20VC：Cerebras CEO谈为何融资10亿美元并推迟IPO | NVIDIA显现增长担忧迹象 | Mag7价值集中：AI热潮会戛然而止吗 | 美国能否为AI提供充足能源——对话Andrew Feldman

事情进展得如此之快，六个月、八个月、十二个月后，每个人都感到不确定。这速度太快了，规模太大了，需求惊人，但没人知道未来会走向何方。折旧问题的核心在于，下一代技术比当前一代快多少。

Things are moving at a rate that six, eight, twelve months out, everybody's unsure. It's so fast. It's so big. There is unbelievable demand, and nobody knows where it will go in the future. The question of depreciation is how much faster are future generations than the current generation.

这才是关于折旧的真正问题。人们常说什么美国电力不足，这完全是错误的。我们电力充足，只是分布不当。真正的风险在于金融市场，人们从根本上低估了风险。

That's the actual question on depreciation. People often say we we don't have enough power in The US, and this is strictly wrong. We have plenty of power. It's in the wrong places. Risk comes in financial markets where people fundamentally underestimate risk.

从来没有一家公司因为给杰出人才支付了太多报酬而破产。

No company ever went bankrupt by paying extraordinary people too much.

欢迎收听20VC，我是哈里·斯蒂宾斯，非常期待本期节目。继上周与Grok的乔纳森·罗斯进行的火爆访谈后，今天我非常高兴迎来另一位行业领袖——Cerebrus的联合创始人兼首席执行官安德鲁·费尔德曼，他正在打造全球最快的AI推理与训练系统。Cerebrus最近完成了11亿美元的G轮融资，估值达81亿美元，富达、老虎基金、Valor等知名机构均参与其中。他们已突破GPU的限制，每月处理数万亿个token，并即将提交上市申请。这是一场令人惊叹的对话。

This is 20 VC with me, Harry Stebbings, and I am so excited for the show's stay. Following our blockbuster episode with Jonathan Ross at Grok last week, I'm so excited to welcome another leader in the space today in the form of Andrew Feldman, cofounder and CEO of Cerebrus, building the world's fastest AI inference and training. Now Cerebrus recently closed a $1,100,000,000 series g round at an $8,100,000,000 valuation with names like Fidelity, Tiger, Valor, and others included in the round. They've leapfrogged GPU limits, operated trillions of tokens per month, and are filing to go public very soon. This was an incredible discussion.

我非常感谢安德鲁的友谊，希望你们喜欢本期节目。但在深入节目之前，我特别喜欢看到团队齐心协力推动这个节目落地。而我不喜欢的是，要跨数十个平台、产品和工具追踪所有信息、数据和项目。这就是为什么我们使用Coda——一个一体化的协作工作空间，已帮助全球五万多个团队达成共识。Coda融合了文档的灵活性与电子表格的结构化，促进更深入的团队协作与更快的创意产出。

I'm so grateful to Andrew for his friendship, and I hope you enjoy the show. But before we dive into the show's stay, I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data, and the projects that we're working on across dozens of platforms, products, and tools. That's why we use Coda, the all in one collaborative workspace that's helped 50,000 teams all over the world get on the same page. Offering the flexibility of docs with the structure of spreadsheets, Coda facilitates deeper teamwork and quicker creativity.

他们的即用型AI解决方案——Coda Brain的智能功能，彻底改变了游戏规则。在Grammarly的支持下，Coda正进入创新与扩展的新阶段，旨在重新定义AI时代的生产力。无论你是希望在保持敏捷的同时理清混乱的初创公司，还是寻求更好协同的大型企业，Coda都能契合你的工作方式。其无缝的工作空间可与数百个你喜爱的工具无缝连接，包括Salesforce、Jira、Asana和Figma，帮助团队重塑工作流程，更快地完成更多任务。立即访问 coda.i0/20vc，免费获取初创团队计划六个月的优惠。

And their turnkey AI solution, the intelligence of Coda Brain, is a game changer. Powered by Grammarly, Coda is entering a new phase of innovation and expansion aiming to redefine productivity for the AI era. Whether you're a start up looking to organize the chaos while staying nimble or an enterprise organization looking for better alignment, Coda matches your working style. Its seamless workspace connects to hundreds of your favorite tools, including Salesforce, Jira, Asana, and Figma, helping your teams transform their rituals and do more faster. Head over to coda.i0/20vc right now and get six months off the team plan for startups for free.

那就是 coda.i0/20vc，免费获取六个月团队计划优惠。coda.i0/20vc。说到信任，如今客户对它的期待比以往任何时候都更快，这就是为什么超过一万家全球企业信赖Vanta。Vanta利用智能AI自动化高达90%的合规工作，涵盖SOC 2、ISO 27001等热门合规标准，集中管理流程、控制风险，让你在数周内而非数月内准备好审计。让你不再追逐文书工作，而是专注于签下订单。

That's coda, c0da,.i0/20vc, and get six months off the team plan for free. Coda.i0/20vc. And talking about trust, today customers expect it faster than ever, and that's why over 10,000 global companies trust Vanta. Vanta automates up to 90% of the work for in demand compliance standards like SOC two, ISO 27,001, and more using smart AI to centralize workflows, manage risk, and get you audit ready in weeks, not months. So you can stop chasing paperwork and start closing deals.

一份新的IDC报告发现，Vanta的客户每年能获得53.5万美元的收益。这太惊人了。而且该平台在三个月内就能收回成本。我以前根本不知道这些。无论你是快速增长还是刚刚起步，Vanta都能帮你连接可信赖的审计师和专家，提供支持，帮助你赢得客户的信任。

And a new IDC report found that Vanta customers achieve $535,000 per year in benefits. That's insane. And the platform pays for itself in three months. I had no idea about these. Whether you're growing fast or just getting started, Vanta connects you with trusted auditors and experts, support to help you build trust with customers.

访问 vanta.com/20vc，首年立减1000美元。就是 vanta.com/20vc。

Get a thousand dollars off your first year at vanta.com/20vc. That's vanta.com/20vc.

你现在已经到达目的地了。安德鲁，老兄，很高兴你再次回来。我特别喜欢我们第一期的节目。你居然能忍受我那些天真的问题，还同意再来一期。老兄，我一定挺有魅力的。

You have now arrived at your destination. Andrew, dude, it is so lovely to have you back on. I so enjoyed our first show. You put up with my naive questions enough to agree to do a round two. Man, I must be charming.

哈里，无论问题多么天真，我都完全不介意。随时都很乐意参与。我读过你的领英帖子和推文，我一直在为你妈妈加油。

Harry, I'm okay with any questions, naive or otherwise. Happy to do it anytime. I I read your your LinkedIn posts, your Twitter posts. I'm rooting for your mom.

老兄，你太客气了。听我说，我想从你昨天刚宣布的十亿美元融资开始聊起。你能跟我讲讲，为什么这件事很重要？为什么选在现在？这对公司意味着什么？

Dude, you are too kind. Listen. I wanna start with the billion dollar raise that you just announced yesterday. Can you just talk to me why it's important, why now, and what it means for the company?

说实话，这是我们品类历史上规模最大的一轮融资，估值也是最高的，而且由顶级投资者领投。在后期投资阶段，你寻找的正是像富达这样的机构。他们就像是投资界的牛津或剑桥，对吧？

Look. It was the largest raise ever done in our category. It was done at the highest valuation and with the premier investors. At late stage investing, you're looking for the likes of Fidelity. They are the, what would the English call it, the sort of Oxford or Cambridge of investing, right?

他们是顶尖的公开市场投资者，当他们决定领投一轮融资时，会给华尔街带来极大的信心。我们非常高兴能与他们以及领投方合作。随后，我们还成功吸引了老虎全球、Valor和1789的大量参与。这就是第一点。

I mean, they are the premier public market investors. And when they choose to lead a round, it brings Wall Street a great deal of confidence. We were really happy to partner with them and with the treaties to lead the round. And then we were able to get enormous participation from Tiger Global, from Valor, from 1789. So that's point one.

我认为第二点是我们现在有了足够的资金储备，能够真正抓住眼前的机会，将我们的制造能力扩展到我们想要的规模和范围，并建设新的数据中心。今年我们在美国新增了五个数据中心，而且我们还有更多宏伟的构想。我认为，那种通过从八位降到四位之类的小幅改进所取得的虚假进步，根本无法让我们抵达人工智能的彼岸。作为整个社区，我们还有实实在在的工作要做。

I think point two is that we now have sort of the dry powder to really push, take the opportunities in front of us to build out our manufacturing to the scale and scope we want, to add new data centers. We added five this year in The US to add more data centers. And we have more big ideas. I think incremental improvements, make believe gains achieved by dropping from, you know, eight bit to four bit, those aren't gonna get us to the promised land in AI. We've got real work to do as a community.

我认为这笔资金让我们占据了有利位置，去实现这一切。

I think this funding puts us in the catbird seat for that.

在保真度方面，这其实很有趣。我最近邀请了HubSpot的首席执行官布莱恩·哈里根做客节目，他让我意识到，在公司上市前和上市时，特别注重保真度的重要性，因为它传递了明确的信号。作为风险投资人，我之前并没有意识到这一点所承载的分量。

On the fidelity side, it's actually interesting. I have Brian Halligan, CEO of HubSpot, on the show recently, and he actually taught me the importance of specifically getting fidelity in both pre IPO and when you IPO just because of the signal that it sends. And I didn't actually realize as a venture guy the weight that's placed

作为顶级风投，你可能会想：那些公开市场的玩家是谁？

top stage venture guy, you're like, who are the public guys?

我说，好吧，Fidelity？随便吧。T. Rowe？当然，它们都差不多，对吧？

I okay. Fidelity, like, what whatever. T Rowe, sure. They're all the same. Right?

但我跟他说：不，不，它们完全不一样。Fidelity才是房间里那个庞然大物。

And I was like, no. No. They're not. Like, Fidelity are the monster in the room.

它们就是那个庞然大物。

They're the monster.

获得它们的重要性非常高。我能问你一下吗，老兄？为什么不去公开上市呢？因为之前有传言说你们打算上市，那为什么现在要进行这轮上市前融资？

Importance of getting them is is very high. Can I ask you, dude? Like, why not go public? Because it was rumored that you guys were gonna go public. Why do this prepublic round?

我们仍然完全有意上市。我认为在后期阶段，如果能快速完成且不分散注意力、继续推进的话，进行一轮Pre-IPO融资是非常常见的。当时我们面前有太多机会，筹集资金以便继续推进这些机会是显而易见的选择。

We we still have every intention of going public. I think it's very common in late stage to do a pre IPO round if you can get it done very quickly, if it doesn't distract you, and keep moving. I think there were so many opportunities in front of us that gathering the capital so that we could continue to prosecute these opportunities was a no brainer.

你提到真正要做的工作。我觉得对于那些不在市场中的人来说，要理解眼下究竟发生了什么非常困难，毕竟我们每天看到的新闻太多了。你能帮我们梳理一下过去三个月的形势吗？我们现在处于什么阶段？有什么变化？

You said about the real work to be done. I think it's quite difficult for everyone who's not really in the market to understand what the hell is going on given all the news that we see. Can you help us just with the lay of the land in the last three months of where are we at now? What's changed?

首先，哈里，我们现在正处于一个宣称规模巨大的市场阶段，数十亿美元的资金在这里那里被投入，但没人仔细阅读细则——这些承诺都是五年内逐步实现的。这简直是营销史上最经典的“自保”话术：五年内将达到一万亿。对吧？但“最高达”意味着可能是30亿，可能是120亿，也可能是400亿，对吧？

The first thing, Harry, is we are in a stage of the market where the claims are enormous, where tens of billions of dollars are being done here and there and nobody's reading the fine print that it's over five years and it's up to. This is the great sort of CYA word in in marketing history is it will be up to a 100,000,000,000 over five years. Right? Well, up to means it could be 30, it could be 12, it could be 40. Right?

他们可以随便挑个大数字，但实际金额绝不会超过这个数。所以当你阅读这些交易时，你必须认真思考这些承诺的时间跨度，以及是否真有人在认真统计。很多人说要为美国带来数百亿美元的就业和各种好处，但八个月过去了，有谁真的拿出过一份小表格，写着‘九个岗位加一家工厂’吗？

He could pick a lot of big numbers and it won't be bigger than. And so as you read these deals, I think you you have to really think about the time frame over which they're being done. You have to think about whether anybody is actually counting. Lots of people are saying they're gonna gonna bring hundreds of billions of dollars of jobs to The US and this and that. In eight months, has anybody got a little little spreadsheet like nine jobs plus one factory?

我的意思是，到底谁来问责？答案是谁都不负责。我认为第一点是这样，第二点是，这比任何事都更清楚地表明：需求极其旺盛，但没人知道未来会走向何方。它规模太大、发展太快，以至于没人能预测。

I mean, who who who holds anybody to account? And the the answer is nobody. I think that's number one. I think number two, what this signals more than anything is that there is unbelievable demand and nobody knows where it will go in the future. That it's so big and happening so quickly that they don't know.

客户来找我们，说他们希望每秒处理五千万到四亿次查询。你怎么可能对需求的预期相差三千五百万次查询/秒还这么不确定？你怎么会连一个数量级都搞不清自己需要多少查询量？答案是：一切变化得太快了，六个月、八个月、十二个月后，所有人都不确定，因为速度实在太快了。

We have customers coming to us and saying, we would like between five and forty million queries per second. Well, how do you not know by a factor of 35,000,000 queries per second where your demand's gonna be? How are you unsure by an order of magnitude of what your queries are gonna be? The answer is things are moving at a rate that six, eight, twelve months out, everybody's unsure. It's so fast.

这规模太大了。所以你应该把这些公告看作是对未来的某种选择权。对吧？真正应该这样理解：在不确定的环境中，我该如何获得未来的某种选择权？我不确定是否会完全用上，但我愿意为未来拥有一定产能的权利支付一些代价。

It's so big. And so you should think about sort of these announcements as sort of options on the future. Right? That that's really the the way to think about it is in an unknown environment, how can I take an option on the future? I don't know if I'll use it all, but I'll pay something for the future rights to have some capacity.

所以，这就是看待这个问题的一种方式。

So that that's a a way to think about it.

既然它发展如此迅速、规模如此庞大，你该如何规划这个充满不确定性的未来？这太残酷了。

Given that it's so fast, it's so big, how do you think about planning for that uncertain future? It's brutal.

在变化极其迅速的环境中，正确的规划节奏应该是什么？你真正需要的不是完美的规划，而是灵活调整规划的规则。对吧？我的意思是，我们必须做出重大押注——我们正在对数据中心的产能进行五到七年的投资。

There's a very interesting question about in extraordinarily rapidly moving environments, what the right planning cadences. What you really need is good planning changing rules rather than good planning. Right? I mean, we have to make big bets. We're making five and seven year investments in data center capacity.

我们现在在供应链上投入的赌注高达数亿甚至数十亿美元，这些都不是三个月的短期决策。我认为，你需要采用与以往不同的规则：更频繁地规划，缩短视野，对未来采取选择权策略。

We are making hundreds of millions now in term of billions of dollars of bets in supply chain, and those are not three month bets. I think what you need to do is use different rules that have historically been used. You plan more frequently. You have a shorter view. You take options on the future.

如果未来的发展对你不利，你就会损失选择权的溢价——你支付一点代价，以确保获得部分产能；如果你最终没有使用它，那就说：好吧，这不过是管理未来不确定性的一种方式。

And if the future moves against you, you lose the premium on the option. You pay a little price to secure some capacity. And if you don't use it, you just you go, alright. That was sort of a way to manage uncertainty about the future.

你认为，你是否仍然低估了最狂野的需求预期？

What do you think the chances are that you are still underestimating even your wildest of demand expectations?

百分之百。我错了。如果你一年前、两年前，随便选个时间，说OpenAI能获得如今这样的估值，这根本无法想象，对吧？

A 100%. I've been wrong. If you would have said a year ago, two years ago, pick pick a time at which it would have been conceivable that OpenAI would get the valuations they're getting. Right? It wouldn't have been conceivable.

三个月前、六个月前、九个月前，那是什么时候？就在前一天，对吧？我认为这同样适用于我们所看到的需求，也适用于我们看到的公司估值。

Three months ago, six months ago, nine months ago, when was it? Like, the day before. Right? I think that's true with the demand we're seeing. It's true with the valuations on companies we're seeing.

这同样适用于进入社区的新想法的速度。

It's true with the rate of ideas entering the community.

你认为这其中有多大一部分是可持续的？每个人都在说：‘这不可持续，很多都是实验性的，不会持久。’你认为有多少是可持续的？

How much of this do you think is sustainable? Everyone in lobby is the oh, it's not sustainable. A lot's experimental. It's not enduring. How much do you think is sustainable?

我总觉得总有一些愤世嫉俗的人说：‘这永远不会成功，你永远赢不了歌利亚。’事实上，大多数事情确实不会成功，大多数时候歌利亚都会赢。但这种观点毫无超额收益。我们押注这些巨头继续不败，既赚不到钱，也毫无意义。这难道不无聊透顶吗？

I would say that there are always grumpy people who they're like, it'll never work, and you'll never beat Goliath. And truth is most things don't work, and most of the time, Goliath wins. But there's no alpha in that. There's no money made for you or me betting on the biggest of the big dogs to continue not to lose. I mean, how uninteresting is that?

当然，如果英伟达继续保持当前的增长速度，十一年后，地球上每个人都会为他们工作。对吧？算算看。但五年后，我们的经济会不会变得截然不同？

Of course, if NVIDIA keeps growing at the rate they're currently growing, eleven years from now, everybody on Earth works for them. Right? Do the math. Right? However, is it possible that our economy looks very different in five years?

我们所重视的东西会不会完全不同？我们会不会围绕人工智能重新组织社会，劳动生产率大幅提升，经济收益显著增长，整个经济蛋糕变得大得多？我认为这不仅很可能，而且几乎是必然的。

Is it possible that the things we value are very different? That we have reorganized around AI, we've seen a major bump in labor productivity, we have benefited dramatically, and the economic pie is much larger. I think that's not only likely, it's almost certain.

你提到过，如果英伟达继续以目前的速度增长，每个人都会为他们工作。

You mentioned that if NVIDIA continues to grow the way that they do, everyone will work for them.

你如果持续以这种速度翻倍，就会不断倍增，但你不可能一直这样下去。

You keep doubling at that rate. You multiply. You can't keep doing that.

到目前为止，他们的规模和资金实力已经坚不可摧到什么地步了？就像乔纳森·罗斯在Grock节目中说的，他们毫无疑问会在五年内达到一万亿美元。

To what extent is it just completely unshakable at this point for them? Like, where the scale and the size of money is like, you know, Jonathan Ross from Grock said on the show, will unwaveringly get to $10,000,000,000,000 within a five year timeline.

我希望他当时已经买入了他们的股票。我从不挑选公开市场的股票，我不喜欢选公开市场的股票。在公开市场，你可能会在好公司上亏钱，也可能在烂公司上赚钱，这让我很不舒服。

I hope he's long on them then. I don't pick public market stocks. I don't like picking public market stocks. In the public market, you can lose money on good companies. You can make money on shitty companies, and that for me doesn't sit well.

作为创业者，作为大卫对抗歌利亚，我想在我们打造一家优秀公司时赚钱，仅此而已。但它们能持续增长吗？我认为我们正看到一些大公司在担心增长时所采取的做法——更多地依赖资产负债表，而不是技术。历史上，大型公司一旦对自己的技术实力感到担忧，就会这么做。

As an entrepreneur, as a David in the battle with Goliath, I wanna make money when when we build a great company, period. But can they continue to grow? I think we are seeing some things that big companies do as they begin to worry about growth. I think use your balance sheet more and your technology less. This is something that historically large companies have done as they feared for their technical prowess.

当你这么说时，你其实是在指像OpenAI这样投入了上千亿美元，以及11 Labs，还有中间所有公司这样的投资。你

And when you say that, you're kind of referring to investments in your OpenAI's of a $100,000,000,000 and 11 Labs and everyone in between. You

开始收购企业，而不是赢得业务。我们看到思科在1999年到2001年2月确立主导地位时就采用了这种策略。另一种策略是所谓的“掠夺性预公告”——在B200还没人能拿到之前，你就开始宣传B300；在B200还没技术完成时，你就开始谈论鲁宾。

start buying business as opposed to winning business. We saw that with Cisco who emerged it as a dominant position, you know, '99, February, 02/2001. That that has been one of the strategies. Another strategy you you see is this predatory preannounce, where you announce B300s before anybody can get B200s. You start talking about Rubin before B200s are technically finished.

你从不提及你们产品在实际使用中的故障率，而这一数据非常惊人。相反，你总是不断谈论未来，试图说服人们等待，以便做出更好的决定，而不是选择当前更先进的技术。我认为，这是大型公司利用自身优势所采用的策略，而我现在看到的正是英伟达正在上演的这一幕。

You don't talk about the field failure rate of your products, which are massive. Rather, you sort of keep talking about the future in an effort to convince people to wait to make a good decision rather than go with technology that's better and present. I think these are the strategies of of very large companies using their strengths. And I I think that's what you're beginning to see unfold with NVIDIA.

说到百亿级别的投资，你是怎么分析这个数字的？对我来说，看到这个数字时，我真的不知道该如何分析，这太前所未有了。

Speaking of a 100,000,000,000 in opening, how did you analyze that? For for me reading that, I didn't really know how to analyze it. It's so unprecedented.

这个结构本来就是设计成让人无法理解的。如果有人想在投资中表达得非常清楚，我们会说：我们已经投入了这个金额，估值如此，交易已完成。但如果你想让它变得复杂，那就设定一个不确定的时间段，不给出任何估值或评估标准，而且这个标准还可能随时变化。它根本不是为了让你或其他分析师去锚定某些具体数值而设计的。这对双方来说都合情合理，但问题是，它根本无法被分析。

It was designed for nobody to understand it. If one wants to make something very clear in an investment, we've invested this amount of this valuation, the deal's done now. You wanna make something more difficult, it's up to this amount over an unbound specified amount of time at no valuation given or evaluation specified, but it can change. It wasn't designed for you or other analysts to anchor on different things. And and that's a a very reasonable thing for both of them, but it's just it's not an analyzable thing.

除了英伟达通过投资OpenAI来锁定其部分需求之外，你所能说的就只有这些了。

Beyond the fact that NVIDIA has chosen to try and lock up a portion of OpenAI's demand by investing in them. That's about as much as you can say.

我完全理解，而且我很高兴它本就设计得如此晦涩，因为我看到它时真的困惑了：这价格到底是多少？他们到底买了多少？

Totally get you, and I'm glad that it's meant to be confusing because I was confused looking at it going, what price was this? How much are they buying? Like

我不确定它是否本意就是要让人困惑，我觉得它的本意是

I I don't know if it was meant to be confusing. I think it was meant to be

你知道吗，我妈妈去购物时，我问她：‘这件裙子真漂亮，朱尔斯，多少钱？’她却说：‘这不重要，根本不重要。’

You know, my mother goes shopping, and I ask her, it's a lovely dress, Jules. How much is it? Well, it doesn't matter. It doesn't matter.

你直接叫你妈妈的名字？等等，稍等一下。我们回到重点上来。你真的直接叫你妈妈的名字？

You call your mom by her first name? Hold on. Wait a sec. Let's go back to the important thing. You call your mom by her first name?

哦，对，叫朱尔斯。

Oh, yeah. Jules.

好吧，你不叫她妈妈。我从来没叫过我妈妈雪莉。我的意思是，我根本不可能那样叫她，我只会叫她妈妈，或者别的什么称呼，但

Okay. You don't call her mom. I've never called my mom Shirley. I mean, I I well, I could never call her. I mean, that would be just mom or something else, but

不，不是的。但当我看到应用里的价格时，我会想，哦，安德鲁。哦，这感觉就是这样，我当时真的这么想吗？

not No. But when I when I get the, like, price on application, I'm like, oh, Andrew. Oh, That that's what this felt like. That's what I was like, really?

不，那里什么都没有。事实上，可能有很多复杂的因素，导致我们无法清晰地描述，否则就会透露出比他们愿意透露的更多的信息。

No. Nothing there. The truth is is there may be and there are likely a huge number of moving parts that make it impossible to clearly describe without giving out more than they wanted to give out.

你提到了预公告，比如三百、二百这样的数字，以及相关的时间节点。我们对芯片折旧的理解方式对吗？再说一次，我刚和乔纳森做过一期节目，他说：嘿，我们实际上是以十八个月到两年为周期来考虑这些问题的。

You mentioned preannouncements that, like, three hundreds, b two hundreds, timings of such. Are we thinking about chip depreciation in the right way? Again, I just did a show with Jonathan. He's like, hey. We actually think about them on, like, an eighteen month time cycle to maybe two years.

我当时想，哇，这速度真快啊。我们对芯片折旧的理解方式正确吗？我们究竟应该如何看待芯片的摊销？

And I was like, wow. It's it's quite quick. Are we thinking about it the right way, and how should we be thinking about the amortization of chips?

我们正处在一个前所未有的阶段。人们显然仍在从H100中获得价值，这已经超过两年了，对吧？所以如果你说它的折旧期是两年，那在经验上是错误的。我认为人们仍在从H100中获得价值，尽管它已不是最前沿的了。

We are in unprecedented waters. People are clearly still getting value from h one hundreds, and that's more than two years. Right? So if you say it's a two year depreciation, you're empirically wrong. And I think people are still getting value from a one hundreds, though not on the cutting edge.

因此，折旧周期更接近三到四年，甚至可能长达五到六年。折旧问题的核心在于：下一代芯片比当前一代快多少？这才是折旧问题的实质。因为所谓折旧，意味着在某个时点，即使一个部件已经完全摊销，也不再值得继续使用，因为新一代部件快得多、功耗低得多，更换它对我更有利。这才是折旧问题的根本所在。

And so that's closer to three or four years, and could be as long as five or six. The question of depreciation is how much faster are future generations than the current generation? That's the actual question on depreciation. Because with depreciation, you're saying, at some point, it's no longer worth using a part that's fully paid off because there is a new part that's so much faster, uses so much less power, that it's better for me to retire it. That's the actual the underpinning to the depreciation question.

如果我有一个50兆瓦的数据中心，里面装满了这些芯片，即使它们已经折旧完毕，运行成本为零，对吧？电力成本加上零折旧成本。但到了某个时点，仍然值得将它们移出，因为新一代芯片快得多、性能好得多、功耗低得多，我能获得更高的每美元收益。

If I have a data center and it's 50 megawatts and I have this much capacity in it, at some point, even though my chips in it have been depreciated and I'm running them at at zero cost. Right? Power plus zero depreciated cost. It makes sense to move them out because the new chips are so much faster, so much better, use so much less power. I get so much more dollars per.

因此，这就是问题所在。如果我们整个行业不能持续一代又一代地制造出极其卓越的新型芯片，人们就不会从一代升级到下一代，芯片的使用寿命会更长，折旧周期也会相应延长。

And so that's the question. If we don't, as an industry, continue to build extraordinarily sort of better parts generation after generation, then people don't move from one generation to the next. They last longer, you depreciate them longer.

我们现在处于什么阶段？我问这个问题感觉有点天真。芯片性能提升的路径上，我们究竟走到哪里了？是已经取得了90%的进展，现在只剩下小幅提升，还是说我们仍处于非常早期的阶段，还有90%的提升空间待挖掘？

Where are we? And I feel very naive for asking this. Where are we in the performance improvement pathway for chips? Are we, like, in the we've got 90% and we're incremental gains, or are we at the we are still at the super early stage and we have 90% of the gains to be made?

这个问题要看你是看人们的营销材料，还是实际的性能数据。毫无疑问，营销材料会让你相信每一代都有巨大提升。但稍微深入工程分析，你可能会发现，每次有意义的代际升级，性能提升大约是2到2.5倍，不会再多了。如果你公平比较——比如8位对8位、4位对4位——实际性能提升可能芯片上的浮点运算数增加了，但内存带宽的提升没有超过2倍，所以你根本用不上那些额外的算力。这些芯片本质上是一种解决方案。

The question is, in that case, whether you read people's marketing material or the actual performance results. Certainly, people's marketing material would lead you to believe that generation over generation, there are huge gains. A little bit of engineering digging probably leads you to the conclusion that you're getting two, two and a half x per meaningful generation move, not more. If you compare apples to apples, eight bit to eight bit, four bit to four bit, if you compare actual performance, you know, you might have more flops on the chip, but your memory bandwidth didn't improve more than two x, so you can't get to them. These chips are a solution.

如果你只让其中一个部件变快，那它就是一个系统。你让一个部件变快，而其他部件没有同步进步，它就会成为新的瓶颈。无论你的芯片有多少浮点运算能力，如果数据无法高效地输入输出，这些算力都是浪费的。问题不在于芯片本身快了多少，而在于整个解决方案快了多少。

And if you make one part fast, it's a system. You make one part fast and the other part doesn't move as far forward, it becomes the new bottleneck. It doesn't matter how how many flops your chip has. If you can't get data onto and off of the chip, those are wasted. The question isn't how much how much faster is the chip, it's how much faster is the solution.

这包括内存，而内存是GPU架构在推理时的根本限制因素。因此，芯片速度再快也没用，关键在于内存带宽能提升多少。

That includes memory, which has, for inference, is the fundamental limiter for the GPU architecture. And so, it doesn't matter how much faster the chip goes. It matters how much faster the memory bandwidth is.

关于这一点，我曾对这个领域的创始人喊话，他说每个人都没意识到的是，虽然SRAM在内存集成方面听起来很棒——当然你比我更懂这个，可能还会因为我这么说而讨厌我——但SRAM本质上就是芯片内存储与芯片外存储的区别。看似很棒，但他指出，它完全无法应对规模扩展。因此，尽管它对小规模应用可能更快，但目前根本无法处理大规模任务，而这正是任何大型服务商的基本需求和要求。你觉得这公平吗？你怎么看？

On this, I I was shouting to a founder in the space and he said that what everyone fails to understand is that, like, although SRAM sounds great in terms of having memory on SRAM is obviously you'll describe it much better than me and hate me for this, but SRAM is obviously memory, like, chip versus off chip. Seemingly great, but he said it's completely unable to handle scale. And so although it may be quicker for anyone who wants to do large scale, it is incapable at present of doing that, And that's a fundamental need and requirement of any of the large providers. Do you think that's fair? And how do you think about that?

这不仅公平，而且正是我们选择晶圆级架构的原因。你朋友说的完全正确：SRAM速度极快但容量极小，而HVM是DRAM的一种，容量大但速度很慢。如今，NVIDIA和所有GPU，包括AMD的，都选择了大容量但速度慢的内存，因为这对图形处理来说非常合适。

Not only is it fair, it's the reason we went to wafer scale. What your friend said is strictly true, in that SRAM is blazing fast and low capacity. HVM is a flavor of DRAM. It has high capacity, and it's very slow. Now NVIDIA and all GPUs, including AMD's, chose a big capacity memory that is slow because it's perfect for graphics.

你不需要频繁访问内存，可以一次性加载大量数据，访问频率很低。SRAM速度极快，但存储容量非常有限。因此，传统芯片的问题在于：如果你把内存放在芯片上，就占用了本可用于计算的宝贵空间。

You don't have to go to memory very often. You can hold a lot. You don't go very often. SRAM is blazing fast, but it can't hold very much. So the problem on traditional chips is if you put memory on the chip, you are using space that could be otherwise used for compute.

芯片的面积是固定的。如果你把一半面积用于内存，那就只剩一半面积可用于计算。我们的想法是：如果我们制造一个像餐盘那么大的芯片，就能塞满高速SRAM，从而克服SRAM容量小的缺陷——通过使用大量硅片面积来实现海量存储。如今，如果你在普通尺寸的芯片上采用SRAM方案，想运行万亿参数模型，就得用四五千颗芯片，简直一团糟。

You have a fixed amount of real estate. And so if you put half memory, half your real estate's available for compute. Our idea was that if we we sort of if we built a chip that was the size of a dinner plate, we could stuff it to the gills with fast SRAM, overcoming the limitation of SRAM, which is it doesn't store very much, by putting a huge amount down, by using more silicon area. Now if you're an SRAM solution today in a normal sized chip and you're trying to do a trillion parameter model, use four, five thousand chips. What a mess.

你知道那需要多少根线缆吗？你知道这对AI系统的影响有多糟糕吗？这简直是一团乱麻，还限制了你用AI做想做的事，比如推测性解码，带来各种棘手的挑战。

You know how many cables that is? You know the impact to the AI? It's a horrible mess. And it limits you from doing things you want to do with the AI, like speculative decode. It has all sorts of painful challenges.

另一方面，如果你只用一两颗或四颗这样的芯片，系统就简单多了，容易多了。这正是你朋友说的完全正确的地方，也是我们决定制造更大芯片的原因——以便用高速SRAM填满它，通过占用大量硅片面积，克服传统SRAM容量不足的限制。

The other hand, use one of these or two or four. And it's simple. It's easy. And this is what your friends said exactly right and the reason we went to build a bigger chip. So we could fill it with this fast SRAM, so we could get over the traditional limitations of SRAM that couldn't store very much by using a lot of space, by using a huge amount of of silicon area.

所以你的朋友说得完全正确。你将来可能会再听他们的建议。

So your friend is exactly right. You might listen to them again in the future.

问你个问题，没有冒犯的意思。

Question to you. No offense.

这看起来有点显而易见。好吧，增加房地产面积，多塞点SRAM。

That seems a little bit obvious. Like, okay. Increase the real estate. Shove more SRAM on.

确实如此，对吧？

It does, doesn't it?

是啊，但这真的有看起来那么明显吗？我是不是忽略了什么？

Yeah. Is it is it as obvious as it seems? Am I missing something here?

我们之前忽略的是，在过去七十五年里，没人能做到这一点。在我们成功之前，制造更大芯片已被证明是不可能的。在计算行业七十五年的历史中，没有人能制造出面积超过约840平方毫米的芯片，许多人尝试过，但都失败了。我们成功之后，埃隆在Dojo项目中也尝试过，但他们失败了。这真的非常非常困难。

Well, what we were missing is that for seventy five years, nobody could do it. Building a bigger chip had proven impossible before we did it. Nobody in the history of the compute industry had been able to build a chip bigger than about 840 square millimeters in the seventy five year history of the compute industry, and many people had tried and failed. After we did it, Elon tried at Dojo, and they failed. It's really, really hard.

我们的策略以前从未有人实施过，也从未成功量产过。所以，虽然这看起来显而易见，但实际做起来却很难。

Our strategy had never been done before, never been successfully yielded. And so while it was obvious, it was hard.

所以，当我们思考当前市场在训练和推理方面的状况时，我们是否同意，NVIDIA的芯片在训练方面确实比你们的好，但你们的芯片在推理方面比他们的更好，市场正是在这种分工下划分的？

So when we think about, like, where the market is today in terms of training inference, do we agree then that actually NVIDIA's chips are much better for training than yours are, but yours are much better for inference than theirs are, and the market splits in that respect?

不，我们在两者上都更快，但训练中的软件挑战是真实存在的。

No. We're faster on both, but the software challenges in training are real.

这什么意思？

What does that mean?

意思是，当一个新的模型被开发出来，所有人都在论文里读到它时，它都是在GPU上完成的。每个人要训练它时，都会拿最初为GPU设计的方案，然后必须将其迁移到自己的硬件方案上——无论是TPU、AMD GPU，还是像我们这样的专用芯片。你必须做迁移，而这是一项更困难的软件工程。但在推理方面，事实是，根本没人关心CUDA，也没人关心PyTorch。

It means that when a new model is built and everybody reads about it in a publication, it was done on a GPU. For everybody to to train it, they take the recipe that was originally done for the GPU, and they have to move it to the recipe for their hardware, whether that's a TPU, whether that's an AMD GPU, whether that's another dedicated chip like ours. You have to move it, and that's a harder software lift. In inference, truth is nobody cares about CUDA. Nobody even cares about PyTorch.

他们想要的只是一个API。从基于GPU的OpenAI OSS 120B方案迁移到我们的方案，只需要敲10个按键。就10个按键，就这么简单。

What they want is an API. It's literally 10 keystrokes to move from a GPU based solution on OpenAI OSS one twenty b to our solution. It's 10 keystrokes. That's it. It's nothing.

答案是，虽然我们在训练和推理上都更快，但展示推理速度更容易，对吧？你只需要做个并排对比。而要证明你比千B200更快，你得先拿到千B200，还得花四到六周时间训练模型。

The answer is is that while we are faster at training and we are faster at inference, it's easier to demonstrate inference. Right? You just put up a side by side. To show that you're faster than a thousand b two hundreds, you got to get a thousand b two hundreds. You need to train the model for four weeks or six weeks.

你得搭建一套我们的机器集群，这工作量更大。我认为，目前市场更容易让客户从GPU转向推理场景，而做推理的人数远远超过做训练的人数。

You got to stand up a cluster of our machines. You got to it's a bigger lift. I think the market right now is easier to move people off GPUs in inference, and the number of people doing inference is vastly higher than the number of people doing training.

当你今天观察推理市场时，它的发展有哪些出乎你意料的地方？

When you look at the inference market today, how has it developed in a way that you did not expect?

我认为人类的思维很难理解几何增长或指数增长，对吧？推理能力的更大增长其实并不难理解——它等于使用人数乘以使用频率乘以每次使用所需的计算量。这是三个不同的变量相乘。

I think it's really hard for the mind to wrap itself around geometric growth or exponential growth. Right? I think there is nothing confusing about the greater growth of inference. Greater growth of inference is the number of people who use it times the frequency of use times the amount of compute needed per use. It is three different variables multiplied by each other.

问题是，这三个变量都在快速增长，这带来了令人眩晕的效果。越来越多的人在使用人工智能；一旦他们开始使用，使用频率就会提高；而他们想用它做的事情也变得更大、更复杂，因此需要更多的计算资源。于是，市场规模就是这三个变量的乘积，而它们都在快速增长。

The problem is they're all growing fast, and that produces some mind numbing effects. More people are using AI. Once they start using AI, they use it more frequently. And what they wanna do with it is bigger and more complicated, so it uses more compute. And so you have three variables, the size of the market's a product of the three, all growing fast.

我们早就知道这一点。我们看到了，但即便如此，它仍然让人惊叹不已。这真的非常有趣。

We knew that going in. We see that, and it still takes your breath away. That that is really, really interesting.

我认为我们还没看到任何真正的东西。

I don't think we've seen anything yet.

我同意。我认为我100%确信我们低估了市场，正是因为这个前提。

I agree. I I think the reason I'm a 100% sure that that we are underestimating the market is because of that premise.

我认为萨姆·阿尔特曼对人们如何使用ChatGPT的描述非常到位。他说，实际上，大多数人把它当作谷歌的替代品来使用；而年轻人则把它当作未来的操作系统，这在他看来才是正确的使用方式。

I think Sam Altman said it very well in terms of how people use ChatGPT, but he said, know, essentially, the majority of people use it like Google, the Google replacement. And, actually, the younger people use it as an operating system for the future, which is the right way to do it in his mind.

完全正确。我认为在1988年，获得诺贝尔经济学奖的罗伯特·索洛提出了这个问题。他说，我们到处都能看到计算机，每个办公桌上都有，但生产力统计数据里却看不到它们的影子。你可能会说，这确实是个有趣的观点。随后另一位经济历史学家加入了讨论，他做了一项大规模研究。

Absolutely right. I I think in 1988, Robert Sallo, who won a Nobel Prize in economics, he asked this question. He said, we see computers on every desktop and everywhere we look except in the productivity statistics. And you say, well, that's a really interesting thing to say. And there was another economic historian who jumped into the fray, and he did this huge study.

他的名字叫保罗·戴维，写了一篇非常著名的论文，题为《计算机与动力机》。他研究的是1880年至1955年间电力在制造业中的采用情况。他发现，起初电力带来的生产力提升非常有限，基本上只是作为皮带传动系统的备用方案。直到人们重新组织车间布局，充分利用电力的优势，生产力才实现了巨大飞跃。

His name was Paul David, and he wrote a a very famous paper called the computer and the dynamo. And what he studied was the adoption of electricity in the manufacturing sector between about 1880 and 1955. And what he showed was at the beginning, electricity produced very little productivity gains. It was basically used as a backup for belt driven systems. And it wasn't until they reorganized the shop floor to take advantage of electricity that you got this huge jump in productivity.

如果把这个逻辑推演到计算机上，他的意思是：我们只是用计算机来做那些我们本来就很擅长的事情。我们用计算机取代了打字机，用电子表格取代了总账会计。而这些事情我们本来就已经很在行了。

And if you roll that forward to the computer, what he was saying is, look. We used computers to do things we're already pretty good at. We replaced a typewriter. We replaced general ledger accounting with spreadsheets. We were good at those things.

因此并没有出现显著的生产力提升。而他预测并随后立即发生的是：到了20世纪90年代中期，生产力出现了巨大飞跃。我们开始将计算机相互连接，建成了互联网，并初步构建了云计算的雏形。

You didn't get a big jump. And what he predicted and then happened immediately thereafter, by the mid nineties, he had a huge jump in productivity. We had begun tying them together. We built the Internet. We had the the first parts of a cloud.

所有这些技术以前所未有的方式使用计算能力，从而带来了生产力的大幅跃升。如果你像使用谷歌那样使用OpenAI及其竞争对手，你会看到生产力只有轻微提升；但如果你以根本不同的方式使用它们——正如萨姆所指出的——你将看到巨大的飞跃。如果我们围绕人工智能重新组织自身，你将看到生产力的巨大提升。

And all these things used compute differently in ways that had never been consumed before. And you got this massive jump in productivity. If you use OpenAI in a various of their competitors, the way you use Google, you'll see a very modest jump in productivity. If you use them in a fundamentally different way, that was Sam's point, you'll see a huge jump. And if we reorganize ourselves around AI, you're gonna see massive productivity gains.

如果我们只是用人工智能取代我们已经习惯做的事情，比如用它代替谷歌或其他工具，你根本看不到明显的生产力提升。这种转变需要时间。他指出，不同年龄段的用户使用方式不同：年轻用户与年长用户的使用方式截然不同，年长用户只是在替换他们已有的工具。

If we use AI to replace things we're already doing, Google or something else, you're not gonna see very big jumps at all. And so that transition takes time. And what he pointed to was a demographic. Younger users are using it in a different way than older users. Older users are replacing something they already had.

而年轻用户则以一种前所未有的方式使用它——将其作为人生的操作系统。

Younger users are using it in a way that never existed before, an operating system for life.

如果我们真要实现你提到的那场转型，能源需求简直是疯狂的。我的意思是，萨姆说要花一万亿美元，还需要日本全国的能源量。这可行吗，安德鲁？

If we're gonna see that transition that you mentioned there, the the energy requirements are just insane. I mean, Sam said a trillion dollar spend. He needs the energy of Japan. Yeah. Is this feasible, Andrew?

是的，这是可行的。但是否对社会有益或可取，那就是另一个问题了。它确实是可行的。人们常说美国电力不足，这完全是错误的。

Yeah. It's feasible. Is it desirable or good for society is a different question. It's feasible. People often say we we don't have enough power in The US, and this is strictly wrong.

我们有足够的电力，只是分布位置不对。对吧？电力不在人们居住的地方，也不在光纤电缆铺设的地方。我们在德克萨斯州西部有大量的天然气电力。

We have plenty of power. It's in the wrong places. Right? It's it's not where we have people or where we have fiber optic cable. We have a ton of power in West Texas in natural gas.

我们在纽约州北部有大量水力发电，还有很多其他地方也有大量电力。但那里没有多少人。问题在于，电力分布与人口、建筑或我们连接数据中心所需的电信光纤之间存在严重错配。第二个观察是关于社会责任的：既然我们要消耗如此巨大的能源，我们就负有责任，必须创造出非凡的价值，这是我们的共同义务。

We have a ton of power in Upstate New York, in hydro. We have a ton of power in lots of places. We don't have people there. The problem is is one of a mismatch between where all the power is and where the people are or where the buildings are or where the telco fiber is that we need to get data to and from the data center. The second observation is one of a community, and that's that to the extent we consume this extraordinary amount of power, we have an obligation to deliver amazing things, and that's on all of us.

我认为我们有责任研发更有效的药物，提供更好的医疗，让衰老过程少些痛苦，减轻照顾年迈或病重父母的负担。你想想社会的各种问题和苦难。如果我们打算消耗这么多电力，那我们就必须为它创造相应的价值。如果我们只是消耗了电力，却没有做到这些，那对社会就不是真正的收益。

I think we have an obligation to deliver drugs that they're more efficacious, to deliver better health care, to to make aging less painful, make the looking after of aged parents or sick parents. You go through society's ills and woes. If we are gonna consume this amount of power, the burden is on us to deliver value for it. If we use it and don't do that, then it's not a gain for society.

你觉得这能控制吗？比如生成吉卜力风格的图像——我名字说错了——这并不能带来多少实际价值，却消耗了海量的计算和能源。我们能控制这种现象吗？这就像

Do you think that's controllable? You know, creating Ghibli or Ghibli images, I get it wrong, isn't particularly value inducing, but it churns a huge amount of compute and energy. Can we control that? That's like

这是一个非常困难的问题，而我只是众多声音中的一个。市场的问题在于，它为了实现一个极具生产力的成果，会推动大量非生产性的活动。吉卜力风格的创作可能对社会整体未必是净收益，但也许用于吉卜力的技术后来被应用于X射线晶体学，并最终促成重大科学突破。这就是现实的复杂性。在任何特定时刻，你都可以看到成千上万种看似无意义的尝试——而这正是市场的本质。

It's a very hard question, and I I you know, I'm one voice in this. The problem with markets is that they do a lot of things that aren't productive in order to get one that is very productive. Ghibli may or may not have been net societally gain, but maybe the technology that is used in Ghibli is used for X-ray crystallography and later is fundamental to finding major scientific breakthroughs. I mean, that's the messiness. At any given point in time, right, you can point to, you know, thousand poppy strategy, which is what a market is.

对吧？市场就是靠大量糟糕的想法来筛选出少数几个好点子。这就是你的生意：投资于大量宏大的构想，其中大多数都会失败。而市场正是这种模式的放大版。

Right? A market has a lot of bad ideas to get a few good ones. That's your business. Your business is investing behind a lot of big ideas, most of which fail. And the market is that writ large.

在这种环境下，你总能事后指着说：‘嘿，哈里，那个投资真是个蠢主意，你干嘛投他们？他们不是彻底崩盘了吗？’ 但这些话都是事后诸葛亮。你瞧瞧。

And so in that environment, you can always point to, oh, that was a dipshit investment, Harry. Why'd you invest with them? I mean, they they blew up. You can always say that after the fact. Look at that.

它们在那上面消耗了大量能源，这没什么用。我们来看看。因此，我认为关键在于，从社会层面来看，当我们动用政府资金、税收优惠或审批便利时，必须确保这些资源被优先给予那些真正对社会有意义的项目。

They're they're using a ton of energy for that. That's not useful. Let's see. And so I I think the answer is we need to be sure that at a societal level where we use government dollars, where we use tax breaks, where we use permitting breaks, we are sure that we're giving these to disproportionately to projects that that matter to society.

你认为特朗普对美国的人工智能发展是帮助更大，还是损害更大？

Do you think Trump's done more to help or to hurt The US AI effort?

我觉得这很复杂。总体来看，可能还是帮助更大。拜登政府的政策方向有误，且过于胆怯；而特朗普政府，我认为值得肯定的是，他身边聚集了一些人工智能领域的聪明人。总体而言，影响是积极的。

I think it's confusing. On net, it's probably more to help. The Biden administration was misguided and afraid. The Trump administration, I think to his credit, they surrounded himself with some smart people in the AI space. On net, it's been positive.

当你审视未来人工智能发展所需的能源时，核能是否是不可避免的，甚至是唯一可行的能源解决方案？

When you look at what is required in terms of energy, is nuclear unavoidable or the sole solution to be the providing force of energy for this next generation of AI?

不，核能并非不可避免。对于那些缺乏其他替代能源的国家来说，核能是一个非常合理的选择。加拿大拥有的水力资源比地球上任何其他国家都多，它有机会开发出地球上最廉价的电力，这简直令人难以置信。

No. It's not unavoidable. It's a very reasonable decision for countries that don't have lots of alternatives. Canada has more falling water than anywhere else on Earth. The opportunity for Canada to develop the cheapest power on Earth is mind boggling.

许多地方都有廉价的电力，但对于那些希望发展电力但缺乏芬兰（地热）、冰岛（地热）或加拿大（水力）等自然资源的国家来说，核能是一种非常合理且成本效益高的策略，尤其是在数十年的时间尺度上。

There is cheap power in lots of places, but in order for countries that wish to and don't have the natural resources of Finland that has geothermal or Iceland that has geothermal or Canada that has falling water, nuclear is a very reasonable and cost effective strategy, especially over a several decade view.

安德鲁，今天最让你担忧的是什么？

What worries you most today, Andrew?

我认为，既然我们消耗了如此多的资源，就必须确保我们能取得非凡的成果。我担心的是，这个机会如此巨大，以至于整个社会都在仓促地冲向它，而实际上，有时候如果我们能停下来思考，有条不紊地前进，而不是盲目奔跑导致跌倒、擦伤膝盖或磕掉牙齿，反而可能在三十天、六十天或九十天内走得更远。

I do think of this idea that to consume the resources we're consuming, we have to be sure that we produce some extraordinary outcomes. I worry that the opportunity is so big that as a community we're running sort of helter skelter at it, that actually sometimes instead of running where you trip and fall and graze your knee and chip a tooth, if you stopped and thought and marched, you might get further over a thirty or sixty or ninety day period.

你是否担心‘七巨头’的价值过于集中？它们在标普指数中的占比现在达到了历史最高水平，这种价值集中是真实存在的。如果人工智能发展遇到任何阻碍，市场可能会严重受挫，而这种连锁效应将影响所有人。没错。

Do you worry about the concentration of value in Mag seven? They now make up more of the S and P than they pretty much ever have done in history, and that concentration of value is very real. If AI hits a speed bump in any way, the market could derail significantly, and the multiplier effect of that is felt by everyone. Right.

我认为其中的风险不在于它们消耗了太多资源，或它们本身价值过高。那不是风险。它们之所以价值如此之高，是因为我们相信未来经济会奖励这些公司。真正的问题是，人们因此误以为标普指数是一种安全或更安全的投资，而事实可能并非如此。风险在于人们头脑中的认知模型与现实存在偏差。

I I think the the risk there is not that they consume that much that they are that much value. That's not the risk. Think they're that much value because we believe the future economy will reward that. I think the issue is is that people then think the S and P is a safe investment or a safer investment than it might be. The risk is the mismatch in the mental model people have.

金融市场的风险往往源于人们从根本上低估了风险。当风险被合理定价时，结果就不会令人意外。但如果人们继续认为标普指数代表了全球经济，而实际上它只是由三到五十家公司构成，那么他们就暴露在自己并未预期的行业风险之中。

Risk comes in financial markets where people fundamentally underestimate risk. When risk is priced properly, then what happens is your outcomes are not surprising. If people continue to think the S and P is sort of an index of the global economy or and it's not. It's 30 or fifty percent seven companies. Then they're exposed to sector risk that they weren't signing up for.

他们以为自己实现了多元化，但实际上却高度依赖一个极其狭窄的行业。这本身就是一种风险。在我看来，这正是新秩序下的一个挑战。所有那些专家给出的建议——比如‘构建多元化投资组合’——当世界发生变化，而你的投资组合因集中化而不再多元化时，如果你仍固守它，就会面临真正的风险。

They thought they were diversified, and in fact, they're heavily dependent on a very narrow sector. And that's a risk. That seems to me to be a challenge in the new world order. And all the advice that sort of the pundits give, you know, diversified portfolio, When the world changes and that portfolio is not you keep holding it and it's not diversified anymore because of consolidation, then there's real risk.

当你看到英伟达市值达到四万五千亿美元时，你认为风险定价合理吗？

Do you think the risk is priced properly when you look at NVIDIA at 4 and a half trillion?

我认为，他们已经证明自己是二十一世纪第一个季度最伟大的公司。他们证明了自己是一家非凡的企业。我不知道四万亿这个数字是否准确，但我认为一个非常庞大的数字——也许甚至偏低——才是合理的，因为他们所取得的成就。

I think they've proven themselves to be the the greatest company in the first quarter of the twenty first century. They've proven themselves to be extraordinary company in the first quarter of the century. And I I I don't know if 4,000,000,000,000 is right, but I think a very big number, maybe it's too low, is right because of what they've achieved.

我们之前谈到过那种无法预测或预估的、永不满足的需求。在你看来，当前的瓶颈是什么？我们之前请过格罗肯的乔纳森，他说：‘实际上，有一个客户要求的供应量是我总产能的五倍。’而我的供应量就是全部了。

When we look at we we said before about the insatiable demand that we cannot predict or anticipate. What are the bottlenecks today in your mind? Again, we had Jonathan at Grokkon, and he was like, actually, you know, I had someone come and demand five times the supply that I have in total. That was from one customer. Supply is mine.

你如何看待我们为满足你提到的这种永不满足的需求所面临的瓶颈？

How do you think about the bottlenecks that we have to reach the insatiable demand that you mentioned?

我认为，如果你回顾一下规划过程，当客户要求的量是你产能的五倍时，那说明你的规划可能出了问题，对吧？你本应规划得更好。我认为每一个层面都存在真实而重要的瓶颈。我认为第一个瓶颈是专业人才。

I think if you go back to planning, if you've got customers demanding five x at your capacity, I mean, probably didn't get your planning right. Right? You probably should have should have planned better. I think there are bottlenecks at every level that are real and meaningful. I I think the first one is expertise.

我们在人工智能专业人才方面存在根本性限制。我们培养的人工智能从业者不够多，懂数据管道的数据科学家也不够多。我们的大学没有足够多地培养这类人才。而美国在移民问题上的困境也加剧了这一问题。

We have fundamental limitations in AI expertise. We're not making enough AI practitioners. We're not making enough data scientists who understand data pipelines. Our universities aren't minting enough. Our challenges in The US with immigration don't help that.

我们历来都在吸引全球最优秀、最聪明的人才——先让他们来我们的学校读书，再让他们留下来工作。如果这不是我们的政策，我们就必须制定这样的政策。如果政府决定不靠引进人才来构建劳动力，而是仅依靠本地居民，那我们就必须更好地培训这些人。我们需要在K-12阶段更好地教授他们，也需要在大学里更好地教育他们，才能培养出足够多的工程师，以满足这一需求。

We have historically sucked the best and the brightest, first on j ones to come to our schools and h ones to stay. If that is not our policy, we need to make them. If the government decides that that is not the way they want to build a workforce, instead they want to build it out of people who live here, we need to do a better job of training those people. We need to do a better job of teaching them in k through 12. We need to do a better job of educating them in our universities in order to to make the number of of engineers we need to meet this demand.

这是一个瓶颈，也正是最优秀的人才获得如此非凡报酬的原因。

That's a bottleneck, and it's why the best and the brightest are are getting such extraordinary compensation.

人才争夺战完全失控了吗？你看到像扎克伯格这样的人为一个人花费数亿美元。你觉得这只是一个被夸大的异常现象，还是认为人才争夺战前所未有？

Is the war for talent completely out of control? You're seeing your Zucks of the world spend hundreds of millions on one person. Do you think that's a blown up anomaly, or do you see the war for talent being unprecedented?

有些工程师具备的技能，是任何数量的其他工程师合作都无法实现的。有些科学家拥有独特的想法和智慧，这些是大量其他有才华的人合作也无法复制的。他们应该比世界级足球运动员拿得更多吗？我不知道，也许吧。

There are engineers who have skills that no number of other engineers working together can achieve. There are scientists who do who have ideas and who have brains that are can't be replicated by lots of other talented people working together. Are they to be paid more than world class soccer players? I have no idea. Maybe.

也许不该。

Maybe not.

我的意思是，从经济理性角度来看，本质上是的。没错。如果OpenAI的首席科学家为公司创造了500亿美元的企业价值，那么支付他十亿美元是值得的。

I mean, inherently, yes, from an economic rationale standpoint. Yes. The value generated from a chief scientist at OpenAI, if they add $50,000,000,000 of enterprise value to pay them a billion dollars is worth it.

这正是我们需要思考的。我们曾为《两个半男人》中的查理·辛每集支付250万美元。我敢肯定，有很多人的社会净生产力远高于这个水平。

That's what we have to think about. I mean, we've paid Charlie Sheen 2 and a half million dollars an episode for two and a half men. I'm pretty sure that there are lots of people who are whose net productivity to society is above that.

但他还是把钱全花光了。

And he still spent it all.

他是的，我看到了。我刚看了那个节目，是在Netflix还是Prime上？真是个令人悲伤的故事，一个如此自我毁灭却又如此有才华的人。但你知道，我们真的应该给足球运动员或篮球运动员这么高的报酬吗？

He is. I saw that. I just saw the show on was it Netflix or Prime? What a sad story of somebody who was so self destructive and so talented. But, you know, should we be paying soccer players or basketball players?

我完全不知道。我从不花一分钟担心我们是否给杰出人才付得太多了。从来没有一家公司因为给杰出人才付太多钱而破产。如果你想破产，那就给平庸的人付太多钱吧——这才是你搞砸的方式。

I have no idea. And I I don't spend a minute worrying about whether we're paying extraordinary people too much. No company ever went bankrupt by paying extraordinary people too much. If you wanna go bankrupt, pay mediocre people too much. That's how you mess up.

从来没有人因为给真正杰出的人付太多钱而陷入困境。

Nobody's ever struggled by paying truly extraordinary people too much.

另一个瓶颈是什么？你说专业知识是其中一个。

What's the other bottleneck? You said expertise is one.

台积电无法足够快地建造晶圆厂。事实上，这对台积电和三星都是如此。这些晶圆厂是全球最惊人的制造工厂。你知道，这些工厂的投资高达300亿到500亿美元。它们快速建造这些工厂的能力受到极大限制。

TSMC can't build fabs fast enough. The truth is is that these are both for for TSMC and and Samsung. These fabs are the most amazing manufacturing plants on the planet. You know, these are $30,000,000,000 $50,000,000,000 factories. Their ability to build them quickly enough is very much limited.

这反过来限制了芯片的供应，使其低于应有的水平——不只是我们的芯片或英伟达的芯片，而是所有公司的芯片，都低于原本可能达到的水平。这推高了成本。目前，数据中心的产能严重短缺。已经投入了巨额资金，也说了大量空话。

That in turn limits and keeps the supply below where it would like to be of chips, not just our chips or NVIDIA, but everybody's chips, below where it might otherwise be. It keeps the cost up. Right now, there's a shortage of data center capacity. There's a huge amount of investment that has gone into that. There's a lot of words.

但那些大家一直在谈论的吉瓦级设施在哪里？人人都承诺要建，可它们在哪里？嗯，它们还没建成。

But where are these gigawatt facilities that everybody's been talking about? Everybody's committing to them. Where are they? Well, they're not up yet.

这需要多长时间？

How long does that take?

你知道，像埃隆这样世界上最快、可能是最擅长建造工厂和大型建设项目的人，需要六个月到八个月。而对世界上其他人来说，可能需要一年半，甚至更久。

You know, somebody like for Elon, who's the fastest in the world and and maybe the best at building plants and large construction projects, it takes six months, eight months. And for the rest of the world, it takes a year and a half, maybe longer.

我们在数据中心建设者身上投资足够了吗？我的意思是，现在数据中心绝对是投资物业中最疯狂热门的类别之一。我来自纯粹的华尔街思维，每个华尔街的人都想进入数据中心领域。

Are we investing enough in data center builder? I mean, it is one of the most insanely hot categories now in terms of investment properties. I'm coming from, like, a pure Wall Street mindset. Like, every Wall Street guy wants to be in data centers.

它们的结构是这些人真正理解的，对吧？在他们看来，这就像债券，也像房地产。你有一个租户。

It has a structure that they really understand. Right? It looks like a bond to them. It looks like a piece of real estate. You you you get a tenant.

他们每月支付租金，你可以以此作为抵押贷款。你获得的是一个投资级租户，本质上就像债券。它的优势在于，它属于债务市场和资本市场中非常熟悉的一种类别或模式，这是个优势。CoreWeave 及其一些金融工程和创新，帮助世人看清了这一点。

They pay rent every month. You you can loan against that. You you get an investment grade tenant that's basically a bond. It has the advantage of of falling into a category or a pattern that is really well understood in the debt market and in the capital markets, and that's an advantage. CoreWeave and some of their sort of financial engineering and innovations there help the world to see that.

你知道，像很多事一样，很多人会涌入，聪明的人会赚钱，不够精明的人会亏钱。建造数据中心并不是人人都适合的。

You know, like many things, lots of people will enter. The smart will make money. The less sophisticated will will lose money. Building data centers is not for everyone.

你如何在建造数据中心时亏钱？

How will you lose money building data centers?

我认为，如果最好的公司能以每兆瓦800万美元的成本建造，而你却花了1200万或1400万，那就是亏钱的原因。亏钱是因为一开始你能否获得低成本电力？接着，一旦你获得了电力，你能否顺利拿到许可？这个过程耗时长吗？你是否真的能获得许可？这能让你快速获得审批。

I think if the best can build them for 8,000,000 a megawatt and you're spending 12 or 14, that's how you lose money. You lose money because it it begins as, can you get access to low cost power? It then continues to once you have access, can you get permitting? Does that take long, or do you have real access? It gets you fast permitting.

一旦它变成一个建设项目，你能否控制好成本？建成后，你能否留住优质的租户？房地产亏钱的方式多种多样，这里也没有免费的午餐。当你试图以不可思议的速度推进时，保持纪律、避免犯错会变得越来越难。

Once it becomes a construction project, can you keep control of your costs? Once it's finished, can you keep good tenants in it? The ways to lose money in property are large and many. There's no free lunch there either. When you are trying to go unbelievably quickly, it's harder and harder to be disciplined and not make mistakes.

在多大程度上实现完全横向整合很重要？我们知道，扎克伯格希望数据中心的建设规模极其庞大。那么，横向整合与纵向整合之间，究竟需要多大程度的平衡？

To what extent is it important to be fully horizontal? Know, we hear about Zuck wanting the data center build out to be just immense in terms of size and scale. To what extent does it need to be horizontal versus vertical?

目前还完全不清楚。迄今为止最成功的两家公司——OpenAI和Anthropic，都不是纵向整合的。OpenAI多年来完全依赖Azure作为基础设施，而Anthropic则使用了AWS和谷歌的组合。因此，到目前为止，它们都没有实现垂直整合。至于未来这是否是正确的策略，或者它们是否会再次做出同样的选择，尚不确定，但很明显，这并非唯一策略；已经存在许多成功模式，它们并不需要从芯片、系统、数据中心到软件，全程完全自研整合。

It is completely unclear. The most successful two companies to date, OpenAI and Anthropic, neither are vertical. OpenAI used Azure, a 100% infrastructure for years, and Anthropic has used a combination of AWS and and Google. And so neither are vertically integrated to date. Now whether that's the right strategy going forward, whether they'd make those decisions again, but it's clear that it's not the only strategy, that there are plenty of working models where you are not fully integrated from chip through system, data center, through software, all the way to the top.

抱歉再次引用，但刚做完这个分析确实很方便。乔纳森说，你肯定会要求OpenAI和Anthropic自行研发芯片，因为这样他们才能掌控自己的命运。

Again, sorry to cite it, but it's it's kind of handy having just done it. Jonathan said that you would definitely have OpenAI and Anthropic build out their own chips because then they would have control of their own destiny.

嗯。

Uh-huh.

你认为OpenAI和Anthropic会自行研发芯片，以减少对NVIDIA的依赖，就像他们今天这样吗？

Do you think OpenAI and Anthropic build their own chips so they don't have self reliance on NVIDIA in the way that they do today?

我认为，软件公司失败于芯片制造的历史由来已久，这样的例子非常多。OpenAI是否能成功，是否能通过与博通或其他更小、更具创新性的公司合作来实现，仍然是一个悬而未决的问题。像微软这样规模的公司都未能成功推出芯片，纵观FANG集团，也有许多尝试制造芯片却失败的案例。

I think that there is a long history of software companies failing to build chips. The list is is very large. Whether OpenAI can do it, whether they can do it through partnership with other vendors, with Broadcom, with smaller, more innovative companies is an open question. Companies at the size of Microsoft have been unable to deliver chips. There are plenty of examples as you look across the FANG group where chips were tried.

最成功的可能是谷歌，但他们已经投入了十年。现代软件的开发模式并不适合芯片制造的框架。每周的敏捷冲刺在长达两年的项目中效果不佳。'快速迭代，频繁出错'的思维在芯片领域行不通。芯片领域的思维方式是'三思而后行'，因为一个错误可能让你损失六个月和数千万美元。

Mean, probably the most successful is Google, and they're ten years in. Modern software does not fit well in a chip making framework. Weekly sprints don't work well on two year long projects. Move fast, break things often is not the way you think in the chip world. The way you think in the chip world is measure twice before you cut once because your bugs cost you six months and tens of millions of dollars.

这是一种完全不同的思维模式。而那些取得成功的案例，往往都是通过收购实现的。苹果通过收购PA Semi进入芯片领域，亚马逊通过收购Annapurna进入芯片领域，谷歌则从多家公司吸纳人才，组建了一个独立的业务单元，并由一位在公司内享有极高威望、拥有十年甚至十五年长远视野的人领导。

It's a very different mentality. And where there's been success, it has frequently been acquired. Apple got into the chip business through buying PA Semi. Amazon got into the chip business through acquiring Annapurna. Google acquired the talent from a collection of companies and then set it in a BU that was a side and under somebody who who had enormous respect in the organization and oars and had a ten or fifteen year view.

这些挑战在许多公司都存在。芯片制造是MBA的噩梦。你的分析会显示，英特尔在2000年至2010年间拥有世界顶尖的架构师和最先进的晶圆厂，却完全无法制造出可用的手机芯片。你会问自己：为什么？他们明明拥有所有必需的资源。

These are things that have been challenging in in many companies. Chip building is an MBA nightmare. Your analysis that says Intel had, you know, between 2000 and 02/2010, some of the world's leading architects, the world's leading fabs, and proved completely unable to build a working cell phone part. And you ask yourself why. They had everything they needed.

当你画出MBA的分析图表时，你会发现它完全难以理解。而答案其实很简单：这真的非常困难。极其微小的思维模式差异，却能带来天壤之别的结果。为什么所有行业领导者都错过了21世纪初最大的计算市场？为什么AMD也错过了？

And, you know, you do an MBA chart, and it's like, it's impenetrable. And the answer is this is really hard. Very small sort of mental model differences produced tremendously different results. How did every leader miss the largest compute market in the first part of the twenty first century? How did AMD miss it?

ARM又是如何赢得这一市场的？所有领导者都错过了。于是你开始想：也许在这些表象之下，有什么我尚未理解的深层原因，对吧？

How did how did how did ARM win it? All the leaders missed it. And then you say, alright. Maybe there's something in the guts here that I don't understand. Right?

你必须真正深入其中，这不能只靠PPT，不能只靠二维矩阵，也不能停留在咨询顾问的层面——它深植于少数能够制造这些芯片的人的DNA之中。我们Cerebras公司，真的很幸运。

You gotta really get in there, and it's not on a PowerPoint. It's not in a two by two. It's not at some sort of consultant level. It is deep in the DNA of the small number of people who can build these things. You know, we are lucky at Cerebras.

我们是全球前六或前八的团队之一，其他初创公司则不是。

We've got one of the top six or eight teams in the world. Other startups don't.

你认为十年后这个市场会是什么样子？我知道十年时间很长，尤其考虑到我们目前所处的阶段。但十年后，会不会是一个垄断市场，一家公司占据90%？会像CloudWef那样吗？

What does that market look like, do you think, in ten years' time? I know ten years is a huge amount of time given where we're at. But in ten years' time, is it a monopoly market with one taking 90%? Is it like CloudWef?

你指的是芯片市场的哪一部分？是在谈AI芯片，还是泛指硅芯片？或者是在谈

Which part of the chip market? Are talking about AI silicon or we're talking about silicon in general? Or we're talking about

我觉得是泛指硅芯片。

I I would say silicon in general.

当然不会。一家公司不可能占据90%。你知道，即使在英特尔最强劲的时候，他们在x86市场的手机领域占据主导，但在交换芯片市场几乎毫无份额。博通在交换硅芯片市场占据主导地位，而交换硅芯片是一种硅基处理器，但他们却在x86或其他计算形式中毫无份额。

Yeah. Absolutely not. One takes 90%. You know, even at the at Intel's strength, they had dominance in x 86 market share on the cell phone and almost no share in the switching market. Broadcom had dominance in the switching silicon market, which is a a form of processor in silicon, and no share in x 86 or, other forms of compute.

是的，不会全部集中到一两家公司手里。

Yeah. It it will not all accrue to one or two companies.

安德鲁，你觉得作为Cerebras公司，如今利润率有多重要？嗯，想想

Andrew, how do you think about the importance of margin today as a business as Cerebras? Well, think

我们之所以能够以更高的估值、从更好的投资者那里筹集到更多资金，是因为我们拥有他们。而其他正在寻找投资、寻求资金的人却有着负利润率。当你准备成为一家可信的上市公司时，人们确实会关注你的利润率。这是从一个想法转变为一家真正公司的重要部分。

the reason we were able to raise at a higher valuation and from better investors and and more money is because we had them. And others who were out looking for mark, looking for money had negative margins. As you prepare for being a credible public company, people do look at your margins. That's a really important part of of moving from being an idea to being a real company.

英伟达今天的利润率是多少？

What are NVIDIA's margins today?

非常惊人，是硬件公司历史上最高的利润率之一。

Extraordinary. Some of the the highest in history for a hardware company.

你怎么看待这一点？这是他们利用定价权的结果吗？

How do you think about that? Is that just pricing power which they are taking advantage of?

当然。简而言之，为什么AWS要自建训练芯片？因为它们想摆脱英伟达收取的78%的毛利率。在高端芯片上，这个数字可能高达85%。历史上，人们并不喜欢这种高利润率。

Absolutely. I mean, the the the short answer is why does it make sense for AWS to build a training part? Well, because they wanna get rid of the 78% gross margin that NVIDIA is charging. It might be on the high end chips, 85%. People don't like that historically.

过去，人们虽然心里清楚这一点，但通常会把它放在脑后。当英特尔陷入困境时，从四面八方跳出来趁机打击它的人多得惊人。这其实是多年积压的不满，当巨头跌倒时一次性爆发。我们一次又一次地看到这种情况。

Historically, people sort of put that in the back of the mind, and they remember it. When Intel stumbled, the number of people who came out of the the woodwork to kick them when they were down was extraordinary. And it was sort of years of pent up frustration when the giant stumbles. We've seen that again and again.

说到这个巨头跌倒和被取代的问题，你认为主权因素是否足以成为现有企业建立自身优势的充分理由？比如，欧洲有Mystral这样的模型提供商，主权是他们的核心策略。你相信这足以支撑其成为巨头吗？

Speaking of that giant stumbling and being built, do you think sovereignty will be a big enough reason why incumbents are built? You know, we have, like, Mystral, a model provider in in Europe, and the kind of sovereignty is their core play. Do you believe that is a sufficient enough core play to be a giant?

我认为，目前主权加上我们通过地球上最快的硬件提供推理能力，使得他们的产品——LeChat产品——极具吸引力。他们正在利用自身优势进行竞争。在欧洲，进行有趣工作的AI实验室实在太少了。他们四处观察，巧妙地利用了战略优势。你知道，他们想成为欧洲的领导者，这一招确实用得很到位。

Well, I think right now, sovereignty plus the fact that we deliver their inference through the fastest hardware on Earth makes their product, the LeChat product, really compelling. They're using their their advantages to compete. There were, in Europe, too few AI labs that are, doing interesting work. They sort of looked around and and sort of used strategic advantage. You know, we wanna be the Europe's leader, you know, played that card really well.

向他们致敬，随后他们以极高的估值完成了融资。

Hats off to them, and then they raised at a huge valuation.

最后谈一下地理因素。DeepSeek显然抓住了那个关键时机，也加剧了人们对中国的担忧。如今，你如何看待中国作为美国在AGI竞赛中的紧迫威胁？你是否反感将这场AI竞赛简单地表述为“中国 vs 美国”？你对此有何看法？

Final one just in terms of geography. DeepSeek obviously had that moment, and it kind of solidified the concerns around China. How do you feel about China today as a pressing concern towards The US in terms of the race towards AGI between the two? Do you hate the way that it's posited as, like, China versus The US, the AI race? How do you feel about that?

我认为我们当前的处境对双方都没有好处。军备竞赛在上世纪八九十年代也并未让美国或俄罗斯受益。我们都把本可用于基础设施、民生或其他领域的资金，浪费在了武器上。如果我们能找到和平合作的方式，双方都会更强大。在这些问题出现之前，我们非常熟悉滴滴、字节跳动、阿里和百度的那些人。

I think it benefits neither, the position we're in. The arms race certainly didn't help either The US or Russia in the late eighties and nineties. We we both spent money we wish on weapons, we wish would have been spent on infrastructure, people, or or other things. We will be much stronger if we can find ways to peacefully engage. Before these issues, we we knew the guys at Didi and ByteDance and and Ali and Baidu extremely well.

他们是一群才华横溢的工程师，致力于打造酷炫的产品。我认为，是我们的政府陷入了僵局，这才是问题所在。2019年，我们本有机会在中国达成一笔交易，但我选择放弃，因为早在美国商务部限制对华出口之前，我就认为这并非明智之举，我担心这项技术会被如何使用。如今的现实政治是，他们在制造无人机和机器人方面更胜一筹。

They're talented engineers trying to build cool stuff. I think our governments are at loggerheads, and that's a problem. We had a huge opportunity in 2019 to do a deal in China, and I decided to pass because I I didn't think it was the right thing to do long before Department of Commerce limited exports to China, I I didn't think it was right, and I was concerned about how the technology would be used. The realpolitik right now is that they're better at making drones. They're better at making robots.

他们的政府在人工智能领域有着极其激进的政策。多年来，他们一直为风险投资机构兜底，对吧？如果你在一家AI公司上亏了钱，政府会补偿你。想象一下，哈里。

Their government has an extraordinarily aggressive policy in AI. For years, they they backstopped their venture groups. Right? So if you lost money in an AI company, the government would make you whole. Imagine that, Harry.

想象一下，如果英国政府能为那些失败的AI公司承担部分损失，你能赚多少钱？我们美国真的还有很多工作要做。

Imagine how much money you could make if the government of the UK offset some of your losses from AI companies that didn't work out. You know, we have real work to do in The US.

你还有什么没做但必须做的事？你希望

What what do you have to do that you haven't done? What would you like to

看看中国，他们对电力基础设施进行了长期深入的思考。他们的政府体制使他们能够进行战略性规划。而我们分散的政府体制，导致电力基础设施呈现出一种拼凑的状态。即使联邦政府想支持你，地方层面的法规——比如城市和县一级的条例——也可能干扰项目，使项目延误数十亿美元。三星在德克萨斯州建了一座晶圆厂，就因为当地消防条例，不得不重新设计工厂。

see you've China thought long and hard about their power infrastructure. Their form of government allowed them to to plan strategically. Our decentralized form of government has left us with sort of a patchwork of power infrastructures. Even if the federal government wants to support you, there are local regulations, like at the at the city and county level of towns that can interfere with a project and and set a project back billions of dollars. I mean, Samsung built a fab in Texas, and they had to change the design of a fab because of a local fire ordinance.

美国政府花了数年时间推动在德克萨斯州部署数十亿美元的项目，结果一个地方消防条例就让项目推迟了八到十个月，还迫使他们重新设计工厂。这是我们不得不共同应对的问题。我们拥有世界顶尖的大学，历来吸引着全球人才。如果你看看我们行业的杰出CEO们——黄仁勋、哈肯、丽莎，再往下数，微软的萨提亚，他们本人或他们的父母都是从国外来的。我们必须认真对待这一点。

And the US government worked for years to get deployment of billions of dollars in Texas, and a local fire ordinance set them back eight months, ten months, and caused them to redesign the fab. That's a problem that we have to sort of collectively work through. We have the premier universities, historically drawn talent from around the world. If you look at the great CEOs in our industry, Jensen, Hakten, Lisa, I mean, you go down the list, Sundar, at Microsoft, they came and their parents came. We gotta take that really seriously.

你并不认同整个说法，实际上，很多人只是滥用H-1B签证，然后直接转用O-1签证，而O-1签证本来就已经有人在用了。H-1B的平均年薪是12万美元，这本来是好事，但人们却只是在用O-1签证。

You don't buy the whole well, actually, a load of people actually just abused h ones, and we'll just move to o ones, which people were using anyway. And the average salary for an h one was a $120,000. It's a good thing, and people would just use o ones.

我确信每个政府项目中都存在滥用现象。但H-1B的滥用比其他领域更多吗？我不这么认为。让最优秀的人才来到我们的大学，当他们受益于我们卓越的教育体系后，自然希望留下来贡献自己的力量——先通过J-1学生签证，再通过正规流程参与H-1B抽签，最终获得绿卡并成为公民，这正是我父母走过的路，对吧？

I am sure that in every government program, there's abuse. Was there more abuse in the h one than in in other areas? I don't think so. Having the best and the brightest come to your universities, and once they benefit from sort of our great institutions, to want to stay and contribute first with a J1, which is a student visa, and then enter the H1B lottery through the through the approved process to get a green card and become citizens, and this is how my parents did it. Right?

我认为这是吸引大量杰出人才来到美国的一种方式。

I think it's one way to bring an extraordinary amount of talented people to The US.

你还有什么其他想改变的吗？你提到了电力基础设施和相关的审批问题。

Is there anything else you'd change? You said the power infrastructure and the permitting around it.

电力基础设施最终集中在地方层面，而这里往往并不是宏大构想和战略得以很好整合的地方。我们一直让大学缺乏计算资源。如果你想在大学里做一些有趣的训练工作，很难获得足够的计算能力。我们根本就没有为这种情况做好准备。这是两个维度。

Power infrastructure ends up at at the local level, which is not necessarily where big ideas and sort of strategy is well knitted together. We have starved our universities of compute. If you wanna do interesting training work at a university, very hard to get enough compute to do that. We're just not set up for that. Those are two dimensions.

我认为特朗普政府在总体上放松了一些令人痛苦的监管措施，做得不错。

I I think the Trump administration's done a good job in generally relaxing some of the regulations that were painful.

安德鲁，我想和你来个快速问答。我会向你抛出一连串快速问题，你得立刻说出你的想法。这很难。

Andrew, I wanna do a quick fire with you. So I'm gonna pummel you with quick questions, and you gotta give me your immediate thoughts. That's hard

因为我只有长篇大论的回答，哈里。所以。

because I only have long answers, Harry. So

你相信什么，而你周围大多数人却不相信？

What do you believe that most around you disbelieve?

我们这一代人会看到中东实现和平。

We will have peace in The Middle East in our lifetimes.

你为什么这么认为？

Why do you believe that?

通过在阿联酋、沙特和卡塔尔的访问和停留，我认为温和立场带来的回报、经济收益是显而易见的。有人说过，我们现在太忙于建设，没空去仇恨。这在阿联酋表现得尤为明显——迪拜和阿联酋的崛起，正是以与以色列和平相处、采取更温和立场为代价换来的。我真心相信，这才是通往未来的道路。

Having visited and spent time now in in The UAE, in Saudi, in Qatar, I think the returns to moderation, the economic gains, someone said, we're we're too busy to hate right now. We're too busy building. Those have been writ large so clearly in The UAE with the rise of Dubai and The UAE in in return for making peace with Israel, in return for a more moderate position. I I really believe that that is the path to the future.

你们的收入中有多少来自阿联酋？

How much of your revenues are from The UAE?

我认为在2024财年，数据表明——我们还没公布其他数据，但占比很高，大约75%到80%。

I think in the s one it says, maybe the '24, we haven't published others, but a lot, 75, 80%.

我这样说完全是出于善意。难道你们不因此必须说些好话吗？比如，如果有人给了我这些……

I mean this in the nicest way. Do you not have to say nice things about it then? Like if someone's giving me That's

回答你的问题：不。我作为犹太人去那里做生意时，我们甚至还没有任何业务。我所发现的一切让我感到惊讶。我们在沙特的业务不多，但我认为他们正在取得巨大进展；目前我们在卡塔尔没有任何业务，但我相信他们也在大步前进。

the answer your question, no. I went there to do business as a Jewish guy before we had any business done. What I found surprised me. We don't do much in Saudi and I think they're making great strides and we don't do anything in Qatar right now. I think they're making great strides.

这可能受到我经常在阿布扎比、迪拜、利雅得和多哈停留的影响。当然，这些经历会影响我的看法，但我认为……

It may well sort of be coloured by the fact that I spend time in Abu Dhabi and I spend time in Dubai and I spend time in Riyadh and I spend time in Doha. Sure, it's coloured by those things But I I think

我认为你们的收入为什么集中在这些地方？是因为他们更愿意接纳创新、新关系和新供应商吗？

I think Why why are your revenues concentrated there? Is it just because they're, like, more willing to embrace innovation, new relationships, new vendors?

他们买了太多，消耗了大量资源。我给你的数据是截至2024年的。他们下了如此巨大的订单，耗尽了我们所有的制造产能。他们建设的速度如此惊人，以至于在2024年期间，消耗了我们大量的制造产能。

They they bought so much, they consumed and, know, the the data I gave you was through the '24. They placed such big orders, they consumed all our manufacturing capacity. They are building at such an extraordinary rate that through the '24, consumed an enormous amount of our manufacturing capacity.

他们的订单超出了你们的预期吗？

Did their orders exceed your expectations of their orders?

我认为他们的订单超出了所有人的预期。你即使在硅谷做销售二十、三十年，也未必见过五亿美元的订单。你现在去硅谷转一圈，跟几十家上市公司的销售副总裁或执行副总裁聊聊，他们都没见过这么大的订单。他们很有魄力，也行动得早。当我们刚开始和42公司合作时，根本没人听说过他们。

I think their orders exceeded everybody's expectations. You can be a professional salesperson in Silicon Valley for twenty or thirty years and not see a $500,000,000 order. You can go around the Valley right now and talk to VPs of sales or EVPs of sales at dozens of public companies who've never seen an order of that size. They were bold and they were early. And, you know, when we we started doing business with g forty two, nobody heard of them.

全世界的人都

Everybody in the world

听说过。你觉得这是你们在资源规划上的失误吗？我这么说很委婉了。

heard Do you think that was a resource planning mistake from you? I mean that nicely.

所有事情回头来看都是失误。如果我们没拿下他们，却为他们准备了资源，那才是资源规划的失误。我的意思是，哈里，我这行就是做重大押注、不断犯错的。

They're all mistakes in retrospect. If we hadn't won them and we had the resources for it, that would have been a resource planning mistake. I mean, I'm I'm in the business of of making big bets and making lots of mistakes, Harry.

你在Cerebras做过的最大一笔赌注，哪次没成功？

What's the biggest bet you've made with Cerebras that didn't work out?

我在这里的赌注一直都很不错。要实现晶圆级规模，解决一个以前没人解决过的问题。基因·阿姆达尔，我们领域的奠基人之一，IBM失败了，德州仪器也失败了，所有人都失败了。

My bets here have have been pretty good. To go to wafer scale, solve a problem that nobody had previously solved. Gene Amdahl, one of the fathers of our field, IBM failed. TI failed. Everybody's failed at this.

在2017年到2019年初大约十五个月的时间里，我们根本造不出一个。那时我们每月烧掉六百万到七百万美元。但我们坚持了下来，董事会也支持我们。结果是

We had a period of about fifteen months between about 2017 and early two thousand nineteen where we couldn't make one. And we were running a burn of about 6,000,000 a month, 7,000,000 a month. And we stayed with it and our board stayed with it. The result was

你们有没有预感它会成功？

Did you have signs that it would work?

是的，我们有预感。我们并没有像没头的苍蝇一样乱转。我们一直在进行工程调试，每次失败后，我们都会做完整的故障分析。每次我们找到原因并修复后，再试一次，还是不行；再试一次，还是不行。

Yeah, we did. And we weren't running around like chickens without our heads. We were going through engineering Each failure was, you know, we did a full FA, a failure analysis. Each time we fixed the cause, we did another one, didn't work. Did another one, didn't work.

但每次我们都变得好一点，越来越好，越来越好，直到最终解决了问题。第一个成功运行的设备，是在一个极小的实验室里诞生的，那原本是个改造成的会议室，为了散热，我们把窗户都打开了，还在墙上打了个洞，好把外部的冷却装置接进来，把冷气灌进去。当它终于运行起来时，创始人站在一起，盯着那个运转的机器，那场景无聊得就像看着油漆慢慢干透。

And each time we got little better. And we got better and better and better. Then we solved it. The first one that worked, the founders were in a tiny little lab that was a converted conference room that for cooling, we had the windows open and we'd blown a hole in the wall so we could get external chiller outside and pour it in. And when we had it running, the founder stood there together and stared at the box running, which is about as interesting as watching paint dry.

我们站在那里，简直不敢相信。我们刚刚解决了一个问题——七十五年来，我们行业里最聪明的人一直都没能解决的问题。我们站在那里看了将近半小时，这是我职业生涯中最辉煌的时刻之一。

And we stood there, and we couldn't believe it. It was like, we have just solved a problem that for seventy five years, the smartest people in our industry had been unable to solve. And we stood there for like half an hour and it was it was one of the highlights of of my career.

真酷。好吧，我服了。

Pretty cool. Alright. I'll give it to you.

好吧。那是一个巨大的赌注，非常大的赌注。

Alright. That that was a a big, big bet. That that was

说得通。每月六千七百万美元的烧钱速度，我想，好吧，合理。我几乎能想象到天使在歌唱，你的眼泪正往下流。

Fair enough. The $67,000,000 a month burn. I'm like, alright. Fair. I'm almost picturing like angels singing and there's like tears coming down your face.

你知道吗？那种感觉确实如此。这是我的联合创始人构想出来的，是他们的发明，是他们想法的实体体现。

You know what? It it felt like that. And it was, you know, the brainchild of my co founders. It was their invention. It was a physical manifestation of their ideas.

如今人们都在投资哪些领域，以至于会输得一无所有？

Where are people investing today where they will completely lose their shirt?

半导体行业不适合25岁的首席执行官，不管你有多聪明。之前在我们这个领域有过经验的人，回报是巨大的。你需要建立众多关系：你需要与晶圆厂建立关系，需要与EDA工具开发商建立关系。

The silicon industry is not a place for 25 year old CEOs, no matter how smart you are. The returns to having built parts before in what we do are enormous. The number of different relationships that are necessary. You need a relationship with the fab. You need a relationship with the EDA toolmaker.

你需要后端设计工程师，需要逻辑设计工程师，需要与IP供应商建立IP合作关系。这对年轻首席执行官来说是一条极其艰难的道路。另一方面，你在投资的许多市场中，年轻首席执行官表现得极为出色，尤其是当他们看起来像自己的客户时。

You need back end design engineers. You need logic design engineers. You need IP relationships with IP providers. It has been an extremely difficult road for young CEOs. On the other hand, young CEOs in many of the markets you invest in have done extraordinarily well, particularly where they look like their customer.

整个社交网络世界由年轻创始人打造的原因是，他们在为自己的朋友开发产品，这是一种优势。那些为其他学生、程序员开发工具的AI初创公司之所以年轻，是因为他们对目标客户的需求和痛点有着极其深刻的理解。而人们会在这里栽跟头的地方，就是认为只要在这个领域够聪明，就能做出好的芯片。但历史上并非如此，过去曾做过十五次或二十次这类项目的人，确实能获得实实在在的回报。

The reason that the entire social networking world was built by young founders is that they were building a product for their friends, and that is an advantage. The reason that AI startups who are doing tools for other students, for coders, are are young is because they understand the needs and demands of their target customer base extraordinarily well. That's an area where people are gonna get clobbered is to take a mentality that said, it's enough to be smart in this field to build a good chip. That that has historically not been the case, that there are real returns to having done 15 or 20 of these in the past.

人们在哪里的投资不足，而实际上那里更应该加大投入？

Where are people not investing enough where they should be investing more?

有一系列非常枯燥无味的事情，正在给整个行业带来巨大的痛苦：数据清洗、你的数据管道——这些事，你知道，没人会在领英上写自己是数据管道专家，但这些却是极其有价值的领域。没人会以数据清洗和分词的专家自居，但这些角色却极其重要。许多AI项目正是在这类环节上失败的，跟AI本身毫无关系，它们失败是因为数据一团糟。

There are this collection of extremely unsexy things that are causing tremendous pain across the industry. Data cleaning, your data pipeline, these things are, you know, nobody puts on their LinkedIn data pipeline expert, and yet these are some extraordinarily valuable cats. Nobody leads with a leader in the cleaning and tokenization of data, and these are extraordinarily important roles. Many AI projects fail on those fronts, have nothing to do with the AI. They fail because the data was a disaster.

它们失败是因为除了AI之外，其他所有部分都失败了。这是一个被严重低估和投入不足的领域。

They fail because everything except the AI was a failure. That's an area that that profoundly underinvested in.

你觉得数据提供市场会是什么样子？我们看到Surge、McCall、Invisible、Turing、Handshake正越来越多地进入这个领域。五年后这个市场会是什么样？它们的年经常性收入都超过了1亿美元。到底会发生什么，安德鲁？

What do you think the data provision market looks like? We see Surge, McCall, Invisible, Turing, Handshake moving into it more and more. What does that market look like in five years? All of them are above a 100,000,000 ARR. What the fuck happens there, Andrew?

我不知道。这是一个非常奇特的市场。

I don't know. That is a very curious market.

为什么说它奇特？

Why is it curious?

嗯，Scale算是最早开创这个领域的。Turing原本在完全不同的市场，后来转型进入并取得了巨大成功。还有其他一些公司也聚集在这里。我认为，提供经过增值、标注或评估的数据显然非常重要。至于这种能力是否持久，或者机器是否能像人一样做得同样好，这个问题目前还很难回答。

Well, scale sort of pioneered it. Turing was in a different market completely and pivoted to it and found great success in it. There are these collections of others. I think clearly the provisioning of value added, tagged, or evaluated data is really important. Whether it's durable, whether we get machines that do it every bit as well as people is a question that that's really hard to answer right now.

这就是为什么这很有趣。显然，现在它很重要。问题是，三年后它还会明显重要吗？这个问题我不知道答案。也许吧，你知道的，两种可能性都有。

That's why it's curious. It's that clearly, it's important now. And the question is, will it clearly be important in three years? That's a question I don't know the answer to. Maybe I'm you know, it could go either way.

关于人工智能在未来五年如何重塑未来，你最疯狂的预测是什么？例如，约翰曾说：我认为人工智能将造成巨大的劳动力短缺，会为这么多人创造如此多的工作，以至于我们会面临严重的劳动力短缺。

What's your craziest prediction in terms of how AI reshapes the future in five years? For example, John had said, hey. I think AI will create massive labor shortages. It will create so many jobs for so many people that we will have massive labor shortages.

五年内？绝对错误。我认为经济动荡不会在很短的时间内得到解决。十五年后也许确实如此，但在三到五年的时间框架内，我绝不相信会这样。人工智能的采用或其在经济中的扩散，会一点一点地渗透进来。

In five years. Absolutely wrong. I think economic dislocation isn't resolved in very short periods of time. That might be true in fifteen years, but in the three to five year time frame, I certainly don't believe that will be the case. The adoption of AI or the diffusion of AI into the economy, it will nibble its way in.

我们来问一个问题：AlphaFold 解决了化学领域最困难的问题之一，一个长期悬而未决的问题。请说出一种由此诞生的药物。一个都没有。现在我相信将来会有，但 AlphaFold 已经有四年了。

Let's ask this question. AlphaFold solved one of the hardest problems in chemistry, a problem that had been open for years. Name a drug that's resulted from it. Not one. Now I believe there will be, but AlphaFall is four years old now.

这是一项重大突破，其发明者因此获得了诺贝尔奖。那药物在哪里？给我看看它的医疗效益。它终将实现，它会变得重要。

This was a massive breakthrough for which the inventors were given Nobel Prizes. Where's the drug? Show me the the medical benefit. It will get there. It will be important.

模型的后续发展将产生根本性影响，但那些因它而被取代的X射线晶体学家在哪里？他们过去只是在物理上做这些工作，但他们并没有失业。事实上，对他们的需求反而增加了。我认为这将对我们的儿童教育方式产生非常有趣的影响，这正是我的兴趣所在。

Continuations of the model will have fundamental impact, but where are the X-ray crystallographers who are displaced because of it? That was what X-ray crystallographers were doing only physically. They're not out of work. In fact, there's more demand for them. I think that it will have really interesting effects on the way we educate children, and that's an interest of mine.

你知道，自亚历山大大帝由亚里士多德辅导以来，我们教育孩子的方式就差不多没变过，对吧？那时候就是找一个聪明的年长者，站在你身后，告诉你该做什么，你阅读，和他们讨论，他们批改你的作业。这种教学方式几乎从未改变过。也许YouTube稍微改变了一点，因为你有了不同的老师。

You know, we we've sort of been educating children the same way since Alexander the Great was tutored by Aristotle. Right? And and it was sort of like, get a smart person, they're older, they stand behind you, they tell you what to do, read, you read it, you talk to them about it, they correct your paper. This form of instruction has been sort of unchanged. Maybe YouTube changed it a little bit in that you had different instructors.

但想象一个系统，比如你在数学作业中犯了一组错误，结果并不是通过纸质作业来评判的。哦，看，你在这里做错了。但系统会将你犯的错误类型与成千上万其他学生犯过的错误类型进行对比，并指出：对于这一类错误，我们发现以下练习册能极其有效地弥补他们思维中的这一漏洞。

But imagine a system where, for example, you made a set of mistakes in your math work. The result wasn't read on a paper. Oh, look. You you got it wrong here. But they compared the type of mistakes you made to the type of mistakes thousands of other students made and said that for this group of mistakes, we have found that the following workbook is extremely effective at remedying this hole in their thinking.

想象一下这种情况。目前没人这么做。没人根据学生所犯错误的类型来差异化和调整教学方式，而这恰恰是你应该做的。因此，我认为我们的教学方式将发生巨大变化，公司对“初级岗位”的定义也将发生巨大变化。

Imagine that. Nobody does it. Nobody differentiates and modifies the training based on the type of error the students are making, and that's exactly what you ought to do. So I I think the way we teach will change a great deal. What it means to be entry level in a company will change a great deal.

因为在咨询公司和投资银行，所谓“初级”通常意味着做低价值的工作，尤其是精通电子表格和撰写他人研究的摘要。而AI在这些方面会做得更好，这将发生巨大改变。我一直觉得，从二十二岁到二十四岁，这么宝贵的几年时间，却用来做这些事，实在是一种浪费——你可是从顶尖学校毕业的。

Because what entry level has generally meant in consulting firms, at investment banks, has been doing shit work, in particular being really good at spreadsheets and writing summaries of other people's research. AI will be better at that. That will change a great deal. I mean, always thought that was a terrible way to spend an extraordinary year twenty two to twenty four years. You are coming out of top schools.

你有太多东西可以学习，有太多贡献可以做出。但花大量时间做电子表格，我认为这些学生、这些年轻人完全有能力进行更有成效的思考，能学到更多，从而在未来几年变得远为高效。我认为AI将改变这一切。

There's so much to be learned. There's so much you can contribute. But to do huge hours doing spreadsheets, I think there's vastly more productive thinking those students, those young people are capable of, and more learning that they can do, and therefore be vastly more productive in the following years. And I think AI will will change that.

最后一个问题是给你，安德烈。如果你知道你不会失败，或者根本不可能失败，你会做什么？我

Final one for you, Andre. What would you do if you knew you wouldn't fail or couldn't fail? I I

我想我从未这样想过。我反而从另一个角度看：每一天，我都在与歌利亚作战。我们每卖出一美元的产品，都是在与一个默认归属NVIDIA的潜在市场竞争——如果我们不努力、不思考、不创新，不比别人优秀十倍，这笔钱就会落入NVIDIA手中。在我与NVIDIA竞争之前，我曾与思科竞争。十五年前，我们每卖出一美元，如果产品不够好、不够积极、不够有创意，市场份额就会被市场领导者夺走。

guess I don't I've never thought of that. I I look at it the other way is that every day I go to battle with Goliath. Every day, every dollar we sell is a dollar that if we didn't work at it, if we didn't think, if we didn't invent, if we weren't 10 times better, would default to Nvidia. And before I competed with Nvidia, I competed with Cisco. Fifteen years and every dollar that we sold there, if we didn't build better product, if we weren't more aggressive, if we weren't more creative, would have defaulted to the market share leader.

在我看来，我的职业生涯中，最让我自豪的是，每天都要面对最刁钻的投手——用板球来比喻，就是最可怕的旋转投球手，最可怕的快速投球手。我享受这种挑战。在安静的时刻，我会回望自己：每天都在工作中，以所有劣势对抗世界上最顶尖的对手，而我热爱这种感觉。更棒的是，除了少数你提到的、很早就站出来支持我的人之外，几乎所有人都在押注我会失败。这就是我选择的生活，我热爱的职业。

And I think for me, in my career, I take great pride in facing every day the most wicked curve, the curveball pitcher, for your cricket example, you know, the the the scariest spin bowler, the scariest speed bowler. I enjoy that. That that is something that in a quiet moment, you sit back and say that that I'm competing with every disadvantage against the absolute best in the world every every single day at work, and I love that. And what's more, everybody's betting against me except a very small group of people who you named who stood up early on and said, you know, maybe he can beat them. That's the life I've I've chosen and the the career I love.

这个结尾也太棒了。你知道，我当然做过很多节目，有些结局我们觉得：天啊，不能这样收尾，这太压抑了。但这个结尾简直太棒了。

That is such a good end as well. You know, I do, obviously, many shows. There are some ends where we're like, oh, we can't end like that. That is like a depressing end. That's a fantastic end.

我真的很感激你，真的非常感谢你。每当我搞不懂到底发生了什么，我第一个就想找你。今天你这么耐心地给我解释，真的太谢谢你了，兄弟。

I so appreciate that. I so appreciate you. You know you're my go to when I'm trying to understand what the fuck's going on. And so thank you so much for explaining this to me today, dude.

我很乐意参与讨论。顺便说一句，关于你的领英帖子，我这一生读过的东西里，只有两样让我觉得真正理解了创业是什么。第一是本·霍洛维茨的书《创业维艰》，第二就是你的推文和你的领英帖子，它们完全贴合我生活的感受。

Well, I'm happy to to jump on. My view of your LinkedIn post, by the way, is there there are two, exactly two things I've read in my life that feel like they understand what entrepreneurship is. And the first is Ben Horowitz's book, The Hard Thing About Hard Things. And the other is your tweets and your your your LinkedIn posts. They have the feel of what my life is.

那种认为你只要每周工作三十八小时、保持工作生活平衡，就能取得伟大成就、打造非凡事业的想法，对我来说简直难以置信。这在生活的任何领域都不成立，而你愿意站出来直言：不，伙计们，事情不是这样的。你依然可以拥有精彩的人生。

This notion that that somehow you can achieve greatness, you can build something extraordinary by by working thirty eight hours a week and and having work life balance. That is mind boggling to me. It's not true in any any part of life, and your willingness to jump in and say, no. That's not how it's done, guys. You can have a great life.

你可以做很多很棒的事，幸福的路有无数条。但把一件从无到有的东西打造成卓越成就的道路，绝不是兼职工作，也不是每周三十、四十或五十小时。它意味着你醒着的每一分钟都要投入。当然，这也会有代价。

You can do many really good things, and there are lots of paths to happiness. But the path to build something new out of nothing and make it great isn't part time work. It isn't thirty, forty, fifty hours a week. It's it's every waking minute. And, of course, there are costs.

你想想世界级运动员，比如听罗纳尔多怎么说的。他连吃进体内的每一样东西都小心翼翼，每天训练，连休息都认真对待，对吧？

You know, it's probably true for world class athletes too if you listen to what Ronaldo talks about. I mean, he worries about everything he puts in his body. He trains every single day. They work on rest. Right?

休息不是放任不管，休息是你刻意去经营的事，目的是让身体更快恢复。更快！这可不是每周三十或四十小时的事，这些人是世界上在各自领域最顶尖的。

Rest isn't rest. Rest is something you work on so your body rejuvenates faster. Faster. This isn't thirty hours a week or forty hours. These guys are the best in the world at everything.

但在我们深入探讨节目的内容之前，我非常喜欢看到团队齐心协力让这个节目成真。我不喜欢的是试图追踪我们在数十个平台、产品和工具上开展的所有信息、数据和项目。这就是我们使用Coda的原因——这是一个一体化的协作空间，已帮助全球五万支团队达成共识。Coda结合了文档的灵活性与电子表格的结构性，促进更深入的团队协作和更快的创意产出。而其即插即用的AI解决方案——Coda Brain的智能功能，更是改变了游戏规则。

But before we dive into the show's stay, I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data, and the projects that we're working on across dozens of platforms, products, and tools. That's why we use Coda, the all in one collaborative workspace that's helped 50,000 teams all over the world get on the same page. Offering the flexibility of docs with the structure of spread sheets, Coda facilitates deeper teamwork and quicker creativity. And their turnkey AI solution, the intelligence of Coda Brain, is a game changer.

由Grammarly驱动，Coda正进入创新与扩展的新阶段，旨在重新定义人工智能时代的生产力。无论你是希望在保持敏捷的同时整理混乱的初创公司，还是寻求更好协同的大型企业，Coda都能契合你的工作方式。其无缝的工作空间可连接数百个你喜爱的工具，包括Salesforce、Jira、Asana和Figma，帮助你的团队转变工作流程，更快地完成更多任务。立即前往 coda.i0/20vc，即可免费获得初创团队计划六个月的折扣。这就是 coda.c0da,.i0/20vc，免费获得六个月团队计划折扣，coda.i0/20vc。

Powered by Grammarly, Coda is entering a new phase of innovation and expansion aiming to redefine productivity for the AI era. Whether you're a startup looking to organize the chaos while staying nimble or an enterprise organization looking for better alignment, Coda matches your working style. Its seamless workspace connects to hundreds of your favorite tools, including Salesforce, Jira, Asana, and Figma, helping your teams transform their rituals and do more faster. Head over to coda.i0/20vc right now and get six months off the team plan for startups for free. That's coda, c0da,.i0/20vc, and get six months off the team plan for free, coda.i0/20vc.

说到信任，如今客户对信任的需求比以往任何时候都更迫切，这就是为什么超过一万家全球企业信赖Vanta。Vanta利用智能AI自动化高达90%的热门合规标准工作，如SOC 2、ISO 27001等，集中化工作流程、管理风险，让你在数周内而非数月内做好审计准备。这样你就能停止追逐文书工作，转而专注于签单。一项新的IDC报告发现，Vanta客户每年可获得53.5万美元的收益。这简直不可思议。

And talking about trust, today customers expect it faster than ever, and that's why over 10,000 global companies trust Vanta. Vanta automates up to 90% of the work for in demand compliance standards like SOC two, ISO 27,001, and more using smart AI to centralize workflows, manage risk and get you audit ready in weeks, not months. So you can stop chasing paperwork and start closing deals. And a new IDC report found that Vanta customers achieve $535,000 per year in benefits. That's insane.

而且该平台在三个月内就能收回成本。我之前根本不知道这些。无论你是快速发展还是刚刚起步，Vanta都会将你与可信赖的审计师和专家连接起来，提供支持，帮助你赢得客户的信任。在 vanta.com/20vc 注册，首年立减1000美元。这就是 vanta.com/20vc。

And the platform pays for itself in three months. I had no idea about these. Whether you're growing fast or just getting started, Vanta connects you with trusted auditors and experts, support to help you build trust with customers. Get a thousand dollars off your first year at vanta.com/20vc. That's vanta.com/20vc.

您已到达目的地。

You have now arrived at your destination.