市场现状

最快的AI公司以比任何SaaS公司都快的速度达到了一亿美元的收入，而且在实现这一目标的过程中，它们在销售和营销上的投入更少。

The fastest AI companies are hitting $100,000,000 in revenue faster than any SaaS company ever did, and they're spending less on sales and marketing to get there.

2025年，表现最出色的公司实现了693%的年同比增长，每名员工创造的收入高达一百万美元。

The top performers grew 693% year over year in 2025, generating up to $1,000,000 in revenue per employee.

这可不是什么效率手册上的内容。

That's not some efficiency playbook.

需求如此强劲，这些公司几乎难以跟上。

Demand is so strong, these companies can barely keep up.

在供应端，每一块新接入的GPU都会立即达到满载状态。

On the supply side, every GPU that gets plugged in is maxed out immediately.

但已经出现了一些裂痕。

But there are cracks.

债务正在进入这个系统。

Debt is entering the system.

阻碍企业采用的最大因素并不是技术本身。

And the biggest thing holding back enterprise adoption isn't the tech itself.

真正困难的是让大型组织改变他们的工作方式。

It's getting large organizations to actually change how they work.

Genkaw 与普通合伙人 David George 讨论了数据揭示了什么，以及为什么我们仍处于早期阶段。

Genkaw speaks with general partner David George about what the data shows and why we're still early.

让我先谈谈我对这篇内容的主要看法，因为这是我们第一次采用这种风格的文章。

Let me just start with what I think the big takeaways are from this piece because this is the first time we've ever done this style piece.

我们产出大量工作和分析。

We produce so much work and so much analysis.

这就像我们团队内部的废气，我们觉得有太多不同的想法和观点。

It's like exhaust inside of our team, and we thought, we have so many different thoughts and points of view.

为什么不把它们写下来，分享给全世界呢？

Why don't we put them on paper and share them out with the world?

这就是这篇文章的由来。

So that was the genesis of this.

我从这次经历中得出的主要结论之一是：人工智能。

My big takeaways from doing this, one, AI.

需求端非常疯狂。

Demand side is crazy.

从我们的角度来看，人工智能领域公司的实际采用率和增长质量令人非常鼓舞。

The actual uptake, growth quality of companies in AI is extremely encouraging from our standpoint.

公司们开始更好地运营自己。

Companies are starting to run themselves better.

我一会儿会展示一些数据，最近甚至今天早上，大家都在热议这个问题。

I'm gonna show you some stats on that, that there's been some sort of X buzz, including this morning, debating what's going on there.

但这一批公司，我认为比之前的公司群体更令人印象深刻，部分原因是他们产品的市场需求非常高。

But this crop of companies, I would say, is more impressive than prior crops of companies, partially because the demand for their products is so high.

这是需求端的情况。

That's demand side.

供应端目前状况良好，但我们已经开始看到一些略显紧张的迹象。

Supply side is healthy right now, but we are starting to see some signs of things that are stretched a little bit.

我会谈谈我们观察到的情况以及我们关注的问题。

I'll talk about what we see and what we're looking out for.

我们很幸运能参与这么多优秀公司的发展，目前私募市场中最令人兴奋的活动就是人工智能，而且它正发生在私募市场中。

We've been fortunate to be a part of a lot of these great companies, and the most exciting action that is happening in the private markets, it's AI, and it's happening in the private markets.

我们会展示一些相关的幻灯片。

And we're gonna show some slides about that.

最后，我最大的结论是，让我对当前所处阶段如此兴奋的原因，是我们才刚刚起步于这个产品周期。

And then lastly, my big conclusion, what has me so excited about where we are now, is just how early we are in this product cycle.

产品周期驱动着我们的业务，这些周期长达十年、十五年，而我们现在才刚刚开始。

Product cycles drive our business, and these are ten, fifteen year cycles, and we're just at the very beginning of it right now.

那么，让我们深入探讨一下。

So let's dive in.

我们在所有私募阶段都有投资。

We invest across all private stages.

这张图表展示了我们的投资活动。

This is a chart that just shows our activity.

我们非常忙碌。

We're very busy.

覆盖所有垂直领域。

It's across all verticals.

在成长阶段，我们最活跃的领域是人工智能和红外应用，其次是AD，但我们在其他垂直领域也非常活跃。

We, on the growth side, have been most active in AI and Infrared apps, and then in AD, but also very active in our other verticals as well.

我会快速浏览一下这些内容。

And I'm gonna zoom through some of these.

我不太想做a16z的广告，但我觉得我们有机会与一些最顶尖的模型、应用和基础设施公司合作，这显而易见。

I hate to do the a 16 z commercial, but I think we have the chance to work with some of the best models and apps and infra companies, obviously.

总之，这里有一些数据。

Anyway, here's some data.

作为成长阶段团队，我们收集了海量的数据，因为我们几乎接触到了市场上所有成长阶段的公司，无论是投资组合公司还是潜在投资对象。

So, we collect tons and tons of data as a growth team, because we're basically seeing every growth stage company in the market as either a portfolio company or as a prospect.

因此，我们拥有一支出色的数据分析团队。

And so, we have a great data analysis team.

我们做了一些数据分析。

We did some data analysis.

我觉得这些东西真的超级有趣。

I think this stuff is just super interesting.

我们对此非常着迷。

We geek out on it.

在我看来，由此得出的一个重要结论是，2025年是收入加速增长的一年。

To me, the big conclusion from this is 2025 was a year for accelerated revenue growth.

显然，在2022、2023、2024年，随着利率上调和一些科技领域的收缩，收入增长放缓了，但2025年扭转了这一趋势，并且在我们按十分位数和四分位数对公司进行排名时，各类公司的收入增长都加速了，尤其是在那些表现突出的公司中，增长确实显著加快了。

Revenue obviously slowed in 2022, '23, '24 following the rate hikes and the pullback in some of the tech stuff, but 2025 reversed that trend, and it accelerated across different types of companies as we ranked them by decile and quartile, but especially among the outlier companies, it really accelerated.

你可能之前见过我们展示过这张幻灯片，但增长最快的AI公司达到1亿美元营收的速度，明显快于同期增长最快的SaaS公司。

And you've probably seen us put this slide on a page before, but the fastest growing AI companies are reaching a $100,000,000 of revenue significantly faster than the fastest growing SaaS companies in their era.

我想强调一个非常重要的原因来解释为何如此，那就是终端客户需求非常强劲，而且产品极具吸引力。

There's a really important thing I wanna call out about why that is the case, and that is because end customer demand is so strong, and the products are so compelling.

这并不是因为它们在销售和营销上投入了更多资金。

It's not because they spend more money on sales and marketing.

实际上恰恰相反。

It's actually the opposite.

那些增长最快的顶尖AI公司，并不是在销售和营销上投入最多的，而且它们在这方面的支出比SaaS同行要少，然而它们的增长速度却要快得多。

The best AI companies that are growing the fastest are not the ones spending the most amount of money on sales and marketing, and they're spending less money on sales and marketing than their SaaS counterparts, and yet they're growing much, much faster.

这张幻灯片展示了AI公司与非AI公司的增长对比。

So this was a slide showing just the growth of the AI companies versus the non AI companies.

粗略来说，AI公司的增长速度是非AI公司的两倍半以上，这应该不足为奇。

Roughly speaking, the AI companies are growing two and a half times plus faster than the non AI companies, and that shouldn't be a huge surprise.

最优秀的AI公司正以非常非常快的速度增长。

The best of the AI companies are growing very, very fast.

当我们看到顶尖AI企业年增长率达到693%时，不得不反复核对这些数据，但这与我们投资组合公司的实际经验和见闻是吻合的。

We had to triple check this data when we saw the AI top performers growing six ninety three percent year over year, but it matches up our experience and anecdotes that we see from the portfolio companies.

以上就是关于增长的情况。

So that's growth.

这是我们在数据集中观察到的利润率概况，需要再次说明的是，这些数据来自我们投资组合公司的内部数据集，以及我们作为潜在投资标的考察的企业。

This is the margin profile that we're seeing in the dataset, and again, these are internal datasets that we have of portfolio companies, and companies that we look at as potential investments.

AI公司的毛利率略低一些。

Gross margins are a little bit worse for AI companies.

你可能之前听我们讨论过这个，但在某种程度上，我们认为AI公司的低毛利率可以说是一种荣誉徽章，因为这意味着我们想看看低毛利率是否是高推理成本导致的。

You've probably heard us talk about this before, but in a way, we feel like low gross margins for AI companies are sort of a badge of honor, in the sense that we want to see if low gross margins are a result of high inference costs.

第一，这说明人们正在使用AI功能；第二，我们相信随着时间的推移，这些推理成本会下降。

One, that means people are using AI features, and two, we have a belief that those inference costs, over time, are gonna come down.

所以，奇怪的是，如果我们看到一个AI项目的推介，而其毛利率超高，我们反而会有点怀疑，因为这可能意味着客户实际购买或使用的并非AI功能。

So, in an odd way, if we see an AI pitch, and the gross margins are super high, we're a little bit skeptical, because that may mean that the AI features are not actually what is being bought or used by the customers.

我们接下来会讨论人均年度经常性收入，但这是我们最近开始关注的一个新指标。

We're gonna talk about ARR per FTE, but this is a new thing that we've started focusing on.

这是过去几天在X平台上引起大量关注和讨论的话题之一。

And this is one of the things that got a lot of pickup and discussion on X in the last few days.

ARR per FTE（人均年度经常性收入）大致衡量了你公司整体运营的效率。

ARR per FTE is sort of a measure of the efficiency of how you run your company in general.

因此，它囊括了你所有的成本。

So it encapsulates all of your costs.

它不仅包含了销售和营销成本（这是我们过去进行分析时一直关注的一项效率指标），还包含了你的管理费用和研发成本。

It encapsulates not just your sales and marketing, which is an efficiency measure that we've always kind of looked at when we do analysis in the past, but it also captures your overhead, it captures your R and D.

因此，最优秀的AI公司每名员工的收入在50万到100万美元之间。

And so for the best AI companies, they're running at 500,000 to a million dollars per FTE.

而在上一代SaaS软件企业中，普遍的基准是大约40万美元。

And the rule of thumb for previous software businesses in the SaaS era was like $400,000 in the last generation.

我稍后还会再详细讲这一点，但出现这种情况的主要原因是，市场对他们的产品需求非常非常强劲，因此他们需要更少的资源就能将产品推向市场。

Again, I'm gonna talk about this a little bit more, but the reason why this is the case is mostly because demand is very, very strong for their products, and so they need a less resource to go take it to market.

大卫，我们先看这张幻灯片之前，能否简单澄清一下？

David, maybe a quick clarifying, just before we go to this slide here.

你们是如何定义AI公司的？

So how do you define AI companies?

这是指ChatGPT之后的公司，还是指某个特定时间点之前成立的传统AI/ML公司？

Is that defined as post chat GBT versus historical AI ML companies founded by a certain time period?

这基本上是指ChatGPT之后的公司。

It's sort of post ChatGPT companies.

其中一些公司正是在那个时期成立的。

And some of them were founded, like, right around that time.

我们会给予一些宽容。

We give a little bit of grace.

但如果他们的首个上市产品是原生AI产品，那我们就这样划分。

But if their first product in market was an AI native product, then that's how we divide it.

明白了。

Got it.

这或许是个可以延后讨论的好点。

And then maybe this is a good point, but where you can punt till later.

但我想很多人想了解的是，从SaaS时代到AI时代，公司预期收入和增长的变化幅度有多大。

But one of the questions I think a lot of folks are trying to understand is the magnitude of change in expected revenue and growth from companies from the SaaS era to AI era companies.

你已经稍微谈到了收入规模等问题。

And you've talked a little bit about the magnitude of revenue, etcetera.

但那些非原生AI的公司会怎样呢？

But what happens to those that are not AI native?

它们会难以与原生AI公司竞争吗？

Will they have a hard time competing against AI native companies?

它们都在转型吗？

Are they all shifting?

我们会看到更多淘汰吗？

Will we see more fallout?

人们应该如何审视他们过去的投资组合？

How should people be thinking about their historical portfolio?

是的。

Yeah.

因此，我们投资组合的处理方式是：你必须适应AI时代，否则就会被淘汰。

So the way that we're approaching this with our portfolio is you need to adapt to the AI era or die.

这既涉及前端，也涉及后端。

And so that's both on the front end and the back end.

在前端，你需要思考如何将AI原生地融入你的产品。

So on the front end, you need to think about how you can incorporate AI into your product natively.

而不仅仅是把聊天机器人应用附加到现有工作流程中，而是要重新构想AI能带来什么，并积极地进行自我颠覆和变革。

And not just attach a chatbot app into your existing workflow, but reimagine what it can mean with AI and be aggressive about disrupting yourself and changing.

然后在后端，我分享了一些关于公司运营效率的数据。

And then on the back end, I shared some of the stats around the efficiency that the companies are running at.

这也将会发生变化。

This is gonna change too.

因此，你需要为所有开发者全面部署最新的编码模型，并在组织内的每个不同职能部门推广所有最新工具。

And so you need to be fully rolled out with the latest coding models for all of your developers, and all of the latest tools across every different function inside your organization.

迄今为止，编码领域的应用最为广泛，这也是我们见证最大飞跃的地方。

The biggest uptake has been in coding so far, and that's where we've seen the biggest leaps.

在过去的一个半月到两个月里，这方面发生了非常、非常重大的变化。

There have been major, major changes, like, in the last two months on this, like, month and a half in this.

安德烈·卡帕西已经就此写过文章。

Andre Karpathy has written about this.

我和我们一家前AI时代的公司进行了跟进交流，这位创始人非常精通AI，对AI有很深的理解，所以他正在调整他的公司。

I was on a catch up with one of our sort of pre AI companies, and this is a founder who's very AI like, he's very AI deep, and so he's adapting his company.

我们本周在讨论时，他告诉我他对他们的一款产品感到沮丧，于是他直接抽调了两名在AI领域造诣很深的工程师，指派他们使用Quad Code、Codex和Cursor从头开始构建，而且他们在编码工具上的预算没有限制。

We were talking this week, and he told me that he was frustrated with one of their products, and so he just took two engineers that are very deep in AI and assigned them to build it from scratch with quad code and codex and cursor, and just they had unlimited budget on coding tools.

他说，他认为这个进展速度比他们以前快了十到二十倍。

And he said he thinks it's going somewhere between ten and twenty x faster than progress that they had before.

与之相关的开支实际上非常高，高到足以让他重新思考整个组织的架构。

The bills that they have associated with that, is actually, they're high enough that it will cause him to rethink what his entire organization will look like.

结论是，我需要我的整个产品和工程团队都以这种方式工作，我认为这将在未来十二个月内实现。

The conclusion was, basically, I need my entire product and engineering organization working this way, and I think it's gonna happen within the next twelve months.

但这对团队的实际结构意味着什么？

But what does that mean for what the team design actually is?

产品从哪里开始，工程从哪里开始，甚至设计在这个过程中又该从哪里开始？

And where does product start, and where does eng start, and even where does design start in that process?

所以感觉十二月是代码领域的一个转折点，未来十二个月，要么这项技术在公司中迅速普及，要么这些公司就会比同行慢得多。

So it feels like December was sort of a turning point on code, and the next twelve months, it's either gonna hit and take hold in companies, or those companies, I think, are gonna be moving much slower than their peers.

就那些前AI公司而言，Adapt公司就是另一个例子，这是一家前AI软件公司，其CEO已经完全被AI赋能了。

So as it relates to the pre AI companies, Adapt, we have another example of a company that is a pre AI software company, and the CEO has gotten totally AI pilled.

他说：‘我们要转型为一家AI产品公司。’

And he's like, we're gonna become an AI product.

你知道，你的员工现在就是你的AI代理。

You know, your employees are now your AI agents.

你有多少个代理？

How many agents do you have?

他谈论的就是这些事情。

Those are the things that he's talking about.

我们还有另一个极端的例子，他说：现在每项任务，我都问一个问题——我是用电力来完成，还是必须用人血来完成？

We have another one that was very extreme about it, and he said, I now ask the question, for every task that we now need to complete, can I do it with electricity, or do I need to do it with blood?

这种极端的思维转变正在我们的公司中发生。

Is like the extreme mindset shift that's happening you know, with our companies.

因此，我很高兴看到我们的前AI公司正在迅速行动并尝试适应，但它们确实需要在前端产品和后端运营方式上全面适应这个新时代。

And so, I'm happy to see that our pre AI companies are moving very fast and trying to adapt, but they very much need to adapt to this new era, both front end product wise and back end, how they run their companies.

完全正确。

Totally.

是的。

Yeah.

也许从战术上讲，几乎每个投资组合，你都需要逐行审视公司，以了解创始人在这一旅程中的位置，以及他们从零开始实施了多少。

Maybe tactically, almost every portfolio, you have to go line by line on the company to understand where the founder is on that journey and how much they're implementing from the ground up.

而且，你知道，你提到的关于颠覆现有运营的说法，在后AI公司中也正在发生。

And, you know, what you said in terms of blowing up existing operations, that's also happening in post AI companies too.

而且越来越多的人每六个月就会重新审视一次。

And increasingly, people are just looking every six months.

就像我们六个月前构建的东西，基于今天可用的技术，可能已经可以得到极大的改进。

It's like the things we built six months ago could be vastly improved based on what is available today.

所以如果这种速率持续下去，前AI公司就需要不断以10倍的速度追赶才能达到那个水平。

So if that rate is continually happening, the pre AI companies are needing to increasingly 10x catch up to that point.

是的，对于前AI公司来说，好消息是商业模式的演进仍处于早期阶段。

Yeah, the good news for the pre AI companies is the business model evolution is still early days.

因此，可能对你最具颠覆性的事情是技术和产品的转变，以及商业模式的同步转变。

So the most disruptive thing that can happen to you is a technology and product shift, and also a business model shift at the same time.

我认为商业模式就像一个光谱，这里我指的是企业级，比如B2B，为了简化讨论。

There's really one I think of the business models as like a spectrum, and I'm talking about enterprise, like b to b, just to keep it simple.

但这个谱系基本上是许可证模式，这属于SaaS之前的许可证和维护商业模式。

But the spectrum is basically licenses, and this was like the pre SaaS license and maintenance business models.

然后出现了SaaS和订阅模式，通常是按座位计费。

Then you had SaaS and subscription, and that was typically seat based.

这是一个重大创新，而且极具颠覆性。

And that was a big innovation, and it was very disruptive.

架构和云交付方式本身具有颠覆性，但商业模式的变革更具颠覆性。

Like, the architecture and cloud delivery was disruptive, but the business model change was very disruptive.

你可以去看看Adobe在经历这一转变时发生了什么。

Like, just go look at what happened to Adobe as they went through that transition.

接着是向按使用量计费的模式过渡，也就是按使用付费，云服务就是这样收费的，许多基于体积或任务类型的企业已经从按座位收费转向了这种按使用付费的模式。

Then you have this transition to consumption based, so usage based, and this is how the clouds charge, and so many of the sort of volume based, like, task based type businesses have already adapted that and shifted to that from, you know, seat based to consumption.

而下一个阶段将是按成果付费。

And then the next iteration will be outcome based.

也就是说，当你执行一项任务时，理想情况下，只有在成功完成该任务后，你才能根据完成结果获得报酬。

So, you know, when you when you do a task, and ideally when you successfully complete a task, you get paid based on the successful completion of that task.

目前唯一真正有可能实现这一点的领域可能是客户服务和客户成功，因为你可以客观地衡量问题是否得到解决。

The only area where that's really possible today to pull off is probably customer support, customer success, because you can objectively measure the resolution of something.

但我们还得看看这些模型的能力会如何发展。

But we'll see what happens with the capabilities of the models.

只要除了客户服务之外的其他职能也能衡量这类成果，这就会对现有企业造成巨大的颠覆性影响。

To the extent that other functions, besides customer support, can measure those kinds of outcomes, that would be a huge disruptive force for incumbents.

老实说，如果公司的人员结构也发生变化，从按席位收费转向按使用量收费可能已经是一个巨大的颠覆。

Honestly, seats to consumption might be a big disruption if the composition of companies changes as well.

但下一个阶段才是真正的重大变革。

But that next one is the really big one.

当然。

For sure.

说到人力成本与电费的对比，我们该谈谈ARR与FTE了。

Speaking of blood versus electricity, we should go to ARR over FTE.

下一张幻灯片？

This next slide?

是的

Yeah.

是的

Yeah.

是的

Yeah.

所以，在这一页、下一页上，大家争论的焦点是：天啊，看看市场上正在发生的AI效率提升。

So the big the big debate that was going on on this one, on the next slide, was oh my gosh, look at the AI efficiency gains that are happening in the market.

现在，这方面确实有一点点体现，比如公司以稍微不同的方式运营自己，就像我举的例子，那两位工程师在重建产品，没错。

Now, there's a little bit of that in this, like companies running themselves a little bit differently, and you take the example that I gave about the two engineers who are rebuilding the product, sure.

根据我们对公司的观察，即使是原生AI公司，它们也运行得更精简，部分原因是它们增长得太快，需求也太强劲。

I would say my observation from our companies, even the AI native ones, is they run leaner, partially because they've just grown so quickly, and the demand is so strong.

但我还不能说，公司已经完全重新构想了它们的运营方式。

I wouldn't say yet we're at the point where companies have fully reimagined the way they run themselves.

我认为这在一定程度上是因为我们的数据集来自最顶尖的公司，而这些公司的需求信号极其强烈，因此它们用于满足需求的资源相对不足。

I think this is a little bit the result of our dataset being the best of the best companies, and demand signals for those being extremely high, so they have less resources to serve that demand.

坦率地说，技术市场自2021年最臃肿时期以来所实现的整体效率提升。

Frankly, general efficiency gains that have happened in the technology market out of the 2021 most bloated era.

所以我们开始看到一些效率提升的早期迹象，但要彻底改变公司的运营方式，我认为我们还处于这一进程的早期阶段。

So we're starting to see some early signs of that efficiency, but to wholesale run your company totally differently, I think we're early in that journey.

我认为我见过的最酷的例子是在公开市场中，任何人都可以查阅，那就是Shopify——你知道，Toby太棒了，他是一位非常贴近我们的CEO，经常参与我们的多个小组，表现非常出色，他在几年前就全面拥抱了这一趋势。

I'd say the coolest one that I've seen is in the public markets that anyone can go read about is probably Shopify, where they, you know, Toby's awesome, like he's a CEO that's close, he's in a bunch of our groups and stuff, and he does a great job, and he fully embraced this a couple years ago.

然后，我们的一位撰稿人写了一篇深入报道，详细分析了Shopify是如何实现AI化的，包括员工指导、流程等方面。

And then there one of our staff writers actually wrote this whole big deep dive on how Shopify AI ified itself, you know, in terms of, you know, employee direction, process, etcetera.

而这可能只是未来五年将要发生的变化的冰山一角。

And that's just probably scratching the surface of what's gonna happen over the next five years.

太棒了。

Awesome.

很好，接下来我们进入下一部分：这些公司究竟在做什么？这是我们最感兴趣的话题——律师在AI新世界中的数量反而增加了，而不是减少。

Good segue to the next section on what are these companies actually doing, our favorite topic, which is lawyers have only increased in this new world of AIs meeting lawyers, not the opposite.

我特别喜欢这条推文，不知道你有没有看到本周早些时候的一条：一位公司律师被引用说，大型语言模型实际上增加了我的工作量，因为每个客户现在都觉得自己是律师。

I love the tweet, don't know if you saw it earlier this week, that a corporate lawyer was quoted saying, LLMs have actually increased my workload because every client thinks they're a lawyer now.

这是个很好的过渡，可以聊聊Harvey，这个非常出色的项目。

It's a good segue to Harvey, which is an excellent.

说得非常好。

That's very good.

说得很好。

That's very good.

Harvey真是太棒了。

Harvey's so great.

好吧，这对我来说是个真正的考验，因为你知道我喜欢谈论我们投资组合中的公司，而我应该快速过一遍这部分内容，因为我觉得大家可能都了解这些公司。

So, okay, this is a real test for me, because you know I love talking about our portfolio companies, and I'm supposed to go through this section quickly, because I think people know these companies, hopefully.

关于这一点，关键收获之一是我们关注的重点，也是我认为有人提出的问题之一：你如何知道收入会是可持续的？

The takeaway on this one, one of the big things that we look for, and one of the questions I think that came in was, how do you know that revenue is gonna be sustainable?

这些公司，它们都增长得非常非常快，但这种增长是短暂的吗？

These companies, they all grew really, really fast, but is it fleeting?

我们极力推动自己去做的重要事情，就是确保我们在收入留存、续约和产品参与度方面做得非常非常深入。

The big thing that we push ourselves to do is make sure we go super, super deep on revenue retention, renewals, and product engagement.

实际上，是花费的时间。

Actually, time spent.

人们多久登录一次平台？

How often are people logging into the platform?

当他们登录平台时，他们的活动情况如何？

When they're in the platform, what does their activity look like?

你在这一页上看到的是，随着他们过去几年构建的更好产品的推出，加上推理模型的改进，事实证明法律工作与推理是相辅相成的，用户在产品中花费的时间大约是之前的两倍。

What you see on this page is, with the onset of much better product that they've built over the last couple of years, plus the improvement of reasoning models, it turns out lawyering and reasoning go hand in hand, users are spending about double the amount in the product as they had before.

所以，事实证明人工智能在法律事务方面确实很擅长。

So, it turns out that AI is really good at lawyering.

再次强调，律师数量并没有减少，但我认为人工智能在这方面非常非常出色，而且我认为律师们的工作效率也大幅提高了。

Again, there's not fewer lawyers, but I think AI is very, very good at this, and I think lawyers are getting a lot more efficient.

就哈维而言，最重要的一点是，他们只是在该产品上投入了大量时间，并从中获得了巨大价值，这非常棒。

The most important thing, as it relates to Harvey, is they're just spending a lot of time in the product and getting a lot of value out of it, which is great.

我们来看下一个项目吧。

Let's go to a bridge.

哦，除非你还想继续聊律师的事。

Oh, unless you wanna keep talking about lawyer.

哦，我本来只是想做个评论。

Oh, I was just gonna make a comment.

在认识你的这七年里，我从来都没察觉到你是肯塔基人，直到现在，就因为你说话的方式。

In all the seven years that I've known you, I wouldn't have ever discerned that you were from Kentucky, other than this moment now, by the way you say lawyer.

这可是暴露了。

That was a tell.

我词汇里有那么几个词，我妻子总开玩笑。

There's a couple of those words in my vocabulary that I can't My wife always jokes.

她说：你一回家，喝一杯波本威士忌，说话就跟你18岁时差不多了。

She's like, You go home, you have one bourbon, and then you talk like you probably did when you were 18.

肯塔基口音露出来了。

The Kentucky came out

就是到上午10点25分的时候。

It's when it came to 10:25 a.

而且，我今天一点威士忌都没喝。

M, and I have not had any bourbons today.

这些区别很重要，是的，没错。

It's important distinctions, yes, exactly.

所以，一个桥梁。

So, a bridge.

桥梁是另一个非常、非常令人兴奋的东西。

A bridge is another one that's super, super exciting.

我的意思是，医生们都很兴奋能使用桥梁，因为它能为他们节省大量时间，让他们的生活变得更好。

I mean, is like, the doctors rave about getting to have access to a bridge, and how much time it saves them, and how much better it makes their lives.

我们聊过的一个客户把它形容为一个值得信赖的副手。

You know, one of the customers that we talked to described it like a trusted deputy.

右边的图表展示的是我们关注的一个指标：蓝色线条代表用户数量的增长，绿色线条代表这些用户的活跃度。

The chart on the right shows something we look for, which is the blue line shows the growth in users, and the green line shows the engagement of those users.

当他们的用户数量大幅增长时，如果新增用户的活跃度下降，你可能会担心，但事实上，这些新用户对产品的使用率极高，而且即使用户数量激增，活跃度依然保持稳定并略有上升。

As they have massively grown the number of users, you'd be a little worried if engagement of those incremental users that they were adding was going down, but instead, they have extremely high usage among the people who use the product, and that has actually held steady and grown a little bit, even as they've added tons and tons of more users.

这些只是我们用来确保对这些公司所产生收入的可持续性充满信心的数据示例。

These are just examples of the kind of data that we look for to make sure that we feel confident that the revenue these companies are generating is sustainable.

而且，这些公司的增长速度比任何前身公司都快，但其增长非常可持续。

And again, these companies are growing faster than any of the predecessor companies, but it's very sustainable.

高参与度、高留存率，这对我们的判断至关重要。

It's high engagement, it's high retention, and that's critically important for us.

Eleven Labs的情况也是如此。

Same thing with Eleven Labs.

语音是许多新型AI工具的核心。

Voice is the centerpiece of so many of the new AI tools.

我之前提到过B2B领域的客户服务，但还有很多其他个人工具和商业工具，都是从语音开始的。

You know, I talked about customer support on the b to b side, but, you know, so much, you know, other personal tools, business tools, you know, start start with voice.

我最喜欢在这张图表上关注的是使用量的增长，其增长幅度令人震惊。

The usage growth is the thing that I love to look at on this chart, and it's just staggering.

这家公司增长非常迅速，是高效运营的绝佳范例。

And this company is growing very fast, and is a great example of one of these companies that runs extremely efficiently.

所以Eleven Labs真是一个绝佳的例子。

So eleven Labs is really a great one.

下一个是以诺。

Navon is the next one.

这是另一个不同的例子。

So this is another, this is a different example.

这实际上是我之前所描述的一个很好的例子。

So this is actually a good example of what I was describing earlier.

他们很早就抓住了人工智能的机遇，并投入大量精力确保能够充分利用人工智能能力来提升业务。

So they were early to this AI shift, and they spent a lot of effort making sure that they could take the most of the AI capabilities and make their business better.

因此，今天在他们的业务中，最明显的表现就是问题解决的处理方式。

And so, the biggest way you can see it in their business today is in the handling of resolutions.

他们的一部分工作是让客服人员处理旅行预订或旅行变更。

So part of what they have is agents that have to handle travel bookings, or travel changes.

现在，人工智能已经处理了其中50%的用户互动。

AI is now handling 50% of those user interactions.

这确实是件棘手的事。

And this is hard stuff.

比如，这是旅行预订，这是旅行变更，所以这并不简单，不像查询银行余额那样。

Like, this is travel bookings, this is changes to travel, so this is not complex, like, tell me the balance of my bank.

这是人工智能现在能够处理的复杂工作流程。

This is complex workflow that AI is now able to handle.

在业务中体现这一点的是，过去三年毛利率扩大了20个百分点。

The way you see that in the business is a 20 percentage point expansion of gross margins over the last three years.

这影响确实非同凡响。

And that's just exceptional impact.

所以，你知道的，适者生存。

And so, you know, you need to adapt or die.

而他们的竞争对手却未能适应。

Well, their competitors are not adapting.

他们非常守旧。

They're very old school.

而且，你知道，当那些老牌企业还在原地踏步、沿用旧方法时，Nivon的毛利率已经比它们高出20个百分点。

And while, you know, they've been sitting still and and doing things the old way, Nivon now has 20 percentage point higher gross margins than those incumbents.

还有，你知道，Flock Flock正在做着绝对令人惊叹的工作。

And then, you know, Flock Flock is doing absolutely incredible work.

我已经多次谈到他们了。

I've talked about them so much.

这是我们投资组合中最具吸引力的客户价值主张，因为他们的投资回报率体现在解决犯罪问题上。

It's it's the most compelling customer value proposition that we see in our portfolio because what their ROI is, is solving crime.

我们之前提到过的10%这个数据，每年Flock系统能协助侦破70万起案件。

The 10% stat we've covered before, each year's flock is solving 700,000 crimes.

右边的数据点也显示，在有Flock系统的地区，每位警官破获的案件数量几乎增加了10%。

The data point on the right also is a data point that just shows per officer that where there's flock, they're clearing almost 10% more crimes.

所以，这对社区产生了巨大的影响。

So, huge impact on the community.

显然，他们拥有一个非常出色的商业模式和与之配套的财务模型，但他们的产品所带来的影响是卓越的。

Obviously, have a great, you know, they have a great business and financial model that goes along with it, but the impact on their product, or from their product is exceptional.

好的。

Okay.

顺便说一下，我不知道你有没有看到聊天区里很多人说他们已经喝了三杯波本威士忌。

By the way, I don't know if you see the chat lighting up of people saying that they're three bourbons deep.

哦，明白了。

Oh, okay.

我没看到。

I didn't see it.

不管怎么说。

For for what it's worth.

有一个问题关于：你怎么看待这个基准？

There is one question about, how do you think about the the benchmark?

比如，如果你考虑金融这样的传统行业，以摩根大通为基准，你会如何衡量《财富》500强企业在AI采用方面的水平？

Like, you were to think about traditional industries like finance, for example, and using JPMorgan as a benchmark, what would you calibrate the Fortune 500 in terms of AI adoption?

然后，也许我可以再叠加一下Xavier提到的那个问题，你知道，去年年初有一项来自麻省理工学院关于企业采用的研究，他们测量了各种奇怪的指标。

And then maybe I'll overlay that that question that that Xavier mentioned as well with you know, there was that study about enterprise adoption from MIT at the early outset of last year, and they were measuring all sorts of wonky things.

能不能再多说一点，关于你从财富500强首席执行官那里听到的反馈，具体是怎么样的？

Maybe say a little bit more about how and what you're hearing from Fortune 500 CEOs.

是的。

Yeah.

我们从财富500强首席执行官那里听到的是，我认为这可能是连接那两点的关键：我们必须适应，我们迫切想知道需要哪些AI工具，我们准备好变革了，我们的企业将全面部署这些技术，我们已经准备好了。

What we're hearing from Fortune 500 CEOs, I would say is and maybe this is the key sort of link between those two points, what we're hearing from Fortune 500 CEOs is we have to adapt, we're dying to understand what AI tools we need, you know, we're ready to change, We, you know, our businesses are gonna fully roll things out, and, you know, we're we're ready.

我们将成为AI公司。

We're gonna become AI companies.

但这和实际发生的情况大不相同。

That's quite different than what is actually happening.

我认为，这种心态与企业实际变革之间最大的脱节就在于，变革管理太难了。

And I think the biggest disconnect of sort of, you know, that mindset compared to actual change in the businesses is just change management is hard.

你知道，要让员工只是使用AI助手来更好地完成工作，就已经够难了。

You know, it's it's hard enough to get people to just use an AI assistant to help them do their jobs better.

你知道，编程可能是最容易让人理解的领域。

You know, coding is probably the easiest one to get people's minds wrapped around.

客户支持，这是一个更好、更快、更便宜、更显而易见的事情。

Customer support, it's such a better, faster, cheaper, obvious thing.

但就实际的业务综合管理、改变业务流程、变革管理而言，这确实极其困难。

But in terms of actually, general management of businesses, changing business processes, change management, it's extremely hard to do.

我毫不意外会有一些传闻表明，哦，事情的进展比预期要慢。

I'm not surprised that there are anecdotes out there that suggest, oh, things are moving slower than expected.

但对于那些全力拥抱它、并且真正知道该怎么做的最佳公司来说，它已经产生了巨大的商业影响。

But for the best companies that are fully embracing it, and actually know what to do, it has tremendous business impact already.

所以，我认为未来五年将会出现一种清算，看哪些公司能够真正拥抱变革、推动变革管理、采纳所有最佳产品，而哪些公司做不到。

So, I think there's gonna be a sort of reckoning over the next five years of who can actually embrace change, push through change management, you know, adopt all the best products, and those that don't.

我认为生产力方面会出现巨大差异。

And I think there'll be major differences in productivity.

我们在后面的幻灯片中有一些图表，我可以详细说明，但关于生产力提升、增长等方面的预期都很高，我认为一批公司会实现这些目标，而那些做不到的公司将处于巨大劣势。

You know, we have some charts later in the slides, you know, which I can talk to, but, the expectations around productivity enhancements and growth and all that stuff, the expectations are high, and I think a bunch of companies will achieve those, and the ones that don't are gonna be at a huge disadvantage.

Chime表示他们减少了60%的客服成本。

Chime said they reduced their support costs by 60%.

Rocket Mortgage表示，他们在贷款审批中节省了110万小时，同比增长6倍，这相当于每年4000万美元的持续节省。

Rocket Mortgage said that they saved one point one million hours in underwriting, up 6x year over year, and that was $40,000,000 of run rate annual savings.

因此，我们在非AI业务中也看到了一些局部成果，我认为未来十二个月将是非常值得观察的一年。

So, we're seeing pockets of it in non AI businesses, and I think this is gonna be a really interesting year to watch over the next twelve months.

我认为你会看到更多类似的案例，但有些公司能搞明白怎么做，而有些公司则做不到。

I think you're gonna see a ton more anecdotes, but there will be companies that can figure it out, and there are gonna be companies that don't.

完全正确。

Totally.

而且，许多企业也必须调整业务，为迎接AI做好准备。

And also, lot of these corporations have had to orient their business to be ready for AI as well.

比如，仅仅使用聊天机器人是一回事，而它实际上能带来多少生产力提升又是另一回事。

Like, there's one version of just using a chatbot, right, and how much productivity gain that actually gets you.

可能提升并不大，对吧？

Probably not a lot, right?

但如果你必须彻底颠覆你的系统、信息和后台架构以适应AI，那么很多工作目前仍处于潜伏状态，正在逐步构建，最终才会显现其实际成果。

But if you have to actually completely upend your systems information and back end to be ready for AI, a lot of that is probably latent and and being built up now into actually seeing the outcomes associated with it.

AI领域的赢家正在推动公开市场。

AI winners are driving the public markets.

它们贡献了标普500指数近80%的回报。

They account for almost 80% of the S and P five hundred's return.

因此，这是推动经济和股市的主要因素。

So this is sort of the major thing driving the economy and the stock market.

公开市场表现非常良好，但基本面依然稳健。

Public markets are doing very well, but the fundamentals are sound.

所以股价在上涨，虽然最近几天有些波动，但总体表现良好。

So the prices are going up, there's some blips like the last couple of days, but they're generally doing well.

但基本面非常坚实，我认为泡沫的迹象微乎其微。

But the fundamentals are very sound, and I would say the evidence of froth is minimal.

近期的表现主要由每股收益增长驱动。

So recent performance is driven by EPS growth.

市盈率略有下降，如果你是SaaS公司，过去几天或几周的降幅可能不止一点，但总体而言，市场定价仍基于盈利。

Multiples have contracted slightly, maybe more than slightly if you're a SaaS company over the last few days or a couple weeks, but I would say the market is priced on, in general, earnings.

盈利和盈利增长。

Earnings and earnings growth.

因此，这些公司的盈利倍数高于平均水平，但远未达到互联网泡沫时期的水平。

So the earnings multiples are higher than average, but nowhere near the .com.

你可以直接看图表，了解我们目前所处的位置，这让我感到安心。

And so you can just look at the charts and see where we are, and that gives me some comfort.

而且，再次强调，作为市场主要驱动力的这些公司，其盈利状况我认为相当稳健。

And again, the earnings of the companies that are the biggest drivers of the market in general, I feel like are pretty sound.

这些公司本身很不错。

The companies are good.

因此，这些公司的经营状况我认为相当良好，虽然估值高于历史平均水平，但并未让人感到极度担忧。

So the health of these companies, I would say, is pretty good, and the valuations are higher than average in the past, but they don't feel super alarming.

我经常说，我刚才提到的领先科技公司，是世界上有史以来最优秀的企业。

I often say the leading tech companies that I was just talking about are the best businesses in the history of the world.

如果你从长期来看，它们展现了利润率持续提升的趋势，这表明这一说法很可能成立，这一点在页面左侧有所体现。

If you just look over a long period of time, they have shown margin improvement that suggests that is probably true, and that's on the left side of the page.

所以投资者是在为利润买单，而不是为亏损的增长买单，这与2021、2022年时期，或者说2021年左右的时代形成鲜明对比，当然也与互联网泡沫时期截然不同。

So investors are paying for profits, not loss making growth, and that's a big contrast from 'twenty one, 'twenty two era, or sort of 'twenty one era, and obviously a big contrast from a dot com.

经过利润率调整后，市盈率并没有那么高。

Adjusted for margins, multiples are not that high.

因此，我再次总结一下，这大概相当于五页幻灯片的内容。

And so, again, I summarize, you know, five slides worth of materials.

市场比过去更高了，但我认为，高预期是有原因的，我们对人工智能在未来几年对公开市场整体盈利的影响持乐观态度。

The market's higher than it has been in the past, but I think, you know, there's high expectations for a reason, and we're optimistic about the impact of AI flowing through to earnings, you know, overall in the public markets in the coming years.

或许我应该把你们的注意力引到右侧，也就是，你们知道，如果你只是简单地把公司分为四类：低增长、高增长、低利润率、高利润率，并将这些类型的公司配对，这张图表展示了它们的交易情况。

And maybe I'd focus your attention on the right side, which is, you know, if you just took a four box of, like, low growth, high growth, low margin, high margin, and paired up those types of companies, this is a chart that shows how they trade.

优质公司存在溢价，你在右侧两列看到的是高增长、高利润率的公司，以及高增长但低利润率的公司。

There's a premium for the best companies, and what you see on the two columns on the right is high growth, high margin companies, and then high growth and low margin companies.

显然，最糟糕的类别是低增长、低利润率的公司，这些公司不应该获得市场回报。

Your bad box is obviously low growth, low margin, and those companies shouldn't be rewarded.

它们应该以低价交易，而事实也确实如此。

They should trade low, and they do.

但那些高增长、高利润率，以及高增长、低利润率的公司，只要它们有良好的单位经济模型，并且正在扩大规模以提升利润率，就理应获得回报。

But the companies that are high growth and high margin, and high growth and low margin, as long as they have good unit economics, and they're scaling into their margins, they should be rewarded.

所以我认为这是好的。

So I think this is good.

如果你不是高增长型公司，即使利润率高，处境也会很艰难，这并不令人意外。

If you're not high growth, even if you're high margin, it's tough out there, and that's not surprising.

我过去曾以多种形式谈论过这一点，但归根结底，增长是驱动五年到十年回报的最关键因素。

Again, I've talked about this in the past in many different forms, but ultimately, growth is the biggest thing that drives returns over five to ten years.

因此，看到高增长公司获得的回报高于低增长公司，让我感到欣慰。

And so, it's nice for me to see high growth is rewarded more than low growth.

但如果你同时具备高增长和高利润率，那你就是那些卓越企业之一，正获得非常丰厚的回报。

But if you have high growth and high margin, you're one of those great businesses, it's being very rewarded.

接下来我们要讨论资本支出扩张的供给侧。

This is just like, we're gonna talk about supply side of the CapEx build out.

这次的扩张规模非常庞大。

So the build out's massive.

投资的规模和集中度本身就具有风险性，毕竟它的体量如此庞大。

The size and the concentration of the investment is inherently risky, just given how big it is.

虽然它带有一些泡沫特征，但我会说，其根本基本面与以往的泡沫几乎毫无相似之处。

While it has some bubbly features, the underlying fundamentals, I would say, bear little resemblance to previous bubbles.

这项投资主要由历史上盈利丰厚的公司提供资金，就像我之前提到的那些盈利能力极强的公司。

The investment is financed primarily by historically profitable companies, like very profitable companies that I had talked about.

债务已经开始介入其中。

Debt has started to enter the picture.

周期时间已经加快，这是好事，但模型方面，我们正在密切关注训练成本以及整个方程的经济性。

Cycle times have accelerated, which is good, but model, we're closely monitoring the cost of training and the economics of that whole equation.

目前看来情况相当不错。

Right now, it seems pretty good.

那些在训练模型上投入资金的大型模型公司，其投资回报率相当可观，但我们仍在密切关注这一点。

The paybacks for the big model companies that spend money on training models is pretty good, but we're monitoring that closely.

最重要的是，我们认为人工智能将是我职业生涯中见过的最大的模式颠覆者，这一点毋庸置疑。

Most importantly, we think that AI is gonna be the biggest model buster that I've seen in my career, certainly.

我写过关于模型破坏者的内容，所以不会在这上面花太多时间。

I've written about model busters, so I won't spend too much time on them.

但它们是那些增长速度和持续时间远超任何情景预测的公司。

But they're companies that grow faster and longer than anyone would've modeled in any scenario.

比如，iPhone 就是这种情况的经典例子。

Like, iPhone is the classic case of this.

如果你看看 iPhone 发布前到四五年后的市场共识预测，当时对苹果业绩的预测偏差达到了三到四倍。

You know, if you if you take consensus models from pre iPhone to five years later, four years later, consensus models were off for Apple's performance by a factor of three x over four years.

而那时，苹果已经是全球最受关注的公司之一。

And this is like the most covered company in the world at the time.

所以我认为，AI 的许多领域也会发生同样的情况——实际表现会远远超出任何电子表格中的预期。

So, you know, I think that the same thing is gonna happen in many pockets of AI, where the performance just massively exceeds what any expectations in a spreadsheet would show you.

科技行业本身就是一个模型破坏者，但自2010年以来，科技行业以空前的速度和规模实现了高利润率的收入。

Tech, in general, is itself a model buster, but since 2010, tech has delivered high margin revenue at unprecedented speed and scale.

因此，它早期往往看起来很昂贵，但一再超出预期，创造的价值远超其增长所需的资本，我没有任何理由认为这次会有所不同。

So it often looks expensive early, but repeatedly surprises to the upside, I would say, and creates value, I would say, far in excess of the capital that's required to grow, and I have no reason to think it'll be different this time around.

因此，相对于互联网泡沫时期，资本支出实际上有现金流支持，而且资本支出占收入的比例要低得多。

So relative to the .com, CapEx is actually supported by cash flows, and CapEx as a percentage of revenue is considerably lower.

所以这是个简单的要点。

So that's a simple headline.

我们可以切换到下一张幻灯片，但显然，我对当前的资本支出动态比互联网泡沫时期要乐观得多。

We can zoom to the next slide, but I feel much better about this capex dynamic than .com, obviously.

超大规模云服务商是承担资本支出最大压力的主体，而这是一件非常好的事情。

Hyperscalers are the ones who are bearing the biggest brunt of the capex, and this is a very good thing.

对我们的投资组合公司来说，这非常棒。

For our portfolio companies, this is great.

我完全支持这一点。

I am all for it.

尽可能多地部署基础设施容量，尽你所能为训练和推理提供尽可能多的供应。

Get as much capacity in the ground, get as much supply as you possibly can on the ground for training and inference.

这是一件非常好的事情。

This is a very good thing.

再次强调，承担这其中大部分压力的公司，是我之前提到过的有史以来最优秀的企业。

And again, the companies that are bearing most of the brunt of this are the best businesses of all time that I had talked about before.

因此，我们开始关注的一个方面是债务被引入这个等式。

So one thing that we're starting to monitor is the introduction of debt into the equation.

所以，你无法仅用现金流来为所有预测的资本支出提供资金，我们已经开始看到一些债务的出现。

So you can't finance all of the forecast capex that's to come with cash flow, and we're starting to see some debt.

因此，我们正在密切关注这一点。

So we're following this closely.

我们通常不会大量投资于有债务敞口的公司。

We're generally not invested heavily in companies with exposure to debt.

我对页面上那些依靠现金流融资、持续产生现金流甚至使用债务的公司感到放心吗？

Do I feel comfortable with a bunch of the companies on the page financing with cash flow, continuing to produce cash flow, and using debt even?

对于Meta、微软、AWS、英伟达作为交易对手方，我当然对此感到非常满意。

Meta, Microsoft, AWS, NVIDIA, as counterparties, of course, I feel great about that.

我刚才提到了我对其感到满意的那些公司。

I mentioned the ones I feel great about.

我并非对所有交易对手都感到满意，所以并非所有交易对手都一样。

I don't feel great about all of them, so not all counterparties are the same.

我们开始看到私人信贷更多地参与到数据中心建设中，而且，那家覆盖范围很广、正押注公司转型为云服务商的是甲骨文公司。他们，你知道的，他们一直盈利，并且持续回购股份，但他们承诺投入的资本金额非常巨大。

We're starting to see private credit get a little bit more involved in the data center build out, and again, the company that's very well covered, that is making a bet the company move into becoming a cloud, is Oracle, And they've, you know, they've been profitable forever in reducing their shares forever, but the amount of capital that they are committing is very large.

这是一场豪赌。

It's a big bet.

未来许多年他们都将出现负现金流。

They're gonna go cash flow negative for many years to come.

而且，你知道，如果你关注一些相关的热议，比如他们的信用违约互换成本在过去三个月里已经上涨到了大约2%。

And, you know, if you follow some of the buzz around it, like, the the cost of their credit default swaps has gone up, you know, to, like, 2% over the last three months.

所以我们在密切关注这类情况。

And so we're watching stuff like this.

再次强调，这对我们投资组合中的公司总体来说都是好事，但我们也要确保整体市场保持健康。

Again, this is all generally good stuff for our portfolio companies, but we wanna make sure that the market overall is healthy as well.

所以，这张幻灯片展示了人工智能变革速度的规模。

So, this is just a slide that shows the magnitude of the pace of change of AI.