How Hudson River Trading Actually Uses AI

Summary

Odd Lots Podcast: How Hudson River Trading Actually Uses AI

Hosts: Joe Weisenthal & Tracy Alloway (Bloomberg)
Guest: Ian Dunning, Head of AI at Hudson River Trading (HRT)
Date: October 31, 2025

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Episode Overview

This episode explores the practical, non-hyped realities of how Hudson River Trading (HRT), a leading quantitative trading firm, actually employs artificial intelligence (AI) and machine learning in their strategies. With guest Ian Dunning (former DeepMind, now Head of AI at HRT), the hosts probe deeply into the evolution from classic quant trading to modern AI-first approaches, the subtleties of market data, tech infrastructure, competitive edges, risk management, and the AI engineering culture on Wall Street. The discussion aims to dispel “AI mystique” and offers rare transparency on one of the most secretive sectors in finance.

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Key Discussion Points & Insights

1. HRT Business Model & Role of AI in Modern Trading

[05:22]

• Market Making as a Service:
  • HRT acts as a sophisticated intermediary or “service provider to markets,” akin to Amazon in retail.
  • Provides liquidity across stocks, futures, options, crypto, and bonds—essentially “moving assets through time and space.”
  • “We’re happy because we essentially pick up a penny in front of a steamroller... if we have a really magical device which tells us how everything should be.” — Ian Dunning [06:00]
• How AI Changed the Quant Landscape:
  • The old approach: Handcrafted features based on human intuition, simple math (e.g., linear regression).
  • The AI leap: Since ~2014, HRT moved toward neural networks that “consume all the data” (order books, trade events) at internet scale, moving away from human bias and toward emergent, superhuman predictive capacity.
  • "Now our trading is entirely driven by this magical machine that consumes all the data." — Ian Dunning [08:54]
  • Difference from LLMs: It’s not a language model, but shares the “all data in, emergent behavior out” paradigm.

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2. Nature and Usefulness of Data in Trading AI

[09:57]

• Data at Internet Scale:
  • Every market event—not just price history—is the raw material.
  • Modern AI systems run on petabyte-scale, event-level datasets.
• Execution & Pattern Recognition:
  • AI offers both speed (execution) and pattern-finding (signals missed by traditional models).
  • HRT's models are not interpretable, and predict “a little better than random” (accuracy around 50.1%)—but at scale, that small edge is lucrative.
• “The most useful thing is just market data.” — Ian Dunning [17:58]
  • Alternative or “exotic” data (news, Twitter, Reddit) is largely less useful than raw exchange event data—especially intraday.
• On Alternative Data Hype:
  • “People get caught up on the whole, like, do you have a Twitter feed type of thing... But that is a relatively infrequent thing compared to the overall massive markets.” — Ian Dunning [18:28]
  • Most “alternative data” is oversold; markets are mostly driven by the (democratized) core exchange data.

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3. AI Predictive Power, Market Efficiency, and Limits

[12:42]

• Short-Term Predictions are Possible:
  • AI models can and do predict short-term price moves above chance.
  • “The predictions are very bad in some sense... the accuracy is like 50.1%... But at scale, that’s huge.” — Ian Dunning [13:16]
• Time Horizon is Everything:
  • Predictive power drops dramatically with longer horizons; models are effective from seconds to intra-day, up to “low single digit days.”
  • “I genuinely don’t know about months-out predictions. That is not a data-rich environment.” — Ian Dunning [16:19]
• AI Survives Regime Breaks?
  • Models performed well even in “pattern breaks” like COVID—because short-term order flow signals persist.
  • “It was more of an engineering crisis... in terms of predictions, they stayed quite good.” — Ian Dunning [15:26]

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4. Interpretability and Opacity of Modern AI Models

[22:43]

• Extreme Opacity:
  • Neural networks at HRT and in general are “big blobs of numbers,” learning in ways unlike human intuition.
  • “They might learn things in a way that is nothing at all like how we learn things.” — Ian Dunning [23:47]
  • This lack of interpretability is acceptable, even beneficial, at the speed and scale HRT operates.

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5. Infrastructure: Hardware, Latency, and Engineering Challenges

[28:52]

• From Short Wires to Smart Systems:
  • The “latency wars” (shortest wire, co-location) are mostly over—edges from shaving microseconds are almost gone.
  • “Happy to report... all the latency has been arbitraged for the most part.” — Ian Dunning [30:04]
• Computation & Execution:
  • Massive focus on throughput: using GPUs for model training; FPGAs and custom chips for ultra-fast inference at exchanges.
  • “For any given sort of speed of response, we're making the smartest possible decision we can.” — Ian Dunning [31:29]
• Physical Constraints:
  • Data centers for training (on-site & cloud)—electricity is increasingly the main limiting factor, more so than GPUs or talent.
  • “Spinning up new data centers really feels like: is there electricity?” — Ian Dunning [38:10]
  • “That’s like the only way to get electricity promptly... throw gas turbines outside the building and turn them on.” — Ian Dunning [39:48]

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6. Where Does Competitive Edge Come From?

[41:04]

• Talent:
  • High premium on engineers who can both research and build, especially at the interface between massive data infrastructure and AI.
• Systems Integration and Scale:
  • The real edge is “putting it all together” at scale: robust engineering to handle petabyte-scale data storage, reliable streaming, model training, and ultra-fast execution.
  • “I can't imagine how you would start a new company like HRT in the year 2025.” — Ian Dunning [42:02]

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7. Risk Management & Guardrails – Learning from Knight Capital

[50:21]

• Multi-layer Defenses:
  • AI does not directly send orders to markets; multiple risk, audit, and “sanity check” layers stand between models and real money.
  • “It's not about losing money or making money... it's operational risk. But paranoia is deep.” — Ian Dunning [51:52]
• Regulatory Focus:
  • HRT is more worried about actions that could draw regulatory ire (and banishment from markets) than losing capital on trades:
  • “If you lose that trust of regulators, you lose it for a very long time.” — Ian Dunning [52:56]

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8. AI vs. Humans: The Limits of Automation in Trading

[54:31]

• The Human Edge Lingers:
  • Even in high-speed, event-driven trading (e.g., after jobs reports), humans using specialized UIs still play a major role, especially in options and in integrating complex information fast.
  • “It’s really tough, but it’s a learnable skill... In an efficient market sense, this should be AI-able—it is challenging though.” — Ian Dunning [54:54]
• LLMs Not Fast Enough:
  • Latency of mainstream LLMs (like ChatGPT) is far too high for markets.
  • Trusting them to respond to breaking news is also hard given overfitting and knowledge of past events.

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9. AI Engineering Culture and Openness

[48:05]

• Balancing Secrecy and Innovation:
  • HRT was once disadvantaged in recruiting compared to Google, where open research publication was possible.
  • Now, big tech labs are equally secretive, and open-source culture is waning in high-stakes AI.
  • “The problem solved itself a little bit for me. And people now recognize that IP should be protected.” — Ian Dunning [48:48]

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10. Broader Market Structure and Fairness

[27:53]

• Flow Monsters and Democratization:
  • Data and AI do allow “flow monster” advantages, but the trend toward off-exchange/dark trading resists full democratization.
  • “AI thrives on data—being in the rooms where trading happens remains an advantage.” — Ian Dunning [28:12]
• Market Design Concerns:
  • HRT prefers fair, predictable market access for all, and dislikes designs that introduce undesirable uncertainty or manipulable latency. [36:15]

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Notable Quotes & Memorable Moments

• On Prediction vs. Reality:
  • “It seems — if someone’s saying they can predict the price of a stock in an hour, your instinctual reaction is incredulity... but no, these models can predict this... The predictions are very bad in some sense... like 50.1% — but at scale, that’s huge.” — Ian Dunning [12:59]
• On Guardrails:
  • “There is not some neural network directly sending orders to NYSE. It is in some sense providing a plan, and then traditional human, heavily audited, risk-checked layers take the actions.” — Ian Dunning [51:15]
• On Culture Shift:
  • “The golden era of being able to 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.” — Ian Dunning [48:20]
• On Market Making as a Service:
  • "We're a service provider to markets... picking up a penny in front of a steamroller." — Ian Dunning [05:59]
• On Data:
  • “By far the most useful thing is just market data.” — Ian Dunning [17:58]
• On the Practical Edge:
  • “You need to be just optimizing the whole stack... putting it all together.” — Ian Dunning [41:44]
• On Constraints:
  • “Electricity is quite clearly a very binding consideration. Spinning up new GPU-based training data centers, it really feels like: is there electricity?” — Ian Dunning [38:10]

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Key Timestamps

• Trading & Market-Making Model Explained: [05:22]
• Old Quant vs. New AI Approaches: [07:27]
• Predictive Power, Signal/Noise: [13:39], [16:43]
• Nature of Useful Data: [17:39]
• Why “Alternative” Data is Overhyped: [17:58]
• Interpretability and Opaqueness: [22:43]
• Hardware, Physical Constraints, End of Latency Race: [28:52]
• Competitive Advantages Now: [41:04]
• Risk Controls Post-Knight Capital: [50:21]
• Human vs. AI in Fast Trading Scenarios: [54:31]
• On Recruiting and Openness: [48:05]

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Summary Takeaways

• AI is Real, and It’s Not All Hype: HRT’s experience shows deep learning adds real practical alpha to short-term trading—if not as dramatically as the “AI mystique” would suggest.
• The Edge is in Integration at Scale: Data, compute, and talent matter, but integrating everything—reliably, securely, and at speed—is the core competitive moat.
• Markets Are Not Go or Chess: Slightly-better-than-random predictions are absolutely enough to be profitable, with the caveat that trading is a positive-sum, service-based business at its core.
• Irreducible Risk and Regulatory Scrutiny: Robust, multi-layered paranoia is required to avoid disasters and stay in the good graces of global financial regulators—AI does not remove operational risk.
• Openness vs. Secrecy in AI Is Shifting: As Wall Street and Big Tech converge on similar approaches, both are becoming more guarded and proprietary about breakthroughs.
• Electricity, Not Wires or Even GPUs, Is the New Bottleneck: The AI hardware arms race in trading is now bumping up against power grid constraints.

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For newcomers or non-listeners, this episode is a refreshingly candid look behind the “black box” of AI in high-frequency trading, replacing hype with grounded, nuanced technical and strategic insight—relevant equally for finance professionals, AI researchers, and market skeptics.

Transcript

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[Ian Dunning]

News.

[Joe Weisenthal]

Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

[Tracy Alloway]

And I'm Tracy Alloway.

[Joe Weisenthal]

Tracy, I've always had this idea for the Poem podcast, or a thing that I've wanted to do conceptually with podcasts is schedule every guest for two interviews. So you have the opening interview and you ask a bunch of questions and then it's oh God, I really wish I had followed up on that. I had more. I was just starting to sort of get my head around this thing now. I could have asked the good questions and then like have the person come back next week. Also the audience complains, I wish you had asked that and then fill in all those gaps that had been inspired by the previous conversation.

[Tracy Alloway]

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?

[Joe Weisenthal]

AI. And also 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 interviewed Don Wilson, we interviewed the head of the cme, we had some other chats. They're all about the world of trading. When it comes to trading, it's like, you know, we talk to long term investors, portfolio managers, endowments, we talk 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.

[Tracy Alloway]

Well, the practical application, right. And also the culture of AI on Wall Street. I find that really interest. I remember, I guess it was like more than a decade ago, but remember Lloyd Blankfein saying that Goldman Sachs is a technology.

[Joe Weisenthal]

Oh, yeah, yeah.

[Ian Dunning]

Jacob Morgan.

[Tracy Alloway]

Yeah, 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 B2 hundreds or we have a data center full of G3 hundreds come work for us.

[Joe Weisenthal]

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

[Tracy Alloway]

Well, there's also the underlying theme of is this all hype?

[Joe Weisenthal]

Right?

[Tracy Alloway]

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.

[Joe Weisenthal]

Cynics. And I'm not saying this myself, I.

[Tracy Alloway]

Know you're not a cynic.

[Joe Weisenthal]

Speaking of trading and technology, cynics would say that CME's deal with Google to build a cloud, to 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.

[Tracy Alloway]

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

[Joe Weisenthal]

I'm just saying there is this world where people do press releases that 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 DeepMind. So his trading and AI bonafides are about as good as it gets with.

[Tracy Alloway]

You've established them.

[Joe Weisenthal]

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

[Ian Dunning]

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

[Joe Weisenthal]

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.

[Ian Dunning]

Yeah.

[Joe Weisenthal]

How does it make money?

[Ian Dunning]

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.

[Joe Weisenthal]

Okay.

[Ian Dunning]

The most clear example is market making. There is 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 it 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 the steamroller if we have a really magical device which tells us how everything should be.

[Joe Weisenthal]

When the steamroller is coming.

[Ian Dunning]

Yeah, tells us when the steamroller is coming. And so 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 stocks, bonds through time and space between different counterparties.

[Tracy Alloway]

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, big shift in the way trading actually works?

[Ian Dunning]

Yeah, I mean, I don't want to overstate ourselves in some sense because in this space, as you mentioned before, it's very 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 so soon or something like that. And maybe you get a bunch of very smart people and they think very hard. It's almost like making a very fancy watch. You kind of artisanally craft all these pieces and then maybe you use relatively simple mathematical techniques like linear regression to combine those predictors. And I've been going to conferences and things in 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 opinion. And like lived experience I guess. And so we sort of viewed it more for a long time as well. Basically everything that's happening 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 buy broad market ETFs, what do I know? And so we. But if you could put all the data into a box and it kind of could turn on all of that data, it would find things that you would never be able to do with this handcrafted thing. And we started doing that very early, relatively in 2014, 2013 period. And over time, over the last 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, eventually kind of just like overtook it entirely. And so now our trading is entirely driven by this magical machine that consumes all the data. I kind of keep saying this magical machine that consumes all the data for a reason, which is that this is how ChatGPT 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.

[Tracy Alloway]

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?

[Ian Dunning]

I think it's both. I think one of the things that people sort of missed with the whole 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 the actual low level substrate people are quoting, trading, retracting quotes. That low level stuff is Internet scale data set sizes. And one of the 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 to 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 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 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 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 don't know if I looked at the order book 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? It's very different. Right. To normal AI. Right.

[Joe Weisenthal]

This gets into some areas that I'm very interested in. But just to establish what we're talking about, you're trading a stock like a Tesla, Nvidia, et cetera, with your magic machine.

[Ian Dunning]

Magic machine?

[Joe Weisenthal]

No, we had another episode where we talked.

[Tracy Alloway]

Well, that was the money box as a magic box.

[Ian Dunning]

That's a different, that's a different one.

[Joe Weisenthal]

With this AI machine, it is sort of arguably grown.

[Commercial Announcer 3]

Right.

[Joe Weisenthal]

It's sort of grown in a lab more than it is programmed, much like a chatbot. I know it's 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 chance of getting that right? 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?

[Ian Dunning]

Yes.

[Joe Weisenthal]

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 timescale, predictive capacity.

[Ian Dunning]

Yes. I think I find this still to mistake a little bit hard to believe. I think you get this kind of efficient market hypothesis stuff jumped into your head. It seems if someone's saying they can predict the price of a stock in an hour, your instinctual reaction is incredulity. Just sounds like you're kind of bluffing or making it up. But no, these models can predict this. And I think the way to kind of reconcile the really man kind of instincts is that the predictions are very bad in some sense. We don't normally talk about 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.

[Tracy Alloway]

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

[Ian Dunning]

Doing it 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.

[Joe Weisenthal]

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.

[Tracy Alloway]

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

[Joe Weisenthal]

Joe, 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, for some reason a computer couldn't do this with much more data analysts ever have. I understand why the cynicism comes off as a little strange.

[Tracy Alloway]

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?

[Ian Dunning]

Yeah, I was at H I T for Covid and I thought that was kind of like the most.

[Tracy Alloway]

That was a big pattern.

[Ian Dunning]

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 like reconcile this in my head as well. I guess it is a matter of like, horizon and like, how far in the future are we talking about intraday? I think a lot of the price movement is driven by just observing 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 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 months 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.

[Joe Weisenthal]

Just by definition there have been more days than months.

[Ian Dunning]

Right.

[Joe Weisenthal]

And so therefore prediction on a day basis, you're offered a lot more data, is that what you're saying?

[Ian Dunning]

Yeah, rule of thumb is basically very useful and it extends all the way down to seconds 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 a month using the same kind of 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 timescale that is the most important thing. It's all about focus flows and things back and forth. If you're thinking about things on a month's timescale, I think that's fundamentals and can AI be used for that? I don't know, to be honest. And that'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 others maybe don't. But it's definitely outside of my area of expertise and I don't know, wait.

[Tracy Alloway]

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 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?

[Ian Dunning]

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, do you have a Twitter feed type of thing? And Bloomberg sells a Twitter feed through its data products and buy that. 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 little events. Someone quoted at this price and this size, it's all anonymous. Market data feeds are anonymous. And so that is the roar stuff. And it is vast. There are just millions and millions of events per day per stock, per. For future, when you get to the day, days, timescale, that's where the alternative data kind of really comes in as an 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, it's a very low shop environment you start getting into. And it can be hard to attribute the extra shop for 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 a market for it.

[Tracy Alloway]

What's the craziest one you've seen? Can you remember?

[Ian Dunning]

I mean, people have definitely reacted very strongly to the WallStreetBets era, tried to kind of social data, bunch of Reddit y extracted thing and go beyond just raw captures of Reddit and trying to distill it into something. You know, it's just, even just thinking about it. The meme stock thing is kind of talked about more after it happens than it happens before. And so like, I don't know.

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[Joe Weisenthal]

Is 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 DeepMind, which of course produced a great go player better than the greatest grandmaster in the world I play chess with. We know that chess engines are much better than any human. On the other hand, as far as I can tell, there is no good AI chess tutor. So in other words, the chess crushes you. 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, like, it doesn't do that. The Chess.com human talk is very rudimentary, et cetera. 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.

[Ian Dunning]

I think it's just because these neural 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 best guess for 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 wince. I've seen imagination and things used about neural networks. I don't know if 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 of years was Golden Gate Claude, which was anthropic, made the model basically get very interested in the Golden Gate Bridge. Every question they asked would come back to the Golden Gate Bridge. And so they're not completely impenetrable. But it's clear that it gets hard beyond a point to kind of map this back to how. Anyway, we think. And it's very tempting to. And exciting too, 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.

[Joe Weisenthal]

Yeah, no, it strikes me is that if you could solve that many jobs, 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, et cetera. 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? 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.

[Ian Dunning]

I would say now in 2025. Absolutely. But I would not have said that in 2020. And this is something that kind of 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 like a hot dog or not? It's kind of like the image classifier test. Then 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 contexts. Okay, that sounds good. You've got a lot of data and you want to churn 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, especially for the LLM places. There are a million people typing into chatgpt.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 sequential stream of tokens. Except our tokens are market events. And so it's a lot of fun because in terms of 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.

[Tracy Alloway]

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?

[Ian Dunning]

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 is a huge amount of like off exchange, dark, quasi dark volume. And I think there are still a lot of quantities of a trading world where like being in the room is kind of like this big advantage. And this is a very much anti AI play. In some sense it's 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 an interesting trend. A lot of us data sales get reported in a centralized place later, but it's not prompt enough to be useful. And so to accept 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.

[Joe Weisenthal]

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 to ChatGPT, I don't care if the model is trained in Ebilene, Texas or wherever it gets back to me. But I know that for high frequency trading, at least on the execution side, there are certain parts that you want to be literally co 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, I think at a.

[Ian Dunning]

Bulk level there's actually some pretty similar things. So I often think about 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 boy's book and perception where it was like really kind of about arbitraging latency. I'm happy to report that in some sense all the latency has been arbitraged for the most part.

[Joe Weisenthal]

There's no more edge in shortening the wire.

[Ian Dunning]

This is probably like a little bit, but it's relatively small. And I think if you look at the big quant trading firms, the need to really make the wires as short as they possibly can is done or no longer relevant. Which is great because 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 it doesn't kind of matter that it took a second. And if you're going to take a microsecond, well, a, you probably can't do too much in a microsecond, but you know, better still be the best response in a microsecond. And so.

[Joe Weisenthal]

But you could be a little worse.

[Ian Dunning]

You could be a little worse.

[Joe Weisenthal]

And then the second.

[Ian Dunning]

The second, yeah, 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 Google's and things. I don't know, it blows my mind the spending 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 kind of the same inference. 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 FPGAs, 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 smartest possible decision we can.

[Joe Weisenthal]

So you can kind of field programmable gate array. Oh, there you go. Sorry. Fpga.

[Ian Dunning]

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 LLMs from everyone typing into their web browsers is massive. And they do all sorts of clever things to kind of batch up requests and process things. We don't really have that luxury really. The markets are going to happen at the speed they happen. We can't kind of 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 intelligent blob. How do I get ounces 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 slightly better aligned somewhere in 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.

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[Tracy Alloway]

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 black box environment. Or maybe it's a latency issue, I don't know. Is that a problem that you're seeing?

[Ian Dunning]

It's something that I worry about. Our general philosophy is market should be very transparent and as fair as possible, so equalizing access is a good thing in terms of participants shouldn't be able to basically pull weird tricks to be faster. On the other hand, I think you want reliability. So this concept of 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. There's 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 nanosecond of each other, this exchange will never process them in the wrong order. Even if it's 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 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 five 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 necessarily a very great thing for society for people to be like stressing very hard about wire lamp.

[Joe Weisenthal]

Yeah, no, probably. I'm glad, I'm glad that you report that. We've 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, et cetera. Where is the big constraint for you that you feel like you're solving for, for right now?

[Ian Dunning]

I think in terms of like really long term strategic planning, electricity is like quite clearly a very binding consideration. When we think about spitting up new like GPU based training data centers, it really feels like, is there electricity? Like finding pieces of land to put a building in is a lot of land. Yeah, the electricity negotiations and that's an.

[Joe Weisenthal]

Issue at HRT even thinking about for.

[Ian Dunning]

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 tranche 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 at some point in the past, but I don't feel like that is.

[Tracy Alloway]

Can you say a little bit more?

[Joe Weisenthal]

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

[Ian Dunning]

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.

[Joe Weisenthal]

When was the worst of the Crunch.

[Ian Dunning]

I guess 2023, late 2023 felt pretty bad. I guess that was like the Nvidia Hopper generation. And I saw some number in Bloomberg yesterday that I think there's an 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 being 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, but 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. 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 up the envelope to power usage and things. And I know that the Sam Altmans in the world have thought about this, have talked about this, oh, we need to be generating this much new power generation per unit time, but 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 than most towns and cities and things, but still. And, but we find it like a challenge to find electricity at a reasonable price.

[Tracy Alloway]

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?

[Ian Dunning]

Right? I mean, people talent is one of your other things, you ask. Is that a constraint? Yeah, 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, it's 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 with 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 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 petabyte scale data sort 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. I think you need to be just optimizing the whole stack. And so like my team is like the AI team. So 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 think about how to actually kind of get the data 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 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 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 HRT are kind of extending themselves more into slower trading. And there are firms that are kind of, those slower firms are trying to kind of go faster.

[Joe Weisenthal]

And yeah, I was just gonna ask about. Just like on the prediction standpoint, okay, maybe you could predict what's. With some reasonable confidence what's gonna happen in the next hour. 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?

[Ian Dunning]

It is, yeah. I think one of the things for people who are aware of HRT even at all, I think the solar perception is 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 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 over time and maybe it's what's the best time to buy that stock over the five day period? Well, I have a model that tells me that the best price 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.

[Joe Weisenthal]

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 halt 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 was more directional. Is it still that or is the fundamental reason why you make money the service you provide doesn't change by definition change over that horizon?

[Ian Dunning]

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. Yeah, but we're trading against orders resting on the book. 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 can also kind of acquire position by market making but with a tilt. So really at the longer horizons I think the sort of market making 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.

[Joe Weisenthal]

Too wishy washy but I love a mental model.

[Ian Dunning]

Yeah. You mentioned go and chess. Right. So the thing about those is that they're. There's zero sum games, there's only one winner. It's truly like a. No, like someone's unhappy, someone wins and they may be 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, might 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.

[Joe Weisenthal]

Don't lie.

[Ian Dunning]

I try not to look at it, especially lately. But yeah, like the utility, my utility is a very long horizon and so someone sells it to me like at $0.01 different, I don't really care. So but like the person who made the cents 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 all kind of makes sense and why people are trading together. But it's also why thinking about markets like an AlphaGo Sense doesn't make sense because it's kind of doesn't really apply. If you thought of markets as HRT 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, this goes across the spread against someone who wanted to sell for whatever reason. If we were right, I guess in five days time they might be 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 five days. They just wanted to hedge their position and we traded with them. So that's the way I tell reconcile.

[Joe Weisenthal]

This in my head.

[Ian Dunning]

It can 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.

[Tracy Alloway]

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?

[Ian Dunning]

Yeah, I mean this is also like a sort of really honest answer. Annette. 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 HRT 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, I've done that. As when you get older, you will not care. Now though, there's this interesting situation where this golden era may be of like being able to 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 tweeting about non competes and things, which is an amazing turn of events because I feel.

[Joe Weisenthal]

Like that was very antithetical to all of them. Right.

[Ian Dunning]

I mean they're like literally effectively banned in the state of California. And I think people were almost 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 confusing things. And then someone comes along and pays $100 million or whatever for like your researchers. 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 just like explicit sort of IP theft. But. And if you hire five people who've been making the soup process knowledge, 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. It 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.

[Tracy Alloway]

The steamroller, I Promised we would. When I hear AI in trading or I know people are very excited about agent based A 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 Capital's algos many.

[Joe Weisenthal]

People would not find that to be an amusing event at all. The worst nightmare possible but amusing from for them the peanut gallery.

[Tracy Alloway]

Right, Right. Schadenfreude. So this algo went rogue and bought like $7 billion worth of stuff, bankrupted the whole company. Yeah, exactly. What are the guardrails that you put in place to avoid the destiny of Knight Capital?

[Ian Dunning]

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

[Joe Weisenthal]

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

[Ian Dunning]

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 is some neural network directly sending orders to nyse. 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 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 KCG type scenario of how you're 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 could 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 those sort of checking processes are kind of a little bit behind because they can't keep up with the flow but for enough to kind of just again every check of a numeric stability of the model sane and things it's not about losing money or making money in JVs and not like oh 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.

[Joe Weisenthal]

Yeah.

[Ian Dunning]

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 an 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. 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 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 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.

[Joe Weisenthal]

I have a million more questions, but for the sake of time I'll just ask one more. And I don't even know whether it's something you're in 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 what I'm talking about specifically is numbers either flash on a screen or a text appears on a website and markets move around a lot, all that and there's people. Then suddenly 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 microsecond after the release, markets are already moving. I know certainly before any human has had a chance to read the thing or form a view. So what I assume is that there's training on here is the text and here are the things and whatever. But as you would put it, or from the perspective of hrt, what happens in the millisecond after an event.

[Ian Dunning]

Yeah, so 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 as 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 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. It's weird. We actually want a monkey on a computer for a hackathon. We, we got a PlayStation controller and gave people the chance to try and practice reacting to events very fast. It's really tough, but it's a learnable skill. I think in an efficient market sense this should be AI able. It is challenging though because if you imagine just kind of plumbing it into ChatGPT, it would be too slow. Like the latency would probably be sufficiently high. I mean 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 ChatGPT to backtest anything. It knows every Jerome Powell 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 had done in the past, but 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, the whole kind of thesis is that it's memorized everything that's being 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 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 sort of niche products. And it's presumably because it's very hard to integrate all the information. It's AGI 20, I don't know, 20, 28, 20, 30, I don't know. There's still a lot of humans trading stock and options and so like I don't know how to reconcile that.

[Joe Weisenthal]

But I think about that when I read Ian Dunning. That was fantastic. There really are like hours more of conversations. Are we gonna have him back next week? Looking forward to next week's episode. But no, that was great.

[Ian Dunning]

Oh, thank you for having me.

[Joe Weisenthal]

Really appreciate it.

[Ian Dunning]

Yeah, a pleasure. Thank you.

[Joe Weisenthal]

Tracy. I thought that was really great. I like this idea of this sort of anti cynicism 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?

[Tracy Alloway]

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 timeframe and the importance of the timeframe. 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, et cetera, et cetera. And a lot of the data is also biased to immediacy versus past analysis, which he spoke about as well.

[Joe Weisenthal]

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. It's like beyond a joke level to talk about a sample size of 19. Yeah, but we could put it all the time.

[Tracy Alloway]

But death cross in a headline is so tempting.

[Joe Weisenthal]

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

[Tracy Alloway]

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

[Joe Weisenthal]

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, a good 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 chatbots.

[Tracy Alloway]

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

[Joe Weisenthal]

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

[Tracy Alloway]

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

[Joe Weisenthal]

Maybe next year.

[Tracy Alloway]

All right, shall we leave it there?

[Joe Weisenthal]

Let's leave it there.

[Tracy Alloway]

This has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Allaway.

[Joe Weisenthal]

And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest Ian Dunning. He's Ian Dunning. Follow our producers Carmen Rodriguez at carmenarmon-obent@dashbot and kalebrooksalebrooks. For more Odd Lots content, go to bloomberg.comoddlots for the daily newsletter and all of our episodes and you can chat about all of these topics 24. 7 in our Discord, Discord, GG Oddlots.

[Tracy Alloway]

And if you enjoy Odd Lots, if you like it when we dive into how companies are actually using AI, then please leave us a positive review on your favorite podcast platform. And remember, if you are a POD Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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