为何收购企业比向企业销售 AI 更有优势：Long Lake 的 Prathik Naidu

简要说明Brief Description

Prathik Naidu 介绍了 Long Lake 收购服务型企业的方法、将 AI 融入其运营的方式，以及如何将部署和节省的时间转化为更优质的服务和更好的业务成果。Prathik Naidu explains Long Lake’s approach to acquiring services businesses, embedding AI into their operations, and turning deployment and time savings into better service and business results.

目录Table of Contents

• 简介
  Intro
• 为什么将 AI 部署到企业中很困难
  Why deploying AI into businesses is hard
• 为什么信任与品牌能够胜出
  Why trust and brand win
• 软件与服务的经济学对比
  Software vs service economics
• 长湖模型
  The Long Lake model
• 选择购买什么
  Choosing what to buy
• 结果
  Results

简介Intro

大家好，我叫 Pratik Naidu，是 Long Lake 的创始合伙人。今天我很高兴能进一步聊聊，为什么收购企业是一种将 AI 部署到现实世界的独特策略。先从一个大家可能都很熟悉的经历说起。Hey everyone. My name is Pratik Naidu. I'm a founding partner at Long Lake and today I'm excited to talk a little bit more about why acquiring businesses is a unique strategy to deploy AI into the real world. So to start out very familiar experience to many of you.

假设你正在打造一款 AI 产品，现在想把它卖给企业客户。那么，该从哪里入手呢？你发现了一个痛点，针对它开发了一个智能体，而且确实能用，但目前只适用于一个非常狭窄的使用场景。Let's say you're building an AI product and now you're trying to sell it into the enterprise. Well, where do you start? You found a pain. You built an agent for it and it works, but for one very narrow use case.

这是一个很好的起点。但现在，你得真正把它投入生产环境。这可能需要应对安全审查、内部协调，以及与已有 20 年历史的系统集成。这需要付出大量艰苦的工作，而这通常是将 AI 部署到企业中的第一道难关。It's a great starting point. But now you have to actually get it into production. And that might involve dealing with security review, internal coordination, integrations with 20-year-old systems. It's a lot of hard work and this is typically the first wall in order to deploy AI into an enterprise.

但一旦你克服了这一点，接下来就得让团队用起来。But once you get past this, now you have to get the team to use it.

因此，通常你会不断迭代产品界面、参加培训，并收集用户反馈。接下来常见的一个问题是：究竟该由谁来推动用户采用？是你这个 AI 供应商，还是与你合作的客户？然后，假设你已经完成了这些工作，现在正想弄清楚，如何才能从刚部署的智能体中获得收入。So you typically will iterate on the product interface, go through trainings, get feedback from users. And there's a typical question that comes up, which is whose job is it to actually drive up that adoption? Is it yours as the AI vendor or is it the client that you're working with? And then let's say you actually go through all this and now you're trying to figure out how do I actually get revenue for the agent that I just deployed.

你通常会围绕几个不同的问题展开讨论：你们是按 token 还是按席位销售，还是两者兼有？如何应对客户的价格敏感度？之后又该如何实际衡量投资回报率（ROI）？You'll typically work through a few different questions. Do you sell tokens or seats, maybe a bit of both? How do you deal with price sensitivity from your client? And how do you actually then measure ROI?

这里更宏观的问题是，作为一家将产品部署到企业环境中的 AI 初创公司，你们如何获取部署所创造的价值？这听起来对你们中的许多人来说可能都很熟悉。And the bigger picture question here is as an AI startup deploying into the enterprise, how do you capture the value that you create from your deployment? This all might sound very familiar to many of you.

对我来说当然是这样，而这些部署壁垒反映了一个相当普遍的挑战：你是在外部构建，这让 AI 的部署变得非常困难。重要的是，问题不在模型本身。正如你所知，模型正变得越来越强大，智能也在呈指数级增长。It certainly is to me and these walls of deployment reflect a pretty common challenge, which is you're building on the outside and that makes deploying AI really hard. And importantly, it's not the model. The models, as you know, are getting incredibly good. Intelligence is exponentially increasing.

那么，究竟是什么让这件事如此困难？So, what makes this really hard?

为什么在企业中部署 AI 如此困难Why deploying AI into businesses is hard

我想谈谈几个不同的观察，说明为什么在企业中部署 AI 很有挑战性。首先，组织结构极其复杂。从纸面上看，你的组织架构图可能会像左边这样。你知道，会有所有者、团队和经理，这是一种标准的团队结构。And I want to talk about a few different observations of why deploying AI into businesses is challenging. The first is that organizations are incredibly complex. On paper, your org chart might look something on the left. You know, you have your owner, you have teams, managers, and this is a standard team structure.

但现实是，组织的情况复杂得多。有些决策深深嵌入团队成员构成的关系网络中，而这些成员往往拥有数十年的经验。他们与销售团队、运营团队密切协作，所有者也常常依赖他们。But, the reality is organizations are far more nuanced. There are decisions that are entrenched in a web of team members, often times folks with decades of experience. They're interacting with the sales team, the operations team. The owner often times leans on them.

因此，作为第三方供应商来构建智能体很难，因为你实际上需要融入组织。我要谈的第二个原因是，如果不了解企业实际运作中的细微之处，就无法充分发挥部署的价值。And so, building an agent as a third-party vendor is difficult because you actually need to embed into the organization. The second reason I want to touch on is you can't deliver the full value of your deployment if you don't understand the nuances of how a business actually runs.

因此，供应商可能会与你的记录系统集成，比如 CRM 或工单系统，甚至是存放着标准操作流程、合同或旧邮件往来记录的文档库。这些都是非常有价值的信息来源，非常有用。但现实是，企业，尤其是身处复杂领域的企业，依赖的是人的判断。真正重要的是各种例外情况、边缘案例、隐性知识，以及人们随口向专家提出的那些问题。So, a vendor might integrate with your systems of record like a CRM or a ticketing system, or even, you know, document repositories where your SOPs, contracts, or, you know, old email threads live. And this is really valuable source of information, very useful. But, the reality is businesses, especially in complex domains, run on human judgment. It's the exceptions, the edge cases, the tacit knowledge, the, you know, quick questions that get asked to the expert that really matters.

这实际上决定了公司的许多运作方式，也是运营中的现实情况。你需要与实际开展工作的人合作，了解真正出色的工作是什么样的。我想强调的第三个原因是，即使你能够提供端到端的价值，作为一家 AI 供应商，要获取这部分价值也相当困难。And that's really what governs a lot of how a company runs. And that's the operating reality. You need to work with the people doing the work to understand what great work actually looks like. The third reason that I want to highlight is that even if you are able to deliver end-to-end value, it's pretty hard to capture value as an AI vendor.

那么，假设你拿下了一份七位数、价值百万美元的合同，这确实相当了不起。但你会如何看待这份合同所带来的长期商业价值？还有许多重要问题需要回答：客户会持续续约吗？So, let's say you land a seven-figure million-dollar contract, which is pretty impressive. But, how do you think about the enduring business value of that contract? A lot of big questions to answer. Will customers keep renewing?

随着模型不断改进、替代方案变得越来越便宜，你还能维持定价吗？不过，假设在这项年收入 100 万美元的合作中，客户确实从中获得了很大价值。事实上，它每年带来的 EBITDA 影响可能高达 500 万美元。有意思的是，对于一家成熟企业来说，你拥有多年的运营历史、长期稳固的客户关系，以及经过验证且具备真实经济效益的商业模式。Can you maintain pricing as models improve and as alternatives get cheaper? But, let's say as part of that million-dollar-a-year revenue engagement, your client actually gets a lot of value from it. In fact, maybe $5 million a year in EBITDA impact. And so, what's interesting about this is that with an established business, you have years of operating history, enduring client relationships, and a proven business model with real economics.

这样一来，你就为一家价值已有据可循的企业增加了收益。以相对保守的 10 倍市盈率估算，你实际上可以为客户创造 5000 万美元的企业价值，这确实很有意思。And so, you're adding earnings to a business with value that you can already reason about. And so, you know, using a, you know, roughly conservative 10x multiple, you can actually generate for your client $50 million in enterprise value, which is really interesting.

所以，这里有一个问题：作为供应商，你完成了所有工作。你部署了智能体，克服了与系统集成、与团队协作中的重重障碍。但实际上，最大的影响却体现在与你合作的企业的损益表上。因此，这些就是由外向内部署 AI 时通常会遇到的障碍和挑战。And so, there's this question here, which is, you know, as a vendor, you did all the work. You've deployed your agents, you went through all the walls of integrating with the systems, working with the team. But, the largest impact actually shows up in the P&L of the business that you actually worked with. And so, these are the typical walls and challenges of deploying AI outside in.

那么，为什么不从第一天起就以 AI 为核心，自己创办那家公司、打造那门生意呢？这其实是一种非常可行的策略。你可以优化整个工作流程，而不只是其中一小部分。你销售的是工作本身，而不只是某个特定工具。So, why not just go build that firm and build that business yourself with AI at its core from day one? This is a very valid strategy, actually. You can improve the whole workflow, not just a narrow slice. You sell the work itself, not just one specific tool.

重要的是，成果由你负责。因此，损益以及你所创造的价值都归你所有。我们认为，这些公司中的许多都会非常出色。这是一个很棒的战略。And importantly, you own the outcomes. So, the P&L and the value that you're generating is yours. And many of these firms are going to be amazing. We think this is a great strategy.

不过，我也想指出这种做法会带来的一些挑战。首先，你一开始没有任何客户，很难建立有效的分发渠道。你也没有品牌声誉，因此很难实现增长。But, I also want to call out some of the challenges that come up with this approach. The first is you don't start with any customers. It's hard to get distribution. You don't have a brand reputation, so that makes it challenging to grow.

而且很难招到专业人才，尤其是在一些小众但重要的行业里，客户可能需要多种专业服务。举个例子：如果你是某个医疗系统的负责人，正计划新建一栋外科大楼，你会把这个项目交给一家 AI 建筑设计初创公司，还是交给合作了 20 年的那家事务所？他们了解相关标准，熟悉你们实际设计建筑时的各种细节，而且你可以信任他们，确保他们交付完整的项目。And it's hard to hire expertise especially in you know niche industries and important industries where clients might need multiple specialty offerings. So take an example here. If you are the leader of a health system and you're planning a new surgery building, do you hand that project to an AI architecture startup or the firm that you've been working with for 20 years who knows the standards, the nuances of how you actually design your buildings, and you can trust them to deliver the full scope of your project.

为什么信任与品牌能够胜出Why trust and brand win

这是一种非常不同的执行模式。这里重要的洞见是，优秀的企业花了几十年赢得客户、打造品牌与信任，并组建了一支卓越的团队，为客户提供出色的服务。因此，与其从外部切入，向一家公司推销 AI 智能体，或试图从零开始打造，不如通过收购，与那些已经存在的优秀企业建立更紧密的合作关系？我还想进一步谈谈，为什么所有权会是一种如此有意思的策略。It's a very different kind of execution model. And so the important insight here is that the best firms have spent decades earning their customers, building brand and trust, and building an incredible team to deliver great service to their clients. So instead of being outside in and selling an AI agent to a company or trying to build from scratch, what if you could partner far more closely with incredible businesses that already exist through acquisition? And I want to talk a little bit more about why ownership is such an interesting strategy here.

先从工程方面说起，也就是接触机会。让你的团队能够与实际工作的人坐下来交流，了解他们的工作，建立关系，认识关键人物、资深员工以及拥有 20 年经验的人，这一点至关重要。只有这样，才能真正设计出不仅能解决某一项工作流程或某一个用例，还能为整个企业全面实现自动化并提升服务交付的智能体。Starting with on the engineering side, which is access. The ability to have your team sit down with the people who do work, understand what they do, build those relationships, understand the key people, the veterans, the folks that have the 20 years of experience is super important to really designing agents that not just solve one workflow or one use case, but really tackle the full automation and service delivery opportunity for the entire business.

第二点是数据。你能想到的每一家企业，本质上都是一个真实运行的环境，拥有真实的任务、真实的边缘案例，以及关于智能体究竟有没有把事情做对的真实答案。这是一个巨大的机会：利用这些信息和信号，真正改进智能体和模型。但更重要的是，在运营层面，激励机制的一致性至关重要。The second is data. Every business you can think of as really a live environment with real tasks, real edge cases, and real answers on whether or not an agent actually got something right versus not. And this is a huge opportunity to use that information, use that signal to actually improve your agents and models. But importantly, on the operational side, incentive alignment is huge.

所有权机制解决了这个问题。只有当你的团队和业务取得成功时，你才会赢。而且没有各自独立的营收目标，也不会被其他客户分散精力。这能带来极高的目标一致性，让你发现并解决许多阻碍，最终加快部署进程，更快实现共同目标。Ownership solves for this. You only win when your team and your business wins. And there's no separate revenue goals, no distractions from other clients. It creates an incredible amount of alignment that enables you to uncover and solve a lot of the blockers to ultimately get to faster deployment and faster shared goals.

成果在这里确实很重要。另一点是信任，我们之前谈过。还有一点很重要：主人翁意识并非从外部切入或从零开始培养，而是从一开始就内含信任这一要素。Outcomes really matter here. The other piece is trust, which we talked about. And, you know, importantly, rather than coming outside-in or building from scratch, ownership has that trust element baked in from the start.

你知道，你拥有一个历经数十年打造起来的品牌，以及所有这些客户关系。这自然也契合规模效应的理念。从庞大的客户基础起步，你拥有这些客户、一支能够提供卓越服务的专家团队，以及持续投资、推动增长复利效应和开发新产品的现金流，这些都非常强大。重要的是，这五项特征并非彼此独立的优势。You know, you have the brand that took decades to build with all of the customer relationships. And naturally plays into this idea of scale. Starting with a large base where you have those customers, you have an expert team that can deliver an incredible service, and the cash flow to continue to invest and compound in growth and new products is very powerful. And the important piece here is these five characteristics aren't just independent benefits.

它们实际上创造了一种强大得多的东西：一种复利式优势。在这种优势下，你的智能体和员工相互配合，持续参与实际运营。你可以了解工作是如何完成的，创造能带来实际成果的真实世界数据，而所有这些学习都会共同累积。这个飞轮确实很有意思，而且从第一天起，你就拥有推动它运转所需的一切要素。They actually create something far more powerful, which is a compounding advantage where a combination of your agents and your people continue to act in live operations. You can understand how work is done, create real-world data that leads to real outcomes, and that learning all compounds together. And this flywheel is really interesting, and it comes with all the elements of ownership to you know, power this flywheel from day one.

因此，这里有趣的是，所有权会随着时间推移迅速累积。我们已经讨论过所有权为什么特别有吸引力。但哪些市场适合收购，尤其是在 AI 时代？我想先从软件经济谈起。And so, what's interesting here is that ownership can compound rapidly over time. So, we talked about why ownership is really interesting. But what markets are ripe for acquisition, especially in the AI era? I want to start with the software economy.

这是一个全球性的软件市场。我们大多数人都花了很多时间和职业生涯在这个领域从事开发。你知道，这是一个相当大的市场。有趣的是，软件始终是作为工具卖给那些实际工作的人。This is a global software market. Most of us have spent a lot of our time and careers building in this part. And you know, it's a pretty big market. But what's interesting is that software is always sold tools to people doing the work.

如果稍微放眼全局来看，这是一个服务型经济体，全球市场规模达70万亿美元。这里的关键洞见是，端到端地掌握一项服务的成果，价值要大得多。这正是令我们兴奋之处。If you zoom out a little bit, this is a services economy. It's a 70 trillion global market. And the important insight here is that owning the outcomes of a service end-to-end is far more valuable. And that's what's exciting to us.

这是一个巨大的市场机遇。但这里有一个重要的发现：服务型企业通常面临着一个潜在挑战。It's a huge market opportunity. But there's an important observation here, which is under the hood, there's a challenge that services businesses typically face.

劳动力成本通常会随着收入增长。例如，业主协会管理公司的社区经理，能够管理的业主协会数量有限；建筑师能够设计的医院或学校数量也有限。因此，如果想要获得更多客户，就必须雇用更多员工。Labor costs typically grow with revenue. So, for example, a community manager at an HOA management company can only run so many associations. An architect can only design so many hospitals or schools. So, if you want more clients, you have to hire more people.

这就是企业通常面临的挑战。你知道，如果拿它和软件公司相比，在典型的软件业务中，每增加 100 美元收入，大约能留下 80 美元。And this is the typical challenge of a business. And you know, if you compare it to a software company, if you add $100 of revenue in a typical software business, you keep about 80.

软件与服务的经济模式对比Software vs service economics

所以，增长的边际成本确实很低，这就是为什么这类企业如此出色，也能迅速扩大规模。相反，服务型企业的运作方式则大不相同。每增加 100 美元收入，通常就要承担大约 80 美元的交付成本和间接费用。你还得招聘、培训和管理团队。So, there's really low marginal cost of growth, which is why these businesses are incredible and you can scale very rapidly. On the contrary, a service business operates very differently. For every $100 of incremental revenue, you typically have around $80 or so of delivery and overhead. You have to hire, train, manage your team.

因此，许多创办了优秀服务型企业的创始人，确实已经不再追求增长了。这并不是因为他们缺乏雄心，也不是因为市场没有机会——这些都是巨大的市场。原因在于规模化的挑战：每赚到100美元，你都得投入大量工作，最后只能留下大约20美元。你可以把这看作是对增长征收了高达80%的边际税，这让外部人士进入并创办这类企业，乃至将它们规模化，都变得非常困难。And so many founders who have built incredible services businesses have really stopped chasing growth, not because they lack ambition and not because there's no market opportunity. These are massive markets. But because the challenges of scaling, when you have to do a lot of the work for every $100, you're only keeping about 20. And so you can think of this as a pretty huge, you know, 80% marginal tax on growth, which makes it very challenging to both come outside in, start these businesses, and even scale them.

这里非常有意思的一点是，AI 实际上在提升服务质量的同时，也降低了服务成本。回头看看这张图，收入会随着人工成本按比例增长；但部署 AI 后，你会发现企业实际上获得了经营杠杆。因此，人工成本不再随着 AI 直接增长。你仍然需要聘请一支优秀的专家团队，为客户提供服务，但这些企业的服务经济模式开始变得更像软件业务。What's very interesting here is that AI actually reduces the cost of service while also improving the quality of your service. So, look back at our chart here where revenue scales proportionally to labor costs, but when you deploy AI you can see here that you get actually operating leverage. So, your labor cost no longer directly scales with AI. You're still going to need to hire an incredible team of experts to deliver your service to your clients, but the service economics for these businesses start to look a lot more like software.

因此，收购企业、与卓越的公司建立深度合作关系，并利用 AI 扩大这些企业的规模、提升服务质量，这正是我们 Long Lake 最为期待的方向。接下来，简单介绍一下我们：Long Lake 是一家应用型 AI 控股公司。And so, the thesis of acquisition, deep partnership with incredible companies, and leveraging AI to scale these businesses and improve the quality of service is exactly what we're excited about here at Long Lake. So, just quickly a little bit more about us. Long Lake is an applied AI holding company.

我们与卓越的企业合作，已从 Elad Gil、General Catalyst、Alpha Wave、D1 等众多投资方筹集了75亿美元。我们大约三年前创立。我们的核心理念是收购业内一流的服务型企业，与它们携手转型运营、加快增长，并最终为客户提供更好的体验。We partner with incredible businesses, have raised 7 and 1/2 billion in capital from Elad Gil, General Catalyst, Alpha Wave, D1, and many others. We were started about 3 years ago. Our whole thesis is to acquire best-in-class services businesses, partner with them to transform their operations, help them grow faster, and ultimately deliver a better client experience.

长湖模型The Long Lake model

截至目前，我们已与多个市场领域的40家企业建立合作，涵盖住宅服务、建筑设计、人力资源等。我们的团队很棒，共有55名成员，分布在技术、财务和运营等领域。我们正在不断壮大。团队成员背景多元，有曾经的创业者、军方人士、来自 Palantir 的人才，还有许多大家熟悉的名字。We've today partnered with 40 businesses across a number of markets, residential services, architecture, HR, and many more. We're an amazing team of 55 team members across technology, finance, and operations. We're growing. We have an eclectic mix of former founders, folks from the military, Palantir, a lot of familiar names here.

我们最近刚刚宣布与 Amex GBT 达成合作。这是一笔价值 63 亿美元的私有化交易，将我们带入企业差旅领域，我们对此感到无比兴奋。你知道，我们以往出现过的许多控股公司为蓝本，但我们有一个非常有意思的观察：AI 提高了业务转型的上限。这正是我们期待去探索的卓越杠杆。And we recently just announced our partnership with Amex GBT. It's a 6.3 billion take private, bringing us into the corporate travel space, which we're incredibly excited about. You know, we're modeled after a lot of the holding companies that have come before us, but with a really interesting observation that AI raises the ceiling for business transformation. And this is the lever of excellence that we're excited to go and pursue.

所以，我们讨论了为什么。我们特别讨论了服务。So, we talked about why We talked about services in particular.

不过，哪些服务类企业真正值得拥有？我想先从一个重要观察说起：随着模型不断进步，智能的价格正在迅速下降。这一趋势令人惊叹。我认为这种趋势还会持续下去。What services businesses though are actually attractive to own? And I want to start with an important observation, which is as models continue to get better, the price of intelligence is rapidly declining. And it's pretty incredible to see. I think this is only going to continue.

那么，如果智能变得越来越廉价，什么东西仍然昂贵？我们真正关注的是那些对使命至关重要的企业，而评估这些公司时，通常会从几个关键问题入手。首先，是否有一批客户信任这些企业，愿意把真正端到端的成果托付给它们？其次，它们实际提供的服务中，是否需要人的判断？So, if intelligence is getting cheap, what remains expensive? We really focus on mission-critical businesses, and it starts with a few key questions that we typically evaluate these companies on. The first is there a customer base that trust these businesses with real end-to-end outcomes? The second is there human judgment in the service that's actually being provided?

第三个问题是，是否存在一些监管方面的规定，明确在做出这些收购决策时该由谁承担责任？我认为这里重要的一点是，你必须对结果负责，而这正是许多此类服务型企业的典型特征。And the third is that are there, you know, regulatory reasons that govern accountability when making these acquisition decisions? And I think the important piece here is you have to own the outcome, and that's the typical characteristic of a lot of these services businesses.

所以，再具体说明一下，这不只是给房主起草一份简报，而是要真正负责处理紧急漏水问题的整个流程并确保解决。它不只是从文件中提取数据，而是要对复杂的税务决策承担责任。而且，这也不只是帮你订机票，还要处理临时出现的各种状况——我相信你们很多人以前都遇到过。So, to color this a bit more, it's not just about drafting a newsletter to a homeowner, but actually handling the end-to-end outcome of an urgent leak. It's not just about extracting data from documents, but actually taking accountability for a complicated tax decision. And, you know, it's not just about helping you book a flight, but handling the last-minute disruptions, which I'm sure many of you have faced before.

选择购买什么Choosing what to buy

这样的企业很少见。你必须满足所有这些要求以及更多要求，还得拥有一支对这种合作充满热情的团队。要做到这一点，你知道，就得考察许多企业。好的投资决策离不开选择。Businesses like these are rare. You have to fit all of these questions, and many more, and also have a team that's excited about that partnership. And to do this, you know, you have to see a lot of businesses. Great investment decisions come with choice.

所以，你们必须保持一个很大的漏斗，同时设置极其严格的筛选标准。我们专注于这样的企业：从业务和市场的角度来看，它们基本面非常出色，同时也拥有绝佳的 AI 机遇，让我们能够真正深入参与，并与这些企业紧密合作。我们每年大约会看到 5,000 个项目，但我们对实际关注的项目和合作对象都非常专注、审慎。太棒了。So, you have to keep a big funnel, and then an incredibly tight filter. Our focus is on businesses that have incredible fundamentals from a business and market standpoint, but also have an awesome AI opportunity where we can actually go in and partner very closely with these businesses. We see about 5,000, you know, deals a year, but again, are very focused and diligent on what we actually focus on and who we partner with. Awesome.

那么，在找到你的业务并与一家公司建立合作关系之后，损益表转型究竟是什么样的？So, once you've found your business and you've partnered with a company, what does P&L transformation actually look like?

我认为，解释这一点其实有个非常简单的方式，就是用 J 曲线来说明。首先，你要投入资金部署 AI。你要搭建平台、开发技术、梳理工作流程，并投入团队大量时间来推动这件事。这是一项非常艰巨的工作。And I think a very simple way to explain this is actually through the visual of a J-curve. So, you start with your investment in AI deployment. You have your platform, you build your tech, capture the workflows, commit a lot of team time to making this work. And it's a lot of hard work.

你必须让它在企业内部真正发挥作用。回想一下从外部切入进行构建所面临的一些挑战：你必须与他们的团队密切合作，不断迭代、验证产品，可能还要训练新模型，并了解这家企业内部各种独特的工作流程和数据。但一旦跨过这道难关，你就会开始看到人们真正用起你的产品，这令人无比兴奋，而且你也开始释放出大量产能。节省了时间，使用量更是一路飙升。You have to make it work within the business. Thinking back to, you know, some of the challenges of building outside-in, you have to partner very closely with their team, iterate, validate your product, potentially train new models, look at all the different unique workflows and data that exist within that business. But once you make it up this hill, you'll start to see folks actually using your product, which is incredibly exciting, and you start to actually unlock a lot of capacity. Time savings, usage is going through the roof.

他们用更少的工时产出了相同的成果。但这并不是旅程的终点。重要的是，我们希望真正实现价值，并与合作伙伴公司创造切实的业务影响。因此，你必须让腾出的产能真正发挥作用。They're producing the same output, fewer hours. But this isn't the end of the journey. Importantly, we want to actually capture value and we want to generate real business impact with our partner companies. And so, you actually have to put that capacity to work.

考虑激励机制的协调，与我们的管理团队和可信赖的合作伙伴紧密合作，推动业务成果、助力增长，并提升客户和员工的 NPS。这些都是在这些企业中真正产生影响的关键因素。做到这些之后，就能切实影响损益表现。Think about incentive alignment, working very closely with our management teams and trusted partners to drive business outcomes, help with growth, improve customer and employee NPS. These are all part of the equation to actually driving impact within these businesses. And once you do all the those things, you get to real P&L impact.

结果Results

员工人均收入更高，但更重要的是，我们能为客户提供好得多的服务质量。我认为这件事很棒的一点是，你不必为每一家企业都单独打造一套定制方案。如果你拥有能够同时推进 AI 部署和业务转型的平台与方案，效果就能迅速累积放大。对于你合作的所有新企业，你都能更快部署智能体，也能更快看到它们对损益表产生的影响。Higher revenue per employee, but importantly, a far better service quality for our clients. And I think what's cool about this is that this isn't just a bespoke playbook that you run one-off for every single business. If you have the platform and the playbook to do both the AI deployment and the business transformation, this can rapidly compound. You get to see faster time to deploy agents and a faster time to P&L impact for all the new businesses that you partner with.

我们在 Long Lake 已经合作的 40 多家企业中都看到了这一点。当然，这不仅仅关乎 AI 和业务转型。要真正拥有、运营并推动转型，背后需要一支相当独特的团队。因此，你们必须实际收购这些企业，组建一支并购团队，与世界一流的服务型企业合作，与他们密切协作、建立关系，并激发他们对未来的期待。And we see this at Long Lake across the 40 plus businesses that we've already partnered with today. Of course, it's not just about AI and business transformation. Under the hood, it takes a pretty unique team to actually own, operate, and transform. So, you have to actually go and buy these businesses, have an M&A team that can partner with world-class services businesses, work with them very closely to build that relationship, should build that excitement for the future.

接下来你确实得亲自投入建设工作，这需要付出大量艰苦努力。你得飞去明尼阿波利斯、波特兰和伦敦，和团队一起工作，真正构建并推动智能体部署方式的前沿发展。同样，这不是只针对某个狭窄的应用场景，而是要覆盖整个运营体系。要真正完成部署，你知道，还需要一支出色的部署和运维团队来推动采用，对吧？Then you actually have to go to do the work of building, and this is a lot of hard work. You have to fly out to Minneapolis, fly out to Portland, go to London, spend time with the team, and actually build and push the frontier of how agents get deployed. Again, not just for one narrow use case, but really for the entire operation. And then to actually do that deployment, you know, you need an amazing deployment and ops team to actually drive adoption, right?

想想那个 J 曲线，对吧？不要只停留在提升产能和节省时间上。你如何确保自己所做的工作，真正为合作企业带来业务影响，并显著提升你所提供服务的客户体验？所以，如果你热衷于对成果负责，并与优秀的服务型企业携手合作，欢迎联系我。Think about that at that J-curve, right? Don't just stop at capacity and time savings. How do you actually ensure that the work you're doing is driving real business impact for the companies that you work with, and delivering a far better client experience for the service that you're providing? So, if you're excited about owning outcomes and partnering with incredible services businesses, feel free to reach out.

我们对这个投资论点感到非常振奋。我们认为，在服务经济领域继续建设有很大的机会。如果有任何问题，欢迎进一步交流。谢谢。We're excited about this thesis. We think there's a lot of opportunity to go continue to build in the services economy, and feel free to chat more if there's any questions. Thank you.