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Speaker 1: Bloomberg Audio Studios.

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Speaker 2: Podcasts.

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Speaker 1: Radio.

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Speaker 2: News.

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Speaker 1: Hello and welcome to the Money Stuff Podcast. I'm Matt Levine.

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Speaker 1: I write the Money Stuff column for Bloomberg Opinion. And

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Speaker 1: today we have a guest, who's Jeremy Mallitz, who is

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Speaker 1: the head of goat markets at Susquehanna International Group.

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Speaker 2: You could say that. Generally goes a little better on

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Speaker 2: business cards if you say head of prediction markets, but

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Speaker 2: goat markets works too. Head of prediction markets. I've been

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Speaker 2: head of macro for a long time at Susquehanna. And

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Speaker 2: in a lot of ways, the prediction markets kind of

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Speaker 2: grew out of that because as head of macro, we

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Speaker 2: had quite a bit of focus around elections during election years.

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Speaker 2: We got involved in trading the election over in Europe

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Speaker 2: where you could trade their prediction market style contracts on

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Speaker 2: say Betfair. And we would also use some of the

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Speaker 2: products that exist, say like predicted, even if you can't

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Speaker 2: trade on it for informational purposes. So I'd known about

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Speaker 2: it for a long time. The macro kind of transitioned

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Speaker 2: into the prediction markets, which started out as a side

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Speaker 2: project and now is a very large business for us.

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Speaker 1: My model is that there are all these prop trading

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Speaker 1: firms that had 23-year-old interns who got into crypto as

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Speaker 1: a hobby and then that became their crypto business. And

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Speaker 1: it might be natural to assume that many prop trading

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Speaker 1: firms also get into prediction markets as a hobby because

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Speaker 1: of sports betting. Is there any of that at Sales

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Speaker 1: Call Hunter, or is it strictly like, oh, it's election markets?

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Speaker 2: So when we first started the prediction market journey, sports

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Speaker 2: weren't a part of predictions in that way. But that

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Speaker 2: being said, there's quite a lot of overlap. And there

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Speaker 2: are certainly a lot of people that are passionate about sports,

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Speaker 2: myself included, the other head of prediction markets. We really

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Speaker 2: got to know each other through sports as some of

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Speaker 2: the DraftKings-style places became more prevalent, trying to understand the

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Speaker 2: some of the opportunities and just being passionate about, you know,

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Speaker 2: all forms of prediction. And so I wouldn't say that

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Speaker 2: it was necessarily directly connected. Like the genesis of our

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Speaker 2: business came from the macro angle, but it's kind of

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Speaker 2: hard to disconnect them because, you know, people at Susquehanna

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Speaker 2: are just very into predicting anything that we can. And

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Speaker 2: there's certainly very large markets around sports betting in this world.

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Speaker 1: When you think about like your overall book of business

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Speaker 1: in the prediction markets, I'm going to ask you the

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Speaker 1: split of sports and non-sports.

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Speaker 2: There's obviously different ways to kind of measure it. Certainly

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Speaker 2: sports is more than half. You know, it's probably sitting

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Speaker 2: somewhere in the, you know, it's not 90%, but it

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Speaker 2: kind of fluctuates by season two. Like it's probably more

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Speaker 2: now with football going on. It's a little less in

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Speaker 2: the summer. It's on this World Cup. But certainly sports is,

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Speaker 2: you know, a very large part of the predictions business overall.

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Speaker 2: I think there's some good reasons why that's the case.

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Speaker 2: The biggest ones being that There was already a market

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Speaker 2: for sports through all the regulated operators in certain states

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Speaker 2: that existed. And this huge amount of sort of latent

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Speaker 2: demand that sort of found a product that's a better

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Speaker 2: product in a lot of ways. The other stuff is new,

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Speaker 2: so it's growing. So I can tell you that the

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Speaker 2: other non-sports businesses are actually growing faster than the sports business.

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Speaker 2: But the sports business is obviously coming from a much

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Speaker 2: higher overall level.

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Speaker 1: Okay. So here's how I think about your business. And

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Speaker 1: tell me if this is wrong. I think of there

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Speaker 1: being like three components. There's classic like flow market making

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Speaker 1: where you are making markets on sports elections, some third category,

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Speaker 1: and you're, you know, quoting a bid and an ask

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Speaker 1: and trading with, let's say, retail customers and hoping to

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Speaker 1: earn the spread. There's parlays, which I don't know how

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Speaker 1: big of your business that is, but I think it's

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Speaker 1: really interesting that like traditional sports makes make a lot

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Speaker 1: of money on parlays and to compete with that Kalshi

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Speaker 1: has to offer parlays, which are called like combos or whatever.

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Speaker 1: And I gather that you're in that business. It just

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Speaker 1: seems like interestingly different from the classic market making business.

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Speaker 1: And then the third thing is what I started talking about,

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Speaker 1: which is the goat hedging, which is like you have

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Speaker 1: a block trade OTC-ish business where you will like. do

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Speaker 1: bespoke hedges for corporate and institutional clients on prediction market events?

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Speaker 1: Are those kind of the three areas?

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Speaker 2: Kind of. I would probably lump the first two together.

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Speaker 2: They're different market structures to market make, but the fundamental

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Speaker 2: principles are the same in that it's still flow-based. We're

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Speaker 2: responding in a different mechanism, but we have our model,

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Speaker 2: we have our pricing, it's largely automated. They function the

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Speaker 2: same from kind of a principles perspective, even if the- They're.

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Speaker 1: Interestingly different to me, but I can see why from

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Speaker 1: your business, they're both flow automated trading.

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Speaker 2: Exactly. The way that we actually handle something, like the

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Speaker 2: details are different, but like philosophically, I think they're kind

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Speaker 2: of the same. And the main thing being that like

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Speaker 2: everything is happening electronically and, you know, it's in sort

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Speaker 2: of this all to all screen type ecosystem. The block business,

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Speaker 2: that institutional business is meaningfully different. And I do believe

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Speaker 2: that's the next phase. And it's where a lot of

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Speaker 2: my personal time is going into now.

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Speaker 1: What's the split there? I assume the volume is much

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Speaker 1: bigger in the float business. Much bigger. I assume you

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Speaker 1: are charging wider spreads on the OTC-ish business?

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Speaker 2: Generally, although spread is a little bit of a weirder

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Speaker 2: concept in that OTC business.

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Speaker 1: Because you're not on both sides. Right.

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Speaker 2: Most of those are positions that we're going to hold

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Speaker 2: for the entirety. It's almost a little bit of a

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Speaker 2: hybrid in some ways between sort of financial markets and

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Speaker 2: insurance in that we have to do underwriting. We use

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Speaker 2: the prediction market's to help with that price discovery and

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Speaker 2: that underwriting. So it's a valuable input to help us

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Speaker 2: to price these trades. But the actual market that exists

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Speaker 2: there is not as relevant outside of the price. Like

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Speaker 2: whether there's $ 1, 000 of liquidity or $ 50, 000 of liquidity on

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Speaker 2: the screen, it doesn't make a difference if the customer

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Speaker 2: wants to do $ 50 million of size. And so what

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Speaker 2: we've really been setting up is to try to take

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Speaker 2: the really big risks on and risks that the current

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Speaker 2: insurance industry doesn't cover. to basically sort of bridge those

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Speaker 2: gaps in a lot of the emergent risks. Okay.

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Speaker 1: So let's talk about the goat hedge. I read about

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Speaker 1: this like in August, there's stories about this guy who

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Speaker 1: owns a goat herding business in California. Yes. And California

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Speaker 1: law basically was, looks like it was going to require

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Speaker 1: him to pay minimum wage to his goat herders 24

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Speaker 1: hours a day. And that would have been bad for business.

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Speaker 1: And he like wanted to draw attention to his plight,

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Speaker 1: I guess, and or actually insure against it. And so

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Speaker 1: he found an insurance broker who found you, or found Susquehanna,

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Speaker 1: and he ended up buying a contract where he paid

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Speaker 1: $ 50, 000 for $ 500, 000 of coverage against the risk that California

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Speaker 1: didn't do something to change this law. Right. And that

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Speaker 1: trade printed to Kalshi. You did a, I say OTC

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Speaker 1: like over the counter, but you did a- A block

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Speaker 1: trade on Kalshi. You did a bilateral trade with him

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Speaker 1: that instead of just signing a contract and putting it

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Speaker 1: in a drawer, it printed to Kalshi. Kalshi published the

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Speaker 1: terms of the bet, basically, which is fairly complicated terms.

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Speaker 1: It's like if any of these 12 things happened that

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Speaker 1: would relieve his pain, then the bet paid off for you.

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Speaker 1: And if none of them happened, then he got his $ 500, 000.

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Speaker 1: And that printed to Kalshi. Then anyone who wanted to

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Speaker 1: could trade it, which I'm very curious who traded it.

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Speaker 1: But we'll talk about that later. So you've talked about

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Speaker 1: when people come to you for a $ 50 million trade

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Speaker 1: or whatever, Like you can look at cow sheep prices

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Speaker 1: and even though you're not like, you know, just laying

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Speaker 1: off the risk immediately, like you have a sense of

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Speaker 1: what the market implied probability is. But here there was

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Speaker 1: no market implied probability because the contract didn't exist before

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Speaker 1: you did the block trade. How'd you price the goat risk?

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Speaker 2: So we had a guy that got on the phone

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Speaker 2: and talked to a lot of people. And he literally

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Speaker 2: talked to goat herders.

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Speaker 1: Sure.

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Speaker 2: He talked to lobbyists. He talked to all the people

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Speaker 2: who would have an opinion, plus some of our other

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Speaker 2: internal research. We have our own research. We basically canvassed

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Speaker 2: as much information as we possibly could to come up

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Speaker 2: with an opinion on what we thought the fair probability was.

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Speaker 1: Did you have pre-existing goat research?

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Speaker 2: We did not have pre-existing goat research, but we certainly

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Speaker 2: have a lot of kind of political research. In general...

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Speaker 2: We are in the business of trying to price random things.

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Speaker 2: So we have networks that we understand where to go to,

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Speaker 2: to figure out how to price random things.

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Speaker 1: What is the history of Susquehanna being in the business

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Speaker 1: of knowing how to price random things? Is that like,

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Speaker 1: because you've gotten into prediction markets? Or is that like

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Speaker 1: from the time Jeff Yass was a baby, he was

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Speaker 1: taking any bets that people came to him with?

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Speaker 2: I mean, I think that is probably true. I don't know, baby,

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Speaker 2: but like six, maybe? Yeah, no, I think that it's

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Speaker 2: always been very much in our culture. I mean, I

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Speaker 2: came to Susquehanna through a recruiting poker tournament 20 years ago.

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Speaker 2: You had some questions earlier about kind of like market making,

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Speaker 2: like I think there's different forms of flow market making,

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Speaker 2: some that are trying to strictly balance and they're using

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Speaker 2: all the different flow signals, and some that are trying

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Speaker 2: to say, we're going to try to come up with

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Speaker 2: a fair price on this and use all the information

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Speaker 2: we have to constantly come up with a fair price

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Speaker 2: and then take the risk wherever it is. We've always

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Speaker 2: been very much in the latter category. It's much more

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Speaker 2: about we're trying to figure out what's fair as opposed

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Speaker 2: to we're just trying to balance the order flow. Obviously,

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Speaker 2: you do both. But I think that mentality has been

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Speaker 2: in our culture of like try to price a thing,

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Speaker 2: figure out what's fair. And we have a lot of

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Speaker 2: random businesses at Susquehanna that are pricing random things. We're

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Speaker 2: in the insurance business. like the official non-predictions insurance business

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Speaker 2: as well. And a lot of that has been pricing

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Speaker 2: random risks that people have. The predictions angle helps us

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Speaker 2: to take that to the next level, but it's always

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Speaker 2: something that we've done.

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Speaker 1: So why, when the good guys showed up, Why couldn't

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Speaker 1: you be like, go see the insurance people? What makes

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Speaker 1: something an insurance product versus a prediction markets block trade?

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Speaker 2: Insurance in general is very good at understanding. We have

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Speaker 2: this large data set of things that have happened in

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Speaker 2: the past. We know how to pool all this risk.

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Speaker 2: We've got great procedures, et cetera. So you're looking for

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Speaker 2: a standard property product. They're going to totally understand how

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Speaker 2: to do that. They're not in the business of here's

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Speaker 2: some new thing that just happened and we're going to

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Speaker 2: figure it out. Like that's just not really how insurance works.

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Speaker 1: Not even at Susquehanna?

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Speaker 2: Well, at Susquehanna, that's how it works. But, you know,

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Speaker 2: we are kind of in the insurance business sort of,

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Speaker 2: but I wouldn't say we have like an insurance ethos.

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Speaker 1: Okay. I'm not saying ethos. I'm saying like the product, right?

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Speaker 1: Like you have an ethos that is product agnostic, like

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Speaker 1: we'll take a bet, right? Yeah. And then the question is,

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Speaker 1: is the bet expressed as a calci block trade or

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Speaker 1: as a contract called insurance? Like, is it just like,

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Speaker 1: You get better capital treatment for that? Why print this

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Speaker 1: to Kelsey rather than to the insurer?

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Speaker 2: Oh, so that is a good question. There are kind

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Speaker 2: of three mechanisms really for which we can take on

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Speaker 2: one of these trades right now. We can do a

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Speaker 2: contract that's printed to a prediction market. We can do

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Speaker 2: a swap and we can do a regulated insurance product.

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Speaker 2: There are kind of advantages and disadvantages to all of them.

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Speaker 2: If you print it on an exchange- You have the

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Speaker 2: most transparency. It's on exchange. It's cleared. All of those

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Speaker 2: things are valuable to certain people. You do it as

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Speaker 2: a swap, then it has more favorable capital treatment sometimes,

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Speaker 2: but it's not on exchange. We might link it to

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Speaker 2: an exchange. You do it as an insurance product. There

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Speaker 2: are some advantages sometimes to being a regulated insurance product.

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Speaker 2: There's also some fees and some other drawbacks. So our

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Speaker 2: goal when we talk to one of these people isn't

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Speaker 2: really to push them in one direction or another. Our

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Speaker 2: goal is to understand what makes the most sense for them.

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Speaker 2: There are some players out there that say, we really

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Speaker 2: want to do this on an exchange. It's important to us.

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Speaker 2: There are some that say, we aren't anywhere near close

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Speaker 2: to being able to connect to one of these exchanges.

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Speaker 2: We've got a million things we've got to figure out

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Speaker 2: on our compliance side, but we want you to take

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Speaker 2: this risk. Can you do it in a different way?

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Speaker 2: And our goal is just to figure out how to

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Speaker 2: take the risk. We're kind of execution platform agnostic.

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Speaker 1: I assume if you face a hedge fund, they want

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Speaker 1: to do a swap. And I assume if you face

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Speaker 1: a goat herder, he probably can't do a swap. He's probably...

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Speaker 1: not allowed to do.

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Speaker 2: That's oftentimes more true, although there are exceptions in terms

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Speaker 2: of how some of this goes. But yeah, for someone

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Speaker 2: like a goat herder, it's way easier to do this

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Speaker 2: audit exchange for sure.

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Speaker 1: So tell me more about like, you're a hedge fund,

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Speaker 1: you want to do a bet. You come to you,

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Speaker 1: you're like, hey, I want to do a bet. You

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Speaker 1: like write up the bet. Like you have yourself and

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Speaker 1: good lawyers. The hedge fund has their trader and good lawyers.

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Speaker 1: You can like hash out the payoff mechanism of the bet.

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Speaker 1: The goat herder bet, is like a tri-party bet. There's you,

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Speaker 1: there's the goat herder, and you have lawyers, and maybe

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Speaker 1: he has lawyers, and then there's Kalshi. The contract that

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Speaker 1: you enter into is not like you sign it and

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Speaker 1: the goat herder signs it. It's like you buy and

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Speaker 1: sell a product on the exchange that is listed, approved,

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Speaker 1: run by the CFTC by Kalshi. How does that work?

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Speaker 1: Who writes that contract and who has input into that contract?

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Speaker 2: Ultimately, in that scenario, Kalshi writes the contract, but It's

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Speaker 2: a discussion.

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Speaker 1: But you wouldn't trade it with a goat herder unless

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Speaker 1: you were both happy with the terms, right?

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Speaker 2: Right, right. So they ultimately are the ones that sign

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Speaker 2: off on it. It's on their exchange. But we have

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Speaker 2: meaningful input, right? We really do all work together. We

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Speaker 2: are all discussing what the different terms are. And at

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Speaker 2: the end of the day, everyone's kind of incentivized to

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Speaker 2: get terms that cover the actual risk, right? We want

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Speaker 2: to cover the goat herder's risk. They want to cover

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Speaker 2: their risk. Cal Street understands that this market is in

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Speaker 2: many ways being created by to help facilitate this specific

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Speaker 2: risk here, this trade. And that's kind of part of

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Speaker 2: the beauty of prediction markets is how easy it is

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Speaker 2: to create these new markets now, like the fact that

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Speaker 2: it can be done in the form of a day.

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Speaker 2: So we're all kind of incentivized to work together. So

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Speaker 2: I would say it's a collaborative process. Like Kalshi has

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Speaker 2: a lot of experience writing these contracts. We have a

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Speaker 2: lot of experience doing it as well. The goat herder

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Speaker 2: may be a little bit less, but he knows what

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Speaker 2: his risks are. He knows the risks better than anybody.

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Speaker 1: Intuitively, having worked at a bank, If you were doing

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Speaker 1: an ISDA with a hedge fund, you'd probably say to

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Speaker 1: me that everyone wants it to cover all the risks

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Speaker 1: and be fair. But you're getting a little edge in

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Speaker 1: on the contract, right? And they're trying to get a

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Speaker 1: little edge in on the contract. Kalshi, I think, has

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Speaker 1: a genuine incentive to... If something happened where the goat

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Speaker 1: herder was disappointed, where he didn't get... the payoff, things

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Speaker 1: didn't work out for him politically, and also your contract

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Speaker 1: didn't pay out, like that would kind of be a

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Speaker 1: windfall for you, right? And vice versa, like if his

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Speaker 1: contract paid out any other thing you wanted, that'd be

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Speaker 1: a windfall for him. But like for Kalshi, they want

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Speaker 1: like the right result in either case, I think, right?

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Speaker 1: Because they're sort of reputationally trying to build this business.

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Speaker 1: So is it like helpful to the business to have

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Speaker 1: like a neutral arbiter, like during the contract and the

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Speaker 1: determination of the payoff?

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Speaker 2: So I guess what I would say is, first of all,

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Speaker 2: I don't think that that sort of haggling over terms

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Speaker 2: and trying to win that has been my experience in

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Speaker 2: this so far. Like we are also trying to build

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Speaker 2: the business here and we actually don't want a situation

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Speaker 2: where we win, but the other side like didn't get

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Speaker 2: covered on their risk.

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Speaker 1: Have you ever like retroactively, have you ever like written

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Speaker 1: a contract where like in hindsight, the other side didn't

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Speaker 1: get covered and you're like, yeah, we'll just pay you anyway.

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Speaker 2: That specific scenario hasn't happened. We've been involved in some

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Speaker 2: contracts where we might have realized there's some form of

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Speaker 2: an edge case where theoretically something could happen, but we've

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Speaker 2: never actually had that edge case happen in a situation

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Speaker 2: that's sort of a relationship type business where we've had

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Speaker 2: a relationship with the other side. There are edge cases

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Speaker 2: in sort of the general screen markets that happen here

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Speaker 2: and there every once in a while. We're making our

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Speaker 2: money on the price at the end of the day.

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Speaker 2: We're charging a little bit of edge on the price.

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Speaker 2: We don't want to make our money on the contract terms.

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Speaker 2: The worst thing that can happen is we don't want

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Speaker 2: the next story about the goat herder to be like, oh,

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Speaker 2: he lost his hedge and he lost his business. That's

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Speaker 2: horrible ultimately. And you mentioned in your article, which is fair,

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Speaker 2: part of the reason we wanted to do this trade

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Speaker 2: was because We think there's some degree of edge in

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Speaker 2: the trade. Although to be honest, we priced this one

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Speaker 2: pretty fair. A large reason was because we think this

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Speaker 2: is an important business for predictions overall. And we wanted

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Speaker 2: more proof of concept out there. We want people to

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Speaker 2: see this is what can be done. We'll win in

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Speaker 2: the long run if this business takes off. And our

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Speaker 2: role right now is really to bootstrap the liquidity of

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Speaker 2: this business and to show what's possible.

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Speaker 1: Okay. So when I read about this trade, it was probably,

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Speaker 1: I don't know, a week after you printed it, something

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Speaker 1: like that? And I looked at the screen for this contract, which,

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Speaker 1: you know, you printed a $ 500, 000 notional trade with this

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Speaker 1: code herder. And when I looked at the screen for

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Speaker 1: the Kelsey contract, it had something like $ 500, 476 of volume.

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Speaker 1: So I think that's your trade plus somebody or some

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Speaker 1: people who found this trade after I wrote about it.

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Speaker 1: And like, you know, nine other articles were written about it. Like,

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Speaker 1: I think that picked up and When I looked this morning,

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Speaker 1: there was a total of $ 655, 000 of volume. The contract resolved.

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Speaker 1: You won, which means he also won. When I wrote

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Speaker 1: about it, it had traded tiny, tiny, tiny size in

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Speaker 1: the 80s, which means it traded down from the price

355
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Speaker 1: you paid, which means that you looked a little like

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Speaker 1: you got picked off by the goat herder, which I

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Speaker 1: think I said to you. But in the long run,

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Speaker 1: you did fine. And it's hard to extrapolate from that

359
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Speaker 1: little volume. Who traded this thing?

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Speaker 2: You know, there are just a lot of people that

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Speaker 2: are passionate.

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Speaker 1: Like some money stuff readers, clearly.

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Speaker 2: Some money stuff readers. There's a lot of people in

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Speaker 2: the predictions ecosystem that are passionate super forecasters that look

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Speaker 2: over every market and they probably have some tools to

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Speaker 2: help them find ones where there's any form of volume

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Speaker 2: and they try to make a prediction in a trade.

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Speaker 2: And if they think that they have some kind of

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Speaker 2: an opinion, they do it. My guess is that's where

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Speaker 2: this came from, although I can't know for sure. It's

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Speaker 2: also possible that I'm sure some people read your article

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Speaker 2: and saw some of the other publications and were like, oh,

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Speaker 2: I want to get involved in this.

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Speaker 1: But the overall- You could buy it at lower than

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Speaker 1: the price Susquehanna paid, which feels like a good deal.

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Speaker 2: In theory, you could, yeah.

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Speaker 1: Also, I wrote about this. In theory, if instead of

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Speaker 1: $ 400 a volume, there was $ 10 million a volume after

379
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Speaker 1: you printed the trade, you could have laid off your risk, right? Yeah.

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Speaker 1: Depending on, you might have lost money if it had

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Speaker 1: all been at 85. But also, with the amount of

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Speaker 1: volume there actually was, ultimately, if you just take the

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Speaker 1: beginning and the end of the trade- the law was

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Speaker 1: changed in some way that helps him. I don't actually

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Speaker 1: know the details, but like the bet resolved to the

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Speaker 1: law was changed. So he got $ 0 on his bet

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Speaker 1: and you got your 500,000 back. You didn't pay out

388
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Speaker 1: the $ 500, 000, but he could have like sold his position, right?

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Speaker 1: Like he paid 10 for it. He could have sold

390
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Speaker 1: it at 15.

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Speaker 2: He certainly could have. And, you know, to be clear, like,

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Speaker 2: There were pieces of news that came out in the

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Speaker 2: interim around– I don't know if the nation was following

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Speaker 2: this with bated breath, but– I.

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Speaker 1: Should have been. I'm probably the most interested person and

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Speaker 1: I did not follow it.

397
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Speaker 2: There were pieces of certain news that came out around

398
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Speaker 2: it and if you were in the weeds of it,

399
00:19:19,780 --> 00:19:21,859
Speaker 2: the probability might have been moving around a little bit

400
00:19:22,040 --> 00:19:24,240
Speaker 2: here and there and you can interpret things in certain ways.

401
00:19:24,340 --> 00:19:27,639
Speaker 2: You're right. The volume wasn't super high. In theory, you

402
00:19:27,700 --> 00:19:29,730
Speaker 2: could have a scenario though where someone puts on a

403
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Speaker 2: hedge trade– And then decides after the fact like, oh,

404
00:19:32,410 --> 00:19:34,080
Speaker 2: you know what? Like this is now trading at such

405
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Speaker 2: a level. Like I'm going to take off some of

406
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Speaker 2: my hedge, all of my hedge. Maybe I don't need

407
00:19:37,940 --> 00:19:40,619
Speaker 2: the hedge anymore. Something else happened. Like that's one of

408
00:19:40,660 --> 00:19:44,200
Speaker 2: the great things about this form of hedging as opposed

409
00:19:44,240 --> 00:19:47,590
Speaker 2: to say like some just like an insurance contract is

410
00:19:48,150 --> 00:19:50,520
Speaker 2: it feels like a financial product, right? You can get

411
00:19:50,580 --> 00:19:51,160
Speaker 2: out of your risk.

412
00:19:51,180 --> 00:19:53,020
Speaker 1: It feels like an exchange-traded product.

413
00:19:53,160 --> 00:19:56,199
Speaker 2: Right, like an exchange-traded financial product. The ecosystem needs to

414
00:19:56,320 --> 00:20:00,560
Speaker 2: scale up and we're trying to bootstrap that scaling, but–

415
00:20:01,040 --> 00:20:03,660
Speaker 2: Once it gets to that point, our hope would be like, yeah,

416
00:20:03,720 --> 00:20:05,980
Speaker 2: trades come to us. We put them up. There's an

417
00:20:06,080 --> 00:20:09,060
Speaker 2: active market in this thing. Other people might have similar risks.

418
00:20:09,580 --> 00:20:12,000
Speaker 2: And ultimately, people can trade in and out of their position.

419
00:20:12,280 --> 00:20:13,860
Speaker 2: If they want to come back to us and they say, hey,

420
00:20:13,900 --> 00:20:15,659
Speaker 2: the market's trading here now, would you let us out?

421
00:20:15,780 --> 00:20:18,580
Speaker 2: Would you let us add? Those are all flexible things

422
00:20:18,600 --> 00:20:22,160
Speaker 2: that we can do here that wouldn't be possible without

423
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Speaker 2: the market existing. Okay.

424
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Speaker 1: And let's talk about capital and funding. When you print

425
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Speaker 1: this trade to Kelshi, if I'm a retail trader on Kelshi,

426
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Speaker 1: If I want to bet on something that has a 90%

427
00:20:32,720 --> 00:20:35,400
Speaker 1: probability of happening, I put up $ 0. 90 on the dollar.

428
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Speaker 1: That's what you do here, right? You're putting up.

429
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Speaker 2: $ 450, 000.

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Speaker 1: He's putting up $ 50, 000. It all goes to Kalshi. You get,

431
00:20:42,290 --> 00:20:45,690
Speaker 1: I guess, Fed funds on your cash or whatever. And

432
00:20:46,210 --> 00:20:49,890
Speaker 1: when the contract resolves, either you get the $ 500, 000, which

433
00:20:49,990 --> 00:20:54,290
Speaker 1: is what happened, or he gets the $ 500, 000. I assume

434
00:20:54,330 --> 00:20:59,310
Speaker 1: that when Susquehanna is trading options, it's more capital efficient.

435
00:20:59,980 --> 00:21:02,659
Speaker 2: Yes, it is. And I think that ultimately that's one

436
00:21:02,680 --> 00:21:05,580
Speaker 2: of the areas that we hope to see predictions evolve

437
00:21:05,680 --> 00:21:06,160
Speaker 2: over time.

438
00:21:06,180 --> 00:21:09,540
Speaker 1: I've heard recently that Kashi is rolling out leverage for

439
00:21:09,560 --> 00:21:11,220
Speaker 1: at least some institutional. Right.

440
00:21:11,300 --> 00:21:14,159
Speaker 2: And I think that'll be very helpful, getting forms of

441
00:21:14,260 --> 00:21:18,659
Speaker 2: margin and leverage. Realistically, what helps the most here in

442
00:21:18,760 --> 00:21:21,400
Speaker 2: this form of business is something that looks a little

443
00:21:21,440 --> 00:21:25,770
Speaker 2: bit like portfolio margin. Say that we have 100 of

444
00:21:25,830 --> 00:21:29,230
Speaker 2: these trades on where all of them are us selling 5%

445
00:21:29,230 --> 00:21:32,450
Speaker 2: to 10% of something that's not super likely to happen happening.

446
00:21:32,490 --> 00:21:35,310
Speaker 2: That's what the insurance business looks like. A lot of

447
00:21:35,369 --> 00:21:38,850
Speaker 2: risks are people wanting to cover some not very likely

448
00:21:38,869 --> 00:21:42,140
Speaker 2: thing that happens, but if it happens, it's catastrophic. You

449
00:21:42,180 --> 00:21:46,600
Speaker 2: have 100 of those things on. They're all uncorrelated. In theory,

450
00:21:46,900 --> 00:21:49,850
Speaker 2: you shouldn't actually have to fund 95 cents on the

451
00:21:49,890 --> 00:21:53,070
Speaker 2: dollar on all of those, right? That's not super efficient.

452
00:21:53,270 --> 00:21:56,580
Speaker 2: For now, we're kind of just saying, we're going to

453
00:21:56,980 --> 00:21:58,859
Speaker 2: figure out how to fund these things.

454
00:21:58,900 --> 00:22:00,679
Speaker 1: How do you find, like, is that like Jeff Yass's

455
00:22:00,740 --> 00:22:03,399
Speaker 1: equity or like, is someone giving you that portfolio margin

456
00:22:03,440 --> 00:22:04,200
Speaker 1: in some form, you know?

457
00:22:04,440 --> 00:22:07,180
Speaker 2: So in traditional options business, the prime broker is going

458
00:22:07,200 --> 00:22:09,929
Speaker 2: to give you a portfolio margin. Here, there is no

459
00:22:09,970 --> 00:22:13,310
Speaker 2: portfolio margin yet, right? So we're just funding the cash, right?

460
00:22:13,350 --> 00:22:15,290
Speaker 2: And it makes the trades a little bit more expensive.

461
00:22:15,490 --> 00:22:17,470
Speaker 2: It means that if you have a really long dated trade,

462
00:22:17,550 --> 00:22:20,790
Speaker 2: so let's say, for example, someone wanted to insure against

463
00:22:21,010 --> 00:22:24,530
Speaker 2: the Olympics being canceled in LA, which is years away,

464
00:22:25,530 --> 00:22:28,369
Speaker 2: that becomes very challenging to do on an exchange in

465
00:22:28,390 --> 00:22:30,939
Speaker 2: the current setup because then your cost of capital for

466
00:22:30,980 --> 00:22:36,120
Speaker 2: having your 90 plus percent on for that long is meaningful, right?

467
00:22:36,940 --> 00:22:38,600
Speaker 2: A shorter dated trade is much easier.

468
00:22:54,210 --> 00:22:56,189
Speaker 1: Okay. So I want to talk about like your general

469
00:22:56,250 --> 00:23:00,169
Speaker 1: OTC book. I say OTC, I feel like technically the

470
00:23:00,210 --> 00:23:02,419
Speaker 1: goat herder trade is not over the counter because you

471
00:23:02,440 --> 00:23:03,500
Speaker 1: printed on an exchange.

472
00:23:03,520 --> 00:23:05,600
Speaker 2: Institutional risk hedging is kind of the term that we

473
00:23:05,660 --> 00:23:06,440
Speaker 2: use internally to.

474
00:23:06,400 --> 00:23:08,340
Speaker 1: Talk about it. In that book, first of all, like

475
00:23:08,359 --> 00:23:10,980
Speaker 1: how many trades do you have? Is it like hundreds?

476
00:23:11,020 --> 00:23:12,730
Speaker 1: Is it like Dozens?

477
00:23:12,930 --> 00:23:14,649
Speaker 2: It sort of fluctuates from time to time, but it's

478
00:23:14,690 --> 00:23:16,810
Speaker 2: more in the dozens than the hundreds now. We're obviously

479
00:23:16,830 --> 00:23:17,740
Speaker 2: hoping to grow it.

480
00:23:18,390 --> 00:23:23,840
Speaker 1: What's the split between true OTC swaps and on exchange?

481
00:23:24,600 --> 00:23:26,600
Speaker 2: I don't know exactly in terms of the number of trades.

482
00:23:26,700 --> 00:23:29,399
Speaker 2: In terms of the size, the larger trades tend to

483
00:23:29,440 --> 00:23:30,600
Speaker 2: be more likely to be a swap.

484
00:23:30,920 --> 00:23:34,399
Speaker 1: And what's the margining on that? Is that like unsecured?

485
00:23:34,440 --> 00:23:36,679
Speaker 1: Is that like you're posting daily collateral?

486
00:23:37,960 --> 00:23:41,040
Speaker 2: The margin is generally just negotiated. There are certainly some

487
00:23:41,080 --> 00:23:43,439
Speaker 2: of those trades where we put up zero. In the

488
00:23:43,460 --> 00:23:45,199
Speaker 2: GoHerder trade, if we did it on swap, we would

489
00:23:45,220 --> 00:23:48,649
Speaker 2: probably say, I think you can probably trust we're good

490
00:23:48,670 --> 00:23:51,230
Speaker 2: for $ 450, 000. There could be larger trades where.

491
00:23:51,130 --> 00:23:53,310
Speaker 1: We- You'd take his 50 there. Probably.

492
00:23:53,330 --> 00:23:57,070
Speaker 2: But when you start talking about larger types of situations,

493
00:23:57,510 --> 00:23:59,570
Speaker 2: we might have to put up margin. The other side

494
00:23:59,630 --> 00:24:02,450
Speaker 2: might be given an ability to do margin. When you're

495
00:24:02,490 --> 00:24:04,910
Speaker 2: on swap, it's all case by case basis. It also

496
00:24:04,970 --> 00:24:08,580
Speaker 2: provides us with quite a lot of flexibility that we

497
00:24:08,619 --> 00:24:12,359
Speaker 2: may not have with a pure binary prediction market. To

498
00:24:12,400 --> 00:24:15,260
Speaker 2: give you an example, we've had parties that have come

499
00:24:15,280 --> 00:24:18,300
Speaker 2: to us interested in saying, we're really concerned about a

500
00:24:18,619 --> 00:24:21,460
Speaker 2: moratorium happening on data centers in our state or in

501
00:24:21,500 --> 00:24:24,640
Speaker 2: our county or whatnot. But if that happens, what we're

502
00:24:24,660 --> 00:24:27,360
Speaker 2: really exposed to is now the depreciation of the chips.

503
00:24:27,990 --> 00:24:29,990
Speaker 2: Can we come up with something that has a trigger

504
00:24:30,070 --> 00:24:32,900
Speaker 2: that's like a prediction market-based trigger, binary trigger? Does this

505
00:24:32,920 --> 00:24:35,679
Speaker 2: thing happen? But then pays out based on the price

506
00:24:35,880 --> 00:24:38,560
Speaker 2: of an asset, whether that's the price of chips or

507
00:24:38,580 --> 00:24:40,679
Speaker 2: the price of compute or sometimes the price of power.

508
00:24:40,700 --> 00:24:44,250
Speaker 2: And we're pretty flexible at being able to do that because, again,

509
00:24:44,270 --> 00:24:48,220
Speaker 2: if we have a reasonable index to settle to- that

510
00:24:48,280 --> 00:24:49,940
Speaker 2: value is, that's kind of our bread and butter, right?

511
00:24:50,119 --> 00:24:52,340
Speaker 2: Moving into a financial asset and doing that as a firm.

512
00:24:52,840 --> 00:24:54,940
Speaker 2: So that's the thing that we're good at and it

513
00:24:55,160 --> 00:24:58,040
Speaker 2: covers their risk more directly, all the better, right? So

514
00:24:58,200 --> 00:25:01,040
Speaker 2: we're very happy to be creative when it comes to

515
00:25:01,080 --> 00:25:03,159
Speaker 2: that swap and that can go to the margin. We're

516
00:25:03,180 --> 00:25:05,179
Speaker 2: going to look at all the components of it and

517
00:25:05,220 --> 00:25:07,580
Speaker 2: try to figure out what's the best way to get

518
00:25:07,619 --> 00:25:09,379
Speaker 2: this thing done that sort of fits all the parties.

519
00:25:09,580 --> 00:25:13,199
Speaker 2: And again, we're looking at this business long-term. So we

520
00:25:13,240 --> 00:25:15,490
Speaker 2: do want them to be ultimately be happy as well

521
00:25:15,570 --> 00:25:18,489
Speaker 2: with what's happening because we want repeat customers and Some

522
00:25:18,510 --> 00:25:21,090
Speaker 2: of these are small communities. We want one data center

523
00:25:21,190 --> 00:25:22,889
Speaker 2: to tell another, like, oh, we got this thing and

524
00:25:22,930 --> 00:25:24,630
Speaker 2: it was helpful. So that is our goal.

525
00:25:24,910 --> 00:25:26,730
Speaker 1: One thing I always thought about is the Cal Shoes

526
00:25:26,750 --> 00:25:28,790
Speaker 1: will tell you, like, we got into this business because

527
00:25:28,830 --> 00:25:31,169
Speaker 1: people want to hedge election risk. And, like, there's the

528
00:25:31,210 --> 00:25:33,850
Speaker 1: story of Jane Street getting the election right and, like,

529
00:25:33,869 --> 00:25:35,709
Speaker 1: losing a bucket of money because they got the directional

530
00:25:35,750 --> 00:25:37,470
Speaker 1: impact of it wrong. And I always thought that was very, like,

531
00:25:37,990 --> 00:25:41,649
Speaker 1: illustrative because, like, there are people in the world who

532
00:25:41,690 --> 00:25:44,330
Speaker 1: are like, I think that Trump will be bad for

533
00:25:44,350 --> 00:25:47,100
Speaker 1: the S & P. And so I want to buy...

534
00:25:47,560 --> 00:25:50,199
Speaker 1: a hedge on Trump getting elected because I have all

535
00:25:50,260 --> 00:25:52,580
Speaker 1: this S and P exposure, but it's totally the wrong

536
00:25:52,619 --> 00:25:55,990
Speaker 1: hedge because like, you know, the S and P goes up, right?

537
00:25:56,390 --> 00:25:58,970
Speaker 1: Like when you have people hedging the election, is that

538
00:25:59,530 --> 00:26:01,430
Speaker 1: the thing you talked about where it's like, there's an

539
00:26:01,490 --> 00:26:04,550
Speaker 1: election trigger and that a financial asset payoff, or is

540
00:26:04,609 --> 00:26:07,130
Speaker 1: it like people who are like, I'm in a very

541
00:26:07,170 --> 00:26:09,139
Speaker 1: regulated industry. I'm going to be shut down if the

542
00:26:09,160 --> 00:26:12,340
Speaker 1: Democrats win. What is the intuition behind people doing election hedges?

543
00:26:13,020 --> 00:26:15,360
Speaker 2: I think those are more just binary. It's just who's

544
00:26:15,380 --> 00:26:17,219
Speaker 2: going to win. And I do think that generally- Why

545
00:26:17,240 --> 00:26:17,780
Speaker 2: is that a hedge?

546
00:26:17,800 --> 00:26:18,740
Speaker 1: Who's hedging what there?

547
00:26:19,200 --> 00:26:21,109
Speaker 2: So I think there's a few different angles. One of

548
00:26:21,150 --> 00:26:24,010
Speaker 2: them is like you said, this would be just very

549
00:26:24,050 --> 00:26:26,530
Speaker 2: bad for my business. And I do think that the

550
00:26:26,570 --> 00:26:28,890
Speaker 2: things that are the real businesses, like this is bad

551
00:26:28,930 --> 00:26:32,280
Speaker 2: for my business, that's probably the thing that's a little

552
00:26:32,300 --> 00:26:34,679
Speaker 2: bit slower to pick up because it takes longer to

553
00:26:34,720 --> 00:26:38,120
Speaker 2: get someone who has a real money business to start

554
00:26:38,180 --> 00:26:40,440
Speaker 2: doing this. That's a totally new product. It makes sense

555
00:26:40,480 --> 00:26:42,660
Speaker 2: why the financial players get involved first.

556
00:26:42,800 --> 00:26:44,720
Speaker 1: Well, but also like if you have a real money business,

557
00:26:44,760 --> 00:26:47,520
Speaker 1: like if you sell ball bearings, you're not going to

558
00:26:47,540 --> 00:26:50,179
Speaker 1: get shut down if like one side wins the election.

559
00:26:50,740 --> 00:26:52,680
Speaker 1: I could name some firms that might get shut down

560
00:26:52,720 --> 00:26:54,480
Speaker 1: if one side wins the election. Right.

561
00:26:54,530 --> 00:26:57,270
Speaker 2: So oftentimes it might be a situation where it's not

562
00:26:57,290 --> 00:26:58,930
Speaker 2: like you're going to get shut down, but there could

563
00:26:58,970 --> 00:27:02,369
Speaker 2: certainly be economic impact to your business, right? And you

564
00:27:02,390 --> 00:27:03,869
Speaker 2: could want to sort of hedge out some of the

565
00:27:03,930 --> 00:27:05,770
Speaker 2: economic impact.

566
00:27:05,850 --> 00:27:07,990
Speaker 1: I'm just wondering like how modelable that is, right? Is

567
00:27:08,030 --> 00:27:10,780
Speaker 1: this like Politically committed CEOs were like, oh, if the

568
00:27:10,800 --> 00:27:12,859
Speaker 1: Democrats win, it's going to be a disaster for me.

569
00:27:13,480 --> 00:27:16,280
Speaker 1: But like, that's not empirically true. I feel like people's

570
00:27:16,780 --> 00:27:20,740
Speaker 1: intuitions about the effect, even Jane Street's intuitions about the

571
00:27:20,780 --> 00:27:24,000
Speaker 1: effect of an election on like their business or asset

572
00:27:24,020 --> 00:27:28,080
Speaker 1: prices are unreliable. And it's a strange way to hedge.

573
00:27:28,359 --> 00:27:31,020
Speaker 2: I can give you an example. Oftentimes, I think if

574
00:27:31,060 --> 00:27:33,540
Speaker 2: it's a real business, it's much more targeted. It's much

575
00:27:33,600 --> 00:27:36,480
Speaker 2: less like vibes, this party is going to crush me,

576
00:27:36,520 --> 00:27:38,770
Speaker 2: but more say, again, people just to go back to

577
00:27:38,790 --> 00:27:41,230
Speaker 2: the data centers, because it's the thing we're hearing more

578
00:27:41,270 --> 00:27:43,949
Speaker 2: about than everything else combined. And I wouldn't say it's

579
00:27:43,970 --> 00:27:45,790
Speaker 2: like the majority of the trades we've done to date,

580
00:27:46,170 --> 00:27:48,379
Speaker 2: but it's by far the majority of the interest that

581
00:27:48,390 --> 00:27:51,379
Speaker 2: we're getting. If you're a data center operator in Texas,

582
00:27:51,900 --> 00:27:55,419
Speaker 2: you've got a governor election, data centers are 100% on

583
00:27:55,460 --> 00:27:57,520
Speaker 2: the ballot. Now they could come and they could say

584
00:27:57,560 --> 00:27:59,520
Speaker 2: to us something like, we actually want to hedge the

585
00:27:59,540 --> 00:28:02,220
Speaker 2: moratorium risk, but you could also get a pretty reasonable

586
00:28:02,260 --> 00:28:05,380
Speaker 2: proxy for just hedging the governor race. And those two

587
00:28:05,420 --> 00:28:08,430
Speaker 2: things wind up being extremely correlated. So I think with

588
00:28:08,450 --> 00:28:10,790
Speaker 2: the real money business, it comes more down to like,

589
00:28:11,230 --> 00:28:13,310
Speaker 2: what is the actual policy that's going to impact me?

590
00:28:14,130 --> 00:28:16,950
Speaker 1: Okay. So like you're a data center developer and you're like,

591
00:28:17,190 --> 00:28:19,710
Speaker 1: if the election goes one way, there'll be a moratorium.

592
00:28:19,850 --> 00:28:21,050
Speaker 1: If the election goes the other way, there won't be

593
00:28:21,070 --> 00:28:24,650
Speaker 1: a moratorium. There's a moratorium contract that you can buy

594
00:28:24,710 --> 00:28:28,710
Speaker 1: from Susquehanna and there's an election contract. There's, let's say,

595
00:28:28,970 --> 00:28:31,600
Speaker 1: no basis risk in the moratorium contract, right? If there's

596
00:28:31,619 --> 00:28:33,300
Speaker 1: a moratorium, you get paid. If there's no moratorium, you

597
00:28:33,340 --> 00:28:36,730
Speaker 1: don't get paid. There's somewhat obviously... basis risk and the

598
00:28:36,770 --> 00:28:39,730
Speaker 1: election contract. Like they're highly correlated, but there's like obvious

599
00:28:39,750 --> 00:28:42,130
Speaker 1: basis risk. Why are you buying the election contract? Is

600
00:28:42,190 --> 00:28:46,230
Speaker 1: it cheaper? Is it more fun? Like what's, is it,

601
00:28:46,460 --> 00:28:49,700
Speaker 1: is it the more holistically a threat than like a more,

602
00:28:49,740 --> 00:28:50,680
Speaker 1: like what's going on there?

603
00:28:51,000 --> 00:28:53,000
Speaker 2: I think the way that the conversation kind of happens

604
00:28:53,120 --> 00:28:55,719
Speaker 2: is the party might not actually even know at the

605
00:28:55,740 --> 00:28:57,820
Speaker 2: beginning that there is a moratorium market. Yeah, but you

606
00:28:57,840 --> 00:29:00,490
Speaker 2: can tell them. Which is why I think that realistically.

607
00:29:00,510 --> 00:29:01,890
Speaker 1: Why are you selling them the election one?

608
00:29:02,110 --> 00:29:04,270
Speaker 2: In that scenario, I don't think we've done a lot

609
00:29:04,310 --> 00:29:07,920
Speaker 2: of sort of like real money business trading the election.

610
00:29:07,940 --> 00:29:10,320
Speaker 2: The people who want to trade the election itself are

611
00:29:10,360 --> 00:29:11,820
Speaker 2: much more likely to be a firm that's on the

612
00:29:11,860 --> 00:29:15,160
Speaker 2: financial side, where they're looking at the financial impact, like

613
00:29:15,280 --> 00:29:18,270
Speaker 2: a hedge fund, for example. They're looking at their portfolio

614
00:29:18,310 --> 00:29:20,990
Speaker 2: risk and all the ways that their portfolio is going

615
00:29:21,030 --> 00:29:22,350
Speaker 2: to move based on that election.

616
00:29:22,370 --> 00:29:25,210
Speaker 1: So they're like, my portfolio has some basis risk to

617
00:29:25,250 --> 00:29:27,709
Speaker 1: the moratorium contract, some basis risk to the election contract.

618
00:29:27,730 --> 00:29:30,550
Speaker 1: So neither one is a perfect hedge to my overall portfolio. Right.

619
00:29:30,710 --> 00:29:33,680
Speaker 2: And I think that most hedge funds that have portfolios

620
00:29:33,740 --> 00:29:36,580
Speaker 2: are are used to trying to break down factors. And

621
00:29:37,080 --> 00:29:40,280
Speaker 2: election is absolutely an important factor, and you've probably seen

622
00:29:40,320 --> 00:29:43,240
Speaker 2: some of the baskets that banks put out of, here's

623
00:29:43,280 --> 00:29:45,240
Speaker 2: all these different products that you should put on to

624
00:29:45,340 --> 00:29:47,770
Speaker 2: bet on Republicans winning or Democrats winning.

625
00:29:48,350 --> 00:29:50,390
Speaker 1: And those are- Your portfolio looks like one of those

626
00:29:50,430 --> 00:29:52,370
Speaker 1: baskets you're calling Susquehanna for a hedge. Right.

627
00:29:52,470 --> 00:29:54,290
Speaker 2: And you can hedge that by putting on that basket.

628
00:29:54,330 --> 00:29:57,050
Speaker 2: But again, that basket is actually oftentimes a dirtier hedge.

629
00:29:57,570 --> 00:30:00,380
Speaker 2: You have the ability to, you could close some of

630
00:30:00,410 --> 00:30:02,500
Speaker 2: your risk, but a lot of these firms might like

631
00:30:02,520 --> 00:30:04,200
Speaker 2: those positions. They don't want to close the risk. So

632
00:30:04,220 --> 00:30:06,300
Speaker 2: if what you're worried about is what's the impact going

633
00:30:06,340 --> 00:30:09,500
Speaker 2: to be on this portfolio, you can hedge the election directly.

634
00:30:09,520 --> 00:30:11,420
Speaker 2: And I think that's where you're more likely to see

635
00:30:11,980 --> 00:30:13,980
Speaker 2: the election. Plus, to be honest, people have a lot

636
00:30:14,000 --> 00:30:15,480
Speaker 2: of opinions on the election. I think you have a

637
00:30:15,500 --> 00:30:17,510
Speaker 2: lot of people who just think one side is going

638
00:30:17,530 --> 00:30:19,350
Speaker 2: to win or not. It's a market that obviously trades

639
00:30:19,410 --> 00:30:22,010
Speaker 2: a lot. If you're a real money business, I think

640
00:30:22,030 --> 00:30:24,250
Speaker 2: in general, you're probably going to go narrower. And that's

641
00:30:24,270 --> 00:30:27,050
Speaker 2: what we see. Like the real money businesses, they're looking

642
00:30:27,070 --> 00:30:29,420
Speaker 2: for things that are much narrower the firms that are

643
00:30:29,460 --> 00:30:31,880
Speaker 2: closer to financial markets are often looking for things that

644
00:30:31,920 --> 00:30:32,420
Speaker 2: are broader.

645
00:30:32,600 --> 00:30:34,880
Speaker 1: And then is that because they're hedging a broader portfolio

646
00:30:34,900 --> 00:30:37,260
Speaker 1: or because they're taking directional risk with you?

647
00:30:37,720 --> 00:30:39,990
Speaker 2: I think it can be both. Obviously, when we are

648
00:30:40,190 --> 00:30:43,190
Speaker 2: doing this institutional business, we're really trying to focus on

649
00:30:43,210 --> 00:30:46,790
Speaker 2: the hedging trades in most markets. Now, in an election market,

650
00:30:46,930 --> 00:30:50,010
Speaker 2: especially a big one like, say, Senate control, those are

651
00:30:50,050 --> 00:30:52,190
Speaker 2: liquid enough markets with things going back and forth that

652
00:30:52,210 --> 00:30:54,430
Speaker 2: we're happy to take on the other side of someone

653
00:30:54,470 --> 00:30:56,920
Speaker 2: else having an opinion and it's their alpha trade. If

654
00:30:56,940 --> 00:30:59,900
Speaker 2: someone wanted a very, very bespoke thing. Like if some

655
00:30:59,920 --> 00:31:01,340
Speaker 2: hedge fund came to us and they were like, you

656
00:31:01,360 --> 00:31:03,560
Speaker 2: want to put on this goat herder hedge, we would

657
00:31:03,580 --> 00:31:04,820
Speaker 2: be less likely to take it on.

658
00:31:04,840 --> 00:31:07,100
Speaker 1: I wrote about, you did a hedge for a Spanish

659
00:31:07,140 --> 00:31:12,530
Speaker 1: soccer team getting relegated. That is like a blazing red

660
00:31:12,570 --> 00:31:16,250
Speaker 1: alarm adverse selection risk, possibly, right? And I assume, and

661
00:31:16,270 --> 00:31:18,170
Speaker 1: I wrote this, that I assume that your due diligence

662
00:31:18,210 --> 00:31:20,750
Speaker 1: involved like modeling how much money they would lose by

663
00:31:20,790 --> 00:31:24,550
Speaker 1: getting relegated and making sure that you were hedging something

664
00:31:24,610 --> 00:31:29,480
Speaker 1: less than 100% of that. 100%. Yes. How much adverse

665
00:31:29,500 --> 00:31:32,860
Speaker 1: selection due diligence is there in these markets? Are people

666
00:31:33,200 --> 00:31:34,980
Speaker 1: coming to try to pick you off? You know what

667
00:31:35,000 --> 00:31:40,330
Speaker 1: I mean? Are politicians betting against themselves? Is getting adversely

668
00:31:40,370 --> 00:31:43,090
Speaker 1: selected by people who know more about these markets a

669
00:31:43,170 --> 00:31:45,550
Speaker 1: big risk on the OTC side, or is it all

670
00:31:45,590 --> 00:31:46,550
Speaker 1: pretty up and up?

671
00:31:46,770 --> 00:31:49,710
Speaker 2: To this point, we haven't really experienced that on the

672
00:31:49,780 --> 00:31:52,760
Speaker 2: OTC side. If we're going to show a big size

673
00:31:52,840 --> 00:31:55,560
Speaker 2: and a tight price... We want to have an understanding

674
00:31:55,600 --> 00:31:57,420
Speaker 2: of the story, right? And we actually really want to

675
00:31:57,460 --> 00:31:58,780
Speaker 2: work with the other side to understand.

676
00:31:58,820 --> 00:32:01,420
Speaker 1: You say a tight price. I assume you're not showing

677
00:32:01,440 --> 00:32:03,150
Speaker 1: people two-sided markets on these things.

678
00:32:04,050 --> 00:32:05,570
Speaker 2: There are times that we do. There are times that

679
00:32:05,590 --> 00:32:07,210
Speaker 2: we will show a two-sided market. A lot of the

680
00:32:07,250 --> 00:32:09,950
Speaker 2: times it is one-sided, but there are some parties that

681
00:32:09,990 --> 00:32:12,010
Speaker 2: actually want a two-sided market and will make that.

682
00:32:12,430 --> 00:32:14,520
Speaker 1: And tell me, so you said you want to understand

683
00:32:14,540 --> 00:32:17,200
Speaker 1: the story, but what's the story of a two-sided market?

684
00:32:17,640 --> 00:32:19,300
Speaker 1: It's just the hedge fund wants to speculate or.

685
00:32:19,800 --> 00:32:21,840
Speaker 2: So sometimes it's coming through a broker, right? And brokers

686
00:32:21,880 --> 00:32:24,520
Speaker 2: are just used to- We want to get a two-sided market.

687
00:32:24,540 --> 00:32:26,240
Speaker 2: We want to show the width. That's sort of part

688
00:32:26,260 --> 00:32:29,530
Speaker 2: of their process. In that case, we're happy to do it. Usually,

689
00:32:29,730 --> 00:32:31,550
Speaker 2: if it's a broker, they're more likely to ask for

690
00:32:31,570 --> 00:32:33,810
Speaker 2: a two-sided market. Not always, but more likely.

691
00:32:33,830 --> 00:32:35,710
Speaker 1: You say brokers. A broker comes to you with, we

692
00:32:35,750 --> 00:32:38,280
Speaker 1: have an anonymous client who wants to take a position

693
00:32:38,340 --> 00:32:40,940
Speaker 1: on the Texas election. Can you show us a two-sided market?

694
00:32:41,180 --> 00:32:42,540
Speaker 1: So you don't really have a sense of the story there.

695
00:32:42,740 --> 00:32:44,620
Speaker 2: So if it's something that's kind of more general, right?

696
00:32:44,660 --> 00:32:48,120
Speaker 2: If it's the Texas election, there's a lot of information

697
00:32:48,160 --> 00:32:48,560
Speaker 2: out there.

698
00:32:48,780 --> 00:32:50,500
Speaker 1: You're not likely to be adversely selected by someone.

699
00:32:50,520 --> 00:32:53,239
Speaker 2: We're not that worried about adverse selection. And so it's

700
00:32:53,280 --> 00:32:55,140
Speaker 2: fine to throw a two-sided market. They could have an

701
00:32:55,180 --> 00:32:57,920
Speaker 2: opinion and that's okay, right? If they're like, we want

702
00:32:57,940 --> 00:33:00,660
Speaker 2: you to build this market from scratch and it's this

703
00:33:00,720 --> 00:33:03,610
Speaker 2: very specific thing, go back to the goat trade. We've

704
00:33:03,690 --> 00:33:05,190
Speaker 2: obviously talked about that one a good amount today.

705
00:33:05,230 --> 00:33:06,750
Speaker 1: It's like the best paradigm. Right.

706
00:33:07,050 --> 00:33:09,310
Speaker 2: No, it is great. Obviously, we're going to need to

707
00:33:09,350 --> 00:33:11,850
Speaker 2: know the story, right? You can't design the trade that

708
00:33:11,930 --> 00:33:14,150
Speaker 2: you have the opinion on to get your alpha trade

709
00:33:14,170 --> 00:33:15,810
Speaker 2: against us. We're not going to be able to get

710
00:33:15,870 --> 00:33:18,050
Speaker 2: out of the risk in some very random specific thing.

711
00:33:18,110 --> 00:33:20,800
Speaker 2: So it really comes down to the specificity. If it's

712
00:33:20,820 --> 00:33:23,580
Speaker 2: a really narrow trade, we want to have a conversation.

713
00:33:23,600 --> 00:33:25,970
Speaker 2: We want to understand why you want to do it.

714
00:33:26,140 --> 00:33:28,460
Speaker 2: And part of that is so we don't get adversely selected.

715
00:33:28,820 --> 00:33:29,920
Speaker 2: Part of that is so that we make sure we

716
00:33:29,960 --> 00:33:32,260
Speaker 2: actually design the right product that actually takes your risks on.

717
00:33:32,900 --> 00:33:34,219
Speaker 2: If you come in in a liquid market, I want

718
00:33:34,240 --> 00:33:36,180
Speaker 2: to predict who's going to control the Senate. That's fine.

719
00:33:36,200 --> 00:33:39,480
Speaker 2: We'll make you a two-sided market in size. We feel comfortable.

720
00:33:39,780 --> 00:33:41,840
Speaker 2: There's enough trading in that market that we could probably

721
00:33:41,880 --> 00:33:44,070
Speaker 2: get out of a position in that. And, you know,

722
00:33:44,270 --> 00:33:46,050
Speaker 2: we know enough about it that we're not that concerned

723
00:33:46,070 --> 00:33:47,470
Speaker 2: that someone's going to adverse select us.

724
00:33:47,870 --> 00:33:51,290
Speaker 1: In crude numbers, this book of RTC trades, like, are

725
00:33:51,310 --> 00:33:52,350
Speaker 1: you laying off like 10% of it or like 90%

726
00:33:52,350 --> 00:33:52,770
Speaker 1: of it or like?

727
00:33:54,010 --> 00:33:55,450
Speaker 2: Probably closer to the 10% than the 90%.

728
00:33:55,450 --> 00:33:56,330
Speaker 1: That's what I thought, yeah.

729
00:33:56,490 --> 00:33:59,380
Speaker 2: This is a business where we are the vast majority

730
00:33:59,400 --> 00:34:01,940
Speaker 2: of the time holding all of the risk.

731
00:34:02,680 --> 00:34:05,080
Speaker 1: I assume if this business is like 100x where it

732
00:34:05,120 --> 00:34:08,230
Speaker 1: is today, you'll be laying off 90% of the risk.

733
00:34:08,489 --> 00:34:09,390
Speaker 2: Yeah, for sure.

734
00:34:09,410 --> 00:34:12,010
Speaker 1: If everything gets super liquid, you won't be holding. Absolutely.

735
00:34:12,030 --> 00:34:13,910
Speaker 1: You won't be like a giant insurance company. Right.

736
00:34:13,989 --> 00:34:16,609
Speaker 2: And I think there's different ways that we ultimately can

737
00:34:16,650 --> 00:34:19,549
Speaker 2: wind up laying off that risk. Some of it can

738
00:34:19,590 --> 00:34:22,280
Speaker 2: be the markets are liquid enough that we're just doing

739
00:34:22,300 --> 00:34:25,040
Speaker 2: it in the markets. That'll probably work for some things.

740
00:34:25,280 --> 00:34:27,660
Speaker 2: And the broader you get, the easier that is. So

741
00:34:27,800 --> 00:34:31,600
Speaker 2: I can absolutely envision a scenario that's not so far

742
00:34:31,640 --> 00:34:34,779
Speaker 2: in the future where there's a liquid enough market in, say,

743
00:34:34,820 --> 00:34:37,690
Speaker 2: will Texas have a moratorium on data centers that we

744
00:34:37,710 --> 00:34:39,879
Speaker 2: can trade in and out of that. When you start

745
00:34:39,900 --> 00:34:42,640
Speaker 2: talking about things that are hyper-specific, you're a lot further

746
00:34:42,660 --> 00:34:44,719
Speaker 2: from that. Our goal is we really do want to

747
00:34:44,739 --> 00:34:46,980
Speaker 2: work with people in the insurance industry because they're used

748
00:34:47,040 --> 00:34:49,879
Speaker 2: to all different forms of syndication and sharing and things

749
00:34:49,900 --> 00:34:52,219
Speaker 2: like that. And so that's probably one of the better

750
00:34:52,260 --> 00:34:54,960
Speaker 2: ways that we can ultimately wind up sharing some of that,

751
00:34:55,080 --> 00:34:56,950
Speaker 2: like laying off some of that risk is to potentially

752
00:34:56,989 --> 00:34:59,570
Speaker 2: share some of it with someone who's in the insurance space.

753
00:35:00,230 --> 00:35:02,509
Speaker 2: And I think that's probably a much easier way to

754
00:35:02,550 --> 00:35:04,030
Speaker 2: go for the specific things.

755
00:35:04,350 --> 00:35:08,090
Speaker 1: Let's say a data center moratorium trade is- not that liquid.

756
00:35:08,110 --> 00:35:10,430
Speaker 1: I don't know if that's true, but Texas governor is

757
00:35:10,450 --> 00:35:14,629
Speaker 1: really liquid. Are you taking $ 100 million of data center

758
00:35:14,670 --> 00:35:18,480
Speaker 1: risk and laying off $ 50 million in the governor election

759
00:35:18,540 --> 00:35:21,880
Speaker 1: and taking some basis? Is there a factor correlation model

760
00:35:21,940 --> 00:35:22,960
Speaker 1: of hedging this stuff?

761
00:35:23,320 --> 00:35:26,660
Speaker 2: It's more that it influences how we will act. So

762
00:35:26,930 --> 00:35:29,110
Speaker 2: we're more likely to try to lean into a position

763
00:35:29,610 --> 00:35:32,390
Speaker 2: in some of those other markets. Most of them, if

764
00:35:32,410 --> 00:35:34,609
Speaker 2: you're taking a massive trade and you're going to just

765
00:35:34,630 --> 00:35:37,080
Speaker 2: sort of like, put this massive trade on and have

766
00:35:37,120 --> 00:35:39,569
Speaker 2: the basis risk. Like there's still not quite the liquidity

767
00:35:39,610 --> 00:35:41,330
Speaker 2: in like Texas governor to be like, we're going to

768
00:35:41,350 --> 00:35:43,810
Speaker 2: put on this massive trade. We're probably going to impact

769
00:35:43,870 --> 00:35:46,730
Speaker 2: that market in the process. And we're probably going to

770
00:35:46,770 --> 00:35:48,669
Speaker 2: wind up with some degree of basis risk there too.

771
00:35:49,170 --> 00:35:51,549
Speaker 2: So it's more of a, we have this on, we

772
00:35:51,570 --> 00:35:54,400
Speaker 2: understand the important factors that are our risk factors that

773
00:35:54,420 --> 00:35:56,859
Speaker 2: we have. We're going to sort of use that both

774
00:35:56,960 --> 00:35:59,380
Speaker 2: to change what we think is fair, because there can

775
00:35:59,400 --> 00:36:02,020
Speaker 2: be information in some of these trades and also to

776
00:36:02,040 --> 00:36:04,339
Speaker 2: potentially try to sort of reduce the factors as we can.

777
00:36:04,969 --> 00:36:07,190
Speaker 2: But that being said, we understand that a lot of

778
00:36:07,230 --> 00:36:10,590
Speaker 2: these are we need to basically manage our risk budget effectively.

779
00:36:10,610 --> 00:36:12,190
Speaker 2: And some of it can be done through laying off

780
00:36:12,230 --> 00:36:14,109
Speaker 2: and all those things. A lot more of it has

781
00:36:14,150 --> 00:36:17,210
Speaker 2: to be done through being selective in terms of what

782
00:36:17,230 --> 00:36:18,710
Speaker 2: are the trades that we're going to put on, what

783
00:36:18,750 --> 00:36:21,570
Speaker 2: trades make sense. And when we look at data centers

784
00:36:21,760 --> 00:36:24,920
Speaker 2: and just the total size of the risk that's probably

785
00:36:24,980 --> 00:36:27,800
Speaker 2: uncovered right now, it's in the hundreds of billions of dollars.

786
00:36:28,260 --> 00:36:30,560
Speaker 2: So it's definitely something we have to put a lot

787
00:36:30,600 --> 00:36:34,420
Speaker 2: of thought into of like, how do we ultimately at scale?

788
00:36:34,500 --> 00:36:36,480
Speaker 2: And right now it's not like it's big enough to

789
00:36:36,520 --> 00:36:38,860
Speaker 2: be like it's eaten up all of anybody's risk budget

790
00:36:38,980 --> 00:36:41,670
Speaker 2: for us. But at scale, we need to understand how

791
00:36:41,710 --> 00:36:43,930
Speaker 2: this looks when you have some massive risk that the

792
00:36:43,950 --> 00:36:46,410
Speaker 2: world has and they need someone to be the capacity

793
00:36:46,469 --> 00:36:49,930
Speaker 2: on the other side. And that's a good part of

794
00:36:49,950 --> 00:36:52,090
Speaker 2: the reason why we actually do kind of invite competition

795
00:36:52,130 --> 00:36:54,980
Speaker 2: to a degree in that like, We want other capacity.

796
00:36:55,040 --> 00:36:57,590
Speaker 2: We want to grow the markets. And we think we'll

797
00:36:57,620 --> 00:37:00,230
Speaker 2: ultimately actually win to that, even if it means we

798
00:37:00,270 --> 00:37:03,410
Speaker 2: maybe are competing with more forms of insurance syndicates that

799
00:37:03,450 --> 00:37:05,910
Speaker 2: are doing some of these things too. We actually welcome that.

800
00:37:05,930 --> 00:37:09,780
Speaker 1: I love the idea that prediction markets are basically sports

801
00:37:09,820 --> 00:37:14,620
Speaker 1: gambling now, and the vision is they're basically society's hedge

802
00:37:14,640 --> 00:37:16,980
Speaker 1: against data center risk. Yeah.

803
00:37:18,930 --> 00:37:22,710
Speaker 2: You know, things happen in mysterious ways. It makes total sense.

804
00:37:23,090 --> 00:37:25,439
Speaker 2: I suppose. But I always thought when I first wanted

805
00:37:25,480 --> 00:37:27,880
Speaker 2: to start building a prediction markets desk, you know, it's

806
00:37:28,020 --> 00:37:31,350
Speaker 2: four years ago now. I really did envision this institutional

807
00:37:31,510 --> 00:37:34,379
Speaker 2: hedging case as the reason to do it. I've got

808
00:37:34,510 --> 00:37:36,110
Speaker 2: a story I've told a few times now about a

809
00:37:36,160 --> 00:37:38,799
Speaker 2: friend who's CFO of a musical instrument company that was

810
00:37:38,820 --> 00:37:41,400
Speaker 2: concerned about tariffs, and that was kind of part of

811
00:37:41,440 --> 00:37:44,080
Speaker 2: the light bulb in my head. That was the original

812
00:37:44,120 --> 00:37:46,399
Speaker 2: reason we really wanted to do it. The path goes

813
00:37:46,440 --> 00:37:49,180
Speaker 2: through sports, because sports has sort of built the space.

814
00:37:49,320 --> 00:37:51,900
Speaker 2: It's created a lot of volume, a lot of interest

815
00:37:52,000 --> 00:37:55,380
Speaker 2: on it. But I do firmly believe that if prediction

816
00:37:55,400 --> 00:37:59,400
Speaker 2: markets is ultimately successful- the long-term success will be predicated

817
00:37:59,580 --> 00:38:04,200
Speaker 2: on this institutional hedging. And that's for ecosystem reasons, because

818
00:38:04,700 --> 00:38:07,830
Speaker 2: you need a robust ecosystem that has a combination of

819
00:38:07,910 --> 00:38:11,310
Speaker 2: different types of market participants. And hedging is a really

820
00:38:11,350 --> 00:38:14,090
Speaker 2: important part, right? If you think about traditional financial markets.

821
00:38:14,130 --> 00:38:17,470
Speaker 1: People are not looking for alpha. Exactly. The traditional market,

822
00:38:17,950 --> 00:38:19,790
Speaker 1: great source of people who are not looking for alpha

823
00:38:19,989 --> 00:38:23,230
Speaker 1: are retail sports gamblers, but data center hedgers are a

824
00:38:23,230 --> 00:38:24,129
Speaker 1: much Right.

825
00:38:24,210 --> 00:38:27,450
Speaker 2: You absolutely do. If your entire market is built from

826
00:38:27,489 --> 00:38:29,790
Speaker 2: people who they're trying to predict, and there's a reason

827
00:38:29,810 --> 00:38:32,330
Speaker 2: why that's the first people in predictions. It's prediction markets.

828
00:38:32,390 --> 00:38:35,969
Speaker 2: People are trying to predict the outcome. You can build

829
00:38:36,010 --> 00:38:38,750
Speaker 2: something there, but it's not as robust because you don't

830
00:38:38,810 --> 00:38:41,130
Speaker 2: have the type of variety of reasons people are trading.

831
00:38:41,710 --> 00:38:43,950
Speaker 2: It's a much more robust ecosystem if you have the

832
00:38:43,989 --> 00:38:47,109
Speaker 2: hedging in there as well. I think there's some hedging now,

833
00:38:47,190 --> 00:38:48,390
Speaker 2: but I'm not going to go and tell you that

834
00:38:48,430 --> 00:38:50,270
Speaker 2: that's the majority of the order flow that you see

835
00:38:50,310 --> 00:38:53,790
Speaker 2: in predictions today. But I do honestly think it needs

836
00:38:53,830 --> 00:38:55,509
Speaker 2: to be if it's going to become what it needs

837
00:38:55,570 --> 00:38:56,070
Speaker 2: to become.

838
00:38:56,930 --> 00:38:59,070
Speaker 1: Is it the majority of the order flow in your

839
00:39:00,150 --> 00:39:02,450
Speaker 1: institutional hedging book slash OTC book?

840
00:39:03,130 --> 00:39:05,670
Speaker 2: In the institutional hedging book, yes. The majority is definitely hedging.

841
00:39:06,090 --> 00:39:08,189
Speaker 1: As opposed to hedge fund alpha trades.

842
00:39:08,350 --> 00:39:11,850
Speaker 2: Yes, for sure. But again, that volume relative to the

843
00:39:11,890 --> 00:39:14,270
Speaker 2: overall predictions volume is still very small as of today.

844
00:39:14,290 --> 00:39:14,370
Speaker 1: Yeah.

845
00:39:23,710 --> 00:39:24,650
Speaker 2: Thank you.

846
00:39:30,380 --> 00:39:32,900
Speaker 1: Sorry to circle back to sports, but like sports hedging.

847
00:39:33,340 --> 00:39:36,600
Speaker 1: So some sports hedging is like you're doing a promotion

848
00:39:36,680 --> 00:39:40,319
Speaker 1: and you're whatever. But like how much sports hedging is

849
00:39:40,340 --> 00:39:41,600
Speaker 1: sports teams hedging themselves?

850
00:39:42,280 --> 00:39:44,910
Speaker 2: I don't think that there's a lot of sports teams.

851
00:39:44,930 --> 00:39:46,850
Speaker 1: Have you done more than one? Because there's one publicly

852
00:39:46,910 --> 00:39:49,430
Speaker 1: reported Spanish soccer relegation trade. Have you done more than

853
00:39:49,489 --> 00:39:50,650
Speaker 1: like three?

854
00:39:51,410 --> 00:39:52,950
Speaker 2: So not all of it is in sort of this

855
00:39:53,030 --> 00:39:56,650
Speaker 2: current era of predictions either. There's been history of different

856
00:39:56,670 --> 00:40:00,220
Speaker 2: types of OTC transactions where sports teams might hedge some

857
00:40:00,239 --> 00:40:03,380
Speaker 2: kind of risk or another, whether it's how far they

858
00:40:03,400 --> 00:40:05,940
Speaker 2: advance or whatnot. Sports teams don't want to put on

859
00:40:05,960 --> 00:40:08,739
Speaker 2: a trade that's a direct bet against themselves a lot

860
00:40:08,760 --> 00:40:11,680
Speaker 2: of the time, and there are reasons for that, partially

861
00:40:11,719 --> 00:40:14,770
Speaker 2: just kind of optics. But then there's a lot of

862
00:40:14,830 --> 00:40:18,350
Speaker 2: businesses that are intrinsically tied up with the sports teams, right?

863
00:40:18,390 --> 00:40:22,200
Speaker 2: You could say you have some... network that has media

864
00:40:22,260 --> 00:40:24,240
Speaker 2: rights and they need a team to make the playoffs

865
00:40:24,390 --> 00:40:27,210
Speaker 2: or even just like they've got some high marking matchup

866
00:40:27,230 --> 00:40:29,770
Speaker 2: that's going to happen on Christmas or some key day

867
00:40:29,810 --> 00:40:31,770
Speaker 2: and like they want the teams to be good. You've

868
00:40:31,790 --> 00:40:34,890
Speaker 2: got the sponsors, you've got all these mouths that are

869
00:40:34,930 --> 00:40:37,450
Speaker 2: tied up with it, right? And we're probably seeing more

870
00:40:37,489 --> 00:40:39,500
Speaker 2: of them involved in saying like, we really want to

871
00:40:39,540 --> 00:40:41,100
Speaker 2: hedge some of the risks that we have on there.

872
00:40:41,120 --> 00:40:44,310
Speaker 2: The different players that have intrinsic risks tied up in

873
00:40:44,330 --> 00:40:46,450
Speaker 2: the performance of a sports team or even just a player.

874
00:40:46,850 --> 00:40:49,370
Speaker 2: And then obviously there's the promotion stuff too, which is real.

875
00:40:49,390 --> 00:40:51,230
Speaker 2: There's a lot of promotions that are based on sports

876
00:40:51,270 --> 00:40:54,589
Speaker 2: teams and we're seeing more of them start to say, well,

877
00:40:54,610 --> 00:40:57,770
Speaker 2: we can hedge our promotion risk. Or even we can

878
00:40:57,870 --> 00:41:00,629
Speaker 2: launch a promotion if we can hedge the risk, right?

879
00:41:00,670 --> 00:41:02,890
Speaker 2: We want to do a promotion where if you pick

880
00:41:03,110 --> 00:41:07,420
Speaker 2: all the NCAA March Madness winners, you get a billion

881
00:41:07,440 --> 00:41:09,420
Speaker 2: dollars or something, but you need someone to actually be

882
00:41:09,480 --> 00:41:11,259
Speaker 2: the one who's going to take that on and Those

883
00:41:11,280 --> 00:41:12,960
Speaker 2: are things that we're willing to do, right? You need

884
00:41:12,980 --> 00:41:14,319
Speaker 2: some mechanism to be able to do that.

885
00:41:15,239 --> 00:41:18,270
Speaker 1: My impression is that sports markets, first of all, are

886
00:41:18,510 --> 00:41:22,270
Speaker 1: pretty liquid. And second, there's a lot of correlated trades, right?

887
00:41:22,330 --> 00:41:25,170
Speaker 1: Like someone who's like, I want to hedge my media

888
00:41:25,230 --> 00:41:28,390
Speaker 1: rights to some sports team. Like you can lay that

889
00:41:28,489 --> 00:41:30,620
Speaker 1: off in a lot of places. Yes. Does that work

890
00:41:30,660 --> 00:41:32,140
Speaker 1: by like you do a big trade and then you

891
00:41:32,160 --> 00:41:33,600
Speaker 1: try to lay it off? Does it work by like

892
00:41:34,040 --> 00:41:35,700
Speaker 1: you do a big trade and then like there's some

893
00:41:35,739 --> 00:41:37,799
Speaker 1: dashboard of like how much Knicks risk you have and

894
00:41:37,820 --> 00:41:40,640
Speaker 1: you're like, Adjust the prices on your flow market making

895
00:41:40,700 --> 00:41:41,180
Speaker 1: on the next?

896
00:41:41,380 --> 00:41:44,190
Speaker 2: It's probably more the second. I would say relative to

897
00:41:44,250 --> 00:41:48,050
Speaker 2: most firms out there, we tend to have more liberal

898
00:41:48,110 --> 00:41:51,480
Speaker 2: attitudes towards risk. We really focus on the expectancy of

899
00:41:51,520 --> 00:41:54,779
Speaker 2: the trade. We take on some big trade and we

900
00:41:54,800 --> 00:41:56,750
Speaker 2: have next risk, right? We're going to tune some of

901
00:41:56,790 --> 00:41:59,730
Speaker 2: our parameters to try to ultimately over a longer, slower

902
00:41:59,770 --> 00:42:03,010
Speaker 2: period of time, work out of that risk in the market.

903
00:42:03,090 --> 00:42:04,370
Speaker 2: But we're not going to go and sort of just

904
00:42:04,390 --> 00:42:05,690
Speaker 2: try to get out of all of it now because

905
00:42:05,710 --> 00:42:07,610
Speaker 2: that's more expensive, which by the way, is exactly what

906
00:42:07,630 --> 00:42:09,690
Speaker 2: we do in traditional financial markets too. If we get

907
00:42:09,730 --> 00:42:11,550
Speaker 2: a big risk put to us, we work out of

908
00:42:11,590 --> 00:42:13,810
Speaker 2: it slowly. You want to paying less to work out

909
00:42:13,830 --> 00:42:14,870
Speaker 2: of it if you work out of it over a

910
00:42:15,290 --> 00:42:17,989
Speaker 2: longer period of time. And we're willing to take the

911
00:42:18,030 --> 00:42:21,210
Speaker 2: higher return for the longer period of risk. And that's

912
00:42:21,430 --> 00:42:22,410
Speaker 2: firm policy overall.

913
00:42:22,790 --> 00:42:24,850
Speaker 1: Yeah, I guess it's like in general in the prediction

914
00:42:24,870 --> 00:42:27,989
Speaker 1: market OTC hedging business, like how much tuning of parameters

915
00:42:28,070 --> 00:42:30,380
Speaker 1: is there, right? So like the goat herder like goes nowhere, right?

916
00:42:30,420 --> 00:42:34,960
Speaker 1: Like there's no other correlated trade, right? But like when

917
00:42:34,980 --> 00:42:37,500
Speaker 1: you're thinking of the risk budget for like Texas elections,

918
00:42:37,660 --> 00:42:39,739
Speaker 1: these things are all correlated, right? You know, if someone

919
00:42:39,760 --> 00:42:40,779
Speaker 1: comes to you and is like, I want to do

920
00:42:40,820 --> 00:42:42,640
Speaker 1: a data center moratorium, you're like, I have so much

921
00:42:43,180 --> 00:42:45,640
Speaker 1: Texas election risk, like we're going to show a bad

922
00:42:45,700 --> 00:42:47,700
Speaker 1: price on that because like it would, you know, add

923
00:42:47,920 --> 00:42:50,009
Speaker 1: too much to our risk. Or like you sell a

924
00:42:50,030 --> 00:42:53,120
Speaker 1: lot of data center moratoriums and you like, adjust the

925
00:42:53,160 --> 00:42:55,620
Speaker 1: flow market making price on the Texas governor race. I mean,

926
00:42:55,640 --> 00:42:56,900
Speaker 1: think about it the right way. Is it sort of

927
00:42:57,040 --> 00:42:59,489
Speaker 1: dashboard of factors and correlations that is kind of like

928
00:42:59,550 --> 00:43:01,750
Speaker 1: feeding into all of these trades or like are a

929
00:43:01,790 --> 00:43:06,430
Speaker 1: lot of these trades so one-off that you evaluate them

930
00:43:06,469 --> 00:43:06,950
Speaker 1: on their own?

931
00:43:07,570 --> 00:43:09,870
Speaker 2: So there's definitely a little of both. Obviously, there's plenty

932
00:43:09,890 --> 00:43:12,710
Speaker 2: of these things that are one-off and there's plenty of

933
00:43:12,730 --> 00:43:15,270
Speaker 2: them that are short-dated enough that like by the time

934
00:43:15,310 --> 00:43:17,779
Speaker 2: you would have gone and done anything, the event is over. Mm-hmm.

935
00:43:18,210 --> 00:43:20,290
Speaker 2: There are certainly some where we have things for longer

936
00:43:20,330 --> 00:43:22,880
Speaker 2: periods of time. And I'd like to give you a

937
00:43:22,890 --> 00:43:25,520
Speaker 2: very clean answer, but it's actually kind of a messy problem.

938
00:43:25,880 --> 00:43:28,160
Speaker 2: Part of it is, okay, we got this position. How

939
00:43:28,200 --> 00:43:30,739
Speaker 2: did we get it? Is there information in that? Should

940
00:43:30,760 --> 00:43:32,880
Speaker 2: we actually change our fair values is sort of the

941
00:43:32,920 --> 00:43:35,200
Speaker 2: first question based on this position that got put to us.

942
00:43:35,489 --> 00:43:38,390
Speaker 1: So the question is, is there information in the trade

943
00:43:38,430 --> 00:43:41,049
Speaker 1: that should cause our fair values to change? Or are

944
00:43:41,090 --> 00:43:42,810
Speaker 1: we further from home? You don't care if you're further

945
00:43:42,830 --> 00:43:43,649
Speaker 1: from home.

946
00:43:43,690 --> 00:43:45,650
Speaker 2: We care about both. But the first question when we

947
00:43:45,690 --> 00:43:48,390
Speaker 2: have a trade is, is there information in this trade? Obviously,

948
00:43:48,450 --> 00:43:50,069
Speaker 2: if some of these things we've talked about have been,

949
00:43:50,110 --> 00:43:51,950
Speaker 2: this seems very much like a hedging trade, but not

950
00:43:51,989 --> 00:43:53,730
Speaker 2: everything that we do is like, this is definitely a

951
00:43:53,750 --> 00:43:56,650
Speaker 2: hedging trade on the other side. So the first question is,

952
00:43:57,170 --> 00:44:00,129
Speaker 2: here's a trade. Let's try to understand how this should

953
00:44:00,170 --> 00:44:03,450
Speaker 2: impact what the probability is of these things happening. Once

954
00:44:03,469 --> 00:44:04,739
Speaker 2: we've done that, then we're going to look at our

955
00:44:04,800 --> 00:44:06,359
Speaker 2: risk and we're going to look at it across all

956
00:44:06,380 --> 00:44:09,339
Speaker 2: the different factors that we have. And generally, we're going

957
00:44:09,360 --> 00:44:11,340
Speaker 2: to say, okay, we would need to be compensated a

958
00:44:11,460 --> 00:44:13,340
Speaker 2: little bit more to put on more risk now that

959
00:44:13,360 --> 00:44:15,440
Speaker 2: we've got a lot of risk on. And we would

960
00:44:15,480 --> 00:44:18,290
Speaker 2: probably be willing to take risk off for zero expectancy

961
00:44:18,410 --> 00:44:20,250
Speaker 2: right now to reduce our risk. We would do it

962
00:44:20,290 --> 00:44:23,250
Speaker 2: for the dead fair price, no bid as spread at all.

963
00:44:23,770 --> 00:44:25,150
Speaker 2: And that's kind of how it works with most of

964
00:44:25,190 --> 00:44:27,150
Speaker 2: our risk. And it's the same thing here. Now, the

965
00:44:27,190 --> 00:44:29,740
Speaker 2: factors and parameters are trickier here, right? Because as you've

966
00:44:29,780 --> 00:44:32,170
Speaker 2: pointed out, You can't just throw it into sort of

967
00:44:32,230 --> 00:44:35,160
Speaker 2: some market model to spit out all your correlations and

968
00:44:35,180 --> 00:44:36,540
Speaker 2: tell you what your risk factors are.

969
00:44:36,719 --> 00:44:39,140
Speaker 1: That's just because it's like there's not second by second

970
00:44:39,460 --> 00:44:40,860
Speaker 1: time series for 20 years.

971
00:44:40,900 --> 00:44:43,060
Speaker 2: You've got a lot of second order effects that happen.

972
00:44:43,530 --> 00:44:45,610
Speaker 2: You can't just throw a quant model at this. You

973
00:44:45,630 --> 00:44:48,450
Speaker 2: have to think really hard about how are these things correlated.

974
00:44:48,610 --> 00:44:52,730
Speaker 2: For example, how is the Senate race in New Hampshire

975
00:44:52,850 --> 00:44:57,080
Speaker 2: correlated with this? data center moratorium risk in Texas. They're correlated, right?

976
00:44:57,480 --> 00:45:00,700
Speaker 2: They're less correlated than, you know, with the Texas race.

977
00:45:00,719 --> 00:45:03,440
Speaker 2: And there's no like long history of data that's going

978
00:45:03,460 --> 00:45:05,880
Speaker 2: to just give you the answer. So that's part of

979
00:45:05,940 --> 00:45:06,440
Speaker 2: our process.

980
00:45:06,500 --> 00:45:08,029
Speaker 1: In like 10 years, you'll be able to throw a

981
00:45:08,030 --> 00:45:10,120
Speaker 1: quant model at it, right? Some of it, sort of.

982
00:45:10,130 --> 00:45:12,009
Speaker 2: It'll certainly be a lot easier to just throw a

983
00:45:12,030 --> 00:45:14,069
Speaker 2: model at it in 10 years than it is today.

984
00:45:14,090 --> 00:45:17,610
Speaker 1: In part because you'll have correlation of like actual events,

985
00:45:18,690 --> 00:45:21,469
Speaker 1: which is hard because, you know, Senate elections are every

986
00:45:21,489 --> 00:45:23,029
Speaker 1: two years or, you know, but like But then there's

987
00:45:23,050 --> 00:45:25,609
Speaker 1: correlations of like the price time series on Kelshi, right?

988
00:45:25,680 --> 00:45:27,700
Speaker 1: Which gives you some maybe information?

989
00:45:27,880 --> 00:45:30,340
Speaker 2: It gives you some, yeah. And I think that as

990
00:45:30,380 --> 00:45:32,860
Speaker 2: you get more history and those markets become more liquid,

991
00:45:32,900 --> 00:45:35,319
Speaker 2: you will absolutely be able to use that information more

992
00:45:35,360 --> 00:45:37,780
Speaker 2: to sort of back out what some of the correlations are.

993
00:45:38,219 --> 00:45:39,880
Speaker 2: All that being said, there's a lot of markets that

994
00:45:39,920 --> 00:45:42,859
Speaker 2: are new also. So many of these markets exist for

995
00:45:42,880 --> 00:45:45,560
Speaker 2: a period of time and then they go away. And

996
00:45:45,600 --> 00:45:46,980
Speaker 2: so you still are going to have to figure out

997
00:45:47,020 --> 00:45:49,530
Speaker 2: how to do the classification of like, these are the buckets, right?

998
00:45:49,640 --> 00:45:52,290
Speaker 2: This is like a legislation contract. It's in California. How

999
00:45:52,350 --> 00:45:54,530
Speaker 2: is that related to when we go back eight years,

1000
00:45:54,590 --> 00:45:58,509
Speaker 2: like another legislation contract in California? There's definitely a lot

1001
00:45:58,530 --> 00:46:02,210
Speaker 2: of expertise that's required. And ultimately it's figuring out how

1002
00:46:02,230 --> 00:46:04,669
Speaker 2: to make the quant models understand some of the things

1003
00:46:04,710 --> 00:46:08,029
Speaker 2: that are our intuition about knowing that this product is

1004
00:46:08,150 --> 00:46:11,130
Speaker 2: kind of correlated to this other product for these intrinsic reasons.

1005
00:46:11,730 --> 00:46:13,930
Speaker 2: It's really just creating a map of what does causality

1006
00:46:13,950 --> 00:46:15,790
Speaker 2: in the world look like? Because that is the thing

1007
00:46:15,830 --> 00:46:18,730
Speaker 2: about predictions. The breadth is phenomenal, right? In Calci, there's

1008
00:46:18,770 --> 00:46:22,190
Speaker 2: over 100,000 markets as of right now. So obviously we

1009
00:46:22,210 --> 00:46:25,450
Speaker 2: don't trade every one of those markets, but understanding how

1010
00:46:25,489 --> 00:46:28,489
Speaker 2: they're all interconnected. And that is why, why is it

1011
00:46:28,870 --> 00:46:31,270
Speaker 2: useful to have a background as a macro trader? Well,

1012
00:46:31,370 --> 00:46:33,510
Speaker 2: it's because I spent a long time trying to think

1013
00:46:33,550 --> 00:46:36,609
Speaker 2: about how one thing impacts another on like a really

1014
00:46:36,750 --> 00:46:41,100
Speaker 2: broad scale. And that translates well to understanding risk and predictions.

1015
00:46:41,840 --> 00:46:45,739
Speaker 1: So like you clearly do elections, do legislation, you do sports. Like,

1016
00:46:45,800 --> 00:46:49,120
Speaker 1: is there like, don't do entertainment, do do entertainment. We'll

1017
00:46:49,140 --> 00:46:50,000
Speaker 1: do it if someone asks.

1018
00:46:50,100 --> 00:46:51,120
Speaker 2: We'll do it if someone asks.

1019
00:46:51,390 --> 00:46:52,930
Speaker 1: Someone came to you and was like, I need $ 5

1020
00:46:52,710 --> 00:46:56,580
Speaker 1: million on the Oscar winner. You'd be like, Yeah, let's

1021
00:46:56,620 --> 00:46:57,960
Speaker 1: do it for that.

1022
00:46:58,160 --> 00:47:02,500
Speaker 2: Assuming that I understood why they wanted to do the trade. Yeah,

1023
00:47:02,540 --> 00:47:03,100
Speaker 2: I think we would be.

1024
00:47:03,090 --> 00:47:04,990
Speaker 1: Able to do it. You'd find some price.

1025
00:47:05,010 --> 00:47:07,750
Speaker 2: We'd find a price, yeah. Like, Oster's a good example of, like,

1026
00:47:07,890 --> 00:47:10,130
Speaker 2: there's a pretty decent amount of liquidity in the market. Like,

1027
00:47:10,150 --> 00:47:13,750
Speaker 2: those prices are pretty good. And ultimately, it takes less

1028
00:47:13,830 --> 00:47:16,850
Speaker 2: volume than you'd think to get a good price. price

1029
00:47:16,950 --> 00:47:21,080
Speaker 2: signal out of a market. It's not millions of dollars necessary.

1030
00:47:21,120 --> 00:47:23,660
Speaker 2: It's probably tens of thousands of dollars, at which point

1031
00:47:24,239 --> 00:47:26,239
Speaker 2: I'm not saying it's perfect, but you've got a pretty

1032
00:47:26,260 --> 00:47:28,760
Speaker 2: good ballpark for what the fair value is after something

1033
00:47:28,780 --> 00:47:30,450
Speaker 2: like tens of thousands and you get more than that

1034
00:47:30,570 --> 00:47:33,310
Speaker 2: on Oscars. So I think like Oscars where there's that

1035
00:47:33,330 --> 00:47:36,250
Speaker 2: much interest, those prices are probably pretty good. We can

1036
00:47:36,270 --> 00:47:39,069
Speaker 2: make a market around it to a hedger and feel

1037
00:47:39,090 --> 00:47:41,820
Speaker 2: pretty comfortable that we probably are making a market around

1038
00:47:41,860 --> 00:47:44,219
Speaker 2: a pretty fair value. We'll do our own research too,

1039
00:47:44,360 --> 00:47:46,299
Speaker 2: but it's a really good starting place.

1040
00:47:46,320 --> 00:47:48,430
Speaker 1: What's the right intuition for what a market around it is?

1041
00:47:48,440 --> 00:47:50,800
Speaker 1: So like, let's say that like someone winning an Oscar

1042
00:47:50,860 --> 00:47:54,580
Speaker 1: is at 25%. Are you making like a 24, 26

1043
00:47:54,580 --> 00:47:57,070
Speaker 1: market for $ 5 million?

1044
00:47:57,090 --> 00:48:00,690
Speaker 2: Kind of depends. It's probably a little wider than, than 24, 26,

1045
00:48:00,690 --> 00:48:01,930
Speaker 2: but not going to be 10%.

1046
00:48:01,930 --> 00:48:02,009
Speaker 1: Yeah.

1047
00:48:02,350 --> 00:48:04,390
Speaker 2: It's tighter than that. It's somewhere in between those. And

1048
00:48:04,430 --> 00:48:06,790
Speaker 2: it sort of depends a little bit on some of

1049
00:48:06,810 --> 00:48:09,190
Speaker 2: the specific dynamics. Like again, you know, we might think

1050
00:48:09,230 --> 00:48:11,670
Speaker 2: they're hedging, but you're never a hundred percent sure. So

1051
00:48:11,770 --> 00:48:13,319
Speaker 2: we're going to talk about all those things and it's

1052
00:48:13,469 --> 00:48:14,980
Speaker 2: sort of a case by case decision, but.

1053
00:48:15,460 --> 00:48:15,620
Speaker 1: Yeah.

1054
00:48:15,700 --> 00:48:17,719
Speaker 2: Two's a little tight, but might be closer to two

1055
00:48:17,760 --> 00:48:18,430
Speaker 2: than 10. All right.

1056
00:48:18,570 --> 00:48:19,450
Speaker 1: What else should we talk about?

1057
00:48:19,700 --> 00:48:22,450
Speaker 2: On the institutional front, I do think that probably one

1058
00:48:22,469 --> 00:48:25,330
Speaker 2: of the biggest things that ultimately we will probably see

1059
00:48:25,350 --> 00:48:28,169
Speaker 2: within the next few months is some of these really

1060
00:48:28,210 --> 00:48:31,670
Speaker 2: large trades around some of these big products, like hundreds

1061
00:48:31,690 --> 00:48:33,629
Speaker 2: of millions of dollars. And we actually do see.

1062
00:48:33,650 --> 00:48:35,830
Speaker 1: Big products, meaning like data center moratorium?

1063
00:48:35,850 --> 00:48:37,790
Speaker 2: Like a data center moratorium, or some of the things

1064
00:48:37,810 --> 00:48:39,770
Speaker 2: that are like, I think you've written about this, like

1065
00:48:39,950 --> 00:48:43,230
Speaker 2: OpenAI and Anthropic have bankruptcy risks that are sort of

1066
00:48:43,550 --> 00:48:46,350
Speaker 2: priced into the market, but nobody can hedge them. Every

1067
00:48:46,390 --> 00:48:49,070
Speaker 2: data center is exposed to one of these massive labs

1068
00:48:49,110 --> 00:48:51,210
Speaker 2: where if one of those- That's interesting.

1069
00:48:51,810 --> 00:48:55,879
Speaker 1: To me, that's like OpenAI CDS, right? But there's no CDS,

1070
00:48:55,890 --> 00:49:00,520
Speaker 1: there's no bonds. So there's effectively an OpenAI CDS is

1071
00:49:01,380 --> 00:49:02,620
Speaker 1: you and your prediction markets.

1072
00:49:02,860 --> 00:49:05,180
Speaker 2: I mean, yeah. So those markets have existed. I mentioned

1073
00:49:05,200 --> 00:49:07,560
Speaker 2: the moratoriums, but I think that is the two things

1074
00:49:07,600 --> 00:49:10,390
Speaker 2: we hear the most in the data center space. And

1075
00:49:10,410 --> 00:49:11,610
Speaker 2: I know I keep coming back to this, but it's

1076
00:49:11,630 --> 00:49:14,339
Speaker 2: just because it's what we hear over and over again.

1077
00:49:14,700 --> 00:49:17,049
Speaker 2: It's that and then it's the tenant bankruptcy.

1078
00:49:17,070 --> 00:49:19,390
Speaker 1: So that's interesting. So like there's trillions of dollars of

1079
00:49:19,489 --> 00:49:21,890
Speaker 1: open AIs debt in the world, but it's not debt,

1080
00:49:21,930 --> 00:49:26,670
Speaker 1: it's leases, right? Right. And the people who are long

1081
00:49:26,730 --> 00:49:30,569
Speaker 1: that debt are data center developers and financiers, right? Right.

1082
00:49:30,730 --> 00:49:33,370
Speaker 1: And they want to lay off some of that debt

1083
00:49:33,870 --> 00:49:36,810
Speaker 1: to like, back when there was a single name CDS market, like,

1084
00:49:37,550 --> 00:49:40,190
Speaker 1: there were people who would get long debt by buying CDS.

1085
00:49:40,310 --> 00:49:44,390
Speaker 1: I assume that you are not going to warehouse hundreds

1086
00:49:44,430 --> 00:49:47,580
Speaker 1: of billions of dollars of OpenAI bankruptcy risk.

1087
00:49:47,820 --> 00:49:49,020
Speaker 2: Probably not hundreds of billions of dollars.

1088
00:49:49,040 --> 00:49:52,240
Speaker 1: I assume that if you're getting into that trade in size,

1089
00:49:53,080 --> 00:49:56,739
Speaker 1: you are somehow intermediating it to someone who wants OpenAI

1090
00:49:56,800 --> 00:49:59,469
Speaker 1: credit exposure and does not own a data center. Is

1091
00:49:59,489 --> 00:49:59,910
Speaker 1: that right?

1092
00:50:00,160 --> 00:50:01,969
Speaker 2: I don't know that it's as simple as we're going

1093
00:50:01,989 --> 00:50:04,710
Speaker 2: to find the other side of the trade. It's more

1094
00:50:04,770 --> 00:50:07,630
Speaker 2: likely that we would want to find parties that are

1095
00:50:07,670 --> 00:50:09,690
Speaker 2: willing to partner with us. Like this is one of

1096
00:50:09,730 --> 00:50:13,450
Speaker 2: those situations where there's a massive risk that sort of

1097
00:50:13,570 --> 00:50:17,170
Speaker 2: outsizes everything else that the world wants to hedge. And

1098
00:50:17,410 --> 00:50:19,890
Speaker 2: how can you basically help provide capacity? So we would

1099
00:50:19,910 --> 00:50:22,200
Speaker 2: put on the size that ultimately we're comfortable with, which

1100
00:50:22,239 --> 00:50:23,960
Speaker 2: would be large, but it's not going to be hundreds

1101
00:50:23,980 --> 00:50:24,780
Speaker 2: of billions of dollars.

1102
00:50:25,140 --> 00:50:27,480
Speaker 1: Yeah. You say like that the world wants to hedge.

1103
00:50:27,719 --> 00:50:30,120
Speaker 1: Like apparently the world wants to take trillions of dollars

1104
00:50:30,160 --> 00:50:33,339
Speaker 1: of open-ended credit risk, right? Like, well, selling bonds, right?

1105
00:50:33,380 --> 00:50:37,180
Speaker 1: Like someone wants that credit risk. And it's not necessarily

1106
00:50:37,200 --> 00:50:40,530
Speaker 1: that the data center developers want it in the concentration

1107
00:50:40,550 --> 00:50:43,450
Speaker 1: that they have it in, but like, you know.

1108
00:50:43,570 --> 00:50:44,370
Speaker 2: Could it be packaged?

1109
00:50:44,450 --> 00:50:45,550
Speaker 1: Could it be packaged, yeah.

1110
00:50:45,710 --> 00:50:48,290
Speaker 2: It's a great thought. And, you know, we love those

1111
00:50:48,330 --> 00:50:50,009
Speaker 2: types of like innovative things. I don't know that we've

1112
00:50:50,050 --> 00:50:51,950
Speaker 2: gotten to the idea of like figuring out how to

1113
00:50:52,270 --> 00:50:55,230
Speaker 2: package and syndicate and all that yet. We're probably not

1114
00:50:55,330 --> 00:50:58,480
Speaker 2: quite there yet, but absolutely we need to figure out

1115
00:50:58,980 --> 00:51:00,959
Speaker 2: how do we create the structures where we can actually

1116
00:51:00,980 --> 00:51:03,630
Speaker 2: take down some of this risk. Certainly the data centers

1117
00:51:03,690 --> 00:51:06,509
Speaker 2: don't particularly want this. And also I think one of

1118
00:51:06,530 --> 00:51:08,750
Speaker 2: the keys is if you can actually start taking out

1119
00:51:09,230 --> 00:51:12,310
Speaker 2: some of these risks, it makes these projects way more financeable, right?

1120
00:51:12,370 --> 00:51:13,910
Speaker 2: Like what we've heard from some of the parties is

1121
00:51:13,989 --> 00:51:17,570
Speaker 2: actually if we can get the right product, we actually

1122
00:51:17,610 --> 00:51:20,310
Speaker 2: wind up getting paid to take off risk, right? Because

1123
00:51:20,610 --> 00:51:23,450
Speaker 2: they get a better financing rate from the banks. Ultimately

1124
00:51:23,810 --> 00:51:26,550
Speaker 2: the banks would be willing to do more of these projects.

1125
00:51:26,630 --> 00:51:29,069
Speaker 2: Like there's a lot of efficiency that ultimately just comes

1126
00:51:29,110 --> 00:51:31,750
Speaker 2: from it. And we've been told legitimately like, If you

1127
00:51:31,770 --> 00:51:33,690
Speaker 2: can get this to work at this right price, it

1128
00:51:33,890 --> 00:51:36,529
Speaker 2: actually will save us money. It's not just that we're

1129
00:51:36,570 --> 00:51:39,239
Speaker 2: taking a risk off the table. So that's where things

1130
00:51:39,320 --> 00:51:41,739
Speaker 2: really start to change is if you can start to

1131
00:51:41,760 --> 00:51:44,660
Speaker 2: sort of create this integrated system where like the banks

1132
00:51:44,700 --> 00:51:46,810
Speaker 2: are saying like, okay, this is what you do. Like

1133
00:51:46,830 --> 00:51:48,669
Speaker 2: they do with other forms of insurance. Like you want

1134
00:51:48,710 --> 00:51:51,450
Speaker 2: this loan, you need to have this product that's part

1135
00:51:51,489 --> 00:51:54,190
Speaker 2: of it. And everyone's trying to figure out data centers

1136
00:51:54,210 --> 00:51:57,050
Speaker 2: right now and in AI because you went from nothing

1137
00:51:57,390 --> 00:52:01,190
Speaker 2: to this incredible amount of dollars that's in there. And

1138
00:52:01,570 --> 00:52:03,549
Speaker 2: it's all you hear about in financial circles and it

1139
00:52:03,650 --> 00:52:05,550
Speaker 2: moves the markets more than any other vector.

1140
00:52:05,690 --> 00:52:08,130
Speaker 1: I tend to think that like, If the need is

1141
00:52:08,170 --> 00:52:12,630
Speaker 1: to bear $ 10 trillion of Frontier Lab and hyperscaler credit risk,

1142
00:52:12,690 --> 00:52:14,590
Speaker 1: that's like– it's like it'd be Susquehanna.

1143
00:52:15,070 --> 00:52:17,839
Speaker 2: No, it's not. It is not going to be us

1144
00:52:17,910 --> 00:52:19,900
Speaker 2: that ultimately is going to take all that risk. It's

1145
00:52:19,920 --> 00:52:22,060
Speaker 2: going to be– It's going to be us that bootstraps it, right?

1146
00:52:22,080 --> 00:52:23,760
Speaker 2: We need to get the proof of concept.

1147
00:52:23,900 --> 00:52:27,279
Speaker 1: You're going to run a CDS desk and like– I'm

1148
00:52:27,300 --> 00:52:31,580
Speaker 1: just going to say AIG, but like some insurance company is.

1149
00:52:31,540 --> 00:52:34,330
Speaker 2: Going to own– That's absolutely what we'd like. We like

1150
00:52:34,350 --> 00:52:36,290
Speaker 2: taking risk. We'd like to take some of the risk,

1151
00:52:36,330 --> 00:52:38,390
Speaker 2: but what we'd really like to do is to build

1152
00:52:38,450 --> 00:52:41,129
Speaker 2: that market in an efficient way. So we become this

1153
00:52:41,190 --> 00:52:43,750
Speaker 2: cog that helps that market to function efficiently, which is

1154
00:52:43,830 --> 00:52:46,129
Speaker 2: our role, right? We take on risk, but we help

1155
00:52:46,150 --> 00:52:49,189
Speaker 2: the market to function efficiently. And ultimately, yeah, we're working

1156
00:52:49,230 --> 00:52:51,670
Speaker 2: with an AIG or we're working with whichever insurers and

1157
00:52:51,910 --> 00:52:54,739
Speaker 2: other sort of like mechanisms that there are to distribute

1158
00:52:54,780 --> 00:52:57,009
Speaker 2: that risk, right? Like It's funny because a lot of

1159
00:52:57,030 --> 00:52:58,770
Speaker 2: the things in prediction, we're just like, yeah, we'll warehouse

1160
00:52:58,810 --> 00:53:00,750
Speaker 2: it because we're big enough that like most of the

1161
00:53:00,770 --> 00:53:02,509
Speaker 2: prediction things, like we can just be like, yeah, we'll

1162
00:53:02,550 --> 00:53:06,620
Speaker 2: take it, whatever. It's millions of dollars. You're big for

1163
00:53:06,660 --> 00:53:07,239
Speaker 2: a goat herder.

1164
00:53:07,460 --> 00:53:08,820
Speaker 1: We're big for a goat herder. You're big for a

1165
00:53:08,820 --> 00:53:09,879
Speaker 1: soccer relegation. Right.

1166
00:53:09,920 --> 00:53:12,410
Speaker 2: But we're small for the entirety of data center. So

1167
00:53:12,750 --> 00:53:15,250
Speaker 2: we can take down trades that are large, but like

1168
00:53:15,510 --> 00:53:18,790
Speaker 2: I suppose- Gives you a little humility when you're used

1169
00:53:18,830 --> 00:53:23,549
Speaker 2: to feeling like you're the big player until you get

1170
00:53:23,590 --> 00:53:26,900
Speaker 2: involved in data centers. Like, oh, we're looking for $ 10 billion. Like, okay,

1171
00:53:27,040 --> 00:53:27,859
Speaker 2: let's think about how.

1172
00:53:27,820 --> 00:53:30,239
Speaker 1: To do this. Jeremy Mallitz, thank you so much for

1173
00:53:30,260 --> 00:53:30,590
Speaker 1: joining us.

1174
00:53:31,080 --> 00:53:32,700
Speaker 2: Thank you for having me here. It's been very fun.

1175
00:53:38,420 --> 00:53:40,620
Speaker 1: And that was the Money Stuff Podcast. I'm Matt Levine.

1176
00:53:41,010 --> 00:53:42,910
Speaker 1: You can find my work by subscribing to the Money

1177
00:53:42,930 --> 00:53:45,770
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1178
00:53:46,010 --> 00:53:49,430
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1179
00:53:49,469 --> 00:53:51,310
Speaker 1: us a question and we might answer it on the air.

1180
00:53:52,090 --> 00:53:54,570
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1181
00:53:54,610 --> 00:53:57,000
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1182
00:53:57,020 --> 00:54:00,040
Speaker 1: people find the show. The Money Stuff Podcast is produced

1183
00:54:00,080 --> 00:54:03,600
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1184
00:54:03,640 --> 00:54:07,480
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1185
00:54:07,820 --> 00:54:11,279
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1186
00:54:11,320 --> 00:54:14,009
Speaker 1: listening to the Money Stuff Podcast. We'll be back next

1187
00:54:14,030 --> 00:54:15,049
Speaker 1: week with more stuff.