Amex Global Business Travel：Long Lake CEO Alexander Taubman 主导的全球首例 AI 私有化

Today Pars, we're joined by Alex Topman, the co-founder and CEO of Long Lake Management. 今天的节目，我们请来了 Long Lake Management 的联合创始人兼 CEO Alexander Taubman。 Long Lake recently announced their intent to acquire American Express Global Business Travel for $6.3 billion in what I believe is the world's first AI take private. Long Lake 近日正式宣布，有意以 $6.3 billion 的总价收购 American Express Global Business Travel，我认为这极有可能是全球有史以来首个 AI take-private 交易。 They have previously bought around 30 companies and they transform and optimize them with AI. 此前他们已经收购了约 30 家公司，并用 AI 对这些公司进行改造和优化。 So, we're very excited to have him on board today. 我们很高兴今天能请到他。 Alex, thanks so much for joining us at Enterprise. Alex，非常感谢你来 Enterprise 和我们一起聊聊。 Pleasure to be here. 很荣幸来到这里。 Thank you for having me, Lad. 谢谢你邀请我，Elad。 Um, you just announced what I believe, and I I could be wrong on this, but I think it may be um the world's first ever AI take private where you've agreed to acquire Ammex Express Global Business Travel, the world's largest corporate travel platform for 6.3 billion, which is pretty amazing. 你们刚刚宣布了一件我认为可能是全球史上首个 AI take-private 交易，以 .3 billion 收购 Amex Express Global Business Travel，也就是全球最大的企业差旅平台，这真的相当了不起。 And before that, you've already done 30 acquisitions under this premise that you can buy businesses and transform them with AI, what some people are referring to as AIdriven roll-ups or AIdriven um uh buyouts. 在此之前，你们已经基于这个理念做了 30 次收购，买下企业然后用 AI 进行改造，有些人把这种模式叫做 AI 驱动的 roll-up 或 AI 驱动的收购。 And so I it's very exciting to have you here and learn more about your business. 我们很期待你的到来，也很想深入了解你们的业务。 You mentioned you have this Nexus platform which helps your employees serve their customers better and automates a lot of their work. 你提到你们有一个 Nexus 平台，能帮助员工更好地服务客户，并自动化大量工作。 Can you give examples of some of the things that that automates or how it helps them in the context of HOA and I know you're now in four other verticals or three other verticals and so what you do kind of crosses across the different businesses you're involved with. 能不能举些例子，说明它在 HOA 业务中具体自动化了什么，或者是如何帮助员工的？我知道你们现在已经覆盖了另外三四个垂直领域，所以这个平台应该是跨业务通用的。 Yeah, that's right. 对，没错。 So we've taken an approach since the beginning of investing very heavily in our horizontal AI platform which call Nexus. 从一开始我们就采取了一种策略：在横向 AI 平台上大力投入，我们把它叫做 Nexus。 Um I'd say roughly 80% of the infrastructure is shared across the verticals and then there's a lot of work to take it and deploy it into those end markets and the deployment involves mapping workflows understanding data sources cleaning up data sources integrating with them to make them easier for the models to access and sort of our next platform sits in between the models on one side we're model agnostic and the data sources the skills the workflows of the business and so that takes a lot of customization and significant applied AI engineering capabilities which we've built built at Long Lake. 大约 80% 的基础设施是跨垂直领域共享的，然后在部署到各个终端市场时还需要大量定制工作，包括梳理工作流、理解和清洗数据源、做系统集成，让模型更容易访问这些数据。Nexus 平台夹在模型层和数据源、技能、业务工作流之间，我们是模型无关的。这需要大量定制化工作，以及扎实的应用 AI 工程能力，这些我们都已经在 Long Lake 内部建立起来了。 So once we can take that platform, we buy a company now or partner with a company now very quickly. 有了这个平台，我们现在买一家公司或者和一家公司合作，部署速度就非常快。 You know, in the beginning it took us over a year with our first acquisitions to actually, you know, find the the real potential of AI uh and see it in the in the business outcomes. 刚开始的时候，我们前几次收购花了超过一年时间才真正挖掘出 AI 的潜力，才在业务数据中看到成效。 But now within days of partnering with a company, we can deploy this very quickly and see immediate impact. 但现在，跟一家公司合作后几天内，我们就能快速部署，立刻看到效果。 So you can buy a company and then within a couple weeks you have instant margin lift because the employees of that new acquisition just go on a platform you've already built for similar businesses. 也就是说，你们买下一家公司，几周内就能看到即时的利润率提升，因为新收购公司的员工直接用上了你们为同类业务已经建好的平台。 Yeah. 对。 What we what we see is instant time savings. 我们看到的是即时的时间节省。 And then the question is how do we grow to give our team members so we actually invest very heavily in growth. 然后问题就变成了，我们怎么利用这些空余的时间来驱动增长，我们实际上在增长上投入很重。 We're actually not really you know we're not focused on cost saving. 我们其实并不是聚焦在降低成本这件事上。 We're actually focused on driving growth and customer experience. 我们的重心是驱动增长和提升客户体验。 That's our that's our big and what we've seen it's a much more powerful model because it's our view of AI is it's incredibly positive sum. 这是我们的核心目标，而我们发现这是一个更强大的模式，因为我们认为 AI 是极其正和的。 I know this is a little bit of a narrative violation but we actually think AI makes people more productive and we have more productive people. 我知道这跟主流叙事有点不一样，但我们真的认为 AI 能让人更有生产力，有了更有生产力的员工， You want more of them and when your customers are happier you grow faster. 你自然想要更多这样的人，而且当客户更满意的时候，你增长得更快。 You actually create jobs and everybody wins. 你实际上是在创造就业，大家都赢。 And so we're seeing this in our companies. 我们在自己的公司里就是这么看到的。 We're fastest growing company in the HOA industry now. 我们现在是 HOA 行业增长最快的公司。 We are growing organically. 我们在做有机增长。 We when we invested in the businesses, they're typically growing 0 to 5% a year in terms of volume. 我们投资这些企业的时候，它们通常每年只有 0 到 5% 的业务量增长。 We're now growing 20 plus% a year. 现在我们每年增长 20% 以上。 And that's because we've made our team members, we've given them extra capacity to go and serve more customers. 这是因为我们给团队成员提供了额外的能力，让他们能服务更多客户。 We actually have better uh more attractive customer acquisition economics. 我们实际上拥有了更好的客户获取经济性。 Um because we can serve those customers at incrementally lower costs with better products and services. 因为我们能以更低的边际成本提供更好的产品和服务来服务这些客户。 So, we've been able to take sort of the the software style playbook of go to market and apply it into these sleepy industries and and I think it's a win-winwin. 所以我们把软件公司那种 go-to-market 打法，移植到了这些传统行业，我认为这是一个三赢的局面。 It must be hard for your employees to go and work anywhere else in the industry then because if they're dramatically more productive, they're doing less busy work. 那你们的员工应该很难再去行业内其他地方工作了，因为他们的生产力已经大幅提升，繁琐的工作少了很多。 Have you found that you've decreased employee churn or have other things? 你们有没有发现员工流失率降低了，或者出现了其他变化？ That's right. 确实是这样。 We've seen very very high retention of our team members across all of our acquisitions. 我们在所有收购公司里都看到了非常高的团队成员留存率。 Um, and actually this is the vision. 而且，这正是我们的愿景所在。 This is the vision long lake. 这就是 Long Lake 的愿景。 We want to basically be the best place to work in every industry that we operate so we can get give the best people the best tools with the best customers and that flywheel becomes self-perpetuating because if you now leave Long Lake or you leave our one of our partner companies to go to a competitor you have to start doing all this mundane work again that you 25% of your day 30% of your day you have to go do that again and the thought of it's like it's like giving up email or something you know you're not going to it's just so actually we've started to become a real talent magnet in these industries, in our companies. 我们的目标是成为每个我们所在行业里最好的雇主，这样我们就能把最优秀的人和最好的工具配上最优质的客户，而这个飞轮会自我强化。因为如果你离开 Long Lake 或者离开我们合作的公司去竞争对手那里，你就得重新做回那些琐碎的工作，每天 25%、30% 的时间都得耗在上面，这感觉就像放弃用邮件一样，你是不会回去的。所以我们已经开始成为这些行业里真正的人才磁石了。 And by the way, we can pay people the most because they're the most productive. 顺便说一句，我们能给出最高的薪资，因为他们的生产力最高。 They're actually making more money. 他们实际上赚到了更多钱。 And we're we're delighted about that. 我们对此非常开心。 So, we can pay you the most, give you the best tools, and we're growing the fastest. 所以，我们能给你最高的薪资，提供最好的工具，同时我们增长最快。 Um, you know, that's part of our vision is it's just it's it's really making things better for team members and customers. 这是我们愿景的一部分，真正让团队成员和客户的处境都变得更好。 And by the way, the other important thing is you give your team members superpowers with AI, the customers are much much happier. 而且顺便说一下，另一个重要的地方是，当你给团队成员配上 AI 的力量，客户会满意很多很多。 So, we're seeing customer retention going up. 所以我们看到客户留存率在上升。 We're seeing response times much faster, 响应时间快了很多， errors going down in sort of things like board reporting, budgeting, email, you know, and uh that's driving up customer 在董事会报告、预算、邮件这类工作中，错误率也在下降，这进而推动了客户满意度的提升。 Mhm. 嗯。 Oh, so cool. 哦，太酷了。 Why why do this v acquisition versus just offering software to people? 为什么要走收购这条路，而不是直接向企业提供软件？ In other words, the traditional Silicon Valley playbook would be, you know, you find the HOA industry, you realize that there's a need in terms of software, you build software for the industry, and then you sell it as a vendor and in this case, sell it as a sort of AI product or tool. 换句话说，传统的硅谷打法是：发现 HOA 行业，意识到软件需求，然后给这个行业做软件，以 vendor 身份出售，比如卖 AI 产品或工具。 Yeah. 对。 um why not do that or why did you decide to go down the acquisition path? 为什么你们没有走那条路，而是选择了收购？ We we think that you can drive better win-win business outcomes with deeper alignment. 我们认为，通过更深度的利益绑定，可以实现更好的互赢业务结果。 And so by actually owning the companies and and owning those customer relationships directly, we can drive better better results. 通过直接拥有这些公司、直接持有客户关系，我们能推动更好的成果。 Software companies are wonderful. 软件公司很好。 We partner with many of them. 我们也和很多软件公司合作。 But when you're just selling software and you don't actually then care what happens with the with the business outcomes, you just don't see the same business. 但如果你只是卖软件，并不真正在乎之后的业务结果，你就很难看到相同的业务效果。 Your engineers are just viewing your employees as their customers in some sense then. 也就是说，你们的工程师在某种程度上把自己的员工当成客户来服务。 Is that correct? 是这样吗？ That's right. 对。 Our our team views our employees and our team members in the field as the customer and that feedback loop internally. 我们的团队把一线的员工和团队成员视为客户，这个内部反馈循环非常重要。 That's the other point is we have a much tighter feedback loop. 这是另一个关键点：我们有一个紧密得多的反馈循环。 So you know the old skunk works thing of you want the engineers and the factory to be colllocated so you can have more innovation. 你知道那个老的 skunk works 理念，让工程师和工厂人员并肩工作，这样才能有更多创新。 That's what we have at Long Lake. 这正是我们在 Long Lake 所做的。 So our team members and our engineers are together in the field all the time. 我们的团队成员和工程师一直在一线协同工作。 I think there's of our engineering team they're probably in 20 different states right now sitting with team members across our architecture business across our HOA business across our HR services or you know specialty tax business and so they're sort of um and there's a deep amount of change management that's involved so this is a lot of you know sitting with the team members understanding their pain points and so there's a real like solutions orientation of how do we take the pain point we and then we build a tool within Nexus to solve it and that feedback loop is really important. 我想我们的工程团队现在可能分布在 20 个不同的州，和团队成员一起坐在我们的建筑业务、HOA 业务、HR 服务、税务业务的现场。这其中涉及大量的变革管理，需要真正坐下来了解他们的痛点，然后带着解决方案导向思维，把这些痛点变成 Nexus 里的一个工具。这个反馈循环非常重要。 So you get to better outcomes this way. 这样你才能得到更好的结果。 No, it's pretty amazing because I think one of the biggest issues for actually adoption of AI is uh change management uh changing processes, changing organizational design. 这很了不起，因为我认为 AI 落地最大的障碍之一就是变革管理，改变流程、改变组织设计。 And I guess if you own the actual company, then you can make those changes. 我想，如果你直接拥有这家公司，你就可以真正推动这些变化。 If you're just selling software, you can't really impact it very much. 如果只是卖软件，你的影响力就很有限了。 I think in general, um in order to do this very well, and I've talked to dozens of people trying to do different forms of AI rollups and, uh things like that, um is you really need three competencies. 我认为，要把这件事做好，我和数十位尝试不同形式 AI roll-up 的人聊过，你真的需要三种核心能力。 It seems like it seems like you need some folks who are great at the the private equity style motion of purchasing things. 一是擅长私募股权风格的并购动作； You need somebody who's great at engineering and building out the uh AI uh stack and then you need really good change management. 二是擅长工程和搭建 AI 技术栈；三是要有真正强大的变革管理能力。 How how were you able to pull those three disciplines together? 你们是怎么把这三种能力整合在一起的？ Because it's very rare and again I've seen very very few very very few companies in this area who've done this. 因为这非常罕见，在这个领域我见过的真正做到的公司寥寥无几。 Was it a magic initial founding team? 是不是那个创始团队一开始就有种魔力？ Was it just how you've hired? 还是在招聘上有什么特殊之处？ I'm just sort of curious how you because you've also gotten exceptional engineers which most you know uh folks aren't able to get in this industry. 我只是很好奇，因为你们还招到了顶尖的工程师，这在这个行业里大多数人都做不到。 Yeah. 对。 Well, thank you for saying that. 谢谢你这么说。 Yeah. 是的。 So, because we were purpose-built from day one to kind of be this uh cross functional company with technology, DNA, change management and M&A, we were able to attract, you know, the right type of people from network. 因为我们从第一天起就是为了成为这样一家跨职能的公司而专门搭建的，技术 DNA、变革管理、M&A 并行，所以我们能通过网络吸引到合适的人。 And so, I think 100% of our first 20 people were through network. 我们最初的前 20 个人，100% 都是通过人脉网络来的。 We knew them really well. 我们都彼此深度了解。 and people from, you know, places like Palunteer, RAMP, Robin Hood, you know, um some of the top Glean, some of the top um sort of modern AI and data uh companies. 他们来自 Palantir、RAMP、Robinhood、Glean 这些顶尖的现代 AI 和数据公司。 And so, you know, Rasmus, our our co-founder and CTO, uh he and I were connected through one of our, you know, early investors and board members who've we've all known each other for kind of 15 plus years. 我们的联合创始人兼 CTO Rasmus，他和我是通过一位我们共同认识了 15 年以上的早期投资人和董事会成员介绍认识的。 um we all started our careers together and it's 我们几乎是从职业生涯起点就一起共事的，这 really rare by the way I think for many business people to have those deep technical networks like what I've observed is that often these are separate worlds and technologists are very bad at hiring business people early on at least in their careers and vice versa business people tend to be awful at hiring engineers and so you end up with these mismatches on the early teams so it's pretty amazing you were able to pull people out of some of these great companies 其实我觉得，对很多商业人士来说，拥有这样深度的技术人脉是非常罕见的。我观察到，这两个世界往往是分开的：技术人士至少在职业生涯早期，很不擅长招商业人才；反之，商业人士往往也很不擅长招工程师。所以早期团队中经常出现这种错配。因此，你们能从这些优秀公司里拉到人，真的很了不起。 yeah well thank you 是的，谢谢。 I mean I think it's a re what it's a really exciting project 我是说，这确实是一个令人兴奋的项目。 I think that's you know the idea of bringing AI into the real world there's a lot I mean 把 AI 带入现实世界这个想法，我觉得里面有太多…… What the labs are doing is extraordinary obviously and they're enabling all of this and all the you know hundreds of billions of investment that's going in models are getting part of our thesis was the models are going to get better every day. 各大 AI 实验室做的工作显然是非凡的，他们为这一切提供了底层支撑，还有数千亿美元的投入流进去，模型也在每天变得更好。我们的核心论点之一就是：模型只会越来越强。 There's going to be a tremendous amount in the trillions of investment 未来在万亿美元规模的投资会