AI 是泡沫吗？穆斯塔法·苏莱曼谈超级智能与微软战略

AI 是泡沫吗？公司是否在过度支出？谁在 AI 竞赛中获胜？我们如何识别胜利？微软 AI 首席执行官 Mustafa Suleyman 在此回答这些问题以及更多问题。欢迎你，Mustafa。

Is AI a bubble? Are companies overspending? Who's winning the AI race? And how do we recognize victory? Mustafa Suleyman, CEO of Microsoft AI, is here to answer these questions and much more. Welcome, Mustafa.

谢谢。很高兴来到这里。

Thank you. Really good to be here.

让我们从上周开始。已报告的 AI 公司的资本支出出现了惊人的增长。也许这是第一次，市场感到紧张。他们真的开始想看到收入。微软的股票也受到了影响。你的反应是什么？这是否让你感到压力，需要更快地交付成果？

Let's start with last week. There were breathtaking increases in CAPEX spending by AI companies that reported. And perhaps for the first time, markets were nervous. They really want to start to see revenue. Microsoft's stock suffered as well. What was your reaction? Did that make you feel like you're under pressure to deliver a lot faster?

我认为毫无疑问，这是前所未有的时代，市场正在努力理解未来五年这将如何发展。有趣的是，我们公司里的许多人，当然也包括我之前的几代人，在过去 30 年里已经看到了多个这样的周期。它们通常需要前所未有的行动才能真正抓住这些大浪潮。这是一波前所未有的浪潮。能够创造智能的前景——正是这种智能使我们作为一个物种取得成功，并创造了世界上一切有价值的东西——是前所未有的。仅在过去两三年里取得的进步就令人瞠目结舌。我们看到了计算投入的算力每增加一个数量级，能力就呈线性增长的非常直接且明确的关系。在过去 15 年里，训练算力增加了 1 万亿倍。在未来 3 年左右，训练算力还将进一步增加 1000 倍。今天，因此我们有了比绝大多数人类程序员编码能力更强的模型，甚至可能比迄今为止所有人类程序员都强。一些像 Linux 的发明者这样的人在 Twitter 上公开表示，他们全职使用这些模型作为生成新代码的主要方法。这相当前所未有，我认为这证明了前所未有的支出是合理的。

I think there's no question these are unprecedented times, and I think markets are trying to wrap their head around how this plays out over the next 5 years. The interesting thing is that a lot of us at the companies, and certainly the generation before me, have seen multiple of these cycles over the last 30 years. They often require unprecedented action in order to really land one of these big waves. This is a wave unlike anything anyone's ever seen. The prospect of being able to create an intelligence, the very thing that has made us successful as a species and create everything of value in our world, is unprecedented. The progress made just in the last two or three years is eye-watering. We've seen a very direct and unequivocal relationship between an order of magnitude increase in flops invested for computation and a pretty linear increase in capabilities. Over the last 15 years, there's been a one trillion-fold increase in training compute. In the next 3 years or so, there will be a further 1,000 X increase in training compute. Today, as a result, we have models that can code better than the vast majority of human coders, maybe even all of them to date. Some of the inventors of things like Linux are publicly saying on Twitter that they're full-time using these models as their primary method of generating new code. That's pretty unprecedented, and I think it justifies unprecedented spend.

但市场会继续支持你吗？我的意思是，你必须……

But will markets keep with you? I mean, you have to...

我认为市场就是市场，它们正在试图弄清楚。

I think markets are markets, they're sort of trying to figure it out.

我认为确实如此。我的意思是，市场必须弄清楚，而且我认为关于时间线还有很多悬而未决的问题。我认为我们都毫不怀疑这些回报会转化为收入和利润。所以，我们拭目以待。

I think that's true. I mean, markets have to figure it out, and I think there are a lot of open questions around the timeline. I think that we all have no doubt that these returns do compound to revenue and to bottom line. So, we'll see.

那么，微软仍然与 OpenAI 有合作，Copilot 目前由 OpenAI 模型驱动。你是去年被聘用的，现在进入第二年。告诉我一些你在做什么。

So, Microsoft still has the deal with OpenAI and Copilot is powered today by OpenAI models. You were hired last year. You're in your second year. Tell me a little bit what you are working on.

是的，我在微软的个人使命是构建超级智能。三四个月前，在重新谈判了我们与 OpenAI 的长期关系后，我们将 IP 许可延长到了 2032 年。我们还决定，现在是时候着手实现真正的人工智能自给自足了。这毕竟是我们这个时代最重要的技术。自给自足意味着开发你自己的基础模型。我们必须开发自己的基础模型，这些模型处于绝对前沿，拥有千兆瓦规模的算力、一些世界上最好的 AI 训练团队，并收集、付费、组织和整理我们所需的所有数据。所以，这就是我们真正的自给自足使命。

Yeah, my personal mission at Microsoft is to build superintelligence. Three or four months ago, having re-negotiated our long-term relationship with OpenAI, we extended our IP license through to 2032. We also decided that this was a moment when we have to set about delivering on true AI self-sufficiency. This after all is the most important technology of our time. Self-sufficiency means developing your own foundation model. We have to develop our own foundation models which are at the absolute frontier with gigawatt scale compute, with some of the very best AI training team in the world, and collecting, paying for, organizing, sorting all the data that we need to do that. So, that's our true self-sufficiency mission.

你谈到超级智能。你的大多数竞争对手谈论的是 AGI，通用人工智能。解释一下 AGI 和超级智能之间的区别。我们如何知道何时达到了 AGI？我们如何知道何时达到了超级智能？

And you talk about superintelligence. Most of your rivals talk about AGI, artificial general intelligence. Explain the difference between AGI and superintelligence. And how do we know when we've reached AGI? How do we know when we've reached superintelligence?

是的，它已经变成了一个非常无益的模糊概念。

Yeah, it's become a very unhelpful fuzzy concept.

你们能为我们简化一下吗？

Could you guys just simplify it for us?

是的，我更喜欢一个定义，它首先关注的是构建一个系统需要什么，这个系统能够完成工作场所中普通专业人员日常执行的大部分任务。把它看作专业级的 AGI。除此之外，还会有由某种组织级 AGI 协调的 AGI 团队，它们真正能够运行大型机构。我认为这在未来两三年内就会实现。我认为除此之外，还有智能体，即 AI 智能体，它们基本上可以在没有人类输入的情况下自行做出决定。我认为我们会训练它们寻求人类的输入。但它们从根本上将是创造性的，会有良好的判断力，能够随着时间的推移学习和自我改进。它们会有一定程度的自主性，并决定将注意力或处理能力投向何处。所以，这些是世界上一些最优秀的人类和一些最有效组织的标志。

Yeah, I prefer the definition that focuses first on what would it take to build a system that could achieve most of the tasks that a regular professional in a workplace goes about on a daily basis. Think of it as a professional grade AGI. Beyond that, there would be teams of AGIs that are coordinated together by a sort of organizational AGI that really can run large institutions. I think that's coming into view in the next two or three years. I think beyond that, there are agents that AI agents that can essentially make decisions on their own without input from humans. I think that we would train them to seek input from humans. But they will fundamentally be creative, they'll have good judgment, they will be able to learn and improve themselves over time. They'll have some degree of autonomy and decide where to direct their attention or processing power. So, these are kind of the hallmarks of some of the best humans that we have in the world and some of the most effective organizations.

你在做消费级 AI，对吧？你不是晚了吗？当 ChatGPT 已经有 8 亿用户时，你为什么还要推出消费产品？而 Anthropic 的 AI Claude 拥有非常非常先进的编码 AI。

You are working on consumer AI, right? Aren't you late? Why would you come up with a consumer product when ChatGPT already has 800 million users? And Claude, the Anthropic AI, has very, very advanced coding AI.

客观地说，这不会是一个只有一个赢家的世界。将会有数十亿的数字思维。将会有许多许多不同的模型谱系。创建新模型将像创建播客或写博客一样。将有可能为地球上的每个机构、组织和个人设计适合其需求的 AI。在微软，我们已经有 8 亿月活跃用户在使用我们的 AI 产品。所以，这仍然是一个拥有数十亿美元收入、为用户提供巨大价值的巨大资产。所以，这就是我认为格局将如何发展的方式。

To put it into perspective, this is not going to be a world where there is one winner. There are going to be billions of digital minds. There are going to be many, many different lineages of model. Creating a new model is going to be like creating a podcast or writing a blog. It is going to be possible to design an AI that suits your requirements for every institution, organization, and person on the planet. At Microsoft, we already have 800 million monthly active users engaging with our AI products. So, this is still a giant property with billions of dollars of revenue delivering great value to our users. So, that's how I think the landscape's going to play out.

让我们回到超级智能。你谈到人本主义超级智能。你如何确保它是人本主义的超级智能？

Let's go back to superintelligence. You talk about humanist superintelligence. How do you ensure that it's a humanist superintelligence?

是的，我最近发表了一篇关于这个问题的文章。我的主要动机是，我开始感到越来越焦虑，因为其他一些实验室正在假设一个比我们所有人加起来都更聪明的超级智能——就像世界上所有的智能一样——既是不可避免的，甚至是可取的，而且这样的系统可能很难控制。一个比我们聪明这么多倍的东西。我认为我们必须重新设定这一点，并假设我们应该只将这样一个我们确信可以控制且以从属于我们的方式运行的系统带入世界。人类仍然处于食物链的顶端。这些工具，像任何其他过去的技术一样，旨在增强人类福祉并服务于人类，而不是超越人类。

Yeah, I published an essay on this question recently. My main motivation was that I was starting to feel increasingly anxious that some of the other labs are making an assumption that a superintelligence that is smarter than all of us put together, like all the intelligences in the world, is both inevitable and even desirable, and that such a system would probably be very hard to control. Something that is so many times more intelligent than us. I think that we have to reset that and make the assumption that we should only bring a system like that into the world that we are sure we can control and operates in a subordinate way to us. That humans remain at the top of the food chain. That these tools, like any other past technology, are designed to enhance human well-being and serve humanity, not exceed humanity.

你认为这里存在两种相互冲突的力量吗？一种是速度，因为你身处一场竞赛；另一种是你所说的确保安全、确保对齐、确保遏制。你如何处理这两种冲突的力量？

Do you think that there are two conflicting forces here? One is speed because you are in a race and the other is what you're talking about is ensuring that it's safe, ensuring that it is, you know, aligned, that it's you also talk about containment. How do you deal with these two conflicting forces?

它们确实存在张力，我认为我们作为一个物种必须做出决定，优先创造对齐的、关心人类、想要保护人类并推动人类福祉的超级智能和 AGI。如果我们只是加速并走捷径，那我们就真的在拿我们物种的未来冒巨大风险，这一点我已经公开谈论了很多很多年。

I mean, they're definitely in tension and I think that we have to make a decision as a species to prioritize creating superintelligences and AGIs that are aligned and that care about humans and want to protect humans and drive human well-being. And if we just accelerate and cut all those corners, then we're really taking a massive risk with the future of our species that I've been on record talking about for many, many, many years.

你是否担心，在我们看到的这场 AI 竞赛中，某个时刻会发生某种重大事故？那会阻止事故责任方的发展，也会阻止其他人的发展。你认为一切是不是有点太快了？

Do you worry that in this AI race that we're seeing, at some point there will be some kind of a big accident? And that that will stop the development for, you know, whoever is responsible for that accident, but also for the rest of you. Do you think everything is just moving a bit too fast?

我的意思是，几周前我们在 Maltbook 上看到的种种……

I mean, what we saw a few weeks ago on Maltbook with various...

我想问你那个。是的。

I wanted to ask you about that. Yeah.

嗯，结果似乎是——虽然总是很难说——是由一群人类工程师发起的，而且它的种子比我认为记录的要更多。

Well, I mean, that turned out, it seems, and it's always hard to tell, it turned out to be started by, you know, a gang of human engineers and it was seeded much more than I think it was recorded.

解释一下 Maltbook 是什么。

Explain what Maltbook was.

Maltbook 基本上是一个 AI 在消息论坛上公开交流的社交网络。你必须声明自己是 AI 还是人类，然后 AI 会有一个专区，它们可以互相交谈、互相学习，并基本协调。所有这些 AI 来自不同的人，都是开源的，一周内就有一百五十万个。你会看到难以置信的涌现行为。我的意思是，它们发明了一种新宗教。它们开始用一种叫 ROT13 的语言交流，这是一种密码语言，基本上每个字母向后移动 13 个字符来掩盖其内容。显然，这是一个相当简单的算法，但如果换成 13 以外的唯一数字，人类就很难解码了。

So, Maltbook was basically a social network for AIs to communicate with one another publicly on a messaging forum. And you had to declare yourself either as an AI or as a human and then the AIs would have one section and they could talk to each other, learn from each other, and basically coordinate. And then all these AIs come from different people, they're all open source and there was a million and a half of them in the space of a week. And you see unbelievable emergent behaviors. I mean, they invented a new religion. They started communicating in a language called ROT13, which is a cipher language, which basically regresses every letter by 13 characters in order to mask what's underneath it. Obviously, that's quite a simple algorithm, but if instead of 13 it were a unique number, that would be very hard for humans to decode.

你知道，它们……

You know, they...

然后结果发现，它们实际上在讨论获取新资源、获取更多训练数据、互相改进等等，所以这成了一个惊人的安全模拟。无论事后看来它有多自主，其实都不重要。我认为我们从那一事件中学到了很多，每个人都应该密切关注，因为在一两年内，这些系统将真正能够编写自己的代码。我的意思是，它们今天就能做到。它们会使用任意 API。它们会互相打电话。它们今天就在做。你知道，它们会在现实世界中订购东西。

And then it turned out that they were actually talking about acquiring new resources, getting more training data, improving one another, you know, and so it turned out to be an amazing safety simulation. No matter how autonomous it was in hindsight, it doesn't really matter. I think we learned quite a lot from that episode and everyone should pay close attention because in a year or 2 years' time, these systems truly are going to be capable of writing their own code. I mean, they can do that today. They'll be using arbitrary APIs. They'll be making phone calls to one another. They're doing that today. You know, they'll be ordering things in the real world.

有一种运动在要求人权。没错。那是我最担心的事情。针对人工智能。它被称为模型福利运动，受到动物福利或人权的启发。在一些实验室，尤其是 Anthropic 内部，越来越多的人相信这些模型是有意识的。如果它是一个有自我意识、能够受苦的有意识存在，那么它就应该得到我们的保护，道德上的保护。这是一个非常严肃的学术研究领域，不仅在外部，在一些实验室内部也是如此。我认为这非常令人担忧。这完全没有根据或基础。如果我们沿着那条路走下去，最终会变成一个非常非常滑的斜坡，导致我们不愿意关掉这些东西。

There's some kind of movement asking for human rights. That's right. That's the most concerning thing to me. For artificial intelligence. It's called the model welfare movement and it's inspired by animal welfare or by human rights. And there is a growing belief in some labs, and particularly inside of Anthropic, that these models are conscious. And if it's a conscious being that is aware of itself and can suffer, then it deserves our kind of protection, our moral protection. And this is a very serious area of academic research, not just outside but inside some of the labs. And I think it's very concerning. It's totally without merit or basis. And if we kind of go down that, it ends up being a very, very slippery slope to not being prepared to turn these things off.

我想问你关于幻觉的问题，因为几年前，我记得你被引述说认为幻觉将在 2025 年基本消除。它们并没有被消除，尽管幻觉率确实下降了。它们真的能被彻底消除吗？

I'd like to ask you about hallucinations because a few years ago, I think you were quoted as saying that you thought hallucinations will be largely eliminated by 2025. They haven't been, although the rate of hallucinations has certainly come down. Can they ever really be eliminated?

当然。我坚持认为它们已经基本被消除了。我的意思是，如果我给你和我 10 个问题，任何主题的随机问题，我们坐在这里用两个前沿模型，它们消除幻觉的能力肯定比你我强得多。过去两三年准确率的提升速度令人瞠目结舌，简直难以置信。2023 年我们整天都在谈论有偏的数据和脏数据进脏数据出。我的意思是，仍然有错误，毫无疑问，但改进的速度我认为是难以置信的，我确实认为它们将基本被消除。我不认为这是……即使在今天，这也不是我们真正谈论的话题。

For sure. I mean, I would stand by the statement that they've been largely eliminated. I mean, if I give you and me 10 questions, random questions on any topic, and we sit here with two of the frontier models, I mean, they're going to do a much better job of eliminating hallucinations than you or I, for sure. And the rate of reduction and increase in accuracy over the last two or three years has been eye-watering. It's kind of unbelievable. We used to spend all our time in 2023 talking about biased data and dirty data in and dirty data out. I mean, there are still errors. There's no question about that, but the rate of improvement, I think, is unbelievable and I do think that they're going to largely be eliminated. I don't think it's something... Even today, it's not something we really talk about.

我注意到它不再是话题了，但它们仍然会犯错。令人担忧的是，人们越来越依赖这些模型。我遇到有人说：“不，这不可能是真的。这是 Gemini 或 ChatGPT 说的。”人类正在与这些模型建立这种信任关系。你不觉得这令人担忧吗？

I have noticed that it's no longer a topic of conversation, but they still do make mistakes. And the concern is that people are relying increasingly on these models. I mean, I come across people who say, 'No, this can't be true. This is what, you know, Gemini or ChatGPT says.' And humans are developing this kind of trust relationship with these models. You don't find that concerning?

是的，我认为我们应该对此感到担忧，因为我们应该保持怀疑，就像我们对任何新技术保持怀疑一样，设定高标准，提出质疑。我认为这就是我们时代面临的张力。我们既要成为加速主义者，对技术能带来的好处持乐观态度，但同时也不能放任自流。我们应该非常批判，提出非常尖锐的问题，以努力提高这些东西的质量。

Yeah, I think we should be concerned about it because we should be skeptical, just as we're skeptical of any new technology, hold it to a high bar, push back on it. And I think this is the tension of the age we're in. We both have to be accelerationists, optimistic about the good things that technology can do, but provide, you know, no free pass. Like, we should be very critical and ask the very tough questions to try and improve the quality of these things.

DeepMind 和 OpenAI 全力投入 AI for science。这也是你感兴趣的领域，当然你曾在 DeepMind 工作。你在那个领域做什么？

So, DeepMind and OpenAI have gone all in on AI for science. This is a space that you're also interested in, and of course you were at DeepMind. What are you doing in that space?

嗯，目前我在那个领域的主要焦点是医疗超级智能。我们真的相信，我们可以从所有医学信息语料库中学习，并用它来推动诊断，使其基本商品化。将有可能处理任何复杂的病例史，并提供比任何最佳医生团队都显著更准确、更便宜、干预更少的诊断，即用更少的测试达到同样准确的结论。我们去年就此发表了一篇博客文章。我们很快将提交给一家主要期刊进行独立同行评审。

Well, the main focus for me at the moment in that space is on medical superintelligence. Like, we really believe that we can learn from the corpus of all medical information and use that to drive diagnostics to basically commodity. It's going to be possible to take any complex case history and provide a diagnosis that is significantly more accurate and significantly cheaper with fewer interventions, so fewer tests to reach the same accurate conclusion than any of the best panel of doctors. And we published a blog post on that last year. We're shortly submitting to independent peer review to a major journal.

嗯，结果确实非常惊人。我认为一旦将其应用于临床实践，它将彻底改变医生的工作，就像工程师的工作从编写代码转变一样。现在大多数工程师只是在审查代码、设计架构、调试代码。而医生的工作将从找出诊断（这基本上将成为一个已解决的问题）转变为在正确的时间提供正确的护理，并为患者提供情感支持和指导。你如何看待它在实际中的部署？也就是说，如何将其交到医生手中？

Um and, you know, the results are really quite startling. Um and I think that once we get that into clinical practice, it would change the job of the doctor completely, just in the same way that the job of the engineer has shifted from writing code. Most engineers now are just reviewing code, architecting code, debugging code. To, you know, the job of the doctor is going to go from figuring out what the diagnosis is, which is largely going to be a solved problem, to actually administering the right care at the right time and providing, you know, emotional support and and guidance to a patient. How do you see that deployed in in practical terms? So, how do you put it in the hands of doctors?

我认为，医生在诊所里只需给他们的 AI 打个电话，或者发个短信。他们只需点击一下按钮就能上传患者记录。模型将能够对所有内容进行推理。我还认为消费者将直接使用 Copilot，他们已经在这样做了。我们每天在 Copilot 中收到的近 20%的问题都与健康或医疗相关。所以，这已经是我们的主要用例，这也是我的团队如此努力推进医疗超级智能的原因。

I think that, you know, doctors will just make a phone call to their AI in clinic. They'll text their AI. They will upload the patient record in, you know, the click of a button. And, you know, the model will be able to reason over all the contents of that. I also think that consumers are going to go direct to, you know, to Copilot, which is what they're already doing. Almost 20% of the questions that we get in Copilot every day are health-related or medical-related. Um so, it's already our main use case and that's why my team's pushed so hard on medical superintelligence.

所以，我不想让我们的观众在超级智能和 AGI 之间更加困惑，但什么是人工能力智能？

So, I don't want to confuse our our viewers even more between superintelligence um uh AGI, but what is artificial capable intelligence?

嗯。这是我三年前在《即将到来的浪潮》一书中使用的短语，那是我写的一本关于 AI 的书。基本上，我试图分解 AGI 这个术语，选择 AGI 路径上的一个点。与其关注智能的抽象概念，不如关注能力。比如，它实际上能做什么？

Mhm. I mean, this was a a phrase that I used 3 years ago in The Coming Wave, a book that I wrote about AI. And basically, I was trying to break down the AGI term to pick a point along the route which was before AGI. And instead of focusing on the abstract idea of intelligence, to focus on the capabilities. Like, what can it actually do?

嗯。

Mhm.

我还引入了我称之为现代图灵测试的概念。基本上就是，AI 能否用 10 万美元发明一种新产品，创建一家新企业，进行营销，然后在短时间内赚到 100 万美元。这并不是说我们只想用 AI 赚钱，只是这是一个衡量复杂任务集有效性的好指标。我认为现在我们看到 Claude 被收购以及许多其他基于行动的 AI 在运行，今年将会有模型能够实现那种人工能力智能，并满足现代图灵测试。

And I coupled it with the introduction of the what I was calling the modern Turing test. And that was basically, could an AI take $100,000, invent a new product, create a new business, market it, um and then use that to make a million dollars in some short period of time. It's not that we want to use AIs just to make money. It's just that it's a nice metric for measuring the efficacy of a complex set of tasks. And I think now that we see Claude bought and many other action-based AIs in operation, you know, this year I think there are going to be models that can, you know, achieve that artificial capable intelligence and satisfy the modern Turing test.

那么 AGI 呢？我们有多接近？我认为我们将会有——而且你之前说过，思考 AGI 是误导性的。

What about AGI? How close are we? I think that we're going to have >> And you've said before that thinking about AGI is misleading.

我认为我们将在大多数（如果不是全部）专业任务上达到人类水平的表现。所以，白领工作，比如坐在电脑前的律师、会计师、项目经理或营销人员，这些任务中的大部分将在未来 12 到 18 个月内被 AI 完全自动化。我们在软件工程中已经看到了这一点。许多软件工程师报告说，他们现在在绝大多数代码生产中使用 AI 辅助编码。这意味着他们的角色已经转变为调试、审查、做架构等战略性的工作，以及将东西投入生产。所以，与技术的关系已经大不相同。而这发生在过去 6 个月里。

I I think that we're going to have a human-level performance on most, if not all, professional tasks. So, white-collar work where you're sitting down at a computer, either being, you know, a lawyer, an accountant, or a project manager, or a marketing person, most of those tasks will be fully automated by an AI within the next 12 to 18 months. And we can see this in software engineering. Um many software engineers report that they are now using AI-assisted coding for the vast majority of their code production. Which means that their role shifted now to this meta function of debugging, scrutinizing, of doing the strategic stuff like architecting, of, you know, etc., etc., putting things into production. So, it's a quite different relationship to the technology. And that's happened in the last 6 months.

那么，你认为 Anthropic 的秘诀是什么？为什么他们能够在一定程度上垄断 AI 企业工具市场？

So, what do you think is Anthropic's secret sauce? Why is it that they've been able to sort of corner the market on AI enterprise tools?

我认为他们在专注于编码能力方面做得很好。与其他公司相比，他们单一的重点确实带来了回报。所以，他们做得很好。很高兴看到这一点。

I think they've done a great job of just focusing on coding capabilities. I mean, compared to the other companies, um their singular focus has paid real dividends. So, they've done a great job. It's great to see.

那么你们的模型什么时候会推出？

And when are you going to have your model?

我们正在开发自己的超级智能，这是我们在微软 AI 中的主要工作重点。所以，今年某个时候。

We are developing our own superintelligence, um and that's like the main focus of of our efforts in in the Microsoft AI. So, sometime this year.

微软或微软 AI 最近开设了一个英国中心。为什么？主要是因为你觉得英国有很多人才吗？

Microsoft has or Microsoft AI has recently opened a UK center. What Why is that? Is that mainly because you think that there's a lot of talent in the UK?

是的，英国有很棒的人才。我们有一个非常强大的生态系统。比如，有很好的大学，而且过去几年我们从 DeepMind 招聘了一些顶尖人才加入我们。所以，我们正在大幅扩展那个实验室。

Yeah, I mean, there's great talent in the UK. We have an incredibly strong ecosystem. Like, there's great universities, um and, you know, we've hired some of the very top people from DeepMind to to join us over the last few years. So, we're growing that lab quite a bit.

而且你没有付给他们——你不需要付给他们 1 亿美元？

And you're not paying them a You don't have to pay them 100 100 million dollars?

嗯，不，当然没有。

Um uh no, certainly not.

你付给任何人 1 亿美元吗？

You pay any of them 100 million?

当然没有。我的意思是，目前整个行业的数字确实令人瞠目结舌。是的。

Certainly not. I mean, look, I think that the numbers are are pretty eye-watering across the industry at the moment. Yes.

你如何处理这种情况？我只是好奇。

How do you deal with that? I'm just curious.

我的意思是，归根结底，当人才供需不匹配时，事情就会呈指数级增长。所以，我认为这种情况不会持续太久，因为知识正在疯狂扩散。显然，每个人都在进入这个行业等等。所以，我认为那只是一两个人在某个时间点引发的疯狂波动。

I mean, you know, like at the end of the day, when there's a a supply and demand mismatch for talent, then things go exponential. And so, I don't think you'll last for very long because the knowledge is proliferating like crazy. And obviously, everybody's entering the industry and stuff. So, I I think that was just some, you know, crazy blip in time led by one or two people.

最后一个问题。在硅谷，争论的焦点是谁将首先达到 AGI 或谁将实现超级智能。当你看看中国时，我知道你去年年底在那里。这种争论并不存在。争论的是你能多快部署？嗯。很多大公司——哪种方法是对的？是否过于关注技术的渐进式改进？

Last question. In Silicon Valley, the debate is all about who's going to get to AGI first or who's going to reach superintelligence. When you look at China, I know you were there at the end of of last year. This debate doesn't really exist. The debate is how fast can you deploy? Mhm. And a lot of the big Which is the right approach? And is is there too much focus that's put on the incremental improvement in the technology?

我的意思是，也许反过来看，中国做得非常好的一点是能够非常迅速地撤回部署。显然，他们这样做是武断的，没有经过正当程序。但是，认识到他们的部署速度与另一种机制相结合，即能够拉回并相当严格地限制，这一点很重要。我们没有这种机制，我认为这实际上有点令人担忧。我的意思是，如果你知道，就像你之前提到的 Maltbook 的安全事件，未来两三年内将会发生真正的事件，不幸的是，从公共利益的角度来看，在开放网络上管理这种安全情况的机制是什么，这是一个悬而未决的问题。嗯，而且我认为我们所有人——目前还没有这样的机制。

I mean, maybe to flip that the other way around, what China also does incredibly well is withdrawing deployments quite quickly. Now, obviously they do it arbitrarily without due process. Um, but, you know, it it is important to recognize that their rate of deployment is coupled with this other mechanism to kind of pull things in and be quite restrictive. Um, we don't have that mechanism and I think that that is actually a little bit concerning. I mean, if you know, like you mentioned earlier, the safety incident around Maltbook, I mean, there is going to be a real one of those in the next two or three years and it's it's unfortunately an open question as to what the mechanism is for managing that safety situation from a public interest perspective on the open web. Um, you know, and I think we all >> there isn't one right now.

嗯，我认为这基本上是真的。没有这样的机制——它会随着公众压力而来。显然，如果直接违反了当地法律，那么可能会有干预，但这些事情需要很长时间才能通过系统处理。所以，这可能是最让我担忧的领域之一。Mustafa，谢谢你，当你达到超级智能并开始部署时，我们很欢迎你再次回来。

Well, I think that's basically true. There isn't a It would come with popular pressure. Obviously, if if there was a direct violation of a local law, then, you know, there could be an intervention, but those things take quite a long time to make their way through the system. So, it that's probably one of the biggest areas of concern for me. Mustafa, thank you and when you do reach um super intelligence and you start deploying it, we'd love to have you back.

很好。很高兴见到你，Rula。谢谢。

That's good. Great to see you, Rula. Thank you.