How Hudson River Trading Actually Uses AI

25:14But what they have in common is this incredible amount of data, incredible amount of compute demand. How applicable, if someone had worked on LLMs, would your training process be to them? How could they move from that environment to yours? Are there enough similarities in the basic notions and compute and requirements to train a model such as yours versus what people are doing at the major labs? I would say now in 2025, absolutely. Okay. But I would not have said that in 2020. And this is something that kind of 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.

25:56If 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 the sort of LLM error, 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.

26:33There are a million people typing into chatgbt.com and they want to hear the response in a relatively prompt manner. Of course, for us also, the models have to make their predictions in a prompt manner, otherwise 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 from market events. And so it's a lot of fun because, you know, in terms of like the research that is still published, you can kind of look at it for inspiration and draw comparisons.

27:09But 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 that's different. I want to go back to the point you made about data and I guess democratizing finance in many ways. And maybe this is a weird question, but I'm thinking back to the 2010s. And we used to talk about the big investment banks as flow monsters. They see all these orders, they get all these orders, they see all the flow, and that allows them to optimize on funding costs and other expenses. Is the idea that data and AI can kind of replicate that advantage so that everyone, or not everyone, but Hudson at least becomes its own little flow monster?

27:53Yeah, I think there's still some trends and markets that worry me a little bit in terms of I guess our platonic ideal market structure is probably like everyone trades on exchange in a centralized place but that is not really how things seem to be going and there's a huge amount of like off exchange dark quasi dark volume and I think there's still a lot of corners of the trading world where like being in the room is kind of like this big advantage and this is a very much anti-ai play in some sense it's data is hidden 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.

28:33And so that's kind of an interesting trend. A lot of this data sales get reported in a centralized place later, but it's not prompt enough to be useful. And so to say 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 trading is happening. I'm glad you brought that up because that's specifically what I'm curious about from the sort of physical infrastructure side. Like if I have a query at a ChatGPT, I don't care if the model is like trained in like Abilene, Texas or wherever it gets back to me and whatever. But I know that for high frequency trading, at least on the execution side, there are certain parts that you want to be literally co-located and you want to have the shortest possible wire.

29:19And 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 bulk level, there's actually some pretty similar things. So I often think about it as like latency and throughput. Latency being the time to react and then throughput kind of like how much thinking you can do in a certain period of time. And so you're right that like this space demands like low latency. Early in the 2010s, it was a sort of Flash Boys book and perception where it was like really kind of about arbitraging latency.

29:59I'm happy to report that in some sense, all the latency has been arbitraged for the most part. There's no more edge in shortening the wire. This is probably like a little bit, but it's relatively small. And like, I think if you look at the big quant trading firms, the need to like 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, it'd be a really, really good decision.

30:35And then 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, it better still be the best response in a microsecond. And so you can be a little worse. You can be a little worse in the second. Yeah, for sure. Yeah. 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.

31:05We are, I think, big if you're not comparing us to Google or Meta, but 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 or response, we're making the smartest possible decision we can. So you can kind of... Field programmable gate array. Oh, there you go. Sorry.

31:36Catchy. FPGA. FPGA, yeah. Basically, all these different devices have different latencies and throughputs. GPUs have very high throughput. They are... That's what they're useful for, right? But the problem with markets is they're kind of narrow. The amount of traffic flowing into these LMs from everyone typing into their robots is massive. And they do all sorts of clever things to batch up requests and process some things. We don't really have that luxury, really. The markets are going to happen at the speed they happen. We can't duck out for a while and catch up. We kind of need to stay in the game.

32:05So 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 give 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 answers out of it in different ways at different speeds? And that, I think, is where a lot of the 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?

32:39which 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 so

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34:49It's not easy to stand out from the crowd. Simplify how you stock up to get ahead. Go to amazonbusiness.com for support. Joe brought up some of the cynicism around CME's cloud deal with Google. And this came up. Speaking of a specific cynic who went on the record in one of our episodes, Don Wilson basically made the argument that matching on a cloud doesn't necessarily make sense because you might put in two orders and you're not really sure which order gets filled first. I guess you're kind of back in that black box environment or maybe it's a latency issue. I don't know. Is that a problem that you're seeing?

35:25It's something that I worry about. Our general philosophy is markets 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 is just a good market design to have. there's a very widespread though in existing exchanges across the world we've 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 a hundred different network ports and they're all connected they have this amazing time stamping 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 yeah no probably i'm glad i'm glad that you report that we've moved on a little bit since then.

36:55Where 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 right now? I think in terms of like really long-term strategic planning, electricity is like quite clearly a very binding consideration.

37:27When we think about spitting up new like GPU-based training data centers, it really feels like is there electricity? Like finding a piece of land to put a building in, there's a lot of land. Yeah. The electricity negotiations. And that's an issue at HRT. Even for us, you know, because we have a sort of hybrid mix of using cloud providers and building our own data centers. and yeah, the negotiations and thinking about power constraints. We have an existing data center in a very cold place and we want to make it bigger and the data center people are fantastic to work with but they're saying like, well, we need to go talk to like the power grid and negotiate this next crunch 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.

38:17It's better now, it's not as competitive. Or the entire stock market is riding on. Say a little bit more about how you perceive the GPU market. 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 in spinning up more. When was the worst of the crunch? 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 was 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 have been made.

38:58So 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. As a very millennial person, I guess climate change was a big thing growing up in college, a lot of discussion about climate change, and to see people spinning up data centers very fast by basically buying as many gas turbines as they can and putting them outside. I'm like, whoa, what are we doing? It's wild. But that's the only way to get electricity promptly. You just have to throw gas turbines outside the building and turn them on.

39:33It'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, the power usage and things and i i know that the sam altman's 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 wait on this note can you talk to us a little bit more about where competitive advantage actually comes from in this space because if the gpu crunch is somewhat solved and if latency isn't as big an issue as it used to be where are people actually getting their edge from right i mean people talent is one of your ever things you asked is that a constraint yeah it is it is a very competitive people market.

40:37We're essentially asking for people that 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 to how you implement it. So that's already like a tough ask. So people are constrained, people that we like I want to find and we pay well for those people as a result. And it is competitive. But I think the more subtle edge is almost like putting 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?

41:19This 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 it's i think you need to be just optimizing the whole stack and so like my team is like the ai team and so net what it 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 like 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.

42:07So 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, kind of building up 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 you know those slower firms are trying to kind of go faster 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 Like maybe a month is just ridiculous.

42:59But do you, in your work, is that horizon, has it broadened? It is. Yeah, I think one of the things for people who are aware of HRT even at all, I think there's still a perception as sort of a pre-2020 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.

43:32Well, I have a model that tells me that the best price is an hour. So maybe the shorter-term model should inform the longer-term trade and cascading all the way down. When you're doing this sort of slightly longer-term or slightly slower frequency trading, is the fundamental job still the same, which is you're in the liquidity provision service business just over longer? you want to hold that warehousing? Or does it some, because when I think of a fund, when I think of a hedge fund, I certainly don't think of, maybe to some extent, some of their strategies might be sort of liquidity provision, but it's more directional.

44:05Is it still that, or is the fundamental reason why you make money, the service you provide, does it change, by definition, change over that horizon? I think the market making service provision does break down. I think it stretches the analogy too far. I think you have to think of it as like liquidity taking, which somehow seems more like aggressive or something. but 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 know you can also kind of acquire a 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.

44:52You tell me if this sounds too wishy-washy. I love a mental model. You mentioned Go and Chess, right? So the thing about those is that they're zero-sum games. There's only one winner. Someone's unhappy, someone wins, and they're maybe equally unhappy. Plus one, minus one. I think the reason that trading works is because it is, in some sense, positive sum. Money is conserved, and I guess a little fee goes to the exchange. So in some sense, money is, at that moment of a trade, is actually negative a little. But utility, people's general happiness, I don't know, my paycheck goes into my 401k provider and it buys some ETFs.

45:31I'm relatively insensitive to how exactly that happens. I'm not going to look at it for another 40 years, right? Don't lie. I try not to look at it, especially lately. But yeah, my utility is a very long horizon. And so if someone sells it to me at one cent different, I don't really care. But the person who made the cents happy and I'm happy because I got good liquidity. It 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 like thinking about markets like an alpha go sense doesn't make sense because it's kind of doesn't really apply.

46:04If you thought of markets as HRT and all our competitors all kind of in some sort of like deathmatch, who's the smartest, who's trying to pick each other up, then markets would be kind of like this giant standoff where no one would be trading. Everyone would be kind of be like waiting. But obviously markets are very vibrant. I think it's because even when we're crossing the spread, it's because we're crossing the spread against someone who wanted to sell for whatever reason. If we are right, I guess in five days' time, they might be less happy, but maybe they weren't actually. Maybe they were just hedging a position.

46:31They don't care what the stock price was in five days. They just wanted to hedge their position, and we traded with them. That's the way I tell Rick and Silas in my head. It can still be a service provision. We make money only because someone else wants to trade. If no one was trading, we wouldn't exist. Right. 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.

47:11How does a company like HRT, how do you actually balance that tension? Yeah. I mean, this is also like a sort of really honest answer in that many years ago, this was a relative comparative disadvantage for us for recruiting. Some, we 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 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.

47:43And I would have basically no strong counter argument, apart from the fact that actually writing papers is kind of overrated. I've been there, done that. When you get older, you will not care. Now, though, there's this interesting situation where this golden era maybe of being able to work at a big tech company and be paid for publishing research is very much over. The papers that do come out of the big AI labs are essentially 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.

48:21I've even seen some of the sort of AI lab people think a lot about non-competes in public, tweeting about non-competes and things, which is an amazing ton of events because I feel like... That was very antithetical. Right, I mean they're like literally effectively banned in the state of California. And I think people were almost like proud of this fact and would also kind of hold it against the New York sort of trading world being like, oh, look at these people with their non-competes and 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.

48:58And like those people know how the soup is made and they are not writing it down and not committing any explicit sort of IP theft. But 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 the steamroller. I promised we would. When I hear AI in trading, or I know people are very excited about agent-based AI nowadays, part of me thinks back to one of the more amusing events in financial history, which is, Joe, I'm sure you remember the time that one of Knight Capital's algos went rogue.

49:51Many people would not find that to be an amusing event at all, the worst nightmare possible. Yeah, for them. The peanut gallery. Right, right. Schadenfreude. So this algo went rogue and bought like$7 billion worth of stocks. Bankrupted the whole company. Yeah, exactly. What are the guardrails that you put in place to avoid the destiny of Knight Capital? So every training cycle, we have a talk about the Knightmare with a K, and we have multiple X Knight employees at HRT, as you might expect, just from a lineage of a successful trading firm that ended in a kind of unhappy way. And we have many people who are at Knight.

50:25The story is crazy. A successful trading firm that ended in about 15 minutes. Yeah. Yeah. So it's fair to say that that stuff haunts us. And we try and take as many lessons away from that as possible, defense and layers. So I think one of the things that I like to emphasize with the AI stuff in particular is that it is not like there 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.

51:02It's just many, many layers of sanity checking throughout the day. And then at a sort of high level, it's a very careful process, including processes to specifically avoid the KCG type scenario of how are you even releasing new versions? And what pre-release checks do you run and audits? And we even during the day, we have some, I don't know, I guess you 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 like flow, but they were enough to kind of just again, like everything of a numeric stability of the model sane and things.

51:40It's not about losing money or making money in javies. It's not like, oh, like risk in the kind of financial sense. It's like operational risk, but paranoia is deep. And that's probably something that's still very different, I think, from this market, from the sort of other AI world, which I guess anything goes and failure rates are kind of just priced in. But yeah, you could imagine just ruining everything. And 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.

52:14We 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 like the profit you make in 10 years by still being in the game versus move fast and break things. It's not move fast and break things, but you still want to move fast.

52:44I have like a million more questions, but for the sake of time, I'll just ask one more. And I don't know even though whether it's something you're in a 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 the screen or a piece of a text appears on a website and markets move around a lot, all that. And there's people then suddenly, actually, the jobs report was good. And if you actually look at the wage number and then the size.

53:19But in that instant, in that first microsecond after the release, markets are already moving. certainly before any human has had a chance to read the thing or form of view so what i assume is that there's training on here is the text and here are the things and whatever but to as you would put it or from the perspective of hrt what happens in the millisecond after an event yeah so yeah i mean so we have like a bloomberg headlines feed that is like probably pretty low latency and if it's like a important article has like a star in the feed things like this right but you can do everything from having kind of a handcrafted logic to look for keywords through to putting it through like an AI model.

53:59One 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 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. I love that job. It's weird. We actually want... Like a monkey on a computer. For a hackathon, we got a PlayStation controller and gave people a chance to try and practice reacting to events very fast.

54:40It's really tough, but it's a learnable skill. I think in an efficient market sense, this should be AI-able. Yeah. It is challenging, though, because if you're imagining 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.

55:14So 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 was 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 grapple with this and they say it still works and they try and account for this. But I don't know, this stuff is really that smart. The whole kind of thesis is that it's memorized everything it's been trained on. So why would it be reliable? And so whenever you see someone being like, Like, oh, I ran every Federal Reserve speech through JantGPT and I got it right like nine out of ten times.

55:50It's like only nine out of ten times? Like, why not 100 %? So I do find that I do think it is interesting how many humans are still involved in 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 stocks and options. Very interesting. And so I don't know how to reconcile that, but I think about that when I read. Ian Dunning, that was fantastic. There really are hours more of conversation.

56:25Are we going to have him back on next week? We'll be back next week. Looking forward to next week's episode. But no, that was great. Thank you for having me. I really appreciate it. Yeah, my pleasure. Thank you.

56:45Tracy, I thought that was really great. I like this idea of this sort of anti-symicism because you do hear a lot of people say, oh no, like AI could solve things like chess or whatever, but the stock market is fundamentally different. And I've never been totally satisfied with some of the theories for why. And like, I get stocks are not like necessarily like a solvable problem in quite the same way, but humans make money on the market by matching patterns. Why can't smart silicon brains do the same thing? Well, there's also history now. We have many years of HFT trading and algorithmically driven trading where people have made a lot of money.

57:20So 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. It is always funny in finance. People are like, oh, 17 out of 19 times there's been this death cross of the S &P 500 stocks went down.

58:03It'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 putting death cross in a headline is so tempting. That's true. You cannot, advice 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 exchange. That was kind of boring when people were talking about the Cold War. and HFT and all of that. It's interesting that the GPU market is eased versus where it may have been a couple of years ago.

58:40And it's interesting that even at a scale of a trading shop, that electricity is proving to be a main constraint, which does raise questions about, are we just going to hit up against a wall given some of the AI plans that so many people are banking on for the chatbots? 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. Yeah. Great conversation. Answered some questions. Yeah. Plenty more to go. That was helpful. Yeah.

59:17And I'm sure we'll talk to him again. Maybe not next week, but soon-ish. Maybe next year. All right. Shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Traci Alloway. You can follow me at Traci Alloway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Ian Dunning. He's at Ian Dunning. Follow our producers, Carmen Rodriguez at CarmenArmond, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks. For more OddLots content, go to Bloomberg.com slash OddLots 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 slash OddLots.

59:52And if you enjoy OddLots, 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 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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