Insider trading has always been a problem for Wall Street.
Corporate executives know things investors don’t, and government officials can often gain access to information before the public. And every so often, someone decides to turn that advantage into money.
That’s why securities laws exist.
But a strange thing happened last month.
Federal prosecutors charged a Google employee with allegedly using confidential internal information to place profitable bets on Polymarket, one of the world’s largest prediction market platforms.
And I realize that might sound like an isolated case of misconduct. But I believe it reveals the real product prediction markets are selling.
And as artificial intelligence increases the value of accurate forecasts, that distinction is going to become increasingly important.
Prediction Markets
As I’ve written about before, prediction markets operate on a simple premise.
Instead of asking one expert what will happen next, you let thousands of people put money behind their opinions.
If they’re right, they make money. And if they’re wrong, they lose it.
That creates a very different kind of forecast.
Because a poll can tell you what people say they believe, but a prediction market tells you what people are actually willing to risk money on.
That distinction helps explain why these markets have grown so quickly.
According to Pew Research, monthly trading volume on Kalshi and Polymarket climbed from less than $5 billion in September 2025 to about $24 billion in April 2026.
For comparison, legal sportsbooks in the United States averaged about $14 billion per month in 2025.
So prediction markets are no longer some tiny corner of the internet. They’re already processing more monthly volume than the legal U.S. sports betting industry.
But they’re not only being used to predict the outcome of sporting events.
People now trade contracts tied to elections, inflation, Federal Reserve decisions, crypto prices, weather, company milestones and even cultural events.
That’s why I believe that prediction markets are becoming a new kind of information network.
One built around probabilities instead of opinions.
Because a prediction market can create a live probability signal. Every one of its contracts is trying to answer the same basic question:
What happens next?
If traders believe there’s a 70% chance the Federal Reserve cuts rates, that price becomes information.
If traders believe there’s a 30% chance a company will go public by the end of the year, that price becomes information too.
The market may be wrong. In fact, it often will be.
But it’s still creating a real-time snapshot of what a large group of people believes is likely to happen.
And businesses have understood the value of that for years.
Google experimented with internal prediction markets as far back as 2005. This Wall Street Journal headline is from early 2008:
Ford and other large companies have tested similar systems.
Researchers who studied corporate prediction markets at these companies found they improved on expert forecasts by as much as a 25% reduction in forecasting error.
That’s a big deal.
Because companies are full of information that never makes it into a formal report.
For example, engineers know when a product launch is slipping. Sales teams quickly realize when demand is weaker than an official forecast. And managers often know when a deadline looks unrealistic.
Prediction markets can pull those scattered pieces of knowledge into a visible number.
Then add in artificial intelligence.
At its core, AI is already a prediction machine. It predicts the next word in a sentence.
It can also predict which video will keep you watching, what product you might buy and which route gets a package to your door faster.
But as AI agents become more useful, they’ll need even better signals about the future.
That’s where prediction markets could become far more important.
Because they create a steady stream of probabilities about real-world events. And in a world filled with AI agents, those probabilities could become fuel.
Which brings us back to the Google case.
According to federal prosecutors, the Google employee allegedly used confidential internal search data to place roughly $2.7 million in trades on Polymarket.
Those trades allegedly generated about $1.2 million in profits.
To be clear, this employee wasn’t accused of manipulating a stock. He wasn’t accused of buying options before a merger announcement either.
He was accused of using private information to bet on future outcomes in a prediction market.
That suggests these markets are starting to attract the same behavior that once belonged almost entirely to Wall Street.
And this wasn’t the only recent case.
In April, federal prosecutors charged a U.S. Army soldier with allegedly using classified information to profit from Polymarket contracts tied to Venezuela.
Prosecutors said he made more than $400,000.
Here’s My Take
Those two cases don’t prove the prediction market industry is broken.
People have always looked for an edge.
But they do suggest that accurate forecasts are becoming increasingly valuable.
And that’s exactly what prediction markets produce.
Every day, they convert uncertainty into probabilities and probabilities into prices.
And as AI increases the value of those forecasts, prediction markets could evolve into something much larger than a speculative sideshow.
They could become a new information layer for the economy.
Because Wall Street trades stocks. But prediction markets trade possibilities.
Regards,
Ian King
Chief Strategist, Banyan Hill Publishing
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