Gabe Stengel - Building Investing Superintelligence [Invest Like the Best, EP.492]

频道: Invest Like The Best
视频: https://www.youtube.com/watch?v=B0illwrqUG0
原文语言: en
统计: 共 90 轮 · Patrick 28 · Gabe 33

[0:00]

What we're seeing right now though is the models are smarter than anyone I know, anyone I spend time with. Imagine if every PM at a hedge fund had 10,000 agents that were just kind of fraternizing, talking about ideas, reading through the notes, pontificating, and then at the end of, you know, 24 hours of endless, you know, debate just gave you one idea. In 10 years, the world's best investment firms, best banks will be have 90% of their enterprise value, not in people, but in software and data and systems. What would you start doing? You would start trying to figure out how do you take all of what lives in the latent minds of your best people and putting it into a system that you own and operate. There's no part of the business that you hold sacred that is immune to being revolutionized. You and I have talked many [music] times about this basic question that I'll start with over the last couple of years. You're effectively trying to build investing super intelligence tools to help investors do their job much faster, better, cheaper, easier, higher quality, but it's starting to feel like, wow, we're really eating a lot of the core functions that even a very smart analyst or even portfolio manager was doing a couple years ago. How do you think about that trajectory as you've seen it and lived it so far and where it's going over the next two years? I think two years is actually easier to reason about than 10 years or 20 years because in two years the best investors are going to be figuring out how to reinvent their own firms and reinvent themselves. And if you look at what happened to market making and quant trading, Jane Street took 15 years to build the dominant franchise. And the world's best investors today are going to spend the next 2 to 5 years figuring out how to integrate AI into what they do. And you know, Daario has the the great line about everyone's going to have a data center full of geniuses or a

Gabe： 不过我们现在看到的是，模型比我认识的任何人、比我平时打交道的任何人都聪明。想象一下，如果对冲基金的每个基金经理（PM，portfolio manager）都有 10,000 个 agent，它们就在那儿互相串门、讨论想法、翻阅笔记、高谈阔论，然后经过 24 小时没完没了的辩论，最后只给你一个想法。10 年后，世界上最好的投资机构、最好的银行，90% 的企业价值将不在人身上，而在软件、数据和系统里。那你会开始做什么？你会开始想办法，把所有藏在你最优秀员工脑子里的东西，装进一个由你自己拥有、自己运营的系统里。你视为神圣不可侵犯的业务里，没有哪一块能免于被彻底变革。

Patrick： 过去这几年，你我已经聊过很多次我今天想拿来开场的这个基本问题。你实际上是在打造投资超级智能工具，帮投资者把工作做得更快、更好、更便宜、更轻松、质量更高，但现在开始让人觉得：哇，我们真的在吃掉很多核心职能，而就在几年前，这些活儿还是由非常聪明的分析师、甚至基金经理在做的。按你目前的所见所历，你怎么看这条轨迹？未来两年它会走向哪里？

Gabe： 我觉得两年其实比 10 年或 20 年更好推演，因为两年之内，最优秀的投资者会忙着搞清楚怎么重塑自己的机构、重塑自己。你看看做市和量化交易领域发生的事，Jane Street 花了 15 年才建立起占主导地位的江山。而今天世界上最好的投资者，会在接下来 2 到 5 年里琢磨怎么把 AI 融进自己的工作。你知道，Dario 有句名言，说每个人都会拥有一座装满天才的数据中心，或者说一个

[1:50]

country full of geniuses. in the data center. You know, what would Goldman do? What would Millennium do? What would Citadel do if they had a country full of geniuses show up? It would probably take them a while to figure out how to change the way they work, how to take advant advantage of that, how to integrate it into their system. I think figuring out how to apply AI into the investment life cycle is the biggest challenge over the next 5 years for every great investor. So there's been many companies on this trajectory where the product was cognition is very famous for like literally their ads now say remember Devon like it's good now.

Gabe： 数据中心里的天才之国。你想想，Goldman 会怎么做？Millennium 会怎么做？Citadel 会怎么做？如果真有一个天才之国出现在它们面前，它们大概得花上一阵子才能想明白怎么改变自己的工作方式、怎么利用好这一点、怎么把它整合进自己的体系。我认为，怎么把 AI 应用到投资的全生命周期里，是未来 5 年每一位优秀投资者面临的最大挑战。

Patrick： 有很多公司都走过这样的轨迹，产品当初是——Cognition 就特别出名，他们现在的广告简直就是在说「还记得 Devin 吗？它现在好用了」。

[2:23] Patrick

Um so lots of now clearly great companies with great products had a stage of an AI business where the product stunk and now it's excellent. If you think about the couple increases in capability that we've seen just from the raw models, could you do the same thing for sort of like the eras of Rogo and its product? Like you pick how many eras it is. I don't I don't know how how you frame it up, but like what it could do at each level up up to and including today.

嗯，所以很多如今显然是好公司、有好产品的企业，在做 AI 业务时都经历过一个产品烂得不行的阶段，而现在变得非常出色。想想光是底层模型本身，我们就见过几次能力跃升，你能不能也照这个思路，给 Rogo 和它的产品划分一下时代？分几个时代你自己定。我也不知道你会怎么划分，但就说说每一级它能做到什么，一直到今天为止。

[2:50] Gabe

Yeah. I actually tried starting Rogo two times before we got started. So in high school, I had a friend whose dad was an investment banker who wanted an app for trying to track the basically equity exchange rate of two public companies as they were merging. And so we tried using really old AI techniques to do that. Terrible. And then in college before GB3 came out, we published a paper on AI assistance for econometrics and financial econometrics. And we tried commercializing at the time and nothing worked at all. And then when we actually started the business, it was when GBD3 came out, pre- chat GBT. And so all the early days of Rogo, it was clear how kind of magical it was. You could demo things that were cool. Nothing nothing worked at all. I mean, I would say since things actually started to work, the eras are very tied to the model eraser, right? It was 01 Pro and then probably Opus 45. 01 Pro was the first time you got enough reliability where it was a good search tool at the very least, right? like you could say help me calculate this sort of you know financial metric for this business over the last 12 quarters and it could do it reliably enough where it wasn't so annoying that you would just do it yourself and then with Opus 45 and the kind of end of last year the end of 2025 into the beginning of this year the models just became capable of basically anything a junior investment professional or junior banker was doing as long as you gave it the right instructions and context. I mean, I think there was a first movers disadvantage for a lot of applied AI companies because you thought you knew where the world was going and you wanted to build a product for it, but the models weren't quite there and so people would try it and go, "This is terrible.

对。其实在真正做起来之前，我已经尝试过两次创办 Rogo。高中的时候，我有个朋友，他爸爸是投行家，想要一个 app，用来追踪两家正在合并的上市公司之间的、基本上就是换股比例。于是我们试着用很老的 AI 技术去做。结果糟透了。后来在大学里、GPT-3 出来之前，我们发表过一篇论文，讲的是用 AI 辅助计量经济学和金融计量经济学。当时我们试着把它商业化，结果完全行不通。等我们真正开始做这家公司时，正是 GPT-3 出来的时候，还在 ChatGPT 之前。所以 Rogo 早期那段日子，你能明显感觉到它有多神奇。你能演示一些很酷的东西。但实际上什么都不管用，一点都不管用。我想说，自从东西真正开始管用以来，各个时代都跟模型的时代紧密挂钩，对吧？先是 o1 Pro，然后大概是 Opus 4.5。o1 Pro 是第一次可靠性够用，至少它能当个好用的搜索工具，对吧？比如你可以说，帮我算一下这家公司过去 12 个季度的某个财务指标，它能做得足够可靠，不至于烦到让你干脆自己动手。然后到了 Opus 4.5，也就是去年年底、2025 年底到今年年初那段时间，模型基本上已经能胜任初级投资从业者或初级银行家做的任何事情，只要你给它正确的指令和上下文。我觉得对很多应用 AI 公司来说，存在一种先发劣势：你以为自己知道世界往哪走，想为那个方向打造产品，但模型还没完全到位，于是人们试用之后会说：「这太烂了。

[4:22]

This is garbage." You have a disadvantage. For us, we saw that too. But now what we've seen is well, if you were right about the end state and where the models were going and you were building towards that, when they get there, it's magical. And for me, a great product is all the feedback we get every day. People all day are saying, "Hey, this is trans transforming the way I work. I'm saving hundreds of hours a month. I am doing things I never could have done before." And so, I'm smarter as a result, and I'm able to make better decisions. And it's delightful, and I like using it. And, you know, it brings me joy in my day-to-day because the UX and the attention to detail and the craftsmanship is so obviously built for me and who I am. And so, that's been the the best part of the product building. and and you would attribute that to you took seriously all the compliance regulatory workflow like last mile hookup stuff and then the models became good enough and all of a sudden that was super valuable.

Gabe： 这就是垃圾。」你就处于劣势。我们也碰到过这种情况。但现在我们看到的是：如果你对终局、对模型会走向哪里判断对了，并且一直朝那个方向去建，那等模型真的到了那一步，效果就很神奇。对我来说，好产品就体现在我们每天收到的反馈里。人们整天都在说：「嘿，这彻底改变了我的工作方式。我每个月能省下几百个小时。我在做以前根本做不到的事。」所以我因此变得更聪明，能做出更好的决策。用起来很愉快，我喜欢用它。它给我的日常工作带来乐趣，因为它的 UX、对细节的用心和那份匠心，一看就是为我、为我这样的人打造的。所以这一直是做产品过程中最棒的部分。

Patrick： 那你会把这归因于：你们认真对待了所有合规、监管工作流这类最后一公里的对接工作，然后模型变得足够好，这些东西一下子就变得超级有价值？

[5:18] Gabe

Well, there's also small details of understanding how someone within one of these firms works and building for it. I'll give you an example. Uh, we make it so easy for a managing director at a bank to email a markup of a deck, which is how they're typically doing these workflows anyway, except sending it to an analyst and sending it to our AI analyst over email, and then returning that markup in 20 minutes as opposed to 2 days. And at the same time, alert the junior analyst on the deal, what's happening, and show them the full auditability of all the little markups that were made in case they want to weigh in. And that whole UX, that whole flow just makes it so much easier for this financial professional who is not logged into a computer in 10 years, but does have an iPad where they know how to mark these things up to actually adopt and use AI. And there are these small details of how you build a product that's great for a specific end user that you only know if you have the kind of spidey sense for what the job is.

嗯，还有一些小细节，就是要理解这些机构里的人是怎么工作的，并围绕它去构建。我举个例子。呃，我们让银行的董事总经理可以非常轻松地把一份演示文稿的批注修改稿用邮件发出去——他们本来一般就是这么走这些流程的，只不过以前是发给分析师，现在是通过邮件发给我们的 AI 分析师——然后 20 分钟就把按批注改好的稿子返回来，而不是两天。同时，它还会提醒这笔交易上的初级分析师现在发生了什么，并把所有这些细小改动完整展示出来、做到可审计，万一他们想提意见也可以。整个 UX、整个流程，让这位可能十年都没登录过电脑、但确实有台 iPad 并且知道怎么在上面做批注的金融从业者，真正更容易接受并用上 AI。怎么为一个特定的终端用户打造出好产品，有很多这样的小细节，只有当你对这份工作有那种「蜘蛛感应」般的直觉时，你才会知道。

[6:12]

What is like the bleeding edge of what it can do that impresses you the most? Like what kinds of jobs? Oh, I mean I think the the the coolest things that we're working on is taking these kind of uh these innovations like malt book. Imagine if every PM at a hedge fund had 10,000 agents that were just kind of fraternizing, talking about ideas, reading through the notes, pontificating, and then at the end of, you know, 24 hours of endless, you know, debate just gave you one idea. And the reason you're able to do that is because investors are happy to pay $50,000 for one really good idea. Whereas there's very few other domains where you can expand that many tokens just for, you know, one simple insight. What we're seeing right now though is that the models are smarter than anyone I know, anyone I spend time with. And it's plumbing. Connect it to your context, inform it about your thesis, tell it the way that you work and trying to integrate it into what you do. And so building out all the plumbing to actually collect that data, collect that context is what is cutting edge to me. It's always interesting to me for a product like this that of course you you're opinionated about what it should be used to do, but in some sense people can be creative with how they use it. So you get to sort of revealed how people want to use it. If I adopted a god's eye view of, you know, it's it's Monday morning here in New York City. Lots of Roger users are probably fired up and using it right now. If I could somehow see into every instance of the product being used, what would I see? Who are the people? What are the predominant use cases? How varied are they? give us a sense of like how it's being used right now as we record.

Patrick： 它能做到的最前沿、最让你惊艳的事情是什么？比如哪类工作？

Gabe： 哦，我觉得我们正在做的最酷的事，是把 Moltbook 这类创新拿过来用。想象一下，如果对冲基金的每个基金经理都有 10,000 个 agent，它们就在那儿互相串门、讨论想法、翻阅笔记、高谈阔论，然后经过 24 小时没完没了的辩论，最后只给你一个想法。之所以能这么做，是因为投资者乐意为一个真正的好想法付 50,000 美元。而几乎没有其他领域能让你为了一个简单的洞见烧掉这么多 token。不过我们现在看到的是，模型比我认识的任何人、比我平时打交道的任何人都聪明。剩下的是管道工程：把它接到你的上下文上，告诉它你的投资论点，告诉它你的工作方式，设法把它融进你所做的事情里。所以，把收集这些数据、收集这些上下文的管道全部搭起来，在我看来这才是最前沿的。

Patrick： 对这样的产品，我一直觉得很有意思的一点是：你当然对它该用来做什么有自己的主张，但某种意义上人们会很有创意地去用它。所以你能从中看出人们到底想怎么用它。假如我拥有上帝视角——现在是纽约的周一早上，很多 Rogo 用户大概正干劲十足地在用它。如果我能看到产品被使用的每一个实例，我会看到什么？都是些什么人？主要的用例是什么？它们有多分散？给我们讲讲，就在我们录节目的这会儿，它是怎么被使用的。

[7:38] Gabe

Even though in so many ways I think public equities is the best application of AI because all the data is available and so it's just about being as smart as possible. That's a very you know brute force framing. Uh our early users in ICP and kind of core market is actually what I would describe as dealmakers or people that are transacting who are buying companies, selling companies, you know, helping coordinate transactions. And so a lot of what we do is both make people smarter but actually do the dealmaking. How do you prepare a data room? How do you unpack a data room? How do you coordinate the call with the third parties to discuss the data room? How do you go through all of the initial steps through closing of a deal? And so if you took a bird's eye view of all the folks using ROGO, I mean it's people who are either on the sell side of a transaction or the buying side of a transaction and are using it to basically prepare all the thoughts and materials to help execute that full deal. Whether it's, you know, putting things into a data room, this is the company's model, you know, this is the, you know, PowerPoint that describes their customers. This is these are the answers to the DDQ questions on what customer concentration is. or it's all the agents on the other side of that that are tearing through the data and mapping it to the firm's investment philosophy to say, "Oh, great. You know, is it is it lower than the kind of concentration risk profile that we would want for this fund, too?"

尽管从很多方面看，我认为公开市场股票是 AI 最好的应用场景，因为所有数据都是公开可得的，所以比拼的只是谁足够聪明。但那是一种很，你知道，蛮力式的看法。呃，我们的早期用户、ICP（理想客户画像）和核心市场，其实是我会称之为「做交易的人」（dealmaker）的那群人，也就是在做交易的人：收购公司、出售公司、协助协调交易。所以我们做的很多事，既是让人更聪明，也是真的去执行交易。怎么准备数据室？怎么拆解一个数据室？怎么协调跟第三方的电话会来讨论数据室？怎么走完一笔交易从最初几步直到交割的全部流程？所以如果你鸟瞰所有在用 Rogo 的人，基本上就是处在交易卖方或买方的人，他们用它来准备执行整笔交易所需的全部思路和材料。不管是往数据室里放东西——这是公司的财务模型，这是介绍他们客户的 PowerPoint，这些是 DDQ（尽调问卷）里关于客户集中度问题的回答；还是另一边的所有 agent，它们飞快地翻查数据，把数据对照到本机构的投资理念上，来判断：「好，那它是不是也低于我们这只基金想要的集中度风险水平？」

[8:56]

And then the kind of components of the system are people access it via all the kind of classic channels you would access an AI tool, email, you know, chat bots, proactive alerts, and those kind of things. But then Rogo is actually in a lot of the behind the scenes systems of these firms because when you're working on a deal, it's not just important for the human beings working on it, but you need to update your CRM, you need to update your portfolio and monitoring systems, you need to, you know, update the way that you distribute information to your LPs after the fact. And so half of our surface area is actually the underneath of the iceberg of interacting with these different systems of record based on what the humans are do doing over the course of the deal. So, so dealmakers today, when do you think you'll be able to give the same answer for junior analysts at a public equity hedge fund or something like this that that is effectively there there's a process to their workflow as well, but it's very very different. Um, it would Yeah, you're you're right that it's interesting that my first intuition would be public markets are the best place to do this because there's so much data available.

Gabe： 至于这个系统的组成部分：人们通过所有经典渠道来使用它，也就是你平时使用 AI 工具的那些渠道——邮件、聊天机器人、主动提醒之类的。但 Rogo 其实也嵌在这些机构的很多幕后系统里，因为你在做一笔交易时，重要的不只是参与其中的人，你还得更新 CRM，得更新投资组合和监控系统，事后还得更新你向 LP（有限合伙人）分发信息的方式。所以我们有一半的覆盖面，其实是冰山水面以下的部分：根据人在交易过程中做的事，去和这些不同的记录系统（system of record）交互。

Patrick： 那么，今天是做交易的人。你觉得什么时候，你能对公开市场股票对冲基金之类机构的初级分析师给出同样的回答？他们的工作流其实也有一套流程，但非常非常不一样。嗯，会——对，你说得对，这挺有意思的，我的第一直觉会是公开市场才是做这件事最好的地方，因为可得的数据太多了。

[10:04]

When do you think that transition happens? I think that for our business, we need to have all the requisite domain knowledge of what it takes to be a great public markets investor. I don't know what it takes. I've never done it. In fact, I I haven't spent nearly as much time as I should have with the folks that are great at it. And we need to both hire out that domain expertise and then figure out based on it how to apply the systems that we have built to that market. I have extreme conviction in the fact that the underlying systems and tools and infrastructure we have built will be invaluable to that market. But now we need the great chef who can figure out how to piece it together and create that kind of endstate product and that last mile delivery delivery for public equities investors. You know, we actually I get pushed a lot by our board to think about expanding the ICP beyond just core banking, but the reality has been is there's been so much depth in TAM in this deal makers vertical. And then for my endstate vision of actually being the full kind of infrastructure for private markets where people can transact very effectively, that is far more important to the dealmakers. Whereas for public equities, all that infrastructure, all those exchanges already exist. And so I'd like to serve them because I want to serve the most sophisticated, smartest users who have inordinate amounts of knowledge on the companies they track and the industries they they they follow. And I'd like to make them even smarter because that sounds super cool. But I can build a huge huge businesses business just concentrating where I am today.

Patrick： 你觉得这个转变什么时候会发生？

Gabe： 我觉得对我们的业务来说，我们得具备成为一名优秀公开市场投资者所需的全部领域知识。我不知道那需要什么。我从来没做过。事实上，我花在那些真正擅长此道的人身上的时间，远远没有我本该花的那么多。我们既需要把这方面的领域专家招进来，然后再基于这些专业知识，想清楚怎么把我们已经搭好的系统应用到那个市场。我极其坚信，我们搭建的底层系统、工具和基础设施对那个市场会有无可估量的价值。但现在我们需要一位出色的大厨，能想清楚怎么把它们拼起来，为公开市场股票投资者做出那种终局形态的产品，完成最后一公里的交付。你知道，其实董事会经常推我去考虑把 ICP 扩展到核心投行业务之外，但现实是，做交易的人这个垂直领域，TAM 的纵深实在太大了。而且对于我的终局愿景——真正成为私募市场的完整基础设施，让人们在上面非常高效地完成交易——这一点对做交易的人要重要得多。而对公开市场股票来说，所有这些基础设施、所有这些交易所都已经存在了。所以我想服务他们，是因为我想服务最老练、最聪明的用户，他们对自己跟踪的公司、关注的行业掌握着极其大量的知识。我想让他们变得更聪明，因为那听起来超酷。但只专注于我现在所在的领域，我就能做出一个非常非常大的生意。

[11:36] Patrick

Interesting. So one takeaway from that would be a lot of the opportunity to build an AI business in a vertical is somewhere where there's lots of plumbing that's not yet built.

有意思。那由此可以得出一个结论：在某个垂直领域做 AI 生意，很多机会恰恰藏在那些底层管道还没铺好的地方。

[11:46] Gabe

Yes.

对。

[11:46] Patrick

And then applying your tool on top of that.

然后再把你的工具架在上面。

[11:48]

Exactly. I mean part of the reason private markets are so attractive is because it's all done by humans. The coordination the standardization looking into things and the actual transacting whereas public equities I mean a lot of it has been automated. Based on what you know, what skills do you think investment professionals, broadly speaking, public and private, should think about being or becoming more valuable as time progresses and which skills become I mean kind of obvious the skills that become less valuable. But yeah, another way of asking like what the hell are we going to do if the thing can do the soup to nuts diligence and IC memo and objection handling and all this kind of stuff? like that's a big part of of a job for at least a junior person in the investing world. So what skills do you think people will still matter a lot in a couple years?

Gabe： 完全正确。私募市场之所以这么有吸引力，部分原因就在于这些事全靠人在做：协调、标准化、调研，还有实际的交易执行；而公开市场股票这边，很多环节早就自动化了。

Patrick： 以你的了解，你觉得投资从业者——广义上讲，公开市场和私募市场都算——应该想想哪些技能会随着时间推移变得更值钱？又有哪些技能会……我是说，哪些技能会贬值其实挺明显的。换个问法就是：如果这东西能把尽调、IC memo（投委会备忘录）、应对质疑这些活儿从头到尾全包了，那我们到底还干什么？这在投资圈里至少是初级从业者工作的一大块。所以你觉得几年之后，人身上还有哪些技能依然非常重要？

[12:36]

I mean I want to preface it all with you know I worked in finance for 2 years and so I am a student of these guys just as much as I'm fascinated by the technology and want to figure out how to use it. Uh but you know the world's best investors have a way of figuring out what matters and exercising their own judgment across a range of topics. And I think jury is still out on on whether or not that is something that AI can eventually replace, right? I think when you know move 37 happened and lease it all saw something I could never see. If that starts to happen in public equities, yeah, it's going to really change what matters. And if that really changes all of how that works, I mean, I think the core skill set is folks who can go out and gather data and inputs into their model that no one else will have. If you can spend time in the field, if you can speak to experts, if you can develop a relationship graph of folks who can inform your model, maybe you're not the one that needs to calculate what your move 37 would be for a great public equities investment, but you can actually feed your model with data that no one else has, too. RAMP is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average, so you can stay focused on growth. RAMP just opened for business in the United Kingdom. So, if you're running a business in the UK, you can now use RAMP's AI powered finance platform to manage cards, expenses, bills, approvals, and accounting all in one place. I run my business on RAM, and so should you. Learn more at ramp.com/invest.

Gabe： 我先声明一下：我在金融行业只干过两年，所以我是在向这些人学习，程度丝毫不亚于我对技术的着迷、想弄明白怎么用它。但你知道，世界上最顶尖的投资人都有一套本事，能判断什么才真正要紧，并在各种议题上运用自己的判断力。我觉得这到底是不是 AI 最终能取代的东西，目前还没有定论，对吧？我想当年那「第 37 手」（move 37）出现的时候，Lee Sedol 看到的是一步他自己永远想不到的棋。如果这种事开始在公开市场股票投资里发生，那什么才重要就真的要大变了。而如果整套运作方式都因此彻底改变，我觉得核心技能就是：能走出去收集别人拿不到的数据和信息，喂进自己的模型。如果你能花时间跑一线，能跟专家聊，能建立起一张可以给你的模型提供信息的人脉关系网，那也许你不必亲自去算出一笔伟大的公开市场投资的「第 37 手」是什么，但你可以给你的模型喂别人都没有的数据。

Patrick： Ramp 是唯一一个专门为了让财务团队更精简、更快、更好而打造的平台，平均每年为企业节省 5%，让你能专注于增长。Ramp 刚刚在英国开业。所以如果你在英国经营企业，现在就可以用 Ramp 的 AI 财务平台，在一个地方统一管理公司卡、费用、账单、审批和会计。我自己的公司就跑在 Ramp 上，你也应该这样。了解更多请访问 ramp.com/invest。

[14:03] Patrick

RO is the AI platform purpose-built for financial institutions, serving tens of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. They can screen deals, draft sims, run buyer outreach, and due diligence on data rooms, all with full security and compliance handled. Every action feeds Rogo Intelligence, the firm's context layer, turning years of deal [music] experience into a secure, governed institutional memory that makes each new transaction stronger than the last. To learn more, visit rogo.com/invest. OpenAI, Cursor, Anthropic, Perplexity, and Verscell all have something in common. They all use WorkOS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, skim, arbback, and audit logs. That's where work OS comes in. Instead of spending months building these missionritical capabilities yourself, you can just use work OS APIs to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on work OS. Work OS is the fastest way to become enterprise ready and stay focused on what matters most, your product. Visit works.com to get started. Maybe it's a good uh time to talk about literally the I think of like the assembly line like if if Rogo puts out some really useful output. Tell me about the entire system.

Rogo 是专为金融机构打造的 AI 平台，服务全球数百家顶尖机构的数万名银行家和投资人。Rogo 的 AI agent 能自主执行你们机构的一大块工作流：筛选交易、起草 CIM（保密信息备忘录）、开展买家接洽，以及在数据室里做尽调，安全与合规全部处理妥当。每一个动作都会沉淀进 Rogo Intelligence——也就是机构的上下文层——把多年的交易经验变成安全、受治理的机构记忆，让每一笔新交易都比上一笔更强。了解更多请访问 rogo.com/invest。OpenAI、Cursor、Anthropic、Perplexity 和 Vercel 都有一个共同点：它们都在用 WorkOS。原因是这样的：要在企业端实现规模化落地，你必须交付一些核心能力，比如 SSO、SCIM、RBAC 和审计日志。这正是 WorkOS 的用武之地。与其花好几个月自己搭这些关键任务级的能力，你可以直接用 WorkOS 的 API，从第一天起就全部拥有。这就是为什么你听说过的很多顶尖 AI 团队都已经跑在 WorkOS 上。WorkOS 是实现企业就绪（enterprise ready）最快的方式，让你专注于最重要的事——你的产品。访问 workos.com 开始使用。也许现在正好可以聊聊——我把它想象成一条流水线——假如 Rogo 产出了一份很有用的成果，给我讲讲整个系统。

[15:26] Patrick

I want to learn about each component part of the system that leads to that output. And I'm most especially interested in the data that you yourself have and use and buy and build and whatever and how you do that. how you think about data and then how you think about model I and and these are both questions one that I'm curious about your specific business for but I also think that there will be some business like rogo built in basically every vertical and so I'm curious you know what can be abstracted to other professional services or other verticals that are that are interesting as well that will fall to AI uh progress so talk us through yeah specifically like the data and model piece and how that's evolved over time

我想了解这个系统里通向那份产出的每一个组成部分。我最感兴趣的是数据：你们自己有什么数据、用什么数据、买什么、自建什么，以及你们是怎么做的；你们怎么看待数据，又怎么看待模型。这两个问题，一方面我好奇你们这家公司具体怎么做，但我也觉得，基本上每个垂直领域都会冒出一家像 Rogo 这样的公司，所以我也好奇，有哪些东西可以抽象出来，迁移到其他专业服务、或其他同样有意思、也会被 AI 的进步攻下的垂直领域。所以给我们具体讲讲数据和模型这一块，以及它这些年是怎么演变的。

[16:01] Gabe

yeah the early days of rogo was kind of this Rube Goldberg contraption where like you had 60 different model calls. A question comes in, you try to say, you know, what companies is Patrick asking about? Now, what are their tickers? Now, how do I feed those tickers into an API call to, you know, Bloomberg or Faxit or some internal data set? Now, it comes back and I need to call a different model to to pull it all together. And as the models get smarter, you kind of want to, you know, be less prescriptive, less of a Rube Goldberg machine and just think about what are the simplest, best, highest quality tools. And the same way that if you have the world's smartest human being starting here tomorrow trying to be a great banker and investor, what are the tools that they would need that are not just intrinsic to being smart, right? What are the data tools? What are the ways of going out and gathering information? What are the ways of auditing its own work? And then what are the ways of presenting it and pushing it back into the systems that it needs. And so for us, we spend a lot of time thinking through what are all the data inputs that a great banker or a great investor would need to actually do their job. Then we spend a lot of time thinking about great when you're doing that job what are all the compliance and regulatory requirements to make sure that if someday you know some Delaware court judge makes AI inputs and research discoverable you have actually done it all the right way such that you're not intermingling information because the reality of AI investment judgments and AI you know banker outputs is that you're going to be able to see the full lineage of how those things are created.

好。Rogo 早期有点像一台鲁布·戈德堡机械（绕一大圈才办成一件小事的连环装置），比如要调 60 次不同的模型。一个问题进来，你先得弄清楚：Patrick 问的是哪些公司？然后，它们的股票代码是什么？再然后，我怎么把这些代码塞进一次 API 调用，去查 Bloomberg、FactSet 或者某个内部数据集？结果返回后，我还得调另一个模型把所有东西拼到一起。随着模型越来越聪明，你就会想少一点硬性规定，别再搞那种鲁布·戈德堡机械，而是去想：最简单、最好、质量最高的工具是什么。这就好比明天有一个世界上最聪明的人来这里上班，想成为一名出色的银行家和投资人，他需要哪些工具——那些光靠聪明本身给不了的东西，对吧？有哪些数据工具？有哪些出去收集信息的方式？有哪些审核自己工作的方式？还有，有哪些方式能把成果呈现出来，并推回到它需要对接的各个系统里？所以我们花了大量时间梳理：一个优秀的银行家或优秀的投资人，要真正干好活，需要哪些数据输入。然后我们又花了大量时间想：好，在做这份工作时，有哪些合规和监管要求——要确保哪天如果特拉华州某个法院的法官裁定，AI 的输入和研究资料可以被调取作为证据（discoverable），你确实全都做对了，没有把不该混的信息混在一起。因为现实是，对于 AI 做出的投资判断、AI 产出的投行成果，人们将能看到它们是如何生成的完整溯源链路。

[17:20] Gabe

uh and then we spend a lot of time benchmarking these models and creating different kind of evals and data sets so we can always decide what is the most performant what is the cheapest from a token perspective what is the lowest latency and route task to the appropriate type of models

然后我们还花大量时间给这些模型做基准测试，搭建各种 eval 和数据集，这样我们随时都能判断：哪个性能最好，哪个从 token 角度看最便宜，哪个延迟最低，再把任务路由给合适类型的模型。

[17:33]

I'm sure your favorite question is how do you ultimately compete with anthropic and open AAI who view finance as a big it's one of the few categories that like you see them talking about and thinking about what can you do in the long run that they that's counterpositioned against what they can or will do you I mean, you want to build things that are perpendicular to what what they want to build. And sometimes you might build a chatbot because that helps you go to market faster, but you should know there's a whole bunch of stuff underneath the surface that the labs are never going to build that we need to build for finance or for any other vertical. And when you think about financial services and capital markets, how many businesses are there that generate more than 5 or 10 billion by just going deep into those workflows, into the data sets, and how work is done? There's a huge amount of TAM and spend on top of very messy specific problems. And all of finance is a collection of different niches with different data sets, different definitions of good, different regulatory requirements. And we can get to $5 billion in revenue by going deep across those things and creating the systems, the systems of record that help manage them. that forth anthropic would kind of be like, you know, stopping on the side of the road to pick up a penny because they're on the pathway of trying to go from a hundred billion in in revenue to a trillion in revenue. Uh, and there's so much depth to these systems that actually need to be built beyond just intelligence.

Patrick： 我猜你最喜欢被问的问题就是：你们最终怎么跟 Anthropic 和 OpenAI 竞争？它们把金融看作一个很大的……这是少数几个你能看到它们公开谈论、认真琢磨的领域之一。长期来看，你们能做哪些跟它们能做或会做的事情形成反向定位（counter-positioning）的东西？

Gabe： 我是说，你要做的东西应该跟它们想做的东西正交、互不重叠。有时候你可能会先做一个聊天机器人，因为这能帮你更快进入市场，但你得清楚，水面之下还有一大堆东西，是这些实验室永远不会去做、而我们必须为金融——或者任何其他垂直领域——去做的。想想金融服务和资本市场，有多少家企业仅靠深挖这些工作流、数据集和实际的做事方式，就做到了 50 亿或 100 亿美元以上的收入？在那些非常杂乱、非常具体的问题之上，有巨大的 TAM（潜在市场规模）和支出。整个金融业就是一堆各不相同的细分领域的集合，每个都有不同的数据集、对「好」的不同定义、不同的监管要求。我们可以靠在这些领域深耕、搭建系统——那些帮助管理它们的记录系统（system of record）——做到 50 亿美元的收入。而这对 Anthropic 来说，就有点像停在路边捡一分钱，因为它们正走在从 1000 亿美元营收冲向 1 万亿美元营收的路上。而且这些真正需要搭建的系统，纵深非常大，远不止智能本身。

[18:50]

Is there a favorite example of that of some like pain in the ass thing that you've had to wire up last mile thing? Yeah, I mean think about, you know, if you're if you are ingesting MNPI because you are working on transactions or you're working on deals that have an effect on the market. Uh the kind of compliance requirements you have just in the auditability or what you can flag and how it feeds into the internal systems of an investment firm or a bank so that if you ever get audited or a regulator wants to see what you did, do you have all that plumbing in place that is pixel perfect? That's one example. Another example is you know if you actually want to transact and you want to you know say you are a big public company buying another big public company and you need to send data back and forth you actually need some sort of data room something that is compliant safe and secure that coordinates and ideally it's not just some static kind of Dropbox folder but something that's plugged into the way that you do work your agents your workflows and I don't think you know open airropic will ever want to build a data room business and if you actually want to be the exchange for all of high finance and all of capital markets, you not just need to own the intelligence, you need to own the transaction venue, the communication venue, the workflows, and all the data inputs that go into it.

Patrick： 有没有一个你最喜欢的例子，就是那种你们不得不去接通的、特别折腾人的最后一公里的活儿？

Gabe： 有，你想想，如果你因为在做交易、在做会影响市场的项目而接触到 MNPI（重大非公开信息），光是在可审计性上就有一堆合规要求：你能标记什么、它怎么流入投资机构或银行的内部系统——这样一旦你被审计，或者监管机构想看你做了什么，你是不是已经把这些管道全部铺好、而且分毫不差？这是一个例子。另一个例子是，如果你真的要做交易——比如你是一家大型上市公司，要收购另一家大型上市公司，双方需要来回传数据——你其实需要某种数据室，一个合规、安全、可靠、能起到协调作用的东西。理想情况下，它不只是一个静态的 Dropbox 文件夹，而是能接进你的工作方式、你的 agent、你的工作流里。我不觉得 OpenAI 或 Anthropic 会想去做数据室这门生意。而如果你真的想成为整个高端金融、整个资本市场的交易所，你不仅要拥有智能，还要拥有交易场所、沟通场所、工作流，以及流入其中的所有数据输入。

[19:58] Patrick

So, it's interesting. I asked about data and models, but actually it sounds like harness or infrastructure is probably the most important of the three.

有意思。我问的是数据和模型，但听下来，harness（套在模型外面的运行框架）或者说基础设施，可能才是三者里最重要的。

[20:07]

Think about the fundamental difference between uh cloud code when it came out and cloud co-work versus openai and chatbt. The models were actually fairly similar, but the harness and the way that it was presented from Claude was far better. And it just allowed the the models to exercise more of their longunning capabilities. And that's why they had a runup in usage and a huge amount of expansion. And it just shows that the way that you harness these models is so so important. And I think people underappreciate that intelligence, you know, the reasons that humans are high agency and can do a lot is not just because we have high, you know, kind of just raw recall and IQ and knowledge, but there's all these different microervices in your brain. How do you, you know, put knowledge away? How do you retrieve it? How do you trigger things? Emotions are a way to trigger all these different microservices. And you know that's why a great investor might have great judgment is because they have you know a good spidey sense for when they see this sort of thing in the market it actually triggers the recall from this event that informs a creative decision. All those kind of small microservices are things that need to be built out. If you if I force you to become an investor and your only goal is to invest in rogo like businesses like one of these businesses that's let's say a vertical application that's wiring up I think about it as just like wiring up the capabilities of AI to an industry. What features would you look for that would get you the most excited either in the industry or in the founder builder and their approach based on what you've learned?

Gabe： 你想想 Claude Code 刚出来时、还有 Claude Cowork，跟 OpenAI 和 ChatGPT 之间的根本区别。模型其实差不多，但 Claude 的 harness 和它的呈现方式要好得多。它让模型能更充分地发挥长时间运行的能力。这就是为什么它们的使用量一路飙升、扩张得非常猛。这也说明，你怎么用 harness 驾驭这些模型，是极其极其重要的。我觉得人们低估了智能这件事——人之所以有很强的能动性、能干很多事，不只是因为我们有很强的原始记忆力、智商和知识，而是因为大脑里有各种各样的微服务：你怎么把知识存起来？怎么把它调出来？怎么触发各种东西？情绪就是触发这些不同微服务的一种方式。这也是为什么一个优秀的投资人可能有很好的判断力——因为他们有一种很灵的直觉（spidey sense），当他们在市场上看到这类情况时，就会触发对过去某件事的记忆，进而促成一个有创意的决策。所有这些小小的微服务，都是需要搭建出来的东西。

Patrick： 如果我逼你去当投资人，而且你唯一的目标就是投资像 Rogo 这样的公司——也就是那种，比如说，垂直应用，把……我把它理解为把 AI 的能力接入某个行业——基于你学到的东西，你会看重哪些特征？无论是行业层面，还是创始人、建设者以及他们的打法层面，什么最能让你兴奋？

[21:30] Gabe

A few things. One is the industry actually does have to have enough complexity and depth in the types of data, the types of systems of record that people use, the types of deployment models that you can spend a lot of effort solving those problems in order to have a wedge to solve everything else. Because if it's an industry that anyone can just, you know, walk into and sell the basic version of chatbt or co-work immediately and you don't have to solve all these weird integrations, you're not going to have enough time to build all those things that are perpendicular to what you're doing. And so the industry itself needs to be adjacent enough to the core market. So that's one thing. Uh the second thing I would look for is, you know, domain expertise from the team and the founders. And I don't have unique domain expertise, but I had enough to get started. And then I was so curious about finance and money and capital markets. And I grew up in New York and I was surrounded by people that all they could think about, all they could talk about was high finance. And I was fascinated by it and I wanted to learn about it. And then we assembled a team that was uniquely passionate about it too. And so I have over a hundred people that have spent time within investment banks or within investment firms across the world's best institutions. And so we can constantly take the models as they're released and harness them for finance.

有几点。第一，这个行业确实得有足够的复杂度和纵深——数据的类型、大家使用的记录系统的类型、部署模式的类型——让你可以投入大量精力去解决这些问题，从而拿到一个切入点（wedge），再去解决其他所有问题。因为如果这个行业谁都能直接走进去，马上卖一个基础版的 ChatGPT 或 Cowork，根本不需要解决各种奇奇怪怪的集成，那你就没有足够的时间去搭建那些跟你眼下所做之事正交的东西。所以这个行业本身得离核心市场足够近。这是第一点。第二点，我会看团队和创始人的领域专长。我自己并没有什么独一无二的领域专长，但足够起步。而且我对金融、钱和资本市场特别好奇。我在纽约长大，身边的人满脑子想的、嘴里聊的全是高端金融。我为之着迷，想要了解它。然后我们组建了一支同样对它充满热情的团队。所以我们现在有一百多人曾在投行或投资机构工作过，都来自全球最顶尖的机构。这样我们就能在模型每次发布时，持续把它们驾驭好、用到金融上。

[22:43] Gabe

and our job is really to catch the change in the models and figure out how to apply it into these institutions. The final big thing I would look for is a business that's willing to constantly reinvent the core product and constantly willing to slash it to nothing. And I think anyone who you know their kind of delivery method of their product is not something that they can fully cannibalize quickly like a terminal or like a very specific UX or interface is not going to be agile enough to constantly reinvent every 6 months when there's a step change in the

我们的活儿，说白了就是捕捉模型的每一次变化，想清楚怎么把它用进这些机构里。我最后要看的一件大事，是这家公司愿不愿意不停地重造自己的核心产品，愿不愿意随时把它砍到归零。在我看来，如果一家公司交付产品的方式没法被自己迅速彻底地革掉——比如一个终端，或者某种非常特定的 UX、界面——那它就不够敏捷，做不到每 6 个月就重造一次，每当出现阶跃式变化的时候

[23:17]

what's an example of that that you've done like a tear it down and rebuild. I mean I the the story the anecdote I'm most inspired by is Max Lebchin who talks about how at a firm they rebuild the kind of fundamental ledger technology every year and they rebuild it for a few reasons. One is it's the most interesting engineering problem and so all the engineers want to work on it and so it's a good way to retain talent to teach engineers about the core fundamental business of a firm but then number two it's a good way to make sure that system doesn't oify and it's constantly improving and so we do that same exact thing for our harness and the kind of core agentic system we are constantly looking at it realizing we're not even at a local minima it would be impossible if we were at a local minima because the models are changing so quickly and we need to redo the whole thing

Patrick： 你们自己做过这种推倒重来的事吗？举个例子。

Gabe： 我、这个……最激励我的一个故事是 Max Levchin 讲的：在 Affirm，他们每年都会把最底层的账本技术重建一遍，原因有好几个。第一，这是最有意思的工程难题，所有工程师都想做，所以这既是留住人才的好办法，也能让工程师真正吃透公司最核心的业务；第二，这能保证这套系统不会僵化，而是一直在变好。我们对自己的 harness 和核心的 agent 系统做的是一模一样的事：我们一直在审视它，然后意识到我们连局部最优都还没到——也不可能到局部最优，因为模型变得太快了，我们得把整套东西重做一遍。

[23:59] Patrick

what's something that the models currently cannot do that if they could would really change the nature of the product.

有什么事是模型现在还做不到、可一旦做到就会彻底改变你们产品形态的？

[24:05] Gabe

Compaction. So it's you know if you are if you have 100 conversations with a single agent, how does it make sure that it's actually remembering the right things and compacting its memory into an amount of tokens that it can use every time and it has enough coherence and context on who you are and what you care about to make it feel like it's a true person that you're speaking to that you know learns more and more about you. Uh that's a hard problem and it compounds exponentially when you think about agents not just as a onetoone right now almost all agents are one to one you use chatbt individually you use copilot individually you use Gemini individually as soon as these are actually things that you know can sort with a lot of colleagues or in a slack channel with a hundred people have to work across an entire company now the compaction problem just scaled exponentially because it's having you know conversations with a hundred different people and needs to be able to coordinate across those things and so being able to take all that memory, all those interactions and actually lodge it into, you know, the kind of mental model or brain of that agent so that it can be persistent, so that it can actually maintain context over the course of a bunch of interactions. That's something that the models are not great at today.

压缩（compaction）。就是说，假如你跟同一个 agent 聊了 100 次，它怎么确保自己记住的是该记的东西、把记忆压缩到每次都用得上的 token 量里，同时对你是谁、你在乎什么保持足够的连贯性和上下文，让你觉得是在跟一个真人说话，一个越来越了解你的人？这是个难题。而且一旦你不再把 agent 只当成一对一的东西，难度会指数级叠加。现在几乎所有 agent 都是一对一的：你一个人用 ChatGPT，一个人用 Copilot，一个人用 Gemini。等到这些东西真能跟一大群同事打交道，或者待在一个一百人的 Slack 频道里、要横跨整个公司干活，压缩问题的规模就指数级放大了，因为它要跟一百个不同的人对话，还得在这些对话之间做协调。所以，怎么把所有这些记忆、所有这些交互真正沉淀进这个 agent 的心智模型、它的「大脑」里，让它能持久存在、在一连串交互中始终保持上下文——这是今天的模型还不太擅长的。

[25:11] Patrick

What do you think the major kinds of AI software businesses there are? So, we've got companies like a cognition or a cursor or something that can grow unbelievably quickly. And I'm especially curious for you to compare this like the old classification system for software companies. I'm curious how people buy rogo like what kind of category you would put it in. Is it usage based? Is it seatbased? Is it something else? Like talk us through the how people want to buy this stuff and sort of what the emerging models are for AI software businesses.

你觉得 AI 软件公司主要分哪几类？像 Cognition、Cursor 这类公司，能以难以置信的速度增长。我特别想听你拿它跟老的软件公司分类体系对比一下。我也好奇大家是怎么买 Rogo 的，你会把它归到哪一类？按用量计费？按席位定价？还是别的？跟我们讲讲，大家想怎么买这类东西，以及 AI 软件公司正在冒出来的商业模式都有哪些。

[25:37] Gabe

We are a classic enterprise software business. We price per seat right now because our buyers are used to pricing per seat. Uh they think of us, you know, in a similar category to Bloomberg to Faxet to Capital IQ to Pitchbook. Uh and so we have to build a very human business. Every time you sign a deal, it requires, you know, an AE and a solutions architect and a sales engineer going in shaking a lot of hands, explaining how it works, explaining how to integrate it. You can't sign one deal where the usage just, you know, rises 100fold. Uh I look at, you know, how hard it was for Anthropic to sell to us very easy and the amount that we pay them has risen exponentially without a human in the loop because it's a token consumption model. There's a lot of industries where just the, you know, riding the the coattails of token consumption isn't going to work for enterprise sales and we're one of those. Uh, and so it's actually pretty interesting because we have to build a go to market machine five times faster than most enterprise sales organizations ever have to build. And so I do think there's the category that's just typical enterprise sales but using, you know, AI models as a tailwind to build a hundred times better products. And then there are the the the token brokers, the token consumption businesses where you're selling in the parts of enterprise where they're used to buying usage based tools, cursor, factory, cloud code and and others. And so you can go much further commercially with fewer people.

我们是一家典型的企业软件公司。现在我们按席位定价，因为我们的买家习惯了按席位付费。在他们眼里，我们跟 Bloomberg、FactSet、Capital IQ、PitchBook 算是一类东西。所以我们得搭一门非常「靠人」的生意：每签一单，都需要一个 AE（客户经理）、一个解决方案架构师、一个售前工程师上门，握很多手，讲清楚它怎么用、怎么集成。你不可能签下一单，然后用量自己就涨个 100 倍。反观 Anthropic 把东西卖给我们有多难？非常容易。而我们付给他们的钱在没有任何人介入的情况下指数级上涨，因为那是按 token 消耗计费的模式。但有很多行业，光搭 token 消耗的顺风车，企业销售是走不通的，我们就是其中之一。所以这事其实挺有意思：我们得用比大多数企业销售组织快五倍的速度，搭起一套市场推广（go-to-market）机器。所以我确实认为有一类公司就是典型的企业销售，只不过借着 AI 模型的东风，做出好一百倍的产品。另一类就是 token 掮客、做 token 消耗生意的公司，它们卖进的是企业里本来就习惯买按用量计费工具的那些部门——Cursor、Factory、Claude Code 等等。这样你用更少的人，就能在商业上走得远得多。

[26:56] Patrick

What's your prediction for how or if that will change in finance?

你预测在金融领域，这一点会不会变、会怎么变？

[27:01]

I think that every business needs to go through two different pricing revolutions. You need to move to some sort of usage based and then you need to move to some sort of outcomebased. For me, if I can figure out a way to skip the skip the token base, skip the usage base, simplify it for my users, and just wait until I can say, "Hey, Patrick, what if I just charge you for every good investment idea I give you, or what if I charge you for the quarterly report you sell to send to LPs that I can do perfectly, or what if I charge you for every SIM that you create as a banker? I would much rather get there than have to figure out some, you know, random way of trying to assign dollars per token. That is, you know, something that we're not going to quite agree on because you're going to spend $100,000 on tokens and say, "Well, did I get $100,000 of value and I don't really know." But you know what the value is to you of a good investment idea because you can actually see how much money did I earn? Or you know what the value to you is if you can produce a sim if you're a bank because you know what you charge these, you know, these firms to actually sell the business. presumably you can't change your seat price like on the fly dynamically at least with the same customer. So how do you deal with the problem of like in some ways misalignment with the customer for your business where if you do a great job and they use the thing way more which costs you a lot of money they become a worse customer.

Gabe： 我觉得每家公司都得经历两场定价革命：先转向某种按用量计费，再转向某种按结果定价。对我来说，如果我能想办法跳过按 token、跳过按用量这一步，给用户把事情简化，一直等到我能说：「嘿，Patrick，要不我按我给你的每一个好投资点子收费？要不我按你要发给 LP 的季报收费，这个我能做得完美无缺？或者你作为投行人，每做一份 CIM（卖方出售公司时用的保密信息备忘录）我就收一份钱？」我宁可直接走到那一步，也不想去琢磨某种随便什么办法，给每个 token 定个价。这种东西我们双方很难谈拢，因为你花了 10 万美元买 token，然后会问：「那我到底拿到 10 万美元的价值了吗？」你其实也说不清。但一个好的投资点子对你值多少钱，你是知道的，因为你能看到自己到底赚了多少；或者如果你是一家投行，能做出一份 CIM 对你值多少你也清楚，因为你知道帮这些公司把生意卖掉时收多少钱。

Patrick： 想必你没法随时动态调整席位价格，至少对同一个客户不行。那你怎么处理跟客户之间某种程度上的利益错位？就是说，如果你们做得特别好，他们用得多得多，这会让你花掉很多钱，他们反而成了更差的客户。

[28:12]

The reality is is we are 1% of the way into our product roadmap. 99% of the innovation for capital markets is in front of us. And so what matters is that we're a good partner. We're a good steward of their AI strategy and they want to work with us in the future. It's so interesting to think about the shape of of this in the future and you said you're 1% penetrated into the road map. So give us a sense of where you think this is all going from your product perspective. I mean not not industrywide but if you're if you have that much of the road map ahead of you still just describe that to us. I mean, think about think about the percentage of all capital markets workflows, investment workflows, uh, investment banking jobs that are still completely human rate limited and done by human intermediaries. And it's kind of similar to other parts of of financial services where 15 years ago, 20 years ago, every mortgage that someone got, you went in, you spoke to a, you know, a banker at a local branch, it felt like a very human decision. I'm buying a house. I'm taking out a loan. This is important. I need to speak to someone. No one ever thought that you wouldn't want a human in the loop for that. Now, 40 to 50% of mortgages are just delivered online by platforms like Rocket Mort mortgage. I think there's going to be a huge amount of innovation in how companies transact, how companies raise capital, how companies raise debt.

Gabe： 现实是，我们的产品路线图才走了 1%，资本市场 99% 的创新还在前面。所以真正重要的是，我们是个好伙伴，是他们 AI 战略的好管家，他们将来还愿意跟我们合作。

Patrick： 想想这件事将来会长成什么样，真的很有意思。你说你们在路线图上才渗透了 1%，那从你们产品的角度讲讲，你觉得这一切会走向哪里？我说的不是整个行业，而是既然你们前面还有那么长的路线图，给我们描述一下。

Gabe： 你想想，所有资本市场的工作流、投资工作流、投行的岗位里，还有多大比例完全卡在人力上限、靠人当中间人来完成。这跟金融服务的其他领域有点像：15 年前、20 年前，每个人办房贷，都得走进本地支行，跟一位银行职员谈，那感觉是个非常「人」的决定——我要买房了，我要贷款了，这事很重要，我得跟个人聊聊。从来没有人想过这种事会不想要人参与。可现在，40% 到 50% 的房贷就是通过 Rocket Mortgage 这类平台在线办下来的。我觉得在公司怎么交易、怎么融资、怎么举债这些事情上，会有海量的创新。

[29:29] Gabe

I think it'll be easier than it's ever been in 10 or 20 years for someone who's a business owner or someone who works at a at a company to go online and, you know, click a button and try to raise capital the way that someone can go on to Robin Hood and click a button and buy an equity. I think it'll be take five minutes for KKR to figure out, can I sell this portfolio company to another sponsor, not 5 months. I think you're going to be able to price assets in an order of magnitude less time. And as a result, markets are going to be more transparent, more liquid, more efficient, and there's going to be a whole bunch more activity.

我觉得，对企业主或者在公司里上班的人来说，上网点一下按钮就去融资，会比过去 10 年、20 年里任何时候都容易，就像现在有人上 Robinhood 点一下按钮就能买一只股票一样。我觉得 KKR 要搞清楚「这家被投公司能不能卖给另一家 PE 买家」，只需要 5 分钟，而不是 5 个月。我觉得给资产定价的时间会缩短一个数量级。结果就是市场会更透明、流动性更好、更有效率，交易活动也会多得多。

[30:02] Patrick

So, I'm I'm going to focus on the specific future of automated risk pricing. I don't know, for lack of a better simple term.

那我想聚焦在一个具体的未来上：自动化的风险定价。姑且这么叫吧，我想不出更好的简单说法。

[30:09] Patrick

Um, you know, we're we're three years from now and everything you just said is true, where I can raise single-digit millions of dollars of debtor equity, kind of like filling out an online form and the thing can just price the risk for me and and give me an offer. Um it's like it's like open door for like everything or something. Um what do you need to build that you don't already have to enable that sort of capital market future?

嗯，假设现在是三年后，你刚才说的全都成真了：我要融个几百万美元的债或股权，就像填个在线表格，这东西直接替我给风险定价，然后给我一个报价。嗯，有点像万物版的 Opendoor 之类的。嗯，要实现那样的资本市场未来，你们还需要构建哪些现在还没有的东西？

[30:31] Gabe

It's actually a very similar strategy to Bloomberg strategy. Bloomberg strategy was I'm going to omit a lot of details here but offer a little bit of data to get in the door build all the analytics and workflows on top that someone would need and then provide the exchange and the communication platform where you can actually transact in a bunch of asset classes that before it was pretty opaque. Bloomberg messenger for us it's use a little bit of AI to get in the door build out the full workflows go from co-pilot chatbot to a full autopilot tool so I can make sure that I'm 100% accurate on your IC memo or on the the DDQs that you're doing and then provide the communication channel between counterparties so that if I have agents that can autopilot do the work I can actually transact for you and the difference between what I need to build in Bloomberg messenger is I don't need to build the communication channel for humans to transact I need to build the communication channel for the agents to transact across these businesses across these investment firms. And so if you think about what is the actual infrastructure that needs to get built, I mean, think about what is the system that would allow, you know, a large private equity firm to feel comfortable having an agent negotiate a deal on its behalf, correspond with all the third party consultants in the transaction, the people doing the QV, you know, the legal adviserss and so on, and then actually run an auction process where you have a bunch of sponsors, you know, providing bits. There's a huge amount of software to be built out. Uh and I think you know sometimes we talk about that as looking like an exchange for a lot of these asset classes that are not standardized.

其实这跟 Bloomberg 的策略非常像。Bloomberg 的策略是——这里我会省掉很多细节——先提供一点数据敲开门，再在上面搭起一个人需要的所有分析工具和工作流，然后提供交易所和通讯平台，让你能在一堆以前相当不透明的资产类别里真正完成交易，也就是 Bloomberg messenger。对我们来说，就是先用一点 AI 敲开门，把完整的工作流搭起来，从 copilot 式的聊天机器人走到完全自动驾驶的 autopilot 工具，这样我能确保你的 IC memo 或者你在做的 DDQ（尽调问卷）百分之百准确；然后在交易对手之间提供沟通渠道，这样如果我有能全自动干活的 agent，我就能真正替你完成交易。我要做的东西跟 Bloomberg messenger 的区别在于：我不需要为人和人之间的交易搭沟通渠道，我要搭的是让 agent 在这些公司之间、这些投资机构之间完成交易的沟通渠道。所以你想想，真正需要建的基础设施是什么——想想什么样的系统，能让一家大型私募股权公司放心让一个 agent 代表它谈判一笔交易，跟交易里所有第三方顾问来回沟通，比如做 QoE（盈利质量审查）的人、法律顾问等等，然后真的去跑一轮竞拍流程，让一堆 PE 买家来出价。这里有海量的软件要建。我觉得，有时候我们会把它说成：给很多非标准化的资产类别建一个类似交易所的东西。

[31:56] Patrick

Tell us a little bit about the customer base today. Like how much of it is giant banks versus investing firms versus you know public versus

跟我们讲讲你们现在的客户构成吧。大银行占多少，投资机构占多少，还有公开市场的和

[32:03]

it's mo it's it's mostly large banks and that's very simply because that was my background right I worked as an investment banker for just a handful of years doing buy side M&A coverage which was super interesting. And so we started targeting the banks pretty early on for a few simple reasons. One is the investment banks are kind of the distribution channel for the rest of finance. You know, a lot of the folks who then end up as great investors started, you know, in their first two years as an analyst at Goldman in a TMT program or something of that nature. Uh two, they have the most seats by far. And so if you can land a bank like Bank of America, you can actually get in the hands of far far far more people than if you land, you know, the the 10 best, you know, single portfolio manager, you know, public equities investors who each only have 10 investors. If I think about a Bank of America or something, everyone's kind of wondering how deep into the adoption curve are we for enterprises using AI. May maybe you have a biased sample because you know your customers are using Rogo and they're using it a lot, but G give us a sense of where you think we are. Seems really hard to pin down a good answer to this.

Gabe： 主要是——主要是大银行，原因很简单，因为那就是我的出身，对吧？我当过几年投行人，做买方并购（M&A）覆盖，特别有意思。所以我们很早就瞄准了银行，理由很简单，有这么几个。第一，投行某种程度上是整个金融行业其他部分的分发渠道。你知道，很多后来成了顶尖投资人的人，最初两年都是在 Goldman 的 TMT 项目之类的地方当分析师起步的。第二，他们的席位数遥遥领先。所以如果你能拿下 Bank of America 这样的银行，你真正能触达的人，会比拿下——比如说——十家最好的、单一基金经理制的公开股票投资机构多得多得多，那些机构每家只有 10 个投资人员。

Patrick： 拿 Bank of America 这类银行来想，大家都在琢磨，企业用 AI 到底走到采用曲线的哪一步了。也许你的样本有偏差，因为你的客户都在用 Rogo，而且用得很多，但还是给我们讲讲，你觉得我们现在处在什么位置。这事好像真的很难给出一个靠谱的答案。

[33:05] Gabe

Uh I would say that that the majority of firms are seeing a huge amount of individual productivity and they're trying to figure out how do we parlay that individual productivity into firm productivity that we can measure. I you speak to any individual banker at a bank we're deployed with.

我会说，大多数机构都看到了个人生产力的大幅提升，他们现在在琢磨的是：怎么把这种个人生产力转化成我们能衡量的机构整体生产力。你去跟我们已经部署的任何一家银行里随便哪个银行家聊聊。

[33:21] Gabe

They're like life's great.

他们都会说：日子太爽了。

[33:22] Gabe

Oh, they're like I'm I'm a hundred times more efficient than I used to be. Right? You'll speak to an MD who will say, "Gabe, I sent five pages to a client that, you know, before I would have had to go back and forth with an analyst on over 3 days to create and I made it in 10 minutes myself." And these are bankers who have who haven't done any sort of analysis in 25 years, right? Like they haven't actually opened an Excel file in 20 years and they're able to do it themselves. The problem is where's that flowing through? Are you winning more deals? Are you, you know, actually transacting more? Are you servicing a part of the market that you haven't seen? And this is where it becomes not just an individual productivity tool problem, but a firm strategy problem. Like what's your plan? Do you want to use this thing to cut costs? Do you want do you want to use this thing to enter parts of the market that before, you know, didn't make sense to serve? You can look at a bank like JP Morgan. JP Morgan just announced that they're going to try and do a lot more M&A work in for SMBs for parts of the market that before they didn't think it made sense to go out and serve because the deal fees were probably too small and so you needed too many people to staff them. Well, now if you have a banker that can be a deal team of one, well, maybe you can enter parts of the market that before, you know, just made no sense.

哦，他们会说：我现在的效率是以前的一百倍。对吧？你会碰到某位 MD（董事总经理）跟你说：「Gabe，我给客户发了五页材料，搁以前得跟分析师来回折腾三天才做得出来，现在我自己十分钟就搞定了。」而这些银行家已经 25 年没做过任何分析了，对吧？他们 20 年没真正打开过一个 Excel 文件，现在却能自己动手做。问题在于，这些效率最后流到哪儿去了？你赢下的交易更多了吗？你实际成交的更多了吗？你是不是开始服务一块以前从没碰过的市场？到这一步，它就不只是个人生产力工具的问题了，而是机构战略的问题。你的打算是什么？你想用它来降本吗？还是想用它进入那些以前觉得不值得服务的市场？你可以看看 JPMorgan 这样的银行。JPMorgan 刚宣布，要给 SMB（中小企业）做多得多的并购（M&A）业务——这块市场他们以前觉得不值得去做，因为交易费可能太低，而配齐一个团队又需要太多人。可现在，如果一个银行家一个人就能顶一整个交易团队，那你也许就能进入那些以前完全不划算的市场了。

[34:28] Patrick

So, in some sense, the bottleneck at some point will be the creativity of the customer, not the like you can provision unlimited capability and you're soon going to be just relying on them figuring out the answer.

所以某种意义上，到某个时候瓶颈会变成客户的创造力，而不是——你能提供无限的能力，很快就只能指望他们自己把答案想出来了。

[34:42] Gabe

It's them figuring out what they want to do. Right? If you had a hundred great investors start here tomorrow working for you, h how would you channel that productivity? It would take you a while to figure out like what's the structure? Do they all work on different things? How much money do I give each of them? What do we attack?

是得靠他们想清楚自己想干什么。对吧？假如明天有一百位出色的投资人来这儿给你干活，你要怎么调度这股生产力？你得花好一阵才能想明白：组织架构怎么搭？每个人各做各的吗？每人给多少钱？我们主攻什么？

[34:56]

So interesting. Two years ago, the the most obvious question in this would have been about accuracy and people to use the word hallucinations, which like seems to have dropped out of the conversation. I can't remember the last time someone said hallucinations to me. What can you teach us about that problem? Ensuring accuracy where accuracy matters a lot to the decimal. Like what's the nature of that these days? It's still super important. Um, I think it's actually more important to be auditable than it is to be accurate. And obviously those two things are conflated. But what's really important is that I give you an answer. You know how to use it. And if it's not accurate and you don't know how to check it and it's hard to see where it came from, you can't use it at all whether it's accurate or not because you don't trust it. if it's accurate most of the time, but even when it's not, it's very easy to see the assumptions that went in where the data was pulled from, it's still actionable and it still saves you time. And then increasingly as these things go from co-pilot tools that you're just using for information retrieval to autopilots where you're trusting them not just to gather the information, but to execute on it, to have agency and actually make an investment decision or send an email, you need to have the full confidence that if you were to go back in and see why it made a decision, you would be able to understand why cuz you're going to need to debug it. And the same way that there's going to be individual investors that make horrible decisions and you need to go in and see what went wrong. Was there an incorrect data input? Did someone lie to them, you know, what was going on, you're going to need to do the same thing with agents.

Patrick： 太有意思了。两年前，聊这个话题最显而易见的问题会是准确性，那时大家总爱用「幻觉」这个词，现在好像已经没人提了。我都想不起来上一次有人跟我说「幻觉」是什么时候了。关于这个问题你能给我们讲点什么？在准确性要精确到小数点的场景里怎么保证准确？这件事现在是什么状况？

Gabe： 依然超级重要。不过我觉得，可审计其实比准确更重要。当然这两件事常被混为一谈。但真正要紧的是：我给你一个答案，你知道怎么用它。如果它不准确，你又不知道怎么核对，也很难看出它是从哪来的，那不管它准不准，你都根本没法用，因为你不信任它。反过来，如果它大多数时候是准确的，而即便不准确的时候，你也能很容易看到它背后用了哪些假设、数据是从哪拉来的，那它依然能指导行动，依然能帮你省时间。再往后，这些东西会越来越从 copilot 式的工具——你只是拿来检索信息——变成 autopilot 式的自动驾驶：你不仅信任它去搜集信息，还信任它去执行，让它有自主权，真的去做投资决策或者发邮件。这时你必须完全有把握：假如你回头去查它为什么做了某个决定，你能搞明白原因，因为你得去 debug 它。就像总会有个别投资人做出糟糕透顶的决策，你得去查到底哪里出了错：是数据输入有误？是有人骗了他们？到底发生了什么？对 agent 你也得做同样的事。

[36:19]

And then especially in parts of capital markets that you know have regulators that look at these things and need to make sure there's no foul play. If you can't explain why you made a decision and the data went in, that's not going to fly. You you were talking before about how you have to sell like a normal enterprise sales organization would, but you have to grow or can grow many multiples faster than like the fastest growing enterprise SAS companies of the last era. How do you solve that problem? Like you're rate limited by the speed of humans to some degree in enterprise sales. How do you hire enough people fast enough? Like how do you think about being able to grow at the right rate when you don't have the anthropic cognition API usage growth you know like like it's so easy for them to grow 10x it's much harder for you how do you how do you solve that

Gabe： 尤其是在资本市场里那些有监管机构盯着、要确保没有违规操作的领域，如果你解释不了自己为什么做了这个决定、用了哪些数据，那是行不通的。

Patrick： 你前面说过，你们得像一个普通的企业销售组织那样去卖，但你们又必须、或者说能够比上一个时代增长最快的企业级 SaaS 公司快好几倍地增长。这个问题你怎么解决？在企业销售里，你某种程度上受限于人的速度。你怎么能足够快地招到足够多的人？你们没有 Anthropic、Cognition 那种 API 用量式的增长——对它们来说涨 10 倍太容易了，对你们就难得多——这种情况下，你怎么考虑以合适的速度增长？你怎么解决这个问题？

[37:05] Gabe

the core problem we need to solve is how fast can you make a human being productive

我们要解决的核心问题是，你能多快让一个人真正上手、产生价值。

[37:09] Patrick

as a saleserson

作为销售？

[37:10] Gabe

a salesperson but but anyone else as a marketer as an SDR as a post salesperson how quickly can they understand our business and help push us forward and push customers forward ward and help our end users and so enablement and training people and constantly retraining people is the fundamental problem that we and I assume other fast growing enterprise startups have to deal with.

销售是一方面，但其他人也一样——市场人员、SDR（销售开发代表）、售后人员——他们能多快理解我们的业务，帮我们往前推、帮客户往前推、帮到我们的终端用户。所以赋能（enablement）、培训人、不断地重新培训人，是我们——我猜其他快速增长的企业级创业公司也一样——必须面对的根本问题。

[37:32] Patrick

So do you like use AI to build tools to do that?

那你们会用 AI 做些工具来干这件事吗？

[37:34] Gabe

Oh the internal tools we have are kind of magical. I mean so first off every internal conversation that happens at Rogo is recorded 100%. when anyone starts, we say, "Hey, just FYI, Patrick, that's how we do. It's always being recorded. It's always being, you know, filtered into the company brain." This is not in a kind of big brother situation. It's just everyone you're going to speak to is going to have granola or some meeting transcription running because they need to use it and they need to have excellent recall and they need to compound the knowledge that they have. And as a result, we just have this huge reservoir of information. And then we have all of these tools that people can use on top to say, "Oh, you know what? we're deploying with this sort of public equities firm in this sort of market. Have we ever served this kind of data before? Are there use cases that might be helpful? And you can pull in the conversation that a peer of your had three weeks ago and you'd never even met that person because they're stationed in Apac. And being able to sponge all that information in and then get it out to people when they need it is the core problem of enablement.

哦，我们的内部工具简直有点神奇。首先，Rogo 内部发生的每一场对话都会被 100% 录下来。每个人入职的时候我们都会说：「嘿，顺便说一声，Patrick，我们就是这么干的。一直在录，所有内容都会汇入公司大脑。」这不是什么老大哥式的监控。只是你要打交道的每个人都会开着 Granola 或者别的会议转录工具，因为他们需要用它，需要有极好的回忆能力，需要让自己的知识不断复利积累。结果就是，我们攒下了一个巨大的信息库。然后在这之上，我们有一大堆工具供大家使用，你可以问：「哎，是这样，我们要给这样一家公开市场股票投资机构、在这样一个市场里做部署。我们以前服务过这类数据吗？有没有可能用得上的用例？」然后你就能调出一位同事三周前的一场对话，而你们俩甚至从没见过面，因为他驻扎在 APAC（亚太区）。能把所有这些信息都吸收进来，再在大家需要的时候送到他们手上，这就是赋能的核心问题。

[38:29] Patrick

So, so maybe describe the internal brain.

那不妨讲讲这个内部大脑吧。

[38:32]

It's called Shrek for some reason cuz my engineers thought it would be hilarious to call it Shrek. You can actually there's a dashboard where you can see the swamp of everything that people are working on at any given time, but it's connected to all of our different systems. It is very prescriptive about what it knows our company goals are, right? Like it's like what are our values? What are the things we want to deliver to clients? What are our north northstar metrics? And so it can shape every answer and deliverable that way. And it's both proactive, proactive and reactive. someone can go in and say, "Hey, you know, I'm trying to get up to speed on how I should talk about uh model routing and how I should think about the value prop there for a very large institution and what the savings will be." And it can pull out all that information for you. But it can also say, "Hey, Patrick, I see on your calendar on Thursday, you're meeting with this sort of private credit firm. here's all the information you should know, all of the use cases that will resonate, and then all of the types of ROI metrics that firms we've worked with in the past would want to hear. So, what's the sales pitch to talent? Like, if you're, let's say there's somebody that if you landed them tomorrow would be transformative because they're so high quality or wellknown or whatever, what is the pitch to them to come work at RO versus go somewhere else that's exciting right now?

Gabe： 不知怎么就叫了 Shrek，因为我的工程师们觉得叫 Shrek 特别搞笑。你其实能看到一个仪表盘，上面是一片「沼泽」，显示任意时刻大家都在忙什么，而它连着我们所有的系统。它对我们的公司目标有非常明确的设定，对吧？比如：我们的价值观是什么？我们想给客户交付什么？我们的北极星指标是什么？所以它能按这些来塑造每一个回答、每一份交付物。而且它既是主动的，也是被动响应的。有人可以进去问：「嘿，我想快速搞懂该怎么介绍模型路由（model routing），面对一家超大型机构该怎么讲它的价值主张，能省下多少钱。」它就能把这些信息全调出来给你。但它也能主动说：「嘿，Patrick，我看到你周四日历上约了这样一家私募信贷机构。这是你该知道的全部信息、会引起共鸣的所有用例，还有我们以前合作过的机构想听的各类 ROI 指标。」

Patrick： 那你招揽人才的说辞是什么？比如说，有这么一个人，你要是明天就能把他招进来，那会是改变局面的事——因为他水平特别高，或者名气很大，诸如此类——你会怎么说服他来 Rogo，而不是去别的眼下也很火的地方？

[39:46] Gabe

Always depends on the person's motivation. So it's hard to give a generic pitch, but my pitch for rogo today for talent is AI is going to completely transform the world. The place that is going to be the most interesting is applied AI because that's where AI intersects with humanity. And so the companies that dictate how AI intersects with humans and touches humans are going to do the most interesting creative engineering and product work in the world. Finance is a domain that is the catalyst for all human progress and innovation. And capital allocation is upstream of the financing of every company, every idea, every economy. And so if you can make that more efficient, you can supercharge the world. And we're the category leading player who has the best shot on gold to not just be the hundred billion dollar business to do it, but the $500 billion business that completely transforms capital markets. And there's such a depth and a complexity and an amount of interesting problems that's so exciting. And we have a killer group of people that is super smart, hungry, curious, and low ego that's going to do it.

这永远取决于这个人的动机，所以很难给出一套通用说辞。但我今天招人时讲的 Rogo 是这样的：AI 会彻底改变世界。而最有意思的地方是应用 AI，因为那是 AI 与人类交汇的地方。所以那些决定 AI 如何与人交汇、如何触及人的公司，会做出世界上最有意思、最有创造力的工程和产品工作。金融是推动人类一切进步与创新的催化剂。资本配置处在每一家公司、每一个想法、每一个经济体融资的上游。所以如果你能让它更高效，就能给整个世界加速。而我们是这个品类的领头羊，胜算最大——不只是做成一家千亿美元的公司，而是做成一家彻底改变资本市场的 5000 亿美元公司。这里面的纵深、复杂度，还有那么多有意思的问题，实在太让人兴奋了。而且我们有一群超强的人——超级聪明、有饥饿感、有好奇心、没架子——会把这件事做成。

[40:47] Patrick

Good pitch. [laughter] Glad I joined. Glad I invested.

说得好。（笑）真庆幸我加入了。真庆幸我投了。

[40:51] Patrick

Say more about this capital markets piece. Like I I I do think that that is uh historically as markets get more efficient and liquid. Um their their positive impact in my opinion grows a lot. Uh and you can see this you you can chart this through market history which isn't that long 300 3 400 years of like proper proper markets.

再多讲讲资本市场这一块。我确实认为，从历史上看，随着市场变得更高效、流动性更好，它的正面影响在我看来会大幅增长。这一点你能看到，你可以沿着市场史把它画出来——市场史其实也没多长，真正像样的市场也就三四百年。

[41:12]

What do you think is possible? Like where might this be going and why do you believe that creating more uh or less friction I guess in capital markets can be so powerful. Look, you're always at risk of, you know, sounding like the the billionaire private equity guy, you know, saying that private equity is good for the world when you talk about how finance is good for the world. But I like to think about the origins of of high finance. And when you think about a business like JP Morgan, you know, some of the origins are JPont Morgan helping connect European investors with the entrepreneurs and in an emerging market, the United States to finance railroads and the infrastructure buildout and everything that allowed the US to be a juggernaut economy. And that happened because there were intermediaries who helped connect, you know, folks who were risktakers and capital allocators with the folks who were entrepreneurial and wanted to innovate. And that was something that had a profoundly great effect on the world. And now think about all the parts of the economy, all the parts of the US domestically, but also internationally that can't tap into capital markets, right? Every emerging nation where you would struggle to raise capital, to finance your idea, to raise debt. And then the 300,000 American businesses that couldn't even tell you what Goldman Sachs does and JP Morgan doesn't have the time to go out and work with them because the business is too small. If you're able to speed up the rate at which entrepreneurs and company founders and individuals can tap into capital markets, you can accelerate all innovation. Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up-to-date proof of trust without all the manual work so you can close deals faster and with less friction. Customers report a 526%

Patrick： 你觉得有哪些可能？这件事可能会走向哪里？你为什么相信，在资本市场里创造更多……或者说减少摩擦，能有这么大的威力？

Gabe： 你看，一谈金融对世界有多好，就总有可能听起来像那种亿万富翁私募股权（PE）大佬在说「私募股权造福世界」。但我喜欢去想高端金融的起源。拿 JPMorgan 这样的机构来说，它的起源之一，就是 J. P. Morgan 本人帮忙把欧洲投资人和一个新兴市场——也就是当时的美国——的企业家连接起来，为铁路、为基础设施建设，为所有让美国成为巨无霸经济体的东西提供融资。之所以能发生，是因为有中间人去牵线，把那些敢冒险的人、做资本配置的人，和那些有企业家精神、想要创新的人连接起来。这件事对世界产生了极其深远的正面影响。现在再想想经济中所有那些接触不到资本市场的部分——美国国内的，也包括国际上的，对吧？每一个你很难募到资本、很难为自己的想法融资、很难借到债的新兴国家。还有那 30 万家美国企业，它们甚至说不清 Goldman Sachs 是干什么的，而 JPMorgan 也没工夫去跟它们合作，因为生意规模太小了。如果你能加快企业家、公司创始人和个人接入资本市场的速度，你就能给所有创新提速。

Patrick： Vanta 把合规自动化，让你的团队少花时间在安全审查上，多花时间去拿客户。Vanta 能把审计准备工作减少 82%，还能即时给你最新的可信证明，省去所有手工活，让你成交更快、阻力更小。客户反馈获得了 526%

[43:03] Patrick

return on investment and more than 16,000 companies use Vanta, including Ramp, Harvey, [music] and Snowflake. Get started with $1,000 off at vanta.com/invest. Ridgeline is the first endto-end system of record with embedded AI for investment management firms running portfolio, accounting, reconciliation, reporting, trading, and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. I've been hearing from a lot of investment managers about AI and they fall roughly into two camps with some unsure where to even start and others convinced they can build their own order management system over a weekend. The reality is that running an investment firm will always require governance, controls, [music] and a single source of truth for your data. And no amount of AI enthusiasm changes that requirement. Ridgeline is built on exactly that foundation, which is why I believe that the firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation. And you can request a demo at ridgeline.ai.

的投资回报率，超过 16,000 家公司在用 Vanta，包括 Ramp、Harvey 和 Snowflake。到 vanta.com/invest 开始使用，立减 1,000 美元。Ridgeline 是首个为投资管理公司打造、内嵌 AI 的端到端记录系统（system of record），把投资组合、会计、对账、报告、交易和合规都放在同一个统一平台上运行。越来越多的机构正在告别老旧技术、转投 Ridgeline，因为 Ridgeline 的 AI 功能远远领先于投资管理软件领域的任何其他产品。我从很多投资经理那里听到他们对 AI 的看法，大致分成两派：一派不确定该从哪儿下手，另一派则确信自己一个周末就能搭出一套订单管理系统。现实是，经营一家投资机构永远需要治理、管控，以及数据的单一可信来源。再多的 AI 热情也改变不了这个要求。Ridgeline 正是建立在这个基础之上的，所以我相信，AI 时代胜出的机构，会是那些跑在 Ridgeline 统一平台上的机构。如果你对自家机构的 AI 战略是认真的，Ridgeline 就应该纳入考量。你可以在 ridgeline.ai 申请演示。

[44:12]

What are you learning from your peers that are building companies kind of shaped like yours, but in other categories? I am learning how much aggression it takes to grow this quickly. I am learning how much chewing glass it is on a day-to-day basis and how much conviction you need to have in the long term. Tam and Sam to make sure that you don't fuss over all the things that are going completely wrong every single day. And you know, I'll call someone like Winston at Harvey. Uh, and Winston's ability to just not worry about the hundred flesh wounds that are inflicted on him at any given time and just think about the kind of endstate goal of where he's going to be 3 years from now. And the only two things that matter to get there is pretty amazing.

Patrick： 那些在别的赛道、做着和你们形态差不多的公司的同行，你从他们身上学到了什么？

Gabe： 我学到的是，要长得这么快，需要多大的进攻性。我学到的是，每天日常要嚼多少玻璃（形容创业的痛苦煎熬），以及你得对长期的 TAM 和 SAM（总可触达市场和可服务市场）有多坚定的信念，才不至于去纠结每天都在彻底出岔子的那一大堆事。比如我会想到 Harvey 的 Winston。Winston 有种本事：任何时候身上都挂着上百道皮外伤，他就是不当回事，只想着终局目标——三年后他要走到哪里，以及要走到那里真正要紧的只有那两件事。这一点真的很了不起。

[44:58] Patrick

What is the glass like and what is the the aggression like? Like what does what does that mean?

这个「玻璃」具体是什么样的？「进攻性」又是什么样的？到底指什么？

[45:03] Gabe

I worked during co so I didn't even get to see what an office looked like. It's been a very, you know, we used, John, my co-founder and I used to joke that it's a very expensive business school education because we were just doing everything wrong. Didn't know how to hire, didn't know how to fire, didn't know how to mentor, didn't know how to manage, didn't know how to give feedback, didn't know how to set direction. And a lot of the people problems that arise with scaling quickly feel like eating glass to me anyway. Um, and so when people quit, when you have retention issues, when you, you know, spend six months recruiting a candidate, they don't join, that's chewing glass. when you spend a bunch of time working on a product that gets completely washed over by the next model that comes out and you know makes you feel like an idiot for expending all that time and capacity on something that was the wrong call. Chewing glass. Uh when you get rejected by 40 investors in a row before you're able to raise capital, chewing glass. And my experience of startup building is it's like a roller coaster where there's you have to feel the extreme highs and feel the extreme lows. And I'm a super emotional guy and I try not to let the team feel it. But like I will feel on top of the world at the high and like everything is cataclysmic at the low. But then if I look back at the journey, the lows get lower, the the highs get much higher. And I look back 3 months ago at the low I was dealing with or the high I was dealing with, I was like, what a joke. Like that, you know, I I could do that in my sleep now. Um, and I think it's just about modulating those things and channeling the emotion to push the business forward, but not letting it distract you too much.

我是在疫情期间开始工作的，所以连办公室长什么样都没见过。这一路，嗯，我跟我的联合创始人 John 以前常开玩笑说，这是一场特别贵的商学院教育，因为我们什么都做错了。不知道怎么招人，不知道怎么开人，不知道怎么带人，不知道怎么管理，不知道怎么给反馈，不知道怎么定方向。快速扩张时冒出来的很多「人」的问题，至少对我来说，就像在吃玻璃。所以，有人离职、留人出问题、你花六个月去招一个候选人结果对方没来，这就是嚼玻璃。你花了一大堆时间做一个产品，结果下一个模型一出来就把它彻底冲没了，让你觉得自己像个傻子，把那么多时间和精力砸在了一个错误的决定上。嚼玻璃。在拿到融资之前连续被 40 个投资人拒绝，嚼玻璃。我对创业的体会是，它就像坐过山车，极致的高峰和极致的低谷你都得亲身感受。我是个特别情绪化的人，我尽量不让团队感受到。但在高点我会觉得自己站上了世界之巅，在低点又觉得一切都要天塌地陷了。可回头看这一路，低谷越来越低，高峰也高得多。我回看三个月前我在应付的那个低谷或者那个高峰，会觉得，简直是个笑话，那种事我现在闭着眼都能搞定。我觉得关键就是调节好这些起伏，把情绪转化成推动业务往前走的力量，但别让它过度分散你的注意力。

[46:31] Patrick

What about aggression? Like what like that seems kind of like a trope.

那进攻性呢？这听起来有点像句陈词滥调。

[46:35] Gabe

So, uh, you've interviewed Patrady. Patrady was at our board meeting on on Wednesday and we presented what was extremely aggressive plan for next year in terms of hiring goals, commercial goals, product goals. And one of the reasons I love Pat is because he he boils everything down into like two bullet points and it's logically infallible and you know he's just like premise one premise two this is the result and he goes Gabe well if everyone in finance is going to make a buying decision on AI for the next in the next 18 months and they are definitely going to buy something no matter what even if you're not there then the only thing that matters is that you can blitz the market as quickly as possible to make sure that you are there given that do you think plan is aggressive enough or no. The answer was no. And the reason it wasn't aggressive enough is because I was being soft. And I think the reality is you need to be so so so aggressive and underwrite all that risk and know the game that you're playing. And my goal as a venture benture back business is to increase the tales of the distribution. It's fine if it gets 30% more likelihood that I fail if the odds that I become a hundred billion dollar company also increase by 20%. Uh, but you actually have to be be okay with raising both of those tales at the same time.

这么说吧，你采访过 Pat Grady。Pat Grady 周三来参加了我们的董事会，我们展示了一份明年极其激进的计划，涵盖招聘目标、商业目标、产品目标。我喜欢 Pat 的原因之一，是他能把一切浓缩成两个要点，而且逻辑上无懈可击，就是前提一、前提二、所以结论是这个。他说：「Gabe，如果金融行业的每个人都会在接下来 18 个月内做出 AI 采购决策，而且不管怎样他们肯定会买点什么，哪怕你不在场，那么唯一要紧的就是你能以最快速度横扫市场，确保到时候你在场。既然如此，你觉得这份计划够激进吗？」答案是不够。之所以不够激进，是因为我太软了。我觉得现实是，你必须非常非常非常激进，把所有这些风险都扛下来，并且清楚自己在玩的是什么游戏。作为一家风投支持的公司，我的目标是把分布的两端尾部都加厚。如果我失败的可能性高了 30%，但成为一家千亿美元公司的几率也提高了 20%，那没问题。但你得真能接受两端尾部同时变厚。

[47:49] Patrick

What's the most emotional low that you faced?

你经历过的情绪最低谷是什么？

[47:52] Gabe

When we were raising our series A, um, we we didn't have any kind of star investors in our cap table yet. Um, but there was a great investor, David Tish, at Box Group, who was an early preed seed investor who introduced me to a bunch of all-time greats for the for the series A. And I thought, "Wow, this is, you know, I I've seen what it does to get a blue chip in your cap table, what it does for recruiting talent for, you know, being a becoming more of a black hole for brand and customers and so on. This is finally the opportunity we're going to have to do that." We've been building for two and a half years and David introduced us to 40 40 investors and I met with everybody. I met with, you know, Sequoia, Kleiner, Benchmark, everybody. And 40 people passed. [laughter] And it wasn't just like, oh, you know, this is I you get the email with the deck and you it's not exciting. It was like, oh, this is interesting. Let me meet Gabe. Oh, I kind of like Gabe. Let me spend an hour with him. Oh, Gabe, come to IC. Oh, Gabe, let's go to dinner. Oh, Gabe, come in, you know, for the weekend afterwards. You know what? We're going to pass. And it's so personal cuz at that stage, it has nothing to do with anything but you, right? It's like someone it's like being broken up with by 40 [laughter] girlfriends who like every time you fell in love and then every time they said, "Ah, not for you." And that was actually Thrive too at the series A. Thrive spent so much time with me. Went to dinner with Avery and Vince. Fell in love with them and the firm because they were awesome. And then crushing blow. Um and luckily Keith Ra boy uh was the was you know came basically a month after everyone else had rejected us. And Keith was like Gabe this is kind of this isn't a contrarian bet. It's basically just Harvey for finance. Why would I do it?

我们在融 A 轮的时候，股东名单里还没有任何明星投资人。不过有一位很棒的投资人，BoxGroup 的 David Tisch，他是我们 pre-seed 阶段的早期投资人，为了 A 轮给我引荐了一堆史上最顶级的投资人。我当时想：「哇，我见识过股东名单里有一家蓝筹机构意味着什么——对招募人才有多大帮助，对让公司越来越像一个把品牌和客户都吸过来的黑洞有多大帮助，等等。这终于是我们能做到这一点的机会了。」我们当时已经做了两年半，David 给我们介绍了 40 位投资人，我每个人都见了。Sequoia、Kleiner、Benchmark，所有人我都见了。结果 40 个人都拒了。（笑）而且不是那种你收到一封附着演示文稿的邮件、看了觉得没意思的情况。而是：「哦，这挺有意思，我见见 Gabe 吧。哦，我还挺喜欢 Gabe 的，跟他聊一个小时吧。哦，Gabe，来我们投委会（IC）讲讲吧。哦，Gabe，一起吃个饭吧。哦，Gabe，之后那个周末再过来一趟吧。」然后：「你知道吗？我们还是不投了。」这太私人了，因为在那个阶段，一切都只跟你这个人有关，对吧？就像被 40 个女朋友甩了一样（笑），每次你都坠入爱河，每次她们都说：「唉，你不适合我。」其实 A 轮时 Thrive 也是这样。Thrive 在我身上花了很多时间，我跟 Avery 和 Vince 吃过饭，爱上了他们俩和这家机构，因为他们真的很棒。然后就是致命一击。还好 Keith Rabois 出现了，差不多是在其他所有人都拒绝我们一个月之后。Keith 说：「Gabe，这算不上什么逆向下注，这基本上就是金融版的 Harvey。我为什么要投？」

[49:30]

And I said Keith if it's not contrarian why did every single one of your friends just say it was a bad idea and not believe in me? Why do you think they didn't believe?

Gabe： 我就说：「Keith，如果这不算逆向，那为什么你那些朋友一个不落地都说这是个馊主意、都不相信我？」

Patrick： 你觉得他们为什么不相信？

[49:38] Gabe

For a lot of reasons. I think people underappreciated the TAM in finance, which was silly. But it's partly because I think SF has less of a less intuition for finance because they didn't fund ion group or Bloomberg or S&P or Faxbook, Paxet or Pitchbook, Morning SAR and so on. And so there actually haven't been great venturebacked businesses in this market. And so there's no intuition for the kind of contours of the market and how large it is. Two is the product was terrible. And so every investor said, "Oh, I work in finance. let me use it. Is it going to transform what I do? And they tried it and it was wrong half the time. They go, "This is never going to work." And it's like, guys, you know, we're on the exponential, right? Like it's like it's like it's going to work in 6 months or 12 months or 18 months or whatever it is. I'm going to figure it out. And the final reason was people didn't think I could figure it out. People didn't have enough data points of watching me chew glass and watching me figure out what the next iteration would be.

原因很多。我觉得大家低估了金融领域的 TAM，这挺可笑的。但部分原因在于，我觉得旧金山对金融的直觉要弱一些，因为他们当年没投过 ION Group、Bloomberg、S&P、FactSet、PitchBook、Morningstar 这些公司。所以这个市场其实从来没出过特别伟大的风投支持的公司，于是大家对这个市场的轮廓、它到底有多大，都没有直觉。第二，产品当时很烂。每个投资人都会说：「哦，我就是做金融的，让我用用看，它会不会彻底改变我的工作？」他们一试，一半时候都是错的，就说：「这永远不可能行得通。」我就想，各位，我们是在指数曲线上啊，对吧？它 6 个月、12 个月、18 个月之后，总之到某个时候就会行得通，我会把它搞定的。最后一个原因是，大家不认为我能搞定。他们没有足够多的数据点，没见过我嚼玻璃，没见过我琢磨出下一次迭代该是什么样。

[50:29]

What do you think changed after that? Cuz now you now now it's a now it's a who's who of of for Capable. Well, cuz I met all these guys very early on and I met them at every single round and every time I said, "Hey, we're going to do this." And they said, "Yeah, there's no way." And then every time we would do it. And by the way, a lot of the times it would

Patrick： 你觉得那之后发生了什么变化？因为现在你们的股东名单简直是一份名流录。

Gabe： 嗯，因为这些人我都很早就认识了，而且每一轮我都会去见他们，每次我都说：「嘿，我们要做成这件事。」他们说：「嗯，不可能的。」然后每一次我们都做成了。顺便说一句，很多时候它会

[50:45] Gabe

manifest in a different way. You know, you would actually lose the key employee you needed or that actually excellent customer that you thought you were going to land totally dissipated. But we kept figuring out what to do and kept navigating the market. And I think that's when when you're investing in, you know, in a market like today's especially in applied AI where it's so turbulent, so ambiguous, you need to underwrite the founders being able to be extremely dynamic.

以另一种形式冒出来。比如你真的失去了你最需要的那个关键员工，或者你以为能拿下的那个特别好的客户彻底黄了。但我们一直在想办法，一直在市场里找路走。我觉得，当你在今天这样的市场里投资，尤其是应用 AI 这种如此动荡、如此模糊的领域，你需要押注的是创始人具备极强的应变能力。

[51:09] Patrick

Why do you think there aren't more like pretty credible financial services competitors?

你觉得为什么没有更多真正有分量的金融服务领域竞争对手？

[51:13]

I think distribution is really hard to crack. I think it's far more a people problem on building trust and delivering value and working with these institutions than just an engineering and product problem. But then there's an enormously high engineering and product burden too. And so the the you know the standard to execute to really crack this market I think is is pretty high. And I was just so lucky that a number of the early people I hired were ex finance and just killers. And we just hired folks from inner network who worked at Goldminer City or Jeff or Apollo or Aries or Blackstone. And so we had a team of people who were diehard, ambitious, curious, smart, and then just good humans, low ego, humble, young enough to be open-minded and to want to e-class, too. I'm also really curious how how you've dealt with technical talent and like what matters in a technical person on your team and how that has evolved. One one intuition might be the value of domain expertise from the technical person has gone up as the ease of execution has has changed a lot. You know, you don't need to be super technical to write good code or less so than in the past. So, what does the shape of like the engineering or technical team look like over time? And I'm asking this again because I'm curious about thinking about other other companies that want to tackle this in whatever their industry might be.

Gabe： 我觉得分发（distribution）非常难攻克。比起单纯的工程和产品问题，它更多是一个「人」的问题：建立信任、交付价值、和这些机构打交道。但同时，工程和产品上的负担也极其重。所以要真正攻下这个市场，执行上的门槛我觉得相当高。我很幸运，早期招的好几个人都是金融出身，而且个个是狠角色。我们就从自己的人脉里招那些在 Goldman、Citi、Jefferies、Apollo、Ares 或 Blackstone 干过的人。所以我们有这样一支团队：肯死磕、有野心、有好奇心、聪明，而且人品好、不自负、谦逊，年轻到足以保持开放心态，也愿意去嚼玻璃。

Patrick： 我也很好奇你是怎么对待技术人才的：在你的团队里，一个技术人员身上什么最重要，这又是怎么演变的。一种直觉是，随着执行的门槛大幅降低，技术人员身上领域专长的价值上升了。你不需要多么硬核的技术背景也能写出好代码，至少比过去要求低多了。那么随着时间推移，工程或技术团队的构成是什么样的？我问这个，还是因为我在想其他那些想在各自行业里做同样事情的公司。

[52:32] Gabe

Yeah. So I would say there's definitely problems where uh domain expertise is increasing in value and product intuition and having more of a GM like mindset versus an engineer-like mindset is super super important. There are also parts of our product surface area where it's like you just need raw gritty engineering talent because you're scaling things up so aggressively. But if I look at the folks on our team uh who have been so excellent, a lot of them are former founders, right? They're folks who started businesses, persevered, ate glass, had a lot of product intuition, figured out how to channel it, and then, you know, the business may be petered out. And to to the point you made, uh, you know, there's been a lot of companies trying to tackle financial AI. There's been a lot of really smart product-minded, engineering-minded, domain experts who have tried to tackle finance AI, and we've acquired six different kind of fledgling financial AI startups. And so those former founders who can be kind of galaxyrained about the future of the product and navigate a course on what the UX should be but also have engineering chops and so they can constantly make the right decisions is super important.

是的。我会说，确实有些问题上，领域专长的价值在上升，产品直觉、以及更像 GM（总经理）而不是工程师的思维方式，都极其重要。我们产品里也有一些部分，就是需要纯粹、硬核的工程人才，因为你在极其激进地把东西规模化。但看看我们团队里那些特别出色的人，很多都是前创始人，对吧？他们创过业，坚持下来过，嚼过玻璃，有很强的产品直觉，摸索出了怎么把它用起来，然后，你知道，那门生意也许后来慢慢熄火了。说到你刚才的观点，一直有很多公司在尝试做金融 AI，有很多非常聪明、有产品头脑、有工程头脑的领域专家尝试过攻克金融 AI，而我们已经收购了六家这类刚起步的金融 AI 创业公司。所以，那些前创始人既能对产品的未来有「银河级大脑」式的宏大构想，为 UX 该是什么样规划出一条路，又有工程功底，因此能持续做出正确决策——这一点极其重要。

[53:34] Patrick

You said uh earlier to me before we were recording that this is the first time that we have like a major innovators dilemma in in this category. Can you describe what you mean by that?

录音之前你跟我说，这是这个领域第一次出现一场重大的创新者窘境。你能说说这是什么意思吗？

[53:43] Gabe

Yeah. Well, I I think for a lot of investment firms and a lot of high finance financial services, the last 10 20 years have been pretty good. you know, you can make a lot of money being a capital allocator, being an investor. Um, it's a very hard industry to enter and then especially if you're in private markets, you know, your businesses have some natural momentum because when you raise a fund and another fund and another fund, it's hard to, you know, mess up the business after that. And I'm sure there's a hundred people who have started funds who know it's a hundred times harder than I just described. But to be fair, there hasn't been a moment and a shock to the market where every investment firm, every bank is saying, "Oh, wow. I need to completely rethink what I'm doing. And now there's going to be an opportunity for hundreds of AI native disruptors, AI native investment firms, AI native investment banks to attack my business model. And I need to figure out what I'm going to do now. And there has not been an innovator's dilemma for private equity firms, for hedge funds, for investment banks in a long time.

是的。我觉得对很多投资机构、很多高端金融服务机构来说，过去一二十年都过得挺好。你知道，做一个资本配置者、做一个投资人能赚很多钱。这是个很难进入的行业，尤其是如果你在私募市场，你的生意自带一些惯性，因为你募了一支基金，又一支，再一支，之后就很难把生意搞砸了。我相信有一百个自己创办过基金的人会说，实际比我刚才说的难一百倍。但公平地说，过去从来没有过这样一个时刻、一次对市场的冲击，让每家投资机构、每家银行都在说：「哇，我得彻底重新想想自己在做的事了。现在会有成百上千家 AI 原生的颠覆者、AI 原生的投资机构、AI 原生的投行来冲击我的商业模式，我得想清楚我现在该怎么办。」私募股权机构、对冲基金、投行，已经很久没有遇到过创新者窘境了。

[54:38]

AI native, what does that term mean to you? Like what is the definition of that? In some ways, I think it just means willing to constantly reinvent everything you're doing and being so AI pill that you don't worry about what's possible or what you know what might seem completely far-fetched, but you are just charting a trajectory towards integrating this alien fundamental technology into everything that you do. And it's showing up in every part of the business. And there's no part of the business that you hold sacred that is immune to being revolutionized. How do you do that as like so you're a leader in a company that's very AI pill that want to make sure do you want to do everything you just said in rogo and yet I'm sure there are areas that like you're unhappy with the status of how much AI is used to do X Y or Z how do you do it as a leader like how do you make sure that your company keeps doing this as a practice and habit versus like a one time or

Patrick： AI 原生，这个词对你来说意味着什么？它的定义是什么？

Gabe： 某种程度上，我觉得它就意味着愿意不断重塑你在做的一切，对 AI 深信不疑到不去操心什么是可能的、什么看起来完全天方夜谭，而是一心规划一条路径，把这项像外星来的、根本性的技术融进你做的每一件事。它体现在业务的每一个部分，没有哪个部分是你奉为神圣、可以免于被彻底革新的。

Patrick： 那你是怎么做到的？比如你领导着一家对 AI 深信不疑的公司，想确保你刚说的这一切在 Rogo 都能做到，但我相信肯定也有一些地方，你对 AI 在做 X、Y、Z 上的使用程度并不满意。作为领导者你是怎么做的？你怎么确保你的公司把这当成一种持续的实践和习惯，而不是一次性的或者

[55:36]

so I'll give you a very simple thing which is on a every month I have a report that gets sent to me that shows me at everyone at the company how much are using the various different AI tools we have both the ones we've procured the ones we've built internally all of these different things and I have a stack ranking within every division of the top five power users and the bottom five users and we post that everywhere and the bottom user in each division gets a print out with a dun cap and we post it around the office and it's hilarious but people know it's coming and by the way as we get bigger and fewer people know me and know I'm like you know it's kind of a joke and I'm funny they're actually terrified and I hope they're not terrified and I hope that changes is, but it's a strong incentive function to actually make sure you're using these things. If you were to list questions that like let's say there's someone sitting down that runs one of these firms that's been a great business, hard fought, but a great business and fairly these businesses tend to be quite simple from a organizational and technical standpoint. It's mostly humans, right? There's not a lot of not a lot of overhead in lots of these businesses. Um maybe they buy a lot of data or something. What questions would you encourage themselves to ask of themselves, of their business to stand the best chance of navigating this transition effectively?

Gabe： 我给你讲个很简单的做法。每个月我都会收到一份报告，里面列出公司每个人用了多少我们的各种 AI 工具——既有我们采购的，也有我们内部自己做的，所有这些都算上。然后在每个部门内部做一个排名，列出用得最多的前五名重度用户和用得最少的后五名，我们把榜单贴得到处都是。每个部门垫底的那个人，会「获赠」一张戴着笨蛋帽（dunce cap）的打印图，我们把它贴满整个办公室，特别搞笑。不过大家都知道它每个月都会来。顺便说一句，随着公司越来越大，认识我的人越来越少，他们不知道这其实是个玩笑、不知道我这人挺逗的，结果是真被吓到了。我希望他们别害怕，也希望这种情况能改过来，但它确实是个很强的激励机制，能保证大家真的把这些工具用起来。

Patrick： 如果让你列一些问题——比方说，有个人坐下来，他经营着这样一家机构，生意一直很好，是辛辛苦苦打拼出来的，但确实是门好生意。而且说实话，这类公司从组织和技术角度看往往都很简单，主要就是人，对吧？很多这类公司没什么额外的开销。嗯，可能会买很多数据之类的。你会建议他们问自己、问自己的业务哪些问题，才能最有把握顺利度过这次转型？

[56:52] Gabe

I would say if you knew for sure that right now 90% of your enterprise value is in your people, right? Your best investors, your best bankers are the people that bring in deals, bring in revenue and actually that's what acrru enterprise value. And in 10 years the world's best investment firms, best banks will be have 90% of their enterprise value not in people but in software and data and systems. What would you start doing? You would start trying to figure out how do you take all of what lives in the latent minds of your best people and putting it into a system that you own and operate autonomously. So that's a big one. The second thing I would think about is unpack every part of the deal life cycle and in each one try and chart where do you think that like you are invaluable or you have data or you have domain expertise that no one else has and then be diligent about saying okay do I actually have something that no one else has in this market and is it a relationship is it context that no one else has or do I just think I'm smarter and better read on the subject area in the market in which case AI is going to obiate that.

我会说：假如你百分之百确定，现在你公司 90% 的企业价值都在人身上，对吧？你最好的投资人、最好的银行家，是那些能带来交易、带来收入的人，企业价值其实就是靠他们积累起来的。而 10 年后，全世界最好的投资机构、最好的银行，90% 的企业价值将不在人身上，而在软件、数据和系统里。那你现在会开始做什么？你会开始想办法，把你最优秀的那批人脑子里那些隐性的东西，全都装进一个你自己拥有、能自主运转的系统里。这是很大的一条。第二件我会考虑的事，是把交易生命周期的每个环节都拆开来看，在每个环节里试着标出来：你觉得自己在哪里是不可替代的，或者你手里有别人没有的数据、有别人没有的领域专长。然后老老实实地问自己：好，在这个市场上，我是真的有别人没有的东西吗？那是一段关系，还是别人拿不到的背景信息？还是说，我只是自以为在这个领域、这个市场里比别人更聪明、读得更多？如果是后者，AI 会把这点优势彻底抹平。

[57:57] Patrick

Have you seen anyone be like the most cutting edge exemplar of this attitude in a big firm? Like is there a favorite example of a person that's just

在大机构里，你见过谁是这种心态最前沿的典范吗？比如有没有你最喜欢的例子，某个人就是

[58:06] Gabe

Oh yeah, there's of this.

哦，有的，这种有……

[58:08] Gabe

There's a It's funny the the the folks that today look the most preient sounded crazy batchet two years ago, right? They they were the guys that came in and said, "We need to record every conversation. you know, you're going to be able to pipe in this conversation directly into my company brain and there's going to be a digital clone of me and then it's going to spit out the, you know, game theory on exactly what the investment should look like. And two years ago, everyone listened to those kind of guys and were like, "What the heck is Patrick talking about?" One example is there there's a co-founder of a of firm, Mullis, John Mumazi, who is was just preient about where it was going and he wanted digital clones of all their best bankers. He wanted systems that could basically show up to calls, speak on his behalf, know how he thinks, and then ingest that context. And then he wanted to build a system such that anyone in junior levels of that investment bank could leverage his expertise and context and relationships immediately. So that that context and that data wasn't just powering his ability to be revenue generating, but it was empowering the ability of every junior in that bank to be revenue generating. Um, and so there there are a number of folks who, you know, had their kind of move 37 moment where they realized, wow, this is going to be so much more profound than anyone's expecting.

有个……挺有意思的，那些今天看起来最有先见之明的人，两年前听起来简直疯得离谱，对吧？他们就是那种一进来就说「我们要把每一场对话都录下来」的人。「你知道吗，你以后可以把这场对话直接灌进我的公司大脑里，会有一个我的数字分身，然后它会直接吐出一套博弈论推演，告诉你这笔投资到底该长什么样。」两年前，所有人听这种人说话，心里都在想：「Patrick 到底在说些什么鬼？」举个例子，有一家机构 Mullis 的联合创始人 John Mumazi，他对这件事的走向就是特别有先见之明。他想给公司所有最好的银行家都做数字分身。他想要的系统，基本上能替他去参加电话会、代表他发言、知道他是怎么思考的，然后把那些上下文都吸收进去。接着他还想搭一个系统，让这家投行里任何一个初级员工都能立刻用上他的专业经验、上下文和人脉关系。这样一来，这些上下文和数据就不只是在支撑他本人创收，而是让这家银行里每一个初级员工都有能力创收。嗯，所以确实有不少人经历了他们自己的「第 37 手」时刻（AlphaGo 对李世石那步出人意料的妙手），突然意识到：哇，这件事的影响会比所有人预想的都深远得多。

[59:15] Patrick

What what to you feels the most uncertain about the future of of your business?

对你来说，你们业务的未来里，最不确定的是什么？

[59:20]

I would say the how quickly private markets actually do transform, right? If you if you think about why different types of asset classes um have increased in transaction volume and you know have increased in liquidity often it has to do with standardization because it gets easier then to track those assets and trade them. Private markets have been immune to standardization because there's so much unstructured data. AI should fix that. That said, is there going to be a regulatory or market force that forces some additional standardization that really accelerates, you know, the the kind of transparency and liquidity you can have in private markets? That's a little bit out of my control. Um, the other thing is it is it's still unclear to me how much alpha will be left in the kind of human relationships, right? I talk about that micro cap M&A. It's very hard to imagine that if you're a small business owner and you've been building a business for 20 years and you want to make sure that if you hand that business off to someone else that you trust them that you can shake their hand that that can be fully automated. Um, but for a lot of sponsor owned businesses or things like secondaries or private credit or GPLP secondaries, I do think it can be fully automated. But how long it takes for, you know, the long tale of all these small businesses, all these medium-sized businesses that have to deal with generational turnover for them to get comfortable clicking a button to sell their business as opposed to shaking the hand to someone. That's that's a little up in the air to me. If you had several new young founders here with us and they were curious about how to navigate and interface with private markets investors, what have you what would you teach them? What would you tell them to to do to not do? My style for fundraising might not be everyone's style. I'm super direct. I'm super transparent about, you know, what I'm

Gabe： 我会说，是私募市场到底会以多快的速度真正完成转变，对吧？你想想看，不同类型的资产类别为什么交易量会上升、流动性会变好，往往跟标准化有关，因为标准化之后，这些资产就更容易追踪、也更容易交易。私募市场一直没被标准化，因为里面有太多非结构化数据。AI 应该能解决这个问题。话虽如此，会不会出现某种监管力量或市场力量，强制推动更多标准化，从而真正加速私募市场能达到的那种透明度和流动性？这有点不在我的掌控之内。嗯，另一点是，我现在还不太清楚，人际关系这一块到底还能剩下多少 alpha（超额收益），对吧？我前面讲过微型企业并购（micro cap M&A）。很难想象这种事能被完全自动化：如果你是个小企业主，花了 20 年把一家公司做起来，你想确保把它交出去的时候，接手的是你信得过、能当面握手的人。嗯，但对很多由 PE 等财务投资方（sponsor）持有的企业，或者二手份额交易（secondaries）、私募信贷、GP/LP 二手份额交易（GP 普通合伙人 / LP 有限合伙人）这类东西，我确实认为可以被完全自动化。可是这条长尾——所有这些小企业、中型企业，都要面对代际交接——要多久它们才能坦然地点一下按钮就把公司卖掉，而不是跟某个人握手成交，这对我来说还有点悬而未决。

Patrick： 如果这里有几位年轻的新创始人跟我们在一起，他们好奇该怎么跟私募市场的投资人打交道，你会教他们什么？你会让他们做什么、别做什么？

Gabe： 我的融资风格不一定适合所有人。我非常直接，也非常坦诚地讲，你知道，我在

[1:01:04] Gabe

worried about, where I want to go. Uh, but then you have to be very headstrong on that end state. And then I would say it's it's about reps and relationships. And the folks who come out of nowhere and lead the series C or the series D are the folks that I met at the seed and then the A and then the B and the C and they passed every time for all sorts of reasons, but they gather a lot more data on me and the business.

担心什么、想往哪里去。呃，但与此同时，你对那个终局必须非常执着。然后我会说，关键在于反复接触和积累关系。那些突然冒出来领投 C 轮或 D 轮的人，都是我在种子轮就见过的，然后 A 轮、B 轮、C 轮又都见了，他们每次都因为各种各样的原因没投，但在这个过程中，他们收集了关于我和这家公司的大量数据。

[1:01:25] Patrick

Anything that you would encourage people to not do?

有什么是你会劝大家别做的吗？

[1:01:28] Gabe

I think there's a lot of the fake it till you make it. Uh and you need to have the bravado and the confidence in what in what you're doing even if you're not fully confident. And I'm a deeply paranoid, deeply insecure, deeply scared person. But you need to put on the face and and say that you are confident about where you're going. Even in the moments where, you know, it's the eb and flow. It's the eb and flow. Um, and I think, you know, people can misinterpret that sometimes as like they need to pretend there's something they're not. I think that's not true. You need to believe that that 5% likelihood that you can be in a hundred billion dollar business is likely. And you don't need to pretend it's 100% likely, but you should be able to to delineate with very clear road map and strategy how it is possible that you can become an hundred billion dollar business and then have confidence that if those things play out, you will be.

我觉得外面有很多「装到你成功为止」（fake it till you make it）那一套。呃，你确实需要对自己在做的事有那股气势和自信，哪怕你其实并没有十足的把握。我本人是个极度偏执、极度没有安全感、极度害怕的人。但你得摆出那副样子，说你对自己要去的方向很有信心。哪怕是在那些……你知道，起起落落，总是起起落落的。嗯，我觉得人们有时候会把这一点误解成：他们得假装成一个自己不是的人。我觉得不是这样的。你需要相信，那 5% 的可能性——你能做成一家千亿美元的公司——是很有可能的。你不需要假装它百分之百会发生，但你应该能用非常清晰的路线图和战略讲清楚：你是怎么有可能成为一家千亿美元公司的，然后要有信心，只要这些事情按预期展开，你就能做到。

[1:02:22]

Why are you scared and insecure? I'm so paranoid about everything that can go wrong. And every day it feels like you're on the knife's edge of of a thousand things collapsing. And I think the reality of this sort of business building is that it's a game of compounding momentum. How do you do every small thing to just race a little bit faster downhill and I'm just scared the momentum will stop or you know you hit a roadblock and then you go off course and then you need to reatalize momentum. And it's so clear to me how hard it is to actually build a machine that gathers momentum. So if there's any stumbling block that kind of halts that velocity if I wasn't constantly petrified of those moments, I wouldn't be doing, you know, everything under the sun to prepare for them and make sure we can have

Patrick： 你为什么会害怕、没有安全感？

Gabe： 我对所有可能出错的事情都特别偏执。每一天都感觉像站在刀刃上，一千件事随时可能崩塌。我觉得做这种公司的现实就是：它是一场势能复利的游戏。你要怎么把每一件小事都做好，好让自己往山下冲得再快一点点？我就是害怕势能会停下来，或者，你知道，撞上一个路障然后偏离航线，接着你又得重新把势能积攒起来。我非常清楚，真正造出一台能不断积攒势能的机器有多难。所以，一旦有任何绊脚石让这种速度停下来——如果我不是一直对那些时刻怕得要死，我就不会想尽一切办法去为它们做准备，确保我们能有

[1:03:04]

What have we missed? Like what what what that you've learned about building a company like this in this era where everything feels like a jump ball. Like it feels like there's going to be a rogo or a Harvey or whatever for every place that there can be. Especially where the wiring last mile wiring is hard and it's not just going to be anthropic. It's one company to rule them all or open AAI. And yeah, what else have we missed that you think is really important to the experience so far of building the business? I think that there are going to be businesses that solve all these problems, but the businesses that do it are going to become black holes for talent and capital and brand. And they're going to be able to siphon in the resources to actually execute because AI is an amazing tailwind, but the execution bar is higher than it's ever been too to compete. And everyone is getting pulled into the big leagues and is having their welcome to the NFL moment. You have to move faster and be stronger and be more resilient and agile than you ever expected. And having the right team is more important than ever. And the right team these days is extremely extremely expensive. And so if you can't figure out what is the strategy to become a black hole for talent and capital as quickly as possible, I do think you are far more at risk at being roadkill of a lab or a company that can.

Patrick： 我们还漏了什么？就是，在这样一个一切都像争球（jump ball，谁都有机会抢）的时代，你在打造这样一家公司的过程中学到了什么？感觉每一个可能的领域都会出现一个 Rogo 或者 Harvey 之类的公司，尤其是那些最后一公里的接线工作很难做的地方，而不会是 Anthropic 或者 OpenAI 一家公司通吃（one company to rule them all）。所以，还有哪些我们没聊到、但你觉得对你目前为止做这家公司的经历非常重要的东西？

Gabe： 我觉得会有公司把这些问题都解决掉，但能做到的那些公司，会变成人才、资本和品牌的黑洞（把这些资源都吸过来）。它们能把资源都吸进来，真正把事情执行下去。因为 AI 是一股很强的顺风，但要参与竞争，执行的门槛也比以往任何时候都高。每个人都被拉进了大联盟，都在经历自己的「欢迎来到 NFL」时刻（新秀第一次被职业联赛狠狠撞醒）。你得比自己预想的动作更快、更强大、更有韧性、更敏捷。拥有对的团队比以往任何时候都重要，而如今对的团队极其、极其昂贵。所以，如果你想不清楚怎样才能尽快成为人才和资本的黑洞，我确实认为，你被某家 AI 实验室、或者某家做到了这一点的公司碾成路边牺牲品（roadkill）的风险要大得多。

[1:04:18]

When I do these, I always ask my favorite question last. What is the kindest thing that anyone's ever done for you? You know, I would say that I um I benefited so much by having parents who were enormously kind and generous and selfless, but it showed up in such different ways for my mother versus my father. And my mother's version of kindness was no matter what I did, I was amazing and smart and could do no wrong. Even though growing up that was absolutely not the case, but she instilled in me the confidence to believe in myself. Even when you were in those low trajectories where it felt like everything was going to go sideways and couldn't do anything right 40 nos in a row, idiot failing out, flunking out, whatever it is, no matter what, she she acted like I was maybe the smartest person on earth. And it's like that was irrational, but you need some of that irrational confidence that comes from just undying love. My dad was very different. You know, my dad if I came home and I had done something wrong would seething, could barely look at me, couldn't understand it, right? Like he was someone who was so disciplined, so good, had such high standards. And so his version of kindness was figuring out, you know, how does he understand who I am and why I am failing at this thing and then help me. And so, you know, he would sit down and and go over every detail with me even though he sometimes just couldn't even understand why I had the opportunities I had and I couldn't take advantage of them and take the time to make me better while staying true to his principles and his standards of excellence and his definitions of good, too. You're building a fascinating business. It's been so cool to watch it get better. Thanks for doing this with me. [music]

Patrick： 每次做这个节目，我都会把我最喜欢的问题留到最后：别人为你做过的最善意的事是什么？

Gabe： 你知道，我会说，我特别幸运，有一对非常善良、慷慨、无私的父母，但这种善意在我母亲和我父亲身上表现得截然不同。我母亲的善意是：不管我做了什么，我都是了不起的、聪明的、不会犯错的。虽然我成长过程中完全不是那么回事，但她给了我相信自己的信心。哪怕是在那些低谷期——感觉一切都要搞砸、什么都做不对，连着被拒 40 次，蠢得要命、挂科、被学校退学，不管是什么——无论如何，她都表现得好像我可能是这世界上最聪明的人。这其实是不理性的，但你需要一点这种不理性的自信，它来自那种永不消逝的爱。我爸就很不一样。你知道，如果我回到家，做错了什么事，他会气得不行，几乎没法正眼看我，完全理解不了，对吧？他是那种非常自律、非常正派、标准非常高的人。所以他的善意是：想办法弄明白我是个什么样的人、我为什么在这件事上失败，然后帮我。所以，你知道，他会坐下来跟我一起把每一个细节过一遍，哪怕他有时候根本理解不了，为什么我明明有那些机会却没能抓住；他还是会花时间让我变得更好，同时坚守他自己的原则、他对卓越的标准，还有他对「好」的定义。

Patrick： 你在做一家非常迷人的公司，看着它一路变得越来越好真的很酷。谢谢你来参加节目。

[1:05:59] Gabe

Thank you.

谢谢。

[1:06:03] Patrick

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