俄羅斯央行：將根據通脹及通脹預期的動態，以及對國內外形勢所帶來的風險分析，進一步做出關鍵利率決策。

U.S. President Trump reportedly said the two leaders would discuss AI. At a regular briefing on the 24th, Foreign Ministry spokesman Lin Jian said summit-level diplomacy provides an irreplaceable strategic steer for U.S.-China ties and that China is willing to work with the U.S. to implement the leaders' important consensus on artificial intelligence.

Analyst Rick says NVIDIA is functioning as a "quasi-central bank" for the AI era: it cannot issue currency or set rates, but it is reshaping allocation of the scarcest capital input—GPUs—and accelerating financialization of the AI industry. The industry is shifting toward an infrastructure financing model: financing to buy GPUs → GPU leasing produces cash flows → cash supports further financing → more GPU purchases, creating a self-reinforcing capital flywheel. GPUs are acquiring infrastructure-asset characteristics—long-duration cash flows, collateral value and securitization potential—which helps explain NVIDIA’s continued lock-up of HBM, advanced packaging and supply capacity. Cloud giants (Microsoft, Google, Meta, Amazon) are expanding capex and using orders, prepayments and deals to ease upstream financing; NeoCloud, HBM vendors and advanced-packaging firms are raising debt and signing long-term contracts; private credit, infrastructure funds and sovereign wealth funds are moving into AI data-center investment. Market implications: sustained, large-scale capital demand may keep long-term funding costs elevated even if short-term policy rates fall; long-duration capital will flow not only to GPUs but to data centers, power, fiber, cooling, HBM and packaging. Financing costs are likely to bifurcate—lower for AI firms with predictable cash flows, relatively higher for traditional sectors—raising the risk of widening credit spreads.