Somebody worked out how to make money from Spotify's charts without recording a single song. Over recent weeks, people have been placing real-money bets on which tracks top the streaming service's charts, using prediction-market platforms like Kalshi and Polymarket. Then someone decided to rig the outcome.
The rigged chart
Spotify says it spotted and stripped out more than 500,000 artificial streams that had suddenly pushed a little-known song, Malcolm Todd's "Earrings", up its charts. The trouble is that those fake numbers had already been used to settle a Kalshi market on the most-streamed Spotify song in the US in June, a market that had drawn about $3 million in trading. Todd was even declared one of the winners on figures published before Spotify finished investigating. Before the fake streams landed, Kalshi had put his chances of topping the chart below 3%, so anyone betting on him stood to make roughly 30 times their money. One trader says the episode cost him $4,500.
Spotify pushes back
Spotify has now asked both Kalshi and Polymarket to remove its logo and make clear that neither platform has a partnership with it. Kalshi said it is "in touch with Spotify and actively investigating", while Polymarket did not respond to Bloomberg. Spotify says stream manipulation is nothing new, that it has "best-in-class detection" and that it does not pay royalties on faked streams; what is new is that betting markets have handed manipulators a fresh reason to try.
Why it matters
This is not the first time a prediction market has been gamed. In one earlier case, a US think-tank employee was found to have edited an interactive map of the Russia-Ukraine war that underpinned a Polymarket wager on Russia's territorial gains, and French authorities have investigated whether someone manipulated a weather station's data to profit from a market on Paris temperatures. The Spotify episode feeds the same wider worry: that prediction markets are quietly turning real-world numbers into targets worth rigging. As betting spreads into more corners of everyday life, the fight over who controls the underlying data, and who pays when it is faked, is only beginning.
