Welcome to Over/Under
A new newsletter on the latest in prediction markets, gambling, and the evolution of everyday risk
Welcome to Over/Under, a newsletter that explores the latest in prediction markets, gambling, and the evolving culture of risk in America. This newsletter delves into how platforms like Kalshi and Polymarket are reshaping how people engage with various aspects of life, including politics, video games, movies, and even weather events.
The surge in sports gambling is particularly noteworthy, as anyone with an internet connection can now place bets on nearly anything. This phenomenon is so widespread that athletes are signing multimillion-dollar sponsorship deals to boost their visibility, while livestreamers dedicate hours to playing slots and other online casino games, garnering millions of viewers.
News organizations, while not directly involved, are forming partnerships with leading prediction markets.
A recent image of actress Sydney Sweeney appeared on a billboard in Times Square, enticing passersby to log on and participate in betting activities. The ease of betting on smartphones adds to the accessibility of this phenomenon. In some instances, the stakes have become quite extreme, with users betting on matters that would have been unimaginable to traditional bookmakers, such as war, disease, and natural disasters.
Politicians themselves are placing bets on their chances of being elected, while a deployed Army master sergeant allegedly used insider knowledge to profit from the kidnapping of Venezuelan President Nicolás Maduro on Polymarket. Federal prosecutors are beginning to address the issue of insider trading, but the industry is still grappling with how to respond to these challenges.
As you navigate this evolving landscape, you may have questions, and so do I. In this newsletter, my aim is to not only explain how betting operates today but also to examine how the risks we take influence and shape the society we live in.
Written by urgent.news from The Atlantic's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.