Author: Andjela Radmilac
Compiled by: Saoirse, Foresight News
Original Title: $700 Million Iran Bet Forces U.S. to Tighten Rules on Prediction Markets
Polymarket and Kalshi are seeking funding at valuations that would place them among the top consumer fintech companies, while at the same time, U.S. regulators are stepping up efforts to draft new rules for such products. It is reported that both companies are in early-stage funding negotiations, with valuations expected to reach approximately $20 billion each.
This funding boom coincides with a political storm.
Contracts related to Iran have turned prediction markets from a niche forecasting tool into a controversial focus involving insider information and war speculation. A Reuters investigation into the markets on Polymarket related to the timing of the Iranian attack and the ousting of Khamenei found that approximately $529 million was wagered on contracts related to the timing of the attack, and about $150 million was bet on contracts related to Khamenei. At the same time, it was reported that six accounts made a combined profit of about $1.2 million through precisely timed trades.
Now, U.S. lawmakers are drafting related bills, and the U.S. Commodity Futures Trading Commission (CFTC) has also stated it will advance new regulatory rules.
Wall Street believes that event probability prediction will become part of the information system; but Washington is blocking it because it fears this system could benefit the wrong people at the worst possible time.
Why Wall Street is Bullish on Prediction Markets
Prediction markets can convert attention into trades, earn fees from those trades, and simultaneously generate real-time probability data, which is packaged into information products.
It is this data product that moves prediction markets out of the "gambling" category and classifies them as information tools similar to market data, opinion polls, and financial terminals—because their output format closely resembles market quotes.
Mainstream media have already begun partnering with these platforms:
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CNBC has signed a multi-year agreement with Kalshi to integrate its probability data into television and digital programming starting in 2026.
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Dow Jones has reached an exclusive deal with Polymarket to introduce prediction data into platforms like The Wall Street Journal and Barron's, treating contract prices as news infrastructure on par with earnings reports, interest rates, and election coverage.
These partnerships also amplify the impact of scandals: once probability data is embedded in mainstream media, it influences public perception of the likelihood and urgency of events. This is also why regulators believe platforms must adhere to higher standards of fairness, monitoring, and settlement.
This explains why, even as Iran-related trades spark political controversy, the valuations of both companies continue to rise.
The Iran Incident Makes Prediction Markets a Washington Problem
The biggest advantage of prediction markets is early access to information. And the Iran-related contracts clearly show that such platforms are touching upon sensitive information that the government tries to control.
On March 2nd, wagers on contracts related to the timing of the attack reached $529 million, and contracts related to the death or ousting of Khamenei reached about $150 million. Just hours before the attack on the Iranian senior official, six accounts suddenly deposited funds and profited about $1.2 million through these contracts.
As the conflict escalated, multiple reports pointed out that a large number of newly registered accounts made precise bets on Iran-related events. Such reports have thrust Polymarket from a niche crypto platform directly into the sights of government oversight and law enforcement.
The core issues these platforms now face are: trust and fairness.
For prediction markets to function, users must believe the rules are stable, outcomes are judged consistently, and there is no insider advantage. Once the subject of trading is military action, the trust issue escalates into a political problem—because the motive for trading early could become a motive for leaking sensitive, even classified, information.
This is also why the policy response has rapidly escalated.
Representative Mike Levin and Senator Chris Murphy are already drafting legislation aimed at restricting prediction markets. Congress will directly define which event contracts can be legally traded.
Additionally, CFTC Chairman Michael Selig stated that the agency has submitted an advance notice of proposed rulemaking to the White House Office of Management and Budget and is about to introduce a regulatory framework for prediction markets, which could affect all aspects from contract design to monitoring and enforcement.
Washington faces a clear choice:
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Acknowledge prediction markets as legal event contracts, strengthen regulation, clarify restrictions, and allow the industry to expand orderly under rules;
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Directly prohibit contract categories related to war, assassination, and leader ousting, as such trades极易 (are highly prone to) trigger insider trading and foster undesirable motives.
The data below reveals why this conflict is difficult to resolve:
Kalshi's own dispute also shows that regulation alone cannot fully solve the trust problem.
On March 5th, Kalshi faced a class-action lawsuit where users accused the platform of refusing to pay approximately $54 million in winnings—users had bet that Iran's supreme leader would step down before March 1st. The plaintiff claimed the platform only activated a "death-related exception clause" after the Iranian leader was attacked, using it to refuse payment.
But Kalshi stated that its rules regarding trades on leader deaths were clear long before, and it had refunded fees and compensated users for losses, meaning users did not suffer net losses.
This is precisely the contradictory dilemma currently faced by investors and policymakers.
Investors hope the industry will achieve growth, expand its reach, and justifiably integrate probability prediction data into the mainstream information system.
Users hope that when event outcomes are controversial and emotionally charged, the platform rules will be stable and credible.
Regulators hope to prevent such markets from turning sensitive state actions into tradable products, avoiding situations where "access to confidential intelligence leads to optimal trading profits." Because once these trading prices begin to influence the public information environment, the associated risks evolve into a governance challenge.
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