Alex Sacerdote - How to Invest Through Technology Cycles - [Invest Like the Best, EP.477]

频道: Invest Like the Best with Patrick O'Shaughnessy
视频: https://colossus.com/episode/investing-on-the-s-curve/
原文语言: en
统计: 共 70 轮 · Patrick O'Shaughnessy 12 · Alex Sacerdote 55

`[0:00]`

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 grew revenue 3.2 times faster than the average American business.Visa, Vercel, Cursor, Stripe, Notion, 11Lab, Shopify, and 70,000 other businesses all runon 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 WorkOSAPIs 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 是唯一一个为「让你的财务团队更精简、更快、更好」而生的平台，平均每年为企业省下 5% 的开支，让你能专注于增长。Ramp 客户的收入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 家企业都跑在 Ramp 上。我自己的公司也是，你的也该是。详情见 ramp.com/invest。

OpenAI、Cursor、Anthropic、Perplexity 和 Vercel 有一个共同点：它们都用 WorkOS。要实现规模化的企业级采用，你必须交付 SSO（单点登录）、SCIM（用户目录同步）、RBAC（基于角色的权限控制）和审计日志这些核心能力。与其花好几个月自己造这些关键基础设施，你可以直接用 WorkOS 的 API，第零天就全部拿到。这就是为什么你听说过的那么多顶尖 AI 团队都已经跑在 WorkOS 上。WorkOS 是「成为企业就绪」最快的路径，让你专注于最重要的事——你的产品。访问 WorkOS.com 开始使用。

Rogo 出品的 Felix 是一个个人金融智能体（agent），它把一句提示词变成可以直接交付客户的成品，用的是你自己公司的模板、上下文和标准。给 Felix 发一封邮件，比如「把这些批注帮我改进去」，

`[1:11]` Patrick O'Shaughnessy

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 slash Felix.Hello and welcome, everyone.

或者「用这些邮件里的信息更新我的追踪表」。然后 Felix 会把做好的 PowerPoint 演示稿、Excel 模型和带出处的研究发回给你。Felix 按你团队本来的工作方式干活，全天候快速准确地交付成果。详情见 rogo.ai/felix。

大家好，欢迎收听。

`[1:30]` Patrick O'Shaughnessy

I'm Patrick O'Shaughnessy, and this is Invest Like the Best.This show is an open-ended exploration of markets, ideas, stories, and strategiesthat 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 opinionsand do not reflect the opinion of Positive Sum.This podcast is for informational purposes onlyand 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.My guest today is Alex Sakerdote, founder of WhaleRock Capital Management.WhaleRock is a technology-focused investment firm that manages more than $17 billionacross hedge fund, long-only, and hybrid strategies.Over the past three years, it's been one of the best-performing funds,compounding up roughly 44% per year.Alex invests through a single lens that he has refined over 20 years.

我是 Patrick O'Shaughnessy，这里是《Invest Like the Best》。本节目是对市场、想法、故事与策略的开放式探索，帮你更好地投资你的时间和你的钱。如果你喜欢这些对话、想更深入，可以看看 Colossus——我们的季刊，深度刻画那些正在塑造商业与投资的人。Colossus 和我们所有播客都能在 Colossus.com 找到。

Patrick O'Shaughnessy 是 Positive Sum 的 CEO。Patrick 和播客嘉宾表达的所有观点仅代表其个人，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。

我今天的嘉宾是 Alex Sacerdote，Whale Rock Capital Management 的创始人。Whale Rock 是一家专注科技的投资机构，管理规模超过 170 亿美元，覆盖对冲基金、纯多头（long-only）和混合策略。过去三年，它是表现最好的基金之一，年化复合回报约 44%。Alex 用一个他打磨了 20 年的单一镜头来投资。

`[2:39]` Patrick O'Shaughnessy

He looks for technology S-curves, durable competitive advantages,and underappreciated earnings power.This conversation is a tour through how he applies that framework today.We start with his highest conviction position, which is anthropic,and use it to work through the entire AI stack from chips to models to applications.Please enjoy my conversation with Alex Sakerdote.Alex, you were saying that your highest conviction position is anthropic right now.

他找的是技术 S 曲线、持久的竞争优势，以及被低估的盈利能力。这场对话是一次巡礼，看他今天如何运用这套框架。我们从他信心最高的持仓——Anthropic——开始，用它把整个 AI 技术栈从芯片到模型再到应用走一遍。请享受我和 Alex Sacerdote 的对话。

Alex，你刚才说你现在信心最高的持仓是 Anthropic。

`[3:04]` Patrick O'Shaughnessy

Can you tell the story of discovering it, making the investment,using this anecdote as an excuse to talk aboutall the things that I think you and I are mutually interested in right now?Investors like you investing in private markets,anthropic to business, AI, everything.It's a great way to zoom in.Why is it your highest conviction, and how did you get started?

能不能讲讲你是怎么发现它、怎么做这笔投资的？就借这个故事，把你和我现在都特别感兴趣的那些事聊一遍——像你这样的投资人怎么投一级市场（private markets）、Anthropic 这门生意、AI，方方面面。这是个很好的切入点。为什么它是你信心最高的持仓，你又是怎么起步的？

`[3:21]` Alex Sacerdote

When the gun went off with OpenAI, ChatGPT, in November 2022,we immediately took the firm and did a massive deep dive with our 10-person team.Anytime you have a new compute paradigm, there's a new stack,and that creates new winners and losers on the old stack.Now Jensen talks a lot about it, but it's power at the bottom, chips at the bottom,the clouds, and then the foundational models, and then the applications on top.And at that time, this was 2023 early, we said,we want to be in the chips and the infrastructure first.And not only do they get the demand first, but we know who the winners are.And no matter who wins above, which we weren't sure at the time,we know we're going to need tremendous amounts of compute.And we did a deep dive into that, which we can talk about later.But over the next two or three years,we started to get more clarity on how the foundational model layer would evolve.And at the time, two or three years ago, there were 60 different companies going after it.OpenAI was kind of in the lead.And we did a webinar in April 2023, and we said,look, this might be a winner-take-all.It might be a total commodity because there's open-source players.It might be a race to zero.

2022 年 11 月 OpenAI 的 ChatGPT 发令枪响的时候，我们立刻把整个机构、10 个人的团队全部投进去做了一次大规模深挖。每当出现一种新的计算范式（compute paradigm），就会出现一个新的技术栈，而这会在旧栈上制造出新的赢家和输家。现在 Jensen（黄仁勋）经常讲这个：最底下是电力，然后是芯片，再往上是云，再往上是基础模型（foundational models），最上面是应用。

当时是 2023 年初，我们说：我们要先站在芯片和基础设施里。它们不仅最先拿到需求，而且我们知道赢家是谁。不管上面谁赢——当时我们并不确定——我们知道一定会需要海量的算力。我们对此做了一次深挖，这个后面可以细聊。

但在接下来的两三年里，我们对基础模型这一层会怎么演化开始看得更清楚了。两三年前，有 60 家不同的公司在抢这块，OpenAI 算是领先的。我们在 2023 年 4 月做过一场线上分享，我们说：这可能是赢家通吃；也可能因为有开源玩家而变成彻底的大宗商品（commodity），变成一场向零竞赛；

`[4:44]` Alex Sacerdote

Or it might be an oligopoly where there's three or four leading players.And what we saw over the following three years was that almost all the startups fell away and died.And then some of the largest companies in the world, including Amazon and others,and Meta, Amazon never really showed up.We'll see what happens with Meta, but they came in strong.Basically, their effort faltered, and they had to do a total reboot.In the meantime, Anthropic kind of was this dark horse candidate, this startup.They focused really purely on the enterprise.OpenAI had kind of won the consumer.And then Gemini can never be counted out.We love Google as well.It's one of our largest positions.So it really started to look like a three-horse race and somewhat of an oligopoly.Very similar to how the cloud market evolved, where three companies underpin the entire SaaS cloud world and have really excellent businesses.And then we also were aware of the open-source risk.From China, we started to get comfortable that the quality of the tokens from the leading edge were superior.Because if you're 80% close to the top of the benchmarks, going from 80 to 85 is a huge unlock.The open-source guys, they don't have as much compute, so they can come close to the leading edge,

或者变成寡头垄断（oligopoly）——只剩三四家领先玩家。而接下来三年我们看到的是：几乎所有创业公司都掉队、死掉了。然后是世界上最大的一些公司，包括亚马逊等等，还有 Meta。亚马逊其实一直没真正出现；Meta 会怎样还得看，但他们来势很猛，结果努力受挫，不得不彻底重启。

与此同时，Anthropic 算是那匹黑马，一家创业公司。他们非常纯粹地聚焦企业市场（enterprise），OpenAI 则赢下了消费端。而 Gemini 永远不能被排除在外——我们也很喜欢 Google，它是我们最大的持仓之一。所以这真的开始看起来像一场三马竞赛，有点寡头垄断的意思。非常像云市场当年的演化：三家公司撑起整个 SaaS 云世界，而且都是极好的生意。

我们也意识到了开源的风险，来自中国的。我们后来慢慢放心了，因为最前沿产出的 token 质量确实更好。原因是：如果你已经做到榜单顶部的 80%，那从 80 分到 85 分是一个巨大的解锁。开源那批人算力没那么多，所以他们能逼近前沿，

`[6:18]` Alex Sacerdote

but they can't leapfrog it, and then they kind of falter.Meanwhile, the scaling laws and other means of improving the models, the feedback loops, etc.,we saw that there was a very strong runway.And everyone we talked to close to the industry saw that the scaling laws would continue.We developed this thesis that it would be a three-horse race.The big kicker was code.And this is the true unlock of AI.In the first few years, we knew AI would be big, but we were skeptical also.We made large investments because we knew the training would be there,but we weren't sure how much revenue might come and if it could truly replace labor.Because if you remember, the early versions of the models were good,but there was a lot of negative feedback from corporates.And could they be truly agentic?

但没法实现反超，然后就渐渐掉队了。

与此同时，规模定律（scaling laws，即模型规模越大能力越强的经验规律）以及其他改进模型的手段、各种反馈闭环，我们看到前面的跑道非常长。我们跟所有接近这个产业的人聊，他们都认为规模定律会继续有效。于是我们形成了「三马竞赛」这个论点。

最大的助推器是代码（code）。这才是 AI 真正的解锁点。头几年我们知道 AI 会很大，但我们同时也是怀疑的。我们做了大额投资，因为我们知道训练的需求一定在，但我们不确定能出来多少收入、它是否真的能替代人力。因为你还记得，早期版本的模型是不错，但企业客户那边有大量负面反馈。它们真的能做到智能体化（agentic，即自己一步步把任务执行完）吗？

`[7:10]` Alex Sacerdote

In 2025, the first cloud code and the coding tools really began to explode.You saw the first gen was like Microsoft Copilot, which is like $20 a month.And that could sort of improve your grammar of coding, maybe find a bug,maybe make a block of code, like a paragraph.And then Anthropoc came out sometime in the middle of the year,and it could do so much more.And it started to get to this point where it could run agentically,and the coding market just exploded.And then we started hearing people who could use it unfettered.We heard that even within Anthropoc at that time,people were spending $100 a day on tokens,which if you do the math, comes out to $20,000 or $30,000 a year.And if you think about how many coders there are in the world, 20 million,you've got a half a trillion dollar market just from coding alone.And mind you, that was on seven, eight, nine-month-old technology.We could see just on the coding market alone that Anthropoc had a tremendous opportunity ahead of it.So we made the investment at the $180 valuation, we said.And I think they were hoping to get to a $9 billion.Yeah, $1 to $9.$1 to $9, yeah.And then the numbers were like nothing we'd ever seen before,$100 to $1 billion on the way to $9.

到了 2025 年，第一批 Claude Code 和编码工具真正开始爆发。第一代是像微软 Copilot 那种，一个月 20 美元，能帮你改改代码的「语法」，也许找个 bug，也许写一小块代码，相当于一个段落。然后 Anthropic 在那一年年中某个时候推出了新东西，它能做的事多得多。它开始能智能体化地自己跑起来，编码这个市场就炸了。

然后我们开始听到那些能不受限制使用它的人的反馈。我们听说当时连 Anthropic 内部，都有人一天在 token 上花 100 美元——你算一下，一年就是 2 万到 3 万美元。再想想全世界有多少程序员，2000 万——光编码一项你就有一个 5000 亿美元的市场。而且请注意，那还是基于七八九个月前的老技术。

光看编码这一个市场，我们就能看到 Anthropic 前面有一个巨大的机会。所以我们在 1800 亿美元估值那一轮投了进去。当时他们希望能做到 90 亿美元。（是的，从 10 亿到 90 亿。）从 10 亿到 90 亿，对。然后那些数字是我们从没见过的——从 1 亿到 10 亿，一路奔向 90 亿。

`[8:37]` Alex Sacerdote

But when we did it in August of 2025, nobody had any idea what 2026 could be.The second big unlock lately is that Claude Code has gone to almost completely agentic.You had Andre Karpathy and Linus Torvalds, two of the smartest people in coding,and they completely flip-flopped.And Karpathy said last year's code tools could write 20% and 80% would be handwritten.That flipped when the latest model came out.And now he hasn't written a line of code except in English.And not to mention the pure unlock that we're going to get for the people that never knew how to code.So just coding alone has completely taken off.One difference between the cloud, GCP, AWS, and the AI companies is the clouds, generally it's commodity.They're selling you servers and storage.They have a lot of software on top and there is stickiness to it.But in the AI models, everyone thought it would be pure commodity.But there's tremendous differentiation within.There's different training methods and different skills that they're good at.And a lot of people have routers that switch in between, which sort of makes it sound like they're commodity.But Anthropic, they're very good for anything that has to do with private equity and finance.

但我们 2025 年 8 月做这笔投资的时候，没人知道 2026 年会是什么样。

最近第二个大解锁，是 Claude Code 已经几乎完全智能体化了。Andrej Karpathy 和 Linus Torvalds，编码界最聪明的两个人，态度彻底翻转了。Karpathy 说去年的编码工具能写 20%，另外 80% 得手写；最新模型出来之后这个比例反过来了，现在除了用英文，他一行代码都没写过。更别说那些本来不会写代码的人被解锁出来的纯增量。所以光是编码就已经完全起飞了。

云（GCP、AWS）和 AI 公司之间有一个区别：云基本上是大宗商品，他们卖给你的是服务器和存储，上面有很多软件、也确实有黏性。但在 AI 模型上，所有人都以为会是纯大宗商品，结果里面有巨大的差异化——不同的训练方法、不同擅长的技能。很多人做了路由器（router）在几家之间来回切，这听上去像是在说它们是大宗商品。但 Anthropic 在任何跟私募股权和金融相关的事情上都非常强。

`[10:03]` Alex Sacerdote

Google is very good for ingesting PDFs.There's a lot of, like, differentiation critical IP, which is a great competitive advantage.Many companies have come after the coding franchise.And Anthropic has been able to keep ahead.The other thing that's good about the foundational models and Anthropic is it's not just the API or the model.They're building a whole panoply or whole ecosystem of products around the API.So they've got the SDK, Cloud for Cowork, orchestration layer, and all the tools.They call it sort of a harness, which is the software around the API that gets the most out of the model.This was one of the things we saw with AWS really early on in 2013 was, oh, people thought it was a commodity server up in a warehouse.Big deal.They saw this was a new way of doing computing.So they invented all these products that they could see before everybody else that slowly built lock-in.The other way we think about this is where are we on this S-curve?

Google 非常擅长「吃」PDF。这里面有大量差异化的关键知识产权，这是很好的竞争优势。很多公司来抢编码这块特许经营权，Anthropic 一直能保持领先。

基础模型和 Anthropic 另一个好的地方在于：它不只是 API 或模型，他们在 API 周围搭了一整套产品全家桶、一整个生态。他们有 SDK、Claude for Cowork、编排层（orchestration layer）和各种工具。他们把这套叫做「脚手架」（harness），也就是 API 外面那层能把模型潜力压榨到极致的软件。

这正是我们 2013 年很早就在 AWS 身上看到的东西：当时人们以为那不过是仓库里一台大宗商品服务器，有什么了不起。而他们看到的是一种全新的计算方式，于是发明了一堆别人还看不见的产品，慢慢建起了锁定效应（lock-in）。

我们思考这件事的另一个角度是：我们现在在这条 S 曲线的哪个位置？

`[11:11]` Alex Sacerdote

And we have this infrastructure layer S-curve, which we think is 10% penetrated.And by the way, we think it's still one of the best ways to play AI.And we'll talk about how that feeds back through.But if you think about it, 200 or I don't know how many, 800 million people are using AI.They're just using AI 1.0, which is like a search engine on steroids.But now with these new primitives where you have clawed on your computer, linking it in, then you build skills.And then they're going to build true AI bots.Big corporations are going to build much larger.But where are we in terms of the amount of people doing that?

我们有一条基础设施层的 S 曲线，我们认为它的渗透率是 10%。顺便说，我们认为这仍然是押注 AI 最好的方式之一，后面我们会讲它是怎么反馈回来的。

但你想想，2 亿、或者我不知道，8 亿人在用 AI，他们用的只是 AI 1.0——相当于一个打了兴奋剂的搜索引擎。而现在有了这些新的原语（primitives，即最基础的能力积木）：Claude 装在你电脑上、跟各处连起来，然后你去构建技能（skills），接着他们会造出真正的 AI 机器人；大公司会造规模大得多的东西。但真正在这么干的人，到底有多少？

`[11:49]` Alex Sacerdote

I mean, Sunder said it's 10 bips of the knowledge workers of the world.So Anthropic has something like 14 or 15 million DAUs.Probably a small portion of those are truly doing AI the way you can do it.So that 10 bips, it's classic S-curve where these are the tinkerers.And then it's going to go to the early adopters.Then it's going to go to the early mainstream.But you're going to go from 10 bips to 1 to 2 or 3 percent to 5 percent to 15 percent in the next four years.And kind of a light switch this year went off in the enterprise where everybody realizes they need to do this now and do it fast.But it's still Internet 1.0 when it's like you knew you needed a website in 1998.But it's like hard to build that website.But this is coming together fast.And so the enterprise AI or enterprise application AI market is less than 1 percent penetrated.And, you know, we talk about S-curves.We call this an L-curve, just straight up.We'll take this to the infrastructure.We're at 10 basis points of people really using AI.And there's not enough compute in the world.So Anthropic has half of what they need right now.And that's before this huge take-up.Mark Andreessen said in the next four years, one thing he's sure of is there's not going to be enough compute.

Sundar（皮查伊）说是全世界知识工作者的 10 个基点（bips，1 个基点 = 万分之一，10 个基点就是千分之一）。Anthropic 有大概 1400 万到 1500 万日活（DAU），其中真正在用「能用的那种方式」用 AI 的可能只占一小部分。

所以那 10 个基点，是典型的 S 曲线起点：这些是「捣鼓者」（tinkerers）。然后会走到早期采用者，再到早期主流。未来四年你会从 10 个基点走到百分之一二三、到 5%、到 15%。今年企业里像是啪地打开了一个开关，所有人都意识到必须现在就做、而且要快。但这仍然像互联网 1.0——1998 年你知道你需要一个网站，但那个网站很难建。不过这一次拼起来的速度很快。

所以企业 AI、或者说企业应用层 AI 的市场，渗透率还不到 1%。我们讲 S 曲线，但这条我们叫它 L 曲线——直直往上冲。

把这个推到基础设施层：真正在用 AI 的人只有 10 个基点，而全世界的算力根本不够。Anthropic 现在拿到的只有他们需要量的一半，而这还是在那波巨大普及发生之前。Marc Andreessen 说未来四年他唯一确定的一件事，就是算力不会够。

`[13:14]` Patrick O'Shaughnessy

And I'm so curious when an investor like you, who historically was a public markets investor, you could hit buy and buy whatever you want, is now operating in lots of the most important private market companies.We can talk about Stripe or Databricks or OpenAI or Anthropic.How do you get the positions at the size that you want coming from the legacy of being able to just buy?

我特别好奇：像你这样一位历史上做二级市场（public markets）的投资人——按一下买入就能买你想买的任何东西——现在却在很多最重要的一级市场公司里持仓。我们可以聊 Stripe、Databricks、OpenAI 或者 Anthropic。你出身于「随时能买」的传统，现在是怎么拿到你想要的仓位规模的？

`[13:35]` Patrick O'Shaughnessy

How much of it is creativity just directly with the company?If it is directly with the company, so it's a double opt-in, they have to decide to let you in too.How do you do that?What have you learned about getting the allocation you want or the amount of equity you want in a private company, given that that wasn't your original background?

其中有多少是直接跟公司创造性地打交道？如果是直接跟公司谈，那就是双向选择（double opt-in），他们也得决定放你进来。你是怎么做到的？关于「拿到你想要的份额、拿到你想要的股权比例」，你学到了什么——毕竟这本来不是你的老本行。

`[13:54]` Alex Sacerdote

We got to know the company.One of our analysts knew people in the finance group there.We had a look at the $60 billion round, and we didn't know the company as well.The gross margins were negative, and frankly, we hadn't seen coding explode the way it had.And one thing about public markets is you get to know companies over a long period of time, and you can kind of invest on your own schedule.Then I got a chance to spend some time with Dario.I started to realize these guys, their management team is excellent.The focus, the dedication, they had almost no turnover, the quality of the code, and then the business plan was really starting to play out.It's one thing to grow from $100 to $1 billion, but it's another to do $9.And then so we reached out to the company as much as we could.They took a meeting with us.We did a 90-page PowerPoint deck where we used Cloud Code to scour the internet for all the feedback we could about the coding market and what their products were good at, where they might need to improve.And we also did our whole overview of what the coding market would be.They welcomed us into this round, and then we stayed close with the CFO.So it's been great to build a relationship with them, and I think we punched above our weight in terms of the allocation.

我们先是认识了这家公司。我们的一个分析师认识他们财务团队的人。600 亿美元那一轮我们看过，但当时我们对公司没那么了解——毛利率是负的，而且坦白说，我们当时还没看到编码像后来那样爆发。

二级市场有个好处：你可以在很长一段时间里慢慢认识一家公司，可以按自己的节奏投。

后来我有机会跟 Dario（Amodei）待了一段时间，我开始意识到这帮人的管理团队非常优秀——那种专注、那种投入，几乎没有人员流失，代码质量也高，而且商业计划真的开始兑现了。从 1 亿美元长到 10 亿美元是一回事，做到 90 亿又是另一回事。

于是我们尽可能地去接触公司，他们接受了跟我们见面。我们做了一份 90 页的 PPT，用 Claude Code 把全网关于编码市场的反馈都扒了一遍——他们的产品好在哪、哪里可能需要改进；我们也把整个编码市场会有多大做了一遍推演。他们欢迎我们进入那一轮，之后我们跟 CFO 一直保持紧密联系。所以跟他们建立关系这件事非常好，我觉得我们在配额上打出了超出自己体量的成绩。

`[15:16]` Alex Sacerdote

So that one was a total home run.We are in this period where the unicorn market is bigger than most stock markets in Europe, maybe even combined.It's definitely bigger than Germany.It's definitely bigger than the U.K.Even before we invested in privates, the first one was 2020.We have to know these companies, and you really have to know them now because sometimes they're the biggest companies in the space and have huge impacts.So we do 2,000 to 3,000 face-to-face meetings with management teams a year, and about 10% or 15% of those are with privates.And then we kind of focus in on the companies that we really want to learn about and find ways to meet with them, get involved in the rounds.Our first one was Stripe.Stripe. We had a large investment at the time.This is 2017, 18, 19, and 2020.We own Audion, which is a fantastic payments company, and they're a next-gen cloud payments company taking from WorldPay, and the cloud modern payments was 5% of total $80 trillion market or what have you.But you can't invest in Audion unless you know Stripe like the back of your hand.And so we did tremendous amounts of due diligence, talked to 200 customers in Audion.But when we asked them about Audion, we asked about Stripe, and we realized this is Coke and Pepsi.

那一笔是彻头彻尾的全垒打。

我们正处在这样一个时期：独角兽市场比欧洲大多数股市都大，也许加起来都比不过。它肯定比德国大，肯定比英国大。

就算在我们开始投一级市场之前——第一笔是 2020 年——我们也必须了解这些公司；而现在你更是必须了解它们，因为有时候它们就是某个领域里最大的公司，影响力巨大。所以我们一年做 2000 到 3000 场跟管理层的面对面会议，其中大约 10% 到 15% 是跟一级市场公司。然后我们会聚焦到那些我们真的想深入了解的公司，想办法见到他们、参与到融资轮里。

我们第一笔是 Stripe。当时我们有一笔大额投资，那是 2017、18、19 和 2020 年。我们持有 Adyen，一家非常棒的支付公司，是从 WorldPay 手里抢生意的新一代云支付公司，而云原生的现代支付当时只占 80 万亿美元总盘子的 5%。但你不可能在不像了解自己手背一样了解 Stripe 的情况下去投 Adyen。所以我们做了海量尽调，为 Adyen 访谈了 200 个客户。但当我们问他们 Adyen 的时候，我们也会问 Stripe，然后我们意识到：这是可口可乐和百事可乐。

`[16:35]` Alex Sacerdote

We said we got to find a way to invest, and I finally got to meet the Carlson brothers in 2019.And so that was our first one.We weren't really known for privates.I've got a friend who's involved with a venture firm that has tremendous amounts, and I talked to him about it, and I said, let me know if you ever want to sell some.And then I get a call from him during COVID in April of 2020, and we knew a lot about Stripe.We didn't have the full financials, but we knew enough that at that valuation, I think it was $35 billion, they disclosed we had over half a trillion of TPV.And we knew that Audion's take rate was 25 or 30 bps, and we knew Stripe's was 40 or 50.And we knew how many employees they had, so we could kind of get at the profitability.It turned out the take rate was higher.It turned out they were being modest about their TPV.It was much higher than the 550.It was closer to the $1 trillion.We underwrote the thing under our assumptions, and it was much better.And then we were able to upsize that from the seller to a $100 million block.The VCs are going to own, and then most of them are going to sell.They like it that we'll own and own in the public market, which we did with NewBank as well, owned it for a long period of time in the public market as well.

我们说必须想办法投进去。我最终在 2019 年见到了 Collison 兄弟，那是我们的第一笔。

当时我们并不以做一级市场闻名。我有个朋友在一家规模很大的风投机构，我跟他聊过这事，我说：你要是什么时候想卖一点，告诉我。然后 2020 年 4 月疫情期间我接到他电话，而我们对 Stripe 已经知道很多了。我们没有完整财务数据，但我们知道的足够多——在那个估值上，我记得是 350 亿美元——他们披露过总支付额（TPV）超过 5000 亿美元。我们知道 Adyen 的费率（take rate）是 25 到 30 个基点，我们知道 Stripe 的是 40 到 50 个基点。我们知道他们有多少员工，所以能大致推出盈利能力。

结果费率比我们估的更高；结果他们对 TPV 的说法很谦虚，实际远高于 5500 亿，接近 1 万亿美元。我们是按自己的假设来承销这笔投资的，结果比假设好得多。然后我们还从卖方那里把这笔加码到了 1 亿美元的大宗。

VC 们会持有，然后大部分人会卖出。他们喜欢我们这种「会一直持有、而且到公开市场也继续持有」的买家——我们在 Nubank 上也是这么干的，上市后在二级市场也长期持有。

`[17:52]`

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`[19:03]` Patrick O'Shaughnessy

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.Maybe now is the right time to lay out everything you've ever learned about S-curves.

所以我相信，在 AI 时代跑赢的机构，会是那些跑在 Ridgeline 统一平台上的机构。如果你认真对待公司的 AI 战略，Ridgeline 应该出现在那场讨论里。你可以在 ridgeline.ai 申请演示。

也许现在是个好时机，把你关于 S 曲线学到的所有东西都摊开讲讲。

`[19:26]` Alex Sacerdote

Obviously, your firm is sort of predicated on this idea of technology adoption life cycles and investing in companies at the right time amidst a certain platform change or S-curve change.I'd love you to go into the super deep detail of what you've learned since this is the lens through which you've viewed markets and stocks for a long time.Bring us into like the nitty-gritty, fine-grained, nuanced detail of why S-curves can be so useful for investing.We have an investment framework.It's S-curve, competitive advantage, and then underappreciated earnings power.And when you get the right part of the S-curve, you get exponential unit growth.If you have a very strong business model, which in tech, there's so many of those for so many different types of moats, your earnings don't grow linearly.They grow exponentially.And that's the last piece.Invest when there's underappreciated long-term earnings power.And very often, the earnings can grow from $1 to $10.And it happens way more than you think.And it allows you to buy some of the best companies in the world for extremely low PEs.When we were buying NVIDIA in 2023, we were paying four times earnings.When we bought Tesla in 2019 for the car, S-curve, we were paying five times earnings.

很明显，你的机构基本上是建立在这个想法之上的：技术采用的生命周期，以及在某个平台变迁或 S 曲线变迁中、在正确的时点投资。我希望你能讲得特别深——因为这是你长期以来看市场和股票的镜头。带我们进到最细的颗粒度里，讲讲为什么 S 曲线对投资这么有用。

我们有一套投资框架：S 曲线、竞争优势、被低估的盈利能力。当你踩到 S 曲线正确的那一段，你会拿到指数级的销量增长；如果这家公司商业模式很强——而科技里这样的太多了，各种不同类型的护城河——那么它的利润不是线性增长的，是指数级增长的。然后是最后一块：在长期盈利能力被低估的时候投。而利润常常能从 1 块钱长到 10 块钱，这种事发生的频率远比你以为的高。这也让你能用极低的市盈率买到世界上最好的一些公司。

我们 2023 年买 NVIDIA 的时候，付的是 4 倍市盈率。2019 年我们因为汽车那条 S 曲线买 Tesla 的时候，付的是 5 倍市盈率。

`[20:44]` Alex Sacerdote

When we were owning Apple, we were paying four times earnings.When we bought Amazon for AWS, we were getting it for free.The world doesn't think exponentially.And they're so focused on the next year, the next quarter.Very few people believe you can accurately predict two, three, four years out.But if you follow and understand the S-curve, you know the moats and you know how to model, you really can predict these great things.So let's go to the S-curve.So the S-curve is crucial because every technology follows this pattern where it comes out.The smartphones were out 10 years before the iPhone.The internet was out 20 years before Netscape.AI has been out, hidden inside of these companies, but it wasn't until ChatGPT took it public and ignited what it was.So electric vehicles, Tesla went public 15 years before 2019 when it went vertical because there were so many barriers to adoption.The first smartphones, they were clunky.They didn't have touchscreen.There wasn't a wireless data system.And then they were too expensive.They were $500 or $600.Steve Jobs got the price to $200.There was AT&T at a 3G network.It was touchscreen.It was so easy.Your grandmother could do it.And he built an ecosystem and made it simple.

我们持有 Apple 的时候，付的是 4 倍市盈率。我们为了 AWS 买 Amazon 的时候，AWS 相当于白送。

这个世界不会用指数思维。大家太盯着明年、下个季度了。很少有人相信你能准确预测两年、三年、四年之后。但如果你跟踪并理解 S 曲线，你懂护城河、你会建模，你是真的能预测这些伟大的事情的。

那我们说回 S 曲线。S 曲线之所以关键，是因为每一项技术都遵循这个模式：它先出来。智能手机在 iPhone 之前已经存在 10 年了；互联网在 Netscape 之前已经存在 20 年了；AI 也一直存在，藏在这些公司内部，直到 ChatGPT 把它带到台前、点燃了它。电动车也一样，Tesla 上市比 2019 年它垂直起飞早了 15 年，因为有太多采用的障碍。

最早的智能手机很笨重，没有触摸屏，没有无线数据网络，而且太贵，要五六百美元。Steve Jobs 把价格做到了 200 美元，有了 AT&T 的 3G 网络，有了触摸屏，简单到你奶奶都会用。然后他建了一个生态，并且把它做简单了。

`[22:06]` Alex Sacerdote

So all the barriers to adoption were eliminated.And then you rocket.When those barriers are removed, that's the tornado of demand that everybody in the world knows they need this right away.And so that's the flip that happens.It happened with electric vehicles.The price was too high.Elon got the price to $40,000.Range anxiety was there.He got the range to 300 miles.The supply chain was finally in place so he could churn out millions of these things.That triggers the inflection.Now, the other nuance, it's not just, oh, it's taken off now.It's how big is this S-curve?

于是所有采用的障碍都被消除了。然后你就一飞冲天。

当那些障碍被移除，就出现「需求龙卷风」（tornado of demand）——全世界都知道自己马上就需要这个东西。这就是那个翻转时刻。

电动车也发生了同样的事：价格太高，Elon 把价格做到 4 万美元；有里程焦虑，他把续航做到 300 英里；供应链终于就位，他能一年造出几百万辆。这些触发了拐点。

不过还有另一个微妙之处：不只是「哦，它起飞了」，还要问这条 S 曲线有多大。

`[22:43]` Alex Sacerdote

How tall it is?So you know when to sell, how long to hold on.Because we're underwriting out two or three years.We have to know what the growth looks like thereafter.And these S-curves can be dynamic.So when Amazon had AWS and it was a hidden line item inside of Amazon covered by retail internet analysts, we realized the TAM for this, it was the largest TAM in enterprise IT ever.Because previously the TAM was routers, memory, storage, Dell, EMC.But they were doing it all.You want to know how tall the S-curve is.So we figured out they were addressing 600 billion of IT systems directly addressing that.And then we said it's probably going to be 50% deflationary.And then therefore, we're 1% or 2% penetrated.But then over time, we realized it actually wasn't deflationary.If you talk to anybody now, I say if you build it yourself, it's about the same price.So that means the TAM was so much bigger.There's mega S-curves and there's sub-S-curves.We've been lucky that we've had internet 1.0, mobile, cloud, e-commerce, and now AI, which we can confidently say is the biggest.And all these things build upon one another.With the electric vehicle S-curve, you have to pay attention, too, because we thought probably maybe 40% to 50% of the cars would go electric.

它有多高？这样你才知道什么时候卖、该拿多久。因为我们是按未来两三年来承销的，我们必须知道之后的增长长什么样。

而且这些 S 曲线是动态的。比如 Amazon 有 AWS 的时候，它只是财报里一个被零售互联网分析师覆盖的隐藏科目，而我们意识到它的 TAM（total addressable market，总可寻址市场）是企业 IT 史上最大的一个。因为过去的 TAM 是路由器、内存、存储、Dell、EMC，而 AWS 把这些全干了。

你想知道这条 S 曲线有多高。于是我们算出他们直接对应着 6000 亿美元的 IT 系统开支。然后我们说这大概会有 50% 的通缩效应，因此我们处在 1% 到 2% 的渗透率。但后来我们意识到它其实并没有通缩——你现在去问任何人，如果你自建，价格差不多。这意味着 TAM 比我们想的大得多。

有超级 S 曲线，也有子 S 曲线。我们很幸运，经历了互联网 1.0、移动、云、电商，现在是 AI——我们可以有把握地说 AI 是最大的一条。而且这些东西都是一层叠一层建起来的。

电动车那条 S 曲线你也得留神，因为我们本来以为大概 40% 到 50% 的车会电动化，

`[24:13]` Alex Sacerdote

But it did hit a big wall at 10% or 15%.Usually, the S-curves go kind of all the way.But in this case, for a variety of reasons, it didn't.So you have to adjust and you have to stay on top of it.And generally, when something gets to sort of 30%, 40% penetrated, then you stop having exponential growth, which means the sell side catches up and there's no longer big beats.And is that when you sell?

但它在 10% 或 15% 就撞上了一堵大墙。通常 S 曲线会一路走完，但这一次因为各种原因没有。所以你必须调整，必须一直盯着。

一般来说，当某个东西渗透到 30%、40% 的时候，你就不再有指数级增长了，这意味着卖方分析师会追上来，不再有大幅超预期。

（那这就是你卖出的时候吗？）

`[24:41]` Alex Sacerdote

Generally, we like the high growth.And it was a mistake with Apple because in the first five or six years of Apple, it was awesome.I mean, it was our largest position.It would go up 50%, 70% a year, except for 08.And then we sold in 2012 when it got to sort of 50% of the U.S. had a smartphone.And with Apple, they maintained their leadership position.It had a couple of years of underperformance.And then the multiple got low and they added several ancillary things.And then they also got to play in the application because they get 30% of the app.So they were able to compound very nicely, say, 20%.But the big years were in the 0% to 50% part of the curve.I'm so fascinated by this sometimes decade-plus long flat line at the beginning of one of these curves, which makes me wonder what you've learned about the right moment to buy or even start paying attention before you buy.How do you measure that?

总的来说我们喜欢高增长。Apple 那次是个错误：Apple 的前五六年非常棒，那是我们最大的持仓，除了 2008 年，每年涨 50%、70%。然后我们在 2012 年卖了，当时美国大约 50% 的人有了智能手机。而 Apple 保住了它的领先地位，虽然有几年跑输，但后来估值倍数变得很低，他们又加了好几项附属业务，而且他们还能在应用层分一杯羹——每笔 App 收入抽 30%。所以他们后来也复合得很好，比如 20%。但最大的那几年，是曲线上 0% 到 50% 那一段。

（我特别着迷于这些曲线开头那条有时候长达十年以上的平线，这让我想问：关于「什么时候买、甚至什么时候该开始关注」，你学到了什么？你怎么衡量？）

`[25:36]` Alex Sacerdote

Is it always different?What are the pitfalls that you've fallen into?How do you know when to start thinking about buying in one of these things?Andy Grove says, when you have strategic inflection points, you can't trust the data.And strategic inflection points are about intuition, anecdotal evidence.I love this book called The Tao Jones Averages, A Guide to Whole Brain Investing, which is right brain and left brain.And the best investors have the creative side where it's visual.It's connecting the dots.We invested in the mobile video game S-curve for so long.The screens were small on the phones and the processing power wasn't good.So you had all these casual games.But then I was in China and I saw this little 12-year-old boy with a huge phone and he was like playing an awesome video game.I'm like, oh my God, it's now coming to the phone.So it's visual.Enterprise is hard because you can't see it.Smartphone, you can see, oh my God, I got it.It's amazing.With AI, there's some intuition there.But enterprise, you don't really get to do that.We go to the Gartner IT Symposium, 30,000 American CIOs go there.We saw this happen with Splunk, where that used to be an amazing database company.And their room where they were explaining it was like standing room only.

（是不是每次都不一样？你踩过哪些坑？你怎么知道该开始考虑买了？）

Andy Grove 说，遇到战略拐点（strategic inflection points）的时候，你不能相信数据。战略拐点靠的是直觉和轶事性的证据。

我很喜欢一本书叫《The Tao Jones Averages》（《道·琼斯均值：全脑投资指南》），讲的是右脑和左脑。最好的投资人都有创造性的那一面——那是视觉的，是把点连起来。

我们在移动端手游那条 S 曲线上投了很久。当时手机屏幕小、处理能力不行，所以全是休闲小游戏。但有一次我在中国，看到一个 12 岁的小男孩拿着一个大屏手机在玩一款很棒的游戏，我心想：天哪，它真的来到手机上了。所以这是视觉性的。

企业级的东西难就难在你看不见。智能手机你看得见——天哪我拿到了，太惊艳了。AI 也有一些直觉可循，但企业级你真的没法这样看。

我们会去 Gartner IT 峰会，三万名美国 CIO 会去。我们在 Splunk 身上看到过这一幕——它当年是一家很棒的数据库公司，他们讲解产品的那个房间站着都挤不下。

`[26:57]` Alex Sacerdote

And we saw that with VMware 30 years ago where they virtualized the server.There was standing room only.And you could just see the corporate demand just beginning.And with AWS, we went there and their grand ballroom was completely packed.And that was at 9 o'clock.And at 10 o'clock, the grand ballroom was completely packed.11 o'clock.So you could see the demand exploding before it happened.We look for all kinds of clues.And there's a whole pattern recognition that happens.And by the way, it's okay to be late.It's okay to miss the first one, two, three years in a lot of cases.Because if the top of the S curve is half a trillion, the growth can go on for a long time.So you don't always have to be right there.It's okay to miss the first 100%.Peter Lynch, I started at Fidelity.And he loved to mentor the kids.So I got some time with him.He said, white out the chart.It's all about the future.So it's okay to miss.But what helps about the S curve is sort of how long it goes for.Then there's the slope of the S curve, which is important.And a lot of people think, because we're in a modern world, everything's so fast.But there's a lot of factors that determine the pace of the adoption.And we commissioned this gentleman, Horace Dadu, used to work with Clayton Christensen to go look in history.

我们在 30 年前的 VMware 身上也看到过，他们把服务器虚拟化，会场也是站着都挤不下。你就能看见企业需求刚刚开始起来。

AWS 也是，我们去了，他们的大宴会厅完全爆满，那是 9 点；10 点大宴会厅还是完全爆满；11 点也是。所以你能在需求爆发之前就看见它在爆发。

我们找各种各样的线索，这里面有一整套模式识别。

顺便说，晚一点是没关系的。很多情况下，错过头一两三年是没关系的。因为如果这条 S 曲线的顶是 5000 亿美元，增长可以持续很久。所以你不一定非得站在最起点，错过第一个 100% 的涨幅没关系。

我是从富达（Fidelity）起步的，Peter Lynch 很喜欢带新人，所以我跟他有过一些时间。他说：把图表涂白。一切都是关于未来的。所以错过没关系。

但 S 曲线帮你的地方在于判断它能走多久。然后还有 S 曲线的斜率，这也很重要。很多人觉得因为我们身处现代世界，一切都很快。但决定采用速度的因素有很多。

我们委托过一位叫 Horace Dediu 的先生——他曾经跟 Clayton Christensen 共事——去把历史扒了一遍。

`[28:24]` Alex Sacerdote

And we have the big S curves on our wall over the last 100 years.And the radio S curve is one of the fastest ever.It took seven years to reach like 100% penetration.But the dishwasher S curve is like that because it needs to be plugged into the back end.B2B stuff can take a long time because it needs to be plugged into the existing systems.Consumers generally tend to go a lot faster.I love that.The radio and the dishwasher, the two models for adoption.I covered internet at Fidelity.My first stock was Amazon.That's a whole other story, which is a lot of fun.But I also did B2B internet.And there was a whole huge bull case on that.The underlying infrastructure wasn't in place for B2B to happen.Ultimately, it happened 20 years later with SaaS.That is a risk with AI in that these big companies are very security conscious, can be slow to move.There's a lot of cultural issues with AI where you really need a few evangelists to push it through.The top management needs to push it through.But then the IT is saying this is risky.And that happened with cloud, too.That was one of the big things with cloud where everybody was afraid it's unsecure to have your data in the cloud.And then we saw the CIA do it.

我们墙上挂着过去 100 年那些大的 S 曲线。收音机那条 S 曲线是史上最快的之一，7 年就达到接近 100% 的渗透率。但洗碗机那条 S 曲线就很慢，因为它需要接到后端管线上。B2B 的东西可能要很久，因为它需要接进已有系统；消费级的通常快得多。

（我太喜欢这个了——收音机和洗碗机，两种采用模型。）

我在富达是覆盖互联网的，我的第一只股票是 Amazon，那是另一个很有意思的故事。但我也做过 B2B 互联网，当时有一整套宏大的多头逻辑，但底层基础设施还没到位，B2B 做不起来，最终是 20 年后靠 SaaS 才实现的。

这对 AI 也是个风险：这些大公司非常在意安全，动作可能很慢。AI 这件事有很多文化层面的问题，你真的需要几个布道者去把它推进去，最高管理层需要去推，但 IT 部门会说这有风险。

云当年也是这样。云最大的问题之一就是所有人都害怕把数据放在云上不安全。然后我们看到 CIA 这么干了。

`[29:47]` Alex Sacerdote

And we saw Capital One.And we talked to the Capital One CIA.We said it's more secure in the cloud.And then it really started to take off.Those takeoffs, maybe because SaaS is like the dishwasher and because cloud, it's got to be plugged in.It meant that, yeah, it was growing, but it was sort of a 30 to 40, maybe a 50% growth rate.But what's amazing about AI is you just, at least with consumers or even business, you just open up the browser and it's there.And so that's why we're getting this straight up.And I think there's enough runway in the near term going from 10 bips of people really using it to 2 to 5 or whatever, which is going to cause it to keep on going straight up.This, we call it a backwards L curve.So it's really pretty exciting.What have you learned about when the group that ends up being the leader separates itself from one of these competitive packs?

我们也看到了 Capital One。我们跟 Capital One 的 CIO 聊，我们说：放在云上其实更安全。然后它就真的起飞了。

那些起飞——也许因为 SaaS 像洗碗机、云也得「接管线」——意味着它虽然在长，但增速大概是 30% 到 40%、也许 50%。

但 AI 的惊人之处在于，至少对消费者、甚至对企业来说，你打开浏览器它就在那儿。这就是为什么我们看到的是直上直下。而且我认为近期跑道足够：从真正在用的 10 个基点走到 2% 到 5% 什么的，这会让它继续直上。我们把这个叫做反向 L 曲线。所以真的挺让人兴奋的。

（关于「最终成为领导者的那一家什么时候从竞争群里分离出来」，你学到了什么？）

`[30:41]` Alex Sacerdote

So you're talking there mostly about overall growth of the S curve in demand.There's always multiple players fighting for it.It seems like you kind of invest after someone has separated themselves from the pack, not try to pick the winners from the pack.You look for the S curve, then we do an exhaustive study of everybody with exposure in that area and try and find the one with a very powerful competitive advantage.And a lot of people didn't like tech.Warren Buffett didn't like tech because he couldn't predict the future.Change too fast.Yeah.And so the S curve is our map for looking in the future.Now, a lot of people were worried about tech because they thought there was so much disruption, you could never trust a company to be a long-lived asset.What we found over the years is some of the competitive advantages within the digital world are more powerful, if not equally or more powerful than in the offline world.You've got the network effect that was so powerful for LinkedIn, Facebook, Alibaba, you name it.Then you can become an industry standard.Oracle and Bloomberg are the industry standard.Oracle charge a lot and there's free versions.There's open source Oracle, but they had all the database administrators.

（你刚才说的主要是 S 曲线整体需求的增长。总会有多个玩家在争。看起来你是在某一家已经从群里分离出来之后才投，而不是试图在群里挑赢家。）

我们先找 S 曲线，然后对这个领域里所有有敞口的公司做一次穷尽式的研究，试着找出那个竞争优势非常强的。

很多人不喜欢科技。Warren Buffett 不喜欢科技，因为他没法预测未来，变化太快。（是的。）而 S 曲线就是我们用来看未来的地图。

另外，很多人担心科技，是因为他们觉得颠覆太多，你永远不能信任一家公司是长寿资产。而我们这些年发现的是：数字世界里的某些竞争优势，比线下世界的更强大，至少同样强大。

你有网络效应，它对 LinkedIn、Facebook、阿里巴巴这些都极其强大。你还可以成为行业标准，Oracle 和 Bloomberg 就是行业标准。Oracle 收费很高，也有免费版本、有开源的 Oracle，但他们握住了所有的数据库管理员，

`[32:00]` Alex Sacerdote

They had all the software that was tuned to work with them.They basically had a chokehold on the relational database market forever.You can get to scale very quickly because these S curves grow and all of a sudden Anthropic is doing 30 billion in sales or Amazon had so much scale and they got it quickly.So they got a Walmart size scale advantage in five years versus 40 years for Walmart.So you can have network effects scale.You can become industry standard.You can be a platform that people build on top of.You can have critical intellectual property, which was what Qualcomm had.You couldn't make a phone without paying them.Or ASML has critical intellectual property.You can't make a chip without their lithography.You can also have brand and brands very important because Google, Amazon, they got to grow.They never had to advertise.Elon's never had to advertise for anything and cost to acquire versus lifetime.It's the whole business model.Almost all the companies I mentioned have all of these rolled into one.Sometimes we can notice these things before the rest of the world.One of our high points was we pitched Amazon for AWS at 2013 at the Robinhood Investors Conference.And we said the bulls have no idea what they're sitting on.

也握住了所有为他们调优过的软件。他们基本上永远扼住了关系型数据库市场的咽喉。

你还能非常快地做到规模——因为这些 S 曲线长得快，Anthropic 突然就做到了 300 亿美元销售额；Amazon 拿到了那么大的规模，而且拿得很快，五年就获得了沃尔玛量级的规模优势，而沃尔玛用了 40 年。

所以你可以有网络效应、有规模，可以成为行业标准，可以成为别人在上面开发的平台，可以拥有关键知识产权——高通就是这样，你不给他们付钱就造不了手机；ASML 也有关键知识产权，没有他们的光刻机你就造不了芯片。

你还可以有品牌，品牌非常重要，因为 Google、Amazon 一路长大从来不用打广告，Elon 也从来不用为任何事打广告——获客成本对终身价值，这就是整个商业模式。

我前面提到的这些公司，几乎每一家都是把这些优势集于一身的。

有时候我们能比世界上其他人更早注意到这些。我们的高光时刻之一，是 2013 年在 Robin Hood 投资者大会上讲 Amazon 的 AWS。我们说：多头们根本不知道自己坐在什么东西上面。

`[33:23]` Alex Sacerdote

Amazon's won the war before it even started.And at that time we said there's Coke and there's no Pepsi.Did turn out there was Pepsi, but it was big enough to last.And we could see they had a seven-year lead.So first mover is important.Then they became a whole ecosystem and a platform.Then they got scale.So they were 10 times the size of everybody else.Nobody could invest in the R&D to catch them.But you're right that if you don't have a competitive advantage, you can be in the best S-curve of all time.And still lose out.But if your name was RIM, Pong, Nokia, HCC, LG, Motorola, I can go on forever.Zero, zero, zero, negative, negative, negative, negative.And that's what we saw at the foundational model layer where there's like 50 companies trying to do that.And they all have fallen away.And two or three have emerged at the top.There's a lot of reasons to think they will continue to hold their position.Google's a little trickier because they have this other huge, massive, complex business attached to the Gemini business.But if you take Anthropic and OpenAI as pure plays and you dig through those and you reason through their competitive advantages, why aren't they susceptible to erosion of those things?

Amazon 在这场战争还没开始的时候就赢了。当时我们说：只有可口可乐，没有百事可乐。后来证明还是有百事的，但盘子大到够所有人活。而且我们能看到他们领先了七年。所以先发很重要。然后他们变成了一整个生态和平台，然后拿到了规模，体量是所有人的 10 倍，没人能投得起研发去追上。

但你说得对：如果你没有竞争优势，你可以身处史上最好的 S 曲线，照样输个精光。如果你叫 RIM、Palm、Nokia、HTC、LG、Motorola——我可以一直数下去——零、零、零、负、负、负、负。

这也是我们在基础模型层看到的：大概有 50 家公司在做，全都掉队了，最后有两三家浮到了顶上。有很多理由认为他们会继续守住位置。Google 稍微复杂一点，因为它旁边挂着另一个巨大而复杂的业务。

（但如果你把 Anthropic 和 OpenAI 当作纯粹标的（pure plays）来挖，把它们的竞争优势推演一遍——为什么这些优势不会被侵蚀？）

`[34:35]` Alex Sacerdote

Of all the S-curves we've done, AI is by far the most complex and the fastest changing.We have to keep in mind that there are risks.The rewards are the highest because we're talking about a market in the trillions, maybe clouds 800 billion.This might be, we now think three to five, but there's higher risk, higher reward.But let's just say with Anthropic now, it looks like they have critical intellectual property.Generally, they've been able to maintain their high market sharing code.Number two is they've built a strong brand for enterprise to where go talk to any CIO and the first thing they'll say is Claude.Three, they're going to have escape velocity and scale.And what was scary for OpenAI and Anthropic fighting these big companies like Google was they had these huge cash cows.And to both of the management teams credited OpenAI and Anthropic, they were able to work in these super capital intensive industries and find ways to raise capital.And certainly with Anthropic, with their 10x sales growth and their fundraising ability, it looks like they've reached escape velocity.So now they have scale.And the other thing that Anthropic and OpenAI could have is Anthropic, now that they're leading in code, they set that code back onto their model.

我们做过的所有 S 曲线里，AI 是最复杂、变化最快的。我们必须记住风险是存在的。回报也是最高的，因为我们说的是一个数万亿美元的市场——云可能是 8000 亿美元，这个我们现在觉得是三到五万亿。所以是更高风险、更高回报。

但就说 Anthropic 现在：第一，看起来他们拥有关键知识产权，总体上守住了在代码上的高市占率。第二，他们在企业端建立了强大的品牌——你去问任何一个 CIO，他第一个说的就是 Claude。第三，他们会拥有逃逸速度（escape velocity）和规模。

OpenAI 和 Anthropic 去跟 Google 这种大公司打，最可怕的一点是对方有巨大的现金牛。而这两家管理层都值得称赞，他们能在这种极度资本密集的行业里找到办法融到钱。至少 Anthropic，凭着 10 倍的销售增长和融资能力，看起来已经达到了逃逸速度。所以现在他们有了规模。

Anthropic 和 OpenAI 还可能拥有的另一样东西是：Anthropic 现在在代码上领先，他们把代码这件事反哺回自己的模型。

`[36:01]` Alex Sacerdote

And it's this concept of the recursive improvement.And if you look at the pace of their innovation, it's accelerating.Maybe they can have this liftoff stage.OpenAI, they were focused on so many different other sectors.They're starting to do better in enterprise and their coding tools good.And they're starting to see accelerating growth on that side.And then, look, the consumer franchise, it looks like enterprise right now is much better because you and I, we're willing to pay a lot because it's replacing human beings.Now, consumer, maybe you can get advertising, but maybe they would pay for a claw bot type assistant if you could make that perfectly well for them.But they have a gazillion eyeballs there.Things do shift.We have this charts that we almost do for all of our pitches.On the internet, the leader grows bigger, faster, and wins.Most of the time, the leader gets it.Shopify becomes the leader.It just keeps on going.Amazon, the leader, keeps on going.SaaS company, XYZ.You get the lead.It compounds on itself.And another thing is you need to be big.Another is scale.You need the compute.And you've got to pay for the compute.So there's only so many people that can do that.Those are some of the moats that we think are now showing up in this business.

这就是「递归式改进」（recursive improvement）的概念。你看他们创新的速度是在加速的，也许他们能进入一个「点火升空」阶段。

OpenAI 之前分散在很多别的赛道上，他们在企业端开始做得更好了，编码工具也不错，那边的增长在加速。而且，看起来目前企业端好得多，因为你我愿意付很多钱，因为它在替代人；消费端也许你能做广告，或者如果你能给他们做出一个完美的 Claude 助理，他们也许愿意付费——但他们那边有海量的眼球。事情是会变的。

我们几乎每次路演都会用一张图：在互联网上，领先者长得更大、更快，然后赢。大多数时候是领先者拿走。Shopify 成为领先者，就一路走下去；Amazon 是领先者，一路走下去；某个 SaaS 公司也是。你拿到领先，它就自我复合。

另外你必须大，另外是规模——你需要算力，而且你得付得起算力钱。所以能做这件事的人就那么几个。这些就是我们认为现在正在这门生意里显现出来的护城河。

`[37:18]` Patrick O'Shaughnessy

Now, there are some exceptions to that rule, usually with a paradigm shift, AOL, and then dial-up went to broadband and they didn't make the change.Netscape came out early and it wasn't as strong of a business model.And if you talk to anyone in the Valley or any startups, they'll tell you that they're building on top of these three.And the world's a huge place and the economy is a huge place that they'll be able to differentiate within those.I'm so curious then what you think all of this means for software.When I look through your portfolio, I don't see a ton of big software companies, enterprise software companies.I don't know if you once had them and sold them.But it's hard to have the experience of building really useful, cool little tools, even if they're still toys, and not have the thought of, wow, if I spend enough time on this, even if I'm not technical,if I could build an ERP equivalent replacement or something for my company, there doesn't seem to be a fundamental reason why that's not possible.And then those companies could be in lots of trouble.Seems like everyone has a strong view on this one way or the other.I'm curious how you've approached those sorts of companies, given that you don't seem to own a ton of them.

当然这条规则也有例外，通常发生在范式转移的时候。比如 AOL：拨号上网变成宽带，他们没跟上。Netscape 出来很早，但商业模式没那么强。

而且你去问硅谷任何人、任何创业公司，他们都会告诉你他们是建在这三家之上的。世界很大，经济体量很大，他们能在这三家之上做出差异化。

（我很好奇你觉得这一切对软件意味着什么。我看你的组合，没看到多少大型软件公司、企业软件公司，我不知道你是不是曾经有过然后卖掉了。

但你很难有过「亲手搭出一些真正有用、很酷的小工具，哪怕还只是玩具」的体验，而不产生这样的念头：哇，如果我在这上面花足够时间，就算我不是技术出身，如果我能给我公司搭一个 ERP 的替代品之类的东西——好像并没有什么根本原因说这做不到。那这些公司可能麻烦就大了。

这件事好像每个人都有很强的立场，非此即彼。我好奇你是怎么看待这类公司的，毕竟你好像没怎么持有它们。）

`[38:27]` Alex Sacerdote

Five years ago, we might have had 40 or 50% of our portfolio in software.And early on in our April 2023 webinar, we said definitely invest in chips first.But at the application layer, initially, we thought these companies are huge.They have huge sales forces.They can take these AI APIs and build products.And they have the data.This is going to be amazing for software.Pretty quickly, we realized their AI products were not very good.They weren't moving the needle.Nobody could charge for them.We basically sold almost all of our software.Entering this year, we were net short.It really helped us in the first quarter.There's so many layers to this.I mean, the old way of software is like using a pen and paper, or it's like a horse and buggy.The new way of software is like a jet engine or, frankly, the transporter from Star Trek.It's so revolutionary changing that it feels like it has to be disruptive.If it's not disruptive now or right away, the software companies have another problem, which is their list on the to-do list or priority list of any CIO has fallen a lot.So even if AI is not going to be disruptive, they're spending it on anthropic tokens because there's faster ROI there.Second, if they're spending all that money over there, it pushes on the budget.

五年前，我们可能有 40% 到 50% 的组合在软件里。而且早在 2023 年 4 月那场线上分享里，我们就说一定要先投芯片。

但在应用层，我们最初以为：这些公司很大，有庞大的销售团队，他们可以拿这些 AI API 去做产品，而且他们有数据——这对软件公司来说会很棒。

很快我们意识到他们的 AI 产品并不好，没有带来实质改变，没人能为此收费。我们基本上把软件全卖了。进入今年的时候我们是净空头，这在第一季度帮了我们大忙。

这里面有很多层。旧的软件方式像是用纸和笔，或者说像马车；新的软件方式像喷气发动机，或者坦白说像《星际迷航》里的传送机。它革命性到你觉得它一定会是颠覆性的。

就算现在或者马上不构成颠覆，软件公司还有另一个问题：它们在任何 CIO 的待办清单或优先级清单上的排名，掉了很多。所以哪怕 AI 不颠覆它们，钱也在花到 Anthropic 的 token 上，因为那边 ROI（投资回报）更快。

第二，如果钱都花在那边，预算就被挤压了。

`[40:03]` Alex Sacerdote

So that hurts them.Third, a lot of software companies were able to raise price every year.And now they're probably nervous about doing that.Then fourth, we'll see what happens with jobs because there's smart people on both sides of that.But we are seeing some companies really gut their jobs.Or freeze hiring or whatever.Freeze hiring.And so that hurts on seats.In terms of them building their own apps, if you want to be optimistic, it's taken them a while to do that.We talked about how early the primitives of AI are.So maybe they have just taken a while to get something they can commercialize.But they might not have the right people.It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you've got to be right at the side to make sure it's really getting done.So you need the FDEs for deployed engineers.They might not have the right people internally to do that.Then, of course, there's the risk of you can build it yourself.The bulls will say, well, they're never going to build their own ERP system.And that's probably right.And it is true that old tech is very sticky.Mobile video games didn't hurt console games.And the tablet didn't hurt the PC.

所以这也伤害它们。

第三，很多软件公司过去每年都能涨价，现在他们大概不太敢了。

第四，工作岗位会怎样还得看，两边都有聪明人。但我们确实看到一些公司在大砍岗位，或者冻结招聘。（冻结招聘。）这会伤到席位数（seats，即按人头计费的订阅量）。

至于它们自己造应用：如果你想乐观一点，可以说它们花了一段时间才做出来。我们刚讲过 AI 的原语有多早期，所以也许它们只是花了点时间才做出能商业化的东西。但它们可能没有合适的人。

卖一套固定系统，和安装一个「替人干活」的东西，这是两种完全不同的销售动作——后者你得一直守在客户旁边，确保活真的干成了。所以你需要 FDE（forward deployed engineers，前线部署工程师）。它们内部可能没有合适的人来做这件事。

然后当然还有「你可以自己造」的风险。多头会说：他们永远不会自己造 ERP 系统。这大概率是对的。而且确实，老技术非常有黏性：手游没有伤到主机游戏，平板没有伤到 PC，

`[41:22]` Alex Sacerdote

And the smartphone didn't hurt the PC.There's a lot of integrations and work that goes into these software.That's all true.And companies do like to buy.They don't like to build themselves that much.That's all true.But you can't imagine a world where in one, two, three, four, five years, you could have a brand new AI native company going after each one of these very strong incumbents.And if their data advantage could get obviated, it might be easy to take it out and put the new one in with AI.The valuations are very high and everybody knows they're under pressure.Some people are tempted to buy these, but the AI coding tools are just getting better and better.We'll have to wait and see.We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory.But it's hard because if you're a company like Salesforce, you've got $40 billion in sales.You might have 500 of ARR, 700 of ARR of AI.So you've got this huge base.Now, maybe this starts to work, but it takes a while.In software, there's the rule of 40, which is your growth rate plus your operating margin.And if you have 20% growth rate, 20%, that's good.For AI, we have a new rule of 40.

智能手机也没有伤到 PC。这些软件里有大量的集成和工作量，这都是真的。而且公司确实喜欢买，不那么喜欢自己造，这也都是真的。

但你没法想象一个世界吗——一年、两年、三年、四年、五年之后，会有一家全新的 AI 原生公司去打每一个强大的在位者。如果它们的数据优势可以被抹平，那么用 AI 把旧的拔出来、把新的插进去也许就很容易。

估值很高，而且所有人都知道它们承压。有些人会心动想买，但 AI 编码工具在越变越好。我们只能等着看。我们非常密切地盯着这些软件公司，看它们能不能拿到足以改变轨迹的收入。

但这很难，因为如果你是 Salesforce 这样的公司，你有 400 亿美元销售额，AI 相关的 ARR（年度经常性收入）可能是 5 亿、7 亿。你的基数太大了。也许这开始跑通，但要花很久。

软件里有个「40 法则」（rule of 40）：增长率加经营利润率。如果你 20% 增长、20% 利润率，那还不错。对 AI，我们有一个新的 40 法则。

`[42:42]` Alex Sacerdote

What percent of your sales are AI?30%.And what's your market share in that category?Say 30%.You'd be 60.That's a great place to look because you've got exposure and you've got a strong market position.The problem with software is their AI is 1% or 2% at this stage, and it's a long way to go.One thing we are picking up, though, now, lately, and this is half-baked, but AI could make some of these software platforms more important because what's the first thing you do with Claude?

你的销售额里有多少百分比是 AI？30%。你在那个品类里的市占率是多少？比如 30%。加起来就是 60。这是个很好的寻找方向，因为你既有敞口，又有强势的市场地位。

软件公司的问题是，它们的 AI 现在只占 1% 或 2%，路还很长。

不过我们最近确实捕捉到一件事——这还没想透——AI 可能会让其中一些软件平台变得更重要。因为你拿到 Claude 第一件事干什么？

`[43:12]` Alex Sacerdote

You plug it into Slack.If that can become a key repository, that will make Slack a permanent fixture within the organization.And so maybe the next wave of AI will be these agents that use tools, and they might operate inside of the existing incumbent software tools to use them like a human being would.Just to pull in that thread, it seems like the commonality of the tools they might use that are the most sticky would be network-based tools.So Slack is a great example of the software in Slack itself.The software is not the special part.It's that everyone is there.I'm curious what kinds of things you would want.Is it just the presence of a network effect?

你把它接进 Slack。如果 Slack 能成为一个关键的信息仓库，那会让 Slack 在组织里变成永久性的固定装置。

所以也许下一波 AI 会是这些会用工具的智能体，它们可能就在现有的在位软件工具内部运行，像人一样使用它们。

（顺着这条线说，看起来它们会用的工具里最有黏性的共同点，是基于网络的工具。Slack 就是个很好的例子——Slack 里特别的不是软件本身，而是所有人都在那里。我好奇你会想要什么样的东西。仅仅是网络效应的存在吗？）

`[43:50]` Alex Sacerdote

Is that the only thing that really matters?Still early in our thinking here, but even maybe Workday or the HR systems or the big systems of record, the agents may be running on top of them.It's good and bad.I mean, CRM is going headless, or they're making a headless version.And that's the bare case, too, that you get relegated to just being a database.There's a human interface to it.Then they need to make the AI interface, which is no interface.It's just them going right into the data.You lose that customer interaction.But if the agents are going right to CRM and doing the work inside of CRM, that will solidify CRM.So you won't have to think it's going away.Can we talk about chips?

（这是唯一真正重要的东西吗？）

我们在这上面的思考还很早，但也许还包括 Workday 或者 HR 系统，或者那些大的记录系统（systems of record）——智能体可能就跑在它们之上。

这有好有坏。CRM（指 Salesforce）在走向「无头化」（headless，即去掉人类界面只留数据层），或者说他们在做无头版本，这也是空头逻辑：你被降级成只是一个数据库。原来它有人类界面，现在他们要做 AI 界面，而 AI 界面就是没有界面——就是直接进到数据里，你失去了跟客户的交互。

但如果智能体是直接进 CRM、在 CRM 里面干活，那反而会巩固 CRM，你就不用觉得它要消失了。

（我们能聊聊芯片吗？）

`[44:37]` Patrick O'Shaughnessy

You've referenced them a few times.Infrastructure chips.Everything around the data center.Maybe I don't know how you conceive of it.Why is this so interesting to you?I love the modified rule of 40 for percentage that's AI and percentage market share in the category.That's an interesting stat.What companies shine on that?

（你提到过几次。基础设施芯片、数据中心周边的一切——我也不知道你是怎么划分的。为什么这个对你这么有吸引力？

我很喜欢你改造过的 40 法则：AI 占比加上品类市占率。这个指标很有意思。哪些公司在这上面很亮眼？）

`[44:53]` Alex Sacerdote

What are laggards that are surprising?For the past 40 years, nothing has changed in the data center.Even with cloud, Intel x86 became the data center chip sometime in the 90s.And compute grew in the cloud era.And compute workloads grow 25 to 40% every year.But Moore's Law is improving at that rate.It didn't require tremendous innovation.And there really was almost no growth in hardware for years and years and years.And the whole industry basically commoditized.Every part, every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking.There was no innovation.You would go from 1 gig to 10 gig.That would take seven years.And when you do switch in the first year, it does take some innovation to get to 10 gig and would create a little cycle.But then it would commoditize.Because now you go to AI, the workloads are growing 10x every year.And they're pushing every single aspect of this hardware to the physical limits of what it can do.Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry.I met with Sean McGuire like three years ago.And he said, I wish I could come back and be a hardware hedge fund.

（有哪些落后者是让人意外的？）

过去 40 年，数据中心里什么都没变过。就算有云，Intel 的 x86 也是从 90 年代某个时候起就成了数据中心芯片。云时代算力在增长，算力工作负载每年增长 25% 到 40%，但摩尔定律也在以那个速度改进，所以并不需要巨大的创新。硬件多年多年几乎没有增长，整个产业基本上大宗商品化了。

每一个部件、每一颗芯片、服务器的每一个部分——从印刷电路板（PCB）到内存、到机箱、到网络——都没有创新。你从 1G 走到 10G 要花七年；切换那一年确实需要一些创新才能做到 10G，会形成一个小周期，但然后又大宗商品化了。

而现在到了 AI，工作负载每年增长 10 倍，而且它们在把硬件的每一个方面都推到物理极限。所以你不仅创造出巨大的销量增长，我们还管这叫「硬件产业的去大宗商品化」（decommoditization）。

我三年前见过 Shaun Maguire，他说：我真希望能回过头去做一只硬件对冲基金。

`[46:27]` Alex Sacerdote

Because all the companies are public.And they all have powerful IP.And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others.And we're in this renaissance of chips.So not only do you have tremendous unit growth, it's requiring tremendous innovation.That means every aspect of the server memory, which used to be a pure commodity.This high bandwidth memory is stack 10 chips on top, input outputs or 10x what they were before.Took Samsung for years to do it.And it's a critical piece.And then that is constantly upgrading.So they've got to be working with NVIDIA for three or four generations in advance.We had this with Celestica.Celestica was a contract manufacturer.This has been a disaster industry since 1999.It went all offshore to China.It was commodity.But they hung on.Celestica's heritage was IBM supercomputing.And they kept all that talent and skill.And then we noticed they were the sole supplier of the Google TPU server three years ago.The stock was trading at eight times earnings.And then they also had this whole business of selling Ethernet white box, which is code word for commodity.White box Ethernet switches into the clouds.It turns out these are excellent businesses.

因为这些公司全都是上市的，而且它们都有强大的知识产权。红杉（Sequoia）最好的一些投资就是在硬件时代做的——Apple、Cisco 等等。我们正处在芯片的文艺复兴中。

所以你不仅有巨大的销量增长，它还要求巨大的创新。这意味着服务器的每个方面——比如内存，过去是纯大宗商品，现在的高带宽内存（HBM）要把 10 颗芯片叠起来，输入输出是过去的 10 倍，三星花了好几年才做出来，而且它是关键部件、还在不断升级，所以他们必须提前三四代跟 NVIDIA 一起做。

我们在 Celestica 上就是这样。Celestica 是一家代工厂（contract manufacturer），这个行业从 1999 年以来一直是灾难，全都搬到中国去了，是大宗商品。但他们撑住了。Celestica 的血统是 IBM 的超级计算，他们把那批人才和技能都留住了。

然后我们注意到三年前他们是 Google TPU 服务器的独家供应商，而股票在 8 倍市盈率上交易。他们还有一整块卖以太网白盒（white box，行话里就是「大宗商品」的意思）交换机给云厂商的生意。结果这些是极好的生意。

`[48:02]` Alex Sacerdote

Not only do they have tremendous growth, but to do an AI server, it's liquid cooled.It's running so much hotter.It's a $200,000 or $300,000 piece of machinery, whereas an old server was $5,000.If it breaks, you just throw it away.If this thing breaks, the whole thing goes down.So you become a commodity supplier to selling a critical part on a plane.You'll never get swapped out.It turned out they were quite good at liquid cooling.A lot of other people tried to do it and failed.And so they've retained that position.Then it also turned out that the Ethernet market, in the old days, you would go from 100 gig to 400 to 800.It would be a seven-year cycle to upgrade.Now they're upgrading every year.That's really hard to do.Then there's a whole software layer, the open-source Sonic layer.The guys at Celestica invented were some of the people that wrote that open-source software.They work very closely with Broadcom.What we thought was just a great growth driver turned out to be great competitive advantages.And they have like 50%, 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive.And then even something like the printed circuit board.

它们不仅增长巨大，而且做一台 AI 服务器是液冷的，跑得热得多，是一台 20 万到 30 万美元的机器，而老服务器是 5000 美元——坏了就扔。而这个东西一坏，整套就宕了。所以你从「卖大宗商品」变成了「在飞机上卖关键零件」，你永远不会被换掉。

结果发现他们液冷做得相当好，很多人试过都失败了，所以他们保住了这个位置。

然后还发现，以太网市场过去从 100G 到 400G 到 800G，升级周期要七年，现在是每年升级一次，这非常难做到。再往上还有一整个软件层，开源的 SONiC 层——Celestica 的人里就有当初写那套开源软件的人，他们跟 Broadcom 合作非常紧密。

我们本来以为只是个很好的增长驱动，结果发现是很好的竞争优势。他们在云以太网交换机市场有 50%、60% 的份额，而这对 AI 是至关重要的市场，因为 AI 极度依赖网络。

再比如说印刷电路板。

`[49:23]` Alex Sacerdote

A regular server, you need 10 layers.These AI servers, you need a 40 layer.And there's very few PCB suppliers that can make this.There's all kinds of complexities in there.Then we also own Elite Materials, which makes the leading ingredient, which is copper-clad laminate, which goes into these boards.The PCB units are growing.The layer counts are rising.So you've got like a 50% to 60% CAGR just in the units.And then the ASPs are rising.And then the gross profits are rising.And your visibility, which used to be, hey, we'll call you next week if we need you to like, hey, we need you for the next four years to be like designing this roadmap with us.You've gone from a 5% grow or low margin to a 35%, 40, 50 top-line CAGR for the next four years with rising margins.On top of that, there's shortages of everything.So even if it is a commodity, it's going to be a great cycle.So we see that up and down the supply chain.You find these companies like, I mean, Corning, they make the fiber.They've got some ridiculously high share of the fiber.I was reading this Microsoft data center they just built.There's enough fiber to circle the world four and a half times in that one thing.And their fiber is thinner and more bendable and can be specially manufactured to the exact specs.

普通服务器你需要 10 层板，这些 AI 服务器你需要 40 层板，而能做这个的 PCB 供应商非常少，里面有各种复杂度。

我们还持有台光电子材料（Elite Material），他们做最主要的原料——覆铜板（copper-clad laminate），用在这些板子上。PCB 的出货量在涨，层数在涨，所以光是出货量就有 50% 到 60% 的复合年增长率（CAGR），然后单价（ASP）在涨，然后毛利也在涨。

而你的能见度，过去是「下周需要你我们再打电话给你」，现在是「未来四年我们需要你跟我们一起设计路线图」。你从一个 5% 增长、低利润率的生意，变成未来四年 35%、40%、50% 的营收复合增速，而且利润率还在上升。

在这之上，所有东西都短缺。所以哪怕它是大宗商品，这也会是一轮很棒的周期。我们在整条供应链上下都看到了这一点。

你会找到像 Corning（康宁）这样的公司，他们做光纤，在光纤上占着高得离谱的份额。我读到微软刚建的一个数据中心，里面的光纤足够绕地球四圈半。而他们的光纤更细、更能弯折，还能按精确规格定制生产，

`[50:53]` Alex Sacerdote

And it's higher margin.And it's the fastest growing part of their business.In networking, there's scale out, which is connecting all the server racks together.Then there's scale across, which is connecting the data centers together.And when you want to build one of these huge clusters and you can't get all the power in one place for training, you want to wire them together.But the wires, you need like 10x.The wire has to be so much thicker.So that's creating huge growth.And where the real kicker comes in is when you do scale up.That's connecting every GPU in the rack to the other ones.That's done over copper.Eventually, that'll be done over fiber.When that happens, that 2 to 3x is Corning's opportunity.So you just have at every layer of the rack.Everyone's overwhelmed.Everyone's overwhelmed.But in the power supplies, every NVIDIA chip or rack uses 50 to 125% more power.That drives the ASPs of Delta and advanced energy.I can't believe these stories when I hear.I'm like, wait, so your ASPs are going to like go up 40% for the next four years in a row?

而且利润率更高，是他们业务里增长最快的部分。

在网络里，有 scale out（横向扩展，把所有服务器机架连起来），还有 scale across（跨域扩展，把数据中心之间连起来）。当你想建这种超大集群、但没法在一个地方拿到全部电力来做训练时，你就想把它们连起来。但那些线，你需要 10 倍的量，线还得粗得多，所以这创造了巨大的增长。

而真正的助推器是 scale up（纵向扩展），也就是把机架里每一颗 GPU 都连到其他 GPU 上。这现在是用铜做的，最终会用光纤做。那件事一发生，就是康宁 2 到 3 倍的机会。

所以机架的每一层你都能看到这种情况。（所有人都应接不暇。）所有人都应接不暇。

电源那边，每一颗 NVIDIA 芯片或机架多用 50% 到 125% 的电，这推高了台达电（Delta）和 Advanced Energy 的单价。

（我听这些故事的时候简直不敢相信。我想，等等，你的单价接下来连着四年、每年要涨 40%？）

`[52:12]` Alex Sacerdote

And it's higher margin?The broader picture is the AI demand, if we're right with this L curve, we're already short.The DRAM market, the NAND market, the PCB market, we're already 30% short, all these things as we are now.The measure of percent AI, percent market share.Do you care more about the absolute or the rate of change of those metrics?

（而且利润率还更高？）

更大的图景是 AI 需求——如果我们对这条 L 曲线判断没错——我们已经短缺了。DRAM 市场、NAND 市场、PCB 市场，就以现在的状况看，我们已经短缺了 30%。

（关于「AI 占比、市占率」这两个指标，你更在意绝对值还是变化率？）

`[52:36]` Alex Sacerdote

We did this presentation in 2024 where we listed everybody's market share.And then I asked Claude to plot it to a thing.And it actually didn't get it right because what it didn't get is the rate of change.So the rate of change is important.And that's incredible too, because you go from 10% to 30% and your growth rate accelerates and your margins accelerate.So rate of change is very important.Why don't more people get this right in public markets?

我们 2024 年做过一次演示，把所有人的市占率列出来，然后我让 Claude 把它画成一张图，结果它画错了，因为它没抓住变化率。所以变化率很重要。而且这也很惊人，因为你从 10% 走到 30%，你的增长率在加速，你的利润率也在加速。所以变化率非常重要。

（为什么二级市场上没有更多人做对这件事？）

`[53:03]` Patrick O'Shaughnessy

If your whole framework is S-curve, competitive advantage, underappreciated earnings power,it feels like the movie's been played out a lot over the last 25, 30 years.My mom said, why do you tell everyone your secret?

（如果你的整套框架就是 S 曲线、竞争优势、被低估的盈利能力，感觉这部电影在过去二十五、三十年已经放过很多遍了。）

我妈说：你干嘛把你的秘诀告诉所有人？

`[53:15]` Alex Sacerdote

And it's like, why does the casino teach people how to play blackjack?It's really hard to do.You have to be comfortable investing.I've been doing tech for 20 years at Whale Rock.We've got a team that's been doing this, covered many cycles.No one's paid attention to hardware and chips at all.So you've got all these newbies coming into it.You and Gavin, that's it.Yeah.And Gavin's done a great job.People weren't comfortable with it.It's harder to do than it seems.And a lot of these companies, their charts are up.So it's scary.Can I buy?

这就像：赌场为什么要教人怎么玩二十一点？因为真的很难做到。

你必须对这种投资感到自在。我在 Whale Rock 做科技做了 20 年，我们有一个团队一直在做这件事，经历过很多周期。

根本没人关注硬件和芯片，所以现在涌进来的全是新手。（就你和 Gavin（Baker），就这些。）是啊，Gavin 干得很棒。

人们对它不自在，它比看上去难做。而且这些公司的股价图都是往上的，所以很吓人：我还能买吗？

`[53:48]` Alex Sacerdote

And then you also have to have the holistic view.Because if you don't have conviction every time with NVIDIA over the last four years,it's, oh, they had a great year.Oh, my God.It's got to be a bubble.And then they had another great year.And it's like six months of marking time.It's got to be a bubble.This is like getting out of hand.This is pretty scary.The Bayer cases are not totally without merit.But if you can see the whole picture and understand how these things are unfolding and gain conviction in that, frankly, if you're just a semi-analyst, so many semi-analysts missed it because they didn't see what was really happening at the foundational model layer and how this broader picture.So it helps to have the big picture.It helps to have decades and scores of S-curves that you're looking at and where it plays in different things.What makes you the most concerned or uncertain?

然后你还必须有整体视角。因为如果你不是每一次都有信念——过去四年的 NVIDIA，「哦他们有很棒的一年」「天哪这一定是泡沫」，然后又是很棒的一年，然后横盘六个月，「一定是泡沫」「这太过头了」「这挺吓人的」。空头逻辑并非全无道理。

但如果你能看到整幅图、理解这些事情是怎么展开的，并对此建立信念——坦白说，如果你只是个半导体分析师，很多半导体分析师错过了，因为他们没看到基础模型层真正在发生什么、没看到这幅更大的图。所以有大局观是有帮助的；手上有几十年、几十条 S 曲线的观察，知道它在不同东西里怎么演，也是有帮助的。

（什么让你最担心、最不确定？）

`[54:43]` Alex Sacerdote

Is it just the rate at which all this stuff changes?And what keeps you worried amidst what seems like pretty extreme bullishness?One thing that bothers me is there's a lot of negativity in the general population about AI.And there's a lot of negativity in some aspects of the government.You know, I think Maine just banned data centers.And only 20% of the people are optimistic about AI and potential for negative regulation.But I do think kind of the genie is out of the bottle.Another risk is that if AI slows down in its improvements, I think there's a whole lot of AI adoption to happen, even if the models didn't improve.But Jensen said this years ago when he was talking about his graphics chips.If good enough is good enough, I won't have a business.Every year he made the graphics a little bit better and people always wanted the best.In AI, if Anthropics sort of hits a wall and stops improving our open AI, then the open source models will catch up.It might be a race to the bottom and it won't be good for the stocks probably.It could be good for the chip companies.The chip companies don't care who wins.So that's another positive.And they'll benefit.Jensen really wants open source to take off.

（只是这些东西变化的速度吗？在这种看起来相当极端的看多情绪里，什么让你忧虑？）

有一件事让我不安：普通大众对 AI 有很多负面情绪，政府某些方面也有很多负面情绪。我想缅因州刚刚禁掉了数据中心。只有 20% 的人对 AI 是乐观的，还有负面监管的可能。但我确实觉得妖精已经从瓶子里出来了。

另一个风险是 AI 的改进如果变慢。我认为哪怕模型不再进步，还有非常多的 AI 采用会发生。但 Jensen 多年前讲他的图形芯片时说过：如果「够好」就够好了，我就没生意了。他每年把图形做好一点点，而人们总是想要最好的。

在 AI 里，如果 Anthropic 或者 OpenAI 撞墙、停止改进，那开源模型就会追上来，可能变成一场向底部的竞赛，那对股票大概不是好事。但对芯片公司可能是好事——芯片公司不在乎谁赢，他们都受益。Jensen 是真心希望开源起飞的，

`[56:06]` Alex Sacerdote

It's all he kept on mentioning at his last GTC.So that could be a risk.Another thing is if one or two of the players falters and loses its position and can't compete, that could be like a lot of compute that they don't need in the future.Now, if AI is so big, somebody else will suck that up.And we saw that with Oracle canceled a big deal and then Meta went right in.But let's just say Meta decided not to be involved with AI.Hey, we can't keep up.It's just going to be a waste of our resources.We watch that very carefully.In general, we see more companies truly going after this and even Microsoft going trying to build their own.Those are some of the key risks.Seems like you really have done very little in the application layer of AI.Historically, the apps ended up being most of the market cap, not the infrastructure.And there wasn't really a model layer in the past.I guess you could say it was the clouds or something.Why focus so much on the bottom layers of Jensen's five-layer cake versus things in the application layer that are actually getting used by consumers?

他上一次 GTC 上翻来覆去说的就是这个。所以那可能是个风险。

还有一件事：如果一两个玩家动摇了、丢了位置、竞争不下去，那可能意味着未来他们不需要那么多算力了。当然，如果 AI 足够大，别人会把这部分吸收掉——我们在 Oracle 取消了一笔大单、Meta 立刻顶上时就见过。但假设 Meta 决定不做 AI 了，说「我们跟不上，这就是浪费资源」——我们非常仔细地盯着这件事。总体上我们看到的是更多公司真的在扑上来，连微软都在试着自己造。这些是一些关键风险。

（看起来你在 AI 应用层做得非常少。历史上，应用最终拿走了大部分市值，而不是基础设施；而且过去其实没有模型层这一层，也许你可以说那时候是云。为什么这么聚焦在 Jensen 那个五层蛋糕的底部几层，而不是那些真正被消费者用起来的应用层？）

`[57:13]` Alex Sacerdote

Well, A, it always comes later.So the first three or four years of the iPhone and then the applications really took time.So maybe it's just starting.To date, we found that area to be pretty risky because where does the foundational model end and where does the application begin?

首先，A，它总是来得更晚。iPhone 的头三四年，应用真正起来是花了时间的，所以也许才刚开始。

到目前为止我们觉得那个领域相当有风险，因为基础模型在哪里结束、应用在哪里开始？

`[57:31]` Alex Sacerdote

Can the applications build enough of a moat where they can fend off and build businesses in that?We thought we would see it in some of the incumbents of CRM.And they're starting in maybe just a matter of time.But we really haven't seen it in the enterprise world.There are some very good startup application companies out there.But the ecosystem's not clear.When we started, the ecosystem in chips was clear.When we started, the foundational model ecosystem wasn't clear.Now it's clearer to us.And at the application layer, it's still kind of unclear and a little bit dangerous.But there will be great application companies built.We really were watching Brett Taylor at Sierra.Brett was CEO of CRM.He wrote Google Maps.He was CIO of Facebook.He's building this fantastic company called Sierra.We're not involved.But that's where the rubber hits the road.Will he be able to turn this into a huge company?

应用能不能建起足够的护城河，去抵挡冲击、在里面建起生意？

我们本以为会在 CRM 这些在位者身上看到，他们是开始了，也许只是时间问题。但在企业世界里我们真的还没看到。

外面有一些非常好的创业型应用公司，但这个生态还不清晰。我们入场时，芯片的生态是清晰的；我们入场时，基础模型的生态不清晰，现在对我们清晰了；而在应用层，现在仍然不太清晰，还有点危险。

但一定会诞生伟大的应用公司。我们一直在看 Sierra 的 Bret Taylor。Bret 当过 Salesforce 的 CEO，写过 Google Maps，当过 Facebook 的 CTO，现在在做一家很棒的公司叫 Sierra。我们没有参与。但那就是真正见真章的地方——他能不能把这个做成一家巨大的公司？

`[58:31]` Patrick O'Shaughnessy

And he's doing quite well.And we'll see.It's a matter of timing when these things really start to come into their own and prove they're sustainable.It usually doesn't start in the first three or four years.It comes a little bit later.At your office, you have this giant award wall for the best research job or project of the year given to an analyst.And I think you won it.You gave it self-awarded in the early days when you were by yourself.But you've got this now long 20-year history of every year one or more people put their name on this wall for having done the best job on a research project that year.I'm so curious about the nature of that research and how it's changing as a result of all of this.Say, you know, the person that's going to win the award this year and the sort of work that that requires a human to do when so much of the work that probably would have won you the award in 2009 or something could probably be fully automated or done in an hour with Cloud Code or something today.How is the nature of research and what gets you on that Whale Rock Award wall changing in real time?

他做得相当不错，我们再看。这些东西真正开始成气候、证明自己可持续，是个时机问题，通常不会在头三四年就开始，会晚一点来。

（在你们办公室，有一面巨大的奖牌墙，颁给年度最佳研究工作或项目的分析师。我记得你也拿过——早年你一个人的时候自己颁给自己的。但你现在有了 20 年的长历史，每年有一个或多个人把自己的名字留在这面墙上，因为那一年做了最好的研究项目。

我特别好奇这类研究的性质，以及它因为这一切正在如何变化。比方说，今年会赢的那个人、以及那份工作里需要一个人类去做的部分——考虑到 2009 年可能让你拿奖的那些活儿，今天大概能被完全自动化、或者用 Claude Code 一个小时就搞定。研究的性质、以及「什么能让你上 Whale Rock 那面奖牌墙」，正在实时地怎么变？）

`[59:32]` Alex Sacerdote

I would like to say that we're so advanced in our AI systems that it's a huge change.But so far, it's helping us get up to speed and we have a handful of great apps.But it's not supplanting the job of the analyst.And so much of what we're doing is we're meeting with as many companies as humanly possible.We're developing relationships with the management teams that we cover.We're talking to the competitors.The system we use is right out of common stocks and uncommon profits, which was written by Philip Fisher in the 1950s.And it's the scuttlebutt approach.It's growth investing.It's get out there and talk to suppliers, customers, competitors, looking for the key characteristics of these leading companies and really developing conviction in them.Now, if it's a new complicated area like ABF substrates or PCBs, we're able to get up to speed on those things quickly.But it can't pick stocks for you in any kind of a way.I will say that if you're an analyst who's good at the blocking and tackling, there's a role for that.But you need to have, obviously, the insight on top.So we're now like using AI to write notes or review the quarter.And those notes are much better.But there better be a really good paragraph on top, which is the wisdom.

我很想说我们的 AI 系统先进到带来了巨大改变。但到目前为止，它是在帮我们快速上手，我们有几个很棒的应用，但它没有取代分析师的工作。

我们做的事情里很大一部分，是跟尽可能多的公司见面，跟我们覆盖的管理层建立关系，跟竞争对手聊。我们用的这套体系直接来自 Philip Fisher 1950 年代写的《怎样选择成长股》（Common Stocks and Uncommon Profits），也就是「小道消息法」（scuttlebutt approach）。这是成长股投资：走出去跟供应商、客户、竞争对手聊，寻找这些领先公司的关键特征，真正建立起对它们的信念。

现在如果是一个复杂的新领域，比如 ABF 载板或者 PCB，我们能很快上手。但它没法以任何方式替你选股。

我要说的是，如果你是一个基本功扎实的分析师，是有位置的；但你显然还得在上面加上洞见。所以我们现在会用 AI 写纪要、复盘季报，那些纪要好多了。但最上面那一段必须非常好，那才是智慧所在。

`[1:00:57]` Alex Sacerdote

What does this mean?How does this deal with our thesis?What changed?Don't just be a reporter.So the AI can be a great reporter.It can't pick into the future.The job that the guys did on Apple Oven two years ago, I think we got two of the best ad tech guys.I knew ad tech.I started actually nearby here in New York at an internet advertising startup.And after I did banking, I knew internet advertising and ad tech, which is historically a terrible industry.But Michael and Sam really figured out the Apple Oven story before anybody.And they followed it when it was private.They know all the competitors.They know all the intricacies of terminology.And Sam went to the Las Vegas app advertising conference.And we went to Khan.And we talked to scores and scores of people.So did the work on the model and developed a great relationship with Adam Ferrogi.He's one of the best managers out there.I don't see AI doing that.What role does talking to other investors outside of your firm play in your life?

这意味着什么？这跟我们的论点有什么关系？什么变了？别只做一个记者。

AI 可以是很棒的记者，但它没法预见未来。

两年前那帮人在 AppLovin 上做的活儿——我觉得我们有两个最好的广告技术（ad tech）分析师。我懂广告技术，我做完投行之后其实就在纽约这附近一家互联网广告创业公司起步的，我懂互联网广告和广告技术，那历史上是个糟糕的行业。但 Michael 和 Sam 比任何人都早搞明白了 AppLovin 的故事。他们在它还是非上市公司的时候就跟着，他们知道所有竞争对手，知道所有术语的细微差别。Sam 去了拉斯维加斯的应用广告大会，我们去了戛纳，我们跟成百上千的人聊。他们把模型也做扎实了，还跟 Adam Foroughi 建立了很好的关系，他是最好的管理者之一。我看不出 AI 能做到这个。

（跟你们机构之外的其他投资人交流，在你生活里扮演什么角色？）

`[1:02:02]` Alex Sacerdote

One of the great things is just the friendships I've built with so many smart investors.And frankly, Philip Fisher said part of his process was get to know a good 10 or 15 like-minded people around the country and share ideas.They're great friends to make.A lot of them have been on your podcasts.You develop good friendships and then you share ideas, talk ideas.It's important that it's a two-way street.I call it the tripod when I like something and then my analyst likes it.And then somebody who I really respect also likes it.That's three legs of the stool can really help the conviction.What have you learned about shaping the products that you offer your investors across the history of the firm?

最棒的事情之一，就是我跟这么多聪明投资人建立起来的友谊。坦白说，Philip Fisher 说过他流程的一部分就是：在全国范围内认识十来个、十五个志同道合的人，然后交换想法。他们是很好的朋友，很多人都上过你的播客。

你会建立起好的友谊，然后分享想法、聊想法。重要的是这必须是双向的。我把它叫做「三脚架」：当我喜欢一个东西，我的分析师也喜欢，然后一个我非常尊重的人也喜欢——这三条腿真的能帮你建立信念。

（关于「在机构这段历史上给投资人提供什么样的产品」，你学到了什么？）

`[1:02:50]` Patrick O'Shaughnessy

It's not just one monolithic structure anymore.There's several things that if I'm an investor and I want to give you money, there's a couple of ways I can do that.How did you arrive at those things?How can you turn that experience into advice for other investors that are trying to provide their LPs with the right set of options?

（现在不再是单一的一种结构了。如果我是投资人、想把钱交给你，有好几种方式可以做。你是怎么走到这些安排的？你怎么把这段经验变成给其他想为 LP 提供正确选项的投资人的建议？）

`[1:03:07]` Alex Sacerdote

So the first 15 years, it was a long short fund and we want to be focused.And if you defocus, that can be hard.So we grew that and we got that to the scale that we wanted to.We're 20 years old.Maybe 10 years in, people started to ask for a long-only product.Sometime in maybe 2015, we formalized that we might be doing privates.And so we gave investors the option to opt in or opt out.And you could do 15% or 25%.So we didn't break the seal on the privates until 2020.We just think there's a huge structural underweight of the largest tech companies in the world.We also realized that a lot of our performance over the years was from some of the largest companies,whether it be Apple or Amazon or Tesla.And so a lot of our largest pools of capital endowments or what have you,they realized they have been massively underweight, the largest tech companies in the world,because they have a lot of privates.They don't have a ton of public.And then maybe half the public is international.And then of their public bucket, there's a belief that there's no alpha in large cap.So they underweight large cap, and they have a lot of small and mid managers that are stock pickers,because it's intuitive that large cap can't have alpha.

头 15 年就是一只多空基金（long short fund），我们想保持专注——如果失焦会很难。所以我们把它做大到我们想要的规模。我们成立 20 年了。

大概第 10 年的时候，人们开始要一个纯多头产品。大概 2015 年某个时候，我们正式确定可能会做一级市场，所以我们给投资人选择「加入或不加入」的权利，可以是 15% 或 25%。但直到 2020 年我们才真正开了一级市场的口子。

我们就是觉得，全世界最大的科技公司存在巨大的结构性低配。我们也意识到我们这些年很多业绩来自一些最大的公司，无论是 Apple、Amazon 还是 Tesla。

所以我们那些最大的资金池——捐赠基金之类的——他们意识到自己一直在大幅低配全世界最大的科技公司：因为他们有很多一级市场持仓，公开市场持仓不多；然后公开市场那部分可能一半是海外的；然后在他们的公开市场桶里，有一种信念认为大盘股没有超额收益（alpha），所以他们低配大盘股，然后配了一堆做选股的中小盘管理人——因为「大盘股不可能有 alpha」听上去很直觉。

`[1:04:29]` Alex Sacerdote

And then in their hedge fund portfolio, even if it's long bias, they're not going to have 15% in NVIDIA and all these other things.We realized that people are worried that there's these big companies.This is just a product of the digital economy in that in tech, the leader usually grows bigger and wins and develops very high market share quickly.And there's great competitive advantages.They're also selling around the globe.So this is going to lead to massive profit pools and massive market caps.And it's just going to happen in the future.Most endowments are betting against this because they're completely underweight this.I think there's tremendous alpha in the largest cap, because if you think about it, a small cap, it just takes one person to figure out it's good and move it up.But it takes 100 people, 100 diversified PMs to realize Google's not a loser.It's a winner.And can we figure that out before 95% of those generalist PMs?

然后在他们的对冲基金组合里，就算是偏多头的，他们也不会在 NVIDIA 和这些东西上放 15%。

我们意识到人们在担心这些大公司。而这其实只是数字经济的产物：在科技里，领先者通常长得更大并且赢，很快就拿到很高的市占率，而且有很棒的竞争优势，还卖向全球。所以这会带来巨大的利润池和巨大的市值，而这在未来还会继续发生。大多数捐赠基金是在赌这件事的反面，因为他们完全低配了这块。

我认为最大市值那一块存在巨大的 alpha。因为你想想，小盘股只需要一个人发现它好、把它买上去；但要让 100 个人、100 个分散型的基金经理意识到 Google 不是输家、是赢家，那需要 100 个人。而我们能不能在那 95% 的通才基金经理之前想明白这件事？

`[1:05:29]` Alex Sacerdote

And we've been able to do it.You like your odds in that.Yeah, we like your odds in that.And so there is alpha to be had there.And then as an asset category, it's great because these companies, by definition, have wonderful moats.And maybe they're not the super S-curve, but sometimes they are.I mean, NVIDIA sure is.And TSM is really levered to it.And Hynix is extremely levered to it.And ASML is levered to it.We're four months into that one.It sort of sounds like really what you've built is a research machine to understand the world through the lens of companies.The thing you're constantly trying to improve is that research machine.And then the way that you would then express that through products is multiplied.But if I was to try to understand Whale Rock, it would be to investigate the research machine first and foremost.We call it the Whale Rock Learning Machine.And it's a group of 10 highly experienced individuals.Warren Buffett reads books and we read books and we read blogs.In tech, you've got to go out and talk to people.So we do 2,500, 3,000 face-to-face meetings with management teams.Munger and Buffett talk about compounding knowledge.We've been compounding that knowledge for 20 years.

我们一直做得到。（你喜欢自己在这上面的胜算。）是的，我们喜欢这个胜算。所以那里确实有 alpha 可拿。

而且作为一个资产品类它也很好，因为这些公司按定义就拥有极好的护城河。也许它们不是那种超级 S 曲线，但有时候就是——NVIDIA 肯定是，台积电（TSMC）高度受益，SK 海力士（Hynix）极度受益，ASML 也受益。我们在那笔上才进去四个月。

（听起来你真正建起来的，是一台通过「公司」这个镜头去理解世界的研究机器。你不断在改进的就是这台研究机器，然后你把它通过产品表达出来只是乘数。但如果我想理解 Whale Rock，首先要研究的就是这台研究机器。）

我们管它叫 Whale Rock 学习机器（Whale Rock Learning Machine）。它是一个由 10 位经验非常丰富的人组成的团队。Warren Buffett 读书，我们也读书、读博客。但在科技里，你必须走出去跟人聊。所以我们一年做 2500、3000 场跟管理层的面对面会议。

Munger（芒格）和 Buffett 讲复利式的知识积累，我们已经复利了 20 年。

`[1:06:40]` Alex Sacerdote

There's changes to the team, but broadly there's a lot of consistency to it.Andrew and Michael have been with me for 18 years.And the average experience level on the team is 10 or so years.And that includes some of the newer people.That research engine can support all these products.And it's the same people that do publics and the private.So we're not going to scour the world and turn over every A, B.But when we see something that fits into our system, we're able to act on it.It's so much fun to do this with you.When I do this, I ask the same traditional closing question of everybody.What is the kindest thing that anyone's ever done for you?

团队有变化，但整体上一致性很高。Andrew 和 Michael 跟我 18 年了，团队平均经验大概 10 年左右，这还包括一些新人。

那台研究引擎可以支撑所有这些产品。而且做公开市场和一级市场的是同一批人。所以我们不会去把全世界翻个底朝天，但当我们看到符合我们体系的东西，我们有能力出手。

（跟你聊真是太开心了。我每次都会问所有人同一个传统的收尾问题：有人为你做过的最善良的事是什么？）

`[1:07:18]` Alex Sacerdote

It's definitely my father.I was super lucky.My father graduated Cornell, a double E electrical engineering, pivoted to Wall Street, and hada great career at Goldman Sachs.He ran corporate finance in the 80s and then ran private equity as chairman in the 90s.He was just whip smart, but he had such humility and was such a great gentleman.When I started Whale Rock, friends and family, he was the first call.But he said, you know, I've been at Goldman for 41 years.How about I come and join you?

肯定是我父亲。我太幸运了。我父亲康奈尔毕业，读的是电气工程，后来转到华尔街，在高盛有过非常成功的职业生涯。他 80 年代管公司金融，90 年代作为主席管私募股权。

他绝顶聪明，但极其谦逊，是个非常好的绅士。

我创办 Whale Rock、找亲友募资的时候，他是我打的第一个电话。但他说：我在高盛已经 41 年了，不如我来加入你？

`[1:07:55]` Alex Sacerdote

I'll be the gray here.I'll be the oversight.I'll be the chairman.You do what you do.You build the firm in Boston, build the team, run the money.I'll help raise some money.And we got to work together for six years until he passed away in 2011.But I just feel so lucky to have worked with him.It's not easy running a fund.We never raised our voice.And he was just an amazing mentor to so many people.And when he passed away, I got so many letters from people who said, your father was just suchan influence on me.He was such a gentleman.He was such a great mentor to me.I just feel so lucky to have worked with him.And if I could be half the person that he is, I'd be completely winning.How did he do that?

我来当那个白发的人，我来做监督，我来当董事长。你做你的事，你在波士顿把公司建起来、把团队建起来、管钱，我帮你募一些钱。

我们一起工作了六年，直到他 2011 年去世。我就是觉得能跟他一起工作太幸运了。管一只基金不容易，而我们从没提高过嗓门。他是那么多人了不起的导师。

他去世的时候，我收到那么多信，人们说：你父亲对我影响太大了，他是个真正的绅士，是我很棒的导师。我真的觉得能跟他共事太幸运了。如果我能做到他一半那样的人，我就完全赢了。

（他是怎么做到的？）

`[1:08:42]` Alex Sacerdote

What was his method?Why do so many people say that?He was modest.He was whip smart.He was wise.He was also known as a great investor, which isn't the most common thing at a lot of investmentbanks.He also was on their commitments committee and kept them out of a lot of tougher situations.And he was very warm and people could go into his office with problems.And he handled it with grace, whether it's a personal problem or a work issue or what haveyou.And he just had this soft way.And he also had a great sense of humor.I'm so lucky.Alex, thanks so much for your time.Thanks so much.If you enjoyed this episode, visit Colossus.com.You'll find every episode of this podcast complete with hand edited transcripts.You can also subscribe to Colossus, our quarterly print, digital, and private audio publicationfeaturing in-depth profiles of the founders, investors, and companies that we admire most.Learn more at Colossus.com slash subscribe.You know how small advantages compound over time.

（他的方法是什么？为什么那么多人这么说？）

他谦虚，绝顶聪明，有智慧。他也以优秀投资人著称，这在很多投行里并不常见。他还在承诺委员会（commitments committee）里，帮他们躲开了很多棘手的处境。他非常温暖，人们可以带着问题走进他的办公室，不管是私人问题还是工作问题，他都处理得很得体。他就是有那种温和的方式，还有很棒的幽默感。我太幸运了。

（Alex，非常感谢你的时间。）非常感谢。

如果你喜欢这一期，请访问 Colossus.com，你能找到本播客的每一期，配有人工编辑的文字稿。你也可以订阅 Colossus——我们的季度印刷、数字和私人音频出版物，深度刻画我们最欣赏的创始人、投资人和公司。了解更多请访问 Colossus.com/subscribe。

你知道，微小的优势会随时间复利。

`[1:10:01]`

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每一家投资机构都是独特的，通用 AI 不理解你的流程，Rogo 理解。这是一个专为华尔街打造的 AI 平台，连接你的数据、理解你的流程、产出真实的成果。到 rogo.ai/invest 看看。

Alex Sacerdote - How to Invest Through Technology Cycles - [Invest Like the Best, EP.477]

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