Inside Hudson River Trading's Blistering Token Burn

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1:49Tracy Alloway:Hello and welcome to another episode of the Odd Lods podcast. I'm Joe Weisenthal. And I'm Tracy Allaway. Tracy, we did another one of our live shows. This time, our biggest show ever in New York City. Our biggest show ever. It was absolutely amazing. We did it at City Winery in New York. I think we had over 300 people in the end. Yeah, I think it was like 350 people were there. Yeah, and the crazy thing is, I think it was our sort of first themed show. And we didn't really plan it that way, but it just worked out. Right. I guess like it's themed and anti-theme at the same time because we're in this moment in which everything is just like AI markets, markets, AI, et cetera.

2:26Tracy Alloway:But, you know, there's all kinds of new things to trade and people are fascinated by the trade itself and people are fascinated by the way the technology development is affecting the trade. So we really wanted to do a kind of future of trading show, which is a very broad thing, but it did sort of come out that way. Yeah, it really did. And you finally fulfilled your longtime dream of doing two-part episodes with our guests. So our first speaker of the evening was actually someone who's been on the show before. That's right. So we had him on the show last year and we had him on our live show. Listen to our episode with Ian Dunning.

3:02Tracy Alloway:He is the head of AI at Hudson River Trading. Talked about all things, implementing AI, GPUs, all that stuff within the context of a trading shop. Take a listen. Joe, this is your dream, right? You finally get to do a two part episode. This is the thing I always think about, which is that after every episode we do, I'm like, oh, there's a question I wish I had asked. So we had Ian on sometime last year. So the last round was easy. This one will be tough. That's what I'm a little worried about. Well, I was going to start first before we, you know, talk about what you do, et cetera. So here's the question I wish I had asked last time.

3:35Tracy Alloway:So Hudson River Trading Shop, you're involved in the AI stuff. Could you theoretically do what High Flyer did and launch an LLM with this tech stack that you have and launch a DeepSea competitor? I think so. I think we're good at training models. We have a lot of compute and people are good at doing the cycle of research, which is required to catch up to the sort of frontier. However, I guess reaching the frontier is clearly a very daunting task. So maybe it's with some effort, deep seek. But beyond that, it's not a claim I'd be willing to make. It's a hugely capital intensive task, clearly. Do people ever chat about that?

4:13Tracy Alloway:It's like, we could do this. I always think about it. I mean, I think perhaps we missed our moment to do so. There's so many open models now coming out from the US as well as China that there's a huge array of them. It's kind of an interesting shift from that deep-seek moment where it felt like it was the first bolt from the blue of here's a competitive open model. Now I see so many groups releasing them. I don't know what the future of open models is if they're all kind of a serious step back and the frontier is progressing so fast. I don't know how you keep up with that. But many people believe that it's possible.

4:44I'm not so sure I'm one of those people there. Okay, so speaking of things moving so fast, my first question is slightly different. I looked up your Twitter feed before you came on the show. Your last tweet before today was, and I quote, feel this every day, worry it's some sort of AI-induced delirium. But then again, various empirical measures are exponential looking, so it feels best to assume we're hurtling towards some sort of endgame. So first of all, please convince us all live on stage that you are in fact not suffering from AI-induced delirium. But secondly, what is the endgame that you speak of here?

5:20God, now I sound like a San Francisco person. You do, yeah. I've been doing AI stuff since around 2016, and that started at DeepMind. And it was a bit of a culture shock for me because there are true believers then, and I was most certainly not a true believer. and I resisted it and I was kind of a natural skeptic for a long time. But certain empirical measures of the pace of progress in the outside world, and I also look at our own business, which looks somewhat exponentially, the amount of compute I'll have next year versus this year, and the amount of compute I have this year versus last year, looks kind of exponentially.

5:59And we're doing things today that I didn't really, I should have dreamt of. I wish I had that kind of visionary, say I'm a visionary and I can see the future and I'm building towards it. But no, I think I'm a pragmatic, engineering archetype. And so it's been very incremental. And I'm like, wow, that happened in a year? So what does this mean? It's some sort of technological convergence, everything going faster all the time. Well, give us an example. Delirium, probably.

6:24Tracy Alloway:Give us an example then. Because, you know, obviously those of us using just the regular models, obviously the improvements in capabilities from one year to another are mind blowing. but from the perspective of like okay the application of ai within the trading context what is something that you can do in 2026 that in say 2024 you would not have been able to anticipate oh i think it's one way to think of just like the amount of compute going into both training a model and running a model and and that it's the same technology working across every equity every future every crypto market every option market across the world with a kind of unified approach.

7:01And this is something that we're doing. But even more interestingly, I would not claim that we are some unique people who are only the ones who have really made progress in AI and trading. I think many of our peers are also investing massively. And we're all doing it all at the same time. And what does that mean? Like, surely you can't just like keep getting better at predicting markets forever. It's got to be some sort of forcing function where, you know, your margins go to zero as you keep investing more in compute.

7:27Tracy Alloway:What you're saying is you are just getting better and better at being able to predict where a market is going to go further and further out in the time frame. Basically. That's cool. And we're not the only ones. So in the end, can there be some Highlander type thing? Like, what are we doing? And this is like my scale. And I guess the other thing I find interesting is of course, the scale that everyone can see with the big labs and what they're doing with compute. And it's like it looks awfully exponential to me. We just had another model released today from Anthropic, and the spacing between them seems to be compressing.

8:00I don't know. I do sound hilarious. I sound feverish, and that's why it's so hard. It's okay.

8:04Tracy Alloway:Literally everyone in this room probably is talking about this. Everyone must feel a fever to some extent, yeah. I never understood the Highlander, there can only be one thing, because there are already two. They could just coexist. That's right. Anyway, sorry. I'm just picking apart your analogy. You mentioned a new model release. When a new model gets released, what is the first thing you do at Hudson River Trading to evaluate it? And how do you actually compare them to the existing one? So, I mean, our primary use cases at Hudson River Trading are definitely kind of just like accelerating your own research.

8:36So that can be coding, but it can also be coming up with experiment ideas, monitoring experiments. we had a sort of a false start with AI I would say sometime last year with the Opus 4.0 models especially from from Anthropic where a cursory examination made us feel like well this is the moment we've crossed the dividing line and we we had a very feverish week where we felt the AGI and we left feeling empty because we realized that it was not there and was not able to meaningfully augment human researchers. And then we had that same feeling again when Opus 4.5 came out and suddenly it was like, oh, wait, no, this is actually what we thought it was going to be six months ago.

9:17So in the most recent model releases, the differences have been more subtle. But we see, I think we have a much better sense of an ever-reducing set of errors they make. And so we're kind of looking for those sorts of mistakes. And we spent some time in the past couple of weeks trying to come up with objective measures to index them against humans in the act of quant research, ideating signals and things. Quant research used to be, as we talked about a little bit, like handcrafting indicators and things. Why not ask AI agents to do that and compare them against humans, like a little sort of battle?

9:49And it's, they're like, I don't know, intern level AI, perhaps? The thing is, what do I think it'll be in a year? I would not want to make a bold claim, but it'll still be.

9:59Tracy Alloway:It'll be wild. So when we think about investing in general, even within sort of like classical quant trading going back decades, there is often, it might be quant, but there's some intuition behind it, right? Cheap stocks tend to do better and we don't actually totally have agreement why they did for a while, but people aren't necessarily surprised by that fact, right? Are we at the point where it's like, why even bother coming up with a human intuitive story? And you just skip the part of giving an explanation that sounds logical to a person and it's just basically pure, rigorous backtesting.

10:37Tracy Alloway:And then it's like, look, here is something that seems to work and we've backtested it a million different ways and it seems to work. And we don't even bother coming up with a story for why, but we're going to trade it. I feel like we're in that world today. It's sort of post, post, post capitalism. When I see IPOs discussed for this coming summer at the valuations they are, I'm like, what is a fundamental? Like, what is anything? It feels like markets are just, the cynical thing is everything is gambling. And so everything is some sort of like gambling market, including public markets. But the joke is...

11:06It flows, it's buying and selling, and it's worth what it's worth. And it's detached and more buyers and sellers price go up. And models are excellent at like pulling that out of data.

11:16Tracy Alloway:But just like, let's say, you know, the classic example of like a backtested is like, oh, companies with the ticker symbol that starts with P, they do well on Tuesdays. And it's like, well, look, the data says that, but this makes no sense. We're not going to trade that. Could it get to the point where it's like, look, ticker symbols that starts with P do well on Tuesdays, and we've run this a bunch of times, and it seems to work, so we're going to put money behind this? Just do what the AI says. That's what I'm sort of getting at. I feel like, yes, although it sounds crazy. It sounds like AI delirium when I say it.

11:44But I feel like there's some sense that that could be true. But at some point, I can't predict. So at the very short timescale, people accept this already, right? I can't tell you the price of a stock in a minute, and no one would reasonably expect any human to do so. Even if they had the order book and spent all the time in the world staring at it. But we accept that neural networks can do this. And then when does that logic break down? Why should it break down at some long timescale? If it's ingesting all the data and has everything and it can keep it all in a context in a way a human can't.

12:15Why should I be able to understand it? And that is a strange thought. A loss of control. It feels like a loss of control. But it's arguably, you know, people say this from math. Maybe humans are actually very bad at math. So it's not surprising AI is much better than humans at these math proofs. Humans probably would be pretty bad at markets where thousands of tradable instruments on very long timescales. We just kind of accepted that some people were good at this. Maybe that was a temporary state of affairs. Well, we talked about this the last time you were on, the idea that the models themselves are not very interpretable, I guess you would say, but you're comfortable with that on a short trading time frame, which is what you do.

12:57and then we started joking about magic models and magic is a dangerous word to use on this podcast because people start thinking about magic boxes. But anyway, now that you've been doing this for another six months since we last spoke to you, do you feel like you have better insight into what the models are actually doing and why they're able to succeed on short timeframes? I do think there are diagnostics we've done where we can see things that we can understand. It's like looking at some very, very complex thing, and you can look at one facet of it and be like, this is a facet I understand, and that gives you some confidence.

13:33But it might be illusory because it's a very, very complex object, and if you're only taking slices through it to understand aspects of it. We had this emergent phenomenon we saw where it felt like the model kind of understood meme stocks from first principles, like quantum stocks and crypto stocks being kind of adjacent in stock space. And of course, from a fundamentals perspective, this has no meaning to it. But we looked at the model in a certain lens and it clearly felt like they knew they were connected. And there were some other actual companies that I probably won't name because it feels like it's bad form.

14:08But Wall Street Bets favorites, I guess. And they were near the cluster too. And this was just one little window. But there were other slices we tried to take which just didn't make sense to us. But again, it's like, who am I to say? Who am I to say?

14:22Tracy Alloway:The model says they're in that vicinity of hyperdimensional space. One thing for us, though, is that when we do have this magical model, it is in a lot of safety around it because we're doing this higher frequency trading. We're trading positions back and forth. There's a lot of risk checks that are fully automated and things. I don't know how you generalize this logic to long term discretionary trading where the idea of like risk checking and that kind of layer of defense around it. it's not so obvious to me how you apply that we can apply very strict controls around this model because it's a well-posed problem we're not taking giant idiosyncratic risks and like one name for months at a time we can sleep at night because of this i don't know how you apply the same thinking to like a fundamental long short thing where you have to put a trade on and it's for three months and you're intentionally taking a very large risk in a very certain direction that's a what's the risk management story around the ai if you just give up all control to just the magic prediction

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16:48Is it just me or is it getting really hard to figure out the best way to save for retirement? Well, Fidelity can help you to find clarity. So you can save the best way for you. With a free personalized plan, goal tracking, and timely insights, you'll be set to take on retirement your way.

17:04Tracy Alloway:Get started at Fidelity.com slash future. Expenses charged by your investments and other costs and fees associated with trading or transacting in your account apply. Fidelity Brokerage Services, member NYSE, SIPC. So you said something on the last time we interviewed you, which is very important. First of all, I feel like getting the quote, AI trade, unquote, people are obsessed with like, what's the bottleneck now, right? And because whatever the bottleneck is, you probably sell it for a lot more money. You said on the last time we talked to you, the chips themselves were not actually a major constraint for you, and that it was more like citing the chips and the powering the chips, the access to electricity.

17:42Tracy Alloway:talk about that what is the state right now let's say like i assemble a poach a bunch of people from hudson river trading yeah i get a bunch of gpus is it then not trivial to find a place to plug those in it's definitely hard to find sites and at short lead times if i went to the market and said i want you know 6 000 blackwell gpus in a box somewhere in north america for delivery in q4 i'm not sure such an offering exists at any reasonable price like if it from maybe someone will give up a lease and i could snag it but i think if i went to the market and tried to get a quick sorry just to be clear the chips are available but not the capacity i think if i had power i could get the chips blackwell chips for delivery this year but i do not think i could get the whole solution and then if you go into 2027 for the next generation of gpus the ruben gpus they at least for the first like stretch are going to be very much sold out and so i think that's a good maybe you actually have on a 2027 delivery you have more luck finding a data center shell by then but you need to you need to be in queue now for those gpus if you want them early so those things are those things are in demand i'll say that for sure and one one of my greatest failures has been uh you know part of my skepticism has been predicting how many gpus we would need on a long enough horizon and it's punishing because it you're constantly playing catch-up and uh one of our competitors put out a podcast this weekend and uh they mentioned something along the lines of the fact They had one data center, and it was the data center, and that was their data center.

19:11And then as they're hungry and hungry for more compute, they had to go out and find it wherever they could. And I would say we are in exactly the same boat. You just can't be picky. It's like you've got like a megawatt there. I'll take it. And it could be, you know, not in terms that are super favorable to you because. Well, say more about that. How are you actually going out and sourcing this stuff? Because as you say, it seems to be exceptionally competitive. And at the same time, don't you guys have an insane data center in like Norway or something? And it's not enough. Yeah, and it's not enough.

19:40Yes, we go to the NeoClouds, the hyperscalers, everyone, and it's a constant dialogue. And they're all in competition with each other. But in some sense, there must be some much bigger shadowy competition going on behind the scenes behind these NeoClouds because they are all looking for space and power. And I don't know if that's the true scarce resource and they have a kind of intermediary layer over it. I don't know what their process is like for sourcing it. But yeah, they have come to us and said, this lease opened up can you please get back to us by the end of the day and for commitment on a long-term contract and our contracts are long-term this is not spot compute this is like 8 000 gpus for three years four years five years payment do you want to pay half up front do you want to pay some per year a lot of different commercial terms credit risk on both sides it's complicated stuff

20:28Tracy Alloway:tell us more about the counterparty risk so it's like you come and you say you want capacity in some data center i'm ian from hudson river trading who yeah well this is the kind of thing like this crowd a lot of people know what hudson river trading is but maybe in san francisco or whatever that's not a household name etc they want to know for sure that you're going to be good you're going to like pay your bills etc how do you establish to the data center that you are going to be a reliable i guess tenant yeah it's a it's a definitely been a dance it's getting better at this point i think we've entered enough deals enough people that i think we have that but we've had everything from people being like, oh, you've issued bonds, what's the rating on those?

21:09To not wanting us to sell too much of one site because if we take all their power rights and then go bust, they might have a long lead time where they can't get another tenant and fill that. And so there's a kind of two-party problem to this where it's like they want customers, but there's presumably a lot of customers, but maybe not as many customers are willing to do the big size and pay more upfront. but we're looking at their CDSs on some of these ones and thinking about how that affects our, maybe we should pay you$3.50 an hour and take out a CDS for 10 cents per hour equivalent of insurance on your heavy leveraged NeoCloud.

21:47You're having a disruption, no names. But I think it's reason to be cagey on both sides because this has all come from nothing like a year ago. We weren't there asking for it and they didn't exist to sell it. And so the only rock is NVIDIA, I guess. An extremely well-capitalized entity who is not going anywhere and is making a lot of GPUs. And we have a very positive relationship with them. And I think that is also a material factor. How much optionality do you actually have on GPUs now? Like if you say you want to prioritize latency or throughput, like can you get the chips that you need to specify on one of those things?