章节 1：资本迷局——OpenAI、Oracle与Nvidia的三角交易

📝 本节摘要：

本章揭开了围绕 OpenAI、Nvidia（英伟达）与 Oracle（甲骨文）之间复杂交易的迷雾。Dylan Patel 指出，这并非外界误传的“无限金钱漏洞”，而是 OpenAI 为应对万亿级算力竞赛所设计的生存策略。由于缺乏科技巨头（如 Google、Meta）那样的巨额现金流与资产负债表，OpenAI 必须寻找盟友（如 Oracle）来预付昂贵的基础设施资本支出（Capex）。同时，Nvidia 通过回购股权等方式参与其中，确保存储芯片的高价预售。这是一场关于现金流、算力储备与未来赌注的高风险博弈。

[原文] [Host]: i was going to lay out this idea of going through the past present and future of compute as like the big big idea for our conversation but since it just happened I don't think you've heard you talk about it anywhere i'd love to start by asking about this whole OpenAI Nvidia thing which uh sounds exciting seems vague not really sure what's going on and maybe you could explain it to us as you see it and what the strategic implications are of the big announcement

[译文] [Host]: 我原本打算以梳理计算（compute）的过去、现在和未来作为我们对话的宏大主旨，但既然这件事刚刚发生，而且我还没听你在其他地方谈论过，我想先问问关于 OpenAI 和 Nvidia 的整件事情。这件事听起来很激动人心，但似乎又很模糊，我不确定到底发生了什么。也许你可以向我们解释一下你眼中的情况，以及这一重大声明的战略含义是什么。

[原文] [Dylan Patel]: all right so I think it's I think it's very very simple right you've got OpenAI paying Oracle lots of money you've got Oracle paying Nvidia lots of money you've got Nvidia paying open lots of money meme we've got we've got the infinite money glitch here

[译文] [Dylan Patel]: 好的，所以我认为这非常非常简单，对吧。你有 OpenAI 支付给 Oracle 大笔资金，你有 Oracle 支付给 Nvidia 大笔资金，你有 Nvidia 支付给 OpenAI 大笔资金。这就成了一个梗，好像我们这里有个“无限金钱漏洞”（infinite money glitch）。

[原文] [Dylan Patel]: uh no no no i that's not actually what's happening right what's really happening is open air has an insatiable demand for compute um the compute precedes the buildup of business you have to have the cluster before you can rent it out for inference right or rather run models on it for inference you have to have the cluster to train the model that's good enough that it unlocks new use cases which then can be adopted and there's an adoption curve there for any new use case so you have to have all these things like sequenced

[译文] [Dylan Patel]: 呃，不不不，实际上并不是那样。真正发生的是 OpenAI 对算力有着无法满足的需求。算力的储备先于业务的建立。你必须先拥有集群，然后才能将其租出去进行推理（inference），或者说在其上运行模型进行推理。你必须拥有集群来训练足够好的模型，从而解锁新的用例，这些用例随后才能被采用。任何新用例都有一个采用曲线，所以你必须按顺序准备好所有这些东西。

[原文] [Dylan Patel]: given this is a game of the richest people in the world or rather the biggest tech giants in the world right it's Zuck it's Google you know Larry and Sergey or Sergey is like constantly in the business now again right it's all the biggest people in the world it's Elon right there's there's very much a risk of OpenAI being too small to matter right you know which is crazy to say because they've got 800 million users but like where's the revenue where's the compute they could easily get swamped in terms of having of of how much compute they have yeah if they don't move fast enough and if they don't have the most compute or like among the most compute they will get beaten

[译文] [Dylan Patel]: 考虑到这是一场世界上最富有的人——或者说世界上最大的科技巨头——的游戏，对吧，有扎克伯格（Zuck），有 Google——你知道 Larry 和 Sergey，Sergey 现在又经常参与业务了，对吧。这全是世界上最有权势的人，还有 Elon。OpenAI 很有可能因为体量太小而无关紧要，对吧。这么说很疯狂，因为他们有 8 亿用户，但是收入在哪里？算力在哪里？如果他们行动不够快，或者如果不拥有最多——或者哪怕是名列前茅——的算力，他们很容易在算力规模上被淹没并被击败。

[原文] [Dylan Patel]: the magic of OpenAI was that they just spent way more compute on a single model run on GP3 and four and they had the foresight and the vision and the execution yeah but they made that bet and they were able to secure it and at the time it was like meh right it was a few hundred million whatever right you know that's a ton of money but like now it's sort of like well Mark Zuckerberg sees how much compute he's going to have to get even though he has this insane cash flow that he's like "Oh wait i need to go sign a deal with Apollo for you know $30 billion on this data center right in Louisiana this mega data center I'm going to build." It's like "Wait why didn't you just fund this with cash flows you have so much cash flow." It's like "Because my plans that's just the physical data center now what am I going to put in it?" Is like so much money the amount of capital that people are going to have and are dumping into this is is insane right

[译文] [Dylan Patel]: OpenAI 的魔力在于他们在 GPT-3 和 GPT-4 的单个模型运行上投入了比别人多得多的算力，而且他们有远见、愿景和执行力。是的，他们下了那个赌注并且成功了。在当时，这看起来没什么大不了的，对吧，也就几亿美元什么的。虽然那也是一大笔钱，但现在的情况是，哪怕像马克·扎克伯格这样拥有疯狂现金流的人，在看到需要多少算力时也会想：“哦，等等，我需要去和阿波罗（Apollo）签个协议，搞个 300 亿美元来建路易斯安那州的这个大型数据中心。”你会问：“等等，你为什么不直接用现金流资助这个？你有那么多现金流。”回答是：“因为我的计划里那还仅仅是物理数据中心，我还得往里面填设备呢？”这就需要更多的钱。人们拥有并正在投入其中的资本数额是疯狂的。

[原文] [Dylan Patel]: google was slow to wake up and then you know they were slow to pivot their data center operations they were slow to do everything and so there while they could have way more compute than anyone by a humongous degree they haven't been able to deploy as fast so so open is like still been on the curve of and then they have like you know how much they allocate to search and you know generative search is not really necessarily competing with open AI right it's it's the mega models so if you have this like tremendous vision of what's going to happen with AI you know that it takes a ton of compute to build them you know pretty much the amount of compute you could dedicate to these models is limitless and they will get better

[译文] [Dylan Patel]: Google 醒悟得很慢，然后你也知道，他们在调整数据中心运营方面也很慢，做所有事情都很慢。所以，尽管他们本可以拥有比任何人多得多的算力，但他们没能部署得那么快。所以 OpenAI 仍然处在（领先的）曲线上。而且 Google 还要分配资源给搜索，你知道生成式搜索并不一定是在与 OpenAI 竞争，真正的战场是巨型模型（mega models）。如果你对 AI 的未来有宏大的愿景，你就知道构建它们需要大量的算力，你也知道你可以投入到这些模型中的算力几乎是无限的，而且模型会变得更好。

[原文] [Dylan Patel]: now it's a log log scale right i.e you need 10x more compute to get to the next tier of performance you might think of it as diminishing returns but what if the next tier of performance is like you know you know a six-year-old versus a 16-year-old like child labor is like quite like effective versus a six-year-old you can't get to do much right you know and this is not exactly the way to think of AI but this is the conundrum that OpenAI is in right um they have to get more compute than anyone or at least among there they have to race with the giants these giants are trillion dollar businesses so how does OpenAI get there well it's it's partnering with Microsoft well that soured some right um it's partnering with Oracle well Oracle can can do a lot but Oracle doesn't even have a balance sheet like like Google and Microsoft and and Amazon and you know etc right

[译文] [Dylan Patel]: 现在这是一个双对数（log-log）比例，也就是说你需要 10 倍的算力才能达到下一个性能层级。你可能会认为这是收益递减，但如果下一个性能层级的差距就像是——你知道——一个 6 岁孩子和一个 16 岁少年的差距呢？比如童工虽然……相对于 6 岁孩子你干不了什么活，对吧。这不完全是思考 AI 的方式，但这就是 OpenAI 面临的难题。他们必须获得比任何人都多的算力，或者至少要在前列，他们必须与巨头赛跑。这些巨头都是万亿美元级别的企业。那么 OpenAI 如何达到目标呢？通过与微软合作，但这关系有点变味了，对吧。通过与 Oracle 合作，Oracle 能做很多，但 Oracle 甚至没有像 Google、微软和亚马逊那样的资产负债表。

[原文] [Dylan Patel]: oracle can be part of it but it's OpenAI needs allies right they need they need people to effectively spend the the capex ahead of the curve and trust that they'll be able to pay the rental income because that's what it is at the end of the day openai is committing to fiveyear deals these five-year deals cost X amount of money it's 10 to 15 billion dollars per gigawatt of data center capacity that you pay a year and then that 10 to 15 billion dollars of gig for a gigawatt of data center capacity you're paying that for five years okay that's 50 to 75 billion of of cash that goes out the door to OpenAI for one gigawatt of capacity

[译文] [Dylan Patel]: Oracle 可以成为其中的一部分，但 OpenAI 需要盟友，对吧。他们需要有人能有效地在曲线之前花费资本支出（Capex），并相信 OpenAI 能够支付租金收入，因为这归根结底就是租赁。OpenAI 正在承诺签署五年期的协议。这些五年期协议花费数额巨大。每吉瓦（gigawatt）数据中心容量每年的费用是 100 到 150 亿美元。然后这一吉瓦的数据中心容量你要付五年。好吧，这就意味着为了获得一吉瓦的容量，OpenAI 要支付 500 到 750 亿美元的现金。

[原文] [Dylan Patel]: and you talk about what Sam's saying is like hey I need I need 10 gigawatts i need more than 10 gigawatts right then you end up with this like really challenging aspect of like how do you pay for that and hey that's only the rental price if I were to actually do the capex it's or if I were to like you know because it's frontloaded right it it becomes who is the balance sheet for this that's the reason these deals are coming about and so Oracle is making a massive bet right Larry you know hey he's getting good margin off of it but he's making a massive bet that this capex that he's going to pay for OpenAI will actually be paid cuz you know he signed a $300 billion deal with OpenAI it's like where's that going to come from yeah is like you you your revenue is like 15 billion ARR this month maybe right on a run rate basis it'll get to 20 by the end of the year uh pretty pretty clearly maybe it's like 16 now but how do you pay $300 billion of revenue

[译文] [Dylan Patel]: 而你谈到 Sam（OpenAI CEO）所说的是：“嘿，我需要 10 吉瓦，我需要超过 10 吉瓦。”那么你就会面临一个非常具有挑战性的方面：你如何支付这笔钱？而且嘿，这还只是租赁价格。如果我要实际承担资本支出（Capex），或者因为它是前置投入的，这就变成了“谁来提供资产负债表”的问题。这就是这些交易产生的原因。所以 Oracle 正在下一个巨大的赌注，对吧。Larry（Oracle 创始人），嘿，他从中获得了不错的利润率，但他正在下一个巨大的赌注，赌他为 OpenAI 支付的这笔资本支出实际上能得到偿还。因为你知道，他与 OpenAI 签署了一项 3000 亿美元的协议。这就好像，这笔钱从哪儿来？你们现在的年化收入（ARR）可能也就是 150 亿美元，也许到年底能达到 200 亿，现在可能大概是 160 亿，但你如何支付 3000 亿美元的账单？

[原文] [Dylan Patel]: Nvidia's kind of got the same conundrum right it's like well Google and Amazon are doing these these deals whether it's uh to two other vendors for TPUs or for tranium whether it's anthropic or others they're trying to court openai uh they're trying to court other companies how do I get into this game right okay fine I can rely on Microsoft somewhat I can rely on Oracle somewhat but at the end of the day GPUs if I want GPUs to be king part of it is just like my chip is the best but part of it is also who's going to pay the capex upfront

[译文] [Dylan Patel]: Nvidia 也有同样的难题，对吧。就像是，Google 和亚马逊正在做这些交易，无论是为了 TPU 还是 Trainium 芯片与其他供应商合作，无论对象是 Anthropic 还是其他人。他们在试图拉拢 OpenAI，试图拉拢其他公司。我（Nvidia）如何进入这个游戏？好吧，我可以某种程度上依赖微软，某种程度上依赖 Oracle。但在一天结束时，如果我想让 GPU 成为王者，部分原因是因为我的芯片是最好的，但部分原因也在于谁来预付资本支出（Capex）。

[原文] [Dylan Patel]: Google and Amazon will pay the capex up front if it's for TPUs or right they won't pay the capex up front necessarily for that same capacity of GPUs so you've got this like challenging aspect and so that's where this this Nvidia and open I deal comes from

[译文] [Dylan Patel]: 如果是为了 TPU，Google 和亚马逊会预付资本支出，但如果是为了同样容量的 GPU，他们就不一定会预付资本支出。所以你面临着这样一个挑战，而这就是 Nvidia 和 OpenAI 这笔交易的由来。

[原文] [Dylan Patel]: sort of this draws back to the OpenAI Nvidia deal because I think most people in the market don't quite get it right they're like "Oh this is just like round tripping." It is to some extent right if OpenAI builds a gigawatt of capac they they they agreed 10 gawatts of capacity Nvidia will do hundred billion dollars of equity investment into OpenAI in the form of cash right and and and Nvidia gets returned capital the first chunk of the deal in the press release is one gawatt $10 billion right so pretty straight line but 10 g one a g one gigawatt to build as we established earlier is like $50 billion so Nvidia is paying 10 billion open still has to come up with other 40 somehow

[译文] [Dylan Patel]: 这又回到了 OpenAI 和 Nvidia 的交易上，因为我认为市场上大多数人并没有完全理解它，对吧。他们会说：“哦，这就像是‘循环交易’（round tripping）。”在某种程度上确实是，对吧。如果 OpenAI 建设 1 吉瓦的容量——他们同意了 10 吉瓦的容量——Nvidia 将以现金形式向 OpenAI 进行 1000 亿美元的股权投资，对吧，然后 Nvidia 会收回资本。新闻稿中交易的第一部分是 1 吉瓦对应 100 亿美元，非常直接。但我们要建立 1 吉瓦——正如我们之前确定的——大约需要 500 亿美元。所以 Nvidia 支付了 100 亿，OpenAI 仍然必须想办法凑齐剩下的 400 亿。

[原文] [Dylan Patel]: the nice thing for Nvidia is they sell you of that 50 billion they capture maybe 35 billion of that is capex that goes directly to Nvidia so year zero openai its partner spends $50 billion on the data center the timing is not exactly that they spend $50 billion on the data center 35 goes it to Nvidia nvidia's gross margin is 75% you know again I'm going to make it simple numbers let's say it's 10 and 40 because 10 billion COGS 40 billion revenue $30 billion of gross profit um if we fix the numbers it's effectively like half their gross profit from that deal is going directly to uh to open in the form of an equity investment the 25% that's COGS is staying uh on in you know Nvidia is paying for that and then they keep the other half of the gross profit on their balance sheet or do buybacks whatever they want to do with it

[译文] [Dylan Patel]: 对 Nvidia 来说，好消息是他们把东西卖给你，在那 500 亿中，他们可能获得了 350 亿，这是直接流向 Nvidia 的资本支出。所以在第零年，OpenAI 的合作伙伴在数据中心上花费 500 亿美元——时间点不完全是那样——他们花费 500 亿美元，其中 350 亿流向了 Nvidia。Nvidia 的毛利率是 75%。再次为了简化数字，假设是 100 亿和 400 亿，即 100 亿是销售成本（COGS），400 亿是收入，那就是 300 亿的毛利润。如果我们修正数字，实际上就像是这笔交易的一半毛利润以股权投资的形式直接流向了 OpenAI。那 25% 的销售成本（COGS）留在了……你知道 Nvidia 支付了这部分成本，然后他们将另一半毛利润保留在资产负债表上，或者进行回购，或者做任何他们想做的事。

[原文] [Dylan Patel]: so Nvidia is not necessarily like they are like roundtpping some of this um but open what effectively is happening is openi gets the opportunity to pay for a big chunk of it in equity y and nvidia's lowering their prices without lowering their prices effectively but and they're getting owners ersship of a company who very likely could just like and and but Nvidia comes out great because they're they're they're getting the capex dollars up front yeah right so all they're really doing is they're saying half of my money that's in this sure it does make its way to me somehow but in reality I still made half of that gross profit and the other half is is is equity in a company that may or may not be worth something a company that may or may not be able to pay hundreds of billions of dollars of compute deals that they've signed right in which case they'd be bankrupt right so this is this is like the the mechanics of that deal

[译文] [Dylan Patel]: 所以 Nvidia 不一定是在完全搞“循环交易”，但实际上发生的是 OpenAI 获得了用股权支付一大笔费用的机会。而 Nvidia 实际上是在没有降价的情况下“降低”了价格，同时他们获得了一家公司的所有权。Nvidia 的结果很好，因为他们预先获得了资本支出的美元，对吧。所以他们真正做的只是在说：“我这里面的一半钱，当然它以某种方式流回了我这里，但实际上我仍然赚到了那一半的毛利润，而另一半变成了对一家公司的股权。”这家公司可能值钱也可能不值钱，这家公司可能付得起也可能付不起他们签署的数千亿美元算力协议，如果付不起他们就会破产，对吧。这就是那笔交易的机制。

章节 2：算力赌注——缩放定律与智能的价值阶跃

📝 本节摘要：

在本章中，Dylan 深入探讨了支撑万亿级 AI 投资的底层逻辑——“缩放定律”（Scaling Laws）。针对“收益递减”的质疑，他提出了一个生动的比喻：虽然投入增加了 10 倍，但如果得到的智能从“6 岁儿童”进化到了“16 岁少年”甚至“30 岁的高级工程师”，这种质的飞跃将带来巨大的商业价值。他认为，如果 AI 能够增强或替代全球每年 2 万亿美元的软件工程师薪资市场，那么当前的算力基建投入即使再疯狂，也是合理的。这是一场关于智能等级跃迁的豪赌。

[原文] [Host]: I want to dig into the underlying assumptions driving this on the training and inference side because obviously there's the willingness like Zuckerberg just needs to go down the hall to a CFO to get access to all this capital he doesn't even need to go down the hall he can just he can just make it so he's got the voting share Sam's got to fly to Norway way and you know Saudi and and and other places and we're we're at that tier of capital i think you're making it sound way easier than it is i I don't mean to at all i'm I'm just saying you know Zuckerberg is hold on if it's this easy let's let's raise 100 bill dude we should do it we can compete but I want to make sure I understand your thinking on the underlying two sides of this one which is like your view on the diminishing return curve on just like the the return on this

[译文] [Host]: 我想深入探讨一下驱动这一切的训练和推理方面的底层假设。因为显然存在这种意愿，比如扎克伯格（Zuckerberg）只需要走到大厅另一头找 CFO 就能获得所有这些资金——他甚至不需要走过去，他可以直接拍板，因为他有投票权。而 Sam（OpenAI CEO）必须飞到挪威、沙特和其他地方去筹钱。我们现在的资本规模已经到了那个层级。我觉得你把它说得太容易了。不，我完全没有那个意思，我只是说，你知道扎克伯格……等一下，如果这么容易，咱们也去融个 1000 亿吧，兄弟，我们应该这么干，我们也能竞争。但我还是想确认一下你是如何思考这背后的两个方面的，首先是你对“收益递减曲线”的看法，也就是这笔投入的回报率。

[原文] [Dylan Patel]: i I don't think it's a diminishing return right i think that's important to recognize right start there i want to ask about inference too but and and like the growth in token you know inference token demand but given it's a log log chart right uh scaling laws are right given there's no model architecture improvements you just throw more compute data model size at it it gets better at this pace but you're confident that that will continue

[译文] [Dylan Patel]: 我不认为这是收益递减，对吧，我认为认识到这一点很重要，我们先从这里说起。（主持人：我还想问推理方面，比如推理代币需求的增长。）既然这是一个双对数（log-log）图表，对吧，缩放定律（Scaling Laws）是成立的。即便没有模型架构的改进，你只要投入更多的算力、数据和模型规模，它就会按这个速度变得更好。你确信这会继续下去吗？

[原文] [Dylan Patel]: I I think everything has shown that it will continue and it's continued overd wasn't some like well GP5 is not not necessarily that much bigger than 4 right and 4 is smaller than four um what what what what's what's changing is sort of paradigm of how you spend the compute... but as far as like if the model gets better at each scale of hardware spend I would say all the tech giants believe it i believe it i think a lot of people in the financial community are like this is freaking scary yeah you know because the moment it stops you know you wherever you were on the rung right if we went from $50 billion spend to $500 billion spend well that $500 billion spend is never going to have ROI right it was one thing if 50 billion didn't have ROI but now this 500 doesn't have ROI it's a big problem

[译文] [Dylan Patel]: 我认为一切迹象都表明它会继续下去，并且一直在持续。GPT-5 不一定比 GPT-4 大多少，对吧，……真正改变的是你如何消耗算力的范式……但就“每一级硬件投入是否会让模型变得更好”这一点而言，我想说所有的科技巨头都相信这一点，我也相信。但我认为金融界的很多人会觉得：“这太吓人了。”是的，因为一旦它停下来，你知道，无论你爬到了梯子的哪一阶，如果我们从 500 亿美元的投入增加到 5000 亿美元的投入，那么这 5000 亿美元的投入将永远无法获得投资回报（ROI），对吧。如果只是 500 亿没有 ROI 那是一回事，但如果是 5000 亿没有 ROI，那就是个大问题了。

[原文] [Dylan Patel]: so anyways one could think of it as as diminishing returns because if you when you go from $50 billion of spend to $500 billion of spend you only move up the let's call that one tier of model capabilities in absence of you know major algorithmic improvements right um and so I'm I'm holding those sort of off to the side for now but that that iterative like performance improvement in the model is is like I like I mentioned earlier right it's like a six-year-old versus a 13-year-old maybe right the the amount of work you can get a 13-year-old to do is I mean if if you do it right we we we we frown upon that now in this civilization um but the amount of work you can get a 13-year-old to do is actually quite valuable relative to a six-year-old

[译文] [Dylan Patel]: 所以无论如何，人们可能会认为这是收益递减，因为当你从 500 亿美元投入增加到 5000 亿美元投入时，在缺乏重大算法改进的情况下，你可能只提升了一个层级的模型能力，对吧。所以我先把算法改进放在一边。但这种模型性能的迭代式提升，就像我之前提到的，就像是一个 6 岁孩子和一个 13 岁少年的区别，也许吧。你能让一个 13 岁少年做的工作量……我是说如果你引导得当的话——当然我们在现代文明中不赞成这样做——但相对于 6 岁孩子，你能让 13 岁少年做的工作实际上是相当有价值的。

[原文] [Dylan Patel]: and and the same applies to like a college intern versus someone who graduated and has even one year of work experience because there's a learning curve for kids coming out of college all the time so there's that learning curve and I think you know while it may be incrementally the same you know an order magnitude more of compute the amount of value like if we just had if we just if you made a company full of high schoolers and you had to refresh them every six months so they didn't learn too much right and become really good it would be really hard to create a valuable company the most you could do is like dig trenches and and like do yard work but then all the time these kids wouldn't even show up right

[译文] [Dylan Patel]: 同样道理也适用于大学实习生与一个刚毕业甚至只有一年工作经验的人之间的对比，因为刚走出大学的孩子总是有一个学习曲线。所以我认为，虽然从增量上看可能是一样的——你需要多一个数量级的算力——但价值量却是巨大的。这就好比，如果你开了一家全是高中生的公司，而且你每六个月就得把他们换掉，这样他们就不会学到太多东西从而变得非常厉害，那你很难建立一家有价值的公司。你最多能做的就是挖挖沟、修剪草坪之类的活，而且这些孩子可能还经常不来上班，对吧。

[原文] [Dylan Patel]: like as as a as as a function of like how valuable of a business could you do if you had unlimited high schoolers versus if you got a business that refreshed so they didn't build knowledge versus college students versus you know 25 to 30 year olds right the the the value of that business that you can build even though incrementally it's just 5 years between each of them yeah it's drastic it's it's a drastic value change

[译文] [Dylan Patel]: 所以作为一个函数来看，如果你拥有无限多的高中生（而且还要定期更换以防他们积累知识），这种业务能有多大价值？相比之下，如果你拥有的是大学生，或者 25 到 30 岁的成年人，对吧。你能建立的业务价值——尽管他们之间按年龄算只是每隔 5 年的增量——这种价值的变化是剧烈的，是巨大的价值质变。

[原文] [Host]: where do you think we are today like which level are we at do you think depends on the domain right like for software developers like I think we're we're we're we're really pretty good right um you know and that's where where we're seeing the most value creation happen right where you see anthropic have gone from like what a billion or less of revenue to seven to eight by you know already in it's the fastest revenue ramp we've ever seen for anything of this and it's basically all code related right

[译文] [Host]: 你认为我们今天处于哪个阶段？我们在哪个等级？

[Dylan Patel]: 这取决于领域。比如对于软件开发人员，我认为我们已经相当不错了，对吧。这也是我们看到价值创造最多的地方。你可以看到 Anthropic 的收入从 10 亿美元甚至更少，已经在极短时间内飙升到了 70 到 80 亿美元。这是我们见过的最快的收入增长速度，而这基本上都与代码相关，对吧。

[原文] [Dylan Patel]: it's all code and you know in that sense it's like if I had 30-year-old senior engineer um at Google and if that was like if I had infinite of those all it costed was capex for chips and the operational cost is actually quite low then you could build businesses worth insane amounts you could have a replacement for the $2 trillion of wages that go to all the software developers in the world today or rather you could augment them and build you know twice as much or five times as much or 10 times as much if you could augment them because these things don't like just run on their own right it's more of like a force multiplier to the existing person so the value creation potential is is there

[译文] [Dylan Patel]: 这全是关于代码的。从这个意义上说，如果我有一个 30 岁的 Google 高级工程师，如果我有无限多个这样的工程师，而唯一的成本只是芯片的资本支出（Capex），其运营成本实际上非常低，那你就能建立起价值惊人的企业。你可以替代今天支付给全球所有软件开发者的 2 万亿美元薪资。或者更准确地说，你可以增强他们，构建出两倍、五倍甚至十倍的价值。如果你能增强他们——因为这些东西不仅仅是自己运行，它更像是现有人员的力量倍增器——那么价值创造的潜力就在那里。

章节 3：代币经济学（Tokenomics）——推理成本与模型扩展的悖论

📝 本节摘要：

本章中，Dylan 提出了“代币经济学”（Tokenomics）这一概念，旨在消解加密货币的旧语境，转而以此定义 AI 时代的经济核心——即算力支出、毛利润与代币产出价值之间的关系。

他指出了 OpenAI 面临的残酷悖论：虽然根据缩放定律，模型越大会越智能，但超大模型会导致推理成本剧增且延迟（Latency）过高，严重损害用户体验。面对每两个月翻一番的代币需求，硬件扩容速度难以跟上。因此，OpenAI 被迫在“制造更聪明的模型（如 GPT-5）”与“制造更高效、可大规模服务的模型（如 GPT-4o）”之间通过“代币经济学”进行权衡。这也是为何 GPT-4o 选择在保持质量的同时大幅缩减体积和成本，而非单纯追求参数规模扩大的战略原因。

[原文] [Host]: I'm also curious about the other side of ability to serve and just demand for like today's models by inference you know the stat I last saw is token demands doubling every two months or something crazy obviously there's all these reasoning tokens that are really exciting for some of the the longer thinking models how do you think about the growth of the pool of demand for inference tokens themselves up just even in today's models like even if we just like stop things and fix things and we'll leave that other side of the equation just for a second what's your model for thinking about that today what most interests you in the in the growth of just broad

[译文] [Host]: 我也很好奇硬币的另一面，也就是服务能力以及对现有模型的推理（inference）需求。你知道我最近看到的数据是代币（token）需求每两个月就翻一番，或者诸如此类的疯狂增长。显然，对于一些通过长思维链进行思考的模型来说，那些“推理代币”非常令人兴奋。你是如何看待推理代币需求池的增长的？即便仅限于今天的模型，即便我们暂停更新、通过修修补补来维持现状，暂时先把（模型升级）那边的等式放一边。你今天思考这个问题的模型是什么？在广泛的增长中，什么最让你感兴趣？

[原文] [Dylan Patel]: so so the thing I like to call it is tokconomics and I I stumbled upon the word actually it's like a crypto kill off crypto finally one once and for all so I'm I'm trying to make tokconomics uh SEO direct to you know us talking about tokconomics and then hopefully you talking about tokconomics hopefully like everyone using say tokconomics 20 more times it's the economics of the tokens right how much compute is being spent how much is the gross profit what's the value being created by these tokens that's that's the end of the day what's what's relevant here right

[译文] [Dylan Patel]: 我喜欢把它称为“代币经济学”（Tokenomics）。我实际上是偶然想到这个词的，这就像是对加密货币（Crypto）的某种终结——彻底干掉加密货币那种含义。所以我正试图让“代币经济学”的 SEO 直接指向我们所谈论的内容，希望你也谈论它，希望大家都多说 20 遍这个词。这就是代币的经济学，对吧：消耗了多少算力？毛利润是多少？这些代币创造了什么价值？归根结底，这才是这里相关的东西。

[原文] [Dylan Patel]: Nvidia keeps saying AI factory which produces intelligence that intelligence has value let's say you have a gigawatt of capacity what can I serve well I could serve a thousand times times of a model that's really shitty i could serve you know amount right i could serve one one times amount that's of a model that's good and I could serve like 0.1 times of a model that's amazing now multiply that by whatever factor of like how many users what's the number of tokens outputed but you know I could do X number of tokens X time 100 X times a million tokens right depending on the model quality

[译文] [Dylan Patel]: Nvidia 一直在说“AI 工厂”，它生产智能，而智能是有价值的。假设你有 1 吉瓦（gigawatt）的容量，我能提供什么服务？我可以服务 1000 倍数量的非常垃圾的模型；我可以服务 1 倍数量的好模型；或者我可以服务 0.1 倍数量的惊人模型。现在乘以用户数量、输出的代币数量等因子。你知道，根据模型质量的不同，我可以处理 X 数量的代币，或者 100 倍 X，甚至 100 万倍 X 的代币，对吧。

[原文] [Dylan Patel]: and so this is sort of where you know the whole GPD5 thing comes around right is open had had a had a challenging you know thing right they're like "Hey we have a couple gigawatts of capacity effectively right by the end of this year roughly a couple gigawatts of capacity too um more or less a little bit less but you know right now but you know it's how how do they maximize their serving capacity with this?"

[译文] [Dylan Patel]: 这就是关于 GPT-5 的整个事情的由来，对吧。OpenAI 面临着一个充满挑战的情况。他们会想：“嘿，实际上到今年年底我们大概有几吉瓦的容量，或者多一点少一点，大概是几吉瓦。但是，我们如何利用这些容量最大化我们的服务能力？”

[原文] [Dylan Patel]: Um one one avenue is we continue to serve big models and we make bigger models and the tokens are more expensive but this log scale is really challenging because yes the value is an order magnitude you know value is way more but the cost is way more and then the the real whammy is the user experience is way worse right if I serve a massive massive model it's slow and users are fickle and you need that you need the response to be way faster than they can be hard to calibrate yeah yeah so so there's this user experience challenge

[译文] [Dylan Patel]: 一条路径是我们继续提供大模型，并且制造更大的模型，这样代币会更贵。但这个对数标度（log scale）非常有挑战性，因为是的，价值可能高出一个数量级，但成本也高得多。而且真正致命的打击（whammy）是用户体验会差得多，对吧。如果我提供一个超级巨大的模型，它会很慢。用户是挑剔的，你需要响应速度比现在快得多……这很难校准。是的，所以存在这种用户体验的挑战。

[原文] [Dylan Patel]: but really in the end it's like you know for a given model level I think there's a saturation point of how many how much demand of intelligence there is right you can only have such large child army right of of like people digging trenches or like con 2012 whatever it is like this is very cancelable but you know um but you could have a much larger army of you know or business of like the larger level of intelligence right

[译文] [Dylan Patel]: 但归根结底，对于给定的模型水平，我认为智能的需求量有一个饱和点，对吧。你只能拥有那么大规模的“童工大军”去挖战壕——或者像 Kony 2012 那样，不管那是什么（这比喻可能让我被取消文化抵制）——但你可以拥有一个规模大得多的、更高智能水平的军队或业务。

[原文] [Dylan Patel]: and so when you think about hey what what could I have done with GPG3 GP GPD3 even if we paused there paused the model capabilities right you know obviously the cost to serve a model quality of GPD3 has tanked 99% or more yeah it's like 2,000 times cheaper now it's so much cheaper now for and then GPD4 same thing right people were freaking out about Deepseek because it's like five six00 times cheaper GPT OSS came out and that's even cheaper than that right and it's the same again like for roughly the same quality actually I would argue the GPT OS open source model is actually a little bit better than GPD4 OG um because it can do tool calling and anyways um the cost of these things tanks rapidly with algorithmic improvement right not necessarily model getting bigger

[译文] [Dylan Patel]: 所以当你思考，如果我们在 GPT-3 停下来，暂停模型能力的提升，我们能做什么？显然，提供 GPT-3 质量模型的服务成本已经暴跌了 99% 甚至更多。是的，现在便宜了大概 2000 倍。对于 GPT-4 也是一样，人们对 DeepSeek 感到震惊，因为它的价格便宜了 500 到 600 倍。GPT-OSS 出来后甚至更便宜。而且在质量大致相同的情况下——实际上我认为 GPT-OSS 开源模型比最初的 GPT-4 还要好一点，因为它可以进行工具调用等等——无论如何，随着算法改进，这些东西的成本会迅速下降，并不一定是因为模型变得更大了。

[原文] [Dylan Patel]: um and as these algorithms get better you can but but at X level of intelligence you can only serve so much demand and then the the the flip side is you know what that demand it takes time for people to realize how to use it so when GPD3 launched no one cared when GBD3.5 launched it was like still most people didn't care chat GPT launched with GBD3.5 people cared a little bit uh GPD4 launched on chat GPD then people cared a lot but a model tier of GPT 3.5 or three still can be very useful in a lot of world a lot of the world

[译文] [Dylan Patel]: 随着算法变得更好，你可以降低成本，但在 X 水平的智能上，你只能满足那么多需求。另一方面是，人们需要时间来意识到如何使用这种需求。当 GPT-3 发布时，没人关心；当 GPT-3.5 发布时，大多数人还是不关心；ChatPGT 带着 GPT-3.5 发布时，人们开始关心一点了；GPT-4 在 ChatGPT 上发布时，人们才真正非常关心。但在世界上很多地方，GPT-3.5 或 GPT-3 级别的模型仍然非常有用。

[原文] [Dylan Patel]: and so you've kind of got this challenge of like if I pause on a model capability then I end up like taking way too long for adoption and also like how can Can I get people to adopt it if I don't let people use it and so so open had this tremendous problem with GPD 40 right 4 and then 4 turbo was smaller than 4 and 4 was smaller than 4 turbo what open basically did was they made the model as much smaller as possible while keeping roughly the same quality or slightly better right so 4 to 4 turbo was like the model was less than half the size and four turbo to 40 like 40's cost is way lower than four um and they just kept shrinking the cost

[译文] [Dylan Patel]: 所以你面临着这样一个挑战：如果我在模型能力上停滞不前，采用周期就会太长；而且如果不让人们使用，我怎么能让人们采用它呢？OpenAI 在 GPT-4o 上就面临这个问题，对吧。GPT-4，然后是 GPT-4 Turbo（比 4 小），然后是 GPT-4o。OpenAI 基本所做的就是尽可能地缩小模型，同时保持大致相同的质量或略好一点。从 4 到 4 Turbo，模型大小不到一半；从 4 Turbo 到 4o，4o 的成本远低于 4。他们只是一直在压缩成本。

[原文] [Dylan Patel]: now five what could they have done they could have gone "Oh we'll go big step." They actually tried that with 4.5 they they screwed up some things cuz it was really hard to get you know 100,000 GPUs to work properly there's challenges there also they hadn't figured out the whole reinforcement learning paradigm at that time so the C so they ran out of you know it's the the scaling laws are like it's a chart of quality versus compute but that compute breaks down into how much bigger do I make the model how much more data do I put in the model and if the internet only has so many tokens you're kind of screwed right

[译文] [Dylan Patel]: 那么对于 GPT-5，他们本可以做什么？他们本可以说：“噢，我们要迈出一大步。”实际上他们在 4.5 上尝试过，但搞砸了一些事情，因为让 10 万个 GPU 协同工作非常困难，这有很多挑战。而且当时他们还没搞清楚整个强化学习的范式。缩放定律是质量与算力的图表，但算力又分解为：我要把模型做多大？我要投入多少数据？如果互联网只有那么多代币（数据），你就有点完蛋了，对吧。

[原文] [Dylan Patel]: so so they kind of had this problem of you have x amount of compute you can service your users but hey today um if people want to use my API I rate limit them because I can't actually serve them all oh if I want to use um you know I have to I have to rate limit the people who have chat GPT free pro and max whatever the whatever the $2 $200 there's like different rate limits you can only do deep research so much

[译文] [Dylan Patel]: 所以他们面临这样的问题：你有 X 数量的算力来服务用户。但是嘿，今天如果人们想用我的 API，我得对他们进行速率限制（rate limit），因为我实际上无法服务所有人。如果是 ChatGPT Free、Pro 还是 Max 用户，无论那个 20 美元还是 200 美元的套餐叫什么，都有不同的速率限制。比如你只能做这么多次 Deep Research。

[原文] [Dylan Patel]: you can't actually serve your user base enough so how are they ever going to move up this adoption curve so then as OpenAI what's your choice do you make go from 40 to 5 do you make the model way bigger and not be able to serve anyone and plus because you can't serve anyone and it's slow to serve the adoption curve doesn't really get going um or do you make the model the same size which is what they did for GBD5 it's basically the same size as 40 and and roughly the same cost that's actually a little bit cheaper potentially and then you just serve way more users um and get everyone up the adoption curve more and then you can instead of putting them on a bigger model you put them on models that do thinking

[译文] [Dylan Patel]: 你实际上无法充分服务你的用户群，那他们怎么能沿着采用曲线向上移动呢？所以作为 OpenAI，你的选择是什么？是从 4o 升级到 5，把模型做得超级大，结果无法服务任何人？而且因为无法服务且速度慢，采用曲线也无法真正启动。还是说，你把模型做成和原来一样大——这也是他们在 GPT-5（注：此处可能指代下一代优化模型）上所做的，基本上和 4o 一样大，成本也差不多，甚至可能更便宜一点——然后你可以服务更多的用户，让大家在采用曲线上更进一步？与其把他们放在一个更大的模型上，不如把他们放在一个能“思考”的模型上。

[原文] [Dylan Patel]: and so this is the whole conundrum they have and this is where the whole tokconomics thing comes into play the question you had I wanted to level set it right which is how do you serve these users the demand is growing so much i'm not doubling my hardware every two months right right yes this capex is crazy but I'm not doubling my hardware every two months but I'm doubling my tokens every two months so so there has to be enough of a cost decrease

[译文] [Dylan Patel]: 这就是他们面临的整个难题，也是“代币经济学”发挥作用的地方。针对你的问题，我想先设定好基准：你如何服务这些用户？需求增长如此之快，但我并不是每两个月就将硬件翻倍，对吧。是的，资本支出（Capex）很疯狂，但我并没有每两个月翻倍硬件，但我每两个月的代币量却在翻倍。所以必须有足够的成本下降。

[原文] [Host]: if you could snap your fingers and change change a dial somehow that would most unlock and unleash more development is it just is it just inference latency because then we could do bigger models and serve them much faster in a way that consumers would enjoy is that the main like bottleneck to be attacked

[译文] [Host]: 如果你能打个响指，转动某个旋钮，从而最大程度地解锁和释放更多的发展，那会是什么？仅仅是推理延迟（inference latency）吗？因为那样我们就可以做更大的模型，并以消费者喜欢的速度更快地提供服务。那是主要需要攻克的瓶颈吗？

[原文] [Dylan Patel]: inference is like always it's it's it's a curve again right like all of these things are curves and it's a trade-off right everything in engineering is a trade-off so So you have inference latency versus cost on any given hardware um GPUs can do lower latency to a certain extent but then the cost is way higher or you can do really really high throughput and the cost is way uh lower right

[译文] [Dylan Patel]: 推理就像……它总是一条曲线，对吧。所有这些东西都是曲线，都是一种权衡（trade-off）。工程学中的一切都是权衡。所以在任何给定的硬件上，你都有“推理延迟 vs 成本”的关系。GPU 可以在一定程度上做到低延迟，但成本会高得多；或者你可以做到非常非常高的吞吐量，成本就会低得多。

[原文] [Dylan Patel]: i think that I think that's a that's a tremendous like question i'd probably still say capacity/cost is more important than latency really i think existing levels of latency are fast enough for a lot um now now if the if the latency was 10x lower for GBD5 then they could have made a model that was 10x bigger and served it at this qual served it at this speed yeah that's what I'm wondering about but but then you would have the same capacity issue right

[译文] [Dylan Patel]: 我认为这是一个极好的问题。我可能还是会说容量/成本比延迟更重要。真的，我认为现有的延迟水平对很多事情来说已经够快了。如果 GPT-5 的延迟能降低 10 倍，那么他们本可以做一个大 10 倍的模型，并以现在的速度提供服务。是的，这就是我好奇的地方。但那样你还是会有同样的容量问题，对吧。

[原文] [Dylan Patel]: um so I guess like if I was if you could have your cake and eat it which is all the capacity in the world and the lowest latency in the world well then you would just make the best you'd make the models way better right like I think I think it's the physical realities of like if I'm at OpenAI what do I choose to do um do I invest more in the model that people can use or do I invest more in the fast do I invest a lot in the model that most people you know won't use because it's expensive first of all and even those that can afford it will often go back to the regular one right

[译文] [Dylan Patel]: 所以我想，如果你能鱼与熊掌兼得——拥有世界上所有的容量和最低的延迟——那你肯定会把模型做得更好，对吧。但我认为是物理现实决定了选择。如果我在 OpenAI，我该怎么选？我是更多地投资于人们能使用的模型，还是投资于那个大多数人都不会用的模型？因为首先它太贵了，而且即使那些付得起的人通常也会退回到普通模型上，对吧。

章节 4：突破数据瓶颈——强化学习与合成环境的崛起

📝 本节摘要：

在本章中，Dylan 探讨了当互联网文本数据被耗尽时，AI 如何继续进化。他引入了“Grokking”（顿悟）的概念，指出模型需要先死记硬背才能实现泛化理解。为了解决数据匮乏的问题，行业正转向“强化学习”（Reinforcement Learning）与“合成环境”。与其依赖现成的互联网数据，不如创建虚拟环境（如模拟的亚马逊网站、数学谜题或医疗案例），让 AI 在其中反复试错、自我博弈并生成高质量的训练数据。这种“后训练”（Post-training）阶段被认为是解锁下一代智能的关键。

[原文] [Dylan Patel]: it's not necessarily even bigger right like there's this whole concept of um overparameterization i.e if you just throw more parameters in a neural network and even when humans I'll equate it to humans right when you had a vocab test or you had some test you memorized before you understood and it wasn't until you did multiple repetitions and in different forms that you actually understood the content rather than just memorized um it takes it takes cycles

[译文] [Dylan Patel]: 这不一定意味着模型要变得更大，对吧。有一个完整的概念叫做“过参数化”（overparameterization）。也就是说，如果你只是在神经网络中投入更多的参数——我把它类比到人类身上——当你进行词汇测试或某种考试时，你在理解之前是先死记硬背的。直到你进行了多次重复，并且以不同的形式进行练习，你才真正理解了内容，而不仅仅是背下来。这需要周期。

[原文] [Dylan Patel]: um and when you when you do an LLM it's the same thing right if you throw some data at it it will memorize it before it generalizes it's this concept called groing right you grocked a subject i.e it's like the aha moment trick of understanding yeah yeah and the models do the same thing they memorize it up until then they understand it at some point and if you make the model bigger and bigger and bigger without the data changing you just memorize everything and actually it starts to get worse again because it never had the opportunity to generalize because the model was so big and there's so many weights and there's so much capacity for information

[译文] [Dylan Patel]: 当你训练大语言模型（LLM）时也是一样，对吧。如果你给它投喂数据，它会在泛化之前先记住它。这就是所谓的“Grokking”（顿悟）概念。你顿悟了一个主题，就像是理解时的那个“啊哈”时刻。是的，模型也是做同样的事情，它们先是死记硬背，直到某一刻它们理解了。如果你在数据不变的情况下让模型变得越来越大，它只会把所有东西都背下来，实际上表现会开始变差，因为它没有机会去泛化，因为模型太大了，权重太多了，信息容量太大了。

[原文] [Dylan Patel]: you know the challenge today is not necessarily make the model bigger the challenge is how do I generate and create data that is in useful domains so that the model gets better at them nowhere on the internet to show you how to fly through a spreadsheet you know using only your uh mouse or not using your mouse using only your keyboard and all these like you know functions and all these things right like that's that's a repetition that's that's bars but there's no data on the internet about this so how do you teach a model that it's not going to learn it from reading the internet over and over and over again which you and I could never do and so it hasn't a level of intelligence that we can't do we can't read the whole internet but it can't do basic stuff which is like play with a spreadsheet

[译文] [Dylan Patel]: 你知道，今天的挑战不一定在于把模型做得更大，挑战在于我如何生成和创造有用领域的数据，以便模型在这些方面变得更好。互联网上没有任何地方教你如何只用键盘、不用鼠标在电子表格中飞速操作，使用所有那些函数和技巧。那是通过重复练习得来的，那是硬功夫（bars），但互联网上没有关于这方面的数据。所以你如何教模型呢？它不可能通过一遍又一遍地阅读互联网来学会这些。虽然它拥有我们无法企及的某种智能水平——我们读不完整个互联网——但它却做不了基础的事情，比如玩转电子表格。

[原文] [Dylan Patel]: um so so so how do you get it to learn these things and so that's that's where this whole reinforcement learning paradigm kind of happened which is giving it environments specific environments to learn it and then fold back in right exactly and that's that's where there's sort of a a challenge in terms of building those environments in terms and so there's like 40 startups now in the bay doing these environments and you know questionable whether or not they'll any of them will make it or what will happen but like there's 40 and then these companies are also making their own environments but these environments can be anything and everything

[译文] [Dylan Patel]: 那么，你如何让它学习这些东西呢？这就是整个强化学习（Reinforcement Learning）范式出现的原因，即给它提供环境，特定的环境来让它学习，然后再反馈回去。没错，正是这样。这也是构建这些环境的挑战所在。现在湾区大概有 40 家初创公司在做这些环境。不管它们中是否有谁能成功或者未来会怎样，反正有 40 家在做。而且大公司也在制造自己的环境。这些环境可以是任何东西，包罗万象。

[原文] [Host]: give me an example just like of one of the startups or something just to get these startups are like like they're they're just making environments for open anthropic and others right so it's like as simple as like here is a fake Amazon right because Amazon terms of service ban chat models and all these things but here's a fake Amazon full of items um figure out how to click around and purchase items right uh figure out how to compare the two items and pick you know I I've generated a list of deodorants three of them are fake one of them's real one of them is not the one I want here's the prompt figure how to buy it and if and and you know it tries many things and you know vary the prompt and all these things but eventually you know it's bought the right deodorant and you've succeeded and you fold it back in that's a simple thing

[译文] [Host]: 给我举个例子，比如其中一家初创公司是做什么的？

[Dylan Patel]: 这些初创公司就是在为 OpenAI、Anthropic 等公司制造环境，对吧。比如简单到一个“假亚马逊网站”。因为亚马逊的服务条款禁止聊天模型爬取之类的，所以这里有一个充满了商品的假亚马逊。你要弄清楚如何点击、购买商品，对吧。弄清楚如何比较两件商品并进行挑选。比如我生成了一份除臭剂清单，其中三个是假的，一个是真的，或者其中一个不是我想要的。这里是提示词（Prompt），你去弄清楚怎么买。它会尝试很多次，改变提示词做各种尝试，但最终它买到了正确的除臭剂，你就成功了，然后你把这个经验反馈回模型。这是一个简单的例子。

[原文] [Dylan Patel]: or it could be hey clean this data right here's this table ton ton of dirty data in there oh there's like colons and stuff the there's an address in one column you know I'm going to you know how do how do I separate out the columns so the address is like you know it's it's street address city zip code and it'll try a bunch of stuff but like hey maybe it can't do that yet so really you just drop it like you teach you give it you know iterative like here's here's addresses here's different formats and you slowly iteratively teach it so there's all this like challenge so that's one that's another example

[译文] [Dylan Patel]: 或者可能是：“嘿，清理这些数据。”这里有一张表，里面有一吨的脏数据。哦，这里有冒号之类的东西，地址都在一列里。你知道，我该如何把列分开，让地址变成街道地址、城市、邮编？它会尝试一堆方法。但也可能它还做不到，所以你就通过迭代的方式教它：“这是地址，这是不同的格式。”你慢慢地、迭代地教它。所以这就是挑战。那是另一个例子。

[原文] [Dylan Patel]: another example is like you're in a game and like whether it's a tic-tac-toe or Call of Duty or you know a math puzzle whatever the game is and that's what a lot of these environments initially have been is like math puzzles it's like do this math puzzle oh well I can't do this one because it's too hard here's an easier one oh okay i can I can spin on this one okay I'm better enough okay now I can learn this one right and and and it has iteratively stepped through those to where you know basically from this you know Q4 of last year to Q2 of this year these things hill climbed up math puzzles like crazy

[译文] [Dylan Patel]: 再比如你在游戏中，无论是井字棋、使命召唤（Call of Duty）还是数学谜题，无论是什么游戏。这也就是很多这类环境最初的样子——数学谜题。比如：“做这个数学题。”“噢，我做不出这个，太难了。”“那这里有个简单的。”“噢好的，我可以在这个上面琢磨一下。好的，我现在够强了，我可以学那个难的了。”它就这样迭代地逐步提升。基本上从去年第四季度到今年第二季度，这些模型在数学谜题上的能力像疯了一样爬坡。

[原文] [Dylan Patel]: um and a lot of that was not hey I just know the math a lot of that was here's how I use Python to uh write something that does the math for me um and now these things are actually quite good at math but you know so so it's like these the environments can be super varied um and it doesn't need to be something that's like clear-cut and dry it can be here's a medical case what's wrong with it and then you have another model say well here's here's your instructions on how you would grade the result of a case what looks like they didn't even try this or didn't even look up this okay you did that wrong and you know you can you can feed these models into so these environments can be very very complicated so building those out is is a challenge right

[译文] [Dylan Patel]: 其中很多并不是“嘿，我懂数学了”，很多是“这是我如何使用 Python 来写一段代码帮我算数学”。现在这些东西数学确实很好了。所以环境可以非常多样化，不一定非得是那种枯燥乏味的东西。它可以是：“这里有一个医疗案例，哪里出了问题？”然后你有另一个模型说：“好的，这里是关于你如何给这个案例的诊断结果打分的说明。看起来他们甚至没试过这个，或者没查过这个。好的，你做错了。”你可以把这些模型反馈进去。所以这些环境可以非常非常复杂，构建它们是一个挑战。

[原文] [Host]: it was one thing to say I'm taking all the internet data i'm going to filter it some i'm going to throw it to the model right there's tons of engineering challenges there for sure there's a different set of engineering challenges that take time to build out in those two like in pure raw internet pre-training world and in this new like environments world like what inning are we in in each of those would you say like how far into the potential benefits have we have we eaten

[译文] [Host]: 之前我们说“把所有互联网数据拿来，过滤一下，扔给模型”，这是一回事，当然这其中有大量的工程挑战。而在这种新的“环境世界”中，有一套不同的工程挑战需要时间来构建。在这两个世界里——纯粹的原始互联网预训练世界，和这个新的环境世界——你觉得我们分别处于第几局（inning）？我们已经吃掉了多少潜在红利？

[原文] [Dylan Patel]: this is where like the whole like oh well then you know Dylan what you're saying is you never need to make models bigger again right because you've already run out of data and until you figure out how to generate tons and tons of data that's great but actually we haven't right like you know we've seen another angle where it's mostly just been pre-training scaling right is is V3 and Banana Nano right these Google image and video models... cuz when I said we've run out of internet we've run out of the text tons of video and image and audio right we just it's just so expensive so you know like we we didn't get to that so like maybe late innings on text mid innings on pre-training i think we're early on text yeah we're quite early

[译文] [Dylan Patel]: 这就是有人会说：“哦，Dylan，那你是不是说再也不需要把模型做大了？因为你已经用完了数据，除非你弄清楚如何生成大量数据。”这听起来很有道理，但实际上我们还没有（停止）。我们看到了另一个角度，主要还是预训练的扩展，比如 Google 的 V3 和 Banana Nano 这些图像和视频模型……因为当我说我们用完了互联网时，我们是用完了文本。还有大量的视频、图像和音频，只是这太贵了。所以我们还没到那一步。如果是文本，可能是后期（late innings）；如果是预训练，可能是中期。不，我认为在文本上我们还很早。是的，我们还相当早。

[原文] [Dylan Patel]: and then the other angle is just because you've used the text doesn't mean you can't learn faster right you take a class you give them all a book you tell them to read it once and you test them all it's like well one kid's going to get 100 and one kid's going to get a 40 right it's just the reality of life... so it's not like you stop training new models it's not like you don't have algorithmic improvements or smarter kids right you know it's not like pre-training is done yeah in fact it's it's the base of everything so you want to keep having gains because any gains on pre-training right i.e the model learns a little faster or the model's a little bit smaller for the same quality Yeah feeds into the next stage which is this whole post-training side um which will subsume the majority of the compute at some point and inning wise is are we in the second inning of that like how is I think we've like thrown the first ball

[译文] [Dylan Patel]: 另一个角度是，仅仅因为你用过了这些文本，并不意味着你不能学得更快，对吧。你给一个班的学生发一本书，让他们读一遍然后考试。有的孩子会得 100 分，有的孩子会得 40 分，这就是生活的现实……所以这不意味着你停止训练新模型，也不意味着没有算法改进或“更聪明的孩子”。预训练并没有结束。事实上，它是所有一切的基础。所以你要继续获得收益，因为预训练上的任何收益——也就是模型学得快一点，或者在同等质量下模型小一点——都会通过去，进入下一个阶段，也就是整个“后训练”（post-training）阶段。这个阶段在未来某个时刻将消耗绝大部分的算力。至于在这个阶段我们处于第几局？我觉得我们才刚刚投出第一个球。

章节 5：从对话到行动——AI代理（Agent）的商业化未来

📝 本节摘要：

本章重点讨论了 AI 发展的下一个里程碑：从单纯的信息检索转向实际的任务执行（Action）。Dylan 指出，未来的模型不再仅仅是回答问题，而是能够像一个成熟的助手一样，“帮你买维生素”或“挑选电动牙刷”并直接完成下单。他透露了一个惊人的数据：Etsy 超过 10% 的流量已经直接来自 GPT。这意味着 AI 公司将通过“抽成模式”（Take Rate）——类似于 Visa 的信用卡交易费——来实现巨大的商业化变现，从而彻底重构搜索与电商的逻辑。

[原文] [Host]: and the so what of let's say we fast forwarded we're in the seventh inning of that or something like this what do you think the way that the average person will most feel that difference in terms of the utility of the model

[译文] [Host]: 那么，假设我们要快进一下，比如说我们到了那个阶段的第七局（seventh inning）或者类似的阶段。你认为普通人将在哪方面最能感受到模型实用性的差异？

[原文] [Dylan Patel]: it'll be very different like motus of using it right it's one thing to like ask for information or ask it to organize information versus it just doing things those those 12-year-olds you need to really direct them how to dig a hole cuz a lot of them haven't dug a hole but you're talking about order me this vitamin and just like it's just done right

[译文] [Dylan Patel]: 使用它的方式（modus）将会非常不同，对吧。请求信息或者让它整理信息是一回事，而让它直接“做事情”是另一回事。那些“12 岁的孩子”（指代早期模型），你真的需要指导他们如何挖洞，因为他们很多人没挖过洞。但你现在谈论的是：“给我订购这种维生素。”然后就这样，事情就完成了，对吧。

[原文] [Dylan Patel]: and and and we're actually like not too far away from that i think if you try and research electric toothbrushes like this is something cuz you know your electric toothbrush I lose it i leave it at a hotel all the time and I've been obsessive about this like in 2021 I I like made a spreadsheet of all the electric toothbrushes cuz based on how many IC's were in each one of them right like this one has a Bluetooth IC why i don't know this one has a display IC like it has a color display IC like what's going on right like so I made a spreadsheet of all this and so like I don't know it's like this weird like little thing that I do

[译文] [Dylan Patel]: 而且我们实际上离那一步并不远了。比如你试着研究电动牙刷。这是个真事，你知道我的电动牙刷，我总是把它弄丢，总是落在酒店里。我在 2021 年对此非常着迷，我做了一个包含所有电动牙刷的电子表格，基于它们里面有多少集成电路（IC）。比如：“这个有蓝牙芯片，为什么？我不知道。那个有显示屏芯片，还是彩色显示屏芯片，这到底是在搞什么？”所以我把这些都做成了表格。我不这也是我做的一件怪事。

[原文] [Dylan Patel]: i've been finding like every every you know how I research which toothbrush I want to buy now I bought a oral B IO like series 9 or whatever right like whatever it's like but it's like comparing them like these models now can like actually like figure out exactly what you want and more than 10% of Etsy's traffic is straight from GPT wow

[译文] [Dylan Patel]: 我发现……你知道我现在怎么研究我想买哪支牙刷吗？我现在买的是 Oral-B IO 系列 9 还是什么的，不管是什么。但通过对比它们……现在的模型实际上可以弄清楚你到底想要什么。而且，Etsy 超过 10% 的流量是直接来自 GPT 的。

[Host]: 哇。

[原文] [Dylan Patel]: Amazon blocks GPT but like otherwise it would be really high people make purchasing decisions through GPTs they just don't make the purchase open's head of applications or co of applications was at Shopify and created the shopping agent right this is is very clear this is how they monetize the models are going to purchase for you right they're going to do actions for you

[译文] [Dylan Patel]: 亚马逊屏蔽了 GPT，否则那个比例也会非常高。人们正在通过 GPT 做购买决策，只是他们（在 GPT 内）还没有完成购买动作。OpenAI 的应用负责人（Head of Applications）或者说应用方面的联合创始人，之前是在 Shopify 的，他创建了购物代理（Shopping Agent）。这非常清楚，这就是他们变现的方式。模型将为你进行购买，对吧，它们将为你执行动作。

[原文] [Dylan Patel]: and the model and then the company that does those actions for you the model that will be able to take some sort of take rate right even if it's like 0.1% even if it's 1% it's 2% it'll be like a credit card transaction visa is the most amazing business in the world because of this right and and chat could be that too

[译文] [Dylan Patel]: 然后那个为你执行这些动作的公司或模型，将能够抽取某种“抽成率”（take rate）。即使只是 0.1%，即使是 1% 或 2%，这就像信用卡交易一样。Visa 之所以是世界上最惊人的生意就是因为这个，对吧。而聊天机器人（Chat）也可以成为那样的生意。

[原文] [Dylan Patel]: if I'm making my decisions on purchasing all sorts of things I mean I already almost outsource like what am I going to eat to like the front page recommendation of like Uber Eats sometimes or I already outsource a lot of decisions it's not too much further till I've like completely outsourced a decision and a purchasing intent

[译文] [Dylan Patel]: 如果我正在做购买各种东西的决定——我的意思是，我有事甚至已经把“我要吃什么”几乎外包给了 Uber Eats 首页的推荐，或者我已经外包了很多决定。距离我完全外包决策和购买意图（purchasing intent），并不遥远了。

[原文] [Dylan Patel]: that's what's made and Google such amazing companies is they figured out how to get the thing you want to purchase in front of you as best as possible right and and all their work on recommendation systems is figuring out what you like how to keep you on the platform longer whether it's YouTube or Instagram or you know or bite dance right with Tik Tok or it's hey here's the ad of the thing you'll probably click on and buy because that's how I get paid and and everyone likes to claim they don't like pay attention to ads but you do right

[译文] [Dylan Patel]: 这正是成就 Google 这样惊人公司的原因，因为它们弄清楚了如何尽可能好地把你想要购买的东西放在你面前，对吧。它们在推荐系统上的所有工作都是为了弄清楚你喜欢什么，如何让你在平台上停留更久——不管是 YouTube、Instagram 还是字节跳动（Bite Dance）的 TikTok——或者说：“嘿，这是你可能会点击并购买的广告。”因为那就是我获得报酬的方式。每个人都喜欢声称自己不看广告，但你确实看了，对吧。

章节 6：思维链与记忆——推理算力与长上下文的挑战

📝 本节摘要：

本章聚焦于 AI 如何通过“推理”（Reasoning）和“记忆”（Memory）来进一步提升智能。Dylan 解释道，推理本质上是用时间换取智能（Time-to-Think），就像人类做数独题或写代码一样，需要消耗“大脑周期”来逐步推导，而非单纯依赖直觉（预训练模型的一步输出）。

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此外，他深入探讨了长上下文（Long Context）的局限性。虽然 Transformer 架构拥有完美的回想能力（Needle in a Haystack），但在处理无限信息时效率极低。相比之下，人类的记忆是稀疏的（Sparse）——我们遗忘大部分细节，只保留核心逻辑。未来的方向可能不是无限扩大模型的“脑容量”（Context Window），而是教会模型像人类一样使用“外部存储”——比如写文档、查笔记或使用数据库（如 OpenAI 的 Deep Research 产品），从而实现对海量信息的高效处理。

[原文] [Host]: before asking even more holistically kind of your view on where we're going there there's a third category which is the reasoning part of the equation so we've got pre-training we've got RL and environments post- training what about just like raw time spent reasoning thing and where that going as its own independent part of the overall scaling law

[译文] [Host]: 在更全面地询问你对未来的看法之前，还有一个第三类范畴，那就是等式中的“推理”（reasoning）部分。我们已经有了预训练，有了强化学习（RL）和环境，以及后训练（post-training）。那么仅仅是“花费时间进行推理”这一点呢？它作为整体缩放定律中独立的一部分，未来将走向何方？

[原文] [Dylan Patel]: the scaling laws again like if you zoom out that's not actually what the like original paper is but in spirit sure scaling laws are more compute better intelligence and that could be bigger and bigger model each iterative token is better... the interesting like an important like thing here is that by putting in these environments you're teaching it like humans right if I asked you you know to go figure something out right you might not necessarily know the answer right away but I know you could probably figure it out in a given amount of time that's reasoning you're spending more brain cycles

[译文] [Dylan Patel]: 再次强调，缩放定律（scaling laws）——如果你把视角拉远，虽然这不完全是原始论文的内容，但在精神上确实如此——意味着更多的算力带来更高的智能。这可以是越来越大的模型，或者每一个生成的代币（token）都变得更好……这里有趣且重要的一点是，通过把模型放入这些环境中，你是在像教人类一样教它。如果我让你去弄清楚某件事，你可能不会立刻知道答案，但我知道你大概能在给定的时间内弄清楚。这就是推理，你在消耗更多的“大脑周期”。

[原文] [Dylan Patel]: the magic again of like intelligence of humans of of people is not that they are information retrieval like the best at information retrieval right like like GPTs are amazing at information retrieval we're really good at because we've been trained in these environments which is our world at figuring out how to do things iteratively and so reasoning and these oral environments are linked together

[译文] [Dylan Patel]: 人类智能的魔力并不在于我们是信息检索的高手——GPT 在信息检索方面才是惊人的——我们真正擅长的是在环境中（也就是我们的世界）被训练去迭代地弄清楚如何做事情。所以推理和这些环境是紧密联系在一起的。

[原文] [Dylan Patel]: right if if I'm telling a model hey do this math puzzle it's not it's not just spewing out like oh the answer's one oh the answer's two oh the answer's three okay the answer was actually seven and when it got there I trained it again it's like okay now it knows next time oh the answer is six seven or eight now it's like seven okay great it's not like now it instantly knows the answer it's actually like oh here's this puzzle oh like these numbers oh this line it's sudoku these numbers add up to this um oh it has one through nine but it's missing eight okay it's eight right like it's thinking through it right like you and I would solve a sodoku now eventually when you get good enough at sodoku you could probably just like spit out an answer um you could do it in your sleep but for a long time you can do it without like you know and so sort of like this reasoning time is a way of spending more compute more brain cycles on the task without actually you know scaling the model

[译文] [Dylan Patel]: 如果我告诉模型：“嘿，做这个数学谜题。”这不仅仅是让它喷出“答案是 1”、“答案是 2”、“答案是 3”，最后“好的答案其实是 7”。当我训练它之后，并不是说它下次就瞬间知道答案是 7 了。它实际上是：“哦，这是个谜题，哦这些数字，哦这一行是数独，这些数字加起来是这个……哦，它有 1 到 9，但缺了 8，好的，那是 8。”就像它在思考过程一样，就像你我解数独一样。当然，当你解数独足够熟练时，你可能可以直接说出答案，甚至在睡梦中都能做，但在很长一段时间里你需要思考。所以这种“推理时间”是一种在不扩大模型规模的情况下，在任务上花费更多算力、更多大脑周期的方式。

[原文] [Host]: on the topic of like embodiment uh and continuing with the human analogy how do you think about things like short and long-term memory in a human versus just like raw model capacity or something like what role does that analogy of memory I don't mean literally like like semiconductor memory but like memory in a model how do you think about the importance that that will play and where are we in that

[译文] [Host]: 关于具身智能（embodiment）的话题，继续用人类做类比，你是如何看待人类的短期和长期记忆，与单纯的模型容量（capacity）之间的对比的？记忆的类比——我指的不是半导体内存，而是模型中的记忆——将扮演什么角色？我们现在处于什么阶段？

[原文] [Dylan Patel]: the magic of transformers was uh attention right i.e i calculate everything in my context length i cont I I I calculate the attention to each other right basically in a vector space... but when you think about how that applies to you know humans what what we're terrible at like exact recall I could tell you a sentence and tell you to repeat it yeah it's like six numbers the average person can remember or something like that right but like you get the gist of the sent if I told you like if I told you a whole paragraph you'd get the gist of it and you could you could repeat the meaning of it to someone you could translate that meaning so models very different right fundamentally transformer attention has been you know calculating the attention to everything to each other and getting the models to actually be able to recall that's been a training data problem but like you can get the model to repeat exactly what you want anything in its context length it's like a needle in the haststack is the like problem

[译文] [Dylan Patel]: Transformer 的魔力在于注意力机制（Attention），对吧。也就是说，我在上下文长度内计算所有东西，我计算它们彼此之间的注意力，基本上是在向量空间中……但当你思考这如何应用于人类时，我们非常不擅长精确回忆。我可以告诉你一个句子让你重复，普通人大概只能记住六个数字之类的。但你能抓住句子的主旨。如果我告诉你一整段话，你会抓住主旨，你可以向别人复述它的意思，你可以翻译那个意思。所以模型是非常不同的。Transformer 的注意力机制一直在计算所有事物彼此之间的关联，让模型能够回忆起来——这曾经是一个训练数据的问题——但你可以让模型精确重复其上下文长度内的任何东西。这就是所谓的“大海捞针”（Needle in a Haystack）问题。

[原文] [Dylan Patel]: but now models are just like amazing right like tell me tell me like blah blah blah and random part of your context but what they really suck at is having infinite context because you have infinite and it's sp what the real word is sparse right you have sparse you you've taken this entire world and you've encoded it in such a small amount of data that lives in your brain and it's so sparse but you understood how to like grab the fundamental reason and put it down there whereas models they haven't been able to create something sparse yet right what is the long how do you how do you how do you reason over the context of infinity

[译文] [Dylan Patel]: 现在模型在这方面已经很惊人了。但它们真正糟糕的是拥有“无限上下文”。因为你拥有无限的……而且它是——那个词叫什么来着——稀疏的（Sparse），对吧。你的记忆是稀疏的。你把整个世界编码进你大脑中非常少量的数据里，它是如此稀疏，但你懂得如何抓住根本原因并将其存储在那里。而模型还没有能力创造出这种稀疏的东西。你如何在一个无限的上下文中进行推理？

[原文] [Dylan Patel]: and you know humans maybe we have like a short-term memory and a long-term memory i think it's a lot more blurry than that... but as we as we go back and back and back it's more and more sparse... now models they have there's a there's a ton of research going on in this domain of long context right how do I get longer and longer context without blowing up my model cost this is a big challenge with reasoning this is why you know we had this HBM bullish uh pitch for a while right is like you know you need a lot of memory when you extend the context right simple thesis right but the fundamental algorithm needs to change and improve over time iteratively to get to something like this short and long context of memory that doesn't necessarily mean the model has to work like we do right why can't the model just reason and have a database that it writes stuff in or like a word document that it writes stuff in and then it like takes it out of its context works somewhere and like Roful calls back

[译文] [Dylan Patel]: 人类也许有短期记忆和长期记忆，但我认为界限比那模糊得多……当我们回溯得越久远，记忆就越稀疏。现在的模型，在长上下文领域有大量的研究正在进行：如何获得越来越长的上下文而不让模型成本爆炸？这是推理面临的一个巨大挑战。这也是为什么我们有一段时间看好 HBM（高带宽内存）的原因，简单的逻辑就是当你扩展上下文时你需要大量内存。但基础算法需要随时间迭代改变和改进，以达到类似这种短期和长期上下文记忆的效果。这并不一定意味着模型必须像我们一样工作，对吧。为什么模型不能进行推理，然后拥有一个它可以写入东西的数据库，或者一个它可以写入东西的 Word 文档，然后把它从上下文中拿出来，在某处工作，然后再回调呢？

[原文] [Dylan Patel]: it's like oh yeah right like we don't do that right like you and I refer to our notes we refer to our calendar we refer to our text we refer to anything all the shopping list right like great I know I need food for dinner I go to the store I'm like I need a shopping list right like cuz otherwise I'm going to buy like stupid shit right is like it's like so so the model doesn't necessarily have to fundamentally work the same way as humans but there is that challenge of like how do I how do I train the model to operate over the context length of a human how do I train it to interact with these databases and these word documents that it writes to because it's never going to learn that from pre-training has to learn that from an environment

[译文] [Dylan Patel]: 这就像……没错，我们不也是这么做的吗？你和我都会查阅笔记，查阅日历，查阅短信，查阅购物清单。如果我知道晚餐需要食物，我去商店，我会想：“我需要购物清单。”否则我会买一堆蠢东西，对吧。所以模型不一定非要在根本上像人类一样工作，但挑战在于：我如何训练模型像人类一样在长上下文长度上操作？我如何训练它与这些它写入内容的数据库和 Word 文档进行交互？因为它永远无法从预训练中学到这些，它必须从环境中学习。

[原文] [Dylan Patel]: and so one of the first things Openi did was deep research right deep research is everything is not in deep research's context right deep research is working for like 45 minutes it's outputting millions and millions of tokens and it's creating this amazing like you know thing that it wrote right and it's like pretty good research um I would say a lot of like memos that you read from people are like on the on par with like deep research at least like a junior how did they do that was they they they enabled it to be able to write something down elsewhere and have this recall and and you know and and and effectively use language to compress information that it looked at put that off to the side use language to compress other information off to the side use language to compress other information off to the side and then looking at all this compressed information and writing something right and that's that's sort of what deep research is

[译文] [Dylan Patel]: 所以 OpenAI 做的第一件事就是 Deep Research（深度研究）。Deep Research 的所有内容并不都在它的上下文中。Deep Research 工作大概 45 分钟，输出数百万个代币，创造出它写的这个惊人的东西。这确实是相当不错的研究，我想说你读到的很多人的备忘录也就和 Deep Research 的水平相当，至少相当于一个初级员工的水平。他们是怎么做到的？他们让模型能够把东西写在别处，并拥有这种召回能力。有效地使用语言来压缩它查阅的信息，把压缩后的信息放在一边；再用语言压缩其他信息，放在一边……然后查阅所有这些压缩后的信息并写出东西来。这就是 Deep Research 的本质。

章节 7：通往“数字上帝”——AGI预测与具身智能

📝 本节摘要：

本章探讨了 AI 发展的终极形态与路径。Dylan 坦承虽然自己极度看好 AI，但相比 Sam Altman（预言 1000 天内实现 AGI）等人仍显得保守。他提出了“数字上帝”（Digital God）的概念，认为即便不达到这一神级境界，仅达到人类高级工程师的水平也足以创造数万亿美元的经济价值。

更关键的是，他认为通往真正 AGI 的最后一块拼图是“具身智能”（Embodiment）。通过生动的“婴儿吃手”比喻，他解释了人类如何通过物理反馈校准智能。相比之下，目前的机器人连“摇晃酒杯”这样简单的动作都难以完成，物理世界的数据飞轮甚至还没开始转动。

[原文] [Host]: if I add all of this up and you know hold the mirror up it's it seems like I would put you in the category of like unbelievably bullish on what these things are going to be able to do in 10 years time or something like pick your time frame am I calibrated the right way like amongst everyone you talk to who you respect and think is I'm much more emarrassed than a lot of people actually which is the crazy thing

[译文] [Host]: 如果我把所有这些加起来，拿镜子照照现状，我似乎会把你归类为那种“难以置信的乐观派”（bullish），相信这些东西在 10 年内——或者不管你选什么时间框架——能做到什么程度。我的判断准确吗？在你尊重并认为有见地的人中，你处于什么位置？

[Dylan Patel]: 实际上，相比很多人，我反而显得更窘迫（不够乐观），这才是疯狂的地方。

[原文] [Host]: help help me understand that distinction like if you're where are you 1 through 10 amongst the people that you respect 10 being the most bullish and then like what is the difference between if you're not a 10 what's the difference between you and the person who's a 10

[译文] [Host]: 帮我理解一下这种区别。如果在你尊重的人中按 1 到 10 打分，10 分是最乐观的，你在哪里？如果你不是 10 分，那你和 10 分的人之间有什么区别？

[原文] [Dylan Patel]: i respect you but I know I'm like way more bullish than you and I respect like Mark Zuckerberg but I know he might be he's probably maybe I don't know if he's more bullish than me but I know Sam Alman's definitely way more bullish than me right he says he says we have artificial general intelligence in in less than a thousand days right say you know like or Dario like I respect him immensely but he's way more bullish than me right

[译文] [Dylan Patel]: 我尊重你，但我知道我比你乐观得多。我尊重像马克·扎克伯格这样的人，但我知道他可能……我不确定他是否比我更乐观。但我知道 Sam Altman 绝对比我乐观得多，对吧。他说我们在不到 1000 天内就会拥有通用人工智能（AGI）。或者像 Dario（Anthropic CEO），我非常尊重他，但他比我乐观得多，对吧。

[原文] [Dylan Patel]: I'm I'm actually even more curious about like the upper limit the extent to which there is the upper limit I think I'm among the most bullish you can get because that's what I mean the upper limit of this is that this will just be smarter than humans i don't think that will happen like anytime soon even if that doesn't happen anytime soon there's so much valuable stuff that can be done with these models that economically we will skyrocket there's so much value that can be created in the world just by hey if the models know how to do uh cobalt to like C and Python migration of like main frames just migrate everything migrate everything from mainframes to cloud the world is how much more efficient

[译文] [Dylan Patel]: 我实际上对“上限”更感好奇，如果真的存在上限的话。我认为在这个问题上我是你能找到的最乐观的人之一，因为我的意思是，这东西的上限就是它将比人类更聪明。我不认为这会很快发生。但即便这不会很快发生，这些模型能做的有价值的事情也太多了，经济将会因此一飞冲天。仅仅是——嘿，如果模型知道如何做从 COBOL 到 C 和 Python 的大型机迁移，把所有东西从大型机迁移到云端，世界将会变得多么高效？这就能在世界上创造出巨大的价值。

[原文] [Dylan Patel]: we could literally just pause it at like a 6 months from now time frame of like how good it is at software development and it would be like godsend in terms of like how much efficiency and value can be created for the economy and it doesn't ever have to get to like digital god Now now I do believe we're going to get the digital god eventually eventually is that 10 years is that 5 years is that 100 years is that a thousand years i don't know cuz there's there's so many unknown unknowns

[译文] [Dylan Patel]: 我们甚至可以把技术停滞在从现在起 6 个月后的水平，比如它在软件开发方面的能力。这就已经是天赐之物（godsend）了，就能为经济创造出巨大的效率和价值，它根本不需要达到“数字上帝”（Digital God）的级别。不过，我现在确实相信我们最终会达到“数字上帝”的境界。至于那是 10 年、5 年、100 年还是 1000 年？我不知道，因为有太多未知的未知数。

[原文] [Dylan Patel]: like I mentioned right like these these babies are putting their freaking hand in their mouth to calibrate and then later they put their foot in their mouth and they're like "Oh that's my foot oh here's the senses on it." And then they can pick up stuff in their hand and they no longer have to put it on their most sensitive part of their body because they know what it is or they're like "Oh this is a speck on the ground what is it it's not food but now I know what it feels like inside my hands and I've calibrated right it's like the models have not gotten there yet right like it has no idea how to do this

[译文] [Dylan Patel]: 就像我之前提到的，那些婴儿把他们的手塞进嘴里来校准感知。然后之后他们把脚塞进嘴里，他们会想：“噢，那是我的脚，噢这里有这种感觉。”然后他们能用手拿起东西，不再需要把东西放到身体最敏感的部位（舌头）去感知，因为他们知道那是什么了。或者他们会想：“噢地上有个斑点，那是什么？不是食物，但我现在知道它在我手里的感觉了。”我已经校准好了，对吧。现在的模型还没有达到那个阶段，它完全不知道该怎么做这个。

[原文] [Dylan Patel]: digital god is like well one I like kind of believe in embodiment and like you need non-digital god you need a physical and you need the capability of like having touch and feel and all that to truly be uh have have an experience like humans and be smarter than us in every way but you know that's so far away

[译文] [Dylan Patel]: 关于“数字上帝”，首先我有点相信具身智能（Embodiment），你需要“非数字上帝”，你需要一个实体，你需要拥有触觉和感觉以及所有那些能力，才能真正拥有像人类一样的体验，并在各个方面都比我们聪明。但这还非常遥远。

[原文] [Host]: what do you think about what physical intelligence is doing attacking the whatever you want to call it large movement model or large robot model or something what are they actually doing today is like holy shit it's so simple in terms of like to a human it's like to to models it's like picking this up is freaking hard like how much do I squeeze my pinky versus this finger versus this finger versus finger i don't know like but you pick up a glass of water and you tilt it and it's like this is impossible for a model today

[译文] [Host]: 你怎么看 Physical Intelligence（一家初创公司）正在做的事情？不管你叫它大型动作模型还是大型机器人模型。他们今天实际在做的事情，对人类来说简直简单得离谱，但对模型来说，“把它拿起来”简直难如登天。比如我的小拇指要用多大力，对比这个手指，再对比那个手指？我不知道，你拿起一杯水并倾斜它，这在今天对模型来说几乎是不可能的。

[原文] [Dylan Patel]: and it's likely like at the level of dexterity like you know if I if I if it was a wine glass and I was swishing it think about how simple that is you don't even think about it but like you instinctually pick up a wine glass and you swish it and it lets the aroma out and you smell it but it's like oh that little swish is so much tactile feedback and movement and it's like these models can't do that shit yet like nowhere close

[译文] [Dylan Patel]: 而且这种灵巧程度……如果你拿着一个红酒杯摇晃它，想想那有多简单，你甚至都不用思考。你本能地拿起酒杯，摇晃它，让香气散发出来，然后你闻一闻。但这轻轻一摇包含了大量的触觉反馈和运动控制。这些模型还做不到那些屁事（shit），差得远呢。

[原文] [Dylan Patel]: so I mean I think yes we have you know but it doesn't need to be that good it doesn't need to be able to swish a wine glass and not break the wine glass and put it back down and tilt it perfectly and not spill it doesn't need to be able to do any of that to be tremendously valuable what it needs to be tremendously valuable is pick this up and put it down here after knowing what it is um so there's so much value that could be created just by being you know really good at like getting data yeah yes i like you know I think the robotics world is huge i think we're you know we're we're like we literally warming up we haven't even left the dug dugout right like we're we're like nowhere close to you know the scaling on on robotics there's a ton of like the data flywheel needs to get going there

[译文] [Dylan Patel]: 所以我的意思是，是的，但它其实不需要那么好。它不需要能摇晃酒杯而不打碎它，或者完美地放下并倾斜而不洒出来。它不需要做任何那些就能变得极具价值。要变得极具价值，它只需要能做到：在知道这是什么之后，把它拿起来，放到这里。哪怕仅仅是擅长获取数据，也能创造巨大的价值。我认为机器人世界是巨大的。我觉得我们……我们简直才刚刚热身，我们甚至还没走出休息区（dugout，棒球术语，指还没上场）。我们离机器人的规模化还差得远呢。在那儿，数据飞轮（data flywheel）还需要转动起来。

章节 8：人才战争——过程知识与“调参”的艺术

📝 本节摘要：

本章揭示了为何顶级 AI 研究员能获得天价薪酬的底层逻辑。Dylan 认为，机器学习（ML）研究本质上与半导体制造如出一辙：两者都涉及在近乎无限的搜索空间中调整成千上万个“旋钮”（Knobs）。

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拥有“过程知识”（Process Knowledge）的人才之所以昂贵，是因为他们能在巨额算力（如价值数千亿美元的集群）上通过直觉减少无效实验，从而节省巨大的时间和成本。Dylan 用“皮肤科医生与家庭医生”的收入差距类比了人才流向，并引用 Jensen Huang 的观点指出，美国之所以富裕，是因为保留了最高价值的设计与创意，而将制造劳动力外包。

[原文] [Host]: one of the most interesting things the subplots of this whole world is the talent wars and a cool idea is that as these things get better maybe we begin to automate some of the research function that people formally would have played do you see a world where like we're squeezing down the fewer and fewer number of people that really matter that will have all the impact on where we go uh in terms of like net new research and that means that all this crazy spending that's happening at Meta or elsewhere makes a lot of sense that like maybe even those numbers should be higher or something like this

[译文] [Host]: 这个世界最有趣的支线剧情之一就是人才战争。一个很酷的想法是，随着这些技术变得更好，也许我们开始自动化一些以前由人扮演的研究职能。你是否预见到这样一个世界：真正重要的人越来越少，也就是那些对我们未来走向——比如净新增研究——拥有全部影响力的人？这意味着 Meta 或其他地方发生的疯狂支出其实很有道理，甚至这些数字可能应该更高？

[原文] [Dylan Patel]: i think it's like tremendously hilarious that people are like "Oh my god this person's getting paid a billion dollars." It is infeasible it's like how could this person possibly be worth that much well they're running the experiments on chips that cost you know hundred billion if every wasted experiment they do if they just used like a third of the compute and and their ideas and their impact on it wasted the compute it was an idea that was already done or like you know like there's so much wasted compute i'll say I call it wasted it's trying stuff and failing but like none of us know what to try and what not to try

[译文] [Dylan Patel]: 我觉得特别滑稽的是，人们会说：“天哪，这个人拿了 10 亿美元薪水。”这看起来不可行，就像这个人怎么可能值那么多钱？好吧，他们正在价值数千亿美元的芯片上运行实验。如果他们做的每一个无效实验——如果他们只用了三分之一的算力，而他们的想法和影响却浪费了算力，或者那个想法是别人已经做过的……可以说有大量的“被浪费的算力”。我称之为浪费，其实是“尝试然后失败”，但我们谁也不知道该尝试什么，不该尝试什么。

[原文] [Dylan Patel]: and and these things are so complicated there's like a group of people just trying different stuff on the existing data how do you mix it what order do you feed it into the model um how do you filter it like what's the architecture there's different people working on long context there's different people working on every single aspect of the model that like if you just make them a little bit more efficient that they come up with the idea that's 5% more efficient well fantastic i just saved not only 5% of my compute time training time I also save 5% across my entire inference fleet and then I do it again and again and again and again because we're so far away from like these models being anywhere near as efficient as a human brain

[译文] [Dylan Patel]: 而且这些东西太复杂了。有一群人只是在现有数据上尝试不同的东西：怎么混合数据？按什么顺序喂给模型？怎么过滤数据？架构是什么？有不同的人在研究长上下文，有不同的人在研究模型的每一个方面。如果你能让他们稍微高效一点，比如他们想出的主意能带来 5% 的效率提升，那就太棒了。我不仅节省了 5% 的训练算力时间，我还节省了整个推理集群 5% 的成本。然后我一次又一次地这样做，因为我们离这些模型像人脑一样高效还差得远呢。

[原文] [Dylan Patel]: adding more people to the problem doesn't make it faster right because there's so many things you're trying you run these experiments you learn something and then you implement it you tweak the knobs in these ways in 100 different ways and then you see the trend line and you're like "Oh so actually I should tweak it this way let's implement that." Right there's so much like gut feel there's so much like reading data understanding it imple reimplementing it into these things that if you add people you're going to slow it down in a sense a lot of like Meta's problems before they did the super intelligence thing was that they just had too many people that weren't led by leadership that was amazing um and they had like a lot of failed experiments and wasted time doing things that didn't matter

[译文] [Dylan Patel]: 增加更多的人手并不会让问题解决得更快，对吧。因为你在尝试太多东西了，你运行这些实验，你学到一些东西，然后你实施它。你以 100 种不同的方式调整这些“旋钮”（knobs），然后你看到趋势线，你会想：“噢，其实我应该这样调整，让我们实施这个吧。”这里面有太多的直觉（gut feel），太多的读取数据、理解数据、重新实施。如果你增加人手，某种意义上你会拖慢速度。Meta 在搞超级智能之前的一个大问题就是他们有太多人，但缺乏出色的领导层，他们有很多失败的实验，在无关紧要的事情上浪费了时间。

[原文] [Dylan Patel]: um there's a there's a tweet from one of my friends uh at OpenAI um he's pretty famous on Twitter his name is Run he's like I get visibly viscerally angry every time I think about how many H100s Meta is wasting it's such a funny tweet because it's like well yeah they're wasting a ton of compute they were you know maybe they still are but you know like and everyone's wasting compute right opening eyes wasting tons of compute cuz you know what's the paralo optimal model architecture who knows

[译文] [Dylan Patel]: 我在 OpenAI 的一个朋友发了一条推文——他在推特上很有名，叫 Roon——他说：“每次想到 Meta 浪费了多少 H100 芯片，我就明显地、发自内心地感到愤怒。”这是一条很有趣的推文，因为是的，他们确实浪费了大量的算力。他们以前是，也许现在还是。但你知道，每个人都在浪费算力，OpenAI 也在浪费大量算力，因为谁知道什么是帕累托最优的模型架构呢？

[原文] [Dylan Patel]: that's almost a function of why like Intel is has fallen off a lot right is like um you have all these geniuses in you know you know nanochemistry and PhDs and all these like random like you know like things whether it be chemistry physics all these incredibly smart people but there's a whole class of incredibly smart people that never went that way because they're like "Oh those guys are making like 200k." Like why would I do that i'm gonna go I'm gonna go to Google and make 800k and now I'm going to go to OpenAI and make 10 mill or no I'm going to go to Meta and go make 100 million right like any like smart 18-year-old is going to be like "Fuck that i'm doing this right?"

[译文] [Dylan Patel]: 这几乎也是 Intel 衰落的一个原因，对吧。你在纳米化学、博士以及所有这些领域——无论是化学还是物理——都有天才。但有一整类绝顶聪明的人根本没走那条路，因为他们会想：“噢，那些家伙只能赚 20 万美元。”我为什么要那样做？我要去 Google 赚 80 万，或者现在我要去 OpenAI 赚 1000 万，不，我要去 Meta 赚 1 亿，对吧。任何聪明的 18 岁孩子都会想：“去他的，我要干这个，对吧？”

[原文] [Dylan Patel]: Why do the smartest doctors and I don't mean to say the smartest doctors in a general sense but their skews really smart population of doctors that want to be dermatologists and anesthesiologists it's like is that the most valuable thing for them to do no but those are the two professions that give you like good working hours and great pay... talent wars like it it is truly like you know we've sort of been through this process of like capital has you know it's it's always been human human capital and and capital goods sort of those two vying off of each other

[译文] [Dylan Patel]: 为什么最聪明的医生——我不是说广义上的最聪明，而是说医生群体中非常聪明的那部分人——都想成为皮肤科医生和麻醉师？这是他们能做的最有价值的事情吗？不是，但这两种职业能给你很好的工作时间和极高的薪水……人才战争真的就像……你知道，我们经历了这个过程，人力资本和资本货物（capital goods）这两者一直在相互竞争。

[原文] [Dylan Patel]: one thing Jensen told me which I thought was like amazing right he's like you know Dylan he's like the reason America is rich like people have it all wrong the reason we're rich is because we've exported all the labor but we've kept all the value and that's what Nvidia does right they've exported the labor of making their chips and Apple right everyone it's it's done in Asia um and those those companies make money not as much money as Nvidia and Apple right all the gross profits are going to them

[译文] [Dylan Patel]: Jensen（黄仁勋）曾告诉我一件事，我觉得非常棒。他说：“你知道吗 Dylan，人们都搞错了。美国之所以富裕，是因为我们出口了所有的劳动力，但我们保留了所有的价值。”这也是 Nvidia 所做的，对吧。他们出口了制造芯片的劳动力，还有 Apple，所有这些都是在亚洲完成的。那些（代工）公司赚钱，但没赚到像 Nvidia 和 Apple 那么多钱，对吧。所有的毛利润都流向了他们。

[原文] [Dylan Patel]: i think the ML researchers are an extreme of how much value one can do but my favorite analogy that I came up with recently is that ML research is the exact same as semiconductor manufacturing you know there's a ton of jobs in in in semiconductor manufacturing that don't exist in ML research but it is a ton of tune a thousand different knobs right oh you put the wafer in this tool you're going to change the pressure of the chamber when you're doing the deposition oh you're going to change the mix of the chemicals flowing in which chemicals you're putting in right like what what speed you do it at do you do it for 30 minutes do you do it for 31 minutes

[译文] [Dylan Patel]: 我认为机器学习（ML）研究员是个人能创造多少价值的极端案例。但我最近想到的最喜欢的类比是：ML 研究与半导体制造完全一样。你知道，半导体制造中有很多工作在 ML 研究中并不存在，但本质上都是调整一千个不同的“旋钮”（knobs），对吧。“噢，你把晶圆放进这个工具里，做沉积的时候你要改变腔体的压力；噢，你要改变流入化学物质的混合比例，放哪种化学物质；你要用什么速度做？做 30 分钟还是 31 分钟？”

[原文] [Dylan Patel]: you have a thousand input and process knobs right process knobs on each tool plus it's like the sequence of them all and and so like you frankly cannot test everything right it's impossible it's it's too large of a search space just like just like designing a chip is too large of a search space you have 100 trillion transistors how you going to possibly try every single thing impossible right you just have to have enough intuition like pick that point pick that point pick that point see the data oh okay i think the answer is here right and then just yolo right

[译文] [Dylan Patel]: 每个工具上都有一千个输入和工艺旋钮，加上所有这些的顺序。坦率地说，你不可能测试所有东西，对吧。这是不可能的，搜索空间太大了。就像设计芯片的搜索空间也太大了，你有 100 万亿个晶体管，你怎么可能尝试每一个组合？不可能。你必须有足够的直觉，比如：“选那个点，选那个点，选那个点，看数据。噢好的，我觉得答案在这里。”然后就直接“Yolo”（不管三七二十一直接上），对吧。

[原文] [Dylan Patel]: um and and you know obviously once you you think the answer is here you test here and you're like okay here but like a different person might have seen these three and then said okay the answer is actually here not here and like the data is like fuzzy it's like somewhere in the center but like you know it's like this this whole like idea of like ML research is you spend a lot of time on compute training doing what effectively were useless things besides teaching yourself what's the right thing to do and what's the wrong thing to do and and semiconductor manufacturing is the same way

[译文] [Dylan Patel]: 显然一旦你认为答案在这里，你会测试这里，然后确认。但另一个人可能看了这三个点，然后说：“好的，答案其实在那里，不在那里。”数据是模糊的，大概在中心某处。但这整个 ML 研究的概念就是，你花费大量时间在算力训练上，做那些实际上看似无用的事情，除了教会你自己什么是对的、什么是错的。半导体制造也是一样的方式。

章节 9：生态权力结构——谁掌握了AI产业链的命门？

📝 本节摘要：

本章深入剖析了 AI 生态系统中的权力流向与金钱分配。Dylan 以代码编辑器 Cursor 与模型厂商 Anthropic 的关系为例，揭示了“亦敌亦友”（Frenemies）的复杂博弈：虽然 Anthropic 赚取了模型调用的费用，但绝大部分毛利最终都流向了硬件层（Nvidia、Broadcom、TSMC）。与此同时，Cursor 掌握了用户交互数据这一核心资产，甚至可以随时切换底层模型。

此外，本章还探讨了 Nvidia 作为“王者”的尴尬处境——手握巨额现金却因反垄断无法进行大规模并购，只能通过“资产负债表”来为客户（如 CoreWeave）担保租赁协议，甚至间接通过风险投资让初创公司将融资额的 70% 重新“回流”购买 GPU。这是一个层层嵌套、高度相互依赖的利益共同体。

[原文] [Host]: i want to go back to where all the way to where we started and and ask about what I'll call like the like the wellspring or the fountain of power in this whole ecosystem so I want to understand how you think about who has the power and how to keep or generate power as a business i mean um because it seemed like talent maybe talent is like the very beginning of the chain and he who has the talent like on a long enough timeline has the power or something like that but also there's structural stuff like just the scale the industrial scale of some of these things which just takes forever to build or whatever how do you think about um even smaller zoomed in examples like okay cursor is unbelievably popular the revenue is insane um so much of it goes back to anthropic like who is the power in that relationships how does that dynamic change over time it just seems like the power dynamics are so um so fascinating in this world and I'm curious where you think it like kind of comes from in the first place like where where it exists today and where it will go in the future

[译文] [Host]: 我想回到我们最开始的地方，问问关于这个整个生态系统中的“权力源泉”（wellspring or fountain of power）。我想了解你是如何看待谁拥有权力，以及作为一个企业如何保持或产生权力。因为看起来人才可能是链条的最顶端，拥有人才的人——在足够长的时间线上——就拥有权力，或者类似的说法。但也有结构性的东西，比如某些事物的工业规模，这些规模需要很长时间才能建立。你是怎么看那些更微观的例子的？比如 Cursor 现在难以置信地受欢迎，收入疯狂增长，但其中很多收入流回了 Anthropic。在那段关系中谁拥有权力？这种动态随时间如何变化？这世界里的权力动态似乎非常迷人，我很好奇你认为它最初来自哪里？今天存在于哪里？未来又会去向何方？

[原文] [Dylan Patel]: when we think about like the power structures right like you mentioned a really interesting one does anthropic hold all the cards in this cursor relationship cursor has like I don't know like nearly a billion dollars of revenue now on a on a on if you do current month times 12 that's a ton but like again like their margins are what they are and they're sending most of it back to Anthropic you know some people say their margins may be negative i think they're slightly positive but regardless they're sending most of it back to anthropic the gross profit dollars are at Enthropic right now and but then Enthropic is taking anthropic is taking all the gross profit dollars and putting them into compute y for training yep so then all those gross profit dollars are going to like Jensen laughing hilariously well maybe Jensen or maybe like Amazon who's then sending it to like uh or or or or Google who's sending it to Broadcom right like the gross profit dollars are going to the hardware layer um from all of this for sure

[译文] [Dylan Patel]: 当我们思考权力结构时，你提到的那个例子非常有趣：Anthropic 在与 Cursor 的关系中是否掌握了所有的牌？Cursor 现在大概有接近 10 亿美元的收入（如果是按当前月收入乘以 12 计算的话），这是一大笔钱。但同样的，他们的利润率就是那样，他们把大部分钱都送回给了 Anthropic。有些人说他们的利润率可能是负的，我认为是微薄的盈利，但无论如何，他们把大部分钱都给了 Anthropic。毛利现在都在 Anthropic 手里。但是，Anthropic 拿着这些所有的毛利美元，又把它们投入到算力上去训练模型。是的，所以所有这些毛利美元最终都流向了 Jensen（黄仁勋），让他笑得合不拢嘴。或者也许是 Jensen，也许是亚马逊，然后亚马逊再付给……或者是 Google 付给 Broadcom。总之，所有的毛利美元肯定都流向了硬件层。

[原文] [Dylan Patel]: but like does anthropic have all the power like you know like the common view is yes from a lot of people but then it's like well but anthropic only makes the model that's generated the code there's a lot more in this system right cursor gets all of the data um they get all of the users they get how do they interact with this enthropic doesn't get that they get a prompt they send a response prompt response now now they have cloud code which is like taking share um and it's very different than cursor but like you know anyways like they get prompt response and then like cursor is like oh well I'm training embedding models on your code database and I have there's actually multiple models that I've made i've made the embedding model i've made the autocomplete model i've made you know oh I can switch the enthropic model to open the eye model whenever I want to i'm only using the entropic model because it's the best one oh and because I have all this data maybe I can train a model not for everything better than you but for the segment better than you and so it's like the power dynamics are you know it's weird it's it's it's their frenemies right everyone's a friend right same as with Open Eye and Microsoft

[译文] [Dylan Patel]: 但是 Anthropic 真的拥有所有权力吗？很多人的普遍观点是“是的”。但实际上，Anthropic 只是制造了生成代码的模型而已，这个系统中还有很多其他东西。Cursor 获得了所有的数据，他们获得了所有的用户，他们获得了用户如何交互的信息。Anthropic 得不到这些，他们只得到一个提示词（prompt），然后发送一个回复，提示词-回复。现在 Anthropic 有了 Claude Code，这正在抢占份额，虽然它和 Cursor 很不一样，但不管怎样，他们只得到提示词和回复。而 Cursor 会想：“噢，我正在你的代码数据库上训练嵌入模型（embedding models），实际上我做了多个模型，我做了嵌入模型，我做了自动补全模型。而且，我随时可以把 Anthropic 的模型切换成 OpenAI 的模型，我用 Anthropic 只是因为它现在是最好的。而且因为我有所有这些数据，也许我可以训练一个模型，不是在所有方面都比你强，但在这一特定细分领域比你强。”所以这种权力动态很奇怪，他们是“亦敌亦友”（frenemies），对吧。每个人都是朋友，就像 OpenAI 和微软一样。

[原文] [Dylan Patel]: the Microsoft opening I1 is absurdly interesting because at one point right like 2023 it was like Microsoft's going to own the world yeah right 2024 a lot of it too and then like H2 2024 Microsoft backed down a lot right they pulled back because because uh Amy Hood and and whoever else at Microsoft Mipundar whoever were like "Maybe we don't need to be on the hook for a $300 billion we're not going to build out $300 billion worth of compute for Open AI." Like that's they can't pay for it yeah right it was like like was at least had to go through their head when they cut back and so they paused a bunch of data centers right and they said "Oh you know we don't need to be the exclusive comput provider you can go to Oracle it's fine." Right like and they like relinquish this power right now Oracle has that deal but then like OpenAI sends like 20% of their revenue to Microsoft or API revenue or something like this and then you know they have Microsoft has this like 49% capped profit structure on OpenAI and then there's like this whole like IP sharing like this deal like you you it's like really hard to understand the mechanics of the OpenAI Microsoft deal even um so you have this like whole power dynamic and they're trying to renegotiate this

[译文] [Dylan Patel]: 微软和 OpenAI 的关系也荒谬地有趣。因为在某个时间点，比如 2023 年，看起来微软要拥有全世界了，对吧。2024 年大部分时间也是如此。然后到了 2024 年下半年，微软退缩了很多，他们撤退了。因为 Amy Hood（微软 CFO）和其他微软高管可能会想：“也许我们不需要为一个 3000 亿美元的项目背锅，我们不打算为 OpenAI 建设价值 3000 亿美元的算力。”因为他们（OpenAI）付不起这笔钱，对吧。当他们削减投入时，这念头至少在脑子里过了一遍。所以他们暂停了一堆数据中心，说：“噢，你知道，我们不需要做独家算力提供商，你可以去找 Oracle，没关系。”他们放弃了这种权力。现在 Oracle 拿到了那笔交易，但 OpenAI 还要把 20% 的收入（或者是 API 收入之类的）分给微软。然后微软在 OpenAI 还有 49% 的利润上限结构，还有整个 IP 共享协议。这笔交易……你甚至很难理解 OpenAI 和微软交易的机制。所以这里有这种整体的权力动态，而且他们正在试图重新谈判。

[原文] [Dylan Patel]: u another power dynamic is the one around Nvidia and the hyperscalers right nvidia is the king all of the gross profit is going to them today right pretty much all of it sure TSMC makes some sure SKH makes some but they have to invest a ton in capex sure Broadcom makes a bunch and you know Broadcom makes a ton of gross profit off of these companies but like Nvidia makes by by far the most grow gross profit in the industry and it's not even close and so going back to like the analogy of like well they're king and they want to continue to be king and they want to make sure GPUs continue to be most used but also like they can't buy anything like they can't buy any companies they weren't even allowed to buy ARM when they were like a nobody right i don't I don't mean nobody but they weren't like they were like pretty much a nobody on the grand scheme of things and they weren't allowed to buy ARM you know in like 2020 or whatever or 2021 whatever the time frame was they totally could not buy any major companies

[译文] [Dylan Patel]: 另一个权力动态是围绕 Nvidia 和超大规模云计算厂商（Hyperscalers）的。Nvidia 是王者，今天所有的毛利都流向了他们，几乎全部。当然 TSMC 赚了一些，SK Hynix 赚了一些（但他们必须投入大量资本支出），Broadcom 也从这些公司身上赚了一大笔毛利。但是 Nvidia 赚取的行业毛利是断层式的第一，遥遥领先。所以回到那个类比，他们是国王，他们想继续做国王，想确保 GPU 继续被最广泛地使用。但同时，他们什么也买不了，他们不能收购任何公司。当他们还是个“无名小卒”的时候——我不是说真的是无名小卒，但在宏大叙事中那时他们还不够分量——他们甚至不被允许收购 ARM。那是 2020 年还是 2021 年的事。他们完全不能收购任何大公司。

[原文] [Dylan Patel]: um you know they'll buy smart startups like you know I bought a startup that I was like a seed investor in and like an adviser in like all these things like recently but like they they can't buy a real company so what do they do with all this cash flow and like sorry but you're a loser if you just do buybacks like that just that's admitting that's admitting you can't get higher returns yep on your capital on your capital which is fine like you know Meta Apple Google they were mature companies for a while guess what they're gonna those companies aren't going to do buybacks ever fucking again right or not like ever again but for a while cuz like they have way more they think there's better ROI for their capital now and Nvidia like you know if you look at Jensen he's like he's like he's always like flirted with buybacks but like mostly he's been like reinvesting in the business but you can't reinvest that much into the business so like how do you He's doing demand guarantees he's doing like all this crazy stuff now yeah right right he's using his balance sheet to win yeah try and win more right

[译文] [Dylan Patel]: 他们会买一些聪明的初创公司，比如最近他们买了一家我是种子投资人和顾问的公司。但他们不能买真正的（大）公司。所以他们拿这些现金流做什么？抱歉，如果你只做股票回购，那你就是个输家。那就等于承认你在你的资本上无法获得更高的回报。这也没关系，像 Meta、Apple、Google 曾有一段时间是成熟公司。但猜猜怎么着？这些公司再也不会做回购了——或者说暂时不会了——因为他们认为现在资本有更好的 ROI（投资回报率）。如果你看 Jensen（黄仁勋），他总是对回购态度暧昧，但主要还是在对业务进行再投资。但你也无法往业务里再投资那么多钱。所以他怎么做？他在做需求担保（demand guarantees），他在做所有这些疯狂的事情。是的，他在利用他的资产负债表来赢，试图赢得更多。

[原文] [Dylan Patel]: um which which is an interesting uh dynamic i don't know if there's ever been anything like this in terms of the non non anti-competitive nature of this right like where you backs stop clusters so that you know like Cor recently got a deal with Nvidia where it was like they backs stopped a cluster right now core would have never built this cluster because it's for like short-term demand and renting GPUs on short-term is like a terrible business model right you want to do long-term contracts you've and you want to do long-term contracts to people with balance sheets that's the golden like goose but that doesn't exist so much so you do long-term contracts of people who don't have a balance sheet like OpenAI and if you can't do that then you'll do you know short contracts with people who don't have a you who do have a balance sheet right like there's this whole matrix of like who you rent GPUs to

[译文] [Dylan Patel]: 这是一种有趣的动态，我不知道以前是否有过类似的事情——这种非反竞争性质的操作。比如你为集群做担保（backstop）。CoreWeave 最近和 Nvidia 达成了一项协议，Nvidia 为一个集群做了担保。CoreWeave 本来永远不会建这个集群，因为这是为了短期需求，而短期租赁 GPU 是个糟糕的商业模式，对吧。你想做长期合同，你想和有资产负债表的人签长期合同，那是金鹅（最理想的情况）。但那种情况不多。所以你和像 OpenAI 这样没有资产负债表的人签长期合同。如果你做不到那个，那你就和有资产负债表的人签短期合同。这就构成了一个关于你把 GPU 租给谁的完整矩阵。

[原文] [Dylan Patel]: but from Nvidia's interest it's like you know what I really love is when venture capitalists fund a company and then 70% of their round is spent on compute they fucking love that right and that's what's happening with all these companies like these and and like it's like whether it's physical intelligence who you know they're spending a lot on like robot arms and shit too but they're also spending a lot of compute or it's like you know any other startup that's raising cursor whoever right and even if it's not directly it's indirectly going to GPUs um they love they love when people spend their entire round on a GPUs

[译文] [Dylan Patel]: 但从 Nvidia 的利益来看，你知道他们真正喜欢什么吗？是当风险投资家资助一家公司，然后那一轮融资的 70% 都花在算力上时，他们简直爱死那个了。这就是所有这些公司正在发生的事情。无论是 Physical Intelligence（虽然他们也花很多钱买机械臂之类的，但也花很多钱买算力），还是其他任何正在融资的初创公司，比如 Cursor 或者是谁。即使不是直接流向 GPU，也是间接流向 GPU。他们喜欢人们把整轮融资都花在 GPU 上。

章节 10：经济周期——过度建设风险与“铁路热”的启示

📝 本节摘要：

本章直面了市场对 AI 基础设施“过度建设”（Overbuilding）的担忧。Dylan 坦言，如果 AI 模型停止进步，当前的巨额投入将导致灾难性的后果，甚至引发美国经济衰退。但他将当下的 AI 热潮与历史上的泡沫进行了区分：它不像郁金香狂热（纯粹的胡闹）或加密货币（庞氏骗局），而更像 19 世纪英国的“铁路热”或光纤泡沫——虽然最终导致了产能过剩，但留下的基础设施极大地降低了成本并改变了社会。他强调，只要缩放定律（Scaling Laws）继续生效，市场对“高级工程师级智能”的需求实际上是无限的（价值 2 万亿美元），因此我们目前离真正的饱和还很远。

[原文] [Host]: If you ask a bunch of investors who are like students of economic cycles through history like Carlo Perez type stuff they'll say that the concern is that every shortage is followed by a glut and we always overbuild on long lead time big capex projects and you've got multi- gigawatt you know power being installed you've got all this crazy stuff in semiconductors and like at some point like it just gets overbuilt all the stuff we talked about earlier feels like we're not really close to that like there's so much freaking demand

[译文] [Host]: 如果你去问那些研究历史经济周期的投资者——比如卡洛塔·佩雷斯（Carlota Perez）那一派的人——他们会说，令人担忧的是每一次短缺之后都会紧接着出现过剩（glut）。对于长周期的巨额资本支出项目，我们总是会建设过度。现在你有数吉瓦的电力设施正在安装，半导体领域也有各种疯狂的投入。在某个时刻，这一切就会变得过剩。虽然我们之前谈到的那些内容让人感觉我们离那一步还很远，因为需求看起来如此疯狂。

[原文] [Dylan Patel]: if the models don't improve yes we will overbuild right like it's pretty simple it's like yes there will be like supply chain things where switches from one supplier to another and like that's a lot of the stuff that we like nitty-gritty stuff we focus on at the end of the day if the models don't improve we're absolutely screwed in fact the US like you know in another year if if this lasts another year and then it happens like the US economy will go into recession like straight up because of this and probably Taiwan as well and probably Korea as well right because there's so much buildup and revenue flowing through to us for this right

[译文] [Dylan Patel]: 如果模型不进步，是的，我们就会建设过度，这很简单。当然会有供应链在不同供应商之间切换这类我们关注的细枝末节，但归根结底，如果模型不进步，我们就彻底完蛋了（absolutely screwed）。事实上，如果这种情况持续一年然后泡沫破裂，美国经济将直接因此陷入衰退。大概台湾和韩国也会如此，因为有太多的建设和收入流都与此相关。

[原文] [Dylan Patel]: but you know when you look at these other things like the bubbles of the past some of them were just silly nonsense right like tulips silly nonsense right crypto complete Ponzi scheme right but then there's other stuff that's like no this was real right like the the UK like spent like some absurd percentage of their GDP on railroads for like a decade 6% or something crazy yeah we're nowhere close to GD 6% of our GDP like holy shit

[译文] [Dylan Patel]: 但当你回顾过去的那些泡沫时，有些纯粹是愚蠢的胡闹，比如郁金香狂热，纯属胡闹；加密货币，彻头彻尾的庞氏骗局。但还有其他一些东西是真实的，对吧。比如英国曾花了大概十年时间，把 GDP 中一个荒谬的比例投入到铁路建设上，大概是 6% 还是什么疯狂的数字。是的，我们现在的投入离 GDP 的 6% 还差得远呢，天哪。

[原文] [Dylan Patel]: um but like that was like okay there's tangible but it's like oh well we over did overbuild because like how many goods are there to transport but like also you must like reduce you must build these railroads to reduce the cost of transport so much because you have no clue when the demand stops and you've overbuilt and because there's 10 people trying to do it at once you're obviously going to overbuild at some point um same thing with fiber

[译文] [Dylan Patel]: 那是实实在在的东西。但当时的情况是：“好吧，我们要建设过度了，因为到底有多少货物需要运输呢？”但也正因为你不知道需求何时会停止，而且你必须建设这些铁路来极大地降低运输成本，再加上有 10 个人同时在做这件事，你显然会在某个时刻建设过度。光纤也是同样的情况。

[原文] [Dylan Patel]: a lot of the argument against this is like well no but this time it's the strongest balance sheets in the world it's the world's most profitable companies they can all pull the plug at any point microsoft pulled the plug at one point before they're like oh shit no no plug it back in right they recently plugged it back in they're like "Oh wait we're starting we're restarting this we're going out into the market we're signing deals with uh Nebius for GPUs." Like I don't remember how big the deal was it's like 10 plus billion yeah it's like 19 billion for Nebius

[译文] [Dylan Patel]: 反对这种（过度建设）观点的一个主要理由是：“不，这次是世界上资产负债表最强劲的公司，是世界上最赚钱的公司。他们随时可以拔掉插头（停止投入）。”微软之前确实拔过一次插头，但后来他们反应过来：“噢该死，不不，把插头插回去。”他们最近又重新插上了，他们说：“噢等等，我们要重新启动这个，我们要去市场上和 Nebius 签 GPU 的协议。”我不记得那笔交易有多大了，大概是 100 多亿……是的，和 Nebius 签了 190 亿美元。

[原文] [Dylan Patel]: it's like well if they had just not pulled the plug on their data centers they wouldn't have had to do that they wouldn't have to pay those gross profit dollars to Nebius right but you know Nebius made the bet that the demand is there and they were right

[译文] [Dylan Patel]: 如果他们当初没有拔掉自己数据中心的插头，他们就不必那样做了，就不必把那些毛利美元付给 Nebius 了，对吧。但 Nebius 赌需求会在那里，而且他们赌赢了。

[原文] [Dylan Patel]: um and so you know when you think about this it's like what is the level of demand where this stops right if if scaling laws continue right how I mean of course there's a adoption curve there's a pace there's realities with capital there's realities with supply chains things take time but if you like boil it down to it it's like your demand for 30-year-old senior engineers at Google who know how to make and program anything is effectively like I don't I want to say infinite but it's $2 trillion of value right if I could have an intelligence as smart as a Google senior engineer that's $2 trillion of software value right because that's how much I pay the world pays to software engineers today

[译文] [Dylan Patel]: 所以当你思考这个问题时，关键在于需求达到什么水平才会停止？如果缩放定律继续生效——当然会有采用曲线、速度限制、资本现实和供应链现实，这些都需要时间——但如果你归根结底来看，你对“30 岁、懂得制造和编程任何东西的 Google 高级工程师”的需求，实际上……我不想说是无限的，但那是 2 万亿美元的价值。如果我能拥有像 Google 高级工程师那样聪明的智能，那就是 2 万亿美元的软件价值，因为这就是今天全世界支付给软件工程师的薪酬总额。

章节 11：应用落地——超越拟物化（Skeuomorphism）的新能力

📝 本节摘要：

本章探讨了 AI 应用是否仅仅停留在“拟物化”阶段（即用新技术做旧工作，如单纯提高程序员效率）。Dylan 反驳了这一观点，指出当成本大幅下降时，“做旧事”本身就会产生质变。

他列举了三个关键案例证明 AI 正在解锁前所未有的能力：1. AI 在 COVID 疫苗研发中的关键作用；2. 他自己的业务——通过 AI 分析全球卫星图像和监管文件来追踪数据中心建设，这在以前靠人力是无法实现的；3. 大型机（Mainframe）与数据库迁移，亚马逊曾花费 20 年才摆脱 Oracle，而现在 AI 能让这类大规模重构变得迅速且经济。

[原文] [Host]: i'd love to hear your thoughts on like going back to the other side of the equation the the app side you know stuff we're going to use these models to do at the significance of this switch from like deterministic code to a much different thing and it seems like what we're doing is the thing we always do you know Apple used to call this like the schemorphic era where you just basically use the new technology to do the old thing you used to do so we're making engineers better you know that would be like an an obvious current example but we seems like we haven't yet gotten into the world where we're going to start using this technology to do things that we couldn't do before with deterministic code i'm curious how you think about like that side of like pushing the envelope

[译文] [Host]: 我想听听你对等式另一端的看法，也就是应用端。你知道，关于我们将用这些模型做什么。从“确定性代码”到一种截然不同的事物的转变，其意义何在？目前看起来我们所做的似乎还是我们一直在做的事情。你知道 Apple 过去称之为“拟物化时代”（skeuomorphic era），即你基本上只是用新技术去做你以前做的旧事。所以我们让工程师变得更好了，这是一个显而易见的当前例子。但我们似乎还没有进入那个世界——即开始使用这项技术去做以前用确定性代码无法做到的事情。我很好奇你是如何看待这方面突破极限的可能性的？

[原文] [Dylan Patel]: why is that like I feel like that's exactly what we do with it right is like the cost to develop things is so high that you can't do it right like or the cost to like you know have someone go buy stuff for you it's like okay great you might have an executive assistant and you can tell them to like you know do this but like the vast majority of people don't and now GPT is on the cusp of doing that right go buy go do this go buy this for me and they'll find the best thing and they'll buy it right and you just trust them enough right um it takes time to trust them but like it's like these things are proliferating across you know the massive you know tech tech is the most deflationary thing in the world ever right uh in terms of quality of life it's it's so it gets cheaper way faster than the revenues go up right but the revenues still go up that's sort of like the fundamental basis of semiconductors of tech everything right

[译文] [Dylan Patel]: 为什么会那样？我觉得这正是我们利用它做的事情，对吧。以前开发事物的成本太高了，以至于你无法做到。或者比如让别人帮你买东西的成本——好吧，你可能有个行政助理，你可以告诉他们去做这个，但绝大多数人没有。而现在 GPT 正处于能做这件事的边缘，对吧。“去买这个，去做这个，帮我买这个。”它们会找到最好的东西并买下来。你只需要足够信任它们。建立信任需要时间，但这些东西正在大规模普及。你知道，科技是世界上最通缩（deflationary）的东西，就生活质量而言。它变便宜的速度远快于收入增长的速度，但收入仍然在增长。这某种程度上是半导体、科技以及所有这一切的基础。

[原文] [Dylan Patel]: are we doing things that we can't do before with tech with AI sure i mean like the COVID vaccine was like created with AI like it was like AI drug discovery like you can there's like there's like entire briefs about like how it was done with AI and guess what if like another pandemic happened I bet it'd be even faster to discover the vaccine if there's a vaccine for it or whatever right like there's all these protein folding things there's all these like optimization things there's AI for material science and AI for like you know all these other like aspects of society there's optimization maybe it's not in your face right it's like oh my god AI just made this drug right it's like no I mean AI worked with the researchers who made the COVID vaccine and so we didn't have to all like you know be stuck inside forever or whatever right point being we're it's already happening

[译文] [Dylan Patel]: 我们是否正在用 AI 做以前用科技无法做到的事情？当然。我的意思是，像 COVID 疫苗就是用 AI 创造的，那是 AI 药物发现。你可以看到关于它是如何通过 AI 完成的完整简报。猜猜怎么着？如果发生另一次大流行，我打赌发现疫苗的速度会更快——如果有疫苗的话。还有所有这些蛋白质折叠的事情，所有这些优化事情。有用于材料科学的 AI，用于社会其他各方面的 AI。这种优化可能并没有直接怼到你脸上，让你觉得“天哪，AI 刚刚制造了这种药”。不，我的意思是 AI 与制造 COVID 疫苗的研究人员合作，所以我们不必永远被困在家里。重点是，这已经在发生了。

[原文] [Dylan Patel]: and the whole like use the new thing to make the old thing faster it's like sure but like if I go back three years how many people would it have taken to deploy a image recognition model that looks at every data center in the world and and looks at like what's the pace it's of constructions in and what equipment they have and like assuming this is something you do this is something we do right it's like it's like how many people would that have taken i don't think it would have been possible my business model like this is the second highest revenue product for us would not have been possible if it weren't for AI like vibe coding like you know being able to dig through permits and regulatory filings being able to run image recognition on satellite photos like this would not be possible this business is not possible without AI

[译文] [Dylan Patel]: 至于所谓的“用新东西让旧东西变快”，确实如此。但如果我回到三年前，要部署一个图像识别模型来观察世界上的每一个数据中心，看看建设进度如何，有什么设备——假设这是你要做的事，这正是我们在做的事——这需要多少人？我认为那是不可能的。我的商业模式——这是我们要收入第二高的产品——如果没有 AI 是不可能实现的。就像“Vibe Coding”（指用 AI 辅助处理），能够挖掘许可证和监管文件，能够在卫星照片上运行图像识别。如果没有 AI，这生意根本做不成。

[原文] [Dylan Patel]: and like am I using it directly like oh yeah sure I'm scraping through the regulatory filings and permits through with LLMs and then manually reviewing it with people and like you know and or like doing it doing that the same with like the the images satellite images yes there's a lot of stuff that you know the image recognition model does we just also look at them a lot and then it's like compiling them and selling a spreadsheet that you get like bi-weekly reports on like all the data centers or what's changed or like hey actually this Amazon data center the fans are starting to spin so actually there's revenue going on from this Amazon data center so we can forecast Amazon's revenue right it's like oh okay like this is like relevant right like you know but like I don't think this would have been possible just a few years ago um at least like off the proof right now and especially like you know there's demand for it because everyone wants to track this and it's so important but it's like it begets each other and I think like at least in my daily life it's like I don't think I could have taken that step from where I was in a business which was still a research provider but like that is a monumental jump and like being able to do it with three people out of the gate versus like 50 or 100 like I don't know how many people it would have taken but I don't think it's possible

[译文] [Dylan Patel]: 我是在直接使用它吗？噢当然，我用大语言模型（LLMs）抓取监管文件和许可证，然后让人工进行审查。或者对卫星图像做同样的事情。是的，图像识别模型做了很多工作，我们也看了很多，然后把它们编译成电子表格出售，提供关于所有数据中心或变更情况的双周报告。或者比如：“嘿，实际上这个亚马逊数据中心的风扇开始转了，所以这个数据中心开始产生收入了，所以我们可以预测亚马逊的收入。”这就像，噢好的，这很相关，对吧。但这在几年前是不可能的，至少现在证据确凿。特别是大家都有这种需求，因为每个人都想追踪这个，这很重要。但我认为至少在我的日常生活中，如果没有 AI，我不认为我能从原来的业务（仍然是研究提供商）跨出那一步。这是一个巨大的飞跃。刚开始只要 3 个人就能做，而不是 50 或 100 人——我不知道到底需要多少人，但我认为那是不可能的。

[原文] [Dylan Patel]: and it's like mainframe migration is something people have always wanted to do Amazon leaving Oracle took fucking 20 years right and they wanted to do it 20 years ago and they had their highest revenue products after EC2 were like the next four were database products at AWS and yet they still freaking used Oracle's database because it's hard now there's like mainframe migration can be way faster or like migration from one tech stack to another can be way faster you can make your business more efficient you add more automation yes the tech exists go to all the businesses around the world and it's like they aren't using the leading edge of what they could they aren't using you know what a 2020 company could have done without AI right no one is doing that and if they did they'd be so much more efficient right but like all of these things just take too long to build they're too expensive to build you have your existing processes how do you hand them over how do you switch them over how do you teach people to do this ais can help you with all of this right so it's sort of like you can take the pessimistic view of like oh we're just doing the same things but it's like the value here is humongous

[译文] [Dylan Patel]: 再比如大型机迁移（mainframe migration），这是人们一直想做的事。亚马逊摆脱 Oracle 足足花了特么 20 年，对吧。他们 20 年前就想这么做了。在 EC2 之后，AWS 收入最高的前四个产品都是数据库产品，但他们自己居然还在用 Oracle 的数据库，因为迁移太难了。现在，大型机迁移可以快得多，或者从一个技术栈迁移到另一个可以快得多。你可以让业务更高效，增加更多自动化。是的，技术是存在的，你去看看全世界的企业，他们并没有使用哪怕是 2020 年没有 AI 时的前沿技术，对吧。没人那么做。如果他们做了，效率会高得多。但所有这些东西建设起来太慢了，太贵了。你有现有的流程，如何移交？如何切换？如何教人做这个？AI 可以帮你解决所有这些问题。所以你可以持悲观观点说：“噢，我们只是在做同样的事情。”但这里的价值其实是巨大的。

章节 12：能源危机——吉瓦级（Gigawatt）数据中心与电网挑战

📝 本节摘要：

本章聚焦于 AI 扩张面临的最大物理瓶颈：电力。Dylan 直言不讳地指出，虽然目前数据中心仅占总用电量的一小部分，但问题在于美国已经 40 年没有大规模建设电力设施了。

他描述了令人咋舌的现状：OpenAI 正在规划的 2 吉瓦（Gigawatt）数据中心耗电量相当于整个费城；由于涡轮机短缺，企业甚至不得不将卡车柴油引擎并联来发电；马斯克不得不从波兰空运电力设备。此外，由于 AI 负载的剧烈波动，电网稳定性面临严峻挑战，甚至可能导致居民家中的冰箱电机因频率波动（59Hz）而损坏。这是一场基础设施与摩尔定律的赛跑。

[原文] [Host]: if it's tokens on one end we haven't talked much about like watts at the very beginning and power what are your thoughts on like what is going on here and how like humanity is responding to this crazy new demand for just raw power

[译文] [Host]: 如果说一端是代币（tokens），那我们还没怎么谈到最开始的瓦特（watts）和电力。你对这里发生的事情有什么看法？人类如何应对这种对原始电力的疯狂新需求？

[原文] [Dylan Patel]: the first approximation is that like we're being a bunch of pansies and it's not that much power yet right like AI data centers are like 3 4% of the US economy power not economy um or just data centers period of that like two is regular data centers and two is like AI data centers you know that's nothing dude like that's literally nothing uh it's just we haven't built power in like 40 years right or like we've transitioned from coal to natural gas more and more over 40 years it's like so mostly we just don't know how to

[译文] [Dylan Patel]: 第一眼的近似判断是，我们表现得像群怂包（pansies），其实现在的电力消耗还没那么大。AI 数据中心大概占美国电力（不是经济）的 3% 到 4%。或者说所有数据中心加起来是这个数，其中 2% 是普通数据中心，2% 是 AI 数据中心。你知道，这点量根本不算什么，兄弟，这简直不算什么。问题只是在于我们大概有 40 年没有建设电力设施了，或者说在过去的 40 年里我们只是越来越多地从煤炭转向天然气。所以很大程度上我们只是不知道该怎么搞了。

[原文] [Dylan Patel]: and there's these regulations and like there's not enough labor and like the supply chains for like Evanova and their dual combine cycle gas reactors are not there yet and same for like Mitsubishi and you know you you know oh like this random e UV curing process for transformer coils is like you know is like there's only this much capacity and it takes two years to build them it's like it's just like it's a supply chain thing it's a like lack of labor thing it's not that it's like actually that much yet

[译文] [Dylan Patel]: 而且还有这些法规，还有劳动力不足。像 Evanova 及其联合循环燃气机组的供应链还没跟上，三菱也是一样。还有变压器线圈的那种随机的 EUV 固化工艺，产能就只有这么多，建造它们需要两年时间。所以这只是一个供应链的问题，一个缺乏劳动力的问题，并不是说现在的用电量真的有那么大。

[原文] [Dylan Patel]: but like at the end of the day it's like okay wait wait you're telling me open is making a data center with 2 gigawatts and that's like the entirety of the power consumption of like Philadelphia like that is real yeah that's insane that's insane right but like we used to get like excited about finding like a couple hundred megawatts new data center now it's like if it's not a gigawatt like I I remember like the the the guy who leads that team he he was like "Oh it's just 500 megawws whatever."

[译文] [Dylan Patel]: 但归根结底，这就好比：“等等，你告诉我 OpenAI 正在建造一个 2 吉瓦的数据中心？”那相当于整个费城的耗电量。那是真实的。是的，那太疯狂了。但我们过去只要发现几百兆瓦（megawatts）的新数据中心就会很兴奋。现在如果是：“噢，如果不到 1 吉瓦……”我记得负责那个团队的人说：“噢，也就 500 兆瓦，无所谓啦。”

[原文] [Dylan Patel]: I was like I like immediately opine I was like I I I also agreed immediately then afterwards I was like wait a second dude that's like a lot of power that's like how much wait 500 megawatts is $25 billion of capex like come on like once you put in the GPUs and everything right it's like that's a ton of money but like snore because there's so many of it happening right we're learning how to build power again right um we're we're getting the the supply chains to do it again we're reshaping the grid

[译文] [Dylan Patel]: 我当时立刻附和了，我也立刻同意了。但事后我想：“等一下兄弟，那是很大一笔电力啊。”等等，500 兆瓦意味着 250 亿美元的资本支出（Capex），拜托！一旦你把 GPU 和所有东西放进去，那就是一大笔钱。但在现在看来就像是“听得我都困了（snore）”，因为这种事发生得太多了。我们正在重新学习如何建设电力，我们正在让供应链重新运转起来，我们正在重塑电网。

[原文] [Dylan Patel]: uh there's all these challenges with these AI data centers that with regards the demand response and making grids unstable right um you know you could you know AI workloads because they change so much so fast especially training you can just blow you could just you can just cause like brownouts or blackouts um especially if the grid doesn't have enough inertia or if you're not putting enough like things to dampen it in between the workload and and the grid

[译文] [Dylan Patel]: AI 数据中心在需求响应和导致电网不稳定方面面临各种挑战，对吧。你知道，因为 AI 工作负载（特别是训练）变化得如此剧烈和迅速，你可能会搞砸，你可能会导致局域断电（brownouts）或大停电（blackouts）。特别是如果电网没有足够的惯性，或者你在工作负载和电网之间没有放置足够的缓冲设备。

[原文] [Dylan Patel]: and even if it's not destroying it uh the grid runs at like 59 hertz or whatever right if you you skew it up and down too much um you these transient power responses your refrigerator will break down sooner the motors in it and and you might not even know it because the data center's nearby so like there's like this there's all these things there's so many like third order effects here with like AI data centers

[译文] [Dylan Patel]: 即使它没有摧毁电网，电网通常运行在 59 赫兹（注：美国标准为 60Hz，此处指偏离标准）或类似的频率上。如果你让频率上下波动太大，那些瞬态功率响应会导致你冰箱里的电机更快坏掉，而你可能甚至都不知道这是因为附近有个数据中心。所以这里有各种各样的事情，AI 数据中心会带来很多三阶效应（third order effects）。

[原文] [Dylan Patel]: but like the funnest one is just that like we're building power right and it's like whether it's you know gas which is a lot of it whether it's through efficient dual combine cycle reactors or it's like you know random generators that are like not nearly as efficient single cycle or even worse um diesel generators there's there's a company that's putting a bunch of truck engines in parallel like like diesel truck engines because the capacity the industrial capacity for diesel truck engines is huge like and no one's tapped it yet so why don't we just put a ton of them in parallel and create this power generation thing right here right

[译文] [Dylan Patel]: 但最有趣的是我们正在建设电力。不管是用天然气（这占了很大一部分），还是通过高效的联合循环反应堆，或者是那种效率低得多的单循环发电机，甚至更糟糕的——柴油发电机。有一家公司正在把一堆卡车引擎并联起来，就是柴油卡车引擎。因为柴油卡车引擎的工业产能是巨大的，而且还没人利用它。所以我们为什么不把一吨这样的引擎并联起来，直接在这里造个发电设备呢？

[原文] [Dylan Patel]: and and and then you're generating power with a bunch of diesel truck engines in parallel and then you're able to power a data center right like okay great because I can't get turbines right and it's like or like you know there's there's all these like crazy things people are doing uh Elon buying some power equipment from Poland and shipping it to America because he needed that power equipment but like whatever couldn't get it here because the supply chains were weird i'll just get it over there any lacks capacity in the supply chain is being eaten up immediately

[译文] [Dylan Patel]: 然后你就能用一堆并联的柴油卡车引擎发电，进而给数据中心供电。好吧，太棒了，因为我买不到涡轮机。人们正在做各种疯狂的事情。比如 Elon（马斯克）从波兰买了一些电力设备运到美国，因为他需要那些设备，但在这里买不到，因为供应链很奇怪。我就去那边买。供应链中任何闲置的产能都会被立即吃掉。

[原文] [Dylan Patel]: and then everyone's like "Okay let's invest." So G is like I'm going to double my turbine production it's like holy crap okay that's awesome and Mitsubishi is doing the same thing and you know you move you go down the list it's like my my transformer supply chain is expanding like crazy and like you know they're fully sold out so I'm going to go to the Korean guys and that's fully sold out so I'm going to figure out how to get the Chinese stuff in even though it's not exactly like you know what people want to do right

[译文] [Dylan Patel]: 然后每个人都说：“好吧，让我们投资。”所以 GE（通用电气）说：“我要把涡轮机产量翻倍。”天哪，那太棒了。三菱也在做同样的事。你看下去，变压器供应链也在疯狂扩张。但它们完全卖光了，所以我要去找韩国人，韩国人也卖光了，所以我要想办法把中国的东西弄进来，尽管那不完全是人们想做的事，对吧。

[原文] [Dylan Patel]: it's like there's all these like weird things like electrician wages have like doubled for mobile electricians that can work on data center stuff or rather contract like if you're down to move to West Texas it's like it's like it's like 2015 again and like being a fracking guy right you don't need to be super duper skilled you can go to West Texas and make a shitload load of money off of fracking but there's not enough of those people that's why right like if there were enough electricians in West Texas if there were enough electricians in America we could build these data centers faster

[译文] [Dylan Patel]: 还有各种奇怪的事情，比如能做数据中心工作的流动电工的工资翻倍了。如果你愿意搬到西德克萨斯去签合同，这就像 2015 年又回来了，就像做水力压裂（fracking）的人一样。你不需要超级熟练，你只要去西德克萨斯搞压裂就能赚一大笔钱。但这种人不够多，这就是原因。如果西德克萨斯有足够的电工，如果美国有足够的电工，我们就能更快地建设这些数据中心。

[原文] [Dylan Patel]: everyone's supply chain is different... no one really knows it because everyone who tracked the supply chain or like knew it like you go talk to like power people it's like it's like on one end of the spectrum is like Daario and then you take a few steps and it's like it's like ML researchers the average ML researcher then it's like me and then it's like you in terms of how bullish we are on AI and the guy at the power utility is like over here right

[译文] [Dylan Patel]: 每个人的供应链都不同……没人真正了解全貌。你去和电力行业的人聊聊，这就好像谱系的一端是 Dario（Anthropic CEO），然后是普通的机器学习研究员，然后是我，然后是你，按我们对 AI 的乐观程度排列。而电力公司的那家伙在这边（指最不乐观的那一端）。

[原文] [Dylan Patel]: um you know this utility guy is like "I'm not building power power doesn't go up you know whatever." And then you have like the regulations around it um you know it's like "How can I build a data center in this density?" Because um okay well then great like I'll build the data center in this density i'll have all this backup generators great now all of a sudden the grid's like um yeah so what we're going to do is we're going to So this has happened in Texas or it's happening in PJM um which is the main you know the sort of northeast kind of areaish um these two grids are putting these like rules where hey we're gonna actually say hey big loads we can tell you 24 hours or 72 hours beforehand we're going to cut off half your power

[译文] [Dylan Patel]: 那个电力公司的家伙会说：“我不建电厂，电力需求又不会涨，管他呢。”然后周围还有法规限制。比如：“我怎么能以这种密度建设数据中心？”好吧，那我就以这种密度建，但我会配备所有这些备用发电机。太好了。但突然间电网说：“嗯，我们要这样做……”这种事已经在德克萨斯发生了，或者正在 PJM（主要覆盖美国东北部地区）发生。这两个电网正在制定规则，说：“嘿，大负载用户，我们可以提前 24 或 72 小时通知你，我们要切断你一半的电力。”

[原文] [Dylan Patel]: which is fine right because like we need to because we need for something else yeah like people people need to have their homes powered we're not fucking like Taiwan where if we're in a drought we limit people's power us water usage and not the fat right which is which is a real story right... they'll limit the water to these people before they'll limit the water to TSMC because and it makes sense the economic value of TSMC is way above the economic value of people showering three times a week but like the US grid is not going to work that way we're not that authoritarian or you know people have more say

[译文] [Dylan Patel]: 这也没问题，因为我们需要把电用在别处，比如人们家里需要供电。我们又不是像台湾那样，如果干旱了，我们就限制居民用水而不限制工厂（FAB），那是真事……他们会先限制居民用水，再限制 TSMC 用水，这有道理，因为 TSMC 的经济价值远高于人们一周洗三次澡。但美国电网不会那样运作，我们没那么威权主义，或者说人民更有话语权。

[原文] [Dylan Patel]: okay so anyways like it's like in Texas and in PGM you can cut half the power if you give them a notice and if you do that then you need to turn on the generators that are there on the site you know it's often diesel generators maybe it's gas um maybe it's like hydrogen stuff there's all sorts of weird stuff people try to do just to ramp up power uh for that period of time but then all of a sudden oh crap the density of my generators means that I fail the air permit if I run the generators for more than eight hours a month so now what do I do right it's like there's all these like weird regulations

[译文] [Dylan Patel]: 好吧，总之在德克萨斯和 PJM，如果你给通知就可以切断一半电力。如果你那样做了，你就需要通过现场的发电机供电。通常是柴油发电机，也许是燃气，也许是氢气之类的奇怪东西，人们尝试各种方法只是为了在那段时间内提升电力。但突然间，“噢该死，我的发电机密度太高了，如果我不一个月运行超过 8 小时，我就通不过空气排放许可（air permit）。”现在我该怎么办？这里有各种各样奇怪的法规。

章节 13：地缘政治——中美AI竞赛与供应链隔离

📝 本节摘要：

本章将视线转向宏观的地缘政治博弈。Dylan 提出了一个极具危机感的观点：如果没有这波 AI 浪潮，美国很可能在十年内失去世界霸权，因为其供应链更慢、更贵且债务不可持续。

相比之下，中国采取了“长线博弈”策略，在过去十年向半导体生态系统倾注了至少 4500 亿到 5000 亿美元，致力于建立一个即便封闭也能自给自足的供应链。Dylan 警告称，如果中国封锁台湾，美国经济将面临“自由落体”式崩溃——从冰箱到 AI 数据中心都将因缺芯而停摆。这是一场关于生存、速度与制造能力的终极竞赛。

[原文] [Host]: if I were to line up all the stages of this between the US and China so you know power semis models applications etc where do you think the most interesting differences are like what are the what are the story lines between us and China at those various layers of like the AI stack that are the most interesting to you

[译文] [Host]: 如果我把美中之间的所有阶段都列出来，比如电力、半导体、模型、应用等等，你认为最有趣的差异在哪里？在 AI 堆栈的这些不同层面上，美中之间最让你感兴趣的故事线是什么？

[原文] [Dylan Patel]: when you look at China it's like they're a very formidable competitor um I think if we didn't have the AI boom the US probably would be behind China and no longer the world hegeimon by the end of the decade if not sooner and a world where the US is not the hegeimon is is a bad one for Americans at least um you know I I I you know I'm sort of like a you know sure fucking bald eagle like carrying like American i'm like it's bad for the world without AI like we're definitely just going to lose right our supply chains are slower they cost too much uh we're we're sliding our debt is like unsustainable

[译文] [Dylan Patel]: 当你看中国时，他们是一个非常可怕的竞争对手。我认为如果没有这场 AI 繁荣，美国很可能在这十年结束前——甚至更早——就会落后于中国，不再是世界霸主。而一个美国不再是霸主的世界，至少对美国人来说是很糟糕的。你知道，我某种程度上就像那种……当然，你可以说我是那种举着美国国旗的秃鹰（意指爱国主义者），但这确实对世界不好。如果没有 AI，我们肯定会输。我们的供应链更慢，成本太高，我们正在滑坡，我们的债务是不可持续的。

[原文] [Dylan Patel]: i think the US would literally fall apart if we don't do something like and and by do something I mean like AI has to dramatically accelerate GDP growth once you start talking about dividing the pie you're screwed right um it has to be growing the pie... so I like I'm I'm like you know the US really really needs AI china's view is like I think it's like a little bit different right they don't necessarily need AI to win they've always played this long game they did it with steel um they've done it with like you know rare earth minerals they've done it with solar panels they've done it for you know producing phones they've done it for PCBs they've done it for so many freaking industries incrementally they're just going to continue to do that and then they're going to win because they they work harder and they're on average smarter

[译文] [Dylan Patel]: 我认为如果我们不采取行动，美国真的会分崩离析。所谓采取行动，我的意思是 AI 必须大幅加速 GDP 增长。一旦你开始谈论“分蛋糕”（存量博弈），你就完蛋了，对吧。必须是“做大蛋糕”。所以我认为美国真的非常非常需要 AI。而中国的观点我认为有点不同，他们不一定需要 AI 才能赢。他们一直玩的是长线博弈。他们在钢铁上是这样，在稀土矿产上是这样，在太阳能电池板上是这样，在手机生产上是这样，在 PCB（印制电路板）上也是这样。他们在如此多的行业上都这么做了。他们只是渐进地继续这样做，然后他们就会赢，因为他们工作更努力，而且平均来说更聪明。

[原文] [Dylan Patel]: china doesn't necessarily think of it the same way um but they are still incredibly pilledled on like well we want to be able to make everything ourselves right so make all of the chips ourselves we're not necessar we don't actually care that much about making all the chips ourselves sure Trump's doing the tariffs sure we had the chips act but those were drops in the bucket compared to how much money China's releasing into the semiconductor ecosystem and have has been for the last 10 years they've dumped you know at least like $4500 billion into this ecosystem through SOE's through um who you know through certain tax policies through certain like land grants through provincial governments through uh the big funds uh which is like government venture funds almost so they've dumped so much more capital into semiconductors than we have in an unprofitable way because they want to build that ecosystem

[译文] [Dylan Patel]: 中国不一定以同样的方式思考问题，但他们仍然极其坚定地认为：“我们要能自己制造一切。”对吧，制造所有的芯片。我们（美国）其实并没有那么在乎能不能自己制造所有芯片。当然，特朗普在搞关税，当然我们有《芯片法案》，但那是杯水车薪。相比之下，中国在过去 10 年里向半导体生态系统投入的资金是巨大的。他们通过国企（SOEs）、税收政策、土地划拨、省政府以及“大基金”（几乎就像政府风险基金）等渠道，向这个生态系统倾注了至少 4500 亿到 5000 亿美元。他们以一种不计盈利的方式投入了比我们多得多的资本，因为他们想建立那个生态系统。

[原文] [Dylan Patel]: um and and over time you know it's like well if you take any country in isolation China is the one that has everything at the highest level on average right sure they're like 30 years behind on jet engines but they don't actually need to go for or 20 years or 10 years whatever it is but they don't need to go outside of China for any of the materials besides like raw materials whereas like the US needs like titanium from here and like you know blah blah blah from there right and the same applies to their semiconductor ecosystem

[译文] [Dylan Patel]: 随着时间的推移，如果你孤立地看任何一个国家，中国是平均水平最高的国家，拥有最全的产业链。当然，他们在喷气发动机上可能落后 30 年（或者 20 年、10 年，不管多少年），但除了原材料，他们实际上不需要走出中国去获取任何材料。而美国则需要从这里进口钛，从那里进口别的什么。这也同样适用于他们的半导体生态系统。

[原文] [Dylan Patel]: there's this like real big perception difference and China could build way faster than us if they wanted to build you know a 2 gawatt or 5 gawatt data center they could probably smuggle a lot of chips it's not like a pure derog a derivative of them wanting to smuggle shitloads of chips because hey if they wanted to build a 10 gawatt data center I bet they could build it in like a few years um whereas the US is not going to build a single 10 gawatt data center for for a while right like the total capacity of an open AI will be like 10 gawatts in a few years right um optimistically

[译文] [Dylan Patel]: 这里存在巨大的认知差异。如果中国想建一个 2 吉瓦或 5 吉瓦的数据中心，他们的建设速度会比我们快得多。他们可能会走私很多芯片——但这不纯粹是因为他们想走私大量芯片——因为嘿，如果他们想建一个 10 吉瓦的数据中心，我打赌他们几年内就能建成。而美国在很长一段时间内都不会建成哪怕一个 10 吉瓦的数据中心，对吧。OpenAI 的总容量也就是几年后才达到 10 吉瓦，这还是乐观估计。

[原文] [Dylan Patel]: they do have the most power they can build stuff way faster right we're impressed at how fast Elon does stuff elon's slow compared to China um and I think he knows that um which is why he's maybe like the one who's like actually using the Chinese ecosystem more in terms of like you know the the battery facilities making in China and you know all these things right he's he probably recognizes it too

[译文] [Dylan Patel]: 他们拥有最强的电力，他们能建造东西的速度快得多。我们对 Elon（马斯克）做事情的速度印象深刻，但相比中国，Elon 也是慢的。我认为他也知道这一点，这也是为什么他可能是实际上更多地利用中国生态系统的人，比如在电池制造设施等方面都在中国。他可能也意识到了这一点。

[原文] [Dylan Patel]: but then like you get to the point of like okay well what happens in like three four years even if the US AI is amazing we have no you know like the doomsday scenario of like you know China decides to blockade Taiwan or even invade it or create some political instability people talk about like Cambridge analytics and like Russian trolls whatever like China could do a billion times that into Taiwan especially with AI with how good AI is now and and somehow subvert it or coup or blockade or whatever and we no longer have Taiwan US economy kind of free falls right because we can't make refrigerators without Taiwanese chips we can't make you know cars we can't make AI data centers we can't grow any of the cloud we can't That means we can't deploy any more SAS applications like what the hell can we do

[译文] [Dylan Patel]: 但这就到了一个关键点：三四年后会发生什么？即使美国的 AI 很惊人，但如果我们遇到“世界末日”剧本——比如中国决定封锁台湾，甚至入侵，或者制造某种政治动荡。人们谈论剑桥分析（Cambridge Analytica）或俄罗斯网络水军，不管是什么，中国可以利用现在的 AI 能力，对台湾进行十亿倍强度的渗透，某种程度上颠覆它，或者政变，或者封锁。如果我们失去了台湾，美国经济基本上就是自由落体（free falls）。因为没有台湾芯片我们连冰箱都造不出来，我们造不出汽车，造不出 AI 数据中心，无法增长任何云服务。这意味着我们无法部署任何新的 SaaS 应用。我们到底还能干什么？

[原文] [Dylan Patel]: i think like that's sort of like the the like catch 22 of this all is like if you push China too hard they totally will like they're going to start swinging they have the talent they could go crazy they could you know if if we no longer have Taiwan actually China could build a way bigger cluster than us and if comput is all that matters right like they could do all of these things and they own the means of production for everything right so sort of like it's like you know there's this like challenging aspect of like geopolitical risk is like you know that's why people don't want to invest in TSMC but it's almost like you can't invest into Amazon or Apple or Google or like Microsoft if you have geopolitical risk if you believe Taiwan has risk and so it's like yolo invest in TSMC

[译文] [Dylan Patel]: 我认为这就是这一切的“第二十二条军规”（Catch-22，进退两难）。如果你把中国逼得太紧，他们完全会开始反击。他们有人才，他们可能会发疯。如果我们失去了台湾，实际上中国可以建立比我们大得多的集群。如果算力就是一切，他们能做所有这些事情，而且他们拥有生产一切的生产资料。所以这就面临着地缘政治风险的挑战。这就是为什么人们不想投资 TSMC（台积电），但这就像是：如果你相信台湾有风险，那你其实也不能投资亚马逊、苹果、谷歌或微软。所以，干脆直接“Yolo”投资 TSMC 算了。

章节 14：回应质疑与硬件创新——超越加速器的机会

📝 本节摘要：

在本章中，Dylan 首先回应了以 Yann LeCun 为代表的“AI 悲观派”观点，认为虽然当前的预训练模式存在局限，但结合强化学习等新范式仍能突破瓶颈。

随后，话题转向硬件投资。Dylan 直言自己不看好那些试图制造 AI 加速器（如 TPU、Trainium 或 AMD 芯片）直接挑战 Nvidia 的公司，因为这需要巨大的资本且缺乏革命性飞跃。相反，他认为真正的机会隐藏在供应链的细分领域：比如固态变压器（Solid State Transformers）、解决芯片间通信瓶颈的光互连（Optics）与网络技术，以及利用 AI 进行材料科学突破（如电池化学）的初创公司（如 Periodic Labs）。

[原文] [Host]: who is your favorite AI bear like someone that is is far distant from you on just their perspective on the direction of this whole thing that you nonetheless like and respect

[译文] [Host]: 你最喜欢的“AI 悲观派”（AI bear）是谁？就是那种在整个事情的发展方向上与你观点相去甚远，但你仍然喜欢并尊重的人？

[原文] [Dylan Patel]: there's some of the like AI researcher like gods like Yan Lun and like these kind of people who are AI bears i I respect them i like what they like their ideas i think they're completely wrong but you and their argument is what like if you had to sum it you know the ways we're doing this won't work right llm's on scale or right but and it's like okay yeah yeah auto reggress like it's like okay auto reggressive pre-training on the internet doesn't work to get you to AGI so he's completely right on that but then like he'll turn around and be like well no no no but like RL systems and all these things are not the right way either right like you know it's sort of like you know it's like the no butts

[译文] [Dylan Patel]: 有一些像神一样的 AI 研究员，比如 Yann LeCun（杨立昆），以及这类人，他们是 AI 悲观派。我尊重他们，我喜欢他们的想法，但我认为他们完全错了。他们的论点主要是：如果你必须总结一下的话，就是我们现在的做法行不通，大规模 LLM 行不通。就像是：“好吧，是的，基于互联网数据的自回归预训练（Auto-regressive pre-training）无法让你实现 AGI。”在这一点上他是完全正确的。但他转过头又会说：“不不不，强化学习系统（RL systems）和所有这些东西也不是正确的方法。”这就有点像是……你知道，总是只有否定而没有建设性的“但是”。

[原文] [Host]: what startups interest you the most

[译文] [Host]: 哪些初创公司最让你感兴趣？

[原文] [Dylan Patel]: so one of the startups is um that I've I've you know it's the most recent investment I've made it's called periodic labs um it's mostly open AI people it's a Google guy and you know the couple material scientists um the area of AI that we've all been talking about is like large scale web training RL all text all digital god right you know I you know we want to make digital god but you know what would drive a shitload of value for the economy besides you know automating programming of everything is like if we just like came up with like a battery chemistry that was like 25% more efficient mhm like holy shit

[译文] [Dylan Patel]: 其中一家初创公司是我最近刚投的，叫 Periodic Labs。团队主要是 OpenAI 的人，还有一个 Google 的人，以及几位材料科学家。我们一直在谈论的 AI 领域都是大规模网络训练、RL、全文本、全“数字上帝”，对吧。你知道我们想造数字上帝，但除了自动化编程之外，你知道还有什么能为经济驱动巨大的价值吗？就是如果我们能研发出一种效率提高 25% 的电池化学配方。天哪，那是不得了的事。

[原文] [Dylan Patel]: and what Periodic is trying to do is they're taking this RL paradigm but they're trying to do it with like real world right test chemistry for something right a here's a here's a chemistry um here's an optimization here's something that the model spit out but then you also want to test it in the real world and then feed that feedback back into the model and so you do this like chain of of circles right but instead of purely being you know digital right which is which is why like RL is like really hard because you need to generate a bunch of responses test and then train the model so the flywheel is so freaking fast yeah right the flywheel in the physical world is so slow

[译文] [Dylan Patel]: Periodic 试图做的是采用这种强化学习（RL）范式，但将其应用于现实世界。比如测试某种化学配方。这里有一个化学配方，这是一个优化方案，这是模型吐出来的东西。但你还需要在现实世界中测试它，然后将反馈输入回模型。所以你做这样一个循环链条，而不是纯粹的数字循环。这也是为什么物理世界的 RL 很难，因为数字世界的飞轮转得超级快，而物理世界的飞轮太慢了。

[原文] [Host]: what about in the hardware world like just in the pure hardware space attacking some other interesting bottleneck

[译文] [Host]: 那么硬件世界呢？在纯硬件领域，有没有人在攻克其他有趣的瓶颈？

[原文] [Dylan Patel]: in the hardware world the biggest challenge is that like I'm not really a big bull on the accelerator companies i've never been companies make competing with Nvidia yeah I got it um competing with Nvidia with TPUs with Tranium you know with with AMD not a big bull on those kinds of companies because it's too hard it's just too many things to do it's too capo intensive it's too there's not enough of a revolutionary leap there's too many predicated things um you know I'm I I wish it could happen right it'd be fun um maybe it does happen but it would take hell of a badass thing

[译文] [Dylan Patel]: 在硬件世界，最大的挑战在于……其实我并不是所谓的“加速器公司”的大多头（bull）。我从来都不是。你是说那些与 Nvidia 竞争的公司？是的，比如做 TPU、Trainium 的，或者是 AMD。我不看好这类公司，因为太难了。要做的事情太多了，资本太密集了，而且没有足够的革命性飞跃，前提条件太多了。你知道，我希望它能发生，那会很有趣，也许会发生，但这需要极其强悍的东西才行。

[原文] [Dylan Patel]: but I think there's a lot of individual parts of the supply chain which are not spaceaged right um Nvidia space age yes it's the biggest value value owner today but their supply chain has so much old shit right and whether it's their supply chain or the hyperscale supply chain transformers have not changed in like 50 100 years right like there's a guy building a company in that space solid state transformers right like things like this yeah so there's all sorts of interesting things there there's so many interesting companies in that space because there's so much innovation to be done and there wasn't that much of a need to do innovation before

[译文] [Dylan Patel]: 但我认为供应链中有很多单独的部分并不是什么“太空时代”的高科技。Nvidia 是太空时代的，是的，它是今天最大的价值拥有者，但他们的供应链里有很多旧东西，对吧。无论是他们的供应链还是超大规模厂商的供应链。变压器（Transformers，指电力变压器）大概 50 年甚至 100 年都没变过了。有一个人在那个领域建立了一家公司做“固态变压器”（solid state transformers），就像这类东西。那里有各种有趣的事情，有那么多有趣的公司，因为有太多的创新可以做，而以前并没有那么大的创新需求。

[原文] [Dylan Patel]: another area is like networking between chips because as we extend context length the memory requirements become bigger and bigger and yes new memory technologies would be awesome but DM is an industry has so much invested capital goods so much so existing factories it's really hard to attack um but you know networking is less so and there's more breakthroughs that can be done in networking that okay maybe you don't have better memory technologies but you've tied the chips closer together so you can use each other's memory on the problem there's so much more that you can do in terms of the optics space bridging the gap between electrical connectivity and optical connectivity

[译文] [Dylan Patel]: 另一个领域是芯片间的网络（networking）。因为随着我们扩展上下文长度，内存需求变得越来越大。是的，新的内存技术会很棒，但 DRAM 产业拥有太多的投资资本和现有工厂，很难去颠覆。但在网络方面就没那么难，网络领域可以有更多突破。好吧，也许你没有更好的内存技术，但你把芯片连接得更紧密，这样你就可以利用彼此的内存来解决问题。在光学（optics）领域，在弥合电连接和光连接之间的差距方面，你可以做的事情太多了。

[原文] [Dylan Patel]: every chip in the rack and connect to every other chip in the rack at 1.8 terabytes a second right that's that's if you think about how much data that is right like like the amount of bandwidth is so high for connecting these chips together like I like you can't fathom what a terabyte a second is... where we are now there's still tons of innovation left to be done there like I think part of the reason Intel is behind is also that uh data sharing internally was terrible... but also it's a lot of it is like building better simulators simulating the world more accurately so world models generally are like hey I'm going to simulate the world i'm going to walk around in you know the common one I think is G3 that Google made right

[译文] [Dylan Patel]: 机架上的每个芯片都以每秒 1.8 TB 的速度与机架上的其他所有芯片连接。如果你想想那是多少数据……连接这些芯片的带宽是如此之高，你根本无法想象每秒 1 TB 是什么概念……我们现在所处的阶段，那里仍有大量的创新有待完成。比如我认为 Intel 落后的部分原因也是内部数据共享太糟糕了……但这其中很多也是关于构建更好的模拟器，更准确地模拟世界。所以“世界模型”（world models）通常就像是：“嘿，我要模拟这个世界，我要在里面走动。”Google 做的 G3 就是一个常见的例子。

章节 15：企业速评——从OpenAI到谷歌的竞争格局

📝 本节摘要：

进入“快问快答”环节，Dylan 对 AI 领域的几大核心玩家进行了犀利点评。他坦言相比 OpenAI，自己更看好 Anthropic，因为后者专注于替代软件工程师这一万亿级市场，而 OpenAI 战线拉得太长。对于 AMD，他虽有感情（曾是他的第一只多倍股），但评价其表现平平（Mid）。

他对马斯克的 xAI 提出了尖锐建议，认为其目前的商业模式（他在文中戏称为“Pornbot”）不可持续，必须找到真正的大杀器。对于 Oracle，他认为这是一场豪赌。而在传统巨头中，他认为 Meta 拥有“同花顺”（硬件+模型+资本+推荐系统），是最接近“赢家通吃”的公司；同时，他一改两年前的看衰态度，转而极度看好 Google，认为其正在全面苏醒并将统领消费者与企业级市场。

[原文] [Host]: could we do like a uh a quick speed round where like I say some company and you just give me like you know a sentence or two on like your impression of them just like just like how how you feel about them in this moment start with Open AI

[译文] [Host]: 我们能不能来个“快速回合”，我说一家公司，你给我一两句话谈谈你对他们的印象，或者你此刻对他们的感觉？从 OpenAI 开始。

[原文] [Dylan Patel]: oh yeah super awesome that's it i mean we've talked about them all day

[译文] [Dylan Patel]: 噢，超级棒（Super awesome）。就这样，我们已经聊了他们一整天了。

[原文] [Host]: anthropic

[译文] [Host]: Anthropic。

[原文] [Dylan Patel]: i'm actually more optimistic on anthropic than I'm Open AI why their revenue is accelerating way faster because what they're focused on is more relevant to that two trillion dollar software market versus OpenAI is split between yeah they're going to do that but they're also going to do these other things but they're also going to do like target AI for you know science and they're going to also target AI for um you know the consumer app and doing the like take rate thing which all of these businesses could be amazing and open maybe executes on all of them but Anthropic is definitely executing on the software side better yeah

[译文] [Dylan Patel]: 实际上，相比 OpenAI，我对 Anthropic 更乐观。为什么？因为他们的收入加速得快得多。他们专注的领域与那个 2 万亿美元的软件市场更相关。相比之下，OpenAI 的精力是分散的：是的，他们要做软件，但他们还要做其他事情，还要把 AI 用于科学，还要做消费者应用并搞那个“抽成模式”。所有这些业务可能都很棒，OpenAI 也许能把它们都做成，但 Anthropic 在软件这方面绝对执行得更好。

[原文] [Host]: AMD

[译文] [Host]: AMD。

[原文] [Dylan Patel]: I love them but they're pretty mid why do you love them if when you grow up like like building computers and like liking computers and like AMD's innovating and they're always like fostered this underdog mentality against Intel and against Nvidia evil Intel and evil Nvidia you know and like AMD is like you know the nice company that's like the underdog... it's like it's hard not to love them you know like and I know so many people there and I like the I like all these major hardware companies right there's not one that I don't like as in terms of the people but like AMD's got a soft spot because like I think it was my first multibagger as well like my first multibagger i can't own stocks anymore because compliance sorry for the rant but I fucking love AMD you know i also love Nvidia but mid but mid

[译文] [Dylan Patel]: 我爱他们，但他们表现挺平庸的（pretty mid）。（主持人：既然平庸为什么爱他们？）因为如果你是组装电脑长大的，你会觉得 AMD 在创新，他们总是培养一种对抗 Intel 和 Nvidia 的“下狗”（underdog，弱者挑战强者）心态——邪恶的 Intel，邪恶的 Nvidia，而 AMD 就像那家善良的弱势公司……很难不爱他们。我认识那里很多人，我喜欢所有这些主要的硬件公司，就人而言没有我不喜欢的。但 AMD 在我心中有个柔软的位置，因为我想那也是我人生第一只翻倍股（multibagger）。因为合规原因我现在不能持股了——抱歉发了牢骚——但我真特么爱 AMD。我也爱 Nvidia。但 AMD 确实挺平庸的。

[原文] [Host]: XAI

[译文] [Host]: xAI。

[原文] [Dylan Patel]: they're in a real danger of not being able to raise capital u Elon's the best C of course everyone's going to give Elon capital but like the scale of capital required uh for him to keep up he can get the next bet he can get to Colossus 2 right uh this mega data center that he's building largest data center in the world when he builds it uh 300,000 you know black wells 500,000 black wells right like it's going to be really great but if he doesn't figure out a business model besides like Pornbot um which is what Annie is which also I think he's monetizing the wrong way like I think he could monetize it so much better

[译文] [Dylan Patel]: 他们面临着无法筹集资本的真正危险。当然 Elon 是最好的融资者（CEO），每个人都会给 Elon 钱。但他为了跟上步伐所需的资本规模太大了。他能搞定下一个赌注，他能建成 Colossus 2——那个他在建的世界最大数据中心，拥有 30 万甚至 50 万颗 Blackwell 芯片——那会很棒。但如果除了“色情机器人”（Pornbot）——也就是 Annie（xAI 的某种拟人化产品）——之外，他想不出别的商业模式的话……而且我认为他在 Annie 上的变现方式也是错的，我觉得他本可以变现得好得多。

[原文] [Dylan Patel]: how you've captured the zeitgeist with a cute anime girl that talks to you in a cute voice and like will rz you up and and you've got like these users who actually fall for it and it's like not realistic enough yet but it will slowly get more realistic and you're not like you're selling like outfits for the same price you should make it a random like "Hey you have a chance to buy the outfit that is actually her being nude." Or like "Hey you have the chance to buy the outfit of her like looking like this one anime girl from this one anime." ... he should partner with Only Fans and make like make manifestations of the Only Fans creator with that are Annie and then he subsumes the Only Fans platform into X the everything app...

[译文] [Dylan Patel]: 你用一个可爱的动漫女孩抓住了时代精神，她用可爱的声音和你说话，还会撩你（rizz you up），用户真的吃这一套。虽然现在还不够逼真，但会慢慢变好的。你不能只卖普通衣服。你应该搞个随机抽取，比如“嘿，你有机会买到她是裸体的皮肤”，或者“你有机会买到她看起来像某个动漫角色的皮肤”……他应该和 OnlyFans 合作，把 OnlyFans 的创作者制作成 Annie 的化身，然后把 OnlyFans 平台整合进 X 这个“万能应用”里……

[原文] [Dylan Patel]: but I think like he has to figure out like some business model beyond just this... to be clear XAI can get to the next stage of compute... But they will have the biggest individual data center and what he does with that and he and they'll be they'll have a very focused team and what they do with that they have to do something like really big otherwise they will fall behind in the race and and Elon will not let that happen like he doesn't want that happen but he can't he can he can subsidize and fund this round but like he can't go to a 3 gawatt data center unless he gets capital which he can't do unless he gets revenue and fundraising

[译文] [Dylan Patel]: 但我认为他必须想出超越这个的商业模式……明确一点，xAI 可以达到下一个算力阶段……他们将拥有最大的单体数据中心，拥有一支非常专注的团队。他们必须用这个做点真正的大事，否则就会在竞赛中落后。Elon 不会让这种事发生，他不想那样，但他只能资助这一轮。除非他能获得收入和融资，否则他无法通过资本去建设 3 吉瓦的数据中心。

[原文] [Host]: oracle

[译文] [Host]: Oracle。

[原文] [Dylan Patel]: Oracle is going to make so much fucking money if you believe if you believe OpenAI is successful but if you think Open AI is going to be successful enough to pay $300 billion dollars to them how many users do they have and what's that IP worth like maybe and also like you know there's reasons you shouldn't own OpenAI like the Microsoft stuff and like the risks around Enthropic and all these things but like you know in most worlds where open where Oracle gets paid $300 billion by OpenAI OpenAI is like a$10 trillion or $5 trillion company or something crazy

[译文] [Dylan Patel]: 如果你相信 OpenAI 会成功，Oracle 将会赚得盆满钵满。但如果你在想 OpenAI 是否能成功到付给他们 3000 亿美元？他们有多少用户？那个 IP 值多少钱？也许吧。而且你知道也有不持有 OpenAI 的理由，比如微软的事情，Anthropic 的风险等等。但在大多数 Oracle 能从 OpenAI 拿到 3000 亿美元的世界线里，OpenAI 得是一家 10 万亿或 5 万亿美元的公司，或者类似疯狂的规模。

[原文] [Host]: we'll end with the the OGs the the old last generation best two business models first being Meta

[译文] [Host]: 我们以“OG”（元老）结束，也就是上一代最好的两个商业模式。首先是 Meta。

[原文] [Dylan Patel]: i think Meta's got the cards to potentially like own it all... the next paradigm a human computer interface is we don't actually have to touch it at all we tell the AI what we want and the AI will translate that into reality... the only company in the world who has the full stack from good hardware that is a you know what what Meta just showed with their glasses with the screen plus the good models um plus the capacity to serve them plus the uh knowledge and knowhow around recommendation systems to know what content to put in front of the user... plus the capital plus the capital um and I think I think Meta is so close to being the only company that can do that u there's a lot of risks there too right so I like Meta a lot

[译文] [Dylan Patel]: 我认为 Meta 手里的牌有可能让他们赢得一切……人机交互的下一个范式是我们根本不需要触摸它，我们告诉 AI 我们想要什么，AI 将其转化为现实……世界上唯一拥有“全栈”的公司就是 Meta：好的硬件（他们刚展示的带屏幕的眼镜）+ 好的模型 + 服务模型的能力 + 推荐系统的知识和诀窍（知道把什么内容放在用户面前）……再加上资本。我认为 Meta 非常接近成为唯一能做到这一点的公司。当然那里也有很多风险，但我非常喜欢 Meta。

[原文] [Host]: google to finish it off

[译文] [Host]: 最后是 Google。

[原文] [Dylan Patel]: i it was pretty bearish Google like two years ago but I'm I'm like super bullish Google why would change they're waking up on every front front you know they're taking the TPUs they're selling them externally they're taking the um their models and they're actually like competitive on them and they're training much better and better and better um they're being aggressive on infrastructure investments... they do have the hardware business that they can pivot into this they won't be as head as Meta is um they won't be as good as Apple is but like they they they do have Android um they do have YouTube they do have like all these IPs they have search that can come together when we turn to that next interface of consumer but also they can also dominate the professional sense too potentially whereas Meta I don't think can dominate that professional sense um only the consumer sense and I think Google's well positioned to go capture both markets or a meaningful share of both

[译文] [Dylan Patel]: 两年前我还相当看空 Google，但我现在超级看多 Google。（主持人：为什么变了？）他们在每条战线上都在苏醒。你知道，他们开始把 TPU 对外销售；他们的模型实际上很有竞争力，而且训练得越来越好；他们在基础设施投资上非常激进……他们确实有可以转型的硬件业务。他们可能不会像 Meta 那么领先（在眼镜上），也不会像 Apple 那么好，但他们有 Android，有 YouTube，有所有这些 IP，有搜索。当我们转向下一个消费者界面时，这些可以结合在一起。而且他们还有可能主导专业领域（Professional Sense），而我认为 Meta 做不到主导专业领域，只能主导消费者领域。我认为 Google 处于非常有利的位置，可以同时通过去并在两个市场都获得可观的份额。

章节 16：软件行业的变局——SaaS模式的危机与重构

📝 本节摘要：

本章深入探讨了软件商业模式在 AI 时代的根本性转变。Dylan 提出了一个极具洞察力的反直觉观点：SaaS（软件即服务）模式的黄金时代可能已逝。

他以中国市场为例指出，由于中国过去的人力开发成本极低，企业更倾向于“自建”而非“购买”软件，导致 SaaS 在中国从未真正兴起。随着 AI 让代码生成成本大幅降低，全球市场可能都会迎来这种“中国化”趋势——即自建软件变得比租赁 SaaS 更划算。

更致命的是，AI 应用打破了传统软件“高获客成本（CAC）、低销售成本（COGS）”的盈利模型。现在的 AI SaaS 公司不仅获客难，还要承担昂贵的推理算力成本（高 COGS），这导致它们难以达到利润爆炸的“逃逸速度”。相比之下，拥有底层基础设施的 Google 因其极低的代币成本，可能拥有巨大的结构性优势。

[原文] [Host]: i feel like we've covered like an incredible amount of ground is there anything that we haven't talked about that you feel is like really critical to what happens in the future that we didn't cover

[译文] [Host]: 我感觉我们要覆盖了难以置信的广泛领域。还有什么我们没谈到，但你觉得对未来发生的事情至关重要的话题吗？

[原文] [Dylan Patel]: i think the question you know sort of of everyone that I constantly get asked is like okay Dylan you know you're lucky your obsession is that you loved hardware and you like followed it and you followed the supply chain and and you built this business on it but like you really like you don't follow the software side nearly as much and all the value is going to get created there right when is that going to when is that flip coin going to flip over

[译文] [Dylan Patel]: 我想大家经常问我的一个问题是：“好吧 Dylan，你很幸运，你的痴迷在于你热爱硬件，你追踪硬件，追踪供应链，并以此建立了业务。但你并没有那么关注软件方面，而所有的价值都将在那里创造，对吧？那个硬币什么时候会翻转过来？”

[原文] [Dylan Patel]: um but I think the thing that most people don't realize software is not the same as it was 5 10 years ago you've had dramatic changes in software and the business model is going to change as well right if we go back like 5 years three years whatever when SAS was the darling November 21 I remember SAS started tanking and um at the time it was like it was like mostly like they were over earning and all these other things doesn't matter

[译文] [Dylan Patel]: 但我认为大多数人没有意识到的是，软件已经和 5 年前、10 年前不一样了。软件发生了巨大的变化，商业模式也将随之改变。如果我们回溯 5 年或 3 年，不管什么时候，当 SaaS（软件即服务）还是宠儿的时候……我记得 2021 年 11 月 SaaS 开始暴跌，当时大部分观点是认为它们盈利过高之类的，但这不重要。

[原文] [Dylan Patel]: the interesting thing about the business model is that it was it is such a good business model when your R&D is sort of this it stays flat right and you grow a little bit but really R&D doesn't flex that much your cogs are super low um you're you know the flip side is in a SAS business your customer acquisition cost is quite high yeah

[译文] [Dylan Patel]: 这个商业模式有趣的地方在于，它曾经是一个极好的商业模式：你的研发（R&D）某种程度上是平稳的，虽然你会增长一点，但研发成本并没有那么大的弹性；你的销售成本（COGS）超级低。但另一面是，在 SaaS 业务中，你的获客成本（CAC）相当高。

[原文] [Dylan Patel]: and so when you look at what like what like certain companies have done when they've acquired a business is they've just crushed the customer cost acquisition cost or crust sa they made the business amazing whether it's like Broadcom with VMware and stuff it's not really customer acquisition they just had a bunch of wasted SGA but like this SGA this customer acquisition that was most of your cost r&d was small but not like crazy and then once you hit critical mass you just you just cash cash money money

[译文] [Dylan Patel]: 所以当你看看某些公司收购业务后的做法，比如 Broadcom 收购 VMware 等，他们就是粉碎了获客成本，或者削减了 SG&A（销售、一般和行政费用），让业务变得惊人。虽然不仅仅是获客，他们只是有一堆浪费的 SG&A。但这种 SG&A、这种获客成本曾是你成本的大头，研发虽然不小但也没那么疯狂。而一旦你达到临界质量（critical mass），你就开始疯狂印钞票（cash cash money money）。

[原文] [Dylan Patel]: but software changes a lot when the cost to build that software that you have tanks like crazy you look at non US markets and the prevalence of SAS it's very different i will bring up China as an example and a counterpoint china doesn't have that much of a SAS business actually their cloud business is pretty small too right relatively to the US not despite them like importing tons of CPUs and storage historically right there most people just did stuff on prem and design their own software because the cost of developing software in China was so much less than America that the SAS business model didn't work as well people could just build rather than rent it out and buy and that creates inefficiency in the market I'm sure those weren't the best of breed solutions always

[译文] [Dylan Patel]: 但是，当你构建软件的成本疯狂暴跌时，软件行业就发生了巨大的变化。你看看非美国市场以及 SaaS 的普及率，情况非常不同。我会拿中国作为一个例子和反面教材。中国其实没有那么大的 SaaS 业务，甚至相对于美国来说，他们的云业务也相当小（尽管他们历史上进口了大量的 CPU 和存储）。那里大多数人只是做本地部署（on-prem），并设计自己的软件。因为在中国，开发软件的成本比美国低得多，导致 SaaS 商业模式没那么行得通。人们可以直接“自建”（build），而不是“租赁”或“购买”。虽然这可能造成了市场效率低下，我敢肯定那些不总是最好的解决方案。

[原文] [Dylan Patel]: anyways that's what the software development cost may be like you know it was like software developers in 2015 in China were getting paid maybe fifth of the US and they were maybe twice as good or something like that so 10x lower cost of software i'm I'm making up numbers right um you know they had 10x lower cost of software and so SAS never happened cloud never happened and and at least as big of a way as it did in the US and around the world for all the companies that use that sort of that have that same economic reality and that's despite the outsourcing right to India and and and Eastern Europe and South South America etc

[译文] [Dylan Patel]: 无论如何，这就是软件开发成本的问题。比如 2015 年中国的软件开发人员，薪水可能是美国的五分之一，但他们的能力可能是两倍好或者类似的，所以软件成本低了 10 倍——我是在编数字举例——但你知道，他们的软件成本低了 10 倍。所以 SaaS 从未兴起，云也从未兴起，至少没有像在美国和世界各地那些拥有相同经济现实的公司中那样大规模兴起（尽管还有向印度、东欧和南美等地的外包）。

[原文] [Dylan Patel]: um you you you changed all of this with AI software development right um and AI SAS products generally right not just AI software development so there's two sort of coins here so AI software development tanks the cost of building a competing software stack do you now move to a world where X can just build I can just build instead of buying renting

[译文] [Dylan Patel]: 而随着 AI 软件开发以及通用的 AI SaaS 产品的出现，你改变了这一切。这里有硬币的两面：AI 软件开发让构建竞争性软件栈的成本暴跌。我们是否正在进入这样一个世界：X 公司可以直接自建，我也可以直接自建，而不是购买或租赁？

[原文] [Dylan Patel]: two is if you are a SAS business and your customer acquisition cost remains the same and most businesses in AI and in SAS are going to remain having a high customer acquisition cost sales is hard uh breaking into a competency is hard but now you add this AI part of it you've now added a humongous cogs right your cost of goods sold in any AI software is really hard and really big and this is partially why I think Google also has an advantage they have the lowest cost of goods sold for any token of any company because they have their own vertical stack on TPUs

[译文] [Dylan Patel]: 第二点是，如果你是一家 SaaS 企业，你的获客成本（CAC）保持不变——大多数 AI 和 SaaS 企业的获客成本都将保持高位，因为销售很难，打入某个领域很难——但现在你加上了 AI 部分，你就增加了一个巨大的销售成本（COGS）。任何 AI 软件的销售成本都非常高昂。这部分也是为什么我认为 Google 拥有优势，他们的任何代币（token）的销售成本都是所有公司中最低的，因为他们在 TPU 上拥有自己的垂直整合栈。

[原文] [Dylan Patel]: anyways coming back to this because you have this high customer acquisition cost and you have this high uh cogs and then the cost of anyone developing it themselves or competitors in the market means you're going to have a very fragmented SAS market or they're just going to build it themselves and therefore you never hit the escape velocity where your customer acquisition cost and your R&D get and get amortized and because you have such a high COGS your amortization point means your gross your net profitability is actually much worse

[译文] [Dylan Patel]: 总之回到这一点，因为你有高昂的获客成本，又有高昂的销售成本（COGS），再加上任何人自建或市场上竞争对手的开发成本都很低，这意味着你将面临一个非常碎片化的 SaaS 市场，或者人们干脆自己造。因此，你永远无法达到那个“逃逸速度”（escape velocity），即你的获客成本和研发成本得到摊销。而且因为你的 COGS 太高，你的摊销点意味着你的净盈利能力实际上要差得多。

[原文] [Dylan Patel]: and so I think like the era of like software only businesses is really really tough in the age of AI now already scaled businesses can do great right I think YouTube is going to have its glory days and I'm sure it'll it'll always be amazing but with with with the cost of generation of content falling and falling creating content he who controls the platform is going to win and win and win and win

[译文] [Dylan Patel]: 所以我认为在 AI 时代，“纯软件”业务的时代真的非常非常艰难。现在已经形成规模的业务可能会做得很好，比如我认为 YouTube 会迎来它的辉煌岁月，它肯定会一直很棒。但随着内容生成成本不断下降，创造内容的门槛降低，那个控制平台的人将会一直赢下去（win and win and win and win）。

[原文] [Dylan Patel]: but like you know there's like the functionality you build within Salesforce is actually going to be like way less like what you can build on your own like or like you know there's there's or like whatever it is i'm not saying it's a take on Salesforce itself specifically but I think many software businesses will have a reckoning with the fact that their COGS is going to sore their customer acquisition cost isn't going to fall and they have a lot more competitors and so then they don't hit that escape velocity and I think that's the the thing that uh maybe software um it's something I've I've like sort of like thought about there's a couple people in my company Doug Douglas Olaflin and he's the one whose idea this actually is

[译文] [Dylan Patel]: 但是，你在 Salesforce 内部构建的功能，实际上可能远远不如你自己构建的。不管是什么软件，我不是针对 Salesforce 本身，但我认为许多软件企业将不得不面对一个现实：他们的 COGS 将会飙升，他们的获客成本不会下降，而且他们会有更多的竞争对手。所以他们无法达到逃逸速度。这可能就是软件行业的现状。这是我思考过的事情，我公司里有几个人，比如 Doug（Douglas O'Laughlin），这实际上是他的主意。

请告诉我是否继续输出 第17章：尾声——个人反思与兄弟情谊？

章节 17：尾声——个人反思与兄弟情谊

📝 本节摘要：

在访谈的最后，Dylan 回答了关于“任何人为你做过的最善良的事”这一传统问题。他并没有谈论商业伙伴或导师，而是深情地提到了他的哥哥。Dylan 坦诚地反思了自己的性格缺陷——他自认在生活中经常表现得像个“混球”（Shithead），因为过度专注于思考和“氛围感”（Vibe）而忽略了周围人的感受，甚至无意中伤害他人。他感激哥哥给予的无条件之爱，以及在关键时刻把他拉回正轨、直言不讳地指出他是个“白痴”的那种坦诚。

[原文] [Host]: this has been incredibly fun i I love love learning from you and listening to you and reading what you put out i think I think you're just one of the most um energetic and awesome thinkers in this whole space right now so thank you for all the work you've done when I do these I ask the same traditional closing question what's the kindest thing that anyone's ever done for you

[译文] [Host]: 这次谈话非常有趣。我非常喜欢向你学习，听你说话，读你写的东西。我认为你确实是目前整个领域中最充满活力、最棒的思考者之一。所以感谢你所做的一切工作。当我做这些访谈时，我会问同一个传统的结束问题：任何人为你做过的最善良的事是什么？

[原文] [Dylan Patel]: done for me h I mean it have to be my brother everything he's done in my life um I've been a shithead my whole life and I still am a shithead um and so like every time he like pulls me back on path he corrects me he loves me unconditionally i think my brother is probably the most he's done the kindest things for me right and I've been an asshole like so much of my life right like unconsiderate and like everything right he's just always been there for me and always been

[译文] [Dylan Patel]: 为我做的？嗯，那肯定是我哥哥，他在我生命中做的一切。我这辈子一直是个混球（shithead），现在还是个混球。所以每次他都会把我拉回正轨，他纠正我，他无条件地爱我。我想我哥哥可能……他为我做了最善良的事。我这辈子很多时候都是个混蛋（asshole），不体贴，各种毛病。但他总是在那里支持我，一直都在。

[原文] [Host]: Why were you an asshole why yeah if you're aware of it it makes it into

[译文] [Host]: 你为什么是个混蛋？为什么？是的，如果你意识到了这一点，那就会让它变成……

[原文] [Dylan Patel]: No no it's terrible yeah and maybe this is like the MMO of like who I am and maybe that's why I'm like a good thinker but like I I like vibe really hard and I'm in the moment really hard and I digest tons of information but I'm very like bad at like um like how would I say like task orientation remembering to do specific things like I'm very bad at those things and thankfully I've like been able to surround myself in my life whether it's through birth or not um with people who help me with the things I'm bad at because I'm very bad at a lot of things like I think like you know as far as like you know radar plot of like how good I'm at things

[译文] [Dylan Patel]: 不不，这很糟糕。也许这就是我之所以为我的“MMO”（大规模多人在线游戏，此处指个人行为模式/设定），也许这就是为什么我是个优秀的思考者。但我真的很喜欢那种“氛围感”（vibe），我非常沉浸在当下，我消化大量的信息。但我非常不擅长……怎么说呢，任务导向，或者记住做具体的事情。我非常不擅长那些事。谢天谢地，我能在生活中——无论是天生的还是后天的——被那些能帮我处理我不擅长事务的人所包围。因为我在很多事情上都很糟糕，如果你看那种我不擅长事情的雷达图的话。

[原文] [Dylan Patel]: and so when I don't like call people or like think be considered of what they're thinking because I'm just vibing and I'm doing whatever you know I'm like like kind of like focused in on like this path and like that path ends up hurting someone else right whether it's like hey I didn't I didn't call someone or I didn't like think about their feelings when I did an action or when I said something that makes be an asshole right and yes I should be more conscious of this and I try to be but it's like it's just one of the things I'm going to wrestle with in my life forever and a lot of times I don't even realize I'm being a freaking idiot until my brother's like "You're a freaking idiot."

[译文] [Dylan Patel]: 所以当我不给别人打电话，或者不体贴别人的想法时，因为我只是沉浸在氛围里，做我自己的事。你知道，我有点像专注于这条路，而这条路最终伤害了别人。不管是“嘿，我没给某人打电话”，或者是“我在做某个动作、说某句话时没考虑他们的感受”，这让我成了个混蛋。是的，我应该更意识到这一点，我也在尝试，但这就像是我这辈子要一直与之斗争的事情之一。很多时候我甚至没意识到自己是个十足的白痴，直到我哥哥说：“你是个十足的白痴。”

[原文] [Host]: God for your brother and so like I I you know that's the kindest thing anyone's ever uh done for me is like my brother through my whole life i love it i love it wonderful place to close thanks so much for your time

[译文] [Host]: 上帝保佑你的兄弟。

[Dylan Patel]: 所以，你知道，那就是任何人曾为我做过的最善良的事，就是我哥哥贯穿我一生所做的一切。

[Host]: 我很喜欢这个回答，我很喜欢。这是一个绝佳的结束点。非常感谢你的时间。

[原文] [Dylan Patel]: thank you so much yeah

[译文] [Dylan Patel]: 非常感谢。