The fundamental challenges of prediction platforms seen from the peripheral markets

Prediction markets are now at a turning point. In the 2024 U.S. presidential election, they demonstrated accuracy surpassing traditional polls and expert analyses, establishing themselves as a “truth discovery engine” both inside and outside the industry. However, the series of incidents that surfaced just a few months later reveal serious structural issues lurking behind this glory. They show that a seemingly trivial question starting from minor trades actually points to fundamental challenges within the market itself.

Maduro Arrest and $400,000 Windfall: The Reality of Information Advantage

At the end of 2024, a new account appeared on Polymarket. Someone wagered nearly $30,000 that Venezuelan President Nicolás Maduro would resign by the end of the month. Based on the market prices at the time, this was seen as an extremely high-risk trade.

A few hours later, the situation took a sharp turn. U.S. authorities arrested Maduro and announced criminal charges in New York. This account secured over $400,000 in profit. The market was correct. But the reason for that accuracy is the real issue.

Generally, prediction markets are explained as aggregating dispersed public information and reflecting diverse participant judgments, thereby achieving probabilistic accuracy. However, this case is markedly different. It is suspected that a specific account executed trades based on internal information inaccessible to anyone else worldwide.

If market accuracy stems from informational advantage, then it is no longer a “truth discovery” but a privileged arena for those close to power to gain benefits. This distinction is more important than it appears.

What the Zelensky Suit Controversy Revealed: Vulnerabilities in Governance

In 2025, Polymarket hosted a seemingly trivial bet: “Will Ukrainian President Zelensky wear a suit by July?” The market attracted significant attention, with trading volume reaching hundreds of millions of dollars.

When Zelensky appeared publicly in a black jacket and trousers, multiple media outlets and fashion experts called it a suit. However, the oracle system “Manhattan Machine” voted “No.”

The structural flaw behind this outcome makes this seemingly trivial incident more serious. A small number of large token holders heavily invested in the opposing vote, effectively controlling the outcome in their favor. If the cost of lying is lower than the profit, the system will inevitably lie. This was precisely the moment when that incentive structure was exposed.

This is not a failure of the ideal of decentralization but an example of human incentive design working too well. If the initial majority decision had been a democratic choice, this trivial dispute could have been dismissed as a market flaw. However, when voting rights are concentrated through capital, disputes become unavoidable.

When “Accuracy” Turns into a Risk Signal

Supporters of prediction markets argue that insider trading prompts early market reactions, spreading beneficial information to others. In other words, “insider information accelerates the discovery of truth.”

But this reasoning contains a fundamental fallacy. If the market improves accuracy based on confidential military operations, government schedules, or unpublished policy decisions, it transforms from a citizen-oriented information market into a shadow trading platform for those close to power.

The reward for analytical skill enhancement and the reward for access to power are fundamentally different. Markets where this distinction is blurred will eventually face strict scrutiny from regulators. Paradoxically, the concern is not that the market is inaccurate, but that it is “too accurate.”

Wall Street Entry and Regulatory Attention: Growth and Turmoil

Prediction markets are rapidly mainstreaming from niche financial products. Their growth rate makes the seriousness of the Maduro incident and Zelensky controversy even more apparent.

Platforms like Kalshi handle hundreds of billions of dollars in annual trading volume, with about $24 billion processed in 2025. More notably, major Wall Street players are beginning to show strategic interest. The New York Stock Exchange shareholder group has proposed a $2 billion acquisition of Polymarket, which is valued at around $9 billion.

This scale and growth rate are comparable to traditional exchanges. That’s why regulatory interest is also rapidly increasing. Some policymakers, including members of Congress, see insider trading as merely “front-running profits” and argue for clearer prohibitions.

The problem is that the market’s growth far outpaces the development of regulation. This regulatory gap is what has led to the series of incidents starting from trivial questions.

The Illusion of the “Machine of Truth”: The Importance of Recognizing Its True Nature

The biggest problem prediction markets face is the illusion of their name and self-image. Platforms have portrayed themselves as “noble engines for discovering truth.” But this very illusion is the source of fundamental issues.

The essence of prediction markets is simple. Participants invest in future outcomes that have not yet occurred; if their predictions are correct, they profit; if not, they suffer losses. Nothing more, nothing less. It is a financial product—a high-risk, high-reward betting arena.

Modifiers like “epistemological engine” or “collective intelligence implementation” obscure this fundamental nature. Adopting blockchain technology, visualizing probabilities, or attracting academic interest does not change the core.

However, if platforms openly acknowledge this core, the situation can improve significantly. Defining the market as a “high-risk, high-stakes financial product” allows regulators to establish clearer frameworks, and designers to aim for more transparent and ethical systems. When disputes or issues arise, they will be treated as regulatory challenges rather than philosophical crises.

The Future of Markets Seen Through Trivial Questions

There is no need to oppose prediction markets outright. In highly uncertain situations, they may be the most efficient way for participants to express beliefs. In fact, they can detect social unrest or signs of change earlier than polls.

But they should not pretend to be a “faithful reflection” of reality. Prediction markets are not epistemological devices but financial products for betting on future outcomes. This seemingly trivial definitional difference greatly influences market transparency and trustworthiness.

Recognizing this core can make markets stronger and more sustainable. Clearer regulations, ethical design principles, and transparent dispute resolution mechanisms will emerge. Accepting that it is a betting arena will no longer be surprising when bets are placed, and appropriate responses can be made.

The growth and serious challenges of markets are two sides of the same coin. Among them, the questions arising from seemingly trivial issues point toward the most fundamental reforms.