A $700 Million Iranian Bet Forces the U.S. to Tighten Prediction Market Regulations
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A $700 Million Iranian Bet Forces the U.S. to Tighten Prediction Market Regulations
Wall Street favors platforms that can monetize attention, but once such attention is directed toward sensitive issues, Washington steps in.
Navigating Web3 tides with focused insightsBy Andjela Radmilac
Translated by Saoirse, Foresight News
Polymarket and Kalshi are seeking funding at valuations that would place them among the top consumer fintech companies—just as U.S. regulators intensify efforts to establish new rules for such products. Reports indicate both firms are in early-stage fundraising negotiations, with valuations each projected to reach approximately $2 billion.
This funding surge coincides with a political storm.
Iran-related contracts have transformed prediction markets from a niche forecasting tool into a controversial focal point involving insider information and war speculation. A Reuters investigation into Polymarket’s markets tied to the timing of Iranian attacks and the potential ouster of Ayatollah Khamenei found roughly $529 million wagered on attack-timing contracts and approximately $150 million on Khamenei-related contracts. Meanwhile, reports indicated six accounts collectively earned around $1.2 million through precisely timed trades.
U.S. lawmakers are now drafting related legislation, and the Commodity Futures Trading Commission (CFTC) has stated it will advance new regulatory frameworks.
Wall Street views event-probability forecasting as an emerging component of the information infrastructure—but Washington is pushing back, fearing this system could enable inappropriate actors to profit at the worst possible moments.
Why Wall Street Backs Prediction Markets
Prediction markets convert attention into trading activity, generate revenue through transaction fees, and produce real-time probability data packaged as information products.
It is precisely this data product that lifts prediction markets out of the “gambling” category and positions them alongside market data, polling services, and financial terminals—as their outputs closely resemble live price quotes.
Mainstream media outlets have already begun partnering with these platforms:
- CNBC signed a multi-year agreement with Kalshi to integrate its probability data into television and digital programming starting in 2026.
- Dow Jones entered an exclusive partnership with Polymarket to embed prediction data across platforms including The Wall Street Journal and Barron’s, treating contract prices as foundational news infrastructure alongside earnings reports, interest rates, and election coverage.
These partnerships also amplify the impact of scandals: once probability data becomes embedded in mainstream media, it influences public perception of event likelihood and urgency. This is why regulators insist platforms must meet higher standards for fairness, monitoring, and settlement.
That also explains why valuations for both companies continue rising—even amid political controversy over Iran-related trading.
The Iran Episode Turns Prediction Markets Into a Washington Dilemma
Prediction markets’ greatest strength lies in their ability to anticipate information ahead of time. Iran-related contracts clearly demonstrate how such platforms touch upon sensitive information governments seek to control.
On March 2, wagers on attack-timing contracts totaled $529 million, while contracts tied to Khamenei’s death or removal reached about $150 million. Just hours before senior Iranian officials were attacked, six accounts suddenly placed large bets—and collectively earned $1.2 million from those contracts.
As tensions escalated, multiple reports highlighted numerous newly registered accounts making precise bets on Iran-related events. Such reporting thrust Polymarket—from a crypto niche platform—directly into the regulatory and law enforcement spotlight.
The core challenge facing these platforms today is trust and fairness.
For prediction markets to function, users must believe rules remain stable, outcomes are adjudicated consistently, and no insider advantage distorts results. Once the underlying assets involve military operations, trust issues escalate into political ones—because the motivation behind early trading may morph into a motive to leak sensitive or even classified information.
That is why the policy response has rapidly intensified.
Representative Mike Levin and Senator Chris Murphy are already drafting legislation aimed at constraining prediction markets. Congress will directly define which event-based contracts may be legally traded.
Additionally, CFTC Chair Michael Selig confirmed the agency has submitted a Notice of Proposed Rulemaking to the White House Office of Management and Budget, signaling an imminent regulatory framework for prediction markets—one likely to affect contract design, surveillance, enforcement, and more.
Washington faces a clear choice:
- Recognize prediction markets as legitimate venues for event-based contracts, strengthen oversight, impose explicit restrictions, and allow industry growth within defined guardrails;
- Outright ban contract categories tied to war, assassination, or leadership removal—given their high susceptibility to insider trading and capacity to incentivize harmful behavior.
The following data reveals why this conflict remains difficult to resolve:
Kalshi’s own dispute further illustrates that regulation alone cannot fully resolve trust issues.
On March 5, Kalshi faced a class-action lawsuit accusing the platform of refusing to pay out approximately $54 million in winnings—users had bet that Iran’s Supreme Leader would step down before March 1. Plaintiffs alleged Kalshi retroactively invoked a “death-related exception clause” only after the Iranian leader was attacked, thereby denying payouts.
Kalshi countered that its rules governing leader-death-related trading had long been explicit, and that it had refunded trading fees and compensated users to offset losses—meaning no user incurred net losses.
This embodies the very contradiction confronting investors and policymakers today.
Investors hope the industry achieves growth and broader adoption—and can credibly integrate probability forecasts into mainstream information ecosystems.
Users demand stable, trustworthy platform rules when event outcomes are contentious and emotionally charged.
Regulators aim to prevent such markets from turning sensitive national actions into tradable commodities—avoiding scenarios where possessing classified intelligence yields optimal trading returns. Because once these trading prices begin influencing the information environment, associated risks evolve into governance challenges.
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