Pod Insights Odd Lots Inside Hudson River Trading's Blistering Token Burn About this episode Today’s episode, which was recorded at our recent live show at New York’s City Winery, follows up on a conversation we had with Iain Dunning, head of AI at Hudson River Trading. Last year, we talked about how his firm uses AI. Now, some seven months later, we follow up on how one of the biggest market makers around is deploying this technology. We talk about the price of memory, bottlenecks in compute, how much HRT employees are actually spending on tokens, why the firm might develop its own chips, as well as AI-induced delirium. Read more:Jane Street Plans New Data Center as Compute Power Runs ScarceNvidia-Backed Robotics Startup Generalist AI Valued at $2 Billion Only Bloomberg - Business News, Stock Markets, Finance, Breaking & World News subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Tracy Alloway? There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.
0:26 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting
1:08 Speaker 01 and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.
1:52 Speaker 01 Bloomberg Audio Studios. Podcasts, radio, news.
2:07 Joe Weisenthal? Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.
2:12 Tracy Alloway? And I'm Tracy Aldaway.
2:13 Joe Weisenthal? Tracy, we did another one of our live shows, this time our biggest show ever.
2:17 Tracy Alloway? Our biggest show ever. It was absolutely amazing. We did it at City Winery in New York. I think we had over 300 people.
2:24 Joe Weisenthal? Yeah, I think it was like 350 people were there.
2:26 Tracy Alloway? 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.
2:34 Joe Weisenthal? Right. I guess like it's themed and anti-themed at the same time because we're in this moment in which everything is just like AI markets, markets, AI, et cetera. 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.
3:00 Tracy Alloway? 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.
3:12 Joe Weisenthal? 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. 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.
3:31 Tracy Alloway? Joe, this is your dream, right? You finally get to do a two-part episode.
3:35 Joe Weisenthal? 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.
3:43 Tracy Alloway? So the last round was easy. This one will be tougher.
3:46 Joe Weisenthal? That's what I'm a little worried about. Well, I was gonna 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. 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 DeepSeek competitor?
4:06 Speaker 03 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 DeepSeek. But beyond that, it's not a claim I'd be willing to make. It's a hugely capital intensive task, clearly.
4:28 Joe Weisenthal? Yeah. But do people ever chat about that? It's like, we could do this.
4:31 Speaker 03 Oh, we think about it. You know, 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 like China that it's like a huge array of them. It's kind of an interesting shift from that DeepSeek moment where it felt like it was the first bolt from the blue of like, 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. I'm not so sure I'm one of those people though.
5:04 Tracy Alloway? Okay, so speaking of things moving so fast, my first question is slightly different. I looked up your Twitter feed.
5:10 Speaker 03 Oh, no.
5:11 Tracy Alloway? 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 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, like, what is the endgame that you speak of here?
5:38 Speaker 03 God, now I sound like a San Francisco person. I don't know. 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.
6:17 Joe Weisenthal? Hmm.
6:18 Speaker 03 And 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? Is some sort of technological convergence, everything going faster all the time?
6:40 Joe Weisenthal? Well, give us an example.
6:41 Speaker 03 Hilarium, probably, but you know.
6:42 Joe Weisenthal? 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?
7:03 Speaker 03 Oh, I think it's, one way to think of it is just like, the amount of compute going into both training a model and running a model, 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. And 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? Surely you can't just like, keep getting better at predicting markets forever. There's got to be some sort of forcing function where, you know, your margins go to zero as you keep investing more in compute.
7:46 Joe Weisenthal? 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 timeframe.
7:54 Speaker 03 Basically.
7:54 Joe Weisenthal? That's cool.
7:55 Speaker 03 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.
8:15 Joe Weisenthal? Yeah.
8:15 Speaker 03 The spacing between them seems to be compressing. I don't know. I do sound hilarious. I sound feverish. And that's why it's so hard to-
8:22 Joe Weisenthal? Literally, everyone in this room probably is talking about this.
8:25 Speaker 03 Everyone must feel the fever to some extent. Yeah.
8:27 Tracy Alloway? I never understood the Highlander, there can only be one thing, because there are already two. They could just coexist.
8:33 Joe Weisenthal? That's right.
8:34 Tracy Alloway? Anyway, sorry, I'm just picking apart your analogy. You mentioned a new model release. When a new model gets released, like, 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?
8:47 Speaker 03 So, I mean, our primary use cases at Hudson River Trading are definitely kind of just like accelerating your own research. So, 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 Anthropic, where a cursory examination made us feel like, well, this is the moment. We've crossed the dividing line. And 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.
9:30 Speaker 03 And suddenly it was like, oh, wait, no, this is, this is actually what we thought it was going to be six months ago. So, 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 are, 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 and- It's very like, I don't know, intern, intern level AI perhaps? The thing is like, what do I think it'll be in a year?
10:15 Speaker 03 And I would not want to make a bold claim, but it will still be.
10:17 Joe Weisenthal? 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, like rigorous backtesting. And then it's like, look, here is something that seems to work and we've backtested it a million
11:00 Joe Weisenthal? 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?
11:05 Speaker 03 I, I, 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, what is anything? It feels like markets are just, uh, the cynical take is everything is gambling. And so everything is some sort of like gambling market, including public markets.
11:23 Joe Weisenthal? The joke is.
11:24 Speaker 03 It's flows, it's buying and selling and it's just, it's worth what it's worth. And it's detached and more buyers and sellers price go up and, and models are excellent at like pulling that out of data.
11:34 Joe Weisenthal? 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.
11:55 Tracy Alloway? Just do what the AI says.
11:56 Speaker 03 Like, I, I feel like, yes, although it sounds crazy, it sounds like AI delirium when I say it, but I, I feel like there's some sense that that could be true. But at some point, I, I, I can't predict. So at the very short time scale, people accept this already, right? Like, I can't tell you the price of like a stock in a minute. And no one would have 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 time scale? If it's ingesting all the data and has everything and it can keep it all in a context in a way human can't, why should I be able to understand it? Yeah. And that is a strange thought.
12:38 Speaker 03 A loss of control, it feels like a loss of control, but it's like we, you know, people save us from math. Maybe humans are actually very bad at math. Right. So it's not surprising AI is much better than humans at these like math proofs. Humans probably would be pretty bad at markets where thousands of tradable instruments on like very long time scales. Uh, we just kind of accepted that we were, some people were good at this. Yeah. Maybe that's a, was a temporary state of affairs.
13:03 Tracy Alloway? 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 timeframe.
13:14 Speaker 03 Yes.
13:14 Tracy Alloway? Which is what you do. And then we started joking about magic models and magic is a dangerous word to use on this podcast. Cause 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?
13:36 Speaker 03 I do think there are diagnostics we've done where, when we can, we can see things that we can understand. I, I, 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, but it might be illusory because it's a very, very complex object. And you can, if you're only taking slices through it and to understand aspects of it. Um, you know, we had this emergent phenomenon we saw where it felt like the model kind of understood meme stocks from first principles. Like quantum stocks, uh, and crypto stocks being kind of adjacent in stock space. And of course, from a fundamental perspective, this has no, there's no meaning to it.
14:17 Speaker 03 But we looked at the model in a sundress certain lens and it clearly felt like they're, they, 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. But you know, Wall Street bet's favorites, I guess. Yeah. And, and they were near the cluster too. And this was like just. 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?
14:39 Joe Weisenthal? Who are you to override the model? The model says they're in that vicinity of hyperdimensional space.
14:44 Speaker 03 Yeah. The one thing for us, though, is that when we do have this 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 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 in 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
15:29 Speaker 03 large risk in a very certain direction. That's a, what's the risk management story on the AI? If you just give up all control to just the magic prediction?
15:53 Tracy Alloway? There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused and brief. Search Barclays Brief wherever you get your podcasts.
16:19 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture. exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs. And hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.
17:03 Speaker 01 But according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company. And Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute, brought to you by ChatGPT. Get started at ChatGPT.com.
17:42 Joe Weisenthal? So you said something on the last time we interviewed you, which is very important. First of all, I feel like in 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, that chips themselves are 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. Talk about that. What is the state right now? Let's say like, I assemble, I poach a bunch of people from Hudson River Trading. I get a bunch of GPUs. Is it then not trivial to find a place to plug those in?
18:20 Speaker 03 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.
18:34 Speaker 03 I'm not sure if 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 a market and tried to get a quote on that.
18:43 Joe Weisenthal? Wait, sorry, just to be clear, the chips are available, but not the capacity to run them?
18:47 Speaker 03 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 like 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, 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.
19:27 Speaker 03 And whenever our competitors put out a podcast this weekend and they mentioned something along the lines of the fact they, they had one data center and it was the data center and that was their data center. And 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.
19:52 Tracy Alloway? We'll 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?
20:03 Speaker 03 And it's not enough. Yeah, and it's not enough. Yes, we, 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're a kind of intermediary layer over it. I don't know what their process is like for sourcing it. But um, 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 a 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 upfront?
20:48 Speaker 03 Do you want to pay some per year? A lot of different commercial terms, credit risk on both sides. It's complicated stuff.
20:54 Joe Weisenthal? 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.
21:03 Speaker 03 Who?
21:04 Joe Weisenthal? Yeah. 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, et cetera. They want to know for sure that you're going to be good, you're going to like pay your bills, et cetera. How do you establish to the data center that you are going to be a reliable, I guess, tenant?
21:22 Speaker 03 Yeah, 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? To 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 potty problems to this where it's like they, 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, and, and pay it more upfront. But, you know, we're looking at their CDSs on some of these ones and thinking about how that affects our,
22:04 Speaker 03 you know, maybe we should pay you 350 an hour and take out a CDS for 10 cents for our equivalent of insurance on, on your heavy leveraged Neo cloud. You're having a disruption, no names. But, you know, it's, it's, I think there's reason to be cagey on both sides because this has all come from nothing. Like a year ago, we, we weren't there asking for it and they didn't exist to sell it. And so the only rock is Nvidia, I guess, 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.
22:41 Tracy Alloway? 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? Or like, do you just take what you can get?
22:54 Speaker 03 Or build your own. Yeah. Yeah. Say more. You can, you can, well, many people, I guess, now are working on building their own chips for inference, which is a strictly simpler technological problem. And ourselves and many of our peer trading firms have hardware teams to tackle this. And you can outsource parts of that process, so it's not as daunting as it seems. But it's definitely an active area of investigation for us. And now clearly everyone, because I feel like everyone's talking about their partnership with Broadcom or something like this. And if someone says partnering with Broadcom, it's like they're making an inference chip.
23:27 Joe Weisenthal? So this is interesting, right? Because you hear about like Amazon, like they've got Tranium, right?
23:31 Speaker 03 Tranium, yeah.
23:33 Joe Weisenthal? Google, TPUs, et cetera. So we could be in a world in which we hear of like a Hudson River Trading branded chip one.
23:41 Speaker 03 I don't think we'll sell it, but yeah.
23:42 Joe Weisenthal? Okay, but like that's
23:43 Speaker 03 But yes, you're right. That is, that is definitely the right model. And on the other hand, Jensen never sleeps. And yeah, Jensen purchased Grok and you know, they've got their new product lineup from the Grok acquisition, which is a very compelling product as well. And there are other startups, Etched comes to mind. So the inference space is a smaller design space. It's not clear that in-house solutions will be a necessary thing in the future if physical enough people are competing. But on the training side, I mean, what a moat, like there's
24:14 Joe Weisenthal? It's just Nvidia.
24:14 Speaker 03 It's just, and I suppose Google, but you know, if you are using TPUs, you're also kind of entering a very close relationship with Google, some feeling of vendor lock-in. It's, it's a complicated thing if you go down that path. But if you're compute hungry, like the Neo Labs, I mean, the, the, the, the big labs, you'll take what you can get. I think Anthropic takes TPUs, Traniums and GPUs, you know? They need them all.
24:36 Joe Weisenthal? So maybe we'll create a little bit of controversy here because later on in a little bit, we're going to be speaking with Carmen Lee, the CEO of Compute Exchange, which is this, you know, one of multiple entities are trying to build financial markets for compute capacity, right? Trade it like oil. So it's like compute futures and stuff that's, right now, could you see a use for that? A financial instrument that's like on some liquid tradable exchange for
25:07 Speaker 03 H one hundred or whatever, some benchmark of how much it costs to run these chips. Could you see that being a useful instrument for you at some point? It's plausible and it partly relates back to my previously stated failure to plan correctly for the future. If in some sense I could lock in a price for some future date for delivery or something of compute, something that is connected to a price to compute in the long term future. I think there could be value to that. We could basically hedge our risk that we wait too long to put the order in and the price goes up. I mean, in 2026, the price of memory has gone up so much that I, we do have concrete, specific things. I wish I put that order in a month earlier. So it's a real, real thing.
25:47 Speaker 03 Do I believe that there would be a good market with left liquidity for long dated compute futures? That I guess remains to be seen. I don't know what I would do with a short dated compute future. I do think defining what compute is, is pretty hard and I have, I have no idea what physical delivery would be. If that is indeed of interest, because you know, because of the long term contracts and because of how much work goes into every site. Yeah. Like when we connect to a NeoCloud site, we're thinking about how to connect it back to our other sites. Everyone's got a different networking system, the file system, like, you know, visiting with GPUs, which was all the focus. But there's also like, you know, what, how does, how is data stored at that site? Or is it stored at that site at all? Is there an adjacent site that all the, the hard drives are in?
26:32 Speaker 03 And they're all idiosyncratic and I can't do anything of 128 GPUs. I need thousands of GPUs or bust. That's like my lot size. And so it's very hard to see how you could kind of break that down into useful units. But maybe it's just a spot thing and if it's long dated, I don't know, they could be. We'll learn more at seven forty.
26:50 Tracy Alloway? I, I did get a preview and it is pretty cool, like the actual program where you can select like the type of compute you need from a specific data center that has like literally, I think dozens, if not maybe hundreds of parameters at the moment. So maybe we can get a demonstration from Carmen. What is your token spend like at the moment? Is it bigger than Joe's? I hope so.
27:11 Speaker 03 I think I, what is my average? I think it's on the order of a hundred, $200 a day.
27:17 Tracy Alloway? Per employee?
27:18 Speaker 03 Per, that's sort of on my team. I feel like that's kind of what I've been seeing lately. And some people are more in a thousand a day range. A bit bursty for that.
27:27 Tracy Alloway? Wait, do you like those people? Cause they're supposedly more productive?
27:31 Speaker 03 No, we definitely are not trying to encourage that. I mean, some people go through stages of experimentation slash AI delirium, which is understandable. And I think we are always trying to understand if the people who are using more, like, are they doing it for something that you haven't figured out yet? That's a pretty profound new expense to have. It's not at a level that concerns us. Well, it didn't exist at all as an expense type. So that's kind of interesting to think about.
27:56 Joe Weisenthal? I'm curious, like, you know, for the consumer models, they talk about how psychophantic they are. Does that happen? It's like, yes, Ian, you're close. This is really smart. You're close to cracking the code of the market. Keep pursuing this.
28:09 Tracy Alloway? Just one more token, bro.
28:10 Joe Weisenthal? This, this idea is doing, or as Claude likes to say, this is doing some real work here in this argument.
28:16 Speaker 03 It's really good.
28:17 Joe Weisenthal? Do you get that in the engineering context?
28:19 Speaker 03 I think we do. And it's interesting. We have, we just started a new internship for the summer. And in previous internships, we noticed that, you know, it's quite daunting coming into this quant trading context. You know, you have no, there's not much to like, read a book, can't read a textbook about it. It's useful. So people ask AI. And, uh, you know, it would always mention some things of like an unusual frequency that maybe an expert in that field wouldn't, like focuses on some things. And we noticed, uh, in our like winter internship program, a lot of sort of very technical quant finance research terms being mentioned a lot by the interns that no full timer used. And it's like the original seed of the mind virus was AI. So there's a little stuff like that, but, um,
29:00 Speaker 03 our token spending is going to go up. I mean, that's almost guaranteed. And yeah, we're getting value out of it. Maybe not 2X productivity, but I, I talked to someone who said their team is 50% more productive. That's pretty good. I mean, you'd happily pay a hundred dollars a day for that. I just don't understand how people who are token poor could keep up with someone who's token rich. And that's again, goes to the acceleration feeling. It's like if you have two people who are sort of equally resourceful and smart, but someone has basically a co-pilot with them that's giving them a 50% boost and all they have to do to get that is essentially spend money. It creates a have have nots dynamic that possibly compounds as you have more success, you make more money, you're more willing to eat.
29:44 Speaker 03 Now a thousand dollars a day per person for token spend, you go even faster and this feeling again of compounding acceleration, which might be delirium, but you could make an argument for why it's could be a real effect instead of more winner take all contexts where speed of improvement is is like the key thing. Um, there's a story there, I think, or delirium, I don't know.
30:03 Tracy Alloway? Well, I mean, speaking of the haves and have nots, the other big story in AI world is just competition for talent, right? And everyone is sort of chasing the same genius engineer, I guess. How are you finding that at the moment?
30:17 Speaker 03 It's it's changed a bit. There's a there's a lot of dynamics going on. There's there's still a feeling that if you are plucky enough, you can get a VC to fund your idea based on very little. You have the right pedigree and that's always been true, I guess, in some sense. This is like the Y C philosophy, some sense you go and it's just that some of the numbers and the FOMO feeling and is is quite shocking. And so that's actually a form of competition, just like, why don't I go create a startup? I don't have any ideas or anything. I'm just going to make a startup. For the big labs, a question of upside, uh, remaining upside, you know, you're at a trillion. Now, I guess there are the two big ones that are trillion dollar valuation. Where do you go from there? I think that's affecting people's level of like forward looking optimism for people who are taking offers now.
31:01 Speaker 03 And for people who are at those places and looking to leave, generally it's a question of like, well, they've become big tech. They've added people at a vast rate and the culture has shifted, especially at some of the labs a lot and to our favor. For a while, it did feel like we were in a very, very fierce competition and now maybe it's now it's maybe more even playing field, but. I don't know. I talked to a lot of undergrads and they don't feel great about the future.
31:28 Joe Weisenthal? Well, so this is
31:28 Speaker 03 They feel very worried, basically.
31:29 Joe Weisenthal? This is what I was gonna, this went by way too fast, but like you mentioned already, the models are at like, okay, junior level.
31:38 Speaker 03 Yeah.
31:38 Joe Weisenthal? So what does talent look like at this point? And what are, like, I've seen some of the Anthropic interview questions and it's like designing some GPU kernel or like optimizing the configuration of GPUs within the data center. What is, what do you want someone to bring to the table at this point?
31:53 Speaker 03 I mean, I think the first thing is just trying to embrace an open book philosophy, like let the interviews be done with the aid of AI. It's something we're trying to aspire to do because it's just, at some point, you become, it becomes unrealistic to pretend anyone would work without that. One of the big things in quant is being like, there's this like archetype of like the math theorist or the string theorist or something. And they go into Long Island somewhere and they come out with alpha. But, you know, like, our, our experience has been a little bit more mixed because it's like, if you can't implement your ideas, how do you, how, how does that happen exactly? Well, now Claude can presumably implement the ideas. So trying to embrace that, maybe we do accept more theorists, more dreamers, people who can come up with ideas, trusting that the implementation work can be done by AI.
32:37 Speaker 03 So I think that's our shift, but I've been joking, it's like the word cell versus shape rotator type. Like, I feel like the error of the word cell may be a bonus. Like, if I do
32:46 Joe Weisenthal? Good, good.
32:46 Speaker 03 Yeah. I mean, prompt engineering is kind of a boomer term at this point, but there is something to be said for like describing what you want clearly and without confounding factors. And that is a skill that can be learned and is not evenly distributed in the population. And I would argue that it's shot up in value simply because of AI. So I like to think of myself as one of these people though. So that could be the delivery I'm talking. I don't know.
33:11 Joe Weisenthal? All right, Ian Dunning, we could talk for like two more hours.
33:14 Speaker 03 Oh, thank you for having me.
33:14 Joe Weisenthal? But thank you so much for joining us at Unmanned.
33:31 Tracy Alloway? That was our conversation with Ian Dunning of Hudson River Trading, recorded live at our New York show. I'm Tracy Alloway. You can follow me at Tracy Alloway.
33:40 Joe Weisenthal? And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow Ian at Ian Dunning. Follow our producers, Carmen Rodriguez at Carmen Armin, Dashal Bennett at Dashbot, Cale Brooks at Cale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to Bloomberg.com/oddlots, where we have a daily newsletter and all of our episodes. And you can chat about all of these topics 24/7 in our Discord, discord.gg/oddlots.
34:06 Tracy Alloway? And if you enjoy Odd Lots, if you like it when we do these live shows and talk about how trading firms 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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