From threats to the safety of poll workers to voters who can't distinguish between odds and results, prediction markets are already scrambling the political process. When Jim Allen, the elections director for Delaware County, Pennsylvania, recently ran a training session for poll workers, he began a conversation about a topic he'd never previously had to address during training: prediction markets . "There was one person who stood up, and they said, 'Well, what if we just want to make a minor bet on what turnout will be, that'll keep things interesting?' And we said: 'No, this is all bad,'" Allen, who oversees 383 precincts, recounts. As a result, Allen and the board of elections in Delaware County amended the oaths signed by people involved in elections to include "an affirmation that the workers have no direct or indirect interests in any bets, wagers, or prediction markets."

Jacob Fortinsky, the CEO and cofounder of the new prediction market Novig, says his outfit isn't like those other markets. Twenty-eight-year-old Jacob Fortinsky has been busy. "I was a groomsman at two weddings this weekend," he tells me over Zoom. Also, the sports trading company he runs, Novig, launched its prediction market last week, facilitating $18 million in trading volume on its first day. Today, Novig launches a "responsible trading framework" in its exchange rulebook, codifying its guardrails as part of its effort to be seen as a kinder, gentler prediction market player.

From pessimism around dating to AI reshaping culture, cyber-ethnographer Ruby Thelot tells WIRED why people are putting too much stock into things that go viral. For the last decade on social media, Ruby Thelot has asked himself the same questions when he encounters viral content online: Is this an ad? Is this an actual trend or just a fluke? While trends like " 6-7 " and " looksmaxxing " have recently broken containment, not every new term or subcultural phenomenon has real mainstream staying power. Remember, in years past, when tech bros tried to make NFTs a thing? Or that period when audio chat apps like Clubhouse were pitched as the future of social media but quickly flamed out? ( A prediction WIRED was not immune to.)

One of Kalshi's most prominent traders tells WIRED he's swearing off Spotify-related markets until the issue is resolved. Top Kalshi trader Caleb Davies usually speaks to the press about how prediction markets help him rake in money. The Minneapolis-based IT worker estimates he's made $1.2 million overall across different prediction platforms, with $414,000 in winnings from Kalshi's culture markets alone. He especially enjoys wagering on music charts, because he carefully analyzes Spotify data to pick winners. "Every single morning, I'm going in, downloading the data, and updating my projections," he tells WIRED.

Pump.Fun's Bounties Platform Is a Black Hole of Circular Grifting The crypto platform claims you can "pay anyone to do anything," from quitting a job on camera to getting a memecoin-themed tattoo. But it mostly seems like people trying to scam each other. Would you run into a crowded university lecture hall, fart into a megaphone, and bellow "fartcoin" at the top of your lungs? If so--and should you have the means to document this stunt on video, preferably capturing the audience's reaction--you may claim a reward of approximately $1,000 . The money, of course, will be dispensed in fartcoin, a meme cryptocurrency trading at a little over 10 cents at time of publication, with a total market capitalization hovering around $130 million. Such is the promise of Pump.Fun GO, a new feature on Pump.Fun, one of the fastest-growing crypto businesses of the past few years.

Prediction Market Philosophers Got What They Wanted. Getting the future right is now big business. But at a festival in the Bay Area, forecasters worry that sports markets could take the whole industry down. On June 11, Kalshi released a buzzy ad featuring noted New York Knicks fan Timothée Chalamet. It was a zeitgeist-capturing moment for prediction markets, akin to the 2022 Super Bowl, when seemingly every commercial featured a celebrity shilling crypto.

When agents trade in a Duality-based Cost Function prediction market, they collectively implement the learning algorithm Follow-The-Regularized-Leader [Abernethy et al., 2013]. We ask whether other learning algorithms could be used to inspire the design of prediction markets. By decomposing and modifying the Duality-based Cost Function Market Maker's (DCFMM) pricing mechanism, we propose a new prediction market, called the Smooth Quadratic Prediction Market, the incentivizes agents to collectively implement general steepest gradient descent. Relative to the DCFMM, the Smooth Quadratic Prediction Market has a better worst-case monetary loss for AD securities while preserving axiom guarantees such as the existence of instantaneous price, information incorporation, expressiveness, no arbitrage, and a form of incentive compatibility. To motivate the application of the Smooth Quadratic Prediction Market, we independently examine agents' trading behavior under two realistic constraints: bounded budgets and buy-only securities. Finally, we provide an introductory analysis of an approach to facilitate adaptive liquidity using the Smooth Quadratic Prediction Market. Our results suggest future designs where the price update rule is separate from the fee structure, yet guarantees are preserved.

When agents trade in a Duality-based Cost Function prediction market, they collectively implement the learning algorithm Follow-The-Regularized-Leader [Abernethy et al., 2013]. We ask whether other learning algorithms could be used to inspire the design of prediction markets. By decomposing and modifying the Duality-based Cost Function Market Maker's (DCFMM) pricing mechanism, we propose a new prediction market, called the Smooth Quadratic Prediction Market, the incentivizes agents to collectively implement general steepest gradient descent. Relative to the DCFMM, the Smooth Quadratic Prediction Market has a better worst-case monetary loss for AD securities while preserving axiom guarantees such as the existence of instantaneous price, information incorporation, expressiveness, no arbitrage, and a form of incentive compatibility. To motivate the application of the Smooth Quadratic Prediction Market, we independently examine agents' trading behavior under two realistic constraints: bounded budgets and buy-only securities. Finally, we provide an introductory analysis of an approach to facilitate adaptive liquidity using the Smooth Quadratic Prediction Market. Our results suggest future designs where the price update rule is separate from the fee structure, yet guarantees are preserved.

Americans Are Trading Billions of Dollars on Polymarket's Banned Offshore Platform It's the first estimate of how many Americans are sneaking onto Polymarket's banned crypto-based platform. Approximately 30 percent of the trading volume on Polymarket comes from the United States, according to a new study--an eye-popping number, considering that none of those people are legally allowed to use the crypto -based platform. The study, conducted by Rutgers University statistician Harry Crane, estimated that people in the US funneled between $10.6 to $26.7 billion through Polymarket. To track the platform's activity, Crane looked at what appeared to be US-based trades on offshore prediction market platforms from May 2025 to the end of April 2026. He found that many of the highest-volume markets on Polymarket were US-centric, including those covering US elections and sporting events.

According to federal prosecutors, Michele Spagnuolo made more than $1 million on the prediction market platform using confidential information about Google Search traffic. A Google security engineer has been charged with crimes stemming from allegedly placing trades on Polymarket using confidential internal information from the tech giant. Michele Spagnuolo, a 36-year-old Italian citizen, was arrested this morning in New York, as first reported by ABC News. Spagnuolo is charged with one count each of commodities fraud, wire fraud, and money laundering. He has worked at Google since 2014 and was based out of the company's Zurich, Switzerland, offices.