Name: 英偉達首席執行官黃仁勳：想參與埃隆-馬斯克參與的幾乎所有活動 (Nvidia CEO Jensen Huang: Want to be part of almost everything Elon Musk is involved in)
Uploaded: 2025-10-09T07:32:51.000Z
Duration: 9 min 3 s

轉承詞

Let's talk about vendor financing, because that has been something that has raised a lot of questions on Wall street since you cut this deal with OpenAI yesterday.

我們來聊聊供應商融資，因為自從你昨天跟 OpenAI 達成這筆交易以來，這一直是華爾街上許多人關注的焦點。

Bloomberg is reporting that you have a $2 billion in financing that you're going to be involved with with X AI to help them with these same stories.

Bloomberg 報導說，你將會參與 X AI 價值 20 億美元的融資，來協助他們處理類似的狀況。

But this idea of circular financing, your, your customers can't afford to buy these chips yet, so you're going to help them out with money along the way.

但這種循環融資的概念，就是你的客戶現在還買不起這些晶片，所以你會在過程中提供資金上的協助。

That leads some people to think back to what happened with a, with companies during the big buildup like a Lucent or Nortel in the early days.

這讓有些人聯想到過去像是 Lucent 或 Nortel 在早期大規模擴張時期發生過的事情。

Well, first of all, I'm super excited about the financing opportunity that they're doing.

嗯，首先，我對他們這次的融資機會感到非常興奮。

You know, the only, the only regret I have about Xi, we're an investor already, the only regret I have is I didn't give him more money.

說真的，我對 X AI 唯一的遺憾就是，我們已經是投資者了，我唯一的遺憾是當初沒有給他更多錢。

You know, almost everything that Elon's part of, you really want to be part of as well.

你知道的，Elon 參與的幾乎所有事情，你都會很想一起參與。

And, and he gave us the opportunity to invest in X. I'm just delighted by that.

他給了我們投資 X 的機會，我對此感到非常高興。

And so that's not, that's an investment into a really great future company and I'm really excited about that.

所以這是一項對未來極具潛力的公司投資，我對此感到非常興奮。

That's not venture venture, that's not vendor financing per se.

But what's going on, what's going on in the world versus what happened in 2000 is just dramatically different.

但現在世界上正在發生的事，跟 2000 年時相比，簡直是天壤之別。

You know, back then as you recall, there were pets.com, hospitals.com and all of the Internet companies combined was what, 30, $40 billion in size.

你記得嗎，那時候有 pets.com、hospitals.com，所有網路公司加起來大概才多少，300、400 億美元的規模。

If you look at the hyperscalers now that's where the first tranche of AI infrastructure is building.

如果你看看現在的 Hyperscalers，那裡就是 AI 基礎設施的第一波建置。

If you look at the, the Hyperscalers at about 2 and a half trillion dollars of business that's already operating today.

如果你看看 Hyperscalers，他們現在的業務規模大約是 2.5 兆美元。

That business, that two and a half trillion dollar business and the CapEx that goes underneath that is about, call it $500 billion.

這 2.5 兆美元的業務，以及其底層的資本支出大約是 5000 億美元。

That transition from a classical CPU based computing to now generative AI computing powered by GPUs.

從傳統 CPU 為主的運算，轉變為現在由 GPU 驅動的生成式 AI 運算。

So we've got to build into half a trillion dollars worth of capacity infrastructure that's already naturally growing by itself.

所以我們必須建置價值 5000 億美元的容量基礎設施，這本身已經在自然增長了。

And we're in the beginning phases of that work.

而我們正處於這個工作的初期階段。

If you just look at MVS infrastructure business, you know, call it a couple of hundred billion dollars so far, you know, a couple of hundred billion dollars into a multitrillion dollar build out.

如果你只看 MVS 的基礎設施業務，你知道，目前大約是幾千億美元，你知道，在數兆美元的建置中投入了幾千億美元。

The second part of it that's really unique is that we have a new generation of AI companies.

第二個非常獨特的部分是，我們擁有一代新的 AI 公司。

The new AI companies like OpenAI and Anthropic and X AI and, and

像 OpenAI、Anthropic 和 X AI 等新的 AI 公司，還有，還有

companies that are, that are, well, you know, thinking machine labs from Mira and Ilya, Suskiverse, SSI and Misha's company Reflection.

像是 Mira 和 Ilya 的 Thinking Machine Labs、Suskiverse、SSI 以及 Misha 的公司 Reflection。

And I mean there's a whole bunch of amazing model builders now, this generation of AI model builders.

我的意思是，現在有很多很棒的模型建構者，這一代的 AI 模型建構者。

What's happened in the last several months?

A transition happened that is really, really important.

發生了一個非常非常重要的轉變。

For the last several years they've been generating tokens, you know, these AI tokens basically at a loss.

過去幾年，他們一直在產生 token，你知道，這些 AI token 基本都是虧損的。

And the reason for that is because the early AI models weren't, they were super interesting, really captivated a lot of attention, but

而原因就在於，早期的 AI 模型雖然非常有趣，吸引了很多關注，

但它們不夠實用，所以大家不願意付費。

For the last several months it's been very clear that the new technology is now reasoning, it's doing research before it answers a question.

過去幾個月以來，很明顯新的技術現在能夠進行推理，在回答問題前會先做研究。

It goes on the web and studies other PDFs and websites.

它會上網研究其他的 PDF 和網站。

It can now use tools, generate information for you and it creates responses that are really useful.

它現在可以使用工具，為你生成資訊，並創造出非常有用的回應。

I use it every day to the point where now the tokens are profitable.

我每天都在使用它，到現在這些 token 都已經能獲利了。

The question though is who is going to continue to pay for that build out?

不過問題是，誰會繼續為這個建置付出費用呢？

Is it the big companies like Procter and Gamble?

Is it a big, is it consumers who are going to do this?

I mean, my doctor showed me the AI that he's using and it's incredibly helpful.

我的意思是，我的醫生就給我看了他正在使用的 AI，它真的非常有幫助。

But there's still a question about whether he's going to pay for it or the company he works for is going to pay for it.

不過還是有個問題，是他要付錢，還是他工作的公司要付錢。

I think the, there's the consumer part of it.

我認為，這有消費者的一部分。

You know, a lot of OpenAI customers are consumers and they're paying for it.

你知道，很多 OpenAI 的客戶都是消費者，而且他們也付費了。

But the thing that's really cool is that the enterprise AI build out that's happening now.

但真正酷的是現在正在發生的企業 AI 建置。

My, my favorite enterprise service is Cursor.

Cursor is an AI coder and every one of our engineers, 100% is now assisted by AI coders and our productivity has gone up incredibly.

Cursor 是一款 AI 程式設計工具，我們百分之百的工程師現在都由 AI 程式設計工具協助，我們的生產力也是以大幅提升。

And so you're now seeing enterprise AI companies like Cursor, Open Evidence, I love Lovable.

所以你現在看到像 Cursor、Open Evidence、I love Lovable 這樣的企業 AI 公司。

All of these companies are some of the fastest growing companies in the world and they address enterprise.

這些公司都是世界上成長最快的公司，而且它們服務的是企業。

I wonder how you think about Jensen, the ultimate destination where all this is actually building toward.

我很好奇你對 Jensen 的看法，這一切最終的目標是什麼。

There was a post from David, Connie's a Sequoia partner this week saying, look, AGI is the only thing that can justify the volumes of capital spending right now.

這週 Sequoia 的合夥人 Connie 的朋友 David 發了一篇文章，說 AGI 是唯一能證明目前巨額資本支出的東西。

At the same time, many of the model builders and experts are pushing out the date where that maybe is going to be achievable.

同時，許多模型開發者和專家都在推遲 AGI 可能實現的日期。

Meantime, you're talking about annual generations of chips.

與此同時，你說的是晶片的年度迭代。

We're putting all this capital in these data centers that it's just not clear like when we get to the end or is it just a treadmill.

我們把這麼多資金投入到這些數據中心，但並不清楚何時能達到終點，或者這只是一個跑步機。

We are going to have incredibly profitable and incredibly useful eyes long before AGI.

在 AGI 出現之前，我們將擁有極其有利可圖且極其有用的 AI。

And for example, right now, Cursor, you know, Cursor.

舉個例子，就像現在的 Cursor，你知道的，Cursor。

Cursor's a software coder, is incredibly useful.

Cursor 是一款軟體程式設計工具，非常實用。

Almost every one of them are going to use it and they're loving it.

幾乎所有人都會使用它，而且他們非常喜歡。

And so we're using it instead of something else, presumably.

所以我們正在使用它，而不是其他東西，我想。

Well, there's placing things yet they're not.

嗯，有在放置東西，但還沒有。

Remember AI, unlike, unlike previous technologies, previous technologies are tools that humans use.

記得 AI，跟之前的技術不一樣，之前的技術是人類在使用的工具。

網頁瀏覽器也是人類在使用的工具。

For the very first time we have technology that can actually use tools by itself.

我們第一次有了可以自己使用工具的技術。

And now we have Gemini agents that are able to use the browser and browse for groceries or destinations or book trips, travel for you.

現在我們有了 Gemini agents，它們可以自己使用瀏覽器，幫你搜尋雜貨、目的地，或是預訂旅行。

So this is really quite an extraordinary thing.

所以這真的是一件非常了不起的事。

The tool industry is a few trillion dollars.

工具使用者產業是 100 兆美元。

Which is the reason why everybody's so excited about the future of technology.

這就是為什麼大家對科技的未來如此興奮的原因。

Because it could augment labor, it can increase the productivity of labor.

因為它可以增強勞動力，提高勞動生產力。

And here at Nvidia, you know, it's increased our product productivity tremendously.

而在 Nvidia，你知道，它極大地提高了我們的產品生產力。

You've already said you wish you could have bought more open air and maybe invested more in X at this point.

你已經說過，你希望當時能買更多 open air，也許在 X 投入更多資金。

That implies that you don't think that this is all redundant because a lot of the players, I mean, if I read what Sam Altman says and the message he tries to convey, it's as if they're sprinting to try to stay ahead of everybody else because they think it's not going to be room for everybody necessarily who's trying to do something similar.

這意味著你不認為這一切都是多餘的，因為很多參與者，我的意思是，如果我讀了 Sam Altman 的話，以及他試圖傳達的訊息，就好像他們正在衝刺，試圖領先所有人，因為他們認為並非所有試圖做類似事情的人都能佔有一席之地。

I think there's general intelligence and I think there's specialized intelligence.

我認為有通用智能，也有專項智能。

When I hire engineers, I like them to be generally intelligent and that's, that's a great thing.

我在聘請工程師時，喜歡他們普遍都很聰明，這真的很棒。

But once they come to Nvidia, we make them highly specialized, intelligent so that they could build things that, you know, Nvidia needs.

但一旦他們來到 Nvidia，我們會讓他們變得高度專業化、聰明，這樣他們才能建造 Nvidia 所需的東西。

And so I think the idea of specialized intelligence versus generalized intelligence will continue to happen.

所以我認為專項智能與通用智能的想法會持續下去。

And where the real value for enterprises and Companies are is specialized intelligence and where the value for consumers general intelligence.

對企業和公司來說，真正的價值在於專項智能，而對消費者來說，價值在於通用智能。

Hey Justin, let's talk a little bit about capital allocation and how do you see that?

Justin，我們來聊聊資金配置，你怎麼看？

Just back to those same points that you wish you had more invested in open.

回到你希望當時能買更多 open 的那些點。

I wish you had more invested in these other companies that you're kind of taking stakes in like an xi.

我希望你當時能更多地投資在像 X AI 這樣你正在參與的其他公司。

You can choose to put it back into R and D.

你可以選擇將它重新投入研發。