当地时间 6 月 15 日，英伟达创始人、CEO 黄仁勋（Jensen Huang）参加美国加州理工学院第 130 届毕业典礼，并发表了一段题为 " 迎难而上抓住机会 "（Embrace Challenges and Seize Opportunities）的主题演讲。以下为演讲全文：
感谢你的介绍。作为一个展示，也许可以应用，有多少人知道英伟达？哇。很多人知道 GPU 是什么吗？很好。我不必改变我的演讲。
女士们，先生们，罗森鲍姆（Thomas F. Rosenbaum）校长，尊敬的教职员工，各位嘉宾，自豪的家长们，最重要的是，2024 届加州理工学院的毕业生们。今天对你们来说是非常快乐的一天。你们看起来更兴奋。
你们从加州理工学院毕业。这是伟大的理查德 · 费曼（美国理论物理学家）、莱纳斯 · 鲍林（美国化学家，量子化学和结构生物学的先驱者之一），以及在我们行业有很大影响的人的学校。
这是一件非常重要的事情。今天是充满自豪和喜悦的一天。这是你们所有人梦想成真的一天。
不仅仅是你们，因为你们的父母和家人为了看到你们达到这个里程碑做出了无数的牺牲。让我们借此机会向他们表示祝贺。感谢他们，让他们知道。你们爱他们。你们不想忘记这一点，因为你们不知道你们还要在家里住多久。我知道你们今天作为一个准备者想要非常感激。我真的很喜欢我的父母，我的孩子没有搬出去。每天看到他们真是太好了。但是现在他们已经搬出去了，这让我很难过。所以希望你们能花些时间和你们的父母在一起。你们在这里的旅程证明了你们的性格、决心、为梦想做出牺牲的意愿。你们应该为自己感到骄傲。做出牺牲、忍受痛苦和苦难的能力。你们在生活中需要这些品质。
你们和我们（NVIDIA）有一些共同交集。首先，英伟达的两位首席科学家都来自加州理工学院。而我今天发表演讲的原因之一，是因为我在招聘。所以我要告诉你们，NVIDIA 是一家非常棒的公司。我是一个非常好的老板，广受喜爱，可以在 NVIDIA 中工作，你们和我们，对科学和工程都有热情。
尽管我们岁数相隔约 40 年，但我们都处于职业生涯的巅峰，对于所有关注英伟达和我的人来说，你们知道我的意思。只是，你们还有更多的巅峰要去攀登。
我只希望，今天不是我的巅峰，不是顶峰。所以我像往常一样努力工作，以确保我在未来还有更多的巅峰。
去年，我很荣幸在国立台湾大学发表毕业典礼演讲。我分享了一些关于这个旅程的故事，以及我们学到的可能对毕业生有价值的经验教训。
我不得不承认，我不喜欢给别人建议，尤其是给别人的孩子。我今天的建议将主要隐藏在一些我喜欢的故事，和一些我喜欢的生活经历中。
我相信，我是当今世界上任职时间最长的科技公司首席执行官（CEO）。在 31 年的时间里，我设法没有破产，没有感到无聊，也没有被解雇。我有幸享受了很多生活经历。我从一无所有创建 NVIDIA 到今天的样子。
我创建 GPU 图形计算经历漫长道路，我们致力于发明 20 多年的 GPU 模型，这正在彻底改变计算。今天，我们依然在这个项目上工作，以及我所知道的理查德 · 费曼非常关心并经常谈论，知识诚实和谦逊如何拯救了我们的公司，以及一次撤退，一次战略撤退是我们最好的策略之一。所有这些都是我在毕业典礼上谈到的违反直觉的经验教训。
我鼓励毕业生参与 AI ，这是我们这个时代最重要的技术。稍后我会再谈一些，但关于人工智能的一切，很难不沉浸其中，被它包围，并对它进行大量的讨论。
我希望，你们都在使用它并应用玩它。所以有令人惊讶的结果，有些神奇，有些令人失望，有些令人惊讶，但你必须享受它。你必须参与其中，因为它发展得如此之快。这是我所知道的唯一一种同时在多个指数上发展的技术。
所以，技术变化非常非常快，因此我建议台大学生跑步，不要走路，参与人工智能革命。
然而，1 年后的今天，变化之大令人难以置信。今天，我想做的是与你们分享，从我的有利位置，我对你们毕业时正在发生的一些重要事情的看法。这些都是正在发生的非凡事情，你们应该对它们有一个直观的理解。因为这对你很重要，对这个行业也很重要。希望你们能利用面前的机会。
计算机行业正在从根本上转型。在每一层，很快每个行业也将被改变。原因很明显，因为今天的计算机，是每个科学领域中每个行业的知识和基础的最重要工具。如果我们如此深刻地改变计算机，它将对每个行业产生影响。稍微谈谈它们，当你进入行业时，重要的是，正在发生什么？
现代计算可以追溯到 IBM System/360。这是我学习的架构手册。这是一本你不需要学习的架构手册。自那以后，已经有了更好的文档和对计算机和架构的更好描述。但 IBM System/360 在当时非常重要。事实上，IBM System/360 的基本思想、架构、原则、思想和架构以及策略仍然支配着计算机行业，它是在我出生一年后推出的。
在 80 年代，我是第一代超大规模集成电路工程师之一，他们从里程碑式教科书中学习设计芯片。我不确定这里是否还在教。应该是。基于 Caltech 的 Cover 开创的 LSI 系统容器的引入，在芯片设计方法和教科书中彻底改变了 IC 设计。它使我们这一代人能够设计超级巨型芯片。
最终，CPU 导致了计算性能的指数增长。被称为摩尔定律的令人难以置信的技术进步推动了信息技术革命，我们所处的工业革命，我这一代人所处的工业革命见证了世界从未见过的东西的大规模生产。
大规模生产看不见的东西，易于复制的软件的大规模生产。它导致了一个 3 万亿美元的行业（IT）。当我坐在你坐的地方时，IT 行业微不足道。你可以通过销售软件赚钱的概念是一个幻想。然而，今天，它是我们行业生产的最重要的商品、最重要的技术和产品创造之一。
但是，Denard 缩放、晶体管缩放和指令级并行性的限制已经减缓了 CPU 性能。在计算需求继续呈指数增长的时候，CPU 性能的缓慢增长正在发生。计算需求和计算机能力之间的这种指数增长差距。如果不解决，计算能源消耗和成本通胀最终将扼杀每个行业。我们说话的时候已经看到了计算通胀的明显迹象。
在经过二十年的推进和 NVIDIA 加速计算之后，提供了一条前进的道路。这就是我在这里的原因。因为最终，行业意识到加速计算的难以置信的有效性，就在我们目睹计算通胀几十年后。通过将消耗时间的算法卸载到专门用于并行处理的 GPU 上。我们通常可以实现 10 倍、100 倍，有时甚至 1000 倍的速度提升，节省资金、成本和能源。
我们现在从计算机图形学、光线追踪到基因测序、科学计算、天文学、量子电路模拟、数据处理，甚至熊猫数据科学等领域，都需要 GPU 进行加速计算应用。加速计算已经达到了一个临界点。这是我们对计算机行业的第一个伟大贡献，它现在为我们提供了一条可持续计算的前进道路，在计算需求继续增长 100 倍的情况下，成本将继续下降。加速计算在时间、成本或能源节约方面打开的任何东西肯定会在其他地方引发新的发展。我们只是不知道它是什么。直到深度学习进入我们的意识，一个全新的计算世界出现了。杰夫 · 辛顿、艾丽斯 · 克拉泽夫斯基和伊莱乌斯 · 苏斯卡伯使用 NVIDIA 的 GPU 训练 AlexNet，并通过赢得 2012 年 ImageNet 挑战赛震惊了计算机视觉社区。
这是深度学习的大爆炸时刻，一个关键时刻，标志着人工智能革命的开始。在 AlexNet 之后，我们决定改变了 NVIDIA 公司，这是值得注意的。我们看到了深度学习的潜力，并相信只是通过原则思考，相信通过我们自己对深度学习可扩展性的分析，我们相信这种方法可以学习，其他有价值的功能，也许，深度学习是一种通用函数学习器。有多少问题很难或不可能用基本的第一原理来表达？
当我们看到这一点时，我们认为，这是一项我们必须真正关注的技术，因为它的限制可能仅受模型和数据规模的限制。然而，当时存在挑战，这是 2012 年。就在 2012 年之后不久。如果我们不必在当时构建这些巨大的 GPU 集群，我们如何探索深度学习的极限？或者说，作为一家小公司，构建这些巨大的 GPU 集群可能需要数亿美元。但如果我们不这样做，就无法保证它在扩展时会有效。
然而，没有人知道深度学习可以扩展到多远。如果我们真的构建了它，我们永远不知道，这是其中之一。如果你建造它，他们会来吗？我们的逻辑是，如果我们不建造它，他们就不会来。
我们基于我们的第一性原理，信念和我们的分析致力于此。我们让自己相信，这将是如此有效。当公司相信某件事时，我们就应该去做。所以，我们深入研究深度学习。在接下来的十年里，我们系统地重新发明了一切，从 GPU 本身开始，发明了现代 GPU，它与我们最初发明的过去的 GPU 非常不同。我们继续发明了计算的几乎所有其他方面，互连、系统、网络和软件。
在这其中，我们向未知领域投资了数十亿美元。同时，十年来，数千名工程师致力于深度学习和推进深度学习的发展，而实际上，我们并不知道能真正把这项技术推进多远。
我们投资、设计并建造了超级计算机，来探索深度学习和人工智能的极限。然后在 2016 年，我们宣布了 DGX-1 ——英伟达第一台人工智能超级计算机。我把第一台交付给了旧金山的一家初创公司—— OpenAI。在 2012 年，也就是 AlexNet 10 年之后，计算能力提高了约 100 万倍。
如果你能想象一下，如果你的笔记本电脑的能力提高了 100 万倍，那会是什么样子，100 万倍之后，OpenAI 推出了 ChatGPT，人工智能进入了主流。
在这十年里，英伟达从一家你们很多人可能最初认识的制造 GPU 的芯片设计公司，转变为一家现在制造大规模数据中心规模超级计算机的人工智能公司。我们彻底改变了我们的公司。我们也彻底改变了计算。今天计算的基本方式已经发生了根本性的变化。
现在的计算堆栈使用 GPU 来处理在超级计算机上训练的大型语言模型，而不是处理程序员编写的指令的 CPU。我们现在正在创建没有人能想象的软件。
即使在 10 年前，计算机现在也是由意图驱动的，而不是指令驱动的。告诉计算机你想要什么，它会想出如何去做。就像人类一样，人工智能应用程序将理解任务、原因、计划，并组织一个大型语言模型团队来执行任务。未来的应用程序，将以与我们非常相似的方式执行和执行任务，组装专家团队，使用工具，推理和计划，并执行我们的任务。
软件和软件能做的事情已经完全改变了。甚至我们的行业，在它被改变和转型的过程中，创造了另一个行业，一个世界从未见过的行业。一个行业正在我们眼前形成。
对于所有工程师来说，人工智能的输入和输出都是标记。这些都是嵌入智能的浮点数。公司现在正在构建一种新型的数据中心，这种数据中心以前不存在，专门用于生产智能标记。从本质上讲，AI 工厂就像尼古拉 · 特斯拉在过去的工业革命中发明的交流发电机一样。我们现在有了人工智能标记发生器。它们将是新工业革命的工厂。有生产能源、电力的大工业。我们现在有一个生产无形软件的大产业。
在不久的将来，我们将有生产制造智能标记、人工智能发生器的产业。一种新的计算模型已经出现，一个新的行业已经出现，这一切都是因为我们从第一原理出发，形成了对未来的信念。
下一波人工智能是机器人技术。除了语言模型，人工智能还具有物理世界模型。我们与数百家公司合作制造机器人、机器人车辆、机械臂、人形机器人，甚至整个巨大的机器人仓库。但与我们在人工智能工厂战略中的经验不同，我们的机器人技术之旅是由一系列挫折导致的。
英伟达发明了 GPU，这是在我们发明人工智能工厂之前。我们对计算机行业的第一个伟大贡献是通过可编程渲染，彻底改变计算机图形学。我们在 2000 年发明了 GPU 和可编程渲染。我们想把 GPU 集成到每台计算机中。所以我们开始把我们的 GPU 和主板芯片结合起来。我们推出了一款出色的集成图形芯片。当时对于 AMD 的 CPU，我们的芯片业务立即取得了成功。
我想它几乎在一夜之间从零增长到了 10 亿美元。但突然之间，AMD 想要控制个人电脑中的所有技术（CPU、GPU），我们想要保持独立，所以他们收购了 ATI，不再需要我们。所以，我们转向英特尔，这可能不是一个好主意，但我们转向英特尔，并协商了一项许可证，以连接英特尔的 CPU。同时，苹果对我们正在建设的东西感到兴奋，并要求我们与他们合作开发一款新电脑，这就是第一台 MacBook Air。
当英特尔看到事情发生改变并决定，他们不再希望我们这样做。他们终止了我们的协议。我们再次转向，这一次我们去授权了 Arm，我们构建了一个低功耗的 SoC 移动 SoC，这是世界上第一个 SoC，本质上是一台计算机，一台完整的操作计算机。这太不可思议了。我们的芯片让谷歌兴奋不已，他们要求，我们为一款新设备工作，结果这款设备就是安卓移动设备。
然而，高通决定他们不希望我们这样做。他们不希望我们连接到他们的模型，而且如果不连接到移动处理器，就很难构建一个基于 AI 的移动设备，因为没有其他 LTE 调制解调器公司，所以，我们不得不退出移动设备市场。
这几乎是在一年的节奏上发生的。然后一年后，我们被踢出了这些市场，没有其他市场可以发展。因此，我们决定建造一些我们确信没有客户的东西，因为你绝对可以保证的一件事是，没有客户的地方。也没有竞争对手。没有人关心你。所以我们选择了一个没有客户的市场，一个 0 亿美元的市场：机器人技术。
我们建造了世界上第一台机器人计算机，处理一种当时没有人理解的算法，叫做深度学习。
这是十多年前的事了。而现在，10 年后，我对我们所建造的和创造下一波人工智能的机会感到非常高兴。更重要的是，我们培养了敏捷性和韧性文化。一次又一次的挫折，我们摆脱了它，滑向了下一个机会。每一次我们都获得了技能，增强了我们的性格，我们增强了我们的企业性格。我们的公司真的很难分心，真的很难气馁。
这些天来，没有任何挫折看起来不像一个机会。具有讽刺意味的是，我们今天建造的机器人计算机甚至不需要图形，这就是我们的旅程开始的原因。所以我们今天所处的位置告诉我们一些事情，并教会我们所有人。正如理查德 · 费曼所说，世界是不确定的，世界可能是不公平的，会给你发一手烂牌，迅速摆脱它，但显然要多读书。迅速摆脱它，来吧。这很聪明。我让自己笑了。
外面还有另一个机会，或者创造一个。我再给你讲一个故事。我曾经每年夏天在我们的一个国际站点工作一个月。当我们的孩子十几岁的时候，我们在日本度过了一个夏天。一个周末，我们参观了京都和银阁寺。
如果你还没有机会去，你一定要去。它以其精致的苔藓花园而闻名。我们参观的那天是典型的京都夏日，闷热难耐，只有炎热潮湿，黏糊糊的。热量从地面辐射出来。空气很浓。静止的。和其他游客一起，我们漫步在精心修剪的苔藓花园中。我注意到一个孤独的园丁。现在记住苔藓花园。这是银阁寺。苔藓花园很大。它大约有这个庭院那么大，有收藏，显然是世界上最大的苔藓收藏。而且维护得非常精致，我注意到一个孤独的园丁蹲着，用竹镊子仔细地挑选苔藓，然后把它放进竹篮里。你必须这样做，这是一个竹镊子。这只是一个花园。篮子看起来空了一会儿。
我以为他是把想象中的苔藓捡到一堆想象中的死苔藓里。于是我走过去问他，你在干什么？用他的英语？他说，我在捡死苔藓。我在照顾我的花园，我说，但是你的花园太大了。
他回答说，我已经照顾我的花园 25 年了。我有足够的时间。这是我一生中最深刻的收获之一。它真的教会了我一些东西，这个园丁致力于他的手艺，做他一生的工作。当你这样做的时候，你有足够的时间。
我每天早上都这样完全一样地开始，我每天早上都从做我最优先的工作开始。首先。我有一个非常明确的优先事项列表，我从最高优先级的工作开始。在我开始工作之前，我的一天已经成功了。我已经完成了我最重要的工作，可以把我的一天奉献给帮助别人。
当人们为打断我而道歉时，我总是说我有足够的时间。我对 2024 届的毕业生们说，我很难想象，还有谁比你们为未来做好了更充分的准备。你们致力于自己。你们努力工作。你们从世界上最负盛名的学校之一获得了世界一流的教育。当你们进入下一个阶段时，希望接受我们的学习。希望它们能在一路上帮助你们。
我希望你们相信一些东西，一些非常规的东西，一些未被探索的东西，但让它是知情的，让它是有道理的，然后致力于实现它。你可能会找到你的 GPU，你可能会找到你的导师，你可能会找到你的生成式人工智能，你可能会找到你的英伟达。
我希望，你们将挫折视为新的机会。你们的痛苦和苦难将增强你们的性格、韧性和敏捷性，它们是我最珍视的自己能力的终极超能力。智力不在这个列表的首位，我忍受痛苦和苦难的能力。
亲爱的，我长时间致力于某件事情的能力，我处理挫折并看到机会就在眼前的能力，我认为是我的超能力。我希望它们也是你们的，我希望你们找到一种手艺。
第一天决定并不重要。甚至很快决定也不重要，但我希望，你们能找到一种手艺。我希望你们找到一种想要奉献一生去完善、磨练技能，并让它成为你们一生工作的手艺。
最后，优先考虑你们的生活。有很多事情要做。有很多事情要做，但要优先考虑你们的生活。你们将有足够的时间去做重要的事情。
祝贺你们，2024 届的同学们。
It really makes me cringe listening to all that.
Thank you for that kind introduction, but I hate hearing about myself.
And just as a show, well, maybe if you could just applaud.
How many of you know who NVIDIA is? And how many of you know what a GPU is? Okay, good, I don't have to change my speech.
Ladies and gentlemen, President Rosenbaum, esteemed faculty members, distinguished guests, proud parents, and above all, the 2024 graduating class of Caltech.
This is a really happy day for you guys.
You got to look more excited.
You know you're graduating from Caltech.
This is the school of the great Richard Feynman, Linus Pauling, and someone who's very influential to me and our industry, Carver Mead.
Yeah, this is a very big deal.
Today is a day of immense pride and joy.
It is a dream come true for all of you, but not just for you because your parents and families have made countless sacrifices to see you reach this milestone.
So let's take this moment and congratulate them, thank them, and let them know you love them.
You don't want to forget that because you don't know how long you're going to be living at home.
You want to be super grateful today.
As a proud parent, I really loved it when my kids didn't move out, and it was great to see them every day, but now they've moved out, it makes me sad.
So hopefully you guys get to spend some time with your parents.
Your journey here is a testament of your character, determination, willingness to make sacrifices for your dreams, and you should be proud.
The ability to make sacrifices, endure pain and suffering, you will need these qualities in life.
You and I share some things in common.
First, both chief scientists of NVIDIA were from Caltech.
And one of the reasons why I'm giving this speech today is because I'm recruiting.
And so I want to tell you that NVIDIA is a really great company, I'm a very nice boss, universally loved, come work at NVIDIA.
You and I share a passion for science and engineering, and although we're separated by about 40 years, we are both at the peaks of our career.
For all of you who have been paying attention to NVIDIA and myself, you know what I mean.
It's just that in your case, you'll have many, many more peaks to go.
I just hope that today is not my peak.
Not the peak.
And so I'm working as hard as ever to make sure that I have many, many more peaks ahead.
Last year, I was honored to give the commencement address at Taiwan University, and I shared several stories about NVIDIA's journey and the lessons that we learned that might be valuable for graduates.
I have to admit that I don't love giving advice, especially to other people's children.
And so my advice today will largely be disguised in some stories that I've enjoyed, and some life experiences that I've enjoyed.
I'm the longest running tech CEO in the world today, I believe.
Over the course of 31 years, I managed not to go out of business, not get bored, and not get fired.
And so I have the great privilege of enjoying a lot of life's experiences, starting from creating NVIDIA, from nothing to what it is today.
And so I spoke about the long road of creating CUDA, a programming model.
The programming model that we dedicated over 20 years to invent, and that is revolutionizing computing today.
I spoke about a very, quite public, canceled Sega game console project we worked on, and where intellectual honesty, something that I know Richard Feynman cares very deeply about and spoke quite often about, where intellectual honesty and humility saved our company.
And how a retreat, a strategic retreat, was one of our best strategies.
All of these are counterintuitive lessons that I spoke about at the commencement.
But I encouraged the graduates to engage with AI, the most consequential technology of our time.
And I'll speak a little bit more about that later, but all of you know about AI.
It's hard not to be immersed in it and surrounded by it, and a great deal of discussion about it.
And of course, I hope that all of you are using it and playing with it, with surprising results and some magical, some disappointing, and some surprising.
But you have to enjoy it, you have to engage it, because it's advancing so quickly.
It is the only technology that I've known that is advancing on multiple exponentials at the same time.
And so the technology is changing very, very quickly.
So I advised the students at the Taiwan University to run, don't walk, and engage the AI revolution.
And yet, one year later, it's incredible how much it's changed.
And so today, what I wanted to do is share with you my perspective, from my vantage point, of some of the important things that are happening that you're graduating into.
And these are extraordinary things that are happening that you should have an intuitive understanding for, because it's going to matter to you, it's going to matter to the industry.
And hopefully, you take advantage of the opportunity ahead of you.
The computer industry is transforming from its foundations, literally from studs.
Everything is changing from studs on up.
And across every layer, and soon, every industry will also be transformed.
And the reason for that is quite obvious, because computers today are the single most important instrument of knowledge.
And it's foundational to every single industry and every field of science.
If we are transforming the computer so profoundly, it will, of course, have implications in every industry.
And I'll talk about that in just a little bit.
And as you enter industry, it's important you know what's happening.
Modern computing traces back to the IBM System 360.
That was the architecture manual that I learned from.
It's an architecture manual that you don't need to learn from.
A lot better documentation and better descriptions of computers and architecture has been presented since.
But the System 360 was incredibly important at its time, and in fact, the basic ideas of the System 360, the architecture of it, the principal ideas and architecture and strategy of the System 360 are still governing the computer industry today.
And it was introduced a year after my birth.
In the 80s, I was among the first generation of VLSI engineers who learned to design chips from Mead and Conway's landmark textbook.
And I'm not sure if it's still being taught here.
It should be in the introduction of VLSI systems.
Based on Carver Mead's pioneering work here at Caltech on chip design methodologies and textbook that revolutionized IC design.
And it enabled our generation to design supergiant chips and ultimately the CPU.
The CPU led to exponential growth in computing.
The performance, the incredible technology advances, that's called Moore's Law, fueled the information technology revolution.
The industrial revolution that we are part of, that my generation was part of, saw the mass production of something the world had never seen before.
The mass production of something that was invisible, easy to copy, the mass production of software.
And it led to a $3 trillion industry.
When I sat where you sat, the IT industry was minuscule.
And the concept that you could make money selling software was a fantasy.
And yet today, it's one of the most important commodities, most important technologies and product creations that our industry produces.
However, the limits of the NARD scaling, of transistor scaling, and instruction level parallelism have slowed CPU performance.
And the slowed CPU performance gains is happening at a time when computing demand continues to grow exponentially.
This exponentially growing gap between demand of computing and the capabilities of computers, if not addressed, computing energy consumption and cost, inflation, would eventually stifle every industry.
We see very clear signs of computing inflation as we speak.
And after two decades of advancing NVIDIA's CUDA, NVIDIA's accelerated computing offers a path forward.
That's the reason why I'm here.
Because finally, the industry realized of the incredible effectiveness of accelerated computing at precisely the time that we're witnessing computing inflation after several decades.
By offloading time-consuming algorithms to a GPU that specializes in parallel processing, we routinely achieve 10, 100, sometimes 1,000-fold speedups, saving money, cost, and energy.
We now accelerate application domains from computer graphics, ray tracing, of course, to gene sequencing, scientific computing, astronomy, quantum circuit simulations, SQL data processing, and even pandas, data science.
Accelerated computing has reached a tipping point.
That is our first great contribution to the computer industry, our first great contribution to society, accelerated computing.
It now gives us a path forward for sustainable computing where cost will continue to decline as computing requirement continues to grow.
A hundred-fold, a hundred-fold of anything in time or cost or energy savings that accelerated computing opened surely would trigger a new development somewhere else.
We just didn't know what it was until deep learning came to our consciousness.
A whole new world of computing emerged.
Jeff Hinton, Alex Krzyzewski, and Ilya Sutskever used NVIDIA CUDA GPUs to train AlexNet and shocked the computer vision community by winning the 2012 ImageNet Challenge.
This was the big moment, the big bang of deep learning, a pivotal moment that marked the beginning of the AI revolution.
Our decisions after AlexNet transformed our company is something that's worth taking note of.
Our decisions after AlexNet transformed our company and likely everything else.
We saw the potential of deep learning and believed, just believed through principle thinking, believed through our own analysis of the scalability of deep learning.
We believed the approach could learn other valuable functions.
That maybe deep learning is a universal function learner and how many problems are difficult or impossible to express using fundamental first principles.
And so when we saw this, when we saw this, we thought this is a technology we really have to pay attention to because its limits are potentially only limited by model and data scale.
However, there were challenges at the time.
This is 2012, shortly after 2012.
How could we explore the limits of deep learning without having to build these massive GPU clusters? At the time we were a rather small company, building these massive GPU clusters could cost hundreds and hundreds of millions of dollars.
And if we didn't though, there was no assurance that it would be effective if we scaled.
However, no one knew how far deep learning could scale.
And if we didn't build it, we'd never know.
This is one of those, if you build it, will they come? Our logic is if we don't build it, they can't come.
And so we dedicated ourselves based on our first principled beliefs and our analysis.
And we got ourselves to the point where we believed this was going to be so effective and when the company believes something, we should go act on it.
So we dove deep into deep learning, and over the next decade, systematically reinvented everything.
We reinvented every computing layer, starting with the GPU itself.
The invention of the modern GPU, which is very different than the GPU of the past that we invented in the first place, and we went on to invent just about every other aspect of computing, the interconnects, the systems, the networking, and of course, software.
We invested billions.
We invested billions into the unknown.
Thousands of engineers for a decade worked on deep learning and advancing and scaling deep learning without really knowing how far we could really take the technology.
We invested billions.
And we designed and built supercomputers to explore the limits of deep learning and AI.
And in 2016, we announced DGX-1, our first AI supercomputer, and I delivered the first one to a startup in San Francisco, a startup nobody knew anything about, a group of friends of mine who were working on artificial intelligence, a company called OpenAI.
In 2022, 10 years after AlexNet, and about a million-fold increase in computing later, a million-fold.
If you could just imagine, what would it be like if your laptop was a million-fold more capable? A million-fold later, OpenAI launched ChatGPT, and AI went mainstream.
During this decade, NVIDIA transformed ourselves from a graphics company that many of you probably first knew us as, that builds GPUs, to now an AI company that builds massive data center scale supercomputers.
We transformed our company completely.
We also transformed computing completely.
The fundamental way of doing computing today has been radically changed.
The computing stack now uses GPU to process large language models that are trained on supercomputers rather than CPUs that are processing instructions written by programmers.
We are now creating software that no humans can write.
We are now creating software that does things that no humans can imagine, even just 10 years ago.
Computers are now intention-driven rather than instruction-driven.
Tell a computer what you want, and it will figure out how.
And like humans, AI applications will understand the mission, reason, plan, and orchestrate a team of large language models to perform tasks.
Future applications will do and perform very similar to the way we do things, assemble teams of experts, use tools, reason and plan, and execute our mission.
Software and what software can do has been completely changed.
Even our industry, as it's being changed and transformed, created yet another industry, an industry the world's never seen before.
An industry is forming right in front of our eyes.
AI's input and output are tokens.
For all the engineers in the room, you know what I mean.
These are floating-point numbers that embed intelligence.
Companies are now building a new type of data center that didn't exist before that specialize in producing intelligence tokens.
Essentially, AI factories.
Like AC generators that Nikola Tesla invented of the past industrial revolution, we now have AI token generators, and they will be the factories of a new industrial revolution.
There's large industries producing energy, electricity.
We now have a large industry producing something invisible called software.
In the future, in the very near future, we'll have industries that are producing, manufacturing intelligence tokens, AI generators.
A new computing model has emerged, and a new industry has emerged, all because we reasoned from first principles, formed our belief about the future, and we acted on them.
The next wave of AI is robotics, where AI, in addition to a language model, also has a physical world model.
We work with hundreds of companies building robots, robotic vehicles, pick-and-place arms, humanoid robots, and even entire gigantic warehouses that are robotic.
But unlike our AI factory strategy and our experience there, which was really formed through reasoning and deliberate action, our robotics journey resulted from a series of setbacks.
As you know, NVIDIA invented the GPU.
This was before we invented AI factories.
Our first great contribution to the computer industry was reinventing computer graphics through programmable shaders. We invented the GPU and programmable shading in 2000.
We wanted to integrate GPUs into every computer, and so we started to combine our GPUs with motherboard chips, and we launched a fabulous integrated graphics chip at the time for AMD CPUs.
Our chipset business was an instant success.
I think it went from zero to a billion dollars practically overnight.
But then all of a sudden, AMD wanted to control all of the technology in the PC, and we wanted to stay independent, so they purchased ATI and no longer needed us.
We turned to Intel.
That probably wasn't a great idea, but we turned to Intel and negotiated a license to connect to Intel CPUs.
Apple was excited by what we were building and asked us to work on a new computer with them, which became the first MacBook Air.
Well, Intel saw what happened and decided they didn't want us to do that anymore, and so they terminated our agreement.
Well, we pivoted again, and this time we went and licensed ARM, and we built a low-power SoC, a mobile SoC, the world's first SoC that was essentially a computer, a full operating computer, and it was incredible.
Our chip excited Google, and they asked us to work on a new device, which turned out to have been the Android mobile device.
Well, Qualcomm decided they didn't want us to do that, and so they didn't want us to connect to their modems, and it's hard to build a mobile device without being connected to a modem.
And there were no other LTE modem companies, so we had to exit the mobile device market.
Well, this happened practically on a year rhythm, and we would build something, it would be incredibly successful, generate enormous amounts of excitement, and then one year later, we were kicked out of those markets.
Well, with no more markets to turn to, we decided to build something where we are sure there are no customers, because one of the things you can definitely guarantee is where there are no customers, there are also no competitors, and nobody cares about you.
And so we chose a market with no customers, a $0 billion market, and it was robotics.
We built the world's first robotics computer processing an algorithm nobody understood at the time called deep learning.
This is over 10 years ago now.
Ten years later, I can't be happier with what we've built and the opportunity to create the next wave of AI. More importantly, we developed agility and a culture of resilience.
One setback after another, we shook it off and skated to the next opportunity.
Each time, we gained skills and strengthened our character.
We strengthened our corporate character.
Our company is really hard to distract and really hard to discourage, and no setback that comes our way doesn't look like an opportunity these days.
Ironically, the robotics computer that we built today doesn't even need graphics, which is why our journey started in the first place.
So where we are today tells us something and teaches us something.
The world is uncertain, as Richard Feynman would say, and the world can be unfair and deal you with tough cards.
Swiftly, shake it off.
You've apparently been paying too much attention to your books.
Swiftly, shake it off.
Come on, that's pretty clever.
I made myself laugh.
There's another opportunity out there, or create one.
Let me tell you one more story.
I used to work from one of our international sites for one month each summer.
When our kids were in their teens, we spent a summer in Japan.
For a weekend, we visited Kyoto and the Silver Temple.
If you haven't had a chance to go, you must.
It's renowned for its exquisite moss garden.
The day we visited was quintessential Kyoto summer day, suffocatingly hot and humid, sticky.
Heat is radiating from the ground.
The air was thick, still.
Along with the other tourists, we wandered through the meticulously groomed moss garden.
And I noticed the lone gardener.
Now, remember, the moss garden, this is the Silver Temple, the moss garden is gigantic.
It's about the size of this courtyard.
And it has the collection, the largest collection of just about apparently every species of moss in the world.
And it's just exquisitely maintained.
I noticed the lone gardener squatting, carefully picking at the moss with a bamboo tweezer and putting it in the bamboo basket.
And you have to, it's a bamboo tweezer, you know, and it's just this one gardener.
And the basket looked empty.
Well, for a moment there, I thought he was picking imaginary moss into a pile of imaginary dead moss.
And so I walked up to him and I said, what are you doing? And in his English, he said, I'm picking dead moss.
I'm taking care of my garden.
And I said, but your garden is so big.
And he responded, I have cared for my garden for 25 years.
I have plenty of time.
Well, that was one of the most profound learnings in my life.
And it really taught me something.
This gardener has dedicated himself to his craft and doing his life's work.
And when you do that, you have plenty of time.
I begin each morning, I do every single morning exactly the same way, I begin each morning by doing my highest priority work first.
I have a very clear priority list and I start from the highest priority work first.
Before I even get to work, my day is already a success.
I've already completed my most important work and can dedicate my day to helping others.
And when people apologize for interrupting me, I always say I have plenty of time and I do.
Graduates of the class of 2024, I can hardly imagine anyone more prepared for the future than you.
You dedicated yourself, you worked hard, you earned a world-class education from one of the most prestigious schools in the world.
And as you commence into the next stage, take my learnings and hopefully they'll help you along the way.
I hope you believe in something, something unconventional, something unexplored, but let it be informed and let it be reasoned.
Then dedicate yourself to making it happen.
You may find your GPU, you may find your CUDA, you may find your generative AI, you may find your NVIDIA.
I hope you will see setbacks as new opportunities.
Your pain and suffering will strengthen your character, your resilience and agility, and they are the ultimate superpowers.
Of all of the things that I value most about my abilities, intelligence is not top of that list.
My ability to endure pain and suffering, my ability to work on something for a very, very long period of time, my ability to handle setbacks and see the opportunity just around the corner I consider to be my superpowers, and I hope they're yours.
And I hope you find a craft.
I hope you find a craft.
It's not important to decide on day one, it's not even important to decide any time soon, but I hope you do find a craft, that you want to dedicate your lifetime to perfecting, to hone the skills of, and let it be your life's work.
And then lastly, prioritize your life.
There's so many things going on, there's so many things to do, but prioritize your life and you will have plenty of time to do the important things.
Congratulations, class of 2024, go get them.
