Regulation, Insider Information, and 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 widespread attention is not only because of the appeal of “betting on the future” itself but also because, amid 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.

As seen in the photo: Kalshi co-founders Tarek Mansour (right) and Luana Lopes Lara (left)

As one of the most prominent compliant prediction markets in the U.S., Kalshi gained rapid popularity during the 2024 U.S. election. Before this surge, it had 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 founding path: why the founders chose “compliance first, growth later” instead of the Silicon Valley common “build first, ask for permission later”; why they endured long approval processes, layoffs, and external skepticism to secure the election market; and how the lawsuit against the CFTC became a turning point for the company’s growth.

The second part delves into the operational logic of prediction markets. Tarek and Luana explain how Kalshi differs fundamentally from traditional online entertainment platforms: it is not a “house” 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 dispersed base of individual traders, “super forecasters,” and small teams. In a sense, prediction markets are not just financial products but mechanisms that directly convert dispersed knowledge into price signals.

Later in the discussion, the scope of prediction markets’ future boundaries is explored: from elections and sports to AI, GPU computing power, macroeconomic variables, and policy pathways—can more and more real-world uncertainties be broken down into tradable, feedback-driven, decision-supporting markets? At the same time, unavoidable controversies surface—how to define insider trading, whether sports contracts could amplify online gambling risks, and how platforms and regulators can balance innovation, transparency, and user protection.

This conversation’s significance 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’s contrarian path: regulation first, growth second: It took 3 years to get licensed and to sue the CFTC to open the election market. The core question is whether prediction markets can be legally viable, which is more important than growth.

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

• ** Ordinary individuals, not institutions, form the core liquidity:** Over 95% of trades are matched by dispersed users and super forecasters, not traditional market makers.

• Kalshi emphasizes itself as an exchange, not an online entertainment platform: Revenue comes from fees, not user losses, encouraging skilled participants rather than restricting winners like in gambling.

• Elections are the “Holy Grail,” but future markets will go far beyond: From sports and macro variables to AI and computing power, the team aims to build a system of derivatives that can price everything.

• Prediction markets are becoming a new form of information infrastructure: Users trade and consume probabilities; 80% of users mainly use it to understand the world rather than to bet.

• Their rise reflects distrust in traditional information systems: Polarized social media and inaccurate polls push people toward price-based judgment mechanisms.

• Long-term goal: improve societal decision-making efficiency, not just run a trading platform: Through continuous pricing and feedback, political and economic consensus can form faster.

Interview Highlights

John Collison (Stripe co-founder & host):

Tarek Mansour and Luana Lopes Lara are co-founders of Kalshi. Kalshi is an emerging prediction market company that gained fame 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.

What are your typical roles? But more interestingly, how do your approaches to problem-solving differ?

Luana (Kalshi co-founder & COO):
Our backgrounds are nearly identical. We both studied math and computer science at MIT, interned in similar roles, and have similar experiences. But I’m very optimistic, love risk-taking, and tend to believe things will work out; he’s very cautious, even somewhat pessimistic. This creates a good balance. Looking back, our true complementarity isn’t just in daily tasks but in this fundamental outlook.

Tarek (Kalshi co-founder & CEO):
A bit of background: I originally planned to be a trader—that was my career path. If you’ve met traders, you’ll know they often have a mental calculator for expected returns.

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 and worst-case scenarios. She doesn’t usually think that way. I think this difference actually leads to good outcomes.

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

John Collison:
I want to ask about this. Your starting point is interesting: Kalshi spent years unable to operate until it got CFTC approval. Most companies don’t start this way. In contrast, Silicon Valley often follows a “build first, ask for forgiveness later” approach—launch first, then seek permission.

Can you share how you started? What was the approval process like? And do you think this path applies to other companies?

Luana:
From the beginning, we knew that if you’re doing financial or healthcare services, you can’t just build first. Financial markets involving user funds have huge risks—FTX is a clear example; healthcare has many disasters. We wanted to do it the right way. More importantly, when we looked at this market, the core issue wasn’t growth potential but whether it’s legal to operate in the U.S. So we decided to address that head-on first. For a long time, many thought this was a mistake.

Before winning the lawsuit on election contracts, many believed offshore markets were better and grew faster. But once we won the case, proving our legal understanding was correct and that we could operate legally in the U.S., things really took off.

John Collison:
What’s the timeline? When did you start? When did you win the election contract lawsuit?

Luana:
We founded in 2019, entered YC that year. It took three years to get regulatory approval and go live—around 2022. We won the election contract lawsuit at the end of 2024, and that’s when the company really accelerated.

Tarek:
There are two levels here. First, practical considerations: to gain mainstream and institutional adoption, prediction markets must operate within a regulated, trustworthy, and safe framework. It’s a complex market involving user funds. Solving that is the path to success.

Second, principle: when we wrote that one-page document on Google Docs, we listed questions—why do we want to build this? Why are we excited? Our answer was: we want to create the next NYSE. A trusted, regulated financial market in the U.S. We’re not excited about offshore copies. The key question is: what kind of company do we want to build? Why do we do this? There are many paths to success, but we don’t want the offshore route. We want this to happen here, in the U.S.

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

Tarek:
Yes.

John Collison:
And every contract still needs individual approval, right?

Luana:
Correct. Each contract is submitted to the CFTC, which has 24 hours to review and can stop it.

John Collison:
So they almost receive a real-time data stream of your contracts?

Luana:
Exactly.

Tarek:
Yes. Getting to today’s contract processing system was a long journey. When we first approached the CFTC, we had this concept in mind, and regulators had to keep pace. We were proposing a product without traditional underlying assets, with dozens or hundreds of contracts weekly. Now, we do much more, but initially, this regulatory model wasn’t designed for such a dynamic environment.

It’s like iterating on a product—except you’re working with regulators to figure out how to regulate this new kind of product, what their concerns are, and how to meet them.

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

Matt Huang:
So now you’re used to this pace—sending out contracts unless explicitly blocked. Have they rejected anything recently?

Luana:
Not recently. The biggest rejection was the election contract, which led us to sue them. They refused for two years. But now, after working with them for so long, both sides understand the boundaries. They trust us, knowing we’re a self-regulatory entity that understands what can and can’t be done. For example, markets on war or assassination are off-limits. As long as we stay within established boundaries, the process is much faster.

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

Tarek:
Exactly. It’s their own rules—

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

Tarek:
True. It’s a tough choice. We started pushing for election markets at the end of 2021, engaging with policymakers, Congress, and regulators. Everyone said it sounded good. But progress was slow, and by late 2022, approval was delayed until after the election—effectively a pocket veto. That was a very tough period; we had to lay off many people. More difficult was that the team, investors, and most stakeholders lost faith in this path.

John Collison:
Not a loss of belief in the idea itself, but in the strategy.

Tarek:
Exactly. People started questioning whether this was the right approach or even the right idea. It seemed like a dead end. But we couldn’t force ourselves to do something else. So we decided to try again.

Imagine the morale was at rock bottom, everyone waiting for a new strategy. Many left or were laid off. Then, at the next stand-up, we told everyone: our 2023 plan is—try again.

John Collison:
So, keep doing the same thing, but with the expectation that this time it will succeed.

Tarek:
Exactly. This time, it will. Despite almost all evidence pointing otherwise. A lot of that push came from her. I really wanted it to succeed, but my rational mind kept telling me it wouldn’t work. She was more persistent. So we tried again. By the end of 2023, they blocked it again. I was almost ready to give up—

John Collison:
Prediction markets just can’t work.

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, Seibel, and I.

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

Tarek:
Yes. I remember those board meetings—initially, they were basically: “You need to understand, this is a bad idea. There are many reasons: your opponents are regulators; you’re only twenty-something; the government can shut you down or revoke your license in countless ways. And it’s not just theoretical. Even if you win, you might be bogged down in the process.”

Before formally discussing with the board, we had an internal meeting the night before starting the lawsuit. I almost backed out then. I thought, maybe we should just wind down, focus on financial products, not go all-in on this. The exact words I don’t remember, but it was basically: “Are you joking?”

Luana:
That sounds exactly like something I would say.

Tarek:
I realized then that I couldn’t win that argument. But part of me also knew we had to do it. Later, we discussed with the board—they said it was a counterintuitive move, a bad idea. But many great companies are built on counterintuitive moves; sometimes something abnormal happens, and maybe this is ours.

John Collison:
That’s a good point. Every company eventually finds a new, unconventional way forward—that might be yours. How did you succeed in the election lawsuit? Was there a particular legal or policy angle?

Luana:
The core is simple: the government can’t arbitrarily ban a contract unless it’s against public interest, and such bans must fall into specific categories—like war, terrorism, assassination. At the time, the CFTC tried 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 block them.

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

John Collison:
So, the “prohibited categories” must be explicitly listed, and elections don’t fall into those.

Luana:
Exactly.

Tarek:
This is very important. Laws constrain companies, but they also constrain 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 unusual, but I’ve realized it’s more common than in traditional Silicon Valley. 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 interacted with the government so much, what advice would you give to others wanting to do similar things? When is challenging the government the right move?

Tarek:
Only when there’s no other choice. It’s still very painful.

John Collison:
But was there really no other choice? Without the election market, couldn’t you have continued? Of course, elections are a very attractive category, but I guess they aren’t the main source of your contracts now?

Luana:
I think it’s just too important. Maybe it sounds obsessive, but it’s the Holy Grail market. It best demonstrates how data from prediction markets can be used, and the value they provide. Take the 2024 election: polls were wildly off, but markets did a better job of aggregating information. It’s the clearest example of why prediction markets are beneficial and why the U.S. needs a regulated framework. No other market has such a strong demonstration.

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

Matt Huang:
John mentioned PayPal and Uber’s “build first, ask later” approach. Actually, other prediction markets already operated offshore and showed real demand. Did that help you in your lawsuit? For example, did it show 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 on the text of the law itself. We’re discussing the Commodity Exchange Act, a core financial regulation law; and the Securities Exchange Act. The key is to interpret these laws carefully and see if regulators are overstepping.

From our perspective, offshore markets helped because they provided external data points. We couldn’t learn directly from our own product at first because we insisted on licensing first. So external data and evidence helped us make decisions. They also helped more people understand prediction markets and their uses. But in policy terms, offshore players probably didn’t help much.

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 now mature?

Luana:
Partly, yes. Early prediction markets like Augur already existed. Their existence probably pushed the CFTC to consider a legal, regulated alternative. Before, they could just say no. That definitely played a role, maybe 5-10%, but not more.

Tarek:
More broadly, I think the public’s interest in prediction markets has been around since the 1950s. It’s known that they can be better signals than many other information mechanisms. But ten or fifteen years ago, there wasn’t a real pain point. Recently, that pain point has become real. Society is more divided, the world more polarized. Social media fragments information into camps, headlines dominate, and the incentives in content—whether traditional news or social media—favor sensationalism. 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 trading actively. About 80% are consuming information. They check who’s likely to win the Texas primary, for example, and see polls say both sides are tied, but the market indicates otherwise. As an information carrier, this function is now much more important.

John Collison:
So you’re saying that in the era of algorithmic information flows, prediction markets are very well suited; but ten or fifteen years ago, people might not have been as interested.

Tarek:
Exactly. More precisely, trust in traditional information sources is declining sharply and continuously. So we need a new source—one that works. The incentive mechanism of prediction markets points to truth: more trading, more liquidity, more accurate predictions. It takes time for people to trust it, but once established, they won’t want to go back to worse options.

John Collison:
Can you give us a sense of Kalshi’s growth? It’s growing very fast.

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

John Collison:
That’s contract trading volume.

Tarek:
Yes. That’s about 11 times more than six months ago, maybe more.

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

Luana:
A year ago, it was completely different. We only had one sports market.

Tarek:
Yes, that was in February. Overall, 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 firms. I don’t know the latest from Cursor or Anthropic, but—

John Collison:
Even in AI, 11x in six months is extraordinary.

Tarek:
Very fast. I think the reason is that we’re a real market with inherent network effects. As more categories and liquidity emerge, user retention and participation grow over time. This naturally drives usage, which in turn attracts more users because of increased liquidity and better products. It’s a virtuous cycle.

Matt Huang:
In early growth, much came from other broker platforms. Now, that structure has changed. How do you see the role of brokers? What’s their current share?

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

Tarek:
Yes. That’s an interesting question.

Luana:
I can explain the broker role, but the exact numbers are his to share. Essentially, since we’re an exchange and clearinghouse, our role is similar to NYSE or, more precisely, CME. Brokers can connect to us. You can trade Kalshi contracts on Robinhood, Webull, or future platforms like Coinbase.

From the start, we’ve been clear: we’re primarily an exchange and clearinghouse, not a retail app or a broker. Connecting with institutions like Goldman Sachs or Robinhood is crucial for understanding the ecosystem.

Last year, our first broker partners were Robinhood and Webull. During rapid growth, broker channels accounted for a large share—good because they bring demand, and market makers are eager to provide liquidity to retail flows. This also gave us time to develop direct-to-user products to today’s level.

Our core remains: exchange + clearinghouse. Users can access us directly via app, website, or API, or through any broker. We’re also investing more in institutional and international brokers. Soon, someone in Brazil will be able to trade Kalshi directly. The numbers? You tell me.

Tarek:
She’s reluctant to share specific figures, but our direct-to-consumer business—kalshi.com, the app—is already growing faster than intermediary channels. Mainly because our brand is well known now. When people disagree about something, their first instinct is to check odds on Kalshi or place a bet there. The brand has become synonymous with this behavior. Natural growth is happening, and we expect this trend to continue.

An Unconventional Market: Ordinary People Are More Important Than Institutions

John Collison:
You mentioned retail growth—people coming via brokers or directly. But as an exchange, you also need market makers. Unlike NYSE, which benefits from strong economic incentives once large enough, how did you build your market-making system from scratch? Do you do it yourself or partner with external market makers? How do you incentivize them?

Luana:
Kalshi’s markets can be divided into two types, with very different behaviors and incentives.

One type is long-tail markets—like “Will One Direction reunite?”—which are hard to price and have low demand. We need to attract market makers through various incentives, including recruitment bonuses. Our ongoing challenge is how to establish stable, sustainable liquidity across potentially tens or hundreds of thousands of markets. How do we ensure liquidity as the number of markets grows?

The other type is more traditional—like crypto, sports, etc. For these, market making is 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 times. In these markets, we’re more about incentivizing order book stability than just rewarding market makers.

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

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

John Collison:
If no new information arrives, you don’t want prices to jump wildly without reason, right?

Luana:
Exactly. Even if there’s new info—say, a touchdown is imminent—you don’t want the order book to lose all liquidity. You can allow wider spreads but still want traders to be able to transact. As we move toward a broker model, brokers will have expectations based on traditional markets. They’ll say: “We want spreads and depth to stay within certain levels at all times.” So we need to negotiate with market makers on how to design incentives—because if we leave it entirely to the market, spreads might widen excessively during high volatility. To serve all users, including brokers, we need to craft incentives carefully.

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

Luana:
Currently, demand is strong enough that even with narrow spreads, they can profit. But that’s also why we have incentive programs—to align benefits. Maybe they lose a little at times, but overall, if returns are high enough, it’s worth it.

Matt Huang:
So, the goal is to keep spreads tight across major markets at all times, right?

John Collison:
Yes, maintaining consistently narrow spreads is a key goal, achieved through careful design.

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

This loops back to the initial point. We’ve solved the regulatory challenge, but liquidity remains. Traditional exchanges like NYSE or CME spend years designing products and recruiting established market makers. Prediction markets are different—they generate liquidity on a weekly, daily, or even hourly basis for new events. How to do that? It’s a highly dynamic process, with new events constantly emerging.

John Collison:
Many find this counterintuitive—you need to incentivize market makers, even though in stock markets, high-frequency traders build low-latency links and compete fiercely. Is this because prediction markets are still early-stage, or is there a fundamental difference?

Tarek:
It’s the core point. You’re dealing with a model that requires instant liquidity provisioning, much faster and more dynamic than traditional markets. Wall Street’s market makers don’t operate on such a rapid, event-driven basis. They can’t just set up a desk in an hour to price political or cultural events.

The most interesting aspect is that prediction markets often have a counterintuitive feature: the best predictors aren’t necessarily 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 is the best at pricing. Over time, we’ve cultivated a community of “super forecasters” who can quickly and accurately price these events. Initially, it’s hard to turn hobbyists into part-timers or full-timers, but as the market grows, this becomes possible.

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

Tarek:
Meaning, over 95% of matched liquidity comes from individual traders or small teams.

John Collison:
Really?

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

Tarek:
This is very rare in exchanges.

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

Luana:
About 2,000 people.

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

Tarek:
Garage coders are actually the most important.

Matt Huang:
And they account for 95%?

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

John Collison:
So, Kalshi is built on a community of continuous observers.

Tarek:
Exactly. For example, in recent years, the best predictors of inflation on Kalshi weren’t big institutions or hedge funds but a person in Kansas who had never traded before but followed the news and sensed where inflation was headed. There are many such people on the platform. Thousands are full-time, but tens of thousands have some knowledge and actively price various topics, earning rewards for their insights.

Luana:
Let me share my favorite user.

Tarek:
And I have a new favorite user recently.

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

Tarek:
I was thinking about this this morning. The person from the Wall Street Journal article about taxes—

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

John Collison:
He finds those markets very important.

Luana:
Yes, he’s earned over $150,000, paid off student loans, got a master’s degree, and bought a car. He’s never traded before but has a strong, almost obsessive interest in music charts. For him, it’s a way to monetize his hobby. He’s also very friendly on Twitter.

Tarek:
I have many favorites, but recently one stood out. Last week, the WSJ published an article about a tax accountant named Alan who’s very active on Kalshi. When DOGE first appeared, many wondered how much it could cut costs. He researched extensively—tax laws, regulations—and concluded that the market’s expectations were unrealistic. He was so confident he told his wife: “I’m very sure about this trade.” It’s like Michael Burry in “The Big Short,” but betting against DOGE. He heavily invested and ultimately won.

This shows the power of prediction markets: if you have specialized knowledge—often niche, like obscure tax laws—you can research deeply and profit. It’s fantastic.

John Collison:
Early AI applications included poker bots. Are there now highly capable AI market makers? Since no one reads all those tax laws, maybe Claude or others are already doing this.

Luana:
That’s a good point. Maybe we should ask them.

Tarek:
We see more and more traders using agents, especially via APIs. It’s obvious now.

John Collison:
Are there users successfully running fully autonomous, agent-driven market-making systems?

Tarek:
They probably don’t share their strategies openly, but yes, we talk to them. I think early Renaissance Technologies was already using some agent models. Today, it’s evolving rapidly, becoming more sophisticated. Many traders’ systems incorporate AI-based summaries and judgments.

John Collison:
I’m really curious about fully autonomous, no-human-in-the-loop systems that set prices and trade independently. That’s probably coming soon, if not already here. For example, Claude might be acting as a market maker.

Luana:
I’m not sure if fully autonomous systems exist yet. For example, in international elections, many systems automatically translate documents, analyze polls, and process data. But I don’t know if it’s fully automated.

Tarek:
We’re not sure if models have reached that level. Recently, we launched Kalshi Research, aiming to collaborate with research labs to create benchmarks for predicting the future. These benchmarks test models’ understanding of the world, not just memory. I’m excited to see the results.

John Collison:
How will you evaluate them?

Tarek:
Not finalized, but the idea is to run models on the same markets for a month or two, then compare performance—accuracy, long-term PnL, etc.

Difference from Online Gambling: Trading vs. Betting

John Collison:
Another market-making question: in sports betting, companies often crack down on “sharps”—sharp bettors who are very skilled. Many don’t realize that the ideal bettor for a gambling company is someone who’s not very professional, just supporting their team; the worst are those who find mispricings in niche markets. Because if they set odds on thousands of markets, a few mistakes will be exploited by pros. They identify sharps through behavioral signals—if you just 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. Like Vegas casinos asking you to leave. Does Kalshi face similar issues with sharps? I thought you’d welcome them, but do market makers worry about facing overly clever opponents?

Tarek:
The thing is, sharps are part of the market. To clarify—

Luana:
We don’t restrict winners. We want the smartest people to participate.

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

John Collison:
So your incentive system is different. You don’t profit from one side losing; you earn fees.

Luana:
Exactly. Our goal is for users to find the market fair, with good prices and stable liquidity. To achieve that, we design incentives for different roles. For example, liquidity providers face higher risk of being “sniped,” so their fees are lower; active traders who take on that risk pay higher fees.

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

Luana:
Yes, that’s how financial markets generally work.

Tarek:
Traditional markets also follow this logic.

Luana:
It’s about rewarding those who create value for the market, and limiting those who extract value unfairly.

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

Tarek:
Insider trading is clearly anti-social and illegal.

Luana:
That’s the most obvious.

Tarek:
And “sniping”—exploiting new information—is also part of the market. When someone gains new info and trades on it, that’s common in traditional markets. To keep liquidity and incentivize market makers, you need to reward them appropriately.

This is also why prediction markets are gaining acceptance: they align incentives with truth. More volume and liquidity lead to better predictions. It takes time for traders to trust this, but once they do, they won’t want to go back to worse options.

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

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

John Collison:
That’s contract volume.

Tarek:
Yes. About 11 times more than six months ago, maybe more.

John Collison:
That’s rapid—almost too fast to track year-over-year.

Luana:
A year ago, it was a completely different world. We only had one sports market.

Tarek:
Yes, that was in February. Overall, growth has been extraordinary.

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

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

John Collison:
Even in AI, 11x in six months is remarkable.

Tarek:
Very fast. The reason is that prediction markets are real markets with network effects. As categories and liquidity expand, user retention and participation increase, creating a virtuous cycle.

Matt Huang:
In early growth, much came from other broker platforms. Now, that structure has shifted. How do you see the role of brokers? What’s their current share?

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

Tarek:
Yes. That’s an interesting question.

Luana:
I can explain the broker role, but the exact figures are his to share. Essentially, since we’re an exchange and clearinghouse, our role is similar to NYSE or CME. Brokers connect to us. You can trade Kalshi contracts on Robinhood, Webull, or future platforms like Coinbase.

From the start, we’ve been clear: we’re primarily an exchange and clearinghouse, not a retail app or a broker. Connecting with institutions like Goldman Sachs or Robinhood is crucial for understanding the ecosystem.

Last year, our first broker partners were Robinhood and Webull. During rapid growth, broker channels accounted for a large share—good because they bring demand, and market makers are eager to provide liquidity to retail flows. This also gave us time to develop direct-to-user products to today’s level.

Our core remains: exchange + clearinghouse. Users can access us directly via app, website, or API, or through any broker. We’re also investing more in institutional and international brokers. Soon, someone in Brazil will be able to trade Kalshi directly. The numbers? You tell me.

Tarek:
She’s reluctant to share specific figures, but our direct-to-consumer business—kalshi.com, the app—is already growing faster than intermediary channels. Mainly because our brand is well known now. When people disagree about something, their first instinct is to check odds on Kalshi or place a bet there. The brand has become synonymous with this behavior. Natural growth is happening, and we expect this trend to continue.

An Unconventional Market: Ordinary People Are More Important Than Institutions

John Collison:
You mentioned retail growth—people coming via brokers or directly. But as an exchange, you also need market makers. Unlike NYSE, which benefits from strong incentives once large enough, how did you build your market-making system from scratch? Do you do it yourself or partner with external market makers? How do you incentivize them?

Luana:
Kalshi’s markets can be divided into two types, with very different behaviors and incentives.

One type is long-tail markets—like “Will One Direction reunite?”—which are hard to price and have low demand. We need to attract market makers through various incentives, including recruitment bonuses. Our ongoing challenge is how to establish stable, sustainable liquidity across potentially tens or hundreds of thousands of markets. How to ensure liquidity as the number of markets grows?

The other type is more traditional—like crypto, sports, etc. For these, market making is 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 times. In these markets, we’re more about incentivizing order book stability than just rewarding market makers.

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

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

John Collison:
If no new info arrives, you don’t want prices to jump wildly without reason, right?

Luana:
Exactly. Even if there’s new info—say, a touchdown is imminent—you don’t want the order book to