章节 1：AI 市场概览与财务效率新指标

📝 本节摘要：

David George 首先阐述了发布这份深度报告的初衷，指出 AI 市场目前“需求侧极其疯狂”，且行业仍处于 10-15 年长周期的极早期阶段。通过分析大量内部数据，他发现 2025 年迎来了收入增长的全面加速，顶级 AI 公司达到 1 亿美元收入里程碑的速度远超 SaaS 时代，且在销售和营销（S&M）上的支出更低。在财务指标上，尽管 AI 公司的毛利率（Gross Margins）因推理成本略低，但这被视为产品被高频使用的“荣誉勋章”。最后，他引入了一个关键的新效率指标——人均 ARR（ARR per FTE），数据显示顶级 AI 公司的这一指标高达 50-100 万美元，显著优于 SaaS 时代的 40 万美元基准。

[原文] [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

[译文] [David George]: 让我先从我认为这篇文章的主要结论开始说起，因为这是我们第一次做这种风格的内容。我们在团队内部产出了大量的工作成果和分析报告，就像是某种“副产品”一样。所以我们想，既然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与全世界分享呢？这就是这件事的缘起。

[原文] [David George]: 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

[译文] [David George]: 我做这份报告得出的主要结论是，你知道，AI 的需求侧非常疯狂。从我们的角度来看，AI 公司的实际采用率、增长和质量都非常令人鼓舞。公司开始更好地管理自己。我会向你们展示一些相关的统计数据，你知道最近在 X（推特）上有一些讨论，包括今天早上，大家在辩论到底发生了什么。但我会说，这一批公司比之前的任何一批公司都更令人印象深刻，部分原因是市场对它们产品的需求太高了。那是需求侧，供给侧目前也很健康。

[原文] [David George]: 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

[译文] [David George]: 最后，我的主要结论——也是让我对现状感到如此兴奋的原因——就是我们目前正处于这个产品周期的非常早期阶段。你知道，产品周期驱动着我们的业务。这些通常是 10 到 15 年的周期，而我们要现在才刚刚开始。

[原文] [David George]: 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

[译文] [David George]: 让我们深入探讨一下。我们投资覆盖了所有私募阶段。这张图展示了我们的活动情况，我们非常忙碌。投资跨越了所有垂直领域。在成长型投资方面，我们在 AI、推理应用（Infra Apps）以及 AD（航空航天/国防）领域最为活跃，但在其他垂直领域也非常活跃。

[原文] [David George]: 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

[译文] [David George]: 无论如何，来看一些数据。作为成长型投资团队，我们收集了海量数据，因为我们基本上看过了市场上每一家成长期公司，无论是作为被投公司还是潜在投资对象。我们有一个很棒的数据分析团队，做了一些分析。我觉得这些东西超级有趣，我们对此很狂热。对我来说，得出的主要结论是：2025 年是收入加速增长的一年。你知道，随着加息和科技股的回调，收入增速在 2022、2023 和 2024 年显然放缓了。但 2025 年扭转了这一趋势。

[原文] [David George]: 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

[译文] [David George]: 增长最快的 AI 公司达到 1 亿美元收入的速度，明显快于那个时代增长最快的 SaaS 公司。我想特别指出一件非常重要的事来解释为什么会这样，那就是因为终端客户的需求太强劲了，而且产品太具吸引力了。这并不是因为它们在销售和营销（Sales and Marketing）上花了更多的钱。实际上恰恰相反。那些增长最快的顶级 AI 公司并不是在销售和营销上花钱最多的，它们花的钱比当年的 SaaS 同行要少，但它们的增长速度却要快得多。

[原文] [David George]: 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

[译文] [David George]: 粗略地说，AI 公司的增长速度是非 AI 公司的 2.5 倍以上。这不应该太令人惊讶。最好的 AI 公司增长得非常非常快。当我们看到顶级 AI 表现者实现了 693% 的同比增长时，我们不得不反复核实了三遍数据，但这与我们的经验以及我们在被投公司中看到的轶事是相符的。

[原文] [David George]: This is the margin profile uh that we're seeing in the data set... 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

[译文] [David George]: 这是我们在数据集中看到的利润率概况……AI 公司的毛利率（Gross Margins）略差一些。你可能以前听我们谈过这个，但在某种程度上，我们觉得 AI 公司的低毛利率就像是一种“荣誉勋章”。因为如果低毛利率是由高推理成本（Inference Costs）导致的：第一，这意味着人们确实在使用 AI 功能；第二，我们相信这些推理成本随着时间的推移会降下来。所以奇怪的是，如果我们看到一个 AI 项目的推介，其毛利率超高，我们会有点怀疑，因为这可能意味着客户购买或使用的实际上并不是 AI 功能。

[原文] [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

[译文] [David George]: 我们要谈谈“人均 ARR”（ARR per FTE），这是我们开始关注的一个新指标，也是过去几天在 X 上引起大量关注和讨论的话题之一。ARR per FTE 某种程度上是衡量你公司整体运营效率的指标。所以它涵盖了你所有的成本。它不仅涵盖了销售和营销——这是我们在过去的分析中一直关注的效率指标——而且还涵盖了你的管理费用（Overhead）和研发费用（R&D）。

[原文] [David George]: 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

[译文] [David George]: 对于最好的 AI 公司来说，它们的人均产出大约是 50 万到 100 万美元。而在上一代 SaaS 时代的软件企业，其经验法则是大概 40 万美元。我要再次多说一点，之所以会出现这种情况，主要是因为对其产品的需求非常非常强劲，所以它们推向市场所需的资源更少。

[原文] [Host]: 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

[译文] [Host]: David，在我们进入这张幻灯片之前，也许可以快速澄清一下。你们是如何定义“AI 公司”的？是定义为 ChatGPT 之后的公司，还是某个特定时期成立的历史性 AI/ML 公司？

[原文] [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

[译文] [David George]: 对，是的，差不多就是“后 ChatGPT”时代。其中一些公司可能就在那个时间点前后成立，我们会给一点宽限期。但只要它们推向市场的第一个产品是 AI 原生（AI Native）产品，那就是我们的定义标准。

章节 2：前 AI 公司的转型挑战与商业模式演变

📝 本节摘要：

在本章节中，David George 深入探讨了“前 AI 时代”公司面临的严峻现实——“不适应即灭亡（Adapt or Die）”。他指出，这种适应不仅限于在前端产品中加入 AI 功能，更在于后端运营的彻底重构。他通过具体案例分享了编程领域的剧变：利用最新的 AI 编码工具，小团队能以 10 到 20 倍的速度完成开发，迫使创始人重新思考组织架构。此外，David 提出了“电力与血肉（Electricity vs. Blood）”的生动隐喻，并梳理了软件商业模式的演进路径：从早期的许可证模式（License），到 SaaS 时代的席位订阅（Seat-based），再到云时代的用量计费（Consumption-based），最终将演变为最具颠覆性的“基于结果（Outcome-based）”的商业模式。

[原文] [Host]: 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

[译文] [Host]: 明白了。或许这是一个很好的切入点，或者你可以留到稍后再谈，但我认为很多人试图理解的一个问题是，从 SaaS 时代的公司过渡到 AI 时代的公司，这种变化的幅度以及预期的收入和增长究竟有多大。你已经谈了一些关于收入规模等方面的内容，但是那些非 AI 原生的公司会怎么样呢？它们在与 AI 原生公司的竞争中会很艰难吗？它们都在转型吗？我们会看到更多的淘汰吗？人们应该如何看待他们历史上的投资组合？

[原文] [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

[译文] [David George]: 是的。我们处理投资组合的方式是，你知道，你需要适应 AI 时代，否则就是死路一条。这既包括前端也包括后端。在前端，你需要思考如何将 AI 原生地融入你的产品中，而不仅仅是在现有的工作流中生硬地加一个聊天机器人应用，而是要用 AI 重新构想产品的意义，并积极地颠覆自己和做出改变。

[原文] [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

[译文] [David George]: 然后在后端，你知道，我分享了一些关于公司运营效率的数据。这方面也将发生改变。所以你需要让你所有的开发人员全面采用最新的编码模型，并在组织内部的每一个不同职能部门中全面推广最新的工具。

[原文] [David George]: 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

[译文] [David George]: 目前为止，采纳率最高的是编程领域，这也是我们看到最大飞跃的地方。在过去两个月，或者说这一个半月里，这方面发生了重大的、重大的变化。你知道 Andrej Karpathy 写过关于这方面的文章。我和我们的一家属于“前 AI 时代”公司的创始人叙旧，这是一位非常有 AI 思维的创始人，他对 AI 钻研很深，所以他正在调整他的公司。

[原文] [David George]: 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

[译文] [David George]: 我们这周聊天时，他告诉我他对他们的一款产品感到沮丧，所以他只带了两个在 AI 方面造诣很深的工程师，让他们利用 Cloud Code、Codex 和 Cursor 从头开始构建它，并给了他们在编码工具上的无限预算。他说，他认为现在的进展速度比以前快了 10 到 20 倍。而与之相关的账单费用实际上已经高到足以让他重新思考整个组织架构应该是什么样子的。

[原文] [David George]: 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

[译文] [David George]: 结论基本上是，我需要我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。但这对于团队设计实际上意味着什么？在这个过程中，产品从哪里开始？甚至设计从哪里开始？感觉 12 月就像是代码领域的一个转折点。你知道，在接下来的 12 个月里，这种冲击将会到来，它要么冲击并在公司中扎根，要么那些没能适应的公司将会比同行发展得慢得多。

[原文] [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

[译文] [David George]: 所以，关于那些前 AI 时代的公司，你知道，必须适应。我们还有另一个例子，一家前 AI 时代的软件公司，其 CEO 已经完全“服下了 AI 的红色药丸”（意指彻底觉醒/信奉），他说我们要变成一个 AI 产品。比如我们要发布这种概念：你的员工现在就是你的 AI 智能体（Agents）。你拥有多少个智能体？这些就是他正在谈论的事情。

[原文] [David George]: 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

[译文] [David George]: 我们还有另一个非常极端的例子，他说我现在会问这个问题：对于我们需要完成的每一项任务，我是可以用“电力”来完成它，还是必须用“血肉”来完成？这就发生在我们公司身上的一种极端的思维转变。所以我很高兴看到我们的前 AI 时代投资组合公司行动非常迅速，努力适应，但它们确实需要在前端产品和后端运营管理上都适应这个新时代。

[原文] [Host]: 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

[译文] [Host]: 完全同意。也许在战术上，对于每一个投资组合，你都必须逐行检查公司情况，以了解创始人在这个旅程中处于什么位置，以及他们从底层开始实施了多少变革。正如你所说的“炸毁”现有的运营模式，这在后 AI（Post-AI）公司中也正在发生。越来越多的人每六个月就会审视一次，感觉就像：“我们六个月前建立的东西，基于今天可用的技术，可以得到巨大的改进。”如果这种更新速度持续发生，前 AI 时代的公司就需要不断地以 10 倍的速度去追赶那个节点。

[原文] [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

[译文] [David George]: 是的。对于前 AI 时代公司来说，好消息是商业模式的演变仍处于早期阶段。对你来说最具破坏性的事情，莫过于技术和产品转型的同时，还伴随着商业模式的转型。我把商业模式看作一个谱系——为了简单起见，我就以 B2B 企业服务为例——这个谱系基本上是从“许可证（Licenses）”开始的，那是前 SaaS 时代的许可证加维护费的模式；然后是 SaaS 和订阅制，通常是“基于席位（Seat-based）”的，那是一个巨大的创新，非常具有颠覆性。虽然架构和云交付具有颠覆性，但商业模式的变革同样极具破坏力，看看 Adobe 在经历那次转型时发生了什么就知道了。

[原文] [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

[译文] [David George]: 然后经历了向“基于消费（Consumption-based）”即基于使用量的转型，这是云服务的收费方式。许多基于交易量或任务类型的业务已经适应了这一点，从基于席位转向了基于消费。然后，下一个迭代将是“基于结果（Outcome-based）”。也就是当你执行一项任务时，理想情况下，当你成功完成一项任务时，你是基于该任务的成功完成而获得报酬。

[原文] [David George]: 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 for sure

[译文] [David George]: 目前真正可能实现这一点的领域可能只有客户支持和客户成功，因为你可以客观地衡量某件事的解决情况。但我们会看到模型能力的发展将会带来什么，如果除了客户支持之外的其他职能部门也能衡量这类结果，那将对在位者（Incumbents）构成巨大的颠覆性力量。老实说，如果公司的构成也发生变化，从席位制到消费制的转变可能已经是一个巨大的颠覆，但下一个（基于结果的模式）肯定才是真正巨大的变革。

章节 3：投资组合深度复盘：法律、医疗与企业服务

📝 本节摘要：

本章节重点分析了 a16z 投资组合中的几家明星公司，以验证 AI 收入的可持续性（Sustainability）。David George 指出，他们通过深入考察“收入留存率”和“产品参与度”来评估公司质量。

* Harvey（法律）：律师在产品中的停留时间翻倍，证明推理模型（Reasoning Models）与法律工作高度契合。

* Abridge（医疗）：被医生称为“值得信赖的副手”，在用户量激增的同时，保持了极高的用户参与度。

* ElevenLabs（语音）：语音成为 AI 工具的核心，展现了惊人的使用量增长。

* Navan（差旅）：作为成功转型的案例，AI 现已处理 50% 的复杂差旅变更，推动毛利率提升了 20 个百分点。

* Flock Safety（安防）：其 ROI 是“解决犯罪”，每年协助解决 70 万起案件，显著提升了警员的结案率。

[原文] [David George]: 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 that's that's very good that's very good

[译文] [David George]: 这是一个很好的过渡，让我们进入下一节，看看这些公司实际上在做什么。我们最喜欢的话题——在这个 AI 遇见律师的新世界里，律师的数量不降反增，而不是相反。我喜欢那条推文，不知道你这周早些时候看到没有，一位公司律师被引用说，大语言模型（LLMs）实际上增加了我的工作量，因为现在每个客户都觉得自己是律师了。这正好过渡到下一张幻灯片——Harvey。这很好，这非常好。

[原文] [David George]: 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

[译文] [David George]: Harvey 非常棒。好吧，这对我是个真正的考验，因为你知道我喜欢谈论我们的被投公司，但我应该快速过一遍这部分，因为我认为大家——希望大家——都已经知道这些公司了。关于这一点的核心结论是，你知道我们寻找的一个重点，也是有人提出的一个问题：你怎么知道收入是可持续的？就像这些公司增长得真的非常非常快，但这会是昙花一现吗？

[原文] [David George]: 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

[译文] [David George]: 我们强迫自己做的一件大事，就是确保我们非常、非常深入地研究收入、收入留存（Retention）、续约率，以及产品参与度（Product Engagement）——实际上就是花费的时间。人们多久登录一次平台？当他们在平台里时，他们的活动是什么样的？你在这个页面上看到的是，随着他们在过去几年构建了更好的产品，加上推理模型（Reasoning Models）的改进——事实证明，律师工作和逻辑推理是密不可分的——用户在产品中花费的时间大约是之前的两倍。

[原文] [David George]: 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 Let's go to uh a bridge Oh unless you want to keep talking about lawyer

[译文] [David George]: 所以事实证明，AI 真的非常擅长律师工作。再说一次，律师并没有变少，但我认为 AI 在这方面非常出色，我认为律师们的效率正在大大提高。关于 Harvey 最重要的一点是，用户在产品上花费了大量时间，并从中获得了巨大价值，这很棒。让我们谈谈 Abridge。噢，除非你想继续聊律师的话题。

[原文] [Host]: 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 That was a tell 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 It's it's it's 10:25 a.m I have not had any bourbons today

[译文] [Host]: 噢，我只是想评论一下。在我认识你的这七年里，除了此时此刻，我从来没听出来你是肯塔基州人。你说“Lawyer”（律师）这个词的方式暴露了你。

[David George]: 我的……我的词汇表里有几个那样的词，我改不了。你知道，我妻子总是开玩笑，她说你一回家，喝上一杯波本威士忌，说话就变回你 18 岁时的样子了。说到律师时，肯塔基口音就跑出来了。现在是上午 10:25，我今天可还没喝波本呢。

[原文] [David George]: So um important distinction 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

[译文] [David George]: 所以，重要的区别。这是重要的区别。是的，没错。所以，Abridge，Abridge 是另一个超级、超级令人兴奋的公司。我是说，医生们对能使用 Abridge 赞不绝口，它节省了他们大量的时间，让他们的生活变得多么美好。你知道，我们要访谈的一位客户将其描述为一位“值得信赖的副手”（Trusted Deputy）。

[原文] [David George]: 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

[译文] [David George]: 右边的图表展示了我们关注的东西：蓝线显示用户的增长，绿线显示这些用户的参与度。当用户数量大规模增长时，你会有点担心新增用户的参与度是否会下降。但在 Abridge 的案例中，使用该产品的人群拥有极高的使用率，而且即使在他们增加了海量用户的情况下，这一指标实际上保持稳定甚至略有增长。

[原文] [David George]: 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 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

[译文] [David George]: 所以这些只是我们寻找的数据类型的例子，以确保我们对这些公司产生的收入是可持续的感到有信心。再次强调，这些公司的增长速度比任何前代公司都要快，但这非常可持续。因为这是高参与度，高留存率。这对我们至关重要。Eleven Labs 也是一样。语音是许多新 AI 工具的核心。你知道我之前谈到了 B2B 端的客户支持，但你知道还有那么多其他的个人工具、商业工具，都是从语音开始的。

[原文] [David George]: 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 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

[译文] [David George]: 在这张图表上，我最喜欢看的是使用量的增长，简直令人震惊。这家公司增长非常快，也是那些运营效率极高的公司的绝佳典范。所以 Eleven Labs 真的很棒。接下来是 Navan（原文口误为 Non）。这是另一个例子，一个不同的例子。这也是我之前描述情况的一个好例子。他们很早就应对了这种 AI 转型，他们花了很大力气确保能够充分利用 AI 的能力来改善业务。

[原文] [David George]: 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

[译文] [David George]: 所以今天你在他们的业务中看到这一点的最大方式是在“解决方案的处理”上。你知道，他们的一部分业务是有客服人员（Agents）处理差旅预订或行程变更。现在 AI 正在处理 50% 的用户互动。而且这都是很难的事情，比如差旅预订，行程变更。这不像“告诉我银行余额”那么简单，你知道，这是 AI 现在能够处理的复杂工作流。

[原文] [David George]: 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

[译文] [David George]: 这在业务上的体现是，过去三年毛利率（Gross Margins）提升了 20 个百分点。这就是非凡的影响力。所以正如我所说，“不适应即灭亡”。他们的竞争对手没有在适应，他们非常老派。当他们在原地踏步、用旧方法做事时，Navan 现在的毛利率比这些在位者高出了 20 个百分点。

[原文] [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

[译文] [David George]: 然后是 Flock（Flock Safety），Flock 正在做绝对不可思议的工作。我谈论过他们很多次。这是我们在投资组合中看到的最具说服力的客户价值主张，因为他们的投资回报率（ROI）是“解决犯罪”。我们之前提到过 10% 的统计数据。Flock 每年解决 700,000 起犯罪案件。右边的数据点显示，在部署了 Flock 的地方，每位警员结案的犯罪数量增加了近 10%。所以这对社区有巨大的影响。显然，他们有一个与之相匹配的伟大的商业和财务模型。但他们的产品所带来的影响是非凡的。

章节 4：企业级落地现状与公开市场表现

📝 本节摘要：

在本章中，David George 揭示了财富 500 强企业在 AI 转型中的真实困境：尽管 CEO 们高喊“不适应即灭亡”，但实际落地深受“变革管理（Change Management）”的阻碍。他列举了 Chime（客服成本降低 60%）和 Rocket Mortgage（节省 110 万工时）等成功案例，预言未来五年将是企业生产力分化的关键期。随后，视角转向公开市场，David 指出 AI 赢家贡献了标普 500 指数近 80% 的回报，且这种上涨由实打实的盈利增长（EPS Growth）驱动，而非互联网泡沫式的炒作。他最后通过“增长-利润”矩阵分析，强调市场正在极度奖励那些兼具高增长与高利润的“史上最佳商业模式”。

[原文] [Host]: 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

[译文] [Host]: 值得一提的是，有一个关于基准的问题。比如，如果你考虑像金融这样的传统行业，并以摩根大通（JP Morgan）作为基准，你会如何校准财富 500 强在 AI 采用方面的表现？也许我可以把 Xavier 提到的那个问题叠加在一起，你知道去年年初 MIT 有一项关于企业采用情况的研究，他们测量了各种各样奇怪的东西。也许你可以多谈谈你从财富 500 强 CEO 那里听到的是什么，以及具体情况如何。

[原文] [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 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

[译文] [David George]: 是的。我们要说的是，我们从财富 500 强 CEO 那里听到的——也许这就是连接这两点的关键——我们听到的是：“我们必须适应。我们渴望了解我们需要什么 AI 工具。你知道，我们准备好改变了。我们的业务将全面铺开，你知道我们准备好了，我们要成为 AI 公司。”但这与实际发生的情况截然不同。我认为那种心态与业务中实际变革之间最大的脱节，仅仅是因为“变革管理（Change Management）”太难了。

[原文] [David George]: 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

[译文] [David George]: 你知道，仅仅是让人们使用 AI 助手来帮助他们更好地完成工作就已经够难了。编程可能是最容易让人们理解的领域。客户支持也是，因为它明显更好、更快、更便宜。但在实际的企业综合管理、改变业务流程方面，变革管理极其困难。所以我对那些暗示“进展比预期慢”的传闻并不感到惊讶。但对于那些完全拥抱它并且真正知道该怎么做的最优秀的公司来说，它已经产生了巨大的业务影响。

[原文] [David George]: 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 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

[译文] [David George]: 所以我认为在接下来的五年里将会有一场清算，看看谁能真正拥抱变革，推行变革管理，采用最好的产品，以及那些做不到的公司。我认为生产力将会出现巨大的差异。我们在后面的幻灯片里有一些图表，我可以谈谈。关于生产力提升、增长等方面的预期很高，我认为一批公司将会实现这些目标，而那些没做到的公司将处于巨大的劣势。

[原文] [David George]: 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

[译文] [David George]: Chime 说他们的支持成本降低了 60%。Rocket Mortgage 说他们在承保业务上节省了 110 万小时，同比增长了 6 倍，这相当于每年节省 4000 万美元的运行费率（Run Rate）。所以我们在非 AI 业务中看到了一些局部的成功案例。我认为接下来的 12 个月将是非常值得关注的一年。你会看到更多的案例，但会有公司能搞定它，也会有公司搞不定。

[原文] [Host]: 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

[译文] [Host]: 完全同意。而且很多大公司必须调整他们的业务以适应 AI。就像有一种版本只是使用聊天机器人，对吧？那实际上能给你带来多少生产力提升？可能并不多。但如果你必须彻底颠覆你的系统信息和后端以准备好迎接 AI，这其中很多工作可能是隐性的，目前正在建设中，以便最终看到与之相关的成果。

[原文] [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

[译文] [David George]: AI 赢家正在驱动公开市场。它们贡献了标普 500 指数近 80% 的回报。所以这是驱动经济和股票市场的主要因素。公开市场表现非常好。尽管价格在上涨——或者像过去几天那样有些波动——但总体表现良好，而且基本面非常稳健。我会说，泡沫的证据微乎其微。

[原文] [David George]: 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

[译文] [David George]: 近期的表现是由每股收益（EPS）增长驱动的。倍数（Multiples）略有收缩，如果你是一家 SaaS 公司，在过去几天或几周内可能收缩得不止一点点。但我会说，市场总体上是根据收益、收益以及收益增长来定价的。所以收益倍数虽然高于平均水平，但远未达到互联网泡沫时期的水平。你可以看看图表，看看我们处于什么位置，这让我感到一些安慰。

[原文] [David George]: 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

[译文] [David George]: 再次强调，作为市场最大驱动力的那些公司的收益，总体上我觉得相当稳健。这些公司很优秀。所以这些公司的健康状况相当好。估值确实比过去平均水平高，但并没有让人感觉超级惊慌。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商业模式。如果你从长远来看，它们展示出的利润率改善表明这可能是真的。

[原文] [David George]: 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

[译文] [David George]: 这就在页面左侧展示了。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 年那个时期形成了巨大的反差，显然也与根据利润率调整后的互联网泡沫时期形成了巨大反差。倍数并没有那么高。

[原文] [David George]: 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

[译文] [David George]: 也许我会让你关注右侧，如果你画一个四象限图：低增长、高增长、低利润、高利润，并把这些类型的公司配对。这张图表显示了它们的交易情况。最好的公司享有溢价。你在右边两栏看到的是“高增长高利润”公司，以及“高增长低利润”公司。糟糕的象限显然是“低增长低利润”，那些公司不应该得到奖励，它们的交易价格应该很低，事实上也是如此。

[原文] [David George]: 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

[译文] [David George]: 但是那些高增长且高利润的公司——以及那些高增长低利润但拥有良好单位经济效益且正朝着利润率规模化发展的公司——它们应该得到奖励。所以我认为这是好事。如果你没有高增长，即使你是高利润，处境也会很艰难。这并不令人惊讶。我在过去以多种形式谈过这一点。归根结底，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得到了更多的奖励。但如果你既有高增长又有高利润，你就是那些伟大的企业之一，将会得到非常丰厚的回报。

章节 5：基础设施建设、资本支出（Capex）与回报率

📝 本节摘要：

本章节聚焦于 AI 基础设施的供给侧。David George 承认当前的资本支出（Capex）规模巨大且集中，具有固有风险，但他强调这与“互联网泡沫”有本质区别：本次建设主要由历史上最盈利的科技巨头（如 Meta、Microsoft）以自有现金流资助，而非依赖投机性债务。尽管 Oracle 等公司开始通过债务融资加大赌注，引发了对其信用违约互换（CDS）的关注，但整体风险可控。他还提出了两个关键论点：一是与 Azure 花了 7 年才达到如今 AI 一年的收入规模相比，AI 的回报速度极快；二是反驳了产能过剩的担忧，引用 Gavin Baker 的观点指出“没有暗 GPU（Dark GPUs）”，即所有上架的算力都能被立即填满，这与当年的“暗光纤（Dark Fiber）”闲置现象截然不同。

[原文] [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

[译文] [David George]: 就像我们现在要谈论资本支出（Capex）建设的供给侧一样。建设规模是巨大的，投资的规模和集中度仅仅因为其体量巨大就具有内在风险。虽然它具有一些泡沫特征，但我认为其潜在的基本面与之前的泡沫几乎没有相似之处。投资主要由历史上盈利能力极强的公司提供资金，就像我之前谈到的那些非常赚钱的公司。债务已经开始进入这一图景，周期时间已经加速，这是好事。但在模型方面，我们正在密切监控训练成本以及整个方程的经济性。目前看来相当不错，大型模型公司在训练模型上的支出回报相当不错，但我们正在密切监控这一点。

[原文] [David George]: 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 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

[译文] [David George]: 最重要的是，我们认为 AI 将成为我职业生涯中见过的最大的“模型破坏者”（Model Buster）。我写过关于“模型破坏者”的文章，所以我不会花太多时间详述，但它们是指那些增长速度比任何人在任何情境下建模预测的都要快、持续时间都要长的公司。iPhone 就是这方面的经典案例。你知道，如果你拿 iPhone 发布前到 5 年后、4 年后的市场共识模型来看，共识模型对 Apple 业绩的预测在四年间偏差了 3 倍。而且这可是当时世界上被研究得最透彻的公司。

[原文] [David George]: 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

[译文] [David George]: 所以你知道，我认为同样的事情将会在 AI 的许多细分领域发生，其表现将大规模地超出任何电子表格中的预期。总体而言，科技行业本身就是一种“模型破坏者”，但自 2010 年以来，科技行业以其前所未有的速度和规模交付了高利润率的收入。所以它往往在早期看起来很贵，但随后会反复带来上行的惊喜，并创造出远超其增长所需资本的价值。我没有理由认为这次会有什么不同。

[原文] [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

[译文] [David George]: 所以相对于互联网泡沫时期，现在的资本支出实际上是由现金流支持的，而且资本支出占收入的百分比要低得多。这是一个简单的标题结论。我们可以跳转到下一张幻灯片，但我对这种资本支出动态的感觉要好得多。显然，超大规模云厂商（Hyperscalers）是承受资本支出冲击最大的一方，但这即使对我们的被投公司来说也是一件非常好的事情。这太棒了，我完全支持。尽可能多地进行基础设施建设，尽可能多地提供用于训练和推理的供应，这是一件非常好的事情。再次强调，承受这部分冲击最大的公司，正是我们之前谈到的那些有史以来最好的企业。

[原文] [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

[译文] [David George]: 所以我们开始监控的一件事是债务被引入到这个等式中。你无法仅靠现金流来为所有预测的未来资本支出融资，所以我们开始看到一些债务。我们在密切关注这一点。我们通常不会重仓投资那些有债务风险的公司。我对页面上这一批用现金流融资、持续产生现金流甚至使用债务的公司——比如 Meta、Microsoft、AWS、Nvidia 作为交易对手——感到放心吗？当然，我对此感觉很好。我提到了那些让我感觉很好的公司，但我并不是对所有公司都感觉良好。所以并非所有的交易对手都是一样的。

[原文] [David George]: 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

[译文] [David George]: 你知道，我们开始看到私人信贷（Private Credit）更多地参与到数据中心的建设中。再次提到一家被广泛报道的公司，它正在下注转型成为一家云公司，那就是 Oracle。你知道它们一直以来都在盈利，并且一直在回购股份。但它们承诺投入的资本数额非常巨大。这是一个巨大的赌注。它们在未来很多年里将处于现金流为负的状态。如果你关注这周围的一些舆论，比如它们的信用违约互换（Credit Default Swaps）成本在过去三个月里上升到了大约 2%。所以我们在关注这类事情。再次强调，这对我们的被投公司来说通常是好事，但我们要确保整个市场也是健康的。

[原文] [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

[译文] [David George]: 这张幻灯片展示了 AI 变革速度的量级。我们将 AI 的建设和 AI 收入与 Azure 的发展历程进行了对比。相对于云服务，AI 收入正在迎头赶上。Azure 花了 7 年时间才达到（目前）一年的 AI 收入水平。这只是 Microsoft 报告的数据，我认为这是一个很酷的方式来构建框架，展示这发生得有多快。你知道，那次建设花了很长时间。同样，这次 AI 建设发生得要快得多。但是 Azure 花了 10 年时间其收入才超过其资本支出（Capex）。我认为这种比率或等式在 AI 领域将会发生得快得多。

[原文] [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

[译文] [David George]: 我们不需要在折旧问题上过于钻牛角尖，但这是金融圈里热议的话题之一，特别是关于芯片折旧的假设。我会说旧款 GPU 的定价非常稳固。早期用户坚持使用模型的时间会长一点，但后来的用户会迅速切换到新东西上。这是右侧，有点像模型侧的情况。在芯片侧，7 到 8 年前的 TPU——Google 实际上披露了这一点——7 到 8 年前的 TPU 实际上拥有 100% 的利用率。我们非常密切地监控二级市场的芯片价格，A100 和 H100 的租赁价格实际上保持得非常非常好。所以老一代的芯片仍然被充分利用。所以我目前并不担心这个问题，尽管它引起了很多讨论，而且你知道有些危言耸听者喜欢谈论系统中的风险。

[原文] [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

[译文] [David George]: 好吧，来点积极的东西。我们一直谈论的一件大事是这个悖论，对吧？随着 Token 变得更便宜，消费量却上升了。所有超大规模云厂商都报告需求远超供应。当他们这么说时，我是相信的。你知道，我在我们的 AI 峰会上采访了我的朋友 Gavin Baker，他将互联网的建设和铺设光纤与现在的数据中心建设进行了比较。他的金句是：“没有暗 GPU（Dark GPU）。”根本就没有暗 GPU 这回事。当年有“暗光纤（Dark Fiber）”。你铺设了光纤，然后你知道它就那样闲置着（Dark），没有被使用。如果你现在把一个 GPU 放入数据中心的系统中，它会立即被完全利用。所以这是一个非常好的迹象，意味着需求能够立即被供应满足。

章节 6：未来万亿级收入预测与一二级市场动态

📝 本节摘要：

最后一章中，David George 展望了 AI 行业的宏观未来。他引用高盛及内部模型指出，为了证明当前巨额资本支出的合理性，到 2030 年 AI 行业需产生约 1 万亿美元的年收入（约占全球 GDP 的 1%），而目前这一数字估计在 500 亿美元左右，增长空间巨大。

视角转向市场结构时，他强调了“幂次定律（Power Laws）”的统治力：在私募市场，前 10 大独角兽占据了近 40% 的总价值。同时，标普 500 企业的平均寿命缩短了 40%，表明技术颠覆正在加速。

最后，David 以 Databricks 为例，分析了其 CEO Ali Ghodsi 作为“技术终结者（Technical Terminator）”的领导力，以及通过服务最前沿的 AI 原生客户来验证自身技术实力的策略。

[原文] [David George]: 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... 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

[译文] [David George]: 高盛估计，AI 建设将带来 9 万亿美元的收入流。如果你假设 20% 的利润率和 22 倍的市盈率（PE），这转化为 35 万亿美元的新增市值。目前已经有大约 24 万亿美元的新增市值被提前实现了……目前的估计认为，到 2030 年，超大规模云厂商的累计资本支出将略低于 5 万亿美元。所以如果你在餐巾纸上算一下，要在那 4.8 万亿或近 5 万亿美元的投资上实现 10% 的最低预期回报率（Hurdle Rate），到 2030 年年度 AI 收入就必须达到约 1 万亿美元。为了把这个背景讲清楚，1 万亿美元大约是全球 GDP 的 1%，这样才能产生 10% 的回报。

[原文] [Host]: 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

[译文] [Host]: 那么我们现在处于什么位置？相对于你预测的 2030 年左右达到 1 万亿美元的 AI 收入，我们今天的 AI 赋能收入大约是多少？我们离那个 1 万亿美元的数字还有多远？

[原文] [David George]: We're probably in the I would probably guess in the 50 billion range... but it's growing you know way way way faster than 100% year-over-year

[译文] [David George]: 我们可能处于——我大概猜是在 500 亿美元的范围内……但它的增长速度，你知道，比年同比 100% 的速度要快得多得多。

[原文] [David George]: 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%

[译文] [David George]: 我的意思是，我们谈论了很多内容，对我来说私募市场的一个大主题是，显然公司保持私有状态的时间变长了，但这现在确实成了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的，比例大概是 86%。

[原文] [David George]: 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

[译文] [David George]: 基本上我会谈一点关于“幂次定律（Power Laws）”，因为我觉得这很有趣，也许是我们还没怎么深入谈论的新东西，但价值非常集中在那些异常值（Outlier）公司身上。北美和欧洲独角兽企业的总估值约为 5.5 万亿美元。如果你只看最大的 10 家，它们几乎占据了总价值的 40%。而且自 2020 年以来，这个比例实际上翻了一番。所以价值，你知道，价值正集中在最大和最好的赢家手中。

[原文] [David George]: If you look at the lifespan of an average company on the S&P 500 that's what that chart shows... 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

[译文] [David George]: 如果你看一下标普 500 指数中一家普通公司的寿命，这就是图表所显示的……如果你回顾过去 50 年，这个平均寿命实际上已经下降了 40%。即它作为标普 500 指数成分股停留的时间。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。

[原文] [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

[译文] [David George]: 我们在这个行业里也总是喜欢谈论幂次定律。我没有选这张幻灯片的标题。我意识到了关于它的所有问题和担忧。关于“波动性清洗（Volatility Laundering）”的说法在我们的圈子里也是一个很大的争论。主要是围绕那些试图辩论私募市场和公开市场优劣的创始人。你知道 Collison 兄弟（Stripe 创始人）做过一个采访，我想可能是 John 做的，他谈到了管理你的股价和避免波动性，你可以某种程度上以有序的方式随着时间的推移提升你的股价。

[原文] [Host]: 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

[译文] [Host]: 有一个关于 Databricks 的问题，你能谈谈他们从一家前 AI 公司到现在完全嵌入 AI 的公司的转型吗？那是什么样的？

[原文] [David George]: 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 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

[译文] [David George]: Ali 也是一样。Ali 是那种独特的混合体，某种商业类型的终结者，我的意思是我们称他为“技术终结者（Technical Terminator）”。你需要有商业直觉，理解价值创造机会和 AI 的重要性，然后你还需要在技术上有足够的深度，知道该构建什么。恰好他们的云数据仓库——或者他们称之为数据湖——实际上是一个让你的数据驻留并在其上运行 AI 工作负载的绝佳场所。

[原文] [David George]: 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

[译文] [David George]: 所以，我们在投资公司时寻找的一件大事就是：他们的客户是谁？我更希望我们被投公司的客户是那些具有现代思维的公司，比如 DoorDash、Instacart、Uber，而不是那些非常非常老派、古板的公司。因为这意味着他们的技术是由聪明的技术人员评估并选中的。而最前沿的 AI 公司都在 Databricks 之上构建，所以你知道，随着这些客户规模的扩大，Databricks 有机会与它们一起成长，但这同时也是一个非常好的验证器，证明他们拥有正确的技术。