SEON CEO: Prediction markets can forecast the future. Can they survive their own manipulation problem?

Prediction markets have moved from niche internet experiments to one of the fastest-growing segments in financial services, with projections pointing toward trillions in annual volume. Robinhood’s CEO recently declared that we’re in a “prediction markets supercycle.”

The vocabulary has shifted with the market. Gambling gave way to investing in outcomes. Bets became event contracts. Your edge, your return. The rebranding isn’t accidental, and it’s not entirely wrong. Prediction markets genuinely aggregate real information, and when real money is on the line, people don’t lie—. CNN’s partnership with Kalshi proved the point during the 2024 elections: crowd-sourced probability estimates can outperform traditional polling throughout the election cycle, precisely because participants have something real at stake: money.

But going mainstream doesn’t solve the problem these markets have been quietly carrying. It amplifies it.

The Difference Between Predicting an Outcome and Engineering One

The fundamental premise of prediction markets is that outcomes can be observed, not influenced. Once that assumption breaks, you don’t just have a fraud problem. You have a structural collapse of the product’s entire value proposition.

Consider a scenario that has made its way through compliance circles: a presidential speechwriter bets heavily on whether a specific, obscure word will appear in a major address, then ensures it does. No money changes hands illegally, and no law is obviously broken, but the market has been gamed without any honest participant knowing.

The Commodity Futures Trading Commission’s (CFTC) own Bloomberg Law analysis confirms that applying traditional insider trading rules, which generally require a trade made while in possession of material non-public information in breach of a legal duty, isn’t always straightforward in prediction markets. This vulnerability isn’t hypothetical at the retail level either. A few years ago, a $50,000 bet was reportedly placed on whether a streaker would interrupt a sporting event. The bettor then streaked to ensure the event happened.

These aren’t edge cases. They represent a core structural exposure: the more niche the contract, the fewer actors can influence the outcome, and the easier it is to coordinate quietly. The problem is asymmetric. A small group communicating privately can engineer a result and split the profit. Collusion of this kind is both easy to execute and extremely difficult to detect and prove. The you don’t know what you don’t know problem is acute here. A marketplace built on information is only as trustworthy as its least transparent participant.

Regulators Are Looking in the Wrong Direction

If 2025 was the breakout year for prediction markets, 2026 is shaping up to be the year of regulatory reckoning, but regulators aren’t reckoning with the right things. While prediction markets have been rapidly expanding and repositioning as investment tools, regulatory energy has been largely consumed by sweepstakes bans in California and New York. Meanwhile, jurisdictional battles are being litigated simultaneously across multiple federal circuits: the Ninth Circuit alone is set to hear consolidated arguments involving Kalshi, Robinhood, and Crypto.com, creating exactly the kind of uncertainty that responsible platforms hate and bad actors exploit.

The deeper problem, though, is conceptual. Regulators are trying to fit prediction markets into the closest existing category: gambling operators. It’s a fundamental misread. As Kalshi CEO Tarek Mansour has argued, traditional sportsbooks are essentially products designed for customers to lose — the house profits from customer losses. Kalshi and platforms like it operate as peer-to-peer exchanges: customers bet against each other, the platform takes fees from both sides, and the house has no stake in the outcome.

That is a financial market, not a casino, and the difference matters enormously for how regulation should be designed. Mandatory licensing frameworks built for casinos, verification standards designed for sportsbooks, and liability structures built for risk-bearing operators — none of these map cleanly. Applying them wholesale will either fail to address the actual manipulation risks, entrench compliance burdens that only the largest incumbents can absorb, or both.

What a Workable Framework Actually Looks Like

The CFTC’s March 2026 ANPRM is the right instinct — a structured federal rulemaking process, grounded in the actual risk architecture of these platforms, is what the moment calls for.

The most useful operating model to emerge so far is a two-layer approach: exchanges are responsible for identifying and removing bad actors; regulators handle criminal penalties. It’s a division of labor that respects what each layer is actually capable of. Platforms have the data and the real-time visibility, while enforcement agencies have the legal authority and the teeth.

Executing on the platform side requires more than good intentions. The online gambling regulatory framework isn’t a direct parallel — operators themselves will tell you it doesn’t map cleanly onto how these platforms work. But the industry is already treating it as the likely template, making compliance investments now to get ahead of a regulatory posture that may not arrive in exactly that form. That anticipatory scramble is itself a signal that the infrastructure for accountability needs to be built, regardless of which framework ultimately governs it.

Mandatory ID verification and document checks are a floor, not a ceiling, and platforms will face real consumer resistance, given understandable anxieties about data breaches. This is where device-level intelligence becomes the critical second layer. Behavioral fingerprinting, device profiling, and multi-account detection are more durable against the actual threat model: technically sophisticated actors using synthetic identities, VPN networks, and coordinated account rings. Consortium blacklists have their place, but bad actors evolve faster than shared databases update.

Risk-tiering matters too. A contract resolving on a large, independently verifiable event — a macroeconomic indicator, a major election — carries a very different manipulation surface than one resolving on a single person’s behavior in a narrow context.

The CFTC has already flagged this distinction, identifying insider trading risk as particularly acute in politically sensitive and entertainment-adjacent contracts. Regulation should explicitly reflect that difference, rather than applying a single standard across a market with wildly uneven exposure.

The Stakes of Getting This Wrong

The optimistic case for prediction markets is that they may become the most honest real-time information mechanism available: more accurate than polls, faster than analysts, and accessible to anyone with a view and the conviction to back it. But that outcome requires the underlying infrastructure to be trustworthy.

Regulation that’s too heavy-handed will push volume offshore — platforms like Polymarket already attract global participation — and excessive domestic restrictions simply relocate the market to venues with less oversight and weaker consumer protections. Regulations that are too passive allow manipulation to compound quietly until a high-profile scandal discredits the entire category. Neither outcome serves the public interest.

The CFTC has a narrow window to get ahead of this market rather than perpetually chase it. Exchanges, compliance teams, and technology providers who want a functional prediction market ecosystem have a responsibility to engage that process now, not just critique the results later. The rules being written in the next 12 months will shape how tens of millions of new participants experience these markets. That is not a process that should happen without the people who understand what’s actually at stake.

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