noise. I definitely cannot
Speaker 5: make reasonable claims about the price of things in like
Speaker 5: a month using the same kind of like AI hammer.
Speaker 5: I guess also to be specific, I'm talking a lot
Speaker 5: about using market data to make these predictions. And that's
Speaker 5: because on the sort of intra day timescale that is
Speaker 5: the most important thing. It's all about flows and things
Speaker 5: have been back and forth. If you're thinking about things
Speaker 5: in a month's timescale, I think that's fundamentals.
Speaker 4: And can AI be used for that?
Speaker 5: I don't know, to be honest, and it's definitely outside
Speaker 5: my wheelhouse. And I guess people have various opinions about that,
Speaker 5: and maybe some people very much would like to claim
Speaker 5: that they can, and you know, others maybe don't. But
Speaker 5: it's definitely outside of my Arab expertise. And I don't know, Wait, talk.
Speaker 3: To us about the data that you're using, or talk more,
Speaker 3: because this is another area where people tend to talk
Speaker 3: in PR speak, sometimes we have access to all this data,
Speaker 3: unusual data, alternative data, and that's going to enable us
Speaker 3: to use AI better. What are you actually looking at
Speaker 3: and what have you found? I guess most useful?
Speaker 5: Well, I think the thing that I found most counterintuitive
Speaker 5: when I started was that when you're thinking about predicting
Speaker 5: the prices of anything a minute, an hour out, I
Speaker 5: far the most useful thing is just market data. This
Speaker 5: is the market data feeds you can buy from the
Speaker 5: exchanges for a pretty reasonable price. People often think of
Speaker 5: some sort of like competitive moat. The data feeds for
Speaker 5: these exchanges are not particularly high. I mean crypto, you
Speaker 5: know where it's just like a wild West. But everyone
Speaker 5: can collect these feeds, and so that is the most
Speaker 5: useful raw ingredient. That is the most true expression of
Speaker 5: everyone's intense right they're going to the market, that quoting,
Speaker 5: the buying, selling, That is the primary ingredient. People get
Speaker 5: kind of caught up on the whole, like do you
Speaker 5: have a Twitter feed type of thing? And Bloomberg sells
Speaker 5: a Twitter feed through a state of products, and it's
Speaker 5: every now and then obviously something happens, news happens during
Speaker 5: market hours that moves, the price justicates the price. But
Speaker 5: if you really coldly rationalize that that is a relatively
Speaker 5: infrequent thing compared to the overall massive markets. So I
Speaker 5: thinking entry day, I think these market data feeds, it's
Speaker 5: literally like a little events someone quoted that this price
Speaker 5: and this size.
Speaker 4: It's all anonymous.
Speaker 5: Market data feeds are anonymous, and so that is the
Speaker 5: raw stuff, and it is vast. There are just millions
Speaker 5: and millions of events per day, per stock, per future.
Speaker 5: When you get to the day days timescale, that's where
Speaker 5: the alternative data quote unquote kind of really comes in
Speaker 5: as an alternative to market data, the SEC filings, the
Speaker 5: news feeds, balance sheets, brokers reports.
Speaker 4: Things like this.
Speaker 5: That's where that comes in, a vast sea of data
Speaker 5: offerings that people try and sell that I think in
Speaker 5: that kind of situation, you know, it's a very low
Speaker 5: shop environment you start getting into, and it can be
Speaker 5: hard to attribute the extra shop needs of these things.
Speaker 5: But in some sense it's also very democratized where maybe
Speaker 5: people collecting very secret data sets, but my inbox, and
Speaker 5: I'm not even the person in charge of buying these
Speaker 5: alternative data sets, is often full of people trying to
Speaker 5: sell me the latest alternative data set, and I think
Speaker 5: a lot of them don't necessarily have much predictive value,
Speaker 5: but clearly as a market for.
Speaker 3: What's the craziest one you've seen?
Speaker 4: Huh can you remember?
Speaker 5: I mean, people have definitely reacted very strongly to the
Speaker 5: Wall Street bets Era tried to kind of create a
Speaker 5: bunch of reddity extracted thing and go beyond just like
Speaker 5: raw captures of Reddit and trying to distill.
Speaker 4: It into something. It's just even just thinking about it.
Speaker 5: The meme stock thing is talked about more after it happens,
Speaker 5: and it happens before, and so like.
Speaker 2: I don't know, it's sort of a sideways question. You
Speaker 2: mentioned interpretability, and just give me something I've been wondering
Speaker 2: about AI for a while, not even in the finance
Speaker 2: realm specifically, you're a deep mind, which of course produced
Speaker 2: a great GOT player better than the greatest grandmaster in
Speaker 2: the world. I play chess. We know that chess engines
Speaker 2: are much better than any human. On the other hand,
Speaker 2: as far as I can tell, there is no good
Speaker 2: AI chess tutor. So in other words, the chess crush
Speaker 2: a doo. But like I've never been able to get
Speaker 2: a thing where it's okay, you did this move, but
Speaker 2: you know what, you're closing this rook file and down
Speaker 2: the line because like it doesn't do that. The chess
Speaker 2: dot com human talk is very rudimentary, et cetera. Can
Speaker 2: you talk a little bit about why there are these
Speaker 2: problems where some version of AI or machine learning or
Speaker 2: whatever can do fantastically well, but then the actual explanation
Speaker 2: of what it's doing, which I think is kind of
Speaker 2: what interpretability is, can't articulate in a plane English why
Speaker 2: it's able to do what it does.
Speaker 5: I think it's just because these neural networks are it's
Speaker 5: just like a big old blob of numbers, and what
Speaker 5: we're aiming to do in maturnit these models is to
Speaker 5: almost like free ourselves from almost all structure, and they
Speaker 5: might learn things in a way that is nothing at
Speaker 5: all like how we learn things. And so my best
Speaker 5: guess for like why it's hot is because they might
Speaker 5: be reasoning in some sense internally, and people use these
Speaker 5: words like reasoning.
Speaker 4: It kind of makes me win.
Speaker 5: So I've seen imagination and things used about neural networks.
Speaker 5: I don't know if it's like kind of anthropomorphization of
Speaker 5: them as kind of dangerous because they are essentially processing
Speaker 5: things internally in this way that I think is inherently
Speaker 5: not like how we do. And that is my best
Speaker 5: sort of Yes, there are some interesting counter examples. One
Speaker 5: of my favorite set of things in It Possible years
Speaker 5: was Golden Gate Claude, which was anthropic made that the
Speaker 5: model basically get very interested in the Golden gate Bridge.
Speaker 5: Every question they asked would come back to the Golden
Speaker 5: gate Bridge, and so they're not completely impenetrable, but it's
Speaker 5: clear that like, I guess how I'd be on a
Speaker 5: point to kind of map this back to how anyway,
Speaker 5: Like we think it's very attempting to and exciting too,
Speaker 5: and as especially for like AI safety applications, which is
Speaker 5: not really relevant to me so much, but I think
Speaker 5: it's for attempting to try.
Speaker 1: Yeah.
Speaker 2: No, it strikes me is that if you could solve
Speaker 2: that many jobs, would you could actually make a lot
Speaker 2: of productivity gains. But I do think that's an important
Speaker 2: hurdle when you're training your models. So your models are
Speaker 2: different than large language models, et cetera, but what they
Speaker 2: have in common is there's a credible amount of data,
Speaker 2: incredible amount of compute demand, how applicable if someone had
Speaker 2: worked on lllms, would your training process be to them?
Speaker 2: How interpret how could they move from that environment to yours?
Speaker 2: Are there enough similarities in the base notions and compute
Speaker 2: and requirements to train a model such as yours versus
Speaker 2: people are doing it the major labs.
Speaker 5: I would say now in twenty twenty five, absolutely, But
Speaker 5: I would not have said that in twenty twenty. And
Speaker 5: this is something that kind of caught me by surprise
Speaker 5: having done this for a while now, is that our
Speaker 5: problems are kind of defined by long sequential strings of
Speaker 5: information in some sense, and extrapolating from that. If I
Speaker 5: think back to the pastive AI, it was like is
Speaker 5: this is it a hot dog or not? It's kind
Speaker 5: of like the like image classifier, you know, test. Then
Speaker 5: there was some stuff with audio and things. I was
Speaker 5: a little bit more familiar robotics, eh. But when we
Speaker 5: got to the sort of LM error, it got very
Speaker 5: interesting because suddenly the problems were very similar and that
Speaker 5: you have you want to think back over like long histories,
Speaker 5: long contexts.
Speaker 4: Okay, that sounds good.
Speaker 5: You've got a lot of data and you want to
Speaker 5: turn through it in as efficient way as possible.
Speaker 4: You also have to serve this model.
Speaker 5: One has to run in like relatively reasonable speed, especially
Speaker 5: for the LM. Is there a million people typing into
Speaker 5: chat GBT dot com and they want to hear a
Speaker 5: response in a relatively prompt manner. Of course for us also,
Speaker 5: the models have to make their predictions in a prompt
Speaker 5: manner of voice of predictions that I'm useful. So all
Speaker 5: these things mean that our sort of way of thinking
Speaker 5: about it has become very similar to the frontier LM things.
Speaker 5: It's just a very different modality. We're operating on I
Speaker 5: guess primarily text, and we're operating on this fileless interpretable
Speaker 5: but still sequential stream of tokens, except our tokens are
Speaker 5: market events. And so it's a lot of fun because
Speaker 5: you know, in terms of like the research that is
Speaker 5: still published, you can kind of look at it for
Speaker 5: inspiration and draw our comparisons. But it's also very much
Speaker 5: its own problem, which is kind of keeps me interested
Speaker 5: every day because it's like its own unique thing.
Speaker 4: It's different.
Speaker 3: I want to go back to the point you made
Speaker 3: about data and I guess democratizing finance in many ways,
Speaker 3: and maybe this is a weird question. But I'm thinking
Speaker 3: back to the twenty tens and we used to talk
Speaker 3: about the big investment banks as flow monsters. They see
Speaker 3: all these orders, they get all these orders, they see
Speaker 3: all the flow, and that allows them to optimize on
Speaker 3: funding costs and other expenses. Is the idea that data
Speaker 3: and AI can kind of replicate that advantage so that everyone,
Speaker 3: or not everyone but Hudson at least becomes its own
Speaker 3: little flow monster.
Speaker 5: Yeah, I think there's still some trends and markets that
Speaker 5: worry me a little bit in terms of I guess
Speaker 5: our platonic ideal market structure is probably like everyone trades
Speaker 5: on exchange in a centralized place, but that is not
Speaker 5: really how things seem to be going. And there's a
Speaker 5: huge amount of like off exchange dark quasi dark volume,
Speaker 5: and I think there's still a lot of qunits of
Speaker 5: the trading world where like being in the room is
Speaker 5: kind of like this big advantage. And this is a
Speaker 5: very much anti AI play in some senses. Data is
Speaker 5: hidden the data, the flow data is hidden it and
Speaker 5: it's not something that you can feed into a machine.
Speaker 4: This very spas amounts of.
Speaker 5: It, So that's kind of an interesting trend a lot
Speaker 5: of us did get sales get reported in a centralized
Speaker 5: place later, but it's not prompt enough to be useful,
Speaker 5: and so to the AI thrives on data, this is
Speaker 5: in some sense like an issue for the long run,
Speaker 5: you need to kind of be in the rooms where
Speaker 5: the sort of trading is happening.
Speaker 2: I'm glad you brought that up, because that's specifically what
Speaker 2: I'm curious about from the sort of physical infrastructure side,
Speaker 2: Like if I have a queria to chat GPT, I
Speaker 2: don't care if the model is like trained in like Eblene,
Speaker 2: Texas or wherever it gets back to me and whatever.
Speaker 2: But I know that for high frequency trading, at least
Speaker 2: on the execution side, there are certain parts that you
Speaker 2: want to be literally co located, and you want to
Speaker 2: have the shortest possible wire, and however short it is,
Speaker 2: ideally you would like it to be shorter. Can you
Speaker 2: talk about the differences and similarities between essentially your physical
Speaker 2: hardware stack verse is what would be required at a
Speaker 2: large language model frontier lab.
Speaker 4: Yeah.
Speaker 5: I think at a bulk level there was actually some
Speaker 5: pretty similar things. So I can think about it as
Speaker 5: like latency and throughput latency being the time to react
Speaker 5: and then throughput kind of like how much thinking you
Speaker 5: can do in a certain period of time. And so
Speaker 5: you're right that like this space demands like low latency.
Speaker 5: Early in the twenty ten, so it was a sort
Speaker 5: of Flashboys book and perception where it was like really
Speaker 5: kind of about arbitraging latency. I'm happy to report that
Speaker 5: in some sense all the latency has been arbitraged.
Speaker 2: For the most part, there's no more engine shortening.
Speaker 5: The wire is probably like a little bit, but it's
Speaker 5: relatively small, and like I think if you look at
Speaker 5: the big quant trading firms that need to like really
Speaker 5: make the wires as short as they possibly can is
Speaker 5: done or are no longer relevant, which is great because
Speaker 5: I find out stuff pretty boring. Personally, I think about
Speaker 5: it more as like, for a given kind of like
Speaker 5: speed of response, you should be the smartest person. So
Speaker 5: it feels like this curve, if you're going to take
Speaker 5: a second to come up with your trading decision, it'd
Speaker 5: be a really really good decision. And then it doesn't
Speaker 5: kind of matter that it took a second. And if
Speaker 5: you're going to take a microsecond, well a you probably
Speaker 5: can't do too much in a microsecond, but you know
Speaker 5: it'll still be the best response in a microsecond, and
Speaker 5: so you.
Speaker 2: Could be a little worse. You can be a little
Speaker 2: worse than the second.
Speaker 4: Yeah, for sure.
Speaker 5: And so essentially for our training, we use the cloud.
Speaker 5: We have our own training data centers that we've built ourselves.
Speaker 5: That is basically the same, although much much smaller scale
Speaker 5: the scale of Googles and sayings. I don't know, it
Speaker 5: blows my mind the spending on stuff like this. We are,
Speaker 5: I think big if you're not comparing us to Google
Speaker 5: a meta, but that's not like bajillions of dollars. So
Speaker 5: training is kind of the same inference. We need to
Speaker 5: put a devices close to the exchanges, and we need
Speaker 5: to think very hot about the power usage and the latency.
Speaker 5: But we have hardware teams, We make our own FPGAs,
Speaker 5: we make our own chips, and we use off the
Speaker 5: shelf GPUs, And what we try and do is we
Speaker 5: try and make sure of it for any given set
Speaker 5: of speed or response, making the smartest possible decision you can.
Speaker 2: So you can kind of field programmabile gate rate. Oh yeah, sorry, Yeah.
Speaker 5: Basically, all these different devices have different latencies and through puts.
Speaker 5: GPUs have very high through puts. They are that's what
Speaker 5: they're useful for, right and so, but the problem with
Speaker 5: markets is they're kind of like narrow. The amount of
Speaker 5: traffic flowing into these like lms from everyone typing into
Speaker 5: their redbos it's massive, and they do also some clever
Speaker 5: things kind of batch up requests and process and things.
Speaker 5: We don't really have that luxury really, like the markets
Speaker 5: are going to happen at the speed they happen. We
Speaker 5: can't kind of like duck out for a while and
Speaker 5: catch up. We kind of need to stay in the game.
Speaker 5: So we have always sort of interesting design challenges around
Speaker 5: how do we use GPUs which are relatively high latency.
Speaker 4: They take a while to give.
Speaker 5: Back a result, but they can process the whole stock
Speaker 5: market on one GPU type of thing versus the fast response,
Speaker 5: And so we have whole teams dedicated to thinking about, Okay,
Speaker 5: I've got this like intelligent blob, how do I get
Speaker 5: ounces out of it in different ways at different speeds?
Speaker 5: And that I think is where a lot of smarts
Speaker 5: are going in this world these days, rather than like
Speaker 5: how do I make sure my microwave towers are like
Speaker 5: slightly better aligned somewhere in rural Pennsylvania, which is a
Speaker 5: cool challenge.
Speaker 4: It's own right, but it's done.
Speaker 3: I think.
Speaker 5: I think people have found the straightest line from New
Speaker 5: Jersey to Chicago.
Speaker 3: Joe brought up some of the cynicism around CME's cloud
Speaker 3: deal with Google, and this came up speaking of a
Speaker 3: specific cynic who went on the record in one of
Speaker 3: our episodes. Don Wilson basically made the argument that matching
Speaker 3: on a cloud doesn't necessarily make sense because you might
Speaker 3: put into orders and you're not really sure which order
Speaker 3: gets filled first. I guess you're kind of back in
Speaker 3: that black box environment, or maybe it's a latency issue.
Speaker 3: I don't know, is that a problem that you're seeing.
Speaker 5: That's something that I worry about. A general philosophy is
Speaker 5: MA should be very like transparent and as fair as possible,
Speaker 5: So like equalizing access is a good thing in terms
Speaker 5: of participants. Shouldn't be at to like basically pull weird
Speaker 5: tricks to be faster. On the other hand, I think
Speaker 5: you want reliability, so like this concept of like orders
Speaker 5: arriving at different times and being filled in different orders
Speaker 5: just doesn't seem like a very sensible way to run
Speaker 5: a market. It's something that requires a lot of effort
Speaker 5: to engineer around, and it's just a good market design
Speaker 5: to have. It is a very widespread though, in existing
Speaker 5: exchanges across the world. We've traded in like a vast
Speaker 5: number of countries, and some of the exchanges have such
Speaker 5: amazing hardware that like, if two orders are sent within
Speaker 5: like a nanosecond of each other, this exchange will never
Speaker 5: process them in the wrong order, even if it's one
Speaker 5: hundred different network ports and they're all connected. They have
Speaker 5: this amazing time stamping stuff. On the other hand, you
Speaker 5: might have like a crypto exchange where it kind of
Speaker 5: feels like a kid learned JavaScript and ran set up
Speaker 5: a website and you're kind of like you send an
Speaker 5: order and you may may not be confirmed that they
Speaker 5: even received it, and then you kind of have to
Speaker 5: refresh your like account balance page like five.
Speaker 4: Minutes later to see if there's many in it or not.
Speaker 5: And we kind of we'll take we'll deal with it
Speaker 5: as it is, but certainly we have a preference for
Speaker 5: kind of equalized access but sort of predictable outcomes, and
Speaker 5: I think that kind of leads to like people spending
Speaker 5: efforts I think it's not astly very great thing for
Speaker 5: society for people to be like stressing very hot about
Speaker 5: why it lam.
Speaker 2: Yeah, no, probably, I'm glad. I'm glad that you report
Speaker 2: that we've moved on a little bit since then. Where
Speaker 2: are your constraints? You know, when you talk to LLLM people,
Speaker 2: there's debates about right now, is it electricity? Is that
Speaker 2: the big constraint? Is it there just aret enough GPUs?
Speaker 2: Is it talent? Is it whatever? When you think about
Speaker 2: where you are now versus the optimal version of where
Speaker 2: or is it I mean, data is the other big
Speaker 2: one because there's all this concern that lllms are going
Speaker 2: to run out of training data, et cetera. Where is
Speaker 2: the big constraint for you that you feel like you're
Speaker 2: solving for right now?
Speaker 5: I think in terms of like really long term strategic planning,
Speaker 5: electricity is like quite clearly a very binding consideration. When
Speaker 5: we think about spitting up new GPU based training data centers,
Speaker 5: it really feels like, is there electricity? Like finding a
Speaker 5: piece of land to put a building in. There's a
Speaker 5: lot of land.
Speaker 4: Yeah, the electricity negotiation.
Speaker 2: That's an issue at agr T even for us.
Speaker 5: You know, because we have a sort of hybrid mix
Speaker 5: of using cloud providers and building our own data centers,
Speaker 5: and yeah, the negotiations and thinking about power constraints. We
Speaker 5: have an existing data center in a very cold place
Speaker 5: and we want to make it bigger. And the data
Speaker 5: center people are fantastic to work with, but they're saying like, well,
Speaker 5: we need to go talk to like the power grid
Speaker 5: and negotiate this next trunch and so on, and it's
Speaker 5: just it often feels like that is the bottleneck. And
Speaker 5: on the terms of a GPU availability, it definitely was
Speaker 5: a crunch at some point in the past, but I
Speaker 5: don't feel like that.
Speaker 2: Is a little more the entire stock market. Say a
Speaker 2: little bit more about how you perceive the GPU market.
Speaker 4: Right, I think.
Speaker 5: I think if we ask for GPUs, we will get
Speaker 5: them to live in a prompt manner, not necessar early
Speaker 5: like next day. But I don't feel like that is
Speaker 5: the thing that we have a long pull and spinning
Speaker 5: up more.
Speaker 2: When was the when was the worst of the crunch?
Speaker 5: I guess twenty twenty three, late twenty twenty three felt
Speaker 5: pretty bad.
Speaker 4: I was.
Speaker 5: I guess that was like the Nvidia Hopper generation, and
Speaker 5: I saw also a number in Ploomberg yesterday that I
Speaker 5: think it was like Nvidia conference yesterday and I said
Speaker 5: something like it was like one million Hopper class GPUs
Speaker 5: have been made, but already like four million Blackwell class
Speaker 5: gp has been made. So I think there's been a
Speaker 5: ramp up of supply. But I don't think they're also
Speaker 5: sitting on unsold inventory either. I think it is being consumed.
Speaker 5: But yeah, in terms of like what is the hod thing,
Speaker 5: I think electricity, And I am it's insane. As a
Speaker 5: very millennial person, I guess climate change was a big
Speaker 5: thing growing up in college, but a lot of discussion
Speaker 5: about climate change, and to see people spinning up data
Speaker 5: centers very fast by basically buying as many gas turbines
Speaker 5: as they can and putting them outside, I'm like, WHOA, Like, yeah,
Speaker 5: what are we doing? It's wild, but this the only
Speaker 5: way to get electricity promptly. You just have to throw
Speaker 5: guests turbines outside the building and turn them on. It's
Speaker 5: pretty radical stuff.
Speaker 4: And I don't know how all the numbers.
Speaker 5: Of people talking about for future data center expansion kind
Speaker 5: of math out because you just back of the envelope
Speaker 5: the power usage and things.
Speaker 4: And I know that the sam Oltman's of the world.
Speaker 4: I've thought about this, We've talked about this.
Speaker 5: So oh, we need to be generating this much new
Speaker 5: power generation per you of time, but there's such daunting numbers.
Speaker 5: I just don't know how that is all going to
Speaker 5: work out. But yeah, even for us in the grand
Speaker 5: scheme of things, like a much smaller player in terms
Speaker 5: of power consumption, we think in terms of like tens
Speaker 5: of megawatts, not gigawatts, which is more than most towns
Speaker 5: and cities and things.
Speaker 4: But still, but we.
Speaker 5: Find it like a challenge to find electricity at a
Speaker 5: reasonable price.
Speaker 3: On this note, can you talk to us a little
Speaker 3: bit more about where competitive advantage actually comes from in
Speaker 3: this space, because if the GPU crunch is somewhat solved,
Speaker 3: and if latency isn't as big an issue as it
Speaker 3: used to be, where are people actually getting their edge from?
Speaker 4: Right? I mean people? Talent is one of your other things.
Speaker 5: You asked that a constraint it is It is a
Speaker 5: very competitive people market. We're essentially asking for people to
Speaker 5: know a lot of things, be both good researchers and
Speaker 5: good engineers. Because I don't know in this AI era
Speaker 5: of a distinction is pretty blurry. It's not something you
Speaker 5: can just wipeboard and then the coding is a little
Speaker 5: bit afterwards. Any kind of research idea you have is
Speaker 5: intimately connected with how you implement it. So that's already
Speaker 5: like a tough ask. So people are constrained. People that
Speaker 5: we like I want to find and we pay well
Speaker 5: for those people as a result, and it is competitive.
Speaker 5: But I think the more subtle edge is almost like
Speaker 5: putting it all together. Do you have people who can,
Speaker 5: like an engineering team that can collect double data recorded,
Speaker 5: make it available to the GPU training data center. This
Speaker 5: is like many I guess it's petabyte scale data sets
Speaker 5: and just stroying too much data, streaming it from wherever
Speaker 5: a stored to wherever in the world the training data
Speaker 5: center is reliably these training runs are very expensive and
Speaker 5: then once you've got that model serving it, so it
Speaker 5: kind of sounds to everything and maybe that's kind of
Speaker 5: like a lame concert, but it really is. I think
Speaker 5: you need to be just optimizing the whole stack. And
Speaker 5: so like my team is like the AI team, so
Speaker 5: net what that really means in practice is we're focused
Speaker 5: on training the models, which is an important but not
Speaker 5: sufficient part of a whole stack, because we would be
Speaker 5: kind of dead in the water without the teams at
Speaker 5: HIT who think about how to actually kind of get
Speaker 5: the data and things TV systems and then the decisions
Speaker 5: out to the markets and keep up when things get busy,
Speaker 5: all these things. So when I think about our competitors,
Speaker 5: I think there is a benefit to scale. I can't
Speaker 5: imagine how you would start a new company like HIT
Speaker 5: in the year twenty twenty five because of the huge
Speaker 5: initial lift to kind of build enough engineering scale to
Speaker 5: achieve this sort of thing. And so I think are
Speaker 5: sort of peer companies also have invested very heavily in engineering,
Speaker 5: and we'll continue to do so. And there was an
Speaker 5: article in the FT like a little like a week
Speaker 5: or two ago about how firms like HIT are kind
Speaker 5: of extending themselves more into slower trading and there are
Speaker 5: firms that are kind of you know, those slower firms
Speaker 5: that's trying to kind of go faster.
Speaker 2: And yeah, I was just gonna ask about, just like
Speaker 2: on the prediction standpoint, Okay, maybe you could predict what's
Speaker 2: with some reasonable confidence what's gonna happen in the next hour. Sometimes,
Speaker 2: if you're lucky, maybe a day like maybe a month.
Speaker 2: It's just ridiculous. But do you in your work is
Speaker 2: that horizon? Has it broadened?
Speaker 4: It is? Yeah.
Speaker 5: I think one of the things for people who are
Speaker 5: aware of HIT even at all, I think is still
Speaker 5: a perception is sort of a pre twenty twenty perception
Speaker 5: of like we are purely high frequency trading firm, but
Speaker 5: we would say we are both high frequency and medium
Speaker 5: frequency trading firm, and it's like a big part of
Speaker 5: our business. One way to think about it, I think
Speaker 5: is that by if I really have a view on
Speaker 5: what a stock should be in like five days time,
Speaker 5: Let's say I want to buy that stock, I'm going
Speaker 5: to acquire that stock over time, and maybe it's what's
Speaker 5: the best time to buy that stuck over the five
Speaker 5: day period, Well, I have a model that tells me
Speaker 5: that the best pricing an hour. So maybe the shorter
Speaker 5: term model should inform the longer term trade and cascading
Speaker 5: all the way down.
Speaker 2: When you're doing this sort of slightly longer term or
Speaker 2: slightly slower frequency trading, is the fundamental job still the same,
Speaker 2: which is you're in the liquidity provision service business, just
Speaker 2: of or longer you want to hold that warehousing or
Speaker 2: does it some because when I think of a fund,
Speaker 2: when I think of a hedge fund, I certainly don't
Speaker 2: think of maybe to some extent, some of their strategies
Speaker 2: might be sort of liquidity provision. It's more directional. Is
Speaker 2: it still that or is the fundamental reason why you
Speaker 2: make money the service you provide? Does it change by definition?
Speaker 2: Change over that horizon?
Speaker 5: I think the market making service provision does break down.
Speaker 5: I think that stretches analogy too far. I think you
Speaker 5: have to think of it as like liquidity taking, which
Speaker 5: somehow seems more like aggressive or something. But the we're
Speaker 5: trading against orders resting on the book. Someone was like,
Speaker 5: I want to sell this dock, and we're like, we
Speaker 5: will buy it from you because we think that in
Speaker 5: the long run will be worth doing it, and so
Speaker 5: we do cross to spread and we do pay this
Speaker 5: transaction costs. Sometimes, you know, you can also kind of
Speaker 5: acquire position by market making, but with a tilt. So really,
Speaker 5: at the longer horizons, I think the sort of market
Speaker 5: making service analogy does break down. But in some sense
Speaker 5: there's always a counterpartty and they wanted to trade for
Speaker 5: a reason, and I think a mental model that I
Speaker 5: don't know. You tell me if this sounds like too
Speaker 5: touishing rushy.
Speaker 2: But love a mental model.
Speaker 5: Yeah, you mentioned go chess, right, So the thing about
Speaker 5: those is that there there's zero sum games is only
Speaker 5: one winner. It's truly like a no, like someone someone's unhappy.
Speaker 5: Someone was in there maybe equally unhappy plus one minus one.
Speaker 5: I think the reason that trading works is because it
Speaker 5: is in some sense positive sum. You know, money is conserved,
Speaker 5: and I guess the little fee goes to exchange, So
Speaker 5: in some sense money is at that moment of a
Speaker 5: trade is actually negative a little. But utility people's general
Speaker 5: happiness I don't know, might paycheck go into my fur
Speaker 5: one k provider and it bias some ETFs. I'm relatively
Speaker 5: like insensitive to how exactly that happens. I just I'm
Speaker 5: not gonna look at it for never forty years, right, No.
Speaker 4: I try not to look at it, especially lately.
Speaker 5: But uh yeah, like the utility, My utility is a
Speaker 5: very long horizon, and so someone sells it to me
Speaker 5: like at one cent different, I don't really care. So
Speaker 5: but like the person who made the sense happy and
Speaker 5: I'm happy because I got good liquidity didn't cross a
Speaker 5: huge spread. So that is kind of why I think
Speaker 5: it all kind of makes sense and white people are
Speaker 5: trading together. But it's also why like thinking about markets
Speaker 5: like an alpha go sense doesn't make sense because it's
Speaker 5: kind of doesn't really apply. If you thought of markets
Speaker 5: as hit and all our competitives all kind of in
Speaker 5: some sort of like deathmatch, who's the smartest, who's trying
Speaker 5: to pick each other off, then markets would be kind
Speaker 5: of like this giant standof where no one would be trading.
Speaker 5: Everyone be kind to be like waiting. But obviously markets
Speaker 5: are very vibrant. I think it's because even when we
Speaker 5: were crossing the spread, because a crossing is prettingaintone who
Speaker 5: wanted to sell for whatever reason. If we're right, I
Speaker 5: guess in five days time they might be like less happy, but.
Speaker 4: Maybe they weren't. Actually, maybe they were just like hedging
Speaker 4: a position. They don't care what.
Speaker 5: The stocks prices in five days. They just wanted to
Speaker 5: like hedge their position, and we traded with them. So
Speaker 5: that's the way I tell rick and styles in my head.
Speaker 5: But it can still be like a sort of service
Speaker 5: provision we make mindly only because someone else wants to trade.
Speaker 4: If no one was trading, we wouldn't exist, right.
Speaker 3: And different market participants with different motivations and goals and aims.
Speaker 3: I want to go back to the talent question. Yeah
Speaker 3: for a second, and I get the sense that engineers
Speaker 3: like open source and they like contributing to the research
Speaker 3: ecosystem on AI. And then I get the sense that
Speaker 3: trading firms probably do not like open source, and they're
Speaker 3: much more into protecting their proprietary models or data or whatever.
Speaker 3: How does a company like HRT, how do you actually
Speaker 3: balance that tension?
Speaker 4: Yeah?
Speaker 5: I mean this is also like a sort of really
Speaker 5: honest answer in that many years ago. This is a
Speaker 5: relative comparative disadvantage for us for recruiting some We often
Speaker 5: have conversations with, maybe especially PhDs who are graduating, and
Speaker 5: they would like, well, I can go to Google and
Speaker 5: I can still publish my research, and that kind of
Speaker 5: gives me optionality.
Speaker 4: People will know who I am.
Speaker 5: If I go into an HRT or like firm, I
Speaker 5: essentially go behind this veil and I never emerge and
Speaker 5: people just had to kind of take it on faith.
Speaker 5: I did smart things for many years, and I would
Speaker 5: have basically no strong counter argument apart from the fact
Speaker 5: that actually writing papers is kind of overrated. I've been there,
Speaker 5: done that, as when you get older you will not care.
Speaker 5: Now though, there's this interesting situation where this golden era
Speaker 5: may be of like being able to be work at
Speaker 5: a big tech company and be paid for public research
Speaker 5: is very much over The papers that do come out
Speaker 5: of the big AI labs are essentially kind of either
Speaker 5: a very stale or not important, and if you're working
Speaker 5: on the most important cutting edge things, you can't share
Speaker 5: what you're doing and it's very secretive. So some since
Speaker 5: the problem solved itself a little bit for me, and
Speaker 5: people now recognize that IP should be protected. I've even
Speaker 5: seen some of us sort of AI lab people think
Speaker 5: out a lot about non competes in public thinking tweeting
Speaker 5: about non competes.
Speaker 4: And things, which is an amazing ton of events, because
Speaker 4: I feel like.
Speaker 2: That was very anesthetical.
Speaker 5: I mean, they're like literally effectively banned in the state
Speaker 5: of California, and I think people were almost like proud
Speaker 5: of this fact, and which also kind of hold it
Speaker 5: against the New York sort of trading world, being like, oh,
Speaker 5: look at these people, are there non competes and things,
Speaker 5: And then someone comes along and pays one hundred million
Speaker 5: dollars or whatever for like your researchers, and a lot
Speaker 5: of that money is being paid for talent, but it's
Speaker 5: also in some sense paying for intellectual property.
Speaker 4: And like, those people.
Speaker 5: Know how the soup is made and they are not
Speaker 5: writing it down and not committing any explicit sort of
Speaker 5: IP theft. But if you hire five people who've been
Speaker 5: making the soup, they know they know a lot of
Speaker 5: process knowledge, and you might suddenly feel a little differently
Speaker 5: about protecting that. We spend a lot of time training
Speaker 5: our employees. Takes a long time for them to be productive.
Speaker 5: In some sense, it would be a shame if people
Speaker 5: could just take that knowledge and immediately leave.
Speaker 4: And so, yeah, just.
Speaker 3: Going back to the steamroller. I promised, I promised we would.
Speaker 3: When I hear AI in trading or I know people
Speaker 3: are very excited about agent based AI nowadays, part of
Speaker 3: me thinks back to one of the more amusing events
Speaker 3: in financial history, which is Joe. I'm sure you remember
Speaker 3: at the time that one of night Capital's algos.
Speaker 2: Would not find that to be an amusing Yeah, did
Speaker 2: all the worst Nightmare possible, but using for them the
Speaker 2: peanut gallery, right.
Speaker 3: Right, schadenfreud. So this algo went rogue and bought like
Speaker 3: seven billion dollars worth of stuff? Yeah, exactly, what are
Speaker 3: the guardrails that you put in place to avoid the
Speaker 3: destiny of night capital.
Speaker 5: So every training cycle we have a talk about the
Speaker 5: nightmare with a K and we have multiple x the
Speaker 5: night employees at HIT, as you might expect just from
Speaker 5: the lineage of a successful trading firm that ended in
Speaker 5: a kind of unhappy way, and we have many people
Speaker 5: who were at night.
Speaker 2: This story is crazy and successful trading firm that ended
Speaker 2: about fifteen yeah.
Speaker 5: Yeah, So it's fair to say that that stuff haunts us,
Speaker 5: and we try and take as many lessons away from
Speaker 5: that as possible. Defense and layers. So I think one
Speaker 5: of the things that I'd like to emphasize with the
Speaker 5: AI stuff in particular is that it is not like
Speaker 5: there's some neural network directly sending orders to NIZ. It
Speaker 5: is in some sense providing a plan and then traditional human,
Speaker 5: heavily audited, risk checked layers take the actions and that's
Speaker 5: just kind of how it has to be. And so
Speaker 5: for us we are kind of on an operational day
Speaker 5: to day basis. It's just many, many layers of sanity
Speaker 5: checking throughout the day, and then at a sort of
Speaker 5: high level it's very careful process including processes to specifically
Speaker 5: avoid the KCG type scenario of how are you even
Speaker 5: releasing new versions and what pre released checks do you run?
Speaker 5: And audits and we even during the day we have
Speaker 5: some I don't know, I guess you'd call them like
Speaker 5: sanity checks of the neural networks to make sure that
Speaker 5: they are producing the values that we expected they would
Speaker 5: be reducing. And those sort of checking processes are kind
Speaker 5: of a little bit behind because they can't keep up
Speaker 5: with the like flow, but like for enough to kind
Speaker 5: of just again like every tex of a numeric stability
Speaker 5: of the model saying and things. It's not it's not
Speaker 5: about losing money or making money in today, because it's
Speaker 5: not like, oh, like risk in the kind of financial sense.
Speaker 5: It's like operational risk. But paranoia is deep and that's
Speaker 5: probably something that's still very different I think, from this
Speaker 5: market from the sort of other AI world, which I
Speaker 5: guess anything goes and like failure rates to kind of
Speaker 5: just priced in. Yeah, but yeah, you could you could
Speaker 5: imagine just ruining everything, and I guess we worry about
Speaker 5: losing money, but I think we worry more about taking
Speaker 5: an action that a regulator would not want us to do,
Speaker 5: because if you lose that trust of regulators, you lose
Speaker 5: it for a very long time. And we trade in
Speaker 5: a lot of markets and we pay very close attention,
Speaker 5: and I have deep respect for the regulators and their
Speaker 5: decisions and all those markets and the rules are sometimes
Speaker 5: very complex, and man, do we watch that stuff like
Speaker 5: a hawk, because you know, you don't be kicked out
Speaker 5: of a country for making an operational error. And this
Speaker 5: is a very low tolerance culture from regulators in terms
Speaker 5: of making mistakes. So we stress it a lot, and
Speaker 5: I think we should because it's it's like the profit
Speaker 5: you make in ten years by still being in the
Speaker 5: game versus move fast and break things. It's not move
Speaker 5: fast and break things, PA still want to move fast.
Speaker 2: I have like a million more questions, but for the
Speaker 2: sake of time, I'll just ask one more. And I
Speaker 2: don't know even know whether it's something you're in position
Speaker 2: great position to answer about. It's something I actually want
Speaker 2: to do an entire episode about at some point. But
Speaker 2: as you would characterize it, what happens in the second
Speaker 2: after a jobs report is released. And what I'm talking
Speaker 2: about specifically is numbers either flash on the screen or
Speaker 2: a piece of a text appears on a website, and
Speaker 2: markets move around a lot all that and there's people.
Speaker 2: Then suddenly it's actually the jobs report was good, and
Speaker 2: if you actually look at the wage number and then
Speaker 2: the six But in that instant, in that first micro
Speaker 2: second after the release, markets are already moving, certainly before
Speaker 2: any human has had a chance to read the thing
Speaker 2: or for view. So what I assume is that there's
Speaker 2: training on here's the text and here are the things
Speaker 2: and whatever. But to as you would put it, or
Speaker 2: from the perspective of hr T, what happens in the
Speaker 2: millisecond after an event?
Speaker 5: Yeah, so yeah, I mean, so we have like a
Speaker 5: Bloomberg headlines feed that it's like pretty low latency, and
Speaker 5: if it's like an important articleize like a star and
Speaker 5: a feed things like this, right, But you can do
Speaker 5: everything from having kind of a hand crafted logic to
Speaker 5: look for keywords through to putting it through like an
Speaker 5: AI model. One of the things that I like still
Speaker 5: kind of kind of wrap my head around is, I guess,
Speaker 5: without saying specific company names, there are options trading firms
Speaker 5: that have thousands of people that are essentially cyborg trading options.
Speaker 5: They have maybe ten people trading like options for a
Speaker 5: single big stock like in VIDEOSA, and they are humans
Speaker 5: staring at the feeds for these things and clicking buttons,
Speaker 5: and they have user interfaces that will sit up for
Speaker 5: them to hit the green button.
Speaker 4: Of the red button. Essentially very fast. It's weird. We
Speaker 4: actually want for a hackathon.
Speaker 5: We got a PlayStation controller and kind of gave people
Speaker 5: a chance to try and practice reacting to events. It's
Speaker 5: really tough, but it's a learnable skill. I think in
Speaker 5: an efficient market sense, this should be ai able. It
Speaker 5: is challenging though, because if you imagine to kind of
Speaker 5: plumbing it into chet GBT, it would be too slow,
Speaker 5: Like the latency would probably be sufficiently high.
Speaker 4: I mean it's not that fast, right.
Speaker 5: It's fast for any normal day to day thing, but
Speaker 5: for markets it's kind of slow also. And this is
Speaker 5: like a very interesting research challenge. Is like you can't
Speaker 5: literally use chet GBT to back test anything. It knows
Speaker 5: every jerme, heal speech, and knows what happened afterwards because
Speaker 5: it's trained on the whole internet. So how do you
Speaker 5: really get confidence that for the next federals of speech
Speaker 5: it's going to do the right thing. Traditionally in finance
Speaker 5: you back to us things to see how you're done
Speaker 5: in the past. But if in this case it's all
Speaker 5: could of ensemble, like it's seen it all before. And
Speaker 5: I've seen academic finance papers if they try and like
Speaker 5: grapple of this and they say it's still works. They
Speaker 5: try and account for this, but I know, just this
Speaker 5: stuff is really that smart. Yeah, The whole kind of
Speaker 5: thesis is that it's memorized, everything has been trained on,
Speaker 5: so why would it be reliable?
Speaker 4: And so when if you see someone being like.
Speaker 5: Oh I ran every federal reserves speech through giant GBT
Speaker 5: and it got it right like nine out of ten times,
Speaker 5: it's like only nine out of ten times, Like why
Speaker 5: not one hundred percent? So I do find that I
Speaker 5: do think that it is interesting there are how many
Speaker 5: humans that's still involved on relatively high speed trading. There
Speaker 5: are a lot of people still doing this and instead
Speaker 5: of niche products. And it's presumably because it's very hard
Speaker 5: to integrate all the information. It's AGI twenty I don't
Speaker 5: know twenty twenty eight twenty thirty. I don't know, there's
Speaker 5: still a lot of humans trading stock and options and
Speaker 5: so like, I don't know how to reconcile that, but
Speaker 5: I think about that.
Speaker 4: When I read fun being.
Speaker 2: Dunning, it was a fantastic There really are like hours
Speaker 2: more of conversation, so we can back next week next
Speaker 2: week's episode. But no, that was great, thank you for
Speaker 2: having really appreciate it.
Speaker 6: Yeah, pleasure, Thank you, Tracy.
Speaker 2: I thought that was really great. I like this idea,
Speaker 2: this sort of anti symicism, because you do hear a
Speaker 2: lot of people say, oh no, like AI could solve
Speaker 2: things like chess or whatever, but the stock market is
Speaker 2: fundamentally different, and I've never been totally satisfied with some
Speaker 2: of the theories for why. And like I get stocks
Speaker 2: are not like necessarily like a solvable problem in quite
Speaker 2: the same way. But humans make money on the market
Speaker 2: by matching patterns. Why can't smart silicon brains do the
Speaker 2: same thing.
Speaker 3: Well, there's also history. Now we have many years of
Speaker 3: HFT trading and yeah, gruthically driven trading where people have
Speaker 3: made a lot of money, So it seems to be working.
Speaker 3: The light bulb moment for me was where Ian talked
Speaker 3: about the timeframe and the importance of the time frame,
Speaker 3: and I think that's really the key in many ways.
Speaker 3: It's adapting what you're doing with AI to the data
Speaker 3: that's available and the data on markets. Most of it
Speaker 3: is going to be very short term and more seconds
Speaker 3: and minutes, more minutes than days, et cetera, et cetera.
Speaker 3: And a lot of the data is also biased to
Speaker 3: immediacy versus past analysis, which he spoke about as well.
Speaker 2: It is always funny in finance people. It's like, oh,
Speaker 2: seventeen out of nineteen times there's been this death cross
Speaker 2: of the S and P five hundred stocks went down.
Speaker 2: It's like any serious data scientists will spit at that sampoint.
Speaker 2: It's like beyond a joke level to talk about a
Speaker 2: sample size of nineteen.
Speaker 3: Yeah, but death cross in a headline. It's so tempted.
Speaker 2: That's true. You all, you cannot advice to journalists. Never
Speaker 2: pass up a chance to put death cross. I was
Speaker 2: glad to hear thought a few things interesting. One is
Speaker 2: I was glad to hear that the wire length problem
Speaker 2: is no longer. Yeah, it's not just as racing it
Speaker 2: closer to the extreme.
Speaker 3: I was kind of boring when people were talking about
Speaker 3: the Cold War and HFT and all of that.
Speaker 2: It's interesting that the GPU market is eased versus where
Speaker 2: it may have been a couple of years ago. And
Speaker 2: it's interesting they even at a scale a good trading shop,
Speaker 2: that electricity is proving to be a main constraint, which
Speaker 2: does raise questions about are we just going to hit
Speaker 2: up against a wall given some of the AI plans
Speaker 2: that so many people are banking on for the chatbots.
Speaker 3: Yeah, I thought also, I guess the cultural shift in
Speaker 3: some of the last Yeah, it was really interesting this
Speaker 3: idea that they've become more proprietary and perhaps more mysterious
Speaker 3: in some ways, rather than the trading firms becoming more open. Yeah.
Speaker 2: Lots of great conversation, answer some questions. Yea plenty more.
Speaker 3: That was helpful, and I'm sure we'll talk to him again,
Speaker 3: maybe not next week, but soon next year. All right,
Speaker 3: shall we leave it there, Let's leave it there. This
Speaker 3: has been another episode of the All Thoughts podcast. I'm
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm joll Wisenthal. You can follow me at The Stalwart.
Speaker 2: Follow our guest Ian Dunning. He's at Ian Dunning. Follow
Speaker 2: our producers Carmen Rodriguez at Carmen Arman, dash Ol Bennett
Speaker 2: at dashbod and kill Brooks at Kilbrooks. More odd Laws content,
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