One of the World's Largest Hedge Funds on Its 86x Growth in Token Spending

**Tushara Fernando** (0:02)
Bloomberg Audio Studios, podcasts, radio, news.

**Joe Weisenthal** (0:18)
Hello, and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

**Tracy Alloway** (0:22)
And I'm Tracy Alloway.

**Joe Weisenthal** (0:24)
Tracy, I'm very interested in AI. No, no, I'm-

**Tracy Alloway** (0:27)
Oh, really? Really, Joe?

**Joe Weisenthal** (0:30)
I am.

**Tracy Alloway** (0:30)
That's a surprise.

**Joe Weisenthal** (0:31)
I'm very interested in the investment context, specifically. I mean, the actual implementation of like, how do investors use it? Because I think, obviously, just sort of substantively, incredibly important question for reasons that need no explaining. But I also think it like raises very interesting sort of like puzzles about what the technology is used for. And I remember like when Chad GPT came out, and people were like, asking it like, what stocks should I buy? Or we did that prediction market episode recently. And it's like, one thing you definitely can't get much value out of is saying like, which contract should I buy? Or what's inflation going to be so I can trade this contract?
So like, but that doesn't mean that there aren't interesting ways. It just seems like, the most crude version of quote, using AI for investing is obviously a dead end DOA.

**Tracy Alloway** (1:26)
Here's the question I have. We've been through technological revolutions in investing before. Notably, we had robo advisors.

**Joe Weisenthal** (1:34)
That's right.

**Tracy Alloway** (1:35)
Remember that. We had systematic investing. Once we had high frequency trading. My big question is how much of the current use of AI is basically an iteration or an improvement on some of those machine learning dynamics, let's say, versus something more substantial or more revolutionary? Is it like a minor evolution or a moderate evolution or something big?

**Joe Weisenthal** (1:58)
Well, this came up a little bit in our conversation with Ian from Hudson River Trading, and I'm glad you brought up the quant example because big data in some sense has been part of quant since the very beginning. How do you establish that value stocks outperform expensive stocks? Well, you just need a lot of data and computers and running that math, etc.
to establish that fact. But this idea of, yes, maybe cheap stocks outperform expensive ones, momentum stocks outperform stocks with bad momentum, things that seem to be true. But we don't really know why and there's a lot of disagreement as to the source of these things. That really makes me think about AI because we can get these outputs from AI models that are obviously remarkable.

**Tracy Alloway** (2:45)
You can recognize patterns, yes.

**Joe Weisenthal** (2:47)
But they can't really explain how they arrived at that pattern. And I'm very interested in this sort of where this leads us with investing and whether it's like, okay, do we get these like ideas and strategies, et cetera, that work, but we can't really articulate them in plain English.

**Tracy Alloway** (3:04)
I'm glad you brought up data as well because one thing you hear at basically every finance conference nowadays is the importance of data when it comes to running LLMs and making sure that your data is actually processed, it's clean, that you have a lot of it, and that you have hopefully proprietary data. And the people we're going to talk to have a lot of data actually.

**Joe Weisenthal** (3:25)
Totally. There was just one more thing. There was a very, I don't know if you caught it last week, we're recording this July 7th. Last week while you're on vacation, there was a very interesting paper out from Bridgewater talking about their use of proprietary data to fine tune an open source version of Quinn. And for a specific purpose of being able to identify what is newsworthy financial information and not, they found that the combination of open source models plus proprietary data got them better results than the most frontier US models.
That is very interesting to me. And these are the types of things that I'm very curious about.

**Tracy Alloway** (4:03)
And where does the secret sauce or the alpha actually come from?

**Joe Weisenthal** (4:06)
Totally, well, yeah, especially when everyone is going to have access to like really, really intelligent models. Anyway, enough talk from us. I'm really excited. We have the perfect guests to talk about this, actually understanding the implementation of AI within the investment or asset management context. We're going to be speaking with Gary Collier, Man Group CTO, as well as Tushara Fernando, the firm's head of data and AI. So like I said, really the perfect guests. Gary and Tushara, thank you both so much for coming on Odd Lots.

**Gary Collier** (4:35)