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.

CFTC chairman Michael Selig sat down with WIRED to discuss how the agency scours Polymarket and other prediction markets for illegal activity. For most of the past year, it looked like prediction markets had kicked off a new golden age of fraud. On Polymarket, traders raked in fortunes from suspiciously timed bets on geopolitical events like the raid on Venezuela and the Iran War. It wasn't clear whether the US government would bother pursuing some of the most flagrant bad actors, since Polymarket's crypto-based platform was technically offshore and not regulated or licensed within the country. Now, however, the Commodity Futures Trading Commission, which oversees prediction markets, wants you to know that it's watching very, very closely.

Truth Predict was supposed to be the Trump family's biggest leap yet into prediction markets. Now it's looking more like a tiptoe. The odds that the Trump family will launch a full-fledged prediction market product this year just plummeted. Last year, the Trump Media and Technology Group announced Truth Predict, a partnership with the cryptocurrency company Crypto.com. The initial announcement touted Truth Predict as a "new product" that would allow Truth Social users to make trades on sports, inflation, elections, and more through an "embedded" prediction market service.

A new executive order seen by WIRED prohibits New York state employees from using insider knowledge to enrich themselves with prediction market bets. New York has banned state employees from using insider information to trade on prediction markets . In an executive order signed today and viewed by WIRED, Governor Kathy Hochul forbade the state's government workforce from using "any nonpublic information obtained in the course of their official duties" to participate on prediction market platforms, or to help others profit using those services. "Getting rich by betting on inside information is corruption, plain and simple," Hochul said in a statement provided to WIRED. "Our actions will ensure that public servants work for the people they represent, not their own personal enrichment. While Donald Trump and DC Republicans turn a blind eye to the ethical Wild West they've created, New York is stepping up to lead by example and stamp out insider trading."

Prediction markets like Kalshi and Polymarket are booming, and so is a fight among regulators, lawmakers, and advocates over their legality. Former New Jersey governor Chris Christie, who currently serves as an advisor to the American Gaming Association, has criticized prediction markets. The political fight in the US over the future of prediction markets like Polymarket and Kalshi has escalated into a full-blown war, and battle lines aren't being neatly drawn along party lines. Instead, conservative Mormons have aligned themselves with Las Vegas bigwigs and MAGA royalty is siding with liberal Democrat lobbyists. One side argues that the platforms are breaking the law by operating as shadow casinos.

As prediction market platforms like Polymarket and Kalshi battle regulators in court, Senate Democrats are urging the CFTC to avoid weighing in, escalating a broader fight over the burgeoning industry. Senator Adam Schiff, a Democrat from California, is leading the group of lawmakers urging the CFTC to stay out of state prediction market lawsuits. A group of 23 Democratic US senators sent a letter Friday to the top federal regulator overseeing prediction markets, urging the agency to avoid weighing in on pending court cases over the legality of offerings on the platforms tied to "sports, war, and other prohibited events." Prediction markets, which sell contracts tied to the outcome of real-world developments, have exploded in popularity over the past year, attracting an increasingly mainstream fanbase eager to wager on everything from geopolitical conflicts to fashion choices to the Super Bowl. As they expanded, the platforms have become a magnet for ethical and legal controversies.

Recent memoirs by the retired bookie Art Manteris and the storied gambler Billy Walters provide a glimpse of an industry in its fledgling form--and a preview of the DraftKings era to come. Las Vegas is no longer the seat of the sportsbook gods. In most states, it's now legal, and extremely popular, to place bets using apps or websites such as FanDuel and DraftKings. From your couch, you can wager on everything from the results of snooker championships to the color of the Gatorade poured over the victorious coach after the Super Bowl. The N.F.L., along with the other major-league American sports associations, has officially partnered with sports-betting sites, and their alliance has proved so lucrative that other industries want in on the action; last month, the Golden Globes made a deal with Polymarket, a predictions-market platform, to encourage wagering (or "trading," if you prefer) on the outcomes of its awards race.

Large language models are increasingly used to evaluate other models, yet these judgments typically lack any representation of confidence. This pilot study tests whether framing an evaluation task as a betting game (a fictional prediction market with its own LLM currency) improves forecasting accuracy and surfaces calibrated confidence signals. We generated 100 math and logic questions with verifiable answers. Six Baseline models (three current-generation, three prior-generation) answered all items. Three Predictor models then forecasted, for each question-baseline pair, if the baseline would answer correctly. Each predictor completed matched runs in two conditions: Control (simple correct/incorrect predictions) and Incentive (predictions plus wagers of 1-100,000 LLMCoin under even odds, starting from a 1,000,000 LLMCoin bankroll). Across 5,400 predictions per condition, Incentive runs showed modestly higher accuracy (81.5% vs. 79.1%, p = .089, d = 0.86) and significantly faster learning across rounds (12.0 vs. 2.9 percentage-point improvement from Round 1 to Round 4, p = .011). Most notably, stake size tracked confidence. "Whale" bets of 40,000+ coins were correct ~99% of the time, while small bets (<1,000 coins) showed only ~74% accuracy. The key finding is not that fictional money makes models smarter; accuracy gains were modest and did not reach statistical significance (p = .089) in this pilot. Rather, the betting mechanic created a legible confidence signal absent from binary yes/no outputs. This suggests that simple financial framing may help transform LLMs into risk-aware forecasters, making their internal beliefs visible and usable. The protocol offers a foundation for future work for meta-evaluation systems and what may become LLM-to-LLM prediction markets.

Prediction markets allow users to trade on outcomes of real-world events, but are prone to fragmentation with overlapping questions, implicit equivalences, and hidden contradictions across markets. We present an agentic AI pipeline that autonomously (i) clusters markets into coherent topical groups using natural-language understanding over contract text and metadata, and (ii) identifies within-cluster market pairs whose resolved outcomes exhibit strong dependence, including "same-outcome" (correlated) and "different-outcome" (anti-correlated) relationships. Using a historical dataset of resolved markets on Poly-market, we evaluate the accuracy of the agent's relational predictions. We then synthesize discovered relationships into a simple trading strategy to quantify how discovered relationships translate into actionable strategies. Results show that agent-identified relationships have around 60-70% accuracy, and their induced trading strategies have an average return of 20% over week-long horizons, highlighting the ability of agen-tic AI and large language models to uncover latent semantic structure within prediction markets.

Reinforcement Learning with Verifiable Rewards (RLVR) has been an effective approach for improving Large Language Models' reasoning in domains such as coding and mathematics. Here, we apply RLVR methods towards forecasting future real-world events - a challenging task for RL due to the very noisy (and delayed) outcomes involved. Using a novel dataset of recent questions from a prediction market, and accompanying relevant news headlines, we show that a compact (14B) reasoning model can be trained to match or surpass the predictive accuracy of frontier models like o1, while greatly improving probabilistic calibration. The model's performance is also practically meaningful: in a Polymarket trading simulation, we estimate that its bets would have yielded a return on investment of over 10% across all questions in the test set. We detail and compare approaches used in training our model, including augmenting our training-data with synthetic prediction questions, guardrails for learning stability, and median prediction sampling at inference-time.