Target. It's good to have you here, man.
Thanks for coming up. Co-founder and CEO of Kalshi, which was speaking my... my father's from Morocco, and so I was like, is it because there's different dialects of Arabic? And of course, Kalshi means everything, right?
Yeah, we say Kolshi in Lebanon.
And I mean, that was sort of the concept, right? The ability to wager on everything.
I mean, it was, you know, the start of the company was... it took a little bit of time, you know, because, you know, I... so my background: I grew up in Lebanon. I was born in California. I grew up in Lebanon, and, you know, Lebanon, you probably know this, but like, very volatile country, right? There's a lot of good, there's a lot of bad. You know, days could change. Like, things could change on a daily basis. You could wake up with something and go to sleep with something completely different, an entirely different world. So, and I also grew up with a single mom. So I grew up in a very kind of volatile environment in general, just like a general sense of anxiety and paranoia. And the place that I found my refuge in was math. I loved math. Like, that was my thing.
That was your safe space.
Yeah. Because there was some elegance to it. It was nice. There were like answers, you know? I like the...
That's why some people like baking too. It's like very specific. Whereas cooking, the recipes, you can sort of do a little bit of everything, and baking has to be like a very exact...
There is like an answer. There's like a standard that like it worked. And math had this... it's like, you know, the outside world was like messy and kind of chaotic, and then math was, you know, you could sit down and find an answer, and it was beautiful. It was elegant. The lines were cleanly drawn and all that. And I really loved that, and I really got obsessed with math. I have a little bit of OCD, like I'm a little bit obsessive, and so that was my thing. And then I really worked hard to get into MIT because that's where all the math nerds were going.
So you were California, Lebanon, and then you were making your way back to the US.
Yeah. Because my mom, you know, was always anxious about our future in Lebanon. And she was like, you have to kind of go back to the US, and this is where you can have a future, and, you know, that country can give you so much. And I think she was right in many ways. But MIT specifically was like, I was into math, and I was like, where are all the math nerds going? And then I was a math nerd, and that's where I went to. And when I got to MIT, my next thing was finance, like what a lot of the smart math people were going to, financial markets, etc. So I started working in finance. I worked at Goldman, at Citadel, some prop shops, smaller funds, and, you know, I realized I didn't like... I was excited about being a trader, but I realized I wanted to build something. I liked sort of building. I like, you know, something tangible that people can use and so on. And in 2016, when I was at Goldman, and I tell this story a lot, I was young, I was still pretty young. And, you know, you have all these really fancy, sophisticated traders, you know, trading swaps and swaptions and interest rates and, you know, all these really complicated things. And I was in this desk where a lot of the demand didn't start with these complicated questions. It was just like, hey, will Brexit happen? And will Trump win the election? And then you take that question and try to turn it into their trade, into the thing that they're trying to take a position on. And like, all the products we were offering were bad. It was really bad. Like, people were like, 'Oh, I want to buy Trump winning the election. I want to get a position on Trump because I think he's going to win.' And we say, 'Okay, so if you think that, what you should do is short the S&P on the week of the election.' You know, that's what a lot of Wall Street did. The banks, you know, sold that. Trump won the election, but the S&P actually went up. So then it was a bad trade. Like, you know, how about you just trade on the thing directly?
And that was where sort of the idea of Kalshi started sort of coming around, which is like, you know, the universe of products right now are very complicated, very not accessible, and they don't even relate, like, tie to the things that people care about, whether it's politics or climate or sports or economics or whatever. And so how about we broaden financial markets to capture things that people care about and capture a larger percentage of the population that may be interested in these things? And that was how kind of the whole thing started, which is like trade on a larger set of things. And, you know, Kalshi is a little bit more interesting. The name, I like names that start with K. I just like a weird thing. I just like names that start with...
So Elon has X, you have K.
Oh, he has like... yeah, he has...
I just like names that start with K, and I was looking for cheap domain names, and like it came to mind Kalshi. And then, you know, we didn't have like a lot of money, it was cheap enough, and Lana's sister, my co-founder Lana's sister, bought it at the time. And then we're like, let's just get going. Right now we had this application to get into Y Combinator, the accelerator, and then we'll change the name later because it's a terrible name, it's hard to pronounce and all that. And fast forward today, we never managed to change it.
So you have the light bulb moment when you're working in finance. How many people, as you're presenting this idea, are like, 'Oh, that's a great idea'? Like, how much support did you have, and then how much difficulty did you have trying to sell the concept?
It took us a while. So that was 2016, and we didn't start till 2018, two years. So that was at Goldman, and that's when I started thinking about the idea. And then what really got me into it is like this idea that, you know, applying free markets to answering a bigger set of questions. You know, like right now the way that we price a stock or price traditional, like for example, like oil or commodities is free markets, and free markets have shown themselves to work really well in terms of like pushing economic prosperity and, you know, pricing things.
Right, and that's what you like to compare yourself to, is like the same way that oil is priced, gold is priced...
Or stocks were pricing questions about the future that people care about, right? And but here's the thing, like I started reading about this and like, oh, why does this not exist and all that. And I realized that actually during World War II, there's a long history, this is where the concept started sort of taking shape. There's an economist, Friedrich Hayek, who talked about the knowledge problem. And the knowledge problem is actually a very simple concept, which is decisions are made centrally, like governments, you know, executives, etc. But the information that is important for those decisions is very distributed. It's like, you know, spread out across a very large number of people, like it's all over the place. And so there needs to be a mechanism to bring that information to light. And at the time he said, well, the best mechanism is a market, is like prices, because market prices are a good way to kind of synthesize, like summarize and aggregate information. He called them information markets at the time. And it took a while till, like, you know, till someone tried it. And a university, the University of Iowa, tried it in the 80s, and with a, you know, small participation, etc., on the election market, and it beat the polls even though it was very small.
It was more accurate. And so people were like, wow, what's like, what is, you know, it was exciting. And then in 2015, there's a professor at MIT, Tetlock, who talked a lot about, I don't know if you've ever come across Superforecasters or Superforecasting, but basically he did something very simple. He took a group of geopolitical experts, experts, so authority figures, elites, you know, you name it, all the big names, and pit them against a group of random dudes, and but there was one condition, is that they read the news.
The random dudes have to read the news.
Like they are avid news readers. They've been reading them for a while.
Mhm.
And he asked them, hey, like, forecast a bunch of geopolitical events, like predict what's going to happen. And guess which group did significantly better?
Well, I mean, the geopolitical elites predicted Trump would lose in 2016. You know, like if you go through various things, like we have, I mean, and you know, and we can get into deeper discussion here, but we have seen increasingly the experts or the experts we depended on or used to trust have gotten some fundamental things wrong in recent years. So I think this is the thing that, like, whoa, like you could really apply like this idea of an unbiased, unfiltered market to answer these questions. And the reason, you know, and then Tetlock did a little bit of research, why are these kind of random people that don't have expertise in the field doing better than the experts? And there's a variety of reasons. One, the experts get dogmatic about their beliefs, you know, they've been in the space for so long, they get kind of a bit tunnel visioned about the things. But there's something a little bit deeper about this, which is the art of thinking critically about the world and forecasting, like predicting what's going to happen, is a skill, and domain expertise is not that important. Some people are good at it, and they, you know, and it's people that do research and read and read the headline, and they're always critical of the headline, and they think very carefully about how they view the world. And that was really exciting to me because I was like, if we can build a marketplace that attracts enough of these people, which I think is where we are today, we can kind of like predict the future better than, you know, is it perfect? Definitely not, and we can talk about that, but better than any other alternative, and that's a very valuable thing to build.
So given all of that, as you guys were building this, how much skepticism did you guys face?
So a lot. I mean, you know, throughout the history of Kalshi, we've faced skepticism at every step of the way, you know, and we've had a lot of supporters at every step of the way. A lot of supporters are these economists, these sort of circles that have really believed in these markets, and obviously our users are like big supporters of prediction markets. So it's always been mixed, and there's always been back and forth, and I think that the sort of push and pull that society has on these new technologies, like whether it's Uber or AI or social media or all these different things, is good. It's healthy because then it helps get to the right answer over time, right? But the predominant thing, and I think what Kalshi's done differently, and something that we've always been committed to, is a regulatory-first approach. Like, okay, 2018 we decided to start a company, and we got really excited, but we didn't launch until four years later, probably.
Yeah, I mean, we had like a small scale like 21, but 22 is really when we launched for GA. In those four years, we're spending time getting regulated up front. So, you know, tech is usually move fast and break things, and, you know, tech usually has this perception that they like to avoid regulation. And actually, we took a bit of a complete opposite approach, is like actually embrace regulation first and foremost before you launch the product, and that's, I think, a little bit missed in the Kalshi story. And the reason we did this is because I want to get the right answers. Like, I really want to build something that is a force for good, and we build something that, like, I believe in the power of prediction markets, but I believe that we, you know, we had to fit it in the proper regulatory regime where we're making sure that it's safe, it's trustworthy, it's fair, it's transparent. And those are the four years that we spent, I think they were very consequential to the company, because a lot of our competitors, which a lot, you know, you probably hear in the headlines about, you know, some of the stuff that's happening on insider trading, and a lot of these things are happening.
I want to get into that with you.
Yeah, like a lot of competitors actually went offshore, and they just launched without appropriate licensing or regulation or all...
Yeah. And I think that that regulatory-first approach is a big piece of... so they launched before us. They started coming after us, but they launched before because they didn't do the regulatory. They just launched, they didn't care. And but then when we launched, we end up becoming much bigger, much faster, and I think it's thanks to that regulatory-first approach because people trust the marketplace more. And that was hard, you know, as I started coming when I was 22, and, you know, regulators are skeptics, that's their job, right? They...
And they play a role in protecting the people, protecting the population.
The job of a regulator is to be, you know, the job of the innovator is to bring the new things, and the job of the regulator is to put pause, is to think about it, to be skeptical. And it took four years of skepticism to kind of really get it through, but we were so committed to getting it done. And I think we got to a good place, like we got approved and launched and all that.
You used the phrase 'force for good.' You want Kalshi to be a force for good. We put out the question to the audience that we had you coming in here, what questions did they want to be asked? And they sort of revolve around a few different buckets, but I want to begin with this one because we got several people asking versions of the following question, existential question: Why should you exist? And what good does Kalshi do for society?
So, it's interesting. There's many ways, and like we can go into all of them, but like one way is like, you know, a little bit like, you know, why did you start your company? Honest question. Why did you start your company?
My company. Oh, because, well, first of all, I realized there was a huge audience that distrusted traditional media and was looking for an alternate place to get things, but also get facts, information in a nonpartisan, verified way.
So I'm going to copy-paste that and start that for prediction markets on Kalshi, right? Like, one of the things we saw in 2018, that was the V1 of when we started talking a lot about polarization in the country, like misinformation or disinformation, whatever you want to call it, but the noise-to-signal ratio has gone up a lot, and that trend since 2018 kept getting worse.
Sure. People don't believe anything anymore.
Exactly. People don't believe anything anymore, and that's tied to the thing I was discussing, which is like the unfiltered, unbiased piece, which is like, you know, today the world is going in increasingly more polarized. Like, you know, I think it's going to the extremes. I think we're seeing it in a variety of different ways, both sides, both sides are going to the extreme. I think social media in many ways wasn't really like, was actually an exacerbator to that trend.
Right, the algorithm was reinforcing the extremes.
The algorithm. And the incentive structure in those places is clickbait.
And as you know, you run a news organization, right? Like people like to click on things that sound extreme, right? Like, and you see it on Twitter all the time. If you have like a multi-paragraph, well-reasoned, logical, down-the-middle take that is nuanced about something, you get like four likes, right?
You say something racist on social media...
Right. Prediction markets have the exact opposite incentive structure. That reasoned take, those like four paragraphs that I mentioned, those are the things that make money. You're going to get rewarded because prediction markets, they reward you for doing research. They reward you for being truth-seeking, to being self-calibrated, to take out the bias from your opinions. The best forecasters, the best predictors, the best performers, the people that like really think critically about their biases and how to take them out. And that's how you basically win. You have to be right, you have to be truthful. Whereas the extreme take that tends to be the wrong one, the misinformation, the false, you know, the false stakes, you lose money, you get punished. And I think long-term, you know, my hope, and if we get this right, it depolarizes the conversation into it's not like 100% or zero. It's not like so black or white. It's not all so polarized. It's maybe like, you know, like there's a 40% chance of this happening, and here are all the factors that will contribute up to this event happening, and here's how we should be thinking critically about that event. And that clean incentive structure, like put $5 on it. How many times have you been in conversation with someone saying all these like hardcore conviction, and you're like, okay, let's put some money on it.
Hey, I had that conversation with my father this morning about, you know, about like what's happening next with Iran. He's like, I was like, you want to put money on it? I'm talking to Kalshi today.
Yeah. I mean, and then you immediately put money on it. Then people are like, now they're starting to think a little bit more carefully, and they have skin in the game.
And I think that's critical as you think about sort of where the world is going.
But the skin in the game thing, I think, concerns folks because there's people with actual skin in the game, actual decision makers that can effectively make money to ensure that certain events actually happen.
Well, that is illegal. You know, it's the same as, you know, trying to do that on the stock market or trying to do that in, you know, any traditional financial market. And that's part of the regulatory piece, right? The question of insider trading or manipulating the markets.
There's been a lot of headlines about that in recent months.
Yeah. And there has been a lot of headlines around that. And now if you read the headlines carefully, and this is also part of the news, right? Like a lot of these cases are really not on Kalshi. They're happening offshore on platforms like Polymarket. They do not have the right set of regulations.
Explain the difference. And I want to get to the core of kind of how Kalshi works, but explain how Kalshi works. And since you're often invoked in the same breath as Polymarket, what the differences are between the two.
Yeah, I think, you know, when new industries are coming around, I think a lot of the players in the industry get lumped in, right? It happened in many ways, like, you know, it's happening in AI, for example. Like I think different players have different takes and are doing different things around AI safety. You cannot just think about them all in one bucket.
Right, Anthropic versus OpenAI versus Google.
It's different, right? And crypto, Coinbase versus Binance. Coinbase was always regulatory-first, how do we keep your money safe, whereas like offshore platforms, you know, there was all sorts of issues, and, you know, no, they don't check who the...
Exactly. So, and that's critical in the prediction market piece. And it's on us, like we have to tell people about this because it's a new industry. Why would people know about it? But, you know, you have a company that's American-based, that has gone through the regulatory process, that has measures around integrity, making sure the platform is trustworthy, is safe, and it's transparent, and people that went offshore and that have none of the right guardrails and none of the sort of like safety mechanisms that are required under US law. Right? And the best analogy I'll give you is like imagine someone like built a version of the New York Stock Exchange offshore that doesn't verify who the customers are and lets them trade on stocks. What would happen? You're going to get insider trading left and right. You're going to get fraud left and right. You're going to get like people that cheat left and right. Right? And the unfortunate thing in our industry is that is happening, those things are happening offshore right now, and a lot of people don't know that, like they don't know that Polymarket is mostly based offshore. They kind of think this is a prediction market problem. So now talking back to Kalshi, like what are we doing about it, right? Like what, you know, so one, insider trading is banned. It's banned under the law, it's illegal. So if you're, for example, like a politician and you try to like buy like, you know, on your own election, like is this person going to run for Congress and you're that person, that is illegal.
Right, it's the same way. How do you prove it, though?
Yeah, I mean, so how does the stock market prove that someone committed insider trading? Exact same thing. So the exact, like a lot of the same measures essentially like replicate. So number one, you have to verify all the customers. That's what we call KYC, right? So this idea that like when you try to onboard, like sign up to trade on Kalshi, it's a bit like opening a brokerage account, right? That's like the banking laws from a few decades ago, which is like we have to know your name, your address, your social security. We have, you have to kind of provide your identity verification. You have to verify your identity. So we know who you are. So that's step number one. Number two is you have to run the same systems that run on the New York Stock Exchange and NASDAQ. You run systems that like essentially are detecting suspicious patterns.
So if it's not me, but I have my friend sign up for me, it still can get detected.
It's going to detect there's something weird going on.
Yeah. Like either like a weird sign-up. I think a lot of times the trades, right? Like when somebody commits insider trading on something, there's always something suspicious about that trade, right?
Right. Usually it's a little bit bigger than usual.
It's a new account that's trading a lot of money.
Exactly. No one else is making that bet right now.
They never really traded before, or their win rate is too high. Something is wrong.
Are you using AI? I mean, you have various technology.
I mean, there's AI, there's stats, there's a lot of, you know, these technologies have evolved over the last few years, mostly like a lot of it versus the stock market, and now you apply the same, a lot of the similar technologies to the prediction markets because they work pretty well. But it's things like, for example, like something has like 10% odds of happening, and then all of a sudden someone is buying it like really heavily all the way to 80% odds, right? You see sometimes, you might have seen some of the spikes in charts, right? We, like, we saw it around the, you know, infamously around the Venezuela operation, right? And that was on Polymarket, but, you know, when that thing happens, that gets flagged, the systems flag it, and then it goes to investigation. So we have investigation staff and all these, by the way, we have this...
Yeah, we have to have it under the law. You have to have surveillance staff.
So you're doing your own policing to a certain...
Because we're what we call like a, like a New York Stock Exchange, Kalshi, these financial exchanges are regulated at the federal level. We're what we call like a self-regulated organization. So you have a bunch of rules that you have to abide by, and you have to build these programs and procedures. And so then the staff, basically what they do is they run their investigation. They ask you who you are, why'd you take that trade, and then they build a case file, and then it can go from fines all the way to referral to the government for criminal prosecution. And we've done some of these. You've seen us announce a bunch of people that we caught, and then we punish them.
And the way to kind of really... And you'll call up the feds.
Yeah. Exactly. And the way that these things work is the same as kind of in the stock market, is like you have to detect and deter, right? No market is immune from bad actors. What you want to do is like go above and beyond to basically find the bad actors and root them out. Not just because it's bad for society, it's also actually bad for business, right? Like if people believe that the marketplace is unfair and you're trading against someone who may be cheating, you're just not going to participate anymore, right? Like that's the sort of like really key thing to mention. And the last thing that Kalshi does, and I think it's very key here, is the transparency. All the trades are public, for the government to see, for the public to see, for the users to see, everything. And so oftentimes if something looks weird, the users immediately call it out, which is a good thing. Like the data is anonymized, but everything is public.
But you can see somebody just placed a huge bet.
One direction or another.
So there's a sort of like crypto element to it. You sort of open...
Yeah. I would say even more than that. It's more like a, you know, it's like the way I put it is like everything, the spotlight is on all the action, and we know who you are if you're trying to do that action. So it's like, it makes it very much harder to commit fraud or to do bad things, right? And that's why I always say it's like, if you wanted to commit fraud, well, you go to the platform that's doing all of this and checking who you are versus the platform that is not checking who you are, where you can just send some crypto somewhere and then trade anonymously, right? And the answer is probably the latter, right? And that's a key thing. But, you know, I think for us, like we have a role, and over time we want to improve about how to do things, like improve the regulations around this, and like want to, you know, engage with all the relevant regulators so the rules over time evolve with the marketplace, right? And, you know, because we're also going above, like we're going beyond what the rules require us to do. For example, we do ban proactively, for example, members of Congress to trade on anything that has to do with Congress, not just after the fact.
But how do you verify that that member of Congress doesn't have a friend that's like doing it for them, right?
That's why you have to have also retroactive surveillance. So right, if they sign up with their own name, then we can block them because that's customer verification.
Now the same issue is, so if someone is like, for example, like a Tesla employee that gives the insider information to a friend to trade on Tesla stock.
That's harder to flag for the stock exchanges, but they still flag it because after the fact, the suspicious...
Yeah. Suspicious trade. Exactly. And then you punish, and you punish both, and you punish them heavily.
For people unfamiliar with Kalshi, actually, I want to walk through how a prediction market works in plain English. So this is like you explaining it to, I imagine, as you've had to do, family members or friends who are unfamiliar with math and...
My dad still sort of understands it. I'm not really sure if my dad understands it yet, but we'll, you know.
Okay. So we're going to make another attempt for your father here. But walk us through how the odds get created, and then how they shift and how they move over time.
Yeah. Like, I mean, for example, one thing that we're watching closely, I was talking with someone on your team, you know, Elizabeth on your team, about like the 2028 Republican primary prediction odds right now, where like JD Vance has been dropping, Marco Rubio has been rising. So maybe we use that example as like that market, how does the market get created, and then how do those numbers move?
Yeah. So, you know, the easiest way to explain it is it works a bit like an option in the traditional stock market. But for people that are not really into that market or heavy...
Yeah. But the non-options traders.
Yeah. So I think it's really like basically it's questions, yes or no questions about an event is going to happen or not. And, you know, you can buy and sell those questions over time. And so, for example, you know, will JD Vance win the 2028 election? Currently, I think it seems like you checked the...
I checked it like JD, so this is the GOP primary nomination. He's been leading it, but in recent months, he's been dropping and Rubio has been rising. And they're both right around 30% right now.
Yeah. So you can basically, let's say you want to buy like 100 shares, you can buy 100 shares at 30%, which is like 30 cents on the dollar.
So you pay $30 for those 100 shares. So you pay $30, you own 100 yes shares on JD Vance. Then, for example, JD Vance's probabilities can go up over time, for example, and maybe he gets to 50% in a few months.
The more of us that buy into JD Vance, generally the probability will go up there.
Yeah. It's like supply and demand driven. The marketplace decides, and, you know, if a lot of people like start believing that like, you know, there's new news and things are happening, a bit like a stock, right? Like if there's positive news about like a company doing really well and performing well, the stock starts climbing based on supply and demand, and, you know, it goes to 50%. And now you have your position that $30, now $50, and then you can sell that and make a $20 profit. Right? So it works exactly like a stock, but the underlying is an event. It's like a question about the future. It's like whether something is going to happen or not. And then if you want to wait till the end, you can wait till '28, and then it can pay you $100, like $1 per contract, because it actually happens, then it goes to 100, $1 per share if it happens, and zero if it eventually ends up not happening, right? But the key thing is like you're buying and selling these markets as things are happening. And a lot of our most active traders are kind of like buying low, selling high. You know, they like buy when they think something is a bit underpriced. It's like they think the probability of something happening is higher than what the market is saying, and then they sell when the opposite is true.
Do the majority of people sell before the event actually happens?
A huge chunk, it depends on the markets. Different markets have different properties, but a huge chunk of our traders are essentially buying and selling before the event is done because they're really thinking...
They're finding value. I mean, sort of like a stock, right? They're finding something that's undervalued. They're like, I'm going to make some money off of this.
And then what taxes are people paying on that?
So they pay what we call like, it's a bit like traditional financial derivatives. Yeah, I have to ask, but like it's basically like, I mean legally these are like what we call like financial swaps, like derivatives, and it's a bit like options. So the same as what people pay on options, I think, but don't quote me on that. That's like a lawyer thing.
But given how much prominence is now given to people's rank on Kalshi, right? What's to prevent somebody from trying to get good buzz, to, you know, basically finding, especially since we know all these like large donors and, you know, rich people around politics, from essentially using Kalshi to their advantage?
Yeah, that's a, I mean, you know, that's a real concern, right? Like, and that concern exists in the polls. That concern exists in the news. Like, we don't live in a vacuum, right? And what I always tell people is, people always say, 'Hey, are prediction markets going to replace the news?' And I always tell them, 'No.' 'Are prediction markets going to replace the polls?' I'm like, 'No, actually prediction markets are going to put competition on the polls for the polls to get better, and polls will put competition on the prediction markets to get better.' So that, you know, both are sort of like kind of competing for trust, and that's a good thing. Having some competition for the polls is a good thing, and vice versa. And, you know, prediction markets rely on the news, and news over time is going to rely on prediction markets, and there's going to be a healthy ecosystem. And I tell people, it's like, don't look at one place, right? Look at a variety of different data, and this one data point so that you can put next to all the other data points. Now, it's interesting, so a few things. One, again, it's not allowed to do this, it's illegal. So when we find someone doing this, we punish them, and, you know, again, it's...
Trying to cook the books, cook the numbers.
Trying to cook the numbers, right? Like, and that's bad. But the other really interesting thing is it's very hard. So if you really wanted to, if you are trying to kind of influence the perception of an election, it's much better to do it via polls, like release biased polls, which I think happens all the time, or seed articles, or like, you know, the news, like polarize the news, say a bunch of things about the other candidates and vice versa, and fund these. Because prediction markets, what they've been shown is they're very resistant to this type of manipulation because of the skin in the game incentive.
So let's take an example. If you are a candidate and trying to move your odds up so that you show people that you're like winning, if your real odds are like, you know, let's call it like 20%, and then you try to buy, buy, buy to move it to 40%...
A lot of the smart money, the traders are doing research, and, you know, the smart money like in the stock market, they're like, 'No, actually the real odds are lower than that.' So what are they going to do? They're going to start selling immediately. So oftentimes what you see is like these spikes are very short-lived. They last like a few hours. It like happens and it goes back down, and all they did is they basically lost a bunch of money, the people that tried to cheat.
There was a story recently, I think it was Wall Street Journal did a story about who's actually making money in these markets.
Yeah. I mean, you guys were quoting that in that story, and that leads to one of the other criticisms we've gotten from our audience, which is like effectively the majority of people are losing money in these markets. And, you know, ultimately, aren't you taking advantage of people who come from lower income levels and don't have that money to spare?
Yeah. So that's not the case. So, you know, I think the...
Well, but what percentage of Kalshi users are making money?
Yeah.
So a few things. One, the article, and we engaged with that article. Unfortunately, like it sort of, they came with sort of a story. They, you know, said, hey, we have this data. We said the data is incorrect. Here's the correct data, and they had data from Polymarket as well. They had separate data from Polymarket. I don't know how that was sourced also, but I can't comment about Polymarket, right? And they still sort of ran the same story, and, you know, which was kind of unfortunate because we sort of gave the data and we said we put the data in context. Now, the one thing, and I will answer the question, but the one thing that's very important here is, you know, a lot of the recent sort of last few months rhetoric around prediction markets has been coming from some of the opposition, right? And the opposition is a lot of the casino and gambling lobbies, and, you know, they're spreading ads, they're lobbying, they're...
So some of the negative stories about you guys, you know, is coming from the casino...
Definitely. I mean, no question about that.
Because they're losing market.
