Ben Thompson on Big Tech, China, and the AI Boom Running Out of Money - [Invest Like the Best, EP.487]

频道: Invest Like the Best
视频: https://colossus.com/episode/winners-losers-ai-era/
原文语言: en
统计: 共 105 轮 · Patrick 9 · Ben 80

[0:00] Patrick

Ramp is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average so you can stay focused on growth.Ramp customers grow revenue 3.2 times faster than the average American business.Visa, Vercel, Cursor, Stripe, Notion, 11Lab, Shopify, and 70,000 other businesses all now run on Ramp.Mine does too, and so should yours. Learn more at ramp.com slash invest.OpenAI, Cursor, Anthropic, Perplexity, and Vercel all have something in common.They all use WorkOS.To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC, and audit logs.Instead of spending months building these mission-critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero.That's why so many of the top AI teams you hear about already run on WorkOS.WorkOS is the fastest way to become enterprise-ready and stay focused on what matters most, your product.Visit WorkOS.com to get started.Felix by Rogo is a personal finance agent that turns a single prompt into finished, client-ready work using your firm's own templates, context, and standards.Send Felix an email like, take these comments and turn them for me.

Ramp 是唯一一个专为让你的财务团队更精简、更快、更强而打造的平台，平均每年帮企业省下 5% 的开支，让你能安心专注在增长上。用 Ramp 的客户，营收增速是美国企业平均水平的 3.2 倍。Visa、Vercel、Cursor、Stripe、Notion、ElevenLabs、Shopify，以及另外 70,000 家企业，现在都跑在 Ramp 上。我自己的公司也在用，你也该用。访问 ramp.com/invest 了解更多。OpenAI、Cursor、Anthropic、Perplexity 和 Vercel 有一个共同点：它们都用 WorkOS。想让产品被大企业规模化采用，你必须交付 SSO、SCIM、RBAC、审计日志这些核心能力。与其自己花几个月去搭这些关键基础设施，不如直接调 WorkOS 的 API，第一天就全都有了。这就是为什么你听说过的那些顶尖 AI 团队，早就跑在 WorkOS 上。WorkOS 是让你最快达到企业级就绪、同时专注在最重要的事——你的产品——上的方式。访问 WorkOS.com 开始使用。Rogo 出品的 Felix 是一个个人金融 agent，它能把一句 prompt 变成可以直接交给客户的成品，而且全部按你所在机构自己的模板、上下文和标准来做。你只要给 Felix 发封邮件，比如说「帮我把这些批注过进稿子里」。

[1:09] Patrick

Or update my tracker with the context of these emails.And Felix sends back finished PowerPoint decks, Excel models, and sourced research.Felix works the way your team already does, delivering work quickly and accurately around the clock.Learn more at rogo.ai.felix.Hello and welcome, everyone.

或者「按这几封邮件里的信息更新我的跟踪表」。然后 Felix 就会把做好的 PowerPoint 演示稿、Excel 模型和带出处的研究材料发回来。Felix 的工作方式跟你团队现在的习惯一致，全天候快速、准确地交付。想了解更多，访问 rogo.ai/felix。大家好，欢迎收听。

[1:29] Patrick

I'm Patrick O'Shaughnessy, and this is Invest Like the Best.This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money.If you enjoy these conversations and want to go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing.You can find Colossus along with all of our podcasts at Colossus.com.Patrick O'Shaughnessy is the CEO of Positive Sum.All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum.This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.Clients of Positive Sum may maintain positions in the securities discussed in this podcast.To learn more, visit psum.vc.So, Ben, if you can believe it, how long it's been since we last did this.

我是 Patrick O'Shaughnessy，这里是 Invest Like the Best。本节目是一场开放式的探索，聊市场、想法、故事和策略，帮你把时间和金钱都投得更好。如果你喜欢这些对话、想再深入一点，可以看看我们的季刊 Colossus，里面有对那些正在塑造商业与投资的人的深度特写。Colossus 和我们所有播客都能在 Colossus.com 上找到。Patrick O'Shaughnessy 是 Positive Sum 的 CEO。Patrick 与播客嘉宾表达的所有观点均仅代表其个人，不代表 Positive Sum 的立场。本播客仅供参考，不应作为投资决策的依据。Positive Sum 的客户可能持有本播客中讨论的证券。了解更多请访问 psum.vc。那么 Ben，你敢信吗，我们上一次这么聊已经过去这么久了。

[2:22]

The world was very different.No AI at the time.We talked about aggregation theory mostly.I thought a fun place to begin, since the world has changed so much, is to hear what you think it would mean for the U.S. to win the AI race.I think it would be very problematic for the U.S. to win.And let's say we take the most sort of fantastical scenario where if you control AI, you basically, your military is better than anyone else.Somehow it fixes their manufacturing, all these things that I don't think AI is necessarily going to do because they sort of deal with the real world.But in this world, what is the game theory optimal response of China?

那时候的世界完全不一样，还没有 AI，我们主要聊的是 aggregation theory。既然世界变化这么大，我觉得一个有意思的开场是：你认为「美国赢下这场 AI 竞赛」到底意味着什么？我觉得美国真赢下来是很麻烦的。我们不妨设想一个最天马行空的场景：如果你掌控了 AI，基本上你的军事实力就压过所有人，甚至不知怎么地还顺手把制造业也修好了——这些我其实不认为 AI 一定做得到，因为它们都得跟真实世界打交道。但在那样一个世界里，从博弈论看，中国最优的应对是什么？

[3:00]

To blow up TSMC.Game theory can get very sort of convoluted and complex.To me, this one actually isn't that complicated.There is a fundamental disconnect that I have with a lot of the rhetoric coming out of Silicon Valley, coming out, I think, of one of the labs in particular, where if we get to a place where we have a meaningful superiority in terms of a military national security perspective, I think that's very dangerous for the world.But in that state, how much does it extend beyond TSMC being blown up?

炸掉 TSMC。博弈论有时候能绕得非常复杂，但这一件在我看来其实并不复杂。我跟硅谷传出来的很多说法——我觉得尤其是某一家实验室的说法——有一个根本性的分歧：如果我们真走到在军事和国家安全层面拥有压倒性优势的那一步，我认为那对整个世界是非常危险的。可是在那种状态下，事情除了 TSMC 被炸掉，还会蔓延到多远？

[3:29]

Because in that state, I would assume we figured out how to build fabs here in the U.S., you know, to some degree and are less reliant on that one choke point.I think there's a little bit of magical thinking, which I just invoked in terms of manufacturing and whether it be fabs, whether that be actuators, all these precursors.I think the degree to which we are dependent on China is underappreciated and is not something that is going to be fixed outside of a conflict.Just because fixing so many of these things is going to be dramatically dumb.If your competitor is sourcing from China and you're going to start sourcing or getting things from the U.S., you're going to be at such a disadvantage, relatively speaking, that you're just not going to do it.So you do it when you have literally no choice.And that works for very big headline items.You can browbeat Apple to move some of their iPhone manufacturing to India, for example.But even that is a good example because Apple is not truly moving out of China.They're diversifying to an extent, but it would just cost so much.It's like paying an insurance policy that if you don't have to pay it and it's astronomically expensive, you're just not going to pay it.

因为到了那一步，我会假设我们已经在某种程度上搞定了在美国本土建 fab，对那一个卡脖子环节的依赖也没那么重了。我觉得这里有一点一厢情愿的想法——我刚才自己也犯了——就是制造业这一块，不管是 fab，还是执行器（actuator），还是各种上游零部件。我们对中国的依赖程度被严重低估了，而且这不是在没有冲突的情况下能解决掉的事。因为要把这么多环节都换掉，本身就是件极其不划算的事。如果你的竞争对手从中国采购，而你打算改从美国拿货，你的相对劣势会大到你根本不会去做。所以只有在你真的别无选择的时候才会做。这套逻辑对那些特别显眼的大项目是成立的——比如你可以逼着 Apple 把一部分 iPhone 制造挪到印度。但就连这个例子也很说明问题，因为 Apple 并没有真的搬出中国，他们只是在一定程度上做分散，因为全搬走代价实在太高。这就像交保险费：如果你可以不交、而保费又贵得离谱，那你就是不会交。

[4:43] Ben

But it's one of those sort of hypotheses that I just have a hard time even grokking because the only world I see where we truly pull out and have no dependency on China such that if they want to blow up Taiwan, who cares?

所以这类假设我连想都很难想通，因为我能设想出来的唯一一种「我们真的抽身出来、对中国零依赖，以至于他们要炸台湾也无所谓」的世界——

[4:58]

There's no impact on us.It seems pretty fantastical to me.And I think there's a bit of facing reality in this regard that is not present in these conversations.Put yourself in their shoes.What do you think the motivations are?

——对我们完全没有影响，这在我看来太不切实际了。我觉得在这件事上，这些讨论里缺了一点直面现实的态度。那你换到他们的位置上想想，你觉得他们的动机是什么？

[5:13]

Everyone can use a good bogeyman.I think from the AI trade perspective, nothing works better than we have to beat China.And I do think we need to beat China.We need to be competitive.I despair at the extent to which over the last few years in particular, so many of our responses, particularly from a political perspective, has been to try to be like China.I think we should be going the other direction, more openness, more innovation, less top down control, less restrictions on speech and things along those lines.America succeeds by being on the leading edge and by leading into that.You said probably the U.S. being purely dominant in AI is not the right end state for the world.What is your ideal equilibrium for how this goes worldwide?

每个人都用得上一个好使的假想敌。从 AI 这笔交易的叙事角度看，没有什么比「我们必须打赢中国」更好使。我也确实认为我们需要赢过中国，我们得有竞争力。但让我很沮丧的是，尤其是过去这几年，我们的很多应对——特别是政治层面的应对——都是在试图变得更像中国。我觉得我们应该往相反的方向走：更开放、更多创新、更少自上而下的控制、更少对言论之类的限制。美国的成功来自于站在最前沿，并且把力气都往这上面使。你刚说美国在 AI 上一家独大，可能不是这个世界该有的终局。那在你看来，全球范围内理想的均衡是什么样？

[6:00] Ben

There's a bit where AI right now is kind of like the Taiwan situation in that the current status quo actually doesn't seem so bad.The question is, how sustainable is it?But maybe it's sustainable for longer than we think.The way I think about it right now is I think OpenAI and Anthropic are clearly on the frontier.Who knows what's happening with Google?

有一点是这样：AI 现在的处境其实有点像台湾的处境——眼下这个现状看起来没那么糟。问题是它有多可持续？但也许它能撑的时间比我们想的更长。我现在的判断是：OpenAI 和 Anthropic 显然在 frontier 上。Google 那边到底怎么样，谁也说不准。

[6:22] Ben

And then Grok and Meta are chasing them.Meanwhile, the Chinese are very capable, very smart, and also definitely distilling these models to sort of stay about six to nine months behind.And it feels like a pretty good equilibrium that I think is generally favorable to the U.S.Now, the question is, how long can it stay this way?

然后是 Grok 和 Meta 在后面追。与此同时，中国那边非常有实力、非常聪明，而且确实在蒸馏（distill）这些模型，大概保持落后六到九个月的距离。感觉这是个相当不错的均衡，而且总体上对美国有利。现在的问题是，这种局面还能维持多久？

[6:44] Ben

And there's lots of questions on there, like, can the Chinese actually pull ahead?I'm still a little skeptical for various reasons, whether it be from chips.Getting to the leading edge in that last six to nine months is very difficult.It's going to be instructive how Meta and Grok do in terms of actually catching up, especially as we get to the world of AI improving itself, using AI to make the AI better, which I think is definitely a real thing.I think you see a real acceleration from both OpenAI and Anthropic recently, which was sort of theorized and it seems to be coming true.And to the extent that's true, can you actually catch up?

这里面有一堆问题，比如中国到底会不会真的反超？出于各种原因我还是有点怀疑，比如芯片这一块。要在最后那六到九个月里追到最前沿，是非常难的。Meta 和 Grok 到底能不能真追上来，会很有参考价值，尤其是当我们进入 AI 自我改进的阶段——用 AI 把 AI 做得更好，我认为这绝对是真实存在的事。我觉得最近在 OpenAI 和 Anthropic 两边都能看到明显的加速，这件事早先只是理论推演，现在看起来正在成真。如果这是真的，你还追得上吗？

[7:17] Ben

And I think the other question about this, by the way, is to what extent does that apply to cost to serve, to marginal costs?If you can apply AI to optimizing your stack, to figuring things out, to analyzing all the data, is your cost to serve structurally lower than anyone else?

顺便说，这里另一个问题是：这一点在多大程度上也适用于服务成本（cost to serve），适用于边际成本？如果你能用 AI 来优化你的技术栈、把问题想明白、把所有数据都分析一遍，那你的服务成本是不是就结构性地比别人低？

[7:33]

This is the thing about the open source models.The talk about them being free is bizarre to me because it's marginal costs.You still have to run inference like GLM or Kimmy.Kimmy is very expensive to serve.The cost per answer is significantly higher.Everyone referring to these as free, it feels like in the narrative, it's in people's head that free is free.Now I can use AI for free.No, you can't use AI for free.You're not paying necessarily the R&D to create the AI, but you're definitely paying the inference to sort of run it.So right now, I kind of like where we are and the pushback would be that's right now.It's not going to stay that way, which I think is fair pushback, but I don't know this way longer than we think.If you could know anything about the future of how this will go to be more confident in like where the equilibrium will end up, what is it?

这就要说到开源模型了。大家说它们「免费」，这在我看来很奇怪，因为这是边际成本的问题。你还是得跑 inference，比如 GLM 或者 Kimi。Kimi 的服务成本很高，每个回答的成本明显更高。所有人都管这些叫免费，感觉在这套叙事里，大家脑子里就认定了免费就是免费——「现在我可以免费用 AI 了」。不，你没法免费用 AI。你可能没付造出这个 AI 的研发费用，但你一定在付把它跑起来的 inference 费用。所以就当下而言，我还挺喜欢我们所处的位置。反驳的说法会是：那是「当下」，不会一直这样。我觉得这个反驳是合理的，但我说不准，这个局面可能比我们想的持续得更久。如果关于这件事将来会怎么走，你可以知道任何一件事，好让你对最终会落在哪个均衡上更有把握，那会是什么？

[8:20] Patrick

Is it like the length of the S curve, like how far up the S curve we are?At some point, these things presumably will level out, maybe not.What would be the thing you'd want to know that would give you a better sense of what the future might look like?

是那条 S 曲线的长度吗？还是我们现在爬到 S 曲线的哪个位置？到某个点上，这些东西大概会走平——当然也可能不会。有什么是你最想知道、能让你对未来长什么样看得更清楚的？

[8:32] Ben

I am concerned that with the scare around people freaking out about mythos and this hugging face incident, that the actual implication of that is not that we reduce these dangers, but we just stop releasing stuff.We on the outside start to lose any sense of like where exactly what is actually the frontier and where it is.There becomes sort of a false sense of security because right now everyone's basing their understanding of mythos on fable.But how good is fable actually relative to mythos?

我有点担心的是，随着大家开始对 Mythos、对 Hugging Face 那件事恐慌起来，实际的结果并不是这些风险真的变小了，而是大家干脆不再往外放东西了。我们这些身处外部的人，会慢慢失去对「前沿到底是什么、到底在哪儿」的判断力。这会形成一种虚假的安全感，因为现在所有人对 Mythos 的理解，其实都是基于 Fable 去推的。可 Fable 相对于 Mythos 到底有多强呢？

[9:09] Ben

That sort of gap is only going to, I think, increase over time.So I think that's a real question that I'm not sure about.This question of the recursiveness and AI sort of making itself better, does that lead to some sort of takeoff?

我觉得这个差距只会随着时间越拉越大。所以这确实是个我说不准的问题。另一个是关于递归性、AI 自己让自己变强这件事——它会不会导向某种「起飞」（takeoff）？

[9:24] Ben

And at the end of the day, there's timing questions in lots of different ways.I'm worried about the timing mismatch in terms of the actual return on investment producing enough revenue to fuel investment.We're working our way down the capital curve.We started with free cash flow.The speed with which the tech companies blew through the debt markets is kind of incredible.It took like a year and now Google is issuing equity.NVIDIA is putting together these $500 billion thing to tap into like pension funds and insurance floats and things like that.What's after that?

说到底，时间点的问题会以很多方式冒出来。我担心的是时间上的错配——真正的投资回报能不能产生足够的收入，来给下一轮投资续命。我们正沿着资本曲线一路往下走：一开始用的是自由现金流；科技公司把债券市场吃透的速度快得离谱，大概一年就走完了，现在 Google 都开始发股票了；NVIDIA 又在攒一个 5000 亿美元的盘子，去对接养老金、保险浮存金这类钱。那再往后呢？

[9:56] Ben

Where does the money come after that?Well, ideally, we actually flip back to free cash flow funding this.But if there's a gap there, if we don't get there soon enough, then we could have a big blow up.But at the same time, even if we have this blow up, the AI is not going away.It's not going to stop improving.It's going to keep sort of progressing in a way that we look back on the dot-com era or we look back on the railroad era or we look back on whatever bubbles through history, ultimately immaterial in terms of the broad scope of humanity, even if they were very devastating.What can the railroads teach us to think?

再往后钱从哪儿来？理想情况是我们绕回来，重新用自由现金流来支撑这件事。但如果中间有缺口，如果我们没能及时走到那一步，就可能出大事。不过话说回来，就算真的崩了，AI 也不会消失，它不会停止变强，还是会一路往前走。到时候我们回头看，就像今天回头看互联网泡沫、看铁路时代，或者看历史上任何一次泡沫一样——放在人类的大尺度上，这些终归无足轻重，尽管当时确实极具破坏性。铁路这段历史能教我们怎么思考？

[10:33] Ben

It's not the last bigger build out, right, in terms of percentage GDP or getting there?I think we might be bigger at this point or it's like it was the biggest.In the ballpark.The railroads had a real duration mismatch.To build a railroad and make money off it was a decade or multiple decades long endeavor, whereas you had to issue money to pay for it in the short term.And the world ran out of money, right?

按占 GDP 的比例算，这还不是史上最大的一次基建投入吧？还是说我们正在逼近？我觉得现在可能已经超过了，或者说铁路曾经是最大的，反正在一个量级上。铁路当年有一个非常真实的期限错配：修一条铁路、再靠它赚钱，是一个十年甚至几十年的工程，可你必须在短期内发债把钱先掏出来。结果全世界的钱不够用了，对吧？

[10:59] Ben

And I think that is probably the aspect.I think that's why people reach for the railroads because everyone talks about, are we going to have enough compute?Are we going to have enough electricity?Maybe the nearest term questions, are we going to have enough money?

我觉得这大概就是关键所在。我想这也是大家为什么老是拿铁路来类比——所有人都在讨论，算力够不够？电够不够？但眼下最近的问题可能是：钱够不够？

[11:13] Ben

Which is kind of a bizarre thing to think about.That's what happened in the 1870s.The world just ran out of money.The funny thing is the railroads kept operating and they expanded the West.Their contributions to GDP was astronomical.They're still contributing to GDP.Railroad money is what's going into Google right now for Berkshire Hathaway.It's very funny.It's quite literal.Berkshire Hathaway has this problem.To me, this NVIDIA deal is very much paired with the Google equity issuance, which I thought was shocking when it happened.Why is it shocking?

这事想起来还挺荒诞的。1870 年代就是这么回事，全世界的钱就是不够了。有意思的是，铁路照样在运营，它们把西部开发了出来，对 GDP 的贡献是天文数字级的，而且到今天还在贡献 GDP。此时此刻通过 Berkshire Hathaway 流进 Google 的那笔钱，本质上就是铁路赚来的钱。特别有意思，字面意义上就是如此。Berkshire Hathaway 面临的正是这个问题。在我看来，NVIDIA 这笔交易和 Google 发股票这件事是配套的一对——Google 发股票那会儿我觉得挺震惊的。为什么震惊？

[11:48] Ben

Because it's Google.They can't raise money.Like, why are they issuing equity?Why are they reducing their upside if they believe so strongly in this?But the Berkshire comparison is interesting because, to a rough approximation, they have seized candies famously, right?

因为那可是 Google 啊。它根本不缺钱，为什么要发股票？如果它这么坚信这件事，为什么要把自己的上行空间稀释掉？不过 Berkshire 这个类比很有意思，因为粗略地说，他们手里握着众所周知的 See's Candies，对吧？

[12:02] Ben

Tremendously high margin business.The problem with a lot of high margin businesses is the percentage profit you can make is very high, but the absolute profit you can make is cash.It's no reinvestment runway.That's right.You're just accumulating cash.The brilliance of the BNSF railway thing was basically they took the seized candy profits and said, here's another industry whose margins are way worse.But the absolute dollar amounts are so large that those way worse margins result in absolute profits that are much larger.BNSF in 2025 or something, the amount of free cash they threw off in one year was more than seized candies had thrown off its entire lifetime.Even though you're talking about a low margin business compared to a very high margin business.I think there's an aspect from Berkshire Hathaway where once your capital gets so large, you start operating in a world of like absolute numbers as opposed to percentage numbers.And the reason why I thought that was so interesting, that story is it seems to capture where Google itself might be going.And so there is very symbolic for them to invest in Google.Google has this unbelievable high margin business of search, one of the most perfect, beautiful business models of all time.

一门利润率高得惊人的生意。很多高 margin 生意的问题在于，利润率的百分比可以非常高，但能赚到的绝对利润就那么多，没有再投入的跑道。没错，你只能不断囤现金。BNSF 铁路那笔交易的高明之处在于，他们基本上是拿 See's Candies 赚来的利润说：这儿有另一个行业，margin 差得多，但绝对金额大到这种差得多的 margin 反而能带来大得多的绝对利润。BNSF 在 2025 年前后，一年甩出来的自由现金就超过了 See's Candies 整个存续期间赚到的总和——尽管一个是低 margin 生意，另一个是超高 margin 生意。我觉得 Berkshire Hathaway 身上有一点是：当你的资本大到一定程度，你就开始活在绝对数字的世界里，而不是百分比的世界里。这个故事之所以让我觉得特别有意思，是因为它似乎正好描述了 Google 自己可能要走的路。所以他们去投 Google，象征意味非常强。Google 有搜索这门高得离谱的高 margin 生意，是史上最完美、最漂亮的商业模式之一。

[13:13] Ben

And the purest aggregator of them all like scales in every direction, doesn't have to invest any money to do it.Everything's zero marginal cost.It's amazing.Meanwhile, there's this AI opportunity, which requires just astronomical, it's just incinerating cash.But you can imagine if AI is intelligence and it's TAM is basically all white collar work and eventually with robotics, everything potentially, the absolute profits available here, even if the margins are lower, is so much larger that will we look back in Google search with seized candies?

而且它是所有 aggregator 里最纯粹的那一个——在每个方向上都能规模化，几乎不用投一分钱，一切都是零边际成本，太漂亮了。与此同时，眼前摆着 AI 这个机会，它需要的投入是天文数字，纯粹在烧钱。但你可以设想一下：如果 AI 就是智能，它的 TAM 基本上等于所有白领工作，将来加上机器人甚至可能囊括一切，那么这里能拿到的绝对利润，哪怕 margin 更低，也要大得多——大到将来我们回头看 Google 搜索，会不会就像今天回头看 See's Candies 一样？

[13:49] Ben

It feels like that's what's happening in that world.You use all your free cash flow.They've done that.You tap the debt markets to the tune of hundreds of billions of dollars.They've done that.You issue equity.What does an equity issues do?

感觉那个世界里正在发生的就是这件事。先把自由现金流用光——他们做了；再去债券市场借上几千亿美元——他们也做了；然后就是发股票。发股票会带来什么？

[14:04] Ben

It dilutes your interest and your interest, your shareholders.So you have a smaller percentage of the pie.Well, you have a smaller percentage of an astronomically larger pie.At the end of the day, no one's going to be complaining.It is very symbolic.Berkshire being the symbol of that equity issuance in that are they actually not just an investor in Google, but a model for Google and where they're going?

它稀释你的权益、稀释股东的权益，你手里那块饼的比例变小了。可你拿到的是一块大到天文数字的饼里更小的比例。说到底，没人会抱怨。这非常有象征意义：Berkshire 成了这次股票发行的象征——他们是不是不只是 Google 的投资人，同时还是 Google 的样板，代表着 Google 要去的方向？

[14:26] Patrick

I'm curious, setting aside the commercial and competitive components of this, like you're describing, how AI pilled on the pure technology would you say you are relative to other people thinking about this space?

我很好奇，先把商业和竞争的层面放一边，就像你刚才描述的那样，单看纯技术，跟这个领域里其他思考这些问题的人相比，你会说自己「信 AI」信到什么程度？

[14:39] Ben

I have a view that is both super bullish and less bullish in some respects.So I am not fully convinced about the generalizable argument.AI is clearly incredible at coding.It kind of blows my mind that people were doing this a year ago, like actually writing out code.It's very good at math, obviously.But the obvious repose is that these are sort of verifiable domains.What is the evidence or where is the compelling evidence of being very good at verifiable domains queenly translates to being very good at sort of unverifiable domains or domains that have very long sort of verification loops?

我的观点在某些方面极度看多，在另一些方面又没那么看多。我并不完全接受「可泛化」那套论证。AI 在写代码上显然强得离谱，一想到一年前大家还在手敲代码，我都觉得不可思议。数学上它当然也很强。但一个显而易见的反驳是：这些都属于可验证的领域。有什么证据、或者说有力的证据在哪里，能说明在可验证领域表现极好，可以干净利落地迁移到不可验证的领域，或者那些验证周期极长的领域？

[15:21] Ben

I think that's still a little bit to be determined.And it's interesting because I raised this question and there are some people at the labs that were on a panel.And I was kind of annoyed at the answer because the answer took me for an AI bear.Oh, well, people thought we couldn't solve chess or we couldn't solve Go and we solved those easy enough.And I'm like, I thought we could solve chess.I thought we could solve Go because they're knowable domains.Scale was the answer to both of those.But also both of those were bounded.What is the go-to example that's not chess, that's not Go, that is genuinely in a new space that's sort of an unknowable space where it's doing things that were not possible?

我觉得这一点目前还没有定论。有意思的是，我在一场论坛上提出这个问题，台上有几位来自实验室的人，他们的回答让我有点恼火，因为他们把我当成了 AI 空头。他们说：以前大家还觉得象棋解决不了、围棋解决不了呢，结果不都轻轻松松解决了。我心想，我一直认为象棋能解决，围棋也能解决，因为那是可知的领域，规模化就是这两者的答案，而且它们都是有边界的。那么有没有一个标杆式的例子，不是象棋、不是围棋，而是真真正正处在一个全新的、某种意义上不可知的空间里，AI 在那里做出了以前根本做不到的事？

[16:00] Ben

That is sort of the I'm not fully convinced sense.However, AI trained at a rough approximation, trained on all the data of the Internet.All the data of the Internet, that's distillation.It distilled all of the end state of human thought.What the actual typing on Reddit, it doesn't have the traces.It doesn't actually have the thought, the emotion or whatever that went into typing that comment or typing, writing that essay.Say Neuralink, whatever.What if the actual payoff from Neuralink is actually capturing the traces of human thought that actually dramatically expands the capabilities of these models?

这就是我「没被完全说服」的那一面。不过，粗略地说，AI 是在整个互联网的数据上训练出来的。整个互联网的数据，其实是一种蒸馏，它蒸馏的是人类思考的最终形态。它拿到的是有人在 Reddit 上敲出来的那些字，但它没有轨迹——它并没有拿到促成那条评论、那篇文章背后的思考过程、情绪之类的东西。比方说 Neuralink 这类东西，如果它真正的回报其实是捕捉到人类思考的轨迹，从而大幅扩展这些模型的能力呢？

[16:40] Ben

In this world, my concerns about verifiability is like, well, we solve verifiability by getting more data.My sense is that a huge number of jobs, a huge amount of economic activity does not exist in these domains that I'm not convinced that AI is good at.Actually, there's a lot of people in the world who are kind of like sentient AIs to a certain extent.They operate very well in verifiable domains.They're given jobs.They do them.And it's almost like a somewhat pessimistic view of humanity to a certain extent.But I think that market is so huge and so large that if the models did not improve at all from where they are right now, the economic opportunity is actually massive.I wrote an article a while ago.There's the whole like accelerationist movement.What I call myself was a reluctant accelerationist.I think we need to push forward because we can't go back.And the worst thing we can do is get stuck where we are.So I'm very AI peeled in terms of its impact on the economy.It's sort of upside in terms of monetization.I'm not sure about the timing.What would be like the gradient towards it?

在那种情形下，我对可验证性的顾虑就变成：好吧，我们靠拿到更多数据解决了可验证性。而我的感觉是，大量的工作、大量的经济活动，其实并不落在那些我不确定 AI 是否擅长的领域里。说实话，这世界上有不少人在某种程度上就像有意识的 AI：他们在可验证的领域里运转良好，交给他们活儿，他们就干完。这么说对人类多少有点悲观。但我觉得那个市场太大太大了，哪怕模型从今天起一点都不再进步，摆在面前的经济机会也是巨大的。我以前写过一篇文章，讲加速主义那一整套运动；我把自己称作「不情愿的加速主义者」。我认为我们必须往前推，因为我们回不去了，而最糟糕的结果就是卡在原地。所以就 AI 对经济的影响、以及变现的上行空间而言，我是非常「信 AI」的；我不确定的是时间节奏。那通往那一步的路径大概会是什么样？

[17:48] Ben

Imagine law or medicine where I don't know whether or not you would consider those verifiable.Like law is like a code of some sort.Medicine, we have a certain state understanding of things.I mean, I think medicine is by far one of the biggest opportunities.It's both of the biggest opportunities and also one of the most challenging ones because of all the regulations and all the access.Like if you could turn an AI, turn machine learning onto all the medical records, I think the number of discoveries and improved treatments we could come up with in a very rapid amount of time would be unbelievable.So that is a very optimistic view.On the flip side, when is that going to happen, right?

想想法律或者医疗，我不知道你会不会把它们算作可验证的领域。法律某种意义上像是一套代码；医疗方面，我们对事物有一套既定的认知。我觉得医疗毫无疑问是最大的机会之一，既是最大的机会，也是最难啃的机会之一，因为牵扯到各种监管、各种数据获取的限制。如果你能把 AI、把机器学习放到全部病历数据上去跑，我觉得我们能在极短时间里得出的新发现和更好的疗法，会多到难以置信。这是非常乐观的一面。反过来说，这件事到底什么时候才会发生呢？

[18:26]

I think the optimistic frame I put on humans is our capacity to create needs is sort of unlimited.So I think we'll do a very good job of creating new opportunities and job serving the fullness of time.The sort of more pessimistic way to put it is our ability to create red tape and muck is also fairly unlimited.How much of our economy is actually we've managed to create more and more jobs that is just make busy and make slow to a certain extent.Vanta automates security and compliance for over 16,000 fast moving companies like Ramp, Cursor and Harvey, keeping them audit ready around the clock.It's the number one agentic trust platform.And it now helps companies like yours watch for the risks that show up between audits across your vendors, your AI tools and your whole environment.Every new tool your team signs up for every vendor that turns on AI features is an opportunity for something to go wrong.And most security programs weren't built for AI's pace of growth.The Vanta agent works like a 24-7 GRC engineer in the background, finding issues, drafting fixes for you and cutting vendor assessment time by up to 50%.Whether you're a fast growing startup or a global enterprise, Vanta helps you earn and prove trust.

我对人类比较乐观的那个框架是：我们创造需求的能力基本上是无限的。所以我觉得假以时日，我们会非常擅长创造出新的机会和新的岗位。而比较悲观的说法是：我们制造繁文缛节和一地泥浆的能力同样近乎无限。我们的经济里有多少其实是我们硬造出来的岗位，某种程度上只是在制造忙碌、制造拖沓？

Vanta 为超过 16000 家快速成长的公司自动化安全与合规工作，客户包括 Ramp、Cursor 和 Harvey，让它们全天候处于随时可审计的状态。它是排名第一的 agentic 信任平台。现在它还能帮助像你这样的公司，盯住两次审计之间冒出来的风险——覆盖你的供应商、你的 AI 工具，以及你的整个环境。你的团队每新开通一个工具、每个供应商每打开一项 AI 功能，都是一次可能出岔子的机会，而大多数安全体系当初并不是为 AI 这种增长速度设计的。Vanta 的 agent 就像一个 7×24 小时在后台运转的 GRC 工程师，帮你发现问题、起草修复方案，把供应商评估的时间最多缩短 50%。无论你是高速成长的初创公司还是全球性企业，Vanta 都能帮你赢得信任、并证明这份信任。

[19:38] Patrick

Invest like the best listeners get a special offer for $1,000 off at vanta.com slash invest.Ridgeline is the first end-to-end system of record with embedded AI for investment management firms, running portfolio accounting, reconciliation, reporting, trading and compliance on one unified platform.Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software.Which is why I believe that firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform.If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation.You can request a demo at ridgeline.ai.If I go back to the early 2010s, maybe the aggregation theory was stewing in your brain and then you published it in 2015.

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[20:30] Patrick

I think it's fair to say like that theory, that idea, maybe you can just quickly remind people what it is, defined the winners and losers of that era of technology.I'm really curious how you're thinking about what theory or principles will define this era of winners from like a financial perspective and market cap perspective.I go back and forth even just on the question of aggregation theory itself.How much does that apply in the current era?

我觉得可以这么说：那个理论、那个想法——也许你可以快速给大家再讲一遍它到底是什么——定义了那个技术时代的赢家和输家。我特别好奇，从财务和市值的角度看，你觉得会是什么理论、什么原则来定义这个时代的赢家。我自己甚至连 Aggregation Theory 本身都还在反复摇摆：它在当下这个时代还适用多少？

[20:55] Ben

Yeah, because like a pushback that people have is one of the key components of aggregation theory is zero marginal costs.And zero marginal costs shows in lots of ways.The one that I focused on the beginning was distribution.And people say, oh, I don't have distribution.I have to pay Google for ads.Like, oh, no, you have a website.Your problem isn't that you have distribution.Your problem is you don't have demand.And you're paying for demand when you're paying for ads and things on those because the aggregators control demand.And they control demand because in a world of abundance, the hard problem is not distribution.It's discovery.How do you actually find what you're interested in?

是啊，因为大家常见的一个反驳是：Aggregation Theory 的关键组成之一是零边际成本。而零边际成本会体现在很多地方，我一开始重点讲的是分发（distribution）。有人会说，我哪有什么分发能力，我还得花钱在 Google 上买广告。可是不对啊，你有网站。你的问题不是分发，你的问题是你没有需求。你买广告、买那些东西的时候，买的其实是需求，因为需求掌握在聚合者（aggregators）手里。而他们之所以能掌握需求，是因为在一个供给过剩的世界里，难题不是分发，而是发现（discovery）——你到底怎么找到自己真正感兴趣的东西？

[21:27] Ben

So the companies that solve discovery in their domain come to dominate that market.They get a virtuous feedback loop.That's sort of aggregation theory in a nutshell.And the other thing is transaction costs.There's no transaction costs.Google can scale to the whole world.And they can scale to the whole world, not just on the user side, but also on the monetization side.And the vast, vast, vast majority of advertisers on Google or Meta never talk to someone at Google or Meta.They just go up and they buy ads.It's all done by computers.The perfect business.And those computers, from a business perspective, cost zero dollars.AI, obviously, that changes significantly.Inference costs are real.But then again, how real are they?

所以，在各自领域里解决了「发现」问题的公司，最后就主导了那个市场，它们拿到一个正向反馈循环。这大概就是 Aggregation Theory 的浓缩版。另一件事是交易成本——交易成本为零。Google 可以扩张到全世界，而且不只是用户侧铺到全世界，变现侧也一样。在 Google 或 Meta 上投广告的绝大多数广告主，从来没跟 Google 或 Meta 的任何人说过一句话，他们就是上去买广告，全部由计算机完成。完美的生意。而从生意的角度看，这些计算机的成本是 0 美元。AI 显然把这一点大大改变了，inference 成本是实打实的。但话说回来，它到底有多「实」？

[22:03]

They're real right now.I don't know, are they?Depends on the company.But they're way more real than those prior examples.Well, we...Like if you look at gross margins.For sure.But you have this incredible spread.So you have people, I think the vast majority of people who are using ad today are using it as basically a Google substitute.Or like a recipe maker or whatever it might be.And my suspicion is that the cost to serve those people is extremely low.And low in the basically similar to serving them a web page.I would imagine it's marginally higher, but not that much higher.Then you have on the other extreme people who are actually leveraging test time scaling.It used to be we just scaled by making the models bigger and bigger.Now you can scale as far as time.How long do you think about the answer?

现在确实很实。我不好说，真有那么实吗？看是哪家公司。但比起前面那些例子，它们要实在得多。呃，我们……你看毛利率就知道了。那当然。但这里面的差距大得惊人。你看，我觉得今天绝大多数用 AI 的人，基本上是把它当 Google 的替代品在用，或者当个菜谱生成器之类的东西。我怀疑服务这批人的成本极低，低到基本上跟给他们发一个网页差不多——我猜是略高一点，但高不了多少。然后另一个极端，是那些真的在用 test time scaling 的人。过去我们扩规模的方式就是把模型越做越大，现在你可以在时间这个维度上扩：你要花多久去想这个答案？

[22:48] Ben

Well, you could think about the answer for days or weeks or months.That is directly marginal cost.Every second longer you're thinking is costing more money.Which speaks to like, we think about AI and inference as this one question.That's why I was pushing back on you.But actually the marginal cost question for the different user, the user using free chat GPT and the user trying to solve a math theorem.They're not even remotely in the same universe.I think you see this challenge actually in the enterprise in a very interesting way.Microsoft recently, they are shifting their enterprise plan.So they come out with like an E7 plan, $100 per user per month.That includes some amount of usage.But then they also are charging for usage on top of that.I think this is kind of a fraught position for Microsoft to an extent.Because the positive way to think about Microsoft is they do everything you need as a business.Every individual component might not be the best, but you get it all for one price and they all mostly work together.And if you're particularly a small or medium sized business or even a large enterprise, there's real value in that.That's right.It makes life easy.The moment you start having to think about how much you're paying, it's not just that that's a new decision, number one, that is untethered from headcount.

你可以想几天、几周甚至几个月。那就是直接的边际成本——你多想一秒，就多烧一秒的钱。这也说明，我们总是把 AI 和 inference 当成同一个问题来看，这就是我刚才要跟你抬杠的原因。可实际上，不同用户的边际成本问题——用免费版 ChatGPT 的人，和拿它去解一道数学定理的人——根本不在一个宇宙里。我觉得这个难题在企业市场里以一种非常有意思的方式显现出来了。Microsoft 最近在调整他们的企业套餐，推出了一个类似 E7 的方案，每用户每月 100 美元，里面包含一定量的用量，但超出的用量还要另外收费。我觉得这对 Microsoft 来说，某种程度上是个挺尴尬的处境。因为看待 Microsoft 的正面角度是：你做生意需要的一切他们都有。单看每个组件也许都不是最好的，但你花一份钱就全拿到了，而且它们基本上能互相打通。尤其如果你是中小企业、甚至大型企业，这里面是有实实在在的价值的。没错，省心。可一旦你开始不得不琢磨自己到底在花多少钱，这就不只是多了一个新决策——第一，这个决策跟人头数脱钩了。

[24:11] Ben

Microsoft got the benefit is when you were hiring a new employee, you would think about the cost of that employee and baked in the cost of that employee is $100 a month or $50 a month for their license.It was kind of a thoughtless revenue stream from Microsoft.Now, if you think about usage, you have to think every single month, how much do I want to spend?

Microsoft 原来吃到的红利是：你招一个新员工的时候，会去算这个员工的成本，而员工成本里天然就打包了每月 100 美元或者 50 美元的 license 费用。对 Microsoft 来说，这是一条完全不用客户动脑子的收入流。现在换成按用量计费，你每个月都得想一遍：我这个月想花多少钱？

[24:32] Ben

That introduces two problems.Number one, most companies aren't set up to do this.They make budgets like once a year.This idea we're going to be thinking about through our budgetary allotment on like a monthly basis doesn't compute.There's an aspect where they're used to thinking about CapEx decisions or one-time costs.And there's a bit where I'm talking about this employee, like the loaded cost of employee.It's not CapEx, but it's kind of like CapEx.It's like you make the decision up front and you don't think about it anymore.The decision is sort of already made.But if you're thinking about usage, you have to do it again.The final thing is if you're every month looking at your Microsoft bill and how much did I use, you start thinking about what am I paying for?

这带来两个问题。第一，大多数公司的机制根本不支持这么干，他们一年才做一次预算。「我们要按月来盘算预算额度」这种事，在他们那儿是说不通的。有一点是，他们习惯思考的是 CapEx 决策，或者一次性成本。而我说的员工这件事，也就是员工的综合成本（loaded cost），它不是 CapEx，但有点像 CapEx——你在一开始把决定做完，之后就不再去想它了，决定算是已经落定了。可如果是按用量，你就得一遍遍重新做这个决定。最后一点是，如果你每个月都在看 Microsoft 的账单、看自己到底用了多少，你就会开始想：我这些钱到底买了什么？

[25:11] Ben

How good is each of these products?Should I actually just start thinking about and spraying this out?And I think they had to do it because that extreme of user who uses a ton of tokens and is actually leveraging AI costs way more to Microsoft than $100 a month.They can't support them.But they want to hold on to the set cost for the vast majority of employees who can fit in that because they need to ask their customers to think a little bit for those extreme employees.But they don't want them to think too much because that breaks the model in very surprising ways.Are you surprised at all that the recipe builder user that is very low cost to serve, that there hasn't been a great business model that's emerged around them just yet?

这里面每一个产品到底好不好？我是不是该重新掂量一下，把这块拆出去？我觉得他们不得不这么做，因为那种极端用户——消耗海量 token、真正在用 AI 的人——给 Microsoft 带来的成本远远超过每月 100 美元，他们扛不住。但对于绝大多数能被这个价位覆盖的员工，他们又想守住那个固定价格。因为他们需要客户为那批极端员工稍微动一点脑子，但又不希望客户想得太多，因为那会以非常出人意料的方式把整个模式打破。那我问你，「菜谱生成器」这类用户，服务成本极低，可到现在也没冒出一个很好的商业模式来吃下他们，你对此感到意外吗？

[25:53] Ben

Google and Facebook are sort of business perfected in this prior era.They haven't seemed to figure this out at all.I am frustrated but not surprised.This is obviously a market that should be supported by advertising.That is why advertising is always the consumer business model.Well, consumers don't want to pay.There's two things to understand about consumers that Silicon Valley has to relearn about every 10 years.Number one, consumers do not want to pay for software.And number two, consumers do not care about being productive.We went through this in early SaaS.The canonical company for this, in my mind, is Dropbox.So Dropbox, unbelievable product.Like especially when it first came out.In business school, I was one of the first people who used Dropbox.And that went off like crazy.I have so much storage still, like my free Dropbox, because I gave out my code to like so many people.So Drew Houston makes this amazing product, so easy to use, just absolutely seamless.He was very clear about this.He wanted to build a consumer company.And there's that famous story of him meeting with Steve Jobs.Apple was interested in acquiring Dropbox.And they're like, oh, we want to build a company.And Steve's, you know, you're a feature, not a company.

Google 和 Facebook 在上一个时代把这门生意做到了极致，可这一次他们好像完全没搞明白。我很郁闷，但不意外。这显然是一个应该靠广告养起来的市场，这也是为什么广告永远是消费级产品的商业模式——因为消费者不想付钱。关于消费者有两件事，硅谷差不多每 10 年要重新学一遍：第一，消费者不想为软件付钱；第二，消费者根本不在乎自己有没有变得更高效。早期 SaaS 我们就经历过一遍。在我心里，这方面最经典的公司是 Dropbox。Dropbox 这产品好得不可思议，尤其是刚出来那会儿。我读商学院的时候是最早用 Dropbox 的一批人，然后它就火得一塌糊涂。我到现在免费 Dropbox 账号里还有超多空间，因为我当年把邀请码发给了特别多人。Drew Houston 做出了这么棒的一个产品，好用到极点，完全无感。他自己说得非常明确：他想做一家面向消费者的公司。还有那个著名的故事，他去见 Steve Jobs，Apple 当时有意收购 Dropbox。他们说，我们想做一家公司。Steve 说，你们是一个功能，不是一家公司。

[27:00] Ben

Which, that plain Jane, just file sync.Apple did make a feature, as far as like sort of iCloud Drive.And with Dropbox, they grew very fast.And then they had like a two-year lull.And in that two-year lull, what they had to do was basically completely rebuild the app from the bottoms up.Because not enough consumers are going to pay for it.Enterprises could see the value they would pay.But if you want an enterprise, you need permissions.You need control.You need someone else to be able to set all these sorts of things.And their app wasn't even created to do that at all.So they had to rebuild the whole thing and realize the only way we're going to make money is by selling to companies.Why do companies pay?

就那个朴素得不能再朴素的东西——纯文件同步。Apple 后来确实把它做成了一个功能，也就是 iCloud Drive 之类的东西。Dropbox 那边增长非常快，然后有大概两年的停滞期。在那两年里，他们基本上不得不把整个 app 从底层彻底重写一遍。因为愿意付钱的消费者不够多，而企业能看到价值、愿意付钱。可你要做企业客户，就需要权限体系，需要管控，需要有别人能去设置这一整套东西。而他们的 app 压根就不是为这些设计的。所以他们只能把整个产品重做，并且认清一件事：我们唯一能赚到钱的路，就是卖给公司。那公司为什么愿意付钱？

[27:38] Ben

Because companies are paying employees.To the extent they can make their employees more productive, they're getting a greater return on their investment.It's the complete inverse of a consumer.A consumer is like, I spent all day working.Why do I want to come home and be more productive?

因为公司本来就在给员工付工资。只要能让员工更高效，公司这笔投入的回报就更高。这跟消费者完全相反。消费者的心态是：我上了一整天班，凭什么回到家还要更高效？

[27:53] Ben

I want to sit on the couch and watch Reels.But you see that with AI.And you also have this overarching skepticism of advertising.I've gotten so much traction on Chatechery by being an advertising appreciator.And I go back and read my early articles about advertising that were kind of directionally correct, but also like were not very good at all.But I got so much traction doing it because I was the only person writing about advertising.In a world of everyone who wanted to have a blog and Twitter, no one wanted to talk about advertising.But even now, there's in Silicon Valley this sort of embarrassment about the fact that the Valley is in many respects monetized by advertising.And particularly during the last sort of Facebook's business, there was a Facebook's icky.The best engineers don't want to go work on this problem.And so you literally had OpenAI replaying the Dropbox story, but at like 100 exercise, being like, no, we're going to sell subscriptions to consumers.They did.They sold a lot, but they didn't sell enough.If you're going to be in the consumer market, you have to be doing advertising.They're doing advertising now.It's a little weird.They finally pivoted to doing advertising at the same time.

我就想瘫在沙发上刷 Reels。AI 这边你也能看到同样的情况。而且还叠加了一层对广告根深蒂固的怀疑。我在 Stratechery 上能积累起这么多读者，很大程度上是因为我是个「欣赏广告的人」。我回头去读自己早期写广告的那些文章，方向大体是对的，但写得其实相当一般。可我靠这个吸引到了大量关注，因为当时只有我一个人在写广告。在那个人人都想开博客、都在玩 Twitter 的世界里，没人愿意聊广告。可即便到今天，硅谷对「这地方在很多方面是靠广告变现的」这件事，仍然带着一种难为情。尤其是上一轮围绕 Facebook 那门生意的时候，大家觉得 Facebook 有点上不了台面，最好的工程师不愿意去做这个方向的问题。于是你就看到 OpenAI 简直是在重演 Dropbox 那个故事，只不过放大了大概 100 倍：不，我们就是要向消费者卖订阅。他们也确实卖了，卖了很多，但还是不够。你要做消费市场，就必须做广告。他们现在开始做广告了。有点奇怪的是，他们终于转向广告的同时——

[29:04] Ben

They're like, oh, crap, we need to go for the enterprise because Anthropic is kicking our way in.So I'm not quite sure what they're doing there.They have been rolling out ad features very rapidly.Things like copy and the connections with retailers.So, you know, if a purchase went through, so you can do all the tracking and things like that.I'm very interested to see how that goes.There is a bit where had they leaned into advertising immediately as soon as ChatGPT was a hit, I think they would have a killer ad product right now.I think that Google would be in much bigger trouble.I think Meta would be in much bigger trouble because if you have this flywheel, the thing about advertising with consumers is your ability to monetize the consumer.Goes up as the volume.Yeah, because the advertiser is bearing the price increase.So there's zero elasticity issues.If you're charging consumers a price, if you want to raise the price like Netflix, this is their problem with the subscription plan.How much can they raise prices before consumers rebel and drop a tear or give up the service entirely?

他们又在想「糟了，我们得去攻企业市场，因为 Anthropic 正在把我们打得很惨」。所以我不太确定他们到底在做什么。他们推广告相关功能推得非常快，比如下单结账那类能力，还有跟零售商的打通——这样一笔购买成交之后，你就能做全套的追踪之类的事情。我很想看看这条路会走成什么样。有一点是：如果 ChatGPT 一火他们就立刻全力押广告，我觉得他们现在会有一个杀手级的广告产品。我觉得 Google 会麻烦大得多，Meta 也会麻烦大得多。因为一旦这个飞轮转起来——面向消费者做广告的妙处在于，你对单个消费者的变现能力会随着量一起往上走。对，因为涨价是由广告主承担的，所以完全不存在价格弹性的问题。可如果你是直接向消费者收钱，想涨价的话——就像 Netflix，这正是他们订阅套餐的难题：价格能涨到多少，消费者才会造反、降一档套餐、甚至干脆不用了？

[30:02]

Charging people money is hard.Giving people things for free is easy.And it's very frustrating that OpenAI did not pursue this sooner.I know you've been spending time with some of the big money firms and sources of capital.What is your sense of their appetite right now and how they're thinking about the future?

向人收钱很难，白送东西很容易。OpenAI 没有更早去做广告这件事，实在让人郁闷。我知道你最近一直在跟一些大资金机构和资本方打交道。你感觉他们现在的胃口怎么样，他们是怎么看未来的？

[30:22] Ben

Because I think this year it's going to be $800 billion or something that we're going to spend in CapEx.Next year is supposed to be $1.3 trillion, I think, is the current estimate.It's going to keep going up from there.We're burning through all the compute that gets installed basically immediately.It's such a strange circumstance that we can use the capacity right away as soon as it's online.Well, that's the thing, though.So there's a few timing mismatches that are happening right now.We can't use it right away.All the bulls on Twitter is always like, we don't have enough compute.We don't have enough compute.Well, we don't have enough compute because there was insufficient investment made in 2023 and 2024, which, yes, absolutely.And by the way, if you think there's not enough compute, TSMC decreased their rate of growth in 2023 and 2024 and 2025.Our shortage of compute is going to get worse in the next few years because a fab, the lead time is even greater than a data center.Today, when we say there's not enough compute, it's not like all the money that the companies are putting in today manifests in compute.No, it all manifests in compute in 2028 and 2029.On the calls, you have both Andy Jassy and Cyanadella are out there saying, look, we're just building data centers.

因为我估计今年我们在 CapEx 上要花掉 8,000 亿美元左右，明年按现在的估算大概是 1.3 万亿美元，之后还会继续往上走。装上去的算力我们基本上是立刻就烧光。产能一上线马上就能被用满，这情况太奇怪了。但问题恰恰在这儿。现在有好几处时间错配：我们并不是马上就能用上。Twitter 上的多头永远在喊「算力不够、算力不够」。我们算力不够，是因为 2023 年和 2024 年投得不够——这一点，是的，完全没错。顺便说一句，如果你觉得现在算力不够，TSMC 在 2023、2024、2025 年是把增速降下来的。未来几年我们的算力短缺只会更严重，因为建一座 fab 的交付周期比建一个数据中心还长。今天我们说算力不够，并不是说这些公司今天砸下去的钱马上就变成算力。不是的，那些钱要到 2028、2029 年才变成算力。在财报电话会上，Andy Jassy 和 Satya Nadella 都在说：你看，我们只是在建数据中心。

[31:31] Ben

Like these are the shells.We might not use them now.Maybe we'll use them in the future.And we only buy GPUs when we know there's demand for them.That is a great story to tell.I'm not sure that I think is a lot of BS because the reality is if you've built the shell, that money is sitting there.You're not going to let it just sit there.If you invest in a fixed cost, and this is the whole logic of commodity markets, I think tech in general doesn't understand commodity markets.Tech is by and large focused on if I produce a highly differentiated product and that differentiation could be like software.It could be a network in terms of developers.It could be a social network sort of thing where peer to peer where I'm highly differentiated.Then my ability to charge higher prices provides sort of my profit margin.So the classic example is like Apple.They have their ecosystem and they have their software and they have third party and all those sorts of things.And so they can charge 50% margins on their iPhone.Everyone looks at Apple as like the ideal business model.That's how you run a business.But in a commodity market, the price is set by the marginal supplier.Cost of service is all that matters.

这些只是壳子，我们现在可能不用，也许将来才用。而且我们只有在确定有需求的时候才买 GPU。这故事讲得很漂亮。我不太确定——我觉得这里面有相当多是扯淡。因为现实是，你把壳子建起来了，那笔钱就摆在那儿，你不会让它就那么闲着。如果你投的是固定成本——这正是大宗商品市场的整套逻辑——我觉得科技行业整体上并不理解大宗商品市场。科技行业关注的基本上是：我做出一个高度差异化的产品，而这个差异化可能来自软件，可能来自开发者构成的网络，也可能是社交网络那种人与人之间的连接，总之我高度差异化。那么我能收更高价格的能力，就构成了我的 margin。经典例子就是 Apple：他们有自己的生态、自己的软件、第三方，等等等等，所以他们能在 iPhone 上拿 50% 的 margin。所有人都把 Apple 看成理想的商业模式，觉得生意就该这么做。可是在大宗商品市场里，价格是由边际供给方决定的，只有服务成本才是关键。

[32:36] Ben

That's right.I had a good friend in Taiwan who is in shipping.Fascinating industry.It's kind of like the airlines too, another industry that I love to look at.You buy a ship and the cost of that ship is depreciation.And your marginal cost is actually quite low.It's the fuel to run the ship and the cost of the crew and like your port fees.Not that much.What that means is you are going to run that ship.As full as you really possibly.No, you're going to run it no matter what.And you're going to bring down the price of a container as low as it needs to be to cover your marginal costs.Now, your paper losses in this situation might be very large because your accounting loss includes depreciation.But the depreciation is an accounting figment.You already paid the money.You're going to run that ship at whatever the market will bear in the container.The beauty of the container.It is a pure commodity.The cost of the market is going to be the marginal cost.Now, if it gets low enough at some point, people will exit because their marginal costs.They're actually losing money on a shipment, not just paper money, but like actual real money.They will exit, but then the supply is diminished.So then the price will go back up and you get this interplay of sort of coming in and off.

没错。我在台湾有个好朋友是做航运的。这行特别有意思。它有点像航空公司，那也是我很爱研究的一个行业。你买一条船，这条船的成本就是折旧；而你的边际成本其实很低——跑船的燃油、船员的工资、还有港口费，就这些，花不了多少。这意味着什么？意味着你一定会把这条船开出去，能装多满就装多满。不，是不管怎样你都会开出去。而且你会把一个集装箱的价格一路压低，压到刚好能覆盖边际成本为止。这时候你账面上的亏损可能非常大，因为会计口径的亏损把折旧算进去了。可折旧只是个会计上的虚数，钱你早就付掉了。所以只要市场肯出价，你就会把船开出去拉箱子。集装箱美就美在，它是纯粹的大宗商品，市场价最终会趋近于边际成本。当然，价格要是跌得足够低，到某个点就会有人退出，因为跌破边际成本了——他们每跑一趟是真在亏钱，不是账面上的钱，是真金白银。他们一退出，供给就少了，价格又会涨回去，于是就形成这种运力进进出出的来回拉扯。

[33:46] Ben

But then let's say the market is very high like it was during COVID.It's like, wow, we're making so much money right now because there's not enough supply.There wasn't enough supply of ships.So containers went from usually being like $3,000, $4,000 to $17,000, $18,000.The amount of money that these shipping companies made in a very short amount of time was insane.What happens though?

但反过来，假设市场特别火爆，像 COVID 那阵子。大家会说，哇，我们现在赚疯了，因为供给不够——当时就是船不够。所以一个集装箱的价格从平时的 3,000、4,000 美元，涨到 17,000、18,000 美元。这些航运公司在很短时间里赚到的钱多到离谱。可接下来会发生什么呢？

[34:06] Ben

Well, imagine if we had more ships, right?The problem is it takes two years to build a ship.If everyone makes this decision simultaneously, you suddenly have a lot of ships, price plummets, etc.Where we see this is in components, in memory in particular.Memory, very famous for boom and bust cycles, people entering the market late.But to what extent are data centers going to be memory makers?

你想想，要是我们有更多的船呢？问题在于造一条船要两年。如果所有人同时做了这个决定，突然之间船就多得不得了，价格暴跌，如此往复。这种事在零部件行业也看得到，尤其是 memory。存储行业的 boom and bust 周期太出名了，总有人在周期末尾才进场。但话说回来，数据中心会在多大程度上变成下一个存储厂商呢？

[34:30] Ben

Where right now everyone can see we don't have enough compute.So everyone's like, we absolutely have to be investing because there's so much money to make.And look at our payback period.The problem is you're measuring your payback period in a time of scarcity.Is that payback period going to hold in a time of abundance?

眼下所有人都看得到，算力不够用。所以大家都觉得，我们必须得投，因为能赚的钱太多了；你看我们的回本周期多短。问题是，你算这个回本周期的时候，正处在稀缺期。等到产能富余的时候，这个回本周期还站得住吗？

[34:47] Ben

And the sort of the bulls would say there's never going to be a time of abundance.AI, we're going to be short forever.We're going to be short forever, which maybe we will be.My concern is even if that's right, we could still have an air gap in that there's so much money going into it right now.And not enough has come online to actually make sufficient revenues to handle the situation where we run out of capital.I believe in AI.I think it's a real thing.I think the economic impact is going to be astronomical.I think all the concerns about societal impact are very real and are going to come to bear in a major way.You can believe all that and still be worried about are we going to make the bridge to this actually generating the level of returns necessary to continue to feel this sort of going forward.Can you zoom in on TSMC and the component makers where fabs are involved?

看多的人会说，根本不会有富余的那一天。AI 这东西，我们会永远供不应求。永远供不应求——也许真是这样。我担心的是，就算这个判断没错，中间也可能出现一个断档：现在砸进去的钱太多了，而真正上线、能产生足够收入的产能还不够多，撑不住资本耗光的那个局面。我是信 AI 的，我觉得这是真东西，它的经济影响会大到天上去。我也觉得那些关于社会冲击的担忧都非常真实，而且会以很剧烈的方式兑现。你完全可以相信这一切，同时仍然担心：我们能不能搭起这座桥，撑到 AI 真的产生足够的回报，来支撑这一轮又一轮的投入继续走下去。能不能具体聊聊 TSMC 和那些牵涉到 fab 的零部件厂商？

[35:33]

And so far, at least my understanding is that they've been quite conservative in their willingness to expand capacity, build new fabs, meet the market's demand with similar growth, which they have not done.If that just really limits this whole thing and prevents us from getting one of these giant overbuilds.We can talk about a few different ones.Like, we'll start with memory.Memory used to have tons and tons of memory makers.Every time there'd be a boom, memory makers would sort of reenter the market.New countries would come in.Like, Taiwan used to have, like, a memory market.But you would get these exact dynamics.If there's a shortage of memory, there's so much money to be made, you can't bring capacity on immediately.It's the same as shipping.It's the same as what we're seeing right now.That would spur people to come in the market.You'd get too much capacity.Prices would plunge.And people would just get blown out.Because the issue is the upfront cost for these is so large.Just like buying a ship, like, building a fab is even more so.And memory now, like, the leading edges of memory are using things like EV machines.So the costs are getting into the billions of dollars for these lines.

至少按我的理解，他们在扩产能、建新 fab、用同等增速去满足市场需求这件事上一直相当保守——他们并没有这么做。这会不会从根本上把整件事卡住，反而让我们躲过一次巨大的过度建设？我们可以分几块来看。先说 memory 吧。以前存储厂商多得不得了，每次繁荣期都会有存储厂商重新涌进来，还会有新的国家加进来——比如台湾以前也有过存储产业。但你看到的永远是这一套动力学：一旦存储短缺，能赚的钱太多，而产能又没法立刻上，跟航运一模一样，跟我们现在看到的也一模一样。这就刺激大家往里冲，然后产能过剩，价格暴跌，一大批人被清出局。因为这行的前期投入实在太大了——就像买船一样，建 fab 甚至更夸张。而且现在存储的最先进制程已经要用上 EUV 光刻机这类设备了，一条产线的成本已经上到几十亿美元的量级。

[36:32] Ben

What happens is every time with these boom and bust cycles, some people would enter, more people get washed out.You go through these famous historical moments for these memory cycles.Companies just get blown out.One of the most interesting, actually, memory stories is how Samsung sort of took over memory.The problem is, three, it's not a monopoly, but it's kind of an oligopoly.And they all got a lot more discipline about let's not make the mistakes of the past.And we're not colluding, but we all are on the same page about let's not do that.And I think that dynamic sort of ran head on to the current moment where it just took a while for them to realize, no, there is a secular shift in memory demand.That didn't exist for a very long time.I think the memory solution will be solved eventually.The other risk they run is Apple's lobbying to get Chinese memory.What is the number one focus of, like, algorithmic changes?

每一轮 boom and bust 循环的结果都是：有人进来，更多人被洗出去。存储周期史上那些著名的时刻，一家家公司就这么被打爆。其中最有意思的一段，其实是 Samsung 怎么一步步拿下存储市场的。问题是现在只剩三家，虽然算不上垄断，但基本是个 oligopoly。而且他们都变得纪律性强多了——别再犯过去的错。他们不是在合谋，但大家心照不宣：那种事咱们别干了。我觉得这套惯性正好一头撞上了当下这个时刻，他们花了好一阵子才反应过来：不对，存储需求这回是发生了长期性（secular）的结构转变，而这种转变已经很久没出现过了。我觉得存储的问题最终会解决。他们冒的另一个风险是，Apple 在游说，想用上中国产的存储。还有，算法改进的头号目标是什么？

[37:50] Ben

How can we use less memory?I think the memory makers probably screw themselves in the long run by creating such a massive target on their back.I've analogized memory makers to Iran.The issue with the Strait of Hormuz is it's very effective.It's more effective if you don't use it because then it's always hanging out there as something you could do.Now they did it.Turns out it worked.But the UAE and Saudi Arabia, they're going to build pipelines.They're going to build new ports.They're not going to let this happen again.It's very painful right now.But say Iran wants to close the Strait of Hormuz in 2035, it's not going to have any effect because it will have been built around.My concern for the memory makers is they might have done the same thing.No one's going to let themselves get in this situation again as far as memory goes.TSMC is arguably worse because there's only one.There is one company on the leading edge.Obviously, Intel and Samsung are trying to get there.It's the same thing.All markets carry risk.And a lot of the question is, who ends up holding the risk?

就是怎么少用点内存。我觉得存储厂商这么搞，长远看多半是在坑自己——他们把自己的后背变成了一个巨大的靶子。我把存储厂商类比成伊朗。霍尔木兹海峡这张牌的问题在于，它确实很好用，但你不用它的时候更好用，因为它一直悬在那儿，是你随时可以打的一张牌。现在他们真打出去了，结果确实奏效。但阿联酋和沙特会去建管道，会去建新港口，他们不会让这种事再发生第二次。眼下当然很痛，可假如伊朗到 2035 年还想封锁霍尔木兹海峡，那时候就不会有什么效果了，因为大家早就绕过去了。我替存储厂商担心的正是这一点：他们可能干了同样的事。在存储这件事上，没人会让自己再陷进去一次了。TSMC 的处境可以说更糟，因为它只有一家——最先进制程上就这一家公司。当然，Intel 和 Samsung 都在往上追。道理是一样的：所有市场都带着风险，关键问题往往是，风险最后落在谁头上。

[38:47] Ben

What I think the tech companies didn't fully appreciate is the extent to which TSMC has offloaded risk onto the big tech companies.And the way they've done that is the risk that TSMC is worried about is overcapacity.If we build too much, it's not just that we built too much and we have all these fixed costs that are not being fully utilized.But if we build a fab, we expect that fab to run for 30 years.We've like baked in too much capacity into the system for years and years and years.So they are very biased towards being much more conservative.There's a little bit of a culture component to this too.One of the most interesting TSMC stories, it's kind of analogous, that Samsung story, was Morris Chang retired in like the late 2000s.New leadership took over.There was the Great Recession.And so they pulled back their planned spending.He comes in, fires everyone.And he's like, the iPhone just launched.This is the biggest opportunity we've ever seen.We need to be investing, not cutting.And they invested through the Great Recession and through that downturn.That's what laid the foundation for them taking over sort of leading edge semiconductors in that time.Morris Chang is a one of one.On the Mount Rushmore, in my mind, of the greatest and most impactful tech executives of all time,

我觉得科技公司没有完全意识到的是，TSMC 把多大一部分风险转嫁给了这些大科技公司。它是怎么做到的？TSMC 真正担心的风险是产能过剩。如果我们建多了，麻烦不只是建多了、这些固定成本没跑满；而是我们建一座 fab，是指望它跑 30 年的。那就等于往系统里塞进了未来很多很多年都消化不掉的过剩产能。所以他们天然就极度偏保守。这里面还有一点文化因素。TSMC 有个特别有意思的故事，跟刚才 Samsung 那段有点像：Morris Chang 大概在 2000 年代末退休，新管理层接手，赶上 Great Recession，于是把原定的支出计划砍了。他回来了，把人全换掉，然后说：iPhone 刚刚发布，这是我们见过的最大机会，我们该加大投资，而不是砍投资。于是他们顶着 Great Recession、顶着那轮下行周期一路投下去，这才奠定了他们在那个时期拿下最先进制程半导体的基础。Morris Chang 是独一无二的。在我心里，他够格上 Mount Rushmore，是史上最伟大、最有影响力的科技高管之一。

[40:04]

the entire fabulous model is so critical to what tech is and what it does.And also just the guts to do that at that time, particularly in someone who lived there,a culture that doesn't necessarily tend to make those sorts of bets.TSMC, they were pretty conservative, to be totally honest.So what happens though?

整个 fabless 模式，对今天科技行业是什么样、怎么运转，实在太关键了。而且在那个时间点上敢下这种注，本身就需要胆量——尤其是对一个生活在当地的人来说，那种文化并不太倾向于做这类豪赌。老实讲，TSMC 后来确实挺保守的。那么接下来会怎样呢？

[40:23] Ben

Where'd the risk go?TSMC's like, we don't want to take the risk.Risk doesn't disappear.It just moves.The risk is right now where you have every single big tech company realizes if we had more compute,we could be making more money.So there's lots of foregone revenue and foregone profits.That is the manifestation of the risk that TSMC handed off to them.Risk doesn't disappear.It just gets handed off.And sometimes that risk doesn't manifest in losing money.It manifests in not making money.And there's money not being made right now because what happened was they were very excitedabout 5G.They did a big wave of like investment, expanding their fabs in around 2020, 2021, 22.And they're like, oh, yeah, we're good.Like I said, 2024, Chattel B.It's out 2022.Big thing in tech in 2023.In 2024, their growth rate went down.In 2025, their growth rate went down.In 2026, it's up now.It was very funny because I was writing about this a while ago.And then I think it was like one or two earnings calls ago.Suddenly, CCA, the CEO and chairman, is talking about like use cases for AI, the whole earnings call in a way he never had before.This is why the merrymakers are scared.Usually there's like a bullwhip and they're worried about being at the end of the bullwhip where the demand happens and it works its way down the chain.

风险跑到哪儿去了？TSMC 说，我们不想承担这个风险。可风险不会消失，它只会转移。现在风险体现在：每一家大科技公司都意识到，如果我们有更多算力，就能赚更多钱。所以存在大量本来能拿到却没拿到的收入和利润，这就是 TSMC 转手给他们的那份风险的具体形态。风险不会消失，只会被转手。而且有时候风险兑现的方式不是亏钱，而是少赚钱。现在就有很多本该赚到的钱没赚到。事情的经过是这样：他们当年对 5G 特别兴奋，在 2020、2021、2022 年前后做了一大波投资扩 fab，然后觉得，行了，我们够用了。就像我刚说的，2024 年……ChatGPT 是 2022 年底出来的，2023 年成了科技圈的头等大事；结果 2024 年他们的增速下来了，2025 年增速又下来了，到 2026 年才重新起来。特别好笑的是，我前阵子还写过这事，然后大概是一两次财报电话会之前吧，突然之间，C.C. Wei，他们的 CEO 兼董事长，整场电话会都在讲 AI 的各种用例，那种讲法是他以前从来没有过的。这就是为什么存储厂商害怕——通常会有一根牛鞭（bullwhip），他们最怕自己处在鞭梢那一端：需求在上游发生，然后一层层往下传导。

[41:43] Ben

And they're at the end and then they double down.It's already too late.They're wasting all their money.And I think the thing with AI is if it's a bullwhip, it's like the longest bullwhip of all time.There's still so much to be built.And it just took a while for Asia to get the message where these sort of companies are.I think they've by and large gotten it.But them getting the message, it then takes several years for that to actually materialize.Do you have a sense for how long you think it will take given the extreme shortage of compute?

等传到他们这儿，他们再加倍下注，其实早就晚了，钱全打了水漂。而我觉得 AI 这件事，如果它也是一根牛鞭，那大概是史上最长的一根，要建的东西还多得是。只是亚洲那边的公司花了不少时间才收到这个信号。我觉得现在他们大体上已经收到了。但收到信号之后，还要好几年才能真正变成产能。考虑到算力短缺已经如此极端，你觉得这个过程大概要多久？

[42:12] Ben

The interesting thing is what this means for Intel and Samsung's sort of logic business.I've been writing about the problem of this dependency on TSMC for years.One of my first articles in 2013 was exhorting Intel.Well, I say you have to build a fab business.You're not going to be a designer anymore.There's a huge business in manufacturing chips.I thought I was late writing it then.Their stock goes to the moon throughout the 2010s as they're riding the sort of cloud wave.And it wasn't until 2020 where they finally realized we fell behind.By the way, there's this huge opportunity.We're totally unprepared for it.We don't have a customer service mindset or culture organization or all the IP building blocks and all these things that TSMC has.And they need a customer.They need customers to help them actually build a real foundry business.So I would write about this as a problem.And I write about the China issue.Like you're dependent on a company that is 60 miles offshore of our greatest geopolitical opponent who thinks it's theirs.So these are big problems.That's where I came to appreciate this insurance issue.For a big tech company to go to Intel and say, Intel, you make our chip.And by the way, the biggest benefactor of this is going to be you because you're going to learn how to work with a partner.

有意思的地方在于，这对 Intel 和 Samsung 的逻辑芯片业务意味着什么。关于过度依赖 TSMC 这个问题，我写了很多年了。我 2013 年最早的几篇文章之一就是在劝 Intel：我说你必须做 fab 这门生意，你不能只当设计公司了，制造芯片是一门非常大的生意。我当时还觉得自己写得太晚了呢。结果整个 2010 年代他们的股价一路上天，因为踩上了云计算那波浪潮。直到 2020 年他们才终于意识到：我们落后了；顺带一提，眼前还有个巨大的机会，而我们完全没准备好。我们没有服务客户的思维方式，没有那种文化和组织，也没有 TSMC 那些 IP 积木和一整套东西。他们需要一个客户，需要客户来帮他们真正把 foundry 业务做起来。所以我一直把这当成一个问题在写。我也写中国这个问题：你依赖的这家公司，就在我们最大的地缘政治对手海岸线外 60 英里，而对方认为那地方是它的。这些都是大问题。也正是从这儿，我才体会到这其实是个保险的问题。让一家大科技公司去找 Intel 说：Intel，你来做我们的芯片；顺便说一句，这件事最大的受益者是你，因为你会学会怎么跟合作伙伴打交道。

[43:26] Ben

And the biggest pain is going to be us because we're going to have to figure out how to work with you.We could just go to TSMC.They are awesome.They are so great to work with.We know they're going to do a good job.It just never made rational sense for anyone to go work with Intel.That was their fundamental problem.In an unchanging world, TSMC would just win forever.But this is where TSMC, in some respects, made the same mistake as the money makers, made the same mistakes as I ran, if I can continue the analogy.Because they didn't invest the last few years, the shortages are going to be so acute.Big 10 companies that were foregoing so much revenue and so many profits because we don't have enough compute, we will go through the pain of getting Intel up to speed, of getting Samsung's logic up to speed.The scarcity is what ultimately saved Intel.I expect at some point that they're going to announce some major partner for the first time.It's going to be a big deal.But ultimately, TSMC brought it on themselves.It's the cure for high prices is high prices thing.We're going to route around them.There's all these things as like an analyst sitting on the side.You can write these things.And it's one of those things I sort of learned.

而最难受的是我们，因为我们得摸索出怎么跟你配合。我们本来直接找 TSMC 就行了。他们太强了，合作体验太好了，我们知道他们一定能干好。所以从理性上讲，从来就没有人有理由去找 Intel，这就是 Intel 最根本的问题。在一个不发生变化的世界里，TSMC 会一直赢下去。但恰恰在这一点上，TSMC 某种程度上犯了和存储厂商一样的错，犯了和伊朗一样的错——如果这个类比可以继续用的话。因为他们过去这几年没投，短缺会变得极其严重。那些因为算力不够而白白损失大量收入和利润的大科技公司会说：行，我们就忍着这个痛，把 Intel 扶起来，把 Samsung 的逻辑芯片扶起来。稀缺最终反倒救了 Intel。我预计到某个时间点，他们会宣布拿下第一个重量级合作方，那会是件大事。但归根到底，是 TSMC 自己招来的。这就是那句「治高价的解药就是高价」。大家会想办法绕过他们。作为一个坐在场边的分析师，这些东西你都可以写出来，这也是我慢慢学到的一件事。

[44:34] Ben

No one's going to pay insurance that they don't need to pay when that insurance expected value is negative.The way to solve the geopolitical problem of dependence on TSMC is to come up with a compute use case that is so massive that everyone is economically incentivized to bring other people up to speed.And then we get the sort of geopolitical insurance for free.If you think about the, let's say, top 10 or 15 technology companies, which ones do you think have the most interesting setups today for their business?

没有人会去买一份自己并不需要的保险，尤其当这份保险的期望值是负的时候。要解决依赖 TSMC 这个地缘政治问题，办法是找到一个大到不得了的算力用例，大到所有人在经济上都有动力去把别的厂商扶起来。这样一来，地缘政治上的那份保险就顺带白拿了。如果看排在前 10 或前 15 的科技公司，你觉得今天哪几家的局面最有意思？

[45:03] Ben

The answer is always Amazon.The reason Amazon is so compelling is the extent to which they build for them.They are their first best customer.They provide the scale to get basically anything off the ground, which they then sell to other people.AWS is the most obvious example.AWS, contrary to sort of popular thought, was not spare Amazon capacity.Actually, it took a long time to get Amazon.com onto AWS.What it drove was the understanding that we can't be having so many meetings.Like, we need to have just compute that you can plug in purely API surface.You don't need to talk to anyone.It's just there.And oh, by the way, if we do that for our internal retail teams, we could do that for anyone.Turns out the retail is so big, we have to start with everyone else.AWS actually started serving external customers before it served internal ones, but now it serves them all.You got other products like, say, the logistics, where it was the opposite.Right now, we're using external providers for logistics, UPS and FedEx and USPS.We need to build this up ourselves.And now they built it up themselves.They're offering to third parties.Other people can use their delivery services.You see this in market after market.

答案永远是 Amazon。Amazon 之所以这么有说服力，是因为它做的东西都是先给自己用的。他们是自己的第一大客户，也是最好的客户。他们能给几乎任何东西提供起步所需的规模，然后再把这东西卖给别人。AWS 就是最明显的例子。跟流行说法相反，AWS 并不是 Amazon 用剩下的富余产能——事实上，把 Amazon.com 迁到 AWS 上花了很长时间。真正推动它的，是他们意识到：我们不能再开这么多会了，我们需要一种插上就能用的算力，纯 API 接口，不需要跟任何人沟通，它就在那里。然后又想到，既然能给内部零售团队这么做，那给任何人都能这么做。结果零售业务太庞大了，反倒只能先从别人开始。AWS 其实是先服务外部客户、后服务内部客户的，但现在两边都服务。还有些业务是反过来的，比如物流：当时我们用的是 UPS、FedEx、USPS 这些外部服务商，我们得自己把这套建起来。现在他们自己建起来了，又开始对第三方开放，别人也能用他们的配送服务。你在一个又一个市场里都能看到这个模式。

[46:10] Ben

They're talking about some of their AI products or their chip products.What's the beauty of the Graviton or the Tranium, particularly the early versions?The early versions were terrible.But if you're on Amazon and you're using some of their managed services, like, say, the Redshift database service, they don't tell you what the processor is underneath that.You're just buying a managed service.So they can put all their crappy processors underneath the services they're selling.And that gives them the volume and the capacity to iterate them and get better.And they get to the point where they can actually sell them externally.Because they were the first best customer for Graviton, Graviton got better.Because they were the first best customer for Tranium, Tranium got better.And now Tranium is obviously running Anthropic and AI products.We'll see if any of them take off.They have call center software.Their call center or their customer experience is going through AI.By the way, it's pretty good.I haven't tried it.Moving back to America, I've been buying lots of stuff.Every summer I'd buy lots of stuff in a very brief amount of time.Sometime in like the last year or so, you can go on and you're clearly talking to a chatbot.

再说他们的一些 AI 产品或者芯片产品。Graviton 和 Trainium 好在哪儿，尤其是早期那几版？早期版本其实很糟糕。但如果你在 Amazon 上用他们的某些托管服务，比如 Redshift 数据库服务，他们并不会告诉你底下跑的是什么处理器，你买的只是一个托管服务。所以他们可以把自家那些还很烂的处理器塞到这些在卖的服务底下去跑。这就给了他们迭代所需的出货量和产能，让芯片越做越好，最后好到可以直接对外销售。因为他们是 Graviton 的第一大客户、最好的客户，Graviton 才变好了；因为他们是 Trainium 的第一大客户、最好的客户，Trainium 才变好了。现在 Trainium 明显在跑 Anthropic 和 AI 产品。至于这些东西能不能起来，再看吧。他们还有呼叫中心软件，他们的呼叫中心、或者说客户体验，正在往 AI 上迁。顺便说一句，做得还挺好的。我还没试过。我搬回美国之后买了不少东西；以前每年夏天我也会在很短的时间里集中买一大堆。大概最近一年吧，你上去一聊，很明显对面就是个 chatbot。

[47:10]

But the chatbot does a great job.And it actually does take care of the problem.So you can see that actually starting to work in that regard.But they're building up these AI services for their own business that they're going to make broadly available.And some of them will work.Some of them won't.It's such an elegant approach given they have so many investments in the real world.Their core business feels so impervious to AI for the model version of AI.It will benefit from AI.But their moat feels deeper than anyone as far as their core business.And their ability to just sort of generate new business lines organically is very compelling.What about Apple?

但那个 chatbot 干得非常好，问题是真的被解决了。所以你能看出来，这条路在这个方向上开始跑通了。而他们是先为自己的业务搭这些 AI 服务，然后再把它们对外大范围开放。有些会成，有些不会。考虑到他们在现实世界里有那么多重投入，这套打法特别优雅。他们的核心业务面对 AI——尤其是模型那一版本的 AI——感觉几乎是免疫的。AI 只会让它更受益。单说核心业务，他们的 moat 感觉比谁都深。再加上他们那种能有机地长出新业务线的能力，非常有说服力。那 Apple 呢？

[47:49] Ben

They've sat this whole thing out, it seems.It feels like it might be a situation of better be lucky than good to a certain extent.Apple has their whole ecosystem.At the end of the day, they do own access to customers.So they can get suppliers.This is the classic aerator play.If you own access to customers, suppliers come to you, not the other way around.So they can get suppliers for their AI as needed.And by the way, to the extent it's true that people don't want to be productive.They just want to sort of a chatbot.Not only can they serve them a chatbot and finally getting a Siri that works, but you can see a future where this absolutely can work on device.And they actually don't even need to pay for inference costs either because they're using the customer's electricity.I don't think we're quite there.There's a reason they're using Google Cloud and NVIDIA chips.But you can certainly imagine a future where that's the case.And they're in physical goods.Actually making phones is hard.Having retail, having distribution for physical goods.They're more insulated.The smartphone is so perfect.It's small to fit in your pocket.It's big enough to watch basically anything on it.You can run your whole life on it.

看起来他们整场都没下场。某种程度上，这可能属于「运气好胜过本事强」的情况。Apple 有他们整套生态。归根到底，他们手里握着通往用户的入口。所以供应商他们要多少有多少。这就是最经典的 aggregator 打法：只要你掌握了通往用户的入口，供应商就会来找你，而不是反过来。所以 AI 上需要什么供应商，他们随时拿得到。而且顺带一提，如果「人们其实并不想变得更高效、只是想要一个 chatbot」这件事成立，那他们不但能把 chatbot 端上来、终于给你一个能用的 Siri，你甚至能看到一种未来：这套东西完全可以跑在端上。到那时他们连 inference 成本都不用付，因为烧的是用户自己家的电。我不认为我们已经到那一步了——他们现在用 Google Cloud、用 NVIDIA 的芯片，是有原因的。但你完全可以想象那样一种未来。而且他们做的是实体商品。造手机这件事本身就很难，还要有零售、要有实体商品的分销体系，他们受到的冲击更小。智能手机这个形态实在太完美了：小到能塞进口袋，又大到基本什么内容都能在上面看，你可以用它跑完自己的一整个人生。

[48:56] Ben

All your entertainment is there.When we talk about customers who want to be entertained, the TV is now an accessory.It's all on your phone.I don't see anyone taking over the phone.The question is, is the phone always going to be the center?

你所有的娱乐都在上面。当我们说到那批「只想被娱乐」的用户，电视现在已经变成配件了，一切都在手机上。我看不到有谁能取代手机。问题在于：手机会永远是那个中心吗？

[49:09] Ben

Or is there a bit where, particularly in the home, this is where OpenAI's efforts here are very interesting, where you want sort of an ambient AI, where you just talk to the AI and it tells you what you need.Apple is the best position to provide that.But can they provide that without having leading edge models?

还是说会出现另一种情形——尤其是在家里，这正是 OpenAI 在这块的动作特别有意思的地方——你想要的是一种环境式的（ambient）AI：你就跟它说话，它就把你要的东西告诉你。提供这个，Apple 的位置是最好的。但问题是，在没有最前沿模型的情况下，他们提供得了吗？

[49:29] Ben

Can they provide that if they're so phone centric?Or is it like a Microsoft situation?Microsoft didn't miss mobile.They were very early to mobile.The problem is their mobile was a small PC.They assumed the PC would always be the center and their phones were going to be something that was off that.Apple realized, no, we need to reset.The phone is not going to be accessory to the Mac.The phone is going to be the phone.The iPod helped them realize that and going with Windows and all that.But will they fall into a Microsoft-like trap?

如果他们这么以手机为中心，他们提供得了吗？还是说这会变成又一个微软式的局面？微软并没有错过移动，他们进移动非常早。问题在于他们的移动就是一台缩小版 PC。他们默认 PC 永远是中心，手机只是挂在 PC 旁边的东西。而 Apple 想明白了：不行，我们得重置。手机不会是 Mac 的附属品，手机就是手机。iPod 帮他们想通了这一点——把 iPod 做到 Windows 上等等这一系列事。但 Apple 会不会掉进一个类似微软的陷阱？

[49:56] Ben

Assuming the phone's so good, it's always going to be the center.And then let's figure out around it.Or is this finally the time when actually ambient, the cloud, just in general, AI being everywhere, it can manifest through your phone.It can manifest through a device.It can manifest on your computer.It is actually better and is actually disruptive to them.I think it's possible.I also think it's totally valid for Apple to double down on what they do.The other thing about the AI stuff is on what basis should we expect Apple to be good at this?

——就是默认手机太好了，所以它永远会是中心，剩下的事围着它转就行。还是说，这一次终于轮到 ambient、云、以及「AI 无处不在」这件事本身：它可以透过你的手机呈现，可以透过某个设备呈现，也可以在你电脑上呈现，而这种形态确实更好、确实对他们构成颠覆。我觉得这有可能。但我也觉得，Apple 就在自己擅长的事上加倍下注，是完全说得通的。关于 AI 还有一点：我们凭什么就该指望 Apple 在这件事上做得好？

[50:30] Ben

At the most crude level, AI is this probabilistic endeavor.Apple is the king of deterministic products.A physical product, you ship that iPhone, you ship it once, and it's got to be good.If it's bad, it costs you billions and billions and billions of dollars.Apple's never had an iPhone recall.It's amazing.That care and decision-making and diligence and fierceness in terms of your supply chain and making hard decisions is very, very different than everything that goes into making great AI.I generally prefer companies to do what they're good at.So from my perspective, I'm fine with Apple not doing AI.I want them to keep making great devices.Of the five potential frontier AI winners, so OpenAI, Anthropic, Gemini, SpaceX AI, Grok, and Meta, which of those firms do you think has the most interesting setup?

说得最糙一点，AI 是一件概率性的事，而 Apple 是确定性产品之王。实体产品——你把那台 iPhone 发出去，只发这么一次，它就必须是好的。要是它不好，代价是几十亿、上百亿美元。Apple 从来没有召回过 iPhone，这太惊人了。那种在供应链上的严苛、决策力、尽职和狠劲，跟做出伟大 AI 所需要的一切非常非常不一样。我总体上更希望公司去做自己擅长的事。所以从我的角度，Apple 不做 AI 我没意见，我只希望他们继续做出色的设备。在五家可能的前沿 AI 赢家里——OpenAI、Anthropic、Gemini、SpaceX AI、Grok 和 Meta——你觉得哪一家的局面最有意思？

[51:23] Ben

OpenAI and Anthropic obviously are the riskiest, but also have the biggest upside.Never discount, number one, the power of belief.They think they're creating God.The most impactful things in history have usually been fueled by religion.The two religious organizations in Silicon Valley are OpenAI is kind of like mainline.They go to church every Sunday.They're sort of like evangelicals.That's Anthropic.They're all in.It is core of their belief.That goes a long way.The fact you need to make a business work for you to survive goes a very long way.Google just needs search to not die too quickly.Meta has the huge advertising business.In a world where Meta was run by anyone other than Mark Zuckerberg, they would not be on the weaning edge.That is one of the purest manifestations of founder energy for better or for worse.Their business is so amazing.You see them just easily sort of doubling down on that.Google, there's a bit where they had Google Cloud.They have TPUs.They've been doing research in this.It makes sense why they're pursuing this.Meta being like, actually, we're going to hire a completely new team.We're going to start from scratch.This all again is pretty insane.And credit to Mark Zuckerberg in that regard.

OpenAI 和 Anthropic 显然是风险最大的，但上限也最高。第一，永远不要低估「信念」的力量。他们认为自己在创造神。历史上影响最大的那些事，通常都是被宗教推动的。硅谷的两个宗教组织：OpenAI 有点像主流教派，每周日按时去做礼拜；而 Anthropic 更像福音派，全情投入，这是他们信仰的内核。这能带你走很远。而「必须把生意做成才能活下去」这件事，也能带你走非常远。Google 只需要让搜索别死得太快。Meta 有那个巨大的广告生意。如果 Meta 是由 Mark Zuckerberg 之外的任何人来经营，他们不可能待在最前沿。那是创始人能量最纯粹的体现之一——无论好坏。他们的生意实在太好了，你会看到他们轻轻松松就在这上面继续加码。Google 那边则是：他们本来就有 Google Cloud，有 TPU，一直在做这方面研究，所以他们去追这件事很合理。而 Meta 的姿态是：行吧，我们干脆重新招一整支全新的团队，从零开始。这事本身也相当疯狂。这一点上得给 Mark Zuckerberg 记一功。

[52:30] Ben

Again, you could decide whether that's a good idea or not.And then SpaceX AI, data centers in space, the theory is there.Do they have to own their own model, though, to do that?They'd get better margins if they do.Then again, if we actually run out, whether through political opposition or power or whatever it might be,if we run out of data centers on Earth, they can run whatever model they want,as we're seeing with selling their capacity to Anthropic right now.They're all pretty interesting.Probably the case for SpaceX AI is probably the weakest because the data center in space play is so highly differentiated.If that plays out, I'm not sure to what extent they need to even have their own model.So why are you wasting billions and billions of dollars in the meantime?

当然，这到底是不是个好主意，你可以自己判断。然后是 SpaceX AI——太空里的数据中心，理论上是成立的。但为了做这件事，他们非得拥有自己的模型吗？如果有，margin 会更好。可话说回来，如果我们真的用完了——不管是因为政治阻力、电力还是别的什么——如果地球上的数据中心不够用了，他们想跑什么模型就跑什么模型，就像我们现在看到的那样，他们正在把算力卖给 Anthropic。这几家都挺有意思的。可能 SpaceX AI 的理由是最弱的，因为「太空数据中心」这个打法差异化太强了。如果那条路真的走通，我不确定他们还需要在多大程度上拥有自己的模型。那这中间你为什么要烧掉几十上百亿美元呢？

[53:14]

That's a fair question.From a tactical perspective, I love the cursor acquisition.That makes so much sense for both companies.And so I've been intrigued to see what they do.Meta is probably the most interesting.You've written a lot about this recently.I think there's a very good case to make that it is more reckless to not be on the frontier if you're a digital company.The counter to Meta is actually Microsoft.Microsoft is not on the frontier.The reason why Microsoft has $20 million of free cash flow last quarter,Microsoft paid a $10 billion dividend last quarter.There's some money, but their play is, oh, we're going to play all these off each other.We're going to provide middleware.We're going to provide the platform that enterprises will build on us, and we're going to sort of disintermediate the models.I think it's a rational play.It's the IBM play of the 90s.History echoes.Everyone talks about Google.Google, like, following Microsoft.Microsoft follows IBM.And you can see that to an extent.What did IBM do?

这个问题问得公道。从战术层面看，我很喜欢 Cursor 那笔收购，对双方来说都太说得通了，所以我一直很好奇他们接下来会怎么做。Meta 可能是最有意思的。你最近写了很多关于这个的东西。我觉得有一个很站得住脚的论点：如果你是一家数字公司，不待在前沿反而更鲁莽。Meta 的反例其实是微软。微软不在前沿。微软上个季度有 2000 万美元自由现金流的原因就在这儿——微软上个季度还派了 100 亿美元的股息。钱是有一些的，但他们的打法是：哦，我们要让这些模型互相制衡，我们来提供中间件，我们来提供企业在上面构建的平台，我们要把模型这一层的中介地位削掉。我觉得这是个理性的打法，这就是 90 年代 IBM 的打法。历史是会押韵的。大家都说 Google 在走微软的老路，而微软走的是 IBM 的老路。这一点你确实能看出来一些。那 IBM 当年做了什么？

[54:06] Ben

What's the analogy?Well, so IBM had this dominant, we talked about it in the 70s.And then you fast forward to the 90s, and IBM is this very distressed asset.And the thought was IBM needed to break up into all these different pieces they had.So Lou Gerstner comes in and takes it over.Gerstner's real key insight to IBM is we're pretty mediocre at everything.It's kind of like when I talked about Microsoft before.And that's the price of monopoly.Once you've been a monopoly, you kind of lose your capacity to be good because you didn't need to compete anymore.And I think a lot of tech incumbent companies have this problem.It didn't matter what they did.They were going to rake in money.And if you don't have the pressure, if you don't have the incentive, if you don't have the fear of death or the fear of God, as we talk about these model companies, then you don't do your best work.And the problem is that once you lose that muscle, it's gone.You're just sort of fat and flabby.So what Gerstner realized is actually the worst thing IBM could do would be to break it up into component pieces because all those component pieces are actually not very good.Our biggest asset is that we're big.It's like, what?

这个类比怎么讲？是这样：IBM 在 70 年代有过我们前面聊到的那种统治地位。然后快进到 90 年代，IBM 成了一个非常困顿的资产。当时的主流看法是，IBM 需要拆分成手里那一堆不同的业务块。然后 Lou Gerstner 进来接手了。Gerstner 对 IBM 真正关键的洞察是：我们干什么都挺平庸的。这有点像我刚才讲微软时说的那个意思——这就是垄断的代价。你一旦当过垄断者，就多少会丧失做好东西的能力，因为你不再需要竞争了。我觉得很多科技巨头都有这个毛病：他们做什么都无所谓，钱照样哗哗进来。而如果你没有压力、没有激励、没有那种对死亡的恐惧或者说对神的敬畏——就像我们聊那几家模型公司时说的那样——你就不会拿出自己最好的活儿。麻烦的是，这块肌肉一旦丢了就找不回来了，你只会变得又肥又松。所以 Gerstner 想明白的是：IBM 最糟糕的做法恰恰就是把自己拆成一块块，因为那些零件其实都不怎么样。我们最大的资产就是「我们大」。你会说：啊？

[55:11] Ben

No, what does it mean we're big?It's the 90s.This internet thing's coming along.There's all these companies that kind of know they have to figure out the internet and they don't know what to do.They need someone who can come in, understand their business, and help them get online.That's basically what IBM did.So they built out, and this is an echo of what's happening now, huge consultant force.And they put all their time into building, basically it was middleware, where they would go in and they'd put this layer between a company's old school mainframe, which all these companies had, and then modern web services.On the other hand, they could have websites and e-commerce sites and all this sorts of thing.And it gave IBM a 30-year lease on life.Yes, in theory, you could go get point solutions from all these hot Silicon Valley startups, but you don't understand that.You don't know how to do that.You know us.We'll come in.We'll create all this middleware, build this big consulting force to help you implement it, and you'll get online.And IBM basically brought all of corporate America online.That's Microsoft's playbook.Microsoft will help you figure out AI.It will help you figure out in a way where you're not giving away the crown jewels to these companies.

「我们大」是什么意思？那是 90 年代，互联网这东西正在冒出来。一大堆公司隐约知道自己必须把互联网这件事搞明白，但不知道该怎么办。他们需要有人能进来，看懂他们的生意，帮他们上网。IBM 干的基本就是这件事。所以他们建起了——这和今天正在发生的事是有回声的——一支庞大的顾问队伍。他们把全部精力投进去做的，本质上就是中间件：他们进场，在企业那台老掉牙的大型机（这些公司家家都有）和现代 Web 服务之间加一层。另一头，这些公司就能有自己的网站、电商站点等等。这给 IBM 续了 30 年的命。是的，理论上你可以去硅谷那些热门创业公司那儿买各种点状解决方案，但你看不懂，你也不知道该怎么做。而你认识我们。我们进来，把这一整套中间件做出来，再建一支庞大的咨询队伍帮你落地，你就上网了。IBM 基本上把整个美国企业界搬上了互联网。这就是微软的剧本：微软会帮你把 AI 搞明白，而且是以一种不用把皇冠上的宝石交给那几家公司的方式帮你搞明白。

[56:13] Ben

We're going to build this platform, this harness, this sort of middle layer.We're dependable.We're stable.You know us.We have backwards compatibility to the 80s.You can build on us, and then we'll manage all the changing models and what's updating and do all those sorts of things.And does that mean you'll get the absolute best experience?

我们要建这个平台、这套 harness、这一层中间层。我们可靠、我们稳定、你认识我们，我们的向后兼容一路兼容到 80 年代。你在我们身上构建，然后由我们来管理不断变化的模型、管理哪些东西更新了，把这些事全都替你办了。那这是不是意味着你会拿到绝对最好的体验？

[56:32] Ben

No.Middleware saws off the sharp edges.You sort of get a lowest common denominator capacity, but if you value in this the oldest enterprise sales motion, how did Oracle go to market?Oracle went to market in the 1980s, Larry Allison, with another technology taken from IBM, or just IBM didn't want it, relational databases.And they're like, you don't want to be locked into IBM.Relational database you could run anywhere.Come with us.The reason this is a joke is because Oracle locks you in more than anyone, right?

不是。中间件会把锋利的边角磨掉，你拿到的多少是个最大公约数级别的能力。但如果你看重的是企业销售里最古老的那套打法——Oracle 当年是怎么进入市场的？Oracle 在 1980 年代进入市场，Larry Ellison 靠的是另一项从 IBM 那儿拿来的、或者说 IBM 自己不要的技术：关系型数据库。他们的话术是：你不会想被 IBM 锁死吧？关系型数据库你在哪儿都能跑，跟我们走。这事之所以像个笑话，是因为 Oracle 把你锁得比谁都死，对吧？

[56:59] Ben

But all of enterprise sales is companies whose long-term goal is to lock you in, getting you on board by trying to make you scared of being locked into somebody else.All the cloud companies are like, oh, portability, whatever, you'd be whatever.They're like, oh, just use our service that only runs in our cloud, and now you're locked in.That's a Microsoft playbook.It's a very rational playbook, and I think it makes sense.That's why they have extra money, because they're not on the frontier.They are building massive data centers, but they're building data centers for inference.They're not building it for training.And their story about investing in time in response to customer demand is more believable in that regard.They're not having to tell a fungibility story where we're building big data centers for training that will be used for inference down the road, maybe.Go back to this notion that it's reckless to not be in the front.The reason why that's concerning, though, is at the end of the day, why are we using Microsoft products again?

但整个企业销售就是这样：一群长期目标是把你锁死的公司，靠让你害怕被别人锁死来把你拉上船。所有云厂商都在说：哦，可迁移性啦，随你怎么弄啦。转头就是：哦，用我们这个只在我们云上跑的服务吧——然后你就被锁死了。这就是微软的剧本。这是个非常理性的剧本，我觉得它说得通。这也是为什么他们手上有多余的钱：因为他们不在前沿。他们确实在建巨型数据中心，但他们建的是用于 inference 的数据中心，不是用于 training 的。所以他们那套「按客户需求逐步投资」的说法在这一点上更可信。他们不需要去讲一个通用性（fungibility）的故事——不必说我们建的这些大型 training 数据中心以后也许能拿来做 inference。回到「不在前沿才是鲁莽」这个说法。它之所以让人担心，是因为归根到底：我们今天到底为什么还在用微软的产品？

[57:47] Ben

Because we did before.To what extent does it actually make sense to have all these artifacts, all these documents, all these email inboxes?Can't AI just do that?There's a real threat here where, to Microsoft's software business, the whole systems of record thing is funny,because one reason why systems of record are so powerful is it's so hard to move them to somewhere else,because it's a very tedious, repetitive job.Oh, AI is actually surprisingly good at that.I'm not sure how good the systems of record...Microsoft isn't so much systems of record.They do have some of the dynamics business.It's user interface.It's like where you actually interact with the computer.That's the part when you see Codex, Quad, Cowork, or whatever, it is aimed like an arrow to the heart of what Microsoft has.In the long run, all digital companies are, but Microsoft is very much.Their strategy is sound.It's also desperate in an existential way, and also in a they might pull it off because they're desperate sort of way.Meta is not threatened immediately, but this is where my bullish view of AI comes in.I think all digital companies are threatened, and Meta is a digital company.They have software.Now, one worry is AI takes up more and more time.

因为我们以前就在用。那这些制品、这些文档、这些邮件收件箱，究竟还有多少存在的必要？AI 不是就能干了吗？这里对微软的软件生意有一个真实的威胁。systems of record 这件事其实挺讽刺的：systems of record 之所以那么强，一个原因就是它太难搬到别处去了，因为那是一件极其枯燥、重复的活儿。哦，结果 AI 在这方面好得出人意料。我不确定 systems of record 到底能有多稳……不过微软其实并不那么算 systems of record，他们确实有 Dynamics 那块业务，但他们真正的东西是用户界面，是你实际跟电脑打交道的那个地方。所以当你看到 Codex、Claude、Cowork 这类东西时，那就像一支箭，直指微软所拥有的核心。长期看，所有数字公司都会被指到，但微软尤其如此。他们的战略是稳健的，同时又带着一种生死攸关的绝望——而且也是那种「正因为绝望所以可能真做成」的绝望。Meta 不会马上受到威胁，但这里就要说到我对 AI 的看多立场了：我认为所有数字公司都受威胁，而 Meta 是一家数字公司，他们有软件。眼下有个担忧是：AI 会占掉越来越多的时间。

[59:02] Ben

Time ultimately is Meta's currency.We saw OpenAI tried the Sora thing, didn't really take off.Social networks are actually pretty hard.Also cost a lot of money.It's kind of really interesting.So this came up with the creator payment stuff.So YouTube, very famously as paid creators kind of from the beginning,and that's a much bigger drag on the business than people appreciatebecause YouTube has marginal costs to their content.Now, unlike a Netflix, they don't have to pay that cost up front.They'll pay it after the fact.So their sharing revenue is a better model than a Netflix model.Netflix has to pay up front for content and then ideally make more money.YouTube pays along the way, but Facebook or Meta.There's nothing.They pay nothing.Instagram is this unbelievable product that generates all this moneyfor which Facebook pays $0 for content.It's unbelievable.It's funny because you could see a world where for YouTube,AI-generated content could theoretically be a positivebecause the inference cost of generating content could be lessthan what they're sharing with creators.For Meta, AI-generated content, to the extent they're the ones generating it,is actually a worse margin profile than what they have today

而时间归根到底就是 Meta 的货币。我们看到 OpenAI 试了 Sora 那一手，其实没怎么起来。社交网络这件事其实挺难的，而且也很烧钱。这里有个特别有意思的点，是从创作者分成那件事引出来的。YouTube 众所周知从很早就开始给创作者付钱，这对生意的拖累比大家以为的要大得多，因为 YouTube 的内容是有边际成本的。不过，跟 Netflix 不一样的是，他们不用预先付这笔钱，而是事后再付。所以他们的收入分成模式比 Netflix 那套模式更好：Netflix 得先为内容掏钱，然后指望之后能赚回更多；YouTube 是一路走一路付。但 Facebook、或者说 Meta 呢？什么都没有，他们一分钱都不付。Instagram 是个不可思议的产品，产出这么多钱，而 Facebook 为内容付的是 0 美元。这太不可思议了。有意思的地方在于，你能想象出一个世界：对 YouTube 来说，AI 生成内容理论上可能是件好事，因为生成内容的 inference 成本可能低于他们分给创作者的那部分。而对 Meta 来说，AI 生成内容——只要生成的人是他们自己——反而比他们今天的 margin 结构更差

[1:00:10] Ben

because what they have today is free.So they have attention.There is a bullish world where Meta is actually very well placedbecause in a world where we interact with the AI all the time,the desire for a human connection becomes greater.And it's sort of like a Meta going back to their roots.Meta, one of their biggest mistakes actually,Meta was always a social network company.They killed Snapchat or stopped Snapchat's growthby realizing Snapchat is a great product.Let's layer it onto our network.They brought their network to bear to kill Snapchat.The reason why TikTok was a blind spot for themis TikTok is classified as a social networkand it's not a social network at all.TikTok is an entertainment product.It doesn't matter who you follow on TikTok.What you see on TikTok is a function of what you watchedand you're going to get more of the same.It's a user-generated content network.And the insight from TikTok was the way to get the best contentto limit it to your social network is an artificial constraint.We're going to give you the best content from across the whole network.And the vast majority of content is going to be crap,but this is like the absolute question before.You don't think about margins.

因为他们今天给的东西是免费的，所以他们手里握着注意力。存在一个偏乐观的世界：Meta 其实位置非常好——因为在一个我们随时随地都在跟 AI 打交道的世界里，人对人际连接的渴望反而会更强。这某种意义上是 Meta 回到自己的老本行。Meta 一直是一家社交网络公司。他们最大的错误之一其实是……当年他们干掉 Snapchat、或者说掐断了 Snapchat 的增长，靠的是意识到 Snapchat 是个好产品，那就把它叠到我们自己的网络上；他们动用自己的网络优势把 Snapchat 压死了。而 TikTok 之所以成了他们的盲区，是因为 TikTok 被归类成社交网络，但它压根不是社交网络。TikTok 是个娱乐产品。你在 TikTok 上关注谁根本不重要，你看到什么，取决于你之前看过什么，然后系统会给你更多同类内容。它是一个 UGC（用户生成内容）网络。TikTok 的洞察在于：想拿到最好的内容，却把范围限制在你的社交关系圈里，这是一种人为的约束。我们要做的是把全网最好的内容给你。绝大多数内容当然都是垃圾，但这恰恰是刚才那个绝对值的问题——你不要去想 margin。

[1:01:12] Ben

You think about absolute numbers.The absolute amount of great content,even if the margin for great content is infinitesimal,if we have a ton of content,the absolute amount of great content is going to be very large.Then Meta is like, we're a social network.Meta is serving you content from your network of people you know,and TikTok is serving you the best content from around the world.That's why they took a huge chunk out of them.Meta had to shift.That's what's happened with Instagram and with Reelsis it's not really a social network.It is a entertainment product that pulls from the entire network.And social networking is like the group checked.It's possible in AI.Actually, social network is important again,because like we actually want humans.We want to have some sort of connection to them.That'll be interesting to see how that plays out.But the other thing with the models is they're so impactful on advertising.The biggest impact of the models,the biggest monetization right now is probably not anthropic open AI.It's the incremental gain that is happening for Google and Meta.Most of the stuff is pre-LLM,but we're getting to LLMs,whether it be generating advertising content,what do we want?

你要想的是绝对数量。优质内容的绝对量——哪怕优质内容的比例小到微乎其微，只要内容池足够大，优质内容的绝对量就会非常可观。而当时 Meta 还在说：我们是社交网络。Meta 给你推的是你认识的人组成的关系网里的内容，TikTok 给你推的是全世界最好的内容。所以 TikTok 才从他们身上咬下一大块。Meta 不得不转向。Instagram 和 Reels 后来发生的就是这件事——它其实已经不是社交网络了，它是一个从整个网络里抓内容的娱乐产品。而社交这件事，基本上退化成群聊了。到了 AI 时代，这事有可能反转：社交网络重新变得重要，因为我们是真的想要人，想跟人有某种连接。这会怎么演化会很有意思。不过模型还有另一层影响：它们对广告的冲击特别大。模型现在最大的影响、最大的变现，可能根本不在 Anthropic 和 OpenAI，而是 Google 和 Meta 拿到的那部分增量收益。这里面大部分东西还是 LLM 之前的技术，但我们正在往 LLM 上走——比如生成广告素材。我们要的是什么？

[1:02:14] Ben

We want verifiable domains.How do you verify if a generated image is good for an ad?Does the ad sell or not?They actually can validate their image creationand their text creation in a way no one else can.And their validation is the ad marketplace.Running a gazillion A-B tests on all these different things,see what works, see what doesn't.Most ads are a throwaway.It's fine.The vast majority of ads don't convert.They have this massive advantage,this huge liquid market that is a verification machinewhere the verifiers are humans decidingwhether they click on that ad and make a purchase or not,but they're doing it at global scalethat can actually have a feedback loopto make their products better.You're also going to get a worldwhere ad matching is actually still fairly crude.Here's the qualities of the person.Here's the qualities of the ad.And it's like you create an embedding,like a vector calculationand see what numbers matchand then you sort of match an ad to the person.What do LMs do?

我们要的是可验证的领域（verifiable domains）。你怎么判断一张生成出来的图片是不是一条好广告？看它卖不卖得动货。所以他们能以别人做不到的方式，去验证自己生成的图像和文案，而他们的验证器就是广告市场本身。在这么多变量上跑海量的 A/B 测试，看什么有效、什么没效。大多数广告都是废的，没关系，绝大多数广告本来就不转化。他们有一个巨大的优势：一个体量极大、流动性极强的市场，本身就是一台验证机器——验证者是活人，由他们决定点不点这条广告、买不买这个东西，而且这是在全球规模上发生的，能形成一个反馈闭环，让他们的产品越来越好。而且你会看到，现在的广告匹配其实还相当粗糙：这是这个人的特征，这是这条广告的特征，做个 embedding、做个向量计算，看看数值对不对得上，然后把广告匹配给这个人。那 LLM 是干什么的？

[1:03:06] Ben

LMs predict.We're going to move to this worldwhere Meta is going to look at peopleand say, this person probably wants to see this nextand they're going to go find that thingand show it to them.The potential upside in terms ofshowing people better adsthat are more relevant to them,they only need to increasea few percentage pointsfor the returns to be billions and billions of dollars.This alone is worth them investingin being on the leading edgeand having these amazing models.I think a big problem Meta hasis they don't tell this story.It's weird, but Mark Zuckerberghas the same problem Sam Altman does.He doesn't love ads.They have the best ad business in the world.They have an ad businessthat I think is a societal positive.You and I have set upthese little content businessesthat make great money.Content is,you get it right on social media.I grew up on Twitter,people sharing my links.It was amazing.If you're selling some product,the beauty of the internetis there is a niche out therethat wants that product.The question is,how do you find the niche?

LLM 是做预测的。我们会走向这样一个世界：Meta 看着一个人，判断这个人接下来大概想看到什么，然后去把那个东西找出来推给他。就“给人看更相关、更好的广告”这一件事，潜在的上行空间是——他们只要把效果往上抬几个百分点，回报就是几十上百亿美元级别的。光这一点就值得他们砸钱站在最前沿、拥有这些顶级模型。我觉得 Meta 一个很大的问题是：他们不讲这个故事。很奇怪，但 Mark Zuckerberg 跟 Sam Altman 有同一个毛病——他不喜欢广告。他们手里是全世界最好的广告生意，而且我认为这门广告生意对社会是净正向的。你和我都做了各自的内容小生意，也都赚得不错。内容这件事，你在社交媒体上做对了就能起来。我是在 Twitter 上长起来的，靠别人转我的链接，那感觉太棒了。如果你在卖某个产品，互联网的美妙之处在于：世界上一定存在一个想要这个产品的细分人群。问题是，你怎么找到这个人群？

[1:04:06] Ben

Facebook advertising.That's what it does.It helps products findthe people who didn't even knowthey wanted that product,but when they get it,they're so happy they got it.That's a huge societal positive.You have new businessfrom a new entrepreneurmaking a new product.You have customers who are happythey got somethingthat they didn't knowthey would get otherwise.Those customers, by the way,got lots of free entertainmentand they'd have to pay for italong the way.And Meta made a bunch of moneyfor themselves and their shareholders,which is basicallyeverywhere in the world.This is why advertising is greatand Meta's advertisingin particular is awesome.And I get frustratedthat Meta doesn't talk about that.Mark Sanberg has never reallytalked about the societal benefitsof advertisingexcept in passing in 20 years.He's handed it offto other people to take care ofand maybe there's a bitwhere him not paying attentionis why there isa certain grit and grindthat goes into buildingan advertising business.People get frustratedor have questions aboutas far as dataand all those sorts of things.And maybe there was a bitwhere he didn't wantto be involved in itand wipe his hands of it.But you saw thiswhen Apple passed ATT.

Facebook 广告。它干的就是这件事。它帮产品找到那些自己都不知道自己想要这个产品的人，而一旦拿到手，他们高兴得不得了。这是巨大的社会正外部性：一个新创业者做出一个新产品；一群顾客拿到了原本根本遇不上的东西，而且很开心。顺便说，这些顾客一路上还免费享受了大量娱乐内容，不用为此掏钱。而 Meta 为自己和股东赚了一大笔钱——它的股东基本上遍布全世界。这就是为什么广告是好东西，而 Meta 的广告尤其棒。让我很不爽的是，Meta 自己从来不讲这件事。二十年里，Mark Zuckerberg 除了偶尔顺口提一句，从来没有认真讲过广告的社会价值。他把这摊事交给别人去打理。也许正因为他不上心，广告生意里那股摸爬滚打的劲头才会显得有点粗粝——大家会在数据这类事情上不满、有疑问。也许他就是不想沾手，想把手洗干净。但 Apple 推 ATT 的时候，你就看清楚了。

[1:05:07] Ben

App tracking transparencywas one of the worstantitrust violationsin the history of technology.Apple unilaterallyobliterating all thesebusiness modelswhile they're simultaneouslybuilding their ownas far as advertising goesand doing all this tracking.Why?

App Tracking Transparency（应用追踪透明度）是科技史上最恶劣的反垄断行为之一。Apple 单方面把一大堆商业模式直接抹掉，同时自己在建广告业务、自己在做这些追踪。凭什么？

[1:05:20] Ben

Trust us.And meanwhile,they're runningthese advertisements.I remember that advertisementof people on the buslike overhearing everyonearound them,what they're saying.That was such a dishonestrepresentation of howadvertising workson the internet.You had Tim Cookin Congress talking aboutcompanies selling data.Facebook's not sellingyour data.That's value to them.Why would they sell the data?

「相信我们就好」。与此同时，他们还在打那些广告。我记得有一条广告，讲一个人在公交车上，周围所有人都在偷听他说话。那对互联网广告的运作方式是极其不诚实的呈现。Tim Cook 还在国会上大谈那些“卖用户数据”的公司。Facebook 不卖你的数据。数据对他们才是价值所在，他们为什么要卖？

[1:05:38] Ben

Meta was not preparedto respond.I think you got thisto Sheryl Sandbergback in the day.She, in every call,would talk about advertising,how great it isand have a bunch of case studies.People who are benefitingfrom advertisingand these new entrepreneurs.And then she leftand it's kind of likethat hole never got filled.It feels like it's a companythat's kind of like embarrassed.We make a lot of moneyfrom ads,but we got glassesand we're doing AI.It's like you have adsand ads are awesome.I think if they hadcommunicated thatmore consistently,they would bein a better placegenerally from a PR perspective.They would have beenin a better placerelative to Apple.And I think they would havean easier time right nowconvincing Wall Streetthat let us invest.The other problem isthey've spentcumulativehundredsome billion dollarson Oculus,which I dated all along.And so there's a bit wherewhy should we let youspend money again?

Meta 完全没准备好怎么回应。我觉得当年有 Sheryl Sandberg 在的时候是有的——她每次财报电话会都会讲广告，讲它有多好，摆一堆案例：哪些人因为广告受益，哪些新创业者因此起来。她一走，这个位置就再没人补上。现在这家公司给人的感觉是有点不好意思：我们靠广告赚了很多钱，但我们还有眼镜，我们还在做 AI。拜托，你有广告，广告很棒。我觉得如果他们把这件事讲得更一以贯之，从公关角度他们的处境会好很多，在跟 Apple 的对抗里也会好很多。而且我觉得现在要说服华尔街“让我们花这个钱”也会容易得多。另一个问题是，他们在 Oculus 上累计砸了一千多亿美元——这件事我从头到尾都不看好。所以就有了那种心理：凭什么再让你花一次钱？

[1:06:27]

The one major playerand companythat we haven't talked aboutmuch is Jensenand NVIDIA.And I'm curioushow you would tie this backto the notionof not understandingcommodity marketsin Silicon Valley,whether or not you thinkcompute ultimatelyis a commodity.I'm curious whether or notyou think intelligencewill ultimatelybe a commodity.It's interesting thatintelligence and compute,which seem to beby far the most interestingand important topicsin tech, both might becommodities and lessdifferentiated thantech-spired products.Well, that's always the case,though.The most interesting thingabout the internetis free distribution.Bandwidth is a commodity.The fact that I canpull out my phone right nowand connect to anyinformation sourcein the world for free,free on a marginal cost basis,is because it's a commodity.It changes the world.Commodities change the world.There is a aspectof differentiated productsby definitionhave lower TAMsbecause there'san elasticity aspect to it.Not everyone can affordto pay for it.People's willingnessto pay is going to differ.Your market is goingto be constrained.Apple's never goingto serve the whole worldby selling a device,whereas a Google canbecause it's free.That matters.

还有一个我们没怎么聊到的重量级玩家和公司，就是 Jensen 和 NVIDIA。我很好奇你会怎么把它跟“硅谷不理解大宗商品市场”这个说法接起来——你觉得算力最终是不是一种 commodity？我也好奇你觉得智能最终会不会变成 commodity。挺有意思的：智能和算力看起来是科技行业里最有意思、最重要的两件事，结果两个都可能是 commodity，差异化程度还不如科技行业里那些被追捧的产品。——其实一直都是这样啊。互联网最有意思的地方就是分发免费。带宽是 commodity。我现在能掏出手机、免费连上世界上任何一个信息源——按边际成本算是免费的——正是因为它是 commodity。它改变了世界。是 commodity 在改变世界。差异化产品有个天然的问题：按定义它的 TAM 更小，因为里面有弹性的因素——不是所有人都付得起，每个人的支付意愿也不一样，你的市场必然受限。Apple 靠卖设备永远服务不了全世界，而 Google 可以，因为它是免费的。这个差别很重要。

[1:07:25]

You're paying for a commodity,but to the extentit is availableto everyoneis the extentit is impactful.The internet is a commodity.It changes the world.So I don't thinkit'd be weirdthat intelligenceends up a commodityand changes the world.Commodities oftenare not thought ofas good of businessesas these differentiatedharm-reaching products.I'm curious for your thoughtson Jensenand NVIDIA specifically.NVIDIA's positionis, I think,definitely unnatural.You look at NVIDIA,they've maintainedall their margins.Isn't that amazing?

你为 commodity 付费，但它影响力的大小，取决于它对所有人的可得程度。互联网是 commodity，它改变了世界。所以我并不觉得“智能最终变成 commodity 并改变世界”有什么奇怪的。——大宗商品通常不被认为是像那些差异化产品那样好的生意。我很好奇你具体怎么看 Jensen 和 NVIDIA。——NVIDIA 的位置，我觉得肯定是“不自然”的。你看 NVIDIA，他们把所有 margin 都守住了。这不是很惊人吗？

[1:07:53] Ben

It's 2026and everyone's comingfor themand they're still charginghowever much moneyfor a chip,but they're actuallynot maintainingtheir marginsbecause this wholequestion of circular financingis people talk aboutLucent and things like thatand, you know,this whole dealand NVIDIA'sproviding a 25% backstop,but if you actuallyascribe a valueto that,to NVIDIA'staking equityin the NeoCloudsor whatever,they guaranteethey're going to buyall their computeto 2030.Why do they do that?

都 2026 年了，所有人都在冲着他们来，他们一颗芯片还能卖那么贵。但其实他们并没有真的守住 margin，因为所谓“循环融资”这个问题——大家会拿 Lucent 之类的例子来说事——比如这整笔交易里，NVIDIA 提供 25% 的兜底；如果你真的给这件事定个价，给 NVIDIA 在那些 neocloud 里拿股权、以及承诺买下它们到 2030 年的全部算力定个价，那他们为什么要这么干？

[1:08:20] Ben

So that the entityin questioncan get a lower costof capitalso they can buyover GPUs,etc.But implicit in that,why do they geta lower cost of capital?They get a lower costof capitalbecause NVIDIAassumed risk.This is my point before,risk never disappears,it just appearssomewhere else.Taking on riskhas a price.There is a worldwhere AI takes off,it never stops,and everything is fine,and NVIDIA capturedall the upsideof their risk.But there's also a worldwhere, say,there's this NeoCloudthey backed up,a ton of computecomes to market,the hyperscalershave plenty of compute,they don't have enough compute,NVIDIA's paying for a computethat no one wants.They just losta bunch of money.If you think about it,there's an expected valueof that investment.That expected valueit's not zero,it's not 100%,it's somewhere in the middle.But that is a diminutionof NVIDIA's profitability.If you actually lookat their business holistically,what that isis a price cut.Now, the price cutdidn't show up in margins,it didn't show upin what they're offering,but a lot of what NVIDIAis doing ishow can we maintainour marginseven if the wide view,sort of discounted cash flow,expected value,holistic view of our company,people do discounted cash flows,

是为了让相关的这家公司能拿到更低的资本成本，这样它就能买更多 GPU 等等。但这里面隐含着一个问题：它凭什么能拿到更低的资本成本？它能拿到更低的资本成本，是因为 NVIDIA 承担了风险。这就是我前面说的：风险从不消失，它只是转移到别处。承担风险是有价格的。有一个世界是：AI 起飞了、一路不停、一切都好，NVIDIA 把自己承担风险换来的上行全部收走。但也有另一个世界：他们兜底的某家 neocloud，市场上一大堆算力涌出来，hyperscaler 手里算力多得是——他们并不缺算力——NVIDIA 就得为没人要的算力买单，白白亏掉一大笔钱。你仔细想，这笔投资是有一个期望值的。这个期望值不是 0，也不是 100%，在中间某个位置。而这就是对 NVIDIA 盈利能力的一次折损。如果你整体地看他们这门生意，它实质上就是一次降价。只不过这次降价没体现在 margin 上，也没体现在他们的报价上。NVIDIA 现在做的很多事，本质是在问：我们怎么才能守住 margin——哪怕从更宽的视角看，从贴现现金流、期望值、整体视角来看……大家是会做 DCF 的，

[1:09:35] Ben

but are you actuallyconsidering all these pieces?The reality isthat moving stuffoff the balance sheetby and large works.But they're doingall these dealsto maintainwhat feelssomewhat unnatural.We have seen price cuts.They're just manifestingin these very bizarresort of ways.Now, in the long run,I think the challenge istheir ultimate competitorsare the hyperscalers,particularly Google and Amazon.So Google and Amazonaren't just buildingtheir own chips,but they're alsolooking to sellthose chips externally.Google already made a dealto sell like 20%of their TPUs to Anthropic.On the last earnings call,Andy Jassy practically confirmedthat they'll be sellingTranium 3sor maybe Tranium 4sor they'll be sellingTranium chipssort of eventually externally,which makes sense.That gives thema long-term buy-into these companies.There's a huge amountof R&D that goesinto developing chips.They get more leverageon their spend.It all makes sense.And by the way,they're not sellingtheir chips on differentiation.They're selling their chipsas commodities.NVIDIA is the oneselling on differentiation.People aren't goingto Amazon to use Tranium.So they're not cannibalizingthe attractivenessof their cloudby selling Tranium outside.

但你真的把这些环节都算进去了吗？现实是，把东西挪到资产负债表之外，大体上是奏效的。但他们在做的这一堆交易，是为了维持某种感觉上不太自然的东西。降价其实已经发生了，只是以非常古怪的方式呈现出来。而长期看，我觉得挑战在于：他们最终的竞争对手是 hyperscaler，尤其是 Google 和 Amazon。Google 和 Amazon 不只是在自研芯片，他们还打算把这些芯片对外卖。Google 已经谈成一笔交易，把大概 20% 的 TPU 卖给 Anthropic。在上一次财报电话会上，Andy Jassy 几乎等于确认了他们会对外卖 Trainium 3，或者 Trainium 4，反正最终会把 Trainium 芯片对外销售。这是说得通的：这能让这些公司长期绑在他们身上。芯片研发要投入巨额 R&D，对外卖能让这笔投入产生更大杠杆。逻辑上完全成立。而且顺带一提，他们卖芯片不是靠差异化在卖，是当 commodity 在卖。靠差异化卖芯片的是 NVIDIA。没有人是为了用 Trainium 才跑去 Amazon 的。所以把 Trainium 卖到外面，并不会侵蚀他们自家云的吸引力。

[1:10:38] Ben

So they're NVIDIA'sbiggest problembecause what'sthe number one advantagethat the hyperscalers have?Scale.Lower cost of capital.It's a capital fight.They have a lower costof capitalthan the NeoClouds do.The NeoClouds are,they'll buy NVIDIAleft, right, and center.And by the way,it also makes total sensethat why SpaceX,I like Elon's out there,we will always buy NVIDIAbecause they're the best.No, you'll buy NVIDIAbecause they're the most fungible.NVIDIA is true.It is the most fungible.Kuda's moatis dramatically diminishedbecause the modelsdon't care what they run onand that's what actually matterswhat's built on topof the models.But it still matters.It's still something of a moat.If you want to playthe game SpaceX is doingwhere we're going to build a lotand rent it outbut reserve the rightto pull it back,of course you're going to beon NVIDIAbecause the easiest wayto rent it outis to be on NVIDIA.You saw this very early,by the way.You go back to 2024,2023,NVIDIA starts talkingabout all thesesovereign clouds.They start talking aboutthey tried to callthese Neotron models.They had this thingin 2024,I remember.It was the first onewhere it was likethe rock star GTCat San Joseand like the huge Coliseum

所以他们才是 NVIDIA 最大的问题。因为 hyperscaler 的第一优势是什么？规模。更低的资本成本。这是一场资本的仗。他们的资本成本比 neocloud 低得多。而 neocloud 那边会左一批右一批地疯狂买 NVIDIA。顺便说，SpaceX 的做法也完全说得通。Elon 到处讲：我们永远买 NVIDIA，因为他们最好。不，你买 NVIDIA 是因为它最通用可互换（fungible）。这话是真的，它确实是最“可互换”的那个。CUDA 的护城河已经被大幅削弱了，因为模型并不在乎自己跑在什么硬件上，真正重要的是建在模型之上的东西。但它仍然有意义，仍然算是某种护城河。如果你想玩 SpaceX 那一套——先大规模建起来、租出去，但保留随时收回来自用的权利——那你当然会选 NVIDIA，因为最容易租出去的方式就是用 NVIDIA。顺便说，这一点很早就能看出来。回到 2024 年、2023 年，NVIDIA 开始大谈各种主权云（sovereign cloud），开始谈他们那些叫 Nemotron 的模型。2024 年他们搞过一个东西，我记得那是第一次——在圣何塞那种摇滚明星式的 GTC，巨大的场馆，

[1:11:38] Ben

and just no one comes out.It was a very boring GTC.The old ones used to beNVIDIAdemonstrating like50 gazillion thingsbecause they're throwingstuff at the wall.They knew they hadsomething with GPUsand they're trying to likefind the use case.Once LM showed up,it's like,oh, we have the use case.But they were coming upwith all theseenterprise offerings.I can't rememberwhat they were called.They were likethese modules basicallythat of coursethey were freebut they only ran on NVIDIAand you could seewhat they were doingis they were tryingto lock people in.Intel is a good example here.AMD cleaned them outin hyperscaler salesbecause the hyperscalerswere put in the effortto get stuff workingon AMD versus Intel.There are stillsmall differenceseven though they're x86because they're buyingat such scalethe investment to do itis worth itto get a better chipor lower priceor whatever it might be.The part of Intel's businessthat never flounderedwas selling to governmentand selling to enterprises.They don't havethe resourcesof a hyperscaler.They're not buyingat that scale.They're just going to keepbuying what they had before.That's why NVIDIAtalks about sellingto sovereign clouds.That's why they talk

结果什么都没端出来。那届 GTC 特别无聊。以前的 GTC 是 NVIDIA 一口气演示五十亿样东西，因为他们在到处扔东西、看哪个能粘在墙上。他们知道 GPU 手里有货，只是在找用例。等 LLM 一出现，就是“哦，用例来了”。但当时他们还在鼓捣一堆面向企业的产品，我记不清叫什么了，基本上就是一堆模块，当然是免费的，但只能跑在 NVIDIA 上——你一眼就看得出他们在干嘛：想把人锁死。Intel 是个很好的例子。AMD 在 hyperscaler 的采购里把 Intel 清扫出局，因为 hyperscaler 愿意花力气把东西迁到 AMD 上跑通。哪怕同是 x86，中间还是有细微差别；但因为他们的采购规模太大了，为了拿到更好的芯片、更低的价格或别的什么，这份投入就是划算的。Intel 这门生意里从来没垮掉的部分，是卖给政府和卖给企业。这些客户没有 hyperscaler 那样的资源，也没有那样的采购规模，他们就是会继续买以前买的东西。这就是为什么 NVIDIA 一直在讲卖给主权云，为什么他们讲

[1:12:36] Ben

about selling to enterprisesbecause they want to getin these marketswhere they're not goingto be balancingthis chip versus that chip.The hyperscalershave always beenthe threat to NVIDIAfor that reason.They're actually bigger.So you have this issuewhere the hyperscalersare the threat.The hyperscalershave a better costof capitalthan the other companiesNVIDIA wants to buy them.That's how you getthis deal this week.I see this dealas a response.That's why it goeswith the Google deal.Google can just issue equity.Shareholders don't love itbut their monetization capacityis much higherthan NVIDIAor NVIDIA's customers are.I think what NVIDIAis hoping formaybe they wouldn't saythis in so many wordsbut if we get to a worldwhere we actuallyrun out of powerthat's probably goodfor NVIDIAbecause in a worldwhere we're totallyconstrained on powereveryone wants the best stuff.We have to getthe best efficiency.The best token efficiency.And I think NVIDIAis still the most token efficientso that is a good worldfor them.Probably the biggest problemfor NVIDIAover the last couple yearsis I think the U.S.has actually broughta lot more power onlinethan expected.It surprised me.Whether it bewhat Elon didsort of behind the meter

卖给企业——因为他们想进那些不会去反复掂量“这颗芯片还是那颗芯片”的市场。正是因为这个原因，hyperscaler 一直是 NVIDIA 的威胁。而且他们体量还更大。于是就出现这个局面：hyperscaler 是威胁，而 hyperscaler 的资本成本又比 NVIDIA 想拉拢的那些公司更好。这就是你看到本周这笔交易的原因。我把这笔交易看成一种回应，所以它跟 Google 那笔交易是配套的。Google 直接增发股票就行，股东当然不喜欢，但他们的变现能力比 NVIDIA、也比 NVIDIA 的客户强得多。我觉得 NVIDIA 心里盼的——他们大概不会这么直白地说出来——是：如果我们真的走到电力见底那一天，那对 NVIDIA 反而是好事。因为在一个电力完全受限的世界里，所有人都要最好的东西，必须把效率做到极致，把 token 效率做到极致。我认为 NVIDIA 现在仍然是 token 效率最高的，所以那样的世界对他们有利。过去这两年 NVIDIA 最大的问题，可能反倒是美国实际新增上线的电力比预期多得多。这一点连我都意外。不管是 Elon 搞的那种表后（behind the meter）自建供电——

[1:13:45] Ben

which has been replicatedor West Texasand natural gasbut even likerestarting nuclear plants.We love how the U.S.responds to these things.It's awesome.It's actuallyone of the biggestencouraging signalsabout the U.S.is I was writingearly onassume this is a bubbleyou want thereto be a long-term payoff.The dot-comwe got fiber in the ground.And by the wayGoogle's played this gamebeforeGoogle builtits businessby buying updark fiber.They had the killersearch enginebut so muchof the powerof what they dois because theybought up all thisdark fiberthat was basicallyfree after thedot-com era.Our core internetstill runs onworld-com fiber.That was a lastingbenefit.The railroadsBNSF is throwingoff moneythat's going toGooglefrom Northern Pacificand Jay Cookselling bondsto retail investors.You want a bubblethat producessomething that lasts.And very longit's likewhat's going tolast from AI?

——这套做法后来被复制了，还是西德州加天然气，甚至包括重启核电站。我特别喜欢美国面对这类事情时的反应方式，太棒了。这其实是关于美国最令人振奋的信号之一。我很早就写过：就算你认定这是个泡沫，你也希望它能有长期的回报。互联网泡沫至少给我们把光纤埋进了地里。而且顺带一提，Google 早就玩过这一招——Google 的生意是靠收购暗光纤（dark fiber）建起来的。他们有那个杀手级搜索引擎，但他们能力的很大一部分，来自把互联网泡沫之后基本上白送的暗光纤全买了下来。我们今天核心的互联网，跑的还是 WorldCom 留下的光纤。那是实打实的长期红利。铁路也一样：BNSF 今天还在源源不断地吐出现金，而这一切的源头，是当年 Northern Pacific 和 Jay Cooke 向散户兜售债券。你想要的是一个能留下点什么的泡沫。所以很自然就要问：AI 最终会给我们留下什么？

[1:14:38] Patrick

The GPUsdon't last that long.Data centersokay finebut what is itgoing to be?Powerit has to be power.If we're in a worldwhere this allblows upand we have waytoo much powerthat is an amazingworld to be.We've always beenenergy constrained.Energy undergirdseverything.What would it be liketo live in a worldof energy abundance?

GPU 撑不了那么久。数据中心——好吧，行，但最终真正的瓶颈会是什么？电力，一定是电力。如果我们真的走到这一切崩掉、而手里的电多得用不完的那一天，那会是个特别美妙的世界。人类一直被能源约束着，能源是一切的底层支撑。生活在一个能源极大丰裕的世界里，会是什么样子？

[1:14:56]

It's hard to evenimaginebecause our mindsare so constrainedby the factwe've actuallyalways beenin energy scarcity.I think we've donean unbelievable job.Power for sureis a constraint.It's going to bea constraintbut I thinkit has taken longerto become a constraintthan anyone expectedand I wouldn't be surprisedif that includesJensen Huang.I think he thoughtinsufficient powerwas going to beNVIDIA's moatsooner than it happened.It turns out thatthe longer we haveenough powerthe more timeAmazon hasto make Trinium better.The more timeGoogle hasto make TPUs competitivefrom an efficiency standpointand if we getin a worldwhereit's a worldwhere those marginsseem very hardto sustain.I love hearingyour takes onjust everything going on.It's the mostinteresting timeI've ever observedin this worldthat you love so muchso thank you so muchfor your time.Thank you very much.If you enjoyedthis episodevisit Colossus.comyou'll findevery episodeof this podcastcomplete withhand edited transcripts.You can alsosubscribe to Colossusour quarterly printdigital and privateaudio publicationfeaturing in-depthprofiles of thefounders,investors,and companiesthat we admire most.Learn moreat Colossus.comslash subscribe.

Ben：这几乎难以想象，因为我们的脑子本身就被「我们从来都处在能源稀缺里」这个事实框死了。我觉得我们已经做得相当了不起了。电力肯定是个约束，将来也会是约束，但我认为它变成约束的时间，比所有人预想的都要晚——我猜这里面也包括 Jensen Huang。我想他原本以为「电力不够」会更早成为 NVIDIA 的护城河，结果没那么快到来。事实是，我们电力够用的时间越长，Amazon 就有越多时间把 Trainium 做得更好，Google 也有越多时间把 TPU 在能效上做到有竞争力；一旦走到那一步，那样的毛利水平看起来就非常难维持了。

Patrick：听你聊这些正在发生的一切，我真的很享受。这是我在这个你如此热爱的领域里，观察到的最有意思的一段时期。非常感谢你抽时间过来。

Ben：非常感谢。

Patrick：如果你喜欢这一期节目，欢迎访问 Colossus.com，那里有本播客的每一期节目，并配有人工精校的文字稿。你也可以订阅 Colossus——我们的季度出版物，含印刷版、数字版和私人音频，深度剖析我们最欣赏的创始人、投资人和公司。详情见 Colossus.com/subscribe。

[1:16:07] Patrick

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