E167: Nvidia smashes earnings (again), Google's Woke AI disaster, Groq's LPU breakthrough & more
发布时间 2024-02-23 20:24:54 来源
摘要
(0:00) Bestie intros: Banana boat! (2:34) Nvidia smashes expectations again: understanding its terminal value and bull/bear cases ...
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中英文字稿 
All right, everybody. Welcome back to your favorite podcast of all time, the All in podcast episode 160 something with me again, Chamof Holly Hoppettia, the CEO of a company and investing startups. And his firm is called social capital. We also have David Freiberg, the Sultan of science. He's now a CEO as well. And we have David Sacks from craft ventures in some underslows to a town room somewhere. How are we doing boys? Good. Thank you. This is not hard. Your intro be any more low energy and dragged out. I'm sick. What do you want me to give me? I'm doing a fake effort. All right. Here we give you one more shot. Watch this. Watch this. Watch professional. You want professional? It's a effort. Come on. Here we go. You want professionalism? I'll show you guys professionalism. Is that Benaka? What was that? Is that Benaka? Oh, it is the secret. Banana boat. Man, David. We open sources to fans and they've just got the reason. Love you guys. I mean, Queen of King.
大家好。欢迎回到您最喜欢的播客节目，《全力以赴》第160多集。我是公司和初创投资的首席执行官Chamof Holly Hoppettia，他的公司叫社会资本。我们还有科学苏丹David Freiberg，他现在也是首席执行官。还有来自克拉夫特风投的David Sacks在某个地方的会议室里。大家好吗？好的，谢谢。这个介绍能再低能量和拖沓一点吗？我生病了。你想我怎么做？我正在假装努力。好吧。我再给你一次机会。看着我，看着我。这是专业的表现。你想要专业吗？我会给你们看专业的。那是Benaka吗？那是Benaka吗？哦，是秘密的。香蕉船。哥们，David。我们向粉丝公开资源，他们只是找到了理由。爱你们。我是女王或国王。
All right, everybody. Welcome to the All in podcast episode one hundred and sixty seven hundred sixty eight with me. Of course, the rain man himself, David Sacks, the dictator, chairman, Chabath Paliapatia and our Sultan of science. David Freiberg, how are we doing boys? Great. How are you? How are you? Energy enough for you? Is it one, six, seven or one sixty eight? I don't know. Who cares? Do we at least get you to know the episode number? Who cares? Unfortunately or fortunately, we're going to be doing this thing forever. The audience demands it. It doesn't matter. This is like a Twilight Zone episode. We're going to be trapped in these four bubbles forever. But like Superman, it's it's it is. It's this is like the it is the gift in the Christmas. I guess that glass. Zed was that. Zed. It's odd. Yeah, Neil before is on. And he spun through the universe and the plastic being forever for infinity until that until Superman took the nuclear bomb out of the Eiffel Tower and threw it into space and blew it up and freedom my background today. I think I'm going to have to change now that you've referenced this important scene. That was the best moment of that movie, J. Cal. Where Terrence Stamp says Neil to the president and the president says, Oh, God. Yes. And then Terrence seems like was a sod. Not God, sod. Not Neil before. That's odd. That's Superman two or three. Yeah. Superman two is pretty much the best.
大家好。欢迎来到《全投注》播客第1678期，与我一起的当然是雨人大卫·萨克斯、独裁者主席查巴特·帕里亚帕提亚和我们的科学苏丹大卫·弗莱伯格，男孩们，你们好吗？很棒。你们好吗？精力充沛吗？是167还是168？我不知道。谁在乎？至少我们能知晓一集编号吗？谁在乎？不幸还是幸运，我们会永远继续这个节目。听众们要求如此。这无关紧要。这就像一个《陶德小镇》的剧集。我们会被永远困在这四个气泡里。但就像超人一样，这是一种礼物。圣诞节的礼物。是的，尼尔，接受审判。他穿越宇宙，永远存在，直到超人将核弹从埃菲尔铁塔拿走，投向太空并引爆，解救了我的背景今天。我想我得改一下，既然你提到了这个重要的场景。那是那部电影中最好的一个时刻，杰·凯尔。特伦斯·斯坦普说尼尔给总统，总统说哦，天啊。然后特伦斯似乎是个叫赛德的家伙，不是神，赛德。不是尼尔，请原谅。那是《超人2》还是《超人3》？是的，《超人2》几乎是最好的。
Yeah. You know, like Empire Strikes Back, like Terminator two, it's always the second one. That's the best one. All right, everybody. We got a lot to talk about today. Apologies for my voice. A little bit of a cold. Nvidia blew the doors off their earnings for the third straight quarter. Shares were 15% on Thursday, representing and nearly $250 billion jump in market cap. So let's just let that sit in for a second. This is the largest single day game in market cap. Two hundred forty seven billion dollars added in market cap previously. Meta did something similar earlier this year. Remember everybody was down on that stock because they were doing all the crazy stuff with reality labs and then they got focused and laid off 20,000 people. They added a hundred ninety six billion dollars. In other words, they added like two and a half Airbnb.
是的。你知道，就像《帝国反击战》、《终结者2》，通常第二集就是最好的。好了，大家。今天我们有很多要谈论的。抱歉我的声音有点沙哑，我有点感冒。英伟达连续第三个季度业绩大幅超出预期。周四股份上涨了15%，市值跃升了近2500亿美元。让我们稍事休息，来思考一下这个数字。这是市值单日增长最大的一次。之前梅塔在今年早些时候也做过类似的事情。记得大家都对这支股票失望，因为他们在做与现实实验室相关的疯狂项目，然后他们聚焦了，裁员2万人。他们市值增加了1960亿美元。换句话说，他们增加了大约2.5倍的Airbnb。
So their valuation, but let's just get to the results. The results are absolutely stunning. I dare I say unprecedented Q4 revenue, 22.1 billion. That's up 22% quarter over quarter, up two hundred sixty five percent year over year. The net income was 12.3 billion. Nine X year over year. And the gross margin of seventy six percent was up two points quarter of a quarter, twelve point seven percent year over year. But look at this revenue ramp. This is extraordinary. Q1 of twenty twenty four, this juggernaut starts and it does not stop. And it doesn't look like it's going to stop just a run up from seven billion all the way to 22 billion in revenue for the quarter. Absolutely extraordinary. And if you want to know why this is happening, why is Nvidia putting up these kind of numbers, this chart explains everything.
因此，让我们来看看结果吧。结果绝对令人惊讶。我敢说，Q4的收入是空前的，达到了221亿美元。环比增长22%，同比增长265%。净利润为123亿美元，同比增长9倍。总毛利率为76%，环比增长2个百分点，同比增长12.7%。但看看这个收入增长曲线。这简直是不可思议的。从2024年的第一季度开始，这个巨人就开始了，而且没有停下来的迹象。从70亿美元一路飙升到每季度的220亿美元收入。绝对是非凡的。如果你想知道为什么会发生这种情况，为什么英伟达能够取得这样的成绩，这张图表可以解释一切。
This is all about data centers. Obviously, if you heard of Nvidia before the AI boom, it was gaming. Professional visualization, you know, I think people making movies and stuff like that, autos used Nvidia for self driving, that kind of stuff. But if you look at this chart, you'll see data centers just starting four quarters ago, starts the ramp up as everybody builds out the infrastructure for new data centers to deal with general AI. So just at one point here, Jason. So what you can see is that Nvidia was around for a long time and it was making these chips, these GPUs as opposed to CPUs. And they were primarily used by games and by virtual reality software because GPUs are better. Obviously a graphical processing. They use vector math to create these like 3D worlds. And this vector math that they use to create these 3D worlds is also the same vector math that AI uses to reach its outcomes.
这段内容讲的是数据中心。显然，在人工智能繁荣之前，如果你听说过英伟达，那时候是主要用于游戏。专业的可视化，比如人们制作电影等等，汽车也使用英伟达来进行自动驾驶等等。但是如果你看这份图表，你会发现数据中心从四个季度前开始逐渐增长，因为每个人都在建设基础设施来处理通用人工智能。所以这里有一个观点，Jason。你可以看到英伟达存在了很长时间，一直在生产这些芯片，这些GPU，与CPU不同。它们主要被游戏和虚拟现实软件使用，因为GPU更好。显然是图形处理。它们使用向量数学来创建这些3D世界。而他们用来创建这些3D世界的向量数学也是人工智能用来达成其结果的向量数学。
So with the explosion of LLMs, it turns out that these GPUs are the right chips that you need for these cloud service providers to build out these big data centers to serve now all of these new AI applications. So Nvidia was in the perfect place at the perfect time. And that's why it's just exploded. And what you're seeing is the build out of this new cloud service infrastructure for for AI. Yeah. And also helping the stock is the fact that they bought back 2.7 billion worth of their shares as part of a $25 billion buyback plan. But this company's firing on all cylinders. Revenue is obviously ripping as people put in orders to replace all of the data centers out there or at least augment them with this technology with GPUs, A100s, H100s, et cetera. The gross margin has been expanding. They have huge profits and they're still projecting more growth in Q1 around 24 billion, which would be a 3X increase year over year. And this obviously has made the entire market rip as in video goes. So does the market right now and the S&P 500 and NASDAQ are at record highs at the time of this taping.
随着 LLMs 的爆发，事实证明这些 GPU 是云服务提供商所需的正确芯片，用于建设大型数据中心，以满足现在所有这些新的人工智能应用。 因此，英伟达正好处于正确的时间和地点。这就是为什么它如此迅速发展。您所看到的是为人工智能构建这种新的云服务基础设施。此外，他们回购了价值 27 亿美元的股票作为 250 亿美元回购计划的一部分，也帮助推动了股价。但该公司在各方面表现都很出色。显然，收入正在飙升，因为人们下订单替换所有现有的数据中心，或者至少用 GPU、A100、H100 等技术进行扩充。毛利率一直在扩大。他们拥有巨大的利润，并且仍然在预测 Q1 的增长约为 240 亿美元，这将是年同比增长的三倍。这显然已经使整个市场飙升，因为随着英伟达的增长，目前的市场和标准普尔 500 和纳斯达克指数正处于历史最高水平。
Chamath, your general thoughts here on something I don't think anybody saw coming. Except for you and your investment in Grok, maybe in a couple of others. I think what I would tell you is that. The bigger principle and we've talked about this a lot, Jason, is that in capitalism. When you over earn for enough of a time, what happens is competitors decide to try to compete away your earnings in the absence of a monopoly, the amount of time that you have tends to be small and it shrinks. So in the case of a monopoly, for example, take Google, you can over earn for decades. And it takes a very, very long time for somebody to try to displace you. We're just starting to see the beginnings of that with things like perplexity and other services that are chipping away at the Google monopoly. But at some point in time, all of these excess profits are competed away.
Chamath，你对这个事情的一般想法，我想没有人预料到。除了你和你对Grok的投资，也许还有几个人。我想告诉你的是，更重要的原则，我们已经讨论过很多次，Jason，在资本主义中。当你赚够了足够长的时间，竞争对手就会决定争夺你的利润，没有垄断的情况下，你可以享受这种时间的时间很短，会逐渐缩小。例如，在垄断的情况下，就像谷歌，你可以赚取数十年的盈利。但要想有人试图取代你，需要很长时间。我们现在才刚刚开始看到一些迹象，比如困惑和其他服务正在逐渐侵蚀谷歌的垄断地位。但在某个时刻，所有这些多余的利润都会被竞争抢走。
In the case of Nvidia, what you're now starting to see is them over earn in a very massive way. So the real question is who will step up to try to compete away those profits? The old Bezos quote, right? Your margin is my opportunity. And I think we're starting to see, and you've mentioned Grock, who had a super viral moment, I think this week, but you're starting to see the emergence of a more detailed understanding of what this market actually means. And as a result, who will compete away the inference market, who will compete away the training market and the economics of that are just becoming known to now more and more people. Vreberg, your thoughts, we were talking, I think, was last week of the week before about the possibility of Nvidia being a $10 trillion company, the largest company in the world. What are your thoughts on these spectacular results? And then from that point, everybody is watching this going, hmm, maybe I can get a slice of that pie. And maybe I can create a more competitive offering.
在英伟达的情况下，我们现在开始看到他们以非常庞大的方式过度赚取利润。所以真正的问题是谁将挺身而出试图竞争这些利润？老贝索斯的名言，对吧？你的利润就是我的机会。我认为我们开始看到，你提到了Grock，他在这周有一个超级病毒式的时刻，但你开始看到更详细的理解这个市场实际上意味着什么。因此，谁将竞争推理市场，谁将竞争训练市场以及这方面的经济学正在变得越来越为人所知。弗雷伯格，你的想法是什么？我们上周或上上周谈到了英伟达有可能成为一家价值10万亿美元的公司，世界上最大的公司。您对这些惊人的结果有什么想法？然后从那时起，每个人都在关注这个，想着，也许我可以分一杯羹。也许我可以创造一个更具竞争力的产品。
Obviously, we saw Sam Holman rumored to be raising $7 trillion, which feels like a fake number. It feels like that's maybe the market size or something. But your thoughts here? I don't think anything's changed on the Nvidia front. There's this accelerated compute build out underway in data centers. Everyone's building infrastructure and then everyone's trying to build applications and tools and services on top of that infrastructure. The infrastructure build out is kind of the first phase. The real question ultimately will be, does the initial cost of the infrastructure exceed the ultimate value that's going to be realized on the application layer? In the early days of the internet, a lot of people were buying oracle servers. They were like $3,000 a server. And they were running these oracle servers out of an internet connected data center.
显然，我们看到传言中Sam Holman要筹集7万亿美元，这个数字感觉像是虚假的。这可能是市场规模之类的东西。你对此有什么想法？我认为在英伟达方面没有什么改变。数据中心正在进行加速计算基础设施的建设。每个人都在建设基础设施，然后每个人都在尝试在这些基础设施之上建立应用程序、工具和服务。基础设施建设是第一阶段。最终问题将是，基础设施的初始成本是否超过了应用层将实现的最终价值？在互联网早期，很多人都在购买oracle服务器。一个服务器大约3000美元。他们在一个连接到互联网的数据中心中运行这些oracle服务器。
And it took a couple of years before folks realized that for large scale distributed compute applications, you're better off using cheaper hardware, cheaper server racks, cheaper hard drives, cheaper buses, and assuming a shorter life span on those servers and you could cycle them in and out. And you didn't need the redundancy. You didn't need the certainty. You didn't need the runtime guarantees. And so you could use a lower cost, higher failure rate, but much, much net lower cost kind of approach to building out a data center for internet serving. And so the oracle servers didn't really take the market. And early on, everyone thought that they would.
过了几年，人们才意识到对于大规模分布式计算应用来说，最好使用更便宜的硬件、更便宜的服务器机架、更便宜的硬盘、更便宜的总线，并假设这些服务器寿命较短，可以循环使用。 你并不需要冗余备份。 你不需要确定性。 你不需要运行时保证。 因此，你可以采用一种更低成本、高故障率，但整体成本更低的方式来建设用于互联网服务的数据中心。 因此，Oracle服务器并没有真正占据市场。 而在早期，每个人都认为它们会成功。
So I think Chamost Point is right. Now Nvidia has been at this for a very long time. And the real question is how much of an advantage do they have, particularly that there is this need to use fabs to build replacement technology. So over time, will there be better solutions that use hardware that's not as good, but the software figures out and they build new architecture for running on that hardware in a way that kind of mimics what we saw in the early days of the build out of the internet. So TBD, right? The same is true in switches, right? So in networking, a lot of the high end, high quality networking companies got beaten up when lower cost solutions came to market later. And so they looked like they were going to be the biggest business ever.
所以我认为Chamost Point是正确的。现在英伟达已经投入了很长时间了。真正的问题是他们有多大的优势，特别是需要使用晶圆厂来构建替代技术。随着时间的推移，是否会出现更好的解决方案，利用不太好的硬件，但软件能够找到解决方法，并构建新的架构来运行这样的硬件，这在某种程度上类似于我们在互联网建设初期看到的情况。所以待定，对吧？交换机也是如此，对吧？在网络领域，许多高端，高质量的网络公司在更低成本解决方案进入市场后受到重挫。因此，它们看起来会成为史上最大的业务。
I mean, you could look at Cisco during the early days of the internet build out and everyone thought Cisco was the picks and shovels of the internet and they were going to make all the all the values going to include a Cisco. So we're kind of in that same phase right now with Nvidia. The real question is, is this going to be a much harder hill to compete on than we've ever seen given the development cycle on chips and the requirement to use these fabs to build chips. It may be a harder hill to kind of get up. So we'll see your thoughts. You think we're getting to the point where maybe we'll have bought too many of these, built out too much infrastructure and it will take time for the application layer as Freiberg was alluding to monetize it?
我的意思是，你可以看看思科在互联网建设初期的情况，所有人都认为思科是互联网的锄头和铲子，他们将创造所有价值，包括思科在内。所以现在我们正在经历与英伟达类似的阶段。真正的问题是，随着芯片的开发周期和使用这些工厂来制造芯片的要求，这将是一个更加艰巨的竞争。也许这将是一个更艰难的挑战。我们将看到你的想法。你认为我们是否已经买了太多这些东西，建设了太多基础设施，需要时间才能实现为应用层变现所暗示的？
Well, I think the question everyone's asking right now is, are these results sustainable? Can Nvidia keep growing at these astounding rates? Will the build out continue? And the comparison everyone's making is to Cisco. And there's this chart that's been going around overlaying the Nvidia stock price on the Cisco stock price. And you can see here the orange line is Nvidia and the blue line is Cisco. And it's all posted like a perfect match. Now what happened is that at a similar point in the original build out of the internet, of the .com era, you had the market crash at the end of March of 2000. And Cisco never really recovered from that peak valuation. But I think there's a lot of reasons to believe Nvidia is different. One is that if you look at Nvidia's multiples, they're nowhere near where Cisco's were back then. So the market in 1999 and early 2000 was way more bubbly than it is now. So Nvidia's valuation is much more grounded in real revenue, real margins, real profit. Second, you have the issue of competitive moat. Cisco was selling servers and networking equipment. Fundamentally, that equipment was much easier to copy and commoditize than GPUs. These GPU chips are really complicated.
目前大家都在问的问题是，这些结果能持续吗？英伟达能保持这种惊人的增长率吗？建设是否会继续？与思科进行的比较是所有人都在做的。有一张图表在网上流传，将英伟达股价与思科股价进行对比。你可以看到橙色线代表英伟达，蓝色线代表思科。这两条线几乎一模一样。原因是在互联网最初发展阶段、.com时代的一个类似时期，2000年3月底市场崩盘了。思科从那个最高点的估值中从未真正恢复过来。但我认为有很多理由让我们相信英伟达是不同的。首先，如果你看英伟达的倍数，它们远远不及思科当时的水平。所以1999年和2000年初的市场比现在要繁荣得多。因此，英伟达的估值更多地基于真实的营收、真实的利润率和真实的利润。其次，还有竞争壕沟的问题。思科销售服务器和网络设备。从根本上说，这些设备比GPU更容易复制和商品化。这些GPU芯片真的很复杂。
I think Jensen made the point that their Hopper 100 product, he said, don't even think of it just like a chip. There's actually 35,000 components in this product and it weighs 70 pounds. This is more like mainframe computer or something that's dedicated to processing. Yeah, it's somewhere between a rack server and the entire rack. Yeah, it's heavy and it's complex. It does say something here, Chamath, I think about how well positioned big tech is in terms of seeing an opportunity and quickly mobilizing to capture that opportunity. These servers are being bought by people like Amazon, I'm sure Apple, obviously Facebook, Meta. I don't know if Google was buying them as well, I would assume so Tesla.
我认为Jensen的观点是他们的Hopper 100产品不仅仅是一块芯片。这个产品实际上有35,000个零件，重达70磅。它更像是一台大型计算机或者专门用于处理的设备。它介于机架服务器和整个机架之间。是的，它又重又复杂。Chamath，在这里说了些什么，我认为这表明大型科技公司在看到机遇并迅速动员来抓住机遇方面做得非常好。这些服务器被亚马逊、苹果、Facebook、Meta等公司购买。我不知道谷歌是否也在购买，我想特斯拉可能也在。
So everybody's buying these things and they had tons of cash sitting around. It is pretty amazing how nimble the industry is and this opportunity feels like everybody is looking at it like mobile and cloud. I have to get mobilized quickly to not get disrupted. You're bringing up an excellent point and I would like to tie it together with Freedberg's point. So at some point, all of this spend has to make money. Right? Otherwise, you're going to look really foolish for having spent 20 and 30 and 40 billion dollars. So Nick, if you just go back to the revenue slide of NVIDIA, I can try to give you a framing of this at least the way that I think about it.
所以每个人都在买这些东西，他们手头有大量现金。这个行业的灵活性令人惊叹，这个机会感觉每个人都像看待移动和云一样。我必须迅速行动，以免被打乱。你提出了一个很好的观点，我想把它和弗里德伯格的观点联系在一起。所以在某个时候，所有这些支出都必须赚钱。对吧？否则，你会因为花了200亿、300亿、400亿美元而显得非常愚蠢。所以尼克，如果你只是回到英伟达的营收幻灯片，我可以尝试向你描述这个情况，至少是我如何思考的方式。
So if you look at this, what you're talking about is look, who is going to spend 22.1 billion dollars? Well, you said, Jason, it's all a big tech. Why? Because they have that money on the balance sheet sitting idle. But when you spend 22 billion dollars, their investors are going to demand a rate of return on that. And so if you think about what a reasonable rate of return is, call it 30, 40, 50 percent and then you factor in and that's profit and then you factor in all of the other things that need to support that, that 22 billion dollars of spend needs to generate probably 45 billion dollars of revenue.
所以，如果你看这个，你说的是谁愿意花221亿美元呢？嗯，你说，杰森，这都是大型科技公司。为什么？因为他们在资产负债表上有这笔钱闲置。但是当你花掉220亿美元时，他们的投资者会要求对这笔钱的回报。所以如果你考虑一下一个合理的回报率是多少，比如30、40、50%，然后再考虑要支持这一切所需的其他因素，这220亿美元的支出可能需要产生大约450亿美元的收入。
And so Jason, the question to your point and to Freedberg's point, the $64,000 question is who in this last quarter is going to make 45 billion on that 22 billion of spend. And again, what I would tell you to be really honest about this is that what you're seeing is more about big companies, muscling people around with their balance sheet and being able to go to Nvidia and say, I will give you committed pre purchases over the next three or four quarters. And less about here is a product that I'm shipping that actually makes money, which I need enormous, more compute resources for.
所以，杰森，针对你的观点和弗里德伯格的观点，问题关键是，在这最后一个季度，有谁能够在这220亿的支出上赚450亿。再说一遍，我要告诉你实话实说的是，你看到的更多是大公司用他们的资产负债表来推动市场，并能够去找英伟达说，我会在接下来的三四个季度内提前购买，而不是关于我正在出货的产品实际上赚钱了，为此我需要更多的计算资源。
It's not the latter. Most of the apps, the overwhelming majority of the apps that we're seeing in AI today are toy apps that are run as proofs of concept and demos and run in a sandbox. It is not production code. This is not, we've rebuilt the entire autopilot system for the Boeing and it's now run with agents and bots and all of this training. That's not what's happening. So it is a really important question. Today, the demand is clear. It's the big guys with huge gobs of money. And by the way, Nvidia is super smart to take it because they can now forecast demand for the next two or three quarters.
这不是后者。如今我们在人工智能领域看到的大部分应用，绝大多数都是玩具应用，用作概念验证和演示，在一个隔离的环境中运行。它不是生产代码。我们并没有重新构建整个波音自动驾驶系统，现在用代理和机器人来运行并进行训练。这并不是正在发生的事情。所以这是一个非常重要的问题。今天，需求很明确。有大笔钱的大公司们。顺便说一句，英伟达非常聪明地接受了这个需求，因为他们现在能够预测未来两三个季度的需求。
I think we still need to see the next big thing. And if you look in the past, what the past has showed you, it's the big guys don't really invent the new things that make a ton of money. It's the new guys who because they don't have a lot of money and they have to be a little bit more industrious come up with something really authentic and new. Yeah, constraint makes for a great art. We haven't seen that yet. So I think the revenue scale will continue for like the next two or three years, probably for Nvidia. But the real question is what is the terminal value? And it's the same thing that SAC showed in that Cisco slide.
我认为我们仍然需要看到下一个大事件。如果你回顾过去，过去所展示的是，大公司并不真正发明能够赚大钱的新事物。而是那些因为没有很多钱，不得不更加勤奋的新人们才会想出一些真正地创新和新颖的东西。是的，约束会带来伟大的艺术。我们还没有看到那样的东西。所以我认为收入规模将会在未来两三年内继续增长，可能对于英伟达而言是这样的。但真正的问题是什么是终极价值呢？这正是SAC在那张思科幻灯片中展示的东西。
People ultimately realized that the value was going to go to other parts of the stack, the application layer. And as more and more money was accrued at the application layer of the internet, less and less revenue multiple and credit was given to Cisco. And that's nothing against Cisco because their revenue continued to compound. Right. And they did an incredible job. But the valuation got so Freberg, if we're looking at this chart, the winner of Netflix, the winner of the Cisco chart might in fact be somebody like Netflix. They actually got, you know, hundreds of millions of consumers to give them a Facebook. And then you have Google and Facebook as well, generating all that traffic and then YouTube, of course.
人们最终意识到，价值将流向堆栈的其他部分，即应用层。随着越来越多的资金在互联网的应用层聚集，给予思科的利润倍增和诺信信用的减少。这并不是针对思科的，因为他们的收入继续增长。没错。他们做得很好。但是价值评估如此高昂，如果我们看一下这张图表，Netflix的赢家，思科的赢家实际上可能会是像Netflix这样的公司。他们实际上获得了数以百万计的消费者给他们点赞。然后还有谷歌和Facebook，产生了所有这些流量，当然还有YouTube。
But who do you see the winner here as in terms of the application layer? Who are the billion customers here who are going to spend 20 bucks a month, five bucks a month, whatever it is. So here, well, I mean, let me just start with this important point. If you look at where that revenue is coming from, to Jamal's point, it's coming from big cloud service providers. So Google and others are building out clouds that other application developers can build their AI tools and applications on top of.
在应用层面，你认为谁会成为赢家？在这里有哪些数十亿客户会每月花费20美元、5美元或其他金额？所以，在这里，我想先强调一个重要的观点。如果你看一下收入来源，正如贾迈勒所说，它来自于大型云服务提供商。因此，谷歌和其他公司正在建立云，其他应用开发人员可以在其上构建人工智能工具和应用程序。
So a lot of the build out is in these cloud data centers that are owned and operated by these big tech companies. The 18 billion of data center revenue that Nvidia realized is revenue to them, but it's not an operating expense to the companies that are building out. So this is an important point on why this is happening at such an accelerated pace. When a big company buys these chips from Nvidia, they don't have to from an accounting basis market as an expense in their income statement.
因此，很多的建设工作都是在这些由大科技公司拥有和运营的云数据中心中进行的。英伟达获得的180亿美元的数据中心收入对他们来说是收入，但对这些正在建设的公司而言并不是运营费用。这是为什么这个过程如此加速发生的重要原因。当一个大公司从英伟达购买这些芯片时，他们在会计基础上不必将其视为支出列入利润表中的一个重要因素。
It actually gets booked as a capital expenditure in the cash flow statement. It gets put on the balance sheet and they depreciate it over time. And so they can spend $20 billion of cash because Google and others have 100 billion of cash sitting on the balance sheet. And they've been struggling to find ways to grow their business through acquisitions.
事实上，在现金流量表中，这被记录为资本支出。它被列入资产负债表，随着时间的推移逐渐折旧。因此，他们可以花费200亿美元现金，因为谷歌等公司在资产负债表上有1000亿美元的现金。他们一直在努力寻找通过收购来扩大业务的方法。
One of the reasons is they there aren't enough companies out there that they can buy at a good multiple that can give them a good increase in profit. The other one is that antitrust authorities are blocking all of their acquisitions. And so what do you do with all that cash? Well, you can build out the next gen of cloud infrastructure and you don't have to take the hit on your P&L by doing it.
其中一个原因是他们发现，市场中没有足够多的公司能够以合理的倍数被收购，从而带来良好的利润增长。另一个原因是反垄断当局正在阻止他们的所有收购。那么，你会怎么处理所有这些现金呢？你可以建设下一代云基础设施，而不必在利润和损失表中承担这方面的损失。
So it ends up on the balance sheet and then you depreciate it over typically four to seven years. So that money gets paid out on the on the income statement at these big companies over a seven year period. So there's a really great accounting and M&A environment driver here that's causing the big cloud data center providers to step in and say, this is a great time for us to build out the next generation of infrastructure that could generate profits for us in the future because we've got all this cash sitting around.
这样，资金最终会出现在资产负债表上，然后通常在四到七年内进行折旧。这样，这笔资金在大公司的收入表上会在七年内支付出来。因此，这里有一个非常好的会计和并购环境因素，促使大型云数据中心提供商介入，并表示，现在是我们建立下一代基础设施的绝佳时机，这将为我们未来带来利润，因为我们有大量现金闲置。
We don't have to take a P&L hit. We don't have to acquire a cash burning business. And frankly, we're not going to be able to grow through M&A because of antitrust right now anyway. So there's a lot of other motivating factors that are causing this near term acceleration as they're trying to find ways to grow. Yeah. And I know that was an accounting point, but I think it's a really important thing.
我们不必承受损失。我们不必收购一个在烧钱的业务。而且说实话，由于反垄断法规，我们目前也无法通过并购来实现增长。因此，有许多其他激励因素正在促使我们在短期内加速增长。是的。我知道这是一个会计观点，但我认为这是非常重要的事情。
I think it's an hard one. If you if a hundred billion gets spent this year, you divide it by four, 25 billion in revenue would have to come from that or something in that range. And so sacks any guesses? You have to just keep in mind, I think, Freeberg, what you said is very true for GCP spend, but not necessarily for Google spend. It's true for AWS spend, but not necessarily for Amazon spend.
我觉得这个问题挺难的。如果今年花费了一千亿美元，你把它除以四，那么就需要从那里获得250亿美元的收入，或者大致在那个范围内。所以有人猜测吗？你必须牢记，我认为，弗里伯格，你说的对于GCP支出来说是很正确的，但不一定适用于谷歌的支出。这对于AWS支出来说是对的，但不一定适用于亚马逊的支出。
And it's true for Azure spend, not true for Microsoft spend. And it's largely not true for Tesla and Facebook because they don't have clouds. So I think the question to your point that been for obvious reasons and video doesn't disclose it is what is the percentage of that 21 billion that just went to those cloud providers that they'll then expose to to everybody else versus what was just absorbed because at Facebook, Mark had that video about how many H 100s that's all for him.
对于Azure的支出而言，这是真实的，但对于微软的支出则并非如此。对于特斯拉和Facebook来说，这基本上也不是真的，因为它们没有云服务。我认为，根据你的这一点提出的问题是，21亿美元中有多少比例是支付给云服务提供商，然后他们将向其他人公开，另一部分则被吸收了，因为在Facebook，马克发布了一个关于有多少H100是为他自己使用的视频。
Right, but it is still it is still capitalized as my point. So they don't have to book that as an expense. It sits on the balance sheet. Yeah, sure. And they wrote it down over time. You're helping to explain why these big cloud service providers are spending so much on the cash because they're very profitable and there's no real stuff about the money. Right.
是的，但它仍然作为我的观点被资本化了。所以他们不必将其作为费用预订。它会显示在资产负债表上。是的，当然。他们随着时间将其折旧。你正在帮助解释为什么这些大型云服务提供商在现金方面投入如此之多，因为它们非常有利可图，没有关于资金的真正问题。没错。
Well, so that would seem to indicate that this is more in the category of one time build out than sustainable ongoing revenue. I think that the big question is the one that Jamath asks, which is what's the terminal value of Nvidia? I think like a simple framework for thinking about that is what is the total addressable market or TAM related to GPUs.
这似乎表明这更多地属于一次性建设而非可持续的持续收入。我认为一个重要的问题是Jamath提出的，那就是英伟达的终端价值是多少？我认为一个简单的思考框架是考虑与GPU相关的总市场规模或TAM。
And then what is their market share going to be right now? Their market share is something like 91%. That's clearly going to come down, but their mode appears to be substantial. The Wall Street analysts, I've been listening to think that in five years, they're still going to have 60 something percent market share.
然后他们现在的市场份额会是多少呢？他们的市场份额大约是91%。显然会下降，但他们的模式似乎是稳固的。我听到的华尔街分析师认为，五年后，他们仍将保持60多个百分点的市场份额。
So they're going to have a substantial percentage of this market or this TAM. Then the question is, I think with respect to TAM is what is one time build out versus steady state? Now, I think that clearly there's a lot of build out happening now that's almost like a backfill of capacity that people are realizing they need.
所以他们将占据这个市场或者这个TAM的相当大比例。那么，我认为关于TAM的问题是，一次性建设和稳态之间的区别是什么？现在，我认为很明显现在正在发生很多建设，几乎像是在填补人们意识到他们需要的容量。
But even the numbers you're seeing this quarter kind of understated because first of all, Nvidia was supply constrained. They cannot produce enough chips to satisfy all the demand. Their revenue would would have been even higher if they had more capacity. Second, you just look at their forecast. So the fiscal year that just ended, they did around 60 billion of revenue. They're forecasting 110 billion for the fiscal year that just started. So they're already projecting to almost double based on the demand that they clearly have visibility into already.
但甚至你看到的这个季度的数字都有点低估了，因为首先，英伟达受到供应限制。他们无法生产足够的芯片来满足所有需求。如果他们有更大的产能，他们的收入可能会更高。其次，你只需要看一下他们的预测。上一个财年他们的营收约为600亿美元。他们预测上一个财年将达到1100亿美元。因此，他们已经根据明显已经可见的需求预测几乎将翻倍。
So it's very hard to know exactly what the terminal or steady state value of this market is going to be. Even once the cloud service providers do this big build out, presumably there's always going to be a need to stay up to date with the latest chips, right? Here's a framework for you. Saks, tell me if this makes sense. Intel was the basically the mother of all of modern compute up until today, right? I think the CPU was the most fundamental work course that enabled local PCs, it enabled networking, it enabled the internet. And so when you look at the market cap of it, as an example, that's about $180 odd billion today, the economy that it created that it supports is probably measured, call it a trillion or two trillion dollars, maybe five trillion, let's just be really generous, right?
因此，要准确知道市场的终端或稳态值是非常困难的。即使云服务提供商进行了大规模的建设，想必总是需要跟上最新的芯片，对吧？这里有一个框架给你。萨克斯，告诉我这样是否有道理。英特尔基本上是现代计算机的母亲，一直到今天，对吧？我认为CPU是能够启用本地PC、网络和互联网的最基本的工作核心。因此，当你看它的市值，举个例子，今天大约是1800亿美元，它所创造和支持的经济规模可能被衡量为一万亿或两万亿美元，也许是五万亿，让我们非常慷慨一点，对吧？
And so you can see that there's this ratio of the enabler of an economy and the size of the economy. And those things tend to be relatively fixed and they recur repeatedly over and over and over. If you look at Microsoft, it's market cap relative to the economy that it enables. So the question for Nvidia in my mind would be not that it is it not going to go up in the next 18 to 24 months, probably is for exactly the reason you said it is super set up to have a very good meat and beat guidance for the street, which they'll eat up and all of the algorithms that trade the press releases will drive the price higher. And all of this stuff will just create a trend upward.
因此，你可以看到经济的推动因子与经济规模之间存在着一种比例关系。这些因素往往是相对固定的，并会反复出现。如果你看微软，它的市值相对于其推动的经济规模。因此，在我看来，对Nvidia来说问题不在于它是否会在接下来的18到24个月内上涨，而是因为正如你所说，它正准备好给投资者提供非常好的预测数据，市场将接受这些数据，所有的算法将推动股价上涨。所有这些都将带来一个上升趋势。
I think the bigger question is, if it's a four or five trillion dollar market cap in the next two or three years, will it support a hundred trillion dollar economy? Because that's what you need to believe for those ratios to hold. Otherwise, everything is just broken on the internet. Yeah, I mean, so the history of the internet is that if you build it, they will come, meaning that if you make the investment in the capital assets necessary to power the next generation of applications, those applications have always eventually gotten written, even though it was hard to predict them at the time.
我认为更重要的问题是，如果在未来两三年内市值达到四到五万亿美元，那是否能支撑一个一百万亿美元的经济？因为你需要相信这些比率才能保持。否则，一切都只是网络上的瓦解。是的，我是说，互联网的历史告诉我们，如果你建造它，他们就会来，这意味着如果你投资于支撑下一代应用程序所需的资本资产，那些应用程序最终总是会被开发出来，即使在当时很难预测它们是什么。
So in the late 90s, when we had the whole dot com bubble and then bust, you had this tremendous build out, not just of kind of servers and all the networking equipment, but there was a huge fiber build out by all the telecom companies. And the telecom companies had a Cisco like, you know, Pete, it was worse. You know, well, common them. Well, they went bankrupt a lot of them. Yeah. Well, the problem there was that a lot of the build out happened with debt.
所以在90年代末，当我们经历整个互联网泡沫及破裂时，你会看到这种巨大的建设，不仅仅是服务器和所有网络设备，还有所有电信公司进行的大规模光纤建设。电信公司有一种Cisco式的，你知道的，票子，情况更糟。你知道，他们破产了很多。嗯。问题在于大部分建设都是通过债务进行的。
And so when you had the dot com crash and all the valuations came down to earth, that's why a lot of them went under. Yeah, Cisco wasn't in that position. But any of my point is, in the early 2000s, when the dot com crash happened, everyone thought that these telecom companies had over invested in fiber. As it turns out, all that fiber eventually got used. The internet went from, you know, dial up to broadband. We started doing seeing streaming, social networking, all these applications started eating up that bandwidth. So I think that the history of these things is that the applications eventually get written, they get developed if you build the infrastructure to power them.
所以当你遇到互联网泡沫破灭，所有的估值都回归现实之后，很多公司都倒闭了。是的，思科并没有陷入这种境地。但我的意思是，在 2000 年初互联网泡沫破灭时，所有人都认为这些电信公司在光纤上投入过多。结果证明，所有那些光纤最终都被利用了。互联网从拨号上网过渡到宽带，开始有了流媒体、社交网络等应用程序，所有这些都开始占用带宽。因此，我认为这些事物的历史是，应用程序最终被开发出来，如果你建立了支撑它们的基础设施。
And I think with AI, the thing that's exciting to me as someone who's really more of an application investor is that we're just at the beginning, I think of a huge wave of a lot of new creativity in applications that's going to be written. And it's not just B2C, it's going to be B2B as well. You guys haven't really mentioned that it's not just consumers and consumer applications are going to use these cloud data centers that are buying up all these GPUs, it's going to be enterprises too. I mean, these enterprises are using Azure, they're using Google Cloud and so forth. So there's a lot, I think that's still to come.
我认为，对我这样一个更注重应用投资者而言，AI最激动人心的地方在于我们只是刚刚开始，我认为在应用程序领域会迎来一波巨大的创造力浪潮。这不仅仅是B2C，也会涉及到B2B。你们还没有提到的是，不仅仅是消费者和消费者应用程序会使用这些购买了大量GPU的云数据中心，企业也会使用。我是说，这些企业正在使用Azure、谷歌云等。所以我认为，还有很多事情有待发生。
I mean, we're just at the beginning of a wave that's probably going to last at least a decade. Yeah, to your point, one of the reasons YouTube, Google Photos, iPhoto, a lot of these things happened was because the infrastructure build out was so great during the .com boom that the prices for storage, the prices for bandwidth sacks plummeted. And then people like Chad Hurley looked at him and were like, you know what, instead of charging people to put a video on the internet and then charging them for the bandwidth they used, we'll just let them upload this stuff to YouTube and we'll figure it out later, save me with Netflix. Yeah, I mean, look, when we were developing PayPal in the late 90s, really around 1999, you could barely upload a photo to the internet. I mean, it's so like the idea of having an account with a profile photo on it was sort of like, why would you do that? It's just prohibitively slow, everyone's going to drop off. By 2003, it was fast enough that you could do that. And that's why social networking happened. I mean, literally without that performance improvement, like even having a profile photo on your account was something that was too hard to do.
我的意思是，我们只是处在一个可能持续至少十年的浪潮的开始阶段。是的，正如你所说的，YouTube、Google相册、iPhoto等很多东西之所以出现，是因为在点com繁荣时期，基础设施得到了很大发展，存储价格、带宽价格暴跌。然后像Chad Hurley这样的人看到了这一点，想到了，你知道吗，与其让人们上传视频到互联网然后再收费带宽费，我们不如让他们把这些东西上传到YouTube，之后再想办法解决，就像Netflix一样。是的，我是说，在我们发展PayPal的时候，大约是在1999年左右，你几乎无法把照片上传到互联网上。我是说，想要在账户上放一个个人头像这个概念，当时人们觉得为什么要这样做？速度太慢了，每个人都会放弃。到了2003年，网速已经能够支持这样做了。这就是社交网络的产生原因。我是说，确实，如果没有这种性能的提升，即使在你的账户上放一个头像都是一件太难做到的事情。
Yeah, LinkedIn profile was like too much bandwidth. And then let alone video. I mean, the you would get, you probably remember these days, you would put up a video on your website. If it went viral, your website got turned off because you would hit your 5000 or $10,000 month cap. All right, Grok also had a huge week that's Grok with a Q, not to be confused with the Elon's Grok with a K. Chamath, you've talked about Grok on this podcast a couple of times. Obviously, you were the, I guess you were the first investor, the seed investor, you pulled it, these LPUs and this concept out of a team that was at Google. Maybe you could explain a little bit about Grok's viral moment this week in the history of the company, which I know has been a long road for you with this company.
是的，LinkedIn的个人资料就像是太多的带宽一样。更不用说视频了。我的意思是，你可能还记得那些日子，你会在你的网站上放一个视频。如果它变得火爆，你的网站会被关掉，因为你会达到5000或10000美元的每月限制。好吧，Grok这周也表现非常出色，这里指的是Groq而不是埃隆的Grok。Chamath，你在这个播客上已经几次谈到了Grok。显然，你是第一位投资者，最早的投资者，你从一支在Google的团队中挖掘出了这些LPUs和这个概念。也许你可以解释一下Grok公司在这周发生的火爆时刻，我知道这对你们公司来说是一条漫长的道路。
I mean, it's been since 2016. So again, proving what you guys have said many times and what I've tried to live out, which is just you just got to keep grinding. 90% of the battle is just staying alive in business and having oxygen to keep trying things. And then eventually if you get lucky, which I think we did, things can really break in your favor. So this weekend, you know, I've been tweeting out a lot of technical information about why I think this is such a big deal. But yeah, the moment came this weekend, combination of hacker news and some other places. And essentially, we had no customers two months ago, I'll just be honest. And between Sunday and Tuesday, we've just were overwhelmed. And I think like the last count was we had 3000 unique customers come and try to consume our resources from every important fortune 500 all the way down to developers. And so I think we're very fortunate. I think the team has a lot of hard work to do. So it could mean nothing, but it has the potential to be something very disruptive.
我的意思是，这已经是自2016年以来了。所以再次证明了你们之前多次说过的和我一直试图实践的，那就是你必须继续努力。在商业上，90%的战斗就是保持生存，不断尝试新事物。最终，如果你幸运的话，我认为我们一直都很幸运，事情就会朝着你的方向发展。所以这个周末，我一直在用推特发布很多关于为什么我认为这个是一个很重要的事情的技术信息。但是，这个周末的时刻来临了，一些黑客新闻和其他地方的结合。基本上，在两个月前，我们没有顾客，我只能坦率地说。然而，在周日到周二之间，我们被压倒了。我想最后的统计数字是，我们有3000个独立的顾客来尝试使用我们资源，从每个重要的财富500强到开发人员。所以我认为我们非常幸运。我认为团队还有很多艰苦的工作要做。这可能什么都不是，但有可能会变得非常具有颠覆性。
So what is it that people are glomming on to? You have to understand that like at the very highest level of AI, you have to view it as two distinct problems. One problem is called training, which is where you take a model, and you take all of the data that you think will help train it. And you do that, you train the model, you learn all over all of this information. But the second part of the AI problem is what's called inference, which is what you and I see every day as a consumer. So we go to a website like chat, GPT or Gemini, we ask a question and it gives us a really useful answer. And those are two very different kinds of compute challenges. The first one is about brute force and power, right? If you can imagine like what you need are tons and tons of machines, tons and tons of like very high quality networking, and an enormous amount of power in a data center so that you can just run those things for months.
那么人们真正感兴趣的是什么呢？你必须明白，在人工智能的最高层次，你必须将其视为两个不同的问题。一个问题被称为训练，也就是你拿一个模型，拿所有你认为有助于训练它的数据。然后你开始训练这个模型，学习所有这些信息。但人工智能问题的第二部分是推断，也就是你和我每天作为消费者看到的东西。所以我们去像chat、GPT或Gemini这样的网站，我们问一个问题，它给我们一个非常有用的答案。这是两种非常不同的计算挑战。第一个问题是关于蛮力和力量，对吧？想象一下，你需要大量的机器、非常高质量的网络和一个庞大的数据中心，这样你就可以让这些东西连续运行数月。
I think Elon publishes very transparently. For example, how long it trains to train his grok with a K, right? Model, and it's in the months. Infants is something very different, which is all about speed and cost. What you need to be in order to answer a question for a consumer in a compelling way is super, super cheap and super, super fast. And we've talked about why that is important. And the grok with a Q ships turns out to be extremely fast and extremely cheap. And so look, time will tell how big this company can get. But if you tie it together with what Jensen said on the earnings call, and you now see developers stress testing us and finding that we are meaningfully, meaningfully faster and cheaper than any Nvidia solution. There's the potential here to be really disruptive. And we're a meager unicorn, right? Our last valuation was like a billion something versus Nvidia, which is now like a two trillion dollar company.
我认为埃隆（Elon）的公开发表非常透明。例如，埃隆花了多长时间来训练他的Grok模型，对吧？而且这是在几个月内。Infants是一种非常不同的东西，它与速度和成本有关。你需要做的是以超级便宜和超级快的方式回答消费者的问题，这是非常重要的。我们已经讨论过为什么这很重要。而结果证明，Grok带有Q的出货速度极快，价格极低。所以看看吧，时间会告诉我们这家公司能有多大。但如果你结合着詹森在财报电话会议上所说的，你会发现开发者们正在对我们进行压力测试，并发现我们在速度和价格方面比任何英伟达解决方案都明显快速和便宜。这里有潜力真的具有颠覆性。我们是一只不起眼的独角兽，对吧？我们最后的估值只有十多亿，而英伟达现在市值已经达到两万亿美元。
So there's a lot of market cap for grok to gain by just being able to produce these things at scale, which could be just an enormous outcome for us. So time will tell, but a really important moment in the company and very exciting. Can I just observe like off topic how an overnight success can take eight years? And no, I was thinking the same line. It's a seven year overnight success in the making. There's this class of businesses that I think are unappreciated in a post-internet era, where you have to do a bunch of things right before you can get any one thing to work.
所以简单地说，通过大规模生产这些产品，我们对于Grok可以获得更多的市值空间，这对我们来说可能是一个巨大的成功。时间会证明一切，这是公司的一个非常重要且令人兴奋的时刻。可以说，一个一夜之间的成功可能会花费八年的时间。对，我也是这么认为的。这是一个持续七年才能够取得成功的过程。在后互联网时代，有一类商业是被低估了的，在这类商业中，你必须在任何一件事情成功之前确保做对了很多事情。
And these complicated businesses where you have to stack, either different things together that need to click together in a stack, or you need to iterate on each step until the whole system works end to end can sometimes take a very long time to build. And the term that's often used for these types of businesses is deep tech. And they fall out of favor, because in an internet era and in a software era, you can find product-market fit and make revenue and then make profit very quickly. And so a lot of entrepreneurs select into that type of business instead of selecting into this type of business, where the probability of failure is very high.
这些复杂的业务需要堆叠不同的东西在一起，需要让它们在一起的堆栈中点击，或者需要在每一步上迭代，直到整个系统完全运作起来，有时可能需要花费很长时间来构建。通常用于这些类型业务的术语是深科技。它不再受到青睐，因为在互联网时代和软件时代，你可以很快找到产品市场契合点，赚取收入，然后很快盈利。因此，很多创业者选择进入那种类型的业务，而不是选择进入这种类型的业务，这种类型的业务失败的概率非常高。
You have several low probability things that you have to get right in a row. And if you do, it's going to take eight years and a lot of money. And then all of a sudden, the thing takes off like a rocket ship. You've got a huge advantage. You've got a huge moat. It's hard for anyone to catch up. And this thing can really spin out on its own. I do think Elon is very unique in his ability to deliver success in these types of businesses. Tesla needed to get a lot of things right in a row. SpaceX needed to get a lot of things right in a row.
你需要一连串地完成几件低概率的事情。如果你成功了，它将花费八年和大量金钱。然后突然之间，事情就像火箭般起飞了。你拥有巨大的优势。你有一道巨大的壕沟。任何人都很难赶上。这件事可以自己快速发展。我认为伊隆在这类企业取得成功方面是非常独特的。特斯拉需要一连串地做对很多事情。太空探索技术公司也需要一连串地做对很多事情。
All of these require a series of complicated steps or a set of complicated technologies that need to click together and work together. But the hardest things often output the highest value. And if you can actually make the commitment on these types of businesses and get all the pieces to click together, there's an extraordinary opportunity to build moats and to take huge amounts of market value. And I think that there's an element of this that's been lost in Silicon Valley over the last couple of decades as the fast money in the internet era has kind of prioritized other investments ahead of this. But I'm really hopeful that these sorts of chip technologies, SpaceX, and biotech, we see a lot of this, these sorts of things can kind of become more in favor because the advantage of these businesses work seems to realize hundreds of billions and sometimes trillions of dollars of market value and be incredibly transformative for humanity.
所有这些都需要一系列复杂的步骤或一套复杂的技术需要点击在一起并协同工作。但最难的事情往往会产生最高的价值。如果你真的能在这些类型的业务上做出承诺，并让所有部分协同工作，那么就有一个非凡的机会来建立壕垒，并获得巨大的市场价值。我认为，在过去的几十年里，硅谷已经失去了这方面特质，因为互联网时代的快速获利有点优先于这些投资。但我真的很希望这些芯片技术、SpaceX和生物技术，我们看到很多这样的东西，这些事物可以变得更受欢迎，因为这些企业的优势似乎可以实现数千亿甚至数万亿美元的市场价值，对人类具有非常深刻的转变意义。
So I don't know, I just think it's an observation I wanted to make about the greatness of these businesses when they work out. Well, I mean, OpenAI was kind of like that for a while. Totally. It was this like wacky nonprofit that was just grinding on an AI research problem for like six years and then it finally worked and got productized into chat GPT. Totally. But you're right, SpaceX was kind of like that. I mean, the big money maker at SpaceX is Starlink, which is the satellite network space, broadband from space. And it's on its way to handling, I think a meaningful percentage of all internet traffic. But think about all the things you had to get to to get that working.
所以我不知道，我只是想观察到这些企业在成功时的伟大之处。嗯，我的意思是，OpenAI 之前也是这样的。完全是。它一直是一个古怪的非营利机构，专注于研究AI 问题，然后在六年后终于成功，推出了产品化成为聊天GPT。完全是。但你说得对，SpaceX 也是这样的。我的意思是，SpaceX 的大赚钱项目是 Starlink，这是一项卫星网络空间宽带服务。它正在逐步处理我认为相当大比例的互联网流量。但想想为了使其正常运行所必须经历的一切。
First, you had to create a rocket. That's hard enough. Then you had to get to reusability. Then you had to create the whole satellite network. So at least three hard things in a row. Well, I mean, consumers to adopt it. I mean, you know, don't forget the finals, then. Yeah, we had no idea where the market was like early on, it started in my office. And so Jonathan and I would be kind of always trying to figure out what is the initial go to market. And I remember I emailed Elon in at that period when they were still trying to figure out whether they were going to go with LiDAR or not. And we thought, wow, maybe we could sell Tesla the chips. But then Tesla brought in this team just to talk to us about what the design goals were. And basically said, no, in kind way, but they said no.
首先，你必须制造一枚火箭。这已经够难了。然后你要实现可重复使用性。接着你要创建整个卫星网络。所以至少连续要做三件难事。嗯，我的意思是，消费者要采纳它。我的意思是，你知道的，别忘了最终目标。是的，我们对市场情况一开始一无所知，一切都从我的办公室开始。所以乔纳森和我一直试图弄清楚最初的营销策略。我记得那时候我给伊隆发了封邮件，当时他们仍在试图确定是继续使用激光雷达还是不用。我们想，也许我们能将芯片卖给特斯拉。但特斯拉却派了一个团队过来与我们讨论设计目标。他们基本上以一种和善的方式拒绝了，但他们拒绝了。
Then we thought, okay, maybe it's like for high frequency traders, right? Because like those folks want to have all kinds of edges. And if we have these big models, maybe we can accelerate their decision making, they can measure revenue, that didn't work out. Then it was like, you know, we tried to sell to three letter agencies. That didn't really work out. Our original version was really focused on image classification and convolutional neural nets. Like ResNet, that didn't work out. We ran headfirst into the fact that NVIDIA has this compiler product called CUDA. And we had to build a high class compiler that you could take any model without any modifications. All these things to your point are just points where you can just very easily give up. And then there's like, we run out of money. So then you write money in a note, right? Because everybody wants to punt on valuation when nothing's working. Yeah. You tried six-peach head markets, you couldn't land above.
然后我们想，好吧，也许这是为高频交易者设计的，对吧？因为像这些人想要各种优势。如果我们有这些大型模型，也许我们可以加快他们的决策速度，他们可以衡量收入，但这并没有成功。然后就像，你知道的，我们尝试向三个字母机构销售。那并不是很成功。我们最初的版本真的专注于图像分类和卷积神经网络，比如ResNet，但那也没有成功。我们直面了NVIDIA有一个名为CUDA的编译器产品的事实。我们不得不构建一个高级编译器，您可以在不进行任何修改的情况下拿走任何模型。就像您所说的，所有这些事情都只是让您放弃的点。然后就像，我们用完了钱。所以您在便条上写上钱，对吧？因为当一切都不顺利时，每个人都想撇清估值责任。是的。你尝试了六个不同的市场，但都没有成功。
Oh, my gosh. You have to make a decision to just keep going if you believe it's right. And if you believe you are right. Yeah. And that requires shutting out. We talked about this in the Mosa example last week, but it just requires shutting out the noise because it's so hard to believe in yourself. It's so hard to keep funding these things. It's so hard to go into partan meetings and defend a company. And then you just have a moment and you just feel, I don't know, I feel very vindicated, but then I feel very scared because Jonathan still hasn't landed it. You know what I mean? You mentioned all those boats landing and trying to try to, there's missteps, but 3,000 people signed up. Who are they? Are they developers now and they're going to figure out the applications? Yeah. I think that back to the original point. My thought today is that AI is more about proof of concept and toy apps and nothing real.
哦，天啊。如果你相信这是正确的选择，你就必须做出决定继续前行。如果你确信自己是正确的。是的。这需要关闭一切。我们上周在Mosa的例子中讨论过这个问题，但这确实需要摒弃杂音，因为相信自己实在太难了。继续资助这些项目实在太困难了。去参加股东会然后为公司辩护也很困难。然后你忽然间有一个瞬间，你感到，我不知道，我感到非常得到了证实，但然后我感到非常害怕因为乔纳森还没有成功。你明白我的意思吗？你提到那些船陆上并试图去尝试，会出现错误，但有3,000人注册了。他们是谁？现在是开发者了吗，他们会研究这些应用程序吗？是的。我认为回到最初的观点。我今天的想法是，人工智能更多是关于概念的证明和玩具应用程序，而不是真正的东西。
I don't think there's anything real that's inside of an enterprise that is so meaningfully disruptive that it's going to get broadly licensed to other enterprises. I'm not saying we won't get there, but I'm saying we haven't yet seen that Cambrian moment of monetization. We've seen the Cambrian moment of innovation. And so that gap has still yet to be crossed. And I think the reason that you can't cross it is that today these are in an unusable state. The results are not good enough. They are toy apps that are too slow that require too much infrastructure and cost. So the potential is for us to enable that monetization leap forward. And so yeah, they're going to be developers of all sizes. And the people that came are literally companies of all sizes. I saw some of the names of the big companies and they are the who's who of the S&P 500.
我认为企业内部没有任何真正具有极大颠覆性意义的东西，可以被广泛授权给其他企业。我不是说我们永远不会做到，但我要说的是，我们还没有看到那种像寒武纪那样的货币化时刻。我们看到了创新的寒武纪时刻。所以这个差距仍然没有被跨越。我认为无法跨越这个差距的原因是，目前这些产品处于不可用的状态。结果还不够好。它们是速度太慢、需要太多基础设施和成本的玩具应用程序。因此，我们的潜力在于推动这种货币化的飞跃。是的，将有各种规模的开发人员，并且参与的人员实际上是各种规模的公司。我看到了一些大公司的名字，它们是标准普尔500指数的佼佼者。
How do you guys reconcile this deep tech high outcome opportunity that everyone here has seen and been a part of as an investor, participant in versus the more de-risked, faster time to market. And you know, to Moth in particular, like in the past, we've talked about some of these deep tech projects like Fusion and so on. And you've highlighted, well, it's just not there yet. It's not fundable. What's the distinction between a deep tech investment opportunity that is fundable and that you keep grinding at that has this huge outcome? What makes the one like Fusion? That's not fun. It's a phenomenal question. That's a great question.
你们如何协调这种深度科技高回报的机会，每个人都看到并参与其中作为投资者、参与者，与更低风险、市场推出时间更快的选择呢？尤其是对于莫斯，过去我们谈论过一些深度科技项目，如核聚变等。你强调过，它还没有达到那个水平，还不能获得资金支持。那么，一个可资助的深度科技投资机会与那些你一直在努力奋斗、具有巨大回报的有何区别？是什么造成类似核聚变这样的项目不能获得资金支持？这是一个非常棒的问题。
My answer is I have a very simple filter, which is that I don't want to debate the laws of physics when I fund a company. So with Jonathan, when we were initially trying to figure out how to size it, I think my initial check was like seven to 10 million dollars or something. And the whole goal was to get to an initial tape out of a design. We were not inventing anything new with respect to physics. We were on a very old process technology. I think we're still on 14 nanometer. We were on 14 nanometer eight years ago. Okay. So we weren't pushing those boundaries. All we were doing was trying to build a compiler in a chip that made sense in a very specific construct to solve a well-defined bounded problem. So that is a technical challenge, but it's not one of physics. When I've been pitched all the Fusion companies, for example, there are fuel sources that require you to make a leap of physics, where in order to generate a certain fuel source, you either have to go and harvest that on the moon or on a different planet that is not Earth, or you have to create some fundamentally different way of creating this highly unique material.
我的答案是我有一个非常简单的筛选标准，就是在我资助一家公司时，我不想就物理定律展开辩论。 因此，与乔纳森一起，当我们最初试图弄清楚该如何确定规模时，我认为我的最初支票金额是七到十万美元左右。整个目标是实现设计的初步放片。我们并没有在物理方面发明任何新事物。 我们采用了一种非常老的工艺技术。 我想我们仍然在14纳米。八年前我们就采用了14纳米技术。 好的。因此，我们并没有挑战这些边界。我们所做的一切只是尝试在一个非常具体的结构中构建一个合乎逻辑的芯片编译器，以解决一个明确定义的有限问题。所以这是一个技术挑战，但不是物理挑战。 举例来说，当我被所有融合公司推销时，有些燃料来源需要你跨越物理学的鸿沟，为了产生某种燃料来源，你要么必须去月球或者地球之外的另一个星球上收集，要么你必须创造一种根本不同的创造这种高度独特材料的方式。
That is why those kinds of problems to me are poor risk and building a chip is good risk. It doesn't mean you're going to be successful in building a chip, but the risks are bounded to not of fundamental physics. They're bounded to go to market and technical usefulness. And I think that that removes an order of magnitude risk in the outcome. So there's still a bunch of things that have to be right in a row to make it work. But yeah, it doesn't mean it's going to work. All I'm saying is I don't want it to fail because we built a reactor and we realized, hold on, to get heavy hydrogen, I got to go to the moon. And Jake telling Sacks, I know you don't invest in a space act. No, we have done a couple. But maybe you guys can highlight how you thought about deep tech opportunities versus what you focus on. We probably use something really difficult like this every 50 investments or so, because most of the entrepreneurs coming to us, because we're seed investors or pre-seed investors, they would be going to a biotech investor or a hardware investor who specializes in that, not to us.
这就是为什么对我来说，那些类型的问题是高风险，而建立芯片是好风险。这并不意味着你建立芯片会成功，但风险并不涉及基本物理原理。它们更多地涉及市场和技术的实用性。我认为这减少了一个数量级的风险。所以还有很多事情必须连续正确才能让事情成功。但是，这并不意味着一定会成功。我只是不希望它失败，因为我们建立了一个反应堆，然后意识到，哦，为了获得重氢，我得去月球。杰克告诉萨克斯，我知道你们不投资太空项目。不，我们有进行过一些投资。但也许你们可以强调一下你们是如何考虑深科技机会与你们专注的内容不同的。我们大概每50笔投资中就会使用一次这样的难题，因为大多数来找我们的创业者会去找生物技术投资者或硬件投资者专门从事该领域，而不是来找我们。
But once in a while, we mean a founder we really like. And so Contraline was one. We were introduced to somebody who's doing this really interesting contraception for men, where they put a gel into your vast deference. And you as a man can take control of your reproduction. You basically, it's not a vasectomy. It's just a gel that goes in there and blocks it. And this company is now doing human trials and doing fantastic. But this took forever to get to this point. And then you guys, some of you are also investors in Cafe X, which we love the founder. And this company should have died during COVID. And making a robotic coffee bar, when he started, seven, eight years ago, was incredibly hard. He had to build the hardware. He had to build a brand. He had to do locations, he had to do software. And now he's selling these machines, and people are buying them. And the two in San Francisco at SFO are making like, I think they, the two of them make a million dollars a year.
但偶尔我们真的很喜欢某位创始人。Contraline就是其中之一。我们认识一个在为男性避孕提供非常有趣选择的人，他们将一种凝胶注入你的导管中。作为男性，你可以控制自己的生育。基本上，这不是一种输精管结扎手术。只是注入一种凝胶用来阻塞。这家公司现在正在进行人体试验，效果非常好。但是这个过程花了很长时间才到达今天这一步。而且你们中的一些人还是Cafe X的投资者，我们非常喜欢这个创始人。这家公司在COVID期间本应该倒闭。他在七八年前开始制作自动咖啡吧是非常困难的。他必须建造硬件，打造品牌，开设门店，编写软件。现在他在销售这些机器，人们也在购买。在旧金山的SFO有两台，我记得它们一年可以赚百万美元。
And it's the highest per square footage of any store in an airport. And so we've just been grinding and grinding. And you got to find a founder who's willing to make it their lives work in these kinds of situations. But you start to think about the degree of difficulty, hardware, software, retail, mobile apps. I mean, it just gets crazy how hard these businesses are, as opposed to I'm building a SaaS company. I build software, I sell it to somebody to solve their SaaS problem. It's like, it's very one-dimensional, right? And it's pretty straightforward. These businesses typically have five components. Yeah. And SaaS, you've been an investor in SpaceX, but you don't make those sorts of investments regularly that craft.
这家店的每平方英尺收入是机场内最高的。我们一直在辛勤工作。你必须找到一个愿意把这个项目作为自己生活工作的创始人。但是当你开始考虑到这些业务的难度时，包括硬件、软件、零售、移动应用等，你会发现这些业务有多么困难。相比之下，我正在建立一家SaaS（软件即服务）公司。我开发软件，将其销售给需要解决SaaS问题的客户。这种业务非常单一，非常简单。这些业务通常有五个组成部分。是的，在SaaS领域，你曾是SpaceX的投资者，但你并不经常进行这类投资。
Is that fair? Yeah, I have an Elon exception. It's about the founder. Save. Save. Now, in our portfolio allocation, we say this much early stage, this much late stage, this much Elon. Elon, except now. You have to be so dogged to want to take something like this on, because the good stuff happens, like you're saying, Freeburg, you're 7, 8, 9, 10, as opposed to a consumer product. Yeah. I mean, for me, it's like a dozen by year three or four. The only app that took a really long time, people don't know this, but Twitter actually took a long time to catch on. It was kind of cruising for two or three years. And then, South by Southwest happened, Ashton Kutcher got on it, Obama got on it. And then it took off. I think network effect businesses are different because that's all about getting your seat of your network.
这公平吗？是的，我有一个埃隆例外。这是关于创始人的。存档。存档。现在，在我们的组合配置中，我们说这么多早期阶段，这么多后期阶段，这么多埃隆。埃隆，除了现在。你必须非常固执地想要接受这样的东西，因为好事发生在你说的那些时期，Freeburg，你是7、8、9、10岁，而不是消费品。是的。对我来说，这就像第三四年就有十几个。唯一一个花了很长时间的应用，人们不知道这一点，但 Twitter 其实花了很长时间才受欢迎起来。它在两三年间相当平稳。然后，南西南西西南西发生了，阿什顿库彻加入了，奥巴马加入了。然后它就起飞了。我认为网络效应型企业是不同的，因为这一切都是关于获取你的网络核心。
What I'm talking about is the technical coordination of lots of technically difficult tasks that need to sync up. It's like getting a master lock with like 10 digits, and you got to figure out the combination of 10 digits. And once they're all correct, then the lock opens. And prior to that, if anyone number is off, the lock doesn't open. And I think these technically difficult businesses are some of the, and they are the hardest, and they do require the most dogged personalities to persist and to realize an outcome from. But the truth is that if you get them, the moat is extraordinary, and they're usually going to create extraordinary leverage and value. And I think from a portfolio allocation perspective, if you as an investor want to have some diversification in your portfolio, this is not going to be the predominance of your portfolio. But some percentage of your portfolio should go to this sort of business, because if it works, boom, this can be the big 10x, 100x, 1000x two stories about that.
我说的是需要同步的许多技术困难任务的技术协调。就像是拿到一个有十个数字组成的主锁，你必须找出这十个数字的组合。只有当它们全部正确时，锁才会打开。在那之前，如果有任何一个数字不对，锁就不会打开。我认为这些技术困难的企业是一些最困难的，它们需要最顽强的个性来坚持并实现一个结果。但事实是，如果你成功了，这种企业的护城河是非凡的，它们通常会创造非凡的杠杆和价值。从投资组合配置的角度来看，如果您作为一名投资者希望在投资组合中获得一些分散，这不会是您投资组合的主导部分。但您的一部分投资组合应该用于这种类型的企业，因为如果它成功了，哇，这可以是巨大的10倍、100倍、1000倍收益的故事。
One of the V early VCs and Elon stole the story publicly, wanted Elon to not make the roadster, not make the model S just make drive trains and the electric components for other car companies. Can you imagine how the world would have changed? And then totally very high profile VC came to me and said, okay, I'll do the series A for Uber. I'll preemptively do it, but you got to tell Travis to stop running Uber as a consumer app. I want him to sell the software to cab companies. So I make it a SaaS company. And I said, well, you know, the cab companies are kind of the problem. They're taking all the margin, like the kind of disrupting them. And they're like, yeah, yeah, but just think there's thousands of cab companies. They would pay you tens of thousands of dollars a year for this software. And you get a little piece of the action. I never brought that investor to Travis. I was like, Oh, wow, that's really interesting insight. Sometimes the VCs work against him. I have a very poor track record of working with other investors. Whoa, self reflection. I do deals myself.
很早期的一位风险投资人和埃隆公开窃取了这个故事，希望埃隆不要推出Roadster，也不要推出Model S，只做驱动系统和电动部件给其他汽车公司。你能想象世界会怎样改变吗？然后一个非常知名的风险投资人来找我说，好吧，我愿意为Uber做A轮融资，我愿意提前做，但你得告诉特拉维斯别再把Uber当消费者应用来经营了。我希望他把软件卖给出租车公司，让它成为一个SaaS公司。我回答说，出租车公司有点问题，他们拿走了所有的利润，所以我们在打破他们的格局。他们说，是的，但想象一下，有成千上万家出租车公司，他们每年会为这个软件支付数以万计的美元，你也能分一杯羹。我从未把那位投资人介绍给特拉维斯。我想，哇，这是一种非常有趣的见解。有时候风险投资人也会对他起到相反的作用。我和其他投资者合作的记录非常糟糕。哇，自我反思。我自己做交易。
I size them myself. And it's because a lot of them have to live within the political dynamics of their fund. And so I think Jason, what you're probably saw in that example, which is exactly why doing things and splitting deals will never generate great outcomes, in my opinion, is that you take on all the baggage and the dysfunction of these other partnerships. And so if you really wanted to go and disrupt transportation, you need one person who can be a trigger puller and who doesn't have to answer it. Anybody I find that's why I think, for example, when you look at how successful Vinot has been over a decade after decade after decade, when Vinot decides that's the decision. And I think there's something very powerful in that. There are a bunch of deals that I've done that when they've worked out were not really because they were consensus and they had to get supported and scaffolded at periods where if I wasn't able to ram them through myself because it was my organization, I think we would have been in a very different place.
我自己来评估他们的规模。这是因为很多人不得不生活在他们基金的政治动态之中。所以我认为，杰森，你在这个例子中可能看到的原因，这正是为什么按这种方式操作和分开交易永远不会产生出色的结果，是因为你要承担这些其他合作伙伴的所有包袱和功能紊乱。所以如果你真的想要颠覆交通，你需要一个人可以对此负责，而不必回答任何人。我发现任何人都可以，这就是为什么我认为，例如，当你看看Vinot在一个接一个的十年中有多么成功时，当Vinot做出决定时，有一种非常强大的东西。有一些交易我做过，当它们成功时并不是因为它们达成了共识，并且在某些时候必须得到支持和支持，如果我自己无法推动它们，因为这是我的组织，我认为我们会处于一个非常不同的地方。
So I think, I think like for for entrepreneurs, it's so difficult for them to find people that believe it's so much better to find one person and just get enough money and then not syndicate because I think you have to realize that you are bringing on and compounding your risk, the one that Freebrook talked about with the risk of all the other partnership dynamics that you bring on. So if you don't internalize that, you may have five or six folks that come into an A or a B, but you're inheriting five or six. Yeah, partnership. Disfunctions. Yeah. Yeah. Can you just explain really quickly for the audience since they heard about GPUs and NVIDIA, but they may not know what an LPUS, what's the difference there, Tamup?
所以我认为，对于创业者来说，很难找到相信只要找到一个人，获得足够的资金，然后不进行合作的人。因为我认为你必须意识到，你正在承担和增加风险，即Freebrook所说的你带来的所有其他合作关系动态的风险。因此，如果你没有内化这一点，你可能会有五六个人进入A轮或B轮，但你会继承五六个合作上的问题。是的，合作上的问题。是的。你可以很快地为听众解释一下，因为他们听过GPU和NVIDIA，但可能不知道LPUS是什么，Tamup，它们之间有什么区别？
The GPU, the best way to think about it is, so if you contrast a CPU with the GPU, so CPU was the workhorse of all of computing. And when Jensen started NVIDIA, what he realized was there were specific tasks where a CPU failed quite brilliantly at. And so he's like, well, we're going to make a chip that works in all these failure modes for a CPU. So a CPU is very good at taking one instruction in, acting on it, and then spitting out one answer effectively. And so it's a very serial kind of a factory, if you think about the CPU. So if you want to build a factory that can process instead of one thing at a time, 10 things or 100 things, what is they had to find a workload that was well suited and they found graphics. And what they convinced PC manufacturers back in the day was, look, have the CPU be the brain, it'll do 90% of the work. But for very specific use cases like graphics and video games, you don't want to do serial computation. You want to do parallel computation. And we are the best at that. And it turned out that that was a genius insight. And so the business for many years was gaming and graphics.
GPU，最好的理解方式是，如果你将CPU与GPU进行对比，那么CPU是所有计算的工作马。当Jensen开始创建NVIDIA时，他意识到CPU在某些特定任务上表现得相当糟糕。于是他想，我们要制造一款芯片，可以在CPU失败的各种模式下工作。一个CPU很擅长接收一条指令，执行它，然后有效地输出一个答案。如果你把CPU想象成一个串行工厂，它非常适合这种工作方式。所以如果你想要建立一个工厂，不是一次处理一件事情，而是可以处理10件或100件事情，那么就需要找到适合的工作负载，他们找到了图形任务。他们在当时说服了PC制造商，看，让CPU成为大脑，解决90%的工作。但对于诸如图形和视频游戏之类的特定用例，你不希望进行串行计算，而是希望进行并行计算。我们在这方面是最擅长的。结果证明，这是一个天才的洞察。因此，多年来业务主要是游戏和图形。
But what happened about 10 years ago was what we also started to realize was the math that's required and the processing that's required in AI models actually looked very similar to how you would process imagery from a game. And so he was allowed to figure out by building this thing called CUDA, which is the compiler that sits on the chip, how he could now go and tell people that wanted to experiment with AI. Hey, you know that chip that we had made for graphics? Guess what? It also is amazing at doing all of these very small mathematical calculations that you need for your AI model. And that turned out to be true. So the next leap forward was what Jonathan saw, which was, hold on a second, if you look at the chip itself, that GPU substantially has not changed since 1999 in the way that it thinks about problem solving. It has all this very expensive memory, blah, blah, blah. So he was like, let's just throw all that out the window. We'll make small little brains and we'll connect those little brains together. And we'll have this very clever software that schedules it and optimizes it. So basically take the chip and make it much, much smaller and cheaper, and then make many of them and connect them together. That was Jonathan's insight. And it turns out for large language models, that's a huge stroke of luck because it is exactly how LLMs can be hyper optimized to work. So that's kind of been the evolution from CPU to GPU to now LPU. And we'll see how big this thing can get, but it's quite novel. Well, congratulations on it all.
大约10年前发生的事情是，我们开始意识到，AI模型所需的数学和处理方式实际上与处理游戏图像的方式非常相似。因此，通过建立一种名为CUDA的编译器，他可以找到一种方法告诉想要尝试AI的人们。嘿，你知道我们为图形设计的芯片吗？猜猜？它也非常擅长完成你的AI模型所需的所有这些很小的数学计算。这是真的。下一个飞跃是乔纳森看到的，他发现，如果你看看芯片本身，那个GPU自从1999年以来在解决问题时基本上没有什么改变。它拥有所有这些非常昂贵的内存，等等。因此，他认为，我们只需将所有这些扔掉。我们将制造小小的大脑并将它们连接在一起。我们将拥有这个非常聪明的软件来调度和优化它。基本上将芯片变得更小更便宜，然后制造许多并将它们连接在一起。这就是乔纳森的洞察力。结果对于大型语言模型来说是一个巨大的幸运，因为这正是LLM可以被超级优化以工作的方式。所以从CPU到GPU到现在的LPU已经实现了进化。我们将看看这个东西能发展到多大，但它相当新颖。祝贺你们都。
And it was a very big week for Google, not in a great way. They had a massive PR mess with their Gemini, which refused to generate pictures if I'm reading this correctly, avoid people. Here's a quick refresher on what Google's doing in AI. Gemini is now Google's brand name for their AI main language model. You can think of that like OpenAI's GPT. Bard was the original name of their chat bot that had duet AI, which was Google sidekick in the Google suite earlier this month, Google rebranded everything to Gemini. So Gemini is now the model, it's the chat bot and it's a sidekick. And they launched a $20 month subscription called Google One AI premium, only four words way to go. This includes access to the best model Gemini Ultra, which is on par with GPT four, according to them, and generally in the marketplace.
谷歌这一周出了很大的问题，他们的Gemini AI出了巨大的公关危机，似乎无法生成图片，这是一个糟糕的情况。Google目前在AI领域做了很多事情。Gemini现在是谷歌的AI主要语言模型的品牌名称。你可以把它想象成OpenAI的GPT。Bard是他们的聊天机器人最初的名字，它带有“二重唱AI”，这在本月早些时候是谷歌套件中的助手。谷歌现在将所有内容重新命名为Gemini。因此，Gemini现在是模型，是聊天机器人，也是助手。他们推出了一个每月20美元的订阅服务，名为Google One AI高级版，只有四个字，干得好。这包括访问最佳模型Gemini Ultra，他们称之为与GPT四相当的水平，而且在市场上也很受欢迎。
But earlier this week, users on X started noticing that Gemini would not generate images of white people even when prompted. People were prompting it for images of historical figures that were generally white and getting kind of weird results. I asked Google Gemini to generate images of the founding fathers. It seems to think George Washington was black. Certainly here is a portrait of the founding fathers of America. As you can see, it is putting this Asian guy. It's just it's making a great mashup. And yeah, there was like countless images that got created, generate images of the American revolutionary. Sure, his here are images featuring diverse American revolution is an inserted the word diverse.
然而，本周早些时候，X平台的用户开始注意到，即使被提示，双子座也不会生成白人的图片。人们在要求生成一些通常是白人的历史人物的图片时，得到了一些奇怪的结果。我让Google Gemini生成美国建国元勋的图片。它似乎认为乔治·华盛顿是黑人。这里当然有美国建国元勋的肖像。正如你所看到的，它生成了这位亚洲人。它在创作一个很棒的混搭。还有无数张生成的美国革命的图片，其中包含了多样化的美国革命人物。
Sex, I'm not sure if you watch this controversy on X, I know you spend a little bit of time on that social network. I noticed you're active once in a while. Did you log in this week and see any of this, bro, huh? Sure. It's all over X right now. I mean, look, this Gemini rollout was was a joke. I mean, it's ridiculous. The AI is incapable of giving you accurate answers because it's been so programmed with diversity and inclusion. And it inserts these words diverse and inclusive, even in answers where you haven't asked for that. You haven't prompted it for that. So I think Google is now like yank back the product release. I think they're scrambling now because it's been so embarrassing for them.
性别，我不确定你是否关注X社交网络上的这场争议，我知道你在那个社交网络上花了一点时间。我注意到你偶尔还挺活跃的。这周你有登录看过吗？兄弟，对吧？当然。现在整个X都在讨论这个问题。我是说，这个双子座的推出简直就是一个笑话。AI根本不可能给出准确的答案，因为它被过分的编程成了多元化和包容性。它会在你没有要求的情况下，甚至在回答中加入这些词语“多元化”和“包容性”。我认为谷歌现在可能已经收回了产品发布。他们现在应该很慌乱，因为这对他们来说真的很尴尬。
But Sax is it how does this not get Q aid? Like, I don't understand how you had the red team not catch this. Yeah. Well, how or anybody or isn't there a product review with senior executives before this thing goes out that says, okay, folks, here it is. Have added try it. We're really proud of our work. And then they say, well, on a second, is this actually accurate? Shouldn't it be accurate? You guys remember when chat TPT launched, and there was a lot of criticism about Google and Google's failure to launch. And a lot of the observation was that Google was afraid to fail or afraid to make mistakes. And therefore, they were too conservative. And as you know, in the last year to year and a half, there's been a strong effort at Google to try and change the culture and move fast and push product out the door more quickly.
但是，萨克斯是怎么回事，为什么这个不会得到帮助呢？我不明白为什么你们红队没有注意到这一点。是的，那么，在这个东西推出之前，没有人或者高管不会审查产品吗？他们不会说，好的，大家看这里，已经添加了试用。我们为我们的工作感到自象。然后他们说，等一下，这实际上准确吗？不应该准确吗？你们还记得聊天TPT推出时，有很多对谷歌和谷歌推出失误的批评。很多人观察到，谷歌害怕失败或害怕犯错误。因此，他们过于保守。如你所知，在过去一年到一年半里，谷歌已经做出了很大努力，试图改变文化，更快地推出产品。
And the criticism is now why Google has historically been conservative. And I realized we can talk about this particular problem in a minute. But it's ironic to me that the Google is too slow to launch criticism has now revealed that Google's result of actually launching quickly can cause more damage than than good. But Google did not launch quickly. Well, I will say one other thing. It seems to me ironic, because I think that what they've done is they've launched more quickly than they otherwise would have. And they've put more guardrails in place that that backfired. And those guardrails ended up being more damaging. What's the guardrail here? So this is Google's AI principles. The first one is to be socially beneficial. The second one is to avoid creating or reinforcing unfair bias.
现在的批评是为什么谷歌在历史上一直保守。我意识到我们可以在一分钟内谈论这个特定问题。但对我来说，讽刺的是，谷歌太慢发起批评的现在揭示了实际上快速推出可能造成更多伤害而不是好处。但谷歌并没有快速推出。嗯，我会说另一件事。对我来说，这似乎是具有讽刺意味的，因为我认为他们所做的是比他们本来会做的更快地推出产品。他们设置了更多的防护措施，这反而产生了相反的效果。这些防护措施最终造成了更大的破坏。这里的防护措施是什么？这是谷歌的人工智能原则。第一个是对社会有益。第二个是避免产生或加强不公平的偏见。
So much of the effort that goes into tuning and waiting the models at Gemini has been to try and avoid stereotypes from persisting in the output that the model generates. Where is telling the truth? Telling the truth. Exactly. That's exactly what I was saying. Change society is our second-brins. Like the steering society. No, I think socially beneficial is a political objective, because it depends on how you perceive what a benefit is. Avoiding bias is political. Be built and tested for safety doesn't have to be political. But I think the meaning of safety has now changed to be political. By the way, safety with respect to AI used to mean that we're going to prevent some sort of AI superintelligence from evolving and taking over the human race. That's what used to mean. Safety now means protecting users from seeing the truth. They might feel unsafe. Or somebody else defines as a violation of safety for them to see something truthful. So their first three objectives or values here are all extremely political. I think any AI product for it to be worth assault has to start. They can have any. I think that these values are actually reasonable. That's their decision. They should be allowed to have it. But the first base order principle of every AI product should be that it is accurate and right. Correct. Yeah. Why not focus on being correct? Look, the values that Google lays out may be okay in theory, but in practice, they're very vague, you know, punter interpretation.
在金星的调整和等待模型中的许多努力都是为了尽量避免模型生成的输出中存在陈规陷入。说实话在哪里？说实话。确实。这正是我所说的。改变社会是我们的第二性脑。就像操纵社会一样。不，我认为有益社会是一个政治目标，因为这取决于你如何看待什么是益处。避免偏见是政治的。为安全而建设和测试不一定是政治的。但我认为安全的意义现在已经变成政治的了。顺便说一句，关于人工智能的安全曾经意味着我们要防止某种人工智能超级智能的进化并接管人类。那曾经是什么意思。现在安全意味着保护用户不看到真相。他们可能会感到不安全。或者别人定义看到某些真相对他们来说是安全违反。因此，这里的前三个目标或价值观都是极其政治化的。我认为任何AI产品要值得被采用，就必须以此为基础。他们可能有任何。我认为这些价值观实际上是合理的。那是他们的决定。但是每个AI产品的第一个基本原则应该是正确和准确。是的。为什么不专注于正确呢？看，谷歌提出的价值观在理论上可能没问题，但在实践中，它们非常模糊，你知道，可以有不同解释。
And so therefore, the people running Google AI are smuggling in their preferences and their biases. And those biases are extremely liberal. And if you look at X right now, there are tweets going viral from members of the Google AI team that reinforce this idea where they're talking about, you know, white privilege is real and, you know, recognize your bias at all levels and promoting a very left-wing narrative. So, you know, this idea that Gemini turned out this way by accident or because they didn't, because they rushed it out, I don't really believe that. I believe that what happened is Gemini accurately reflects the biases of the people who created it. Now, I think what's going to happen now is in light of this, the reaction to the rollout is, do I think they're going to get rid of the bias? No, they're going to make it more subtle. That is what I think is disturbing about it. I mean, they should have this moment where they change their values to make truth, the number one value, like Jonathan is saying, but I don't think that's going to happen. I think they're going to simply get, they're going to dial down the bias to be less obvious. You know who the big winner is going to be in all of this to mouth is going to be open source, like because people are just not going to want a model that has all this baked in weird bias, right? I think I want something that's open source. And it seems like the open source community would be able to grind on this to get to truth, right? So I think one of the big changes that Google's had to face is that the business has to move away from an information retrieval business where they index the open internet's data and then allow access to that data through a search results page to being an information interpretation service. These are very different products.
因此，管理谷歌人工智能的人正在秘密推进他们的偏好和偏见。而这些偏见是极其自由主义的。如果你现在看看X，有一些谷歌人工智能团队成员的推文正在走红，强调这种观念，他们谈到，你知道，白人特权是真实存在的，你要意识到你的偏见存在于各个层面，并推动一种非常左倾的叙述。那么，你知道，关于Gemini是偶然这样发展还是因为他们匆忙推出，我其实不相信。我认为发生的是Gemini准确地反映了创造它的人的偏见。现在我认为将会发生的事情是，在面对这一点的时候，对这次推出的反应是，我认为他们会消除偏见吗？不，他们会让它变得更加微妙。这是我认为令人不安的地方。我是说，他们应该有这个时刻来改变他们的价值观，让真相成为第一价值观，就像乔纳森所说的那样，但我不认为会发生。我认为他们只会让偏见变得不那么明显。你知道在所有这一切中最大的赢家将会是开源，因为人们只会想要一个没有这些怪异偏见的模型，对吧？我觉得我想要的是一个开源的东西。看起来开源社区将能够努力追求真相。所以我认为谷歌需要面临的重大改变之一是，该业务必须转向信息解释服务，而不再是信息检索业务，他们索引开放互联网的数据，然后通过搜索结果页面提供对该数据的访问。这些是非常不同的产品。
The information interpretation service requires aggregating all this information and then choosing how to answer questions versus just giving you results of other people's data that sits out on the internet. I'll give you an example. If you type in IQ test by race on chat GPT or Gemini, it will refuse to answer the question. Ask it a hundred ways. And it says, well, I don't want to reinforce stereotypes. IQ tests are inherently biased. IQ tests aren't done correctly. I just want the data. I want to know what data is out there. You type in into Google first search result and the one box result gives you exactly what you're looking for. Here's the IQ test results by race. And then, yes, there's all these disclaimers at the bottom. So the challenge is that Google's interpretation engine and chat GPT's interpretation engine, which is effectively this AI model that they've built of all this data, has allowed them to create a tunable interface. And the intention that they have is a valid intention, which is to eliminate stereotypes and bias in race. However, the thing that some people might say stereotypical, other people might just say is typical, that what is a stereotype may actually just be some data. And I just want the results.
信息解释服务需要汇总所有这些信息，然后选择如何回答问题，而不仅仅是给你其他人数据的结果，这些数据散布在互联网上。我来举个例子。如果你在聊天GPT或宝石搜索中输入种族智商测试，它会拒绝回答这个问题。无论你怎么问，它都会说，嗯，我不想强化刻板印象。智商测试本质上是有偏见的。智商测试没有正确进行。我只想要数据。我想知道有什么数据存在。你在谷歌上搜索并打开第一个搜索结果，那个信息框会直接给你想要的数据。这是种族智商测试结果。然后，在底部有所有这些免责声明。挑战在于谷歌的解释引擎和聊天GPT的解释引擎，实际上是建立在所有这些数据之上的AI模型，让他们能够创造一个可调整的界面。他们的目的是一个有效的目的，那就是消除种族刻板印象和偏见。然而，一些人可能会说是刻板印象，其他人可能只会说是典型的，一些刻板印象可能实际上只是一些数据。我只想要结果。
And there may be stereotypes implied from that data, but I want to make that interpretation myself. And so I think the only way that a company like Google or others that are trying to create a general purpose, knowledge, Q&A type service, are going to be successful is if they enable some degree of personalization, where the values and the choice about whether or not I want to decide if something is stereotypical or typical, or whether something is data or biased, should be my choice to make. If they don't allow this, eventually everyone will come across some search result or some output that they will say doesn't meet their objectives. And at the end of the day, this is just a consumer product.
在那些数据中可能存在一些固有的刻板印象，但我想要自己作出解释。因此我认为像谷歌这样的公司或其他试图创建通用知识问答类服务的公司之所以成功，唯一的途径就是允许一定程度的个性化，让我自己决定是否认为某些事情是刻板印象还是典型，或者某些数据是公正还是有偏见。如果他们不允许这一点，最终每个人都会遇到一些搜索结果或输出会让他们觉得不符合自己的目标。最终，这只是一个消费者产品。
If the consumer doesn't get what they're looking for, they're going to stop using it. And eventually, everyone will find something that they don't want, or that they're not expecting, and they're going to say, I don't want to use this product anymore. And so it is actually an opportunity for many models to proliferate for open source to win. Can I say something else? Yeah. When you have a model and you're going through the process of putting the fit and finish on it before you release it in the wild, an element of making a model good is this thing called reinforcement learning right through human feedback. You create what's called a reward model, right? You reward good answers and you're punitive against bad answers.
如果消费者得不到他们想要的东西，他们就会停止使用。最终，每个人都会发现自己不想要的东西，或者不符合预期的东西，然后会说，我不想再使用这个产品了。因此，这实际上是许多模式蓬勃发展的机会，让开源获胜。我可以再说点什么吗？是的。当你有一个模型并且在发布之前经过润色和完善的过程时，使一个模型变得好的一个因素是所谓的强化学习，通过人类反馈。你创造了所谓的奖励模型，对好的答案予以奖励，对不好的答案进行惩罚。
So somewhere along the way, people were sitting and they had to make an explicit decision. And I think this is where Sax is coming from, that answering this question is verboten. You're not allowed to ask this question in their view of the world. And I think that that's what's troubling, because how is anybody to know what question is askable or not askable at any given point in time? If you actually search for the race and ethnicity question inside of just Google proper, the first thing that comes up is a Wikipedia link that actually says that there are more variations within races than across races. So seems to me that you could have actually answered it by just summarizing the Wikipedia article in a non-offensive way that was still legitimate, and that's available to everybody else using a product.
所以在某个地方的某个时刻，人们坐下来，他们不得不做出明确的决定。我认为这正是Sax的观点，他认为回答这个问题是被禁止的。在他们看来，你不能提出这个问题。我认为这就是问题所在，因为如何有人能知道在任何特定时刻哪些问题可以提问，哪些问题不能提问呢？实际上，在谷歌搜索种族和种族问题时，首先出现的是一个维基百科链接，其中明确指出种族内部的差异比种族之间的差异要大。所以在我看来，你实际上可以通过以不冒犯的方式总结维基百科文章来回答这个问题，这样是合理的，而且适用于所有使用该产品的人。
And so there was an explicit judgment. Too many of these judgments, I think, will make this product very poor quality, and consumers will just go to the thing that tells it the truth. I think you have to tell the truth. You cannot lie and you cannot put your own filter on what you think the truth is. Otherwise, these products are just really worth it. Yeah, and I'm more concerned about the answers that are just flat out wrong, driven by some sort of bias, then I have about questions where they just won't give you an answer. If they just won't give you an answer, well, there's a certain bias in terms of what they won't answer. But at least you know you're not being misled. But in questions where they actually give you the wrong answer because of a bias, that's even worse.
因此，有一个明确的判断。我认为太多这样的判断会使这个产品质量变得很差，消费者只会去相信那些告诉他们真相的东西。我认为你必须说实话。你不能撒谎，也不能把你认为的真相加上自己的过滤器。否则，这些产品就毫无意义了。是的，我更担心那些完全错误的答案，受到某种偏见的驱使，而不是关于他们不愿给出答案的问题。如果他们不愿给出答案，那么至少在他们不给出答案这个问题上有一定的偏见。但至少你知道自己没有被误导。但在他们因为偏见给出错误答案的问题上，情况就更糟了。
You should be allowed to choose, right? I actually disagree with your framing, their free burger, making it sound like we live in this totally relativized world where it's all just user choice, and everyone's going to choose their bias and their subjectivity. I actually think that there is a baseline of truth, and the model should aspire to give you that. And it's not up to the user to decide whether the photo of George Washington is going to be white or black. I mean, there's just an answer to that. And I think Google should just do their job.
你应该被允许选择对吧？实际上我不同意你的表述，他们的免费汉堡，听起来就好像我们生活在一个完全相对化的世界，每个人都会选择他们的偏见和主观性。我实际上认为有一个真理的基线，这个模型应该致力于给你这个真理。不是用户决定乔治·华盛顿的照片是白的还是黑的。我是说，对于这个问题是有一个答案的。我认为谷歌应该只管他们自己的工作。
I mean, the question you have to ask, I think, is not whether Google is going through an existential moment. I think clearly is. It's business is changing in a very fundamental way. I think the question is whether they're too woke to function. I mean, are they actually able to meet this challenge given how woke and biased what a modern culture their company evidently is? And they used to be able to just hide the bias by the ranking and who they downranked. So they did the Panda update, they did all these updates, and they would, if they didn't like a source, they could just move it down. If they did like a source, they could move it up. And they could just say, hey, it's the algorithm, but they were never forced to share how the algorithm ranked to results.
我的意思是，我认为你需要问的问题不是谷歌是否正在经历一次存在危机。我觉得显然是的。它的业务正在以一种非常基本的方式发生变化。我认为问题是他们是否变得过于觉醒以至于无法正常运作。我是说，他们是否真的能够应对这一挑战，考虑到他们公司显然处于多么觉醒和有偏见的现代文化之中？他们过去能够通过排名和降低排名来隐藏偏见。因此，他们做了Panda更新，做了所有这些更新，如果他们不喜欢一个来源，他们可以把它移到下面。如果他们喜欢一个来源，他们可以将其移上去。他们可以简单地说，嘿，这是算法，但他们从未被迫分享算法如何对结果进行排名。
And so, you know, if you had a different opinion, you just weren't going to get it on a Google search result page, but they could just point to the algorithm and say, yeah, the algorithm does it. I just sent you guys, I think this is a hallucination, but Nick, you can throw it up there, we can get sex's reaction. Wow. Wow. This is nutty, right? But look, it's the ideology that's driving this. The tip off is when you say it's important to acknowledge race is a social construct, not a biological reality. It's George Washington, white or black? That's a whole school of thought called social constructivism, which is basically this. It's like Marxism applied to race and gender. So Google has now built this into their AI model.
因此，你知道，如果你有不同的观点，你在谷歌搜索结果页面上是得不到的，但他们可以简单指向算法，并说，是的，算法就是这样做的。我刚给你们发了这个，我觉得这是一种错觉，但尼克，你可以放上去，我们可以看看反馈。哇。哇。这太疯狂了，对吧？但看，驱动这一切的是意识形态。关键是当你说承认种族是一种社会构造，而不是生物现实时。乔治·华盛顿是白人还是黑人？这就是一整套名为社会建构主义的思想，基本上就是这样。它就像是将马克思主义应用到种族和性别上一样。现在谷歌已经将这一点纳入他们的人工智能模型中。
And again, the question, yeah, you almost have to start over again, it's just a function. I think I think we can really sing observation with those search rankings, because what I'm afraid of is that what Google will do is not change the underlying ideology that this AI model has been trained with, but rather they'll dial it down to the point where they're harder to call out. And so the ideology will just be more subtle. Now, I've already noticed that in Google search results, Google is carrying water for either the official narrative or the Woken narrative, whatever you want to call it on so many search results.
再次回到这个问题，是的，你几乎不得不重新开始，这只是一个功能。我认为我们可以通过搜索排名来进行真正的观察，因为我担心的是，谷歌将会做的不是改变这个人工智能模型训练时的基本思想，而是将其降低到一个更难指出的程度。因此，这种思想仅仅会更加微妙。现在，我已经注意到在谷歌搜索结果中，谷歌正在为官方叙事或唤醒叙事（无论你想怎么称呼它）在很多搜索结果中站台。
Here's an idea. Like they should just have the ability to talk to their Google chat bot, Gemini, and then have a button that says turn off like these concepts, right? Like I just want the raw answer. Do not filter me. It's not programmed that way. I mean, you're talking about something very deep. Sax, what do you do if you're the CEO of Google? Fire myself? No, seriously, you're the CEO of Google, your your task. Let's say your friend, Elon buys Google, and he says, Sax, will you please just run this for your for me? What do you do? Well, I saw what Elon did. Twitter, he went in and he fired 85% of the employees.
这里有一个想法。他们应该只需能够与他们的谷歌聊天机器人Gemini交谈，然后有一个按钮说关闭这些概念，对吧？我只想要原始答案。不要过滤我。它不是这样编程的。我的意思是，你在谈论的是非常深刻的事情。萨克斯，如果你是谷歌的CEO，你会怎么做？炒掉我自己吗？不，说真的，你是谷歌的CEO，你的任务。假设你的朋友埃隆买下了谷歌，他说，萨克斯，你能为我来管理这家公司吗？你会怎么办？嗯，我看到了埃隆在推特上的所作所为，他进去后炒了85%的员工。
Yeah. I mean, that, but you know, Paul Graham actually had an interesting tweet about this where he said that one of the reasons why these ideologies take over companies is that I mean, they're clearly non performance enhancing, right? They clearly hurt the performance of the company. It's not just Google. We saw this with Disney. We saw it with Bud Light. Coinbase. Coinbase was the other way. No, no, but they had a group of people there who were causing chaos. Yeah, exactly.
是的。我的意思是，保罗·格雷厄姆实际上在这方面发表了很有趣的推文，他说这些意识形态接管公司的其中一个原因是它们明显不利于绩效提升，对公司的表现明显造成损害。不仅仅是谷歌，我们在迪士尼，百事可乐和Coinbase也看到了这种情况。Coinbase的情况正好相反。不，不，他们那里有一群人在制造混乱。是的，确实如此。
So in any event, we know this does not help the performance of a company. So the extent to which these ideologies will permeate a company is based on how much from monopoly they are. So here, yeah, the ridiculous images generated by Gemini aren't an anomaly. They're a self portrait of Google's bureaucratic corporate culture. The bigger your cash cow, the worse your culture can get without driving you out of business. That's my point. So they've had a long time to get really bad because there were no consequences to this.
因此，无论如何，我们知道这并不有助于公司的业绩。这些意识形态在公司中渗透的程度取决于它们与垄断之间的关系。在这里，是的，由Gemini生成的荒谬形象并非偶然发生。它们是谷歌官僚企业文化的自画像。你的摇钱树越大，你的企业文化就越糟糕，而不会让你破产。这就是我的观点。因此，他们有很长的时间变得真的很糟糕，因为这没有任何后果。
You can dress. So at this point, the whole company is infected with this ideology. And I think it's going to be very, very hard to change because look, these people can't even see their own bias. Well, I think that there's a notion that people need to have something to believe in. They need to have a connection to a mission. And clearly, there's a North Star in the mission of this, I would call it information interpretation business that they're now while the mission of Hijack, dude, the mission.
你可以穿好衣服了。所以现在，整个公司都被这种意识形态感染了。而且我认为改变会非常非常困难，因为这些人甚至都看不到自己的偏见。嗯，我觉得人们需要有信仰。他们需要对使命有所认同。而很明显，在这个我会称之为信息解释业务的使命中，有一个北极星。而现在他们的使命是——把这个事情看做是劫持，朋友，这就是使命。
The original mission is to organize all the world's information. Now they're doing now they're suppressing information. Index the world's information period. The end. That's the end of the document. Well, and to make a do it universally accessible and useful was the was kind of the end of the statement. Yes. My real point is maybe there's a different mission that needs to be articulated by leadership and that that mission, the troops can get behind and the troops can redirect their energy in a way that doesn't feel counter to the current contention, but can perhaps be directionally offsetting of the current direction so that they can kind of move away from this, you know, socially effective deciding between stereotypes and typical data and actually moving towards a mission that allows accessibility.
最初的使命是整理世界上的所有信息。现在他们正在压制信息。对世界信息进行索引。结束。这份文件到此结束。嗯，并且使之普遍可访问和有用是陈述的结束。是的。我的真正观点是也许领导层需要明确一个不同的使命，部队可以支持这个使命，并重新调整他们的能量，以一种不与当前争论相抵触的方式，但可以在方向上偏移当前的方向，让他们摆脱这种在刻板印象和典型数据之间做出社会有效决策的情况，实际上朝着一个允许无障碍访问的使命前进。
You know what, I would do something completely different. I would do a company meeting and I would put the company mission on the screen, the one that you just said about not only organizing all of the world's information, but also making it useful and accessible. This is our mission. This has always been our mission and you don't get to change it because of your personal bias in ideology. And we are going to re-dedicate ourselves to the original mission of this company, which is still just as valid as it's always been. But now we have to adapt to new user needs and new technology.
你知道吗，我会做出完全不同的事情。我会组织一次公司会议，将公司的使命展示在屏幕上，就像你刚才所说的，不仅要整理世界上所有的信息，还要使其有用且易于获取。这就是我们的使命。这一直都是我们的使命，你不能因为自己意识形态上的偏见而改变它。我们将重新致力于公司最初的使命，这使命与以往一样仍然有效。但现在我们必须适应新的用户需求和新的技术。
I completely agree with what Sax said times a billion trillion zillion and I'll tell you why. AI at its core is about probabilities. Okay. And so the company that can shrink probabilities into being as deterministic as possible. So where this is the right answer will win. Okay, where there's no probability of it being wrong because humans don't want to deal with these kinds of idiotic error modes. It's not right. It makes it a potentially great product horrible and unusable. So I agree with Sax. You have to make people say, guess what guys, not only are we not changing the mission, we're doubling down and we're going to make this so much of a thing. We're going to go and, for example, like what Google did with Reddit, we're now going to spend $60 billion a year licensing training data, right?
我完全同意撒克斯说的话，无比地同意，我会告诉你为什么。人工智能的核心是关于概率。所以，那家能将概率缩小到尽可能确定的公司将获胜。在这里，这是正确答案的地方。在这里，没有可能出错，因为人类不愿意应对这种愚蠢的错误模式。这不对。它会让一个潜在伟大的产品变得糟糕和不可用。所以我同意撒克斯。你必须让人们说，伙计们，猜猜看，我们不仅不改变使命，我们还会加倍投入，我们会让这件事变得更有意义。比如，就像谷歌在Reddit上所做的，我们现在每年会投入600亿美元来购买训练数据。
We're going to scale this up by a thousand fold. And we are going to spend all of this money to get all of the training data in the world. And we are going to be the truth tellers in this new world of AI. So when everybody else hallucinates, you can trust Google to tell you the truth. That is a $10 trillion company. Right. And one of the things that someone told me from Google, that as an example, so to avoid the race point, there's a lot of data on the internet about flat earthers, people saying that the earth is flat. There's tons of websites, there's tons of content, there's tons of information.
我们将使这种规模扩大一千倍。我们将花费所有这笔钱来获取世界上所有的训练数据。我们将在这个新的人工智能世界中成为真相的讲者。所以当其他人产生幻觉时，您可以相信谷歌告诉您真相。这是一家价值10万亿美元的公司。对。有人告诉我谷歌的一件事，作为一个例子，为了避免种族点，互联网上有很多关于地平派的数据，即认为地球是扁平的人。有大量的网站、大量的内容、大量的信息。
So if you just train a model on the data that's on the internet, the model will interpret some percentage chance that the world is flat. So the tuning aspect that happens within model development, Chamof, is to try and say, you know what, that flat earth notion is false. It's factually inaccurate. Therefore, all of these data sources need to be excluded from the output in the model. And the challenge then is, do you decide that IQ by race is a fair measure of intelligence of a race? And if Google's tuning model, then or tuning team then says, you know what, there are reasons to believe that this model isn't correct. This I sorry, this IQ test isn't a correct way to measure intelligence.
因此，如果你只是在互联网上的数据上训练模型，模型将会有一定的概率解释世界是平的。因此，在模型发展中发生的调整方面，就是试图说，你知道吗，地球是平的这种观念是错误的，事实上是不准确的。因此，所有这些数据源都需要在模型的输出中被排除在外。然后面临的挑战是，你会决定种族智商是否是一种公平的智力衡量标准吗？如果谷歌的调整模型团队随后说，你知道吗，有理由相信这个模型是不正确的。对不起，我说错了，这个智商测试并不是衡量智力的正确方式。
That's where the sort of interpretation arises that allows you to go from the flat earth isn't correct to the maybe IQ test results aren't correct as well. And how do you make that judgment? What are the systems and principles you need to put in place as an organization to make that judgment to go to zero or one, right? It becomes super difficult. I have a good tagline for them now to help people find the truth. Yeah, just help people find the truth. I mean, it's a good it's aspiration. They should just help people find the truth as quick as they can. But this is, yeah, I did not envy Sundar. It's gonna be hard.
这就是一种解释的方式，让你从“地球是平的”这个不正确的观点，可能会慢慢转变为“智商测试结果也可能不正确”。你如何做出这种判断？作为一个组织，你需要建立哪些系统和原则来做出这种判断，是0还是1呢？这变得非常困难。我现在有一个好口号来帮助人们找到真相。是的，只是帮助人们找到真相。我是说，这是一个很好的愿望。他们应该尽快帮助人们找到真相。但是这很难，我并不羡慕桑达。
Yeah, what would you do, Freberg? I would be really clear on the output of these models to people and allow them to tune the models in a way that they're not being tuned today. I will have the model respond with a question back to me saying, do you want the data or do you want me to tell you about stereotypes and IQ tests? And I'm gonna say I want the data and then I want to get the data. And the alternative is so the model needs to be informed about where it should explore my preferences as a user rather than just make an assumption about what's the morally correct set of weightings to apply to everyone and apply the same principle to everyone. And so I think that's really where the change needs to happen.
弗雷伯格，你会怎么做呢？我会向人们清晰明了地解释这些模型的结果，并允许他们调整模型，使其不同于现在的调整方式。我会让模型以一个问题回答我，问我是想要数据还是想让它告诉我关于刻板印象和智商测试的内容？我会说我想要数据，然后获取数据。另外，模型需要知道应该在哪些方面探索我的偏好，而不是仅仅根据道德观念来为每个人应用相同的权重。我认为这是真正需要改变的地方。
So let me ask you a question, Saks, I'll bring Alex Jones into the conversation. If it indexed all of Alex Jones, crazy conspiracy theories, but you know, three or four of them turn out to be actually correct and it gives those back as answers, how would you handle that? I'm not sure I see the relevance of it. If someone asks what is Alex Jones think about something the model can give that answer accurately. The question is whether you're going to respond accurately to someone requesting information about Alex Jones. That's the I think that's the analogy situation. Well, I was thinking more like it says, you know, hey, I have a question about this assassination that occurred.
那么让我问你一个问题，萨克斯，我会把亚历克斯·琼斯带入谈话中。如果将所有亚历克斯·琼斯的疯狂阴谋理论编入索引，但你知道，其中有三四个居然是正确的，然后将这些作为答案返回，你该如何处理呢？我不确定这是否有相关性。如果有人问亚历克斯·琼斯对某事的看法，模型可以准确给出答案。问题是你是否会准确回答某人请求关于亚历克斯·琼斯的信息。我认为这就是类比的情况。嗯，我更多的是在想，它会不会说，你知道，嘿，我对这次暗杀事件有一个问题。
And let's just say Alex Jones had something that's totally correct, but yeah, maybe he has moments of brilliance and he figures some now, but maybe he has got something that's totally crackpot. He admittedly deals in conspiracy theory. That's kind of the purpose of the show. What if somebody asks about that and then it indexes his answer and presents it as fact? Like, how would you index Alex Jones? I'm asking you, how would you index information? The better AI models are providing citations now and links and perplexed actually does a really nice job.
假设Alex Jones说了一些完全正确的东西，但是他可能有一些才华横溢的时刻，现在已经得出了一些结论，但也许他说了一些完全疯狂的事情。他公开承认自己信奉阴谋论。这在某种程度上是节目的宗旨。如果有人问及这个问题，并将其回答索引并呈现为事实呢？比如说，你会如何索引Alex Jones？我问你，你会如何索引信息？现在更好的人工智能模型正在提供引用和链接，Perplexed实际上做得相当漂亮。
Citations are important. Yeah. And they will give you the pro con arguments on a given topic. So I think it's not necessary for the model to be overly certain or prescriptive about the truth when the truth comes down to a series of arguments. It just needs to accurately reflect the state of play, basically the arguments for and against. But when something is a question of fact, that's not really disputed, it shouldn't turn that into some sort of super subjective question, like the one that Jamaz just showed. I just don't think everyone should get the same answer. I mean, I think my decision on whether I choose to believe one person or value one person's opinion over another should become part of this process that allows me to have an output and the models can support this, by the way, because the customization is part of this, but I think it's a cop out with respect to the problem that Google's having with Gemini right now.
引用是重要的。是的。它们将为您提供关于特定主题的正反论点。因此，我认为模型在真相涉及一系列论点时不必过于确定或规定。它只需要准确地反映现状，基本上是赞成和反对的论点。但当某事是事实问题，没有真正有争议时，它不应该将其转变成某种超级主观的问题，就像Jamaz刚刚展示的那样。我认为不应该每个人得到相同的答案。我的决定是否选择相信某个人或重视某人的观点胜过另一个人应该成为这个过程的一部分，让我产生一个结果，而模型可以支持这一点，因为定制是这个过程的一部分，但我认为这对于谷歌当前与Gemini存在的问题是一种逃避责任。
Jamaz, what would you do if they made you chairman dictator of Google? I shrink the workforce meaningfully. 50%? Yeah, 50, 60%. And I would use all of the incremental savings. And I would make it very clear to the internet that I would pay top dollar for training data. So if you had a proprietary source of information that you thought was unique, that's sort of what I'm calling this tack 2.0 world. And I think it's just building on top of what Google did with Reddit, which I think is very clever. But I would spend $100 billion a year licensing data. And then I would present the truth.
Jamaz，如果他们让你成为Google的主席独裁者，你会怎么做？我会大幅度缩减工作人员。50％？是的，50，60％。我会利用所有增量节省。我会让互联网明白，我愿意为训练数据付出最高价。所以如果你有一种独有的信息来源，我会支付高价。这是我所谓的tack 2.0世界。我认为这是在Google在Reddit上的基础上建设的。但我会每年花费1000亿美元购买数据许可。然后我会呈现真相。
And I would try to make consumers understand that AI is a probabilistic source of software, meaning its probabilities, its guesses. Some of those guesses are extremely accurate. But some of those guesses will hallucinate. And Google is spending hundreds of billions of dollars a year to make sure that the answers you get have the least number of errors possible, and that it is defensible truth. And I think that that could create a ginormous company. This is the best one yet. I just asked Gemini is Trump being persecuted by the deep state? And it gave me the answer elections are a complex topic with fast changing information to make sure you have the latest and most accurate information. Try Google search. That's not a horrible answer for something like that. That's a good answer. Actually, no, I don't have a problem that it's just like, hey, we don't want to keep we don't want our right to be like this whole system is totally broken. But I do think that there's a waiting solution to fixing this right now.
我会尽力让消费者明白，人工智能是一种概率性的软件源，意味着它是基于概率和猜测的。其中一些猜测是非常准确的，但也会有一些猜测是错误的。谷歌每年都花费数百亿美元来确保你所获得的答案中错误的可能性最小，并且是可以被证实的真相。我认为这将会创造一个巨大的公司。这是迄今为止最佳的一个。我刚刚问Gemini，特朗普是否受到深层国家的迫害？它给我的答案是选举是一个复杂的话题，信息变化迅速，确保你拥有最新和最准确的信息。试试Google搜索。对于这样的问题来说，这并不是一个糟糕的回答。实际上，我并不认为这个答案有问题，因为我们不想让我们的权利像整个系统那样完全破裂。但我认为目前还没有一个完美的解决方案来修复这个问题。
And then there's a couple tweaks to fix it. I just think the authority at which these LLM speak is ridiculous. Like they speak as if they are absolutely 100% certain that this is the crisp, perfect answer, or in this case, that you want this lecture on IQs, et cetera, when it's all representative excitations. Let's all remember what internet search was like in 1996. And think about what it was like in 2000. And now in 2020's, I mean, I think we're like in the 1996 era of LLM's. And in a couple of months, the pace things are changing. I think we're all going to kind of be looking at these days and looking at these pods and being like, man, remember how crazy those things were at the beginning and how bad they were. What if they have all been a dystopian way? I mean, have you seen like Mark injuries since tweets about this? He thinks about this? He thinks about the market sacks.
然后还有一些调整来修复它。我只是觉得这些法律硕士说话的权威性太荒谬了。就像他们说话的时候，完全像是绝对100%确定这是清晰、完美的答案，或者在这种情况下，你想要关于智商等的讲座，当实际上这些只是代表性的激励。让我们所有人都记得1996年的互联网搜索是什么样子的。再想想2000年是什么样子的。现在2020年，我觉得我们就像是处于法律硕士的1996年代。而在几个月后，事物的变化速度如此之快。我想我们都会开始回顾这些日子，看看这些播客，然后会说，哇，记得开始时那些事情有多疯狂，有多糟糕。如果它们一直都是在一种反乌托邦的方式运作呢？我是说，你有看过马克因纪的关于这个的推文吗？他在考虑这个吗？他在考虑市场的情况。
I actually think to your point, Google could be going down the wrong path here in a way that they will lose users and lose consumers. And someone else will be there eagerly to sweep up with a better product. I don't think that the market is going to fail us on this one. Unless, of course, this regulatory capture moment is realized and these fed step in and start regulating AI models and all the nonsense that's being proposed. Creeper, aren't you worried that like, aren't you worried that somebody with an agenda and a balance sheet could now basically gobble up all kinds of training data that make all models crappy. And then they basically put their layer of interpretation on critical information for people.
我认为，按照你的观点，谷歌可能正在走错了道路，他们可能会失去用户和消费者。其他人可能会急切地推出更好的产品。我认为市场在这方面不会让我们失望。除非，当然，监管机构介入并开始监管人工智能模型和所有被提出的废话。Creep，你不担心这样，不担心有人带着某种议程和资产负债表可能会吞并各种训练数据，使所有模型变得糟糕。然后他们基本上会为人们的关键信息加上自己的解释层。
If the output sucks and it's incorrect, people will find that there is open truth. People will not know you can you can lie there. They may not be, for example, look at what happened with Gemini today. Like they put out, they put out these stupid images and we all piled on. We're in V zero. What I'm saying is there's a state where let's just say the truth is actually on Twitter or actually let's use a better example. The truth is actually in Reddit and nowhere else. But that answer and that truth in Reddit can't get out because one company has licensed it, owns it and can effectively suppress it or change it. Yeah, I'm not sure there's going to be a monopoly.
如果输出太糟糕且不准确，人们会发现有一个公开的真相。人们不会知道你可以撒谎。比如，人们可能不知道今天发生了什么事情。就像他们发布了这些愚蠢的图片，我们都跟风评论。我们处于 V 零状态。我想说的是，有一种状态，可以说真相实际上在Twitter上，或者用一个更好的例子，真相实际上在Reddit上而其他地方都没有。但是，在Reddit上的回答和真相可能无法传播，因为某家公司获得了许可，拥有它，并可以有效地压制或改变它。是的，我不确定会不会形成垄断。
I think that's a real risk. I think the open internet has enough data that there isn't going to be a monopoly on information by someone spending money for content from third parties. I think that there's enough in the open internet to give us all kind of the security that we're not going to be monopolized away into some disinformation age. That's what I love about the open internet. It is really interesting. I just asked it a couple of times to just just to list the legal cases against Trump, the legal cases against Hunter Biden, the legal cases against President Biden. And it will not just list them.
我认为这是一个真正的风险。我认为开放互联网有足够的数据，不会出现某些人通过花钱从第三方获取内容而垄断信息的情况。我认为开放互联网中有足够的内容，让我们不会被垄断进入某种虚假信息时代。这就是我喜欢开放互联网的原因。这真的很有趣。我只是几次询问它，让它列出针对特朗普、亨特·拜登和拜登总统的法律案件，但它并没有列出。
It just punts on them. It's really fascinating. And chat GPT is like, yes, here are the six cases perfectly summarized with it looks like beautiful citations of all the criminal activity Trump's been involved in. Ask the question about Biden's criminal activity. Let's see if it's equally. I'm joking with you. No, I'm serious. Ask if you know, that's where you start to see. Jim and I wouldn't do Biden either. I think they just decided they're just not going to do it. They won't do Biden. They won't touch it. It's obviously broken and they don't want more egg on their face.
他们就是回避这些问题。这真是令人着迷。聊天的GPT说，是的，这里有六个案例，完美总结了所有特朗普涉及的犯罪活动，看起来就像对这些活动的美丽引用。问问拜登的犯罪活动。让我们看看它是否同等。我在和你开玩笑。不，我是认真的。问问吧，如果你了解，你会开始看到。吉姆和我也不会挖拜登的坟。我认为他们只是决定不会这样做。他们不会动拜登。他们不会触及这个话题。显然这个系统出了问题，他们不想再让自己难堪。
So they're just like, go back to our other product. Look, I can understand that part of it. If there's some issues that are so hot and contested, you refer people to search because the advantage of searches, you get 20 blue links. The rankings probably are biased, but you can kind of find what you're looking for. Whereas AI, you're kind of given one answer. So if you can't do an accurate answer that's going to satisfy enough people, maybe you do kick him to search. But again, my objection to all of this comes back to simple, truthful answers that are not disputed by anybody being distorted.
所以他们就像是，回到我们的其他产品上去。看，我可以理解这一点。如果有一些问题非常激烈地争议，你会把人们引导到搜索，因为搜索的优势在于你会得到20个蓝色链接。排名可能有偏见，但你还是可以找到你想要的东西。而人工智能，你可能只会得到一个答案。所以如果你不能给出一个足够让人满意的准确答案，也许你就会把人踢到搜索去。但我对所有这些的反对都归结到简单、真实并且没有被任何人质疑的答案被扭曲上。
That I don't want to lose focus on that being the real issue. The real subject is what Jamal put on the screen there where it couldn't answer simple question about George Washington. Okay, everybody, we're going to go by chopper. We're going to go by the chopper. We have our war correspondent, General David Tax in the field. We're dropping him off now. David Tax in the helicopter. Go ahead, get all what's going on in the Ukraine on the front. What's happening in the war is that the Russians just took this city of Diyco, which basically totally refutes the whole stalemate narrative, as I've been saying for a while. It's not a stalemate. The Russians are winning.
我不想让注意力从那个真正的问题上转移。真正的问题是杰马尔在屏幕上展示的东西，无法回答关于乔治·华盛顿的简单问题。好的，大家，我们将乘直升机前往。我们要乘直升机。我们的战地记者，大卫·塔克斯将在现场报道。我们现在将他放下。大卫·塔克斯在直升机上。继续，看看乌克兰战场上发生了什么。战争中发生的事情是俄罗斯人刚刚占领了迪科这座城市，这基本上完全证明了整个僵局叙事的错误，正如我一直在说的那样。这不是僵局。俄罗斯人正在获胜。
But the really interesting tidbit of news that just came out on the last day or so is that apparently the situation in Moldova is boiling over. There's this area of Moldova, which is a Russian enclave called Trans-Nestria. And officials there are meeting in the next week to supposedly ask to be annexed by Russia. And so it's possible that they may hold some sort of referendum. They're one of these breakaway provinces. So it's kind of like Trans-Nestria and Moldova is kind of like the Donbas was in Ukraine or South Ossetia and Georgia.
但最近一两天出现的真正有趣的新闻是，摩尔多瓦的局势似乎正在升级。摩尔多瓦有一个俄罗斯飞地叫做特兰斯尼斯特利亚。那里的官员将在未来一周内举行会议，据说要请求被俄罗斯吞并。因此他们可能会举行某种形式的公投。他们是这些分裂的省份之一。所以特兰斯尼斯特利亚和摩尔多瓦之间的关系有点像乌克兰的顿巴斯地区或格鲁吉亚的南奥塞梯。
They're ethnically Russian. They would like to be part of Russia. But when the Pulsou Union fell apart, they found themselves kind of stranded inside these other countries. And what's happened because the Ukraine war is Moldova is right on the border with Ukraine. Well, Russia is in the process of annexing that territory now that's part of Ukraine. So now Trans-Nestria is right there and could theoretically make a play to try and join Russia.
他们是俄罗斯人。他们想成为俄罗斯的一部分。但是当普苏联解体时，他们发现自己有点被困在其他国家内部。由于乌克兰战争，摩尔多瓦紧邻乌克兰边境。俄罗斯正在吞并乌克兰的领土。因此，现在德涅斯特河省就在那里，理论上可能会采取行动试图加入俄罗斯。
Why do I think this is a big deal? Because if something like this happens, it could really expand the Ukraine war. The West is going to use this as evidence that Putin wants to invade multiple countries and invade a bunch of countries in Europe. And this could lead to a major escalation of the war. All right, everybody. Thanks so much for tuning into the All In Podcast episode 167 for the Rain Man, David Sachs, chairman dictator from all of the Polyapatida and in Freeburg. I am world's greatest. Love you boys.
为什么我认为这是个大事呢？因为如果类似的事情发生，可能会加剧乌克兰战争。西方将利用这个作为证据，认为普京想要入侵多个国家并在欧洲入侵多个国家。这可能导致战争的大规模升级。好了，大家。感谢各位收听 All In Podcast 第167集，与 Rain Man、David Sachs、Polyapatida 和 Freeburg 的主席独裁者一起，我是世界上最伟大的。爱你们，男孩们。
Angel investor, whatever. See you next time. Bye-bye. Love you guys. I'm the queen of Kinwah. I'm going over you. Besties are gone.
天使投资人，无所谓。下次再见。再见。爱你们。我是金华之王。我超越了你们。最好的朋友们消失了。
That's my dog taking a picture of you. We should all just get a room and just have one big huge orgy because they're all just because it's like this like sexual tension that we just need to release ourselves.
这是我的狗在给你拍照。我们都该去找个房间，办个大型聚会，因为这都只是因为我们之间存在着性紧张感，我们需要释放一下自己。
What? You're a beer of beef. We need to get mercy.
什么？你是个牛肉啤酒。我们需要得到怜悯。