Winning the AI Race Part 3: Jensen Huang, Lisa Su, James Litinsky, Chase Lochmiller
发布时间 2025-07-23 22:34:29 来源
Guys, this is one of the most amazing entrepreneurs that you're going to meet Jim Littinski, this founder and CEO of MP Materials. Thanks. Good to be here. Hey, how are you? Let me set this up. Jim was a hedge fund guy running a pretty successful hedge fund. And he ended up basically investing in something called Molly Corp, which went out of business. Yep. And you did this incredible thing, which is you said, you know what, screw this. You essentially shuttered the fund, took over the company, and fast forward many years later, you are the largest and only, I think, supplier and refiner of rare earth materials and maker of magnets inside the United States. We're 100% of the American industry.
大家好，介绍一下这位你们将遇到的最棒的企业家之一，吉姆·利廷斯基，他是MP Materials的创始人兼CEO。谢谢，很高兴来到这里。嘿，你好吗？让我来简单介绍一下背景。吉姆曾是一位对冲基金经理，他经营的对冲基金相当成功。他投资了一家名为Molly Corp的公司，但该公司最终倒闭了。是的。令人惊讶的是，吉姆做了件了不起的事情，他决定放手不管，把基金关闭了，然后接管了这家公司。许多年过去后，他现在成为美国国内唯一的稀土材料供应商和提炼商，同时也是磁体的制造商。我们占据了美国这一行业的100%市场份额。
100% of the American industry. We just did two really incredible things actually in the last couple of weeks. One was you announced an enormous public private partnership with the DOD. 400 million dollars, et cetera. And then the second is you announced a really big deal with Apple. Yes. Okay, so yeah, I take you to set back. Talked to why rare earths matter. Tell us about the supply chain for AI. Tell us why you're doing this. Rare earth magnets are really the feedstock to physical AI. Robots, drones, everything we're talking about today, the biggest industry in the world to come. Essentially, electrified motion requires rare earth magnets.
翻译并简化成中文：
美国工业的100%。我们在过去几周内确实完成了两件令人难以置信的事情。一个是你宣布与国防部(DOD)建立了一个巨大的公私合作伙伴关系，涉及4亿美元等。另一个是你宣布与苹果签署了一项很大的协议。是的。那么，我来解释一下为什么稀土重要。告诉我们AI的供应链。告诉我们你为什么这么做。稀土磁铁实际上是物理AI的原料。机器人、无人机和我们今天谈论的所有东西，都是未来最大的产业。基本上，电动运动需要稀土磁铁。
So you mentioned the predecessor when bankrupt, there was a feeling when I took over this site with my co-founder and this goes back to 2015. Where is the site? Oh, it's in Mount Pass, California. So if you'll be familiar, if you take a 45-minute drive from the Las Vegas strip, just over the border in California is this site, you actually can see it from the road. And it's actually really the best rare earth or body in the world. The thing about rare earths is that when you mine them, you also have to refine them and it's really expensive and difficult to refine them. It's really a specialty chemical process. It's really a, think of it as a multi-billion dollar refinery that you need to have just to separate them.
所以你提到这个项目的前身破产了。2015年，我和我的联合创始人接手这个项目时，有一种特别的感觉。这个项目在哪里呢？哦，它位于加利福尼亚的芒特帕斯。如果你熟悉这个地方，从拉斯维加斯大道开车大约45分钟，就可以到达加州边界的这个项目，你实际上可以从路边看到它。实际上，这是世界上最好的稀土矿之一。采掘稀土时，不仅要开采，还必须精炼。但精炼过程非常昂贵且困难，这是一种专业的化学工艺。你可以把它想象成一个耗资数十亿美元的精炼设施，专门用于稀土的分离。
And then once you separate them, you need to turn them into metal and then a magnet. And so there's a multiple layers of this stream to get this supply chain. And of course, you could have all the rare earths in the world, but if you don't make the magnets, you're sending it to China. Or you could have all of the magnetic capability in the world. But if you don't have the rare earths, you're relying on China. And our vision from day one going back to, we originally bought these assets at a bankruptcy. Officially, it was a two-year battle, took it out in 2017. And there was a perception that we just couldn't compete against China.
首先，你需要将它们分离出来，然后把它们转化为金属和磁铁。因此，这个供应链需要经过多个步骤来实现。当然，即使你拥有世界上所有的稀土，但如果你不生产磁铁，你的稀土就会被送到中国。或者你拥有世界上所有的磁铁生产能力，但如果你没有稀土，你也要依赖中国。我们从第一天起的愿景是回到最初，当时我们通过破产程序收购了这些资产。这个过程实际上持续了两年，并于2017年完成。当时，人们普遍认为我们无法与中国竞争。
And what we discovered actually is we could. It's a world-class site, but we had to reorganize the process flow. And then we had to make investments to move downstream. So over the last eight years, we invested about a billion dollars. And, as you know, we took the company public in 2020. We built out the refining capability. And then about four years ago, we announced we were going to build a magnetic factory in Texas. We built that factory. We have GM as a foundational customer. We're now producing auto-grade magnets to GM SPAC. And we'll be ramping up sales to GM at the end of this year in magnets.
我们发现实际上我们是可以做到的。这个地方是一个世界级的场地，但我们需要重新组织流程。然后，我们必须投入资金以推进下游业务。在过去的八年里，我们投资了大约十亿美元。正如你所知，我们在2020年将公司上市。我们建立了炼制能力。大约四年前，我们宣布将在德克萨斯州建造一个磁材工厂。我们建好了这个工厂，并且通用汽车成为了我们的基础客户。目前，我们正在生产符合通用汽车标准的汽车级别磁材。今年年底，我们将扩大对通用汽车的磁材销售。
And then, Shem off you referenced a couple. It's been a busy few months for us. We announced a pretty transformative public-private partnership with the Department of Defense. DoD is, there's really three pillars to this deal. DoD is becoming our largest economic investor. As well as they're going to provide a price floor for our commodities so that the Chinese, sort of Chinese mercantilism, we can get into that, won't take the price of the commodity below the cost of production. And then, as a result of the DoD investment, we're going to accelerate the build out of the magnetic supply chain.
然后，Shem，你提到了一些事情。过去几个月对我们来说非常忙碌。我们宣布了与国防部建立一个相当具有变革性的公私合作伙伴关系。这项合作主要有三个支柱。国防部将成为我们的最大经济投资者，同时，他们也会为我们的商品提供价格底限，以防止类似中国重商主义的行为将商品价格压低到生产成本以下。此外，由于国防部的投资，我们将加速磁性供应链的建设。
So we're expanding our facility in Texas for Apple. I'll talk about that in a second. But we're then going to build a 10X facility to 10X our capacity with DoD as our 100% off-take partner customer and business partner. Because we'll be splitting profits 50-50 with DoD. And we're going to just translate this. It's not a handout from the government. They didn't give you $400 million. They invested in your company. They have warrants. They have equity.
我们正在为苹果公司扩建我们在德克萨斯的设施。我待会儿会详细谈这个。接下来，我们会建立一个能使我们产能扩大10倍的新设施，与国防部（DoD）作为我们百分之百的承购合作伙伴和业务伙伴合作。我们将与国防部平分利润，各占50%。需要澄清的是，这并不是政府的馈赠。他们没有白给我们4亿美元，他们是投资了我们的公司。他们持有认股权证和股权。
Yeah. So they invested, they both are an owner. They also are an upside participant in our commodity to the extent that the prices take off. And then, there are also 100% off-take customer. We have a guaranteed level of profits to want to build out this facility. But above a certain threshold, there are 50-50 economic participants. They mean you. There's really you the taxpayer. Yeah. So this is a, and maybe I'll say something wild here. This is a true win-win. Obviously great for MP shareholders. Great for a national security and commercial national security standpoint. Because we're going to have enough magnets to provide, you know, real certainty in the supply chain for the physical AI revolution and other industries.
好的。那么，他们进行了投资，所以他们都是这家公司的所有者。同时，他们也可以从我们的商品中获利，特别是在价格上涨时。他们也是我们产品的100%买家。我们有一个保障的盈利水平来建设这个工厂。然而，超过一定的门槛后，他们就会成为经济利益的五五分享者。这里的“他们”其实指的是你，你们纳税人。是的，所以这真的是一个双赢的局面。对MP公司股东来说当然是很好的消息。从国家安全和商业安全的角度来看，这也是极好的。因为我们将拥有足够的磁铁，确保为实体的人工智能革命和其他行业提供稳定的供应链。
But it would not surprise me if when we five years from now hopefully we'll do this conference. And, Jamal, you'll say to me, Jim, you know, I remember that deal that was the first of its kind that you did with DoD. And the government made money on you. The taxpayer made money on doing this. And I'll say, yeah, I actually think that that's going to be the outcome. Because there's sort of an element of mutually assured economic destruction. If the Chinese believe that America has national champions too, then there's no point in subsidizing the rest of the world. And so I think you can start to see prices normalize for some of these things and free up our ability to invest and expand.
但我不会感到惊讶，如果五年后我们再次举办这个会议，希望那时你能对我说：“贾马尔，你知道吗，我记得那个你和国防部（DoD）做的开创性交易。政府和纳税人在这笔交易中都赚到了钱。”我会说，是的，我确实认为这将是结果。因为这有一种互相保证经济毁灭的因素。如果中国相信美国也有国家级的领先企业，那么再去补贴其他国家也就没有意义了。所以我认为，你可以开始看到这些东西的价格趋于正常，从而释放我们投资和扩张的能力。
Why go to the government to invest in the government? Why go to the government for this investment as opposed to the private markets? Well, because it's that issue, this is sort of one of those, you know, obviously you have to go back to World War II or the railroad boom where you really need government. And credit, I mean, this administration did something, you know, totally unique that- Which piece of it put the government- Merkantlesm, straight up Merkantlesm, because the Chinese will sell magnets for below the cost of raw materials. And so every time there's somebody who makes progress, they can put them out of business overnight. And so it's difficult to want to make the investment.
为什么要去政府投资于政府？为什么不选择私人市场进行投资而要选择政府？这主要是因为这是一个特殊的问题，就像在二战或者铁路大发展时期，你真的需要政府的参与。就信用而言，这届政府确实做了一些非常独特的事情——其中的一部分是推行了重商主义，直接的重商主义。这是因为中国可以以低于原材料成本的价格出售磁铁。所以每当有谁取得进展，他们就能立刻让其破产。因此，很难有人愿意去做这样的投资。
And so frankly, with the Department of Defense, the scale that they wanted us to build on the time frame that they wanted us to build, there was no way we were going to make that commitment- We're fiduciaries, right? We're shareholders. There's no way we're going to make that commitment without certainty that we would not be destroyed by Merkantlesm and that we would have a customer for the magnets.
坦率地说，对于国防部来说，他们希望我们在所要求的时间框架内进行如此大规模的建设，我们根本无法做出这样的承诺。因为我们是受托人，对股东负责。如果没有确定性保障我们不会被重商主义摧毁，并且能确保有磁铁的客户，我们是绝对不会做出这种承诺的。
How big of an industry is physical AI? Meaning we see the robots, we're told the robots are coming, we're told there's going to be billions of them. Are they actually being deployed at the scale and at the pace that we've been told? Well, I think that that is a question for- There's much smarter guests on this, for the rest I'll give a plug, the rest of the day. Obviously you have the best of the best providing that feed suck. I will say that I think one of the big drivers of our deal was the- As we've seen in Ukraine in the Middle East, the future of warfare is physical AI, right? Robots and drones.
物理人工智能究竟是多大的一个行业？我们看到机器人，并被告知机器人即将普及，并且数量要达到数十亿。那么，它们真的以我们被告知的规模和速度在部署吗？我认为这个问题可以留给那些更有见识的嘉宾来回答，今天剩下的时间里，这样的嘉宾会很多。显然，你将得到最好的信息。我只能说，我认为我们合作的一个重要推动因素是——就像我们在乌克兰和中东看到的那样，未来的战争就是物理人工智能，也就是机器人和无人机的应用。
And I think irrespective of the scale that robotics is ultimately going to be, and certainly the commercial business will be bigger than the defense needs. But just from a defense standpoint, this is a really important supply chain that we must have. We can't be funding cutting edge drone and robotics companies and then say, okay, but we're going to buy those magnets from China.
我认为，无论机器人技术最终发展到什么规模，商业领域的应用肯定会比国防需求更加广泛。但仅从国防的角度来看，这是一个非常重要的供应链，我们必须拥有。我们不能一边资助先进的无人机和机器人公司，一边又说，好吧，但是我们要从中国购买那些磁体。
Do we have talent capacity or do we have a talent shortage? Secretary Bergen gave me a stat which was pretty shocking to me that we only graduate 200 people a year in the United States in mining, which is orders of magnitude different than China. What do we need to do to be competitive to build the industry here? It's a great question. Jason, I think about this question a lot. One day is- What's that? Dave. Oh my god, Dave. No, no, talking. I'm a huge fan of a pot and I can't embarrass myself. This is the old- It's the old- I know. You know, I'm a fan of the pot since they won't have totally-
我们的人才是否充足，还是面临人才短缺的问题？伯根秘书给我提到一个让我震惊的数据：美国每年只培养出 200 名矿业专业毕业生，而这与中国的情况有天壤之别。我们需要做些什么才能在这个行业中具有竞争力呢？这是个很好的问题。杰森，我经常思考这个问题。有一天……怎么了？戴夫。天哪，戴夫。不，不，继续说。我是某个节目的超级粉丝，我可不能在这里出丑。这是那个老-我知道。自从不会完全不展示以来，我就是那个节目的粉丝……
There's only one correction on it. I know my messing with you. Was this intentional? So- Huge fan of the pot. Yeah, he's sharing the pot. Who are you guys? I'm not the AI's arc. Go ahead. Yeah. So we have 850 employees today at MP. We're going to hire when we include what we're building out for Apple, coupled with what we're going to build with DOD. We're going to need a couple thousand more people easily not to mention the construction chops.
只有一个更正。我知道我在跟你开玩笑。这是故意的吗？所以，我是这个项目的超级粉丝。是的，他在分享这个项目。你们是谁啊？我不是AI的核心。继续说吧。是这样的，我们现在在MP公司有850名员工。我们计划招聘更多员工，特别是当我们考虑到为苹果和国防部（DOD）所建设的项目时，我们很容易就需要多几千人，更不用说建设方面的专长了。
So this is a key existential question for all of us as we build out this is where we're going to get the talent. I think what we have found, you know, at Mountain Pass and we hire at all electricians, maintenance, you know, operators is you get people in, you train them. And then obviously you give people a career. And so we've been training a lot of people and it's a little bit more painstaking, but there's absolutely talent out there. People are hungry to do it. Why do you think it's been so hard to establish that idea like meaning you find it straight forward to find good, hardworking people to get into these jobs. But the same the thought is always that wow these jobs are not desirable, but they really are desirable by many people.
这是我们所有人在发展过程中面临的关键生存问题：我们将从哪里获得人才。我认为我们在Mountain Pass的经验是，无论是电工、维修工还是操作员，我们都会招聘，然后对他们进行培训。显然，这也为他们提供了职业发展的机会。所以，我们培训了很多人，虽然这个过程比较费心，但绝对有优秀的人才存在。很多人都渴望从事这些工作。你为什么认为建立这样的观念如此困难？也就是说，你觉得找到努力工作的优秀人才来进入这些岗位其实很简单，但人们总是认为这些工作不受欢迎，而实际上有很多人非常想要这样的职位。
Yeah, absolutely. I mean, you know, our median wage is now pushing a hundred thousand dollars a year. And there's, you know, relative to some of the opportunity set. These are great. These are great jobs. And by the way, what's that? What's the starting salary? So it really depends on the job function because. You know, there's, there's, I mean, I think the easiest way to think about it is you can, you can certainly as an operator make close to a hundred thousand dollars a year with us. Because by the way, everybody's an owner. We have an owner operator culture. Everyone got stock when we went public in 2020. Somebody coming out of high school, they can make 40, 50, 60K or more.
是的，当然。我们的平均工资现在已经接近每年十万美元。相对于一些其他的工作机会而言，这些是很好的职位。顺便问一下，起薪是多少？其实这取决于具体的工作，因为，嗯，我觉得最简单的解释是，如果你是操作人员，完全可以在我们这里每年赚到接近十万美元。顺便说一句，我们每个人都是股东。我们有一个股东运营的企业文化。我们在2020年上市时，每个人都获得了股票。即使是刚从高中毕业的人，也可以赚到四万、五万、六万美元或者更多。
Yeah, or depends. Are you, if we can't find enough electricians, we can't find enough maintenance workers. A maintenance worker can an electrician they can make six figures today. Tell us, you said earlier that you suspect five years from now, we're going to look back at this deal with the DOD was a blueprint. Yeah. Give us other areas of either physical AI or software AI or other markets where you think these public private partnerships are really necessary to embellish US supremacy.
好的，或者说要看情况。如果我们找不到足够的电工，就找不到足够的维修工。如今，一个维修工也可以和一个电工一样赚到六位数的年薪。您之前提到，您怀疑五年后，我们会回顾与国防部（DOD）的这个协议，把它视作一个蓝图。是的。请告诉我们，您认为在哪些领域，无论是物理人工智能还是软件人工智能，或者其他市场，这些公共和私人合作伙伴关系是非常必要的，以增强美国的领先地位。
Yeah. There are some major categories, obviously, we've all heard about shipbuilding and advanced pharmaceutical ingredients. I mean, I think, I think those are important ones. And then there are a number of sort of niche areas like industrial diamonds that are important for quantum computing. And some of these things that you never would have thought of where there, it's a vertical where there might not be a market large enough to need five players. But a good public private partnership can just solve that problem. And then there's some other verticals and critical minerals.
好的。有一些主要类别，显然我们都听说过造船和高级药物成分。我认为这些是重要的领域。另外，还有一些你可能想不到的小众领域，比如用于量子计算的工业钻石。这些领域的市场可能不够大，不需要五家企业来竞争，但良好的公私合作可以解决这个问题。此外，还有一些其他的垂直领域和关键矿物。
It was straightforward for you to find the right person within the Trump administration that said, of course, this is obvious. Let's sit down and hash this out like that. Well, and I think that's our particular deal was led by DOD. And so I have to say that the Pentagon leadership is extraordinary. And this was a mandate though directly from the president to solve this problem. And so again, they deserve a lot of credit for being bold here.
在特朗普政府中找到一个说“这显然是对的，让我们坐下来好好解决”的合适人选对你来说相对容易。而且，我认为我们的特别协议是由国防部主导的。所以，我必须说五角大楼的领导层非常出色。这项任务是总统直接下达的，旨在解决这个问题。因此，他们在这一过程中表现出的大胆值得称赞。
And to be clear, because this story is another, our process, this was, I've never worked so hard on my life. I mean, this was like a true aggressive private equity style investment and negotiation. The transaction documents are public. You can work at that. So, yeah. That's the thing. They're tough. Yeah, this was as tough as it gets tougher than, you know, think of any, you know, blue chip private equity. Or, or, or distress lender type negotiation.
为了说明白一点，因为这个故事是另一个版本，我们的过程是前所未有的，我这辈子从来没有这么努力工作过。我的意思是，这就像一个真正激烈的私募股权投资和谈判。交易文件是公开的，你可以查看。所以，是的，就是这样。他们确实很难。是的，这次是最难的一次，比你能想到的任何顶尖私募股权或困境贷款商的谈判都要艰难。
That's what this was. And the key thing was they were going to hold our feet to the fire to execute on an aggressive timeline. They were going to hold our feet to the fire on the costs. And so we're exposed. If we get the costs wrong, you know, we're making this investment. And, and so the key piece of this, which I think is a good model for all of us. And it is actually will be really effective is the goal. I don't speak for them. Ask them. But I think their goal was we're going to take the things off the table that you can't control. Mercantilism, you know, certain customer issues.
这就是事情的真相。关键点在于，他们要逼迫我们在一个激进的时间表上执行计划。他们还要在成本方面逼迫我们。因此，如果我们在成本问题上出错，我们就会面临风险，因为我们正在进行这项投资。我认为这个过程的关键部分是一个对我们所有人都很有启发意义的模式，并且实际上会非常有效。这就是目标。我不能替他们发言，要问还是问他们。但我认为他们的目标是要将那些我们无法控制的因素，比如一些贸易问题和客户问题，从议题中剔除。
We're going to be held to account for the things that we can control. Our ability to execute. Our ability to execute on a good timeline and our ability to control costs. So when we think about a lot of these historically, the government sort of investing in a sector and, quote, picking a winner. Usually there's sort of money given to someone and it's sort of public risk. Private upside, right? This is not that. This is private risk. Public risk. Public upside. Private upside. It's a true shared win-win win.
我们将对那些在我们控制范围内的事情负责，比如我们的执行能力、按时完成任务的能力以及控制成本的能力。回顾历史，政府通常会投资于某个行业，并“挑选赢家”。通常情况下，资金会给到某一方，呈现出公共风险、私人收益的情况，对吧？这次不是这样的。这是私人风险、公共风险、公共收益和私人收益，这是真正意义上的双赢局面。
And again, like I said, hold me to these words. I hope I'm right on this. But I think the credit to the Trump administration, I think they will make money on this. And have solved the national security problem. All right, we appreciate you coming. Steve. Thanks. Thanks so much. Thanks, brother. Yeah, it's good. All right, take care, Steve. Thanks, Steve. Thanks, Jacob. We're done. Yeah, we'll do.
再次重申，就像我之前说的那样，请记住我的这些话。我希望我是对的。不过，我认为特朗普政府值得称赞，我认为他们会在这件事上获利，并解决了国家安全问题。好吧，我们感谢你过来。史蒂夫，谢谢你。非常感谢。谢谢，兄弟。好的，保重，史蒂夫。谢谢你，史蒂夫。谢谢，雅各布。我们结束了。好的，我们会做到的。
Hi, Lisa. Hi, Lisa. Lisa, it's a pleasure. Hi, Lisa. Well, thanks so much for being here with us today. We don't have a lot of time. So we want to get into it. In April, it was announced that you achieved your first Silicon output at the TSMC facility in Arizona on that two nanometer line. This administration and the private sector have talked a lot about ensuring semiconductor manufacturing. Would love your thoughts of the on the ground experience in Arizona. How's it going? What's not going well? What does America need to do to get this right?
你好，Lisa。你好，Lisa。Lisa，很高兴见到你。你好，Lisa。非常感谢你今天来到这里。我们的时间不多，所以我们想迅速进入正题。今年四月，有消息称你在亚利桑那州的台积电工厂实现了你在两纳米生产线上的首次硅片输出。政府和私营部门一直在讨论确保半导体制造的问题。我们很希望听听你在亚利桑那的实地经验。进展如何？有哪些不足之处？美国需要做些什么才能把这件事做好？
Well, absolutely. First of all, it's a pleasure to be here. Love the theme. I think we're all super excited about winning the US AI race. And I thought if we're going to talk about chips, David, I should actually bring one. Oh, awesome. That's OK. Yeah, a little bit of show and tell. So this is our latest generation AI chip. It's our MI355 chip. 185 billion transistors. Takes about nine months to build lots of technology on it. If I just stop. That's a two nanometer. This is three nanometer and six nanometers. So lots of different chips. I'll be putting the sunny bay later. I'm going to take it with me when I was at. Thank you. But look to answer your question.
当然，首先，很高兴能够来到这里。非常喜欢这个主题。我想我们对赢得美国的人工智能竞赛都特别感到兴奋。我想如果我们要谈论芯片，David，我应该真正带一个来。哦，太棒了。没问题。有点像“展示和讲解”环节。这是我们最新一代的AI芯片，是我们的MI355芯片。拥有1850亿个晶体管，建造起来大约需要九个月，包含许多技术。如果我暂停一下，这个是2纳米，这是3纳米和6纳米的芯片。所以有很多不同的芯片。我会在之后放到Sunny Bay。我会在我离开时带走它。谢谢。好的，来回答你的问题。
I think these AI chips are extremely, extremely complex. They have so much technology on it. We're super excited about the progress in US manufacturing. I would say 12 months ago, people weren't sure that we could do leading edge manufacturing. In the United States, we've been very early in Arizona with TSMC. And that we did get our first chips out. They're actually four nanometer. But what we see from it is where there's a will, there's a way. And I think all of the conversation about on foreign manufacturing has been super good for the semiconductor industry.
我认为这些AI芯片非常非常复杂，里面包含了大量的先进技术。我们对美国制造业的进步感到非常兴奋。大约12个月前，大家还不确定我们能否在美国进行尖端制造。在亚利桑那州，我们与台积电紧密合作，率先实现了这一目标，并成功生产出了我们的第一批芯片，它们是4纳米的。我们从中看到的启示就是，有志者事竟成。而且，我认为有关海外制造的讨论对半导体行业非常有益。
And frankly, for all of us in semiconductors, we're in such an interesting place because chips are so essential to ensuring that we are able to win the AI race. That we want to make sure that there's a lot of geographic diversity and capability there. But the reports out where that TSMC couldn't get good, qualified, trained employees, they have to bring folks over. Is that accurate? And again, if we're going to scale it, what's the order of magnitude we're going from here is a 10X, a 100X.
坦率地说，对于我们所有从事半导体行业的人来说，我们处于一个非常有趣的局面，因为芯片对于确保我们在AI竞赛中获胜至关重要。我们希望确保在这一领域有很大的地理多样性和能力。但有报道称台积电无法找到合适的、经过良好培训的员工，他们不得不从其他地方带人过来。这个说法准确吗？而且，如果我们要进行扩展，接下来的扩展规模是什么，是10倍还是100倍？
And how are we going to build a workforce to support this industry, which is a completely new industry for America? At least so you have permission to speak freely. The best way to say it is, no matter when you start something new, it's going to take work. It's going to be hard. So sure, in the beginning, there were some issues of, you know, the TSMC has like a formula for how they build and they just, you know, rinse and repeat. And they've learned how to do that well in Taiwan.
我们该如何构建一个支持这个产业的劳动力队伍呢？这个产业对美国来说是全新的。至少你要能够自由表达。最好的说法是，不论何时开始新的事物，都会需要付出努力。会很困难。所以，当然，起初会有一些问题，比如台积电在台湾形成了一套建厂和运营的模式，并在此基础上不断重复，并且已经在台湾学会了如何很好地执行这个模式。
So they had to learn how to do it well in the United States. But I have to tell you, we've been super impressed with the progress. And, you know, if we look at the main thing that we look at is, you know, yields and just how many chips do we get out on a given wafer. And I would say it's equivalent between what we get in Taiwan and what about cost and Arizona? Because it's unrealistic to think the United States could compete on cost. Am I correct?
所以他们不得不在美国学会如何做好这件事。但我必须告诉你，我们对他们的进步印象非常深刻。你知道，我们主要关注的是芯片的产量，即每个晶圆能制造多少芯片。我想说，这方面与我们在台湾得到的结果相当。那么成本和在亚利桑那州的情况呢？因为认为美国可以在成本上竞争是不切实际的。对吗？
We're going to pay a little bit more. Give us the ballpark. 50% more, 20%. Not, not 50% more. I mean, look, it's going to be, you know, more than 5%, but, you know, let's call it less than 20%. So low, low, low double, let's say low double digits. And how does that impact the business if at all in terms of competition globally? Well, I think the important thing is, I mean, just think about like everybody wants a GPU, right?
我们准备支付稍微高一点的价格。你能大概估算一下吗？是贵50%还是20%？不是50%。我的意思是，价格上涨会超过5%，但也不会超过20%。可以说是低两位数的增长。那这对全球竞争中的业务有影响吗？我认为重要的是，大家都想要图形处理器（GPU），对吧？
Like if you look across the industry, you really say, you know, the people who are going to win an AI want to have as much compute in their foundation as possible. And they want a sure and supply. We want to be able to supply this no matter what happens. And so if you put that in context, you know, the fact that you're not going for the lowest cost. You know, every minute of the day is okay. It's okay.
在整个行业中，你会发现，想要在人工智能领域取得胜利的人们都渴望在基础设施中拥有尽可能多的计算能力。他们也希望有一个可靠的供应保障。无论发生什么，我们都希望能够保障供应。因此，在这样的背景下，你就会明白，不追求最低成本也是可以接受的。每天的每一分钟都可以安心。
Like obviously we're not going to build not everything needs to be in the most advanced technologies. And so we have a very geographically diverse supply chain. You know, I think Taiwan continues to be important in that view. But the focus from this administration on getting on for manufacturing in a big way, not in a small way. I think is very good. And so in the meantime, do we have if there was a disruption for whatever reason we can come up with hypotheticals in Taiwan and we were unable to get chips from those factories.
显然，我们并不是要把所有东西都用最先进的技术来制造。因此，我们拥有非常多元化的供应链。在这方面，我认为台湾仍然非常重要。不过，这届政府非常注重大规模推动制造业的发展，而不是小规模。我认为这是非常好的。因此，在此期间，如果出于某种原因台湾的供应链出现了中断，比如假设我们无法从那里的工厂获得芯片，我们是否做好了准备呢？
What would that look like globally? Yeah, you have to look across the supply chain. But you know, from a structure standpoint, we all want to keep reserves for those times. But it's months. It's not yours. Two really interesting posts over the last couple of days. One was from Elon race said in five years, he projected 50 million H 100 equivalents just for X AI. And the second was Sam Altman. They signed a deal for a four, I think I go out data center 30 billion a year with Oracle. That just pretends an enormous amount of chips that are necessary and power. But if you forecast that, how do we actually meet all of that? What needs to happen that's not happening today inside of the United States to actually do that?
全球范围内会是什么样子呢？是的，你必须纵观整个供应链。不过，从结构的角度来看，我们都希望为那些时刻保持储备。但这指的是几个月，而不是几年。过去几天有两个非常有趣的帖子。一个来自埃隆·马斯克，他预测在五年内，仅X AI就需要5000万个H 100等效设备。第二个是Sam Altman，他们与Oracle达成了一项协议，每年耗资300亿美元建立一个数据中心。这意味着需要大量的芯片和电力。但如果你预测一下，我们究竟如何才能满足这一切？美国境内需要发生哪些事情，才能真正实现目标？
Yeah, it's a great, great point. I mean, that's that's what we're seeing. We're seeing this incredibly large demand for AI. And they're coming from Sam and Elon are certainly the leaders, a couple of the leaders. There's a lot of demand elsewhere too. I mean, if you think about it, nations want their own AI. There's a very high demand. We're imagining that just the accelerator market. So the chips for these, you know, AI large computing systems will be like, you know, over $500 billion in a couple of years. So very high growth.
是的，这是一个非常好的观点。我们确实看到了对人工智能的巨大需求。Sam 和 Elon 无疑是这一领域的领导者之一。同时，其他地方的需求也很大。如果你想想，各个国家都想拥有自己的人工智能，因此需求非常高。我们预计仅加速器市场，也就是为这些大型人工智能计算系统提供芯片的市场，在几年内可能会超过5000亿美元，所以增长非常迅速。
And when you say, you know, what do we need to do? It's the entire ecosystem needs to scale up. So we need to scale up. Certainly what we're doing in chip design is trying to get chips out as fast as possible. But we're also scaling up the entire manufacturing ecosystem. And, you know, as I said, I don't, I think the US is going to be a huge piece of it. So it's not just about the Silicon. There's all of the various other pieces of the ecosystem that have to come to the US. I think, look, I think today's AI action plan is actually a really, you know, excellent blueprint.
当你问我们需要做些什么时，整个生态系统都需要扩大规模。因此，我们需要扩大规模。我们在芯片设计方面所做的工作，当然是尽快推出芯片。但我们也在扩大整个制造生态系统的规模。正如我所说的，我认为美国将会是其中一个重要部分。因此，不仅仅是关于硅芯片，还有生态系统中其他各种元素也必须引入美国。我觉得，今天的AI行动计划实际上是一个非常优秀的蓝图。
How do you see the market evolving in these next five or six years? Is it there's a standard set of chips for training, a standard set for inference, or do you just see an explosion like a Cambrian explosion of different A6 different designs, different use cases? Yeah, I like that question because I am a believer in there will be diversity of chips. And the reason is there's so many use cases, right? If you think about use cases from, you know, whether you're talking about science or manufacturing or design or backend or, you know, frankly, personal AI.
在接下来的五六年里，你怎么看待市场的发展？是否会有一套用于训练的标准芯片和一套用于推理的标准芯片，还是说会像寒武纪大爆发一样，出现各种不同的芯片设计和用例？
我喜欢这个问题，因为我相信芯片会有多样性。原因是有太多的用例了，对吧？无论是科学、制造、设计、后端，还是个人人工智能，都会需要不同的芯片。
I think we're going to see AI in everything that we do, you know, certainly in your phones and your PCs. And so you have all these pieces. You're going to have different types of chips that do that. You know, certainly the for the largest systems, we tend to believe that, you know, you need the most compute you can get. And so, you know, GPUs are there, but lots of A6 are in the process. And, you know, we'll see a variety of different chips.
我认为我们将会在生活的各个方面看到人工智能的存在，特别是在手机和电脑中。为了实现这一点，会有不同类型的芯片应用在这些设备上。对于大型系统来说，我们普遍认为需要尽可能强大的计算能力。因此，除了GPU以外，还有很多专用芯片(A6)在使用过程中发展起来。我们将会看到各种不同类型的芯片。
You opened up a really interesting line of questioning there when mainframes were so expensive and then eventually wound up having PCs that were more expensive on their desktop, you alluded to AI being run locally. Yes. When would we have a local computer, a laptop, a desktop computer that would have the power we're seeing to run some of these LLM models in your mind? And do you see that as a specific market to go after? Look, I definitely see the idea that AI will be at every part of our ecosystem is a real thing.
你提到了一个非常有趣的话题：从前大型主机非常昂贵，然后后来人们桌面上的个人电脑变得更为昂贵，你还提到了本地运行AI的可能性。那么，你认为我们什么时候会有一台本地电脑、一台笔记本或台式电脑，能够具备现在运行这些大型语言模型的能力？你认为这是一个值得追求的特定市场吗？
我确实认为，AI将在我们生态系统的每一个部分发挥作用，这将是一个真实的趋势。
I think that's one of the advantages. If you think about the power of AI, you want it everywhere. And you want it across all different applications. And I think when you think about PCs today, we're putting significant amount of AI in them to run local models. And why would you want that? It's like, well, maybe I don't want all my personal data, you know, all over the place. On that point, can you make a prediction on when the market for physical AI chips is greater than the market for chips and data centers?
我认为这就是其中一个优势。如果你考虑到AI的力量，你会希望它无处不在，并且跨越各种不同的应用。当我们谈到今天的个人电脑时，我们正在其中投入大量AI来运行本地模型。为什么要这么做呢？也许我不想把我的所有个人数据都放在其他地方。在这个基础上，你能预测一下什么时候物理AI芯片市场的规模会超过数据中心芯片市场吗？
That's a great question. I'm a big believer in physical AI. I still think it's, let's call it five years. You think five years is that fast? That at least five years. So you're saying five plus. Five plus. Yeah. But that is ultimately the biggest end market. Do you think is it, you think physical AI becomes the biggest end market? I think it becomes a significant end market. I think you look at chips and data centers and you look at chips at the edge.
这是个很好的问题。我非常相信物理人工智能。我依然认为，我们可以说大概需要五年。你觉得五年很快吗？至少需要五年。那么你是说五年以上。对，五年以上。但最终它会成为最大的终端市场。你认为物理人工智能会成为最大的终端市场吗？我认为它会成为一个重要的终端市场。我觉得你可以看看芯片和数据中心，以及边缘计算的芯片。
They're also significant markets. When you look at the most cutting edge techniques today, EV lithography, all of this whole stuff to make chips. One of the things that's observable is we're only as good as what humans have been able to invent. And I often ask the recursive question, what happens when the AI is able to invent its own method of manufacturing? Different materials, different material sciences, different approaches that we may not necessarily understand. Is any of that R&D happening, whether an AMD or another place is like, how are we trying to get beyond the physical limits of electrons shunting across a junction?
它们也是重要的市场。当你查看当今最前沿的技术时，比如电动汽车光刻以及所有用于制造芯片的技术，有一个可观察到的现象：我们的技术水平取决于人类能够发明的东西。我经常提出一个递归式的问题：如果AI能够发明自己的制造方法，会发生什么呢？不同的材料、不同的材料科学、我们可能不一定理解的不同方法。这些研发正在进行吗？无论是在AMD还是其他地方，我们是如何尝试突破电子穿过结点的物理极限的？
I think this idea that the AI can be extremely smart and extremely capable. Like we think about how AI can design the future chips. And it will design pieces of it. But there's still a creativity of bringing it all together that I think humans are still absolutely at the center of that. So I don't necessarily see the AI designing our next generation GPU. But I do see it helping us design the next generation GPU much faster and more reliably. You talked about the need to reshore more parts of the ecosystem. You see you guys are world class chip design, the fabs are getting reshore. But how do you think about things like lithography? Does that need to be reshore or does ASML need to start building machines in the United States? Or is it okay to have that type of supply chain risk on an ally?
我认为AI可以非常聪明和非常有能力。比如，我们考虑到AI可以设计未来的芯片，它可以设计其中的一部分。但是，我认为将这些部分结合在一起的创造力仍然是人类不可缺少的。所以，我不认为AI会完全设计出我们下一代的GPU，但我确实认为它可以帮助我们更快速和更可靠地设计下一代的GPU。你提到需要将更多的生态链部分带回本土。你们在世界一流的芯片设计上非常成功，工厂也在回迁。但你怎样看待光刻技术之类的事情？这也需要回迁吗？还是ASML需要在美国建厂？又或者将这种供应链风险放在盟友那里是可以接受的呢？
Well, I think we're going to, we have to accept the fact that it's a global supply chain. Even if you were to reshore X number of components, you would still have Y components that are across the world. I think it's important for us to have our allies together. So that's a key piece of the conversation and ensuring that we have access to the latest generation technologies. And that is something that we protect given our intellectual property.
我认为我们需要接受这是一个全球供应链的事实。即使你把一定数量的组件搬回国内，你仍然会有一些组件在世界各地。我认为，让我们的盟友团结在一起非常重要。这是对话的关键部分，同时确保我们能获得最新一代的技术。而保护我们的知识产权也是我们需要重视的事情。
And going to first principles and asking you the open-ended question, what should be done about American education? I'm going to ask this a lot today. Assume there's no college high school, nothing. You arrive in America, the situation is what it is today. What do you do? How do you build an education system to prepare the next generations for the evolving workforce? I'm probably a little bit biased as maybe some of your guests are today. I'm a big believer in science and technology background as being sort of the STEM background is so helpful when we think about the future workforce. And the earlier we can get into the process, I think the better.
要从基本原则出发，提出一个开放性的问题：关于美国教育，我们应该怎么做？我今天会多次问这个问题。假设没有大学或高中，你来到美国，面临的就是当下的情况。你会怎么做？如何建立一个教育体系，为未来不断变化的劳动力做好准备？可能我有些偏见，正如今天你邀请的一些嘉宾一样。我非常相信科学和技术背景，也就是所谓的STEM背景，这在我们思考未来的劳动力时非常有帮助。而且我认为我们越早开始这个过程越好。
So some of the work that's being done to revitalize the curriculum is pretty important in the next generation workforce. And one of the things when I think about how we win in AI, there's so many aspects of it. But ensuring that America is the best place for AI talent is also a key piece of that. And inspiring people when they're young to really study science.
为了振兴课程而进行的一些工作在下一代劳动力中非常重要。当我思考我们如何在人工智能领域取得成功时，有很多方面需要考虑。其中一个关键因素是确保美国成为吸引人工智能人才的最佳之地。同时，还要激励年轻人认真学习科学。
So go to bed at night and you think about the best case scenario for this technology and this trajectory on which is accelerating and you're enabling. What could the world look like in 10 years? Let's say pretty obvious we're hitting artificial general intelligence at this moment. I think we'd all agree. We're starting to see that. But super intelligence can't be far behind that. I assume you agree with that. So when we hit that super intelligence, what would the world look like in 10 years? In the most optimistic scenario if we do this right?
所以，晚上上床睡觉时，想象一下这种技术和其加速发展的最佳情况，以及你正在促成的未来。10年后的世界可能是什么样子？可以说很明显的是，我们在这个时候就达到了人工通用智能。我想大家都会同意这一点。我们已经开始看到这一趋势。但超级智能也不会离得太远。我假设你对此也表示同意。那么，当我们达到超级智能的时候，在最乐观的情况下，如果我们做对了，10年后的世界会是什么样子呢？
Well, I think the exciting part about it and I can say this very sincerely. I mean, this is the most transformational technology sort of in our lifetimes. I mean, that's the way we should think about it. Orders of magnitude. Orders of magnitude. And the reason is it's not just going after one aspect. It actually take AI and make science better. You can take AI and make medicine better. You can take AI and make manufacturing better. You can take AI and make every aspect of your business better.
我认为，这件事最令人兴奋的地方在于——我可以非常真诚地说，它是我们这一生中最具有变革性的技术。这是真正的颠覆性技术。原因在于，它不仅仅是改善某一个领域。你可以利用人工智能来提升科学水平，可以用它来改善医疗，可以用它来优化制造业，也可以用它来让你的业务各个方面都变得更好。
And so, in my mind, 10 years from now, we'd like to believe that we are really leveraging it to solve some of the world's most important problems. I like to say, like, you know, AMDers get up in the morning and they say, you know, how can I use technology to solve some of the most important challenges in the world? And, you know, AI is really our mechanism for doing that.
所以，在我看来，十年后，我们希望能真正利用这项技术来解决一些全球最重要的问题。我常常说，AMD员工每天早晨起床时都会想，我今天怎样可以利用科技来解决这个世界上一些最重要的挑战呢？而人工智能正是我们实现这一目标的工具。
I have a business strategy question. If we went back 20 years and we wrote the tale of three companies in Vidya AMD Intel. And then you fast forwarded 20 years to have just absolutely thrived. And one has not. And if you had made the bet back then, it would have been very inconclusive that you would have picked in Vidya AMD. And if anything, there is an amount of inherent belief that Intel had just figured it all out. Can you just tell us sort of like the lessons learned of why you've thrived and maybe what you take away from their journey that you make sure AMD doesn't play out?
我有一个关于商业策略的问题。假设我们回到20年前，写下关于三家公司的故事：英伟达、AMD和英特尔。然后快进20年，两家公司蓬勃发展，而一家则没有。如果在当时做出投资选择，很难确定你会选择英伟达和AMD。事实上，很多人可能会相信英特尔已经找到了成功的方法。你能告诉我们英伟达为何能够蓬勃发展的经验教训吗？也许还有从其他两家公司的历程中学到的东西，以确保AMD不重蹈覆辙？
Well, you know, as a CEO, we have to be paranoid every single day, right? So we don't rely on the past, but I think there are lessons of the past. And I think that probably the most important lesson that I can say for technology is you have to shoot ahead of the duck. Like you have to be thinking, what is the most like your question Jason? Great question. We think about that all the time. How do we shoot ahead of the duck?
作为CEO，我们每天都要保持警惕，对吧？所以我们不能依赖过去，但我认为过去有很多可以学习的经验。我认为对于科技行业来说，最重要的一课就是要“抢占先机”。就像你问的那个问题，Jason，真是个好问题。我们一直在思考，怎么才能“抢占先机”？
And, you know, you have things that change. You know, technology is a beautiful place because you see big inflection points. Like five years ago, AI was around, but we wouldn't be able to gather this audience to talk about AI because people would be like who cares? But the fact is you had to invest many, many years ago to be where we are today. And I think, you know, I like to say that, you know, you will be able to judge whether we've done a good job or not by how we perform five years from now. Like the decisions we're making will take, you know, five plus years to play out. But that's a key thing in tech. Like nothing is fast, but hopefully it's quite lasting.
你知道，有些事物会发生变化。科技领域是一个很美妙的地方，因为我们会看到一些重大的转折点。比如五年前，人工智能就已经存在了，但那时候我们不可能聚集这样一批人来讨论人工智能，因为大家可能会觉得无所谓。但事实上，多年前我们就必须进行投资，才能达到今天的水平。我认为，我们能否被评判为做得很好，将取决于我们五年后的表现。我们现在所做的决策需要五年甚至更长的时间才能见效。这是科技领域的一个关键特点：没有任何事情是一蹴而就的，但希望它能具有持久的影响力。
And what do you think is happening in countries not in the United States? Like what do you think is happening in chip design and all of these capabilities in China and other places right now? We should believe that it's super, super competitive. I mean, at the end of the day, I think the world has recognized that semiconductors and chips are essential to national economies or essential to national security. And so assume that everyone's investing. I'd like to believe that we have a great head start, you know, because of the innovation pipeline, because of the great companies that we have here, but we should not be, you know, confused that everybody's investing and we need to keep our investments as well.
你认为在美国以外的国家发生了什么？比如说，对于中国和其他地方的芯片设计和相关能力，你有什么看法？我们可以相信，这个领域的竞争非常激烈。我认为，世界已经认识到半导体和芯片对于国家经济和国家安全是至关重要的。因此，可以假设各国都在投入资金。我希望相信，我们在这方面有一个很好的起步，因为我们拥有创新管道和优秀的公司，但我们也不能忽视其他国家都在加紧投资，我们也需要持续保持我们的投入。
And I think that's why, you know, this whole idea of any one company can provide every solution that's necessary just isn't the case. Right. I love the idea of open ecosystems of companies collaborating of collaboration across the ecosystem. So hardware, software systems, you know, collaboration across public private partnerships. Because that's what it's going to take. Like for us to win, we have to be, you know, front facing and realizing that bringing, you know, the countries that win, bring all of the smartest people and the best capabilities together. And let them go as fast as they possibly can.
我认为，这就是为什么任何单个公司都无法提供所有必要解决方案的原因。我非常喜欢开放生态系统这个概念，让公司之间进行合作，生态系统内的协作，包括硬件、软件系统以及公私合作伙伴的跨界合作。因为，这就是成功的关键。要想取得成功，我们必须具有前瞻性，将世界上最聪明的人才和最佳的能力汇聚在一起，让他们全速前进。
Right. Well, thank you for being with us. Wonderful. Yeah. Great. Appreciate it. Thank you. Thank you. Thank you. Pleasure to meet you. Thank you. I'm Chase Locke-Miller, the co-founder and CEO of Crusoe. And I'm here to talk to you about the AI Industrial Revolution. I'm going to start with a quote. And it's from Warren Buffett in his 2020 shareholder letter, shareholder letter to investors. And he said, and it's brief 232 years of existence, there has been no incubator for unleashing human potential like America.
好的，非常感谢您参与我们的活动。太好了。感谢您的支持，非常感激。很高兴见到您。我是Chase Locke-Miller，Crusoe的联合创始人兼首席执行官。今天我要和大家谈谈人工智能工业革命。首先，我想引用沃伦·巴菲特在2020年致股东的信中的一句话。他说，在美国232年的历史中，没有哪个国家像美国一样能激发人类潜力。
Despite some severe interruptions, our country's economic progress has been breathtaking. Our unwavering conclusion never bet against America. Buffett's words were true then. And as we enter this global race for technological dominance of artificial intelligence, they ring even truer today. American dynamism has always prevailed and it will continue to do so. So in sort of the history of really what's made America great is, you know, we live in a nation that's the freest nation in the world. And we have, we are just as rich in land and resources as we are in human ambition to drive progress.
尽管我们国家的经济进步曾受到一些严重的干扰，但其成就依然令人惊叹。我们始终坚信：永远不要轻视美国。巴菲特的话在当时是正确的，而在我们进入全球人工智能技术主导地位的竞争时，这句话今天更为贴切。美国的活力总能取胜，并将继续如此。美国之所以伟大，在于我们生活在一个世界上最自由的国家，我们的土地和资源丰富，同时人们充满着推动进步的雄心壮志。
And one of the things that's fundamentally enabled that progress to happen and that ambition to be unleashed is the leading investments that we've made in infrastructure. Over the course of his lifetime, Warren Buffett got to witness investments in power, in transportation, and in power and transportation and in natural resources to enable people to go pursue their dreams and live a better life. Now in 2025, we stand at a, you know, the start of a new era of infrastructure, the infrastructure of intelligence.
促进这一进步并释放这种雄心壮志的关键因素之一，是我们在基础设施方面进行的领先投资。沃伦·巴菲特在他的一生中见证了电力、交通以及自然资源方面的投资，这些投资使人们可以追求他们的梦想，过上更好的生活。现在，在2025年，我们正处于一个新基础设施时代的起点，即智慧基础设施时代。
And it's driving the biggest capital investment in human history. This investment is being led by the hyperscalers who are investing hundreds of billions of dollars per year, per year to make this happen. These are the companies with the biggest balance sheets in the history of business that are quite literally going all in to make this happen. And they're not the only ones. You know, there's also startups like Crusoe and there's even nation states that are following suit. So what's going on there? What's the, what's the prize that they're going after? The, you know, the opportunity here is that for the first time in human history, we've actually been able to manufacture intelligence. Intelligence is the scarce economic resource in the history of the economy. And for the first time, we're actually able to make it.
这正在推动人类历史上最大规模的资本投资。这项投资由“超大规模公司”引领，它们每年投入数千亿美元来实现这个目标。这些公司拥有商业史上最强劲的资产负债表，实际上是全力以赴地去实现这个目标。而它们并不是唯一在行动的，还有像Crusoe这样的初创公司，甚至一些国家也在跟随潮流。那么，这到底是在做什么呢？它们追求的目标是什么？这里的机会在于，人类历史上第一次，我们能够制造出智能。智能在经济史上一直是稀缺的经济资源，而这次是我们第一次能够创造它。
And the opportunity here is to actually unlock access to what has historically been that scarce economic resource. So this is why the data centers of the future are not being referred to as data centers. They're actually being referred to as AI factories. It's a factory that takes as inputs data and algorithms and chips and energy and it outputs intelligence. This is the alchemy of intelligence. So this newly manufactured intelligence will spawn a new chapter of unprecedented productivity and development. And that will serve to improve human quality of life. So the IBC estimates that AI will generate $20 trillion in economic impact by 2030. So even if you can earn a small slice of that that hundreds of billions of dollars of investment will earn an amazing return. For each dollar invested into business related AI is expected to generate $4.60.
这里的机会在于能够真正解放对历史以来一直稀缺的经济资源的访问。因此，未来的数据中心不再被称为数据中心，它们实际上被称为AI工厂。这种工厂将数据、算法、芯片和能源作为输入，输出智能。这是一种智能的炼金术。这种新制造的智能将开启一个前所未有的生产力和发展的新篇章，从而提升人类的生活质量。据国际商会估计，到2030年，人工智能将带来20万亿美元的经济影响。因此，即使只获得其中的一小部分，数千亿美元的投资也会带来惊人的回报。每投入一美元于与商业相关的人工智能，可以预计会产生4.60美元的收益。
As my friend Jensen would say, the more you buy, the more you save. Or in this case, the more you buy, the more you make. And we can grow the pie together and usher in a new era of AI-driven abundance. So when we look at the history of American energy production and consumption, as the US industrialized, we really ramped up energy generation and also consumption. But if you look at this chart, you can see that it's kind of flatlined over the last 20 years where we're generating and consuming about 4,000 terawatt hours per year. AI is fundamentally transforming this demand picture and energy is quickly becoming the bottleneck to growth. Data centers are forecast to do account for 20% of the growth in power demand between now and 2030.
正如我的朋友詹森所说，买得越多，省得越多。而在这种情况下，买得越多，赚得越多。我们可以一起分享“蛋糕”，迎接一个由人工智能驱动的繁荣新时代。回顾美国能源生产和消费的历史，在美国工业化过程中，我们大幅增加了能源的生产和消费。但如果你查看这张图表，就会发现过去20年里，这一趋势趋于平稳，每年大约生产和消费4,000太瓦时的电力。人工智能正在从根本上改变这一需求格局，能源很快成为增长的瓶颈。据预测，到2030年，数据中心将占到电力需求增长的20%。
And data center total power consumption is going to go from 2.5% of US power consumption to 10%. So what this means is that the technology industry that's sort of willing this infrastructure into existence fundamentally needs to bring its own power to support that growth, which means massive investments, not just in data centers, but also in the energy infrastructure to support them. And this will require people, lots of people, to build, operate, maintain, and run these large scale energy investments. So if we look at data centers, by the numbers, I think it's important as people are sort of throwing around gigawatt scale data centers of looking at the amount of data center infrastructure that exists today. North of Virginia is sort of the center of the world for data centers, but it's only at the end of 24, it was only 4.5 gigawatts. Today we have companies that are looking at building single 5 gigawatt facilities.
数据中心的总耗电量将从美国耗电总量的2.5%上升到10%。这意味着推动这些基础设施出现的科技行业需要为自身的增长提供能源支持，这不仅需要对数据中心进行大规模的投资，还需投入大量资金到能源基础设施之中。为此，需要大量人力来建设、运营、维护和管理这些大规模的能源投资。谈到数据中心，从数据来看，人们现在常提到千兆瓦级别的数据中心，因此回顾目前的数据中心基础设施规模是重要的。弗吉尼亚州北部是全球数据中心的中心，但截至24年底，那里只有4.5吉瓦的电力设施。而如今有公司计划建设单个5吉瓦的设施。
And if you look at this growth, we're building more than a North of Virginia every single year in the forecast in future. So if there's one thing that you're going to take away from this presentation, it's that we need new infrastructure, we need lots of it, and we need lots of people to build, operate, and maintain it. This is what Crusoe is focused on solving. Crusoe is in the business of activating energy for intelligence, of building, operating, AI factories at scale, from the steel to the silicon, from the electron to the token. And if you look at our pipeline, we have about 40 gigawatts of capacity that spans all sorts of energy resources from new energy technologies like small modular reactors to renewables and natural gas to power this innovative future.
如果你看看这种增长趋势，我们每年计划建设的规模都相当于整个弗吉尼亚州北部。因此，如果你要从这个演讲中记住一件事情，那就是我们需要新的基础设施，而且需要大量的基础设施，还需要大量的人来建设、运营和维护它。这正是Crusoe所专注解决的问题。Crusoe的业务是激活能源以赋能智能化，建立和运营大规模的人工智能工厂，从钢铁到硅片，从电子到代币。如果你查看我们的计划，我们拥有大约40吉瓦的产能，涵盖了各种能源资源，从小型模块化反应堆等新能源技术到可再生能源和天然气，以推动这个创新的未来。
So revisiting my formula here, I think we left off one critical component, which is the people. AI will be the largest job creation catalyst that we've ever seen. So I think it's important to sort of look at what this looks like in practice. For the last year, Crusoe has been building a large scale AI factory in Abelene, Texas. And speed is paramount. Again, this event is winning the AI race. In order to win a race, you really need speed. And Crusoe has really been focused on using modular components on rapidly scaling investment in construction and infrastructure to support this. And we've actually built a lot of different modular components in factories and brought them to site.
所以重新审视我的公式后，我认为我们遗漏了一个关键因素，那就是人。人工智能将成为我们见过的最大规模的就业创造催化剂。因此，我认为重要的是要看看这在实际中是怎样的。过去一年，Crusoe公司一直在德克萨斯州的阿比林建立一个大型的人工智能工厂。速度是至关重要的。这次活动就是为了赢得人工智能竞赛。要赢得比赛，确实需要速度。Crusoe公司一直专注于快速扩大在建设和基础设施上的投资，使用模块化组件来支持这一点。我们实际上已经在工厂中建造了许多不同的模块化组件，并将它们运送到现场。
And they're kind of like Lego blocks that sort of fit together to build one of these AI factories at rapid scale and speed. So if you look at what this looks like today, this is, this is, this is, this site will consume over 1.2 gigawatts of power and 400,000 Nvidia GPUs, all in a single coherent cluster. So this will essentially be a gigawatts scale computer to drive human progress forward. You know, it's really amazing what you can kind of accomplish in a year. You see just one year ago, this is what the site looked like. And this is what it looks like today.
它们有点像乐高积木，可以快速、高效地拼在一起，建造出一个AI工厂。如果你看看当前的状况，这个场地将消耗超过1.2吉瓦的电力，并使用40万块Nvidia GPU，所有这些都集中在一个统一的集群中。因此，这实际上是一个推动人类进步的千兆瓦级计算机。你知道，一年内可以完成的事情真的令人惊叹。你看，仅仅一年前，这个场地的样子还是这样的，而今天它已是这副模样。
So what does this mean from a jobs perspective? We have 4,000 people working on site every day to make this facility happen. And you know, it's a bunch of different trades, electricians and plumbers and construction workers. And it's required a lot of capital to we raise $15 billion to basically put this facility and bring it into existence. And it's also required manufacturing and that's in a lot of the critical components have happened off site in these controlled manufacturing environments.
这对就业来说意味着什么呢？我们每天有4000人在现场工作，以推动这个设施的建成。而这些人来自各种不同的行业，有电工、水管工和建筑工人。为此，我们筹集了150亿美元的资金，以便让这个设施得以实现。此外，这还需要大量的制造工序，其中许多关键部件是在这些受控的制造环境中，非现场完成的。
But this isn't the only one. This isn't one of a kind. We also are building AI infrastructure and AI factories across America. This site in West Texas is going to be a gigawatt facility behind the meter with wind with incremental gas and grid and connection. We did a partnership with Redwood materials where we built the largest micro grid in the United States with 60 megawatt hours of batteries and of life EV batteries and 20 megawatts of solar to power and AI factory. We have a partnership with GE Vernova and engine number one for 4.5 gigawatts of new gas generation capacity power future AI data centers.
但这不是唯一的，也不是独一无二的。我们还在全美范围内建设人工智能基础设施和工厂。这个位于德克萨斯州西部的场地将成为一个千兆瓦级的设施，采用风能、增量天然气及电网连接供电。我们与Redwood材料公司合作，建造了美国最大的微电网，拥有60兆瓦时的电池、电动汽车的第二生命电池以及20兆瓦的太阳能，为一个人工智能工厂供电。我们还与GE Vernova和Engine Number One合作，新增4.5千兆瓦的天然气发电能力，为未来的人工智能数据中心提供电力。
Finally, we want to announce a new partnership that we're doing with tall grass energy and Wyoming that will initially power 1.3 gigawatts of total compute load alongside two gigawatts of power generation. Ultimately, we feel like this can scale to 10 gigawatts of power. So we're really thrilled to partner with tall grass. So as a vertically integrated AI infrastructure company built here in America, we believe that AI factories will be the ultimate economic engine creating utility for society and new jobs for the economy.
最后，我们想宣布一个新的合作伙伴关系，我们将与Tall Grass Energy和怀俄明州合作，初期将为1.3吉瓦的计算负载提供电力，同时产生两吉瓦的电力。我们觉得这个合作最终有潜力扩大到10吉瓦的电力。我们非常高兴能与Tall Grass合作。作为一家在美国建立的垂直整合AI基础设施公司，我们相信AI工厂将成为推动经济的终极动力，为社会创造效益并为经济带来新就业机会。
This will usher in a massive new era of AI driven prosperity for the United States. And I want to leave you with my final quote from Warren Buffett that in this AI race, never bet against America. Thank you. So is this stuff real? You guys started off as a Bitcoin miner and now somehow all the hyperscalers are asking you to build non-stop data centers. Why you guys? I think again, it comes back to this being a race and one of the things that Kruisos been able to do better than anyone is execute at speed and scale.
这将开启一个由人工智能推动的美国繁荣新时代。最后，我想引用沃伦·巴菲特的话：在这场人工智能竞赛中，永远不要对美国押错注。谢谢。那么，这一切是真的吗？你们一开始是比特币矿工，如今所有的大型科技公司都要求你们持续建设数据中心。为什么是你们？我认为，这归根结底还是因为这是一场竞赛，而Kruisos比任何人都更能快速大规模地执行。
And I know there's been some of the biggest constraints around water, energy, the land for this type of stuff. Where have you seen what parts of the country you guys able to do this or have you seen any of the local regulators start to step up to make this stuff easier for you? We've been building quite a bit in Texas. Abelian Texas is this initial facility that's gotten a lot of coverage. We just announced another facility in Texas. Wyoming's been a big area of investment for us, but there's a number of other states that were evaluating investing to build large skill.
我了解到在水、能源和土地等方面存在一些最大的限制。你们在哪些地区能够实现这些目标，或者你有没有注意到某些地方的监管机构开始采取措施使这些事情变得更容易？我们在得克萨斯一直在进行大量建设。得克萨斯的Abelian项目是一个受到广泛关注的初始设施。我们刚刚宣布在得克萨斯的另一个项目。怀俄明州也是我们的一个重要投资地区，但我们正在评估在其他多个州进行大规模建设的可能性。
Is it only going to be the more rural red states? Or do you think that Oregon, Washington, etc. will start to get together and realize they've got cheap hydropower and cheap water and we'll try and get you there? Believe it or not, we're actually looking at something in California. Wow. California, Gavin, is he going to bring you in? I would imagine it's going to take 50 years with that right now. Yeah, maybe. We'll see. We'll see.
这仅仅会是更偏远的红色州（共和党占多数的州）吗？还是你认为俄勒冈州、华盛顿州等地会开始团结起来，意识到他们拥有廉价的水电和水资源，并尝试吸引你们过去？不管你信不信，我们实际上正在考虑加利福尼亚的某些事情。哇，加利福尼亚，Gavin（加州州长）会把你们吸引过去吗？我想这可能需要50年时间才能实现吧。是的，也许吧。我们拭目以待。
Do you think that the hyper-scaler demand obviously we were just on with Lisa Sue talking about the demand for chips over the next couple years? That's obviously correlated to the demand with data centers. Do you think that's actually going to play out the way that all the public markets are projecting? Or are we in 1999 peak? Everybody thinks that fiber is going to be deployed all over the world. Turns out all those projections were totally off.
你认为超大规模云计算的需求，显然我们刚刚和Lisa Su讨论了未来几年对芯片的需求。显然，这与数据中心的需求是相关的。你认为这种情况会按照公开市场的预测发展吗？还是我们处在类似1999年的高峰期？那时候大家都认为光纤会在全球范围内部署，结果所有的预测都完全错了。
I think the important trend to watch is the capital investment that's happening and the term over which that's happening. I felt like meta backed off on it a little bit. If for a little bit talk about they were going to deploy like crazy and then pull back, although he's obviously spending a billion dollars on chief AI scientists now. Yeah, I think the investments they're making in people are actually rounding errors compared to the investments they're making in infrastructure. And I think that's something to appreciate in this moment in time.
我认为一个重要的趋势是目前正在进行的资本投资及其持续的时间。我觉得Meta在这方面有些退缩。之前他们说要大规模投资，后来又有所收敛，尽管他们现在显然在首席AI科学家上投入了数十亿美元。不过，我认为他们在人力上的投资与基础设施上的投入相比只是九牛一毛。我认为这是我们目前需要关注的一点。
People are betting their entire balance sheets. These are the biggest and best balance sheets in the history of business. They're betting their entire balance sheet on the future infrastructure that's going to power the modern economy. And then the data centers like Texas, what's the limiting factor? Is it like workforce to actually go build these things? Is it like materials? Is it the cooling towers? Is it the chips? Is it the hyper-scalers giving you the contracts? What's the limiting reagent? Labor is definitely like a major constraint. Like I said, we have about 4,000 people on site every day. We're going to multiple sites that are operating with thousands of folks basically building this infrastructure. So, Labor is definitely one of the big bottlenecks. We think it's really important for America to make these massive investments in the workforce to really build the infrastructure of the future.
人们正在押注他们的整个资产负债表。这些是商业史上最大和最优秀的资产负债表。他们将整个资产负债表押注于未来将驱动现代经济的基础设施。那么，就像德克萨斯州的数据中心，限制因素是什么？是建造这些东西所需的劳动力吗？是材料吗？是冷却塔吗？是芯片吗？还是来自超大规模企业的合同? 什么是限制因素？劳动力绝对是一个主要限制因素。就像我说的，我们每天现场有大约4000人。我们要去多个地点，那里有成千上万的人基本上在建设这种基础设施。因此，劳动力绝对是一个重要瓶颈。我们认为，美国在劳动力上进行大规模投资以真正建设未来的基础设施是非常重要的。
Anything that requires some real re-skilling where it's like people from oil and gas or construction having to go into just totally net new fields, or something where you guys are actually able to pull on pre-existing talent pools pretty quickly. Both. There's a lot of existing labor. At that facility in Abelene, we're actually pulling labor from all 50 states at this point, believe it or not. So, like a company town importing people in. Yeah, we have about 50% of the people are from Texas, but we are importing a lot of labor to make the project happen. Do you see the company started to go more full stack beyond just the operations of data centers? How do you think about like you started off with focus on energy arbitrage now to data centers where do you see yourselves going over time?
任何需要进行真正技能再培训的领域，比如石油和天然气或建筑行业的人需要进入全新的领域，或者你们能够迅速利用现有的人才库的情况。两者都有。在那个位于阿比林的设施里，我们实际上正从全美50个州招募员工，难以置信吧，就像是一个公司城镇在引进外来人口。是的，我们大约有50%的人来自德克萨斯州，但我们正在引进大量劳动力来完成这个项目。你是否看到公司正在拓展业务，不仅仅限于数据中心的运营？你是如何看待从最初专注于能源套利到现在的数据中心的变化的？你觉得随着时间推移，公司将如何发展？
Yeah, I'm a cruiser vertically integrated AI infrastructure business. So, you know, data centers is a key component to that. And, you know, I think one of the most important pieces to be building right now and one of the hardest things to do at speed. But we also have, you know, this managed AI cloud services layer that enables innovators to build large-scale AI applications on the platform. Makes sense. Well, yeah, Chase, thanks so much for joining us on stage and. Yeah, thank you. Appreciate it. Thanks, Diane. Okay, everybody, we got a real treat for you. Jensen, Ron is here. Sit here. Sit here. Sit here. The hot seat. Thanks for coming. Thank you. The number one podcast in the world. We were saying the number one company in the world. Wow. Thank you. You're a fan of the pod. You listen to the pod. This is Norman, our host.
好的，我是一家垂直整合的AI基础设施企业，所以数据中心是其中的关键组成部分。我认为数据中心是目前需要构建的最重要的部分之一，同时也是以快速速度实施起来最困难的事情之一。此外，我们还有一个托管的AI云服务层，能够让创新者在这个平台上构建大规模的AI应用程序。明白了。好的，Chase，非常感谢你加入我们。谢谢你，感谢。谢谢，Diane。好，大家，我们为你们带来了一个真正的精彩时刻。Jensen，Ron来了。请坐，请坐，请坐。这个热门位置。感谢你的到来。谢谢。这是全球排名第一的播客。我们说这是世界上排名第一的公司。哇，谢谢。你是这个播客的粉丝，你听这个播客。这位是我们的主持人诺曼。
Yeah, yes, and they're Steve. What's the story with the jacket? You got one of those. You have like six. I have something like 50 or 60 of them. You really? Yeah. What is that Tom Ford? I think so. This one is I think. Yeah, it's nice. I like it. I tried that on. It was like you way too much money. Well, you guys are also fashionable. Yeah, coming from you guys. It actually means something. Yeah. Oh yeah, oops. Oh, look at you. Look at you. We've been talking a lot about opportunity. You've talked to him. I just like a model. He is. He is. Okay. He's definitely in his head. He's like, it's Tom Ford. Your favorite. Who's your favorite? My favorite is whatever my wife gets me. Ah, she dresses you. As soon as she gets it for me, it's my favorite. Yes. Same with the men. It's more man.
是的，是的，他们是史蒂夫。那个夹克有什么故事吗？你也有一件这样的吧。你大概有六件。我大概有五十或六十件。真的吗？是Tom Ford的吗？我想是的。这件我觉得是。挺不错的，我喜欢。我试过一次，但实在太贵了。嗯，你们也很有时尚感。从你们那里听到这样的评价确实有意义。哦，对，看你。看你。我们一直在谈机会。你和他聊过了。我就是喜欢模特，他确实是这样。他肯定心里会想，这就是Tom Ford。你最喜欢的品牌是什么？我最喜欢的是我老婆给我买的。哦，她给你挑衣服。一旦她给我买了，就成我最喜欢的了。对，男人也大多是这样。
No one wears a suit better than Jacob. Good God. Yeah. It's a handsome man. Just trying to give over to you guys. I got two questions for you. Take them, which I've already like. We've been talking a lot about job displacement opportunity, short term, long term. Obviously you get to see everybody applying the technology because, hey, listen, you've got the best product in town to build on. Therefore, everybody explains to you their hopes, their dreams. So you have a unique way of looking at the playing field. You have complete information that we don't have. So I want to know what you think. Don't worry. We'll fix it. What you think. What you think about job creation, transfer, displacement, etc.
没有人比雅各布更适合西装。天啊。是的，他真是个帅气的人。我只是想为你们传达一些东西。我有两个问题要问你们。选择一个，我已经有些想法。我们一直在讨论工作替代和机会，无论是短期还是长期。显然，你能看到每个人如何应用这项技术，因为你们拥有本地最好的产品来支持。因此，大家都会向你们分享他们的希望和梦想。所以，你们在这个领域有独特的视角，拥有我们所没有的完整信息。我想知道你们的看法。别担心，我们会解决的。关于工作创造、转岗、失业等问题，你们怎么看？
And then the second one. I've just always been curious. You got all these important people knocking on your door. You got stock. You got E. You got Sam Altman. He seems like he's a little bit of a headache. I'll be honest. But he's great. He's great. How do you allocate the H 100s and whatever else you're selling them and still have them all like you because they must ask sometimes, hey, can I get extra? I'll pay you extra. So just the allocation of a finite amount of resources and then jobs. First of all, I wrote off $5 billion with a poppers. If anybody would like to have some extras. Just give me a call.
然后是第二件事。我一直很好奇。总有一些重要的人来敲你的门。你有股票，你有E，你还有Sam Altman。他看起来有点让人头疼，说实话。但他是个很棒的人。他真的很棒。你是怎么分配那些H 100的？你在卖这些东西的时候，怎么做到让他们都喜欢你？因为他们肯定有时候会说，"嘿，我能多拿点吗？我可以多付钱。" 所以这个问题就是，有限资源的分配问题，还有工作方面。首先，我注销了价值50亿美元的破烂。如果有人想要额外的配额，给我打个电话就好。
Jobs. We use AI across a whole company. Every single software engineer today uses AI. Not one left behind. A hundred percent of our chip designers use AI. We are busier than ever. And the reason for that is because we have so many ideas that we want to go pursue. AI makes it possible for us to go pursue those ideas now that we're not doing the mundane stuff. And so I think the first idea is the more productive you are as a company. So long as you have more ideas, you could pursue those ideas. You'll go after those ideas. And I think that that AI in my case is creating jobs. It causes us to be able to create things that other people would customers would like to buy. It drives more growth. It drives more jobs. You know, all that goes together.
工作。我们在整个公司范围内都使用人工智能。每一位软件工程师如今都在使用人工智能，没有一个人落后。我们所有的芯片设计师百分之百使用人工智能。我们比以往任何时候都更加忙碌。原因在于我们有很多想法想要实现。人工智能让我们摆脱了日常琐事，使我们能够追求这些想法。因此，我认为第一个想法是，一个公司越高效，只要有更多的想法，你就可以去追求这些想法。你会去追求这些想法。而我认为在我的情况下，人工智能正在创造就业机会。它使我们能够创造出顾客愿意购买的东西，推动更多的增长，创造更多的工作机会，所有这一切都是相辅相成的。
The other thing that to remember is that AI is the greatest technology equalizer of all time. Okay, explain. Everybody's a programmer now. Yes. You used to have to know C and then C++ and Python. And you know, in the future, everybody can program a computer, right? Just have to get up. And if you don't know how to program a computer, you don't know how to program an AI. Just go up to the AI and say, how do I program an AI? And the AI explains to you exactly how to program the AI. Even when you're not sure exactly how to ask questions, what's the best way to ask the question. And I'll actually write the question for you. It's incredible. And so it's a great equalizer. Everybody is going to be augmented by AI. Everybody's an artist now. Everybody's an author now. Everybody's a programmer now. That is all true.
要记住的另一件事是，人工智能是有史以来最伟大的技术平等器。为什么这么说呢？因为现在每个人都可以算是程序员。过去你需要掌握C语言，然后是C++和Python这些编程语言。未来，每个人都能进行计算机编程。即使你不知道如何编程，也可以直接向AI询问如何进行AI编程，AI会详细告诉你该怎么做。即便你不确定如何提问，或者不知道最佳的提问方式，AI也会为你组织问题。这简直令人难以置信。所以，这是一种伟大的平等器。每个人都将通过AI得到增强。现在每个人都是艺术家、作家和程序员。这都成为了现实。
And so we know that AI is a great equalizer. We also know that it's not likely that although everybody's job will be different as a result of AI, everybody's jobs will be different. Some jobs will be obsolete. But many jobs will be created. The one thing that we know for certain is that if you're not using AI, you're going to lose your job to somebody who uses AI. That I think we know for certain. There's not a software programmer in the future who's going to be able to hold their own. I mean, you know, typing by themselves. You can't raw dog it. No. No, not anymore. Not anymore. You can't raw dog it. I'll be sure to go home and tell people. Yeah, exactly. You're not going to raw dog this. Yeah, get your co-pilot on.
我们知道人工智能是一个很好的平衡器。我们也知道，尽管每个人的工作都会因为人工智能而有所不同，但有些工作会被淘汰，而许多新工作将被创造。可以确定的是，如果你不使用人工智能，你就会被那些会使用人工智能的人取代。这一点是我们可以确定的。在未来，没有哪个软件程序员可以独自完成工作，在没有工具的帮助下，你无法单打独斗。不，你已经不能这么做了。回到家，我一定会告诉大家这件事。没错，你不能再单打独斗了，赶快找个助手吧。
Now what about the allocation of all these? Okay, so the way we allocate is this place of PO. That's it. You go to the register. You pay, you order. First, you know, first in the old days with Hopper, it happened so fast. It wasn't possible to keep up with the demand. But now we disclose our roadmap to all of our partners a year in advance. It gives everybody a chance to plan with us. They decide how much power and how much data center space and how much capex they want to allocate. We plan together. We work on transitions. It's really quite early these days.
现在我们来谈谈如何分配这些资源。我们分配资源的方式是在采购订单（PO）中进行。就是这样，你去柜台，进行支付和下单。以前，在Hopper时代，这个过程发展得非常快，几乎无法满足需求。但是现在，我们提前一年向所有合作伙伴公开我们的路线图。这给每个人提供了与我们一起规划的机会。他们可以决定要分配多少电力、数据中心空间和资本支出。我们一起进行规划，协作推进过渡阶段。如今，这个流程确实已经非常提前了。
What's the lifespan now? You know, I was looking into how they're amateurizing, you know, these units four or five years. What happens to this massive build out in your six, seven and eight? What will be the use of those computers? If you keep building such great products that replace them at two, three, four times, what do we do with that? The concepts are happening right now. The first thing, first thing is every generation, we increase the performance by X factors. Yeah. If the perf per doll per per watt goes up by X factors, whatever your data center power is, we just increase your revenues by X factors. Right. So perf per watt is equal to revenues. Perf per dollar equals the cost. And so when we increase your perf per dollar by X factors, we reduce your cost by X factors. Does that make sense?
这段文字翻译成中文，并尽量简单易读：
现在的生命周期是多少？我在研究这些设备，它们在四五年后就会被替换。在第六、七、八年时，这些大规模的建设会发生什么？如果你们不断开发出比之前好两倍、三倍、四倍的产品，那这些旧电脑该怎么办？现在，这些概念正在出现。首先，每一代产品的性能都会提高X倍。如果每瓦性能（perf per watt）提高了X倍，不管你的数据中心电力有多大，我们实际就将你的收入提高了X倍。每瓦性能相当于收入，每美元性能（perf per dollar）相当于成本。当我们将每美元性能提高X倍时，我们就降低了你的成本X倍。这样说有道理吗？
That's the first idea. And so every single reason why we're moving so fast is we're trying to increase everybody's revenues. We're trying to decrease everybody's cost so that we have the benefit of driving AI cost down as far as possible so that we can have thinking AI. It's not that we're trying to make, you know, AI so that it generates a thousand tokens. And that's it. In the future, you're going to be generating millions of tokens and that generates an answer as a result of that. You got to think a long time. And so you got to get that cost down.
这是我们的第一个想法。我们之所以如此快速推进，是因为我们想增加大家的收入，减少大家的成本，以便能够大幅降低人工智能的成本，最终实现有思维能力的人工智能。我们的目标并不是让AI仅仅生成上千个字词而已。在未来，AI可能会生成数百万个字词，并在此基础上提供答案。这需要考虑很长时间，因此我们必须降低成本。
The second idea is if you look at the residual value of NVIDIA gear right now, hopper for example, one year later, it's probably about 80%, 75% to 80% of the value of your original value.
第二种观点是，目前来看，NVIDIA设备的剩余价值，比如说Hopper，一年后可能会保有原始价值的75%到80%左右。
And so the next thing that we're going to do is we're going to do a whole lot of things. And so the next thing that we're going to do is we're going to do a whole lot of things that we're going to do.
接下来，我们要做很多事情。我们将要进行许多事情。
And so the next thing that we're going to do is we're going to do a whole lot of things that we're going to do. And so the next thing that we're going to do is we're going to do a whole lot of things that we're going to do. And so the next thing that we're going to do is we're going to do a whole lot of things that we're going to do. And so the next thing that we're going to do is we're going to do a whole lot of things that we're going to do. And so the next thing that we're going to do is we're going to do a whole lot of things that we're going to do. And so the next thing that we're going to do is we're going to do a whole lot of things that we're going to do.
接下来我们要做的事情是，我们会做很多我们计划要做的事情。
Right. Jensen, can you explain to us, um, he launched tweet and the impact to your industry. He said, we're going to have 50 million H 100 equivalents by in five years from now. And everybody started to feverishly do the math. Because if he has 50 million H 100 equivalents, then I will have that much or more. Meta will have that much or more Google, et cetera, et cetera, et cetera. Can you just explain to us, layman, what that means, what he just said, and how it impacts your business?
好的。詹森，你能为我们解释一下吗？嗯，他发了一条推文，说五年后将拥有5000万个H 100等效产品。大家都开始疯狂地计算。因为如果他有5000万个H 100等效产品，我也会有那么多，或者更多。Meta会有那么多，谷歌等等也都会有那么多。你能否用简单的语言解释一下这意味着什么，他刚才说的话对你的行业有什么影响？
Um, one of the biggest observations about AI is that there's, there's the industry of applications that AI has created as a revolutionary technology every industry would, will be revolutionized new applications will be created so on so forth that all the things that we know. Agenetic AI, reasoning AI, robotics AI so on so forth, we know all those things now. Every industry, healthcare, education, transportation, you name manufacturing all revolutionized.
AI的一个最大观察是，作为一项革命性技术，AI创造了一个应用产业。每个行业都会被其彻底改变，并且会诞生新的应用。我们现在知道各种各样的AI类型，如生成型AI、推理型AI、机器人AI等等。每个行业，包括医疗、教育、交通、制造业等等，都会被彻底革新。
The one part that that that we observed and made a great contribution to is that in order to sustain those applications, you need factories of AI. You have to produce AI. Unlike software, you write the software and that's it. In the case of AI, you have to continuously produce it, generate the tokens. In a lot of the same ways that energy production was a large part of the economy. A couple of two, three hundred years ago, I think it actually peaked out of 30%. There's a whole, there's going to be a whole industry of just producing tokens.
我们观察到并为之做出重要贡献的一点是，要维持这些应用程序，你需要AI工厂。你得不断生产AI。与软件不同，你只需编写软件就完成了。而AI需要持续不断地生成和更新，就像能源生产曾经是经济的重要组成部分一样。一两百年前，能源生产一度占经济的30%。未来将会有一个专门用于生成AI内容的完整行业。
And this is going to be the new infrastructure just as we have the energy production infrastructure, we have the internet infrastructure and we got to build out that plumbing. And now we got to, we have to build out the AI infrastructure. My sense is that we're probably, you know, a couple of hundred billion dollars, maybe a few hundred billion dollars into a multi trillion dollar infrastructure build out per year.
这将成为新的基础设施，就像我们拥有能源生产基础设施、互联网基础设施一样，我们需要建造这套系统。现在，我们必须建设人工智能的基础设施。我觉得，我们可能已经投入了几百亿美元，甚至几千亿美元，而每年的基础设施建设总规模将达到数万亿美元。
What about manufacturing? And the reason for that is because you want the new infrastructure, which increases revenue drive your class down. What about manufacturing in the US? So where are we, we, you know, we've seen stories of TSMC in Arizona. We asked this question earlier about how it's going. Is the US equipped? What is it going to take for us to get there to have onshore fats?
关于制造业呢？之所以提到制造业，是因为你希望新的基础设施能够增加收入，从而降低你的成本。那美国的制造业情况如何？我们知道，最近有关于台积电在亚利桑那州的新闻。我们之前也问过这个问题：进展如何？美国是否有足够的能力应对？要实现本土化制造，我们需要做些什么？
First of all, you guys know you're talking about the United States. The, I know that there's lots of concerns and everybody's, you know, worried about competition and things like that. But we are talking about America here. This is, this is unquestionably the most technology rich country in the world. And this is the most innovative countries in the world. The computer industry, I have the honor to serve, is the single greatest industry our country has ever produced. I think we could acknowledge that.
首先，你们知道你们讨论的是美国。我知道有很多担忧，大家都在为竞争等问题感到不安。但是这里我们谈论的是美国。毫无疑问，这是全球科技最发达的国家，也是最具创新力的国家。我有幸服务的计算机行业，是我们国家所创造的最伟大的行业。我觉得我们可以承认这一点。
The level of leadership of the computer industry, the technology industry, is just unimaginable worldwide. And so this is our national treasure. This is one of our country's assets. We have to make sure that we continue to, to, to advance it. Onshoreing, next generation manufacturing is going to be insanely technology driven. Robotics technology, AI technology. You're going to have factories that are going to be orchestrated by AI, orchestrating a whole bunch of robots that are AI, building products that are effectively AI's. Right? So you're going to have this layers of inception. And the amount of technology necessary to create that isn't really insane. We've, I, I love President Trump's vision, bold vision of reindustrializing the United States.
计算机行业和技术行业的领导水平在全球范围内是难以想象的。这是我们的国家瑰宝，也是我们国家的资产之一。我们必须确保不断推进这一事业。未来的国内制造业将高度依赖技术，充满了机器人技术和人工智能技术。工厂将由人工智能指挥，协调一群人工智能驱动的机器人生产实际上也是由人工智能设计的产品。可以说，这是层层递进的过程。实现这一目标所需的技术量非常庞大。我非常欣赏特朗普总统关于重新实现美国工业化的大胆愿景。
That entire band of industry that's missing, we outsourced too much of it, frankly. We don't need to insource all of it. But we ought to bring onshore the most advanced, the most economy, sustaining, driving, national security enhancing parts of the industry. You know, people always degrade down to tennis shoes. We don't have to go there. We just manufacture chips and AI supercomputers. In Arizona and Texas, we will, in the next four years, probably produce about half a trillion dollars with AI supercomputers. About half a trillion dollars with AI supercomputers will probably drive a few trillion dollars with the AI industry. And so that's only in the next several years.
整个行业中有一部分已经消失了，因为我们过度外包了这些工作。坦白说，我们不需要把所有的工作都迁回国内，但应该把最先进的、对经济具有支撑和推动作用的，以及能增强国家安全的部分带回国内制造。很多人总是提到网球鞋，但我们不一定要走那条路。我们只需要在亚利桑那州和德克萨斯州制造芯片和人工智能超级计算机。在未来四年内，我们可能会生产价值约5000亿美元的人工智能超级计算机。这些超级计算机可能会推动价值数万亿美元的人工智能行业发展。以上只是未来几年的计划。
And they're doing great. Arizona is doing great. And so there's, there's a lot of talk about American competitiveness today. And the White House ruled out its AI action plan and, and the video is making very big bets on the United States. And so as a CEO of a global company, what do you see our America's unique advantages that other countries don't have? America's unique advantage that no country possibly have is President Trump. And let me, let me explain why. One, on the first day of his administration, he realized the importance of AI and he realized the importance of energy. For the last, I don't know how many years, energy production was, was vilified, if you guys remember.
他们干得很棒。亚利桑那州的表现也很出色。现在有很多关于美国竞争力的讨论。白宫公布了其人工智能行动计划，视频对美国寄予厚望。作为一家全球公司的首席执行官，您认为美国有哪些其他国家没有的独特优势？美国独一无二的优势就是特朗普总统。让我来解释为什么。首先，在他上任的第一天，他就意识到了人工智能的重要性，并认识到了能源的重要性。在过去的若干年里，能源生产被妖魔化，如果你们还记得的话。
Yeah. We can't create new industries without energy. You can't reshore manufacturing without energy. You can't sustain a brand new industry like artificial intelligence without energy. If we decide as a country that only thing we want is IP, to be an IP only, a services only country, then we don't need much energy. But if we want to produce things, something as vital as artificial intelligence and we need energy. And so I'm just delighted to see pro, to accelerate AI innovation, to accelerate the growth of energy so that we can sustain this, this new industry. And, you know, go after the, the new industrial revolution. Big, big deal.
好的。我们无法在没有能源的情况下创造新的产业。没有能源，就无法把制造业迁回国内。没有能源，也很难支持人工智能这样的全新产业。如果我们作为一个国家决定只想要知识产权，只做一个依靠知识产权和服务的国家，那我们不需要太多的能源。但如果我们想要生产实物，尤其是像人工智能这样重要的东西，就需要能源。因此，我非常高兴地看到加速人工智能创新和能源增长的支持，这样我们才能维持这个新兴产业，并迎接新的工业革命。这是个重要的大事。
Can you talk about physical AI versus data center AI? We talked a little bit about this today. Is there a threshold where you see physical AI accelerating and ultimately the deployment of chips outpaces, the deployment of chips and data centers? Is that where the world evolves to? What do you think? Everything in the world looks like. Yeah. Excellent. Everything in the world that moves will be autonomous someday. And that someday is probably around the corner. So everything that moves. We already know that your lawnmower is going to, you know, who's going to be pushing a lawnmower around this? Craziness. Unless you want to.
你能谈谈物理人工智能和数据中心人工智能之间的区别吗？今天我们稍微讨论了一下这个问题。有没有一个临界点，会让你觉得物理人工智能的发展会加速，并最终超过数据中心芯片的部署速度？你认为世界会朝着这个方向发展吗？你的看法是怎样的？太好了。我认为，未来一切能够移动的东西都会实现自动化。而这个未来可能即将到来。所有能够移动的东西。我们已经知道，未来没有人会去推割草机，这是不可思议的，除非你自己愿意去做。
I mean, it's, you know. And so, so I think everything that moves will be autonomous. And every machine, every company that builds machines will have two factories. There's the machine factory, for example, cars. And then there's the AI factory to create the AI for the cars. And so maybe you're a machine factory to build human robots. You need an AI factory to build a brain for the human robot. Right. And so every company in the future, in fact, the future of industry is really two factories. No. Tesla already has two factories. Right. Elon has a giant AI factory. He was very early in recognizing that he needs to have an AI factory to sustain the cars that he has.
我的意思是，你懂的。所以，我认为一切会动的东西都会变成自动化的。每个制造机器的公司都会有两个工厂。比如说，汽车厂就是其中一个。然后还有一个用于为汽车创造人工智能的AI工厂。也许你是一个制造人形机器人的工厂，那么你就需要一个AI工厂来为机器人创造“大脑”。对，每家公司未来都会有这两种工厂。实际上，未来的工业就是由这两种工厂组成的。特斯拉已经有这种模式了。伊隆·马斯克很早就意识到他需要一个AI工厂来支撑他拥有的汽车。
Now he's got AI's in the car, but in the future, instead of, you know, I imagine that in the future, instead of a whole whole lot of people remotely monitoring the factory, or a whole lot of people remotely monitoring air traffic control, it'll be a giant AI that's doing the remote control. And then only in the case of the giant AI can handle it where the person come in to intercept. And so I think you see that these industries in the future, every industrial company will be an AI company. Or are you not going to be an industrial company? There was a couple of moments throughout the course of this year where people almost threw in the towel and said, there's China, right?
现在他的车里装有人工智能，但我想象，在未来，与其让很多人远程监控工厂或空中交通指挥，可能会由一个巨大的人工智能系统来进行远程控制。只有在这个巨大的人工智能系统无法处理的情况下，人们才会介入。因此，我认为在未来，各个行业都会转变成人工智能公司，否则就无法继续成为工业公司。在今年的一些时刻，有人几乎放弃并提到了中国的情况，对吧？
There was the deep seek moment, but maybe this week, last week, there was this Kimming model moment. But then it kind of fizzled out. Can you just explain to us how big of a threat they really are in terms of getting to supremacy, getting their first, whether it's the AGI or super intelligence? Yeah, excellent question. The Chinese AI labs are the world's leading open model companies. They offer the most advanced open models. Open source is fantastic. If not for open source, we know startups won't exist. And to the extent that we believe that the future is going to be, the future industry is going to be today startups. They're going to need open source models.
有一个很关键的时刻，但也许就在这周或者上周，有一个关于Kimming模型的时刻。不过后来它似乎没什么后续了。你能给我们解释一下，他们在实现超越性、达到第一，比如实现AGI或超级智能方面，到底有多大的威胁吗？这是个很好的问题。中国的人工智能实验室是全球领先的开放模型公司。他们提供最先进的开放模型。开源非常棒。如果没有开源，我们知道初创公司可能就无法存在。而我们相信未来的产业很可能就是今天的初创公司，因此他们将需要开源模型。
And deep seek, when it came out, it was a great win for the United States. It was an incredible win. What people didn't, and two reasons. First, imagine if deep seek came out and only ran on Huawei. I just want us to pretend. Use that thought experiment. Totally. Right. You got to parallel universe. No, we know exactly. Could you imagine if QN came out and only worked on non-American tech stack? Could you imagine if Kimmy came out and only worked on non-American tech stack? And these are the top three open models in the world today. It has downloaded hundreds of millions of times.
当 "deep seek" 发布时，它是美国的一个巨大胜利，真的是一个令人难以置信的胜利。人们通常忽略了这一点，原因有两个。首先，试想一下如果 "deep seek" 只能在华为设备上运行。我们来进行这个假设，完全假设是这样。在一个平行宇宙中，你懂的，没错。你能想象如果 QN 只在非美国技术栈上运行会怎么样吗？你能想象 Kimmy 只在非美国技术栈上运行的情况吗？这些是如今全球排名前三的开放模型，已经被下载了数亿次。
So the fact of the matter is, American tech stack, all over the world, being the world's standard, is vital to the future of winning the AI race. You can't do it any other way. We've got to be, as you know, any computing platform wins because of developers. Yeah. And half of the world's developers are in China. So speaking of developers. The second, the second, I'm sorry. The second thing and it's really big deal. When deep seek came out, we were thrilled for the second reason, which is we now have a super efficient reasoning model.
事实上，美国的技术体系已经成为全球的标准，这对于在人工智能竞赛中获得胜利至关重要。没有其他途径可行。正如你所知，任何计算平台的胜利都依赖于开发者。而全球一半的开发者都在中国。谈到开发者，第二件事，我很抱歉。第二件事情是个大问题。当深度搜索模型出现时，我们感到非常兴奋，原因之一就是我们现在拥有了一个超级高效的推理模型。
And the reason for that is because the old models are one shot. You give it a question. Everything was memorized. You know, pre training is basically memorization and generalization, two concepts. Post training is teaching you how to think. And so now with deep seek R1, Kemi K2, Q1, 3, you now have reasoning models that can allow, that help you to think. And so the reason why I was so excited is, if each pass of a thought is energy efficient, then you can think for a long time.
这段话的大意是：原因在于旧的模型是一次性的，你向它提问时，一切都是提前记住的。预训练基本上是记忆和泛化这两个概念，而后期训练则是教你如何思考。因此，现在有了像 deep seek R1、Kemi K2、Q1、3这样的推理模型，它们可以帮助你思考。我之所以感到兴奋，是因为如果每一次思考的过程都很节能，那么你就可以长时间地进行思考。
Yeah. The last question for me is that we see this capital being applied to human capital in a way that we never thought was possible. It used to be NBA players signing $300 million contracts. Now it's, you know, model researchers. And then there was a post this weekend that said that there was a person that was offered a billion dollars over four years by Meta. Now if that's happening at this layer, why hasn't it happened at your layer? Because you are the enabler of all of that. And how do you think all of this human capital is going to actually play out?
好的。我最后一个问题是，我们看到资本对人力资本的投入达到了前所未有的程度。以前，是NBA球员签三亿美元的合同。现在则是，例如，顶级研究人员。最近有一个帖子提到，有人被Meta公司开出了四年十亿美元的报价。如果这种情况已经发生在这个层面上，那为什么还没有发生在你的层面上呢？因为你是这一切的促成者。你认为所有这些人力资本最终会如何发展？
First of all, I've created more billionaires on my management team than any CEO in the world. They're doing just fine. Okay. And so, and they're doing, don't feel sad for anybody at my layer. Yeah. Everybody's doing okay. Yeah, my layer is doing just fine. But the important, the big idea though, is that you're highlighting, is that the impact of a 150 or so AI researchers can probably create with an upfunding behind them, create an open AI. It's not a 150 people.
首先，在我的管理团队中，我已经创造了比世界上任何其他CEO更多的亿万富翁。他们的情况都很好。所以，不必为我这个层级的人感到难过。大家都过得不错，我这个层级的人都表现得非常好。不过，你提到的一个重要的大想法是，有大约150名AI研究人员在充足的资金支持下所能够创造的影响力，可能会产生一个类似于OpenAI的组织。这不仅仅是150个人的事情。
Yeah, it's not a, well deep-six 150 people. It shots 150 people. Right, right. And so, I mean, look at the original open AI was about 150 people. Deep-mind, you know, and they're all about that size. I think, you know, there's something about the elegance of small teams and that's not a small team. That's a good, good size team with the right infrastructure. And so, that kind of tells you something. 150 people, if you're willing to pay, say, $20 billion, $30 billion, the buy a startup with 150 AI researchers, why wouldn't you pay one?
好的，这不是简单地让150人消失不见。实际上，他们起到了作用。对，对。我是说，看看最初的OpenAI也是由大约150人组成的团队。DeepMind你知道，也是类似的规模。我认为，小团队有其独特的优雅之处，但150人也算不上小团队，而是一个有合适基础设施的优秀团队。所以，这让你明白了一些事情。如果你愿意花200亿、300亿美元来收购一个有150名AI研究员的初创公司，那为什么不这样做呢？
Right. Speaking of options. By the way, we, we, we told me, we need to wrap, because I don't know, but we have the same. I'm going to do this one question. Somebody who was inside your organization told me with the options that you have a secret pool of options and that you will randomly just, if somebody does a great job, dropped a bunch of RSUs on top of them and that you have this like little bag of options you carry around and that you can send them out. That's, that's, that's, is that true?
好的，说到期权。顺便提一下，我们需要结束这个话题，因为我也不太清楚，但我们想法一致。我来问一个问题。有内部人士告诉我，你们公司有一批秘密的期权，通常当有人表现出色的时候，你们会随机给予他们一大堆限制性股票单位（RSU）。据说你们有一个可以随时派发的小袋子。这是真的吗？
Yeah, I'm carrying in my pocket right now. So, listen, so this is what happens. I review, I review everybody's compensation up to this day. Yeah. At the end of every cycle, when they presented and they sent, they send me everybody's, everybody's recommended a comp. I go through the whole company, I've got my methods of doing that and I use machine learning, I do all kinds of technology. And I sort through all 42,000 employees and a hundred percent of the time I increase the company's spend on op-x.
好的，我现在就放在口袋里。所以听好，事情是这样的。我会定期审查每个人的薪酬，直到今天为止。每个周期结束时，他们会提交并发送所有人的薪酬建议。我会查看整个公司有4.2万名员工，并采用我的方法进行分析，其中包括使用机器学习和各种技术。几乎每次我都会增加公司在运营开支（op-x）上的支出。
And the reason for that is because you take care of people, everything else takes care of, takes care of the company. Exactly.
原因是，当你关心人们时，其他一切就都会得到妥善处理，公司的事务也一样。没错。
Yeah. All right. Well done.
好的。不错，干得好。
Thank you, thank you, Jason.
谢谢，谢谢你，杰森。
Thank you. We have an L.A.
谢谢。我们有个洛杉矶。
We'd love to continue the conversation.
我们很想继续这个讨论。
Yeah. So we'll send you the world's number one podcast.
好的。我们会把这个全球排名第一的播客发给你。
There you go. Thank you.
你拿着吧。谢谢你。