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Chapter 1: What is the main topic discussed in this episode?
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Bloomberg Audio Studios. Podcasts. Radio. News.
Hello and welcome to another episode of the Odd Lodge 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.
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Chapter 2: How is Hudson River Trading implementing AI in trading?
The thing is, what do I think it'll be in a year? I would not want to make a bold claim, but it will still be...
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?
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 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? What is anything? It feels like markets are just... The cynical thing is everything is gambling. And so everything is some sort of gambling market, including public markets.
But the joke is... It's flows, it's buying and selling, and it's worth what it's worth. And it's detached, and more buyers and sellers' prices go up. And models are excellent at... pulling that out of data.
But just let's say the classic example of a back test 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.
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Chapter 3: What challenges does HRT face with compute and memory costs?
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 exist. 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. And 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. But 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. There were some other actual companies that I probably won't name because it feels like it's bad form. But, you know, WallStreetBets 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 are you to override the model? Who am I to say?
The model says they're in that vicinity of hyperdimensional space.
They're there. 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.
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Chapter 4: What insights does Ian Dunning share about AI-induced delirium?
Is it then not trivial to find a place to plug those in?
It's definitely hard to find sites at short lead times. If I went to the market and said, I want 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. 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 quote on that.
Wait, sorry, just to be clear, the chips are available, but not the capacity to run them?
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 Rubin GPUs, they, at least for the first 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 be in queue now for those GPUs if you want them early. So 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 and 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 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 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?
And it's not enough. Yeah. And it's not enough.
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Chapter 5: How does HRT evaluate new AI models?
trying to embrace an open book philosophy like let the interviews be done with the aid of AI is 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 quantum is being like there's this like archetype of like the math theorist or the string theorist or something and they go in to Long Island somewhere and they come out with alpha. But you know like our experience has been a little bit more mixed because it's like if you can't implement your ideas how do you 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. So I think that's our shift. But I've been joking. It's like the word cell versus shape rotator type. I feel like the error of the word cell may be a bonus.
Prompt engineering is kind of a boomer term at this point. But there is something to be said for 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 delirium talking. I don't know.
All right. Ian Dunning, we could talk for two more hours.
Thank you for having me.
Thank you so much for joining us.
That was our conversation with Ian Dunning of Hudson River Trading, recorded live at our New York show. I'm Traci Allaway. You can follow me at Traci Allaway.
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