AI 基建能否持续指数增长？SemiAnalysis 的 Dylan Patel 与 Jordan Nanos 谈投资回报

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人工智能还能保持指数级增长吗？让我们来问问 SemiAnalysis——与 Dylan Patel 和 Jordan Nanos 对谈Can AI Keep Growing Exponentially? Let’s Ask SemiAnalysis — With Dylan Patel and Jordan Nanos

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人工智能要发展到多大规模，才能证明数万亿美元的基础设施支出是合理的？接下来，我们就和 Semi Analysis 的顶尖专家聊聊这个问题。欢迎收听《Big Technology Podcast》，这是一档探讨科技世界及其他领域的节目，风格冷静而细致。人工智能基础设施的投资数字似乎还在不断攀升，势头没有尽头。所以，我们要聊聊这场全面建设从物理层面来看会是什么样子，以及它能否继续维持下去，而不至于——也许不是毁灭世界，而是摧毁经济。嗯，我们请来了几位非常有意思的嘉宾，一起探讨这个话题。今天，我们邀请到 Semi Analysis 的首席执行官兼创始人 Dylan Patel，以及 Semi Analysis 的技术团队成员 Jordan Anos，来聊聊基础设施建设中正在发生的一切。How big does AI have to get to justify the trillions in infrastructure spending? Let's talk about it with the leading authorities from semi analysis right after this. Welcome to Big Technology Podcast, a show for coolheaded and nuanced conversation of the tech world and beyond. The AI infrastructure numbers just seem to be getting bigger with no end in sight. So, we're going to talk about what the physics of this entire buildout looks like and whether it can continue to sustain itself without not maybe not destroying the world, but destroying the economy. Uh, cuz we have some very interesting guests to speak with us about it. Today, we're joined today by Dylan Patel, the CEO and founder of Semi analysis, and Jordan Anos, member of the technical staff at Semi analysis to talk all about everything uh going on within the infrastructure buildout.

呃，Dylan Jordan，欢迎来到节目。Uh Dylan Jordan, welcome to the show.

主持人：谢谢。很高兴来到这里。Host: Thanks. Great to be here.

嘉宾：我们就先来谈谈我们现在看到的这场 AI 基础设施建设到底有多大规模吧。Dylan，眼下 X 上有张图在流传，把 AI 基础设施建设和过去的建设浪潮作比较，比如铁路、公路。图表显示，如今 AI 建设投入占 GDP 的比例大约是 3.6%——6%，不对，P……中位数大约是 3.6%，按年份平均；而铁路建设是 2.2%。基本上，大家的解读是，这规模大得多，对吧？也就是说，即便只看占 GDP 的比例，它也比铁路建设大约高出 50%。大家还认为，这是我们有史以来见过的规模最大的建设浪潮，几乎前所未有。我很想听听你怎么看它的规模，以及这种看法是否恰当。Guest: Let's just start by talking about the scale of what we're seeing uh in this AI infrastructure buildout if we can. Uh Dylan, there's like a chart going around X right now that compares uh the AI infrastructure buildout to previous buildouts in the past like railroads, um like highways. And the chart is showing that the percent of GDP for the AI buildout today is about 3.6% 6% or sorry the P the median is about 3.6% uh the average year compared to 2.2% 2% for the railroads. And basically the commentary is that this is far bigger, right? That would put it basically 50% bigger than railroads even in terms of its share of GDP. And the commentary is this is bigger than anything we've ever seen before on a scale that is, you know, effectively unprecedented. Um I'd love to hear your perspective on just how big this is, whether that's the right way to look at it.

嗯，然后如果我们要打造这么大规模的东西，这意味着什么。Um and then what it means if we're building out something this big.

主持人：是啊，我是说，呃，那张图传开的时候，我第一反应就是，嗯，人们的数据太低了。实际情况已经比那个高得多了。嗯，♀♀♀♀♀♀Host: Yeah, I mean uh the first thing I when that chart was going around, I was like, well, people's numbers are too low. It's actually much higher than that already. Um,

嘉宾：真的吗？好吧。那就分享真实的数据吧。Guest: really? Okay. So, share the real numbers.

主持人：是。是。所以，我的意思是，如果你看一下，明年美国的资本支出大约会达到 2 万亿美元——这不仅包括数据中心和芯片，还包括人们正在投资的整个其他供应链。所以资本支出接近 2 万亿美元。再加上 GDP 大约是 30 万亿美元出头。所以你知道，这大概占 5% 或 6%，对吧，而不是 3% 或 2%。所以我的想法是，哇，这些数字实际上偏低了，因为人们低估了美国、北美乃至全球正在发生的资本支出的规模和金额。我认为部分原因在于，你知道，铁路不同，你的资本支出会发生在你铺设铁路的那个国家。Host: Yeah. Yeah. So, so like I mean it's it's if you look at like next year um capex across the US will be on the order of $2 trillion uh include not just data centers and chips but also all the rest of the supply chain that people are investing in. Um so you're close to $2 trillion of capex. Um and then you stack on top of that uh GDP is like close to 30 30 and some change trillion. So you're at like you know five or 6% right not not 3% or 2%. Um so so sort of my thought process is actually like oh wow these numbers are lower than the actual because people are understating the amount and size of capex that's happening in the US and and and North America and the world. Um, and I think like part of this is because, you know, with railroads, your capex is in the country that you're deploying it in.

对于光纤来说，你的资本开支发生在你部署光纤的国家。但对于 AI 基础设施来说，你在美国的资本开支，实际上有很大一部分是用来服务美国以外的需求，对吧？比如说，欧洲的大部分 Anthropic 推理都在美国进行，OpenAI 在欧洲的大部分推理也在美国进行。嗯，亚洲很多国家的情况也类似。所以，这里有一点是：美国的资本开支实际上因此高得多。原因之一是你把所有训练都放在这里做；其次，你还通过美国的数据中心为其他国家提供服务。所以归根结底，你现在处于这样一个阶段：这种基础设施的经济影响要大得多，远超过其他任何类型的实体基础设施。With fiber, your capex is in the country you're deploying it in. But for AI infrastructure, your capex in America is actually a lot of it's being used to serve outside of America uses, right? So, if you look at, for example, Europe, most of Europe's inference for anthropic is in America and most of OpenAI's inference in Europe is in America. Um and and similarly if you look at a lot of Asian countries um and so there is a bit of this like this this you know capex in America is actually like much higher as a result um because one you're doing all the training here and then two you're actually serving other countries from data centers in America um and so ultimately you're sort of at this stage where capacity is much um the economic impact of it is much larger um than any other sort of physical infrastructure.

此前从未有过的投资周期。investment cycle ever before.

嘉宾：好的，Jordan。你是《Clustermax 3.0》的作者。这是 SemiAnalysis 对 GPU 云系统的排名，把许多超大规模云服务商和新兴云厂商分成不同梯队，稍微谈了谈它们的表现，我们稍后会聊到这一点。不过，你知道，你在报告里确实全面考察了这个领域，有一点我想请你谈谈。基本上，我们看到的是超大规模云服务商的季度资本支出同比增幅走势。到 2026 年第三季度，这一增幅达到 116%，同比增长，着实惊人。Guest: All right, Jordan. And you are the author of Clustermax 3.0. This is semi analysis rankings of the GPU uh cloud systems. Uh basically putting a lot of the hyperscalers and the neoclouds uh in tiers talking a little bit about their performance and we're going to get to that in a bit. But I I you know you do look across the board in your report and this is something that I I'd like you to comment on. Uh basically we're looking at um where the uh the hyperscaler quarterly yearon-year capex growth is going and in the third quarter of 2026 uh the growth is at 116%. Year-over-year which is astonishing.

嗯，市场一致预期，也就是华尔街预计接下来会怎样：他们预计增速会在第一季度——哦不，是第四季度降到同比增长低于 100%，然后明年第一季度降到 70% 左右，此后还会继续下滑。我觉得这是我们熟悉的一种周期，对吧？大家会想：“嗯，我们现在的基数更大了，所以增速不可能再超出预期，也不可能再翻一番了。”所以我很想听听你的看法：根据你的研究，你觉得我们真的会看到这种趋势吗？也就是增长会放缓？还是说我们又掉进了这个陷阱？或者华尔街又掉进了这个陷阱，觉得：“好了，你们的数字基本已经大到难以想象了，但你们还是得把目光放得更远。”um the consensus estimates so what Wall Street is expecting to come after that you know they they drop down in the first quarter well in the fourth quarter to below 100% year-over-year growth and then the first quarter next year uh in the 70% and it continues to drop from there and I think this is a cycle that we're used to right where it's like well you know we're we're on a bigger base so it can't possibly exceed expectations again and it can't possibly double again um so I'm curious to hear from your perspective in your research, uh, do you think that we're actually going to see that trend like the growth is going to decelerate or are, you know, are we again falling into this trap or is Wall Street again falling into this trap where it's like, all right, you've you're basically at the biggest numbers you could possibly imagine, but you still need to think bigger.

[叹气] 是的，我觉得我们肯定会在明年年底前后触及上限，并延续到……[sighs] Yeah, I think we're definitely going to hit limits towards the end of next year and into

主持人：等等，Jordan，在你认真回答之前，我有、我有个、我有，你知道，就像你问一个孩子他能想到的最大数字是多少，他会说“00”之类的，对吧？然后现在，就像，不不不，宝贝，我们都到一万亿了。Host: Wait, Jordan, before you answer seriously, I got I gota I got, you know, it's like when you ask a kid what's the biggest number they can think of and they say like,00 right and and now and it's like no no no we're at a trillion babe.

嘉宾：是的。Guest: Yeah.

主持人：是啊，四个逗号俱乐部。嗯。Host: Yeah. Four commas club. Um

嘉宾：所以等等，在Dylan这个例子里，这个孩子就是华尔街。不是不是在说任何任何人是孩子。只是，呃，嗯，请继续。抱歉。Guest: so wait in in Dylan in in this example the kid is Wall Street. Not not making any any statements that people are children. Just uh yeah, go ahead. Sorry.

主持人：迪伦可能有个习惯，有时会把员工和客户称作孩子，不过撇开这一点，嗯……Host: Dylan may have an a habit of referring to his employees and his customers as kids sometimes, but that aside, um

嘉宾：我想，我想我用的词要刻薄得多。Guest: I think I think I use a much more mean word.

主持人：好。是的，Host: Okay. Yeah,

嘉宾：我已经很喜欢这个方向了。Guest: I like where this is going already.

主持人：我们快要用完那些大数字了。说到建设扩张，有不少方面值得关注。这涵盖的范围很广，从工业设备，到能够承接 EPC（工程、采购、施工）工作的公司，再到芯片、冷却系统，以及你知道的、除此之外的所有组件，基本上还有整个 IT 基础设施体系。为了让所有这些资本支出都能投入其中，人们正在做大量工作来扩展这条供应链。其中很多工作都在奏效，所以我们会看到大幅增长。不过，你不可能一直保持三位数增长，还让增长的加速度在目前的基础上进一步提升。总会遇到资源耗尽的时候。我认为眼下最大的问题有两个方面。Host: We're we're running out of big numbers. There are there are things to uh look at when it comes to the buildout. This could be everything from like industrial equipment to firms that are prepared to do EPC work like engineering, procurement, construction to chips to cooling systems to you know all the components that go outside of that and um an IT you know estate basically. Uh there's a lot of work being done to expand this supply chain in order to be able to accommodate for all of this capex to to go into it. Um, and it's a lot of it's working and so we're going to see massive growth, but yeah, you you can't just have tripledigit growth and accelerate the second derivative, you know, even further from where it is right now. Um, you run out of things and I think the biggest thing right now is uh twofold.

一方面，对于超大规模云服务商中规模最大的 NeoClouds 来说，要组织并调动所需资源，在现有建设计划之上再建成一个吉瓦级项目，确实会变得非常困难。筹集资金对他们来说也相当困难。尽管许多此类投资已经能带来惊人的回报，但你仍然得找到资金投入这些项目。你知道，当我们谈论的是数万亿美元的规模时，这可不是件容易的事。One is like it it becomes really hard for the biggest NeoClouds in hyperscalers to just organize and marshall the resources required to build another gigawatt on top of what they're already trying to build, for example. and it becomes pretty hard for them to raise the capital. Even though there's incredible returns on a lot of these investments already, you still just need to find the capital to pour into these projects and that's uh you know a difficult thing to do when you're when we're talking about numbers in the trillions of dollars.

嘉宾：我想，我想说的另一点是，按定义来说，数字必然会缩小。嗯，你知道，今年 Anthropic 的营收增长了 10 倍，从不到 100 亿增长到超过 100 亿，呃，是超过 1000 亿。所以，他们实现了 10 倍增长。嗯，按定义来说，他们明年年底营收就是不可能达到 1 万亿美元。嗯，所以，是的，增长确实在放缓。嗯，你知道，英伟达的营收增长仍然有 2 倍，但你知道，已经不像早些时候那么高了。嗯，毕竟，要在同样的基础上实现 2 倍增长，按定义来说就更难了。要在同样的基础上实现 3 倍增长也是如此。所以，嗯，其他公司也适用同样的情况。所以，从定义上来说，增长确实在放缓。Guest: I would I would say the other thing is like by definition the numbers have to shrink. Um you know this year we have 10x growth entropic going from sub10 billion of revenue to north of 10 uh north of 100 billion. So, they had 10x growth. Um, definitionally, they're just not going to have a trillion dollars of revenue by the end of next year. Um, so yes, there's some decelerating growth there. Um, you know, Nvidia's revenue, uh, growth is still 2x year, but it's, you know, it's not as much as it was at an earlier point. Um, just because it is definitionally harder to grow 2x off the same base. So, 3x off the same base. So, it's sort of like, um, same applies to like other firms. So it's it is it is definitionally like definitely decelerating.

主持人：好。所以支出增速总有一天会放缓。但营收和利润的增速则必须加快——你知道，基本上，它们必须跟上这场大规模的基础设施建设，并且加速增长，可能还要超过我们目前看到的资金支出增速。我的意思是，为了推动所有这些 AI 行动，你大概会这么想。围绕这个话题，最近也有更多讨论流传开来。Dylan，我来把这些数字念给你听。好，这是高盛的一份报告。呃，不好意思，高盛，他们说 Meta 已经自寻死路，背上了难以承受的债务，公司将很难偿还。这是贝恩。贝恩说，超大规模云服务商需要 6 万亿美元的营收，永远都不可能。Host: Okay. So the growth on spending is going to need to decelerate at some point. But the growth on revenue and profit for that matter is going to have to accelerate you know beyond uh it's basically going to have to trail this big infrastructure uh buildout and accelerate probably beyond where we've seen uh the the dollars being spent. I mean you would you would imagine uh to facilitate all the AI action. And there's been uh further further conversations that have circulated about this. And Dylan, let me just throw these numbers out to you. Right. So this is a Goldman Sachs uh report. Um and sorry, so Goldman Sachs, they say Meta, this is a a tweet, right? Meta's committed corporate suicide after taking on insurmountable debt that the company will struggle to pay back. There's Bane. Bane says hyperscalers will need 6 trillion in revenue, not ever.

这是每年……到2020年、到2031年，呃，才能实现收支平衡。哥伦比亚大学教授斯廷·范·纽伯格曾表示，到2032年，美国每年需要在人工智能服务上支出大约3.5万亿美元，相当于GDP的8.8%，才能证明如今对数据中心的大规模投资是合理的。嗯，你怎么看这些关于要投入多少、收入需要达到多少才能收回投资的评估？这些投资有可能获得回报吗？This is per year by 2020 by 2031 uh to break even. And there's a Colombia professor uh Stinvan Newberg who said that America will need to spend roughly 3.5 trillion annually on AI services by 2032 8.8% of GDP to justify today's massive data center investment. Um what do you think of these these sort of assessments of where the spending and where the revenue needs to be to pay off the investments and do they have a chance at making good on those investments?

嘉宾：是的。我是说，我觉得按照这项基础设施建设将推进的速度来看，大家对营收需求的预估都相当低。我觉得营收会像……比如到2031年，营收会比这个数字高得多。我的意思是，这里有几个要点，对吧？一个是，你知道，人们在资本支出上花了多少钱，然后营收是多少，而营收总是滞后于资本支出。从会计角度看，这些基础设施投资至少属于可折旧资产，折旧年限为六年；尽管有不少证据表明，它们实际上的使用寿命会更长。不过，我们还是继续按六年来算。所以，如果我今天在 AI 数据中心和 GPU 上花一万亿美元，GPU 的使用寿命是六年，数据中心则是十五年。Guest: Yes. I mean I think I think everyone's numbers for revenue requirements are quite low um given the pace of where this buildout is going to go. I think I think it's going to go like I think revenue will be much higher than that uh by 2031 for example. Um I mean there's a couple points here right? One is like you know how much is people are people spending on capex and then revenue and the revenue always trails the capex. These infrastructure investments are six years on an accounting basis at least depreciable assets although there's quite a bit of evidence that they're actually going to last longer than that but you know six years is let's just continue to use six years. So if I spend a trillion dollars today on you know AI data center and GPUs um the GPUs are lasting six years the data centers are lasting 15.

嗯，不过这就意味着，按定义来说，从营收角度看，要么我可以假设它是一条直线，对吧？那么在这 6 年里，我需要把它算作 1.5 万亿美元的营收，也就是每年 2500 亿美元的营收，才能对应那 1 万亿美元的资本支出；要么它会是一条有斜率的曲线，第一年少得多，最后一年则高得多，嗯，这样才能收回成本。至于关于 Meta 的那些说法吧，这其实挺荒唐的，因为 Meta 随时都可以停止购买新东西，他们的现金流完全足以支付所有已经签下的承诺，对吧？他们没必要——你知道——明年非得建 1000 亿美元的数据中心。他们想什么时候叫停都可以。嗯，显然他们已经承诺了一些合同，但还有很多事情他们还没有承诺。Um but that means definitionally in terms of revenue um either I could assume it's a straight line right so then over the 6 years I need to call it 1.5 trillion of revenue so $250 billion of revenue a year for that trillion dollars of capex or it's going to be something sloped where the first year is much less and the last year is much higher um to get the payback um as far as like you know like statements on meta it's like this is this is just kind of silly because meta meta at any point can just stop buying new stuff and their cash flow can easily pay for all the stuff that they've signed, right? They just don't have to, you know, they don't have to build a hundred billion dollars of data centers next year. They could pull the plug whenever they feel like it. Um there's obviously contracts that they've committed to, but there's also a lot of stuff they haven't committed to yet.

嗯，所以就 Meta 的偿付能力而言，他们完全有偿付能力，完全没问题。嗯，接下来的问题是，他们还能创造多少营收？嗯，至于 AI 对美国经济的影响，嗯，是啊，我的意思是，如果到 2031 年 AI 的营收只有 3 万亿美元，那我们确实会遇到大麻烦。嗯，不过你知道，我相信到明年 12 月，光 OpenAI 和 Anthropic 两家公司合计就能达到 6 亿到 7 亿。嗯，我认为光这两家公司，到 2027 年底也许就能达到 1.5 万亿美元。嗯，也就是说，到 2027 年底。这样一来，回报就很明确了，对吧？嗯，你知道他们的钱从哪里来。嗯，说到底，人们正在扩建的产能，正好与他们认为即将到来的业务规模相匹配。Um and so as far as like the solveny of Meta, like they're completely solvent. They're completely fine. Um and then the question is like what revenue do they make on top of that? Um and as far as like AI's impact in the in the US economy, um yeah, I mean if if AI revenue is only $3 trillion in 2031, then yeah, we'll have big problems. Um but you know, I I'm I'm like of the belief that we can get to like 600 700 million across openthropic alone by the by December of next year. Um I'm of the opinion that you can uh get to, you know, 1 and a.5 trillion maybe even by 2020 end of 2027 uh with just those two companies. Um so that's end of 27. So then the payback is clear, right? Um you know where they're getting that from. Um ultimately um the the capacity that people are building out is matched to what they think business is coming to.

嗯，所以如果我们处于建设阶段，那么基础设施投入与 AI 收入之间总会存在不匹配，因为今天的 AI 收入实际上是建立在前几年投入的基础设施之上的。嗯，现在资本支出并没有每年增长 10 倍，但 AI 实验室的推理收入同比刚刚增长了 10 倍。嗯，我们看到 Anthropic 现在已经实现盈利，也就是说，收入超过了算力成本——或者说，算力成本低于收入。嗯，这里把训练和推理合在一起计算。OpenAI 大概会在接下来的几个季度内达到这一水平，可能两三个季度吧，至少根据我们在 token 经济学模型里的预期是这样。所以我认为这些公司已经开始实现盈亏平衡了。比如亚马逊，它是最大的 AI 基础设施建设者，其所有 AI 基础设施投资目前都已实现盈利。当然，它们的客户可能会资不抵债，这些客户包括 OpenAI、Anthropic 等。但目前这些客户在相关业务上仍有偿付能力，而且毛利率非常高。Um, and so there's always going to be a mismatch if we're in the building phase of how much infrastructure are you spending on versus what's the AI revenue because that AI revenue for today is really based on the infrastructure you spent in prior years. Um, and right now capex is not going up at 10x a year but inference just revenue just went up 10x year on year um for the AI labs. And so um we've seen entropic is now you know profitable in terms of um compute cost minus revenue or revenue minus compute cost. um both training and inference combined um and OpenAI gets there you know within the next couple quarters uh maybe two or three quarters um so based on at least what like we expect in the tokconomics model so so I think you know those companies are already uh breaking even if you look at like Amazon for example the biggest builder of AI infrastructure they all of their AI infrastructure investments are profitable today um now you know the customers that for them could go insolvent those customers being openthropic etc Um but right now they are solvent on those um you know making very strong gross margins.

事实上，在最近一个季度，Bedrock 和来自 Bedrock 的 AI 收入推动了他们的毛利率上升。嗯，所以，没错，AI 收入确实需要大幅增长。不过我认为，我们已经能够实现各种事务的自动化，也能够构建出世界上此前能够构建的一切，而这种能力最终会为消费者带来可观的收入和剩余价值。In fact AI has made their gross margin go up in the most recent quarter um from bedrock and AI revenue from Bedrock. Um so so yes AI revenue needs to grow a lot. Um, but I would posit that like being able to automate all the stuff that we've been able to and being able to build all the stuff that the world has been able to um is going to generate meaningful revenue and surplus for the consumer ultimately.

主持人：是的，我觉得我们需要稍微拓展一下想象力，才能理解未来我们要如何衡量 AI 带来的收入。如果这些公司真的拥有世界上最好的模型，而且这些模型几乎能够胜任所有知识工作，你可以想象，它们会进入编码以外的许多不同行业，并获得与它们在编码领域所见相似的投资回报。你可以看看机器人和自动驾驶汽车，也可以看看药物研发，或者更广泛地说，医疗健康和生命科学。我们可以把这个思路推广到所有这些行业：如果它们想推出一款产品，比如抗癌药物或自动驾驶汽车，你就能想象，基于它们目前的投资，潜在的收入回报会有多大。Host: Yeah, I think we need to stretch it a little bit in our minds to understand what we are going to measure as AI revenue going into the future. If these companies really do have the best models in the world that are capable of effectively all knowledge work, you can imagine them going into a whole bunch of different industries beyond coding and having returns on their investments similar to what they've seen in coding. So you can look at things like robotics and self-driving vehicles. You can look at drug discovery. You can look at just health and life sciences generally. And we can take this across all these industries and say if they want to bring a product to market like a cancer drug or like a self-driving vehicle, you can just imagine how much revenue they can potentially return based on the investments they have today.

而且，如果他们拥有 AI，也拥有能够出色完成打造该产品所需知识工作的系统，我认为只有他们真正有能力在做这些事情的前沿展开竞争。因此，我认为人们缺乏想象力，无法理解回报还可以如何看待，而不局限于当下这种 Claude Code 订阅和按 token 计费的模式。And if they have the AI, the system to be able to produce the knowledge work required to build that product incredibly well, I think they're the only ones that can really compete on the frontier of being able to do these things. And so I think people lack the creativity to understand how returns can be viewed beyond the like claude code subscription and per token pricing regime of today.

嘉宾：是的，有一张图表展示了我们的支出占 GDP 的比例。这只是 2025 年的数据。医疗健康占 GDP 的 18%，是最大的类别。食品占 9.1%。人工智能服务占 8.8%，是 2025 年的第三大类别。我想，到了 2026 年，它可能会超过食品。所以各位，我只是好奇，这些钱是从哪里来的？比如说，假设 OpenAI 和 Anthropic 明年的营收合计达到 7000 亿美元。嗯，这些钱是来自这些公司的劳动力池吗？还是说仅仅是因为它们能卖出更多东西，所以收入就增加了？呃，这些公司的客户手里又是从哪里拿到这些钱的？呃，他们又是从哪里得到这笔钱，好把它们输送给人工智能公司呢？Guest: Yeah, there's there's this chart that looks at how much we're spending as a share of GDP. This is just 2025. So a share of GDP health is 18% that's the top category. Food is 9.1%. AI services is 8.8%. It's the third category in 2025. I imagine it's going to lap or it will pass food in 2026. So guys, I I'm just curious where's this money coming from? Like it, you know, let's say OpenAI and Anthropic combined get to $700 billion in revenue next year. Um, is the money coming from these companies labor pools? Is it just additive because they can sell more? Uh, where is the money that these companies are the customers of these companies? uh where's that money coming from that they can funnel it into the AI companies?

主持人：这涉及几个因素，对吧？首先，显然企业能够加快他们正在开发的东西，因此可以投入更多资金。越来越多的研发资金会流向 AI，越来越多的销货成本支出也会流向 AI。但与此同时，也会有相当多的资金来自劳动力，对吧？我不认为劳动力本身会减少。我认为，至少在短期内，经济其他部分的增长速度会非常快，所以劳动力规模大致会保持平稳。过去，经济中出现过不同的周期，但长期以来一直存在一个趋势：劳动力在 GDP 中所占的份额几十年来持续下降，而且自 70 年代以来，这种下降一直缓慢而稳定。因此，人们所说的很多不满，比如“哦，美国经济糟透了”。Host: It it's a few factors, right? First off is um obviously companies are going to be able to accelerate what they're building um and so they can invest more. Um increasing share of R&D dollars will go towards AI. Increasing share of cost of goods sold dollars will go towards AI. Um but also you'll have a significant amount of money sort of going coming from um you know labor as well, right? labor and I don't think labor shrinks per se. I think the rest of the economy grows so fast, at least in the short term, that labor labor sort of stays, you know, flattish. Um and and so like um you know, in the past, we've had different cycles across the economy, but one long-standing one has been labor share of GDP has fallen over the decades, and it's been a steady slow since the 70s. And so a lot of the like strife that people say, "Oh, America's economy sucks."

然而，哦，你看，经济规模变大了，实际上，嗯，经济中的劳动份额却缩小了，这就是为什么现实中，更多劳动创造的价值都流向了资本。AI 所做的，是会进一步加速这一趋势，也就是长期以来劳动份额相对于资本份额的变化趋势，因为 AI token 从根本上说就是资本，嗯，你知道，就是以芯片和数据中心的形式存在在那里，创造劳动，嗯，所以劳动份额会下降得更快，嗯，而且，而且，而且，所以你知道，我，我，我希望——对整个社会来说，我的看法是——你知道，也许这是一种非理性的信念，但我相信，我们会让经济增长得如此之快，以至于以美元计的劳动收入不会减少。嗯，它甚至可能还会缓慢增长。只是经济的其他部分增长得太快了。And yet, oh look, the economy is bigger is actually well like labor share of economy is shrank and that's why um in reality a lot more of the labor the value is getting acrewing to capital. What AI does is it's going to supercharge that trend, long-term trend of labor share versus capital share because AI tokens are gener basically just capital um you know in the form of chips and data centers that are sitting there and creating labor um and so that labor share is going to fall even faster um and and and so you know I I I would hope and and for society that that that's my belief you know and maybe it's like a irrational belief but I believe that we will just grow the economy so fast that labor in terms of dollars will not shrink. Um, and it might even grow slowly. It's just that the rest of the economy grows so fast.

嗯，这大概就是它的由来，对吧？所有提供各种服务的公司，服务都会变得更好。他们能够收取更高的费用，也能卖出更多产品。嗯，而且他们只需要更少的人手就能做到。嗯，或者换个角度看，他们的人数不变，但业务会变得更好。嗯，这就是……还有很多行业都有可能实现这一点，对吧？嗯，你知道，显然编程就是一个人们已经大量应用它的领域。嗯，显然一般的白领工作正在迅速自动化，但与此同时，公司也能做比过去多得多的事情。嗯，不过还有很多行业，比如制药、交易、芯片设计等等，对吧？Um, and that's where that's sort of where it comes from, right? So, all these companies that do uh offer various services, their services are going to get better. They're going to be able to charge more. They're going to be able to sell more. Um, and they're going to be able to do it with fewer people. Um, or another way to look at it is they're going to have the same number of people and their business just going to get better. Um and that's that's and and and there's a number of industries where this is possible, right? Um you know, obviously coding is an area where people have done it quite a bit. Um obviously general white collar work is getting rapidly automated, but also companies are just able to do a shitload more than they were able to do in the past. Um but there's a lot of industries like pharma and uh trading um and chip design and such, right?

芯片设计就是一个很好的例子，对吧？芯片。美国从事芯片设计的研发工程师人数，20年来基本持平。可围绕芯片设计形成的经济规模却爆发式增长。当然，这里不包括制造业，我们不把制造业算进去，只算设计人员。嗯，这些设计人员所在的公司，比如英伟达、博通——它们是全球最有价值的公司中的两家，对吧？还有谷歌以及所有这些自主研发芯片的公司，还有很多其他公司，对吧？从人数上看，这些设计人员并没有增加多少。对我们来说，这个行业的人数一直持平，但创造的价值却爆发式增长。为什么会这样？出现了很多工具。那么，如今 AI 辅助芯片设计能做什么呢？如今，整个经济中的芯片都在变得越来越好。Like chip design is a great example, right? chips. The number of American R&D engineers working in chip design has been basically flat for 20 years. And yet the economy for it has exploded. Now, obviously manufacturing isn't, we're not counting manufacturing. We're just counting the designers. Um, but the designers are like, you know, two of the most valuable companies in the world, right? Nvidia, Broadcom, um, as well as like Google and all these other companies that are making their own chips and many others, right? These designers have not really grown that much in terms of number of people. It's been flat as an industry for us and yet the value that's accured has exploded. And why is that? There have been a lot of tools. Now what is what does AI assisted chip design now do? Well, now all of the chips that are across the economy get better and better.

你们的手机会变得更好。你知道，AirPods 也会变得更好，它们里面的芯片也会变得更好。机器人用的芯片会变得更好。显然，AI 用的芯片也会变得更好。各种各样东西用的芯片都会变得更好。蓝牙、GPS，还有所有这些其他东西。博世生产的用于检测燃气泄漏之类情况的传感器也会变得更好。这一切都会变得更好，因为 AI 可以帮你设计芯片。人数不变，收入增长，利润率提高，一切看起来都棒极了，对吧？我觉得，这种模式可以在很多行业中复制。我的看法是，Your phones are going to get better. Your you know AirPods are going to get better, the chips in them. Chips for robots are going to get better. Chips for obviously AI um are going to get better. Chips for all sorts of things. Bluetooth and GPS and all these other things. Sensors um for gas leaks or whatever that Bosch makes. these are all just going to get better because AI can help you design chips. You do the same number of people, revenue grows, margins grow, um everything looks fantastic, right? And that's sort of like there's many industries where this can get replicated. Um in my view,

嘉宾：Dylan，你以前好像说过，知识工作完蛋了之类的话。那你现在不这么认为了吗？嗯，我觉得大多数知识工作……好吧，我们把我对“《乐一通》2035”那种情景的看法，和你知道的 2028 年的情况分开来说。嗯，我觉得在接下来的 X 年里，只要 AI 虽然非常强大，但还没在几乎所有方面都胜过人类，那么最有能动性的人就能做最酷的事，也能对世界产生大得多的影响。嗯，现在嘛，要是 AI 真的比所有人都强，那情况可能就会大不一样，对吧？嗯，那知识工作就完蛋了。实际上，重要的会是人们能不能玩游戏、享受乐趣，能不能表演，能不能做那些人类会彼此欣赏、只有人类才会做的事——前提是 AI 没把我们全都杀光，对吧？不过这算是……[嗤笑]Guest: Dylan, you've said that uh I think in the past you've said something to the extent of knowledge work is effed. So do you no longer believe that? Um I think most knowledge work okay like let's separate like my belief in like Looney Tunes 2035 versus like you know 2028 um I think in 20 I think for the next you know X number of years as long as AI is very powerful but it is not better than humans in most every way the most the most high agency people are going to be able to do the coolest stuff they're going to be able to make way bigger impacts on the world Um now you know in like the case where AI is just better than everyone um that that could change drastically right um you know then then like knowledge work is screwed and actually like you know what matters is like people being able to like play games and have fun people being able to act people being able to like you know do the things that humans do that humans only appreciate in each other if the AI doesn't kill us all right but that's sort of like you know [snorts]

主持人：我们今天就不谈这个了。嗯，Jordan，你知道，这算是你的专业领域。我想先聊聊可能会让这个故事脱轨的风险。嗯，你知道，我们之前已经谈过，像 Meta 这样的公司可能有一些合同不一定非得履行。嗯，你显然一直在密切关注新云厂商的业务，而这些业务依赖于 Meta 这样的公司真正兑现承诺，按照他们提出的计划与这些公司合作建设。嗯，这算不算一种被低估的风险？现在如果你去问这些新云厂商，一切看起来似乎都相当不错，因为他们会告诉你，他们拥有全世界最好的客户。[嗤笑]Host: we we won't go there today. Um Jordan, you know, this is kind of in your your area of expertise. I want to get started talking a little bit about the risks to like what might derail this story. Um and you know, we'd already talked about the fact that uh there are some contracts that a company like Meta uh could, you know, may not have to go through with. Um you're obviously looking a lot at the businesses of the Neoclouds which are dependent on uh companies like Meta actually making good uh on the on the promises that they've they've put forth to build with these companies. Um is this sort of an underappreciated risk that like everything looks like [snorts] fairly fine right now if you speak to one of these Neoclouds because they'll tell you that they have the best customers in the world.

嗯，他们有 Meta 这样的客户，而 Meta 会兑现承诺，他们正以此为依据举债。不过实际上，风险可能在于，像 Meta 这样的公司可能会决定：“是的，我们不打算这么做。”而且这些合同可能并不像新云厂商告诉我们的那样具有约束力。uh they have the Metas and Meta will make good on the on the um on the promise and they're raising debt based off of this. Uh but in reality the risk might be that a company like Meta might just decide yeah we're not going to do that and that the contracts might not be as binding as uh you know the Neoclouds tell us they are.

嘉宾：好的。我想澄清一下 Dylan 刚才提到的一个问题：合同终止，与 Meta 和新云厂商目前的情况之间有什么区别。一般来说，这些超大规模云服务商，或者说那些与新云厂商签约的前沿实验室，并没有那种可以按月随意取消合同、说停就停的权利。通常，他们签的是五年或六年的合同期限，并承诺使用全部容量；他们还有约定好的小时费率和合同总价值。对新云服务商来说，这些条件非常有吸引力，因为这些公司已经拿下了土地和电力，建好了场地，有些情况下甚至在签下合同之前就已经买好了芯片。Guest: Okay. So I I want to be clear about one thing Dylan was talking about there in terms of termination versus what's going on with meta and the neoclouds which is that generally speaking these hyperscalers or you know large frontier labs who are signing up with contracts for the Neoclouds do not have arbitrary cancellation rights on like a monthly basis where they can just pull the plug and the contract's done. Uh generally speaking, they're signing five years, six-year term lengths and they're off taking the full capacity and they have a set, you know, hourly rate and total contract value that's very very attractive to the NeoCloud providers because those guys have gone out and secured land and power and built a site and even in some cases purchased the chips respectively before they've actually gone and done that contract.

Dylan 所说的有可能放慢、暂停或取消项目，更多是指自建园区。也就是说，Meta 拥有规模庞大的园区，未来会作为基础容量的一部分；他们可以选择为这些园区购买芯片，也可以在想放慢脚步、不再全力推进时，让它们多年都只是空壳。一般来说，虽然建设成本非常高，但在一个新芯片项目的总成本中占比很小。换句话说，混凝土、钢材和墙体当然很重要、规模也很大，要把这些建起来确实是个不小的挑战；但按金额算，HBM、光学器件和 GPU 本身才是账单中占比更大的部分。what Dylan's referring to with the the potential to slow down or pause or cancel projects, it's more with respect to the self-build sites, which is to say like Meta has massive campuses that are going to serve as more of a base load of capacity going into the future, which they can either choose to buy chips for or just leave as an empty shell for years if they want to slow down and not go as hard as they can. And generally speaking, construction costs while very expensive are a small percentage of the total cost of a new chip uh project. Like in other words, the concrete and the steel and the walls is quite you know it's important and big and and you know very a real challenge to get it done but on a dollar basis like the HBM and the optics and the GPUs themselves are the the bigger uh portion of the bill.

嗯，所以你知道，现在的情况是，所有这些大公司都必须应对巨大的增长，而客户群体的需求要求它们实现这种增长。无论是 Meta、OpenAI 还是 Anthropic，它们的面向消费者的应用都有大量需求，比如 ChatGPT、Claude Code、Codex，甚至 Meta 的 Muse 等等。为了满足这些需求，它们需要 GPU，而满足这些需求是有利可图的，所以它们需要扩大规模，并做好准备，将这些产品推广给 Meta 各个平台上数十亿的月活跃用户；ChatGPT 的月活跃用户也接近十亿，Claude Code 等产品则有数亿用户。总之，考虑到这个区别，项目取消、经济下行之类的情况会带来什么风险？Um, and so you know what it's what it's looking like right now is that all of these big companies are really needing to contend with the massive growth that they're expected to pursue by their demand from their customer base. either that's meta or openai or enthropic who have demand on their consumerf facing applications like uh chat gpt or cloud code or uh codeex or uh even muse from meta things like that in order to serve that demand they need GPUs and it's profitable s to serve the demand so they need to ramp up and be prepared to roll this thing out to the billions of monthly active users on the meta uh different you know uh surfaces that they serve and approaching a billion of monthly active users for chatpt and hundreds of millions for uh cloud code and things like that. So anyway, um with that distinction in mind, what's the the risk with project cancellation or or a downturn or things like that?

我的意思是，要预测那里的供需曲线会发生某种变化，我们确实得看到需求放缓。芯片的产量、数据中心的建设数量，以及这些设施能够多快投入运营，都受到硬性限制。而截至目前，我们能做的所有建模基本都表明，需求超过了供给。那么，眼下需求比供给高出 10% 吗？是 50% 吗？还是高出无限？我的意思是，这个问题我们没法回答，因为目前我们无法通过新增 GPU 供给来追上需求。如果这个问题最终有了答案——如果我们发现知识工作需求或经济对智能的需求存在上限——那我们就能弄清楚谁需要在什么时候、什么地方放慢脚步，以及事情会如何发展。I mean, in order to for for us to um forecast some sort of change in the demand supply curve there, you'd really need to see demand slow. Um, there are hard constraints on how many chips you can produce, how many data centers you can build, and how fast this stuff is going to come online. And as of right now, all of the modeling we can do basically shows that demand outstrips supply. And so, is demand 10% more than supply right now? Is it 50%, is it infinite? I mean, we we don't get to answer that question because we are not able to catch up to demand with the supply of GPUs that you can bring online right now. And so when that question gets answered, if it gets answered, if we find limits to the demand for knowledge work, to the demand for intelligence from the economy, then we'll we'll be able to figure out who needs to slow at what time, where, and how that plays out yet.

但到目前为止，我想这些限制还没被发现。所以，嗯，好吧。在聊 Cluster Max 之前，我还想谈最后一个话题：一场即将到来的大型 IPO，当然就是 Anthropic 的 IPO。Dylan，你发过一条非常有意思的推文。我来读一下。你说：“你没法以两万亿美元的估值投资 Anthropic 的 IPO，因为它可能归零，也可能达到二十万亿美元；但如果达到二十万亿美元，我们就全完了。”你能解释一下这话是什么意思吗？也跟我们说说你的想法好吗？But so far, I guess those limits haven't been found. So, um, all right. Uh, before we get into Cluster Max, one last topic I want to cover, uh, big IPO on it way, uh, which is, of course, the anthropic IPO. Uh Dylan, you had a very interesting tweet about it. I'm just gonna read it. You said, "You can't invest in the anthropic IPO at$ two trillion dollars because it could be zero and I'm or it could be 20 trillion, but at 20 trillion, we're all fucked." Uh can you explain that and and tell us a little bit about what you mean by that?

主持人：嗯，明确一下，这完全就是一篇帖子。嗯，我当时就像……Host: Um to be clear, it's it's entirely a post. Um I was I was like

嘉宾：你知道吗？我明白，但我觉得你说的确实有些道理。那就跟我们聊聊吧。我总体上极其看好 Anthropic。我认为 Anthropic 会成为一家非常成功的公司，成为世界上非常成功的公司。Anthropic 甚至可能成为全球第一家估值达到 10 万亿美元的公司。我就是这么看好 Anthropic。但与此同时，嗯，要让他们成为一家估值 10 万亿或 20 万亿美元的公司，尤其是 20 万亿美元，他们的营收倍数首先得大幅下降。嗯，你知道，他们以前融资时的倍数是 30 倍，而现在，他们 IPO 时可能——咱们就说他们的营收达到 1000 亿美元，估值可能是 20 倍吧。嗯，如果他们估值达到 10 万亿美元，我打赌他们的营收得达到 1 万亿美元左右，或者什么特别夸张的数，对吧？类似这种情况，对吧？Guest: You know what? I I get it, but I think there's some truth to to what you're saying. So talk to us about it. I I'm I'm generally extremely bullish Anthropic. I think Anthropic will make you make, you know, be a very successful company in the world. Anthropic could even be the first company to be a 10 trillion valuation company in the world. Like I'm like that bullish entropic. But at the same time, um for them to be a 10 or 20 trillion valuation company, especially 20, um they're first of all, their multiples on revenue are going to have to collapse quite a bit. Um, you know, so they used to, you know, they used to raise at 30x and now they're, you know, IPO is going to be, you know, let's call it they're at hundred billion dollars of revenue and they're going to maybe be at 20x. Um, you know, if they get to 10 trillion, I bet you they would need to be at like a trillion dollars of revenue um or something crazy, right, like that, right?

嗯，这可能会在 28 年或 29 年的某个时候发生。嗯，你知道，这当然得看情况，比如股市也可能疯狂上涨，把估值倍数也推得很疯狂。但你知道，在它们估值达到 20 万亿美元的情况下，我不认为它们的营收能达到 10 倍销售额的估值倍数；它们现在的倍数可能只有 5 倍，甚至更低。嗯，这样的话，如果它们的估值是 20 万亿美元，营收是 2、3、4 万亿美元左右——它们的营收到底是 2、3、4 万亿美元？20 万亿美元的估值，2、3、4 万亿美元的营收。到了那种时候，你知道，如果在一个 [嗤笑] 150……500 亿美元的全球经济体，或者说万亿美元级别的经济体里，它们就能创造数万亿美元的营收，成为遥遥领先的全球最大公司，那么人工智能的能力也太他妈惊人了。Um, which could happen at some point in 28 um or 29. um you know it depends of course like the stock market can also go crazy and like make multiples go crazy but you know in the world where they're doing you know where they're worth 20 trillion you know I don't think their revenue I don't think they're even at a 10x sales multiple now they're probably at like a 5x or even less um in which case they're you know if they're at $20 trillion of revenue they're at are they at like 2 3 4 trillion $20 trillion valuation 2 three 4 trillion revenue and at that point you know what is the state of AI if on [snorts] a 150 $50 billion global economy or trillion dollar economy, they're generating multiple trillion dollars of revenue and by far the largest company in the world. Like AI's capabilities are freaking insane.

嗯，那样的话，你知道，所有这些担忧——比如 AI 会不会杀光我们，或者 AI 好到让所有人都失业；而这些收益的分配会严重偏向持有 Anthropic 股票的人，或者它相关供应链上的人，而不是得到合理分配——那么人们就会陷入困境和不安，甚至推翻政府，因为所有增长都集中到了极少数人手里。所以，这些事情可能会撕裂社会结构。因此，如果你相信 Anthropic 的市值会达到 20 万亿美元，那么整个社会可能会……Um, in which case like you know all of this worry of like whether it's AI kill us all or AI so good that everyone loses their jobs and because distribution of that earnings is going to be heavily leveraged to the people who own anthropic stock or um constituent supply chains of it and not distributed well then people are going to have dist dis distress and like overthrow governments because all of the growth is accumulating to a very small number of people. So like these things could like tear the fabric of society apart and therefore like if you believe anthropic will get to 20 trillion, you know, society as a whole could be

主持人：Jordan，你也有类似的担忧吗？Host: Jordan, do you have similar worries?

嘉宾：我觉得，是的，我觉得，呃，要达到那个程度还有很多事情要做。嗯，我其实不觉得自己像迪伦那么担心。Guest: I think Yeah, I think I think uh there's a lot to do to get to that point. Like um I I actually don't think I'm necessarily as worried uh as Dylan.

主持人：顺便说一句，我还是很乐观的。我刚才描绘的只是悲观的情景。现在我来谈谈乐观的一面。Host: I'm optimistic, by the way. I just painted the pessimistic picture. I'll go optimistic.

嘉宾：是的。我的意思是，比较乐观的看法，大致就是我之前描述的那种：如果他们的营收能达到 20 万亿美元。对用户而言，Claude Code 作为一款产品，并不是按 token 用量计费的。我也不认为，你知道，Claude Code 的质量会从现在的水平提升 20 倍，从而带动营收增长。我认为他们会涉足其他业务领域，而且就像我之前说的，我相当信任 Anthropic，觉得他们能够把一种抗癌药物之类的东西推向市场。最近 GLP-1 类药物也出现了类似的情况：它们对经济中的许多人来说都意义重大。因此，像 Lilly 这样的公司，凭借那些促成了这些了不起的药物、并帮助了许多人的非凡研究，也获得了巨大的回报。所以，我认为，对于未来——理论上，Anthropic 的估值达到 20 万亿美元——我会更加乐观。Guest: Yeah. I mean like the the rosy optimistic view is kind of what I described earlier which is like if they're getting to $20 trillion of revenue. This is not done on a per token metered basis for people using cla code as a product and I don't think you know you go and see cloud code go 20x better from where it is right now in terms of like quality and that drives the revenue. I think that they get into other areas of business and I think I would trust Anthropic quite a bit to bring like I said earlier a cancer drug to market or something like that and we've seen similar stuff happen recently with GLP1s just be such a meaningful thing for so many people in the economy and as a result you know companies like Lily have have had massive uh you know returns on on that incredible research that led to those uh incredible drugs that help people and so I think um I would be more optimistic for the future where theoretically anthropics at a $20 trillion valuation.

嗯，不过我确实觉得，接下来一路上会遇到很多坎。嗯，也就是说，回到你之前的问题：他们怎么可能满足所有这些需求、训练这些模型，并且，呃，你知道，让智能扩散到经济中的各个领域，从而让人们能够切实享受到这些好处？我认为，实现这一点的方法就是建设算力，而这非常依赖于施工、安装 GPU、确保它们稳定运行等方方面面的细节。对我来说，通往从 2 万亿美元 IPO 到公开市场 20 万亿美元估值这条路上，世界的中心就是 NeoClouds。没错，Anthropic 算是一个挺有意思的商业案例，对吧？他们在基础设施上投入了这么多钱。Um, but I do think that there are a lot of uh going to be a lot of bumps along the road along the way. Um, which is to say going back to your to your question previously, like how can they possibly serve all of this demand and train these models and uh, you know, have intelligence diffused to all of these different parts of the economy so that people can realize the benefits. And I think the way you do that is to build compute and it so depends on you know the nitty-gritty details of construction and installing GPUs and running them reliably and all of that. Like to me the center of the world is the NeoClouds on that road from a $2 trillion IPO to a 2020 20 trillion valuation in the public markets. Yeah, anthropic uh kind of an interesting business case, right? They're spending all this money on infrastructure.

嗯，即使在 S1 阶段，你也看到他们在营收增长的同时出现了巨额亏损。不过，嗯，Dylan，正如你提到的，他们似乎已经扭转了一些局面，现在赚的钱比花在算力上的钱还多。嗯，聊聊这个吧，因为我觉得大家的看法是，像 Anthropic 这样的公司属于低利润率业务，或者说，至少利润率远低于软件公司。但实际情况似乎并非如此。你们也算过这笔账。我想说，如果你看看 Salesforce，它可能是最出色的 SaaS 公司之一。嗯，我的意思是，我觉得它至少一直在稳步发展，成为最大的 SaaS 公司之一，而他们的毛利率大约是 75%。Uh even in the S1, you saw big losses accompanying their revenue. Although um Dylan, as you mentioned, it looks like what they've done is they've turned the tide a bit and they're making more money uh right now than they're spending on compute. Um talk a little bit because I think the the um the belief here is that a company like Anthropic is a low margin business or at least much less than uh than software. But that doesn't actually seem to be the case. And you guys have run the numbers. I would say like if you look at like Salesforce which is probably like one of the greatest SAS companies out there um you know I mean I think like at least like consistently grown to become one of the biggest SAS companies um their gross margins are like 75%.

而且 Anthropic 在推理业务上的毛利率达到 75%，甚至还略高一些，尤其是在前沿模型上。所以这有点像是——Salesforce 花了相当长的时间才达到这个水平，对吧？他们上市时的毛利率可没到 75%。他们确实花了不少时间才做到。到了 2000 年代中后期，他们好像还是没完全达到那个水平。我觉得 Anthropic 用的时间要短得多，就实现了如此强劲的毛利率增长。嗯，再看看许多其他 SaaS 公司，它们的毛利率甚至都没这么高。嗯，还有一点是，它们的客户获取成本高得离谱，对吧？你看一家 SaaS 公司，可能会觉得：“好吧，我的销售成本很低”，但客户获取成本却可能高达 30%。and Enthropic on their inference is doing 75% gross margins a little bit higher even um you know especially on the frontier models so it's sort of like well and it took it took Salesforce quite some time to get there right they were not at 75% when they IPOed um you know they they took quite a bit of time actually they were but then they like you know the mid in the late 2000s like they were you know I think like it took they were like not quite there it took you know anthropic quite a bit less time to get to that strong levels of growth margin. Um, and if you look at many other SAS companies, they're not even that high. Um, and then the other thing is like their customer acquisition cost is insane, right? If you look at like a a SAS company, it's like, you know, fine, my uh my COGS is really low, but then my customer acquisition cost can be as high as like 30%.

嗯，Anthropic 的客户获取成本几乎为零。显然，他们目前的研发支出非常庞大，但研发支出的增速低于收入增速。所以他们现在已经实现盈利，而且这种趋势没有理由不继续，对吧？他们会变得越来越盈利。至少从营业利润率来看是这样。另一个方面是可扩展性，对吧？很多 SaaS 公司有个很棒的地方：即使客户获取成本很高，只要利润率足够高，能先把这笔成本赚回来，你最终就不必再次获取这个客户了。这样你就拥有了一门利润极高的生意。但 AI 实验室的业务似乎有一定的客户黏性。没错，外界有各种说法，比如“Meta 要削减支出”或者“微软要削减支出”之类的，但归根结底，营收图表上的曲线一直在直线上扬，几乎每个客户给他们带来的收入也都在增长。Um, and Anthropic has near no customer acquisition cost. Right now, obviously, they have a humongous R&D spend. um but their R&D spend has grown slower than their revenue. Um and so now they've reached profitability and there's no reason why that trend doesn't continue, right? And they continue to grow more profitable. Um so at least uh operating margin wise and then and then like scalability is the other thing, right? Like the the beautiful thing about a lot of SAS companies even if your customer acquisition cost is so high, you know, as long as your margins are high enough to pay for it initially, eventually you don't have to reacquire that customer. Now you've got this super profitable business. But with like the AI labs, their business seems somewhat sticky. Yes, there's all this like messaging of like, oh, you know, Meta's cutting spend or Microsoft cutting spend or what have you, but ultimately the revenue has only been a straight line up on the chart and almost every customer's only grown on revenue for them.

而且，呃，即便你知道有其他中国模型问世，或者推出了更好的模型，也不会让支出份额发生太大变化。嗯，对于现有客户来说，你最终会拥有这样一个相当有黏性的客户，收入可能会以一定速度增长，而且没有客户获取成本，利润率也很高。所以可以说，AI 实验室算是比软件更好的生意。And uh switching cost even when you know some other Chinese model comes out or comes out with a better model doesn't really shift the spend share that much. um for existing customers um you end up with like this like pretty sticky customer potentially who is growing revenue at some pace and there's no customer acquisition cost and the margins are high so it's arguably a better business than software is is an AI lab

主持人：如果你能成功、】【Host: if you can successfully

嘉宾：不过，这其中的风险有多大？事实上，就像这里有这种引领前沿的……嗯，运动，运动正在出现……嗯。Guest: but how much is the risk the fact that like there this pacing the frontier um mo movement movement is coming out um

主持人：我觉得那只是在发信号，我觉得它完全……Host: I think that's just signaling I think it's completely

嘉宾：谈谈这件事，你完全不认为那会……那根本就不是真的。Guest: talk about that you don't think that's going to that's true at all.

主持人：嗯，如果你相信我们可能会自我毁灭，那么控制前沿发展的节奏是负责任的做法。嗯，不过我不一定觉得他们会把前沿发展的速度放慢太多。我觉得，考虑到他们身处的竞争，他们能放慢的空间其实非常小。嗯，人们也为此编过一些短剧，但你知道，Daario没法放慢，因为他不信任Sam；Sam也不会放慢，而且Sam也没法放慢太多，原因和Daario一样。显然，发生这些重大的安全事件时，他们会稍微放慢一些。嗯，可他们谁都不能放慢，因为中国也没落后太远。所以，这事确实非常棘手。Host: Um, I think pacing the frontier is a responsible thing to do if you believe that we could kill ourselves. Um, I don't necessarily think that um they will pace the frontier too much though. I think like there's there's like very little pacing they can do given the race that they're in. Um, and people have made skits about this, but like you know, you know, Daario can't slow down because he doesn't trust Sam and Sam's not gonna slow down and Sam can't slow down much because same with Daario. Obviously, they're going to slow down a little bit when they have these massive security in incidents. Um, and neither of them can slow down because China's not too far behind. So, it's like sort of like very difficult.

嘉宾：这让人放心。好吧。呃，听着，我们先快速休息一下，回来后聊聊 AI 云服务的现状，当然还有 ClusterMax，然后继续聊一些关于技术和业务的问题。我们回来后马上就谈这些。我们回来了，欢迎收听 Big Technology 播客，本期嘉宾是 SemiAnalysis 的 Dylan Patel 和 Jordan Anos。SemiAnalysis，相信你们都很熟悉。它是关于 AI 基础设施的一本宝典。呃，这两位都是业内顶尖人物。非常感谢你们二位抽出时间。嗯，绝对推荐大家至少去看看他们的博客。你也可以在 subscanalysis.com 订阅 SemiAnalysis。它是排名第一的科技通讯，实至名归。Guest: That's reassuring. All right. Um, look, we're going to take a quick break and then when we come back, we're going to talk a little bit about, um, the state of the AI clouds, uh, cluster max, of course, uh, and then we'll continue on with some more questions about the technology and the business. So, we'll do that when we're back right after this. And we're back here on Big Technology podcast with Dylan Patel and Jordan Anos from Semi analysis. Semi analysis, I'm sure you're familiar. It's the Bible for anything AI infrastructure. Uh, these guys are the best. very grateful to have your time both of you and um you know definitely recommend you at least uh check out the blog. You can also get semi analysis on subscanalysis.com. It's the number one technology newsletter uh and welld deserved.

所以，恭喜大家。So congratulations guys.

主持人：你可以在 newsletter 里找到《ClusterMax 3.0》。即使你不是技术人员，这篇也很值得一读——我之前和 Jordan 聊过这件事。它介绍了基础设施建设的现状。副标题告诉你，相关工作已经以正确的方式启动，并深入剖析了 GPU 云服务提供商的可靠性、性能、支持、定价和安全性。里面涵盖了很多内容。Jordan 和 Dylan，我们可以从很多方面展开，不过先从英伟达在这里扮演的角色谈起吧。这份报告虽然大部分篇幅都在讨论技术层面，但也提到了英伟达在维持这些集群运转、为一些前沿实验室乃至整个行业提供 AI 基础设施，以及推动整个体系运行方面发挥的重要作用。正如你们所写，英伟达还通过营收底线保障、房东担保以及计划转让给第三方的租赁协议，为许多相关企业提供支持，帮助新云厂商和 AI 实验室在没有超大规模云服务商参与的情况下获得投资级融资。Host: In the newsletter you can uh find the clustermax 3.0. Uh it's it's actually even if you're not technical I was talking with Jordan about this. It's a fun read. It goes into the state of the buildout. Um, and the subhead tells you, you know, it's already kicked off in the right way in the gory detail, the reliability, performance, support, and pricing and security of the GPU cloud providers. There's a lot in there. Um, there's a lot of areas we can go in this uh Jordan and Dylan, but let's begin with the role of Nvidia here. Um the report you know though it spends most of the time on the technical side of things does go into the fact that uh Nvidia is playing a ma major role in keeping these clustries providing uh infrastructure AI infrastructure maybe to some of the frontier labs but really across the board uh and making this whole thing work and Nvidia has been backstopping uh a lot of their um their businesses with uh as you write um revenue floors landlord guarantees and leases that it plans to transfer to third parties helping the Neo Labs and AI labs in obtaining investment grade financing without a hyperscaler being involved.

嗯，Jordan，这个问题想问你。你知道，对这个行业的一种批评——有点像 Ed Zitron 的那种批评——是，Nvidia 基本上是在为所有这些新型云服务商的需求提供资金支持；一旦这种支持停止，你知道，整个局面基本上就会烟消云散，Nvidia 看起来也会没那么庞大，而这些新型云服务商也会变得难以为继。你深入研究之后，对此有什么看法？Um Jordan to you on this one. Uh you know there's the the criticism sort of like the Edotron criticism of this uh of this industry is that Nvidia is basically funding uh the demand for all these Neoclouds and without that uh backs stop um you know this whole thing would basically evaporate and make Nvidia look a bit smaller and make these neoclouds um you know non nonviable. What's your thought on that after going so deep into this?

嘉宾：呃，是啊，我的意思是，我们在 Clustermax 以及最近写的另一篇文章《英伟达的后盾宇宙》中，都稍微深入探讨了后盾机制的运作方式，以及它们如何帮助新云服务商筹集所需资金，以部署 GPU，而目前客户正大量租用这些 GPU。呃，我们目前追踪到的数字是，英伟达正在支持的表外后盾资金总额达 5880 亿美元。这又是我们在这期播客中一路讨论的一连串令人难以置信的数字之一。嗯，我的意思是，英伟达的营收正接近 4000 亿美元，即将突破 5000 亿美元。所以我们谈论的是非常庞大的数字。作为参照，这相当于美国 M2 货币供应量的 2.5%。显然，在我们所说的这个宇宙里，资金规模极其庞大。呃，我能说的就是，这件事在英伟达看来似乎非常务实。Guest: Uh yeah, well I mean both in Clustermax and in another article we did recently called Nvidia's backs stop universe, we go into a little bit of the dynamics of what backs stops are and how they help the Neoclouds raise the money they need to deploy the GPUs which then customers are renting pretty strongly at this point. And uh the number that we have is that currently we've got tracking of $588 billion of offbalance sheet backs stops that Nvidia is supporting right now, which is one of yet another unbelievable number in the list that we've been going through on this podcast so far. Um I mean, but Nvidia's revenue is approaching 400 about to cross 500 billion. So we're talking about really big numbers there. For context, that's like 2.5% of the M2 money supply in the US. So obviously there's massive massive amounts of money going on uh in this um universe as we refer to it and uh I mean the only thing I can say about this is that it just seems really pragmatic from Nvidia.

他们确实，呃，希望客户群足够多元化。他们不希望所有收入都依赖超大规模云服务商，也不希望客户只能从三四家特定的超大规模云服务商那里获取芯片。因此，他们有充分的动力去打造一个健康的市场，提供多样化的选择，让所有客户都能找到合适的合作方。要做到这一点，人们面临的问题不只是筹集购买 GPU 所需的资金。首先，他们需要保证，筹到资金后，债权人能够获得回报。这就需要某种信用评级达到投资级的承购方，而在许多这类情况下，英伟达可以充当这样的承购方。They really uh want a wide diversity of customers. They want don't want to be dependent on the hyperscalers for all of their revenue. Um they also don't want their customers to be dependent on getting their chips from a select set of three or four different hyperscalers. And so they have every incentive to have a a nice healthy market with a diversity of options and uh lots of selection for all of the customers to go uh work with. And in order to deliver on that, people have more problems than just uh raising the money required to buy the GPUs. Well, first of all, they need the guarantee that if they raise the money, those creditors are going to see returns. And that requires you know some sort of investment grade offtaker which Nvidia can serve in these cases in many in many of these cases.

但到目前为止，事情的运作方式是，英伟达先以承购方的身份签约，等到真正要拿这些 GPU 自己用于内部研究时，恰好有人愿意出更高的价钱买它们。于是他们就把 GPU 卖给那些人，不再拿来做内部研究，然后继续转向下一个项目。对于那些认为英伟达这么做是出于什么不可告人的目的，或者是为了维持某种泡沫的人，我建议你们看看英伟达的研究质量。他们在 Hugging Face 上发布的模型比任何其他公司都多。他们针对自家 GPU 所支持的整套软件，拥有非常出色的 CI；他们还训练了 Neotron 这样的模型。我们并不是特别喜欢这些模型或这类软件。But the way it's worked so far is that Nvidia signs up as that offtaker and then when it comes time to actually you know take those GPUs for themselves for their own internal research work. Somebody else comes along and wants them more at a higher price. And so then they sell them to their to those guys. They don't take them for their internal research and they roll on to the next one. And for everybody who says that Nvidia, you know, is doing this for some nefarious purpose or to to keep some bubble going, I'd encourage you to look at the quality of Nvidia's research. They publish more models on Hugging Face than anybody else. They have incredible CI across the suite of software that they they support on their GPUs and they train models like Neotron. We're not the biggest fan of these models or this software.

你知道，我们认为应该有一个多元化的生态系统，但认为英伟达不会使用自家的 GPU 来开展服务于公司自身利益的研究，这种说法本身就是错的。我们看到的情况是，这部分算力实际上并不是英伟达收入中有意义的一部分——它只占英伟达总收入的很小比例，用于他们自己的内部研究工作负载。我们看到的只是，他们正在支持新云服务商扩建产能，以满足客户目前极其强劲、难以满足的需求。嗯，关于这一点最后还可以说的是，涉及的不只是芯片。比如说，我们追踪到他们为各种各样的环节提供支持。从建设成本、内存供应商，到建造晶圆厂的人，以及生产工业设备的人，都是如此。you know we think there should be a diverse ecosystem but the idea that Nvidia does not use their own GPUs for research that serves the purposes of their company is you know itself wrong and then the dynamic that we've seen is that this amount of compute is actually not a meaningful portion of Nvidia's revenue it's it's a very small percentage of Nvidia's total revenue that is you know going back for their own internal research workloads so what we're seeing is just that they are supporting Neocloud's build out the capacity for the demand from the customers which is so incredibly strong right now and hard to serve. Um, and then maybe the the last thing to say about this is that it's not just the chips. Like we we we track a whole wide range of things that they're backstopping. This is everything from like, you know, the construction costs, the memory suppliers, the people building fabs, the people building industrial equipment.

我的意思是，这不只是说我需要为GPU筹钱。我还需要为供应链中的其他所有环节筹钱，而这些才是那个特定项目中最大的瓶颈。I mean, it's not just like I need help raising money for my GPUs. is I need help raising money for all of the other things in the supply chain which is just the long pole in the tent for that specific project.

主持人：对。我们来谈谈安全问题吧，因为，你知道，这份报告的第一张图，是引用转发了伊利亚·苏茨克维的一条帖文；那条帖文是在 Hugging Face 事件发生后不久发布的。伊利亚说，新云的网络安全能力有限。下一次智能体成功失控时，它们会试图接管一家新云服务商，以运行更多副本。这很糟糕。因此，新云服务商应该大幅加强网络安全，而每家拥有强大网络安全模型的公司都应该提供帮助。这是伊利亚很少见的一条推文。迪伦，你怎么看？嗯，在我看来，如果他说这话是认真的，记住，他自己也在开发这些东西。我会说，在超级智能方面，我们可能遇到了问题。是啊。所以，我们发布了一篇题为《大多数新云服务商的安全性都很差》的文章，记录了……Host: Yep. Let's talk about security because um you know the first image in this report uh is a quote tweet of an Ilia subsever post which was made right after uh the hugging face incident. Uh Ilia says neoclouds have limited cyber security. Next time agents successfully go rogue, uh they'll try taking over a Neocloud to run more copies. This is bad. Thus, Neocloud should greatly strengthen their cyber security and every company with strong cyber models uh should help with that. This is a rare tweet uh from Ilia. Dylan, what do you make of this? Uh is this sounds to me like if he's out saying this right, remember he's building this stuff himself. Uh I'd say super intelligence that we might have a problem here. Yeah. So, we we posted an article titled most Neoclouds suck at security. Um documenting,

嘉宾：顺便说一下，那个，呃，那个作品真是太棒了。我们会把它放到屏幕上，给正在观看的观众看看这家伙正在……Guest: by the way, the art for that uh is is incredible. We'll put it on the screen uh for those who are watching this guy getting

主持人：对，没错。被所有人捅刀子，而且这居然还符合 SOC 2 合规要求。Host: Yeah, exactly. getting stabbed by everybody and it's like it's SOC2 compliant.

嘉宾：嗯，不过是这样，我们发布了那份报告，然后大概八个小时后吧，Ilia 发了他的推文。嗯，因为他们确实在使用各种云服务，嗯，你知道，他们显然非常保密，也很在意保守他们正在做什么的秘密，但他们也非常相信超级智能。嗯，我想是安全超级智能（SSI）。他们是第一个，你知道，特朗普实际上抄了 Ilia——Ilia 最先提出了另一个，对吧？最先提出推理优先，然后是 LLM 扩展，现在又最先提出超级智能这个品牌。嗯，Ilia 就这么发了条推文。嗯，而且这和我们写的东西有些呼应，对吧？就是说，瞧，这些云服务商大多在安全方面糟糕透顶。你可以用开源模型去入侵 Neoclouds，或者访问 Neoclouds 内部的其他东西。Guest: Um but yeah, so we we posted that report and then uh that you know like maybe eight or eight hours later or whatever, Ilia uh posted his tweet. um because they are very much um you [snorts] know using various clouds um they're obviously super secretive um and care a lot about the secrecy of like what they're working on but also they are big believers in super intelligence um you know I guess safe super intelligence SSI they were the first you know Trump actually copied Ilia Ilia's first with another one right um first to reasoning first to uh LLM scaling and now first to uh super intelligence branding. Um, but Ilia sort of tweeted that out. Um, and and it sort of echoed what we wrote, right? Which is like, look, most of these clouds are terrible at security. You can get open- source models to just hack Neoclouds or get access to stuff within other within Neoclouds.

嗯，你知道，尤其是 GLM 5.3，在这方面真的很厉害。所以，开源中国模型可以轻易入侵市面上几十家新兴云服务商，你也可以去看看其他人在做什么。我们在其他云平台上也见过类似的情况。有一家云服务商，我们其实根本没打算这么做，结果却意外地看到了那个集群上的所有其他客户，还看到了他们存储里的东西、Slurm 里的东西，等等。里面甚至还有情报机构，对吧？所以我们马上就向他们报告了。我们就说：“喂，……”Um, you know, GLM 5.3 especially is like really good at this. So, open source Chinese models can just hack dozens of the Neoclouds that exist out there and you can go look at what other people are doing. And so we've seen stuff like this um at other clouds. There was a cloud that you know we we didn't even intend to, but we just accidentally ended up like seeing all of the other clients on that cluster, seeing what stuff they had in storage, see what stuff they had in slurm, etc. And there were like intelligence agencies, right? Like on this. So we immediately like reported it to them. We're like, yo,

主持人：哪些情报机构？Host: which intelligence agencies?

嘉宾：我们……我们不能说，我觉得那样说不合适。国家层面的，比如联邦……嗯，安全机构、政府。Guest: We we cannot say I don't think that's a valid thing to say. national like like federal well security agencies governments

主持人：他们不是美国人Host: they were not American

嘉宾：他们不是美国人，但我们说这是给一个GDP排名前十的国家的，又不是哪个小国家。Guest: they were not American but we said it was for a country that was in a has a top 10 GDP it's not like some tiny country

主持人：我、我都快疯了，竟然还能做到那种事。Host: I'm I'm losing my mind here that that was possible

嘉宾：是啊，这就像是说，如果我们心怀恶意——顺便说一句，当时可能还有十几位其他客户，说不定其中就有一个心怀恶意——我们本来完全可以看到他们在训练什么，对吧？他们显然是在为自己训练模型、运行模型，因为这是一套 GPU 基础设施嘛。那一大批 Blackwell GPU 还能拿来做什么呢？Guest: yeah and it's like it's like if we were malicious and by the way there were probably a dozen other customers and maybe one of them was malicious we could have just like seen what they were training right and they were training models and running models for their themselves presumably um because this was a GPU infrastructure. So what else would you do with a bunch of Blackwell GPUs?

主持人：是的，我们总是把“主权人工智能”这个词挂在嘴边，它也经常和“新云”的概念交织在一起。人们甚至在融资时也会频繁使用这个词，因为他们把算力视为基础设施。我认为这完全正确。这就像电信基础设施的建设一样。部署这些基础设施、参与其中的主权国家，会把它视为本国希望掌控并拥有主权的资产。他们也相信，正如你在节目开头所说的，他们希望成为代币净进口方或净出口方，而这取决于他们所建设的基础设施，也就是内部配备 GPU 的数据中心。因此，我认为联邦政府的研究机构和情报机构出现在其中一些集群上，与其他机构共享基础设施，其实并不令人意外。Host: Yeah, we we throw around this this word sovereign AI and it it gets entangled with the concept of a neocloud many times and and people even when they're raising money, they use this word a lot because they view compute as infrastructure. And I think that's exactly right. This is just like the telecommunications buildout where this is this infrastructure is viewed by the sovereign nations that are deploying it and are involved in this as a asset that the country would like to control and have sovereignty over and they believe that similar to what you're saying at the beginning of the show uh they want to be a net token importer or exporter and that is driven by the infrastructure they build which means data centers with GPUs inside of it and so I think it's actually no surprise that uh federal government research and intelligence agencies are on some of these clusters sharing infrastructure with others.

你会希望他们拥有一个物理隔离的数据中心，但如果全国只有一家设施配备 GB300、NVL72——我的意思是，那就是卖方市场。You would like them to have an air gap data center, but if there's exactly one facility in the entire country with GB300, NVL72, I mean, it's a sellers market.

嘉宾：天哪。呃，好吧。看来我今晚是睡不着了。那么，想让你知道，你在这份报告里也把很多这类超大规模云服务商分了级，也把新兴云服务商分了级。顶级的是 CoreWeave 和 Nebius。我更感兴趣的是第二梯队，也就是 Oracle 和 Google Cloud。主要是想问问 Jordan，因为你知道，我为今天的谈话准备的那份文档里列了很多话题和句子，都是我从报告里摘出来、准备提问的；可说到 Oracle，我只写了 Oracle，后面跟着一堆问号，因为这大概就是我对这家公司的感受。我不知道该怎么看待他们。显然，评级机构对他们的债务评级不太好。Guest: Goodness. Uh all right. Well, there goes my sleep tonight. So um let's let you know you you you do in this report also rank uh you know a lot of these hyperscalers um into tiers and and the neoclouds into tiers and the at the top tier is coreweave and nebus um I'm more interested in the second tier and that is Oracle and Google cloud um mostly Jordan because you know I I you know I have like in the document that I put together for you know our conversation today I have like a lot of like topics and sentences um that I've like pulled out of the report with questions and then with Oracle, I just have Oracle and then a bunch of question marks because that's kind of how I feel about that company. I don't know how to feel about about them there. Obviously, their debt has been rated um you know, not well by the rating agencies.

他们把这一切都押上去了。嗯，他们的评分相当高，而且你们的报告实际上也称赞他们开创了一些技术。嗯，不过我很想听听你对甲骨文的看法：这对他们来说是不是一笔明智的押注？你觉得他们会辉煌崛起，还是像很多人预期的那样惨淡收场？我认为，ClusterMAX 的关键在于，它不是股票推荐，对吧？嗯，它跟“嘿，这家新云服务商明年要建一座吉瓦级设施”或者“200 兆瓦”之类的说法毫无关系，而这些对股票的影响要大得多；它也不是在评估他们运营集群的能力，对吧？它跟“嘿，他们说要在明年交付这座拥有这么多容量的数据中心，结果延期了三个月”也毫无关系。ClusterMAX 衡量的明确是：如果有人租用这些 GPU，这个集群用起来有多好？它有多可靠？They're making this all in bet. Um they rate pretty high and they actually in your report have been credited with pioneering some techniques. Um, but I would love to get your readout on on Oracle and you know whether whether this has been a smart bet for them and whether you know they're they're going to to rise in glory or or sort of fall in flames like a lot of people anticipate. I think the key thing about cluster max is it is not stock recommendations, right? Um it is not anything to do with hey this Neil cloud is going to build a gigawatt next year or 200 megawws u which is far more relevant for the stock than you know what how good are they at operating a cluster right um has nothing to do with hey this data center that they said they're going to deliver with this much capacity next year got delayed by 3 months that's not what cluster max measures cluster max is explicitly measuring sort of how how good is the cluster for someone to use if you rent the GPUs um how reliable is it?

它有多安全？存储怎么样？网络怎么样？生命周期怎么样？Kubernetes 上的 Slurm 怎么样？这些方面都怎么样？嗯，这种情况下，你知道，我们只是想尽量做到更客观。显然，做一个评级金字塔，就像你知道的，这是你多少得做的事。要是能只写一篇长篇报告，而没人介意里面没有排名，那当然很好。我是说，事情本来就该是这样。所以，你多少得做排名。嗯，还得设定一些界限；至于为什么这样划分、哪些东西属于哪个等级，你总可以据理力争个没完。但我们认为 Oracle 是服务质量最好的几家新兴云厂商之一，这是客观事实。不过，另一方面，这并不意味着——比如说，4 月份，我们在 SemiAnalysis 上发布消息告诉客户，他们位于新墨西哥州的数据中心会延期，因为他们拿不到那些管道。How secure is it? How's the storage? How's the network? How's the life cycle? How's the slurm on Kubernetes? How are all these things? Um in which case, you know, it's just sort of I we try to be more objective. Obviously, making a rating pyramid is like, you know, it is it is something you kind of have to do. It would be nice if you could just make a long report and no one cared if there were no rankings. I mean, that's what would happen. So, you kind of have to do the rankings. Um and and set certain boundaries and you can always litigate the hell out of why and what is in what tier. Um but like it is an objective fact that we think Oracle is one of the top Neoclouds in terms of quality of service. Now that doesn't mean on the other hand, hey at semi analysis in April, we published to our clients that their New Mexico data center was going to be delayed because they weren't going to get the uh pipelines.

这对 OpenAI 来说是个大问题，因为 OpenAI 已经和他们签约，要在新墨西哥州建设一座大型数据中心。但如果这些管道建不起来，这座数据中心也就无法建成。嗯，后来我们在五月、六月又提过这件事，最近 Oracle 也宣布了不可抗力，股价因此暴跌，对吧？所以，我们其实是在把两件事拆开来看。没错，Oracle 的云服务很出色，在托管集群方面也是顶尖的新云厂商之一；但与此同时，他们也可能面临执行方面的问题——比如新墨西哥州这座数据中心遇到的监管问题，导致管道无法铺设到那里，或者他们资产负债表上的其他问题。所以，我们要把两件事拆开：哪些因素对股价有利或不利，以及我们在这里究竟想要验证什么。And this was a big issue for OpenAI because OpenAI has contracted a huge data center with them in New Mexico. But if these pipelines don't get built, this data center can't go up. Um and then later you know we said it again in uh May um and June and then most recently Oracle announced a force measure on it and their stock tanked because of it right so so we're disentangling two things right yes Oracle is an amazing cloud at managed for their managed cluster one of the top Neoclouds but they also at the same time may have execution issues whether it's regulatory in the case of this New Mexico data center where they can't get the pipeline there or other issues you know on their balance sheet right and so that disentangling like what is good or bad for the stock versus what are we trying to test here.

第一个集群最大值中的目标是The goal in first cluster max was

主持人：做出一些 GPU 租用者可以使用的东西。嗯，结果现在它还有很多其他用途，人们会试图生搬硬套地用到各种场景里。比如说，嗯，嘿，我们要给这家新云公司承销债务。如果他们是新用户，如果他们把评级从青铜升到白银，他们的费率实际上会下降。嗯，你知道，诸如此类。或者说，嘿，我要投资那些在 ClusterMax 上排名靠前的股票。嗯，显然，这些并不是 ClusterMax 的决策，也不是它的设计用途。嗯，如果你要和这些公司打交道，这只是一个可以参考的数据点。但就甲骨文这家公司而言，嗯，咱们可以从两个方面来看，对吧？一个是他们和 OpenAI 签了很多 AI 数据中心交易。嗯，是去年签的吗？Host: create something that people renting GPUs can use. Um and it turns out now like there's a lot of other uses for it that people try to hamfist in whether it's like you know um hey we're going to write debt for this NeoCloud. If they're new if they if they upgrade rankings from bronze to silver their rate will actually go down. Um you know things like this or like hey I'm going to invest in stocks that are at the top of cluster max. Um so obvious these are not the decision these are not what cluster max is designed for. Um it's a data point that you should use if you're doing anything with these companies. But um as far as Oracle as a company um there's sort of like you know two things to go here, right? Like one is they signed a ton of AI data center deals um with OpenAI. Um was it last year?

是啊，那是去年。天哪，居然才是去年。感觉像过了好几个世纪。嗯，我觉得可能正好是一年前。不过有意思的是，他们签下了这些巨额合同。嗯，这些合同里埋着不少风险。其中一点是，他们签合同时基本上锁定了固定利润率。嗯，所以现在 GPU 对云服务商来说涨价了很多，他们却没法享受到更高的利润率。所以他们在这方面有点受制于人。[清嗓子]第二，他们筹集了大量债务，之后市场开始担心企业能负担多少债务，于是大家都被吓跑了，尽管甲骨文完全有偿付能力。第三，他们在执行方面也有些问题。你知道，我提过监管方面的问题。此外还有一些数据中心也出现了延误之类的情况，比如威斯康星州的那些，但最主要的还是新墨西哥州这个项目，因为管道问题可能会延迟好几年。Yeah, it was last year. Holy crap, it was only last year. Feels like eons ago. Um I think it might have been exactly a year ago. Um but the interesting thing is they signed these massive deals. Um and there's a number of issu risks embedded with them. One is they signed them with a fixed margin essentially. Um so now that the price of GPUs has gone up a lot for clouds, they can't take advantage of this higher margin. So they're kind of hammed in [clears throat] there. Number two, um they raised a bunch of debt and then the market started getting freaked out about how much debt companies can have and so they spooked out even though Oracle is completely solvent. And then number three, they have some execution issues. You know, I mentioned the regulatory one. There's some other data centers that have some delays and such there as well, but the main one being this, you know, such as in in Wisconsin, but the main one being this New Mexico site that is delayed potentially multiple years because of this pipeline issue.

嗯，所以你知道，这些事情可能会对股价产生非常负面的影响，但我认为公司本身仍然没问题。现在，你知道，他们不是 Nebius。Nebius 能够迅速调整，而且他们是 ClusterMax 中头部云服务商里平均合同期限最短的。嗯，所以 Nebius 能够迅速调整，大幅提高价格并从中获利，对吧？而 CoreWeave 和 Oracle 尽管也拥有不错的集群管理能力，以及集群可靠性、安全性等等，却没能充分利用这一点，因为它们主要把自己锁定在长期合同里。Um, and so, you know, these things can have very negative impacts on the stock, but I think the company is still fine. Now, you know, they're not Nebius. Nebus gets to turn around and they had the they have the shortest average contract length of the top clouds in cluster max. Um, and so Nebius has been able to turn around and really jack up pricing and take advantage of this, right? Whereas coreweave and Oracle despite having also good cluster um you know management and clust and reliability and security and all this stuff were not able to take as much advantage because they lock themselves into long-term contracts primarily.

主持人：哦，这真有意思。好吧，Jordan，那我来问你一个技术方面的问题。嗯，你知道，正如 Dylan 发布出来的那样，这是一张金字塔图，顶层有一些顶级群体，像我们谈到的 CoreWeave 和 Nebius，Oracle、Google Cloud 也在上面。不过在“不推荐”这一栏里有很多标志，还有“表现不佳”和“不可用”这几档。我觉得“不可用”这一档里的标志，可能比上面四档加起来还多。你觉得这说明了什么？我是说，这么多服务都在那里，却达不到你的标准，这说明了这项技术的什么情况？Host: Oh that is fascinating. All right Jordan let me ask you a technology question then. um you know as Dylan put out uh put out there it is a pyramid um and there's like these top tier uh groups like we talked about coreweave and nebius Oracle Google cloud is up there but in the notrecommended section there's a lot of logos uh there's underperforming and unavailable um I think there's more something like more logos in the unavailable than you know top four tiers combined What do you think that says about the fact that I mean, what do you think that says about this technology that there's so many that are out there uh that just aren't meeting the bar for you?

嘉宾：听着，作为试图代表买方利益的人，我觉得这只会让我们感到难过。看到，呃……很难过。Guest: Look, as people who are trying to represent the interests of buyers, I think it just makes us sad. It's sad to see the uh

主持人：你看，挺令人遗憾的是，距离 ClusterMax 的第一个版本发布已经差不多一年半了，实际上现在快两年了；自从我们在 SemiAnalysis 创造了“新云”（neocloud）这个词以来，很多人却一直没有朝着我们写过的那些方向取得进展。我们写的内容，恰恰就是我们与这些新云客户交流时会谈到的事情，他们……嗯。所以我觉得，这种卖方市场的局面意味着，这些公司的优先事项是：如何尽可能多地融资，如何尽可能快地建设数据中心，以及如何尽可能快地部署芯片，而不是致力于提升底层服务的真正品质。这就是你在其中看到的一种动态：有些公司即使不太操心能否提供优质服务、让客户真正满意并愿意推荐给别人，也能做成很好的生意。Host: look it's sad to see that like a a year and a half almost you know since um the first version of cluster max was published actually approaching 2 years now um and since we coined the term neocloud at semi analysis that a lot of people have just not made progress towards the things that we were writing about and the things we were writing about is just exactly what we talk to the customers of these neoclouds about what they Um, and so I think it's just like the dynamic of it being a sellers market means that the priority for these companies is how can they raise as much money as they can, how can they build data centers as fast as they can, and how can they deploy chips as fast as they can rather than working on the true quality of the underlying service. And that's that's a little bit of the dynamic that you see represented there where people can have these great businesses without actually worrying too much about delivering a great service that their customers really enjoy and recommend to other people.

另一件事是，我认为有一种持续存在的动态，我们今后会一直看到：领导者的数量终究有限，因为真正进行创新的人只有那么多，其他人都会去模仿他们。根据我们的追踪，截至今天上午，市场上实际上有 323 家服务提供商。我们会在 AI 云总拥有成本（TCO）模型中追踪这些提供商；订阅 SemiAnalysis 的用户可以看到这份完整名单。真正相关的只有其中一部分，也就是规模足够大、能列入名单的那些；而在这些之中，又只有一部分能够达到我们认定的推荐门槛。你知道，在某些方面，我们觉得这个推荐门槛其实很低。比如，安全性和可靠性是最主要的两项，而在我们看来，这些方面的基本要求都非常简单。看到其他人没有足够重视，或者不够在意，以至于没有真正落实这些措施，实在令人遗憾。The other thing is that I think there's like this constant dynamic that we'll continue to see over time which is that there can only be so many leaders because there's only so many people who truly innovate and then everybody else copies them. And when you have a market that by our tracking is actually 323 providers as of this morning uh which we track in our AI cloud TCO model for people who are subscribers to semi analysis they have this full list. there's only a certain amount of them that are even relevant, like large enough to put on the list and then a certain number of those that can pass the bar for what we believe is what we need to see to to recommend them. And you know, in some ways, we think that the bar for being recommended is quite low. like the um the basic things that fail with respect to security and reliability being the two top ones are just um in our view dead simple and it's it's just sad to see that others aren't paying attention enough or caring enough to actually implement this stuff.

嘉宾：好，在结束之前还有两个很快的问题。嗯，先问个比较宽泛的问题。你知道，谷歌显然已经宣布他们要推出 Gemini，呃，Argon。Gemini for Argon。嗯，你怎么看？有人呼吁谷歌干脆就做一家 NeoCloud，呃，把 Google Cloud 的 TPU 和算力租出去。嗯，你觉得他们会朝这个方向发展吗？还是认为他们仍会把很大一部分算力用于模型训练和推理？我的意思是，从定义上来说，过去六个月他们分配给模型训练和部署的算力，已经比两三年前少了。嗯，而且这个分配比例一直在下降。嗯，这算是我们开玩笑时提到过的一件事——或者甚至不能说是开玩笑，只是说托马斯·库兰赢了，丹尼斯输了，对吧？Guest: Okay, two quick ones before we go. Um just more broad question. Uh you know Google obviously they're they've announced they have this Gemini uh Argon coming out. Gemini for Argon. Um, is it your view that that you know there have been some people who have called upon Google to just be, you know, an basically a NeoCloud and rent out uh the TPUs from Google Cloud and the computing. Um, do you see them going that way or do you think that they're still going to use like a large percentage of their compute for model training and inference? I mean definitionally they have allocated less compute to uh model training and deployment um in the last 6 months than they had two or three years ago. Um and that that percentage allocation has fallen over time. Um this is sort of you know one of the things that we made a joke about like uh maybe not even joke but just a statement that Thomas Curran won and Dennis lost right?

嗯，现在我想，卡莱，嗯，谷歌的算力正在大幅增长。嗯，你知道，年复一年都是如此，但分配给 DeepMind 的算力增长得没那么快。你知道，并不是说——并不是说，哦，DeepMind 过去拥有谷歌三分之一的算力，现在还是拥有谷歌三分之一的算力。嗯，实际上，这个比例正在下降。呃，这真的很有意思，因为现在如果只看 DeepMind、Anthropic 和 OpenAI，到今年年底，DeepMind 的算力会是三家里最少的。所以，这真的是一件非常、非常重要的事。嗯，所以谷歌，你知道，不管有没有新云，或者说“新云”这个词到底是什么意思，呃，你知道，出售其他模型的 tokens 算不算新云？嗯，你知道，从某种意义上说，这就是新的云业务：出售 AI 模型的 tokens，对吧？Um now I guess Karai um Google is producing significantly more compute. um you know, year on year, but the amount that's going to deep mind is not rising as fast on a you know, it's not like it's not like oh Deep Mind had a third of Google's compute, now it still has a third of Google's compute. Um it's actually falling as a percentage. Uh which is really interesting because now if I look at just DeepMind versus Anthropic and OpenAI, DeepMind has the least compute of the three um by the end of this year. So like it's it's a really it's really a big deal. Um, and so Google, you know, whether or not there's a Neil cloud, what the word Neocloud even means, uh, you know, does selling tokens of other models count as Neocloud? Um, you know, does does, you know, in a sense that's what the new cloud business is, is selling AI model tokens, right?

所以那可能就是一种新云，对吧？嗯，定义是…So maybe that's a neocloud, right? Um, the definition is

主持人：黑石集团，以及开展新的建设项目来部署 GPU，直接为核心平台之外的客户提供服务。Host: Blackstone and going and doing new construction projects to build your GPUs to deploy directly for customers outside of your core platform

嘉宾：或者是卖芯片，对吧？那算新云吗？不算。我的意思是，他们向 Anthropic 卖了很多芯片。所以，他们是实实在在地把芯片卖出去，而不是只把芯片租给别人用。我想说，谷歌的商业模式正在演变，他们希望在技术栈的每一层都赚钱。嗯，不过从定义上来说，谷歌在 TPU 方面这样大力帮助 Anthropic，又在其他方面提供这么多帮助，确实会损害谷歌 DeepMind 的业务。嗯，好。Guest: or selling chips, right? Is that neocloud? No. I mean, they're selling a lot of chips to anthropic. So it's, you know, physically selling the chips rather than like just renting them out. So I would say like Google's business model is evolving and they want to make money at every layer of the stack. Um, but by definition, you know, helping Enthropic this much with TPUs, um, and helping them this much with these other things is hurting Google Deep Mind's business. Um, okay.

主持人：我觉得他们得找到一个细分市场，哥们。比如 Gemini，我是说，没人想要第三、第四或第五好的编程模型。所以他们得找到一个能推动 Gemini 令牌销量的细分市场，这样 Deep Mind 才能继续在 AI 研究前沿领域竞争。因为眼下，我是说，即使推出了 Gemini 4 Argon，它在编程方面也比不上排名前两三位的模型。Host: I think they need they need to find a niche, man. Like for Gemini, I mean, nobody wants the third or fourth or fifth best coding model. So they need to find a niche that drives Gemini token sales if they want Deep Mind to continue competing on the frontier of AI research and because right now it's I mean even with this Gemini 4 argon release it's just not there on coding compared to the top two or three

嘉宾：而且这里的风险在于，明年 OpenAI 和 Anthropic 都会尝试为各自的实验室增加 5 到 10 吉瓦的额外算力。你知道，OpenAI 和 Anthropic 各自都会这么做。嗯，而 Google 只会增加几吉瓦。现在他们的算力差距是这样的：之前 Google 在算力上遥遥领先，而他们算是落后，或者说差不多——嗯，基本上只是稍微落后一点。现在他们落后得更多了，但他们的算力还是原样。那么当他们的算力更少时会怎么样？他们能 somehow 神奇地追上来吗？有可能，对吧？他们有人才，也有人手，只需要重新组织起来，真正集中精力。不过，越来越多最优秀的人才不断被挖到 Anthropic、OpenAI 去，而这……Meta……Guest: and and and the risk here is what happens next year when Open Entropic both attempt to add 5 to 10 gawatt of additional compute for their labs and you know for open eye andropic each and then Um, Google only adds a few gigawatts. Now their compute deficit, it it was Google was way ahead in compute and they were kind of behind slash equivalent. Um, mostly just a little bit behind. Um, and now they've fallen further behind, but their compute is the same. So what happens when they have less compute? Are they do they somehow magically catch up? It's possible, right? They have the talent, they have the people, they need to reorganize and really focus up. Um but also like more and more of the best people just keep getting poached to anthropic to open AI and it it's it meta

主机：发现循环。Host: discovery loop.

嘉宾：是的。Guest: Yeah.

主持人：发现循环。Host: Discovery loop.

嘉宾：发现循环。对。Guest: Discovery loop. Right.

主持人：好。如果可以的话，在我们结束之前我还想再问一个问题。嗯，我记得迪伦你以前说过，最终从这些 AI 模型中获得最大价值的是终端用户，对吧？比如在底层使用云服务的人。当然，Anthropic 会通过 API 提供模型，让它们用于各种不同的场景。你知道，他们还得开展更多授权合作，至于能不能为所有这些场景开发产品，谁也说不准。我很好奇，你是否认为存在这样一种情况：考虑到我们之前谈过的那些巨大数字，他们会把前沿模型留在内部更久、久得多，并用它们来打造自己的产品？我们知道他们有湿实验室，也知道他们有 Claude Code 和 Cowork；之后他们不再对外开放这些模型，因为他们想从自己购买和搭建的基础设施中获得更高的利润。Host: Okay. I I want to ask one more before we go if that's okay. Um you know the I think Dylan you've said in the past that um the the people who are getting most of the value out of the AI models that are created are the end users, right? The people that are using the clouds for instance at the you know at the base. Um, and of course, Anthropic makes that model available via API and to go into all these various uses. You know, it's it's going to have to do more licensing and and who knows if they'll be able to do products for all this. I'm curious if you think if you see a scenario where given the big numbers that we talked about in the past um or they keep their frontier models at home for longer, much longer, and use them to build their own products. It's like we know they have the wet lab, we know they have cloud code uh and co-work um and then they don't make them available because they want to get more margin out of the infrastructure that they've they've bought and built.

嘉宾：是的。所以，我的意思是，我觉得我们已经看到这种情况了，对吧？嗯，Anthropic 在二月份就准备好了 Mythos。呃，他们的模型安全卡没有提到——呃，提到他们在 Mythos 之后还发布了另外两个模型，但截至目前，我们仍然没有完整的 Mythos。我们拿到的是一个被阉割的版本，叫 Fable。嗯，而且 Fable 过了好多个月才发布。他们还有 Glasswing，有点像 Mythos，但只针对某些子领域，而且有些方面也被削弱了，对吧？不是所有方面。所以很明显，Anthropic 并没有发布新模型。OpenAI 也一样。实际上，Astra 几个月前就已经完成了，但直到最近才推出；你知道，他们过了几个月才发布 Astra。嗯，至于新的模型 Bell，他们甚至还发了一篇博客，展示它在数学方面的表现，以及它是如何解决这些问题的，嗯，但他们还没有发布它。Guest: Yeah. So, I mean I think I think we've already seen this, right? Um Anthropic had Mythos ready in February. Uh they've their model safety cards not noted uh noted that they have two other models since Methos that have released and publicly we still don't have full Methos. We have a neutered version of it called Fable. Um, and it it took many many months before Fable even came out. They had the glass wing which was like mythos but for only some subsectors and it was neutered in some areas, right? Not all areas. And so it's clear that Anthropic has not released new models. Open eye. Likewise, Astra was actually done a couple months ago and it only came out like, you know, it took a couple months before they released Astra. Um and the new model bell um they even showed a blog where they showed the performance of it in math um and how it you know solved all this stuff um and they have not released it.

所以很明显，他们只是内部使用，而不是对外发布。而且你知道，各家实验室已经沿着这条路线走了，对吧？情况就像是：“哦，没错没错，他们和中国开源模型之间仍然有六个月的差距。”他们某种程度上维持着这种说法，但实际上，他们把数月的进展都留在内部，没有对外公开。因为一旦你把它公开，人们就可以蒸馏它，等等。所以我觉得，Anthropic 很可能会在接下来几周内发布一个新模型，你知道，为了 IPO。我觉得 OpenAI 也很可能不得不在接下来几周内发布一个新模型，毕竟竞争环境如此激烈。但与此同时，他们也把很多东西留在内部，没有对外公开。对，我们之前在内部讨论过一些……资本主义激励因素。So clearly they are just using it internally not externally and and so you know the labs have already moved along this line right where it's like oh yeah yeah the gap between them and and Chinese open source is still 6 months um they're kind of keeping that but in reality they've kept you know months of progress internal only um because the moment you make it external people can distill it um you know and all these other things so I think you know enthropic will likely release a new model in the coming weeks you know for their IPO Um I think open I will likely have to release a new model in the coming weeks as well just because it's such a competitive environment but at the same time they are like keeping a lot of their stuff internal only. Yeah, we were discussing internally some of the uh capitalistic incentives.

如果这些激励确实会促使人们发布更多模型，并让消费者用上它们——因为按 token 计费的 API 定价有着惊人的毛利率——还是说，激励会让他们把模型留在内部，以便开发终端用户应用，而这些应用的价值甚至比按 token 出售模型访问权限更高？这件事会如何发展，值得关注。但在其中一种情形下，资本主义的做法会是把前沿技术的推进节奏掌握在自己手中，对吧？If if the incentives are actually going to motivate people to release more models and get them in the hands of consumers because of the incredible gross margins on the per token API pricing or if the incentive is to keep it internally so that you can pursue the end user applications which are even more uh high value than selling the access to the model per token. It's going to be interesting to see how it plays out. But in one of those scenarios, the capitalist approach will be to pace the frontier for yourself, right?

主持人：那你的直觉觉得事情会如何发展？你认为哪种情形会成为现实？Host: And which what's your gut on where it's going to go? Which one of those scenarios you think plays out?

嘉宾：我认为 Anthropic 曾公开表示，他们通过限制自己来放慢前沿技术的发展。而这也是他们的博客文章在现实中唯一体现出来的方式。其他人都没有放慢脚步，只有他们在放慢自己的步伐。我认为确实如此，我觉得事情就是这样发展的。而且我认为，如果通过解决癌症这类问题来提升 AI 的地位——这显然是好事，人人都能认同——那就会形成一种积极的激励。然后我也认为，做出这样的成果同样有很多钱可赚。所以我打赌，OpenAI 和 Anthropic 会继续发布满足最低可行标准的模型，以保持对中国以及其他开源生态系统的领先优势。网址是 semianalysis.com，正如 Dylan 所说，去订阅博客吧，就像我订阅了他们的简报一样。Guest: I think Anthropic has publicly stated that they are pacing the frontier by keeping themselves uh restricted. And that's the only way that their blog posts have manifest in reality. Nobody else is slowing down. They're only slowing themselves down. I think that's true. I think that's what's happening. And uh I think the incentives to both improve the status of AI by solving things that are, you know, like cancer that are just unequivocally good that everybody can agree is a good thing would be, you know, a positive incentive. And then I think there's also plenty of money to be made if you do something like that. So I my my bet is that we continue to see the minimum viable models released by both openai and anthropic to keep a lead over Chinese and the rest of the open source ecosystem. The website semianalysis.com uh sign up for the blog as Dylan says, you know, sign up for the newsletter as I have.

嗯，这里有丰富的见解和分析，我想如果你还没来过这里，一定会喜欢的。嗯，而这次对话正是这一点的体现。所以，嗯，Dylan，很高兴再次见到你。Jordan，很高兴认识你们。欢迎你们随时再来，也感谢你们今天拨冗参与。Uh it's a wealth of insight and analysis and I think you'll love it if you're not there already. Uh and this conversation has just, you know, been the manifestation of that. So, uh, Dylan, great to see you again. Jordan, really nice to meet you. You guys are welcome anytime, and we appreciate your time today.

主持人：亚历克斯，谢谢你邀请我们。Host: Thanks for having us, Alex.

来宾：谢谢你，Alex。Guest: Thanks, Alex.

主持人：好了，很棒。感谢大家收听和观看，我们下期《Big Technology Podcast》再见。Host: All right, good stuff. Thanks everybody for listening and watching, and we'll see you next time on Big Technology Podcast.