Tarek Mansour and Luana Lopes Lara are co-founders of Kalshi, the first federally regulated prediction market in the US, which now trades over $10 billion per month in prediction contracts on elections, sports, economic indicators, and more.
They spent four years pre-launch fighting for CFTC approval, deliberately choosing a “permission-first” regulatory strategy rather than the Silicon Valley “ask forgiveness” model, because they were handling people’s money and wanted to build a credible, regulated financial market akin to a next-generation NYSE.
They sued their own regulator, the CFTC, after it repeatedly blocked their election contracts, and won the lawsuit in late 2024, which unlocked explosive growth.
Kalshi operates as both an exchange and clearinghouse; contracts are individually filed with the CFTC, which has 24 hours to block them. The company now lists thousands of markets and is expanding into institutional products, new market structures (futures, swaps, options), and international access.
Founding story and regulatory-first approach
Started in YC 2019, launched in 2022 after three years of regulatory work, and won the election lawsuit at the end of 2024.
The founders come from identical backgrounds (math and CS at MIT) but have opposite temperaments: Tarek is the risk-averse “expected value calculator,” Luana is the dogmatic optimist. They credit this tension as key to their success.
Their founding Google Doc stated they wanted to build the “next generation New York Stock Exchange” — a credible, regulated US financial market — not an offshore operation.
Most investors and team members lost faith during the years of regulatory delays. After a pocket veto at the end of 2022, they laid off staff and told the remaining team the 2023 strategy was simply “try again.”
The CFTC blocked election contracts again at the end of 2023. Luana pushed to sue the government; Tarek initially resisted, calling it a bad idea that could get them shut down. The board (including Alfred Lin and Michael Seibel from YC) eventually backed it as a potential “anti-pattern” that defines great companies.
The election lawsuit
The legal basis was the Commodities Exchange Act: the CFTC can only block contracts that fall within specific prohibited categories (war, terrorism, assassination, gaming). Elections have clear economic impact and don’t fall into any prohibited category.
The CFTC had tried to justify blocking elections by arguing they might violate state bucket-shop laws. The lawsuit established that the regulator couldn’t simply invent reasons to overstep its statutory authority.
The win proved their legal interpretation correct and opened the floodgates for growth.
Why now — the timing of prediction markets
Intellectual interest in prediction markets dates to the 1950s, but the real catalyst is a crisis of trust in information.
Social media has bifurcated feeds, clickbait dominates, and polarization has accelerated. About 80% of Kalshi users are consumers just looking at market odds to understand what’s actually happening (e.g., “the polls say tied, but the market says otherwise”).
The incentive structure of prediction markets is truth: more volume and liquidity translate to more accurate forecasts, which is uniquely valuable in the current media environment.
Early crypto prediction markets like Augur helped by showing the CFTC that a legal, regulated alternative was needed, but this was a minor factor (~5-10%).
Kalshi by numbers and growth mechanics
February 2024 volume: $10.4 billion, up 11x over six months. A year ago they only had the Super Bowl for sports.
Growth is driven by marketplace network effects: better liquidity improves user retention, which increases volume, which attracts more users and sharing.
Initially relied on broker partners (Robinhood, Webull) to bootstrap demand and attract market makers. Now the direct consumer product (Kalshi.com and app) has dramatically outpaced the broker channel as the brand has gone mainstream.
They are the fastest-growing company outside of AI and compete with top AI companies on growth rate.
Market making and liquidity
Kalshi has two distinct categories of markets with different liquidity dynamics:
Long-tail markets (e.g., “Will One Direction reunite?”): hard to price, low demand, require explicit liquidity incentives for market makers.
Major markets (crypto, sports, elections): clear demand, easier to price. Market makers are rebated fees but must meet strict uptime, spread, and top-of-book size requirements to ensure book stability — preventing spreads from blowing out during volatile moments.
The key insight: over 95% of maker orders on Kalshi come from individuals and small shops, not institutional market makers. This is fundamentally different from traditional exchanges.
Kalshi has over 2,000 active market makers (individuals or small shops), many of whom have turned forecasting into a full-time job.
Examples: a Kansas resident who became the best inflation forecaster without any prior financial trading experience; an Ariana Grande superfan who made over $150,000 on Billboard chart markets and paid off student loans; a tax accountant who read tax codes, concluded DOGE couldn’t hit its targets, and made a large winning trade — a “Michael Burry shorting DOGE” moment.
Agentic/AI trading is growing, especially on the API side. Traders increasingly use AI modules for summarization and synthesis. Kalshi is working with research labs to create a benchmark for which AI models best predict the future.
Sharps and the difference from gambling
Unlike sports bookies, Kalshi does not limit winners or ban sharps. The company’s revenue comes from transaction fees, not from customer losses.
Bookies make money from losers and cut winners; Kalshi wants all participants because sharps are what make markets accurate.
Fee structure is used to incentivize pro-social behavior: lower fees for providing liquidity (being at risk of being sniped), higher fees for sniping.
Insider trading is prohibited under CFTC/SEC rules: if you have a confidentiality agreement (e.g., BLS employee with early inflation data), you cannot trade. But observing public information (e.g., hearing rehearsals outside the Super Bowl) is fine. Kalshi goes further by banning government officials and congressional staff from trading on bills they’re working on.
Kalshi has a dedicated surveillance team, refers cases to the CFTC, and has already fined insiders over five times their profits and banned them.
Sports contracts and the ethics debate
Sports betting companies operate at ~10% rake; prediction markets operate at ~1%.
Sports bookies actively incentivize losing customers with bonuses and deposit boosts to keep them coming back. Kalshi does not.
The founders argue that regardless of moral views on betting, people will bet, and they should have access to the best, safest product. Prohibition just drives activity offshore with fewer protections (no self-exclusion, no deposit limits).
They see prediction markets as objectively better and safer than state-regulated casinos.
Insider trading policy
Kalshi follows federal insider trading law: trading is prohibited when you have a duty of confidentiality. The company goes further by banning government officials from trading on legislation they’re working on.
For “mention markets” (e.g., how many times a word appears in a speech), the person writing or giving the speech and their staff cannot trade, but the market itself can exist.
Kalshi has a full surveillance division that flags suspicious trading, refers cases to the CFTC, and can impose fines and bans.
The philosophical stance: markets are good at incentivizing information flow. The goal is to build systems resilient enough to have markets while restricting specific players who would make them unfair — the same approach as traditional stock markets.
Product roadmap and new verticals
Four pillars for becoming the world’s largest derivatives exchange:
Market structure: expanding beyond binary yes/no contracts to futures, swaps, and options.
Margining systems: currently requires full capital up front; building proper margin to enable institutional-grade participation (e.g., hurricane insurance).
Liquidity: sustainable liquidity across the long tail of markets.
Compute is seen as a massive new commodity market that traditional futures exchanges aren’t addressing.
Institutional products are ramping up: block trades launched a week ago, allowing OTC-style negotiation before clearing on the exchange. Institutional interest is concentrated in macro topics (tariffs, petroleum reserves, AI).
The company is structured into maintenance/operations teams and forward-looking teams (institutional, margin, international) to balance current excellence with innovation.
Who loses from Kalshi?
Traditional sports bookies: Kalshi’s lower fees and fairer structure are objectively superior for users.
Political polling: campaigns are already using Kalshi data. The founders argue this will make polls better by incentivizing accuracy (skin in the game), similar to how FiveThirtyEight aggregated polls into a meta-forecast.
Parametric insurance: once margin systems exist, Kalshi can offer hurricane and natural disaster insurance.
Traditional futures exchanges: as Kalshi expands into non-binary products, it encroaches on CME/CBOT territory.
The vision: infinite markets and pricing everything
Inspired by a Kevin Hassett paper arguing that as society grows more complex, asset price understanding naturally decays because the number of influencing factors becomes too high-dimensional. The solution is “infinite markets” — a market for each factor — to feed back into better traditional asset pricing.
Example: after the Citrini AI 2027 report caused a stock sell-off, Kalshi launched a market on whether its five conditions would materialize. The market priced the scenario at ~10%, suggesting the sell-off was overblown.
The founders acknowledge short-term noise and sentiment swings but argue that more data is always better than less. Long enough time horizons make markets effective allocators of capital and weighing mechanisms.
They draw boundaries: no markets on wildfires, war, terrorism, or assassination.
Political effects of prediction markets
Prediction markets are already being used by candidates and campaigns for real-time feedback.
They are not polarized by party dynamics — they show real odds regardless of establishment preferences. Examples: the Texas primary where markets diverged from polls, and the New York mayor race where Mamdani’s odds rose steadily despite pundits declaring Cuomo a lock.
They create an Iowa-New Hampshire effect: generating narrative and momentum through continuous, real-time measurement.
They may depolarize politics by shifting the dimension from Republican vs. Democrat to “what do I actually think will happen?” — forcing people to step back from tribal feeds.
They pull people into genuine research: having skin in the game makes people read, learn, and engage more deeply with the underlying issues rather than just posting on social media.
The founders believe politics will get faster feedback loops, similar to how startups iterate quickly by measuring multiple metrics in real time.
Internal use and policy vision
Kalshi employees cannot trade on Kalshi at all due to regulatory restrictions, which prevents dogfooding. The company has asked regulators about allowing small internal markets.
Power users and superforecasters function as a de facto advisory group and heavily influence product direction.
Policy vision: pro-innovation AND pro-regulation.
Innovation must happen in America with proper regulation as insurance against things going wrong.
Key policy goals: ban insider trading for members of Congress (or ban their trading entirely), make all trade data publicly auditable, establish industry-wide customer protection standards (education, deposit limits, self-exclusion) that even traditional retail brokerages should adopt.
Oppose banning speculation outright, which would drive activity offshore where it cannot be monitored or policed.
