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Kalshi's $40 Billion CEO Is Taking On Wall Street and Vegas | PBD Podcast #881 — Transcript

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0:00I mean, this year we have people that
0:00are making over $100 million this year.
0:02>> Just on Kalshi?
0:03>> And they're like not even 30 years old.
0:04>> Stop it.
0:05>> Yeah.
0:05>> And no one knows who these people are.
0:07>> They're pretty secretive.
0:08>> one of the fastest growing companies in
0:08the world, but also one of the most
0:10controversial. [music]
0:11Kalshi calls itself a prediction market,
0:14but a lot of people consider it a
0:16gambling [music]
0:16>> You find a card counter and what do you
0:17do with them? You kick them out of the
0:18casino.
0:19>> And so if anyone is doing smart
0:20research, you incentivize them not to,
0:22you push them out.
0:23>> We have an open market where winners can
0:25win. Actually, that incentivize people
0:27to do research and get better at [music]
0:28the thing.
0:29>> Those guys are banned from the casino
0:30anyways.
0:31>> So then why do they hate you?
0:33>> I'm building the modern-day financial
0:34market. [music] That's what I'm
0:35building. Instead of giving the
0:36microphone to select few, we're giving
0:38the megaphone to millions of people that
0:40are battling it out in the open in
0:42complete transparent way, putting money
0:43where their mouth is.
0:44>> Trading on the weather
0:45>> Imagine millions of people that are
0:46doing that, competing on the open market
0:47all the time. You get truth out of that.
0:49>> But what if some of these guys try to
0:51manipulate this because they see how
0:52much of an impact it has? What what
0:54would that do to the market?
0:55>> Complete waste of money. Where should
0:56they have put it? Giving it to the
0:58campaign. Put a bunch of ads on the TV,
1:00attack ads on the other candidates. Or
1:02they should have commissioned their own
1:03polls that will say whatever they want
1:04them to say, which by the way, you know,
1:05now we're in the last stretch of the
1:06election. What is going to happen?
1:08>> [music]
1:18>> So it's great to have you on.
1:19>> Thanks for having me. I'm excited.
1:21>> Yes. Kalshi, you're everywhere. I can't
1:23turn on TV without seeing Kalshi, right?
1:24Tarek Mansour, co-founder of Kalshi,
1:27went from what? Zero to now they're
1:29saying it's 22 billion, soon to be 40
1:31billion, depending on who's reading what
1:33article, but uh for the audience that
1:36doesn't know, maybe walk me through how
1:39the whole story of Kalshi came about.
1:40Your background before starting Kalshi.
1:43>> Yeah, I was born in
1:44California, and and then I at a young
1:46age I moved back to Lebanon cuz my
1:47parents are Lebanese.
1:49Uh and you know, I
1:50I grew up with a single mom, and you
1:51know, Lebanon is
1:53you read some of it, like it's it's a
1:54crazy country. It's very volatile.
1:56>> Mhm.
1:57>> Um and
1:59it's kind of a show, honestly. Like
2:00but it there's a lot of positive about
2:02it. It's it's beautiful and I you know
2:03probably hear some from friends like we
2:05have great nightlife, great restaurants,
2:07etc. But but it's just the the most
2:09defining thing is that everything's
2:11unpredictable. Like you don't know what
2:12you're going to wake up to the next next
2:13day.
2:14And you know especially with a single
2:15mom that makes you an anxious kid and I
2:16was very anxious. I still am. That led
2:19me to love math. I always liked math and
2:20I would play around with with with I
2:22would read like papers and
2:24and because math, you know, what's nice
2:26about it is like you have an answer.
2:28There's no debate. There's no my opinion
2:29versus your opinion.
2:30>> Black and white.
2:30>> Yeah, there's an answer. Like either the
2:31proof works or it doesn't work, right?
2:34That led me to okay, I want to go where
2:35the math people go and at times thinking
2:36like MIT, this is the place. I went to
2:38MIT and then where are the math people
2:39going? Finance. Because finance is a
2:41very good application of math and
2:43and um you know, it's funny like there's
2:44this saying in um
2:46um at time because I also did the
2:48masters at during MIT and
2:50in academia circles and in Wall Street
2:51there's always this joke which is like,
2:53you know, doing data science and
2:54research in academia is the same as Wall
2:56Street because people do data science
2:57and research on both. The only
2:58difference in Wall Street is they have
2:59to be right.
3:00You know, because you put a paper out
3:02and but in Wall Street you have to make
3:03money.
3:03>> Yep.
3:04>> And you know, that's why I always liked
3:06markets. I I because markets are good
3:07application of incentives.
3:10Cuz what do markets incentivize? If
3:11you're right, if you get a piece of
3:12information, if you're knowledgeable
3:13about the world, you make money. And if
3:14you're wrong, you lose money.
3:15>> Mhm.
3:16>> Went to work at Goldman as an intern in
3:172016.
3:19And you know, it's all these complicated
3:21products. The everyday layman doesn't
3:22understand any of this stuff, you know,
3:23swaps and swaptions and credit default
3:25and like all these weird crazy terms.
3:27And then Wall Street, what do they do
3:28over time is they complexify everything.
3:29They add more and more layers so that
3:30the average average person doesn't
3:31understand what's going on.
3:33That kind of frustrated me a little bit
3:34and and the second thing is
3:36people were coming and asking us simple
3:38questions about the future. They're
3:39like, hey, I think Trump is going to
3:40win. How do I
3:42bet on it? How do I And these are by the
3:44way hedge funds, large corporations,
3:45like institutional investors.
3:47How do I trade on Trump winning the
3:49election?
3:49>> are you working under at Goldman? Are
3:51you working under Sharmin or are you
3:53>> derivatives on the in the trading side.
3:54So I was in the trading business and and
3:58you know, they would say, okay, I think
3:59Trump is going to win. How do I play
4:00that in the markets? And we would say,
4:02you know, at the time I remember the
4:03Trump trade was you short the S&P.
4:06Cuz Trump is a populist candidate. If he
4:07wins, he's obviously going to tank the
4:08market, so you should, you know, if you
4:09think he's going to win, you should
4:10short the S&P. So, that's what a lot of
4:11people did. Come November, he won,
4:13people were right about the prediction,
4:14but the S&P went up, they all lost
4:15money. Bad trade, really bad way to
4:18express that opinion. So, I started
4:19thinking, well, okay.
4:20>> And that was Goldman's position at the
4:21time?
4:22>> Uh no, they sold We sold it to a lot of
4:23customers, you know. But that's what
4:25happens. Like the banks at the end of
4:26the day, you're not taking the position
4:27necessarily, you're selling it to. But
4:28the key thing is to me is that what is a
4:30market? A market is a place that
4:32aggregates
4:33a large number of a the large diversity
4:34of participants, people from all walks
4:36of life, to trade on their difference of
4:37opinions on, you know, to predict the
4:39price of stock, Apple, or price of
4:42grain, or oil.
4:43What if you build the same mechanic
4:45where buyers and sellers, we match
4:46buyers and sellers, like the New York
4:47Stock Exchange, exactly the same thing,
4:49but the underlying thing
4:51is something that people care about or
4:52relate to, whether it's politics, like
4:54Trump is going to win or not, Brexit
4:55happens, weather tomorrow if it's going
4:57to rain,
4:58uh if OKC is going to win the next game,
5:01uh uh if inflation is going to go up or
5:03down. Just simple questions about the
5:04future that can bring in a much larger
5:05number of people.
5:07And the more I read about it, you know,
5:08the the idea wasn't new.
5:10A lot of economists love the idea of
5:11information markets,
5:13dating back to World War II.
5:15Why? Because at the time, it
5:17you know, when they would talk about the
5:19idea that market prices are the only
5:20form of truth.
5:21Why? Because markets, what they do is
5:23they aggregate information from
5:24everybody. Like, you know, you have a
5:25piece of information in Hawaii, in
5:26California, in Texas,
5:28and markets are like a magnet. They
5:29incentivize you. If you have a valuable
5:30information, what do you do? You go
5:32trade on it. Mhm.
5:33And you think something is underpriced,
5:35you move it up a little bit, and if you
5:36think something is overpriced, you move
5:37down a little bit. And imagine millions
5:38of people are doing that, competing on
5:40the open market all the time. You get
5:41truth out of that.
5:42And that's what an information market
5:44does. And and I love that idea. It's
5:47like the application of free markets to
5:49the pursuit of truth. Right? Like if you
5:51and I want to figure out what the price
5:52of Apple is, we're not going to go and
5:54like debate what the price of Apple is.
5:55We're going to look at the New York
5:56Stock Exchange. We have a There's a big
5:57why because there's rigor in it. The
5:58incentive is to get it right.
6:01But now in politics, what do we do? We
6:02debate.
6:03But now we build a market with the same
6:05level of financial rigor where people
6:07are there's quants and hedge funds and
6:09people that are doing research and if
6:10they have good information, they're
6:11going to come trade on it and make money
6:12and if they have bad information,
6:13they're they're having stupid opinions,
6:15they're going to trade on those opinions
6:16and lose money.
6:17And that's what the beauty of a market
6:18is. It filters out
6:20bad information from good information.
6:22And then aggregates it beautifully
6:23across the wisdom of the crowds,
6:24millions of people. So I had to start a
6:25company. I was like, I got to I got to
6:27do this. I was going to go work at
6:28Citadel, which I loved by the way.
6:29Citadel is a great place.
6:31But I I was like, I got to do this. This
6:32is like I think too important to not do.
6:34>> Was anybody doing that at the time?
6:36>> You know, there was a lot of trials, you
6:37know, the the
6:39So there was University of Iowa that
6:40tried an election market in the '90s,
6:42way back in the '90s.
6:43And it was super accurate, small scale,
6:45mm-mm, non-for-profit, yeah. And I was
6:47like, this is interesting. And then
6:49>> How did you find out about it?
6:50>> started reading online and
6:52And and because economists really loved
6:54the idea forever. They really wanted
6:55this to exist. And then, you know, then
6:57then
6:58there was a book Superforecasters
7:01written by a MIT professor, which I was,
7:04uh Philip Tetlock.
7:05And it's about this idea like about
7:06super forecasting and like the art of uh
7:09uh who are the people that are good at
7:11forecasting the future? Who are the
7:12people that are good at doing this?
7:13>> Mhm.
7:14>> And
7:16you know, he did he did this very
7:17interesting um
7:18analysis where he put a bunch of
7:20geopolitical experts, like elites from
7:22DC, Wall Street, the big big guys, the
7:24big talking heads.
7:25And he put them against a bunch of
7:26random dudes to predict
7:29you know, random dudes and random women
7:30to predict
7:32geopolitics, the thing that the experts
7:33were experts in.
7:34Guess who did better?
7:36>> The random people. Why?
7:38>> Because this is what's beautiful about
7:39it. It's like what he realized is like
7:40actually information doesn't sit with
7:42the elites. We are always making
7:44decisions based on what the elite tell
7:45us.
7:46Top down, you know.
7:48>> Wow, so it sits with the people instead
7:49of the elites.
7:50>> It sits with people. A lot of decisions
7:51are made by a handful of people, but the
7:54information that's important sits with
7:55millions of people on the nodes, on the
7:57extreme, everywhere. It's decentralized,
7:58distributed.
7:59And so markets surface that. They are
8:02magnets for all that information, for
8:04all the people to kind of bring it up
8:05and then predict the future. And I love
8:07that. And and like to me right now as
8:08you look at it, why is Kensho more
8:10accurate than all these other places,
8:11you know? And
8:13and and and and you know, we we the
8:15Federal Reserve put out a paper in
8:16February. They compared our forecast to
8:18Kensho, which is Main Street, average
8:19people, to Wall Street on the economy,
8:22like inflation, Fed interest rates, GDP,
8:25uh unemployment, which are the things
8:27that Wall Street
8:28Bloomberg and all these guys are very
8:29good at. Guess who did better?
8:31Kensho.
8:32And then people come to me and say, "Who
8:34are these traders? Like who are these
8:35people doing it?" And like you know, and
8:36there's actually some research. I don't
8:37know if you can pull it up
8:38about who the traders are. I think
8:41it's one of these Yeah, this one. If you
8:42go down a little bit on the chart.
8:44And I love this. I mean a little bit
8:46higher. That that chart that's right
8:47here. Right? Like
8:4980% of our customers went to public
8:50school.
8:52If you look at our customers, our top
8:53customers are twice as likely to have
8:55worked in retail than have worked on
8:56Wall Street.
8:58You know, our best inflation forecaster
8:59is a random dude in Kansas, never traded
9:01financial markets, just loves reading
9:02about the economy and ask people about
9:04what do you think about prices.
9:05>> [laughter]
9:05>> And imagine millions of these people,
9:07but that's the beauty. We're giving
9:07people the opportunity
9:08>> you find a guy in Kansas?
9:09>> I mean now we build a you know, the
9:10people know
9:11>> Cuz on the back end you keep asking,
9:12"Why does this guy keep getting it
9:13right?"
9:14>> Yeah, and I tell you know, I talked to
9:15him he's very anonymous. And like
9:16there's others that are like that, but
9:17the people that I know are good
9:18>> you look at people that are the best at
9:20picking? Like is there like a leader's
9:21bulletin on the back end?
9:22>> There's some that are public, some of
9:23them don't opt in to like showing it
9:24because they want to be very private. So
9:26they they don't they don't want people
9:27to know. It's like Wall Street, a lot of
9:29people don't want to know
9:29>> But do you do you have the ability to
9:31find out who are the people that have
9:32the highest percentage?
9:33>> We do.
9:33>> And what pattern? Would I give a name?
9:35What pattern? Because
9:36last week I had Frances Haugen on from
9:38the Facebook whistleblower who was here.
9:41The movie that they just did, The Social
9:42Dilemma, whatever it was called. She
9:44told me about a program Facebook had
9:46what was called crosscheck, and under
9:48that division they developed a group
9:50where they looked at how many people
9:53who high score of reason reasonability,
9:56meaning say this person's a
9:57conservative, but they get the most
9:59people from the left that like their
10:00post. Or say this person's a liberal,
10:02how many people on the right listen to
10:03them? And they say this is a reasonable
10:05human being, give them a bigger wide
10:07audience because they're reasonable,
10:09they're fair, even though we know which
10:10side they lean on. What patterns did you
10:12notice with who gets the most things
10:14right?
10:14>> described.
10:16Because
10:17the people and so this is what's
10:18important. It's not the elites.
10:21It really is.
10:21>> That's wild to me.
10:22>> I mean this is the the key thing about
10:23and that's why by the way prediction
10:24markets use look at the news
10:26and the elites they don't like them
10:27because it's like
10:29at the core it's moving power from
10:32information from being centered with a
10:34few gatekeepers and a few people that
10:36tell us here's what to believe and what
10:37not to believe.
10:38>> Yep.
10:38>> And it's giving it to millions of
10:39people.
10:40Nobody controls the prediction markets.
10:42So I don't it's all the trading activity
10:44is public and you can see who's trading
10:45and what they're doing, but it's
10:46millions of people saying here's the
10:48percentage of something happening or not
10:49happening. And that shift of balance of
10:51power it's this what free markets do so
10:53well. It's they're giving instead of
10:55giving the microphone to select few
10:57we're giving the megaphone to millions
10:58of people that are battling it out in
11:00the open in complete transparent way and
11:02competing on their opinions. And and if
11:03you look at it who they really are
11:05it's not like the same people that
11:06predict sports
11:08are good at predicting politics,
11:09economics
11:10uh uh financial markets and so on.
11:11>> Would you notice a pattern? Like if you
11:13if you're able to be specific
11:14>> Not age.
11:15Not gender.
11:17Not who they are or what their
11:18background is. It's more skill. Which is
11:21the skill of
11:23self-calibration. Which is you go out
11:25they're always reading below the
11:26headline. They look for information and
11:30like opposing views. They look for
11:32information that comes from opposing
11:33views at the same time to understand
11:35what the other side has.
11:36>> Cover their own blind spots.
11:37>> Yes, where am I wrong? They're always
11:38thinking they're not thinking they're
11:40trying to self-calibrate because you
11:42know humans
11:43we have this thing called the affect
11:44heuristic. Have you heard of it?
11:45>> No.
11:45>> The affect heuristic is this thing where
11:46humans when they're when you put
11:48something in front of them
11:49in the first 5 seconds we have this
11:51thing where emotionally we decide
11:52whether we like it or not. Immediately.
11:55And that's why you know social media has
11:56has really fueled that. That's why we
11:57begin becoming so polarized because you
11:59see a 10-second reel
12:01about a person. If it's positive the
12:03first time you're going to like the
12:03person pretty much forever. And if it's
12:05negative the second the first time
12:06you're going to hate that person pretty
12:07much forever. And it's going to be so
12:09hard to get over your off of to make you
12:10and you know how hard it is for us to
12:11change opinions. We don't like changing
12:12opinions.
12:14The guys are really good at prediction
12:15markets. The women and guys are very
12:16good at prediction markets are the ones
12:17that like resist it.
12:20They're the ones that are like, why do I
12:21believe what I believe? Let me go seek
12:22out the opposite side
12:24and see what they believe and why they
12:25believe it and like I'm always
12:26calibrating. Am I too certain? Am I not
12:28certain enough? And by the way,
12:30they're not When people say, you know,
12:31people are people are betting on Cal
12:32Shiller. Yeah, people are betting in the
12:33same way they bet on the stock market
12:34and same way they bet on anything else.
12:36But a lot of what they do is they trade
12:37in the sense that like they're buying
12:39low and selling high. They think, you
12:41know, you could buy Republicans or
12:43Democrats. The same people are buying
12:44Democrats or Republican winning the
12:46Senate. Not because that's what they
12:47want to happen.
12:48That's because they're thinking what
12:49will happen. And right now, for example,
12:50a lot of people tell me, I think that
12:52Republicans are underpriced for the
12:53winning winning the Senate. I think the
12:55odds of Republicans under, you know, are
12:56like 36% right now.
12:58That's underpriced, right?
13:00And they maybe like buy at 36 and then
13:01if it goes up to, you know, 50, they'll
13:03sell it at that point. And they're not
13:05thinking they're not they don't wait
13:06till the election. They basically buy
13:07and sell buy low sell high. They're
13:09always figuring out where the value
13:10stands.
13:10And so that's a very different type of
13:12thinking than this sort of emotional
13:13clickbait that we have on Twitter. You
13:14know, on Twitter and social media,
13:16you don't get rewarded for truth. You
13:17get rewarded for, you know, if you put
13:19out a bunch of misinformation, you don't
13:20get punished. You actually get rewarded.
13:21You get more likes.
13:23In prediction markets, if you trade
13:24based on misinformation, you get
13:25punished. You lose money.
13:26>> So, the the let let Do you have a top 50
13:29earners that you look at who have made
13:30the most money?
13:31>> I I look Yeah, we have top top I mean,
13:33really we look at the top 15,000 because
13:35we have 30 million customers now.
13:36>> Top 15,000?
13:37>> Yeah.
13:38>> And and
13:39what is a top make? Are there people
13:42>> that make millions of dollars. People
13:43that make tens of millions of dollars.
13:44>> Off of Cal Shiller?
13:45>> Yeah, yeah. And people
13:45>> And some of them are regular people or
13:46well-known people?
13:47>> And pretty much all like like the vast
13:49majority are regular people.
13:50>> Like if
13:51>> People think that Wall Street is
13:51outperforming on Cal Shiller, but we're
13:53a new Wall Street. We're we're enabling
13:55a much broader people. You know the The
13:57Big Short, you know, those two guys that
13:58were in the basement?
13:59>> Yes.
14:00>> You remember those two guys that the
14:01guys that were in the basement that were
14:03How was it? Lone Pine Capital or
14:04something like that? I forgot. The
14:06um
14:07The ones that like the two kids that
14:08were in their mom's garage
14:11and then they were like
14:12>> I do. So, the guy goes to them and talks
14:13to them. I I remember.
14:14>> Exactly. Those guys.
14:15>> Pull it up. We'll pull up in the images
14:16so people can do the the two boys in a
14:18basement. Just type in next to Big
14:20Short.
14:22>> Basement, yeah.
14:23>> Yeah.
14:24>> Uh let's see.
14:27>> No, in the movie. Type in in the movie.
14:30>> Uh I think I should just do like two
14:31Yeah, uh what
14:33was it called? Lone Pine Capital or
14:34something like that? I forgot.
14:36Uh
14:36>> Okay, keep going. I'll find it. I'll
14:38send it to you.
14:39>> Uh no, not that. Not that.
14:42Yeah. But I think those guys are the
14:45uh uh you know, the
14:47uh
14:48No, it's not it's not this one. It's
14:50It's one of I think I forgot what the
14:51name is. Maybe it's not Lone Pine. It's
14:53something Capital.
14:53>> Charlie Geller and Jamie Shipley. I'll
14:56send it to you. From uh Brown Fuel
14:57Capital.
14:58>> Brown Fuel, right. Brown Fuel.
15:00>> And and that's the customer.
15:02It's It's the people that are like, you
15:04know, looking at the world and trying to
15:06get smarter about the world. And that's
15:07prediction markets like financial
15:09markets. What do they do? They
15:10incentivize people to go out and get
15:11smarter about the world and get more
15:13diligent. And if they do the the
15:15analysis and they get smart, they get
15:16rewarded for it because they're bringing
15:17all information to open market. And when
15:19people tell me always like, "Well, how
15:20is this different from gambling, right?
15:22Like how is that What is the difference,
15:23right?"
15:24>> And I always say it's actually very
15:25simple structural difference.
15:27You know, back in the day when society
15:28when we got grain futures, which I think
15:30now we all see it as a financial
15:31instrument, boring financial instrument.
15:32Society thought it was gambling. Why?
15:33Because farmers were betting on the
15:34price of grain. Some farmers were
15:36betting, some farmers were hedging. And
15:39and like how do we decide that this is
15:40gambling versus not gambling? And the
15:41way they decided like yes, people people
15:43are speculating. Do people bet or gamble
15:45on Robinhood? Certainly they do a form
15:46of it. The question is not whether
15:48there's some behavior that's like this.
15:49The question is like, are you doing it
15:51in an open competitive marketplace that
15:53rewards you for being smarter and for
15:55knowledge? Or are you betting against a
15:57sports book or a casino that actually
15:59doesn't reward you? It rewards you for
16:00basically
16:01losing money. Right? Like the person
16:03that gets a suite at the casino is not a
16:04card counter that's actually trying to
16:06be smart and like do smart things. It's
16:08the person that's basically losing a lot
16:09of money to the book and has signs of
16:10addiction and is, you know, is is in
16:12there for the entertainment. And those
16:14are regulated differently and treated
16:15differently because the markets have a
16:17lot of positive externality, which is
16:18the forecast, right? That's why we look
16:20at the forecast of what's going to
16:21happen in the midterms or sports event
16:23or other. And then some people are
16:25actually using it and I think you're
16:26going to like this, they're using it not
16:27they're putting a position not to
16:28speculate to make money, they're putting
16:30it to hedge themselves as an in form of
16:32insurance.
16:33>> What does that mean?
16:34>> So, you know
16:35So, why did what when when grain futures
16:37came around they were
16:38helpful because of the prediction. It
16:40would tell the farmers here's how much
16:41you can like sell your here's the
16:42prediction of prices in 6 months or how
16:43much you can sell for. And then hedging
16:46which is like farmers would basically
16:47short the price of grain.
16:49So, that if the price of grain goes down
16:51and they lose revenue
16:53the short pays them the offset of the
16:54revenue, right? And so, you see this on
16:56events, right? Like for example, in
16:58hurricanes in the Keys in Florida.
16:59>> like buying a stock and buying a put at
17:01the same time an option? Okay, got
17:02>> It's the same as buying insurance. So,
17:04like a lot of people in the Keys in
17:05Florida they buy yes on a hurricane
17:06markets
17:08as a way to basically get paid if the
17:10hurricane hits their town instead of
17:12going to insurance companies because
17:13insurance companies won't insure a lot
17:14of these homes. Another board sports is
17:16a a big source of hedging for us. So,
17:18last week there was an article and
17:19there's, you know, hundreds of
17:20businesses that do the same.
17:22We have a restaurant chain that owns 25
17:24bars and they made their math that they
17:25make $120,000
17:27if the Dodgers make it past the
17:28playoffs. You know why? Because, you
17:30know, if they make it you're going to
17:31have a lot of customers that come watch
17:32the games and they sell and $0 if they
17:34don't or like much lower. So, what did
17:36they do? They basically shorted the
17:37Dodgers.
17:39They bought no on the Dodgers making it
17:40past the playoffs.
17:42So, that the event contracts the hedge
17:44pays them their revenue loss. It offsets
17:46the revenue loss. So, now they're
17:47$60,000 up.
17:48>> [laughter]
17:49>> But that's that's that's very important.
17:50It's very deep.
17:51>> Brilliant.
17:51>> It's brilliant and it's critical but
17:53that's because why grain far you know,
17:55people on
17:56why did Congress step in and create a
17:57national wide model and regulation for
18:00grain futures? Why are they so
18:01important? Even though people are
18:02betting on the price of grain like when
18:04people are saying it's gambling.
18:05Imagine farmers had less predictability
18:07of their business in 6 months. What
18:08would they do? They would invest less.
18:11They get less lines of credit.
18:12Predictability in business is one of the
18:13key anyone who's run a business would
18:14know this.
18:15Would you pick a cash flow that's zero
18:17or $120,000 or you pick cash flow that's
18:19$60,000 either way? You'd pick the
18:21predictable cash flow. And the reason is
18:22that you can give it better loans over
18:24time. Predictability helps you invest
18:26more certainly for the future. And so
18:28over time this is what the reverse
18:29markets do as we get smarter about
18:30what's going to happen, who's going to
18:31win the midterms. We can make better
18:33decisions and then hedge all the things
18:35that impact us negatively over time
18:37which helps promote investment and so
18:38on.
18:38>> So is there a way for somebody to get
18:40good at Kalshi where hey, let me teach
18:42you how to be a better trader on Kalshi.
18:44Is there things like that as well on how
18:46to succeed?
18:47>> It's a skill. Right? It's a it's it's
18:49>> But maybe let me ask this question. Who
18:50is
18:52What I'm giving a name again, I'm not
18:53looking for a name. But I just went on
18:54ChatGPT and asked the question, who are
18:56the top 10 traders on Kalshi? These are
18:58some of the names that came up. BM
19:00Markets, 951.
19:02Can you go back to it? You know, Lost
19:04Buck. And so the win rate to the right
19:07is what matters the most to me, right?
19:0955%, 73%, 41%. Does this mean based on
19:12the position they make that's how often
19:14they win?
19:15>> Yeah, win rate is like yes, exactly. Out
19:17of each position you take, how often it
19:19is a right position.
19:20>> Is the highest ever public on the
19:22reported profits? It's not.
19:23>> We have people that are making like over
19:25I mean this year we have people that are
19:26making over $100 million this year.
19:28>> Just on Kalshi?
19:29>> And they're like like not even 30 years
19:31old.
19:31>> Stop it. And no one knows who these
19:33people are.
19:34>> They're pretty secretive.
19:36>> In the states?
19:37>> It's like the Brownfield Capital. It is
19:38the
19:38>> In the states?
19:39>> Yeah, yeah, yeah. I mean we we And we by
19:40the way it's not like we have a lot of
19:42people that are making millions and they
19:43they tend to be more like private. By
19:44the way, that's not everybody. That's
19:45why I always say people tell me but
19:46there's a lot of people that lose, for
19:47sure. It's a competition. How many
19:49people win the NBA finals?
19:50>> Is it an 80-20 split? What's the number?
19:51Is it
19:51>> It's like more of a 65-35.
19:53>> 65 lose, 35
19:54>> the way, here's what's interesting. What
19:56percentage of hedge funds are performing
19:57the S&P every year?
19:59>> Yeah, that's a it's a much smaller
20:01>> 20%. 80% underperform the S&P. 80% of
20:03hedge funds lose against their
20:05benchmark.
20:05>> off just betting an S&P
20:07>> than betting on 80% of hedge funds,
20:09right? But that's but that hedge funds
20:10are benchmarked against the S&P because
20:11you know, if you're below S&P you're
20:13you're loss. But like it and the reason
20:15it's market financial markets including
20:17prediction markets like the sports or
20:18weather or politics etc. They're a
20:20skill-based game. If it was a game of
20:22chance it'd be 50/50.
20:2450% of people would win and 50% of
20:25people would lose, which by the way,
20:27like is what you would see in a casino
20:28or whatever. But, um
20:31because it's a skill, some people are
20:32going to put more effort
20:34and get better at it over time. And
20:36other
20:36>> made a hundred million this year so far?
20:38>> It's it's it's now the he has a small
20:40team. He's like a few people on the
20:41team.
20:41>> So, do you think it's going to get to a
20:42point where a casual person's going to
20:44make a billion? It's going to be
20:46>> I mean, we we could get I mean, it's
20:47like the markets, right? Like Jane
20:49Street and these guys make billions of
20:50dollars in the open market. So, as the
20:51market grows, I mean, now we're doing a
20:53trillion dollars of volume a year. So,
20:54as the market
20:55>> [laughter]
20:55>> you know, as the market grows, it's
20:57going to be
20:58Yeah.
20:59>> Okay, what number shocked you the most?
21:01You just said we're going to do a
21:02trillion. How much did you guys do
21:03during the World Cup? I mean, I don't
21:05know what it was, but
21:05>> We did I mean, yeah, we I don't know. We
21:07we're bigger than we were now than we
21:09were at the time, actually. So,
21:11I did a billion billion some. Yeah, I
21:12think that was July
21:14>> That's one game, right? World Cup final
21:15>> Yeah, but that month, I mean, we're
21:16doing right now we have because we have
21:18prediction markets, we have perpetual
21:19futures, traditional commodity futures.
21:21Um I think the prediction markets are
21:22analyzing something like 600 billion a
21:24year and then the the rest is 400
21:25billion. So, we're like around a
21:26trillion dollars of annualized volume.
21:28But, but
21:29>> And what is the business model? What How
21:30do you make a
21:31>> small fee. So, that's the key. I mean,
21:32this is also one of the differences of
21:33being a market versus sports book. A
21:34sports book business model
21:36that's why, you know, betting and
21:37gambling people don't like in society.
21:38It's it's a way to you your revenue is
21:40equal to your customers' losses. So,
21:42what do you do? You figure out who the
21:43winners are and you ban them. You You
21:45find a card counter and what do you do
21:46with them? You kick them out of the
21:47casino. And so, if anyone is doing smart
21:49research, you incentivize them not to.
21:51You push them out. If that anyone is
21:53signs of addiction and they keep losing
21:54money and they're not they're doing it
21:56like without really thinking, you
21:57incentivize them to keep doing it.
21:58That's how you get rewarded. And you
21:59give them bonuses and promos so you can
22:01keep these losers coming back.
22:03Markets, I
22:05I'm a marketplace. You are matching you
22:07as a buyer to somebody who's selling to
22:08you.
22:09And if you open the for example, the
22:10Senate or any of the sports markets we
22:12have, you you know, if you go down a bit
22:14and you click on Democrat Party up I
22:15don't know. Yeah, right here. And then
22:17you Yeah, if you look at this
22:19uh if you go down a little bit. Yeah,
22:20right here. No, yeah, right right in the
22:22middle.
22:22The top the asks is if you scroll
22:24slightly up just so that we see Yeah,
22:26right here. The asks are people that are
22:28selling and the bids are people that are
22:29buying. It's the same as the stock
22:30market. This is what the New York Stock
22:31Exchange looks like. And we match buyers
22:33and sellers.
22:34And I take a small percentage. So, if
22:36people trade, let's say $1,000 here,
22:38I'll take 0.7%.
22:40So, I'll take, you know, seven Let's
22:41call it $7. Right?
22:43>> On a thousand.
22:44>> Yeah.
22:44>> Is that relatively the number?
22:45>> Yeah, it's like the New York Stock
22:46Exchange, exactly. Yeah. And and and I
22:47think that like that is key because I do
22:50it whether somebody wins or loses. I'm
22:52indifferent. Actually, I want the card
22:54counters or the smart people, like the
22:56New York Stock Exchange wants the smart
22:57traders. Why?
22:58Because the smart traders are the ones
22:59that create efficient markets and
23:01accurate prices. No one would use my
23:02forecasts
23:04if it's not calibrated over time. That
23:05That those people that are very skilled
23:07are And that's why the difference in
23:08model is so important.
23:09>> poker. You're You're not the enemy. In
23:11blackjack, unlike blackjack where the
23:14enemy is the casino, here
23:15>> It's different from poker in one other
23:17sense. It's we're discovering the prices
23:18of something that has real
23:20human impact.
23:22So, on poker when people are like, you
23:23know, betting against each other, you're
23:25not learning anything because you know
23:26what the probability of a card flipping
23:28is.
23:29Here you're learning this idea of price
23:31discovery. You're learning the prices.
23:33If you go back back up to the chart,
23:35that chart you would never like, you
23:37know, have a chart on which poker hand
23:39is going to play next because, you know,
23:41that chart is a fixed probability. It's
23:42a, you know, whereas here we're like
23:44that positive externality of getting
23:45smarter about the world is what makes
23:47markets different.
23:47>> Okay, so let's just say let let walk me
23:48through it. If somebody right here puts
23:50a $100,000 on no 37 cents Democratic
23:54Party, what does that mean?
23:56>> So, if you look at it here, you if you
23:58Yeah, on the right. If you click on
24:00dollars on the all the way to the right,
24:02just a little bit right here.
24:03Shares.
24:04And then if you type in 1,000 shares in
24:07the in the thing, it tells you
24:08immediately. So, you're buying 1,000
24:10shares. The average price you're going
24:11to get filled at is 64 cents.
24:13And you're going to pay 600, let's say,
24:15$50 after fees.
24:17And then you're going to get paid
24:17$1,000. If you If you end up being
24:19right. Now you Now you own those 1,000
24:21shares.
24:22They're not shares of a stock or shares
24:24of like oil
24:25uh futures. They're shares of, you know,
24:28the Democrats winning the Senate the the
24:30Senate. And then in a few weeks, if that
24:32goes up to like, let's say 75%, you can
24:34sell those shares and you get like a
24:36>> You know, I've been waiting till the
24:37end.
24:37>> 11%. That's what most people do. Vast
24:39majority of volume there buying and
24:40selling.
24:40>> What percentage doesn't wait till the
24:41end?
24:43>> Of the volume, I think it's like 70%.
24:44>> 70% doesn't wait till the end.
24:46>> Yeah, I mean because they're traders.
24:47They're like they're trading. They're
24:48they they're figuring out like a lot
24:51again, it's it's You know, I discussed
24:52this. There are people that are in the
24:53middle. A lot of these guys are like
24:55also like, you know, I I Some of these
24:56jokes like they're kind of swing voters
24:57because like they're dispassionate about
24:59the outcome. They're unburdened by
25:00ideology. They're like thinking, "Okay,
25:02the Democrats are a little bit too
25:03overpriced or underpriced right now. So,
25:05if I think it's overpriced, I'm going to
25:06sell."
25:07>> Wow.
25:07>> I think it's like the stock.
25:08>> That totally makes sense. Yeah.
25:10>> But that that level of rigor
25:12is markets.
25:13>> Okay. So, let me ask you. So, today
25:14today's a special day. Why? Brazil
25:17election president Bolsonaro Lula,
25:19right? Day one, you guys had him at 80%.
25:21>> That's right.
25:22>> And if you go to it widen a little bit
25:24more. Yeah, so day one go back to like
25:26two days ago. Go back two days ago. You
25:29had him at 87%. That's about two weeks
25:32ago a week ago. Well, that's fine. 87%.
25:34>> Yeah, 75.
25:35>> So, and all of a sudden this flips.
25:38>> That's right.
25:38>> Lula wins 15 out of seven 17 districts
25:43that were doing the counting of the
25:44the votes
25:46said Bolsonaro is going to win. Last
25:48minute, he pleads with the Supreme Court
25:50to open up the voting in areas that are
25:52pro-Lula and all of a sudden he comes
25:54out of nowhere wins. I don't know how
25:55many he gets. Plus minus 10.6 million.
25:57This has never happened in the history,
25:58right? So, for you when you watch it cuz
26:01you have a data that we don't have. What
26:03do you speculate is going on there?
26:05>> what's important to understand. There's
26:06two things.
26:08One, a market doesn't create
26:10new data.
26:12A market aggregates all the data. Right?
26:14Like Apple stock
26:16doesn't run the product for Apple.
26:18Right? Now, Apple stock is at $100 a
26:20share today, let's call it.
26:22If Apple does a series of actions, like
26:24for example, if they some bad regulatory
26:27action comes out that they were doing
26:28something bad in some of their
26:31factories,
26:32what's going to happen to the stock?
26:34It's going to go down.
26:35>> Mhm.
26:35>> Why? Because new information has come to
26:37light that is now going to be priced in
26:38by the market. The market is going to
26:40absorb that information in real time. If
26:41Apple releases a bunch of great products
26:43over the next 3 weeks, what's going to
26:45happen to the stock?
26:46It's going to go up. So, what And this
26:48is always important. Markets, what they
26:49do is they absorb new information in
26:51real time. They're an aggregate of And
26:53in the elections, it's of the polls, the
26:55news, uh uh candidate quality. If a
26:57candidate has a scandal, what's going to
26:58happen to their odds?
27:00They're going to tank. So, markets,
27:01that's what they do. They do a very good
27:02job at summarizing all the information
27:04that's out there. And you know that the
27:05incentive to summarize it is clean
27:06because there's no partisan lens or
27:08bias. Like, why is Fox saying something
27:09or CNN saying something? There's a
27:10partisan lens in here. Whereas, the only
27:12lens for prediction markets that people
27:13are trying to
27:14get to to ground truth because if
27:16they're closer to the truth, they're
27:17going to make money. If they're further
27:18from the truth, they're going to lose
27:18money. Now,
27:21sometimes there's new piece of
27:22information that's very material, right?
27:24Like a if a can- a candidate is doing
27:25very well, but then the scandal happens,
27:27they're going to tank very quickly. And
27:29that's what happened. There's a big new
27:31shift that happened yesterday on the
27:32election, which was
27:33>> Could you guys pinpoint on what it was
27:34or could could
27:35>> the big shift happened when the I think
27:37the extensions, I think started
27:38happening, but I I hadn't looked at it
27:40quite yet. But, the more interesting way
27:41to evaluate the prediction markets, I
27:43think, if if you go back to the
27:44calibration plot, I think it's we have
27:46it in open cal- Yeah, right now. If you
27:47go down a little bit, is this. So, if
27:49you if you zoom in a little bit on some
27:51of these charts, that's what we call a
27:52calibration plot. So, you cannot
27:53evaluate prediction market accuracy on
27:55one market.
27:56There's a study on like 2 million
27:57markets that's published anyone can
27:59replicate it. What it says, what is the
28:01probability that we're saying something
28:02is going to happen with
28:03and how often does it happen? So, if you
28:05say something has 20% chance, it doesn't
28:07mean it's not going to happen.
28:09It means it's going to happen one in
28:10five times.
28:12It's going to happen two in 10 times.
28:13Like, it's going to happen 20% of the
28:14time, right?
28:15>> Got it.
28:15>> And that's so important. It's like,
28:16sometimes the 10%er,
28:18if you run the race 10 times, what's
28:20going to happen? It's going to happen
28:21one out of It's not And so, that's what
28:24shows you in this plot, which is
28:24actually very accurate. It's like, when
28:26we say something has 20% chance, it
28:27actually does happen in 20% of the time.
28:29>> So, on the back end, what did it show to
28:30you guys? Did did did you did Caltech
28:33know that there is a possibility that
28:35Lula wins?
28:36>> There was a 25% chance, one in four
28:38possibility that Lula wins. And and that
28:40was, you know,
28:42looking at all the prevailing factors.
28:44And that changed obviously dramatically
28:45when the new material informations came
28:47to light. But like again, if Lula if you
28:50play that election four times and Lula
28:51wins zero out of those four times, then
28:52we would be wrong. He should win one out
28:54of those four times and that happens.
28:56And so I what I see with the midterms
28:57right now is people ask me, "Oh, how do
28:58you evaluate the midterms?" It's like,
28:59"Look, look at 100 races that Calshie's
29:00predicting.
29:01And assume that the, you know, there
29:03it's saying that the Republican is
29:04winning
29:0550% of the time or like 40% of the time.
29:07How many of those 100 races should go
29:09Republican?
29:1040.
29:11It shouldn't be zero. It shouldn't be
29:13It's not like every one of those races
29:14should go
29:16Democrat.
29:16>> Yeah, and that one makes more sense
29:17because that's a volume of races, right?
29:20You're not dealing on one.
29:21>> That's right. But you have to look at
29:22elections over time. It's a
29:23probabilistic model, right? A probably
29:25Like right now we're saying the Senate's
29:26going to go 64% Democrat, 60 whatever.
29:28That It's like a three-sided coin where
29:30two sides are Democrat, one side is
29:32Republican. It's a three-sided die with
29:34that has three sides, two Dem, one
29:35Republican. If you roll that die, you
29:37could still land Republican, one in
29:38three chances. It's not a poll. And
29:40people People really confuse markets
29:41with polls because polls are What are
29:43you What are you What are you going to
29:44vote for? It's checking who's going to
29:45vote for what. Markets are What is the
29:47probability? What do you think will
29:49happen? That something will happen.
29:50>> So of the guy that does 100 million,
29:52does he do a lot of trades? Is is is
29:55person like So it's not one. It's like
29:57diversifying. Or is that kind of a
29:58>> For sure, for sure.
29:59>> Got it.
30:00>> It's very diverse. You're trading You're
30:01you're trading in and out and you're
30:02you're And and that Those are the guys
30:04that make the markets accurate. Those
30:05guys would never be allowed on a sports
30:06book.
30:07Why? Because if they were making that
30:08amount of profit, who is it going from?
30:10>> The casino.
30:11>> Yeah, there's not It's a very different
30:12model.
30:12>> you brought up
30:13>> The casino is a way to move money from
30:14people to the casino.
30:14>> So who hates you? Who are you Who is the
30:17enemy? Is it the casino? Who are the top
30:19three enemies of you?
30:20>> I think the casinos.
30:21>> Mainly casinos at the
30:22>> I mean like some of Yeah, because I
30:23think that the they are the ones that
30:25are pushing the states to come after us
30:26and because they want a monopoly on this
30:28too, right? Like that's that's how, you
30:29know, they they That's a model that's
30:31more transparent.
30:33It's healthier, but also it's a it's a
30:35market. Like in you know, institutional
30:37we have institutional clients are using
30:38Kalshi
30:39signals, but also using it to hedge
30:41against different different events. You
30:43cannot use a sports book to hedge
30:44against different events. Like, that
30:45will never happen.
30:46>> we were doing an event in Las Vegas, and
30:48I don't know if you discussed you or
30:50not, MGM Grand Arena. We got the big
30:52Vault conference.
30:53And while we're doing that, all of a
30:55sudden, last minute, they're like, "You
30:56can't have Kalshi here." We're like,
30:57"What does that have to do with
30:58anything?"
30:59>> They said, "Kalshi cannot be a sponsor."
31:00So, Kalshi can be a sponsor? No, they
31:01can't.
31:02>> So, Kalshi it's our sponsor. They're
31:05they're No, you can't you can't have
31:06Kalshi in here because Kalshi So, what
31:08is the feud with an MGM and Kalshi?
31:10>> monopoly on this, too. I mean, some
31:12industries, you know, don't want more
31:13transparency into something.
31:16>> You don't think they want more trans
31:17>> Yeah, because think about it. Like, if
31:19if if if we have an open market where
31:21there is where winners can win.
31:23Actually, that incentivize people to do
31:24research and get better at the thing.
31:26And now it compares to the model where
31:28like every time you do a little bit of
31:29research, you get punished for it. You
31:30get kicked out of the casino. What does
31:31that tell people?
31:33Actually, oh, I'm getting really screwed
31:35with these odds. These odds are
31:36terrible. They're giving me bad odds.
31:38All these and then also they want a
31:40monopoly on any market. It's big
31:41business now, and they feel like, "Oh,
31:42we should have a monopoly on this, too."
31:43That's the age-old question. Why did
31:44taxes go after Uber? Is it because Uber
31:46was worse for a customer?
31:48>> No, absolutely not.
31:50>> Never It's never what it is that Never
31:51It's never what this is about. It's
31:53never about because I'm actually, you
31:55know, and by the way, this issue of
31:56young men and addiction, I think it's a
31:57very real issue.
31:59And I've been always saying, the beauty
32:00of Kalshi is I don't make money off the
32:02people that are like losing money and
32:03getting addicted, etc., as much as the
32:05DraftKings does because, you know, the
32:06the sports books, they're betting
32:08against those customers, and so they
32:09need them. They keep getting more of
32:10them over time. The prediction markets
32:13if somebody's losing all the time,
32:14they're losing it to somebody else.
32:16And so, in my view, I always tell the
32:18federal government is like, "If you're
32:19worried about customer protection, let's
32:20let's regulate this, and let's put
32:22sensible guardrails where like people
32:23that are using it in a way that's
32:25dangerous for them and they're
32:26responsible and it responsibly
32:28we use tools to add friction to their
32:29accounts and make sure that we're giving
32:31them, you know, "Hey, you lost a lot.
32:32Relax. Stop. Maybe you have to take a
32:35pause. Maybe you have to exclude
32:36yourself and move on."
32:37Guess who is the most against those
32:39types of things.
32:39>> Mhm. Casinos.
32:41>> For sure, because those are the most
32:42profitable customers.
32:43>> Interesting.
32:43>> And those are the people that are going
32:44out publicly saying, "Oh, you know, the
32:45the prediction markets are the issue
32:46with young men." Prediction markets were
32:47never the issue with young men.
32:48>> How much money did you guys lose did
32:50they lose to you?
32:51>> I I don't think they are.
32:53Cuz 70% of our volume is a sophisticated
32:56multi-monitor. They're doing modeling,
32:58they're doing all this sort of thing.
33:00Those guys are banned from the casino
33:01anyways.
33:02The smart traders cannot be in the in
33:04the sports book.
33:05>> You said 80% of them only have a public
33:08school diploma, right? Or they
33:10didn't go
33:10>> So
33:11>> But I'm saying this is a separate
33:12statistic. 80% of people that are like
33:14the volume that comes from 80% of volume
33:16comes from a small percentage of people
33:17that are very that are doing very highly
33:19like a lot of research, a lot of
33:20analysis.
33:21>> Got it. So the the 24% are the banking
33:23wealth. They know what they're doing.
33:24These guys are in the space.
33:26>> Yeah, and those guys the point is like
33:28there it's not a high percentage as you
33:29would imagine. People would think, "Oh,
33:31but it's not." It's actually 50% of our
33:33customers work in retail.
33:36>> So that's amazing. So one of the So one
33:37of the Yeah, that's very that's
33:39absolutely amazing. But so
33:40>> It's like anyone can like if you put the
33:42effort
33:43you can win. Now, look, not a lot of
33:45people put the effort. A lot of people
33:46just do it on the side or
33:47>> Define effort.
33:48>> Yeah, like like research, time, energy.
33:51You you you have to read a lot a lot
33:53about the
33:54what's going on in Texas, who's voting
33:55how. I'm going to break down the
33:57different demographics. Like you have to
33:59get deep. The same way that you get deep
34:01on a stock. If you're If you're a bank.
34:03If you're a bank analyst in in in in
34:05Morgan Stanley, what are you doing all
34:06day?
34:06You're doing quantitative analysis,
34:08qualitative analysis. You're doing
34:09surveys, you're polling, you're you're
34:11researching the stock. Is the product
34:12going to work? You're asking customers,
34:14"Do you like the product? Do you like
34:14the product?" And then based on that
34:16information, you go and say, "Oh, I
34:17think the stock is going to is
34:18overpriced or underpriced." And you you
34:19trade based on that. And that's how you
34:21make money. But you get rewarded by the
34:22open market for that information that
34:23you brought.
34:24That's the same thing that's happening
34:25here.
34:26But you're doing it on a much broader
34:28set of things. Is inflation too high? Is
34:29this sports team going to win or not?
34:31And that
34:32provides us a public good that is this
34:35bottom-up people-driven forecast of
34:37what's going to happen in the world.
34:38>> So I asked the question here. I said,
34:40"How much money
34:42have casinos lost to Kalshi?" I'll read
34:44it to you. You can pull up the same
34:45question and see what it tells you.
34:47Uh how much casinos have already lost?
34:49In a January 2026 analysis by Citizen
34:52estimates that prediction markets have
34:55diverted approximately 5% of legal
34:57sports betting volume.
34:58Uh this translates to $8 billion in
35:01annual betting volume redirected to
35:03prediction markets.
35:04Approximately $800 million in potential
35:07annual sports book revenue assuming a
35:0910% uh sports book hold. 1.3 billion in
35:12potential state gaming revenue for taxes
35:15diverted since 2025. Does that sound
35:18about right?
35:18>> I don't think so.
35:20>> You think it's smaller?
35:21>> Yeah, I mean like the the CEOs of
35:23DraftKings and FanDuel in their
35:24quarterly earnings said that there's
35:25less than 1% cannibalization from
35:27prediction. They've said this. And
35:28they're right about that, yeah.
35:30>> So, why are they so worried about you?
35:31>> I you know, there's a message like in
35:33the casino by the way, and this is
35:34certainly true for casinos. Like casinos
35:36uh they people are no one is deciding to
35:38go and trade it on Kalshi versus like
35:40going with their buddies and have a few
35:41drinks at the casino. That's like if
35:42anything actually the people that we
35:44cannibalize if we really did that study,
35:46which we're we're cannibalizing
35:47Robinhood.
35:48And and like the Nasdaq and like people
35:51that are like in the financial markets
35:52that like now have another financial
35:54tool and a financial instrument, but
35:55they on things that they relate to. Cuz
35:57a lot of people like when they trade in
35:58options on zero day expiry options on on
36:01S&P or or Nasdaq,
36:04they think they don't have an edge
36:05against the big Wall Street guys.
36:08Whereas this is the same thing you're
36:09buying and selling. You're trading in an
36:10open marketplace, but on things that you
36:11may have an edge. People read the
36:13economy. People read politics. People
36:14like the weather and they do studies on
36:15the weather. I mean our best By the way,
36:17there's some analysis
36:19uh an independent uh a school that is
36:21studying our weather markets. They show
36:22that it's 10% more accurate than uh than
36:24the weather stations. Think about that.
36:27We're 10% more accurate than the weather
36:28stations
36:29uh at predicting the weather.
36:31And that's because of our customers.
36:33Like they're doing all sorts of crazy
36:34analysis on weather patterns, etc. that
36:36do it. We did We just did an ad with
36:37Shane Gillis on this.
36:38I don't know if you saw it. It was
36:40People loved it.
36:41>> With uh uh is that one with Peyton
36:43Manning or no?
36:44>> It was the one with Shane Gillis and
36:45Danny McBride.
36:47>> Which one is it?
36:47>> It came out on Friday. It's it's fun,
36:49but it's you know, they
36:52it's a whole skit about how Shane
36:53>> This guy?
36:54>> They're both meteorologists.
36:55>> Oh, yes, yes, yes.
36:57Yes.
36:57>> Danny McBride keeps getting the you
36:59know, he's a he's a weather They're both
37:00weather men and Danny McBride keeps
37:01getting it right and and and Shane gets
37:02gets it wrong.
37:04>> Two phenomenal guys to get to do this.
37:06>> They're they're they're a lot of fun.
37:08>> Yeah, so the DraftKings CEO Jason Robins
37:10in August 2026 said only 1% and he also
37:13said 80 to 90% of prediction markets
37:15volume in states with legal sports book
37:17came from professional traders and
37:20institutions.
37:21>> He's exactly right.
37:22>> Rather than traditional sports book
37:23customers.
37:24>> And he's right.
37:24>> So then why do they hate you? Then why
37:26do they
37:27>> He's right. I mean they're not We're not
37:28It's not just about them. It's the
37:29broader casino because I think that the
37:30casinos feel like they should also get a
37:32monopoly on this. So they want
37:33>> Are they in the space or they're not in
37:35it yet?
37:35>> They want to get into it and like
37:36>> It's too late for that.
37:37>> It's always interesting, you know, that
37:38a lot of these guys like the casinos,
37:40first they came to us and they figured
37:42they were figuring out if there's a way
37:42for them to get into it. Then they
37:44realized they're too late.
37:45>> When did they come to you first time?
37:47>> 18 months ago.
37:48And
37:48>> And then what did they say? Can we work
37:50together?
37:50>> figuring out if there's a way for them
37:51to get into the space, etc. Then a lot
37:53of them realized that they were late.
37:55So what do they do? They're like, let's
37:56litigate.
37:57You know, it's always litigate,
37:58legislate and then when you finally
37:59realize that this is here to stay and
38:00the consumers have
38:02have adopted it and they you know, they
38:03they don't want to change it, you
38:04finally innovate. But that that the
38:06state versus federal is never about
38:07customer protection. They're trying to
38:08ban prediction markets. Why? Because
38:10prediction markets Why are markets
38:11regulated at the federal level?
38:14Why is New York Stock Exchange regulated
38:15at the federal level or the grain
38:16futures even though states wanted to
38:17regulate as gambling?
38:19Because you need a large global
38:20liquidity pool. You need a large
38:21diversity of participants. Imagine
38:23having 50 New York Stock Exchanges
38:26where in Oregon you can only buy
38:28Tesla stock from someone in Oregon.
38:31It wouldn't make any sense.
38:31>> No.
38:32>> You wouldn't be able to get as much as
38:33you want. You would get worse prices. It
38:34would be completely inefficient. It's
38:35the end It's the complete opposite of
38:37free markets, right? A free market is
38:39get everybody on a uniform set of rules
38:41and have people in Texas trade against
38:43California. How would we even get our
38:44prediction if the person in Texas if if
38:46if people in California are the only
38:48ones trading are only trading against
38:49Californians for the Texas election, it
38:51would never work.
38:53>> Is it too late for them cuz if you think
38:54about the language learning, right?
38:56>> of the view that you should always
38:57innovate because even if you're late in
38:59the market
39:00>> Do it anyways.
39:01>> Yeah, you're you're the market is always
39:02big. I'm a positive something thinker.
39:04Right? And if they wanted to get but I I
39:05think it's a bit different because the
39:06professional traders are not going to
39:07really they're not abide by the
39:09traditional casino or sports book.
39:11Like the my biggest competitors are like
39:12Robinhood and Goldman over time. And my
39:15customers because the customers are the
39:16same. The those professional traders
39:18that Jason's talking about, he's right.
39:1980-90% are coming from professional
39:20traders and he's right about that.
39:22Because those are those are traders that
39:23otherwise are trading in equities and
39:26commodities and and
39:27>> Would you get into their space or you're
39:28not touching what they're doing?
39:29>> I'm not doing that.
39:29>> You have no desire to get into what
39:31casinos are doing.
39:32>> No, no, no. Gaming is different.
39:33>> That's gaming is not your thing.
39:34>> is completely different.
39:35>> So what is the upside
39:36>> no price discovery coming from it.
39:37>> So then what's the upside for Kalshi?
39:38>> I'm building the modern-day financial
39:40market. That's what I'm building. I'm
39:42building the next generation CME, the
39:44Chicago Board of Trade. Because
39:45historically, what we had What Why do
39:47derivatives markets exist, right? It
39:48gives They give us a predictability
39:50about an industry.
39:51And once that industry matures, it needs
39:52instruments to manage that
39:53unpredictability
39:55for lenders, for certainty for
39:57investment.
39:58How do we start? We started grain
39:59futures. Why? 100 years 150 years ago
40:01because the American economy was
40:03primarily agriculture.
40:04Then we moved into industrializing. So
40:06what did we get?
40:07Oil futures, metal futures. Things that
40:09are basically inputs and outputs of the
40:11industrial phase.
40:12Then we moved into interest rate swaps
40:14and interest rate futures and and and
40:15credit and all these other indices, S&P
40:18futures. Why? Because banks and the
40:19services sector started going growing.
40:21Financial services became bigger.
40:23And to me Kalshi is a generalization of
40:25that. It's like do we believe there's an
40:26economic impact from politics?
40:28From sports, from weather,
40:30from all these different things.
40:31Certainly. And do we need an industry to
40:32help predict the risks around those
40:34different industries? For sure over
40:36time. And that demand is going to only
40:37grow. And Kalshi I think in some ways is
40:39generalizing the stock market.
40:40>> Who would Kalshi take business away
40:41from? If it's not sports gambling, who
40:44would it
40:44>> I believe these things tend to be more
40:45additive than people make it out to be.
40:47>> so?
40:47>> Yeah, I think so because like
40:49liquidity begets liquidity. So when I
40:51add So So okay.
40:53Um there's a farmer
40:56in in California uh a month or two ago.
40:59Uh he's he's a he he's a he's in the
41:01goat herding business. He's a goat
41:03herder.
41:03He has 3,000 goats. Why do these goats
41:05exist?
41:06They eat the grass that usually leads to
41:08wildfires, okay?
41:10And
41:11that farmer, you know, he
41:13he had a he had a risk, which is that
41:15there was a bill in the legislation in
41:16California that was going to go up
41:17goat herding wages from $70,000 to
41:19$240,000.
41:21It's going to right drive him out of
41:22business because he can't afford these
41:23wages.
41:23>> $70 to $240. More than 3x.
41:26>> More than 3x. So it's going to drive him
41:27out of business because how could you
41:28afford these wages?
41:29He went to insurance companies. Can you
41:31please underwrite this risk for me? What
41:33did they say?
41:34No, there's no way. Who like who is in
41:36the business of underwriting these
41:37different risks?
41:38So he came to us and said, "Can you list
41:39a market on it?" We're like,
41:40"Certainly."
41:41Within 2 hours the market started
41:42trading. We have bid you know again 30
41:44minutes costs us we have a hive mind
41:45that can you know price this. It says it
41:46does 10% chance of this thing happening.
41:48If you If you search for goat herder,
41:49you'll see it directly. Goat herder
41:51Yeah.
41:52First one.
41:55And then he bought $50,000
41:57at 10%, which pay pays out $500,000 if
42:00the bad thing happens as a hedge, as
42:01insurance. And it happened and he got
42:03paid $500,000 that he's now using to
42:05diversify his business and stay afloat
42:07for another 18 months.
42:08>> He put 50k on Cal She made a half a
42:10million dollars.
42:11>> As a hedge.
42:12>> As a hedge because he's now he has to
42:13pay all the different So he's offsetting
42:15some of the additional cost.
42:17Who are we taking business from there?
42:19Nobody.
42:20We're bringing liquidity to a much
42:22larger set of things that wouldn't have
42:24been liquid otherwise. Like when a when
42:25a sports business, a bar or a marketer
42:28comes and puts a hedge on Cal She to
42:29hedge themselves against sports risk,
42:31what is their alternative historically?
42:32Like going and doing an undrafting?
42:34They're never going to do that
42:34undrafting. So it doesn't make any
42:35sense. They need a financial instrument
42:38that is traded in open market where
42:39people are going to compete and provide
42:40them competitive bids and ask
42:41competitive pricing.
42:43Uh so they can book it on their
42:44financials as a financial instrument and
42:46like place that hedge as a real hedge.
42:48And so it's it's a new market that's
42:49being created that like basically you
42:51know, uh makes like
42:53creates more opportunities to price risk
42:55and manage risk.
42:56>> Well, who who is an enemy? Who is an Who
42:58is not happy with Kalshi?
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43:22to move, built to endure, designed to
43:26impress.
43:31>> I generally feel at my core like elites
43:33will always not love it.
43:35Cuz I think that's really what we're
43:37like 80% of our customers
43:39active
43:40they don't trade. So, we have 30 million
43:42customers that have trading accounts,
43:43but really close to 100 million
43:44Americans that like use it for the
43:45forecast. You see it Fox News, and you
43:47see it the campaigns are all using
43:48Kalshi.
43:50What's the problem?
43:51>> 80% use it, but don't
43:53>> They use it for the information. They
43:54use it for the information.
43:55>> That's a public good that comes out of
43:56markets. You Would you would Would a
43:57sports book be able to say 80% of my
43:59customers don't and that there's no use
44:00to you? What what is the information
44:02that you're getting out of a sports
44:03book, right? But that's what markets do.
44:04They surface information to people. And
44:07but here's the key thing about it.
44:09Elites will never love these markets,
44:10and they'll always attack us. That's why
44:11we often get attacked in the press etc.
44:13because like
44:15it's the first time that information is
44:16flowing from people bottom up.
44:19There's no gatekeepers. There's no
44:20elite. We're not listening to a handful
44:22of people telling us here's the truth
44:23and here's not what's here's not what's
44:24true. We don't have gatekeepers of the
44:26truth. The truth here is being produced
44:27by millions of American people putting
44:30money where their mouth is, having skin
44:31in the game.
44:32>> Okay, so how about a how about a
44:34Fanatics? How about a Robinhood? How
44:36about state gaming regulators? How about
44:38those guys? Would they
44:39>> think it's state Look, I I think we we
44:41always say like we're happy to engage.
44:42Like I always tell states
44:44uh it was never about if people want to
44:46tax us. People say, "Oh, we're avoiding
44:47taxes." That's not at all true. I always
44:48tell states, "If you want to tax us at a
44:50sensible rate, I'm actually not the type
44:51that is completely against taxes. I'm
44:53I'm fine with it. Let's come to a
44:55reasonable
44:56uh deal. This is a very fast-growing
44:57industry. And look, if there's ways
44:59where we can promote more education and
45:01like fund education resources or
45:03education in general or healthcare, I'm
45:04all for it. I actually that's my
45:06personal view. I'm a free markets guy
45:07and I love capitalism. But at the same
45:09time, I think things are a little bit
45:09too out of whack. There's too much
45:10wealth inequality right now. We have to
45:12adjust it a little bit to make sure we
45:13bring in more people into the system. I
45:15think when people are out of the system
45:16and they're sitting on the sidelines,
45:18it's normal that over time they're going
45:19to bubble up feelings that they don't
45:20like the system because, you know, if I
45:22was out of the system, I wouldn't like
45:23it either, right? And so,
45:25that's my view. And then if there's
45:26really if people are worried about
45:27consumer protections, if we feel like
45:29there's not And I'm I'm a
45:29pro-regulations guy. I think there needs
45:31to be sensible guardrails around the
45:32mar- any market.
45:33Why? Because you want need to make sure
45:35that it's fair. If a marketplace is not
45:36fair, there's corruption in the
45:37marketplace, what happens? People stop
45:39trusting it.
45:40Why would you ever participate in a in a
45:41marketplace that is not fair that where
45:42the guy on the other side may have a
45:44knife and you don't, right? Like that's
45:45that's unfair. And so, I would believe
45:47in regulation and tell states, "Look,
45:49let's have a good faith discussion of if
45:50you think there's a If you think that we
45:52already got the federal regu- We're we
45:53spent the first four years getting
45:54regulated. But if you think there should
45:55be additional consumer protections in
45:57your state, let's have a conversation
45:58about that. We're open to it as long as
45:59it doesn't hamper on the free markets
46:02principles of a federal regulated
46:04exchange."
46:05>> Are you in all 50 states right now?
46:06>> Uh except for Nevada, which you're going
46:08to
46:08>> Makes sense.
46:09>> You know, they got to go
46:10>> When do you think you'll ever will get
46:11in there or
46:12>> you know, it's bubbling up. There's some
46:13litigation. There's litigation that's
46:15going on and I think you know, we feel
46:16very good about like, you know, the way
46:18that this needs to be regulated. It's
46:19federal. Uh and I think that there's a
46:21bit of kind of I I think there's some
46:23confusion plus you know, the casino
46:24lobby is is is doing a lot of work right
46:26now against us.
46:26>> Interesting. How about Polymarket?
46:29>> Look, I think I like competition. I
46:31think competition is great. Uh and you
46:32know, we have competition from
46:33Polymarket. We have Robinhood. We have
46:35you know,
46:36the big banks are getting They've
46:37announced they're getting into the
46:38prediction market space, which is
46:39exciting. I mean, we just announced a
46:40partnership with S&P, which is the
46:42largest And the NBA. And the NBA. But
46:44I'm excited about S&P because it's the
46:45largest financial data provider
46:48on the planet. It's you know, and and
46:50and I think um
46:52the the difference I have with
46:53Polymarket is we took a regulatory first
46:55approach to building the business. I
46:56wanted to build it in America, the
46:58American exchange, and pro-regulation,
46:59regulated first.
47:01And I like got regulated as a financial
47:02market, and like have all the right
47:04rules around banning inside of trading,
47:05and banning market manipulation, making
47:07sure minors are not on the product. Um
47:09all the kind of sensible guardrails that
47:10it needs to be.
47:11And I spent the first 4 years doing
47:12that, right? They just launched offshore
47:15and then start growing before And you
47:16remember remember this, a few years ago
47:17Polymarket was the brand, it wasn't
47:19Kalshi.
47:19>> I I remember that clearly.
47:20>> But I was so committed to the regulatory
47:22first and doing the right thing.
47:23>> And they didn't, right? They did
47:24something different.
47:24>> That's the the the the I think the
47:26philosophical misalignment we have
47:27because
47:28that's why you start hearing about the
47:29insider trades that are going on police,
47:31they have markets on war, and and now
47:33maybe they're now they're stopping, but
47:34they used to have them. Uh they they
47:36don't verify customers, and so all these
47:37bad things started happening, but that
47:38wasn't us because we were doing it under
47:40the right umbrella with the right
47:41consumer protections and the right
47:42guardrails.
47:43Um but, you know, as because it's a
47:45nascent industry, people commingle us,
47:46people think it's the same. Uh but no,
47:48there's an American regulated market,
47:49and there's an offshore market that was
47:51not really abiding by rules. Now they're
47:52coming in and they're abiding by rules,
47:53which I think is a good thing. Like I
47:54hope they do more and more of that.
47:56But as as long as you have the right
47:57sensible rules, I think you let you need
47:58more com- competition. Competition
48:00pushes us to get better. Um and so I
48:02hope over time that they really embrace
48:04doing things the right way, which I
48:05think they're starting to.
48:06>> Is there anything different between the
48:07two of you today?
48:08>> Oh, for sure. I mean, we're we're
48:09regulated, we're
48:10>> They're not. They're still not.
48:12>> a lot of it is not, and then we we ban
48:13insider traders and we police it. We
48:15verify our customers when they're coming
48:17in. We have consumer guardrails on if
48:18people are doing, you know, I think any
48:20of these products, there's some
48:21irresponsible use. People can Losing is
48:23not bad.
48:24Competition's some winners and some
48:26losers, you know? Like you you don't
48:28when somebody loses in the NBA, you
48:29don't go go give them a trophy. Like
48:30that's that's how it works.
48:32What's bad is like irresponsible use and
48:34addiction. Those exist
48:36on prediction markets, they exist in
48:39gaming, but they also exist in
48:40Robinhood, and they exist in like, you
48:41know, the the financial markets, right?
48:43How many people How many times do you
48:44hear about like a a guy who lost his
48:46shirt on trading options in the open
48:48market? It's very common. And you need
48:50to regulate that, all right? You have to
48:52have sensible guardrails on making sure
48:53that like Look, I believe people should
48:54make their own decisions, but you have
48:55to give them warnings. You have to say,
48:56"Look, you're getting off your skis.
48:58You're doing more than you should.
48:59Here's all the friction that we're going
49:00to add to your account. Make sure that
49:02like, you know, you you look at how much
49:03you're losing. Make sure to use these
49:05risk management tools and diversify.
49:07You're going a little too crazy. And I
49:10think it's important to have these
49:11guardrails because otherwise you're
49:11going to have some of the irresponsible
49:13users go un unregulated and that's not a
49:15good thing.
49:16>> Now, would would you guys and Polymark
49:18is it NBA teamed up with both of you or
49:20just Calshi?
49:21>> I think it's in I believe they're teamed
49:22up with both of us today which is which
49:24I think is good because it's it's the
49:25league saying
49:26>> How does that happen? How does it happen
49:27to team up with both?
49:28>> Yeah, it happens. I mean you these big
49:29leagues have multi-partners and and they
49:31do it with broadcast.
49:31>> Within the same space and
49:32>> Sometimes they do it with broadcast,
49:33right? You do a broadcast deals with two
49:34different uh
49:35>> Right. Right.
49:36>> I think it's important because one of
49:37the key things that are important about
49:39>> we going to see as a fan? Are you going
49:40to see Polymark and then Calshi there?
49:41>> Oh, it's more complicated than that.
49:42Yeah, I mean it's it's we have different
49:44teams that we partner with etc. But the
49:46key pillar of that actually partnership
49:47that, you know, usually people don't
49:49talk about that. That's really what's
49:50important about this part. We're very
49:51excited about them. It's not the
49:52marketing. Marketing is a piece of it.
49:54It's the market integrity piece.
49:56Because now what we have an NHL, we have
49:58an MLB, we have it now with the NBA.
50:00It's a data sharing agreement to police
50:02the bad actors.
50:04And that's what's important. Because
50:06market integrity is one of the
50:07foundations of a free market. I always
50:09say this, what's the opposite of free
50:11market?
50:12It's corruption.
50:14Free markets and corruption cannot
50:15coexist.
50:16Right? Why? Because free markets is has
50:18a as a a requirement of a free, open,
50:22and fair competition. The competition
50:24needs to be fair. Some people have
50:26access to specific special tools. That's
50:28not a fair and nobody's going to
50:29participate. It ruins the competition.
50:31And so markets have to work if they have
50:34to work they have to be fair. And so
50:36what we do with these leagues is like we
50:37for example we ban athletes from trading
50:39on the league. We have a bunch of market
50:40integrity and game integrity measures
50:42which is like if we ever catch an
50:43athlete that was trying to do something
50:45weird
50:46they're going to get punished and
50:47they're going to get criminally
50:48indicted.
50:48>> currently do it?
50:49>> Uh I they do it on the sportsbooks in
50:51general. Most of the cases happen
50:52>> with you guys? Not
50:53>> We have a very stiff regime around
50:55policing that.
50:55>> it? How do I identify?
50:56>> You think so? One cuz
50:58customer verification. And by the way,
50:59it's the same thing with elections. So
51:01actually if you open the Senate race in
51:02Texas
51:04or, you know, either any of these races
51:06in the elections. Yeah, if you go down a
51:07little bit
51:08a little bit more.
51:10Uh
51:12slightly more, slightly more.
51:13Uh a little bit more. I'll tell you
51:15when. No, no, that other market.
51:17Yeah, yeah, but if you scroll down
51:19>> [snorts]
51:19>> so
51:20uh a little bit more. I'll tell you
51:21when. Stop. Right slightly.
51:24Little bit lower, lower, lower.
51:27A little bit lower. Okay, if you look at
51:29here, insider trading is prohibited. If
51:31you open that
51:32those are all the people that are not
51:33allowed to trade on this market.
51:35So, people that are involved in the
51:36campaigns, or people that And so, what
51:37Who are these? These are the people that
51:38have insider information.
51:41And you want to police them and you want
51:42to ban them out of the system. And we do
51:43that, we actually if if our systems when
51:45you do the customer verification flags,
51:46okay, this person might be involved in
51:48that campaign, so you ban them
51:48proactively.
51:50Now, what people do is they, you know,
51:51what when you want to
51:52if you have a cousin at Tesla that wants
51:53to trade on insider information, what do
51:54they do? They don't do it themselves,
51:56they give you the information so you
51:57trade on it. How do you catch them? We
51:58use the same systems as in Nasdaq. We
52:00have actually a partnership with the
52:01Nasdaq and a bunch of other systems.
52:02It's the same system. So, what happens
52:04is there's a suspicious pattern
52:06and when we look at a suspicious
52:06pattern, we do an investigation. A third
52:08of our team is like compliance. And
52:09those guys have investigatory
52:11>> of your team is compliance?
52:12>> A lot, yeah.
52:12>> So, how many how many compliance people
52:14do you have?
52:14>> We have now, I mean, over 50, 60. I
52:16mean, it's it's And like they compliance
52:18market regulation lawyers and they they
52:20they are basically in the business of
52:21like they look at these flags that the
52:23system brings and they look at a case,
52:25who is the person, how they trade, why
52:26they trade. And then if they find that
52:27there's
52:28something wrong, they it goes from fines
52:30all the way to criminal prosecution. We
52:31can refer it to the DOJ, which we have.
52:33We referred, you know, George Santos to
52:34the DOJ.
52:35>> What happened there, by the way?
52:36Something happened with that situation.
52:37>> to manipulate the market and we caught
52:38him immediately. You know, the other
52:39thing that's great about prediction
52:40markets, there's not more insider
52:42trading on prediction markets than on
52:42the stock market. What's great is that
52:44it's all transparent.
52:47All the traders look at it and they say,
52:49"Hey, something is weird here."
52:50>> Oh, they tell you.
52:51>> because all the trades are transparent.
52:53And like when somebody cheats against
52:55you, you're incentivized to say, "Hey,
52:56somebody cheated." And so, when we look
52:57at it, like, okay, well, there was
52:58cheating here and like we're going to
52:59enforce it. It's like a crime, it's a
53:01financial crime.
53:02How do you police crime long term?
53:04You catch it
53:06and then you heavily punish it so you
53:07deter the next person from doing the
53:08same thing. And that's what we do.
53:10That's what the New York Stock Exchange
53:10does. The difference between prediction
53:12markets is like I think more of the
53:14I think there's more insider trading in
53:15the in the stock market than the
53:16prediction market. The difference
53:17prediction markets we catch more of it
53:18because it's so transparent.
53:19>> Tell me why.
53:20>> All the trading activity is transparent.
53:22Everybody can see it.
53:22>> So is it kind of like a decentralized
53:24like on Bitcoin where you can kind of
53:26see what's going on through the app?
53:27>> decentralized. We just like show all the
53:29trading activity publicly.
53:31And then it's so obvious, you know, if
53:32somebody is
53:33something is trading at 10%.
53:35And all of a sudden somebody is buying
53:36it in bulk, you're going to look at
53:37that. Why are you buying it at 10%?
53:39Something is What do you know? What what
53:40happened here?
53:41Who are you? Who Who did you call? What
53:43is the piece of information that
53:44>> Do you reach out to them? Does
53:45compliance
53:46>> team will apply
53:48>> And do you are you getting faster at
53:50wanting to put a stop to it so this
53:51manipulation doesn't
53:52>> fast on it. We immediately ban the
53:53account. But here's the key thing.
53:55Insider trading case on New York Stock
53:56Exchange they take time to bring. Why?
53:58Because you have to bring a real case.
53:59Our standard in the America, which is a
54:01good standard, is innocent until proven
54:02guilty.
54:03So you have to establish a fact pattern
54:04and and go through through due process.
54:05Like you have to go through the courts.
54:07And then you know, show that like you
54:09you I mean sometimes people settle. They
54:10say, "I'm sorry. I did the wrong thing.
54:12Let me know what I need to do." And a
54:13lot of people do that. But a lot of
54:14people decide to fight it. And there you
54:16have to like it takes time.
54:17>> with Santos?
54:18>> I don't remember, honestly. I think he
54:20decided to fight it. I forgot or he paid
54:22the fine. I'm not sure. I completely
54:23forgot, to be honest.
54:24>> A lawsuit for life after prediction
54:25market company found he misled
54:28public about whether he would have
54:29attended the State of
54:31>> And then Cal Shapiro imposed a financial
54:33penalties. That's what You guys fined
54:34him $72,000.
54:35>> Which is way more than he made, yeah.
54:37>> Wow.
54:38>> Yeah. And then when we make those fines,
54:39we pay them we give them to like a
54:41a
54:42nonprofit that basically like works on
54:44educational resources around like
54:45insider trading and like like and
54:47training people to basically
54:48>> even take that money. You give it to the
54:49charity.
54:50>> Yeah, yeah.
54:51>> Very cool.
54:51>> Yeah. I mean you know, we now hire like
54:52a bunch of FBI ex-FBI agents and
54:55forensics experts to police the markets.
54:57It's a very good you know, they they do
54:59>> have to, right?
55:00>> Yes, yes. It's it's you know, it's
55:01systems People ask me are you done? Like
55:03is the No, because it's like
55:04cybersecurity.
55:05Are we ever safe from cybersecurity? No.
55:07Like fraudsters get better at like
55:08circumventing the defenses and so the
55:09defenses need to keep getting better.
55:11>> What role does AI play in Cal sheet for
55:13you guys?
55:14>> We use a lot of AI, you know, I mean
55:16it's amazing. Honestly, it's it's truly
55:18amazing how much like
55:20productivity gains you can get and and
55:22and and look
55:24I think
55:25I've been thinking about this a lot,
55:26like how much AI is being used in
55:28traders. Like are they using AI? And
55:30what I've seen is I talked to our top
55:31traders
55:32it's AI plus human that is their best
55:34strategy. So the AI lets you code
55:36faster, lets you summarize information
55:38better.
55:40But the layer of human judgment is
55:41necessary.
55:43Cuz I think
55:44AI what it does is it memorizes all of
55:46our past.
55:47But you know how there's an idiom which
55:48is like past performance in the
55:50financial markets don't are not
55:51indicative of future performance. And I
55:53think that's true in prediction markets
55:54and I think
55:55in these financial markets too.
55:57And so humans still have this unique
55:59judgment of like I think this one is
56:01different this time. And this is why I
56:02think I'm going on on my gut intuition.
56:05That gut intuition has I'd like to
56:06believe that it hasn't been replaced
56:07yet.
56:07>> So so partnerships is it is it
56:11are you watching like what they're I saw
56:13Polymarket got LeBron James and it was a
56:14bunch of thing about it. 20 to 50
56:16million bucks. I don't know what they
56:17pay the guy but it was a big number I
56:18was seeing a guy talking about on the
56:20podcast. Who do you see spaces and
56:23industries to go and say these guys
56:25would be good. This would be a good
56:26spokesperson. Is that kind of like the
56:28lobbying and the creativity between the
56:30two organizations today?
56:31>> Yeah, it's creativity and look I I think
56:34our view I always think the product
56:36needs to be the thing that sells itself
56:37and then that's our biggest motion is
56:39people like the product and they tell
56:41others about it and our biggest motion
56:43is people use the forecast. You know how
56:44you see the Cal sheet charts
56:46and in your podcast, you see it in and
56:48Twitter. People use it as a way to get
56:50informed and that's a way to kind of get
56:51more and more people know.
56:52>> Phenomenal.
56:53>> Right? More and more people now Fox and
56:54CNN use the the data as well. We have
56:56partnerships with them as well
56:58because the data works. It's a market
56:59driven way to think about the world,
57:01right? Now
57:03and so I all of our marketing if you see
57:05Cal sheet market it's never this hard
57:07sell like oh come use the product and I
57:09don't like any of that stuff. What I try
57:11to think is we try
57:12to just be human. It's like let's have
57:14let's let's explain these complicated
57:16market-based concepts in a fun and human
57:18way because what's the best What are the
57:20two emotions that like get people to do
57:22something?
57:23One is hatred.
57:24But I don't want to go there. That's not
57:25a good thing for consumer brand. That's
57:27for politicians. Let them do the hatred
57:28stuff. You know, they keep hitting each
57:30other. That's the way to get likes. The
57:31second one is
57:33laughter.
57:34Like light. People have already a hard
57:36lives. Like you know, I grew up with a
57:37single mom living on like you know, like
57:39day-to-day was brutal. And what what do
57:40people like? How do people think about
57:41things? They want to laugh and so
57:43the for example, the Shane Gillis, I
57:45just find him to be very funny. I think
57:46he's a very funny guy and he gets cats
57:47or some people don't like him and um
57:50but what I liked about him is he's
57:52simple. He just explains things in
57:54simple way and like I wanted to explain
57:55a lot about how a lot of people use
57:57Kalshi as a weather forecast cuz it's
57:59more accurate than the weather station.
58:00How do we explain that concept? And then
58:01he came up with this weatherman concept
58:03that I love I mean
58:04>> He came up with it?
58:05>> Yeah, he was like we need to do this and
58:06we need to do it with Danny McBride and
58:07it's going to be amazing. And like And
58:08he was right about that.
58:09>> approach you guys with the idea or did
58:11you
58:11>> talking to each other and like
58:12>> Got it.
58:13>> And you know, it was really about I
58:14always say it has to be authentic. Don't
58:16sell something to the customers. If you
58:17sell something to the customers, they're
58:18not going to like it. They're going to
58:19be pissed. And you just
58:22put yourself out there, have have some
58:24fun with it, explain what it is, why
58:25it's a good thing for society. I mean,
58:27think about it. We're a little bit more
58:28accurate about predicting the weather
58:29thanks to markets. How incredible is
58:31that? And how you're not going to
58:32explain it by like putting a bunch of
58:33math and data and statistics in front of
58:35people. You're going to explain it by
58:36doing something light-hearted that like
58:37people can watch and not, you know,
58:39throw up when they look at like another
58:40ad on live TV.
58:42>> That's pretty wild. That's Is there
58:44going to be more bigger things being
58:45announced here soon with different
58:47partnerships?
58:48>> I'm really excited about the S&P
58:49partnership. That's
58:50>> Yeah, you were talking about that
58:50earlier.
58:51>> their core partner historically has been
58:52CME.
58:53I feel like this Kalshi S&P is I I
58:55really feel today I really felt it's the
58:57start of a new era.
58:58Cuz it feels like we're finally being
59:00perceived as the next generation
59:01financial markets because that they're
59:02going to distribute our products to
59:04institutions and we're going to be
59:06developing a bunch of indices. You know,
59:07what's the S&P's most famous index? It's
59:09the S&P 500, which is a pulse on where
59:11America's market is.
59:14What if you build the Kalshi S&P
59:15economic index, inflation index, sports
59:18index, which is what are different teams
59:20and how they're performing over time,
59:21political index, are we leaning more
59:23Republican or Democrat? Not like who's
59:25going to win this next midterm. It's
59:26like is the country going towards
59:28Republican or country going towards
59:30Democrat? We have this thing called
59:31Kepow. If you pull it up
59:33on Google, Kalshi Kepow. And
59:37and if you open this thing, look at this
59:39chart. I mean, this is this is for
59:40example one of the recent if you go down
59:42a little bit.
59:43This is like a the Kalshi political
59:45index, which is an aggregate of all the
59:46prediction market data into one piece of
59:48information, which is are we projected
59:50to lean more Republican or Democrat over
59:52time? And you can see
59:52>> By the day.
59:54>> By the day, real time. Like the S&P S&P
59:56for politics. But this is the beauty of
59:57that partnership. We're going to build
59:58the S&P.
1:00:00Here's the thing. Do you think the only
1:00:01thing that's worthy of predicting right
1:00:02now is American stocks? Do you think
1:00:05it's worthy of predicting the economics,
1:00:06the politics, the sports, the weather?
1:00:08All these things are worthy of
1:00:09prediction. And now we have finally a
1:00:10financial market with financial market
1:00:11rigor to predict these things. And this
1:00:13partnership with S&P is I think going to
1:00:15propel that to on your own.
1:00:16>> like let's let's look at this here. If
1:00:17we look at this chart, January 2026,
1:00:20>> That's right.
1:00:20>> Republicans are up, right? It was plus
1:00:22let's just say eight or nine. Okay,
1:00:24whatever that number is. Go a little
1:00:25bit. Yeah, plus eight. Okay.
1:00:27>> the dot.
1:00:27>> So then when it goes blue,
1:00:29>> Look at the Look at the dot.
1:00:30>> the war?
1:00:31>> If you look at that dot.
1:00:32>> the dot. Go to the black dot, one behind
1:00:33you, right there.
1:00:35Operation in Iran.
1:00:36>> Got it. So that was the first real like
1:00:38>> So then what caused it to go back to
1:00:40red?
1:00:40>> Go to the next dot.
1:00:41>> Supreme Court decision, Louisiana versus
1:00:43Okay.
1:00:43>> distracting.
1:00:44>> I remember that. So Virginia Supreme
1:00:45Court of Arizona.
1:00:48It explains every one of them. Generic.
1:00:50And then go back down to Democrat. Go
1:00:52all the way down to the next dot. What's
1:00:54it say?
1:00:55>> Average gas
1:00:55>> Gas price. And and you had something you
1:00:58were showing me about the gas price.
1:00:59>> Yes, I'm going to show it to you. But
1:01:00just think about it. It's the rigor. And
1:01:01if you can open that as well, you look
1:01:03at the Democrat control of odds and you
1:01:06you know, that's the the probability of
1:01:07the Democrats winning the Senate, the
1:01:09market based on the millions of people
1:01:10are predicting.
1:01:11And you layer it now, you can do
1:01:12analysis and insights like you layer it
1:01:14against national gas prices, how it
1:01:16moved over time. Look at that
1:01:17correlation.
1:01:19But this is the beauty. You're bringing
1:01:21a level of rigor
1:01:22into these processes. And if you go back
1:01:24to the K file, right? Like that is a
1:01:26financial market. That is a
1:01:27market-driven, a prediction market,
1:01:28financial market-driven number that is
1:01:31not what the pundits are saying or the
1:01:32elites are saying or my opinion is or
1:01:34your opinion is.
1:01:36Markets don't lie. And that's the rigor
1:01:37that I want to bring to everything.
1:01:38>> People ask us questions sometimes.
1:01:40They'll say like go to the Tallarico
1:01:41one. Go to the Tallarico the one Senate
1:01:43race you just had it like a minute ago.
1:01:44Yeah. Keep going all the way up. Yeah.
1:01:46So they'll say, "Okay, $45 million here,
1:01:50552 600 600. Okay.
1:01:53What if a billionaire in the back end
1:01:54puts $20 million
1:01:56into Tallarico? Does that kind of
1:01:58manipulate the market to believe that
1:02:00Tallarico's got that big of a lead?"
1:02:01>> So there's a study and I don't know if
1:02:03you can find it. I think in in
1:02:04calibration, if you go to publications,
1:02:06I think we might be able to find it
1:02:07there. Let me see. If you
1:02:09click on publications in the top tab.
1:02:13You go down a little bit. Let's see if
1:02:14it's anywhere here.
1:02:15Okay, it's not here. I'll I'll I'll I'll
1:02:17surface it later. But there was a study
1:02:18we did on Spencer Pratt mayor race.
1:02:21It was he was a 10% chance of winning
1:02:23the mayor race. And someone put $2
1:02:24million, I think 1.5 $2 million to move
1:02:27the odds up. So he moved the the person
1:02:29moved the odds to I think 30%
1:02:31>> From 10 to 30.
1:02:32>> Yeah, 25 30. I forgot the exact numbers,
1:02:34but it moved up.
1:02:36Within Oh, here it is. If you go down.
1:02:38Oh, it's great.
1:02:39Let's find the chart. If you go down a
1:02:41little bit. Okay. If you zoom out a
1:02:42little bit then you can see See that?
1:02:43See how it jumped? It's actually It's
1:02:45actually Okay, 27 to 45.
1:02:47>> It didn't make a difference.
1:02:48>> happened right after?
1:02:49Do you know that delta? Guess how much
1:02:51time it took for it to
1:02:52>> One day.
1:02:53>> 9 seconds.
1:02:53>> Stop it.
1:02:55Stop it. Markets.
1:02:57>> It's the beauty of markets. So what
1:02:58happened when it went to 45? What
1:02:59happened to the people that are trading
1:03:00on this thing? What did they see?
1:03:02>> The mispricing you were talking about.
1:03:03>> It's mispriced.
1:03:05>> Got it.
1:03:05>> If I give you Apple stock, if you know
1:03:07that my Apple stock and you have your
1:03:08that is worth $100.
1:03:10And all of a sudden now it's trading at
1:03:11$60. What are you going to do?
1:03:13I'm going to buy it. And I'll keep
1:03:14buying it all the way till it goes
1:03:15>> So then let me ask you a question. So
1:03:16>> Isn't that But isn't that beautiful?
1:03:17That's what markets are for. By the way,
1:03:18I want to say one last thing. When
1:03:20people put out a bad poll,
1:03:22you know, Wedgwood, where you saw that
1:03:23poll that last week there was two guys
1:03:25that had come up with a fake numbers and
1:03:26like it was quoted all over media. Who's
1:03:27correcting that?
1:03:30Isn't that influencing public
1:03:30perception? This is the beauty of it.
1:03:32It's it's markets don't lie. They have
1:03:35an embedded
1:03:37structure to constantly calibrate
1:03:39towards the truth, average out towards
1:03:41the truth.
1:03:41>> Okay, so do this. What if somebody does
1:03:43Again,
1:03:43>> And by the way, we we we had an
1:03:44investigation on this guy.
1:03:45>> I'm going to ask you multiple questions.
1:03:46So, let's just say they sell it. Let's
1:03:47do a million dollars but 20 times over
1:03:50the next week. What would that do?
1:03:52>> Same thing.
1:03:53>> Same thing.
1:03:53>> I mean,
1:03:54it would always The market is always
1:03:56thinking. Is this person know something?
1:03:58Are they trying to do something else?
1:03:59Why are they trading it? So, maybe it
1:04:01moves, but then it after that it's kind
1:04:02of like It's the same as the stock
1:04:03market. So, when someone big puts a big
1:04:05order on Apple,
1:04:07the market thinks about it. They're
1:04:07like, "Look, are they putting it because
1:04:09there's a new piece of information that
1:04:10we're not aware of?
1:04:11Or are they putting it because they're
1:04:12trying to buy a lot? Or are they putting
1:04:13it for some other reason?"
1:04:14>> Yeah, and this is great because this is
1:04:16overcoming a lot of the objections that
1:04:17people think about. Back in the days,
1:04:19there was a business where
1:04:20>> And by the way, it's illegal if you're
1:04:22actually trying to manipulate the
1:04:23market. It's a financial crime.
1:04:25>> I get that, but this won't be the first
1:04:27time someone's trying to
1:04:28>> For sure, but like the polls What is the
1:04:30punishment that these guys got?
1:04:31>> Nothing happens to them. Go away and
1:04:32say, "Ah, it's a funny spoof, whatever."
1:04:34>> Nothing happens.
1:04:35>> But, remember back in the days they
1:04:37would say, "Hey,
1:04:38pay us $200,000. We're going to have
1:04:416,000 people that are going to buy your
1:04:43book to help you become a New York Times
1:04:44and that company got caught. I don't
1:04:45know if you remember that or not. The
1:04:46company ended up getting caught. You can
1:04:48even pull this up. You may find a story
1:04:50on it. But, what if some of these guys
1:04:52try to manipulate this because they see
1:04:53how much of an impact it has? They say,
1:04:55"Hey, we're going to give you this much
1:04:57money. You keep 250 of it, but bet do
1:04:59this, do that." What what would that do
1:05:01to the market?
1:05:02>> the Go back to think about that thing.
1:05:03That $2 million or whatever, $1.25
1:05:05million they put on that.
1:05:07What happened at the end? They just
1:05:08wasted it.
1:05:09>> Waste.
1:05:10>> Complete waste of money. Where should
1:05:11they have put it?
1:05:12They should put it
1:05:13giving it to the campaign. That'd been a
1:05:15better use of money. Put a bunch of ads
1:05:16on TV, attack ads on the other
1:05:18candidate. Or they should have
1:05:19commissioned their own polls that will
1:05:21say whatever they want them to say.
1:05:22Which by the way, you know, now we're in
1:05:23the last stretch of the election. What's
1:05:24going to happen in the last next 7 days?
1:05:26Democrats and Republicans are going to
1:05:27both put polls that are saying the
1:05:29opposite thing. The Democrats are going
1:05:31to say Democrats are up by whatever
1:05:32points and the Republicans are going to
1:05:33say I'm up by whatever points because
1:05:34they commissioned the polls to make them
1:05:35say whatever they want to say. That
1:05:37money would have been much better used
1:05:39done in a more in a more opaque system.
1:05:41On an open market,
1:05:43it's much harder to game the market
1:05:44because people are seeing it in broad
1:05:45daylight. And if we catch you,
1:05:47if we catch you doing this, you commit a
1:05:49financial crime and we're going to
1:05:50enforce against you. And see look at
1:05:51this. It's very hard not to catch them.
1:05:53>> to happen to that guy that did the 1.2
1:05:55>> So, I I don't I actually don't know
1:05:56because there's an ongoing investigation
1:05:57going on. I I don't I'm not privy to
1:05:59what's happening because the market
1:06:00regulation department is in like the way
1:06:02to regulate regulate as a financial
1:06:04exchange like the New York Stock
1:06:05Exchange.
1:06:06Those exchanges, this compliance and
1:06:08regulatory functions don't report
1:06:10directly to the CEO. They report to
1:06:11regulatory oversight committee.
1:06:12So, these investigations I don't know
1:06:15about them up until they're basically
1:06:16done. And the reason is because you
1:06:17don't want the business side
1:06:19to impact the the regulation.
1:06:22>> there was a couple stories with
1:06:22military. I don't know if you heard. Hex
1:06:24said a couple days ago Hex said said you
1:06:26can't, you know, if you're in a military
1:06:28service, you can no longer
1:06:29uh do anything with uh Polymarket with
1:06:32CalShe, Polymarket, none of that. That
1:06:33just happened like two days ago.
1:06:34>> favor of that.
1:06:35>> That's great. I'm sure you are. So, but
1:06:36there was something with a
1:06:38>> The Maduro trader. The Maduro trader.
1:06:41>> That's right. What was that all about?
1:06:43>> because there there's no customer
1:06:44verification. So, by the way, that
1:06:45trader tried to do it on CalShe
1:06:47and he blocked him.
1:06:49>> Same trader.
1:06:50>> Yeah,
1:06:50>> Tried to do it on CalShe.
1:06:51>> The DOJ and the government they tell you
1:06:52like, oh, there was another exchange
1:06:54where he tried to open an account and we
1:06:55got back.
1:06:56>> What caught it?
1:06:56>> And it doesn't always work. We have a
1:06:58system. That's the whole point. We have
1:06:59a system that catches people up front to
1:07:02ban them from proactive proactively ban
1:07:04them from trading on things they might
1:07:05have control over. And by the way,
1:07:07to be clear, we don't do war, terrorism,
1:07:09assassination, violence, like missile
1:07:10strikes markets, etc. that exist
1:07:11offshore. We don't do them. We cannot do
1:07:13them as
1:07:13>> They do them?
1:07:14>> So, they have they used to. Now, I think
1:07:15they're starting to clean up and take it
1:07:16them out, which I'm in favor of. But
1:07:18they are still they're still like
1:07:19wildfires. They've done them. I think
1:07:20those are bad. But that's the key thing
1:07:22of distinction of between the regulated
1:07:23onshore American markets versus offshore
1:07:26unregulated markets, right? And then
1:07:28that distinction is very important, but
1:07:29the press, you know, lumps lumps us in.
1:07:32>> So, in this case, this is a soldier
1:07:34that's working on the project with
1:07:36Maduro.
1:07:36>> Yeah, bad. That's bad.
1:07:37>> And he ends up, he's a special forces
1:07:39guy. Does he end up getting the money or
1:07:41he gets caught?
1:07:42>> He got caught.
1:07:42>> He got caught.
1:07:43>> By luck, I mean honestly by luck cuz he
1:07:44uses his name on the email.
1:07:47>> Maduro is also not a smart criminal.
1:07:49>> They don't do a customer verification
1:07:50there. So, how would you That's the
1:07:51problem. You have to KYC, what you call
1:07:53know your customer, like the banks. So,
1:07:54they caught him by luck because he his
1:07:56name was in the email. Now, for us, we
1:07:58have systems because we ask them for
1:07:59your social security, your address.
1:08:00>> You do ask them for social security.
1:08:02>> We're like opening a Schwab
1:08:03>> And they're not asking for social
1:08:04security.
1:08:04>> They're Now they're starting a They're
1:08:06working on a regulated US platform,
1:08:07which I'm very favorable. I'm happy that
1:08:08they're doing that. So, where they
1:08:09regulate property. So, there's
1:08:10Polymarket offshore, Polymarket US,
1:08:11which is great. But I think over time we
1:08:13want more regulated more of the
1:08:14regulated onshore
1:08:16>> What happened with the Khomeini
1:08:17situation, the $54 million?
1:08:18>> Oh, that's an interesting one. So, so
1:08:20that that one actually um um
1:08:22And you know, I don't think that lawsuit
1:08:23is going to go anywhere because what's
1:08:24happened is we had a market on like
1:08:26whether the leader was going to be out.
1:08:28Now, here's the thing that's very
1:08:29important. I told you we don't do death
1:08:30markets.
1:08:31What does that mean? All of our markets
1:08:33have a death caveat, and it's very
1:08:34clear. It's always clear in the box,
1:08:36right, when you're trading that says
1:08:39if a candidate, God forbid, dies or
1:08:42other things,
1:08:43we settle the market at the price before
1:08:45the event happened. So, no one can ever
1:08:47profit from a death.
1:08:50Cuz you don't want to create the
1:08:51incentive for someone to do something
1:08:52bad.
1:08:53>> yeah.
1:08:53>> Yeah.
1:08:54But obviously people were like, "Oh
1:08:55well, I should have profited because it
1:08:56it you know, he he was out." And you
1:08:58know, people get you know, when people
1:08:59don't They wanted to get paid because
1:09:01you would have made a lot of money.
1:09:02But we stood by our principles. It's
1:09:04like we are not going to pay based on
1:09:05death. That's the principle. That's the
1:09:06rules from the start. And some people
1:09:08are going to get pissed about that. And
1:09:09that's how what's hard about building a
1:09:10business that has 30 million customers
1:09:11now. It's like sometimes you're going to
1:09:12have to make decisions that are right. I
1:09:14strongly believe that was the right
1:09:15decision. I would never change it. I
1:09:16would do it again.
1:09:17Uh but some people are going to be
1:09:18pissed about that. And that's just how
1:09:19it is. And we reimbursed what we did is
1:09:21like, "Look, if To the extent that there
1:09:22was confusion, if there was, we're going
1:09:24to reimburse you all your fees. We
1:09:26didn't make any money on this market and
1:09:28all your losses. If you lost on this
1:09:29market, we're going to reimburse you.
1:09:31They were still pissed because they felt
1:09:32like they should have won.
1:09:34>> Oh.
1:09:35>> And that happens in consumer products.
1:09:36>> We had a person here, okay? We had a
1:09:39person here who won. He's like, "Oh my
1:09:41god, you know, I keep obviously uh he's
1:09:43like, "Hey, you know, I put on the whole
1:09:44thing with Comey and you know, and then
1:09:46all of a sudden he comes like, "So, man,
1:09:48I'm not getting anything because they
1:09:49decided to do everything back and I
1:09:51didn't lose any money, but I didn't make
1:09:52the money I was going to."
1:09:53>> Nobody lost. We made sure of that. But
1:09:54but like look, we made it clear. The
1:09:55rules are the rules.
1:09:56>> You remember this when that happened
1:09:57here?
1:09:58You Connor, you remember that whole uh
1:10:00yeah.
1:10:00>> But you know, the rules
1:10:02you know, these markets, if you go back
1:10:04to the Senate race, let's go back to
1:10:05that for a second.
1:10:06And if you go down, there's something
1:10:08called if you control F rules, you'll
1:10:09see it. But uh
1:10:12uh
1:10:13Here uh maybe go down a little bit more.
1:10:16Here one, the view full rules on the
1:10:17bottom.
1:10:19Each market is a legal contract. That's
1:10:21what we call them event contracts.
1:10:22There's rules of exactly how it's going
1:10:24to settle.
1:10:25>> Zoom in a little bit.
1:10:28Minimum trade size a penny.
1:10:30>> And payout criteria is like and Comey
1:10:31says that the first candidate who
1:10:33presents certification of election, etc.
1:10:34etc. And that's what it's The market has
1:10:37to have certainty on what is going to be
1:10:38a yes and what's going to be a no. And
1:10:39if we can never move away from those
1:10:41rules.
1:10:43Because if the rule if people don't
1:10:44trust that we're going to follow the
1:10:45rules to a T,
1:10:46then you're buying, you know, then
1:10:47you're not never going to trust that
1:10:48we're going to settle it the right way,
1:10:49right? And that's the key thing.
1:10:51And and it's important to understand
1:10:52that because you know, sometimes what is
1:10:55a yes, what is a no? Like how do you
1:10:56define if the whole Strait of Hormuz is
1:10:57open?
1:10:58And so we have a very specific
1:10:59definition of like the 7-day average of
1:11:01boats reported by this agency. That's
1:11:02the kind of definition that we have.
1:11:04And that's always been a challenge for
1:11:05financial markets, you know, I'll give
1:11:07you this funny example.
1:11:09Have you ever heard of butter futures?
1:11:11>> No.
1:11:12>> Butter futures are one of the big
1:11:13futures that are traded.
1:11:14>> Okay.
1:11:15>> And because butter, you know, butter
1:11:16producers need futures to hedge and etc.
1:11:18You know, it took a while to define
1:11:19butter because, you know, people would
1:11:21freak out on the Chicago Board of Trade
1:11:22and the CME at the time saying,
1:11:23"No, no, no, you're not accounting for
1:11:25butter the right way because that form
1:11:26of milk should be accounted as butter."
1:11:28And that and then at some point the way
1:11:30how they resolve it they're like you
1:11:31know what
1:11:32I don't care what everybody's definition
1:11:33of butter is. The definition of butter
1:11:34that we're going to use in this market
1:11:35is this level of saturation, this level
1:11:37of
1:11:38density, this level of salt
1:11:40>> you guys do.
1:11:41>> And that you have to. It's the only way
1:11:42to create a legitimate
1:11:43>> so what's the step process? Then when
1:11:45somebody wants to create a position then
1:11:48who writes the contract then
1:11:49>> You have a team that like so so a lot of
1:11:52customers want to trade it then we have
1:11:53a team that's now very good because
1:11:55we've listed so many tens of thousands
1:11:57of markets so most markets look and feel
1:11:59like a market that we had in the past.
1:12:00So we pull up templates and then we see
1:12:02if there's needs refinement on like the
1:12:04specific rules. If it's something
1:12:05completely new that we haven't done
1:12:06before we create a very uniform set of
1:12:08rules on it that is certain from up
1:12:10front then we list and then we have to
1:12:11make sure that the regulation department
1:12:13is okay with it which is like we're not
1:12:14doing some of the markets are banned and
1:12:16then we basically put it out in the open
1:12:17market.
1:12:17>> total markets have you guys ever had?
1:12:19>> tens of thou I mean tens oh
1:12:21>> All time.
1:12:21>> millions now
1:12:22>> millions now How many active do you have
1:12:24right now?
1:12:24>> tens of thousands
1:12:25>> tens of thousands
1:12:26>> Yeah.
1:12:28>> And the the method
1:12:30>> the method of creating tracking and
1:12:32approving
1:12:33what is that process? Like there's a
1:12:36group coming in what things will you say
1:12:37no to?
1:12:37>> I mean like look at the Dodgers market
1:12:39real quick. If you like if you Google
1:12:41maybe like Dodgers uh cow hats
1:12:43maybe just Google it.
1:12:45uh
1:12:46cow Dodgers I mean
1:12:48uh
1:12:50uh maybe Reuters the the article right
1:12:52below that one.
1:12:54uh
1:12:57You know that was a market that we we we
1:12:59we we already had we I mean we already
1:13:01had a markets on the
1:13:02teams. They were like okay I want that
1:13:04specific market on that not making it
1:13:06past the playoffs because that's the
1:13:07risk that we really incur.
1:13:09And you see that $60,000 hedge
1:13:12they didn't do it to make money. They
1:13:13did it to offset some of the
1:13:15the the
1:13:17the risks that they had and we discussed
1:13:18this a little bit.
1:13:20Sometimes these market formations is
1:13:21like a specific to the risks that the
1:13:22person has and sometimes it's more broad
1:13:24where like they basically buy the index
1:13:25like oh the the Dodgers is going to do
1:13:27well over time or do less well over
1:13:28time.
1:13:29And we always have to balance like is
1:13:30there going to be liquidity? Are they
1:13:32going to be enough incentive? How do you
1:13:33liquidity comes? It's from people that
1:13:34are speculating.
1:13:36Right? Because whenever someone is
1:13:37hedging, who's on the other side? The
1:13:39speculator. Someone is doing it for
1:13:40making money. And that's the key thing.
1:13:42We always think about what are the
1:13:43markets that will have enough liquidity
1:13:44where people are going to be speculating
1:13:45trading enough. You know, people say do
1:13:46people bet on cash? You like yeah, they
1:13:47bet like they bet on stock market. But
1:13:49that speculation is the thing that helps
1:13:51get the market efficiency in the prices
1:13:53to help the hedgers do what they need to
1:13:54do.
1:13:55>> Give me the weirdest markets you've had.
1:13:56Weirdest positions you've had.
1:13:57>> Oh, the the goat herder is one of them.
1:13:59I mean
1:13:59>> That's one of them. What else what else
1:14:00have you had?
1:14:01>> Taylor Swift is always very popular.
1:14:04Taylor Swift.
1:14:04>> Any kind?
1:14:05>> Where was she was getting married.
1:14:07You know, I have a deal with MSG. So,
1:14:09people always
1:14:10you know, when I talk to policy makers,
1:14:11they come to me and they say
1:14:12why are people trading on Taylor Swift?
1:14:14Like what what what is the economic
1:14:15purpose of her? Like it's it's it's if
1:14:17you you know, wedding dress is another
1:14:19example.
1:14:20And
1:14:22I'll tell you I'll tell you that.
1:14:23The wedding whether she picks Dior or
1:14:25not, do you believe it's going to move
1:14:26the Dior stock price? Now they're
1:14:28private company. What do you believe it
1:14:29will move it?
1:14:30>> I think so.
1:14:30>> For sure. I mean
1:14:31>> Yeah.
1:14:32>> I mean think about
1:14:32>> 402,000. So, what what
1:14:34>> Think about the number of people think
1:14:36about the number of people that will
1:14:37pick Dior as their wedding dress just
1:14:39because Taylor Swift picked picked her
1:14:40wedding dress as Dior. Just think about
1:14:42that. Just truly that.
1:14:43>> So so isn't that a lot of opportunities
1:14:45there
1:14:46for somebody for
1:14:48even for her team to go to Dior and say
1:14:50hey
1:14:51$50 million
1:14:53we'll have you be the dress. Isn't there
1:14:55like opportunities like that?
1:14:56>> I mean but they probably pay her to do
1:14:58that by the way. Think about it. You
1:14:59don't you don't think Dior is going to
1:15:00pay Taylor Swift?
1:15:01>> That's what I'm saying. I would
1:15:02>> But that's advertisers. What do they do?
1:15:03I mean
1:15:04you know, people we had a market where
1:15:05she was going to get married.
1:15:07Okay?
1:15:08People were like well that's frivolous.
1:15:09Why would anyone care about that? I'll
1:15:10tell you like I care about I mean I have
1:15:12a partnership with MSG
1:15:14where the concourse on the sixth floor
1:15:15is now the Cash concourse.
1:15:17And I can tell you right now we have an
1:15:18internal model that
1:15:19tells you how much value is that part. I
1:15:21you know, we pay except let's call it
1:15:22that's not the right number. But let's
1:15:23say we call we pay a million dollars a
1:15:24year for that partnership.
1:15:26After Taylor Swift decided to get
1:15:27married there
1:15:29that million dollars a year of value is
1:15:32now 2.5 million dollars year of value.
1:15:36Think about it. I mean, it's you have
1:15:38but think think about, you know, what
1:15:40the when average people always tell me
1:15:42sports have economic consequence.
1:15:43Obviously, they have an economic
1:15:44consequence. Like a if you we if you
1:15:46sponsor like do you think my MSG deal
1:15:49might the deal value of my MSG
1:15:50partnership went up after the next
1:15:52one?
1:15:52>> No, 3x, 2 and 1/2 x.
1:15:54>> Exactly.
1:15:55What So, is that a binary risk that I
1:15:57actually hold is there an economic
1:15:58impact?
1:15:58>> far. They got They got to
1:15:59>> Yeah, they got to They got to do better,
1:16:00but maybe there is an economic impact
1:16:02from the next winning or losing. There's
1:16:03no question.
1:16:04>> thing is to manipulation.
1:16:06>> The And and that's why it's regulated.
1:16:08That's why it's regulated, right? Like
1:16:09the the regulatory What do you do about
1:16:11There's manipulation in the stock
1:16:12market. I mean, actually, you know, you
1:16:13know this is funny.
1:16:15You know that onion futures are illegal
1:16:18in America?
1:16:19>> Illegal?
1:16:20>> Illegal. Look at the Onion Act. No,
1:16:22there's no way you
1:16:23you're going to find if the Onion Act is
1:16:24going to be repealed. You're not going
1:16:25to have
1:16:26>> [laughter]
1:16:27>> If you if you go to look at Wikipedia
1:16:28onion onion act. You know why?
1:16:31So, onion is the only vegetable where
1:16:33it's illegal to have a future on in
1:16:34America.
1:16:35>> Why is that?
1:16:36>> So, onion No, just do onion onion act,
1:16:38not actors.
1:16:40>> Yeah.
1:16:40>> Uh onion future act.
1:16:43Is the United States law banning the
1:16:44trading of futures contract on onions as
1:16:45well as motion picture box office which
1:16:47is motion picture box office very is
1:16:49another story. We can talk about that.
1:16:52But what happened? Two guys, 1955, Sam
1:16:55Siegel and Vincent Kosuga. What did they
1:16:57do? They
1:16:58They Have you heard of a short squeeze?
1:17:00They put a short
1:17:01>> squeeze Yeah, of course. Yeah.
1:17:02>> Yeah, they bought all the onion supply,
1:17:03put a short squeeze, and then tanked the
1:17:04market, which bankrupted a lot of
1:17:05farmers. They manipulated the the the
1:17:07onion markets to make a profit.
1:17:10What did Congress decide? What did What
1:17:11was Congress' conclusion at the time?
1:17:13Congress was like, "You know what? The
1:17:14problem is clearly onions here, not
1:17:16humans. So, we should ban onion futures.
1:17:18That will fix the issue." As if
1:17:20that could never happen in corn futures
1:17:23or soybean futures or or financial
1:17:25markets at large.
1:17:26The answer is not banning onion futures,
1:17:27as we know. The answer is actually
1:17:30regulating them. Add the right set of
1:17:32guardrails, and the same guardrails of
1:17:33risk exists anyways. Humans will always
1:17:35try to game a system. And by the way,
1:17:37you know this is a bad outcome because
1:17:39onion futures are one of So, if you go
1:17:40to my Twitter, you'll see I took a tweet
1:17:42I put out recently on I compared the oil
1:17:44price volatility to onion prices, which
1:17:46is not a one-to-one comparison, but
1:17:47still it gives you a little bit of a of
1:17:48a feeling. So, if you go down a little
1:17:50bit
1:17:51uh a little bit more, you're going to
1:17:52find the chart a little bit more.
1:17:54Here. If you open that chart,
1:17:56look at the volatility of onion prices
1:17:58against
1:17:59oil.
1:18:01And by the way, oil is considered to be
1:18:02a volatile You know how oil would be
1:18:04excluded from inflation because it's a
1:18:05very volatile commodity. Look at the
1:18:07volatility of onion prices. You know
1:18:08why?
1:18:10There's no futures market for it. It's
1:18:11less predictable.
1:18:13That's a bad thing.
1:18:15>> So, because of that,
1:18:18interesting.
1:18:18>> Isn't that like isn't that I mean this
1:18:20is an actual Should we have done onions?
1:18:22And the answer is unequivocally no. It's
1:18:23a terrible decision.
1:18:24>> Very interesting.
1:18:26Very interesting. I I
1:18:28I read somewhere where for you you had
1:18:30150 people reporting to you. Is that
1:18:32true?
1:18:32>> Yeah, me and Luana might
1:18:33>> How do you have 150 people reporting to
1:18:35you?
1:18:35>> I work a lot. I really do. Like, you
1:18:37know, I I I and and I want to talk a
1:18:39little bit about this and I know you you
1:18:40talked a lot about this about, you know,
1:18:41that I believe in hard work. I really
1:18:43believe in like being committed to the
1:18:44thing and really believing it. And and
1:18:46the soul of an organization is going to
1:18:47be the founder at the end because you're
1:18:48making decisions.
1:18:49I think
1:18:51I pretty much work like 7 days a week
1:18:53and and long hours. I think it's
1:18:55scalable because we have a lot of tools
1:18:57now to scale companies, especially with
1:18:58AI. Like, I I think companies over time
1:19:02You know, the the traditional structure
1:19:03is you have a boss who has
1:19:05reports, and those reports are bosses
1:19:06for other reports, and you go down in a
1:19:08tree-like structure.
1:19:10And then that layer in the middle is
1:19:12what we call the management layer. And
1:19:14you know, when people complain all the
1:19:15time, what do they say? The management
1:19:16layer is the worst.
1:19:17They don't get things done. It's where
1:19:19all innovation dies. And And why?
1:19:20Because
1:19:22managers are in the business of
1:19:24politics.
1:19:25At
1:19:26No matter what when when industry gets
1:19:28big enough.
1:19:29What you need is leadership. And what is
1:19:31leadership? It's like you point a vision
1:19:32and communicate and motivate your people
1:19:34to go one and roll in one direction.
1:19:36And management is the thing that like
1:19:37checks in on people all the time, make
1:19:38sure they don't That whole thing, the
1:19:40checking in, making sure something was
1:19:41done, making sure it was not done. Guess
1:19:43what's very good at doing it right now.
1:19:45AI.
1:19:47It's very good at tracking and measuring
1:19:48and like summarizing and all these
1:19:50different things. You don't need that
1:19:51massive management layer. So, I think
1:19:52organization going to be structured more
1:19:53as a circle over time.
1:19:55Where founders and leaders can like use
1:19:57AI to track a much greater number of
1:19:59people than they could have in their
1:20:01brain beforehand. And that's what we are
1:20:03trying to build at Causal. We've been
1:20:04really very kind of at the front of
1:20:06using AI. And that's what I That's what
1:20:08excites me, by the way. We're We're a
1:20:09250 people company now.
1:20:11Which is very small given the size of
1:20:12the company. And I always say to these
1:20:14young people, there's so much to
1:20:15>> 22 billion-dollar company with 350
1:20:17people.
1:20:17>> 250, yeah.
1:20:18>> 250 people. That's it.
1:20:20>> Yeah, we're we're we're actually the
1:20:21highest revenue per employee company in
1:20:23in I think the might be the planet.
1:20:25But that that to me is the opportunity
1:20:26and I want more young people to to
1:20:28really
1:20:29You have all these tools now. You can go
1:20:30and build things. You can go and do
1:20:31things. You have You have You have And I
1:20:32want young young people to trust
1:20:33themselves more and like if you have a
1:20:35good idea, put it out in the world.
1:20:37You'll be surprised. You'll be And you
1:20:39have a bad idea, put it out in the world
1:20:40and fail and learn how to make it better
1:20:42for next time, right? And I think I
1:20:43think I worry that young people are not
1:20:46thinking about the world that way. I
1:20:47think they're too negative. And I think
1:20:48it's very important. What do you think
1:20:49they're negative?
1:20:51I think it's a little bit what we
1:20:52discussed. I think a lot of people feel
1:20:53like that, you know, capitalism is
1:20:54pushing them out of the system and and
1:20:56and you know, I mean, I grew up in
1:20:57Lebanon and and and
1:20:58that's that's what upsets me a little
1:21:00bit. It saddens me because like when I
1:21:01came to country, I was so pumped. Like,
1:21:03I was so excited, you know. And I grew I
1:21:04was born here, I grew up in Lebanon. My
1:21:05mom was always telling me, "You know, if
1:21:06you work hard, you're going to make it."
1:21:07And you know, you you're the same thing,
1:21:08right? Like, you build, you work hard,
1:21:10you you push yourself to your extreme
1:21:11and you you get rewarded. It's a country
1:21:13that rewards you. And I worry that a lot
1:21:14of people don't believe that anymore.
1:21:16And and
1:21:17>> think they're right or do you think
1:21:18they've been brainwashed? Do you think
1:21:19they've given up? Do you think the
1:21:21influence is from the outside to inside?
1:21:22What What do you think it is?
1:21:23>> My feeling is it doesn't really matter
1:21:25cuz they think that.
1:21:26Right? We are where we are.
1:21:28>> is it doesn't really matter what's
1:21:29causing it. It's just the fact that you
1:21:31think that that's a bad thing.
1:21:32>> We are where we are, right? And and so,
1:21:33we got to fix it. We got to figure out
1:21:34ways of fixing it and I think that um
1:21:37you know, uh
1:21:38to to To to my feeling is like it the
1:21:41fix is going to have to be a way to
1:21:42educate and communicate to a lot of
1:21:44people that they can they they don't
1:21:46they shouldn't sit on the sidelines.
1:21:47They shouldn't be on the sidelines. They
1:21:49should jump into the game and start
1:21:50playing. And now with AI with all these
1:21:52tools it's so much easier to start
1:21:54playing and start doing cool things,
1:21:56right? And and and it you know, you
1:21:57>> What's the process? Walk me through it.
1:21:59So, let's just say
1:22:00uh the way you came up with the idea Cal
1:22:02idea Cal you're in a meeting you're
1:22:04like, "Okay, what if we did this? What
1:22:06if we created these markets?" Right? And
1:22:08you read about what happened in Iowa and
1:22:10you read this book Superforecaster
1:22:11whatever it was and Superforce which by
1:22:13the way, I looked at the book. The book
1:22:14came out in 2016. You started Calchas in
1:22:1618. So,
1:22:18what is the lens to look at something
1:22:20and say I'm going to get into this
1:22:21industry? I'm going to get into that
1:22:22industry?
1:22:24>> I my honest feeling and like look, I'm a
1:22:26risk-averse guy by the way. I told you
1:22:28I'm a little bit anxious and you know,
1:22:29my my co-founder was my co-founder is
1:22:31always more risk-on. She's she's
1:22:33faith-based. She likes to you know, she
1:22:35believes that things are going to work
1:22:36>> Brazilian Brazilian
1:22:37>> Yeah, she believes that things are going
1:22:38to work out. She She pushes She pushes
1:22:40things, right? And and
1:22:42uh [snorts] so, it took me a while to
1:22:43really come to terms but I my my the
1:22:45core thing is I really believe people
1:22:46should just
1:22:47like follow their instincts and do the
1:22:49thing they really are like like
1:22:51childishly excited about. If you're
1:22:53childishly excited about something like
1:22:54everybody has a child in them. I don't
1:22:55think that ever expires. Some people
1:22:57ignore it because like oh, I have to
1:22:58become an adult and do it all these
1:22:59different things.
1:23:00But you don't you don't have to ignore
1:23:02it like you should just like follow the
1:23:03things that like you kind of you find
1:23:05yourself sometimes at night just reading
1:23:07about.
1:23:08Doesn't matter what it is. It could be
1:23:09sports. It could be I don't know. Some
1:23:10people are into like Reddit channels.
1:23:12Some people Whatever it is that thing
1:23:14that like gets you going and you know,
1:23:16you you know, you you you start talking
1:23:17about it like when that thing that gets
1:23:19your friends to eye roll when somebody
1:23:21brings it up because then you know, they
1:23:22know that you're going to like not stop
1:23:24not stop talking for another 45 minutes.
1:23:26That's probably the thing that you
1:23:26should work on.
1:23:28I really believe that. And people think
1:23:29so much about how do I be successful
1:23:31etc. Like you have the tools now to
1:23:33imbue your personality and your thing on
1:23:35a lot of like society.
1:23:36Like and if you're excited enough about
1:23:38something, I think over time you're
1:23:38going to bring enough people on the on
1:23:39the journey
1:23:40>> When's your birthday?
1:23:41>> May 7th, 1996. I was born in 1996. yeah.
1:23:44>> And anime, what role did anime play for
1:23:46you?
1:23:46>> Oh, I love anime.
1:23:47>> Tell me why.
1:23:48>> I I you know
1:23:49>> Is And is there a favorite one? Is there
1:23:51one that you
1:23:52>> You know, I was like kind of a a lot. I
1:23:54mean, Dragon Ball, One Piece.
1:23:56Uh
1:23:57There's a lot of new ones. I mean,
1:23:59I mean, I was like, you know, a nerd in
1:24:00high school. So, like, a lot of nerds at
1:24:02the time You know, it's funny. Like, a
1:24:03lot of people were like closeted anime
1:24:05watchers. And and um
1:24:08now it's kind of cool cuz people are
1:24:09into Japanese culture and it's become a
1:24:10cool thing. So, a lot of people that
1:24:12like I didn't know would watch when they
1:24:13were younger, now I realize they were
1:24:14all watching, but we never told each
1:24:16other because, you know, you'd get
1:24:16bullied in recess if like you ever said
1:24:18that.
1:24:19But I love it because there's something
1:24:19about it, you know, it's it's The thing
1:24:21that I love the most about it is like
1:24:23there's sort of this notion of like all
1:24:24the characters have some unifying
1:24:26traits, which are on self-improvements.
1:24:29Like, I always think, you know, that the
1:24:30people that do the best
1:24:32that are essentially at the top of the
1:24:33food chain and like, you know, most of
1:24:35the animes,
1:24:36there's always like a this dynamic of
1:24:37the the main the main protagonist and
1:24:40then like the secondary protagonist.
1:24:41It's usually their archrival or
1:24:42arch-enemy or their competitor.
1:24:44The main guy is usually mostly focused
1:24:45on himself and like getting better. He's
1:24:47always competing against himself. He's
1:24:48always stressed about not achieving his
1:24:50full potential and not doing enough
1:24:52to versus what he's capable of. And the
1:24:54second guy is the guy who's trying to
1:24:55beat the main guy.
1:24:56He's usually a little bit more envious,
1:24:58more, you know, he's desiring that what
1:24:59the main main guy
1:25:00And the main guy, you know, always
1:25:01outperforms. And there's always that
1:25:03foundational thing of like, be the main
1:25:05guy. Be the guy who's just pushing
1:25:07yourself. Cuz if you push yourself and
1:25:08do deliver the best you can, you're
1:25:10probably going to get somewhere. And
1:25:11there's something neat about that. It's
1:25:13a positive view of society. You don't
1:25:14have to beat others to do your thing.
1:25:16Just find your own path and beat
1:25:17yourself. And keep beating yourself
1:25:18every week. And if you do that for a
1:25:19long enough period of time,
1:25:21beautiful results will happen.
1:25:23>> It really does. What's What's your You
1:25:25go from zero, you're at Goldman, little
1:25:28bit of that Citadel in Chicago or here?
1:25:30Probably in Chicago.
1:25:31>> I was in New York, actually.
1:25:32>> Oh, New York. Okay. So, New York
1:25:33Citadel.
1:25:34>> these as as an intern. I never really
1:25:35had a full-time job.
1:25:36>> So, at what point these guys watch
1:25:38Jujutsu Kaisen?
1:25:40>> Uh I watch Jujutsu Kaisen, yeah.
1:25:41>> They're They're They're My My son's gone
1:25:43through like he's like
1:25:44>> Uh what are they saying? So,
1:25:45Jujutsu Kaisen is incredible. JJK, you
1:25:47said you
1:25:47>> JJK is all he talks about and that
1:25:49>> I love I love I love
1:25:49>> between two of them, this and another
1:25:51one. Uh, what is a main character's name
1:25:53in the
1:25:53>> Gojo.
1:25:54>> Uh, yeah. Gojo.
1:25:54>> Well, but Gojo is the same what I just
1:25:56described by the way.
1:25:57Never cares about beating others. He
1:25:59just like cares about being He just
1:26:00beating himself, keep keep improving.
1:26:01>> with me, explains it to me for 45
1:26:03minutes. When I tell you the way he
1:26:06breaks
1:26:06>> up, right?
1:26:07>> Yeah, he says there's this one character
1:26:09that he cuts himself and you know, he's
1:26:10able to cut you and it it's fascinating.
1:26:13He lights up when he talks. He got
1:26:14everybody recruited in the Hamptons to
1:26:15watch it and they're just said they're
1:26:1720-minute episodes, one after another
1:26:19after another. [clears throat]
1:26:19>> Well, go back go back to. So, you go
1:26:21from Goldman
1:26:23then Citadel, New York
1:26:25and then you got um
1:26:27and then you
1:26:28>> was going in and out of MIT, so I did
1:26:29Citadel then I went back to finish my
1:26:30masters.
1:26:31>> And you're doing the stuff with MIT. So,
1:26:33when you're when you're doing that, when
1:26:34you're going through it um
1:26:36in your mind, when you're saying, "Okay,
1:26:38I'm going to get in." And all of a
1:26:39sudden you got a company that's 20 22
1:26:41billion dollars, you know, whatever to
1:26:42it's it's about to get a valuation of 40
1:26:44billion, 250 employees. You have
1:26:46influence, enemies, all these people
1:26:47that are How do you keep yourself sane?
1:26:49How do you not lose
1:26:51>> I just like like I think it's the core
1:26:53is like it's interesting. Like I always
1:26:55you know, I always tell people like I I
1:26:57like what I'm doing. I like markets. I
1:26:58really like free markets and I believe
1:26:59in free markets. I believe that we
1:27:01should have more of them. And the more
1:27:02we have of them, the more Every time we
1:27:04did more free markets, we got better
1:27:06results for society, every single time.
1:27:08It never misses.
1:27:10Um, now you have to have some sensible
1:27:12guardrails, but I think and and and I
1:27:13believe in the mission of markets. I
1:27:14believe in prediction markets. They will
1:27:16depolarize us over time. They will make
1:27:17us smarter. You know, I people ask me,
1:27:19"What's the difference in like, you
1:27:20know, we talk a little bit about
1:27:21gambling?" The gambling is incentivizing
1:27:23bad behavior. It's keep losing and, you
1:27:25know, doing it for entertainment.
1:27:26Prediction markets incentivize people to
1:27:27think more over time, to think more
1:27:28rationally, to think probabilistically.
1:27:30You know, the the truth is not
1:27:32Republicans are going to win or
1:27:33Democrats are going to win. The truth
1:27:34right now is 40% chance is going to move
1:27:36over the next week as newer information
1:27:38develops. And I want people to get more
1:27:39and more mathematical about the word.
1:27:41That excitement that I have, the thing
1:27:43that I just described, this childish
1:27:44excitement about what I'm building
1:27:46is the thing that gives you sanity
1:27:47because I'm not doing it at the at the
1:27:48core I'm obviously I'm very happy and
1:27:50like things are, you know, we we've got
1:27:51we're very lucky to have have all the
1:27:53success we had. In some ways I'm living
1:27:55the American dream and it's amazing. But
1:27:57like I don't spend much more than I used
1:27:58to. Like I, you know, uh you know, I
1:28:02like fly better. I think that's one of
1:28:04the best things about, you know, making
1:28:05some money is like you can fly more more
1:28:07comfortably because, you know,
1:28:08honestly like
1:28:09flights are getting really tough these
1:28:10days. Um I eat nice things and like, you
1:28:13know, but but
1:28:14things have not changed materially. I
1:28:15just like what I'm doing and I'm locked
1:28:17in on like I love doing it. It's like
1:28:19work is is fun for me. It's play.
1:28:22Like when like, "Oh, how Why do you go
1:28:23to work on the weekend? Are you
1:28:24miserable?" No, I love I love going to
1:28:25the office. It's like amazing, you know,
1:28:26I like go and like there's new ideas and
1:28:27there's like this thing that I can build
1:28:29and like, "Oh my god." And like, "Wait,
1:28:30what do customers Oh, customers didn't
1:28:31like that feature. They're liking that
1:28:32new one." And that's why I always tell
1:28:34people like that's the best thing to do
1:28:36because I think over time we've got
1:28:38acquisition offers over time and like we
1:28:39didn't really want to sell because I
1:28:41thought about it. I was like, "Okay, if
1:28:41I sold it and made a lot of money, what
1:28:42would I do?"
1:28:44I don't know. I know myself. If I go and
1:28:45like do nothing with a bunch of money, I
1:28:47think I'm going to get depressed. People
1:28:48always think about if you don't have
1:28:49purpose, it doesn't matter. You can have
1:28:51all the money in the world, doesn't
1:28:52really matter. And you you you you you
1:28:53know this, right? Like um
1:28:55I was like I probably would go back to
1:28:57maybe being in financial markets and
1:28:58being a builder.
1:29:00And I thought like, well, it's exactly
1:29:01what I'm doing right now. So like so
1:29:02Yeah, well, well, I feel like I would go
1:29:04back to I would be building a you know,
1:29:06I'm I know I have a girlfriend for 4
1:29:08years now.
1:29:09And so I I think like I I I tell people
1:29:12figure out the thing that makes you
1:29:14light up. And if you don't know what it
1:29:16is, ask your friends what it is. They
1:29:17will know. And you should probably work
1:29:19on that.
1:29:19>> I tell you what, Cal Sheet, incredible
1:29:21concept. And it's amazing what it's
1:29:24turned into and I'm so curious to know
1:29:25where you take it next cuz for us when
1:29:28we look at anything with election,
1:29:30decision, any of that stuff, we go
1:29:31straight to Cal Sheet.
1:29:32And if you're not already doing it, you
1:29:34ought to go out there and do it for
1:29:34yourself as well.
1:29:35>> Thank you so much. It's been a pleasure,
1:29:37man. Really enjoyed it. Thank you.
1:29:38>> Handmade in Portugal
1:29:41where craftsmanship is a way of life
1:29:43for those who move through life
1:29:45differently.
1:29:47From sunlit decks to nights that never
1:29:49need an occasion,
1:29:51Every detail [music] is considered.
1:29:53Every step effortless.
1:29:56Crafted from fine suede and built with
1:29:59build a technology designed to move with
1:30:02you.
1:30:03>> [music]
1:30:03>> Because true luxury isn't about being
1:30:05noticed.
1:30:06It's about how you feel when you arrive.
1:30:09The FLB Icon Loafer
1:30:12>> [music]
1:30:13>> made for the life you've earned.
1:30:17>> [music]

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