欢迎致辞：Claude 驱动金融未来
Please welcome to the stage head of revenue at Anthropic Kate Jensen.
请欢迎 Anthropic 营收主管 Kate Jensen 上台。
Good morning everyone. Thank you so much for being here today. I'm Kate Jensen, the head of revenue at Anthropic, and I am so excited to welcome you to the future of finance powered by Claude.
大家早上好。非常感谢大家今天莅临现场。我是 Anthropic 的营收主管 Kate Jensen，非常高兴能欢迎大家来到由 Claude (Anthropic公司开发的一系列大型语言模型) 驱动的金融未来。
I want to invoke a scene that's probably familiar to many of you in the audience today.
我想唤起一个在座许多人可能都熟悉的场景。
You're three coffees deep. It's 3:00 a.m. You're furiously checking that last model. You have a 100 tabs open. You have a 5 a.m. prep call ready for a 6 a.m. client call.
你已经喝了三杯咖啡，凌晨三点还在拼命检查最后一个模型。你打开了上百个浏览器标签页，准备在早上五点进行一次预备电话会议，以便应对六点的客户会议。
Today, that's all about to change. We are so excited to announce Claude for financial analysis.
今天，这一切都将改变。我们非常高兴地宣布推出用于金融分析的 Claude。
This is the industry's first unified intelligence layer that transforms how financial professionals will work with AI.
这是业界首个统一智能层，将彻底改变金融专业人士与 AI (Artificial Intelligence: 人工智能) 协作的方式。
Now you will have a virtual first class virtual collaborator powered by AI that will help you to get your work done better and faster.
现在，你将拥有一个由 AI 驱动的一流虚拟协作伙伴，它将帮助你更好、更快地完成工作。
What this is is a tailored version of Claude for enterprise. It's specifically built for financial analysts, and it's equipped with the nuance, accuracy, and reasoning that you need to handle the complexity of your work.
这是专为企业定制的 Claude 版本，专门为金融分析师打造，具备处理复杂工作所需的细致入微、准确性和推理能力。
Since 2021, Anthropic has been building AI systems that are helpful, harmless, and honest.
自2021年以来，Anthropic 一直致力于构建有益、无害且诚实的 AI 系统。
At our foundation is safety and trust, which is exactly what financial services companies need.
我们的基石是安全和信任，这正是金融服务公司所必需的。
When you're managing billions in assets, good enough just isn't good enough.
当你管理着数十亿资产时，“足够好”是远远不够的。
AI is transforming every industry, and nowhere is this more prominent than in financial services.
AI 正在改变每一个行业，而这种改变在金融服务领域尤为突出。
The complexity of modern markets, the velocity of information, the sheer volume of data that you need to process, it has reached a point where human intelligence alone, no matter how great, just isn't good enough.
现代市场的复杂性、信息的速度以及需要处理的庞大数据量，已经达到了仅凭人类智能（无论多么卓越）都力不从心的地步。
From speaking with many of you, our customers, we're hearing that the world is bifurcating.
通过与许多客户的交流，我们得知世界正在分化。
There will be two types of investment firms. Those using AI institutionally and those who are losing their top talent to competitors who are.
将有两种类型的投资公司：一种是机构性地使用 AI 的公司，另一种是其顶尖人才流失到那些使用 AI 的竞争对手公司。
Over the past year, we have been partnering with leaders who are breaking new ground across every segment of the financial industry.
在过去一年中，我们一直与在金融行业各个领域开辟新天地的领导者合作。
The teams at Bridgewater have been using Claude since 2023.
桥水基金 (Bridgewater) 的团队自2023年以来一直在使用 Claude。
This is to power their investment analyst assistance which solves complex models. Their CTO (Chief Technology Officer: 首席技术官) tells us that Claude is really powering their efforts to push the boundaries of what's possible.
这旨在为其投资分析师助理提供支持，解决复杂的模型问题。他们的首席技术官告诉我们，Claude 确实在推动他们突破可能性的边界。
Down in Australia, Commonwealth Bank is making a massive bet on AI. Their CTO Rodrigo sees our partnership as the foundation of their global AI strategy.
在澳大利亚，联邦银行 (Commonwealth Bank) 正在对 AI 进行大规模投资。他们的首席技术官 Rodrigo 将我们的合作视为其全球 AI 战略的基础。
And here in New York at AIG, Peter tells us that they have completely reimagined underwriting with Claude. What used to take weeks is now taking days.
在纽约的 AIG (American International Group: 美国国际集团)，Peter 告诉我们，他们使用 Claude 完全重塑了承保业务。过去需要数周的工作现在只需数天。
Timelines compressed more than 5x and accuracy has gone up from 75% to 90%.
时间线缩短了5倍以上，准确率从75%提高到90%。
Underwriters can now serve their customers better and faster.
承保人现在可以更好、更快地为客户服务。
强大的生态系统与合作伙伴
Now, the solutions we're sharing today, we didn't create alone. They're the result of deep collaboration with the entire financial ecosystem.
今天我们分享的解决方案并非我们独自创造。它们是与整个金融生态系统深度合作的成果。
At our foundation, we have our cloud providers. AWS (Amazon Web Services: 亚马逊网络服务) and GCP (Google Cloud Platform: 谷歌云平台) provide secure, scalable infrastructure that financial institutions demand.
我们的基础是云服务提供商。AWS 和 GCP 提供金融机构所需的安全、可扩展的基础设施。
I am thrilled to share that today Claude for enterprise including Claude for financial services is now available on AWS marketplace and it's coming soon to the Google cloud marketplace as well.
我很高兴地宣布，今天，包括金融服务版 Claude 在内的企业版 Claude 现已在 AWS Marketplace 上线，并将很快在 Google Cloud Marketplace 上线。
We hear all the time that AI is only as good as the context it has.
我们一直都听到，AI 的能力取决于其所拥有的上下文信息。
We're excited to partner with platforms like Box, Databricks, Palantir, and Snowflake to bring your company's data into Claude so that you have the context you need to get work done that's actually relevant to your day-to-day.
我们很高兴与 Box、Databricks、Palantir 和 Snowflake 等平台合作，将贵公司的数据引入 Claude，以便您获得日常工作所需的上下文信息。
And today, we are especially excited to announce new integrations to critical external market data sources for informing your most important financial decisions.
今天，我们尤其高兴地宣布，已与关键的外部市场数据源实现新的集成，以帮助您做出最重要的金融决策。
We're partnering with the companies you already trust.
我们正在与您已经信任的公司合作。
FactSet (FactSet Research Systems Inc.: 一家提供金融数据和分析软件的公司) delivers comprehensive fundamentals and consensus estimates.
FactSet 提供全面的基本面数据和市场共识预测。
S&P Global (标普全球: 一家提供金融信息和分析服务的公司) enables access to Cap IQ financials, market data, and transcripts.
S&P Global 提供 Cap IQ 财务数据、市场数据和逐字稿访问权限。
Dupa (Dupa: 一家提供AI验证基本面数据的公司) provides AI verified fundamentals with source citations.
Dupa 提供经 AI 验证并附带来源引用的基本面数据。
And between Morningstar (晨星公司: 一家提供投资研究和管理服务的公司) and PitchBook (PitchBook Data, Inc.: 一家提供私募市场数据和分析的公司), you have both public investment research and private market intelligence at your fingertips.
通过 Morningstar 和 PitchBook，您可以轻松获取公共投资研究和私募市场情报。
Our consulting service providers are also turning this technology into real business transformation.
我们的咨询服务提供商也在将这项技术转化为真正的业务转型。
Deloitte (德勤: 全球四大会计师事务所之一) and KPMG (毕马威: 全球四大会计师事务所之一) are modernizing entire organizations and deploying AI agents at scale.
德勤和毕马威正在对整个组织进行现代化改造，并大规模部署 AI 代理。
PWC (PricewaterhouseCoopers: 普华永道，全球四大会计师事务所之一) and Turing (Turing: 一家提供AI解决方案和人才的公司) are solving critical regulatory challenges, navigating constantly evolving compliance requirements.
普华永道和 Turing 正在解决关键的监管挑战，应对不断变化的合规要求。
And Slalom (Slalom: 一家专注于战略、技术和业务转型的咨询公司) and Tribe AI (Tribe AI: 一家提供AI人才和解决方案的公司) are modernizing core operations from migrating legacy cobalt infra (Cobol Infrastructure: 基于Cobol语言的传统IT基础设施) to accelerating due diligence (尽职调查: 在交易前对目标进行全面审查) with intelligent document processing.
Slalom 和 Tribe AI 正在实现核心业务的现代化，从迁移遗留的 Cobol 基础设施到通过智能文档处理加速尽职调查。
Collectively, we are building the AI ecosystem that will define the future of financial services.
我们正在共同构建将定义金融服务未来的 AI 生态系统。
As our head of revenue, my team and I spend all of our time talking to customers and partners. We heard from you how important it was to bring all of this context, both internal and external, into Claude to help you make use of this technology.
作为我们的营收主管，我和我的团队将所有时间都花在与客户和合作伙伴的交流上。我们听取了你们的意见，了解到将所有内部和外部的上下文信息引入 Claude 对帮助你们利用这项技术是多么重要。
We care deeply about defining the future of AI and human collaboration. And thank you for your feedback. We are thrilled to bring this solution to you today.
我们深切关注定义 AI 和人类协作的未来。感谢您的反馈。我们很高兴今天能将这一解决方案带给您。
Now, I'm really excited to welcome up to the stage two of our partners who help to exemplify the power of our ecosystem in action.
现在，我非常高兴地欢迎两位合作伙伴上台，他们将帮助我们展示生态系统的实际力量。
Peter Lerzie is the chief strategy officer at Kensho Technologies (Kensho Technologies: 标普全球旗下专注于AI创新的公司), S&P Global's AI innovation hub. He's been defining how AI transforms financial data into actionable intelligence.
Peter Lerzie 是 Kensho Technologies 的首席战略官，该公司是 S&P Global 的 AI 创新中心。他一直在定义 AI 如何将金融数据转化为可操作的情报。
And Vikrambot is vice chair and financial services industry leader at Deloitte where he has been helping global financial institutions navigate digital transformation. Please welcome them up to the stage.
Vikrambot 是德勤的副主席兼金融服务行业负责人，他一直致力于帮助全球金融机构实现数字化转型。请欢迎他们上台。
AI 采用的突破性时刻
Kate Jensen: Did you pick that song?
Kate Jensen: 这首歌是你选的吗？
Peter Lerzie: Yes.
Peter Lerzie: 是的。
Kate Jensen: It's actually in my writer.
Kate Jensen: 它实际上在我的作者名单里。
Kate Jensen: I love it. Thank you so much for being here. You have both been instrumental in shaping the frontier of AI adoption at your companies. From your unique vantage point, Peter with data and intelligence, Vikram with implementation and transformation, what's the one breakthrough that you've seen that has really created an aha moment at your companies?
Kate Jensen: 我喜欢这首歌。非常感谢你们的到来。你们两位都在各自公司塑造 AI 采用的前沿方面发挥了关键作用。从你们独特的视角来看，Peter 专注于数据和智能，Vikram 专注于实施和转型，你们看到了哪个突破真正为你们的公司带来了“啊哈”时刻？
Peter Lerzie: So, yeah, I think for me it's really adoption. I mean, I've spent the better part of the last decade trying to get financial services companies to adopt AI and we've had some really great successes with traditional machine learning (机器学习: 人工智能的一个分支，让计算机通过数据学习). I would say in the world of generative AI (生成式人工智能: 能够生成新内容的人工智能), that conversation is very, very different. It's kind of how can you serve us as quickly as possible the data that is in a format that is optimized for these large language model (LLM: 大型语言模型) use cases for generative AI and how can I make sure that the data that you're providing is trusted, that it's accurate, that it's kind of foundationally a part of the workflows that we've always relied on.
Peter Lerzie: 是的，我想对我来说，真正重要的是采用。我的意思是，在过去十年中的大部分时间里，我都在努力让金融服务公司采用 AI，我们在传统的机器学习方面取得了一些非常大的成功。我想说，在生成式 AI 的世界里，这种对话截然不同。这更像是你们如何能尽快地为我们提供经过优化、适合大型语言模型用于生成式 AI 场景的数据，以及我如何能确保你们提供的数据是可信的、准确的，并且是作为我们一直以来所依赖的工作流程的基础部分。
And so I think for us, the big kind of surprising thing about generative AI has just been the speed of adoption, the rate of adoption. You know, it has been very influential in our roadmap. The products that we've kind of foregrounded for us, our LLM (Large Language Model: 大型语言模型) ready API (Application Programming Interface: 应用程序编程接口), which is the data behind what we've done with MCP, has been we've seen like enormous traction with that. Our clients want to make sure that when they're interfacing with this technology that they can rely on trusted data and data in workflows that they that are already existing that they can then automate and optimize with large language models.
因此，我认为对我们来说，生成式 AI 最大的惊喜就是其采用速度和普及率。它对我们的路线图产生了非常大的影响。我们优先推出的产品，即我们与 MCP 合作背后数据支持的 LLM 就绪 API，已经获得了巨大的关注。我们的客户希望确保在使用这项技术时，他们能够依赖可信的数据，并将其整合到他们现有工作流程中，然后通过大型语言模型实现自动化和优化。
And so that's really been a huge surprise. It's going into these client conversations and not saying, "Okay, but here's what AI can do here. Here here's the here's the kind of magic that you can unlock." They already know that part. It's now how can we truly make our company or institution or group an AI forward group and also make sure that we can trust the outputs and for that you need trusted data. So that that's kind of been the big thing that we've noticed. And Vic, what about you? As you think about bringing this technology, what are those aha moments that get people to really adopt it?
所以这确实是一个巨大的惊喜。与客户交流时，我们不再是说：“AI 能做什么，它能带来什么奇迹。”他们已经知道这些了。现在的问题是，我们如何才能真正让我们的公司、机构或团队成为一个 AI 优先的团队，并确保我们能够信任输出结果，为此，你需要可信的数据。所以这大概是我们注意到的一个重要变化。Vic，你呢？当你考虑引入这项技术时，哪些“啊哈”时刻能真正促使人们采用它？
Vikrambot: We have really an AI first strategy in everything we do. Some of it is internal in every in every service that we have. We have starting to embed AI, you know, kind of front to back. On the client side, what we're seeing is there was an initial focus on productivity as a key theme of value in through AI. But what is really happening is that is expanding to can we develop new products based on AI? Can we reimagine distribution? Can we reimagine front to back processes? It is really changing the conversation from a pure productivity as a value driver to revenue generation. Can we do better risk and control? Can we drive more employee and client experiences? So as the appetite and the aperture of value has really expanded, it is really brought AI into everything that does and every piece of the client organization.
Vikrambot: 我们在所做的每一件事中都真正秉持“AI 优先”的战略。其中一部分是内部的，我们正在将 AI 嵌入到我们提供的每一项服务中，从前端到后端。在客户方面，我们看到最初的重点是将生产力作为 AI 带来价值的关键主题。但现在真正发生的是，这正在扩展到：我们能否基于 AI 开发新产品？我们能否重新构想分销方式？我们能否重新构想端到端流程？这正在将对话从纯粹以生产力作为价值驱动因素，转变为创收。我们能否更好地进行风险控制？我们能否提升员工和客户体验？因此，随着对价值的渴望和范围的真正扩大，AI 已经真正融入到所有业务以及客户组织的每一个环节中。
Kate Jensen: I love that. And we'll start with you this time, but what do you think you need to do differently as an organization to make sure that you're really capturing that full value, not just adopting the technology?
Kate Jensen: 我喜欢这一点。这次我们从你开始，你认为作为一个组织，需要做出哪些不同的改变，才能真正捕捉到全部价值，而不仅仅是采用这项技术？
Vikrambot: Yeah. So I'm probably going to give a an incorrect non-political answer. In financial services, we are a highly regulated industry. Everybody knows that. So the real challenge is in this battle of I'll say innovation and risk management. How do you coexist in a manner that there's not 20 checkers for one doer and the equation is actually flipped that you actually have more people driving innovation across your organization and you work in coexistence with your audit teams with your model risk teams, you know, all the other players that are necessary in order to get adoption successful.
Vikrambot: 是的。所以，我可能会给出一个不那么“政治正确”的答案。在金融服务领域，我们是一个高度受监管的行业。大家都知道这一点。所以真正的挑战在于创新和风险管理之间的这场“战斗”。你如何以一种方式共存，即不会出现一个执行者有20个检查者的情况，而是这种等式实际上颠倒过来，你有更多的人在整个组织中推动创新，并且你与审计团队、模型风险团队以及所有其他为成功采用所必需的参与者共同协作。
The other example is that AI first strategy that I mentioned. I think more and more clients are actually saying AI is not just about driving 30 to 50% productivity in the CIO (Chief Information Officer: 首席信息官) organization or anywhere else. It is really about changing the way we do business and to do that they are how do you actually bring the humans we we have this philosophy of humans and machines coexisting in the age of AI and how do you drive the human change at speed and scale together with the pace of change that's coming through AI.
另一个例子是我提到的“AI 优先”战略。我认为越来越多的客户实际上在说，AI 不仅仅是在 CIO 组织或其他任何地方提高30%到50%的生产力。它真正关乎改变我们的业务运作方式，而要做到这一点，他们需要思考如何在 AI 时代真正实现人机共存的理念，以及如何以 AI 带来的变革速度和规模，同步推动人类的变革。
Kate Jensen: How do you think you do that? How do you get them to coexist and adopt at the same time?
Kate Jensen: 你认为该如何做到这一点？如何让他们同时共存并采纳？
Vikrambot: I I I think it it it is not one answer. There's a number of different levers that one can pull in order to drive that. For example, some organizations have done executive academies where the top 300 executives really drive training. Some are doing hackathons to really think about certain processes or value streams that they can completely reimagine. There is you know complete AI academies and trainings that are being pushed forward at you know for developers but also for general users. So it's not one lever I think in the end that's going to drive the speed of human adoption of AI. It's a number of things that need to be done together.
Vikrambot: 我认为这并非一个单一的答案。有许多不同的杠杆可以用来推动这一进程。例如，一些组织举办了高管学院，由前300名高管真正推动培训。有些组织正在举办黑客马拉松 (Hackathon: 一种编程活动，参与者在短时间内合作开发软件项目)，以重新构想某些流程或价值流。还有针对开发人员和普通用户的全面 AI 学院和培训正在推进。因此，我认为最终驱动人类快速采用 AI 的不是一个单一的杠杆，而是需要多方面协同进行。
Kate Jensen: Love that. Peter, what about you? What do you think people need to do differently and institutions need to do differently?
Kate Jensen: 太棒了。Peter，你呢？你认为个人和机构需要做出哪些不同的改变？
Peter Lerzie: Well, I think honestly it comes down to and I think Vikram Vikram touched on it. It's it's kind of a culture thing and I think that a lot of that culture does have to be top down, right? You have to you have to have your leadership teams really buy in to this concept of AI first. I totally agree with that. I think but part of that top down is to also embrace the fact that in many of these organizations innovation's coming from the bottom up, right? People who are in the trenches doing day-to-day work are surfacing amazing ideas not just for productivity which I think is is oftentimes the focus but for huge expansions of revenue opportunity.
Peter Lerzie: 嗯，我认为坦白地说，这归结于一点，Vikram 也提到了，这是一种文化问题。我认为很多这种文化必须是自上而下的，对吧？你的领导团队必须真正认同“AI 优先”这个概念。我完全同意这一点。但我认为自上而下的一部分也包括接受这样一个事实：在许多组织中，创新是自下而上产生的，对吧？那些在一线从事日常工作的人们会提出惊人的想法，这不仅是为了提高生产力（这往往是重点），更是为了极大地拓展收入机会。
I think that's absolutely correct. I think that it's kind of changing a mindset around innovation and I do think a big part of that is understanding especially in our space there are going to be workflows where being maybe a little bit less risk averse is is really important and being able to create those that room for experimentation is going to be really important. I mean certainly for us at Kensho, right? We've been able to have that room and the way that it works for us is that we kind of operate somewhat independently and as a result we kind of have a little bit more room to experiment but then you know crucially it's also understanding where those boundaries are and where that risk is unacceptable and that's I think where for us we've also seen an enormous benefit in having S&P's trusted data as our kind a foundational core competency.
我认为这绝对正确。我认为这正在改变围绕创新的思维模式，而且我确实认为很重要的一部分是理解，特别是在我们这个领域，有些工作流程中，稍微不那么规避风险是非常重要的，并且能够为实验创造空间也将非常重要。我的意思是，当然，对于我们 Kensho 来说，我们能够拥有这样的空间，我们运作的方式是相对独立，因此我们有更多的实验空间。但关键在于，也要理解这些界限在哪里，哪些风险是不可接受的，我认为这正是我们看到巨大好处的地方，即拥有 S&P 可信数据作为我们的基础核心竞争力。
You know, we've been on this journey creating this grounding agent where for us it's ensuring that when a large language model is interfacing with our data that we're taking responsibility for automating where in our entire data universe we're going to pull and query those data sets and then surfacing that data with citations. And so it's it's both enabling experimentation but then also making sure that your response is and whatever the output is is truly grounded in in verifiable fact. So I think there's there's a balance there and and creating that trust is is something that's really important to us.
我们一直在致力于创建这种基础代理，对我们来说，这意味着当大型语言模型与我们的数据交互时，我们负责自动化地从我们整个数据宇宙中提取和查询这些数据集，然后将带有引用的数据呈现出来。因此，这既能促进实验，又能确保你的回答和任何输出都真正基于可验证的事实。所以，我认为这里存在一个平衡，建立这种信任对我们来说非常重要。
Kate Jensen: I love that push to innovate, that desire to really harness all the energy that you have from your organization, but to do it responsibly. It's really at the core of a lot of what we do. Thank you both so much for coming up and thank you for the partnership.
Kate Jensen: 我喜欢那种推动创新、真正利用组织所有能量的冲劲，但同时也要负责任。这确实是我们许多工作的核心。非常感谢你们二位前来，也感谢我们的合作。
Peter Lerzie: Absolutely. Pleasure. Thanks.
Peter Lerzie: 当然。非常荣幸。谢谢。
Kate Jensen: And now I'm thrilled to welcome up to the stage my colleague Nick Lynn who leads our product for Cloud for Financial Services. Thank you all.
Kate Jensen: 现在，我非常高兴地欢迎我的同事 Nick Lynn 上台，他负责我们金融服务版 Claude 的产品。谢谢大家。
Claude 金融分析解决方案：核心功能与优势
Nick Linton: All right. Thank you, Kate. My name is Nick Linton and I lead product for Claude for financial services. I am also a recovering investment banker (投资银行家: 从事证券发行、并购等金融服务的人) and private equity investor (私募股权投资者: 投资未上市公司股权的投资者). So, I was the analyst Kate mentioned frantically trying to debug the model at 3:00 a.m. while building the pitch deck running on no sleep. That's why I am especially excited to share what we've been working on.
Nick Linton: 好的。谢谢你，Kate。我叫 Nick Linton，负责金融服务版 Claude 的产品。我曾是一名投资银行家和私募股权投资者。所以，我就是 Kate 提到过的那个在凌晨三点拼命调试模型，同时在熬夜赶制路演材料的分析师。这就是为什么我特别兴奋能分享我们一直在努力的工作。
As Kate mentioned, we're announcing the Claude Financial Analysis Solution, Anthropic's first offering built specifically for finance.
正如 Kate 所说，我们正在发布 Claude 金融分析解决方案，这是 Anthropic 首次专为金融领域打造的产品。
Our solution is built on three main pillars of what comprises of an agent. First, the models. Now, something I always think about is that the models we interact with today are the worst they will ever be.
我们的解决方案建立在一个代理的三个主要支柱之上。首先是模型。我总是在想，我们今天所交互的模型，是它们有史以来最差的状态。
Even so, Claude today is not just state-of-the-art for code. Claude has been specifically trained for finance domain knowledge and excels at tasks like data analysis at scale, financial reasoning, and even Excel manipulation.
即便如此，今天的 Claude 不仅仅是代码领域的尖端技术。Claude 经过专门的金融领域知识训练，擅长大规模数据分析、金融推理，甚至 Excel 操作等任务。
Now the research product flywheel is a critical part of an Anthropic strategy.
现在，研究产品飞轮是 Anthropic 战略的关键部分。
We firmly believe in working with customers like yourselves to really understand where the models are working, where the gaps are, and to borrow a term we use internally, hill climb, to improve the next generation of our model capabilities.
我们坚信与像你们这样的客户合作，真正了解模型在哪里有效，哪里存在不足，并借用我们内部使用的术语，通过“爬坡”来改进我们下一代模型的能力。
The finest agent benchmark published by our friends at vowels.ai has picked up a lot of traction in the industry and is well representative of many financial reasoning tasks.
我们的朋友 vowels.ai 发布的最佳代理基准测试在行业中获得了大量关注，并且很好地代表了许多金融推理任务。
You can see Claude and Sonnet far outperform competitors.
你可以看到 Claude 和 Sonnet 远远超越了竞争对手。
Similarly, Claude is also very capable of actually doing the work.
同样，Claude 也非常擅长实际执行工作。
One of our partners, Fundamental Labs, has built an Excel agent called Shortcut on Opus.
我们的合作伙伴之一 Fundamental Labs 在 Opus 上构建了一个名为 Shortcut 的 Excel 代理。
This agent was able to pass five out of seven levels of the Financial Modeling World Cup (金融建模世界杯: 一项全球性的金融建模竞赛) competition and scored over 83% accuracy on complex Excel and financial reasoning tasks.
该代理在金融建模世界杯比赛的七个级别中通过了五个，并在复杂的 Excel 和金融推理任务中取得了超过83%的准确率。
Again, far outperforming competitors.
再次远远超越了竞争对手。
The next pillar of our solution is agent capabilities built on top of these models.
我们解决方案的下一个支柱是建立在这些模型之上的代理能力。
Now model intelligence needs to be translated into something that's actually useful for humans.
现在，模型智能需要转化为对人类真正有用的东西。
Claude's agent capabilities are flexible and composable and really aim at solving the core problems you all work in every single day.
Claude 的代理能力灵活且可组合，旨在解决大家每天都在处理的核心问题。
Including building multimodal reports (多模态报告: 结合文本、图像、图表等多种信息形式的报告) like pitch stacks (路演材料: 向潜在投资者展示公司或项目的演示文稿) investment memos, analyzing and visualizing data like benchmark analyses (基准分析: 将公司或产品的表现与行业标准或竞争对手进行比较) or stock price and volume charts (股价和成交量图表: 显示股票价格和交易量的图表), and natively reading and writing Excel and PowerPoint documents.
包括构建路演材料、投资备忘录等多模态报告，分析和可视化基准分析或股价和成交量图表等数据，以及原生读写 Excel 和 PowerPoint 文档。
I'm excited to announce that for the very first time, these expanded output capabilities are now in research preview exclusively for select customers of our financial analysis solution.
我很高兴地宣布，这些扩展的输出功能首次作为研究预览版，专供我们金融分析解决方案的精选客户使用。
We've also expanded usage limits to really support the deep work analysts work in every single day. And we've integrated Claude Code to support use cases like analyzing much larger data sets, Monte Carlo simulations (蒙特卡洛模拟: 一种通过随机抽样来估计数值的方法) and risk analyses (风险分析: 评估和量化潜在风险).
我们还扩大了使用限制，以真正支持分析师每天进行的深度工作。并且我们集成了 Claude Code，以支持分析更大的数据集、蒙特卡洛模拟和风险分析等用例。
Finally, the model intelligence and agent functionalities are delivered through our very flexible platform.
最后，模型智能和代理功能通过我们非常灵活的平台交付。
Our solution is the first agent with unified intelligence across all the core finance data sources you all work in.
我们的解决方案是第一个在所有核心金融数据源中都拥有统一智能的代理，这些数据源都是大家日常工作中会接触到的。
As Kate and Peter shared, we're excited to partner with industry leaders like yourselves to build those critical integrations into the agents with many more to come in the future.
正如 Kate 和 Peter 所分享的，我们很高兴与像你们这样的行业领导者合作，将这些关键集成构建到代理中，未来还将有更多集成。
Importantly, we're delivering white glove finance specific implementation and onboarding to really assist you all with deployment, education, and change management.
重要的是，我们提供“白手套”式的金融专属实施和入职服务，以真正协助大家进行部署、教育和变革管理。
Now this is of course built with enterprise grade security and trust.
当然，这都是以企业级安全和信任为基础构建的。
We're SOC 2 Type 2 certified (SOC 2 Type 2 认证: 一种关于服务组织数据安全、隐私和可用性控制的审计报告), and by default we do not train any of our models on your data.
我们通过了 SOC 2 Type 2 认证，并且默认情况下，我们不会使用您的数据来训练任何模型。
Now our solution is really targeted at solving the problems you all know well.
现在，我们的解决方案真正旨在解决大家都很熟悉的问题。
In investing, insights are alpha (阿尔法: 超额收益，指投资组合在承担同等风险的情况下，跑赢基准指数的部分).
在投资领域，洞察力就是阿尔法。
The ability to sort through the noise, build a thesis, get to an investment decision quickly is paramount.
能够从噪音中筛选信息，建立投资论点，并迅速做出投资决策至关重要。
As we've heard from many of our customers, this is of course true for the sell side (卖方: 投资银行、券商等提供研究和交易服务的机构) and the buy side (买方: 基金、资管公司等进行投资决策的机构).
正如我们从许多客户那里听到的，这对于卖方和买方来说都是如此。
To summarize the pain, analysts work in a deluge of documents every single day and spend hours researching into data sources that are difficult to verify.
总结一下痛点，分析师每天都在处理海量文档，花费数小时研究难以验证的数据来源。
They painfully prepare analyses and tick through models cell by cell.
他们痛苦地准备分析报告，并逐个单元格地核对模型。
And they manually prepare investment memos and pitch decks often pulling all-nighters on a tight deal timeline.
他们手动准备投资备忘录和路演材料，常常在紧张的交易时间线内通宵达旦。
With Claude, we can start to address all of these pain points.
有了 Claude，我们可以开始解决所有这些痛点。
实时金融分析演示：Sarah 的案例
Now, let's show you what that looks like in action.
现在，让我们向您展示实际操作中的效果。
Meet Sarah, a hedge fund (对冲基金: 一种高风险高回报的私募基金) at Acme Capital.
认识一下 Sarah，Acme Capital 对冲基金的一名分析师。
It's 2 p.m. Her portfolio manager (PM: 投资组合经理) burst into her office with an urgent question, which I think many of you know well.
下午两点，她的投资组合经理（PM）冲进她的办公室，带着一个紧急问题，我相信你们很多人都非常熟悉。
Our target company, Velocity Athletic, reported terrible earnings yesterday.
我们的目标公司 Velocity Athletic 昨天公布了糟糕的财报。
Revenue is down 12%. But somehow the stock price is up 17%.
营收下降了12%。但股价却上涨了17%。
Trading at $71 per share.
每股交易价格为71美元。
But PM needs to know, is this rally justified by their new strategy or should they sell into the spike? And he needs an answer before the market closes.
但投资组合经理需要知道，这次上涨是否由他们的新战略支撑，还是应该趁高点卖出？他需要在市场收盘前得到答案。
Now, let me show you how Claude transforms this typical four to five hour scramble into analysis under 30 minutes.
现在，让我向您展示 Claude 如何将这种典型的四到五小时的匆忙分析，转化为30分钟内完成的分析。
First, Sarah connects her tools. Look at what is available in one workspace. All the tools she works in S&P Global, Morningstar, FactSet, Dupa, and even her firm's internal Box documents.
首先，Sarah 连接她的工具。看看一个工作区中可用的所有工具：S&P Global、Morningstar、FactSet、Dupa，甚至她公司内部的 Box 文档。
No more juggling 14 different browser tabs. Everything she needs is right here with unified access.
不再需要同时管理14个不同的浏览器标签页。她所需的一切都集中在这里，统一访问。
Sarah starts a comprehensive query asking Claude to pull from multiple data sources simultaneously.
Sarah 发起了一个全面的查询，要求 Claude 同时从多个数据源中提取信息。
Watch what happens. Claude orchestrates across platforms. S&P Global for transcripts, Morningstar for reports, Box for internal analyses, Dupa pulling eight quarters of financials.
看看发生了什么。Claude 在不同平台间进行协调。S&P Global 用于获取逐字稿，Morningstar 用于报告，Box 用于内部分析，Dupa 提取了八个季度的财务数据。
And here's the key. Sarah gets synthesized intelligence, not just raw data dumps.
关键在这里。Sarah 获得的是综合智能，而不仅仅是原始数据堆砌。
Output full earnings transcript analysis (财报电话会议逐字稿分析: 对公司财报会议记录的文本分析) with red flags from Q&A (Question and Answer: 问答环节). The CFO (Chief Financial Officer: 首席财务官) disclosing a 400 basis point (基点: 百分之零点零一，常用于利率或百分比变化) margin hit from the tariffs. Competitors look like they're doing better. Pace running has been in Vietnam since 2019.
输出完整的财报电话会议逐字稿分析，并标示出问答环节的“红旗”信息。首席财务官披露，关税导致利润率下降400个基点。竞争对手的表现似乎更好。Pace running 自2019年以来一直在越南运营。
And most importantly, you can see full financials linked to the original data source like we're seeing here with Dupa. So Sarah can verify the results instantaneously.
最重要的是，你可以看到完整的财务数据链接到原始数据源，就像我们在这里看到的 Dupa。因此 Sarah 可以即时验证结果。
To go deeper, Sarah asked Claude to create visualizations and specific analyses like an annotated stock price chart, comps and benchmarking analyses, and even discounted cash flow (DCF: 贴现现金流) model.
为了更深入地分析，Sarah 要求 Claude 创建可视化图表和特定分析，例如带有注释的股价图、同业比较和基准分析，甚至贴现现金流模型。
As you can see, Claude is now pulling key corporate events from SEC Edgar (SEC Edgar: 美国证券交易委员会的电子数据收集、分析和检索系统) and the web, price data from S&P Global, historical financials from Dupa, consensus estimates from FactSet and creates several visual artifacts.
正如你所看到的，Claude 正在从 SEC Edgar 和网络上提取关键公司事件，从 S&P Global 获取价格数据，从 Dupa 获取历史财务数据，从 FactSet 获取共识估值，并创建了多个可视化图表。
First, an event annotated stock price chart showing the 17% spike with all key events marked.
首先，一张带有事件注释的股价图，显示了17%的涨幅，并标记了所有关键事件。
Emergency board meeting, CFO stock sale, earnings surprise.
紧急董事会会议、首席财务官股票出售、财报意外。
Next, a comprehensive comps table. Velocity is trading at 21 times IBIDA against peers at 16 times despite having worse fundamentals.
接下来，是一张全面的同业比较表。Velocity 的企业价值倍数 (EV/EBITDA) 为21倍，而同行仅为16倍，尽管其基本面表现更差。
And here is where it gets powerful. Claude has built a fully auditable discounted cash flow model (DCF Model: 贴现现金流模型，一种估值方法) with a functional case selector, projections tied to assumptions, and even a perfect WACC (Weighted Average Cost of Capital: 加权平均资本成本) calculation without needing to be prompted.
而这就是它的强大之处。Claude 构建了一个完全可审计的贴现现金流模型，该模型具有功能性情景选择器、与假设挂钩的预测，甚至无需提示即可进行完美的加权平均资本成本计算。
Now, this DCF model projects a base price of $54, suggesting that perhaps $71 is overly optimistic.
现在，这个 DCF 模型预测的基础价格是54美元，表明71美元可能过于乐观。
Finally, Sarah actually needs to prepare a deliverable for her PM. She asks Claude to create an investment memo using her firm's templates from Box.
最后，Sarah 实际上需要为她的投资组合经理准备一份可交付成果。她要求 Claude 使用她公司在 Box 中的模板来创建一份投资备忘录。
Claude searches Box for any relevant internal information, including a memo template and any past event-driven trades as reference.
Claude 在 Box 中搜索任何相关的内部信息，包括备忘录模板和任何过去的事件驱动交易作为参考。
In seconds, she has a professional memo with an exact sum recommendation to fade the rally (趁反弹抛售: 一种交易策略，指在市场反弹时卖出资产，预期价格会再次下跌), clear rationale with supporting data for market performance and comps, and specific action items and risk analyses, all properly cited.
几秒钟内，她就得到了一份专业的备忘录，其中包含明确的“趁反弹抛售”建议，以及市场表现和同业比较的清晰理由和支持数据，还有具体的行动项目和风险分析，所有内容都附有适当的引用。
The bottom line, the market is overpricing a complex operational challenge. So, take profits now, buy it back later, cheaper.
总而言之，市场对一个复杂的运营挑战估价过高。所以，现在获利了结，以后再以更低的价格买回来。
Now, let's recap what just happened here. Sarah delivered institutional quality analyses in under 30 minutes, a typical process that takes 3 to five hours across multiple platforms. So, she didn't just save time, she uncovered insights she might have missed like the CFO sale or the competitor's existing advantages.
现在，让我们回顾一下刚才发生了什么。Sarah 在不到30分钟内完成了机构级别的分析，而这个过程通常需要跨多个平台花费3到5个小时。所以，她不仅节省了时间，还发现了她可能错过的洞察，比如首席财务官的股票出售或竞争对手已有的优势。
Now, we believe this is the future of financial analysis where AI fully augments an analyst's capabilities where the best insights across all of the platforms come together in one intelligent workspace.
现在，我们相信这就是金融分析的未来，AI 将全面增强分析师的能力，将所有平台上的最佳洞察汇集到一个智能工作区中。
Now one example of these capabilities coming to life is from the Norwegian sovereign wealth fund (主权财富基金: 由国家拥有和控制的投资基金), otherwise known as NBIM (Norges Bank Investment Management: 挪威银行投资管理公司), the largest sovereign wealth fund in the world.
这些能力得以实现的一个例子来自挪威主权财富基金，也就是 NBIM，它是全球最大的主权财富基金。
To share a quote from their CEO Nikolai, Claude has fundamentally transformed the way we work at NBIM.
引用其首席执行官 Nikolai 的话：“Claude 从根本上改变了我们在 NBIM 的工作方式。”
They've achieved 20% productivity gains that is 213,000 hours back a here to focus on what really matters.
他们实现了20%的生产力提升，这意味着每年节省了21.3万小时，可以专注于真正重要的事情。
That is better decisions and returns for the Norwegian people.
那就是为挪威人民带来更好的决策和回报。
As Nikolai put it, Claude has become indispensable.
正如 Nikolai 所说，Claude 已经变得不可或缺。
Now, building these solutions requires deep partnership with all of you.
现在，构建这些解决方案需要与所有在座的各位深度合作。
Everyone in attendance today will receive a one month free trial to the solution.
今天所有与会者都将获得该解决方案的一个月免费试用。
I am personally very excited to spend much more time with all of you to dive deeper into the problems we can solve together and to shape the future of financial services with both our products and model capabilities.
我个人非常期待与大家有更多时间深入探讨我们可以共同解决的问题，并利用我们的产品和模型能力塑造金融服务的未来。
And with that, I'll pass it to my colleague Jonathan Pelosi, our head of financial services, and our distinguished panel of industry leaders to share their own journeys with Claude.
现在，我将把时间交给我的同事 Jonathan Pelosi，我们的金融服务主管，以及我们尊贵的行业领袖小组，请他们分享各自与 Claude 的合作历程。
专家小组讨论：AI 投资策略与实践
Jonathan Pelosi: Thank you, Nick, and to the DJ. Well, I didn't choose that song. I love it. Let me put these down. And thank you to everybody for joining us.
Jonathan Pelosi: 谢谢 Nick，也谢谢 DJ。嗯，这首歌不是我选的，但我喜欢。我把这些放下来。感谢大家的加入。
Hopefully you get a sense of why we're so optimistic about being able to support every one of you and everyone of you dialed in on all of your tasks as it relates to financial analysis. But don't just take my word for it. I've got a much more impressive panel that can speak to what they're doing with this type of technology and how they're doing it. And as Nick alluded to, in my role overseeing all financial services for Anthropic, that effectively means we support the biggest banks and insurance companies, hedge funds, and asset managers in taking this technology and practically applying it to your firm in a way where you can use it to drive real value and real transformation.
希望大家能明白我们为何如此乐观，能够支持在座的各位以及所有线上参与者，完成所有与金融分析相关的任务。但不要只听我的一面之词。我们有一个更令人印象深刻的专家小组，他们可以谈谈他们如何利用这种技术以及具体操作。正如 Nick 所暗示的，我负责 Anthropic 的所有金融服务业务，这意味着我们支持最大的银行、保险公司、对冲基金和资产管理公司，将这项技术实际应用到贵公司，从而推动真正的价值和转型。
And I say practically because all too often we'll read about the theoretical headlines of what this stuff can do and you're like, "Oh, it'll transform the way we work and it'll improve productivity by 50%."
我说“实际应用”是因为，我们常常会读到关于这项技术理论上能做什么的头条新闻，你会觉得：“哦，它将改变我们的工作方式，提高50%的生产力。”
But by the time my team and I sit with you, we often pull back the layers and say, "Well, how do you actually make that happen? How do you actually tailor this to your organization? How do you actually work on change management?"
但当我的团队和我与你们坐下来时，我们常常会深入探讨并问：“那么，你们如何才能真正实现这一点？如何才能真正根据你们的组织量身定制？如何才能真正进行变革管理？”
Getting these individuals comfortable with using this technology.
让这些员工适应使用这项技术。
Those are the types of things we obsess over and the types of things we're excited to chat further about. So, with that said, I'm going to call up my esteemed panel. If you could come on down and give them a chance to introduce themselves.
这些都是我们非常关注和渴望进一步讨论的话题。所以，话不多说，我将请出我尊贵的专家小组。请他们上台并自我介绍。
Jonathan Pelosi: Thank you, team. We're going to do quick intros because I don't think the titles necessarily do justice to all of the AI work they do. And we're going to start with my good friend Michael here. Take it away.
Jonathan Pelosi: 谢谢团队。我们快速介绍一下，因为我认为头衔不一定能完全体现他们所做的所有 AI 工作。我们先从我的好朋友 Michael 开始。请。
Michael: Thanks JP. My name is Michael. I co-head the COO (Chief Operating Officer: 首席运营官) group at DE Shaw. That means is my team and I work on big firmwide transformation initiatives. And there is no bigger firmwide challenge and opportunity that we're spending time on right now than AI.
Michael: 谢谢 JP。我叫 Michael。我是 DE Shaw (DE Shaw Group: 一家全球性的量化对冲基金公司) 首席运营官团队的联席主管。这意味着我和我的团队致力于全公司范围内的重大转型计划。目前，我们投入时间的最大全公司挑战和机遇莫过于 AI。
Lloyd: Thanks, JP. Lloyd Hilton. I am an AI lead at HG Capital (HG Capital: 一家专注于软件和科技服务领域的私募股权投资公司). So, for those of you who don't know us, we are a major PE firm (Private Equity Firm: 私募股权公司), one of the top 10 largest globally. And we're a sector specialist. So, we have about 50 portfolio companies (投资组合公司: 私募股权基金或风险投资基金所投资的公司) in the B2B SAS (Business-to-Business Software as a Service: 面向企业的软件即服务) and services space. And we've been driving a major transformation effort with those portfolio companies for the last two and a half years now, partnering with the Anthropic team.
Lloyd: 谢谢 JP。我是 Lloyd Hilton。我是 HG Capital 的 AI 负责人。对于不了解我们的人来说，我们是一家主要的私募股权公司，全球排名前十。我们专注于特定行业。我们拥有大约50家 B2B SAS 和服务领域的投资组合公司。在过去两年半的时间里，我们一直与 Anthropic 团队合作，推动这些投资组合公司进行重大转型。
Don: Morning everyone. Thanks for having us JP. My name is Don Vu. I'm the chief data analytics officer at New York Life (纽约人寿: 美国最大的寿险公司之一). For those that aren't familiar with New York Life, we're Fortune 69, over a trillion dollars of insurance in force. And we're one of the largest insurance companies in the world. I'm responsible for our overarching AI and data strategy and its execution.
Don: 大家早上好。JP，谢谢你邀请我们。我叫 Don Vu。我是 New York Life (纽约人寿) 的首席数据分析官。对于不熟悉纽约人寿的人来说，我们是财富榜第69位，拥有超过一万亿美元的有效保单。我们是全球最大的保险公司之一。我负责我们全面的 AI 和数据战略及其执行。
Frod: Yes. So, my name is Frod. I am part of the Norwegian Sovereign Wealth Fund. We are a $2 trillion sovereign wealth fund. And 70% equities, 30% fixed income and bonds. And we are only 700 employees worldwide. So we have a single owner, the Minister of Finance who represents the Norwegian people and I'm part of the team here in North America that manages the bulk of the equity assets.
Frod: 是的。我叫 Frod。我是挪威主权财富基金的成员。我们是一个2万亿美元的主权财富基金。其中70%是股票，30%是固定收益和债券。我们在全球只有700名员工。我们只有一个所有者，即代表挪威人民的财政部长，我是北美团队的一员，负责管理大部分股权资产。
Jonathan Pelosi: Thank you, Frod. Frod and I were chatting earlier and I think the stat I think has to be true that per employee no organization manages more money on Earth than NBIM. So it's an impressive stat indeed. And for those in the audience, we'll have a chance for you to ask questions as well after I ask a couple to the panelists. So keep that in mind. We're really looking forward to hearing from you as well. But let's start the AI AI investment thesis. Naturally, your big organizations, complicated firms. At what point did this decision to go all-in and do this at like a firmwide level take place and who drove that? And for this, we'll start with Frod. I'm curious to hear from you.
Jonathan Pelosi: 谢谢 Frod。Frod 和我早些时候聊过，我认为这个数据很可能是真的，即按每位员工计算，世界上没有哪个组织管理的资金比 NBIM 更多。所以这确实是一个令人印象深刻的数据。对于在座的各位，在我向小组成员提问几个问题后，你们也将有机会提问。所以请记住这一点。我们也期待听到你们的声音。但我们先从 AI 投资论点开始。自然，你们都是大型组织，复杂的公司。在哪个时间点，你们决定在全公司层面全面投入并执行这项工作？又是谁推动了这一决策？这次我们从 Frod 开始。我很想听听你的看法。
Frod: Yeah, sure. So, I mean, our AI strategy is really to be a leading user of AI in investment management. And that just means embedding AI into everything I do, I think, in a in a responsible way. So that means creating efficiency gains, reducing costs, enhancing returns and really improving risk management. So I think the what's changed is that we have a coordinated strategy with really dedicated dedicated organizational support and a dedicated AI team that helps out in in the organization and this is really driven from the top right.
Frod: 当然。我的意思是，我们的 AI 战略实际上是成为投资管理领域 AI 的领先用户。这意味着以一种负责任的方式将 AI 嵌入到我所做的一切中。因此，这意味着提高效率、降低成本、增加回报并真正改善风险管理。所以我认为改变的是，我们拥有一个协调一致的战略，有非常专注的组织支持和一个专门的 AI 团队来帮助组织内部的工作，而这确实是由高层推动的。
So it's we have this AI maniac at at the top of the fund, Nikolai Tongen and he is just all in on on on the change that we see in society right so his leadership his enthusiasm about AI is really really setting the scene internally in the fund and and driving the cultural adaptation that we see I think that's just absolutely crucial.
所以，我们基金的最高层有一位 AI 狂热者，Nikolai Tongen，他完全投入到我们社会所看到的变革中。因此，他的领导力以及他对 AI 的热情，真正在基金内部营造了氛围，并推动了我们所看到的文化适应，我认为这绝对至关重要。
Jonathan Pelosi: Thank you, Frod. And not surprisingly, this kind of top-down buy-in is critical if you're trying to drive adoption and really meaningful change across the Oregon. I think the Frod and Nikolai has done an amazing job of that. Lloyd, how about you as it relates to to your firm both internally and with your portfolio companies?
Jonathan Pelosi: 谢谢你，Frod。不出所料，这种自上而下的认同对于在俄勒冈州推动 AI 采纳和真正有意义的变革至关重要。我认为 Frod 和 Nikolai 在这方面做得非常出色。Lloyd，对于你的公司，无论是内部还是与你的投资组合公司相关，情况如何？
Lloyd: Yeah, so I mean I think that the top-down buy-in really resonates for for us as well. So although we are investors, a lot of our leadership team are sort of technologists and engineers. So very early on, I think we sort of recognized that this was a platform shift. A lot of HG's success has been predicated based on helping our portfolio companies navigate the sort of on-prem to SAS platform shift. We've also been investing in AI for a while. So we have a central data team. We actually had access to GPT3 back in 2021. So we we've been experimenting with this for a while and I guess when the early LLMs were sort of launched around 2022 2023, we realized this was a pretty major opportunity.
Lloyd: 是的，我的意思是，我认为自上而下的认同对我们来说也深有共鸣。所以，尽管我们是投资者，但我们很多领导团队成员都是技术专家和工程师。因此，我们很早就认识到这是一次平台转变。HG 的许多成功都建立在帮助我们的投资组合公司应对从本地部署到 SaaS (Software as a Service: 软件即服务) 平台转变的基础上。我们也在 AI 领域投资了一段时间。我们有一个中央数据团队。早在2021年，我们实际上就获得了 GPT-3 的访问权限。所以我们已经试验了一段时间，我想当早期的 LLM 在2022-2023年左右推出时，我们意识到这是一个相当大的机会。
I think for us though, you know, people find conviction at their own pace and what really helped was we ran an event in Silicon Valley. So we worked with the Anthropic team. We actually had Mike Creger the CPO come to speak at that event and we've fully immersed our execs in this AI topic and got people really hands-on. So for us that was a that was a real catalyst. And I guess over the last two years, the thing that's really surprised us is just how quickly this has moved. So, we started probably thinking about, oh no, we can get 10 to 20% productivity gains through integrating AI into our existing processes. What we're now really focused on is fully transforming our portfolio companies with with AI.
不过我认为，对我们来说，人们会按照自己的节奏建立信念，真正有帮助的是我们在硅谷举办了一次活动。我们与 Anthropic 团队合作。我们实际上邀请了首席产品官 Mike Creger 在活动上发表演讲，我们让我们的高管完全沉浸在这个 AI 话题中，并让人们真正动手实践。所以对我们来说，那是一个真正的催化剂。我想在过去两年里，真正让我们惊讶的是这项技术发展得如此之快。我们最初可能认为，哦，我们可以通过将 AI 整合到现有流程中，实现10%到20%的生产力提升。现在我们真正关注的是用 AI 全面改造我们的投资组合公司。
And we think that there's going to be a huge amount of value upside for us and for our portfolio companies through through doing that. So, that's really where the the focus is now. And we've kind of replicated that same approach. So, you know, we got that top-down buy-in centrally for HG. We're now making sure that we create that same catalyst and that same spark for our for our portfolio companies and we're finding that sort of now permeating pretty nicely through the HG portfolio to drive that that transformational change.
我们认为通过这样做，对我们和我们的投资组合公司来说，将会有巨大的价值增长空间。所以，这才是我们现在的重点。我们已经复制了相同的方法。也就是说，我们为 HG 获得了自上而下的中央支持。我们现在正在确保为我们的投资组合公司创造同样的催化剂和火花，我们发现这种方法现在正很好地渗透到 HG 的投资组合中，以推动变革性的改变。
Jonathan Pelosi: Fantastic. Thank you, Lloyd. And for the folks in the audience, I can imagine this is something on your mind and for those dialing in. All too often we hear, you know, should I build this internally or should I buy something out of the box with APIs? Now, it's increasingly easy to build really bespoke AI powered solutions. And at the same time, there's a lot of great solutions out of the box. Nicklin just walked through one with you today. So with that said, we'll start with you, Don. How do you think about this notion of build versus buy and basically deciding which path to go? Do you go both? Walk us through that thought process.
Jonathan Pelosi: 太棒了。谢谢你，Lloyd。对于在座的各位，以及那些在线参与的人，我想这可能也是你们心中的一个问题。我们经常听到这样的讨论：我应该自己内部构建，还是应该购买带有 API 的现成解决方案？现在，构建真正定制化的 AI 驱动解决方案变得越来越容易。与此同时，市场上也有很多优秀的现成解决方案。Nicklin 今天刚刚向你们展示了一个。那么，Don，我们从你开始。你如何看待“构建与购买”这个概念，以及如何决定走哪条路？你会两者兼顾吗？请为我们讲解一下你的思考过程。
Don: Yeah. Yeah. No, I think it's a great question. I think when we first started this generative AI (生成式人工智能: 能够生成新内容或数据的AI模型) wave, a lot of organizations, especially financial services companies, went through a decision like should we leverage APIs and build our own custom wrappers or should we partner with a company like Anthropic and leverage like these enterprise solutions? There's obviously trade-offs with those. I think for us, we were actually very intentional that you know continuing to innovate on the UI layer and all these different sort of capabilities like canvas and things of that nature connectors etc etc was going to be really challenging.
Don: 是的。是的。我认为这是一个很好的问题。我认为当我们刚开始这波生成式 AI 浪潮时，很多组织，特别是金融服务公司，都面临着一个决策：我们应该利用 API 构建自己的定制封装，还是应该与像 Anthropic 这样的公司合作，利用他们的企业级解决方案？这显然有利弊权衡。我认为对我们而言，我们非常清楚，要在 UI 层以及像画布、连接器等各种功能上持续创新，将是非常具有挑战性的。
I think companies like Anthropic are just moving at such incredible velocity with respect to innovation. We wanted to ensure that our internal developers and engineers were focused on our most proprietary and specific organizational solutions. So for us, it made a lot of sense to more so partner versus try to build our own custom ones and let's leave like the custom integrations and solutions for those things that are really important for us competitively.
我认为像 Anthropic 这样的公司在创新方面正以惊人的速度发展。我们希望确保我们的内部开发人员和工程师能够专注于我们最具专有性和特定性的组织解决方案。因此，对我们来说，选择合作而不是尝试构建我们自己的定制方案更有意义，而将定制集成和解决方案留给那些对我们具有竞争力的真正重要的事情。
Jonathan Pelosi: Thanks, Don. And Michael, how do you guys think about that at DE Shaw?
Jonathan Pelosi: 谢谢你，Don。Michael，你们 DE Shaw 是怎么考虑这个问题的？
Michael: So, for those in the hedge fund industry at least, I think we're pretty well known for having a big focus on building things. We hire amazing technologists and we give them the opportunity to build great things. And so the instinct when something comes along for us is to build the best version of it ourselves. But resources aren't infinite and with great people comes big opportunity cost. And so we always think hard about build versus buy.
Michael: 对于对冲基金行业的人来说，至少我认为我们以非常注重“构建”而闻名。我们招聘优秀的科技人才，并给予他们创造伟大事物的机会。因此，当有新事物出现时，我们的本能是自己构建最好的版本。但资源并非无限，优秀人才的加入也伴随着巨大的机会成本。所以我们总是认真思考“构建与购买”的问题。
What I think is different this time and I think Don alluded to it is the speed at which the technology is changing and with that speed comes a different balance between build and buy. To be able to build and deploy at that speed and scale that some of what's being offered in the commercial market can provide is often somewhere between difficult and impossible internally. So that shifts our calculus. We're still doing both and we're being extraordinarily deliberate in where we choose to build and where we choose to buy.
我认为这次不同的是，正如 Don 所暗示的，技术变化的 F速度非常快，而这种速度带来了“构建与购买”之间不同的平衡。要在商业市场提供的一些解决方案所能达到的速度和规模下进行内部构建和部署，通常在内部是介于困难和不可能之间。所以这改变了我们的考量。我们仍在两者兼顾，并且在选择构建和选择购买时都非常谨慎。
Jonathan Pelosi: Fantastic. And yeah, we hear that so often as well when they'll develop a partner will develop an internal AI chat and they'll share a feature that they're particularly proud of and by the time we meet with them you're almost you know afraid to let them know it's like that's already a thing of the past and look at this this and this that's that's already been developed. So I could couldn't agree more. Recognizing you're all big firms and leaders in your respective spaces. Another huge challenge we hear about is this change management and driving this kind of transformation at scale. It's one thing that this technology can do cool stuff. How do you actually get people using it?
Jonathan Pelosi: 太棒了。是的，我们也经常听到这样的情况：当一个合作伙伴开发了一个内部 AI 聊天工具，并分享他们特别引以为傲的功能时，等我们见到他们时，你几乎不敢告诉他们，这已经是过去式了，看看这个、这个和这个，这些都已经开发出来了。所以我完全同意。考虑到你们都是各自领域的巨头和领导者。我们听到的另一个巨大挑战是变革管理和大规模推动这种转型。这项技术能做很酷的事情是一回事。但你如何才能真正让人们使用它呢？
So when you think of like transformation of the size that you're operating at? And Don, I'll start with you. Obviously got a lot of employees. How do you think about like practically driving adoption with technology like this when you've got so many different departments employees to to think about?
那么，当你考虑你们所运营的这种规模的转型时？Don，我从你开始。显然你有很多员工。当你需要考虑这么多不同部门的员工时，你如何实际地推动这种技术的采用？
Don: Yeah, I know for us our AI strategy has been pretty everything everywhere all at once. So we actually treat our AI strategy as a as a portfolio of initiatives. So I think we started with like a lot of folks on targeted use cases specific to workflows or certain business domains. We had measurable value in production and that was great. We certainly realized that we wanted to raise the floor and the ceiling of our aspirations by really democratizing and empower all of our employees with AI tools.
Don: 是的，我知道对我们来说，我们的 AI 战略一直都是“一切、无处不在、同时进行”。所以我们实际上将我们的 AI 战略视为一系列举措的组合。我想我们开始时，很多人都专注于针对工作流程或特定业务领域的有针对性的用例。我们在生产中实现了可衡量的价值，这很棒。我们当然意识到，我们希望通过真正地普及 AI 工具并赋能所有员工，来提升我们的抱负的下限和上限。
And so there's multiple tools that we're really leveraging to do that. And then we're actually also focusing on reinvention. So like business leaders at the top are really looking at each of their respective domains to understand how AI, agentic AI and these new paradigms can actually help us re-anchor our aspirations of how we can transform and the velocity for us to do it. And so for us to do that, I mean, again, very very pervasive, tons of hands-on training and hackathons, all these different things that you actually have to do like get in the trenches to really help people understand what these possibilities are.
因此，我们正在利用多种工具来实现这一目标。然后，我们实际上还在专注于重塑。所以，顶层的业务领导者正在真正审视各自的领域，以了解 AI、代理式 AI 和这些新范式如何能帮助我们重新定位转型的目标和实现转型的速度。而要做到这一点，我的意思是，再次强调，它非常非常普遍，大量的实践培训和黑客马拉松，所有这些你实际上必须亲身参与的事情，才能真正帮助人们理解这些可能性。
There's mindset shifts that are related to that that I think maybe we'll touch on later in the discussion. But it really has been kind of a full-court press that's complemented by both a top-down commitment from our CEO and his executive leaders, but again this bottoms up sort of empowerment that goes alongside that.
还有一些与此相关的思维转变，我想我们可能会在稍后的讨论中触及。但这确实是一种全面出击的策略，它既有我们首席执行官及其高管团队自上而下的承诺作为补充，又有这种自下而上的赋能与之并行。
Jonathan Pelosi: Thank you, Don. And Lloyd, you have a bit of a different challenge and you're working with the employees internally, but also of course the portfolios that you that you support. So how do you guys think about scaling this and driving that transformation at the portfolio level realizing that's a lot of different companies you got to help train and educate?
Jonathan Pelosi: 谢谢你，Don。Lloyd，你面临的挑战有点不同，你既要与内部员工合作，当然也要与你所支持的投资组合公司合作。那么，你们是如何考虑在投资组合层面进行规模化推广并推动这种转型的呢？要知道，你们需要帮助培训和教育很多不同的公司。
Lloyd: Yeah. Yeah, for sure. So, I mean, I think a lot of what you said resonates, Don. So, we have 50 companies, about 120,000 FTE (Full-Time Equivalent: 全职当量员工) across those companies. All of our companies look a little bit similar and and mostly, you know, verticalized software platforms. So what that's enabled us to do is really sort of bring transformation at scale to that group. So, that starts with a central effort from HG. We have 20 AI specialists internally supported by about 150 or so kind of contractors and close partners.
Lloyd: 是的。是的，当然。我的意思是，Don，你说的很多话都引起了共鸣。我们有50家公司，总共有大约12万全职当量员工。我们所有的公司看起来都有点相似，而且大多是垂直化的软件平台。因此，这使我们能够真正地为这个群体带来大规模的转型。这始于 HG 的一项核心工作。我们内部有20名 AI 专家，由大约150名承包商和紧密合作伙伴提供支持。
And we've been really driving that that central change across the portfolio. It's also allowed us to experiment with lots of different tools, lots of different initiatives and really see what works quickly across that sort of laboratory and you know understand what the best processes are. But I think the yeah the top-down point has been absolutely critical. So we've been kind of systematically working with each of the leaders of our portfolio getting them engaged getting them hands-on and then there's also the piece I'd add is that we we're really trying to be disruptive in the way that we think about this.
我们一直在整个投资组合中推动这种核心变革。这也使我们能够尝试各种不同的工具、各种不同的举措，并在这个“实验室”中快速看到什么有效，并了解最佳流程是什么。但我认为，自上而下的观点绝对是至关重要的。因此，我们一直在系统地与我们投资组合中的每一位领导者合作，让他们参与进来，让他们亲身实践，然后我想补充的一点是，我们真的在努力以颠覆性的方式思考这个问题。
So you know rather than retrofitting existing processes and adding some AI we've re-engineered a lot of our our functions. So if I take software engineering for example on average across the the portfolio we're now measuring about 30% productivity gains in software engineering. We have some companies who have taken their development squads down from nine people to two people. So getting really material leverage. We have one company that's deployed a thousand instances of an agentic software engineer. So they've kind of added 50% productive capacity to their engineering team.
所以，你知道，我们没有改造现有流程并添加一些 AI，而是重新设计了我们很多功能。例如，就软件工程而言，在整个投资组合中，我们现在测量的软件工程生产力平均提升了大约30%。我们有一些公司已经将他们的开发团队从九人减少到两人。因此获得了真正的实质性杠杆。我们有一家公司部署了一千个代理软件工程师实例。所以他们为他们的工程团队增加了大约50%的生产力。
And that's the sort of reinvention that we're now sort of driving out across across the portfolio. I should say there what what we're not doing is you know taking out any cost. What we're doing is then reinvesting in productivity basically clearing down tech debt and also building AI products. And that's the other key focus for us where we're seeing you know a huge amount of transformational value through embedding the Anthropic API but other solutions as well into into our core software solutions. And that's adding material growth to some of our businesses already.
这就是我们现在正在整个投资组合中推动的那种重塑。我应该说，我们在这里做的不是削减任何成本。我们正在做的是将资金重新投入到生产力中，基本上是清理技术债务，并构建 AI 产品。这是我们另一个关键的重点，我们看到通过将 Anthropic API 以及其他解决方案嵌入到我们的核心软件解决方案中，带来了巨大的转型价值。这已经为我们的一些业务带来了实质性的增长。
培养 AI 文化与克服恐惧
Jonathan Pelosi: Thank you, Lloyd. And on the topic of change management, this also comes up a lot with our partners. How do you think about, hey, this technology, there's a lot about that scares me and my employees might interpret this as something that could do their work. And how do you tow the line there in empowering them to use it while also recognizing that there's some fear on is this going to kind of do my my job for me. So we we we think about like the cultural implications at the firm and how do you make this a part of the culture and so Frod this one's for you because I think NBIM's done a masterful job navigating this how do you and I guess starting from Nikolai but as you as a senior portfolio manager and your teams how do you see this fitting into the culture of the firm and becoming just a core part of how you guys operate?
Jonathan Pelosi: 谢谢你，Lloyd。关于变革管理的话题，这在我们的合作伙伴中也经常被提及。你们如何看待，嘿，这项技术有很多让我感到害怕的地方，我的员工可能会将其解读为可以取代他们工作的东西。你们如何在赋能他们使用它的同时，又能意识到他们对“这是否会取代我的工作”存在一些担忧？所以我们思考的是公司内部的文化影响，以及如何让它成为文化的一部分。Frod，这个问题是给你的，因为我认为 NBIM 在处理这方面做得非常出色。你，以及从 Nikolai 开始，你作为高级投资组合经理和你的团队，如何看待它融入公司文化并成为你们运作方式的核心部分？
Frod: Yeah, I mean starting with a top-down perspective obviously very important right? I mean that's really driven it and I think we had focus on tech for a long time we've been a very tech-driven organization now we we've taken a journey from moving from our infrastructure to the cloud having data on Snowflake integrating Claude for enterprise and being able to really chat with our data and that fits very well with our quantitative nature and our inclination to use data right? So I think from a culture perspective is a very good fit and I'd say in my team for instance a lot of us are really introverted right?
Frod: 是的，我的意思是，从自上而下的角度来看，这显然非常重要，对吧？我的意思是，这确实推动了它，而且我认为我们长期以来一直专注于技术，我们一直是一个非常技术驱动的组织。现在，我们已经踏上了一段旅程，从我们的基础设施迁移到云端，将数据存储在 Snowflake 上，集成企业版 Claude，并能够真正地与我们的数据进行对话，这非常符合我们的量化性质和我们使用数据的倾向，对吧？所以，我认为从文化角度来看，这是一个非常好的契合点，而且我想说，例如在我的团队中，我们很多人都非常内向，对吧？
And we we like to sit on and and code and and do things and I think just having Claude and having the projects that we can now build assistance and just share and collaborate on it is just really accelerating the the cultural shift that we're seeing organizations. I think that's super fascinating and I'll also add that being a sovereign wealth fund with a single owner it's just having AI into the organization is just such a massive enabler for us, right? Because we can do a lot of stuff with this technology that others just can't, right? Because we're not hampered with, you know, client and and customer dynamics.
我们喜欢坐下来编写代码和做事情，我认为拥有 Claude，以及我们现在可以构建辅助工具并进行分享和协作的项目，正在真正加速我们所看到的组织内部的文化转变。我认为这非常引人入胜，我还要补充一点，作为一个单一所有者的主权财富基金，将 AI 引入组织对我们来说是一个巨大的赋能者，对吧？因为我们可以用这项技术做很多其他公司无法做到的事情，对吧？因为我们不受客户动态的束缚。
So, we can we have a really open and and lean organization with a collaborative culture and we're just 700. So, this is just fascinating.
所以，我们可以拥有一个真正开放、精简且具有协作文化的组织，我们只有700名员工。所以，这太令人着迷了。
Jonathan Pelosi: Fantastic. Thank you, Frod. Really I really appreciate that this idea of like demystifying what this technology is all about at the individual employee level is so critical because so quickly you can address those concerns or fears at you know a cultural level because very quickly they see oh this is just a great assistant if you will it's a great partner when I wake up and I start my role in helping me do my work more effectively so it's wonderful to hear that example.
Jonathan Pelosi: 太棒了。谢谢你，Frod。我非常非常赞同这种在个体员工层面揭开这项技术神秘面纱的想法是如此关键，因为你可以很快地在文化层面解决这些担忧或恐惧，因为他们很快就会明白，哦，这只是一个很棒的助手，如果你愿意，它是一个很棒的伙伴，当我醒来开始工作时，它能帮助我更有效地完成工作，所以听到这个例子真是太棒了。
Don: Yeah, I think JP I think it's a great point. I think we were actually very intentional. I think very early on in this journey, folks would talk about, you know, AI is not going to take your job, but someone using AI will. And so we wanted to really, you know, address that head-on and ensure that as many employees as possible. And we were intentional about every employee having multiple AI solutions and then not just that, but getting like the training involved to help them feel even more empowered.
Don: 是的，JP，我认为这是一个很好的观点。我认为我们实际上非常有意为之。我想在这段旅程的早期，人们会谈论，你知道，AI 不会取代你的工作，但使用 AI 的人会。因此，我们希望真正地正面解决这个问题，并确保尽可能多的员工。我们有意让每位员工都拥有多个 AI 解决方案，而且不仅如此，还要提供培训，帮助他们感到更加赋能。
I thought that was really critical to your point to like get through some of the call it the FUD (Fear, Uncertainty, and Doubt: 恐惧、不确定和怀疑) that might be a little bit of a headwind.
我认为这对于你所说的，克服一些可能带来阻力的“恐惧、不确定和怀疑”情绪，是至关重要的。
Jonathan Pelosi: Awesome. Thank you, Don.
Jonathan Pelosi: 太棒了。谢谢你，Don。
Michael: Yeah. And oftentimes I'll I'll laugh because even when partners ask like, you know, how do I train? How do I build these curriculums to drive adoption? I'll pause and I'll be like, ask Claude. And jokes aside, whether it's Claude or another tool you're using, these tools are very good at that sort of task. We're like, here's my firm. And for those dialing in, same applies. Here's my size. Here's what we do. I want to build a simple training program for my employees. Maybe they've got three hours they'll spend over the next two weeks. How would you break it up? What would that training look like? I'm telling you, if you do that exercise when you go home, it'll be a beautiful, beautiful output.
Michael: 是的。我常常会笑，因为即使合作伙伴问：“我该如何培训？如何构建这些课程来推动采用？”我都会停顿一下，然后说：“问 Claude。”开玩笑归开玩笑，无论是 Claude 还是你正在使用的其他工具，这些工具都非常擅长这类任务。我们会说：“这是我的公司。对于那些远程接入的人，同样适用。这是我的规模。这是我们所做的事情。我想为我的员工制定一个简单的培训计划。也许他们将在未来两周内花费三个小时。你会如何安排？这个培训会是什么样子？”我告诉你，如果你回家后做这个练习，它会产生非常棒的输出。
Don: Can I give you a double down on that?
Don: 我可以再补充一点吗？
Michael: Yes.
Michael: 可以。
Don: If your staff say, "I don't know what to do. I don't know where to start." Tell them to describe their job briefly. And ask the AI, how ought I start working with this? What are a few things I can try? It actually works wonders.
Don: 如果你的员工说：“我不知道该做什么。我不知道从哪里开始。”让他们简要描述自己的工作。然后问 AI：“我该如何开始使用它？我可以尝试哪些事情？”这实际上会产生奇效。
Jonathan Pelosi: I mean, I love that example because I'll even go so far as to say I've got two little kids, for instance, a four and a two-year-old. And if I'm looking for an activity to do on the weekend, I'm not very good at coming up with those. But I'll ask Claude, but I'll do one better. I'll be like, "Claude, I want you to optimize the following prompt." And I'll be like, "I need to come up with activities for my kids." And then Claude will rewrite it. I'll copy paste it. And then I get like the world's greatest to-dos. So that's that's another good hack. Ask Claude to write your prompts for you.
Jonathan Pelosi: 我的意思是，我喜欢这个例子，因为我甚至可以说，我家里有两个小孩，一个四岁，一个两岁。如果我周末想找些活动，我不太擅长想出这些。但我会问 Claude，而且我会做得更好。我会说：“Claude，我希望你优化以下提示。”然后我会说：“我需要为我的孩子们想些活动。”然后 Claude 会重写它。我复制粘贴后，就能得到世界上最棒的待办事项。所以这是一个很好的技巧。让 Claude 帮你写提示。
But deciding where to start is another thing we hear a lot about, right? So for folks in the audience, some of you are smaller firms, a lot of your massive firms, but okay, there's a lot. You've heard a lot from this group. Transformation across the or top-down leadership. I'm going to start with Michael again here. Where do you for the folks in the audience if we have to start somewhere like what's the use case? What is the first place they start if they're starting with a clean slate? What would you suggest?
但决定从哪里开始是我们经常听到的另一个问题，对吧？所以对于在座的各位，你们中有些是小公司，很多是大型公司，但没关系，有很多东西。你们从这个小组听到了很多。全组织范围的转型或自上而下的领导。我将再次从 Michael 开始。对于在座的各位，如果我们必须从某个地方开始，比如用例是什么？如果他们从零开始，他们应该从哪里开始？你有什么建议？
Michael: So, it's incredibly tempting to look for a first place to start and my advice would be to resist that temptation. Your colleagues, I don't know that they know more about how best to use the tools. They certainly don't before you give them the tools, but once you give them the tools, they will discover things to do with those that you never imagined. So, we're a pretty decentralized place. We are a place that wants to make every one of our colleagues better using AI. So our core focus off the bat and what I would recommend to others is get really easy to use tools out there. Let people figure out what they're doing and then pay attention.
Michael: 寻找一个起点是极具诱惑力的，我的建议是抵制这种诱惑。你的同事，我不知道他们是否更了解如何最好地使用这些工具。在你给他们工具之前，他们肯定不知道，但一旦你给了他们工具，他们就会发现你从未想象过的用途。所以，我们是一个相当分散的地方。我们是一个希望通过使用 AI 让每一位同事变得更好的地方。因此，我们的核心重点，以及我向其他人推荐的，就是提供真正易于使用的工具。让人们自己摸索，然后你再关注。
Help accelerate the spread. In some cases centralized as you learn but not as your first step. I'll give one concrete piece of advice on that. I think some people will do that. They'll try something, it won't work the way they'd like it to, and they'll abandon it. What we found to be valuable is to encourage folks to come back, let's call it every six months. The rate at which the technology and the products are changing is just foreign to most of your colleagues and the degree to which they can improve for a particular use case in 3 months, 6 months, 9 months can sometimes shock them and so pushing them to revisit is one play.
帮助加速传播。在某些情况下，当你学习时可以集中化，但不要作为你的第一步。我在这方面给出一个具体的建议。我认为有些人会这样做。他们会尝试一些东西，如果它没有达到他们想要的效果，他们就会放弃。我们发现有价值的是鼓励人们回来，比如说每六个月回来一次。技术和产品变化的速度对你的大多数同事来说是陌生的，它们在3个月、6个月、9个月内针对特定用例的改进程度有时会让他们震惊，因此推动他们重新审视是一种策略。
We don't tend to be too pushy but that's one place where I think pushiness can pay off. So, I love that because I think to Michael's point, I want to hear from the from the rest of the panel. The notion of starting with one specific use case, oftentimes it can be like, oh, I'm going to build a bespoke analyst or risk mitigation agent.
我们通常不会太强求，但在这一点上，我认为强求是会有回报的。我喜欢这一点，因为我认为对于 Michael 的观点，我想听听小组其他成员的看法。关于从一个特定的用例开始的概念，通常可能是，哦，我要构建一个定制的分析师或风险缓解代理。
We'll always advise, similar to what Michael said, first things first, get this technology in the hands of your employees in a safe, responsible way. Obviously, you saw we have a solution. There's there's many solutions out there. And that should almost always be step one because so often, and I imagine this is true for everyone in the room. It's only when you start using this stuff. And honestly, that's true in your personal life. It's true at work that you really start to see what it's capable of.
我们总是建议，类似于 Michael 所说的，首先，以安全、负责任的方式将这项技术交到员工手中。显然，你看到了我们有一个解决方案。市面上有许多解决方案。这几乎总是第一步，因为很多时候，我想在座的每个人都是如此，只有当你开始使用这些东西时。说实话，这在你的个人生活中是如此，在工作中也是如此，你才会真正开始看到它的能力。
I'm going through a process today. I just bought a new house. I'm getting home insurance. I don't know when the last time you read a home insurance policy was. They're brutal and they're like 400 pages. And I get these three policies and I'm like even distilling the difference between coverage and umbrellas and all that and I work with a lot of insurance companies so I should know all of this. I just uploaded to Claude and I'd be like give me a breakdown of like what I'm looking at here and I'm sharing this as an example because those same aha moments and it gave me a wonderful answer.
我今天正在经历一个过程。我刚买了一套新房子。我正在办理房屋保险。我不知道你上次阅读房屋保险单是什么时候。它们太残酷了，大约有400页。我拿到了这三份保单，我在想，即使是区分保障范围和附加险等等，我都感到很困难，而且我与很多保险公司合作，所以我应该了解所有这些。我只是把它上传到 Claude，然后说：“帮我分析一下我看到的是什么。”我分享这个例子是因为同样的“啊哈”时刻，它给了我一个绝妙的答案。
The fastest way to create them is to give your employees access to a tool like Claude and there's again there's there's other tools out there but just to get it in their hands in a safe and responsible way is is is huge. With that said, I want to go over to Lloyd as you think about advising this group on they've got limited resources, limited people. Where do they begin? Do you have any other thoughts on where they should start? And let's assume for the purpose of this they've already enabled a Claude or an AI tool at the employee level. What's next?
最快的方法就是让你的员工使用像 Claude 这样的工具，市面上也有其他工具，但以安全负责的方式让他们上手，这非常重要。话虽如此，我想请 Lloyd 谈谈，当你考虑向这个资源有限、人员有限的团队提供建议时，他们应该从哪里开始？你对他们的起点还有其他想法吗？我们假设他们已经在员工层面启用了 Claude 或其他 AI 工具。接下来是什么？
Lloyd: Yeah. Um, so I was just going to add on to on to Michael's point. I I fully agree with, you know, revisiting the technology every 3 to six months. The other point I'd add is really investing in your sort of prompt engineering (提示工程: 设计和优化输入给AI模型的提示，以获得更佳输出) or context engineering. I think there's a massive difference between a sort of average user who puts a kind of a lazy prompt into Claude versus someone who really thinks about that context.
Lloyd: 是的。嗯，我只是想补充 Michael 的观点。我完全同意，你知道，每三到六个月重新审视这项技术。我想补充的另一点是，真正投资于你的提示工程或上下文工程。我认为一个普通用户向 Claude 输入一个懒散的提示与一个真正思考上下文的人之间存在巨大差异。
So I think finding those people who sort of become power users in your organization and just have a sort of natural intuition for that can really help to accelerate if you you know put those people up as AI champions that can really drive a lot of change. On other use cases once you've gone with broad enablement, I mean the way we think about that is looking at you know those kind of high friction tasks where people do a lot of mechanical processing that are quite repeatable that are using structured data.
所以，我认为找到那些在你的组织中成为高级用户，并且对此有天然直觉的人，如果你将这些人提升为 AI 倡导者，他们可以真正推动很多变革，从而加速进程。在其他用例方面，一旦你进行了广泛的赋能，我的意思是，我们思考这个问题的方式是，着眼于那些高摩擦的任务，即人们进行大量机械化处理、可重复且使用结构化数据的任务。
Those are often sort of perfect candidates in your organizations for something like Claude. So for us, for the really critical decisions where it requires a lot of sort of careful thought and system two type thinking, we maybe use Claude as more of a sounding board but for a lot of those sort of mechanical tasks that happen in all of our organizations, it's kind of using Claude plus other automation tools to really drive transformation of of those processes is how how we think about it.
这些任务通常是你的组织中像 Claude 这样的工具的完美候选。所以对我们来说，对于那些需要大量仔细思考和系统二式思维的真正关键决策，我们可能更多地将 Claude 用作一个“思想共鸣板”，但对于我们所有组织中发生的许多机械性任务，我们则是将 Claude 与其他自动化工具结合使用，以真正推动这些流程的转型，这就是我们的思考方式。
Jonathan Pelosi: Awesome. Thank you, Lloyd. One of one of my favorite questions to ask, so I'm obviously going to ask it here. For the purpose of the audience, all of you have a lot of experience. You've kind of gone through this. You've applied it in your organization. You've seen a lot of what works and a lot of what doesn't. I'm going to start with Frod. Knowing what you know now, if you could do it all over, what's one thing you would do differently?
Jonathan Pelosi: 太棒了。谢谢你，Lloyd。这是我最喜欢问的问题之一，所以我肯定会在这里提问。对于在座的各位，你们都经验丰富。你们都经历过这个过程。你们已经在自己的组织中应用了它。你们看到了很多有效的方法，也看到了很多无效的方法。我将从 Frod 开始。如果现在让你重新来过，你会做一件什么不同的事情？
Frod: I don't know about what I would do different really. I think in NBIM we really nailed it in terms of getting it. I think it's just maybe just start even even before right and start right. I mean that's what I would say you know this is something you just have to lean into and and and take take on so I think move fast and make mistakes learn from them and move on is really the the key takeaway and I think if anything is just be there.
Frod: 我真的不知道我会做些什么不同。我认为在 NBIM，我们在获取它方面做得非常成功。我想可能就是更早开始，并正确开始。我的意思是，我想说的是，这是一件你必须全身心投入并承担的事情，所以快速行动，犯错，从中学习，然后继续前进，这才是真正的关键。我认为，如果非要说有什么不同，那就是要在那里。
Jonathan Pelosi: Thank you for Don how about you.
Jonathan Pelosi: 谢谢你，Don，你呢？
Don: Yeah, yeah. I'll lean into the do more faster bit but I think one thing we haven't touched on that I think that we focused on but looking back perhaps we could have focused on earlier was this notion of it's a true like mindset and culture shift. We talked a lot about democratization and like a tool roll out but certainly this is more than just like rolling out tools and giving people access. It's like giving everyone in America a treadmill and expecting like heart disease to be cured.
Don: 是的，是的。我赞同“更快做更多”这一点，但我认为我们还没有触及的一个方面，也是我们曾经关注但回过头来看可能可以更早关注的，就是这是一种真正的思维模式和文化转变。我们谈论了很多关于民主化和工具推广，但这肯定不仅仅是推广工具和提供人们访问权限。这就像给美国所有人一台跑步机，然后期望心脏病能被治愈一样。
What we were very intentional about is understanding that this is actually a mindset shift as well. So I think a lot of people look at like a Claude or a ChatGPT they see a text box and like oh this is like Google I should just like put in one question. I'm going to get back an answer and then I'm kind of done. I think the reality is the the this technology is really designed to be more of like a human. This truly is a companion and a co-pilot, however you might describe it.
我们非常有意识地认识到，这实际上也是一种思维模式的转变。所以，我认为很多人看到像 Claude 或 ChatGPT 这样的工具时，他们看到一个文本框，就会觉得“哦，这就像 Google，我应该只输入一个问题，然后得到一个答案，然后就完事了。”我认为现实是，这项技术实际上设计得更像一个人。它确实是一个伴侣和一个副驾驶，无论你如何描述它。
And so having a bit of a mindset shift and actually helping folks understand that, I think is really critical. We've been really intentional about partnering with folks from the outside to help our executives especially on that journey. One analogy we've used is, you know, if you're going to Costa Rica with your family, you could go to Google and ask for some recommendations or you could just prompt Claude or Chat PT and say, "Hey, you are the Costa Rica prime you know head of tourism. I'm going to Costa Rica with my family of four. My kids are 10 and 12. One likes surfing. The other one likes, you know, wildlife. Can you come up with a customized itinerary?"
因此，我认为进行一点思维转变，并实际帮助人们理解这一点，是至关重要的。我们一直非常有意地与外部人士合作，尤其是在这一旅程中帮助我们的高管。我们使用的一个比喻是，你知道，如果你带着家人去哥斯达黎加，你可以去 Google 寻求一些推荐，或者你可以直接向 Claude 或 ChatGPT 提问说：“嘿，你是哥斯达黎加旅游局的负责人。我将和我的四口之家去哥斯达黎加。我的孩子分别是10岁和12岁。一个喜欢冲浪。另一个喜欢野生动物。你能为我制定一个定制行程吗？”
And so that's that's a very different sort of paradigm between those two tools. And frankly, for most folks that, you know, first start to engage, that's kind of where they start. But we think it's really important to kind of help people on their journey. And if I could have, if we could go back, we would maybe even started that even sooner. So,
所以这两种工具之间的范式非常不同。坦率地说，对于大多数刚开始接触的人来说，他们就是从那里开始的。但我们认为，帮助人们走上他们的旅程非常重要。如果可以的话，如果能回到过去，我们甚至可能会更早开始。所以，
Jonathan Pelosi: Thank you, Don. How about you, Lloyd?
Jonathan Pelosi: 谢谢你，Don。Lloyd，你呢？
Lloyd: Yeah, I mean, I think I'd echo Frod. We are running at this very fast at HG, and I'd like to think we're we're sort of ahead of the curve. We were building our first AI products back in 2023, but if I could say one thing, it would be run even faster towards this. Just the exponential rate of improvement of models and the ecosystem that's developing around that is frankly unprecedented. So yeah, I would have said, you know, run even harder even faster Michael.
Lloyd: 是的，我的意思是，我想我会附和 Frod 的观点。我们在 HG 正在非常迅速地推进这项工作，我愿意认为我们走在了前面。我们早在2023年就开始构建我们的第一个 AI 产品，但如果我只能说一件事，那就是要更快地朝着这个方向前进。模型改进的指数级速度以及围绕其发展的生态系统，坦率地说，是前所未有的。所以，是的，我本会说，你知道，更努力、更快地跑，Michael。
Michael: So we're pretty relentlessly data-driven and one thing we do is we look at the usage intensity across the firm and what you see is a lognormal curve something like this and the theoretical underpinning of that is it it describes processes that are roughly the more you use it, the more you use it. So they're multiplicative.
Michael: 我们是非常数据驱动的，我们做的一件事是观察公司内部的使用强度，你会看到一个对数正态曲线，大致是这样的，其理论基础是它描述的过程大致是“你用得越多，你就会用得越多”。所以它们是乘法关系。
And if you combine that with this bottom-up view, what you want to do is just encourage people to take more bites of the apple. So here are two things that I do to to help make that happen. One, at a lot of my team meetings, I just make people do a lightning round of some strange AI thing you tried in the last week. It worked, it didn't work, but you know, 30 seconds each share with the team. And two, and this plays off something you said, the route to comfort and learning about how to use these at work doesn't have to only run through work.
如果你将这一点与自下而上的观点结合起来，你想要做的就是鼓励人们更多地尝试。所以，我有两件事可以帮助实现这一点。第一，在我很多团队会议上，我都会让大家快速分享上周尝试过的某个奇怪的 AI 应用。它成功了也好，失败了也好，但每个人30秒与团队分享。第二，这与你刚才说的有关，在工作中熟悉和学习如何使用这些工具的途径不一定只通过工作。
And often people are more comfortable taking risk in their personal life. It's not as potentially embarrassing at least in their minds. And so encouraging people to experiment there I think can pay off as much or more than encouraging them to take those first jumps in the workplace.
人们在个人生活中冒险往往更自在。至少在他们看来，这不那么令人尴尬。所以，我认为鼓励人们在那里进行实验，其回报可能与鼓励他们在工作场所迈出第一步一样多，甚至更多。
Jonathan Pelosi: Yeah. No, I I could not agree more and that's a huge huge I think benefit to driving adoption for everyone here today and dialed in. If I had to recap what we've talked about today is enable. So give your employees access to one of these tools. Train. You don't have to be a master trainer or you know a buy a million-dollar training program. You can work with some great partners of which we've t touched on a few of them today and you can use a Claude to help you build a training program to give them baseline training. I'm not talking about 100 hours here. Baseline how to prompt how to interact with these tools.
Jonathan Pelosi: 是的。不，我完全同意，这对于推动今天在场和在线的所有人采用 AI 来说，我认为是一个巨大的好处。如果我要总结我们今天讨论的内容，那就是“赋能”。所以，让你的员工能够接触到这些工具之一。培训。你不必成为一名资深培训师，也不必购买一个百万美元的培训计划。你可以与一些优秀的合作伙伴合作，我们今天已经提到了其中一些，你也可以使用 Claude 来帮助你构建一个培训计划，为他们提供基础培训。我这里说的不是100小时的培训。而是如何进行提示，如何与这些工具交互的基础知识。
Measure. There's to Michael's point now. I think he's doing it in a really great natural way. Let's do a speed round at Anthropic. You can imagine it's like we are monitoring in a way are you using these tools to do a better job and like you're actually tracking that because whether or not you use them it's making sure you hold your employees accountable for building that muscle and getting used to using it. I'll share a personal anecdote in closing and Michael touched on this. Sometimes folks are more comfortable using this technology in their personal lives to see what's capable of them work.
衡量。现在回到 Michael 的观点。我认为他正在以一种非常棒的自然方式进行。让我们在 Anthropic 进行一次快速问答环节。你可以想象，我们某种程度上正在监控你是否正在使用这些工具来做得更好，并且你实际上正在跟踪这一点，因为无论你是否使用它们，都要确保你让员工对培养这种能力并习惯使用它负责。最后我将分享一个个人轶事，Michael 提到了这一点。有时，人们在个人生活中使用这项技术会更自在，以了解它能为他们的工作带来什么。
I have a 2-year-old son who has a lot of medical concerns and in learning to navigate what his challenges were and what we could do for him. The hospital sent over 1100 pages of notes from the NICU (Neonatal Intensive Care Unit: 新生儿重症监护室) from our neurologists and it's very hard to make sense of all of it. So even when you talk to different doctors, it's they all give you different answers.
我有一个两岁的儿子，他有很多医疗问题，在了解他的挑战以及我们能为他做什么的过程中。医院寄来了我们神经科医生在新生儿重症监护室（NICU）的1100多页记录，所有这些都很难理解。所以即使你咨询不同的医生，他们也会给出不同的答案。
And I took all of this and I uploaded to Claude. I go, "Help me understand what's happening here." And I took that timeline and I'm not suggesting Claude's a doctor by any means. I just took that timeline, 1100 pages by the way, PDFs, and I took it back to our doctor. I'm like, "Does this seem right? Help me understand what happened here and what I can do about it." And literally the doctor's jaw dropped. He's like, "How did you come up with this?" And I'm not sharing that story because look how great Claude is. I'm sharing that story because in moments like that when it has such a meaningful impact on your life personally, you can see my inclination irrespective of working in a company like Anthropic to try using this technology in work goes up dramatically.
我把所有这些都上传到 Claude。我说：“帮我理解这里发生了什么。”我拿着那个时间线，我绝不是说 Claude 是医生。我只是拿着那个时间线，顺便说一句，是1100页的 PDF 文件，然后我把它带回给我们的医生。我说：“这看起来对吗？帮我理解这里发生了什么，我能做些什么。”医生简直惊掉了下巴。他说：“你是怎么弄出这个的？”我分享这个故事并不是为了炫耀 Claude 有多棒。我分享这个故事是因为在那些对你的个人生活产生如此有意义影响的时刻，你会发现，无论我是否在像 Anthropic 这样的公司工作，我尝试在工作中运用这项技术的意愿都会大大增加。
So as a takeaway, if you don't have one of these apps, these AI apps in your phone today, I'm going to say step one, use it. Set a goal for yourself. Use it. I don't care if you're taking a photo of a tree or you're analyzing life insurance statements or policies. Use it a couple times a day. Build the muscle and in turn you'll be better equipped to do that with your own employees. So, I want to say a big thank you to our panel. I really, really appreciate it. Thank you for everyone who dialed in and joining us today. And for those seated, stay put because we'll have a chance for questions.
所以作为总结，如果你今天手机里没有这些 AI 应用之一，我建议第一步就是使用它。给自己设定一个目标。使用它。我不在乎你是在给一棵树拍照，还是在分析人寿保险单或保单。每天使用几次。培养这种习惯，反过来，你将能更好地指导自己的员工这样做。所以，我要对我们的专家小组说声非常感谢。我真的非常非常感激。感谢所有在线参与和今天加入我们的人。对于在座的各位，请稍候，因为我们还将有机会提问。