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Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara
01:16:30
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- [00:01]
A
Tarek Mansour and Luanna Lara are co founders of Kalshi, one of the new prediction market firms that rose to prominence in the November 24 elections. They spent four years pre launch fighting for regulatory approval to build the first onshore prediction market in the US and now trade more than $10 billion each month in prediction contracts.
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B
Cheers.
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A
Cheers to you guys. What is this that we're drinking, Luana?
- [00:23]
B
It's Brazilian beer.
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A
Okay.
- [00:24]
B
It's our most famous Brazilian beer. I would say very light. I don't know if you like it very light.
- [00:29]
C
I like it.
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A
So what is the split between you guys? Maybe in terms of responsibilities, but more interestingly in terms of outlook?
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B
Well, we actually come from the exact same background. We studied math in CS at mit, same internships, everything. But I'm a very, very optimistic person. Love taking risk. I think everything's going to work out. He's very paranoid, more on the negative side. So it's always like a very good, I think balance and I think that realist outside of what we do day to day, that's what really the difference between us that works out.
- [00:56]
C
I mean, there's a little bit of background. So I was going to be a trader. That was really what I was going to do. And you know, when you're a trader and you probably, I don't know if you've ever met sort of or spent enough time with the Persona, but it's like a very.
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D
John is a secret trader.
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A
Not harsh.
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C
Yeah, but if you're a trader, you're like an expected value calculator. Like I think about these sort of tail really bad outcomes all the time and Luan oftentimes doesn't. And I think this is the thing that actually leads to great outcomes.
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A
Okay. So I want to ask about that starting out because this is really interesting. You guys started Calci and for several years were not able to operate until you got CFTC approval. And that's interesting where just most companies don't start out that way. And secondly, I feel like the Silicon Valley standards that people sometimes as a criticism is sort of the PayPal uber early days model where you start doing the thing and maybe retroactively a structure is put on top of it. But you do a bit of ask forgiveness rather than permission in the very early days. And so can you just tell a little bit the story of how you started and that approval process? And then I want to get into whether that generalizes to other companies.
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B
Yeah, I think that the approach we took from the start was that financial services or healthcare. I think you can't ask for forgiveness. I think there's a big difference between losing people's money. See what goes wrong with like an FTX example that can go very wrong with healthcare, there's a lot of other massive examples of it going wrong. And we wanted to do things the right way because also when we look at the market we thought the biggest question to be answered was not is this going to grow? It was can we do this legally in the US and we were like, let's just actually address the biggest problem first and, and go from there. And I think this strategy for a long time people looked at it as the wrong strategy. I think up until we won the election lawsuit, everyone was saying the folks that went offshore, they're doing a lot better, they're growing a lot more. But I think once we won the election lawsuit and proved that the legal interpretation we had was right and we could do the company as we wanted in the US I think it just really, really took off.
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A
What were the timelines here? So when did you start and when did you win the election lawsuit?
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B
Right, so we started the company in 2019. We started NYC in 2019 and then it took us three years to be able to get regulated and I think it was 2022 at that point. And then we won the election lawsuit and the end of 2024 and that's when we really started ramping up.
- [03:25]
C
There were a lot of like elong. I mean there's some overlap between the timelines but maybe going back to the question and maybe we can talk a little bit more about the sort of history afterwards. But I think it was like a twofold sort of or two step process. Like one, it was a pragmatic thing which is we felt like to get proper mainstream adoption and institutional adoption. The elephant in the room was like, could we do it in a regulated, credible and safe way? Because it's a complex marketplace, you're moving people's money and we're like, we have to solve that problem first. That is the hard problem to solve and that will be the road to success. The second thing was a bit more principled. What excited us like when we, you know, we created this stock one page on Google Docs and we wrote a set of things like why should we do this company and why are we so excited about this? We wanted to build the next generation New York Stock Exchange. We wanted to build a financial market that is in the US that is credible, that is regulated. We were not very excited about this idea of building something offshore that, you know, and so that was really important because it's like, what kind of company do you want to build and why are you doing this in the first place? There's many paths to success. We just weren't very excited about the other idea. We wanted it to be here.
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A
You're the first CFTC approved prediction markets at any scale.
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C
Yes, yes.
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A
And still to this day, all the contracts are individually approved. And so yep, yep.
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B
Every single contract we file with the CFTC and they have 24 hours to stop it.
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A
Yeah, yeah. Okay, so they get kind of a real time feed of the contract.
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C
Exactly, yeah. And it was a kind of, it was a very long journey to sort of get to where we are in terms of the contract process and how it works. Because you gotta imagine the first time we walked into the building, actually the picture is right here. This is the first time we ever walked into cftc. You know, you walk in and you're talking about this idea and it's, you gotta imagine the regulator's head starts spinning. It's like, you know, you're talking about things that don't have a financial underlying. And then there's this idea of like potentially hundreds of tens of contracts, hundreds of contracts a week. And I mean now we're like, you know, but there's all these things where the model wasn't really set up for this. So a lot of the process was actually like this iterative process where you're trying to figure out how to actually regulate this as you get feedback from the regulators and what can we do to satisfy the concerns. So it was a bit like building a product, but you're not building it for a customer.
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B
You're actually, it's a regulatory market fit in a way.
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D
And so now you've gotten uncomfortable with you shipping them unless they say no, right?
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C
Yes.
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D
Have they said no to anything recently?
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B
Well, the biggest they said no to was the elections. That's why we had to end up suing them. They said no for two years. But at this point, I think we've worked with them for so long that we know exactly, kind of like they trust us as well as a self regulated entity to know kind of what we can do and cannot do. So we don't do anything around war, assassination, those things that we don't do. So within the parameters that we've worked with them, it's a lot faster.
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A
So sorry. The election lawsuit was they were willing to approve contracts generally they were not willing to approve contracts around who would win the election, which is a pretty popular prediction contract, the U.S. presidential election. And so you sued the CFTC.
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C
Yeah, our own regulator. I mean, and, and which is generally
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A
not considered a best practice.
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C
It was, I mean, okay, so, so
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B
we, it worked out.
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C
We started talking about the election market at the end of 21 and we started talking to, started, you know, engaging with policymakers, like talking with Congress a bunch and the regulator and like, yeah, I think it's a good idea, It's a good idea. But then they weren't moving. We started noticing like, okay, something is off by the end of 22. They sort of like delayed the approval till after the election. What we call a pocket veto. That was brutal. So that was one of the hardest times in the company where we had to lay off a bunch of people. But the harder part of this is that your team and some of your investors or majority of investors kind of stop believing the idea. Yeah, believing in the strategy. The idea. And it's a bit like, this is getting a little bit unhealthy. You guys should do something else. You know, clearly it's not going to work out. But we could not, we couldn't get ourselves to do something else. We just couldn't. I mean, and so we're like, okay, we're going to try again. And end of 22, so imagine the team is at an all time low in morale. They're waiting for a new strategy, bunch of people left, bunch of people, you know, got laid off because we had to downsize. And our message in that next standup was actually, guys, here's the 23 strategy is we're gonna try again.
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A
We're gonna do the same thing.
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C
Same thing.
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A
But this time it'll work.
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C
Exactly. But this time it's gonna work. Even though every inch of evidence was pointed in the other direction. And I will say, a lot of this is her like, you know, I wanted this to happen so bad, but I'm like, my rational brain was like, gotta listen to these people. And Luana is much more dogmatic. So we try again. End of 23, they block it again. And I was really at the point
- [08:39]
A
where I'm like, okay, these prediction market things just not gonna work.
- [08:42]
C
It's just. And then, and then when I was like, well, the only thing we can do right now out of the entire range of possibilities is we gotta sue the government. And I mean, yeah, at the beginning I was like, this is crazy. And we took it to the board and you know, we had Alfred and Michael at the time and they're all
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A
like, well, Alfred Lynn and Michael Cybo from yc.
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C
And I remember like that board meeting. It took a few board meetings, but it took a few times. At the beginning I was like, well, we have to tell you guys it's a bad idea. These are all the ways it's a bad idea because you're a regulator, you're now a 25 people company. The government can do anything. They can shut you down, take out the license. I mean it is true. So rarely does it work. And even if you win, you will probably lose. You will end up getting killed in the process. And it took a few times. And I remember very early there was a meeting we had internally before talking to the board. And this was the night before. We had lined up the lawyers and everything. And I got like cold feet. I was like, let's just focus on getting like a clearinghouse. We could focus on financial products. We can focus on all these other things. We don't need to sort of tank it all on this. And you know, really bet the farm on this. And I remember Nuwana and that call, I forgot the exact wording, but it was something along the lines of like, are you fucking kidding me?
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B
That sounds like me.
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C
And I realized like, all right, I'm not gonna win this fight. But then I. But the other part of me is like, we gotta do this. Like I knew. And so we go into Bora and the response was basically, it's an anti pattern, it's a bad idea. But a lot of great companies are built by an anti pattern. There's something off that is weird that happens and maybe this is yours.
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A
Yeah, it's a good way of putting it that every company is different in some new way. And so yeah, this could be yours. What was the basis for the decision where you won the election last year? Was there any interesting policy angle?
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B
Right, so the whole point is that the government cannot stop any type of contract unless he makes a finding that's against public interest. And he has to fall within certain categories of war, terrorism, assassination. And the CFTC was taking the stance that they were trying to fit elections into any of these things. They're like, oh, elections might be legal under state law. And you know, because betting on elections, there's this one state that in bucket shop law they try to find something to stop it. And we knew we were very, very clear on the law. Like elections have economic impact. If the elections have economic impact, they need to be allowed to trade on a futures exchange or derivatives exchange. And it was basically I think what the lawsuit did is it told the CFTC that they couldn't just do whatever they wanted and that kind of like
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A
the categories of prohibitors, it needs to actually fall under one of the prohibited categories.
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B
Exactly.
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A
Which elections did not.
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B
Right, Exactly.
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C
And I think that's important because the law, you know, the thing that we always say like the law applies to companies, but it also applies to the government. Right.
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A
But you should make your point about maybe suing the government's under arena.
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D
Well, I think certainly in crypto and in prediction markets, it's sort of this unique thing of suing the government. But I was sort of surprised to realize that Coinbase has sued their primary regulator and GovTech, SpaceX, Anduril Palantir all had to sue for various reasons. So it seems like it's actually more common than Silicon Valley conventional wisdom. So like what I guess advice would you have, having dealt with the government to people out there trying to build businesses? Like what. What sort of situation would be you think ripe to actually make that kind of challenge?
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C
I think, I think it's a sort of no other option situation. Right, like that.
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A
So it's still painful. But did you actually have no other option? Because couldn't Kalsh have done fine without elections? I mean, elections are obviously very helpful because they're such like a big shiny thing. But I presume elections are not a majority of your contracts today.
- [12:40]
B
I think it was just too important. And maybe that's the dogmatic or whatever, but it's like it is the holy grail market. It is like that's the one that you can see the use case of the data the best. And you can see the use of the. And you can see in the 2024 election.
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C
Right.
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B
Like the polls were completely wrong and the markets were so much better at bringing that sort of information. And I think that it's the shining example of why these markets are forced for good and we need to have them in the US and regulated, which other markets are just won't have.
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D
So to John's point, on PayPal and Uber and asking for forgiveness, there were other prediction markets operating and showing real usage offshore. And so I'm wondering how much did that help demonstrate that election markets weren't against the public interest? Like, did that factor at all into the court case, like the fact that people were already doing it?
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C
I don't know. But I mean specifically on the court, it was much more grounded in sort of like the law. Yeah, the law, like reading our law, is the commodities exchange Act. So that's one of the financial statutes. The other one is the securities Exchange act and sort of reading it and really interpreting it. And is the regulator overstepping now? I think for us it was like a good way to learn, right. Because we could not learn about our product because we took this sort of regulatory first approach, like where we will ask for permission first before doing something. And so in some ways it was helpful that you could have some data, some evidence that could guide our decisions over time. And I think it also helped educate some people about the existence of prediction markets. You know, they are here, here's how you can use them, et cetera. But I don't think like unregulated offshore players really help from a policy perspective.
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B
Right.
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A
Could CASI have been started 10 or 15 years prior to. Or was there some moment of openness in the cftc? Was there some tech enablement that was required? Were stablecoins required?
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B
Just I do think that there is a part of it that crypto at the time was augur and there was like some very early prediction markets. I think that, that the existence of that made the CFTC also be like we need a legal regulated alternative to this. Because before you could just say no to things. I do think that that played a role. But maybe like 5%, maybe 10%. I don't think it's more than that.
- [15:05]
C
The broader thing is like, I think, you know, there's always intellectual interest in prediction market and I think that starts in the 50s. It is a better source of signal than most other mechanisms of getting signal. Right. But there wasn't a real pain, I think 10, 15 years ago in the way that there is pain in the last few years. And that pain I think is a sort of. I think the country is more polarized, I would say the world is more polarized. Social media has really bifurcated social feeds. You know, clickbait is rampant. Like the incentive structure for most things that we read these days is clickbait, whether it's a lot of traditional news or social media or other. So there was more of a pressing problem, I think that helped create the wave and this sort of adoption that you're seeing in prediction markets that I don't think happened or would have happened 15 years ago because the problem wasn't that.
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B
Yeah, and that's because like most of our users, like 80% of our users are actually just looking like consuming information. They're just coming in and seeing who's going to win the Texas primary yesterday and seeing like, okay, the polls are saying they're tied, but they're not tied. And all those things and those, that consumption of information is way more important and relevant now.
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A
Okay, so you're saying like algorithmic feeds. Kalshi markets do very well on algorithmic feeds and just maybe people wouldn't have been as interested 10 or 15 years ago.
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C
Yeah, I just think that sort of there's a meaningful and accelerating rise of distrust in traditional sources of information. And so you need a new one. And this does work. Right. Like the incentive structure for a prediction market is truth. Right. It is more volume, it is more liquidity, which translates to better and more accurate forecasts. And it took a few iterations for people to start trusting it. But like, you know, as you start building a track record, people start trusting it and they're never really going to use a better product. Right.
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A
Well, to the point of substantive volume, can you guys give us the outline of it seems like it's growing very quickly.
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C
So volume in February was $10.4 billion
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A
of contracts traded. Yes, traded.
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C
And that's up 11x over six months, I think, or a little bit. I mean, since growing so quickly, you
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A
don't even bother to go back a year because that's just ancient history.
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B
I mean, a year ago it really is. It's like we just had, for example, one sports market.
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C
We only had the super bowl in February. Yeah, I mean it's growing very fast. Yeah.
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D
Fastest growing company outside of AI.
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C
Yes, I think so. And we compete with some, I think even some of the top AI companies. I don't know what Cursor and Anthropics last the latest numbers are, But
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A
I think 11x is very quick. Even in AI.
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C
It's quick. And I think because it's, it's an. So we, we are marketplace. It's a true marketplace that has all the attributes of, you know, you know, has network effects. So, so what happens in those situations is that users retain better because there's a more diversity and more liquidity. Their, their participation and volume grows over time.
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A
Yeah.
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C
Which obviously grows their usage, but it also grows other people's usage because there's more liquidity in the system and then they share it more with other people because the product is getting better. And so the sort of trifecta of factors is leading to this sort of
- [18:27]
D
growth when some of your early growth depended mostly on other brokers. And I think you've evolved that mix today. Like what's the broker mix and how do you think about that?
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A
So what's A broker in this context, like Robinhood.
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C
Okay, well, she explain what that is, which is interesting. I mean,
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B
I can explain the broker part, but I don't know what we want to show in the numbers part. But basically that was a tough one. That's why we looked at each other. I was like, all right, so because we're an exchange and clearinghouse, we basically function like the New York Stock Exchange
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A
or another beer for her.
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D
Yeah, exactly.
- [19:03]
B
The Chicago Mercantile Exchange. So brokers can connect to us. Right? So you can go to Robinhood straight stocks, you can go to Robinhood to trade on Kaoshu. Same with Coinbase or whatever. And it's always been part of how we think about. We always wanted to be in an exchange, in a clearinghouse first. And actually connection to a Goldman Sachs or a Robinhood is very important for how we thought about this ecosystem as a whole. In the beginning of last year, we launched the first broker partner that we had was actually Robinhood and then webull. And at the start, actually when we were starting to ramp up, the brokers were a very, very big part of kind of how we started growing, which is actually very great because the brokers bring so much demand. Then we get all the market makers to come in because they want to trade against the retail flow. And then we could kind of like buy ourselves time to ramp up the direct product a lot to where it is now. But basically how you think about it is, well, we really are at the core is an exchange and a clearinghouse. And then you can access us for our app, website, API, but also any broker, we're investing more into institutional now, international brokers. So you can be in Brazil and you can trade on Kashi, all those things. They're coming soon. But on the numbers, you can take it.
- [20:07]
C
I mean, maybe I won't share numbers, but the direct, what we call direct kalshidirect, which is our kalshi.com, kalshi app, the consumer business that has grown, you know, that has sort of dramatically outpaced the rest, our other sort of intermediate or broker business. And I think it's just that the brand has gone mainstream. I think people, when they think about, have a difference of opinion on something. It's sort of becoming synonymous to like, oh, let me pull up Kalshi and see the odds, or let me sort of place a position on Kalshi. And that's like, there's just a lot of organic growth now. And I think that's going to continue over the next few months.
- [20:47]
A
You're describing how you grow the individual retail kind of side of the market. Whether people coming through brokers like a Robinhood or people coming directly to the Kalshi website. There is also, when you're an exchange like this, you have to spin up market making. And in the end the New York Stock Exchange doesn't have to think too much about market making because just the economic incentive is there. And so when something is at large scale, that's not as big of an issue. But I'm curious what that was like in the beginning. Were you guys doing the market making? Did you work with market making partners now? How do you incentivize market makers to participate? I'm just curious what the market making scale up has looked like.
- [21:28]
B
So there's actually two groups of contracts on markets on Kaohsi and they behave very differently. And the market making incentives are actually pretty different. So you have the long tail of markets, right? Like the ones like, well, one direction have a reunion or you know, all those things and they are actually very hard to price. And because there's not necessarily a lot of demand, we actually have to incentivize market makers to come in and there's like liquidity incentives, all those things for them to come in. And I think it's actually how we think about how to build our moat long term is actually how do we get very sustainable solid liquidity in this long tail of markets. So we can get like we have like I think 10,000, how do we get to 50, 100,000 markets with still. But on the other side you have the more classic like crypto sports, all of those guys. And on that side is actually a lot easier to market make because you have very clear proven demand is a lot easier to price. So the market making incentives on this side is actually we don't pay them for it, we just rebate fees. But they have very, very, very hard conditions to meet. They need to have uptime of certain amounts, spreads and top of book size and all of those things. Because we see it more as like incentivizing stability of the book than it is incentivizing them being there.
- [22:36]
A
So incentivizing stability of the book mean,
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B
for example, if you think of a live game or like if you're trading like an hourly crypto, you actually don't
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A
want the price flying around a whole bunch if there's no new information, right?
- [22:49]
B
Exactly. Or even if there is, right. If someone is about to sort of touch down, you don't want the book to just have like no liquidity whatsoever. You want it maybe to go a little bit wider, but you want people to be able to trade. And actually when we go into the intermediated model, the brokers come with expectations that they have from traditional markets. Right. So they're expecting we want this spread and this size at any point in time. It doesn't matter. So we need to go to the market makers and like how do we incentivize this? Even though if you think about you should just let the markets do whatever they want to do and if they're going to go way wider because they need to, it is. But we have to kind of inset play with incentives in a way to, for all of our users, including the
- [23:24]
D
brokers, during those moments when the spreads would normally blow out wide. Are market makers losing money then and they're cross subsidizing to the other parts where like it's more stable?
- [23:36]
B
Well, now there's so much demand that I don't think they're like, you can make money on spread even if the spread is like a little lower. But that's the point of the program, right? Is like you have to think about all the benefits you get in this program and then even if you're losing a little bit in this time, having the benefits is worth it.
- [23:52]
A
So you want tight spreads all the time for the major markets. That's what market civilian means.
- [23:57]
C
Exactly.
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A
And that actually takes work to engineer.
- [24:00]
C
It's hard to get there. But there's more to it. Which is. So I think the magic and the uniqueness, I would say, or the special thing about prediction markets is that a lot of the liquidity is not what you consider a market maker.
- [24:16]
B
Right, right.
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C
It's people. And so this goes back to the whole point. So let's just go back to from first principles, right? Like, you know, there was, okay, the regulatory thing that we figured out, but then there's the liquidity problem, which is historically it's like a bit like we said the New York Stock Exchange or the cme. Okay. They're like, we're gonna create a grand future. We're gonna take two years to figure out what it looks like and we're gonna call all of our buddies, like you know, the 50 market makers that we all know, we all hang parties together, et cetera. We're gonna get them ready and they're gonna start helping us get this product launched. And then we're gonna market it for next three years. And then it's just the same thing. The liquidity is there, but Prediction markets is really different because now you have to create liquidity in these products on like a weekly, daily, maybe even hourly basis. Like how do you do that? Right. It's much more dynamic. There's new things all the time.
- [25:08]
A
I think it's counterintuitive to people that you have to incentivize market makers to create liquidity. Because like in the stock exchange you don't have to incentivize high frequency trading firms to create sub second liquidity. They are very excited to take on that project themselves and build the high speed interconnect between New York and Chicago to accomplish and everything like that. And so is this just the stage that prediction markets are at or is there something fundamentally different?
- [25:34]
C
I think this is what I was talking about, which is this idea that you need. So, so maybe finishing that sort of line of thought and then like I'll get to the answer there. You now have a model where you need liquidity to be built on the fly much faster, much more dynamically. Right. And the market makers, the traditional Wall street market makers are not geared up for that. It's not like they can spin up a new desk to price like politics or price culture in an hour. Right. And so but this is the part that gets really interesting, which is like, and this goes back to the foundational principles around prediction markets is like a lot of these markets, the people that will price them the best may not actually be the experts or the authority figures that you usually would think about. It's actually random people like you know that live Internet anons. Exactly, they're super forecasters. Those are, it's like extremely dispersed. You cannot find like a clearly defined demographic who they are. And I think that the thing where we got to now and that took a very long time and you had to incentivize is we have the community, the strong community of super forecasters that are on calshi that can help price these things extremely effectively and fast. Where you know, you don't have. But it took a while to kind of get them incentivized and come in and spend the time and resources to take it from a hobby, which was a hobby, to a part time job, now it's a full time job because the pie is so big and a
- [27:00]
B
metric that we can share is actually the. When you think about traditional market makers, the biggest percentage on a platform of a traditional market maker is less than 5% of the maker orders in that market. They have matched of the liquidity of the liquidity.
- [27:13]
A
Sorry, say that again.
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B
Yeah, so less than 5% of the order. So people come and make orders. Less than 5% of the ones that match actually come from the big institutional market makers. You'd think about, I see over 95 are just like peer to peer. Peer to peer or funds that have like two people that just got sold,
- [27:29]
C
which is unusual for.
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D
How many of those small full time little shops are there?
- [27:35]
B
There's over 2,000 people that market making people slash small shops. Yeah. On like a specific.
- [27:40]
D
Yeah.
- [27:40]
A
I think what Matt's getting at is like who is a market maker on Kalshi? Like, you know, there's all these Jane street conspiracy theory memes. Is it like that's my Twitter feed or is it like some guy in his garage, you know, drinking red bull at 3am Market making bar.
- [27:54]
C
The guys in the garage are the most.
- [27:56]
D
And you're saying those are 95%.
- [28:00]
C
They're extremely crucial to the ecosystem because they price fast. They're monitoring the situation all the time. They're the original situation monitors.
- [28:09]
A
Kalshi is built on people who are monitoring the situation.
- [28:15]
C
So one example I'll give, and I've done this in the past, the best inflation forecaster on Kalshi over the last few years is none of the institutions or the, you know, the big name hedge funds. It's this guy who lives in Kansas, never traded financial markets before, just likes to read the news and just knows how to predict inflation. He can feel it like, you know, and you have so many of these people like now you have like, I mean, I would say like a few thousand that are formally kind of like committed but like there's tens of thousands of like these people that know a bunch of like, but a bunch of different topics and they're sort of actively pricing these things and they do it as a full time job and they get rewarded for that.
- [28:57]
B
You need to talk about my favorite user.
- [28:59]
C
Oh yeah, Well, I have a new favorite user, by the way.
- [29:03]
A
Okay, each of us, each of you can tell us your favorite user.
- [29:06]
C
I was thinking about this.
- [29:06]
B
What was your new favorite user?
- [29:08]
C
The Wall Street Journal article about the tax guy.
- [29:10]
B
Oh yeah, that's true. He's a good candidate. But no, my favorite user is this Rihanna grande superfan and he found Kaoshu during the election season and he's like, I don't like the elections, like, whatever. Then he found our Billboard ranking markets of like charts.
- [29:24]
A
He's going to work on important markets.
- [29:26]
B
Important markets to me, very important. And he's made over $150,000. He's getting every single thing he paid back. Student loans, he put himself for a master's degree, bought a car and all those things. And he just like loves these markets and he's never really traded, never done anything like that before, but it's the first time that he actually has a way to monetize this very compulsive hobby that he had on, you know, music charts and he's able to do it. And he is also very, very nice to us on Twitter. So I like him.
- [29:54]
C
So I had many over the years, but maybe I shift a lot. But like the.
- [29:59]
B
He's not loyal. See, I'm loyal to my guy.
- [30:01]
C
Well, you know, I love all of our users, but there was an article last week in the, in the Wall Street Journal about a tax accountant who was very active on Kalshi Alan. And he, you know, when Doge came around and like there were a lot of sort of talk about how much they could cut, he actually read a bunch of tax codes and a bunch of statutes just like dug extremely deep and then realized there is no way they could hit the targets. Like, and even if like he really kind of deterministically realized and then he basically talked to his wife and he's like, I have extremely high conviction in this trade. I know, I, you know, it's a bit like Michael Burry with the big short. So this guy put a big short but on Doge this time, right? And it was big. Like he really kind of went all in and you know, he won. And it's just like one of those like amazing sort of showcases of what this can do. Like now you have a market that, like, if you have that sort of knowledge, which maybe oftentimes is esoteric, like I'm assuming none of us have read all these tax codes, you can actually go out in the world, do research, get smarter about the world and then, you know, get rewarded for that. And that's awesome, right?
- [31:22]
A
You know, an early field of AI was poker bots. Are you seeing any good AI market makers? When you say no one's read all these tax codes, I mean no one except Claude.
- [31:33]
B
That's fair. That's fair. We should ask ourselves.
- [31:35]
C
We are seeing more, like increasingly more people using agents to trade. So that's definitely.
- [31:40]
B
Especially on the API side.
- [31:41]
C
On the API side it's very big. And you know, like, but like, do
- [31:44]
A
you have users who are successfully running market making businesses that are mostly agentic
- [31:48]
C
users don't exactly tell us their strategies,
- [31:50]
A
but like, you talk to them, you
- [31:51]
C
know, generally yes, yes, yes. But, you know, I mean, it is. The way I think about it is like, you know, do we think Rentech back in the days was using agentic models? So I'm talking about Renaissance to trade, like. Yeah, the early versions of them. Right. And so I think they're just evolving and they're getting better and. And most of our traders in their stack have some sort of summary and synthesis module that's AI driven.
- [32:19]
A
Yeah. I guess what I'm curious about is fully autonomous, no human in the loop, consuming information and providing a market based on that that feels like it's coming quite soon if it's not like your claw making a market.
- [32:33]
B
Yeah. I don't know if there's a full. I know that for example, there's a lot of. For international elections just on like for example, translating all documents pulled into foreign languages to kind of do all of that. But I don't know if it's all we're doing.
- [32:45]
C
So we don't know if the models are there yet. Right, That's. And I was. So, you know, we launched CASI Research recently which. And one of the threads that we want to work on is we're talking to some of the research labs to create a new benchmark around which models actually predict the future better. Which could be a unique benchmark around like, are these models developing some understanding of the world that goes beyond memorizing old patterns? And I'm honestly excited to see how it goes.
- [33:15]
A
And what's the eval for that?
- [33:17]
C
We don't know yet. But I think you could roughly kind of let the models run for make predictions on same set of markets for a month or two and see which ones perform that percentage of predictions that were correct P and L over time, et cetera.
- [33:31]
A
Just kidding. Okay. Another market making question. So sports bookies have this need to crack down on what in their industry is called sharps. You know, people who are too good. Where like I think people don't think so much about this dynamic. But for a sports bookie, the best possible punter is someone who is kind of unsophisticated bets on like their home soccer team's game, irrespective of the odds. Exactly. And just wants the home team to win or whatever. And the worst kind is someone who's super sophisticated finding the narrow markets because for a bookie, maybe they're making odds on 10,000 different markets. They only need to be wrong once or twice for people get to choose which they play on and so they presumably can't be Right. On all the odds that they're offering. And these sophisticated people go find those. And so what happens is they basically use behavioral signals to identify. If you are just signed up and you're betting on your home sports team's game, that's good. And if you appear to be really sophisticated with all the signals that they would use. Exactly. They shut them down. But it's interesting, right? You think, like, I'm just booking on the bets that, you know, on the odds that you're offering. But, like, if you're too good, they'll shut you down. It's maybe like car counting in Vegas. Do you have this dynamic with Sharps? Like I would have thought. No. That you just are fine with it. But, like, do the market makers worry about too sophisticated counterparties on the other side?
- [35:03]
C
The Sharps are the market.
- [35:05]
B
I mean, we don't limit any winners. We don't have any of the. Like, we want all the winners.
- [35:12]
C
We need the Sharps. Because how do you get market accuracy without the Sharps? This is the difference of, like, the.
- [35:18]
A
Well, yes and no, right? Because what you want is different is the Sharps can snipe. They can just turn up once when the odds are wrong, grab a big win, and then disappear. Whereas what you're describing is you want. During the game or during the election, you want narrow spreads all the time. And so I feel like providing good market making is different than being right.
- [35:40]
C
But a lot of the Sharps can actually do better if they provide market making and, you know, like, become part of the liquidity. So this is the bin difference, which is very important. Like, that the. It may be a disclaimer like, I don't gamble, I trade. Which I've always found a difference. And I think gambling is this idea where the business model is, you are the house and your revenue is your customer's losses. So a lot of the dynamic that you describe has to be true because your incentive is like, well, somebody's making money, I gotta stop them because they're making me lose. That's just going straight from my bottom line. And the opposite is true. If somebody's losing money, I gotta figure out how to bring them back. That's a very different model from, like, traditional financial markets where, like, the structure is you have to incentivize fairness and transparency. That's the structure. Like, you want to create fair rules of the game for people to participate. Maybe Matt is better than Luana. Maybe Luana is better than Matt. And, like, they can battle it out. They can figure it out.
- [36:36]
A
You think the incentive system is very different where you do not like a casino or something, you do not monetize on some zero sum other person losing. You monetize just on transaction fees.
- [36:45]
B
Yeah. The best outcome for us is people like this is fair, they have good prices, they have stable liquidity. I'm going to go there. But of course, for us to get there, we also need to incentivize different players differently. So that's why, for example, a lot of the liquidity programs come into effect. They're like, okay, if we're providing liquidity, you're taking a lot more risks because you're putting yourself out to be sniped. Then you're going to lower your fees. But if you're taking and you're going to snipe, you're going to have higher fees so you can pay for that activity. So in a lot of ways do
- [37:09]
A
you use fees to incentivize pro social behavior?
- [37:11]
B
Exactly. And I think that that's actually how we see a lot of how financial markets actually do the same thing.
- [37:15]
C
Financial markets do the same, the same thing, but it's more balancing out the marketplace so that people are providing value to the marketplace have some, you know, a little bit more tilt and then people that are like taking away value have a little bit less.
- [37:26]
A
What behavior is pro social and what behavior is antisocial?
- [37:29]
C
Well, insider trading is antisocial, right?
- [37:31]
B
That's a big one. Yeah, yeah.
- [37:33]
C
And illegal. Yeah, but you know, but it's, and look, sniping is part of it. Right. You need people that like all of a sudden have gained some information edge and they do it in traditional financial markets all the time. Right. But to have liquidity and make sure that people are there and they're investing the resources they have to get, as you said, some incentives. But the interesting point I think, and I think this is part of why prediction markets are being adopted so much, is people like this idea that if your edge is proportional to your research, how informed you are, how much time and energy you put into this. And I think that only exists in prediction markets or traditional financial markets. Except that for a lot of people, traditional financial markets, they're just less interesting. Right. And here you're researching about Doge and what's going to happen or what the election and how people think about elections and how they vote, that at least to me feels a little bit more interesting than like, let me think about IBM's quarterly earnings every quarter.
- [38:37]
A
Kalshi has built a new kind of marketplace where real World outcomes are traded like whether the US will confirm whether aliens exist before 2027. You have thousands of participants opening, transferring, settling their positions all in real time. And underneath it, as you can imagine, there's a complex multi party flow of funds. That choreography on Kalshi is powered by Stripe Connect. Onboarding participants, processing payments, routing funds, managing payouts. When money movement becomes programmable, new products or even new market structures become possible. So if you're building something new with complex money flows, Stripe Connect was built for you.
- [39:15]
D
Let's talk like different market verticals. And I think today everyone gets elections, they get sports, they get economic indicators. But I think you can look at prediction markets as this kind of search function across the set of interesting markets humanity wants to trade. And it's kind of a weird artifact that like the CME used to green light like wheat and oil and corn, but now you get to green light a thousand markets a day. So what do you think we're going to find as we do that?
- [39:48]
B
One thing that we're very excited for, we're actually starting to go in the direction of for example, things like watches and bags and all of those things are like more going to the collectible side. They're actually able to do derivatives on those things. One thing that you should talk about is the GPU is a very. And I think the computer. What we're thinking a lot about is that there's a lot of these types of things that they function better as a more traditional future. So things that don't have a binary. Will it be this price? Yes. No, but it's more like an actual future. You can have margin, you can have more like institutional grade liquidity and all of that. And I think that that's a great example of like when we start going more outside of binary markets and more into the traditional ones, then what we're doing is expanding kind of that from grain to compute from the.
- [40:36]
D
So it strikes me that obviously the futures markets that have worked best are these large commodity categories because we're sort of in an era where humanity is spending more money than it's ever spent before on a new commodity category kind of commodity. So. And the other traditional markets don't seem to be attacking compute.
- [40:57]
B
So the way that we think about we want to be the biggest derivatives exchange in the world, right? And for that, when we think about product roadmap, there's four things that matter. The first one is breadth of topics of markets, right? So we think about compute, we think about sports, we think about elections, we Think about securities, we think about all of that. The second bucket is really market structure. So right now we only have the binary. Yes, no, we want to have things like futures, like swaps, options, all of that. The third one is really margining systems. Right now it's very bad. You have to put all the money
- [41:24]
C
up front, you have to tie up
- [41:25]
A
all the capital in the market which
- [41:27]
B
makes a lot of. For example, will a hurricane happen this year? Very, very bad for you to be actually like market making or selling those contracts doesn't make almost any sense capital wise. And then the last one is liquidity. And when we think about it is like if we win these, we have the broadest set of markets, we have the broadest set of market structures. We have great and very, I don't want to say cheap margin but in a way around that and then good liquidity. We're going to win on everything that we do. So everything that we do in the company is like it needs to be in one of these four buckets. And I think a lot of the topic side is like how do we actually match the right topic with the right market structure with the right margining and how do we make sure that it's all coming together? But you're completely right. I think that being able to build all the margin systems, all those things from scratch, we're going to be able to do margin models a lot faster and list a lot of these kind of new markets a lot faster.
- [42:19]
D
Because of your direct mobile app interface and the fact that you target a lot of retail, do you worry that sort of the markets you're going to gravitate towards are the ones that are most interesting to just retail and how much do you think about as opposed
- [42:34]
A
to like the pro markets, the kind
- [42:36]
D
of institutional markets like I think of compute as much more of an institution to institution market. So yeah, how do you think about building liquidity and interest up market?
- [42:44]
B
Yeah, we almost like divide the company again in a way we divide the markets and like sports crypto and everything else. But in how do we make what we have great but very new things. Because I think what got Kalshi here was not the regulatory side was not it was really that we just really pushing what is the next thing. It was elections and then after election of sports and for us it's like we need to be pushing what the next thing is and doing that very well. And I think that if we stop doing that we're not going to win. The company's kind of structured that we have the market operations, we have the engineering side, all of that that's set up for maintenance and improvement of what we have. And then the new teams like Institutional, the Margin Team, International that are kind of pushing forward and we just try to kind of find a balance on those teams and then a platform layer that is the core exchange and compliance and all of that. But it is tricky because we're still like 120 people to do it.
- [43:38]
D
Is there a poll already on the institutional side and certain topics?
- [43:42]
B
Yeah, no, for sure. And I think that we actually just launched a week ago this thing called block trades. I don't know if you know blockchains. It's a very institutional way to do it that I can call you and negotiate a trade and then we go and put it on the exchange versus trying to do everything that way. So we're trying to build a lot of features to start getting more in the institutional side.
- [44:00]
D
Are they trading the same things that are on the Kalshi retail or are you offering new types of products?
- [44:05]
B
Yes and no. So whenever they're interested in something like the. There's a lot of interest. There was a lot of interest on the tariff situation. Is there going to be a tariff or not? A lot now with the petroleum like the reserve and kind of how that's going to go. So whenever we hear we want to trade this market, we just list it directly and then it's accessible to everyone. But I do think there's going to be a very big gap on what the institutionals are going to end up trading versus not. But we just list it to everyone. It's very cheap for us at this point to lease new markets.
- [44:32]
A
In the early days of Uber, it wasn't bad for the taxi business because it was just excess capacity and it was serving unmet need. But then after a while it was bad for the taxi business. Are there existing businesses that will feel the effects of Kalshi and other prediction markets? Because it's a bigger market, there's more liquidity. Like I can think about existing futures exchanges. Like maybe Kalshi is a better place to, you know, hedge your soybean prices or what have you. There's sports bookies, obviously, there's political polling firms where maybe you can get kind of the same information way cheaper. So who do you think will start feeling the effects of prediction markets because they have been in some way superseded?
- [45:16]
B
Yeah, there's that funny meme of like the guy knocking on the door and it's like, who's the next so you don't want to do that. But I think that a lot of what you mentioned. Right. I think that just traditional betting that we talked about all the issues that industry has that we're very different from, there is a traditional futures now. We're going way more into their space. So I think there's going to be difference there. There is the political polling that I think since the last election, there's just a lot of campaigns that are using our data and all of that. There's parametric insurance. Once we have margin, we can start going to more hurricane, natural disaster insurance. All of that side.
- [45:51]
D
Is there a tragedy of the commons with the polling? Like, part of the reason the prediction markets are accurate is because they interpret the polls. So if people stop using polls, in some sense polls are the sensor that you get of what people's opinions are. And then the prediction markets are like the mathematical interpretation of the polls.
- [46:14]
B
Yeah. My take is that polls are just going to get a lot better because what people are going to be is like, okay, I can make money if my polls are right. So I'm just going to commission this poll and I'm going to do this. And now you can actually like compete a lot of polling models into one market.
- [46:26]
A
It's like 538 did kind of the meta poll interpretation.
- [46:29]
B
Exactly.
- [46:29]
A
More of that.
- [46:30]
B
And you can kind of have one number that's kind of aggregating all of that. And even in the last election, there was someone that actually did this. They commissioned a specific poll to do like nearest neighbors type of thing. It's like it was, I don't know how it was, but a different type of poll. And then they was. They were able to make a lot more money in the markets. And that's the whole point of like having money and skin in the game. Aligns the incentives with truth. And then the polls are not just paid for, like, tell me what I want to hear, but the real numbers. So I think it's complementary. Same with the news. A lot of people are like, prediction markets will destroy the news. I think it's way more complementary. It's like when you're talking about an election, you're going to give your opinion. The market is not going to give you an opinion. You still need the commentators, but they're going to be able to show a number and be like, this is what the forecast is and this is my opinion on it. I don't think the opinions are going to disappear.
- [47:18]
A
You guys referenced insider trading earlier and there's just the policy question as to what the right policy should be around insider trading when it comes to prediction markets, I think it's pretty nuanced. It's nuanced in the stocks case, right. Where famously there's lots of, you see SEC enforcement actions all the time against the things that aren't allowed. But there are cases like a hedge fund can have proprietary satellite data of the Walmart parking lot and use that to trade earnings. And that is information that only that hedge fund has, but that is permissible. And so similarly, I think there's just a complex set of line drawing exercises here where presumably we don't think government officials should be trading in advance of military actions. What about leading up to the super bowl, predicting the bad bunny halftime show length? I mean, people have that information. And so where do you think the lines should get drawn on insider trading?
- [48:16]
B
Yeah, and you said it perfectly. It's a very complicated question. And it's complicated question for stocks way more into a bigger scale than it is in prediction markets. The line that we take now is that we follow what the federal law is. So it's basically if you have, and
- [48:31]
A
these are, sorry, CFTC rules.
- [48:33]
B
Well, CFT and sec, they both have it. So basically if you have signed an agreement that says you cannot share some part of data. So if I work at the Bureau of Labor Statistics and I have in my like confidentiality, then I'm not able to say what the inflation number is before then it is, you have, like you cannot be sharing that data. But if, you know, if you know that they're going to be rehearsing the Thursday before the super bowl and you're outside and you're like, I hear Lady Gaga's singing. That's fine. And that's the same thing that, you know, a lot of hedge funds do with like Starbucks and people know that there's more people, fewer people in the store. And that's the point. Markets are very good at incentivizing information. We want information to come to the markets, but we don't want it to be unfair. And if you have access to it in an unfair way, you should not be trading on it.
- [49:16]
A
Okay, so you cannot trade on information or you have some duty to hold that information confidential.
- [49:21]
B
We actually take it even a step further. For example, if you are a government official, you cannot, like, if you're in Congress, you cannot trade on bills passing, even though I don't know if they have an agreement.
- [49:30]
A
Famously, Congresspeople can trade on stocks.
- [49:32]
B
Right, right, right. So it's like we're actually like taking a step further there and we're working a lot of the regulators because obviously it's a very new problem for them and for us. But we have an entire part of being regulated. We have an entire surveillance division that is looking at every single flag. They pretty much don't sleep and they just try to figure out everything. And we put out two cases two weeks ago of two insiders that because we're also regulated, we're able to charge them a lot of fines. We charge them over five times what they made and all those things they banned and all that.
- [50:03]
A
What's very interesting to me about these stories is that you guys were doing that where with the public equities markets. The SEC is extremely enthusiastic about enforcing their insider trading doctrine. Just what has the CFTC been like on this topic?
- [50:20]
B
It's a great question. So obviously the cftc, you can think about it at three steps, right? The first step is our own surveillance and enforcement. Then the next step is, goes to the CFTC and their own surveillance and enforcement. The last step, if you go to the Department of Justice. And I think that the biggest difference is when people look at the SEC cases, most of the time they take a long time because the exchanges did their research and their investigation and they put some fine, they block someone and it kind of goes through the process. So it's still like that. Like every single trade on Kalshi goes to the cftc. They have every single thing, every single case goes to the CFTC for them to review. So they might take action. We don't know. But now the world.
- [50:59]
A
Okay, so you refer cases to the.
- [51:01]
B
We refer cases to them. But we kind of do our first step of our first level of protection there.
- [51:08]
D
I'm curious. There are clearly markets where nobody knows the answer yet of some event in the future. So, like, it's sort of impossible to insider trade. And then there are markets where a single person can change the outcome. Like the mentioned markets in a speech or maybe a sports player doing a specific number of shots. So like, how do you think about kind of that spectrum?
- [51:31]
A
Like, are mention markets a bad idea because they're just so inherently gammable?
- [51:34]
D
Yeah. Or are they like limited in scale
- [51:37]
A
fundamentally because mention markets like the Brian Armstrong Coinbase earnings thing and stuff like that?
- [51:43]
B
But yes, I think that. Well, inherently, I think mention markets are actually great if you think about the Fed, right. Like the amount of hedge funds that are just sitting down being like, is he going to tilt his head this way or this way? And if he does that, it means he's not very sure.
- [51:56]
A
Fed meeting minutes are the original mentioned market.
- [51:58]
B
Exactly. That's actually pretty much the. And it was. Because we just know that specific words being used mean very specific things. And it can move the market. So much the same thing with Trump. Right. If Trump says we're going to war, that's going to move the markets a lot. Right. Or if he says a lot of things like tariff. Everyone else moves the markets a lot. Even in. Yeah, just a lot of everywhere things that people say move markets and move a lot of different things. So I think the major market is very important. Obviously, the person that is working on the speech or that is saying the speech cannot trade. And that's kind of how we enforce it. Like, if you are Gavin Newsom and there's a market on what you're gonna say, you cannot trade it and your staff cannot trade it. That's part of the political kind of cuts that we do there, that they cannot. But I think that's the point is like, if there is a way to restrict some players in the market so that the market's fair and the market's positive and there's an economic utility for it, the market should exist. We shouldn't say like, okay, there are five people that could manipulate the market and the market shouldn't exist. Then you say stock market shouldn't exist. Right. So I think it's more about how do we build a system that is strong and resilient enough and with the right prohibitions that you can have the market.
- [53:11]
A
The other big debate you guys are in the middle of the is just that about sports contracts generally. And, you know, I was trying to reason about my own thoughts here on this debate, where on the one hand, you know, the criticism is that with more sports gambling comes proven bad effects. You know, you can measure some of the bad effects that it has on people. And, you know, we have this especially in the US where there was a lot of legalization of sports betting over the past 10 years, and there's some data on that. On the other hand, you know, I have no real issue with alcohol, despite the fact that it has kind of a similar distribution where many people enjoy it, and then there's a very bad set of outcomes for a small fraction of the population. And so I feel like the societal discussion of, you know, the morals of alcohol and the morals of betting are different, despite the fact that, again, it's similar shape to the distribution. And, you know, also just thinking from my experience online sports Betting has been legal in. Well, legal's complex term has been available in Europe.
- [54:20]
B
Right. For a very long time.
- [54:22]
A
For a very long time. Basically since the start of the Internet. I knew there was all these cross border hacks in Malta and now it's a bit more regularized but it's been available to people who want it for a long time and life goes on, you know what I mean? And it hasn't led to any kind of major societal collapse over there. But clearly this is one of the big debates that rages around Calgary. And so I'm curious how you guys think about increasing access to sports contracts and the effects there.
- [54:48]
B
Well, there's a lot of parts to everything. I think that the way that we think about sports, how we decided to first list sports, is obviously something that a lot of people are interested in that's unquestionable. But also there's something that a lot of people do a lot of research and know a lot about. And there aren't traditional ways for you to make money on that that are actually good. Or we talk about the winners, they get cut and all those things and it doesn't really work. And regardless of whether people like that some people bet or dislike that some people bet, people bet and it's just about what is the best way for them to have access to something that they can get exposure to sports. And I think that the whole point of markets versus a bookie is that markets are just objectively better. Right. I think that it's almost. I've never heard someone make a case that a state by state regulated casino is actually a good thing. I'm actually hearing nowadays that a lot of like the paid propaganda by the gambling guys trying to say that. But if you push them numbers on
- [55:45]
A
that, the order of magnitude rake for sports betting companies is around 10% and the order of magnitude for prediction markets is, you know, 1% or a few points.
- [55:55]
B
But the predatory part doesn't even come from that. Like for sports betting, if you start losing because they want the losers, they don't want the winners. If you start losing, the first thing that they're going to do is give you a bonus. They're going to give you $1,000 for you to come back or deposit boost and all those things so that they can hook you to keep you coming back because they want to incentivize the losers. We don't do any.
- [56:15]
A
People losing the most money are the most profitable for sports bookies, which creates a bad incentive.
- [56:24]
B
Yeah. And we don't have that at all. And I think that the whole point is like there's a moral question, like some people are going to go into Robinhood or Coinbase or whatever and speculate on stocks and speculate in crypto and whatever they want to do. And some people want to speculate on sports and they should have the best, the access to the best possible thing for that. And right now the sports books are just not it. And we really firmly believe that what we do in our markets are significantly safer for all of that. And if you just take a stance of prohibiting, it's like same with what you said, alcohol, right? It didn't change. People just went to like a speakeasy and drank. And people are just going to go offshore when there's way less protections. There's no, none of the self exclusion, the positive limits. All those things that we do don't have any information about them and they're going to actually, it's actually very bad for them. So I think it's just this prohibition concept just never really works.
- [57:12]
A
It's also the whole policy discussion around this stuff is also very interesting when it kind of reminds me of in Canada, all the liquor stores are run by the government or at least in British Columbia. And you have the government saying this must be very carefully controlled, but also we will sell it to you and have the revenue source. And obviously that's much more of a factor in lotteries and things like that.
- [57:35]
B
It's all about money. At the end of the day. The States want their money, the casinos want their money. It's just. Yeah, it is what it is.
- [57:41]
D
Speaking of sports, one thing we were talking about was just what interesting new verticals are there going to be? And so I'm just curious, like which ones are you most excited about?
- [57:50]
C
Well, I think that anything around dissecting like a stock into its sort of like kbs more atomic components.
- [58:00]
D
So this would be like betting directly on Nvidia GPU shipments versus Tesla deliveries
- [58:07]
C
or whatever, like their earnings. The, you know, because like the. And like you can expand that to things like, okay, dissecting sort of the macro economy. Like what are the main sort of like factors are influencing the economy broadly? Like AI and like have a series of questions to price, what's going on with AI, things like health scares like Covid and so on. But where things get really interesting is this idea where. So there was a paper written by Kevin Hassett around this idea that as society gets increasingly more complex, our asset prices, our understanding of asset prices will naturally decay like entropy will go up because the things that influence or the sort of vector that you know, like that the number of factors much higher
- [59:00]
D
dimensional vector becomes very high dimensional.
- [59:02]
C
Right. And if those dimensions you don't have a good understanding of X1 to XN, you cannot get a good estimate of Y. Right. And so the paper basically says that you need infinite markets. In prediction markets are this notional infinite markets which is like you have to have a market for each one of these X's so that you can then take that back into pricing traditional asset getting good traditional asset prices. And an example of that this last week. So you know there was the Citrini put out a research report, right. And it got a surprising amount of
- [59:39]
B
people got obsessed with that.
- [59:40]
C
I would say it got a surprising amount of love and interest.
- [59:43]
A
This is the AI 2020 ish world.
- [59:45]
B
Yeah, 38% on employment.
- [59:48]
C
Look, I think there's a little bit of like a society wants to believe that AI is going to end us all. I think right now there's a bit of that. But this is where you know and that impacted markets, right. Like the stocks got, you know, there was a sell off and so we launched a prediction market on that. And well before that Citadel came out with a rebuttal and we launched a prediction market on that and you know the odds are at 10%. Right.
- [60:07]
A
So of the economic scenario that they predicted being true as of 2020, it's
- [60:13]
B
three out of five things. So they have like five conditions. If three hit.
- [60:16]
C
Yeah, if three out of these five conditions hit, you could reasonably say that okay, this outcome has somewhat materialized and it's just 10%. Five out of five is much lower. Right. So debt is important. If you can put that back into pricing models, maybe the markets wouldn't have reacted that like maybe people wouldn't have sold off DoorDash because at least I believe. But you don't have to trust me, maybe trust markets that this sort of analysis around Doordash was actually quite poor.
- [60:41]
D
So one thing you're sort of envisioning is this world where everything has a price all the time. And I'm curious like is that a good world to live in? And I would note that stripe I think benefits from being private and not having the real time price and smoothness for employees and comp and all that.
- [60:59]
B
Which is by the way the biggest
- [61:01]
A
some noise in the, you know, because
- [61:02]
D
like sentiment swings publicly. Like there's on average in the long run it's a truth telling machine. But in the short run can be A panic. So just curious for you guys to think about. Yeah. Is that the world we want to live in?
- [61:16]
B
I mean, we're obviously biased because we love markets, we think markets are good. So we're definitely biased here. But I think our view on this is that it's always better to have more data than less data. If you don't think the data is good, if you don't think whatever the second by second stock price is good, you can just choose to ignore it. The world might not just ignore it, but you can.
- [61:38]
A
The CEOs of public companies would say it is not always possible to choose to ignore it.
- [61:42]
B
I guess that's fair. That's fair. But in a way it's. It's better to have the data and then use it as an input to something than not. But when we say we want to have prices on a lot of things, it doesn't mean everything. There are a lot of things that we wouldn't do, like wildfires, we don't do war, terrorism, assassination. Those things are bad. And there's a moral side of these markets and we're not going to ever go there. But in general, in a world of social media, you don't know what's true anymore. My feed is like, is it real? Is it not real? Did this happen? But it's just better to have a source, an unbiased source of information that you can kind of use it for other things. And I think that that value is there, but I don't know.
- [62:21]
C
Well, I mean, I think that maybe the simple frame for this is like you are increasing market efficiency for all these questions, right? Like that's what's happening, right? Including potentially some events or things that relate to maybe private companies. And I was just thinking about the question, it's an interesting question, like, why do companies go public, right? And why is it important to get like a real time market price? Because there are downsides. Sometimes markets are erratic, sometimes they overshoot in either directions. But the market on the long enough time horizon is a good sort of allocator. It's a good weighing mechanism. Mechanism. It's a good allocator of capital. And I don't really see that. Like, I just think that like pricing, a lot of these questions will just increase efficiency, make our function, our allocating function better over time. And there will be some net losers, right? Like some people that maybe capital shouldn't be allocated to. Right.
- [63:25]
B
It's also a good feedback loop, right? Like if you're the CEO of a public company and you Announce something and it just keeps going down. You're like, okay, maybe I'm wrong. And I think the same thing is seen with politicians where you can see in the live debate, if they say some answers and they see their prices going lower, they're like, well, maybe the answers aren't great. And I think a lot of these things, when we see even the. For example, the use case of prediction markets in government, a lot of it is conditional markets, right. They can say, if we pass this bill, will unemployment go up or down? And you can price these things for better decision making and just like a tighter feedback loop tied with good incentives.
- [63:59]
A
Do you guys feel like we have started to see, like, clearly we have seen the effects of social media on politics where politics is a different game now and different politicians are popular and the political discourse is changed by the existence of, I mean, first Twitter and now to some extent, short form video. Do you guys feel like we have seen the effects on politics of prediction markets yet?
- [64:26]
C
Definitely. I mean, what are they? Well, the candidates are using the, like, are using the prediction market prices. Sure.
- [64:32]
A
But that could just be like a handy thing. Whereas again, I think with social media, like, the candidates are different, the debates are different.
- [64:37]
C
Reflexive. Yeah, yeah, reflexive.
- [64:38]
B
I do think that what the markets are good at is that they are more unbiased by party dynamics. So if there is a underdog that the public really likes, there's a lot of like, maybe there's like a party that's like, we don't like this guy. We like this guy from the establishment. But the markets are very good at actually showing the real odds for that person.
- [64:58]
A
Okay, so you think the party machines have lost a little bit of power.
- [65:01]
B
I think you can shine more light into what people really want, which might not be necessarily what the parties want to put forward. I don't think we've seen that necessarily yet. But I feel like if I were to say there was a. For example, the Texas primary, and I think that the polls were saying one person was really going to win, another person that was very way higher in prediction markets won. And I think. Right. And I think a lot of it was more of like, they give a more a fairer view of the state of the race than a lot of the party calls.
- [65:30]
C
I think we do see this a lot with also. Well, there's sort of the piece where people use it and that sort of react in real time to certain things, but I think there's some degree of depolarization and that's what luana is alluding to. And in some ways, it's an antidote to social media. Like, social media has really polarized. Like, when you have two candidates running for a Senate race, we're sort of set, right? Like, your feet is set. Like, either your feet is saying the Republican candidate is awesome, or the feed is saying the Democrat candidate is awesome. Prediction markets don't really have that because the people that are engaging in this are not in the sort of like, who's best, who's great and who's, you
- [66:10]
A
know, I think what you're saying is social media feeds ultimately try to resolve up front. They're like, I need to figure you out. Are you a Democrat? Are you a Republican? Like, what post should I show to you? And pretty quickly, and you know, people have complained about this phenomenon where you end up down a particular rabbit hole because they have pigeonholed you as kind of this type. And you're saying just prediction markets do not have that phenomenon.
- [66:30]
C
There's a little bit of a reverse phenomenon. Are we feeling too certain about this person? And I think that depolarizes things because the dimension is not. We're not one dimensional anymore, which is like, Republican, Democrat. And that's where what you're seeing, what you're hearing about now, it's like, well, this guy's kind of cool, right? And that's Talarico, and maybe he might do good in Texas, even though he's a Democrat. And, you know, the same thing happened
- [66:53]
B
with the New York mayor situation, right? Like, everyone's like, cuomo is going to win 100%. 100%, Cuomo is going to win. There's not even a chance. And we were just seeing Mondani's odds going up. And I think it's just the progressive message was really working with New Yorkers, and the markets were really seeing that uptick. And that decrease in polarization really comes from people taking a step back and being like, what do I actually think is going to happen? I'm incentivized the right way.
- [67:15]
A
There's a bit of like an Iowa, New Hampshire effect here where, you know, in presidential elections in the U.S. there's like some big name leading into the election. You know, maybe it was Hillary Clinton in 08. And then Iowa and New Hampshire are measurements of the sentiment of those two states. But they also create narrative. And I think what you guys are saying is prediction markets create this Iowa, New Hampshire effect where they can create narrative in a way that changes the ultimate outcome.
- [67:43]
B
I don't know if it Changes, though I wouldn't say it changes the ultimate. I think it sheds light into what the ultimate outcome is going to be.
- [67:50]
A
Because if this eventually changes it.
- [67:51]
D
Well, I think there clearly is something.
- [67:53]
C
I mean, there's always. But it's like with the polls too, right? Like the. Yes. I mean, there's polls.
- [67:58]
A
Aren't you guys hiding your lamp under a bushel here? You're like, well, we're not changing anything here. You're just like.
- [68:03]
C
I just think that the one thing I would say, the reason why we're like, prediction markets are a big deal.
- [68:08]
A
It's okay to say they'll change things a little bit.
- [68:10]
C
High odds don't always correlate to, like, a good outcome. So you saw Mamdani when his odds were 94% on Kalshi, the thing that he was messaging pretty consistently. And I think there was a little bit of worry there. It's like, you gotta show up. Right? Like, because if you're very high odds, that could also lead people to be like, okay, this isn't. So it's not as clear.
- [68:29]
A
Yes.
- [68:30]
C
And I don't think this changes things more than polls changes. Does that make sense? The response is more like in a vacuum. Yes.
- [68:36]
A
Yes.
- [68:36]
C
If you had nothing else, if you didn't have social media, if you didn't have views, if you didn't have any of that. Yes, but because we have all these other things and you add prediction market to it, like the impact market. But I want to make one point that I think is actually very interesting. And we're seeing this often.
- [68:48]
A
We'll be the judge there.
- [68:49]
C
Okay, you can judge that. Let me know if it's interesting. But a lot of times when you see people start participating in prediction markets, they get more engaged in the underlying. They legitimately just get more informed.
- [69:02]
A
It pulls people in.
- [69:03]
C
It pulls people in. But to do research now you have. Because you have some skin in the game, or you're about to put some skin in the game. You read everything changes. You're not just like saying something crazy on Twitter anymore. You're putting money. And now it's like, let me read. Let me actually figure out, oh, who's this person? What's happening?
- [69:18]
B
It's like, are they pro this? Are they against this? What's their view in everything?
- [69:21]
C
And you go, because, you know, it's like, amazing. Because this happened a bit in the New York race, right? Like, or it happened a bit with Brexit. People voted for Brexit because, oh, no, we want Brexit. And after they voted for it, they're like, oh God, wait, I don't think we wanted this. Wait, wait, we didn't even understand what we were voting for, right? And I think this heightened engagement is very good. Like, you know, like it engages people further to like learn and understand. You know, in sports, basically, if you ask sort of like a lot of the leagues, they would tell you the same thing where people got more engaged with the statistic which player is good, what's happening. And I think that will happen is already happening sensitive in politics, which is a good thing.
- [69:54]
B
I think that the. I'm going to say something and then I'm going to clarify what I'm saying. I think that politics are going to get better because you're going to have a way faster feedback loop on the messaging and the policies. Right now when a candidate says 10 things and they win or they lose, you're trying to make one assessment of so many things that the candidate did and did that go well or not and which point was it? And even if you do a poll, it's like always delayed is a very specific sample. But now you can have real time of like, they said this, what was the response? And you can get that in kind of like that faster feedback loop, which I think makes startups great, right? You're able to iterate very fast. And I think if candidates are able, everyone wants to win at the end of the day and if they're able to optimize their message to what really people want and what policies people want, I think they're going to end up being better because they're just going to know what people want better.
- [70:42]
C
It's like a, you get a score on all of them, all the, all the different things you've done. Not one score on Compass.
- [70:49]
B
When you ship a new feature, you're able to have like 10 metrics and you're like, okay, this went up, this went down. And you're able to. Markets can kind of contribute that to that, but also like, I think to a lot of other things as well. Music with charts, when someone listens to the song and they're like definitely not going to hit number one, you're like, okay, that was that song, so we should do something else.
- [71:07]
D
Do you guys try to use prediction markets in any way internally to make decisions?
- [71:12]
B
Every single decision we make is always probability. Even the election lawsuit, right when we're doing it, it's like, but that's you
- [71:19]
D
guys evaluating the probability. Do you ever think about creating markets for your employees to Participate in.
- [71:24]
C
So we have one knit. Yes. Which is as a regular exchange.
- [71:29]
B
Right.
- [71:29]
C
We can't trade, but we do it like internal.
- [71:32]
A
There's like a separation of church and state thing there.
- [71:34]
B
Exactly, exactly.
- [71:36]
C
So we've been asking that, we've been working with regulators. Could we do something small, like could we do small dollars where. So that, you know, we. Because obviously that's one that we really want to do.
- [71:45]
D
And you can't even dog food the product, I guess.
- [71:47]
C
And then dog food, which is. Been hard. It's hard, you know, that everyone's on their own.
- [71:52]
A
Sorry. Employees cannot trade in their personal capacity.
- [71:54]
C
They can't.
- [71:55]
B
Not at all.
- [71:55]
C
Wow.
- [71:55]
A
Interesting.
- [71:56]
C
Yeah.
- [71:56]
A
But yeah, that makes it very hard. As you say, you just can't dog food your people at Facebook. Use Facebook. And that's how you make the product good.
- [72:03]
B
Right, right. So that's why it's so important for us to just be like asking the users all the time.
- [72:08]
A
Yeah, yeah. That's so interesting.
- [72:10]
B
It sucks.
- [72:11]
C
But the power users, the super forecasters, are a lot of what influences where it goes because they're very engaged.
- [72:16]
A
You must presumably spend a lot of time with those power user superforecaster types and just have them on speed dial.
- [72:22]
B
They're there, they won. You want to make sure they're happy.
- [72:26]
A
Last question. Where do you guys want to see prediction markets policy go? Like, when you're talking to someone in government or if you had a magic wand, what are you arguing for?
- [72:38]
C
So our stance as a company, and I think this may differ a little bit from I would say your average tech company or big tech company. So we are pro innovation. Innovation needs to happen in America. You know, we. We have to lead and we have to do it right and we have to win. Like, we have to be, you know, all the things that Americans want to do or we need to win as a country, we should have here. But we're also pro regulation. And so at a higher level principle, like there's usually this tension that, you know, generally it's like, you know, it's like the policymakers, like want to regulate. And I think you're maybe more like
- [73:15]
A
a traditional financial firm in that way. Right. Where maybe Silicon Valley. You know, a lot of firms grew up in an unregulated way, but financial firms have always had a regulator. And that's just a fact of life.
- [73:26]
B
It's just a constraint for you to work with.
- [73:27]
C
It's part of the culture. And again, we spent four years getting regulated up front. So it's part of but we believe in regulation. Like, I think it's important because, you know, regulation is a bit like insurance. It's like it's protecting you from things going wrong at bad times. And so when I think about, like, okay, where this lands, I think anything that is oriented around preserving these in America and making sure we win, but then elevating the fairness and transparency of the markets. Right. Anything that's oriented, like, how do we make it more fair? Ban insider trading, Add more restrictions on, for example, like, you know, government officials, members of Congress trading on, like, you know, information they shouldn't trade. I'm obviously big fan of like, banning insider trading for members of Congress.
- [74:06]
B
We talked about it.
- [74:07]
A
Yeah, but presumably you mean just banning trading, period, for a member.
- [74:11]
C
I think it's not a bad idea. Like, you know.
- [74:14]
B
Yeah, that's how we kind of.
- [74:16]
C
And then things around, like, you know, loving, like creating also social fairness and transparency because of all the questions that we ask. Like, if people are basically trained on politics, let's have all the trade data be as public as possible so anyone can audit it, anyone can see it, which is a good thing right now. You don't know, like, imagine a poll where you can check every single person that basically got. And the general public can check who was polled and what the sample was like, and then anything around customer protection. Because that is important long term in the sense that, like, when you build a consumer product and it goes mainstream, there is like a massive sort of like, burden on the other companies to educate. And you've seen it like, you know, over and over. And in our case, you want to make sure that people know what they're getting into. They're not overextending themselves in terms of how much they're sort of trading. They're not getting into an area of discomfort. And how do you kind of. We can do as much as we can do on the marketing and on the product side, but we need policymakers and regulators help to basically make it an industry standard, but also help us elevate hours.
- [75:22]
B
By the way, we're pro that. I think that even the classic retail brokerages should also be adopting a lot of these customer protections that we're talking about that they don't. I think that every retail trading platform should be taking a lot of these steps.
- [75:35]
C
Yeah, I mean, that's sort of our general view. And we hope that this is sort of the direction that things take because, you know, there's kind of you can have a variety of views. Right. And some people believe that, like, hey, like, you know, any type of speculation should be banned, whether it's in the stock market or crypto or prediction market. We don't believe that. Like, you know, we think that would be a bad outcome for all the reasons, because there's a lot of upside to having liquid markets and a variety of different things, but also because if you ban it, you're actually heightening the risks that you're trying to prevent, because now that activity is going offshore. Right? Like, and where you cannot monitor or police it or do anything to protect it.
- [76:12]
A
Awesome. Cool. Tarana. Thank you, guys.