章节 1：开场与计算领域的重塑 (Opening & Reinventing Computing)

📝 本节摘要：

本节作为访谈的开篇，思科（Cisco）首席执行官 Chuck Robbins 首先对黄仁勋（Jensen Huang）在繁忙的亚洲行之后仍莅临现场表示感谢。随后，黄仁勋提出了核心观点：计算机行业正在经历 60 年来的首次彻底重塑。他定义了“AI工厂”的概念，并阐述了计算模式正从“显式编程”（人工编写指令）向“隐式编程”（用户表达意图，由计算机推导解决方案）的根本性转变，这一变革将重构包括处理、网络、存储和安全在内的整个计算堆栈。

[原文] [Chuck Robbins]: so uh first of all thanks everybody for being here for an incredibly long day We started this thing early this morning and uh we had speaker after speaker after speaker after speaker and then we had about a two and a half hour break and they came back to see you So uh I've been up since 1:00

[译文] [Chuck Robbins]: 那个，首先，感谢大家在这个漫长的一天里光临现场。我们今天一大早就开始了，演讲嘉宾一个接一个地上台，之后我们休息了大约两个半小时，大家又回来见你了。所以我从凌晨 1 点就醒了。

[原文] [Chuck Robbins]: So this guy this guy is on the tail end of a two-eek trip in four or five different cities in Asia Uh one day ago was in Taiwan Last night I was in Houston Here I am But he's been gone two weeks and we're standing between him and his his personal bed versus a hotel So we're gonna we're gonna have fun and then we're gonna we're gonna get him out of here

[译文] [Chuck Robbins]: 这位老兄刚结束了为期两周的亚洲之行，跑了四五个不同的城市，正处于行程的尾声。一天前他还在台湾，昨晚我在休斯顿，现在我也到了。但他已经出差两周了，我们现在是他和他自家的床（而不是酒店）之间的最后一道关卡。所以我们会玩得很开心，然后我们会让他赶紧回去休息。

[原文] [Chuck Robbins]: So uh but uh you don't you don't need much of an introduction but thank you for being here man We really appreciate it Thanks for our partnership and really proud of you guys So let let's let's start with uh let's start with that We we have had a partnership and you you introduced this whole concept of AI factories and we're working on this together

[译文] [Chuck Robbins]: 不过，你其实不需要太多介绍，但还是谢谢你能来，伙计。我们真的很感激。感谢我们的合作伙伴关系，也真的为你们感到骄傲。那么让我们从这里开始吧，我们建立了合作关系，你提出了“AI工厂（AI factories）”的完整概念，我们正为此共同努力。

[原文] [Chuck Robbins]: It's probably not going as fast as either one of us would like in the in the enterprise space but can we start by talking about what what do you what is an AI factory to you is that

[译文] [Chuck Robbins]: 在企业级领域，进展可能没有我们两人希望的那么快，但能不能先谈谈，对你来说什么是“AI工厂”？

[原文] [Jensen Huang]: so first of all remember we're reinventing computing for the first time in 60 years what used to be explicit programming right we wrote the programs and the variables that's passed through APIs and are very explicit to implicit programming you now tell the computer what your intent is and it goes off and and it figures out um how to solve your problem

[译文] [Jensen Huang]: 首先，请记住，我们正在重塑计算领域，这是 60 年来的第一次。过去是显式编程（explicit programming），对吧？我们编写程序，通过 API 传递变量，一切都非常明确；现在转变为隐式编程（implicit programming），你只需告诉计算机你的意图（intent），它就会去弄清楚如何解决你的问题。

[原文] [Jensen Huang]: So from explicit to implicit uh from general purpose computing basically calculation to artificial intelligence the entire computing stack has been reinvented

[译文] [Jensen Huang]: 所以，从显式到隐式，从通用计算（基本上就是计算）到人工智能，整个计算堆栈（computing stack）已经被重塑了。

[原文] [Jensen Huang]: Now people talk about computing u where the processing layer is which is where we are but remember what computing is there's computing there's the processing but there's storage networking and security all that is being reinvented as we speak and so the first part the first part is we need to develop AI

[译文] [Jensen Huang]: 现在人们谈论计算时，通常指处理层，也就是我们所在的领域。但请记住计算的构成：除了计算和处理，还有存储、网络和安全，所有这些就在我们要说话的当下正在被重塑。所以第一部分，当务之急是我们需要开发 AI。

章节 2：从聊天机器人到代理式AI (From Chatbots to Agentic AI)

📝 本节摘要：

在本节中，黄仁勋深入阐述了AI的演进方向：从最初基于记忆和泛化的聊天机器人（Chatbots），进化为具备解决未知问题能力的“代理式AI”（Agentic AI）。他指出，真正的智能不仅仅是生成文本，更在于推理（Reasoning）、规划（Planning）、使用工具（Tool Use）以及通过检索增强生成（RAG）来获取事实依据。对话中还穿插了关于早期编程语言（如Fortran、COBOL）的幽默互动，Chuck Robbins 借此自嘲了年龄。

[原文] [Jensen Huang]: to a level and we'll talk about that we need to develop AI to a level that is useful to people And until now uh chat bots where you give it a prompt and it figures out what to tell you um is interesting and curious but not useful Helps me finish crossword puzzles sometimes Yes

[译文] [Jensen Huang]: 达到某种水平，我们会谈到这一点，我们需要将 AI 发展到对人类真正有用的水平。直到现在，那些你给一个提示词、它就想办法回答你的聊天机器人，虽然有趣且令人好奇，但并不算真正“有用”。也就是帮我填完纵横字谜罢了。（Chuck：是的。）

[原文] [Jensen Huang]: And and uh but only only on things that it had memorized and generalized So if you look go back in the beginning of I mean it's a little little literally only three years ago when chatbt emerged uh that that we thought oh my gosh it's able to generate all these words it's able to to create Shakespeare um but it's all based on things that it memorized and generalized

[译文] [Jensen Huang]: 而且，这仅仅局限于它已经记忆和泛化（generalized）的内容。如果你回顾一下，我是说真的就在三年前 ChatGPT 出现的时候，我们会想“天哪，它能生成这么多文字，它能创作莎士比亚风格的作品”，但这一切都基于它记忆和泛化的东西。

[原文] [Jensen Huang]: and but we know that intelligence is about solving problems and solving problems is partly about knowing what you don't know uh partly about reasoning ing uh how to solve a problem you've never seen before Breaking it down into elements that you know how to solve very easily so that in its composition that you're able to solve problems that you've never seen before

[译文] [Jensen Huang]: 但我们要知道，智能的核心在于解决问题。而解决问题，部分在于知道你不知道什么，部分在于推理（reasoning）——思考如何解决一个你从未见过的问题。将其分解为你很容易解决的要素，这样通过组合这些要素，你就能解决以前从未见过的问题。

[原文] [Jensen Huang]: and um uh to come up with a strategy what we call plan to to perform a task ask for help use tools do research so on so forth These are all fundamental things that now in the in the in the in the phrasiology of agentic AI you've heard isn't that right tool use research uh uh retrieval augmented generation which is grounded on facts um memory These are all things that that all of you in the in the context of talking about agentic AI uh you're starting to hear

[译文] [Jensen Huang]: 然后制定一个策略，我们称之为规划（plan），去执行任务、寻求帮助、使用工具、进行研究等等。这些都是基础性的东西，现在用“代理式AI（agentic AI）”的术语来说——你听说过对吧？工具使用、研究、基于事实的检索增强生成（retrieval augmented generation, RAG）、记忆。在讨论代理式AI的语境下，你们开始听到所有这些概念。

[原文] [Jensen Huang]: But the important thing the important thing is in order to evolve from general purpose computing which is explicit programming we wrote in forran we wrote in C we wrote in C++ cobalt to that's right that's good stuff that's good stuff Chuck that's good stuff it's my fall it's my fallback job that's good stuff that's good stuff yeah that's one of those that's one of those skills that remains valuable I know yeah I know that it remains valuable I've got a lot offers Dinosaurs are valuable forever We just established that you're older than me

[译文] [Jensen Huang]: 但重要的是，为了从通用计算进化——也就是显式编程，我们以前用 Fortran 写，用 C 写，用 C++ 写，还有 COBOL（Chuck插话：那是好东西）。没错，那是好东西，Chuck，那是好东西。（Chuck：那是我的后备工作）。那是好东西，那是好东西，是的，那是那些仍然有价值的技能之一。（Chuck：我知道，是的，我知道它还有价值，我收到了很多offer）。恐龙永远是有价值的。我们刚刚确认了你比我老。

[原文] [Jensen Huang]: I know And I'm I'm the prehistoric It doesn't appear so but it's true All right That was pretty good I'm the only Probably the oldest person in this room

[译文] [Jensen Huang]: （Chuck：我知道。我是史前的。）看起来不像，但确实如此。好吧，这很有趣。我可能是这个房间里唯一、也许是最老的人了。

章节 3：企业AI落地策略：百花齐放 (Enterprise Strategy: Let a Thousand Flowers Bloom)

📝 本节摘要：

当 Chuck 询问企业部署 AI 的具体步骤时，黄仁勋建议不要在一开始就纠结于投资回报率（ROI）。相反，他主张找出公司的“本质”业务（最核心、最具影响力的工作），并在内部推行“百花齐放”的策略。他认为创新本质上是失控的，试图控制是一种错觉（甚至需要“看心理医生”）。企业应鼓励员工广泛尝试各种 AI 工具（如 Anthropic、Codex、Gemini），就像鼓励孩子探索生活一样，先允许试验（Say Yes），之后再进行筛选和“修剪花园”，避免过早将资源孤注一掷。

[原文] [Chuck Robbins]: So how do you so let's talk a little bit about like as you as you think about the so so here we are uh I went to Chuck and I say hey listen uh we need to reinvent computing and Cisco's got to be a big part of it and so we've got we've got um uh we have a new new whole computing stack coming out Vera Rubin and uh Cisco is going to be ting market with us on on that and so that the computing layer but there's also the networking layer

[译文] [Chuck Robbins]: 那么你是如何...我们要谈谈...就像你思考的那样...所以我们要说的是，我去找 Chuck 说，嘿听着，我们需要重塑计算，而 Cisco 必须是其中的重要组成部分。所以我们有了一套全新的计算堆栈即将推出——Vera Rubin，而 Cisco 将与我们一起将其推向市场。那是计算层，但还有网络层。

[原文] [Chuck Robbins]: And um Cisco is going to uh integrate uh AI networking technology from us but put it into the Cisco Nexus plane control plane so that so that uh from your perspective you're going to get all the performance of AI but in the controllability and security and the manageability of Cisco we're going to do the same thing with security and um and so each one of these pillars has to be reinvented so that so that enterprise computing could take advantage of it

[译文] [Chuck Robbins]: Cisco 将整合我们的 AI 网络技术，但会将其放入 Cisco Nexus 的控制平面中。这样从你们的角度来看，你们将获得 AI 的所有性能，但同时也拥有 Cisco 的可控性、安全性和可管理性。我们在安全方面也会做同样的事情。因此，这些支柱中的每一个都必须被重塑，以便企业计算能够利用它。

[原文] [Chuck Robbins]: But the but but ultimately and we'll come back to this hopefully that that um you know why is it that enterprise AI wasn't ready three years ago and why it is that you have no choice but to get engaged as quickly as you can Okay Don't don't fall behind I think there's you don't have to be the first company to take advantage of AI but don't be the last Yeah Mhm

[译文] [Chuck Robbins]: 但最终——我们希望能回过头来谈谈这个——为什么企业级 AI 三年前还没准备好，以及为什么现在你别无选择，只能尽快参与进来。好吧，不要掉队。我认为你不必成为第一家利用 AI 的公司，但绝不要成为最后一家。（Jensen：是的，嗯。）

[原文] [Chuck Robbins]: So if you're an enterprise today what what's your recommendation on the first second third step they should take to begin to get ready

[译文] [Chuck Robbins]: 那么，如果你今天是家企业，你建议他们应该采取的第一、第二、第三步是什么，以便开始做好准备？

[原文] [Jensen Huang]: well I get questions like things like ROI and and um I I wouldn't I wouldn't go there And the reason for that is because because um with all technology deployments in the beginning it's hard to put into a spreadsheet um the ROI of of a new tool a new technology

[译文] [Jensen Huang]: 嗯，我经常被问到像 ROI（投资回报率）之类的问题，但我不会从那里开始。原因在于，对于所有技术部署而言，在一开始很难用电子表格来计算一个新工具、新技术的 ROI。

[原文] [Jensen Huang]: Um but what I would do is I would go find out what is the single most what is the essence of my company what's the most impactful work that we do in our company don't don't mess around don't mess around with with peripheral stuff

[译文] [Jensen Huang]: 但我会做的是，我会去找出什么是最重要的——我们公司的“本质（essence）”是什么？我们在公司里做的最具影响力的工作是什么？不要瞎忙活，不要在无关紧要的边缘琐事上浪费时间。

[原文] [Jensen Huang]: I mean in our company we have we just let a thousand flowers bloom We the number of different AI projects in our company is it's out of control and it's great It notice I just said something It's out of control and it's great Innovation is not always in control

[译文] [Jensen Huang]: 我是说，在我们公司，我们就让“百花齐放（let a thousand flowers bloom）”。我们公司里不同的 AI 项目数量已经失控了，但这很棒。注意我刚才说的：它失控了，但这很棒。创新并不总是处于受控状态。

[原文] [Jensen Huang]: If you want to be in control first of all you got to seek therapy But second it's a it's a it's an illusion you're not in control If you want your company to succeed you can't control it You want to influence it you don't can't control it

[译文] [Jensen Huang]: 如果你想掌控一切，首先你得去看心理医生。其次，这是一种错觉，你根本无法掌控。如果你想让你的公司成功，你就不能控制它。你要去影响它，而不是控制它。

[原文] [Jensen Huang]: And so I think number one um too many people want it too many companies I hear they want it they want it they want us explicit They want it specific They want demonstrable ROI And you know showing the value of something worth doing in the beginning is hard

[译文] [Jensen Huang]: 所以我认为第一点，太多人、太多公司，我听到他们想要明确的、具体的东西，他们想要可论证的 ROI。你知道，在一开始就要展示某件值得做的事情的价值是很困难的。

[原文] [Jensen Huang]: Um but what I would do what what I would say is that let a thousand flowers bloom Let people experiment Let the people experiment safely And we're we're experimenting with all kinds of stuff in the company We use Anthropic we use codeex we use you know we use Gemini we use everything

[译文] [Jensen Huang]: 但我会做的、我会说的是，让百花齐放。让人们去实验，让人们在安全的前提下实验。我们在公司里正在尝试各种各样的东西。我们用 Anthropic，我们用 Codex，你知道，我们用 Gemini，我们什么都用。

[原文] [Jensen Huang]: And and when a when one of our groups says I'm interested in using this AI my first answer is yes And I'll ask why instead of why then yes I say yes then why And the reason for that is because I want I want the same thing for for my company that I want for my kids go explore life

[译文] [Jensen Huang]: 当我们的一个团队说“我有兴趣使用这个 AI”时，我的第一反应是“好的（Yes）”。我会先说“好”，再问“为什么”，而不是先问“为什么”才说“好”。原因在于，我希望我的公司能像我对孩子们的期望一样——去探索生活。

[原文] [Jensen Huang]: They say they want to try something The answer is yes And then they say how come you don't go prove it to me Prove to me that doing this very thing is going to lead to financial success or some happiness someday Prove to me And until you prove it to me I'm not going to let you do it We never do that at home but we do it at work Do you know what I'm saying yeah It makes no sense to me

[译文] [Jensen Huang]: 他们说想尝试某件事，答案是“好的”。如果你反过来说：“怎么不先向我证明呢？向我证明做这件事将来会带来经济上的成功或某种幸福。证明给我看，除非你证明了，否则我不让你做。”我们在家里从不这样做，但我们在工作中却这样做。你明白我的意思吗？（Chuck：是的。）这对我来说毫无道理。

[原文] [Jensen Huang]: And so the way that we we treat AI and and and whether it's AI or the internet before or cloud before just let a thousand flowers bloom And then at some point you have to use your own judgment to figure out when to start curating the garden because a thousand flowers bloom makes for a messy garden

[译文] [Jensen Huang]: 所以我们对待 AI 的方式——无论是 AI，还是之前的互联网或云——就是让百花齐放。然后，在某个时间点，你必须运用自己的判断力来弄清楚何时开始修剪（curating）花园，因为百花齐放会让花园变得杂乱无章。

[原文] [Jensen Huang]: But at some point you have to start curating to find what's the best approach or what's the best platform so that you could put all your wood behind one arrow But you don't want to put all your wood behind one arrow too soon You pick the wrong arrow So let a thousand flowers bloom At some point you curate

[译文] [Jensen Huang]: 在某个阶段你需要开始修剪，找出最好的方法或最好的平台，这样你才能“把所有木头放在一支箭后”（集中力量）。但你不想太早把所有赌注压在一支箭上，万一选错了箭呢？所以，先让百花齐放，之后再修剪。

[原文] [Jensen Huang]: And so I haven't started curating yet Just to put it in perspective I've got a thousand flowers bloom everywhere But I encourage everybody to try However I know exactly what is most important to our company Of course of course I do What is the essence of our company what are the most important work of our company and I make sure that I've got a lot of expertise and a lot of capability focused on using AI to revolutionize that work

[译文] [Jensen Huang]: 所以，我还没开始修剪。为了让大家看清现状，我在各处都有成千上万的花在绽放。我鼓励每个人去尝试。然而，我非常清楚什么对我们要公司最重要。当然，我当然知道。什么是我们公司的本质？什么是我们公司最重要的工作？我确保我有大量的专业知识和能力专注于利用 AI 来彻底变革这些工作。

[原文] [Jensen Huang]: in our case chip design software engineering system engineering notice you might have noticed that that that we partnered with synopsis and cadence and seammens and today do so so that we could insert our technology and infuse as much technology as they want Whatever they want whatever they need I will provide So that I could revolutionize the tools by which we use to design what we do

[译文] [Jensen Huang]: 在我们的案例中，是芯片设计、软件工程、系统工程。你可能已经注意到，我们与 Synopsys、Cadence 和 Siemens 合作，以便我们能够植入我们的技术，并注入他们想要的所有技术。无论他们想要什么，无论他们需要什么，我都会提供。这样我就能彻底变革我们用来设计产品的工具。

[原文] [Jensen Huang]: We use synopsis everywhere We use we use cadence everywhere We use seammens everywhere Use the so everywhere I will make sure that they have a th000% of whatever they want so that I have the tools necessary so I could create the next generation And so so that tells you something about how I my attitude about about uh what's most important to me and what I will do to revolutionize my own work

[译文] [Jensen Huang]: 我们到处都在使用 Synopsys，到处都在使用 Cadence，到处都在使用 Siemens，还有 Dassault。我会确保他们拥有他们想要的 1000% 的支持，这样我就能拥有必要的工具来创造下一代产品。这就告诉了你我的态度——关于什么对我最重要，以及我将如何彻底变革我自己的工作。

章节 4：聚焦核心与“无限算力”思维 (Focusing on Essence & The "Infinity" Mindset)

📝 本节摘要：

在本节中，黄仁勋提出了一个极具颠覆性的思维模型——“丰饶（Abundance）”。他指出，相比于摩尔定律缓慢的增长，AI 算力正以每十年一百万倍的速度飞跃。这种算力的爆发性增长意味着我们可以用“无限资源”的心态去解题：假设计算速度无限快、成本为零、没有物理限制（反重力）。他呼吁企业领导者找出公司中最具影响力的难题，不再受限于旧有的资源约束（例如不得不将大数据切分处理），而是尝试“吞噬整个问题”（如一次性处理全量图数据）。如果不具备这种“光速”思维，企业将被具备此思维的竞争对手或初创公司淘汰。

[原文] [Jensen Huang]: Think about think about think about what AI does AI reduces the cost of intelligence or create the abundance of intelligence by orders of magnitude That's another way of saying what we used to do that takes you know one unit of time now what what we used to to take a year could take a day now what we used to take take a year could take an hour could it could be done in real time and the reason for that is because we are in the world of abundance

[译文] [Jensen Huang]: 思考一下，思考一下 AI 到底是做什么的。AI 降低了智能的成本，或者说创造了数量级规模的智能“丰饶（abundance）”。换句话说，过去我们需要消耗一个单位时间做的事，过去需要一年才能完成的事现在可能只要一天，过去需要一年现在可能只要一小时，甚至可以实时完成。原因就在于我们正处于一个“丰饶”的世界。

[原文] [Jensen Huang]: Moore's law goodness gracious that was slow that's like snails remember Moore's law was two times every 18 months 10 times every 5 years A 100 times every 10 Okay But where are we now a million times every 10 years In the last 10 years we advanced AI so so far that engineers said "Hey guess what why don't we just train an AI model on all of the world's data?"

[译文] [Jensen Huang]: 摩尔定律，天哪，那太慢了，简直像蜗牛一样。记得吗，摩尔定律是每 18 个月翻一番，5 年翻 10 倍，10 年翻 100 倍。好吧，但我们要现在在哪里？每 10 年翻一百万倍。在过去的 10 年里，我们将 AI 推进了如此之远，以至于工程师们说：“嘿，你猜怎么着，我们为什么不直接用全世界的数据来训练一个 AI 模型呢？”

[原文] [Jensen Huang]: They didn't mean "Let's just collect all the the data from my disc drive Let's just let's pull down all of the world's data and let's train an IM model That's the definition of abundance The definition of abundance is you look at a problem so big and you say you know what I'll do it all I'm going to cure every field of disease I'm not going to just do cancer Are you kidding me that's insane We'll just do all of human suffering That's abundance

[译文] [Jensen Huang]: 他们指的不是“收集我硬盘里的数据”，而是“把全世界的数据都拉下来，训练一个大模型”。这就是“丰饶”的定义。丰饶的定义是，你看着一个巨大的问题说：“你知道吗，我全都要做。我要治愈所有领域的疾病，不仅仅是癌症。”你在开玩笑吗？这太疯狂了。“我们要解决所有人类的苦难。”这就是丰饶。

[原文] [Jensen Huang]: Um when I think about engineering when I think about a problem these days I just assume my technology my tool my instrument my spaceship is infinitely fast How long is it going to take for me to go to New York i'll be there in a second So what would I do different if I can get to New York in a second what would I do different if something used to take a year and then now takes real time what would I do different if something you know you used to weigh a lot and now it's just anti-gravity and so you approach everything with that attitude when you approach everything with that attitude you are applying AI sensibility does that make sense

[译文] [Jensen Huang]: 当我思考工程，当我思考问题时，这几天我就假设我的技术、我的工具、我的仪器、我的飞船是无限快的。我去纽约要多久？我一秒钟就能到。那么，如果我一秒钟就能到纽约，我会做什么不同的事？如果某件事过去需要一年，现在是实时的，我会做什么不同的事？如果某件东西过去很重，现在有了反重力，我会做什么不同的事？你要用这种态度去对待一切。当你用这种态度对待一切时，你就在应用“AI 感知（AI sensibility）”。这有道理吗？

[原文] [Jensen Huang]: for example there are many companies that we're working with where the graph analytics the dependency the relationships and dependencies that you know these graphs they have So many edges so many nodes and edges trillions of them Back in the old days you would you would process a graph small pieces of it These days just give me the whole graph How big is it i don't care That sensibility is being applied everywhere

[译文] [Jensen Huang]: 例如，我们合作的许多公司，在做图分析（graph analytics）——也就是关系和依赖性，你知道这些图有无数的边，无数的节点和边，数以万亿计。在过去，你会把图切成小块来处理。如今，直接把整个图给我。它有多大？我不在乎。这种感知正在被应用到各个地方。

[原文] [Jensen Huang]: If you're not applying that sensibility you're doing it wrong If speed matters not at all You're at the speed of light If mass is you're at zero weight zero gravity if you're not applying that logic if this something is not is insanely hard to you in the past and you go nah doesn't matter If you're not applying that logic you're not doing it right Now imagine you apply that logic that sensibility to the hardest problems in your company That's how you're going to move the needle

[译文] [Jensen Huang]: 如果你没有应用这种感知，那你就做错了。如果速度根本不重要——你拥有光速。如果质量是零重量、零重力。如果你不应用这种逻辑——如果某件事过去对你来说难得离谱，而现在你说“不，没关系”——如果你不应用这种逻辑，你就没做对。现在想象一下，你把这种逻辑、这种感知应用到你公司最困难的问题上。这就是你如何实现突破（move the needle）的方式。

[原文] [Jensen Huang]: And that's how they all think Now the people who are if you're not thinking that way just all you have to do is just imagine your competitor is thinking that way If you're not thinking that way just imagine a company who is about to get founded is thinking that way It changes everything And so I would go find where are the most impactful work in your company Apply infinity to it Apply zero to it Apply the speed of light to it and then ask Chuck how to make that happen

[译文] [Jensen Huang]: 这也正是那些领先者思考的方式。现在，如果你不这样想，你只需要想象你的竞争对手正在这样想。如果你不这样想，想象一家即将成立的公司正在这样想。这改变了一切。所以我建议去找出你公司里最具影响力的工作在哪里，对它应用“无限”，对它应用“零”，对它应用“光速”，然后再问 Chuck 如何实现它。

[原文] [Chuck Robbins]: No let's talk about how to make that happen

[译文] [Chuck Robbins]: 不，让我们谈谈如何实现它。

章节 5：深度学习的底层逻辑与软件范式革命 (The Logic of Deep Learning & Software Paradigm Shift)

📝 本节摘要：

黄仁勋回顾了 15 年前 AlexNet 在计算机视觉领域的突破，这让他意识到世界上许多最困难、最有价值的问题并没有类似于 $F=ma$ 这样的“原理性算法”，其答案往往是“视情况而定（It depends）”。为了解决这类问题，软件开发范式必须从“显式编程”转向“深度学习”。他用生动的比喻解释了这种变革：过去的软件是“预录制（Pre-recorded）”的（像 CD-ROM 一样，内容固定）；而未来的软件是“生成式（Generative）”的（像当下的对话一样，每一刻都是全新的）。此外，两人再次拿 Chuck 的“古老”编程背景开玩笑，将 COBOL 语言戏称为“希伯来语（Hebrew）”，并调侃这是喝了四杯酒后的“肺腑之言”。

[原文] [Chuck Robbins]: So you have this analogy of this five layer cake because everybody's talking about like infrastructure apps I mean how do I how do I go about it talk about that a little bit

[译文] [Chuck Robbins]: 你有一个关于“五层蛋糕”的比喻，因为大家都在谈论基础设施、应用之类的。我是说，我该如何着手？稍微谈谈这个吧。

[原文] [Jensen Huang]: Well the first you know one of the things that that successful people do is they reason about what is something you know what what's what's happening here So so almost 15 years ago um an algorithm uh was able to with two engineers u solve a computer vision problem

[译文] [Jensen Huang]: 嗯，首先，你知道成功人士做的一件事就是推理——去思考事物的本质，思考这里到底发生了什么。差不多 15 年前，有一个算法，仅仅两个工程师，解决了一个计算机视觉的问题。

[原文] [Jensen Huang]: Computer vision is basically the first part of intelligence Perception Intelligence is perception reasoning planning Perception What am I what what's going on what's my context reasoning How do I reason about how do I compare this to my goals and then three come up with a plan to solve that to achieve that Okay

[译文] [Jensen Huang]: 计算机视觉基本上是智能的第一部分：感知（Perception）。智能由感知、推理、规划组成。感知是“我是谁？发生了什么？我的语境是什么？”；推理是“我该如何思考？如何将其与我的目标进行比较？”；然后第三步，制定一个计划来解决问题、达成目标。

[原文] [Jensen Huang]: And so about about 13 14 years ago we made a huge gigantic leap in computer vision which is the p the first layer of the perception problem And it was super hard you know how do you solve computer vision and Alex Net and the first um the first breakthrough that we saw It was kind of like the the first contact you know I love that movie the first contact you know it was like our first contact to AI

[译文] [Jensen Huang]: 大约 13、14 年前，我们在计算机视觉领域取得了巨大的飞跃，那是感知问题的第一层。这非常难，你知道，如何解决计算机视觉？然后 AlexNet 出现了，那是我们看到的第一个突破。这有点像《第一类接触（First Contact）》，我喜欢那部电影。这就像是我们与 AI 的第一次接触。

[原文] [Jensen Huang]: and the thing that we did was we said okay what does that mean how is it possible that two engineers was able to overcome the algorithms that were that we worked all of us worked on for some 30 years you know and Ilia Suscober I talked to him yesterday and and Alex Kashevsky and and uh how is it possible two kids with a couple of GPUs solve this problem what does it mean

[译文] [Jensen Huang]: 我们当时做的是，我们问：“这意味这什么？为什么两个工程师能够超越我们需要所有人努力 30 年才能完成的算法？” 我昨天还和 Ilya Sutskever 聊过，还有 Alex Krizhevsky。怎么可能两个孩子用几块 GPU 就解决了这个问题？这意味着什么？

[原文] [Jensen Huang]: and so we broke it all down and I reasoned about it a decade ago and I came to the conclusion that in fact most of the hard problems in the world that can be solved can be can be solved can be solved this way And the reason for that is most of the hard problems in the world most of the most of the valuable problems have no no principled algorithms

[译文] [Jensen Huang]: 我们把它拆解开来，我在十年前对此进行了推理，得出的结论是：事实上，世界上大多数能被解决的难题，都可以用这种方式解决。原因在于，世界上大多数难题、大多数有价值的问题，都没有“原理性算法（principled algorithms）”。

[原文] [Jensen Huang]: There's no F equals MA There's no Maxwell's equation There's no shortinger equation There's you know there's no Ohms law There's no it just doesn't exist There's no law of thermodynamics It's not that specific most of the valuable things that we call intuition and wisdom and it's all you know the problems that you know you Chuck that the type of problems that you and I get the answer is it depends

[译文] [Jensen Huang]: 没有 $F=ma$，没有麦克斯韦方程组，没有薛定谔方程，没有欧姆定律，这些都不存在，没有热力学定律。情况没那么具体。大多数我们称之为直觉和智慧的有价值的东西——比如你和我遇到的那种问题，Chuck，这类问题的答案通常是：“视情况而定（It depends）”。

[原文] [Jensen Huang]: you know what I'm talking about you know if it was if it was three it'd be great it was 3.14 it'd be fantastic okay those those are the great ones but most of the hard problems in life most of the valuable problems in life are it depends because it depends depends on the context It depends on a circumstance context

[译文] [Jensen Huang]: 你懂我的意思。如果是“3”那就太好了，如果是“3.14”那就太棒了，那些是很好的确切答案。但生活中大多数难题、最有价值的问题都是“视情况而定”，因为它取决于语境，取决于环境和上下文。

[原文] [Jensen Huang]: And we reasoned that in fact we're going to reinvent and which is the beginning of our conversation We're going to reinvent computing altogether from from explicit programming to a new way of of doing computing where the models the software will be learned

[译文] [Jensen Huang]: 我们推理出，实际上我们将彻底重塑计算——这也是我们对话的开头。我们将彻底重塑计算，从显式编程转变为一种新的计算方式，即模型和软件将是“习得”的（learned）。

[原文] [Jensen Huang]: simplistically what Chuck is saying is that we came from a world where everything was pre-recorded The software that Chuck worked on really good stuff It it ran a very long time Just for the record it was indeed it was it was it was described in the Hebrew That is true That was another skill I mean cobalt in the room that knows Hebrew cobalt And so anyways anyways anyways anyways that that was pre-recorded

[译文] [Jensen Huang]: 简单来说，Chuck 所说的是，我们来自一个所有东西都是“预录制（pre-recorded）”的世界。Chuck 以前开发的软件，那是好东西，运行了很长时间。顺便说一句，那确实是用“希伯来语（Hebrew）”描述的。（Chuck：那是真的。）那是另一种技能。我是说房间里懂希伯来语的人也就是懂 COBOL 的人。总之，总之，那些都是预录制的。

[原文] [Jensen Huang]: The reason why it's pre-recorded the reason why you know software in the past was pre-recorded is because it came in a CDROM Isn't that right yes It was pre-recorded Okay What is software now because it's contextual and every context is different and every time everybody who uses the software is different and every prompt is different and all the and the pre the precursor you give it the priors you give it the context is different Every single instance of the software is different

[译文] [Jensen Huang]: 过去的软件之所以是预录制的，是因为它是装在 CD-ROM 里发售的，对吧？（Chuck：是的。）它是预录好的。现在的软件是什么？因为它是情境化的，每个情境都不同，每个使用软件的人都不同，每个提示词都不同，你给它的先决条件、先验知识、上下文都不同。所以，软件的每一个实例都是不同的。

[原文] [Jensen Huang]: In the future everything is gonna be generative just like is happening right now This conversation has never happened before The concepts existed before The priors existed before but every single word in this sequence has never happened before And the reason for that is obviously we're four wines in cobalt and Hebrew have never come out of the cold brew Yes Cobalt Hebrew No Thank goodness this is not on campus or being streamed

[译文] [Jensen Huang]: 在未来，一切都将是生成式（generative）的，就像现在正在发生的一样。这场对话以前从未发生过。概念以前存在，先验知识以前存在，但这串序列中的每一个字都从未发生过。原因很明显，我们喝了四杯酒，COBOL 和希伯来语（Hebrew）这种词儿以前从来没从这“冷萃咖啡（cold brew）”里冒出来过。（Chuck：是的。COBOL，希伯来语。不，谢天谢地这没在园区里直播。）

[原文] [Chuck Robbins]: Do you understand what you're saying the only thing that Chuck has fed me today so far is four glasses of wine And to be fair I only fed you I fed you one of them You took the other three off the buffet

[译文] [Chuck Robbins]: （Jensen：你明白你在说什么吗？）Chuck 今天到现在为止唯一喂给我的就是四杯酒。不过说句公道话，我只喂了你一杯，另外三杯是你自己从自助餐台上拿的。

[原文] [Jensen Huang]: You know what you know what happened your team your your team actually told us ahead of time if you get three glasses of wine in he's optimal If you get the fourth one in it's going to be incredible This is suboptimal

[译文] [Jensen Huang]: 你知道发生了什么吗？你的团队实际上提前告诉过我们：如果让他喝三杯酒，他是最佳状态（optimal）；如果喝了第四杯，那将会是“难以置信”的。现在这就是“次优”状态了（suboptimal）。

章节 6：物理AI与工具使用 (Physical AI & Tool Use)

📝 本节摘要：

针对 Chuck 关于“物理AI（Physical AI）”的提问，黄仁勋进行了一场思想实验：假如存在一个通用人形机器人，它是会选择使用现有的螺丝刀，还是重新发明一把？答案显而易见是直接使用。同理，数字AI也应学会使用现有的软件工具（如 SAP、ServiceNow），而非重新发明计算器。他指出，当前的挑战在于让 AI 理解物理世界的因果律（如多米诺骨牌效应），这是大语言模型目前所欠缺的。最后，他提出了一个巨大的商业范式转变：IT 行业正从销售“工具”（如锤子、软件）转向销售“增强型劳动力”（如自动驾驶即数字司机），这将打开一个比原有市场大 100 倍的全新价值空间。

[原文] [Chuck Robbins]: Um can you just give us your top of mind on physical AI remember what remember what software is software is a tool There's this notion that the tool industry is in decline and will be replaced by AI You could tell because there's a whole bunch of software companies whose stock prices are under a lot of pressure because somehow AI is going to replace them It is the most illogical thing in the world and time will prove itself

[译文] [Chuck Robbins]: 嗯，能不能请你谈谈你对物理AI（physical AI）的看法？记住软件是什么，软件是一种工具。有一种观点认为工具行业正在衰退，并将被 AI 取代。你看得出来，因为有一大堆软件公司的股价正面临巨大压力，因为某种程度上人们认为 AI 会取代它们。这是世界上最不合逻辑的事情，时间会证明一切。

[原文] [Jensen Huang]: Let's just give it let's give ourselves the the ultimate thought experiment Suppose we are the ultimate AI artificial general robotics The ultimate AI the physical version of us You could of course solve any problem because you know you're humanoid You could do things If you were a human or robot would you use a use a screwdriver or invent a new screwdriver i just use one

[译文] [Jensen Huang]: 让我们来做一个终极思想实验。假设我们是终极 AI——通用人形机器人（artificial general robotics），也就是我们的物理版终极 AI。你当然可以解决任何问题，因为你是人形的，你可以做任何事。如果你是一个人类或机器人，你会使用一把螺丝刀，还是去发明一把新螺丝刀？（Chuck：我就用现成的。）

[原文] [Jensen Huang]: Would you use a hammer or invent a new hammer would you use a chainsaw or invent a new chainsaw it just don't First of all ideally they don't use it at all But but do you understand what I'm saying if you were a human or robot artificial general robotics would you use tools or reinvent tools the answer obviously is to use tools

[译文] [Jensen Huang]: 你会使用一把锤子还是发明一把新锤子？你会使用一把电锯还是发明一把新电锯？这根本不...首先，理想情况下它们根本不需要用这些。但你明白我的意思吗？如果你是一个人类或通用机器人，你会使用工具还是重新发明工具？答案显然是使用工具。

[原文] [Jensen Huang]: And so now do the digital version of that If you were a artificial general intelligence would you use the tools like Service Now and SAP and Cadence and Synopsis or would you reinvent a calculator of course you would just use a calculator That's the reason why the latest breakthroughs in AI is what tool use Because the tools are designed to be explicit

[译文] [Jensen Huang]: 现在把这个逻辑应用到数字版本上。如果你是一个通用人工智能（AGI），你会使用像 ServiceNow、SAP、Cadence 和 Synopsys 这样的工具，还是会重新发明计算器？当然，你会直接使用计算器。这就是为什么 AI 的最新突破是“工具使用（tool use）”。因为工具被设计成显式的（explicit）。

[原文] [Jensen Huang]: There are many problems in our world where F equals MA Please could you please not come up with another version fa is not kind of MA It's just MA Do you guys Oh V equals IR It's not kind of IR You know approximately IR statistically IR it is IR Okay do you understand what I'm saying and so so I I think we want the artificial general robotics artificial general intelligence to use tools

[译文] [Jensen Huang]: 我们世界上有许多问题是遵循 $F=ma$ 的。拜托，能不能不要搞出另一个版本？$F$ 不是“有点像”$ma$，它就是 $ma$。你们懂吗？哦，$V=IR$（欧姆定律），它不是“有点像”$IR$，你知道，不是“大约是”$IR$ 或“统计学上是”$IR$，它就是 $IR$。好吧，你明白我的意思吗？所以我认为我们希望通用机器人、通用人工智能去使用工具。

[原文] [Jensen Huang]: Well that's the big idea I think that that in the next generation of physical AI we're going to have AIs that understand the physical world understand causality If I tip this over it's going to tip all of that over They understand the concept of a domino Just the concept of a domino Notice a child understands if you tip that over the concept of the domino is extremely in it's like deeply profound the cause causality contact gravity mass all of that is integrated into a domino tipping dominoes over

[译文] [Jensen Huang]: 这就是一个大思路。我认为在下一代物理AI中，我们将拥有理解物理世界、理解因果律（causality）的 AI。如果我把这个推倒，它会把所有的东西都推倒。它们理解多米诺骨牌的概念。仅仅是多米诺的概念——注意，孩子能理解如果你推倒那个...多米诺的概念极其深刻：起因、因果关系、接触、重力、质量，所有这些都融合在推倒多米诺骨牌这一动作中。

[原文] [Jensen Huang]: the idea that you could have a little tiny domino tip a larger domino tip a larger domino tip a larger domino to the point where there's a ton on the other side a child has no trouble with that concept A large language model will have no idea And so we have to teach we have to create a new type of physical AI

[译文] [Jensen Huang]: 那个想法——你可以用一个小小的多米诺骨牌推倒一个更大的，再推倒一个更大的，直到推倒另一边重达一吨的骨牌——孩子理解这个概念毫无困难，但大语言模型完全不知道。所以我们要教导、我们要创造一种新型的物理AI。

[原文] [Jensen Huang]: Well what's the opportunity so far the industry that Chuck and I have been part of is about creating tools We have been in the screwdriver hammer business Our entire life has been about creating screwdrivers and hammers For the first time in history we are going to create what people call labor but augmented labor Give you an example What is a self-driving car it's a digital chauffeur

[译文] [Jensen Huang]: 那么机会在哪里？到目前为止，Chuck 和我所在的行业都是关于创造工具的。我们一直处于“螺丝刀和锤子”的业务中。我们的整个职业生涯都在创造螺丝刀和锤子。而有史以来第一次，我们将创造人们所说的“劳动力（labor）”，或者说“增强型劳动力（augmented labor）”。举个例子，什么是自动驾驶汽车？它是一个数字司机（digital chauffeur）。

[原文] [Jensen Huang]: What's a digital chauffeur valued at a lot A lot more than the car And the reason for that is because in the lifetime of the digital chauffeur the economics of the dig digital chauffeur is a lot more than the car For the very first time we are exposed to a TAM that is 100 times larger Literally mathematically true

[译文] [Jensen Huang]: 一个数字司机值多少钱？很多钱，比那辆车值钱多了。原因在于，在数字司机的生命周期中，其经济价值远超汽车本身。有史以来第一次，我们面对的一个潜在市场规模（TAM）比以前大 100 倍。从字面上、数学上来说都是真实的。

章节 7：从原子到电子：全员科技化 (From Atoms to Electrons: Every Company is a Tech Company)

📝 本节摘要：

本节中，黄仁勋提出了一个核心商业洞察：所有传统企业都应利用 AI 转型为科技公司。他列举了三组鲜明的对比——迪士尼与 Netflix、梅赛德斯与特斯拉、沃尔玛与亚马逊，指出尽管前者都是伟大的公司，但它们都渴望拥有后者的科技属性。黄仁勋分析了其底层逻辑：传统公司处理的是“原子（Atoms）”，受限于物理质量和规模；而科技公司处理的是“电子（Electrons）”，数量无限且易于扩张。一旦企业完成从原子到电子的跃迁（例如从实体 CD 转变为流媒体），其公司价值将可能实现千倍的爆发式增长。他强调，未来的企业应以“技术优先（Technology first）”，将技术作为超级力量，而将原本的行业领域视为技术的应用场景。

[原文] [Jensen Huang]: So it is the it is the case that all of you all of you everybody in in this room today you have the opportunity to apply this technology to become a technology company

[译文] [Jensen Huang]: 所以情况就是这样，你们所有人，今天在这个房间里的每一个人，都有机会应用这项技术，从而转型成为一家科技公司。

[原文] [Jensen Huang]: Let me give you some examples I really believe as much as I look I love Disney and we I love working with Disney I'm pretty sure they'd rather be Netflix I love Mercedes I came into Mercedes I am certain they'd rather be Tesla I love Walmart I am certain they'd rather be Amazon

[译文] [Jensen Huang]: 让我给你们举几个例子。我真的相信，尽管我非常喜爱迪士尼，我也喜欢与迪士尼合作，但我很确定他们更愿意成为 Netflix；我喜爱梅赛德斯，我曾造访梅赛德斯，但我很确定他们更愿意成为特斯拉；我喜爱沃尔玛，但我很确定他们更愿意成为亚马逊。

[原文] [Jensen Huang]: Do you guys agree so far am I three for three all of you are that way

[译文] [Jensen Huang]: 你们同意吗？目前为止我是不是三发三中（全说对了）？你们所有人都是这样的。

[原文] [Jensen Huang]: I believe that we have an opportunity to help transform every single company into a technology company Technology first Technology first Technology is your superpower and the domain is your application versus the other way which is the domain is who you are and you're seeking for technology

[译文] [Jensen Huang]: 我相信我们有机会帮助每一家公司转型为科技公司。技术优先，技术优先。技术是你的超级力量，而（行业）领域是你的应用场景；而不是反过来——即领域是你原本的身份，而你在寻求技术。

[原文] [Jensen Huang]: And the reason that's so the reason that's so is because companies who are technology first you're dealing with electrons not atoms And electrons there's a lot more of them atoms you're limited by mass

[译文] [Jensen Huang]: 之所以如此，原因在于那些“技术优先”的公司，你们处理的是电子（electrons），而不是原子（atoms）。电子的数量要多得多，而对于原子，你会受到质量（mass）的限制。

[原文] [Jensen Huang]: Which is the reason why the moment they went from CDROMs to electrons the value of the company exploded by a thousand times You need to be like us an electron an electronics company electron company which is another way of saying a technology company

[译文] [Jensen Huang]: 这就是为什么当他们从 CD-ROM（原子载体）转向电子的那一刻，公司的价值爆炸式增长了一千倍。你需要像我们一样，成为一家电子公司——“电子”公司其实就是科技公司的另一种说法。

章节 8：领域专长：新的超级力量 (Domain Expertise: The New Superpower)

📝 本节摘要：

在本节中，黄仁勋重新定义了核心竞争力的概念。他指出，随着编程从“显式”转向“隐式”，编程语言本身的障碍已不复存在（他再次幽默地提到了 Chuck 用希伯来语编程的梗）。在这个新时代，“写代码”实际上只是“打字”，而打字正在沦为一种廉价商品（Commodity）。因此，真正的价值不再是编程技能，而是对客户和问题的深刻理解，即“领域专长（Domain Expertise）”。这是非科技企业的巨大机遇：利用 AI 将自己的领域知识转化为技术优势，摆脱对软件工程师数量的依赖，让“理解问题”成为新的超级力量。

[原文] [Jensen Huang]: And so I I think that that the opportunity for you is here Another way to think about that is AI And we just said it earlier Even Chuck who only knows how to program in Hebrew it's a gift His instrument choice is it and right to left because as you know it smears otherwise it is pretty smart actually smart people do smart things

[译文] [Jensen Huang]: 所以我认为机会就在这里。另一种思考方式就是 AI。我们刚才也说了，即使是只懂用希伯来语（Hebrew）编程的 Chuck——这是一种天赋，这是他选择的工具，而且是从右向左写的，因为你知道，不这样写墨水会蹭花（smears）。这其实很聪明，聪明人做聪明事。

[原文] [Chuck Robbins]: Yeah

[译文] [Chuck Robbins]: 是的。

[原文] [Jensen Huang]: And so so the beautiful thing is that as you know the programming language of the world and for all of your companies you kind of feel like oh my gosh you know software is not our strength but knowledge intuition domain expertise is your strength

[译文] [Jensen Huang]: 所以美妙之处在于，你知道世界的编程语言...对于你们所有的公司来说，你们可能会觉得：“天哪，软件不是我们的强项。”但知识、直觉、领域专长（domain expertise）才是你们的强项。

[原文] [Jensen Huang]: Well you get to you now for the first time can explain exactly what you want to a computer in your language Do you remember where we started from explicit programming to implicit programming for first time in history you could program a computer implicitly Just tell it what you want Tell it what you mean and the computer will write the code because coding as it turns out is just typing and typing as it turns out is a commodity

[译文] [Jensen Huang]: 那么现在，你有史以来第一次可以用你自己的语言向计算机准确解释你想要什么。还记得我们从哪里开始的吗？从显式编程到隐式编程。有史以来第一次，你可以隐式地（implicitly）对计算机编程。只需告诉它你想要什么，告诉它你的意思，计算机就会编写代码。因为事实证明，写代码（coding）只是打字，而打字事实证明只是一种商品（commodity）。

[原文] [Jensen Huang]: And that's the great opportunity for you All of you could be levitated above the atomic limitations that you were limited by before All of you could escape from this limitation which is we don't have enough software engineers because as it turns out typing is a commodity

[译文] [Jensen Huang]: 这对你们来说是一个巨大的机会。你们所有人都可以在原来的原子限制（atomic limitations）之上实现“升华（levitated）”。你们都可以逃离这种限制——即“我们没有足够的软件工程师”——因为事实证明，打字只是一种商品。

[原文] [Jensen Huang]: And all of you have something of great value which is domain expertise to understand the customer understand the problem And that is the ultimate value That is the ultimate value to understand the intent

[译文] [Jensen Huang]: 而你们所有人都拥有某种极具价值的东西，那就是领域专长——去理解客户，理解问题。那才是终极价值（ultimate value）。理解意图（intent）才是终极价值。

[原文] [Jensen Huang]: You know as you know when you graduate from software software when you graduate from college you could be a super programmer but you have no idea what customers want You have no idea what problems to solve But that's what all of you know You know what customers want You know what problems to solve The coding part of it is easy just tell the AI to do it And so that's your superpower

[译文] [Jensen Huang]: 就像你知道的，当你从软件专业毕业，当你大学毕业时，你可能是一个超级程序员，但你根本不知道客户想要什么，你根本不知道要解决什么问题。但这正是你们所有人知道的。你们知道客户想要什么，你们知道要解决什么问题。编码部分很容易，只需告诉 AI 去做即可。所以，这就是你们的超级力量。

[原文] [Jensen Huang]: So Chuck and I are here to enable you to do that That closing was done with five glasses of wine in me It's a Hey listen as it's a miracle indeed between this is somebody who works off a true representation of artificial intelligence or maybe it's enhanced enhanced intelligence

[译文] [Jensen Huang]: 所以 Chuck 和我在这里就是为了让你们能够做到这一点。这段结束语是在我喝了五杯酒之后说出来的。（Chuck：嘿听着，这简直是个奇迹...）这是某人基于人工智能真实表现的工作成果，或者也许这是“增强型智能（enhanced intelligence）”。

章节 9：基础设施策略：为何要在本地构建AI (Infrastructure Strategy: Why Build AI On-Prem)

📝 本节摘要：

在本节中，黄仁勋针对“租用云端 vs 自建算力”的经典问题给出了独到见解。他建议企业不要只做“Uber乘客”，而要尝试“打开引擎盖换机油”，即亲自构建一些基础设施以获得对技术的触感（tactile understanding）。他强调，世界并非非黑即白（全租或全买），混合模式才是正解。更重要的是，他指出了一个深刻的商业秘密：企业最宝贵的知识产权（IP）往往不是“答案”，而是“问题”（即企业正在思考什么、关注什么）。正如人们不希望心理咨询的对话被公开一样，企业关于核心战略的“提问”也应保留在本地（On-Prem）的小房间里，以确保数据主权和绝对隐私。

[原文] [Jensen Huang]: And then somebody asked me earlier and I just I just said you know I think it's worth repeating somebody asked me earlier should you do should you do just rent the cloud or should you even make the effort to to u uh build your own computer here's what I I would tell you

[译文] [Jensen Huang]: 之前有人问过我，我刚才也说了，但我认为值得重复一遍。有人问我：你应该只租用云端，还是应该费力去...呃...自己构建计算机？这是我会告诉你的。

[原文] [Jensen Huang]: I would advise you to do exactly the same thing I advise my children build a computer even though the PC is everywhere even though it's mature even though the technology is developed For God's sakes build one Know why all the components exist

[译文] [Jensen Huang]: 我建议你做的，正如我建议我孩子们做的一样——去组装一台电脑。即使 PC 无处不在，即使它已经成熟，即使技术已经很发达。看在上帝的份上，去组装一台吧。去搞清楚为什么会有这些组件。

[原文] [Jensen Huang]: If you were to to to uh be in the world of automotive the automobile industry the transportation industry don't just use Uber For God's sakes lift the hood change the oil understand all the components For God's sakes understand how it works It is vital This technology is so important to the future You must have some tactile tactile understanding of it Lift the hood change the oil build something Doesn't have to be large Build something

[译文] [Jensen Huang]: 如果你身处汽车世界、汽车行业、运输行业，不要只用 Uber。看在上帝的份上，把引擎盖打开，换换机油，了解所有的组件。看在上帝的份上，去了解它是如何工作的。这至关重要。这项技术对未来如此重要，你必须对它有一些触觉上的（tactile）理解。打开引擎盖，换机油，造点什么。不需要很大，但要造点什么。

[原文] [Jensen Huang]: You might discover you're actually insanely good at it You might discover that you need that skill You might discover that the world is not about all rent versus all own that you want to rent some and own some because some part of your company should be built on prem

[译文] [Jensen Huang]: 你可能会发现你其实非常擅长这个。你可能会发现你需要这项技能。你可能会发现这世界不是关于“全租”对立“全买”，而是你想租一部分、拥有一部分，因为你公司的某些部分应该建立在本地（on-prem）。

[原文] [Jensen Huang]: For example sovereignty and proprietary information and just you just you're not comfortable you're not comfortable sharing your questions with everybody You know the reason why I've never I this is a conceptual example You know that when you go see a therapist you don't want the questions to be online You know you know what I'm saying Okay I'm just I'm imagining this one Okay hypothetically

[译文] [Jensen Huang]: 例如主权（sovereignty）和专有信息，或者仅仅是你觉得不舒服，你不愿意与所有人分享你的问题。你知道为什么我从未...这是一个概念性的例子。你知道当你去看心理医生时，你不希望那些问题出现在网上。你知道，你懂我的意思。好吧，我只是在想象这种情况。好吧，假设性的。

[原文] [Jensen Huang]: So hypothetically I I think that a lot of questions you have a lot of conversations you have a lot of dialogue a lot of uncertainties you have ought to be kept private Companies are the same way I am not confident I am not secure about putting all of NVIDIA's conversations in the cloud which is the reason why we build it locally We've built a super AI system locally because I'm just not confident to share that conversation

[译文] [Jensen Huang]: 所以假设性地说，我认为你有很多问题、很多对话、很多不确定性应当被保密。公司也是一样。我没有信心、我没有安全感把 NVIDIA 所有的对话都放在云端，这就是我们在本地构建它的原因。我们在本地构建了一个超级 AI 系统，因为我只是没信心分享那些对话。

[原文] [Jensen Huang]: because conversa my as it turns out the most valuable IP to me is not my answers as they're my questions Are you following me my questions are the most valuable IP to me What I'm thinking about are my questions The answers are a commodity If I simply knew what to ask I'm identifying what's important

[译文] [Jensen Huang]: 因为对话...事实证明，对我来说最有价值的知识产权（IP）不是我的答案，而是我的“问题”。你跟上我的思路了吗？我的问题对我来说是最有价值的 IP。我正在思考的东西就是我的问题。答案是商品。如果我仅仅知道该问什么，我就识别出了什么是重要的。

[原文] [Jensen Huang]: And I don't want people to know what I think is important And I want that to be in a small room I want that to be on prem I want that to be by myself And I want to create my own AI

[译文] [Jensen Huang]: 而我不希望人们知道我认为什么是重要的。我希望那是在一个小房间里，我希望那是在本地（on-prem），我希望那是我独自一人。我想创造我自己的 AI。

章节 10：结语：AI在环与未来展望 (Closing: AI in the Loop)

📝 本节摘要：

在访谈的最后，黄仁勋提出了一个反直觉的终极观点：业界常说的“人在环（Human in the loop）”其实是错误的，未来应该是“AI在环（AI in the loop）”。他认为，AI 的作用是捕捉员工的经验与智慧，将其转化为公司的永久知识产权，从而确保公司每天都在进步，永远不会倒退或“归零”。最后，Chuck Robbins 再次感谢黄仁勋在结束漫长的亚洲行、即将回家休息前的最后一刻，仍选择来到这里分享真知灼见。

[原文] [Jensen Huang]: And then one last thought since it's already 11 o'clock One last thought There was an idea that AI should always have human in the loop It's exactly the wrong idea It's backwards Every company should have AI in the loop

[译文] [Jensen Huang]: 然后还有一个最后的想法，既然已经 11 点了。最后一个想法：有一种观点认为 AI 应该始终“人在环（human in the loop，即人工介入审核）”。但这完全是错误的观点，是颠倒的。每个公司都应该有“AI在环（AI in the loop）”。

[原文] [Jensen Huang]: And the reason for that is because we want our company to be better and more valuable and more knowledgeable every single day We never want to go backwards We never want to go flat We never want to start from the beginning

[译文] [Jensen Huang]: 原因是，我们希望我们的公司每一天都变得更好、更有价值、更有知识。我们绝不希望倒退，我们绝不希望停滞不前，我们绝不希望从头再来。

[原文] [Jensen Huang]: Which means that if we have AI in the loop it will capture our life experience Every single employee in the future will have AI lots of AIs in the loop And those AIs will become the company's intellectual property That's the future company

[译文] [Jensen Huang]: 这意味着，如果我们让 AI 在环，它将捕捉我们的经验。未来的每一位员工都会有 AI，有很多 AI 在环。而这些 AI 将成为公司的知识产权。这就是未来的公司。

[原文] [Jensen Huang]: And therefore I think it sensible for all of you to call Chuck immediately I'll call Jensen Anyhow that's my closing

[译文] [Jensen Huang]: 因此，我认为你们所有人立即给 Chuck 打电话是明智的。（Chuck：我会打给 Jensen。）总之，这就是我的结束语。

[原文] [Chuck Robbins]: Listen let's uh two weeks on the road Jensen flew here spent his last night last evening with us before he gets to sleep in his bed for the first time in a long time We're forever grateful Appreciate you being here Thank you Thank you very much And I I

[译文] [Chuck Robbins]: 听着，让我们...在路上跑了两周，Jensen 飞到这里，把他在能睡上自家床之前的最后一个晚上留给了我们，这是很久以来的第一次。我们永远感激。感谢你的到来。谢谢。非常感谢。