Regulation, Insider Information, and the Essence: The Story Behind Kalshi's $20 Billion Valuation

Video Author: John Collison

Translation: Peggy, BlockBeats

Editor’s note: Over the past few years, prediction markets have gradually moved from a relatively fringe financial experiment to a central topic in discussions of technology, finance, and public policy.

Their broad attention is not only because “betting on the future” is inherently attractive, but also because, against the backdrop of social media amplifying noise, polls repeatedly missing the mark, and declining trust in traditional information systems, a more fundamental question has emerged: Can market prices serve as a signal mechanism that is closer to reality than opinions, emotions, and narratives?

This interview centers on that question. Participants include Stripe co-founder John Collison, Paradigm co-founder Matt Huang, and Kalshi co-founders Tarek Mansour and Luana Lopes Lara.

Kalshi’s co-founders Tarek Mansour (right) and Luana Lopes Lara (left)

As one of the most representative compliant prediction markets in the U.S., Kalshi gained rapid prominence during the 2024 U.S. election. Before this breakout, it had already spent years negotiating with the Commodity Futures Trading Commission (CFTC), ultimately winning a key lawsuit that paved the way for the legalization of prediction markets in the U.S.

The first part of the discussion focuses on Kalshi’s path to birth: why the founders chose “compliance first, growth later” instead of the Silicon Valley common “build first, ask later”; why they were willing to endure long approval processes, layoffs, and external doubts to secure the “election market”; and how the lawsuit against the CFTC became a turning point for the company’s true takeoff.

The second part delves into the logic of prediction markets: Tarek and Luana explain how Kalshi differs fundamentally from traditional online entertainment platforms—it’s not a “house” model that profits from user losses, but a fee-based exchange that encourages liquidity and information entry. They also highlight a counterintuitive reality: Kalshi’s liquidity mainly comes not from traditional large market makers, but from a large base of dispersed individual traders, “super forecasters,” and small teams. In a sense, prediction markets are not just financial products but mechanisms that directly convert dispersed cognition into price signals.

In the latter half, the discussion extends to the future boundaries of prediction markets: from elections and sports to AI, GPU computing power, macro variables, and policy pathways—can more and more real-world uncertainties be broken down into tradable, feedback-driven, decision-supporting market problems? Meanwhile, a series of unavoidable controversies surface—how to define insider trading, whether sports contracts will amplify online gambling risks, and how platforms and regulators can establish new balances between innovation, transparency, and user protection.

Because of this, the significance of this conversation extends beyond Kalshi itself. It aims to answer: Will prediction markets become the next-generation financial markets, or the next-generation information infrastructure?

Below is the original content (reorganized for clarity):

TL;DR

· Kalshi chose an unusual “regulation first, growth later” path: spent 3 years obtaining licenses, sued the CFTC to open the election market. The core judgment is that whether prediction markets can legally exist is more important than growth.

· The essence of prediction markets is incentivizing truthful information with money: compared to polls and social media, markets filter information through profit and loss mechanisms, serving as signals closer to truth.

· Ordinary people, not institutions, form the core liquidity: over 95% of matches come from dispersed users and super forecasters, not traditional market makers.

· Kalshi emphasizes itself as an exchange, not an online entertainment platform: revenue from fees, not user losses; encourages expert participation, unlike online gambling which limits winners.

· Elections are the “Holy Grail,” but future markets will go far beyond: from sports and macro variables to AI, computing power, and policy variables, the team aims to build a derivative system that prices everything.

· Prediction markets are becoming a new information infrastructure: users not only trade but also consume probabilities; 80% of users mainly use it to understand the world rather than to bet.

· Behind its rise is distrust in traditional information systems: polarized social media and inaccurate polls push people toward price-based judgment mechanisms.

· The long-term goal is to improve societal decision-making efficiency, not just to run a trading platform: through continuous pricing and feedback, political and economic fields can reach genuine consensus faster.

Interview Summary

John Collison (Stripe co-founder & host):

Tarek Mansour and Luana Lopes Lara are Kalshi’s co-founders. Kalshi is an emerging prediction market company that quickly gained popularity during the 2024 U.S. election. To establish the first compliant prediction market in the U.S., they spent four years negotiating with regulators and seeking approval before launching. Now, Kalshi’s monthly trading volume exceeds $10 billion.

So, how do you two usually divide responsibilities? But more interestingly, what are your different ways of approaching problems?

Luana (Kalshi co-founder & COO):
Actually, our backgrounds are almost identical. We both studied math and computer science at MIT, interned similarly, with little difference. But I’m very optimistic, adventurous, always believing things will turn out fine; he’s very cautious, even somewhat pessimistic. So I think that creates a good balance. Looking back, beyond daily tasks, our true complement is this difference.

Tarek (Kalshi co-founder & CEO):
I’ll add a bit of background. I originally planned to be a trader—that was almost my career path. If you’ve met traders, you’ll understand—they’re like people with a built-in expected return calculator in their heads.

Matt Huang (Paradigm co-founder):
A very typical trader.

John Collison:
Right, but—

Tarek:
If you’re really a trader, you’re constantly thinking about tail risks, worst-case scenarios. She doesn’t usually think that way. I think it’s precisely this difference that leads to good results.

Why Kalshi Chose the Hardest Path: Regulation First, Growth Later

John Collison:

I want to ask about this. Your starting point is very interesting. After founding Kalshi, you couldn’t operate for years until you got CFTC approval. Most companies don’t start that way. On the other hand, Silicon Valley often follows a “build first, ask later” pattern—get things going first, then patch the structure and permissions afterward, a “build first, seek forgiveness later” approach.

Can you tell us how you started? How did the approval process go? And do you think this path applies to other companies?

Luana:
From the beginning, we knew very clearly that if you’re doing financial services or healthcare, you can’t just build first and ask later. In finance, once user funds are involved, the cost of problems is huge—FTX is a typical example; healthcare is even more sensitive, with many disastrous precedents. We wanted to do it the right way. More importantly, when we looked at this market, the core issue wasn’t whether it would grow, but whether it could be legal to operate in the U.S. So we decided to address this biggest legal hurdle upfront. For a long time, many thought this was the wrong strategy.

I believe that before we won the lawsuit on election contracts, the outside world kept saying that offshore operators were doing better and growing faster. But after we won that lawsuit, proving our legal understanding was correct and that the company could operate legally in the U.S. as we envisioned, things really started to take off.

John Collison:
What was the timeline? When did you start, and when did you win the election contract lawsuit?

Luana:
We founded the company in 2019, entered YC that year. It took us three years to finally get regulatory approval and go live, around 2022. Then, at the end of 2024, we won the election contract lawsuit, and only after that did the company really accelerate.

Tarek:
There are two levels here. First, a very practical consideration: if we want real mainstream and institutional adoption, the key question is whether this can operate within a regulated, trustworthy, and safe framework. After all, it’s a complex market involving user funds. We had to solve this hardest problem first—that’s the path to success.

The second level is more principled. What excited us initially was a set of questions we listed in a Google Doc when we started: why do we want to build this company? Why does this excite us? Our answer was, we want to create the next-generation New York Stock Exchange—a trusted, regulated financial market in the U.S. We’re not excited about offshore setups that mimic that. The core question is: what kind of company do you want to build? Why do you want to do this? There are many paths to success, but the one we don’t want is the offshore route. We want this to happen here, in the U.S.

John Collison:
You’re the first to get CFTC approval and reach a certain scale in prediction markets.

Tarek:
Yes, exactly.

John Collison:
And until today, each contract still needs individual approval, right?

Luana:
Yes. Every contract we submit to the CFTC can be halted within 24 hours.

John Collison:
So they almost receive your contract data stream in real time?

Luana:
Yes, that’s correct.

Tarek:
Exactly. To reach this state of contract processing network, it’s been a very long process. Imagine the first time we walked into the CFTC building, our minds were full of this concept, and the regulators had to operate at high speed too. Because we’re discussing a product without traditional underlying assets, with dozens or hundreds of contracts weekly, it’s a very different scenario from what the system was originally designed for.

So, it’s like iterating on a product, but not for customers—it’s a process of exploring how to regulate this kind of product with regulators. What concerns do they have? What can we do to address those concerns?

Luana:
In a sense, it’s about finding a regulatory product-market fit.

Matt Huang:
So now you’re more accustomed to this rhythm—sending out contracts unless explicitly stopped. Have they rejected anything recently?

Luana:
Not recently. The biggest rejection was the election contract, which led us to sue them. They refused us for two years over that. But now, we’ve worked with them for so long that both sides understand the boundaries well. They trust us, knowing we are a self-regulatory entity that understands what can and cannot be done. For example, markets like war or assassination are off-limits. As long as we stay within established boundaries, the process speeds up significantly.

John Collison:
So, to clarify, the core of the election lawsuit was that they’re generally willing to approve various contracts but refuse to approve contracts about who will win the election, which is actually the most popular type—especially during U.S. presidential elections. So you sued the CFTC.

Tarek:
Yes. It’s actually their own rules—

John Collison:
And generally, suing your own regulator isn’t considered best practice.

Tarek:
Exactly. The story is that, starting in late 2021, we pushed for election markets, began communicating with policymakers, Congress, and regulators. Everyone said it sounded good. But they didn’t push forward, and we started feeling something was wrong. By late 2022, they actually delayed approval until after the election—effectively a pocket veto. That period was very tough for us; we had to lay off many people. Even more difficult, the team, investors, and most investors started losing faith in this path.

John Collison:
It’s not that they don’t believe in the idea itself, but they no longer believe in this strategy.

Tarek:
Yes, they no longer believe in the strategy, and some even doubt the idea itself. People felt things were getting unhealthy—should we do something else? Clearly, this path seemed impossible. But we couldn’t force ourselves to do something else; we just couldn’t. So we said, okay, let’s try again.

Imagine the morale was at rock bottom, everyone waiting for a new strategy. Many left, many were laid off, because we had to downsize. Then, at the next stand-up, we told everyone: our 2023 strategy is—let’s try again.

John Collison:
So, you’re basically doing the same thing, just hoping this time it will succeed.

Tarek:
Exactly. That’s what it means—this time it will work. Even though almost all evidence pointed the other way. I have to say, she was the main driver of this. I really wanted it to succeed, but my rational mind kept telling me—listen to these people, this path won’t work. But she was more determined. So we tried again. By the end of 2023, they blocked it again. At that point, I almost thought—

John Collison:
Well, prediction markets just can’t be done.

Tarek:
Yes, I really felt that way. Then she said, among all options, the only thing left is to sue the government. My first reaction was: that’s crazy. We brought it to the board—Alfred, Michael, and Seibel from my side were there.

John Collison:
That’s Alfred Lin and Michael Seibel.

Tarek:
Yes. I remember those board discussions—initially, it was basically, “We have to tell you this is a terrible idea.” Many reasons: your opponent is the regulator; you’re only twenty-something; the government really wants to shut you down, revoke your license, and they can do it. And it’s not just theoretical risk. Even if you win, you might be dragged through the process.

I also remember, before the formal board discussion, we had an internal meeting. The night before we contacted lawyers to prepare for litigation, I suddenly hesitated. I said, maybe we should just do a clearinghouse, or focus more on financial products—don’t go all in on this. The exact words I don’t remember, but roughly it was, “Are you joking?”

Luana:
That definitely sounds like something I would say.

Tarek:
I realized then that I couldn’t win this argument. But another part of me knew—we had to do this. Later, we discussed with the board, and their response was basically that it’s an anti-pattern, a bad idea. But many great companies are built on anti-patterns; something abnormal always happens—maybe this is your abnormality.

John Collison:
That’s a good point. Every company eventually finds a new, unusual way forward—that might be yours. So, how did you finally win the election lawsuit? Was there a particular legal basis or policy angle that was especially interesting?

Luana:
The core is quite simple: the government cannot arbitrarily ban a contract unless it’s deemed against public interest, and such bans must fall into specific categories like war, terrorism, assassination, etc. At the time, the CFTC’s stance was trying to shoehorn election contracts into those categories. They argued that elections might be illegal under some state laws, even citing a state’s bucket shop law to find any reason to block it.

But our legal position was clear: elections have economic impact, and as long as they have economic impact, they should be tradable on futures or derivatives exchanges. The lawsuit ultimately told the CFTC: you can’t do whatever you want.

John Collison:
So, the “prohibited categories” must be explicitly listed, and elections clearly aren’t among them.

Luana:
Exactly.

Tarek:
This is very important. We often say the law constrains companies, but law also constrains the government.

John Collison:
Right. Matt, you mentioned the two or three years of suing the government?

Matt Huang:
Yes. I think in crypto and prediction markets, suing the government seems especially common, but I later realized it’s actually more common than in traditional Silicon Valley thinking. Coinbase sued its main regulator; in GovTech, SpaceX, Anduril, Palantir have all sued the government for various reasons. So I’m curious—since you’ve had so much interaction with the government, what advice would you give to others wanting to do similar things? When do you think challenging the government is the right move?

Tarek:
I think only when there’s no other choice. It’s still very painful.

John Collison:
But are you really out of options? Without the election market, can’t you just keep going? Of course, elections are a very attractive category, but I guess they’re not the main source of your contracts now?

Luana:
I think it’s just too important. Maybe it sounds obsessive, but it’s truly the Holy Grail market. It best demonstrates the data utility of prediction markets and the value they can bring. Take the 2024 election: polls were wildly off, but markets clearly did better at aggregating information. I think it’s the most shining example, proving why prediction markets are beneficial and why the U.S. needs to have them within a regulated framework. No other market has such a strong demonstration.

Core Logic of Prediction Markets: Using Real Money to Produce Information

Matt Huang:
John mentioned PayPal and Uber’s “build first, then ask” logic. Actually, there were already other prediction markets operating offshore, showing real demand. So I’m curious—did that help you in your lawsuit? For example, does it help demonstrate that election markets don’t conflict with public interest—since people are already doing them?

Tarek:
I’m not sure. But from a legal perspective, the focus is more on the legal texts themselves. We’re discussing the Commodity Exchange Act, one of the core financial regulations; and the Securities Exchange Act. The key is to read and interpret these laws carefully and judge whether the regulator has overstepped.

But from our own perspective, offshore markets do help because they provide external data that we can reference in a “regulation first, then product” approach. We couldn’t learn directly from our own product at first because we insisted on getting approval before doing anything. So, external data and evidence can help us make decisions. They also help more people understand what prediction markets are and how they can be used. But in terms of policy, do offshore players help us significantly? Probably not.

John Collison:
If Kalshi had appeared ten or fifteen years ago, would it have been impossible? Is it because the current CFTC is more open, or because certain technological conditions—like stablecoins—are mature?

Luana:
I think part of it is related to crypto. Back then, early prediction markets like Augur already existed. Their existence definitely made the CFTC feel the need for a legitimate, regulated alternative. Before, they could just say no. I think that played a role, maybe 5-10%, but not more.

Tarek:
More broadly, I think the intellectual interest in prediction markets has been around since the 1950s. People have long recognized that they are a better signal source than many other information mechanisms. But ten or fifteen years ago, there wasn’t such a pressing practical pain point. In recent years, that pain has become real. Society is more divided, the world more fractured. Social media fragments information streams into camps, clickbait is rampant, and the incentives in mainstream content—whether traditional news or social media—are increasingly about grabbing attention. Because of that, more urgent problems have emerged, leading to the adoption of prediction markets. I don’t think we’d see today’s situation fifteen years ago, because the problems weren’t as severe then.

Luana:
Most of our users aren’t even trading—they’re consuming information. About 80% use it to gauge the world. For example, yesterday’s Texas primary: polls said both sides were neck and neck, but the market integrated information more effectively. It’s a very strong case showing why prediction markets are beneficial, why the U.S. needs them within a regulated framework. No other market demonstrates this as powerfully.

Core Logic of Prediction Markets: Incentivizing Truth with Money

Matt Huang:
John mentioned PayPal and Uber’s “build first, then ask” approach. But there were already offshore prediction markets showing real demand. So I wonder—did that help your case? Does it help prove that election markets don’t conflict with public interest—since people are already doing them?

Tarek:
I’m not sure. But legally, the focus is on the texts. We’re analyzing the Commodity Exchange Act, a core financial law; and the Securities Exchange Act. The key is to interpret these laws carefully and judge whether the regulator has overreach.

From our perspective, offshore markets do help because they provide external data points. We couldn’t learn directly from our own product initially because we insisted on licensing first. External data can inform our decisions. They also help more people understand prediction markets and their uses. But do offshore players help us significantly? Probably not.

John Collison:
If Kalshi had appeared ten or fifteen years ago, would it have been impossible? Is it because the current CFTC is more open, or because certain tech—like stablecoins—is mature?

Luana:
Partly, yes. Early prediction markets like Augur already existed, and their presence made the CFTC realize they needed a legitimate, regulated alternative. That definitely played a role, maybe 5-10%, but not more.

Tarek:
More broadly, the intellectual interest in prediction markets has existed since the 1950s. They’re a better signal source than many other mechanisms. But until recently, the pain points weren’t urgent enough. Now, society is more divided, information streams are fragmented, and prediction markets fill a need that’s become critical. Fifteen years ago, the problems weren’t as severe.

Luana:
Most users aren’t just trading—they’re consuming information. About 80% use it to understand the world. For example, in the Texas primary, market prices integrated information more accurately than polls. It’s a compelling demonstration of prediction markets’ value, especially within a regulated framework.

The Distinction from Online Gambling: Trading vs. Betting

John Collison:
Another market-making question. In sports betting, online companies often crack down on “sharps”—those who are too smart or too good at betting. Many don’t realize that for online betting companies, the ideal bettor is someone who’s not very professional, just supports their favorite team; the worst are those who find mispricings in niche markets. Because they might offer odds on thousands of markets, a professional bettor can exploit errors and target those. They use behavioral signals—if you just signed up and bet on your team, that’s fine; if you’re too professional, they might ban you.

It’s interesting—you think you’re just betting based on odds, but if you’re too good, they don’t want you. It’s like Las Vegas casinos asking you to leave. Does Kalshi face similar issues with sharp bettors? I thought you’d welcome them, but do market makers worry about facing overly clever opponents?

Tarek:
The problem is, sharps are part of the market. Let me clarify—

Luana:
We don’t restrict winners. We don’t do that. We want the smartest people to come.

Tarek:
We need sharps. Without them, the market wouldn’t be accurate. That’s the biggest difference from online gambling.

John Collison:
But it’s not quite the same. Because you need liquidity—maintaining narrow spreads throughout the event or election. Sharps might just jump in when odds are mispriced, make a quick profit, then disappear. Providing liquidity and correct judgment are different.

Tarek:
But many sharps, if they provide liquidity, can earn more. They can be part of the liquidity pool. This is crucial. Many say, “I’m not gambling, I’m trading,” which sometimes sounds like self-justification, but it points to an essential difference: gambling’s business model is house profits from customer losses; your income comes from user losses. So, behaviors like exploiting mispricings are rational for online gambling—if someone profits, you ban them; if they lose, you try to bring them back.

This is very different from traditional finance. The core of finance design is fairness and transparency. You need rules that are fair to all participants. Maybe Matt is better than Luana, or vice versa—they can compete and settle their differences.

John Collison:
So your incentive system is entirely different. You don’t profit from user losses; you earn transaction fees.

Luana:
Exactly. Our best outcome is that users find the market fair, with good prices and stable liquidity, so they want to trade here. To achieve that, we also design different incentives for different roles. That’s why we have various liquidity programs. If you provide liquidity and bear higher risk of being “sniped,” your fees should be lower; if you’re actively taking orders or trying to “snipe,” your fees should be higher because you’re paying for that behavior.

John Collison:
So you use fees to incentivize pro-social behavior.

Luana:
Yes, I think that’s how traditional markets work.

Tarek:
The core of traditional finance is also based on this logic.

Luana:
It’s just not always expressed so explicitly.

Tarek:
Fundamentally, it’s about giving those who create real value for the market a slight advantage, and reducing the advantage of those just extracting value.

John Collison:
What behaviors are pro-social, and which are anti-social?

Tarek:
Insider trading is obviously anti-social.

Luana:
That’s the most typical.

Tarek:
And illegal. As for “sniping,” it’s also part of the market. When someone gains new information suddenly and trades on it, that happens every day in traditional markets. But if you want liquidity to persist and market makers to invest resources, you need to give them some incentives.

I think that’s also one reason prediction markets are gaining acceptance—they align incentives with truth. More trading volume and liquidity lead to more accurate predictions. It takes time for people to trust this process, but once established, they won’t want to go back to worse products.

John Collison:
Speaking of trading volume, can you give us a sense of Kalshi’s growth? It seems to be growing very fast.

Tarek:
In February this year, trading volume was $10.4 billion.

John Collison:
That’s $10.4 billion in contract trading volume.

Tarek:
Yes. Compared to six months ago, it’s roughly an 11-fold increase, maybe more.

John Collison:
That’s so fast you don’t even bother looking back a year, it’s like ancient history.

Luana:
A year ago, it was completely different. For example, a year ago, we only had one sports market.

Tarek:
Yes, that was in February. Overall, the growth has been very rapid.

Matt Huang:
Apart from AI, probably the fastest-growing company.

Tarek:
I think so. Maybe even comparable to top AI companies. I don’t know the latest numbers from Cursor or Anthropic, but—

John Collison:
And even in AI, an 11x increase in six months is extraordinary.

Tarek:
Very fast. I think the reason is that we’re a real market with inherent network effects. As the market categories expand and liquidity deepens, user retention improves, and trading volume continues to grow over time. This naturally drives more usage, as liquidity in the system increases and the product becomes more useful. Users are more willing to share with others, creating a positive feedback loop. These forces combine to produce this rapid growth.

Matt Huang:
A large part of your early growth came from other broker platforms. Now the structure has changed. How do you view the broker channel? What’s its current share?

John Collison:
What do you mean by broker? Like Robinhood?

Tarek:
Yes. That’s a very interesting question.

Luana:
I can explain the broker part first, then he can give specifics. Basically, because we’re fundamentally an exchange and clearinghouse, our role is more like the NYSE or, more precisely, CME. Brokers can connect to us. You can trade Kalshi contracts on Robinhood, just like stocks; in the future, also on Coinbase or other platforms.

From the start, we’ve been very clear that we are an exchange and clearinghouse first, not anything else. Establishing connections with institutions like Goldman Sachs, Robinhood is also very important for understanding the ecosystem.

Last year, our first broker partners were Robinhood and Webull. During the rapid growth phase, broker channels accounted for a large share, which was good because brokers brought demand, and that demand attracted market makers who wanted to take the other side of retail flows. This gave us time to develop a direct-to-user product to today’s level.

Our current understanding is that the core is always the exchange + clearinghouse. Users can access directly via our app, website, or API, or through any broker. We’re also investing more in institutional and international brokers. Soon, you could trade Kalshi directly from Brazil, for example. You tell me the numbers.

Tarek:
He’s reluctant to share specific figures, but our “direct” business—kalshi.com, the Kalshi app—has already grown faster than the intermediary (broker) channel. I think it’s mainly because the brand has become well-known. Now, many people’s first reaction when they disagree on something is to open Kalshi and check the odds or place a bet. The brand is becoming synonymous with this behavior. There’s already a lot of organic growth, and I believe this trend will continue in the coming months.

An Unconventional Market: Ordinary People More Important Than Institutions

John Collison:
You just discussed retail user growth—some come via broker platforms like Robinhood, others directly on Kalshi. But as an exchange, you also need to address market-making. The NYSE doesn’t worry much about market makers because the economic incentives are strong once the scale is big. I’m curious—how did you build your market-making system initially? Did you do it yourself? Partner with external market makers? How do you incentivize their participation? How did you start from zero?

Luana:
Markets on Kalshi can be divided into two types, with very different behaviors and incentives.

One type is long-tail markets, like “Will One Direction reunite?” These are hard to price and usually have low demand, so we need to attract market makers through various incentives, including recruitment bonuses. One of our long-term concerns is how to establish stable, sustainable liquidity for these long-tail markets. Today, we might have around 10,000 markets; in the future, if we have 50,000 or 100,000, how do we ensure liquidity?

The other type is more classic markets, like crypto or sports. For these, market-making is much easier because demand is clearer and pricing logic more mature. Incentives here are not direct payments but fee rebates, combined with strict obligations—maintaining certain spreads or depth within specified timeframes. In these markets, we’re more like encouraging order book stability rather than just rewarding market makers for being present.

John Collison:
What do you mean by “encouraging order book stability”?

Luana:
For example, during live events or hourly crypto settlements—

John Collison:
If no new information arrives, you don’t want prices to jump wildly, right?

Luana:
Exactly. Even if there’s new info—say, a touchdown is imminent—you don’t want the order book to suddenly lose all liquidity. You can allow some spread widening, but you still want traders to be able to trade. As we move toward a broker model, brokers will bring expectations from traditional markets. They’ll say, “We want spreads and depth maintained at certain levels at all times.” So we need to negotiate with market makers on how to design incentives. Because if we let the market decide freely, spreads might widen significantly during high volatility; but to serve all users—including broker channels—we need to design incentives accordingly.

Matt Huang:
During those times when spreads widen dramatically, do market makers lose money? Do they use profits from calmer periods to offset losses?

Luana:
Currently, because overall demand is strong, even with narrower spreads, they can still profit from the spread. But that’s precisely why we have incentive programs—to align the benefits. Maybe they lose a little at times, but overall, the returns are high enough to justify it.

Matt Huang:
So your goal is to keep spreads tight across the main markets at all times.

John Collison:
Meaning, maintaining consistently narrow spreads in core markets—this requires careful design.

Tarek:
Exactly. It’s difficult, but more interestingly, prediction markets are unique because liquidity doesn’t mainly come from traditional market makers, but from ordinary people.

This loops back to the initial logic. We’ve solved the regulatory problem, but liquidity remains a challenge. Traditional exchanges like NYSE or CME spend years designing products, recruiting dozens of market makers months in advance, and promoting the product over years. That’s the traditional way. But prediction markets are entirely different—they generate liquidity on a weekly, daily, or even hourly basis for new events. How to do that? The pace is highly dynamic, with new events constantly emerging.

John Collison:
Many find this counterintuitive—you need to incentivize market makers to provide liquidity. Because in stock markets, high-frequency trading firms invest heavily in low-latency links between New York and Chicago without needing incentives. Is this because prediction markets are still early-stage, or is there a more fundamental difference?

Tarek:
It goes back to what I said earlier. You’re dealing with a mode that requires instant liquidity provisioning, much faster and more dynamic than traditional markets. Wall Street market makers don’t operate on this model—they can’t just set up a desk in an hour to price political or cultural topics.

The truly interesting part is that prediction markets have a very counterintuitive feature: in many markets, the best predictors aren’t experts or authorities, but ordinary people.

Matt Huang:
Internet anonymous forecasters.

Tarek:
Exactly. They’re highly dispersed. It’s hard to say that a specific demographic group is the best at pricing. Over time, we’ve cultivated a community of “super forecasters” on Kalshi—people who can quickly and efficiently price these events. Initially, it’s hard to turn hobbies into part-time work, then full-time. But as the market grows, this finally happens.

Luana:
Here’s a data point we can share: on the platform, the largest market makers—large institutions—account for less than 5% of all matched orders.

Tarek:
That means they only contribute a small part of the overall liquidity.

John Collison:
Really?

Luana:
Yes. Less than 5% of all matched orders come from well-known large institutions. Over 95% are peer-to-peer or small funds and teams.

Tarek:
This is very rare in exchanges.

Matt Huang:
How many of these small, full-time market-making teams are there?

Luana:
About 2,000 people actively market-making on Kalshi.

John Collison:
Matt was asking—who exactly are Kalshi’s market makers? Are they firms like Jane Street, Akuna, or just someone coding in a garage at 3 a.m. with Red Bull?

Tarek:
The garage coder types are actually the most important.

Matt Huang:
And you said they account for 95%?

Tarek:
Yes. They are crucial because they price quickly, monitor the order book constantly, and observe the situation in real time. They are the true frontline observers of the market.

John Collison:
So, Kalshi is built on a community of people constantly watching the situation.

Tarek:
Exactly. For example, in recent years, the best predictors of inflation on Kalshi weren’t big institutions or hedge funds, but a person from Kansas who had never traded before, just liked reading news and had a feel for inflation trends. You find many such people on the platform. Thousands are full-time, but tens of thousands have some knowledge on various topics and actively price them, earning rewards for their insights.

Luana:
Let me tell you about my favorite user.

Tarek:
And I have a new favorite user I want to mention.

John Collison:
Great, each of you tell us about one favorite user.

Tarek:
I was just thinking about this morning. The person from The Wall Street Journal’s tax report—who—

Luana:
Oh, yes, he’s a very strong candidate. But my favorite is a super fan of Ariana Grande. He discovered Kalshi during election season but isn’t interested in elections at all. Later, he found our Billboard ranking markets—those ranking prediction markets.

John Collison:
He considers that a very important market.

Luana:
Yes, it’s very important to him. He’s earned over $150,000, paid off student loans, completed a master’s, bought a car. He never traded before, never did anything like this, but has an intense, almost obsessive interest in music charts. For the first time, he can monetize this hobby. He’s also very friendly on Twitter.

Tarek:
I have many favorite users, but recently one stands out. Last week, The Wall Street Journal published an article about a tax accountant named Alan, who’s very active on Kalshi. When DOGE first appeared, everyone debated how much it could cut costs. He dug into extensive tax laws and regulations, reached a very deep understanding, and concluded that the market’s expectations were unrealistic. He was almost certain about his judgment. He told his wife he was highly confident in this trade. In a way, he’s like Michael Burry in “The Big Short,” but this time betting against DOGE. He heavily invested and ultimately won.

This shows the power of prediction markets: if you have specialized knowledge—often niche, like tax law—you can do research, understand the world better, and profit from it. It’s fantastic.

John Collison:
Early AI applications included poker bots. Do you see any advanced AI market makers now? Because no one’s fully read all those tax laws, and now, besides Claude, maybe not anymore.

Luana:
That’s true. Maybe we should ask it.

Tarek:
We see more and more people using agents for trading, especially on the API side.

John Collison:
Have any users successfully operated highly agentic market-making systems—mostly automated, with minimal human intervention?

Tarek:
Users generally don’t share their strategies in detail.

John Collison:
But you do talk to them?

Tarek:
Yes, John, we do. My understanding is that early Renaissance Technologies was already using some agent-based models for trading, though that was very early. Today, it’s evolving and becoming more powerful. Many traders on our platform already have AI-based summary and judgment modules integrated into