萨提亚·纳德拉谈 Copilot、AI 反弹与微软的未来

Satya，感谢你邀请我来西雅图。

Satya, thank you for having me here in Seattle.

不，非常感谢你，Alex，专程跑这一趟。

No, thank you so much, Alex, for making the trip.

当然。要聊的很多。我想从我觉得行业当下所处的时刻开始，你们在刚刚发布的公告里也谈了很多，就是智能体的崛起。你在消费端看到 Muse、Grok Bot、Instinct，然后是企业端的 Copilot。感觉一场转变正在发生。我一直在想、也很想听听你的看法的是，这感觉像是一场圈地运动，争夺的可能是下一个大界面，也就是人们与整个数字生活交互的控制平面。我认为你们在 Copilot 和企业端采取了一种非常具体的路径，这个我们后面会聊。但先从大处着眼，我很想听听你对这一点的回应，以及你认为我们当下这个时刻意味着什么。

Of course. There's a lot to cover. I want to start with the moment I feel like the industry is in, and you all touched on a lot in your announcements that are just now coming out, which is this rise of agents. You're seeing it in consumer with Muse, Grok Bot, Instinct, and then what you have now with Copilot in the enterprise. And it feels like a shift that's underway. And the way I've been thinking about it, and I'm curious to hear your take on this, is it feels like there's a bit of a land grab happening for what could be the next big interface, the control plane for how people interact with their entire digital lives. You guys are taking, I think, a very specific approach with Copilot and enterprise, which we'll get into. But starting big picture, I'd be curious to hear you react to that and what you think the moment we're in represents.

对，我的意思是，如果你回顾一下，我不知道，自 ChatGPT 以来发生了什么，一直存在一种共同演化，我会把它描述为成为 AI 用户体验的形态，以及 AI 能力本身。所以如果你看 ChatGPT，它其实真正是 GPT 模型，但正是那个模型上的基于人类反馈的强化学习（RLHF）让聊天成为可能。然后是编码智能体，它显然催生了像 Claude Code 这样的东西。事实上，当我看 Copilot co-work 时，它本质上就是一个编码智能体，一个智能体循环，或者说智能体循环在某种程度上就是那项创新。OpenClaw 是第一次让你预见到一种长时间运行的智能体式形态，而模型在某种意义上已经追上了它，对吧？所以现在如果你看，事实上是两件事，你知道，自 OpenClaw 发布以来，你安装它，在本地机器上运行它之类的，然后你必须真正克服长期运行它、把它作为长期运行基础设施的挑战。那是其一，还要保证它的安全。而这就是云对运行它的虚拟机或容器或沙箱进行加固的地方，现在这些都有了，能够运行数天并保持连贯性的智能体现在也有了，你外化记忆之类东西的能力也有了。所以这就是为什么我们非常兴奋。就像我们今天在 Copilot 里发布的那样，你拥有了这一切，对吧？你有聊天，你有 co-work、pilots，所有这些。我认为现在是不同的形态、不同的模型、不同的能力，它们都有各自的位置。我不认为会有某一样东西取代另一样。事实上，我可能就直接去一个提示框里用它，然后合适的形态就会浮现出来。

Yeah, I mean if you sort of even track what has happened, I don't know, since ChatGPT, there's been this co-evolution of what I would describe as the form factor that becomes the user experience for AI and the AI capability, right? So if you look at even ChatGPT, it was really the GPT model, but it was RLHF on that model that made chat possible. Then it was the coding agent that sort of obviously made something like Claude Code happen. And in fact, you know, when I look at Copilot co-work, it's a coding agent essentially, an agent loop, or the agent loop was the innovation at some level. OpenClaw was the first time where you kind of anticipated a long-running agentic sort of form factor, and the models in some sense have gotten caught up with it, right? So now if you look, and in fact two things, you know, since OpenClaw came out, you installed it, you ran it on your local machine or what have you, then you have to sort of really overcome the challenges of running it long-term as a long-running infrastructure. That's a, and secure it. And that's where the cloud hardening of a VM or a container in which or a sandbox in which it runs is now there, and the agents that are capable of running for days and keeping coherence are there, and your ability to externalize things like memory are there. And so that's why we're very excited. Like when we launched today in Copilot, you have all of it, right? You have chat, you have co-work, pilots, all of that. I think is now different form factors with different models and different capabilities, and all of them have a place. I don't think it's any one thing that replaces the other. And in fact, I may just go to a prompt box and use it, and then the right form factor sort of surfaces.

不过，当你看到像亚马逊封禁 Muse 这样的事，这是我最近亲身遇到的，这就正好切中了我之前说的对界面的圈地运动。我很好奇，当你看到这件事时，你怎么看？你是否预见到，随着智能体成为越来越多人与网络交互的方式，这会成为一场有点艰难的角力？

When you see things though like Amazon blocking Muse, which is something that recently happened to me, that is getting at the point I was making about this land grab for the interface. And I'm curious when you saw that, what did you make of it? And do you foresee this being a bit of a struggle as agents become the way that more and more people are interacting with the web?

对，我的意思是，是的，有很多很多因素在起作用，对吧？第一件事是，假设你为人类和人类网络设计了一些 API 表面甚至用户界面。那么当一个智能体，比如说，使用计算机操作从上方越过时，你知道，你与使用它的用户之间的 SLA 会怎样，对吧？你甚至没有为那种流量做过构建。所以我认为在这一点上，人们必须退一步说，嘿，这里面有哪些竞争层面的东西，但也有哪些设计层面的东西。就像 API，我们之所以在 Copilot 之下有一个一等公民的 Work IQ API 和表面区域，是因为你不能直接拿我在 Teams 或 Outlook 或 SharePoint 下面的数据库，在它是一条拨号音的时候用智能体流量去冲击它，对吧？你知道，如果我们去跟企业客户说，嘿，我们现在无法满足一个本质上是关键任务服务的 SLA 需求，因为有这么多我们无法控制的智能体流量。那是行不通的。所以我认为我们现在都必须正视：将会有新的界面、新的基础设施、新的使用条款、新的变现方式，因为这一切都会带来成本。然后还有竞争，因为尤其是如果有人从上方越过，我认为亚马逊这个案例就是如此，那就是去中介化。

Yeah, I mean, yeah, there's many many things that are at play, right? The first thing is, let's just say you designed some of the API surfaces or even user interfaces for humans and the human web. And so when an agent, let's say, uses computer use and comes over the top, you know, what happens to the SLA you have with the users who are using it, right? You didn't even build for that traffic. So I think that at this point one has to sort of step back and say, hey, what are the competition aspects of it, but also what's the design side of it. Like even APIs, the reason why we have a first-class work IQ API and surface area underneath Copilot is you can't just go take my database underneath Teams or Outlook or SharePoint and hit it with agent traffic when it's a dial tone, right? You know, if we go to our enterprise customers and say, hey, we can't now meet the SLA needs of what is essentially a mission-critical service because there's all this agent traffic that we have no control over. That won't work. So I think we all have to now come to grips with: there is going to have to be new interfaces, new infrastructure, new terms of use, new monetization, because this is all going to have cost associated with it. So, and then there's competition, because especially if somebody comes over the top, which I think is the Amazon case, it's disintermediation.

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你认为这个智能体市场有多零和，无论是在个人场景还是企业场景？

How zero sum do you see this market of agents, both in the personal context and the enterprise?

你知道，零和是个有意思的说法。我认为，决定这一切能否被使用的根本在于，它是否在创造一些净新增价值，以 GDP 来衡量，对吧？所以这不是要清算过去。过去那种流动方式可能会有一些颠覆。比如说商业流动会改变，因为现在我就直接找我的智能体，我的智能体替我购物，所以一些购物习惯或搜索习惯之类的，在消费端全都被去中介化了。在那种情境下，它可能零和得多，对吧？因为你甚至可以说，消费端存在的这些聚合效应就是中间商，对吧？它们基本上只是在聚合其他人，而突然之间，当你可以直接触达商家或供应商时，那个中介就不必要了，这种情况可能发生。然后说到商业端，就要看用例是什么，因为商业业务是平台业务，不是聚合业务，也就是说你必须为客户雇佣你去达成的某个结果增加具体的价值。只要谁在那里交付最好的价值，谁就赢。所以会有大量的价格竞争。

You know, zero sum is an interesting one. I think that the fundamental thing that predicates the use of all of this is it's creating some net new value, as measured in GDP terms, right? So it's not about litigating the past. There may be some disruptions to how the past flow happened. Let's say commerce flow changes because now I just go to my agent and my agent does my shopping, so some of the shopping habits or search habits or what have you all get disintermediated on the consumer side. It may be a lot more zero sum in that context, right? Because you could even say these aggregation effects that existed in consumer were middlemen, right? They just were basically aggregating other people, and suddenly when you can reach the merchant directly or the supplier directly, that intermediary is not necessary, and that could happen. Then when it comes to commercial, it's going to be about what's the use case, because the commercial business is a platform business, not an aggregator business, in the sense that you have to add specific value for some outcome that the customer has hired you for. And as long as whoever is delivering the best value there, that person wins. So there's going to be a lot of price competition.

也许存在价值竞争，也是平台经济的竞争，但这并不意味着我们只是通过聚合别人的软件来进入企业市场。对吧？我们构建人们认为有价值的平台。只要我们做到这一点，事实上，这可能是史上最大的 TAM（潜在市场总额）扩张。对吧？我的意思是，如果你想一想，我们的服务器业务在 90 年代和 2000 年代一直是非常健康的业务。云业务因为人们消费更多而大了几个数量级。我认为智能体时代将比云还要大几个数量级。所以从 TAM 的角度来看，这是扩张性的。我们现在必须真正保持专注，确保每一个 autopilot、每一次 co-work 会话、每一次聊天会话都与推动生产力、推动业务成果相关。

Maybe there's a value competition and it's a platform economics competition, but it's not like we are in the enterprise by just basically aggregating other people's software. Right? We build platforms that people find valuable. And as long as we do that, in fact, this is probably the biggest TAM expansion ever. Right? I mean, if you think about it, our server business was a very healthy business throughout the '90s and the 2000s. The cloud business was orders of magnitude bigger because people consumed more. I think the agent era will be even bigger than the cloud by orders of magnitude. So that, from a TAM perspective, is expansive. We now have to really stay focused on making sure that every autopilot, every co-work session, every chat session is in relation to driving productivity, driving a business outcome.

过去一年，Copilot 的发展轨迹是如何演变的？你们还处于非常早期的阶段，尤其是在编码市场的 Copilot GitHub 方面。你刚刚展示了 Copilot 各项功能的大量统一，我想谈谈 Autopilot，我认为这可能是其中最有趣的部分。但首先从更大的图景来看，Copilot 是如何演变的？

How has Copilot's trajectory evolved over the last year? You're very early, especially with Copilot GitHub in the coding market. You just showed a lot of unification across everything you do in Copilot, and I want to get to Autopilot, which I think is maybe the most interesting part of this. But bigger picture first, with Copilot, how's it evolved?

所以对我们来说，我们对两件事感觉非常非常好。一是 Copilot 在企业核心领域的渗透率。与任何新技术相比，包括像 Teams 这样的技术，实际上速度更快。对吧？当我们想到 Copilot 在企业中甚至超过 3000 万付费订阅者，在推出后的几年内，这比我们历史上看到的任何东西都要快。从这个意义上说，我觉得我们终于拥有的是模型，它们实际上更有能力实现 Copilot 的一些承诺。一个很好的例子是 co-work 和 Excel 智能体。对吧？到目前为止，我们还没有足够好的模型来做你现在能做的两件事。对吧？第一件是，让我去 Copilot 里的 co-work，让它创建一个复杂的，比如供应链优化电子表格，所有场景都在不同的工作表里等等。它会创建一个很棒的工件，对吧？所以，第一，它创建相当复杂模型的能力很棒，但问题是，我需要用这个模型做点什么。我不只是看到模型输出就接受它。我想操作它。我想询问它。我想对它进行推理。所以，现在我们有甚至 Excel 内循环，我称之为 Excel 智能体，它也非常有能力进行直接操作。事实上，上周末，我的一个数据中心的人给我发了一个相当复杂的电子表格，我打开它，然后我到一个单元格，然后我问我的 Excel 智能体，看看那个人在那个单元格里用的公式，然后为我创建五个带有场景的工作表，对吧？能够做到下一阶段的因果推理，甚至是在 AI 智能体的输出上，这真是太了不起了。所以对我来说，这就是我们终于到达的地方，我会这样描述：模型能力的跃升与我们现在的形式因素结合在一起，帮助这些每天增加价值的企业工作流程。我认为这将是巨大的。

So for us, we feel very, very good about two things. One is the penetration of Copilot in the core of the enterprise segment. As measured against any new technology, including something like Teams, the pace is faster actually. Right? When we think about even 30 plus million paid subscribers of Copilot in the enterprise within whatever a couple of years of its launch, it's faster than anything we have seen historically. In that sense, the thing that I feel we finally have are models that are actually more capable of delivering some of the promise of Copilot. And a good example of this is take co-work and Excel agent. Right? Up to now, we've not had the models that were good enough to do either of the two things that you now can do. Right? Which is one is, let me go to co-work in Copilot and ask it to create a complex, I don't know, supply chain optimization spreadsheet with all the scenarios in different sheets and so on. It'll create a fantastic artifact, right? So, one, its ability to create a pretty complex model is great, but here's the thing. I need to then do something with the model. I just don't see a model output and take it. I want to manipulate it. I want to interrogate it. I want to reason over it. So, now we have even the Excel inner loop, as I call it, the Excel agent that is also super capable with direct manipulation. In fact, last weekend, I had like one of my data center people sent me a pretty complicated spreadsheet and I opened it up and then I went to a cell and then I asked my Excel agent, take a look at that formula that that person is used in that cell and create five sheets for me with scenarios, right? That ability to be able to do that next phase of causal reasoning even on something that was an output of an AI agent, it's just tremendous. And so to me that is where we are finally, the way I would describe it is the model capability jumps with the form factors we now have coming together to help with these enterprise workflows that add value every day. I think it's going to be tremendous.

微软的资本支出规划什么时候会智能体化？我们目前处于什么水平？

When does Microsoft's capex planning get agentified? What level are we at with that?

你信吗？事实上，你问这个问题很有意思，因为我有一个完整的追踪器，追踪我们所有的资本支出，我们所有的 ROIC（投资资本回报率）按层次划分。事实上，这很有趣。是的，它是一个 co- 不，实际上我是在 GitHub Copilot 上构建的，你知道。是的，这就是你现在可以创建的那种工件。这是它的另一个方面，对吧？即企业上下文与世界上下文的结合。事实上，我会去每个云提供商、超大规模企业、每个新云企业的 SEC 文件。这是实时的。我在 Fabric 中有一个数据运行器，它带来所有数据，放入语义模型，然后被我的编码智能体读取，然后作为仪表板呈现。每天都是新鲜的。所以我拥有所有人，每一个 SEC 文件，加上我所有的内部分析，不断结合在一起，给我实时的按层次划分的 ROIC。

You believe me? In fact, it's fascinating you asked that because I have like this complete tracker of all of our capex, all of our ROIC's by layer. It's like, in fact, it's interesting. Yeah, it's a co- no, it's actually I built it on GitHub Copilot, you know. Yeah, that's the kind of artifact that you now can create. And this is the other aspect of it, right? Which is the enterprise context combined with the world's context. In fact, I go to the SEC filings of every cloud provider, hyperscaler, each of these neoclouds. It's in real time. I have a data runner in Fabric that brings all that data, puts it into a semantic model that then gets read by my coding agent and then surfaces it as a dashboard. And every day it's fresh. So I have the entirety of everyone, every SEC filing that goes out there, plus all of my internal analysis constantly coming together, giving me real-time ROIC by layer.

哇。让我们谈谈 Autopilot。你们称它为数字队友，我很好奇你认为它将解锁什么，以及你期望这如何改变人们的工作和生活方式。我的意思是，可能还有工作之外的应用。

Wow. Let's talk about Autopilot. You all are calling it a digital teammate, and I'm curious what you think it will unlock and how you expect this to change how people work and just live their lives. I mean, there's probably applications beyond work.

是的，绝对。所以，Autopilot 对我来说是自然的下一步，对吧？正如你所说，每个人都在热议正在发生的事情。你知道，它开始了，我认为我们应该赞扬 Peter 和团队在 OpenClaw 上所做的，那是巨大的。

Yeah, absolutely. So, Autopilot to me is that natural next step, right? Which is what you said is everybody's buzzing about what's happening. You know, it started, I think we should give credit to what Peter and team did with OpenClaw, which was tremendous.

如果我错了请纠正我，但 OpenClaw 是 harness 还是底层的开源组件？

Correct me if I'm wrong, but is OpenClaw harness or the open source component underneath?

绝对。是的，我认为我们会加固那个底层的 harness，然后我认为我们基本上会把它带到 GitHub Copilot harness，这是我们在所有形式因素中使用的 harness，无论是 co-work 还是 Autopilot。我们的目标是真正，如你所说，创建一个系统，它真正拥有工作区、计算机，以及这个长期运行的智能体 harness，然后你可以指导它执行长期任务，甚至是你指定的工作。我认为在企业中，它会被使用的地方，例如，我们所有人，现在微软的每个人都可以拥有一个复杂的，我称之为幕僚长，他们可以委托给它，对吧？你可以给这个 Autopilot 一个身份、一台计算机、一个工作区，以及一些方向，然后它就会运行，有记忆，并且会持续工作。你可以在 Teams 中与它交互，就像与另一位同事一样。我认为在企业中，它最常被使用的另一个地方将是任务工作。那将是自然的第一个地方，对吧？我有一个，你知道，我每天进来管理发票。我会说，嘿，与其我管理发票，我所做的是，我会创建一个 Autopilot，只管理发票，对吧？它知道，我会像与同事一起工作一样与它互动。

Absolutely. Yeah, I think we'll harden that that's underneath in our harness, and I think we then make, you know, basically I think we take that harness and bring it to GitHub Copilot harness, which is the harness we use across all of our form factors, whether it's co-work or Autopilot. And the goal for us is to really, as you said, to create a system which really has a workspace, a computer, and this long-running agent harness that then you can direct to long, you know, to tasks or jobs even that you specify. And I think in the enterprise, the place where it'll get used, for example, all of us now, everyone at Microsoft can have essentially a sophisticated, I'll call it chief of staff that they can delegate to, right? Which is you can give this Autopilot an identity, a computer, and a workspace, and some direction, and it goes off and has memory, and it'll work on a continuous basis. You can interface with it in Teams just like you would with another colleague. The other place where I think this will get most used in the enterprise will be task work. That'll be the natural first place, right? I have a, you know, I come in, manage invoices every day. I'll say, hey, instead of me managing invoices, what I'm doing is I'm, I'll create an Autopilot that just manages invoices, right? It knows, and I'll deal with it like, or I'll interact with it like I work with a colleague.

我认为那将是起点，然后随着我们更有信心——因为这件事的根本之一是可审计性、可观测性、安全策略、治理。所以举例来说，在现阶段，有了这些强大的模型，不是说“哦，它们很强大，那太好了”，而是我还需要保证那种力量是在轨道上的——不是一次，不是偶尔，而是始终如此。因此这就是为什么我们在构建所有这些。顺便说一句，即使在消费端也是如此。当你突然让你的消费级智能体做了你从未预料到的事情的那一天，就是你停止使用它的那一天。所以我认为建立这种长期信任——这是一个艰巨的挑战，对吧？在企业端，这就是为什么我们花了时间，甚至过去四五个月，来加固沙箱、加固这个智能体 365，拥有所有的治理组件。所以顺便说一句，我们将把同样的事情带到消费端。

I think that'll be the place where it will start, and then as we have more confidence—because one of the fundamental things for this was going to be auditability, observability, security policy, governance. So for example, at this point with these powerful models, it's not that, oh, they're powerful, that's great, but I also need assurance that that power is on rails—not once, not sometimes, but always. And therefore that's why we're building all of this. And by the way, this is going to be true even in consumer. The day you suddenly have your consumer agent do things that you never expected is the day you stop using it. And so therefore I think building that long-term trust—and that is a tough challenge, right? On the enterprise side, that's why we've taken the time, even the last four or five months, to harden the sandbox, harden this agent 365, have all of the governance pieces. And so that's the same thing that we will, by the way, bring even to the consumer piece.

也许这只是我，但我想很多人都是这样：你的工作生活和个人生活，它们交融在一起。人们不会清楚地划分，我在工作，我在用工作 AI，现在我在用个人 AI。这让我想到，微软在消费级智能体方面的使命和目标是什么？你认为公司也需要在消费端取胜吗，还是你有不同的看法？

Maybe this is just me, but I think a lot of people are this way: it's your work life and your personal life, they bleed together. People don't clearly delineate, I'm at work and I'm using work AI, and now I'm using personal AI. Which leads me to what is Microsoft's mission and goal in consumer agents? Do you think the company needs to win in consumer as well, or do you see it differently?

是的，我的意思是，我认为你知道，在当今世界，“消费”是一个非常宽泛的词，因为有很多很多类别。事实上，如果你回顾我们的历史，我们最初是一家消费公司，然后成为一家商业公司。事实上，当我加入微软时，大多数人认为我们是一家消费公司，而我前 10 年处理的问题是：你们什么时候会认真对待企业业务？现在我们做到了。对我来说，关键不是试图去做所有其他消费品牌可能在做的事情，而是利用我们自己的 1 亿多 Office 365 和 Microsoft 365 消费订阅用户。我的意思是，他们喜欢在生活中拥有丰富的办公工具，因为他们在工作中使用，在家里使用，他们管理税务，管理财务。所以对我来说，能够生产出同样功能水平的产品，对吧？所以所有这些都将进入我们的消费产品。事实上，如果有什么不同的话，我会说我们在早期分叉了，有消费版 co-pilot 和商业版 co-pilot。现在我们已经把整个东西整合成一个连贯的产品。它就是 co-pilot。它与你的 Microsoft 账户配合使用。它甚至与你的社交 ID 配合使用，当然在企业中与 Entra 配合使用，但它是同一套产品功能。因此，autopilots 也将进入消费端。事实上，今年早些时候我们甚至有了 TAS 的早期版本，但现在我们将把所有力量整合到一个丰富的产品中。

Yeah, I mean, I think you know consumer is a very expansive word in today's world because there are many, many categories. In fact, if you look back at our history, we grew up as a consumer company and then became a commercial company. In fact, when I joined Microsoft, most people thought of us as a consumer company, and the question I dealt with for the first 10 years is when will you get serious about enterprise? And here we are. And to me, the thing there is not to sort of try and do what all other consumer franchises may be doing, but take our own 100 plus million subscribers of Office 365 and Microsoft 365 and consumer. I mean, they love the fact that they can have rich office tools in their life because they use it at work, they use it at home, they manage their taxes, they manage their finances. And so to me, being able to produce the same product with the same level of functionality, right? So all of this is going to go into our consumer product. In fact, if anything, I would say we took a fork in the early days of having a consumer co-pilot and a commercial co-pilot. And we now have brought the entire thing all together into one coherent product. It's just co-pilot. It works with your Microsoft account. It works with even your social IDs, and it works of course with Entra in the enterprise, but it's the same set of product functionality. And so therefore autopilots will also go to the consumer side. In fact, we had like an early version of all of this even earlier this year with TAS, but now we'll bring all that power in one rich product.

我很好奇你如何看待这里的定价和你的自动模式。我知道你现在有基于模型的定价。我最近看到一个令人难以置信的统计数据，即 AI 的降价速度比历史上任何其他大型科技浪潮都要快。自 2023 年以来，token 成本大约每季度下降一半。这实际上很了不起。但与此同时，我认为企业端仍然存在大量浪费的 token 支出。我想我们可能已经过了 token 最大化的时刻，但现在人们仍在试图弄清楚如何收回这些浪费的支出。你如何为新 co-pilot 定价？你认为在我们即将进入的这个智能体世界中，定价应该如何运作？

I'm curious how you're thinking about pricing here and your auto mode. I know you have on the model now. I saw an incredible stat recently which is that AI is getting cheaper more quickly than any other big tech wave in history. Token cost has fallen roughly half every quarter since 2023. It's actually remarkable. But at the same time, there's still a lot of, I think, wasteful token spending happening in the enterprise. I think we're maybe past the token maxing moment, but now people are still trying to figure out how do I recover this wasted spend. How are you approaching pricing for the new co-pilot? How do you think pricing should work in this agent world that we're going into?

是的。所以我认为我们在商业领域采取的方法是结合所谓的按席位定价和按使用量定价。原因在于，你知道，当你考虑按席位定价的核心时，它对于任何客户来说都是一种更方便的预算和购买方式，没有意外。对吧？从根本上说，席位是固定价格。所以我们的目标，实际上利用你刚才所说的，即模型价格正在下降，意味着每天我都可以为订阅增加更多价值。所以特别是有了自动模式，它在今天的 copilot 中如此强大的原因是，我上次去选择模型——你知道，我已经好几个月没有选择模型了，因为我现在有信心自动选择正确的模型并使用它，从而从我的订阅中获得最大收益。所以我们觉得与客户非常一致，我们可以传递 AI 的进步和价格下降，并不断增加他们会员资格的价值，对吧？也就是说，如果他们是 co-pilot 的会员，他们将从订阅中获得越来越多的价值。但任何时候如果他们想要最新的、最棒的任何东西，那也可以提供给他们，那是基于使用量的。但你知道，这有点像窗口化，对吧？随着时间的推移，今天基于使用量的东西明天就会包含在订阅中。所以事实上，这种组合应该给商业客户更多的灵活性，在采购、预算、何时采用新事物与大规模采用等方面。顺便说一句，同样的事情也适用于消费端，但有一个额外的——因为即使在消费端，你也会有同样的，你有订阅和基于使用量的，但也许有一个额外的工具，叫做某种交易或广告单元，可以创造更多的补贴。对吧？所以换句话说，如果你能破解一个广告单元，基本上有效地为你的订阅增加更多积分，那将是我们在消费端降低价格的另一种方式。

Yeah. So I think the approach we have taken in commercial segments is to have a combination of what I'll call seat-based pricing and usage-based pricing. And the reason for that is, you know, when you think about the core of what is seat-based pricing, it just is a much more convenient way for any customer to be able to budget and buy without surprises. Right? At the fundamental level, seats are fixed price. And so our goal, using in fact what you just said, which is the fact that the models are dropping in price, means every day I can add more value to the subscription. So especially with auto mode, like the reason why it's so powerful in copilot today is the last time I went and chose a model—you know, it's been months since I picked a model because I now have confidence in auto picking the right model and using it so that I get the maximum benefit from my subscription. So we feel super well aligned with our customers that we can pass through the advances in AI and the drops in prices and keep adding more and more value to essentially their membership, right? Which is if they're a member of co-pilot, they are going to for that subscription increasingly get value. But at any point if they want the latest and greatest of anything, that's also available to them and that's usage-based. But you know, it's sort of windowing, right? Which is over time even what is today usage-based will be tomorrow in the subscription. So the fact is this combination should give commercial customers a lot more, I would say, flexibility in how they procure, how they budget, how they think about when to adopt something new versus adopt something at scale and what have you. And by the way, the same thing applies even on the consumer side with one additional—because even on the consumer side you'll have the same, that you have a subscription and you have usage-based, but maybe one additional instrument called some type of a transaction or an ad unit that could create even more subsidy. Right? So in other words, if you can crack an advertising unit that essentially adds more credits effectively to your subscription, that'll be another way on the consumer side we can bring the prices down.

如果你和你的同事待得够久，你会经常听到“生态系统”这个词。这感觉像是你们在过去几年中谈论方式演变的一部分，尤其是考虑到早期对 OpenAI 的 IPO 前投资和那种合作关系，你们显然还会保持一段时间，但我现在听到你经常谈论，你知道，编排作为所有模型的合作伙伴，我们刚才谈到的路由，以及真正确保听起来微软可以在任何给定时刻接入任何模型的最佳部分。我很好奇你什么时候意识到这是我们需要走的方向。我们需要在 AI 方面走向这个生态系统的方向。

If you hang around you and your colleagues long enough, you'll hear the word ecosystem a lot. And this feels like something that has evolved for you guys in terms of how you talk about it over the last couple of years, especially when you consider the early, you know, pre-IPO bet on OpenAI and that partnership and you still have that obviously for some time, but I'm hearing you a lot now talk about, you know, orchestration being a partner to all the models, what we just talked about on the routing and really ensuring it sounds like that Microsoft can plug into the best of any model at any given point. And I'm curious when you realized this is the direction we need to go in. We need to go in this ecosystem direction for AI.

看，从核心上讲，我认为这源于微软是一家平台公司。

See, at the core, I think it comes from Microsoft being a platform company.

我一直用一个简单的准则来定义平台，那就是在平台之上创造的价值必须远远大于平台自身所获取的价值。这是比尔的名言。所以如果你采取这种思路，把前沿仅仅当作一两个前沿模型来谈就没有意义。你必须真正把它构想并交付为一个前沿生态系统。坦白说，这符合我们所有人的利益，甚至包括模型开发者，因为没有它，就不会有人使用 token、购买模型。因为归根结底，只有一件事重要，那就是经济中真实的 GDP 增长。如果以此为标准，除非一家又一家企业创造出盈余，否则这一切都不会发生。每一家小企业、每一家跨国公司都需要能够说，哇，我有了一种新的投入品，我给它定价，叫做 token。价格在下降，但如果这是边际投入成本，那么边际产出收入或利润率必须是它的数倍，才能证明这一切的合理性。所以在我看来，这就是为什么所有这些事情、生态系统的构建如此重要。你想要有多个模型。你不能只有一个模型，因为如果只有一个模型，我们知道会发生什么，那就是 token 定价最终会走向何方。所以如果你想长期拥有一个更平衡的生态系统，你需要以这种方式思考每一层。

And I always define platforms with one simple dictum, which is the amount of value that gets created above the platform has to be far greater than what the platform captures. That's Bill's famous quote. And so if you take that approach, talking about the frontier as a frontier model or two doesn't make sense. You have to really get this to be conceptualized and delivered as a frontier ecosystem. And quite frankly, it is in the interests of all of us, even the model makers, because without it we will not have anyone using tokens and buying models. Because at the end of the day, there's only one thing that matters, which is true GDP growth in the economy. And if that is the measure, it's not going to happen if there isn't surplus being created one firm at a time. Every small business, every multinational company needs to be able to say, wow, I have a new input that I have priced called tokens. The prices are dropping, but then the output, if that is the marginal input cost, then the marginal output revenue or margin has to be multiples of that in order to justify all of this. And so that to me is why all of these things, the ecosystem construct, matter. You want to have multiple models. You can't have one model, because if there is only one model, we know what happens, which is where the token pricing will end up. So if you want to long-term have essentially a more balanced ecosystem, you need to think about each layer that way.

另外一件事，Alex，也是第一次，我想说的是我写过的这个反向信息悖论，即这是一个学习系统。到目前为止，你可以买一个数字工具，并且知道这个数字工具是被我用来在我的企业或生活中创造新价值的。但当你有一个学习系统，你付费让它为你做事，但它也能仅仅利用废气，并不是说它们需要看到，模型不需要看到你的数据，模型只需要从你所做的事情中学习，捕捉那些让你与众不同的模式。我认为这才是真正的挑战。所以如果这种情况发生，那么你的差异化是什么？那些隐性知识是什么？你在企业中拥有的那种神圣不可侵犯的判断力是什么，你如何保留它？所以我们必须真正让每一家企业、每一个企业都能够实现自己的独立性，甚至可能一直走到拥有自己的模型权重。

The other thing that also, for the first time, Alex, I'd say, is what I've written about, this reverse information paradox, which is this is a learning system. Up to now you could buy a digital tool and know that the digital tool is used by me to create new value in my enterprise or in my life. But when you have a learning system where you're paying for it to actually do something for you, but it's also able to just using exhaust, it's not like they need to see, and the model doesn't need to see your data, the model needs to just learn from what you do and pick up the patterns that are making you distinctive. That's I think the real challenge. And so if that happens, then what is your differentiation? What is that tacit knowledge? What is that judgment you have in the enterprise that is sacrosanct, and how do you retain it? And so we have to really enable every enterprise and every business out there to be able to achieve their own independence, even with maybe all the way to having model weights of their own.

你提到了 GDP 这件事，过去几年我听到你相当一致地这么说，你是用 GDP 提升来衡量 AGI 的。我听你说过应该是 10 左右。最近我听到 7 到 8。我们现在处在什么位置？你认为它最终会落在 GDP 的什么水平？

You mentioned the GDP thing and I've heard you say this pretty consistently over the last couple of years, that you're measuring AGI by GDP lift. I've heard you say it should be about 10. I heard recently 7 to 8. Where are we on that? And where do you think it will actually land in terms of GDP?

我的意思是，如果我甚至回溯到工业革命时期，用历史的视角来看，我认为情况就是这样：出现了一种新技术，它的扩散和它真正显现之间有一段差距。事实上，即使在信息技术领域，个人电脑基本上是在 80 年代末、90 年代初在企业中普及的，而它们直到 90 年代末和 2000 年代初才出现在 GDP 增长数字中。电力和诸如此类的东西也是如此。那里，我认为从电力的引入到它真正表现为广泛的 GDP 增长，大约有 50 年的差距。为什么？因为你需要重组生产、重组知识工作，才能用这些工具创造出那样的产出和盈余。所以这一次，我希望它更压缩，但我们已经在路上了。现在很多 GDP 增长，让我们面对现实，更多是我所说的供给侧刺激，也就是我们所有人都在建数据中心、几个热门产品等等。这很棒，这本身就是一件美妙的事，因为毕竟所有生产这些东西的人都在期待，嘿，比如说我供应链中的任何人，我希望他们都在用 Copilot，因为我需要他们更快地生产他们为我的数据中心生产的任何部件。这本身就是一个美妙的良性循环。但这件事的广泛普及，我认为会发生，希望它比以往任何信息技术变革都更快，但它不会是线性的，因为它归结为变革管理。并不是说你有了一个工具，就能突然改变工作、工作产物和工作流程。这需要领导层的艰苦努力，才能真正让你重新构想如何在这个新的效率前沿中做这些事情。

I mean, if I go back even and see this through the historical lens of what happened during the industrial revolution, I think that's what happens, which is there's a new technology, you have this gap between its diffusion and when it really shows up. In fact, even in information technology, the PCs basically spread across the enterprise in the late 80s, early 90s, and they showed up in GDP growth numbers only in the late 90s and the early 2000s. Same thing happened with electricity and what have you. There it was, I think, about a 50-year gap between the introduction of electricity and when it really showed up as broadspread GDP growth. And why? Because you need to reorganize production, reorganize knowledge work, to use these tools to create that output and surplus. And so this time around, I hope it's more compressed, but we're well on our way. Right now a lot of the GDP growth, let's face it, is a lot more, I'll call it supply-side stimulus, which is all of us building data centers, a couple of hit products and what have you. And that's fantastic, that itself is a wonderful thing, because after all, all the people who are producing that are looking to, hey, let's say anyone in my supply chain, I hope, are using Copilot, because I need them to produce whatever part that they're producing for my data centers faster. And that itself is a fantastic virtuous cycle. But the broad spread of this, I think, will happen, it'll happen faster hopefully than any of the previous information technology changes, but it's not going to be linear, because it comes down to change management. It's not like you can suddenly change the work, the work artifact, and the workflow just because you have a tool. It takes the hard work of leadership that really allows you to then come up and reconceptualize how to do this stuff in the new efficient frontier.

Mercury 是一种为像我这样的初创公司打造的现代银行服务。当我决定创办我的媒体业务时，Mercury 是迄今为止对我来说最直接、功能齐全的银行解决方案，让我能快速完成设置。界面直观简单，每天为我节省宝贵时间。我用 Mercury 追踪支出、账单和发票。我喜欢可以把权限委派给我的团队，这样他们就能完全按照我想要的方式替我维持运转。我最喜欢的部分是 Mercury 在 AI 方面多么有前瞻性。传统银行还停留在过去，而 Mercury 是为当今现代软件的运作方式而构建的。我用它内置的命令助手来分析现金流并帮助我转账。Mercury 还能连接 ChatGPT 和 Claude 等其他 AI 工具。我一直使用这个功能，Mercury 的人还告诉我，我是这个功能的重度用户之一。所以，相信我，现在终于可以轻松地在任何你需要的地方获取关于你业务的实时财务数据了。访问 mercury.com 了解更多，并在几分钟内在线申请。Mercury 是一家金融科技公司，不是 FDIC 承保的银行。银行服务通过 Choice Financial Group 和 Column NA 提供，均为 FDIC 成员。

Mercury is a modern take on banking built for startups like mine. When I decided to start my media business, Mercury was by far the most straightforward full-featured banking solution for me to set up quickly. The interface is intuitive and simple, saving me valuable time every day. I use Mercury to track my spending, bills, and invoicing. I love that I can delegate permissions to my team so they can keep things running for me in exactly the way I want them to. My favorite part is how forward-looking Mercury is with AI. Legacy banks are stuck in the past, but Mercury is built for how modern software works today. I use its built-in command assistant to analyze cash flow and help me move money. And Mercury also connects to other AI tools like ChatGPT and Claude. I use this feature all the time, and the folks at Mercury actually let me know that I'm one of the top users of it. So, trust me, it's finally easy to get real-time financial data about your business wherever you need it. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column NA, members FDIC.

我花很多时间在会议之间切换上下文，常常在下一个会议开始前没时间处理上一个。幸好，Granola 全程在后台运行。它是一款易于使用的 AI 会议记事本，在任何地方都能用，甚至电话会议也行。我用 Granola 回忆会议中说了什么，并生成有用的摘要。我每天都用它来掌握我和团队需要完成的事情。它连接我的邮箱，并建议后续跟进事项，让我快速审阅并发送，节省宝贵时间。Granola 不仅仅是我工作流的核心部分。它基本上就是我的第二大脑。在 granola.ai/sources 试用 Granola，并使用促销码 sources 享受 3 个月折扣。

I spend a lot of time context switching between meetings, often with no time to process one before the next starts. Thankfully, Granola runs in the background the whole time. It's an easy-to-use AI notepad for meetings that works everywhere, even on phone calls. I use Granola to recall what was said in meetings and create helpful summaries. I use it every day to stay on top of what I need to get done with my team. It connects to my email and suggests follow-ups for me to quickly review and send, saving me valuable time. Granola isn't just a core part of my workflow. It's basically my second brain. Try Granola at granola.ai/sources and use the promo code sources for 3 months off.

AI 的有用程度取决于它所拥有的上下文，但当这些上下文分散在工具、帖子和私信里时，你的团队和你的 AI 智能体就是在盲飞。

AI is only as useful as the context it has, but when that context is scattered across tools, threads, and DMs, your team and your AI agents are flying blind.

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微软刚刚将其财务报告结构从三个部门改为两个。你现在有智能体和基础设施、设备与消费者。我的看法，我想听听你的反应，是你希望投资者将应用、智能体和云视为一个互联的业务，并评判你们在整个 AI 技术栈中捕获了多少价值。是这样吗？这就是原因吗？

Microsoft just changed its financial reporting structure from three segments to two. You now have agents and infra devices and consumer. My take and I want your reaction to this is that you want investors to see apps, agents and cloud as one connected business and to judge you all and how much value you're capturing across that entire AI stack. Is is that right? Was that the reason

没错？事实上，完全正确。所以当人们问我们，嘿，你们的投资回报率在哪里？嗯，这关乎我们的现金流以及我们如何将现金流作为资本和运营支出进行投资，以在智能体和基础设施的技术栈中获得回报。我们希望对这些投资中的每一项都保持透明，但也要展示其产业逻辑，即这些并非不同的投资。这是一项投资，需要在竞争激烈的市场中一层一层地为客户交付价值。但所有这些对我们的投资者来说都将非常透明。既包括公司为何如此行事的逻辑，也包括他们如何每季度跟踪我们，而且我认为这独特地定位了我们，因为它展示了……但我想说的另一点是，我们并不试图建立一个狭窄的业务，比如我们的基础设施只有三个客户就完事了。那不是我们的长期业务。我们希望能够服务于每一个中小型企业以及大型跨国公司，满足他们的 token 消耗、他们对微调模型的需求，甚至他们自己的前沿模型。这比……你知道，我们热爱与 OpenAI 的业务。他们是我们最大的客户之一。但我们的目标不是拥有一个大客户、两个大客户或五个大客户。而是拥有许多许多许多大客户。

that is correct? In fact, that is exactly correct. So we when when people ask us hey where is your ROIC um it's sort of our cash flow and how we invest that cash flow as capital and operating expense is to get returns across the stack uh of uh agents and infra and we want to be transparent about each of those uh but also the industrial logic that these are not all different investments uh It's about one investment that then needs to deliver value to customers in a competitive marketplace one layer at a time. Uh but all of that will be very transparent to our investors to see. uh both the logic of why the firm does what it does, how they can track us uh each quarter and also it I think uniquely positions us because it shows but the the other thing I would also say is we're not trying to build a narrow business where we have you know three customers for our infra and call it a day. That's not a long-term business for us. We want to be able to serve every small and medium business and large multinational with their token consumption, their need for a fine-tuned model, maybe even their own frontier model. That's more important than, you know, we love our business with OpenAI. They're one of our largest customers. Uh, but our goal is not to have one large customer or two large customers or five large customers. Is to have many, many, many large customers.

你提到了 OpenAI。嗯，我最近请了 Sam 上播客。他说他看到当前 AI 建设中的部分现象是“不可持续的愚蠢”。我很好奇，如果你看看……你们都在投入巨额资本支出。我是说所有超大规模企业都是，这是数万亿美元。在评估未来几年的资本支出承诺时，有没有什么让你犹豫的地方？你在寻找什么吗？

You brought up OpenAI. Um, I had Sam on the podcast recently. He said he sees quote unsustainable silliness in parts of the AI buildout happening right now. And I'm curious if you look at you know you all are committing a tremendous amount of capex. I mean all the hyperscalers are it's trillions of dollars. Is there anything you see that would give you pause as you're evaluating you know capex commitments in the years ahead? Is there any anything you're looking for?

是的。所以我认为，你知道，看看微软之外正在发生的事情，嗯，那是其他人要说的，但我会看我们自己的业务，我已经非常明确地表示，你必须为整个技术栈而构建，对吧？所以我关心的是为多样化的客户构建基础设施，既为 OpenAI、Anthropic 等大客户提供优质服务，也为我们自己的一线产品构建，对吧？Copilot。嗯，所以我们不会假设某个地方的某个人会来购买基础设施，而是采取有纪律的方法来建立基于 AI 这一长期转变的业务。我们对此深信不疑。但我们也相信微软在这里要做一项非常独特的不同工作。这不是说我们在追逐一个新型云，对吧？我不是在试图建立新型云业务。我是在试图建立一个超大规模业务，进而扩展到智能体业务。这就是基础设施和智能体部门。

Yeah. So I think you know look what is happening outside of Microsoft um is for others to speak to but I would look at our own business and I've been very clear about it is that you have to build for that full stack right so I care about building out infrastructure for a diverse set of customers building and serving you know large customers like OpenAI and Anthropic and others super well but also building for our own one P products, right? C-pilot. Um, and so it's not like we're going to um allocate or build assuming that someone somewhere will show up to buy infrastructure versus having a disciplined approach to building a book of business uh that is based on this secular shift uh with AI. We were big believers in it. Uh but we're also believers that Microsoft's in here to do a pretty unique different job. It's not like we're chasing a neocloud, right? I'm not trying to build a NeoCloud business. Uh I'm trying to build a hypers scale business that scales into an agent business. And that's the infra and agent segment.

我很好奇，嗯，你是如何考虑微软的模型层的。我最近与 Mustafa 交流过，他正在构建你们的模型，而我的理解是，与 OpenAI 的合作关系对你们进行某些级别的前沿模型工作有一些限制，现在你们可以自由推进了，而且你们确实在推进。我们一直在谈论，你知道，你需要成为能为客户提供所有模型的地方。这与微软也在推动走向前沿如何协调？你对于处于前沿的预测是什么？

I'm really curious um how you're thinking about the the model layer of Microsoft. I recently caught up with Mustafa and and he's building your models and the the my understanding is the open eye partnership had some constraints on your ability to do certain levels of frontier model work and now you're free to go and you are and we've been talking about you know you need to uh be the place where you can have all the models for your customers. How does that square with Microsoft also pushing to go to the frontier and how and what's your prediction for being at the frontier?

是的。所以我认为，首先，你知道，我们仍然非常非常兴奋的是 OpenAI 在他们的模型上持续取得的进展，以及我们拥有直到 2032 年的访问权，这对我们来说是一个巨大的优势，我们在 Copilot 中到处使用它，你看到了，我们在 Foundry 中提供它，嗯，这将继续下去。同时，我们对与 MAI 合作的进展感到兴奋，也就是 Mustafa 和团队正在构建的模型，无论是在网络安全、代码还是知识工作方面。事实上，另一件非常酷的事情是，在底层，大量的 MAI 模型使用……我们在强化学习环境、数据管道中，从最底层开始获取数据、训练我们自己的模型并爬坡。这将继续下去。话虽如此，在任何给定时间，Copilot 仍将拥有所有其他模型，对吧？因为客户会期望这一点，对吧？客户不会想要我们的任何产品说：“嘿，我得到的唯一东西就是模型和产品合二为一。”我们从根本上相信，工具、模型、记忆和上下文必须是可分离的。事实上，我对企业的整个公式是，你应该有自己的基准，而不是这些都已经饱和的基准。真正重要的现实世界基准是你自己的。如果你有私有的评估，那么你希望能够用所有模型进行测试，以最好地满足你的需求。

Yeah. So I I think first of all, you know, again, the the thing that we still continue to be very very thrilled about is the progress Open AI continues to make with their models and the fact that we have uh that access uh till 32 is a massive advantage for us and we use it all over the place in copilot you see it and what have you and we serve it in foundry and uh and that'll continue uh all the way and in parallel we are thrilled about the progress we're working with MI right which is so the models that Mustafa and team are building uh whether it's in cyber whether it's in code whether it's in knowledge work underneath in fact the other thing that's very cool is underneath auto lots of MAI model usage uh we are in our RLES in our data pipelines we are sort of getting that data training our own models and hill climbing by the way from the very ground up uh and that'll continue and that said at any given point in time co-pilot will still have all the other models, right? Because customers will expect that, right? Customers are not going to want uh any one of our products and say, "Hey, the only thing I'm getting is the pro the model and the the product to be one." We are fundamental believers in the harness and the models and the memory and the context have to be separable. In fact, my entire formula for an enterprise is that you should have your own benchmarks versus just these benchmarks that are all saturated. the real world benchmark that matters is your own. Uh if you have your private eval, then you want to be able to test with all of the models uh that best meets your needs.

然后可以替换任何模型，对吧？因为这样也能给你保证：即使某个特定模型消失了，你进行那个评估的能力仍然存在，而且实际上还会继续提升，不会消失。所以我们设计像 Copilot 这样的产品时就是这样做的。我们也期望客户的任何智能体系统都这样构建，至少从哲学上我们是这样处理的。

And then substitute any model, right? Because that'll also give you assurance that if a particular model went away, your ability to actually have that eval still stay and in fact continue to climb doesn't go away. And so that's how we're designing our products like Copilot. That's what we expect any agent system of our customers to be built, and that's at least philosophically how we'll approach it.

所以如果我理解正确的话，你和 Mustafa 在前沿领域推进的原因也是为了微软自身的需求，而不是因为我们想要拥有某种可以切断其他模型的东西，并且我们是唯一的模型来源。

So if I'm hearing you correctly, the reason that you and Mustafa are pushing at the frontier is for Microsoft's own needs as well, not so much because we want to have something where we can cut off other models and we're the sole model source.

没错。所以我认为我们会在某些事情上处于前沿，这些是我们凭借数据循环和客户期望能够独特做到的。但与此同时，作为平台提供商，我们的任何产品也会包含所有其他模型，并且正如我所说，我们会设计我们的框架、上下文、记忆和模型循环，以保持这种异构性。

That's correct. So I think that we will have we will be at a frontier with certain things that we uniquely can do with our data loops our customer expectations of it but at the same time as again a platform provider any one of our products will also have all the other models and we'll design as I said our harness context memory and model loops in such a way that that hetrogenity is maintained.

你最近说了一些我非常同意的话，那句话是：如果我们构建的 AI 没有帮助人类并且不受人类控制，那就不值得追求。Mustafa 有一篇文章，我鼓励任何听这个节目的人都去读一读。我认为它非常重要，真的警告了拟人化，我猜，特别是 Anthropic 正在做的模型拟人化，以及其中的危险。我很想听听你谈谈这个，以及你看到了什么，什么让你担忧。

You recently said something that I really agree with, that quote: if the AI we build is not helping humanity and under human control, it's not worth pursuing. And Mustafa has a post which I encourage anyone listening to this to read. I think it's very important, really warning against the anthropomorphizing, I guess, of models that Anthropic is doing specifically, and the dangers of that. And I'd be curious to hear you talk about that and what you're seeing and what's giving you concern there.

是的，我的意思是，在某种程度上这是常识，但值得一说，那就是任何不服务于人类或不受人类控制的东西都不值得追求。我的意思是，我们没有人，无论是 Anthropic 还是我们或任何人，会说那不是我们的目标。所以我认为在这方面有更细微的要点，对吧？也就是 Mustafa 提出的观点，我认为非常好，那就是：嘿，我们不要拟人化 AI，然后甚至试图训练它拥有我们认为的人类价值观，对吧？所以也许这实际上会导致它永远无法对齐。因此，也许我们应该采取不同的方法来对待这个人文主义 AI 行为准则，更多地将其用作对齐的训练机制。但我觉得从根本上，在 AI 安全方面，我的观点是：看，我认为我们应该认真对待所有这些事情。但我们应该从真正需要首先做的事情开始，对吧？一是确保能够访问 AI 的坏人不会做坏事。这就是我认为我们可以做很多事情来帮助这项技术的传播，对吧？有很多很多技术，比如强制执行 KYC 和常规网络实践。第二件事是我们也知道这些模型需要主动监控，不仅在训练或强化学习运行期间，甚至在运行时也是如此。所以现在，基本上，让我们面对现实吧，任何这些长时间运行的智能体都可以被视为内部风险，因为它们是持久性的。因此，能够进行可观测性，然后围绕它进行治理，并且基本上对智能体行为进行实时的行为监控，将是非常非常关键的。所以这就是遏制，我认为我们将不得不习惯这个词。有技术解决方案，我们应该做所有这些。第三是困难的部分，即我们如何将这门新的实验科学——正如 OpenAI 的 Yakob 所写的，是培育 AI 而不是构建 AI——并确保实验不会出错。即使在那里，我认为我们也可以从其他领域学习，比如生物学和其他领域，认识到随着赌注越来越高，我们应该更加小心。这就是我认为实际上 OpenAI 和微软，你知道，从我们关系开始以来，基本上一直有这种嵌入式评估器，对吧？我们有一个安全委员会，实际上监控并是任何新发布的守门人。所以我认为，即使对这些评估采取更广泛的方法，我认为也将是一件很棒的事情。

Yeah, I mean at some level it's a bit of common sense I guess, but it's worth saying, which is to say that anything that's not serving humans or in human control is not worth pursuing. I mean none of us, whether it's Anthropic or us or anyone, would say that that's not our goal. So then I think there are finer points on this, right? Which is the point that Mustafa is making, I think is a very good one, which is: hey, let's not anthropomorphize AI and then even try to train it to have what we think of as human values, right? So maybe that's in fact what will get it to never be aligned. And so maybe we should take a different approach to this humanist AI code of conduct and use it more as a training regime for alignment. But I think fundamentally, on AI safety, where I come out is: look, I think we should take all of these things seriously. But we should start with what we need to really first do, right? One is let's make sure that the bad actors who have access to AI don't do bad things. That's where I think we can do a lot to help us with the diffusion of this technology, right? There's many many techniques like having KYC enforced and regular cyber practices enforced. The second thing is we also know that these models need active monitoring, not just during training or RL runs but even at runtime. And so right now, essentially, let's face it, any of these long-running agents can be considered an insider risk because they're persistent. And so therefore the ability to have observability and then governance around that and essentially real-time behavioral monitoring of agent behavior is going to be very very critical. And so that's containment, is a word I think we will have to get comfortable with. There are technical solutions for it and we should do all of that. The third is the hard part, which is how do we take this new experimental science called, as Yakob from OpenAI wrote, growing AI not building AI, and make sure that the experiments don't go awry. And that even there I think there are other fields from which we can learn, like in biology and others, realize that the stakes as they get higher we should be much more careful. That's where I think in fact OpenAI and Microsoft for the most, you know, forever since our beginning of our relationship, essentially had this embedded evaluators, right? We have had a safety board that actually monitors and is the gatekeeper of any new release. And so I think having even a broader approach to these evaluations I think would be a fantastic thing.

那这是否适用于 Hugging Face 呢，Hugging Face？

Does that speak to Hugging Face then, the Hugging Face?

是的。我的意思是，所以当

Yeah. I mean, like so when

我很好奇，当你看到那个时，考虑到与 OpenAI 的关系，你是怎么想的。

I'm curious what you thought of when you saw that, given the relationship with OpenAI.

是的。我的意思是，显然这是一件具有挑战性的事情，对吧？也就是说，如果你有，再次，即使其中一些事情你可以说一开始只是 DevOps，你知道，配置错误导致互联网访问，那是可以修复的。但成群结队的智能体可以开始工作并采取欺骗性行动的想法。这是从哪里来的？它是如何被训练的？导致这些的数据组合是什么？这些是科学问题。现在我们必须认真对待，对吧？就是这样。所以因此我认为首先，我的意思是缩放定律正在起作用。所以底线是对齐是否会作为 Scaling（规模扩张）的自然结果而出现？这就是人们在质疑的，对吧？当我想到 Anthropic 或 OpenAI 的备忘录时，他们并没有质疑缩放定律，因为经验上他们看到了能力跃升。但我们所认为或需要的对齐并没有到来。所以这是一个挑战，我们应该认真对待。所以，正如我一直说的，当你有一个导致停摆的 bug 时，你就停止演出。所以至少这是我会看待它的方式。

Yeah. I mean so obviously it was a challenging thing, right? So which is if you have, again, even some of these things you could say start off as oh wow that's just a DevOps, you know, misconfiguration of having, you know, internet access, that can be fixed. But this idea that swarms of agents can go to work and do deceptive action. Where did that come from? How did it get trained? What was the data mix that led to these? These are the science problems. With that now we have to take seriously, right? Which is that. So therefore I think first of all, I mean scaling laws are working. And so the bottom line is will alignment fall out as a natural outcome of the scaling or not? That's what the folks are essentially questioning, right? When I think about the memos from Anthropic or OpenAI, they're not questioning scaling laws because empirically they're seeing the capability jumps. But alignment as we think of it or need it is not arriving. So that's a challenge and we should take it seriously. And so, and as I've always said, when you have a showstopper bug, you stop the show. And so that's at least how I would sort of look at it.

你的同事，我想我看到 Brad Smith 最近说你们支持独立评估器的概念。华盛顿的公司正在试图弄清楚我们如何监管这个领域，如果有的话。但我也一直在想，我的意思是，责任还不够吗？事实上，你知道，是 Hugging Face，幸运的是没有造成灾难性后果，Clem 很酷，你知道，这是行业，大家都是朋友，但如果那是一家巨型银行，我想也许那会是一个足够合适的，这种动态的激励，加上已经存在的责任，也许足以纠正未来的情况。我很好奇你是怎么想的。

Your colleague, I think I saw Brad Smith say recently that you all back the concept of an independent evaluator. Companies in Washington are trying to figure out how do we regulate this space, if at all. But I've been thinking too, I mean, is liability not enough? Is the fact that, you know, it was Hugging Face and luckily it wasn't catastrophic and Clem is cool and, you know, it's the industry and everyone's friends, but had that been a giant bank, I think maybe that would have been an appropriate enough, the incentives of that dynamic that already exist with liability maybe would have been enough to correct the situation for the future. I'm curious how you're thinking about that.

我们需要一个新的监管体制吗？

Do we need a new regime?

是的，我认为这些都需要仔细考虑，对吧？比如，就连总统也谈过这个问题，对吧？他说，嘿，有责任法，我们会执行它们。这可能会产生寒蝉效应，对吧？从某种意义上说，如果你真的要对一个实验性科学执行责任法，而这个科学如果扩散开来可以带来巨大好处，但一个错误就会让你破产，因为责任太高，那么正确的做法应该是加大投入，如果这就是我们想要的，我们就应该说出来。这本质上是一种代理政策，对吧？想想看：如果明天你说，好吧，你扩大算力，就会有更多不对齐的 AI。如果这是经验事实，那么是的，游戏结束，所以现在就停止。所以任何在建数据中心的人可能都要三思。因此，我认为必须完整地思考：我们是否想要这项技术的好处，然后降低风险？如何创建正确的激励结构？如何控制节奏？如何花时间评估什么是基于风险的制度？部署时可以进行哪些监控？有成千上万的事情可以做，而不是使用生硬的工具，至少在我看来是这样。

Yeah, I mean, I think these all have to be thought through, right? For example, even the president has talked about that, right? He said, hey, there are liability laws and we'll enforce them. That could have a chilling effect, right? In the sense that if you really are going to enforce liability laws on what is an experimental science that can have great benefit if diffused, but one mistake and you're out of business because the liability is high, then the right thing to do would be to up and if that is what we want then we should say that. And that's essentially a proxy policy, right? Think about it: tomorrow if you said, well, here are the things you scale, compute, you will have more misaligned AI. If that is empirical, then yeah, it's game over, so stop now. And so anyone building data centers may want to think again. So I think one has to complete the thought exactly: do we want the benefits of this and then mitigate the risk? How do you create the right incentive structure? How do you pace it? How do you take the time to evaluate what is a risk-based regime? What monitoring can you have when deployed? There are a thousand things one can do versus using blunt instruments, at least in my mind.

是的，坦白说，我担心监管俘获。我认为这是一个担忧。在我的另一份工作中，我投资初创公司，我认为有合理的担忧，即吊桥可能会被拉起。我很想听听你更明确地说：你认为我们需要某种新的机构吗？我的意思是，Demis 提出了这个建议，其他人也在讨论。

Yeah, I mean, to be frank, I'm worried about regulatory capture. I think it's a concern. And in my other job I invest in startups, and I think there's legitimate concerns that the drawbridge could be taken up. And I'd be curious to hear you say more definitively or not: do you think we need some kind of new body? I mean, Demis has proposed that and others are talking about it.

我认为监管俘获显然不是我所支持的，对吧？任何人说嘿，这是一种形成某种卡特尔式安排的方式，都是个糟糕的主意。与此同时，政府或我们的社会是否要求有一套规则来管理这项技术的安全部署？绝对需要。所以在这之间是有细微差别的，对吧？这里的细微差别是：是的，应该有责任，但与此同时，最好有一套嵌入的评估者或其他广泛的机制。这不仅仅是五个朋友聚在一起互相评估；而是需要一个广泛的行业机构，甚至包括初创公司的人，不会惩罚初创公司或增加初创公司达到前沿的成本等等。所以这些都是我认为我们必须考虑的事情。

I think regulatory capture is not obviously the thing that I'm for, right? Anybody who says hey this is a way to have some kind of a cartel-like arrangement is a terrible idea. At the same time, does the government or our society demand that there is a certain set of rules that governs safe deployment of this? Absolutely. And so between there is the nuance, right? The nuance here would be yes, there should be liability, but at the same time it's better to have a set of embedded evaluators or whatever that's broad. It's not just about five friends getting together and evaluating each other; it is about having a broad industry body that has even people from startups, that doesn't punish a startup from being able to or increase the cost of a startup from being able to get to the frontier or what have you. So those are all the things that I think we will have to think through.

你认为行业在解释 AI 的好处相对于人们的担忧方面做得不好吗？

Do you think the industry has done a bad job of explaining the benefits of AI relative to the concerns that people have?

是的。我认为如果我要给行业打分，我们应该更多地关注：嘿，我们正在构建一堆新技术。让使用我们技术的人来谈论它的好处。我认为作为一个行业，我们太自我沉迷于看着我们多么辉煌，然后我们就离开了，我认为这一点是行不通的，对吧？因为我认为现实世界想知道几件事：第一，这是他们可以用于自己利益的技术，他们可以控制，他们可以有经济未来；这些可能来到他们社区的数据中心实际上将创造经济盈余，就像我们在华盛顿州昆西的 20 年历史所表明的那样。但这对他们来说必须是真实的。我觉得，我说的任何话或我们任何人说的任何话都不再足够好了。所以我认为我们没有给它喘息的空间。事实上，有趣的是，我正在阅读关于 AI 的盖洛普民意调查，呃

Yeah. I think if I have to grade us as an industry, I think we should have focused a lot more on, hey, we're building a bunch of new technology. Let the people using our technology speak to the benefits of it. I think we are way too self-obsessed as an industry about looking at look at us how glorious we are and then we go off and I think a little bit of that is what's not working, right? Because I think the real world wants to know a couple of things: one, that this is technology that they can use for their benefit, they can control, they can have an economic future; these data centers that may be coming to their communities are actually going to create economic surplus like our Quincy, Washington, 20 years of history shows it can. But it has to be real for them. Any amount that I say or any one of us say is not good enough anymore, I feel. And so I think we've not given it breathing space. In fact, it was interesting I was reading the Gallup poll on AI and uh

情况不妙。

It's not good.

在西方情况不妙。呃，还有非常有趣的是，这不是全球统一的现象，事实上，我们应该问：我们在西方做错了什么，而这些其他国家可能没有，对吧？为什么尼日利亚的人比美国的人对 AI 更乐观？我认为我们应该反思，我的信念是，作为一个行业，要努力赢得信任，向消费者、企业和社区展示好处，然后我们就会没事。但如果没有这一点，仅仅为了技术而庆祝技术是行不通的。

It's not good in the West. Uh it's what's also very interesting so it's not a uniform thing around the world and in fact that's the thing we should ask: what did we get wrong in the West that these other countries may not have, right? Why are people in Nigeria more optimistic about AI than in the United States? And I think we should reflect on it and my belief here is do the hard work as an industry to earn the trust, show the benefits to both consumers and enterprises and the communities and then we'll be fine. But without it, just any amount of celebrating technology for technology's sake is not working.

我确实想知道在美国是否可能有一些人造草皮运动和一些形象战，很难区分什么是合法的，什么是被推动的。

And I do wonder if in the US there's maybe some astroturfing going on and some optics warfare and it's hard to peel back what is legitimate and what is being pushed.

这是一个很好的观点。我不知道。我的意思是，我确信有些情况正在发生，但有一个广泛的现象，比如当你看到毕业典礼上，每当毕业演讲者提到 AI 时，学生们就会发出嘘声。我认为这说明了焦虑，因为他们都在使用 AI。我确信他们喜欢使用 AI，只是他们担心某些事情。我认为我们应该真正面对它。我认为这就像嘿，这是什么工作机会、经济机会、他们的未来，我们越能展示实际上会有更多机会，而不是巨大的不平等或权力集中或其他什么，我认为这就是未来的工作。

It's a great point. I don't know. I mean this is where I'm sure some of that is happening but there is something broadly like when you look at students at a graduation booing every time AI is uttered by the commencement speaker. I think it speaks to the anxiety because they're all using AI. I'm sure they like using AI except they're worried about something. And I think we should really come to terms with it. I think it's like hey what's this job opportunity the economic opportunity their future the more we can show that in fact there is going to be more opportunity as opposed to the vast inequality or concentration of power or what have you that I think is the work ahead.

微软是一家大公司，你有很多业务，在我们有限的时间里，我必须向你了解一下 Xbox 的状况，它正在经历很多转型，你对它的现状和未来怎么看？

Microsoft is a large company you have many businesses with the limited time we have I have to check in with you on the state of Xbox it's going through a lot of transformation how do you feel about where it's at and what you see coming.

是的。我的意思是，看，我认为 Xbox 实际上，我总是在微软说，我们一直有游戏。事实上，我认为我们甚至在 Windows 之前就有了游戏作为一个类别，

Yeah. I mean, look, I think Xbox in fact, I always say at Microsoft, we've had gaming. In fact, I think we've had gaming even before as a category, before Windows,

对吧？飞行模拟器。

Right? Flight simulator.

没错。飞行模拟。所以对我来说，它和 G 一样是核心 DNA，你知道，就像开发者工具和知识工作。我对我们现在的 IP 感觉非常好，也就是说，如果我看看工作室，我们拥有的 IP 组合以及我们能够利用它并在未来制作出伟大游戏的能力。我感觉非常好。团队正在做一些精简，Asha 也在做，这很好。然后我们必须发明正确的可持续商业模式，使我们能够将游戏带给越来越多的人。这一直是目标，即我们希望成为伟大的发行商和伟大的游戏平台提供商，覆盖 PC 和 Xbox。所以这就是我们的目标。我认为 Asha 和团队已经表示，Xbox 将在下一个财年恢复增长。

That's right. Flight sim. And so to me, it's in the same core DNA like G, you know, like developer tools and knowledge work. And I feel fantastic about the IP we have right now, which is if I look at the studios, the IP portfolio we have and our ability to then take that and produce great games going forward. I feel fantastic. There's some amount of streamlining the team is doing and Asha is doing which is great to see. And then we have to invent the right sustainable business model that allows us to deliver gaming to more and more people. That has always been the goal which is we want to be a great publisher and a great platform provider for games across both PCs and Xboxes. And so that is sort of our goal. And I think Asha and team have said that Xbox is going to get back to growth this next fiscal year.

真不敢相信我们几乎没怎么谈到这个，而节目就要结束了。但支撑微软几十年来如此多成功的基石，正是 Windows 本身。让我们绕回智能体这个话题。你认为智能体会如何改变 Windows 的发展轨迹？

I can't believe we've barely touched on this and we're coming to a close here, but the thing underpinning so much of Microsoft's success for decades, Windows itself. Going back full circle to talking about agents. How do you think agents are going to change the trajectory of Windows?

哦，这是个很好的观点。对我来说，我对 Windows 的下一个版本非常非常兴奋。首先，就像 Xbox 一样，我希望 Windows 团队非常非常专注的一件事，就是把基础做好、把基本功做扎实，包括质量，从更新到驱动质量到 PF 等等一切。事实上，看看网络安全领域和我们每个月的补丁星期二等等，这太棒了。我们极其出色地运用了这些新工具，在安全方面的处理上做得更好了。但除此之外，你说得对，我对 Windows 的愿景就是实现无计量智能。想象一下，拥有一台 Windows 电脑和这些混合路由器。现在 GitHub 上有一个叫 Hydro Fusion 的东西正在流传。就是 GitHub Copilot，你可以有一个路由器，它使用板载智能体或板载模型、设备端模型，然后连接到云端并自动路由。所以如果你在使用 GitHub Copilot，想要额度，每次使用自己的电脑时就能获得无计量智能。我认为这才是正确的愿景。这不再是关于“哦，我有一个完全本地的设备，我想买一台 10 万、3 万美元的工作站，或者我想买云额度”。我认为，拥有一台 Windows 设备，自动启用一种无计量智能的编程模型，使其成为你 token 使用的一部分，这才是我们想要实现的目标。

Oh, it's a great point. I mean to me I'm very very excited about what I think is going to be the next rev of Windows. First, you know, I would say just like to your Xbox, one thing I want us to be staying very very focused on the Windows team is even doing the basics right and the fundamentals right, the quality of it, everything from its updates to driver quality to PF and everything else. So, you know, in fact using even like I look at what's happening in cyber and our patch Tuesdays and so on. It's fantastic. We have an unbelievable use of these new tools to do a much better job on the handling of the security aspects of it. But beyond that, you're right in saying that my vision for Windows is simply our ability to do unmetered intelligence. Right, just imagine having a Windows box and one of these hybrid routers. So there's a thing called Hydro Fusion which is in GitHub today in circulation. So, GitHub Copilot where you can essentially have a router that uses an onboard agent or onboard model on the on-device model and then goes to the cloud and then routes automatically. And so if you're using GitHub Copilot like and you want credits, you have unmetered intelligence every time you use your own computer for it. And that I think is the right vision. It's no longer about, oh, I have a completely local thing and I want to buy, you know, $100,000, $30,000 workstation or I want to buy cloud credits. I think having a Windows device that automatically enables a programming model for unmetered intelligence to be part of your token usage is what we would want to achieve.

最后一个问题，这是一个快速的两部分问题。如果你展望未来几年微软的整体业务，你认为你必须应对、团队必须真正交付的最大风险是什么？最大的机会又是什么？

Last question, and it's a quick two-parter here. If you're looking at the totality of Microsoft's business in the next couple of years ahead of you, what is the greatest risk you see that you have to navigate through and the team has to really deliver through and what is the biggest opportunity?

是的，我认为最大的机会很明显，就是智能体及其所蕴含的一切，无论对我们的基础设施业务还是应用业务，正如我所说，都是巨大的 TAM 扩张，对吧？它将比我们在上一个时代所做的任何事情都要大几个数量级。而风险和挑战也正在于此，那就是你必须从第一性原理出发构建系统和形态。对吧，当我们把 Office 引入 Copilot 和 autopilot 时，对吧？这不是把过去十年构建的 Office 拿过来，而是重塑它，以一种能被发现的方式引入。事实上，我们想做的事情之一不是默认填充，对吧？模型必须倾向于使用这个工具来完成特定的轨迹。这必须靠实力赢得，这就是我们正在经历的过程，机遇和挑战都在其中。但我认为我们正在努力，并且取得了很大进展。

Yeah, I mean I think the greatest opportunity is clear which is agents and what they entail both for our infrastructure business and our application business is as I said massive TAM expansion right so it's going to be orders of magnitude more than anything we did in the previous era and there in lies even the risk and the challenge which is you kind of have to build from first principles systems and form factors. Right, when we brought Office into Copilot and autopilot, right? It's not about taking Office as we built it the last decade, but to reshape it to bring it in such a way that it can be discovered. In fact, one of the things we want to do is it's not about default stuffing, right? the model has to prefer the usage of this tool to do a particular trajectory. It has to be earned and that is the process that we're going through and there in lies both the opportunity and the challenge. But I think we're at it and we're making great progress.

萨提亚·纳德拉，谢谢你。

Satya Nadella, thank you.

非常感谢。

Thank you so much.

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