00:00:02
Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to another episode of the Odd Lots Podcast.

00:00:21
Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway.

00:00:24
Speaker 1: Tracy, can I reveal a very bloomer take of mine, so to speak?

00:00:29
Speaker 3: Go on?

00:00:30
Speaker 1: I actually really loathe anything that's sort of like identify as I get rich quick, or the gamification of stock trading or gamification of stock trading.

00:00:44
Speaker 2: Ramification would be something different.

00:00:45
Speaker 4: That would probably be something different. I don't know why I pronounced it that way.

00:00:48
Speaker 1: Gamification of stock trading, gambling ads. I really don't like gambling ads that imply they are going to be winner. And I really don't like, even though I'm very interested in prediction market, I generally don't like, you know, a lot of the marketing. There was a really good Wallshet Journal article about polymarket and these ads that they had created this idea that everyone's a winner and there's all this free money out there. You just have to trade your knowledge of whatever rainfall or whatever.

00:01:16
Speaker 5: Like.

00:01:16
Speaker 4: I really like it. Really viscerally bothers me.

00:01:19
Speaker 2: Yeah. I think this is the key difference with prediction markets is it's zero sum, right, if you make a bet, someone is on the other side taking the opposite of that bet, and that feels a little different to me than like traditional stock markets, where you know, you would get some cash flow, some dividend, sell monetize whatever they got, and someone else buys.

00:01:39
Speaker 1: Like no, I totally agree. I mean, look, there are certainly zero some There are all kinds of zero some markets in what we'd call trad five futures are zero some options or zero some, et cetera. But on the other hand, like but yes, zero some games. Even if we're talking about like options or futures or swaps or whatever, that's not investing, that's trading, and that can often be speculation, and a lot of people lose a lot of money. And when we know, in a lot of these environments, a lot of like most people lose. Some people are very sophisticated, but a lot of people think they're going to go into some of these new markets or whatever, whether we're talking about prediction markets, whether we're talking about options trading on Robinhood and they have dreams making a lot of money, or just talk about crypto, which is also zero sum.

00:02:22
Speaker 4: I think they don't.

00:02:24
Speaker 2: Yeah, absolutely. The other interesting thing about prediction markets just from a sort of market structure question, and we've done episodes on this with Susquehanna. Is the liquidity aspect and as more professional investors get in, does that start to maybe arbitrage some of the edge that certain traders have seen so far? Because you know, you read these stories specific story the average guys out smarting Wall Street on prediction markets. Like average guys, I have a question over whether or not they're actually average, but you can imagine a scenario where more professionals get into prediction markets and maybe it becomes harder to actually beat them.

00:03:04
Speaker 1: Absolutely, I think everyone should actually go back and listen to our episode that we did with Jeremy Mallach, who runs the prediction market's desk over at Susquehanna before listening to this, because the other thing he talked about is like how even some of these low liquidity markets, they put a lot of stock in the price and therefore they can feel comfortable doing OTC transactions based on.

00:03:24
Speaker 4: What the price.

00:03:26
Speaker 1: So there's interesting stuff going on.

00:03:27
Speaker 4: Anyway, you mentioned this.

00:03:28
Speaker 1: Article came out in the New York Times May twenty six, the average Guys out smarting Wall Street on prediction markets, and it is important. There are some people who are doing very well, and of like a handful of they they would say sharks in various like other contexts, who are like sharks, very good. Most of us are minnos and some people do really well and.

00:03:49
Speaker 2: The vast majority are total idiots.

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Speaker 4: Yeah.

00:03:51
Speaker 1: Yeah, if I went on there, I would quickly lose one thousand dollars and some fraction of it would go to the house, and a bunch of it would go to some of the best traders, most likely, especially because it's peer Anyway, we have a very complicated special episode. I don't even know how this is gonna work, but I thought it'd be fun. So this was a great piece. We have the reporter on the piece with us, as well as a couple of the sharps. They are in a discord together, a bunch of them. We're just gonna be talking with two of them. The discord is called the Maga Kiwi Club. That sounds fun, so we're gonna be talking with Adam the journalist as well as some of the members of the Maga Kiwi Club. We have Brian Golden, who is in the article who was identified as someone who's really top notch and trading inflation contracts with Daniel Reichman, who also goes by Carnita's Taco, who does a lot of election and politics. And of course we have Adam here who is a journalist, reporter and producer here in NYC recently joined the tech company Notion which all journalists might end up working at tech company.

00:04:48
Speaker 2: Well we do, so we don't get to talk to a carneitis taco not often on the podcast.

00:04:53
Speaker 1: I'm very excited about this episode. Thank all three of you, Brian, Daniel, and Adam for joining us on Outlove.

00:04:59
Speaker 4: It's great to be It's a pleasure.

00:05:01
Speaker 1: Let's just start, Adam. You know, I guess actually we're sort of trusting you. We've sort of delegated some of our judgments. But you, you know, you reported out this story, Like how did you like identify that there exist people in the world who consistently win on prediction market?

00:05:18
Speaker 6: Yeah?

00:05:18
Speaker 5: I mean I think it started for me. I got very interested in the in these markets because I was playing around in them, right, yeah, and I was losing a lot of money, you know, not a lot a lot of money, but for me, it was a lot of money, and I was wondering, who the heck am I losing this money to? Am I losing to market makers? Am I losing to you know, the SIGs of the world. And it occurred to me maybe that maybe there are some people who are just a little bit sharper than me who are out there. And I started asking around, and I sort of started, you know, looking around on you know, just started you know, just just asking around basically, and one person sort of leaves the next, and suddenly you find yourself in a discord with a lot of guys who are just really crushing it sharper than anybody else. And you know, two of those folks are.

00:06:03
Speaker 4: Brian and Daniel.

00:06:04
Speaker 2: Well, Brian and Daniel. Tell us about the Discord group, because, from what I can tell from the article, you know, a bunch of prediction market traders get together, share information, maybe you share some actual p and l. It kind of sounds like a multistrat it's fun where everyone's like in charge.

00:06:20
Speaker 3: Of their own portfolio.

00:06:22
Speaker 2: But tell us about the Discord Group.

00:06:25
Speaker 7: Yeah, Well, the first thing is the Maga Kiwi club name is ironic. It's not a Maga club, but most of the names of the group end up being some form of inside joke that emerges during the conversation because we spend almost all day every day talking to each other about markets, ranging from economics to culture to.

00:06:44
Speaker 8: Sports, and it is sort of like that.

00:06:48
Speaker 7: I would describe it almost as an Avengers model, where there are different people in the group that have different specialties and everyone has their own portfolio, but there is kind of a spirit of community in the way that information is exchanged, in that I know that if I am giving helpful tips to Daniel and others on inflation, that they're going to give me helpful tips back on things that I may be adequate at but not expert at, and we sort of work together in that way.

00:07:20
Speaker 6: Yeah, so a lot of us actually know each other, dating back all the way really to twenty sixteen twenty seventeen when predicted was the dominant prediction market, and we mostly know each other through politics originally because that was the prediction market game. And you know, pretty much all of us had some interest in politics or very large interest in politics to start with.

00:07:43
Speaker 8: And the prediction.

00:07:45
Speaker 6: Market space has grown very large, and you know, the money has gotten larger, but a lot of us would be doing a lot of this stuff anyway, would be unpacking elections, trying to find the truth of things, trying to you know, solve puzzles together. And it just so happens that the space has grown large around us. But we're kind of doing the same thing we've always been doing.

00:08:06
Speaker 5: And the way I found myself, you know, talking to Brian and Daniel was, you know, I met another trader who who loved elections. You know, this is sort of where where the journey began for me, and you know, we were just talking about elections, and you know, it was after maybe six weeks of talking and source building, they finally he says, oh, yeah, I'm actually talking to all of these other people. I'm not just doing this alone. I'm not just building models alone in my basement. You know, I'm like, I'm talking to all of all my colleagues. Really, And I said, colleagues, you're working at a fund No no, no, no, no, not quite. We're actually all on a discord together.

00:08:38
Speaker 1: Oh yeah, I have to say it does something pretty fun. Well, let's just between us. Is it one hundred percent mail?

00:08:44
Speaker 4: This one is?

00:08:45
Speaker 1: Yes, yes, Okay, I just sort of sort of I sort of assumed such, let me ask you guys a question, like I am like an emh bro, I think that generally, across all markets, most people like markets are generally well priced. And I think I think that with prediction markets too, in the specific sense that like I think it is not it is difficult to make money. But evidently in stocks, even though I think stocks are efficiently priced, some people seem to consistently make money in stocks, whether they're a Warren Buffett strategy or whether they work it along short and evidently some people will consistently make money in prediction markets. How do you guys think about market efficiency generally and how high quality that signal is on a price? And like would you say, like those of us in the media who sometimes increasingly quote prediction market quotes like these are do you find are they decent or when you look at them.

00:09:42
Speaker 4: Or they's like, oh, there's just easy pickings everywhere.

00:09:45
Speaker 7: Well, I think if they were all efficiently priced, you wouldn't be able to get some of the returns that some people in this group have gotten. I think it would be much harder to enter and sort of crush markets repeatedly if the price signal were better. I guess where I see it is that there is a lot so who I would call kind of prediction market evangelists would like to go out and say, oh, prediction markets are the future. They're this incredible price signal, and you know, they're more accurate than all these other places. I don't really believe that. I've just seen too many markets that are way way off. The reason that I think prediction markets have value is because there are consequences when you're wrong, and there are consequences when you're right. And we have so much kind of expertise in the world that says a lot of things under the expert banner, but then doesn't really have any bills to pay when they mislead people or when they're wrong. And so really the most appealing part of this ecosystem for me is that when you're wrong, you pay a price. And I think that we might have a better expert culture in this country if that were true in media that covers economics and politics.

00:10:53
Speaker 6: It depends on the market certainly in terms of you know, how correct these prices are going to be. But you know, going back to what Joe was talking about in the intro about the zero sum nature of these prediction markets, a lot of the events, most of the events that we're betting on our zero sum. You know, we have an election and two campaigns spend a ton of money, and then one wins and one goes home. And the nature of politics in this country especially, but all over the world is that people live in totally silent environments and believe different things and often engage with reality in totally opposite ways. And having a mechanism to, you know, essentially bet your beliefs and try to find real truth is you know, it's not going to be a perfectly efficient way, but at the moment it seems to be better than at least anything else we've got.

00:11:45
Speaker 7: Well, I think to Daniel's point, you know, unlike inflation, politics brings out matters of the heart and is I think much more subjected to that siloed effect. I mean, you can look back at the Los Angeles mayor primary that just happened, and Spencer Pratt price to not just make the top two, but to actually win the mayorship of Los Angeles got so unbelievably high, and there was this right wing media ecosystem and sometimes even a mainstream media place that was like sort of flirting with this idea like can this happen? Everyone I know is saying that this is live And there wasn't a sharp that I knew that didn't have one of the biggest positions of their lives on Spencer Pratt not winning the mayorship of Los Angeles because the math was just not there to be mathing. Los Angeles is a Democrat plus forty two city. A Republican is not going to win that race. But this sort of siloed media of people sort of only following certain accounts on Twitter and watching certain media can lead to what feels like to them an abundance of evidence in a certain thing happening. And in that sense, I think that elections will always be some of the most mispriced markets, because you know, people don't really have a heart connection to gosh, I really believe I want inflation to be three point six instead of three point eight. I do think that the future of economics markets is probably a tightening, But people like Daniel are fortunate because I think elections will always be a little softer.

00:13:31
Speaker 2: This was my problem in the last election. I spent way too much time on Reddit, and so I thought Harris was going to win, and then I was very surprised just to press on this further. When we talk about you guys having an edge in this market. You know a lot of people will say a lot of investors will say that they have an edge, and usually it's like they got lucky a few times and then they built a whole narrative around it. But how would you describe what you are doing differently to so you mentioned, you know, looking rationally at the numbers, keeping feelings out of it, social media echo chambers, that sort of thing. But you're also doing some original on the ground reporting. You have your own models. What is the edge exactly?

00:14:16
Speaker 6: A lot of it is really just work that we put in. So's it's being open to changing your mind, trying to quantify and test your assumptions with politics, it's a lot of history. I mean, it's just knowing this has happened before, this is the trend, we think the trend might be, you know, this big this time when it was only you know, half as big last time, and then seeing numbers come in and trying to stay calibrated with your assumptions. When you win an election or win a bet, you don't just say yay, I won, You say, how much did I win? You know, what was my belief in the probability, just like really digging in and unpacking it. And then as the money has gotten bigger recently, we've started trying to learn more. We tried to do some polling and the Texas Primary commissioner own phone polls. Some of the guys in the group have done this live door to door polling, which we did a bigger trip with Adam that's written about in the article. But it's mostly just work and openness to data and changing your mind.

00:15:20
Speaker 7: I think Daniel's even under selling how good the elections team is in our group at elections and what they do. I mean, I can tell you for the Los Angeles mayor's race, these guys had a model built of on election night what certain areas should look like in terms of the early vote, that in person vote, what it would take for Pratt to have what he needed. You know, what we know from California is that so much vote comes in late that really predicting what that vote is going to look like. So these guys did historical work by precinct and region, and on election night when Nitya Rahman was in tears speaking to her support because she thought she lost Daniel and the crew were betting on her to make it to the top two because they had a better vibe on her chances than I think her actual campaign did. So I really I think he's It would be hard actually to over sell how good this election's crew is with data and not just needing days to solve it, but being able to really solve on the fly based on incoming numbers.

00:16:43
Speaker 1: Even though, like my assumption was that prices are somewhat efficient, I do notice that on election nights there's a lot of noise based on timing of vote batches with multiple elections. We even saw it in the recent Canadian Prime minister election, for example, where there was a splike for qualif on election because of some early votes that were clearly not representative. So that always does make me sort of question whether I should ever be quoting these things. But Brian in Adam's article, and maybe Adam you can talk about this. Your inflation models are described by some economist as quote being like, no stradomis of inflation. Why give us a general overview of what you're doing and explain to us why you are not working at a I think Matt Levine talked about this in whre's newsletter. It doesn't make sense if you're like an inflation phos damas, why aren't you managing a you know, multi billion dollar rate hedge fund or something.

00:17:34
Speaker 8: Well, to be clear, nobody's called.

00:17:36
Speaker 7: But okay, Well, the first thing that I did was the BLS has a formula to the way that they take all of their price inputs to calculate the inflation number. As I know your audience knows, but just to set the table, it's not one big number. It's two hundred plus subcategories of how prices have moved, which we call the basket of goods, and the basket of goods changes what percenta each subcategory is worth every month based on how much Americans are spending on it. So this formula to me, I mean, look my degree, I have an undergraduate degree in drama. I'm just a theater kid from the Midwest. Like, this is very complicated. I'm sure that I know less about the macroeconomy than any guest you've ever had. But I rebuilt their formula on Excel. It took me like three months to figure out exactly how they math the formula, and a lot of really helpful public servants at the BLS answered my questions about just how the math works, and then you go from there to predicting the prices. There are some price categories that have public data, like gas and natural gas and sometimes cars, but most of it is just trend work and guessing and looking at the last six months and sort of where prices seem to be heading.

00:18:55
Speaker 8: But the real thing for me is I just think that this.

00:18:58
Speaker 7: Says a lot more of about the sort of softness of the people, the investment banks and people who predict this in the institutional end than it does about me, because when you predict inflation, it's not even a prediction, it's in the past.

00:19:15
Speaker 8: It's data that.

00:19:16
Speaker 7: Has already happened. And it has been very surprising to me since I entered this space that the places who advise billions of dollars of capital with their inflation forecasts aren't better at this.

00:19:29
Speaker 2: Well, this is what I wanted to ask, because, by the way, BLS employees very helpful, very helpful. You actually call them on the phone, they will like walk you through stuff. But on that note, when you describe just like you know, working out the BLS formula, calling some people up and asking them how it all works. Why aren't more people doing this, either in the prediction market or in traditional finance.

00:19:56
Speaker 8: I mean, you'd have to ask them.

00:19:57
Speaker 7: You know, it's shocking to me when and you know, it just really shouldn't be the case that my average absolute error on predicting inflation is better over the last two years than the Bloomberg consensus. I'm just one guy with Excel, and they are have pretty much unlimited resources to find this data. It's you know, not just shocking from a kind of what are they doing and why aren't they trying harder? But I think we find in both economics and elections that expert forecasts really shape expectations and that can play a big role in narrative creation and how the public response to that. I mean, I've seen many times the headline on Bloomberg twenty minutes before the inflation number will say inflation to show blank, as if it's a foregone conclusion because Goldman and JP Morgan and Bank of America have said so, And then when the inflation number is wrong, they just change the headline like that never happened. And the market reacts to it either being above or below that expectation, which maybe wasn't that good of an expectation in the first place. So I don't I don't know why they're not trying harder. I mean, they certainly have more resources to chase down these numbers than I do.

00:21:16
Speaker 1: For what it's worth, Like, the best inflation people we know all did it the same way you did, Brian, in terms of like, actually the bottom is up approach to learning in the formula, like all the best inflation and of course, like someone like a marys Reef comes to mind, Yeah, he's a handful of people who like, really, yeah.

00:21:34
Speaker 5: And this is what I mean, This is what not just Brian, but all of the sharps that I talked to, whether they're doing inflation, or they're doing elections, or they're doing meteorology. How much is it going to rain next weekend in New York City whatever, they're all just making calls and going on the ground and talking to people and gathering so much data. They're calling meteorologists, they're calling geophysicists, they're calling you know, Bloomberg reporters. In many instances, you know, I talked a few sharps like, yeah, I'm on the phone with Bloomberg reporters all the time. But you know, I think these a lot of these sharps are just you know, they're constantly on the fun gathering information, which makes sense, right. This is also what you do if you're at a fund.

00:22:11
Speaker 4: Yeah.

00:22:12
Speaker 1: Can I ask this is more of like a sort of I don't know of a tactical question or market structure question. Maybe it's two interrelated things. So sometimes an event happens and then there is a period of time before it results.

00:22:25
Speaker 4: And Kelshy and.

00:22:26
Speaker 1: Polymarket have different approaches to resolution. Polymarket is based on a sort of third party quote oracle end quote that is also sort of like whatever, and then Kelshi is more centralized. And I'm curious, like what your trading philosophy is. It's like, Okay, you get the LA mayorship, right, do you wait until resolution or do you sell it when it hits ninety nine percent and then move on to the next big thing? And is there alpha or profits to be gained in holding from the ninety nine to one hundred during the period of resolution? And is it different on either of the two sides.

00:23:04
Speaker 7: I think it depends how much time it is and what other plays are available. I mean, usually you know, if if a market is going to resolve in the next forty eight hours, I mean, you're pretty much always going to hold that from ninety nine to one hundred. But there are certainly elections that the answer is clear, and then you know you're not getting that payout for a month. And I mean usually I can turn ninety nine cents into a dollar over a month faster doing something else than waiting. But I suppose it depends on the context. I mean, I don't know, Daniel might have a different approach.

00:23:36
Speaker 6: It's really important to know the difference between is your market ninety eight or ninety nine just because of the time it's going to take to resolve, or is there a one or two percent chance of it actually going the other way. I try really hard to avoid these rules disputes and these you know, thorny markets where it ends up kind of a debate. I think polymarkets system is problematic. They have basically undermined their oracle and now every market just settles on how Polymarket clarifies. I have generally, for the most part, been happy with Calshi's resolutions, but when you trade in stuff like will a cabinet member get confirmed or who wins an election, it is almost never up for debate, you know, twenty twenty notwithstanding you have clear answers, and so it just depends. I'm holding the Peruvian election right now from ninety nine two one hundred because I don't have anywhere else to put the money. But if I had another play that I liked and I wanted the money freight up, I would sell it in a second, and you know, put it in whatever else there was.

00:24:38
Speaker 7: Let's talk when we get off the call, I got some places for you.

00:24:43
Speaker 2: Since we're talking about probabilities changing, I wanted to bring up this. There's a long running debate, I guess about whether prediction markets are actually better than traditional polls when it comes to forecasting election results, or whether they just look better because they were able to update faster as the results come in. And Daniel, I'm very I'd be very interested in getting your take on this, like, are these genuinely producing better probabilities or is it just a matter of speed.

00:25:14
Speaker 6: Yes, but it depends on how much money is coming into each side. A really noteworthy election, I think recently last November was the New Jersey governor's race where the polls really all said this was a pretty close race, you know, two, three, four or five. One of the guys in our channel had made a very simple model, not even really a model, had basically just said, you know, Kamala won this state by I forget five or six. Trump's approval had fallen maybe eight or nine points since then, you know, Trump was president instead of Biden, Kamala or Cheryl's probably going to win by about fourteen. And this was just you know, sketched out on prior's on and Upkin very quickly three months before any polls, and then all these came in showing this close race. Very very few polls showed a big, big margin, and we actually we didn't ignore the polls. We kind of tried to standardize them all. But we did genuinely believe in our channel that this was a twelve thirteen to fourteen point race that the polls didn't show at all. Now, we couldn't make the prediction market prices reflect that because there was so much money on the other side. So you know, ultimately the prediction markets I think were higher than the polls and pointing to a higher margin, but still well below what it would eventually come out to. So you know, on an election where the liquidity on the you know, the square side is large enough, the prediction market prices are still going to be wrong. I mean, you can look at Pratt again. You know, Pratt's price was never twenty seven to be the mayor. It was, you know, less than five, always in truth, but it was also still that twenty seven was still much more accurate than anybody's bubble Who would have said.

00:26:55
Speaker 1: But actually this brings me to a question. I want to go to the bubble question again.

00:27:00
Speaker 8: Aim.

00:27:00
Speaker 1: Obviously your piece was great. There was another great, great piece of prediction markets journalism in the last year about Alan Cole, who had bet his life savings that Elon Musk wouldn't actually reduce the deficit very much, and he like knows the deficit very well and he's like, this is never going to happen. And the money quote in that article is from Ellen's wife who said, I read through the comment section in the Prediction Markets and they all seem like idiots, at least relative to her husband. And therefore I was very comfortable with him risking all of our family life savings on this one particular.

00:27:30
Speaker 4: Bet.

00:27:31
Speaker 1: I'm curious, like if you like, okay, when maybe there's sort of bubble mentality emerging, or it's like this is a price that reflects people not getting good information. How often in your group can you use the comment sections to gauge like, wow, there's a lot of dumb money on this contract.

00:27:50
Speaker 7: Always bet against the comment section, okay, real? I mean not always, but.

00:27:57
Speaker 8: It tends to be.

00:27:57
Speaker 7: It tends to be a guiding principle because sharps tend to keep their mouths shut except when talking to each other, and generally the more ideas or comments there are sort of advocating for one side. I do think that tends to be the wrong side.

00:28:17
Speaker 8: Yeah, I was going to.

00:28:18
Speaker 6: Say it was surprising how or is surprising how reliable the comments indicator is. Back on predicted there was actually a lot of good information. People hadn't built these discard networks. They hadn't you know, ended up in their silos where we talk about all the useful information ourselves, and so there was a lot more sharing and helping. And you know, part of that is also because the money's gotten larger, the importance of protecting your reliable information is so useful that you just don't see, you know, someone like me or Brian going on to kel She's you know, message board and explaining, no, you guys have it wrong because you're not accounting for this, that and the other I mean predicted.

00:28:58
Speaker 7: Having an eight hundred and fifty limit, you know, made it very possible for you know, the limit mint you could share like I'm done, I can't fill up anymore. But Calshi not having kind of a meaningful position limit, you know, changes that equation dramatically.

00:29:16
Speaker 2: Since we're talking about betting against the comment section. You know, there was the article in the journal recently talking about how on poly market, sixty seven percent of profits go to zero point one percent of accounts. And we also have more professional investors who seem to be expressing some interest in getting into this market. If a bunch of people are just losing money on this, which it seems like they are, does the dumb money eventually go away and it becomes harder for you to you know, make these these bets.

00:29:49
Speaker 6: It should That's been the history of you know, the poker boom got harder. Daily fantasy sports was big money for a lot of people that got harder of our time. You know, certainly it's should happen that way. So far, certain markets have gotten harder. Elections appear to still be dominated by vibes and emotions. You know, we we'll see. Hopefully these prediction market spaces are still new enough that there's a lot more people still to be onboarded and anything can happen, But so far they still seem to be pretty beatable.

00:30:20
Speaker 4: Yeah.

00:30:20
Speaker 1: I remember during the online poker when people like talk about like soft tables and all these suff and then like eventually like they all lost their money and they were like notice soft tables, and then the yeah, then it was just all like sharks versus sharks, and the only when it's like you go to like the high limit room at a casino and there has to be like, you know, there has to be some celebrity there or like a shake or someone who has like a bunch of money who just wants to lose to pros there that night. But if it's all like you know, Adam Ivy and all these guys playing high limit poker against each other, the only one who's they're just going to grind each other down in the money.

00:30:58
Speaker 4: It's going to go to the house.

00:30:59
Speaker 1: How did how did you guys do on the twenty twenty five Romanian election?

00:31:05
Speaker 8: It was the dark day for our channel.

00:31:09
Speaker 1: Yeah, tell us about the day. I tell us about the Romanian election and the dark day for your channel.

00:31:14
Speaker 8: Yeah, Daniel, what happened?

00:31:16
Speaker 6: So in the first round, well, the first round of the Romanian election was actually a null due to Russian involvement. It was a very strange event. But the next first round, this guy Simon won it by I believe about twenty and it was headed to a runoff. And basically, in the entire history of European runoff elections and even you know, expanding beyond European, nobody had ever really come back from a deficit that big. And there was also a correlation where the places that the other candidates had done well, this guy Simi and the leader had also done well. So it really just seemed at the beginning like he would win as easily, and a lot of us put a lot of money on it at various times. He then proceeded to leave the country skip all his debates, became a laughing stock on Romanian media, which way to be clear, did not pick up on just how much he had become a joke in Romania, and so it ended up being a case where a lot of Romanians were betting a lot of money against a lot of internet politics sharps. Yeah, I mean they were right, we were.

00:32:45
Speaker 2: How do you feel about insider trading in prediction markets, because this is also one of the big debates now, the idea that people I don't need it's not illegal, right.

00:32:55
Speaker 1: Well I figured it.

00:32:58
Speaker 2: I can't get clarity on this, but but I know there are some charges against people for using like legally protected information to make these bets. How do you feel about the idea that there you might be betting against someone who is actually like, fully in the room and fully informed.

00:33:16
Speaker 8: I feel like.

00:33:19
Speaker 7: There's sort of a mixed feeling here where one of them on one side, you're like, well, yeah, that's really terrible for retail traders to know that they are going to be going against someone who literally has the answer. On the other hand, you know, I think one of the early arguments for why prediction markets should be allowable is that they can provide price signal by allowing all information that exists to come into the public sphere.

00:33:53
Speaker 8: And not stay private. I mean you can imagine.

00:33:56
Speaker 7: A scenario where I mean you could cook up a very Hollywood scenario, which I won't, but you can imagine where someone who was aware of illegal activity and had no way to make that public, we're trading on that information and sort of creating a little bit of price signal there.

00:34:14
Speaker 8: I don't know.

00:34:15
Speaker 7: I mean, you never want to be trading against an insider. And of course cal She and poly Market would very much like people to think that there are no insiders at all, that they have policed this aggressively. I don't really find that to be the case. I could tell you specific markets that I am one hundred percent sure that I lost to someone with inside information.

00:34:37
Speaker 8: I think you can.

00:34:37
Speaker 7: Pick out a market and see is this possible for this market to be insidered? You get a little bit of a sense of what price movement looks like when someone actually knows the answer. Huge volume coming out of nowhere at a price that hadn't been traded on before. I can tell you I would bet my life savings that someone who knew the Critics' Choice Awards winners last spring knew that Jacob Aloradi was going to win best supporting actor the night before because he went to one from one cent to forty cents on massive volume, which was a trade that I lost because I really didn't think he was going to win. So, you know, we've taken some of those l's. I think ultimately you want to not allow it, but it's hard because it also can be part of giving a price signal on important events.

00:35:26
Speaker 3: So what I'd like to see.

00:35:27
Speaker 6: And what I think we're moving toward is the sites and the companies making the serious insider trading not make financial sense for the people that do it. So we just saw the guy who insidered the Google year in search lost his Google job and I believe charges were referred. The military guy who bet on the invasion was arrested. You know, he made something like four hundred k and now he's facing charges. I mean, there's no way that math works out for those people, right, So it's really the small insider ones. But then you also have these situations where it's debatable what's an insider, Like there were insiders on the super Bowl performance market where you know, somebody who has no actual attachment to the gig or the performance, you know happens to hear something that you know makes them no more than the market, but they really have no technical relationship of being an insider. How I don't think you can police that, so I like to stick to stuff. You know. Again, like in elections, you can't have insiders. We've got a lot on the cabinet confirmations, and that was interesting because you could have insiders on those. You know, some staffer to a senator knows how their person's going to vote. You know, you just kind of have to ask in every event, you know, who's my counterparty? Could they know more than me? And in situations where someone could know more than you, you really got a size appropriately and make sure that you're not just blowing a ton of money to somebody who only isn't it because they know more than you?

00:36:55
Speaker 1: Just for what it's worth. Not that my opinion matters at all, but I don't think the government should be expending resources to police insider trading on the length of the Super Bowl halftime show, because it's like, it doesn't matter if you guys lose a bunch of money to an insider on that. I don't care because, like I think, like regulated while markets are a good thing, I do not want like, you know, public resources protecting people who are like gambling on it. But just you know, I'm curious and maybe all three of you like this story and people discovering that you guys have this discord, et cetera. Like from the perspective of the companies, Like I could see the Calshies and poly markets of the world loving this because they're like, look, a bunch of people get in their minds that they could be Brian and Daniel and they could have a crew and do a bunch of information and when or I could see them just like it because they're like, wow, you know what, I'm not going to uh start, I'm not going to trade because I do not have anywhere the means to come close to this group in terms of like the resources required to invest to trade consistently. Well, I'm curious, Like what you've seen is like are stories like this good for the markets or they would they rather sort of perpetuate the illusion that it really is like totally random. I just made my rent money because I knew it wasn't raining today and that's simple, which is how some of their ads are. And this is like really like the sort of the advertising, the misadvertising, et cetera. I've seen you know, you've seen it from both of the two major platforms.

00:38:22
Speaker 5: At a high level, I'm very you know, concerned with like, how are people going to read this thing? And I think my hope, and you know what folks have told me is, wow, I didn't really realize that I was the dumb money. Yeah, And I don't even think I realized, you know, at the beginning of this journey that like, I was the dumb money. And so the hope I think is that you know, people read something like this, people listen to a conversation like this, and they think, oh my gosh, Okay, I maybe don't stand a chance against Brian or Daniel or the rest of these guys in these discord groups. But you know, Cashi and polymarket can kind of flip the script and tell you the other story.

00:38:56
Speaker 7: Yeah. I mean, I think what is in the long term good of prediction markets continuing to be legal and regulated is stories that are like Adams, Because a lot of journalism reporting on prediction markets has sort of focused on, Wow, isn't it crazy that people are making money on how long a handshake will last and what words someone will mumble at a speech, And.

00:39:26
Speaker 8: I mean, my.

00:39:27
Speaker 7: Personal opinion is that people should be able to bet on things because it's their money. But these are not important questions that prediction markets are built to answer. And I think that in some ways Calshi and poly Market have been so aggressive in trying to make as much profit as possible, as quickly as possible, that they have gotten deep in markets that are not only pushing against state regulation, but are clearly not the kind of important social questions and economic questions that prediction markets are are are really should be answering. I mean, what I appreciated about Adam's story was that it was essentially about people who are going the extra mile and working really hard to try to answer questions that actually matter, who will win elections, what prices will look like, and in some cases people who are working harder than you know publicly accepted experts on those So I think for the long term existence and regulation and legality of prediction markets, you know, stories that focus on I want to say, people like Daniel and I because it's not about him and I, but it's about what questions are are we all trying to answer? What puzzles are we trying to solve and if those puzzles matter to the general discourse in the economy, that then absolutely and should be legal. But some of the extraneous stuff that is just you know, a silly, a silly excuse to gamble, you know, I don't think that that is really the future of where this should be headed.

00:40:55
Speaker 8: Yeah.

00:40:56
Speaker 2: I think that's an important point because we talk about, you know, the price signal all of this, and the price signal really doesn't matter if it's like a dumb question being asked. Right, One more question for me on your research process. How much of this has been enabled by AI and the tools that are now at your disposal.

00:41:16
Speaker 6: AI is very helpful for getting started on something like an international market, or especially for searching in foreign languages, where it can intermediate the language barrier for you. There's basically been I think very few of us use much AI for modeling. Some of the guys who code more have been using claud code a bunch just to do some statistical stuff, you know, quicker and easier. But the people who just ask LLLMS a question and think that that gives them an edge on a market are some of the squarest money out there. You know, these lms will tailor their answer to what you ask, and you know if you ask the question a certain way, it will tell you this is the probability, and it will ignore.

00:42:06
Speaker 2: What an insightful question. You are on the right track.

00:42:09
Speaker 1: Wait, is square money? Is that what you guys call soft money these days?

00:42:13
Speaker 4: Or like? Is that the square money?

00:42:14
Speaker 7: Is that?

00:42:14
Speaker 4: Like them for like a soft table or whatever?

00:42:16
Speaker 6: Right, there's there's plenty, got it?

00:42:18
Speaker 4: Oh yeah, of course the squares of the show.

00:42:20
Speaker 6: You saw tons of this again, going back to Pratt, you know all the people on Twitter my you know, LLM give me the chances of this for one and a trillion, et cetera.

00:42:28
Speaker 7: I've asked chat GPT things like what will inflation be next month? And it will give me a number and it'll be like, wow, great question. I think it's going to be this because of these reasons. And then I'll just say, without without anything else, I'll just say that's too high, and it'll say, you're right, I'm glad you brought that up. It actually will be lower for these reasons. And then I'll say that's too low and it'll be like, you know what, thanks for bringing that to my attention. So not only is it is it telling you what you want to hear, but but it only has grounding in other expertise that it that it gathers. And if that expertise that I'm already trading against is beatable, then I don't really see why the AI is any less beatable than that, at least in its current incarnation.

00:43:16
Speaker 3: I lied.

00:43:16
Speaker 2: I have one more question, very important question, Daniel, why are you called carnitas Taco?

00:43:21
Speaker 6: So when I used to play a lot of poker, I would often be in tournaments all night and then stay up for breakfast tacos in the morning, and that has stuck with me as a DJ name, a Twitter name, prediction market name. Just that's that's my online name.

00:43:39
Speaker 1: Brian, Daniel, and Adam, thank you all so much for coming on at I think that actually worked. That was a little bit complicated organized, but that was a great conversation. And really appreciate all of you taking your time.

00:43:48
Speaker 4: Thanks, thanks for having us.

00:43:49
Speaker 7: Thanks it's great to be here.

00:44:05
Speaker 3: Tracy.

00:44:05
Speaker 1: That was really fun. That was That was sort of a complicated episode to do, but I thought that was like a I actually felt like I.

00:44:10
Speaker 4: Learned a lot in that conversation.

00:44:12
Speaker 2: Yeah, absolutely, I didn't realize that the poker boom had sort of gone through a similar thing where you had a bunch of people playing online and then they just kept losing and they left.

00:44:22
Speaker 1: Yeah, because the story was like do you remember like how the why the online poker boom like really happened?

00:44:28
Speaker 2: Vaguely, but remind me.

00:44:29
Speaker 1: Because this guy named Chris Moneymaker won the World Series he was to his name was Chris Moneymaker and he was a total nobody and he won the World Series of Poker Las Vegas. So everyone got in their head that like, actually poke, anyone can win a lot of money and playing poker, and that was the sort of catalyst for like poker becoming this thing that like ESPN would cover and et cetera. It's huge wave after wave, and then eventually, like there was I think sometime in two thousand and nine or two thousand, I think it was two thousan nine, it was a big government crackdown on some of these like quasi illegal offshore sites.

00:45:05
Speaker 4: But that was already at that point.

00:45:07
Speaker 1: I think like the minnows were coming out of because a bunch of people were losing that Chris.

00:45:12
Speaker 4: Money Maker was really a fluke.

00:45:13
Speaker 1: There were a lot of interesting things on it, including is to your point that like will eventually the square money or the dumb money or the minno is just like flush out of the system and then it's sharp versus sharp and only the platforms are making money all the stuff about like, okay, what is a healthy future for these predictions markets look like? But also what is you know, the real work involved to actually have an edge. It's like, if you are listening to this and you think you're gonna make money, you probably aren't unless you like actually have some reason to think that you're like putting in work.

00:45:46
Speaker 2: It kind of emphasizes that in the age of AI, like the edge is still going out and finding new data, like picking up on turning points, because most of the lms are still very backward. Yeah okay, and I guess having that sort of like human connection.

00:46:04
Speaker 3: Well if you think about it, like you have to know the vibe.

00:46:06
Speaker 1: Yeah right, But if you think about it too, it makes sense because one of the things that like a lot of our a I guess we'll talk about is the value of proprietary data, right, and so so it actually makes sense like what is quote scarce of the age of AI. Well, someone knocking on doors and asking questions of people rather than someone just asking the model what they think is going to happen.

00:46:28
Speaker 8: I also think that story.

00:46:29
Speaker 2: I didn't mean to turn this into another AI. I was just curious.

00:46:33
Speaker 1: Yeah, No, it's a good important question. I want to I'm curious how much money was lost on that twenty twenty five Romanian election, because I know that that was like a big upset and so and he walked through respect the candor of admitting they really with one, that one, and then that all the randoms in Romania who are paying attention to it knew more than the sharps on that one.

00:46:53
Speaker 2: Shall we leave it there?

00:46:54
Speaker 1: Let's leave it there, all right?

00:46:55
Speaker 2: This has been another episode of the AU Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

00:47:01
Speaker 1: And I'm Joe Wisenthal. You can follow me at The Stalwart. Follow our producers Carmen Rodriguez at Carmen Erman, Dashill Bennett a Dashbot, Kilbrooks at Kilbrooks and Kevin Lozano at Kevin Lloyd Lozano.

00:47:12
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00:47:18
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00:47:24
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00:47:47
Speaker 3: Thanks for listening in