与「workday」相关的搜索结果

Incredible result in the @midjourney v8.2 test 🤯 Creative Statement: I stopped showing my best creations on X because of the daily thefts, but now Nikita is demonetizing those profiles and the platform has finally become a comfortable network for creators. In 2022, after the arrival of Midjourney, I decided to dedicate myself full-time to diving deep into this emerging culture. With my background in audiovisual production, I knew this new culture would be my future. That very risky decision brought difficulties at the beginning. Then, after 12-hour workdays, I started landing collaborations. Now it’s time to develop series and projects and start reaping the rewards.Everything has changed a lot. All platforms are flooded with AI creations, which has slowed down my strategy. Every day there are purges of AI channels on Instagram and YouTube. To avoid that problem, I’ve had to present myself as a traditional painter so I don’t fall into the pure AI niche. I will present my series drawn by hand, showing a human side. You might think you can create an AI-generated video that simulates painting by hand, but in reality, it will be flooded with comments calling it AI, and you’ll create distrust among your followers. It’s not a real solution. I believe that if you decide to take this path of producing professionally and making a living from it, you should do a market study beforehand. (This isn't an MJ promo AD; it's just a test of its latest update.)

POV: It's 5pm and you just got off work in #Shanghai#. Night is still a while away, and another side of the city is just beginning to unfold. #vlog# #citycenter# #metropolis# #dailyroutines# #workday# Video cr. 岸上乌鸦

Every software company just got a second life and Jensen just explained why (Save this). The conventional fear was straightforward, AI agents replace human workers, human workers use software tools, therefore agents destroy SaaS. Jensen Huang stood on stage at Computex 2026 and walked through exactly why that logic is backwards. Agents don't replace software, they consume it at machine speed, around the clock, without weekends. Here's the actual architecture Jensen laid out. An agent isn't just a large language model but rather an LLM sitting inside a harness that manages memory, orchestrates tool use, routes context, and plans iterative actions. That harness has to constantly call tools, spreadsheets, databases, browsers, and code engines, with every reasoning loop triggering another tool call. A human might use Salesforce 40 hours a week, an agent running inside a company uses it 168 hours a week and never misses a context window. The GitHub data Jensen showed on stage makes it tangible, 90 million pull requests merged, 1.4 billion commits, and 20 million new repositories created every month. As of April 2026, GitHub is processing 275 million commits per week on pace for roughly 14 billion by year end, a 14x explosion in a single year and AI agents are the source. Pull requests opened by AI agents went from 4 million in September 2025 to 17 million in March 2026 more than 4x in six months. That's AI becoming the largest software user on earth. Goldman Sachs quantified the downstream effect last month, token consumption is expected to multiply 24x by 2030, reaching 120 quadrillion tokens per month globally. A traditional chatbot consumes roughly 1,000 tokens per session, an embedded copilot burns 5,000 tokens per day while a continuously running enterprise agent? Over 100,000 tokens per day. The software companies that figured this out first are already printing money, Salesforce Agentforce hit $800 million ARR growing 169% year over year, with 29,000 deals closed. ServiceNow's Now Assist crossed $600 million in ACV, just raised its full year target to $1.5 billion, and told investors that when its agents replace a 20-person support team, total ServiceNow spend by that customer grows more than 5x even after accounting for reduced seat licenses. Workday delivered 1.7 billion AI actions across its platform in fiscal 2026. The key unlock Jensen pointed to and what investors need to understand is MCP, the model context protocol is the interface layer that makes software agent-readable. Software that supports MCP can be called by any agent, from any model, through any harness. Anthropic created it, OpenAI, Microsoft, and Google all adopted it and it was donated to the Linux Foundation. It is effectively becoming the HTTP of agentic computing. Software companies with native MCP support are plugged into the agent economy. Software companies still waiting are one product cycle away from becoming invisible to the fastest-growing category of software users in history.

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

把特朗普的持仓报告扒了下挖到两个质量还不错的公司 $ADBE 大名鼎鼎的 Adobe ，目前价格$241， 市盈率只有 13，总市值千亿美金，10.8% 的自由现金流收益率和 89% 的毛利率，还有250 亿美元的回购计划。但是价格这么低不是没有原因的，因为受到 AI 冲击，订阅式的商业模式不再适用。 目前来看Adobe 不是一家正在死掉的公司，是一家正在变慢的公司。 $WDAY 和 SNOW 和 NOW、CRM 等同一赛道，Workday解决的是企业核心管理系统的刚需——人力资本管理（HCM）和财务管理（FMS），目前价格$130 目前正站在一个关键的十字路口，市场既害怕AI颠覆它的商业模式，又期待AI成为它的新增长引擎。 今天晚点会更新一下，投研报告，记得关注 @lianyanshe

Gavin Newsom has proposed expanding California’s sales tax rules to include many forms of digital software and SaaS products. If approved by the legislature, the proposal would apply sales tax to cloud-based and digitally delivered software services that have traditionally avoided taxation as non-physical products. It could impact platforms such as Microsoft 365, Salesforce, Adobe, Workday, and other subscription tools. The measure is part of California’s revised 2026–27 budget and is projected to generate billions in long-term state and local revenue. If enacted, the rules would take effect in January 2027 and could influence other states’ taxation of cloud software and digital services.

2026年5月本周美股大事件一览！ 这周美股还是围绕AI基建这条主线在走，整体热度还不错（7.5/10），但已经开始明显高位分化了。 现在AI正从“概念炒作”转向“真金白银落地”。数据中心要扩建、算力要升级，光互连、AI服务器这些底层硬件成了最紧迫的 bottleneck。MRVL作为AI数据中心互连芯片的核心玩家，被市场盯得最紧，整个光互连和AI硬件链条成了当前最热的“主航道”。 背后逻辑很简单：Google、Microsoft、Amazon、Meta这些巨头还在疯狂砸钱建AI基础设施，企业端AI软件和云支出也开始慢慢起来，半导体产业链的上游需求依然强劲。 本周重点事件 • 5/19-5/20 Google I/O 2026：谷歌一年一度的大会，主要看AI新进展和开发者生态，会不会给数据中心硬件带来新预期。 • 5/20 ADI财报（盘前）：模拟芯片大厂，看工业、数据中心真实需求怎么样。 • 5/21 Workday财报（盘后）：观察企业AI软件和云支出的实际情况。 • 下周5/27 MRVL财报：这是主线核心，市场已经提前开始预热了。 主线还在热，但内部已经分化。核心票（如MRVL）资金抱得紧，高弹性题材（AI光学、HPC等）波动变大，二线股也开始有弱转强的迹象。整体就是“主线延续 + 高位谨慎”的状态，大家对AI长期资本开支还是有信心的，但对涨太多、估值太贵的个股开始理性了。 5月中下旬，美股AI基建还在高景气通道里，但节奏在切换。Google I/O和MRVL财报会带来新一轮验证，AI基础设施仍是中期最确定的方向，只是现在要更注意分化和节奏，别一头扎进去追高。 简单说，这周还是AI硬件的故事在主导，后面财报季会继续给出答案。

下周一开盘前必看：五大引爆点同时登场——短期震荡，但牛市没死，只是个上涨中继里的一个调整 一、先说清楚大背景 1. 上周五美股单日蒸发9000亿美元，表面上看是一次正常回调，但底层信号值得认真对待： 2. 30年期美债收益率收于5.15%，创2007年以来最高周线收盘 3. 纳指和标普同时连续5周收在布林带上轨外——过去30年只在1999年科网泡沫破裂前出现过唯一一次 4. 全美保证金债务单月暴增830亿，总额达1.3万亿历史新高，过去12个月融资额狂飙53% 5. 纳斯达克成分股跌破50日均线的比例已创新低，指数还在高位，但内部高度分化 6. 垃圾债HYG出现日线级双顶背离，信用市场已在提前重定价 7. AMD出现历史级别暗池巨量——机构在高位执行派发 但需要强调：这是牛市中期的正常震荡整理，不是趋势反转。调整完成后行情大概率继续向上。 二、顶级聪明钱在做什么 几个值得关注的机构动作： 巴菲特 连续14个季度净卖出，现金储备3970亿历史新高。一季度卖出240亿，仅买入159亿，清仓亚马逊、Visa、万事达。但需要注意——巴菲特囤现金很可能是在等6月12日SpaceX IPO，届时他需要巨额弹药参与认购。这不一定是看空市场，而是在等更好的机会。 大量共同基金 由于软件股净值回撤和客户赎回压力，在过去一周将CRM、Workday等软件股执行了投降式清仓，调仓出来的钱全部追高涌入了偏离均线60%的高位芯片。 这个行为本身就是信号： 软件筹码已经洗到极度纯净，而硬件筹码正在高位拥挤换手。 三、大盘空心化——需要注意的结构性信号 大盘能装多少水，不取决于最长的纳指AI木板，而取决于最短的那根——罗素2000小盘股。 周五小盘股IWM在日线上跌破21日生命线，大盘指数还在高位，但内部个股大面积跌破均线，市场整体参与度已暴跌至25%的极低水平。 但这个数字同样是一个积极信号： 历史上参与度跌至这个极值区域，往往是短线黄金抄底买点，而不是崩盘前兆。市场在通过震荡消化过热情绪，这是健康的。 四、下周五大引爆点 引爆点一：三星周一重启谈判→DRAM短期承压 此前DRAM板块大涨押注的核心逻辑是三星大罢工导致HBM断货。周六三星宣布周一重启谈判，稀缺性溢价开始消化： 1. 云厂商超额囤积的内存订单将放缓 2. DRAM现货价格短期面临压力 3. MU高位杠杆仓位需要消化，下方$700是关键支撑，失守看$630-640 市场总是比新闻快半步——周五尾盘MU放量跳水和周六凌晨三星和解消息之间的时间差，值得细品。这是短期利空，不影响DRAM的长期供需逻辑。 引爆点二：周三英伟达财报→Sell the News风险 市场预期营收787.5亿，同比增118%。但是 1. H200出口中国消息已充分定价 2. 北京据报已拒绝批准采购H200，转向本土供应链 3. Rubin架构真实营收要到下半年才能体现 几乎每次都是Sell the News。财报前不追，财报后看方向再决策。 财报后的回调是布局机会，不是恐慌信号。 引爆点三：COT数据空头陷阱→周一可能先逼空 最新COT数据显示，大型投机机构在周五大跌时不仅没减少空头，反而堆高了净空头头寸。加上周日中美初步达成关税削减框架协议，期货市场开盘时这个消息足以引爆空头止损，造成短期逼空。 周一可能先跳空高开，随后英伟达财报前后再出现调整。不要被开盘跳涨迷惑，也不要因为开盘下跌而恐慌。 引爆点四：6月全球央行联合紧缩潮 这是5-6月最大的宏观压力来源： 1. 欧洲央行6月11日加息概率90% 2. 日本央行6月16日重启加息概率78% 3. 美联储新主席Warsh将转为中性偏紧表述 4. 美国30%以上国债需在未来一年内展期，高息展期压力持续 但同样重要的是： SpaceX 6月12日IPO，预估市值1.75-2万亿，这个超级催化剂会在6月为市场提供强劲的资金流入和情绪支撑。主力在SpaceX上市前大概率维稳大盘。 引爆点五：市场参与度触底→短线黄金买点临近 参与度跌至25%极值区域，如果下周初芯片继续下砸导致参与度杀进20%冰点，这反而可能是今年上半年最完美的短线抄底买点，随后配合周三英伟达财报引发反弹。 越是恐慌的时候，越是布局的时候。 五、我对标普的判断 第一阶段（5月中-5月底）：震荡寻底7200-7250 这个位置是多方模型共振确认的关键支撑： 1. 日线21均线 2. 周线8周均线 3. 多个机构模型黄金买点区域全部在这里共振 路径A（逼空base case）：周一受关税利好跳空高开，冲破$743.50站稳，短暂回补$744-748缺口。 路径B（震荡寻底）：美债收益率持续施压，$732破位后向$725寻底，再向7200-7250黄金买点区域砸盘。 我倾向于先A后B——周一短暂逼空，随后英伟达财报前后出现真正的调整，给出更好的入场机会。 第二阶段（6月-6月中）：反弹，但要看高度 7200-7250守住后，6月将迎来反弹。但反弹的高度决定了后续方向： Higher High（突破7510）→ 牛市加速，多头重新控盘，下半年看更高 Lower High（低于7510）→ 反弹力度不足，说明市场还需要更多时间消化宏观压力 第三阶段（SpaceX上市后）：可能再次调整至7000-7100 SpaceX 6月12日上市是最大的短期催化剂，主力在上市前大概率维稳。但上市即是利好兑现——历史一再证明，最大的催化剂往往也是最大的Sell the News时刻。 SpaceX上市后，6月全球三大央行联合紧缩压力全面释放，市场可能再次面临调整，目标区间看7000-7100。 这个位置才是我认为下半年真正的中期战略买点。 整体路径总结： 现在高位 → 5月底7200-7250（第一买点）→ 6月反弹看7510（观察高度）→ SpaceX上市后再调整至7000-7100（战略买点）→ 下半年继续上行 六、钱在往哪里流 机构高低切换方向非常清晰： 流出： 半导体（MU、AMD）——筹码高位换手，短期承压 流入方向一：网络安全CRWD、PANW月内逆势大涨20%，周五继续创历史新高。Cisco财报证明云厂商网络安全实际需求90亿vs预期54亿，近乎翻倍。AI越发展，需要保护的数据越多，网安是AI时代的底层收费站，也是目前最抗跌的板块之一。 流入方向二：软件机构投降式清仓之后，CRM、NOW、MSFT的筹码已经极度纯净。软件空头仓位处于历史极值，Short Squeeze的燃料已经装满，只等引爆点。 SNOW特别值得关注——全美大量医疗和合规私有数据沉淀在其数据云底座，AI智能体落地时对其数据调用具有不可替代的粘性，是软件股里基本面最扎实的标的之一。NOW和CRM也是，夜盘大盘跌，这两个是为数不多在涨的。英伟达财报后半导体资金撤离，才是软件最佳入场窗口。 流入方向三：防御资产（短期避风港） MCD： 估值杀回7年铁底，远期PE 20.6倍，周线止跌十字星，下行空间封死，上行空间10-15% PEP： 17倍超低PE，大资金期权押注，强劲现金流 Costco： 周五逆势创历史新高 XLE能源： 中东局势未解，油价高位，周五逆势涨2.5% TSEM高塔半导体： 技术面健康，相对强度龙头，偏离均线仅7倍ATR CSX铁路： 周五逆势突破长期下行趋势线 七、总结 下周是今年最复杂的一周，五个引爆点叠加： 三星和解→DRAM短期承压→英伟达财报Sell the News→6月全球紧缩→SpaceX IPO催化 我的整体路径判断： 现在高位震荡 → 5月底测试7200-7250（第一买点）→ 6月反弹观察高度（Higher High vs Lower High）→ SpaceX上市后再次调整至7000-7100（战略买点）→ 下半年继续上行 这不是看空，这是牛市中期的正常节奏。每一次调整都是为下一段上涨积蓄能量。 一句话： 牛市没死，但好价格需要耐心等待。7200-7250是第一个买点，7000-7100才是真正的战略买点。在那之前，控制仓位，让子弹飞一会儿。 #美股# #标普500# #英伟达# #NVDA# #美债# #软件股# #网络安全# #板块轮动# #下周展望# #SpaceX#