萨提亚·纳德拉谈 AI 生态系统战略与前沿模型

世界会对那些说“相信我们，一切都会很美好”的科技公司非常怀疑。这次你必须交付实实在在的好处，因为这次太重要了，它占据了经济太大比重，不可能不如此。真正的雄心是让不可能成为可能。我从管理 Azure 网络的人身上获得很大启发。过去 15 个月我们建设的 Azure 容量超过了前 15 年的总和。这太疯狂了。我们的工作不是做 Azure 网络，而是构建做 Azure 网络的智能体系统。获取信息、自我教育、持续更新的方式已经发生了巨大变化。也许下一个大创业公司会是建立新大学的人，甚至是一种新的教学法，让人完成课程并找到高价值的经济机会。

The world is going to be very skeptical of tech and tech companies that say trust us, we've got it, the future is going to be glorious. You kind of have to deliver tangible benefits because it's too important this time around. It's too much of the economy for it not to be the case. True ambition is about making the impossible possible. I take great inspiration from the people who are managing the Azure network. We built in the last 15 months more Azure capacity than we built in the first 15 years. I mean, it's crazy. Our job is not to do Azure networking. Our job is to build the agentic system that does Azure networking. The way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. Maybe the next big startup could be someone who builds a new university, a new pedagogy even of how to get someone to go through a curriculum and find economic opportunity that's highly valuable.

欢迎 Swyx、Saragosa、Alod Gill，以及微软董事长兼首席执行官 Satya Nadella。你好。怎么样？我很兴奋来到这里。欢迎来到 No Priors in Lane Space 与 Satya Nadella 的跨界节目。恭喜你取得了惊人的成就。

Please welcome Swyx, Saragosa, Alod Gill, and Chairman and Chief Executive Officer of Microsoft, Satya Nadella. Hello. What's up? I'm so excited to be here. Welcome to a crossover episode of No Priors in Lane Space with Satya Nadella. Congratulations on an amazing build.

不，非常感谢，很高兴和你们两位在一起。我一直在听你们两位或你们的播客。能上节目真是太好了。

No, thank you so much and it's great to be with both of you. I listen to both of you or both the podcast all the time. It's great to be on it.

非常感谢。那么，你整个早上都在谈论微软各个领域的这些惊人发布，大概 3 个小时。你最重要的反思或收获是什么？

Thank you so much. So, you were talking about these amazing announcements from across the Microsoft estate all morning for I think 3 hours. What is the most important reflection or takeaway you have?

我想对我来说最重要的可能是，我们应该更多地将其概念化为一个生态系统玩法，而不是单一模型甚至单一平台。我在微软长大，经历了四次重大平台转变，我属于那种认为平台的定义是其创造平台之外价值的能力，而非平台内捕获价值。所以如果你看现在正在发生的事情，今天早上的主题演讲是关于任何公司，无论是 AI 原生公司还是传统企业，如何作为一流参与者参与其中，指向他们自己创造的 AI。并不是说他们不使用别人的 AI，当然会用。但对我来说，路径是什么？配方是什么？我怎么做？栈长什么样？工具长什么样？什么有价值？你怎么做？就是这样。这就是我们的工作。

I'd say perhaps the biggest one for me is let's conceptualize this more as an ecosystem play as opposed to a single model or even a single platform. I mean, having grown up at Microsoft, having seen four major platform shifts, I fall into that camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. So if you view what's happening right now, this morning's keynote was about how any company, whether it's an AI-native company or a traditional enterprise company, can participate as a first-class participant where they can point to AI they create. It's not that they don't use other people's AI. Of course, they will. But to me, what's the path? What's the recipe? How do I do it? What does the stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's our job to do.

生态系统策略非常复杂，对吧？因为你最终要构建某些组件，为某些组件建立合作，支持它们。你刚刚宣布了这一大套模型。跟我们讲讲微软的训练策略吧。

Ecosystem strategy is very complicated, right? Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Tell us a little bit about the training strategy for Microsoft.

是的，我们想用 MAI 模型做的事情，就像 Mustafa 谈到的，首先是建立一个好的血统。从预训练开始，数据质量非常好，做所有的消融实验，确保——因为在某种意义上，构建一个干净血统的模型变得更难了，因为外面有太多东西需要真正消融掉才能得到一个出色的预训练模型。事实上，这是许多开放权重模型面临的挑战之一：它们在一两个基准上看起来很好，但在实践中并不出色。这就是为什么即使在 RFDs 中，人们对这些 MAI 模型也非常兴奋，因为一个小型 5B 模型怎么能爬山呢？这又回到了我认为最终的关键事情：试图找到那个认知核心。所以对我来说，从干净的血统开始，然后创造能力让公司能够使用它，不仅作为通才，而是通过围绕它构建爬山脚手架来创建自己的专才。所以不仅仅是模型，你还有围绕它的爬山脚手架，然后你会开始构建你的 RLE，开始收集轨迹。最重要的是，你会有私有评估，因为我们知道所有公开的评估都很好、很有趣，但在这个阶段它们并不那么关键，因为它们都可以被刷满。所以关键是每个公司都有自己的私有评估。围绕我们模型的端到端平台故事我觉得很有趣。还有一件事，既然你提到了，我确实觉得有一个新前沿。人们谈论前沿，你在前沿运作。有趣的是，如果你加入一点时间性，你可以使用——比如我们展示的 Lando Lakes 演示很酷。我们用了 GPT-55，对吧？然后你收集了一堆轨迹，然后你拿一个 5B 推理模型达到了更高。所以这是在前沿运作的另一个方面。

Yeah, so the thing that we wanted to do with the MAI models was to build, as Mustafa talked about, first of all, a great lineage, right? Starting with pre-training with very good data quality, doing all the ablations, making sure because in some sense it's become even harder to build a clean lineage model because there's so much stuff out there that you truly need to ablate out to be able to have a fantastic pre-trained model. In fact, that's one of the challenges of a lot of the open weight models: they look great on one benchmark or two, but they're not great in practice. That's why even in the RFDs, people are really excited about these MAI models because how can a small 5B model hill climb? It goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. So, to me, starting with a clean lineage, then creating that ability for companies to be able to use this, not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it. So it's not just the model, but you have a hill climbing scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private eval because we know all the evals out there are good, interesting, but they're not really that critical at this point because they all can be maxed. So the point is each company will have its own private eval. And that end-to-end platform story around our models is what I think is interesting. And then the one other thing, since you brought that up, I do feel there's a new frontier. People talk about the frontier and you're operating at the frontier. Interestingly enough, if you add a little temporality to it, you can use, let's say, the Lando Lakes demo we showed was pretty cool. We used GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher. So that is another aspect of what it means to operate at the frontier.

我想首先我必须祝贺你，基本上在两年内就在微软内部建立了一个前沿神经实验室。我想知道，你正在推出所有这些 AI 策略。你现在知道什么，是你希望两年前或三年前告诉自己的？三年是 Jensen 合作，两年是 MAI。

I think I first of all have to congratulate you on basically building a frontier neural lab inside of Microsoft in two years. I'm wondering, you know, you have all this AI strategy that you're rolling out. What do you know now that you wish you would tell yourself two years ago or three years ago? Three years for the Jensen partnership, two years for MAI.

是的，我想我反思很多的是，我显然是在对缩放定律论文感到兴奋时进入这一切的。当 OpenAI 合作出现时，那些人说，‘嘿，我们要在 Transformer 上投入大量算力。’他们帮了忙。我回顾时总是说，‘哇，这些东西确实有在攀升的能力。’用粗俗的说法就是智能是算力的对数。有点道理。现在，我认为我们可能低估的是部署这些模型以在现实世界中真正交付价值的复杂性。所以任何基准衡量的结果都很有趣、很重要，但真正的评估是当人们能够做他们唯一珍视的独特事情时。而且这是非常可衡量的。我希望我们当时在意识中更重视这一点。因为现在我认为当人们说‘哇，我不想 token 最大化’时，这是我们行业没有意识到我们每一步都在用 token 创造价值的产物。所以我想那就是我希望我们当时已经达到的状态，但我很高兴我们现在在这里。

Yeah, I mean, I think the thing that I reflect quite a bit is obviously I got into all this when I got excited by the scaling laws paper. And when the OpenAI partnership came about, those folks said, 'Hey, we're going to really throw a lot of compute at transformers.' And they've helped. The thing that I always look back and say, 'Wow, these things do have capability that they're climbing up with.' I mean, this crude way of saying it is intelligence is log of compute. Kind of works. Now, what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world. So the outcomes as measured by any benchmark is interesting, important, but the true eval is when people out there are able to do unique things that they only can value. And it's very measurable. That I wish we had even like had more in our consciousness, right? Because right now I think when people say, 'Wow, I don't want a token max,' it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.

你看到还有哪些其他用例为客户创造了最大价值？我知道人们经常谈论代码，而且很明显代码正在产生大规模影响。还有哪些共同领域是你的客户真正受益的？

What are some other use cases that you've seen that have created the most value for your customers? Because I know that people talk a lot about code and I think it's pretty clear that that's something that's having very large-scale impact. Are there other areas that you find in common that your customers are really benefiting from?

是的，你说得对，显然代码现在成了主角，但有趣的是，你甚至喜欢谈论代码，对吧？代码工作得如此之好，以至于我们现在必须重建 IDE，对吧？我的意思是，看到像 Large 这样的东西说“天哪，我有上百个智能体会话，它转移给我的认知负担太大了，我需要一个新的 UI”，这有点疯狂。哦，顺便说一句，聊天作为唯一的工件也是不可能的。所以，这就是为什么我们需要一个画布。所以，关于哪里需要软件或哪里需要 UI 的所有事情都很有趣，对吧？即使是在一个完全智能体式的世界里，代码也需要这些。但话虽如此，我们开始看到的一件事是，我们从 co-work 开始看到，甚至是我们用 auto autopilot 展示的一些工作，对吧？你在 Claws 上看到的就是一个很好的例子，因为如果你想想，很多人力资本在做粘合工作，对吧？如果你现在可以用长时间运行、持久的 token/智能体来增强它，对吧？那么你扩展甚至仍然是判断和粘合工作的能力就会像代码一样被放大。所以，我确信 6 个月后我们都会说：“哇，整个晚上，所有这些自动驾驶仪都在代表我工作，我委托了权限，对吧？我甚至可以用我的身份做一堆工作。然后我当然需要我的新 IDE 来问：‘你做了什么？我可能做了这项工作吗？’等等。”所以，我认为这就是工作流压缩、任务完成的地方，我认为很多价值都是在这里创造的。

Yeah, I think to your point, obviously coding is now God, but it's interesting by the way you love to even talk about the coding, right? Which is coding is work so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we saw large is like, "Oh my God, I have these hundred agent sessions. The cognitive load it transfers back to me as a human is so excessive that now I need a new UI." Oh, by the way, like the chat as the only artifact is also impossible. So, that's why we need a canvas. So, it's kind of interesting for all the things about where is software needed or where is UI needed? You kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see we started seeing with co-work, but even some of the work we showed with auto autopilot, right? On what you see with Claws, is a good one because if you sort of think about a lot of human capital is doing the glue work, right? If you now can augment that with tokens/agents that are long-running, durable, right? Then your ability to scale even what is still judgment and glue work gets amplified like coding does. So, you can like I'm positive that 6 months from now we'll all be saying, "Oh, wow. Like all through night the night there was a bunch of stuff that all these autopilots that I have working on my behalf with my delegated authority so to speak, right? I can sort of given even my identity did a bunch of work. Then of course I'll need my new IDE to say, 'What did you do? Like I might did I do this work?' And so on." So, I think that that's where compressing of workflows, completing of tasks, where I think a lot of the value gets created.

你提出了一个非常有趣的观点，即实际智能体在编写代码，而它周围有一个“框架”。那就是环境、上下文，以及你作为开发者为实际编码智能体设置的一切。企业的框架是什么？对于更广泛的生产力工作，是否有类似的概念？或者你通常如何思考这个概念？

You raise a really interesting point, which is there's the actual agent is doing the code and then there's a harness around it. And that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent. What is the harness for the enterprise? Is there an equivalent concept for broader productivity work or how do you think about that concept sort of generally?

没错。所以从某种意义上说，你希望框架定义模型、数据和工具。这样你就在这三者之间形成了一个循环。所以我们首先要确保我们构建的每个产品，无论是 GitHub Copilot 还是安全 Copilot，我们展示的 M-Dash 的东西，甚至是科学发现工具，都不例外。它们都是多模态框架，具有工具访问权限，这样你就可以逐步暴露工具，甚至使它们 token 高效。然后你给它提供非常丰富的上下文，因为这是我们在过去两年中学到的另一个艰难教训：天哪，你需要做大量的工作来准备上下文层，以便你的计划能够以最有效的方式执行，这才是神奇之处。所以在我们这里，我们有 GitHub 框架，它基本上被用于我们所有的产品。它在 Foundry 中可用。我们是开放的，你可以使用你的 Llama 框架，或者任何开放框架，或者你自己的框架，并用你的工具、多个模型和你的上下文进行训练。这就是我们的主张，因为现在很多讨论都是：如果我一起训练框架、工具和模型，你就会得到评估。我们正在证明的是，最好的例子就是 M-Dash，对吧？因为它发布时，发现了 Mythos 没有发现的错误或漏洞。所以我认为有证据表明，你可以拥有一个多模态框架，它在现实世界中实际上可以表现得更好。

That's right. So in some sense, you kind of want the harness to define the models, the data, and the tools. And so that you have a loop across those three. And so what we are trying to first of all make sure is each of our products that we build, right? Whether it's GitHub Copilot or the security copilot, the stuff we showed with M-Dash, or even the discovery for science, it doesn't matter. All of them are multimodal harnesses with tools access so that you can do this progressive disclosure of tools even so that they are token efficient. And then you're feeding it with very rich context because that's sort of the other hard lesson we've learned in the last 2 years is oh my god, the amount of work you need to do to prep the context layer such that your plan can execute in the most efficient way is where the magic is. So we have in our case, we have the GitHub harness, which essentially we're using across all our products. It's available in Foundry. And we're open like you can use your Llama harness, whatever, or you can use any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch because right now a lot of dialogue is hey, if I train the harness plus tools and the model together, you get evals. And what we are proving out is and the best example of that is what we did with M-Dash, right? Because when it launched, it found bugs or vulnerabilities that were not found by Mythos. And so there is existence proof, I would claim, that you can have a multimodal harness that can in fact be more performant in the real world.

所以独立前沿实验室训练的前提是，我们会有这些模型，会有 API 业务，会支持企业和初创公司，但第一方产品，无论是生产力、代码还是搜索，驱动了大部分收入。这与你描述的价值等式不同。我认为在微软生态系统中，如果是这样，请告诉我是否如此，因为显然你有第一方产品和赋能产品。开发者的角色是什么？在那个世界里，什么将是困难的，开发者需要哪些技能，以及他们如何获取价值？

So the premise behind the training at the independent frontier labs is really, you know, we're going to have these models and we'll have an API business and we'll support enterprises and startups, but a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing. I think with the Microsoft ecosystem, if that's the case, tell me if it's the case, because obviously you have first-party products and you have enablement products. What is the role of the developer? Like what's going to be hard and the set of skills and the value capture the developer has in that world?

是的，所以我认为总是会有这样的情况：一个非常成功的平台构建者也可以拥有第一方产品。Windows 是这样，SaaS 和云方面，我们和其他公司也是这样。但关键是，这不应该成为其他人取得同样成功的限制，对吧？我认为这是核心区别，这次围绕智能的网络效应之所以如此，是因为它们从数据中学习，而且实际上只需要几个样本就能理解什么是新颖的。所以这就是为什么游戏变成了如何保护。所以这就是为什么我会说，每个公司拥有私有评估可能是最大的知识产权，对吧？我想想。比如，那个私有评估，你可以用它让前沿模型爬山，而不泄露痕迹，这可能是知识产权最大的驱动因素之一。换句话说，另一个试金石是：你有一个私有评估，你正在使用模型 A。你能切换到模型 B 并爬山吗？如果能，你就掌控了局面；如果不能，你就没有掌控。这就是框架决策变得极其重要的地方，对吧？因此，拥有一个开放的框架，让所有模型都能接入，让你的评估、上下文和工具帮助你爬山，我认为这是 AI 原生初创公司、SaaS 公司或每个企业都需要掌握的技能。

Yeah, so I think that there's always going to be the case that someone who's super successful and as a platform builder can also have first-party products. It was true with Windows, it was true with the SaaS side and the cloud side as well with us and others and so on. But the thing that is is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the network effects this time around around intelligence are such because they learn from data and not really lots of it's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company having private evals may be the biggest IP, right? I think about it. Like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces maybe one of the biggest drivers of IP. Like so in other words, another acid test is you have an eval that's private. You're using model A. Can you switch it to model B and you know, climb up? If you can, then you're in control. If you can't, you're not in control. And that's where even the harness decision becomes super important, right? So therefore, having an open harness, letting all models come in, having your evals, your contexts, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs.

是的，我认为从非常实际的角度来看，微软历史上是一家操作系统公司，然后成为一家云公司，也许第三幕就是成为一家框架或评估公司。无论你想把哪些概念组合在一起。我认为，让每家公司都拥有前沿智能，或者我忘了你用的确切术语，这就是使命，对吧？这就是平台承诺：你与我们合作，你将获得针对你数据的智能。

Yeah, I think in a very real way, your Microsoft historically as an operating systems company and then becoming a cloud company, maybe like the third act is that you're a harness or evals company. Whatever the sort of conglomerate of concepts that you want to put together. I think like enabling every company to have like frontier intelligence or what I forget the exact term that you used. Is the mission, right? That is the platform promise that you build with us, you will get your intelligence for your data.

就是这样。

That's it.

对我来说，如果这次开发者大会只有一个主题，那就是：每个人都能用前沿智能在前沿领域运作，对吧？这太重要了，否则我不知道如何实现稳定均衡，对吧？也就是说，我怎么能说，‘哇，我的公司会有终值，因为我知道如何在一个不断变好的平台上持续复利’，对吧？所以当 Windows 显然出现时，Adobe 和 Autodesk 都构建了产品，或者就像 Jensen 说的，‘我们构建了 DX。’然后他在上面构建了 CUDA，对吧？我常对 Jensen 说，‘天哪，我吃了亏。’对吧？我希望我们当时能意识到。但无论如何，这个想法——你可以构建一个平台层，让别人在上面扩展并构建他们自己的智能层——我认为就是一切。对吧？没有它，为什么要开开发者大会？我可以直接来让你们都崇拜一个模型。但那不是开发者大会。

That to me, that is the like if there was one tagline for this entire developer conference is can everybody operate at the frontier with their frontier intelligence, right? To me, that is so important because otherwise I I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, 'Wow, my company is going to have a terminal value because I now know how to continuously compound on top of what's a platform that gets better, right?' So when like Windows obviously came out, Adobe built, Autodesk built, or even like take what Jensen said, 'We built DX.' And he built, you know, CUDA on top of it. Right? I mean, I always say to Jensen, 'God, I got the short end of that.' Right? I wish we had recognized it. But nevertheless, but that idea that you can build a platform layer that someone else can then extend out and build their own intelligence layer in this case, I think is everything. Right? Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. But that's not a developer conference.

嗯，在后台我们讨论过什么是知识产权，或者公司的价值是什么。过去是公司里人类经验的长度。现在变成了另一回事，即评估，以及将智能体应用到公司中的经验。这里，我希望你稍微展开一下，因为……

Uh backstage, we had a discussion about what is IP or what is the value in a company. It used to be the length of human experience at a company. And now it's this other thing, which is the evals, the experience in sort of applying agents to the company. Here, I just want you to like flesh that out a bit more cuz

是的，这是一个很好的框架，对吧？因为归根结底，每家公司都会同时拥有人力资本，这仍然非常有价值，因为人类及其发现始终存在的差距的能力，将是我们创造价值的方式。对吧？我绝对属于那种认为即使代币资本增加，这也会是关于表达新形式的人类能动性和雄心的阵营。对吧？所以，假设任何一家公司都有很多代币和很多人力资本。问题是如何将两者复利？所以，如果你在团队中，我有一堆智能体在工作，一堆人类在工作，以及它们之间的痕迹，这就是企业如何创造价值的非常重要的背景。然后，这又回到训练——不是训练通用模型，而是训练公司的资深智能体。对吧？这非常非常有价值，对吧？当一家公司说它实际上应该进入资产负债表时，我就是这么想的，对吧？事实上，人力资本从来不可能被放入资产负债表，因为你不知道如何捕捉隐性知识。而现在我认为你可以通过那些随时间从所有痕迹中学习的智能体来实现。所以至少我们认为这会发生。

Yeah, it's a great way to frame it, right? Because you have At the end of the day, every company is going to have both the human capital that is still going to be super valuable because humans and their ability to find the gaps that exist at all times is going to be the way we all will create value. Right? I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up. Right? So, let's say a any corporation has lots of tokens and lot of human capital. The question is, how do you compound the two? So, if you have a like if you take in teams, I have a bunch of agents doing work and a bunch of humans doing work and the traces between those, that is really important context of how that enterprise is creating value. Then, that goes back to train not a generalist model, but to train the train the company veteran agent. Uh right? That is super valuable again, again, right? Which is when a company goes and says it should in fact go on to the balance sheet is how I think about it, right? That's In fact, there may be like human capital was never possible to go put on a balance sheet because you didn't know how to capture the tacit knowledge. Whereas now I think you can with the agents that have learned through the through time through all the traces. So that's what at least we think will happen.

我认为 SEC 将不得不为代币专业知识制定会计准则。

I think the SEC is going to have to have accounting standards for token expertise.

你在谈论均衡状态和稳定均衡，公司拥有这种复利价值并能看到自身的终值。另一个挑战是，你知道的，考虑中的均衡：有一些应用和工作流在垂直或水平领域是通用的，这就像 SaaS 公司的一代，而且你知道微软也有很多 SaaS 产品，然后还有一些对每个企业来说非常具体、能形成差异化的东西。我相信你听过并参与了很多关于软件终结的辩论，因为所有这些工作流现在生成起来都很便宜。你认为未来在企业内部构建的智能体与在供应商那里构建的智能体之间，均衡会有所不同吗？

You're talking about the equilibrium state and a stable equilibrium where companies have this compounding value and can see terminal value for themselves. Another challenge to you know the considered equilibrium of okay, there are applications and workflows that are sort of common to a vertical or a horizontal and this was like the generation of SAS companies and you know Microsoft has lots of SAS properties as well and then there are things that are very specific to every enterprise that they're differentiated against. I'm sure you have heard much and participated much of the debate about the end of software because all these workflows are are cheap to generate now. Do you think the equilibrium looks different between what agents get built in enterprises versus in their vendors in the future?

是的，所以我认为正在发生的是，我们有一种特定的方式来捕获工作流，我称之为应用中的工作流，对吧？因为我们构建了一个数据模型，对吧？我们将某个业务流程的一部分模式化了。嗯。然后我们构建了一堆业务逻辑。是的。然后我们在上面放了一堆 UI。对吧？这就是每个 SaaS 公司所做的，加上一点配置。过去 20 年就是这样。所以有趣的是，现在你不得不重新审视这种垂直堆叠，对吧？所以我仍然认为，例如，你在每个 SaaS 应用下构建的数据模型非常好，对吧？为什么要重新发明呢？比如我的总账就应该是个总账。我不需要创建新的模式。事实上，那个实体关系是非常健壮的，我想用它来输入。而且你想要稳定。没错。业务逻辑也一样。对吧，看看我们的产品 Power BI，对吧？人们创建了大量的仪表板。仪表板之下的美妙之处在于一个非常丰富的语义模型，对吧？有人费心创建了一个仪表板并做了所有度量。你想要那个，那就是业务逻辑，对吧？我希望它能为我所用。所以，我认为 SaaS 商业模式的挑战在于我们以一种方式打包。我们现在必须学会如何拆解这些东西，以新的方式重新打包，并发现新的商业模式，对吧？我的意思是，看看今天 Microsoft 365 发生的事情就是一个很好的例子。对吧，我们有一个叫 Work IQ 的东西。事实上，我们意识到的是，天哪，如果你看看它，实际上有一个历史类比，对吧？我们首先销售了 Exchange 和 SharePoint，你知道，在 Teams 之前我们有一个叫 Link Server 的东西等等。我们以为那一切都会迁移到云端，但我们没有意识到的是，使用云端服务器的人数会是 10 倍、100 倍，对吧？因为人们不是购买服务器，他们只是购买订阅。同样的事情现在发生在 M365 上，因为通过 Work IQ，我们暴露了公司中可能最重要的数据库，它从未被用作数据库，因为它只被我们的应用所束缚，对吧？所有邮件都在上面运行，Teams 在上面运行，Word、Excel、PowerPoint、SharePoint。但现在，这是我能用 Work IQ 做的最酷的事情之一。我去一个 GitHub 仓库说，‘嘿，我上周参加了一堆与这个仓库相关的设计会议。你能捕捉所有这些并告诉我应该做什么修改吗？’想想看，对吧？它实际上可以查看所有这些转录，带着改变代码库的计划回来。对吧，以前你永远无法想象用 M365 做那样的事情。所以，现在在智能体世界中的价值创造机会实际上是 10 倍以上。但这确实要求我们，例如，围绕 M365 会有使用量，对吧？这可能甚至超过最终用户，对吧？甚至需要重新架构。事实上，就像我过去用来服务收件箱或邮箱的东西，不能用来服务智能体。

Yeah, so I think what's happening there is see we we had a particular way we captured I would say workflow in apps, right? Because we built up a data model, right? We schematized some part of some business process. Mhm. We then built a bunch of business logic Yep. and then we put a bunch of UI on top of it. Right? So that's kind of what every SAS company a little configuration. 20 20 years that was And that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think for example that data model that you build underneath every SAS application is super good, right? It's like why reinvent it? Like I my general ledger better be a general ledger. I don't need new schema creation. In fact, that entity relationship is actually pretty good robust thing that I want to feed. And you want to be stable. That's right. Then same thing with business logic. Right, if you look at we have this product called Power BI, right? It is like dashboards galore people created. The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures. And you want that that's business logic, right? I want that to be available to me. So, I think the challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right? I mean, if you look at it what's happening today with Microsoft 365 is a great example. Right, we have this thing called Work IQ. In fact, what we are realizing is oh my god, like if you look at it in fact, there's a historical parallel to right? We sold first exchange and SharePoint and you know, before Teams we had a thing called link server and what have you. And we thought oh, that's all going to move to the cloud, but little did we realize that oh, the number of people who will use servers in the cloud is 10x 100x, right? Because people were not buying servers, they were just buying a subscription. The same thing is now happening with M365 because with Work IQ we have exposed what was perhaps the most important database in a company that never got used as a database because it is only captive to our apps, right? It is all email operated on it, Teams operated on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coolest things I get to do with Work IQ. I go to a GitHub repo and I say, 'Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make. I mean, think about that. Right, it literally can go look at all those transcripts, come back with a plan to change a code base. Right, previously you could never have thought of using M365 for something like that. So, the value creation opportunity now in the agent world is in fact 10x more. But, it does require us to have, for example, there's going to be usage around M365, right? Which is going to be perhaps more than even the end users, right? to even re-architect. Like, in fact, like what I used to serve an inbox or a mailbox cannot be used to serve an agent.

所以，这大概就是我们在做的事情。

And so, that's sort of what we're doing.

我不认为这些领域有任何永久的商业模式，但短期内，你对基于结果的定价、基于 token 的定价、企业捆绑销售有什么预测吗？

I don't believe in permanent business models for any of these domains, but in the near term, do you have a prediction between outcomes-based pricing, token-based pricing, enterprise bundles?

是的，我思考这个问题的方式是，我们一直有，就拿按用户定价来说。按用户定价实际上是某人创建预算时需要确定性的人为产物。对吧，因为最重要的是有人想要预算，他们就需要按用户定价。而按用户定价只是一套使用权限。对吧，基本上就是这样。所以，第一种捆绑方式是把一些使用量打包成按用户堆栈，然后销售订阅。所以，我认为订阅会存在，按用户定价也会存在。然后，下一个大事件将是按消费量定价。所以，人们会说我要按消费量。也有可能人们会说，我甚至不想为任何订阅或消费结果付费。但是，请记住，大多数人喜欢结果，直到他们有了结果。因为一旦你有了结果，就像放弃版税一样，对吧？我的意思是，我和喜欢基于结果定价的客户谈过，我说我完全支持，直到他们说，哦，天哪，你在说什么？你在分享我的结果。不，不，不，我希望你回到按用户定价，我希望你按消费量定价，对吧？所以，我认为这场辩论会继续下去。但所有这些商业模式都有特定的时间和地点，而不是一个模式统治所有。而且，如果你是 SaaS 供应商或平台供应商，拥有这种灵活性，坦白说，我们在 GitHub 上也面临这个问题，对吧？我们最近刚刚宣布了 GitHub 的按用户定价。因为 GitHub Copilot 是在我们甚至不了解智能体使用强度之前按用户级别构建的，对吧？它是一种交互方式，让开发者使用代码补全，也许还有任务。它不像，哦，我启动了 10,000 个全天运行的智能体，对吧？所以，这就是调整的内容。所以，现在我们真正想要的是，按用户定价会一直存在。但他们必须有一个消费计量器。

Yeah, the way I think about this is always we have had like, let's even take the per-user pricing. The per-user pricing is really an artifact of someone creating a budget needing certainty. Right, because it's the most important thing like somebody wants a budget, they need a per-user. And per-user is just a set of entitlements to usage. Right, that's kind of what it is. And so, the way is if the first bundling will be take some usage, bundle it into per-user stacks, and then sell subscriptions. So, subscriptions I think are going to be there, per-user is going to be there. Then, the next big thing will be consumption. So, people will say I want consumption. And it's also possible that people will say I don't even want to pay for any of the subscriptions or the consumptions outcome. But, remember, most people love outcomes until they have an outcome. Because once you have an outcome, it's like giving away royalty, right? I mean, like I've talked to customers who love outcome-based pricing, and I say I'm all in until they oh my God, like what are you talking about? You're sharing in my outcome. No, no, no, I want you to go back to per-user pricing and I want you to consumption price, right? So, I think that debate will go on. But all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility and quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub. Because GitHub Copilot was constructed at a per-user level before we understood even the intensity of usage of agents, right? It was an interactive way for a developer to use code complete, maybe task. It is not like oh, I launched 10,000 agents that are going on all day, right? So, that is what the adjustment is about. So, now that we really want there will always be a per-user. But they will have to be a consumption meter.

你更广泛地如何看待 SaaS 的持久性？我观察到的一件事是，在许多企业内部，会有一些团队几乎处于智能体狂热状态。他们对自己能构建的东西的爆发感到非常兴奋，以至于他们试图重建许多应用程序，或者去找他们的 SaaS 供应商说，我们不再与你们合作了，或者我们正在考虑一个内部项目。似乎 6 到 9 个月后，也许其中一些人会回来说，实际上我们无法重建一切。你如何看待这个世界中什么是持久的，什么不是？

How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying we're not going to work with you anymore or we're considering an internal project. And it seems like in 6 to 9 months, maybe some of those people will come back and say actually we can't rebuild everything. How do you think about what's durable in this world and what isn't?

我认为我们必须经历一个完整的预算周期才能真正看到均衡的出现。因为归根结底，即使生成应用程序也有边际成本，对吧？所以，事实上，可以简单地说，如果你自己构建和维护某物的边际成本更高，那么你应该总是从外部获取。对吧，这应该是一个可量化的事情。而维护部分很重要。对吧，就像你必须记住，嘿，所有那些安全方面的事情，现在 AI 会发现你最好尽快修复它们。当然有编码智能体可以帮助你，但那会消耗 token。对吧，那么这是谁的责任？这有点像你必须仔细考虑的一个循环。我认为我们已经经历了“我可以生成大量软件”的兴奋期。我认为下一步是：我真正想生成什么软件？我想从别人那里使用什么软件？我如何将这两者组合成一个我拥有控制权的智能体式工作流？对吧，因为我认为对于任何在供应商层面不灵活的人，容忍度会非常低。但与此同时，我认为任何展现出这种灵活性、交付价值的人都会再次回归。对吧，我们销售软件，但实际上只是不同的商业模式。

I think we have to go through one full budget cycle on this to really see the emergence of the equilibrium. Because at the end of the day, there's marginal cost to even generating the app, right? So, in fact, it can be even a simple way to say it like if you should always acquire something if the marginal cost of building and maintaining something on your own is higher. Right, that should be like it's a quantifiable thing. And the maintenance part is important. Right, even like you got to remember like hey, you know, all the security stuff that now AI will find you better fix them too fast. Of course there's a coding agent to help you with but then that burns tokens. Right, so whose responsibility is it? It's kind of like a cycle that you've got to think through. And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over? Right, because I think there'll be very little tolerance for anybody who is inflexible at the vendor level. But at the same time, I think that anyone who has got that flexibility shows up, delivers the value will be back at again. Right, we're selling software but we're just different business models in fact.

说到构建软件，我认为在之前的一次 Build 大会上，也许是一两年前，我最喜欢的时刻之一是有一个环节是你自己构建软件。我很好奇你现在在构建什么吗？

Speaking about building software, one of my favorite moments from I think a previous build maybe one or two years ago was they had a big they there was a section of you building your own software. I'm curious if you're building anything now.

是的，所以我认为，首先，让我们面对现实。对吧，构建软件使得即使像我们这样公司的 CEO 这样无能的人也能构建。所以感谢上帝。但话虽如此，我确实觉得像 GitHub Copilot 这样的东西，尤其是新的 sessions 应用或新应用，让你对你以前觉得无法触及的工件有了更多的控制权。对吧，所以对我来说，作为 CEO，甚至可以去代码库学习它。我记得很久以前加入微软时，每个人都要去查看，你知道，无论 Cutlers、Malak 还是什么，来学习如何编写好的 C/C++ 代码。所以现在这种更全面的全栈能力非常好。但这并不意味着我们每个人都应该做同样的事情。问题在于，你如何拥有检查、学习、观察事物的能力。我认为这要多得多。所以对我来说，我大量构建的是这些长期运行的 Foundry 智能体。对吧，所以有自动驾驶仪。对我来说最简单的事情是，我想我上周刚构建了一个，想法是，嘿，我能不能有一个持续监控的智能体，本质上是我自己的参谋长自动驾驶仪，对吧？我们显然会在 Scout 中拥有它。这就是我们展示的。但它构建起来非常容易和简单。我用了 Work IQ。我说，用 Work IQ，去构建一个 Foundry 长期运行的智能体，使用 Raven 存储所有记忆，对吧？基本上作为我的后端即服务。你瞧，它构建出来了。不仅构建出来了，我还可以说发布到 Teams，它就把那该死的东西发布到了 Teams。所以，拥有完成这样一个端到端项目的能力，简直太神奇了。

Yeah, so I think the, you know, first of all, let's face it. Right, building software has made it possible for even the incompetence of a CEO of a company like ours, you can build. So thank God. But that said, I do feel that something like GitHub Copilot to me and especially the new sessions app or the new app has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before. Right, so to me as a CEO even to go to a code base, to be able to learn about it. Like I remember joining Microsoft long back, you know, first and then you say when everybody had to go in and look at, you know, whatever Cutlers, Malak, or what have you to learn how to do good C C++ code. So now that ability to be more full stack up and down is so good. But that doesn't mean every one of us should be doing the same thing. The question is how do you then have the ability to inspect things, learn things, see things. I think it's just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Right, so there's autopilots. So the easiest thing is to me, I think I just built one even last week where the idea was, hey, can I have an agent that is continuously monitoring, essentially my own chief of staff autopilot, right? We're going to have that obviously in Scout. That's what we showed. But it is so easy and trivial to build. I took work IQ. I said, take work IQ, go and build a Foundry long-running agent, store all the memory in using Raven, right? Basically as my back-end as a service. And lo and behold, it built it. And not only built it, I could say publish to Teams and it published the damn thing to Teams. So the ability to have you know, some end-to-end project like this complete is just pretty miraculous.

你认为这会影响未来存在的不同类型的工程角色吗？因为现在我认为有十几种不同的工程师类型，从 QA、前端等等。你知道，范围很广。我听到一些人争论说，在四五年内，我们基本上会剩下四个工程角色。将是管理智能体的人。将是前向部署工程师或 FDE。将是安全工程师。

You think that impacts the different types of engineering roles that exist in the future because right now I think there's, you know, a dozen different types of engineers that you can be from QA, front end, etc. You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents. It'll be forward deployed engineers or FDEs. It'll be security engineers.

然后，为少数服务构建大规模基础设施的人，其他一切都坍缩到智能体式的世界中。你觉得这是对世界的正确看法吗？

And then people working on large-scale infrastructure for a small number of services. And then everything else just collapses into the agentic world. Yeah, you think that's a correct view of the world?

是的，我认为我们得通过实验来摸索。但你说得对，确实有一些大规模的事情。在 LinkedIn，他们进行了结构性变革，建立了一个名为“全栈构建者”的新学科。他们把设计、产品管理、前端工程的人聚在一起，但每个人仍有专长——设计师仍有设计专长，前端工程师仍有前端专长，但你可以给自己更大的角色范围，不被局限在一个角色里。同样，基础设施变得非常关键。比如 Excel 团队，构建可学习奖励的 RLE 实际上是最难的基础设施问题之一。所以你需要新的人才，即使是曾经被认为是终端用户应用团队的部门也需要分布式系统人才，因为这是不同的技能集。所以基础设施科学是另一个方向。我认为我们会看到这些如何演变。世界总是会有一批专家。我认为通才角色将是最令人兴奋的，因为通才的杠杆作用会带来最大回报。当你说“嘿，我在编码”，我现在是一个通才——我基本上转化了知识工作，以前我创建 Word 文档或电子表格，现在我可以构建一个应用。这种通才技能获得更高杠杆的想法，我认为会全面出现。

Yeah, I think we'll have to experiment our way through it. But what you said is there are some very at-scale things. At LinkedIn, they structurally changed and basically built up a new discipline called full stack builder. They brought people from design, product management, front-end engineering, all put them together. But also have an edge—it's not like the design person still doesn't have the design edge or the front-end person doesn't have the front-end edge, but you can give yourself bigger scope in role so that you're not confined to one role. And equally, infrastructure has become very critical. For the Excel team, building the RLE in which a reward can be learned is actually one of the hardest infrastructure problems. So you need new talent, distributed systems people even in what was considered an end-user app team because it's a different skill set. So infrastructure science is the other one. I think we'll see how these evolve. The world will always have a bunch of specialists. I think the generalist role is going to be the most exciting because the leverage of a generalist is where we'll see the maximum returns. When you said, 'Hey, I'm coding,' I'm now a generalist—I basically translated knowledge work, where I created a Word document or a spreadsheet, and now I can build an app. That idea that my generalist skills have gotten higher leverage is what we'll see across the board.

这话 CEO 和 VC 听了会很高兴，有点危险但也很有趣。想法人士的黄金时代。有很强能动性的想法人士。如果你把个人能动性的想法放大到组织层面。我的合伙人 Mike Rynal，他职业生涯始于微软，刚写了一篇文章，其中一个重要结论是：这是一个你可以更有雄心的时代，而且考虑到环境的速度以及用户和公司接受新技术的速度，你必须更有雄心。你觉得微软现在如何能更有雄心？

Music to the ears of CEOs and VCs that are a little dangerous and a lot of fun. Golden age for idea people. Idea people with a lot of agency. If you take that idea of personal agency and zoom it out to the organizational context. My partner Mike Rynal, who started his career at Microsoft, just wrote an essay where one of the big takeaways is it's an age where you can be much more ambitious and you need to be given the pace of the environment and how quickly users and companies are open to adopting new technologies. How do you think about how Microsoft can be more ambitious now?

这是个好问题。在这种转型中，关键是要有一个概念模型，说明工作如何改变，以追求以前难以想象的结果。Kevin Scott 有句好话：当你让不可能变得可能——让困难的事情变容易，这是一个杠杆点，但真正的雄心是让不可能成为可能。所以，我们所有组织中有点缺失的是那个新的概念模型：我们能构建什么？什么是不可能的，我们能构建什么？我给你举个例子。我从管理 Azure 网络的人那里得到了很大启发。他们去年找到我。我们正在 Scaling——我谈到在过去 15 个月里，我们构建的 Azure 容量超过了前 15 年的总和。这很疯狂。而且是同一个团队。他们看到后说，‘如果我们不重新概念化我们的工作，这行不通。’所以他们构建了一个智能体式系统。他们说，‘我们的工作不是做 Azure 网络。我们的工作是构建做 Azure 网络的智能体系统。’这些人管理着 500 多家光纤运营商，管理着全球的车辆。光纤运营是物理操作——东西被切断，需要修复。我们有像 DevOps 这样的花哨词汇，但基本上就是邮件进来，你得回复。所以他们构建了这个智能体系统。他们甚至给它起了个名字叫 Miles，它做所有这些事。他们开始要求更多 token。他们说，‘我们不需要人头数。我们需要 token 来管理我们的运营。’这种对工作的重新概念化——他们把工作变成了元工作。那个元工作现在是他们的新工作。在 80 年代，如果有人跟我说，‘40 亿人早上起来开始打字，’我的模型会是，‘我们需要 40 亿打字员。’但我们不是在打字——我们在做知识工作。所以对我来说就是这样：无论是微软还是任何组织，我们需要允许自己进行新型的元认知、元工作，使用这些新工具来改变重要的产出。然后真正让不可能成为可能。连接这些点，我认为，是大量企业价值将被创造的地方。

It's a great question. In these types of transitions, it's important to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously. Kevin Scott has a nice line: when you can make the impossible—like when you're making hard things easier, that's one point of leverage, but true ambition is about making the impossible possible. So what's missing a little bit in all our organizations is that new conceptual model of what we can build. What was impossible and what can we build? I'll give you one example. I take great inspiration from the people managing the Azure network. They came to me last year. We were scaling—I talked about how we built in the last 15 months more Azure capacity than we built in the first 15 years. It's crazy. And it's the same team. They saw that and said, 'This just isn't going to work if we don't reconceptualize our work.' So they built essentially an agentic system. They said, 'Our job is not to do Azure networking. Our job is to build the agentic system that does Azure networking.' These are the folks managing 500-plus fiber operators managing the van all over. Fiber operations is a physical operation—things get cut, have to be repaired. We have fancy words like DevOps, but basically emails come in and you have to respond. So they built this agentic system. They even have a character for it called Miles, and it does all this stuff. They started screaming for more tokens. They said, 'We don't need head count. We need tokens to manage our operation.' That reconceptualization of their work—they took their work and made it meta. That meta work is now their new work. In the '80s, if somebody had said, '4 billion people are going to get up in the morning and start typing,' my model would have been, 'We need 4 billion typists.' But we're not doing typing—we're doing knowledge work. So that to me is it: whether it's Microsoft or any organization, we need to give ourselves permission to do new types of meta cognition, meta work using these new tools to change the outputs that matter. And then really make the impossible possible. Completing that connective tissue across those, I think, is where a lot of enterprise value will be created.

那么，你谈到了数据中心？

So, you talked about the data centers?

是的，请讲。

Yeah, please ask.

哦，好的。这很自然地引出了数据中心的建设。我一直对微软以及其他公司的建设规模感到印象深刻。这重新定义了什么是超大规模云服务商。我觉得这在财务、公司运营方式以及受影响的社区方面都是前所未有的规模。你能多谈谈你在实地看到的情况吗？比如当你访问你的……

Oh, okay. Well, this leads nicely into the data center build out. I always think I'm just impressed at the sheer scale of the build out from Microsoft, but also everyone else. This is redefining what it means to be a hyperscaler. And I just feel that that is unprecedented scale on finances, on the way you run the company, but also the communities that are impacted. Can you talk a little bit more about what you're seeing on the ground? Like when you visit your...

是的，我认为有两个方面。显然，建设规模是非凡的。以前从未发生过这样的事情，能成为参与者之一很棒。但你提到了另一方面。我认为在这一点上，很明显，除非我们整个行业非常有原则地确保我们谈论的所有这些好处在社区层面以真实的方式被感受到，因为这不只是一场运动。它必须是真实的，人们会说，‘看，这并没有改变我的能源价格。事实上，如果有什么的话，它正在降低价格，因为长期来看会有更好的电网。会有更多的能源。’

Yeah, I think there are two aspects of it. Obviously the build out is extraordinary. Nothing like this has happened, and it's great to be one of the participants in it. But you brought up the other part. I think at this point it's clear that unless we as an industry are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways at the community level, because this is not just a campaign. It has to be real where people are saying, 'Look, this is not changing the prices on energy for me. In fact, if anything, it's bringing down prices because long term there's going to be a better grid. There is going to be more energy.'

实际上，水的消耗并不是问题……事实上，水是会被补充的，对吧？你真的需要让人们了解我们在构建的闭环系统中真正发生了什么。我们必须投资于培训、就业和税基。事实上，最不常被提及的是建设期间和建设后创造的就业数量，以及社区中的税基。所有这些都必须真实。如果是这样，我们就会获得许可。如果不是，我们就不会。就这么简单。我认为我们整个行业对此非常重视。我认为社区保持怀疑、提出尖锐问题、让我们努力去赢得信任是件好事。但归根结底，如果我们真的能……我一直觉得，在人类历史上，如果你消耗了大量能源，但也为社会创造了巨大价值，那么故事就会很精彩。如果你做不到，那就没那么好了。这一次，我坚信，如果你有一个能推动生产力、经济增长、广泛参与和更好健康结果的代币经济，那么我们将处于一个很好的位置。这至少是我们所有人都必须关注的。

Water consumption is in fact not... in fact, water is being replenished, right? You really have to educate folks on what's truly happening in the closed-loop systems we're building. We have to invest in training, jobs, and the tax base. In fact, the least talked about stuff is the amount of jobs created during construction and after construction, and what the tax base is in the community. And all this has to be real. If that is the case, then we will have permission. If not, we won't. It's as simple as that. I think we as an industry take it pretty seriously. I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work and earn that. But at the end of the day, if we can really be... I've always felt that in human history, if you use a lot of energy but also create a lot of value for society, the story has been fantastic. If you don't do that, it's not been that great. This time around, I'm a firm believer that ultimately, if you have a token economy that drives productivity, economic growth, broad participation, and better health outcomes, then we will be in a great place. That's at least what we all have to be focused on.

是的。这让我想到，实际上，通过你正在做的所有这些举措，可能先在社区中看到投资回报比在企业中更容易。

Yeah. It makes me think actually that with all these initiatives that you're doing, it might be easier to see ROI in the communities first before in enterprise.

我认为两方面都有。事实上，它们是相互关联的。首先，社区中的人们将获得就业并参与实体经济，对吧？这就是问题所在。如果整体经济表现良好，社区也表现良好，那么这些点就会连接起来。市场力量会让我们连接这些点。就是这样。你必须能看到证据。这不能只关乎某一家公司。它必须是广泛的经济增长和广泛的社区许可。

I think both sides. In fact, it comes back together. It starts with the people in the communities who are going to be employed and participate in the real economy, right? That's the question. If the broad economy is doing well and the communities are doing well, the dots get connected. Market forces are such that we will connect the dots. That's it. You have to be able to see the evidence. It can't be about any one company. It has to be broad economic growth and broad community permission.

是的。你最近在哪些方面更新了你的想法，或者你个人关于 AI 社会影响的模型有哪些最大的更新？

Yeah. What have you most updated your thinking about currently, or what have you most updated your personal models on regarding societal impact of AI?

所以，你是说……

So, you're saying what's the...

在 AI 的社会影响方面，你更新最多的是什么？

What have you updated most on in terms of societal impact of AI?

是的。我认为最关键的是我们一开始提出的问题：我们需要讲述这个故事，并让它成为现实，让每个人都有机会作为一等参与者加入这个新经济。在未来 12 到 18 个月内，我们需要让人们说，‘哦，哇，我明白了。’将会有巨大的能力和基础设施，但我能看到将要发生的事情——无论是健康结果等好处，还是我创建初创公司的能力，或者我更高效地经营本地商店的能力。它正在发生，我亲眼看到了好处。以路径依赖的方式赢得许可，我们不能等待。我现在学到的一件事是，世界将对那些说‘相信我们，我们搞定了，未来会很美好’的科技公司非常怀疑。你必须提供切实的好处。政治家因为倡导这一点而赢得选举，这至少是我的调整。没有它，认为因为它太重要而无法概括，或者它在经济中占比太大以至于不可能不实现，这种想法是不行的。

Yeah. I think the most critical thing is the first question we started with: we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy. In the next 12 to 18 months, we need a way for people to say, 'Oh, wow, I get it.' There will be tremendous capability and infrastructure, but I can see what's going to happen—whether it's benefits like health outcomes, my ability to create a startup, or my ability to run my local store more efficiently. It's happening, and I see that benefit myself. Earning that permission in a path-dependent way, we can't wait. One thing I've now learned is that the world is going to be way skeptical of tech and tech companies that say, 'Trust us. We've got it. The future is going to be glorious.' You have to deliver tangible benefits. Politicians winning elections because they advocate for that will be at least my adjustment. Without it, thinking that somehow because it's too important to summarize, it's too much of the economy for it not to be the case.

所以，我有一个非常简单的框架，关于 AI 除了科技社区之外的广泛好处，那就是财富创造。它将在大量不同的公司中发生，包括初创公司和大公司。然后是医疗保健。你今天看到了很棒的演示。有像 Open Evidence 这样的公司。我认为这正在发生。教育似乎是另一个明显的好处，但我们还没有看到像预期那样大的影响。你对此有什么假设吗，或者你认为它会出现吗？

So, one very simple framework I have for what is going to be the broad benefit of AI beyond the communities just working in technology are sort of wealth creation. It's going to happen in a ton of different companies, startups and large companies. Then you have health care. You had amazing demos today. There are companies like Open Evidence. I think that is happening. Education seems like another one that's an obvious good where we haven't seen as much impact as I'd expect. Do you have a hypothesis on why that might be or if it'll come?

是的，我的意思是，我认为这又回到了我们如何看待教育的问题上。最近我遇到了 Alpha School 的创始人，学到了很多关于他们正在做的事情。听他们如何重新思考教育真正是什么样子，非常吸引人。我认为这实际上非常重要。我并不是说传统做法不重要，对吧？我甚至看过……很有趣……我忘了是斯坦福的哪门课，好像是亚洲的 CS 指南之类的。因为人们仍然需要学习。有一门有趣的 AI 课程，他们确保人们学习如何正确应用 softmax，而不是说，‘嘿，修复我的训练运行。’所以我认为学习概念很重要。这将是关键。但我们创造激励的方式、凭证是什么、我们如何评估这些凭证、这些凭证的就业机会是什么。所以我认为，鉴于获取信息的方式、自我教育的方式、持续更新自己的方式已经发生了巨大变化，必须发生彻底的改变。所以，有趣的是，也许下一个大型初创公司和成功故事可能是有人建立了一所新大学，或者一种新的教学法，甚至是如何让某人完成课程并找到高价值的经济机会。

Yeah, I mean, I think this is where again, how we think about education. Recently I met with the founders of Alpha School and learned a lot about what they were doing. It is fascinating to listen to how you even rethink what education really looks like. I think it's actually very important. I'm not saying anything traditionally being done is less important, right? I was even looking at the... it's fascinating to see... I forget which Stanford class it was, the Asian guidelines for CS something. Because you still need people to learn. There was an interesting AI class where they were making sure people were learning how to apply softmax appropriately versus saying, 'Hey, fix my training run.' So I think learning concepts is important. It's going to be critical. But the way we create incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials. So I think there is a complete change that has to happen given the way to get to information, the way to educate yourself, the way to continuously keep yourself updated has changed so much. So, interestingly enough, maybe the next big startup and success story could be someone who builds a new university or a new pedagogy even of how to get someone to go through a curriculum and find economic opportunity that's highly valuable.

嗯，这在很长一段时间里可能感觉不可能，但这是一个很好的结束点，而且可能是可能的。是的。谢谢你，Satya。

Well, that has felt perhaps impossible for a long time, but it's a great note to end on and something that might be possible. Yeah. Thank you, Satya.

非常感谢。谢谢。我很感激。谢谢大家。

Thank you so much. Thank you. I appreciate it. Thank you all.

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