An anonymous reader quotes a report from Wired: Prediction market boosters often tout the platforms as barometers of public opinion, or even "truth machines" offering a clearer picture of where things are headed than polling or other data. In 2024, for example, Kalshi beat many pollsters in forecasting the outcome of the US presidential race. But their trading activity can also tell more perplexing stories. Two global election markets on Polymarket's offshore platform, for instance, generated over $600 million in trading volume, far more than most political markets typically attract. Most of the activity took place buying and selling contracts tied to long-shot candidates who ultimately did not win. The report focuses on suspicious trading activity surrounding elections in Ethiopia and Romania, where researchers uncovered $284 million in questionable trades tied to Ethiopia's election and periods when 99.6% of trading volume in a Romanian election market was flagged as suspected "wash trading." According to Wired, "wash trading is a type of market manipulation in which accounts repeatedly buy and sell assets to create the illusion of heavy trading activity, when in reality, money is simply moving back and forth." Some accounts may also be artificially inflating their activity to qualify for potential cryptocurrency token rewards, a practice known as "activity farming." In response to the Ethiopian trading activity, Polymarket told Wired that the company "immediately flagged this activity, investigated, and blocked the accounts from trading." The company did not explain why the suspicious trades were allowed to continue for months before action was taken.

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An anonymous reader quotes a report from Reuters: Private credit's expanding role in financing AI infrastructure faced one of its clearest market tests this week as CoreWeave's bonds fell to distressed levels, raising questions about how lenders are pricing a boom built on chips, data centers and customer contracts. CoreWeave has about $50 billion of debt, according to LSEG data, making it one of the largest borrowers linking private credit to the AI buildout. The cost of insuring its debt against default climbed to about 1,200 basis points this week, while yields on some of its bonds rose to roughly 15% to 16%, signaling sharply increased investor concern over its credit risk. "There is still a price. Investors are still comfortable financing it, but they're demanding more compensation for it," said James Martin, senior US credit strategist at SMBC, referring to projects further from completion. Carlyle estimates that private credit could finance roughly $1 trillion of the AI infrastructure buildout but warns that aggressive lending could create concentration risks similar to those seen in software lending. "The clearest lesson is not to avoid AI compute exposure, but to set concentration limits before the buildout accelerates further, not after," Carlyle said.

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