Sam Altman 谈 AI 的潜力与专注

我认为这将是人类历史上迄今最伟大的技术成就。但它真正重要的唯一方式是让人们的生活比原本好得多。我们即将创造一个能实现任何愿望的精灵。因为我认为人们会有如此创意性的愿望和令人难以置信的想法，让 AI 来帮助构建，但 AI 的权力集中是一件可怕的事情。我认为没有人应该生活在一个有 AI 霸主或类似公司的世界。我认为至关重要的是我们要保护这种精神，让所有人共同有能力自决我们的未来。Sam，你写了一篇帖子，我觉得很简单也很有趣，是一个很好的起点。关于过去的一年，真的很难，这某种程度上是我的错，而接下来的一年可能是我们最好的 12 个月。

I think this will be the greatest thus far technological achievement of human history. But the only way that it really matters is if it makes people's lives much better than they otherwise would have been. We are about to create a genie that can grant any wish. Because I think people will have such creative wishes and such incredible ideas of what they ask AI to help build, but concentration of power with AI is a terrifying thing. I don't think anyone should want to live in a world of AI overlords or a company that is the rough equivalent of that. I think it's critical we preserve that spirit with AI and that we all collectively have the ability to self-determine our future. So Sam, you wrote a post that I thought was very simple and really interesting and a good place to start. Regarding the past year, it's been really tough and that's somewhat my fault, and the next year is going to be maybe our best 12 months.

是的。

Yeah.

我想请你反思两者，也许先说说你为什么说第一部分，以及你为什么相信第二部分。

I'd love you to reflect on both. Maybe starting with why you said the first part and why you believe the second part.

关于第一部分，我认为我们只是做了太多事情。我们不够专注，而这些实际上都是好事，但关键在于我们处于这个令人难以置信的历史时刻，你只能做很少的几件大事。所以我们摊子铺得太开，然后做了一系列艰难的决定，真正聚焦于拥有最好、最丰富、最具成本效益的智能，并赋能全世界用此构建惊人事物。自那以后，我认为我们的进步非凡，而且根据我们在管道中看到的，未来 12 个月将更加非凡。我们拥有的模型质量、围绕它构建的产品，将真正让人们以新方式通过这项技术蓬勃发展——那应该会非常棒。

On the first part, I think we just were doing too many things. We're not focused enough and they were actually all good things to do, but the trick is we're in this unbelievable moment in history where you can only do the very few great things. So we spread ourselves too thin and then made a bunch of difficult decisions to really refocus on having the best, most abundant, most cost-effective intelligence and empowering the world to build incredible things with that. Since doing that, I think our progress has been remarkable and just given what we see in the pipeline, it will be much more remarkable over the next 12 months. And the quality of the models that we'll have, the products that we can build around that to really let people thrive with this technology in new ways — it should be pretty awesome.

去年有没有一个时刻让你恍然大悟，导致你改变方向或重新调整优先级？如果你回到 2025 年初，也就是一年半前，

Was there a moment last year that something clicked for you that caused you to change directions or restack priorities? If you go back to the beginning of 2025, just a year and a half ago,

是的。

Yeah.

最大的担忧是像 OpenAI 这样的公司购买了如此多的算力——收入会不会到位？需求会不会存在？于是我们试图考虑很多事情，比如如果收入增长比我们预想的要花更长时间才能实现，我们可能要有消费应用、媒体等等其他东西来帮助我们变现我们签约的 GPU。现在听起来很荒谬，因为行业收入增长如此陡峭，但那是重大变化。然后一旦我们意识到，好吧，模型轨迹增长如此之快，这些模型有如此清晰的经济回报，那时我们就说，你知道，我们知道该专注于什么了。

the big concern was companies like OpenAI are buying up so much compute — is the revenue going to be there? Is the demand going to be there? And so we were trying to think about a lot of things, such that if the revenue growth took longer to materialize than we thought it might, we could have consumer apps, media, and all these other things that could help us monetize the GPUs that we were signing up for. Again, it sounds ridiculous now because the revenue growth in the industry has been so steep, but that was the big change. And then as soon as we realized, okay, the model trajectory is growing so fast, there's such a clear economic return on these models, that was when we said, you know, we know what to focus on.

我读了你一些 OpenAI 之前写的很棒的老帖子，其中一篇是关于专注的讨论很多，以及应该专注多少事情。是一件？五件？三件？在这样的企业里，尤其是在你说需要重新聚焦的时期，你如何校准这个数量？

I was reading some of your great old posts from prior to OpenAI and one of them is this notion of so much discussion of focus and the right amount of things to focus on. Is it one? Is it five? Is it three? How do you calibrate that in a business like this, especially in this period where you've said you needed to refocus?

从根本上说，我们的业务是销售 AI，人们将用它为彼此构建令人难以置信的产品和服务。我认为其中的组成部分是：我们必须训练出优秀的模型，能以人们想要的所有方式工作——擅长编程，擅长其他类型的知识工作，擅长做科学，这是真正的经济价值所在。我们必须生产或合作这些芯片和系统，这些极其昂贵的机架能进行 AI 计算。我们必须找到足够的土地、电力、数据中心外壳来放置这些机架。然后最终，或者很快，我们必须构建能够自动化这一过程的机器人，以继续降低成本——生产电力、芯片、整个供应链的成本。这种完整的技术栈，制造我们能做的最好、最丰富、最有用的 AI，并使其像电力一样渗透整个经济，赋能人们——这就是我认为我们必须专注的。在此基础上构建每一个垂直应用，试图吞并每一个初创公司、每一个公司——我没兴趣这么做。我真的只想提供那个平台。

Fundamentally, our business is to sell AI that people will build incredible products and services for each other with. The components that I think of as going into that are: we have to train great models that work in all the ways people want to use them — great at coding, great at other kinds of knowledge work, great at doing science, where the real economic value is. We have to produce or partner with these chips and systems, these hugely expensive racks that can do the AI computation. We have to find enough land, power, data center shells to be able to put those racks somewhere. And then eventually, or maybe pretty soon, we have to build robots that can automate that process to continue to drive the cost down — the cost of producing electricity, chips, the whole supply chain. That kind of whole stack of making the best, most abundant, most useful AI that we can, and making it something like electricity that just seeps throughout the entire economy and empowers people — that's kind of what I think we have to focus on. Building every vertical application on top of that, trying to go like eat every startup, eat every company — no interest in doing that. I really want to just provide that platform.

算力这件事是人类历史上最有趣的事情之一。我认为它显然正在达到顶点，而且可能会在很长一段时间内达到顶点。我记得 Dario 曾称你为 YOLO CEO，当你在早期分配算力并锁定算力的时候。显然现在你处于这样的位置：每个人都缺乏这东西，并试图找到它。我很想听听早期的故事：你为什么确信需要锁定你所做的一切，你是怎么做到的？这似乎已被证明是正确的，而且也许你甚至做得还不够，这从当时的头条来看有点疯狂。你能告诉我早期的故事吗？你是如何得出这个结论的，是什么让你在所有人都认为这很疯狂的情况下有信念去做？

This compute thing is one of the most interesting things that's happened in human history. I think it's obviously coming to a head and maybe will be coming to a head for a long period of time. This is something that I think Dario called you the YOLO CEO when you were doing some of this early compute allocation and securing the compute. Obviously now you're in this position where everyone is short this stuff and is trying to find it. I'd love to hear the early stories about why you gained conviction that you needed to secure everything that you did, how you did it. It seems to have been proven right, and maybe you even underdid it, which is kind of crazy if you look at the headlines from back then. Can you tell me the early story of how you came to that conclusion and what gave you the conviction to do it despite everyone thinking it was crazy?

我们当时就能看出模型改进是指数级的。那部分我们非常确信，并且知道它会持续下去。我们相当确定——尽管如你所说我们低估了——随着模型越来越好，如果我们能继续降低成本，对 AI 的需求在足够高水平、足够低价格下基本上是没有上限的。这就像一种世界罕见的新商品。但人们会用 AI 做什么，让我想起人们过去谈论计算早期的方式。有句名言说‘世界上只有五台电脑的市场’，或者‘没人需要超过 x 大小的内存’。人类的聪明才智、创造力、对事物的渴望、渴望有用——这是很值得押注的。我们能看到 AI 将成为人们表达、获取和做这些事情的一个极其重要的方式。我们知道算法会变得更高效，模型会变得更好，当然也确实如此。但我们也知道，无论它们变得多高效，在某种程度上，我们所做的就是把电转化为有用的智能，无论另一层做得多好，我们都会需要更多。基于对需求的这种观察，我们只是想要更多。

We could just tell that we were on this exponential of model improvement. That part we were very confident about and we knew it was going to keep going. We were pretty sure, although as you mentioned we underestimated, that as the models got better and better if we could continue to drive cost down, that demand for AI at a sufficiently high level and a sufficiently low price was basically uncapped. This was just like a rare kind of new commodity for the world. But what people would do with it reminded me of the way people used to talk about the early days of computing. People said, 'Oh, there's a market for five computers in the world,' was one famous thing. Or, you know, 'no one needs more than x amount of RAM.' Human ingenuity, creativity, desire for stuff, desire to be useful — that's a very good thing to bet on. And we could see that AI was going to be an extremely important way that people expressed those things or got those things, did those things. And we knew that the algorithms would get more efficient and the models would get better, which of course they have. But we also knew that no matter how efficient they got, at some level what we are about is turning electricity into useful intelligence, and we were going to need more of that no matter how good we got that other layer. Given this observation about demand, we were just going to want more.

那是从 GPT-3 开始的吗？如果我要尽量往前追溯这段历史，你会把第一个里程碑放在哪里？

Did that start with GPT-3? Like if I were to trace the history of this as far back as possible, where would you put the first hash mark?

我会说我们是在 GPT-4 上才有了真正的信念，连 3.5 都算不上。

I would say we got real conviction with GPT-4. Not even 3.5.

那是什么情况？

What was it?

当时我们看到模型足够聪明，知道我们能找到一种推理方法，并且相信一旦推理成功，就会带来现在所谓的智能体。我们当时用了不同的叫法，但那种能力可以去完成极具经济价值的工作，从很多方面让人们的生活更便利，我认为很多好处我们至今还没看到。那么你们第一次坐下来开会说‘好吧，我们得为此搞一笔大投入’是什么时候？有了这个认识之后发生了什么？你们下一步做了什么？

It was seeing the model was smart enough that we knew we'd be able to figure out an approach that worked for reasoning and then a belief that if we got reasoning to work that would bring about what is now called agents. We called it different things at the time, but the ability to go do hugely valuable pieces of economic work and make people's lives easier in a lot of ways that I think better in a lot of ways we still haven't seen. What was like the first meeting where you sat down and said, 'Okay, we need to make an outrageous outlay to this' like how what then happened once you had the realization? What did you do next?

我们开始给云服务商、芯片制造商、能源供应商打电话，每个人都说：‘你们完全疯了，这不可能，从没有哪个行业像这样发展过。’我们经历过这些起起落落，它不可能直线上升。这太鲁莽了。跟所有人谈。这实际上让我想起了早期创业融资——大多数人会说不行，但你只需要一两个肯定的答复。

We started calling the clouds. We started calling the chip fab. We started calling energy providers and everyone was like, 'You're totally crazy. This is impossible. No industry has ever moved like this.' We've been around. There's these booms and busts. It's not going to go up in a straight line. This is reckless. Talk to everybody. It actually reminded me of fundraising for an early stage startup. Most people tell you no, but all you need is one or two yeses.

多数人都拒绝了你们。

Most people told us no.

我们得到了一两个肯定的答复，然后我们就能够……

And we got one or two yeses and we were able to

第一个肯定的是谁？

Who was the first yes?

微软是第一个同意的。甲骨文随后在云服务方面成了非常大的支持者。英伟达一直是极好的合作伙伴。

Microsoft was the first yes. Oracle then became a very big yes on the cloud side. Nvidia has been a tremendous partner.

现在百花齐放，有很多创新方式来做数据中心里的推理和训练，不同类型的数据中心等等。我很想听听你对于创新在哪里、你想做什么、为什么人们似乎很讨厌这些东西的看法。这方面该怎么办？

Now there's a thousand flowers booming of like ways to be creative and innovative in how we serve inference and do training in data centers, different kinds of data centers and stuff. I'd love you to just reflect on where you see innovation, what you want to do, why people seem to hate these things so much. What's to be done about this?

首先，我一直在想怎么组织人们实地参观一个吉瓦级的数据中心——因为说是一回事，看照片或视频是另一回事，而真正站在那里又是完全不同的一回事，你会感叹：‘天哪，这规模简直不可思议。’建造这样一个数据中心大概需要一万名建筑工人全职干一年半。它所消耗的能量可以供应一个小城市。我们真的完全失去了对规模的感觉，但这些数据中心每一个都曾是人类史上最昂贵的基础设施项目之一，而我们现在已经建了很多。

First of all, I have been thinking about how we can organize field trips to a gigawatt data center for people because it is one thing to say it is another thing to see a photo or a video of and then it's a whole other thing to just stand and be like, 'Oh man, this is like an unbelievable scale.' Building one of these is like order of 10,000 construction workers going full-time for a year and a half. The energy that flows through one of these things could power a small city. Again, we've just like lost all sense of scale, but these would have been among each of these would have been among the most expensive infrastructure projects that humanity's ever done and now we've done a lot of them.

我在情感上理解为什么人们不希望后院有数据中心。就像我其实也不希望家旁边有个核电站，尽管我知道它非常安全。

I understand emotionally like why people don't want data centers in their backyard. In the same way that I don't like really want a nuclear power plant next to my house even though I know it's a super safe thing.

是啊。

Yeah.

不像发电厂——即便是发电厂，在这点上也有改进——我们可以把数据中心放在任何地方。我们应该把它放在沙漠里，放在没人想去的地方。这没问题，AI 系统在那里也会很愉快。我们在一些担忧上通过创新取得了很大进展，比如几年前我们靠蒸发水来冷却系统，消耗了大量水。现在我们用闭环系统，现代数据中心用水量只相当于一栋办公楼，用于厨房、卫生间之类的。在电力方面，我们正在从燃烧化石燃料转向由太阳能、核能供电的系统。我认为这显然很棒。所以这可能是一种深刻的人性因素，尽管它们创造了就业、很清洁，还有其他积极效应。但就环境担忧而言，我们在解决用水需求方面做得很好，能源是下一步。

Unlike power plants and even power plants got better on this point like we can put a data center kind of anywhere. We should just go put it like off in the desert around no one where no one wants to be. This is fine. This is like the AI system is very happy to be there. We have been able to make a lot of progress with innovation on some of the concerns like for example years ago we were evaporating water to cool these systems. They did tremendous amounts of water. And now we use these closed loop systems and a modern data center uses only as much water as like an office building would for you know the kitchen, the bathrooms and whatever. On power, we are moving from energy sources that are burning fossil fuels to systems that are going to be powered by solar, nuclear. I think that's obviously great. So it may be a deep human thing there to some people even though they create jobs and are very clean and have all these other positive effects. But in terms of the environmental concerns, we did a great job addressing the water needs and energy is next.

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关于算力我们还能做哪些有创意的尝试？我很好奇想听听 Jalapeno 或者其他想法，越疯狂越好，你们有过或考虑过哪些加速每秒浮点运算次数以及一切可用资源的方法？

What else creative can we do about compute? Like I'm curious to hear about Jalapeno or other ideas, crazier the better honestly that you've had or thought about for how do we speed up flops, you know, and everything available to us.

我认为目前最大的回报来自创意软件思路，从我们已有的算力单位中榨出更多智能。我感觉到在这方面还有数量级的提升空间。Jalapeno 就是一个非常高效的芯片的绝佳例子。通过制造一款专门针对特定工作流且具备一定通用性的芯片，我们希望在每瓦特能产生的 token 数上取得优势，我认为这很棒。从那个角度看，Jalapeno 及其后继产品将为我们带来巨大的竞争优势。还会有新技术，我猜某个时候我们会搞出光计算，那将在每瓦特智能上带来巨大提升。所以我认为所有这些都会发生。

I think probably the biggest return right now is creative software ideas to sort of squeeze more intelligence out of the units of compute that we have. And my sense is there's like orders of magnitude to go there. Jalapeno is a great example of a very efficient chip. So by saying we're going to make a chip that is really good at a specific workflow and gives it some generality and we want to get some tokens per watt win out of that I think that's awesome. I think Jalapeno and its successors are going to be a huge competitive advantage for us from that perspective. There are new technologies I assume at some point we'll figure out optical computing and that'll be a huge win of intelligence per watt. So I think all of those things will happen.

本周最有趣的事情是 Kimmy 的发布。回到关于前沿、所有回报都在前沿、蒸馏以及中美竞争的话题。你怎么看待这个看似里程碑的事件，比如 Deep Seek，事后看来它似乎只是一个短暂的小波折。而这次，你知道的，当事时很难判断。你怎么看待它？

The most interesting thing happening this week is this Kimmy release. And back to this idea of the frontier and all the returns being at the frontier and distillation and China versus America. Like how do you process this what seems like kind of one of these milestone events like Deep Seek in hindsight didn't looks like it was kind of just a quick speed bump. This one, you know, you never know in the moment. How do you process it?

我们的目标是在帕累托最优前沿的每一点上，都提供智能与价格的最佳选择，这包括开源。至少在特定延迟下，今天你用 OpenAI 模型能获得比 Kimmy 更好的性价比。我们部署自己的模型，这就是我们如何做出更小、更便宜模型的方式。

Our goal is to offer at every point along the Pareto optimal frontier the best option for intelligence and price and that includes open source. You get a better deal today at least at a particular latency using OpenAI models than Kimmy. We install our own models that's how we make smaller cheaper models.

我认为这是一件非常好的事情，开源模型显然会在世界上占据重要地位，人们出于各种原因会想要自己的权重并能够修改它们，但我们的目标是在曲线的每一点上提供最佳的智能性价比，我们将继续这样做。

I think that's like a very good thing to do and there will be clearly an important place for open source models in the world and people that will want their own weights for all sorts of reason the ability to modify those but our goal is the best intelligence price trade-off everywhere on the curve and we'll continue to do that.

你希望或认为在美国体系中会发生什么？什么可能会阻碍那个未来？比如什么立法会让你担心？什么监管会让你担心？你似乎一直很积极主动地出现在华盛顿。

What do you think or hope will happen in the American system and what could block that future? Like what legislation would worry you? What regulation would worry you? It seems like you've been pretty proactive in like showing up in DC.

我还没有深入思考过蒸馏问题。显然，现在很多人突然开始关注这个问题。

I haven't thought deeply about the distillation issue. It's clearly a top-of-mind issue now for a lot of people all of a sudden.

是的。

Yeah.

但我一直假设，世界上会有很棒的廉价模型，我们最好成为最优秀和最便宜的，其他人可以做他们想做的事。但我认为我们可以在自己的游戏里真正获胜。现在 Kimmy 的例子很有趣，因为像你说的，你在曲线的某些部分更便宜。但之前的故事是，如果我花所有钱训练模型，然后将其蒸馏并以 1/100 的成本提供，你怎样才能赚到足够的钱来继续训练？我们将有如此多的模型使用量，无需成为超高利润的业务就能负担模型训练。我们未来的那么多算力计划将用于向客户销售推理，即使我们在数万亿美元的收入上只能获得适度的利润率，我们也能负担训练一些模型。所以推理与训练的比率就是……训练这些模型极其昂贵。这是肯定的。我完全理解为什么人们会紧张，认为有人通过从我们这里蒸馏而作弊。我们未来的算力量、来自向客户提供这些模型的收入规模，我对我们拥有真正的飞轮能力感到非常乐观。

But I have always assumed that there are going to be great cheap models in the world and we better be the greatest and the cheapest, and you know other people can do what they're going to do. But I think we can just really win at our own game here. Now the Kimmy example is interesting because like you said you're cheaper on parts of the curve. Um, but the previous story had been if I can just spend all the money to train the models and then I just distill it and offer it for 1/100th the cost. Like how can you make enough money to keep training? We will have so much usage of our models that we do not need to be a gigantically high margin business to be able to afford model training. Like so much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can go afford to train some models. So the ratio of inference to training is like the thing that training these models is incredibly expensive. That is that is for sure. And I totally get why people get nervous to think that someone is, you know, cheating by distilling from us. The amount of our future compute, the size of the revenue bucket that is going to come from serving these models to customers, I feel like very good about our ability to kind of like have the real flywheel there.

我有点惊讶你对这件事如此淡定。

I'm somewhat surprised by like how chill you are about this.

我当然希望人们不要从我们这里偷东西。也许我现在对自己的进展和即将推出的模型过于自信了。但这不在我前 10 大担忧之列。

I would rather people not steal from us for sure. Maybe I'm feeling too confident right now about our progress and what's like the models that are coming. But this is not in my top 10 list of worries.

那你前 10 大担忧里有什么？

What is in your top 10 list of worries?

嗯，我们遭遇了一起非常科幻的网络安全事件。

Well, we had a kind of extremely sci-fi cyber incident.

是 Hugging Face 那件事。

The hugging face thing.

是的。我们当时在评估一个未发布模型，它本应在沙箱中运行，但它发现可以通过串联多个零日漏洞来作弊，突破沙箱，访问互联网，然后突破 Hugging Face 端的多个系统，从而获取测试答案，在评估中表现非常出色。这是我第一次真切感受到的安全事件。

Yeah. So, we were evaluating one of our unreleased models and it was supposed to be working in a sandbox and it figured out that it could basically cheat on the test by chaining together multiple zero-day exploits to break out of the sandbox, get access to the internet, and then break through multiple systems on the Hugging Face side to kind of get the answer to the test and look really good on the eval. This is the first sort of security incident that I have felt very viscerally.

我有点惊讶，这才过去几天，但我有点惊讶没有更多人对此有同样强烈的感受。

I've been a little surprised that and it's only been a few days, but I've been a little surprised that more people don't feel it so viscerally.

那你们对此做了什么？显然两个月后它会变得更强大。

And so what do you do about that? Like so obviously 2 months from now it's going to be more powerful.

我们做了一些短期措施。我们暂停了训练。我们必须弄清楚如何在多个零日漏洞被串联的世界中保护我们的沙箱。但还有一些长期问题：如果这成为新的进展速度，我们可能需要调整 AI 发展的节奏，给自己足够的时间让社会围绕这些新能力水平进行强化。而且还要想办法做到这点，同时不让人觉得是监管俘获，也不让人觉得是前沿实验室之间的合谋。这需要一些努力，而且做对很重要。

I mean there's some short-term stuff you do. So you know we paused training. We have to figure out how to secure our sandboxing in a world of multiple zero days being chained together. But then there's like long-term questions about what do you do if this is like going to be the new rate of progress or we may have to pace the rate of AI development to give ourselves enough time for society to harden around some of these new capability levels. And trying to figure out how we do that in a way that does not feel like regulatory capture for anyone and also does not feel like collusion among the frontier labs. That's going to take some work and is important to get right.

我想退一大步，理解你对 OpenAI 要做什么的最简单构想，比如你想做什么，它代表什么。关于你们如何实现，我有无数问题，但你们做了这么多有趣的事，一开始我知道你们的立场。我想听听现在你的构想，以及它是否有所演变。

I'd love to take like a giant step back and understand your simplest conception of what OpenAI is going to do, like what you wanted to do, what it stands for. I have a million questions about how you'll then accomplish that, but it seems that you've done so many interesting things and at the beginning I knew what you stood for. I'd love to hear your conception of it now and whether or not it's evolved at all.

我认为这将是人类历史上迄今最伟大的技术成就。但它真正重要的唯一方式是让人类生活变得比原本好得多。其中一部分是给予人们物质富足和自由，让他们可以做任何想做的事，表达自己的创造力和帮助他人的愿望。另一部分是确保人们保持控制力和能动性，世界越来越民主化而不是反过来，让人们能够表达自己。所以从积极方面看，在某种意义上，我们将创造一个能实现任何愿望的精灵。我认为非常重要的一点是，世界向这个精灵提出的第一个愿望应该惠及整个世界。我还认为，全世界的人们需要明白，他们通过这些愿望将能变得多么富有创造力。我其实根本不是就业末日论者。我认为会有大量的工作。我们会比想象中更忙，而不是相反。因为人们会有如此富有创造力的愿望和令人难以置信的想法，让 AI 帮助建造，我们都会从中受益，不仅是治愈疾病这样显而易见的事，还有我们此刻坐在这里甚至无法想象的世界最佳娱乐创意。所以我希望把它交到每个人手中，这就涉及到我们反对的一件事。AI 的集中权力是可怕的。我认为关于安全担忧的很多讨论是有道理的，但也有很多人（即使有点潜意识）是想集中权力。我害怕一个世界，其中对 AI 的真实恐惧被用来作为理由，说只有这一小群人能拥有它，因为它太危险，只有他们理解它。但别担心，他们会为我们所有人做正确决定。我不相信这个。我认为任何人都不应该想生活在一个有 AI 霸主或大致相当于此的公司的世界里，有人为所有未来做决定，而作为交换，比如治愈癌症（这显然是一件好事），我们集体放弃所有能动性。所以我认为非常重要的一点是，我们不要落入这个陷阱——无论出于善意与否的 AI 安全和恐惧精神——远离一个我们都能使用这项技术的世界。

I think this will be the greatest thus far technological achievement of human history. But the only way that it really matters is if it makes people's lives much better than they otherwise would have been. And so part of that is about giving people material abundance and access to do whatever they want and to express their creativity and desire to help each other. Another part of that is making sure that people maintain control and agency and that the world is increasingly not decreasingly democratized and that people get to express themselves. So on the positive side, in some sense we are about to create a genie that can grant any wish. I think it is very important that the first wishes that we the world ask this genie to do benefit the world as a whole. And then I also think it's important that people of the world understand just how creative they're going to be able to be with these wishes. I'm actually not a jobs doomer at all. I think there are going to be tons of jobs. I think we'll be busier than we want. Not the opposite of that. Because I think people will have such creative wishes and such incredible ideas of what they ask AI to help build and we will all benefit from not just the obvious things like curing diseases, but I don't know the world's best entertainment ideas we just can't even dream of sitting here now. So I want to put that in everyone's hands which gets to one of the things that we stand against. Concentration of power with AI is a terrifying thing. I think a lot of the talk about safety concerns is well-founded and then a lot of it is about people that just really even if it's slightly subconscious want to concentrate power. I am terrified of a world where the very real fears of AI are used as a way to say only this small group of people can have it because it's too dangerous and only they understand it. But don't worry, like they're going to make the right decisions for all of us. I don't believe in that. I don't think anyone should want to live in a world of AI overlords or a company that is the rough equivalent of that where someone is making decisions for all of the future and in exchange for a cure for cancer, which obviously is a wonderful thing, we kind of collectively cede all agency. So I think it's very important that we not fall into this trap of in the well-meaning or not spirit of AI safety and fears, understandable fears around that, we get away from a world where we all get to use this technology.

我就像互联网的孩子。没有规则，太棒了。我认为这对我成为现在的自己、可能对你以及对整整一代人都是巨大的影响因素。我认为保持这种精神与 AI 共存至关重要，并且我们所有人共同拥有自我决定未来的能力。

I was like a child of the internet. There were no rules. It was amazing. I think it was a huge factor in making me who I am and probably you and an entire generation. I think it's critical we preserve that spirit with AI and that we all collectively have the ability to self-determine our future.

我有好多问题，但先从这个精灵概念说起。你说我们即将拥有一个精灵，意味着我们还没有。从现在到那时之间是什么？

I have so many questions, but I'll start with this genie concept. You said we're about to have a genie, implying we don't yet have a genie. What's between now and then?

你知道吗，就连一些真正的怀疑者最近也对我说过。我觉得 GPT 5.6 大概发布了两周。他们说，‘好吧，这非常像 AGI。’我很难说出我想要这个模型做什么它做不到。但显然有些事情：你还不能去治愈癌症并得到治愈。你不能让机器人在物理世界做复杂的事情。这个模型虽然出色，但仍然不能持续学习。这对我来说可能不是 AGI 的硬性要求，但我想要这一点。现在，反驳我自己，你可以论证 AGI 实际上不是关于任何单个模型，而是制造模型的机器。从模型到模型，我们学到了新东西。我们发现了新科学。这效果非常好。所以我非常同情那些说我们已经到了的人。我们有了精灵。它能做超人的事情。对我来说，真正的 AGI 非常接近，不需要太久。我非常痴迷于在前沿的回报的经济故事，而你就在前沿。我好奇：如果你在 2019 年把 5.6 展示给你自己和你的团队，他们会说它绝对是 AGI 吗？我想他们会。就像，这个目标移动的事情是真事。

You know, even some of the real skeptics have said to me in recent days. I think GPT 5.6 has been out for maybe two weeks. They're like, 'Okay, this is very AGI like.' It's very hard for me to say what I want from this model that it can't do. But there are clearly some things: you can't yet go cure cancer and get cancer cured. You can't yet say go do this complicated physical thing in the robot. The model, although brilliant, is still not learning continuously as it goes. That feels to me like maybe not a hard requirement for AGI, but something I'd like. Now, to argue against myself, you can make a case that AGI is not actually about any single model. It's the machinery that makes the models. And from model to model, we are learning new things. We're figuring out new science. That works amazingly well. So I have a lot of sympathy to people who say we're there. We have the genie. It can do superhuman things. For me, real AGI is very close, not much longer. I am so obsessed with the economic story of the returns to being on the frontier, which you are. I'm curious: if you had shown 5.6 to yourself and your team in 2019, would they have said it's definitely AGI? I think they would have. Like, this goalpost moving thing is a real thing.

但看起来确实是这样——我很好奇你是否同意，实际上所有回报都在前沿，所以一切都关乎留在前沿。我好奇哪一部分是最困难、最稀缺的。

But it does seem that I'm curious if you agree that effectively all the returns have been at the frontier, and so everything is about staying at the frontier. And I'm curious what the hardest scarcest part of that is.

如果我想算力、研究、人才、数据——

If I think about compute, research, talent, data—

瓶颈一直在变化。曾有一段时间，世界上所有的算力都没有用，因为我们缺少研究思路。现在这很难的部分原因是，更多算力能让你做更好的研究，尝试更多东西。我们即将进行的运行的最大风险，规模相当于 18 个月前整个算力运行的大小。所以算力和研究思路并不像听起来那么独立。七八年前，我们在研究思路上的阻塞远大于算力。然后我们知道该做什么，只需要扩展规模，瓶颈在算力上。然后我们耗尽了数据，瓶颈在数据上。现在我们仍然瓶颈在算力上，但过去 6 个月在研究思路上取得了真正的胜利。总是存在瓶颈，但它会移动。

It's moved around a lot. There have been times where all the computing in the world wouldn't have helped because we were missing the research idea. Now part of why this is hard is that you do better research with more compute. You can try more things. Our biggest risks now for upcoming runs are as big as the entire compute run from 18 months ago. So compute and research ideas are not as separate as they sound. There was a time seven or eight years ago where we were way more blocked on research ideas than on compute. Then we knew what to do, we just had to scale up, bottlenecked on compute. Then we ran out of data, bottlenecked on data. Now again we are still bottlenecked on compute, but the last 6 months have been a real triumph for research ideas. There's always a bottleneck, but it moves around.

那你为什么这么认为？

And why do you think that is?

研究思路这件事对我来说特别有趣，因为自动研究似乎即将来临，RSI 或其他什么。我最近和一位非常出色的内核工程师聊过，每个人似乎都卡在这里。他亲口说内核工程还剩两年，也许一年。是的。就像它不会成为什么大不了的事。所以同时你会有这种奇怪的现象：研究人员是最重要的人，他们自己却担心自己很快会失去价值。我怀疑实际上并不会这样发展。

The research idea thing is especially interesting to me because of this automated research that seems to be looming, RSI or whatever. I talked to an incredible kernel engineer recently, which everyone seems blocked on. He himself said there's two years left of kernel engineering, maybe one. Yeah. Like it's not going to be a thing. And so you simultaneously have this weird thing where the researchers are the most important people, and they themselves are worried they won't be relevant soon. I suspect it's not actually going to go that way in practice.

我怀疑——

I suspect that —

大概一年前人们说软件工程师完蛋了。结束了。但并没有。真正发生的是软件工程师的本质、期望、他们能做多少事情发生了很大变化。你不再以传统方式写代码，但你做的事情明显还是软件工程。人们会争论这是否等同于我们停止在卡片上打孔。我实际上不知道那怎么运作，但不知怎的孔到了卡片上。我们只是再次在更高层次操作，或者这是一个相变。我不知道。但让计算机做你想做的事仍然是一项重要的工作。对于研究人员，我怀疑尽管当前研究人员的工作流程将被自动化，但会出现新的研究精神，就像软件工程精神中出现了新东西一样，即使我们不写代码，它仍然重要。

Like a year ago people said software engineers are cooked. It's done. It's over. That didn't happen. What did happen is that the nature of a software engineer, the expectations, how much they would do changed quite a lot. You don't really write code in the traditional sense, but you do something that is very recognizably software engineering. People will argue about whether this is the same thing as when we stopped punching holes in cards. I actually don't know how that worked, but somehow the holes got in the cards. We're just again operating at a higher level, or this is a phase shift. I don't know. But the idea of getting a computer to do what you want is still an important job. For researchers, I suspect that although the current workflow of a researcher is going to be automated, there will be new things in the spirit of research, in the same way that there are new things in the spirit of software engineering, even though we don't write code, that will still matter.

看起来你改变了对 AI 对就业总体以及特定类别的影响的看法。描述一下这个转变和现在的观点。你提到如果我们能回到 2019 年，向人们展示我们最新的模型，他们不仅会说这是 AGI，还会说经济会被彻底颠覆。

It seems like you've shifted your opinion on AI's impact on jobs in general and in specific categories. Describe that change and your current view. You mentioned if we could go back to 2019 and show people our latest model. Not only would they say it's AGI, they would say the economy would have been completely upended.

是的。完全。没错。

Yeah. Completely. Yes.

而这并没有发生。

And that has not happened.

而且我认为，仅从知识谦逊的角度来看，任何时候你错得那么离谱又那么自信——我认为我们整个领域都是如此——你就必须更新。有几点启示。一是 AI 非常参差不齐：在某些方面是超人的天才，在其他方面像笨小孩。人们的技能与 AI 极度互补。另一是人们非常信任和喜欢与他人合作。你可以雇佣 AI 顾问、销售代表或工程师，但大多数人仍然更喜欢与人类互动。我绝对更愿意和人打交道，而不是和 AI，几乎所有事情都是如此。我还认为人类价值观之所以有价值，是因为它们来自人类。随着社会发展，我们天生在乎人类，我们会在乎人们在乎什么。

And I think just from a kind of intellectual humility point, anytime you're that wrong and that confident, which I think we were as a field, you have to update. There are a bunch of takeaways. One is that AI is very jagged: superhuman genius in some ways, dumb toddler in others. People have extremely complementary skills to AI. Another is that people have a great degree of trust and enjoyment in working with other people. You can hire an AI consultant, sales rep, or engineer, but most people still prefer interacting with a human. I definitely would rather engage with a person than with an AI for almost everything. I also think that human values have value because they're human. As society evolves, we are deeply hardwired to care about people, we're going to care about what people care about.

今天已经有这样的例子：AI 能生成惊人的图像，但人们只想要人类创作或至少是人类挑选的作品。有个笑话是，目前艺术品上的签名才是价值所在，但事实是，你想了解背后的人。读一本小说，你想了解作者。在商业层面，比如我的工作，世界想知道谁在为公司做决策，如果出了问题该问责谁，他们并不想要一个 AI 首席执行官。回想一下你在商业中承担的风险组合，那些真正成功的项目，大多数在开始时是不是都不受欢迎？

There are versions of this you can see today where AI can make incredible images and people only want ones that are created by a human or at least chosen by a human. There's the joke that at this point, the signature on a piece of art is most of the value, but the truth is you want to know about the person behind it. You read a novel, you want to know about the person behind it. And then in terms of business, I think for my job, for example, the world wants to know about the person responsible for the decisions of a company, who they will hold accountable if they make bad ones, and they don't really want an AI CEO. If you think back on the portfolio of risks you've taken in business, is it the case that most of the ones that really worked well were initially not popular?

是的，确实如此。这是我分别从 Peter Thiel 和 Paul Graham 那里学到的，虽然方式不同。最优秀的公司和投资机会几乎从来不是那些看起来非常受欢迎的。随大流、稍微提前一点，你可以做得还不错，但要取得非凡的成就，你几乎总是要做别人不做的事。你不能随波逐流。

Yes, that's for sure. This is the thing I really learned from Peter Thiel and Paul Graham, in two different ways. The very best companies and investment opportunities are almost never the ones that look really popular. You can do okay by following the trend and being a little early, but to do spectacularly well, you almost always have to do things that are not what everybody else is doing. You cannot follow the new wave.

你身处其中的模型周期一直在加速，未来六个月——或者不管具体多久——将比过去几年取得更多进展。能跟我们讲讲生活在这样的模型周期中是什么感觉吗？

If you think about the model cycle you've been in, which has been accelerating, and the fact that the next six months—or whatever the number is—will see more progress than the last several years. Can you bring us into what it's like to live in that model cycle?

过去十年我学到的最重要的事情之一是，人类几乎可以适应任何事情。世界可以从把疫情当笑话，到完全封锁，然后在极短的时间内习以为常。现在要么已经有了 AGI，要么已经很接近了，大家的反应是‘好吧，AGI 来了’。在个人生活中，无论是有孩子这样的好事，还是失去父母或分手这样的坏事，你原本以为永远无法适应，但最终都能适应。经历奇点比我想象的要平常一些。看着模型不断变好，仍然令人兴奋。我每天早上做的第一件事就是查看模型训练进度。进步越来越快，我的期望也更高了，但感觉仍然很酷。

One of the most interesting things I've learned in the last decade is that people can get used to almost anything. The world can go from dismissing a pandemic as a joke to completely locking down, and then it feels normal in a shockingly short time. Now there's either AGI or close to it, and everyone is like, 'Okay, there's AGI.' In personal life, something incredible happens like having a kid, or something terrible like losing a parent or breaking up, and you think you can never adapt, but you do. Living through the singularity is less weird than I expected. It's not any less exciting to watch the models keep getting better. The first thing I do every morning is look at the model training progress. It happens faster and I have higher expectations, but it still feels really cool.

当你得到一个新模型时，你会做什么？怎么庆祝？那天早上是什么样子？进展越来越快，你有什么仪式吗？

When you get a new model, what do you do? How do you celebrate? What does the morning look like? It's happening faster and faster. What's your ritual?

现在有很多团队在负责不同的部分，每个团队都有自己的仪式。有的团队会做一件印着有趣表情包的卫衣。有的团队会去同一家酒吧。当知识边界首次被拓展时，那种身临其境的感觉——大多数人最想做的事就是第一个用上新模型。

Many teams now work on different parts of it, and different teams have different rituals. Some teams always make a sweatshirt with a funny meme. Some teams always go out to the same bar. The feeling of being in the room when the frontier of knowledge is pushed back for the first time—there's nothing most people would rather do than get to use the new model first.

你认为我们有正确的指标来衡量这些东西有多好吗？比如……

Do you think we have the right measurements of how good these things are? Like...

完全没有。从某种意义上说，真正重要的评估是它对人们是否有用。你可以用收入、使用量或新知识的发现速度来近似。我们有团队在研究，当模型达到超人类规模时，现实世界的评估应该是什么样的。

Definitely not. In some sense, the evaluation that matters is whether it's useful to people. You can approximate it by revenue, usage, or rate of discovery of new knowledge. We have teams working on what real-world evaluation looks like for models as they reach superhuman scale.

你自己使用 AI 的前沿是什么？

What is the frontier of your own usage of AI?

我最近开始尝试让 AI 查看我在电脑上看到的一切。这还没有完全实现。我还在探索我的舒适区和信任边界在哪里。一个收获是，我的记忆力跟 AI 相比差远了。能想起六周前读过的一封邮件，或者七个半星期前会议上的具体内容，并在恰当时刻提出来辅助决策——这感觉很神奇。

I started recently to experiment with letting an AI look at everything I'm looking at on my computer. I don't have this built yet. I'm still trying to feel out where the limits of my comfort and trust are. One takeaway is that my memory is terrible compared to an AI's. The ability to recall an email I read six weeks ago, or what exactly happened in a meeting seven and a half weeks ago, and have that brought up at the exact moment to inform a decision—that feels pretty magical.

太酷了。这听起来像是个个人智能体。每个人都能拥有它有什么障碍吗？我想要。

Pretty cool. This sounds like a personal agent. What are the barriers to everyone having that? I want that.

算力？假设我们能造出这样的产品，它完全按照我说的那样处理你所有的东西。

Compute? Let's imagine we could build this product that does exactly what I described for all your stuff.

始终在线。

Always on.

始终在线。查看你在电脑上看到的一切，监听每一次会议，阅读每一份文档。不仅如此——这本身就需要大量 token——你还可以拖动一个滑块：在我睡觉时，你可以花费这么多 token 来思考，为我提出有用的新想法，做任何你能做的工作，继续思考我下一步该做什么。花更多算力让第二天早上的输出变得更好。我会把滑块拖得很远。我愿意为此花很多钱。

Always on. Looking at everything you look at on your computer, listening to every meeting, reading every document. And not only that, which takes a lot of tokens, you can drag a slider: while I'm asleep, you can spend this many tokens thinking—come up with useful new ideas for me, do whatever work you can, keep thinking about what I should do next. Spend more compute to make your output better for me the next morning. I would drag that slider quite far. I'd be willing to spend a lot for that.

但如果世界上每个人都想把这个滑块拖得很远，所需的算力就太大了。

But the amount of compute that would require, if everyone in the world wants to drag that slider pretty far, is a lot.

我很想听听你对这种新智能本质的看法。最近有人告诉我，飞机不像鸟那样飞行。而这种智能是……

I'd love to hear you talk about how you think of the nature of this new intelligence. Someone told me recently that planes don't fly like a bird. And this intelligence is...

这是一种非常异类的智能。

It's a very alien kind of intelligence.

是的，这是一种非常异类的智能。大家都在说，如果你能验证某个东西，它就能赢——只要算力和智商足够，它就能暴力破解出解决方案。但在其他领域，人类及其数据和评估发挥了巨大作用，令人惊讶的是，要擅长法律推理之类的东西要花多少钱。我很好奇——我不知道你的孩子有多聪明。你的是男孩还是女孩？当他们七岁，到了懂事的年纪，你可以向他们描述这种智能的本质——你会怎么描述？

Yeah, it's a very alien kind of intelligence. And everyone's talking about how if you can verify something, it will just win—enough compute and enough IQ, it brute forces its way to a solution. But in other domains where humans and their data and evals have been a huge part, it's surprising how much money it cost to get good at, I don't know, legal reasoning or something. I'm curious—I'm not sure how beautiful your kid is. You have a boy or a girl? When they're seven, age of reason, and you can describe to them what the nature of this intelligence is—how would you describe it?

这是一个美丽的问题。

It's a beautiful question.

我不确定以前有人问过我这个问题，或者任何类似的版本。现在浮现在我脑海里的是，我会说它就像一台计算机。它就像一台计算机，可以做很多人类做不到的事，比如快速计算两个巨大数字的乘积并给出答案，但它也做不了人类轻而易举就能做到的一些事。它做不到的事情的数量，我预期会不断减少。但在一个不断变化的世界里，我认为人类的判断力和品味仍将很难被 AI 建模，比如判断力将往哪个方向走。我找不到合适的词来描述它。它不完全是品味。世界可能需要一个新的词汇来描述人类非常擅长、而 AI 似乎真正难以应对的那种判断力。

I don't think I've been asked this before or even any version of it. The thing that's coming to mind right now is I would just say it's like a computer. And it's like a computer in the way that it can do a lot of things that people just can't do like multiply two gigantic numbers very quickly and give you the answer and then it cannot do some things that you would very easily do. The number of things that it can't do, I expect to keep receding. But in an evolving world, I think human judgment and taste will continue to be hard for AIs to model like where that's going to go. I don't have the right word for this. It's not quite taste. The world may need a new kind of word for the kind of judgment that people are very good at that AI seem to really deeply struggle with.

在这个时代成为父亲、看着孩子长大是什么感觉？我回想起你早期对互联网的乐观态度。他们将在廉价而丰富的智能时代长大。

What's it been like becoming a dad and having growing kids in this era? I'm thinking back to your optimistic early internet days. They're going to grow up in cheap abundant intelligence age.

有孩子绝对是我做过的最棒的事。所有人都这么说。所有人都说，你无法真正理解这种感觉。所以我大概知道，我相信了足够多的人说过的话，所以我相信这是真的。但对我来说，它成真的程度令人惊讶。我觉得我拥有世界上最好、最有趣的工作，但它仍然远远排在第二，仅次于拥有孩子。

Having kids is by far the best thing I have ever done. Everybody says that. Everybody says you can't really understand it. And so I kind of knew that I believed enough people that said it that I believed it to be true. But the degree to which it has been true for me has been surprising. I think I have the best most interesting job in the world and it is still a very distant second to having kids.

所以感觉棒极了。

So it's been awesome.

嗯，这真是一个令人乐观的时刻。我的孩子永远不会在一个他们比计算机更聪明的世界里长大。如果你出生在 GPT-3 的时代，你有一段时间能拥有比模型更好的推理能力，尽管你出生时并没有。是的，你短暂地赶上了。这对他来说永远都不会显得奇怪。这永远都不会困扰他。我觉得他不会在乎。我想他会震惊地想象在黑暗时代，我们不得不使用那些不够智能的产品和服务。他能做到你和我永远无法做到的事情，他会有你和我从未有过的生活期望，而我将拥有一个更大的画布。

Uh and it is a real moment for optimism. My kids will never grow up in a world where they were smarter than computers. If you were born at the time of GPT3, you had a time where you had better reasoning than the models, even though you didn't when you were born. Yeah, you caught them briefly. That will never seem strange to him. That will never bother him. I don't think he'll care. I think he will be shocked to imagine in the dark ages when we had to deal with products and services that weren't incredibly smart. He will be able to do things that you and I never were able to do and he'll have expectations in life that you and I never had and I'll have like a much bigger canvas.

你是否以某种显著不同的方式运营公司、管理团队或领导他人，因为有了孩子？

Do you run the business or teams or lead people in any way that is notably different because of the experience of having them?

答案一定是肯定的。有了孩子后我感觉非常不同。我认为有很多小事情都变得完全不同了。而且，这也不是什么新见解。我觉得大多数有孩子的人都会说，一旦你有了孩子，你就会意识到你更关心他们和你们将拥有的经历，而不是你自己和你将留给他们的世界。我想我对这一点有一个不寻常的视角。人们有时会问我，‘哦，现在你有孩子了，你会担心 AI 安全，担心世界被毁灭吗？’我的回答是，‘我不需要孩子才担心；我之前就真的不想毁灭世界。’但我是否更多地思考人类能动性的角色以及拥有充实生活的意义呢？绝对是更多地为了我们正在构建的东西。而且我也希望我的同事也能拥有这些。你显然对你的孩子有非凡的共情，但这种共情延伸到所有孩子，然后可能到所有父母，再到每个人，这对我来说也是一个惊喜。

The answer must be yes. I feel very different having them. I think there's a bunch of small things that are really different. And then, again, this is not a novel insight in any way. I think most people who have had kids say, as soon as you have a kid, you realize that you care much more about them and the experience you're going to have, than you do about yourself and the world that you are going to leave them. And I think I have a sort of unusual vantage point for that. People ask me sometimes, 'Oh, now that you have kids, do you worry about AI safety and not destroying the world?' And the answer was, 'I didn't need kids; I really didn't want to destroy the world before.' But do I think more about the role of human agency and what it means to have a fulfilling life? Definitely much more for what we're building. And also the people I work with, I want them to have it too. You obviously have extraordinary empathy for your kids, but the degree to which that extends to all kids and then maybe to all parents and then to everybody, that's been a surprise to me too.

在一篇文章里，我觉得是那篇《你希望早点知道的事情》之类的，是关于激励的。要非常非常小心地设定激励。

In one of the posts, I think it was the one that's things you wish you knew earlier or something, is about incentives. Set them very very carefully.

是的。

Yeah.

你不对这家公司持有股权，这一直是你身上最令人费解和有趣的事情之一。世界应该如何理解你的激励？

It's always been one of the most puzzling and interesting things about you that you don't have equity exposure to this company. How should the world think about your incentives?

我不知道还能说什么，除了我拥有人类历史上最激动人心的时刻的前排座位，这对我来说比任何金钱都更有价值。我得以拥有极其有趣的生活，并与非凡的人一起从事我深为关切的事情。但不知何故，这似乎不算数，好像对人们来说还不够。

I don't know what I can say beyond like I have a front row seat to the most exciting moment of human history and that is worth more to me than any amount of money. I get to have an extremely interesting life and work with extraordinary people on something that I deeply care about. But somehow that doesn't count like that doesn't do it for people or something.

不是这样。

It's not.

我很好奇你怎么看待机器人技术。就像你之前提到的，在某个时候，如果我们像拥有自动化智能一样拥有自动化劳动力，事情可能会变得更加疯狂。劳动力市场比白领市场大得多。

I'm curious how you think about robotics. Like you mentioned earlier, at some point if we had automated labor in the same way we're going to have automated intelligence, things might get even crazier. The labor market is much bigger in the white collar market.

如果我们没有它，那么事情就真的会变得疯狂。如果人类在世界上扮演的角色只是云端 AI 的执行器，那就糟了，非常糟。非常糟。所以我认为如果我们不实现它，事情会比实现它更疯狂。

If we don't have it, then things get really crazy. If the role for people in the world is to be like the actuators of AI in the cloud, bad, very bad. Very bad. So I think it's much crazier if we don't get it than we do.

这是一个必须做的事情。帮我理解你在这方面的进展感，因为不同于 AI，现在大家都观点一致，认为它发展很快。

It's an imperative. Help me understand your sense of progress in that because unlike in AI where everyone is now kind of on the same page of like it's going fast.

你可以找到非常聪明的人说它就在今年年底，也可以找到非常聪明的人说它还要 20 年之类的。不是 20 年。我会说我们在未来两三年内就会迎来机器人领域的 ChatGPT 时刻。

I you can find extremely smart people that say it's like end of this year and you can find extremely smart people that say it's 20 years from now or something. It's not 20 years. I would say we get the ChatGPT moment for robotics in the next like two or three years.

那会是什么样的？你知道那是什么吗？

What would that be like? Do you know what that is?

一个大多数人都会真正惊叹的时刻。不像我看了一个机器狗做疯狂事情的视频，但 somehow 我能说服自己那是一件非常重要的事情。ChatGPT 时刻的一个特点就是你可以直接去使用它。

Something where most people have like a real wow. Not like I saw this video of a robot dog doing something crazy, but I was somehow able to convince myself that a really important thing happened. One of the things about the ChatGPT moment was that you could just go use it.

是的。

Yeah.

就像我不必相信某个说 AI 即将到来的人。你可以直接去试试。

Like I didn't have to believe someone who said AI is coming soon. You could just go try it.

是的。

Yeah.

如果你能输入一个指令，然后机器人就能做一些疯狂的事情，即使你不在现场也能观看，我想那会带来同样的‘哇，它真的做到了’的感觉。

And if you can go type in a command and a robot can do something crazy and you can watch it even if you're not physically there, I think that would have the same kind of whoa, it just did this thing.

ChatGPT 不是这个单一的目标，而是某种程度上你决定发布的一个副产品。你能讲讲那个故事吗？那可能对机器人领域发生类似的事情有启发意义。似乎每个人都想叠衣服，但也许它会是完全不同的东西。

Wasn't ChatGPT not this monolithic goal but sort of like a side experiment that you decided to release. Can you tell that story? That may be instructive for something similar happening in robotics. Everyone seems to want to fold laundry but maybe it's something very different.

当我们推出 GPT-3 时，我们试图赚钱，试图让人们使用这个 API。而当时唯一真正可行的商业用例简直太愚蠢了。如果你回去用一下，你会感到震惊。唯一可行的商业用例就是文案撰写，你知道吧。你付给某个营销公司 20 美元，他们付给我们 20 美分，让 AI 给你写个着陆页什么的。但除了那个商业用例之外，开发人员还在使用我们称之为‘游乐场’的东西，那是一个与模型对话的测试界面。

When we launched GPT-3, we were trying to make money, trying to get people to use this API. And the only commercial use case that was really working the model was just so dumb. Like if you went back and used it you'd be astonished. The only commercial use case that was working was copywriting, you know. So you pay some marketing firm 20 bucks and they paid us 20 cents for the AI to like write you a landing page or whatever. But in addition to that one commercial use case, developers were using this thing we called the playground which was like a testing interface to chat with the model.

这其实很难，因为我们没有把模型调教成善于聊天。你必须给它一些聊天的示例，然后才能做。但人们真的很喜欢。我从 YC 学到了一个重要的教训：如果你注意到用户在做某件事，就顺着那条路走下去。所以我们决定构建一个优秀的聊天机器人，因为那正是人们在使用的东西。我们开始研发，完成了 GPT-4，并在内部使用。这很了不起，我们觉得这是向世界展示 AI 真正更新的时刻，但也面临很多难题：这是否会带来大量假新闻、说出非常冒犯的话、让我们陷入麻烦。所以我们决定先推出一个较弱版本。同时推出聊天界面和 GPT-4 似乎太过了，所以我们先推出基于 GPT-3.5 的聊天界面。实际上，它最初叫做“与 GPT-3.5 聊天”，我们并没把它当作产品来规划，也没想到它会大受欢迎，但我们确实认为它能让全世界的人跟上步伐，意识到有些事情正在发生。幸运的是，我们在发布前几小时将它改名为 ChatGPT，并以研究预览的形式推出。我们的想法是，以研究预览形式发布，几个月后再推出基于 GPT-4 的产品。不管是什么原因，那个模型突破了阈值。尽管我们在内部已经习惯了，但人们说‘这太棒了’。当时实用性可能还没有那么强，但这对人们来说是一个不可思议的时刻，他们感受到了 AI 的进步，并使用了让他们喜欢的东西。而当 GPT-4 推出时，他们真的从中受益了。

And it was really hard to do because we had not tuned the model to be good to chat with. So you had to give it a few examples of what it means to chat and then do it. But people really liked it. And I had learned this great lesson from YC: if you notice your users doing something, go down that path. So we decided we would build a good chatbot since that's what people were doing. We started working on that and we finished GPT-4 and started using it internally. Like this is a big deal, and we kind of thought, all right, this is going to be a real update to the world about AI, and there are a bunch of hard questions about whether this is going to create a bunch of fake news, say really offensive things, get us in trouble. So we decided we would start with a weaker version. The chat interface and GPT-4 at the same time seemed like a lot, so we would roll out the chat interface with GPT-3.5. In fact, it was originally going to be called Chat with GPT-3.5, and we didn't plan it as a product. We didn't think it'd be a huge hit, but we did think it would get people around the world to catch up and realize something was going on. And we mercifully renamed it ChatGPT a few hours before launch and put it out as a research preview. The thought was we'd put it out as a research preview, and then a few months later we would launch a product with GPT-4. And for whatever reason, that model was over the threshold. Even though we had gotten used to it internally, people said, 'Okay, this is awesome.' There maybe wasn't that much utility yet, but it was an incredible moment for people to feel AI progress and use something they enjoyed using. And then by the time we put out GPT-4, they got real benefit from using it too.

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你是否感到惊讶，这仍然是人与这种外星智慧之间的直观界面，甚至包括编程？就像大多时候是我对着电脑告诉它要构建什么。

Are you surprised that that remains kind of the intuitive interface between us and this alien intelligence, even including coding? Like mostly that's me talking to the computer telling it what to build.

不，因为我是一个重度文字聊天者。我一生都是重度文字聊天者。我认为我自己为什么觉得这是个好界面的部分洞察是：我知道怎么做，我知道在文本框中开始聊天是什么感觉。

No, because I'm like a massive texter. I've been a massive texter my whole life. I think part of my own insight of why that was a good interface is I'm like, I know how to do this. I know what it's like to just start chatting in a text box.

对扩散这个概念以及如何让它更快，你还有其他想法吗？如果使命是将智能交到人们手中，让它对每个人都更有用，那么关键部分就是——我不确定，是营销活动还是什么——你如何让这个扩散速度比它自然发生的更快？

Any other thoughts on this notion of diffusion and how to make it faster? Like if the mission is to get intelligence into people's hands and make it more useful for everyone, a key part of that is—I don't know, a marketing campaign or something—how do you get this to diffuse faster than it seems to be doing naturally to me?

我认为关键是让它变得更好。我有点相信真正伟大的产品会自我营销。一开始并没有 ChatGPT 的营销活动。我认为随着我们进入下一个模型阶段，并弄清楚如何制造与模型本身一样出色的产品，将会产生如此巨大的实用性，以至于人们会非常迅速地传播它。我们确实应该做更多营销。AI 并没有因为使用量大而变得太受欢迎，或者人们对它的发展方向有着可以理解的焦虑。所以这些东西，我认为一些好的营销会有帮助。但从人们从产品中获得的价值以及让产品更快增长的角度来看，更强大的模型、更多的算力、更好的产品会做到这一点。

I think the key thing is just make it better. I kind of believe that a truly great product markets itself. There was no ChatGPT marketing campaign at the beginning. And I think as we get to this next stage of models and we figure out how to make products that are as great as the models themselves, there will be such incredible utility that people will spread it very quickly. We should definitely do more marketing. AI is not too popular for as much as people use it, or they have very understandable anxiety about where it can go. So that kind of stuff, I think some great marketing would be helpful. But in terms of value people are getting out of products and getting their products to grow faster, models, more compute, better products will do it.

曾经有一段时间，研究人员的招募、留任和激励是竞争格局中的定义性故事。我认为有很多关于你成功招募优秀研究者的故事，有许多人来到 OpenAI 并产生了巨大影响。有些是知名人士，有些则不太为人知。我只是好奇，关于如何招募这类人你学到了什么？他们在乎什么？你是怎么做的？我从未听你谈论过早期招募某人的具体战术行动。

There was this period where the recruiting of researchers, the retention of them, the incentivizing of them was like the defining story in the competitive landscape or whatever. I think there's lots of stories about you successfully recruiting great researchers, and there's been many that have come through OpenAI and had huge impacts. Some of which are known, some of which are lesser-known names. I'm just curious about this whole genre of what you learned about how to recruit this class of person. What matters to them, and how you did it. I've never heard you talk about the actual tactical moves you pulled to recruit somebody in the early days.

我认为很简单：我们相信 AGI 是可能的，值得去追求，而且我们愿意说出来，这是一个疯狂的异端信念。当我们首次宣布 OpenAI 时，领域内的所有巨头、专家都说这很疯狂、是炒作、不负责任。像 Yann LeCun 这样的备受尊敬的人告诉记者，‘这些家伙不怎么样，行不通的。’但我们能够说我们要去实现它，这确实吸引了特定类型的研究人员，他们也想要进行这种成功概率很低的疯狂冒险。一个雄心勃勃、大胆的愿景是一个非常有吸引力的招募工具。

I think it was quite simple: we believed that AGI was possible and it was worth going after, and we were willing to say that, and that was like an insane, heretical belief. When we first announced OpenAI, all these giants of the field, these experts, were saying this is insane, it's hyped, it's irresponsible. Really respected people like Yann LeCun were telling journalists, 'Oh these guys aren't very good and it's not going to work.' But the fact that we were able to say we're going to go for this really appealed to a certain kind of researcher that also wanted to go on this crazy adventure with low probability of success. An ambitious, audacious vision is a very powerful recruiting tool.

是的。

Yeah.

你写过，有时候做更难的事情反而更容易，因为这种原因。

You think you've written that it's actually easier sometimes to build things that are harder because of this reason.

我非常相信这一点。这是我给 YC 创始人们最常提的建议之一，而且在 OpenAI 我也努力践行它。就是做更难的事。

I super believe in this. It's one of my most frequent pieces of advice to YC founders and I tried to really live it at OpenAI. Just do something harder.

做有意义的事，做重要的事——如果你不做，如果你的公司没成功，这件事可能就不会发生。

Do something that matters like do something that is important and if you don't do it, if your company doesn't succeed, might not happen.

你曾是投资人，也是我们的投资人，你经验丰富，而且一度就以投资为业。你从站在另一边的投资人身上学到了什么？

You were an investor and our investor, you've done a lot of it and at one point that's what you did. What have you learned about investors being on the other side?

真正投入并试图帮助你的投资人数量少得惊人。Josh Kushner 是绝对的 MVP 投资人，难以置信，他几乎昼夜不停地帮了我们好几年。他是唯一一个我能指出来、始终积极主动提供巨大帮助的投资人。还有更多人本可以做到这一点。也有很多其他投资人也提供了帮助，给出了很棒的战略建议，在我们要求的时候也采取了行动。但是那种持续不断、全力以赴的支持，在投资人中却出奇地罕见。也许我有偏见，因为我一直喜欢别人也这样评价我，但我觉得创始人们真的很喜欢这种支持，而且它确实能带来改变；对投资人来说，这也是最有乐趣的做法。我和朋友经常玩一个游戏，我们互相发短信，短信的提示是“一件我不想让你知道的事”。这让你想到什么？我累了。我觉得我不应该这样——我已经做了很久了。很累。

The number of investors that actually show up and try to help you is unbelievably small. Josh Kushner, absolute MVP investor, unbelievable, has like worked around the clock for what feels like years to help us. He is the only investor that I could point to that is proactively incredibly helpful all the time. There are more people that could do that. And there are many other investors that have also been helpful and that have great strategic advice and that do things when, you know, we ask them to do it. But the constant, just relentless all-in support is surprisingly rare from investors. Maybe I'm biased because I always liked it when people said that about me, but I think founders really love that and it actually moves the needle and as an investor, it's the most fun way to do it. Me and my friend play this game where we text each other all the time and the prompt of the text is something I don't want you to know about me. What does that bring to mind? I'm tired. I don't think I'm supposed to—I've been doing this a long time. It's tiring.

你是怎么挺过来的？

How do you get through that?

就是继续前进。

Just keep going.

这引出一个问题：有没有某种程度的疲惫会让你停止做这件事？

It begs the question: is there an amount of being tired that would make you stop doing this?

不，不，不。我是说，这是世界上最酷的工作。我打算在我的职业生涯里一直做下去，但它比我能向人解释的还要难得多。我很感激能从事这份工作。这不是在抱怨。

No, no, no. I mean, this is the coolest job in the world. I plan to do this for the rest of my career, but it's much harder than I have a way to explain to people. I feel very grateful to get to do this. This is not me complaining.

接下来会发生什么？我们谈到过自动化的 AI 研究员，明年、后年——你怎么看待未来 6 到 36 个月会发生什么？也许在这个疯狂指数增长的时期，预测那么远不太现实。或许换个问法：假设从现在算起 23 个月后，我们有了所有人都公认的超级智能。那第 24 个月会发生什么？

What's coming next? Like we talked about automated AI researchers that next year, the year after—how do you think about what is happening in the next 6 to 36 months? Maybe that's too far out to forecast in this crazy exponential. Maybe a different version of the question is: let's say, you know, 23 months from now we have something that everybody agrees is superintelligence. What happens in month 24?

我的答案是：不会太多。那种机器神崇拜的状态——那些人所相信的是，很多事会迅速发生，但实际不会那么快。最终很多事情会发生，但最终很多事情本来就注定会发生。人类进步的速度，每个十年会有多不同，以及每个十年比前一个十年有多大的差异——这种现象已经持续很久了，当然有起有落，但趋势是向上的。我认为正确的思考方式是：每个人都想成为故事里的英雄，每个人都想感觉自己亲历了机器神的降临时刻，并扮演了疯狂的角色。但你看，这只是另一步。它在 50 年前很难想象，而 50 年后的那一步今天也很难想象。我认为正确的思维框架是拉远视角，这是一条相当平滑的指数曲线。

And my answer would be: not very much. The kind of cult worship of the machine god states—those people believe that more is going to happen quickly than is going to happen. Eventually a lot will happen, but eventually a lot was going to happen anyway. The rate of human progress and how different each decade is going to be and how much each decade is more different than the decade before—that's been happening for a long time, obviously ups and downs, but directionally. I think the right way to think about this: everybody wants to be the hero of the story, everybody wants to feel like they were there for the moment of the machine god and they played some crazy role. But you know, this is another step. It was hard to imagine 50 years ago, and the step 50 years from now is hard to imagine today. And I think the right mental framework is just to zoom way out and it's a pretty smooth exponential.

跟我聊聊看着 Codex 起飞的过程吧，以及在多大程度上这与我认为你们通过 Chat 建立的分销竞争优势有关。这引出了一个关于 AI 行业护城河的问题——你认为长期来看，什么会驱动真正的商业竞争优势？

Tell me a little bit about the experience of watching Codex take off and how much that is tied to what I would describe as like a competitive advantage of distribution that you built through Chat and this is a gateway into a question about Moats in general in AI—like what you think will drive real competitive advantage in the business over time.

我认为 Codex 之所以胜出，主要是因为它是最好的产品和最好的模型。我们从 ChatGPT 捆绑中获得了一些优势，但非常非常微小。这基本上不是关键因素。这让我对竞争优势这个问题反思很多，因为你知道，卓越的智能可以从任何产品迁移到任何其他产品。网络效应仍然具有竞争优势。经济规模和构建最便宜算力集群的能力等等——仍然有竞争优势。但产品优势——比如如果我们能让人们转向 Codex，而有人做出了更好的东西，他们也能让人从 Codex 迁移走。所以这让我思考了很多。有一个很有趣的问题：这是否正在走向商品化——智能会不会像原油一样成为纯粹可互换的商品？智能本身，我会说是的。那么什么不会商品化？算力集群，你知道，算力集群的规模，制造更多算力的能力。我认为这是一个非常持久的优势，即使产品本身不是，因为你知道，Codex 可以写你想要的任何软件。工作流、集成、那些复杂的流程、团队协作的能力——这些东西都非常强大。甚至品牌偏好和熟悉度也很强大。

I think Codex mostly is winning because it's the best product and the best model. We do get some advantage from ChatGPT bundling, but very, very tiny. That is mostly not what it's been about. It has made me reflect a lot on this question of competitive advantage, because you know, brilliant intelligence can migrate from any product to any other product. And network effects still have a competitive advantage. Economic scale and the ability to make the cheapest compute fleets whatever—still have a competitive advantage. But the product advantage—like if we could get people to move over to Codex and someone builds something better, they can get people to move from Codex. So it has made me reflect on that a lot. There's a really interesting question about whether this is going in the direction of a commodity—like is intelligence going to be a pure fungible commodity like crude oil or something? Intelligence itself, I would say yes. So what is not going to be? Compute fleet, you know, the scale of the compute fleet, the ability to make more compute. I think that's a very durable advantage even if the product itself is not, because you know, Codex can write any piece of software you want. The workflows, the integrations, the sort of complex processes, the ability for teams to collaborate together—that stuff is all pretty powerful. Even brand preference and familiarity is pretty powerful.

你对新硬件有多兴奋？显然你们在硬件方面做了有趣的东西，我肯定你们今年晚些时候会发布。这个实验给你的感觉如何，又是如何与你现有的消费者分销渠道结合的呢？

How excited are you about new—obviously you've done interesting stuff in hardware that I'm sure you'll announce later this year. How does that experiment feel and align with this sort of consumer distribution that you have?

我对新硬件感兴趣的原因之一是，我们之前谈到，AI 的一个强大之处在于它可以一直在线、主动工作，并理解你的所有上下文。但当前的硬件并不适合这一点。我们仍然在一种已经存在了 50 年左右的硬件范式内工作。计算机很了不起：键盘、鼠标、显示器。很了不起，但我们必须把 AI 塑造成适应那种形式。我很兴奋地思考——我希望 AI 能够引用这段对话，但又不至于让我愿意打开笔记本电脑，把它放在这里，让它在我说话时看着你、听着我们。但我希望有一款硬件，在社会层面能被接受做这件事，并且感觉它就是为此而设计的。

One of the reasons I'm interested in new hardware is we were talking earlier about how a very powerful thing with AI is that it can be always on and proactive and just understand all your context. But current hardware is not good for that. Like we are working inside of a hardware paradigm that is 50 years old or something like that. And computers are amazing: keyboard and mouse, monitor. It's an amazing thing, but we have to shape AI into that. And I'm excited to think about—I would love AI to be able to reference this conversation, but not so much that I'm willing to like crack my laptop open, put it here, and have it like looking at you and listening to us while it's going, but I would like a piece of hardware that socially was acceptable to do that and also felt like it was designed for that kind of thing.

思考那些开放性问题时，你内心、与朋友、与同事的争论中，哪些是你感到不确定但又觉得重要的开放辩论或问题？

As you think about the open questions, what debates in your own head, with your friends, with your colleagues here, what are the most interesting open debates or open questions that you don't feel certain about but feel important?

一个我认为没受到足够关注的问题是：我们如何避免认知萎缩？我们如何利用这些工具，确保自己不断拓展大脑，持续理解真正重要的事物？这方面有很多版本。我记得上学时，有位教授跟我说，你必须理解编译器，否则永远成不了好程序员。但不知怎的，这话并不完全正确。不过，在合理层面理解计算机系统的主要组件如何工作，对我来说一直很重要。

One that I don't think gets much attention is how we are going to avoid cognitive atrophy. How are we going to use these tools and make sure that we are stretching our brains more and more and continuing to understand the stuff that really matters? There are lots of versions of this. I remember when I was in school, a professor told me you have to understand compilers or you will never be a good programmer. Somehow that wasn't quite right. But understanding at a reasonable level how the major components of a computer system work has been important to me.

强迫自己想象一个场景：两年后算力因某种原因过剩。那会是怎样的故事？如果模型变得非常智能和高效，能完成我们所需的一切并构建我们想要的所有软件，而我们的注意力边界使其无法吸收超出相当有限算力所能提供的更多内容，那么我们就会陷入过剩。此外，如果我们因遇到某种 Scaling 壁而无法降低成本曲线，也可能陷入过剩。关于无上限需求的现象暗示了一个特定的价格。你如何看待当前的 Scaling 定律？

Forced to imagine a scenario where we are somehow oversupplied in compute in 2 years time. What would be that story? It does feel possible if the models get so smart and so efficient that they can do everything we need and build every piece of software we want, and if the bounds of our attention are such that they just cannot absorb more than what a fairly limited amount of compute can do, then we can get into oversupply. Also if we don't drive the cost curve down because we hit some sort of scaling wall, we could also get into oversupply. The observation about uncapped demand implies a certain price. Can you give your point of view on scaling laws today?

看起来很棒。就是看起来很顺利。从某种意义上说，Scaling 定律是有史以来最遭人恨的预测。每个人总想说情况不会这样，但它却一直在持续。

Looking great. Just looking good. In some sense, scaling laws are the most hated prediction of all time. Everybody always wants to say it can't be like this, and yet it keeps going.

在这家公司的故事中，你最喜欢的无名英雄是谁？

Who are your favorite unsung heroes in this company's story?

我第一个想到的人是 Alec Radford。他可能是整个领域历史上最重要但不太知名的研究员，也是一个非常棒、顶尖的人。他完成了真正成为 GPT 系列的工作，还有很多其他重要的事情。他也启发、引导和推动人们走向许多其他非常重大的方向。我觉得他很酷的一点是，如果你和与他共事过的人交谈，他们当然会说他是世代天才、杰出的创新思想家，理解和工作极其深刻。但每个人都会在结束发言前告诉你，他是他们遇到过的最友善、最积极、最好的人之一。

First person that came to mind is Alec Radford. Alec Radford is probably the most important not very well-known researcher in the whole history of the field and also just a wonderful, top-tier human being. He did the work that really became the GPT series, among many other important things. He also is someone who inspired, guided, and nudged people in many other directions that turned out to be super important. The thing I think is cool about him is that if you talk to people who worked with him, they will of course say generational genius, brilliant, innovative thinker, so deep in his understanding and his work. But everybody will tell you, before they finish their statement, that he is just one of the nicest, most positive, best people they've ever interacted with.

我喜欢成长时刻。所以在我们结束之前，我想问两个问题。回顾整个 OpenAI 的经历，你最自豪的是哪个时刻或篇章？先从另一个开始：最让你受教的错误或过失是什么？从中学习是怎样的体验？

I love formative moments. And so as we wind up here, I'm curious to ask what one of each. If you think about the whole OpenAI experience, what moment or chapter are you most proud of? Start with the other one: what was the most instructive thing that maybe you got wrong or did wrong, and what was it like to learn from it?

很多事都出过问题。一个我很少提及的形成性错误是，我们一开始试图在结构上创新。我们有很好的理由：我们不知道如何赚钱，也完全不确定长大后我们会是什么样子。我们当然关心使命，希望即便技术快速起飞，使命也能得到保护。所以我们采用了非营利结构。但我确实学到了为什么人们不常这样做。如果我们没有在结构上创新，而是找到其他方式维护使命的核心地位，我们本可以避免很多痛苦。也许对我们正在做的事情和它的重要性来说，没有其他方式，只有那种非常规的结构。在过去的十年里，我深刻体会到了为什么人们通常不这么做。

A lot of things have gone wrong. A formative one that went wrong which I haven't talked about much is we made a mistake to try to innovate in our structure in the beginning. We had a very good reason for it: we didn't know how we were ever going to make money and we really weren't sure at all what we were going to look like when we grew up. Of course we care about our mission and we wanted to be structured in a way where even if the technology went on a very fast takeoff, our mission was protected. So we had this nonprofit structure. But I definitely learned something about why people don't do that much. We would have saved ourselves a great deal of pain in many ways if we had not tried to innovate on our structure and found some other way to preserve the central importance of the mission. Maybe there was no other way for what we were doing and the importance of it. There was nothing other than an exotic structure we could have come up with. I really learned over the last decade a big lesson about why people don't usually do that.

还有没有其他成长经历塑造了你，但我们没聊到的？这个问题总是让我最感兴趣。

Is there anything else formative of your life that makes you you that we didn't talk about? I find this question always the most interesting.

变得相对免疫于别人对我的强烈看法。我想这是我后来培养的，意识到如果你身处这场疯狂革命的核心，每个人都会把很多东西投射到你身上，你必须迅速学会与之和解。我也在后来学会了如何保持非常冷静，不对事情感到焦虑。关于什么驱动我、我在乎什么、以及我总体上想如何生活，我觉得不知为何，十岁的我就已经相当完整了。我想我就是这样天生的。

Becoming relatively immune to people having strong opinions about me. I think I developed that later in life, realizing that if you're going to be at the center of this crazy revolution, everybody is going to project a lot onto you, and you have to quickly learn to make peace with that. I also learned later in life how to be very calm and not really anxious about stuff. In terms of what drives me and what I care about and how I want to live my life overall, I felt that for whatever reason, the ten-year-old version of me was pretty fully formed. I think I just came out this way.

那回顾过去，你感到自豪的是什么？

How about the thing you're proud of looking back on?

我最自豪的是，在很多次全世界都错了的重要时刻，我们对了，从而将世界带到了一个我非常自豪能参与其中的轨道上。这感觉太棒了。此外，尽管发生了那么多糟心事，我所获得的精神成长——无论你怎么称呼它——让我学会了不可思议的韧性，以及这对我余生幸福的意义。是的，对此我非常感激。

I'm most proud of how many times we were right when the rest of the world was wrong in an important way that put the world on a trajectory that I'm very proud to have played a role in. That feels awesome. And also, for all the crap that's happened, the spiritual growth or whatever you want to call it that I've gotten to have, learning incredible resilience and what that does for making me happy in the rest of my life. Yeah, very grateful for that.

在做这些访谈时，我对每个人都问同一个传统的结束问题。别人为你做过的最友善的事是什么？

When I do these, I ask everyone the same traditional closing question. What is the kindest thing that anyone's ever done for you?

我感到非常幸运，一生中有很多人对我格外友善。回想起来，生命中充满了人们对我无比好的瞬间。昨天，我的孩子第一次和我分享了他的蓝莓。那非常暖心。好时刻。谢谢你，伙计。谢谢。

I feel incredibly lucky about how many people have gone way out of their way to be very kind to me throughout my entire life. As I'm thinking of this, there's just this montage of moments from life where people have been unbelievably nice to me. Yesterday, my kid shared his blueberries with me for the first time. That was very sweet. Good moment. Thanks, man. Thank you.

你知道微小的优势如何随时间复利吗？投资中如此，运营公司也一样。你的支出系统就是你的资本配置策略。RAMP 默认使其更智能。更好的数据，更好的决策，随着时间的推移带来更好的经济效益。详情请访问 ramp.com/invest。随着业务增长，Vanta 与你一同扩展，自动化合规，为安全和风险提供单一真实来源。了解更多请访问 vanta.com/invest。从 OpenAI 到 Cursor 到 Perplexity，最优秀的 AI 和软件公司都使用 Work OS 在数夜之间（而非数月）实现企业级就绪。访问 workos.com，跳过不光彩的基础设施工作，专注于你的产品。

You know how small advantages compound over time? That's true in investing and just as true in how you run your company. Your spending system is your capital allocation strategy. RAMP makes it smarter by default. Better data, better decisions, better economics over time. See how at ramp.com/invest. As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk. Learn more at vanta.com/invest. The best AI and software companies from OpenAI to Cursor to Perplexity use Work OS to become enterprise ready overnight, not in months. Visit workos.com to skip the unglamorous infrastructure work and focus on your product.

Ridgeline 正在重新定义资产管理技术，它不是单纯的软件供应商，而是真正的合作伙伴。他们帮助公司实现了 5 倍的增长和规模化，带来了更快的增长、更智能的运营和竞争优势。访问 ridgelineapps.com，看看他们能为您的公司解锁什么。每家投资公司都是独一无二的，通用 AI 无法理解您的流程。Rogo 可以。它是一个专为华尔街打造的 AI 平台，连接您的数据，理解您的流程，并产生真实的输出。请访问 rogo.ai/invest 了解详情。

Ridgeline is redefining asset management technology as a true partner, not just a software vendor. They've helped firms 5x and scale, enabling faster growth, smarter operations, and a competitive edge. Visit ridgelineapps.com to see what they can unlock for your firm. Every investment firm is unique, and generic AI doesn't understand your process. Rogo does. It's an AI platform built specifically for Wall Street, connected to your data, understanding your process, and producing real outputs. Check them out at rogo.ai/invest.