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加布，谢谢你做这件事。
Gabe, thanks for doing this.
当然。
Of course.
是的。
Yeah.
谢谢你的到来。
Thanks for coming.
也许我们可以先从头说起，比如对于任何没听说过Harvey的人，这家公司是做什么的？
Maybe we can just start with, like, for anyone who hasn't heard of Harvey, what is the company?
你能谈谈你们的规模以及今天服务的对象吗？
Can you talk about the scale and who you serve today?
在Harvey，我们为律师事务所和大型企业法务团队构建人工智能。
At Harvey, we're building AI for law firms and large in house teams.
我们几乎拥有一千家客户，五百名员工。
We're almost at a thousand customers, 500 employees.
公司成立于三年半前，此后一直快速扩张。
Started about just over three and a half years ago, and so been kind of scaling quickly since then.
你们可以说是我们的早期种子投资者之一。
And kinda you guys were some of our OG seed investors.
所以，很高兴能来到这里。
So, yeah, good to be here.
也许从产品最基础的角度来看，为什么它不只是Copilot、TechVT或Claude呢？
Maybe from a most basic perspective on the product, why is it not just, you know, Copilot or TechVT or Claude?
是的。
Yeah.
我认为产品最初就是这么起步的。
I think that's how the product started.
当我们最初从OpenAI获得融资时，我们获得了对gbd四的访问权限。
So when we first raised from OpenAI, we got access to gbd four.
我认为从gbd三到gbd四的模型跃迁非常巨大，当时的想法就是直接把模型交给律师，让他们去试用。
And I think gbd three to gbd four was such a big model jump that the intuition at the time was just give the model to lawyers and have them play with it.
我认为这个行业文本量极大，因此仅仅与模型互动就能获得巨大价值。
And I think that industry was so text heavy that you got so much value from just interacting with the models.
然后，我认为一旦把模型交给律师，就会立刻遇到模型幻觉等种种问题。
And then I think as soon as you gave it to lawyers, you also ran into all of the sharp edges of the models of they hallucinate.
它们无法连接到我们所需的大量上下文信息。
They're not connected to a bunch of our context.
因此，我认为公司前两年的核心问题是：如何为律师构建一个围绕这些模型的IDE，将它们与律师个人高效工作所需的所有上下文连接起来。
And so I would say the past the kind of first two years of the company were how do we build essentially the IDE for lawyers around these models that connect it to all of the context you need to be productive as an individual lawyer.
但我想说，在过去一年及未来，我们解决的核心问题不再是如何提升单个律师的效率。
But I would say in the past year and going forward, the big problem we're solving is not how do you make individual lawyers more productive.
而是如何提升一组律师在处理同一个客户事务时的效率，更重要的是，如何提升整个律所在处理成千上万个客户事务时的效率和盈利能力。
It's how do you make a team of lawyers working on a client matter more productive, and more importantly, how do you make an entire law firm working on thousands of these client matters more productive and more profitable.
因此，我认为当问题扩展到这个规模时，我们所面对的许多挑战就不再是单纯的模型智能问题了。
And so I think when you get to that scale, a lot of the problems you're solving are not just model intelligence problems.
而是涉及编排、治理以及在规模化过程中必然遇到的所有企业级产品问题。
They are these orchestration, governance, and kind of all of the enterprise product problems that you run into at at scale.
你们也已经将业务从律师事务所扩展到企业，包括那些与内部法律团队和外部律师协同使用的大型公司。
You've also been broadening from just law firms into enterprises and to big companies using you in concert with both their in house legal teams and external counsel.
你能详细谈谈这一点，以及它是如何逐步发展的吗？
Can you talk more about that and how that's been evolving as well?
是的。
Yeah.
我们最初向最大的律师事务所销售产品，大约一年半前，这些律所开始向他们的客户展示Harvey。
So we started selling to the largest law firms, and something that started happening about a year and a half ago was these law firms started showing Harvey to their clients.
他们的客户既希望更有效地与律所协作，也希望直接在自己的内部部门使用这一工具。
And their clients both wanted to collaborate more effectively with their law firms, and they also wanted to use this directly in their in house department.
因此，我们最近宣布与沃尔玛达成了合作。
So we recently announced we signed Walmart.
我们正在与AT&T、多家财富500强企业、私募股权公司以及全球2000强企业合作，这些企业都是法律服务的最大消费者。
We're working with AT and T, a bunch of these Fortune 500 large private equity firms, Global two thousand, kind of the largest consumers of legal services.
我们正在构建一个面向内部法律团队的平台，用于处理他们内部的工作，例如合同管理，以及那些通常不会外包给律所的大量法律运营事务，同时也支持协作场景，比如我正在参与一项大型交易或诉讼。
And what we're starting to build is a platform for the in house teams to do the work that they do internally, so things like contracting and this long tail of all of the legal operations you need to do that you typically don't send out to law firms, but also the collaborative tissue of, I'm working on a large transaction, a litigation.
我需要外部的专业知识。
I need outside expertise.
我想安全地与我的律师事务所共享这些数据。
I wanna securely share this data with my law firm.
在这方面，围绕安全和数据隐私存在很多技术问题，我们希望解决这些问题，以便这些律师事务所及其客户能够有效协作。
And there's a lot of technical problems there around security data privacy that we wanna solve so these law firms and their clients can collaborate effectively.
我认为，对于你们这个主要是技术背景的观众来说，大多数人并不清楚法律工作流程究竟是什么样的。
I think for you know, we have a largely technical audience, but also most people don't know exactly what legal workflow looks like.
是的。
Yeah.
在我们真正开始合作之前，我想象中就是，嗯，这是什么意思？
I think before we really started working together, I imagined it as just like, well, what do you mean?
我就是给我的律师发邮件，他思考一下，
I, like, email my lawyer, he thinks about it and,
就像 是的。
like Yeah.
阅读一份文件，然后回传一些内容。
Reads a document and then sends something back.
对吧？
Right?
中间可能涉及修订批注，也许还有协商。
And there's, like, redlining involved somewhere, maybe there's negotiation.
是的。
Yeah.
你能描述一下，对你来说，工作流程意味着什么吗？
Can you paint a picture of just what workflow means to you guys?
对。
Yeah.
对。
Yeah.
我认为很多人一想到法律，就会想到消费者法律。
And I think a lot of people, when they think about legal, they think of consumer legal.
所以我有一份租赁合同，需要对此征求一些意见。
And so I have a lease, and I need to get input on that.
这和那些大型律师事务所的做法完全不同。
That's completely different than what these massive law firms are doing.
因此，我认为一个很好的例子是这些律所为风险投资公司或私募股权公司所做的工作，你们应该很熟悉，而且很多初创领域的人也都知道。
And so I think a really good example of what these firms are doing that, I mean, you guys will be familiar with and I think a lot of people in the startup space will be familiar with is what law firms do for venture capital firms or private equity firms.
嗯。
Mhmm.
所以，风险投资公司和私募股权公司主要做两件事。
And so VCs, PE firms, they do two main things.
一是募集资金，二是进行投资。
You raise money, and you invest it.
所以主要的
And so the main
我们还做播客。
And we do podcasts.
是的
Yeah.
还有播客，这其实也很重要，但那里的法律工作较少。
And podcasts, which is actually important, but there's less legal work there.
而你需要做的重要事情包括基金设立。
And and the important things you need to do there are fund formation.
那么，我该如何结构化这个将持有所有资金的实体呢？
So how do I structure the entity that is gonna hold all that money?
听起来很简单，但如果你是一家大型私募股权公司，会有主权财富基金加入，并说我们需要按照这种方式结构化，因为涉及税务影响。
And it sounds easy, but if you're a large private equity firm, you have a sovereign wealth fund that comes in, and they say, we need to structure it in this way because of tax implications.
嗯
Mhmm.
然后还有养老基金，它们有其他要求。
Then you have a pension fund that has these other requirements.
因此，最终这变成一个极其复杂的过程：如何起草有限合伙协议，这份协议可能长达100页。
And so it ends up being this incredibly complex process of how do you draft the limited partnership agreement, which can be a 100 pages.
每个投资者在投资时，你可能会有100个，而他们每个人都有一份修改条款的附属协议。
Every investor that's investing, you can have a 100, and they all have side letters that modify that.
你需要明白，如果我这样修改，会产生哪些影响。
And you need to understand if I modify it this way, it's gonna have these implications.
其中很大一部分是项目管理，涉及在筹集十亿美元基金时协调所有这些事项。
And a lot of it is the project management that also goes around coordinating all of these products if you're raising, you know, a billion dollar fund.
一旦你设立了这个基金，接下来就是从该基金进行的所有投资。
And then once you've created that fund, there's all the investments you do out of that fund.
所以，例如，当我们进行任何一轮融资时，都需要建立一个数据室。
And so, for example, when we did, you know, any of our series, you need to get a data room.
我们会共享大量数据。
We share a bunch of data.
你要仔细查看这些数据。
You look at that.
你需要理解相关合同，以确保我们声称的收入确实如我们所描述的那样结构化。
You need to understand the contracts we have to make sure that the revenue we say we have is actually structured in the way we've claimed.
而且，你知道，有诉讼吗？
And, you know, are there litigation?
所有这些事情。
All these things.
因此，这是一个极其复杂的理解过程，我认为一个类似的比喻是，理解代码库，但这个代码库就是所有这些合同和所有的法律工作。
And so it's this massively complex process of understanding And I think one analogy you can think of, like, understanding a code base, but the code base is all of these contracts and all this legal work.
我认为法律如此困难的原因是工作流程没有结构化。
And I think the reason legal is so difficult is the workflows aren't structured.
就像编程一样，在为程序员构建工具的模型出现之前，这非常困难。
So the same way with programming, it's really hard until these models to build tools for programmers.
你基本上只有一个集成开发环境（IDE），然后程序员用各种不同的语言工作，但你并没有专门针对某种语言的工具，比如专门为Python设计的工具。
You basically just had an IDE, and then programmers did stuff in all the different languages, but you didn't have like, oh, here's a tool for Python.
专门为C++设计的工具，法律领域也是类似的情况。
Here's a tool for C plus plus Legal's kind of the same way.
因此，我认为你在编程和法律领域看到这么多进展的原因是，这些工作流程有很多相似之处，它们都高度依赖文本。
And so I think a lot of why you're seeing traction in programming and legal is I think there's a lot of analogies of of these workflows, of they're so text heavy.
在这些模型出现之前，工作流程是无法像现在这样结构化的。
And the workflows, until you had these models, you couldn't structure them in the way I think you can now.
在编程方面，人们正在朝一个方向发展，那就是构建所谓的‘智能体’系统。
So one of the directions that people are going on the coding side is to build things that are being called like agentic.
关于‘智能体’具体意味着什么，目前还处于早期阶段，但本质上是能够分解出逻辑树，明确在特定情境下需要执行的一系列操作。
And it's very early on in terms of what agentic means, but basically being able to deconstruct a logic tree in terms of what are the set of actions that you need to take in a certain situation.
是的。
Yeah.
然后让AI智能体逐一回溯检查这些事项，执行每个任务，再进入下一项，并与前一项进行核对。
And then having the AI agent go back and sort of check each one of those items, do it, go on to the next item, double check it against the prior one.
从法律角度看，你们也这样做吗？还是说这在法律领域还属于未来的发展，相比编程还落后一些？
Do you do that from a legal perspective, or is that something that's a little bit more in the future relative to where code is today?
是的。
Yeah.
我们现在已经开始这样做了。
We're starting to do this now.
实际上，当我还在DeepMind时，我做的很多强化学习研究就是这样的。
And actually, like, when I was at DeepMind, a lot of the RL research I did was that.
所以当我们第一次获得GPT-4的访问权限时，我们强烈地预感到：你将能够串联多个模型调用，或者最终实现像推理模型那样的功能，让整个智能体是可微分的。
And so when we first got access to GBE4, we had the very strong intuition of, okay, you're gonna be able to string a bunch of these model calls or eventually do things like reasoning models where the full agent is differentiable.
就在我们刚获得gabepd四的当天，温斯顿就把自己关在房间里十四个小时，重做了他的一系列助理任务。
And even the first day we got access to gabepd four, Winston went in his room for fourteen hours and just redid a bunch of his associate tasks.
当我看到他做的工作时，本质上就像这种粗糙的智能体系统，他说：好吧。
And when I looked at the work he was doing, it was essentially like this hacky agentic where he said, okay.
我需要去查找这个判例法，总结它，然后用这个总结来起草文件。
I would need to go look up this case law, summarize it, take that summary, use it to draft.
是的。
Mhmm.
看到他这样做，让我们很早就有了这样的直觉：这就是未来的发展方向。
And so seeing him do that gave us the intuition very early on of that's the direction this is going.
你可以把助理想象成智能体。
And you can kind of think of associates as agents.
他们从合伙人那里接到一个任务，对方说：
They get this task from a partner that's, hey.
我有一个高层次的案件策略。
I have this high level case strategy.
我想看看能不能找到一些支持这个策略的判例。
I wanna see if I can find a bunch of case law that supports it.
你能去研究一下吗？
Can you go research that?
去查一下。
Look it up.
引用一下。
Cite it.
给我写一份备忘录。
Write me a memo.
因此，我们开始构建的许多系统看起来都非常像这样。
And so a lot of the systems we're starting to build look a lot like that.
我认为编码实验室和研究实验室正在走向的一个有趣方向是构建这些强化学习环境，在其中部署智能体，让它们与代码库交互，看看能否通过单元测试。
And I I think one interesting direction that the coding labs, the research labs are going is building these RL environments where you deploy these agents, and they can interact with a code base and see if they can pass unit tests.
而在法律领域，这个强化学习环境就是客户事务。
And in legal, that RL environment is a client matter.
因此，你拥有基金设立、并购、诉讼等所有背景信息，模型开始学会进入文档管理系统查找资料，进入数据室，或进行判例研究，并从合伙人那里获得反馈。
So you have all of the context of a fund formation, an acquisition, a litigation, and the models are starting to learn, let me go in the document management system and see if I can find this, go in the data room, or do case law research, get feedback from a partner.
所以我认为这个研究方向非常、非常有趣。
And so I think that research direction is super, super interesting.
把助理作类比真的很有趣，因为我记得当我主导你们的B轮融资时——我想那大概是两年前吧，已经有一段时间了——我联系了很多你们的大客户，与律师事务所的负责人或这些机构的负责人交谈。
It's really interesting to make the associate analogy, because I remember when I led your guys' series B, which I think was maybe two years ago now, was a while ago, I called a lot of your big customers and talked to the head of the law firm or talked to the head of, you know, some of these institutions.
其中一件让我印象特别深刻的事情是，第一，他们开始采用法律软件，而以前要向他们推销这类软件非常困难。
And one of the things that I thought was really striking is, number one, they were adopting legal software, which before was really hard to sell into them.
由于你们所做的事情如此突出和重要，他们迅速采纳了你们的产品。
And because of what you were doing being so striking and important, they were adopting you really fast.
第二，他们并不感到威胁。
The second is they weren't threatened by it.
我当时想，他们可能会感到威胁，因为AI可能会增强甚至最终取代法律领域的某些方面，或者彻底改变它。
And I thought, oh, they'd be threatened because it may augment or eventually replace certain aspects of law, etcetera, or help sort of change that dramatically.
他们反复提到的一个见解让我觉得非常有趣，他们说：随着这类AI工具和智能工作流在Harvey和你们这样的公司中普及，你如何看待律所的未来？
And one of the insights they kept bringing up that I thought was really interesting is they said, as we think ahead, as this sort of AI tooling and agentic workflow spread through Harvey and companies like you, how do you think about the future of a law firm?
因为与其雇佣100名初级律师，其中你预期最终只有10人能成为合伙人，也许你只需要50人，甚至更少——那么，你们现在招聘的人数，真的足够识别出谁会是优秀的合伙人吗？
Because instead of hiring 100 associates, of which you assume 10 will be partners eventually, maybe you only need 50, maybe you only need And so are you even hiring enough people to know who'd be a great partner?
是的。
Yeah.
因为随着时间推移，完成某些任务所需的人数会减少。
Because you're gonna shrink the set of people that are needed to do certain tasks over time.
对吧？
Right?
但现在还不是这样。
Right now, that isn't true.
目前只是辅助作用。
It's augmentation.
这是在扩展业务。
It's expanding business.
但长远来看，这种情况可能会发生。
But that could happen in the long run.
你如何看待法律的未来，或者律师事务所会变成什么样子，以及这一切的演变？
How do you think about the future of law or what law firms will look like or the evolution of all that?
是的。
Yeah.
这是个很好的问题。
This is a great question.
我认为过去几年里已经发生了很大变化。
I think it's changed a lot in the past couple years.
我认为我们现在和律师事务所讨论的一个重要话题是，如何培养未来的合伙人，正如你所说，这些律所的合伙人与助理比例失衡，助理很多，但合伙人很少。
I think something we are starting to talk with law firms a lot is how do we think of training the future generation of partners, where, to your point, these law firms have these leverage ratios where you have a lot of associates but much less partners.
这确实有价值，因为并不是每个人都能成为合伙人。
And there is value to that because not everyone is gonna become a partner.
而这个过程的一部分，就是找到那个你愿意信赖、能完成如此复杂收购案的人，因为他们有过相关经验。
And part of going through that process is how you find, oh, this is the person that I would trust to do this very complex acquisition because they've gone through that experience.
所以，让我感到乐观的是，回想十多年前我刚开始学编程的时候，那真是特别痛苦。
And so I I think the part I'm optimistic about is if I think about over ten years ago when I learned to program, it was super painful.
对吧？
Right?
你得去Stack Overflow上找答案。
Like, you had to go on Stack Overflow.
学习多种语言很难，因为你得想，好吧。
It was hard to learn multiple languages because you're like, okay.
我就先学Python吧。
I'm just gonna learn Python.
我要学TensorFlow之类的。
I'm gonna learn TensorFlow or something.
就连学这些都很难。
It was just hard to even learn that.
提问很难。
It was hard to ask questions.
我在谷歌的时候，你不想问太多问题，因为别人会想，哦，你连这个都不知道。
When I was at Google, you don't wanna ask a bunch of questions because people would be like, oh, you don't know that.
而且我
And I
总是卡住。
think stuck all the time.
是的，正是如此。
Yeah, exactly.
而现在有了这些模型，学习编程变得非常有趣，因为你只要说：‘这是用Python怎么写的。’
And now with the models, it's like programming is so fun to learn because you can just be like, Here's how to write this in Python.
把它翻译一下。
Translate it.
为什么这样写？
Why is it written this way?
而且你可以学得快得多。
And you can learn this so much more quickly.
我们看到律师们用Harvey做这件事，他们会说：生成这份合并协议。
And we see lawyers doing that with Harvey, where they'll say, Generate this merger agreement.
为什么我们要这样结构化？
Why did we structure it that way?
因此，我们已经开始看到一些这样的情况。
And so we're already starting to see some of that.
但我认为律师事务所真正巨大的机会在于，如何利用他们内部合伙人提供的反馈和数据来开始训练模型？
But I think the really big opportunity for law firms is how do they take all of the internal partner feedback and data that they've created and use that to start training?
我认为这是重要的一环。
I think that's one big piece.
我认为另一个我们正在讨论的问题是，正如你所说，如何总体上重新构建律师事务所？
I think another conversation we're having is, to your point, how do you just generally start restructuring firms?
我认为这一点上我们有一些直觉，但很大程度上还是要取决于律所本身、地区、规模、专业领域以及他们服务的客户类型。
I think this is one where we have some intuitions, but a lot of it is going to depend on the firm, the region, the size, their specialty, the types of clients they serve.
我认为律师事务所面临的一个巨大挑战是，它们实际上是由众多不同业务领域组成的集合体。
I think one of the things that's very challenging with law firms is they are really a collection of all these practice areas.
因此，专注于诉讼的律所与专注于大型交易或中型交易的律所看起来截然不同。
And so the firms that specialize in litigation look different than the firms that specialize in large transactions versus midsize transactions.
通常，大型律所会同时涵盖这些不同领域。
And usually, the big firms do a collection of these.
因此，我们花了很多时间逐个业务领域进行研究。
And so a lot of what we're spending time is practice area by practice area.
我们可以去与基金设立团队及其私募股权客户坐下来，思考他们在工作流程、人员配置和定价方面会是什么样子。
Can we go and sit with, here's the Fund Formation Group and their private equity clients, and start thinking about what that would look like in terms of the workflows, the staffing, the pricing.
我认为这是一个非常有趣的问题，因为产品和平台的大部分价值不仅在于产品本身，更在于我们如何帮助这些律所实现转型。
And I think it is a really interesting problem where a lot of the value in the product and the platform is not just the product itself, but how do we help enable these firms to transform?
因此，从这个角度来看，我们的目标是让这些律师事务所变得更加盈利。
And so when you think about it from that perspective, our goal is how do we make these law firms more profitable?
这不仅仅是一个产品问题。
And it's not just a product problem.
这是在思考他们的整体业务，以及我们在更大格局中所处的位置。
It's thinking about their holistic business and where do we fit in in kind of that bigger picture.
嗯。
Mhmm.
是的。
Yeah.
这非常有趣，因为当你观察
It's really interesting because when you look
合伙人所承担的一系列职能时，我特别想到的是咨询公司，而不是律师事务所，因为我对咨询公司更熟悉一些。
at the set of functions that a partner fills and I'm thinking in particular of consulting firms and less about law firms simply because I'm a little bit more familiar with consultancies.
其中一部分是模式识别、高层次的思考和战略，另一部分则是销售
Some of it's the pattern recognition, the high level thinking, the strategy, and then part of it is like the sales
对。
Yeah.
并且真正建立起客户联系。
And really being able to make that client connection.
因此，正如你所说，有趣的是要更广泛地思考：我如何才能增强他们业务的各个方面，而不仅仅是法律工作流程？
And and so it's interesting to, to your point, think about more broadly how can I augment all parts of their business versus just the the legal workflows?
是的。
Yeah.
而且正如你提到的，我认为这部分并没有改变——当我们意识到我们现在是法律服务的更大消费者时。
And to your point, I don't think that part changes where it's like when we think of the like, we're now larger consumers of legal services.
当我们想到我们合作过的最佳合作伙伴时，我认为他们的模式并不会很快发生变化。
And when we think of the best partners we've worked with, I don't think the models are doing Yeah.
他们目前的做法不会很快改变。
What they do anytime soon.
我认为有趣的是，律师事务所合伙人的角色实际上并没有太大变化，就像我认为高级工程师的角色在这种情况下也不会有太大变化，因为你主要是在委派工作。
And I think what's interesting is I think the role of law firm partners actually doesn't change that much in the same way I don't think the role of very senior engineers changes with this because you're largely delegating work.
你被付钱来做的是制定高层次的战略。
And what you're getting paid to do is here's the high level strategy.
提供正确的抽象框架。
Here's the right abstractions.
去写代码或者做法律研究来帮我完成，我会去对接客户。
Go write the code or write do the legal research to help me do it, and I will interface with the client.
所以我认为，我的猜测是，这一点不会有太大变化，但一些较低层级的功能确实因为这项技术而发生了改变。
And so I think that my guess is that doesn't change too much, but some of the, like, lower level functions do change because of this technology.
你提到的一件事我觉得很有趣，就像我们之前另一场对话中说的，你可以把一位优秀的资深合伙人，比如戈登·穆迪这样的人，比作在谷歌负责系统的杰出工程师。
One of the things that you said that I thought was interesting, like in another conversation that we were having was that there's an analogy that you could make between like a great senior partner, like a Gordon Moody type, that's like a distinguished engineer working on systems at Google.
对吧？
Right?
我认为，对于更技术背景的听众，或者对戈登·穆迪具体做什么并不了解的普通商业受众来说，他们可能会像埃尔德说的那样，认为他的58%其实是靠人脉或声誉。
I think for a more technical audience or just general business audience that doesn't really know what Gordon Moody does, they might assume what Elad said, which is like, isn't, like, 58% of that, like, his network, right, or his reputation?
但你指出的是，真正重要的是那种预测一系列论证的能力，这些论证能引导你得出想要的答案，或者管理风险。
But, you know, what you were pointing out is, like, there's expertise in the ability to predict, like, a sequence of arguments that is going to, like, get you to the answer you want or manage risk.
没错。
Exactly.
这种能力如何转化为强化学习环境中的任务呢？
How does that how does that translate to an RL environment or a task for you?
是的
Yeah.
这是个好问题。
That's a good question.
为了给观众提供一些背景信息，Gordon Moody 曾是 WalkTel 的合伙人，这是一家全球顶尖的交易型律所，很早就加入了我们，现在是我们的顾问。
So for maybe for background context for the audience, Gordon Moody was a partner at WalkTel, which is one of the top transactional firms in the world that joined us early on and is now an adviser.
我之前打的比方是，为什么谷歌的一位资深杰出的分布式系统工程师如此宝贵？
And kind of the analogy I was giving is why is a senior distinguished distributed systems engineer at Google so valuable?
其中很大一部分原因在于他们架构这些系统时所积累的经验，而这些经验从未公开过。
And a lot of it is the experience they have architecting these systems that none of this is public.
这些经验在很长一段时间内都不会被纳入模型中。
This won't go into the models for a long time.
因此，如果你在谷歌负责构建搜索系统，这些专家就能直接指出：如果你以这种方式、在这个规模下构建系统，它会因为某种极其非直观的原因而崩溃。
And so if you're building search at Google, these people can just point out, hey, if you build this system this way at this scale, it's gonna collapse for some reason that is super not intuitive.
嗯
Mhmm.
戈登早些时候提到的一个例子是，他曾参与迈克尔·戴尔将戴尔公司私有化、重组后再重新上市的过程。
And one of the examples that Gordon talked about early on was he was a part of when Michael Dell took Dell private and then restructured it and took it public again.
这是一场历时数年、极其复杂的大企业财务与法律重组。
And this was like a multi year, super complex financial and legal restructuring of an incredibly large business.
当你和他交谈时，他特别擅长的，就是那种和资深工程师交谈时感受到的相同感觉——他脑海中完整地掌握着这个法律实体的全貌。
And what he when you talk with him is incredibly good at, it's the same feeling as when you talk with a very senior engineer where he can just he has the whole picture of this legal entity in his head.
当时，他们
At the time, they
必须进行
had to do
史上规模最大的债务发行。
the largest debt offering of all time.
他们不得不创造一种全新的金融工具。
They had to create they had to invent a new financial instrument.
所以，这完全是一种理解：如果我需要筹集这么多资金来完成交易的这一部分，我就应该这样设计结构。
And so it's just understanding if I need to raise this much money to do this part of the transaction, this is how I would structure it.
因此，他带来的大量价值不仅仅是人脉关系，更是那种对如何架构这些事务的技术理解，就像架构大型软件项目一样。
And so a lot of the value he brings is not just the relationship, but it's just that technical understanding of how you architect these things the same way that how you architect, like, very large software projects.
我认为，当这种能力迁移到强化学习环境时，公开模型所缺失的部分正是：观察一个实体，并根据‘我想完成这笔并购’这一目标，推导出最佳结构方案的过程。
And I think when that translates to an RL environment, part of what is missing from the public models is the process of looking at one of these entities and figuring out, given all of the context of I wanna do this merger, this is the right way to structure it.
就是这个过程。
Just that process.
而且很多
And a lot
这是一种推理轨迹。
It's a reasoning trace.
对吧？
Right?
没错。
Exactly.
是的。
Yeah.
就像在代码中一样。
Just like it would be in code.
对。
Yeah.
如果你查看这些交易中某一笔的数据集，就会看到客户来找戈登，说我想进行这项大型并购，然后会有会议和邮件讨论，好吧。
And if you looked at that dataset for one of those transactions, it would be client comes to Gordon, says, I wanna do this large merger acquisition, and then there would be meetings and emails talking about, okay.
这是两家公司的背景。
This is the background of the two companies.
这是我们大致会如何结构化它们。
This is roughly how we would structure them.
这些都是我们需要调查的事项。
These are all the things we need to look into.
很多数据都是戈登把这些任务分配给助理，说好吧。
And a lot of the data would be Gordon giving these tasks to associates to say, okay.
调查一下我们之前类似交易中的这些风险因素。
Look into these risk factors of similar transactions we've done.
他们会做研究，然后说，好吧。
They would do research and say, okay.
也许我们可以这样安排。
Maybe we could structure it this way.
然后他会指出一个非常细微的细节，说：嘿。
And then he would point out this really subtle thing that, hey.
实际上，在这种情况下，如果你这样安排，就会发生这种情况。
Actually, in this case, if you structured it this way, this thing's gonna happen.
嗯哼。
Mhmm.
但这些都没有体现出来。
But none of that shows up.
比如，你从这些公开的并购交易中得到的，只是一份SEC文件。
Like, all you get from these public mergers is like an SEC filing.
对。
Right.
所以你看到的只是最终结果，但大多数价值，或者说我认为要最终改进这些模型所需的东西，是决策过程——就像你需要这些推理轨迹来训练模型完成各种推理任务一样。
And so you do see the final result, but most of the value or what you need, I think, to eventually improve these models is the decision making process the same way you need these reasoning traces to train these models to do kind of any of these reasoning tasks.
正如你提到的，这些实验室都非常专注于在编程和数学领域进行强化学习的扩展。
One of the, as you mentioned, like, the labs are all very focused on RL scaling in, like, coding and math domains.
是的。
Yeah.
我认为这些领域是非常容易验证的。
I think those is, like, highly verifiable.
对吧？
Right?
但并不是完全如此。
Not perfectly so.
但你觉得，在法律领域使用强化学习是否合适呢？毕竟它不像其他领域那样容易验证？
But, like, how how do you think about the appropriateness of, like, law for RL given it's not as easily verifiable?
对。
Yeah.
这是最大的问题之一。
This this is one of the biggest problems.
我记得在我们刚开始尝试确定合适的评估结构时，我们有过一些讨论。
And I remember we had conversations early on when we were trying to figure out what is the right evaluation structure.
所以我认为法律领域最难的地方在于，这些任务大多是长文本生成。
So I think the hardest thing about legal is most of these tasks are very long form text generation.
当然，法律工作中有一些子领域是完全可以验证的，比如进入数据室，找出所有控制权变更条款，从而构建这些传统数据集。
And so there are definitely subsets of legal work that are super verifiable of go in this data room and just find all the change of control provisions that you can kind of build these traditional data sets.
嗯。
Mhmm.
但像生成合并协议这样的任务，很难简单地用二元判断来界定好坏。
But for something like generate this merger agreement, it's really hard to just give some binary, this is good or this is bad.
嗯。
Mhmm.
我认为这一直是一个重大的研究难题。
And I think this has been, like, a big research problem.
比如，我们与所有实验室以及内部团队合作时，始终存在一个悬而未决的问题：如何构建这个奖励函数。
Like, with all the labs we work with and also internally, there is just this open question of how do you build that reward function.
如果你想想律师事务所里的奖励函数是什么，那就是合伙人。
And if you think of what that reward function is at the law firms, it's the partners.
对吧？
Right?
归根结底，除了那些做过大量此类工作的资深合伙人说‘是的’，别无他法来验证这一点。
Like, at the end of the day, there is no way to verify this besides the senior partner who's done a bunch of these said, yeah.
这看起来还不错。
This looks pretty good.
嗯。
Mhmm.
因此，这些律师事务所内部积累了大量数据，记录了所有修改过程和反馈意见。
And so internally, these law firms have a bunch of data of here's all the edits that went into this and the feedback.
所以我们开始思考如何利用这些数据来训练这些奖励函数。
And so we are starting to think about how do you use that to train these reward functions.
但我觉得这是其中一个非常大的问题。
But I would say that is one of the really big problems.
但我觉得有趣的一点是，我认为在编程中你其实也面临同样的问题，短期内编程是可验证的，因为你可以看到单元测试。
But I think one of the interesting things is I think you actually have the same problem in programming where I think in the short term programming is verifiable where you can look at unit tests.
但一旦进入真正的软件工程，就不存在所谓的单元测试了。
But once you get into real software engineering, like the unit there is no unit test.
这就像我部署了一个设计。
It's like I deployed design.
是的。
Yeah.
这就像我部署了这个系统，一百万用户用了六个月，它都没崩溃。
It's like I deployed this, and a million users used it for six months, and it didn't crash.
是的。
Yeah.
所以你就会发现，并购也是同样的情况，你可以确保文件填写正确。
And so you get and and it's like mergers are the same where it's like, you can make sure the filing is correct.
但归根结底，三年后，这些公司仍然合并在一起，他们并没有遇到预料之外的诉讼之类的问题。
But at the end of the day, it's like three years later, the companies are still merged, and they didn't take on litigation they didn't expect or something like that.
而这最终才是真正宝贵的人类经验。
And it's like that is eventually the really valuable human experience.
对吧？
Right?
这就是为什么你要聘请顶尖的软件工程师或顶尖的律师，因为他们有长达十年的记录，能够构建这些系统，而且从未崩溃。
That's what you pay really good software engineers or really good lawyers, where they have that decade long track record of they can build these systems, and they haven't fallen apart.
而很多这类事情，就像无法进行单元测试一样，很难验证。
And a lot of these stuff, the same way you can't unit test, it's like hard to verify.
所以我认为，这是一个非常有趣的开放性研究问题。
So it is, I think, this really interesting open research problem.
你们在另一端所做的事，是推动Harvey产品和模型能力边界的同时，真正把它们部署起来。
One thing that you guys are doing on sort of the other end from pushing the bounds of what Harvey products and the models can do is, like, just get them deployed.
你们最近刚刚启动了部署工程团队。
And you recently started this for deployed engineering force.
是的。
Yeah.
这让我感到困惑，因为我觉得你们并不一定是一家传统意义上构建应用程序的公司，而FTE通常被理解为这样的公司。
This is confusing to me because I'm like, well, you're not necessarily like an application building company, which is how people have traditionally thought of FTE.
那你们为什么要这么做呢？
Like, why are you doing this?
我想我们所遵循的模式并不是完整的Palantir模式。
I would say the model that we're operating under is not a full Palantir.
让我们深入代码库，开发定制软件。
Let's go into the code base and kind of build custom software.
嗯。
Mhmm.
我觉得这更接近Sierra的智能体工程计划。
I I would say this is closer to, like, Sierra's agent engineering program.
但我们现在越来越多地遇到一个问题：早期我们很好地构建了一个通用平台。
But what we're starting to run into a lot is I think early on, we did a really good job of building a horizontal platform.
我们并没有为客户提供太多定制化服务，也就是说，没有为特定客户开发特定功能。
We did not that much customization for customers in the sense of building specific things for specific customers.
我认为法律领域的好处在于，我们可以将工作流构建器之类的功能集成到产品中，让客户自行定制产品。
I think the thing that was nice about legal was we could build things like workflow builder and things into the product that would let customers customize the product.
而对于像普华永道这样的大型客户，我们确实做了一些定制化工作。
And then for very large customers like PwC, we did some customization.
但现在，当我们开始与律师事务所讨论，比如‘我们想利用大量数据帮助你们构建模型或代理’时，就需要进入你们的环境，弄清楚如何连接所有数据。
But now we're getting to the point where when we're starting to talk with law firms about, hey, we wanna take a bunch of this data and help you build a model or build agents, there is some amount of, we need to go in this environment into your environment and figure out how to connect all the data.
开始连接他们大量的业务系统，比如计费系统、治理系统等。
Starting to connect to a lot of their, like, business systems, so their billing systems, governance systems.
尤其是当我们开始与沃尔玛、大型银行、财富500强企业合作时，它们的系统标准化程度远低于这些律师事务所。
And then especially when we start working with the Walmarts, the very large banks, the Fortune 500, they're much less standardized than these law firms.
因此，当我们去一家大型银行时，他们会说：‘我们的法律部门根本没有文档管理系统。’
And so there is just this massive amount of work where we go to a large bank and they say, We don't have any document management system for our legal department.
你们能帮我们直接构建一个吗？
Can you just build us one?
而且现在有大量的需求，客户希望有聪明的技术人员坐在这里，帮助他们思考业务和运营，以及如何将这些内容映射到生成式AI系统中。
And there is a massive amount of demand of, We just want smart technical people to sit here and help us think about our business and our operations and how we should start mapping that into Gen AI systems.
对我们来说，这是一个非常好的方式来规划路线图，比如我们最近开始合作的Blue Owl，是增长最快的私募股权公司之一。
And for us, it's a really good way to figure out the road map where, for example, Blue Owl is, like, one of the fastest growing private equity firms that we recently started working with.
我们经常和他们见面，他们总是说，有很多事情我们觉得可以映射到生成式AI中。
And they we meet with them all the time, and they're just like, there's all these things that we feel like we could map into Gen AI.
我们还不太清楚具体会是什么样子，但让我们坐下来一起把它搞清楚。
We don't quite know what it's gonna look like, but let's just sit together and figure it out.
因此，我认为这正是我们这个项目最初形成的根源——如何让更多人能够与这些客户合作，开始为不同垂直领域的一些新路线图铺平道路。
And so I would say that's a lot of the genesis of the of the program of how do we just get more people that can work with all these customers and start kind of paving the way of some of these new roadmaps in different verticals.
是的。
Yeah.
我认为你所描述的，正是一个非常标准的企业操作模式。
I think what you're describing too is a very standard enterprise playbook.
是的。
Yeah.
我认为在硅谷，人们几乎忘记了，在SaaS时代之前，如果你是甲骨文、戴尔、IBM，或者任何这些大型企业，这就是你销售软件的方式。
And I think in Silicon Valley, people almost forgot because of the SAS era that if you're Oracle, if you're Dell, if you're IBM, if you're any of these larger organizations, this is how you sell software.
没错。
Exactly.
对吧？
Right?
你有一个平台，围绕它有很多定制化服务，客户拥有独特的数据集，但他们可能内部没有足够的能力或人员来实施某些连接器或系统。
You have a platform, you have a bunch of customization around it, people have bespoke data sets, they may not always have the ability internally or enough people to implement certain connectors or systems.
对。
Right.
而且这一点
And this
这就是标准的做法。
is like the standard way to do it.
当你一遍又一遍地这样做时，就会逐渐把这些内容变成平台的一部分。
And then as you do it over and over again, you start repeatedly turning that into part of the platform.
所以，对。
So Right.
而且我认为，很多这些案例最初都是从类似FD的做法开始的，等你规模足够大时，就会形成一个实施生态系统，会有许多第三方公司进来提供实施服务。
And and I I think a lot of these started with doing something that resembled FD, and then you get big enough that you get this, like, implementation ecosystem, there's all these third parties that will come in and implement.
他们会成为认证的供应商之类的。
They'll be like the certified, like, vendor or whatever.
而且我认为我们实际上开始看到的一个有趣现象是，律师事务所正在为他们的内部客户做这件事。
And and I think the interesting thing we're actually starting to see is law firms are starting to do this for their in house clients.
所以他们开始带着Harvey去接触客户，说：嘿，购买Harvey，我们会帮助你们构建所有工作流程并完成实施，因为我们有规模和专业知识来做这件事，而通常这些内部团队
So they're starting to go and take Harvey and go to their clients and say, hey, buy Harvey, and we'll help you build all the workflows and implement it because we have the scale and the expertise to build this, where typically these in house teams
这真的很棒。
That's really cool.
规模较小的团队没有足够的预算或内部资源来构建这些。
The smaller ones don't have, like, the budget or the in house to build this.
所以，是的，我认为这确实有很多
And so, yeah, I think there is kind of a lot
有道理。
of sense.
这可以成为你所合作的律师事务所的一项新业务收入来源。
That could be a good revenue driver for the law firms that you work with in terms of a new line of business that they can offer.
是的。
Yeah.
一些律所已经开始这样考虑了。
And some of them are starting to think about it that way.
对。
Yeah.
我印象深刻的是，我不确定是不是从零开始。
I was really struck by it wasn't I don't know if it was day zero.
你可以纠正我。
You can correct me.
但就在第一年内，Harvey的首个版本其实是一个律师个人生产力工具。
But it was within the first year where the very first version of Harvey was really an individual lawyer productivity tool.
对吧？
Right?
我是律师事务所的一名初级律师或资深律师。
I'm an associate or a more senior person at law firm.
我想完成一项工作。
I wanna get a piece of work done.
你能让它不那么痛苦吗？
Can you just make it less painful?
但很快地，人们就开始思考如何转型业务、提升盈利能力，以及如何组织团队构建生态系统，我认为这种转变发生得非常快。
But the transition quickly to, like, how do we transform the business, make the business more profitable, like organize teams being the ecosystem, I think happened happened pretty quickly.
而且，任何业务转型都需要大量的参与和投入。
And, like, anything that is a business transformation just requires, like, you know, a lot of engagement.
是的。
Yeah.
因此，考虑到你们在客户成功方面投入了这么多，以及这如何推动了采用率，我觉得其中很大一部分原因就在于AI发展得太快了。
And so I given how much you guys have invested in customer success and how that's, like, driven adoption, I feel like a big piece of it is just how quickly AI has happened.
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对。
Yeah.
是吧？
Right?
我原来真没料到，你们合作的所有客户都会在这家公司的产品推出的头一两年里，就爽快地选择用起来。
I I would not necessarily have predicted that all the customers you're working with would be like, yes, in year one and two of this company selling, we're adopting.
没错。
Yeah.
不过，你也知道，这其中有一部分功劳是你们在帮他们。
But, you know, part of it is you guys are helping them.
对吧？
Right?
对。
Yeah.
不是的。
No.
当我回看时，我觉得仍然令人惊讶的是，一些律所采纳这项技术的速度如此之快。
And I I think this was like, when I look back, it was still surprising how quickly some of these law firms adopted this.
我们的第一个客户其实是通过你介绍认识的一位前合伙人认识的，他在这里读商学院，他把我们引荐给了A&O的戴维·韦克林。
Like, our first customer we actually met through you introduced us to an ex partner who's doing business school here, and he introduced us to David Wakeling at A and O.
那是在我们第一年，他们从一个小规模试点项目迅速扩展到全公司范围，并投入资源支持这项技术。
That was in our first year, and they went from a small pilot to firm wide and investing in this.
我认为，你在几个垂直领域都看到了类似的情况，比如Cursor Open Evidence，这种技术对这些高度依赖文本和知识的行业来说具有变革性，因为他们之前从未有过这样的工具。所以早期我们就发现了一些客户，他们意识到：这真的值得大力投入。
And I think I mean, I think you're seeing this in a couple verticals with, like, cursor open evidence where this technology is so transformative for these industries that just are so text heavy and knowledge based, they just haven't had tools like this, that I think early on, we did find these customers that were like, oh, this is worth really betting on.
但没错，我认为进展的速度仍然令人惊讶。
But, yeah, I think the pace has still been pretty surprising.
我通过X平台向网友征集了应该问你的问题。
I asked the Internet through X what questions we should ask you.
其中一个热门问题是：你们为什么不去创办一家律所？
And a popular one was like, why aren't you guys building a law firm?
你们会不会自己开一家律所，和你们的所有客户竞争？
Are you gonna build a law firm and compete with all your customers?
是的。
Yeah.
不是。
No.
我们经常被问到这个问题。
We we get this question.
当我们刚开始做Harvey并进行调研时，我们确实与Atrium的30个人进行了交流。
And, I mean, I think when we first started Harvey and we were doing research, we actually talked to 30 people from Atrium.
而且有趣的是，当时OpenAI的总法律顾问Sam和Jason
And I think also interestingly, Sam and Jason, who was the GC of OpenAI at the time
对。
Mhmm.
也是Y Combinator投资Atrium时的总法律顾问。
And was the GC of Y Combinator when they did the Atrium investment.
让我们印象深刻的是，那里的人们认为这是一个非常好的想法，并对前景非常兴奋。
What struck us was the people who worked there said it was a really good idea, and they were super excited about the prospects.
然后在法律和执行方面出现了一些挑战。
And then there were some challenges around the legal and the execution.
但当我们深入研究后，发现他们遇到的主要问题是，你实际上是在同时建立两家不同的公司。
But when we dug more into it, the big challenge that they ran into was you're essentially just building two different companies.
对吧？
Right?
你是在建立一家律师事务所，同时也在建立一家科技公司。
You're building a law firm, and you're building a tech company.
嗯。
Mhmm.
而且，要做好产品工程、做人工智能、扩大销售，本身就非常困难。
And it's already really hard to, like, build product engineering, do AI, scale sales.
我认为，如果你试图同时做这两件事，最大的问题在于你只能把一件事做好，而把律师事务所做好和把软件公司做好是完全不同的。
And I think the big issue you run into if you try to do both of these is I think you can only do one thing well, and doing a law firm well is very different than building a software company well.
我认为这就是其中一点。
I think that's one point.
更大的观点是，对我们来说，最好的结果是我们能弄清楚如何帮助每一家律师事务所成为一家以人工智能为先的律所，而不是我们自己去建立一家。
The bigger point is, for us, it feels like the best outcome is if we can figure out how do we make every law firm how do we help every law firm become an AI first law firm, not how do we build one ourselves.
我认为我们真正要解决的问题是：我们能否让每一家律师事务所变得更盈利？
And I think the real problem we're trying to solve is, can we make every law firm more profitable?
其中一部分涉及他们如何与客户互动。
And a part of that is how they work with their clients.
你能否让他们的客户获得更好、更快、更便宜的法律服务？
And can you make their clients get better, faster, cheaper legal services?
我认为，从规模上解决这个方程式，比单独建立一家律师事务所的机会大得多，因为后者会让你陷入利益冲突。
And I think solving that equation at scale is a much bigger opportunity than if you build a single law firm because you get conflicted out.
你无法规模化这种模式。
You can't scale this.
所以我认为，这可能是我们不会去做的事，但确实有人问过我们这个问题。
And so I think this is probably like, this is something we don't do, but we've gotten this question.
但，是的，我认为这并不是公司的重点。
But, yeah, I I think it's kind of not the focus for the company.
与软件领域的其他市场类似，法律领域让我个人感到非常惊讶，因为如果你真正有雄心壮志，想实现能做的事情，其问题的范围会变得如此庞大。
Analogous to other markets in software, law feels like an area where I've been very surprised personally about how how large the scope of the problem is eventually if you're really ambitious about what you can do.
我之前没意识到，你告诉我，比如说，如果进行一笔规模巨大的并购，像微软收购动视这样的交易。
I didn't realize, like, you were telling me, you know, if you do a really large M and A, let's say, of, like, two global companies, Microsoft Activision, or something.
你会发现这里有上百家外部律师事务所。
You're like, there's a 100 outside counsel firms here.
你知道为什么吗？
You know why?
因为在新西兰——这两家公司都有客户——存在税务影响，而那个懂得这些的人就住在新西兰。
Because in New Zealand, where both companies have customers, you have like a tax implication, and the dude who understands that lives in New Zealand.
是的。
Yeah.
哦，确实是这样。
Oh, it's yeah.
这太疯狂了。
It's crazy.
所以我认为，就像其他市场一样，这个领域的中小企业版本和高端企业版本看起来非常不同。
And so I think like, you know, like other markets, like, the SMB version of this looks really different from the, like, high end enterprise version of this.
是的。
Yeah.
因此，我认为很难想象如何在一家律师事务所和一家软件公司同时整合所有这些专业知识；相比之下，Harvey现在在新西兰已经有大约40个客户了。
And so I do think it's, like, hard it just seems hard to imagine, like, coalescing all of that expertise in a law firm and a software company at the same time versus I'm like, well, Harvey now has, like, what, 40 customers in New Zealand.
没错。
Or Exactly.
是的。
Yeah.
而且，如果你想想这些交易，也不仅仅是律师事务所的事。
And and I mean, if you think about those transactions, it's also not just law firms.
还有投资银行，可能还有普华永道或税务顾问，甚至可能有人力资源咨询公司帮助你规划人员合并的问题。
So there's investment banks, and you maybe have PwC or a tax adviser, And there could be an HR consultancy that helps you think about how you're merging headcount.
因此，对我们来说，更大的机会在于如何构建一个平台，让专业服务提供商及其客户能够协同工作？
And so for us, the bigger opportunity seems how do we build the platform that lets professional service providers and their clients collaborate?
我认为在那里需要解决的许多问题中，最大的一个是这些实体之间的安全协作和安全数据共享。
And I think a lot of the problems you need to solve there are The biggest is the secure collaboration across many of these entities, the secure data sharing.
你如何在这些极其复杂的项目中构建和部署人工智能系统。
How do you build and deploy AI systems across these very complex projects.
我认为你说得对，法律领域的市场规模高达一万亿美元。
And I think to your point, the scope of this, like, legal is a trillion.
专业服务领域的规模大约是三到五万亿美元。
Professional services is something like 3 to 5,000,000,000,000.
这里有着巨大的增长空间。
There's just this massive amount of room to grow.
我们认为我们的专长在于构建产品、技术系统和人工智能系统，以支持这种协作。
And we think our expertise is going to be in building the product, the technical systems, the AI systems that enable that.
我们希望将这种基础设施提供给所有不同的律师事务所，而不是与它们竞争，因为我真的认为你无法与它们竞争。
And we want to give that infrastructure to all of the different law firms rather than compete with them because I just don't think you can.
我认为，这一波人工智能浪潮中一个非常引人注目的特点是，有许多技术背景深厚的人正在不同行业中建立庞大的公司。
I think one thing that's really striking about this sort of wave or era of AI is that there's deeply technical people building giant companies in really different industries.
是的
Yeah.
你来自研究背景。
And you come from a research background.
你曾在主要的实验室工作过，专注于基础模型和其他领域，比如强化学习环境。
You worked at one of the major labs in terms of foundation models and other areas, RL environments, reinforcement learning.
在从研究人员转型为创始人、从零开始创办公司并运营的过程中，你最大的意外是什么？
What has been your biggest surprise in terms of transitioning into being a founder and running a company and building something from the ground up like that?
我认为与其说是意外，不如说是最大的思维模式转变：在创立Harvey之前的十年里，我主要从事AI研究，同时尝试创业，但大部分时间仍是以个人贡献者（IC）的身份。
I think maybe not surprise, but biggest mental model shift is I think the ten years before Harvey, I was doing a mix of mainly AI research and then trying to start companies, but always largely as an IC.
对。
Mhmm.
从开始工作和扩展的过程中，我不得不彻底改变对我们要构建的公司类型、如何规模化运作的认知，这是我最大的转变，这段经历非常疯狂——从我和温斯顿最初在Airbnb里起步，到三年半内发展到500人。
And I think the shift from this started working and scaling, just how much I had to change my mental model of the type of company we're building, how you do this at scale, how you operate, I think that was the biggest surprise or thing that I've had to to change, but it's been a crazy experience kind of going from, you know, Winston and I in an Airbnb to 500 people in, like, three and a half years.
还有，如何在规模化层面构建这些产品，以及这个行业的复杂性，这是一段极具挑战但又非常有趣的经历。
And then I think also how you build these products at scale and the complexity of this industry, that has been a really hard but interesting experience.
看到你们所取得的成就，真是太棒了。
It's been amazing to see what you all have accomplished.
这时间也太短了。
It's such a short period of time.
我回想起三年半前我们向你们两位路演时的情景，当时我们说：‘AI应用于法律领域？’
I was thinking back to when we pitched to both of you guys like, three and a half years ago, and we were like, hey, AI for legal?
你们当时说：‘听起来不错。’
And you guys were like, sounds good.
但我们确实有一些这些想法，只是我觉得它们真正地
But we like I mean, we had some of these ideas, but I think they've really like
我认为这一点也很重要：你们在GPT-4发布之前、以及模型发生诸多变革之前就创立了这家公司。
I think a really important aspect of that too is you all started this company before GPT-four came out and before a lot of the shifts in the models happened.
我记得你们曾展示过GPT-3.5和GPT-4的对比，你们的方案在GPT-4上能运行，但在3.5上不行。
And so I remember you showing side by side GPT-3.5 versus four, and what you were doing worked on four, but not on 3.5.
是的。
Yeah.
你也是其中一员
And you were part
你们是最早坚信这一趋势至关重要的一批人。
of that very early wave that had conviction this was so important as a trend.
这是源于你在实验室的经验吗？
Was that because of your experience in the labs?
还是其他原因？
Was it something else?
是什么驱动了你？
What drove you?
因为你们起步时，真正开始做AI公司的人并不多。
Because not many people were actually starting AI companies when you all got started.
而且就像你所说，AI+法律，当时根本没人做。
And it was kind of just, to your point, AI plus legal, nobody was doing Yeah.
是的。
Yeah.
这是一个现在每个人都觉得显而易见的想法，但当时确实如此。
It's something where now everyone's like, oh, this is such an obvious idea, but at the time Yeah.
对。
Yeah.
输入文本，输出文本。
Text in, text out.
但当时，确实没人考虑过这个。
But at the time, yeah, no one was thinking about this.
我认为这是几种因素的结合。
I think it was a combination of a couple things.
当时我合作过的许多顶尖人才都去了OpenAI。
A lot of the best people at the time I had worked with had gone to OpenAI.
因此我在Meta研究大型语言模型。
And so I was working on large language models at Meta.
你看到了gbd1、gbd2、gbd3。
You saw gbd1, gbd2, gbd3.
如果你在过去十年里从事人工智能领域，其中一个大问题就是如何把这些东西整合起来？
If you were working in AI for the past ten years, kinda one of the big problems were how do you pull all this together?
因为你构建的系统，比如这个在视觉方面非常出色。
Because you'd built systems where, okay, this is really good at vision.
这个在特定任务上表现很好，但没有人真正找到通用的解决方案。
This is really good at, like, specific things, but no one really had the, like, general solution.
你看到了像Lambda这样的东西。
And you saw saw you saw things like Lambda.
嗯哼。
Mhmm.
我认为随着这种趋势，我所看到的是，只要你初步确定了某种方法，通常就可以通过扩展来让这些技术持续有效。
And I think with that trend, like, what I'd seen is anytime you kind of make that initial, okay, this is how you do it, you can usually just scale, and this stuff keeps working.
所以到了3.5版本时，你感觉这变得非常有趣，但还没完全到位。
And so with three, three point five, you were like, this is getting really interesting, but it's not quite there.
因此，我们的赌注是：OpenAI可能是那个破解这一难题的人。
And so the bet was, okay, OpenAI may be one of the people to crack it.
我知道那里很多人。
Like, I know a lot of the people there.
那是其中一部分。
That was part.
然后我认为另一个关键部分是，温斯顿是个律师，我们变得非常亲密。
And then I think the other big part was just Winston was a lawyer, and I think we had become super close.
我从来没想过我们会一起创业，但我听他谈论法律行业的方式让我印象深刻。
Never thought we'd start a company together, but just the way I heard him talk about the legal industry.
尽管他只是个初级律师，却对自身工作以及律所的结构都有敏锐的洞察。
Like, he even though he was a first year associate, just had this intuition of not just the work he was doing, but the structure of the firm.
我听他谈论律所时，他会说：这些合伙人各自在做什么。
Like, I would hear him talk about the firm and be like, here's what all the different partners are doing.
我们的律所为什么采取这样的战略。
Here's, like, why our firm strategy is this way.
他当时正在说服一些合伙人离开，和他一起创办一家新律所，这简直不可思议，所以我就想，好吧。
He had he was in the process of convincing some partners to leave to start a law firm with him, which is insane for like and so it was just like, okay.
这会非常有趣。
This will be really fun.
他给我看了他的一些法律科技产品。
He'd showed me a bunch of his legal tech.
这看起来像是完美的应用场景。
It was like, this seems like the perfect application.
当我们看到gabepereyra时，就觉得时机到了。
And then when we saw gabepereyra, it was just like, oh, the time is now.
这简直是完美的应用。
Like, this is the perfect, like, application.
我觉得特别值得一提的是，即使在和你们合作六个月后，我依然觉得我们的能力会迅速提升。
I think it is, like, really noteworthy where I was actually, like, you know, even six months into working with you guys being like, well, our capability is really gonna advance that quickly.
你和温斯顿都完全同意。
And both you and Winston were like, absolutely.
对吧？
Right?
我们应该有雄心去应对任何可能的法律工作所涉及的全部复杂性，因为模型会不断进步。
Like, we should we should have the ambition to take on the full complexity of, like, any type of legal work that's possible because the models will keep getting better.
而且，这在今天看来似乎是一个非常显而易见的主流观点。
And, like, that seems like a super obvious mainstream point of view today.
是的。
Yeah.
但在2022年，我认为拥有这样的想法是一种强大而独特的洞察力。
But in, I don't know, the 2022, I think it was a strong, unique intuition to have.
对。
Yeah.
对。
Yeah.
我认为我们做得特别好的一点是，我们当时就抱有这样的信念，就像你在编程产品中看到的那样：如果你只做了一个东西，比如仅仅检查你的Python代码有没有bug，而这些功能用3.5就能更好地完成，
I think that was something we did really well where we just had this belief where I think the same that you see with the programming products where if you had built something where it's like, all this does is, like, check that your Python code doesn't have bugs, which you could have done better with 3.5.
那你根本不会去开发像Cursor这样的产品。
Like, you wouldn't have built something like Cursor.
而这种直觉就是，这些模型可以帮助你完成任何编程语言中的任何编程任务。
And the intuition was just these models can help you do any programming task in any programming language.
嗯。
Mhmm.
我认为我们在法律领域也有同样的感觉。
And I think we felt that same way in legal.
我有一点直觉。
And I had a bit of intuition.
我做过一点投资银行和私募股权的工作，那里的工作流程也是一样的，你可以用这些模型完成其中任何一项。
I did, like, a bit of investment banking private equity, and it was the same workflows where you could just do any of them with these models.
所以我认为，保持产品足够开放，给了我们空间，现在我们可以逐步拓展到所有这些领域以及其他专业服务中。
And so I think keeping the product open ended enough that it gave us the room that now we can build into all these things and and, like, professional other professional services.
我认为这非常重要。
I think that was super important.
这是一个非常有趣的类比，因为对于代码来说，主要的编程公司多花了两年时间才真正崛起，并成为最有可能胜出的公司。
It's a really interesting analogy because for code, it took an extra two years, I think, for the main coding companies to really emerge as the ones that are likely to win.
是的。
Yeah.
对吧？
Right?
所以你们大概是三年半前开始的，几乎立刻就有了产品，而且迅速投入运行。
And so you folks started, I think, three and a half years ago, and you had a product almost immediately, and you're up and running really fast.
然后我认为Cursor直到24个月前才推出它的IDE，大概是那样吧，
And then I think Cursor didn't really launch its IDE until twenty four months ago, something like that,
是的。
Yeah.
Cognition也差不多是那个时期出现的，当然还有Cloud Code。
Then Cognition was slightly in that era, and then obviously Cloud Code.
六个月后。
Six months later.
是的。
Yeah.
所以所有这些在编程领域都是以一种时间延迟的方式出现的，尽管 GitHub Copilot 是最早的产品之一，而且大家都明白它非常重要。
So everything kinda came it's kind of in a time delayed way for coding, even though GitHub Copilot was one of the first products, and everybody knew that that was really important.
是的。
Yep.
现在我觉得这非常有趣，因为当时有那么多编程公司都是基于这个前提成立的，但最终真正腾飞的却是这些稍晚起步的公司。
And now I think that's really interesting because there were so many coding companies that got started under the premise, but somehow it's these ones that start a little bit later that really were the ones who took off.
所以我一直想知道，为什么会这样？是什么导致了这种情况？
And so I always wonder why is that you know, what caused that
我的猜测是，我直觉的一部分是，你们从一开始就专注于构建强大的能力，而不是追求 AGI 这种现在看来比较时髦的概念。
My guess is my part of my intuition here was just you guys were, like, let's say, like, very AGI is less trendy as we're now, but very capability build from the beginning.
对吧？
Right?
是的。
Yeah.
所以无论是你，温斯顿，还是你这位从投资银行家转行成为 AI 研究者的人，都坚信它将来能够做到
So both you, Winston, and you as a shred of an investment banker turned AI researcher were both like, it's gonna be able to do
有这么多。
so much of this.
人们也这么想。
People thought that too.
是的。
Yeah.
他们确实是。
They were yes.
我觉得人们三年前可能没那么有雄心，至少有些人是这样。
I people were I think people were a little less ambitious, like, three years ago, at least some some.
有些人打算去构建大型模型。
Some people were gonna go and build giant models.
而且，我觉得人们其实非常有雄心。
And, you know, I actually feel like people are very ambitious.
我只是觉得你们可能一开始就专注于产品，这就是其中一部分区别。
I just think that maybe you folks immediately focused on product, and that was part of the difference.
我认为关键是找到了合适的产品形态。
I think it was finding the right form factor.
在法律领域，最初的产品形态可能更明显，比如我们最早开发的功能——上传文档并对其进行处理——当时其他产品都没有这个功能。
And I think in legal, it was maybe a bit more obvious where the initial form factor was essentially like like, the the initial feature we built that none of the products had at the time was upload a document and do something with it.
对吧？
Right?
而这正是大量法律工作的核心。
And that is a lot of legal tasks.
是的。
Yeah.
然后就是进行非常精准的引用。
And it was that and then do really accurate citations.
当你向人们展示这一点时，他们都会说：天啊，这简直太疯狂了，因为这占了我工作的很大一部分。
And when you showed people that, they were like, oh, this is crazy because that's so much of my job.
我认为在编程领域，最初的模型也没那么好，你需要基础模型具备更多能力，然后还需要找到合适的方式，将它整合到开发环境中。
I think with coding, the initial models were also not quite as good that you needed maybe a bit more capabilities of the base models, and then you needed, I think, figuring out the right way to, like, integrate this into the into the ID.
不过，我记得我开发的第一个产品版本是这样的，我当时用了GPT-4，因为我的专业背景主要是分布式系统和人工智能研究。
But, I mean, I I remember where it's like the first version of the product that I built, it was mainly like, I used gbd4 because, like, most of my background was, like, distributed systems and AI research.
而且我到现在都不会用React。
And I was I still don't know React.
我当时就只会用原生JavaScript，凑合着把这个东西拼出来。
I was just like raw JavaScript and kinda like putting this together.
然后我就对着GPT-4喊，嘿，来干活。
But I'd be like, hey, g b d four.
比如说，帮我完成这个开发。
Like, help me make this.
而且在那个时候，从编程领域里其实已经能看出些苗头了，这也是让我产生那种直觉的部分原因——我可以把温斯顿正在做的事和这类情况做类比。
And, like, you could kind of already see it at the time with programming, and that was part of what gave me the, like, intuition that I could analogize to, like, what Winston was doing.
对了，你刚才提到，在过去三年半左右的时间里，你们团队已经从最初只有两位创始人发展到了现在的500人。
What and you mentioned that you folks have gone from basically the two founders to 500 people over the last three and a half years or so.
很明显你们的成长速度非常快。
You're obviously growing really quickly.
业务在正常运转。
The business is working.
你知道，你们有大量的客户需求。
You know, you have tons of customer demand.
你们在招聘什么职位？
What are you hiring for?
你们接下来想招聘什么样的员工？或者现在正在招聘哪些类型的岗位？
What are you looking for in terms of next set of employees, or what types of roles are you hiring for right
是的。
Yeah.
在技术方面，我们之前提到了全职员工。
On the on the technical side, so we mentioned FTEs.
总的来说，我们需要能力强的工程师，我再提几个具体的岗位。
I think looking in general, like, roles of just strong engineers, and then I would say maybe specific callouts.
我们刚刚聘任了一位纽约办公室的负责人，正在扩大那个办公室的规模。
We just hired a site lead for New York, so starting to scale up that office.
更多前端和整体产品扩展方面的人才，还有更多AI领域的人员。
More folks on kind of front end and scaling product in general, and then more AI folks as well.
所以，但当然，任何能力强的工程师都请申请。
So but, yeah, anyone strong engineer, like, please apply.
好的。
Okay.
我最后一个问题是。
Last question for you.
鉴于
Given
你能做多少个引体向上？
How many pull ups can you do?
开玩笑的。
Just kidding.
是的。
Yeah.
我们确实知道了。
We did find out.
是的。
Yeah.
我不知道那是不是极限，但这两个家伙都能做15个引体向上，中间还眨了眨眼。
Well, I I don't know if that was the max, but these guys can both do 15 pull ups, guys, with a wink in the middle.
还能做更多。
Can do more.
做不了。
Couldn't.
好的。
Okay.
好的。
Okay.
伙计们，我们明白了。
Guys, we get it.
你是说在WhatsApp里吗？
You mean in WhatsApp?
一次性完成。
In one set.
一次性完成。
In one set.
当然。
Of course.
做二十个。
Do the twenty
四小时挑战。
four hour challenge.
是的。
Yeah.
在WhatsApp上。
At WhatsApp.
你一次能做多少个在
Just how many can you do in a
一天内现在？
day now?
是的。
Yeah.
是的。
Yeah.
很多。
It's a lot.
是的。
Yeah.
你可以上传到你的TikTok。
You can upload to your TikTok.
哦，那太好了。
Oh, that's great.
在无先验的TikTok上。
On the no priors TikTok.
埃隆有TikTok吗？
Does Elon have TikTok?
没有。
No.
好吧。
Okay.
不知道。
Don't know.
我没有TikTok。
I don't have a TikTok.
是的。
Yeah.
你有吗
Do you
你知道TikTok特别适合做什么吗？
know what TikTok is very good for?
Eirwan的视频。
Eirwan videos.
哦，太棒了。
Oh, so great.
是的。
Yeah.
这真的很好。
That's really good.
非常好。
Super good.
是的。
Yeah.
有一些特别搞笑的
There's some really funny
Eirwan视频？
Eirwan videos?
是的。
Yeah.
比如，洛杉矶的奶昔Eirwan，或者像那个迈阿密女孩拜访Erwan的视频。
Like, LA smoothie Eirwan or, like, people like there's one where it's the Miami girl visits Erwan.
天啊。
Oh my god.
她问：Eirwan？
She's like, Erwan?
你有趣吗？
Like, are you fun?
所以非常好。
So it's very good.
我强烈推荐。
I highly recommend.
知道。
Know.
我们给你发一个
We send you a
几个。
couple.
不。
No.
还有Twitter，我觉得那里是
And Twitter, feel That's where
我所有时间都花在那里。
all my time goes.
这些，是的。
These Yeah.
TikTok上每个人的视频。
TikTok everyone videos.
好的。
Okay.
你可以选，我们来挑其中一个。
You can pick we'll pick one of these two.
我问了公司里其他一些人。
I asked some other people involved in the company.
你没问题。
You're good.
我该问盖布什么问题？
What questions should I ask Gabe?
天哪。
Oh, boy.
我们已经聊过一些了。
We covered some of them.
但其中一个问题是，你为什么还睡在气垫床上？
But one of them was like, why do you still sleep on an air mattress?
好的。
Okay.
所以我并不睡在气垫床上。
So I don't sleep on an air mattress.
我有一个不错的床垫。
I have a good mattress.
我没有床架。
I don't have a bed frame
好的。
Okay.
这就是问题的由来。
Is where that's coming from.
当我们从洛杉矶搬到旧金山时，我的床架坏了。
And so what happened is when we moved from LA to SF, my bed frame broke.
嗯。
Mhmm.
在创业的头一年半里，事情简直乱成一团。
And then the first year and a half of the startup, things were so crazy.
我当时想，这就是创业者的标准做法。
I was like, this is what a startup founder should do.
但后来某一天，我意识到我得买个床架了。
And at some point, was like, I need to get a bed frame.
于是我订了一个，它送来了。
And I ordered one, and it came.
然后我接到公寓的电话，他们说：嘿。
And I got a call from the apartment, and they were like, hey.
你没有办理退租手续。
You didn't sign out.
你也没填写搬运保险，所以我们不能让他们把东西搬上来。
You didn't fill out the insurance, like the rent the mover's insurance, so we can't let them bring this up.
我当时就说：好吧。
And I was like, okay.
我给他们打了电话。
I called them.
我说，嘿。
I was like, hey.
你们有租房保险吗？
Do you guys have renter's insurance?
他们说，我们是UPS。
They're like, we're UPS.
他们就是说，我们不提供这个服务。
They're just like, we don't do that.
然后我就想，我没时间处理这事，也没去处理。
And then I was just like, I don't have time to deal with it, and I haven't dealt
我其实挺兴奋的。
I with was psyched.
我们还有其他问题要解决。
We have other problems to solve.
所以，好吧。
So Okay.
好吧。
Okay.
所以从物理上讲，除了公司之外，什么都做不了，
So physically can't do anything except the company,
对，只能这样。
right Just only Yeah.
没错。
Exactly.
没错。
Exactly.
是的。
Yeah.
另一个问题是，你们当初开始做Harvey的时候，其实有很多前瞻性。
Other question was, you know, there's a bunch of foresight in starting Harvey when you guys did.
当你展望未来时，你有没有什么预测，认为别人现在可能并不认同，而且不属于主流观点？
When you look forward, do you have a prediction that you think others don't necessarily agree with you right now That is not mainstream.
关于前瞻性，我一定要提的一点是，我们经常听到有人说‘这是一夜成名’、‘你们早就预见到了’。
So one comment I'll definitely make on the foresight is I think a lot of like, we've gotten comments of like, oh, overnight success and, oh, you saw this coming.
但我想说，在创办Harvey之前，我花了整整十年时间试图创办一家类似Harve的公司。
And I would say I actually just spent the decade before Harvey trying to start a company like Harvey.
所以我认为我只是太超前了。
So I think it was just I was super early.
然后最终，我意识到：现在是合适的时机了。
And then eventually, I was like, oh, now is the right time.
而那时，你们正好处于正确的位置。
And then you were kind of in the right position.
我猜，现在人们正在赶上你所说的那种‘极度超前’的状态，就像Winston和我当年那样。
I my guess is I think people now are catching up to how pilled, as you called it, like Winston and I were.
我认为硅谷的人们对这些模型的发展方向有不错的理解，但我认为，大多数人并没有意识到它们未来还会变得多么强大。
I think people in Silicon Valley, I think people have a good sense of where these models are going, but I think, generally, people don't appreciate how much better they're gonna continue getting.
很难真正理解。
It's hard to internalize.
真的很奇怪。
It's really weird.
是的。
Yeah.
真的很奇怪。
It's really weird.
我一直在开发东西，然后我心想，天哪。
I build things, and I'm like, oh my god.
代码生成真的有效。
Code gen works.
现在它真的完全奏效了。
It just really works now.
太疯狂了。
It's crazy.
是的。
Yeah.
对我来说，有趣的是从这些模型各自非常智能，向更深层次转变的过程。
And to me, it's like, I think the interesting thing will be the transition from Like, these models are really smart individually.
但如果你想想过去二十年我们做SaaS的大部分工作，其实就是如何用软件来构建这些庞大的组织。
But if you think about, like, a lot of what we've done in the past twenty years with SaaS, it's how do we use software to make these massive organizations?
我认为接下来的趋势会是，我们开始意识到像律师事务所这样的机构，由于计算机和互联网的出现，规模已经扩大了十倍。
And I think that will be the continued trend where a lot of what we're starting to think about is like, law firms have, like, 10x in size compared to before computers and the Internet.
我认为这种情况会再次发生，但可能不会像过去二十年那样以同样的方式。
And I think that's gonna happen again, but in, like, maybe a different way than the past twenty years.
但在我看来，很多人仍然在谈论协作者和个体生产力。
But I think that to me like, a lot of people still talk about copilots and individual productivity.
而我们开始思考的很多问题，其实是组织层面的生产力——比如，如何构建大规模的系统。对于Cursor、Codex这样的工具，一个有趣的问题是：让一个人编程速度快20%，并不会让整个产品开发速度也快20%。
And I think a lot of the the things we're starting to think about is, like, organizational productivity and, like, how do you build these systems at scale where both for internal engineering team like, think a really interesting question for the cursors, codexes, is like making someone program 20% faster doesn't make you build a product 20% faster.
因此，我们需要开始思考：需要什么样的整体基础设施，才能让这些公司更快地开发软件和产品？类似地，在法律领域，这也是我们正在思考的一个方向，但可能别人很少提到。
And so starting to think about like, what is the broader infrastructure you need so these companies can develop software and product faster, and then kind of same analogy to legal, think that's kind of one of the things we're thinking about that I maybe don't hear people talk about as much.
某种程度上是一种协作式人工智能。
Kind of collaborative AI in some sense.
这有点像Figma的转变，从单个设计师的独立工作转变为与设计团队协作。
It's sort of like the Figma transition of your individual contributor designer versus working collaboratively with the design team.
没错。
Exactly.
你所谈论的是将这种模式应用到法律、代码以及其他不同领域，并让AI作为其上层的支撑。
And what you're talking about is doing that for law, doing that for code, doing that for different verticals, and having AI as a layer on top of that.
这非常有趣。
So it's super interesting.
是的。
Yeah.
我认为关键在于，人类和AI该如何高效协作？
And I think to that point, it's like, how do you how are humans and AIs going to work super effectively?
因为即使在这些大型公司里，也有大量不同专业背景、承担不同职能的团队成员。
Because even at these large companies, you have huge teams of different specialized people that have different functions.
当我听到很多人谈论这些模型时，他们往往觉得AI会自己变聪明并完成所有这些工作，但我不认为这是它发展的路径。
And I think when I hear a lot of people talk about these models, they kind of talk about it as like, oh, AI will just get smart and do all of this, and I don't think that's the way this evolves.
这就像不是只要雇上十万人，就能建起沃尔玛一样。
The same way it's not just like hire a 100,000 people, and now you've built Walmart.
这其中的很大一部分，其实是一百万。
It's like so much of it is like A million.
你是说，嗯，
How you yeah.
三百万。
3,000,000.
是的。
Yeah.
其实是三百万。
3,000,000, actually.
是的。
Yeah.
你如何组织所有这些内容。
How you organize all of these.
我认为这将成为这些领域中一个非常有趣的问题。
And I think that will be, like, one of the really interesting problems for these.
是的。
Yeah.
我在AI驱动的整合以及Brainco这家公司身上都看到了很多这样的情况，是的。
I'm I'm seeing that a lot in the context of both AI driven roll ups as well as this company, Brainco, that Yeah.
帮助其启动运行，其中许多AI实施问题都围绕人员管理和工作流程优化。
Helped get up and running where a lot of the AI implementation issues are around people management, workflow optimization.
这更多不是关于你能否构建AI，而是如何真正改变组织以使其能够正确采纳AI。
It's much less about can you build the AI and much more how do you actually change the organization to be able to adopt it properly.
是的。
Yeah.
不。
No.
我们开始与许多私募股权公司合作，我觉得这很有趣，因为可以看到他们是如何思考这个问题的，我认为这将是一个非常有趣的领域。
And we're starting to work with a lot of private equity firms, and I think it's interesting, like, starting to see how they're thinking about that because I think that will be, like, really interesting space.
太棒了。
Awesome.
谢谢，盖布。
Thanks, Gabe.
谢谢你的参与。
Thanks for going.
非常感谢。
Thanks so much
邀请我。
for having me.
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