Name: 薩提亞-納德拉微軟的人工智能賭注、超大規模、量子計算突破 (Satya Nadella: Microsoft's AI Bets, Hyperscaling, Quantum Computing Breakthroughs)
Uploaded: 2025-07-04T21:12:23.000Z
Duration: 40 min 30 s

What are the tools that we can put in the hands of people that will give them that sense of empowerment?

我們可以把哪些工具交到人們手中，讓他們感受到權力的存在？

I'm not into this anthropomorphizing AI at all.

我一點也不喜歡把人工智能擬人化。

There is going to be a job called a software engineer.

It's going to be different, but I look at it, you are really taking a software engineer and saying you're now a software architect.

這將會有所不同，但我認為，你實際上是在把一個軟件工程師說成是一個軟件架構師。

It's my pleasure to welcome the chairman and CEO of Microsoft, Satya Nadella.

我很高興歡迎微軟公司董事長兼首席執行官薩蒂亞-納德拉（Satya Nadella）。

Man, San Francisco, you should move to Seattle.

我的職業生涯是從西雅圖開始的。

Anyone who's successful starts at Microsoft.

任何成功人士都是從微軟起步的。

So, Satya, you've emphasized before that AI is going to shape all that we do.

薩提亞，你之前強調過，人工智能將影響我們所做的一切。

At Microsoft, how does this actually drive your strategy and particularly thinking about how AI will influence ideas beyond the immediate incredible product suite, like the broader economy?

在微軟，這實際上是如何推動你們的戰略，尤其是如何思考人工智能將如何影響眼前令人難以置信的產品套件之外的想法，比如更廣泛的經濟？

At Microsoft, I feel we are a platform company, a product company, and a partner company.

在微軟，我覺得我們是一家平臺公司、產品公司和合作夥伴公司。

And I've kind of, in my 35 years, I've lived through client-client server, web internet, mobile cloud.

35 年來，我經歷了客戶端-客戶服務器、網絡互聯網和移動雲。

So that's just at least how I pattern match.

So the first thing that I think about is the platform opportunity.

是以，我首先考慮的是平臺機會。

When I sort of look at all the folks here, the interesting thing is the compounding effects of all these platforms, right?

當我看到這裡的所有人時，有趣的是所有這些平臺的複合效應，對嗎？

So this AI piece, the reason why I think the rate of diffusion is so fast and so wide is because it builds on the previous generation.

我之所以認為人工智能的普及速度如此之快、範圍如此之廣，是因為它是在上一代產品的基礎上發展起來的。

I think about, like, if the cloud was not there, we wouldn't have been able to build the AI supercomputers, which then led to the models, which then led to the products, right?

我想，如果沒有云計算，我們就無法建立人工智能超級計算機，進而產生模型，再產生產品，對嗎？

So that compounding effect is the interesting thing to me.

是以，這種複合效應對我來說才是最有趣的。

So that's why you always sort of take the previous platform and build the next platform, and you want to be able to get that right.

這就是為什麼你總是要利用上一個平臺來建立下一個平臺，而且你希望能夠做到這一點。

And then you've got to build the next generation products on top of it, right?

然後在此基礎上開發下一代產品，對嗎？

With each one of these platforms shift, there's a new workload, right?

每一個平臺的轉變都會帶來新的工作量，不是嗎？

I mean, when I first remember looking at the large-scale training job, I mean, it's kind of a very different workload to what we built, for example, the cloud with, right?

我的意思是，當我第一次看到大規模培訓工作時，我的意思是，這與我們建立雲計算的工作負載截然不同，對嗎？

It's a data-parallel, synchronous workload, which is so different than, let's say, a Hadoop job or what have you.

這是一個數據並行、同步的工作負載，與 Hadoop 工作或其他工作相比，有很大不同。

And so the platform itself then completely gets, you know, completely relitigated and changed.

這樣一來，平臺本身就完全被重新審視和改變了。

So to me, that's, I think, the exciting thing on the platform side.

是以，對我來說，我認為這是平臺方面最令人興奮的事情。

It's golden age of system software, quite frankly.

坦率地說，這是系統軟件的黃金時代。

You know, today, if I had to think about anybody who's building at the infrastructure layer, not just the hyperscalers, but even the startups, I think that's a tremendous opportunity.

要知道，今天，如果讓我想想任何在基礎設施層進行建設的人，不僅是超大規模企業，甚至是初創企業，我都認為這是一個巨大的機會。

Obviously, there's a tremendous opportunity in the model side, and then the products on top of it.

顯然，在模型方面有巨大的機會，而在此基礎上的產品也是如此。

So yeah, we think of these, and then ultimately, what's it for?

所以是的，我們想到了這些，然後最終是為了什麼？

It's for one thing and one thing alone, which is to drive, ultimately, economic growth and GDP growth.

這隻有一個目的，那就是最終推動經濟增長和國內生產總值的增長。

So if I had to ask me, my benchmark for AI is, is it creating surplus in the world around us, one community, one country, one industry, one company at a time?

是以，如果要問我，我對人工智能的基準是，它是否在我們周圍的世界，一個社區、一個國家、一個行業、一家公司創造了盈餘？

I mean, it seems like the app level, you know, you guys have built, you know, sort of the defining apps at the app layer for so many decades.

我的意思是，似乎在應用程序層面，你知道，你們已經構建了，你知道，幾十年來在應用程序層的定義應用程序。

It feels like we're at this weird, lumpy moment where, you know, maybe the models have popped up and we're sort of astonished by what's happening, but then, you know, sort of the compute and the apps need to actually catch up.

感覺我們正處在一個奇怪的、動盪不安的時刻，你知道，也許模型已經出現，我們對正在發生的事情感到驚訝，但隨後，你知道，計算和應用程序需要真正跟上。

And, you know, the hope here is actually the people in this room will be the people who build those apps.

而且，你知道，我們希望在座的各位將成為開發這些應用程序的人。

One of the questions is, is the model like SQL?

問題之一是，該模型是否與 SQL 相似？

Or is it the SaaS app itself and the model, right?

還是 SaaS 應用程序本身和模式的問題，對嗎？

I mean, I think the place where, where does the model end and where does the product begin?

我的意思是，我認為哪裡是模式的終點，哪裡是產品的起點？

Because if you sort of say model with some scaffolding and tool calling in some infinite loop is the product, if that is what it is, then I think that that's where it gets a little confusing.

因為如果你說帶有腳手架的模型和在無限循環中調用的工具就是產品，如果是這樣的話，那麼我認為這就是讓人有點困惑的地方。

But that's like saying a bunch of SQL business logic is with SQL is what is an app.

但這就好比說一堆 SQL 業務邏輯與 SQL 就是應用程序。

So I think it's still possible for anyone to build an app tier on top of a model.

是以，我認為任何人都有可能在模型的基礎上建立一個應用程序層。

And you have to sort of abstract yourself and say, yeah, the model is just like SQL was to me.

你必須把自己抽象出來，然後說，是的，這個模型對我來說就像 SQL 一樣。

And so I think that, I mean, I always dreamt of a moment when AI slash machine learning will have a SQL moment.

是以，我認為，我的意思是，我一直夢想著人工智能和機器學習將迎來 SQL 時刻。

Because if you think about it, we never had a stable platform layer in the past because everything was vertically built and integrated.

因為仔細想想，我們過去從來沒有一個穩定的平臺層，因為一切都是垂直構建和整合的。

For the first time in this model layer, now we have something like a SQL engine that we can then use to build pretty sophisticated products.

在這個模型層中，我們第一次擁有了類似於 SQL 引擎的東西，我們可以用它來構建相當複雜的產品。

I mean, just the inference time compute plus tool calling is giving us, I think a pretty robust harness to be able to build pretty sophisticated products.

我的意思是，僅僅是推理時間計算加上工具調用，就為我們提供了一個非常強大的工具，能夠打造出非常複雜的產品。

It's kind of wild how much it's the integration piece that is also the app layer now.

它的集成部分現在也是應用層，這有點瘋狂。

It's just, the models sitting on their own are incredibly smart, but right now they feel sort of, there's just giant gulf between that and the data that really matters to you for business.

只是，這些模型本身非常聰明，但現在它們覺得，這些模型與真正對業務有重要意義的數據之間存在著巨大的鴻溝。

Yeah, I mean, I think that's a good observation because I think at least my read of the situation is the model is an important piece, the model scaffolding and all this tool calling, so there's a real app server that you kind of need in order to be able to build sophisticated applications.

是的，我的意思是，我認為這是一個很好的觀察點，因為我認為至少我對這種情況的理解是，模型是很重要的一部分，模型腳手架和所有這些工具的調用，所以你需要一個真正的應用服務器，以便能夠構建複雜的應用程序。

But the interesting thing is the feedback loop, the data path inside the product that then is used in order to post-train, in order to be able to do the right tool selection, that seems to be the place where product creation is all gonna happen.

但有趣的是，產品內部的反饋迴路和數據路徑，可以用來進行後期培訓，以便進行正確的工具選擇，這似乎是產品創造的關鍵所在。

AI scaling laws are continuing to hold and the demand for intelligence appears to be potentially infinite.

人工智能的擴展規律仍在繼續，對智能的需求似乎可能是無限的。

Yesterday, Elon was mentioning that there will be 99 hyper-intelligent beings to one human, which is kind of a wild prognostication, but seems possible given this, where does the building for the future of AI truly demand for global compute infrastructure?

昨天，埃隆提到，人類與超智能生物的比例將達到 99:1，這雖然是一種天馬行空的預言，但似乎也有可能，那麼，未來人工智能的建設對全球計算基礎設施的真正需求在哪裡？

How do you anticipate these demands evolving as models don't just become larger, but more intelligent and capable of complex multi-agent interactions?

隨著模型不僅變得越來越大，而且變得越來越智能，並能夠進行復雜的多代理交互，您預計這些需求將如何演變？

Yeah, I mean, look, if you sort of really step back and say, first, if you sort of go with the compute or intelligence is one of a log of compute, and then you ask the question, how much energy does compute consume?

是的，我的意思是，你看，如果你退一步說，首先，如果你認為計算或智能是計算的對數之一，然後你問一個問題，計算會消耗多少能源？

Let's take in the United States, maybe 2% today, 3% tops.

就拿美國來說吧，現在可能是 2%，最多 3%。

That's like massive because then the amount of extra energy that needs to be produced in order for AI to use it is pretty high.

這就像是大規模的，因為這樣一來，為了讓人工智能使用它而需要產生的額外能量就相當高了。

I think that's why we all have to sort of keep in mind that if there's one lesson history has taught us is that if you're gonna use energy, you better have social permission to use energy.

我認為這就是為什麼我們都必須牢記，如果說歷史給我們上了一課，那就是如果你要使用能源，你最好得到社會的許可。

So that means you've got to make sure that the output of this AI is socially useful.

是以，你必須確保人工智能的輸出對社會有用。

In other words, if we really are not creating social surplus, economic surplus, as measured by countries and communities, then we just can't consume energy.

換句話說，如果我們真的沒有創造社會盈餘、經濟盈餘（以國家和社區為衡量標準），那麼我們就不能消費能源。

是以，對我來說，這才是更重要的事情。

Like everybody's today hot and bothered about, okay, what do I do about energy production?

就像今天每個人都在熱火朝天地討論 "好吧，能源生產該怎麼辦？

I think the real question in the next five years is we've got to produce enough products that are creating great value, which I'm very confident of, by the way, in healthcare and education, in productivity.

我認為，未來五年的真正問題是，我們必鬚生產出足夠多的產品，創造出巨大的價值，順便說一句，在醫療保健、教育和生產力方面，我對此非常有信心。

So there's many, many domains, but that's the real challenge for us as a tech industry is to prove unequivocally that what we have created is showing up in real stats that is not just an AGI or AI benchmark.

是以，有很多很多的領域，但作為科技行業，我們面臨的真正挑戰是明確證明我們所創造的東西在真實的統計數據中顯示出來，而不僅僅是 AGI 或 AI 基準。

I mean, the hope is that this will show up in sort of the real things that you sort of interact with on a daily basis. 100%.

我的意思是，希望這能體現在你日常接觸的真實事物中。100%.

You know, you go use, you get a mortgage loan and instead of three months or two months of waiting around and you don't know if you're going to get approved, or, you know, there's just so many things that are important parts of your life that get drowned in paperwork or bureaucracy that those things could potentially go away. 100%.

你知道，你去使用，你得到一個抵押貸款，而不是三個月或兩個月的等待，你不知道你是否會得到準許，或者，你知道，只是有這麼多的事情，是你生活的重要組成部分，被淹沒在文書工作或官僚主義，這些事情可能會消失。100%.

So I think, yeah, if you take even, take some of the public services, right?

是以，我認為，是的，如果你甚至採取一些公共服務，對嗎？

I mean, if you take any country, you know, it's GDP or take healthcare, like in the United States, what is it, 18, 19% of our cost is healthcare.

我的意思是，如果你把任何一個國家，你知道，它的國內生產總值或醫療保健，像在美國，它是什麼，我們的成本的18％，19％是醫療保健。

And a lot of it, like everybody talks about the magical drug, blah, blah, blah, except all of the cost is in workflow.

很多時候，每個人都在談論神奇的藥物，諸如此類，但所有的成本都在工作流程中。

And so if you really take something like a simple thing like discharge, the amount, like you take the backend of an EMR system with just an LLM and a prompt, that itself is going to save so much time and money and energy that it would sort of pay for itself.

是以，如果你真的想做一些簡單的事情，比如出院，比如你只需要一個 LLM 和一個提示，就能在 EMR 系統的後臺節省大量的時間、金錢和精力，這本身就已經物有所值了。

我的意思是，這很直接，對嗎？

You spend an incredible amount of GDP on healthcare and rightly so, but every dollar that's spent on clerical work could have been spent towards some sort of treatment that would have saved someone's life.

你們在醫療保健上花費了大量的 GDP，這是理所應當的，但花在文書工作上的每一美元都本可以用於某種治療，挽救他人的生命。

Or the simple time allocation of a physician away from paperwork to the patient is right there to be had.

或者說，將醫生從文書工作中抽出的簡單時間分配給病人，就在眼前。

What do you see as being the biggest rate limiter for AI deployment today?

您認為目前人工智能部署的最大障礙是什麼？

See, here's the interesting thing, right?

看，這就是有趣的地方，對吧？

And this audience is so young and none of my metaphors would sort of work, but nevertheless, if you sort of came in in the early part of, let's say you were a multinational company, pre-PCs, how the heck did we do forecasts?

我的聽眾都很年輕，我的比喻都不太管用，但不管怎樣，如果你是一家跨國公司的早期員工，比方說，在 PC 出現之前，我們到底是如何進行預測的？

The way one would do sales forecast was you would send faxes.

進行銷售預測的方法是發送傳真。

People would then take those faxes and send inter-office memos.

然後，人們會接收這些傳真，併發送部門間備忘錄。

And those inter-office memos would be annotated and a forecast would come hopefully before the quarter end.

這些部門間備忘錄將被註釋，並有望在季度末之前做出預測。

And then suddenly people said, with email and PCs and Excel, they said, let me just send an Excel spreadsheet in email.

然後，人們突然說，有了電子郵件、個人電腦和 Excel，他們說，讓我用電子郵件發送 Excel 電子表格吧。

People enter number and you have a forecast.

人們輸入數字，你就能做出預測。

So what happened was the work, the work artifact and the workflow changed.

於是，工作、工作成果和工作流程都發生了變化。

When someone says, I'm going to now do my job, but with whatever, 99 agents that I am directing on my behalf, the workflow is not going to be constant, right?

當有人說，我現在要做我的工作，但不管有多少人，有 99 個代理人代表我指揮，工作流程不會是恆定的，對嗎？

I mean, you now are really going to have to change even the scope of your job is going to change.

我的意思是，你現在真的必須改變，甚至你的工作範圍也要改變。

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薩提亞-納德拉微軟的人工智能賭注、超大規模、量子計算突破 (Satya Nadella: Microsoft's AI Bets, Hyperscaling, Quantum Computing Breakthroughs)

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