Can prediction markets truly reveal the “truth”? Or are they merely tools to monetize “information advantage”? From the Maduro incident to the Zelensky suit controversy, this article deeply analyzes the nature and governance challenges of prediction markets. This article is based on Thejaswini M A’s research paper “Truth Comes Later,” translated by Dongqu.
(Background: Data: Prediction markets leaked the fall of Maduro with $10 million in advance)
(Additional context: In-depth analysis of on-chain prediction platform “Polymarket,” how it becomes a global trend indicator, and the technical & regulatory challenges it faces)

Table of Contents

• The Ideal Narrative and Harsh Reality
• Accuracy as a Warning Signal
• From Margins to Mainstream
• The “Zelensky Suit” Incident: An Overlooked Alarm
• Shedding the Glamorous Exterior
• Conclusion

Whenever prediction markets spark controversy, we often circle around the same questions but fail to confront the core: Can prediction markets truly reach the “truth”?

This discussion is not about accuracy or practicality, nor whether they can beat polls, journalists, or social opinion. What we are talking about is—the truth itself.

Prediction markets assign prices to events that have not yet occurred. They do not report established facts but allocate probabilities to a future full of variables and unpredictability. At some point, we began to treat these probabilities as a form of truth.

Over the past year, prediction markets have been remarkably successful. They outperform polls, beat election news, and surpass analysts with slide decks. During the 2024 US election, platforms like Polymarket captured real-time changes faster than almost all mainstream prediction tools. This achievement has led to a narrative: prediction markets are not only accurate but also legitimate—they gather purer, more honest signals of truth.

Then, things took a turn.

A new account appeared on Polymarket, betting about $30,000 that Venezuelan President Nicolás Maduro would be ousted before the end of the month. At that time, the market considered such an event highly unlikely, and the bet seemed utterly foolish.

However, hours later, Maduro was arrested and charged criminally in New York. The account closed its position with a profit of over $400,000. The prediction was correct. But the problem lies precisely here.

The Ideal Narrative and Harsh Reality

There is a comforting story about prediction markets: they aggregate dispersed information, people support their judgments with real money, prices adjust as evidence accumulates, and ultimately, collective wisdom converges on the truth.

This story presupposes that the information flowing into the market is public, chaotic, and probabilistic—such as tightening poll trends, candidate slip-ups, or sudden weather changes. But the “Maduro trade” feels entirely different; it’s less about inference and more about precise timing.

At that moment, prediction markets cease to be smart forecasting tools and transform into another domain: where insiders with privileged information seize the advantage, winning based on information channels rather than analytical skill.

If prediction markets are accurate because some people hold information inaccessible to others worldwide, then they are not discovering the truth but monetizing the “information gap.” This distinction is far more critical than the industry admits.

Accuracy as a Warning Signal

Proponents of prediction markets often argue: if someone trades on insider information, the market will move in advance, alerting other participants. In other words, “insider trading accelerates the revelation of truth.”

This theory sounds appealing but is fundamentally flawed. If a market’s accuracy depends on leaked military operations, classified intelligence, or government internal schedules, then from a meaningful citizen’s perspective, it ceases to be an information market and becomes a shadowy secret trading platform.

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

From Margins to Mainstream

The Maduro incident is worrying not just because of the profits involved but also because of the era of explosive growth prediction markets are experiencing. They have shifted from niche corners to ecosystems taken seriously by Wall Street.

Trading volume soars: Annual turnover on platforms like Kalshi and Polymarket has reached hundreds of billions of dollars. In 2025 alone, Kalshi handled nearly $24 billion.

Capital influx: Shareholders of the NYSE have proposed strategic deals worth up to $2 billion with Polymarket, with the company valued at around $9 billion. This indicates that Wall Street believes these markets can compete with traditional exchanges.

Regulatory tug-of-war: Congress members like Rick Torres have introduced bills to ban trading by insiders, arguing such activities resemble “front-running” for profit rather than speculation based on public information.

The “Zelensky Suit” Incident: An Overlooked Alarm

If Maduro’s case exposed insider trading issues, the “Zelensky suit” market reveals a more fundamental flaw.

In 2025, Polymarket created a market asking whether Ukrainian President Zelensky would appear in a suit in July. This seemingly trivial market attracted hundreds of millions of dollars in trading volume but eventually led to a governance crisis.

When Zelensky appeared publicly, he wore a black coat and trousers from a well-known designer. Media called it a suit, fashion experts agreed it was a suit. But the oracle’s verdict was “No.”

The reason: a few whales holding large tokens heavily bet on the opposite outcome. Their voting power was enough to enforce a settlement favorable to them. The cost of manipulating the oracle was less than their potential gains.

This is not the failure of decentralization but a breakdown of incentive mechanisms. The system operates exactly as designed: how much the human-governed oracle can be bought depends on how high the cost of lying is. In this case, the reward for lying was more enticing.

Prediction markets do not discover the truth—they settle the outcome.

Seeing these events as “growing pains” is a mistake. They are the inevitable result of three factors: financial incentives, ambiguous terms, and unresolved governance mechanisms.

Prediction markets are not about seeking the truth but about settling outcomes. What matters is not what most people believe but what the system ultimately recognizes as the “result.” This recognition point is where image, power, and money intersect. When large sums are involved, this intersection becomes inevitably crowded.

Shedding the Glamorous Exterior

We have overcomplicated things.

Prediction markets are simply venues where people bet on outcomes that have not yet happened. If you guess right, you make money; if you guess wrong, you lose. All other embellishments are secondary.

They will not become more refined or sophisticated just because their interfaces are sleeker, probabilities are clearer, they run on blockchain, or they attract economists. You are rewarded not because of unique insights but because you guessed correctly on “what will happen next.”

I see no need to insist that this activity is noble. Packaging it as “foresight” or “information discovery” does not change the fundamental nature and motivation of taking risks. To some extent, we are reluctant to admit: people just want to bet on the future.

In fact, it is this layer of “disguise” that creates the dilemma. When platforms claim to be “truth machines,” every controversy feels like an existential crisis; but if they admit it’s a high-risk betting product, then disputes over settlement are just straightforward conflicts, not philosophical crises.

Conclusion

I do not oppose prediction markets. They are one of the most honest ways to express beliefs amid uncertainty, and their speed in revealing signals of unrest often surpasses polls.

But we should not pretend they are something nobler than they are. They are not “epistemological engines” but financial tools linked to future events.

Recognizing this can make them more robust. It helps clarify regulatory directions, establish clearer rules, and design more reasonable ethics. Once you admit you are running a betting product, it’s no longer surprising when betting behaviors emerge.