Oct 31, 2024 · 35m · no-priors
No Priors Ep. 88 | With Founder & CEO of Kalshi Tarek Mansour
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In this episode of No Priors, host Sarah Guo speaks with Kalshi co-founder and CEO Tarek Mansour about creating the first federally regulated prediction market exchange in the United States. They discuss Kalshi's landmark legal victory against the CFTC, the economic utility of event contracts for hedging real-world risks, and how market incentives generate superior probabilistic forecasts compared to traditional polling and consensus models.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. The hosts hold 18.6% of the talking time here. How this is scored →
The hosts as informed peer 5.5 Guest teaching 4.3 Guest disagreement 1.6 The hosts pushing back 2.3
speaking balance: gold is the hosts, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 15:05 Dismissing data science and health papers
Tarek aggressively dismisses data science as mostly unreliable and academic health papers as total garbage due to bad incentives and reproducibility failures.
Hardest push from the hosts ▶ 16:43 Refusing Taleb's grandma heuristic for AI riskSarah directly refutes the guest's citation of Nassim Taleb, pointing out that grandmotherly intuition fails when evaluating complex, frontier technological risks like AI.
Biggest teaching moment ▶ 23:00 Deconstructing polling vs probability misinterpretationsTarek systematically educates the audience on why a 55% market probability is not equivalent to a 5-point lead in poll data, correcting widespread public misreadings.
The host holds their own ▶ 9:11 Citing Dojima rice exchange and derivatives historySarah demonstrates strong financial fluency by bringing up the 17th-century Japanese Dojima rice exchange to contextualize the historical evolution of futures and hedging.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The hosts as informed peer | Guest teaching | Guest disagreement | The hosts pushing back | Why |
|---|---|---|---|---|---|---|
| Explaining Prediction Markets and Live Trading Demo | 5 | 3 | 0 | 1 | The host opens with an engaging, interactive live demo by placing a real bet on Trump odds. The dynamic is playful and collaborative as the guest highlights the platform's money-market interest yield feature. | |
| The Legal and Regulatory Battle with the CFTC | 4 | 4 | 1 | 1 | The host prompts the guest to explain the multi-year battle against the CFTC. The guest details the legal strategy, regulatory filings, and the initial pushback from their own venture board against suing a federal regulator. | |
| Gambling versus Hedging and Risk Transfer | 7 | 4 | 2 | 2 | The host demonstrates deep domain knowledge of financial market history, mentioning her background at Goldman Sachs and the historical Dojima rice exchange in Japan. The guest elaborates on the philosophical difference between capital allocation and risk transfer. | |
| The Role of Speculation and Human Risk Perception | 6 | 3 | 3 | 5 | The guest argues that grandmas are better at evaluating risk than data scientists and dismisses academic papers as unreliable. The host pushes back on Nassim Taleb's framework, pointing out that frontier risk-taking in AI requires specialized domain understanding rather than intuition. | |
| Exchange Architecture, Liquidity Scaling, and Leverage | 6 | 5 | 1 | 2 | The host asks targeted questions regarding market architecture, clearinghouse operations, and credit leverage. The guest explains how post-2010 Dodd-Frank regulations shape their clearinghouse license and the risks of introducing margin. | |
| Comparing Prediction Markets to Traditional Polling | 5 | 6 | 2 | 1 | The guest educates listeners and the host on the widespread confusion between polling margin percentages and prediction market win probabilities, emphasizing that pricing odds behaves like a biased coin flip rather than a poll lead. | |
| Conditional Markets and Cross-Domain Forecasting Accuracy | 6 | 5 | 3 | 4 | The host expresses skepticism about how prediction markets could outperform complex scientific weather models. The guest explains the mechanism of financial incentives aligning information aggregation across distributed experts. | |
| Rapid Market Listing and Creative Forecasting Strategies | 5 | 4 | 1 | 2 | The conversation covers market creation speed, user-generated markets, and idiosyncratic trading strategies such as tracking late-night lights at government buildings. The dynamic remains conversational and high-energy. |
Statements from this episode (23)
Mansour: Kalshi is first legal US prediction market platform
“CalShay is actually the first legal platform in the U.S. Where you can basically bet yes, no on any future questions.”
Guo: Trump at 55% election odds is underpriced
“I think you know, Trump at 55%, that feels underpriced to me. Let's go in on it.”
Mansour: Kalshi pays 4.1% interest on active trade collateral
“The other thing that's actually really cool, and we launched this just now, is now you have a bet on Trump, you also, like, only get paid interest on that bet. So it's, it gets deposited in money markets, in US treasuries, and you get paid 4.1% interest while …”
Mansour: Sequoia and YC warned Kalshi against suing the CFTC
“When Juan and I brought it up to Alfred and from Sequoia and then YC, we're like, Hey, we're going to sue the government, our regulator. And they're like, we, it's never a good idea. It's just a bad pattern, but we took the risk”
Mansour: Kalshi is the sole legal, regulated US election trading exchange
“So we won this month, and the cool thing now is, like, it's the first time in a hundred years that betting on the election, trading on the election is actually legal in the U.S., It's finally back to actually being legal, and this time it's actually regulated,…”
Mansour: Derivatives exist for risk transfer rather than capital allocation
“The futures market, the derivatives market is a bit different. It's not capital allocation. It's risk transfer. And I love that notion. I love that notion so much. It's like, it's even more neat. It's like you're transferring risk from people that like have it…”
Mansour: Prediction markets hedge organic risks unlike artificial gambling
“Like, grain prices, or hurricanes, or Brexit happening or not, or election, Trump versus Kamala winning, that's a risk that exists already, right? Like, it's not like you and I are creating this risk so that we can bet on it. This exists already, and some peop…”
Mansour: Most data science and published health papers are "total garbage"
“One, generally speaking, don't listen to data scientists. Data science is mostly bullshit. Health papers and stuff like most of them are just total garbage.”
Mansour: Y Combinator advertises like casinos by spotlighting outlier winners
“So how do casinos actually advertise their products? They, like, show you the one person that made 300,000 dollars in the slot machine. People get excited and everyone goes and does it. And, you know, everybody knows the odds, et cetera. YC kind of does the sa…”
Mansour: Kalshi grew sign-ups and volume 6x day-over-day after election launch
“I mean, actually, we're growing, like, I think the last two days have been, like, six x day over day in terms of, like, sign ups and in terms of volume.”
Mansour: Kalshi expects $100M trades if institutional onboarding completes
“There is demand for, like, ten million dollars, twenty million dollars. Actually, there is demand even up to a hundred million dollar trades. We have three weeks, so we're gonna have to figure out if we have enough time with our compliance departments to onboa…”
Mansour: Kalshi acquired one of few US derivatives clearinghouses in 2024
“So we own now as of this year, actually, one of the, like, there's a handful of clearinghouses in the U.S. That can actually clear, that can move and hold dollars for trading derivatives.”
Mansour: Kalshi plans to introduce margin and leverage within 18 months
“I want to do it. I want to do it within the next, next, maybe, 12 to 18 months.”
Mansour: Prediction market odds reflect win probabilities, not poll margins
“Prediction markets are just pricing the odds of Trump versus Kamala winning, right? They are for, like, they're just giving a probability. We think there's a, you know, today right now, it's a 55% chance that Trump is winning. That does not mean that Trump is …”
Mansour: Prediction market odds move more than underlying polling shifts
“If Trump polls one percent higher than Kamala, or vice versa, prediction markets are more volatile. They're gonna move more than one percent, right? They're gonna give Trump a higher probability, or Kamala a higher probability based on the polling.”
Mansour: Susquehanna trades as market maker on Kalshi
“We have large market makers like Susquehanna, SIG and a few others that are confidential.”
Mansour: Kalshi beat Bloomberg and Economist inflation forecasts
“We have been the most accurate forecast for inflation over the last two years. Which is really hard to forecast. We're better than Bloomberg. We're better than the Economist survey.”
Mansour: Kalshi predicts daily weather better than most stations
“Like we can predict daily weather more accurately than most weather stations.”
Mansour: Half of Kalshi's markets originate from user submissions
“50% of our markets actually come from our users. We have this thing called Market Builder on the site, on the app, where people can just build a market and make the case like, hey, I think this should be up, and then we list it within 24 hours.”
Mansour: Kalshi CEO cannot trade prediction markets or non-stock instruments
“Actually I cannot trade on Cauchy. I can't trade on any instrument other than stocks because I run a regulated exchange.”
Mansour: Probability of Elon Musk stepping down from Tesla is 15-20%
“I think Elon leaving Tesla is pretty underpriced. Or like stepping down and having someone run it. Like it says, you know, I mean, now that this year is close to done, but two percent chance, like one in 50, I don't know... I think the odds are more closer to …”