A new report by the Anti-Corruption Data Collective found that more than 52% of large “long-shot” military bets on Polymarket are winning — far above typical odds — raising fears that insiders may be profiting from sensitive information.
The implication is clear: ordinary traders could be betting against people who already know the outcome, turning what looks like forecasting into a transfer of money from the uninformed to the informed.
This is why a growing number of users are asking a sharper question: are prediction markets actually fair, or are they structurally tilted toward those with better access to information? In simple terms, if even a small number of traders have an informational edge, the market stops being about collective wisdom and starts rewarding those closest to the truth before it becomes public.
Market Mechanics
The data behind the concern is hard to ignore. The report analysed hundreds of thousands of markets and found that high-value, low-probability bets on military and defence outcomes were winning at roughly double the rate seen in political markets and far above the platform average. That kind of performance is not supposed to happen consistently unless the odds are mispriced or the bettor has superior information. In traditional financial markets, patterns like that would trigger immediate surveillance for insider trading or market abuse.
The issue has already moved beyond theory. US prosecutors have charged Gannon Ken Van Dyke, an active-duty soldier, with allegedly placing bets linked to a military operation involving Venezuela while in possession of classified information. He has pleaded not guilty, but the case is significant because it represents the first known US prosecution tied to insider trading on a prediction market. With enforcement now underway, regulators are no longer treating these risks as hypothetical.
Who Wins
The financial implication is straightforward but uncomfortable. Prediction markets rely on the idea that prices reflect a wide range of independent views. But if outcomes are influenced or known by a small group — such as officials, military personnel or insiders — then the advantage shifts dramatically. The market may still arrive at the “correct” answer, but the profits flow disproportionately to those with early or privileged insight.
For ordinary users, that creates a structural disadvantage. Rather than participating in a level playing field, they may be providing liquidity that allows more informed traders to monetise their edge. This dynamic has already drawn criticism from lawmakers, who argue that markets tied to political or military decisions could create perverse incentives for insiders to benefit financially from outcomes they can influence or anticipate.
At the same time, the sector is expanding. Platforms like Kalshi are positioning themselves as more regulated alternatives, while a growing number of tools now allow users to track and copy large or “suspicious” trades in real time. That development reinforces the core tension: if certain traders consistently outperform, others will try to follow them — effectively acknowledging that the market may not be as evenly informed as advertised.
The deeper contradiction is what makes this story financially significant. Prediction markets are becoming more accurate in forecasting complex events, which increases their value as information tools. But if that accuracy is driven by a small, informed minority rather than true collective insight, then the same mechanism that improves predictions also undermines fairness. In that scenario, the market is not just predicting outcomes — it is redistributing money based on who knows first.
