山姆·奥特曼：创办 OpenAI，以及押注看似不可能的事

我刚刚提到 Tobi Lütke，以及我之前和他录过节目。你为什么说他是目前最有趣的 CEO 之一？

I just brought up Tobi Lütke and the fact that I recorded with him previously. Why do you say that you think he's one of the most interesting CEOs right now?

Toby 最让我印象深刻的一点是，在 AI 的早期，以及随后发展的每一个阶段，他都是最积极进取的 CEO。他会亲自编写软件，亲自实验。他会给我们发送极其详细的产品反馈和模型能力反馈。他比任何人都更早地说：“我们不是一家 NPC 公司，所以我们要采用智能体，否则我们就完蛋了。我们要自己构建。”每次我和他交谈，他都处在任何 CEO 或非 CEO 所能达到的前沿。他亲自构建，他深知自己有着深刻的洞察力，而且他总是比其他 CEO 领先六到八个月。

One of the things that struck me the most about Toby is in the very early days of AI and then at every moment along the curve of its development, he has been the most forward-leaning CEO. He's in there like writing the software himself. He is experimenting with it. He sends us extremely detailed feedback on the product offering and on the capabilities of the models. He was before anybody else saying this: he was like, "We are not an NPC company, and thus we are going to adopt agents, otherwise we're totally screwed. We're going to build it ourselves." Every time I talk to him, he is at the edge of what anyone, CEO or not, is doing. He builds himself. He understands that he has a great deep feel, and he is always six to eight months ahead of any other CEO.

你还记得他写那封信的时候吗？大概一年半前，也许是 2024 年，他说你必须做的第一件事就是看看 AI 能否解决你的问题。即使在那个时候，也就是 18 到 24 个月前，人们都疯了，觉得这很荒谬。

Do you remember when he wrote that letter, probably a year and a half ago, maybe 2024, saying that the first thing you have to do is see if AI can solve your problem? Even back then, 18 or 24 months ago, people went crazy; they thought it was ridiculous.

这就是我的观点：他一直走在前沿，他是正确的，他积极投入。没有炒作，只有“这是它现在真正能做的，这是我认为它很快能做的，这是我打算如何推动公司”这样的内容。而且他对现状有着极其深刻的理解。

This is my point: he's just consistently been ahead, he's been correct, he's leaned in. There's no hype, nothing other than here's what it can really do right now, here's what I think it'll be able to do soon, here's how I'm going to push the company here. And just extremely deep understanding of where it's at.

我从未想过，对于你这样的位置来说，有这样的人给你强烈、直接、清晰的产品反馈是多么大的好处。很多人会发送产品反馈，但他是唯一一个同时具备大公司 CEO 身份和极其准确、详细、前沿反馈的人。

I never even thought of that, how much of a benefit it has to be for somebody in your position where you have somebody like that giving you intense and very direct and clear product feedback. A lot of people send product feedback. He is the only person at the intersection of CEO of a large company and extremely accurate, detailed, cutting-edge feedback.

是的，他告诉我——我不知道是在节目里还是节目后——但他非常坚定。他说：“我们回顾 2026 年，会发现那是每个企业都面临重新洗牌的一年。”他说有人会构建 Shopify 的 AI 原生版本，而且他说：“那个人就是我。”所以晚上他实际上在尝试重建。

Yeah, he told me—I don't know if it was on the episode or after—but he said he was very adamant. He's like, "We're going to look back on 2026 as a year that every business was up for grabs." He said somebody was going to build the AI-native version of Shopify, and he said, "And it's going to be me." And so at night he's literally trying to rebuild.

如果你从零开始，你会用当前的技术做什么？

If you started from scratch, what would you do with the current technology?

关于他，我本来还想说另一件事：他亲力亲为。他亲自使用这些工具，亲自编写软件，亲自尝试模型，亲自重新构想工作流程。大多数 CEO 到了那个级别，都有层层团队来实施事情，试图让你满意，试图抹平粗糙的棱角。我认为如果你不亲自去做，就很难获得那种感觉。而据我所知，他整晚都在亲力亲为。

That was the other thing I was going to say about him: he does it himself. He is using these tools himself, writing software himself, trying the models himself, trying to reimagine his workflows himself. Most CEOs at that level have teams of people managing teams of people trying to implement the thing, trying to make you happy, trying to smooth the rough edges. I think it's very hard to get the feel if you're not actually doing the thing. And he does it so hands-on, all night long, as far as I can tell.

我不确定 2026 年是否会是每个企业都感觉面临洗牌的一年。我可能有点不同意他的看法。但我理解那种精神，我也理解那种感觉正在发生。你认为这有可能吗？无论是 2026 年还是 2046 年？

I'm not sure if 2026 will be the year that every business feels up for grabs. I might disagree with him a little bit there. But I get the spirit of that, and I do understand that it feels like that's happening. Do you think that's even possible? Like whether it's 2026 or 2046?

我的意思是，显然不是字面上的每个企业。我认为有些东西是非常反 AI 的。比如 AI 越好，一些与 AI 无关的企业就越难竞争，因为我们会真正想要这些真实的非技术体验，或者我们会更关心运动队之类的。所以，不，不是所有，但我认为会有很多软件企业面临重新洗牌。

I mean, obviously not literally every business. I think there are some things that are very anti-AI. Like the better AI gets, the more some businesses that have nothing to do with AI will be harder to compete with, because we'll really want these authentic non-technological experiences, or we'll care more about sports teams or whatever. So, no, not everything, but I think there will be many software businesses that are very up for grabs.

那么你会不同意这个时间线吗？

Would you disagree on the timeline then?

我不同意这个时间线。我认为需要更长一点的时间。

I disagree on the timeline. I think it's going to take a little bit longer.

好的。你能详细说说吗？

Okay. Can you say more about that?

我热爱初创企业。我认为初创企业是经济中最酷的东西，我的职业生涯都在努力真正理解初创企业。我以为当我们达到 GPT-4 时，也就是 2023 年，之后很快就会有更多的软件企业面临重新洗牌，但事实并非如此。我想我在一些事情上错了，但其中之一是速度。其中之一是经济有巨大的惯性。人们继续做他们正在做的事情，继续从同一家公司购买，继续想以同样的方式使用他们的工具。我认为这在很多方面实际上是积极的，它会使我们面前的这个重大转型更平稳、更缓慢。我对此很感激。但我认为这意味着我们在时间线上都过于雄心勃勃了。即使有了这项令人难以置信的技术——我认为 AI 是人类发明的最不可思议的技术之一——社会和经济也会适应得更慢。

I love startups. I think startups are the coolest thing in the economy, and I've spent my career trying to really understand startups. I thought when we got to GPT-4, which was back in 2023, that very quickly after that there was going to be much more disruption in software business being up for grabs right away than it turned out to be. And I think I was wrong about a few things, but one of them in terms of the speed. One of them is the economy just has so much inertia. People keep doing the same things they're doing. They keep buying from the same company. They keep wanting to use their tools in the same way. I think this is actually a positive in many ways, and it's going to make this big transition in front of us go smoother and slower. I'm grateful for it. But I think it means we've all been too ambitious on timelines. Even with this incredible technology—and I think AI is one of the most incredible technologies humanity has ever invented—society and the economy will adapt more slowly.

是的。有趣的是，我们在开始录音之前谈到，历史上有所有这些相似之处。显然，我以读历史为生。当你刚才说话的时候，我甚至没有在想 OpenAI、AI 和 Sam Altman。我在想读 Larry Ellison 的传记，在 80 年代。他就像：“伙计们，这不是软件问题。这是人的问题。我们必须说服他们我们可以安装软件。他们没有使用它。我们必须改变他们的行为。技术已经在那里了。我们现在必须让人类适应，让他们真正开始使用技术。”我自己的例子是，Netflix 出现并开始寄送 DVD 之后，甚至在他们开始流媒体之前。令我惊讶的是人们仍然去 Blockbuster。这让我难以置信。我几乎每天上学放学都会路过一家 Blockbuster。令我惊讶的是人们仍然这样做。这是一个深深印在我脑海里的例子，关于习惯的力量和人们做事的方式。改变行为比技术极客意识到的要难得多。所以，如果我们回到 Toby 写那封公开信或写给他公司内部人员的信引起的骚动，你比任何人都更快地采用这些技术，因为你部分地发明了它们，对吧？那么，有没有什么事情让你对自己的行为感到震惊，比如：“我知道有更好的方法。我甚至在创造可能更好的产品，但我仍然无法克服这种习惯，这种习惯的力量。”

Yeah. It's funny. We were talking before we started recording that there are all these parallels to history. Obviously, I read history for a living. When you were just talking, I wasn't even thinking about OpenAI and AI and Sam Altman. I was thinking of reading this biography of Larry Ellison in the 80s. He was just like, "Guys, this isn't a software problem. It's a people problem. We have to convince them we can install software. They're not using it. We have to change their behavior. The technology is there. We have to now adapt humans so they actually start using the technology." My own example of this was after Netflix came out and started shipping DVDs, even before they started streaming. It was amazing to me that people still went to Blockbuster. It was incredible to me. I would just watch this because I kind of drove by a Blockbuster on my way to and from school. And it was amazing to me that people still did it. That is an example that has stuck in my head of force of habit and the way people do things. Changing behavior is just much harder than the tech nerds realize. So, if we go back to this uproar of Toby writing that open letter or that letter to the people inside his company, you're adopting this faster than anybody else because you're partially inventing them, right? So, is there something where you're actually shocked at your own behavior, like, "I know there's a better way to do this. I'm even creating the product that could be better, and yet I still can't get over this habit, this force of habit."

100%。

100%.

好的。

Okay.

我喜欢你之前问过我这个问题。我一直在等这个问题。

I love that you asked me this before. I have been waiting for this question.

对我来说，最让我感到心理上不一致的事情是，20 年来我一直以同样的方式使用电脑。现在我有了一个神奇的东西叫 Codex。你也有，每个人都有。这意味着我应该完全以不同的方式使用电脑。我不应该再到处点击，你知道，从一个消息应用复制粘贴到另一个。我不应该再漫无目的地滚动邮件，试图找出哪封邮件对我来说最不痛苦、最愿意打开和回复。我不应该再像以前那样，用同样的方式维护待办事项清单，做那些琐碎的电脑任务。然而，我的脑海里似乎有某种编码，认为做这些事情就是工作的意义，就是高效的意义。如果你问我，我绝不会说我喜欢那样做。事实上，我会说相反的话。而且我认为我是真心的。但根据显示性偏好，我现在有更好的方式。我可以更快地完成。我可以更多地用 Codex 来处理日常事务，比如处理一堆邮件，完成待办事项，处理所有这些事情。但我仍然用老方式做。这毫无道理，除非我其实暗中喜欢那样，或者觉得那样感觉不错。

The thing to me that feels most psychologically inconsistent about myself is that I have for 20 years been using computers the same way. I now have a magic thing called Codex. So do you. So does everybody. That means I should completely be using my computer in a different way. I should not be clicking around, you know, pasting from one messaging app to another. I should not be scrolling mindlessly through my emails and trying to figure out which one is like least painful for me to open and respond when I don't want to be dealing with it. I should not be like keeping a to-do list and doing sort of this like these wrote computer tasks in the same way that I have for so long. And yet there's like something in my mind that is encoded that like doing this kind of stuff is what it means to work and what it means to be productive. And if you asked me I would never say I like doing it that way. I would in fact I would say the opposite. And I think I would mean it. But like by revealed preference, I have a better way to do it now. I can do it faster. I can be using codeex for more of just like my day-to-day like got to get through this stack of emails, got to do the stuff on my to-do list, got to, you know, deal with all these things. And I still do it that way. And it makes no sense other than I must like secretly like like it or feel good about it.

你觉得要发生什么变化，你才会真正更深入地采用自己的产品？

What do you think is going to have to change for you to actually adopt your own product in a more deep way?

我不太确定。我的意思是，这是逐渐发生的，这可能是正确的答案，即这些事情必须逐渐发生，而彻底改变一个人根深蒂固的习惯和工作流程是困难的。我认为我们可以用这项技术构建更好的产品，让这种转变更加无缝。但现在感觉我们都在两个世界之间徘徊，你知道，我们仍然有一台可以用老方式使用的电脑，我们也有 Codex 可以用这种惊人的新方式使用电脑，而我们不确定什么时候该用哪个，用来做什么。我认为这主要是产品失败。我们现在所处的阶段让我想起 iPhone 之前的智能手机。我当时是早期采用者。我在 2003 年或 2004 年左右有一部 Palm Trio。

I don't really know. I mean, it's happening gradually and this might be the right answer, which is these things have to happen gradually and totally changing someone's like ingrained habits and workflows is difficult. I think there are better products we can build with this technology that will make it more seamless to do that. But right now it feels like we're all kind of straddling these two worlds of, you know, we still have a computer we can use the old way and we have Codex that can use our computer in this amazing new way and we're like not sure which to use when for what. And I think this is mostly a product failure. The phase that we're in now reminds me of like smartphones before the iPhone. I was like an early adopter. I had like a Palm Trio in, you know, 2003 or four or whatever.

你有 Sidekick 吗？

You have a sidekick?

我从来没有过 Sidekick。我觉得它们超级酷。我想要一个。

I never had a sidekick. I thought they were super cool. I wanted one.

而且很多技术都已经具备了。比如它缺少多点触控，但主要缺少的是让 iPhone 成为 iPhone 的产品理念。我觉得我们现在所处的世界，已经拥有所有的技术拼图，但我们还没有迎来 iPhone 那样的时刻，彻底改变人与技术的交互方式。

And a lot of the technology was there. Like it was missing multi-touch, but mostly it was missing like the product ideas that made the iPhone the iPhone. And I feel like we are now in a world where we have all of the technological pieces, but we have not had the iPhone moment of like completely changing how someone interfaces with technology.

我们刚才谈到 Toby，Toby 在这里自己构建这些东西，对吧？我们有个共同的朋友 Josh Kushner。他说，史蒂夫·乔布斯的思维方式和他经营公司的方式，与你（Sam）的思维方式有很大的可比性。乔布斯显然不是写代码的人，也不是造硬件的人，但他说“我是零号病人。我在制造我自己想用的产品。”基本上，我们在苹果看到的一切，基本上就是他想要的。有一本书里有个很棒的故事，他们本来要和史蒂夫开个会，讨论的是一款新的 MacBook 笔记本电脑，团队为史蒂夫准备了巨大的演示，他们非常紧张，因为他气场强大。他们以为会议会持续一个小时。他走进来，他们给他看笔记本电脑，他说“开、关”。他按下按钮，屏幕亮了。然后立刻关掉，接着他试图打开 MacBook，但有一个延迟。他说“把这个做到这样”，然后就走出了房间。整个会议就是这样。苹果历史上有很多这样的例子。你是怎么做的？你如何改进产品？你只是根据自己的需求来做吗？你怎么看待这件事？

We were talking about Toby's, like Toby's out here building these himself, right? We have a mutual friend in Josh Kushner. He says he's like there's a big comparison to be made between the way that Steve Jobs thought and the way he ran his company to the way that he thinks that you do. He wasn't the one obviously writing the code. He wasn't building the hardware, but he's like I am patient zero. I am making products that I myself want to use. And essentially like everything that we saw with Apple was just basically what he wanted. There's this great story in one of the books where he they they were supposed to have a meeting on I think one of the MacBook like the new MacBook laptops and the team prepares like all this huge presentation for Steve and they're like really nervous cuz of his commanding presence and he walks in they think it's going to be like an hour meeting. walks in and he shows him the laptop and he's like on off. He presses the button, it comes on. Press off like immediately and then he then tries to open up the the MacBook. There's like a delay. He goes make this meaning the MacBook like that and then walks out the room and that's the whole meaning like that. There's a lot of examples in the history of Apple like that. How do you approach it? Like how do you improve the product? Like are you just doing it through your own needs? Like how do you think about this?

我现在的大部分精力都花在研究和算力上。我希望能在产品上花更多时间。我们这里有很棒的人在思考产品，但最重要的事情是创造智能模型，并能够高效、大量地为很多人运行它们。如果我们能做到这一点，我相信其他一切都会随之而来。从哲学上讲，我非常倾向于说，要找到那种高杠杆、困难的问题，能够延续指数增长，对我们来说，这就是模型和算力。

Most of my effort right now is on research and compute. I would love to be able to spend more time on product. We have great people thinking about the product here, but the most important thing that we can do is to create smart models and to be able to run them efficiently and abundantly for a lot of people. If we can get that right, I believe that everything else will follow. Philosophically I'm very inclined to say you know try to find the like the high leverage difficult problem that will continue the exponential and for us this is like models of compute.

为什么这些问题天然适合你？

Why do they naturally suit you?

以我们这种方式扩展算力，需要复杂的供应链，有很多有趣的合作伙伴关系需要解决，我喜欢做这些。还有有趣的财务挑战，比如如何为可能是历史上最昂贵的基础设施项目融资，或者至少是迅速成为最昂贵的项目。构建这种规模的算力所涉及的技术问题，从设计自己的芯片，到晶圆厂和机架制造商的供应链，再到这些系统的电力系统，我一直对能源感兴趣，所有这些都汇聚在一起。所以，围绕构建这种规模的算力，在技术、商业、政策、供应链、物流等方面都有很多有趣的问题。

To scale compute in the way that we're doing this requires like it's a complex supply chain there's like a lot of interesting partnerships to figure out which I like doing there's like interesting financial challenges of how you're going to finance what is probably already or at least rapidly becoming the most expensive infrastructure project in history. The technology questions that go into building out compute at this scale from you know designing your own chip to the supply chain of fabs and people that make racks to to sort of the the power systems for these things. I've always been interested in energy all come together. So there are a lot of problems that are interesting across technology, business, policy, supply chain, logistics altogether around building compute at this kind of scale.

所以我曾经是一名初创企业投资人，在我的职业生涯中，我发现最接近初创企业投资的事情是管理研究项目。它们也有很多不同的地方。比如，普通研究员和普通创始人在表面上看起来不同，原因很明显，但在如何找到非共识的赌注、如何决定在哪里建立信念、如何理解指数增长的样子、如何管理异常人才以及如何识别人才等方面，有很多相似之处。这是我们所在的研究大楼，也是我工作的地方。很好。请多谈谈你在初创投资中学到的东西与研究之间的相似之处。

So I used to be a startup investor and the thing in my career that I have found closest to startup investing is managing a research program. There are all these ways in which they're really different too. Like you know the average researcher and the average founder have I think on the surface look different for obvious reasons but there's like a lot of similarities about how you find the non-consensus bets how you decide where to have conviction how you understand what exponential growth looks like how you manage like outlier talent and how you how you identify it even more. This is the research building that we're in and it's where I sit. Great. Say more about why the parallels between what you learned at startup investing with doing research.

一个重要的相似之处是幂律分布。人们在投资中经常谈论这个，你必须重新编程你的大脑，因为我们似乎天生不适合这样思考。你知道，你最好的投资会超过你所有其他投资的总和。你第二好的投资会超过之后所有其他投资的总和。至少 AI 研究也是如此。当我们开始时，人们认为 AGI 是完全不可能或几乎不可能的。

One big one is the power law. So people talk about this all the time in investing which is you have to kind of like reprogram your brain cuz we don't seem naturally well suited to think this way. Where you know your best investment will outperform all of your other investments put together. Your second best investment will outperform everything else put together after that. And AI research at least is like that as well. When we started, people thought it was totally unlikely or almost impossible that AGI was possible.

这是哪一年？

What year is this?

2015 年。

2015.

我的意思是，我们当时因为说我们要做 AGI，被领域里所有的思想巨匠狠狠批评了一通。然后当我们真正开始专注于大语言模型时，我们又挨了一顿批，说这完全荒谬。而我理解，至少从我的创业背景出发，我认为其他人也以不同的方式理解到，高风险赌注是可以的，只要你选择那些一旦成功就价值巨大的赌注。研究也是如此。那些能成为伟大研究者的人，往往是反共识、方法新颖、精力充沛、有点非主流——这是我一直想到的词。

I mean, we just got hammered by all the intellectual giants of the field for saying we were going after AGI. And then when we started really focusing on large language models, we got hammered again, saying this is completely ridiculous. And I understood, at least from my startup background, and I think other people understood in other ways, that high-risk bets are okay as long as you take the ones where, if they work, they're super valuable. And research looks this way. The kind of people that make great researchers are sort of non-consensus, fresh approach, high energy, sort of non-standard—that's the word that keeps coming to mind.

你得再多说说“非主流”。能更具体一点吗？他们是不是很尖锐？

You've got to say more about non-standard. Can you be more specific? Are they spiky?

你不想投资一个创始人，他对同一个想法的看法，和你之前谈过的一千个人只有细微差别，他试图说服你，也许也说服自己，他们完全不一样，在做全新的事情。但这多半是试图随大流，和所有人走同一条轨道，做他们该做的事，也就是创业。他们听 Peter Thiel 说过太多次，你应该做点不一样的事，所以他们试图模仿，但他们并不是真心的。对我来说很清楚，当有人就是和大多数人想得不一样，愿意坚持非常不受欢迎的信念，这些信念很可能是错的，但如果对了，至少他们会非常正确，而另一个人只是给所有人都有的想法涂了一层薄薄的漆。

You don't want to fund a founder who has a very slightly different take on the same idea as the last thousand people you've talked to, who has tried to convince you and maybe convince themselves that somehow they're completely different and doing something totally new. But it's mostly like trying to fit in with the herd and be on the same track as everybody else and do what they're supposed to do, which is start a startup. And they've heard Peter Thiel say enough times that you're supposed to be doing something different that they kind of try to emulate that, but they don't really mean it. It's very clear to me when you have someone who just thinks differently than most other people and is willing to stand by convictions that are very unpopular, may well be wrong, but if right, at least they're going to be really right, versus someone who is like a thin veneer on the same idea that everybody else has.

2015 年底，当我们创办 OpenAI 时，世界上几乎没有在做 AGI 的团队。有 DeepMind，还有一两个我能想到的。这绝对是一件非常反共识的事。同一年，可能有很多很多——我就拿这个举例，因为我想到了，但还有其他类别——可能有成千上万的创始人在做照片分享应用。那可能不是个好主意。今天，很多人想创办 AI 实验室。有少数人，你知道，两三个，不管怎样，在做完全新的事情。那在直到我变得这么好之前是不可能的，但看起来还不是个好主意。而这是作为创业者的我一直想投资的东西，也是对我最有效的东西。对研究者来说也有类似的情况。很多研究者会追逐上一个成功的东西，而少数研究者对新想法有高度信念。我认为我们过去是，现在也是最适合这些人的研究实验室。

In late 2015, when we were starting OpenAI, there were very few AGI efforts in the world. There was DeepMind, one or two others that I can think of. It was like a very non-consensus thing to do. In that same year, there were probably—I'll pick on it just because it came to mind, but there are other categories too—there were probably many, many thousands of founders starting photo-sharing apps. That was probably not as good of a thing to do. Today, a lot of people want to start AI labs. There are some handful of people, you know, two, three, whatever, doing something completely new. That actually wasn't possible until the day I got this good, but it doesn't seem like a good idea yet. And that is the thing that, as a startup founder, I always wanted to fund, and the thing that mostly worked for me. There's a similar thing for researchers. There were a lot of researchers that would chase whatever the last thing was that worked, and there were a small number of researchers that had high conviction towards a new idea. And I think we were and are the best research lab for those people.

我记得几个月前和 Dario 聊过这个，他觉得，就你们这种追赶方式而言，有三大玩家，资金、资本要求——不会再有第四个大玩家了。但他说，有这么一个——我忘了具体数字，我就编一个——比如说 10% 的概率，会有某个像僧侣一样的研究者，用一种我们从未考虑过的角度来接近它。

I remember talking to Dario about this a few months ago, and he thought, in terms of chasing after the way that you guys are, there's the big three players that the money, the capital requires—there's not going to be like a fourth bigger player. But he's like, there's like this—I forgot what the number was, I'll just make it up—say 10% chance that there's just some monk researcher that's going to approach it in a way, just this angle we've never even considered.

完全同意。我不知道怎么给它定个数字，但确实有可能。我觉得我不是在编数字，但就是一个小百分比。

Totally. I don't know how to put a number on it, but there is some chance. I don't think I'm making the number up, but it was like a small percentage.

肯定有这种可能，我很喜欢这一点。我觉得这就是为什么事情一直令人兴奋。

There is some chance of that for sure, and I love that. I think that's why stuff stays exciting.

我想告诉你本期播客的赞助商 Ramp。我最近读了很多关于 SpaceX 的文章。SpaceX 是世界上最有价值的企业之一，其历史的一个主题就是不断攻击和质疑你的成本。Ramp 帮助世界上许多最具创新力的企业做到这一点。使用 Ramp 的公司中位数削减了 5% 的开支。SpaceX 证明的一件事是，对控制成本的宗教般执着实际上可以帮助增加收入，因为你可以追求原本无法追求的机会。我们在 Ramp 的数据中也看到了这一点。使用 Ramp 的公司中位数收入也增长了 16%。所以，当你的企业在 Ramp 上运行而竞争对手没有时，你就拥有了一个随时间复利的巨大竞争优势。Ramp 是唯一一个旨在让你的财务团队更快、更快乐的平台。我认识的许多顶级创始人和 CEO 都在 Ramp 上运营他们的业务。我自己也在 Ramp 上运营我的业务，你也应该这样做。访问 ramp.com 了解他们如何帮助你的企业节省时间、节省金钱并增加收入。那就是 ramp.com。

I want to tell you about the presenting sponsor of this podcast, Ramp. I have been reading a lot about SpaceX lately. SpaceX is one of the most valuable businesses in the world, and one of the main themes in the history of SpaceX is constantly attacking and questioning your cost. Ramp helps many of the most innovative businesses in the world do exactly that. The median company running on Ramp cuts their expenses by 5%. And one thing SpaceX has demonstrated is that a religious dedication to controlling costs can help actually increase revenue because you can pursue opportunities you couldn't otherwise. And we see that in the Ramp data, too. The median company running on Ramp also grows their revenue by 16%. So, when you're running your business on Ramp and your competitors are not, you have a massive competitive advantage that compounds over time. Ramp is the only platform designed to make your finance team faster and happier. Many of the top founders and CEOs I know run their business on Ramp. I run my business on Ramp and you should too. Go to ramp.com to learn how they can help your business save time, save money, and grow revenue. That is ramp.com.

我在读《从 0 到 1》时发现了我一直最喜欢的一句名言。这句话是：“我注意到的唯一最强大的模式是，成功的人在意想不到的地方发现价值。他们通过从第一性原理而不是公式来思考商业来做到这一点。”这正是 Apploven 对他们的广告平台所做的。Apploven 让你接触到移动游戏内超过 10 亿的潜在新客户。Apploven 让你捕捉到全神贯注的注意力。Apploven 广告是全屏视频广告，平均观看 35 秒。这种留存率让其他广告平台望尘莫及。你可以在几分钟内就在 Apploven 上启动。你设定目标，Apploven 实现它。没有复杂的设置，不需要专业知识，Apploven 可以快速扩展。他们可以把你的广告展示给超过 10 亿的潜在客户。其他企业已经看到了立竿见影的效果，扩展到每天数十万美元的支出，并增加了数百万的收入。所以，你想在所有竞争对手都上 Apploven 之前快速开始。你可以通过访问 apploven.com 来实现。那就是 apploven.com。

I found one of my all-time favorite quotes when I was reading the book Zero to One. The quote says, "The single most powerful pattern I have noticed is that successful people find value in unexpected places. And they do this by thinking about business from first principles instead of formulas." That is exactly what Apploven has done with their advertising platform. Apploven connects you with over a billion potential new customers inside mobile games. Apploven allows you to capture undivided attention. Apploven ads are full-screen video ads that are watched for an average of 35 seconds. That is retention that blows other ad platforms out of the water. And you can launch on Apploven in minutes. You set the goal and Apploven achieves it. There's no complex setup, no expertise needed, and Apploven scales quickly. They can put your ads in front of over a billion potential customers. Other businesses have seen immediate results, have scaled to hundreds of thousands of dollars of spend per day and increase their revenue by millions. So, you want to get started quickly before all of your competitors are on Apploven. And you can do that by going to apploven.com. That's apploven.com.

好的。所以，让我困惑的是：你从创始人到投资人，又回到创始人。但为什么在 2015 年，是什么让你最初对人工智能产生兴趣，让你说：“嘿，这是如此反共识的事情。人们觉得我疯了。我无论如何都要做。”

Okay. So, what is confusing to me? You went from founder to investor to back to founder. But why in 2015, like what got you interested in artificial intelligence to begin with that you're saying, "Hey, this is such a non-consensus thing. People think I'm crazy. I'm going to do it anyways."

嗯，我一生都对 AI 感兴趣。我是个非常书呆子气的孩子。我是那种周五晚上在电脑上玩、看科幻、读科幻的孩子。我一直认为 AI 会是最惊人、最疯狂的东西。我从没想过我真的能从事这方面的工作，但我一直热爱它。我甚至上大学就是为了学这个。我在大一和大二之间的夏天在 AI 实验室工作，但什么都不起作用。事实上，我记得很清楚，一位教授告诉我，我可以尝试所有这些事情。有所有这些方向。

Well, I had been interested in AI my whole life. I was like a very nerdy kid. I was the kind of kid that spent Friday nights playing on my computer and watching sci-fi, reading sci-fi. And I always thought that AI would be the most amazing, craziest thing. I never thought I would actually get to work on it, but I always loved it. I even came to college sort of to study it. I worked in the AI lab the summer between my freshman and sophomore year, and nothing was working. In fact, I very memorably, a professor told me I could try all of these things. There's all these directions.

我们唯一知道行不通的就是深度学习。我们试了很长时间。你知道，这是最有可能让你职业生涯糟糕的方式。我当时是个容易受影响的大一新生，我就信了。所以我追求了其他东西。当时，大概是 2005 年，我很清楚 AI 行不通。我碰巧意外进入了创业领域，但后来非常热爱它。所以我甚至不会称之为职业弯路，因为它非常有帮助。回头看，成为创业投资人很棒。

The one thing we know doesn't work is deep learning. We tried that for a long time. You know, it's the most guaranteed way to have a bad career. And I was like an impressionable freshman in college, whatever. I assumed that was true. So I pursued these other things. It was clear to me at the time, this is now like kind of 2005, that AI was not working. And I happened to like accidentally get into startups, but then very much fell in love with it. So I wouldn't even call it a career detour because it was super helpful. Looking back, becoming like a startup investor was great.

我们不想要那样。我听说你说过你会在余生都做这件事。

We don't want that. I heard you say that you're going to work on this for the rest of your career.

是的，希望我们刚开始。我们会多次上这个节目。

Yeah, hopefully we're starting. We're going to come on the show multiple times.

我会让你兑现这个承诺。我们不需要更多退休后去投资的创始人。我们已经有太多投资人了。

I'm going to hold you to this. We don't need more founders that retire and invest. We have too many investors in this.

但我想说的是，我走的是相反的方向。我先做投资人，然后经营公司。这很不寻常。

But what I was going to say is that I got to go in the other direction. I was an investor first and then I ran a company. It's pretty unusual.

非常不寻常。

Very unusual.

我对此非常感激，因为作为投资人，如果你真的研究和观察公司，你会获得令人难以置信的学习和模式匹配。这对我经营 OpenAI 非常有帮助，但这是与正常方向相反的，所以这是非常罕见的事情，我强烈推荐。

And I'm super grateful for it, because you get this unbelievable set of learnings and pattern matching if you really study and watch companies as an investor. That has been super helpful to me running OpenAI, but it's like the opposite normal direction, so it's just a very rare thing and I strongly recommend it.

为什么有帮助？

Why is it helpful?

如果你经营一家公司，你在过去遇到过一些类似的决策，比如一些关键的决策，你看到了什么有效，什么无效。如果你必须做出高风险的战略转变，或者以非常混乱的方式解雇高管，你只有过去 5 到 10 年自己有限的经验。但作为投资人，你会看到所有的关键时刻。所以你不会像日常经营公司那样获得运营实践，但你整天都会看到很多重大的关键时刻。所以我拥有的数据集的丰富程度非常棒。

If you're running a company, you have faced some number of similar decisions in your past, like some number of crux decisions, and you've seen what works and what doesn't. And if you have to make a high-stakes strategy shift or fire an executive in a really messy way, you have whatever your own limited previous experience was over the last 5 or 10 years that you've been doing it. But as an investor, you kind of watch all the crux moments. So you don't get the kind of operating practice that you do just day in and day out running a company, but you've seen a lot of the big crux moments all day long. So the wealth of the data set that I had there was awesome.

所以当你需要做决定时，这些就会在你脑海中浮现。

So that plays in your head when you have a decision to make.

是的。我会想，“哦，这家公司遇到类似情况时发生了什么，或者我看到这个创始人犯了错误，或者这个创始人做得非常对。”

Yeah. I'm like, "Oh, this is what happened when this company had a similar thing or I saw this founder make this mistake or this founder got it really right."

我们之前谈到你研究过工业革命。我们谈到了一些我们都读过的伟大传记。我的朋友 Daniel X 这样评价我，因为我认为我在另一个播客《Founders》做了 10 年的这个项目的好处是……

We were talking about that you studied the industrial revolution. We were talking about some great biographies that we both read earlier. My friend Daniel X says this about me, because I think the benefit of me doing this project on my other podcast called Founders for 10 years is like...

他说，你就像一个在历史上最伟大的企业家身上训练的 LLM，但温度调高了，因为你疯了，因为我以一种奇怪的方式对此充满热情，痴迷于已故的企业家。但这很有帮助。就像，我会和一个创始人交谈，他们会谈到他们处理过的事情。我会说，“哦，卡内基这样做过，洛克菲勒这样做过，你也许可以试试这个。”

So he's like, you're like an LLM trained on history's greatest entrepreneurs but with the temperature turned up, because you're crazy, because I'm like super passionate about it in a weird way, to be like obsessed with dead entrepreneurs. But it is helpful. It's like, I'll be talking to a founder and they'll talk about something they dealt with. I'm like, "Oh, well, Carnegie did this and Rockefeller did this and you might want to try this."

你发现你从所有这些中有一个大的洞察，还是说对于任何给定的场景，你都有这些人做了什么以及它们如何结合起来的例子？

And do you find that you have like one big insight from all of that, or it's just like for any given scenario, you have like what all these people did and how it comes together?

我认为这取决于创始人的个性，对吧？所以当我读……我多年来一直在读你的博客。我认为你是一位伟大的作家。你非常简洁，简洁真的很吸引我，我喜欢编号列表。我们俩以同样的方式写作，这很奇怪。我觉得我在读你的博客时，我会想，这就是我基于所有阅读会得出的确切结论。有一小部分原则可以应用，但这真的取决于创始人是谁以及他们想做什么。

I think it's dependent on the personality of the founder, right? So when I was reading... I've read your blog for years. I think you're a great writer. You're very succinct, the brevity is just really appealing to me, and I love numbered lists. It's weird that we both write in the same way. I feel like I'm reading your blog and I'm like, this is the exact conclusion that I would come to based on all the reading. There's this handful of principles that could be applied, but it's really depends on who the founder is and what they want to do.

这就是我想理解的。让我们回到我们之前讨论的。你做了一个很大的跳跃，因为你是我听说过的硅谷有史以来最好的投资人之一。你可以只是富有，不必真正工作，因为投资人有点懒。顺便说一句，我只是开玩笑。有点不是。

This is what I'm trying to understand. Let's go back to what we were talking about. You're making a big jump, because you're one of, from what I hear, one of the best investors of all time in Silicon Valley. You could just be rich and not really have to work, because investor is kind of lazy. I'm just kidding by the way. Kind of not.

不，这很……我两者都做过，我想我可以说经营公司比做投资难得多得多。

No, it's pretty... I having done both, I think I can say it's much, much, much harder to run a company than being an investor.

没错。在我看来，这就是人们应该做的。所以你说，“去他的。我不会走轻松的路。我要做最难的事情。”人们认为不可能的事情。我会被嘲笑的事情。

Exactly. And that's what people should be doing in my opinion. So then you're like, "Fuck that. I'm going to not take the easy route. I'm going to do the hardest thing ever." The thing that people think is impossible. The thing I'm going to be made fun of.

是的。

Yeah.

我仍然需要理解。好吧。所以你很投入，孩子。为什么它会吸引一个孩子？你当时住在圣路易斯。

I still need to understand. Okay. So you're into it, kid. Why would it appeal to a kid? You were living in St. Louis at the time.

我当时住在圣路易斯。

I was living in St. Louis.

为什么 AI 当时会吸引你？

Why would AI appeal to you back then?

嗯，我认为它吸引了每一种电脑极客。我不认为我有什么不寻常的。它只是感觉不可能。我认为大多数人会说，那当然是最酷的事情，但完全不可能。我认为我的奇怪之处在于，好吧，让我们试试。但我认为每个人都认为它会很棒，值得追求。

Well, I think it appealed to every kind of computer nerd. I don't think it's that unusual about me. It just felt impossible. I think most people would say, of course that'd be the coolest thing ever, but it's totally impossible. I think the weird thing about me was like, okay, let's try. But I think everybody thought it would be awesome and something to go for.

等等，那是你小时候的一个性格特质，告诉你你不能做某事，你的最初反应是抗拒？

So wait, that was a personality trait of yours as a kid that told you that you couldn't do something, like your initial response was resistance?

不是抗拒，而是，“你确定吗？为什么不呢？让我们试试。看看会发生什么。也许我可以，也许我们可以。”我是一个非常乐观的孩子。而且，某件事看起来越不可能，我就越感兴趣。我们可以发明一种技术，让我们能做所有其他事情，以任何其他单一技术都无法做到的方式赋予人们权力，这个想法对我来说总是天生就非常吸引人。就像，我想要那个东西。我想要能够做所有其他事情。我认为另一件我记事起就有的性格特质是，真正给人们更多权力和能力是很有趣的。在某种意义上，这是技术的整个弧线，我肯定一直是一个技术极客，但 AI 是我能想象的最强版本。

Not resistance, but like, are you sure? Why not? Let's try. Let's see what happens. Maybe I can, maybe we can. I was a very optimistic kid. And also, the more something seemed impossible, the more intrigued I was. The idea that we could invent a technology that would let us do everything else, that would just empower people in this way that no other single technology could, that always seemed like innately incredibly appealing to me. It's like, I want that thing. I want to be able to do everything else. I think another thing that was kind of a personality trait as long as I remember is that it is interesting to really give people a lot more power, a lot more ability. In some sense, this is the whole arc of technology, and I was for sure always a technology nerd, but AI is the strongest version of that I can imagine.

你当时认为它会实现什么？比如当你还是个孩子，这看起来像一项很酷的技术。我想做 X。除非 AI 被发明，否则我无法做 X。

What did you think that it would enable back then? Like when you were a kid, this seems like a cool technology. I want to do X. I can't do X unless AI is invented.

很难记住这当中有多少是我当时真正想的，又有多少是你现在试图构建的东西。是的。就像我当前的工作在多大程度上影响了我对它的记忆。当然，小时候我非常喜欢机器人，你知道，我们学校有个机器人俱乐部。那时候的机器人简单得可笑。我甚至记得在夏令营里，我们有一个小乌龟，你可以用电脑在地板上或桌子上控制它，我觉得那是最酷的东西。有些东西是物理的、由电脑控制移动的，我总觉得那太神奇了。

It's always hard to remember how much of this is the stuff that I actually thought at the time versus what you're trying to build right now. Yeah. Like how much my current work has colored my memories of it. For sure as a kid I was very into robots, you know, we had a robots club in my school. And the robots at the time were laughably simple. I even remember at summer camp we had this little turtle that you could control with a computer on the floor or on the table and thought that was just the coolest thing. There's something about physical stuff moving controlled by a computer that I always thought was amazing.

现在我对 AI 能如何推动科学发现极为感兴趣。在我成年后的记忆里，我觉得我小时候也觉得那很酷。但这感觉不太可信。我猜这就是记忆被染色的一个例子。

Now I am extremely interested in what AI can do to advance scientific discovery. In my memory as an adult, I think I thought that was cool as a kid, too. But it feels just implausible. And I assume that's an example of where the memories have gotten more colored.

但现在，AI 能去发现新物理学、治愈疾病，以及它已经在数学上做的事情，我认为这将成为最重要的领域之一，甚至比 AI 能做的其他任务自动化更重要，就是为了帮助我们理解更多事物。我们之前谈到过《无穷的开始》这本书。从今天的视角重读那本书，我会想，天哪，AI 真的会帮助我们完成理解一切或尽可能多的事。

But now, the fact that we can have AI go discover new physics and cure diseases and what it's already doing for math, like I think this will be one of the most important areas, even more important than automation of other tasks that AI can do, just to help us understand more things. We were talking earlier about this book, The Beginning of Infinity. And rereading that book from today's vantage point, I'm like, man, AI is really going to help us do this important thing of understanding everything or as much as we can.

我肯定对《星际迷航》那种巨大繁荣和富足以及 AI 能如何推动它感兴趣。也许对科学感兴趣的记忆更真实。我就是热爱科学，以及这个想法：因为我们聪明，我们就能弄清楚如何理解世界、做出预测，并做那些没有这种深刻理解就无法做到的事情。我不知道，这似乎天生就很棒。

I was definitely interested in the sort of Star Trek version of huge prosperity and abundance and what AI could do to drive that. Maybe the memory of being interested in science is more real. I just loved science and this idea that we could, because we were smart, figure out how to understand the world and make predictions and do things that we couldn't without this deep understanding. I don't know, that seems like innately awesome.

有趣的是，人类对 AI 的期望在时间上如此一致，因为你描述的东西非常相似。我刚刚第二次重读了克劳德·香农的传记，我忘了，因为我大概 5 年没读这本书了，他和艾伦·图灵在贝尔实验室时每天都会见面喝咖啡。那是 1940 年代，他们总是谈论 AI，而且他们都对此着迷。他们当时认为这是不可避免的，而且他们认为 15 年后就会实现。所以到 1955 年，我们就会有计算机，对吧？他们当时有模拟版本，会比人类更聪明，任何认为这不会发生的人，他们都觉得荒谬至极。他们还会问：“你想让电脑做什么？”他说：“解数学题、写诗、治愈疾病。”你一次又一次听到这些。

It's interesting how consistent over time what humans want from AI is, because something you're describing is very similar. I just reread the biography of Claude Shannon for the second time and I had forgotten, because I hadn't read the book in maybe 5 years, that him and Alan Turing used to meet every day for coffee when they were both at Bell Labs. This is like 1940s and they would just talk about AI and they were both obsessed with it. They thought it was inevitable back then and they thought it was going to happen like 15 years from there. So like 1955 that we're going to have computers, which didn't exist, right? They had the analog versions, that are going to be smarter than humans, and anybody that thought that wasn't going to occur, they thought was absolutely ridiculous. And they're like, "Well, what would you want the computer to do?" He's like, "Solve math problems, write poetry, cure diseases." Like you hear this over and over again.

我读过很多那些人当时写的东西，我非常难过他们不在这里看到这一切，因为他们对每件事都如此正确。我们终于到了 AI 解决新颖数学问题的时刻。它正在发现其他东西。它，你知道，你可以争论它好不好，我会说不是很好，但它正在写诗。它达到了这些家伙所期望的，我想他们会说，好吧，你做到了，就是这样，我们成功了。那会非常酷。

I have read a bunch of things that those guys wrote at the time and I am so sad they are not here to see it because they were so right about everything. We're finally at the moment where AI is solving novel math problems. It is discovering other stuff. It is, you know, you can argue about how good or not, I would say not very good, but it is writing poetry. It gets here to like what these guys, I think they would have said, all right, you've done it, like this is it, we've got it. And that would have been so cool.

是的。这就是奇怪的地方，每个人都只是说：“哦，它永远不会做 X。”比如，我和音乐行业的人谈过。他们说：“它永远不会做出伟大的音乐。”然后他们又问：“那你觉得它会做播客吗？”我说：“当然会。它会做我们能做的一切，至少，我会说比现在更好，比我们能做的更好。”这是一种非常奇怪的现象，它永远不会超越我恰好活着的这个当前时刻。那里有一个深刻的人类心理缺陷。

Yeah. This is the weird thing where everybody's just like, "Oh, it'll never do X." Like, I talked to people in the music industry. It's like, "It's never going to make great music." And then they're like, "Well, do you think it's going to like make a podcast?" I was like, "Of course it's going to. It's going to do everything that we can do at least to, I would say better than like even right now, like better than what we can do." It's a very bizarre thing where it's like it will never surpass what is happening at this current point that I happen to be alive. There's a deep human psychological flaw there.

但这里有一个我认为更有趣的问题。假设它确实做出了一个很棒的播客。你知道，两个 AI 的对话比你我更有趣。你觉得人们会在意吗？还是他们会想要有真人的那个，因为我们都对人着迷？而事实是那不是真人。

But here is, I think, a more interesting question. Let's say it does make a great podcast. You know, two AIs are having a more interesting conversation than you and I are. Do you think people will care or will they want the one with the real people because we're all obsessed with people? And the fact that it's not real people.

是的，这更有趣。就像，哦，这两个我可能天生喜欢或不喜欢的人正在进行一场我觉得有趣的对话。我认为对于严格的参考，比如我的另一个播客，我只是在说，嘿，这是我在书里读到的一些有趣想法。那可能会被颠覆，不管怎样。但尤其是对于在这个发生之前出生的人，也许对你儿子来说不同，你知道，但对我来说，我就是觉得人类总是会被人类吸引。我真心深信这一点。我认为有很多其他工作可能面临重大转型，但关于人的东西，关于人与人联系、人与人喜欢的东西，那些东西在 AI 之后的世界里会变得更有价值，而不是更少。

Yeah, for this is like more interesting. It's like, oh, these two people that I may be predisposed to like or dislike are having a conversation that's interesting to me. I think for like strict like reference, like maybe my other podcast where I'm just saying, hey, this is some interesting ideas I read in this book. That could maybe get disrupted, whatever the case is. But especially for people that were born before this happened, maybe it's different for your son, you know, but for me it's just like I think humans are going to always be drawn to humans. I really deeply believe that. I think there's like a lot of other jobs that could face significant transition, but stuff that's about people, stuff that's about people's connection, connection to people and people liking other people, that stuff feels like it gets more valuable in the post world, not less.

不过，我可能实际上是谈论这个的错人，因为我内心深处渴望，尽管我所有的工作都是数字化的，你知道，向全世界广播。我就是深深渴望更模拟的生活。我喜欢读实体书。比如我和 Kelly 谈话时，我说，我不想用 Zoom。给我打电话或者我们当面谈。我喜欢实体的，我不喜欢……

I may be actually the wrong person to talk about this though because I kind of deeply desire, even though my entire work is digital, you know, broadcast all over the world. It's just like I deeply desire like more of an analog life. I like reading physical books. Like when I was talking to Kelly, I was like, I don't want to get on Zoom. Call me or we'll talk in person. Like I like physical, I don't like...

我也一样。我不读电子书。

I'm like that too. I don't read ebooks.

是的。

Yeah.

我不喜欢 Zoom 会议。我喜欢和真实世界里的人在一起。

I don't like Zoom meetings. I like to be like with people in the real world.

我确实认为有一小部分怪人，而且可能有很多住在这个城市里，你知道，不喜欢人类，只想和电脑交流。但我就是觉得那是人类中极小的一部分。我认为那是人类中极小的一部分。这就是为什么我认为整个世界即使有了超级智能也不会那么不同。就像人们仍然会从根本上被设定为关心他人、想和他人在一起、与他人互动。而且你知道会有一些人就是痴迷于模型，认为人类是障碍，或者，你知道，是需要应对的危险之类的，但对大多数人来说，那才是全部意义。

I definitely think there's a subset of weirdos, and there's probably a lot of them that live in this city, that, you know, don't like humans and only want to communicate with computers. But it's just like I think that's a tiny percentage of humanity. I think it's a tiny percentage of humanity. This is why I think the world on the whole is not going to be that different even with superintelligence. Like people are still going to be very fundamentally wired to care about other people, to want to be around other people, to interact with other people. And you know there will be some people who just get obsessed with the models and just think humans are in the way or, you know, danger to be contended with or whatever, and for most people it'll be the whole point.

我认为这非常重要，当我们确实发现这样的人时，要让他们被点名，并确保他们不会获得权力。

I think it's very important, and when we do find people like that, to them be called out and make sure they don't acquire power.

我当然同意这一点。

I certainly agree with that.

我担心的 AI 两大风险，可能有点刻意。一是失控，AI 以某种方式变得过于强大，我们无法保证想要的控制。二是权力过度集中，某个公司、模型或个人掌握过多权力。在这两种情况下，根本问题是，我认为这两种情况的发生都是非常反人类的立场。正确的做法是，我们要让人类深度掌控未来，让人类深度赋能。人才是这一切的核心。我们不会坐在这里，逐渐把控制权交给 AI 模型，因为不信任或不喜欢人类。说我们要把所有信任寄托在这个模型上，让它拥有所有权力和对世界的决策权，这是非常厌世的说法。但我认为世界上有些人认为这是正确的结局。还有另一个版本，因为不信任人类，我们必须限制谁可以使用这项技术以及如何使用。所有这些可怕的事情都可能发生，出于对这些的恐惧，我们打算把权力集中在少数公司手中。他们会说我们不让其他人使用，但会给他们一些好处。我对这个的讽刺是，我认为 AI 领域有些人实际上在说，我们要给世界治愈所有疾病的方法，让东西变得非常便宜，以换取人们放弃自主权、对未来的影响力和权力，还以安全为名，而且绝对猖獗的不平等。会有人拥有巨额财富和权力，而其他人只得到相当不错的一切。这是一个糟糕的推销。这是一个非常反人类的推销，但居然有人愿意做。

Maybe the two big risks that I'm most worried about with AI, which are a little bit intention. One is a loss of control where AI somehow just becomes too powerful in a way that we can't guarantee the control we want. The other is power gets too centralized, where you have one company or model or person with too much power. In both of these, the fundamental thing is that I think it's a very anti-human position for either of these things to happen. The right approach is to say we want people deeply in control of the future. We want people deeply empowered. People are the whole point of this. We are not going to sit here and gradually hand over control to an AI model because we don't trust or like people. It's a very misanthropic thing to say we're going to put all of our trust in this model and let it have all the power and decision-making over the world. But I think there are some people in the world who think that's the right outcome. There's another version of this, which is because we don't trust people, we have to limit who gets access to this technology and how they can use it. All these terrible things could happen, and out of fear of those, we are going to concentrate power in the hands of a few companies. They're going to say we're not going to let other people use this, but we'll give them some benefits. My caricature of this is I think there are some people in the AI field who effectively say we're going to give the world a cure to all disease and we're going to make stuff really cheap in exchange for people giving up their autonomy and impact over the future and power, also in the name of safety, and also like just absolutely rampant inequality. There will be people that have access to huge amounts of wealth and power, and other people just get a pretty good everything. This is a terrible sales pitch. This is a very anti-human sales pitch that somehow people feel willing to make.

你觉得他们为什么愿意这么做？

Why do you think they feel willing to make that?

我认为是恐惧和权力。当人们谈论 AI 的风险时，我认为有很多人对这些风险的规模如此紧张，被其深深吸引，感到需要保护世界免受其害，以至于他们说，我们应该在这里用大量自由换取安全，因为这不同于我们见过的其他风险。但我也认为，这最终也成了一种为大量追求权力的行为辩护的方式。当我读到，比如我刚才说的克劳德·香农或艾伦·图灵，至少在我读过的书里，更多的是乐观的，我们要发明让生活更美好、能为我们做事的东西。

I think it's fear and power. When people talk about the risks of AI, I think there are a lot of people who are so nervous about the magnitude of those risks and get so taken by that and feel a need to protect the world from that that they're like, we should trade off a lot of liberty for safety here because this is unlike other risks we've seen. But then I think that also ends up being a way to justify a lot of power-seeking behavior. Everything when I read, like when I was just saying what Claude Shannon was saying or Alan Turing, at least in the books that I've read, it's more of an optimistic like we're going to invent things that make our lives better and can do things for us.

你说得完全对，如果回到克劳德·香农和艾伦·图灵的时代，他们谈到了 AGI 会有多美妙，会做所有的事情。我们刚开始时，确实承受了来自末日论者的巨大压力。我同意末日论者的部分是，这是一项强大的技术，我们应该偏向安全，应该在每个技术层面谨慎行事。我不同意末日论者的部分是，这是一个无法解决的问题。如果回到 OpenAI 的初期，我认为会有两种普遍观点。第一，我们根本不会，当然也不会在 10 年内，构建出非常接近 AGI 的东西。然后，如果我们做到了，我们肯定无法让它安全。你知道，如果你有一个 AI 在很多方面比很多最聪明的人、大多数最聪明的人更聪明，那么末日论者会说，到那时世界肯定已经被摧毁了。对齐会失败，而且对于十年后会发生什么，有这些非常自信的立场。我们已经构建了一些东西，我认为当时大多数人会说这让我们非常接近 AGI，而且发生了很多好事，那种世界末日的疯狂糟糕预测并没有发生。所以我认为这应该更新人们对未来的预测。我们面前仍有更高风险的挑战需要解决。但我们的方法，这是我从初创公司学到的另一件事，做事的方式是把东西放到世界上，从真实客户那里获得反馈，看看哪里会坏，哪里不会坏。这是做出好产品的方法。这也是做出安全产品的方法。我们在 AI 安全方面取得的进展，比我认为大多数人开始时预期的要多得多。

You are totally right that if you go back to the Claude Shannon and Alan Turing era, they talked about how wonderful AGI would be and all the things that it would do. When we started, we really had a lot of pressure from the doomers. Now the part of the doomers that I agree with is this is a powerful technology and we should err on the side of safety and we should act with caution at each level of technology. The part of the doomers that I don't agree with is that it's an unsolvable problem. If you go back to the beginning of OpenAI, I think there would have been two widely held opinions. Number one, not at all and certainly not in 10 years were we going to build something that was very AGI-like. And then conditioned on if we did, we certainly were not going to be able to make it safe. You know, if you had an AI that was smarter in many ways than a lot of the smartest people, most of the smartest people, then the doomers would say surely at that point the world would have been destroyed. The alignment thing would have failed, and there were just these very confidently held positions about what would have happened a decade on. We have built something that I think most people would say at the time would have set us very AGI-like, and a lot of good things have happened, and the kind of crazy bad predictions of the world ending have not happened. So I think that should update people's predictions about the future. There are still higher-stakes challenges in front of us to solve. But our approach, this is another thing I learned from startups, the way you do things is to put things out into the world, get feedback from real customers, see where they break, see where they don't break. That is the way you make a good product. That is also the way you make a safe product. And we have made way more progress on AI safety than I think most people thought we would when we started.

为什么？

Why?

因为有这么多人，每周有十亿人在使用你的产品，每次我们推出新水平的模型，我们把它放到世界上，看看什么有效，什么无效，哪里人们需要我们放宽护栏，因为他们有好事想用它做，哪里我们有对齐失败，哪里我们有安全系统故障。ChatGPT 才推出不到 4 年，十亿人用它处理敏感和重要的事情，而事实上，在这么短的时间内，用如此强大的技术，我们能交付一个被广泛认为是安全的东西，当然它有问题。我认为在象牙塔里我们不可能做到这一点。这就是我相信的构建良好、安全、稳健、有用的技术和产品的方法，我认为这是 Y Combinator 的一个伟大教训。对大多数 AI 安全人士来说，能走到这个阶段，同时仍然拥有我们现在这样的安全保证，似乎是完全不可能的。我确实认为从这里开始会更难，但我不认为通过脱离现实能解决它。

Because so many people, there's a billion people using your products on a weekly basis, and each time we get a new level of model we put it out in the world and we see what works, what doesn't work, where people need us to relax the guardrails because they have good things they want to use it for, where we have alignment failures, where we have safety systems failures. ChatGPT has only been out like less than 4 years, a billion people use it for sensitive and important stuff, and the fact that we can deliver something that is broadly considered safe, of course there are issues with it, in that short of a time frame with such a powerful technology. I think there is no way we could have done that in an ivory tower. This is how I believe you build good, safe, robust, useful technology and products, and I think it's a great learning of Y Combinator. It would have seemed to most of the AI safety people totally impossible to get to this stage and still have the level of safety guarantees we have now. I do think it gets harder from here, but I don't think you're gonna solve it by disconnecting yourself from reality.

为什么从这里开始会更难？

Why does it get harder from here?

因为我们大约和世界上最聪明的人一样聪明，而最聪明的人大约和最聪明的模型一样聪明。而现在这个情况即将反转。

Because we're about as smart as the smartest people in the world are about as smart as the smartest models in the world. And that's going to flip right now.

是的。一个方向。我认为这是对的。我认为模型就是如此不可思议地有能力，并且在如此陡峭的轨迹上改进，以至于未知的未知可能不会相对更难，但从绝对角度来看，它们似乎更难。我认为我们将不得不做出一系列艰难的决定，关于何时推迟开发，何时我们说，好吧，你知道，让我们现在接触现实，或者让我们等更久，真正更深入地研究这个。最近和某人交谈，有一件事让我印象深刻，FAA 帮助让飞行变得极其安全。飞行表面上看起来是极其危险的事情。而你上飞机时可能不会多想。

Yeah. A direction. I think that's right. I think that the models are just so incredibly capable and improving on such a steep trajectory that the unknown unknowns maybe they don't get harder relatively, but from an absolute perspective they seem harder. And I think we'll have to make a bunch of difficult decisions about when we delay development, when we sort of say okay, you know what, let's contact with reality now, or let's wait longer to really study this more. Talking to someone recently, something that stuck in my mind is that the FAA has helped make flying incredibly safe. Flying on the surface seems like this extremely dangerous thing. And you probably get on an airplane without giving it much thought.

而且这当然是，你知道，在人类历史的漫长轨迹中，飞机并不算古老。而在飞机诞生之初，情况当然不是这样。他们有极其健全的事故报告机制，极其清醒。他们从不会试图，比如，你知道，对某件事敷衍了事。他们想尽可能多地提取信息。在某种意义上，我认为对于任何新技术，这种方法都非常有效，而且常常被低估。

And this was certainly, you know, airplanes are not that old in the long trajectory of human history. And this was certainly not the case at the beginning of airplanes. They have extremely robust accident reporting, extremely cleareyed. They never try to, like, you know, handwave over something. They want to extract as much information as possible. And in some sense I think with any new technology an approach like that works very well and is often underappreciated.

所以当我们开始部署我们的模型，当我们说我们要把 ChatGPT 推向世界时，我们知道模型并不完美。我们知道它会幻觉，我们知道它还能做其他这些事，但我们也知道世界必须体验这项技术。我们必须学会如何让它安全。我们必须把权力交到人们手中。我们不能仅仅用它来强加我们的世界观。我们不能用它去坐在实验室里，试图把所有影响都想清楚，反正这也不会奏效，因为社会和模型将共同进化。我们必须一起做这件事，作为这个共同的产品。

So when we started deploying our models, when we said we're going to put ChatGPT in the world, we know the model's imperfect. We know it hallucinates, we know it can do these other things, but we also know that the world's got to experience this technology. We've got to learn how to make it safe. And we got to put the power in people's hands. We cannot just use this to impose our worldview. We cannot use this to go sit in a lab and try to think through all the impacts, which won't work anyway because society and the models are going to co-evolve. Like we have to all do this together as this joint product.

然后我们会做非常好的事故记录。当出现问题时，我们会进行研究。我们会发布非常清晰的复盘报告。我们会尽可能多地学习。我们不仅会改进自己的技术和产品，还会努力与其他人分享这些经验，让他们也能构建 AI。我认为到目前为止，这效果出奇地好。

And then we'll do very good accident recording. We will study when something goes wrong. We will put out a very clear postmortem. We will learn as much as we can. We will not only improve our own technology and products, but we'll try to share those learnings with other people building AI. I think that's worked surprisingly well so far.

而那是技术史上的好例子，也是初创公司的好例子。Deel 是最好的创始人如何将世界变成他们人才库的方式。十年来，我一直在研究历史上最伟大的创始人如何运作。他们都有一个共同点，那就是他们明白招聘和雇佣最优秀的人才才是你最重要的优先事项。A 级人才会识别其他 A 级人才，这就是为什么像 Ramp、Shopify、11 Labs、Uber 和 DoorDash 这样的顶级公司都使用 Deel。我认识的许多顶级创始人都在使用他们的产品后亲自投资了 Deel，他们发现 Deel 是世界上构建全球招聘基础设施最好的公司。Deel 将帮助你的企业在全球任何地方雇佣、支付和管理任何员工。这样你就可以在任何地方留住最优秀的人才，并把剩余时间花在你最擅长的事情上，为客户创造价值。

And that was like good examples from the history of technology, good examples from startups. Deel is how the best founders turn the world into their talent pool. I've been studying how history's greatest founders operate for a decade. And one thing they all have in common is they understand that recruiting and hiring the very best talent is your most important priority. A players recognize other A players, which is why top companies like Ramp, Shopify, 11 Labs, Uber, and DoorDash all use Deel. Many of the top founders I know have personally invested in Deel after using their product, and what they discovered is that Deel is the best company in the world at building infrastructure for global hiring. Deel will help your business hire, pay, and manage any worker anywhere in the world. So you can retain the best talent anywhere and spend the rest of your time focusing on what you do best, delivering value to your customers.

11 Labs 的创始人对 Deel 能为你的公司带来的价值有一个很好的描述。他说：“我们建立 11 Labs 是为了打破语言和沟通障碍。借助 Deel，我们可以在全球任何地方雇佣和支持杰出人才，从而加速创新，将更多声音、故事和想法带到世界的每个角落。”超过 40,000 家企业信任 Deel。今天就访问 deel.com/enra，了解他们如何帮助你的企业。那就是 deel.com/enra。

The founder of 11 Labs has a great description of the value Deel can give your company. He said, "We built 11 Labs to break down language and communication barriers. With Deel enabling us to hire and support exceptional talent anywhere, we can accelerate our innovation and bring more voices, stories, and ideas to every corner of the world." Deel is trusted by over 40,000 businesses. Learn how they can help your business today by going to deel.com/enra. That is deel.com/enra.

有一件事一定让你感到困惑，因为对我来说这毫无道理。你知道，我主要关注的只是创业者和创业精神，对吧？所以我做的所有播客都是为了创业者的利益。我很高兴其他人也听，但它非常专注于试图找到有用的信息，无论是关于一位已故创业者的传记，还是与像你这样的人交谈，让其他创业者能从这次对话中受益，对吧？或者我做的任何播客。所以在这方面，并不是有很多创业者热爱 AI，但我想弄清楚的是，如果你能帮我调和一下：每个人都在用 AI，每个人都讨厌 AI。这到底是怎么回事？

One thing that has to be disorienting for you, because it doesn't make any sense to me, like you know essentially what I focus on is just entrepreneurs and entrepreneurship, right? So all the podcasts I make are for the benefit of entrepreneurs. I'm glad other people listen, but it's like heavily focused on just trying to find useful information, whether it's in a biography of a dead entrepreneur or talking to somebody like you, that other entrepreneurs can benefit from this conversation, right? Or any of the podcasts that I make. So in that, there's not like a lot of entrepreneurs love AI, but like what I'm trying to figure out, like if you can help me reconcile, is everybody uses AI. Everybody hates AI. What the hell is going on there?

嗯，人们总是害怕快速的社会经济变革。之前谈到过工业革命。我也喜欢阅读以前的技术革命。就像人们对于工业革命期间发生的变化，并没有普遍温暖和模糊的感觉。我认为人类社会有一个很好的特点，就是我们有一些内在的惯性。我们对快速变化有一些怀疑。我认为这在动荡时期，或者你知道，局部疯狂的时候，可能对社会有帮助。所以其中一些可能是好的，而且我认为这是人类生物学的一个特点，我相信不要试图与之对抗得太厉害。

Well, people are always afraid of like rapid socioeconomic change. Talked about the industrial revolution earlier. I love reading about previous technological revolutions too. And like people did not have universally warm and fuzzy feelings to the change that was happening during the throughout the industrial revolution. I think it's probably a good feature of human society that we have some built-in inertia. We have some skepticism of rapid change. I think that probably helps society in times of turmoil or in times of, you know, localized craziness or whatever. So some of it is probably good and I think a feature of human biology which I believe in never trying to fight too hard.

我还认为很多构建 AI 的人，你知道，一直在说我们有 25% 的可能性会毁灭世界，然后我们还要竞相去做，因为否则那些坏人会先做，或者你知道，就像“天哪，这东西会非常糟糕，明年 50% 的工作会消失，我们希望你们都还好，但这看起来真的很可怕”。就像我们作为一个领域，没有很好地解释给人们听，好处是什么，坏处如何能被缓解，而且我们肯定没有做好。即使人们有答案，比如你知道，说会有全民基本收入，或者工作将是可选的，或者其他什么，但人们很少讨论如何以及为什么重要的是，人们在世界上拥有更多的权力和个人自由，而不是更少，这对大多数人来说非常重要。人们影响自己未来的能力，以及集体设计社会走向的能力，以及随之而来的自主权，非常重要。而且我认为 AI 领域的很多人，他们自己感受到了这一点，但他们没有花太多时间去思考、反思或承认这对其他人有多重要。

I also think a lot of the people building AI, you know, have been off saying there's a 25% chance we're going to destroy the world and yeah we're going to race ahead to do it because otherwise those bad guys will do it first, or you know it's like man this thing is going to be really terrible and there's going to be 50% of the jobs are going to go away in the next year and we hope you all are okay but seems really scary. Like we have not as a field done a very good job of explaining to people what the benefits are and how the downsides can be mitigated, and we certainly have not done a good job. Even if people have had answers like you know saying well there's going to be universal basic income or work will be optional or whatever, there's been very little discussion from people about how and why it's important that people have more power and personal freedom in the world not less, and that matters a lot to most people. The ability of people to influence their own future and collectively to design kind of where society is going to go and the autonomy that comes with that, very important. And I don't think a lot of people in the AI field, they feel it for themselves, but they don't spend much time thinking about or reflecting on or acknowledging how important that is to other people.

所以回到那个对销售说辞的描述，我甚至不知道那是不是一个词。

And so to go back to that like characterization of the sales pitch, I don't even know if that's a word from earlier.

是的。

It is.

就像“亲爱的农民们，我们将赐予你们这些礼物，治愈癌症、物质财富和一些东西，还有你知道的，伟大的娱乐，你们别抱怨了，我们会做出关于未来的所有决定，只要相信我们，你知道，我们会是仁慈的独裁者。”不好。不好。

The like "dear peasants, we will bequeath upon you these gifts of a cure for cancer and material wealth and some things and you know great entertainment, and you stop complaining and we'll make all the decisions about the future and just trust us, you know we'll be benevolent dictators." Not good. Not good.

作为一个热爱创业者的人，并且有点像研究是什么让近几个世纪以来这个不可思议的经济奇迹发挥作用的学生，真正赋予人们去做新事情、去推动他们相信的事情、去拥有创造公司和发明技术以及追求想法的自由，以及围绕这一切使其发生的系统，没有什么比这更让我坚信的了。而且我认为即使人们从不把自己视为创业者，即使他们可能永远不想创办大公司，他们也明白这有多重要。然后当你听到人们含蓄或明确地说，有了 AI，这种情况会减少，因为一小部分人将拥有权力，但他们会做出伟大的决定并保证每个人的安全，我认为这对他们来说非常可怕。

As a lover of entrepreneurs and sort of like a student of what has made this incredible economic miracle of recent centuries work, really empowering people to go do new stuff and to push on the things they believe in and to have the freedom to create companies and invent technology and sort of pursue ideas and like the system around that that makes that happen, there is like nothing I believe in more strongly. And I think even if people don't see themselves as an entrepreneur ever, if they may never want to start a big company, they do understand how important that is. And then when you hear people kind of implicitly or explicitly saying there's going to be less of that with AI, cuz a small number of people are going to have the power but they're going to make great decisions and keep everybody safe, I think that's very scary to them.

我还认为，即使大多数人可能不想创办真正的大公司，但很多人想创办小公司，而这一直很难。这需要相当多的特权、运气和资源才能做到，而我们即将看到有史以来最大的小企业创业潮。我认为 AI 正在赋能这一点。现在，出于某种原因，这个领域，包括我们，还没有充分谈论这一点，尽管我们看到了所有这些迹象，这很棒，而且我们还没有建立足够的产品来加速这一进程，但我认为我们会看到更多这样的现象。

I also think that even if maybe most people don't want to start really big companies, a lot of people want to start smaller companies and that has been hard. That has been something that has required a fair amount of privilege and luck and resources to be able to do and we are about to see the greatest boom in people starting smaller businesses that we have ever seen. I think AI is empowering that. Now, for some reason, the field, including us, has not talked about that enough, even though we see all these signs of it, and it's great, and we have not built enough products to accelerate that, but I think we're going to see a lot more of that.

有趣的是，我们之前在对话结尾谈到了 Toby Luke，我想那是在这一集里。他提到了一些我没想过的事情。他说：“哦，是的，你和我做的是同一行。”他说：“我们都在努力创造更多的创业者。他在为创业者建设基础设施。我在为他们制作教育和励志播客。”而结合你刚才说的，疯狂的是，我认为在任何新行业中，AI 行业做得最差，这大概是我见过的最差的。我认为部分原因是你们有能力走出去，谈论你们看到的东西并进行教育。有一本很棒的书叫《英特尔三部曲》。它讲述了英特尔的故事。之所以叫三部曲，是因为三位主要人物是鲍勃·诺伊斯、安迪·格鲁夫和戈登·摩尔。书里有一个我永远不会忘记的精彩故事。他们从发明集成电路到微处理器，他们意识到这项技术太重要了，会吓到潜在客户。所以他们走出去，那三个人停止了手头的工作，开始教育潜在客户、投资者和整个国家。他们说，有一段时间他们开设的课程比当地社区大学的整个课程目录还要多。他们就是这样把它列为头等大事的，比如我们要出去教育人们了解这项新技术。就像为什么 AI 领域没有人这样做？我的意思是，没有借口。我们应该做得更多。

It's funny, we talked about Toby Luke earlier at the end of the conversation, and I think that's in the episode. He mentioned something I didn't think about it. He's like, "Oh, yeah, you and me are in the same business." He's like, "We're both trying to create more entrepreneurs. He's building infrastructure for entrepreneurs. I'm building educational and inspirational podcasts for them." And the crazy thing with what you just said, it's like not only I think out of any new industry, it's not that as an industry, the AI industry, which it's doing the worst job I've probably ever seen. And I think part of it is just the ability you guys have to get out there and talk about the stuff that you're seeing and educate. There's this great book called the Intel Trinity. And it talks the story of Intel. And it's called Trinity because the three main players are Bob Noise, Andy Grove, and Gordon Moore. And when they there's a great story in the book that I never forgot. they go from inventing I think the integrated circuit to the microp processor and they realized that that technology was so important and it would scare their potential customers. So they went out they stopped those three people stopped doing what they're doing and went out and started educating potential customers investors the entire country and they said at one time they were putting on more classes than like the local community college had in their entire course catalog. That's how much they made it top priority like we're going to get out and educate about this new technology. It's just like why isn't anybody in AI doing that? I mean, no excuses. We should be doing more.

我想我们尝试过类似的版本。我们还没有完全做对。

I think we've like tried versions of this. We haven't gotten it quite right.

嗯，你现在就在做。比如，这是我想和你谈的原因之一，因为我一直在用 AI。我觉得它很迷人，但你有这样的视角，让我的视角看起来像蚂蚁的视角。你脑子里有太多东西，我想把它们挖出来，比如，嘿，你有所有这些背景。而且你在发明这项不可思议的技术。我很想知道其他人是怎么使用它的。实际上，在我们谈到其他人之前，我听到你说了一些有趣的话。我觉得这和你刚才说的关于我们在发明技术的话有关。技术正在飞速发展，但采用应该是缓慢而审慎的。我在另一个播客上听到你说：“嘿，我甚至在考虑，我应该让 AI 看到我电脑上的每一件事吗？”你想谈谈这个吗？

Well, you're doing it right now. Like, this is point one of the points of reason I wanted to talk to you about because like I use AI all the time. I think it's fascinating, but you have such like this you have a view that makes mine look like like the the view of an ant. Like you there's so much stuff in your head that I want to like get out and like, hey, you have all this context. It's like and you're inventing this incredible technology. I would love to know how other people are using it. Actually, before we even get to other people, I heard you say something that was interesting. I think ties what you just said about kind of like we're inventing technology. The techn is increasing rapidly, but the adoption should be slow and deliberate. And I heard you on another podcast saying, "Hey, I'm even considering like how much should I let AI see every single thing that's on my computer?" You want to talk about that?

对于最新一代的模型，我不想说它们感觉足够聪明，因为我认为我们应该始终渴望它们变得更聪明，但它们确实相当聪明。而在这一点上，我感觉更受限于 AI 对我的有用上下文的数量。比如，我希望 AI 尽可能多地了解我，以便帮助我。我希望它做那些我自己不能或不想做的事情。比如，我不会去读我们内部 Slack 上的每一条帖子。我不会去读每一个客户关于 ChatGPT 对他们有用或失败的案例。我做不到。然后还有其他事情，比如，你知道，我可能可以读更多的研究论文，但哦，那需要很多脑力，你知道，但我希望有一个 AI 智能体，它不断试图帮助我，能够看到和理解比我个人能看到的、有时间或精力去做的更多上下文，并且能够利用这些上下文，在我需要做决定时给我好的建议。所以我认为我们正确地关注了模型智能，但在产品方面，我们还没有充分思考，给模型提供比任何人都能拥有的更多上下文，并帮助那个人做出重大决策意味着什么。我的感觉是，我们正处于一个临界点，即将看到一种非常不同的与 AI 合作的方式，在这种方式下，人类无法达到这种水平。有很多非常聪明的人，但没有人能在几秒钟内阅读数万页的上下文，并真正准确地利用它。这是 AI 能做到的事情，它将是非常新的，并且是一个不可思议的补充。

With the latest generation of models, I don't want to say they feel smart enough cuz I think we should always aspire for them to get smarter, but they are pretty smart. And I feel more limited at this point by the amount of useful context AI has on me. Like I want the AI to know as much as it can to help me. I want it to be doing things I can't or don't want to do on my own. Like I I'm not going to read every post on our internal Slack. I'm not going to go read every story a customer has to tell about where Chad GBT worked for them or failed them. I can't. And then there's like other stuff of like I just, you know, I probably could read more research papers than I do, but like oh, it's like takes a lot of mental energy and you know, but I would love to have an AI agent that is constantly trying to be helpful to me and that can look at and understand more context than I can or that I have time for or energy for to do on my own and can help bring that context to bear and give me good advice when I have to make a decision. So I think we've focused correctly so much on model intelligence that on the product side we have not yet thought enough about what it means to give a model more context than any person could have and help advise that person on on their big decisions. My sense is we are just on the precipice of being able to see a very different way of working with AI on a on a on an axis where people just can't get this good. There are plenty of like very smart people, but there is no one that can read like, you know, tens of thousands of pages of context in some small number of seconds and really like use that accurately. And this is something that AI can do that just is going to be very new and an incredible supplement.

你读过所有这些传记。可能有些时候你模糊地记得某件事，如果你能记住其中某个具体的轶事，那真的能在某个时刻帮助一个创业者，就在你和他交谈的时候。但也许你忘了，或者你记不太准确。我建立了我自己的 AI 工具。所以我在内部使用它。你知道它是什么吗？它只训练了 2018 年以来的数据。我把每一本书的每一个笔记和重点都保存在这个数据库里，我会搜索它。然后当你们的工作成果出来时，我添加了它，所以我让它训练了这些，然后每一个笔记、每一个重点，以及我所有《创始人》节目的转录稿。我每天都用这个东西来制作每一集。所以我在用 Claude 处理 Shannon，对吧，Shannon 那一集播出大概两三个星期前，不管什么时候，我在问问题，比如，嘿，Bob Noise 对此说了什么，或者 Rockefeller 对此做了什么，我做了那一集，我读了书，我做了笔记，但我不记得了，因为那是大约七年前的事了。这太不可思议了。这就是我的意思。我觉得这太棒了。太酷了。

You've read all these biographies. There are probably times where you vaguely remember something that if you could remember a specific anecdote from one of them, it would like really help an entrepreneur in one moment for that particular entrepreneur right when you were talking to them. But maybe you forgot it or maybe you don't remember it exactly right. I built my own AI tool. So I use it internally. So you know what it is? It's only trained on since 2018. I've kept every single note and highlight from every single book that I've ever used into this database and I would search it for that. And then when your the work that you guys do came out, then I added so I have it trained on that then every note every highlight and then all the transcripts for my episodes of founders. I use this thing every single day to to make every single episode. So I'm working on Claude called Shannon right the call Shannon episode came out I don't know two or three weeks ago whenever it was and I'm asking questions about all I was like hey what did Bob noise say about this or what did Rockefeller do about this and I I made the episode I read the book. I took the note I don't remember because it was like seven years ago. It's incredible. This is what I mean. I was like it's awesome. That is so cool.

这就是我的意思。我觉得这太棒了。太酷了。

This is what I mean. I was like it's awesome. That is so cool.

让我们来谈谈你如何管理公司，对吧？所以你在花时间。你说你的主要焦点是获得更多算力，然后是研究，对吧？好的。

Let's get into like how you think about running the company, right? So you're spending your time. You said your your main focus is getting more compute and then research, right? Okay.

所以你希望模型是世界上最好的，但你如何看待，比如你必须建立自己的产品吗？现在你建立了 Codex，对吧？我甚至不知道产品线，比如所有收入从哪里来？

So you want the models to be the best in the world, but how do you think about like do you have to build your own products? Now you built Codex, right? What I don't even know the the product lines like where's all the revenue coming from?

实际上，我认为我们应该更像一个平台公司，而不是产品公司。

Actually, I think we should be more of a platform company than a product company.

嗯，我们当然会做产品，但你们有多少产品？我们退一步说，你们现在有多少产品？

Um, like we will build products of course, but how many products do you have? Let's back up. How many products do you have now?

我们刚把 ChatGPT 和 Codex 合并了。所以以前我们有 ChatGPT、Codex 和 API。

We just merged ChatGPT and Codex together. So we used to have like ChatGPT, Codex, and the API.

嗯，你知道，Codex 这个名字有点不幸，但它不只是编码。它能做各种工作，这让人们感到困惑。

Um, you know, and Codex sort of unfortunately named, but that was not just coding. It could kind of do any kind of work, which confused people.

我也对此感到困惑。

I'm confused by that.

是的。

Yes.

好的。

Okay.

其他很多人也一样。我认为大多数人想要的是那种单一的界面，连接到他们个人或公司的 AGI（通用人工智能），能帮他们处理任何需要的事情，然后通过 API 在其上构建任何他们想要的东西。这就是我们应该提供给世界的平台。我们要在成本曲线、成本性能曲线的每一个点上出售出色的 AI，我们会是最好的。你知道，你想要高端 AI 去发现科学，那很好。你想要非常便宜的 AI 去做大量的、可能不需要天才级智力的大量工作，我们也覆盖了。而且，你知道，人们谈论不同的方式，新的公用事业、新的商品，随便你怎么称呼，人们想要大量使用 AI，想要低成本、快速、好用、有上下文、流畅，我们都有。然后还有一个单一产品，就是我最终需要问 AI 一些事情。也许 AI 应该主动给我提供东西。但你会拥有这个界面，它最初是聊天机器人，现在也有编码智能体，我认为在某个时候它会感觉像一个更持久的智能体，运行在你需要的任何东西上。但仅此而已。我不认为我们应该去构建每一个产品类别。我不认为我们应该去和所有客户竞争。我不认为我们应该试图吞并整个经济。我认为我们应该提供这个平台，并努力让 1 亿家新企业和 80 亿人以各种新的方式使用它。所以，一个直接的产品界面，一个 API 让人们随意使用。这些最终也会越来越融合。然后一切都取决于人们用它做什么，在其上构建什么，等等。

As were many other people. What I think most people want is the sort of like single interface to their own personal or their company's AGI that can kind of help them with whatever they need, and then the ability with an API to build anything they want on top of it. And that is the platform that we should offer to the world. We're going to sell great AI at every point on the cost curve, cost performance curve, we will be the best. You know, you want really high-end AI to discover science, that's great. You want really inexpensive AI to do like you know a massive amount of volume of work that maybe doesn't require genius level intelligence, we got you covered there too. And you know, thinking about this as a sort of people talk about different ways, a new utility, a new commodity, whatever you want to call it, like people want to use a lot of AI and they want it at a low cost and they want it to be fast and to work well and have their context and be smooth, we got you. And then there's like a single product which is I need to ask the AI something eventually. Maybe it's the AI should proactively offer me things. But you will have this interface which started as a chatbot and now also has coding agents, and I think at some point will feel like a more persistent agent to this AI that is running on whatever you need it to run on. But that's it. I don't think we should go build every product category. I don't think we should like go try to compete with all our customers. I don't think we should try to like subsume the entire economy. I think we should offer this platform and try to have 100 million new businesses and 8 billion people use it in all kinds of new ways. So, one kind of direct interface to the product, one API for people to use it however they want. Those eventually come more and more together too. And then it's all about what people do with it, build on top of it, whatever else.

你犯了什么错误才学到这些？我觉得你也必须扼杀一些好主意，牺牲全力追求伟大的东西。

What mistakes did you make to have to learn that? I feel like you've had to kill some good ideas too, and sacrifice going after the great with your full intensity and focus.

是的，我认为扼杀好主意，比如牺牲好主意去追求伟大的主意，是任何企业家或企业最难学的一课。扼杀好主意很糟糕。无论你认为自己会怎么做，人们，每个人，也许只是因为选择成为企业家的人的天性，似乎在这方面都做得很差。我在这方面很差。我知道我做得不好。但去年，例如，我们扼杀了 Sora，那是一个好产品，有趣又酷，但消耗了大量算力，不如我们把算力放在 Codex 上重要。嗯，我们扼杀了我们的网络浏览器 Atlas。这又是一个我认为很棒的产品，是最好的网络浏览器，但对我们来说，不如把那些人才放在其他地方重要。在一个算力有限、人才有限、资源有限的世界里，我们认真思考，然后说，你知道吗，用于知识工作、最终用于科学的通用智能是我们能做的最重要的事情。任何为了生成这种智能而上游的事情，构建我们自己的芯片、构建我们自己的数据中心、编写良好的基础设施软件、当然还有训练模型，显然这些都非常重要，但然后我们就提供这种 AI 即服务，让人们把它用于智力追求、工作、科学发现，让他们的个人生活更高效。让我们拥有这种灵活通用的平台，而不是做很多其他事情。

Yeah, I think killing the good ideas, like sacrificing the good ideas to go after the great ideas, is kind of the hardest lesson for any entrepreneur or business to learn. It sucks to kill good ideas. And no matter how much you think you're going to do it, you people, everyone, like kind of maybe just by the nature of who chooses to be an entrepreneur, seems to do terrible at this. I'm terrible at this. I know I'm bad at this. But last year, for example, we killed Sora, which was a good product and fun and cool, but used a lot of compute and not as important as Codex where we put the compute. Um, we killed our web browser called Atlas. Which again, I think it was a great product and it was the best web browser, but not as important for us to focus on as somewhere else we could put that talent. In a world of limited compute, limited people, limited resources, we thought really hard and we said, you know what, the general intelligence for knowledge work and eventually for science is the most important thing we can do. Anything that goes into making that upstream of generating that intelligence, building our own chip, building our own data centers, writing good infrastructure software, certainly training models, obviously that's all really important, but then let's just offer this AI as a service and get people to use it for intellectual pursuit, for work, for scientific discovery, to be more productive in their personal life. And let's have the kind of flexible general platform and not do a lot of other things.

任何从事复杂工作的人，而且你现在肯定是活着的人里最顶尖的，都需要有人帮助整理思路，对吧？这非常有益。你在每一本传记里都能看到。你在历史中也能看到，你需要有人交谈。实际上有一个有趣的故事，说明这可以有多极端。查理·芒格有一个叫“猩猩理论”的东西。你听说过吗？他说一个相对聪明的人可以进去，和猩猩坐在一起，告诉它所有的问题，告诉它脑子里的一切，然后猩猩显然什么也不说。人离开后，状态更好了。仅仅是被迫把自己的想法整理成某种结构。现在，显然有了一个非常聪明的伙伴，芒格为巴菲特扮演了这个角色。巴菲特是有史以来最聪明的人之一。有史以来最伟大的投资者。仍然需要向别人整理他的想法。你正在经历，我想不出，你几乎有一种独特的生活经历，尤其是像你这么年轻的人。所以我很好奇，谁在你的生活中扮演这个角色？你会去找谁，谁能哪怕稍微理解你每天到底在应对什么？

Anybody engaged in complicated work, and you've got to be the top of the list of anybody alive right now, needs somebody to help organize their thoughts, right? It's extremely beneficial. You see this in every single biography. You see this in history, like you need somebody to talk to. There's actually a funny story of how extreme this can be. Charlie Munger has a thing called the orangutan theory. You've ever heard of this? Where he said a relatively smart human could go in, sit down with the orangutan, tell him all his problems, tell him everything on his mind, and then the orangutan obviously says nothing about nothing else. The human leaves and the human's better off. Just the idea of being forced to put your thoughts into some kind of structure. Now, obviously with a very intelligent partner, Munger played this role for Buffett. Buffett's one of the most intelligent people ever lived. Greatest investor of all time. Still needed to organize his thoughts to somebody else. You are going through, I can't think, you have almost like a singular lived experience, especially for somebody as young as you are. So I'm curious, like who plays this role in your life? Like who do you go to that can even remotely empathize with what the hell you're dealing with on a day-to-day basis?

这里大概有三类。第一，很多一直在这里的研究人员，我们基本上一起经历过一切，我们发展出了一套共享的语言、直觉、标准，随便你怎么称呼，这是我无法在公司外部与任何人复制的。当涉及到正在发生的事情的形态、接下来可能发生什么、技术可能走向何方，以及关于商业和世界的问题时，在我职业生涯的很长一段时间里，保罗·格雷厄姆和彼得·蒂尔是我从中学到最多的两个人，涉及我职业生涯的许多不同阶段，而且如果我真的有一个非常不明显的难题卡住了，我仍然会去找他们。而且我找了很久，没有找到其他人有同样那种超级非线性思考的能力。就像，你知道，如果大语言模型做的是预测下一个词，那两个人是我最无法预测下一个词会是什么的人。那是一种超级宝贵的技能。你带着“哦，天哪，我真的卡住了，我已经想遍了所有选项”去，然后有人能告诉你，“我认为那些选项都不好。这里有这个你没想到的东西，现在看起来完全明显且正确”，一个你在别处从未听过的完全不同的观点。

Kind of three categories here. One, a lot of the researchers that have been here forever, we've kind of all been through it together, and we've developed this set of shared language, intuition, standards, whatever you want to call it, and that I have not been able to replicate with anybody outside of the company. When it comes to the shape of what's happening and what might happen next and where the technology is likely to go, in terms of questions of just like business and the world, for a long time in my career, Paul Graham and Peter Thiel have been two of the people that I have learned the most from about lots of different phases of my career, and are still the two people that I go to if I really have like a very non-obvious problem that I'm stuck on. And there I have not found anyone else after a lot of looking that has the same kind of ability to just think in a super nonlinear way. Like, you know, if what LLMs do are predicting what word comes next, those are two of the people that I can predict the least what word is going to come next. And that is a super valuable skill. You go with like, oh man, I feel really stuck and I've kind of thought through all these options, and someone that can tell you like, I think none of those options are good. Here's this thing that now seems totally obvious and correct that you didn't think of, just a completely different view that you haven't heard anywhere else.

这更像是给你自己思考的一个提示，而不是明确的建议，比如“去做 X”之类的？

Is this more like a prompt for your own thinking as opposed to explicit advice, like 'do X' for example?

通常是一个具体的事情。

It's often like here is a specific thing.

真的吗？

Really?

是的。

Yeah.

那你能分享一个来自 Peter 的例子吗？我觉得 Peter 非常有意思。

So what would be an example that you could share from Peter? Peter's very fascinating to me.

他确实非常有意思，而且他正是我想到的人。我刚才想到这个问题，是因为你提到“我们为了伟大的想法不得不杀掉这些好主意。我们在砍掉 Atlas。我们在算力上投入。我们必须聚焦、聚焦、再聚焦。”当你听他谈论专注的重要性时，这一点非常明显。如果你有某个东西在起作用，让它变得更好，沿着这条线走下去，从中抽出一个小时去探索别的东西，代价太高了。你应该在已经有效的事情上更深入。极端之处有大量价值。

He is very fascinating, and he's actually who I thought of. The reason I thought of this question just now is because you're like, we had to kill these good ideas for the great. We're cutting Atlas. We're computed. We have to focus, focus, focus. That's something that is very obvious when you listen to him talk about the importance of focus. And if you have something that's working, making it work better and going down this line, taking an hour away from that to explore something else is too expensive. You should just go deeper on what's already working. There's a lot of value at the extremes.

我们推出 ChatGPT 之后，情况有点奇怪，因为人们不太知道拿它来干什么，但它增长得非常快，却感觉很不稳定，甚至有点像低价值增长。人们用它只是因为对跟它聊天感兴趣，以及它能做什么。所以公司里有很多人说：“这，你知道，我们得想点别的。这不是可持续的价值。”我记得跟他讨论过一份清单，上面有五六个我们可以转而专注的其他事情。那大概是在 ChatGPT 发布两个月后。他说：“除了它在增长这个事实之外，做任何其他事情都是明显的错误，而增长是罕见且伟大的。”当时它还没有后来增长得那么快。他说：“它的力量就是 Google 搜索框的力量。就像一个文本框，你可以在里面输入任何东西，它就会做正确的事。”而它不符合当前硅谷的智慧，比如你得有信息流，你得有网络效应，你得有——因为我们这些都没有，所以大家才担心。你知道，你得有一种方式让人们积累更多。那是在我们有记忆功能之前。人们会积累更多上下文。人们会被锁定吗？人们会有所有——他说：“那些东西，人们追逐 Google 的商业模式已经 20 年了，这是第一个出现的东西，而且显然空文本框对 Google 有效，那你为什么不直接加倍下注呢？它在增长，非常灵活，它有所有迹象，只是不符合当前硅谷的智慧。”我说：“好吧。”然后我们就全力投入，结果非常好。

After we launched ChatGPT, it was sort of this weird thing because people didn't really know what to use it for, and it was growing super fast, but it felt like very unstable or kind of almost like low-value growth. People were using it just because they were interested in talking to it and what they could do. So there were a lot of people in the company who were like, 'This is, you know, we got to figure out something else. This is not sustainable value.' And I remember talking to him about this list of five or six other things that we could focus on instead. This is maybe two months after ChatGPT launch, something like that. And he was like, 'It's an obvious mistake to do anything about this besides the fact that it's growing, which is rare and great.' It was not growing as fast then as it did start after. He's like, 'The power of this is the power of the Google text box. It's like a text box you can type anything into and it does the right thing.' And the fact that it doesn't match the current Silicon Valley wisdom of, you know, you got to have feeds and you have to have a network effect and you have to have—because we had none of these things, and that's why everyone was worried. You know, you have to have a way that people are going to build up more. This is before we had memory. People are going to build up more context. Are people going to get locked in? Are people going to have all the—he's like, 'All that stuff, people have just been chasing the Google business model for 20 years, and this is the first thing that's come up, and you know, clearly the empty text box worked for Google, so why don't you just double down on that? It's growing, it's very flexible, and it has all of the signs other than it doesn't fit the current Silicon Valley wisdom.' And I was like, 'Okay,' and so we went super hard on it, and it was great.

他刚才说的就像一种简单的天才。有时候会有更复杂的东西，但这是一个非常重要的简单天才的例子。

He's like a simple genius to what he just said. Sometimes there's like more complexness, but that was an example of very important simple genius.

那 Paul Graham 有没有给过你什么建议、指导，或者他推动你朝某个方向走？

What about some advice that Paul Graham or some guidance or a direction he kind of pushed you in?

你刚才说的这个，对很多 YC 创始人来说就像个梗。你去参加他的办公时间，他会说：“你知道你该怎么做吗？”然后他会这样摇手指。“你知道你该怎么做吗？你知道你该怎么做。”有时候后面跟着的话很棒，有时候很糟糕。但重要的是，那里面有一种创造力和开放的视野，就是“让我们尝试很多东西”。我们谈过迭代部署的精神，也谈过，就像创业公司一样——我认为他真的把创业生态系统推向了这样一个世界：“你得发布一个早得令人尴尬的 v1，它能不能更好并不重要；你会因为客户的反馈而把它做得更好。”我甚至不觉得我们在发布前问过他：“嘿，你觉得我们该发布这个东西吗？”但我知道他会怎么说。我知道那还早，我知道那还很尴尬，我知道正确的事情是把它推出去，放到人们面前。

When you just said that, this is like a meme for many YC founders, where you would go see him for office hours and he would say, 'You know what you should do?' and he would shake his finger like this. 'You know what you should do? You know what you should do.' And sometimes the thing that came after that was great. Sometimes the thing that came after that was terrible. But the important thing was there was a kind of creativity and open landscape and just a 'let's try a lot of things.' We talked about the spirit of iterative deployment, and we talked about how, in the same way startups—he really, I think, pushed the startup ecosystem into this world of 'you got to ship a v1 that's embarrassingly early, and it doesn't matter if it could be much better; you'll get it much better because of the feedback from customers.' I don't even think I asked him before we launched, like, 'Hey, do you think we should launch this thing?' but I knew what he would say. I knew it was still early. I knew it was still embarrassing. And I knew the right thing was to get it out and get it in front of people.

等等，你对 Paul Graham 的心智模型如此完整，以至于在这种情况下你甚至不用问他。

So, wait. Your mental model of Paul Graham is so complete you don't even have to ask him in this one case.

这就是那种你可以说有确定性的情况。

That's the one where you would say there's certainty.

我给你讲个有趣的事。就在他去世前，去世前几个月，我去查理·芒格家和他吃了晚饭。我问：“你多久和巴菲特谈一次？”他说：“从不。”我说：“什么？”他说：“我们以前每天谈好几个小时。巴菲特可以假装拿起电话打给我，而他已经知道我要说什么了。”这显然是 65 年紧密合作之后的结果，但我觉得这太搞笑了。

Let me tell you something funny. Right before he died, a few months before he died, I went to Charlie Munger's house and had dinner with him. And I was like, 'How often do you talk to Buffett?' He goes, 'Never.' I go, 'What?' He goes, 'We talked every day for hours and hours. Buffett can just pretend to pick up the phone to call me, and he already knows what I'm going to say.' That obviously comes after 65 years of working closely together, but I thought it was hilarious.

太搞笑了。这真是个有趣的故事。

That is hilarious. That is really a funny story.

不，有很多次我无法预测他会说什么，这也是我认为它有价值的原因。但就发布一个让你尴尬的产品而言，我知道他会说什么。那一条——我不会说它是 YC 最有价值的战术建议，但它绝对名列前茅。回顾这些年来我在 YC 创始人身上看到的所有数据点，我惊讶于快速行动和迭代的能力与成功的相关性有多高。

No, there are many times that I couldn't predict what he's going to say, which is why I think it's valuable. But in terms of the launch when you're embarrassed of a product, I know what he's going to say there. That one has been—I won't say the most valuable piece of tactical YC advice, but it's been up there. I'm astonished looking back at all of my data points at YC founders over the years how much the ability to move fast and be iterative correlates with success.

好吧，你在这次对话中提到 YC 的次数太多了。我得探讨一下这个，因为我们之前聊过。听着，我不是记者，我是个爱好者。我没有问题清单。我面前坐着一位世界级的创始人。我想知道这个人脑子里到底在想什么，我想自私地为自己提取信息。所以为什么——我真的很震惊你在对话中这么频繁地提到它，经历 YC 然后运营 YC，与他们有联系，显然对你的生活影响巨大。你能详细说说吗？有某个乐队，他们并不那么成功，专辑卖得不多，但他们影响了后来所有的音乐人。

Okay, you've mentioned YC way too many times in this conversation. I have to explore this because we talked before. It's like, listen, I'm not a journalist. I'm an enthusiast. I don't have a list of questions. I have a world-class founder across from me. I want to know what the hell is in this person's mind, and I want to extract information out selfishly for me. So why—like I'm just shocked at how much you reference it in conversations, how impactful going through YC and then running YC, being affiliated with them, clearly has been on your life. Can you expound on this? There is some band that wasn't that successful. They didn't sell that many albums, but they influenced all of the musicians that came after.

我想那乐队叫“地下丝绒”吧。

I think it's called the band.

确实是，Rick Rubin 讲过这个故事。我想可能是地下丝绒乐队，但你明白我的意思，不管是不是——

Literally, Rick Rubin told this story. I think it might be the Velvet Underground, but you know the idea I'm getting at, whether it's—

用这种方式谈论 YC 其实不公平，因为按传统指标衡量，比如创造的市值之类的，YC 是少数几家最有价值的科技公司之一。但 YC 对过去 20 年科技行业、创业公司、创业精神等一切事物的全面影响程度，我认为只是被部分理解。OpenAI 就是一个例子。

It's not fair to talk about YC in this way, because YC measured by traditional metrics like market cap created or whatever is one of the handful of most valuable tech companies. But the degree to which YC totally influenced everything that has happened in the last 20 years of the tech industry and startups, entrepreneurship, whatever, I think is only sort of understood. OpenAI is an example of that.

不只是我们从产品发布的方式中学到的，还有我们运营研究实验室的理念。我觉得如果你去和这一代大型科技公司的其他负责人聊聊，即使他们没经历过 YC，也会告诉你类似的故事。

Not just from how we've shipped our products in the world, but the philosophy of how we run our research lab. I think if you talk to many other people running this generation of large tech companies, they'd tell you similar stories, even if they didn't go through YC.

但那里到底发生了什么？是 YC 给你的一套操作系统——比如你听到“做这五件事”之类的——还是更像一种建立公司的哲学？作为局外人，这是让我困惑的地方。

But what's happening there? Is it an operating system that YC is giving you—like, you hear 'do these five things' or whatever—or is it more of a philosophy of building companies? This is the confusing part for me as an outsider.

我认为是两件大事。确实有一些“该做什么”的操作系统，但我认为更重要的是如何运营公司的哲学。迭代部署的理念、技术人员掌权、愿意在公司各个层级押注那些精力充沛、雄心勃勃但经验较少的年轻人。然后还有整个科技生态系统的相关变化。所以如果我们把时钟拨回 2004 年，然后把技术投射到 2016 年，但其他关于创业生态系统的形态——成为创业者意味着什么、资本如何流动、谁能经营公司——都不变，我不认为 OpenAI 会可能实现。我认为 YC 在整个生态系统中引发的变革——创始人获得更多杠杆、年轻的技术创始人能够筹集大量资本、在没有非常成熟的履历的情况下能够从事雄心勃勃的项目——我不认为 OpenAI 会可能实现。

I think it's two major things. There is some of the operating system of what to do, but I think it was the philosophy of how to run companies. The idea of iterative deployment, technical people in charge, and being willing to bet on young people with a lot of energy and ambition but maybe less experience throughout all levels of the company. And then it was also the related change to the whole ecosystem that happened in the tech ecosystem. So if we ran the clock back to 2004 and then projected technology forward to 2016, but not anything else about the shape of the startup ecosystem—what it meant to be an entrepreneur, how capital flowed, who got to run companies—I do not think OpenAI would have been possible. I think the changes that YC induced in the whole ecosystem—more leverage going to founders, young technical founders having the ability to raise lots of capital, the ability to work on ambitious things without a very proven resume—I don't think OpenAI would have been possible.

所以这算是很大的变革。这一切是否与你认为从创始人到投资人再回到创始人这段长期经历有益有关？

So this is kind of like a big change. Is this all tied to the fact that you think there was a benefit in you going from founder to investor for a long period of time back to founder?

这些好处确实都存在，但我不会说我真正经历了从创始人到投资人再到创始人的过程，因为我第一次当创始人时并不太成功。

There are all those benefits too, and I wouldn't say I really went from founder to investor to founder, because the first time I was a founder didn't really work out that well.

一家公司，你从失败中学到了一些教训，但我认为你从成功中学到的更多。

A company you learned some lessons from failure, but I think you learned way more from success.

哦，你得等等——我们不能就这么跳过。你得再多说点。

Oh, you got to hold on—we're not moving on from that. You got to say more about that.

有句话——我不敢相信，我觉得是某部伟大的俄罗斯小说。我很尴尬不知道这个。开头是“所有不幸的家庭各有各的不幸，所有幸福的家庭都是相似的。”

There's some—I can't believe I think it's some great Russian novel. I'm very embarrassed not to know this. Starts with like 'all unhappy families are unhappy in their own way. All happy families are the same.'

是的。这就解释了为什么它会跳进我的脑海。

Yeah. That explains why it jumped into my mind.

但我认为这真的很对。比如，当我回顾自己失败的地方时，我学到了一些关于毅力和决心的泛泛之谈，以及一些不该做的事。但大多数事情都不成功，所以失败的原因有很多。而且很难把正确的因果关系拼凑起来。而当我真正成功的时候，当我理解 Y Combinator 哪些部分真正有效，或者 OpenAI 哪些部分真正有效时，尝试将这些经验应用到未来，比尝试应用那些“反经验”要有用得多。所以，你当然应该从每个数据点尽可能多学习。从失败中学习，从成功中学习。但以我自己的经验，当我尝试应用这些经验时，从成功中学到的经验非常好，我应该更多地应用它们。而从失败中学到的经验要么相当泛泛，我基本已经知道了，要么会妨碍其他事情。我认为这对很多人来说都是普遍正确的。

But I think this is really true. Like, when I look at the lessons of where I have failed at something, I learned something generic about grit and determination and something not to do. But most things don't work, so there are a lot of reasons why things don't work. And it's harder to put together the correct causation. And when I've had something really work, when I understand what parts of Y Combinator really worked or what parts of OpenAI really worked, trying to apply those lessons going forward has been much more helpful to me than trying to apply the anti-lessons of what didn't work. So you should, of course, learn as much as you can from every data point. Learn from the failures, learn from the successes. But in my own experience, when I have tried to apply those lessons, the lessons I learned from success were very good and I should have applied those more. And the lessons I learned from failure were either fairly generic and I kind of already knew them, or got in the way of something else. And I think this is generally true for a lot of people.

是的。但难道不是我们已经知道该做什么或该避免什么，但需要提醒，不断的提醒。所以我对我的另一个播客《Founders》最好的描述是，它就像企业家的教堂。如果你仔细想想，我以前周日都会去，我应该恢复这个习惯。但这真的只是在重复同样的事情——同样的性格类型在历史上反复出现。只是现在这个人碰巧在造船，那个人在搞技术，但他们生活在不同的时代。同样的性格。这是肯定的。我有点痴迷于“长久存在的事物”这个想法。比如，公司——最好的公司可以存在很久，但不如城市久。而城市和国家的存在时间不如宗教。所以我在想，在所有人为创造的事物中，什么存在得更久？我想不出除了宗教之外还有什么。所以我开始研究。我从小——我妈妈是原教旨主义基督徒，所以我被迫一生都去教堂——然后我开始分析，世界上所有主要宗教有什么共同点？比如，哦，我们有一个共享的知识基础，通常是某种书，对吧？我们定期与志同道合的信徒聚会。

Yeah. But isn't it like we already kind of know what we should do or should avoid, but it's the reminder, the constant reminder. So the best description of my other podcast, Founders, I ever heard is like it's church for entrepreneurs. If you really think about it, I used to drop it on Sundays and I should go back to doing that. But it's really just telling the same—it's the same personality type that has appeared throughout history. It's just like now this person happens to be building ships and this person built technology, but they live in different times. Same personality. That's for sure. I'm kind of obsessed with this idea of things that last for a long period of time. And like, you know, companies—the best companies can last a long time, but not as long as cities. And cities don't last—cities and countries don't last as long as religions. And I'm like, so out of all the man-made things, what has lasted longer? I would say I can't think of anything other than religion might be another. So then I start studying. I grew up—my mom was a fundamentalist Christian, so I was forced to go to church my entire life—and I just start analyzing like, what do all the main religions in the world have in common? It's like, oh, we have a shared base of knowledge, usually some kind of book, right? We meet with like-minded fellow believers at regular intervals.

而且不是说我周日去教堂，然后说“好吧，我们上周讨论了耶稣，但这周聊聊另一个人”。不是的，我们一遍又一遍地回到同样的书和同样的故事。

And it's not like I go to church on Sunday. It's like, okay, we talked about Jesus last week, but let's talk about this other guy. It's like, no, we go back to these same books and the same stories over and over again.

所以我读了你的博客，你甚至提到了 YC 结束时的情况。就像你在重复同样的事情，你一直在告诉他们，然后他们离开了教堂，你知道，用这个类比，然后他们就不再做同样的事了。这甚至不是教训，而是不断提醒“这很重要”。

So I read your blog and you even said something about when YC ended. It's like you're repeating the same thing, you're telling it to them all the time, and then they leave the church, you know, to use this analogy, and then they stop doing the same stuff. It's not even the lessons. It's like the constant reminder that this is important.

我非常强烈地同意这一点。但我认为最好是提醒那些积极的事情——比如多和用户交流，更早发布产品，获得更多反馈，对招聘和录用的人设定更高的标准，并且更快。但我觉得积极的事情才是好的。

I extremely strongly agree with that. But I think it is better to be reminded of the thing—like talk to your users more, ship products earlier, get more feedback, hold a higher bar for who you recruit and who you hire, and more quickly. But it's the positives that I think are good.

你有一条最棒的推文，我经常对着手机说。你就像在说，跳过会议、晚宴和其他一切。本质上就是做产品或者卖产品。如果你不在做产品，你就在卖产品。这就是你真正需要做的全部。我觉得这又回到了那种简单的天才。所以这是我发现最迷人的另一部分，因为昨天有人问我，他们说，你通常对每个我遇到的创始人都有一个历史类比，比如“哦，那家伙有点像范德比尔特，那家伙像洛克菲勒”之类的。然后我问，山姆的历史类比是什么？我说，没有，我想不出来，因为我还不够了解他，我还不理解他的思维方式。

You have one of the greatest tweets I say to my phone. You're like, you know, skip the conferences, the dinners, everything else. Just essentially make the product or sell the product. If you're not making it, you're not selling it. Like that's all you actually have to do. And I think it's like again it goes back to that simple genius. So then this is the other part that I find most fascinating, because somebody asked me yesterday, they're like, what's your—usually there's some kind of historical equivalent for every founder I meet, like I can say 'oh that guy's kind of like Vanderbilt, that guy's like Rockefeller' or any of these people. And I was like, what's your historical equivalent for Sam? I was like, there isn't, I can't think of one, because I don't know him well enough, like I don't understand how he thinks yet.

那现在你怎么看？

What do you think now?

嗯，这希望是八次对话中的第一次。

Well, this is the first of hopefully eight conversations.

所以我在第七次对话里会讲，但这非常罕见。我刚刚和 Doug Leone 聊过，他提到他雇的一个人是 New Bank 的创始人，他曾经是合伙人兼 VC，然后离开并创立了最成功的公司之一。我说我从没听说过。Doug，你听说过吗？他一生都致力于此。他说，不，那是唯一一个。所以再说一次，非常罕见。大多数人从创始人卖掉他们的生意，不幸的是，然后成为投资人，而不是像我更喜欢的那样一直经营到死。我好奇的是，你刚才说我从成功中学到的更多，对吧？嗯，是成功，因为你接触了那十年或十五年里的一万家公司，你显然看到了最好的那六家或十二家。所以你会把他们的成功也当作有教育意义的吗？

So I'll tell you on conversation 7, but this is very rare. I just talked to Doug Leone and he talked about one dude that he hired is the founder of New Bank and he was a like an associate and VC and then leaves and founds one of the most successful companies. I'm like I've never heard of that. Doug, have you? And he dedicated his life to this. He goes, no, that's the only one. So again, very rare. Mostly people go from founder sell their business unfortunately and then investor as opposed to run the business till you die which is my preferred method of things. What I'm curious about this is like when you just said I learned more from successes right? Well it's the successes cuz you were exposed to what 10,000 different companies in that decade or decade and a half that you were doing this and you saw obviously maybe the half a dozen or the dozen were the best in the world. So like are you taking their successes as well as like instructive?

不，不，完全不是。我觉得人们确实会这样。我的意思是，你是个了不起的学生，但有很多相当好的创业学学生，我认为人们常常试图去寻找那些真正有效的东西的经验，正如你所说，这有点像在不同行业里重复同样的事情，但你需要经常被提醒，而且它并不光鲜。

No, no, totally. And I think people do. I mean, you're an incredible student, but there's a lot of pretty good students of entrepreneurialism and entrepreneurism, and people I think often try to go look for those lessons of the things that really worked, and as you said, it's kind of the same thing over and over again, like done in different industries, but you have to be reminded of it a lot, and it's unglamorous.

我在进入这次对话之前毫无理解。我觉得我进入时理解稍微好一点。我在开始前告诉过你，这只是为了我自己受教育，但即使是影响你的人，就像 Peter Taylor 说“不，傻瓜”，他显然会说“不，傻瓜，这行得通，你为什么还要做别的事情”，但行得通的事情，所以肯定有这样的例子：你会说“嘿，我给其他创始人提过一百万次这个建议”，然后你发现自己“哦，我现在甚至没有应用我自己的建议”。

I had no understanding going into this conversation. I think I was slightly better understanding going into this. I told you before we started this is just for my own edification but like even the people that influenced you where it's just like Peter Taylor saying no dummy he obviously would say no dummy this is working why are you doing anything else but the thing that is working so there's got to be examples where you're like hey I've given this advice to other founders a million times and then you catch yourself oh I'm not even applying my own advice at this point in time

完全正确，是的。我可以举很多这样的例子。我觉得同样有启发的是，什么是新的。你没有建议的部分是什么？

Totally, yeah. I'll give many examples of that. I think it's also instructive to like what was the new. What didn't you have the advice for?

而 OpenAI 与我之前见过的任何东西真正不同的是，从我们创办公司到推出第一个产品，花了四年半时间。

And the thing that was really different about OpenAI than anything that I had pattern matching before is it was 4 and 1/2 years from when we started the company till we launched our first product.

这和 YC 的建议相反，对吧？

The opposite of YC advice, right?

是的。

Yes.

好的。

Okay.

是的。

Yes.

尽管管理研究团队与选择和建议创始人在很多方面相似，我们学到什么程度，因为我认为我们做得完美，比如如何管理这个没有客户外部信号的领域，你只是试图做通常会被视为灾难性的创业建议——四年半不发布产品。那非常困难，我们尝试了所有这些方法，如何用“我们的研究是否真的有效”来替代“客户是否真的喜欢产品”的信号。实际上有效的一件事是在 Dota 2 时期，当我们试图用强化学习在视频游戏中击败人类时，我们放了一个排行榜，人们可以直接看到不同想法的表现，那是客观且真实的，人们想要提升排名，但我们不得不尝试所有这些方法来模拟最终用户，那是一个完全有趣的新问题，我没有任何模式匹配。

And although there were all these ways which managing a research team was similar to selecting and advising founders, learning what it to whatever degree we learned, cuz I think we did it perfectly, like how you manage through this part of the world where you don't have the external signal from customers and you're just trying to like you know do what would normally be the catastrophic startup advice of not shipping a product for 4 and a half years. That was very difficult and we tried all of these things about how we had a how we replaced the signal of do customers actually like the product for is our research actually working. One of the things that worked actually is during the Dota 2 days when we were trying to use RL to beat this video beat people at a video game, we put up like a leaderboard and people could just see how different ideas were performing and what was you know that was like objective and real and people wanted to like go up that but we had to try all of these things to basically like simulate end users and that was a totally interesting new problem I had no pattern matching for.

你是如何解决这个问题的？你的想法是什么？你是怎么做到的？

How did you work your way through that? What was your thinking? Like, how'd you do this?

我们问了一群曾在过去伟大研究实验室工作过的人，而且，你知道，OpenAI 成立时正值硅谷每个人都把创办研究实验室当作虚荣项目的时期，包括我在内。有很多关于贝尔实验室或施乐帕克研究中心鼎盛时期的书非常流行。每个人都在谈论这个。有大量的讨论。事实上，我甚至看到那边有一本关于贝尔实验室的书。但在活着的记忆中，并没有很多人真正知道如何做到。所以我们和 Alan Kay 谈了很多。我们还和其他几个人谈了谈，从他们那里得到了一些关于什么造就了一个真正好的研究实验室的建议，其中一些非常好。有些则不太适用于当下。

We asked a bunch of people who had been at great research labs of the past and it had been, you know, OpenAI started as sort of a time when everybody in Silicon Valley as their vanity project, including me, wanted to start a research lab. And there were all these books about the heyday of Bell Labs or Xerox Park that were very popular. Everybody was talking about this. There was a huge amount of discussion. In fact, I even see one of the books over there about Bell Labs. But there was not a ton of people that had like in living memory how to actually do it. So we talked a lot to Alan Kay. We talked to a handful of other people and we got some advice from them about, you know, what made a really good research lab and some of it was really good. Some of it didn't translate as well to the current moment.

嗯，你也没有那种巨大的垄断利润机器，比如贝尔实验室是独立分拆出来的。宝丽来在他们基本上垄断摄影时做了更多研究。我刚读了本田创始人的传记，对吧？这家伙创造了有史以来最成功的机动车。本田小狼连续销售了大约 60 年，数百万辆。他的整个理念是，他得出了和贝尔实验室相同的结论，他认为研发实际上必须分开。它从公司分拆出来，拥有独立的所有权，就像贝尔实验室一样。

Well, you also didn't have this giant monopolistic profit printing machine like Bell Labs was spun out independently. Polaroid did a lot more research when they had essentially like a monopoly on its photography. I just read the biography of the founder of Honda, right? The guy created the most successful motor vehicle of all time. The Honda Cub has sold uninterrupted for like 60 years, millions of vehicles. And his whole thing he arrived the same conclusion Bell Labs did that he thought the research and development had to actually be separate. It was spun out of the company and had separate ownership just like Bell Labs did.

我们没有那个。

We did not have that.

不，你没有。

No, you did not have

当我回想那些早期日子，我主要感觉我一直在尝试筹集资金但失败了。那是我对 OpenAI 早期的主要记忆。付出了那么多努力，如此令人沮丧。我希望我们有那样的现金机器。

When I think back to those early days, I mostly feel like I was trying and failing to raise money. That's like my dominant memory of the early days of OpenAI. It so much effort, so frustrating. I wish we had some sort of cash machine like that.

我记得我对 OpenAI 最清晰的记忆之一。宣布于 2015 年底。但第一天是在 2016 年新年之后，我们 12 个人或 11 个人出现在 Greg Brockman 的公寓，你知道，大约周一或周二早上 9:30 左右。假设是 1 月 4 日，每个人都在那里，这是一次巨大的努力，每个人都带着极大的兴奋走进来。感觉像开学第一天，什么的。然后很快，人们环顾房间，有点像是在想，我们现在做什么？有人说：“好的，我们应该弄一块白板。”Greg，你知道，让人去找白板。白板来了。嗯，又环顾四周。你知道，我们现在该做什么？你只是感觉到房间里的能量崩溃了。我们没有人知道该做什么。就像没有，这不像建立一个产品初创公司。不像让我们构建这个产品，让我们和客户谈谈。就像，好吧，我们说我们想制造 AGI。也许我们应该写一些论文。好吧，让我们写一些论文。也许我们应该思考一些想法。好吧，让我们思考一些想法。你知道，每个人都有“我不知道自己在做什么”的时刻。那是我的时刻之一。就像我，你知道，我们刚刚推出了这个东西。我们没有人知道我们要做什么。所以，我们做了我们知道怎么做的事情，最终我们发现很多事情都不起作用。

I remember like one of my clearest memories of all of OpenAI. announced coming out of 2015. But the first day was right after New Year's in 2016, and 12 of us or 11 of us showed up at Greg Brockman's apartment, you know, like 9:30 on a Monday or Tuesday morning, something like that. Let's say it's January 4th, and everybody's there, and it had been this like big effort, and everybody walks in with a lot of excitement. It feels like the first day of school, whatever. And then very quickly, people like look around the room, and they're sort of like, well, what do we do now? Someone says, "Okay, we should get a whiteboard." Greg, you know, gets someone to go off and find a whiteboard. Whiteboard comes. Um, look around again. You know, what are we supposed to do now? And you just feel the energy in the room collapse. And none of us know what to do. Like there's no, it was not like building a product startup. It was not like let's build this product. Let's talk to customers. It's like, okay, we said we want to make AGI. Maybe we should write some papers. Okay, let's write some papers. Maybe we should think about some ideas. Okay, let's think about some ideas. You know, everybody's got their like moments of I have no idea what I'm doing. That was one of mine. Like I have, you know, we have just launched this thing. None of us have any idea what we're going to do. So, we did what we know how to do and eventually we figured out a lot of things didn't work.

最终，我们摸索出了一种做研究下注并评估这些下注的节奏。显然，这远非完美。但我们确实找到了一条可以沿着前进的梯度，我们弄清楚了如何获得那些非常聪明的人所需的资源，以及如何确保我们不会完全在荒野中迷失。经过几年时间，大多是混乱的摸索，我们最终取得了大部分重大发现。你知道，最初那篇无监督情感论文变成了 GPT-1，然后最终变成 GPT 之类的。缩放定律的工作给了我们信心，不仅是在算力上，还有关于如何扩展我们模型的理解，这些逐渐汇聚在一起，还有其他许多事情。在这个过程中，我们学到了一些有效的东西，比如排行榜的想法。我们还学到了外部演示的巨大力量，比如向一位研究人员非常想打动的杰出人物展示。然后我们也学到了一些不管用的东西，比如虚假的截止日期。

Eventually, we figured out a kind of rhythm for making and then evaluating research bets. And it's far from perfect, obviously. But we did find a gradient that we could kind of progress along, and we figured out how to get the resources that very smart people needed, and how to make sure that we were not completely getting lost in the wilderness. And over some number of years, mostly chaotic stumbling, we eventually made most of the big discoveries. You know, what started as the unsupervised sentiment paper turned into GPT-1 and then eventually GPT-whatever. The scaling laws work that gave us the confidence, not only in the compute but the understanding about how to scale up our models, sort of came together, and through many other things too. Through this process, we learned things like that idea of leaderboards that worked. We also learned the incredible power of external demos, like for an eminent person that the researchers really wanted to impress. And then we learned a bunch of things that didn't work, like fake deadlines.

经历这一切一定让人感到非常迷失。你们 12 个人挤在一间公寓里，连块白板都没有，不知道该做什么。十年后，却有十亿人在使用。

That has to be so disorienting to live through that experience. You're at 12 people in an apartment, don't even have a whiteboard, don't know what to do. Fast forward a decade, you have a billion people using.

非常奇特的经历。

Very strange experience.

你写日记吗？

Do you keep a journal?

当我的孩子出生时，我的第一个孩子，我会在一天结束时回到家，摇着他入睡，就像跟孩子聊天什么的。所以我就在想，得找点话题来说。于是我就跟他讲我的一天，我们在为什么挣扎，我在担心什么，发生了什么。这对我来说挺有趣的，我觉得这有点意思，将来他拥有这些也会很有趣。所以我开始给他写信，每个星期天我都会给他写一封信。我就是为了说而说，然后把它写下来。我大概只写了八封左右。

When my kid was born, my first kid, I would, you know, get home at the end of the day and be rocking him to sleep and just like talk to the kid or whatever. So I was just like, need to come up with things to talk about. So I would just tell him about my day and what we were struggling with and kind of like what I was worried about and what was happening. It was kind of like fun for me to do, and I was like, this is sort of interesting, and someday it'll be like interesting for him to have this. So I started writing him like every Sunday I would write him a letter. I would talk just to talk, and then I would write it down. I only ever did like eight of them or something.

你有几个孩子？

How many kids do you have?

两个。

Two.

好的。贝索斯有一句关于创建亚马逊的精彩名言。就像我们努力去做那些可以讲给孙辈听、让我们自豪的事情，对吧？而那些事情很难。你当时在给儿子写信——

Okay. Bezos has this great line about building Amazon. It's like we're trying to do stuff that we can tell our grandkids about that we're proud of, right? And those things are hard. The fact that you were writing to your son—

天哪。

Oh man.

继续写信。如果你不这样做，这很快，因为我读过足够多关于这方面的书。大多数时候，你猜怎么着？创始人不会在 40 岁时写自传。他们会在 70 岁时写，那时他们回顾过去，希望自己能重来一次。太多东西已经随着时间流逝而丢失。他们都重复这一点。他们都说，我希望我写过日记。所以即使你不写，你也有足够的资源。我会怎么做呢，写，让人写一本书，即使只是为了内部目的。你读过迈克尔·莫里茨的《小王国》吗？

Keep writing the letters. And if you don't do that, this is real quick, just because I've read enough books about this. Most time, guess what? Founders don't write autobiographies when they're 40. They write them when they're 70 and they're looking back and they wish they could do it again. And so much has been lost to the sands of time. They all repeat this. They're like, I wish I journaled. So even if you don't do it, you have enough resources. What I would do, write, have a book written, even if it's for internal purposes only. You ever read The Little Kingdom by Michael Moritz?

我没读过。

I never read.

哦，你一定要读。那是迈克尔·莫里茨写的苹果前六年历史。他写了那本书，是不是很疯狂？

Oh, you have to. It's like the first six-year history of Apple written by Michael Moritz. Isn't it crazy that he wrote that book?

他是一位杰出的作家。

He's a phenomenal writer.

一个疯狂的作家最终成为有史以来最优秀的风险投资家之一，我猜。但重点是让那本书结束。史蒂夫甚至还没被赶出苹果。所以你能看到真正发生了什么。你会想要这个的。你现在可能不想要，但你到 60 或 70 岁时肯定想要。

Crazy writer winds up being one of the best venture capitalists of all time, I guess. But the point is to have that book end. Steve hasn't even been kicked out of Apple yet. So you get like what actually happened. You're going to want this. You might not want it now, but you're damn sure going to want it when you're 60 or 70.

最有趣的是那种给孩子写信的心态。你真的无法躲在任何东西后面。就像你会想，我真的很在乎我的孩子会怎么看我，所以这件事发生了，你知道，感觉不太好。下周最好换个方式做。这是一个非常有趣、极其有趣的思维框架。也许我会找到某种方式再做一次。

The thing that was so interesting was like the mindset of writing to your kid. Like you really can't hide behind anything. Like you're like, I really care what my kid's going to think about me, so like this thing happened, like you know, didn't feel great about it. Better do it differently next week. Like it was a very interesting, extremely interesting mental framework. Maybe I'll find some way to do it again.

哦，也许你会做的。

Oh, maybe you're going to do it.

好的，山姆，谢谢你抽出时间。这太棒了，伙计。非常感谢。

Okay, Sam, thanks for taking the time. This was awesome, man. Appreciate it.

希望你喜欢这一集。请记得在你收听的地方订阅并留下评论。另外一定要收听我的另一个播客《创始人》。近十年来，我痴迷地阅读了 400 多本历史上最伟大企业家的传记，寻找你可以在工作中使用的想法。你在这个节目中听到的大多数嘉宾最初都是通过《创始人》找到我的。

I hope you enjoyed this episode. Please remember to subscribe wherever you're listening and leave a review. And make sure you listen to my other podcast, Founders. For almost a decade, I've obsessively read over 400 biographies of history's greatest entrepreneurs, searching for ideas that you can use in your work. Most of the guests you hear on this show first found me through Founders.