An Interview with Palantir CTO Shyam Sankar and Head of Global Commercial Ted Mabrey
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摘要
An interview with Palantir CTO Shyam Sankar and Head of Global Commercial Ted Mabrey about Palantir's mission, operating system, and AI opportunity
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中英文字稿 
这篇Stratechery采访于2023年6月15日星期四发布，采访对象是Palantir的首席技术官Sham Sankar和全球商业负责人Ted Mabry。早上好。这次Stratechery的采访虽然是关于一家上市公司，但实际上我是把它当作Stratechery创始人访谈系列的又一部分来处理的。需要提醒的是，报道初创公司的一大挑战是缺乏可用的数据。我的解决办法是走另一条路，直接采访创始人，让他们主观地概述他们的公司，同时深入探讨他们的商业模式、背景和长期发展潜力。在这种情况下，尽管关于Palantir确实有美国证券交易委员会（SEC）要求的数据，但这家公司一直以来给人的感觉是比较神秘。
▶ 英文原文 ⏱
This Stratechery interview with Palantir CTO Sham Sankar and head of Global Commercial Ted Mabry was published on Thursday, June 15th, 2023. Good morning. This Stratechery interview is with the executives of a public company, but truthfully, I approached it as another installment of the Stratechery Founders series. As a reminder, one of the challenges in covering startups is the lack of available data. My solution is to go in the opposite direction and interview founders directly, letting them give their subjective overview of their companies while pressing them on their business model, background, and long-term potential. In this case, there is certainly SEC-mandated data available about Palantir, but this has always felt like a company that was mostly mysterious.
为此，我想借此机会采访首席技术官Shyam Sankar和全球商业负责人Ted Mabry，了解Palantir是如何发展到今天的状态，以及未来有哪些机遇。坦诚地说，我对Palantir正在构建的内容非常感兴趣，并可能很快会写更多关于这家公司的文章。希望你在这次非常深入的采访中能感受到，当我在实时加深对这家公司的理解时的感受。下面开始采访。Shyam Sankar和Ted Mabry，欢迎来到Stratechery。很高兴在这里。感谢您的邀请。
▶ 英文原文 ⏱
To that end, I wanted to take the opportunity to interview Chief Technology Officer Shyam Sankar and head of Global Commercial Ted Mabry about how exactly Palantir came to be. What it is today and the opportunities it has going forward. I'm going to be honest, I'm incredibly intrigued by what Palantir is building, and will probably write more about the company soon. What I hope you enjoy about this interview, which is very dense, is the extent to which I felt like I was expanding my understanding of the company in real time. On to the interview. Shyam Sankar and Ted Mabry, welcome to Stratechery. Great to be here. Thanks for having us.
所以，Palantir不是我之前报道过的公司。不过，我确实写过关于你们的S1文件，我们稍后会谈到它。所以，我想先说明一下，虽然Palantir是一家上市公司，但我想把这次采访稍微当作创始人系列的一部分。我现在处于一种有点探索的状态，想更好地了解你们的业务。所以，我想这次你会比较轻松，我并不是想要抓你们的把柄，其实我对你们非常感兴趣。Shyam，你之前联系过我，想聊聊这家公司。我听到很多关于你们的好评。
▶ 英文原文 ⏱
So, Palantir is not a company that I've covered to date. I did write about your S1, which is the one. We'll actually get to that in a little bit. So, I just want to sort of put up front that, well, Palantir is a public company. I'm going to treat this interview a little bit more like one of the Founder Series. I'm sort of in, personally, in like somewhat sort of exploration mode and wanting to better understand your business. So, you're going to get off a little easy, I think. I'm not trying to like nail you to the wall on anything, but I actually am quite intrigued. Shyam, you reached out to me a little bit ago to sort of talk about the business. I've been hearing a lot of good things.
我确实回去看了几段你们提到过我的投资者视频。这显然是个让我要加入的好方法。但我想回到更早的时候，也就是最初的开始。Shyam，你说你是第13号员工，对吗？是的。那Ted，你是什么时候加入公司的？2010年。2010年。好的。那么，Palantir是在2003年正式成立的，尽管有报道称是2004年。那时候是什么样的？Palantir的愿景有没有向你们展示过？就像，这种愿景是否持续到了今天，还是有过改变？当时是产品出来之前，什么东西都没有的阶段。
▶ 英文原文 ⏱
I did go back and watch a few of your investor videos where I was invoked. So, that was like obviously a good way to sort of get me on board. But I want to go back to even before then, sort of back to the beginning. Shyam, you said you were employee 13. Is that right? That's right. Yeah. And Ted, when did you join the company? 2010. 2010. Okay. And so, Palantir was officially started in 2003, I believe, although it's reported 2004. What was it like back then? Was the vision of Palantir presented to you? Like, was that the vision that sort of persists to today? Or was there a shift? Like, this is like pre-product, pre-anything.
是什么让Shyam Sankar多年前加入进来的呢？关键在于我们所追求目标的雄心一直都非常明确。当一群人在9/11事件后聚在一起，问道：“为什么要争论隐私和安全哪个更重要？”这两者显然都很重要。我们该如何构建能够同时增强两者的技术？这不是一个简单的网络应用，也不是一个平凡的技术愿景。正是这个原因让我深深投入其中。
▶ 英文原文 ⏱
What got Shyam Sankar sort of on board back in the day? Well, the thing that's been hugely consistent over time is the ambition of what we're trying to accomplish. So, you know, when a group of folks got together in a post-9-11 world and said, why are we arguing about what's more important, privacy or security? They're both obviously important. How do we build technologies that allow you to have more of both? It's not a simple web app. It's not a trivial technical vision. And that's what got me deeply wedded to this.
在这个过程中，就像我们必须构建的技术一样，我们不断提升和扩展我们的目标。起初，我们的重点非常集中在政府上，这种理念是技术可以深刻地影响我们需要运作的世界机构的运作方式。而且，这种影响不仅限于政府，还会扩展到商业机构。我认为在金融危机后的世界中，这一点非常明显，并且一直保持着惊人的一致性。你提到目标的提升，并将其置于从政府开始的背景中。
▶ 英文原文 ⏱
And in that journey, like the technology we had to build, the ambition that we continually upscaled and upscoped. So while it started, actually very focused on government, this sort of idea that technology can have a profound effect on how the institutions we need in the world to function, function, and why that would extend beyond government to commercial institutions. I think that's very clear in a post-financial crisis world. That's been remarkably consistent. Well, you mentioned sort of the upscale in sort of ambition, and you put it in the context of starting with government.
显然，Palantir 的增长故事主要围绕其企业产品，我们稍后会讨论这一点。但你提到过一个观点，即在很多方面，Palantir 就像是一个非常庞大的初创公司，因为你觉得必须从头到尾构建整个系统，以实现你的承诺。这是否是公司从一开始就有的想法，还是在你逐步明确需要完成的事情时逐渐扩展出来的？
▶ 英文原文 ⏱
And obviously, the huge sort of growth story, I think, around Palantir is largely around your enterprise offering, which we'll get to in a little bit. But something you've talked about is the extent to which Palantir is like the fattest of fat startups in many respects, where you feel like you have to build the entire system from top to bottom to sort of deliver on your promise. Was that part of that? Or is that something that sort of expanded over time that you're talking about as you sort of figured out what needed to be done?
好的，有时Ted会说，这就好比，你到底对什么负责呢？你是在对你构建的软件的某种狭隘定义负责吗？我们的理念是客户的成功就是我们的成功。基于我们认为在实际操作中真实有效的经验，我们不断扩展对软件功能的认知。有时你以为有效的东西实际上并没有效果。如果它是我系统底层的一部分，那我就必须拥有它，因为我需要它能够正常运作。如果你想帮助士兵从战区归来，没有人会说，我用的是Gartner象限排名靠前的软件，大家的标准更为绝对。那你如何应对呢？软件就像一个“Potemkin村”一样，界面越来越厚。
▶ 英文原文 ⏱
Well, as Ted sometimes says, it's like, what are you accountable to? You know, are you accountable to some sort of narrow definition of the software that you're building? Because the way we think about it is our success is our customer success. And we just kept expanding what we thought our software needed to do based on what we thought was empirically true on the ground. Like that thing that you think is working is not working. So if that is below me in the stack, like I got to own that because I need that to work. If you're trying to help soldiers come back from war zones, nobody says like, well, I use the software that's at the top of the Gartner quadrant, you know, the standard is much more absolute. And so how do you go after that? It just got thicker and thicker from the perspective of what are you learning is kind of a Potomacan village of software.
这段话的大意如下：
我并不是说软件无法编译，显然，它只是没有按照企业的需求去做。当你在第一线工作时，你会发现所有这些秘密和真相，竟然迷失在组织的官僚主义中，关于什么有效、什么无效以及原因。有一个特殊的时刻，是在9/11事件之后，大家常说要在安全与隐私之间做选择。我们认为可以同时实现两者，于是我们想创业，实现这一目标。在构建的过程中，我们意识到需要做很多事情，并整合进现有的体系中。是否有这样一个时刻，让你有了一个灵光一现的概念，并开始在所有的财务报告开头使用这个短语，说自己是企业的操作系统？
▶ 英文原文 ⏱
And not because I think the software literally doesn't compile, obviously, it's just that it just doesn't do what the enterprise needs it to do. And when you're at the coalface, you can find all these secrets, all these truths that somehow get lost in the bureaucracy of the organization of what works and doesn't work and why. So was there a particular moment like where there was a real shift to, OK, we're in the post 9-11 sort of area. People say you have to choose between security and privacy. We think we can deliver both. We're going to do a startup that can sort of build this. And as you're building it, you're like, OK, well, we actually need to build this. We need to build this. We need to integrate into into sort of that. Was there an aha moment where you have this concept, use this phrase now at the beginning of all your financial reports, which is that you're sort of the operating system for enterprises.
这段话翻译成中文如下：
现在，很明显，这仍然算是政府时代，但它很有趣。S1用到了那条线，但它处于更靠下的位置，不是主要的内容。那是后来发展出来的东西吗？还是说，必须成为所有东西的接口，是对Gotham、你们开发的那种政府智能软件至关重要的？我认为其关键部分在于意识到我们最初构建的产品是基于一个假设，即我们的客户已经整合了数据，因此我们可以专注于数据整合后进行的分析。
▶ 英文原文 ⏱
Now, obviously, this is still sort of the government era, but it's interesting. The S1 uses that line, but it's kind of further down. It's not sort of the lead thing. Was that something that sort of emerged later? Or was this bit that like, no, we have to be the interface for everything. Was that critical to sort of Gotham, your sort of government, the government sort of intelligent software that you built? I think the critical part of it was really realizing like we had built the original product kind of presupposing that our customers had data integrated, that we could focus on the analytics that came subsequent to having your data integrated.
我发现创伤的根源在于，实际上每个人都声称他们的数据是集成的，但其实是一团糟。而更有趣和有价值的是，我们开发了能让数据集成产品化的技术，而不是像一个永无止境的五年咨询项目。这样我们才能真正做我们原本开办这家公司是为了做的事情。对，我们的目标是分析，但结果却发现必须先构建自己的工具和工厂，才能开始进行分析，而这最终在长远看来更有价值。正是如此。
▶ 英文原文 ⏱
And I feel like founding trauma was realizing that actually everyone claims that their data is integrated and it is a complete mess. And that actually the much more interesting and valuable part of our business was developing technologies that allowed us to productize data integration instead of having it be like a five year never ending consulting project so that we could do the thing we actually started our business to do. Right. So the analytics was the goal, but it turned out to actually like you had to build your own picks and shovels, your own pick and shovel factory to even start to do the analytics and that ended up being more valuable in the long run. Exactly.
好的，理解了，过渡阶段是这样的。那么，能给我介绍一下什么是Gotham吗？我们稍后会谈到Foundry，不过我想我们还是先把这个产品讲清楚。谁会购买它？它有什么用途？
Gotham是为国防情报机构设计的操作系统。它可以让这些机构整合多种类型的数据。这些数据包括卫星图像、视频、结构化和非结构化的文本数据、传感器数据等，将这些信息融合在一起，形成一个通用的操作图。就是说，通过这个系统可以实时了解世界上实际发生的情况，并以此为基础做出未来的计划。你可以想象成这是一个整合数据的统一平台，通过这个“玻璃板”，你可以看到所有信息并进行规划。
▶ 英文原文 ⏱
Yeah. That makes sense as sort of as the transition goes. So lay out to me, what is Gotham? And we're going to get to Foundry sort of in a moment, but I think just to sort of be super clear, what is this product? Who buys it? What is it used for? So Gotham is an operating system for defense intelligence organizations. It allows them to integrate multimodal data. So this could be satellite imagery, video, structured and unstructured text data, sensor data, fuse that together to create a common operating picture. Like how can I see what's actually happening in the world right now? And use that as a, you know, you can think about that as a single pane of data that allows you to have a single pane of glass and then plan forward.
根据目前的情况，我们可以做些什么来创造我们想要的世界效果呢？你一直在用“操作系统”这个术语，其实我也是从你那学来的。那么，为了构建这个概念，你是需要和其他国防承包商合作，来开发一个与他们系统兼容的东西，还是说你自己独立研究出来的呢？你是如何从单纯的分析软件这个思路，转变到“操作系统”这个想法的呢？实际上，这是Ted为这项业务开发的核心之一，即对决策的专注。
▶ 英文原文 ⏱
So based on what's happening now, what could we be doing to create the effects in the world that we want? So you keep using the, or both of us keep using the term operating system, but I got it from you. So what are, is this something where you had to go in and work with other defense contractors to build something that sort of sat on top with them, or you just had to sort of figure that out on your own? Like how did you get to this idea of, again, from being just an analytic sort of piece of software to this operating system idea? So it's really one of the core things that Ted developed for this business, which is an obsession with decisions.
你知道的，与其说从数据出发，考虑我有哪些数据以及如何整合它们，我们不如尝试从不同的视角去看待一个机构或运作，把它看作是一系列相互关联的决策。我们如何为这些决策带来更好的智能、协调和复合效应，并将它们串联起来？为了做到这一点，我需要什么？这就是统一视图的重要性所在。这是我做出决策的地方。那么，我如何优化进入这个决策的信息？接着，我怎样帮助你理解这些决策在前后过程中的影响？
▶ 英文原文 ⏱
You know, instead of going data forward, like what data do I have? How do I bring it together? How do we actually kind of squint at an institution or an operation and think about it as a series of interconnected decisions? How do I bring better intelligence, coordination, and compounding to those decisions and chain them together? And what would I need to be able to do that? So that's where the single pane of glass becomes really important. This is the, this is the place where I'm going to make the decision. Okay, well, how do I optimize what's coming into that decision? And then how do I help you understand the consequences upstream and downstream of these decisions?
然后这是单人模式，但那不够有趣。真正有趣的是，你正在谈论大量同时进行的多人决策，并且你希望利用技术来推动这种协调。这并不是因为Sean、Ben和Ted碰巧彼此认识，也不是因为我们想出了某种标准作业程序。而是因为我决策产生的数据正在改变我行事的方式。我们常常把这表述为，大多数分析系统将数据视为"排放"，就好像是交易系统的"废气"，可能会去分析它，但实际上数据是"燃料"。
▶ 英文原文 ⏱
And then that's single player mode, but that's not interesting. Really, it's, you're talking about massive concurrent multiplayer decisions that are happening and you want to enable the technology to drive that coordination. It's not because Sean and Ben and Ted happen to all know each other or that we've come up with some sort of SOP. It's that the data that is a consequence of my decision is changing how I'm doing things. So we often frame this in terms of like most of these analytics systems think of data as exhaust. You know, it's like the exhaust of the transactional system and maybe you're going to go analyze it, but actually data is fuel.
如果你把这个想法颠倒过来，说这是为了给我的操作提供动力，那么你会如何改变你对技术栈的看法，以及你对操作和使用软件的人类角色的理解？在这里，我认为操作系统的比喻非常重要。这不仅仅是一个终端仪表盘，也不是简单的分析工具，它实际关乎决策过程。你会有很多次做出这个决策的机会，你希望明天在同一个问题上的决策比之前的更好。所以，如果你认为自己处于一个学习循环中，你会如何运用一切可用资源每天变得更优秀？
▶ 英文原文 ⏱
If you, if you, if you flip this around and say like, this is going to fuel my operations, what do you change in terms of how you think of your stack and how you think of the roles of the humans who are operating and using your software? This is where I think that the metaphor of the operating system is, is quite important here. This is not a terminal dashboard. This is not analytics. It actually is about the decision-making. And then you're going to get many chances at making this decision. You want, you want tomorrow's decision against the same problem to be better than the one before. And so if you're thinking, look, I'm in a learning loop, how do I wield everything at my disposal to get better at this every day?
可以这样翻译成中文：所以，就像 Sean 所说的，你可能经历了一种“创伤”，就是数据被分散了。我认为第二种“创伤”是关于洞察力的实用性或其缺乏。首先，你需要整合这些数据，达到一个单一的真实数据来源。标准流程就是获取数据、整理数据、分析数据并采取行动。这个过程中出现了一个差距，就是当你了解了一些信息后，如何实际应用这些信息。接下来，你需要考虑如何在一个多角色模式的复杂价值链中部署这些洞察。
▶ 英文原文 ⏱
So you've sort of founding trauma, if you will, to quote Sean on the data was disintegrated. I think then the second sort of trauma was the sort of utility or lack thereof of insights. So starting with like, okay, I have to get this data integrated. I need to get to a single source of truth. The sort of standard, get data, organize data, analyze data, act on data. That, that gap that looks like the last step of once I know something I can do about it. But then you think about how is that insight deployed into a multiplayer mode complex value chain?
我们一次又一次地发现，当把假设的见解尝试应用到真正做决策的前线时，会存在巨大的落差。因此，我们需要明确在决策时需要什么样的信息智能？我们需要怎样的信息框架？为什么现有的数据支持不了这种框架？而当我们拥有这个框架并做出决定时，如何能让这一过程变得更加智能化、自动化、协作化，并且随着时间的推移不断优化？
▶ 英文原文 ⏱
And what we found time and time again is that the hypothetical value of an insight, when tried to deploy operationally to the front lines of people actually making decisions, there's an ocean of a gap. And so we really need to be able to be accountable to what is the information intelligence that is needed at the point of decision? What is the frame on that information that we need? Why is the existing underlying data not provide that frame? And then when you can have that frame and make that decision, how do you do that more intelligently, more automated, more collaboratively, more compounding over time?
那么，你是怎么做到的呢？我的意思是，这看起来似乎很简单，我认为，理论上我能理解这个过程，首先，你做出了一个决定，然后你记录下这个决定。之后，你可以追踪这个决定的结果，看看发生了什么或没有发生什么。这样做是否仅仅是为了在这个方面“闭环”，还是其中还有沟通交流的部分呢？
▶ 英文原文 ⏱
So how do you do that? I mean, like, that seems like a pretty, I mean, theoretically, I can get the bit where, okay, you make a decision. Now it's sort of, you've entered the fact you made the decision. You can track the outcome of that and see sort of what happened or didn't. Is that, is it just about sort of closing the loop in that regard? Or is there sort of a communications aspect to this?
我的意思是，这一切听起来都很棒，但是，它如何真正体现在军事或国家安全机构中呢？好在，对我们来说，我认为无论是在国防领域的操作人员，还是试图更有效地钻探地下矿井、建造飞机、研发新药、交付疫苗等等，实际上都有很多共同点。现有的软件系统在帮助操作人员做出决策时存在很多共性问题。
▶ 英文原文 ⏱
I mean, this, this sounds all great, but like, well, how does it actually manifest itself in like a military or a national security agency? Well, fortunately for us, uh, I think there are a ton of commonalities, whether you are an operator in the defense context, or you're trying to figure out how to drill an underground mine or figure out how to build an airplane, uh, more effectively discover new drugs, deliver vaccines, whatever it might be. There's actually a lot of commonalities in sort of where the existing software stack breaks down in enabling operators to make those decisions.
我们把这看作是工作流程复杂程度的一个发展。这个流程的第一步是：我是否通过及时的数据、以我能够理解的方式提供给我，全面掌握了我做出本地决策所需的所有信息？如果我是菲亚特克莱斯勒或空客工厂的一线工人，那么我所看到的信息会与需要做长期战略决策的CEO所看到的非常不同，但无论是来自客户关系管理系统、传感器数据还是企业资源规划系统，都会有大量数据涌来。
▶ 英文原文 ⏱
And so we sort of think of that as a development of levels of sophistication of the workflow. The first part of that workflow is, do I have 360 degree awareness of everything that I need to know to make my local decision at my fingertips with timely data presented to me in a way that I can understand that. And if I'm a blue collar worker on the factory floor at Fiat Chrysler or at Airbus, that view is going to be very different than if I'm a CEO making long-term strategic decisions, but there's a lot of data coming from CRM, sensor data, ERPs, whatever it might be.
我能否在AI的意义上获得那个“上下文窗口” —— 正是我需要的上下文来做出决策。这样做的好处是，我不再需要对抗各种不同的系统，不用来回切换不同的屏幕，不用用Excel宏来创建我自己的本地数据源，也不用在数据仓库中找到不同的视图等等。这一切都能在交易层面提供我所需要的完整上下文信息。现在，我希望在这个过程中变得更加智能。
▶ 英文原文 ⏱
Can I get that, you know, in the AI sense, that context window that is exactly the context that I need in order to make that decision. Now that's all well and good because it allows me to, instead of fight a lot of different systems, swivel chair into different screens, have Excel macros that create my own local source of truth, different views in the data warehouse, whatever it might be, giving me at the transaction level, this is the thing that I need with all the context that is there. I now want to start to become more intelligent as I do it.
所以，我想把一些事情自动化，像是如何确定我需要优先做哪些简单的事情。为了进行这种优先级排序，我需要一个集成的上下文窗口。接下来，我需要能够部署从简单代数到人工智能模型的模型、智能和规则，以帮助我推动决策的优先级排序。
▶ 英文原文 ⏱
So I want to automate things, simple things as how do I prioritize which thing I need to do first? Well, in order to do that prioritization, I need that integrated context window. And then I need to be able to deploy models and intelligence and rules from simple algebra up to and including artificial intelligence models to help me drive the prioritization of how I'm making that decision.
如果我对客户服务流程有一个整体的了解，那么在决策时，我该如何确定最紧迫的客户，当时我掌握该客户的所有产品使用情况和价值信息？当我们考虑全方位的了解时，还需要在此基础上增加智能分析。举个具体的例子：如果我在运营一个海上石油平台，我不仅需要对油井有全方位了解，还需要对光纤数据进行警报，当数据流传输下井的信号显示有结砂风险时，就会提醒我。
▶ 英文原文 ⏱
So if I have an integrated view about my customer service workflow, what is the most pressing customer now when I have the entirety of product usage and everything I know about the value of that customer at that point of decision making? So as we sort of think 360 degree awareness, now start to layer in intelligence with it. Maybe best described in a concrete example of if I'm running an offshore oil platform, I need a well 360, but then I also need alerting on when is the fiber optic data that is streaming data off the downhole signaling that I have sanding risk.
但这是一个方面。我的理解是，你是不是在说把可能存在于不同系统中的大量数据整合起来，使其在即时情况下更易于访问？而这正是你需要跨越的鸿沟？首要的鸿沟就是在所有不同系统之间实现实时数据的呈现，并让操作员能够在有用的背景下使用这些数据。
▶ 英文原文 ⏱
But that's one thing. Is this sort of like what you're driving at, it seems to me, is making lots of this data that may have existed in all these different systems, basically making it much more real time accessible sort of in the moment? And that is that sort of the gulf that you sort of had to bridge? That was the first order gulf was real time data across all the different systems presented in a context that is usable for an operator.
但是我还需要两样东西。我需要能够将模型整合到这个视图中。所以这不仅仅是一个数据的展示。我现在需要通过模型获得的所有智能融入其中。因此，它们必须是平等的重要。同时，关键的是，我还需要能够对我拥有的信息采取行动。因此，我怎样才能通过同一个应用程序在现实世界中真正实现物理变化呢？
▶ 英文原文 ⏱
But then I also need two things. I need to be able to integrate models into that view. So it's not just a representation of the data. I now need all of the intelligence that can be asserted by models into that. So that has to be co-equal and then critically also the ability to act on the things that I have. So how can I literally create physical change in the world from that same application?
我不是只看着某个东西然后想着怎么在SAP中实现它。我是在执行操作，回写数据，并调用BAPI函数或在我的SAP系统中创建采购订单。明白了。这正是你之前提到的那个巨大的缺口。这就说得通了。
▶ 英文原文 ⏱
I'm not looking at something and then going, figuring out how to implement it in SAP. I am executing and writing back and calling the BAPI function or create the purchase orders across my SAP. Got it. That was a huge gulf that you were talking about before that was missing. Okay. That makes sense.
好的，我认为这就是我们自然过渡到Foundry的原因。Foundry的历史是什么呢？你们主要有两个产品。Gotham是为政府设计的，而Foundry则是为企业提供的。请帮我理解一下，它们是完全不同的软件架构吗？它们之间有没有很多共同之处？Apollo是不是你们的部署系统，是它们之间的共同点？Foundry是否在最初就是你们的愿景之一，还是说你们起初的想法只是为了解决政府的国家安全问题呢？
▶ 英文原文 ⏱
Well, I think that's a natural transition to Foundry. What was the history of Foundry? So those are your two main products. You have Gotham, that is for government, and Foundry, that is for sort of the enterprise. And help me understand, are they totally different software stacks? Is there a lot of commonalities? Is it Apollo, sort of your deployment system, that is the commonality there? And was Foundry in the vision sort of from the beginning, or was this a, look, our founding sort of idea was to solve this government national security problem.
事实证明，我们具备这种能力，在其他地方也非常有用。这涉及到很多内容。基本上，请告诉我关于Foundry的故事。这样，Foundry最初是在商业领域起步的，但现在已经发展起来了。你可以把它看作一个结构，Apollo在最底层，是生产基础设施。接着是Foundry，它是一个完整的技术栈，但请跟我一起想象。Gotham则将位于它之上。
▶ 英文原文 ⏱
Turns out we have this capability that's sort of useful elsewhere. That was a whole bunch of stuff. Basically, tell me the Foundry story. So, so Foundry, while it started in commercial, has, has now grown, you know, you can kind of think about it as like Foundry, Apollo is at the very bottom of the stack. It's the production infrastructure. Foundry is next, which we can, and it's, it's kind of full stack, but go with me. Gotham will sit on top of it.
明白了。所以可以说Gotham就像是一个针对政府的Foundry的具体体现，有点类似于政府版的Foundry。而Foundry在某些方面就像一个操作系统，而Apollo则更像是基础设施。 是的。所以我们开始在商业领域使用Foundry，我想这几乎是基于意识到数据整合行不通这一点进行的加倍投资。
▶ 英文原文 ⏱
Got it. So Gotham is like a government specific manifestation of the Foundry, sort of, Foundry is like the operating system in some respects, and, and Apollo is like the infrastructure. Yeah. Yeah. And so we started Foundry in commercial, I'd say almost doubling down on this realization that data integration doesn't work.
这段话可以翻译成：工具的数量很大，你可以在第一天就让它们运作起来，但如何应对数据随着时间推移而产生的混乱和积累呢？又需要什么样的工具来应对这些挑战呢？所以，我们需要思考如何像对待代码一样对待数据集成，以及如何把企业的更多部分像代码一样对待。
▶ 英文原文 ⏱
And the amount of tooling, like you can get it to work on like day zero, it kind of works on day one, but like, how do you deal with the entropy of the universe in data over years and what accumulates there and what sort of tooling would you need? And so all of this kind of like, how do we treat data integration like code? How do we think, how do we treat more and more of the enterprise like code?
我们如何将Ted刚才提到的那个组件产品化？这个组件不仅能让你将数据抽象和整合到语义层中，还能将模型绑定到这些数据上，然后在此基础上构建整合决策工具。这样一来，客户就可以根据需要为底特律汽车工厂的工人、海上石油工人，甚至是医院运营人员开发所需的工具。
▶ 英文原文 ⏱
How do we productize that component that Ted was just talking about that allows you to abstract and integrate not only your data into semantic layer, but bring your models, bind your models to that data, and then build that decision that integrated decision making tooling on top of that. So customers could build what they needed for the factory worker at a Detroit car factory, but also an offshore oil worker or, you know, a hospital operations person.
这就是它的起源。但我想说，它是从底层开始的，我们不断地建立。当我们意识到可以处理非常复杂的实时数据整合时，就能实现第一层的360度全方位感知。接下来，我如何提供更多工具，将实时智能见解融入其中？然后，如何提供一个数字孪生体来进行动态模拟和反事实分析，比如：如果我做出这个决定，怎样推演以理解其后果，并利用这个反过来自动、程序化地生成情景？例如，这里有10个可能的决策，让我分析它们的后果，然后让人工决策者能从中选择。对，然后就像Ted说的那样，真正做出决策，同时让决策反馈到基础层面。
▶ 英文原文 ⏱
So that is the origin of it. But it started, I'd say, at the bottom of the stack, and we just kept building as we realized like, okay, I can deal with really complicated real-time data integration that gives you this level one 360 awareness. Now, how do I give you more tooling to bring the real-time intelligence insight into it? Now, how do I give you a digital twin that allows you to do dynamic simulation and counterfactuals of like, oh, if I made this decision, how do I cascade it through to understand the consequences of it, and then turn that around so that you can automatically, programmatically generate scenarios? Like, here are 10 decisions I could make. Let me play through the consequences of them and now enable the human operator to select amongst them. Right. And then to sort of Ted's point, actually make the decision and have it sort of filter back down into sort of the underlying layers.
关键是，当你用这种综合视角做出决策时，你也在创造一个数据资产，这能让你在此基础上进行学习，对吗？因为我记录了采取了某项行动。结果如何？这是当时世界上其他所有事情的背景信息。现在，我拥有了训练数据资产，可以根据我实际做出的决策的背景进行训练，而不是后来构建的一些汇总分析视图。对吧，这很有道理。你提到过，Shyam，2017年对Palantir来说是最关键的一年。你还说，尽管从财务角度来看，那一年可以说是公司历史上最糟糕的一年。
▶ 英文原文 ⏱
And critically, when you make that decision with the integrated view, you're also creating a data asset that allows you to learn on top of it, right? Because I have like, this action was taken. What happened? This is the contextual information of everything else that was happening in the world at that moment. I now have the training data asset for me to be able to actually train things given the context of the decision I actually made, not some aggregated analytic view that I construct later. Yeah, that makes sense. So you mentioned, Shyam, that in 2017 was sort of the most, the critical year for Palantir. And you said that was despite the fact that from a financial perspective, that was arguably the worst year in the company's history.
那是为什么呢？请告诉我关于2017年的一个转折点。就Palantir而言，那是Foundry真正进入市场的时候，我们能够将商业领域的客户和收入转移到Foundry之上。在此之前，我们只是为不同客户进行一些定制整合和定制部署。可以说，那时候我们正处于试验阶段，探索产品是什么以及它在技术堆栈中的位置。我想说，我们当时构建的东西后来都变成了Foundry中的应用。Foundry为我们提供了基础设施，不仅可以在商业领域扩展我们的工作，而且还能服务于所有的政府客户。
▶ 英文原文 ⏱
And why was that? Tell me about like the 2017 sort of pivot point. As far as Palantir is concerned. That was the point where Foundry had really come to market and we were able to transition our customers in the commercial world, transition that revenue to run on top of Foundry. So before it was just a bunch of like custom integrations, custom deployments and all these different customers. Yeah, there was a period of like experimenting with what the product was and where it sat in the stack. And I'd say the things that we built would subsequently become like apps within Foundry, right? So like Foundry gave us the infrastructure to really scale what we were doing, not only across the commercial world, but across all of our government customers.
这段话的意思是：
它让我们具备了快速行动的能力。我认为关键在于，不仅仅是行动快，同时也能对同样的成果负责。比如在2017年，我们成功整合了Foundry平台，并在A350生产方面实现了33%的加速。那么，如何在维持统一基础的前提下，推出能够精准解决关键问题的高效产品呢？无论是用于Airbus、PB、Swiss Re，或其他任何公司。Foundry真正成为了一个操作系统，这也符合你的观点，关于公司如何在创造超额收益和普通收益之间找到平衡。许多公司都希望开发自己的定制软件，因为这能为他们带来竞争优势。
▶ 英文原文 ⏱
And it gave us the ability to go fast. And I think also critically, just the ability to go fast, but also still be accountable to the same outcomes. So 2017, we were able to consolidate on Foundry, but also deliver 33% ramp up in the acceleration of the A350 production. So how do you sort of push out the efficient frontier of building exactly what people need to solve their exact critical problem, but on the same common foundation where there's commonalities where you can use it or Airbus or PB or Swiss Re or whoever it might be? Right. So basically this was really where Foundry became an operating system to sort of your point where you talk about this idea of companies, you know, where they generate alpha, where they generate beta sort of idea and this bit that everyone wants to build their own custom software because that will give them advantage.
没有其他人拥有这种软件，但在某种程度上，每个人都需要反复做一些相同的事情。如果你把操作系统放在电脑的背景下来看，它其实涵盖了整个电脑，但又有很多电脑。所以，什么东西能够跨越所有这些电脑，从非常简化的层面来看，这似乎是对操作系统概念的一种重新思考。我觉得这里有一个有趣的类比，可以拿微软举例。微软在生产力领域面临的一个问题是SaaS（软件即服务）的爆炸式增长。
▶ 英文原文 ⏱
No one else sort of has that software, but there is an extent to which there's a lot of stuff that everyone has to do the same thing sort of over and over again. If you think about an operating system in the context of a computer, that is sort of covers the totality of the computer, but there's lots of computers. And so what sort of sits across all those computers and that at a very simplified level, it just seems to be a sort of a rethinking of what an operating system is. I actually think there is an interesting analogy to Microsoft where Microsoft sort of in the productivity area of space, you know, one of the issues, there's a SaaS explosion, right?
有各种不同的单一解决方案，但我们如何将它们整合成某种共同体呢？我认为微软在这方面做得很好，这一点常常被低估。我认为，即使整合得一般般，也好过没有整合，这种观点常常被人忽视。我的理解是——如果我错了，请纠正我——Palantir正在做类似的事情，但基本上是为各个行业提供大规模的解决方案。你们经常谈论石油公司、建造航空母舰的事情，这与拼凑一个聊天客户端或文字处理文档有些不同。
▶ 英文原文 ⏱
There's all these different sort of single solutions, but how do you actually tie them all together into some sort of commonality? And I think Microsoft has done very well sort of focusing on that. I think that's underappreciated that integration mediocre, mediocrely done is better than no integration at all. And I think that's sort of underrated. And what I'm hearing the analogy I have in my head and correct me if it's, if I'm wrong is Palantir is doing a similar thing, but basically at for industries as sort of at scale, you've talked a lot about oil companies, building aircraft carriers. This is a little bit different than stitching together a sort of a chat client and a sort of word documents.
这实际上达到了SAP以及ERP系统等同级别的水平。抱歉，我有点啰嗦，只是想确保我对Palantir的描述准确。这是一个合适的思考方式吗？我们的目标是打造一个操作系统，让全球的每个机构都可以基于它做出各自的决策。思考一下这点，可以从两个维度来展开讨论。其一是在价值链中，从供应商到客户，中间有许多决策需要作出，这不是简单的线性过程，而是一个决策网络。这是一个维度。另一个维度则是从战略到执行之间的联系。
▶ 英文原文 ⏱
It's really at the level of SAP and, and, and, you know, ERP systems and all that sort of thing. Sorry, I'm rambling a little bit. I just want to make sure I'm painting the right picture of what Palantir is. Is that sort of a good way to think about it? Our ambition is to have the operating system that enables every institution in the world to make every decision that they make. And when you think about that, okay, well, you can blow that out in two dimensions. Like one is they exist in a value chain, you know, so from the hand of their suppliers to the hand of their customers, there are a lot of decisions that happen. It's, it's more, it's not really linear. It's like a web of decisions. So that's one dimension. The other dimension is between strategy and operation.
那么，高层到底想要做什么？这些想法又是通过什么过程转化为具体行动的？通常，这两者之间的联系并不紧密。我认为这就是为什么我们如此关注阿尔法的最终表现：我们如何实现阿尔法而不仅仅是贝塔？答案在于将战略与运营相结合。这样，当高管们试图达成某个目标时，就有一个方向盘来连接和指导决策的制定，并形成一个反馈循环，让事情更加明晰。不过，要确保这一点清楚的话，例如像SAP这样的公司相对于Palantir，又处于怎样的位置呢？你们是打算取代它们，还是在它们之上进行叠加？我用它们来代表那些已经存在很长时间、深度集成于企业中的工控级软件公司。
▶ 英文原文 ⏱
So what, what is, what is the top of the house trying to do? And what is the process by which that gets translated into actions? Often that's, that's pretty disconnected. And, and I think this is the ultimate manifestation of why we're so focused on alpha. Like how do you deliver alpha and not just beta? Well, it's by connecting strategy and operation. So there's actually a steering wheel when the C-suite is trying to accomplish something that links that up and informs the decisions that start to get made in a way that provides a feedback loop. So to really sort of make sure this is clear though, where does a company like say SAP fit relative to a Palantir? Are you looking to replace them or is this sort of more of a layering on sort of on top of, I'm using them as a stand-in for all these sort of industrial grade sort of software companies that have been around for a very long time are very deeply integrated into these companies.
Palantir在这方面处于什么位置？这是要彻底替换掉现有系统吗，还是怎样操作？我认为可以从两个方面来考虑这个问题。一方面，你有各种符合工业标准的核心交易系统。我们通常会问的问题是，你有多少决策是通过这些系统来做出的？每天有多少资源是通过这些系统分配的，又有多少是围绕这些系统分配的？这些系统的不足之处在哪？首先，有些显而易见的问题是：理论上，这些是我的ERP系统应该完成的工作；但实际上，我可能通过Excel、电子邮件、电话来处理，还有许多需要定制化的内容。
▶ 英文原文 ⏱
Where is Palantir relative to that? Is this a rip out and replace or, or how does that work? So I think there's two ways that you can think about it. One is you have all of these sort of industrial rate, industrial grade core sort of transactional systems. And then a question that we ask, and this is a question we ask when we start customers is, you know, how many of your decisions are made through those systems? How many of your resources are allocated every day through those systems? And how many of them are allocated around those systems? So where do they fall down? And so there's sort of the first thing, which is the low hanging fruit of, well, in theory, this is what my ERP does. In practice, this is what I do through Excel, emails, phone calls, like the number of customizations that I have to do.
然后对于那些无法撤销的定制，如果我...你知道的，我该如何处理这样的事实：这个测试版虽然符合我的系统，但每个企业的运作方式不同。所以，你如何在提供坚固稳定、自动化良好的功能的同时，又能在需要满足客户需求或需要智能的时候灵活应对呢？我认为这是一个容易解决的低门槛问题。而更高层次的问题是，为什么我必须要通过现有软件架构去思考如何反映我的业务运行方式？在其自然状态下，业务本质上是试图弄清如何分配稀缺资源。为什么我还要经常与现有CRM系统中的思维模式作斗争呢？
▶ 英文原文 ⏱
And then the customizations that are impossible to unwind if I, if I, you know, so how do I deal with the fact that kind of this, this beta, the conform to my system isn't how every single business operates. And so how do you provide flexibility to use the things that are hardened, excellent, provide significant automation, but then fall over when they have to meet your customer where you need or where you need intelligence in order to do it. So I think that's the low hanging fruit. The higher order bit is then, well, why do I reflect the way that I run my business, which, you know, at its business in its natural state, trying to figure out how to allocate scarce resources? Why do I have to battle it through the existing software architecture of thinking about what's in my CRM?
在我的饼图历史记录中有什么？在我的ERP中有什么？在我的MES中有什么？比如，我制造飞机，我制造汽车。我希望能够考虑到整个价值链中的所有因素来做出决策。所以，当我对生产计划进行更改时，我会考虑到市场在那个特定时间点的动向，并试图从整体和自然的角度来看待我的业务，这样我就能以我思考业务的方式，作为管理者与其进行互动，而不是花费三周时间去获取一份报告来告诉我事情的实际情况。
▶ 英文原文 ⏱
What's in my pie historian? What's in my ERP? What's in my MES? Like I build airplanes. I build cars. I want to be able to think of these things and make decisions that takes into context everything across that value chain. So when I'm making a change to my production schedule, I'm taking into account what's happening in the market at that given point in time and sort of view my business in its natural state where I can start to interact with it as an executive in the way that I think about my business, not sort of fighting three weeks to get a report to tell me what's going on in the frame of how I actually want to run the thing.
我对我们团队的比喻感觉非常好，因为我一直强调的一点是，微软在市场上确实比很多SaaS公司更有竞争力。很多公司忘记了，人们并不是为了使用，比如说Slack的聊天软件而去购买这些产品，他们是为了更好地经营自己的业务而寻找合适的工具。所以当你提到“我要建造飞机”这样的说法时，会产生很强的共鸣。我们来谈谈这个问题吧。我发现Palantir有一点非常有趣和显眼，就是传统软件不仅需要购买，还需要一个系统集成商，或者你可能需要直接与公司合作，让他们的服务部门来实施和安装软件。
▶ 英文原文 ⏱
I'm feeling very good about my sort of team's analogy here, because the point I've always made there is where Microsoft really kicks the rear end of a lot of these SaaS companies in the market is a lot of these companies forget they're not built, like people aren't out there to buy, you know, to use the Slack example, chat software. They're out there to actually run their business and they're just looking for something to sort of help them get done. So when you say something like, look, I'm trying to build airplanes, I think that sort of really resonates. And so let's talk about this. One of the things that's really interesting about Palantir that I found very striking is this bit about how, you know, traditional software, you don't just buy the software, but then you have to like have a systems integrator or your maybe you contract with a company directly, they have a services division to actually sort of implement and install the software.
这是一份与另一份合同不同的独立合同。Palantir是否依旧没有单独的安装合同？这只是服务的一部分。如果你注册使用Palantir，Palantir会负责安装软件。这个情况还是这样吗？如果是这样的话，你们这样做的好处是什么？答案是有，也没有。我认为这有两个有趣的方面。首先，我们的FDE模式被认为是公司最大的秘密之一，也是公司最重要的特点之一。
▶ 英文原文 ⏱
And that's a separate contract sort of above and beyond sort of the contract. Is it still the case that Palantir, there is no installation sort of contract? That's just part of the service. If you sign up for Palantir, Palantir is going to come in and sort of put the software in. Is that the case still? And if that's the case, like what's the payoff for you from doing it that way? Yes and no. So I think there are two interesting dimensions to it. So one is that our sort of our FDEs, you know, the FDE model, we think of as one of our greatest secrets, one of the greatest features of the company.
主要原因是，我们认为这样做可以让我们对客户负责，确保我们与客户保持一致，即软件是否满足了客户的实际需求。这不仅仅是说，我们是否按照软件行业当前对这一功能的理解来交付功能，而是要真正检验它是否有效。我们需要从客户的角度来体验这个软件，切实地从他们的角度来思考问题。在谈及我们的软件时，我们为它迄今为止所取得的成就感到非常自豪。
▶ 英文原文 ⏱
And the primary reason we think of that is that it provides accountability to us that aligns us with our customers to say, is the software doing what the customer actually needs? Not just as someone using it, like have we delivered the feature in a way that sort of matches what the software industrial complex would want out of this feature in the current understanding of the stack? But like, is it actually working? And how do we walk a mile in our customer’s shoes? And when you think about, and the way we think about our software is, we're very proud of what it's done so far.
我们认为，它所产生的结果确实是很真实的，但我们认为我们只完成了所需软件的1%。那么，我们如何持续建立一个追责机制，确保我们对客户的最终结果负责呢？正如Shama提到的，这就是FDE（前线部署工程师）为Palantir提供的主要服务，他们深入前线，实际评估软件是否有效，是否对客户有实际意义。
▶ 英文原文 ⏱
We think the outcomes that it's delivered are really real, but we think we've only built 1% of the software that we need to build. And so how do we continue to create a accountability function, mentioning what Shama had, that is the accountability to the end outcome of our customers? And that is primarily the service that the FDE provides to Palantir as the institution is living at the coalface, sitting there saying like, does it actually work? Did it actually matter?
现在，我们也希望以一种能够随着时间最大化扩展性的方式来实现这一目标。因此，我们一直在进行投资。我认为，创新反馈会始终是Palantir的一部分，但也有其他客户开始提供同样程度的责任感，不只是安装软件，而是真正发挥作用的东西。因此，我们开始看到其他第三方能够独立实施这些软件。而真正对我们有利的地方在于，那些客户与他们的最终客户非常一致。
▶ 英文原文 ⏱
Now, also, we want to do that in a way that provides maximum scalability over time. And so what we've been doing is investing in the, I think that sort of innovation feedback will always be a part of Palantir, but there are other customers that also start to deliver things that provide that same level of accountability, not just install the software, but something that really works. And so we can start to see other third parties implementing the software independently. And where that's really working for us is where those customers are very aligned with their end customer.
因此，我们可以看到，例如在 EPC 工程领域，与像 Jacobs Engineering 这样的公司进行合作，它们正从事实际工作。比如，他们会签约进行植被管理，以防止野火发生。这对我们来说是一个很好的规模化合作伙伴，因为他们在实施软件时提供了与我们自己的现场工程师（FDE）类似的责任功能。
▶ 英文原文 ⏱
And so we're seeing that, for instance, scaling in the EPC sort of engineering space with someone like a Jacobs engineering, like they're doing physical things, right? Like they're signing up for the vegetation management so the wildfire doesn't ignite. That's a really good scaling partner for us of implementing the software because they provide that same accountability function that our FDEs do when we're doing the implementation.
这其中的一个重要部分也涉及到产品反馈循环。对于面向客户的产品方面，我们要考虑如何让软件的实施时间尽可能短，并使随后的价值能够累积增长。通过承担这一责任，我就不会与关于如何加快速度或者如何让你做更多事情的信号脱节。
▶ 英文原文 ⏱
And a big part of this also plays into the product feedback loop. So like that's customer facing on the product side, you know, how do I, I want the time to implement the software to be as short as possible and the value you get subsequently to be compounding. And so by taking ownership of that, I'm not getting disintermediated from the signal of what could I be building to make this go twice as fast or to enable you to do twice as much.
通过将这一成本内化，我们获得了一种激励，鼓励我们在这一方面进行投资。我认为，很多其他公司面临一种代理问题：我是否在剥夺合作伙伴的某些利益？在这个过程中，是否存在产品反馈循环，即当您为客户构建特定集成时，以后可以将其用于其他客户？
▶ 英文原文 ⏱
And by internalizing that cost, we have a radical incentive to invest in that where I think there's kind of an agency problem for lots of other companies where it's like, how am I, am I actually depriving something from my partners here? To what extent is there a product feedback loop in that you have to build particular integrations for a customer as you're doing this, and then you will reuse that for other customers?
这当然是其中的一部分。我的意思是，你可以说，看看，你可以泛泛地整合任何类型的数据。但是当我们开始接触到非常复杂的工业系统时，就会觉得，好吧，这些系统非常特殊，你要如何对其进行建模。我想说，几乎可以说，更有趣的是：你想要什么样的语义模型来帮助你定义从零到价值的过程？例如，我如何才能理解人们用这些系统试图实现的用例？
▶ 英文原文 ⏱
That's certainly part of it. I mean, I think you could say, look, you can generically integrate any sort of data. But then as we started getting closer to very complicated industrial systems, then it's like, okay, these are pretty particular and how you want to model it, I'd say almost in part, what's more interesting is like, what is this semantic model that you want that helps you define the zero to value? Like, how do I get to the use case that people are trying to do with this?
所以我不仅仅是在提供一个数据连接器，而是更像一个综合的链条，这条金光闪闪的路径能够让你在几天内就赚到第一个美元。那么，使用Palantir的公司生命周期是什么样的呢？因为听起来这与操作系统的比喻有关，在一开始的时候，你几乎就像是在组装一台计算机。
▶ 英文原文 ⏱
So I'm not just shipping a data connector, but actually more of like an integrated chain of like, this is the golden path that gets you to your first dollar of return inside of a couple of days. So what's sort of the life cycle then of a company using Palantir? So because it sounds like they're sort of, you know, this ties into the operating system metaphor, where at the beginning, you're almost like building the computer.
所以，你的意思是，你们不会购买新的ERP系统，也不会获得新的CRM系统，因为你们已经有很多现成的系统了。因此，最初的目标是让Palantir把这些系统整合成一个统一的平台。但在使用Palantir之后，是否期望今后所有的操作都会直接在Palantir上进行呢？
▶ 英文原文 ⏱
So but you're not going to buy, you're not going to get a new ERP system is what I'm hearing from you, you're not getting a new CRM system, you have lots of sort of pieces that are already there. And so the initial goal is for Palantir to sort of tie those pieces together into your phrase, sort of a single pane of glass. But after someone has Palantir, is the expectation that everything from going forward is just going to be on Palantir directly.
这大概就像是，他们虽然可能会继续维持他们的SAP合同，但将来不会购买其他企业软件。这是你的观点吗？不，我的意思是，我们生活在一个现实的环境中，不仅有许多现存的东西，他们还会希望在未来拥有去往任何他们需要的地方的选择权。
▶ 英文原文 ⏱
And that's sort of like, that's the end of like, sure, they may maintain their SAP contract, but they're not going to be buying any other sort of enterprise software in the future. Is that is that sort of the viewpoint that you have? No, I mean, people are gonna we live in a context of a brownfield reality, right? Like, so not only is there a lot of stuff that's there over time, but they're going to want the optionality to go wherever they need to go in the future.
那么，具体来说会发生什么呢？比如说，你已经使用过一段时间了，现在是一个相对成熟的用户，可能有成千上万个用户。在这种情况下，它就自然而然地成为了解决业务中下一个使用场景或问题的逻辑场所。因为你所有的数据都已经在数字孪生中建模，你可以访问企业中的所有AI模型，它们已经与事务系统集成在一起，这样当你做出决策时，就能够真正去执行它。
▶ 英文原文 ⏱
So then what concretely happens, like, you know, let's just say you've used this for a little bit, you're a more mature customer, maybe you have 10s of 1000s of users on this, this becomes the logical place to solve the next use case or problem that arises in the business, because you have all of your data, they're modeled in your digital twin, you have access to all of the AI models in the enterprise, and it's wired up into your transactional system so that when you make a decision, you can actually activate it.
然后你开始积累相互关联的应用程序。比如说，我有一个采购应用和一个生产应用。当我获得折扣原材料时，我可以轻松了解这将如何影响我的生产计划。不过，最后一步是不希望这些数据被锁在一个封闭的环境中。而是利用本体论SDK，你可以将所有这些数据，以及数字孪生模拟的动态，带入企业中的各个应用程序，不论是定制开发的应用还是第三方应用，只需简单地让数据流入，这样就为你提供了一种类似企业集线器的功能。这有点接近操作系统的隐喻，也就是说，我可以构建自己的应用程序，可以自由地导入和导出数据，而Foundry将处理我们的应用程序，并处理很多底层的细节，比如这些事务系统来自哪里，你如何实际地将数据写回这些系统等。
▶ 英文原文 ⏱
And then you start to accumulate applications that are interconnected. Like I have a procurement app, and a production app. And when I get a discount raw material, I can understand how is this going to affect my production plan pretty seamlessly. Now, the last mile of this, though, is like, of course, you don't want this inside of the world garden is that the the ontology SDK allows you to go bring all this data, all the digital twin simulation dynamics to the applications you have in the enterprise, whether those are custom built apps or third party apps, where you just simply want the data to flow to so that it gives you kind of like an enterprise buff, which is a little bit closer to the operating system metaphor where it's like, look, I can build my own application, I can pull data in and out of this, you know, Foundry will handle our applications will handle a lot of the low level details of which transactional systems is coming from, how do you actually write back to these systems?
我如何协调和整合这些东西？这让应用程序开发者能够专注于他们已经了解的业务，然后构建能够改变业务的应用程序，而不是纠结于以前做出的技术决策。对吧？我的意思是，您几乎就像一个中间件层，对吧？因为他们是在对接Palantir平台。那么问题是，您是否认为未来不只是内部业务应用程序，一旦Palantir平台就位，是否会有一个第三方企业应用程序的市场，能够直接与Palantir平台协作并假设Palantir已经在使用？
▶ 英文原文 ⏱
How do I coordinate and integrate these things? And that liberates the application developers to know their business, which they already do, and then build applications that change their business rather than fighting with whatever technical decisions have been made in the past. Right? Well, I mean, because you're being like a middleware layer almost, right? Because they're writing to Palantir. So the question is, do you see a development of not just sort of internal line of business apps where going forward once Palantir is in place, but is there a market of third party sort of enterprise applications that you foresee in the future, sort of working with Palantir out of the box and assuming that Palantir is sort of in place?
好的，我想我们已经达到了那个阶段。也许Ted，你可以谈谈我们构建的一些生态系统企业。好的，当然。如果你以Skywise为例，Skywise是空客的数字化项目，使用Foundry作为其操作系统。它已经在150家不同航空公司中部署，并提供各种应用程序。一方面，它能够促进信息的安全交换，使得飞行员能够将信息反馈给飞机的设计者和维修人员。另一方面，它也让这些设计和维修人员可以为150家航空公司部署预测性维护应用程序。而且，他们能够在统一的本体上实现规模化部署，这个本体抽象掉了每个系统底层不同系统之间的差异。
▶ 英文原文 ⏱
Yeah, I'd say we're there already. I mean, maybe Ted, you'd like to talk about some of the ecosystem enterprises we built. Yeah, absolutely. So if you take Skywise as an example, which is the Airbus digital program that uses Foundry underneath it as its operating system, it's deployed across 150 different airlines where they provide different applications. One, to be able to facilitate secure exchange of information so that the people who fly the aircraft can get it back to the people who design and fix the aircraft, but also so that the people who design and fix the aircraft can deploy predictive maintenance applications to 150 airlines, but be able to do that at scale on top of that same common ontology that abstracts away the differences in all the underlying systems that are different across each one of those different systems.
明白了。那么，是各个航空公司与Palantir签订合同来使用这些服务，还是通过空客实现的？这都是通过Skywise实现的。Skywise可以看作是空客在Foundry基础上开发的一套应用程序，他们将其出售给航空公司客户。这很有趣。那么我有点好奇，这种业务拓展战略是什么样子的？这让我想起了那种资金雄厚的初创公司概念，Palantir就是典型代表之一。它一开始是为了应对明确的国家安全问题，特别是与恐怖主义相关的问题。
▶ 英文原文 ⏱
Got it. So do you do all those individual airlines, do they have a contract with Palantir then to go in or this is all sort of via Airbus? This is all via Skywise, yeah. So Skywise is like, you can think about it as this suite of applications that Airbus built on top of Foundry and they sell that to their airline customers. Interesting. Interesting. Well, I'm sort of curious, what does the go-to-market for this look like? This gets back to the fat, very, very fat sort of startup sort of concept, which Palantir is in spades. You start out and you have this, look, there's this very clear national security issue around terrorism that you are out to address.
美国当时正很愿意打开钱包，所以这是个很好的起点。但现在我们谈论的是大型企业，比如空客、石油公司或其他制造商。而这听起来就像一个相当大的要求。因为你希望他们从根本上改造整个公司，而不仅仅是一个团队觉得Palantir不错、能提高生产力就够了。
▶ 英文原文 ⏱
And the US was in the mood to sort of open up the pocketbooks. And so that was a great place to sort of get started. But now you're talking about these large entities, whether it be an Airbus or an oil company or other sort of manufacturer. And it sounds like this is a pretty big ask. You're asking them to basically transform the entire plane of their company to your point, because you're not going to get the benefit of Palantir by just like, this isn't a one team in the company saying, oh, this is very useful and makes us more productive.
这段话主要讲的是，你需要全面实施才能获得好处。那么该如何推销这个理念？如何让公司愿意参与进来呢？其实并非完全如此。我的简单描述是，实施过程实际上反映了客户的雄心。正如Sean所提到的，通过内化很多成本并加快实现价值的速度，我们将数据集成层产品化，这意味着我们在扩大可实现目标的同时，大大降低了实现目标的门槛。
▶ 英文原文 ⏱
This is talking about sort of like, you're not going to get the benefits unless everything is on there. So what's the pitch? How do you go in? How do you get companies to sort of commit to this? Yeah, so not exactly true. But I would say the way I would describe it at a high level is the implementation essentially indexes the ambition of the customer. And as Sean had mentioned, sort of by internalizing a lot of the cost and accelerating the sort of speed to value and productizing that data integration layer, that means that we've been able to dramatically lower the floor while also extending the ceiling of what we want to do.
在过去的两年里，因为我们积累了大量安装这些系统的经验，现在你会觉得自己可以快速完成在任何公司中的安装。就是说，我们的中位生产时间，对吧？我们的概念验证（POC）项目的中位时间是这样的：在项目结束时，你并不是只有一个POC，而是一个可以由用户实际使用的生产应用，通常需要六到八周。
▶ 英文原文 ⏱
And so what we've done over the last two years, because you've just accumulated so much experience of installing these, you feel you can walk into any company and get it done pretty quickly. Yeah, so sort of median time to production, right? Median time of our POCs sort of, and POCs at the end of, you don't have a POC, you have a production application that's used by users in anger is six to eight weeks.
这让我们可以从一个具体的问题入手。通常，这需要我们的客户承认，他们目前的软件系统不能解决现有的问题。没有这样的认知，我们会面临困难。但这个问题可以是非常具体的，我们可以在几周内解决。不过，如果你的问题涉及整合许多不同的软件，你是不是只是为了解决一个问题而在安装一个操作系统？也许这个问题只是以某种方式表现出来？
▶ 英文原文 ⏱
And so that allows us to start with an individual problem. It does oftentimes require that our customer says, like, I recognize that I have a problem that my existing software stack does not solve. Like, without that, we're challenged. But that problem can be very specific and something that we can solve on the order of weeks. But if your problems involve, your problem solving involves sort of integrating lots of different software, are you effectively installing an operating system just to solve one problem? Maybe it's only sort of manifesting in one way?
你能不能直接说，我们只是有一个CRM（客户关系管理）的问题，或者其他任何问题，这个概念验证到底是什么样的？因为你是怎么在不构建整个系统的情况下做事的？通常情况下，这些事情的开始是确定自己有哪些问题是Palantir能够解决的，然后这个问题通常会以一种非常具体的方式表现出来。
▶ 英文原文 ⏱
Or can you sort of go in and say, look, we just have a CRM problem. We just have a whatever problem might be like, what is this? What is this proof of concept bit look like? Because how do you do what you do without building, putting in the whole thing? Oftentimes, what these things are is they start with like, okay, what version of the problem that Palantir can solve do I realize that I have? And that often manifests itself in a very specific way.
我在供应链中遇到了很大的波动问题，这让我面临许多挑战。有时候我无法按时交付订单（OTIF），而有时候库存暴增。在高利率的情况下，这些问题让我感到难以应对。我知道这是我需要解决的问题。为了解决这个问题，我可能需要整合多个不同的系统。但是，我可以通过一种软件定义的方式来处理。我可以使用一个应用程序在几周内开始解决你的额外库存问题。
▶ 英文原文 ⏱
Like, I have a huge amount of problem of challenge with volatility in my supply chain. So I'm either blowing OTIF or I have exploding inventory and with high interest rates, like I can't manage that. Like, that's the thing I know. That's the problem I know I have. Now, in order to solve that, I have to integrate probably several different systems underneath that. But I can bring that and approach that in a software-defined way where I can say like, I can start to address your excess inventory problem with an application that is in production on the order of weeks.
很多时候，他们会看到这些情况，然后说，好吧，现在我已经有了这些信息，我还想整合一下我的定价策略，以及合同管理是如何在无意中给我带来问题的。我还会考虑我的生产计划是如何进行的，以及如何处理供应商短缺的问题。但这种经典的“登陆并扩展”策略，就是横向的数据整合，非常专注，也非常负责。最终用户生产应用程序的投资回报率是多少？大多数事情就是这样开始的。明白了。
▶ 英文原文 ⏱
Then oftentimes, what they do is they'll see that and say, okay, well, now that I have that, I'd also like to integrate what my pricing strategy is, how I'm doing my contract management as a function of creating these problems for myself, how I do my production planning, how I deal with managing supplier shortages. But that sort of classic land and expand strategy of horizontal data integration, very focused, very accountable. What is the ROI on the end user production application? That's how most of these things start. Got it.
所以，如果你能够在几周内解决第一个问题，那么能不能说你通常可以在几天内解决第二个或第三个问题呢？因为你已经有了所有这些数据。确实如此，这也是事情变得有趣的地方。而且这真的是，我们最终受到客户雄心的驱动，他们看到这一点后会说，好吧，我有了这个之后，接下来我能做什么？我还能做些什么？
▶ 英文原文 ⏱
So if you can, if you can solve that first problem in the order of weeks, is it fair to say you can often solve a second or third problem almost in the order of days? Because you've already got all this sort of data there. Exactly. And this is where it gets fun. And where it really, you know, we end up being bound by the ambition of our customers of seeing that and saying, okay, once I have this, what's the next thing that I can do? What's the next thing that I can do?
接下来我可以做什么呢？我的意思是，这有点像Palantir公司的类比。因为一开始你有很大的抱负，一旦你意识到你正在解决所有问题，你就可以进入客户那里，基本上可以通过默认方式实现你的宏伟目标，因为这就是解决单一问题的方法。然后，当他们逐渐认识到这个机会后，事情就会不断发展壮大。
▶ 英文原文 ⏱
What's the next thing that I can do? I mean, it's almost like an analogy of Palantir the company where, where, because you start out with the sort of grand ambition and once you realize you're solving all the problems that like now you can go into customers, you can basically install your grand ambition by default, because that's the way you solve a singular problem. And then as they just sort of appreciate sort of the opportunity sort of goes from there.
我的意思是，我可能有点像个粉丝，但显然，Shab，你在一些投资者交流中提到过这个想法，就是说你们在某种程度上是在聚合解决问题的方法。因为你们已经收集了所有数据，并将其集中在一起，因此解决新问题的边际成本急剧下降。这完全正确。
▶ 英文原文 ⏱
I mean, I'm sounding like a bit of a fanboy, but obviously, I mean, Shab, you sort of said this in the, you know, in that, you know, some of your investor talks about this idea of you're, you're aggregating problem solving in a certain respect, because you've already sort of collected all the data, it's already all in one place. And so the marginal cost of solving a new problem sort of decreases dramatically. That's exactly right.
我指的是，最典型的退化案例就是危机。这说明我们在这方面确实很出色。我们很自豪能够帮助客户应对危机，因为解决问题的成本非常低，你有很强的灵活性，已经掌握了大量可用的数据，然后你可以借此改变运营方式和决策方式。那我怎么知道新冠疫情的情况呢？首先，我没有一个跨越6000家医院的综合国家数据系统。接下来，我需要了解个人防护装备（PPE）的情况，然后我需要弄清楚如何分配这些物资。之后，问题就不再是PPE，而是疫苗的问题。就是说，我要不断在每一个新的具体问题上继续进行优化，找出当前特定问题的具体表现。
▶ 英文原文 ⏱
I mean, and the kind of degenerate case of this, which really shows this is crisis. You know, I, we're kind of very proud of how we can respond. We can help our customers respond to crisis because the cost of solving the problem so low, you have all the agility, you've already got the data that you can wield, and then you can drive it to changing your operations, changing how you make decisions. How do I know what's happening with COVID? Well, the first thing is, you know, I don't have any integrated national data asset that operates across 6,000 hospitals. Okay. After that, now I need to know what's going on with PPE. Now I need to figure out how to allocate PPE. Okay. Now it's not a PPE problem. Now it's a vaccine problem. Like, how do I continually iterate on top of like, what is the specific problem of the current specific manifestation?
在这些危机中，情况经常变化，对吗？今天的问题和明天的问题可能会不同。但是如果所有数据都整合在一起，响应起来就会更容易。这完全有道理。销售市场策略的对立面是客户流失。一旦公司使用了Palantir，他们还会流失客户吗？好比说，情况会是怎样呢？是否会出现你解决了我的问题，但花费巨大，然后我觉得你做得很好，但未来可能负担不起这样的情况？或者这是一个很强的客户锁定机制？我们其实不太感兴趣与那些不想和我们合作的客户打交道。
▶ 英文原文 ⏱
And oftentimes in these crises, right? The problem rolls like what the problem is today is different than the problem tomorrow. But if you have the data all integrated, then it becomes sort of much more easier to respond. No, it makes total sense. The inverse of sort of go to market is churn. And once a company has Palantir in place, I mean, does anyone churn? I mean, like what, what, like what, what would, what would be, is it just sort of at some point that way you solved my problem that cost a whole lot of money. Hey, you did a good job, but I'm not sure we can afford this going forward. Or is this sort of like the world's best lock-in? We really don't have any interest in working with customers that don't want to work with us.
正如我之前说的，我们现在确实处于起步阶段。我们需要那些能够推动我们加速产品创新的客户。这只有在我们和客户高度一致的情况下才有效。如果我们做的事情导致了不一致，那就不行了。需要明确的是，“锁定”本身并不是坏事。我想，虽然“锁定”这个词听起来不太好，但你可以将其重新理解为一整套与现有数据完全整合的API，这是构建新事物的显而易见的下一步。比如说，操作系统就是世界上最好的“锁定”，因为它对所有相关方都有好处。它们极大地扩展了市场，无论是从计算机操作、硬件角度，还是从开发者和用户的角度来看，都带来了更多的可能性。
▶ 英文原文 ⏱
Like I said, like we're really at the beginning. Uh, we need customers that are forcing us to accelerate in the innovation of what our product is going to be. That only works when we're very aligned with our customers. And so if we're doing something that creates misalignment. Yeah. To be clear, lock-in is not a bad thing per se. I mean, I guess the word lock-in is bad, but you could reframe it as an available set of APIs that is fully tied into my existing data and is an obvious next step to build something. I mean, like operating systems are the best lock-in on earth because they are so good for everyone involved. They, they dramatically expand the markets for, you know, from the computer operation, from the hardware perspective, from a developer perspective, from a user perspective and what's more available.
这不是批评，而是赞美。至少可以说，我们自然而然地走到一起。听起来不太好，我想说这是很有黏性的。好吧，公平地说，这种黏性是积极的。当然，顾客对此很聪明，他们认识到人们有时希望能在做决策时保留一些可逆性。所以，一切都是开放的。你需要和他们一起走过这个过程，以了解如果他们想退出是否可以。所以我们对此非常有承诺。正如Ted所说，我们以高标准要求自己，但我们希望成为解决下一个问题时最合理、最经济的地方。
▶ 英文原文 ⏱
So it's not a criticism. It's a, it's a, it's a praise. At least maybe, you know, we just sort of naturally, you know, lock in. That sounds bad. Well, I'd say it's sticky. Okay. Fair enough. The sticky thing is the positive one. Yeah. Yeah. And, and of course, like customers are smart about this. They recognize like that people want to, you know, sometimes the Europeans call it the reversibility of the decision. So, you know, everything's open. You have to kind of walk that journey with them to understand it's like, look, if I wanted to exit, could I? And so like, we're very committed to that. But, and to Ted's point, we hold ourselves to the standard, but we want to be the place, the most logical, cheapest place to solve the next problem.
我们如何能够持续创新地做到这一点？你认为为什么其他公司没有采取这种全面投入的方法呢？我的意思是，这确实很困难——在各个方面都很难。你需要为结果负责，还要吸引那些本可以选择在硅谷工作的软件工程师，以及提供良好的体验，而他们要面对的是跨国公司的巨大官僚体系和数据权限等各种挑战。所以首先，你必须激励真正有才华的人参与这项工作，这真的非常困难。然后，你还需要想办法开发出真正解决问题的产品。
▶ 英文原文 ⏱
How are we going to continuously innovate on doing that? Why do you think that other companies haven't taken this sort of approach of being this sort of, you know, sort of all consuming in some respects? I mean, you know, whereas as it's so hard, it's so hard. I mean, and hard on every dimension, right? Like you have to sign up for outcomes. You have to get software engineers who have the alternative of working in Silicon Valley and a pleasured experience deploying downrange and, or downrange into a fortune 500 institution defined by giant bureaucracies and data rights access and all of these other things. So one, you got to motivate really talented people to do that. That's really hard. Then you have to figure out how you can build product that actually solves the problems.
这是一个实质上非常困难的问题。然后你必须想办法实现它，创建一种可以兼容性强，并且为尚未解决的下一个技术支撑的平台。这会产生很多内部摩擦。我如何让FDE与职责不同的开发人员合作？当开发人员要对350个不同顾客的问题负责时，我如何能够将开发人员的工作放在解决顾客关键需求的核心路径上？这非常困难，我们在内部解决这些问题的时候并不总是能做到最好。我会说，我们在这方面可能是世界上最好的，但这仍然很难，因为我们总是在泛化与具体化之间失去平衡，做得过多或过少。
▶ 英文原文 ⏱
That's a substantively hard problem. Then you have to figure out how you do that, where you're creating technology that is horizontal and load bearing for the next technology that you didn't solve. That's a lot of internal friction. How do I get the FDE to work with the dev who has a different accountability function? How do I put the devs work on my critical path to solve the critical need of the customer when that dev is accountable to solving things for 350 different customers? This is a very hard, we don't always get it right sort of internally. I would say that I think we're the best in the world at doing this and we suck at it because it's so hard and we're always missing over under and over calibrated with the generalization versus specific.
这非常非同寻常。因此，你总是会因为一些方式而受到批评，这些批评好像与我们认为自己在做的事情完全无关。我们甚至不在乎这些批评，但这需要很高的纪律性和大量的投入，才能完全与客户保持一致。你觉得现在进入有趣的部分了吗？我们很乐观。我想可以这么说，现在可能比以前更有趣了。不过，这仍然一直很难。
▶ 英文原文 ⏱
And it's very unconventional. And so you get critiqued all the time for ways that are kind of like, we're like, that critique has nothing to do with what we think we're doing. Like we don't even care about the critique, but it requires a lot of discipline, a lot of commitment to fully align yourself with your customer. Do you think you're getting to the fun part now? I mean, we're optimistic. And I think maybe it's more fun now than it was. I'll put it that way. It's still hard all the time.
我觉得现在市场上正在发生一些有趣的变化，特别是大型语言模型在效果上的突破或跨越，这很令人兴奋。好的，让我先停一下。还有一个关于动机的问题。为了保留这些工程师来构建这个庞大的系统，这有多重要？只要想两分钟，你就能想象出需要投入多少工作，以及要建造多少东西。
▶ 英文原文 ⏱
But one of the things that I think is getting really fun right now is we're seeing a shift in the market with sort of the advent or sort of maybe the threshold crossing of the effectiveness of large language models. Yeah. Well, let me do that a second. One more question on sort of the motivation bit. How important was it with keeping these engineers to build this massive system? I think you just think about it for two minutes. You can imagine how much work you had to do and how much you had to build.
拥有一个核心的最初激励因素，即“我们这样做是为了国家安全”，其重要性有多大？比如，Alex Karp在S1文件中发布了一封信，明确表达了他们的立场，即我们相信西方治理模式，我们与民主国家结盟，我们不会向中国出售产品。我们不认同硅谷那种一群高管坐在房间里告诉别人该做什么的做法。
▶ 英文原文 ⏱
How important was it to have that core original motivating factor of we're doing this for national security? Were there like, I mean, you know, Alex Karp sort of issued that letter with the S1, just sort of laying it out there. Look, we believe in sort of Western governance. We're allying ourselves with democracies. We're not going to sell to China. We don't believe in Silicon Valley. A bunch of executives sort of sitting in their rooms telling people what to do.
我的意思是，那封信对于首次公开募股来说是个了不起的举动吗？还是说这是一种持续不断的动力？这和你近二十年来坚持不懈地构建这个系统的能力相关吗？我们可能都应该从个人的角度来回答这个问题。但我觉得，没有那种动力，你根本没办法做到这些。这是至关重要的，是基础性的。
▶ 英文原文 ⏱
I mean, how was that just a great sort of letter for the IPO? Or was that something that was an ongoing motivation? And did that tie into your ability to sort of persevere for, you know, it's been almost 20 years and sort of build this system? We should probably both give our personal answer to that. But I was like, I don't think there's any way you can do it without that. It's crucial. Yeah. It's fundamental.
这句话的意思是：它之所以重要，是因为它渗透到各个方面。我在商业部门工作，但同样有任务导向，对吧？就像当你在一个使命导向的地方工作时，你就会承担起客户的使命。不论是为在蒙古建矿的人工作，还是为在瑞士定价保险的人工作，你都需要认同和加入他们的使命。成功意味着西方经济的强大，能够提供就业机会，并持续推动经济核心的创新，不仅限于硅谷等等。
▶ 英文原文 ⏱
And it's fundamental also because it permeates. I sit on the commercial side of the business, but the same mission orientation, right? It's like when you work at a mission oriented place, then you take on the mission of your customer. If that's someone building a mine in Mongolia or figuring out how to price insurance in Switzerland, like you still have to sign up for that mission and belief that in that mission, then it's the success of the West is the strength of the economy, the ability to employ people, the ability to continue to actually drive innovation into the core of the economy, not just in Silicon Valley, et cetera, et cetera.
如果没有这样的动力，你绝不会主动选择承受这种痛苦。看看在Palantir工作的人们，不得不说，他们的任职时间真的很长。那些已经在这里的人往往会继续留在这里。我觉得这很大程度上是因为，他们可以在一些小事情上做出贡献，以改善世界上的种种问题，同时还能接触到大量不同领域的工作。
▶ 英文原文 ⏱
Like without that, there's no way you sign up for this pain. You look at the longevity of people at Palantir. It's like strangely, I mean, the people who have been here tend to be here and stay here. And I think a big part of it is look at all the things in the world that I have some small part in making better and being able to touch and across the diversity of things that I get to do it.
这就是你签约的原因，也是你加入公司的原因。但更重要的是，它告诉你如何在极大的困难中保持动力。你要面对客户的官僚作风，面对这个不完美的世界，而不是像大多数科技公司那样想要逃避。相反，你是要接受这种复杂性，并通过你能够实现的改变来定义你从中获得的回报。
▶ 英文原文 ⏱
It's, it's why you sign, it's why you join the company, but then it's, it's, it's how you continue to be motivated through the incredible pain. Like you're dealing with the bureaucracy of your customers. You're dealing with the imperfections of the world and you don't get to just put down your toys and run away like most tech companies want to do. It's you're embracing that complexity and you're defining your reward for embracing it based on the change you're able to manifest.
因此，它推动了整个公司及其员工的协同一致。好吧，现在让我们进入AI部分。我不是故意推迟这个话题的，但我确实认为在这里建立一个更广泛的基础是有趣的。Sham，你上周刚做了一个主旨演讲。我想在这个内容发布的几个星期前，你也谈到了你们的新AI平台，给我详细介绍一下吧。
▶ 英文原文 ⏱
And so it drives alignment through the whole company and the talent in the whole company. Well, let's, okay, we are now to the AI part. I've been put, I've not purposely put it off, but I do think it's an interesting sort of to lay the broader foundation here. Sham, you just gave a keynote last week. I think a couple of weeks ago when this is sort of going to publish talking about your new AI platform, walk me through it.
请给我一个简单明了的介绍。我觉得，你已经与这些数据有很深的接触，但为什么是现在？为什么是今天？我的意思是，为什么以前没有，现在却是2023年这个时候推出呢？AIP是一套核心的基础技术，它实际上允许企业将大型语言模型（LLM）引入到他们私有网络中的数据，以便让他们的软件能够将LLM与企业的工具、AI模型、地球物理模拟器以及他们已经投资的现有技术相连接。这一切都是以一种受控和管理的方式进行的，使得企业最终能够信任这些技术并遵守相关法规。
▶ 英文原文 ⏱
Give me the pitch. I think the, you know, you're obviously already plugged into sort of all this data, but is this something like, why now? Why today? And I mean that in a context of why not previously, what is it about 2023 that this is the time for it to come out? So AIP is a core set of foundational technologies that really enable enterprises to bring LLMs to their data on their private networks, to enable their software, to connect the LLMs to the tools of the enterprise, to their AI models, to their geophysical simulators, to the things that exist, the technology they've already invested in, and to do so in a way that's controlled and governed so that they can ultimately trust it and comply with regulations.
这其中有很多复杂性。你几乎可以把大型语言模型（LLM）看作是“第一公里”。如何利用它来做出决策，就像是一大堆后续工作需要完成。我认为，从很多方面来看，我们在Foundry和Gotham上的所有工作似乎一直在等LLM。我的思维模式是，LLM不会取代你的软件，也不会取代人类，而是取代人类在使用这些软件时所做的事情。所以，如果回到LLM出现之前的世界，Foundry就像是企业的操作系统，而现在把AIP引入其中，它将极大地提升你在决策方面的体验。
▶ 英文原文 ⏱
And there's a lot of complexity. You can almost think about the LLM as being like the first mile. How do you wield this to make decisions being all of the other work to be done there? And I think in many ways, all of the work that we'd done with Foundry and Gotham was just waiting for the LLM. And the mental model I have for this is that the LLM doesn't replace your software. It doesn't replace the human. It replaces what the human was doing when they were using your software. So if you step back to a pre-LLM world, the way that Foundry would be an operating system for the enterprise, and now you bring AIP into that, it's going to supercharge the experiences you have around the decisions you can make.
也许最简洁的表达方式是，我们认为利用人工智能来支持企业做出的每一个决策存在一个很大的机会。而AIP将会在这方面发挥作用。为了实现这个目标，我们认为有几个关键因素是必不可少的，而这些也是我们一直在投入资源的领域，使我们在这一方面具备理想的优势。首先是，操作系统中你对类型安全的重视程度越高，你从大语言模型（LLM）中获得的优势也就越大。
▶ 英文原文 ⏱
So maybe the most pithy way of saying this is that we think there's an opportunity to enable every decision the enterprise is making with AI. And that AIP is going to bring that experience to bear. And there's a couple of pieces that I think make it, that are kind of generically required that we've been investing in, but I think that make us ideally suited to doing that. And the first is that you need, you know, the more obsessed you are with type safety in your operating system, the more leverage you actually get out of the LLM.
我们对整个技术栈的开发非常专注，其实整个系统都极其关注细节。特别是在用户互动的那一层，这帮助用户能够更快更远地前进。可以这么来理解：在进入企业时，你实际上是想把企业的整个状态机融入到你需要解决的问题的背景当中。我认为，如果没有一套基础技术——这些技术为你建模语义层、提供数字孪生，并提供工具以便在信息检索、增强生成或实际的计算工具之间反复迭代——你是无法实现这一点的。
▶ 英文原文 ⏱
And we are an obsessively, actually the whole stack is kind of obsessively type 6. But if we just talk about that, the layer that the human is interacting with, this enables them to go much further, much faster. And one way of thinking about this is if you go to this enterprise, you're trying to essentially get the entire state machine that is your enterprise somehow into the context and the question that you're trying to solve. And I don't think you can do that without a foundational set of technologies that have modeled the semantic layer that have given you the digital twin, and then give you the tools to iterate back and forth between whether it's retrieval, augmented generation, or actual computational tools that allow you to get that to compute, to get an answer.
这些模型是怎么来的？你们是在使用第三方的API模型，然后在其基础上添加企业的数据吗？还是你们开发了自己的模型？我们是将模型带给客户，让他们可以在自己的服务器上运行这些模型，并选择他们喜欢的开源模型。我们认为，随着使用过程的推进，持续微调这些模型是一个巨大的机会。总体来说，我们的观点是，模型的进步速度非常惊人。在企业中，可能会有各种各样的模型共存。
▶ 英文原文 ⏱
Are these models, are you using a third-party API for the model and then layering on sort of the enterprise's data on top of that? Or have you developed your own models? We're bringing the models to our customers here so that you can self-host the models, pick your own open source models. We think there's a huge opportunity in continuing to fine-tune these models as you go along. Essentially, our view of the world is that the rate of progress with the models is incredible. That you're probably going to have a menagerie of models that are actually in your enterprise.
你将能够以很低的成本将它们调整到满足特定需求。最大的、最强的模型并不一定是最可能胜出的。我们认为在模型的强大和你可以迭代该模型的速度之间有一个最佳平衡。所以，如果你处于这样的环境中，我认为你实际可以开发出能够帮助你实现特定应用的模型。
▶ 英文原文 ⏱
You're going to be able to very cheaply fine-tune them to the specific needs. That the biggest, baddest model is not inherently the one that's likely to win. We think there's kind of this Goldilocks balance between the power of the model and the rate at which you can iterate on that model. And so if you're living in this sort of universe, I think you're going to actually be able to develop models that help you with the specific applications that you're trying to apply it towards.
我们尝试实现的是一种几乎不需要提示的体验。我的想法是把提示看作开发者的工具。实际上，我们想在操作系统中为用户提供神奇的互动体验，并帮助开发这些应用的开发者利用这些工具，创造出基于大型语言模型的体验。据我理解，目前的大型语言模型的运作方式是，你拥有一个核心模型，然后可以用你自己的数据进行微调。这些数据可以针对特定的用途或数据类型，比如如果你正在编写代码。
▶ 英文原文 ⏱
And what we're trying to enable is really this almost like prompt-free experience. My mental model for this is like the prompts are tools for developers. And really, how do I enable magical interactions in the operating system itself for our users and enable the developers who are building these applications to stand on top of and create these LLM-powered experiences? As I understand it, the way like an LLM will work today is you have sort of the core model, then you can sort of fine-tune it with sort of your own sort of data. Or it can be a particular, you know, use case or type of data to your bid like, you know, if you're doing coding, for example.
或者说，你知道，这可能是基础的一部分。但你不能直接从基础过渡到应用。在这中间还需要一个过渡步骤。我想我有点像你说的那样，你可以替换任何数据，可以使用开源数据和开源模型，可以做X、Y、Z。这是否意味着存在某种不同的步骤？我对这个运作方式有点不清楚。
▶ 英文原文 ⏱
Or it could be, you know, but there's sort of like the foundational piece. But you don't just go straight from foundational piece to sort of application. There's some sort of intervening step there. I guess I'm a little like you talked about you can sub in any data. You can use open source data, open source model. You can do X, Y, Z. Does that mean that there's sort of a distinct I'm little unclear about how this works.
如果你是一个企业，你会选择使用OpenAI的模型，还是谷歌的模型？是否在这之间还有一个步骤，你需要用自己的数据对模型进行微调？或者不是这样的，这些开源选项是完全独立的？不好意思，我对这个实际应用还不是很清楚，有点困惑。
▶ 英文原文 ⏱
So if you're an enterprise, do you say, I want to use the open AI sort of model, or I want to use the Google's model? And then there's a intervening step where you fine-tune it with your data? Or is it no, like this is completely self-contained thing. These open source options. Sorry, I'm just a little confused on how it actually manifests itself.
是的，这是个好问题。先放慢一下进度。AIP 自带了预定义的整合功能，能够直接对接这些大型基础模型。而且这些整合功能已经被设置好，可以与平台中的常规体验一起工作。那么在 AIP 交付过程中，是否仍然需要进行某种微调步骤呢？AIP 初始状态下并不预设任何微调步骤。
▶ 英文原文 ⏱
Yeah, no, no, it's a good question. Just kind of slow down on the packet. So AIP ships with predefined integrations out of the box, hitting these large foundational models. And those are wired up to work with the generic experiences in the platform. And is there still going to be a step there, this sort of fine-tuning step as part of the AIP delivery? There's no fine-tuning presupposed out of the box.
这段话可以翻译为：
这只会让你捕捉到越来越多的细节，还让你从那些公共互联网上可能不存在的、基础模型训练数据集中没有的独特应用场景中获得更多的价值，这时候你就需要进行微调。不过，这不是预设条件。我觉得其中的一个关键是，如何为这些大型语言模型（LLMs）提供工具。首先，我有自己的应用程序并在使用它。我尝试在我的私有数据上执行操作，而这些数据显然不在公共数据中。然后，你会面临LLMs的弱点。比如我让它执行很可能导致幻觉的任务，或者我让它做一些我没有办法完全信任的事情。
▶ 英文原文 ⏱
It only allows you to capture more and more specificity and more and more, it allows you to capture more value out of your unique use cases that may not exist on the public internet, may not exist in the training data set for these foundational models, where you're going to need fine-tuning. But it doesn't presuppose that. And I think a big part of it is, how do I actually give those LLMs the tools? The first part is, I have my application, I'm using it. I'm trying to do stuff on my private data, which is clearly not in there. Then you have the weaknesses of LLMs. I'm asking you to do things like, that are likely to lead to hallucination, or I'm asking you to do things that where I can't trust it perfectly.
如何将大型语言模型（LLM）的优势与企业中的各种工具相结合？例如，当我要求LLM回答问题时，这与让我生成查询以使用传统的检索技术和相关的安全措施来正确回答问题是非常不同的。那么，我如何帮助你将这些元素结合起来，以便你既能享受LLM带来的体验，又能利用现有企业工具的优势？要让这一切顺利运行的诀窍是什么呢？在你的主题演讲中，这几乎感觉像是世界上最好的SQL查询生成工具，这一想法确实非常吸引人。
▶ 英文原文 ⏱
How do I combine the strength of the LLM with the fact that I have a whole bunch of tools in my enterprise? Like, if I'm asking the LLM to answer a question, that's very different than asking you to generate the query I need, where I can use traditional IR techniques and the security against that to actually answer the question properly. And so how do I help you compose these so that you get the experience of the LLM, but you get the leverage of your existing enterprise tools? And what's the exact magic to sort of make that work? I mean, in your keynote, it almost felt like this was the world's best sort of SQL query building tool, which I think is actually quite compelling.
这很有道理。但是，这在日常工作中到底是如何体现的呢？这个方法让你可以提出问题。就像，在主题演讲中会分成几个部分讨论。首先是，如何将数据整合到我的本体中？你可以把它看作，恰如其分地说，这是世界上最好的 SQL 构建工具，对吧？我可以简单地拿出我的目标和来源，然后问：这两者之间能否连起来？但现在我已经有了本体，我该如何针对它提问呢？比如，我之前展示的例子是，我收到一封邮件，说我的一个配送中心出现了中断。
▶ 英文原文 ⏱
That makes a lot of sense. But like, how does that actually sort of manifest itself in day to day work? Well, it allows you to ask questions, so if you, the keynote kind of breaks down a few different sections. The first is, okay, how do I integrate data into my ontology? You could, you could call that, I think if you squint at it, the world's best SQL building tool, right? Where I can just take, here's my target, here's my source, just can you connect the dots on this? But now that I have the ontology, how can I ask questions of it? How can I, you know, the example I showed there is like, I got an email saying that there's a disruption at one of my distribution centers.
好的，那么我该如何粘贴邮件并请求它帮助我可视化受影响的客户订单，然后按照一系列步骤来找出在我面临这种中断的情况下可以如何重新分配库存呢？你已经建立了这个操作系统，它能够整合所有这些数据并帮助做出决策。你的希望基本上是，这个语言模型能够揭示所有已经存在的潜在能力，以一种不需要人们了解如何操作这个操作系统的方式呈现出来。就像微软所说，它好比是你的副驾驶。
▶ 英文原文 ⏱
Okay, well, how do I just paste that email in and ask it to help me visualize the impacted customer orders, and then work through a series of steps to figure out what can I do about reallocating inventory, given that I have this disruption? So you've built this operating system, you've a way to sort of pull in all this data and to make decisions. And your hope is that basically the LM will expose all these latent capabilities that are already there in a way that people, you know, don't need to know how to operate the operating system, because it's sort of like they're, you know, to use Microsoft's term, it's sort of your co-pilot.
好的，也许我可以把话题拉回来一点，回到之前谈话中提到的两个基本创伤，我认为它们在人工智能处理器（AIP）背景下具有重要意义，但在一个新的层面上，这个雄心的程度显得更为显著。首先是，我的数据整合得有多好？在大型语言模型（LLM）世界中，这变成了我如何管理一个非常小的上下文窗口。如果你想到每个人在每个企业中做出的每个决策，企业中会有数据流动，这些数据提供了上下文窗口。许多时候，人们在与他们的ERP、CRM、Access数据库、Excel宏功能作斗争，以获取人类需要的上下文来做出决策。
▶ 英文原文 ⏱
Yeah, and maybe I kind of bring it also back to sort of previous in the conversation here, we had these sort of two foundational traumas that I think are really relevant in the AIP context, but at a whole new level, order of magnitude of ambition. So the first one is, how well integrated is my data? Now in the LLM world, that's how can I manage a very small context window. So if you think of every single decision that every human is making across every enterprise, there is a flow of data coming through the enterprise that prevents that provides that context window. Oftentimes, they are fighting their ERP, their CRM, their access database, their Excel macros to get the context that the human needs in order to make the decision.
所以，如果我能为你提供一个自动化的上下文窗口，确保它与你作为操作员需要做的决策相关，那就是我首要的任务。要确保你在需要做出决策的瞬间，始终拥有所有必要的数据。你的上下文窗口必须准确，因为现在是由代理程序来处理这些任务，而不是用户亲自去做。因此，这个过程必须实现自动化，并以编程方式进行，我需要能够在任何时候持续为用户提供和更新合适的上下文信息。
▶ 英文原文 ⏱
So if I can provide you an automated context window that is relevant to your decision as an operator, okay, that's the first order thing that I need. Making sure you always have all the data that you need to make a decision at the moment, you need to make a decision. That you need that your context window is accurate because, well, I'll get to is like, because now it's an agent doing it instead of a user doing those things. So that has to be automated, it has to be programmatic, I have to be able to constantly be providing and flowing the right context over the user at any given point in time.
但是，我认为第二个创伤同样重要，你看到很多东西，比如我来总结非结构化信息，我来做语义搜索，这些都是人们尝试接触生成式 AI 的一些简单方法，但这些其实并不是特别有趣。在企业环境中，我们都明白这一点，对吧？我必须能够做些事情，我不能只是获取一些见解，还需要能够采取行动。那么，我该如何为大型语言模型（LLM）提供合适的上下文窗口，并且配备工具，以便实现调用订单重新分配模型呢？
▶ 英文原文 ⏱
But then I think the second trauma is also as important of, okay, so you see a lot of things that are like, let me summarize unstructured information, let me do semantic search, kind of the low hanging fruit of how people are like weighting themselves into generative AI, but that really isn't particularly interesting. We know this in an enterprise context, right? I have to be able to do something. And I can't just, it's not an insight, it's an action. So how do I then provide the LLM the right context window, but also the tools to be able to say, I want to call a order reallocation model.
这种模型是一个工具，是大型语言模型（LLM）所不具备的。它是LLM所需的查询之一，但除此之外，它还需要知道在什么条件下可以重新分配资源。如果我要加强自动化，就需要有基础设施，能够严格定义LLM可以从数据角度访问什么、在什么情况下可以采取哪些行动、以及在哪些环节需要人工介入。现在，我可以基本上转向这样的操作系统，其中很大一部分操作是由专注于特定任务的自主AI代理执行的，它们能够理解上下文并在这个基础上进行迭代。
▶ 英文原文 ⏱
And that model is a tool that an LLM doesn't have. It is one of the queries that it needs, but then it also needs to know it's like, under what conditions can I act on reallocating this? Now, if I need to have the infrastructure to say, I want to increase the automation, I want to be able to very rigorously define what the LLM can access from a data perspective, what actions it can take under what circumstances, where a human needs to be managed in the loop. I now have the ability to essentially move to that operating system, but where a significant amount of that operating is done by autonomous AI agents that are focused on very specific tasks that can take that context and start to iterate across them.
那么，在最终状态，你会看到的一种情况是，将会有数百个不同的代理在整合并完成其中的各个部分。所以，我如何建立一个基础设施来管理所有的工具、安全、操作等等，让它们在这个情境中做一些比写诗更有趣的事情呢？就是，实际上去做一些实事。
▶ 英文原文 ⏱
But then at the end state, what you're going to expect, right, is that you're going to have hundreds of different agents potentially that are integrating and accomplishing components of this. So how do I have the infrastructure that manages all of the tooling, the security, the actions, et cetera, to get them to do something that is much more interesting in this carpet set than write poetry, right? Like, like actually do something.
好的。我觉得这和你之前提到的事情有关，就是关于你的核心工作。可能说"世界上最好的SQL构建者"或者"SQL查询构建者"不是最恰当的表达。实际上是"世界上最伟大的提示工程师"这样的说法更贴切一些。这就是你如何使用一个通用模型，因为这个上下文窗口基本上可以通过一个简单的查询来传递。
▶ 英文原文 ⏱
Yeah. I think this ties into something you said, your sort of keynote job. I mean, like maybe the world's, you know, best SQL builder was not the right way to, or SQL query builder was not the right way to put it. It's the world's greatest. I think I'm repeating what you said, actually, because it's kind of clicked sort of greatest prompt engineer to some extent where, and that's how you can use a generic model because that context window is basically, you could pass that in with a single query.
然后，就像，不仅仅是用户输入的内容，而是用户的每一个方面。Sham，你提到过这个，就像WYSIWYG重新构想了AIP，你说过所有的用户界面都会改变。这就是整合点。听起来这就像是从命令行转向图形用户界面的转变，就好比在操作系统中，你构建的所有功能都已经存在，但用户仍然需要努力学习才能了解自己能够做什么。
▶ 英文原文 ⏱
And then like, so it's not just what the user types, but every aspect of the user's bit. And you sort of, you said this, Sham, you know, WYSIWYG sort of re-imagine that AIP, you know, you already said every UI is going to change. This is the integration point. And it sounds like what this is, if I can analogize it to the operating system, is the shift from sort of the command line to the GUI, where all the capabilities you've built, they're all there, but there's still a steep learning curve for the user to sort of even know what they're able to do.
是的，你已经将所有数据集中在一个地方。是的，他们可以在上面进行各种操作。你已经完成了这项艰巨的工作，但是，要知道如何使用命令行，你必须是个高级用户。而你的工作就是，把所有这些功能和决策点都传递到提示框中，使其限制在可行范围内。然后，你只需要说出你的需求就行了，这就是“所见即所得”，你说什么就得到什么。
▶ 英文原文 ⏱
Yes, you've got all the data in one place. Yes, they can sort of do all this operations on it. You've done that hard work, but there's, you have to be an advanced user to sort of know how to use the command line. And your bit is look, all those capabilities and all those decision points can be passed into the prompt basically, so that it's already constrained to what's possible. And then it's just, you just say, what is it? What's your WYSIWYG? What you say is what you get.
你说什么就得到什么。确实如此。我认为这很不错，这就像从命令行到图形用户界面的革命一样，而图形用户界面到大语言模型(LLMs)也将推动类似的变革。我相信现在的很多初步实验都在进行中。因为，我想插入一点，从命令行到图形用户界面的变化中，你不会直接与操作系统的所有功能以及它与硬件的交互能力打交道。
▶ 英文原文 ⏱
What you say is what you get. Exactly. I think that's a very good, I think it's the same sort of revolution from command line to GUI that GUI to LLMs are going to power that. And I do think there's a part of this where like the initial experimentation that has mostly been happening. Right. Cause well, just to jump in one thing, like a bit about the, the sort of command line to GUI is you're not exposed to the capabilities of the operating system and what it can do with sort of, you know, with the hardware that you're on in this bit.
这段文字的意思是，如何展示和揭示所有一个人可以访问的数据是一个难题。而AI在某方面有独特的能力来揭示这些信息。就像我们大多数人无法将所有电脑功能都记在脑海中一样，我们也无法记住所有的数据。AI就像一个过滤器，能够根据你当前的操作环境，揭示你所需的数据以及可能和相关的决策。
▶ 英文原文 ⏱
It's how do you expose and show someone all of the data they have access to that sort of the hard problem here. And sort of to what, what, what AIs are sort of uniquely able to expose. And you, there's no one, you can't keep that all in your head, just like you, you can't keep all this sort of functionality of a computer in your head, except for at least most of us anyway. That's right. I think it itself acts as a funneling mechanism to expose the data you need, the decisions that are possible and relevant in that moment against the, the, the operating context that you actually have.
如果你继续沿着这条路走下去，我认为这也会改变开发者构建软件的方式，就像图形用户界面（GUI）改变了开发者实际构建的内容以及他们对所构建内容的思考方式一样。如果你考虑一种没有提示的用户界面，可能会有按钮背后实际上是在调用大语言模型（LLM）。当你使用联合开发助手（Co-Pilot）来编写代码时，你并不是在要求它为你解决某个功能。
▶ 英文原文 ⏱
And if you continue going down that path, I think it also changes how developers built software to the same way that the GUI changed what developers were actually building as well and how they thought about what they were building. If you thought about a prompt free user interface, like you may have buttons that behind it are actually calling the LLM. When you're using co-pilot to write code, you're not asking for it to solve a function for you.
这基于你作为用户的意图以及你正在编写的代码的上下文，来预测接下来可能出现的内容。因此，真正专注于这些“辅助”体验是这里可能实现的目标。我认为这将重新定义用户界面。这也与我最初提出的“为什么是2023年”的问题有关。你们都提到的一点是，理解Palantir的困难在于，我们正在为未来五到十年后的东西而构建。
▶ 英文原文 ⏱
It's based on the intent of you as a user and the context of the code that you're writing, what kind of comes next. And so really honing in on those sorts of co-pilot experiences are what's possible here. And I think what redefines the user interface. Yeah. And I think this makes sense in your, my, my initial question, which is why 2023? I mean, something you guys have both talked about is look, you, it's hard to understand Palantir because we're building for something that's five to 10 years out sort of in the future.
这段话的中文翻译和简化表达如下：
而且，而且，而且你得相信我们。然后就会发现，哦，看，Palantir在ChatGPT推出六个月后也来了。我们有个大型语言模型（LLM）产品，但重点不是你没在研发这类产品，而是因为你们已经花了数年时间构建非常安全和精准的数据集，基本上是为企业量身定制的全球最佳标注数据集。现在有了大型语言模型，你可以以一种别人做不到的方式利用它们。更重要的是，我们的客户也可以利用这些模型。比如，当我们与一家保险公司合作时，我们能够在两天内构建一个用于代位求偿的代理。但更准确地说，其实是用了四年时间完善他们的本体论、配置以及业务流程，加上最后的两天。
▶ 英文原文 ⏱
And, and, and you have to sort of trust us on that. And then it's like, oh, well, here's Palantir coming along six months after chat GPT. We got an LLM product, but your point here is not that you're not building that product. You're taking, because you've basically spent years to your point, making very type safe data, like the best labeled data sets in the world for all intents and purposes, that's custom to an enterprise. Now that LLMs have showed up, you can leverage them in a way no one else can. Or more importantly, our customers can leverage them. You know, it's like when we were working with an insurance company, like we were able to build an agent to subrogate insurance clamps in two days. But I think a fairer version of it is it's not, it was four years of their ontology, their setup, running their operations on this thing, plus two days.
我认为我们需要回到的是，当我们普遍审视我们软件的实施时，是否能将下一个阶段的整合，特别是将大语言模型（LLM）整合进去，提供一个根本性改变客户使用该软件结果的机会？如果能够实现，那就是我们需要专注的方向，并且我们需要迅速展开行动。我认为目前我们有信心的是，这将极大地提升我们软件对客户的价值。早期的采用者，比如Sean提到的保险行业，在主题演讲中你可以看到类似JD Power的公司正在重新定义客户如何与他们的数据互动。再比如Jacobs公司在重新定义整个污水处理网络管理时所做的事情。这太令人兴奋了，主要是因为我们的客户将会因此取得巨大的成功。
▶ 英文原文 ⏱
And I think sort of come back to, right, is when we look at the implementations of our software generally, is the ability to integrate the next order, sort of integrate LLMs into that, does it provide an opportunity to fundamentally alter the outcomes that our customers have with that software? And if it does, then it's something that we need to focus on and we need to run at very quickly. And I think where we've gotten confidence right now is that this will turbocharge the value of our software to our customers. And sort of the early adopters, Sean mentioned the insurance, you know, you saw in the keynotes, like what JD Power is doing is they think about redefining the experience of how their customers interact with their data. What Jacobs is doing when you think about how you redefine like entire sewer or wastewater management networks. It's very exciting, mostly because of gosh, our customers are going to be really successful with this.
好的，这就是为什么我在一开始加了一个免责声明，因为可能我对你的问题还不够深入，部分原因是我觉得你所描绘的图景很有吸引力。关于在现实世界中工作，真正解决实际问题，并围绕这些建立真实的结构，以及采取操作系统的方法这一点都很有说服力。而且是的，现在你所说的关于人工智能或者用户界面的出现，也就是人工智能作为用户界面这一点，非常有意义。总体来说，这都很吸引人。
▶ 英文原文 ⏱
Yeah, well, this is why I put the sort of disclaimer at the beginning where I'm not, I'm probably insufficiently grilling you, in part because I think it's, you know, what the picture you're painting is sort of very compelling. This bit about actually working in the real world, actually solving sort of real problems, building real structure around this and having this operating system sort of approach. And, and yeah, now, now you're sort of like, you know, the AI or the UI has shown up the AI as UI sort of bit makes a lot of sense. And yeah, it's very compelling.
是的，我还想说，我们感到兴奋的另一件事是对我们软件的需求也在不断显现出来。以前，我们可能只是在使用云计算实现90年代的本地数据架构，提高一些效率。但现在，CEO们开始说这种技术发展得太快了，令人感到害怕，他们认为在接下来的五年里，为了企业的生存，他们迫切需要这种技术。这样的市场对我们来说非常有吸引力，因为我们已经做好了应对这种责任的准备。
▶ 英文原文 ⏱
Yeah, and I would also say the other thing we're excited about is also the demands on our software is showing up. So instead of something that is like, okay, now I have like incremental efficiency in implementing a 90s on-prem data stack using cloud compute with what I expect from data. Now we have CEOs saying like, shoot, this tech got scary. Like I need this for what I think will be the survival of my institution over the next five years. That's a market that's very compelling for us because we're very, we're built to sort of sign up for that accountability.
有没有一点让你担心你的目标市场，觉得大家会到处抢LM的那种？其实正好相反。我认为这种情况有两个方面：从α的角度看，我们长期以来对类型安全的关注正好迎来了它的时机，这是我们独有的优势。从β的角度看，大家终于期望他们的软件能够快速实际地运行了，这正在扩大我们的市场。
▶ 英文原文 ⏱
Is there a bit where you are worried about sort of your addressable market in that people are just going to be grabbing for LM's sort of left and right? It's the opposite. I think so. The alpha side of this is like, wow, this obsession we've had on type safety has met its moment. And that's unique to us. The beta on this is finally, everyone expects their software to actually work fast. And that is, that's expanding our market.
有趣。那么你是否认为会有很多公司采用现成的解决方案，并进行微调等操作，但这实际上是完全错误的方向，因为他们无法克服幻觉问题，因为他们没有正确定义他们的数据。而你们可能会采取相反的方向，不管使用什么模型，但因为我们已经很好地定义了我们拥有的和没有的内容，所以这种"幻觉"反而成为了一种特点，而不是一个缺陷。
▶ 英文原文 ⏱
Interesting. So do you think there's going to be a lot of companies that take sort of an off the shelf solution, do fine tuning, whatever it might be, but that's actually the completely wrong direction because they're just going to, the hallucination problems are not going to be overcomable because they, they haven't properly defined their data. Whereas you guys might sort of come in the opposite direction, do whatever model you want, but because we're so well, we've so well defined sort of what we have and what we don't, the who station bid is a feature, not a bug.
这确实是一个方面，但我认为它不仅仅如此。因为问题不仅是能否准确回答，而是：它能否真正发挥作用？在我看来，这正是我们认为聊天界面限制了这项技术的原因，因为这种界面让大家都只想着能否得到准确的回答。其实还有更多可能性可以实现。如果我们把它看作一种可以操作的工具，那么准确性当然是基础要求，只有达到了这一点，我们才可以考虑如何真正利用它来做一些事情。但接下来我们应该思考的是，如何才能用它来实现更多功能呢？
▶ 英文原文 ⏱
It is, but I also think it's more than that. Cause it's not just, can it be accurate, but like, can it do something? I think this is where our opinion that like the, the chat interface is such a limiting interface for this technology because it makes everyone say it's like, can I get an accurate answer to a chat? Like who cares? There's so much more that you can do with it. If you think of it, like, how can I operate with these things? And so yes, accuracy is presupposed as a requirement to even get to a point where you could contemplate doing that. But then how do I get to a point where I'm doing something?
好的。因为它在没有你主动触发的情况下就自动生成内容。这需要更高程度的信任，因为你根本不知道输入了什么指令。考虑这个问题的工具也需要升级。可以类比于旧版的自动任务工具，比如 cron job。
▶ 英文原文 ⏱
Right. Because it's generating stuff without you sort of instigating the generation. It's just doing it on its own. And that requires a much higher degree of trust because you don't even know what went into the prompt. And kind of the tooling to think about it. It's kind of like the old, the old version of this would be cron job.
如果我们从多个角度来思考这个问题，那我们要考虑：我该如何构建这个代理？代理是如何知道何时需要启动的？我如何设定企业这一"状态机"？在何种情况下，我信任状态机的哪个部分？我设置了哪些断言，以便判断它是否在其保护范围内运行？
▶ 英文原文 ⏱
If we go a few paradigms out, it's like, okay, well, how am I building the agent? How does the agent know when to start? How have I modeled the state machine that's the enterprise? What part of that state machine do I trust them on under what circumstances? What are the assertions I'm building in so that I know whether it's hitting its guardrails or not?
然后，代理的输出实际上将是一组场景，供人类进行评估。这也是它与任何机构内的变更管理现实相结合的地方。我们会通过实际体验来建立对它的信任。你知道的，这不是在实验室里就能解决的。
▶ 英文原文 ⏱
And then the output of the agent is going to be realistically a set of scenarios for a human to evaluate. So that's where it also meets the reality of change management within any institution. Like we're going to develop trust experientially with it. You know, it's like this can't come out of the lab.
那么，我如何为这些人类配备副驾驶或代理，创建人机团队呢？我认为企业能够以快速的速度利用这一点来实现真正的转型，这是令人振奋的。非常好。
▶ 英文原文 ⏱
So how am I giving co-pilots or agents to these humans and creating these human agent teams? And I think the speed with which enterprises can leverage that to achieve real transformation is exciting. Very good.
好的，我们今天聊得很深入，探讨了很多细节。我觉得这次的交流非常有趣，总体来说也很有吸引力。感谢你们来参与这次讨论。谢谢你，Ben。
▶ 英文原文 ⏱
Well, we have gone long and gotten, I think, very much into the weeds. But I think this was a very interesting overview. I think the overlying picture is really compelling. And I appreciate you guys coming on and talking about it. Thanks, Ben.
好的，谢谢你邀请我们。再见。再见。再见。再见。再见。
▶ 英文原文 ⏱
Yeah, thanks for having us. Bye. Bye. Bye. Bye. Bye.