亚历克斯·萨塞多特：如何通过技术周期进行投资 - 《像最佳投资者一样思考》第 477 集

我在富达的时候研究过互联网。

I covered internet at Fidelity.

我买的第一只股票是亚马逊。

My first stock was Amazon.

那可是另一个精彩的故事了。

That's a whole other story which is a lot of fun.

但我同时也关注过企业级互联网，当时对这个方向有非常乐观的预期。

But I also did B2B internet and there was a whole huge bull case on that.

那时候企业级互联网所需的底层基础设施还没准备好。

The underlying infrastructure wasn't in place for B2B to happen.

最后真正实现是在二十年后，随着SaaS的兴起。

Ultimately happened twenty years later with SaaS.

AI现在也面临类似的风险，大公司普遍对安全非常谨慎，行动起来往往很慢。

That is a risk with AI in that these big companies are very security conscious, can be slow to move.

AI推广中还有很多文化上的障碍，真的需要几个积极推动者才能打开局面。

There's a lot of cultural issues with AI where you really need a few evangelists to push it through.

高层得推动这件事，但IT部门却说这风险太大。

The top management needs to push it through, but then the IT is saying this is risky.

云服务刚出来的时候也是这样。

And that happened with cloud too.

云服务最大的障碍就是大家都害怕。

That was one of the big things with cloud where everybody was afraid.

把数据放在云上不安全。

It's unsecured to have your data in the cloud.

后来我们看到中情局用了，看到Capital One也用了，还采访过他们的CIO，他说在云上反而更安全。

And then we saw the CIA do it, and we saw Capital One and we talked to the Capital One CIO, he said it's more secure in the cloud.

从那以后，云服务才真正开始爆发。

And then it really started to take off.

这种爆发可能是因为SaaS就像洗碗机，而云服务得插着电才行，所以情况就是这样。

Those take offs maybe because SaaS is like the dishwasher and because cloud it's gotta be plugged in, it meant that yeah.

它在增长，但增速大概在30%到50%之间。

It was growing, but it was sort of a 30 to 40, maybe a 50% growth rate.

但AI最厉害的地方在于，不管是普通用户还是企业，只要打开浏览器，它就在那儿了。

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.

所以这就是为什么我们现在看到它这么迅猛地崛起。

And so that's why we're getting this straight up.

我觉得在短期内，从只有百分之零点几的人在用，到两成、五成甚至更多，这个增长空间足够大，会推动它持续飙升。

And I think there's enough runway in the near term going from 10 bips of people really using it to two to five or whatever, which is gonna cause it to keep on going straight up.

我们管这叫‘倒L型曲线’。

This we call it a backwards l curve.

所以这真的非常令人兴奋。

So it's really pretty exciting.

你有没有发现，当一个团队最终从竞争者中脱颖而出时，关键是什么？

What have you learned about when the group that ends up being the leader separates itself from one of these competitive packs?

你刚才主要谈的是S型曲线的整体增长和市场需求。

So you're talking there mostly about overall growth of the S curve and demand.

总是会有好几个玩家在争夺这个市场。

There's always multiple players fighting for it.

你好像都是等有人已经脱颖而出之后才出手投资，而不是试图在一群竞争者里提前选赢家。

It seems like you kind of invest after someone has separated themselves from the pack, not try to pick the winners from the pack.

我们会先找那个S型曲线，然后对所有在这个领域有布局的公司做全面研究，找出那个拥有强大竞争优势的公司。

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.

很多人其实不太喜欢科技行业。

And a lot of people didn't like tech.

沃伦·巴菲特就不喜欢科技，因为他没法预测未来。

Warren Buffett didn't like tech because he couldn't predict the future.

变化太快了。

Turns too fast.

是的。

Yeah.

所以，S型曲线就是我们展望未来的路线图。

And so the s curve is our map for looking in the future.

很多人过去担心科技行业，觉得颠覆太剧烈，根本没法相信哪家公司能长久存活。

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.

这些年来我们发现，数字世界里的某些竞争优势，甚至比线下世界的还要强大。

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.

像LinkedIn、Facebook、阿里巴巴这些公司，网络效应的作用特别明显，例子数不胜数。

You've got the network effect that was so powerful for LinkedIn, Facebook, Alibaba, you name it.

然后你就能成为行业标准。

Then you can become an industry standard.

Oracle和Bloomberg就是行业标准。

Oracle and Bloomberg are the industry standard.

Oracle收费很高，当然也有免费版本。

Oracle charge a lot and there's free versions.

有开源的Oracle，但他们手握所有数据库管理员。

There's open source Oracle, but they had all the database administrators.

所有软件都专门为他们的系统做过优化。

They had all the software that was tuned to work with them.

他们几乎完全垄断了关系型数据库市场，而且一占就是几十年。

They basically had a choke hold on the relational database market forever.

你可以迅速达到规模，因为这些S型曲线一旦启动，增长就非常快，比如Anthropic一下子就能做到三千亿美元的销售额。

You can get to scale very quickly because these s curves grow, and all of a sudden Anthropic is doing 30,000,000,000 in sales.

亚马逊就是个例子，它很快就实现了巨大的规模。

Or Amazon had so much scale and they got it quickly.

他们只用了五年就达到了沃尔玛那样的规模优势，而沃尔玛花了四十年。

So they got a Walmart size scale advantage in five years versus forty years for Walmart.

网络效应能带来规模上的优势。

So you can have network effects scale.

你还能成为行业标准。

You can become industry standard.

你可以成为一个让别人依赖的平台。

You can be a platform that people build on top of.

你还可以掌握关键的知识产权，就像高通那样。

You can have critical intellectual property, which was what Qualcomm had.

不付钱给高通，你就造不出手机；ASML也掌握着关键的知识产权。

You couldn't make a phone without paying them or ASML has critical intellectual property.

没有他们的光刻技术，就造不出芯片。

You can't make a chip without their lithography.

品牌也很重要，像谷歌和亚马逊，它们的成长根本不用靠推广。

You can also have brand and brands very important because Google, Amazon, they got to grow.

它们从来不需要做广告。

They never had to advertise.

埃隆连产品都不用打广告，获客成本和用户终身价值完全不成比例。

Elon's never had to advertise for anything and cost to acquire versus lifetime.

这整个就是商业模式的问题。

It's the whole business model.

我提到的几乎每家公司，都同时具备了这些优势。

Almost all the companies I mentioned have all of these rolled into one.

有时候，我们能比全世界更早发现这些趋势。

Sometimes we can notice these things before the rest of the world.

我们的一个高光时刻，是在2013年罗宾汉投资者大会上，向亚马逊推介了AWS。

One of our high points was we pitched Amazon for AWS at twenty thirteen at the Robinhood investors conference.

我们当时就说，那些看涨的人根本不知道自己坐的是什么宝座。

And we said the bulls have no idea what they're sitting on.

亚马逊在战争还没开始的时候就已经赢了。

Amazon's won the war before it even started.

那时候我们说，可乐有，但百事根本不存在。

And at that time we said there's Coke and there's no Pepsi.

后来确实出了个百事，但亚马逊的体量已经大到足以撑很久了。

Did turn out there was Pepsi, but it was big enough to last.

我们早就看出，他们领先了七年。

And we could see they had a seven year lead.

所以先发优势很重要，接着他们建起了整个生态系统和平台，然后规模也上来了。

So first mover is important, then they became a whole ecosystem and a platform, then they got scale.

他们的规模是其他所有人的十倍。

So they were 10 times the size of everybody else.

没人能负担得起研发投入来追上他们。

Nobody could invest in the R and D to catch them.

但你说得对，就算你处在有史以来最棒的增长曲线上，如果没有竞争优势，也照样会输。

But you're right that if you don't have a competitive advantage, you can be in the best s curve of all time.

还是会被淘汰。

And still lose out.

如果你的名字是Ramp、Pong、诺基亚、HEC、LG、摩托罗拉——我能列一长串，结果都是零，零，零，负，负，负，负。

But if your name was Ramp, Pong, Nokia, HEC, LG, Motorola, I can go on forever, zero zero zero negative negative negative negative.

我们在基础模型层看到的情况就是，当时有五十家公司都在做，结果全垮了，最后只剩下两三个站到了顶端。

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.

有很多理由相信，它们会继续牢牢守住自己的位置。

There's a lot of reasons to think they will continue to hold their position.

谷歌的情况稍微复杂一点，因为它的Gemini业务背后还连着一个庞大又复杂的其他业务体系。

Google's a little trickier because they have this other huge massive complex business attached to the Gemini business.

但如果你把Anthropic和OpenAI当作纯粹的AI公司来看，深入分析它们的竞争优势，为什么这些优势不会被慢慢侵蚀呢？

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?

在我们研究过的所有增长曲线里，人工智能无疑是最复杂、变化最快的。

Of all the s curves we've done, AI is by far the most complex and the fastest changing.

我们得记住，这里面是有风险的。

We have to keep in mind that there are risks.

回报也最高，毕竟这可是万亿级别的市场，可能高达八千亿。

The rewards are the highest because we're talking about a market in the trillions, maybe clouds 800,000,000,000.

我们现在觉得是三到五年，但风险更高，回报也更大。

This might be we now think three to five, but there's higher risk, higher reward.

就拿Anthropic来说，他们现在看起来已经掌握了关键的知识产权。

But let's just say with Anthropic now, it looks like they have critical intellectual property.

总的来说，他们一直保持着很高的市场份额和代码优势。

Generally, they've been able to maintain their high market share and code.

第二，他们在企业市场建立了强大的品牌，你去问任何一位CIO，他们第一个提到的肯定是Claude。

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.

第三，他们会获得足够的势能和规模。

Three, they're gonna have escape velocity and scale.

当初OpenAI和Anthropic跟谷歌这样的巨头对抗时，最让人担心的是对方有那么多赚钱的现金牛业务。

And what was scary for OpenAI and Anthropic fighting these big companies like Google was they had these huge cash cows.

两位管理团队都值得称赞，他们能在这种资本密集型行业里，找到融资的办法。

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.

尤其是Anthropic，销售额增长了十倍，融资能力又强，看起来已经突破临界点了。

And certainly with Anthropic, with their 10 x sales growth and their fundraising ability, it looks like they've reached escape velocity.

现在他们有了规模。

So now they have scale.

另外，Anthropic和OpenAI可能拥有的优势是，Anthropic如今在代码领域领先，他们把这套代码反哺回自己的模型，这就是所谓的递归式改进。

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, and it's this concept of the recursive improvement.

如果你看看他们创新的速度，明显在加快。

And if you look at the pace of their innovation, it's accelerating.

也许他们正要进入起飞阶段。

Maybe they can have this lift off stage.

OpenAI之前太分散精力了，涉足了太多其他领域。

OpenAI, they were focused on so many different other sectors.

现在他们在企业市场表现越来越好，编程工具也挺出色，增长势头正在加速。

They're starting to do better in enterprise and their coding tools good, and they're starting to see accelerating growth on that side.

再看看消费端，现在企业端明显更强，因为咱们愿意多花钱，毕竟它是在替代人工。

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.

消费端的话，也许能靠广告赚钱，但如果你真能做出一个完美的机械臂助手，他们也可能愿意买单——不过他们那儿有海量用户。

Now consumer, maybe you can get advertising, but maybe they would pay for a claw bot type assistant if you can make that perfectly well for them, but they have gazillion eyeballs there.

情况总是在变的。

Things do shift.

我们每次做演示，几乎都会用到这张图。

We have this charts that we almost do for all of our pitches.

在互联网上，领先者会变得更大、更快，最终胜出。

On the internet, the leader goes bigger, faster and wins.

绝大多数时候，领先者都能拿到结果。

Most of the time the leader gets it.

Shopify 成了领先者，就一路狂奔。

Shopify becomes the leader, just keeps on going.

亚马逊，作为领先者，也一直在持续扩张。

Amazon, the leader keeps on going.

SaaS公司，比如X、Y、Z，只要拿到领先位置，优势就会自我放大。

SaaS company, x y z, you get the lead, it compounds on itself.

还有一点，你得足够大。

And another thing is you need to be big.

再就是规模，你需要算力，还得花钱买算力。

Another is scale, you need the compute, and you gotta pay for the compute.

所以真能干这事儿的人没几个。

So there's only so many people that can do that.

这些就是我们现在看到的、在这个行业里逐渐形成的护城河。

Those are some of the moats that we think are now showing up in this business.

当然也有例外，比如AOL，当互联网从拨号上网转向宽带时，他们没跟上转型。

Now there are some exceptions to that rule, usually with the paradigm shift AOL, and then dial up went to broadband and they didn't make the change.

Netscape虽然早早就出现了，但商业模式不够扎实。

Netscape came out early and it wasn't as strong of a business model.

如果你去问硅谷的任何人或者任何初创公司，他们都会说，自己正是在这三大基础上搭建，而世界这么大、经济这么庞大，他们完全能在其中找到自己的差异化空间。

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's a huge place that they'll be able to differentiate within those.

那我很好奇，你认为这一切对软件行业意味着什么？

I'm so curious then what you think all of this means for software.

我翻看你的投资组合，发现并没有太多大型软件公司，尤其是企业级软件公司。

When I look through your portfolio, I don't see a ton of big software companies, enterprise software companies.

我不确定你以前有没有投过这类公司并退出了，但很难有过亲手打造过有用又酷的小工具的经历——哪怕它们还只是玩具——却不会想到：天啊，如果我花足够多时间在这上面，即使我不懂技术，也许也能做出一个替代公司ERP系统的东西。

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, maybe I could build a ERP equivalent replacement or something for my company.

从本质上讲，似乎没有理由说这不可能发生，而一旦真有人做到了，那些传统公司就会陷入大麻烦。

There doesn't seem to be a fundamental reason why that's not possible, and then those companies could be in lots of trouble.

好像每个人对这个问题都有非常明确的看法，要么支持，要么反对。

Seems like everyone has a strong view on this one way or the other.

我很好奇，既然你并没有持有太多这类公司，你是怎么看待它们的？

I'm curious how you've approached those sorts of companies given that you don't seem to own a ton of them.

五年前，我们的投资组合里可能有四到五成是软件公司。

Five years ago, we might have had 40 or 50% of our portfolio in software.

早在2023年4月的网络研讨会中，我们就明确说过：先投资芯片。

And early on in our April 2023 webinar, we said definitely invest in chips first.

但在应用层，我们一开始觉得这些公司体量巨大，销售团队强大，能轻松整合AI接口，又有数据优势，这对软件行业来说简直是天赐良机。

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 gonna be amazing for software.

但很快我们就发现，他们的AI产品根本不行，完全没带来实质改变，也没人愿意为此买单。

Pretty quickly we realized their AI products were not very good, they weren't moving the needle, nobody could charge for them.

最后我们几乎把所有软件类投资都清仓了。

We basically sold almost all of our software.

今年初，我们的整体仓位已经是净做空了。

Entering this year we were net short.

这在第一季度帮了我们大忙。

It really helped us in the first quarter.

这里面的门道可太多了。

There's so many layers to this.

以前的软件就像用笔和纸，或者骑马赶车一样落后。

I mean, the old way of software is like using pen and paper or it's like a horse and buggy.

现在的软件，那简直就是喷气式发动机，甚至像《星际迷航》里的传送装置。

The new way of software is like a jet engine or frankly, the transporter from Star Trek.

这种变革太颠覆了，感觉不造成冲击都不可能。

It's so revolutionary changing that it feels like it has to be disruptive.

如果现在或短期内没带来冲击，软件公司就又面临一个问题——任何CIO的待办事项或优先级列表里，他们的位置已经大幅下滑。

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.

所以即使AI不会造成颠覆性影响，他们也会把钱花在Anthropic的调用上，因为那边回报更快。

So even if AI is not going to be disruptive, they're spending it on Anthropic tokens because there's faster ROI there.

其次，如果他们把大笔资金都投到那边去了，预算就会吃紧，这反过来会拖累他们。

Second, if they're spending all that money over there, it pushes on the budget, so that hurts them.

第三，很多软件公司过去每年都敢提价。

Third, a lot of software companies were able to raise price every year.

但现在，他们可能不敢轻易这么做了。

And now they're probably nervous about doing that.

第四，我们会看到就业方面会发生什么，两边都有聪明人，但我们已经看到一些公司大刀阔斧地裁员了。

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.

免费招聘之类的

Free hiring or whatever

免费招聘，这反而影响了座位利用率。

Free hiring and so that hurts on seats.

至于他们自己开发应用，如果往乐观了看，他们花的时间确实有点长。

In terms of them building their own apps, if you wanna be optimistic, it's taken them a while to do that.

我们之前聊过，增强现实的基础技术还太早了。

We talked about how early the primitives of AR.

也许他们只是花太久才找到能商业化的东西，但可能缺的是合适的人才。

So maybe they have just taken a while to get to something they can commercialize, but they might not have the right people.

卖固定系统和卖能替代人力的系统，销售方式完全不同——后者你得贴身跟进，确保事情真能落地。

It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you gotta be right at the side to make sure it's really getting done.

所以你需要现场部署工程师，也就是FDEs。

So you need the FDEs for deployed engineers.

他们内部可能根本找不到合适的人来做这事。

They might not have the right people internally to do that.

当然，自己动手开发也存在风险。

Then of course, there's the risk of you can build it yourself.

看涨的人会说，他们根本不会自己开发ERP系统。

The bulls will say, well, they're never gonna build their own ERP system.

这很可能没错。

And that's probably right.

确实，老技术的黏性很强。

And it is true that old tech is very sticky.

手机游戏没影响主机游戏，平板也没冲击PC，智能手机也没让PC被淘汰。

Mobile video games didn't hurt console games, and the tablet didn't hurt the PC, and the smartphone didn't hurt the PC.

这些软件背后有大量的集成和定制工作。

There's a lot of integrations and work that goes into the software.

这些都说得通。

That's all true.

企业确实更愿意采购。

And companies do like to buy.

它们不太愿意自己从头开发。

They don't like to build themselves that much.

这些都没错。

That's all true.

但你可以想象，再过一两年、三四年，可能会冒出一家全新的AI原生公司，专门挑战这些行业巨头，一旦它们能打破对方的数据优势，就很容易用AI驱动的新系统取而代之。

But you can imagine a world wherein 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.

它们的估值都很高，人人都知道它们正面临压力。

The valuations are very high and everybody knows they're under pressure.

有些人确实动心想收购，但AI编程工具的进步越来越快。

Some people are tempted to buy these, but the AI coding tools are just getting better and better.

咱们只能拭目以待。

We'll have to wait and see.

我们正在密切观察这些软件公司，看它们能否获得足以扭转局面的收入。

We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory.

但问题在于，像Salesforce这样的公司，年收入高达四百亿。

But it's hard because if you're a company like Salesforce, you've got 40,000,000,000 in sales.

它们的AI相关年经常性收入可能只有五亿到七亿。

You might have 500 of ARR, 700 of ARR of AI.

所以你有这么庞大的基础。

So you've got this huge base.

现在也许开始见效了，但需要点时间。

Now maybe this starts to work but it takes a while.

在软件行业有个‘40法则’，就是增长率加上运营利润率。

In software there's the rule of 40, which is your growth rate plus your operating margin.

如果你有20%的增长率，20%，那就不错了。

And if you have 20% growth rate, 20%, that's good.

对于AI领域，我们有了一个新的‘40法则’。

For AI, we have a new rule of 40.

你的销售额里，AI占了多少百分比？

What percent of your sales are AI?

30%。

30%.

你在这一品类里的市场份额是多少？

And what's your market share in that category?

假设是30%吧。

Say 30%.

那你就是60了。

You'd be 60.

这可是个值得关注的方向，因为你既有市场渗透，又占着有利地位。

That's a great place to look cause you've got exposure and you've got a strong market position.

软件公司的AI业务现在才占一两个百分比，路还长着呢。

Problem with software is their AI is one or 2% at this stage and it's a long way to go.

不过最近我们注意到一点，虽然还不是很成熟，但AI可能会让某些软件平台变得更关键——比如，你用Clog的时候，第一件事是干嘛？

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 Clog?

当然是接入Slack。

You plug it into Slack.

如果Slack能成为核心数据枢纽，那它在公司里就彻底稳住了。

If that can become a key repository that will make Slack a permanent fixture within the organization.

所以下一波AI浪潮，可能是这些能调用工具的智能代理，它们会像人一样，直接在现有的主流软件里干活。

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.

顺着这个思路想，他们最可能长期使用的工具，应该是那些基于网络的平台。

Just to pull on that thread, seems like the commonality of the tools they might use that are the most sticky would be network based tools.

Slack 就是个很好的例子，Slack 本身这个软件并不是关键。

So Slack is a great example of the software in Slack itself.

真正重要的是，所有人都在上面。

The software is not the special part, it's that everyone is there.

我想知道，你希望看到什么样的功能？

I'm curious what kinds of things you would want.

是只要有一个网络效应就够了？

Is it just the presence of a network effect?

就只有这一点最重要吗？

Is that the only thing that really matters?

我们目前的想法还比较初步，但也许像 Workday 或 HR 系统这类核心系统，未来也会有智能代理在上面运行。

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.

这有好处，也有坏处。

It's good and bad.

我的意思是，CRM正在向无界面方向发展，或者他们正在推出无界面版本。

I mean CRM is going headless or they're making a headless version.

这其实是最基本的情况——你最后就只剩下一个数据库了。

And that's the bare case too that you get relegated to just being a database.

如果有用户界面，他们还得再开发一个AI接口，而这个接口根本不需要界面。

There's a human interface to it, then they need to make the AI interface, which is no interface.

AI直接跟数据打交道就行了。

It's just them going right into the data.

这样一来，客户互动就没了。

You lose that customer interaction.

但如果智能代理直接接入CRM，在里面完成所有工作，反而会让CRM变得更稳固，大家就不会觉得它要被淘汰了。

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.

咱们能聊聊芯片吗？

Can we talk about chips?

你之前提到过好几次了。

You've referenced them a few times.

基础设施芯片，还有数据中心周边的那些东西，我不太确定你怎么看。

Infrastructure chips, everything around the data center maybe, I don't know how you conceive of it.

为什么这些东西对你这么有吸引力？

Why is this so interesting to you?

我特别喜欢那个调整过的44%规则，就是AI在市场份额里占的百分比。

I love the modified rule of 44 percentage that's AI and percentage market share in the category.

这个数据挺有意思的。

It's an interesting stat.

哪些公司在这一块表现特别突出？

What companies shine on that?

哪些公司是拖后腿的？你知道的，有没有让你意外的？

What are laggards, you know, that are surprising?

过去四十年，数据中心里根本没什么变化。

For the past forty years, nothing has changed in the data center.

就算有了云计算，英特尔x86芯片也早在九十年代就成了数据中心的主流。

Even with cloud, Intel x 86 became the data center chip sometime in the nineties.

在云计算时代，计算量每年增长25%到40%。

And compute grew in the cloud era and compute workloads grow 25 to 40% every year.

但摩尔定律的提升速度并没有跟上。

But Moore's law is improving at that rate.

这并不需要什么重大的技术突破。

It didn't require tremendous innovation.

之后好多年，硬件几乎没什么进展。

And then there really was almost no growth in hardware for years and years and years.

整个行业把每个部件都做成了大宗商品——无论是芯片、服务器的电路板、内存、机箱还是网络设备，全都没有创新。

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.

从1吉比特升级到10吉比特，居然要花七年时间。

You would go from one gig to 10 gig, that would take seven years.

刚换新标准的第一年确实需要一些创新才能实现10吉比特，会带动一个小循环，但很快又回到同质化。

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.

现在转向人工智能，计算需求每年增长十倍。

Now you go to AI, the workloads are growing 10 x every year.

他们正在把硬件的每一个方面都推到物理极限。

And they're pushing every single aspect of this hardware to the physical limits of what it can do.

这不仅带来了巨大的出货量增长，我们称之为硬件行业的去商品化。

Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry.

三年前我见过肖恩·麦圭尔，他说真希望当年能转行做硬件对冲基金，因为现在所有公司都上市了，而且都拥有强大的专利技术。

I met with Sean McGuire like three years ago and he said, wish I could come back and be a hardware hedge fund because all the companies are public and they all have powerful IP.

红杉资本当年投资苹果、思科这些硬件公司，赚到了他们最成功的几笔钱。

And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others.

我们现在正处在一个芯片复兴的时代。

And we're in this renaissance of chips.

所以，这不仅需要巨大的出货量增长，更需要前所未有的技术创新。

So not only do you have tremendous unit growth, it's requiring tremendous innovation.

以前服务器里的内存完全是标准化产品，现在高带宽内存要把十片芯片堆叠在一起，输入输出速度是过去的十倍，三星花了好几年才搞定，这可是关键部件。

That means every aspect of the server, memory, used to be a pure commodity, this high bandwidth memory is stack 10 chips on top, input outputs are 10 x what they were before, took Samsung for years to do it, And it's a critical piece.

而且这些技术还在不断升级。

And then that is constantly upgrading.

所以他们得提前三四代就和英伟达紧密合作。

So they've gotta be working with NVIDIA for three or four generations in advance.

我们以前和Celestica就是这样合作的。

We had this with Celestica.

Celestica是一家合同制造商。

Celestica was a contract manufacturer.

这个行业从1999年起就一直走下坡路。

This has been a disaster industry since 1999.

整个生产都转移到了中国。

It went all offshore to China.

虽然成了大宗商品，但他们还是撑住了。

It was commodity, but they hung on.

Celestica原本是做IBM超级计算机的，保留了所有技术人才和能力。

Celestica's heritage was IBM supercomputing and they kept all that talent and skill.

后来我们发现，三年前他们已经是谷歌TPU服务器的唯一供应商了。

And then we noticed they were the sole supplier of the Google TPU server three years ago.

当时股价的市盈率只有八倍。

The stock was trading at eight times earnings.

他们还做了一整块业务，就是卖以太网白盒设备——这其实就是指卖给云服务商的普通白盒以太网交换机。

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.

结果发现，这些业务其实非常棒。

It turns out these are excellent businesses.

不仅增长迅猛，而且做AI服务器得用液冷，发热量大得多，一台机器要值二三十万美元，而以前的老服务器才五千块。

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 2 or $300,000 piece of machinery, whereas an old server was $5,000.

要是老服务器坏了，直接扔了就行。

If it breaks, you just throw it away.

但要是这台设备出问题，整个系统就瘫了。

If this thing breaks, the whole thing goes down.

所以你成了关键部件的供应商，说白了，根本没人能把你换掉。

So you become a commodity like supplier to like selling a critical part on it, plain, you'll never get swapped out.

结果他们做液冷特别在行。

It turned out they were quite good at liquid cooling.

很多人试过但都没成功，所以他们一直保持着这个地位。

A lot of other people tried to do it and failed, and so they've retained that position.

以前以太网市场是从100吉比特升级到400，再升级到800。

Then it also turned out that the ethernet market, in the old days, you would go from 100 gig to 400 to 800.

升级一次要七年时间。

It would be a seven year cycle to upgrade.

现在每年都在升级。

Now they're upgrading every year.

这可不容易做到。

That's really hard to do.

还有一整套软件层，就是开源的Sonic系统。

Then there's a whole software layer, the open source sonic layer.

Celestica的那些人，或者参与开发这套开源软件的团队，跟博通合作得非常紧密。

The guys at Celestica invented or some of the people that wrote that open source software, they work very closely with Broadcom.

我们原本以为这只是个增长动力，结果发现这是巨大的竞争优势。他们占了云以太网交换机市场60%的份额，而这个市场对人工智能至关重要，因为AI对网络带宽的需求极高。

What we thought was just a great growth driver turned out to be great competitive advantages, And they have like 60% share of the cloud ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive.

还有像印刷电路板这样的东西。

And then even something like the printed circuit board.

普通的服务器需要十层。

A regular server, you need 10 layers.

而这些AI服务器需要四十层。

These AI server, you need a 40 layer.

能生产这种电路板的供应商寥寥无几。

And there's very few PCB suppliers that can make this.

这里面的复杂性多得不得了。

There's all kinds of complexities in there.

我们还拥有Elite Materials公司，它生产关键材料——铜箔基板，用在这些电路板上。现在电路板用量在增长，层数也在不断增加。

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.

仅是出货量，年复合增长率就达到百分之五十到六十，再加上单价上涨、毛利提升，以前的供货模式是‘下周再联系你，看需不需要你’，现在变成了‘未来四年，我们一起规划路线图’。

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 road map with us.

你从一个年增长5%、利润率低的业务，变成了未来四年年营收增长百分之三十五到五十、利润率持续上升的业务。

You've gone from a 5% grower low margin to a 35%, forty, fifty top line CAGR for the next four years with rising margins.

除此之外，所有东西都缺货。

On top of that, there's shortages of everything.

所以哪怕是个普通商品，现在也正处在绝佳的周期里。

So even if it is a commodity, it's gonna be a great cycle.

我们在整个供应链上都能看到这种现象。

So we see that up and down the supply chain.

比如康宁公司，他们生产光纤。

You find these companies like, I mean, Corning, they make the fiber.

我刚看到微软新建的数据中心，他们的光纤市场份额高得离谱。

They've got some ridiculously high share of the fiber that I was reading this Microsoft data center they just built.

单是这一个数据中心用的光纤，就够绕地球四圈半了。

There's enough fiber to circle the world four and a half times in that one thing.

他们的光纤更细、更柔韧，还能按特定规格定制生产。

And their fiber is thinner and more bendable and can be specially manufactured to the exact specs.

而且利润率更高，也是他们增长最快的业务板块。

And it's higher margin and it's the fastest growing part of their business.

在网络领域，有一种叫横向扩展，就是把所有服务器机架连在一起。

In networking, there's scale out, which is connecting all the server racks together.

还有一种叫跨域扩展，是把不同的数据中心彼此连接起来。

Then there's scale across, which is connecting the data centers together.

当你想搭建一个超大规模的集群，但又没法把所有电力集中到一个地方来做训练时，你就得把这些系统连起来。

And when you wanna build one of these huge clusters and you can't get all the power in one place for training, you wanna wire them together.

但你需要的线缆要达到十倍粗，线缆本身得变得粗得多。

But the wires that you need like 10 x, the wire has to be so much thicker.

这就带来了巨大的增长空间。

So that's creating huge growth.

而真正关键的是纵向扩展，也就是把机架里的每个GPU都互相连起来。

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.

目前是用铜线来实现的。

That's done over copper.

但将来肯定会改用光纤。

Eventually that'll be done over fiber.

一旦发生，这就能为康宁带来两到三倍的增长机会。

When that happens, that two to three x's Corning's opportunity.

所以在机架的每一层，

So you just have at every layer of the rack

所有人都忙不过来。

Everyone's overwhelmed.

所有人都忙不过来，但电源方面，每个英伟达芯片或机架的功耗比以往高出50%到125%，这直接推高了台达和先进能源的产品均价。

Everyone's overwhelmed, but in the power supplies, every NVIDIA chip or rack uses 50 to a 125% more power that drives the ASPs of Delta and advanced energy.

我听到这些说法时简直不敢相信，心里想：等等。

I can't believe these stories when I hear I'm like, wait.

所以你的产品均价在未来四年会连续每年上涨40%，而且利润率还更高？

So your ASPs are gonna, like, go up 40% for the next four years in a row and it's higher margin?

从更大的格局来看，如果我们对这个L型曲线的判断没错，目前DRAM、NAND、PCB市场都已经短缺30%了。

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, we're already 30% short all these things as we are now.

AI所占的百分比，还有市场份额。

A measure of percent AI, percent market share.

你更看重这些指标的绝对值，还是它们的变化速度？

Do you care more about the absolute or the rate of change of those metrics?

我们在2024年做过一次演示，列出了所有人的市场份额，然后我让Claude把它画成图表，结果它没画对，因为它没理解变化速度才是关键。

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.

所以变化速度很重要。

So the rate of change is important.

这也很惊人，因为份额从10%涨到30%，增长率在加快，利润率也在提升。

And that's incredible too because you go from 10% to 30% and your growth rate accelerates and your margins accelerate.

所以增速非常关键。

So rate of change is very important.

你的财务团队并不是因为犯了大错才亏钱。

Your finance team isn't losing money on big mistakes.

而是通过成千上万个没人盯着的小决定慢慢流失的。

It's leaking through a thousand tiny decisions nobody's watching.

Ramp能在花钱之前就把好关，给支出设好限制。

Ramp puts guardrails on spending before it happens.

实时限额、自动规则，彻底告别救火。

Real time limits, automatic rules, zero firefighting.

立即前往 ramp.com/invest 体验。

Try it at ramp.com/invest.

随着业务增长，Vanta 会同步扩展，自动完成合规工作，为你提供安全与风险的统一视图。

As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk.

了解更多，请访问 vanta.com/invest。

Learn more at vanta.com/invest.

每家投资公司都不同，通用型 AI 根本理解不了你的流程。

Every investment firm is unique and generic AI doesn't understand your process.

Rogo 可以。

Rogo does.

这是一款专为华尔街打造的 AI 平台，直接对接你的数据，理解你的操作流程，产出真正有用的结果。

It's an AI platform built specifically for Wall Street, connected to your data, understanding your process, and producing real outputs.

详情请访问 rogo.ai/invest。

Check them out at rogo.ai/invest.

从OpenAI到Cursor再到Perplexity，最顶尖的AI和软件公司都靠WorkOS在一夜之间达到企业级标准，而不是花上几个月。

The best AI and software companies from OpenAI to Cursor to Perplexity use WorkOS to become enterprise ready overnight, not in months.

去workos.com官网，跳过那些枯燥的底层基建，专心做好你的产品。

Visit workos.com to skip the unglamorous infrastructure work and focus on your product.

Ridgeline正在重新定义资产管理技术，它不只是软件供应商，更是真正的合作伙伴。

Ridgeline is redefining asset management technology as a true partner, not just a software vendor.

他们帮助多家机构实现了五倍增长，推动更快扩张、更智能运营，赢得竞争优势。

They've helped firms five x in scale, enabling faster growth, smarter operations, and a competitive edge.

访问ridgelineapps.com，看看他们能为你的机构带来什么改变。

Visit ridgelineapps.com to see what they can unlock for your firm.

为什么在公开市场里，这么多人就是做不好这件事？

Why don't more people get this right in public markets?

如果你的整个投资框架都是S型竞争壁垒、被低估的盈利潜力，那过去二三十年，这剧情早就被演烂了。

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 twenty five, thirty years.

有人问，你干嘛把秘密到处说？

Said, why do you tell everyone your secret?

这就像赌场为什么要教人玩二十一点？

And it's like, why does the casino teach people how to play blackjack?

这事儿真没那么容易。

It's really hard to do.

你得能坦然地去投资。

You have to be comfortable investing.

我在WhaleRock做科技这行已经二十年了。

I've been doing tech for twenty years at WhaleRock.

我们团队一直干这个，经历过好几个周期了。

We've got a team that's been doing this, covered many cycles.

根本没人关注过硬件和芯片这块。

No one's paid attention to hardware and chips at all.

所以现在一堆新手涌了进来。

So you've got all these newbies coming into it.

你和加文就是这样。

You and Gavin.

没错

That's

就是这样。

it.

对。

Yeah.

加文做得非常好。

And Gavin's done a great job.

大家一开始都不太敢碰。

People weren't comfortable with it.

实际做起来比看起来难多了。

It's harder to do than it seems.

很多公司的股价都涨上去了。

And a lot of these companies, their charts are up.

所以挺让人担心的。

So it's scary.

我能买吗？

Can I buy?

那你还得有全局的眼光。

Then you also have to have the holistic view.

如果你过去四年每次都不相信英伟达，就会觉得，哎呀，他们这一年表现真不错。

Because if you don't have conviction every time with Nvidia over the last four years, it's, oh, they had a great year.

天哪。

Oh my god.

这肯定是个泡沫。

It's gotta be a bubble.

结果他们又交出了一年亮眼的成绩，然后又花了六个月原地踏步。

And then they had another great year and it's like six months of marking time.

这肯定是个泡沫。

It's gotta be a bubble.

这事儿有点失控了。

This is like getting out of hand.

这真有点吓人。

This is pretty scary.

单看表面现象，这些说法也不是全无道理，但如果你能看清全局，理解这些事情是怎么一步步发展的，并因此建立起信心，说实话，很多半吊子分析师都错过了，因为他们没看到基础模型层面真正发生的事，也没看清整个大图景。

The bare 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.

所以，看得长远一点很有帮助。

So it helps to have the big picture.

你得盯着几十年来无数条S型曲线，看它们在不同领域里是怎么交织演进的。

It helps to have decades and scores of s curves that you're looking at and where it plays in different things.

让你最担心或最不确定的是什么？

What makes you the most concerned or uncertain?

是这一切变化的速度太快了吗？

Is it just the rate at which all this stuff changes?

在这么强烈的看涨情绪中，是什么让你还感到忧虑？

What keeps you worried amidst what seems like pretty extreme bullishness?

让我有点不安的是，大众对人工智能普遍抱有负面情绪，政府里某些方面也是如此。

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.

你知道吗，缅因州刚禁止了数据中心建设，只有百分之二十的人对人工智能和潜在的监管限制持乐观态度。

You know, I think Maine just banned data centers and only 20% of the people are optimistic about AI and potential for negative regulation.

但我认为，这股潮流已经无法遏制了。

But I do think kind of the genie is out of the bottle.

另一个风险是，如果人工智能的进步放缓了，即便模型不再提升，社会对AI的采纳依然会大规模展开。

Another risk is that if AI slows down in its improvements, I think there's a whole lot of AI adaption to happen even if the models didn't improve.

几年前杰ensen就说过，他做显卡的时候就明白：如果‘够用’就行，那我这生意就做不下去了。

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.

他每年把显卡性能提升一点点，但消费者永远想要最好的。

Every year he made the graphics a little bit better and people always wanted the best.

在人工智能领域，如果Anthropic或者OpenAI卡住了，不再进步，开源模型迟早会追上来。

In AI, if Anthropic sort of hits a wall and stops improving or OpenAI, then the open source models will catch up.

这可能会演变成一场竞相降价的竞赛，对相关股票恐怕不是好事。

It might be a race to the bottom and it won't be good for the stocks probably.

但对芯片公司来说，这倒可能是好事。

It could be good for the chip companies.

芯片公司根本不关心谁赢。

The chip companies don't care who wins.

这又是另一个有利因素。

So that's another positive.

他们反而会从中受益。

And they'll benefit.

詹森特别希望开源能真正火起来。

Jensen really wants open source to like take off.

他上一次GTC大会上一直都在提这个。

It's all he kept on mentioning at his last GTC.

这其实也可能是个风险。

So that could be a risk.

还有就是，如果有一两家公司掉队了，失去优势，没法继续竞争，那他们未来可能就用不上那么多算力了。

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.

不过如果AI真的这么庞大，总有人会接手这些算力，我们已经看到过，甲骨文取消了一笔大单，马上就被Meta接了。

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.

但假设Meta决定不参与AI领域，说我们跟不上，这会浪费我们的资源。

But let's just say Meta decided not to be involved with AI, hey we can't keep up, it's just gonna be a waste of our resources.

我们对此密切关注，总体来看，越来越多公司真正投身于此，连微软都在努力自研，这些都是关键风险。

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.

你似乎在AI的应用层做得很少。

Seems like you really have done very little in the application layer of AI.

过去，应用端往往占了大部分市值，基础设施和模型层都没那么重要。

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.

也许可以说，那时候主要是云服务在主导。

I guess you could say it was the clouds or something.

为什么你们这么专注在詹森提出的五层架构底层，而不是那些真正被消费者使用的应用层呢？

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?

首先，应用层总是来得晚一些。

Well, a, it always comes later.

iPhone刚推出那三四年，应用生态也是过了很久才真正爆发的。

So the first three or four years of the iPhone and then the applications really took time.

所以也许这才刚刚开始。

So maybe it's just starting.

如今，我们觉得这个领域风险挺大的，因为基础模型和应用之间的界限到底在哪？

Today, we found that area to be pretty risky because where does the foundational model end and where does the application begin?

应用方能不能建立起足够的护城河，来抵御竞争并真正做成生意？

Can the applications build enough of a moat where they can fend off and build businesses in that.

我们原本以为一些老牌的CRM公司会率先行动，可能只是时间问题。

We thought we would see it in some of the incumbents of CRM and they're starting in maybe just a matter of time.

但在企业级市场，我们真没看到什么实质进展。

But we really haven't seen it in the enterprise world.

确实有一些不错的初创应用公司，但整个生态还不明朗。

There are some very good startup application companies out there, but the ecosystem's not clear.

我们刚入行的时候，芯片领域的生态已经很清楚了。

When we started, the ecosystem in chips was clear.

当初我们进入基础模型领域时，生态还不清晰，现在我们看清楚多了。

When we started the foundational model ecosystem wasn't clear, now it's clearer to us.

在应用层，现在还是有点模糊，甚至有点风险，但未来一定会出现顶尖的应用公司。

And at the application layer, it's still kind of unclear and a little bit dangerous, but there will be great application companies built.

我们一直在关注西耶拉公司的布雷特·泰勒。

We really were watching Brett Taylor at Sierra.

布雷特曾经是CRM公司的首席执行官。

Brett was CEO of CRM.

他开发了谷歌地图。

He wrote Google Maps.

他还做过Facebook的首席信息官。

He was CIO of Facebook.

现在他正在打造一家非常出色的公司，叫西耶拉。

He's building this fantastic company called Sierra.

我们并没有参与其中。

We're not involved.

但真正的考验就在这里。

But that's where the rubber hits the road.

他能把它做成一家巨头公司吗？

Will he be able to turn this into a huge company?

他目前做得不错，咱们拭目以待吧。

And he's doing quite well and we'll see.

关键在于时机，这些东西真正成熟并证明自己能持续下去，往往要等上一阵子。

It's a matter of timing, when these things really start to come into their own and prove they're sustainable.

通常不会在头三四年就出现，得再晚一点才看得出来。

That usually doesn't start in the first three or four years, it comes a little bit later.

你们办公室墙上挂满了年度最佳研究项目奖，得奖的是分析师，我记得你拿过一次，还给自己颁了个‘当我独处时’奖。

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 and you gave it self award to the When I was alone.

当你一个人的时候。

When you're by yourself.

但你已经有整整二十年的历史了，每年都有一个人或多人的名字被刻在这面墙上，表彰他们当年最出色的研报。

But you've got this now long twenty 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.

我特别好奇这些研究的本质，以及它们如何因为这一切而发生变化。

I'm so curious about the nature of that research and how it's changing as a result of all of this.

你说，今年拿奖的这个人，他做的工作还得靠人来完成，可要是放在2009年左右，那些可能帮您拿奖的活儿，现在用Claude Code之类的工具，一个小时就能全自动搞定。

Say, you know, the person that's gonna 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, I don't know, 2009 or something could probably be fully automated or done in an hour with Claude Code or something today.

研究的本质，还有哪些成果能登上WhaleRock奖墙，这些变化现在到底有多快？

How is the nature of research and what gets you on that WhaleRock award wall changing in real time?

我真希望可以说，我们的AI系统已经先进到彻底改变了这一切。

I would like to say that we're so advanced in our AI systems that it's a huge change.

但到目前为止，AI主要帮我们加快了起步速度，我们也就用了几个特别棒的应用。

But so far, it's helping us get up to speed and we have a handful of great apps.

它还没能取代分析师的工作。

But it's not supplanting the job of the analysts.

我们做的很大一部分事，就是尽可能多地跟公司见面交流。

And so much of what we're doing is we're meeting with as many companies as humanly possible.

我们跟所覆盖公司的管理团队建立起长期关系。

We're developing relationships with the management teams that we cover.

我们也在跟竞争对手沟通。

We're talking to the competitors.

我们用的这套方法，源自菲利普·费舍尔上世纪五十年代写的《普通股与不普通利润》。

The system we use is right out of common stocks and uncommon profits, which was written by Philip Fisher in the nineteen fifties.

就是那种打听小道消息的路子。

And it's the scuttlebutt approach.

属于成长型投资。

It's growth investing.

得亲自走出去，跟供应商、客户、竞争对手聊，找出那些领先企业的关键特质，真正建立起信心。

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.

现在要是遇到像ABF基板或PCB这种新领域，我们能很快上手，但AI没法替你选股。

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.

如果一个分析师基本功扎实，那他的价值依然在，但光有基本功不够，还得有洞察力。

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.

我们现在用AI来写会议纪要、复盘季度报告，生成的文本确实更到位了，但前面必须有一段真正有分量的分析，那才是真正的智慧。

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.

这到底意味着什么？

What does this mean?

这跟我们的投资逻辑怎么对应？

How does this deal with our thesis?

有什么变化？

What changed?

别只当个传话的。

Don't just be a reporter.

AI倒是很擅长做信息整理。

So the AI can be a great reporter.

但它没法看透未来。

It can't pick into the future.

两年前团队对AppLovin的分析，我觉得我们请到了两位顶尖的广告科技专家。

The job that the guys did on AppLovin two years ago, I think we got two of the best ad tech guys.

我对广告科技很熟悉。

I knew ad tech.

我当初就是在纽约这儿的一家互联网广告初创公司起步的。

I started actually nearby here in New York at an internet advertising startup.

我做完银行业之后，就明白互联网广告和广告技术这个行业历来都很糟糕。

And after I did banking, I knew internet advertising and ad tech, is historically a terrible industry.

但迈克尔和萨姆在别人之前就看懂了AppLovin的故事，还在它还是私营公司时就一直跟踪关注。

But Michael and Sam really figured out the AppLovin story before anybody, and they followed it when it was private.

他们对所有竞争对手都了如指掌。

They know all the competitors.

连那些专业术语的细节他们都门儿清。

They know all the intricacies of terminology.

萨姆去了拉斯维加斯的移动广告大会，我们也去了，跟几十号人聊了个遍。

And Sam went to the Las Vegas app advertising conference, and we went to con, and we talked to scores and scores of people.

他们做了大量的模型研究，还和亚当·费罗吉建立了很好的关系，他是业内最出色的管理者之一。

So did the work on the model and developed a great relationship with Adam Ferrogi, he's one of the best managers out there.

我不觉得AI能做到这些。

I don't see AI doing that.

除了你们公司内部，跟其他投资人交流在你们生活中扮演什么角色？

What role does talking to other investors outside of your firm play in your life?

最棒的一点就是，我结识了这么多聪明的投资者，建立了深厚的友谊。

One of the great things is just the friendships I've built with so many smart investors.

说实话，菲利普·费雪说过，他的方法之一就是认识全国大约十到十五个志同道合的人，互相交流想法。

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.

这些人都是很棒的朋友，很多人都上过你的播客。

They're great friends to make a lot of them have been on your podcasts.

你建立起真挚的友谊，然后彼此分享观点、讨论想法。

You develop good friendships and then you share ideas, talk ideas.

重要的是，这得是双向的。

It's important that it's a two way street.

我喜欢用‘三脚凳’来形容：当我看好一个标的，我的分析师也看好，再加上一个我特别敬重的人也看好，这三方面一齐支撑，信心就特别稳。

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.

在公司这么多年，你在为投资者设计产品方面学到了什么？

What have you learned about shaping the products that you offer your investors across the history of the firm?

现在的产品结构早就不是单一不变的了。

It's not just one monolithic structure anymore.

如果我是投资者，想把钱交给你，其实有几种方式可以选择。

There's several things that if I'm an investor and I wanna give you money, there's a couple ways I can do that.

你是怎么决定这些选项的？又怎么把这段经验转化成建议，帮其他投资者为他们的有限合伙人提供合适的产品组合？

How did you arrive at those things and how can you turn that experience into advice for other investors that are trying to provide their LPs with the right set of options?

前十五年，我们只做多空策略，一直坚持专注。

So the first fifteen years, it was a long short fund and we wanna be focused.

一旦分散了注意力，事情就会变得很难。

And if you defocus, that can be hard.

我们把这支基金做起来了，达到了预期的规模。

So we grew that and we got that to the scale that we wanted to.

我们公司今年正好二十岁。

We're twenty years old.

大概十年后，开始有客户问我们能不能出一只只做多的产品。

Maybe ten years in, people started to ask for a long only product.

到了2020年，我们正式推出了这只只做多的基金。

In 2020, we launched the long only fund.

到现在，这个产品已经运行六年了。

So we're six years on that.

现在它的规模已经超过多空策略了。

That's now larger than the long short.

大部分资产都集中在这两个产品上。

The bulk of the assets are in these two.

大概在2015年左右，我们正式开始考虑做私募投资，并给投资者提供了选择权，可以加入或退出。

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.

投资者可以选择配置15%或25%。

And you could do 15% or 25%.

我们直到2020年才真正开始投入私募投资。

So we didn't break the seal on the privates until 2020.

2021年，我们推出了一只混合型基金，最多可以配置80%的私募资产，思路类似，但适合想更大力度布局私募的投资者。

In 2021, we offered a hybrid fund that could be 80% into privates, sort of similar approach, but if you wanted more exposure to privates.

最近，我们刚推出了WhaleRock megaCap科技基金。

Very recently, we launched the WhaleRock MegaCap Tech Fund.

我们只是觉得，全球最大的那些科技公司，在整体投资组合里被严重低估了。

We just think there's a huge structural underweight of the largest tech companies in the world.

我们也意识到，这些年来我们的大部分收益，其实都来自一些最大的公司，比如苹果、亚马逊或者特斯拉。

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.

所以，我们很多大型资金来源——比如捐赠基金之类的——后来才意识到，他们对全球最大的科技公司配置严重不足，因为他们投了太多私募，公开市场的持仓却很少，而且其中一半还是国际股票。

And so a lot of our largest pools of capital endowments or what have you, they realized they've 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.

而在他们的公开市场部分，普遍存在一种看法：大盘股根本赚不到超额收益。

And then of their public bucket, there's a belief that there's no alpha in large cap.

因此，他们压低大盘股的配置，转而大量投资于中小盘基金经理，因为大家觉得大盘股不可能跑赢市场，这听起来很合理。

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.

在他们的对冲基金组合里，哪怕整体是多头导向的，也不会把15%的资金押在英伟达这类公司上。

And then in their hedge fund portfolio, even if it's long biased, they're not gonna have 15% in Nvidia and all these other things.

我们发现，很多人其实对这些巨头公司心存顾虑。

We realized that people are worried that there's these big companies.

这其实是数字经济的自然结果：在科技行业，领先者往往越做越大，迅速占据极高市场份额，并建立起强大的竞争壁垒。

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.

它们的产品也在全球范围内销售。

They're also selling around the globe.

这将带来巨大的利润和极高的市值。

So this is gonna lead to massive profit pools and massive market caps.

这种趋势还会持续下去。

And it's just gonna happen into the future.

大多数捐赠基金却在押注相反的方向，因为它们对这些公司配置严重不足。

Most endowments are betting against this because they're completely underweight this.

终于有人来找我们问：我们该怎么办？

Finally, somebody came to us and said, what should we do?

我在汉密尔顿学院的董事会，也在他们的投资委员会里，他们当时就在琢磨这个问题。

I'm on the board of Hamilton College and on their investment committee, they were trying to figure out this.

所以我们不断听到类似的声音。

And so we kept on hearing it.

最后我们决定：好吧，我们来帮你们做这件事，因为这里面有大量超额收益。

And finally, we said, we'll do this for you because there's a lot of alpha to be had.

所谓的七巨头之类的，情况会不一样。

And the mag seven or the or whatever, it's gonna be different.

2022年，它们全都涨了。

In 2022, they all rallied.

去年它们表现分化，今年又跌了。

Last year, they were very divergent and this year they're down.

所以我们推出了WhaleRock超大型科技基金，覆盖全球市值前30的公司。

And so we created the WhaleRock mega cap tech fund, which is the universe is the top 30 market caps globally.

然后我们从中选出最优秀的那十二三家。

And then we pick the 12 or 13 that are the best.

我觉得最大型的公司里藏着巨大的超额收益，你想想，小盘股只要一个人发现它有价值，股价就上去了。

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.

但要让一百个分散的基金经理都意识到谷歌不是烂公司，而是好公司，这需要很长时间。

But it takes a 100 people, a 100 diversified PMs to realize Google's not a loser, it's a winner.

我们能不能在那95%的普通基金经理之前看透这一点？

And can we figure that out before 95% of those generalist PMs?

我们确实做到了。

And we've been able to do it.

你觉得这个机会怎么样？

You like your odds in

没错。

that.

是的。

Yeah.

我们看好这个机会。

We like your odds in that.

那里确实有超额收益的空间。

And so there is alpha to be had there.

而且从资产类别来看，这些公司天生就拥有强大的护城河。

And then as a asset category, it's great because these companies by definition have wonderful motes.

它们可能不是那种爆发式增长的类型，但有时候也确实是。

And maybe they're not the super s curve, but sometimes they are.

我的意思是，英伟达确实如此。

I mean, NVIDIA sure is.

台积电也高度依赖这一点。

And TSM is really levered to it.

海力士对这一点的依赖更是极其严重。

And Hynix is extremely levered to it.

ASML也同样依赖于此。

And ASML is levered to it.

我们已经跟进这个方向四个月了。

We're four months into that one.

听起来你实际上打造的是一台研究机器，通过公司来理解这个世界。

It sort of sounds like really what you've built is a research machine to understand the world through the lens of companies.

你一直在不断优化的，正是这台研究机器。

The thing you're constantly trying to improve is that research machine.

而你将这些洞察转化为产品的方式，则是成倍放大。

And then the way that you would then express that through products is multiplied.

但如果我要理解WhaleRock，首先得弄清楚他们的研究机制。

But if I was to try to understand WhaleRock, it would be to investigate the research machine first and foremost.

我们把它叫做WhaleRock学习机器，由十位经验非常丰富的专业人士组成。

We call it the WhaleRock Learning Machine and it's a group of 10 highly experienced individuals.

沃伦·巴菲特读书，我们也读书，还看各种博客。

Warren Buffett reads books and we read books and we read blogs.

在科技领域，你必须亲自去和人交流。

In tech, you gotta go out and talk to people.

所以我们每年要和管理层进行两千五百到三千场面对面的访谈。

So we do 2,500, 3,000 face to face meetings with management teams.

芒格和巴菲特常提到知识的复利效应。

Munger and Buffett talk about compounding knowledge.

我们已经持续积累这种知识二十年了。

We've been compounding that knowledge for twenty years.

团队成员会有调整，但整体上一直保持着很高的稳定性。

There's changes to the team, but broadly there's a lot of consistency to it.

安德鲁和迈克尔跟我一起工作已经十八年了，团队成员平均有十年左右的经验，这还包括一些新加入的人。

Andrew and Michael have been with me for eighteen years and the average experience level on the team is ten or so years, and that includes some of the newer people.

这个研究系统能支撑我们所有的产品。

That research engine can support all these products.

做公开市场和私募投资的是同一拨人。

And it's the same people that do publics and the privates.

我们不会满世界去翻遍每一个角落。

So we're not gonna scour the world and turn over every a b.

但只要看到符合我们体系的机会，我们就能迅速行动。

But when we see something that fits into our system, we're able to act on it.

和你一起做这件事真是太有趣了。

It's so much fun to do this with you.

每次做这种访谈，我都会问大家同一个经典问题。

When I do this, I ask the same traditional closing question of everybody.

别人对你做过最温暖的一件事是什么？

What is the kindest thing that anyone's ever done for you?