Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara

Tarek Mansour and Luana Lopes Lara are the co-founders of Kalshi, the first federally regulated prediction market in the US. They sit down with John and Matt Huang to discuss growing their revenue 11x in six months, why they sued their own regulator to list election markets, and how they are building the "New York Stock Exchange of events." They cover why prediction markets are an antidote to social media polarization, the mechanics of market making for culture, and their vision for trading everything from GPU shipments to the Oscars and the weather. Timestamps (00:01:39) Suing the government (00:14:42) Why now? (00:17:12) Kalshi by numbers (00:20:58) Solving market making (00:31:33) Agentic trading (00:33:43) Sharps (00:38:45) Stripe Connect (00:39:33) Evolving Kalshi (00:44:50) Who loses from Kalshi? (00:47:35) Insider trading (00:53:28) The ethics of sports contracts (00:58:08) New derivatives (01:04:27) Politics Article(s): On the Observational Implications of Knightian Uncertainty https://www.aei.org/wp-content/uploads/2018/06/Knightian_theory_wp.pdf – Kevin Hassett & Weifeng Zhong (AEI)The 2028 Global Intelligence Crisis https://www.citriniresearch.com/p/2028gic – Citrini Research

• 0:01Speaker 00

  Tarek Mansour and Luana Lara are co-founders of Kalshi, one of the new prediction market firms that rose to prominence in the November 24 elections. They spent four years pre-launch fighting for regulatory approval to build the first onshore prediction market in the US, and now trade more than $10 billion each month in prediction contracts.

• 0:17Speaker 01

  Cheers.

• 0:18Speaker 00

  Cheers to you guys.

• 0:21Speaker 00

  What is this that we're drinking, Luana?

• 0:22Speaker 01

  It's a Brazilian beer.

• 0:23Speaker 00

  Okay.

• 0:24Speaker 01

  It's our most famous Brazilian beer, I would say. Very light. I don't know if you like it.

• 0:28Speaker 00

  I like it. So what is the split between you guys? Maybe in terms of responsibilities, but more interestingly in terms of outlook.

• 0:34Speaker 01

  Well, we actually come from the exact same background. We studied math in CS at MIT, same internships, everything. But I'm a very, very optimistic person. Love taking risks. I think everything's going to work out. He's very paranoid, more on the negative side. So it's always like a very good, I think, balance. And I think that real is outside of what we do day to day. That's really the difference between us that works out.

• 0:56Speaker 02

  I mean, there's a lot of background. So I was going to be a trader. That was really what I was going to do. And you know, when you're a trader, and you probably... I don't know if you've ever met sort of or spent enough time with the persona, but it's like a very...

• 1:08Speaker 03

  John is a secret trader.

• 1:11Speaker 00

  At heart, yeah.

• 1:11Speaker 02

  But if you're a trader, you're like an expected value calculator. Like, I think about these sort of tail, really bad outcomes, all the time. And Luana oftentimes doesn't. And I think this is the thing that actually leads to great outcomes.

• 1:24Speaker 00

  Okay, so I want to ask about the starting act. This is really interesting. You guys started Kalshi, and for several years were not able to operate until you got CFTC approval. And that's interesting where just one, most companies don't start out that way. And secondly, I feel like the Silicon Valley standard that, you know, people sometimes trot out as a criticism is sort of the PayPal, Uber, early days model, where you start doing the thing and maybe retroactively, a structure is put on top of it, but you do a bit of ask forgiveness rather than permission in the very early days. And so can you just tell a little bit the story of how you started and that approval process? And then I want to get into whether that generalizes to other companies.

• 2:09Speaker 01

  Yeah, I think that the approach we took from the start was that financial services or healthcare. I think you can't ask for forgiveness. I think there's a big difference between losing people's money. You see what goes wrong with like an FTX example. That can go very wrong. With healthcare, there's a lot of other massive examples of it going wrong. And we wanted to do things the right way because also when we look at the market, we thought the biggest question to be answered was not, is this going to grow? It was, can we do this legally in the US? And we were like, let's just actually address the biggest problem first and go from there. And I think the strategy for a long time, people looked at it as the wrong strategy. I think up until we won the election lawsuit, everyone was saying the folks that went offshore, they're doing a lot better, they're growing a lot more.

• 2:53Speaker 01

  But I think once we won the election lawsuit and proved that the legal interpretation we had was right, and we could do the company as we want in the US, I think it just really, really took off.

• 3:03Speaker 00

  What were the timelines here? So when did you start and when did you win the election lawsuit?

• 3:06Speaker 01

  Right, so we started the company in 2019. We started in VC in 2019, and then it took us three years to be able to get regulated and launch. I think it was 2022 was that point. And then we won the election lawsuit at the end of 2024. And that's when we really started ramping up.

• 3:24Speaker 02

  There were a lot of like, I mean, there's some overlap between the timelines.

• 3:28Speaker 01

  Right.

• 3:29Speaker 02

  But maybe going back to the question and maybe we can talk a little bit more about the sort of history afterwards. But I think it was like a twofold sort of, or two-step process. Like, one, it was a pragmatic thing, which is, we felt like to get proper mainstream adoption and institutional adoption, the elephant in the room was like, could we do it in a regulated, credible and safe way? Because it's a complex marketplace, you're moving people's money. And we're like, we have to solve that problem first. That is the hard problem to solve. And, you know, that will be the road to success. The second thing was a bit more principled. We were, what excited us, like when we, you know, we created this doc, one page on Google

• 4:14Speaker 02

  Docs, and wrote a set of things like, why should we do this company? And why are we so excited about this? We wanted to build the next generation. New York Stock Exchange. We wanted to build a financial market that is in the US, that is credible, that is regulated. We were not very excited about this idea of building something offshore. That, you know.

• 4:34Speaker 01

  Yeah.

• 4:35Speaker 02

  And so that was really important because it's like, what kind of company do you want to build? And why are you doing this in the first place? There's many paths to success. We just weren't very excited about the other idea. We wanted it to be here.

• 4:46Speaker 03

  You're the first CFTC-approved prediction markets at any scale.

• 4:50Speaker 02

  Yes, yes.

• 4:51Speaker 03

  And still to this day, all the contracts are individually approved? And so?

• 4:56Speaker 01

  Yep, yep. We, every single contract we file with the CFTC and they have 24 hours to stop it.

• 5:02Speaker 03

  Yeah, yeah. Okay, so they get kind of a real-time feed of the contracts, essentially?

• 5:05Speaker 01

  Exactly, exactly.

• 5:06Speaker 02

  Yeah, and it was a kind of, it was a very long journey to sort of get to where we are in terms of the contract process and how it works. Because you've got to imagine, the first time we walked into the building, actually, the picture is right here. This is the first time we ever walked into the CFTC. You know, you walk in and you're talking about this idea and it's... You've got to imagine the regulators had started spinning. It's like, you know, you're talking about things that don't have a financial underlying. And then there's this idea of like, potentially hundreds of, tens of contracts, hundreds of contracts a week. And I mean, now we're like, you know. But there's all these things where the model wasn't really set up for this. So a lot of the process was actually like this iterative process.

• 5:48Speaker 03

  Yes.

• 5:48Speaker 02

  Where you're trying to figure out how to actually regulate this as you get feedback from the regulators and what can we do to satisfy their concerns. So it was a bit like building a product, but you're not building it for a customer. You're actually, you know.

• 6:01Speaker 01

  A regulatory market fit, in a way.

• 6:03Speaker 03

  And so now you've gotten uncomfortable with you shipping them unless they know.

• 6:09Speaker 01

  Right.

• 6:09Speaker 03

  Have they said no to anything recently?

• 6:12Speaker 01

  Not, well, the biggest they said no to was the elections. That's why we had to end up suing them. They said no for two years. But at this point, I think we've worked with them for so long that we know exactly, kind of like, they trust us as well as a self-regulated entity to know kind of what we can do and cannot do. So we don't do anything around war, assassination, those things that we don't do. So we've been... Within the parameters that we've worked with them, it's a lot faster.

• 6:36Speaker 00

  So sorry, the election lawsuit was they were willing to approve contracts generally. They were not willing to approve contracts around who would win the election, which is a pretty popular prediction.

• 6:46Speaker 02

  It's the biggest, right?

• 6:46Speaker 00

  Contract, yeah, yeah, yeah. The US presidential election. And so you sued the CFTC?

• 6:52Speaker 01

  Yeah.

• 6:52Speaker 02

  Our own regulator. I mean, and...

• 6:54Speaker 00

  Which is generally not considered a best practice.

• 6:56Speaker 02

  It was, I mean, okay, so, so we...

• 6:59Speaker 01

  It worked out.

• 6:59Speaker 02

  We started talking about the election market at the end of 21. And we started talking to, started, you know, engaging with policymakers, like talking with Congress, a bunch, and the regulator. And like, yeah, I think it's a good idea, it's a good idea. But then they weren't moving. We started noticing, like, okay, something is off. By the end of 22, they sort of like delayed the approval till after the election, what we call a pocket veto. That was brutal. So that was one of the hardest times in the company, where we had to lay off a bunch of people. But the harder part of this is that your team and some of your investors, or majority of your investors, kind of stopped, like...

• 7:36Speaker 00

  Believing in the idea?

• 7:37Speaker 02

  Yeah. But believing in the strategy, the idea, and it's a bit like, this is getting a little bit unhealthy. You know, you guys should do something else, like, you know, clearly this is not going to work out. But we could not, we couldn't get ourselves to do something else. We, we, we just couldn't. I mean, and, and so we're like, okay, we're going to try again. And end of 22, so imagine the team is at an all-time low in morale. They're waiting for a new strategy. A bunch of people left, a bunch of people, you know, got laid off because we had to downsize. And, you know, our message in that next stand-up was actually, guys, here's the 23 strategy, is we're going to try again.

• 8:14Speaker 00

  We're going to do the same thing.

• 8:15Speaker 02

  Same thing.

• 8:16Speaker 00

  But this time it'll work.

• 8:17Speaker 02

  And exactly, but this time it's going to work, even though every inch of evidence was pointed in the other direction. And, and I will say, a lot of this is, is her, like, the, you know, I wanted this to happen so bad, but I'm like, my rational brain was like, better listen to these people. And the one I was much more dogmatic. So we try again. End of 23, they block it again. And I was really at the point where I'm like...

• 8:39Speaker 00

  Okay, these prediction marketing is just not going to work.

• 8:42Speaker 02

  It's just, and then, and then the one I was like, well... The only thing we can do right now, out of the entire range of possibilities, is we've got to sue the government.

• 8:53Speaker 02

  And, I mean, yeah, at the beginning it was like, this is crazy. And we took it to the board, and we had Alfred and Michael at the time.

• 9:02Speaker 00

  Alfred Lin and Michael.

• 9:04Speaker 02

  Seibel from YC. And I remember that board meeting, it took a few board meetings, but it took, you know, a few times. At the beginning it was like, well, we have to tell you guys this is a bad idea. Like, you know, these are all the wizards about it, yeah. Because you're regulator, you're like now a 25-people company. Like, the government can do anything. Like, they can shut you down, take out the license. I mean, it is true. So, so rarely does it work. And even if you win, you will probably lose. Like, you will end up getting killed in the process. And it took a few times, and I remember very vividly, there was a meeting we had internally before talking to the board. And this was the night before. We had lined up the lawyers and everything. And I got like, cold feet.

• 9:45Speaker 02

  I was like, let's just focus on getting like a clearinghouse. We could focus on financial products. We can focus on all these other things. We don't need to sort of tank it all on this. And, you know, really bet the farm on this. And I remember, Luana and that call, I forgot the exact wording, but it was something along the lines of like, are you fucking kidding me?

• 10:04Speaker 01

  That sounds like me.

• 10:07Speaker 02

  And I realized, like, all right, I'm not going to win this fight. And, but then I, but the other part of me is like, we got to do this. Like, I knew, you know. And so we go into the board, and their response was basically, it's an anti-pattern, it's a bad idea, but a lot of great companies are built by an anti-pattern. There's something off, that is weird, that happens. And maybe this is yours.

• 10:27Speaker 00

  Yeah, it's a good way of putting it, that every company is different in some new way. And so, yeah, this could be yours. And what was the basis for the decision, where you won the election last year? Like, was there any interesting policy angle?

• 10:42Speaker 01

  Right, so the whole... Point is that the government cannot stop any type of contract unless it makes a finding that's against public interest. And it has to fall within certain categories of war, terrorism, assassination. And the CFTC was taking the stance that they were trying to fit elections into any of these things. They're like, oh, elections might be illegal under state law. And, you know, because betting on elections, there's this one state that in bucket shop law, they try to find something to stop it. And we knew we were very, very clear on the law, like elections have economic impact. If the elections have economic impact, they need to be allowed to trade on a futures exchange or derivatives exchange. And it was, basically, what the lawsuit did is it told the CFTC that they couldn't just do whatever they wanted.

• 11:26Speaker 01

  And that kind of like...

• 11:28Speaker 00

  The categories of prohibited, it needed to actually fall under one of the prohibited categories, which elections did not.

• 11:33Speaker 01

  Right, exactly.

• 11:34Speaker 02

  And I think that's important because the law, you know, the thing that we always say, like, the law applies to companies, but it also applies to the government.

• 11:39Speaker 01

  Right.

• 11:39Speaker 02

  Right?

• 11:40Speaker 00

  Well, you should make your point about maybe suing your, suing the government's underwriting.

• 11:44Speaker 03

  Well, I think certainly in crypto and prediction markets is sort of this unique thing of suing the government. But I was sort of surprised to realize that Coinbase has sued their primary regulator. In GovTech, SpaceX, Anduril, Palantir, all had to sue for various reasons. So it seems like it's actually more common than Silicon Valley conventional wisdom. So like, what, I guess, advice would you have, having dealt with the government, to people out there trying to build businesses? Like, what, what sort of situation would be, you think, right to actually make that kind of job?

• 12:23Speaker 02

  I think, I think it's a sort of no other option situation, right? Like that...

• 12:28Speaker 00

  So it's still painful. But did you actually have no other option? Because couldn't Calci have done fine without elections? I mean, elections are obviously very helpful because they're such like a big shiny thing. But I presume elections are not a majority or contracts today.

• 12:39Speaker 01

  I think it was just too important. And maybe that's the dogmatic or whatever, but it's like, it is the holy grail of market. It is like, that's the one that you can see the use case of the data the best. And you can see the use of the, and you can see the 2024 election, right? Like, the polls were completely wrong. And the markets were so much better at bringing that sort of information. And I think that it's the shining example of why these markets are forced for good and we need to have them in the US and regulated, which other markets are just won't have.

• 13:07Speaker 03

  So to John's point on PayPal and Uber and asking for forgiveness, there were other prediction markets operating. And showing real usage offshore. And so I'm wondering, how much did that help demonstrate that election markets weren't against the public interest? Like, did that factor at all into the court case? Like, the fact that people were already doing it?

• 13:30Speaker 02

  I don't know. But, I mean, specifically on the court, it was much more grounded in sort of like

• 13:37Speaker 03

  The law.

• 13:37Speaker 02

  Yeah, the law. Like, reading our law is the Commodities Exchange Act. So that's one of the financial statutes. The other one is the Securities Exchange Act. Instead of reading it and really interpreting it, and is the regulator overstepping? Now, I think for us, it was like a good way to learn, right? Because we could not learn about our product because we took this sort of regulatory-first approach, like where we will ask for permission first before doing something. And so, in some ways, it was helpful that you could have some data, some evidence, that could guide our decisions over time. And I think it also helped educate some people about the existence of prediction markets.

• 14:20Speaker 01

  Right.

• 14:21Speaker 02

  You know, they are here, here's how you can use them, etc. But I don't think, like, unregulated offshore players really helped from a policy perspective.

• 14:30Speaker 01

  Right.

• 14:30Speaker 03

  Could Calci have been started 10 or 15 years prior? Or was there some moment of openness in the CFTC? Was there some tech enablement that was required? Were stablecoins required?

• 14:43Speaker 01

  I do think that there is a part of it that crypto at the time was augur and there was like some very early prediction markets. I think that that, the existence of that made the CFTC also be like, we need a legal-regulated alternative to this. Because before, you could just say no to things. I do think that that played a role, but maybe like 5%, maybe 10%? I don't think it's more than that.

• 15:04Speaker 02

  The broader thing is like, I think, you know, there's always intellectual interest in prediction markets. And I think that started in the 50s. It is a better source of signal than most other mechanisms of getting signal, right? But there wasn't a real pain, I think, 10, 15 years ago in the way that there is pain in the last few years. And that pain, I think, is a sort of, I think the country is more polarized. I would say the world is more polarized. Social media has really bifurcated social feeds. You know, clickbait is rampant. Like, the incentive structure for most things that we read these days is clickbait. Whether it's a lot of traditional news or social media or other. So the There was more of a pressing problem, I think, that helped create the wave and this

• 15:47Speaker 02

  sort of adoption that you're seeing in prediction markets that I don't think happened or would have happened 15 years ago because the problem wasn't that painful.

• 15:55Speaker 01

  Yeah, and that's because like most of our users, like 80% of our users are actually just looking like consuming information. They're just coming in and seeing who's going to win the Texas primary yesterday and seeing like, okay, the polls are saying they're tied, they're not tied and all those things and that consumption of information is way more important and relevant now.

• 16:14Speaker 00

  Okay, so you're saying like algorithmic feeds, KALSHI markets do very well on algorithmic feeds and just maybe people wouldn't have been as interested as 10 or 15 years ago?

• 16:25Speaker 02

  Yeah, I just think that sort of there's a meaningful and accelerating rise of distrust in traditional sources of information and so you need a new one.

• 16:38Speaker 00

  Yep.

• 16:38Speaker 02

  And this does work, right? Like the incentive structure for prediction market is truth, right? It is more volume, it is more liquidity, which translates to better and more accurate forecasts. And it took a few iterations for people to start trusting it, but like, you know, as you start building a track record, people start trusting it and they're never going to use a better product, right?

• 17:01Speaker 00

  Well, to the point of substantive volume, can you guys give us the outline of, it seems like it's grown very quickly.

• 17:07Speaker 02

  So volume in February was 10.4 billion.

• 17:11Speaker 00

  So dollars of contracts traded.

• 17:14Speaker 02

  Yes, traded. And that's up 11x over six months, I think, or a little bit.

• 17:22Speaker 00

  It's growing so quickly, you don't even bother to go back a year because that's just ancient history.

• 17:27Speaker 01

  I mean, a year ago, it really is. It's like we just had, for example, one sports market, we only had the Super Bowl.

• 17:33Speaker 02

  In February, yeah, I mean, it's growing very fast, yes.

• 17:36Speaker 03

  Fastest growing company outside of AI.

• 17:37Speaker 02

  Yes, I think so. And we compete with some, I think, even some of the top AI companies. I don't know what cursor and anthropics last latest numbers are, but.

• 17:48Speaker 03

  I think 11x is very quick, even in AI.

• 17:52Speaker 02

  It's quick, and I think because it's a, so we, we are marketplace. It's a true marketplace that has all the attributes of, you know, has network effects. So, so what happens in those situations is that users retain better because there's a more diversity and more liquidity. Their, their participation and volume grows over time.

• 18:14Speaker 03

  Yeah.

• 18:14Speaker 02

  Which obviously grows their usage, but it also grows other people's usage because there's more liquidity in the system. And then they share it more with other people because the product is getting better.

• 18:23Speaker 03

  Yep.

• 18:23Speaker 02

  And so the sort of trifecta of factors is leading to this sort of growth.

• 18:27Speaker 03

  When some of your early growth depended mostly on other brokers, and I think you've evolved that mix today. Like, what's the broker mix and how do you think about that? Sorry, what's a broker in this context? Like Robinhood.

• 18:40Speaker 01

  I don't know how much I want to say.

• 18:41Speaker 02

  Well, to explain what that is, which is interesting, I mean, the.

• 18:45Speaker 01

  Well, I can explain the broker part, but I don't know what we want to share in the numbers part, but basically.

• 18:50Speaker 02

  That was a tough one.

• 18:51Speaker 01

  That's why we look at each other as like, alright. So because we're an exchange and clearinghouse, we basically function like the New York Stock Exchange.

• 18:59Speaker 03

  You'd never be here for her. Yeah, exactly.

• 19:02Speaker 01

  The Chicago Work and Tile Exchange. So brokers can connect to us, right? So you can go to Robinhood to trade stocks, you can go to Robinhood to trade on Kosh, same with Coinbase or whatever. And it's always been part of how we think about, we always wanted to be an exchange and a clearinghouse first. And actually connection to a Goldman Sachs or a Robinhood is very important for how we thought about, like, this ecosystem as a whole. In the beginning of last year, we launched the first broker partner that we had was actually Robinhood and then Weibo. And at the start, actually, when we were starting to ramp up, the brokers were a very, very big part of kind of how we started growing, which was actually very great because the brokers bring so much demand. Then we get all the market makers to come in because they want to trade against the retail flow.

• 19:44Speaker 01

  And then we could kind of like buy ourselves time to ramp up the direct product a lot to where it is now. But basically, how we think about it is, well, we really are the cores and exchange and the clearinghouse. And then you can access us through our app, website API, but also any broker. We're investing more into institutional now, international brokers. So you can be in Brazil and you can trade on Kashi, all those things, they're coming soon. But on the numbers, you can take it.

• 20:07Speaker 02

  I mean, maybe we won't share numbers, but the direct.

• 20:12Speaker 02

  What we call direct, Kashi direct, which is our Kashi.com, Kashi app, the consumer business. That has grown, you know, that has sort of dramatically outpaced the rest, our other, the intermediated or brokered business. And I think it's just that the brand has gone mainstream. I think people, when they think about, have a difference of opinions, of opinion on something, it's sort of becoming synonymous. They're like, oh, let me pull up Kashi and see the odds, or let me sort of place a position on Kashi. And there's, like, there's just a lot of organic growth now. And I think, I think that's going to continue over the next few months.

• 20:47Speaker 00

  You're describing how you grow the individual retail kind of side of the market, whether people coming through brokers like a Robinhood, or people coming directly to the Kashi website. There is also, when you're in exchange like this, you have to spin up market making. And, you know, in the end, like, you know, the New York Stock Exchange doesn't have to think too much about market making because just the economic incentive is there. And so, when something is at large scale, that's not as big of an issue. But I'm curious what that was like in the beginning. Like, were you guys doing the market making? Did you work with market making partners? Now, how do you incentivize market makers to participate? I'm just curious what the market making scale up has looked like.

• 21:27Speaker 01

  So, there's actually two groups of contracts on markets on Kashi, and they behave very differently, and the market making incentives are actually pretty different. So you have the long tail of markets, right? Like, the ones like, well, One Direction have a reunion, or, you know, all those things. And they are actually very hard to price, and because there's not necessarily a lot of demand, we actually have to incentivize market makers to come in. And there's like, we're creating incentives, all those things, for them to come in. And I think it's actually how we think about how to build our moat long term. It's actually how do we get very sustainable, solid liquidity in this long tail of markets?

• 22:00Speaker 00

  Yep.

• 22:01Speaker 01

  So we can get like, we have like, I think, 10,000, how do we get to 50, 100,000 markets we're still? But on the other side, you have the more classic, like, crypto, sports, all of those guys. And on that side, it's actually a lot easier to market make, because you have very clear, proven demand. It's a lot easier to price. So the market making incentives on this side is actually, we don't pay them for it. We just rebate fees, but it helps very, very, very. Hard conditions to meet. They need to have uptime of certain amounts, spreads and top of block size and all of those things. Because we see it more as like incentivizing stability of the book than it is incentivizing them being there.

• 22:35Speaker 00

  So what does incentivizing stability of the book mean?

• 22:38Speaker 01

  For example, if you think of a live game or like if you're trading like an hourly crypto.

• 22:43Speaker 00

  You actually don't want the price flying around a whole bunch if there's no new information?

• 22:48Speaker 01

  Right, exactly. Or even if there is, right? If someone is about to sort of touchdown, you don't want the book to just have like no liquidity whatsoever. You want it maybe to go a little bit wider, but you want people to be able to trade. And actually, when we go into the intermediated model, the brokers come with expectations that they have from traditional markets, right? So they're expecting, we want this spread and this size at any point in time, it doesn't matter. So we need to go to the market makers and like, how do we incentivize this? Even though if you think about, you should just let the markets do whatever they want to do. And if they're going to go way wider because they need to, it is. But we have to kind of play with incentives in a way to, for all of our users, including the brokers.

• 23:24Speaker 03

  But during those moments when the spreads would normally blow out wide, are market makers losing money then and they're cross subsidizing to the other parts where like it's more stable?

• 23:35Speaker 01

  Well, now there's so much demand that I don't think they're like, you can make money on spread even if the spread is like a little lower. But that's the point of the program, right? It's like, you have to think about all the benefits you get in this program. And then even if you're losing a little bit in this time, having the benefits is worth it.

• 23:51Speaker 00

  So you want tight spreads all the time for the major markets? Like that's what market civilization means? And that actually takes work to engineer?

• 23:59Speaker 02

  It's hard to get there. You have to, but there's more to it, which is, so I think the magic and the uniqueness of, I would say, or the special thing about prediction markets is that a lot of the liquidity is not what you consider a market maker.

• 24:15Speaker 01

  Right, right.

• 24:16Speaker 02

  It's people. It's, and so this goes back to the whole point. So maybe let's just go back to from first principles, right? Like, you know, there was, okay, the regulatory thing that we figured out, but then there's a liquidity problem, which is historically, it's like. A bit like we said, the New York Stock Exchange or the CME, okay, they're like, "We're gonna create a grain future. We're gonna take two years to figure out what it looks like, and we're gonna call all of our buddies, like, you know, the 50 market makers and that we all know, we all, you know, hang out Christmas parties together, etc. We're gonna get them ready, and they're gonna start helping us get this product launched, and then we're gonna market it for the next three years, and then it's just the same thing. The liquidity is there. But prediction markets is really different because now you have to create liquidity in these products

• 25:00Speaker 02

  on like a weekly, daily, maybe even hourly basis. Like, how do you do that, right? It's much more dynamic, there's new things all the time.

• 25:08Speaker 00

  I think it's counterintuitive to people that you have to incentivize market makers to create liquidity. Because like, in the stock exchange, you don't have to incentivize high-frequency trading firms to create, you know, sub-second liquidity. They are very excited to take on that project themselves and build the high-speed interconnect between New York and Chicago to accomplish and everything like that. And so, is this just the stage that prediction markets are at? Or is there something fundamentally different?

• 25:34Speaker 02

  I think this is where I was talking about, which is this idea that you need, so... So, maybe finishing that sort of line of thought, and then like, I'll get to the answer there. You now have a model where you need liquidity to be built on the fly, much faster, much more dynamically, right? And the market makers, the traditional Wall Street market makers, are not geared up for that. It's not like they can spin up a new desk to price like politics or price culture in an hour, right? And so, but this is the part that gets really interesting, which is like, and this goes back to the foundational principles around prediction markets, is like, a lot of these markets, the people that will price them the best may not actually be the experts or the authority figures that you usually would think about. It's actually...

• 26:20Speaker 02

  Random people like, you know, that live.

• 26:23Speaker 00

  Right, internet anons.

• 26:24Speaker 02

  Exactly, they're super forecasters. Those are, it's like extremely dispersed. You cannot find like a clearly defined the demographic, who they are. And I think that the thing where we got to now, and it took a very long time when you had to incentivize, is we have the community.

• 26:40Speaker 01

  Right.

• 26:41Speaker 02

  A strong community of super forecasters that are on CalShe. They can help price these things extremely effectively and fast. Where you, you know, you don't have, but it took a while to kind of get them incentivized and come in and spend the time and resources to take it from a hobby, which was a hobby to a part-time job. Now it's a full-time job because the pie is so big.

• 26:59Speaker 01

  And a metric that we can share is actually the, when you think about traditional market makers, the biggest percentage on a platform of a traditional market maker is less than 5% of the maker orders in that market that have matched.

• 27:11Speaker 02

  Of the liquidity.

• 27:12Speaker 01

  Of the liquidity.

• 27:12Speaker 00

  Sorry, say that again.

• 27:13Speaker 01

  Yeah, so less than 5% of the orders, so people come and make orders, less than 5% of the ones that match actually come from the biggest institutional market makers you'd think about.

• 27:23Speaker 00

  I see.

• 27:23Speaker 01

  Over 95 are just like.

• 27:25Speaker 00

  Peer-to-peer.

• 27:25Speaker 01

  Peer-to-peer, like, or funds that have like two people that just got started.

• 27:29Speaker 02

  Which is unusual for.

• 27:30Speaker 00

  How many of those small, full-time, little shops are there?

• 27:34Speaker 01

  There's over 2,000 people that market making.

• 27:37Speaker 02

  People slash small shops, yeah.

• 27:38Speaker 01

  Yeah, on like.

• 27:39Speaker 00

  I think what Matt's getting at is like, who is a market maker on CalShe? Like, you know, there's all these Jane Street conspiracy theory memes. Is it like Jane Streets? Or is it like some guy in his garage, you know, drinking Red Bull at 3 a.m. market making?

• 27:53Speaker 02

  The guys in the garage are the most.

• 27:55Speaker 00

  And you're saying those are 95% of the flow?

• 27:59Speaker 02

  They're like extremely crucial to the ecosystem because they price fast. They're monitoring the situation all the time, right? They're the original situation monitors.

• 28:08Speaker 00

  Yeah, I see. CalShe is built on people who are monitoring the situation.

• 28:11Speaker 02

  And so, and.

• 28:14Speaker 02

  So one example I'll give. So the, and I've given this in the past. The best inflation forecaster on CalShea over the last few years is not, none of the institutions or the, you know, the big name hedge funds. It's this guy who lives in Kansas, never traded financial markets before. Just likes to read the news and just knows how to predict inflation. He can feel it, like, you know, and, and you have so many of these people. Like now you have like, I mean, I would say a few thousands that are formally kind of like committed, but like there's tens of thousands of like these people that know a bunch of like, a bunch, but a bunch of different topics. And they're sort of actively pricing these things and they do it as a full-time job and they get rewarded for that.

• 28:57Speaker 01

  You need to talk about my favorite user.

• 28:59Speaker 02

  Oh, yeah.

• 29:01Speaker 02

  Well, I have a new favorite user, by the way. Okay, each of us, each of you can tell us your favorite user. I was thinking about this this morning.

• 29:06Speaker 01

  Who's your favorite user?

• 29:07Speaker 02

  The, the Wall Street Journal article about the tax guy.

• 29:09Speaker 01

  Oh, yeah, that's true. He's a good candidate. But no, my, my favorite user is this Ariana Grande superfan. And he found CalShea during the election season. And he's like, I don't like the elections, like whatever. Then he found our billboard ranking markets of like charts.

• 29:23Speaker 02

  He's gonna work on important markets.

• 29:25Speaker 01

  Important markets, to me, very important. And he's made over $150,000. He's getting every single thing. He paid back student loans. He put himself for a master's degree, bought a car and all those things. And he just like loves these markets and he's never really traded, never done anything like that before. But it's the first time that he actually has a way to monetize this very compulsive hobby that he had on, you know, music charts. And he's able to do it and he is also very, very nice to us on Twitter, so.

• 29:52Speaker 02

  I'm curious. So I had many over the years, but maybe I shift a lot, but like the.

• 29:59Speaker 01

  He's not loyal, see, I'm loyal to my guy.

• 30:01Speaker 02

  Well, I, you know, I love all of our users, but

• 30:05Speaker 02

  there was an article last week in the, in the Wall Street Journal about a tax accountant who was very active on CalShea, Alan, and.

• 30:16Speaker 02

  He, you know, when Doge came around and like there were a lot of sort of talk about how much they could cut. He actually read a bunch of tax codes and a bunch of statutes, just like dug extremely deep. And then realized there is no way they could hit the targets. Like, and even if, like, he really kind of deterministically realized, and then he basically talked to his wife. And he's like, I have extremely high conviction in this trade. I know, I, you know, and you know, it's a bit like Michael Berry with the big short. So this guy put a big short, but on Doge this time, right? And, and, and it was big, like, he really kind of went all in. And, you know, he won.

• 31:01Speaker 00

  Yeah.

• 31:01Speaker 02

  Um, and it's just like one of those like amazing sort of showcases of what this can do. Like now you have a market that like, if you have that sort of knowledge, which maybe oftentimes is esoteric. Like, I'm assuming none of us have read all these tax codes. You can actually go out in the world, do research, get smarter about the world, and then, you know, get rewarded for that. And, and that's awesome, right?

• 31:21Speaker 00

  You know, an early field of AI was poker bots. Are you seeing any good AI market makers? When you say no one's read all these tax codes, I mean, no one except Plod.

• 31:32Speaker 01

  That's fair. That's fair. We should ask.

• 31:34Speaker 02

  We are seeing more, like increasingly more people using agents to trade. So that's definitely.

• 31:39Speaker 01

  Especially on the API side.

• 31:40Speaker 02

  On the API side, it's very big and you know, like.

• 31:43Speaker 00

  But like, do you have users who are successfully running market making businesses that are mostly agentic?

• 31:48Speaker 02

  Users don't exactly tell us their strategies.

• 31:50Speaker 00

  But like you talk to them, you know.

• 31:51Speaker 02

  Yeah, yes, yes, yes. But you know, the, I mean, it is. The way I think about it is like, you know, do we think RenTech back in the days was using agentic models? So I'm talking about Renaissance to trade, like, yeah, the early versions of them, right? And so I think they're just evolving and they're getting better. And, and like, most of our traders in their stack have some sort of like summary and synthesis. Uh, module that's AI driven.

• 32:19Speaker 00

  Yeah, I guess what I'm curious about is. Fully autonomous, no human in the loop, consuming information and providing a market based on that. Like, that feels like it's coming quite soon if it's not like your cloth making a market.

• 32:32Speaker 01

  Yeah, I don't know if there's a full. I know that, for example, there's a lot for international elections, just on like, for example, translating all documents, polls and being able to kind of do all of that. But I don't know if it's all.

• 32:44Speaker 02

  We're doing, so we don't know if the models are there yet, right? That's, and I was, so, you know, we launched Causi Research recently, which, and one of the threads that we want to work on is we're talking to some of the research labs to create a new benchmark around which models actually predict the future better.

• 33:02Speaker 00

  Yeah.

• 33:02Speaker 02

  Which could be a unique benchmark around, like, are these models

• 33:06Speaker 00

  Yeah.

• 33:06Speaker 02

  developing some understanding of the world that goes beyond memorizing, you know,

• 33:11Speaker 00

  Yes.

• 33:11Speaker 02

  old patterns. And I'm honestly excited to see how it goes.

• 33:14Speaker 00

  And what's the eval for that?

• 33:16Speaker 02

  We don't know yet, but I think you could roughly kind of let the models run for, you know, make predictions on same set of markets for like a month or two and see which ones perform that like, you know, percentage of predictions that were correct, P and L over time, etc.

• 33:30Speaker 01

  Correct.

• 33:30Speaker 00

  Yes, yeah. Okay, another market making question. So, sports bookies have this need to crack down on what in their industry is called sharps.

• 33:43Speaker 02

  Yeah.

• 33:43Speaker 00

  You know, people who are too good, where like, I think people don't think so much about this dynamic, but for a sports bookie, the best possible punter is someone who is kind of unsophisticated, bets on like their home soccer team's game. In respect of the odds. Exactly, and just wants the home team to win or whatever. And the worst kind is someone who's like, super sophisticated, finding the narrow markets. Because for a bookie, you know, maybe they're making odds on 10,000 different markets.

• 34:14Speaker 02

  Yeah.

• 34:15Speaker 00

  Like, they only need to be wrong once or twice for, you know, people get to choose which they play on. And so they presumably can't be right on all the odds that they're offering, and these sophisticated people go find those. And so what happens is they basically use. Behavioral signals to identify if you are just signed up and you're betting on your home sports team's game. That's good. And if you appear to be really sophisticated with all the signals that they would use. Exactly. They shut them down. But it's interesting, right? You think like, I'm just booking on the bets that, you know, on the odds that you're offering. But like, if you're too good, they'll shut you down. It's maybe like card counting in Vegas. Do you have this dynamic with Sharps? Like, I would have thought, no, that you just are fine with it.

• 34:57Speaker 00

  But like, do the market makers worry about two sophisticated counterparties on the other side?

• 35:03Speaker 02

  The Sharps are the market. I mean, they're super forecasters.

• 35:05Speaker 01

  We don't limit any winners. We don't have any of the, like, we, we, once all the winners, please come, Sharps become everyone.

• 35:12Speaker 02

  We need the Sharps, because how do you get market accuracy without the Sharps? This is the difference of like the.

• 35:18Speaker 00

  Well, yes and no, right? Because what you want is different. Because the Sharps can snipe. They can just turn up once when the odds are wrong, grab a big win and then disappear. Whereas what you're describing is you want during the game or during the election, you want narrow spreads all the time. And so I feel like providing good market making is different than being right.

• 35:40Speaker 02

  But a lot of the Sharps can actually do better if they provide market making and, you know, like, become part of the liquidity. So this is the big difference, which is very important, like, that the, the,

• 35:52Speaker 02

  it may be a disclaimer, like, I don't gamble. I trade, which I've always found a difference. And I think gambling is this idea where the business model is you are the house and your revenue is your customers' losses. Like, so a lot of the dynamic that you describe has to be true because your incentive is like, well, if somebody's making money, I gotta stop them because they're making me lose. That's just going straight for my bottom line. And the opposite is true. If somebody's losing money, I gotta figure out how to bring them back. That's a very different model from like traditional financial markets where like the structure is you have to incentivize fairness and transparency. That's the structure. Like, you want to create fair rules of the game for people to participate. Maybe Matt is better than Luana, and maybe Luana is better than Matt. And like, they can battle it out. They can figure it out.

• 36:35Speaker 00

  You think the incentive system is very different? Where you do not, like a casino or something, you do not monetize on some zero-sum other person losing. You monetize just on transaction fees.

• 36:45Speaker 01

  Yeah. The best outcome for us are people like, "This is fair. They have good prices, they have stable liquidity, I'm going to go there." But of course, for us to get there, we also need to incentivize different players differently. So that's why, for example, a lot of the liquidity programs come into effect. They're like, "Okay, if we're providing liquidity, you're taking a lot more risks because you're putting yourself out to be sniped. Then we're going to lower your fees. But if you're taking and you're going to snipe, you're going to have higher fees so you can pay for that activity." So in a lot of ways...

• 37:08Speaker 00

  I think you'll use fees to incentivize pro-social behavior.

• 37:10Speaker 01

  Exactly. And I think that that's actually how we see a lot of, how financial markets actually do the same thing.

• 37:14Speaker 02

  Financial markets are the same.

• 37:15Speaker 01

  The same thing.

• 37:16Speaker 02

  But it's more balancing out the marketplace so that people that are providing value to the marketplace

• 37:20Speaker 01

  Right.

• 37:20Speaker 02

  have some, you know, a little bit more tilt. And then people that are like, taking away value have a little bit less.

• 37:25Speaker 00

  What behavior is pro-social and what behavior is antisocial?

• 37:29Speaker 02

  Well, insider trading is antisocial.

• 37:30Speaker 01

  Right. That's a big one.

• 37:32Speaker 00

  Yeah, yeah.

• 37:32Speaker 02

  And illegal.

• 37:35Speaker 02

  But, you know, but it's... And look, sniping is part of, is part of it, right? You need people that like, all of a sudden have gained some information edge and they do it in traditional financial markets all the time, right? Like, that... But to have liquidity and make sure that people are there and they're investing the resources, they have to get, you know, as you said, some incentives. But the interesting point, I think, and I think this is part of why prediction markets are being adopted so much is, people like this idea that if, like, your edge is proportional to your research, how informed you are, how much time and energy you put into this. And I think that only existence prediction markets or traditional financial markets, except that for a lot of people, traditional financial markets,

• 38:20Speaker 02

  they're just less interesting, right? Like, in here, you're researching about Doge and what's going to happen, or what the election and how people think about elections and how they vote. That, at least to me, feels a little bit more interesting than like, "Let me think about IBM's quarterly earnings every quarter."

• 38:37Speaker 00

  Calci has built a new kind of marketplace where real-world outcomes are traded, like whether the US will confirm whether aliens exist before 2027. You have thousands of participants opening, transferring, settling their positions, all in real time. And underneath it, as you can imagine, there's a complex, multi-party flow of funds. That choreography on Calci is powered by Stripe Connect, onboarding participants, processing payments, routing funds, managing payouts. When money movement becomes programmable, new products or even new market structures become possible. So if you're building something new with complex money flows, Stripe Connect was built for you.

• 39:15Speaker 03

  Let's talk like different market verticals. And I think today, everyone gets elections, they get sports, they get economic indicators. But I think you can look at prediction markets as this kind of search function across the set of interesting markets humanity wants to trade.

• 39:32Speaker 01

  Right.

• 39:32Speaker 03

  And it's kind of a weird artifact that like the CME used to greenlight like wheat and oil and corn. But now you get to greenlight, you know, 1,000 markets a day. So what do you think we're going to find as we do that?

• 39:48Speaker 01

  One thing that we're very excited for, we're actually starting to go in the direction of, for example, things like watches and bags and all of those things are like more going to the collectible side. They're actually able to do derivatives on those things. One thing that you should talk about is the GPU compute is a very, and I think the compute that the, what we're thinking a lot about is that there's a lot of these types of things that they function better as a more traditional future. So things that like don't have like a binary, will it be at this price? Yes, no. But it's more like an actual future. You can have margin, you can have more like institutional grade liquidity and all of that. And I think that that's a great example of like when we start going more in outside of binary markets and more into the traditional ones.

• 40:31Speaker 01

  Then what we're doing is expanding kind of that from grain to compute.

• 40:36Speaker 03

  It strikes me that obviously the futures markets that have worked best are these large commodity categories. Because we're sort of in an era where humanity is spending more money than it's ever spent before. On a new commodity category. Kind of commodity. So and the other traditional markets don't seem to be attacking compute.

• 40:57Speaker 01

  So the way that we think about, we want to be the biggest derivatives exchange in the world, right? And for that, when we think about product roadmap, there's four things that matter. The first one is breadth of topics of markets, right? So we think about compute, we think about sports, we think about elections, we think about securities, we think about all of that. The second bucket is really market structure. So right now, we only have the binary yes/no. We want to have things like futures, like swaps, options, all of that. The third one is really margining systems. Right now, it's very bad. You have to put all the money up front.

• 41:24Speaker 03

  To tie up all the capital.

• 41:25Speaker 01

  Right, which makes a lot of, for example, will a hurricane happen this year? Very, very bad for you to be actually like market making or selling those contracts. Doesn't make almost any sense, like capitalized. And then the last one is liquidity. And when we think about it, is like if we win these, we have the broadest set of markets, we have the broadest set of market structures. We have great and, you know, very, I don't want to say cheap margin, but in a way around that. And then good liquidity, we're going to win on everything that we do. So everything that we do in the company is like, it needs to be in one of these four buckets. And I think a lot of the topic side is like, how do we actually match the right topic with the right market structure of the right margining? And how do we make sure that it's all coming together? But you're completely right.

• 42:05Speaker 01

  I think that being able to build all the margining systems, all those things from scratch. We're going to be able to do margin models a lot faster and list a lot of these kind of new markets a lot, a lot faster.

• 42:18Speaker 03

  Because of your direct mobile app interface and the fact that you target a lot of retail. Do you worry that sort of the markets you're going to gravitate towards are the ones that are most interesting to just retail? And how much do you think about?

• 42:33Speaker 00

  As opposed to like the pro markets?

• 42:35Speaker 03

  The kind of institutional markets. Like I think of compute as much more of an institution to institution market. So yeah, how do you think about building liquidity and interest upmarket?

• 42:43Speaker 01

  Yeah, we almost like divide the company again in a way. It's like we divide the markets and like sports, crypto and everything else. But in how do we make what we have great, but very new things? Because I think what got Caoshi here. Was not the regulatory side, it was really that we're just really pushing what is the next thing. It was elections and then after elections, sports and for us it's like we need to be pushing what the next thing is and doing that very well and I think that if we stop doing that, we're not gonna win. The company is kind of structured that we have the market operations, we have the engineering side, all of that that's set up for maintenance and improvement of what we have. And then the new teams like institutional, the margin team, international that are kind of pushing forward.

• 43:24Speaker 01

  And we just try to kind of find a balance on like those themes and then a platform layer that is like the core exchange and compliance and all of that. But it is tricky because we're still like 120 people to do it.

• 43:37Speaker 00

  Are you seeing pull already on the institutional side in certain topics?

• 43:41Speaker 01

  Yeah, no, for sure. And I think that we actually just launched a week ago this thing called Block Trades. I don't know if you know of Block Trades. It's a very institutional way to do it that I can call you and negotiate a trade and then we go and put it in the exchange versus trying to do everything that way. So we're trying to build a lot of features to start getting more in the institutional side.

• 44:00Speaker 00

  Are they trading the same things that are on the CalShee retail? Or are you offering new types of products for them?

• 44:05Speaker 01

  Yes and no. So whenever they're interested in something like the... There was a lot of interest on the tariff situation, is there gonna be a tariff or not? A lot now with the petroleum, like the reserve and kind of how that's gonna go. So whenever we hear we want to trade this market, we just list it directly and then it's accessible to everyone. But I do think there's gonna be a very big gap on what the institutionals are gonna end up trading versus not. But we just list it to everyone. It's very cheap for us at this point to lease the market.

• 44:32Speaker 00

  In the early days of Uber, it wasn't bad for the taxi business because it was just excess capacity and you know, it was serving on met need. But then after a pilot, it was bad for the taxi business. Are there existing businesses that will feel the effects of CalShee and other prediction markets? Because... It's a bigger market, there's more liquidity. Like, I can think about the just existing futures exchanges. Like, maybe Calci is a better place to, you know, hedge your soybean prices or what have you. There is sports bookies, obviously. There is political polling firms where maybe you can get kind of the same information way cheaper. So who do you think will start feeling the effects of prediction markets? Because they have been in some way superseded.

• 45:16Speaker 01

  Yeah, there's that funny meme of like the guy knocking on the door and it's like, who is the next one?

• 45:20Speaker 00

  Yeah, yeah.

• 45:21Speaker 01

  So you don't want to do that, but I think that a lot of what you mentioned, right? I think that just traditional betting that we talked about, all the issues that that industry has, that we're very different from. There is a traditional futures now, we're going way more into their space. So I think there's going to be difference there. There is the political polling that I think since the last election, there's just a lot of campaigns are using our data and all of that. There's parametric insurance. Once we have margin, we can start going to more hurricane, actual disaster insurance, all of that side.

• 45:51Speaker 03

  Is there a tragedy of the commons with the polling? Like, part of the reason the prediction markets are accurate is because they interpret the polls.

• 45:59Speaker 01

  Right.

• 45:59Speaker 03

  So like if people stop using polls, like in some sense polls are the sensor that you get of what people's opinions are. And then the prediction markets are like the mathematical interpretation of the polls?

• 46:13Speaker 01

  Yeah, my take is that polls are just going to get a lot better. Because what people are going to be is like, okay, I can make money if my polls are right. So I'm just going to commission this poll and I'm going to do this. And now you can actually like compete a lot of polling models into one market.

• 46:26Speaker 00

  It's like 538 did, kind of the meta poll interpretation.

• 46:28Speaker 01

  Exactly. And you can kind of have one number that's kind of aggregating all of that. And even in the last election, there was someone that actually did this. They commissioned a specific poll to do like nearest neighbors type of thing. It's like, it was, I don't know how it was, but a different type of poll. And then they were able to make a lot more money in the markets. And that's the whole point of like having money and skin in the game. Aligns the incentives with truth and then the polls are not just paid for like, tell me what I want to hear, but for the real numbers. So I think it's complimentary. Same with the news. A lot of people are like prediction markets will destroy the news. I think it's way more complimentary. It's like when you're talking about an election, you're going to give your opinion, the market is not going to give you an opinion. You still need the commentators, but they're going to be able to show a number and be like,

• 47:11Speaker 01

  this is what the forecast is and this is my opinion on it. I don't think the opinions are going to disappear, but.

• 47:17Speaker 00

  You guys referenced insider trading earlier and there's just the policy question as to what the right policy should be around insider trading when it comes to prediction markets. I think it's pretty nuanced. Like it's nuanced in the stocks case, right? Where famously there's lots of, you know, you see SEC enforcement actions all the time against, you know, the things that aren't allowed. But there are cases like a hedge fund can have proprietary satellite data of the Walmart parking lot and use that to trade earnings and that is information that only that hedge fund has. But, you know, that is permissible.

• 47:53Speaker 01

  Right.

• 47:53Speaker 00

  And so similarly, I think there's just like a complex sort of line drawing exercises here where presumably we don't think government officials should be trading in advance of military actions.

• 48:04Speaker 01

  Right.

• 48:04Speaker 00

  What about leading up to the Super Bowl, you know, predicting the Bad Bunny halftime show length? I mean, you know, people have that information. And so where do you think the lines should get drawn on insider trading?

• 48:15Speaker 01

  Yeah, and I just, you said it perfectly. It's a very complicated question and it's complicated question for stocks way more and to a bigger scale than it is in prediction markets. The line that we take now is that we follow what the federal law is. So it's basically if you have.

• 48:31Speaker 00

  And these are, sorry, CFTC rules?

• 48:32Speaker 01

  Well, CFT and SEC.

• 48:34Speaker 00

  Okay.

• 48:34Speaker 01

  They both have it. So basically if you have signed an agreement that says you cannot share some part of data. So if I work at the Bureau of Labor Statistics and I have in my like confidentiality, then I'm not able to say what the inflation number is before. Then it is you have like you cannot be sharing that data. But if you know, if you know that they're going to be rehearsing the Thursday before the Super Bowl and you're outside and you're like, I hear Lady Gaga's singing, that's fine. And that's the same thing that, you know, a lot of hedge funds do with like Starbucks and people know there's more people, fewer people in the store. And that's the point, like markets are very good at incentivizing information. We want information to come to the markets, but we don't want it to be unfair.

• 49:11Speaker 00

  Yep.

• 49:12Speaker 01

  And if you have access to it in an unfair way, you should not be trading on it.

• 49:15Speaker 00

  Okay, so you cannot trade on information where you have some duty to hold that information confidential?

• 49:20Speaker 01

  Right, we actually take it even a step further. For example, if you are a government official, you cannot, like if you're in Congress, you cannot trade on bills passing, even though I don't know if they have an agreement.

• 49:30Speaker 00

  Famously, Congress people can trade on stocks.

• 49:32Speaker 01

  Right, right, right. So we're actually like taking a step further there. And we're working a lot of the regulators, because obviously it's a very new problem for them and for us, but we have an entire, part of being regulated, we have an entire surveillance division that is looking at every single flag. They pretty much don't sleep and they just try to figure out everything. And we put out two cases of, two weeks ago, of two insiders that, then, because we're also regulated, we're able to charge them a lot of fines. We charged them over five times what they made and all those things they banned and all that.

• 50:02Speaker 00

  What's very interesting to me about these stories is that you guys were doing that where with the public equities markets, the SEC is extremely enthusiastic about enforcing their insider trading doctrine. Just what has the CFTC been like on this topic?

• 50:19Speaker 01

  It's a great question. So obviously the CFTC, you can think about it as three steps, right? The first step is our own surveillance and enforcement. Then the next step is it goes to the CFTC and their own surveillance and enforcement. The last step, if you go to the Department of Justice. And I think that the biggest difference is when people look at the SEC cases, most of the time, they take a long time because the exchanges that their, that their research and their investigation and they put some fine, they block someone, and it kind of goes through the process. So it's still like that, like every single trade on Cowshy goes to the CFTC. They have every single thing, every single case goes to the CFTC for them to review. So they might take action, we don't know, but now the.

• 50:59Speaker 00

  Okay, so you refer cases to the CFTC.

• 51:01Speaker 01

  We refer cases to them. But we kind of do our first step of our first level of protection there.

• 51:07Speaker 03

  I'm curious, there are clearly markets where nobody knows the answer yet of some event in the future. So like it's sort of impossible to insider trade.

• 51:15Speaker 01

  Right.

• 51:15Speaker 03

  And then there are markets where a single person can change the outcome. Like the mention markets in a speech or maybe a sports player doing a specific number of shots. So like, how do you think about kind of that spectrum?

• 51:30Speaker 00

  Like, are mention markets a bad idea because they're just so inherently gameable?

• 51:33Speaker 03

  Yeah, or are they like limited in scale fundamentally because?

• 51:37Speaker 00

  Mention markets like the Brian Armstrong Coinbase earnings thing and stuff like that.

• 51:41Speaker 01

  That's the worst example. But yes, I think that, well inherently, I think mention markets are actually great. If you think about the Fed, right, like the amount of hedge funds that are just sitting down being like, is he gonna tilt his head this way or this way? And if he does that, it means he's not very sure.

• 51:55Speaker 00

  Fed meeting minutes are the original mention markets.

• 51:57Speaker 01

  Exactly, that's actually pretty much, and it was because we just know that specific words being used mean very specific things and it can move the market so much. The same thing with Trump, right? If Trump says, we're going to war, that's going to move the markets a lot, right? Or if he says a lot of things like tariff, that everyone knows moves the market a lot. Even in, yeah, just a lot of everywhere, things that people say move markets and move a lot of different things. So I think the mention markets are important. Obviously, the person that is working on the speech or that is saying the speech cannot trade. And that's kind of how we enforce it. Like if you are Gavin Newsom and you're, there's a market on where you're gonna say you cannot trade it

• 52:39Speaker 01

  and your staff cannot trade it, that's part of the political kind of cuts that we do there that they cannot trade. But I think that's the point is like, if there is a way to restrict some players in the market so that the market's fair.

• 52:52Speaker 00

  Yes.

• 52:52Speaker 01

  And the market's positive and there's an economic utility for it, the market should exist. We shouldn't say like, okay, there are five people that could manipulate the market and the market shouldn't exist, then you say the stock market shouldn't exist, right? So I think it's more about how do we build a system that is strong and resilient enough?

• 53:07Speaker 00

  Yes.

• 53:07Speaker 01

  And with the right prohibitions that you can have the market.

• 53:09Speaker 00

  Yes. The other big debate you guys are in the middle of is just that about sports contracts generally. And, you know, I was trying to reason about my own thoughts here on this debate where, on the one hand, you know, the criticism is that with more sports gambling comes proven bad effects. You know, you can measure some of the bad effects that it has on people. And, you know, we have this, especially in the US, where there was a lot of legalization of sports betting over the past 10 years, and there's some data on that. On the other hand, you know, I have no real issue with alcohol, despite the fact that it has kind of

• 53:51Speaker 00

  a similar distribution where many people enjoy it, and then there's a very bad set of outcomes for a small fraction of the population.

• 53:57Speaker 01

  Right.

• 53:57Speaker 00

  And so I feel like the societal discussion of, you know, the morals of alcohol and the morals of betting are different despite the fact that, again, it's similar shape to the distribution. And, you know, also just thinking from my experience, online sports betting has been legal in, well, legal is a complex term, has been available in Europe.

• 54:20Speaker 01

  Right, for a very long time.

• 54:21Speaker 00

  For a very long time, basically since, you know, the start of the internet. I knew there was all these like cross-border hacks in Malta, and now it's like a bit more regularized. But it's been available to people who want it for a long time, and life goes on. You know what I mean? And it hasn't led to any kind of major societal collapse over there. But clearly, like, this is one of the big debates that raged around Calcey. And so I'm curious how you guys think about increasing access to sports contracts and the effects there.

• 54:48Speaker 01

  Well, there's a lot of parts to everything. I think that the way that we think about sports, how we decided to first list sports is obviously something that a lot of people are interested in, that's unquestionable. But also, there's something that a lot of people do a lot of research and know a lot about. And there aren't traditional ways for you to make money on that, that are actually good. Or we talk about the winners, they get cut and all those things, and it doesn't really work. And regardless of whether people like that some people bet or dislike that some people bet, people bet. And it's just about what is the best way for them to have access to something that they can get exposure to sports. And I think that the whole point of markets versus a bookie is that markets are just objectively better. Right, I think that it's almost, I've never heard someone make a case

• 55:32Speaker 00

  Yes.

• 55:33Speaker 01

  that a state-by-state regulated casino is actually a good thing. I'm actually hearing nowadays that a lot of like the paid propaganda by the gambling guys trying to say that. But if you push them to question-

• 55:43Speaker 00

  And just to put numbers on that, the order of magnitude rake for sports betting companies is around 10%. And the order of magnitude for prediction markets is, you know, 1% or a few points.

• 55:54Speaker 01

  Right, but the predatory part doesn't even come from there. Like for sports betting, if you start losing, because they want the losers, they don't want the winners. If you start losing, the first thing that they're going to do is give you a bonus. They're gonna give you a thousand dollars for you to come back, or like a deposit boost and all those things. So that they can hook you to keep you coming back because they want to incentivize the losers. We don't do any of that.

• 56:14Speaker 00

  The people losing most, the most money are the most profitable for sports bookies.

• 56:20Speaker 01

  Yeah.

• 56:21Speaker 00

  Which creates a bad incentive.

• 56:23Speaker 01

  Yeah, and we don't have that at all. And I think that the whole point's like, there's a moral question. Like some people are gonna go into Robinhood or Coinbase or whatever and speculate on stocks and speculate in crypto and whatever they want to do. And some people want to speculate on sports and they should have the best, the access to the best possible thing for that. And right now, it's just, the sports books are just not it. And we, we really firmly believe that what we do in our markets are significantly safer for all of that. And if you, if you just take a stance of prohibiting, it's like similar, you said alcohol, right? It didn't change, people just went to like a speakeasy and drank. And people are just gonna go offshore when there's way less protections, there's no, none of the self-exclusion, the deposit limits, all those things that we do, don't have any information about them and they're gonna actually, it's actually very bad for them.

• 57:08Speaker 01

  So I think it's just, this like prohibition concept just never works.

• 57:12Speaker 00

  It's also the whole- Policy discussion around this stuff is also very interesting when, it kind of reminds me of in Canada, all the liquor stores are run by the government, or at least in British Columbia. And you know, you have the government saying this must be very carefully controlled, but also we will sell it to have the revenue source. And obviously, that's much more of a factor in lotteries and things like that.

• 57:34Speaker 01

  It's all about money at the end of the day. The states want their money, the casinos want their money, it's just, yeah, it is what it is.

• 57:40Speaker 03

  Speaking of sports, one thing we were talking about was just what interesting new verticals are there gonna be? And so I'm just curious, like, which ones are you most excited about?

• 57:49Speaker 02

  Well, I think that anything around dissecting like a stock into its sort of like

• 57:57Speaker 01

  KPIs.

• 57:58Speaker 02

  More atomic components.

• 58:00Speaker 03

  So this would be like betting directly on Nvidia GPU shipments versus?

• 58:05Speaker 01

  Or like Tesla deliveries or whatever.

• 58:07Speaker 02

  Like their earnings, the, you know, but because like the, and then you can expand that to things like, okay, dissecting sort of the macro economy, like what are the main sort of like factors are influencing the economy broadly? Like AI and like have a series of questions to price what's going on with AI.

• 58:25Speaker 02

  Things like, you know, health scares like COVID and so on. But the sort of where things get really interesting is this idea where, so there was a paper written by Kevin Hassett around this idea that like as society gets increasingly more complex,

• 58:44Speaker 02

  our asset prices, our understanding of asset prices will naturally decay. Like entropy will go up because the things that influence or the sort of vector that, you know, like the number of factors.

• 58:59Speaker 03

  Much higher dimensional vector.

• 59:01Speaker 02

  Becomes very high dimensional, right? And if those dimensions, you don't have a good understanding of X1 to XN,

• 59:08Speaker 02

  you cannot get a good estimate of Y, right? And And so the paper basically says like you need infinite markets. In prediction markets, I have this notion of infinite markets, which is like you have to have a market for each one of these Xs. So that you can then take that back into pricing traditional asset. Getting good traditional asset prices. And an example of that this last week, so you know, there was the the Citrin, Citrin put out a research report, right? And it got a surprising amount of

• 59:38Speaker 01

  People got obsessed with that.

• 59:40Speaker 02

  Yeah, I would say it got a surprising amount of love and interest.

• 59:42Speaker 00

  This is the AI 2028.

• 59:44Speaker 01

  Yeah, like 38% on a bull.

• 59:46Speaker 02

  And I think, I think, look, I think there's a little bit of like a society wants to believe that AI is gonna, is gonna end us all. I think right now there's a bit of that. But this is where the, you know, and that impacted markets, right? Like the stocks got, you know, there was a sell off and and so we launched a prediction market on that. And, and well, before that Citadel came out with a rebuttal and we launched a prediction market on that. And, you know, the, the odds are 10%, right? So.

• 1:00:08Speaker 00

  Of the economic scenario that they predicted being true as a 2028.

• 1:00:12Speaker 01

  It's three out of five things. So they have like five conditions, if three hit, we.

• 1:00:16Speaker 02

  Yeah, if three out of these five condition hit, you could reasonably say that, okay, that this outcome has somewhat materialized. And it's just 10%. Five out of five is, is much lower, right? So that is important. If you can put that back into pricing models, maybe the markets wouldn't have reacted that. Like maybe people wouldn't have sold off DoorDash. Because I, at least I believe, but you don't have to trust me, maybe you should trust markets. And this sort of analysis around DoorDash was actually quite poor.

• 1:00:40Speaker 03

  So one thing you're sort of envisioning is this world where everything has a price all the time. And I'm curious, like, is that a good world to live in? And I would note that Stripe, I think, benefits from being private and not having the real time price and smoothness for employees and comp and all that.

• 1:00:59Speaker 01

  Which is by the way, the biggest.

• 1:01:00Speaker 00

  Some noise in the, you know, sub second pricing.

• 1:01:03Speaker 03

  Sentiment swings publicly, like there's on average, in the long run, it's a truth telling machine, but in the short run can be a panic. So just curious for you guys to think about, yeah, is, is that the world we want to live in?

• 1:01:16Speaker 01

  I mean, we're obviously biased because we love markets. We think markets are good. So we're definitely biased here. But I think our view on this is that it's always better to have more data than less data. If you don't think the data is good, if you don't think like the second by second stock price is good, you can just choose to ignore it. The world might not just ignore it, but you can...

• 1:01:38Speaker 00

  I think CEOs of public companies would say it is not always possible to choose to ignore it.

• 1:01:41Speaker 01

  I guess that's fair, that's fair. But in a way, it's better to have the data and then use it as an input to something than not. But let me say like, you know, we want to have prices on a lot of things. It doesn't mean everything. There are a lot of things that we wouldn't do, like wildfires, we don't do war, terrorism, assassination. Those things are bad and like, there's a moral side of these markets and we're not going to ever go there. But in general, it's in a world of social media, it's like you don't know what's true anymore. My feed is like, is it real, is it not real, did this happen? It's just better to have a source, an unbiased source of information that you can kind of use it for other things. And I think that that value is there, but I don't know.

• 1:02:20Speaker 02

  Well, I mean, I think that like, maybe the simple frame for this is like you are increasing market efficiency for all these questions, right? Like, that's what's happening, right? It's including potentially some events or things that relate to maybe private companies. And I was just thinking about the question, it's an interesting question, like, why do companies go public, right? And why is it important to get like a, you know, real time market price? Because there are downsides. Sometimes markets are erratic, sometimes they overshoot in either directions. But the market on the long enough time horizon is a good sort of allocator. It's a good weighing mechanism.

• 1:03:05Speaker 02

  It's a good allocator of capital. And I don't really see that, like, I just think that like, pricing a lot of these... Questions will just increase efficiency. Make our function, our allocating function better over time. And there will be some net losers, right? Like some people that maybe capital shouldn't be allocated to, right?

• 1:03:25Speaker 01

  It's also a good feedback loop, right? Like if you're a CEO of a public company and you run out of something and it just keeps going down, you're like, okay, maybe, maybe I'm wrong. And I think the same thing you see with politicians, where you can see in the live debate, if they say some answers and they see their, their price is going lower, they're like, well, maybe the answers aren't great. And I think a lot of these things, when we see even the, for example, the use case of prediction markets in government, a lot of it is conditional markets, right? They can say, if we pass this bill, will unemployment go up or down? And you can price these things for, for better decision making and just like a tighter feedback loop. Tight loop, as Gudin said this, but.

• 1:03:59Speaker 00

  Do you guys feel like we have started to see, like, clearly we have seen the effects of social media on politics? Where politics is a different game now and different politicians are popular and the political discourse is changed by the existence of, I mean, first Twitter and now to some extent, short form video. Do you guys feel like we have seen the effects on politics of prediction markets yet?

• 1:04:25Speaker 02

  Definitely. I mean, the.

• 1:04:27Speaker 00

  What are they?

• 1:04:28Speaker 02

  Well, the candidates are using the, like, using the prediction market prices to.

• 1:04:31Speaker 00

  Sure, that could just be like a handy thing. Whereas again, I think with social media, like, the candidates are different. The debates are different.

• 1:04:36Speaker 02

  Reflexive.

• 1:04:37Speaker 00

  Yeah, yeah.

• 1:04:37Speaker 01

  I do, I do think that what the markets are good at is that they are more unbiased by party dynamics. So if there is an underdog that the public really likes, there's a lot of like, maybe there's like a party that's like, we don't like this guy, we like this guy from the establishment. But the markets are very good at actually showing the real odds for that person.

• 1:04:57Speaker 00

  Okay, so you think the party machines have lost a little bit of power?

• 1:05:01Speaker 01

  I think you can shine more light into what people really want, which might not be necessarily what the parties want to put forward. I don't think we've seen that necessarily, but I feel like, if I were to say, like, there was a, for example, the Texas primary and I think that the polls were saying one person was really gonna win. Another person that was way higher in prediction markets won.

• 1:05:20Speaker 02

  Tarrico, right?

• 1:05:21Speaker 01

  Right, and I think a lot of it was more of like, they give a fairer view of the state of the race than a lot of the party.

• 1:05:28Speaker 02

  Well, I think that we do see this a lot with also, well, there's sort of the piece where people use it, and that's sort of reacting in real time to certain things. But I think there's some degree of depolarization. That's what Luana is alluding to. And in some ways, an anecdote to social media. Like, social media has really polarized. Like, when you have two candidates running for a senate race,

• 1:05:51Speaker 02

  we're sort of set, right? Like, your feed is set. Like, either your feed is saying the Republican candidate is awesome, or the feed is saying the Democrat candidate is awesome. Prediction markets don't really have that. Because the people that are engaging in this are not are not in the sort of like, who's great and who's, you know?

• 1:06:10Speaker 00

  I think what you're saying is, social media feeds ultimately try to resolve upfront. They're like, I need to figure you out. Are you a Democrat? Are you a Republican? Like, what posts should I show to you? And pretty quickly, I mean, you know, people have complained about this phenomenon, where you end up down a, you know, particular rabbit hole, because they have pigeonholed you as kind of this type. And you're saying just prediction markets do not have that phenomenon.

• 1:06:29Speaker 02

  There's literally a reverse phenomenon. It's like, are we feeling too certain about this person? And like, and I think that depolarizes things, because the dimension is not, we're not one dimensional anymore, which is like Republican, Democrat. And that's what you're seeing and what you're hearing about. Now, it's like, well, this guy is kind of cool.

• 1:06:46Speaker 01

  Right.

• 1:06:46Speaker 02

  And that's Tarrico. And maybe he might do good in Texas, even though he's a Democrat. And, you know.

• 1:06:51Speaker 01

  The same thing happened with the New York mayor situation, right? Like, everyone was like, Cuomo is going to win 100%. 100% Cuomo is going to win. There's not even a chance. And we were just seeing Mondani's odds going up. And I think it's just the progressive message was really working with New Yorkers and the markets were really seeing that uptick. And that decrease in polarization really comes from people taking a step back and being like, what do I actually think is going to happen? I'm incentivized the right way.

• 1:07:14Speaker 00

  There's a bit of like an Iowa-New Hampshire effect here, where, you know, in presidential elections in the US, there's like some big name leading into the election. You know, maybe it was Hillary Clinton in '08. And then Iowa and New Hampshire are measurements of the sentiment of those two states, but they also create narrative.

• 1:07:34Speaker 01

  Right.

• 1:07:35Speaker 00

  And I think what you guys are saying is, prediction markets create this Iowa-New Hampshire effect, where they can create narrative in a way that changes the ultimate outcome.

• 1:07:42Speaker 01

  I don't know if it changes the, I wouldn't say it changes the ultimate. I think it sheds light into what the ultimate outcome is going to be.

• 1:07:49Speaker 02

  And potentially changes it. Well, I think. There clearly is some. I mean, there's always, but it's like with the polls too, right? Like the, yes, I mean, there's polls. Why aren't you guys hiding your lamp under a bushel here? You're like, well, we're not changing anything here. No, because I just think that the one thing I would say, the reason why we're like. Like prediction markets are a big deal. It's okay to say they'll change things a little bit. High odds don't always correlate to like a good outcome. So you saw Mamdani when his odds were 94% on Kashi. The thing that he was messaging pretty consistently, and I think there was a little bit of worry there. It's like, you gotta show up, right? Like, because if you're very high odds, that could also lead people to be like, okay, this isn't it. So it's not as clear. Yes. And I don't think this changes things more than polls changes. Does that make sense? Yeah.

• 1:08:33Speaker 02

  The response is more like, in a vacuum, yes. Yes. If you had nothing else, if you didn't have social media, if you didn't have news, if you didn't have any of that, yes. But because we have all these other things and you add prediction market to it, like the impact model. But I want to make one point that I think is actually very interesting. And we're seeing this off. We'll be the judge of that. Okay, there you go. You can judge that. Let me know if it's interesting. But a lot of times when you see people start participating in prediction markets, they get more engaged in the underlying. They legitimately just get more informed. It pulls people in. It pulls people in, but to do research. Now you have, because you have some skin in the game or you're about to put some skin in the game. You read, you, everything changes. You're not just like saying something crazy on Twitter anymore. You're putting money. Sure. And now it's like, let me read, let me actually figure out, oh, who's this person? What's happening?

• 1:09:17Speaker 01

  It's like, are they pro this? Are they against this? What's their view and everything?

• 1:09:20Speaker 02

  And you go, because you know, it's like amazing because this happened a bit in the New York race, right? Like, or it happened a bit with Brexit. People voted for Brexit because, oh, no, we want Brexit. And after they voted for it, they're like, oh, God, wait, I don't think we, we wanted this. Wait, wait, we didn't even understand what we were voting for, right? Like, and I, and I think this heightened engagement.

• 1:09:38Speaker 01

  It's very good.

• 1:09:39Speaker 02

  Like, you know, like it engages people further to like learn and understand. You know, in sports, basically, if you ask sort of like a lot of the leagues, they would tell you the same thing. Where people got more engaged with the statistic, which player is good? What's happening? And I think that will happen, is already happening in politics, which is a good thing.

• 1:09:53Speaker 01

  I think the.

• 1:09:56Speaker 01

  I'm gonna say something, then I'm gonna call it out. I think that politics is gonna get better because you're gonna have a way faster feedback loop on the messaging and the policies. Right now, when a candidate says 10 things, and they win or they lose, you're trying to make one assessment of like so many things that the candidate did. Yes. And did that go well or not? And which point was it? Even if you do a poll, it's like always delayed. It's a very specific sample. But now you can have real time of like, they said this, what was the response? And you can get that and kind of like that faster feedback loop, which I think makes startups great, right? You're able to iterate very fast. And I think if candidates are able, everyone wants to win at the end of the day. And if they're able to optimize their message to what really people want and what policies people want, I think they're gonna end up being better

• 1:10:40Speaker 01

  because they're just gonna know what people want better.

• 1:10:42Speaker 02

  It's like a, you get a score on all of the different things you've done. Not one score that encompasses.

• 1:10:47Speaker 01

  Which is basically like, when you ship on your future, you're able to have like 10 metrics. And you're like, okay, this went up, but this went down. And you're able to, markets can kind of contribute that to that, but also like, I think to a lot of other things as well. Music with like charts. When someone listens to the song and they're like, definitely not gonna hit number one. You're like, okay, that was that song. So we should do something else.

• 1:11:07Speaker 03

  Do you guys try to use prediction markets in any way internally to make decisions?

• 1:11:12Speaker 01

  Every single decision we make is always probability. Even the election lawsuit, right? When we're doing it, it's like...

• 1:11:18Speaker 03

  But that's you guys evaluating the probability.

• 1:11:19Speaker 01

  Yeah.

• 1:11:20Speaker 03

  But do you ever think about creating markets for your employees to participate in?

• 1:11:24Speaker 02

  So we have one niche.

• 1:11:26Speaker 03

  Yes.

• 1:11:26Speaker 02

  Which is, as a regulated exchange, we can't trade.

• 1:11:31Speaker 01

  But we're doing like internal polling.

• 1:11:32Speaker 03

  There's like a separation of church and state thing there.

• 1:11:34Speaker 01

  Exactly, exactly.

• 1:11:35Speaker 02

  And so, we've been asking that, we've been working with regulators, could we do something small? Like, could we do small dollars where, so that, you know, because obviously that's one that we really want to implement.

• 1:11:45Speaker 03

  And you can't even dog food the product, I guess.

• 1:11:46Speaker 02

  And then dog fooding the product.

• 1:11:47Speaker 01

  Right, which is a big deal.

• 1:11:49Speaker 02

  Which has been hard, it's hard. You know, that...

• 1:11:51Speaker 01

  Everyone's on demo.

• 1:11:52Speaker 03

  And so, employees cannot trade in their personal capacity?

• 1:11:54Speaker 02

  They can.

• 1:11:54Speaker 01

  Yeah, not at all.

• 1:11:55Speaker 03

  Wow, interesting.

• 1:11:55Speaker 01

  Yeah.

• 1:11:56Speaker 03

  But yeah, that makes it very hard for you. As you say, you just can't dog food your...

• 1:11:59Speaker 02

  You can't try the product.

• 1:12:00Speaker 03

  People at Facebook use Facebook, and that's how you make the product good.

• 1:12:03Speaker 01

  Right, right. So that's why it's so important for us to just be like, asking the users all the time.

• 1:12:07Speaker 03

  Yeah, yeah. Oh, that's so interesting.

• 1:12:09Speaker 01

  Yeah. It sucks.

• 1:12:10Speaker 02

  But the power users, the super forecasters are a lot of what influences where it goes because they're very engaged.

• 1:12:15Speaker 00

  You must presumably spend a lot of time with those power user, super forecaster types and have them on speed dial.

• 1:12:22Speaker 01

  They're their day one, so you want to make sure they're happy.

• 1:12:26Speaker 00

  Last question. Where do you guys want to see prediction markets policy go? Like when you're talking to someone in government or if you had a magic wand, what are you arguing for?

• 1:12:39Speaker 02

  Our stance as a company, and I think this may differ a little bit from like, I would say like your average tech company or big tech company.

• 1:12:49Speaker 02

  So we are pro innovation. Innovation needs to happen in America. You know, we have to lead and we have to do it right. And we have to win. Like we have to be, you know, all the things that Americans want to do or we need to win as a country, we should have here. But we're also pro regulation. And so at a higher level principle, like there's usually this tension that, you know, generally it's like, you know, it's like policymakers like want to regulate and the...

• 1:13:14Speaker 00

  I think you're maybe more like a traditional financial firm in that way, right? Where maybe Silicon Valley, you know, a lot of firms grew up in unregulated way, but financial firms have always had a regulator and that's just a fact of life.

• 1:13:25Speaker 01

  It's just a constraint for you to work.

• 1:13:27Speaker 02

  So it's part of the culture and I mean, again, we spent four years getting regulated upfront, so it's part of... But we believe in regulation. Like I think it's important because, you know, regulation is a bit like insurance. It's like it's protecting you from things going wrong at bad times. And so when I think about like, okay, where this lands, I think anything that is oriented around preserving these in America and making sure we win, but then elevating the fairness and transparency of the markets, right? Anything that's oriented like, how do we make it more fair? Ban insider trading. Add more restrictions on, for example, like, you know, government officials, members of Congress, trading on like, you know, information they shouldn't trade. Now, I'm obviously a big fan of banning insider trading from members of Congress.

• 1:14:06Speaker 01

  We talked about it, yeah.

• 1:14:07Speaker 02

  But... Presumably me just banning trading, period, for a member of Congress. I think it's not a bad idea, like, you know...

• 1:14:13Speaker 01

  Yeah, that's how we kind of think.

• 1:14:15Speaker 02

  But I think, and then things around like, you know, loving, like, creating also social fairness and transparency, because of all the questions that we ask, like, if people are busy trading on politics, let's have all the trade data be as public as possible, so anyone can audit it, anyone can see it, which is a good thing, right? Now, you don't know, like, imagine a poll, where you can check every single person that basically got, and the general public can check who was polled and what the sample was like. And then, anything around customer protection. And because that is important long-term, in the sense that like, when you build a consumer product and it goes mainstream, there is like a massive sort of like burden on the other companies to educate. And you've seen it like, you know, over and over.

• 1:14:59Speaker 02

  And in our case, you know, you want to make sure that people like know what they're getting into, they're not like overextending themselves in terms of like how much they're sort of trading, they're not like getting into an area of discomfort. And how do you kind of like, you know, we can do as much as we can do on the marketing and on the product side, but like, we need policymakers and regulators' help to basically make it an industry standard, but also help us elevate ours.

• 1:15:22Speaker 01

  By the way, we're pro that, I think that like even the classic retail brokerages should also be adopting a lot of these customer protections that we're talking about, that they don't, I think that every retail trading platform should be taking a lot of these steps, so...

• 1:15:35Speaker 02

  Yeah, I mean, that's sort of our general view, and we hope that this is sort of the direction that things take. Because, you know, there's kind of, you can have a variety of views, right? And some people believe that like, hey, like, you know, any type of speculation should be banned, whether it's in the stock market, or crypto, or prediction market. We don't believe that, like, you know, we think that would be a bad outcome, for all the reasons, because there's a lot of upside to having liquid markets and a variety of different things. But also because if you ban it, you're actually heightening the risks that you're trying to prevent, because now that activity is going offshore, right? Like, and where you cannot moderate or police it, or do anything to protect it. Awesome. Well, tired, Lana. Thank you, guys.

• 1:16:13Speaker 01

  Thank you.