Matt Garman：为 AI 智能体重建云服务

简要描述Brief Description

为 AI 智能体构建云平台 | AWS 首席执行官 Matt GarmanBuilding the Cloud for AI Agents | AWS CEO Matt Garman

目录Table of Contents

• 简介
  Intro
• 1690 亿美元，增长 37%
  $169B, growing 37%
• 为什么初创企业是经济的命脉
  Why startups are the lifeblood
• 重新思考面向智能体的云
  Rethinking cloud for agents
• GPU 分配问题
  The GPU allocation problem
• 人工智能资本支出是泡沫吗？
  Is the AI CapEx a bubble?
• 澄清关于数据中心的误区
  Clearing up data center myths
• The Graviton and Trainium bet
• 企业在智能体应用上遇到的瓶颈
  Where enterprises are stuck on agents
• AI risk, Hugging Face, and Continuum

简介Intro

智能体工作流在 AWS 上的表现往往优于其他任何平台。计算沙箱、网关，以及智能体权限与人员权限的区分。这些都是我们已经构建、正在构建并积极思考的领域。Agentic workflows tend to perform better on AWS than anywhere else. Compute sandboxes, gateways, agent permissions versus people. A lot of those are things that we have built and are building and thinking actively about.

主持人：顶尖的前沿实验室几乎把所有可用的 GPU 都抢光了。与此同时，你也想扶持那些有望成为明日企业的新兴公司。你是如何考虑平衡这两者的？Host: The top frontier labs gobble up every available GPUs. At the same time, you want to promote newer companies that are going to become the enterprises of tomorrow. How are you thinking about balancing?

嘉宾：从 AWS 上线之初，初创企业就一直是我们核心业务的命脉。这些创新者站在技术前沿，探索技术的可能性。我们有意为初创企业预留可用容量。我们最近宣布，未来几年将购买 200 万块 NVIDIA GPU。Guest: From the very beginning of when we launched AWS, startups have been the lifeblood of the core of what we do. These are the innovators that are at the edge of technology understanding what's possible. We're very intentional about keeping capacity available for the startups. We recently announced we're going to be buying 2 million NVIDIA GPUs over the next couple of years.

主持人：你的资本开支是多少，200 还是……Host: Your capex is what 200 or

嘉宾：2026 年是 2200 亿美元，呃，我们预计短期内不会放缓，因为需求实在太庞大了。Guest: $220 billion uh for 26 and we don't anticipate slowing down anytime soon because the demand is just massive.

主持人：围绕 AI 扩展风险和 Hugging Face 攻击的诸多争论中，CEO 们都在向你询问些什么？Host: With all the debate around AI extension risk and the hugging face attack, what are CEOs asking you about all these things?

1690亿美元，增长37%$169B, growing 37%

欢迎来到 podmat。我们正身处一个多么特别的时代啊。所以，我有很多话题想和你聊聊。Welcome to the podmat. What a time we're living in. So I have a lot of topics to talk to you about.

嘉宾：太棒了。感谢邀请我来。我很兴奋。Guest: Awesome. Thanks for having me. I'm excited.

主持人：是啊，是啊，当然。[哼笑] 那我们就从头开始吧，对吧？你是 EC2 的第一任总经理。Host: Yeah. Yeah. Absolutely. [snorts] So let's start actually right from the beginning, right? So you are the first GM for EC2.

嘉宾：嗯。Guest: Mhm.

主持人：那是2006年，对吧？而如今，你们的营收大概有1600亿到1700亿美元了。Host: And that was 2006, right? And today you guys are what 160 170 billion in revenue.

嘉宾：是的。大约1690亿到1700亿。没错。Guest: Yeah. About 169 170 billion. Yep.

主持人：是的。增长 30Host: Yeah. growing 30

访客：37 37%。Guest: 37 37%.

主持人：169时为37%。Host: That's 37% at 169.

嘉宾：是啊，这真是太疯狂了。Guest: Yeah, it's that's crazy.

主持人：有一大堆 o，而且这确实……是的，这确实挺有意思的。你知道，我们从第一天、第一天、第一天起就在那儿，当时我们有了第一美元的收入，不过，嗯……Host: There's a ton of o and it's Yeah, it's it's it's interesting to think about it from, you know, we're there in day day day one when we had like, you know, the first dollar of revenue, but um

嘉宾：是啊，有意思的是，大家都知道，我们现在仍处于业务发展和客户机遇的早期阶段。嗯，目前大多数工作负载——你知道——仍然有大量工作负载部署在本地，而人们每天进行的计算量都比前一天更多。因此，你能看到 AI 带来的助力，也能看到迁移到云端带来的助力。嗯，你知道，业务增长非常快，能参与其中真是特别有趣。Guest: yeah, and it's, you know, the interesting thing is we're still at the early stages of what the business can be and what the opportunity is for customers. um most workloads still, you know, there's a huge amount of workloads still that live on prem today and the amount of compute um that people are doing every single day is is more than it was the day before. And so you see the tailwind from AI, you see the tailwind from migration into the cloud and um you know the the business has grown really fast and it's it's been a super fun part to be a um thing to be a part of.

主持人：是啊，确实如此。我是说，很长一段时间里，人们都会为此撰写历史书和商业书籍。我想聊聊本地部署，这事儿有点让我摸不着头脑。显然，我曾尽最大努力让他们长期留在本地部署环境中。Host: Yeah, it is. It is. I mean they'll be writing uh history books about this and business books about this for a long time to come. I want to touch on the on prem that sort of boggles my mind. I obviously did my best to keep them there for a long time.

嘉宾：你过去在本地部署环境中搭建了很多东西，现在正试着把它们全部迁移到 AWS。Guest: You built a lot of stuff on prem back in the day of trying to now get all of that to move into AWS.

主持人：是的，这个我们可以稍后再聊。不过，回想一下 EC2 早期的时候，你们最初显然是从向初创公司销售起步的。如今再看 AWS 云服务的发展历程，回顾这一路的演变，最让你印象深刻的是什么？Host: Yeah. So we can talk about that later. But uh so if you think about EC2 in the early days and you got your start with obviously selling to startups and and today the AWS cloud service I mean as you reflect on the evolution what's what stands out.

嘉宾：是的。Guest: Yeah.

主持人：第一部分，然后第二部分，我们会谈谈你为初创企业提供服务的方式发生了怎样的变化。Host: Part one and then part two we'll talk about how this nature of how you serve startups has changed.

为什么初创企业是生命之源Why startups are the lifeblood

嘉宾：当然。嗯，就像你刚才说的，其实挺有意思的。2005 年，我曾在 AWS 实习，那时它最初还只是一个内部项目。这是我商学院的实习项目，我的任务是分析我们认为 AWS 对哪些群体最有吸引力。答案是初创公司，这大概并不令人意外。自 AWS 推出之初，初创公司就一直是我们业务核心的命脉，原因有很多。其中之一就是 AWS 为初创公司提供的价值主张极具吸引力。Guest: Sure. Well like you said actually it's funny. So I actually interned for um AWS in 2005 when we were first kind of was an internal project. It was my business school internship and my project was actually to come up with uh an analysis of who we thought AWS would be most interesting to and uh the the answer was startups like probably not surprisingly and uh and so you know from the very beginning of when we launched AWS startups have been the lifeblood of of the core of what we do for for a number of reasons. One is the value proposition is just so attractive what AWS provides to startups.

主持人：呃，而且我们花了很多时间和精力，确保自己能成为初创公司的优秀合作伙伴，不只是为他们提供基础设施，还会给他们建议，帮助他们让公司运转起来，并思考如何设计架构，才能在未来实现规模化，以及我们为初创公司做的许多其他事情。但我们也认为，这对我们来说也是一门好生意，因为……Host: Um and and we we spend a lot of time and effort making sure that we're great partners to the startups helping them not just provide infrastructure but also advice and how to get your company up and running and how to think about your architecture so it'll scale eventually and and a whole bunch of things that we do for the startups. But we also think that for us it's just good business cuz

嘉宾：今天的初创公司就是明天的企业，而且，而且你知道，这个数字并不精确，但是……Guest: the startups today are the enterprises of tomorrow and and you know it's it's an imprecise number but

主持人：今天我们估计，你知道，AWS 的收入可能有 30% 到 40% 来自那些公司——嗯，它们在 AWS 的发展历程中曾经是初创公司。所以，嗯，我们很高兴看到这些公司随着时间不断成长、规模越来越大，最终成为企业级公司。嗯，这就是为什么我们如此重视初创公司，也为什么我们如此关注那些刚刚起步的、你知道，只有两个人在车库里创业的公司。嗯，我们这么做不只是因为商业成果，还因为我们能从它们身上学习。它们是处于技术前沿的创新者，了解哪些事情是可能实现的；它们会推动我们改进服务，告诉我们它们还需要什么、什么能帮助它们加快发展、什么能帮助它们实现目标。Host: today we estimate that you know maybe 30 40% of of AWS revenue comes from companies that well there's a one-time you know startups in in AWS's lifetime. So and it's uh you know and so it's fun to see for us to see the companies grow over time and and to get bigger and become enterprises effectively. Um, and so that's why we invest so much in startups and why we pay so much attention to the the brand new, you know, two people in a garage type startups. And um, and and not um, and and not just because of the business outcomes, but also because that's who we learn from. These are the these are the innovators that are at the edge of technology, understanding what's possible, uh, pushing our services to say what they'd want more of, what could help them go faster, what could help them achieve their uh, outcomes.

很多时候，他们推动的力度比银行、医疗保健公司或政府都大。真正不断突破边界的是初创公司。这确实有助于我们做得更好，并确保我们走在这股浪潮的前面：五年后银行会想要的某种技术或能力，初创公司今天就已经想要了。And a lot of times they're pushing more than the the banks or the healthcare companies or the governments. The startups are the ones that are pushing that envelope. And it really helps us to be better and make sure that we're ahead of that wave where the banks are going to want some tech some capability 5 years down the road that startups want today.

嘉宾：是的，是的。嗯，跟那时候相比，现在这些初创公司有什么变化？它们想从你这里得到什么？显然……Guest: Yeah. Yeah. And uh compared from then to now what have the how have the startups changed and what they want from you? Obviously

主持人：他们都需要大量 GPU，我们可以聊聊这个，不过除此之外……Host: they all want a lot of GPUs and we can talk about that but besides that

嘉宾：嗯，我想说，呃，有几件事发生了变化。其一，嗯，你知道，创业公司起步时的规模比我们刚开始时小得多，对吧？他们可能拿到了 1000 万美元的融资，有一个应用创意，然后就这么慢慢迭代。现在呢，你知道，从第一天起估值就达到 10 亿美元，拿着 2 亿美元的融资。就好像，一个团队加上一个想法，突然之间就值 10 亿美元了，所以，嗯，你知道，我觉得……Guest: well I will say uh there's a couple of things that have changed. One is um you know startups started at a much smaller size than than when when when we first started right they they might have gotten $10 million of funding and they had a app idea that they were kind of slowly iterating on. Now it's like you know from day one they're valued at a billion dollars they have $200 million of funding. It's like it's a, you know, it's a a a team and an idea and all of a sudden they're worth a billion dollars which um so it's you know I think the

主持人：他们只是从我们的办公室直接去你们的办公室。Host: they just go from our offices to your offices.

嘉宾：没错，没错。嗯，你知道，我觉得这些想法的规模、体量和雄心都意味着需要更多资金。它们一开始就更大。嗯，显然成本也更高，对吧？比如说，要去训练一个模型，或者做现在很多人在做的事情，成本就更高。嗯，这是第一点，就是它们起步时的规模真的很大。嗯，不过我想第二点是，有些事情并没有改变，对吧？他们其实还是会思考：好，等我扩大规模之后，该如何考虑架构？嗯，该如何考虑安全性？该如何考虑性能？该如何确保具备所需的所有能力？该如何搭建我的 IAM 环境，才能确保员工超过三人之后，一切还能正常运作并扩展？这也是他们很多时候喜欢 AWS，而不是直接去用 Neocloud 之类服务的原因。Guest: That's right. That's right. Um uh and you know like the the size and scale and ambition of the ideas I think requires more capital. They're they're bigger to start with. Um they're obviously more expensive too, right? To you know to um uh to go after kind of a training a model or doing something that that a lot of the folks are doing today. Um, so that's number one, which is just the size that they're that that they start from is is really big. Um, but I would say number two is uh some things haven't changed, right? They they actually are still thinking about, okay, once I scale, how do I think about an architecture? Um, how do I think about um security? How do I think about performance? How do I think about having all the capabilities that I need? How do I think about setting up my IM setup so that when I have more than three employees, this thing is going to work and scale, which is a lot of times why they like AWS as opposed to just, you know, going to a Neocloud or something like that.

他们实际上还需要培训所带来的所有其他安全性和能力。我认为，这一点没有改变，完全还是一样的。我认为，如今规模扩张的速度确实有所不同。我还认为，我们越来越常看到一些团队希望云不仅适合人使用，也能很好地配合智能体工作。我认为，这正是我们投入了大量时间的地方：你会如何思考如何实现广泛扩展？如何看待性能？如何设计一个定义良好的 API 接口，让智能体能够轻松地遍历并使用它？这也是我们双方都认为自己天生就很擅长的事情，但我们实际上也在这方面加倍投入，以确保能够实现这样的能力：比如，如何在 3 秒内启动一个数据库，并真正具备一些让智能体兴奋的能力？They actually need the all of those other security and and um and capabilities that that come around from um from training. Um and so I think that's that's you know that's something that hasn't changed and and is exactly the same. I think the the scale at which the ramp up um is definitely different today. I also think increasingly we're seeing um teams that want a cloud that is great to work with agents and not just with people. Um and I think that's where we've spent a lot of time how do you think about you know exactly how do you think about broad scale? How do you think about performance? How do you think about um you know an interface that is you know a well- definfined API interface that agents can actually easily traverse and work across. Um, and so that's also something that we both I think we were naturally set up to do well, but that we've actually doubled down on to ensure things like, you know, how can you start a database in 3 seconds and and and and really get to um some capabilities that agents are excited about?

主持人：嗯，我本来应该查一下的，但最近没查。你们有没有推出专门面向智能体或智能体开发者的新服务？我想说，我们做的是优化了一些现有服务，让它们既能服务于人，也能服务于智能体。因为你知道，有时候……所以答案是，我们确实有一些专为构建智能体而设计的服务，比如 Agent Core、Bedrock 以及类似的组件。不过，如果我们只看像 S3 这样的底层组件——绝大多数公司都在这里存储数据并建立数据链接——事实证明，人和智能体的很多使用场景都很相似。但你还需要……我们目前正在预览版或测试版中提供的一项功能是……Host: Um, I should have looked, but I haven't lately. Have you introduced any specific new services that are explicitly targeted at agents or people building agents? I I'd say what we've done is we've optimized some existing services that that so that they can both work for people and agents and it it because you know sometimes um and and so the answer is there we definitely have some services designed for building agents. So we have things like agent core and um and bedrock and pieces like that. But when we look at just taking an underlying component like S3, right, which is where the vast majority of companies store their data and have their data links. Um it turns out like a lot of the use cases for people and agents are similar, but you want to have um and one of the things that we have in preview right now or in in beta.

它叫 AWS context，它可以让你构建一个上下文层，让智能体更轻松地找到分散在各种数据连接中的所有所需数据。嗯，无论你的数据存储在 Aurora、S3，还是 AWS 的其他地方，都可以借此构建这样一个上下文层。人们未必会以这种方式访问数据，但智能体很乐于跨越许多不同的数据源进行访问。我们正在构建一些这样的服务，但在大多数情况下，你还会关心延迟表现如何、吞吐量表现如何，以及如何确保底层引擎足够快速且可扩展。嗯，他们其实非常关注尾延迟，这一点很有意思。嗯，人们不一定总是在意 S3 的 P999 延迟，但智能体确实在意，也会因此受阻，所以……It's called AWS context that allows you to build a context layer so that agents can actually more easily find all of the data they want across your various data links. Um and so whether you have your data stored in um Aurora or you have it in S3 or you have it in somewhere else in AWS can kind of build this context layer where when people aren't necessarily going to access data in that way but agents actually are happy to go across lots of those different things. And so there are some services that we're building like that but for the most part you also care about what does latency look like, what does throughput look like, how do you make sure that the underlying engine is actually fast, scalable, um that they actually care a lot about tail latencies which is interesting. um where people are not they don't always care about the the P999 S3 latency like agents do care and get blocked by that and so

嘉宾：嗯，这就是我们长期以来一直关注的事情。嗯，从性能角度来看，这是最突出的优势之一，这也是为什么智能体工作流在 AWS 上的表现往往比在其他任何地方都好。是的，显然我们看到很多公司刚起步，当然，他们最常说的一句话就是：智能体正在为他们编写所有代码，对吧？Guest: um that's the thing that we've cared about for a long time and um from a performance perspective it's one of the things that um that really popped that that's why um aentic workflows tend to perform better on AWS than anywhere else. Yeah, obviously we see um a lot of companies starting out and the very common refrain of course is that uh agents are writing all the code for them, right?

主持人：而代理正在选择数据库，并且…Host: And agents are selecting the databases and

嘉宾：邮件服务器以及所有你能想到的东西，对吧？所以，显然他们也读了文档，那么大家有没有认真思考过 AWS 上那些——说它们是所谓的“遗留服务”感觉挺奇怪的——已经存在十年的服务该如何重新架构？Guest: the email servers and everything that you can name it, right? So has there been a lot of uh thinking that and obviously they read the documentation uh has there been a lot of thinking on the AWS like your feels funny to say quote unquote legacy services that have been around for a decade on how do you rearchitect them.

主持人：我想，是的，我觉得确实有一些事情我们已经考虑过了。实际上，我认为核心底层构建模块的状态已经非常好了。我觉得我们还在考虑一个易用性层，看看如何让它更容易上手，尤其是我们姑且称之为最简单的使用场景：有人很快写了个应用，然后让它部署。我们发现，绝大多数客户都会告诉他们的编程代理——无论是 Kira、Claude 还是 Codeex——他们会说：“我想基于 AWS 来构建，这是我的凭证，这是我的相关信息。这是我想要的部署方式。”然后这些代理就会出色地完成任务。不过，也有一些使用场景是，你还没有使用任何云服务，只是想试试某个东西。Host: I think there so yes I think there are some things that we've thought about actually the core underlying building blocks I think are in in really good shape. I think there's a usability layer um that we're thinking about of how we make um easier to use for we'll call it like the very simplest the the the use case you call out where somebody's just coding up an app really quickly and they ask it to deploy. We we find the vast majority of our customers will go and tell their coding agent whether it's Kira, whether it's Claude, whether it's uh Codeex, they'll they'll say like, I want to build on AWS, here's my credentials, here's my stuff. This is how I want to deploy it. And then the agents are great and they go do it. There are use cases though where you're just you're already not on a cloud, you're just trying something.

你说的是部署。坦率地说，很多时候我们会把这件事交给一些合作伙伴，他们在上层提供了更易用的界面。You say deploy. And frankly, a lot of times that'll go to some of our partners that have a an easier to use layer on top.

嘉宾：嗯，我们也很喜欢这个，顺便说一句。我们也很喜欢在这些方面与我们合作的伙伴。嗯，不过我确实觉得，我们正在做的一些事情，对于刚入门、还没有 AWS 账号、也还没有设置 IAM 的人来说，确实有帮助。如今，或者说几个月前，开通账号要难得多，对吧？这 20 年来一直都是这样：你得定义自己的 VPC，定义 IAM 角色，做各种类似的事情。这些事情等你发展到一定规模后其实都非常重要。嗯，我们从客户那里听到的是，这确实是个艰难的取舍，因为他们知道几个月或几年后会需要这些东西，但眼下他们只想使用服务，不想操心这些。Guest: Um which we which we love, by the way, too. and we love um our partners on on those fronts too. Um but I do think that there um there are some things that we're doing where if you're brand new, you don't already have an AWS, you already haven't set up your IM. Um today or we'll say a couple of months ago, it was much harder to start up an account, right? You had to it's been like this for 20 years, but you have to define your VPCs, you have to define your IM roles, all these kind of things, which are actually super important things once you get to be large. Um, and what we hear from customers is like it was a hard trade-off because they know that they're going to want those n months or years down the line, but for now they just kind of want to use the services, not worry about that.

主持人：嗯，所以我们实际上已经推出了这样一项服务：如果你今天去创建一个新的 AWS 账户，Host: Um, and so what we've actually launched is if you go create a new AWS account today,

嘉宾：我们正在逐步推出这项功能。我不确定现在是否已经全面上线了。Guest: and we're slowly rolling this out. I don't know if it's actually full all the way out yet now,

主持人：但是，嗯，你不必做那些事。你不必提供信用卡。你可以用 Gmail 账号注册。嗯，所有这些事情都会在后台默认处理好，不到 30 秒你就能开始使用，而且可以在一个完整的 AWS 账户中运行。对于这类系统来说，你知道，这更符合智能体想要实现的方式，因为它们不想经历所有那些设置 VPC 之类的事情……Host: but um, you don't have to do those things. You don't have to give it a credit card. You can sign up with your Gmail account. um you get to just all of those things are handled in default behind the scenes and within less than 30 seconds you're up and running and can be and can be operating in AW full AWS account which is you know for those type of systems much more what the agents want to be able to do because they don't want to have to go through all that setting up your VPCs and your

嘉宾：嗯，呃，还有诸如此类的功能。所以我们在某些方面提升了易用性，这让我们非常兴奋。我也看到客户对此给出了非常积极的反馈，今后我们还会继续做更多这样的改进。Guest: um uh and and pieces like that. So there are some things where we're adding kind of some ease of use to that which we're we're quite excited about and I've seen some really positive feedback from customers on and and we'll keep doing more things like that over time.

主持人：还有，我再补充一点，它很棒的一点是，这并不是一个功能简化的账户，让你之后还得迁移。如果你基本上是说：“好吧，现在我确实想扩大规模，或者发展一个组织，想对某些方面进行微调”，你可以轻松地直接开始——你已经在一个真正的 AWS 账户里了，之后需要时就可以去做所有这些事情，因此以后不必迁移或搬迁。这也是我们真正认真思考的难题之一：如何让客户不必为此做选择，而是轻松上手，逐步使用丰富的功能。我们知道，客户和初创公司在开始扩大规模时都会需要这些功能。Host: And I and just one more thing that which is the the the great part about that though is it's not like a a simplistic account and then you have to migrate. If you basically say okay now I actually do want to scale into the or I want to develop an organization. I want to go and and kind of fine-tune some of the things. you can easily come just you're you're already in a real AWS account and you can actually then go and do all of those things later when you need them and so there's no migration or move later and so that's part of the hard work that we really think about which is how do we make it not a choice for the customer but an easy on-ramp into the depth of features that we know customers um and startups are going to want when they start scaling.

嘉宾：是的。是的。明白了。嗯……Guest: Yeah. Yeah. Got it. And um

为智能体重新思考云计算Rethinking cloud for agents

随着代理人逐渐接手，有哪些事情最难适应？我是说，你已经闯出了名堂。what has been some of the hardest things to accommodate as agents have taken over? I mean you made a name.

主持人：你们之所以能成为如今这样的现象级存在，是因为你们服务了开发者，对吧？Host: This is the way you guys became the phenomenon that you became was serving developers, right?

嘉宾：嗯，然后，呃，基础设施团队——现在开发人员被智能体取代了，很快基础设施团队也会被智能体取代。Guest: Um and then uh infra teams u now developers are substituted by agents and pretty soon infra teams are being substituted by agents.

主持人：是的。作为全球最大的服务提供商，你们遇到过的一些最棘手的事情是什么？Host: Yeah. Are there what has been some of the hardest things for you as the largest service provider in the world

主持人：如何应对这种转变？Host: to handle that transition?

嘉宾：嗯，你知道，我觉得，呃，就像我说的，我们的大部分基础设施过去是……现在也是……Guest: Well, I you know look I think um like I said much of our infrastructure was is

主持人：对，配置得相当完善，足以应对这种规模，这很好。我觉得有几件事挺有意思的，我认为这是一些有趣的范式转变。你可以说，我们的一些系统……我不会说它们过度设计了，不过举个例子，很多智能体想创建一个数据库，做一点工作，然后就让数据库消失。你真的需要这个数据库具备五个九的持久性吗？没错。对。所以有些地方我们需要重新思考。比如，创建 Aurora 数据库并将其用作生产数据库时，你确实希望数据具备五个九的持久性，你知道，你需要持久性、可用性，以及所有这些东西。但对于智能体的使用场景来说，考虑到我们的需求，这可以说——或者也许不用说——有些过度设计了。Host: yeah pretty well set up to handle the scale um which is good. I I think there's been a couple of things which are interesting which are I think interesting paradigm shifts where um you could argue that some of our systems are um I wouldn't say overengineered but um as an example many agents want to create a database uh do a little bit of work and then have the database go away. You really need that database to have 59s of durability. Exactly. Right. And so there's some things that we rethink there where when you're creating an Aurora database like as your production database like you do want five ends of data, you know, you want you you need durability, you want availability, you want all of those things. And for the agent use case, that's that's arguably or maybe not arguably like overengineered for for what we need.

所以，你知道（清了清嗓子），这很难。我们，嗯，我们有这么一种信念：我们其实不太想提供那种不持久、而且会引发问题的选项，因为你永远不太清楚创建的数据库是要长期保留，还是只存在很短一段时间。所以，我们正努力思考，如何兼顾这两方面：既能快速创建并随时丢弃，不会浪费太多资源；同时，如果你确实想保留它，它也可以持久存在很长时间，甚至发展成一个大型生产数据库。这些都是我们在积极权衡的问题：当我们思考传统意义上“这将是我的生产系统”这类能力，如何与代理想要使用的基础设施更具临时性的特点相匹配时，就会考虑到这些取舍。And so, you [clears throat] know, it's hard. We don't um and we kind of have this this uh um a belief that we don't we don't really want to have like a non-durable option that's that's going to cause problems either cuz you never really quite know if the database that's created is wants to stay around for a long time or a short amount of time. And so we're we're um trying to do the hard work to think about how do you accomplish both of those things where you can create it quickly and throw it away and you don't really waste a lot of resources. But um but um but if you do want it and it can be durable and stay for a long time, it actually grow into a big production database. So those are some trade-offs that we think about actively as we think about how does the the the more traditional kind of this is going to be my production system kind of capabilities um match with uh with with some of the more transient nature of the infrastructure that agents uh want to use.

嗯，所以我觉得这是其中一个，不过还有很多。我觉得，嗯，创建持久资源的规模、速度和延迟是另一个有意思的问题。嗯，你知道，我觉得我们也一直在积极思考另一个问题：智能体会不会需要一些我们以前确实用不上的新构建模块。没错。比如计算沙箱、嗯，呃，网关、嗯，智能体权限与人员权限或服务角色权限的区别。我们已经构建、正在构建并积极思考其中很多东西，因为它们都是全新的构建模块。并不是以不同方式使用现有构建模块，而是全新的构建模块；很显然，你会希望智能体拥有不同的权限。你不会想直接把 Ragu 的权限交给它，然后任由它做任何你能做的事。Um, so that that's one I think, but there's there's there's a bunch. I think the um scale and speed and latency of creation of of durable resources is another one that's interesting. Um, you know, I think the other one that we actively think about too is just are there new building blocks that agents are going to want that we just didn't really need before. Exactly. And so compute sandboxes, um, uh, gateways, um, agent permissions versus people or or or service role permissions, um, a lot of those are things that we have built and are building and thinking actively about cuz they are just brand new building blocks. It's not using the existing building blocks differently, but it's brand new ones where pretty pretty clearly you want a different permissions for agents. You don't just want to give it Ragu's permissions and let it go do whatever you can do.

实际上，你可能想要的是那种有明确时间限制的短期权限，只够完成某项任务。你甚至可能不想给整个工具授予权限，而是想对代理能够执行的操作进行非常细粒度的授权。you actually want um you know very time box short-term permissions to just go do a task. You actually may not want to give permissions to a whole tool at all. You may want to get very fine grain commissions what an agent's able to do

嘉宾：来自沙箱，对吧？你实际上想要一个沙箱，嗯，呃，你想要……Guest: from a sandbox, right? You actually want a sandbox that um uh you want to

主持人：虚拟化也开始发挥作用了。Host: virtualization is having its act too.

嘉宾：没错。正是如此。它们确实不一样，但很相似。理念是一样的，不过你知道，怎样才能让它足够轻量，同时又考虑到……遗憾的是，我们在 Firecracker，也就是我们这种微型虚拟机上，已经做了很多工作。Guest: That's right. Exactly. That's and and they're they're different. It's similar. It's the same idea, but it's, you know, how do you have that lightweight and and think about um unfortunately we've done a lot of work with um firecracker or our kind of microVMs.

主持人：事实上，很多沙盒公司的初创企业都会使用 Fire。Host: In fact, a lot of the sandbox company startups use fire.

嘉宾：它们都使用了 Fire，这是我们大概 10 年前发明的。它确实是专门打造的——我的意思是，它并不是专门为智能体打造的，但实际上很适合智能体，因为你可以非常迅速地启动它们。它们有很好的安全边界，而且不像传统的大型虚拟机那样会带来大量虚拟化开销。所以，我觉得这些都是我们正在考虑的一些新构建模块，而且每天都有更多新东西涌现出来。是的。然后……Guest: They all use fire which which we invented 10 years ago maybe or something like that. And it's it's it's really purpose-built for I mean it's not wasn't purpose-built for agents, but it's actually quite good for agents because you can spin them up really rapidly. they have a great security boundary um and you don't have a lot of that virtualization overhead that you have from a traditional large VM. Um, so you know, I think those are some some new building blocks that we're thinking about and um and more emerge every single day, you know. Yeah. And

GPU 分配问题The GPU allocation problem

主持人：嗯，是的，Host: yeah,

嘉宾：我已经跟智能体们唠叨得够多了。之后再聊这个话题吧，不过它确实很有意思。Guest: so I've rattled enough into agents. I'll come back to it later, but it's a fascinating topic.

主持人：太酷了。Host: Super cool.

嘉宾：不过，呃，让我稍微换个话题，问一个我们所有初创公司都会面对、也是别人最常问我们的问题。Guest: But uh let me switch gears a little bit and ask about something that uh every one of our startups face and we get asked most frequently

主持人：也就是说，我们该怎么获得 GPU，对吧？Host: which is how can we get GPUs, right?

嘉宾：是啊。我是说，显然你们运营着规模庞大的 GPU 集群，而且每天都在扩建（倒吸一口气）。不过，顶尖的前沿实验室把所有能用的 GPU 都抢光了，那就祝他们算力越来越强吧。Guest: Yeah. I mean obviously you guys have running massive GPU farms and increasing it every day [gasps] but uh the top frontier labs gobble up every available GPUs more power to them.

主持人：你如何处理内部事务，同时又推动那些你所说的、将成为明日企业的新公司？Host: How do you deal with the internal at the same time you want to promote newer companies that are going to become the enterprises of tomorrow like you said

嘉宾：嗯，你是如何考虑平衡……的？Guest: um how are you thinking about balancing

主持人：是的，这是容量需求的问题，还有这些信用额度不高之类的小公司。所以，嗯，是的，这是个很好的问题。嗯，其中有几件事让这件事更具挑战性。首先，部署目前所有人都需要的计算资源所需的资本支出非常庞大，对吧？所以我们，呃，我认为……Host: yeah it's capacity needs and these small companies that don't have a lot of credit and whatnot. So, well, yeah, it's a great question. Um, and and a couple of these things make it more challenging to one is um the the capex expense needed to go deploy kind of all of the compute that everyone needs right now um is massive, right? And so we are uh I think

嘉宾：你的资本支出也差不多吗？200 还是别的什么？Guest: your capex the same as what 200 or something

主持人：2026 年是 2200 亿美元。嗯，我曾经看到过，这笔开支相当惊人，我的意思是，这可能是我们有史以来最大的单年开支，也许也是任何公司有史以来最大的单年开支。嗯，而且我们预计短期内不会放慢脚步，因为需求实在太大了。所以到某个时候，你会受到各种因素的限制：数据中心建得有多快、资本部署得有多快、内存和芯片等能以多快的速度到位。嗯，这些因素在不同阶段都会成为限制，无论是电力、数据中心、资本、内存还是芯片，它们都会在不同时期成为瓶颈。还有施工人员——负责建造建筑物的施工人员，就像现在，他们可是非常紧缺的。Host: $220 billion uh for 26. It's uh I I by by at one point I saw that it's you know it's it's a pretty I mean that is a larger expense than we've ever had maybe any company has ever had in a in a single year. Um and um and we don't anticipate slowing down anytime soon because the demand is just massive. And so at some point you're limited by how fast can you build data centers, how fast can you deploy capital, how how fast can you get memory and chips and all of those kind of things. Um and so all of those are at different points constraints that we're you know whether it's power data centers capital memory chips they're all kind of constraints at various times construction people to build buildings like you know construction people are are at a premium today.

嘉宾：嗯，所以这些事情，我们都非常努力地确保能够实现。嗯，至于我们能够部署的产能——虽然仍然非常庞大，但我们也知道还不够——我们会非常有意识地思考如何制定分配策略。所以，[嗤笑] 嗯，我们和大型前沿实验室合作得很好，比如 Anthropic、OpenAI、Meta，以及其他大型客户。这些机构都是我们的优质客户。Guest: Um and so all of those we you know we we work really hard to make sure happen. Um and then with the capacity that we are able to deploy, which is still a huge amount and and and not enough, we know, um we really think intentionally about what is that allocation strategy. And so, [snorts] um we're great partners with the the large frontier labs, the Anthropics and OpenAIs and and Meta and and other large customers. And so those folks are are really good customers of ours.

主持人：是的。Host: Yep.

嘉宾：嗯，我们想确保对它们进行投资。我们有大型企业客户，比如 Salesforce、JPMC，或者其他大型公司。他们需要的 GPU 数量较少，但有时需要加速器；有时想要 Trainium，有时想要英伟达 GPU。我们也想确保能够支持他们，而且我们在如何确保为初创公司预留算力方面非常有意识。我们实际采取的做法是分配资源，基本上会说：好，我们要留出一部分。你说得对，我们完全可以把手头每一块 GPU 或 AI 加速器都卖给那些大型前沿实验室，然后就此了事。Guest: And uh and we want to make sure that we invest in them. we have large enterprise customers whether it's uh sales forces or or JPMC's or you know other large companies that that have demand for fewer numbers of GPUs but or or accelerators sometimes they want tranium sometimes they want um uh Nvidia GPUs um but we want to make sure that we can support them as well and we're very intentional about how we make sure that we have capacity for startups and and so what we do is we actually do allocate and we basically say okay we're going to keep we could you're right we could sell every single um uh you know GPU or or AI accelerator we had to probably just the lab the big frontier labs and call it a day.

主持人：嗯，我们选择不这么做，因为我们实际上希望继续发展整个生态系统。他们会得到一个很大的数字，但我们希望继续支持……Host: Um we choose not to do that because we actually want to keep growing the the full ecosystem. They get a large number but but um we want to keep supporting

嘉宾：嗯，扩大客户群体，因为我们确实认为，这样整个生态系统对我们来说会更健康。这也能带来一些多元化，而且我们知道，这些公司未来都会发展壮大。嗯，所以我们尽力支持他们。嗯，我最近看到，我们最终收到的请求中，大约有 60% 我们都会以某种方式答应。有时候会晚一些，有时候是在不同的地区，有时候配置会和客户想要的稍有不同。嗯，但我们确实努力积极响应，尽可能多地调配资源。你知道，每一家初创公司都想要更多，所以我们一直在努力，确保能满足所有这些需求。嗯，不过你知道，这确实很难。就像很多人说的，这是个好问题。Guest: um a broader set of customers cuz we actually think that both the whole ecosystem be more healthy for us. There's some diversification but it's also just we know these are going to be big companies over time. Um and so we try to support them. Um I saw recently that we say you know yes in some way shape or form to something like 60% of the the requests we eventually get. Sometimes it's a little bit later sometimes it's in a different region. Sometimes it's a slightly different configuration than the customers are looking for. Um but we really try to lean in and and and try to allocate as much as we can and every single startup that you have wants more and so you know so we're working hard um to try to make sure that we have all of those. Um, but you know, it's it's hard. Like that's that's it's you know, like this is the as a lot of people say, it's a good problem to have.

呃，但这终究还是个问题。而且，嗯，我们会继续关注。你知道，我们最近宣布，未来几年我们将采购200万块英伟达GPU。也就是说，我们将获得海量算力。Uh, but it's it's a problem nonetheless. And um and we're you know, we continue to look at at um you know, we we recently announced we're going to be buying um you know, 2 million Nvidia GPUs over the next uh couple of years. like we're we're landing a massive amount of capacity

主持人：还有200万Host: and 2 million

嘉宾：200万，嗯，你知道，这是一大笔，而且是在接下来的几年里，嗯，不过，呃，谁知道这够不够呢？但到某个时候，我们也会受到其他组件的限制。不过，我们确实非常有意识地为初创公司预留可用容量。我知道容量不够确实很让人难受，但我们会继续努力。[叹气并倒吸一口气]Guest: 2 million um so you know it's a lot um and it's over the next couple years but um and uh and who knows if that's enough but that's at some point again we're limited by other other components as well um but it is we're we're very intentional about keeping capacity available for for the startups and I know it's it's painful to not have enough but we we keep pushing [sighs and gasps]

主持人：那你的资本开支是什么时候超过的？我是说，单看 AWS。我的意思是，亚马逊当时是一家规模更大的公司。Host: and when did uh your capex cross I mean pure AWS. I mean Amazon was a bigger company.

嘉宾：是的。Guest: Yeah.

主持人：比如说交叉销售，咱们就算一年 10 亿，或者 100 亿吧。Host: Cross like let's call it even 1 billion or 10 billion a year.

嘉宾：哦，我不太清楚。我得回头看看。我不确定这一点。不过，过去几年它确实大幅扩大了规模，嗯，幅度相当明显。Guest: Oh, I don't know. I'd have to go back and look. I'm not sure about that. But it it's definitely scaled up over the last couple of years. Um uh in a pretty meaningful way.

主持人：是的，AI建设投入确实让我们的资本开支大幅增加。所以我想说，过去大概3到4年里，我们的资本开支确实加速了不少。我不知道我们是什么时候突破10亿美元的，不过，既然现在已经是220了，我想我们在资本开支上已经投入了一段时间，而且公司一直很擅长为此提供资金支持。当然，AWS业务很不错，我们也愿意投资。Host: The the yeah the AI buildout has has definitely ramped our our capex spending. And so I'd say over the last, you know, call it 3 or 4 years, our our capex has definitely accelerated um pretty pretty meaningfully. Um I don't know when we crossed a billion, but um given we're 220 now is probably I mean we we've been spending capex for a while and the and the company has been really good about funding that um and obviously the AWS business is is a good one that we like to invest in.

人工智能资本支出是泡沫吗？Is the AI CapEx a bubble?

嘉宾：是的，是的，当然。嗯，呃，而且你知道，我觉得很长一段时间以来，实际上，嗯，我们，你知道，我们一直在需求出现之前就进行投资，而且……Guest: Yeah. Yeah, of course. And um uh and you know I think we were for the longest time actually um we you know we we were investing ahead of where the demand was and

主持人：嗯，你知道，我觉得最令人痛苦的事情之一是，随着 GPU 真正开始大规模部署，很多弹性很遗憾地消失了。嗯，所以希望在我们的核心计算、存储等方面，我们还能恢复到弹性依然存在的状态。不过，嗯，你知道，我们已经持续投入了相当长一段时间，而且我们对目前的投入感觉非常好。嗯，我想我有时会收到很多关于你们如何看待这笔投入的问题，比如你们是否担心泡沫之类的。我想说的是，你知道……Host: um you know I think one of the one of the most painful things is that with the real ramp of of GPUs like a lot of the elasticity has unfortunately you know kind of gone away. Um and so hopefully we'll get back to in our core compute and and storage and things that elasticity is still there and that but um but you know that um but but so we've been spending for for quite a bit of time and and we feel really good about the spend that we're making now. um which is you know I think I get lots of questions sometimes about how you feel about that spend and like are you nervous about bubble and other things like that and I will say you know

嘉宾：我们，呃，因为我们所处的位置，比如说，首先我们确实采取了多元化的策略，所以我们的产能不会全部集中在一个客户身上。我想你看看其中一些，无论是新云服务商，还是其他一些供应商……Guest: we um because of the position we have like one we do take this diversified approach and so not all of our capacity is bundled up in one customer and I think you go to a some of these whether they're neoclouds or some of the the other providers out there and

主持人：你知道，有时你会看到某一两个客户的浓度达到 30%、40%、50% 甚至 60%。Host: you know you'll see sometimes concentrations of 30 40 50 60% with one or two customers

嘉宾：显然，我们离那个程度还差得远。你知道，我们最高也就个位数的百分比，通常还不到这个水平。首先，我认为我们在某一个特定客户身上的风险要小得多；而且，我们提供了丰富的服务，比如 AWS，大家正是来这里部署生产工作负载的。因此，今天我们的大部分用量实际上都来自核心计算、存储和推理，它们都是应用的一部分。我认为，这些工作负载不会仅仅因为我们看到企业获得了正向投资回报就消失。你去和客户聊，问他们：“以目前的能力和成本，你们的业务是否获得了正向回报？”几乎每个人都会说：“哦，是的。”Guest: we're nowhere near that obviously we're you know singledigit percentages at the highest and and and usually it's less than that for one I think we have a lot less risk on on one particular customer but also because we have that rich set of services like AWS is where people really are coming to launch their production workloads and so the majority of our usage today actually is either core compute and storage and inference which is part of that application and so those are the workloads that I think just aren't going to go away just cuz we see enterprises getting positive ROI you go talk to the customers and you say at the capability today and the cost today are you seeing positive returns to your business and almost to a person they'll say like oh yeah

主持人：所以你会觉得，嗯，这种情况不会消失。他们不可能在某个泡沫里突然停止在这方面的投入，你知道，所以……Host: and so you're like well that's not going to go away there's no bubble in which they they stop spending on that you know and so

嘉宾：你知道，而且你知道，这就像风险投资模式：每家估值十亿美元的初创公司都会成功吗？不，不会。但你知道，这就是这门游戏的一部分，而且这五十年来一直如此。它们也并不总是估值十亿美元的公司。Guest: you know and you know this like the VC model like is every billion dollar startup going to make it no they won't but you know that's that's kind of the game and it's been that's been true for 50 years they haven't always been billion dollar

主持人：起始数字变了，但原则没变，对吧？你押注10个，其中一个成功了，就能弥补另外10个的成本，不管具体比例是多少。希望成功率能高于十分之一。没错。所以你也看到了，互联网曾经出现过泡沫，很多互联网公司没能撑下去，但互联网依然存在。Host: start numbers have changed but the principles are the principles are the same right you bet on 10 and one makes it and pays for the other 10 or whatever the whatever the percentage is. Hopefully it's higher than one. Yeah. Right. And so that that there you know you saw that with the internet where there was a bubble and a bunch of of internet companies didn't make it and the internet's still a thing

嘉宾：而且，而且很多拥有持久业务的公司Guest: and and a lot of the companies that had durable businesses

主持人：谷歌、亚马逊之类的公司表现得相当不错。Host: the Google, Amazon's that others like that they did pretty well

嘉宾：所以对我们来说，我们觉得这项投资会让我们感觉非常好，嗯，而且，而且今后还会继续投资。Guest: and so for us we think we'll feel really good about that investment and um and and and the continued investment going.

主持人：你们对需求有自己的看法吗？Host: You guys have a view of the demand

嘉宾：这无与伦比，对吧？因为你能纵观全球，看到各个领域的情况——企业、大型实验室、AI 原生公司等等。所以，Guest: that's unparalleled, right? because you're seeing across the globe, you're seeing across every segment enterprise and the big labs and the AI native companies and so on and so forth. So,

主持人：如果有人应该宣布，那你应该能第一个宣布。Host: if anybody should call it, you should be able to call it first.

嘉宾：我希望如此。Guest: I would hope so.

主持人：是的，这就是我们的计划。说实话，我们花了很多时间思考这件事。我们是经过深思熟虑的。Host: Yeah, that's our that's our plan and and honestly, we spend a lot of time thinking about it. We're very intentional

嘉宾：嗯，关于我们如何使用……你知道，我们的股东资本，我们认为自己做出了很好的投资。对于如何有计划地投资这笔钱，我们有很多良好的保障措施。不过，嗯，是的，我们对此非常看好。你知道，我想安迪也公开说过，AWS 的潜力真的非常、非常大，未来十年都蕴藏着巨大的潜力。每当你遇到这么大的机会，你就会想……你会想投资，抓住这个机会。Guest: um about how we spend um you know, our shareholder capital and and we think that we're making great investments. We have a lot of good protections of how we intentionally invest that money. But um but yeah, we're we're very bullish on and you know, I think Andy's been public about saying this, we the potential for AWS is is really really large and over the next decade, the potential is there and anytime you have an opportunity that's that big like you kind of want to make you want to you want to invest to go after it

主持人：随着这些数字的规模越来越大，对吧？Host: and as the scale of these numbers go larger and larger, right?

嘉宾：是的。我的意思是，你的规划和流程是否发生了很大的变化？比如说，你现在不写……了？Guest: Yeah. Has your planning and process dramatically changed in terms of I mean you're not writing

主持人：是的。Host: Yeah.

嘉宾：十亿美元级别的支票，你们开出的可能是500亿美元、2000万美元之类的支票。Guest: billion dollar checks, you're writing like $50 billion checks or $20 million checks or whatever it is.

主持人：是的。嗯，是的。可以说是，也可以说不是。我是说，我觉得，很多时候我们仍然非常看好这些投资，也会积极主动地推进，但流程方面……所以，有很多事情已经彻底改变了。Host: Yeah. Um Yeah. Yes and no. I mean I think look a lot of times we're the the we're still very bullish about the investments and and lean forward but there there the process. So there's a lot of things that have completely changed.

嘉宾：比如，你到底怎么估算 2028 年的需求？还有些事情是我们现在必须考虑的，以前我们根本不用考虑。嗯，所以……Guest: Like how do you even estimate 2028 demand? And there's things that we have to think about now that we just never had to think about. Um and so

主持人：如果你回到15年前，Host: if you go back 15 years,

嘉宾：嗯，如果我们需要更多电力，就会请电力公司再给我们一个，Guest: um if we needed more power, we asked the power company to give us another,

主持人：你知道，几兆瓦之类的，对吧？像几十兆瓦，他们就会直接供给我们，因为 10 兆瓦并不算多。呃，那对我们继续发展来说已经绰绰有余了。嗯，现在我们得自己带来电力，我们自己提供电力。所以我们为电力项目买单，也为可再生能源项目买单。过去 10 年里，我们每年都是最大的可再生能源电力采购方之一。嗯，所以你知道，我们一直在陆续启用新的太阳能项目、新的核能项目、新的……Host: you know, couple of megawatts or whatever it was, right? Tens of megawatts like and they would just give it to us cuz 10 megawatts wasn't that much. And uh and that was plenty for us to keep growing. Um now we have to bring our own we bring our own power. And so we we pay for power projects. We pay for renewable projects. We're one of the biggest uh renewable power purchasers uh each year for the last 10 years. Um and so it's you know we're regularly bringing on new solar projects, new nuclear projects, new

主持人：所以你说的是表后，还是说与运营商合作？Host: So you're talking about behind the meter or are you talking about working with the operator?

嘉宾：两种我们都会做。嗯，这两方面都是如此。我们接手这类能源项目时，通常会投入资金、为项目买单，之后项目接入电网，我们就能获得相应的抵扣。所以我们会推进这类项目。有时候我们也会做表后项目。就我们目前的规模而言，这些方式都会结合起来。你得把所有这些因素都考虑进去。嗯，不过你知道，这就是规划：要考虑未来20年，思考电力、输电以及相关资本投入的问题。这些都是我们以前从来不必考虑的事情，这种规划我们过去也从未做过。嗯，不过还有一点我们一直都得考虑：以前我们估算所需的服务器容量时，会看好几个季度的需求，并和供应商沟通之类的。Guest: We'll do both. Um and so both of those things and so often times with these power projects that we bring on um we'll pay the the capital and and pay for the project and then it'll go into the grid and then we'll get credit for that. So we'll bring those on. Um sometimes we'll do behind the meter too. like it's it's a mix at the at the scale that we're doing. You have to think about all of those things. Um but um but you know that's that's planning where you're thinking you know 20 years out of how you're going to think about um power, how you're going to think about transmission, how you're think going to think about that um capital and and that's stuff we'd never had to think about before. That's planning we just never had to do. Um but you know we always had to it's you know the the other things is when we used to think about server demand that we needed we you know we'd have you know multiple quarter demand things and we'd talk with our our suppliers and things.

现在我们会提前好几年进行规划，嗯，因为规模实在太大了，我们必须考虑 26 年需要什么、27 年需要什么、28 年需要什么。嗯，所以这是……不过你知道，这也是我们为客户带来的价值之一，对吧？这是客户确实无法自己做到的事情。比如，他们不会去规划电力，也不会规划 2028 年的内存容量。他们就是做不到这一点。所以这也是我们为我们的……带来的真正价值之一。Now we we work multiple years out um just because the the size is so much that we have to think about kind of what do we need for 26 we need for 27 what do we need for 28. Um, and so that's but you know, but it's also one of the value that we bring to customers, right? That's a thing that legitimately customers can't do themselves. Like they're not going to do power. They're not going to plan their memory footprint in 2028. Like they're just that they can't do that. And so that's one of the real values that we bring to to our

主持人：广泛的客户群体意味着有一整套事情是你不必操心的，而这些事情我们花了大量时间去考虑。Host: broad set of customers is just that's just a whole set of things that you don't have to worry about and that that we spend a huge amount of time thinking about.

嘉宾：是的，是的。我想说明一下，你们通常是服务器几乎所有组件的最大买家。没错吧？Guest: Yeah. Yeah. You guys are generally, I think for the record, the largest buyers of practically every component of a server. Correct.

主持人：我，我不知道那个。我不……Host: I I don't know that. I don't

嘉宾：我确信，我们肯定算是规模比较大的那个、那个、那个零部件采购方之一。嗯，至于其他所有人怎么样，谁知道呢，不过这在某种程度上取决于你怎么理解它们、怎么衡量……嗯。Guest: I am sure that we're one of the the the bigger um uh purchasers of of components out there for sure. Um and uh who knows about everyone but uh and it kind of depends on how you think about them and how you measure but um

主持人：我想问的是，你觉得限制因素最严重的会是什么时候？就说 27、28 年吧。你觉得限制因素又会在什么时候有所缓解？嗯，这挺有意思的。呃，我会绕个弯来回答这个问题。你知道，我记得本科时——那是很久以前了——我们读过《目标》。我当时从没想过这会是一本有用的书，但我们读了《目标》，也读过《目标》，对吧？结果发现，限制因素从来不止一个，永远只有当前最主要的那个限制因素。所以你得把所有限制因素都考虑进去，而你一旦解决了一个，就会有另一个出现，对吧？所以，你知道，你觉得 2028 年会出现什么限制因素？其实我不知道，我觉得肯定会有一个。Host: so where I was leading to where do you see the the constraints being most severe let's call it 2728 and where do you see the constraints easing up? Yeah, it's funny. It's um so uh I'll answer this in a roundabout way, but um you know uh I remember that um in undergrad we actually this was this long time ago and I never thought this would be like a useful book that I read but we read we read the goal and have read the goal right and so it turns out there's never one constraint there's always just the latest constraint and so you have to think about all of them and as soon as you hit one there's another one right and so you know what do you like what is the constraint that's going to happen in 20 and 28 like I actually don't I think there will be one.

我觉得对我们来说，每个月的问题都像是：电力够不够？然后，一旦电力不再是瓶颈，你知道，可能就变成内存，也可能是台积电的产能、HBM，也可能是网络组件，或者供应链某个环节出了点小问题，比如连接器之类的，对吧？总有一天，你得把所有这些环节都考虑进去。而且，相关环节出现在什么地方也很重要，你知道。所以情况可能是这样：嘿，我们在印度尼西亚有大量电力，但在德国却不够，对吧？因此，你也得考虑希望把这些产能布局在世界的哪些地方，因为事实证明，并不是所有东西都能完全自由调配。有些可以，有些则不行。I think it's like every month for us it's do you have enough power and then as soon as power is no longer the constraint you know it might be memory it might be TSMC capacity it might be you know HBM it might be um uh networking components it could be you know there's a a blip somewhere in the supply chain and like you know connectors or whatever it is right at some point you have to think about all of those pieces um and it's um you know it's it's not also the um uh kind of where they happen matters too, you know. So it's it may be like, hey, we have a ton of power in Indonesia, but we don't have enough in Germany, right? And so you think about kind of where in the world you want that capacity, too, cuz it's turns out that not everything is totally funible. Some is and some's not.

嗯，不过可能是磁盘驱动器，也可能是 SSD。我们会考虑每一个组件，追踪并关注数以万计、甚至数十万个组件。其中一些我们依靠供应商来管理，另一些则由我们直接管理很大一部分。我们有一整个团队负责这件事，他们非常出色，我认为他们在业内处于领先地位。大概十年前，我们就预见到了这个问题，并开始思考的不只是需要追踪多少台服务器，而是一路追溯整个供应链，深入四五层，找出哪些组件可能会给我们带来问题，并确保这些组件有稳定可靠的供应。Um but might be disc drives, it might be SSDs, it might like we we think about every single component and we have you know tens hundreds of thousands of components all that we track and think about and um and some we rely on our you know our um suppliers to to manage some we direct much of it we directly manage and uh um and yeah that we have a whole team that does that and they're fantastic they're I think they're industryleading and and we um we kind of we we saw this problem coming um probably a decade ago and really started not just thinking about okay how many servers do we need to track but just thinking all the way through the supply chain you know four tiers five tiers down what is the component that that could cause an issue for us and making sure that we had kind of guaranteed supply on that

嘉宾：嗯，如果你觉得，如果你还记得的话。Guest: um if you think if you remember

主持人：我不记得具体是什么时候了，大概是十多年前，泰国好像发生了洪灾，然后、然后大家都买不到硬盘了，呃，呃，所以那就是……Host: I don't remember when this was over a decade ago when there was like the floods in Thailand and and no one had disc drives anymore um uh and so that was

嘉宾：当时出现过磁盘驱动器危机，后来又出现了内存危机。Guest: there was disc drive crisises and then there was memory crisis

主持人：没错，所以你知道，我想我们会把所有这些事情都考虑一遍，也会考虑他们在制造业方面是如何实现多元化的。诸如此类的各个方面，我们都会努力梳理。嗯，我们永远不可能做到尽善尽美，但总会遇到不同的供应限制。Host: exactly and so you know it's like I think we think through all of those things and so we also think about where's their diversification in manufacturing like all of those kind of pieces we try to work through and um and we're we're never going to be perfect on it but there's always a different supply constraint.

澄清有关数据中心的误解Clearing up data center myths

嘉宾：是的。所以，嗯，显然，围绕数据中心存在很多广泛的争论。Guest: Yeah. So um obviously there's a lot of wide ranging debate about data centers.

主持人：嗯。Host: Mhm.

嘉宾：对。嗯，像我们这样的人立场很明确。但你觉得，作为一个行业，我们是不是做得不够好？Guest: Right. And uh it's clear that folks like us where we stand. But do you think as an industry we have not done a good job

主持人：解释数据中心为何对美国有利，以及Host: of explaining why data centers are good for America and

嘉宾：一般来说是整个世界？不过，呃。Guest: generally the world? But uh

主持人：那你怎么 [哼笑] 你们内部是怎么说的？Host: and how [snorts] do you what's the internal talk

嘉宾：安迪团队中的一员Guest: amongst Andy's team

主持人：我们该如何应对这个问题？Host: on how do we deal with this?

嘉宾：是的。嗯，你看，我觉得……我想你们今后会听到我们更多谈这个，我也同意。我们需要更积极地发声，也要更坦诚直接。因为实际上，我们做了很多事情，对我们运营所在的社区都非常有益。我们也一直在认真思考，如何为这些数据中心引入可再生能源，如何实现水资源正效益？Guest: Yeah. Well look I I think um and I think you'll hear more from us over this and I agree. I think we need to be more vocal and be more upfront. Um cuz we actually do a ton that's that's really beneficial both for the communities we operate in um for the you know we we think a ton about how do we bring renewable energy to these data centers. How do we think about being water positive?

主持人：嗯，实际上，我们的数据中心用水量非常非常少。我们主要采用自然空气冷却。我们该如何思考，才能成为所在社区的优秀参与者？嗯，我们又该如何为运营所在地的社区带来高薪工作？嗯，并不是所有数据中心运营商都会这么做。我想，其他一些运营商就有一些记录详尽的案例，他们在这方面做得不太好，而且他们只是……Host: Um actually our our our data centers use a really really small amount of water. We mostly use free air cooling. How do we think about being great participants in the communities where we are and um and how do we bring highpaying jobs to the communities we operate in? Um and not all data center operators do that. And I think there are some well there are some well chronicled examples of others out there that are not great um at that and they just

嘉宾：不太关注法规。他们觉得这些规则不适用于自己，或者就是，你知道，很快就推出了，根本没考虑这些。我觉得这会给整个行业带来问题，因为大家都会被一概而论。所以……Guest: don't really pay attention to regulations. They think that the rules don't apply or they just you know launch really quickly without thinking about those. And I think that it causes problem for the whole industry because everybody kind of gets lumped into that. And so

主持人：嗯，所以你看，我想我们会……我们……我觉得你说得对。我们，嗯，你知道，我们在表达上是自我批评的。我们需要更主动地讲清楚我们确实带来的好处，并想想还能通过哪些方式帮助社区了解我们给他们带来的益处，包括他们使用的服务，对吧？如果你通常去一个社区问：“你们不想用 Netflix 吗？”他们就会说：“不，不，我还是想用 Netflix。”就是这样。嗯，而且，嗯，你知道，我们应该想想并强调我们带来的好处。我最近看到一份报告，其中一个我们开展业务的社区……Host: um so look I think we'll be we we I think you're right. We um you know vocally is self-critical. We need to be more vocal about the benefits that we do bring and think about additional ways that we can help communities understand the benefits that that we bring to them both for the the services they use, right? If if you usually go to a community and say, "Well, do you not want to use Netflix?" And they'll be like, "No, no, like I I still want Netflix." Like that's it. Um and um you know like it's important for us to think about and highlight the benefits that we bring where I I recently saw a report where one of the communities that we operate in

嘉宾：那个县里的每个人每年都少交 5,000 美元的税，这要归功于我们为当地带来的税收。而且我们也不，我们不告诉他们。他们甚至不知道，对吧？这对他们来说完全是看不见的。所以我觉得我们只需要更，嗯，嗯，清楚地说明我们带来的这些好处，因为我觉得如果你告诉社区的人们：顺便说一下，如果我们不在这里，你们的税单本来会比现在多 5,000 美元，Guest: the everybody in that county pays $5,000 a year less in taxes because of the taxes that we bring to that. And we don't we don't tell them. They don't even know it, right? It's just invisible to them. And so I think we just need to be more um um clear about those benefits that we bring cuz I think if you told the communities, by the way, your tax bill is $5,000 less than it would otherwise be if we weren't here,

主持人：他们对那边那栋建筑可能会有一点不同的看法。嗯，所以，而且……但并不是每个人都会那样做。并不是每个人都会，嗯……Host: they might have a little bit of different thought about the building that's over there. Um so and and but but not everyone does that. Not everyone kind of um

嘉宾：想必还需要提高透明度。Guest: presumably there's more transparency that's needed.

主持人：你看，我觉得数据中心行业的大多数参与者，不只是我们，其实都挺负责任的。只是有少数几个不太……嗯，不太……Host: And look, I think much of the data center um community, not just us, is actually pretty good actors. And there's just a few that aren't that um that

嘉宾：我觉得这类事情最近引发了一些焦虑。我想我们需要做得更好一些，明确指出哪些人是守规矩的好公民，哪些人不是。Guest: that kind of I think have have caused some of the angst recently. And I think we just need to do a better job of highlighting, you know, who's who's being good citizens and who's not.

The Graviton and Trainium bet

主持人：是的。嗯，在我们结束硬件这个话题之前，我还想谈谈训练。Host: Yeah. And uh before we leave the hardware topic, uh um I want to touch upon training.

嘉宾：嗯。Guest: Mhm.

主持人：还有你们自己制造芯片的整个历程，呃，对吧？Host: And your whole history with building your own uh chips, right?

嘉宾：嗯，很久以前我们就是最早使用 Nitro 的合作伙伴之一。嗯。Guest: Um we were one of your first partners using Nitro a long time ago. Mhm.

主持人：呃，从那以后，Graviton 和你们都取得了巨大的进展。那么，当初是什么想法让你们决定，听着，我们要自己做自己的东西？之后进展如何？你们现在发展到哪一步了？Host: Uh and since then uh Graviton and you made tremendous progress. So what was the thinking that led to saying look we're going to do our own thing and then how has that progress been and where are you

嘉宾：这其实是个很有意思的故事，我觉得这是一个很好的例子，说明亚马逊 AWS 会不断创新、随着时间推进行迭代，并持续思考我们还能做些什么更大的事情；但我们会一步步验证可行性，而不是……你知道的。举个例子，大概是现在——我……这应该是 13、14 年前吧。那时我们发现，虚拟化给整体资源数量带来了相当大的开销。我们当时就在想该怎么办。客户告诉我们：“我想要裸机性能。”他们是在拿它跟拥有一台服务器的全部资源作比较。Guest: it's actually a fascinating story and I think it's a great example of where um Amazon AWS will innovate and we'll iterate over time and continue to think bigger about what we can do but but um but kind of prove our way there as opposed to you know and so takes as an example um this probably now I 10 it's probably about 13 14 years ago we were seeing that there was a pretty significant um virtualization tax on the overall number of resources and um and we were kind of thinking about how do we our customers were telling us I want bare metal performance and they you know they're comparing to having all the resources of a of a server

主持人：所以我们做的第一件事是，嗯，我们拿了一张网络卸载卡，把所有网络虚拟化都虚拟化出来，并将其转移到卸载卡上，这样网络虚拟化的性能就更接近裸机了。那时还没有完全达到裸机性能，但已经很接近了。Host: and so the first thing that we did is that um we took a a network offload card and virtualized all of our network virtualization and pulled it off onto an offload card so that network virtualization got closer to to bare metal performance and back then it was not quite bare metal but it was close it was closer

嘉宾：然后我们对此特别兴奋，就说，好，如果我们也能把存储虚拟化卸载出去呢？当时那些网络卸载卡都做不到这一点。后来我们找到了一家公司，他们有一张卸载卡上配备了几个 ARM 内核，而且他们用这些内核做其他事情。我不记得他们最初的用途是什么了，但我们就问他们，能不能用这些内核来实现存储虚拟化和其他一些功能。他们说也许可以。于是我们就和他们反复迭代。这就是当时的产品。对，这就是团队。Guest: and then we got really excited about that and we said okay what if we could move storage virtualization off as well right and and um and none of the network offload cards could do that and then we we found this one company who had some ARM cores on an offload card and they were doing it for other reasons I can't remember their original purpose but we're like could you use those to do storage virtualization and some of these other functions and they're like maybe and so we really iterated with them this was the product Yeah, this is the team

主持人：而且，嗯，就是特别喜欢那个团队，真的很有创新精神、使命感很强，也真心想解决问题。Host: and um and just loved that team like really innovative, really missiondriven, really wanting to solve problems.

嘉宾：嗯，所以我们收购了他们，然后我们说：“听着，你们能不能给我们做一块稍微大一点的卡，真正能够把所有网络虚拟化都卸载掉，基本上给我们一台没有任何虚拟化的裸金属服务器，不做虚拟机虚拟化，一切都通过卡上的 API 来实现。”因为我们当时认为，这样性能会更好，资源利用率也会更高，可能安全隔离也会更好。Guest: Um and so we acquired them and we said, look, could you build us a slightly bigger card that actually could take all the network virtualization off and basically give us a bare metal server that has no virtualization on it, no VM virtualization, everything is through APIs on the card. And because we had this view that one performance would be much better, resource resource utilization would be better. Ruby be we security isolation

主持人：而且安全隔离会好得多得多。我们告诉大家，你知道，这样就能理直气壮地告诉大家，我们无法访问在那里运行的任何虚拟机。Host: and security isolation would be much much better and we tell people you know could then legitimately tell people we have no access to any of your VMs that are running there

嘉宾：嗯，这在过去十年里给我们带来了巨大的好处。坦白说，我们一直走在前面，而其他人推进得相对较慢，因为这并不是人人都能做到的通用做法。Guest: and and um and this has been a huge benefit for us for the last decade where frankly like we've been leading and and others have been kind of slow to do this because they this is not a generalized thing that people can do

主持人：不过，嗯，我们到了那一步，基本上就说，听着，我们在这方面取得了很大进展。嗯，如果我们把……你知道，这块卸载卡上有一大堆 ARM 核心。于是我们就说：“要是把它变成一台服务器呢？”Host: but um so we got to that and we basically said look we're we're making a lot of progress here um what if we take you know And there's a bunch of ARM cores that were on this offload card. And we said, "What if we turn that into a server?"

嘉宾：呃，我们最先用 Graviton 做了这件事。我们推出的是一台性能很弱、嗯、体积很小的服务器。Guest: Uh, and we did that first with Graviton. It was a very underpowered um very small server that we launched.

主持人：嗯，客户们也很兴奋。他们说：“我很想要一台 ARM 服务器。这太有意思了。”于是我们就沿着这条路走了下去，嗯，Graviton 也是如此。嗯，我们做的一部分工作是研究并发现，ARM 核心的性能提升趋势，以及功耗利用率图表，都表明我们知道这种架构何时会达到一个转折点，开始特别适合某些工作负载；而它们只是需要有人来推动生态系统发展，并把其中一些环节搭建起来。所以我们就用 Graviton 做到了这一点。嗯，如今 Graviton 已经大获成功。嗯，Host: Um, and and customers were excited. They're like, "I'd love to have an ARM server. This is super interesting." And so we went down the path and um and Graviton um you know and part of what we did is we looked and saw that there's the the slope of you know ARM cores were getting faster and where the you saw the power utilization and and the graph where they and you knew the intercept was going to happen for where this architecture was going to be really good for for parts and they just needed somebody to drive the ecosystem and and get some of the pieces in place. So we did that with Graviton. Um and Graviton's been a a runaway hit at this point. Um,

主持人：你们有没有公开过车队中有多少比例是……？Host: have you been public about what percent of your fleet is?

嘉宾：我们每年推出的 Graviton 芯片都比其他任何类型的芯片多。它们非常受欢迎。你看，它们便宜 20%，性能还好 20%，而且过去五六年来一直如此。所以这是一个很容易理解的价值主张。我认为，我们排名前 100 的客户中，绝大多数——大概超过 90%——都在其整个机群中以某种形式使用 Graviton。这对我们和客户来说都是巨大的成功。对客户而言，降低账单最简单、效果最显著的方式就是转用 Graviton。Guest: We land more graviton ships uh uh every year than um uh than than any other um type. So it's it's it's very popular. Um and it's um you know look we they're 20% cheaper at a 20% better performance and have been like that for the last kind of five six years. So that's a it's an easy value proposition that um and I think the vast majority something like 90 plus% of our top 100 customers all use Graviton in some way shape or form across their fleets. Um and so that's been a huge win for us and for customers it's been it's the single big single easiest way that customers lower their bill is to move to Graviton.

主持人：嗯，他们通常……我们有过一些案例，有些人迁移了整个服务器群，并把服务器数量减少了一半。Host: Um they can often we've we've have examples where people have moved their whole fleets and cut the number of servers they had in half.

嘉宾：性能好太多了。Guest: The performance is so much better.

主持人：这太惊人了。这样一来，我们所需的服务器数量减少了一半，而且每台服务器的成本也更低。这是个巨大的优势。所以大约五六年前，我们看到 AI 计算开始兴起。我们当时并没有预料到它会发展成今天这个规模，但已经看出它会带来重大影响。于是我们开始行动，基于 Tranium 打造了第一款芯片。现在，第三代 Tranium 3 已经上市，而且表现非常出色。所以，我们的算力容量大概到明年年底前都会售罄。我们也在努力想办法腾出一些容量，让初创公司也能用上，因为我们看到的效果非常好。Host: It's amazing. And so half a number of servers, each server costs less. Like it's it's a it's a big win. And so then about five, six years ago, we said, you know, we saw the rise of of AI um compute happening. Um not nearly expecting what it was today, but still saw it was going to be a big material mover. And so we went in, built our first chip in and Tranium, and uh we're now um you know, in market with the third generation in Tranium 3. And um have seen fantastic results. So, it's, you know, we're we're sold out um uh for capacity through probably maybe towards the end of next year or something like that. And and and we we're trying to again we're trying to figure out how we can save some capacity and get startups to be able to use some of the capacity cuz it's uh we see great results.

所以，你们大多数人都知道，Bedrock 上的大部分流量都运行在 Trainium 上。嗯，我们和 Anthropic、OpenAI 都有很好的合作，双方都在基于 Trainium 构建产品。嗯，此外还有一批规模较小的初创公司。我想，现在大约有六到十二家初创公司也在基于 Trainium 构建产品。So, most of you know the the the majority of of traffic on Bedrock all runs on Tranium. Um and we have um great deals with both Anthropic and OpenAI to build on top of Tranium. um as well as a a a set of um of smaller startups and you know I think we have half dozen to a dozen startups that are building on top of tranium now too

嘉宾：然后，呃Guest: and uh

主持人：所以现在这个名字暗示它是一款训练芯片。Host: so now the name suggests it's a training chip

嘉宾：是的，我们回来了。Guest: yeah we're back

主持人：但大家也都在用它进行推理。Host: but everybody's using it for inference too

嘉宾：那么，精益在架构上处于什么位置，未来又将走向何方？Guest: so where is the lean architecturally and where is it going

主持人：这点说得很好。嗯，听着，我们在口头上很擅长自我批评，不太擅长起名字，所以这并不是我们的强项。最初，我们有一款名为 Inferentia 的芯片，用于推理，还有一款用于训练的芯片，叫 Trainium。随着模型变得越来越大，人们发现实际上需要在这些大型系统上运行推理。结果发现，Trainium 也许才是目前市场上最好的推理芯片。Host: it's a good point um look the vocally self-critical we're terrible at naming and so it's not it's not our strength originally we had a chip called inferentia for inference and training for training for training And then as the models got bigger and bigger and it turns out you actually want to run the inference on these really large systems, it turns out it turns out that tranium is actually maybe the best inference chip on the market right now.

嘉宾：嗯，从绝对性能和性价比的角度来看。Guest: Um from a a per absolute performance and cost performance point of view.

主持人：嗯，所以，嗯Host: Um and so um

嘉宾：内存、内存带宽更好吗？那个在哪里？Guest: is it better memory memory bandwidth? Where is the

主持人：它只是……它的架构和其他产品有一点不同，而且便宜得多。所以从性价比和绝对性能的角度来看，Trainium 都很出色。我们大量使用它来做推理；也就是说，Bedrock 今天的很多推理任务都是由它驱动的。不过它也是一款不错的训练芯片。我认为，我们的很多客户并不训练模型，而是使用模型。所以很多人都是在幕后用到它，这也是我们对它感到兴奋的原因。不过，许多大客户也对用它来构建训练集群感兴趣，尤其是等到 Trainium 3 和 4 推出之后。我们已经公布了 Trainium 4，但还没有正式发布。Host: it's just it has it has um it's just architecture is a little bit different than than um uh than others and it's much cheaper. Um, and so from a cost performance perspective and absolute performance perspective, tranium is great. Um, uh, and so we're, you know, we we use it a ton for, um, for inference and, and, and that's going to say, Bedrock, um, it drives much of the Bedrock um, uh, inference today. And, um, but it's also a good training chip, too. And it's, you know, I think we're we we, you know, a lot of our broad set of customers usage is not in training models, but is in using it. And so that's where a lot of people get to use it under the covers, and that's where we're excited about it. But um but a lot of the big customers are interested in it for training clusters as well. And particularly as you get to tranium 3 and four um uh which we've announced we haven't we haven't launched trainium 4 yet but announced it.

嗯，有些人看了这种架构后就说：“没错，这也是我希望自己的训练集群未来发展的方向。”所以，我们也非常期待它未来在这些超大规模训练集群中的应用。Um folks have kind of looked at that architecture and said like yeah that's the future of where I want my training clusters to be as well. So we're we're quite excited about the future where that goes for these really broadscale training clusters also.

嘉宾：不过，嗯，不过是的，它已经建好了。Guest: But um but yeah it's built.

企业在智能体方面遇到的瓶颈Where enterprises are stuck on agents

主持人：是的。现在，呃，我们回到智能体这个话题，不过这次从大型企业或大中型企业的角度来谈。Host: Yeah. Now uh let's get back to uh talking about agents but from a perspective of uh large enterprises or large medium enterprises.

嘉宾：是的。Guest: Yeah.

主持人：他们在采用方面进展到哪一步了？嗯。Host: Where are they in their adoption? Um

嘉宾：嗯，那他们……你们看到他们带来了哪些好处？Guest: and uh have they what sort of benefits are you seeing them

主持人：Reap 已经……就你目前所知，他们的路线图是什么？Host: reap already and what is the roadmap for them as far as you can tell from your vantage point?

嘉宾：是的，这是个非常好的问题。我认为，这是我们花了很多时间思考的问题。如今，当我和各地的客户交流时，他们觉得自己目前所做的事情很有价值。我想说，大多数企业构建的智能体都相对简单、直接。他们也开始思考……而且他们大多……Guest: Yeah, it's it's a really good question. And I think it's one that um that we've spent a lot of time thinking about. And when I talk to customers all out there um today, they view, you know, they're getting a lot of value out of what they've done today. And I would say the agents that most enterprises have built are are relatively simple and straightforward. Um and they're starting to think about um and they're um mostly

主持人：非自主的，对吧？也就是说，它们某种程度上仍然需要有人参与其中。顺便说一句，如今客户仍从中获得大量价值。所以他们真正思考的是，如何让这些系统以安全的方式实现自主化。我认为，目前有两件事阻碍客户继续扩大规模。如今这已经是一项相当大的业务，但我认为它有巨大的潜力，能够真正改变每一位客户、每一个工作流程，我们也确实在认真思考这一点。第一，关键在于该如何看待它。我认为我们最初看到的是，企业有一个工作流程，然后会说：“很好，我可以让一个智能体去执行同样的工作流程。”Host: non-aututonomous, right? They're still kind of people in the loop, if you will. And so I think we're and and by the way, there's customers are still getting lots of value out of that today. And so they're really thinking about how do I have these be autonomous but in a safe way. And I think there's two things that that I think hold customers back today from just continuing to to scale. And it's it's already a pretty big business today, but I think it's it has a massive opportunity to really change every single customer out there and every single workflow and and really thinking about it. And so number one is just how to think about it. I think what we originally saw was that enterprises had a workflow and they're saying great I would have the enterprise I would have an agent go do the same workflow.

嘉宾：是的。Guest: Yeah.

主持人：我们真正鼓励他们做的，是不要只想着照搬——你知道，鲍勃做第 1、2、3、4、5 步，于是智能体也照着做第 1、2、3、4、5 步，最后再由鲍勃来检查。这并不是你想要的模式。你真正想做的是退一步想：如果我要实现某个目标，智能体能不能用不同的方式来完成？它可以大规模并行处理，可以尝试 50 种不同的方法来达成目标。那你该如何帮助它得出正确的结果，并重新思考计算机解决问题的方式与人类解决问题的方式有什么不同？因此，我们要做的一件事，就是帮助客户理解该如何思考这些问题，让他们真正从一张白纸开始，因为真正的价值就在于此：不是照搬你们今天正在做的事情，而是从全新的角度出发，思考如何用完全不同的方式解决问题。我相信，你们很多初创公司也是这么想的：如何帮助客户从零开始解决问题，而不是照搬当下的做法。Host: And what we encourage them to really do is think not just replicate you know Bob does step 1 2 3 4 5 so agent is going to do one step 1 2 3 4 5 and then Bob's going to check it at the end. That's not really the model you want. You want to actually step back and say if I want to accomplish something how can an agent do it differently? It can do it in a massively parallelized way. can try 50 different things and get to that and and how do you help it get to that right outcome and rethink how a computer would solve a problem versus a human solving a problem. And so one of the things is us just helping customers understand how to think about that and really kind of have that blank slate because that's where you really get value is not just replicating what you're doing today but but thinking from a green field approach about how you go solve a problem completely differently and I'm sure that's how many of your startups are thinking about this too which is how do you help customers green field solve a problem not replicate the thing that happens today.

嘉宾：所以那是第一点。Guest: So that that is number one

主持人：那些运行着大量智能体、智能体群之类的人，随你怎么称呼。是的，Host: people running fleets of agents swarms whatever you want to call them. Yeah,

客人：如今很常见Guest: very common these days

主持人：而且，而且，你只是想想这个问题，你知道，而且，所以企业不会那么……再说一次，这是一个你可以向初创公司学习的领域，然后试着想想如何把这种做法应用到企业环境中。你知道，保险公司未必那么积极进取，但他们很乐意想办法改进审批工作流之类的。嗯，这是第一点。第二点是，如何把这些转变成完全自主的工作流，以及如何真正信任这些智能体？所以我们花了很多时间思考，如何构建服务，让企业确信自己的系统是安全的，能够信任智能体做出决策；如何为它设置恰当的防护机制，赋予它适当的数据权限，并确保它不会删除生产系统，也不会犯下严重的错误。Host: and and and you just want to think about it, you know, and and so enterprises are not as as again this is one where you learn from the startups and you try to how do you apply that to an enterprise world where they're, you know, an insurance company is not necessarily as forwardleaning, but they would love to figure out how they can have a better um you know, approval workflow or something like that. Um so that's number one, but then the second one is this this how do you turn those into fully autonomous workflows and how do you actually trust the agents? And so we're spending a lot of time thinking about how do we build services to help enterprises feel like their systems are secure and that they can trust an agent to make a decision that they can have the right guard rails that it can have the right permissions on their data that it's not going to delete production systems that it's not going to make tragic mistakes.

而且现在，我觉得这种顾虑可能正在阻碍人们——顺便说一句，这种阻碍或许是恰当的——让企业不敢直接说：“好，尽管放手去做。”毕竟，你肯定不希望智能体彻底失控，意外删除生产数据库。那可就糟了。And right now I think that nervousness is probably holding people back maybe appropriately by the way is holding enterprises back from just saying okay go nuts. It's like you don't actually want an agent to just go crazy and accidentally delete a production database. That's going to be pretty bad.

嘉宾：嗯，所以我们正在积极地与客户一起解决这个问题，帮助他们设计架构，坦白说，也帮助他们发明新的技术和能力，来协助他们解决这个问题。我认为这是我们会继续创新的领域之一，嗯，我们最终会做到的。我觉得我们已经有了一些非常好的想法，也有一些很有潜力的技术正在研发，我认为它们确实能帮上忙。那么，企业是否正在学习如何搭建评估系统之类的东西？等等，他们确实需要帮助。比如说，如何进行评估？如何建立持续的测试循环？如何以合理的方式来思考目标导向？如何对数据进行标注，才能让评估真正有意义，能够大致反映你们在生产环境中会做的事情？Guest: Um and so you that's how we're we're kind of actively working through this with customers on how do we both help them architect and frankly invent new technologies and capabilities that are going to help them solve that problem. And so that that is one of the areas where I think we'll continue to innovate and um and and um and we'll get there. I think we have some really good ideas um and and some good technologies brewing that I think can really help. So are enterprises learning how to do eval systems and so on and so forth to keep the agents they need help honestly like you like both like evals how do you have a constant loop of testing? How do you how do you think about um you know goal seeking in a reasonable way? How do you have your data labeled in such a way that it actually even makes sense that the eval can actually kind of um approximate what you're going to be doing in production?

如何在生产环境中进行测量并回测，确保不会出现漂移？这些都是企业如今不知道如何解决的问题。我不确定现在是否真有人擅长解决这些问题。这就是为什么你会看到这么多 FTE 团队陆续组建起来，AWS 和我们的合作伙伴也在大力投入 FDE 这一模式，提供帮助。这是客户最需要帮助的领域。我们在思考如何推进这件事时，我的看法是，我们希望培训客户，让他们能够自己去做，对吧？这不是传统的那种模式——我不想打造一家永远依赖人力的企业，让客户一遍又一遍地付钱请顾问。How do you measure in production and and back test it so you're not seeing drift? All of those things are problems that that um enterprises don't know how to solve today. I shouldn't know if anyone really is great at solving these today. Um it's why you've seen so many FTE teams kind of spin up and and AWS is um and our partners are really leaning into the the FDE motion to go and help and this is the single biggest area where customers need help and and when we think about how you you you and my view is we want to train our customers to be able to go and do this themselves, right? This is not the traditional notion where I want to have a people-driven business that goes on forever where you just keep paying consultants over and over and over again.

我们对 FD 应该如何运作的看法是……嗯，我们希望进入一家客户那里，而这家客户已经准备好接受——你知道，真正愿意在我们完成后承担起这件事。我们会在 45 天内开展工作，教他们如何制作评估、如何以带标签的方式整理数据，并与他们并肩工作。然后，在 45 天结束时，你离开，而客户已经准备就绪、接受了培训，可以开始使用了。这就是客户告诉我们的他们想要的：他们不想在接下来的五年里都受制于外部劳动力。是的。our our view on how FD should work and and and is um we want to go into a customer who's ready to accept the you know to really kind of accept owning this when we're done and in 45 days do work where we can teach them how to make an eval teach them how to get their data in a labeled way do work alongside with them and then at the end of 40 to 5 days you leave and that customer is is good and ready to go and trained up and that's what our customers tell us they want they don't want to be beholden to a external workforce for the next five Yes.

主持人：嗯，不过他们今天需要帮助。Host: And um but they need help today.

嘉宾：是的。你在……进行了巨额投资。Guest: Yeah. You made a massive investment in

主持人：FTEES。Host: FTEES.

嘉宾：嗯，那再进一步来说，对吧？你们一些同行说过，你看，你不可能把所有数据都放在……Guest: Um so taking that even one step further, right? Some of your industry peers have said, look, you can't have all of your data

AI risk, Hugging Face, and Continuum

把资源投入大型前沿模型。企业真正应该做的是，采用一个开源模型，然后用自己的数据、工作流、各种表达方式等对它进行后训练。going into a big frontier model. What enterprises should really do is to take an open source model and then post train on your own data and workflows and phrases and whatnot.

主持人：你对此怎么看？你们有没有看到客户真的在尝试这么做，还是你们……Host: Where do you stand on that? Are you seeing customers actually trying to do that or do you guys

主持人：你对此怎么看？Host: how do you how do you think about that?

嘉宾：这……这是个很好的……第一点，我完全赞同第一点，客户和企业数据是他们最有价值的资产。Guest: It's it's a great the first point I I wholeheartedly agree on that first point like the customers and like enterprise data is their most valuable asset.

主持人：所以，从一开始，这就是我们以这种方式构建 Bedrock 的原因。嗯，我们保证你的数据绝不会离开你的 VPC。因此，如果你在 Bedrock 内运行，你的数据不会返回给模型提供商。他们永远看不到你的提示词。嗯，这些数据会留在你自己的可信环境中。嗯，所以这就是企业倾向于在 Bedrock 上运行的原因，也是你看到这项业务大幅增长的原因——增长了数百倍之多。我是说，每个月，嗯，每个月我们都看到这项业务不断增长；实际上每周都在爆发式增长。这也是为什么你会看到 OpenAI 工作负载迁移到 Bedrock，也能看到 Anthropic 快速发展。所以，无论你使用的是开放模型，还是……嗯……封闭的前沿模型，都一样。Host: And so from the very beginning it's why we built Bedrock like we did. Um we have a guarantee that your data never leaves your VPC. And so your if you're running inside of Bedrock um your data doesn't go back to the model provider. They never see your prompts. um that stays inside of your own trusted environment. Um and so that that is why um enterprises kind of run they they prefer to run on top of bedrock and it's why you see that business growing massively like hundreds and hundreds I mean it's every um every month we see that just business just every week we see that business exploding and and it's why um you see open AI workloads migrating to bedrock. It's why you see Anthropic really growing really rapidly. And so whether you're using open models or um uh or or um closed frontier models.

嗯，我觉得 Bedrock 是个很棒的解决方案。顺便说一下，这是我们的客户告诉我们的。你知道，如果你还记得，三年前我可是挨了不少批评。Um I think bedrock is a great solution that our our customers told us by the way like you know we if you remember 3 years ago I got a lot of heat.

嘉宾：说到难听的名字，不过这个名字倒是不错。Guest: Speaking of bad names, that's a good name though.

主持人：是的，Bedrock 是个好名字，很不错。不过，实际上我们因为进入 AI 领域的速度慢而受到了很多批评，因为我们确实是从基础开始构建的。当时我们说，听着，我们不会只是急着推出一项服务。我们真的想认真考虑，如何确保保护客户的数据，并打造一项我们认为能够持久满足已知用例需求的服务。要是你还记得的话，我们当时受到了很多批评，但我们说，听着，我们会去打造正确的产品。现在，随着人们从概念验证转向生产环境，他们当中绝大多数都选择在 AWS 上使用 Bedrock，其中一个很大的原因就是这个。当然，这也得益于我们提供的一系列服务。我们还提供开放模型，也提供专有模型。嗯，我们还围绕 Agent Core 提供了一整套功能。Host: Yeah, Bedrock is a good name. That's good. But we we got a lot of heat actually for being slow to the AI world because we actually built the foundations of this where we said look we're not just going to rush out of service. We really want to think about how do we make sure that we protect our customers data and build a service that we think is going to be durable for the use cases that we knew about. And if you remember we got a lot of heat and we said look we're going to go build the right thing and now as people move from proof of concepts to production vast majority of them are landing in AWS on bedrock for for much one of the reasons is because of this. It's also because of the the set of services that we have. We also offer open models. We offer proprietary models. Um we offer um a whole set of capabilities around those agent core.

我们构建这些基础模块，让你能够更轻松地使用自己想要的任何模型来构建智能体。We build these building blocks so it's easier to build agents with any of the models that you want.

嘉宾：嗯，不管它们是在 Bedrock 上还是不在 Bedrock 上。你可以用 Gemini 或其他工具来做。不过，嗯，我认为这是我们的一项差异化优势，而且是一个非常重要、值得思考的问题，因为……Guest: Um whether they're in bedrock or out of bedrock for that matter. You can use Gemini or other things for it. But um but I think that's a it's a it's a differentiating piece for us and it's a super important thing to think about because

主持人：我认为，让这些数据回流给模型提供商是一件危险的事。不过，你提到了开放权重模型。我确实觉得这是一个让我很兴奋的领域。嗯，我们正在大力加强对开放权重模型的支持，努力打造一个良好的环境。坦白说，我认为如今很多客户都相信，他们拥有有价值的专有数据；如果能把这些数据融入开放权重模型，进行一些后训练和微调，再将模型蒸馏出来，就能以更低的价格获得性能更好的模型。这里有很多环节都必须顺利运作。他们确实需要有一套好的评估方法，来证明这一点确实成立。如今大多数人都在 SageMaker 上做这些事。Host: having that data go back into the the model provider I think is a is a dangerous thing. You talk about open weights models though. I do think that there's a and it's an area that I'm excited about. um uh we're we're really ramping up our support of open weights models and and trying to build a good um environment and and frankly this is where today I think and I think it's true a lot of customers believe that they have meaningful proprietary data that if they could mix in you know do some post- training do some fine-tuning um um to an open weights model that they could distill down they could actually get a better performing model at a lower price. Um there's a bunch of pieces in here that have to work out well. They actually have a good eval to actually prove that that's true. Most people are doing that in SageMaker today.

我认为我们还能做更多，让这件事变得更容易。不过实际上，如果你去看看大家在哪里做这件事，就会发现他们现在确实是在 AWS 上的 SageMaker 中完成的，而且是通过 SageMaker 托管推理服务。I think there's more that we can do to make that easier. But actually like if you go look at where are people doing that, they actually do it in SageMaker on AWS today and they actually host the inference via SageMaker.

嘉宾：SageMaker 正焕发新生，成为一个……Guest: SageMaker is getting a new lease of life as a

主持人：呃，我的意思是，它一直以来都算是一个模型构建平台，对吧？现在你知道，如果想想企业在这个领域都在做什么，他们基本上就是在构建自己的定制模型。所以，SageMaker 是做这件事的绝佳平台。嗯，我觉得我们还需要继续完善一些东西，让这件事变得越来越容易。Host: uh I mean it is it's it was always kind of a model building platform, right? And now you know there's if if you think about what enterprises are doing in this world that's what they're doing is they're basically effectively building their own custom models. And so that is SageMaker is is a great place of doing that. Um and I think there's some things that we need to keep building on to make that easier and easier to do

嘉宾：嗯，还有就是在不同的开放权重模型之间进行测试之类的。不过这是一个不断发展的领域。我觉得它非常有意思，也是我们希望确保具备所有适当条件的领域，这样客户如果拥有合适的数据和专业知识，确实想沿着这条路走下去的话，就能够做到。Guest: um and to test across different open weights models and things like that. But that's a um it's a it's an evolving space. I think it's a super interesting one and it's one we want to make sure that we have all the right things for um for customers to be able to do if they if they have the the right data and expertise to actually go down that path

主持人：围绕 AI 扩展风险，以及种种其他问题，还有安全漏洞等等，再加上 Hugging Face 攻击，[哼了一声]。企业界的人都怎么样？CEO 们会问你什么关于这些事情的问题？是的，问题确实很多，而且他们大多想问的是——这又回到了那个问题：当我启动智能体时，怎么才能相信它们会按我的要求行事？所以我们正变得越来越……Host: and with all the debate around AI extension risk and this that and the other and the security um uh vulnerabilities and so on and so forth and the hugging face attack. [snorts] How are enterprise what do what are CEOs asking you about all these things? Yeah, that there's a bunch and and they look they they mostly want to say like and it goes back to this like when I launch agents, how can I trust that they're going to do what I want them to do? And so we're in increasingly

嘉宾：其实我们已经有一段时间在大力投入，建设相关能力，让人们能够安全地在自己的环境中部署智能体，并考虑这些控制措施。其中一些问题包括：如何确保智能体拥有恰当的权限？如何为它们提供适当的沙箱隔离？如何确保设置了合适的一系列防护措施？如何真正有意识地思考希望智能体做什么、不做什么？有没有人在环路中参与？因此，我们花了很多时间和客户一起探讨如何安全地部署智能体，并随着时间推移不断改进；以及我们还需要构建哪些其他能力，来帮助人们在自己的环境中安全地部署智能体。这方面还有很多内容。Guest: we have been for a while like we're basically we're heavily investing in building capabilities that allow people to deploy agents safely into their environment and think about those controls. And some of those are how do you make sure the agents have the right permissions? How do they have the right sandboxing? How do you make sure that you have the right set of guardrails? How do you how do you really intentionally think about what you want the agents to do and not do? Is there a human in the loop or not? And so we we spend a lot of time with our customers thinking about how do you think about safe agent deployment and get better over time and um and what other capabilities do we need to go build to help people deploy agents safely into their environment. Um so there there's a lot there.

嗯，另一个角度是，很多人担心这些非常强大的模型会不会成为攻击面，以及某种“mythosphere”。所以我对此有自己的看法：是的，我认为这确实会给客户环境带来真正的风险，但同时也是真正的机遇。我们最近推出了一项名为 Continuum 的服务，它利用这些强大的模型帮助客户保护自己的环境。我们会全面检查客户的环境，利用这些模型查找漏洞，帮助客户发现以前没发现过的漏洞。顺便说一句，最重要的是，我们还会对漏洞进行优先级排序，因为我们了解他们环境的具体情况，包括环境如何配置、权限如何设置，以及可能有哪些补偿性控制措施会让利用漏洞变得更难或更容易。Um the other a the other angle on that which um a lot of people are worried about which is just um are some of these really powerful models going to be um attack services and and and kind of mythosphere um and so I I kind of have a view on there they're yes that is a real risk I think to to customer environments but it's also um a real opportunity and so we we recently launched a service called continuum that uses these powerful models to help customers go secure their environment and So, we'll look across our environment, uh, look for vulnerabilities with them. We'll use some of these powerful models and and, um, um, and help customers find vulnerabilities they haven't found before. And most importantly, by the way, prioritize which ones cuz we know context about their environment, how it's set up, where their permissions are, where they may have compensating controls that that make it harder or easier to do.

嗯，所以 Continuum 在客户中非常受欢迎。事实上，我们对 AI 能带来的可能性非常、非常看好，因为 AI 可以助力安全防护。你看，未来某个时候，客户需要的是机器速度的安全防护，而不是人类的速度——不是那种警报响了，然后有人过去查看的方式。Um, and so Continuum is incredibly popular with customers. Actually, we're um um um really bullish about what's possible from AI to help with AI powered security cuz look at at some point customers are going to need security at machine speed, not at at human speed, not at like an alarm someone goes in look at it.

主持人：所以你们知道，我们正迅速行动，帮助客户构建这项能力，保护他们的环境。我也非常期待 Continuum 团队在这方面的建设成果。那么，就 AWS 本身而言……Host: And so that you know we're running fast to go help build that for customers to help them protect their environments and and I'm very excited about the the continuum team is building on that front too. So within AWS itself

嘉宾：嗯。Guest: Mhm.

主持人：比如说，从更广泛的层面来看，智能体的采用和使用现状如何？以及……Host: like how what's the state of adop usage of agents in the broad and

嘉宾：嗯，Continuum 基本上就是我们试图把内部的工作方式展现出来。我们在自身安全工作中广泛使用 AI，在软件开发中也广泛使用 AI。嗯，我们还会使用智能体。其实有件特别酷的事：我们在整个业务中都部署了智能体。我们已经为每一位亚马逊员工推出了 Amazon Quick。现在我看到人力资源团队正在构建智能体，帮助推进以前需要一群人花上几周才能完成的工作，如今一个人几个小时就能搞定，比如团队规划、资源管理。嗯，我的财务团队也在构建智能体，用来从各处提取税务规则，并确保我们在许多不同方面都符合规定。嗯，看到过去受限于软件开发人员的那些事情……真是太酷了。Guest: well continuum is basically us trying to expose what we do internally and so we we use uh AI extensively for our own security. We use AI extensively for our own software development. Uh we use uh agents actually one of the things that's really cool is we use agents across our entire business. And so we we rolled out um Amazon Quick to every single Amazon employee. And now I see HR teams building agents to to help drive what what used to take teams of people weeks to do that a single person can now do in a couple of hours to think about kind of team planning and and and resource management. Um I have finance teams that are building um agents to to go think about how do they go pull tax rules from everywhere and ensure that we have compliance on a bunch of different pieces. And um super cool to see that things that used to be blocked by software developers

主持人：实际上，业务部门的员工现在能够自行解决问题，也能更快地进行创新。嗯，Quick 已经取得了巨大成功，而且发展势头如野火般迅猛。你看，从小型初创公司到全球最大的企业，都在使用它；这些大型企业还将它推广到整个客户群，以便访问所有企业数据，并轻松运用智能体和各种功能，帮助加快工作进度。所以我刚才说，我们在各个领域都用它，从软件开发到安全，再到制定人力资源政策。我的意思是，你们出了名地会衡量运营中的方方面面。你们在哪些方面看到的成效最大？ 是啊，我的意思是，显然你们知道答案：软件开发。这才是真正的答案。Host: actually the the the line of business folks are able to go and and unblock themselves and and innovate more quickly. Um and so Quick has been an enormous blow and and that has has grown like wildfire. see um customers like small startups using it all the way to the the largest enterprises in the world rolling it out to their entire customer base to get the benefit of kind of being able to access all of your enterprise data and easily apply agents and and capabilities to to help you accelerate your jobs. And so I said we use it across uh everything from software development to security to um to to driving HR policies. I mean you guys are notorious for measuring every everything about your operation. Where have you seen the biggest gains? Yeah, it's I mean obviously you know the answer software development like that's the real answer

嘉宾：那个的导数Guest: the derivatives of that

主持人：不过，是啊，我是说软件，我是说软件开发的速度一直以来都……呃，还有整个产品开发也是如此，不只是编写代码。Host: but yeah I mean the software I mean the speed of software development has been uh and and and really product development uh as a whole not just coding

访客：实际Guest: actual

主持人：绝对如此，我们推出新产品的速度与过去相比已经大不相同。我想你们也能看到这一点。你知道，AWS 一直以快速推出功能著称，而在过去一年左右，随着我们所说的“前沿团队”开始采用智能体式开发，而不是传统开发，我们在这方面又有了加速提升。它不再只是代码补全，而是真正以智能体为先：智能体编写所有代码，你只是在管理一支智能体团队并推动工作进展。看到面向客户的创新以如此惊人的速度涌现，确实很有意思。而这也势在必行，因为我们的客户对新能力几乎有着无穷无尽的需求，而这正是我们必须做到的。Host: absolutely the the case at which we're deploying new products is is um is massively different than it has been in the past. I think you can see this where you know AWS has always been known for rolling out features really quickly and uh and it's you know we've we've seen a turbo boost on that in the last year or so as as we've get we call them frontier teams as they think about agentic development as opposed to kind of traditional development and it's you know it's not code completion it really is agent first the agents write all of the code you're just managing a team of agents and and driving that um and it's it's been fun to see the the the pace at which um innovations for customers has has been massive and um and it has to be cuz that's the that's the our customers out there have a a almost insatable appetite for new capabilities and and that's what we got to do.

嘉宾：是的。那么从组织层面来说，你有没有什么见解，比如组织应该如何改变，以及如何……[叹气]Guest: Yeah. So organizationally are you like do you have any insights on how organizations should change and how [sighs]

主持人：我不，呃，我会这么说。Host: I don't uh I'll say that

嘉宾：智能体管理人，人管理智能体Guest: agents manage people manage agents

主持人：呃，会有很多人，而且这种情况会持续很长一段时间。我确实认为组织会发生变化。我还不知道那个神奇的答案。嗯，但我们正在积极思考……Host: uh there's like there's going to be there's going to be lots of people for a long period of time. I do think organizations will change. I don't know the magic answer yet. Um but but we're actively thinking

主持人：你在里面就是尝试各种实验之类的吗？Host: inside you're just trying various experiments or what?

嘉宾：我们正在尝试一些实验。我们在考虑采用小组模式。嗯，你想想，比如在产品组织中，以前会有一个团队长期负责某一类特定能力，可能有10个人在做这件事。但如今创新速度可以这么快，团队不一定非得有10个人，可以是三到四个人。Guest: We're trying experiments. We're thinking about pods. Um as you think about you know here's one example is in a product organization you used to have a team that would own a particular kind of capability for a long time and you might have 10 people working on that thing. Well today you can innovate so rapidly that one doesn't have to be 10 people. It can be three to four people

主持人：他们构建东西的速度太快了，以至于你会想把他们调到不同的项目和问题上去。所以，我们也在思考，如何既运营和维护已经构建的东西，又能保持敏捷灵活，在 AWS 这样规模庞大的组织里灵活调动，同时参与各种正在尝试和探索的事情。不过，嗯，这很有趣，也能赋能我们的员工。他们其实很喜欢，因为这样他们可以更快地构建东西、做更多事情。但，嗯，这确实也需要投入实际工作。Host: and they build something so fast, you actually want to move them to different projects and and problems. And so thinking about how do you both operate and and maintain the things that you built while being agile and flexible to move around on an organization that's as big as AWS is as active things that we're experimenting with and playing with. But it's um it's fun and it's enabling for our employees. They actually love it because they can build faster and do more. But um but there's real work there.

嘉宾：是啊，这是一个非常引人入胜的时代。嗯，非常感谢你抽出时间。我们可以就这个话题聊上好几个小时，不过还是感谢你分享的这些见解。Guest: Yeah, it's a fascinating time and uh so thank you very much for your time. We could be talking about this for hours together, but uh thanks for all your insights.

嘉宾：是的，感谢邀请我，也谢谢你们。我们非常高兴你们的公司成为我们的客户，也很乐于向他们学习。感谢你们邀请我来到这里。Guest: Yeah, thank you for having me and uh and thank you. We we we love having uh all your uh your companies as customers and uh we love learning from them and uh I appreciate having me here.

主持人：好的，我们会继续把他们介绍给你。Host: Yeah, we'll keep sending them your way.

嘉宾：太好了。谢谢。Guest: Excellent. Thank you.

主持人：谢谢。Host: Thanks.