#需求增长

avatar
但是也不要低估美股的机会，尤其是这一轮的美股，我会说不高，因为需求是倍增的、产能增加是线性的，指数打线性，唯一的问题是涨的不够多。 以及资金容量上去了，配置几乎是必须。 你说 OpenAI 在和 NVDA, AMD, Oracle 搞盘子？ 我说资本的积累，最快就是靠盘子，20 年的流动性挖矿、21 年的 FIL，去年的 MSTR，今年的 BMNR 都是盘子。 现在和互联网泡沫有一个很大的区别是，美股涨到现在，其实 1000 亿朝上的大科技和半导体还是很理性的。 看 forward PE 也就是打入了两年的预期，只要还能持续更久的时间，可以收没有任何泡沫。 其实主体大概是两件事： 一个是最终大模型的用户端的需求，主要看 token 用量增速，目前还在 AI 摩尔定律，两个月翻一倍，这个轨迹上。 而且有 Sora 这种多模态生成模型出来，用量远远超过文本，看不到增速会放缓的迹象。 token 用量应该是只会增加不会减少的，区别只是增速。只有增速放缓的问题，不存在需求存量减少的问题。 - 用过 Cursor、Codex 的程序员，就没办法不用了。 - 用了大模型去优化业务的公司，哪怕 token 涨价，只要还是远远低于雇个人，还是会继续用。 另一个是，最终这个游戏的买家是大公司的 capex 还加的动多大杠杆，以及替大公司放大杠杆的公司（比如 CoreWeave 这种 neocloud）的融资能力/业务状况 现在的情况是大公司加的杠杆并不夸张，CoreWeave 这种呢，只要上卡就租的出去，GPU 利用率并不低，绝不像互联网泡沫时代一样服务器在空转 uptime。 AI 这一次和过去很多次不同的是，比如互联网在 90 年代末是一个新的管道/传媒方式，Crypto 是把资产发行方式做演进，或者中国读者更熟悉的比如土地财政和无效基建。 这些共性都是在「虽然不知道建了干啥，但先建了再说」，但这波 AI 不太一样，用量需求就在那里，直接兑现的就是生产力，起码在今天这个阶段，还不存在什么过度基建。 股票的价格是未来的预期，现在赌的是，在按目前预期打满以后，未来 2 年会不会超预期。 个人的观点应该还是会的。这个行业它不是软件，迭代的速度是按小时、天算的， 这里的速度是新品发布、两年起步，产能爬坡是线性、不是指数。 但是需求的增速，目前还是指数，即使放缓，也还是指数。 简单倍增思想的运用吧，看不懂就算了。 半导体行业作为一个周期性行业，虽然现在看已经在周期高位了，但仍然很有可能长期偏离周期，走一个中国入世前 6 年铜的走势，可以去看看 K 线。 中国入世的 20 多年，整套工业体系是按全世界的需求去建设的，而不是一个国家的需求，给世界带来了巨大的生产力。 这轮的 AI 也是上了就是生产力，和互联网泡沫、Crypto 这些有本质不同，如果你有点商品交易经验的话，看到这应该已经高潮了。 泼泼冷水，风险在 AI 以外，比如宏观转向了，经济不行了，打仗了，川普又发癫了。 茅台镇假酒言论，请勿作为投资建议。 （说的只是七姐妹和半导体公司里的大盘股，炒比如储能、核电、量子计算这些小盘股就是完全另一个思路了，这些和山寨币没太大区别。）
#美股 #AI #半导体 #需求增长 #生产力
avatar
大罗SEO
5个月前
SEO是死是活我不知道，但是对SEO的需求今年开始井喷是真的
#SEO #需求增长 #行业趋势
avatar
经济日报：房贷利率下行扩需求稳预期
#房贷利率 #需求增长 #经济预期 #经济日报
avatar
Andrew Ng
9个月前
Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - Skill in managing ambiguity. Because AI’s performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it. - Ongoing learning. AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users’ lives. Finally, AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves. The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work. The variety of valuable things we can build is nearly unlimited. What a great time to build! [Original text: ]
#软件开发 #产品管理 #人工智能 #原型设计 #需求增长