Prediction Markets That Have the First Advantage: From "Truth Machines" to Insider Trading Platforms

Prediction markets have once been highly celebrated. During the 2024 U.S. election, platforms like Polymarket outperformed traditional polls, expert forecasts, and news media by accurately reflecting reality through data. This success created an enticing narrative: prediction markets are not only accurate but also represent the truth itself—a purer, more honest signal aggregation. But a month changed everything.

A mysterious account on Polymarket bet about $30,000 on Venezuelan President Nicolás Maduro stepping down before the end of the month. At the time, the market priced this possibility as extremely low, making it seem like a sure-loss bet. But hours later, police actually arrested Maduro. The account closed with a profit of over $400,000. The market prediction was correct.

This is precisely the problem.

The Information Asymmetry Revealed by Maduro Trade

When the accuracy of prediction markets depends on some individuals possessing information that others cannot access, the market ceases to be about discovering truth. Instead, it becomes about monetizing an “information advantage.” This approach is like having the inside track—those with insider information always profit first.

Supporters argue that even with insider trading, market fluctuations can help others uncover the truth. But this ideal sounds good in theory and flawed in practice. If a market’s accuracy stems from leaked military operations, confidential intelligence, or government decision timelines, it has effectively transformed into a secret trading platform rather than an information market.

Rewarding better analysis and rewarding access to power are fundamentally different. If prediction markets blur this line, they will eventually attract regulatory scrutiny—not because they are inaccurate, but because they are “too accurate” in the wrong way.

Why Accuracy Becomes a Warning Signal

Ironically, the accuracy of prediction markets may itself be a dangerous signal. When a platform consistently predicts market shifts with precision derived from insider information, it has shifted from a tool for discovering information to a nexus of power.

At this nexus, those who control information profit, while uninformed investors bear the risk. The larger the amounts involved, the more crowded this nexus becomes, and the lower the cost of corruption. Ultimately, platforms face not just technical issues but moral and legal challenges.

From Niche to Wall Street: Governance Concerns

Prediction markets have evolved from niche entertainment to ecosystems of serious interest on Wall Street, amplifying the severity of these issues:

Surging Trading Volumes: Platforms like Kalshi and Polymarket now handle billions of dollars in trades. In 2025 alone, Kalshi processed nearly $24 billion in trading volume.

Capital Commitments: Shareholders, including the New York Stock Exchange, have invested up to $2 billion strategically in Polymarket, which is valued at around $9 billion. Wall Street believes these markets can rival traditional trading venues.

Regulatory Battles: Congress members like Rep. Richie Torres have proposed bills to ban government insiders from trading on prediction markets, arguing these markets resemble “front-running” opportunities rather than information-based investments.

When prediction markets reach this scale, each instance of insider advantage is no longer isolated but becomes a systemic risk.

Zelensky Clothing Incident: The Complete Breakdown of Incentives

If the Maduro incident exposed internal issues, the Zelensky clothing market revealed deeper governance crises.

In 2025, a market was launched on Polymarket asking whether Ukrainian President Zelensky would appear in formal Western attire before July. This seemingly absurd market attracted hundreds of millions in trading volume and eventually led to a settlement dispute.

When Zelensky appeared in a designer jacket, the standard for judgment became contentious. Major holders of governance tokens, with large exposure on the inverse position of this market, wielded enough voting power to enforce a settlement favoring their interests. The incentive to lie outweighed the cost of honesty.

This is not a failure of decentralization but a complete breakdown of incentives. The system operated exactly as designed: the accuracy of human governance depends on how costly honesty is. In this case, the cost of honesty far exceeds the gains from deception.

Prediction markets do not uncover truth—they reach “settlements.” What matters is not what most believe, but what the system decides counts as the “result.” And this decision-making power resides with those who hold the most resources.

Embracing the Reality to Improve Design

We have overcomplicated prediction markets. At their core, they are simply places where people bet on future events—if the event occurs as expected, they profit; if not, they lose. All the elaborate language is secondary.

Prediction markets won’t become something more advanced just because they have cleaner interfaces, clearer probability displays, run on blockchain, or attract academic interest. Participants are rewarded not because they have true insight, but because they bet correctly on “what will happen next.”

This “packaging” is the root of real difficulty. When platforms claim to be “truth machines,” every dispute feels like a crisis; but if we accept they are high-risk financial products, settlement disputes are just typical financial disputes, not philosophical paradoxes.

In fact, honestly acknowledging the nature of prediction markets can help the industry better self-regulate:

• Clearer Regulatory Frameworks: Clarify their status as financial instruments, establish disclosure requirements and risk controls.
• More Ethical Design: Recognize the objective existence of incentive problems and implement mechanisms to prevent manipulation by large players.
• Realistic Expectations: Avoid overhyping predictive accuracy and instead emphasize their risk profile.

Once we accept that we are operating a betting product, regulators will have clearer guidance, and participants will better understand the risks they undertake.

Conclusion

Prediction markets are not inherently bad. They are a relatively honest way to express beliefs under uncertainty. To some extent, they can reflect market anxiety and shifts faster than traditional polls. But we should not pretend they are higher truths.

They are financial tools linked to future events, vulnerable to exploitation through information advantages. Recognizing this makes them more robust and sustainable—by guiding clearer regulation, ethical design, and honest operation.

When we stop packaging prediction markets as “truth machines” and start viewing them as financial products, ironically, we get closer to the truth they can offer.