Spotify has removed more than 500,000 streams from a track that reached No. 1 on its US chart, after discovering the surge coincided with a spike in suspicious bets on prediction platform Kalshi.
The track, “Earrings” by artist Malcolm Todd, was not manipulated by Todd or his label, Columbia Records, according to reports. Instead, the activity points to traders who placed large wagers on Todd securing the top spot on Spotify’s US chart in June.
A Spotify spokesperson said: “All streaming services face ever-changing stream manipulation. Spotify has best in class detection and mitigation practices for manipulated streams, and we don’t pay out associated royalties.”
Evidence gathered by a successful prediction-market trader Caleb Davies indicated the likelihood of “Earrings” topping the chart randomly was “a roughly 1 in 77 octillion chance.”
Kalshi’s Response
Kalshi confirmed it is in contact with Spotify and is “actively investigating this matter,” according to a company spokesperson. The platform also removed Spotify’s logo at the streaming service’s request.
Music Betting’s Rapid Growth
The incident highlights the scale of music-related trading on Kalshi. In the first four months of 2025 alone, more than $400 million in trades were placed on music outcomes, including $110 million wagered on which song Bad Bunny would perform first at the Super Bowl halftime show.
A New Manipulation Vector
Traditional stream manipulation typically involves uploaders, such as artists, rightsholders, or bad actors using AI-generated tracks, who face penalties when caught. This case is different: the artist and label appear to be victims of third-party traders seeking to profit from prediction markets.
Spotify removed the fraudulent streams but must ensure that Malcolm Todd and Columbia Records face no further consequences under its anti-manipulation systems. The incident leaves open how Spotify will handle the intersection of streaming data and prediction markets, including whether to collaborate with platforms like Kalshi to detect suspicious patterns or to restrict their access to its data.
