A prediction market wager concerning influencer Tiffany Fong and tech mogul Elon Musk has drawn sharp criticism from Ethereum co-founder Vitalik Buterin, highlighting ongoing ethical debates within the crypto betting space.
The controversy erupted after Polymarket, an online prediction platform, listed a market on April 17 speculating whether Fong would become pregnant, following reports alleging Musk had asked her to bear his child.
Buterin publicly condemned the market, stating via social media, “fwiw I personally think this is tasteless and violation of a person’s privacy and dignity.” His comments underscore concerns about the boundaries of decentralized prediction markets.
The platform reportedly used an AI-generated image depicting a pregnant Fong for the market’s promotion, further fueling the debate.
Speculation intensified following a Wall Street Journal piece detailing alleged communications between Musk and Fong. The report suggested Musk proposed the idea of having a child together, an offer Fong apparently declined. This refusal reportedly led to Musk unfollowing her on X (formerly Twitter) and the cessation of a revenue-sharing agreement.
Fong, who gained prominence interviewing former FTX CEO Sam Bankman-Fried—interviews later referenced in legal proceedings—has navigated public speculation about her connections within the tech and crypto spheres before.
Polymarket is no stranger to contentious markets, having previously faced scrutiny for facilitating bets on geopolitical events and natural disasters. This latest incident involving personal speculation adds another layer to discussions around the responsibilities of such platforms operating within the burgeoning crypto ecosystem. The platform’s willingness to host controversial wagers continues to spark debate about ethics in the rapidly evolving world of crypto prediction markets.
The incident raises questions about privacy and the potential for misuse of blockchain-adjacent technologies when applied to sensitive personal matters, prompting reflection on acceptable use cases for prediction markets.