泡沫啥啊。。又不是概念，你看英伟达都赚麻了。。。

很喜欢Mark Papermaster下面这段。这人和苏姿丰一起是AMD崛起的关键。
When we think about $400 billion, it's an accelerator TAM. It is GPUs, it is the memory around it, it is other bespoke accelerators, specifically targeted for this AI infrastructure build-out. And it is just that. Think about it as a build-out like when the Internet launched. And you had the -- an entire build-out, not just of compute but networking, infrastructure, et cetera. It's a new platform. And so it is indeed a major investment.
And with that investment comes the monetization, which will occur as you'll see thousands upon thousands of applications, which are now at a torrid pace of being development. So, there's the economics that have to be behind it and are behind it.
And when you look at that $400 billion TAM, a lot of it, of course, is the hyperscalers and these massive AI cluster build-out. So that's going to be first-party workloads of hyperscalers, but also third-party. And that's where the largest models are needed, the LLMs that are taking on broad questions that are helping answering broad productivity savings that they're driving. And so that is a big piece of that $400 billion TAM.
But it is much more than that because when you look at what's going to happen over the next several years, AI is not just in the domain of these largest hyperscalers, these massive clusters. What happens is businesses is they focus and they have AI needs to address their business needs, not of the world's AI problem set that needs solve but actually driving productivity of their business. And those models are typically smaller in size. They can be handled -- they can either be run in smaller clusters on the cloud or on-prem.
And frankly, where you need more quick response and even lower latency, think about automotive applications with self-driving, think about the factory floor, that build-out will be embedded devices. And then right to the endpoint where you're seeing now PCs, we launched our AI-accelerated PC last year with the Ryzen 7040. And now we look at 2024 as a big transition year where AI comes to the PC, and we're out ahead of that.
So there's a lot behind that TAM. We're expecting the AI market to grow at a 70% kind of CAGR. I know that seems like a huge number, but the work our team has done, we think -- you can debate the numbers, but the fact is it's very large, and we are investing to capture that growth.

英伟达涨到偶现在，今年才30倍，A股这么跌好多还有30多倍，可见哪边才是泡沫哟

他可能说的是A股的

这段看起来不太好懂， 顺手用Copilot翻译一下、
当我们谈到4000亿美元时，这是一个加速器的总可用市场（TAM）。它包括图形处理器（GPUs）、周围的内存以及专门针对AI基础设施建设的其他定制加速器。就像互联网启动时的建设一样，你可以把它看作是一次建设。这不仅仅是计算能力的建设，还包括网络、基础设施等。这是一个新平台，因此确实需要大量的投资。
随着这种投资，将会出现货币化，你会看到成千上万的应用程序正在飞速开发。所以，必须有经济学在背后支持。
当你看到那个4000亿美元的TAM，当然，其中很大一部分是超级规模者和这些大规模的AI集群建设。所以这将是超级规模者的第一方工作负载，但也有第三方。这就是需要最大模型的地方，LLMs正在接受广泛的问题，帮助回答他们正在推动的广泛的生产力储蓄。所以，这是那个4000亿美元TAM的一个大部分。
但是，它远不止于此，因为当你看看接下来几年将会发生什么时，AI不仅仅是这些最大的超级规模者、这些大规模集群的领域。发生的事情是，企业会关注他们有AI需求来解决他们的业务需求，而不是需要解决世界的AI问题集，而是实际上推动他们的业务生产力。这些模型通常较小，可以在云上或本地的较小集群中运行。
坦率地说，当你需要更快的响应甚至更低的延迟时，想想自动驾驶的汽车应用，想想工厂的生产线，那种建设将是嵌入式设备。然后直接到终端，你现在看到的PC，我们去年推出了我们的AI加速PC，搭载了Ryzen 7040。现在我们看到2024年将是一个大的转型年，AI将来到PC，我们已经领先于此。
所以，TAM背后有很多东西。我们预计AI市场将以70%的复合年增长率增长。我知道这看起来是一个巨大的数字，但是我们团队所做的工作，我们认为——你可以辩论这些数字，但事实是它非常大，我们正在投资以捕获这种增长。

ai会不会是一个大泡沫？

为什么股价下跌？人们预期的更好？

OpenAI已经将人工智能扩展到视频了，算力需求几何级数增长。
不过视频以后还有更大的应用场景吗？感觉这波高峰过去后要见顶

小丰总@张小丰 关注的票都比较优质，又涵盖人工智能、创新药龙头及热点，感觉应该可以吃到这波美股上涨红利的。不过看净值反应不大，可能仓位配置比较分散，个股上涨被均衡了，感觉还是应该按把握程度和优质程度，仓位配置有所侧重的，把对板块和个股独到的理解体现到组合的净值上。
当然按小丰总的投研及配套能力，长期终归还是会把认知转化为基金收益的！
经常看小丰总帖子，受益良多，一点小小看法，出于善意，希望小丰总基金净值节节高！

逆天业绩，可惜A股没有

比预期超了不到10%，把你们都“炸裂了”

犹记得不到150那会你还说贵得下不去手。业绩有点像《三体》里科技爆炸，很难预测。

积压订单那么多，业绩是和产能正相关的，未来预期要去看订单交付周期。

PC，PC互联网，移动互联网，人工智能，信息科技进入新的世代了。

市占率能一直这么高吗？