Mar 17, 2026 · 1h 16m · cheeky-pint
Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara
Tarek Mansour · 26m spoken Luana Lopes Lara · 24m spoken John Collison · 14m spoken Matt Huang · 4m spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Kalshi co-founders Luana Lopes Lara and Tarek Mansour discuss building a federally regulated prediction exchange, winning a landmark lawsuit against the CFTC, and establishing event contracts as an objective truth engine for global finance and public discourse.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. John holds 20.2% of the talking time here. How this is scored →
John as informed peer 5.3 Guest teaching 4.7 Guest disagreement 1.8 John pushing back 3.4
speaking balance: gold is John, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 9:50 Luana confronts Tarek over backing down from suing the CFTC
Tarek recounts when he got cold feet the night before confronting their board about suing the CFTC, and Luana aggressively rejected any pivot with 'are you fucking kidding me?'
Hardest push from John ▶ 1:08:25 John presses founders on downplaying the political narrative impact of marketsJohn calls out Luana and Tarek for being overly modest, claiming they are 'hiding their lamp under a bushel' by refusing to admit prediction markets create powerful self-fulfilling narrative momentum.
Biggest teaching moment ▶ 27:10 Luana reveals market makers represent under 5% of order liquidityLuana corrects the standard assumption about Wall Street market makers by revealing that over 95% of matched orders on Kalshi come from dispersed individual retail forecasters and tiny niche funds.
John holds their own ▶ 35:25 John dissects the structural differences between sports bookies and exchangesJohn articulates with precision how sportsbooks profile winning sharps using behavioral cues to limit them, forcing the founders to detail their maker-taker fee alignment.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | John as informed peer | Guest teaching | Guest disagreement | John pushing back | Why |
|---|---|---|---|---|---|---|
| Regulatory-First Strategy vs. The Ask-Forgiveness Playbook | 5 | 4 | 2 | 4 | John challenges Kalshi's regulatory-first posture against Silicon Valley's typical 'ask forgiveness' playbook like early PayPal and Uber. Luana and Tarek explain that moving customer money in financial markets carries systemic risk (citing FTX) that makes offshore or unregulated paths non-viable. | |
| Establishing Regulatory Fit and the 24-Hour Review Process | 4 | 5 | 3 | 3 | Tarek recounts the near-death experiences of Kalshi when the CFTC repeatedly delayed and blocked election contracts, forcing internal layoffs and board resistance before deciding to sue. John probes the extreme contrarianism of suing one's own regulator. | |
| Legal Victory on Election Contracts and Regulatory Precedent | 6 | 4 | 2 | 4 | John questions whether suing the CFTC was truly mandatory or if Kalshi could have survived without election contracts. Luana clarifies the statutory basis of the Commodities Exchange Act and why elections serve as the holy grail proof of economic utility. | |
| Prediction Markets as an Antidote to Information Distrust | 5 | 5 | 1 | 2 | John inquires why prediction markets gained traction now versus 15 years ago. Tarek explains that societal polarization, algorithmic clickbait, and institutional media distrust created an acute consumer demand for ground-truth probability feeds. | |
| Exponential Volume Growth and the Dual Distribution Model | 4 | 4 | 1 | 3 | John notes Kalshi's rapid 11x volume growth over six months to $10.4B. Luana and Tarek break down the dual distribution architecture between broker integrations (like Robinhood) and direct-to-consumer Kalshi apps. | |
| Market Making Architecture: High-Volume vs. Long-Tail Contracts | 6 | 5 | 2 | 4 | John probes the mechanics of market making and spread stability, drawing comparisons to traditional equity exchanges. Luana contrasts the subsidized liquidity requirements of long-tail contracts with fee rebates and uptime covenants on high-volume sports and crypto markets. | |
| Decentralized Superforecasters and Grassroots Liquidity Provision | 6 | 6 | 2 | 3 | John presses on why institutional market makers don't dominate Kalshi like they do on equity exchanges. Luana and Tarek reveal that over 95% of matched liquidity comes from dispersed retail superforecasters and small independent desks rather than major Wall Street institutions. | |
| Unlikely Forecasters: The Billboard Fan and The DOGE Short | 3 | 5 | 1 | 1 | Luana and Tarek share colorful anecdotes of idiosyncratic user alpha, including an Ariana Grande superfan trading Billboard charts and a tax accountant shorting DOGE targets after deep statute analysis. | |
| AI Agents in Market Making and Forecasting Benchmarks | 5 | 4 | 2 | 4 | John asks whether autonomous AI agents are actively market-making without humans in the loop. Tarek describes current automated summarization stacks and Kalshi Research's collaboration with labs to benchmark model forecasting capabilities. | |
| Sharps vs. Bookies: Prediction Markets vs. Gambling Models | 7 | 5 | 3 | 5 | John explores how traditional bookmakers ban 'sharks' and challenges whether Kalshi faces adverse selection from predatory snipers. Tarek and Luana differentiate exchange fee structures from casino house models, noting maker-taker fee pricing balances liquidity providers against snipers. | |
| Sponsor Break: Stripe Connect Infrastructure | 0 | 3 | 0 | 0 | Segment includes John's Stripe Connect sponsor ad read followed by Matt Huang and Luana discussing derivative expansion into new physical commodities and compute futures. | |
| Institutional Adoption: Block Trades and Enterprise Demand | 4 | 5 | 1 | 3 | Matt and John probe the divergence between retail curiosity markets and institutional macro hedging. Luana highlights the launch of institutional Block Trades and custom enterprise hedging on macro topics like tariffs and oil reserves. | |
| Disrupting Legacy Polling, Sportsbooks, and Media | 6 | 4 | 2 | 4 | John asks which legacy incumbents—like polling firms or sportsbooks—will be disrupted. Luana argues polling will evolve rather than die, serving as an input for traders with financial skin in the game. | |
| Navigating Insider Trading Boundaries and Regulatory Surveillance | 7 | 5 | 2 | 4 | John probes the boundaries of insider trading in prediction markets compared to equity markets. Luana outlines Kalshi's internal surveillance division, confidentiality breach standards, and proactive fines levied against illicit traders. | |
| Mention Markets, Speech Pricing, and Market Manipulation | 6 | 4 | 2 | 4 | John and Matt question whether mention markets (e.g., words in political speeches or earnings calls) are inherently gameable. Luana explains that restricting key speakers and their staffs preserves market integrity while reflecting genuine macro speech signal. | |
| Sports Contracts, Consumer Protection, and the Case Against Prohibition | 7 | 5 | 2 | 3 | John draws parallels between the regulation of sports betting and alcohol prohibition. Luana contrasts Kalshi's low-fee market structure with predatory sportsbook practices like loss-inducing deposit bonuses and winner limits. | |
| Deconstructing Macro Risk, AI Scenarios, and Infinite Markets | 5 | 6 | 1 | 3 | Tarek outlines Kevin Hassett's 'infinite markets' thesis, arguing that as economic complexity increases, traditional equity prices decay unless granular prediction markets unbundle and price individual macroeconomic variables. | |
| Continuous Pricing, Information Feedback Loops, and Market Efficiency | 6 | 5 | 2 | 4 | John and Matt challenge whether continuous sub-second pricing across all facets of society introduces destructive short-term volatility. Luana and Tarek defend transparent pricing as an essential capital allocation and real-time policy feedback mechanism. | |
| Impact on Political Discourse, Campaign Strategy, and Depolarization | 7 | 4 | 3 | 6 | John playfully challenges the founders for downplaying Kalshi's power, suggesting prediction markets create narrative loops similar to Iowa/New Hampshire primary voting. Tarek argues markets depolarize politics by forcing participants to research facts rather than post ideologically on social media. | |
| Internal Operations and Regulatory Trading Restrictions | 6 | 5 | 1 | 3 | Matt Huang and John ask about internal company dogfooding. Luana and Tarek explain that regulatory prohibitions strictly prevent Kalshi employees from trading on their own exchange, making direct user feedback loops essential. |
Statements from this episode (41)
Mansour: Balancing tail-risk paranoia with founder optimism drives great outcomes
“If you're a trader, you're like an expected value calculator. Like, I think about these sort of tail really bad outcomes all the time, and Luana oftentimes doesn't, and I think this is the thing that actually leads to great outcomes.”
Lopes Lara: Fintech and healthcare startups cannot operate by asking forgiveness
“I think that the approach we took from the start was that financial services or healthcare, I think you can't ask for forgiveness. I think there's a big difference between losing people's money, see what goes wrong, like an FTX example that can go very wrong w…”
Lopes Lara: Kalshi files all contracts under 24-hour CFTC review window
“We, every single contract we file with the CFTC, and they have 24 hours to stop it.”
Lopes Lara: CFTC blocked Kalshi election markets for two years
“Not, well, the biggest they said no to was the elections. That's why we had to end up suing them. They said no for two years.”
Mansour: Suing your primary regulator rarely works and usually kills startups
“So, so rarely does it work, and even if you win, you will probably lose. Like, you will end up getting killed in the process.”
Huang: Tech companies suing regulators is more common than conventional wisdom suggests
“Coinbase has sued their primary regulator and GovTech, SpaceX, Andrew Palantir all had to sue for various reasons. So it seems like it's actually more common than Silicon Valley conventional wisdom.”
Lopes Lara: 2024 election showed prediction markets outperform traditional polling
“The polls were completely wrong and the markets were so much better at bringing that sort of information, and I think that it's the shining example of why these markets are forced for good, and we need to have them in the US and regulated”
Lopes Lara: 80% of Kalshi users only consume information without trading
“Most of our users, like, 80% of our users are actually just looking, like, consuming information. They're just coming in and seeing who's gonna win the Texas primary yesterday, and seeing, like, okay, the, this, the polls are saying they're tied, but they're n…”
Mansour: Kalshi logged $10.4B in trading volume in February
“So, so volume in February was 10.4 billion.”
Lopes Lara: Robinhood and Webull were Kalshi's first broker partners
“Ah, in the beginning of last year, we launched the first broker partner that we had was actually, ah, Robinhood and then Webull.”
Mansour: Kalshi Direct has dramatically outpaced broker channel growth
“The direct, what we call direct Cauchy Direct, which is our Cauchy.com, Cauchy app, the consumer business, that has grown, you know that has sort of dramatically outpaced the rest, Or other the sort of intermediated or broker business.”
Lopes Lara: Kalshi rebates fees instead of paying market makers directly
“So the market making incentives on this side is actually, we don't pay them for it, we just rebate fees, but they have very, Very hard conditions to meet. They need to have uptime of certain amounts, spreads, and top of box size, and all of those things becaus…”
Lopes Lara: Over 95% of Kalshi maker orders come from peer-to-peer traders
“Over 95 are just like. Peer-to-peer, like, or funds that have like two people that just got stuck.”
Lopes Lara: Over 2,000 people act as market makers on Kalshi
“There's over 2000 people that market making.”
Mansour: Kalshi's top inflation forecaster was an amateur from Kansas
“The best inflation forecaster on CalSphere over the last few years is not, none of the institutions or the, you know, the big name hedge funds, it's this guy who lives in Kansas, never traded financial markets before, just likes to read the news, and just know…”
Lopes Lara: Kalshi user made over $150k trading Billboard music rankings
“To me, very important, and he's made over a 150,000 dollars, he's getting every single thing, he paid back student loans, he put himself for a master's degree, bought a car, and all those things, and he just like, Loves these markets, and he's never really tra…”
Mansour: Most Kalshi traders use AI summary modules in trading stacks
“And like most of our traders and their stack have some sort of like summary and synthesis module that's AI, AI driven.”
Mansour: Kalshi is talking to AI labs to build future-prediction benchmarks
“We launched Calci Research recently, which And one of the threads that we want to work on is we're talking to some of the research labs to create a new benchmark around which models actually predict the future better.”
Mansour: Gambling houses profit from losses, exchanges profit from transparency
“Gambling is this idea where the business model is, you are the house, and your revenue is your customer's losses. Like, so, and a lot of the dynamic that you describe has to be true, because your incentive is like, well, somebody's making money, I gotta stop t…”
Mansour: Prediction markets attract users because culture is more engaging than earnings
“The interesting point, I think, and I think this is part of why prediction markets are being adopted so, so, so much is people like this idea that if like your edge is proportional to your research, how informed you are, how much time and energy you put into t…”
Lopes Lara: Kalshi is developing derivatives for luxury watches and collectibles
“One thing that we're very excited for, we're actually starting to go in the direction of, for example, things like watches and bags and all of those things are like more going to the collectible side. They're actually able to do derivatives on those things.”
Lopes Lara: Kalshi plans to add futures, swaps, and options
“Right now we only have the binary yes, no. We want to have things like futures, like swaps, options, all of that.”
Lopes Lara: Upfront collateral requirements impair prediction market making
“The third one is really margining systems. Right now it's very bad. You have to put all the money up front. Right. Which makes a lot of, for example, will a hurricane happen this year? Very, very bad for you to be actually like market making or selling those c…”
Lopes Lara: Kalshi launched institutional Block Trades feature
“We actually just launched a week ago this thing called Blocktrades.”
Lopes Lara: Kalshi will enter natural disaster insurance once margin is live
“Once we have margin, we can start going to more hurricane, natural disaster insurance, all of that side.”
Lopes Lara: Prediction markets will improve polling accuracy, not destroy it
“My take is that polls are just going to get a lot better, because what people are going to be is like, okay, I can make money if my polls are right, so I'm just going to commission this poll, and I'm going to do this, And now you can actually, like, compete a …”
Lopes Lara: Prediction markets will complement news media rather than destroy it
“A lot of people are like prediction markets will destroy the news. I think it's way more complimentary. It's like when you're talking about an election, you're going to give your opinion, the market's not going to give you an opinion. You still need the commen…”
Lopes Lara: Kalshi bars members of Congress from trading on legislation
“We actually take it even a step further. For example, if you are a government official, you cannot, like if you're in Congress, you cannot trade on bills passing, even though, I don't know if they have an agreement.”
Lopes Lara: Kalshi fined two insider traders 5x their total profits
“And we put out two cases of two weeks ago of two insiders that then because we're also regulated, we're able to charge them a lot of fines. We charge them over five times what they made and all those things they banned and all that.”
Lopes Lara: Kalshi submits every trade and investigation to the CFTC
“Like every single trade on Kaoshi goes to the CFTC, they have every single thing, every single case goes to the CFTC for them to review.”
Lopes Lara: Kalshi bars speakers and staff from trading mention markets
“Obviously the person that is working on the speech or that is saying the speech cannot trade, and that's kind of how we enforce it. Like if you are Gavin Newsom and you are, there's a market where you're going to say you cannot trade it and your staff cannot t…”
Collison: Sportsbooks take ~10% whereas prediction markets take ~1%
“The rate for sports betting companies is around 10%, and the order of magnitude for prediction markets is, you know, one percent or a few points.”
Lopes Lara: Kalshi does not offer bonuses to incentivize losing bettors
“What they do is you start losing, and then they're gonna give you a thousand dollars for you to come back, or like a deposit boost, and all those things, so that they can hook you to keep you coming back, because they want to incentivize the losers. We don't d…”
Mansour: Prediction markets are required to accurately price complex traditional assets
“And if those dimensions, you don't have a good understanding of x one to x n you cannot get a good estimate of y, right? And so you, the paper basically says that you need infinite markets. And prediction markets are this notion of infinite markets, which is l…”
Lopes Lara: Kalshi will never list war, terrorism, or assassination markets
“There are a lot of things that we wouldn't do, like wildfires we don't do, war, terrorism, assassination. Those things are bad, and like, there's a moral side of these markets, and we're not going to ever go there.”
Mansour: Political candidates are using prediction market prices for campaign decisions
“Well, the candidates are using the, like, are using the prediction market prices to inform.”
Mansour: Prediction markets are an antidote to social media polarization
“I think that we do see this a lot with also, well, there's sort of the piece where people use it and that sort of react in real time to certain things, but I think there's some degree of depolarization. And that's what Luana is alluding to. Like, and in some w…”
Mansour: Kalshi employees are legally barred from trading on their exchange
“Which is, as a regulated exchange, we can't trade.”
Mansour: Banning prediction markets heightens risks by driving trading offshore
“Because if you ban it,
You're actually heightening the risks that you're trying to prevent, because now that activity is going offshore, right?
Like, and where you cannot monitor it or police it or do anything to protect it.”