这一轮已经跌了33%了！5个月了。还是有点不敢信！ 入行十年多。我很少看到这么跌的微软。从我有工作记忆以来，它几次决策都对的。从软件到云 ai！ 微软(MICROSOFT) W MSFT.O 358.500 量 572.9万 股本 74.26亿 市盈™ 22.3 万得 盘口 时间范围 2021/11/26 ~ 2022/11/04 50周期 周期:周 区间涨幅 均价 280.666 成交量 75.8亿 回撤 -38.56% -35.05% 年化 -36.17% 振幅 39.30% 换手率 102.05% 关闭 597.619 552.242 -> 308.854 65.465 20.088 2026-03-27 2021-12-10 2017-08-18 2019-10-18 TRIX(12,9) 0.96 TRMA:0.00 × -0.93 MTR:20.80 ATR:10.33 22年牛转熊，当时6-7个月，最多也就跌了35%。 我都搞懵了。不知道公司管理层做何想？ 一代王！现在被抛弃，4月底出财报。大概率毛利率还会掉，股价没有起来的预期！再跌就破了 15年以来的 记录了！ ...

ai还没发力就已经这样了。美国那边更夸张，所有人都在等待裁员到自己。这可能就是标普要出问题的 原因，ai的负反身性—就业崩塌，消费崩塌。这个问题 华尔街 硅谷已经讨论3个月以上了。80%的人都 失业了，吃饭都成问题，怎么养，养龙虾要2w人民币月薪。我最近一直都不理解，nv google财报那 么好，推理数据那么好，为啥股票一直要跌？硅谷产业里的人告诉我，他们觉得觉得是负反身性的问题 会在26年正式拉开序幕！因为他们做ai的这群人，下半年也要被裁了！ 太恐怖了。认识的所有公司都不招聘，几十家应该有吧，只调减。 ...

因为之前 这个信息表达过一次可以翻一下我们前面的文章。一月底的时候。 英特尔(INTEL) W INTC.O 47.180 量 9743.6万 股本 50.21亿 市盈™ -887 万得 第二 2.07 3.120 7.08% - 换 0.00% 市值 2369亿 市净" 盘后 46.900 -0.280 -0.59% 美东 19:52 √ ■ ▼ 五日 日K 图K 月K 車名 (0) 叠加 均价:46.933 8.58% 47.840 47.210 400 47.180 1800 द्री - 15:59 47.205 200 15:59 47.205 100 0.00% 15:59 47.200 700 44.060 15:59 47.197 425 15:59 47.195 320 15:59 47.195 280 15:59 47.200 620 15:59 47.200 1.2万 -8.58% 40.280 16:00 15:59 47.190 1076 14:15 9:30 11:00 12:45 15:59 47.190 2630 总量:9743.57万 744.01万 15:59 47.200 ...

都在问为啥盘后跌？ 财报非常优秀 FY26Q2美光收入、净利润大超市场预期。FY26Q2美光实现收入238.60亿美元，同比+196%、环比+75%，大超市场预期（192.05亿美元）；实现净利润 140.21亿美元，同比+686%、环比+156%，大超市场预期（98.35亿美元）；实现EPS 12.20美元，大超市场预期（8.59美元）。 毛利率及指引超市场预期。公司FY26Q2毛利率为74.9%，超市场预期（68.6%）；指引FY26Q3毛利率为81%，超市场预期（70.7%）。 nv表示"这我熟呀！" 别问为什么 ...

阿里云 1/10 谷歌 1万亿！ ppu芯片白送！淘宝白送！应该6万亿打不住吧。 MINIMAX-W(0100) W 03-18 14:47:07 1327.000 额 24.9亿 股本 3.14亿 市盈™ -29 力得 器□ |换 0.91% 市值 4162亿 市净 -20 294.000 28.46% 分时 五日 日K 月K 导 叠加 设均线 MA5:1096.200↑ 10:1044.450↑ 20:929.800↑ 30 前复权 1431.288 --- 1-328-000 -- 774.000 0.000 116.712 02-09 03-02 03-18 2026-01-09 -22 成交量 总手:212万 总额:24.92亿 VMA5:160万 VMA10:212万 VMA: . Or .. MACD(12,26,9) DIF:148.86274 DEA:128.52362 MACD:40.67823 148.86 LLSS 市盈率 市盈率:-22.24 智谎(2513) W 03-18 14:47:25 748.500 额 23.6亿 股本 4.46亿 市盈"" -169 万得 盘口 127. ...

Core Insights - The financial services industry had the highest representation among attendees at the GTC conference, indicating a strong interest in technological advancements within this sector [1] Group 1 - The statement reflects a preference for developers over traders, suggesting a shift in focus towards technology and development in the financial services industry [1] - There is an implication that developers do not need to attend the conference in person, which may highlight the increasing reliance on digital platforms for engagement and learning [1] - The mention of financial professionals lacking the funds to attend suggests potential economic constraints within the industry, which could impact participation in future events [1]

Core Insights - The article highlights the competitive landscape of AI models, showcasing the usage data and trends among various models across different regions, particularly focusing on the dominance of Chinese models in the market. Group 1: Model Usage and Rankings - The total token usage across platforms reached 78.2 trillion tokens, with Chinese models accounting for 41.9 trillion tokens (53.6%), marking a 34.9% increase compared to the previous period [5] - The top five models based on usage are: 1. MiniMax M2.5 (China): 18.7 trillion tokens (+15%) 2. Gemini 3 Flash (USA): approximately 10 trillion tokens 3. DeepSeek V3.2 (China): 8.3 trillion tokens (+4%) 4. Claude Opus 4.6 (USA): data not fully disclosed 5. Step 3.5 Flash (China): 7.5 trillion tokens (+69%, notable rise) [5] Group 2: Regional Performance - Chinese models have consistently led the market, with a growing gap over American models, which accounted for 36.3 trillion tokens (46.4%), reflecting an 8.5% decrease [5] - The article indicates that the trend of Chinese models gaining market share is expected to continue, further solidifying their position in the AI landscape [5] Group 3: Industry Impacts - The rise in token usage is accompanied by significant layoffs in major tech companies, with Meta potentially cutting up to 20% of its workforce, and Microsoft expected to follow suit with even larger reductions [6]

Core Viewpoint - The article expresses frustration over the rapid rotation of investment opportunities, highlighting that while there are a few promising directions, the volatility is significant [1] Group 1 - The investment landscape is characterized by quick shifts, making it challenging for companies to maintain a stable strategy [1] - There are two to three sectors identified as having potential, but they are accompanied by high volatility [1]

Group 1 - The A-share market is primarily supported by the energy and power sectors, indicating resilience amidst geopolitical tensions [1] - The ongoing conflict in the Middle East has not adversely affected the market, with oil tankers still able to pass through the Strait of Hormuz [1] Group 2 - Japan and South Korea have experienced another round of market circuit breakers, suggesting significant volatility in their markets [2]

Core Insights - The article suggests that data centers should be located in China due to lower costs for chips, electricity, and land, particularly in Inner Mongolia [1] - It proposes a strategy of renting some computing power overseas for marketing purposes while utilizing domestic computing resources for inference tasks, which are time-consuming [1] - The expansion of undersea cables and increased bandwidth are emphasized as necessary steps, with a focus on maintaining data security through local data virtualization and isolation solutions [1] Summary by Sections - **Data Center Location**: The article argues for establishing data centers in China, highlighting the cost advantages of chips, electricity, and land [1] - **Inference Strategy**: It discusses the approach of using overseas computing power for marketing while relying on domestic resources for inference, which can take 1-4 minutes for tasks [1] - **Infrastructure Development**: The need to expand undersea cables and bandwidth is mentioned, along with a commitment to data security through local solutions [1] - **AI Model Utilization**: The article notes that domestic models are significantly cheaper, costing only one-sixth of their overseas counterparts, which could facilitate global AI application proliferation [1]