--- author: a16z date: '2026-02-09' guest: '' layout: post.njk source: https://www.youtube.com/watch?v=rSohMpT24SI speaker: a16z tags: - ai-market-trends - business-transformation - capital-expenditure - market-efficiency - product-cycle title: a16z David George 深度解析AI市场：增长、效率与未来趋势 summary: a16z合伙人David George分享了对当前AI市场的深度洞察。他指出AI需求旺盛，AI公司增长迅猛且运营效率更高，产品周期仍处于早期阶段。公共市场由AI驱动，资本支出巨大但由盈利公司支撑，AI被视为颠覆性力量。同时，他强调企业必须积极适应AI时代，进行产品和运营模式的转型，否则将面临淘汰。报告还探讨了AI对各行业的影响，以及私有市场中价值向头部公司集中的趋势。 insight: '' draft: true series: '' category: ai-ml area: tech-engineering project: [] people: [] companies_orgs: - a16z - OpenAI - Anthropic - Shopify - Harvey - Abridge - 11 Labs - Non - Flock - Databricks - Google - Microsoft - Nvidia - Oracle - Goldman Sachs products_models: - ChatGPT - LLM - Agent Bricks media_books: [] status: evergreen --- ### AI市场概览与早期阶段 **David George**: 让我先从我认为这份报告的几个**主要收获**开始，因为这是我们第一次做这种风格的报告。我们产生了如此多的工作和分析，就像我们团队内部的“废气”一样。我们认为，我们有这么多不同的想法和观点，为什么不把它们写下来并分享给全世界呢？这就是这份报告的起源。我从这次工作中得到的几个主要收获是：**AI**的需求端非常疯狂。从我们的角度来看，**AI**公司的实际采用率、增长速度和质量都非常令人鼓舞。公司开始更好地运营自己。我将向你们展示一些统计数据，关于**X**上的一些讨论，包括今天早上，人们还在争论那里发生了什么。但我会说，这一批公司比之前的公司更令人印象深刻，部分原因是它们产品的需求非常高。这是需求端。供应端目前是健康的，但我们开始看到一些迹象表明有些事情有点紧张。我将谈谈我们看到了什么以及我们正在关注什么。我们很幸运能成为许多这些优秀公司的一部分。在**私募市场**中，最令人兴奋的行动就是**AI**，而且它正在**私募市场**中发生。我们将展示一些关于这方面的幻灯片。最后，我的主要结论是，现在让我如此兴奋的是，我们正处于这个**产品周期**的非常早期阶段。**产品周期**驱动着我们的业务。这些是10到15年的周期，而我们现在才刚刚开始。所以，让我们深入探讨。我们投资于所有**私募阶段**。这张图表显示了我们的活动。我们非常忙碌，涵盖所有垂直领域。在增长方面，我们主要活跃于**AI**和**推理应用**，然后是**AD**，但在其他垂直领域也同样活跃。
Original English **David George**: 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. We produce so much work and so much analysis. It's like exhaust uh in, you know, inside of our team and we thought, you know, we have so many different thoughts and and points of view. Why don't we put them on paper and share them out with the world? So that was the genesis of this. My big takeaways from doing this one, you know, **AI** demand side is crazy. The actual uptake growth quality of companies in **AI** is extremely encouraging from our standpoint. Companies are starting to run themselves better. I'm going to show you some stats on that that, you know, there's been some sort of **X** buzz uh including this morning. You know, kind of debating what's going on there. But this crop of companies I would say is more impressive uh than than prior crops of companies partially because the demand for their products is so high. Um that's demand side. Supply side is healthy right now. Uh but we are starting to see some signs of things, you know, that are stretched a little bit. I'll I'll talk about what we see and what we're looking out for. We've been fortunate to be a part of a lot of these great companies. Um, and the most exciting action that is happening in the **private markets**, it's it's it's it's **AI** and it's happening in the **private markets**. Um, and we're going to show some slides about that. 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**. Um, you know, **product cycles** drive our business. And, you know, these are 10, 15 year cycles and we're just at the very beginning of it right now. So, let's dive in. We invest across all **private stages**. This is a chart that just shows our activity. We're very busy. It's across all verticals. We on the growth side have been most active in **AI** and infer apps uh and then in in **AD**, but also very active in our other verticals um as well.
**David George**: 我会快速浏览其中一些。我讨厌做**A16Z**的商业宣传，但我真的很喜欢这张幻灯片。我们有机会与一些最好的模型、应用和基础设施公司合作。
Original English **David George**: And I'm going to zoom through some of these. I hate to do the **A16Z** commercial, but I really like this slide. You know, I think we have the chance to work with some of the best models and apps and infra companies. Uh obviously,
**主持人**: 我来做这边的。
Original English **主持人**: I'm I'm going to do that on that side.
**主持人**: 搞定。
Original English **主持人**: Gong it.
**David George**: 我确实很喜欢那张幻灯片。
Original English **David George**: I do like that slide a lot.
**主持人**: 这里是音效板。我很高兴。
Original English **主持人**: Soundboard effect here. I'm happy.
**David George**: 我们讨论过。是的，我们讨论过这张幻灯片应该放在演示文稿的什么位置，我说应该放后面一点，但我被否决了。所以很庆幸。总之，这里有一些数据。作为增长团队，我们收集了大量数据，因为我们基本上看到了市场上所有处于增长阶段的公司，无论是投资组合公司还是潜在客户。所以我们有一个很棒的数据分析团队。我们做了一些数据分析。我认为这些东西非常有趣。我们对此非常着迷。对我来说，主要的结论是2025年是收入加速增长的一年。在2022、23、24年，随着**利率上调**和一些科技领域的**回调**，收入明显放缓。但2025年扭转了这一趋势。它在不同类型的公司中都加速了，特别是那些**异类公司**，增长确实加速了。你可能以前见过我们展示这张幻灯片，但增长最快的**AI公司**达到**1亿美元**收入的速度，比**SaaS**时代增长最快的公司快得多。我想要强调一个非常重要的事情，为什么会这样，那是因为**终端客户需求**非常强劲，产品非常有吸引力。这并不是因为它们在销售和营销上花费更多，实际上恰恰相反。增长最快的**AI公司**并不是在销售和营销上花费最多的公司，它们在销售和营销上的花费比其**SaaS**同行更少，但它们的增长速度却快得多。这张幻灯片展示了**AI公司**与**非AI公司**的增长情况。大致来说，**AI公司**的增长速度比**非AI公司**快两倍半以上。这不应该让人感到惊讶。最好的**AI公司**增长非常非常快。当我们看到**AI**的顶尖公司同比增长**693%**时，我们不得不反复核查这些数据，但这与我们从投资组合公司中看到的经验和轶事相符。
Original English **David George**: We debated. Yeah, we debated how early to put that slide on the deck and and uh I said put it further back and I was overruled. So thankfully um anyway, here's some data. So we collect tons and tons of data as a growth team because we're basically seeing every growth stage company in the market uh as a either portfolio company or as a prospect. And so we have a great data analysis team. We did some data analysis. I think this stuff is just super interesting. We geek out on it. To me, the big conclusion from this is **2025** was a year for accelerated revenue growth. Um, you know, revenue obviously slowed, you know, in **2022**, **23**, **24** following the **rate hikes** and and the **pullback** in some of the tech stuff. But **2025** reversed that trend. Um and you know it accelerated across uh different you know types of companies as we rank them by decile and cortile. Um but especially among you know the **outlier companies** you know it really accelerated and you've probably seen us put this slide on a page before but the fastest growing **AI companies** are reaching **100 million bucks of revenue** significantly faster than the fastest growing **SAS companies** in their era. And there's a really important thing I want to call out about why that is the case and that is because **end customer demand** is so strong and the products are so compelling. It's not because they spend more money on sales and marketing. It's actually the opposite. The 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 **SAS counterparts**. And yet they're growing much much faster. So this was a slide showing just the growth of the **AI companies** versus the **nonAI companies**. 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. The best of the **AI companies** are growing very very fast. We had to triple check this data when we saw the, you know, the the **AI top**, you know, top performers growing **693% year-over-year**. um but it matches up our experience you know and and anecdotes that we see from the portfolio companies.
### AI公司的利润率与效率 **David George**: 这是我们从数据集中看到的**利润率概况**，这些是我们内部的投资组合公司和我们作为潜在投资对象所关注的公司的数据集。**AI公司**的**毛利率**略差一些。你可能以前听我们谈论过这一点，但在某种程度上，我们觉得**AI公司**的**低毛利率**是一种荣誉徽章，因为我们希望看到低毛利率是否是**高推理成本**的结果。如果是，那意味着人们正在使用**AI功能**，而且我们相信这些**推理成本**会随着时间下降。所以，奇怪的是，如果我们看到一个**AI**提案，而其**毛利率**非常高，我们会有点怀疑，因为这可能意味着客户购买或使用的并非真正的**AI功能**。
Original English **David George**: So that's growth. This is the **margin profile** uh that we're seeing in the data set and again these are internal data sets that we have of portfolio companies and companies that we look at uh as potential investments. **Gross margins** are a little bit worse for **AI companies**. Um, 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 if if if low gross margins are a result of **high inference costs**, one that means people are using **AI features** and two, we have a belief that those **inference costs** over time are going to come down. Uh 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 uh or used by the customers.
**David George**: 我们将讨论**每全职员工年经常性收入（ARR per FTE）**，这是我们开始关注的新事物之一，也是过去几天在**X**上引起大量关注和讨论的话题之一。**每全职员工年经常性收入（ARR per FTE）**是衡量公司整体运营效率的指标。它包含了你所有的成本，不仅包括你过去的分析中我们一直关注的销售和营销效率指标，还包括你的**管理费用**和**研发费用**。因此，对于最好的**AI公司**，它们的**每全职员工年经常性收入**在**50万到100万美元**之间。而上一代**SaaS**时代的软件企业经验法则是大约**40万美元**。我将再多谈一点，但之所以如此，主要是因为它们产品的需求非常非常强劲，因此它们需要更少的资源来将其推向市场。
Original English **David George**: We're going to talk about **AR per FTE**, but this is a new thing that we've started focusing on and this is one of the things that got a lot of pickup and discussion uh on **X** in the last few days. **ARR per FTE** is sort of a measure of the efficiency of how you run your company in general. So it encapsulates all of your costs. Uh it encapsulates, you know, 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&D**. Uh and so for the best **AI companies**, they're running at like **500,000 to a million dollars** uh per per **FTE**. And the rule of thumb for previous software businesses in the **SAS era** was like **$400,000** in the last generation. Again, I'm going to 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. Um, you know, and so they need a less resource to go take it to market.
**主持人**: David，在我们看下一张幻灯片之前，也许可以快速澄清一下。你们如何定义**AI公司**？是定义为**ChatGPT**之后成立的公司，还是某个时间段内成立的历史**AI/ML公司**？
Original English **主持人**: David, maybe a quick clarifying just before we we um go to this slide here. So, how do we how do you define **AI companies**? Is that defined as postjack **GBT** versus historical **AI ML companies** founded by a certain time period?
**David George**: 是的，是的，它有点像**ChatGPT**之后，其中一些公司是在那个时间点左右成立的。我们会给予一点宽容，但如果它们在市场上的第一个产品是**AI原生产品**，那么我们就是这样定义的。
Original English **David George**: Yeah. Yeah, it's sort of post postg and and some of them have were founded like right around that time. We'd give a little bit of grace but but if they're their first product in market was an **AI** you know native product then that's how we define it.
**主持人**: 明白了。那么，也许现在是个好时机，或者你可以稍后再说，但很多人都在试图理解从**SaaS时代**到**AI时代**公司收入和增长变化的幅度。你已经谈到了一些收入的幅度等等，但那些非**AI原生**的公司会怎样？它们在与**AI原生公司**竞争时会遇到困难吗？它们都会转型吗？我们会看到更多的失败吗？人们应该如何看待他们历史上的投资组合？
Original English **主持人**: Got it. And then um maybe this is a good point but where you can punt till later but like one of the questions I think a lot of folks uh are trying to understand is the magnitude of change and expected revenue and growth from companies from the **SAS era** to **AI era companies** and you've talked a little bit about the magnitude of revenue etc but what happens to those that are not **AI native** will they have a hard time competing against **AI native companies** are they all shifting uh will we see more fallout how should people be thinking about their historical portfolio.
### AI时代的“适应或死亡” **David George**: 是的。所以，我们处理投资组合的方式是，你必须适应**AI时代**，否则就会消亡。这包括前端和后端。在前端，你需要思考如何将**AI**原生整合到你的产品中，而不仅仅是把一个**聊天机器人应用**附加到你现有的工作流程中，而是要重新构想**AI**能带来什么，并积极地颠覆自己和改变。
Original English **David George**: Yeah. So, the way that we're approaching this with our portfolio is, you know, you you need to adapt to the **AI era** or die. Um, and so that's both on the front end and the back end. So on the front end, you need to think about how you can incorporate **AI** into your product natively and not just, you know, attach a **chatbot app** into your existing workflow, but reimagine what it can mean with **AI** and be aggressive about disrupting yourself and changing.
**David George**: 然后在后端，我分享了一些关于公司运营效率的统计数据。这也会改变。所以你需要为所有开发者全面推出最新的**编码模型**，以及组织内部每个不同职能部门的最新工具。到目前为止，最大的采用率是在**编码**领域，这是我们看到最大飞跃的地方。在过去一个半月到两个月里，这方面发生了重大变化。**Andre Carpathy**对此有所著述。我最近与我们一家**前AI公司**的创始人进行了交流，他非常精通**AI**，正在改造他的公司。我们这周谈话时，他告诉我他对他们的一款产品感到沮丧，所以他找了两名非常精通**AI**的工程师，让他们用**Cloud Code**、**CodeX**和**Cursor**从零开始构建它，并且他们有无限的**编码工具预算**。他说他认为这比他们以前的进展快了**10到20倍**。而且相关的费用高到让他不得不重新思考整个组织的结构。结论基本上是，我需要我的整个产品和工程组织都以这种方式工作，我认为这将在未来12个月内发生。但这对于团队设计意味着什么，产品从哪里开始，甚至设计从哪里开始？所以感觉12月是**代码**的一个转折点，未来12个月，它将会在公司中普及并扎根，否则这些公司我认为会比同行慢很多。
Original English **David George**: Um, and then on the back end, you know, I I I shared some of the stats around the efficiency that the companies are running at. This is going to change too. And so you need to be fully rolled out with the latest **coding models** for all of your developers um and all of the latest tools across every different function inside your organization. Um the the biggest uptake has been in **coding** so far and that's where we've seen the biggest leaps. There have been major major changes like in the last two months on this like month and a half in this. Um you know **Andre Carpathy** has written about this. I was on a catchup with one of our, you know, sort of **pre-AII companies**. Uh, and this is a this is a founder who's very **AI**, like he's very **AI** deep and so he's adapting his company. 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 **Cloud Code** and **Codeex** and **Cursor** and just they had unlimited budget on **coding tools**. Uh and he said he thinks it's going somewhere between **10 and 20x faster** than progress that they had before. And 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. The conclusion was basically I need my entire product and engineering organization working this way and I think it's going to happen within the next **12 months**. But what does that mean for what the team design actually is and and where does product start and where does start you know and even where does design start in that process. So it feels like **December** was sort of a turning point on **code** um and you know the next **12 months** it's going to kind of hit it's it's either going to hit and take hold in companies or those companies I think are going to be moving much slower than their peers.
**David George**: 所以，关于**前AI公司**，你需要适应。我们还有另一个例子，一家**前AI软件公司**的CEO完全接受了**AI理念**，他说：“我们要成为一家**AI产品公司**。我们要推出，你的员工现在就是你的**AI代理**。你有多少个代理？”这些都是他在谈论的事情。我们还有另一个非常极端的例子，他说他现在每次需要完成任务时都会问一个问题：“我能用**电力**完成它，还是需要用**血汗**完成？”这就像是我们的公司正在经历的极端思维转变。所以，我很高兴看到我们的**前AI公司**正在非常快速地行动并努力适应，但它们确实需要适应这个新时代，无论是在前端产品方面还是在后端公司运营方面。
Original English **David George**: Um, so you know, as it relates to the **preAI companies**, you know, adapt, we have we have another example of a company that is a **pre-ai software company** and the **CEO** has gotten totally **AI pill** and he's like, we're going to become an **AI product**. Like, we're going to ship, you know, your employees are now your **AI agents**. How many **agents** do you have? Like those are the things that he's talking about. Um you know we have another one that was very extreme about it and he said I now ask the question um for for every task that we now need to complete uh can I do it with **electricity** or do I need to do it with **blood** like this is like the extreme mindset shift that's happening you know with uh with our companies and and so I I'm I'm happy to see that our **PAI companies** are moving very fast and trying to adapt uh but they very much need to adapt to this new era both front end product wise and back end how they run their companies.
**主持人**: 完全正确。是的。也许从战术上讲，几乎每个投资组合公司，你都必须逐行了解创始人在这段旅程中的位置，以及他们从基层开始实施了多少。你所说的**颠覆现有运营**，这也在**后AI公司**中发生。越来越多的人每六个月就会审视一次，就像我们六个月前构建的东西，根据今天可用的技术，可以大大改进。所以如果这种速度持续发生，**前AI公司**就需要不断地以**10倍**的速度追赶。
Original English **主持人**: Totally. Yeah. 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 are implementing from the ground up and and you know what you said in terms of blowing up existing operations. That's also happening in **post AI companies** too and and increasingly people are just looking every six months. It's like the things we built six months ago could be vastly improved by based on what is available today. So that if that rate is continually happening, the **preAI companies** are needing to to increasingly **10x** catch up to that point.
### 商业模式的演变 **David George**: 是的。对于**前AI公司**来说，好消息是**商业模式的演变**仍处于早期阶段。所以，对你来说最具颠覆性的事情是技术和产品转变，同时也是商业模式转变。我真的认为商业模式就像一个**光谱**，我只是为了简化而谈论企业**B2B**。但这个光谱基本上是**许可证**，这就像**前SaaS**时代的许可证和维护商业模式，然后是**SaaS**和**订阅**，这通常是基于**席位**的，这是一个巨大的创新，而且非常具有颠覆性，就像架构和**云交付**具有颠覆性一样，但商业模式的改变也非常具有颠覆性，就像看看**Adobe**在经历那个转型时发生了什么。
Original English **David George**: Yeah. The good news for the **prei companies** is the **business model evolution** is still early days. 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. There's really one I I think of the **business models** as like a **spectrum** and you know and I'm talking about like enterprise like **B2B** just to keep it simple but the **spectrum** is basically **licenses** and this was like the **pre-SAS** you know license and maintenance **business models** then you had **SAS** and **subscription** and that was typically **seatbased** and that was a big innovation and it was very disruptive like the architecture and **cloud delivery** was disruptive but the **business model change** was very disruptive like just go look at what happened to **Adobe** as they went through that transition.
**David George**: 然后你有了向**基于消费**的转型，也就是**基于使用**的模式，这就是**云**的收费方式，许多基于**容量**的、基于**任务**的业务已经适应并转向了这种模式，从基于**席位**转向了**消费**。然后下一个迭代将是**基于结果**的。所以，当你完成一项任务时，理想情况下，当你成功完成一项任务时，你会根据该任务的成功完成获得报酬。目前唯一真正可能实现这一点的是**客户支持**、**客户成功**，因为你可以客观地衡量某个问题的解决情况。但我们将拭目以待模型能力的发展，如果除了**客户支持**之外的其他功能也能衡量这类结果，那将对现有企业产生巨大的颠覆性力量。老实说，从**席位**到**消费**的转变可能是一个巨大的颠覆，如果公司的构成也发生变化的话。但下一个才是真正大的。
Original English **David George**: 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 **taskbased type businesses** have already adapted that and shifted to that from you know **seat based** to **consumption**. Um and then the next iteration will be **outcome based**. So, you know, when you when you do a task, um, you know, and ideally when you successfully complete a task, you get paid based on the successful completion of that task. The only area where that's really possible today to pull off is is probably **customer support**, **customer success**, because you can kind of objectively measure the resolution of of something. Um but we'll see what happens with the capabilities of the models to the extent that other functions besides **customer support** can measure those kinds of outcomes that would be a huge disruptive force uh for incumbents and and honestly **seats to consumption** might be a big disruption if the composition of companies changes as well. Uh but that next one is the is the really big one
**主持人**: 当然。说到**血汗**与**电力**，我们应该转向**每全职员工年经常性收入（AR over FTE）**。下一张幻灯片。
Original English **主持人**: for sure. Um speaking of **blood** versus **electricity** we should go to **AR over FTE**. Uh this next slide here.
### AI带来的效率提升与公司运营模式转变 **David George**: 是的，是的，是的。所以关于这个话题，下一张幻灯片上的主要争论是，“天哪，看看市场上正在发生的**AI效率提升**！”现在，这其中确实有一点，比如公司以略微不同的方式运营自己，你知道，就像我举的例子，两个工程师正在重建产品。当然，我会说，根据我对我们公司的观察，即使是**AI原生公司**，它们的运营也更精简，部分原因是它们增长太快，需求太强劲。我不会说我们已经到了公司完全重新构想其运营方式的地步。我认为这有点是我们数据集是**顶尖公司**的结果，而且这些公司的需求信号非常高。所以，它们用更少的资源来满足这些需求，坦率地说，你知道，从2021年那种最臃肿的时代走出来，技术市场中发生的普遍效率提升。所以我们开始看到这种效率的一些早期迹象，但是**彻底改变公司运营方式**，我认为我们还处于这段旅程的早期阶段。
Original English **David George**: Yeah. Yeah. Yeah. So the big the big debate that was going on on this one uh on the next slide was um like oh my gosh look at the **AI efficiency gains** that are happening in the market. Now there's a little bit of that in this like companies running themselves a little bit differently and you know you take the example that I gave about you know the two engineers who are rebuilding the product like sure 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. I I wouldn't say yet we're at the point where companies have fully reimagined the way they run themselves. I think this is a little bit the result of our data set being the **best of the best companies** and demand signals for those being extremely high. Uh so they you know they have less resources to serve that demand and frankly you know efficient general efficiency gains that have happened in the technology market you know out of the kind of **2021** most you know most bloated era. Um, so we're starting to see some early signs of that efficiency, but the wholesale run your company totally differently. I think, you know, we're we're kind of early in that in that journey.
**David George**: 我会说我见过的最酷的例子，在公共市场中任何人都可以读到，可能是**Shopify**。**Toby**很棒，他是一位与我们关系密切的CEO，参与了我们很多小组等等。他做得非常出色，他在几年前就完全接受了这一点。然后我们的一位撰稿人写了一篇关于**Shopify**如何通过**AI**来管理员工方向、流程等方面的深度文章。这可能只是未来五年内将发生的事情的冰山一角。
Original English **David George**: I'd say the coolest one that I've seen is um in the 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 that's close. He's in a bunch of our groups and stuff. Um, and he does a great job and he, you know, he fully embraced this a couple years ago. And then um there one of our staff writers uh actually wrote this whole big deep dive on how **Shopify AI** itself you know in terms of you know employee direction process etc. Um and that's just probably scratching the surface of what's going to happen over the next **5 years**.
**主持人**: 很好地过渡到下一部分，关于这些公司到底在做什么，以及我们最喜欢的话题，那就是在这个**AI**遇到**律师**的新世界里，律师的数量只增不减，而不是相反。我喜欢本周早些时候看到的一条推文，一位公司律师被引用说，**大型语言模型（LLM）**实际上增加了我的工作量，因为现在每个客户都认为自己是律师了。这很好地引出了**Harvey**，也就是下一张幻灯片。
Original English **主持人**: A good seg to the next section on what are these companies actually doing in our favorite topic which is lawyers have only increased in this new world of uh **AI's** meeting lawyers um not the opposite uh I I love the tweet I don't know if you saw it earlier this week that uh a **corporate lawyer** was quoted saying **LLM** have actually increased my workload because every client thinks they're a lawyer now it's a good seg to **Harvey** which is the next slide
### AI在特定行业的应用案例 **David George**: 那非常好，那非常好。**Harvey**太棒了。好的，这对我来说是一个真正的考验，因为我喜欢谈论我们的投资组合公司，但我应该快速浏览这一部分，因为我认为人们可能已经了解这些公司。关于这个话题，一个重要的收获是，我们关注的一个大问题是，收入如何才能持续？这些公司都增长得非常快，但这种增长是昙花一现吗？我们努力做的一件大事是，确保我们深入研究**收入留存**、**续订**和**产品参与度**，实际花费的时间，人们多久登录一次平台，当他们在平台中时，他们的活动是什么样的？你在这页上看到的是，随着过去几年他们构建的更好产品的出现，加上**推理模型**的改进，结果发现**法律工作**和**推理**是相辅相成的。用户在产品中花费的时间比以前大约**翻了一倍**。所以事实证明，**AI**在**法律工作**方面非常出色。再说一次，律师并没有减少，但我认为**AI**在这方面非常非常出色，而且我认为律师的效率正在大大提高。与**Harvey**最相关的重要事情是，他们只是在产品中花费了大量时间，并从中获得了大量价值，这很棒。
Original English **David George**: that's that's very good that's very good **Harvey's** so great I 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 uh you know I think people people know these companies uh hopefully um the takeaway on this one you know one of the big things that we look for and um one of the questions I think that came in was how do you know that revenue is going to be sustainable like these companies they all grew really really fast but is it fleeting and the big thing that we push ourselves to do is make sure we go super super deep on **revenue venue retention**, **renewals**, uh, and **product engagement**, actually time spent, how often are people logging into the platform, when they're in the platform, what does their activity look like? And 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** uh go go hand in hand. um users are spending about **double the amount** uh in the product as they had before. So it turns out that **AI** is is really good at **lawyering**. Um again there's not fewer lawyers. Uh but I think you know **AI** is very very good at this and I think lawyers are getting a lot more efficient. 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.
**主持人**: 让我们转向**Abridge**。哦，除非你想继续谈论律师。哦，我只是想发表一个评论。在我认识你的七年里，我从未察觉你来自**肯塔基州**，除了现在这一刻。顺便说一句，你发音“lawyer”的方式。
Original English **主持人**: Let's go to uh **a bridge**. Oh unless you want to keep talking about lawyer. Oh, I was just going to make a comment. In all the seven years that I've known you, I wouldn't have ever uh discerned that you're from **Kentucky** other than this moment now. By the way, you say lawyer.
**David George**: 那是一个线索。
Original English **David George**: That was a tell.
**David George**: 我的词汇里有几个这样的词。我不能，你知道，我妻子总是开玩笑说，你回家喝一杯**波本威士忌**，然后你说话就像你18岁时那样。当谈到律师时，**肯塔基**口音就出来了。
Original English **David George**: My uh there's a there's a couple of those words in my vocabulary. I can't I that I I don't you know, my my wife always jokes. She's like, you know, you go home, you have like one **bourbon**, and then you you talk like you probably did when you were 18. the **Kentucky** came out when it came to lawyers.
**主持人**: 现在是上午10点25分。我今天还没喝**波本威士忌**。所以，嗯。
Original English **主持人**: It's it's it's 10:25 a.m. I have not had any bourbons today. So, um
**David George**: 重要的区别。
Original English **David George**: important distinction.
**David George**: 是的，重要的区别。是的，没错。所以，**Abridge**是另一个非常非常令人兴奋的公司。我的意思是，医生们对能够使用**Abridge**以及它为他们节省了多少时间，以及它如何让他们的生活变得更好赞不绝口。我们采访的一位客户将其描述为一位**值得信赖的副手**。右边的图表显示了我们关注的一个指标，蓝色线表示用户增长，绿色线表示这些用户的参与度。所以，当他们大规模增长用户数量时，如果新增用户的参与度下降，你会有点担心。但相反，他们的产品用户参与度极高，即使他们增加了大量的用户，这种参与度也保持稳定并略有增长。所以，这些只是我们寻找的数据类型的一些例子，以确保我们对这些公司产生的收入是可持续的充满信心。再说一次，这些公司的增长速度比任何前身公司都快，但它是非常可持续的。它具有高参与度、高留存率。这对我们来说至关重要。
Original English **David George**: It's important distinctions. Yes, exactly. So, uh **a bridge** **a bridge** is another one that's super super exciting. I mean, this is like the doctors rave about um getting to to have access to **a bridge** and how much time it saves them uh and how much, you know, better it makes their lives. Um, so you know, one of the customers that we talked to described it like a **trusted deputy**. 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. And so 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. So the 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 and again these companies are growing faster than you know any of the predecessor companies but but it's very sustainable. It's, you know, it's high engagement, it's high retention. Uh, and that's critically important for us.
**David George**: **11 Labs**也是一样。**语音**是许多新的**AI工具**的核心。你知道，我谈到了**B2B**端的**客户支持**。但是，你知道，许多其他的**个人工具**、**商业工具**，都从**语音**开始。**使用量增长**是我喜欢看这张图表的原因。它简直令人震惊。这家公司增长非常快，是这些运营效率极高的公司的一个很好的例子。所以**11 Labs**确实非常棒。
Original English **David George**: Same thing with **11 Labs**. **Voice** is the centerpiece of so many of the new **AI tools**. You know, I talked about **customer support** on the **B2B** side. Um, but, you know, so much um, you know, other personal tools, business tools, you know, start start with **voice**. Um, the **usage growth** is the thing that I love to look at on this chart. It's just staggering. Uh and this company is growing very fast and is a great example of one of these companies that runs extremely efficiently. Um so **11 Labs** is is really is really a great one.
**David George**: **Non**是下一个。这是一个不同的例子。所以这实际上是我之前描述的一个很好的例子。他们很早就抓住了**AI转型**的机会，并投入了大量精力确保他们能够最大限度地利用**AI能力**来改善他们的业务。所以你今天在他们业务中看到的最大变化是在**问题解决**方面。他们的一部分工作是，代理需要处理**旅行预订**或**旅行变更**。**AI**现在处理了**50%**的用户互动。这很困难，这是**旅行预订**，这是**旅行变更**。所以这不像“告诉我我的银行余额”那样简单。这是一个复杂的**工作流程**，现在**AI**能够处理。你在业务中看到的结果是，过去三年**毛利率**扩大了**20个百分点**。这简直是卓越的影响。所以，你知道，你需要适应，否则就会消亡。而他们的竞争对手没有适应。他们非常守旧，当他们停滞不前，墨守成规时，**Non**现在的**毛利率**比那些老牌公司高出**20个百分点**。
Original English **David George**: Non is the next one. So this is another this is a different example. So this is actually a good example of what I was describing earlier. So they were early to this, you know, **AI shift** and uh and and they spent a lot of effort making sure that they could take the most of the **AI capabilities** and make their business better. And so the biggest way you can see it in their business today is in uh the handling of **resolutions**. So part part of what they have is, you know, agents that have to handle **travel bookings** or **travel changes**. **AI** is now handling **50%** of those user interactions. And this is hard stuff like this is **travel bookings**. This is changes to **travel**. Uh so this is not you know complex like tell me the balance of my bank. Uh you know this is like complex **workflow** that that **AI** is now able to handle. The way you see that in the business is a **20 percentage point expansion of gross margins** over the last **3 years**. And that's just exceptional impact. And so you know you need to adapt or die. Well their competitors are not adapting. They're very old school and while you know they've been sitting still and and doing things the old way, **Non** now has **20 percentage point higher gross margins** than those incumbents.
**David George**: 然后，你知道，**Flock**。**Flock**正在做着绝对不可思议的工作。我谈论过他们很多次了。这是我们在投资组合中看到的最具吸引力的**客户价值主张**，因为他们的**投资回报率（ROI）**是**解决犯罪**。我们之前已经提到过**10%**的统计数据。每年，**Flock**解决**70万起犯罪**。右边的数据点也显示，在有**Flock**的地方，每位警官侦破的犯罪案件几乎增加了**10%**。所以对社区产生了巨大的影响。显然，他们有一个很棒的业务和财务模型与之配套。但他们的产品或产品带来的影响是卓越的。
Original English **David George**: And then you know **Flock** **Flock** is doing absolutely incredible work. I've talked about them so much. It's it's the most compelling **customer value proposition** that we see in our portfolio because what their **ROI** is is **solving crime**. Um the **10% stat** we've covered before. Each year **Flock** is solving **700,000 crimes**. Um the the 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%** um you know more crimes. So huge impact on the community. Obviously they have a great you know they have a great business and financial model that goes along with it. But the but the impact uh on their product or from their product is is exceptional.
**主持人**: 好的。
Original English **主持人**: Okay.
**David George**: 顺便说一句，我不知道你是否看到聊天区里有人说他们已经喝了三杯**波本威士忌**了。
Original English **David George**: Uh by the way, I don't know if you see the chat lighting up of people saying that they're three bourbons deep. Uh
**主持人**: 哦，我没看到。
Original English **主持人**: oh, I didn't see it.
**主持人**: 值得一提的是，有一个问题是关于你们如何看待**基准**？比如，如果你们考虑像**金融**这样的传统行业，并以**摩根大通（JP Morgan）**为基准，你们会如何衡量**财富500强（Fortune 500）**在**AI采用**方面的表现？然后我可能会把**Xavier**提到的那个问题也叠加进来，就是去年年初**麻省理工学院（MIT）**有一项关于**企业采用**的研究，他们测量了各种奇怪的东西。也许可以多谈谈你们从**财富500强CEO**那里听到了什么，以及你们如何看待。
Original English **主持人**: For what it's worth, uh there is one question about um how do you think about the the benchmark? Like if you were to think about traditional industries like **finance** for example and using **JP Morgan** as a benchmark, what would you calibrate the **Fortune 500** in terms of **AI adoption**? 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. Uh maybe say a little bit more about how and what you're hearing from **Fortune 500 CEOs**.
### 财富500强企业的AI采纳与挑战 **David George**: 是的。我们从**财富500强CEO**那里听到的，我想说，也许这是这两个观点之间的关键联系。我们从**财富500强CEO**那里听到的是，我们必须适应。我们迫切希望了解我们需要哪些**AI工具**。我们已经准备好改变。我们的业务将全面推出这些东西，我们已经准备好了。我们将成为**AI公司**。
Original English **David George**: Yeah. Um 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. Um, you know, we're ready to change. We, you know, our businesses are going to fully roll things out and, you know, we're we're ready. We're going to become **AI companies**.
**David George**: 这与实际发生的情况大相径庭。我认为这种心态与业务实际变化之间最大的脱节在于**变革管理**非常困难。让人们仅仅使用**AI助手**来更好地完成工作就已经够难了。**编码**可能是最容易让人们接受的。**客户支持**，它是一个更好、更快、更便宜、更明显的东西。但就实际的业务**通用管理**、**业务流程变更**、**变革管理**而言，这极其困难。所以，我并不惊讶有传闻说事情进展比预期慢，但对于那些完全接受并真正知道该怎么做的优秀公司来说，它已经产生了巨大的业务影响。所以，我认为未来五年将有一个**清算期**，看谁能真正拥抱变革，推动**变革管理**，采纳所有最好的产品，而那些不这样做的公司，我认为在**生产力**方面将会有巨大的差异。
Original English **David George**: That's quite different than what is actually happening. 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. Um, you know, it's it's hard enough to get people to just use an **AI assistant** to help them do their jobs better. Um, you know, **coding** is probably the easiest one to get people's minds wrapped around. **Customer support**. It's such a better, faster, cheaper, obvious thing. But in terms of actually, you know, **general management of businesses**, **changing business processes**, **change management**, it's extremely hard to do. And so I'm not surprised that there are anecdotes out there that suggest, oh, you know, things are moving slower than expected, but for the best companies that are fully embracing it and actually know what to do, it has tremendous business impact already. Uh so you know I think there's going to 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 um and those that don't and I think there'll be major differences in **productivity**.
**David George**: 我们在幻灯片后面有一些图表，我可以谈谈，但对**生产力提升**和**增长**等的预期很高，我认为许多公司会实现这些预期，而那些没有实现的公司将处于巨大的劣势。**Chime**表示他们将**支持成本**降低了**60%**。**Rocket Mortgage**表示他们在**承保**方面节省了**110万小时**，同比增长**6倍**，这相当于每年**4000万美元**的运行率节省。所以，我们正在**非AI业务**中看到这些零星的例子，我认为未来12个月将是观察的非常有趣的一年。我认为你会看到更多的轶事，但会有公司能够解决问题，也会有公司不能。
Original English **David George**: you know we have some charts later in the slides you know which I can talk to but you know the expectations around **productivity enhancements** and you know and **growth** and all that stuff um you know the expectations are high and I think a bunch of companies will achieve those and the ones that don't are going to be at a huge disadvantage. **Chime** said they reduced their **support costs by 60%**. Um, **Rocket Mortgage** said that they saved **1.1 million hours in underwriting**, up **6x year-over-year**, and that was **40 million bucks of run rate annual savings**. So, you we're seeing pockets of it in **nonAI businesses** and I think this is going to be a really interesting year to watch over the next **12 months**. I think you're going to see a ton more anecdotes, but there will be companies that can figure it out and there are going to be companies that don't.
**主持人**: 完全正确。而且，这些公司中的许多也必须调整其业务，以适应**AI**。就像只使用**聊天机器人**的版本一样，那能带来多少**生产力提升**呢？可能不多，对吧？但如果你必须完全颠覆你的系统信息和后端，以适应**AI**，那么很多可能都是潜在的，现在正在构建，以实际看到与之相关的成果。
Original English **主持人**: Totally. And also, they've a lot of these corporations have had to orient their business to be ready for **AI** as well. Like there's one version of just like using a **chatbot**, right? And how much productivity gained that actually gets you? Probably not a lot, right? But if you have to actually completely upend your systems information and backend 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驱动的公共市场与资本支出 **David George**: **AI赢家**正在推动**公共市场**。它们贡献了**标准普尔500指数（S&P 500）**近**80%**的回报。所以这是推动经济和股市的主要因素。**公共市场**表现非常好，但基本面是稳健的。所以价格正在上涨，或者你知道，过去几天有一些小波动，但总体来说它们表现良好。但基本面非常稳健。我想说**泡沫**的证据微乎其微。所以最近的表现是由**每股收益（EPS）增长**驱动的。**市盈率（multiples）**略有收缩，如果你是一家**SaaS公司**，过去几天或几周可能收缩得更多。但我想说，市场总体上是根据**盈利**和**盈利增长**定价的。所以**盈利市盈率**高于平均水平，但远不及**互联网泡沫**时期。所以你可以看看图表，看看我们现在所处的位置，这让我感到一些安慰。再说一次，那些总体上是市场最大驱动力的公司的盈利，我觉得相当稳健。这些公司都很好。所以，我想说，这些公司的健康状况相当不错，估值高于过去的平均水平，但并不令人超级担忧。我经常说，我刚才谈到的那些领先的科技公司是**世界历史上最好的企业**。如果你从长远来看，它们表现出的**利润率提升**表明这可能是真的。这在页面的左侧。所以，投资者正在为**利润**买单，而不是为**亏损增长**买单。这与2021-2022年时代，或者说2021年时代形成了鲜明对比。显然，与调整利润率后的**互联网泡沫**时期也有很大对比。**市盈率**并没有那么高。
Original English **David George**: **AI winners** are driving the **public markets**. They account for almost **80% of the S&P 500's return**. So this is sort of the major thing driving the economy and the stock market. **Public markets** are doing very well. Um but the fundamentals are sound. So the prices are going up or you know there's some blips like the last couple of days but they're generally doing well. Um but the fundamentals are very sound. Um and I would say the evidence of **froth** is minimal. So recent performance is driven by **EPS growth**. Um **multiples** have contracted slightly maybe more than slightly uh if you're a **SAS company** over the last few days or a couple weeks. Um but I would say the market is priced on in general uh **earnings earnings** and **earnings growth**. So the **earnings multiples** are higher than average but nowhere near the dot. And so you can just look at the charts and see where we are and you know that that gives me some comfort. And again the earnings of the companies that are the biggest drivers of the market in general I feel like are pretty sound. The companies are good. So, you know, the the health of these companies, I would say, is pretty good and and the valuations are higher than average in the past, but they don't feel super alarming. I often say the leading tech companies that I was uh I was just talking about are the **best businesses in the history of the world**. Um, if you just look over a long period of time, they have shown **margin improvement** that suggests that is probably true. And that's, you know, that's on the left side of the page. So, investors are paying for **profits**, not **lossmaking growth**. Um, and that's a big contrast from **2122 era**, sort of **21 era**. Um, and obviously a big contrast from a dot adjusted for margins. Um, **multiples** are are not that high.
**David George**: 所以，我再次总结一下，你知道，五张幻灯片的内容。市场比过去高，但我认为，你知道，高预期是有原因的。我们对**AI**对未来几年**公共市场**整体盈利的影响持乐观态度。也许我会把你的注意力集中在右侧，那就是，如果你只是把**低增长**、**高增长**、**低利润率**、**高利润率**这四种类型的公司配对起来。这张图表显示了它们的交易方式。最好的公司有溢价。你在右侧的两列看到的是**高增长**、**高利润率公司**，然后是**高增长**和**低利润率公司**。你的糟糕类别显然是**低增长**、**低利润率**。这些公司不应该得到奖励。它们应该交易低价。它们也确实如此。但那些**高增长**和**高利润率**的公司，以及那些**高增长**和**低利润率**的公司，只要它们有良好的**单位经济效益**并且正在扩大其利润率，它们就应该得到奖励。所以我认为这很好。如果你不是**高增长**，即使你是**高利润率**，那也很艰难。这并不令人惊讶。再说一次，我过去以许多不同的形式谈论过这一点。但最终，**增长**是驱动5到10年回报的最大因素。所以，我很高兴看到**高增长**比**低增长**获得更多奖励。但是如果你有**高增长**和**高利润率**，你就是那些优秀企业之一，它正在获得丰厚的回报。
Original English **David George**: And so again, I like summarize, you know, five slides worth of materials. The market's higher than it has been in the past, but I think, you know, there's high expectations for a reason. and and we're optimistic about the impact of **AI** flowing through to **earnings**, you know, overall in the **public markets** in the coming years. Uh, and maybe I'd focus your attention on the right side, which is um, you know, if you just took a **fourbox** 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. There's a **premium** for the best companies. And what you see on the the two columns on the right is **high growth**, **high margin companies** and then **high growth** and **low margin companies**. Your bad box is obviously **low growth**, **low margin**. And those companies shouldn't be rewarded. They they they should trade low. Uh and they do. But the companies that are **high growth** and **high margin** um and you know the **high growth** and **low margin**, as long as they have good **unit economics** and they're scaling into their margins, they should be rewarded. And so I think this is good. Um, if you're not **high growth**, even if you're **high margin**, it's tough out there. And that's not surprising. Again, I've talked about this in the past in many different forms. But ultimately, **growth** is the biggest thing that drives returns over **5 to 10 years**. And so, it's nice for me to see **high growth** is rewarded more than **low growth**. U, but if you have **high growth** and **high margin**, you're one of those great businesses, it's being very rewarded.
**David George**: 这就像我们要谈论**资本支出（capex）**建设的供应侧。所以建设规模巨大，投资的规模和集中度本身就具有风险，考虑到它的巨大。虽然它有一些**泡沫特征**，但我认为其基本面与之前的**泡沫**几乎没有相似之处。这项投资主要由我之前谈到的那些历史性盈利公司，比如非常盈利的公司提供资金。**债务**已经开始进入视野。**周期时间**已经加速，这很好，但你知道，我们正在密切监控**训练成本**和整个方程的经济性。目前看来相当不错，那些在**训练模型**上花钱的大型模型公司的**回报**相当不错，但我们正在密切监控。最重要的是，我们认为**AI**将是我职业生涯中见过的最大的**模型颠覆者**。我写过关于**模型颠覆者**的文章，所以不会花太多时间在上面，但它们是那些增长速度和持续时间都超出任何人在任何情景下建模的公司。
Original English **David George**: This is just like we're going to talk about **supply side** of the **capex buildout**. So the buildout's massive, the size and the concentration uh of the investment is inherently risky just given how big it is. Um while it has some **bubbly features**, the underlying fundamentals I would say bear little resemblance to previous **bubbles**. Um the investment is financed primarily by historically profitable companies like very profitable companies that I had talked about. Um **debt** has started to enter the picture. um **cycle times** have accelerated which is good but you know model we're closely monitoring the sort of **cost of training** and the economics of that whole equation right now it seems pretty good the paybacks for the big model companies that spend money on **training models** is pretty good uh but we're monitoring that closely most importantly we think that **AI** is going to be you know the biggest **model buster** that I've seen in my career certainly um I've written about **model busters** so I won't spend too much time on them but they're companies that grow faster and longer than anyone would have would have modeled in any scenario.
**David George**: 就像**iPhone**就是典型的例子。你知道，如果你拿**iPhone**问世前到五年后，四五年后的**共识模型**，**苹果公司**的业绩预测误差达到了**3倍**，而这在当时是世界上最受关注的公司。所以，我认为同样的事情会发生在**AI**的许多领域，即实际表现将大大超出你在任何电子表格中看到的预期。所以，**科技**总体上本身就是一个**模型颠覆者**，但自2010年以来，**科技**以**前所未有的速度和规模**提供了**高利润收入**。所以它早期看起来往往很昂贵，但我会说，它反复超出预期，创造的价值远远超过增长所需的资本。我没有理由认为这次会有所不同。
Original English **David George**: Like **iPhone** is the classic case of this. You know, if you if you take **consensus models** uh from pre **iPhone** to **5 years later**, **four years later**, **consensus models** were off for **Apple's performance** by a factor of **3x over four years**. And this is like the most covered company in the world uh at the time. So, you know, I think that the same thing is going to happen in many pockets of **AI** where the performance just massively uh exceeds, you know, what any expectations in a spreadsheet would would show you. So, **tech** in general is itself a **model buster**, but since **2010**, **tech** has delivered **high margin revenue** at **unprecedented speed and scale**. So it often looks expensive early, but repeatedly surprises to the upside, I would say, um, and creates value, I would say, far in excess of the capital that's required, uh, to grow. And I I have no reason to think it'll be different, you know, this time around.
**David George**: 所以相对于**互联网泡沫**时期，**资本支出（capex）**实际上是由**现金流**支持的，而且**资本支出**占收入的百分比也显著降低。所以这是一个简单的标题。我们可以跳到下一张幻灯片，但你知道，我对这种**资本支出动态**感觉好多了。显然，**超大规模云服务商**是承担**资本支出**最大冲击的，这对我们的投资组合公司来说是非常好的事情。这太棒了，我完全支持它，尽可能多地投入产能，尽可能多地提供**训练**和**推理**的供应。这是一件非常好的事情。再说一次，承担大部分冲击的公司是我之前谈到的有史以来最好的企业。
Original English **David George**: So relative to the dot, **capex** is actually supported by **cash flows**, and **capex** as a percentage of revenue is considerably lower. So that's simple headline. We can zoom to the next slide but you know I feel much better about this **capex** um you know dynamic than than than do obviously **hyperscalers** are the ones who are bearing the biggest brunt of the **capex** and this is a very good thing you know for our portfolio companies this is great like I am all for it get you know get as much capacity in the ground get as much supply as you as you possibly can on the ground for **training** and **inference** this is a very good thing 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.
**David George**: 所以，我们开始监控的一件事是**债务**的引入。你无法用**现金流**来为所有预期的**资本支出**提供资金，我们开始看到一些**债务**。所以我们正在密切关注这一点。我们通常不会大量投资于有**债务风险**的公司。我是否对页面上的一些公司通过**现金流**融资，持续产生**现金流**，甚至使用**债务**，以**Meta**、**微软（Microsoft）**、**亚马逊网络服务（AWS）**、**英伟达（Nvidia）**作为交易对手感到放心？当然，我对此感觉很好。我提到了我感觉很好的那些公司。我并非对所有公司都感觉很好。所以，并非所有交易对手都是一样的。我们开始看到**私募信贷**更多地参与到**数据中心**的建设中。再说一次，**甲骨文（Oracle）**这家备受关注的公司正在进行一场**赌上公司命运**的举动，转向成为一家**云服务商**。他们一直盈利，并一直在回购股票。但他们承诺的资本量非常大。这是一个巨大的赌注。他们将在未来多年内出现**负现金流**。如果你关注一些相关讨论，比如他们的**信用违约互换（credit default swaps）成本**在过去三个月内上升到**2%**。所以我们正在关注这类事情。再说一次，这对我们的投资组合公司来说总体上都是好事，但我们希望确保整个市场也是健康的。
Original English **David George**: So, one thing that we're starting to monitor is the introduction of **debt** into the equation. 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**. So, we're following this closely. Um, we're generally not invested heavily in companies with exposure to **debt**. Um, 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, you know, **Meta**, **Microsoft**, **AWS**, **Nvidia** as counterparties? Of course, I I feel great about that. I mentioned the ones I feel great about. I don't feel great about all of them. So, not all counterparties are the same. You know, we're starting to see **Private Credit** get a little bit more involved in the **data center buildout**. And you know again the company that's very well covered uh that is kind of making a **bet the company move** into becoming a **cloud** is is **Oracle** and they've you know they've been profitable forever and reducing their shares forever. Um but the amount of capital that they are committing um is very large. It's a big bet. They're going to go **cash flow negative** for many years to come. Um, and you know, if you follow some of the buzz around it, like the the cost of their **credit default swaps** has gone up um, you know, to like **2%** uh, over the last **three months**. And so, we're watching stuff like this. Again, this is all generally good stuff uh, for our portfolio companies, but we want to make sure that the market overall is healthy as well.
**David George**: 所以，这张幻灯片展示了**AI**变革速度的巨大程度。所以，将**AI建设**和**AI收入**与**Azure**发生的情况进行比较。**AI收入**相对于**云**正在迅速发展。**Azure**花了**7年**才达到**AI**一年的收入。所以这只是**微软**报告的数据，我认为这是一个很酷的方式来描述这一切发生的速度有多快。建设花费了很长时间。再说一次，这次**AI建设**发生得快得多。但**Azure**的收入花了**10年**才超过其**资本支出**。我认为这种比率或等式在**AI**领域会发生得快得多。
Original English **David George**: So, this is just a slide that shows the magnitude of the pace of change of **AI**. So, comparing **AI buildout** and **AI revenue** to what happened with **Azure**. So the **AI revenue** is coming along relative to the **cloud**. It took **Azure 7 years** to reach **one year of AI revenue**. Um so this this is just **Microsoft** reporting data which I think is a a cool way to to frame how quickly this has happened. Um you know the build's taken a very long time. Again this this **AI buildout** is happening much faster. Um but it took **10 years** for **Azure revenue** to surpass their **capex**. Um and I think it's I think that sort of ratio or equation is going to happen much faster with **AI**.
**David George**: 我们不需要过多地深入研究**折旧**，但这是在**金融圈**中引起很多关注的话题之一，你知道，就是你对特别是**芯片折旧**的假设是什么。我想说，**旧款GPU**的定价非常坚挺。早期用户会使用模型更长时间，但后期用户会迅速转向新产品。这是右侧，有点像模型侧。在**芯片**侧，**谷歌（Google）**实际上披露了，**7到8年**前的**TPU**实际上有**100%**的利用率。我们非常密切地监控**二级市场**的**芯片价格**以及租用**A100**和**H100**的价格，这些价格实际上保持得非常好。所以老一代的**芯片**仍然得到充分利用。所以这不是我目前担心的事情，但它确实引起了很多关注，以及那些喜欢谈论系统中风险的**危言耸听者**。
Original English **David George**: We don't need to geek out too much on **depreciation**, but this is one of the topics that gets a lot of buzz in **finance circles**, you know, just what are your assumptions around **depreciation of chips** in particular. Um, I would say the pricing for **older GPUs** is very solid. Um, early users stick with models a bit longer, but later users quickly switch to the new thing. So, that's the right side. That's like kind of the model side. On the **chip side**, um **7 to 8y old TPUs**, **Google** actually disclosed this, **7 to 8y old TPUs** actually have **100% utilization**. Um and we very closely monitor the price of **chips** in the **secondary market** and the price to rent **A100s** and **H100s** um has actually held up very very well. So older generations of **chips** are still still getting fully utilized. So this is not something I worry about uh yet but it gets a lot of buzz and you know sort of alarmists uh who like to to talk about risk in the system.
### AI的增长潜力与市场预期 **David George**: 好的，一些积极的事情。我们一直谈论的一件大事就是这个**悖论**：随着**token**变得更便宜，**消费**就会增加。所有**超大规模云服务商**都报告说需求远远超过供应。我相信他们说的是真的。我采访了我的朋友**Gavin Baker**，在我们的**AI峰会**上，他将**互联网建设**和铺设**光纤**与这里的**数据中心建设**进行了比较。他的主要观点是，没有“**暗GPU**”。没有**暗GPU**。以前有“**暗光纤**”，你必须铺设光纤，然后它就闲置在那里没有被使用。如果你把一个**GPU**放入**数据中心**的系统中，它会立即得到充分利用。所以这是一个非常好的迹象，你知道，就需求立即满足供应而言。我之前提到过，这些公司应该实现**实际增长**，这是我们的预期。如果不能，它们可能会被颠覆，如果它们无法改变的话。所以**变革管理**仍然是我们看到事情尚未发生巨大转变的最大原因。老实说，对我来说，这并不是技术本身的准备程度。它可能是需要围绕技术进行的产品建设，然后是**变革管理**和将其投入生产。
Original English **David George**: All right some positive stuff. So uh the the big thing that we talk about all the time uh is is is this **paradox** right like as **tokens** get cheaper **consumption** goes up. All the **hyperscalers** report demand is well in excess of supply. I believe them when they say that. Um, you know, I interviewed uh **Gavin Baker**, friend of mine on our at our **AI summit**, and he was comparing the buildout of uh the **internet** and and laying all the **fiber** to the buildout of **data centers** here. And, you know, his his big line was there is, you know, there is no **dark GPU**. There are no **dark GPUs**. There was a **dark fiber**. You had to lay **fiber** and then, you know, it laid there dark and it wasn't used. If you put a **GPU** in the system in a **data center**, it gets fully utilized immediately. And so that's a very good sign, you know, in terms of, you know, demand meeting supply uh immediately. I mentioned this earlier, earnest growth should come for these companies like this is our expectation. Um, and if it doesn't, then they will probably be disrupted uh if they can't change. So **change management** again is the biggest reason why we see things um you know that that haven't sort of dramatically shifted yet. Um it's honestly to to me it's not the readiness of the technology itself. It's probably you know product buildout that needs to get built around the technologies. Uh and then **change management** and and putting it in production.
**David George**: 所以，相对于其他类别，**收入增长**以惊人的速度扩大。这张图表显示了**生成式AI**的**应用内收入**从2023年（当时几乎看不见）到现在增长的速度。这是我们之前展示过的一张幻灯片，它基本上比较了**云**、**公共软件公司**以及2025年新增的**净收入**。最右边是我喜欢看的部分，即**公共软件公司**在2025年增加了**460亿美元**的收入。如果你只计算**OpenAI**和**Anthropic**的运行率，它们就贡献了近一半。我认为如果你对2026年进行同样的比较，整个**公共软件行业**，包括**SAP**，这不仅仅是**SaaS**，包括**SAP**和老牌软件公司，我认为**AI公司**，也就是**模型公司**，将达到75%到80%的水平。所以，这一切发生的速度简直令人震惊。
Original English **David George**: So **revenue growth** has scaled at a staggering clip relative to other categories. Uh so this is just it shows how quickly **generative AI** uh in app revenue has grown uh from **23** where it was basically you know you can barely even see it on the page to to now and this is a slide that we've showed before but basically this compares the **clouds** um **public software companies** uh and then how much net new revenue gets added in **2025**. So the far right is what I like to look at, which is **public software companies** added **$46 billion of revenue in 2025**. If you just add up **OpenAI** and **Anthropic** on their on a run rate basis, they added almost half of that. And I think if you were to do that same comparison for **2026**, all of the entire **public software industry**, I mean **SAP**, this is not just **SAS**, like including **SAP**, um, and older software companies. I think the **AI companies**, the **model companies** will be something like **75 to 80%** as much. So, it's just staggering how quickly that has happened.
**David George**: 这些幻灯片，接下来的几张，都相当详细。它们展示了基于当前股价和分析师模型中隐含的**AI表现预期**。**高盛（Goldman Sachs）**估计，**AI建设**将带来**9万亿美元**的收入，如果假设**20%**的利润率和**22倍**的市盈率，这将转化为**35万亿美元**的新**市值**。目前已经提前实现了约**24万亿美元**的新**市值**。我们现在可以争论这是否全部归因于**AI**或其他大型科技公司的表现。但仍有大量的**市值**有待实现，如果你知道这些假设是正确的，你可能会获得上涨空间。
Original English **David George**: These are pretty detailed slides, these next couple ones. Um, these are sort of slides showing what is implicitly expected in **AI performance** based on where stock prices are today um in analyst models. So, **Goldman Sachs** estimates **9 trillion of revenue** flowing from the **buildout of AI**. So if you assume **20% margins** and a **22 times PE** that translates into **35 trillion of new market cap** um there's been about **24 trillion of new market cap** that's been pulled forward. Now we could debate if that's attributable all to **AI** or otherwise you know large tech performance. Um but there's still a lot of of sort of **market cap** to go get um where you could have upside if you know if those assumptions are right.
**David George**: 所以这是另一种尝试解决**AI回报问题**的方式。目前的估计是，到2030年，**超大规模云服务商**的累计**资本支出**将略低于**5万亿美元**。所以如果你粗略计算一下，要实现**10%**的**最低回报率**，在**4.8万亿**或近**5万亿美元**的投资上，到2030年，**AI年度收入**必须达到约**1万亿美元**。换句话说，**1万亿美元**大约占**全球GDP**的**1%**，才能产生**10%**的回报。这有可能发生。也有可能我们达不到这个目标。但我认为仅仅看到2030年是有限的。我认为这笔投资的回报可能会在更长的时间内发生，比如在2030年到2040年之间。但你知道，总的来说，大约是**1%的GDP**才能达到**10%最低回报率**的回报数字。
Original English **David George**: So this is another sort of cut or or few cuts on trying to address this sort of **AI AI payback question**. So current estimates put cumulative **hyperscaler capex** at a little less than **5 trillion by 2030**. So if you do napkin math on that to achieve a **10% hurdle rate** on that **4.8 trillion** or almost **5 trillion of investment** annual **AI revenue** would have to hit about a **trillion dollars by 2030**. So to put that into context, a **trillion dollars**, that would be about **1% of global GDP** to generate a **10% return**. It's possible that that happens. It's also possible we could fall a little short of that. But I think it's limiting just to look to **2030**. I think the the payback of this probably happens, you know, over a longer period of time, like, you know, between **2030** and **2040** as well. Um, but you know, framing it up, that's about, you know, **1%**, you know, **1% GDP** to get to to get to the payback number of a **10% hurdle rate**.
**David George**: 好了，街头巷尾的消息。我们开始做的事情是，我们构建了软件来追踪所有**AI**或所有**科技公共科技公司**在**财报电话会议**中讨论的内容，以及**AI**与我们早期和增长阶段业务的相关性。然后我们将其打包并分享给我们的**CEO**们。所以他们可以以一种简单易懂的格式了解，关于**AI**与**公共科技公司**相关的一切，我需要知道什么，它如何影响我的业务等等。所以我们在这里分享了我们追踪的很多内容。
Original English **David George**: All right. Heard it on the street. What we've started to do is we've sort of built software to track what all of the **AI** or what all of the **tech public technology companies** discuss in their **earnings calls** and mentions of **AI**, how relevant it is to our business at the early stage and, you know, the growth stage. And then we package it all up and we share it out to our **CEOs**. Um, so you know they can kind of have a simple digestible format of like what do I need to know about **AI** as it relates to **public technology companies** you know how does it how does it impact my business etc. And so we shared a bunch of the you know the stuff that we that we track in here.
**主持人**: 太棒了。在我们进入**私募**部分之前，有一个问题，当然，这次电话会议上的很多人都关心这个转型。但在我们谈到这个问题之前，请问我们如何衡量你们在2030年左右预测的**万亿美元AI收入**？相对于你们对**AI赋能收入**的估计，我们现在处于什么位置？我们离那个**万亿美元**的数字还有多远？
Original English **主持人**: Awesome. There was one uh question before we moved to the **private section** which a lot of folks of course on this call care about in this transition here. Um, but before we get to that, so where are we calibrating to your **trillion dollar in AI revenue**, you know, thereabouts in in **2030**? Where are we today relative to your guesstimate of **AI enabled revenue**? And and um how far off are we to that **trillion dollar number**?
**David George**: 我们可能在**500亿美元**左右。
Original English **David George**: We're probably in the I would probably guess in the **50 billion range**.
**主持人**: 是的。
Original English **主持人**: Yep.
**David George**: 把所有都加起来。没有完美的方法来做这件事。我的意思是，我知道一些大的投入。
Original English **David George**: Just add it all up. And there's no perfect way to do it. I mean, I know I know some of the big inputs.
**主持人**: 是的。
Original English **主持人**: Yeah.
**David George**: 更难追踪的是那些大型科技公司，它们到底有多少真正的**AI收入**？**云服务商**有时会给出**AI**带来的百分比提升，但我认为根据他们想要描绘的图景，他们可能会在这方面玩一些花样。所以，我认为这是一个粗略的估计，但你知道，**万亿美元**，我们现在可能在**500亿**，但它正在以每年**100%**以上的速度快速增长。
Original English **David George**: Uh to it. The harder stuff to track is honestly the the big tech companies like how much real **AI revenue** do they have? the cl the **clouds** can kind of they will from time to time give percentage uplift from **AI** but I think depending on how they want to paint the picture they can play games with that a little bit so you know I think it's I think it's I that's a that's a rough swag but like you know **trillion** we're probably at **50** but it's growing you know way way way faster than **100% year-over-year**.
**主持人**: 是的。那么可以说，这些收入，我的意思是，**ChatGPT**三年前就发布了，但大部分的增长实际上发生在过去一年半左右，如果我们非常慷慨地估计的话。这是一个公平的描述吗？
Original English **主持人**: Yep. And then arguably that revenue I mean **chat GPT** launched three years ago but substantially most of this traction happened in the last year and a halfish or so if we're being really generous too. Is that a fair characterization?
**David George**: 是的，没错。是的。
Original English **David George**: Yeah, that's right. Yeah.
**David George**: 而且你看，现在不仅仅是消费端的**ChatGPT**。**谷歌（Google）**有业务，**XAI**也有业务。
Original English **David George**: And look, you know, it's not just **CH GPT** now on the consumer side. You know, **Google** has a business, **XAI** has a business.
**David George**: 而且，你知道，在**B2B**端，不仅大型模型公司都有庞大的**API业务**，**云服务商**也有。所以很多模型销售也通过**云服务商**进行。
Original English **David George**: Um, and then, you know, on the **B2B side**, you know, not only do the big model companies all have large **API businesses**, but the **clouds** have it too. Um, and so a lot of the, you know, a lot of the sales that are model sales are also flowing through the **clouds**.
**主持人**: 是的。是的。是的。是的。好的，酷。我们有一些关于**私募公司**方面的问题，但我会让你先完成这一部分，然后我再提问。
Original English **主持人**: Yep. Yep. Yep. Yep. Okay, cool. We have, uh, some questions on on the **private company side**, but I'll let you get through the section and then I'll te you up for it.
### 私募市场与权力法则 **David George**: 好的，如果你想提问，我很乐意回答。我的意思是，我们谈论了很多东西，你知道，对我来说，**私募市场**方面的主要主题是，公司显然会保持**私有化**更长时间，但现在这是一个真正的**资产类别**。在过去20年里，**上市公司**的数量减少了一半。你知道，绝大多数收入超过**1亿美元**的公司都是**私有公司**，大约**86%**。所以，这是一个重大的转变。我们可以跳过几张幻灯片。我将简单谈谈**权力法则**，因为我认为这很有趣，也许是我们之前没有太多谈论过的一些新东西，但**价值**非常集中在**异类公司**中。所以**北美**和**欧洲独角兽**的总估值约为**5.5万亿美元**。如果你只看其中最大的10家，它们几乎占据了总价值的**40%**。这实际上自2020年以来翻了一番。所以**价值**正在集中在最大和最好的赢家身上。我正在实时计算，我们投资组合中的10家公司有四五六七家是其中之一。所以，我们有相当多的覆盖。
Original English **David George**: Well, you I I'm happy to go into questions if you want on it. I mean, this a lot of the stuff that we've talked about, you know, the big themes for me on the **private market side**, um, you know, companies are obviously staying **private longer**, but this is such a real **asset class** now. Over the last **20 years**, the number of **public companies** has been cut in half. Um, you know, the the vast majority of companies that are **hundred million dollar plus revenue companies** are private, something like **86%**. Um, so, you know, that's that's a major shift. Um, we could go you can skip a couple slides forward. Uh basically I'll talk a little bit about **power laws** because that's I think that's interesting and maybe some new stuff that we haven't talked about as much but value very much concentrates in the **outlier companies**. So the collective valuation of **North American** and **European unicorns** is about **$5.5 trillion**. The **10 largest ones** if you just take those um comprise almost **40% of the entire value**. So and that's actually doubled since **2020**. So sort of value you know sort of value is being uh concentrated in the biggest and best winners. I'm trying to count real time we have four five six seven of the 10 our portfolio companies of that 10 um so you know we we've got a a reasonable amount of coverage on that.
**David George**: **权力法则**也在**公共市场**中发生。自2019年以来，**大型股**的规模已经翻了三倍。所以构成**大型股公司**的标准自2019年以来实际上已经翻了三倍。我认为右边的图表非常有趣。这是我们做的新数据分析。如果你看看**标准普尔500指数（S&P 500）**上**普通公司**的寿命，这就是那张图表所显示的。这些数字代表着，一旦一家公司进入**标准普尔500指数**，它能在上面待多久？平均而言，如果你看过去50年，这个时间实际上下降了**40%**。它作为**标准普尔500指数**一部分的时间。所以对公司的**颠覆**发生得越来越快，我认为这是一个非常有趣的动态，也与我们所看到的市场中**技术驱动**的**变化速度**相符。
Original English **David George**: **Power laws** are happening in the **public markets** too. So **large cap** has tripled since **2019**. So what what constitutes a **large cap company** has actually tripled since **2019**. And I think this the chart on the right side is super interesting. This was new data analysis that we had done. Um if you look at the lifespan of an average company on the **S&P 500**, that's what that chart shows. That's what the numbers represent. The like once a company is on the **S&P 500**, how long is it on there? This is on average is actually if you look over the last **50 years** that has declined by **40%**. The amount of time it stays as part of the **S&P 500**. So disruption to companies happens faster and faster and faster which I think is a very interesting dynamic and and sort of matches what we're seeing you know just in terms of like **speed of change** in in the markets driven by **technology**.
**David George**: 所以我们总是喜欢在我们的业务中谈论**权力法则**。我没有选择这张幻灯片的标题。我理解所有的问题和担忧。所以**波动性洗白**在我们的圈子里也是一个大辩论。主要是围绕那些试图争论**私募市场**和**公共市场**优点的创始人。**Collison**兄弟做了一个采访，我想可能是**John**，他在采访中谈到了管理你的股价和避免波动性，你可以以一种有序的方式，随着时间的推移提高你的股价，这使得留住员工、招聘员工、管理士气等等变得更容易。所以我理解这些优点。我也认为成为一家**上市公司**有非常非常强大的优点。我认为在接下来的18个月里，我们将看到一些长期私有的大公司上市，这将是非常非常有趣的。我认为这也是一件好事。
Original English **David George**: So we always like to talk about **power laws** in our business too. I didn't choose the title of this slide. Uh, I recognize all of the, you know, questions and concerns about it. Um, so the the **volatility laundering** thing is is a is a big debate in our circles too. Um, mostly around founders who are trying to debate the merits of the **private markets** and the **public markets**. And you know, the **Collison's** did an interview where I think maybe it was **John** uh did an interview where he talked about, you know, managing your stock price and avoiding **volatility** and you can kind of orderly fashion, bring your stock price up over time and that makes it easier to retain employees, hire employees, manage morale, uh, etc., etc. Um, and so I get the merits of that. I I also think there are really really strong merits of being a **public company** as well. I think we're going to have a really really interesting **18 months** where we're going to have some of the big kind of private for a very long time companies that go public. Um and that's a good thing in my opinion too.
**David George**: 我们在这张图表中展示的一些内容只是**波动性**，以及随着时间的推移，市场中的**波动性**变得更加极端。对我来说，这也有点受**周期驱动**。我知道我们衡量的是**短期持续时间**。但两者都有优点，公司可以在**私募**方面变得更大。我们已经接受了这种新现实。我认为这对我们的业务来说是一个巨大的好处，因为我们可以随着时间的推移继续投资这些公司。但显然，你知道，成为一家**上市公司**并获得**流动性**也是一条途径，我们也非常关心这一点。
Original English **David George**: Um some of the stuff that we show in this chart is just **volatility** and the observation that over time **volatility** has gotten a little bit more extreme in the markets. To me this is a little bit cycle driven too. I know it's short short duration is sort of what we're measuring. Um but there's merits to both companies can get much larger in the **private side**. We have embraced that new reality. I think it's it's been a big benefit to our business in terms of getting getting to continue to invest in these companies over time. Uh but obviously you know there's there's a path of of being a **public company** and getting **liquidity** which we care a lot about too.
**主持人**: 太棒了。关于这一点，我有两个问题要向你提问。一个是关于**Databricks**，你能谈谈他们从一家**前AI公司**到现在完全嵌入**AI公司**的转型过程是怎样的吗？
Original English **主持人**: Awesome. that note um there were uh two questions uh I will queue up for you here uh one on **data bricks** can you talk about their transition uh from being a **preAI company** now to a fully embedded **AI company** and what that's been like
### Databricks的AI转型之路 **David George**: 是的，首先，我认为你需要，我提到了**Toby**，**Shopify**之所以拥抱**AI**，是因为**Toby**从高层领导，他以**AI**为中心运营业务，并且他绩效管理每个人，以确保他们做到这一点。**Ali**也是一样。**Ali**是这种独特的商业终结者。我谈论的是，我们称他为**技术终结者**。你需要有**商业直觉**，理解**AI**创造价值机会的重要性，然后你需要足够深入地了解技术，知道要构建什么。
Original English **David George**: yeah um first of all I think you need to you know I mentioned **Toby** like the reason **Shopify** has embraced it is because **Toby** has led from the top and he runs the business you know with **AI** at the center and and he he sort of performance manages everyone uh to you to make sure that they do that. **Ali** is the same. **Ali** is this unique blend of um sort of commercial kind of terminator. I talk about I mean we call him the **technical terminator**. You need to have a **commercial instinct** and understand the importance of the **value creation opportunity** and **AI** and then you need to actually be deep enough in the technology to know what to build.
**David George**: 所以碰巧的是，他们的**云数据仓库**，或者他们称之为**数据湖**，实际上是一个很好的地方，可以将你的数据放在那里，并在其之上运行**AI工作负载**。所以，你知道，这对他们来说是一个很好的起点，然后他们非常积极地迭代新的**AI产品**。他们有一个名为**Agent Bricks**的新产品，我们对此非常非常兴奋。我们认为它将对他们产生巨大且变革性的影响。所以，我想说这是一部分，然后他们拥有所有大型**AI原生公司**作为客户。所以，你知道，他们拥有技术，他们拥有低成本的技术。所以，你知道，我们在对公司进行投资时关注的一个重要因素是他们的客户是谁。我更喜欢我们投资组合公司的客户是那些**思维现代**的公司，比如**DoorDash**、**Instacart**、**Uber**，而不是那些非常非常守旧的公司，因为这意味着他们的技术受到了**聪明技术专家**的评估，并且他们选择了它。所以**尖端AI公司**都在**Databricks**之上进行构建，所以，你知道，他们有机会随着这些公司的扩展而成长，但这也是一个非常好的验证，证明他们拥有正确的技术。
Original English **David George**: And so it just so happens that their um their sort of **cloud data warehouse** or they call it the **data lake** um is actually a great way to have your data in a place to run **AI workloads** on top of it. So you know that was sort of a good place to be for them and then they've very aggressively iterated on new **AI products**. They have this uh new product called **agent bricks** which we're super super excited about. we think is going to be really big and transformative for them. So um I would say that's a piece of it and then they have the big **AI native companies** all as customers and so you know they have the technology they have the lowcost technology um and so you know a big thing that we look for when we're making investments in companies is who are their customers and I would far prefer the customers of our portfolio companies to be the modern thinking ones you know the **Door Dashes** of the world um you know the **Instacarts** of the world the **Ubers** of the world than the very very old school stodgy companies because that means that their technology is evaluated by smart technologists and they pick it and so the cutting edge **AI companies** are all building on top of **data bricks** uh and so you know they have the chance to grow with them as they scale uh but it's also a really good you know validator that they have the right technology
**主持人**: 我们就到这里结束。谢谢**David**带我们了解这些。
Original English **主持人**: we'll close out here thank you **David** for taking us through that