# 【中英文完整版】剑桥震撼演讲：黄仁勋谈AI与未来 #AI #aigc #黄仁勋 #英伟达 #剑桥大学 #演讲 #科技前沿

https://www.youtube.com/watch?v=Zi-7Nu_0lkc
Translation: zh-CN

[00:17] Good evening everybody.
  大家晚上好。

[00:18] Thank you so much for joining us for what is about to be an exciting evening.
  非常感谢大家加入我们，今晚将是一个激动人心的夜晚。

[00:23] Just before we get into tonight's proceedings, just a tiny in-house rule from me.
  在我们开始今晚的议程之前，我有一个小小的内部规定。

[00:27] Please do resist the urge to take photographs or videos in your personal mobile devices.
  请尽量不要用您的个人移动设备拍照或录像。

[00:31] We have professional photographers in house and I would trust you to leave it to the professionals to take care of that.
  我们有专业的摄影师在场，我相信您可以放心地将此事交给专业人士处理。

[00:36] Thank you.
  谢谢。

[00:40] Okay.
  好的。

[00:40] So, created in 2017, the Professor Steven Hawking Fellowship was established to recognize individuals who have made distinguished contributions to science and technology.
  所以，史蒂芬·霍金教授奖学金成立于2017年，旨在表彰那些在科学技术领域做出杰出贡献的个人。

[00:50] administered every year by the Hawing Fellowship Committee.
  由霍金奖学金委员会每年管理。

[00:53] The fellowship acts to memorialize Professor Hawings legacy and to celebrate innovation in the field.
  该奖学金旨在纪念霍金教授的遗产，并庆祝该领域的创新。

[01:01] Fellows from past years have included Bill Gates in 2019, Jane Goodle
  往届的获奖者包括2019年的比尔·盖茨，珍妮·古道尔

[01:04] included Bill Gates in 2019, Jane Goodle in 2020 and the Open AI team in 2023.
  包括2019年的比尔·盖茨，2020年的珍妮·古道尔以及2023年的OpenAI团队。

[01:09] in 2020 and the Open AI team in 2023. Tonight's honoree makes a stunning addition to that list of inducted fellows.
  在2020年以及2023年的OpenAI团队。今晚的获奖者为这一批被提名者增添了耀眼的光彩。

[01:12] addition to that list of inducted fellows. Jensen Hang is the founder, CEO, and president of Nvidia.
  被提名者。黄仁勋是英伟达的创始人、首席执行官兼总裁。

[01:15] fellows. Jensen Hang is the founder, CEO, and president of Nvidia. Founded in 1993, Nvidia has pioneered accelerated computing.
  被提名者。黄仁勋是英伟达的创始人、首席执行官兼总裁。英伟达成立于1993年，在加速计算领域取得了开创性成就。

[01:18] CEO, and president of Nvidia. Founded in 1993, Nvidia has pioneered accelerated computing. From the invention of the GPU in 1999 to major developments like the Gracehopper Super Chips, this Stanford graduate and alumni has been a trailblazer in innovation and tech pioneering since the very beginning.
  首席执行官兼总裁。英伟达成立于1993年，在加速计算领域取得了开创性成就。从1999年GPU的发明到像Gracehopper超级芯片这样的重大发展，这位斯坦福大学的毕业生和校友自始至终一直是创新和技术先驱的开拓者。

[01:21] 1993, Nvidia has pioneered accelerated computing. From the invention of the GPU in 1999 to major developments like the Gracehopper Super Chips, this Stanford graduate and alumni has been a trailblazer in innovation and tech pioneering since the very beginning.
  1993年，英伟达在加速计算领域取得了开创性成就。从1999年GPU的发明到像Gracehopper超级芯片这样的重大发展，这位斯坦福大学的毕业生和校友自始至终一直是创新和技术先驱的开拓者。

[01:24] computing. From the invention of the GPU in 1999 to major developments like the Gracehopper Super Chips, this Stanford graduate and alumni has been a trailblazer in innovation and tech pioneering since the very beginning.
  计算。从1999年GPU的发明到像Gracehopper超级芯片这样的重大发展，这位斯坦福大学的毕业生和校友自始至终一直是创新和技术先驱的开拓者。

[01:27] in 1999 to major developments like the Gracehopper Super Chips, this Stanford graduate and alumni has been a trailblazer in innovation and tech pioneering since the very beginning.
  1999年到像Gracehopper超级芯片这样的重大发展，这位斯坦福大学的毕业生和校友自始至终一直是创新和技术先驱的开拓者。

[01:29] Gracehopper Super Chips, this Stanford graduate and alumni has been a trailblazer in innovation and tech pioneering since the very beginning.
  Gracehopper超级芯片，这位斯坦福大学的毕业生和校友自始至终一直是创新和技术先驱的开拓者。

[01:31] graduate and alumni has been a trailblazer in innovation and tech pioneering since the very beginning.
  毕业生和校友自始至终一直是创新和技术先驱的开拓者。

[01:33] trailblazer in innovation and tech pioneering since the very beginning.
  自始至终一直是创新和技术先驱的开拓者。

[01:36] pioneering since the very beginning. Please give me a warm Cambridge Union welcome in inviting the 2025 Professor Steven Hawking Fellow, Mr. Jensen Wang.
  自始至终一直是开拓者。请给予热烈的剑桥 union 欢迎，邀请2025年史蒂芬·霍金教授研究员，王 Jensen 先生。

[01:38] Please give me a warm Cambridge Union welcome in inviting the 2025 Professor Steven Hawking Fellow, Mr. Jensen Wang.
  请给予热烈的剑桥 union 欢迎，邀请2025年史蒂芬·霍金教授研究员，王 Jensen 先生。

[01:42] welcome in inviting the 2025 Professor Steven Hawking Fellow, Mr. Jensen Wang.
  欢迎，邀请2025年史蒂芬·霍金教授研究员，王 Jensen 先生。

[01:45] Steven Hawking Fellow, Mr. Jensen Wang. [applause]
  史蒂芬·霍金教授研究员，王 Jensen 先生。（掌声）

[01:54] [applause]
  （掌声）

[01:58] Heat. Heat.
  热。热。

[02:04] [applause]
  （掌声）

[02:08] Thank you.
  谢谢。

[02:08] Thank you very much.
  非常感谢。

[02:11] Thank you.
  谢谢。

[02:11] Thank you very much.
  非常感谢。

[02:11] Thank you.
  谢谢。

[02:13] Thank you.
  谢谢。

[02:13] Thank you.
  谢谢。

[02:18] Thank you.
  谢谢。

[02:21] And now I would like to take the opportunity to invite Lucy Hawking,
  现在，我想借此机会邀请露西·霍金，

[02:23] Professor Hawking's daughter, to present this year's fellowship.
  霍金教授的女儿，来颁发今年的奖学金。

[02:46] Thank you.
  谢谢。

[02:50] Maybe I'll have my wife hold it.
  也许我会让我妻子拿着。

[02:51] Maybe I'll have my wife hold it.
  也许我会让我妻子拿着。

[02:51] Oh, perfect. Please.
  哦，太好了。请。

[02:54] Oh, perfect. Please.
  哦，太好了。请。

[02:54] I feel safer with her holding as far as
  我感觉有她拿着更安全，就到

[02:58] Thank you.
  谢谢。

[03:00] Thank you.
  谢谢。

[03:02] Wow.
  哇。

[03:02] This is quite a moment.
  这是一个重要的时刻。

[03:05] You know, today when I arrived, I was so overwhelmed by
  你知道，今天我刚到的时候，我太不知所措了，因为

[03:08] When I arrived, I was so overwhelmed by being on Cambridge.
  当我到达时，我被来到剑桥的经历所压倒。

[03:10] Being on Cambridge that I decided to uh bask in the moment.
  来到剑桥，我决定沉浸在这一刻。

[03:14] That I decided to uh bask in the moment and write a proper speech.
  我决定沉浸在这一刻，并写一篇像样的演讲稿。

[03:19] And and so I I was in is it the St.
  于是我，我是在，是圣。

[03:22] And and so I I was in is it the St. James Lodge?
  于是我，我是在，是圣詹姆斯旅馆吗？

[03:25] Is it uh where it was a beautiful place?
  是在一个美丽的地方吗？

[03:28] The fireplace was uh was going and and uh I sat in a must have been a 30,000 year old chair chair.
  壁炉正在燃烧，我坐在一张一定是3万年前的椅子上。

[03:36] And um uh it was it was incredible and and here here it is.
  这太不可思议了，现在它就在这里。

[03:39] I wrote it.
  我写了它。

[03:41] So Lucy, Ivan, members of the Hawin family, and everyone everyone here, incredible everyone here.
  所以露西、伊万、霍金家族的成员，以及这里的每一个人，令人难以置信的每一个人。

[03:48] I'm deeply honored to receive the Steven Hawking Fellowship.
  我非常荣幸能获得史蒂文·霍金奖学金。

[03:53] And to receive it here at Cambridge is profoundly humbling.
  能在剑桥获得它，我深感谦卑。

[03:56] Cambridge is a cathedral, a cathedral of world changing ideas.
  剑桥是一座大教堂，是改变世界的思想的殿堂。

[04:02] Newton redefining motion and gravity.
  牛顿重新定义了运动和引力。

[04:04] Darwin
  达尔文

[04:10] Darwin questioning creation.
  达尔文质疑创世。

[04:12] Maxwell, one of my favorites, uniting light and magnetism.
  麦克斯韦，我最喜欢的科学家之一，统一了光和磁。

[04:19] Touring, another favorite, imagining a machine that could think.
  图灵，另一个我喜欢的，设想了一台能够思考的机器。

[04:25] And Stephen Hawking expanding our understanding of time and universe.
  以及斯蒂芬·霍金拓展了我们对时间和宇宙的理解。

[04:32] Professor Hawkings life showed that curiosity has no boundaries.
  霍金教授的一生表明，好奇心没有界限。

[04:41] By the way, that was one of his theories as you know, no boundaries.
  顺便说一句，正如你所知，那是他关于没有界限的理论之一。

[04:48] Even when his body was confined, his mind traveled beyond the stars.
  即使他的身体受到束缚，他的思想也超越了星辰。

[04:53] He reminded us that discovery does not come only from intellect but from conviction and optimism.
  他提醒我们，发现不仅来自智慧，还来自信念和乐观。

[05:02] And through his life's work and through the way he lived his life, he inspired us to look beyond our limitations.
  通过他一生的工作和他生活的方式，他激励我们超越自身的局限。

[05:11] limitations to meet challenge with curiosity and to meet challenge with curiosity and humor.
  局限性，以好奇心和幽默感来应对挑战。

[05:15] I can think of no higher compliment than to be associated with that spirit.
  我认为没有什么比与这种精神联系起来更高的赞美了。

[05:21] And Vidia is a story of nearly impossible odds.
  而英伟达的故事几乎是不可能战胜的困难。

[05:27] The three of us, three friends in a townhouse in 1993, uh set out with an idea that we'd invent a new way of doing computing to solve problems that normal computers cannot.
  我们三个人，1993年在联排别墅里的三个朋友，嗯，带着一个想法出发，我们要发明一种新的计算方式来解决普通计算机无法解决的问题。

[05:41] Along the way, we invented a new product category, the GPU.
  在此过程中，我们发明了一个新的产品类别，即GPU。

[05:44] We invented, in fact, a new form of computing called CUDA accelerated computing.
  事实上，我们发明了一种新的计算形式，称为CUDA加速计算。

[05:49] We invented new strategies to take that technology and that architecture and proliferate it literally all over the world.
  我们发明了新的策略，将这项技术和该架构推广到全世界。

[06:03] And along the way, we created the instrument of scientists, artists, designers, dreamers, and most
  在此过程中，我们为科学家、艺术家、设计师、梦想家以及大多数人创造了工具。

[06:11] artists, designers, dreamers, and most importantly,
  艺术家、设计师、梦想家，最重要的是，

[06:13] importantly, we sparked a new industrial revolution,
  重要的是，我们引发了一场新的工业革命，

[06:18] we sparked a new industrial revolution, the AI industrial revolution.
  我们引发了一场新的工业革命，人工智能工业革命。

[06:23] We've come a long ways and our
  我们已经走了很长的路，我们的

[06:28] We've come a long ways and our discoveries has led to the most
  我们已经走了很长的路，我们的发现导致了我们这个时代最具影响力的技术，

[06:30] discoveries has led to the most impactful technology of our time and uh
  发现导致了我们这个时代最具影响力的技术，嗯，

[06:34] impactful technology of our time and uh probably of all time, the ability to
  我们这个时代最具影响力的技术，嗯，可能是有史以来最具影响力的技术，那就是制造智能的能力。

[06:37] probably of all time, the ability to manufacture intelligence.
  可能是有史以来最具影响力的技术，那就是制造智能的能力。

[06:39] manufacture intelligence.
  制造智能。

[06:41] We now seen the technology advance incredibly in the last decade.
  我们现在看到这项技术在过去十年里取得了惊人的进步。

[06:45] incredibly in the last decade.
  在过去十年里取得了惊人的进步。

[06:48] It is now transforming every application,
  它现在正在改变每一个应用，

[06:50] application, every field of science, every industry.
  每一个应用，每一个科学领域，每一个行业。

[06:55] every field of science, every industry.
  每一个科学领域，每一个行业。

[06:58] Everyone will be impacted.
  每个人都将受到影响。

[07:01] Every company will use it.
  每家公司都将使用它。

[07:04] Every nation will build it. It will now
  每个国家都将构建它。它现在将

[07:07] be part of infrastructure, the intelligence infrastructure.
  成为基础设施的一部分，智能基础设施。

[07:10] the intelligence infrastructure.
  智能基础设施。

[07:10] and to realize that like energy, like
  并且要认识到，就像能源一样，就像

[07:14] and to realize that like energy, like the internet, we will now be building AI infrastructure all over the world.
  并意识到，就像能源、互联网一样，我们现在将在世界各地构建人工智能基础设施。

[07:20] That observation, that realization led to the company that you see today.
  这一观察，这一认识，促成了您今天看到的这家公司。

[07:28] The company helping every company, every industry, every country build out artificial intelligence as part of their social fabric.
  这家公司正在帮助每个公司、每个行业、每个国家将人工智能作为其社会结构的一部分进行构建。

[07:37] And so here we are 33 years later.
  就这样，33年后我们在这里。

[07:41] I'm the longest serving tech CEO the world's ever known.
  我是世界上任职时间最长的科技公司首席执行官。

[07:50] The way you achieve that, by the way, is uh don't get bored and don't get fired.
  顺便说一句，你实现这一点的方式是，嗯，不要感到厌倦，也不要被解雇。

[07:56] And in a lot of ways I feel Nvidia and I have been uh reborn that the company is renewed that the entire technology industry is being completely reinvented.
  在很多方面，我觉得英伟达和我已经重生了，公司焕然一新，整个科技行业正在被彻底重塑。

[08:13] Every single layer of the single most
  最最...

[08:15] Every single layer of the single most important instrument of humanity, important instrument of humanity, computers, is being reinvented from chips to systems, the software, algorithms, applications, and the potential impact.
  人类最重要的工具的每一个层面，人类最重要的工具，计算机，正在被重新发明，从芯片到系统，软件，算法，应用程序，以及潜在的影响。

[08:30] In no time in history has this happened.
  历史上从未发生过这样的事情。

[08:33] Surely not in the last 100 years. And now the entire tech stack is being reinvented.
  肯定不是在过去的100年里。现在，整个技术栈正在被重新发明。

[08:39] the entire tech industry is being reinvented and in fact we do feel completely renewed and so in a lot of ways I'm starting where you're starting.
  整个科技行业正在被重新发明，事实上，我们感到焕然一新，所以在很多方面，我正从你开始的地方开始。

[08:49] We're all newbies now.
  我们现在都是新手了。

[08:56] We're all looking at a future beaming with opportunity and equal amounts of concern.
  我们都看到了一个充满机遇和同样程度担忧的未来。

[09:06] No technology of this capability can be advanced without thoughtfulness, without care, without scrutiny.
  没有任何一项具有如此能力的技术，可以在没有深思熟虑、没有关怀、没有审视的情况下得到发展。

[09:17] Without care, without scrutiny.
  没有谨慎，没有审查。

[09:20] And yet the opportunities ahead is incredible.
  然而，未来的机遇是不可思议的。

[09:22] And so I feel like a startup again.
  所以我又感觉像一家初创公司了。

[09:25] 33 years later, Nvidia is now the world's largest startup.
  33年后，英伟达现在是世界上最大的初创公司。

[09:29] And so I'm I'm incredibly incredibly proud to receive this.
  所以我非常非常自豪能收到这个。

[09:33] It's a great honor.
  这是一个巨大的荣誉。

[09:36] And um I'm looking forward to spending time with all of you.
  嗯，我期待着与大家共度时光。

[09:37] Thank you.
  谢谢你。

[09:54] It might be me.
  可能是我。

[09:58] It might be that one of us has to go.
  可能是我们中的一个人必须离开。

[10:07] Perfect.
  完美。

[10:07] I think we're fine.
  我认为我们没事。

[10:07] Wonderful.
  太棒了。

[10:09] Perfect.
  完美。

[10:09] I think we're fine.
  我认为我们没事。

[10:09] Wonderful.
  太棒了。

[10:11] Well, thank you very much for joining us today.
  嗯，非常感谢您今天加入我们。

[10:13] And I think that it would be a miss if I didn't begin by saying congratulations on receiving the 2025 Professor Steven
  我想，如果我不先说恭喜您获得2025年史蒂文教授奖，那将是一大遗憾。

[10:19] receiving the 2025 Professor Steven Hawking Fellowship.
  荣获2025年史蒂文·霍金教授奖学金。

[10:20] Hawking Fellowship.
  霍金奖学金。

[10:20] Thank you.
  谢谢。

[10:21] Thank you.
  谢谢。

[10:21] With such a rich career and legacy and sense of of achievement in all that you've been able to do so far.
  拥有如此丰富的职业生涯、卓越的成就和深远的影响，在你迄今为止所做的一切中。

[10:28] you've been able to do so far.
  你迄今为止所做的一切。

[10:29] You're making me nervous.
  你让我很紧张。

[10:32] You're making me nervous.
  你让我很紧张。

[10:32] I promise you I'm on my best behavior today.
  我向你保证，我今天表现得最好。

[10:36] today.
  今天。

[10:36] Taking that setup, that setup is too much.
  这样的铺垫，这样的铺垫太过了。

[10:38] that setup, that setup is too much.
  这样的铺垫，这样的铺垫太过了。

[10:39] [laughter]
  [笑声]

[10:40] You know, taking all of that into consideration, it's tricky to figure out where to start.
  你知道，考虑到所有这些，很难确定从哪里开始。

[10:42] consideration, it's tricky to figure out where to start.
  考虑，很难确定从哪里开始。

[10:44] But to quote The Sound of Music, let's start at the very beginning.
  但引用《音乐之声》的话来说，让我们从头开始。

[10:45] of Music, let's start at the very beginning.
  《音乐之声》，让我们从头开始。

[10:47] It's a very good place to start.
  这是一个很好的起点。

[10:47] So, being born in Taipei and moving to America when you were nine.
  所以，出生在台北，九岁时搬到美国。

[10:49] start.
  开始。

[10:49] So, being born in Taipei and moving to America when you were nine.
  所以，出生在台北，九岁时搬到美国。

[10:51] moving to America when you were nine.
  九岁时搬到美国。

[10:54] Um, what comes really apparent in your early years is a great sense of determination, drive, and personal discipline.
  嗯，在你早年显而易见的是强烈的决心、动力和个人纪律。

[10:56] early years is a great sense of determination, drive, and personal discipline.
  早年显而易见的是强烈的决心、动力和个人纪律。

[10:59] determination, drive, and personal discipline.
  决心、动力和个人纪律。

[10:59] How were you able to cultivate that through your education, through your time at Oregon State, through your time at Stanford to keep going and to continue being relentless even in your early education?
  你是如何通过你的教育，在俄勒冈州立大学的时光，在斯坦福大学的时光来培养这种能力，并一直坚持不懈，甚至在你的早期教育中也保持这种不懈的精神的？

[11:00] discipline.
  纪律。

[11:00] How were you able to cultivate that through your education, through your time at Oregon State, through your time at Stanford to keep going and to continue being relentless even in your early education?
  你是如何通过你的教育，在俄勒冈州立大学的时光，在斯坦福大学的时光来培养这种能力，并一直坚持不懈，甚至在你的早期教育中也保持这种不懈的精神的？

[11:02] cultivate that through your education, through your time at Oregon State, through your time at Stanford to keep going and to continue being relentless even in your early education?
  培养这种能力，在俄勒冈州立大学的时光，在斯坦福大学的时光来保持这种不懈的精神？

[11:04] through your time at Oregon State, through your time at Stanford to keep going and to continue being relentless even in your early education?
  在斯坦福大学的时光来保持这种不懈的精神？

[11:06] through your time at Stanford to keep going and to continue being relentless even in your early education?
  在斯坦福大学的时光来保持这种不懈的精神？

[11:08] going and to continue being relentless even in your early education?
  并一直坚持不懈，甚至在你的早期教育中也保持这种不懈的精神？

[11:10] even in your early education?
  甚至在你的早期教育中？

[11:10] My mom taught me English and she doesn't speak English.
  我妈妈教我英语，而她自己不会说英语。

[11:12] My mom taught me English and she doesn't speak English.
  我妈妈教我英语，而她自己不会说英语。

[11:15] speak English.
  说英语。

[11:18] And that kind of tells you all that tells you something about about um
  这大概能说明一切，说明了关于嗯

[11:21] that tells you something about about um the impression that parents could leave the impression that parents could leave with their children.
  这告诉你一些关于父母可能给孩子留下什么样的印象。

[11:27] Uh I she told me when I was young that that uh uh that I was special, that somehow somehow I took tests and I was I did well on tests and um I encouraged me.
  呃，我，她告诉我，我年轻的时候，我，我，我很特别，不知何故，我参加考试，我考得很好，这鼓励了我。

[11:46] Uh often times if people tell you that you're you're better, greater uh more capable than you are, you might live up to that expectation.
  呃，很多时候，如果人们告诉你，你比他们更好、更伟大、更有能力，你可能会达到这种期望。

[11:54] uh it reminds us to do the same with our companies and it reminds us to do the same with each other.
  呃，这提醒我们也要对我们的公司这样做，也提醒我们要对彼此这样做。

[11:58] Uh and uh and she she left with me an impression that that nothing could be that hard you know to this day and people have seen me adapt and
  呃，呃，她给我留下了一个印象，那就是没有什么事情是那么难的，你知道，直到今天，人们看到我适应了，

[12:14] I think it was Professor Hawin that said uh adapt in intellect is the ability to
  我想是霍金教授说的，呃，智力上的适应是能力

[12:21] Uh adapt in intellect is the ability to adapt.
  嗯，适应智力就是适应的能力。

[12:25] Wasn't didn't he say that true adapt.
  他不是说过真正的适应吗？

[12:27] Wasn't didn't he say that true intellect is the ability to adapt in a lot of ways?
  他不是说过真正的智力在很多方面都能适应吗？

[12:31] That that kind of defines Nvidia kind of defines me.
  这在某种程度上定义了英伟达，在某种程度上定义了我。

[12:34] I I approach almost everything from the perspective, you know, how hard can it be now.
  我几乎所有的事情都从这个角度出发，你知道，现在能有多难呢。

[12:36] Oftenimes turns out to be really hard, but you approach it with the attitude, how hard could it be?
  很多时候事实证明确实很难，但你带着一种态度去面对它，它能有多难呢？

[12:49] And and if you look at all the things that we've done as a company and what I've done, I I've never been CEO before.
  如果我们看看我们公司所做的一切和我所做的一切，我以前从未当过首席执行官。

[12:56] This is my first CEO gig.
  这是我的第一个首席执行官职位。

[12:58] Okay?
  好的？

[13:00] And so I I I think I'm I'm going to get it right someday.
  所以我想我总有一天会做对的。

[13:05] Um but when we first started the company and I never raised money, I never wrote a business plan.
  嗯，但当我们刚开始创业时，我从未筹集过资金，也从未写过商业计划。

[13:09] I still haven't written a business plan.
  我仍然没有写过商业计划。

[13:11] Um and and I've never been a CEO, never even been a manager.
  嗯，我从未当过首席执行官，甚至从未当过经理。

[13:17] in all cases.
  在所有情况下。

[13:19] I think I think her the the the the
  我想，我想她，她，她，她

[13:23] I think I think her the the the the fact that she was able to teach me.
  我想，我想她，她，她，她能够教我。

[13:24] fact that she was able to teach me English and she doesn't speak English.
  她能够教我英语，而她自己却不会说英语。

[13:27] English and she doesn't speak English.
  英语，而她自己却不会说英语。

[13:27] Uh she can't read English.
  呃，她看不懂英语。

[13:29] Uh she can't read English.
  呃，她看不懂英语。

[13:29] So, you got to ask yourself how did how did she do it?
  所以，你不得不问自己，她是怎么做到的？

[13:31] So, you got to ask yourself how did how did she do it?
  所以，你不得不问自己，她是怎么做到的？

[13:34] Um turns out piece of paper in a dictionary.
  嗯，原来是一张纸，一本字典。

[13:40] paper in a dictionary.
  纸，一本字典。

[13:40] And uh I approached almost everything like that.
  嗯，我几乎是那样对待一切的。

[13:41] approached almost everything like that.
  对待一切都差不多是那样。

[13:41] you know, how hard could it be and break it down to first principles and you learn along the way and and and I meant it earlier.
  你知道，它能有多难呢？把它分解到最基本原则，然后你在过程中学习，我之前就是这个意思。

[13:43] you know, how hard could it be and break it down to first principles and you learn along the way and and and I meant it earlier.
  你知道，它能有多难呢？把它分解到最基本原则，然后你在过程中学习，我之前就是这个意思。

[13:44] it down to first principles and you learn along the way and and and I meant it earlier.
  分解到最基本原则，然后你在过程中学习，我之前就是这个意思。

[13:48] learn along the way and and and I meant it earlier.
  在过程中学习，我之前就是这个意思。

[13:48] Uh so long as you could stay in the game long enough to learn the sport and staying in the game is in fact most of it.
  呃，只要你能足够长地留在游戏中去学习这项运动，而留在游戏中实际上就是大部分。

[13:50] it earlier.
  之前。

[13:50] Uh so long as you could stay in the game long enough to learn the sport and staying in the game is in fact most of it.
  呃，只要你能足够长地留在游戏中去学习这项运动，而留在游戏中实际上就是大部分。

[13:52] in the game long enough to learn the sport and staying in the game is in fact most of it.
  足够长地留在游戏中去学习这项运动，而留在游戏中实际上就是大部分。

[13:56] sport and staying in the game is in fact most of it.
  这项运动，而留在游戏中实际上就是大部分。

[13:56] Uh I I was able to to do what I'm doing today because I didn't get bored and I didn't get fired.
  呃，我之所以能够做我今天所做的事情，是因为我没有感到厌烦，也没有被解雇。

[14:00] most of it.
  大部分。

[14:00] Uh I I was able to to do what I'm doing today because I didn't get bored and I didn't get fired.
  呃，我之所以能够做我今天所做的事情，是因为我没有感到厌烦，也没有被解雇。

[14:02] what I'm doing today because I didn't get bored and I didn't get fired.
  我今天所做的事情，是因为我没有感到厌烦，也没有被解雇。

[14:05] get bored and I didn't get fired.
  感到厌烦，也没有被解雇。

[14:05] That I think was the magic.
  我认为那就是魔力所在。

[14:07] think was the magic.
  我认为那就是魔力所在。

[14:07] All all of it.
  所有的一切。

[14:07] It's 100% of it.
  它是其中的百分之百。

[14:10] 100% of it.
  百分之百。

[14:10] And I think staying on that topic of Nvidia being the beginning of a lot of firsts for you, it's as if you've read my mind, this is flowing so beautifully.
  而且我认为，就英伟达是你许多第一的开端这个话题而言，你好像读懂了我的心思，这真是太流畅了。

[14:13] topic of Nvidia being the beginning of a lot of firsts for you, it's as if you've read my mind, this is flowing so beautifully.
  英伟达是你许多第一的开端这个话题，你好像读懂了我的心思，这真是太流畅了。

[14:14] lot of firsts for you, it's as if you've read my mind, this is flowing so beautifully.
  许多第一对你来说，你好像读懂了我的心思，这真是太流畅了。

[14:15] read my mind, this is flowing so beautifully.
  读懂了我的心思，这真是太流畅了。

[14:15] Let's talk about the decision that led to you becoming CEO of the company.
  让我们谈谈让你成为公司首席执行官的那个决定。

[14:17] beautifully.
  流畅。

[14:17] Let's talk about the decision that led to you becoming CEO of the company.
  让我们谈谈让你成为公司首席执行官的那个决定。

[14:19] decision that led to you becoming CEO of the company.
  让你成为公司首席执行官的那个决定。

[14:19] So the dream and plan for Nvidia came together over many coffee
  所以，英伟达的梦想和计划是在许多咖啡时间形成的

[14:21] the company.
  公司。

[14:21] So the dream and plan for Nvidia came together over many coffee
  所以，英伟达的梦想和计划是在许多咖啡时间形成的

[14:23] Nvidia came together over many coffee meetings at the chain restaurant Denny's.
  英伟达在连锁餐厅Denny's的许多咖啡会议上走到了一起。

[14:27] Those meetings revealed to your co- business partners Pri and Malahowski that you were to be the CEO of Nvidia.
  那些会议向你的联合商业伙伴Pri和Malahowski透露，你将成为英伟达的首席执行官。

[14:33] What do you think made them unanimously decide that you were the best fit amongst the group to lead Nvidia as its CEO?
  你认为是什么让他们一致决定你是最适合领导英伟达担任其首席执行官的人选？

[14:38] I think because they didn't want the job and they were right.
  我认为是因为他们不想做这份工作，而且他们是对的。

[14:47] They didn't want the job and and and um all three of us were engineers and and I think that's the answer.
  他们不想做这份工作，而且，而且，而且我们三个人都是工程师，我认为这就是答案。

[14:55] They didn't want the job. Um in retrospect, I I could have been smarter myself and and uh to be CEO is a is a lifetime of sacrifice.
  他们不想做这份工作。嗯，回顾过去，我本可以更聪明一些，而担任首席执行官是一生的牺牲。

[15:05] And most people think that it's about leading and being in command and being on top and uh you none of that is true.
  大多数人认为这关乎领导、指挥和处于顶端，而这些都不是真的。

[15:15] You're you're in service of the company. You're creating conditions for other people to do their life's work.
  你是在为公司服务。你正在为他人创造实现其毕生事业的条件。

[15:23] Uh you're you're inspiring through example. Most of the examples are making
  你正在通过榜样来激励。大多数榜样都在做出

[15:25] Most of the examples are making difficult decisions during very difficult decisions during very difficult times.
  大多数例子都是在非常艰难的决定中做出艰难的决定，在非常艰难的时期做出艰难的决定。

[15:28] It's mostly about sacrifice.
  这主要是关于牺牲。

[15:28] It's about strategy.
  这是关于策略。

[15:31] And strategy as you know is not choose not just about choosing what to do.
  而且正如你所知，策略不仅仅是选择做什么。

[15:36] It's about choosing what not to do which is sacrifice and and the and the determination the the conviction um the pain and suffering that goes along with with overcoming obstacles.
  这是关于选择不做什么，那就是牺牲，以及决心，以及信念，以及克服障碍所伴随的痛苦和折磨。

[15:49] That's all sacrifice.
  这一切都是牺牲。

[15:49] It's a you know being the CEO.
  你知道，作为首席执行官。

[15:55] I'm not trying to talk anybody out of the the profession.
  我不是想劝任何人放弃这个职业。

[15:59] um if you were to do it, it it is the greatest honor.
  嗯，如果你要去做，这是最大的荣誉。

[16:02] But I you you you have to realize that it's not about, you know, fame and glory.
  但我你你必须认识到，你知道，这与名望和荣耀无关。

[16:07] It's mostly about pain and suffering and and to a lot of that um you know, I I I attribute to me growing up.
  这主要是关于痛苦和折磨，以及很多方面，你知道，我将其归因于我的成长经历。

[16:18] I it wasn't an easy easy uh uh easy journey.
  我，这并非一段轻松的旅程。

[16:22] Uh you know, we came to we went to we went we came to America.
  你知道，我们来到，我们去了，我们来到美国。

[16:24] Went to America.
  来到美国。

[16:24] Um my parents
  嗯，我的父母

[16:27] America.
  美国。

[16:27] Went to America.
  去了美国。

[16:27] Um my parents wanted us to pursue the American dream.
  嗯，我的父母希望我们追求美国梦。

[16:29] wanted us to pursue the American dream.
  希望我们追求美国梦。

[16:29] Uh they didn't have very much.
  呃，他们没有多少钱。

[16:31] Uh they didn't have very much.
  呃，他们没有多少钱。

[16:31] They didn't they were they were uh quite modest.
  他们不，他们是，他们是呃相当朴实的。

[16:34] didn't they were they were uh quite modest.
  不，他们是，他们是呃相当朴实的。

[16:34] Um and uh uh moving to United States, you know, was quite difficult
  嗯，呃，呃，搬到美国，你知道，相当困难

[16:38] modest. Um and uh uh moving to United States, you know, was quite difficult
  朴实的。嗯，呃，呃，搬到美国，你知道，相当困难

[16:38] for us in 19 1973.
  对我们来说是在1973年。

[16:39] States, you know, was quite difficult for us in 19 1973.
  美国，你知道，对我们来说是在1973年相当困难。

[16:42] for us in 19 1973.
  对我们来说是在1973年。

[16:42] But somehow we found we we made our way
  但不知何故，我们找到了，我们走出了困境。

[16:45] But somehow we found we we made our way through it.
  但不知何故，我们找到了，我们走出了困境。

[16:45] And I think the the uh the the life the life of struggle, endeavor,
  我想，呃，生活的挣扎，努力，

[16:48] through it. And I think the the uh the the life the life of struggle, endeavor,
  走出了困境。我想，呃，生活的挣扎，努力，

[16:48] nothing for granted, having having to earn everything.
  不理所当然，必须努力赢得一切。

[16:53] nothing for granted, having having to earn everything.
  不理所当然，必须努力赢得一切。

[16:53] Uh that I think was good CEO training, you know, and so I I
  呃，我认为那是很好的CEO培训，你知道，所以我

[16:55] earn everything. Uh that I think was good CEO training, you know, and so I I
  赢得一切。呃，我认为那是很好的CEO培训，你知道，所以我

[16:59] good CEO training, you know, and so I I think the answer is that they were
  很好的CEO培训，你知道，所以我认为答案是他们

[17:02] think the answer is that they were smart.
  认为答案是他们很聪明。

[17:05] smart. They didn't want the job and they live very high quality lives now.
  聪明。他们不想要这份工作，他们现在过着非常高质量的生活。

[17:07] They didn't want the job and they live very high quality lives now.
  他们不想要这份工作，他们现在过着非常高质量的生活。

[17:07] And building upon what you said about
  并且在你刚才说的关于

[17:10] live very high quality lives now. And building upon what you said about
  过着非常高质量的生活。并且在你刚才说的关于

[17:10] struggle and strategy and personal development,
  挣扎、策略和个人发展，

[17:12] building upon what you said about struggle and strategy and personal
  并且在你刚才说的关于挣扎、策略和个人

[17:12] development, at a recent visit to
  发展，最近一次访问

[17:14] struggle and strategy and personal development, at a recent visit to
  挣扎、策略和个人发展，最近一次访问

[17:14] Stanford, you told an audience of
  斯坦福时，你告诉听众

[17:15] development, at a recent visit to Stanford, you told an audience of
  发展，最近一次访问斯坦福时，你告诉听众

[17:15] students, greatness comes from
  学生们，伟大源于

[17:17] Stanford, you told an audience of students, greatness comes from
  斯坦福，你告诉听众学生们，伟大源于

[17:17] character.
  品格。

[17:19] students, greatness comes from character.
  学生们，伟大源于品格。

[17:19] Character comes out of from people who have suffered in the
  品格来自于那些在

[17:21] character. Character comes out of from people who have suffered in the
  品格。品格来自于那些在

[17:21] beginning years of building Nvidia amongst many setbacks.
  建立英伟达的最初几年经历过苦难的人。

[17:23] people who have suffered in the beginning years of building Nvidia amongst many setbacks.
  那些在建立英伟达的最初几年经历过苦难的人。

[17:23] How did you keep
  你是如何保持

[17:24] beginning years of building Nvidia amongst many setbacks. How did you keep
  建立英伟达的最初几年经历过许多挫折。你是如何保持

[17:28] Amongst many setbacks, how did you keep going on? Cuz you've said that you had never fundraised before at such a high level.
  在经历了许多挫折之后，你是如何坚持下来的？因为你说过你以前从未进行过如此高水平的筹款。

[17:31] You'd never pitched such a brilliant idea.
  你从未提出过如此绝妙的想法。

[17:33] You would never had a business plan, but there was still this idea of strategy and duty and personal sacrifice.
  你从未有过商业计划，但仍然存在战略、责任和个人牺牲的想法。

[17:36] I mean, it's one of those it's it's one thing to to say it, but to live it is often really difficult.
  我的意思是，说起来容易做起来难，但要做到却常常非常困难。

[17:42] How did you continue to keep the faith in those really beginning stages?
  在那些真正的初期阶段，你是如何继续保持信念的？

[17:49] I say it to this day. We believe what we believe and so we reason about our perspective of the future.
  我至今仍是如此说。我们相信我们所相信的，因此我们对我们对未来的看法进行推理。

[17:53] And you reason about it from first principles that you learn um through education and you know fundamental first principles and uh you have to reason all the way back to either the first principles of computer science or the first principles of physics or you know whatever first principles you could you could hang on to and you try you try to reason as far back as you can.
  你从你通过教育学到的基本原理以及你知道的基本原理进行推理，并且你必须一直追溯到计算机科学的基本原理、物理学基本原理，或者你知道的任何你能抓住的基本原理，然后你尝试尽可能地追溯。

[18:17] Now once you do that and you come to the conclusion that all of the all of the environmental conditions and all the information that you have uh causes you
  现在，一旦你这样做了，并且你得出结论，所有的环境条件和你拥有的所有信息都导致你

[18:28] information that you have uh causes you to believe in what you believe then at that point you believe what you believe.
  你所拥有的信息会让你相信你所相信的，然后在那一刻，你相信你所相信的。

[18:32] And you got to decide am I going to be somebody who does something with it do something about it or do I just become one of those people that you know that say something like oh yeah I knew that.
  你必须决定，我是要做一个利用它做点什么的人，还是仅仅成为那些你知道的、会说“哦，是的，我知道”的人之一。

[18:44] Or I also knew that or I've said that before but you did nothing about it.
  或者我也知道，或者我以前说过，但你却什么也没做。

[18:49] And so I tend tend to be somebody that that would would reason about these things and believe it so deeply that frankly I could see it in my head.
  所以，我倾向于成为一个会思考这些事情的人，并且深信不疑，以至于坦率地说，我能在脑海中看到它。

[18:56] And once I could see it in my head, as far as I'm concerned, is might as well be real.
  一旦我能在脑海中看到它，就我而言，它就如同真实存在一样。

[19:02] Everything else is just details.
  其他一切都只是细节。

[19:05] And so you you manifest your belief as deeply as you can.
  所以，你尽你所能地显化你的信念。

[19:08] And after that is you're kind of hard to um dissu all of the assumptions that I made.
  在那之后，你很难……我所做的所有假设。

[19:17] all of the assumptions that I made.
  我所做的所有假设。

[19:19] all the assumptions that I used to reason about the strategy, I'm constantly re-evaluating.
  我用来推理策略的所有假设，我都在不断地重新评估。

[19:24] If any of that changes, if any of the the assumptions or any of
  如果其中任何一个发生变化，如果任何假设或任何

[19:29] If any of the assumptions or any of the principles that I used were flawed, um I'm quick quick to adapt, you know.
  如果我使用的任何假设或任何原则存在缺陷，我都会迅速适应。

[19:36] So, I'm constantly learning through failure and I'm quick to adapt.
  所以我一直在通过失败学习，并且我能迅速适应。

[19:42] And by adapting, you get to stay in the game.
  通过适应，你就能留在游戏中。

[19:44] And so, so I think it's it's not a complicated equation.
  所以，我认为这不是一个复杂的方程式。

[19:49] Oftentimes, oftentimes I think I think people have a hard time pivoting because they feel that their ego is somehow tied to some decision they have made or something that they said.
  很多时候，我认为人们在转变时会遇到困难，因为他们觉得自己的自尊心与他们做出的某个决定或说过的话有关。

[20:02] And that's really hard for CEOs.
  这对首席执行官来说真的很难。

[20:05] You know, for CEOs, you I I stand up and, you know, Nvidia today, we have 50,000 people and I declare something about the future.
  你知道，对于首席执行官来说，我站出来，你知道，今天在英伟达，我们有五万人，我宣布一些关于未来的事情。

[20:11] You know, I I I describe what I I think is a as a direction the company ought to go and and and you reason about why you do do so.
  你知道，我描述了我认为公司应该发展的方向，并解释了你为什么这样做。

[20:20] And you I do do that in front of 50,000 people.
  我会在五万人的面前这样做。

[20:22] And I'm doing it constantly.
  我一直在这样做。

[20:25] I'm saying it constantly because you can't just say something once.
  我一直在说，因为你不能只说一次。

[20:28] You got to say a thousand times.
  你必须说一千次。

[20:30] After you say something a thousand times, when you discover that in fact

[20:32] times, when you discover that in fact you were wrong,

[20:36] it's fairly difficult to pivot. But over

[20:40] it's fairly difficult to pivot. But over time I I've earned the right to change

[20:44] time I I've earned the right to change my mind

[20:46] my mind and I as soon as something is wrong and

[20:49] and I as soon as something is wrong and I feel that it's and and the way we

[20:51] I feel that it's and and the way we describe it is we have we have a phrase

[20:53] describe it is we have we have a phrase in the company and I I remind ourselves

[20:55] in the company and I I remind ourselves of that all the time you h we have to be

[20:58] of that all the time you h we have to be intellectually honest. you know, if I

[21:01] intellectually honest. you know, if I know that if I know that that um we

[21:04] know that if I know that that um we ought to we ought to change our mind and

[21:06] ought to we ought to change our mind and I don't um that's a that's a character

[21:09] I don't um that's a that's a character question, you know, it's an ego

[21:10] question, you know, it's an ego question. And you know, somehow I'm

[21:13] question. And you know, somehow I'm preventing myself from allowing

[21:14] preventing myself from allowing everybody else to to do the right

[21:16] everybody else to to do the right things. And so so I I I'm quick to get

[21:18] things. And so so I I I'm quick to get out of that. And over time, I discovered

[21:21] out of that. And over time, I discovered that that leaders are not meant to be

[21:25] that that leaders are not meant to be right. That's not our job. You know,

[21:28] right. That's not our job. You know, we're not our job isn't to be right. Our

[21:30] we're not our job isn't to be right. Our job is to help other people succeed.

[21:33] job is to help other people succeed. It's related, not the same. And so, to

[21:35] It's related, not the same. And so, to the extent that they always believe that

[21:37] the extent that they always believe that I want to help people succeed, then they

[21:39] I want to help people succeed, then they want to help me succeed. When I change

[21:41] want to help me succeed. When I change my mind, nobody thinks about it twice.

[21:44] my mind, nobody thinks about it twice. It's almost like they never forgot what

[21:45] It's almost like they never forgot what I said before. You know, this new thing

[21:47] I said before. You know, this new thing that I say was, "Yeah, he's right." And

[21:50] that I say was, "Yeah, he's right." And I change it a little bit. Yeah, he's

[21:51] I change it a little bit. Yeah, he's still right. And so, you know, people

[21:53] still right. And so, you know, people just want you to succeed. and and um you

[21:56] just want you to succeed. and and um you want to create the conditions where you

[21:57] want to create the conditions where you allow yourself to be vulnerable uh to

[22:00] allow yourself to be vulnerable uh to change your mind uh to be wrong uh but

[22:03] change your mind uh to be wrong uh but because people know that that um I

[22:05] because people know that that um I always have their best interest and I

[22:06] always have their best interest and I want the company to succeed and I want

[22:08] want the company to succeed and I want us to realize that future you know they

[22:10] us to realize that future you know they they'll they'll go along with it.

[22:12] they'll they'll go along with it. >> Wonderful. Let's talk about the GPU. So

[22:14] >> Wonderful. Let's talk about the GPU. So in 1999,

[22:16] in 1999, Nvidia creates the light bulb moment in

[22:18] Nvidia creates the light bulb moment in the tech industry when the company

[22:20] the tech industry when the company manages to offload graphic rendering

[22:22] manages to offload graphic rendering from the CPU to the GPU. In 2006, you

[22:25] from the CPU to the GPU. In 2006, you followed up with the CUDA microservices

[22:27] followed up with the CUDA microservices with over 400 libraries.

[22:30] with over 400 libraries. When you're creating and innovating at

[22:32] When you're creating and innovating at NVIDIA, how do you strike the balance

[22:34] NVIDIA, how do you strike the balance between building and developing on

[22:36] between building and developing on pre-existing technologies and deciding

[22:38] pre-existing technologies and deciding like you said when to pivot into a new

[22:39] like you said when to pivot into a new into a new lane? How do you keep that

[22:41] into a new lane? How do you keep that balance?

[22:43] balance? >> Uh it's really hard and the reason for

[22:45] >> Uh it's really hard and the reason for that is this. When you uh when you

[22:48] that is this. When you uh when you reinvent some for example, let's let's

[22:51] reinvent some for example, let's let's use a phone for example. When the iPhone

[22:53] use a phone for example. When the iPhone came along, it's still a phone and

[22:56] came along, it's still a phone and initially it didn't do much more than a

[22:57] initially it didn't do much more than a phone. Uh it had a browser and most

[23:00] phone. Uh it had a browser and most people didn't need a browser all the

[23:02] people didn't need a browser all the time and uh it had a map that was nice

[23:05] time and uh it had a map that was nice and it played some music that was nice,

[23:07] and it played some music that was nice, but it cost five times more. And so the

[23:11] but it cost five times more. And so the challenge is at the time of a new

[23:13] challenge is at the time of a new product category, the cost of the

[23:16] product category, the cost of the technology is much higher than the value

[23:18] technology is much higher than the value that it provides. And the same thing

[23:19] that it provides. And the same thing with the GPU. We invented the GPU

[23:22] with the GPU. We invented the GPU because we wanted computer graphics to

[23:25] because we wanted computer graphics to be a medium that was expressable through

[23:28] be a medium that was expressable through software. Prior to us, prior to the GPU,

[23:31] software. Prior to us, prior to the GPU, graphics accelerators were fixed

[23:33] graphics accelerators were fixed function. Grow shading with grow

[23:35] function. Grow shading with grow shading, function shading with function

[23:37] shading, function shading with function shading. The way it, you know, whatever

[23:38] shading. The way it, you know, whatever specular highlight you decide, that's

[23:41] specular highlight you decide, that's exactly the way it would render it.

[23:42] exactly the way it would render it. Well, we decided that that computer

[23:45] Well, we decided that that computer graphics should be a artistic

[23:47] graphics should be a artistic storytelling medium and the software uh

[23:51] storytelling medium and the software uh it should be programmable by software

[23:53] it should be programmable by software using what is called a programmable

[23:54] using what is called a programmable shader. So we invented this idea called

[23:57] shader. So we invented this idea called it a real-time programmable shader. And

[23:59] it a real-time programmable shader. And so that that led to a bunch of future

[24:04] so that that led to a bunch of future opportunities. But on the day we

[24:05] opportunities. But on the day we announced it, there were no

[24:06] announced it, there were no applications, but it cost twice as much.

[24:09] applications, but it cost twice as much. And so here's something that you have no

[24:11] And so here's something that you have no need for and as the current customer

[24:14] need for and as the current customer would pref prefer to have something that

[24:16] would pref prefer to have something that is quite frankly half the half the price

[24:18] is quite frankly half the half the price than to have something that has future

[24:20] than to have something that has future promise. And so,

[24:23] promise. And so, uh, there's there's no easy answer for

[24:25] uh, there's there's no easy answer for that aside from you've got to believe

[24:27] that aside from you've got to believe what you believe. And when you create

[24:29] what you believe. And when you create it, you know, the rest of it is about

[24:32] it, you know, the rest of it is about ecosystem development and uh, inspiring

[24:35] ecosystem development and uh, inspiring the the developers to create

[24:37] the the developers to create applications that realize the potential.

[24:39] applications that realize the potential. Um, a whole bunch of software, you know,

[24:42] Um, a whole bunch of software, you know, that kind of stuff. And that's all

[24:45] that kind of stuff. And that's all that's all kind of mechanical work. Um,

[24:47] that's all kind of mechanical work. Um, but in order to go do that, you have to

[24:49] but in order to go do that, you have to believe in a future. And then crossing

[24:52] believe in a future. And then crossing that crossing that chasm is extremely

[24:55] that crossing that chasm is extremely painful. It's life-threatening. Most

[24:58] painful. It's life-threatening. Most companies don't make it. Most companies

[25:01] companies don't make it. Most companies don't make it from classical phone to

[25:04] don't make it from classical phone to become a smartphone. Notice none of them

[25:06] become a smartphone. Notice none of them did.

[25:08] did. No feature. It's back then we call our

[25:10] No feature. It's back then we call our phones feature phones. I I don't know

[25:13] phones feature phones. I I don't know phones. And so they were called phones.

[25:16] phones. And so they were called phones. And then now they're called smart mo

[25:17] And then now they're called smart mo smart smartphones. No phone company made

[25:20] smart smartphones. No phone company made it through to the smartphone. Nvidia is

[25:23] it through to the smartphone. Nvidia is the only company that made it from one

[25:24] the only company that made it from one generation to another generation to

[25:26] generation to another generation to another generation to another

[25:27] another generation to another generation. And we constantly reinvented

[25:29] generation. And we constantly reinvented ourselves. We've now reinvented

[25:32] ourselves. We've now reinvented ourselves through six computing eras.

[25:35] ourselves through six computing eras. And I the mechanics of it is fairly

[25:39] And I the mechanics of it is fairly mundane. It's easy to explain. Um you

[25:42] mundane. It's easy to explain. Um you know I'll teach a course here one of

[25:43] know I'll teach a course here one of these days. Is it it's a five it's a

[25:46] these days. Is it it's a five it's a five-step program and it's not it's not

[25:48] five-step program and it's not it's not hard but the part that is hard is the

[25:52] hard but the part that is hard is the courage to do it because when you leap

[25:54] courage to do it because when you leap across to that next thing at the moment

[25:57] across to that next thing at the moment when you're in the middle of that canyon

[25:59] when you're in the middle of that canyon your your cost is incredibly high your

[26:02] your your cost is incredibly high your value is incredibly non-existent and

[26:05] value is incredibly non-existent and very very rarely do people cross to the

[26:07] very very rarely do people cross to the other side and just that's 100% courage

[26:10] other side and just that's 100% courage the ability to endure pain and suffering

[26:12] the ability to endure pain and suffering um the rest of it is you skills.

[26:15] um the rest of it is you skills. >> One of the beautiful things about

[26:16] >> One of the beautiful things about Nvidia's work is the way in which its

[26:19] Nvidia's work is the way in which its discoveries have reverberated across the

[26:21] discoveries have reverberated across the world and have spurred on lots of

[26:22] world and have spurred on lots of different technologies. This year,

[26:24] different technologies. This year, Bristol University unveiled the Ismbard

[26:26] Bristol University unveiled the Ismbard AI, the 11th fastest supercomputer in

[26:29] AI, the 11th fastest supercomputer in the world, powered by 5,448

[26:32] the world, powered by 5,448 GH200 Gracehopper superchips. Ismbard AI

[26:36] GH200 Gracehopper superchips. Ismbard AI is being lorded for being able to power

[26:38] is being lorded for being able to power cutting edge medical and sustainable

[26:40] cutting edge medical and sustainable research. How does it feel to see the

[26:44] research. How does it feel to see the work of the Gracehopper super chips be

[26:46] work of the Gracehopper super chips be extrapolated upon to build what is

[26:48] extrapolated upon to build what is essentially really crucial technology

[26:49] essentially really crucial technology that is shifting medical research at the

[26:51] that is shifting medical research at the highest levels?

[26:52] highest levels? >> Yeah. Um before is

[26:56] >> Yeah. Um before is so so that we get the record straight

[26:58] so so that we get the record straight the fastest supercomput in the UK built

[27:02] the fastest supercomput in the UK built um I built and it was called Cambridge

[27:05] um I built and it was called Cambridge 1.

[27:06] 1. >> Did you guys know that? Okay. I never

[27:09] >> Did you guys know that? Okay. I never got I was I I did it because I thought

[27:13] got I was I I did it because I thought um Nvidia was going to have a

[27:16] um Nvidia was going to have a headquarters here in the UK.

[27:19] headquarters here in the UK. We uh did you guys did you guys ever

[27:21] We uh did you guys did you guys ever hear about the story? I almost bought a

[27:24] hear about the story? I almost bought a a uh UK company. Uh it was blocked by

[27:28] a uh UK company. Uh it was blocked by the UK just to I

[27:33] I know breaks my heart to this day. Uh

[27:37] I know breaks my heart to this day. Uh so so anyways, we almost bought ARM. You

[27:40] so so anyways, we almost bought ARM. You guys know that, right? We almost bought

[27:41] guys know that, right? We almost bought ARM and uh I worked on it for a long

[27:43] ARM and uh I worked on it for a long time. I thought it would have been a

[27:44] time. I thought it would have been a great idea. I still think it would have

[27:46] great idea. I still think it would have been a great idea.

[27:48] been a great idea. I don't think it's too late. No, I'm

[27:50] I don't think it's too late. No, I'm just kidding.

[27:54] [laughter]

[27:55] [laughter] Anyways, ARM's a great company. Turned

[27:57] Anyways, ARM's a great company. Turned out turned out. Uh so so anyhow

[28:02] out turned out. Uh so so anyhow you know what what we build is the is

[28:05] you know what what we build is the is the most crucial instrument of knowledge

[28:08] the most crucial instrument of knowledge discovery and for the very first time I

[28:13] discovery and for the very first time I we we've built a computer that can

[28:15] we we've built a computer that can understand the meaning of the

[28:19] understand the meaning of the information that it is processing. It's

[28:21] information that it is processing. It's not just processing data. It understands

[28:24] not just processing data. It understands the meaning of the data it's processing.

[28:26] the meaning of the data it's processing. So for example, it's not processing

[28:28] So for example, it's not processing letters, it's processing words and it

[28:31] letters, it's processing words and it understands the words. Um it's not just

[28:34] understands the words. Um it's not just processing uh a whole bunch of numbers.

[28:36] processing uh a whole bunch of numbers. It understands that these numbers

[28:38] It understands that these numbers represent

[28:40] represent fluid flow. Um or that it it understands

[28:44] fluid flow. Um or that it it understands that this sequence of numbers actually

[28:46] that this sequence of numbers actually represents a protein uh or a small

[28:50] represents a protein uh or a small molecule chemical. Um and it understands

[28:54] molecule chemical. Um and it understands the meaning of it. its vocabulary, its

[28:57] the meaning of it. its vocabulary, its meaning. It understand its

[28:59] meaning. It understand its functionality.

[29:01] functionality. For example, you know, understand the

[29:03] For example, you know, understand the the semantics. It understands the

[29:06] the semantics. It understands the context that it's in and therefore uh it

[29:10] context that it's in and therefore uh it understands how we would react in that

[29:12] understands how we would react in that context. I've just used a bunch of words

[29:14] context. I've just used a bunch of words that when you apply to chatbt is very

[29:17] that when you apply to chatbt is very very obvious. But

[29:20] very obvious. But remember the protein and those English

[29:23] remember the protein and those English words to the computer is the same.

[29:25] words to the computer is the same. to the to the to the extent that we can

[29:27] to the to the to the extent that we can under we can help the com computer

[29:29] under we can help the com computer understand the nature of proteins, the

[29:32] understand the nature of proteins, the meaning of proteins, the structure of

[29:34] meaning of proteins, the structure of it, its dynamics. Um it should be able

[29:37] it, its dynamics. Um it should be able to understand how it interacts with

[29:39] to understand how it interacts with other chemicals and other proteins in

[29:41] other chemicals and other proteins in various context. We should be able to

[29:43] various context. We should be able to talk to our protein in the future. What

[29:45] talk to our protein in the future. What are you? How would you behave? Are you

[29:48] are you? How would you behave? Are you soluble? How do you how do you behave in

[29:51] soluble? How do you how do you behave in temperature, high temperature? How do

[29:53] temperature, high temperature? How do you behave in different different types

[29:55] you behave in different different types of liquids in different context? Um, how

[29:58] of liquids in different context? Um, how would you react to this particular

[29:59] would you react to this particular chemical? How would you bind to it? You

[30:01] chemical? How would you bind to it? You could literally talk to a protein in the

[30:03] could literally talk to a protein in the future. Now, what I just described

[30:05] future. Now, what I just described sounds a little ridiculous right now,

[30:08] sounds a little ridiculous right now, but as you know, you could talk to an

[30:10] but as you know, you could talk to an image today. Just go up to an image and

[30:13] image today. Just go up to an image and you know what are you? I'm a picture of

[30:14] you know what are you? I'm a picture of a cat. What kind of cat are you? Can you

[30:17] a cat. What kind of cat are you? Can you move? and and all of a sudden the video

[30:20] move? and and all of a sudden the video the the image turns into a video. And so

[30:23] the the image turns into a video. And so uh notice notice this is the world that

[30:26] uh notice notice this is the world that we're in now. Not only were we

[30:28] we're in now. Not only were we processing data, we understand the data

[30:30] processing data, we understand the data that we're processing and the

[30:33] that we're processing and the implications of that in the field of of

[30:35] implications of that in the field of of course drug discovery or you know

[30:37] course drug discovery or you know material sciences or any other any form

[30:39] material sciences or any other any form of science is really quite profound. So,

[30:42] of science is really quite profound. So, you know, we we've created um what TI,

[30:46] you know, we we've created um what TI, you know, imagined, right? Artificial

[30:48] you know, imagined, right? Artificial intelligence.

[30:50] intelligence. >> One of the key um visions at Nvidia is

[30:54] >> One of the key um visions at Nvidia is about being able to turn biology into

[30:55] about being able to turn biology into engineering. And Nvidia is um alongside

[30:58] engineering. And Nvidia is um alongside other AI companies doing

[31:00] other AI companies doing >> How hard could it be?

[31:01] >> How hard could it be? >> Turns out super hard. [laughter]

[31:04] >> Turns out super hard. [laughter] >> We've been at it for 10 years. I'm still

[31:05] >> We've been at it for 10 years. I'm still working on it. Nvidia alongside other um

[31:08] working on it. Nvidia alongside other um AI companies are doing a lot of really

[31:10] AI companies are doing a lot of really good work at studying um molecular

[31:12] good work at studying um molecular biology particularly as it pertains to

[31:14] biology particularly as it pertains to things like amino acids um and trying to

[31:17] things like amino acids um and trying to not only keep the work that you do um

[31:20] not only keep the work that you do um limited to computers but also to do

[31:21] limited to computers but also to do cutting edge medical research. At what

[31:23] cutting edge medical research. At what point did you realize that Nvidia

[31:25] point did you realize that Nvidia possessed the capability to switch into

[31:28] possessed the capability to switch into helping with the with the healthcare

[31:30] helping with the with the healthcare industry? And what are your um views on

[31:33] industry? And what are your um views on the progress that's being made in

[31:34] the progress that's being made in molecular um biology research using AI?

[31:38] molecular um biology research using AI? 40 years ago, something really amazing

[31:41] 40 years ago, something really amazing happened. uh 43 years ago, I was the

[31:46] happened. uh 43 years ago, I was the first generation of engineers that

[31:49] first generation of engineers that designed a computer inside a computer.

[31:54] designed a computer inside a computer. Before then, all of the engineers did it

[31:59] Before then, all of the engineers did it practically by hand, prototyped

[32:02] practically by hand, prototyped our systems into existence.

[32:05] our systems into existence. My generation was the generation of what

[32:07] My generation was the generation of what is called computer AED design.

[32:10] is called computer AED design. Well,

[32:11] Well, 40 years later,

[32:14] 40 years later, 100% of what we build exists completely

[32:17] 100% of what we build exists completely as a digital twin inside a computer

[32:20] as a digital twin inside a computer before we make it. To the point where

[32:24] before we make it. To the point where in the old days, we would we would tape

[32:27] in the old days, we would we would tape out a chip. We go fab a chip and we hope

[32:29] out a chip. We go fab a chip and we hope it works. Today, these chips are a

[32:33] it works. Today, these chips are a billion times more complicated. Tens of

[32:35] billion times more complicated. Tens of thousands of engineers work on it at at

[32:37] thousands of engineers work on it at at one time. We send it to the fab and when

[32:40] one time. We send it to the fab and when it comes back I know it works. And the

[32:43] it comes back I know it works. And the reason why I know it works because it's

[32:45] reason why I know it works because it's been living inside another computer for

[32:47] been living inside another computer for a long time. Well, that's 40 years

[32:50] a long time. Well, that's 40 years later. I believe the time is about now

[32:53] later. I believe the time is about now where we have the ability to represent

[32:57] where we have the ability to represent the various hierarchies of biology so

[33:01] the various hierarchies of biology so that we can have computer aided drug

[33:04] that we can have computer aided drug design.

[33:06] design. the idea of drug discovery. I the the

[33:09] the idea of drug discovery. I the the even the word is wrong. Drug discovery.

[33:12] even the word is wrong. Drug discovery. It's like, "Hey honey, uh, I'm going to

[33:15] It's like, "Hey honey, uh, I'm going to go look for mushrooms now."

[33:18] go look for mushrooms now." And some days you come back empty-handed

[33:21] And some days you come back empty-handed most of the time. Some some days you

[33:23] most of the time. Some some days you come back with some amazing truffles.

[33:24] come back with some amazing truffles. Look what I discovered today. I found it

[33:27] Look what I discovered today. I found it today. Now, of course, drug discovery is

[33:30] today. Now, of course, drug discovery is a little bit like that. It's much more

[33:32] a little bit like that. It's much more science and less engineering. If you

[33:35] science and less engineering. If you look at the way we do computers design

[33:37] look at the way we do computers design design today it's 100% engineering and

[33:40] design today it's 100% engineering and so we have every single year we get

[33:42] so we have every single year we get better and better and better at it drug

[33:44] better and better and better at it drug discovery is very very hard every single

[33:46] discovery is very very hard every single drug for every single disease almost

[33:48] drug for every single disease almost feels like a brand new discovery like a

[33:50] feels like a brand new discovery like a brand new journey we've we have to

[33:53] brand new journey we've we have to create the computer tools and the the

[33:56] create the computer tools and the the the information representation

[33:59] the information representation transistors and logical gates and you

[34:02] transistors and logical gates and you know large functions and large chips We

[34:05] know large functions and large chips We have different languages and different

[34:06] have different languages and different tools to represent the different

[34:08] tools to represent the different hierarchies of electronic design. We

[34:10] hierarchies of electronic design. We need to discover the hierarchy of

[34:13] need to discover the hierarchy of information representation for

[34:14] information representation for biological design. Does that make sense?

[34:17] biological design. Does that make sense? And so once we have that, once we have

[34:19] And so once we have that, once we have tools that understand that

[34:20] tools that understand that representation and we can manipulate it,

[34:23] representation and we can manipulate it, then the world becomes drug design. And

[34:26] then the world becomes drug design. And every single year that goes by, we

[34:28] every single year that goes by, we become better and better and better at

[34:30] become better and better and better at it. We stand on the top, you know, on

[34:32] it. We stand on the top, you know, on the shoulders of giants. And one of

[34:34] the shoulders of giants. And one of these days, you know, who knows? And so

[34:36] these days, you know, who knows? And so that was that was I just reason I

[34:39] that was that was I just reason I literally just did just now what I did

[34:43] literally just did just now what I did 10 years ago at NVIDIA. And I explained

[34:46] 10 years ago at NVIDIA. And I explained this to a bunch of computer scientists

[34:48] this to a bunch of computer scientists and and then everybody goes, "Okay,

[34:50] and and then everybody goes, "Okay, well, let's go give it a try. How hard

[34:52] well, let's go give it a try. How hard can it be?" And so it's been 10 years

[34:54] can it be?" And so it's been 10 years and I got to tell you, it's super hard,

[34:58] and I got to tell you, it's super hard, but it's okay. But I still believe it. I

[35:00] but it's okay. But I still believe it. I I have I I have 100% certainty that we

[35:03] I have I I have 100% certainty that we will discover the representation of

[35:06] will discover the representation of biology and the tools necessary to go

[35:08] biology and the tools necessary to go design it. And the reason for that is

[35:09] design it. And the reason for that is this. Although we although we are we're

[35:12] this. Although we although we are we're all a little different, we're largely

[35:13] all a little different, we're largely the same. So there's obviously structure

[35:15] the same. So there's obviously structure in biology and obviously we it's it's a

[35:18] in biology and obviously we it's it's a repeatable thing.

[35:20] repeatable thing. Not only is Nvidia crossing over in

[35:22] Not only is Nvidia crossing over in terms of its legacies into different

[35:24] terms of its legacies into different fields, it's

[35:24] fields, it's >> by the way, I just wrote the entire

[35:26] >> by the way, I just wrote the entire business plan of Nvidia going into drug

[35:27] business plan of Nvidia going into drug discovery and this business is now for

[35:30] discovery and this business is now for us billions of dollars

[35:33] us billions of dollars and I just wrote the entire business

[35:34] and I just wrote the entire business plan just now. No number, no no

[35:37] plan just now. No number, no no spreadsheets were used,

[35:40] spreadsheets were used, no numbers were applied,

[35:46] no calculus was necessary.

[35:50] no calculus was necessary. Just like that. All simple reasoning. I

[35:53] Just like that. All simple reasoning. I did the same with autonomous vehicles. I

[35:55] did the same with autonomous vehicles. I did the same with robotics. I did

[35:57] did the same with robotics. I did exactly the same for artificial

[35:59] exactly the same for artificial intelligence. Just like that. We sat

[36:01] intelligence. Just like that. We sat there and reasoned about it step by step

[36:02] there and reasoned about it step by step by step. Um, usually we have the benefit

[36:05] by step. Um, usually we have the benefit of a whiteboard. It makes it easier to

[36:07] of a whiteboard. It makes it easier to to to communicate. After that, somebody

[36:10] to to communicate. After that, somebody takes a picture of it, the company goes

[36:12] takes a picture of it, the company goes off, and 10 years later, in the case of

[36:14] off, and 10 years later, in the case of artificial intelligence, 15 years later,

[36:16] artificial intelligence, 15 years later, here we are. Well, let's talk a little

[36:18] here we are. Well, let's talk a little bit more about artificial in

[36:20] bit more about artificial in intelligence. So, not only is the work

[36:22] intelligence. So, not only is the work that Nvidia is doing crossing over

[36:23] that Nvidia is doing crossing over different fields of science, but it's

[36:24] different fields of science, but it's crossing across borders. In September

[36:26] crossing across borders. In September 2025, Nvidia announced a2 billion pound

[36:29] 2025, Nvidia announced a2 billion pound investment in the UK AI ecosystem

[36:31] investment in the UK AI ecosystem alongside firms like Baldin, Phoenix

[36:34] alongside firms like Baldin, Phoenix Cord, Excel.

[36:35] Cord, Excel. >> I did it right in front of the prime

[36:37] >> I did it right in front of the prime minister,

[36:37] minister, >> Kioa.

[36:38] >> Kioa. >> Yes.

[36:39] >> Yes. >> I mean, Kioa champions.

[36:41] >> I mean, Kioa champions. >> I'll invest in your company. I'll invest

[36:42] >> I'll invest in your company. I'll invest in your company. Their $2 billion went

[36:44] in your company. Their $2 billion went really fast.

[36:47] really fast. Not only did Kstarma champion it, it was

[36:49] Not only did Kstarma champion it, it was also recognized by a bunch of other

[36:50] also recognized by a bunch of other business leaders who thanked Nvidia for

[36:52] business leaders who thanked Nvidia for its bold leadership. How does it feel to

[36:55] its bold leadership. How does it feel to be able to support burgeoning um

[36:57] be able to support burgeoning um leadership in AI and creativity and

[36:59] leadership in AI and creativity and innovation particularly in the UK and

[37:01] innovation particularly in the UK and also across the world?

[37:03] also across the world? >> Nvidia succeeds when other companies

[37:05] >> Nvidia succeeds when other companies succeed. Remember, we're a platform

[37:07] succeed. Remember, we're a platform company. We're a tools company. We're a

[37:09] company. We're a tools company. We're a platform computing platform company.

[37:11] platform computing platform company. Nobody wakes up in the morning, okay?

[37:14] Nobody wakes up in the morning, okay? Nobody in your family wakes up in the

[37:15] Nobody in your family wakes up in the morning said this morning and go guess

[37:17] morning said this morning and go guess what we need? We need to buy a computing

[37:19] what we need? We need to buy a computing platform.

[37:22] platform. Nobody says that. And so the only reason

[37:25] Nobody says that. And so the only reason why we succeed is because companies and

[37:28] why we succeed is because companies and developers who use our platform create

[37:31] developers who use our platform create something incredible.

[37:33] something incredible. That's how we succeed. And so, so in a

[37:36] That's how we succeed. And so, so in a lot of ways, I've got one of the the

[37:38] lot of ways, I've got one of the the really the one of the greatest jobs

[37:40] really the one of the greatest jobs ever, which is to have a company whose

[37:43] ever, which is to have a company whose mission is to desire other people to

[37:47] mission is to desire other people to succeed.

[37:48] succeed. And this is a phrase that is always

[37:50] And this is a phrase that is always inside the company. I'm always reminding

[37:53] inside the company. I'm always reminding everybody in the company that that we

[37:56] everybody in the company that that we want and we need and we desire other

[37:58] want and we need and we desire other people to succeed. And it's through

[37:59] people to succeed. And it's through their success we get to ride their their

[38:02] their success we get to ride their their coattails. And so that's that's it, you

[38:05] coattails. And so that's that's it, you know, and so when I when I saw that the

[38:07] know, and so when I when I saw that the UK um really is a has a Goldilock

[38:11] UK um really is a has a Goldilock moment, you know, this is a this is you

[38:13] moment, you know, this is a this is you you have incredible researchers here.

[38:15] you have incredible researchers here. This is the home of computer science. If

[38:17] This is the home of computer science. If if if there's a country that represents

[38:20] if if there's a country that represents computer science, if that's got to be

[38:21] computer science, if that's got to be UK, you've got you've got uh a rich uh

[38:25] UK, you've got you've got uh a rich uh ecosystem of entrepreneurs.

[38:28] ecosystem of entrepreneurs. And the only thing that you don't have,

[38:29] And the only thing that you don't have, as it turns out, is the instrument of

[38:33] as it turns out, is the instrument of knowledge, the instrument of science,

[38:34] knowledge, the instrument of science, the instrument necessary to do that. And

[38:36] the instrument necessary to do that. And I I know how to do that. And so I felt

[38:38] I I know how to do that. And so I felt that that um with our involvement, we

[38:42] that that um with our involvement, we might be able to create that spark and

[38:45] might be able to create that spark and um and then maybe UK becomes, you know,

[38:48] um and then maybe UK becomes, you know, one of Nvidia's headquarters after all,

[38:50] one of Nvidia's headquarters after all, you know.

[38:51] you know. >> [laughter]

[38:51] >> [laughter] >> So, but but is this it is uh this you

[38:54] >> So, but but is this it is uh this you you really have an extraordinary moment.

[38:56] you really have an extraordinary moment. But for some reason,

[38:59] But for some reason, I do feel that that

[39:02] I do feel that that the the the culture of of the UK,

[39:07] the the the culture of of the UK, you're so modest.

[39:09] you're so modest. You know, you know, in in Silicon

[39:12] You know, you know, in in Silicon Valley,

[39:14] Valley, what however great we are, we describe

[39:17] what however great we are, we describe it as a hundred times more than that.

[39:19] it as a hundred times more than that. [laughter]

[39:21] And however, but here in the UK, however

[39:23] And however, but here in the UK, however great you are, you're onetenth that. And

[39:26] great you are, you're onetenth that. And so, so, um, I'm here to tell you, you

[39:29] so, so, um, I'm here to tell you, you you're pretty extraordinary. And, and

[39:31] you're pretty extraordinary. And, and look at the list of of, uh, of the

[39:35] look at the list of of, uh, of the inventors and the scientists and

[39:38] inventors and the scientists and discoverers that have come before you

[39:40] discoverers that have come before you and that have that have inspired us and

[39:44] and that have that have inspired us and uh, has uh, has made it possible for us

[39:46] uh, has uh, has made it possible for us to do what what we do. And then just

[39:49] to do what what we do. And then just remember

[39:50] remember the industrial revolution was invented

[39:52] the industrial revolution was invented here, was discover, it was created here

[39:55] here, was discover, it was created here and there's a new industrial revolution

[39:57] and there's a new industrial revolution now. So take advantage of it. It's got

[39:58] now. So take advantage of it. It's got all the it's got everything that that

[40:00] all the it's got everything that that you're you're part of and everything

[40:02] you're you're part of and everything that you're great at. And so it this is

[40:04] that you're great at. And so it this is your moment. You've got to you've got to

[40:05] your moment. You've got to you've got to capitalize on it.

[40:07] capitalize on it. >> And you've spoken earlier on in this

[40:08] >> And you've spoken earlier on in this interview about what makes the UK such a

[40:10] interview about what makes the UK such a promising um field for AI development.

[40:13] promising um field for AI development. Um let's talk a little bit about South

[40:15] Um let's talk a little bit about South Korea. So Nvidia has just committed over

[40:17] Korea. So Nvidia has just committed over $260,000 of its most advanced chips to

[40:20] $260,000 of its most advanced chips to South Korea in what is being called a

[40:22] South Korea in what is being called a $10 billion mega deal in AI. What would

[40:25] $10 billion mega deal in AI. What would you say is what makes South Korea an

[40:28] you say is what makes South Korea an ideal partner for this level of

[40:29] ideal partner for this level of investment? And what are some of the

[40:30] investment? And what are some of the promising projects that you hope that

[40:32] promising projects that you hope that Nvidia might be able to achieve in the

[40:34] Nvidia might be able to achieve in the in the area region?

[40:36] in the area region? >> South Korea has a uh is one of the major

[40:39] >> South Korea has a uh is one of the major regions major countries in the world

[40:41] regions major countries in the world that has industrialized deeply. Uh South

[40:44] that has industrialized deeply. Uh South Korea, as you know, manufactures chips.

[40:47] Korea, as you know, manufactures chips. They manufacture ships, chips, ships,

[40:51] They manufacture ships, chips, ships, cars, electronics. Uh this is a country

[40:54] cars, electronics. Uh this is a country that is incredibly good at

[40:56] that is incredibly good at industrialization.

[40:58] industrialization. In in just a few short decades, um they

[41:01] In in just a few short decades, um they reinvented the whole nation and became a

[41:04] reinvented the whole nation and became a global industrial giant. uh

[41:08] global industrial giant. uh South Korea also has has a unique

[41:11] South Korea also has has a unique capability of doing software and so

[41:15] capability of doing software and so the technology ecosystem is able to deal

[41:17] the technology ecosystem is able to deal with um hard hardware and manufacturing

[41:21] with um hard hardware and manufacturing on the one hand but software and the art

[41:23] on the one hand but software and the art artistry of of developing software on

[41:25] artistry of of developing software on the other hand and so I I think the the

[41:28] the other hand and so I I think the the it's a real opportunity to see them uh

[41:30] it's a real opportunity to see them uh take advantage of artificial

[41:32] take advantage of artificial intelligence and reinvent how

[41:34] intelligence and reinvent how industrialization works.

[41:36] industrialization works. You know this is the next era of AI.

[41:39] You know this is the next era of AI. Currently AI understands language and

[41:42] Currently AI understands language and numbers and images and videos. But in

[41:44] numbers and images and videos. But in the future in the near future we need AI

[41:47] the future in the near future we need AI to understand the laws of physics,

[41:48] to understand the laws of physics, understand causality and object

[41:50] understand causality and object permanence and you know inertia and you

[41:54] permanence and you know inertia and you know that needs to understand gravity

[41:55] know that needs to understand gravity and things like that, friction and

[41:57] and things like that, friction and things like that. So, so I think the the

[42:00] things like that. So, so I think the the um when when that happens and when we

[42:03] um when when that happens and when we create embodied AI, AI that could

[42:06] create embodied AI, AI that could operate, manipulate and operate in the

[42:08] operate, manipulate and operate in the world, uh then then we're going to

[42:10] world, uh then then we're going to reinvent reinvent how industry works and

[42:14] reinvent reinvent how industry works and and as you know the world has a ve quite

[42:17] and as you know the world has a ve quite a severe shortage of labor. The world's

[42:21] a severe shortage of labor. The world's GDP would be much higher today if not

[42:24] GDP would be much higher today if not for the fact that we had a shortage of

[42:26] for the fact that we had a shortage of labor. we have shortage of labor in just

[42:27] labor. we have shortage of labor in just about every single company, every single

[42:29] about every single company, every single field. And so although we talk about

[42:32] field. And so although we talk about about jobs being lost, it is very likely

[42:35] about jobs being lost, it is very likely that all jobs will be transformed and

[42:38] that all jobs will be transformed and it's very likely that AI is going to

[42:40] it's very likely that AI is going to drive a product productivity gains and

[42:43] drive a product productivity gains and and GDP growth like we've never seen

[42:44] and GDP growth like we've never seen before. And that's that's certainly the

[42:46] before. And that's that's certainly the hope and I and I believe in it.

[42:48] hope and I and I believe in it. >> Just the last question for you before we

[42:50] >> Just the last question for you before we hand over to the floor in the live

[42:51] hand over to the floor in the live audience Q&A. Many of the young people

[42:54] audience Q&A. Many of the young people sat here today like you have a passion

[42:57] sat here today like you have a passion for science. Cambridge is a university

[43:00] for science. Cambridge is a university much like your alma mart Stanford that

[43:01] much like your alma mart Stanford that has produced 126 Nobel Prize laurates

[43:04] has produced 126 Nobel Prize laurates with an exciting AI development scheme.

[43:07] with an exciting AI development scheme. The city has produced a culture of many

[43:08] The city has produced a culture of many eager to turn their ideas into

[43:10] eager to turn their ideas into businesses and to top firms as you have

[43:12] businesses and to top firms as you have successfully done. What is one piece of

[43:14] successfully done. What is one piece of advice you would share to many of the

[43:16] advice you would share to many of the young people who've got an idea, who've

[43:18] young people who've got an idea, who've got a bullet point, who've got a bit of

[43:20] got a bullet point, who've got a bit of code that they're excited about. How

[43:22] code that they're excited about. How would you encourage them today?

[43:25] would you encourage them today? >> There are some skills. There are some

[43:26] >> There are some skills. There are some skills in determining whether an idea is

[43:29] skills in determining whether an idea is a good one or all. If you want to do

[43:33] a good one or all. If you want to do something, just go do it. Um, and so

[43:36] something, just go do it. Um, and so long as you're you're you're

[43:38] long as you're you're you're intellectually honest, you're

[43:40] intellectually honest, you're contextually alert, um, you're

[43:43] contextually alert, um, you're environmentally alert, understanding

[43:45] environmentally alert, understanding what's going on around the world and and

[43:46] what's going on around the world and and your industry. and uh and you're willing

[43:50] your industry. and uh and you're willing to pivot, then then that's all fine.

[43:53] to pivot, then then that's all fine. Okay? But nonetheless, there are skills

[43:55] Okay? But nonetheless, there are skills in determining uh upfront whether an

[43:58] in determining uh upfront whether an idea is worth pursuing or not.

[44:02] idea is worth pursuing or not. But I I would say that the singular best

[44:05] But I I would say that the singular best piece of advice for entrepreneurs, and

[44:08] piece of advice for entrepreneurs, and it's served me incredibly,

[44:12] it's served me incredibly, is to have this

[44:14] is to have this childlike

[44:16] childlike view of the future, which is optimistic.

[44:20] view of the future, which is optimistic. You're curious,

[44:22] You're curious, and you ask yourself, you know, how hard

[44:25] and you ask yourself, you know, how hard could it be? And don't let anybody tell

[44:29] could it be? And don't let anybody tell you that in fact it's really hard.

[44:33] you that in fact it's really hard. You're going to find out for yourself

[44:35] You're going to find out for yourself that in fact it's really hard. Um but

[44:37] that in fact it's really hard. Um but you have plenty of time to do that. You

[44:40] you have plenty of time to do that. You have plenty t plenty of time to do that.

[44:42] have plenty t plenty of time to do that. And so people have asked me would I

[44:44] And so people have asked me would I start the company again if I knew

[44:45] start the company again if I knew everything today back then? The answer

[44:48] everything today back then? The answer is of course not. It's too scary. It's

[44:51] is of course not. It's too scary. It's too painful. Too much sacrifice. How

[44:54] too painful. Too much sacrifice. How could you take all of the, you know, all

[44:56] could you take all of the, you know, all of the feelings and everything that I've

[44:58] of the feelings and everything that I've learned, all bottled up at this moment,

[45:00] learned, all bottled up at this moment, transport it back into a 29y old body

[45:03] transport it back into a 29y old body and go, "Hey, with that, get at it.

[45:05] and go, "Hey, with that, get at it. You're never going to do it. You're

[45:07] You're never going to do it. You're never going to do it." And so, don't

[45:09] never going to do it." And so, don't don't be don't be afraid of ignorance

[45:11] don't be don't be afraid of ignorance and don't be afraid of being naive and

[45:13] and don't be afraid of being naive and don't be afraid of, you know, all all of

[45:15] don't be afraid of, you know, all all of those things. You're going to learn

[45:16] those things. You're going to learn everything you need to learn along the

[45:18] everything you need to learn along the way. And just, you know, if you if

[45:20] way. And just, you know, if you if you're really passionate about

[45:21] you're really passionate about something, go do it. you know, just tell

[45:23] something, go do it. you know, just tell yourself how hard can it be.

[45:25] yourself how hard can it be. >> Thank you so much, Jensen. And on that

[45:27] >> Thank you so much, Jensen. And on that note, we are going to open the questions

[45:29] note, we are going to open the questions up to the floor. So, if you do have a

[45:30] up to the floor. So, if you do have a question, please stick your hand up nice

[45:32] question, please stick your hand up nice and high. And then when called upon to

[45:34] and high. And then when called upon to my word and then when called upon to do

[45:36] my word and then when called upon to do that, we will get an em to bring you a

[45:39] that, we will get an em to bring you a microphone. Um, let's start right here

[45:42] microphone. Um, let's start right here at the front with the gentleman who's

[45:44] at the front with the gentleman who's got his hand up. And then afterwards, we

[45:46] got his hand up. And then afterwards, we will go upstairs to the man in the dark

[45:48] will go upstairs to the man in the dark blue jumper. So, we'll start here and

[45:49] blue jumper. So, we'll start here and then go upstairs.

[45:51] then go upstairs. Jensen, your your point about uh

[45:53] Jensen, your your point about uh cooperating with each other and not

[45:54] cooperating with each other and not competing really resonated. Um but here

[45:57] competing really resonated. Um but here at the university, we spend a lot of our

[45:59] at the university, we spend a lot of our time training students for exams uh

[46:01] time training students for exams uh marking them, ranking them and so on.

[46:03] marking them, ranking them and so on. What would your advice be to Cambridge

[46:05] What would your advice be to Cambridge University? Should this 800 and

[46:06] University? Should this 800 and something year old institution abolish

[46:08] something year old institution abolish exams um and have a more cooperative way

[46:10] exams um and have a more cooperative way of building up our student base so that

[46:12] of building up our student base so that we can uh adapt to the future which is

[46:15] we can uh adapt to the future which is coming with AI?

[46:17] coming with AI? >> I I guess I guess if if there's no

[46:21] >> I I guess I guess if if there's no um ranking and rating. Uh it would be

[46:24] um ranking and rating. Uh it would be hard for you to know whether the

[46:26] hard for you to know whether the problems and the curriculum is even hard

[46:29] problems and the curriculum is even hard enough that it's sufficiently

[46:31] enough that it's sufficiently challenging to to push the students. I

[46:34] challenging to to push the students. I get that. Um a long time ago when I

[46:36] get that. Um a long time ago when I first started the company, there was a

[46:37] first started the company, there was a concept in management called ranking and

[46:39] concept in management called ranking and rating. It was created by another

[46:41] rating. It was created by another Silicon Valley company and and because

[46:44] Silicon Valley company and and because because that company was was famed for

[46:48] because that company was was famed for being incredibly good at management um I

[46:51] being incredibly good at management um I took a lot of the the learnings that

[46:54] took a lot of the the learnings that were being being talked about and I

[46:56] were being being talked about and I applied it at NVIDIA. I can tell you

[46:58] applied it at NVIDIA. I can tell you today I've dropped 100% of them. There's

[47:01] today I've dropped 100% of them. There's no benefit in ranking people.

[47:04] no benefit in ranking people. Uh there are ideas called uh even back

[47:06] Uh there are ideas called uh even back then uh 360 peer review. There's no

[47:10] then uh 360 peer review. There's no benefit in that.

[47:12] benefit in that. There's no benefit in asking people to

[47:14] There's no benefit in asking people to to rate and review you. Um I I found

[47:18] to rate and review you. Um I I found that most of those techniques don't

[47:20] that most of those techniques don't really work. Uh but in the context of

[47:24] really work. Uh but in the context of academia,

[47:27] frankly with AI,

[47:32] frankly with AI, I think

[47:34] I think the idea of information being somehow

[47:39] the idea of information being somehow uh

[47:42] uh unaccessible so that it's even

[47:45] unaccessible so that it's even worthwhile to compete anymore to get the

[47:47] worthwhile to compete anymore to get the right answer. I think those days are

[47:50] right answer. I think those days are kind of gone. Um

[47:53] kind of gone. Um I I do wonder I do wonder how I I wonder

[47:57] I I do wonder I do wonder how I I wonder if the way you teach courses are going

[48:00] if the way you teach courses are going to be very similar to the way we renew

[48:04] to be very similar to the way we renew ourselves at a corporate level cons

[48:07] ourselves at a corporate level cons continuously learning continuously

[48:09] continuously learning continuously renewing ourselves. I'm I'm quite famed

[48:11] renewing ourselves. I'm I'm quite famed for saying I don't fire anybody.

[48:14] for saying I don't fire anybody. And and the reason for that is very is

[48:16] And and the reason for that is very is is is grounded in in in wisdom.

[48:21] is is grounded in in in wisdom. We want to encourage

[48:23] We want to encourage our employees to innovate, which which

[48:26] our employees to innovate, which which requires them to take risk, which

[48:28] requires them to take risk, which requires them to be vulnerable. They're

[48:30] requires them to be vulnerable. They're going to do something that puts

[48:32] going to do something that puts themselves out and they'll likely fail.

[48:34] themselves out and they'll likely fail. And if they're taking sufficient risk

[48:36] And if they're taking sufficient risk that is proportional to the type of

[48:37] that is proportional to the type of company that we want to be, then they

[48:40] company that we want to be, then they should fail often.

[48:42] should fail often. But if if we let go of people that we

[48:46] But if if we let go of people that we rank to the bottom, the bottom 5% rule,

[48:49] rank to the bottom, the bottom 5% rule, you might have heard of that management

[48:50] you might have heard of that management technique as well. Every single year,

[48:52] technique as well. Every single year, remove the bottom 5%. Because if you do

[48:55] remove the bottom 5%. Because if you do that, then what's left in the soup, you

[48:58] that, then what's left in the soup, you know, is consummate. Okay? But that's

[49:01] know, is consummate. Okay? But that's complete nonsense because we love stew.

[49:05] complete nonsense because we love stew. We want we want the mess of a stew, not

[49:08] We want we want the mess of a stew, not the pure purity of a consmate. And so in

[49:11] the pure purity of a consmate. And so in fact, we don't want that 5% to be lost

[49:14] fact, we don't want that 5% to be lost because they they happen to be precisely

[49:16] because they they happen to be precisely the people who had just taken a risk

[49:19] the people who had just taken a risk failed

[49:20] failed and they learned something from it and

[49:23] and they learned something from it and they might tomorrow be the outlier that

[49:26] they might tomorrow be the outlier that reinvented something to save your

[49:27] reinvented something to save your company.

[49:29] company. And so I think the the old cultures, the

[49:33] And so I think the the old cultures, the old cultures, the old systems deserve

[49:36] old cultures, the old systems deserve some re reevaluation and certainly

[49:38] some re reevaluation and certainly because intelligence is now a commodity.

[49:41] because intelligence is now a commodity. We got to say that kind of out loud.

[49:44] We got to say that kind of out loud. Intelligence is about to be a commodity.

[49:46] Intelligence is about to be a commodity. Then what's left? And what's left is a

[49:48] Then what's left? And what's left is a lot of the things that we were just

[49:49] lot of the things that we were just talking about. It's courage. It's

[49:52] talking about. It's courage. It's intellectual honesty. It's the absence

[49:55] intellectual honesty. It's the absence of ego. the ability to be vulnerable in

[49:58] of ego. the ability to be vulnerable in public so that you can create. You know,

[50:02] public so that you can create. You know, as you know, artists and inventors and

[50:04] as you know, artists and inventors and creators, they're humiliated, laughed at

[50:08] creators, they're humiliated, laughed at often because what they do isn't always

[50:10] often because what they do isn't always perfect. And so, you need to have that

[50:13] perfect. And so, you need to have that that humility, the vulnerability, and

[50:16] that humility, the vulnerability, and yet the courage to put yourself out

[50:18] yet the courage to put yourself out there and be laughed at.

[50:21] there and be laughed at. And so so um some of those things I I

[50:24] And so so um some of those things I I think are going to become more

[50:25] think are going to become more important.

[50:26] important. >> Can we take a question

[50:29] >> Can we take a question um to in the greenest jumper right

[50:32] um to in the greenest jumper right there?

[50:35] there? >> Yeah, indeed.

[50:36] >> Yeah, indeed. >> Hi Jensen.

[50:37] >> Hi Jensen. >> Hi.

[50:37] >> Hi. >> Um thank you for coming to Cambridge and

[50:39] >> Um thank you for coming to Cambridge and congratulations again on your

[50:40] congratulations again on your fellowship. Thank you.

[50:41] fellowship. Thank you. >> My name is Josh. I'm in Trinity College.

[50:44] >> My name is Josh. I'm in Trinity College. >> Um you mentioned earlier that you think

[50:46] >> Um you mentioned earlier that you think that the future of jobs are going to

[50:49] that the future of jobs are going to change. um as a result of AI and a lot

[50:53] change. um as a result of AI and a lot of young people, aspiring lawyers,

[50:55] of young people, aspiring lawyers, accountants, consultants, bankers, they

[50:57] accountants, consultants, bankers, they don't really share that optimism. I

[50:59] don't really share that optimism. I think they're more pessimistic about it.

[51:01] think they're more pessimistic about it. So, I was hoping you could just maybe

[51:03] So, I was hoping you could just maybe shed some more light onto what you think

[51:06] shed some more light onto what you think that job transformation will look like

[51:07] that job transformation will look like and what the benefits will be that will

[51:09] and what the benefits will be that will come from it.

[51:10] come from it. >> Okay. Um,

[51:13] >> Okay. Um, one is is um

[51:16] one is is um uh

[51:18] uh optimism in humanity. One part of the

[51:21] optimism in humanity. One part of the answer, one part of the answer is uh

[51:25] answer, one part of the answer is uh pragmatism and then one part of the

[51:27] pragmatism and then one part of the answer is evidence. Okay, I'll start

[51:29] answer is evidence. Okay, I'll start with evidence.

[51:31] with evidence. I radiology was going to be the first

[51:34] I radiology was going to be the first industry to be completely destroyed by

[51:37] industry to be completely destroyed by artificial intelligence. And in fact,

[51:40] artificial intelligence. And in fact, almost every radiologist today uses AI.

[51:43] almost every radiologist today uses AI. And yet the number of radiologists being

[51:46] And yet the number of radiologists being hired has increased.

[51:49] hired has increased. Why is that? Because they can now do

[51:52] Why is that? Because they can now do more things. There are so many cases

[51:55] more things. There are so many cases that went undiagnosed using radiology

[51:59] that went undiagnosed using radiology and there's so many cases that were not

[52:01] and there's so many cases that were not deeply diagnosed because there's just a

[52:04] deeply diagnosed because there's just a bottleneck of how many radiologists

[52:06] bottleneck of how many radiologists could study all those all those images

[52:09] could study all those all those images and so now the basic stuff is done very

[52:11] and so now the basic stuff is done very very quickly. Now the number of cases

[52:14] very quickly. Now the number of cases that they're getting, the depth of the

[52:16] that they're getting, the depth of the cases that they're getting um is much

[52:19] cases that they're getting um is much much more much more more interesting as

[52:21] much more much more more interesting as a result more more people getting hired.

[52:24] a result more more people getting hired. Uh the uh pragmat prag pragmatic part of

[52:28] Uh the uh pragmat prag pragmatic part of it. Uh let's say that you have a job and

[52:32] it. Uh let's say that you have a job and uh within your job part of it is doing

[52:36] uh within your job part of it is doing some task that

[52:40] some task that however you describe that task that task

[52:42] however you describe that task that task became infinitely fast. Okay. And and it

[52:46] became infinitely fast. Okay. And and it used to take you a week to do something.

[52:49] used to take you a week to do something. Now it takes you a second to do it.

[52:51] Now it takes you a second to do it. Well, my question to you is based on

[52:55] Well, my question to you is based on that, what is more likely to happen?

[52:57] that, what is more likely to happen? That you now have

[53:00] That you now have more time to enjoy coffee

[53:03] more time to enjoy coffee or you just became busier? It is likely

[53:06] or you just became busier? It is likely you just became busier. It's just

[53:08] you just became busier. It's just another example of the radiology. The

[53:10] another example of the radiology. The reason for that is because that thing

[53:12] reason for that is because that thing that you were doing is causing you to

[53:14] that you were doing is causing you to not be able to do all the other stuff

[53:15] not be able to do all the other stuff later be pass that pipeline, you know,

[53:18] later be pass that pipeline, you know, past that workflow or past that task.

[53:22] past that workflow or past that task. Now that task is done infinitely fast,

[53:24] Now that task is done infinitely fast, all of a sudden the answer came back to

[53:27] all of a sudden the answer came back to you. You're now the critical path again.

[53:30] you. You're now the critical path again. It's no different than if I were to

[53:32] It's no different than if I were to issue an instruction to my my my group

[53:34] issue an instruction to my my my group of people that I'm working with and

[53:36] of people that I'm working with and they're supposed to go off and do a

[53:37] they're supposed to go off and do a research or do some analysis or

[53:39] research or do some analysis or simulation and bring that answer back to

[53:41] simulation and bring that answer back to me so that I can make the next decision.

[53:44] me so that I can make the next decision. Today, it'll take a day or a week for

[53:46] Today, it'll take a day or a week for them to do that. Meanwhile, I'm doing

[53:48] them to do that. Meanwhile, I'm doing something else. But if it takes a second

[53:50] something else. But if it takes a second for them to come back, I'm back in the

[53:51] for them to come back, I'm back in the critical path again. I'm busier than

[53:54] critical path again. I'm busier than ever. Now, the reason for that is

[53:56] ever. Now, the reason for that is because I have so many ideas. We have so

[53:58] because I have so many ideas. We have so many ideas to go pursue that when the

[54:00] many ideas to go pursue that when the tasks become super fast, it turns out we

[54:03] tasks become super fast, it turns out we could do more things. We become busier.

[54:05] could do more things. We become busier. There's some evidence that smartphones

[54:07] There's some evidence that smartphones made us busier, not less busy. There's

[54:10] made us busier, not less busy. There's some evidence that computers made us

[54:11] some evidence that computers made us busier, not less busy. And so, it's

[54:14] busier, not less busy. And so, it's because we're we are now increasingly

[54:16] because we're we are now increasingly the critical path and because we have so

[54:18] the critical path and because we have so many ideas, we can pursue more things.

[54:20] many ideas, we can pursue more things. Now, uh, just hope in humanity, we

[54:25] Now, uh, just hope in humanity, we always find a way to discover new things

[54:27] always find a way to discover new things to do, new things to be busy. And, and

[54:30] to do, new things to be busy. And, and I'm hoping that that as a result,

[54:34] I'm hoping that that as a result, um, we'll go off and work on the

[54:35] um, we'll go off and work on the problems that are that are the most

[54:38] problems that are that are the most meaningful, the most valuable in all of

[54:41] meaningful, the most valuable in all of our work, which is the ones that are

[54:42] our work, which is the ones that are poorly defined. The poorly defined work

[54:46] poorly defined. The poorly defined work is the most valuable of all work,

[54:49] is the most valuable of all work, discovery work, creativity work, the

[54:52] discovery work, creativity work, the original creativity work. I'm not

[54:54] original creativity work. I'm not talking about change the cat, you know,

[54:56] talking about change the cat, you know, to a cat with shag r shag rag, you know,

[55:00] to a cat with shag r shag rag, you know, shag shag rugs, you know, I mean is I'm

[55:02] shag shag rugs, you know, I mean is I'm talking about really create creating

[55:04] talking about really create creating something out of the box. And and so the

[55:07] something out of the box. And and so the the ability for us to use AI to solve

[55:10] the ability for us to use AI to solve problems that are fairly easy to des

[55:13] problems that are fairly easy to des well not it doesn't have to be even easy

[55:15] well not it doesn't have to be even easy that this describable um so that we

[55:17] that this describable um so that we could go and work on things that are

[55:18] could go and work on things that are very difficult to describe I think is is

[55:20] very difficult to describe I think is is really quite powerful.

[55:23] really quite powerful. >> Thank you. Shall we take the question

[55:24] >> Thank you. Shall we take the question from the member in the gallery?

[55:26] from the member in the gallery? >> Hi um hi Jason my name is

[55:28] >> Hi um hi Jason my name is >> Every job will change. You will not lose

[55:30] >> Every job will change. You will not lose your job to AI. You will lose your job

[55:32] your job to AI. You will lose your job to someone who uses AI.

[55:35] to someone who uses AI. Thanks for that. Uh, hi Jason. My name

[55:37] Thanks for that. Uh, hi Jason. My name is Lily. I'm a EMBA student at the J

[55:40] is Lily. I'm a EMBA student at the J Business School. I also run my own

[55:42] Business School. I also run my own venture builder called Founder X. Um,

[55:45] venture builder called Founder X. Um, it's very interesting that you just

[55:46] it's very interesting that you just mentioned the UK's position almost

[55:48] mentioned the UK's position almost having this Goldilock moment because we

[55:51] having this Goldilock moment because we commonly believe the AI game is actually

[55:53] commonly believe the AI game is actually happening in China or happening in the

[55:55] happening in China or happening in the US. So what do you think the UK or the

[55:58] US. So what do you think the UK or the UK startup community can do more to

[56:01] UK startup community can do more to leverage its current position and

[56:03] leverage its current position and benefiting more from the AI game?

[56:05] benefiting more from the AI game? >> Regulate less

[56:10] [applause]

[56:15] and I I mean that I mean that deeply and

[56:18] and I I mean that I mean that deeply and and uh uh genuinely for the best

[56:21] and uh uh genuinely for the best interest of the UK. you I know I know

[56:25] interest of the UK. you I know I know that I know that regulators um and

[56:29] that I know that regulators um and because so much so much of regulators

[56:31] because so much so much of regulators are are lawyers which is good um but

[56:34] are are lawyers which is good um but because they want to protect us they

[56:35] because they want to protect us they want to pro protect society um they

[56:38] want to pro protect society um they could regulate too early

[56:42] could regulate too early especially on technology like this where

[56:44] especially on technology like this where it's hard to predict the future you

[56:45] it's hard to predict the future you could watch sci-fi movies but that's not

[56:47] could watch sci-fi movies but that's not the future that's called the sci-fi

[56:49] the future that's called the sci-fi movie and and to use the the science

[56:52] movie and and to use the the science fiction movies projected into reality um

[56:55] fiction movies projected into reality um and and um uh through some words of some

[56:58] and and um uh through some words of some people uh cause cause social panic uh to

[57:02] people uh cause cause social panic uh to the point that you cause you you

[57:04] the point that you cause you you overregulate um you're stifling the UK's

[57:07] overregulate um you're stifling the UK's ability to innovate. Uh the fact of the

[57:10] ability to innovate. Uh the fact of the matter is China as you know is a under

[57:13] matter is China as you know is a under technology is underregulated

[57:16] technology is underregulated and the reason for that is because the

[57:17] and the reason for that is because the leaders in China mostly are engineers.

[57:21] leaders in China mostly are engineers. The leaders in United States are mo

[57:23] The leaders in United States are mo mostly lawyers. And so you can kind of

[57:26] mostly lawyers. And so you can kind of tell what's going on. Uh and the the

[57:28] tell what's going on. Uh and the the rate of of technology evolution and

[57:31] rate of of technology evolution and industry evolution in China is just

[57:33] industry evolution in China is just running incredibly fast because they

[57:35] running incredibly fast because they regulate late. They wait until the

[57:37] regulate late. They wait until the problems show up and they regulate the

[57:40] problems show up and they regulate the problems. Uh they solve they create

[57:43] problems. Uh they solve they create regulations the way engineers solve

[57:44] regulations the way engineers solve solve problems. You know, let's not

[57:46] solve problems. You know, let's not dream it up. Let's let's observe the

[57:49] dream it up. Let's let's observe the problem. Let's understand the root cause

[57:51] problem. Let's understand the root cause of the problem and then solve the

[57:52] of the problem and then solve the problem. And um and so anyhow, I would

[57:56] problem. And um and so anyhow, I would say under regulate less.

[57:59] say under regulate less. >> Jensen, thank you very much for this

[58:01] >> Jensen, thank you very much for this evening. I know that we have so many

[58:02] evening. I know that we have so many questions, but in the interest of time,

[58:04] questions, but in the interest of time, I think it's fitting for us to conclude

[58:05] I think it's fitting for us to conclude there. But I want to say a huge thank

[58:07] there. But I want to say a huge thank you from us um from all of the members

[58:10] you from us um from all of the members gathered here today and on behalf of the

[58:12] gathered here today and on behalf of the selection committee. Congratulations

[58:13] selection committee. Congratulations again on being the twins.

[58:15] again on being the twins. >> Thank you very very much. [applause]

[58:17] >> Thank you very very much. [applause] >> Thank you.