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``` It is difficult for the US stock market to hit new highs again in the short term, as funds are flowing out from the semiconductor stocks that have seen the biggest gains this year and turning to AI hyperscalers. Morgan Stanley's Chief Equity Strategist Michael Wilson pointed out

``` The oil supply shock caused by the US-Iran war has quickly reversed. With the resumption of Middle East exports, OPEC+ is shifting from a narrative of scarcity to concerns about surplus, once again facing coordination pressures between price and quotas. According to Bloomberg, key OPEC+ members reached an agreement last

``` International oil prices have recently fallen sharply, breaking below key long-term moving averages and providing important support to the stock market. Historical experience shows that after such a technical correction in oil prices, the average rise in the US stock market over the next 12 months can reach about 17%

``` Google has unveiled a major trump card in the large model competition. According to leaked information, Gemini 3.5 Pro will be officially released on July 17th, reportedly having made a leap forward in both frontend and visual code generation capabilities, outperforming Anthropic's Fable 5 in multiple tests,

``` Unbeknownst to many, equity fund managers at large fund companies have collectively shown strong momentum in this round of tech stock gains. According to the fund company performance rankings released by Guotai Haitong Securities, over the past five and ten years, the overall returns of large fund companies have posted

``` As SpaceX was officially included in mainstream indices such as the Nasdaq 100 this week, a value-driven “mine-sweeping” movement of investment portfolios is quietly spreading among US retail investors. After completing the largest IPO in history this June, SpaceX’s stock price surged and then fell back by more than

Against the backdrop of continuously rising AI usage costs, "model router" technology is rapidly penetrating the enterprise market. The core logic is not complicated: automatically match the right, not the most expensive, model for each task, drastically reducing token expenditure without significantly affecting performance. This is prompting enterprises