Google Engineer Accused of Trading on Inside Search Data at Prediction Market

Michele Spagnuolo, a 36-year-old Google software engineer, has been charged with fraud and money laundering in a federal complaint unsealed in New York. He is accused of using Google's internal search data—information not available to the public—to place profitable bets on Polymarket, a blockchain-based betting platform that lets people wager on real-world events. According to prosecutors, he made more than $1 million from these trades.

The charges focus on his alleged use of confidential search data to bet on which person would be Google's most-searched individual in 2025. Federal prosecutors say he wagered on the singer D4vd, relying on actual search volume patterns that only he could see—information the general public and other Polymarket users did not have access to.

How the Alleged Scheme Worked

Spagnuolo joined Google in 2014 and had legitimate access to internal company data about what people search for on the platform. Federal prosecutors allege he crossed a line when he began using this proprietary information to place bets on Polymarket.

The mechanics are straightforward: while other Polymarket users have to guess or make educated guesses about search trends based on public information, Spagnuolo allegedly knew the actual numbers. Polymarket allows people to bet on Google's year-end search trends as part of its cultural prediction markets. He had direct visibility into the search volumes that would ultimately determine whether his bets won or lost.

The Justice Department says this was an abuse of "elevated access to confidential trends"—material information unavailable to other market participants. Prosecutors treat this not simply as a workplace violation but as deliberate exploitation of privileged access for profit.

What Makes This Case Novel

This case sits at an unusual intersection of corporate data access, betting platforms, and fraud law. Traditionally, insider trading involves advance knowledge of things like earnings reports or mergers that will move stock prices. Here, the inside information is real-time search data that predicts the outcome of what amounts to a trivia bet with money on the line.

The legal argument appears to rest on a principle that matters across many kinds of markets: if you have material nonpublic information—true facts that aren't public yet—and you use it to gain an unfair advantage in a market where that information affects outcomes, that can be fraud. Polymarket operates on blockchain technology and uses prediction market tokens rather than traditional stocks, but the core unfairness is the same: some participants are informed while others are guessing.

The broader context here is important. This prosecution suggests federal prosecutors are treating prediction market manipulation with the same legal weight as traditional stock market fraud. That could set a precedent as these platforms grow in popularity and size.

A Pattern Emerging

The charges against Spagnuolo come roughly one month after federal prosecutors charged a U.S. Army Special Forces master sergeant who allegedly used classified military intelligence to earn $400,000 on Polymarket bets about Venezuelan President Nicolas Maduro.

That pattern reflects a real challenge prediction markets face as they scale up and attract larger amounts of money. Traditional stock and bond markets have decades of regulatory rules and sophisticated monitoring systems in place. Prediction markets are newer and operate in a less settled regulatory environment, where the rules around acceptable information use are still being written through court cases like these.

Both cases involved defendants with legitimate professional access to information valuable for specific bets, then allegedly crossed into criminal territory by monetizing that access for personal gain.

Historical Context

We have seen this movie before. When electronic trading emerged, when cryptocurrency exchanges started, even when traditional derivatives markets were new, regulators and courts had to figure out the boundaries between legitimate analysis and illegal manipulation. There is typically a gap between how fast a market or technology grows and how fast clear rules catch up.

What makes prediction markets distinctive is their basic logic: they work because people have different views about future events based on different information sets. The challenge for regulators and platform operators is drawing a clean line between someone doing legitimate research and analysis—which should be encouraged—and someone exploiting truly privileged access to nonpublic facts.

Polymarket and similar platforms will probably need better monitoring systems and clearer rules about what counts as prohibited information sources. Traditional financial markets have elaborate frameworks about material nonpublic information and insider trading restrictions. Prediction markets are now facing the need for similar structures.

What Comes Next

The Spagnuolo case tests whether existing fraud and money laundering laws apply to prediction market manipulation. If he is convicted, it will establish clear precedent that traditional securities law extends to blockchain-based prediction platforms. An acquittal might push regulators to develop new, more specific rules instead.

For technology companies, the case highlights a gap in existing compliance programs. Many firms already restrict stock trading or require pre-clearance for certain transactions. But prediction market participation is a newer category that many internal policies may not address.

The prediction market industry faces a larger question: how do you balance the core value—bringing together many perspectives and information sources to predict outcomes—with the need to prevent exploitation of truly privileged access. As these platforms handle more money and attract more sophisticated participants, getting these boundaries right matters more.

Federal prosecutors have sent a clear message: prediction markets are not a regulatory gray zone where fraud laws do not apply. The same principles that govern fairness in traditional financial markets extend to these newer platforms, whatever technology they use or what kind of bets they host.