Kalshi CEO Tarek Mansour on The Case for Prediction Markets | Ep. 48

[00:00] We decided to sue. All the kind of bad things that were predicted happen. All the little things like, oh, we're not going to let you do this. We're going to delay this. We're going to kill you on this. The audit that was supposed to be two weeks now is like 18 months. Oh, my God. It's like nonstop, just like knife after knife. But the most important thing is we won. All right. I'm really excited to be here with Tarek, CEO of CalShe. Thanks for doing this. I've been looking forward to it. Thanks for having me. I want to start with the history of CalShe. So can you kind of take me through the genesis, how the idea came together, how the company got started? [00:30] So I grew up in Lebanon. I was born in California. I grew up in Lebanon. [00:33] and [00:33] Lebanon was kind of a [00:35] It's quite like rough terrain to grow up in. It's like super volatile, a lot of uncertainty. You know, I kind of found refuge in math. [00:44] you know, kind of a mix of different things. Like I grew up a single mom. My mom was like, I wanted you to be successful. Maybe math is the thing to like get you back to America. And so I got really into math. And then it was like a lot of my decisions at that point were like, what are the like best like, [00:57] smart math people do, basically. And it's like, oh, they're going to MIT, so that's what I need to get into. And got into MIT, and then the next... [01:06] kind of stage was the same question, and the answer was finance. And I started spending time in finance. I worked at Goldman, I worked at Citadel, [01:12] I worked at some small prop shops. The one that did Bridgewater and Citadel as well. [01:17] And... [01:18] the [01:19] There was kind of a pattern that was emerging in a lot of these places, especially in the example I always love to give is in 2016 at Goldman. [01:26] I was working on this desk and there were like two questions that were like bothering everybody in Wall Street. And like this is those are two questions that people are figuring out how to like trade on these questions like will Brexit happen and then will Trump win the 2016 election. People wanted to have like the Trump hedge or the Trump trade and.

[01:42] And then the thing that really, like... [01:44] stuck with me was that [01:46] So Brexit happened and it was a shock. Yeah. People were like really sort of completely shocked that, you know, the polls were saying this is not going to happen. And people had all these smart trades about how to hedge against Brexit. But then a bunch of desks on Wall Street blew up and they lost money and all the bad stuff. When it came to Trump, there was... [02:01] Kind of a very similar thing happened. Like the trade that we sold at Goldman, the very common trade was like – [02:07] The Trump trade was you short the S&P. [02:10] Because if he's going to win, the S&P is going to go down. [02:13] right [02:14] Everyone bought that trade. That was the trade. And it was a horrible trade because Trump won. [02:22] And... [02:23] The S&P actually, I think it was the single biggest rally in the S&P's history ever, essentially. Right, yeah. It's the worst trade of all- The exact wrong way to trade the idea. It is, exactly. It's actually the perfect- Which is a shame, because it's like what you were trying to trade was this underlying thing that you got right, and then you expressed it backwards. They were right about the prediction, and they lost money. Yeah. This kind of is like when a company has quarterly earnings, and people are like, "Oh, it's going to beat earnings." Yes. And so I'm going to try to buy the stock. Yes. And then it beats earnings, and the stock goes down. [02:53] all smart in retrospect. Like if you actually trace the plot [02:57] of like... [02:58] Whether the [02:59] stock went up or down after earnings... [03:02] Beat. I think it's like 50-50. Pretty much. It's mostly priced thin. Two things kind of like. I realized. Like actually a lot of. Some of the smartest. Kind of like traders and institutions. Are. [03:12] a lot of their trading ideas or like the things that they're trying to do originate from a simple like human view about the future. I think Trump is going to win. I think there's going to be

[03:22] like some change in diplomatic relationship between these two countries. I think COVID is going to come back. What they thought they were trading on with traditional market is the event, but what they're really trading on is this sort of [03:30] reaction function is how the market was going to react to an event. They weren't trading on [03:36] whether Trump was going to win. They were actually trading on how the SNP was going to react to Trump, which – [03:40] In retrospect, and now we can't really predict. It's kind of impossible to predict. It was kind of a very exciting idea because it's like, okay, what if we just build this marketplace where... [03:52] What you're trading is like... The specific thing that you're thinking about. Yeah, it's just things that people care about. You know, whether politics or economics or weather or really any other topics that just people naturally walk around the street and think about. Because people don't think about like... [04:04] you know, what is Cisco going to print? Like, what are their financials next score? Like, they don't think about that. They just think about, you know, the Fed might raise interest rates or things are more simple. And so that is exciting because like the time could be much larger because like a large number of people would care. Yeah. Right. The second thing that was really interesting is like if you, [04:21] He has this kind of [04:23] if you believe in markets, what markets really do is they aggregate information, right? They are a very good weighing function. So they can figure out how to get information from a bunch of people, aggregate it, and get a single price. And, [04:34] It's like, what if we applied that to questions about the future, like all these kind of specific events or questions about the future? Then in theory, we should get a smarter or more accurate answer or market-based answer about all these questions. [04:45] And that got me really, really excited because... [04:48] At the time, we were thinking about if you could get a little bit smarter about the future...

[04:51] That's like a very worthwhile product to build. Right. It doesn't you don't need to be like 100 percent smart. Even we get 10 percent smart about the future. That's more than enough. And that's how I got like into the prediction market. We can talk about that. The whole history on prediction markets and all of that. And I got really obsessed. So when you got started with the company, what was the first year or the first couple years of building? Like, what did you do when you got started? I was actually going to go work at Citadel. [05:13] because I had kind of spent time somewhere there and I actually loved it. It was one of those situations where Luana and I started talking about it and [05:21] You know, the idea was just bothering me. [05:24] Like I could not get it. Like I have a little bit of like. [05:27] I'm a little bit OCD and I get obsessed with things, but I couldn't get it off my brain. It was so... [05:33] So like, you know, I was like going to go because, you know, one thing I would say is we were not like entrepreneurs that were like trying to figure out what product to build to build a company. [05:40] That was not how Calci started. You just had this one idea. Like the idea kind of forced itself on us. Yeah. Like I was talking about it and like, no, no, no, like, forget about that. It's just like not, you know, let me just go to Citadel and pay me all this money, like, [05:50] And [05:51] But then I remember we had a friend who was going to this YC hackathon. [05:56] I don't know if they still do them, but they used to do these like hackathon and bring a bunch of builders. And he was like, oh, I'm going to this thing. Like, you should come. I think the deadline is passed. [06:04] And we're like, [06:05] Well, deadline's passed. He's like, no, no, you should just email the guy. And we emailed, I forgot who the organizer was at the time. I don't know, we emailed something like, hey, we were trying to figure out flights or something. And he's like, yeah, fine, you can just come. So we're like, okay, well, we should just go. And it's funny, we did this hackathon, we put together a front end for what the V1, it was like a bunch of questions and a list format.

[06:25] With yes, no, and then like some probability. And it was like an order book. Like literally we just copy pasted what the New York Stock Exchange order book would look like. And it's funny because we had judges like that were going to judge the different teams and pick the finalists. And our judges were Michael Seibel and Christina from Vanta. I don't know if she had started Vanta at the time or she was in the early innings of it. When was it? It was October... [06:44] 2018. I think she had started. She had started? I think so. You know, we started pitching the idea, and then it's very, like, I remember, like, Michael was like, oh, you know... [06:53] Everything is great about this idea, except for the fact that it's like totally not allowed in the US. And like, you know, this has like absolutely no way of existing in any way, shape or form. And we walk out and be like, look, we tried. [07:03] you know, move on. I remember like, I drank a bunch of beers in that hackathon. Like, we're done. And then this guy like, ends up picking us to be finalists. You know, he's like, dunks in the whole thing and then we end up winning that hackathon and we're like, well, maybe we're onto something which got us into YC. And then we're like, well, we have to give YC a shot. Like, you know, and at the time it was like, you know, wow, like, [07:22] Never expected to get into YC. And then like this is the first year was crazy because you know how there's this thing about YC like they're the cool companies are building products and getting all these investors excited. Like we were the total opposite of that. We had no product, no customers. Week to week, we go to office hours and everyone's like, here's my KPIs and here's attraction. And we were like, we got nothing.

[07:52] week, you come back with your group and everybody else has grown 7% week over week. And how much have you grown? And you're just like, none. Doesn't feel good. I don't even know what the product is. But we knew the vision was always clear. But for us, the key question is like, how do we get it regulated? We were very, from the beginning, we made a decision. We're not going to launch a product outside of the realm. [08:11] of the law or regulation. We wanted to figure out how to get this regulated in the US onshore, no matter how long it took, no matter what. And the early days were really tough because we had no progress in regulation is not linear. It's not like you get [08:26] sort of encouraging status updates from regulators. It's like there's big bang moments almost. Exactly. It's like zero all the way up to like approval. Yes. And nothing in between. Can you talk about what those were for you? [08:38] So that was 2018. Then we spent the first two years [08:42] Essentially think of it as like [08:44] We were figuring out like which lawyer would take this on. And it was this one. Think of it as like this thing where like basically I became essentially somewhat of an expert in the law, like in the law around commodities, which is where I thought this would get regulated. Essentially, there was no proof in the law of why this shouldn't exist. But there were a lot of like sort of like things to figure out. What was the law at the time? Like what was not allowed at the time? Well, it says that a commodity could be an occurrence or a contingency. And so was the issue the definition of the commodity?

[09:14] We're used to futures like grain and things are tangible. Right. An election outcome is hard to sort of put a thing around. Imagine walking into a regulator. You're like two 21-year-old, 22-year-old MIT kids just walking into the – I mean, we got this lawyer, Jeff, who was ex-CFTC, the regulator is a CFTC. And he just got us the first meeting with the regulators. And two kids are like, here's our 40-page deck plan of how we could regulate this thing. And they're looking at us like, what are you talking about? [09:44] issues? How are you going to police for manipulation? How are you going to list this sheer number of markets? Because usually in our markets, it's like [09:52] you know, like the CME or, you know, like those exchanges, they list one or two new things like a year or every few years. Like you're talking about like listing hundreds and tens of markets sound like crazy. So it was a lot of these kind of like. [10:07] like hurdles, none of which felt impossible. But if you add them all up, it started to look like Mount Everest. Yeah. And the problem is that you start climbing the Mount Everest and then it [10:15] somehow they're like [10:16] You see a higher peak kind of thing. Yeah, it starts sort of like, you know, it keeps going. And we had no certainty it was going to end. That was the toughest part. It wasn't actually the work. I mean, the work was regulatory all day. It was like, we could fix this thing and it might be like we haven't made a dent. It's like a desert. You don't know if it ends. And so psychologically, it's very taxing. [10:33] Because you're walking in that desert and you have no idea if this thing is ever going to end. You may actually just die. Yeah. And then you just walked for like years in this desert. So what happened? Well, we're just so stubborn because we're like, look, and in those situations you have to just like, you got to stop thinking. You have to have no kind of like...

[10:49] introspection. Mark Andreessen. Yeah, I mean, you know, he got like, he got a little flame for that one. But no, I mean, more like, you have to have a bit of tunnel vision or like, look, [10:59] We're going to keep going until we're proven wrong, i.e. we die. Yeah, yeah. [11:03] Or we find the other side. So can you talk about finding the other side? Towards the end of 2020... [11:10] we started seeing like, think of like, okay, they would send an issue to us and then one after the other and after the other, after the other. And then it started fizzling out. Like the issue started, like these guys started to be like, wait, maybe these people are actually serious. Like they're, you know, and like at some point you kind of run out of issues to find. We, you know, we walked through all of them and worked through all of them. And think of like thousands and thousands of legal documents and pages, et cetera. And then we, we started kind of angling towards an approval and we got approved in November, 2020 to get the first sort of, uh, [11:40] Regulated exchange for prediction markets. Then the new administration, on the same day of our approval, the election happened. So the new administration came around. [11:48] And our approval was bipartisan. It was like them and Republican. But like [11:51] But then the new administration was like, wait, wait, pause, pause. We're going to have to think through this. In some ways, we're like, oh, we're finally through the desert. [11:58] But all of a sudden, actually, oh shit, we got dropped back into the middle of the desert. And it was like... [12:03] We're going to have to see if we can let you do all these things. Maybe we'll let you do like a four economic markets, but not this sort of broader vision. [12:08] That was disheartening. It was really hard because like we'd spent two years. We're finally there. We're going to launch. Yeah, of course. [12:14] And so this is another, like we started the first battle to launch the exchange. And we're like, fine, you know what? We'll launch with the four economic markets. We never thought that would get a product market fit, which it didn't. But we're like, we've got to get off the door now and launch and see what happens. So...

[12:26] Then by the end of 21, we kind of launched with, I think, a few economic markets. [12:30] no traction whatsoever, etc. And at the end of 21 is when we're like, okay, [12:35] We have to open up the space to get all the markets we want. [12:38] And this is when we started talking about the election market. And for prediction markets, I think like [12:43] And we can talk about the dynamics, but we always thought that you need the diversity of markets. [12:47] But you also need a catalyst. You need enough, like something that is enough of a driving force to get people noticing so that you can kind of break through the supply and demand. You need something strong enough to get the chemical reaction going. Because you have the supply and demand problem, the chicken and the egg, and the chicken would come and there's no egg and vice versa. You need something strong enough to get everyone at the same time to start trading. And then after that, the thing can get going. But also, I think it was one of the best ways to explain to people why prediction markets are powerful. [13:12] It's like you need an event that everyone cares about and where we can provide a better product, like give a better forecast. [13:19] End of 21, we start talking to CFTC and they say, well, maybe, etc. Maybe we'll do it by end of 22 or for the midterms. [13:26] a whole year of that, just regulation again. So now you're talking we're three years in, three to four years in just doing regulation. End of 22, [13:33] The regulator sort of kind of like nudges the approval post the deadline. [13:37] which essentially just didn't make a decision. [13:40] Um, [13:41] And that was a very hard time with the company because we thought the approval was going to come. [13:45] We've done all the work possible. Very kind of tunnel vision again. We didn't get it. And so in those areas, [13:50] circumstances what happens is like people kind of [13:53] they blame the execution of the strategy. It's never kind of like, you know, things are outside of your control. It's like,

[13:58] we made the wrong decisions and it was bad. And, you know, and we lost a bunch of the team at the time. And, you know, we had to do some layoffs. It was really hard time. Like, it was really, really like hard time. I think back at that time, it's like, I think it was one of the most painful times like I've ever sort of experienced in my life. And by the way, like I went through war in Lebanon. Like I have had kind of like missiles drop next to my house, like, you know, things like that. Like, it doesn't compare. Like, I think there's some form of pain because it's you feel shame as an entrepreneur. Totally. Right. You get it, right? Like, you know, that feeling of like, [14:28] people are like trusting you with all this and then we come out of this in January, February. We're kind of sort of reconvening. What do we do as a company, etc.? And I remember Luana is like sort of [14:37] dogmatic belief in this vision. Should we pivot? Like clearly, you know, we're not going to be able to do this. [14:43] And then I was like, we're going to try again. [14:45] So that's the strategy for 23 is we're going to do the exact same thing as what we did in 22. [14:49] and we're going to try again. It was pretty, I mean, it was unpopular, but like we did it. A whole other year of the same thing, talking to policymakers, regulators, same regulator, et cetera. Then they ban it, like they block it at the end. They block the election market at the end of, uh, [15:03] of 23. And same thing happens again. And this was the point where I think this was, I would say, the most sort of like, you know, Sequoia likes to call these like crucible moments. But I think like [15:13] the kind of key decision [15:15] that I think got to come to where I was today, which is like we sat down and we're like, what do we do from here? [15:20] And again, when I was kind of driving a lot of this, but it was like, look, we strongly believe we're right on the law. We do. We also strongly believe this thing should exist. Like we have come so far.

[15:30] You know, we're like, you're talking now where [15:32] Five years in. [15:33] We just got to sue the government and we got to sue our own regulator. You know, we talked to Subor, talked to Alfred and, and, and, [15:41] You know, the interesting thing that came out of that conversation is that like it's definitely an anti-pattern for a company to sue its own regulator or the government in general. [15:49] It's even more so for a company of like 20 people. [15:52] that has no real product, no real... Like, we're kind of like a nobody company. Is it an empty pattern? I think a lot of... [15:59] Great consumer companies have gotten to, I don't know if it's a full dispute of their own regulator or full lawsuit, but at least some legal battles. It's maybe the sequencing. Yeah. Maybe they got big. Like Airbnb and Uber were really big. Yeah. We're talking about a platform that has. Coinbase, maybe. Coinbase got really big, right? We're talking about a platform that like. Yeah, it was tiny. Tiny. Like, you know, we had like hundreds and thousands, like maybe thousands of users a week. Like it was, you know, the evangelists, the really early adopters in terms of sort of this is the unlock that will get us going. Yeah. [16:29] was saying like, even if we win, even in the offshoot, kind of the crazy shot that we win, [16:34] we may still lose because the regulator could kill you in the meantime. Right. Right. And it's like the death by a thousand paper cuts type sort of. And it was real, right? This kind of notion of like lawfare or people just like coming after you for all these unrelated things. But you kind of know that it's because you sued. [16:48] your own government. Yeah. Lawsuit is usually pretty [16:51] It's a big battle It's a big deal But then I remember like After like kind of saying this It's like [16:55] But sometimes sort of some of the best companies I've ever seen and kind of start with an anti-pattern. Like there's something weird that happens in that company. It is unusual. And maybe this is yours. So we decide to sue. All the kind of bad things that were predicted happen. All of them. Like all the little things like, oh, we're not going to let you do this. We're going to delay this. We're going to kill you on this. We're going to the audit that was supposed to be two weeks now is like 18 months. Oh, my God. And it's like a nonstop, just like knife after knife. But the most important thing is we won.

[17:23] Like October, like October 2024. So how long did that suit take? [17:27] A year. [17:29] And during that year, you're just stressed out of your mind. [17:31] Yes, but not more than the other ones because think of at that point. Yeah, it's my last shot. What else am I going to do? It's like walking the desert had become our life. And it was like our last shot. It was a bit of a desperation. Like it was like, you know, your back is against the wall. If I don't do this, I'm not going to make it anyway. So who cares? Basically, like I think we didn't think there was like any shot at making like we had to get the election market. At that point, it was more it was more than just business. It was there was like a mission thing. Like we wanted to deliver this to the world. We were so dogmatic about we want the world to see this market in action. [18:01] how powerful this thing can be. Okay, so you win the lawsuit. Now we're going to... [18:05] Now it's interesting because we won the lawsuit and like it's like this feeling. So, OK, we won the lawsuit. And specifically what the lawsuit enabled us was. So the lawsuit was basically saying it was in some ways redefining what constitutes like gaming or gambling versus a financial market. And it's interesting because there was a lawsuit prior to that. So basically it's now saying things like betting on the president or the outcome of a sports game. That can be a financial market. It's now a financial market.

[18:35] And very simply put One is what is the structure Are you an open and free market Where people are just trading against each other [18:42] Versus like a house where you're accepting bets from someone. And again, your business model is like gambling. It's like your revenue is equal to customer losses. I see. And then the second thing is like, is this a real thing that's happening in the world? Where like some people may benefit from hedging or some people may benefit from. Basically by being the marketplace where other people are betting against each other. [19:01] That's critical and not being gambling. That's a very critical thing. Societally, but also legally, a bunch of kind of, you know, but also just like very simply, like, you know, there's still also a difference between like. [19:13] you know, like, [19:14] Two people kind of transacting on like, hey, what is this dice going to land on? [19:18] Because that's an artificial risk that you're creating just for the purpose of trading or betting on it. Yeah. Versus if you're trading on a stock, which exists, a company exists, or oil, or an election. Got it. So it's also the event existing, whether or not people are gambling on it. It's a natural thing. It's a natural event, not an artificial event. Yeah. And that's important. That's very important. Yeah. And it's interesting because the decision that came out of that lawsuit is very similar to one that came out, [19:43] close to 120 years earlier in [redacted address], which is the one that legalized grain futures, the most boring financial market, the OG hedging market. Because at the time, there was a state versus federal like, [19:55] kind of fight where the states are claiming, well, this is gambling because some people are speculating. Like farmers were going and kind of betting on the price of grain. So that's gambling. It must be gambling. And the Supreme Court said, like, look, a lot of people will speculate.

[20:08] But there is... [20:09] Some people are like using it for hedging or getting smarter about the price of grain over time. So there is that's why it's a financial market. And actually, in many ways, the expectation is necessary. [20:19] for that market to exist. Like if you want a marketplace, if you want the stock market to exist, if you want commodity markets to exist, if you want prediction markets to exist, you need speculation. You cannot just have [20:26] people that are enshrined themselves against stuff because the person on the other side needs to be a speculator. In some ways, it's kind of history repeating itself. Um... [20:33] But it kind of redefined the aperture of what's allowed. It would be legal for somebody to speculate on whether or not we were going to say a certain word in this podcast. Would that fit the definition if people wanted to trade on that? Yeah, because it's happening anyway. They can bet with each other. And they're trading against each other, right? And it would not be legal for us to, and we can talk about that, if we had a position. So if we cannot go and trade on that and then you just go say something, that would be market manipulation. Is it illegal to bet on something where you see... [21:02] a bet playing out in the world, but you know the answer for sure? So it depends. And that's the whole kind of conversation we've been having around insider trading. And there's a whole long history here. But the line that [21:14] So we're a regulated financial market. We're a regulated exchange and clearinghouse. And a lot of the rules we have are mimicked after the rules of the stock market. In the stock market, the line is drawn. It's like you cannot trade on what is material non-public information. [21:27] And the way that that's defined is like, [21:29] you have a piece of information that you acquired [21:33] under certain rules. [21:35] right and one of those rules is that you cannot disclose it so material non-boblig information is information you're not allowed to disclose that like if you own as an executive of tesla and you went and

[21:43] said it to the press, you would get in trouble. You're not allowed to do it. And trading is a form of disclosure. That's the whole point for extra markets, right? Like, prediction markets are a way to disclose information. Like, when you trade, maybe you're not saying it on Twitter, but you're actually trading that information and you're moving the price in a way to disclose the information. But, like, let's say this morning I saw... [22:01] you know, a trade that was... [22:03] Will Jack and Tarek see each other today? And I'm like, I know the answer to that. So I'm going to make a big bet. Are you Jack or are you someone else? I'm me. [22:10] Well, you have influence on that. You have direct control over that. Got it. So that would be market. That wouldn't be inside trading. That would be manipulation. Yeah. So the majority of participants in grain futures are grain farmers. [22:20] Right. Like they and the way that the price of grain futures is done, this is going to be a little surprising. Yeah. Can you guess like how do you determine the price of grain? How do you determine it? I don't know. [22:29] How? [22:29] You literally survey a bunch of the farmers are trading in the market. Wow. It's a little weird, right? They definitely have inside information then, right? Because they're the guys, they know what the price is. [22:40] And it's interesting because the line there has been like, okay, inside trading is very hard to define green futures. But what we're going to draw the line is you cannot – [22:49] put a position and then artificially manipulate the underlying price. You cannot move the prices of the, like you cannot move the event or the underlying in a way that will kind of [22:58] help you profit. And so it's the same thing here. It's like if you have direct control over an event, if you're a politician that was looking to pass a bill, [23:05] You take a position, then you tank the bill or you try to pass it even harder. That's illegal. Yeah. And that's ban on calcium. I could see that could get a little gray, though. Like, let's say you were let's say back to the, you know, us talking on the podcast today. Let's say you're a friend of mine.

[23:18] And you knew that we were, you knew it was happening. You don't really have control over it, but you could be like, you know... [23:24] It's the same as the stock market. Is the cousin sort of responsible? And if someone on the street heard an executive talk about some MNPI, are they allowed to trade it or not? And these lines have always been on. So does this stuff come up a lot or is it not? Yeah, definitely. Because it's interesting. And look, this is a conversation that CalShieldone will not have all the responses to. [23:41] And it's a conversation where like having regulators and over time policymakers, but like [23:45] You know, my principles are kind of generally simple when it comes to this is like, [23:49] I always go back to why is insider trading ban in the stock market? Now, there are some people that argue, well, maybe we should let insider trading happen in the stock market because it would make the prices more accurate. Right. Yeah, it feels like, I mean, my reaction to it is it feels unfair. Exactly. People got money for no reason other than they were told something and that shouldn't be a source of people generating money. [24:11] It's all about unfairness. It feels like there was no skill and no effort that went into it. Yes. And it's actually even worse than that. There's a very practical implication, not just sort of a moral implication. The practical implication is that, well, if people believe the stock market is rigged, [24:25] They stop trading. [24:27] Like, the liquidity dries up. - Right, right. - There's kind of this nice property of market, it was like, markets that have a lot of insider trading, in some ways are not gonna exist. - At some point, like at the limit, you'd be like, if I don't have insider information on a stock, [24:38] Why am I trading? Like, I'm definitely the sucker. There's a mix, right? And so I think, no, you have to have information on something. Yeah. And seeking information is very good. Doing research, like when...

[24:48] Like, for example, you know, some of the I don't know if you ever heard that, like, at the time, apparently Two Sigma used to like, I don't know if Two Sigma specifically, but like, these are like you satellite images of like the parking lot at Walmart. Yeah. Yeah. [24:58] to figure out how many cars were coming here. Yeah, probably even before that, people would just hire somebody to sit outside of Walmart. That's not insider information. What's the foot traffic? Exactly. That's information. That's just work. That's work. Yeah, that's work. Exactly. But I think there's a balance. But I think to me, insider information is... [25:12] The way to define it is information that you could not get access to would work. Yes. If you're not an insider. That's right. Yeah. Like, and that's the, and I think the reason you should ban it and the rules that you have to build in the marketplace is like, how do we keep it fair? Yes. And it's very practical. If it's not fair, then people will stop participating. Yes. And this was a message I always say is like, there's something nice about insider trading, which is like, if the marketplace is really not fair, you could trust that people will just like stop doing it. They won't, they won't want to do it. And that's why we take this very seriously. Yeah. I have a topic I want to. [25:39] get your take on because I can tell that you're extremely thoughtful about fairness, the way it should work, what's the better future. I think one of the discomforts people have with prediction markets is that they... [25:52] They look like gambling and some people who are not comfortable with them. It's like this new idea and it looks like people just pulling their phone out and they're starting to do gambling. Yeah. And obviously that is not at all what your sort of conception of it is. There's all sorts of function outside of it. But I'd be curious actually to start to hear sort of your thoughts. [26:11] steel man version of like what's the way this goes wrong like what's the version of it that is like the bad version yeah what's the bad version of gambling that you were not comfortable with that you think crosses some line that you don't think is good and you know this is obviously i think probably you and i share like a baseline um value of libertarian and like adults should be able to do what they want to do obviously with some balance of like we shouldn't you know expose people to

[26:41] There's some balance here. Yeah. Where are you not happy? Look, I'll tell you, like, I'm a risk taker. I'm a trader. Like, and I've never, I don't consider myself having really gambled ever, like, when I trade. And that's, you know, I speculate. There's, like, a lot of similarities between this. And... [26:56] And there's a lot of arguments. I mean, the argument I hear most about is like people like talk about ducks a lot, like the it quacks like a duck. And like, you know, that's usually the argument of like, well, it looks like gambling. So maybe it is gambling. Um... [27:09] And it's interesting because the thing I always say is like, [27:12] That argument has been made about every single new type of financial market that has ever come. [27:18] to the US or really anywhere. [27:20] Right. Like the argument has been made about grain futures like we just discussed. It's in some ways, you know, when we start with life insurance back in the back in the day, you know, the headlines at the time were like, oh, this is like morally horrible. Like you're gambling on people's lives. This is terrible. We should not have this at all. I mean, you know, now I think a lot of us would agree we should have grain futures. We should have we should have the stock market, which is, you know, has been we should have life insurance. We should have all these different things. But I think there is a basic like, yes, speculation. [27:48] has a flavor of [27:50] It looks sometimes like gambling, but it doesn't make it as such. And so I like this kind of frame of like, let's actually like play this out. And how does it go? [27:59] wrong. In my opinion, the things that end up contributing to this bad perception in gambling [28:04] is the incentive structure in the system. [28:07] is how is the system built? [28:09] And what are the incentives that are built into the system? And when you think about a gambling business model –

[28:15] It's a business model who's like where the primary KPI, right? The thing that will... [28:20] not just predict your net income, will be pretty much equal to your net income, is your customers' losses. If that's your business model, [28:26] and that's what your incentive is, what are you going to do? You're going to promote? Losses. Losses. What else are you going to do, right? If you do a great job at stopping losses, you're going to lose money. Yeah, more throughput, bigger rig. Yes. It's just the inevitable. And so what a lot of these businesses do is, if you're sitting in a casino and you're making money, what do they do? The bodyguard comes and takes you aside and says, stop. If you're doing something informed, if you're doing something smart, if you're seeking information, the very point of financial markets, you get blocked, you get banned. [28:56] or at least limited. Well, part of that happens because they're trading against you. So in the casino, That's the business model. Blackjack, it's against the house. Yes. And so you winning is exactly me losing. Exactly. Online casino, all these models are like, [29:08] The counterparty is the house. Versus for you in a marketplace, you don't care who in the house is. I don't care. Yeah. [29:13] And that's fundamentally different. So the way that it goes bad is like when the house is trading against its own customers – [29:19] You are inevitably the algorithms. One thing that does persist, though, is theoretically, you don't care if on net the two of them leave the day with more or less money between the two of them. Like you don't care that two people betting against each other. I'm neutral. You don't care. Yeah, exactly. If they both start with one hundred dollars, you're OK if at the end of the day one has one hundred and ten and one has 80. That's OK. I actually in some ways prefer if they're both at one hundred. Yeah. In some ways. I mean, because but think of it as like. But to me, it's the incentive in the system.

[29:49] Because... [29:50] Okay, and going back to kind of how it goes bad, if you're at a house, what are you going to do? You're going to figure out, you're going to build algorithms and you're going to, you know, whether it's physical algorithm, the casino, like casino has all these smart ways of figuring out who are the big losers in the room. And then you're going to figure out how to get them hooked, get them to come back, get them to come back even if they're losing, even if they know that they're losing. You give them a suite, you give them all these different things, right? You make the lights more flashy. All of that, like, you know, the waitress comes in with cocktail, right? And so that is where the unhealthy behaviors emerge. [30:19] Because you have an algorithm that's just promoting unhealthy behaviors, right? And we've seen it in a bunch of other kind of like, even in the context of tech platforms, et cetera. What I like about it in free and open marketplace, and that applies to the stock market, it applies to crypto, it applies to options. There isn't that dynamic. That dynamic doesn't exist. What is my incentive as a company? My company, I take a small fee. [30:38] A transaction fee, right? So what I want is volume. I want people to just trade [30:43] more. Right? And [30:45] I'm actually incentivized. If I want people to trade more, I want the platform to be fair and perceived to be fair. I want the platform to be neutral, as neutral as possible. And I want it to be transparent, which is another key thing. All the trades are public. Everyone can see what they do. And I like this model significantly more because now... [31:03] And when we think about going back and with our responsibility as a platform, I have a much better shot like structurally at creating healthy feedback loops into the product. Like if I and we do this a lot, we have limits on like how much people do and how much they trade, et cetera. [31:17] But it's not just something that we just say, like our business model is tied to it. If someone is doing too much excessive behavior and losing too fast,

[31:23] We, as a business, will not be hurt as much as the other types of business models if we tell this person, hey, maybe you should pause. Now, I don't know if it's our job to block this person. That's a different story. But we have all the incentives to say, hey, maybe you should stop. Maybe you should self-exclude. Maybe you should put limits on you. Because, again, they're not losing it to us. They're losing it to someone else. And that's not a positive thing for me if that's happening. And so that's what's exciting me. And I hope over time, not just like Calci. [31:53] in the context of how do we limit unhealthy behaviors or excessive behaviors. And I hope that this gets applied to also all the other financial markets where people, retail participation is going higher, where it's crypto options, all these different things. - Let's say that what people were trading was just like stocks instead of the outcome of an election or something like that. In that world, even with a fee, [32:15] you know, over 10 years of trading, like the stock tends to go up versus with, you know, an election, it's just to trade back and forth with a little. And so the outcome that you're producing is not incrementally more net worth over time. The outcome you're producing is better information and people being able to express their views. Yes. It does seem to me like it would be cool if you also had the other type of product where you were helping people. Like the investment type product. Yeah. Yeah. There's a difference in investing and trading. That's right. That has always been existing, right? Like, [32:43] Yes, the stock market is, look, I think holding a stock for five years, that's investing. Yeah. Now, if you trade a stock in and out over the next few days. You're going to get crushed by all the fees. That's trading. Yeah, not just fees also. It's like...

[32:53] Your directional view It's not enough time Yeah Yeah it's like you're trading You're And that's a zero sum game And options are a zero sum game All crypto in my opinion We'll see over time But like [33:03] Most of crypto trading, not if you hold Bitcoin for five years, that's investing, but if you're trading Bitcoin and out, you're zero sum. And so my mental model for this is like, [33:13] Yes, that's true. But it's interesting because we ask our customers, a lot of them, hey, like, do you trade, not invest, they're different. Do you trade, for example, S&P or do you trade traditional like options? And consistently, like nine out of 10 of our customers, their response is no. [33:30] And the reason is... [33:31] I don't gamble. [33:33] And like, wait, but... [33:34] You know, in people's mind, but like, [33:37] elections trading or betting, that sounds more like gambling than trading and options. Yeah. Because that sounds financial, you know. But actually, like if you ask people, like, well, I don't have a way to win. Right. I don't have an edge. Yes. There's no way for me to do it. Those markets are still efficient. They're efficient. The hedge funds have way more information than I do. There isn't a way for me to be truly informed. And if I put a lot more research. So the whole point is, but if you put a lot of research, can you get better and can you win? And the reality is, in a lot of traditional markets, the answer is no. [34:07] be it Main Street. [34:08] Wall Street will always be the average person. The beauty of what we're building, it's just not the case. [34:13] The average person is winning. [34:15] More than Wall Street. [34:17] Like our best inflation forecaster is not a Wall Street person. Well, I guess by definition, the average person is neutral with you because there's a buyer and a seller. But it's more about a point of like there isn't that structural advantage that like Wall Street has in our markets. Yeah. I mean, what I would argue is they might be neutral with you and they're definitely going to be negative if they're going to try to trade against Citadel or something like that. Generally, yes. Yeah. And yes, our average user is neutral. But I'm talking more about like. The people who want to put in real work.

[34:47] Why do people vote on bills? [34:50] Back in the 2024 election, the guy who put a lot of money on Trump because he did the neighbor poll. Totally. It's amazing. The market's working exactly how they should, which is they're rewarding – [35:01] Someone going out there, doing the research and doing the truth seeking on behalf of [35:05] society. Yeah. And then you get rewarded for it. Like you're doing a reward mechanism for someone to do research, which does not exist in a lot of the traditional markets. Yes. Yes. [35:14] And that's why like when you talk to these people that are on CalSheet, the prediction market, you know, I don't know if you saw the New York Times article about the rise of the prediction market trader. Yeah. That class of people that are doing this as a full time job. Yes. [35:25] And they're excited about this because it's a way for them to get rewarded for all the things they are learning about the world. By the way, one of the things that I think is very interesting is whenever there's like a new financial product, there's all these emergent behaviors and properties. And like an example with yours is like insurance and hedging and things like that. Can you talk about like when I first learned about that, I was like, oh, that's surprising, but it makes sense with like a hurricane or something like that. It's getting used for those types of things too, right? Yes, absolutely. [35:50] And that's, I would say, like the trajectory over time is like that is becoming an increasingly bigger part of the platform. Obviously, we started with retail, like people, individuals. But now as we're getting into the institutional, that's becoming a bigger and bigger piece. But let me talk about retail and then let's talk about institutional. So, yeah, there's two functions of the market. One is what we call like price discovery, which is predicting all these events, right? And that's one of the benefits of prediction markets is you're giving people an incentive to do the price discovery, which is predict all these events. And that's working, right? I think a lot of people now...

[36:18] at least understand increasingly more. I don't know if you saw the Fed paper that came out. You saw that? Yeah, it's cool though. Yeah, actually the rise of micro markets, right? And it was like the Fed itself is saying this is the best gauge we have on the economy. It's crazy. It's like amazing. And by the way, the people, it's not Wall Street again, it's Main Street. We've figured out how to build this community of [36:34] people that are dispersed across America that like, [36:36] are making us smarter about the economy. It turns out that if you ask a big enough crowd of people, how much does a cow, a particular cow weigh, they get really close. That's the OG, original prediction market. Yeah, it's pretty cool. That's how it started. It's literally bringing up crowd wisdom. It's happened with the elections too, with Trump and stuff like that. Yes. Everybody's like, no, Trump won't possibly win. And it's like, well, maybe. If you have an incentive to actually do the research, I think you may actually, you know. So that's that. And the second prong is hedging. [37:04] And hedging is a little different from insurance. So insurance is usually regulated at the state level because it's also there's a house. Right. So you go to an insurance company and they give you a price. Hedging is on the open market. So hedging is just like I'm on a coast and I'm just going to bet that a hurricane is going to knock my house over. But the key thing is it's an open and competitive market. You say I want to buy X amount of something that protects me and then people can like fill you at whatever price and they compete for that price. And we see this a lot, for example, in Florida in the Keys. [37:31] You know, insurance companies have pulled out because they don't know how to price hurricane risk anymore. It's like really expensive. And so we get like calls. As of when? [37:39] It's been like two years, three years. Wow. Where we get a lot of calls around hurricane season where people are like, hey, I want to buy... [37:45] X amount of hurricane hitting this town. Yeah. I don't want to deal with the insurance process. Oftentimes they don't pay me back. There's deductibles. There's all this. I just want, if the hurricane hits the town,

[37:54] I get paid. Yes. And that's a hedge. Yes. And that's one, like, you know, Chris, like, really clear sort of – [38:01] use case that we've seen. The other one is like at the time with Biden and the forgiveness market, a lot of like students were hedging, like smoothing out their student loans. They were super worried about having to pay back. But the interesting thing about division long term is like, [38:13] As we're sort of... I mean, now we're seeing... [38:15] We're seeing a massive acceleration in institutional adoption of prediction markets and Calci. [38:21] is think about it this way. Like you own S&P. [38:24] as an institution, but you're really worried about an upcoming election. You're really worried about the midterms. [38:28] One way or the other. If the Republicans win or Democrats win, you think it's going to impact your portfolio in a certain way. Today... [38:34] You don't have any options. [38:36] You may just have to sell your position. [38:38] before the event happened, if you want to protect yourself. With prediction markets, you can actually put the hedge. So if you're worried about, for example, Republicans winning or Democrats winning, you can buy – [38:48] Republicans or Democrats, if you think that's going to impact your portfolio, your hedge is going to basically complement for that. So you don't have to sell your position anymore. You can't put on the hedge. And that's, I think, where the next generation is like AI, COVID-19. [39:00] elections, bills passing, regulatory changes, all these different things can become just insurable risks. [39:07] And what CalShay has provided for that is [39:10] Like the layer one of being able to, you know, kind of think about those risks, just pricing them. Like any of these risks now we can put on the platform. We have this platform. We basically have all the top super forecasters on the planet that will give you the price. Yeah. Like you send this thing to the system and it'll come back and spit out. Hey, well, Citrini scenario happens. I don't know if you read that. Have you seen the research report from Citrini? Oh, Citrini, yes. Yeah, yeah. We put it on Calci and like, you know, it started at 11%. Now it's at 33%. This thing has gone up. Yeah. Yeah.

[39:36] But it's amazing because you have a market that you can probe now instead of listening to different pundits and Twitter and people are battling on Twitter. And then based on that, if you believe that, then you can go do other trades against that. You can either use that to hedge if you're worried about that impacting your portfolio, [39:50] The interesting thing is like pricing this thing. [39:54] can enable us as a society to make the prices of all of our traditional assets better. Now, there is this theory about infinite markets. Have you ever heard about that? No. [40:02] So this idea that like, you know, as society gets increasingly more complex, [40:07] the vector, [40:08] Like the number of dimensions that matter for asset prices increases. [40:13] Like 100 years ago, you maybe needed to understand supply and demand and industrial labor and maybe the agricultural economy in the US. Now you have to understand those things. We also have to understand what's happening in Iran and what's happening in COVID, AI and what it's going to do, how technology is rapidly progressing, cyber, all these different – like society is just getting increasingly more complicated and more interconnected. So everything impacts everything. The interesting thing is like… [40:37] As this vector, as the number of dimension increases, our pricing of traditional assets, like the market, like the S&P or home price, etc., the entropy there goes up. Like we have less and less information, like we have less and less of the relevant information. So you have to actually over time price all these different dimensions. [40:54] so that you can then price the S&P more accurately. And like one of those dimensions, for example, is the Citrine report, like what will happen with AI, what will happen with COVID? [41:01] And that idea of infinite markets is very tied to prediction markets because prediction markets fill that gap. Prediction markets can actually price all these different things.

[41:09] that if you get smarter about all the subcomponents, then you can be smart about the actual [41:13] component. If you want to price a Tesla stock, you have to price whether Elon is going to leave, whether they're going to over or under deliver on deliveries, how fast autonomous vehicles are going to come around, all these different questions. And prediction-wise, you can price all these different factors that then feed into the stock price. This is very important for us to keep being smart about resource allocation over time. Otherwise, our model of the world is going to just get worse. We're going to get less smart. Maybe the last topic I'm sort of interested in, [41:43] Your company is small relative to the scale you're at. So you're not much over 100 people. Yeah, we're 127 now. So how does that work? I mean, that is very small relative to what you've accomplished. [41:55] So what's interesting about it is that we didn't sit down – [41:59] proactively and like we didn't ride a dog of like how we're going to build a small company that's lean. [42:05] It just sort of happened. Did you do any particular other things that this was a byproduct of? Yeah. So I think a few things like and I'm not 100 percent sure. So I'm still kind of figuring out like why are all these other companies so much bigger? [42:17] I'm still like trying to figure out, am I missing something? Or one is Luan and I work very, very hard. [42:22] Very, very hard. Like we have a chip on our shoulder. Like we'd like to think we're the underdogs. What I learned over time is like, [42:30] So if you look at kind of – we're generally like first to office – [42:33] last to office, last from office, work on weekends. And I think that just generally the output per person in the company just is heightened because, you know, generally the leader is really in the front line doing a lot. Number two is...

[42:45] We have a lot of direct reports. Like how many? There's not really an agile layer in the company yet. It's like 100? [42:52] Like if you ask the one, what maybe like 80, 85 of the people at the company today are doing, [42:58] She knows. Wow. Because she probably checked with them on Slack in the last 48 hours. Wow. And the rest is maybe me. Well, I mean, there's a ton of people that just don't need, like, you don't need to know what they're doing. You know that they're just... [43:10] doing like you got you got to let them cook. Yeah. You know, so that's number two. And then I think number three is we don't think about org charts much. [43:17] And I don't know how that will scale. We're still thinking about that. But like we think about like here are the sort of [43:22] Like we keep sort of dynamically listening here to talk like X problems of the company today. And how, who do we have on those problems? And people move between problems. Yeah, it's sort of like, yeah, it's like. Do people self-organize to the problems? Yeah, yeah. It's like a, you know, like sales in an organization. Like, you know how like. Yeah. If you have like a, if you get cut. Yeah. Yeah. [43:41] Your cells are just going to come around the cut and do their thing. It'll be a bit like that. I mentioned to you, I really want you to read the Valve Employee Handbook. I think you're right. Yeah, I'm excited about reading it. I think it's very good. But it just sort of happens, and it's kind of like... [43:53] Make sure there's like [43:54] like as little kind of constraints... [43:57] or bottlenecks of that sort of self-organization to happen as possible. Is this what people expected when you hired them? Did you hire types of people that you thought could only function in a place like this? Like, how did you... [44:10] How did you end up with that culture when it's [44:14] what I would describe as extremely uncommon. You know, we don't have all the answers, obviously, but like the, um,

[44:19] One is we do bias on slow versus intercept because... [44:23] People that have Intercept, I think, generally can land in this culture and be like, [44:26] what like what on earth is going on like this is crazy and that's just happened like we've had this happen uh someone who's used to like a big more structured organization comes in or just yeah even not even big like an organization is just structured in a different way and they show up they're like this is like crazy like you know this is like complete chaos right and and because yeah it looks like an organism we're like these organisms are moving around and and so so slope because slope is they don't know there's you know they're super smart very high agency like oh like [44:56] You just think it's normal. So that's one. Number two is like, I always say, I mean, Brian Chesky put better, I didn't, I verbalized it when he put it into towards, but like, we don't manage people who manage work. So people that just like have, just generally high agency, we never have to check on whether they're doing something. Sometimes we have to reorient a bit. Hey, like actually, this is not actually that useful. Like we should like do something else or like, [45:17] you know, or like being very much in the details. But it's about the work, not the people. But just they're doing stuff. They have this sort of high agency. I want to always be doing something. Yeah. And I kind of bias, honestly, towards like execution over strategy. Me too. [45:30] I really do. [45:32] Because... [45:32] Look, I think strategy is hard, but like, you know, what I found over time is like the national next step for a company is generally kind of natural. Like, you know what I mean? It's like, it's not like if you probe, like for a public company, for example, and the CEO lays out a strategy. It's not like, what should we do? It's like, can we do it? And how quick can we move and all of that? Most of the time. Periodically, there's probably like a non-obvious strategic decision that like the founder needs to make. But that's probably a couple times a year kind of thing. Yes, max, max, I think. And it's usually a little bit longer horizon than a year.

[46:01] So I think of our role, Luan and I, as like, [46:03] I try to very, very high level. Like, are we just like directionally, generally in the right direction? And what are the big risks in the next three to four years? [46:11] and like make sure that we like are thinking about those and are executing against those and I really I really mean three to four years yeah [46:17] I'm pretty paranoid. Again, from my time in Lebanon, I always think like, what is the thing that's going to go wrong in three, four years? I'm like, let me work through that. And then very much in the details. So like, I literally, I'm often like in specific copy in the product. Like a lot of it, Luan and I wrote still to today. Or like even ads, like we get into the copy, like, is this good? It's very, very, very specific things. And everything in between, we try to like, [46:39] Not spend any time on. Yeah. [46:41] And that pushes other people to not spend any time on. Yeah. And sometimes there's a subset of people that, [46:46] That doesn't work well for? And then people can just opt out. Basically. Yeah. [46:50] I mean, [46:50] There's not a clean solution. Yeah, of course. It's just hard, right? Building a company. But a lot of people kind of like it. They just don't want to be managed. Are you going to be able to keep the company small? I really hope so. Even they're scaling fast. That's one of the worries I have. Small is possible, at least. I think about this a lot. That's one of the worries we have. [47:08] I would say maybe the trade-off, which is like, you know, people sometimes walk away with like, oh, this perfectly run company. And the answer is like, no, like not at all. The trade-off is usually, I think we take on more organizational chaos. Does that make sense? And to me, I think there's a bit of a decision you make as a company. Like either you're more chaotic or you have... [47:25] You have more process. [47:27] which means bureaucracy and some degree of slowness. And so Calji is very comfortable with putting something out there and getting bashed for it. But like,

[47:33] that, you know, three or four weeks later, it gets much better. And now you're much better than if you had waited the two months. We're very comfortable with that. It's great. Tarek, this is super fun. Thanks for your time to do it. Thanks for having me. This is awesome.