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Analysis: Large-scale outflows from Bitcoin ETFs and private credit funds, market risk signals intensifying

According to CoinDesk, in just the month of June, the U.S. spot Bitcoin ETF saw a net outflow of $4 billion, led by BlackRock's IBIT, as funds shifted towards opportunities in AI trading and the SpaceX IPO. Bitcoin fell about 14% in the second quarter, dropping below $60,000, marking its third consecutive quarter of losses. However, this outflow pales in comparison to the $2 trillion private credit market. Redemption requests in private credit reached $15.6 billion in the second quarter, with 10 out of 16 business development companies exceeding the 5% quarterly cap, and most investors receiving only partial payouts. Fitch expects redemptions to continue in the coming months, and unmet requests will keep several companies under pressure.Bitcoin ETFs have strong liquidity, and outflows directly impact BTC prices; in contrast, private credit BDCs are illiquid long-term instruments. The simultaneous redemptions of both reflect widespread market concerns about liquidity and risk. The energy market is also sending signals of risk aversion, with the U.S. Strategic Petroleum Reserve at its lowest level since 1983. QCP Capital summarized: "Different sectors, same pattern: the market's buffer space is narrowing." It pointed out that the Strategic Petroleum Reserve has bottomed out, Strategy has sold BTC for the first time to pay dividends, and private credit redemptions have surpassed thresholds, all indicating that risk assets face a more challenging environment.

Detailed explanation of the security item change event released by Gate: Confirm that the applicant has a good grasp of highly matching external information and is advancing on-chain analysis and asset tracking procedures

Gate recently released a detailed verification statement regarding the security item changes and fund loss incident reported by customer 9****6. After an internal review, the platform confirmed that the applicant submitted materials highly matching the customer's account, including Email, phone number, real-name information, transaction records, and Alipay screen recordings. The platform pointed out that the Alipay screen recording required access to the customer's Alipay account and successfully passed multiple identity verifications, indicating that the applicant not only possessed account information but also obtained the customer's external real-name information, Alipay account, and device permissions in advance.The platform emphasized that its security unbinding review implements a four-fold verification process of "multi-channel advance notification + system risk control preliminary screening + manual multi-layer review + time protection." After notifying the customer through dual channels, the application was processed only after waiting two days without objections, and a 24-hour withdrawal prohibition protection was imposed after modification. Upon investigation, there were no information leakage logs within the platform, and some sensitive identity information provided by the applicant was external information previously unknown to the platform.Regarding fund recovery, the platform has sorted out the flow of funds and collaborated with multiple departments to conduct on-chain analysis and asset tracking, currently coordinating with third parties like Tether to advance the freezing process, and will cooperate with judicial investigations in the future. The platform advises customers to conduct comprehensive security reinforcement on their devices and expresses regret that the customer did not contact the platform within the three-day notification period.

KPMG's research shows that nearly 30% of corporate executives find it difficult to understand the cost of AI on a pay-per-use basis, and nearly half have delayed deployment

According to KPMG's latest survey report involving 2,145 executives from 20 countries, as technology companies like Anthropic, OpenAI, and GitHub recently shifted some of their AI services from fixed subscription models to usage-based billing, businesses are facing challenges in cost forecasting and management during the scaling of AI deployment.The report indicates that 29% of corporate executives find it difficult to understand and control operational costs when scaling AI deployment, and one-third of executives believe that insufficient understanding of AI economics hinders the deployment of AI entities. Due to costs exceeding expected value, nearly half (about 49%) of corporate organizations have chosen to delay or readjust their AI deployment plans; meanwhile, low-cost, high-fidelity large models are accelerating their impact on corporate AI strategies.In addition, tech giants are increasing capital expenditures to build AI capacity. Amazon plans to spend about $200 billion on capital expenditures this year and is investing $1 billion in its AWS frontline engineering organization to assist customers in adopting AI entities; Microsoft's total capital expenditure is expected to reach $190 billion this year, with $2.5 billion allocated to the new entity Microsoft Frontier Company. KPMG emphasizes that, in addition to cost pressures, accountability in AI governance, employee engagement rules, and the prevention of system "hallucinations" remain core challenges faced by businesses today.
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