Tarek Mansour on Kalshi's Plan to Create Markets in Everything

Episode Summary

Executive Summary: In this live Odd Lots conversation, Kalshi CEO Tarek Mansoor argues that prediction markets are becoming mainstream because the legal and regulatory barriers that once suppressed them are easing. He explains Kalshi’s path from years of regulatory battles to rapid growth, the role of market makers, why the company sees itself as a regulated financial market rather than a gambling product, and why sports, culture, and eventually company- and security-related markets could expand the platform’s reach.

Main Topics: Regulatory breakthrough and legalization (Priority: 5/5): Mansoor says the decisive factor in prediction markets' rise was legalization under a regulated framework. He describes years of regulatory work, the election-market lawsuit, and the importance of operating with CFTC oversight to make the market durable and scalable. Prediction markets as financial markets, not gambling (Priority: 5/5): He repeatedly distinguishes Kalshi’s model from casino-style gambling, arguing that markets on real-world outcomes are better framed as financial instruments with transparent pricing, market-based odds, and fee-based revenue rather than house-versus-customer losses. Market structure, lifecycle, and liquidity (Priority: 4/5): Mansoor walks through how binary event contracts are created, listed, and made liquid, emphasizing the need for quick contract creation, self-certification, and market makers to solve the chicken-and-egg problem of thin liquidity at launch. Sports as the biggest near-term driver (Priority: 4/5): He says sports has become a major growth engine, especially on Sundays and NFL days, while noting that election-related and Trump-related markets can rival or exceed sports on a one-to-one basis when events are scheduled and highly salient. Institutional adoption and hedging potential (Priority: 4/5): The conversation explores whether hedge funds, corporations, and other institutional players will use prediction markets for risk transfer and hedging. Mansoor says liquidity is the main bottleneck, but he expects deeper institutional use over the next two to three years. Competition, on-chain markets, and future expansion (Priority: 3/5): Mansoor discusses Polymarket and on-chain models, arguing that regulated exchanges offer legitimacy, institutional access, and a stronger long-term path. He also outlines a future where company-specific, financial, and potentially perpetual futures on broader real-world questions become common. Culture, social utility, and regulation’s role (Priority: 3/5): He defends user choice and market access, criticizing paternalistic regulation and arguing that adults should be able to decide whether to trade, invest, or bet, as long as rules are fair and customer protections exist.

Key Arguments: Prediction markets had to be legalized and regulated to scale meaningfully; offshore or VPN-based approaches could not attract institutions or survive government scrutiny. These markets are economically distinct from gambling because they price natural uncertainty, use transparent market mechanisms, and do not rely on a house making money from customer losses. Market makers are essential at launch to seed bid-ask spreads, but their role should diminish as organic liquidity grows. Sports markets are a powerful growth category, yet election and politically salient markets can produce even larger one-off volumes. Institutional participation is real but limited by liquidity and margining constraints; full-scale adoption will require deeper markets and broker/prime-broker integration. On-chain infrastructure may be useful for transparency and clearing, but regulation and legitimacy matter more for mainstream adoption than pure decentralization. Prediction markets can expand beyond politics and sports into company outcomes, macro events, and even perpetual futures on stocks, private companies, or other risky outcomes. Regulators should not dictate what adults can do with their money if the activity is conducted within a fair, supervised market structure.

Data Points: Time to list first market: 18 months - Mansoor said Kalshi’s first market listing initially took 18 months, but now can be done in about 30 minutes. Time spent on regulatory work: 3.5 years - He said the company spent roughly three and a half years on regulatory work after founding Kalshi. Total fight to legalize prediction markets: 5.5 years - He described the full regulatory push as about five and a half years. Growth after election lawsuit win: 100x overnight - He said Kalshi grew about 100x overnight after the election lawsuit victory and launch. Kalshi size vs prior year: 3x bigger within a year - He said the company is now around three times bigger than a year earlier, even accounting for competitors. Sunday sports volume vs weekday: ~3x higher - He estimated Sunday volume is roughly three times weekday volume. Elections / Trump-related one-to-one comparison: Trump-related markets win on volume - He said a one-to-one comparison of a sports game versus something Trump does would favor Trump-related markets. Availability target for some market makers: 98% availability - He gave an example of market-maker obligations in Fed markets requiring near-continuous quoting. Market maker quote obligation example: 75,000 contracts each side within 4¢ spread - He described one market-maker program structure with large quoted size and tight spreads. Institutional minimum trade size: 50 to 100 million - He said buy-side firms and corporations may need very large liquidity before care becomes meaningful. Current main bottleneck for institutional use: Liquidity - He repeatedly identified liquidity as the key constraint on broader institutional adoption. Prediction market fee model: Fully cash - He said Kalshi is fully cash-collateralized today, which complicates large institutional usage.

Pivotal Quotes: "The fundamentals are there. Like, this should be a very large market." — Tariq Mansoor: He explains why prediction markets are having a moment despite existing for decades. "For this to go big is you got to legalize it. Like, there was no other way." — Tariq Mansoor: He argues that regulation and legalization were necessary for mainstream scale. "I think gambling is sort of where I have a problem." — Tariq Mansoor: He draws a conceptual distinction between prediction markets and gambling.

Implications: Prediction markets appear to be shifting from fringe novelty to regulated financial infrastructure. If liquidity deepens and institutional access improves, they could become a major venue for pricing real-world risk across politics, sports, companies, and macro events.