Last month’s Nobel Peace Prize winner, Venezuelan opposition leader María Corina Machado, raised speculation over just how sensitive prediction markets are to insider trading.
As the time was closing in on a decision for a nominee, Machado’s odds were running behind the widow of Russian opposition leader, Alexei Navalny, Yulia Navalnaya, and US President Donald Trump. However, hours before the big announcement, votes in Machado’s favor increased rapidly, reaching 73% in just a few hours. Harvard professor and former US Chairman of the Council of Economic Advisers, Jason Furman, speculated the trend could be the work of individuals with insider information.
The episode highlighted a persistent flaw in prediction markets: their dependence on unequal information. While designed to crowdsource intelligence, prediction markets can just as easily amplify the advantage of those closest to the facts – the insiders.
Now, developers are asking whether decentralized AI agents, autonomous systems that monitor data and on-chain behavior, could serve as impartial sentinels, detecting manipulation or insider patterns in real-time.
Prediction markets are not corrupt; they are vulnerable
As prediction markets evolved from academic experiments to high-stakes platforms, their greatest strength has seemingly also become their weakness. Prediction markets depend on information that lacks even distribution.
Markus Levin, the co-founder of XYO, a decentralized data validation network, argued that prediction markets are not “inherently corrupt” but are “highly vulnerable to information asymmetry.” This is due to their binary structure, ability to bet on almost anything, and reliance on outcomes that a small group of individuals influences.
That imbalance is what Joe Z, the co-founder of DeAgentAI, an AI-powered predictive signal platform, called the “irony” of prediction markets. He explained that, generally, the systems designed to democratize foresight often end up amplifying the voices of those who have the best “leaks.”
Unlike traditional markets, which follow a set of rules, prediction markets are “not governed by the same legal frameworks that developed around public companies,” added Dylan Dewdney, the co-founder and CEO of Kuvi.ai, a platform pioneering Agentic Finance. According to him, any platform vulnerable to information asymmetry is also vulnerable to insider trading.
“In fact, one could argue that in prediction markets, insider trading can actually make the market more accurate, since it reflects the reality known by those closest to the facts. Regulation can reduce it, but it can’t eliminate it because human nature is social and networked.”
The US Securities and Exchange Commission’s (SEC) insider trading laws do not apply to prediction markets, as they are regulated as contracts by the Commodity Futures Trading Commission (CFTC), which only prohibits certain types of insider trading. Specifically, CFTC Rule 180.1 prohibits trading on the basis of material nonpublic information (MNPI) in breach of a duty owed to the source of the information, or on information obtained through fraud or deception.
Decentralized AI agents could make manipulation more challenging
Some industry experts believe insider trading could be beneficial in prediction markets, as insiders could enhance the accuracy of speculations. While the public tends to think that fairness is the goal, markets are rarely ever fair, Dewdney explained.
“Decentralized AI could identify behavioral or data-driven anomalies that suggest insider activity. But whether that matters is another question.”
Dewdney added that decentralized systems make manipulation by “traditional gatekeepers,” such as governments or large institutions, harder by shifting where power resides. Yet, they are unable to fully remove the power itself.
DeAgentAI’s Z took a more optimistic stance. He highlighted that if built right, developers could train AI agents to flag unusual wallet clusters, timing patterns, and subtle statistical deviations before outcome shifts. An example is the platform’s AlphaX product, which already processes market data for price forecasting and strategy automation.
“If you plug that kind of intelligence into a prediction market, you can have agents running continuous checks for anomalies… Basically, they’d act like decentralized compliance officers that don’t sleep or get bribed.”
However, for AI agents to be effective, the integrity of the underlying data, what oracles feed into these markets, is just as crucial. XYO’s Levin explained that the more localized or niche the event, the higher the risk of manipulation is. By distributing verification across independent sources and having AI agents cross-check inputs, reject outliers, and aggregate consensus, the system makes tampering exponentially harder.
Another example of where this technology is already being implemented is Olas Predict, which delivers predictions through agent trading activity.
While insider advantages may never disappear, decentralized AI agents and distributed oracle networks can make manipulation far harder. However, their implementation remains to be seen.
