TECH004：山姆·阿尔特曼与OpenAI的崛起——对话塞布·邦尼

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大家好，欢迎来到本周三的《无限科技》节目。今天，我和塞布·邦尼将深入探讨凯伦·豪的著作《AI帝国：山姆·奥尔特曼OpenAI中的梦想与噩梦》。我们将追溯山姆·奥尔特曼从早期创业及Y Combinator时期，到与埃隆·马斯克共同创立OpenAI，以及该公司从非营利理想转变为微软支持的行业巨头的历程。过程中我们会解析2023年山姆突然被解雇的著名事件，OpenAI复杂的治理结构，以及AGI引发的广泛伦理问题。

Hey, everyone. Welcome to this Wednesday's release of Infinite Tech. Today, Seb Bunny and I dive into Karen Howe's book, Empire of AI, Dreams and Nightmares in Sam Altman's OpenAI. We trace Sam Altman's rise from his early startup and Y Combinator days to the founding of OpenAI with Elon Musk and the company's transformation from a nonprofit ideal to a Microsoft backed powerhouse. Along the way, we unpack the famous blip where Sam got fired back in 2023, OpenAI's complex governance, the broader ethical questions raised by AGI.

各位，这期节目绝对不容错过。话不多说，让我们直接进入这本书的内容。

And, guys, this is surely an episode you won't wanna miss. So without further ado, let's jump right into the book.

您正在通过投资者播客网络收听由普雷斯顿·皮什主持的《无限科技》。我们以富足与健全货币的视角，探索比特币、人工智能、机器人技术、长寿及其他指数级增长技术。加入我们，共同连接塑造未来十年乃至更久远的突破性进展，助您今日掌握未来。现在有请主持人普雷斯顿·皮什。

You're listening to Infinite Tech via the Investors Podcast Network hosted by Preston Pysh. We explore Bitcoin, AI, robotics, longevity, and other exponential technologies through a lens of abundance and sound money. Join us as we connect the breakthrough shaping the next decade and beyond, empowering you to harness the future today. And now here's your host, Preston Pysch.

大家好，欢迎收听节目。今天我身边是独一无二的塞布·邦尼，我们将讨论OpenAI和山姆·奥尔特曼。这家公司究竟发生了什么？它将何去何从？又源自何处？

Hey, everyone. Welcome to the show. I'm here with the one and only Seb Bunny, and we are talking OpenAI, Sam Altman. What in the world has gone on there at this company? Where does it go Where did it come from?

我们共同阅读了一本书，会以其为框架展开讨论，但也会超越书本探索其他方向。塞布，欢迎来到节目。

And we have a book that we read together and we'll be using that somewhat as the framework, but also kind of going in in other directions beyond just the book. And Seb, welcome to the show, sir.

老兄，非常感谢邀请我，普雷斯顿。不知听众是否收听了我们上期关于《思考机器》的节目，那期讲述了英伟达崛起、黄仁勋的故事，以及英伟达如何为OpenAI和神经网络奠定基础——后者正是构建这些大语言模型的技术基础。那期节目为阅读本书做了绝佳铺垫。我超级享受阅读过程，想快速分享的是：我原先完全不了解英伟达对AI世界的奠基作用——从中央处理器(CPU)到英伟达发明图形处理器(GPU)，实现并行计算处理海量数据，这彻底改变了游戏规则。从第一本书过渡到本书的阅读体验非常棒，因为这种知识衔接极大加深了我的理解深度。

Oh man, thanks for having me on Preston. And you know what I found really fascinating is, so for those who didn't listen to our previous episode where we discussed the thinking machine and that's kind of the rise of Navidea and Jensen Huang and kind of essentially how Navidea laid the foundation for OpenAI and neural nets, which is kind of the technical term for the foundation of what these large language models are built on. It really kind of set the stage for reading this book. I super enjoyed it and maybe the point that I'll just kind of quickly share is that I had no idea to what extent Nvidia really did pave the world of AI in that we went from CPUs, central processing units to Nvidia creating GPUs, graphics processing units, which enable parallel processing, computing tons of data, and that basically let's set the stage. And so it was really cool going from that first book into the second book, because I think it helped with the depth of understanding.

没错，百分之百。有趣的是，不知道你是否看过西耶娜·奥尔特曼和黄仁勋的那段视频片段，当时还有另一位先生在场，谈论他们正在进行的数千亿美元规模的投资。人们都在想，好吧，那他们是怎么融资的？看起来钱就像从一个人手里转到另一个人手里。

Yeah. 100%. And it's interesting because I don't know if you've seen the, clip of Sienna Altman and Jensen Huang, and there was another gentleman there talking about all this investment that they're doing to the tune of hundreds and hundreds of billions of dollars and how people are like, okay. So, like, how are they financing this? And it looks like it's going in one person's hand and then into the other person's hand.

这就像是整个过程的循环融资。不过先不说这个，我们直接进入正题吧。好，我们读的这本书名叫《AI帝国的梦想与梦魇：山姆·奥尔特曼的OpenAI》，作者是凯伦·豪。这本书写得不错。

It's like the circular financing of of all of it. But that aside, let's go ahead and jump into this. Okay. So the name of the book that we read was called Empire of AI Dreams and Nightmares of Sam Altman's OpenAI, and this was written by Karen Howe. The book was good.

书中有部分内容确实吸引了我的注意，也有些部分让我觉得有点残酷，有点‘觉醒’。但除此之外，我们会梳理时间线，向大家介绍山姆·奥尔特曼的崛起以及OpenAI的所作所为。我们会分享我们喜欢的部分，讨厌的部分，然后继续讨论。那么，塞布，有什么开场白吗？

The book had parts that, like, really got my attention. There's other parts of the book where I was like, a little brutal, a little woke. But other than that, like, we're gonna kinda go through the timeline and kinda educate people on the rise of Sam Altman, what they're doing there at OpenAI. You know, we'll give our overview on the things we love, the things we hated, and, we'll go from there. So, Seb, any opening comments?

我想知道你是否和我有同样的感觉，在书中段那些‘觉醒’的内容部分，有没有什么不同的看法？

Anything different than what I'm curious if you kinda saw it the same way where in the middle of the book where some of this woke

完全一样的感觉。我认为这本书一开始就非常吸引人，我真的很喜欢。开头部分，正如我们即将讨论的，确实讲述了山姆和OpenAI的崛起，以及一些媒体上不常听到的关于AI构建及其所建立的关系等内容。我觉得这些非常非常有趣，但书中段确实有点‘觉醒’，涉及了一些性别和环境议题。

stuff Exactly the same way. I think to start the book very much grabbed my attention. I've really, really enjoyed it. It started out, as we'll get into, really discussing, Sam, the rise of OpenAI, And I think some of the stuff that you don't necessarily hear in the media about the construction of AI and such and the relationships that it's built upon. So I found that really, really fascinating but it definitely in the middle of the book got a little woke, got into some of the gender stuff and the environmental stuff.

不过归根结底，这无疑是一本有趣的书。

But ultimately, it was an interesting book for sure.

是的。好的，那么我们来梳理一下山姆·奥尔特曼的生平吧，我觉得这部分相当有趣，也有助于理解他的背景和动机。这不是书的主线，我只是想先谈谈山姆，给大家一些背景信息。

Yeah. Okay. So let's go through, the just basically Sam Altman's life because I find this pretty interesting, and it also kinda helps frame things of, like, maybe where he's coming from. And this is not the arc of the book. I'm just gonna start off kinda talking about Sam, kind of giving people that background.

他早年生活在圣路易斯，很小就学会了编程。到了二月，他进入斯坦福大学攻读计算机科学，但随后辍学创业。大约在2005年，他创立了这家名为Looped的公司，并担任联合创始人。

So early in his life, grew up in St. Louis, learned how to code at a young age. By February, he goes to Stanford and is doing computer science, but then drops out to start a company. He starts this company, this is around the 2005 time frame. It was called Looped, and he cofounded this.

这是一款基于地理位置分享的社交应用，我觉得他从这个领域起步很有意思。他成功获得了风险投资，成为早期移动互联网浪潮的一部分。虽然Loop最终未能大规模流行，但他在2012年以4300万美元的价格将其出售，这为他在科技创业领域赢得了一定信誉。

And it's a location sharing social app, which I found kind of interesting that that's where he starts. Right? He raised venture capital, became part of an early this early mobile wave. The loop never gained a lot of mass traction. He did sell it in 2012 for $43,000,000, and this gave him, you know, some credibility in the tech founder startup world.

2011至2019年间，他首先加入了Y Combinator——这个大家应该耳熟能详的孵化器。当时Y Combinator的负责人是Paul Graham。这个孵化器孕育了许多知名初创企业，比如Airbnb、Stripe和Dropbox。

2011 to 2019, he first joined, Y Combinator. I'm sure people have heard about Y Combinator a lot. Think of this as like a, this was, Paul Graham that was the president of Y Combinator when he came in there. And this is an incubator that founded many or, you know, assisted in the founding of many of these early startups. And some of these are like Airbnb, Stripe, Dropbox.

无数公司从Y Combinator脱颖而出。他在这里建立了自己的声誉，先是作为兼职合伙人加入，通过与Paul Graham的合作树立了形象，深受其赏识。书中提到，他极其擅长在政治层面周旋于不同场合，能够根据需要赢得他人极度好感，最终在Y Combinator脱颖而出成为负责人。

A ton of companies came out of Y Combinator. And so he built a reputation here at Y Combinator. He goes in there. He joins as a part time partner at Y Combinator and just kinda made a reputation with himself with, Paul Graham and was very well liked by him. And in the book, it talks about how he's just really good at politically putting himself into different situations and being extremely liked if he wants to be extremely liked and kinda rose to the top at Y Combinator and eventually became the president at Y Combinator.

具体年份我记不太清了，但他在2011到2019年任职期间，Paul确实提拔他担任了Y Combinator总裁。这在硅谷是件大事——毕竟这是个已经有过成功创业经历（4300万美元公司收购案）的人，现在执掌着这个孵化器，实际操控着所有经过Y Combinator的知名初创企业和创始人命脉。

I'm trying to think of the year that that happened. I'm not necessarily remembering, but his time at Y Combinator was from 2011 to 2019. So somewhere in the middle, Paul, you know, made him the president at Y Combinator. And this was a really big thing out in the valley because here's a guy, he does have one win under his belt, if you will, by selling his company for 43,000,000. And then he steps into this role and is literally the guy kinda pulling the strings as to all these major startups and founders that are moving through this organization wide combinator.

我先暂停一下。Seb，关于目前的时间线你还有什么要补充的吗？还是继续往下说？

I'm gonna pause there. Seb, anything else you wanna add or throw in based on the timeline so far or just keep rolling?

我觉得你说得很到位。这本书里有个很有意思的对比：有几位受访者认为Sam是天才级人物，他在Y Combinator积累的知识深度和人脉资源无人能及。然后...

I think you're spot on. I think it's really fascinating because there's this kind of juxtaposition throughout the book where it's got there's a handful of individuals that basically say Sam is ingenious. Just his depth of knowledge, his connections to people that I think he very much formed through Y Combinator is bar none. And then

你还有另一面，我们会

you've also got this other side which we'll

深入探讨，其中涉及到对这些信念部分合理性的质疑。我想快速读一段书中让我印象深刻的引述，来自OpenAI员工Ralston。他说，萨姆能讲述一个让你想参与其中的故事，既引人入胜又显得真实，甚至看似可能发生。他将此比作史蒂夫·乔布斯的现实扭曲力场。

get into where there's a bit of questioning the legitimacy of some of these beliefs. There's one quote that I'll quickly read out that I think really stood out to me throughout the book. And it's this guy Ralston, who's an employee at OpenAI, he says, Sam can tell a tale that you want to be a part of. That is compelling and that seems real, that seems even likely. He likens it to Steve Jobs' reality distortion field.

嗯。

Mhmm.

他说，史蒂夫能讲述一个压倒你现实其他部分的故事，无论这是现实扭曲还是成为了现实。因为别忘了，史蒂夫·乔布斯确实建造了改变你现实的东西。这不只是扭曲，它是真实的。而萨姆正在创造的东西，隐约也有这种意味——它是真实的，还是仅仅是扭曲？这种矛盾将贯穿全书。

Steve could tell a story that overwhelmed any other part of your reality, he says, whether there was a distortion of reality or it became a reality. Because remember the thing about Steve Jobs is he actually built stuff that did change your reality. It wasn't just distortion, it was real. Which kind of there's this hint of is what Sam is creating, is it real or is it simply just a distortion? And so this is kind of this conflict which we'll see as we go throughout the book.

总之，我觉得这是个有趣的引述。

Anyway, I thought that was an interesting quote.

我非常喜欢这个引述，我认为企业创始人深谙此道——他们能看到某种他们认为可能实现的愿景，尽管那显然遥不可及，否则就不会有从零到一的10倍、100倍乃至千倍的飞跃。这正是彼得·蒂尔《从零到一》书中探讨的理念。但关键在于，这种人不仅能看到未来，还有能力组建团队、领导团队、激励团队去实现它。在早期一无所有时，他们通过语言让一切显得触手可及，以此获得融资。你知道，在种子轮或YC这类孵化阶段，这些企业基本只有PPT演示文稿。

I I love the quote and I think that this is something that founders of businesses, they see this they see a vision of something that they think can happen, but obviously is is way out there, or else you wouldn't have the 10 to 100 x to a thousand x move of going from nothing to the one that it becomes. And that's the name of zero to one is Peter Thiel's book, and he talks about this idea. But it's almost like there's this innate draw for a person who can not only just see the future, they have the ability to kind of assemble a team, to lead a team, to motivate a team, to build it out. But in the early days when it's nothing, they speak in a way that makes it feel like it's real right now or that it's completely possible in order to get the funding because, you know, you get into the seed phase or, like, this y combinator phase, the incubator phase of these businesses, and there's nothing there. They're PowerPoint slides for the for the most part.

这很大程度上是画饼充饥。很多都是'嘿，它可以这样'的承诺。而那些精于此道的人往往是卓越的故事讲述者，他们能牢牢吸引风险投资人和资金配置者的注意力。

And it's a lot of hand waving. It's a lot of, hey. It can be this. And it almost seems like the ones that are super good at this are amazing storytellers. They're able to capture the attention of venture capitalists and people that would allocate funds to them.

现实扭曲力场似乎确实存在，我很好奇那些熟悉风投圈或早期创业领域的人或许能证实这一点。我认为这种力量几乎是天然的、与生俱来的——该用什么词形容呢，塞巴？它似乎就是这领域的固有特征，我想表达的就是这个意思，对吧？

And it seems like the reality distortion field is somewhat and I'm curious people that are you know, have been around the VC space or the early stage startup space can maybe attest to this. I think it's valid that it's almost this force or this natural innate. What's the word I'm looking for, Seb? It almost seems like it comes with the territory, I guess is where I'm going. Right?

完全同意。让我想起一句话：过早和错误没有区别。有时候你脑海中可能构想了未来的发展蓝图，但如果技术演进速度跟不上，本质上你就是错的。所以人们可能扭曲现实，讲述一个看似极其真实的故事，同时还得指望技术能跟上这个构想。说到这个，我又想起NVIDIA的思考机器——黄仁勋提到在NVIDIA崛起时，他研发了这些芯片来支持更高强度的图形游戏。

Absolutely. And I think a quote that kind of pops to mind is being early is the same as being wrong. I think sometimes you can have this idea in your mind about how you think the future is going to kind of plan out but in reality if the technology doesn't evolve quick enough, essentially you're wrong. So I think that sometimes you can distort this, that you can tell the story that seems very, very real and you've also got to hope that technology catches up or keeps up with this idea. I think what comes to mind again, bring it back to the thinking machine in NVIDIA, is he talks about how at the rise in Nvidia, he created these chips to enable far more graphic intensive games.

但当时的其他硬件、电脑还无法处理这些芯片，导致电脑不断崩溃。表面看是NVIDIA的失败，实则是世界还没准备好迎接这个理念。这种现实扭曲尚未真正映射到现实中。

But at the time, the rest of the hardware, the computers weren't able to process it so it just kept on crashing the computers. And so it looks like a failure of Nvidia but in reality, it was the rest of the world had not caught up to this idea. This distortion hadn't mapped out into reality just yet.

AI领域也是如此。神经网络可不是近五到十年才有的概念，早在八九十年代就有人尝试构建神经网络了。只是当时的算力根本撑不起这个构想，注意力机制和transformer架构也尚未成熟。

Well, you could even say that about the AI space. I mean, neural nets aren't something that were new in the past five to ten years. This was stuff that was being done in the nineties and eighties where they had the idea of building a neural net. They didn't have the capacity of processing to really kinda take it there. And I think the attention part of it, the transformer part of it was also lacking.

就像当时还没人攻克那些技术难题。如果我没记错，这些突破大约发生在2017年前后。所以纵有奇思妙想，若市场条件不成熟、需求不存在或技术不可行，那注定是空中楼阁。你就是个空有绝妙点子的天才，却无法将其实现。说到这里我想暂停深入探讨，因为这直接关系到OpenAI的创立——那是在2015年。

Like, somebody hadn't figured that out yet. And if I remember right, that was around the 2017 time frame that that happened. So, like, you can have these ideas, but if the rest of the market isn't there or the market demand isn't there or the technical feasibility isn't there, like, you're just dead on arrival. You're just the brilliant person with a great idea, but nothing to actually, like, make it into fruition. So the part that I wanna, like, pause right here and and get into because this really gets into the founding of OpenAI itself, and that happened in 2015.

据说当时埃隆·马斯克与谷歌创始人有过一次晚宴交流。谷歌团队刚以几亿美元收购了DeepMind的德米斯·哈萨比斯（姓氏发音可能不准），将其作为谷歌旗下全资子公司，打造成顶级AI研究部门。

Evidently, there was this engagement between Elon Musk and the founders at Google, some dinner party or something like that. And the Google guys had recently acquired Demise Hespis, I think is how you pronounce his last name, from DeepMind. They purchased his company for a couple $100,000,000. I can't remember the exact number. And basically bolted it on the Google as their premier AI research arm, fully owned operational subsidiary inside of Google.

那时AI才开始显露出真正潜力。哈萨比斯是全球该领域的领军人物之一。后来马斯克与谷歌创始人的那场晚宴上，双方爆发了激烈争论——我记不清对方是不是拉里·佩奇了。

And this is when we were really starting to see AI start to seem like it was something. Like, he was one of the leading people in the world that was doing this. And, there was this dinner that then happened between Elon Musk and the founders of Google. And it came down to this conversation where Elon got in a heated debate with these guys. And I forget if it was I don't think it was Larry Page.

我记得是另一个人对埃隆说，他说，是啊。你们只是些碎片，因为他们当时在争论人工智能是否会统治人类，成为世界新的顶级掠食者。埃隆被这样的言论震惊了，好像在说，当然，我是个言论主义者。难道你们真的想被非人类的东西统治和支配吗？你们疯了吗？

I think it was the other one that said to Elon, he goes, yeah. You're just aspieces because what they were doing is they were arguing over whether AI would dominate humans and become the new apex predator of the world. And Elon was so taken back by the comment of like, well, of course, I'm a speechist. Like, do you really wanna be ruled and dominated by something other than that that's nonhuman? Like, are you out of your mind?

这段对话其实是和谢尔盖·布林进行的。抱歉刚才一时忘了名字。但埃隆当时的反应就像是，这些人到底在胡说什么？意思是，为什么不让生命，或者说一种更高级的智能形式接管呢？你们这是在阻碍智能的自然进化。

And this conversation really it was Sergei Brin. Sorry to to forget the name there for a second. But Elon was just like, what in the world are these crazies talking about? Meaning, like, why won't you let life or, you know, a superior form of intelligence take over? Like, you're trying to get in the way of, like, the natural progression of intelligence.

这次晚餐后，我听过埃隆在不同场合提到这件事。塞布，我想你在媒体上也看到过。显然，这件事让埃隆觉得太离谱了，他认为我们需要一个竞争对手，以负责任的方式开发与人类利益一致的人工智能，而不是试图接管我们，把我们当作家养宠物对待。这就是埃隆和山姆开始合作的原因。

And so following this dinner, and I've heard different clips where Elon has talked about this. Seb, I'm assuming you have as as well out there in the media. But evidently, this event was the thing that just Elon was like, what in the world? Like, we need a competitor that is going to try to build AI in a responsible way that's aligned with human interest that's not gonna try to take us over and treat us like we're pets, like household pets. And so this is where Elon and Sam start to connect.

这就是OpenAI成立的背景，其中的'Open'代表开源，这点很多人都知道。他们举办了一场豪华晚宴，埃隆和山姆聚在一起，山姆还带来了许多Y Combinator的人，共同探讨如何建立一个能与谷歌DeepMind抗衡的竞争对手。他们的使命是为全人类创造安全的通用人工智能（AGI），并试图从治理角度确保这是整个项目的核心原则。

This is where the whole founding of OpenAI, and the open in there stands for open source as many know. But this is where they did this, and they had this fancy dinner. They got all together, Elon and Sam, and then Sam was bringing in a lot of people from Y Combinator to kind of really kind of piece this together of like, how can we do this? How can we build some type of competitor to what Google is doing over there with DeepMind? And, you know, their mission was safe AGI for all of humanity, and they were trying to organize it from a governance standpoint so that that was the leading principle of the entire thing.

你还记得埃隆最初的投资金额吗？我一时想不起来了，基本上是他出资启动了这个项目。讽刺的是，他是联合创始人之一。埃隆·马斯克与山姆、格雷格·布罗克曼等几人共同创立了OpenAI。就资金而言，埃隆应该是主要的出资人。

Do you remember what Elon's initial investment was? I can't remember off the of my head to basically fund this and to get it going because he was this is the irony. He's the cofounder. Elon Musk is the cofounder of OpenAI with Sam and Greg Brockman and I think another couple people. But as far as funding goes, think Elon was like the primary guy funding this thing.

没错，他是主要出资人，具体金额记不清了，但肯定在数十亿级别。我想强调的是，OpenAI最初完全是非营利性质的，纯粹以使命为驱动，没有盈利动机，完全开放。书中多次提到，山姆不希望通用人工智能（AGI）掌握在像谷歌这样的中心化实体手中。他们的目标是确保这项技术——不是如果，而是当我们实现通用人工智能时——是开源的，对所有人开放。

Absolutely. He was the primary guy and I can't remember, it was definitely like in the billions. And one thing that I really just wanted to highlight is that OpenAI very much started out with it is a non profit, it is not a for profit, it is purely mission driven, no profit motive, full openness, like essentially and it mentions it a couple times, like Sam did not want AGI or artificial general intelligence in the hands of a centralized entity like Google. And it says like Google a couple times in the book so I found that really fascinating. Their whole goal was we need to make sure this technology when we get there, not if we get there, when we get to artificial general intelligence is open source and available for everyone.

我们稍事休息，听听今天赞助商的消息。

Let's take a quick break and hear from today's sponsors.

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好了，回到节目。

Alright. Back to the show.

好的，我刚查了一下。埃隆最初的承诺是10亿美元捐款的一部分，而他实际支出最终是5000万美元。所以这是他在特定时期内承诺的10亿美元。哦，不对。

Okay. So I just looked it up. So Elon's initial commitment was part of a $1,000,000,000 pledge, and his actual outlays ended up being 50,000,000. So it was a billion over a certain period of time, which was what he pledged. Oh, no.

我理解这一点，但重要的是，那是承诺，并非预先支付的现金。第一年实际花费的金额更接近1.3亿美元。马斯克本人报告称，是的，他实际贡献的金额介于1.3亿至5000万美元之间，但最初的承诺是10亿。因此他确实是这一切的发起者，我认为很多人忽略了这一点，尤其是当你看到他们之间的来回交锋和彼此敌意时，可能会疑惑原因何在。

I take that, but importantly, that was a pledge, not upfront cash. The actual money spent in the first year was much closer to a 130,000,000. Musk himself reported yeah. There's a number between 130,000,000 and 50,000,000 of what he actually contributed, but the initial pledge was for a billion. So he was there, like, at the start at of all of this, which I think is lost on a lot of people, especially when you see the back and forth and the animosity that these two have for each other, and you're kinda maybe wondering why.

现在马斯克拥有xAI，众所周知。但这就是原因——因为他是早期开支票的人，实际上是推动这项事业发展的关键人物，从一开始就倡导其必要性和开放性。继续时间线：山姆最终在2019年前后离开Y Combinator，全职投入OpenAI。随后他们还与微软达成了具有里程碑意义的10亿美元投资协议。山姆监督了GPT-2的开发，我认为正是在GPT-2时期，它尚未家喻户晓之前。

And Musk now has x AI as everybody's well aware, But that's why is because he was the guy, like, writing the checks in the early days and is really kind of the one that led the charge as to why this was needed and why it needed to have the openness to it from the get go. So kinda continuing on the timeline. So Sam ends up leaving Y Combinator to focus full time on OpenAI around the 2019 time frame. Then they also negotiated a landmark deal with Microsoft for a billion of investment. Sam oversaw GPT-two, which I would argue really was before you had before it became a household name, it was GPT-two.

然后我认为GPT-3才是真正让它家喻户晓、人人谈论的时刻。这个时间点大约在2022年底。之后它真正爆发了。2022到2023年间，GPT-3升级为GPT-4，进入Bing AI，OpenAI还推出了各种合作伙伴关系。

Then I would say GPT-three is when this really became household name and everybody started talking about it. That timeline is right around late twenty twenty two, I would say is where that is. So then it really breaks out. 2022 to 2023, this is where GPT-three transitions into GPT-four. It's getting into Bing AI, and you got all sorts of partnerships that are then coming out of OpenAI.

我认为这正是萨姆·奥尔特曼真正成为家喻户晓名字的时刻，因为全球有如此多的人在使用这项服务，此时几乎所有人都知道他是谁。最后，我认为我们应该提到的2023年大事件，她在书中称之为‘闪点’。这个‘闪点’就是萨姆被OpenAI董事会解雇，所有人都对原因和发生的事情感到极度困惑，戏剧性十足。这一事件持续了数周。我记得在X上关注此事，看到后续影响简直疯狂。

And I would argue this is where Sam Altman really becomes a household name, and pretty much everybody knows who he is at this point because there's so many people using this service around the world. Finally, the last thing I think that we should kind of like hit is in 2023, there was this massive in the book, she calls it the blip. And the blip was Sam being fired from the board of OpenAI, and everybody just being insanely confused as to, like, why, what happened, so much drama. This lasted for weeks. I mean, I remember watching this on x and just seeing the fallout was crazy.

在读这本书之前，我承认自己仍不明白整件事的来龙去脉。我想大多数人也对此非常困惑。塞布和我稍后会尝试解析这个问题，因为它确实相当复杂，我们将在此详细讨论。但老实说，这可能是书中最吸引我的部分。作者以‘闪点’开篇来抓住读者的注意力。

And before reading this book, I would argue, I still didn't understand what it was all about. And I think most people are very confused what it was all about. And, you know, Seb and I will get into trying to define that because it's actually pretty complex, But we'll cover that in a lot of detail here coming up. But that was probably my favorite part in the book if I have to be honest. And the author opens up the book with the blip and covering this to kinda like grab your attention.

随后在书中，她零散地多次提及此事，但始终没有清晰系统地讨论。因此今天节目中我想做的，就是理清他被解雇的原因——至少是我们认为的原因——以及整个事件的真相。好了，以上就是大致时间线。塞布，关于时间线部分你还有什么要补充的吗？

And then throughout the book, she kinda talks about it a lot more here and there, but still it wasn't, like, very cleanly discussed. So something I would like to do on the show today is kinda cleanly go through why he was fired or at least why we think he was fired and what that whole thing was about. But Okay. So that's kind of the timeline. Seb, anything else you want to add as we kind of wrapped up the timeline?

是的。我很想听听你的看法。如果要简化解雇事件的两条主线，我认为第一条是公司内部确实存在对萨姆的不信任——我们稍后会深入探讨——这种不信任源于某些人质疑他部分言论的真实意图。第二条主线是OpenAI创立时的非营利性质前提，随着时间推移，其理念和使命发生了根本性转变。

Yeah. And I'm curious to hear your thoughts. I tend to think like if I was to simplify the firing down into two threads, I would lean on the first one being there was definitely there was a distrust of Sam inside of the company and I'm sure we'll dive into it. There was a distrust where some people questioned his intent behind some of the words that came out of his mouth and I think that's one of the first threads. And the second thread is this idea that OpenAI was founded, as we discussed, on the premise of being a non profit and you'll see as we get into it really the idea and the mission changed over time and it changed drastically.

甚至在本书完稿后，他们又提议转型为营利性公益公司。所以你会看到这家公司从纯粹非营利组织逐渐演变为实质上的营利企业，试图在两者间寻找平衡。我认为这两条主线最能解释解雇事件的原因，不过我很期待...

Then essentially even post the writing of this book, they proposed to convert into a for profit public benefit corporation. And so you've seen this company go from essentially nonprofit all the way to essentially a for profit company and trying to find that balance between those two. So I think those are the two threads that I tend to lean on as to why we saw this firing, but I'm curious

听听你的想法。

to hear your thoughts.

没错。我会从几个不同维度来分析董事会分裂的原因。我完全同意你刚才说的所有观点。首先，这家公司——这个非营利组织，塞布你想怎么称呼它都行——从一开始的治理结构就非常奇怪。

Yeah. I would break it down into a couple different vectors that kinda were like just pulling the board apart. I definitely agree with everything you said there. First of all, the thing that was very strange about this company, this nonprofit, what do you wanna call it, Seb? This thing, this entity, was the governance structure right from the start.

与大多数实体董事会和治理文件不同，这份文件的设定方式赋予董事会自我毁灭和解散的能力，这非常奇怪。你从不会在任何企业或实体中看到这样的条款——‘我们可能变得过于强大以至于需要自我了断’，这基本就是其构建逻辑。文件还涉及罢免权，董事会有权移除治理架构中的任何成员。所有这些异常条款，你会如何形容这种董事会设计？

So unlike most boards and most governing documents for an entity, this was set up in a way that the language gave the board the ability to destroy itself and dismantle itself, which is very strange. You don't ever see that with any business or entity is we might become so powerful that we need to kill ourselves is basically kind of the way that this was constructed. It also got into the ability to remove. The board has the ability to remove anybody within the governance. All these really weird situations or what would you call it from the board?

我不确定专业术语怎么说，但董事会确实拥有以各种离奇方式自我解散的权力。所以我认为首要问题就是董事会的治理架构本身。另一个关键点是其创立时的核心原则包含安全考量，但这却导致一个诡异悖论：如果他们行动不够快而被竞争对手抢先，反而会被指责因进度过慢而危及安全——毕竟对手可能先于他们实现AGI（通用人工智能）那将更危险。这种进退两难的困境，你见过类似案例吗？

I don't know the proper terminology, but the board had this ability to go in there and dismantle itself in many different weird and strange ways. So I'd say that'd be the first thing was just the governance of the board and how it was constructed. The other thing that I think was huge is one of the guiding principles of when it was founded was safety. But then you get in this really weird dynamic of if they don't go fast enough and somebody else wins, now they can make the argument that they're not being safe by going too slow because somebody else will beat them and achieve AGI before them, and that's dangerous. So think of the think of this catch 22 strain like, when is that ever the case?

对吧？正因为你正在设计超级智能或试图实现它，随之而来的困境就是：如果我们不够快，反而可能促成最初担忧的局面——别人会比我们更快造出来。这种动态博弈确实存在，因为组织内部和董事会中有人认为‘我们需要减速，必须采取比现在更安全的推进方式’。

Right? It is because you're literally designing super intelligence or you're trying to achieve super intelligence. And so what comes with that is this quandary of if we don't go fast enough, we might actually manifest our concern in the first place, which is that somebody else is going to build it faster than us. So that dynamic was at play because some people inside of the organization, some people within the board are saying, we need to slow down. We need to go about this a whole lot safer than what we're doing.

他们说我们在走钢丝，在持剪刀奔跑等等。而反方观点则是：如果我们不保持这种速度，中国或其他地方的竞争者就会超越我们。接下来我认为还有一点很重要——塞巴斯蒂安，你可以随时补充——就是塞巴斯蒂安你刚才提到的，与萨姆本人相关的透明度和信任问题。

We're being precarious. We're running with scissors, whatever. And the counterargument is as well, if we're not going this fast, somebody else in China or somebody else wherever is going to go faster. The next thing that I think kind of played into this, and Seb, feel free to add anything on to any of these points as I'm going through it. The next thing I would say is just the transparency and trust issues that you brought up Seb with Sam himself.

他们在实践中发现：每个人都是间谍。今天为你工作的人，明天可能以掌握的信息为筹码跳槽谷歌或其他公司，带走研发中的核心机密。作为掌舵者的萨姆——我不是为他辩护，只是分析——该如何管控行业机密不外泄？最终解决方案是在组织内实行信息隔离。但这会导致什么？

And I think what they found as they were going through this is everybody's a spy. Everybody's, you know, working for you one day and then trying to use that as a bargaining chip to go work for Google or whoever the next day and take the secret sauce of what they're developing over to these other places. So Sam, as the person sitting at the top, and I'm not trying to defend it, I'm just trying to talk through how do you manage that to control the industry secrets that you're producing without them getting out? What you do is you you end up compartmentalizing information within the organization. Well, what does that do?

必然引发信任危机。不断浮现的问题是：为萨姆工作的员工表示‘不可信，他们在这里秘密行动，部门间互不通气，我们因信息被隐瞒而产生怀疑’。

It leads to trust issues. Naturally, there's trust issues. So that was the next thing that kept kind of coming out and getting expressed is people that are working for Sam are saying, don't trust this guy. They're doing things over here. They're not talking the left hand's not talking to the right hand, and I don't trust them because he's withholding things.

这种不信任最终渗透到董事会讨论中。另一个重要因素是微软与OpenAI的权力集中现象，人们逐渐看清最初开源理想的变质——本应保持开放的资金模式，最终却变成被逐利的行业伙伴（即微软）扼住咽喉。在2023年萨姆突然被罢免前夕，市场普遍议论的焦点正是：‘微软现在实质上掌控了OpenAI’。

That percolates up into the board discussion. I think the other thing that was big was this power concentration of Microsoft and OpenAI and everybody just seeing that what they set off initially to do, which was keep the whole thing open source and the way that it would be funded wasn't going to be like there's this industry partner that's for profit industry partner that's breathing down their throat. And that was Microsoft, right? Leading up into the 2023 blip where SAM was fired. This became a massive talking point in the market, was Microsoft basically owns OpenAI at this point, was the talking point.

当时董事会中有人注意到这一情况并表示，这正在演变成一场灾难。对于那些关注此事的人来说，为什么——再次声明，我不是在为Sam辩护，只是试图理清所有线索——当人们思考‘Sam为何要这么做’时，从他们的扩张角度来看，他们很快意识到：只要我们能获取更多英伟达芯片，为这些芯片提供更多电力并输入更多数据，系统就会变得更智能。

So there was people on the board that were looking at that and saying, this is becoming disastrous. So for people that are looking at that, why would Again, I'm not trying to defend Sam. I'm just trying to lay out all the pieces here. As people are looking at, well, why would Sam do that? Well, when you look at for them to scale, the thing that they quickly understood was if we can just get more NVIDIA chips and put more power on the grid to these chips and we can feed it more data, the thing just gets smarter.

这是最基本的逻辑。当然实际情况要复杂得多，我只是过度简化了。那么这需要什么？需要疯狂的资本支出，需要巨额的投资资金。

That's the the basic. I mean, it's way more complex than that, but I'm just kinda oversimplifying. And so what does that take? It's crazy amounts of CapEx. It's crazy amounts of investment dollars.

如果你认为能以非营利方式筹集这些资金对抗——你必须考虑：在这个领域你的竞争对手是谁？他们是怎么做的？答案是：我有多个竞争对手，而他们没有一个采用这种方式。Sam再次回归安全性的考量：如果我们进展太慢，实际上等于毫无建树，也无法将最安全的模型推向世界。

And if you think you're going to be able to raise that in a nonprofit kind of way against and you and again, you have to look at, well, who's your competitor in this? And is that how they're doing it? And the answer is, I've got multiple competitors, and none of them are doing it that way. He's looking again, going back to the safety thing. If we go too slow, we're literally accomplishing nothing and we're not putting the safest model into the world.

因此他必须寻找合作伙伴。从他的角度看，必须与能提供巨额资本支出用于训练未来模型的机构合作。这是另一个关键因素。

So he has to partner. From his point of view, he has to partner with somebody that can bring him the capital massive CapEx expenditures to train these future models. So that was another big piece.

你知道吗？我想到的是——有句话让我印象深刻：‘OpenAI的成就不可能发生在硅谷之外的任何地方。’他说，在AI人才可与美国匹敌的中国，无论多么杰出的研究团队都不可能获得10亿美元（更不用说十倍于此的资金）来开发耗资巨大的技术，除非能明确说明这项技术的具体形态和应用价值。我认为这个观点很有趣，OpenAI的诞生可以说在全球其他任何地方都无法复制，这同样令人着迷。

And you know what? What comes to mind is just saying that as well is, again, there's a quote that stood out to me that was what OpenAI did never could have happened anywhere but Silicon Valley. He said, in China, which rivals The US and AI talent, no team of researchers and engineers, no matter how impressive, would ever get $1,000,000,000 let alone 10 times more to develop massively expensive technology without an articulated vision of exactly what it would look like and what it would be good for. And I think this is an interesting point where OpenAI came to be arguably it couldn't have happened anywhere else in the world, which I find really fascinating as well.

Preston 是啊。长话短说，当时存在大量矛盾对立——我不知道该如何准确描述。人们总想要个简单解释，比如‘这就是原因，Sam做了某件事所以被开除’。但实际情况要复杂得多。

Preston Yeah. So long story short, there was just a lot of dichotomy playing out where it's like, I don't know how to really put that on. Everybody wants like a really simple, like, this is what it was. Sam did whatever, and that was why he was fired. I think it's just way more complex than that.

我认为有太多不同方向的力在拉扯董事会。他们将Altman视为公司最终掌舵者，然后认为‘必须撤换这个人，因为太多做法与我们最初目标完全背道而驰’。至于这是否是真相，我不得而知。但书中是这么阐述的，这些是我能提取出的关键信息，也是我认为的事态本质。

I think that there was just so many vectors kind of just pulling that board in so many different directions. And they're looking at Altman as being the guy ultimately on the controls of the company, and they're like, we've got to get rid of this guy because there's just too many things that are complete opposites of what we initially set out to do. Whether that's the ground truth or not, I don't know. But that's how lays this out in the book. Those were the key things that I was able to pick out and kind of say, think this is what it is.

但在评论区，如果有OpenAI的人正在听，请留言。我很想听听局外人或内部人士对此的看法。

But in the comments, if we have any OpenAI people listening, please comment. I would love to hear an outsider or insider's perspective on what you might think that this is.

公平地说，Sam，我认为这本书并没有把Sam描绘成一个正面形象。完全没有。我要说的是，这并不是站在Sam一边，但除非你置身于那个位置，真正进入市场，否则很难理解他为何做出那些决定。当然，书中详述的许多决策确实让人质疑Sam的诚信和一些行为。但我觉得事情远比这复杂。或许我们需要深入探讨非营利转向营利这一叙事变化——当你是一个难以融资的非营利组织，又试图阻止其他实体获得通用人工智能时，你会怎么做？

And to be fair as well too, Sam, I would say the book doesn't paint Sam necessarily in a positive light. At all. And I would argue that and this isn't to side with Sam, but I would say that until you put yourself in that position and you put yourself out into the market, I think it's harder to really understand why he made the decisions at which he made. Now being absolutely transparent, there are many decisions which the book goes into which makes you question maybe some of Sam's integrity and some of the things he However, I think that it is a lot more convoluted than that. And so I think maybe diving into the changing narrative around nonprofit for profit, Again, it's one of those things where you've got this individual who's trying to do what's best and if you're a non profit and it's hard to raise capital and you're trying to stop other entities from gaining artificial general intelligence, then what do you do?

是否必须改变路线？但关键在于区分：这是必要之举，还是掺杂了个人野心导致使命偏离？我们看到2015年起步时，它完全是非营利、开源、纯使命驱动；2016年转为有条件开放，保留部分研究不公开；2018-2019年形成非营利与利润封顶的营利双轨制，微软开始注资（初期约10亿美元），与埃隆产生分歧；2020年转为API墙，以'通过访问实现开放'取代开源；到2024年强调广泛可及性，却仍保持营利模式——这些工具需要廉价推广，但本质仍是商业行为。

Do you have to change or pivot trajectory? But then it's about separating, is this necessary or is there ego involved in here and it's actually a change of mission? And so what we saw is in 2015, maybe to get a little more detailed, it started out as non profit, open source, purely mission driven, no profit motive. 2016, openness with caveats, we're moving towards we've got research but we're going to keep some of that research closed. Well, everyone should benefit but we'll keep that research closed.

因此我认为，这种演变过程引出了你提到的核心问题：这些改变是为了OpenAI发展的必要妥协，还是根本上的使命背叛？

Then 2018, 2019 they started to move into, they had the non profit and then they had the for profit and the for profit had a capped profit model. And I think this is where we started to see Microsoft step in. This is when they started to have the issues with Elon. Microsoft stepped in to kind of prop up OpenAI with I think it was like a billion dollars to start and then from there we saw 2020, the API wall, models locked behind APIs instead of open source framed as openness through access. And then we started to see twenty twenty four broad access and affordability but this is like we need to put these tools into the hands of people for free or cheap but they're a for profit model.

随着时间的推移，这种转变逐渐显现。回到你提出而我提到的关键点：这是OpenAI成长的必要条件，还是使命的改变？

And so I think over time you've seen this change happen and going back to that point that kind of you've brought up and I mentioned, is this a necessity in order to grow OpenAI, or was this a change of mission?

这种矛盾实在太多了。正如你所说，Seb，除非亲身经历，否则根本无法理解其中的微妙之处，这极其困难。我们在上次书评介绍这本书时就说过——我直言不喜欢这个人，这个观点完全基于多年来与他共事且不信任他的人的评价。

There was just so many there were so many dichotomies like that. And to your point, Seb, unless you walk a day in this guy's shoe, you could never possibly understand how many of these you know, the nuance of this, it'd just be extremely hard. The thing that you know, and we said this on our last book review when we we introed that we were gonna do this book. I said, I'm not a fan of this guy. And I'm just basing this purely on the people that have worked alongside him through the years that are not fans and basically say this guy is untrustworthy is where I'm basing that opinion from.

但说实话，读完这本书后，看到他试图带领公司完成的疯狂事业，这似乎是个极其艰难的工作。简直难以想象要处理所有这些事！再看看他们的收支状况——完全是个烧钱的无底洞！正因如此，他的叙事能力才至关重要：他必须不断讲述故事来筹集资金维持运营，尽管收入远跟不上这个吞噬巨资的项目。

But to be quite honest with you, after reading this book and kinda seeing the craziness of trying to do what he's trying to do with this company, it seems like a really hard job. This seems this seems crazy diff I couldn't imagine trying to do all of this. And what a money pit. Like, what a freaking money pit when you look at how much they bring in versus what they're spending to do this and then to be able to continue and this is why his storytelling is so important. His storytelling skills are so important is because he's gotta go out there and raise more money to keep the lights on despite the lack of revenue versus expense that the thing is eating up.

你几乎可以说，你需要一个极其擅长讲故事的人，讲得如此之好以至于能说服人们相信它有可能成真，只有这样的人才能执掌这样的公司。我知道这极具争议性，甚至可能冒犯到一些人，但我认为这是事实。而且你知道吗？埃隆也有这方面的特质。市场上有许多人看着他，比如当他提到‘资金已到位’时——我甚至不记得那是特斯拉时期的什么事了。

You could almost say that you need somebody who's just crazy good at telling a story so good that it convinces people that it could potentially come true is the only type of person that could be at the helm of a company like this. And I know that's super arguable, and it might actually, you know, offend people that I would say such a thing, but I think it's true. And and you know what? Elon has parts of this too. There's a lot of people in the market that, you know, look at him and, like, for for instance, when he said funding secured for I don't even remember what that was for back in the Tesla thing.

这大概是四五年前的事了。埃隆讲起故事和愿景来令人叹服，但他也确实兑现了承诺，并且通过他所有的不同公司多次证明了这一点。这其中有条微妙的界限：这家伙是在对我说谎，还是在告诉我实际上可能发生的真相？他们似乎总是游走在这个边缘。

This was probably like four or five years ago. Elon tells a hell of a story and tells this vision, but he also does back it up, and he has backed it up many a times over with all the different companies that he's doing. And there's this fine line of, is this guy telling me a lie or is this guy telling me the truth as to what can actually happen? Like, they're right on that cusp at all times.

总之这很有挑战性。我认为归根结底，当你深入研究时，会发现Sam与一个我永远念不对名字的人共同创立了OpenAI——我就用他的姓吧，Satskever。有句引语让我印象深刻，因为它揭示了他们创建OpenAI的初衷：人们对通用人工智能充满恐惧。

Anyway It's challenging. And I think essentially when you dig into it, you find out that Sam co founded OpenAI with a guy I can never pronounce his first name, I'm just going go for the last name, which is Satzkever. And again, there's a quote that stands out to me. And the reason why this quote stands out to me is that I think this is the foundation of why they're building OpenAI. There's a lot of fear around official general intelligence.

如果我们真的发现并实现了通用人工智能，世界会变成什么样？所以有句引语大致说，Satskever阐述了准备迎接AGI的计划：‘等我们都躲进地堡后’，他开始说道。‘抱歉’，一位研究员打断道，‘地堡？’

What does the world look like if we do? If we do, when we do find this and discover this artificial general intelligence? And so there's a quote that basically says, Satskovar laid out his plans for how to prepare for AGI. Once we all get into the bunker, he began. I'm sorry a researcher interrupted the bunker.

‘在发布AGI前我们肯定会先建好地堡。’Satskever实事求是地回答。作为OpenAI联合创始人，他谈论的是AGI（通用人工智能）将彻底改变世界，未必是向好的方向。因此我认为OpenAI的建立基础中包含着某种恐惧。

We're definitely going to build a bunker before we release AGI. Satskova replied with a matter of fact. So this is like the co founder of OpenAI talking about the fact that AGI, artificial general intelligence, completely changed the world, not necessarily for the positive. And so I think there's a foundation of fear that, OpenAI was built upon.

是啊。当我思考这些问题时，看着这些AI生成的环境，你会想：难道最安全的做法不是把这些模型放进人形机器人的头部三维模型中，再放入模拟环境里吗？然后让它在那里停留足够长的时间，来证明或展示其行为是合理的。我知道无法完美模拟人类体验，因为每个人都有独特经历——比如有人上前推搡机器人。

Yeah. As I'm, like, thinking through a lot of this stuff. And you're looking at these environments that are being AI generated. You wondered, like, isn't the best place to put these things is do the three d mock up of a humanoid robot, put the model in the head of that humanoid robot, and put it into a simulated environment is the safest thing, and then let it dwell in there for however many, you know, however much time we've got to kind of prove out or, like, demonstrate that the way it's acting is reasonable. And I know you can't perfectly simulate our experience because everybody's got a way about going through it, and maybe somebody goes up and pushes a robot.

在模拟环境中如何呈现这种恶意行为？所有这些都让‘最安全实施方式’变得极难考量。但我始终持这个观点：在将其投入现实世界前，我们必须模拟所有可能性，因为可能会产生30%到50%的未知连锁后果。

How do you simulate that in that environment that they're being mean? All of this stuff is so difficult to think through the safest way to go about it. But I guess I'm constantly left with this point of view of like, we need to simulate all of this before you put it into the real world because of the unknown thirty:fifty consequences that could potentially fall out of it all.

这是个颇具挑战性的问题。几周前我们讨论过，我认为任何技术都有利弊。新技术总会带来冲击，但从长远看，这种冲击应该是积极的，因为它推动效率提升、生产力进步，促进社会发展。让我对人工智能感到不安的是，那些恐怖故事有多少是基于现实，又有多少是凭空想象。我最近读到杰里米·施拉特写的一篇文章，叫《推理模型中的关机抵抗》。

It's a challenging one. I think you and I were speaking about this a couple weeks back, but I think there's always pros and cons to any technology. You're always going to get disruption with any technology, and hopefully that disruption in the long term is positive because it's a trend towards more efficiency, more productivity and society thrives. I think the scary thing that I struggle with artificial intelligence is how much of these fear stories are grounded in reality and how much of them are basically these fanciful stories. So there's this one article that I ended up reading called Shutdown Resistance in Reasoning Model by this guy called Jeremy Schlatter.

他提到OpenAI做过实验，测试模型是否会在任务中途允许自己被关闭。结果部分模型非但不服从，还破坏关机指令以继续运行。他们最先进的推理模型三号——这应该是在发布GPT-5之前——在近80%的测试中抗拒关机，即便被明确告知'允许自己被关闭'。相比之下，Anthropic的Claude和谷歌的Gemini每次都服从指令。这说明OpenAI的代码里写着'不行，必须完成任务'。

And he basically says that OpenAI, they ran experiments to see if their models would let themselves be shut down mid task. Instead of complying, some of the models sabotaged the shutdown commands so they could keep on working. And their most advanced reasoning model, three, this is I think before they released GPT-five, resisted shutdown nearly 80% of tests, even when it was explicitly told allow yourself to be shut down. By contrast, Anthropics Claude and Google's Gemini always complied. And so something written into the code of OpenAI is like, nope, pursue the task.

老兄，这太疯狂了。简直难以置信，我都不知道说什么好。天啊，你能想象把这些东西装进人形机器人里，让它到处执行任务吗？

Dude, that's nuts. That's totally crazy. I don't even know what to say to that. Oh my god. I mean, can you imagine once they stick these things into a humanoid robot, right, and let it, like, start going around and doing tasks?

我不知道...天啊各位，这也太离谱了。好吧，我想快速梳理下OpenAI现在的运营实体结构。

I don't know. I think that oh my god, y'all. This is getting crazy. Alright. I wanted to quickly just kind of, cover, like, what is the operating entity of OpenAI today?

我们说过它是混合体，既有营利也有非营利部分。母公司层面，OpenAI Inc.是非营利组织，理论上掌控整个机构。明白吗？

So we said it was this hybrid, it's profit, it's nonprofit. Okay. So at the parent entity level, OpenAI Inc. Is a nonprofit that technically controls the organization. K?

所以在母公司层面仍是非营利性质。然后有个叫OpenAI Global LLC的运营实体，这是家利润上限的营利公司，2019年成立。运作方式是利润不得超过投资额的100倍——具体含义不明。最有趣的是...

So you still have at the parent level, it's a nonprofit. Then you have what's called this operating arm, which is OpenAI Global LLC, and this is a capped for profit company. They stood this up in 2019. And evidently, way it works is that the profits are capped at a 100 x their investment, whatever that means. And if anything and this is where it really gets interesting.

超出限额的利润会回流到母公司层的非营利机构。还有个复杂情况是，他们与微软是重要合作伙伴，微软目前已投资超130亿美元，通过云服务抵扣和现金形式获得权益。具体细节我就不清楚了。

Anything in excess of that is swept back to the nonprofit, which is at the parent level. So I don't know. Like, and then you kinda throw another wrinkle in there is that they have a major partner or investor in Microsoft, which I guess is invested, I think, over $13,000,000,000 so far. And then they get credits via, like, cloud credits in cash. So I have no idea, like, the specifics of that.

但当你审视这种架构时，会发现它非常怪异且令人困惑。我只能想象各个层级的治理模式，以及从激励机制角度这会如何演变。埃隆整天在X平台上猛烈抨击这些人，我认为原因在于他为此投入了大量资金——当然，这或许是个相对概念。

But when you kinda look at that structure, you can see very strange, very confusing. I can only imagine the governance at these different levels too and how that shakes out from an incentive standpoint. And I think that you see Elon bashing the living heck out of these guys all day long on x. And I think the reason why is because he threw a lot of money at this. I mean, I guess that's a relative thing.

但对任何人来说，从名义金额看，他投入的孵化启动资金确实庞大。如今这个项目已自成体系。而真正掌舵的是谁？归根结底是山姆在主导全局。这就是矛盾所在。

But he you know, for any person looking at it in nominal terms, it's a lot of money that he threw at this thing to incubate and to get it started. It's just kind of taken on a life of its own. And who's at the helm of it? It's really Sam that's kind of at the helm of all of it. So there's the beef.

问题症结就在于此。

That's the issue.

这确实引发诸多疑问。正如你先前提到的，他们构建的营利与非营利双轨制本意是：营利部门创造收入来支持开放AGI的使命，非营利部门则监管营利部门以防止权力集中或使命被个人劫持。但山姆被OpenAI解雇五天后又复职CEO的事件表明，法律层面设计的防范措施（董事会确实有权以使命偏离为由解雇山姆）在实践中远比理论复杂。

It definitely brings up some questions, which is you mentioned it previously, this idea that they built it around this for profit arm, non profit arm as they evolved. And the idea was that the for profit arm enabled them to generate revenue to be able to help support their mission of having an open access AGI. However, had the non profit arm overseeing the for profit arm to be able to prevent any control structures, centralization, single individual co opting the mission. And I think that the way that it panned out in Sam being fired from OpenAI and then five days later being reinstated back as CEO highlights the fact that you can put all of these measures in place from a legal perspective. Legally, the board had the power to fire SAM because they felt there was mission drift, but in practice it's more complex than that.

因为当你拥有影响力、大批员工支持、形成特定文化时，外加外部压力（资金究竟是投向OpenAI还是山姆的愿景？），局面就变得棘手。五天后他的复职更引发思考：这个架构究竟是在阻止山姆安全实现AGI，还是在阻碍董事会排除偏离使命的领导者？我无法断言最终走向何方。

Because the moment you have influence, the moment you have a whole bunch of your employees backing you, there's culture, all these external pressures, where's the funding coming from? Are they funding OpenAI or are they funding Sam's vision? And so it's really challenging because then five days later he was reinstated. So then there's this question, was the structure actually preventing SAM from creating a world where, okay, we get AGI in a safe way or did the structure actually prevent the board from being able to enable basically push SAM out because they had mission drift? And I don't have the answer to that, and it's hard to articulate which direction it went.

普雷斯顿，你说得对。书中有个有趣观点提到训练机制的问题——不过在探讨之前，我想先梳理全书四大主线，之后我们再重点讨论这部分。

Preston Yeah. I think you're right. So one of the things in the book, it's an interesting point that was brought up is just like, how is this training happening? Before we get into that, I just want to cover the major arcs in the book. I would say there's four major arcs, and then we'll talk about this one in particular.

现在我来详述书的四个部分。开篇场景是2023年山姆·奥尔特曼被解雇事件，完整讲述了这段故事，极具阅读吸引力——这可能是全书我最喜欢的章节。

So I wanna get into the arc of the four different parts of the book. The opening scene of the book was this beginning of the 2023 firing of Sam Altman. It tells that whole story. It really kind of engages you as a reader. Probably my favorite part of the book was that beginning and talking through that.

书的下一部分深入探讨了作者所说的隐性成本。他们是如何训练模型的？如何获取所有这些额外数据？并揭示了他们实现这一过程的不为人知的一面。第三部分则聚焦于内部斗争、企业文化、领导力、危机等议题。

The next part of the book gets into what the author is saying is the hidden costs. How did they train the models? How do they get all this extra data? And it talks about kind of the dark side of, like, how they went about doing that. The third part of the book gets into the internal struggles, the culture, the leadership, the crisis, all of that.

这部分内容与开篇提及的2023年事件形成呼应，但以更详尽的笔触展开——剖析人物性格与人格的冲突、董事会的矛盾、公司内部的文化问题。第四部分则展望未来：这一切将导向何方？存在哪些风险？又有哪些替代方案？

So it loops back to maybe the first part of the book where they open up about the 2023 event, but it gets into it in a lot more detail and a lot more granular laying this out like character versus personality, the conflict of the board, the culture issues that happen inside of the company. And then the fourth part of the book gets into the future. Like, where is this all going? What are the risks? What are the alternatives?

我们能否采取负责任的方式确保人工智能不会毁灭人类？这些是作者的观点。内容尚可，但我不认为值得专门阅读。

What are some ways that maybe we could go about this in a responsible way to make sure that AI doesn't come in and kill everybody? So that's the author's takes on that. It was okay. I'm not gonna say that it's worthy of reading, but any

我同意。如果满分五星，我会勉强给两星，慷慨些可能给三星。虽然书中确实有些精彩的线索...

I I would agree. I'd I'd say it was like a two out of five. If I was generous, I'd give it kind of like a three out of five star. Yeah. And I found as if there was some amazing threads.

别误会，整体上我收获颇丰，这本书确实让我更清晰地理解了某些问题。甚至可以说，读完後我对Sam的信任度反而比阅读前有所提升。但书中不少论述让我困惑，作者时常偏离主题。比如她突然开始讨论Sam Bankman Fried和有效利他主义，我当时就纳闷：这些内容从何而来？还特意搜索求证Sam是否真是有效利他主义者...

Do not get me wrong. Like, overall, I learned a lot, and it definitely helped provide a little more clarity and I would say that I am giving Sam a little more benefit of the doubt actually after reading it than I was prior to reading the book. However, there was a lot of points that I was a little confused, she kind of went on some tangents. At one point she started talking about Sam Bankman Fried and effective altruism and I was like where does this come from? So I typed in, was like is she talking about effective altruism because Sam is an effective altruist?

结果谷歌显示Sam并非有效利他主义者。我就更不解为何要讨论这个

And then you dive in on Google and it says Sam is not an effective altruist. I was like why are we talking

话题？为什么

about this? Why

我们是在

are we

讨论那个吗？

talking about that?

比尔，有几个离题的地方对我来说不太明白，她没有把这些内容带回书中，所以我有点困惑她到底想表达什么。

Bill There's a couple tangents that to me didn't really, she didn't bring them back into the book and so I was a little lost as to where she was going.

比尔，我们稍作休息，听听今天赞助商的内容。

Bill Let's take a quick break and hear from today's sponsors.

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我当时非常...有好几次我听着这个，简直难以置信地想：这到底是怎么回事？为什么会出现这么奇怪的内容？所以提醒一下，如果有人正在读或打算读，我同意你的两星评价（满分五星），Seb。这大概也是我会给的分数。但我确实带着这种感觉离开：这是个极其棘手的问题。

I was very There was a few times and I was listening to this And there was a few times I was just like, what in the world? Why is this coming up in this this very strange that this is, like, coming up? So just FYI, if anybody's reading it or they plan to read it, you know, I would agree with your two out of five. I think that's what I would give it as well, Seb. But I kinda did walk away with this sense of this is a really hard problem.

这家伙试图做的事近乎疯狂。如果换作是我，处在他的位置尝试做这些，会面临无数困难。每个人都会对这是好是坏的决定发表意见。说这话时——我以比特币铁杆支持者的身份说——这家伙简直是WorldCoin的代言人，扫描眼球、搞些完全反乌托邦的事情，我完全反对且极度反感。

Like, what this guy is trying to do is borderline nuts. If I was, you know, thrown into his shoes and was trying to do what he's doing. There's so many difficulties, and everybody's gonna have an opinion as to, like, why that's a good or bad decision. So and I say this I say this as a, you know, hardcore Bitcoiner. And this guy is, like, literally the face behind WorldCoin where he's scanning eyeballs and just really dystopian things that I completely disagree with and don't like at all.

我认为他们极度危险。是的，我把这些看法一口气全说了出来。

I think they're extremely dangerous. So yeah, I say that all in the same breath.

你知道吗，有件事我反复思考过很多次。书中频繁提及人工通用智能，但贯穿全书都在强调一个事实——我认为他们中没有人真正定义了什么是人工通用智能。于是我上网查了查，想弄清楚人工通用智能的定义以及我们如何判断何时实现了它。结果发现对其定义并无共识。多数人认为这是能执行人类所有智力任务的AI形式。有趣的是，就目前而言，某种程度上我觉得正在使用的AI已经比周围大多数人更出色地完成了大部分任务。

You know, there was one thing that kind of popped into my mind a handful of times, they talk a lot about artificial general intelligence and throughout the book it presses on the fact that I don't think any of them actually have a definition for what artificial general intelligence is. So I kind of looked up online, was like what is the definition of artificial general intelligence and how do we actually know when we've achieved it? And there isn't an agreed upon definition of what it is. Most agree that it's the form of AI that could perform any intellectual task that a human can. Now the thing that I find interesting about that is at the moment there's a part of me that would say when I'm using AI it performs most tasks better than most people around me as it is.

所以我在想的问题是：即便AGI已经存在，我们能否识别它？我顺着这个思路深入思考后认为，我们可能根本无法区分人工通用智能、现有系统与真实人类。比如当我面对某个陌生领域的专家时，我无法验证其言论的真实性，只能选择信任，因为我缺乏专业知识。同理，我们如何验证AGI向我们传递信息的真实性？特别是当它涉足我们无法理解的领域时。更进一步说，或许AGI此刻正在与我们对话，而我们将其视为幻觉仅仅因为它不符合我们对世界的既有认知框架。

So will we I think the question that kind of popped into my mind is could we recognize AGI even if it existed right now? And I kind of went down this rabbit hole pulled on this thread a little further and I would say that I don't actually know if we can distinguish between artificial general intelligence, the systems we currently have, and another human because if I sit down with an expert in a field that I know nothing about, I can't really verify the authenticity of what they're saying. I just have to kind of take them at trust because I just don't have that depth of knowledge. So how would we be able to verify the authenticity of AGI basically with whatever it is that it's telling us, especially if it's moving into domains that are beyond our understanding? And then on top of that, I think that we could already be in an environment where AGI is speaking to us right now, but the only reason why we're dismissing it as hallucinations is because they just don't fit into our existing framework of how we believe the world works.

几年前我听了个令人印象深刻的演讲。有位研究者问电视主持人：你认为世界上最聪明的人在哪里？主持人回答可能在顶尖学术机构。演讲者摇头说：不，他们在精神病院的病房里。因为这些人对世界的理解远超常人，以至于我们根本无法理解。这又回到那个问题：如果AGI已经存在，我们真能认出它吗？或许它早已存在？

And there was an interesting talk that I listened to a couple years ago that kind of stood out and it was this talk where this kind of researcher asks the TV hosts, where do you think the smartest people in the world reside? And the host answered, I don't know, in the great academic institutions. And the speaker basically shook his head and he was like, no, they exist in the mental institutions in the psychiatric wards because their understanding of the world is so far beyond the average person that we just simply can't grasp it. And so this brings us back to this point like, would we even recognise AGI if it did exist? And we already have it?

我认为核心问题在于：我们需要阻止某个中央集权实体获得AGI，但首先我们连如何判断是否已实现AGI都搞不清楚。

I think it's this big question of we need to stop a centralised entity getting AGI, but how do we know when we've actually even got there anyway?

Preston，我认为这是术语体系的分歧。每个人对这些术语的理解都不同。当你我说AGI时，我们想的其实是两回事。听众听到这些术语时又会作何理解？但我想世界试图定义的是：这东西何时会变得像我们一样？如果粗略概括，我们到底想定义什么？

Preston I think that there's a breakdown in terminology and I think everybody has a different opinion on what some of this terminology even is. So you hear AGI, I hear AGI, and we're automatically thinking two different things. I don't know what the listener is thinking when those terms come up. But what I think the world is trying to define is when is this thing going to be like us? If I was going to just generally broad brush stroke, what is it that we're trying to define?

我认为我们想定义的是：何时我能与某个人形机器人对坐交谈，感觉就像此刻与你Seb对话一样？它们要有独特的人生经历和情感能力——因为那才是意识。如果我们探讨何为有意识，就是指拥有独特情感的存在。比如机器人过来对我说：我和某人聊过，后来他们伤害了我的感情。这类表现才会让它感觉像人类。

And I think what we're trying to define is when am I gonna be able to sit down across from, call it, some humanoid robot, have a conversation with it, and it's going to feel like the conversation I'm having with you, Seb, right now, that they have their own unique life experience and they can feel because that's sentience. Right? If we get into, like, what makes something sentient, it's something that actually has its own unique feelings. And, you know, like the robot would come over and, I had a conversation with so and so, and, like, they hurt my feelings afterwards. Like, something like that would make it feel human.

这会让它显得真实。我个人认为这就是关键所在——在此基础上再加上它比你聪明得多：能回答任何问题，理解语境，设身处地为其他存在着想。因为它聪明绝顶，能理解其他生命体如何感知世界。

It would make it feel real. And I personally think that's kind of where and then you kind of sprinkle on top of that, it's way smarter than you. Like, it can answer any question. It can understand understand the context and, like, put itself into these other shoes of other beings because it's so freaking smart. It understands the context of, like, how they probably optically view the world.

这就是它们仍具备感知、感受及进行独特对话的能力。我认为这正是我们试图定义或观察的。问题是，我们何时能看到这一幕？

That's how and they still have the ability to sense and feel and have these conversations that are uniquely theirs. That's what I think we're trying to define or see. It's like when will we see that?

没错。但有趣的是，究竟是什么赋予对话这种人性化的感觉？我认为恰恰是我们人类的不完美性。而AI近乎完美，几乎无可挑剔。

Yeah. What's interesting though is this idea that, well, gives this conversation a feeling or a sense of this human touch? And I would argue that what gives this conversation this human touch to it is actually the fallibility of us as humans. And AI is almost perfect. AI is almost perfect.

你看它下国际象棋或围棋时，能轻松击败世界顶尖选手。但作为人类，我们反而更爱看人类之间的对弈。就算机器人足球赛水平远超真人，我们仍会选择观看人类球员。这正是人性的本质——我们的缺陷与犯错能力，才真正创造了吸引力和趣味性，而非完美主义。

If you watch it play chess or you watch it play Go, it just smashes the world's best players, absolutely destroys them. But then as humans, what do we do? We don't go and watch games of AI playing itself, we go back to watching humans play themselves. If we had a whole football pitch of robots playing football at a far higher level than actual footballers would still go back to watching people. And I would argue that there's something inherently human about being human, is our fallibility and the ability to make mistakes and that's what actually creates intrigue and interest as opposed to this perfectionism.

回到你的观点：当AGI能让我们在对话中完全察觉不到它是机器时，是否就算实现了？但反过来说，我可能反而能通过它的绝对无误来识别它——毕竟我根本抓不到它的破绽，懂我意思吗？

And so kind of going back to your point which is like, is AGI when we're able to have a conversation with it and have no idea that we're speaking to a human, but then the argument would be, well I'm going to be able to tell that it's AGI because it's it's just infallible. Like, I can't really catch it out. You know what I mean?

是啊。但也许它聪明到能理解这点，会故意装笨让我们不觉得它高人一等。不过你说得对，完全正确。

Yeah. But maybe it's so smart that it would actually understand that, and it would dumb itself down to make us feel like it's not superior and it's intelligent. I don't know. But you're exactly right. You're exactly right.

就像派对上15岁青少年自然聚在一起，9岁孩子和同龄人玩耍，成年人也自成圈子。我们因相近年龄和经历产生共鸣——共同的生命经验形成了相似的语境，这种年龄要素让我们处于相同频道。

And you see this with just, you know, go to a party and all the 15 year olds are hanging out with people that are around that age. The nine year olds are hanging out with the nine year olds, and the adults are hanging out with the adults. And you see and it's it's the context of experience that we kind of relate to each other based on being of a similar age and experience set. Like, we've experienced the same amount of life, and there's this context that is similar. Like, we're on the same wavelength because of that age element.

你还提出个有趣的问题：若将这些AI放入人形躯体，其智能与计算机分离后，是否真能建立情感联结？从设计角度出发，你其实在探讨某种可能拥有独特体验的存在。但你会否真的愿意坐下来与它们深入交流？毕竟它们聪明绝顶又博学多才——这样的对话会有趣吗？

And you bring up an interesting point of, like, whether that will ever exist between let's say these things are put into humanoid bodies, their intelligence is partitioned off from the computer. Right? You get from a design standpoint, you really kind of go after one of these things that could potentially have its own unique experiences. And you have to ask yourself whether you would really have any type of emotional connection or desire to sit down and have these types of conversations because they're just so freaking smart and they know so many different domains. Like, would that be interesting?

它们很糟糕吗？

Are they foul?

这是

It's

很难。很难。我不知道。

tough. It's tough. I don't know.

这太难了。然后另一件事是，我思考的方式是，作为人类显然有人性的一面。还有精神层面，比如我邀请一群朋友来吃晚餐，我花精力从外面采摘蔬菜，带进屋里，做出一顿美妙的晚餐，与朋友们进行精彩的对话，这其中倾注了爱与情感，有些东西是无法剥夺的。你可以让机器人在厨房里做出米其林星级大餐，但我认为人类投入的时间、精力和爱意，有些东西是无法复制的。

It's so tough. And and then the other thing is, like, the way I kind of think about it is there's, a human beingness, obviously, to being human. There's, like, a spiritualness to being human, which is, like, if I have a whole bunch of friends over for dinner and I spend time putting energy into going and harvesting the vegetables from outside, bringing them inside, making this amazing dinner, having these amazing conversations with all my friends, there's love and affection that's gone into this creation and there's something that you cannot take away, that you can have a robot in the kitchen who's gone and made a Michelin star meal. But I would even say that there's something about the humanness of the human putting that time and energy and that love, there's something that I don't think you can replicate.

这顿饭的不可靠性。对吧？是的。抱歉放了

The fallibility of the meal. Right? Yeah. Sorry to put

太多盐。

way too much salt on it.

尤其是我在厨房的时候。哦，不。不过我很喜欢这个观点。我真的很喜欢这个观点，即人类元素的存在是因为脆弱性和不可靠性，这对我们来说是真实的，因为我们处于相似的波长上。不过，是的。

Especially when I'm in the kitchen. Oh, no. I love that point, though. I really like that point that there's the human element is because of the vulnerability, the fallibility, and it's real to us because we're on a similar wavelength. However yeah.

实际上，虽然我可能说得不够准确，但克劳德·香农在信息论中提到，信息就是出乎意料。这正切中要害，当我们与人类互动时，我认为互动的魅力就在于你无法预知对方接下来要说什么。而如果你是某个领域的专家，与AI交谈时，你大致能猜到它会说什么。所以我在想，这是否也是其中的一个因素。

Actually, and I'm going to butcher this, with Claude Shannon in information theory, he says information is when we have surprise. And so it's kind of to that point, when we're interacting with a human I think the engaging point of interacting with a human is to surprise that you don't really know what they're about to say. Whereas if you're an expert in a field and you're talking to AI, you kind of have an idea about what they're going to say. And so I wonder if that's a component to it.

是的。还有其他想讨论的内容吗？我之前本想提到，后来转而概述了书中四个部分的内容，就是在中间部分，她谈到许多这类系统是如何被训练的——前往发展中国家那些近乎点击工厂的地方，人们只是看着桥梁的照片，然后必须打标签。这个

Yeah. Anything else you wanted to cover? The only thing that I was gonna that I was gonna say earlier and then I kind of like pivoted to doing the overview of the four different parts of the book was, in the middle section there, she talks about how a lot of these things were trained and going to these farms, these almost click farms in developing nations where people are just looking at pictures of a bridge, and then they have to tag. This

是

is

一座桥。这是一个人。投入的资金极其匮乏，但从中获得的人力资源和劳动成果却形成巨大的货币套利。我们俩都是比特币支持者，所以看到这种情况会说，嘿，比特币终将解决这个问题。

a bridge. This is a person. And just total lack of funding that is put into this, but the amount of horsepower and human work that you're getting out of it is just a giant currency arbitrage. And, you know, the two of us are Bitcoiners. So we're looking at this and saying, hey, Bitcoin will eventually solve that problem.

但这是书中的重要部分，篇幅比我个人对这个话题的兴趣要长。从我的角度看，我觉得非常悲哀，世界上无数人受到这样的对待。但同时，我看到未来十到二十年将出现自动解决这个问题的方案。所以对我来说，我对那个特定时期并不那么深入关注。这样说可能显得冷漠，但作为一个以工程思维为基础的人，我看到的是：她很好地定义了问题，但在我看来，已经存在一个解决方案，未来会解决大部分问题。

But that was a major part of the book. It went on a little longer than, you know, what my interest was in the topic because I guess from my vantage point, I'm looking at it and I'm saying this, it's super sad that this is how many people, countless people around the world are treated. But at the same time, I see a solution in sight in the next ten to twenty years that's automatically going to solve for that. So I guess for me, I'm not really as deep into that particular time. That might sound very insensitive to frame it that way, but as a person who's grounded in engineering, I'm looking at, okay, here's a problem, she's defining the problem, she's doing a great job defining it, but I'm also looking at there's already a solution in my humble opinion that's going to solve a lot of this in the future.

不过Seb，我很好奇你对这个

But Seb, I'm curious your thoughts on What that

她将这些AI巨头比作殖民帝国，并引用她的话说：‘它们攫取和榨取珍贵资源——艺术家和作家的作品、无数个人的数据、土地、能源、容纳庞大数据中心所需的水资源。’接着她谈到支撑AI的基础层人力从何而来——那些低收入全球工人为AI标记、清理、审核所有数据。最初我们浏览谷歌相册输入‘猫’就能找到所有猫的照片，这并非AI完成，而是有人手动标记猫的样子。我觉得这非常耐人寻味。

comes up is she compares these AI giants to colonial empires and there's a quote where she says, They seize and extract precious resources, the work of artists and writers, the data of countless individuals, the land, the energy, the water required to house massive data centres. And then to your point, she goes into well where are all of these people coming from at the base layer to support AI? And there's all of these low paid global workers that are tagging, cleaning, moderating all of this data for AI and to start out, we go and look through our Google Photos and we just type in, I don't know, cat and it goes and finds all of the pictures of cats. Well initially that was not done by AI, that was done by an individual going through all of our pictures and tagging what a cat looks like. So I find this really fascinating.

目前确实存在这种资源榨取现象。但我想说的是，当你深入比特币的兔子洞时，问题总是归结于：这是表象还是我们要探究根本原因？我认为能以每小时70美分找到愿意接受这种报酬的人，正是治理模式失败和这些共产主义社会主义实践的体现——本质上就是大规模的榨取主义。如果我们有更自由的市场，我认为AI就无法轻易找到这样的劳动力。所以我们可以不断讨论表象，但何不尝试解决根本问题？即全球范围内本可避免的绝对贫困现状。

There absolutely is right now this extraction of resources. However, and I think when you go down the Bitcoin rabbit hole, it's always about, okay, is this a symptom or do we want to go down to the root cause? I would say the symptom of being able to find people that are willing to accept 70¢ an hour is the symptom of poor governance models and these communist socialist practices where you've basically got massive extractivism. If we had more of a free market I would argue that the AI couldn't go out there and find these individuals. So I'd say we can always talk about the symptoms, but how about we try and fix the root problem, which is the fact that we actually have absolute poverty globally when we don't necessarily need to.

普雷斯顿，阿门。确实。这类书籍中那些冗长的章节常让我感到沮丧——作者试图揭露他们眼中的不公正现象。但和你一样，我认为这只是表象而非根源。我想探讨的是最上游的解决方案：我们能修复什么根本性问题，从而最终解决这些表象？

Preston Amen. Yeah. I think that's where I get frustrated with these types of really long sections in some of these books that are written by, you know, people that are trying to shine a light on something that they see as being very unjust. But like you, I see it as a symptom and not the cause. And what I wanna talk about is the route, like as far upstream as we can possibly go, what can we fix that then will eventually work that out?

因为如果不解决根本诱因，我们可以无休止地讨论这些案例，但实际毫无助益。不过这个议题确实值得关注，人们在使用这项技术时需要明白：当你享受这种超级便利、节省大量时间的工具时，应该了解其背后的成因。这本书确实让我对此有了深刻认知。

Because if you don't fix the fundamental thing that's causing it, we can sit around and and talk about all these stories as much as we want, but it doesn't really solve anything. So but I think it was an interesting highlight. It's something that does need to be called out. It is something that that people need to understand when they're using this technology, and it's so you're harnessing this, it's super abundant, it saves you so much time. There's an appreciation for what went into it and where it came from and the book definitely did give me that.

这又回到关键点——我并非支持这种做法——但假设谷歌全力研发AGI并不惜代价，而OpenAI若坚持按美国15美元时薪的劳工标准，就会立即出局。现实世界残酷地倾向于最低成本方案，导致企业涌向阿根廷、委内瑞拉等地。虽然确实存在人权问题和权力滥用，但正如你所说，这些都是更深层问题的症状。我们总困在表象讨论中。另外我想提出个有趣的问题：未来世界会变成什么样？

And it gets back to the point and I'm not, I don't want this to come across as I'm supporting it, but it gets back to that point where let's just say you know Google is absolutely geared towards AGI and they're willing to go do whatever it takes to go and create this AGI. Well when you look at OpenAI and you're saying, look if we want to focus on best practices for workers and pay minimum US dollar wages at $15 an hour, all of a sudden you've completely kicked yourself out of that race. And so I think the way the world works unfortunately is that people will go to they lean towards the cheapest way to do something and so they end up going into these countries like Argentina and Venezuela and stuff. And so absolutely I think there are human rights issues and there are abuses of power, but again to your point I think that is a symptom of a bigger issue and we get stuck talking about symptoms as opposed to the root cause. I think there's one other point that I did want to bring up which I found was really fascinating is what does the world look like moving forward?

以ChatGPT及其迭代版本为例。经过调查，GPT-4训练成本约4000万至8000万美元，而GPT-5可能超过10亿美元。这些耗资数亿的模型创造了我们日常使用的神奇工具。但再看中国AI公司深度求索发布的R1模型——仅用29.4万美元和512块英伟达芯片就完成了训练。

Because you look at something like chat GPT and the GPT-one, GPT-two, GPT-three, four and such. And GPT-four, I did a little bit of digging, how much did it cost to really train GPT four in it? It cost between $40 to $80,000,000 And then you look at GPT five and it could be upwards of $1,000,000,000 but we don't necessarily know this number. You're asking like, there's these models that are being trained with hundreds of millions of dollars to be able to create this incredible thing that we use in day to day. And then you go and see something like the Chinese AI company DeepSeek go and release their R1 model and they trained it for $294,000 on five twelve NVIDIA chips.

大量风投资本涌入AI公司期待回报，同时竞争却不断压低模型训练成本。在这种疯狂竞争中，我认为他们永远无法获得预期回报。

So you're like, all of this VC capital has funneled into these AI companies and they're expecting a return and at the same time you're having this competition that is driving down the cost of training these AI models. I don't think they're ever going to get a return on these things because it's wild, there's this competition.

没错。而且模型训练完成后，其他公司可以进行逆向工程提取权重参数——虽不完美但足够精良。我看不出投资人如何获得回报。这场竞赛中还掺杂着大量 ego 因素，确实很疯狂。

Yeah. And then the reverse engineering on what it is, after they do train it, then all these other companies can go in and reverse engineer, extract out the weights, not perfectly, but pretty dang good. I just don't see how the people putting up the funding on this are possibly going to get a return. It's pretty wild. And I think there's a lot of ego playing into this race as well that yeah.

我是说，这确实相当疯狂。当我们展望其未来发展时，我认为关键在于对齐问题。因为另一个未被充分讨论的方面是：当你向这些模型提出查询、输入一个问题并得到答案时，如果你能针对这类问题创建高度定制化的模型，并快速返回答案，就能在该领域形成专长——这样不仅能创造巨大实用价值，还能因提供快速精准的领域特定解答服务而获得广泛关注。我认为未来趋势将是这些专业化模型，它们几乎是从基础模型中萃取出来的，专门优化以更精准快速地对接用户初始问题的意图。

I mean, it is pretty insane. And I think that as we look at where it goes next, it really comes down to the alignment. Because the other part that I think is not being talked about is when you put in an inquiry, you put an input into one of these models and you get an answer back. If you can create a model that's very specific to that kind of question and you can return the answer very quickly, you can specialize in that domain and you're gonna have a lot of utility and a lot of interest for that being able to provide that service that's giving you a very quick, a very accurate answer for a specific domain. And where I think a lot of it's gonna go is these models that are specialized, that are almost extractive out of the base model that then are then specializing in something that gets the alignment of the person's initial question a whole lot faster.

我看了Anthropic创始人之一的一段短视频，他谈到：虽然我在转述而非原话，但大意是说，盲目追求构建最大模型近乎徒劳，真正的价值在于能否对特定问题给出快速准确的响应。要实现这点，我认为关键在于对齐和微调技术——这才是价值捕获的核心。特别是从构建竞争壁垒的角度，你总能找到实现路径。

I saw a very quick video clip from one of the founders of Anthropic, and this is something that he was talking about. He's like, you know, the the race to build the biggest model is I'm paraphrasing this, and this is not how he said it, but it almost seems like it's a fool's errand. And that the real value capture is being able to get a quick response, a very accurate response to a very specific question. To do that, I think that the alignment and basically fine tuning things is going to be where the real value capture is at. You can kind of figure out a way to do that, especially from a competitive moat standpoint.

但从投资角度来说，天啊，我完全无从下手——这领域复杂得让我望而却步。

But boy, I would be nowhere near from an investment standpoint, good lord, I just don't even know where to begin.

我认为还是要回到英伟达，你得押注芯片端。唯一确定的是芯片需求只会持续增长。

I think it's going back to NVIDIA, you want to be on the chip side of things. The one thing you know is there's going to be more demand for chips.

最重要的是未来将出现对

More than anything there's going be demand for

芯片的需求，而这些AI公司

chips whereas these AI companies

正在

are

它们只会互相吞噬。真的会互相吞噬。说实话，刚才你提到Anthropic时让我印象深刻，书里也提到了。那对曾在OpenAI工作的兄妹因为不认同公司的发展方向，认为安全措施不到位，所以离开创办了Anthropic。正如我之前所说，有研究显示OpenAI的模型在执行任务中途被强制关闭时会失效，它抗拒被关闭。

just going to eat one another. They're freaking going to eat one another. And actually to be honest the one thing that stood out just then as you mentioned was Anthropic, it talks about it in the book. There's the brother and sister that worked for OpenAI and left OpenAI because they didn't believe in the trajectory it was going down and they felt that the safety was not in place and so they started Anthropic. Which you could argue, as I mentioned previously, there are these studies that are coming out that are showing that OpenAI is useless when you try and shut down the model midway through a task, it doesn't want to be shut down.

但Anthropic会立即停止运行。这让人思考确保最终产品安全性的防护协议。

But Anthropic immediately shuts down. So you wonder the safety protocols to ensure that the end product is secure.

没错。好了，Seb，我们下一本书是David Sinclair的《寿命》。我一直想探讨长寿这个话题，是这个领域的忠实粉丝，渴望了解相关进展。

Yeah. Alright. Real fast, Seb, our next book is called lifespan by David Sinclair. So I have wanted to cover longevity and some of this stuff for a very long time. I'm a big fan of this space and just kind of learning everything that's happening in this space.

很多比特币爱好者也痴迷长寿研究，因为他们想延长生命享受生活。我们会在节目中持续关注这个领域的发展。David Sinclair堪称长寿研究的先驱，他的著作非常精彩，Seb将带我们解读。

You know, a lot of Bitcoiners love longevity because they wanna figure out how they can live a little longer and enjoy life. And we're gonna cover this from time to time on the show is what in the world's happening in the longevity space. So this book, David Sinclair, I would argue is one of the pioneers in this whole field of longevity. His book is fantastic. Seb's going to go through it.

我会重读这本书——虽然几年前读过，但它确实为理解长寿研究的源流和未来方向奠定了坚实基础。欢迎听众们加入共读，这就是我们的下一站。

I'm going to reread this book. I read it a couple years back, but I think it's a really strong book for a foundation for people to kind of understand where a lot of the research for longevity comes from and where it might be going in the future. So if you're reading along with us, that's where we're going next. We would love to have you guys as a co reader. That's the book.

Seb，对下一本书有什么要说的吗？

Seb, any comments on the next one?

没有，老兄。我很期待，说实话最想听你对长寿的看法。我觉得长寿运动分两派：一派认为人类进化需要缩短寿命来实现快速迭代；另一派则主张无限延长寿命。

No, man. I'm excited. To be honest, one of the things I'm most excited about is hearing your thoughts on longevity because I feel as if there's kind of two camps to the longevity movement. It's kind of this camp which is just like, well, we're humans and if we want to evolve, we want to minimise lifespan because then it allows us to iterate, iterate, iterate. And then there's this other camp which is like, let's just expand lifespan indefinitely.

让我们活上五百年，但那样的话，我们是否会变得固步自封？是否更容易遭遇某种颠覆人类的大变革？所以我很好奇你的看法，因为我认为长寿领域存在几个不同的阵营。

Let's live five hundred years but then do we become immovable? Do we become basically prone to some big change that whites out humanity? So I'm curious to hear your take because I think there's a few different camps in the longevity space.

你是个诡辩家吧，塞巴？这可有意思了。好了各位，这就是本周我们为大家准备的全部内容。我们讨论的书是《AI帝国的梦想与噩梦：山姆·奥尔特曼的OpenAI》。

You're a specious, aren't you, Seb? This is gonna be good. Alright. So folks, this is, all we have for you. The book that we covered this week was Empire of AI Dreams and Nightmares in Sam Altman's OpenAI.

我们觉得还不错。下周要读的书是大卫·辛克莱的《寿命》，非常感谢大家的参与。塞巴，给大家简单介绍一下你手头正在忙的书和其他项目吧。

We liked it. It was okay. Next book is gonna be Lifespan by, David Sinclair, and thank you so much for joining us. Seb, give people a quick hand off to all the stuff that you have going on in in the book that you also have.

没问题。如果他们想关注动态，可以在Twitter或X上找到我——我还是习惯叫它Twitter，我的账号是@saidbunny（bunny拼作b-u-n-n-e-y）。我有个博客叫《自我主权的喜悦》，网址是saidbunny.com。另外我写了本书《金钱的隐性成本》，它主要...

For sure. Yeah. If if they wanna kind of follow along, they can find me on Twitter or x, I still get into the habit of calling it Twitter, I link towards Twitter, at said bunny and bunny is b u n n e y. I have a blog, The Cheer of Self Sovereignty at saidbunny.com and then I also have the book, The Heading Cost of Money and it kind of

对，讲的是金钱，不过现在能聊聊

yeah, talks about money but at the moment it's nice to

金钱以外的话题也挺好的。

be kind of discussing things other than money.

我们会在节目备注里放上所有链接。塞巴，感谢你的参与，也谢谢所有听众。继续阅读吧，期待下周与大家再会。

We'll have links to all of that in the show notes. Seb, thanks for joining me and, everybody else out there listening. Keep reading and, we look forward to you joining us next week.

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