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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to

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Speaker 1: another episode of the Odd Lots Podcast.

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Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 1: Tracy, can I reveal a very bloomer take of mine,

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Speaker 1: so to speak?

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Speaker 3: Go on?

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Speaker 1: I actually really loathe anything that's sort of like identify

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Speaker 1: as I get rich quick, or the gamification of stock

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Speaker 1: trading or gamification of stock trading.

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Speaker 2: Ramification would be something different.

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Speaker 4: That would probably be something different. I don't know why

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Speaker 4: I pronounced it that way.

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Speaker 1: Gamification of stock trading, gambling ads. I really don't like

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Speaker 1: gambling ads that imply they are going to be winner.

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Speaker 1: And I really don't like, even though I'm very interested

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Speaker 1: in prediction market, I generally don't like, you know, a

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Speaker 1: lot of the marketing. There was a really good Wallshet

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Speaker 1: Journal article about polymarket and these ads that they had

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Speaker 1: created this idea that everyone's a winner and there's all

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Speaker 1: this free money out there. You just have to trade

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Speaker 1: your knowledge of whatever rainfall or whatever.

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Speaker 5: Like.

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Speaker 4: I really like it. Really viscerally bothers me.

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Speaker 2: Yeah. I think this is the key difference with prediction

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Speaker 2: markets is it's zero sum, right, if you make a bet,

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Speaker 2: someone is on the other side taking the opposite of

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Speaker 2: that bet, and that feels a little different to me

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Speaker 2: than like traditional stock markets, where you know, you would

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Speaker 2: get some cash flow, some dividend, sell monetize whatever they got,

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Speaker 2: and someone else buys.

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Speaker 1: Like no, I totally agree. I mean, look, there are

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Speaker 1: certainly zero some There are all kinds of zero some

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Speaker 1: markets in what we'd call trad five futures are zero

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Speaker 1: some options or zero some, et cetera. But on the

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Speaker 1: other hand, like but yes, zero some games. Even if

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Speaker 1: we're talking about like options or futures or swaps or whatever,

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Speaker 1: that's not investing, that's trading, and that can often be speculation,

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Speaker 1: and a lot of people lose a lot of money.

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Speaker 1: And when we know, in a lot of these environments,

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Speaker 1: a lot of like most people lose. Some people are

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Speaker 1: very sophisticated, but a lot of people think they're going

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Speaker 1: to go into some of these new markets or whatever,

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Speaker 1: whether we're talking about prediction markets, whether we're talking about

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Speaker 1: options trading on Robinhood and they have dreams making a

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Speaker 1: lot of money, or just talk about crypto, which is

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Speaker 1: also zero sum.

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Speaker 4: I think they don't.

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Speaker 2: Yeah, absolutely. The other interesting thing about prediction markets just

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Speaker 2: from a sort of market structure question, and we've done

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Speaker 2: episodes on this with Susquehanna. Is the liquidity aspect and

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Speaker 2: as more professional investors get in, does that start to

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Speaker 2: maybe arbitrage some of the edge that certain traders have

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Speaker 2: seen so far? Because you know, you read these stories

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Speaker 2: specific story the average guys out smarting Wall Street on

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Speaker 2: prediction markets. Like average guys, I have a question over

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Speaker 2: whether or not they're actually average, but you can imagine

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Speaker 2: a scenario where more professionals get into prediction markets and

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Speaker 2: maybe it becomes harder to actually beat them.

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Speaker 1: Absolutely, I think everyone should actually go back and listen

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Speaker 1: to our episode that we did with Jeremy Mallach, who

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Speaker 1: runs the prediction market's desk over at Susquehanna before listening

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Speaker 1: to this, because the other thing he talked about is

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Speaker 1: like how even some of these low liquidity markets, they

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Speaker 1: put a lot of stock in the price and therefore

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Speaker 1: they can feel comfortable doing OTC transactions based on.

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Speaker 4: What the price.

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Speaker 1: So there's interesting stuff going on.

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Speaker 4: Anyway, you mentioned this.

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Speaker 1: Article came out in the New York Times May twenty six,

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Speaker 1: the average Guys out smarting Wall Street on prediction markets,

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Speaker 1: and it is important. There are some people who are

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Speaker 1: doing very well, and of like a handful of they

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Speaker 1: they would say sharks in various like other contexts, who

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Speaker 1: are like sharks, very good. Most of us are minnos

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Speaker 1: and some people do really well and.

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Speaker 2: The vast majority are total idiots.

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Speaker 4: Yeah.

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Speaker 1: Yeah, if I went on there, I would quickly lose

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Speaker 1: one thousand dollars and some fraction of it would go

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Speaker 1: to the house, and a bunch of it would go

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Speaker 1: to some of the best traders, most likely, especially because

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Speaker 1: it's peer Anyway, we have a very complicated special episode.

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Speaker 1: I don't even know how this is gonna work, but

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Speaker 1: I thought it'd be fun. So this was a great piece.

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Speaker 1: We have the reporter on the piece with us, as

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Speaker 1: well as a couple of the sharps. They are in

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Speaker 1: a discord together, a bunch of them. We're just gonna

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Speaker 1: be talking with two of them. The discord is called

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Speaker 1: the Maga Kiwi Club. That sounds fun, so we're gonna

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Speaker 1: be talking with Adam the journalist as well as some

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Speaker 1: of the members of the Maga Kiwi Club. We have

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Speaker 1: Brian Golden, who is in the article who was identified

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Speaker 1: as someone who's really top notch and trading inflation contracts

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Speaker 1: with Daniel Reichman, who also goes by Carnita's Taco, who

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Speaker 1: does a lot of election and politics. And of course

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Speaker 1: we have Adam here who is a journalist, reporter and

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Speaker 1: producer here in NYC recently joined the tech company Notion

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Speaker 1: which all journalists might end up working at tech company.

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Speaker 2: Well we do, so we don't get to talk to

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Speaker 2: a carneitis taco not often on the podcast.

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Speaker 1: I'm very excited about this episode. Thank all three of you, Brian, Daniel,

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Speaker 1: and Adam for joining us on Outlove.

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Speaker 4: It's great to be It's a pleasure.

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Speaker 1: Let's just start, Adam. You know, I guess actually we're

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Speaker 1: sort of trusting you. We've sort of delegated some of

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Speaker 1: our judgments. But you, you know, you reported out this story,

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Speaker 1: Like how did you like identify that there exist people

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Speaker 1: in the world who consistently win on prediction market?

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Speaker 6: Yeah?

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Speaker 5: I mean I think it started for me. I got

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Speaker 5: very interested in the in these markets because I was

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Speaker 5: playing around in them, right, yeah, and I was losing

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Speaker 5: a lot of money, you know, not a lot a

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Speaker 5: lot of money, but for me, it was a lot

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Speaker 5: of money, and I was wondering, who the heck am

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Speaker 5: I losing this money to? Am I losing to market makers?

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Speaker 5: Am I losing to you know, the SIGs of the world.

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Speaker 5: And it occurred to me maybe that maybe there are

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Speaker 5: some people who are just a little bit sharper than

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Speaker 5: me who are out there. And I started asking around,

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Speaker 5: and I sort of started, you know, looking around on

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Speaker 5: you know, just started you know, just just asking around basically,

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Speaker 5: and one person sort of leaves the next, and suddenly

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Speaker 5: you find yourself in a discord with a lot of

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Speaker 5: guys who are just really crushing it sharper than anybody else.

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Speaker 5: And you know, two of those folks are.

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Speaker 4: Brian and Daniel.

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Speaker 2: Well, Brian and Daniel. Tell us about the Discord group, because,

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Speaker 2: from what I can tell from the article, you know,

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Speaker 2: a bunch of prediction market traders get together, share information,

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Speaker 2: maybe you share some actual p and l. It kind

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Speaker 2: of sounds like a multistrat it's fun where everyone's like

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Speaker 2: in charge.

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Speaker 3: Of their own portfolio.

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Speaker 2: But tell us about the Discord Group.

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Speaker 7: Yeah, Well, the first thing is the Maga Kiwi club

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Speaker 7: name is ironic. It's not a Maga club, but most

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Speaker 7: of the names of the group end up being some

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Speaker 7: form of inside joke that emerges during the conversation because

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Speaker 7: we spend almost all day every day talking to each

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Speaker 7: other about markets, ranging from economics to culture to.

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Speaker 8: Sports, and it is sort of like that.

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Speaker 7: I would describe it almost as an Avengers model, where

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Speaker 7: there are different people in the group that have different

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Speaker 7: specialties and everyone has their own portfolio, but there is

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Speaker 7: kind of a spirit of community in the way that

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Speaker 7: information is exchanged, in that I know that if I

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Speaker 7: am giving helpful tips to Daniel and others on inflation,

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Speaker 7: that they're going to give me helpful tips back on

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Speaker 7: things that I may be adequate at but not expert at,

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Speaker 7: and we sort of work together in that way.

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Speaker 6: Yeah, so a lot of us actually know each other,

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Speaker 6: dating back all the way really to twenty sixteen twenty

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Speaker 6: seventeen when predicted was the dominant prediction market, and we

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Speaker 6: mostly know each other through politics originally because that was

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Speaker 6: the prediction market game. And you know, pretty much all

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Speaker 6: of us had some interest in politics or very large

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Speaker 6: interest in politics to start with.

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Speaker 8: And the prediction.

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Speaker 6: Market space has grown very large, and you know, the

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Speaker 6: money has gotten larger, but a lot of us would

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Speaker 6: be doing a lot of this stuff anyway, would be

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Speaker 6: unpacking elections, trying to find the truth of things, trying

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Speaker 6: to you know, solve puzzles together. And it just so

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Speaker 6: happens that the space has grown large around us. But

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Speaker 6: we're kind of doing the same thing we've always been doing.

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Speaker 5: And the way I found myself, you know, talking to

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Speaker 5: Brian and Daniel was, you know, I met another trader

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Speaker 5: who who loved elections. You know, this is sort of

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Speaker 5: where where the journey began for me, and you know,

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Speaker 5: we were just talking about elections, and you know, it

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Speaker 5: was after maybe six weeks of talking and source building,

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Speaker 5: they finally he says, oh, yeah, I'm actually talking to

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Speaker 5: all of these other people. I'm not just doing this alone.

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Speaker 5: I'm not just building models alone in my basement. You know,

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Speaker 5: I'm like, I'm talking to all of all my colleagues. Really,

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Speaker 5: And I said, colleagues, you're working at a fund No no, no, no, no,

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Speaker 5: not quite. We're actually all on a discord together.

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Speaker 1: Oh yeah, I have to say it does something pretty fun. Well,

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Speaker 1: let's just between us. Is it one hundred percent mail?

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Speaker 4: This one is?

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Speaker 1: Yes, yes, Okay, I just sort of sort of I

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Speaker 1: sort of assumed such, let me ask you guys a question,

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Speaker 1: like I am like an emh bro, I think that generally,

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Speaker 1: across all markets, most people like markets are generally well priced.

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Speaker 1: And I think I think that with prediction markets too,

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Speaker 1: in the specific sense that like I think it is

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Speaker 1: not it is difficult to make money. But evidently in stocks,

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Speaker 1: even though I think stocks are efficiently priced, some people

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Speaker 1: seem to consistently make money in stocks, whether they're a

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Speaker 1: Warren Buffett strategy or whether they work it along short

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Speaker 1: and evidently some people will consistently make money in prediction markets.

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Speaker 1: How do you guys think about market efficiency generally and

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Speaker 1: how high quality that signal is on a price? And

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Speaker 1: like would you say, like those of us in the

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Speaker 1: media who sometimes increasingly quote prediction market quotes like these

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Speaker 1: are do you find are they decent or when you

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Speaker 1: look at them.

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Speaker 4: Or they's like, oh, there's just easy pickings everywhere.

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Speaker 7: Well, I think if they were all efficiently priced, you

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Speaker 7: wouldn't be able to get some of the returns that

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Speaker 7: some people in this group have gotten. I think it

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Speaker 7: would be much harder to enter and sort of crush

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Speaker 7: markets repeatedly if the price signal were better. I guess

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Speaker 7: where I see it is that there is a lot

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Speaker 7: so who I would call kind of prediction market evangelists

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Speaker 7: would like to go out and say, oh, prediction markets

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Speaker 7: are the future. They're this incredible price signal, and you know,

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Speaker 7: they're more accurate than all these other places. I don't

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Speaker 7: really believe that. I've just seen too many markets that

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Speaker 7: are way way off. The reason that I think prediction

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Speaker 7: markets have value is because there are consequences when you're wrong,

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Speaker 7: and there are consequences when you're right. And we have

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Speaker 7: so much kind of expertise in the world that says

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Speaker 7: a lot of things under the expert banner, but then

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Speaker 7: doesn't really have any bills to pay when they mislead

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Speaker 7: people or when they're wrong. And so really the most

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Speaker 7: appealing part of this ecosystem for me is that when

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Speaker 7: you're wrong, you pay a price. And I think that

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Speaker 7: we might have a better expert culture in this country

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Speaker 7: if that were true in media that covers economics and politics.

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Speaker 6: It depends on the market certainly in terms of you know,

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Speaker 6: how correct these prices are going to be. But you know,

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Speaker 6: going back to what Joe was talking about in the

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Speaker 6: intro about the zero sum nature of these prediction markets,

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Speaker 6: a lot of the events, most of the events that

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Speaker 6: we're betting on our zero sum. You know, we have

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Speaker 6: an election and two campaigns spend a ton of money,

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Speaker 6: and then one wins and one goes home. And the

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Speaker 6: nature of politics in this country especially, but all over

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Speaker 6: the world is that people live in totally silent environments

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Speaker 6: and believe different things and often engage with reality in

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Speaker 6: totally opposite ways. And having a mechanism to, you know,

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Speaker 6: essentially bet your beliefs and try to find real truth

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Speaker 6: is you know, it's not going to be a perfectly

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Speaker 6: efficient way, but at the moment it seems to be

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Speaker 6: better than at least anything else we've got.

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Speaker 7: Well, I think to Daniel's point, you know, unlike inflation,

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Speaker 7: politics brings out matters of the heart and is I

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Speaker 7: think much more subjected to that siloed effect. I mean,

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Speaker 7: you can look back at the Los Angeles mayor primary

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Speaker 7: that just happened, and Spencer Pratt price to not just

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Speaker 7: make the top two, but to actually win the mayorship

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Speaker 7: of Los Angeles got so unbelievably high, and there was

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Speaker 7: this right wing media ecosystem and sometimes even a mainstream

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Speaker 7: media place that was like sort of flirting with this

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Speaker 7: idea like can this happen? Everyone I know is saying

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Speaker 7: that this is live And there wasn't a sharp that

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Speaker 7: I knew that didn't have one of the biggest positions

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Speaker 7: of their lives on Spencer Pratt not winning the mayorship

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Speaker 7: of Los Angeles because the math was just not there

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Speaker 7: to be mathing. Los Angeles is a Democrat plus forty

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Speaker 7: two city. A Republican is not going to win that race.

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Speaker 7: But this sort of siloed media of people sort of

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Speaker 7: only following certain accounts on Twitter and watching certain media

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Speaker 7: can lead to what feels like to them an abundance

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Speaker 7: of evidence in a certain thing happening. And in that sense,

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Speaker 7: I think that elections will always be some of the

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Speaker 7: most mispriced markets, because you know, people don't really have

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Speaker 7: a heart connection to gosh, I really believe I want

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Speaker 7: inflation to be three point six instead of three point eight.

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Speaker 7: I do think that the future of economics markets is

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Speaker 7: probably a tightening, But people like Daniel are fortunate because

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Speaker 7: I think elections will always be a little softer.

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Speaker 2: This was my problem in the last election. I spent

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Speaker 2: way too much time on Reddit, and so I thought

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Speaker 2: Harris was going to win, and then I was very

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Speaker 2: surprised just to press on this further. When we talk

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Speaker 2: about you guys having an edge in this market. You

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Speaker 2: know a lot of people will say a lot of

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Speaker 2: investors will say that they have an edge, and usually

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Speaker 2: it's like they got lucky a few times and then

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Speaker 2: they built a whole narrative around it. But how would

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Speaker 2: you describe what you are doing differently to so you mentioned,

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Speaker 2: you know, looking rationally at the numbers, keeping feelings out

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Speaker 2: of it, social media echo chambers, that sort of thing.

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Speaker 2: But you're also doing some original on the ground reporting.

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Speaker 2: You have your own models. What is the edge exactly?

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Speaker 6: A lot of it is really just work that we

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Speaker 6: put in. So's it's being open to changing your mind,

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Speaker 6: trying to quantify and test your assumptions with politics, it's

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Speaker 6: a lot of history. I mean, it's just knowing this

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Speaker 6: has happened before, this is the trend, we think the

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Speaker 6: trend might be, you know, this big this time when

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Speaker 6: it was only you know, half as big last time,

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Speaker 6: and then seeing numbers come in and trying to stay

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Speaker 6: calibrated with your assumptions. When you win an election or

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Speaker 6: win a bet, you don't just say yay, I won,

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Speaker 6: You say, how much did I win? You know, what

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Speaker 6: was my belief in the probability, just like really digging

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Speaker 6: in and unpacking it. And then as the money has

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Speaker 6: gotten bigger recently, we've started trying to learn more. We

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Speaker 6: tried to do some polling and the Texas Primary commissioner

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Speaker 6: own phone polls. Some of the guys in the group

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Speaker 6: have done this live door to door polling, which we

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Speaker 6: did a bigger trip with Adam that's written about in

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Speaker 6: the article. But it's mostly just work and openness to

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Speaker 6: data and changing your mind.

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Speaker 7: I think Daniel's even under selling how good the elections

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Speaker 7: team is in our group at elections and what they do.

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Speaker 7: I mean, I can tell you for the Los Angeles

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Speaker 7: mayor's race, these guys had a model built of on

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Speaker 7: election night what certain areas should look like in terms

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Speaker 7: of the early vote, that in person vote, what it

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Speaker 7: would take for Pratt to have what he needed. You know,

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Speaker 7: what we know from California is that so much vote

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Speaker 7: comes in late that really predicting what that vote is

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Speaker 7: going to look like. So these guys did historical work

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Speaker 7: by precinct and region, and on election night when Nitya

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Speaker 7: Rahman was in tears speaking to her support because she

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Speaker 7: thought she lost Daniel and the crew were betting on

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Speaker 7: her to make it to the top two because they

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Speaker 7: had a better vibe on her chances than I think

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Speaker 7: her actual campaign did. So I really I think he's

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Speaker 7: It would be hard actually to over sell how good

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Speaker 7: this election's crew is with data and not just needing

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Speaker 7: days to solve it, but being able to really solve

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Speaker 7: on the fly based on incoming numbers.

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Speaker 1: Even though, like my assumption was that prices are somewhat efficient,

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Speaker 1: I do notice that on election nights there's a lot

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Speaker 1: of noise based on timing of vote batches with multiple elections.

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Speaker 1: We even saw it in the recent Canadian Prime minister election,

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Speaker 1: for example, where there was a splike for qualif on

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Speaker 1: election because of some early votes that were clearly not representative.

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Speaker 1: So that always does make me sort of question whether

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Speaker 1: I should ever be quoting these things. But Brian in

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Speaker 1: Adam's article, and maybe Adam you can talk about this.

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Speaker 1: Your inflation models are described by some economist as quote

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Speaker 1: being like, no stradomis of inflation. Why give us a

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Speaker 1: general overview of what you're doing and explain to us

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Speaker 1: why you are not working at a I think Matt

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Speaker 1: Levine talked about this in whre's newsletter. It doesn't make

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Speaker 1: sense if you're like an inflation phos damas, why aren't

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Speaker 1: you managing a you know, multi billion dollar rate hedge

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Speaker 1: fund or something.

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Speaker 8: Well, to be clear, nobody's called.

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Speaker 7: But okay, Well, the first thing that I did was

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Speaker 7: the BLS has a formula to the way that they

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Speaker 7: take all of their price inputs to calculate the inflation number.

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Speaker 7: As I know your audience knows, but just to set

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Speaker 7: the table, it's not one big number. It's two hundred

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Speaker 7: plus subcategories of how prices have moved, which we call

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Speaker 7: the basket of goods, and the basket of goods changes

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Speaker 7: what percenta each subcategory is worth every month based on

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Speaker 7: how much Americans are spending on it. So this formula

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Speaker 7: to me, I mean, look my degree, I have an

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Speaker 7: undergraduate degree in drama. I'm just a theater kid from

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Speaker 7: the Midwest. Like, this is very complicated. I'm sure that

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Speaker 7: I know less about the macroeconomy than any guest you've

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Speaker 7: ever had. But I rebuilt their formula on Excel. It

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Speaker 7: took me like three months to figure out exactly how

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Speaker 7: they math the formula, and a lot of really helpful

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Speaker 7: public servants at the BLS answered my questions about just

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Speaker 7: how the math works, and then you go from there

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Speaker 7: to predicting the prices. There are some price categories that

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Speaker 7: have public data, like gas and natural gas and sometimes cars,

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Speaker 7: but most of it is just trend work and guessing

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Speaker 7: and looking at the last six months and sort of

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Speaker 7: where prices seem to be heading.

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Speaker 8: But the real thing for me is I just think

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Speaker 8: that this.

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Speaker 7: Says a lot more of about the sort of softness

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Speaker 7: of the people, the investment banks and people who predict

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Speaker 7: this in the institutional end than it does about me,

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Speaker 7: because when you predict inflation, it's not even a prediction,

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Speaker 7: it's in the past.

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Speaker 8: It's data that.

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Speaker 7: Has already happened. And it has been very surprising to

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Speaker 7: me since I entered this space that the places who

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Speaker 7: advise billions of dollars of capital with their inflation forecasts

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Speaker 7: aren't better at this.

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Speaker 2: Well, this is what I wanted to ask, because, by

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Speaker 2: the way, BLS employees very helpful, very helpful. You actually

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Speaker 2: call them on the phone, they will like walk you

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Speaker 2: through stuff. But on that note, when you describe just

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Speaker 2: like you know, working out the BLS formula, calling some

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Speaker 2: people up and asking them how it all works. Why

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Speaker 2: aren't more people doing this, either in the prediction market

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Speaker 2: or in traditional finance.

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Speaker 8: I mean, you'd have to ask them.

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Speaker 7: You know, it's shocking to me when and you know,

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Speaker 7: it just really shouldn't be the case that my average

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Speaker 7: absolute error on predicting inflation is better over the last

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Speaker 7: two years than the Bloomberg consensus. I'm just one guy

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Speaker 7: with Excel, and they are have pretty much unlimited resources

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Speaker 7: to find this data. It's you know, not just shocking

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Speaker 7: from a kind of what are they doing and why

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Speaker 7: aren't they trying harder? But I think we find in

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Speaker 7: both economics and elections that expert forecasts really shape expectations

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Speaker 7: and that can play a big role in narrative creation

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Speaker 7: and how the public response to that. I mean, I've

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Speaker 7: seen many times the headline on Bloomberg twenty minutes before

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Speaker 7: the inflation number will say inflation to show blank, as

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Speaker 7: if it's a foregone conclusion because Goldman and JP Morgan

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Speaker 7: and Bank of America have said so, And then when

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Speaker 7: the inflation number is wrong, they just change the headline

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Speaker 7: like that never happened. And the market reacts to it

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Speaker 7: either being above or below that expectation, which maybe wasn't

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Speaker 7: that good of an expectation in the first place. So

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Speaker 7: I don't I don't know why they're not trying harder.

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Speaker 7: I mean, they certainly have more resources to chase down

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Speaker 7: these numbers than I do.

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Speaker 1: For what it's worth, Like, the best inflation people we

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Speaker 1: know all did it the same way you did, Brian,

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Speaker 1: in terms of like, actually the bottom is up approach

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Speaker 1: to learning in the formula, like all the best inflation

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Speaker 1: and of course, like someone like a marys Reef comes

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Speaker 1: to mind, Yeah, he's a handful of people who like, really, yeah.

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Speaker 5: And this is what I mean, This is what not

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Speaker 5: just Brian, but all of the sharps that I talked to,

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Speaker 5: whether they're doing inflation, or they're doing elections, or they're

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Speaker 5: doing meteorology. How much is it going to rain next

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Speaker 5: weekend in New York City whatever, they're all just making

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Speaker 5: calls and going on the ground and talking to people

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Speaker 5: and gathering so much data. They're calling meteorologists, they're calling geophysicists,

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Speaker 5: they're calling you know, Bloomberg reporters. In many instances, you know,

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Speaker 5: I talked a few sharps like, yeah, I'm on the

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Speaker 5: phone with Bloomberg reporters all the time. But you know,

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Speaker 5: I think these a lot of these sharps are just

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Speaker 5: you know, they're constantly on the fun gathering information, which

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Speaker 5: makes sense, right. This is also what you do if

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Speaker 5: you're at a fund.

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Speaker 4: Yeah.

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Speaker 1: Can I ask this is more of like a sort

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Speaker 1: of I don't know of a tactical question or market

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Speaker 1: structure question. Maybe it's two interrelated things. So sometimes an

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Speaker 1: event happens and then there is a period of time

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Speaker 1: before it results.

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Speaker 4: And Kelshy and.

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Speaker 1: Polymarket have different approaches to resolution. Polymarket is based on

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Speaker 1: a sort of third party quote oracle end quote that

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Speaker 1: is also sort of like whatever, and then Kelshi is

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Speaker 1: more centralized. And I'm curious, like what your trading philosophy is.

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Speaker 1: It's like, Okay, you get the LA mayorship, right, do

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Speaker 1: you wait until resolution or do you sell it when

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Speaker 1: it hits ninety nine percent and then move on to

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Speaker 1: the next big thing? And is there alpha or profits

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Speaker 1: to be gained in holding from the ninety nine to

439
00:22:58,600 --> 00:23:02,119
Speaker 1: one hundred during the period of resolution? And is it

440
00:23:02,160 --> 00:23:03,680
Speaker 1: different on either of the two sides.

441
00:23:04,359 --> 00:23:06,320
Speaker 7: I think it depends how much time it is and

442
00:23:06,400 --> 00:23:09,119
Speaker 7: what other plays are available. I mean, usually you know,

443
00:23:09,160 --> 00:23:11,439
Speaker 7: if if a market is going to resolve in the

444
00:23:11,440 --> 00:23:14,560
Speaker 7: next forty eight hours, I mean, you're pretty much always

445
00:23:14,560 --> 00:23:16,520
Speaker 7: going to hold that from ninety nine to one hundred.

446
00:23:16,600 --> 00:23:20,159
Speaker 7: But there are certainly elections that the answer is clear,

447
00:23:20,560 --> 00:23:23,080
Speaker 7: and then you know you're not getting that payout for

448
00:23:23,119 --> 00:23:26,000
Speaker 7: a month. And I mean usually I can turn ninety

449
00:23:26,080 --> 00:23:29,280
Speaker 7: nine cents into a dollar over a month faster doing

450
00:23:29,320 --> 00:23:33,480
Speaker 7: something else than waiting. But I suppose it depends on

451
00:23:33,520 --> 00:23:35,919
Speaker 7: the context. I mean, I don't know, Daniel might have

452
00:23:35,960 --> 00:23:36,760
Speaker 7: a different approach.

453
00:23:36,920 --> 00:23:40,040
Speaker 6: It's really important to know the difference between is your

454
00:23:40,040 --> 00:23:42,439
Speaker 6: market ninety eight or ninety nine just because of the

455
00:23:42,480 --> 00:23:44,800
Speaker 6: time it's going to take to resolve, or is there

456
00:23:44,840 --> 00:23:48,040
Speaker 6: a one or two percent chance of it actually going

457
00:23:48,040 --> 00:23:51,480
Speaker 6: the other way. I try really hard to avoid these

458
00:23:52,200 --> 00:23:55,960
Speaker 6: rules disputes and these you know, thorny markets where it

459
00:23:56,119 --> 00:23:59,760
Speaker 6: ends up kind of a debate. I think polymarkets system

460
00:23:59,800 --> 00:24:03,480
Speaker 6: is problematic. They have basically undermined their oracle and now

461
00:24:03,560 --> 00:24:09,280
Speaker 6: every market just settles on how Polymarket clarifies. I have generally,

462
00:24:09,320 --> 00:24:11,960
Speaker 6: for the most part, been happy with Calshi's resolutions, but

463
00:24:12,480 --> 00:24:14,480
Speaker 6: when you trade in stuff like will a cabinet member

464
00:24:14,560 --> 00:24:17,960
Speaker 6: get confirmed or who wins an election, it is almost

465
00:24:18,160 --> 00:24:22,240
Speaker 6: never up for debate, you know, twenty twenty notwithstanding you

466
00:24:22,320 --> 00:24:25,080
Speaker 6: have clear answers, and so it just depends. I'm holding

467
00:24:25,119 --> 00:24:27,399
Speaker 6: the Peruvian election right now from ninety nine two one

468
00:24:27,480 --> 00:24:29,639
Speaker 6: hundred because I don't have anywhere else to put the money.

469
00:24:30,119 --> 00:24:32,280
Speaker 6: But if I had another play that I liked and

470
00:24:32,320 --> 00:24:34,280
Speaker 6: I wanted the money freight up, I would sell it

471
00:24:34,280 --> 00:24:37,000
Speaker 6: in a second, and you know, put it in whatever

472
00:24:37,119 --> 00:24:37,800
Speaker 6: else there was.

473
00:24:38,119 --> 00:24:39,719
Speaker 7: Let's talk when we get off the call, I got

474
00:24:39,760 --> 00:24:40,560
Speaker 7: some places for you.

475
00:24:43,720 --> 00:24:47,320
Speaker 2: Since we're talking about probabilities changing, I wanted to bring

476
00:24:47,400 --> 00:24:50,639
Speaker 2: up this. There's a long running debate, I guess about

477
00:24:50,640 --> 00:24:55,280
Speaker 2: whether prediction markets are actually better than traditional polls when

478
00:24:55,320 --> 00:24:58,560
Speaker 2: it comes to forecasting election results, or whether they just

479
00:24:58,760 --> 00:25:02,840
Speaker 2: look better because they were able to update faster as

480
00:25:02,880 --> 00:25:05,800
Speaker 2: the results come in. And Daniel, I'm very I'd be

481
00:25:05,960 --> 00:25:08,640
Speaker 2: very interested in getting your take on this, like, are

482
00:25:08,680 --> 00:25:13,080
Speaker 2: these genuinely producing better probabilities or is it just a

483
00:25:13,119 --> 00:25:13,840
Speaker 2: matter of speed.

484
00:25:14,800 --> 00:25:18,280
Speaker 6: Yes, but it depends on how much money is coming

485
00:25:18,280 --> 00:25:22,400
Speaker 6: into each side. A really noteworthy election, I think recently

486
00:25:22,480 --> 00:25:26,720
Speaker 6: last November was the New Jersey governor's race where the

487
00:25:26,760 --> 00:25:30,040
Speaker 6: polls really all said this was a pretty close race,

488
00:25:30,560 --> 00:25:33,480
Speaker 6: you know, two, three, four or five. One of the

489
00:25:33,520 --> 00:25:36,320
Speaker 6: guys in our channel had made a very simple model,

490
00:25:36,480 --> 00:25:39,080
Speaker 6: not even really a model, had basically just said, you know,

491
00:25:39,160 --> 00:25:42,160
Speaker 6: Kamala won this state by I forget five or six.

492
00:25:43,080 --> 00:25:46,640
Speaker 6: Trump's approval had fallen maybe eight or nine points since then,

493
00:25:46,840 --> 00:25:50,359
Speaker 6: you know, Trump was president instead of Biden, Kamala or

494
00:25:51,160 --> 00:25:53,800
Speaker 6: Cheryl's probably going to win by about fourteen. And this

495
00:25:54,000 --> 00:25:56,359
Speaker 6: was just you know, sketched out on prior's on and

496
00:25:56,480 --> 00:25:59,560
Speaker 6: Upkin very quickly three months before any polls, and then

497
00:25:59,560 --> 00:26:03,320
Speaker 6: all these came in showing this close race. Very very

498
00:26:03,359 --> 00:26:06,919
Speaker 6: few polls showed a big, big margin, and we actually

499
00:26:07,040 --> 00:26:08,800
Speaker 6: we didn't ignore the polls. We kind of tried to

500
00:26:08,840 --> 00:26:12,600
Speaker 6: standardize them all. But we did genuinely believe in our

501
00:26:12,680 --> 00:26:15,680
Speaker 6: channel that this was a twelve thirteen to fourteen point

502
00:26:15,760 --> 00:26:18,480
Speaker 6: race that the polls didn't show at all. Now, we

503
00:26:18,520 --> 00:26:22,040
Speaker 6: couldn't make the prediction market prices reflect that because there

504
00:26:22,119 --> 00:26:24,760
Speaker 6: was so much money on the other side. So you know,

505
00:26:24,840 --> 00:26:27,119
Speaker 6: ultimately the prediction markets I think were higher than the

506
00:26:27,160 --> 00:26:29,639
Speaker 6: polls and pointing to a higher margin, but still well

507
00:26:29,640 --> 00:26:33,080
Speaker 6: below what it would eventually come out to. So you know,

508
00:26:33,320 --> 00:26:36,480
Speaker 6: on an election where the liquidity on the you know,

509
00:26:36,520 --> 00:26:40,159
Speaker 6: the square side is large enough, the prediction market prices

510
00:26:40,560 --> 00:26:42,159
Speaker 6: are still going to be wrong. I mean, you can

511
00:26:42,240 --> 00:26:45,360
Speaker 6: look at Pratt again. You know, Pratt's price was never

512
00:26:45,480 --> 00:26:47,719
Speaker 6: twenty seven to be the mayor. It was, you know,

513
00:26:47,840 --> 00:26:51,200
Speaker 6: less than five, always in truth, but it was also

514
00:26:51,400 --> 00:26:53,680
Speaker 6: still that twenty seven was still much more accurate than

515
00:26:53,680 --> 00:26:55,560
Speaker 6: anybody's bubble Who would have said.

516
00:26:55,480 --> 00:26:58,440
Speaker 1: But actually this brings me to a question. I want

517
00:26:58,480 --> 00:27:00,000
Speaker 1: to go to the bubble question again.

518
00:27:00,160 --> 00:27:00,440
Speaker 8: Aim.

519
00:27:00,480 --> 00:27:02,840
Speaker 1: Obviously your piece was great. There was another great, great

520
00:27:02,880 --> 00:27:06,040
Speaker 1: piece of prediction markets journalism in the last year about

521
00:27:06,080 --> 00:27:09,720
Speaker 1: Alan Cole, who had bet his life savings that Elon

522
00:27:09,800 --> 00:27:12,639
Speaker 1: Musk wouldn't actually reduce the deficit very much, and he

523
00:27:12,800 --> 00:27:14,960
Speaker 1: like knows the deficit very well and he's like, this

524
00:27:15,040 --> 00:27:17,000
Speaker 1: is never going to happen. And the money quote in

525
00:27:17,040 --> 00:27:19,480
Speaker 1: that article is from Ellen's wife who said, I read

526
00:27:19,520 --> 00:27:22,479
Speaker 1: through the comment section in the Prediction Markets and they

527
00:27:22,480 --> 00:27:25,320
Speaker 1: all seem like idiots, at least relative to her husband.

528
00:27:25,800 --> 00:27:28,280
Speaker 1: And therefore I was very comfortable with him risking all

529
00:27:28,320 --> 00:27:30,840
Speaker 1: of our family life savings on this one particular.

530
00:27:30,880 --> 00:27:31,160
Speaker 4: Bet.

531
00:27:31,480 --> 00:27:35,120
Speaker 1: I'm curious, like if you like, okay, when maybe there's

532
00:27:35,160 --> 00:27:37,919
Speaker 1: sort of bubble mentality emerging, or it's like this is

533
00:27:37,960 --> 00:27:42,320
Speaker 1: a price that reflects people not getting good information. How

534
00:27:42,440 --> 00:27:46,560
Speaker 1: often in your group can you use the comment sections

535
00:27:46,600 --> 00:27:48,639
Speaker 1: to gauge like, wow, there's a lot of dumb money

536
00:27:48,640 --> 00:27:49,400
Speaker 1: on this contract.

537
00:27:50,119 --> 00:27:54,320
Speaker 7: Always bet against the comment section, okay, real? I mean

538
00:27:54,480 --> 00:27:55,360
Speaker 7: not always, but.

539
00:27:57,040 --> 00:27:57,760
Speaker 8: It tends to be.

540
00:27:57,800 --> 00:28:02,879
Speaker 7: It tends to be a guiding principle because sharps tend

541
00:28:02,880 --> 00:28:05,600
Speaker 7: to keep their mouths shut except when talking to each other,

542
00:28:06,240 --> 00:28:11,880
Speaker 7: and generally the more ideas or comments there are sort

543
00:28:11,920 --> 00:28:15,800
Speaker 7: of advocating for one side. I do think that tends

544
00:28:15,840 --> 00:28:16,840
Speaker 7: to be the wrong side.

545
00:28:17,000 --> 00:28:18,560
Speaker 8: Yeah, I was going to.

546
00:28:18,560 --> 00:28:22,800
Speaker 6: Say it was surprising how or is surprising how reliable

547
00:28:22,880 --> 00:28:26,440
Speaker 6: the comments indicator is. Back on predicted there was actually

548
00:28:26,480 --> 00:28:29,640
Speaker 6: a lot of good information. People hadn't built these discard networks.

549
00:28:29,680 --> 00:28:32,400
Speaker 6: They hadn't you know, ended up in their silos where

550
00:28:32,400 --> 00:28:34,639
Speaker 6: we talk about all the useful information ourselves, and so

551
00:28:34,680 --> 00:28:38,640
Speaker 6: there was a lot more sharing and helping. And you know,

552
00:28:38,720 --> 00:28:40,920
Speaker 6: part of that is also because the money's gotten larger,

553
00:28:41,040 --> 00:28:45,960
Speaker 6: the importance of protecting your reliable information is so useful

554
00:28:46,800 --> 00:28:49,480
Speaker 6: that you just don't see, you know, someone like me

555
00:28:49,920 --> 00:28:52,920
Speaker 6: or Brian going on to kel She's you know, message

556
00:28:52,920 --> 00:28:55,360
Speaker 6: board and explaining, no, you guys have it wrong because

557
00:28:55,360 --> 00:28:58,120
Speaker 6: you're not accounting for this, that and the other I

558
00:28:58,120 --> 00:28:58,720
Speaker 6: mean predicted.

559
00:28:58,800 --> 00:29:01,760
Speaker 7: Having an eight hundred and fifty limit, you know, made

560
00:29:01,760 --> 00:29:05,520
Speaker 7: it very possible for you know, the limit mint you

561
00:29:05,560 --> 00:29:08,120
Speaker 7: could share like I'm done, I can't fill up anymore.

562
00:29:08,480 --> 00:29:11,760
Speaker 7: But Calshi not having kind of a meaningful position limit,

563
00:29:12,920 --> 00:29:15,680
Speaker 7: you know, changes that equation dramatically.

564
00:29:16,000 --> 00:29:19,720
Speaker 2: Since we're talking about betting against the comment section. You know,

565
00:29:20,000 --> 00:29:22,880
Speaker 2: there was the article in the journal recently talking about

566
00:29:22,920 --> 00:29:25,800
Speaker 2: how on poly market, sixty seven percent of profits go

567
00:29:25,960 --> 00:29:29,040
Speaker 2: to zero point one percent of accounts. And we also

568
00:29:29,120 --> 00:29:33,600
Speaker 2: have more professional investors who seem to be expressing some

569
00:29:33,840 --> 00:29:36,760
Speaker 2: interest in getting into this market. If a bunch of

570
00:29:36,760 --> 00:29:39,760
Speaker 2: people are just losing money on this, which it seems

571
00:29:39,800 --> 00:29:43,240
Speaker 2: like they are, does the dumb money eventually go away

572
00:29:44,040 --> 00:29:47,520
Speaker 2: and it becomes harder for you to you know, make

573
00:29:47,600 --> 00:29:49,480
Speaker 2: these these bets.

574
00:29:49,560 --> 00:29:52,360
Speaker 6: It should That's been the history of you know, the

575
00:29:52,400 --> 00:29:56,360
Speaker 6: poker boom got harder. Daily fantasy sports was big money

576
00:29:56,360 --> 00:29:58,360
Speaker 6: for a lot of people that got harder of our time.

577
00:29:58,800 --> 00:30:02,240
Speaker 6: You know, certainly it's should happen that way. So far,

578
00:30:02,560 --> 00:30:06,520
Speaker 6: certain markets have gotten harder. Elections appear to still be

579
00:30:06,840 --> 00:30:10,680
Speaker 6: dominated by vibes and emotions. You know, we we'll see.

580
00:30:10,720 --> 00:30:13,800
Speaker 6: Hopefully these prediction market spaces are still new enough that

581
00:30:13,840 --> 00:30:16,680
Speaker 6: there's a lot more people still to be onboarded and

582
00:30:16,920 --> 00:30:19,520
Speaker 6: anything can happen, But so far they still seem to

583
00:30:19,560 --> 00:30:20,320
Speaker 6: be pretty beatable.

584
00:30:20,680 --> 00:30:20,880
Speaker 4: Yeah.

585
00:30:20,880 --> 00:30:23,360
Speaker 1: I remember during the online poker when people like talk

586
00:30:23,400 --> 00:30:25,360
Speaker 1: about like soft tables and all these suff and then

587
00:30:25,480 --> 00:30:27,440
Speaker 1: like eventually like they all lost their money and they

588
00:30:27,480 --> 00:30:33,440
Speaker 1: were like notice soft tables, and then the yeah, then

589
00:30:33,440 --> 00:30:35,760
Speaker 1: it was just all like sharks versus sharks, and the

590
00:30:35,800 --> 00:30:37,800
Speaker 1: only when it's like you go to like the high

591
00:30:37,840 --> 00:30:41,160
Speaker 1: limit room at a casino and there has to be like,

592
00:30:41,520 --> 00:30:44,320
Speaker 1: you know, there has to be some celebrity there or

593
00:30:44,360 --> 00:30:47,000
Speaker 1: like a shake or someone who has like a bunch

594
00:30:47,040 --> 00:30:49,440
Speaker 1: of money who just wants to lose to pros there

595
00:30:49,480 --> 00:30:52,400
Speaker 1: that night. But if it's all like you know, Adam

596
00:30:52,440 --> 00:30:54,960
Speaker 1: Ivy and all these guys playing high limit poker against

597
00:30:55,000 --> 00:30:56,480
Speaker 1: each other, the only one who's they're just going to

598
00:30:56,560 --> 00:30:58,480
Speaker 1: grind each other down in the money.

599
00:30:58,480 --> 00:30:59,360
Speaker 4: It's going to go to the house.

600
00:30:59,480 --> 00:31:01,640
Speaker 1: How did how did you guys do on the twenty

601
00:31:01,680 --> 00:31:02,960
Speaker 1: twenty five Romanian election?

602
00:31:05,800 --> 00:31:07,520
Speaker 8: It was the dark day for our channel.

603
00:31:09,080 --> 00:31:11,880
Speaker 1: Yeah, tell us about the day. I tell us about

604
00:31:11,880 --> 00:31:14,600
Speaker 1: the Romanian election and the dark day for your channel.

605
00:31:14,680 --> 00:31:16,040
Speaker 8: Yeah, Daniel, what happened?

606
00:31:16,840 --> 00:31:19,520
Speaker 6: So in the first round, well, the first round of

607
00:31:19,520 --> 00:31:24,720
Speaker 6: the Romanian election was actually a null due to Russian involvement.

608
00:31:24,840 --> 00:31:29,520
Speaker 6: It was a very strange event. But the next first round,

609
00:31:30,120 --> 00:31:33,040
Speaker 6: this guy Simon won it by I believe about twenty

610
00:31:33,160 --> 00:31:36,360
Speaker 6: and it was headed to a runoff. And basically, in

611
00:31:36,440 --> 00:31:41,520
Speaker 6: the entire history of European runoff elections and even you know,

612
00:31:41,560 --> 00:31:44,800
Speaker 6: expanding beyond European, nobody had ever really come back from

613
00:31:44,800 --> 00:31:48,600
Speaker 6: a deficit that big. And there was also a correlation

614
00:31:48,880 --> 00:31:52,240
Speaker 6: where the places that the other candidates had done well,

615
00:31:53,120 --> 00:31:55,840
Speaker 6: this guy Simi and the leader had also done well.

616
00:31:55,920 --> 00:31:58,560
Speaker 6: So it really just seemed at the beginning like he

617
00:31:58,560 --> 00:32:00,680
Speaker 6: would win as easily, and a lot of us put

618
00:32:00,720 --> 00:32:03,200
Speaker 6: a lot of money on it at various times. He

619
00:32:03,280 --> 00:32:07,280
Speaker 6: then proceeded to leave the country skip all his debates,

620
00:32:08,160 --> 00:32:12,720
Speaker 6: became a laughing stock on Romanian media, which way to

621
00:32:12,800 --> 00:32:15,720
Speaker 6: be clear, did not pick up on just how much

622
00:32:15,760 --> 00:32:18,760
Speaker 6: he had become a joke in Romania, and so it

623
00:32:18,880 --> 00:32:21,680
Speaker 6: ended up being a case where a lot of Romanians

624
00:32:22,240 --> 00:32:24,480
Speaker 6: were betting a lot of money against a lot of

625
00:32:24,520 --> 00:32:28,520
Speaker 6: internet politics sharps. Yeah, I mean they were right, we were.

626
00:32:45,760 --> 00:32:49,760
Speaker 2: How do you feel about insider trading in prediction markets,

627
00:32:49,840 --> 00:32:51,880
Speaker 2: because this is also one of the big debates now,

628
00:32:52,240 --> 00:32:55,880
Speaker 2: the idea that people I don't need it's not illegal, right.

629
00:32:55,840 --> 00:32:57,040
Speaker 1: Well I figured it.

630
00:32:58,520 --> 00:33:00,400
Speaker 2: I can't get clarity on this, but but I know

631
00:33:00,440 --> 00:33:04,080
Speaker 2: there are some charges against people for using like legally

632
00:33:04,160 --> 00:33:08,560
Speaker 2: protected information to make these bets. How do you feel

633
00:33:08,560 --> 00:33:11,160
Speaker 2: about the idea that there you might be betting against

634
00:33:11,200 --> 00:33:14,680
Speaker 2: someone who is actually like, fully in the room and

635
00:33:14,720 --> 00:33:15,680
Speaker 2: fully informed.

636
00:33:16,320 --> 00:33:17,120
Speaker 8: I feel like.

637
00:33:19,560 --> 00:33:23,520
Speaker 7: There's sort of a mixed feeling here where one of

638
00:33:23,560 --> 00:33:27,400
Speaker 7: them on one side, you're like, well, yeah, that's really

639
00:33:27,480 --> 00:33:32,000
Speaker 7: terrible for retail traders to know that they are going

640
00:33:32,080 --> 00:33:35,720
Speaker 7: to be going against someone who literally has the answer.

641
00:33:36,440 --> 00:33:39,120
Speaker 7: On the other hand, you know, I think one of

642
00:33:39,160 --> 00:33:45,000
Speaker 7: the early arguments for why prediction markets should be allowable

643
00:33:45,920 --> 00:33:49,720
Speaker 7: is that they can provide price signal by allowing all

644
00:33:49,760 --> 00:33:53,000
Speaker 7: information that exists to come into the public sphere.

645
00:33:53,040 --> 00:33:56,680
Speaker 8: And not stay private. I mean you can imagine.

646
00:33:56,080 --> 00:34:00,160
Speaker 7: A scenario where I mean you could cook up a

647
00:34:00,240 --> 00:34:02,920
Speaker 7: very Hollywood scenario, which I won't, but you can imagine

648
00:34:02,920 --> 00:34:05,920
Speaker 7: where someone who was aware of illegal activity and had

649
00:34:05,960 --> 00:34:10,719
Speaker 7: no way to make that public, we're trading on that

650
00:34:10,840 --> 00:34:13,319
Speaker 7: information and sort of creating a little bit of price

651
00:34:13,360 --> 00:34:14,080
Speaker 7: signal there.

652
00:34:14,680 --> 00:34:15,120
Speaker 8: I don't know.

653
00:34:15,200 --> 00:34:17,440
Speaker 7: I mean, you never want to be trading against an insider.

654
00:34:17,480 --> 00:34:21,640
Speaker 7: And of course cal She and poly Market would very

655
00:34:21,760 --> 00:34:25,200
Speaker 7: much like people to think that there are no insiders

656
00:34:25,239 --> 00:34:28,879
Speaker 7: at all, that they have policed this aggressively. I don't

657
00:34:28,880 --> 00:34:30,960
Speaker 7: really find that to be the case. I could tell

658
00:34:30,960 --> 00:34:33,759
Speaker 7: you specific markets that I am one hundred percent sure

659
00:34:34,200 --> 00:34:36,480
Speaker 7: that I lost to someone with inside information.

660
00:34:37,000 --> 00:34:37,840
Speaker 8: I think you can.

661
00:34:37,719 --> 00:34:40,719
Speaker 7: Pick out a market and see is this possible for

662
00:34:40,760 --> 00:34:43,319
Speaker 7: this market to be insidered? You get a little bit

663
00:34:43,320 --> 00:34:46,399
Speaker 7: of a sense of what price movement looks like when

664
00:34:46,440 --> 00:34:49,480
Speaker 7: someone actually knows the answer. Huge volume coming out of

665
00:34:49,520 --> 00:34:52,960
Speaker 7: nowhere at a price that hadn't been traded on before.

666
00:34:53,640 --> 00:34:56,640
Speaker 7: I can tell you I would bet my life savings

667
00:34:56,840 --> 00:35:00,640
Speaker 7: that someone who knew the Critics' Choice Awards winners last

668
00:35:00,640 --> 00:35:03,440
Speaker 7: spring knew that Jacob Aloradi was going to win best

669
00:35:03,440 --> 00:35:06,320
Speaker 7: supporting actor the night before because he went to one

670
00:35:06,440 --> 00:35:11,080
Speaker 7: from one cent to forty cents on massive volume, which

671
00:35:11,160 --> 00:35:13,040
Speaker 7: was a trade that I lost because I really didn't

672
00:35:13,040 --> 00:35:16,440
Speaker 7: think he was going to win. So, you know, we've

673
00:35:16,480 --> 00:35:19,200
Speaker 7: taken some of those l's. I think ultimately you want

674
00:35:19,200 --> 00:35:21,320
Speaker 7: to not allow it, but it's hard because it also

675
00:35:22,040 --> 00:35:25,960
Speaker 7: can be part of giving a price signal on important events.

676
00:35:26,040 --> 00:35:27,280
Speaker 3: So what I'd like to see.

677
00:35:27,080 --> 00:35:29,640
Speaker 6: And what I think we're moving toward is the sites

678
00:35:29,920 --> 00:35:35,000
Speaker 6: and the companies making the serious insider trading not make

679
00:35:35,080 --> 00:35:37,200
Speaker 6: financial sense for the people that do it. So we

680
00:35:37,320 --> 00:35:40,279
Speaker 6: just saw the guy who insidered the Google year in

681
00:35:40,320 --> 00:35:44,160
Speaker 6: search lost his Google job and I believe charges were referred.

682
00:35:44,840 --> 00:35:48,640
Speaker 6: The military guy who bet on the invasion was arrested.

683
00:35:49,200 --> 00:35:50,960
Speaker 6: You know, he made something like four hundred k and

684
00:35:51,000 --> 00:35:53,279
Speaker 6: now he's facing charges. I mean, there's no way that

685
00:35:53,360 --> 00:35:56,120
Speaker 6: math works out for those people, right, So it's really

686
00:35:56,239 --> 00:35:59,400
Speaker 6: the small insider ones. But then you also have these

687
00:36:00,200 --> 00:36:04,120
Speaker 6: situations where it's debatable what's an insider, Like there were

688
00:36:04,160 --> 00:36:08,120
Speaker 6: insiders on the super Bowl performance market where you know,

689
00:36:08,239 --> 00:36:12,759
Speaker 6: somebody who has no actual attachment to the gig or

690
00:36:12,800 --> 00:36:17,520
Speaker 6: the performance, you know happens to hear something that you

691
00:36:17,520 --> 00:36:20,399
Speaker 6: know makes them no more than the market, but they

692
00:36:20,440 --> 00:36:24,520
Speaker 6: really have no technical relationship of being an insider. How

693
00:36:24,880 --> 00:36:27,880
Speaker 6: I don't think you can police that, so I like

694
00:36:27,960 --> 00:36:30,320
Speaker 6: to stick to stuff. You know. Again, like in elections,

695
00:36:30,360 --> 00:36:32,560
Speaker 6: you can't have insiders. We've got a lot on the

696
00:36:32,560 --> 00:36:35,400
Speaker 6: cabinet confirmations, and that was interesting because you could have

697
00:36:35,480 --> 00:36:38,600
Speaker 6: insiders on those. You know, some staffer to a senator

698
00:36:38,640 --> 00:36:40,840
Speaker 6: knows how their person's going to vote. You know, you

699
00:36:40,960 --> 00:36:43,360
Speaker 6: just kind of have to ask in every event, you know,

700
00:36:43,360 --> 00:36:46,000
Speaker 6: who's my counterparty? Could they know more than me? And

701
00:36:46,040 --> 00:36:48,479
Speaker 6: in situations where someone could know more than you, you really

702
00:36:48,480 --> 00:36:50,839
Speaker 6: got a size appropriately and make sure that you're not

703
00:36:51,080 --> 00:36:53,360
Speaker 6: just blowing a ton of money to somebody who only

704
00:36:53,400 --> 00:36:55,160
Speaker 6: isn't it because they know more than you?

705
00:36:55,200 --> 00:36:57,680
Speaker 1: Just for what it's worth. Not that my opinion matters

706
00:36:57,719 --> 00:37:00,360
Speaker 1: at all, but I don't think the government should be

707
00:37:00,440 --> 00:37:03,480
Speaker 1: expending resources to police insider trading on the length of

708
00:37:03,480 --> 00:37:06,080
Speaker 1: the Super Bowl halftime show, because it's like, it doesn't

709
00:37:06,080 --> 00:37:08,040
Speaker 1: matter if you guys lose a bunch of money to

710
00:37:08,080 --> 00:37:11,439
Speaker 1: an insider on that. I don't care because, like I think,

711
00:37:11,520 --> 00:37:14,040
Speaker 1: like regulated while markets are a good thing, I do

712
00:37:14,160 --> 00:37:17,920
Speaker 1: not want like, you know, public resources protecting people who

713
00:37:18,000 --> 00:37:20,839
Speaker 1: are like gambling on it. But just you know, I'm

714
00:37:20,920 --> 00:37:23,920
Speaker 1: curious and maybe all three of you like this story

715
00:37:23,960 --> 00:37:26,919
Speaker 1: and people discovering that you guys have this discord, et cetera.

716
00:37:27,120 --> 00:37:30,279
Speaker 1: Like from the perspective of the companies, Like I could

717
00:37:30,320 --> 00:37:33,239
Speaker 1: see the Calshies and poly markets of the world loving

718
00:37:33,280 --> 00:37:35,439
Speaker 1: this because they're like, look, a bunch of people get

719
00:37:35,440 --> 00:37:38,440
Speaker 1: in their minds that they could be Brian and Daniel

720
00:37:38,440 --> 00:37:39,759
Speaker 1: and they could have a crew and do a bunch

721
00:37:39,800 --> 00:37:42,920
Speaker 1: of information and when or I could see them just

722
00:37:42,960 --> 00:37:44,680
Speaker 1: like it because they're like, wow, you know what, I'm

723
00:37:44,680 --> 00:37:47,520
Speaker 1: not going to uh start, I'm not going to trade

724
00:37:47,560 --> 00:37:50,879
Speaker 1: because I do not have anywhere the means to come

725
00:37:50,920 --> 00:37:53,680
Speaker 1: close to this group in terms of like the resources

726
00:37:53,719 --> 00:37:57,759
Speaker 1: required to invest to trade consistently. Well, I'm curious, Like

727
00:37:57,880 --> 00:38:01,000
Speaker 1: what you've seen is like are stories like this good

728
00:38:01,040 --> 00:38:05,040
Speaker 1: for the markets or they would they rather sort of

729
00:38:05,520 --> 00:38:08,240
Speaker 1: perpetuate the illusion that it really is like totally random.

730
00:38:08,280 --> 00:38:10,200
Speaker 1: I just made my rent money because I knew it

731
00:38:10,239 --> 00:38:12,799
Speaker 1: wasn't raining today and that's simple, which is how some

732
00:38:12,840 --> 00:38:14,680
Speaker 1: of their ads are. And this is like really like

733
00:38:15,120 --> 00:38:19,080
Speaker 1: the sort of the advertising, the misadvertising, et cetera. I've

734
00:38:19,120 --> 00:38:20,600
Speaker 1: seen you know, you've seen it from both of the

735
00:38:20,640 --> 00:38:21,880
Speaker 1: two major platforms.

736
00:38:22,239 --> 00:38:26,160
Speaker 5: At a high level, I'm very you know, concerned with like,

737
00:38:26,320 --> 00:38:28,600
Speaker 5: how are people going to read this thing? And I

738
00:38:28,640 --> 00:38:31,040
Speaker 5: think my hope, and you know what folks have told

739
00:38:31,080 --> 00:38:34,640
Speaker 5: me is, wow, I didn't really realize that I was

740
00:38:34,719 --> 00:38:37,320
Speaker 5: the dumb money. Yeah, And I don't even think I realized,

741
00:38:37,320 --> 00:38:38,840
Speaker 5: you know, at the beginning of this journey that like,

742
00:38:38,920 --> 00:38:41,640
Speaker 5: I was the dumb money. And so the hope I

743
00:38:41,680 --> 00:38:43,520
Speaker 5: think is that you know, people read something like this,

744
00:38:43,560 --> 00:38:45,319
Speaker 5: people listen to a conversation like this, and they think,

745
00:38:45,680 --> 00:38:48,799
Speaker 5: oh my gosh, Okay, I maybe don't stand a chance

746
00:38:48,840 --> 00:38:51,200
Speaker 5: against Brian or Daniel or the rest of these guys

747
00:38:51,200 --> 00:38:54,520
Speaker 5: in these discord groups. But you know, Cashi and polymarket

748
00:38:54,520 --> 00:38:56,160
Speaker 5: can kind of flip the script and tell you the

749
00:38:56,160 --> 00:38:56,640
Speaker 5: other story.

750
00:38:56,800 --> 00:38:58,680
Speaker 7: Yeah. I mean, I think what is in the long

751
00:38:58,760 --> 00:39:04,680
Speaker 7: term good of prediction markets continuing to be legal and

752
00:39:04,719 --> 00:39:11,720
Speaker 7: regulated is stories that are like Adams, Because a lot

753
00:39:11,719 --> 00:39:17,400
Speaker 7: of journalism reporting on prediction markets has sort of focused on, Wow,

754
00:39:17,520 --> 00:39:20,279
Speaker 7: isn't it crazy that people are making money on how

755
00:39:20,280 --> 00:39:23,480
Speaker 7: long a handshake will last and what words someone will

756
00:39:23,520 --> 00:39:25,560
Speaker 7: mumble at a speech, And.

757
00:39:26,920 --> 00:39:27,399
Speaker 8: I mean, my.

758
00:39:27,400 --> 00:39:30,319
Speaker 7: Personal opinion is that people should be able to bet

759
00:39:30,360 --> 00:39:33,160
Speaker 7: on things because it's their money. But these are not

760
00:39:33,360 --> 00:39:37,120
Speaker 7: important questions that prediction markets are built to answer. And

761
00:39:37,200 --> 00:39:39,680
Speaker 7: I think that in some ways Calshi and poly Market

762
00:39:39,719 --> 00:39:43,160
Speaker 7: have been so aggressive in trying to make as much

763
00:39:43,160 --> 00:39:45,799
Speaker 7: profit as possible, as quickly as possible, that they have

764
00:39:47,560 --> 00:39:51,520
Speaker 7: gotten deep in markets that are not only pushing against

765
00:39:51,520 --> 00:39:56,120
Speaker 7: state regulation, but are clearly not the kind of important

766
00:39:56,120 --> 00:40:00,279
Speaker 7: social questions and economic questions that prediction markets are are

767
00:40:00,480 --> 00:40:02,920
Speaker 7: are really should be answering. I mean, what I appreciated

768
00:40:02,960 --> 00:40:06,200
Speaker 7: about Adam's story was that it was essentially about people

769
00:40:06,239 --> 00:40:09,319
Speaker 7: who are going the extra mile and working really hard

770
00:40:09,360 --> 00:40:12,080
Speaker 7: to try to answer questions that actually matter, who will

771
00:40:12,080 --> 00:40:15,400
Speaker 7: win elections, what prices will look like, and in some

772
00:40:15,560 --> 00:40:18,480
Speaker 7: cases people who are working harder than you know publicly

773
00:40:18,520 --> 00:40:21,400
Speaker 7: accepted experts on those So I think for the long

774
00:40:21,520 --> 00:40:27,239
Speaker 7: term existence and regulation and legality of prediction markets, you know,

775
00:40:27,560 --> 00:40:31,200
Speaker 7: stories that focus on I want to say, people like

776
00:40:31,280 --> 00:40:33,120
Speaker 7: Daniel and I because it's not about him and I,

777
00:40:33,239 --> 00:40:36,560
Speaker 7: but it's about what questions are are we all trying

778
00:40:36,600 --> 00:40:38,640
Speaker 7: to answer? What puzzles are we trying to solve and

779
00:40:38,680 --> 00:40:42,400
Speaker 7: if those puzzles matter to the general discourse in the economy,

780
00:40:42,440 --> 00:40:45,239
Speaker 7: that then absolutely and should be legal. But some of

781
00:40:45,239 --> 00:40:48,520
Speaker 7: the extraneous stuff that is just you know, a silly,

782
00:40:49,280 --> 00:40:52,239
Speaker 7: a silly excuse to gamble, you know, I don't think

783
00:40:52,280 --> 00:40:54,919
Speaker 7: that that is really the future of where this should

784
00:40:54,960 --> 00:40:55,360
Speaker 7: be headed.

785
00:40:55,719 --> 00:40:55,959
Speaker 8: Yeah.

786
00:40:56,040 --> 00:40:58,160
Speaker 2: I think that's an important point because we talk about,

787
00:40:58,440 --> 00:41:01,480
Speaker 2: you know, the price signal all of this, and the

788
00:41:01,520 --> 00:41:04,360
Speaker 2: price signal really doesn't matter if it's like a dumb

789
00:41:04,440 --> 00:41:08,359
Speaker 2: question being asked. Right, One more question for me on

790
00:41:08,400 --> 00:41:12,279
Speaker 2: your research process. How much of this has been enabled

791
00:41:12,520 --> 00:41:15,680
Speaker 2: by AI and the tools that are now at your disposal.

792
00:41:16,040 --> 00:41:20,360
Speaker 6: AI is very helpful for getting started on something like

793
00:41:20,400 --> 00:41:26,560
Speaker 6: an international market, or especially for searching in foreign languages,

794
00:41:26,680 --> 00:41:31,520
Speaker 6: where it can intermediate the language barrier for you. There's

795
00:41:31,640 --> 00:41:35,799
Speaker 6: basically been I think very few of us use much

796
00:41:35,880 --> 00:41:39,560
Speaker 6: AI for modeling. Some of the guys who code more

797
00:41:39,640 --> 00:41:42,200
Speaker 6: have been using claud code a bunch just to do

798
00:41:42,320 --> 00:41:46,360
Speaker 6: some statistical stuff, you know, quicker and easier. But the

799
00:41:46,360 --> 00:41:49,520
Speaker 6: people who just ask LLLMS a question and think that

800
00:41:49,520 --> 00:41:52,080
Speaker 6: that gives them an edge on a market are some

801
00:41:52,320 --> 00:41:55,000
Speaker 6: of the squarest money out there. You know, these lms

802
00:41:55,680 --> 00:42:00,400
Speaker 6: will tailor their answer to what you ask, and you

803
00:42:00,440 --> 00:42:02,879
Speaker 6: know if you ask the question a certain way, it

804
00:42:02,920 --> 00:42:05,879
Speaker 6: will tell you this is the probability, and it will ignore.

805
00:42:06,360 --> 00:42:09,200
Speaker 2: What an insightful question. You are on the right track.

806
00:42:09,239 --> 00:42:11,879
Speaker 1: Wait, is square money? Is that what you guys call

807
00:42:11,920 --> 00:42:12,879
Speaker 1: soft money these days?

808
00:42:13,000 --> 00:42:14,279
Speaker 4: Or like? Is that the square money?

809
00:42:14,360 --> 00:42:14,520
Speaker 7: Is that?

810
00:42:14,640 --> 00:42:16,640
Speaker 4: Like them for like a soft table or whatever?

811
00:42:16,960 --> 00:42:18,920
Speaker 6: Right, there's there's plenty, got it?

812
00:42:18,960 --> 00:42:20,839
Speaker 4: Oh yeah, of course the squares of the show.

813
00:42:20,960 --> 00:42:22,880
Speaker 6: You saw tons of this again, going back to Pratt,

814
00:42:22,920 --> 00:42:25,000
Speaker 6: you know all the people on Twitter my you know,

815
00:42:25,160 --> 00:42:27,040
Speaker 6: LLM give me the chances of this for one and

816
00:42:27,080 --> 00:42:28,400
Speaker 6: a trillion, et cetera.

817
00:42:28,840 --> 00:42:32,120
Speaker 7: I've asked chat GPT things like what will inflation be

818
00:42:32,239 --> 00:42:34,880
Speaker 7: next month? And it will give me a number and

819
00:42:35,000 --> 00:42:37,239
Speaker 7: it'll be like, wow, great question. I think it's going

820
00:42:37,280 --> 00:42:40,040
Speaker 7: to be this because of these reasons. And then I'll

821
00:42:40,080 --> 00:42:45,200
Speaker 7: just say, without without anything else, I'll just say that's

822
00:42:45,200 --> 00:42:48,160
Speaker 7: too high, and it'll say, you're right, I'm glad you

823
00:42:48,280 --> 00:42:51,120
Speaker 7: brought that up. It actually will be lower for these reasons.

824
00:42:51,160 --> 00:42:53,200
Speaker 7: And then I'll say that's too low and it'll be like,

825
00:42:53,280 --> 00:42:55,440
Speaker 7: you know what, thanks for bringing that to my attention.

826
00:42:56,000 --> 00:43:00,600
Speaker 7: So not only is it is it telling you what

827
00:43:00,640 --> 00:43:03,280
Speaker 7: you want to hear, but but it only has grounding

828
00:43:03,520 --> 00:43:07,719
Speaker 7: in other expertise that it that it gathers. And if

829
00:43:07,719 --> 00:43:11,120
Speaker 7: that expertise that I'm already trading against is beatable, then

830
00:43:11,160 --> 00:43:13,640
Speaker 7: I don't really see why the AI is any less

831
00:43:13,640 --> 00:43:15,759
Speaker 7: beatable than that, at least in its current incarnation.

832
00:43:16,480 --> 00:43:16,880
Speaker 3: I lied.

833
00:43:16,960 --> 00:43:19,920
Speaker 2: I have one more question, very important question, Daniel, why

834
00:43:19,920 --> 00:43:21,400
Speaker 2: are you called carnitas Taco?

835
00:43:21,840 --> 00:43:23,640
Speaker 6: So when I used to play a lot of poker,

836
00:43:24,000 --> 00:43:26,719
Speaker 6: I would often be in tournaments all night and then

837
00:43:26,840 --> 00:43:31,120
Speaker 6: stay up for breakfast tacos in the morning, and that

838
00:43:31,200 --> 00:43:35,000
Speaker 6: has stuck with me as a DJ name, a Twitter name,

839
00:43:35,880 --> 00:43:38,960
Speaker 6: prediction market name. Just that's that's my online name.

840
00:43:39,160 --> 00:43:41,480
Speaker 1: Brian, Daniel, and Adam, thank you all so much for

841
00:43:41,520 --> 00:43:43,400
Speaker 1: coming on at I think that actually worked. That was

842
00:43:43,400 --> 00:43:45,960
Speaker 1: a little bit complicated organized, but that was a great conversation.

843
00:43:46,200 --> 00:43:48,160
Speaker 1: And really appreciate all of you taking your time.

844
00:43:48,160 --> 00:43:49,799
Speaker 4: Thanks, thanks for having us.

845
00:43:49,840 --> 00:43:59,160
Speaker 7: Thanks it's great to be here.

846
00:44:05,239 --> 00:44:05,640
Speaker 3: Tracy.

847
00:44:05,680 --> 00:44:07,520
Speaker 1: That was really fun. That was That was sort of

848
00:44:07,560 --> 00:44:09,680
Speaker 1: a complicated episode to do, but I thought that was

849
00:44:09,680 --> 00:44:10,880
Speaker 1: like a I actually felt like I.

850
00:44:10,920 --> 00:44:12,440
Speaker 4: Learned a lot in that conversation.

851
00:44:12,560 --> 00:44:16,479
Speaker 2: Yeah, absolutely, I didn't realize that the poker boom had

852
00:44:16,760 --> 00:44:18,799
Speaker 2: sort of gone through a similar thing where you had

853
00:44:18,840 --> 00:44:21,000
Speaker 2: a bunch of people playing online and then they just

854
00:44:21,080 --> 00:44:22,320
Speaker 2: kept losing and they left.

855
00:44:22,440 --> 00:44:24,799
Speaker 1: Yeah, because the story was like do you remember like

856
00:44:24,840 --> 00:44:27,680
Speaker 1: how the why the online poker boom like really happened?

857
00:44:28,400 --> 00:44:29,560
Speaker 2: Vaguely, but remind me.

858
00:44:29,760 --> 00:44:32,720
Speaker 1: Because this guy named Chris Moneymaker won the World Series

859
00:44:32,880 --> 00:44:35,440
Speaker 1: he was to his name was Chris Moneymaker and he

860
00:44:35,520 --> 00:44:38,520
Speaker 1: was a total nobody and he won the World Series

861
00:44:38,560 --> 00:44:41,080
Speaker 1: of Poker Las Vegas. So everyone got in their head

862
00:44:41,120 --> 00:44:43,799
Speaker 1: that like, actually poke, anyone can win a lot of

863
00:44:43,800 --> 00:44:46,840
Speaker 1: money and playing poker, and that was the sort of

864
00:44:47,000 --> 00:44:50,200
Speaker 1: catalyst for like poker becoming this thing that like ESPN

865
00:44:50,239 --> 00:44:53,800
Speaker 1: would cover and et cetera. It's huge wave after wave,

866
00:44:54,400 --> 00:44:57,880
Speaker 1: and then eventually, like there was I think sometime in

867
00:44:57,920 --> 00:44:59,560
Speaker 1: two thousand and nine or two thousand, I think it

868
00:44:59,600 --> 00:45:02,440
Speaker 1: was two thousan nine, it was a big government crackdown

869
00:45:02,719 --> 00:45:05,480
Speaker 1: on some of these like quasi illegal offshore sites.

870
00:45:05,880 --> 00:45:07,760
Speaker 4: But that was already at that point.

871
00:45:07,840 --> 00:45:10,560
Speaker 1: I think like the minnows were coming out of because

872
00:45:10,600 --> 00:45:12,200
Speaker 1: a bunch of people were losing that Chris.

873
00:45:12,160 --> 00:45:13,600
Speaker 4: Money Maker was really a fluke.

874
00:45:13,840 --> 00:45:16,200
Speaker 1: There were a lot of interesting things on it, including

875
00:45:16,360 --> 00:45:19,000
Speaker 1: is to your point that like will eventually the square

876
00:45:19,000 --> 00:45:20,920
Speaker 1: money or the dumb money or the minno is just

877
00:45:20,960 --> 00:45:23,040
Speaker 1: like flush out of the system and then it's sharp

878
00:45:23,120 --> 00:45:27,560
Speaker 1: versus sharp and only the platforms are making money all

879
00:45:27,640 --> 00:45:30,600
Speaker 1: the stuff about like, okay, what is a healthy future

880
00:45:30,880 --> 00:45:34,080
Speaker 1: for these predictions markets look like? But also what is

881
00:45:34,400 --> 00:45:37,719
Speaker 1: you know, the real work involved to actually have an edge.

882
00:45:37,760 --> 00:45:40,480
Speaker 1: It's like, if you are listening to this and you

883
00:45:40,480 --> 00:45:42,719
Speaker 1: think you're gonna make money, you probably aren't unless you

884
00:45:42,800 --> 00:45:44,759
Speaker 1: like actually have some reason to think that you're like

885
00:45:45,280 --> 00:45:46,040
Speaker 1: putting in work.

886
00:45:46,640 --> 00:45:50,360
Speaker 2: It kind of emphasizes that in the age of AI,

887
00:45:50,760 --> 00:45:54,680
Speaker 2: like the edge is still going out and finding new data,

888
00:45:54,800 --> 00:45:57,360
Speaker 2: like picking up on turning points, because most of the

889
00:45:57,480 --> 00:46:01,400
Speaker 2: lms are still very backward. Yeah okay, and I guess

890
00:46:01,440 --> 00:46:04,200
Speaker 2: having that sort of like human connection.

891
00:46:04,280 --> 00:46:05,880
Speaker 3: Well if you think about it, like you have to

892
00:46:05,920 --> 00:46:06,600
Speaker 3: know the vibe.

893
00:46:06,680 --> 00:46:08,359
Speaker 1: Yeah right, But if you think about it too, it

894
00:46:08,360 --> 00:46:10,719
Speaker 1: makes sense because one of the things that like a

895
00:46:10,800 --> 00:46:13,200
Speaker 1: lot of our a I guess we'll talk about is

896
00:46:13,239 --> 00:46:16,600
Speaker 1: the value of proprietary data, right, and so so it

897
00:46:16,760 --> 00:46:20,080
Speaker 1: actually makes sense like what is quote scarce of the

898
00:46:20,120 --> 00:46:23,440
Speaker 1: age of AI. Well, someone knocking on doors and asking

899
00:46:23,520 --> 00:46:26,560
Speaker 1: questions of people rather than someone just asking the model

900
00:46:26,680 --> 00:46:28,680
Speaker 1: what they think is going to happen.

901
00:46:28,719 --> 00:46:29,680
Speaker 8: I also think that story.

902
00:46:29,760 --> 00:46:32,920
Speaker 2: I didn't mean to turn this into another AI. I

903
00:46:33,000 --> 00:46:33,520
Speaker 2: was just curious.

904
00:46:33,600 --> 00:46:36,279
Speaker 1: Yeah, No, it's a good important question. I want to

905
00:46:36,440 --> 00:46:38,359
Speaker 1: I'm curious how much money was lost on that twenty

906
00:46:38,400 --> 00:46:40,640
Speaker 1: twenty five Romanian election, because I know that that was

907
00:46:40,640 --> 00:46:43,640
Speaker 1: like a big upset and so and he walked through

908
00:46:44,560 --> 00:46:47,200
Speaker 1: respect the candor of admitting they really with one, that one,

909
00:46:47,200 --> 00:46:49,920
Speaker 1: and then that all the randoms in Romania who are

910
00:46:49,920 --> 00:46:52,840
Speaker 1: paying attention to it knew more than the sharps on

911
00:46:52,840 --> 00:46:53,280
Speaker 1: that one.

912
00:46:53,920 --> 00:46:54,680
Speaker 2: Shall we leave it there?

913
00:46:54,719 --> 00:46:55,839
Speaker 1: Let's leave it there, all right?

914
00:46:55,880 --> 00:46:58,400
Speaker 2: This has been another episode of the AU Thoughts podcast.

915
00:46:58,480 --> 00:47:01,600
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.

916
00:47:01,320 --> 00:47:04,040
Speaker 1: And I'm Joe Wisenthal. You can follow me at The Stalwart.

917
00:47:04,239 --> 00:47:07,800
Speaker 1: Follow our producers Carmen Rodriguez at Carmen Erman, Dashill Bennett

918
00:47:07,800 --> 00:47:11,399
Speaker 1: a Dashbot, Kilbrooks at Kilbrooks and Kevin Lozano at Kevin

919
00:47:11,480 --> 00:47:12,120
Speaker 1: Lloyd Lozano.

920
00:47:12,400 --> 00:47:14,480
Speaker 2: And for more Oudlots content, you should check out our

921
00:47:14,560 --> 00:47:16,919
Speaker 2: daily newsletter. You can find that at Bloomberg dot com

922
00:47:16,960 --> 00:47:18,400
Speaker 2: Forward slash odd Lots.

923
00:47:18,200 --> 00:47:20,160
Speaker 1: And you can shout about all of these topics twenty

924
00:47:20,200 --> 00:47:24,240
Speaker 1: four to seven in our discord Discord dot gg slash odlts.

925
00:47:24,400 --> 00:47:26,600
Speaker 2: And if you enjoy odd Lots, if you like it

926
00:47:26,719 --> 00:47:31,759
Speaker 2: when we talk to sharps about beating the squares or

927
00:47:31,760 --> 00:47:34,360
Speaker 2: the circles, then please leave us a positive review on

928
00:47:34,400 --> 00:47:37,200
Speaker 2: your favorite podcast platform. And remember, if you are a

929
00:47:37,239 --> 00:47:40,759
Speaker 2: Bloomberg subscriber, you can listen to all of our episodes.

930
00:47:40,200 --> 00:47:41,520
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931
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932
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Speaker 2: on Apple Podcasts and follow the instructions there.

933
00:47:47,280 --> 00:48:06,360
Speaker 3: Thanks for listening in