Gabe Stengel - 打造投资超级智能 - [像最佳投资者那样思考，第492集]

Ramp是唯一一个为了让你的财务团队更精简、更高效、更出色而打造的平台，平均每年为企业节省5%的开支，这样你就能专注于增长。

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刚刚在英国开始运营。

Ramp just opened for business in the United Kingdom.

所以，如果你在英国经营企业，现在就可以使用Ramp的AI驱动财务平台，在一个地方管理卡片、开支、账单、审批和会计。

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.

我自己就用Ramp来经营业务，你也应该试试。

I run my business on Ramp, and so should you.

了解更多信息，请访问ramp.com/invest。

Learn more at ramp.com/invest.

OpenAI、Cursor、Anthropic、Perplexity和Vercel都有一个共同点。

OpenAI, Cursor, Anthropic, Perplexity and Vercel all have something in common.

它们都在使用WorkOS，原因如下。

They all use WorkOS, and here's why.

要实现大规模的企业采用，你必须提供SSO、SCIM、RBAC和审计日志等核心功能。

To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC and audit logs.

这就是WorkOS发挥作用的地方。

That's where WorkOS comes in.

与其自己花几个月时间开发这些关键功能，不如直接使用WorkOS的API，从第一天起就拥有所有这些功能。

Instead of spending months building these mission-critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero.

这就是为什么你听说的许多顶尖AI团队都已经在使用WorkOS。

That's why so many of the top AI teams you hear about already run on WorkOS.

WorkOS是让你快速达到企业级标准并专注于最重要的事情——你的产品——的最快方式。

WorkOS is the fastest way to become enterprise-ready and stay focused on what matters most, your product.

访问workos.com开始使用吧。

Visit workos.com to get started.

过去几年里，我们多次讨论过这个基本问题，今天我就从这个开始说起。

You and I have talked many times about this basic question that I'll start with over the last couple of years.

你实际上是在努力打造投资超级智能工具，帮助投资者更快、更好、更便宜、更轻松、更高质量地完成工作。

You are effectively trying to build investing super intelligence tools to help investors do their job much faster, better, cheaper, easier, higher quality.

但现在感觉，哇，我们真的在取代很多核心职能，这些职能即使是几年前非常聪明的分析师甚至投资组合经理也在做。

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 of years ago.

你怎么看这个发展轨迹，到目前为止你看到并经历的，以及未来两年会走向哪里？

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?

我觉得两年其实比十年或二十年更容易想清楚，因为两年内，最优秀的投资者会开始琢磨如何重塑自己的公司和自我。

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.

如果你看看做市和量化交易领域发生了什么，Jane Street花了15年才建立起主导地位，而今天全球最优秀的投资者未来两到五年会花时间搞清楚如何把AI融入他们的工作。

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 two to five years figuring out how to integrate AI into what they do.

达里奥有句名言，说每个人都会有一个装满天才的数据中心，或者一个国家般的天才装进一个数据中心。

And Dario has the great line about everyone's going to have a data center full of geniuses, or a country full of geniuses in a data center.

高盛会怎么做？

What would Goldman do?

千禧年会怎么做？

What would Millennium do?

如果城堡投资突然有一整个国家的天才，他们会怎么做？

What would Citadel do if they had a country full of geniuses show up?

他们可能需要一段时间才能搞清楚如何改变工作方式，如何利用这些，如何把它整合到他们的体系里。

It would probably take them a while to figure out how to change the way they work, how to take advantage of that, how to integrate it into their system.

我觉得，对于每一位顶尖投资者来说，未来五年最大的挑战就是如何把AI应用到投资的全流程中。

I think figuring out how to apply AI into the investment life cycle is the biggest challenge over the next five years for every great investor.

很多公司都走过这样的路，产品一开始很烂……Cognition就很有名，现在他们的广告都直接说：“还记得Devin吗？”

There's been many companies on this trajectory where the product was... Cognition is very famous for literally their ads now say, "Remember Devin?

“现在它已经很好了。”

It's good now."

所以，现在很多明显很优秀的公司，产品也很棒，但都经历过AI业务产品很糟糕的阶段，而现在产品已经非常出色了。

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.

如果想想我们从原始模型上看到的几次能力跃升，你能不能用同样的思路来看Rogo的各个发展阶段和它的产品？

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 the eras of Rogo and its product?

比如，你随便划分几个阶段，我不知道你怎么划分，但每个阶段它能做什么，一直到今天。

Like, you pick how many eras it is, I don't know how you frame it up, but what it could do at each level up to and including today.

其实在Rogo正式启动之前，我尝试过两次。

I actually tried starting Rogo two times before we got started.

高中时，我有个朋友的爸爸是投行家，他想要一个应用，用来追踪两家上市公司合并时的股票兑换率。

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.

所以我们当时尝试用很老的人工智能技术来做这件事。

And so we tried using really old AI techniques to do that.

效果糟透了。

Terrible.

然后大学的时候，在GPT-3出来之前，我们发表了一篇关于AI辅助计量经济学和金融计量经济学的论文，当时也尝试商业化，但完全行不通。

And then in college, before GPT-3 came out, we published a paper on AI assistance for econometrics and financial econometrics, and we tried commercializing it at the time, and nothing worked at all.

后来我们真正开始创业，是在GPT-3发布的时候，那时候ChatGPT还没出来。

And then when we actually started the business, it was when GPT-3 came out, pre-ChatGPT.

所以Rogo早期的时候，大家都觉得它挺神奇的。

And so all the early days of Rogo, it was clear how kind of magical it was.

你可以演示一些很酷的东西。

You could demo things that were cool.

但实际什么都不好用。

Nothing worked at all.

我觉得真正开始有效果之后，阶段跟模型的迭代是紧密相关的，对吧？

I would say since things actually started to work, the eras are very tied to the model eras, right?

那是o1 Pro，然后可能是Opus 4-5。

It was o1 Pro, and then probably Opus 4-5.

o1 Pro是第一次让你获得足够的可靠性，至少作为一个好的搜索工具。

o1 Pro was the first time you got enough reliability where it was a good search tool, at the very least.

你可以说：“帮我计算这家公司过去12个季度的这个财务指标”，它能可靠地完成，不会烦人到让你想自己做。

You could say, "Help me calculate this 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.

然后随着Opus 4-5和去年年底，2025年年底，今年年初，模型基本上能胜任初级投资专业人士或初级银行家所做的任何事情，只要你给出正确的指令和上下文。

And then with Opus 4-5 and the end of last year, the end of 2025, end of 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.

我认为很多应用型AI公司存在先发劣势，因为你以为你知道世界的发展方向，想为它构建产品，但模型还没有完全到位。

I think there was a first mover's 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.

所以人们会尝试它，然后说：“这太糟糕了。”

And so people would try it and go, "This is terrible.

“这是垃圾。”

This is garbage."

你就有劣势。

You have a disadvantage.

对我们来说，我们也看到了这一点。

For us, we saw that too.

但现在我们看到的是，如果你对最终状态和模型的发展方向判断正确，并且一直在朝着那个方向努力，那么当它们达到那个水平时，效果就非常神奇了。

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.

对我来说，一个伟大的产品就是我们每天收到的所有反馈。

And for me, a great product is all the feedback we get every day.

人们整天都在说：“嘿，这正在改变我的工作方式。

People all day are saying, "Hey, this is transforming the way I work.

我每个月能节省数百小时。

I'm saving hundreds of hours a month.

我能做到以前从未做到过的事情，因此我变得更聪明，能够做出更好的决策。

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.

而且这让人感到愉快，我喜欢使用它。

And it's delightful, and I like using it.

它给我的日常工作带来了乐趣，因为用户体验、对细节的关注和工艺水平显然是为我和我的身份量身定制的。”

And 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."

所以这就是产品开发过程中最棒的部分。

And so that's been the best part of the product build.

你觉得这是因为你认真对待了合规、监管、工作流程和最后一公里的对接这些事，然后模型变得足够好，突然之间这些就变得非常有价值了？

And you would attribute that to you took seriously all the compliance, regulatory, workflow, last mile hookup stuff, and then the models became good enough and all of a sudden that was super valuable?

其实，还有一些小细节，就是要理解这些公司里的人是怎么工作的，然后为他们量身打造。

Well, there's also small details of understanding how someone within one of these firms works and building for it.

我给你举个例子。

I'll give you an example.

我们让银行的董事总经理可以非常轻松地通过电子邮件发送一份演示文稿的批注版，这本来就是他们通常做这些工作流程的方式，只不过以前是发给分析师，现在是发给我们的AI分析师，通过电子邮件，然后20分钟内就能返回批注，而不是等两天。

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 two days.

同时，还会通知交易中的初级分析师发生了什么，并让他们看到所有小批注的完整可追溯性，以防他们想发表意见。

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.

整个用户体验，整个流程，让这位十年没碰过电脑、但确实有iPad而且知道怎么在上面做批注的金融专业人士，更容易真正采用和使用AI。

And that whole UX, that whole flow, just makes it so much easier for this financial professional who has 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.

这些就是打造一个对特定终端用户来说很棒的产品的小细节，你只有对这份工作有那种直觉，才能知道这些细节。

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

它现在最前沿的能力是什么，最让你惊艳的是哪一点？

What is the bleeding edge of what it can do that impresses you the most?

什么样的工作？

What kinds of jobs?

我们现在在做的最酷的事情，就是把像Malt Book这样的创新应用起来。

The coolest things that we're working on is taking these innovations like Malt Book.

想象一下，如果对冲基金的每个投资经理手下有一万个智能体，它们整天在一起交流、讨论想法、翻阅笔记、高谈阔论，然后经过24小时的辩论，最后只给你一个点子。

Imagine if every PM at a hedge fund had 10,000 agents that were just fraternizing, talking about ideas, reading through the notes, pontificating, and then at the end of 24 hours of debate, just gave you one idea.

之所以能做到这一点，是因为投资者愿意为一个真正好的点子付五万美元，而在其他领域，很少有地方能让你为了一个简单的洞察花掉这么多算力。

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 expend that many tokens just for one simple insight.

不过我们现在看到的是，这些模型比我所认识的任何人都聪明，比和我打交道的任何人都聪明，关键就在于管道连接。

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.

把它连接到你的上下文，告诉它你的论点，告诉它你的工作方式，然后试着把它融入你的工作流程。

Connect it to your context, inform it about your thesis, tell it the way that you work, and try and integrate it into what you do.

所以，搭建所有这些管道来真正收集数据和上下文，对我来说才是最前沿的。

And so building out all the plumbing to actually collect that data, collect that context, is what is cutting edge to me.

对我来说，像这样的产品总是很有趣，你对它应该用来做什么有自己的看法，但在某种程度上，人们可以创造性地使用它，所以你会看到——它实际上揭示了人们想怎么用它。

It's always interesting to me for a product like this that 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-- It's sort of revealed how people want to use it.

如果我站在上帝视角看，现在是纽约的周一早上，很多Rogo用户可能正兴奋地用它。

If I adopted a god's-eye view of, it's Monday morning here in New York City, lots of Rogo users are probably fired up and using it right now.

如果我能看到每个使用这个产品的实例，我会看到什么？

If I could somehow see into every instance of the product being used, what would I see?

都是些什么人？

Who are the people?

主要的使用场景是什么？

What are the predominant use cases?

这些场景有多多样化？

How varied are they?

给我们讲讲现在录制时它是怎么被使用的。

Give us a sense of how it's being used right now as we record.

尽管在很多方面，我认为公开股票是AI最好的应用场景，因为所有数据都是可用的，所以关键就在于尽可能聪明。

Even though in so many ways I think public equities is the best application of AI because all the data's available, and so it's just about being as smart as possible.

这个说法有点太简单粗暴了。

That's a very brute force framing.

我们早期的用户，也就是核心市场，其实是我所说的交易撮合者，或者说是做交易的人，他们买公司、卖公司、帮忙协调交易。

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, helping coordinate transactions.

所以我们做的很多事情，一方面是让人变得更聪明，但实际上是帮他们完成交易。

And so a lot of what we do is both make people smarter, but actually do the deal making.

怎么准备数据室？

How do you prepare a data room?

怎么拆解数据室？

How do you unpack a data room?

怎么协调与第三方讨论数据室的电话会议？

How do you coordinate the call with the third parties to discuss the data room?

怎么处理从交易开始到结束的所有步骤？

How do you go through all of the initial steps through closing of a deal?

所以如果你从全局来看所有使用Rogo的人，他们要么是交易的卖方，要么是买方，他们用这个工具来准备所有的思路和材料，帮助完成整个交易，无论是把东西放进数据室，还是公司的模型。

And so if you took a bird's-eye view of all the folks using Rogo, 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 putting things into a data room, this is the company's model.

这是描述他们客户的PPT。

This is the PowerPoint that describes their customers.

这些是对DDQ问题中关于客户集中度的回答。

These are the answers to the DDQ questions on what customer concentration is.

或者，另一边的所有智能体都在快速分析数据，并将其与公司的投资理念进行匹配，然后说：“哦，太好了。

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.

这个集中度风险是不是低于我们为这只基金设定的风险承受范围？”

Is it lower than the concentration risk profile that we would want for this fund, too?"

系统的各个组成部分包括，人们通过所有常见的AI工具访问渠道来使用它，比如电子邮件、聊天机器人、主动提醒等等。

And then the components of the system are people access it via all the classic channels you would access an AI tool, email, chatbots, proactive alerts, and those kind of things.

但实际上，Rogo也深度嵌入了这些公司的许多后台系统。

But then Rogo's actually in a lot of the behind-the-scenes systems of these firms.

因为在处理交易时，不仅对参与其中的人员很重要，你还需要更新你的客户关系管理系统。

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.

你还需要更新你的投资组合监控系统。

You need to update your portfolio monitoring systems.

交易完成后，你还需要更新向有限合伙人（LP）传递信息的方式。

You need to update the way that you distribute information to your LPs after the fact.

所以我们一半的业务范围实际上是在冰山之下，根据人类在交易过程中的操作，与这些不同的记录系统进行交互。

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 doing over the course of the deal.

那么，你觉得什么时候能给公开股票对冲基金或类似机构的初级分析师提供同样的答案呢？

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?

他们的工作流程也有一定的步骤，但非常非常不同。

There's a process to their workflow as well, but it's very, very different.

你说得对，有趣的是，我的第一直觉是公开市场是做这件事的最佳场所，因为那里有大量的数据。

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.

你觉得这个转变什么时候会发生？

When do you think that transition happens?

我认为，对于我们的业务来说，我们需要具备成为优秀公开市场投资者所需的所有领域知识。

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.

我不知道需要什么。

I don't know what it takes.

我从来没做过这个。

I've never done it.

我花在那些擅长这件事的人身上的时间远远不够。

I haven't spent nearly as much time as I should have with the folks that are great at it.

我们需要引进这方面的专业人才，然后根据这些专业知识，想办法把我们建好的系统应用到那个市场。

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.

我非常有信心，我们构建的底层系统、工具和基础设施，在那个市场上会非常有价值。

I have extreme conviction in the fact that the underlying systems and tools and infrastructure we have built will be invaluable to that market.

但现在我们需要一位优秀的大厨，能把所有东西整合起来，打造出最终的产品，并为公开股票投资者提供最后一公里的服务。

But now we need the great chef who can figure out how to piece it together and create that kind of end-state product and that last-mile delivery for public equities investors.

董事会经常给我施压，让我考虑把理想客户画像扩展到核心银行业务之外。

I get pushed a lot by our board to think about expanding the ICP beyond just core banking.

但现实是，交易撮合这个垂直领域有太多的深度和潜在市场空间。

But the reality has been is there's been so much depth and TAM in this deal makers vertical.

而我的最终愿景是成为私募市场的完整基础设施，让人们能够高效交易，这对交易撮合者来说远比扩展客户画像重要得多。

And then for my end-state vision of actually being the full infrastructure for private markets where people can transact very effectively, that is far more important to the deal makers.

而公开股票市场呢，那些基础设施和交易所都已经存在了。

Whereas for public equities, all that infrastructure, all those exchanges already exist.

所以我想服务他们，因为我想服务那些最专业、最聪明的用户，他们对自己跟踪的公司和关注的行业有非常深入的了解。

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 follow.

我想让他们变得更聪明，因为那听起来特别酷。

And I'd like to make them even smarter because that sounds super cool.

但就算只专注在现在的领域，我也能打造一个非常非常大的业务。

But I can build a huge, huge business just concentrating where I am today.

有意思。

Interesting.

所以从这个角度看，在一个垂直领域打造AI业务的机会，往往在于那些基础设施还不完善的地方。

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.

对。

Yes.

然后把你现有的——

And then applying your-

……在这个基础上再叠加你的东西。

... thing on top of that.

没错。

Exactly.

私募市场之所以这么有吸引力，部分原因在于所有环节——协调、标准化、调研、实际交易——都是靠人完成的。

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.

而公开股票市场，很多流程都已经自动化了。

Whereas public equities, a lot of it has been automated.

根据你的了解，你觉得投资专业人士，无论是做公开市场还是私募市场的，应该考虑培养哪些技能，才能随着时间推移变得更有价值？又有哪些技能会逐渐变得不那么重要？

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 kind of obvious, the skills that become less valuable?

我们到底该怎么办？

What the hell are we going to do?

我觉得可以完成从尽职调查到投资委员会备忘录，再到处理反对意见等等这一整套流程。

I think can do the soup to nuts diligence and IC memo and objection handling and all this kind of stuff.

这至少是投资领域初级员工工作的一大部分。

Like, that's a big part of a job for at least a junior person in the investing world.

那你觉得几年后，哪些技能还会很重要？

So what skills do you think people will still matter a lot in a couple of years?

我想先说一句，我在金融行业工作过两年，所以我对这些人的了解程度和我对技术的着迷程度是一样的，我也很想搞清楚怎么去用这些技术。

I want to preface it all with, I worked in finance for two 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.

但是，世界上最优秀的投资者总有一种方法，能搞清楚什么才是重要的，并且在各种话题上运用自己的判断力。我觉得，这到底是不是人工智能最终能取代的，现在下结论还为时过早。

But 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 whether or not that is something that AI can eventually replace.

当“第37步”发生的时候，丽莎·多尔看到了我永远看不到的东西。如果这种情况开始出现在公开股票市场，那真的会改变什么才是重要的。

When Move 37 happened and Lisa Doll saw something I could never see, if that starts to happen in public equities, yeah, it's going to really change what matters.

如果这真的彻底改变了整个运作方式，那我认为核心技能就是那些能去实地收集数据、获取别人没有的模型输入的人。

And if that really changes all of how that works, 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.

如果你能花时间在实地，能跟专家交流，能建立一个能给你模型提供信息的人脉关系图，也许你不需要自己去计算对于一笔好的公开股票投资来说，你的“第37步”会是什么，但你可以用别人没有的数据来喂养你的模型。

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.

Vanta 自动化合规流程，让你的团队能花更少时间在安全审查上，更多时间去获取客户。

Vanta automates compliance so your team can spend less time on security reviews and more time getting customers.

Vanta 将审计准备时间缩短了82%，并且无需手动操作，就能即时提供最新的信任证明，这样你就能更快、更顺畅地达成交易。

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.

客户报告的投资回报率高达526%，超过16000家公司都在使用Vanta，包括Ramp、Harvey和Snowflake。

Customers report a 526% return on investment, and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake.

现在就去vanta.com/invest注册，立减1000美元。

Get started with $1,000 off at vanta.com/invest.

Ridgeline是首个集成了AI的端到端记录系统，专为投资管理公司打造，将投资组合会计、对账、报告、交易和合规统一在一个平台上。

Ridgeline is the first end-to-end system of record with embedded AI for investment management firms running portfolio accounting, reconciliation, reporting, trading, and compliance on one unified platform.

越来越多的公司正在从传统技术迁移到Ridgeline，因为Ridgeline的AI功能在投资管理软件领域遥遥领先。

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.

我经常听到投资经理们谈论AI，他们大致分为两派：一派不知道从何入手，另一派则自信能在一个周末内搭建自己的订单管理系统。

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.

现实是，运营一家投资公司始终需要治理、控制和单一的数据来源，无论AI多么火热，这些需求都不会改变。

The reality is that running an investment firm will always require governance, controls, and a single source of truth for your data, and no amount of AI enthusiasm changes that requirement.

Ridgeline正是基于这样的基础构建的，所以我相信，在AI时代脱颖而出的公司，将是那些运行在Ridgeline统一平台上的公司。

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.

如果你认真考虑公司的AI战略，Ridgeline应该成为讨论的一部分，你可以在ridgeline.ai上申请演示。

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.

也许现在是个好时机来谈谈流水线。

Maybe it's a good time to talk about the assembly line.

如果Rogo能产出一些真正有用的结果，我想了解产生这个结果的系统的每个组成部分。

If Rogo puts out some really useful output, I want to learn about each component part of the system that leads to that output.

我特别感兴趣的是你自己拥有、使用、购买、构建的数据，以及你是如何做的，你怎么看待数据，然后你怎么看待模型。

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.

这些都是问题，我既对你的具体业务感到好奇，但我也认为基本上每个行业都会出现像Rogo这样的业务。

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.

所以，我很好奇哪些可以抽象到其他专业服务或其他有趣的行业，这些行业也会受到AI进步的影响。

And so I'm curious what can be abstracted to other professional services or other verticals that are interesting as well that will fall to AI progress.

那么，请具体谈谈数据和模型的部分，以及它是如何随着时间演变的。

So talk us through specifically the data and model piece and how that's evolved over time.

Rogo早期就像个复杂的装置，有60个不同的模型调用。

The early days of Rogo, it was kind of this Rube Goldberg contraption where you had 60 different model calls.

一个问题进来，你会试着说：“Patrick在问哪些公司？”

A question comes in, you try to say, "What companies is Patrick asking about?

现在，他们的股票代码是什么？

Now, what are their tickers?

现在，我该怎么把这些股票代码输入到彭博或FactSet或某个内部数据集的API调用中？

Now, how do I feed those tickers into an API call to Bloomberg or FactSet or some internal data set?

现在数据回来了，我需要调用另一个模型来整合所有信息。

Now it comes back and I need to call a different model to pull it all together."

随着模型越来越聪明，你其实希望少一些预设步骤，少一些那种复杂的机械流程，而是想想什么是最简单、最好、最高质量的工具。

And as the models get smarter, you kind of want to be less prescriptive, less of a Rube Goldberg machine, and just think about what are the simplest, best, highest quality tools.

就像如果你有一个全世界最聪明的人明天来这里，想成为一名优秀的银行家和投资者，他们需要哪些工具，而这些工具不仅仅是聪明本身就能带来的，对吧？

In 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?

有哪些数据工具？

What are the data tools?

有哪些方法可以去收集信息？

What are the ways of going out and gathering information?

有哪些方法可以审计自己的工作？

What are the ways of auditing its own work?

那怎么把结果呈现出来，并推回到需要的系统里呢？

And then what are the ways of presenting it and pushing it back into the systems that it needs?

所以对我们来说，我们会花很多时间思考，一个优秀的银行家或投资者真正需要哪些数据输入来完成工作。

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.

然后我们会花很多时间考虑，当你做这份工作时，有哪些合规和监管要求，确保如果有一天特拉华州的法官要求AI的输入和研究可以被查阅，你确实是以正确的方式完成的，没有混淆信息。

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 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.

因为AI投资判断和AI银行家输出的现实是，你将能够看到这些结果产生的完整链条。

Because the reality of AI investment judgments and AI banker outputs is that you're going to be able to see the full lineage of how those things are created.

然后我们会花很多时间对这些模型进行基准测试，创建不同的评估和数据集，这样我们总能决定哪个性能最好，哪个从token角度看最便宜，哪个延迟最低，然后把任务分配给合适的模型类型。

And then we spend a lot of time benchmarking these models and creating different 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 tasks to the appropriate type of models.

我相信你最喜欢的问题是，你最终如何与Anthropic和OpenAI竞争，他们把金融视为一个大的、少数几个他们会谈论和思考的领域之一。

I'm sure your favorite question is how do you ultimately compete with Anthropic and OpenAI, who view finance as a big, one of the few categories that you see them talking about and thinking about.

从长远来看，你能做什么来与他们的能力或计划形成对冲？

What can you do in the long run that's counter-positioned against what they can or will do, do you think?

你要构建的东西，应该与他们的方向垂直。

You want to build things that are perpendicular to what they want to build.

有时候你可能会做一个聊天机器人，因为这样能更快进入市场，但你要知道，表面之下还有很多东西是那些实验室永远不会去做的，而我们为了金融或者其他垂直领域必须去构建。

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.

当你想到金融服务和资本市场时，有多少企业能仅仅通过深入那些工作流程、数据集和工作方式，就产生超过50亿或100亿美元的收入？

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?

在那些非常混乱、具体的问题之上，有巨大的市场空间和支出。

There's a huge amount of TAM and spend on top of very messy, specific problems.

整个金融业就是由各种不同的细分领域组成的，每个领域都有不同的数据集、不同的成功标准、不同的监管要求，我们可以通过深入这些领域，创建那些帮助管理它们的系统，也就是记录系统，来实现50亿美元的收入。

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.

对于Anthropic来说，那就像是在路边停下来捡一分钱，因为他们正走在从1000亿美元收入迈向一万亿收入的道路上。

That for Anthropic would kind of be like stopping on the side of the road to pick up a penny because they're on the pathway of trying to go from $100 billion in revenue to a trillion in revenue.

这些系统除了智能之外，还有太多深度需要真正去构建。

And there's so much depth to these systems that actually need to be built beyond just intelligence.

有没有一个你最喜欢的例子，就是那种你必须去处理最后一步的麻烦事？

Is there a favorite example of that, of some pain in the ass thing that you've had to wire up last mile things?

想想看，如果你因为处理交易或对市场有影响的交易而接触到重大非公开信息，那么你在可审计性、你能标记什么以及它如何进入投资公司或银行的内部系统方面，就有合规要求，这样如果你被审计或者监管机构想看你做了什么，你是否已经把所有那些管道都布置得完美无缺？

Think about If you are ingesting MNPI because you are working on transactions or you're working on deals that have an effect on the market, the 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 ever wants to see what you did, do you have all that plumbing in place that is pixel perfect?

这是一个例子。

That's one example.

另一个例子是，如果你真的想进行交易，比如你是一家大型上市公司要收购另一家大型上市公司，你需要来回发送数据，你实际上需要某种数据室，一个合规、安全、可靠的协调工具。

Another example is if you actually want to transact, 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.

而且理想情况下，它不应该只是一个静态的Dropbox文件夹，而应该与你的工作方式、你的智能体、你的工作流程紧密集成。

And ideally, it's not just some static Dropbox folder, but something that's plugged into the way that you do work, your agents, your workflows.

我觉得OpenAI或Anthropic永远不会想去做数据室这种业务。

And I don't think OpenAI or Anthropic will ever want to build a data room business.

如果你真的想成为整个高端金融和资本市场的交易平台，你不仅需要拥有智能，还需要拥有交易场所、沟通渠道、工作流程以及所有相关的数据输入。

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.

有意思，我本来问的是数据和模型，但听起来其实工具或基础设施可能是三者中最重要的。

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.

想想Claude Code刚推出时和Claude Cowork与OpenAI和ChatGPT之间的根本区别。

Think about the fundamental difference between Claude Code when it came out and Claude Cowork versus OpenAI and ChatGPT.

模型本身其实差不多，但Claude的工具和呈现方式要好得多，这让模型能更好地发挥其长期运行的能力，这也是他们使用量激增、大幅扩张的原因。

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 models to exercise more of their long-running capabilities, and that's why they had a run-up in usage and a huge amount of expansion.

这就说明，如何驾驭这些模型真的太重要了。

And it just shows that the way that you harness these models is so, so important.

我觉得人们低估了智能这一点，人类之所以有很强的自主性和执行力，不仅仅是因为我们有很强的记忆力和智商、知识，还因为大脑里有各种不同的微服务。

And I think people underappreciate that intelligence, the reasons that humans are high agency and can do a lot is not just because we have high raw recall and IQ and knowledge, but there's all these different microservices in your brain.

你怎么把知识存起来？

How do you put knowledge away?

你怎么把它调出来？

How do you retrieve it?

你怎么触发这些功能？

How do you trigger things?

情绪就是触发这些微服务的一种方式。

Emotions are a way to trigger all these different microservices.

这就是为什么优秀的投资者能有很好的判断力，因为他们在市场上看到类似情况时，有一种很好的直觉。

That's why a great investor might have great judgment is because they have good spidey sense for when they see this sort of thing in the market.

这实际上会触发对相关事件的回忆，然后帮助做出创造性的决策。

It actually triggers the recall from this event, then informs a creative decision.

所有这些小型的微服务都是需要逐步构建起来的。

All those kind of small microservices are things that need to be built out.

如果我强迫你成为一名投资者，而你唯一的目标是投资像Rogo这样的企业，也就是说，那种把AI能力与某个行业结合起来的垂直应用，你会寻找哪些特征，让你最兴奋，无论是行业本身，还是创始人或建设者的方法，基于你所学到的？

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 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?

有几个方面。

A few things.

第一，行业本身确实需要有足够的复杂性和深度，无论是数据类型、人们使用的记录系统，还是部署模式，这样你可以花大量精力解决这些问题，从而获得解决其他所有问题的切入点。

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.

因为如果这个行业任何人都能直接进入，马上销售ChatGPT或Cowork或Copilot的基础版本，而不需要解决那些复杂的集成问题，那么你就没有足够的时间去构建那些与你当前工作垂直相关的所有东西。

Because if it's an industry that anyone can just walk into and sell the basic version of ChatGPT or Cowork or Copilot 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.

因此，行业本身需要与核心市场足够接近。

And so the industry itself needs to be adjacent enough to the core market.

这是第一点。

So that's one thing.

第二点，我会寻找团队和创始人的领域专长。

The second thing I would look for is domain expertise from the team and the founders.

我并没有独特的领域专长，但我有足够的知识来起步，而且我对金融、金钱和资本市场非常好奇。

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.

我在纽约长大，周围的人都只想着、只谈论高端金融，我对此非常着迷，想要学习。

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.

然后我们组建了一个同样对此充满热情的团队。

And then we assembled a team that was uniquely passionate about it, too.

所以我有超过100名曾在全球顶级机构的投资银行或投资公司工作过的员工，我们可以不断利用新发布的模型，将它们应用到金融领域。

And so I have over 100 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.

我们的工作实际上就是捕捉模型的变化，并想办法将其应用到这些机构中。

And our job is really to catch the change in the models and figure out how to apply it into these institutions.

最后，我会寻找一个愿意不断重塑核心产品、并愿意将其彻底推翻重建的企业。

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.

我认为，如果一家公司的产品交付方式不是那种可以迅速被自己完全取代的，比如终端或特定的用户体验或界面，那么它就无法足够灵活地每六个月在技术出现重大变化时进行自我革新。

And I think anyone who their 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 six months when there's a step change.

你有没有做过这样的例子，比如推倒重来？

What's an example of that that you've done, like a tear it down and rebuild?

最让我受启发的故事是马克斯·列夫琴的，他提到在Affirm，他们每年都会重建核心账本技术，这么做有几个原因。

The story, the anecdote I'm most inspired by is Max Levchin, who talks about how at Affirm they rebuild the fundamental ledger technology every year, and they rebuild it for a few reasons.

第一，这是最有趣的工程问题，所以所有工程师都想参与，这也是留住人才、让工程师了解Affirm核心业务的好方法。

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 Affirm.

但第二，这也是确保系统不会僵化、持续改进的好办法。

But then number two, it's a good way to make sure that system doesn't ossify and it's constantly improving.

所以我们对我们的工具和核心代理系统也采取了同样的做法。

And so we do that same exact thing for our harness and the core agentic system.

我们不断审视它，意识到我们甚至还没达到局部最优。

We are constantly looking at it, realizing we're not even at a local minima.

如果我们已经达到局部最优，那是不可能的，因为模型变化太快，我们需要重新做整个系统。

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.

现在模型有哪些做不到的事情，如果它们能做到，会真正改变产品的本质？

What's something that the models currently cannot do that if they could, would really change the nature of the product?

压缩。

Compaction.

所以，如果你和一个智能体进行了100次对话，它怎么确保自己记住了正确的东西，并把记忆压缩成每次都能用的token数量，同时还能对你这个人以及你在乎的事情有足够的连贯性和上下文，让你感觉是在和一个真正的人说话，而且这个人越来越了解你？

So 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 learns more and more about you?

这是个难题，而且当你把智能体看作不是一对一的时候，这个问题会成倍地复杂化。

That's a hard problem, and it compounds exponentially when you think about agents not just as a one-to-one.

现在，几乎所有的智能体都是一对一的。

Right now, almost all agents are one-to-one.

你单独用ChatGPT，单独用Copilot，单独用Gemini。

You use ChatGPT individually, you use Copilot individually, you use Gemini individually.

一旦这些智能体真的要和很多同事打交道，或者在一个有100人的Slack频道里，要跨整个公司协作，那么压缩问题就成倍地扩大了，因为它要和100个不同的人对话，并且需要协调这些对话。

As soon as these are actually things that consort with a lot of colleagues or in a Slack channel with 100 people, have to work across an entire company, now the compaction problem just scaled exponentially because it's having conversations with 100 different people and needs to be able to coordinate across those things.

所以，能够把所有这些记忆、这些互动，真正嵌入到那个智能体的心智模型或大脑中，让它能够持久化，能够在多次互动中保持上下文，这是目前模型做得不太好的地方。

And so being able to take all of that memory, all those interactions, and actually lodge it into the 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.

你觉得主要的AI软件企业类型有哪些？

What do you think the major kinds of AI software businesses there are?

比如有像Cognition或Cursor这样的公司，它们可以增长得非常快，我特别想听听你把这个和旧有的软件公司分类体系做个比较。

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 to the old classification system for software companies.

我很好奇人们是怎么购买Rogo的，你会把它归到哪一类？

I'm curious how people buy Rogo, what kind of category you would put it in.

是按使用量收费吗？

Is it usage-based?

是按席位收费吗？

Is it seat-based?

还是其他方式？

Is it something else?

跟我们说说人们想怎么买这类产品，以及AI软件业务的新兴模式是什么。

Talk us through how people want to buy this stuff and what the emerging models are for AI software business.

我们是一家典型的企业软件公司。

We are a classic enterprise software business.

目前我们按席位定价，因为我们的客户习惯按席位付费。

We price per seat right now because our buyers are used to pricing per seat.

他们把我们和Bloomberg、FactSet、Capital IQ、PitchBook归为同类。

They think of us in a similar category to Bloomberg, to Fact Set, to Capital IQ, to PitchBook.

所以我们得建立一个非常人性化的业务。

And so we have to build a very human business.

每次签合同，都需要客户经理、解决方案架构师和销售工程师亲自上门，握手寒暄，解释产品怎么运作，怎么集成。

Every time you sign a deal, it requires an AE and a solutions architect and a sales engineer going in and shaking a lot of hands, explaining how it works, explaining how to integrate it.

你不可能签一个合同，使用量就暴涨一百倍。

You can't sign one deal where the usage just rises a hundredfold.

我看看Anthropic卖给我们有多难，其实很容易，我们付给他们的钱却呈指数增长，而且全程没有人工介入，因为这是按token消耗计费的。

I look at 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.

在很多行业里，靠token消耗模式来推动企业销售是行不通的，我们就是其中之一。

There's a lot of industries where riding the coattails of token consumption isn't going to work for enterprise sales, and we're one of those.

所以这其实挺有意思的，因为我们得建立一套市场推广机制，速度要比大多数企业销售组织快五倍。

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.

所以我觉得确实存在一个类别，就是典型的企业销售，但利用AI模型作为助力，打造出好一百倍的产品。

And so I do think there's the category that's just typical enterprise sales, but using AI models as a tailwind to build 100 times better products.

然后还有那些token中间商，也就是按token消耗计费的业务，他们销售的对象是企业中那些习惯按使用量付费的部门，比如Cursor、Factory、Cloud Code等等。

And then there are 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 others.

所以你可以用更少的人手在商业上走得更远。

And so you can go much further commercially with fewer people.

你预测金融领域会如何变化，或者会不会变化？

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

我觉得每个企业都需要经历两次定价革命。

I think that every business needs to go through two different pricing revolutions.

你需要转向某种基于使用量的定价，然后再转向基于成果的定价。

You need to move to some sort of usage-based, and then you need to move to some sort of outcome-based.

对我来说，如果我能找到一种方法跳过按token计费，跳过按使用量计费，简化用户的体验，然后直接等到我能说："嘿，Patrick，如果我按每个好投资点子收费，你觉得怎么样？"

For me, if I can figure out a way to 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?"

或者，如果我按你发给LP的季度报告收费，而我能完美地完成这些报告呢？

Or, what if I charge you for the quarterly report you send to LPs that I can do perfectly?

或者，如果我按你作为银行家创建的每个SIM卡收费呢？

Or what if I charge you for every SIM that you create as a banker?

我更愿意直接做到那一步，而不是费劲去琢磨怎么给每个token定个价格。

I would much rather get there than have to figure out some random way of trying to assign dollars per token.

这一点我们可能不太能达成一致，因为你可能会花10万美元在代币上，然后问自己：'我真的得到了10万美元的价值吗？'

That is 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?"

而我自己也不太确定。

And I don't really know.

但你知道一个好的投资想法对你来说价值多少，因为你确实能看到自己赚了多少钱；或者如果你是银行，你知道制作一个SIM卡对你的价值，因为你清楚你向这些公司收取多少费用来实际出售业务。

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 firms to actually sell the business.

大概你不能动态地改变你的座位价格，至少对同一个客户来说是这样。

Presumably, you can't change your seat price on the fly dynamically, at least with the same customer.

那么你如何处理与客户在某些方面不一致的问题呢？如果你做得很好，他们使用得更多，这会花费你很多钱，他们反而变成了更差的客户？

So how do you deal with the problem of, 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?

现实是，我们的产品路线图才完成了1%。

The reality is, is we are 1% of the way into our product roadmap.

资本市场99%的创新还在前方，所以重要的是我们是好伙伴，是他们AI战略的好管家，他们未来愿意和我们合作。

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.

想想未来的形态真的很有趣。

It's so interesting to think about the shape of this in the future.

你说你们才完成了路线图的1%。

You said you're 1% penetrated into the roadmap.

从你们产品的角度来看，你觉得这一切会怎么发展？不是整个行业，就是你们自己的产品。

Give us a sense of where you think this is all going from your product perspective, not industry-wide.

如果前面还有那么多路线图要走，跟我们说说吧。

If you have that much of the roadmap ahead of you still, describe that to us.

想想看，资本市场的工作流程、投资流程、投行的工作，还有多少是完全靠人力、靠人工中介来完成的？

Think about the percentage of all capital markets workflows, investment workflows, investment banking jobs that are still completely human rate limited and done by human intermediaries.

这跟金融服务的其他领域有点像，十五年前、二十年前，你办房贷的时候，得去当地分行跟银行经理谈。

It's kind of similar to other parts of financial services, where 15 years ago, 20 years ago, every mortgage that someone got, you went in, you spoke to a banker at a local branch.

那感觉完全是一个人工决策的过程。

It felt like a very human decision.

我要买房子。

I'm buying a house.

我要贷款。

I'm taking out a loan.

这很重要。

This is important.

我得找个人谈谈。

I need to speak to someone.

以前没人会觉得你不想让人参与其中。

No one ever thought that you wouldn't want a human in the loop for that.

现在，40%到50%的抵押贷款都是通过像Rocket Mortgage这样的平台在线完成的。

Now, 40% to 50% of mortgages are just delivered online by platforms like Rocket Mortgage.

我觉得在公司交易、融资、发债这些方面，会有大量的创新。

I think there's going to be a huge amount of innovation in how companies transact, how companies raise capital, how companies raise debt.

我觉得10年或20年后，对于企业主或公司员工来说，上网点个按钮就能融资，会比现在容易得多，就像现在有人能在Robinhood上点个按钮买股票一样。

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 company to go online and click a button and try to raise capital the way that someone can go onto Robinhood and click a button and buy an equity.

我觉得KKR要搞清楚能不能把这家投资组合公司卖给另一个赞助商，只需要五分钟，而不是五个月。

I think it'll take five minutes for KKR to figure out, can I sell this portfolio company to another sponsor, not five months.

我觉得资产定价的时间会大幅缩短，这样一来，市场会更透明、流动性更强、效率更高，而且会有更多的交易活动。

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.

我要重点讲讲具体的未来——

I'm going to focus on the specific future of-

对

Yes

……自动风险定价，暂时想不到更好的简单说法。

... automated risk pricing, for lack of a better simple term.

假设现在是三年后，你刚才说的都成真了，我能像填个在线表格一样，融到几百万美元级别的优质股权，系统直接帮我评估风险，给我一个报价。

We're three years from now, and everything you just said is true, where I can raise single-digit millions of dollars of better equity, kind of like filling out an online form, and the thing can just price the risk for me and give me an offer.

就像Opendoor一样，但适用于所有东西。

It's like Opendoor for everything or something.

要实现那样的资本市场未来，你现在还缺什么？需要建什么？

What do you need to build that you don't already have to enable that sort of capital market future?

其实这和彭博的策略非常相似。

It's actually a very similar strategy to Bloomberg's strategy.

彭博的策略是，我在这里省略很多细节，但先提供一点数据来打开门，然后在上面构建所有需要的分析和流程，最后提供交易所和通信平台，让你能在很多以前很不透明的资产类别上进行交易，就是彭博的Messenger。

Bloomberg's 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.

对我们来说，就是用一点AI来打开市场，把整个工作流程建立起来，从副驾驶聊天机器人变成全自动驾驶工具，这样我就能确保你的IC备忘录或者你做的DBQ百分百准确，然后提供对手方之间的沟通渠道，这样如果我有能自动驾驶完成工作的代理，我就能真正为你交易。

For us, it's use a little bit of AI to get in the door, build out the full workflows, go from copilot chatbot to a full autopilot tool, so I can make sure that I'm 100% accurate on your IC memo or on the DBQs 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.

和需要构建彭博终端通信平台不同的是，我不需要为人类交易构建沟通渠道。

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.

我需要构建的是让代理在这些企业、这些投资公司之间进行交易的沟通渠道。

I need to build the communication channel for the agents to transact across these businesses, across these investment firms.

所以，如果你想想真正需要构建的基础设施是什么？

And so if you think about What is the actual infrastructure that needs to get built?

想想什么样的系统能让大型私募股权公司放心地让代理代表它谈判交易，与交易中的所有第三方顾问——做Q of E的人、法律顾问等等——进行沟通，然后真正运行一个拍卖流程，让一堆赞助商提供报价。

Think about what is the system that would allow 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 Q of E, the legal advisors, and so on, and then actually run an auction process where you have a bunch of sponsors providing bids.

有大量的软件需要构建，我想有时候我们把它说成是许多非标准化资产类别的交易所。

There's a huge amount of software to be built out, and I think sometimes we talk about that as looking like an exchange for a lot of these asset classes that are not standardized.

跟我们说说现在的客户群吧，比如大型银行和投资公司各占多少比例。

Tell us a little bit about the customer base today, like how much of it is giant banks versus investing firms.

主要是大型银行，这很简单，因为那是我以前的背景。

It's mostly large banks, and that's very simply because that was my background.

我之前做过几年投资银行，主要负责买方并购，那个经历特别有意思。

I worked as an investment banker for just a handful of years doing buy side M&A coverage, which was super interesting.

所以我们很早就开始瞄准银行，原因很简单。

And so we started targeting the banks pretty early on for a few simple reasons.

第一，投资银行是金融行业其他领域的分销渠道。

One is that investment banks are the distribution channel for the rest of finance.

很多后来成为优秀投资者的人，最初两年都是在高盛的TMT项目或类似项目里做分析师的。

A lot of the folks who then end up as great investors started in their first two years as an analyst at Goldman in the TMT program or something of that nature.

第二，银行的席位最多，所以如果你能拿下像美国银行这样的客户，你就能接触到比拿下十个顶级单一投资组合经理多得多的用户，因为每个经理手下只有十个投资者。

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 more people than if you land the 10 best single portfolio manager public equities investors who each only have 10 investors.

如果说到像美国银行这样的企业，大家都在想，企业在使用AI方面到底走到了哪一步？

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?

你看到的样本可能有点偏，因为你的客户都在用Rogo，而且用得很多。

You have a biased sample because your customers are using Rogo, and they're using it a lot.

但你觉得我们目前处于什么阶段？

But give us a sense of where you think we are.

这个问题确实很难给出一个确切的答案。

It seems really hard to pin down a good answer.

我觉得大多数公司都看到了个人效率的巨大提升，他们正在想办法把这种个人效率转化为可以衡量的公司整体效率。

I would say 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.

随便问问我们部署了系统的银行里的任何一位银行家——

Speak to any individual banker at a bank we're deployed with-

他们就会说，"生活太美好了。"

They're like, "Life's great."

对，他们会说，"我比以前高效了100倍。"

Oh, they're like, "I'm 100 times more efficient than I used to be."

你会听到一位董事总经理说："加布，我发给客户五页材料，以前我得跟分析师来回沟通三天才能做出来，现在我自己十分钟就搞定了。"

You'll speak to an MD who will say, "Gabe, I sent five pages to a client that before I would've had to go back and forth with an analyst on over three days to create, and I made it in 10 minutes myself."

这些银行家已经25年没做过任何分析了。

And these are bankers who haven't done any sort of analysis in 25 years.

他们20年都没打开过Excel文件了，现在却能自己完成这些工作。

They haven't actually opened an Excel file in 20 years, and they're able to do it themselves.

问题是，这些效率提升体现在哪里呢？

The problem is, where's that flowing through?

你们是不是赢得了更多交易？

Are you winning more deals?

你们是不是真的在促成更多交易？

Are you actually transacting more?

你们是不是在服务以前没接触过的市场领域？

Are you servicing a part of the market that you haven't seen?

这时候，问题就不只是个人效率工具的问题了，而是公司战略的问题。

And this is where it becomes not just an individual productivity tool problem, but a firm strategy problem.

你们的计划是什么？

What's your plan?

你们是想用这个工具来削减成本吗？

Do you want to use this thing to cut costs?

你们是想用这个工具进入以前服务起来不划算的市场领域吗？

Do you want to use this thing to enter parts of the market that before didn't make sense to serve?

你可以看看像摩根大通这样的银行。

You can look at a bank like JPMorgan.

摩根大通刚刚宣布，他们打算为中小企业做更多的并购业务，进入那些以前觉得不值得服务的市场，因为交易费用可能太小，需要太多人手来操作。

JPMorgan just announced that they're going to try and do a lot more M&A work 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.

现在，如果有一个银行家能一个人组成一个交易团队，那也许就能进入以前完全没意义的市场。

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 just made no sense.

所以从某种意义上说，瓶颈最终会变成客户的创造力。

So in some sense, the bottleneck at some point will be the creativity of the customer.

你可以提供无限的能力，很快你就只能靠他们自己来找到答案了。

You can provision unlimited capability, and you're soon going to be just relying on them figuring out the answer to the question.

就是让他们自己想清楚要做什么。

It's them figuring out what they want to do.

如果明天有100个优秀的投资者开始为你工作，你会怎么引导这种生产力？

If you had 100 great investors start here tomorrow working for you, how would you channel that productivity?

你需要一段时间才能弄清楚结构是什么样的。

It would take you a while to figure out what's the structure.