Kalshi vs. Everyone: Co-Founder Luana Lopes Lara on the lawsuits and the long game

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Spend $250 on your first campaign on LinkedIn ads and get a $250 credit for the next one. Just go to linkedin.com slash scale. That's linkedin.com slash scale. Terms and conditions apply. I think there's a lot of misconceptions about prediction markets out there. There's nothing wrong with speculation. Couchy has always been about how do we bring any thesis about the future into the markets, right? So that any thesis you have about the future, you have a market that perfectly mimics that. And that's what you can expect for us. And we want every new financial innovation, every new financial structure to be on Couchy first or at least a very close second. We want to be the biggest derivatives exchange in the world. That's Luana Lopez Lara. Co-founder, president, and COO of Couchy, the prediction market that has exploded in growth over the past two years. Luana gamely talks to me about what it's like to become an overnight billionaire, what's behind Couchy's ongoing courthouse battles, and why betting on Couchy is different than FanDuel or DraftKings. We touch on financial market features like hedging and insider trading and get into how Couchy resembles ballet, Timothee Chalamet's Strange Dance, Dentist-chaired Couchy ad, and what the platform reveals about the U.S. midterm elections. My prediction? You'll be fascinated. So let's get to it. I'm Bob Safian, and this is Rapid Response. I'm Bob Safian. I'm here with Luana Lopez Lara, co-founder, president, and COO of Couchy. Luana, great to see you. Great to see you, too. Thank you so much for having me. I'm excited. I'm excited about this. You are the world's youngest self-made female billionaire. That is amazing. Congratulations. Thank you. Is it weird to be part of the Billionaires Club? How different is your life? I mean, it's definitely weird. I don't know how much my life changed. I feel like when I think that people always say this, but I actually think it's true that it's, in a lot of ways, my life is still the same. I mean, I wake up. I go to the gym, come to work, stay in the office the entire day, go back home, you know, hang out with my husband and my dog and go to sleep. But I think that what's most exciting is about it really is just like another, I think it's just another kind of metric maybe that shows how far the company has gone, how hard we've worked, and it's kind of paid off in that way. The growth has been just phenomenal. I mean, you launched in 2021. Last year at this time, Couchy was valued at $5 billion. I think by early this year, it was up to $20. $2 billion, like that scale at that speed. What makes that possible? Like, is there luck in it? It's a great question because actually, though, like we started the company in 2018, right? So in a lot of ways, it looks like an overnight success that it was like up two years ago. We just started growing a lot around the election. But it was a result of now eight years of work. When we started the company, it was very important for us to be legal and regulated from the start. So it took us four years before we could launch the product, launch anything, really, have any user. To work with the federal government to figure out how to kind of bring prediction markets to the U.S. in a safe and regulated way. A lot of things like helped us grow this much now. But I think it's just like the compounded effort that we've done and the team has done for so many years on the tech and the users and talking to them. It's just like in the moment that we won the lawsuit against the CFTC to be able to bring a lot more markets to Couchy. And when that happened, the product was ready to really, really grow and go from there. So I think it's a mix. I think we were very prepared when our time came. That kind of hockey stick growth. Do you have to pinch yourself like, is this totally real? Is there anything about that that scares you? That kind of pace? I actually would say it's a very good thing that it happened like so fast. Because in a lot of ways, we keep the mentality of being very early stage, right? And I think it's like when companies, I think they're just like compounding at a kind of like very normal rate. I think it's easier to kind of start thinking like, oh, I'm a bigger company. I need to hire more people. And you kind of like you don't realize like you can start. You're making a lot of mistakes and take a long time for you to realize you're making that many mistakes. For us, because we grew so fast, we also, our mentality and our kind of like way that we look at the company hasn't changed as fast. So because of that, we're able to, for example, have and operate with very fewer people. We just had to like really keep going on, like building the product as fast as we could. Early stage team and early stage mentality, very intense in time of like work intensity, keeping the speed, which I think is the most important thing for startups is the speed. You're definitely right that sometimes we'll look at the numbers. And like two years ago, like before the election, we were making like way less than $10 million a year, right? Now, in a day, we transact way, way more than we used to do in a year, like two years ago, you know? And it's just like just the transaction volume. And it is crazy, like the numbers we're talking about. And we're very grateful for where we are. But we really, really try to keep the mentality on of we're still underdogs. We still need to have a lot to prove and to grow. I mean, the success you've had has put a bullseye on your back. There are states are coming. After you, you know, for being an unlicensed gambling operation. Federal appeals court just ruled that Ohio and Tennessee can regulate Kalshi through their gambling laws. Like, is that kind of an existential threat? I mean, New York alone is suing you for $36 billion, the state where you're headquartered. We are very, very confident in legal analysis. And of course, like, as you said, the appeals court that we're in today says, but we also won the Third Circuit. What all of these lawsuits are saying is like each of them. And each of them has a different legal theory, like thesis, the more important part, if you take a step back, is that like, the mechanics of how Kalshi operates in a sports book is completely different, right? And that's why they are regulated different ways. That's why we are federally regulated, right? We are an exchange, which means that you trade against someone else. We don't set the price. We don't set the odds. We don't trade against the users. The users are just trading against each other. And we take a transaction fee. What matters the most here is liquidity and making sure that we have, like, national liquidity, right, to build upon. Imagine if you have, for example, the New York Stock Exchange. But you can only buy stocks in the New York Stock Exchange if you're in New York. The prices would be significantly worse. It would not be a liquid market. It would just be worse for every participant. And also, the market just wouldn't work well. On the sports book, on the other hand, it operates completely different, right? Like, for example, in the exchange, because we also don't trade against our users, we don't make money when users lose. A sports book is actually completely different. They make money. Their revenue is equal to customer losses. The more the customers lose, the more money they make. For us, it's not the same. The incentive is not to make people lose. Because we don't make money when people lose. Because of that, as well, we don't cap our winners. So, you know, if you go to a sports book or casino, you start making money, they'll make sure that you cannot participate anymore. That's the opposite. We want winners. We want people to come and bring price. And we want price competition. And all of those kind of, like, really, there's no, like, price competition, right? The sports book has a monopoly on the price, and they're going to put their margins on top because, like, they're having a bad month. So, they're going to make the prices a little worse or whatever. And because of that, it's, like, they are very fundamentally different mechanics and very fundamental. Fundamentally different products, and they need to be regulated in different ways, which is how the federal regulation for exchanges develop a certain way. And, you know, we're growing a lot because, you know, an exchange is a fair, more accessible, more transparent way to trade. You can see all the prices. You can see the competition and the order book in real time. And that's why users like it so much. And I think it's fair, and it should be, that the consumers at the end of the day pick what's better for them. Something like 75% of the volume of your business is sports. And for some users' point of view… …it can seem like it sort of serves the same function for them and, you know, the DraftKings and Fandles of the world look at you as competition, even if the engine behind operates in a different way. Well, the thing is, speculation happens in all financial markets. You know, crypto, stocks, options, futures, there's speculation in all of them. And speculation is actually very important because it drives liquidity, right? Obviously, every market that we're talking about. And it's actually one of the… …biggest growing parts of Kaoshi right now is the hedging and kind of small business hedging. And hedging is very important for financial markets, and it is a differentiator from gambling. But speculation is very important, and it happens in every market, and it should happen, and it's fine. But the mechanics being different really matters. You know, the house always wins. Like, that's not the case of an exchange. And on the DraftKings and Fandles of the world point, it's actually interesting because Jason, CEO of DraftKings, actually in their latest earnings said that they're seeing no cannibalization between sports. So, they're seeing no cannibalization between sports, betting, and prediction markets because they are fundamentally different. Users understand they're different, and they engage with them in a different way. different way. And because of that, I think both models can coexist. They are coexisting. And at the end of the day, the users will pick what's best for them. But it's not the same. And I think the users kind of know that. You mentioned hedging. I did love during the Knicks NBA championship run, there was a bar here in New York that used Kalshi as a hedge so it could offer free drinks to everyone if the Knicks won. These are great counterpoints to all the betting talk. Do you look for those opportunities to tell those kinds of stories? Well, it's a very big growing part of our business. So in a lot of ways, it's like the same way that we want to grow our retail side, we want to grow our business, our small business arm as well. We're hiring a lot for this part of the business. One of my favorite stories actually is this goat farmer in California. And by the way, I don't know. I'm not a farmer. I don't know. But so I might say something wrong. But there was some new legislation that was going to pass and increase his labor costs. I think by 4x. And he was able to come to Kalshi and hedge that passing. And we always explain a lot of what we do on this is like working on the education or explaining to people how this can be useful to them. And then they of course, they can make their choice. But it's like putting money on what you don't want to happen. So if you don't want to happen, you still make some money to like insurance. And that part of business is growing so much like we have like small business in every single state of the country, hedging things like gas prices, diesel, weather, sports, politics, one of the most interesting things that we see actually, when you think about like gas and oil hedging, right? And you talk to small businesses, and we always ask like, why don't you just buy oil futures? You know, they've been there for a long time, the liquid market, why don't you buy it? And the response actually, every single time has been, wait, what is that? How do I do that? How does this work? Because like, just don't understand. It's like a game for like big institutions. They're just like, they're trying to hedge at some certain, like a big scale. But you don't have access to these things before, like small businesses don't have access to these things. And it's something that we are really excited to bring. And it's part, for example, of our partnership now with the U.S. Hispanic Chamber of Commerce. I went there to talk to them. And the most exciting thing for them is like, we want to be able to do it. Like businesses in Texas have been telling me like, we have so much risk associated with the elections. We need a way to hedge it. And I think bringing these is very exciting for us. We see it as a very big, important part of the business that now, because we're way bigger and more liquid, we can really start growing there. You don't use, the term betting. Like, that's not what people are doing. They're not. I mean, at the end of the day, like, look, people use the word bet everywhere, right? Like if you open the Wall Street Journal, it says people are betting on stocks or there's some big bet on real estate. People use that term for basically like taking a view of what's going to happen in the future. Obviously, there's people speculating on cow sheep. We're not trying to say that there isn't. But I think that the reason we say trade is because it is a fundamentally different mechanics that people like, understand. But if you want to call it, you know, whatever you want to call it, because people call every financial activity like this nowadays, I think people can do whatever they want. There are more contentious stories. Kalshi listed a contract on whether Iran's supreme leader would die or be removed from power. You had to backpedal and reimburse more than $2 million to users. Who decides what can and can't be traded? Is there a line or do you sort of discover that line only in hindsight? No, there's a good question. Because we're federally regulated, we don't allow markets on war, terrorism, assassination, and any of that. The reason we decided to list that market, same thing with the Maduro, is that actually there are a lot of different ways that a person can be out of power, right? And an example is Maduro, for example, he wasn't assassinated or anything like that. And you could see like the impact, the clear economic impact that had, for example, in like price of oil. So what we do for these markets is actually we let that market operate. But what we do is if there is an assassination or an attack, we let that market operate. And we let that market operate. Or a terrorism or a case or something like that. What we do is we avoid every trade so no one can profit from it. And we kind of like, that's kind of how we drew the line in this case. I think that the reason we did the reimbursement was actually not because we thought there was anything wrong, but I think that we didn't do a good enough job in the product to explain to users how that voiding would work. And I think that that's what got us confused. Now, after this market, we actually have been way more selective with the markets that we list. There are studies that say that prediction markets are more accurate than almost any forecasting method. Right. When money's on the line, people are incentivized to tell the truth in a different way. Like, why do they work? If I ask you right now if you think it's going to rain tomorrow, you might say, oh, maybe it's going to rain. I don't know. Yes, it's going to rain. But if I tell you I'll give you $100 if it's going to rain tomorrow, the first thing you're going to do is you're going to open your phone and you're going to open the website, like theweather.com. You're going to look outside. You're going to maybe call your mom and ask, like, do you think it's going to rain tomorrow? What do you think? And there is that first layer, which is if you're incentivizing people that you will make money if you're right or lose money if you're wrong, they will go out and do research and get more information to bring to the market. So that's layer number one. And also why, like, yeah, they're layer number one. Layer number two is that you actually have now competition between these people, right? Like, I went and asked my mom and did all my research. You did the same. And now we are both going to try to compete on the market for the best price so that we're going to match with someone that's willing to trade against us. So now there's price competition. And that price competition, that trading of price competition really makes the market get to the best forecasted value. That is, everyone in the market kind of agrees that that price is fair. Because if someone doesn't agree with it, they're going to trade and they're going to move the price, right? So that combination of the incentives for people to bring, like, more information to the market, incentive to make money, and the price competition makes that number the best forecast that you can. Because it's basically everyone that did a lot of research is agreeing that that price is the best one. And you can track it with time, right? Because the markets are open 24-7, every second there's some news, maybe that fair value changed and now there's incentive for someone to come and move and trade to make money. And because of that, markets become kind of efficient and they become the best forecast and the real-time forecast of what's going to happen in the future. You can go on TV and say there's a recession tomorrow and, you know, you're going to get a lot of clicks, everyone's going to watch because you're, you know, doomsday and whatever. But it doesn't really matter if you're not right. But on markets, it really, really matters if you're right. We actually put out our own calibration. And what calibration really means is, like, if a market says there's a 70 percent chance of something happening, is it actually true that, you know, seven out of ten times something will happen? And the results are very, very, very good. That calibration and prediction marks is very good. And as you get closer and closer to the date or the resolution date, they get even like high 90s on calibration and kind of accuracy. Prediction accuracy in the high 90s is pretty darn good. One of the benefits of deploying market dynamics. So what does Kalshi reveal about the upcoming U.S. midterm elections? And what about Timothy Chalamet's strange dentist chair ad for Kalshi? We'll talk about that and more after the break. Stay with us. 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Is that 70% accuracy, or like at what point does it start to move toward 90%? A poll or something, it's top to bottom, right? It's like, you know, it's like, you know, Yeah. editorial board or someone that's doing a poll, they're trying to aggregate the information and just tell people this is the number, this is the forecast, this is what's going to happen. Well, prediction markets are doing their bottoms up, right? They're actually, we want as many people as possible to do as much research as possible and bring that information to the market. So it is kind of an aggregation of what millions of people are thinking and doing. And I think it's one of the first times that you really see kind of like information that's actually led by people versus like the elites just coming and saying, this is what's going to happen. With our big markets, like, you know, like who's going to take control of the Senate or control the House, I think it's like as accurate as you're going to ever get. The other thing that we have to always talk about, which is like probabilities are not certainties, right? Like when we had the, when something happens 1% of the time, it doesn't mean it will never happen. It means that one out of 100 times something will happen, like it will happen. An election that's like a 60% chance of someone winning. If I told you, if you walk outside, right now, there's a 40% chance to get hit by a bus. You're not going to walk outside because, you know, 40% is pretty high. It's the same thing. Like 40% does not mean that the person, the underdog is never going to win. And I think that's kind of a challenge that we have on the educational front, which is explaining to people that different from polling, it's not like polling. If you say like someone is like X amount of points, like 10 points ahead, it almost probably in the market would be like over 90% chance of someone winning because they're very, very different things. They measure different things. And we need to look at it as, as probabilities. There's thousands and thousands of election markets. It's very hard to actually go market by market and kind of get a full sentiment of what's going on. Is the country leaning Democrat? Is it leaning Republican? How do you think about the margin of victory? All those markets. So one thing that we launched was like our, our balance of power index, like a K-PAL. It aggregates all of that in one index that you can see kind of sentiments trending more Democrat, more Republican. And you can kind of track with time, kind of how the aggregate or the index of power is going to change. And then you can kind of track with time, of all those elections coming together. I saw a report that 70% of the visitors to CalSheet just look at the data, that they're not even coming to put money behind a prediction. Is that true? I think it's actually higher, maybe 78% now. And it goes back to what we talked about on the final, like this betting versus not. Like how many people would open a sports betting app to just look at the odds, right? They won't because it's like this price means nothing. It's just the price that a sports book's trying to give because of their margins or something. But the amount of people coming to CalSheet to consume information is because they really understand the value of that information that they can't get anywhere else, which is this unbiased, market-driven, kind of real-time information. And we see a lot of people saying, like, I don't want to trade, but I come here as almost like a substitute of Twitter, a substitute of the New York Times. And I think that what they say, and it's true, is that what prediction markets do is because they incentivize truth. If you're right, you make money versus clickbait, right? If you open Twitter, they're going to be incentivizing clickbait. Insane thing you're going to read on Twitter is going to have the most likes. On CalSheet or prediction markets in general, it's actually the opposite. If you're saying something absurd, you're just going to lose money. CalSheet recently introduced margin trading, letting users borrow money to make bigger trades. That amplifies risk, doesn't it? I mean, what do you say to folks who say like, oh, CalSheet's getting too dangerous? Well, that is a concern of ours. We've been working on margin for over two years at this point. And all of that started with a lot of work with the regulators, a lot of work in the margin models, making sure the exchange and the clearinghouse will be safe. And also who gets margin, right? You need to apply to get margin. It's not like you sign up to CalSheet, you can get margin directly. You have an extra application that you tell us, for example, how much experience you have investing or trading, you know, your net worth, all those things, to make sure that you should have access to these products. It's not open to everyone. But margin is very important, actually. If you look at, for example, the traditional financial markets, almost everything has margin. And it's because it's very complicated for an institution to, like, for example, if they're going to take a $100 million trade to just park $100 million on something that maybe can happen in a year, there's a lot of opportunity cost for that money. And you need to make capital efficient, right? And if you think about, for example, a business trying to hedge an election, right? They think they're like a green energy startup. They're very concerned about some Republican coming and taking away their subsidies. And they want to hedge that election. If they want to put $100 million, you need someone to put $100 million and park that for two years. So what you can do is, like, we offer leverage to the person, the seller, for example, so that it makes it worthwhile for them to actually participate in that trade. And that's how all financial markets operate, right? If you look at futures markets, you trade everything on leverage. And it's just a very important step for us in our institutional adoption. When we started Kaoshi, actually, the idea of Kaoshi always came, it really came from the financial world, right? Like I worked at Bridgewater Citadel Securities, my co-founder at Goldman Citadel, and what we first thought about Kaoshi was, people always have some thesis about what's going to happen in the future, right? They think someone's going to win a recession or someone's going to win an election, Brexit is going to happen, and you try to figure out how to put that in the markets, but there isn't a direct way to do it. So the premise of Kaoshi from the start is how do we allow all this trading that's happening through proxies, how do we make all of that in an exchange in a better way, in a transparent, accessible to everyone way? So that's why, like, bringing this product to institutions is so important. And a precondition for that really is having a very good and efficient margin model. One of the issues for all markets is insider trading. On a stock exchange, insider trading rules are pretty clear. On a sports platform, athletes and coaches are barred from betting on their own games. Kaoshi is like a new vector that's sort of harder to police. I wouldn't say it's harder to police, but first starters is like, you know, insider trading is prohibited on Kaoshi, we're federally regulated, you cannot insider trade, you cannot try to manipulate markets. And if you do, we have an entire surveillance team that has actually brought a lot of enforcement cases recently. There was the teleprompter White House case. There was the case of George Santos. Yeah, George Santos, you barred, purportedly trying to get a contract trade on his own attendance at the State of the Union. That shows that the system is working right. We found the people try to do bad things. We banned them from the exchange. We put fines on them, we send them to the regulators and they can go to the Department of Justice, end up in jail because these things are illegal in a federally regulated exchange. And I also don't think it's like harder for us to look for insiders because the topic is always narrower. And we have everyone that sign up, we have their name, address, date of birth, social security number, ID, we have self verification, employment verification. And with all of that, it becomes kind of easier to find those people. And we do a lot of proactive stops, right? Like as you mentioned, like us. Athlete or a coach cannot, you know, participate in sports betting. They also cannot like participate on a couch. So, for example, if you're a politician, you're going to be blocked from your own election even before you try to put money on it. If you're an athlete, same thing from your sport. And that type of proactive ban is something that we are doing that the New York Stock Exchange and all other financial exchanges aren't even doing. And I think that we're deterring a lot of this activity. What's really bad is what happens kind of in the kind of offshore platform. So you don't know people's names and addresses. You mean Polymarket, right? There's more than just Polymarket, by the way. Obviously, the Polymarket offshore is a great example of like, I think it's impossible to know who is trading or what's going on there unless the person really, really tries to leave very clear clues, like putting their name as like their username, which, you know, most people that really want to do something bad, they won't do that. So Polymarket is a great example of like, I think it's impossible to really surveil for insider trading there, but you have a lot of other platforms outside of the U.S. that are doing the same thing. And I think all of them are very dangerous. And that's why we keep saying that banning things is never the answer, because if you ban them, you just move the activity offshore. And then it's all the concerns you might have about the activity in the U.S. are now 100 times worse. How do you do the surveillance for, say, like, like there's a there's a mention market, right, contracts on specific word choices and speeches? Well, I think a great example that we call here is the Trump, the person that works in the White House now. They're the television. They're the teleprompter, right? And that person obviously has insider information because they know what's going to show up in the teleprompter. And that is material, not public information. That was actually pretty easy, quote unquote, for us to catch. We know the information from the person. We do a lot of like AI machine learning on the trades themselves to see if there are anomalies or things that are like, you know, someone that is a new account that just traded a certain way. That's very different. So there's a lot of things that we look for on making like on trying to detect information that it goes to a compliance team that does a full investigation, including, you know, looking at the person's social media, reaching out to the person. There's multiple interviews, all of that to to to catch it. Also, there is like users themselves when they notice something weird in the in the markets, they will point it out to us and say like, hey, like we're noticing something weird here. When I say it took us four years to get regulated, people are always like, why does it take so long? And one of the things is like the development of the surveillance system with the CFTC took that a long time because we had to like prove them prove to them that how these things worked and out. Now our systems are very, very advanced. So it's actually claim is probably our kind of like most important IP in a way is the kind of how these models have developed. There's a lot of buzz around AI agents trading on financial markets. Are there AI agents on Kalshi? Like does Kalshi have a philosophy about where agents fit into your future and your model? Yeah. So we allow like you can use an API to trade on Kalshi. The only thing is that, of course, you are responsible for your agent. So all the all the rules that apply to a person. Apply to an agent and in a lot of ways, like we're definitely not against it because it's like. Like, the good thing about the markets is that, as we talked about, it gets to that one number that's the best forecast of the future. For us, what we want is more and more informed people and more and more informed traders and the winners to really come and, like, get us the best and most accurate number that we can. And if that is that some agents are competing for orders and bringing the price to an even better price and a better forecast, that's very good for us. Are you game for a rapid-fire round of questions? Can I throw a bunch of different things at you? Let's do it. All right. So, prediction markets have come a long way in a short time. What are things we should use prediction markets for that we're not yet? I would say the hedging side is something that, like, I think we're seeing a lot of small businesses adopt it, but I think that a lot of, like, just individuals can use it, too. Like, we're seeing now some start of, like, people hedging the weather in their wedding day. And I think that on that hedging side is being as used now as we want and believe it should be. Prior to CalShe, you worked at the MIT Media. You worked at the MIT Media Lab in personal robotics. Do you have any predictions about robots in our everyday lives? I think very soon we'll be able to use robots for the vast majority of the chores that we don't want to do in our personal lives. I'm very excited to have a robot to do laundry and, you know, wash my dishes. So, hopefully that comes very soon. You were raised as a highly trained ballerina, I understand. What can ballet teach us about business? I think it's the discipline side. I think the discipline side is most applicable to everyone, which is, like, it doesn't matter if you're having a good or a bad day. You need to go train. And I think that discipline of it doesn't matter what's happening. I will have to do my job and I'll have to get these things done is very important, especially for running a business. Because if you're a founder, if you're a leader of a company, your fluctuations will mean the company's fluctuations. Discipline really comes from example and making sure that as a leader, you're very stable emotionally and very stable on kind of your work ethic. One of the biggest gifts you can give for productivity in the company. I wasn't sure whether you were going to give an answer about balance. I'm always amazed by how ballet dancers manage to maintain their balance doing extraordinary things. I mean, I do think that not falling is a very important thing in daily life. I'm actually way more clumsy than I was before because in ballet, I actually lost with multiple accidents, I guess. I lost, like, a couple ligaments on my foot and I have a bad knee. So, I actually fall. I have worse balance than normal people. For sure, at this point. Do you think about how you keep from, what, from getting the equivalent of injuries like that in your business so that you can maintain your balance? I think it's, that is a good question, actually. No one ever asked me that before. I think that, I think that the interesting point of ballet that you always have to be thinking about is kind of like, you want to push yourself to as much as you can before you get injured. It's very competitive. And, and, you know, to Timothee Chalamet's, you know, he was right that way fewer people go watch ballet nowadays. That makes it way more competitive. And which means that if you get injured, most of the times you can't actually afford to just, like, take two, three months off to get fully better. You just need to kind of push through and, and, and do it. So, it's a kind of the sense of, like, if you do too much today and then you get injured, tomorrow you're just going to be worse for, for some time. I always tell people in the company, it's like, okay, if you're going to pull in. If you're going to pull in all night or today to finish something, that's great. But you really need to make sure that every other day of the week that you're going to be less productive because you're more tired. You always need to be thinking at the end of the day of, like, to maximize kind of, like, your productivity and your output. There are times that you're not maximizing the kind of, like, each specific step. Sometimes you have to take a break in order to be able to come back and do the next performance or have the next challenge there. Yeah. You, um, you, when you mentioned Timothee Chalamet, it made me think of that. The Kalshi ad where he's in the dentist chair saying, kind of like, that was just bizarre. When you saw that, were you like, oh, that's exactly what you want, what we want? Or were you like, what is this about? When we started working with him, it was part of, like, kind of what he wanted was, like, let's make it very different. Let's make people be like, what's going on? And let's just really, like, kind of question a little bit, like, how advertising is done. And at the start, we were like, wow, are we sure? Like, this is just like, you know, it's a very big for us. It was the world. It was like, are we sure that this is the way to go? But we're like, look, he's clearly like a, you know, creative genius. Let's trust his intuition. And he was 100% right. I think if you look at the metric from, um, from kind of how that ad performed versus a lot of our other ads or our competitors, it's just, it was such a great ad and performed so well for us. And I think that's kind of what we always try to do with our marketing team, which is like, we want to be, um, different. We need to be telling our story in, like, a different way. And without that. I think there's a lot of misconstrued narratives about prediction markets out there that we need to use a part of it of advertising and marketing to explain to people how it is different. What's next for CalShoe? CalShoe has always been about how do we bring any thesis about the future into the markets, right? Um, so for us, it's, that is always a goal in the roadmap is how do we expand, um, our, you know, market structure offering, our margin offering, our topics offering and the liquidity so that any thesis you have about the future, you have a market that perfectly mimics that. And we want every new financial innovation, every new financial structure, or, you know, to, to be on CalShoe first, or at least a very close second. It's just a matter of like, if we out-execute everyone else, we'll get there, uh, in just a matter of time, but, but we need to out-execute everyone. No small goals for you, Luana. Really fun talking to you. Thank, thanks for doing it. Thank you so much for the time. This was great. I'll confess that I was a bit skeptical heading into the conversation with Luana, but she made CalShoe's prediction market sound more appealing than I'd expected. I liked the idea of an information counterpoint in a world of clickbait, though, of course, clickbait could be influencing the market. What strikes me most isn't just the ambition and creativity behind CalShoe. It's Luana's ballet lessons about discipline and constant fine tuning. And also, an awareness that overreaching, pushing too hard, can lead to long-term negative results. New innovations, whether prediction markets or agentic AI, can have lots of unintended consequences. We need to capture the good and protect against the bad with each leap into the unknown. I'm Bob Safian. Thanks for listening. I'll see you next time on The Devil Wears Prada. It takes about 10 seconds to find. Just search Rapid Response wherever you listen to podcasts and hit follow to make sure you never miss an episode. I hope to see you there. Humans will never be more intelligent than AI. There's going to be two types of companies. Those are great at AI and those that went out of business because they weren't. How do we build a future that is human-centered? I'm Rana El-Khayoubi. And on my podcast, Pioneers of AI, we answer that question and so many more. As an AI scientist, entrepreneur, and investor, I know what it takes to build AI that works for everyone. 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