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

Tarek Mansour is the co-founder and CEO of Kalshi. Kalshi is a regulated prediction market exchange valued at $22B in 2026 where people trade on the outcomes of real-world events – things like inflation prints, Fed decisions, elections, or weather events. Instead of betting against a house, users trade against each other in a market, and prices reflect the collective probability of an outcome happening. Before starting Kalshi, Tarek worked as a quantitative trader at Goldman Sachs as a structured credit and equities analyst and at Citadel as a global macro trader. During his time at these firms, he realized a common thread: a lot of trading stemmed from an opinion on a future event. We covered the idea behind prediction markets and how they offer a more direct way to trade on beliefs about the future. The conversation follows the long, difficult path to building a regulated exchange in the U.S., from early skepticism to ultimately winning a landmark legal battle. We also discuss how these markets can improve forecasting, enable new forms of hedging, and change how information gets priced. Timestamps: (0:00) Intro (0:23) Kalshi’s genesis (5:05) Regulation-focused from inception (11:06) Suing the government (18:02) Gambling vs. financial markets (20:58) Defining insider trading (25:38) Incentive structure of the system (32:40) Investing vs. trading (35:31) Hedging use cases (41:38) Scaling a lean team (44:02) Defining Kalshi’s culture Links: https://x.com/jaltma https://x.com/mansourtarek_ https://kalshi.com/ https://uncappedpod.com/ friends@uncappedpod.com

Tarek MansourguestJack Altmanhost
Apr 29, 202647mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:000:23

    Intro

    1. TM

      So we decide to sue. All the kind of bad things that were predicted happen. All the little things like, "Oh, we're not gonna let you do this, we're gonna delay this, we're gonna kill you on this, we're gonna..." The audit that was supposed to be two weeks now is, like, 18 months.

    2. JA

      Oh my God.

    3. TM

      It's, like, a nonstop, just like knife after knife. But the most important thing is we won.

    4. JA

      [upbeat music] All right, I'm really excited to be here with Tarek, CEO of Kalshi. Thanks for doing this. I've been looking forward to it.

    5. TM

      Thanks for having me.

    6. JA

      I

  2. 0:235:05

    Kalshi’s genesis

    1. JA

      wanna start with the history of Kalshi.

    2. TM

      Yeah.

    3. JA

      So can you kinda take me through the genesis, how the idea came together, how the company got started?

    4. TM

      A little bit of background before. So I, I grew up in Lebanon. I was born in California, I grew up in Lebanon. And, you know, Lebanon was kind of a... It's kind of, like, rough terrain to grow up in. It's, it's, like, super volatile, a lot of uncertainty. You know, I, I kind of found refuge in math, you know, kind of a mix of different things. Like, I, I grew up with a single mom. My mom was like, "I want you to be successful. Maybe math is the thing to, like, get you back to America." And, and so I got really into math, and then it was like a lot of my decisions at that point were like, what do the, like, best, like, smart math people do, basically. And, and it's like, oh, they went... They're going to MIT, so that's what I need to get into. And got into MIT, and then the next kind of stage was the same question, and the answer was finance. And I started spending time in finance. I, I worked at Goldman, I worked at Citadel, I worked at small, some small prop shops. Uh, Luana did Bridgewater and Citadel as well. And, um, the... There was kind of a pattern that was emerging in a lot of these places, especially in, in, uh... The example I always love to give is, is in 2016 at Goldman, um, I was working on this desk, and there were, like, two questions that were, like, bothering everybody in Wall Street. And, like, this is the... Those are two questions that, like, people were 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, and then the thing that really s- like, stuck with me was that... So Brexit happened, and it was a shock.

    5. JA

      Yep.

    6. TM

      People were, like, really sort of completely shocked. You know, the polls were saying this is not going to happen, and, 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 kind of a very similar thing happened, like the trade that we sold at Goldman, the, the very common trade was, like, the, the Trump trade was you short the S&P because if he's going to win, the S&P is gonna go down, right? E- every, like, everyone bought that trade. That was the trade. Um, and it was a horrible trade because Trump won, and, um, the S&P actually, like, it was a... I think it was the sort of single, like, biggest rally in the S&P's history at, like, ever, essentially. [laughs]

    7. JA

      Right. Yeah.

    8. TM

      It's like worst trade of all time.

    9. JA

      The exact wrong way to trade the idea.

    10. TM

      It is exac- Like, it's actually the perfect-

    11. JA

      Which is interesting 'cause it's like what you were trying to trade was this underlying thing that you got right, and then you expressed it backwards.

    12. TM

      They were right about the prediction, and they lost money.

    13. JA

      Yeah. This kind of is like when, um, you know, like a company has quarterly earnings and people are like, "Oh, it's gonna beat earnings."

    14. TM

      Yes.

    15. JA

      "And so I'm gonna try to buy the stock."

    16. TM

      Yes.

    17. JA

      And then it beats earnings, and the stock goes down.

    18. TM

      And they're all smart in retrospect. Like, if, if you actually trace the plot of, like, whether the stock went up or down after earnings beat, I think it's like fifty-fifty-

    19. JA

      Mm-hmm

    20. TM

      ... pretty much.

    21. JA

      Mm-hmm.

    22. TM

      It's mostly priced in. Two things kind of, like, I realized, like, actually a lot of some of the smartest kind of, like, traders and institutions, a lot of the, the- 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 gonna win. I think there's gonna be, like, some change in diplomatic relationship between these two countries. I think COVID is gonna come back." The way they thought they were trading on with traditional market is the event, but they were... What they were really trading on is this sort of reaction function, is how the market was going to react to an event.

    23. JA

      Yeah.

    24. TM

      They weren't trading on whether Trump was gonna win. They were actually trading on how the S&P was gonna react to Trump, which in retrospect and now it's, like, like, we can't really predict. It's just kind of impossible to predict. It, it was kind of a very exciting idea because it's like, okay, what if we just build this marketplace where what you're trading is like-

    25. JA

      The specific thing that you're thinking about.

    26. TM

      Yeah, it's just things that people care about, you know, whether politics or economics or weather or really any, any of topics that just people naturally walk around the street and think about. Because people don't think about, like, you know, what is Cisco gonna print? Like, what are their financials next quarter? Like, they don't think about that. They just think about, you know, the Fed might raise interest rates or things that are more simple. And so that is exciting because, like, the TAM could be much larger because, like, a large number of people would care.

    27. JA

      Yeah.

    28. TM

      Right? The second thing that was really interesting is, like, if you... There's this kind of... If you believe in markets, what markets really do is they aggregate information, right? They, they, they are a very good weighing function, so they can figure out how to get information from a bunch of p- people, aggregate it, and get a single price. And 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-

    29. JA

      Yeah

    30. TM

      ... or a market-based answer about all these questions. And that got me really, really excited because, you know, at the time we were thinking about if we could get a little bit smarter about the future, that's, like, a very worthwhile product to build.

  3. 5:0511:06

    Regulation-focused from inception

    1. JA

      So when you got started with the company, what was the first year? What were the first couple years of building? Like, what did you do when you got started?

    2. TM

      I was actually gonna go work at Citadel, uh, because I, I had kind of spent time at summer there, and I actually loved it. It was one of those situations where, like, Luana and I started talking about it, and, you know, the idea was just bothering me. Like, I could not get it... Like, I, I, I have a little bit of, like, you know, I'm a little bit OCD, and I get, like, obsessed with things, but I couldn't get it off my, like, brain.

    3. JA

      Mm-hmm.

    4. TM

      It was, like, so, so, like, you know, I was, like, gonna go... Because, you know, one thing I always say is we were not, like, entrepreneurs that were, like, trying to figure out what product to build to build a company. That was not how Kalshi started.

    5. JA

      You just had this one idea.

    6. TM

      Like, the idea kind of forced itself on us.

    7. JA

      Yeah.

    8. TM

      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. They're paying me all this money." Like, and... But then I remember, like, we had a friend, uh, who was, like, going to this YC hackathon. I don't know if they still do them, but they used to do these, like, hackathon and bring a bunch of builders and, and he was like, "Oh, I'm going to this thing. Like, you should come. I think the deadline has passed."And we're like, "Well, deadline's passed now." He's like, "No, no, you should just, like, email the guy." And we emailed... I forgot who the organizer wa- was at the time. I know we emailed something like, "Hey," like we, we were trying to figure out flights or something, and like, he's like, "Yeah, fine, you should- 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 like a front end for what the V1 of... It was like a bunch of questions in like a, a list format 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 q- order book would look like. And it's funny because we had judges like that were gonna judge the different teams and pick the finalists, and our judges were Michael Seibel and, and Christina from Vanta.

    9. JA

      Mm-hmm.

    10. TM

      Uh, I don't know if she had started Vanta at the time or she was in the early innings of it.

    11. JA

      When was it?

    12. TM

      It was October, 2018.

    13. JA

      Yeah, I think she had started.

    14. TM

      She had started?

    15. JA

      I think so.

    16. TM

      You know, we start pitching the idea, and then it, it's great. Like, I remember like Michael was like, "Oh, I, you know, everything i- is great about this idea except for the fact that it's like totally not allowed in the US and like, you know, th- this has like absolutely no way of existing in any way, shape, or form." And you know, we walk out, we're like, "Look, we tried," you know, move on. I remember like I drank a bunch of beers in that hackathon.

    17. JA

      Yeah.

    18. TM

      Like, "We're, we're done." And then this guy like ends up picking us to be finalists. [laughs] You know, he just like dunks on the whole thing, and then, and then we end up winning that hackathon. 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, the, you know, uh, and at the time it was like, you know, wow, like never expected to be in, get, get into YC. And then like this is the f- the first year was crazy 'cause you know how there's this thing about YC, like they're the cool companies that are building products and getting all these investors excited? Like we were the total opposite of that.

    19. JA

      Mm.

    20. TM

      We had no product, no customers. Week to week we go to office hours and everyone's like, "Here are my KPIs and here's the traction," and we were like [laughs] -

    21. JA

      We got nothing.

    22. TM

      Nothing.

    23. JA

      Yeah.

    24. TM

      Like we were just like, "Well, we, we talked to this lawyer who said no," and then the next week it was a different-

    25. JA

      It's tough 'cause, you know, I did YC and that was one of the, one of the most notable parts of the experience, which I think is a very positive part, is every week you come back with your group and everybody else has grown 7% week over week.

    26. TM

      [laughs]

    27. JA

      And how much have you grown? And you're just like, "None. Doesn't feel good."

    28. TM

      None. I don't even know what the product is. But-

    29. JA

      Yeah

    30. TM

      ... but we knew what the pr- the vision was always clear, but for us, the key question is like how do we get it regulated? We, we were very, from the beginning we made a decision we're, we're not gonna launch a product outside of the realm of the law or regulation.

  4. 11:0618:02

    Suing the government

    1. TM

      side.

    2. JA

      So can you talk about finding the other side?

    3. TM

      Towards the end of 2020, 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 issues started, like these guys started being 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, we walked through all of them and worked through all of them and you're... And think of like thousands and thousands of legal documents and pages, et cetera. And we, we started kind of e- angling towards an approval and we got approved in, in November 2020 to get the first sort of, uh, 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 and our approval was bipartisan. It was like Dem and Republican, but like... But then the new administration was like, "Wait, wait, pause, pause. We're gonna have to think through this." [laughs] In some ways we're like, oh, we're finally through the desert, but all of a sudden actually, oh shit, we got dropped back into the middle of the desert [laughs] and it was like, we're gonna 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. That was disheartening. It was really hard because like we had spent two years, we're finally there, we're gonna launch.

    4. JA

      Yeah, of course.

    5. TM

      And so this is another-- Like, we started the first battle to launch the exchange, and we're like, "Fine, you know what? Like, 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 gotta get o-off the, off the door now and launch and see what, what happens." So then by the end of '21, we kind of launched with, I think, a few economic markets, no traction whatsoever, et cetera. And at the end of '21 is when we were like, "Okay, we have to open up the space to get all the markets we want." And this is when we started talking about the election market. And for prediction markets, I think, like... And we can talk about the dynamics, but we always thought that you need the diversity of markets, 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, like, kind of break through the supply and demand. You need something strong enough to get the chemical reaction going because, you know, you have the supply, 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. It's like you need an event that everyone cares about and where we can provide a better product, like give a better forecast. And end of '21, we start talking to, to the, to CFTC, and they say, "Well, maybe," et cetera, "maybe we'll do it by end of '22 or for the midterms." A whole year of that, just regulate-regulation again. So now you're talking we're three years in, four, three to four years in just doing regulation. End of '22, the regulator sort of kind of like nudges the approval post the deadline, which essentially it just didn't make a decision. Um, and that was a very hard time with the company because we thought the approval was gonna come. Uh, we'd done all the work possible, very kind of tunnel vision again, but we didn't get it. And so in those circumstances, what happens is, like, people kind of, they blame the execution of the strategy. It's never kind of like, you know, things are outside of your control. It's like we made the wrong decisions, and it was bad, and-

    6. JA

      Yeah

    7. TM

      ... you know, and we lost a bunch of the team at the time, and, you know, we had to do some layoffs. It was a really hard time. Like that, that was a really, really, like, hard time. I think back at that time as like, I think it was one of the most painful times, like, I've ever sort of experienced in my life.

    8. JA

      Hmm.

    9. TM

      And by the way, like I, I went through war in Lebanon. Like I, I, I've 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 ba-- because it's, you feel shame as an entrepreneur.

    10. JA

      Totally.

    11. TM

      Right? You get it, right? Like, you know that-

    12. JA

      Yeah

    13. TM

      ... feeling of like-

    14. JA

      No, I've had to do a layoff. It's awful

    15. TM

      ... and, and it's like you have responsibility, and people are like trusting you with all this. And, and, and then we come out of this in January, February. We're kind of sort of reconvening what do we do as a company, et cetera. And I remember Luana has like this sort of dogmatic belief in this, in this vision. Should we pivot? Like, clearly the go-- you know, we're not gonna be able to do this. And then Luana's like, "We're gonna try again." So that's the strategy for '23 is we're gonna do the exact same thing as what we did in '22, and we're gonna try again. It was pretty-- I mean, it was un-unpopular, but, like, we did it. A whole other year of the same thing, ta-talking to policymakers, regulators, same regulator, et cetera. Then they ban it at, like or they block it at the end of-- They block the election market at the end of, uh, of '23. And, uh, same thing happens [chuckles] 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, but I think, like, the kind of key decision that I think got the company to where it was today, which is, like, we sat down, and we're like, "What do we do from here?" And a-again, Luana was kind of driving a lot of this, but it was like, "Look, we're, we, 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, you know, we're not... You're talking now we're five years in. We just gotta sue the government, and we gotta sue our own regulator. You know, we, we talked to Stu Boyd, talked to Alfred, and, and, you know, the, the interesting thing that came out of that conversation is that, like, um, it's definitely an anti-pattern for a company to sue its own regulator or the government in general. It's even more so for a company of, like, 20 people that has no real product, no real tr-- Like, we, we were, we're ta- We're kind of like a nobody company at the time.

    16. JA

      Is it an anti-pattern? I feel like a lot of great consumer companies have gotten into, I don't know if it's a full dispute of their own regulator or full lawsuit, but at least some legal battles.

    17. TM

      It's maybe the sequencing.

    18. JA

      Yeah. Maybe they got big first.

    19. TM

      Like Airbnb and Uber were really big.

    20. JA

      Yeah.

    21. TM

      We're talking about a platform that has-

    22. JA

      Coinbase maybe

    23. TM

      ... Coinbase got really big, right? We're talking about a platform that like-

    24. JA

      Yeah, yeah. It was tiny

    25. TM

      ... tiny. Like, you know, we, we had like, like hundreds and thousand, like maybe thousands of users a week.

    26. JA

      Yeah.

    27. TM

      Like it was, it was, you know, the evangelists, the really early adopters in terms of sort of this is the unlock that will get us going.

    28. JA

      Yeah.

    29. TM

      And, and like you remember Alfred was saying like, "Well, even if we win, even in the offshoot, kind of the crazy shot that we win, we may still lose because the regulator could kill you in the meantime."

    30. JA

      Right.

  5. 18:0220:58

    Gambling vs. financial markets

    1. TM

      can be.

    2. JA

      Okay, so you win the lawsuit. Now where are we going?

    3. TM

      So now it's interesting because we won the lawsuit, and like it's like this feeling... So okay, we won the lawsuit-

    4. JA

      And specifically what the lawsuit enabled is what?

    5. TM

      So the, the lawsuit was basically saying it was i-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-

    6. JA

      So basically it's now saying things like betting on the president or the outcome of a sports game, that is now-

    7. TM

      Can be a financial market

    8. JA

      ... is now a financial market.

    9. TM

      And it, it-- and there is kinda two, two kind of elements to that. Um, and very simply put is basically one is what is the structure? Are you an open and free market, market where people are just trading against each other versus like a, a house where you're accepting bets from someone, and again, your business model is, like, gambling. It's like your revenue's equal to customer losses.

    10. JA

      I see.

    11. TM

      And then the second thing is, like, is this a real thing that's happening in, in, in the world where, like, some people may benefit from hedging, or some people may benefit from the-

    12. JA

      Basically by being the marketplace where other people are betting against each other, that's critical, and not being gambling versus-

    13. TM

      That's a, 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, you know, like, two people kind of transacting on, like, "Hey, what is this dice going to land on?" 'Cause that's an artificial risk that you're creating just for the purpose of-

    14. JA

      Right

    15. TM

      ... trading or betting on it.

    16. JA

      Yeah.

    17. TM

      Versus if you're trading on a stock which exists, a company exists, or, or oil or an election-

    18. JA

      Got it. So it's also the, the event existing whether or not people are gambling on it.

    19. TM

      It's a natural risk. It's a natural thing. It's a natural event, not an artificial event.

    20. JA

      Yeah.

    21. TM

      And that's important.

    22. JA

      Yeah.

    23. TM

      That's very important.

    24. JA

      Yeah.

    25. TM

      And it's interesting because the, the decision that came out of that lawsuit is very similar to one that came out close to 120 years earlier in 1905 in the Supreme Court, which is the one that legalized grain futures, the most boring financial market, the, the OG hedging market.

    26. JA

      Yeah.

    27. TM

      Because at the time there was a state versus federal, like, uh, kind of fight where the states were claiming, "Well, this is gambling 'cause some people are speculating."

    28. JA

      Right.

    29. TM

      Like, farmers were going and kind of betting on the price of grain, so that's gambling. It must be, it must be gambling. And the Supreme Court said, like, "Look, a lot of people will speculate, but there is some people that 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 speculation is necessary for that market to exist. Like, if for, if you want a market, if you want the stock market to exist, if you want commodity markets to exist, you want prediction markets to exist, you need speculation. You cannot just have people that are insuring themselves against stuff because the o- person on the other side needs to be a speculator. In some ways, it's kind of history repeating itself, um, but it kind of redefined the aperture of what's, what's allowed.

    30. JA

      It would be legal for somebody to speculate on, like, whether or not we were gonna say a certain word in this podcast. Would that fit the definition if people wanted to trade on that?

  6. 20:5825:38

    Defining insider trading

    1. JA

      Is it illegal to bet on something where you see, you know, a bet playing out in the world but you know the answer for sure?

    2. TM

      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 I... The, the line that, you know... So we're a regulated financial market.

    3. JA

      Yeah.

    4. TM

      We're a regulated exchange and clearing house, and a lot of the rules we have are, are mimicked, uh, after the s- 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. And the way that that's defined is, like, you have a piece of information that you acquired under certain rules, right? And one of those rules is that you cannot disclose it. So material non-public information is information you're not allowed to disclose.

    5. JA

      Got it.

    6. TM

      That, like, if you own as an executive of Tesla and you went and said it to the press, you would get in trouble. That's... Y- you're not allowed to do it. And trading is a form of disclosure. That's the whole point of prediction markets, right? Like, prediction markets are a way to disclose information.

    7. JA

      Yeah.

    8. TM

      Like, when you trade, maybe you're not saying it on Twitter, but you're actually tr- trading that information, and you're moving the price-

    9. JA

      Right

    10. TM

      ... in a way that disclosed the information.

    11. JA

      But, like, let's say, let's say this morning I saw, you know, a trade that was will Jack and Tarek see each other today? And I'm like, "I know the answer to that, so I'm gonna make a big bet."

    12. TM

      Are you Jack, or are you someone else?

    13. JA

      I'm me.

    14. TM

      Well, you have influence on that. You have direct control over that.

    15. JA

      Got it.

    16. TM

      So that would be m- market... That wouldn't be insider trading. That would be manipulation.

    17. JA

      Yeah.

    18. TM

      So the majority of participants in grain futures are grain farmers, right? Like they... And the way that the price of grain futures is done, this is gonna be a little surprising.

    19. JA

      Yeah.

    20. TM

      Can you guess? Like, how do you determine the price of grain? How, how do you think we determine it?

    21. JA

      I don't know. How?

    22. TM

      You literally survey a bunch of the farmers that are trading on the market.

    23. JA

      Wow.

    24. TM

      It's a little weird, right?

    25. JA

      Yeah.

    26. TM

      Like, it's like you... Well, you, you... They definitely have inside information then, like, right?

    27. JA

      Right.

    28. TM

      Because they're, they're the guys... I mean, they're, they know what the price is. And it's interesting because the line there has been like, okay, inside trading is very hard to define in grain futures, but what we're gonna draw the line is, is you cannot 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 help your 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, you take a position, then you tank the bill, or you try to pass it even harder-

    29. JA

      Yeah

    30. TM

      ... that's illegal.

  7. 25:3832:40

    Incentive structure of the system

    1. JA

      Yeah. I have a topic I wanna get your take on 'cause I can tell that you're extremely thoughtful about fairness, the way it should work-

    2. TM

      Yeah

    3. JA

      ... what's the better future. I think one of the discomforts people have with like prediction markets-

    4. TM

      Yeah

    5. JA

      ... is that they, 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.

    6. TM

      Yeah.

    7. JA

      And obviously that is not at all what your sort of conception of it is.

    8. TM

      Yeah.

    9. JA

      There's all sorts of function outside of it. But I'd be curious actually to start to hear sort of your steel man version of like what's the way this goes wrong? Like what's the version of it that is like-

    10. TM

      The bad version of it.

    11. JA

      Yeah, what's the bad version of gambling-

    12. TM

      Yeah

    13. JA

      ... that you are not comfortable with-

    14. TM

      Yeah

    15. JA

      ... 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-

    16. TM

      Yes

    17. JA

      ... and like adults should be able to do what, uh, they want to do. Obviously with some balance of like we shouldn't, you know, expose people to things that are, you know, short-term addicting that are long-term bad for them. You know, there's some balance here.

    18. TM

      Yeah.

    19. JA

      Where are you not happy?

    20. TM

      Look, I, I'll tell you like I, I'm a risk taker. I'm a trader. Like I, I-- and I've, I've never-- I don't consider myself having really gambled ever, like but I trade. And that, that's, you know, there-- I speculate. There's like a, a lot of similarities of-- between this. And, 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, 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, and it's interesting because the, the thing I always like say is like that argument has been made about every single new type of financial market that has ever come, uh, to the US or really anywhere, right? Like the argument has been made about grain futures like we just discussed. It's in some ways, you know, when, when we started with life insurance back in the, back in the day, you know, the, the headlines at the time were like, "Oh, this is like morally horrible. Like you're, you're gambling on people's lives."

    21. JA

      Mm.

    22. TM

      "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, um, 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 has a flavor of, uh, it looks sometimes like gambling, but that 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 wrong? In my opinion, the things that end up contributing to this bad perception in gambling is the incentive structure in the system. It's how is the system built and what are the incentives that are, you know, built in this, into the system? And, and when you think about a gambling business model, it's a business model whose like where the primary KPI, right? The thing that will not just predict your net income, w-will be pretty much equal to your net income, is your customer's losses. If that's your business model, and that's what your incentive, uh, is, what are you gonna do? You're gonna promote-

    23. JA

      Losses

    24. TM

      ... losses. Like that-- What else are you going to do, right? If you, if you, if you do a great job at stopping losses-

    25. JA

      Yeah

    26. TM

      ... you're gonna lose money.

    27. JA

      Just be more throughput, bigger rake.

    28. TM

      Yes. It's just the, the inevitable. And so what a lot of these businesses do is if, you know, if you're sitting in a casino and you're making money, what do they do? Like the bodyguard comes and takes you aside and says, "Stop." If you're doing something informed, if you're doing something smart-

    29. JA

      Yeah

    30. TM

      ... if you're seeking information, the very point of financial markets-

  8. 32:4035:31

    Investing vs. trading

    1. TM

      you know, there's a difference between investing and trading.

    2. JA

      That's right.

    3. TM

      And like that has always been existent, right? Like yeah, so stock market is... Look, I think holding a stock for five years, that's investing.

    4. JA

      Yeah.

    5. TM

      Now, if you trade a stock in and out over next few days-

    6. JA

      You're gonna get crushed by all the fees

    7. TM

      ... that's trading. Yeah, and not just fees also. It's like your directional view.

    8. JA

      It's not enough, that's not enough time. Yeah.

    9. TM

      Yeah, it's like you're trading. You're, you're, you're... And that's a zero-sum game, and options are a zero-sum game. All, all crypto, in my opinion, we'll see over time, but like most of crypto trading, not in, like not if you... If you hold Bitcoin for five years, that's investing, but if you're trading Bitcoin in and out, you're zero-sum. And so my, my mental model for this is like 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 9 out of 10 of our customers, their response is, "No." And the reason is, "I don't gamble." And like wait, but you know, in, in people's mind, w- but like elections trading or betting, that sounds more like gambling than trading in options-

    10. JA

      Yeah

    11. TM

      ... because that sounds financial-

    12. JA

      Right

    13. TM

      ... you know? But actually, like if you ask people, it's like, "Well, I don't have a way to win-

    14. JA

      Right

    15. TM

      ... an option. I don't have an edge."

    16. JA

      Yes.

    17. TM

      "I, there is no way for me to do it-

    18. JA

      Those markets are so efficient.

    19. TM

      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 about how 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. Wall Street will always beat Main Street. Wall Street will always beat the average person. The beauty of what we're building, it's just not the case. The average person is winning more than Wall Street. Like our best inflation forecaster is not a Wall Street person.

    20. JA

      Well, I guess by definition, the average person is neutral with you because there's a, there's a buyer and a seller of everything.

    21. TM

      But, but it's more about a point of like the, there isn't that structural advantage that like Wall Street has in our markets.

    22. JA

      Yeah, I mean, what I would argue is they might be neutral with you, and they're definitely gonna be negative if they're going to try to trade against Citadel or something like that.

    23. TM

      Generally, yes.

    24. JA

      Yeah.

    25. TM

      Uh, and, and yes, the, our average user is neutral, but I'm, I'm talking more about like-

    26. JA

      The people who wanna put in real work.

    27. TM

      Yes.

    28. JA

      Yeah.

    29. TM

      People, if you put in real work on figuring out how, how do people vote on bills or why... It's like, you know, the, the, uh, back in the 2024 election, you know, the, the guy who put like a lot of money on Trump-

    30. JA

      Yeah

  9. 35:3141:38

    Hedging use cases

    1. TM

      world.

    2. JA

      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, like insurance and hedging and things like that.

    3. TM

      Yeah.

    4. JA

      Can you talk about like... So 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."

    5. TM

      Yeah.

    6. JA

      Isn't it, it's getting used for those types of things too, right?

    7. TM

      Yes, and that's, I would say like the trajectory over time is like that is becoming an increasingly bigger part of the platform. Uh, obviously we started with retail, like people, individuals, but now as we're getting into the institutional, that's becoming a bigger and bigger-

    8. JA

      Right

    9. TM

      ... piece. But let me talk about retail, then let's talk about institution. So, so yeah, there's the two functions of the market. One is what we call like price discovery, which is the predicting all these events, right? And that's one of the benefits of prediction market, which is you're giving people an incentive to do the price discovery, which is predict all these events, and that's working, right? I, I think a lot of people now at least understand increasingly more... I don't know if you saw the Fed paper that came out.

    10. JA

      Yeah.

    11. TM

      You saw that?

    12. JA

      Yeah. It's cool though. Yeah.

    13. TM

      Kalshi, the rise of micro markets, right?

    14. JA

      Yes.

    15. TM

      Like, and it was like the si- the Fed itself is saying, "This is the best gauge we have on the economy."

    16. JA

      It's crazy.

    17. TM

      It's like amazing. And, and by the way, the people, it's not Wall Street. Again, it's Main Street. We, we've figured out how to build this community of people that are dispersed across America that like are making us smarter about the economy.

    18. JA

      But it seems like it turns out that like if you ask like a big enough crowd of people like, "How much does a cow, like a particular cow weigh?" They get like really close.

    19. TM

      No, that's how... That's the OG, o- original prediction market.

    20. JA

      Yeah. It's pretty cool.

    21. TM

      That's how it started. It's like literally bringing wisdom of the, like crowd wisdom to markets.

    22. JA

      And the same thing happened with the elections too, with like Trump and stuff like that-

    23. TM

      Yes

    24. JA

      ... where like everybody's like, "No, Trump won't possibly win." And it's like, well, maybe.

    25. TM

      You, if you have an incentive to actually do the research, I think you may actually, you know... But, butSo that's that. And you-- Uh, and, and the second prong is hedging, 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.

    26. JA

      Right.

    27. TM

      So you go to an insurance company, and they give you a price. Hedging is on the open market.

    28. JA

      So hedging is just like I'm on a coast, and I'm just gonna bet that a hurricane is gonna come-

    29. TM

      Yes

    30. JA

      ... and knock my house over.

  10. 41:3844:02

    Scaling a lean team

    1. JA

      Maybe as a last topic I'm sort of interested in, I didn't realize until chatting with you that your, your company is small relative to sort of the scale you're at, so you're not much over 100 people.

    2. TM

      Yeah, we're a hundred and twenty... I, I was just asking, a hundred and twenty-seven now.

    3. JA

      So how does that work? I mean, like, that is very small relative to what you've accomplished.

    4. TM

      So the... What's interesting about it is that, um, we didn't sit down proactively and, like, we didn't write a doc of, like, how are we gonna build a small company that's lean. It just sort of happened.

    5. JA

      Did you do any particular other things that this was a byproduct of?

    6. TM

      Yeah. So I, I think a few things, like, and I'm not a hundred percent sure, so I'm still kind of figuring out, like, why are all these other companies so much bigger? I'm still, like, trying to figure out am I missing something or... One is Luana and I work very, very hard. Very, very hard. Like, uh, and till t- like, we really have a chip on our shoulder, like, we'd like to think we're the underdogs. What I learned over time is, like... So, so if you look at kind of we're generally, like, first to office, l- last to office, uh, last from office, work on weekends. And, and I think that just generally the output per, per person in the company just is heightened because, you know, generally the leader is really in the frontline doing a lot. Number two is, um, we have a lot of direct reports.

    7. JA

      Like how many?

    8. TM

      There's not really a managerial layer in the company yet.

    9. JA

      So like 100?

    10. TM

      Like, if you ask Luana what maybe, like, 80, 85 of the people at the company today are doing, she knows.

    11. JA

      Wow

    12. TM

      ... because she probably checked with them on Slack in the last twen- 48 hours

    13. JA

      Wow

    14. TM

      And the rest is maybe me. Well, I mean, there's a chunk of people that just don't need-- Like, you, you don't need to know what they're doing. You know that they're just doing... Like, you, you got, you gotta let them cook, like-

    15. JA

      Yeah

    16. TM

      ... you know. So that's step number two. And then I think number three is, uh, we don't think about org charts much, 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... Like, we keep sort of dynamically listing here are the top, like, X problems of the company today and how-- who do we have on those problems?

    17. JA

      And people move between problems fluidly.

    18. TM

      Yeah, sort of like... Yeah, it's like, um-

    19. JA

      Do people self-organize to the problems?

    20. TM

      Yeah, yeah. It's, it's like a, you know, like, cells in an organ- Like, you know how, like-

    21. JA

      Yeah

    22. TM

      ... if you have, like, a... If you, if you, if you get cut-

    23. JA

      Yeah

    24. TM

      ... your cells will just kinda come around the cut and, like, do their thing.

    25. JA

      Yeah, yeah.

    26. TM

      It's-- It'll be, like, a bit like that.

    27. JA

      I mentioned to you, I really want you to read the Valve Employee Handbook. I think you'd like it.

    28. TM

      Yeah, I, I'm excited about reading it.

    29. JA

      I think it's very good.

    30. TM

      But it, it just sort of happens, and just kind of, like, make sure there's, like, like, as little kind of constraints or bottlenecks to that sort of self-organization to happen as possible.

  11. 44:0247:48

    Defining Kalshi’s culture

    1. JA

      Is this what people expected when you hired them? Is it the ty- Uh, did you hire types of people that you thought could only function in a place like this? Like, how did you, how did you end up with that culture when it's what I would describe as extremely uncommon?

    2. TM

      You know, we, we don't have all the answers obviously, but, like, the, um... One is we do bias, uh, on slow versus intercept, because people that have an intercept I think generally can land in this culture and be like, "What?" Like, "What on earth is going on?" Like, "This is crazy." And that's happened.

    3. JA

      Yeah.

    4. TM

      Like, we've, we've had this happen, uh-

    5. JA

      Someone who's used to, like, a big, more structured organization comes in and is like-

    6. TM

      Or just a... Yeah, I... Even, not even big. Like, an organization that's 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... 'Cause yeah, it looks like an organism [laughs] where, like, these organisms are moving around, you know? [laughs] And, and so, so slow, because slow is they don't know. They're just, you know, they're super smart, very high agency. Like, oh, like, they don't even think about what's happening.

    7. JA

      Mm-hmm, mm-hmm.

    8. TM

      They just think it's normal. So, so that, that's one. Number two is, like, I always say, I mean, Brian Chesky put better... I, I didn't-- I verbalized it when he put it into words, but, like, I... We don't manage people, we 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, you know, uh, or, like, being very much in the details of what they're doing.

    9. JA

      But it's about the work, not the people.

    10. TM

      But just they're, they're doing stuff. They're, they have this sort of high agency, "I wanna always be doing something."

    11. JA

      Yeah.

    12. TM

      And I, I, I kind of bias honestly towards, like, execution over strategy.

    13. JA

      Me too.

    14. TM

      I really do. Like, I, I... Because, look, I think strategy is hard, but, like, it... You know what I've found over time is, like, the natural next step for a company is generally kinda natural. Like, you know what I mean? It's like-

    15. JA

      Totally

    16. TM

      ... it, it's not like if you probe... Like, for a public company, for example, when the CEO lays out a strategy, it's not like-

    17. JA

      It's not like, "What should we do?" It's like, "Can we do it?" And can-

    18. TM

      M- most of the time

    19. JA

      ... how quick can we move, and all of that.

    20. TM

      Most of the time.

    21. JA

      Periodically, there's probably, like, an, a non-obvious strategic decision that, like, the founder needs to make, but that's probably a couple times a year kinda thing.

    22. TM

      Yes. Max. Max, I think. And it's usually a little bit longer horizon than a year.

    23. JA

      Yeah.

    24. TM

      So I, I think of our role, Luan and I, as like, I try to very, very high level, like, are we m- are we just, like, directionally generally in the right direction? What are the big, big risks in the next three to four years? And, like, make sure that we, like, are thinking about those and are executing against those.

    25. JA

      Yeah.

    26. TM

      And I really, I really mean three to four years.

    27. JA

      Yeah.

    28. TM

      Like, I'm, I'm pretty paranoid. Again, from my time from Lebanon, I always think, like, what is the thing that's gonna go wrong in three, four years, and, like, let me work through that. And then very much in the details. So, like, I, I literally am often, like, in specific copy in the product. Like, a lot of it Luan and I wrote still to, to today. Or, like, even ads, like, we get into the copy. Like, is this good? Is it bad? Very, very, very specific things.

    29. JA

      Yeah.

    30. TM

      And everything in between we try to, like, not spend any time on.

Episode duration: 47:48

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