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- [00:00]
Tracee Aldeway
Oddlauts is brought to you by Vaneck. For years investors basically forgot about real assets, energy, gold and infrastructure. But look at what's driving markets now. Central banks loading up on gold, massive capex cycles, currencies doing weird things. These assets are at the center of it. RACS the VanEck Real Assets ETF is an actively managed one stop shop for real assets spanning gold, commodities, natural resource equities and more. Go to vaneck.com raaxpod to learn more fun disclosures later in this episode.
- [00:33]
Ryan Reynolds
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- [00:55]
Tracee Aldeway
of $45 for three month plan equivalent
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- [00:59]
Tracee Aldeway
first three months only, then full price plan options available, taxes and fees extra.
- [01:02]
Inner Balance Advertiser
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- [01:53]
Joe Weisenthal
Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.
- [01:57]
Tracee Aldeway
And I'm Tracee Aldeway.
- [01:59]
Joe Weisenthal
Tracy we did another one of our live shows. This time our biggest show ever in New York City.
- [02:03]
Tracee Aldeway
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 New York.
- [02:10]
Joe Weisenthal
Yeah, I think it was like 350 people were there.
- [02:12]
Tracee Aldeway
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.
- [02:20]
Joe Weisenthal
I guess.
- [02:21]
Cameron
Like it's themed and anti theme at
- [02:23]
Joe Weisenthal
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.
- [02:46]
Tracee Aldeway
Yeah, it really did. And you finally fulfilled your longtime dream of doing two part episodes with our guests. So. So our first speaker of the evening was actually someone who's been on the show before.
- [02:57]
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.
- [03:16]
Cameron
Take a listen, Joe.
- [03:18]
Tracee Aldeway
This is your dream, right? You finally get to do a two part episode.
- [03:21]
Cameron
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
- [03:29]
Tracee Aldeway
year, so the last round was easy. This one will be.
- [03:31]
Ian Dunning
That's what I'm a little worried about.
- [03:32]
Cameron
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. 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 deep seat competitor?
- [03:51]
Ian Dunning
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.
- [04:15]
Cameron
Do people ever chat about that could do this?
- [04:17]
Ian Dunning
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 there like a huge array of them. It's kind of an interesting shift from that deep sea 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, 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.
- [04:50]
Tracee Aldeway
Okay, so speaking of things moving so fast, my first question is slightly different. I looked up your Twitter feed.
- [04:56]
Ian Dunning
Oh, no.
- [04:56]
Tracee Aldeway
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 end game. So. 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 end game that you speak of here?
- [05:24]
Ian Dunning
God, now I sound like a San Francisco person. 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. 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 engineer 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.
- [06:26]
Cameron
Well, give us an example. Delirium, probably, but 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?
- [06:49]
Ian Dunning
Oh, I think it's one way to think of just 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, 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 keep getting better at predicting markets forever. There's got to be some sort of forcing function where your margins go to zero as you keep investing more in
- [07:31]
Cameron
control, 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.
- [07:39]
Ian Dunning
Basically.
- [07:40]
Cameron
That's cool.
- [07:40]
Ian Dunning
And we're not the only ones. So in the end, can there be some Highlander type thing? What are we doing? This is 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 looks awfully exponential to me. We just had another model released today from Anthropic, and the spacing between them seems to be compressing. I don't know, I do sound delirious, I sound feverish. And that's why it's so literally everyone
- [08:09]
Cameron
in this room, probably everyone, must feel
- [08:11]
Ian Dunning
the fever to some extent. Yeah.
- [08:12]
Tracee Aldeway
I never understood the Highlander. There can only be one thing because there are already two. They could just coexist.
- [08:19]
Cameron
That's right.
- [08:20]
Tracee Aldeway
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?
- [08:33]
Ian Dunning
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, 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. And suddenly it was like, oh, wait, no, 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 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 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 will still be, it'll be wild.
- [10:04]
Cameron
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. And then it's like, look, here is something that seems to work and we've back tested 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.
- [10:51]
Ian Dunning
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 take is everything is gambling and so everything is some sort of like gambling market, including public markets. But the joke is 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 models are excellent at like pulling that out of data.
- [11:20]
Cameron
But just like, let's say, you know, the classic example of like a back tested 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 it.
- [11:40]
Tracee Aldeway
Just do what the AI says.
- [11:43]
Cameron
That's what I'm sort of getting.
- [11:44]
Ian Dunning
I feel like, yes, although it sounds crazy, it sounds like AI delirium when I say it, but 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? Like, I can't tell you the price of, like, 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 human can't, why 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, people save us from math. Maybe humans are actually very bad at math. So it's not surprising AI is much better than humans at these levels, like mass proofs, humans probably would be pretty bad at markets where thousands of tradable instruments on, like, very long timescales. We just kind of accepted that we were. Some people were good at this. Maybe that's a. Was a temporary state of affairs.
- [12:49]
Tracee Aldeway
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?
- [13:22]
Ian Dunning
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 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, 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 and crypto stocks being kind of adjacent in stock space. And of course from a fundamentals perspective this has no, there's no meaning to it. But we looked at the model in a, under a certain lens and it clearly felt like they knew they were connected. And there's some other actual companies that I probably won't name because it feels like it's bad form. But you know, Wall street bets favorites I guess. And they were near the cluster too. And this was like 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, who am I to say
- [14:26]
Cameron
the model says they're in that vicinity of hyperdimensional space.
- [14:31]
Ian Dunning
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 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 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. What's the risk management story around the AI if you just give up all control to just the magic? Prediction.
- [15:38]
Joe Weisenthal
Data centers need electricity, AI needs copper, reshoring needs steel. And gold's run may tell you something about how the world is repricing money and debt. All of those point back to real assets. The RACS ETF is an actively managed one stop real asset shop. From gold to commodities to natural resource equities, adjusting as conditions change. Visit vaneck.com raaxpod to learn more. An investor should consider the investment objective, risks, charges and expenses of the fund before investing to obtain a prospectus and summary prospectus which contains this and other information. Visit vaneck.com Please read the prospectus and summary prospectus carefully before investing. RACS is distributed by Vaneck Securities Corporation Distributor.
- [16:24]
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- [16:56]
Ian Dunning
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- [17:11]
Joe Weisenthal
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- [17:24]
Cameron
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 the last time we talked to you the chips themselves are we're 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 approach 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?
- [18:01]
Ian Dunning
It's definitely hard to find sites and at short lead times. If I went to the market and said I want you know 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 market and tried to get a quote
- [18:24]
Cameron
Wait, sorry, just to be clear, the chips are available but not the capacity.
- [18:28]
Ian Dunning
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 stretch, are going to be very much sold out. And so I think that's like a. 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 of my greatest failures has been part of my skepticism has been predicting how many GPUs we would need in a long enough H in. And it's punishing because it you're constantly playing catch up. And one of 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 know not in terms that are super favorable to you because.
- [19:33]
Tracee Aldeway
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?
- [19:44]
Ian Dunning
And it's not enough. Yeah, and it's not enough. Yes, we, we go to the Neo clouds, the hyperscalers, everyone. And it's a constant dialogue and they are 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 Neo clouds, 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 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 3 years, 4 years, 5 years payment. Do you pay half upfront? You want to pay some per year? A lot of different commercial terms, credit risk on both sides. It's complicated stuff.
- [20:35]
Cameron
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 in 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?
- [21:04]
Ian Dunning
Yeah, it's 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 party problem 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 pay more upfront. But you know we're looking at their CDSs on some of these ones and thinking about how that affects our, you know, maybe we should pay you 350 an hour and take out a CDS for 10 cents per hour equivalent of insurance on, on your heavily leveraged neo. You're having a disruption. No names but I think there'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.
- [22:22]
Tracee Aldeway
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
- [22:34]
Ian Dunning
you can get or build your own?
- [22:37]
Tracee Aldeway
Yeah, yeah, say more.
- [22:38]
Ian Dunning
You can, you can. Well many people I guess now are working on building their own chips for, 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 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 or Broadcom they're making an inference chip.
- [23:08]
Cameron
So that's interesting right because you hear about like Amazon, like they've Trainium training. Yeah Google, TPU etc. So we could be in a world in which we hear of like a Hudson River Trading branded chip line.
- [23:22]
Ian Dunning
I don't think we'll sell it but yeah, 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 Groq and you know they have got their new product lineup from the Groq 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 physic enough people competing. But on the training side, I mean what emotes like there's just Nvidia, 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 Neolabs, I mean the labs, you'll take what you can get. I think anthropic takes TPUs, trainiums and GPUs, you know, they need them all.
- [24:18]
Cameron
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 one of multiple entities are trying to build financial markets for compute capacity, trade it like oil. So it's like compute futures and stuff that right now could you see a use for that, a financial instrument that's like on some liquid tradable exchange for H100 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?
- [24:56]
Ian Dunning
It's plausible and it probably 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 the price of 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 we do have concrete, specific things. I wish I put that order in a month earlier so it's a real, real thing. Do I believe that there would be a good market with less 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 no idea what physical delivery would be if that is indeed of interest because, you know, because of a long term contracts and because of how much work goes into every site. Yeah, like when we connect to a NEO cloud 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, basically GPUs which was all the focus. But there's also like, you know, how is data stored at that site or is it stored at that site at all? Is there an adjacent site that all the hard drives are in 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.
- [26:29]
Cameron
We'll learn more at 720 when we learn more.
- [26:31]
Tracee Aldeway
Yeah, yeah, 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?
- [26:51]
Cameron
I hope so.
- [26:53]
Ian Dunning
I think I. What is my average? I think it's on the order of 100, 200 a day per employee, per restaurant 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. Bit bursty for that.
- [27:08]
Tracee Aldeway
Wait, do you like those people because they're supposedly more productive or are you
- [27:13]
Ian Dunning
telling them to rename Definitely not trying to encourage that. I mean some people go through surges of experimentation/AI delirium, which is understandable. And I think we are always trying to understand 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 the level that concerns us. Well, it didn't exist at all as an expense type. So that's kind of interesting to think about.
- [27:37]
Cameron
Well, I'm curious for the consumer models, they talk about how sycophantic they are, does that happen? It's like, yes, Ian, this is really smart. You're close to cracking the code of the market. Keep pursuing this.
- [27:50]
Tracee Aldeway
Just one more token bro.
- [27:52]
Cameron
This idea is doing. Or is it. Claude likes to say this is doing some real work here in this argument.
- [27:57]
Ian Dunning
It's really good.
- [27:58]
Cameron
Do you get that in the engineering context?
- [28:00]
Ian Dunning
I think we do and it's interesting. We have a. We just started our 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 if it's useful. So people ask AI and you know, it always mentions some things of like an unusual frequency that maybe an expert in that field wouldn't like focuses on some things. And we noticed in our 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. It's like the original seed of the mind virus was AI. So there's a little stuff like that. But our token spending is going to go up, that's almost guaranteed. And yeah, we're getting value out of it. Maybe not 2x productivity, but I talked to someone who said that team is 50% more productive. That's pretty good. I mean you'd have to 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 now $1,000 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 could be a real effect instead of more winner take all contexts where speed of improvement is like the key thing. There's a story there, I think. Or delirium, I don't know.
- [29:45]
Tracee Aldeway
Well, I mean speaking of the haves and have nots, the other big story in AI world is just competition for talent and everyone is sort of chasing the same genius engineer, I guess. How are you finding that at the moment?
- [29:58]
Ian Dunning
It's changed a bit. There's a lot of dynamics going on. 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 YC 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. The question of upside remaining upside. You're at a tril now, I guess there are two big ones that are a 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 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.
- [31:09]
Cameron
Well, so this is.
- [31:09]
Ian Dunning
They feel very worried.
- [31:10]
Cameron
Basically this is what I was going to. This went by way too fast. But like you mentioned already, the models are like okay, junior level.
- [31:19]
Ian Dunning
Yeah.
- [31:19]
Cameron
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?
- [31:35]
Ian Dunning
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 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 quant 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. Like, I feel like the error of the word cell may be a bonus, like if I prompt. 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 delirium talking. I don't know.
- [32:52]
Cameron
All right, Ian Dunning, we could talk for two more hours.
- [32:55]
Ian Dunning
Thank you for having me.
- [32:56]
Cameron
Thank you so much for joining us at ABLA Flag.
- [33:12]
Tracee Aldeway
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.
- [33:21]
Joe Weisenthal
And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow Ian. Ian Dunning. Follow our producers, Carmen Rodriguez, Armenarmon Dashiell, Bennett at Dashbot, Kalebrooks Alebrooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to bloomberg.comoddlods where we have a daily newsletter and all of our episodes. And you can chat about all these topics 24. 7 in our Discord, Discord GG oddlots.
- [33:47]
Tracee Aldeway
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
- [34:10]
Joe Weisenthal
Sam.
- [34:40]
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Ian Dunning
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