I.C.S.307.Capital.TECH.NVDA.Close
可以相信计算资源不重要，压缩后反映世界本质的知识很可能是非常精简的。人工智能的软件部分可能大幅度改变世界，但是硬件特别是芯片投资看上去有些问题，GPU之前的计算机硬件很多年没什么进步，但这期间软件的进步非常大，对比intel和microsoft就很明显，一个在地一个在天。
同时可以参照巴菲特对Intel的评论，巴菲特不看好的原因是《只有偏执狂才能生存》，书中描述了战略拐点，但他说：“安迪·格罗夫和其他一些人一起完成了转型。不过，这种事情并不是每次都会发生。”。
1、I.C.S(Intelligent hypotheses, Correct facts and Sound reasoning.)
人工智能对广告业务的影响：通过推荐引擎提升广告效率已经有明显的效果，很多企业的提升幅度接近20%，比如，Meta和腾讯，Meta每年广告收入1300亿美元，假设整个市场是5000亿美元，提升10%是500亿的新增收入，20%是1000亿的新增收入，这些收入大部分给到NVIDIA，目前看着还没有太偏离事实。
全球广告总收入9270亿美元，其中数字广告6550亿美元，主要由推荐引擎产生的广告包括社交软件（2120亿美元），短视频（780亿美元），以及大型零售商的广告业务（1260亿美元），合计4160亿美元，大型零售商的广告业务部分搜索引擎部分推荐引擎，乐观估计距离5000亿美元不算太偏离，参考：《MAGNA Ups Advertising Growth Outlook Following Strong First Half of the Year》。
人工智能对云计算的影响：全球三大云计算公司，亚马逊、微软、Google，云计算的年收入分别是1100亿美元，964亿美元，456亿美元，合计2520亿美元，三大公司占据整个市场的64%，乐观估计市场目前的整体规模是5000亿美元，人工智能的增量收入有多大？
亚马逊、微软、Google在云计算上的营收入是104亿美元，105亿美元，19亿美元，合计228亿美元，全行业500亿美元，按20%的贡献计算是100亿美元，云计算和数字广告不一样，数字广告的边际成本几乎为零，增加的销售收入即贡献的价值，云计算是有固定成本的，参考：《微软、AWS和谷歌云2024年第三季度盈利对比》。
2、《1983-06-15 Steve Jobs.Let the world innovate》
Making machines intuitive 让机器变得直观
STEVE JOBS: Well, the major thing is that we’ve got to make things intuitively obvious. And it turns out that people know how to do a lot of things already. In other words, if you walk into a typical office, there’s all these…there’s stacks of paper on the desk, and the one on the top is the most important. And people know how to switch priority. Right? And people know how to deal with concurrent things going on at once. They’re constantly switching between things every few minutes. And they know how to deal with interruptions. The phone rings, they get an urgent message.
史蒂夫·乔布斯：嗯，主要的是我们必须让事情变得直观明了。事实证明，人们已经知道如何做很多事情。换句话说，如果你走进一个典型的办公室，桌子上有一堆堆的文件，最上面的那份是最重要的。人们知道如何切换优先级，对吧？人们知道如何处理同时发生的事情。他们每隔几分钟就会在不同的事情之间切换。他们知道如何处理中断。电话响了，他们收到一条紧急信息。
And so what we’ve got to do is leverage off of what people already know how to do. And part of the reason we model our computers on these metaphors that already exist out there, like the desktop, is because we can leverage all this experience that people already have, and they intuitively just take to it like water.
所以我们要做的是利用人们已经知道如何做的事情。我们将计算机建模为已经存在的比喻（如桌面）的部分原因是，我们可以利用人们已经拥有的所有经验，他们会直观地接受它。
The second thing we do is, right now, when you buy an application, each one works differently. In other words, not only do you have the specific knowledge about the application to learn, but it interacts with you through the computer differently than the last one. The word processor, you move the cursor around this way. VisiCalc, you move it around another way.
我们现在做的第二件事是，当你购买一个应用程序时，每个应用程序的工作方式都不同。换句话说，你不仅需要学习关于该应用程序的特定知识，而且它通过计算机与您的交互方式也与上一个不同。文字处理器，你这样移动光标。VisiCalc，你用另一种方式移动它。
And what we’ve got to do is make it so that when you learn how to use one application, all the rest of them work in pretty much the same way. And to come up with a general…we spent a lot of time coming up with a general mechanism that was so powerful that there was not one type of program where it wouldn’t be perfect for it. We think we did that. We think we absolutely did that. And so in trying to make some consistency throughout the system, we can leverage the learning.
我们要做的是，当你学会如何使用一个应用程序时，其他所有应用程序的工作方式基本相同。我们花了很多时间想出一个通用机制，这个机制非常强大，没有一种程序类型不适合它。我们认为我们做到了。我们认为我们绝对做到了。因此，在尝试使整个系统保持一致性时，我们可以利用这种学习。
Let the world innovate 让世界创新
STEVE JOBS: The neatest thing about it is that, again, when you make tools and then…see, you get a bunch of smart people that can design into something. And then they can give it to a bunch of people that maybe can’t design it, but they can build a lot of them. And then you can give it to a bunch of people that can’t do that, but they can help get them out in the world through stores. And then get it to even a giant group of people that can use them.
史蒂夫·乔布斯：这最妙的地方是，当你创造工具，然后…你会得到一群聪明的人，他们能够设计出一些东西。然后他们可以把它交给一群可能无法设计它的人，但他们可以生产大量的这些东西。接着你可以把它交给一些无法做到这些的人，但他们可以通过商店帮助把这些东西推向市场。然后让它进入一个庞大的群体，让他们使用这些东西。
And when you have a million people using something, then that’s when creativity really starts to happen on a very rapid scale, because the marketplace…there are literally like 500 companies making software for the Apple II, and they’re all watching each other. And the minute one of them comes up with a good idea, it ain’t six months before they all got it. And so it’s constantly raising the level of competence and the level of innovation that’s required to sell stuff to those million Apple owners out there. And that’s phenomenal.
当有一百万人在使用某个东西时，这时创造力就开始以极快的速度发生，因为市场上……有大约 500 家公司在为 Apple II 制作软件，他们都在互相观察。当其中一个公司想出一个好主意时，不到六个月他们全都有了。因此，它不断提高了能力水平和创新水平，以便向那一百万苹果用户销售产品。这是惊人的。
截至2024年8月，ChatGPT的每周活跃用户已超过2亿人，较去年11月的1亿人翻了一番。
And so the fastest way to get innovation is, we need some revolutions like Lisa, but we also then need to get millions of units out there and let the world innovate, because the world is pretty good at innovating, we found.
因此，获得创新的最快方式是，我们需要一些像 Lisa 这样的革命，但我们也需要将数百万台设备推向市场，让世界进行创新，因为我们发现世界在创新方面相当出色。
3、《1997-05-05 Berkshire Hathaway Annual Meeting》
17. We don’t know how to value Intel and Microsoft
我们不知道如何评估英特尔和微软
WARREN BUFFETT: Zone 11, please.
沃伦·巴菲特： 请第十一区提问。
AUDIENCE MEMBER: Yes, Mr. Buffett, I would like to thank you again for issuing the Class B shares.
观众： 是的，巴菲特先生，我再次感谢您发行了B类股票。
WARREN BUFFETT: (Laughs) Well, I’m glad we did, and I hope you own them.
沃伦·巴菲特： （笑）我很高兴我们这么做了，我希望您拥有这些股票。
AUDIENCE MEMBER: I am a Class B shareholder.
观众： 我是一名B类股股东。
I need your comment on some analysis that we did. If someone uses your investment philosophy of building a highly concentrated portfolio of six to eight stocks, and adopts your buy-and-holding principle so that the max of compounding and no tax works for you, but however, with one major modification: invest in high-octane companies like Intel and Microsoft that are growing at 30 percent, instead of typical 15 percent growth company in your portfolio.
我们进行了一些分析。如果有人采用您的投资哲学，建立一个高度集中的投资组合，包含6到8只股票，并采用您的长期持有原则，这样可以最大化复利效应且免于缴税，但有一个主要修改：投资于像英特尔和微软这样的高增长公司，它们的增长率是30%，而不是您投资组合中典型的15%的增长公司。
My question is, will this investment philosophy translate into twice the shareholder return as you have historically provided to your shareholders?
我的问题是，这种投资哲学是否会转化为您历史上为股东提供回报的两倍？
WARREN BUFFETT: Yeah. Well, it will certainly work out to twice the return if Intel and Microsoft do twice as well as Coke and Gillette. I mean, it’s a question of being able to identify businesses that you understand and feel very certain about.
沃伦·巴菲特： 是的。如果英特尔和微软的表现是可口可乐和吉列的两倍，那么回报肯定也会是两倍。这主要取决于是否能够识别出您理解并非常确信的业务。
And if you understand those businesses, and many people do, but Charlie and I don’t, you have the opportunity to evaluate them. And if you decide they’re fairly priced and they have marvelous prospects, you’re going to do very well.
如果您理解这些业务，很多人确实理解，但查理和我不理解，那么您就有机会去评估它们。如果您认为它们的价格合理且前景光明，那么您会表现得非常出色。
But there’s a whole group of companies, a very large group of companies, that Charlie and I just don’t know how to value. And that doesn’t bother us. I mean, you know, we don’t know what — we don’t know how to figure out what cocoa beans are going to do, or the Russian ruble, or I mean, there’s all kinds of financial instruments that we just don’t feel we have the knowledge to evaluate.
但是，有一大类公司，确实是很大的一类公司，查理和我不知道该如何评估。而这并不会困扰我们。我的意思是，比如我们不知道可可豆的价格走势，也不知道俄罗斯卢布会如何表现，还有各种各样的金融工具，我们觉得自己没有足够的知识去评估它们。
And really, you know, it might be a little too much to expect that somebody would understand every business in the world.
确实，期望有人能够理解全世界的每一个行业可能有点过分。
And we find some that are much harder for us to understand. And when I say understand, my idea of understanding a business is that you’ve got a pretty good idea where it’s going to be in ten years. And I just can’t get that conviction with a lot of businesses, whereas I can get it with relatively few. But I only need a few. As you’ve pointed out, you only need a few, six or eight or something like that.
我们发现有些行业对我们来说非常难以理解。而我所说的“理解”是指你对一个企业十年后的状况有一个相当清晰的预判。而对于很多企业，我无法得到这种信心，而对于少数企业，我可以。但我只需要少数几个企业。正如你提到的，你只需要少数几个，比如六到八个这样的企业。
It would be better for you — it certainly would have been better for you — if we had the insights about what we regard as the somewhat more complicated businesses you describe, because there was and may still be a chance to make a whole lot more money if those growth rates that you describe are maintained.
如果我们对你所描述的那些稍微复杂一些的企业有更多的洞察力，这对你来说会更好——肯定会更好。因为如果这些增长率能够保持，那么确实有机会赚到更多的钱，现在可能仍然有这样的机会。
But I don’t think they’re — I don’t think you’ll find better managers than Andy Grove at Intel and Bill Gates at Microsoft. And they certainly seem to have fantastic positions in the businesses they’re in.
但我不认为——我不认为你能找到比英特尔的安迪·格罗夫（Andy Grove）和微软的比尔·盖茨（Bill Gates）更优秀的管理者。而且他们似乎确实在各自的领域中占据了极好的位置。
But I don’t know enough about those businesses to be as sure that those positions are fantastic as I am about being sure that Gillette and Coca-Cola’s businesses are fantastic.
但对于这些企业，我并没有足够的了解，无法像确信吉列和可口可乐的业务一样，确信它们的地位是如此的出色。
You may understand those businesses better than you understand Coke and Gillette because of your background or just the way your mind is wired. But I don’t, and therefore I have to stick with what I really think I can understand. And if there’s more money to be made elsewhere, I think the people that make it are entitled to it.
由于你的背景或思维方式，你可能比了解可口可乐和吉列更了解那些企业。但我不是，因此我必须坚持投资我真正认为可以理解的业务。如果其他地方可以赚更多的钱，我认为那些赚到钱的人是应得的。
Charlie?
查理？
CHARLIE MUNGER: Well, if you take a business like Intel, there are limitations under the laws of physics which eventually stop your putting more transistors on a single chip. And the 30 percent per annum, or something like that, you — I don’t think — those limitations are still a good distance away, but they’re not any infinite distance away.
查理·芒格： 以英特尔这样的公司为例，物理定律设定了一些限制，这些限制最终会阻止你在单个芯片上放置更多的晶体管。至于30%的年增长率之类的情况，我认为——这些限制还比较遥远，但并不是无限遥远的。
That means that Intel has to leverage its current leadership into new activities, just as IBM leveraged the Hollerith machine into the computer. Predicting whether somebody’s going to be able to do that in advance is just — it’s too tough for us.
这意味着英特尔必须将其当前的领先地位扩展到新的活动中，就像IBM将霍勒瑞斯打孔机（Hollerith machine）扩展到计算机领域一样。提前预测某人是否能够做到这一点——对我们来说，这太难了。
WARREN BUFFETT: Bob Noyce —
沃伦·巴菲特： 鲍勃·诺伊斯——
CHARLIE MUNGER: We could (inaudible) to you.
查理·芒格： 我们可以（听不清）。
WARREN BUFFETT: Bob Noyce, one of the two founders of — two primary founders — of Intel, grew up in Grinnell, Iowa. I think he’s the son of a minister in Grinnell, and went through Grinnell College and was chairman of the board of trustees of Grinnell when I went on the board of Grinnell back in the late ’60s.
沃伦·巴菲特： 鲍勃·诺伊斯是英特尔的两位主要创始人之一，他在艾奥瓦州的格林内尔长大。我记得他是格林内尔一位牧师的儿子，就读于格林内尔学院，并在我于60年代末加入格林内尔董事会时，担任学院董事会主席。
And when he left Fairchild to form Intel with Gordon Moore, Grinnell bought 10 percent of the private placement that funded — was the initial funding for Intel.
当他离开仙童公司（Fairchild）与戈登·摩尔（Gordon Moore）共同创立英特尔时，格林内尔购买了英特尔初始私募资金的10%。
And Bob was a terrific guy. He was very easy to talk to, just as Bill Gates is. I mean, these fellows explained the businesses to me, and they’re great teachers but I’m a lousy student. And they — I mean, they really do. They’re very good at explaining their businesses.
鲍勃是一个了不起的人。他非常平易近人，就像比尔·盖茨一样。我是说，这些人向我解释他们的业务，他们是很棒的老师，但我是个糟糕的学生。他们确实是非常擅长解释他们的业务。
Bob was a very down to earth Iowa boy who could tell you the risks and tell you the upside, and enormously likeable, a hundred percent honest, every way.
鲍勃是一个非常脚踏实地的艾奥瓦州男孩，他会告诉你风险和潜力。他非常讨人喜欢，完全诚实，方方面面都是如此。
So we did buy 10 percent of the original issue. The genius that ran the investment committee and managed to sell those a few years later, I won’t give you his name. (Laughter)
因此，我们确实购买了最初发行的10%。至于那个领导投资委员会并在几年后卖掉这些股份的天才，我就不说他的名字了。（笑声）
And there’s no prize for anybody that calculates the value of those shares now.
顺便说一下，现在谁要是计算这些股份的价值，是没有奖品的。
Incidentally, one of the things Bob was very keen on originally, in fact he was probably the keenest on it, was he had some watch that Intel was making. And it was a fabulous watch, according to Bob.
顺便说一下，鲍勃最初非常热衷的一件事，实际上他可能最为热衷的，是英特尔当时制作的一款手表。据鲍勃说，这是一款了不起的手表。
It just had one problem. We sent a guy out from Grinnell who was going out to the West Coast to where Intel was. And Bob gave him one of these watches. And when he got back to Grinnell he wrote up a report about this little investment we had, and he said, “These watches are marvelous.” He said, “Without touching anything, they managed to adapt to the time zones as they change as we went along.” In other words, they were running very fast, as it turned out. (Laughter)
不过它有一个问题。我们从格林内尔派了一个人到西海岸英特尔的所在地。鲍勃给了他一块这样的手表。当他回到格林内尔时，他写了一份关于我们这笔小投资的报告。他说：“这些手表太棒了。”他说：“不用动任何东西，它们就能自动适应我们经过的时区变化。”换句话说，事实证明，它们走得非常快。（笑声）
And they worked with that watch for about five or six years, and they fell on their face.
他们花了五到六年时间研究那款手表，结果以失败告终。
And as you know, you know, they had a total transformation in the mid-’80s when the product on which they relied also ran out of gas. So, it’s not —
大家知道，在80年代中期，他们依赖的产品也失去了动力，于是他们进行了彻底的转型。所以，这并不是——
And Andy Grove has written a terrific book, incidentally, Only the Paranoid Survive, which describes strategic inflection points. I recommend that every one of you read that book, because it is a terrific book.
顺便说一下，安迪·格罗夫写了一本非常棒的书——《只有偏执狂才能生存》，书中描述了战略拐点。我建议你们每个人都去读这本书，因为它确实很好。
But they had an Andy Grove there who made that transformation, along with some other people. But that doesn’t happen every time. Companies get left behind.
但他们有安迪·格罗夫和其他一些人一起完成了转型。不过，这种事情并不是每次都会发生。公司会被落下。
恰恰说明这是一项非常艰难的生意，微软有同样的经历。
We don’t want to be in businesses where companies — where we feel companies can be left behind. And that means that, you know — and Intel could have, and almost did, go off the tracks. IBM owned a big piece of Intel, as you know, and they sold it in the mid-’80s.
我们不希望进入那些我们认为可能会被落后的行业。这意味着，英特尔曾经——几乎确实——脱轨了。你们知道，IBM曾经拥有英特尔的大量股份，但他们在80年代中期卖掉了。
So, you know, here are a bunch of people that should know a lot about that business but they couldn’t see the future either.
所以，你看，这些人应该对那个行业非常了解，但他们也看不到未来。
I think it’s very tough to make money that way, but I think some people can make a lot of money understanding those kinds of businesses. I mean, there are people with the insights.
我认为靠那种方式赚钱非常困难，但我也认为有些人可以通过理解那类业务赚很多钱。我的意思是，有些人确实有这种洞察力。
Walter Scott, one of our directors, has done terrifically with a business that started, you know, just a gleam in the eye maybe ten or 12 years ago here in Omaha, and it turned into a huge business.
我们的董事之一沃尔特·斯科特，在一家业务上取得了巨大的成功。这家业务大约在十到十二年前从奥马哈萌芽，后来发展成了一个巨大的企业。
And you know, Walter explained that to me on the way down to football games, but bad student again, so — (Laughs)
沃尔特曾在我们去看橄榄球比赛的路上向我解释过这个业务，但我还是一个差学生，所以——（笑声）
Walter — if Walter could have connected, and you know, I’d cheer from the stands. But that doesn’t bother me at all. I mean, what would bother me is if I think I understand a business and I don’t. That would bother me.
如果沃尔特能理解，我会在看台上为他欢呼。但这对我来说根本没有困扰。真正会困扰我的是，如果我认为我理解一个业务，但实际上并没有，那才会困扰我。
Charlie?
查理？
CHARLIE MUNGER: Well, having flunked when we were young and strong at understanding some complex businesses, we’re not looking to master what we earlier failed at — (laughs) — in our latter years. (Laughter)
查理·芒格： 我们年轻力壮的时候，就已经在理解一些复杂业务上失败了，现在年纪大了，我们可不打算重新掌握那些我们之前没搞懂的东西。（笑声）
WARREN BUFFETT: Zone 12? This may turn out like a revival meeting where we all confess our sins and come forward (inaudible). (Laughter)
沃伦·巴菲特： 第十二区？这可能会像一个忏悔大会，大家都来承认自己的过错并站出来（听不清）。（笑声）
4、《2023-02-09 Ted Chiang.ChatGPT Is a Blurry JPEG of the Web》
2013年，一家德国建筑公司的工人注意到他们的施乐复印机有些不对劲：当他们复制一个房屋的平面图时，复印件与原件存在微妙但重要的差异。在原始平面图中，每个房间都伴随着指定其面积的矩形：三个房间的面积分别为14.13、21.11和17.42平方米。然而，在照片复制品中，所有三个房间都被标记为大小为14.13平方米。该公司联系了计算机科学家David Kriesel调查这个看起来难以置信的结果。他们需要一位计算机科学家，因为现代施乐复印机不使用在1960年代流行的物理静电复印过程。相反，它数字扫描文档，然后打印生成的图像文件。再加上几乎每个数字图像文件都被压缩以节省空间，这个谜团的解决方案就开始浮现。
压缩文件需要两个步骤：首先是编码，其中文件被转换为更紧凑的格式，然后是解码，即将过程反转。如果恢复的文件与原文件相同，则压缩过程被描述为无损：没有丢失任何信息。相比之下，如果恢复的文件只是原始文件的近似值，则压缩被描述为有损：一些信息已经丢失，现在无法恢复。无损压缩通常用于文本文件和计算机程序，因为这些领域中，即使一个字符不正确都可能具有灾难性。有损压缩通常用于照片、音频和视频，在这些情况下，绝对准确性并不是必要的。大多数情况下，即使图像、歌曲或电影不是完美重现，我们也不会注意到。只有在文件被压缩得非常紧凑时，我们才会注意到所谓的压缩伪像：最小jpeg和mpeg图像的模糊程度，或低比特率MP3的尖锐声音。
施乐复印机使用一种名为jbig2的有损压缩格式，专为黑白图像设计。为了节省空间，复印机识别图像中外观相似的区域，并为所有区域存储一个副本；当文件被解压缩时，它重复使用该副本来重构图像。结果发现，复印机认为标记房间面积的标签足够相似，只需要存储一个标签“14.13”，并在打印平面图时将其重复使用于三个房间。
施乐复印机使用有损压缩格式而不是无损压缩格式本身并不是问题。问题在于复印机以微妙的方式降低了图像质量，压缩伪像并不是立即可识别的。如果复印机只是产生模糊的打印输出，每个人都会知道它们不是原件的准确复制品。导致问题的是，复印机产生的数字是可读但不正确的；复制品似乎是准确的，但实际上并不是。（2014年，施乐发布了一个修补程序来解决这个问题。）
我认为这个关于施乐复印机的事件值得我们今天牢记，因为我们考虑OpenAI的ChatGPT和其他类似的程序，这些程序被人工智能研究人员称为大型语言模型。复印机和大型语言模型之间的相似之处可能并不立即显现，但请考虑以下情况。想象一下，您即将永久失去对互联网的访问权限。为此，您计划创建整个网络上所有文本的压缩副本，以便将其存储在私有服务器上。不幸的是，您的私有服务器只有所需空间的百分之一；如果想让所有内容都适合，您就不能使用无损压缩算法。相反，您编写了一种有损算法，该算法识别文本中的统计规律并将它们存储在一种专门的文件格式中。由于您可以投入几乎无限的计算能力来完成此任务，因此您的算法可以识别非常微妙的统计规律，从而实现所需的100:1压缩比率。
现在，失去互联网访问权限并不太可怕；您在服务器上存储了网络上的所有信息。唯一的问题是，由于文本已经被高度压缩，您无法通过搜索确切的引用来查找信息；您永远不会得到完全匹配，因为存储的不是单词。为了解决这个问题，您创建了一个接口，以问题的形式接受查询，并用传达您服务器上内容要旨的答案进行响应。
我描述的情况听起来很像ChatGPT，或者说是大多数其他大型语言模型。将ChatGPT视为网络上所有文本的模糊JPEG。它保留了网络上的大部分信息，就像jpeg保留了更高分辨率图像的大部分信息一样，但是，如果您正在寻找确切的比特序列，您将找不到它；您将得到的一直是近似值。但是，由于近似值以语法文本的形式呈现，而ChatGPT擅长创建这种文本，因此通常是可以接受的。您仍然在查看模糊的jpeg，但是模糊之处以一种不会使整个图像显得不太清晰的方式发生。
这种与有损压缩的类比不仅是了解ChatGPT在使用不同的单词重新包装网络上的信息方面的能力的一种方式。它也是理解大型语言模型（如ChatGPT）容易出现“幻觉”或对事实问题的无意义回答的一种方式。这些幻觉是压缩伪像，但是——就像施乐复印机生成的不正确标签一样——它们是足够合理的，以至于需要将它们与原始数据进行比较，这在这种情况下意味着网络或我们自己对世界的了解。当我们以这种方式考虑它们时，这些幻觉并不令人惊讶；如果压缩算法旨在在丢弃99%的原始数据后重构文本，我们应该预计它生成的重要部分将是完全捏造的。
5、《2023-10-15 Jensen Huang.ACQUIRED Interview with NVIDIA CEO Jensen Huang》
Jensen: That’s right. But we observed that there was a segment of the market. At the time, the PC industry was still coming up and it wasn’t good enough. Everybody was clamoring for the next fastest thing. If your performance was 10 times higher this year than what was available, there’s a whole large market of enthusiasts who we believe would’ve gone after it. And we were absolutely right, that the PC industry had a substantially large enthusiast market that would buy the best of everything.
Jensen: 没错。但我们观察到市场中有一个细分群体。当时，PC行业还在发展，且性能还不够好。每个人都在争相追求下一代更快的产品。如果今年你的性能比现有产品高出10倍，我们相信有一大群发烧友会争相购买。而我们完全正确，PC行业确实拥有一个庞大的发烧友市场，他们愿意购买最好的产品。
To this day, it remains true. For certain segments of a market where the technology is never good enough like 3D graphics, and we chose the right technology, 3D graphics is never good enough. We call it back then 3D gives us sustainable technology opportunity because it’s never good enough, so your technology can keep getting better. We chose that.
直到今天，这依然成立。对于某些市场的细分领域，技术永远不够好，比如3D图形，我们选择了正确的技术，3D图形永远不够好。我们当时称之为“3D为我们提供了可持续的技术机会，因为它永远不够好，因此你的技术可以不断改进”。我们选择了这一点。
聪明的假设。
Jensen: Oftentimes, if you created the market, you ended up having what people describe as moats, because if you build your product right and it’s enabled an entire ecosystem around you to help serve that end market, you’ve essentially created a platform.
Jensen: 通常，如果你创建了市场，你最终会拥有所谓的护城河，因为如果你正确地构建了你的产品，并且它使周围的整个生态系统得以支持这个最终市场，那么你就实际上创造了一个平台。
Sometimes it’s a product-based platform. Sometimes it’s a service-based platform. Sometimes it’s a technology-based platform. But if you were early there and you were mindful about helping the ecosystem succeed with you, you ended up having this network of networks, and all these developers and customers who are built around you. That network is essentially your moat.
有时它是基于产品的平台，有时是基于服务的平台，也有时是基于技术的平台。但如果你早早进入并且注意到与生态系统一起成功，你最终会拥有一个网络中的网络，所有围绕你的开发者和客户。这个网络本质上就是你的护城河。
I don’t love thinking about it in the context of a moat. The reason for that is because you’re now focused on building stuff around your castle. I tend to like thinking about things in the context of building a network. That network is about enabling other people to enjoy the success of the final market. That you’re not the only company that enjoys it, but you’re enjoying it with a whole bunch of other people.
我不太喜欢在护城河的框架中思考这个问题。原因是你现在专注于围绕你的城堡建造东西。我更倾向于从建立网络的角度来看待事情。这个网络的核心是使其他人也能分享最终市场的成功。你不是唯一享受它的公司，而是和很多其他人一起享受它。
大型科技企业的特征。
David: I’m so glad you brought this up because I wanted to ask you. In my mind, at least, and it sounds like in yours, too, Nvidia is absolutely a platform company of which there are very few meaningful platform companies in the world.
David: 我很高兴你提到了这一点，因为我一直想问你。至少在我看来，听起来在你看来也是，Nvidia绝对是一家平台公司，而世界上有意义的平台公司是非常少的。
I think it’s also fair to say that when you started, for the first few years you were a technology company and not a platform company. Every example I can think of, of a company that tried to start as a platform company, fails. You got to start as a technology first.
我也认为可以公平地说，当你们刚开始时，最初几年你们是科技公司，而不是平台公司。我能想到的所有试图作为平台公司起步的公司，都失败了。你必须首先作为技术公司开始。
When did you think about making that transition to being a platform? Your first graphics cards were technology. There was no CUDA, there was no platform.
你什么时候开始考虑从技术公司转变为平台公司？你们的第一代显卡是技术产品，并没有CUDA，也没有平台。
Jensen: What you observed is not wrong. However, inside our company, we were always a platform company. The reason for that is because from the very first day of our company, we had this architecture called UDA. It’s the UDA of CUDA.
Jensen: 你观察到的并没有错。然而，在我们公司内部，我们一直都是平台公司。原因是从公司成立的第一天起，我们就有一个架构叫做UDA，它就是CUDA的UDA。
6、《2024-09-11 Jensen Huang.Goldman Sachs Communacopia + Technology Conference》
Dig down on this a little bit deeper, just talk about the differences between general purpose and accelerating computing.
深入探讨一下这个问题，谈谈通用计算和加速计算之间的区别。
Jensen Huang 黄仁勋
If you look at software, out of your body of software that you wrote, there's a lot of file IO, there's a -- setting up the data structure, there's a part of the software inside, which has some of the magic kernels, the magic algorithms. And these algorithms are different depending on whether it's computer graphics or image processing or whatever it happens to be. It could be fluids, it could be particles, it could be inverse physics as I mentioned, it could be image domain type stuff. And so all these different algorithms are different. And if you created a processor that is somehow really, really good at those algorithms and you complement the CPU where the CPU does whatever it's good at, then theoretically, you could take an application and speed it up tremendously. And the reason for that is because usually some 5%, 10% of the code represents 99.999% of the runtime. And so if you take that 5% of the code and you offloaded it on our accelerator, then technically, you should be able to speed up the application 100 times. And it's not abnormal that we do that. It's not unusual. And so we'll speed up image processing by 500 times. And now we do data processing. Data processing is one of my favorite applications because almost everything related to machine learning, which is a data-driven way of doing software, data processing has evolved. It could be SQL data processing, it could be Spark type of data processing, it could be a vector database type of processing, all kinds of different ways of processing either unstructured data or structured data, which is data frames and we accelerate the living daylights out of that. But in order to do that, you have to create that library, that fancy library on top. And in the case of computer graphics, we were fortunate to have Silicon Graphics' OpenGL and Microsoft DirectX. But outside of those, no libraries really existed. And so for example, one of our most famous libraries is a library kind of like SQL is a library. SQL is a library for in-storage computing. We created a library called cuDNN. cuDNN is the world's first neural network computing library. And so we have cuDNN, we have cuOpt for combinatory optimization, we have cuQuantum for quantum simulation and emulation, all kinds of different libraries, cuDF for data frame processing, for example, SQL. And so all these different libraries have to be invented that takes the algorithms that run in the application and refactor those algorithms in a way that our accelerators can run. And if you use those libraries, then you get 100x speed up.
如果你看软件，在你编写的软件中，有很多文件IO操作，还有数据结构的设置，软件中也有一些“魔法”内核，神奇的算法。这些算法根据不同的应用是不同的，比如计算机图形处理、图像处理，或者其他应用领域。可能是流体模拟，可能是粒子模拟，可能是逆物理学，就像我之前提到的，或者是图像领域的相关处理。因此，这些不同的算法各不相同。
如果你创造了一种处理器，这种处理器在这些算法上表现得非常好，同时与CPU互补，CPU可以做它擅长的事情，那么理论上，你可以大幅加速应用程序。原因在于，通常5%到10%的代码占据了99.999%的运行时间。因此，如果你能将那5%的代码卸载到我们的加速器上，那么从技术上讲，你应该能够将应用程序的速度提高100倍。这并不罕见，也不例外。我们可以将图像处理的速度提高500倍。现在我们还做数据处理。数据处理是我最喜欢的应用之一，因为几乎所有与机器学习相关的内容——即一种数据驱动的软件开发方式——都涉及到数据处理的演变。它可能是SQL数据处理，可能是Spark类型的数据处理，可能是矢量数据库类型的处理，无论是处理非结构化数据还是结构化数据（如数据框架），我们都可以大幅加速这一过程。
为了做到这一点，你必须在顶层创建那个高级的库。在计算机图形领域，我们很幸运有Silicon Graphics的OpenGL和微软的DirectX。但在这些之外，几乎没有任何库存在。例如，我们最著名的一个库之一类似于SQL。SQL是一个用于存储计算的库，而我们创建了一个叫cuDNN的库。cuDNN是世界上第一个神经网络计算库。我们还有cuDNN、用于组合优化的cuOpt、用于量子模拟和仿真的cuQuantum、以及用于数据框处理的cuDF，例如SQL。
因此，所有这些不同的库都必须被发明出来，以重新构造应用程序中的算法，使我们的加速器能够运行这些算法。如果你使用这些库，那么你就能获得100倍的加速。
7、《2024-11-20 NVIDIA Corporation (NVDA) Q3 2025 Earnings Call Transcript》
And every time you read a PDF, open a PDF, it generated a whole bunch of tokens. One of my favorite applications is NotebookLM, this Google application that came out. I use the living daylights out of it just because it's fun. And I put every PDF, every archive paper into it just to listen to it as well as scanning through it. And so I think -- that's the goal is to train these models so that people use it. And there's now a whole new era of AI if you will, a whole new genre of AI called physical AI, just those large language models understand the human language and how we the thinking process, if you will. Physical AI understands the physical world and it understands the meaning of the structure and understands what's sensible and what's not and what could happen and what won't and not only does it understand but it can predict and roll out a short future. That capability is incredibly valuable for industrial AI and robotics.
每次你阅读一个 PDF，打开一个 PDF，它都会生成一大堆的标记。我最喜欢的应用之一是 NotebookLM，这是谷歌推出的一个应用。我非常频繁地使用它，因为它很有趣。我把每个 PDF，每篇存档论文都放进去，不仅是为了听它，还为了浏览它。因此，我认为——目标是训练这些模型以便人们使用它。现在有一个全新的 AI 时代，如果你愿意的话，一个全新的 AI 类型，叫做物理 AI，就是那些大型语言模型理解人类语言和我们的思维过程，如果你愿意的话。物理 AI 理解物理世界，它理解结构的意义，理解什么是合理的，什么不是，什么可能发生，什么不会发生，它不仅理解，还能预测并推出一个短期的未来。这种能力对于工业 AI 和机器人技术来说是非常有价值的。
8、《2024-12-03 Amazon Announces Supercomputer, New Server Powered by Homegrown AI Chips》
Company leaders, though, are realistic about how far AWS’s chip ambitions can go—at least at the moment.
不过，公司领导对 AWS 芯片雄心的实现程度持现实态度——至少目前如此。
“I actually think most will probably be Nvidia for a long time, because they’re 99% of the workloads today, and so that’s probably not going to change,” AWS CEO Garman said. “But, hopefully, Trainium can carve out a good niche where I actually think it’s going to be a great option for many workloads—not all workloads.”
“我实际上认为大多数情况下可能会长期使用英伟达，因为他们占据了今天 99%的工作负载，所以这可能不会改变，”AWS 首席执行官 Garman 说。“但希望 Trainium 能够开辟一个好的市场，我实际上认为它将成为许多工作负载的一个很好的选择——不是所有的工作负载。”
9、《2025-01-03 Intel’s Problems Are Even Worse Than You’ve Heard》
You may think you know how much Intel is struggling, but the reality is worse.
你可能认为你知道 Intel 有多么挣扎，但现实更糟。
The once-mighty American innovation powerhouse is losing market share in multiple areas that are critical to its profitability. Its many competitors include not just the AI juggernaut Nvidia but smaller rivals and even previously stalwart allies like Microsoft.
这家曾经强大的美国创新巨头正在多个对其盈利能力至关重要的领域失去市场份额。它的众多竞争对手不仅包括 AI 巨头 Nvidia，还有较小的竞争对手，甚至是像 Microsoft 这样曾经坚定的盟友。
One flashing warning sign: In the latest quarter reported by both companies, Intel’s perennial also-ran, AMD, actually eclipsed Intel’s revenue for chips that go into data centers. This is a stunning reversal: In 2022, Intel’s data-center revenue was three times that of AMD.
一个显而易见的警告信号：在两家公司最近报告的季度中，Intel 长期以来的追随者 AMD 实际上在数据中心芯片的收入上超过了 Intel。这是一个惊人的逆转：在 2022 年，Intel 的数据中心收入是 AMD 的三倍。
AMD and others are making huge inroads into Intel’s bread-and-butter business of making the world’s most cutting-edge and powerful general-purpose chips, known as CPUs, short for central processing units.
AMD 和其他公司正在大举进入 Intel 的核心业务，即制造世界上最尖端和强大的通用芯片，称为 CPU，即中央处理器。
Even worse, more and more of the chips that go into data centers are GPUs, short for graphics processing units, and Intel has minuscule market share of these high-end chips. GPUs are used for training and delivering AI.
更糟糕的是，越来越多用于数据中心的芯片是 GPU，即图形处理单元，而 Intel 在这些高端芯片中所占的市场份额微乎其微。GPU 用于训练和交付 AI。
By focusing on the all-important metric of performance per unit of energy pumped into their chips, AMD went from almost no market share in servers to its current ascendant position, says AMD Chief Technology Officer Mark Papermaster. As data centers become ever more rapacious for energy, this emphasis on efficiency has become a key advantage for AMD.
通过专注于每单位能耗性能这一重要指标，AMD 从几乎没有服务器市场份额发展到目前的上升地位，AMD 首席技术官 Mark Papermaster 表示。随着数据中心对能源的需求越来越大，这种对效率的重视已成为 AMD 的关键优势。
Notably, Intel still has about 75% of the market for CPUs that go into data centers. The disconnect between that figure and the company’s share of revenue from selling a wider array of chips for data centers only serves to illustrate the core problem driving its reversal of fortunes.
值得注意的是，Intel 仍然占据了大约 75%的数据中心 CPU 市场份额。这个数字与公司在销售更广泛的数据中心芯片阵列方面的收入份额之间的差距，只是说明了导致其命运逆转的核心问题。
This situation looks likely to get worse, and quickly. Many of the companies spending the most on building out new data centers are switching to chips that have nothing to do with Intel’s proprietary architecture, known as x86, and are instead using a combination of a competing architecture from ARM and their own custom chip designs.
这种情况看起来可能会迅速恶化。许多在建设新数据中心上花费最多的公司正在转向与 Intel 的专有架构 x86 无关的芯片，而是使用来自 ARM 的竞争架构和他们自己的定制芯片设计的组合。
A spokeswoman for Intel says the company is focused on simplifying and strengthening its product portfolio, and advancing its manufacturing and foundry capabilities while optimizing costs. Intel interim Co-Chief Executive Michelle Johnston Holthaus recently said that 2025 will be a “year of stabilization” for the company. Intel is currently seeking a permanent leader after its CEO Pat Gelsinger was pushed out last month.
英特尔的一位女发言人表示，公司专注于简化和加强其产品组合，并在优化成本的同时提升其制造和代工能力。英特尔临时联席首席执行官 Michelle Johnston Holthaus 最近表示，2025 年将是公司“稳定的一年”。在其首席执行官 Pat Gelsinger 上个月被迫离职后，英特尔目前正在寻找一位永久领导者。
The decades that developers spent writing software for Intel’s chips mean that Intel remains a giant, even as its market share has shrunk, and that legacy will limit how quickly Intel’s revenues can decline in the future. Analysts estimate Intel’s 2024 revenue was about $55 billion, just behind Nvidia’s approximately $60 billion. Intel still has the lion’s share of the market for desktop and notebook CPUs—around 76%, overall, according to Mercury Research.
开发人员为 Intel 芯片编写软件的几十年意味着，即使其市场份额缩小，Intel 仍然是一个巨头，这种遗产将限制 Intel 未来收入下降的速度。分析师估计，Intel 2024 年的收入约为 550 亿美元，仅次于 Nvidia 的约 600 亿美元。根据 Mercury Research 的数据，Intel 在台式机和笔记本电脑 CPU 市场中仍占据约 76%的份额。
AMD recently formed an alliance with Intel to collaborate on support and development of the x86 ecosystem that both companies make chips for. Papermaster says that his own company continues to invest in this ecosystem even as AMD also develops ARM-based chips for some applications, such as networking and embedded devices.
AMD 最近与 Intel 结成联盟，以合作支持和开发两家公司都为之制造芯片的 x86 生态系统。Papermaster 表示，尽管 AMD 也在为某些应用（如网络和嵌入式设备）开发基于 ARM 的芯片，但他自己的公司仍在继续投资于这一生态系统。
For a concrete example of Intel’s challenges, look at Amazon, the world’s biggest provider of cloud computing. More than half of the CPUs Amazon has installed in its data centers over the past two years were its own custom chips based on ARM’s architecture, Dave Brown, Amazon vice president of compute and networking services, said recently.
要了解 Intel 面临的挑战，可以看看全球最大的云计算提供商亚马逊。亚马逊副总裁 Dave Brown 最近表示，过去两年中，亚马逊在其数据中心安装的 CPU 中有一半以上是基于 ARM 架构的自定义芯片。
This displacement of Intel is being repeated all across the big providers and users of cloud computing services. Microsoft and Google have also built their own custom, ARM-based CPUs for their respective clouds. In every case, companies are moving in this direction because of the kind of customization, speed and efficiency that custom silicon allows.
这种对 Intel 的取代正在所有大型云计算服务提供商和用户中重复上演。Microsoft 和 Google 也为各自的云构建了自己的定制 ARM 架构 CPU。在每种情况下，公司都朝这个方向发展，因为定制芯片所允许的定制化、速度和效率。
All those companies are also making their own custom, ARM-based chips for AI workloads, an area where Intel has missed the boat almost entirely. Then there’s the 800-pound gorilla in AI, Nvidia. Many of Nvidia’s current-generation AI systems have Intel CPUs in them, but ARM-based chips are increasingly taking center stage in the company’s bleeding-edge hardware.
所有这些公司也在为 AI 工作负载制造自己的定制 ARM 架构芯片，而这是 Intel 几乎完全错失的领域。然后是 AI 领域的 800 磅大猩猩，Nvidia。Nvidia 的许多当前一代 AI 系统中都有 Intel 的 CPU，但 ARM 架构芯片正越来越多地在该公司的尖端硬件中占据中心位置。
Intel’s repeated flubs in entering markets for new kinds of computing and new applications for chips are a textbook example of a big, profitable incumbent becoming a victim of the innovator’s dilemma, says Doug O’Laughlin, an industry analyst at SemiAnalysis, which recently published a blistering report on Intel. The innovator’s dilemma holds that powerful companies that are unwilling to cannibalize their biggest sources of revenue can be overtaken by upstarts that build competing products that start out small, but which can ultimately take over the market which the incumbent dominates—like the mobile chips which ARM started off with.
英特尔在进入新型计算市场和芯片新应用方面的反复失误，是一个大而盈利的现有企业成为创新者困境受害者的教科书式例子，行业分析师 Doug O’Laughlin 在 SemiAnalysis 最近发布的一份严厉报告中表示。创新者困境认为，那些不愿意蚕食其最大收入来源的强大公司，可能会被那些开发竞争产品的后起之秀所超越，这些产品起初规模较小，但最终可以接管现有企业主导的市场——就像 ARM 最初推出的移动芯片一样。
In 1988, former Intel CEO Andy Grove published a book called Only the Paranoid Survive, which highlighted the ways that companies have to be vigilant about what’s coming next, and be willing to disrupt themselves and pursue new technologies. What he intended as a warning to all companies has since become a prophecy foretelling Intel’s current difficulties.
1988 年，前 Intel 首席执行官 Andy Grove 出版了一本名为《只有偏执狂才能生存》的书，强调了公司必须警惕未来的发展，并愿意自我颠覆和追求新技术。他本意是对所有公司的警告，如今却成为预言，预示了 Intel 当前的困难。
对价值投资来说是一个错误的行业。
“The book is literally about the importance of not missing strategic inflections, and then Intel proceeds to miss every single strategic inflection since,” says O’Laughlin.
“这本书实际上是关于不遗漏战略拐点的重要性，然后 Intel 却错过了自那以来的每一个战略拐点，”O’Laughlin 说。
Then there are laptops. After decades of trying to make it happen, 2024 was finally the year of credible, ARM-based laptops running Windows, thanks to efforts by Microsoft to make Windows on ARM work. The company convinced other companies to port their own software, and created tools that allow most existing programs to run on the new laptops, in emulation. Chips in these devices are made by Qualcomm, and benchmarks show that they can finally compete with Apple’s M-class mobile processors, which are also based on a combination of ARM technology and a great deal of custom chip design by Apple’s formidable in-house team.
然后是笔记本电脑。经过几十年的努力，2024 年终于成为了运行 Windows 的可信赖的 ARM 架构笔记本电脑之年，这要归功于 Microsoft 使 Windows 在 ARM 上运行的努力。该公司说服其他公司移植他们自己的软件，并创建了允许大多数现有程序在新笔记本电脑上以仿真方式运行的工具。这些设备中的芯片由 Qualcomm 制造，基准测试显示它们终于可以与 Apple 的 M 系列移动处理器竞争，这些处理器同样基于 ARM 技术和 Apple 强大的内部团队进行的大量定制芯片设计。
Another bastion of market share and profits for Intel, the PC gaming market, is also showing early signs of erosion. Portable gaming systems like Valve’s Steam Deck and the Lenovo Legion Go, which can run even very demanding games, use processors from AMD. Future devices that will be part of the company’s plan to license its custom OS to other manufacturers may also use ARM-based ones.
对于英特尔来说，另一个市场份额和利润的堡垒——PC 游戏市场，也显示出早期的侵蚀迹象。像 Valve 的 Steam Deck 和联想的 Legion Go 这样的便携式游戏系统，即使是运行非常高要求的游戏，也使用来自 AMD 的处理器。未来将成为公司计划的一部分，将其定制操作系统授权给其他制造商的设备也可能使用基于 ARM 的处理器。
Inherent in Intel’s woes is the way its vertically integrated structure, long an asset, now weighs on the company’s bottom line and ability to innovate. Unlike other companies that either design chips or manufacture them, Intel has stuck to a seemingly antiquated model of doing both.
英特尔困境的内在原因在于，其长期以来作为资产的垂直整合结构，如今却拖累了公司的利润和创新能力。与其他仅设计芯片或制造芯片的公司不同，英特尔坚持采用一种看似过时的双重模式。
Intel reported a $16 billion loss in its most recent quarter as it spent big to transform into a contract manufacturer—that is, a company that also manufactures chips for other companies, even competitors—and catch up to rival TSMC, which now produces the world’s most cutting-edge chips.
Intel 在最近一个季度报告了 160 亿美元的亏损，因为它投入巨资转型为合同制造商——即一家也为其他公司甚至竞争对手制造芯片的公司——并赶上现在生产世界上最先进芯片的竞争对手 TSMC。
Analysts expect Intel to return to profitability in 2025, but it won’t be clear for years whether the company’s big manufacturing bets will ultimately pay off.
分析师预计 Intel 将在 2025 年恢复盈利，但公司大规模制造投资是否最终会取得成功，几年内都不会明朗。
One of the big bets of Intel’s recently departed CEO Gelsinger, was Intel’s attempt to leapfrog TSMC in terms of chip technology. What it calls its “18A” tech could in theory allow its own chips, and those it makes for outsiders, to once again be the most cutting-edge, and the fastest, on the planet. The company has said it could regain that title by 2026. Intel recently announced it had signed a deal with Amazon to make custom chips for the company, using its 18A technology.
英特尔最近离任的首席执行官盖尔辛格的一个重大赌注是英特尔试图在芯片技术方面超越台积电。它所谓的“18A”技术理论上可以使其自身芯片以及为外部制造的芯片再次成为全球最前沿和最快的。公司表示，到 2026 年可以重新获得这一称号。英特尔最近宣布已与亚马逊签署协议，使用其 18A 技术为该公司制造定制芯片。
Even if Intel can once again lead the industry with its technology, the best case scenario for Intel’s own products is that it regains dominance in a market that continues to shrink—the x86 CPU one, says O’Laughlin. The removal of Gelsinger, who was betting on an all-in strategy for Intel to regain dominance both in the market for its own chips and in serving outside companies, suggests that Intel’s board agrees that the company can’t continue to count on being the best in the world at everything.
即使 Intel 能够再次凭借其技术引领行业，Intel 自身产品的最佳情况是重新在一个持续萎缩的市场中占据主导地位——即 x86 CPU 市场，O’Laughlin 说。Gelsinger 的离职表明，Intel 董事会同意公司不能继续指望在所有领域都成为世界最佳，他曾押注于 Intel 通过全力以赴的策略重新在自家芯片市场和为外部公司服务中占据主导地位。
All of these challenges and conflicting priorities may push Intel to someday split in two, severing its product side from manufacturing. Intel INTC 0.26%increase; green up pointing triangle Co-CEO David Zinsner recently said that spinning off the company’s manufacturing side is an “open question.”
所有这些挑战和相互冲突的优先事项可能会促使 Intel 有一天分拆为两部分，将其产品部门与制造部门分离。Intel 的联合首席执行官 David Zinsner 最近表示，剥离公司的制造部门是一个“开放的问题”。
It’s also possible, in the worst case, that a fate even worse than being dismembered could be in store for Intel.
在最坏的情况下，Intel 可能面临比被拆分更糟糕的命运。
Rene Haas, CEO of ARM, recently observed that Intel has long been an innovation powerhouse, but that in chipmaking and design, there are countless companies that don’t innovate fast enough—and no longer exist.
ARM 的首席执行官 Rene Haas 最近指出，Intel 长期以来一直是创新的强大力量，但在芯片制造和设计方面，有无数公司创新速度不够快，已经不复存在。
10、《2025-01-13 U.S. Targets China With New AI Curbs, Overriding Nvidia’s Objections》
The caps on exports of AI chips apply in different ways to different countries and companies.
对 AI 芯片出口的限制在不同国家和公司中适用的方式不同。
The 18 close U.S. allies will face no restrictions on purchases of chips. And smaller orders from customers around the world—up to around 1,700 advanced AI chips—won’t require a license or count against caps on countries’ chip purchases, the Commerce Department said.
18 个美国亲密盟友在购买芯片时将不受限制。美国商务部表示，来自世界各地客户的小额订单——最多约 1,700 个先进 AI 芯片——不需要许可证，也不计入各国芯片购买的上限。
That leaves the question of whether companies based in the U.S. or its allies can build significant AI capacity in a country falling into a middle zone—neither trusted ally nor top adversary. The Commerce Department said yes, but with limits. Companies that meet high security standards can apply for a status that allows them to place up to 7% of their global AI computing capacity in any single such country. That could be as many as hundreds of thousands of chips, the department said.
这就留下了一个问题，即总部位于美国或其盟国的公司是否可以在一个处于中间地带的国家建立显著的 AI 能力——既不是可信赖的盟友，也不是主要对手。商务部表示可以，但有限制。符合高安全标准的公司可以申请一种状态，允许他们在任何一个这样的国家中放置其全球 AI 计算能力的最多 7%。商务部表示，这可能多达数十万片芯片。
A further category of companies based in countries that aren’t U.S. adversaries can apply for a status allowing them to buy up to the equivalent of 320,000 of today’s advanced AI chips over the next two years. Those that don’t get this status can still buy up to the equivalent of 50,000 advanced AI chips.
位于非美国对手国家的公司可以申请一种状态，允许他们在未来两年内购买相当于 32 万片当今先进 AI 芯片的产品。未获得此状态的公司仍然可以购买相当于 5 万片先进 AI 芯片的产品。
The limits suggest many countries could be challenged in setting up AI computing facilities capable of competing with the largest and most advanced in the U.S. and its closely allied countries. Some of the biggest AI computing facilities in the U.S. contain huge numbers of Nvidia’s AI chips, including the Colossus supercomputer being built by Elon Musk’s xAI in Memphis, Tenn., which is being scaled up to include 200,000 of them.
这些限制表明，许多国家在建立能够与美国及其紧密盟国中最大和最先进的 AI 计算设施竞争的 AI 计算设施方面可能面临挑战。美国一些最大的 AI 计算设施包含大量 Nvidia 的 AI 芯片，包括由 Elon Musk 的 xAI 在田纳西州孟菲斯建造的 Colossus 超级计算机，该计算机正在扩展以包含其中的 20 万个芯片。
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