A US soldier involved in the capture of ousted Venezuelan leader Nicolas Maduro allegedly made $400,000 after betting the mission would be successful, using classified information to place wagers, the Justice Department said.
Details of the Case
Gannon Ken Van Dyke, a master sergeant with U.S. Army Special Forces, allegedly made bets on the prediction market Polymarket that the US would enter Venezuela and Maduro would be out of power in the weeks leading up to the January 3 capture. Van Dyke, 38, has been charged with unlawful use of confidential government information for personal gain, theft of nonpublic government information, commodities fraud, wire fraud, and making an unlawful monetary transaction.
President Trump's Reaction
When President Donald Trump was asked about the case, he compared it to Pete Rose, who was banned from Major League Baseball over a gambling scandal. Trump said: 'That's like Pete Rose betting on his own team. If he bet against his team, that would be no good, but he bet on his own team. I'll look into it.'
Evidence and Indictment
According to the indictment, Van Dyke uploaded a photograph on his Google account in the early morning of January 3, hours after the U.S. military brought Maduro to the USS Iwo Jima amphibious assault ship. The photograph depicts Van Dyke on what appears to be the deck of a ship at sea, at sunrise, wearing U.S. military fatigues and carrying a rifle, standing alongside three other individuals wearing U.S. military fatigues.
Polymarket's Statement
Polymarket wrote on X: 'Insider trading has no place on Polymarket. Today's arrest is proof the system works.'
Justice Department's Position
Acting U.S. Attorney General Todd Blanche said: 'Our men and women in uniform are trusted with classified information in order to accomplish their mission as safely and effectively as possible, and are prohibited from using this highly sensitive information for personal financial gain.'
The case highlights the serious consequences of using classified information for personal benefit, even in the context of prediction markets.
