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They Made $0 for 4 Years. Then Built a $22B Startup | The Kalshi Story

Most startups spend their early years shipping products and growing users. Kalshi spent them fighting to exist. Founded by Tarek Mansour and Luana Lopes Lara, Kalshi set out to build a regulated financial exchange for real-world events. A place where people could put money behind their convictions and turn the future into a market. The path was brutal. They were told it was impossible. They were blocked by regulators. And eventually, they made one of the boldest decisions a startup can make: suing the government agency that oversaw them. After years of fighting, Kalshi broke through. Millions of customers. Billions in volume. A $22 billion valuation. This is the Kalshi story. 00:00 Intro 02:31 From Ballet to Building a $22B Startup 04:55 Conviction: 65 Lawyers Said No 08:58 Legitmacy: Four Years to Earn the Right to Launch 12:40 Payoff: Four Years of Fighting. Four Weeks to Scale 100x. 19:21 Signal: Not a Casino. A Market for Truth 24:02 Edge: Where Knowledge Becomes a Market 28:40 Scale: From Niche Market to Global Infrastructure 🔗 Read the EO article about Kalshis fundraising: https://www.eomag.io/article/kalshi-tarek-luana?utm_source=youtube&utm_medium=description EO stands for Entrepreneur& Opportunities. As we're looking to feature more inspiring stories of entrepreneurs all over the world, don't hesitate to contact us at partner@eoeoeo.net Newsletter | https://www.eomag.io/subscribe?utm_source=youtube&utm_medium=description LinkedIn | @EO STUDIO X | @eostudi0

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May 15, 202631mWatch on YouTube ↗

CHAPTERS

  1. 0:00 – 0:47

    Why money produces better forecasts than polls or “talk”

    The episode opens with the core claim behind prediction markets: incentives improve accuracy. When people must risk money, they update beliefs more carefully than when answering a survey or posting opinions online.

    • Real-world forecasting differs from controlled research settings because incentives and scale matter
    • Putting money behind a belief forces nuance and reduces overconfidence
    • Prediction markets aggregate conviction into a single price signal
    • Financial motivation can reduce polarization compared to pure opinion polling
  2. 0:47 – 1:06

    Founding mission: make prediction markets mainstream as a ‘market for truth’

    The founders describe a deep belief that markets can surface objective truth about uncertain future events. That mission becomes the “guiding light” that sustains them through multi-year setbacks.

    • Markets as a mechanism for truth-seeking and objective conversation
    • Goal is broad mainstream adoption, not a niche trader product
    • Price as a public data point that benefits even non-traders
    • Persistence driven by conviction that society needs better forecasting tools
  3. 1:06 – 2:30

    The first wall: 60–65 lawyers say it’s not possible

    Early on, the team stress-tests legality and feasibility—and gets universal rejection. The inability to “ship” while others launch products creates existential pressure.

    • Mass outreach to legal experts yields consistent ‘no’ answers
    • Regulation-first approach prevents normal startup iteration and visible progress
    • Competitive temptation to go offshore contrasts with their chosen path
    • The problem is not product-market fit but regulatory feasibility
  4. 2:30 – 5:59

    Origins of the founders: discipline, adaptability, and risk tolerance

    Personal backstories explain the founders’ stamina for long, painful timelines. Ballet discipline and growing up amid instability shape a mindset of delayed gratification and rapid adaptation.

    • Ballet training builds extreme discipline and comfort with long preparation for short ‘performance’ moments
    • Transition to MIT and identity shift away from ballet
    • Lebanon’s volatility fosters adaptability and emotional resilience
    • Founder lesson: absorb setbacks, adjust quickly, don’t overreact
  5. 5:59 – 6:59

    The insight from Wall Street: institutions want event exposure, not indirect trades

    A Goldman Sachs internship reveals that big players care about binary outcomes like Brexit or elections. Existing hedges are crude and can lose money even when the prediction is right—creating the opening for event contracts.

    • Institutional demand centers on ‘will X happen?’ events
    • The ‘Trump Trade’ example shows misaligned hedging instruments
    • Event contracts would give precise, direct exposure to outcomes
    • This becomes the initial product thesis for Kalshi
  6. 6:59 – 8:04

    Connecting the dots at Five Rings/Citadel—and committing to regulation-first

    The founders refine the idea through trading internships and attempt early demos, only to be told it’s illegal. That moment hardens a defining principle: do everything regulatory-first, even if it slows the company to a crawl.

    • Internship experiences reveal recurring ‘prediction market behavior’ without a legal venue
    • YC hackathon feedback: ‘That’s illegal’ forces a strategic decision
    • Company principle: no launch/marketing until the legal foundation exists
    • YC experience looks unlike peers: progress measured in filings, not metrics
  7. 8:04 – 10:41

    Finding a path: the 23 core principles and earning early legitimacy

    A key advisor, Jeff, doesn’t immediately say no—he lays out the daunting requirements to become a regulated exchange and clearinghouse. The founders respond with a detailed plan to satisfy the ‘23 core principles,’ signaling seriousness and capability.

    • Regulated exchange/clearinghouse involves extensive compliance obligations
    • ‘23 core principles’ become the blueprint for building the system
    • Rapid, thorough analysis convinces an experienced expert to join/support
    • Early legitimacy comes from operational rigor, not a flashy demo
  8. 10:41 – 13:28

    Stalled for years: the unsexy grind of compliance versus faster (offshore) competitors

    The team spends years writing policies, engaging regulators, and building compliance infrastructure while competitors move faster outside the U.S. The psychological toll is the lack of tangible progress despite intense work.

    • Years of document-heavy work: procedures, policies, regulator engagement
    • Hardest part is stagnation—no weekly growth loops or public milestones
    • Competitive pressure from offshore/unlicensed alternatives
    • Motivation anchored in belief that regulated markets matter most
  9. 13:28 – 14:51

    Taking the fight to court: suing the regulator to unlock election markets

    After years of attempts—including public comment support from prominent academics—the founders conclude persuasion won’t work. They choose the most aggressive remaining option: sue their regulator, risking everything to establish legality.

    • Extended engagement process fails despite strong public support and comments
    • Decision: remain regulation-first rather than ‘just list it’ like others
    • High-stakes lawsuit against the overseeing government agency
    • Belief they are right on the law and that the market is socially important
  10. 14:51 – 16:55

    Winning—then racing the clock: appeals, stress, and a four-week launch window

    They win in court, face immediate government appeal threats, and endure an all-consuming period of uncertainty. Once cleared, they have only weeks to transform Kalshi from niche product into a major election-time data source.

    • District and appeals court rulings go in their favor
    • Appeal threat creates intense time pressure ahead of the election
    • Founders can’t sleep/eat/focus as the decision hangs
    • Post-win reality: celebration is brief; execution must immediately follow
  11. 16:55 – 18:01

    Four years of fighting, four weeks to scale 100x: demand shock and system breakage

    With legal clearance, growth explodes: millions of customers and billions in volume, while the team is still tiny. Engineering and operations are pushed to the edge as deposits, signups, and infrastructure strain under the sudden surge.

    • Team of ~20–25 experiences ‘100x overnight’ scaling
    • 2M+ customers in ~two weeks; $2B+ volume
    • Operational bottlenecks force slowing growth because systems are breaking
    • All-hands sprint: team pauses life to meet the moment
  12. 18:01 – 19:31

    The clearinghouse crisis: forced migration over a weekend

    A third-party clearinghouse blocks the election market, forcing Kalshi to accelerate a major infrastructure migration. What typically takes six months is compressed into a weekend to keep the election product alive.

    • Two pillars: exchange matching + clearinghouse money movement and risk management
    • Third-party clearinghouse refuses to support the election market after the lawsuit
    • Emergency move to Kalshi’s own newly approved clearinghouse
    • High-risk, high-complexity migration executed by engineering under extreme time pressure
  13. 19:31 – 24:05

    Not a casino: defining speculation, market neutrality, and regulatory protections

    The episode tackles the biggest misconception—prediction markets as gambling—by contrasting them with casinos and highlighting parallels to traditional exchanges. Regulation is framed around market integrity and customer protection, with transparent rules and enforcement.

    • Key distinction: markets on real-world risks vs artificial games (dice)
    • Exchange model is neutral (participants trade against each other), unlike a ‘house’ that profits from losses
    • Regulation enforces fairness, transparency, and equal customer treatment
    • Insider trading and manipulation are treated like traditional financial crimes
  14. 24:05 – 28:40

    Where traders find edge: inefficiencies, research, and hedging real-life risk

    Real users explain how knowledge becomes tradable advantage—through deep research into speeches, niche data sleuthing, and weather expertise. Markets also function as practical hedges, resembling insurance for those exposed to real-world events.

    • Full-time trader exploits inefficiencies via speech databases, intuition, and constant monitoring
    • Teacher uses niche public data (e.g., site source code) to spot supply signals and profit
    • Meteorology-trained operator uses weather markets to hedge hurricane deductible risk
    • Prediction markets translate specialized knowledge into actionable prices and risk tools
  15. 28:40 – 31:10

    From niche to infrastructure: brand, global expansion, and institutional ambition

    The founders describe Kalshi’s shift into mainstream awareness and the next phase: clarifying what the brand stands for, expanding internationally, and building liquid markets that serve both retail and major institutions. The long-term vision is prediction markets as foundational as stock markets.

    • Consumer network effects compound slowly, then accelerate into mainstream growth
    • Brand clarity: explaining who Kalshi is and isn’t, and what it stands for
    • Expansion plans: international reach and broader market categories
    • Full-circle goal: deep liquidity for retail + institutions; prediction markets approaching stock-market scale

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