Prediction markets incentivize truth. Everything else incentivizes clickbait. That’s the core argument Luana Lopes Lara makes for Kalshi, the prediction market platform she co-founded that has gone from $5 billion to $22 billion in valuation in under a year and is now being sued by New York’s attorney general for $36 billion. Lara, the company’s chief operating officer, makes the case that Kalshi is fundamentally different from DraftKings—even though 75% of its volume comes from sports—and shares what Kalshi’s data actually says about the midterms.
This is an abridged transcript of an interview from Rapid Response, hosted by former Fast Company editor-in-chief Robert Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode.
Kalshi launched in 2021. Last year at this time, Kalshi was valued at $5 billion. I think by early this year, it was up to $22 billion. That scale at that speed—what makes that possible? Is there luck in it?
It’s a great question because actually, though we launched in 2018, right, in a lot of ways it looks like an overnight success. Like it was all up two years ago, and we just started growing a lot around the election. But it was the result of eight years of work. When we started the company, it was very important for us to be legal and regulated from the start, so it took us four years before we could launch the product, launch anything really, or have any users. We worked with the federal government to figure out how to bring prediction markets to the U.S. in a safe and regulated way.
A lot of things helped us grow this much now, but I think it’s the compounded effort that the team has put in for so many years on the tech, on the users, and on talking to them. Then we won the lawsuit against the CFTC [Commodity Futures Trading Commission] to be able to bring a lot more markets to Kalshi. When that happened, the product was ready to really grow and go from there. So I think it’s a mix of both. We were very prepared when our time came.
With that kind of hockey-stick growth, do you have to pinch yourself? Is this totally real? Is there anything about that pace that scares you?
I actually would say it’s a very good thing that it happened so fast because, in a lot of ways, we keep the mentality of being very early stage. I think when companies are compounding at a very normal rate, it’s easier to start thinking, “Oh, I’m a bigger company. I need to hire more people,” and you can start making a lot of mistakes. It can take a long time for you to realize you’re making them. For us, because we grew so fast, our mentality and the way we look at the company haven’t changed as fast.
Because of that, we’re able to operate with far fewer people. We’ve just had to keep going and building the product as fast as we could: early-stage team, early-stage mentality, and very intense work. Keeping speed is the most important thing for startups. You’re definitely right that sometimes we look at the numbers and, two years ago, before the election, we were making way less than 10 million dollars a year.
Now, in a day, we transact way more than we used to in a year just two years ago. It is crazy, the numbers we’re talking about, and we’re very grateful for where we are. But we really try to keep the mentality that we’re still underdogs, and we still have a lot to prove and a lot to grow.
The success you’ve had has put a bull’s-eye on your back. States are coming after you for being an unlicensed gambling operation. A federal appeals court just ruled that Ohio and Tennessee can regulate Kalshi through their gambling laws. Is that kind of an existential threat? New York alone is suing you for $36 billion . . . the state where you’re headquartered.
We are very confident in our legal analysis. Of course, as you said, the appeals court went against us, but we also won the 3rd Circuit. Each of these lawsuits has a different legal thesis. The more important part, if you take a step back, is that the mechanics of how Kalshi operates and how a sportsbook operates are completely different, right? And that’s why they are regulated in different ways. We are federally regulated. We are an exchange, which means you trade against someone else. We don’t set the price. We don’t set the odds. We don’t trade against the users. The users are trading against each other, and we take a transaction fee.
What matters most here is liquidity and making sure we have national liquidity to build on. Imagine if you had the New York Stock Exchange, but you could only buy stocks on the New York Stock Exchange if you were in New York. The prices would be significantly worse. It would not be a liquid market. It would just be worse for every participant, and the market wouldn’t work well.
A sportsbook, on the other hand, operates completely differently. Because we also don’t trade against our users, we don’t make money when users lose. A sportsbook is completely different. Their revenue is equal to customer losses. The more the customers lose, the more money they make. For us, it’s not the same. The incentive is not to make people lose because we don’t make money when people lose.
Because of that as well, we don’t cap our winners. If you go to a sportsbook or casino and start making money, they’ll make sure you cannot participate anymore. We want winners. We want people to come and bring price, and we want price competition. In a sportsbook, there’s no price competition. The sportsbook has a monopoly on the price, and they’re going to put their margins on top because they’re having a bad month, so they make the prices a little worse or whatever. Because of that, they are fundamentally different mechanics and fundamentally different products, and they need to be regulated in different ways, which is how the federal regulation for exchanges developed.
We’re growing a lot because an exchange is a fairer, more accessible, and more transparent way to trade. You can see all the prices. You can see the competition and the order book in real time, and that’s why users like it so much. I think it’s fair that consumers, at the end of the day, pick what’s better for them.
Election polling has become kind of unreliable. What does Kalshi’s current data say about the U.S. midterms? Is that 70% accuracy? Or at what point does it start to move toward 90%?
A poll is top-down, right? It’s some editorial board or someone doing a poll, trying to aggregate the information and just tell people, “This is the number” or “This is the forecast” or “This is what’s going to happen.” But prediction markets are bottom-up, right? We want as many people as possible to do as much research as possible and bring that information to the market. So it is kind of an aggregation of what millions of people are thinking and doing, and I think it’s one of the first times that you really see information that’s actually led by people versus the elites just coming and saying, “This is what’s going to happen.”
With our big markets, like who’s going to take control of the Senate or control of the House, I think it’s as accurate as you’re ever going to get. The other thing we always have to talk about is that probabilities are not certainties, right? When something happens 1% of the time, it doesn’t mean it will never happen. It means that one out of 100 times, it will happen.
An election that’s at a 60% chance of someone winning means there’s still a real chance the other person wins. If I told you if you walk outside right now, there’s a 40% chance you’ll get hit by a bus, you’re not going to walk outside because 40% is pretty high. It’s the same thing. Forty percent does not mean that the person, the underdog, is never going to win. I think that’s kind of a challenge we have on the educational front, which is explaining to people that, different from polling, this is not the same thing.
Polling might say someone is 10 points ahead. In the market, that would probably mean over a 90% chance of someone winning, because they’re very different things. They measure different things, and we need to look at them as probabilities.
