# (77) A Cheeky Pint with Anthropic CEO Dario Amodei - YouTube
- Transcript ID: `gcqq1ebbqkc`
- Source tier: `youtube_creator`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (157/157 chunks)
- Original source: https://www.youtube.com/watch?v=GcqQ1ebBqkc
- Raw evidence: `data/transcripts/youtube/GcqQ1ebBqkc.json`
> 中文是派生翻译，英文是留存的源文本。中文未复核时，以英文和原始音视频为准。
## 00:00:00
**中文**
我一直很想知道，和自己的兄弟姐妹一起创业是什么体验？我不知道你为什么问我，因为你自己就知道。模型仿佛想要学习，也想在市场上取得非凡成功。是的，除了学习冲动，模型还有一种资本主义冲动。
**English / Original**
I'm excited to finally learn what is it like to start a company with your sibling? I don't know why you're asking me that question, because you know. It's like the models want to learn, the models want to be extraordinarily successful in the market. Yes, right, in addition to having this learning impulse, the models have this capitalistic impulse.
## 00:00:16
**中文**
有时人们谈到 API 业务，会说“它的黏性不强”或“它会商品化”。我经营的就是 API 业务，而且我喜欢 API 业务。没错。我认为未来模型犯错会比人类少得多，但它们犯的错会更加古怪。
**English / Original**
Sometimes people think of the API business and they say, "Oh, it's not very sticky," or, "It's gonna be commoditized." I run an API business. I love API businesses. No, no, exactly, exactly. I think we're gonna be in a world where the models will make mistakes much less often than humans, but they'll be stranger mistakes.
## 00:00:33
**中文**
所以我们需要为大语言模型发明一种“口齿不清”的信号。这样应该就能正确倒酒了。哇。达里奥是 Anthropic 的 CEO，Anthropic 是当今前沿 AI 实验室之一。几年前他还是 AI 研究员，如今已在经营全球增长最快的企业之一。干杯。今天我很期待聊聊 Anthropic 的业务。
**English / Original**
So we need to invent slurring for LLMs. And that should correctly pour it. Oh, wow. Dario is CEO of Anthropic, one of today's frontier AI labs. He's gone from being an AI researcher just a few years ago to now running one of the world's fastest growing businesses. Cheers. Cheers. So, I'm excited to talk a bit about the Anthropic business.
## 00:00:55
**中文**
你学习过物理学和计算神经科学，之后在百度、Google Brain 和 OpenAI 工作，随后创办 Anthropic。我们会谈 Anthropic 的业务，但我终于想知道：和自己的妹妹一起创业是什么体验？我也可以反问你。经营一家公司似乎需要做好两类事情。
**English / Original**
You studied physics and computational neuroscience. Yes. You then worked at Baidu, then Google Brain, then OpenAI, and then started Anthropic. Yes. And we'll get into the Anthropic business, but I'm excited to finally learn what is it like to start a company with your sibling? I could ask the same question of you, but you know, it's almost like there's two things you need to do when you're running a company.
## 00:01:19
**中文**
一类是把运营执行到位，另一类是制定好战略，看清最重要、或别人没有看到的事情。后者是我的工作，前者是 Daniela 的工作。我们各自擅长自己负责的事情，因此大部分时间都可以投入自己最擅长的领域。
**English / Original**
You need to operationally execute and you need to have a good strategy and kind of see the most important thing or the thing that no one else sees. And so my job is the second, and Daniela's job is the first. And we're both good at the things that we do. And so I think it's allowed us each to spend most of our time on the thing that we're best at.
## 00:01:43
**中文**
这里大概也涉及信任。科技乃至 AI 行业的联合创始人组合通常并不稳定；有一个长期相处、彼此深度信任的人很重要。是的，就是完全、彻底的信任。其实还不止如此，Anthropic 有七位联合创始人。
**English / Original**
Presumably there's something about the trust side of things as well, where cofounder teams in general, in tech, I guess, and AI as well. I mean they're unstable pairings and just having someone where you've a long running and deep trust— Yeah, yeah, where you have just total and complete trust. I mean, I think even beyond that, Anthropic has seven cofounders.
## 00:02:00
**中文**
创业时，几乎所有人都告诉我们：“七位联合创始人一定是灾难，公司转眼就会散架，所有人都会彼此争斗。”我决定给每个人同样多的股权，更招致了许多负面评价。但 Daniela 和我是兄妹，而七个人中有些彼此认识很久，有些过去不只是认识，而是真正共事过。我认为这使我们始终能够保持一致。
**English / Original**
When we founded it, basically, the advice from pretty much everyone was like, "Seven cofounders is a disaster. The company will fall apart before you know it. Everyone will be fighting with each other." There was even more negativity on my decision to give everyone the same amount of equity. But what we found, and I think it was because, obviously, me and Daniela are siblings, but then all seven of us, some of us knew each other for a long time or had a history of working, not just knowing each other, but working together in the past, and I think that really allowed us to always be on the same page.
## 00:02:34
**中文**
尤其随着公司扩大，七个人都真正承载公司的价值观，并把它传递给更广泛的人群，这让公司在保有原有价值观和团结的同时扩展到大得多的规模。我想谈 Anthropic 的业务：据近期报道，你们的年度经常性收入已突破 40 亿美元。大家理所当然地大量讨论你们开发的技术，但这本身也是历史上增长最快的企业之一。
**English / Original**
And I think especially as the company grows, the idea that you have seven people who really carry the values of the company and project them to a wide set of people, it allows you to scale the company to a much larger size while holding onto the values and the unity that we have. So I wanna ask about the Anthropic business, because again, it's an incredible story where it was reported recently that you'd blown through $4 billion in ARR, and so it's a lot of discussion correctly about the technology that you're developing, but also, this is just one of the fastest growing businesses in history.
## 00:03:11
**中文**
所以我想谈谈 AI 市场。大家究竟在用 AI 做什么？有编程、客服，但所有收入到底来自哪里？用途非常广，而且一直在变化。增长最快的应用无疑是编程——尽管远非唯一用途，我们还有许多其他应用。
**English / Original**
And so I wanna talk a bit about the AI market, and maybe the place to start is, what is everyone doing with AI? Like, there's coding, there's customer service work, but like, where does all this revenue come from? Yeah, there's a wide range of things, and it's kind of changed over time. I would say definitely the application that has grown the fastest, although it's not very far from the only application, we have a wide range of them, is definitely coding.
## 00:03:40
**中文**
它增长如此之快，除了我们专注编程、模型也擅长编程外，我认为更反映了技术在社会中的扩散。如今 AI 模型在每个领域都有巨大的能力余量：它们能够做的事情，远多于今天实际部署出来的事情。
**English / Original**
And my theory on why it's grown so fast, other than that we focused on coding and the models are good at coding, it's actually really a statement about kind of societal diffusion. Which is that if we look at today's AI models, I think in every area, there's a huge overhang in terms of what they could do compared to how they're actually being deployed today.
## 00:04:01
**中文**
扩散存在摩擦，大型企业的人还不熟悉这项技术。看看银行或保险公司的工作：即使模型不再进步、我们也停止在模型之上开发产品，单个企业中仍有数十亿美元级的潜力。
**English / Original**
There's some friction, people at large enterprises are not familiar with the technology. I look at what a bank does or what an insurance company does, and there's huge potential, even if the model stopped getting better, right? Even if we stopped building products on top of the model, there's still huge, billion dollar potential in an individual enterprise.
## 00:04:23
**中文**
我接触的企业 CEO 往往完全明白这一点。但一家拥有一万或十万人的公司，整个组织本就是围绕某种固定方式运营的，改变需要时间。写代码的人在社交和技术上则都与开发 AI 模型的人非常接近。
**English / Original**
And often, the CEOs of companies that I talk to understand that perfectly well. But if the company is a 10,000 or a 100,000 person company. Companies that size, they're set up to operationally do a certain thing a certain way, and it takes time to change them. But in code, the people who write code are very socially and technically adjacent to the folks who develop AI models.
## 00:04:46
**中文**
因此技术在编程领域扩散得很快。开发者也是习惯新技术的早期采用者。所以编程业务大幅增长的首要原因，是从业者及相关初创公司采用得快，而且非常了解这项技术。
**English / Original**
And so the diffusion is very fast. They're also the kind of people who are early adopters, who are used to new technology. And so I think the big growth in code, I would say the biggest cause of that is just that the people doing it and the startups devoted to it are fast adopters who understand the technology super well.
## 00:05:06
**中文**
但用途绝不限于编程。许多公司在做工具调用，也有你提到的客服；我们与 Intercom 等公司密切合作。生物领域也开始出现应用：我们既与制药和医疗保健公司合作，也参与基础科学研究。
**English / Original**
But it's by no means limited to code at all. If you look at, there are a bunch of companies that do things like tool use. There are, as you mentioned, customer service. You know, we work closely with companies like Intercom. We're starting to see some things on the biology side. We're working both with pharmaceutical and healthcare companies, and we're working on the side of kind of basic scientific research.
## 00:05:35
**中文**
例如我们与 Benchling 合作，也与一些大型制药公司合作。此前我们和诺和诺德合作撰写临床研究报告。完成临床试验后，需要整理结果，包括不良事件和统计数据等，这就是临床研究报告。
**English / Original**
We work with companies like Benchling, for example. But we also work with some of the very large pharma companies. There was something done a while back where we worked with Novo Nordisk to write clinical study reports. So clinical study reports are like, you've done a clinical trial, and then you would kind of write up the results, and it's like, these are the adverse events, these are the statistics.
## 00:05:56
**中文**
临床研究报告通常要花约九周，Claude 大约五分钟就能完成，之后人类再用几天核查。由此可以清楚看到加速潜力；随着模型改进，它们也会深入研究环节。概括来说，编程遥遥领先，但后面还有很长的应用长尾，其中一些用例同样意义重大。
**English / Original**
And the clinical study report normally takes like nine weeks. Well, Claude could do it in like five minutes, and then it took a human a few days to check it. And so you can really see the opportunity for acceleration, and as the models get better, you know, they'll reach into the deep research as well. So, I guess a way to summarize it would be to say that kind of code is out in the lead, but we see a long tail of quite a lot of other stuff, including some some very, very significant use cases.
## 00:06:23
**中文**
编程或许是其他一切领域未来变化的早期指标或预兆：同样的指数增长，只是发生得更快。许多地方都能从 AI 获得显著提升，但工程师习惯采用新工具。想想 Hacker News 上人们如何争论最佳工具，他们热衷于采用新工具。
**English / Original**
I think code is maybe an early indicator, like a premonition of what's gonna happen everywhere else. It's the same exponential. It's just faster. It's happening faster. Right, so there are many places where there's significant AI uplift, but engineers are used to adopting... Like, you think about Hacker News and people arguing over the best tools, people are passionate about adopting new tools.
## 00:06:42
**中文**
Claude Code 发布两小时后，就会有人拿它尝试一万种事情、接入所有框架；Twitter 两小时形成一种看法，再过两小时又修正。把这种速度与制药公司在研究中采用它的速度相比，差异很明显。
**English / Original**
Like two hours after we released Claude Code, there's some person out there who's tried 10,000 different things with it and plugged it into all the frameworks, and Twitter forms one opinion after two hours and then revises an opinion in two hours. And you think of the speed of that as compared to the speed that a pharmaceutical company can use it in research, right?
## 00:07:05
**中文**
传统零售企业也一样。我们希望把 AI 带到所有领域；世界上一些最大的收益来自影响实体经济，但这在本质上不可能以同样速度发生。你们如何决定哪些垂直领域自己做，哪些留给平台伙伴？你们有 Claude Code，也有 Windsurf、Cursor 等平台公司。
**English / Original**
Or that traditional retail company. And we wanna bring everything— Some of the biggest benefits in the world are touching the physical economy, and we wanna get there, but it just intrinsically does not happen at the same speed. How do you decide which verticals to do yourself versus which to allow platform... Like, you have Claude Code, and obviously, there's also platform companies like Windsurf and Cursor and everyone like that.
## 00:07:29
**中文**
你们发布了 Claude for Financial Services；想必也有些垂直领域你们会说“不由我们开发工具”。我们还有 Claude for Enterprise，它不是垂直产品，而是面向企业的通用方案。我们的思路是首先把自己视为一家平台公司。
**English / Original**
You launch Claude for Financial Services. Presumably there are other verticals where you say, "Well, we're not building a tool there." we have things like Claude for Enterprise, which is not a vertical, but a general play to go with enterprise. I think the way we like to think about it, I think we think of ourselves as a platform company first.
## 00:07:49
**中文**
可以类比云服务。要打造我们希望在不多年后达到规模的超大型平台业务，仍有若干理由要提供第一方产品，而且某些垂直领域会更依赖第一方。其中一个理由是直接接触用户。
**English / Original**
The analogy here would be maybe clouds or something. If you think of a really large platform business of, the size we're trying to get to in hopefully small number of years, there are a number of reasons why you would also want to have things that are first-party and some verticals end up being more first-party heavy. One is when you want to have direct exposure to the users.
## 00:08:13
**中文**
终端用户能让你了解他们究竟如何使用、最需要什么。纯平台若没有直接联系，会在多方面处于劣势，很难做出最佳产品，甚至很难知道模型真正需要朝哪个方向发展。
**English / Original**
The end user gives you some sense of how exactly are they using it, what are they most looking for? If you're a pure platform and you don't have that direct connection, you can be disadvantaged in various ways. It's hard to build the best products. Yeah, it's hard to build the best products. It may even be hard to know where the model really, really needs to go, right?
## 00:08:32
**中文**
人们会笼统地说“编程”，但许多模型看似擅长编程，却并非以真正切合用户需求的方式擅长。我们让 Claude 在人们实际使用的维度上表现良好，这是原因之一。另一原因还是大型企业：对传统公司而言，直接基于 API 开发有时更困难。
**English / Original**
People say things like coding, but like, there are many models that seem to be good at coding, but they aren't good in the way that's actually relevant, right? We've actually managed to make Claude good in a way that's relevant to what people actually use. I think that's one reason. Another reason goes back to the large enterprises, where building on an API, sometimes it's more challenging for a more traditional company to do that.
## 00:08:59
**中文**
因此需要给它们更容易使用的东西：要么提供开发套件，要么直接提供应用。企业也很喜欢 Claude Code；我们正逐渐把 Claude for Enterprise 发展成所谓的“虚拟同事”。不过我很难想象 Anthropic 去开发“油气勘探版 Claude”。为什么？
**English / Original**
And you need to give them something that's a little bit easier to use, either a kit to help them build things, or you need to give them an app. Enterprises have also liked Claude Code, and we're gradually developing Claude for Enterprise into, what we call a virtual coworker. I find it hard to picture Anthropic developing Claude for oil and gas exploration, and why is that?
## 00:09:21
**中文**
为什么你觉得难以想象？也许它其实就是你们下一项发布。不过我们目前确实没在做油气勘探版 Claude。我会区分我们明确不允许的用途——例如违法用途——与其他用途。作为平台，人们会用它做很多事情。
**English / Original**
Why is it that you find it hard to imagine? Yeah, or I mean, maybe in fact it's the next launch, but... Yeah, we're not currently working on Claude for oil and gas exploration. I would draw a distinction between things we just, things we just don't allow, right? Things that are illegal, or things like that. And there are a number of use cases that it's like, okay we're a platform, people are gonna do a bunch of things.
## 00:09:43
**中文**
有些用途我们只是没有热情，不会赶在其他用例之前主动推动。我们投入科学和生物医学的力度，可能超出其即时盈利能力所能解释的程度，因为我们认为它们值得做。
**English / Original**
We're just not passionate about it. But like, we're not passionate about it. We're not gonna go out and make this happen before the other use cases. I think there is a component of that where, probably we work on things like science and biomedical out of proportion to its immediate profitability. Because you guys think it's worthwhile.
## 00:10:03
**中文**
因为我们认为值得。对发展中世界的相关项目也是如此。还有一个有争议、外界看法恰好相反的例子：国防和情报工作。人们常说“这些人向现实低头、唯利是图了”。我认为恰恰相反。例如我们与美国国防部和情报界签了一份上限 2 亿美元的合同。
**English / Original**
Because we think it's worthwhile. We feel the same way about things in the developing world. One I'll give you that's controversial, people think about it the opposite way. So the work we do on defense and intelligence, people are often like, "Oh, these guys are selling out." I think about it the opposite way, right? So, you know, there was this contract with a ceiling of $200 million with, you know, the DOD and intelligence community.
## 00:10:26
**中文**
人们说“Anthropic 果然妥协了”。实际正相反：从某家编程初创公司再获得 2 亿美元收入，所需努力要少一个数量级。我们做国防，是因为认为捍卫民主值得，而且会设定边界。
**English / Original**
People are like, "Oh, man, Anthropic's selling out." It's exactly the opposite. Getting another $200 million from some coding startup would take an order of magnitude less effort than getting that contract. But you think defense is very worthwhile. And we're doing it because we want to defend democracies. And we do it within bounds.
## 00:10:44
**中文**
有些事情令我们担忧。我尤其担心政府权力在国内被滥用，我们更多考虑对外用途。这说明我们优先做的是自己认为正确的事，不一定是让人感觉良好、或我们预计能获得正面舆论的事。
**English / Original**
There are some things we're concerned about. I'm deeply concerned about abuse of government authority on the domestic side. We think more on the kind of outward directed side. But that's an example of the things we prioritize are things that we think are good, not necessarily things that feel good or that people will that we think the kind of external buzz will be positive.
## 00:11:07
**中文**
我们对一些事情有坚定信念，并会不受外界影响去做。你提到想打造怎样的企业；未来三到五年，你对 Anthropic 的业务有何期待？AI 在很多方面都很奇特，其中一点是它呈指数增长，因此我们很难准确估计业务会有多大。
**English / Original**
We actually have conviction around some things, and we do them regardless. You reference the kind of business you want to build. What are your aspirations for the Anthropic business in, say, three to five years time? AI is strange in, like, a number of ways. I think one of the ways it's strange is that because it's an exponential, we have a hard time calibrating exactly how big the business will be.
## 00:11:30
**中文**
我们有过这样的经历：2023 年初，我们还没发布产品，收入是零，而我此前从未向机构投资者融资。我准备材料时说：“我想第一年大概能做到 1 亿美元收入。”一些投资者因此说：“这太疯狂了。”
**English / Original**
We had the following experience. In 2023 I'd never raised money from institutional investors before, and so our revenue was zero at the beginning of 2023 because we had not released a product. So I was putting together something, and I'm like, "Oh, I think we can probably get $100 million of revenue in the first year." This caused some investors to say, "This is crazy.
## 00:11:52
**中文**
“资本主义历史上从未发生过这种事，你已经毫无可信度，只是在编数字。再见。”但我们真的做到了。第二年我说：“我想我们能从 1 亿做到 10 亿美元。”有了上次经历，人们没那么直接斥为疯狂，但仍常不相信，随后我们又做到了。
**English / Original**
This has never happened in the history of capitalism. You've lost all credibility." "You're just making up numbers." "Goodbye, goodbye." And then we actually did it. And so then the next year, I was like, "I think we can go from $100 million to a billion." And actually, having done it the first time, people were like, it was a little bit less dismissed as crazy, but still often dismissed as crazy, and then we did it again.
## 00:12:16
**中文**
今年才过一半，正如你所说，我们已远超 40 亿美元收入。按对数尺度，再增长一个数量级并非不可想象。未来可能有很多种：规模达到某点后曲线放缓；但也可能指数继续，两三年后这些公司成为世界上最大的企业。
**English / Original**
This year, we're halfway through the year. We're, as you mentioned, well past 4 billion revenue in kind of logarithmic space, to add another order of magnitude. So, there's a bunch of different futures. There's one where once things get to a certain size, the curve slows down, but there's a provocative world where the exponential continues and in two or three years, these are the biggest businesses in the world.
## 00:12:42
**中文**
在 Anthropic 这类组织工作或掌舵的一项根本体验与不确定性，就是你不知道究竟会怎样。指数外推听起来疯狂，也可能确实疯狂，但也可能并不疯狂，因为此前趋势线就是如此。我在训练 AI 模型及模型认知能力的技术语境里说过同样的话，如今商业侧也出现了同样连续的曲线。
**English / Original**
And I think one of the fundamental experiences and uncertainties of working at or running something like Anthropic is you kind of don't know. You make this exponential projection. It sounds crazy. It might be crazy, but also, it might not be crazy, because that trend line has followed before. And I've said much the same thing in the context of training AI models, in the context of the cognitive capabilities of AI models on the technological side, but now we're seeing the same kind of continuous lines on the business side.
## 00:13:13
**中文**
那么这里与缩放定律的对应是什么？当你同步扩大影响模型质量的相关投入，性能会显著提升。是否存在某种机制：投入更好的模型和正确的组织？我知道输入是什么。比如训练投入增加五到十倍、数据增加五到十倍，按照缩放定律，模型质量沿某条曲线上升；收入也有一条传递曲线。我在模型上多花十倍，模型就从聪明本科生变成聪明博士生。
**English / Original**
So, what's the analogy to scaling laws here, where you scale up the relevant inputs for model quality in parallel, and you get kinda much better model performance? Is there something where you put better models in, and I know the right organization? I know what the inputs are. Yeah, yeah, yeah, there's something like, there's some curve where you spend 5x or 10x more to train a model, or you have 5x or 10x more data or whatever the scaling laws say, and there's some transfer curve for revenue, right, where I spend 10 times more on the model, andthe model goes from being a smart undergrad to a smart PhD student.
## 00:13:55
**中文**
然后我去问制药公司：“这多值多少钱？”它们往往会说“大概值十倍”，甚至不止。这类幂律分布会出现在许多情境。技术侧也是如此：训练越深入，模型捕捉到的相关性长尾就越长。
**English / Original**
Then I go to a pharmaceutical company, and I'm like, "Well, how much more is that worth?" Often, they end up saying "That's worth about" More than 10x, yeah. "That's worth about 10x," where these kind of power law distributions occur in a bunch of contexts. Going on the technical side, when you train the model, there's a longer and longer tail of kind of correlations that you're capturing as you train the model, right?
## 00:14:19
**中文**
这些相关性存在于语言结构、世界和各种模式中。它们被认为产生了缩放定律，因为背后有类似对数分布。当模型在认知任务上越来越强，再看它在经济中的用途，也会出现类似结构——至少目前经验上如此。
**English / Original**
Correlations in the structure of language, in the world, in patterns. And that correlation is what's thought to lead to the scaling laws, because there's this kind of logarithmic distribution. And then as you think of the model getting more and more capable in terms of cognitive tasks, there must be, or we're seeing empirically so far, if you think of the uses of the model in the economy, right?
## 00:14:42
**中文**
公司如何组织？组织结构本身就呈幂律分布，比如组织架构图。你仿佛在沿着价值的幂律分布向上攀爬。我对产品和市场进入策略的理解是：模型本身想进入收入的指数曲线，而产品和市场进入只是把窗户擦净、让光透进来的方法。
**English / Original**
If I think of the way that companies are organized, right? There's a kind of power law, there's a power law structure of, like, the— The org chart. The org charts of companies. And it almost feels like you're climbing that power law distribution of value. And then I guess the way I think about product and go-to-market is that the model wants to be on that exponential of revenue, and product and go-to-market, they're kind of a way to clean the window and let the light shine through.
## 00:15:14
**中文**
也就是把光圈打开，让指数增长发生。模型想学习，也想在市场上取得非凡成功。除了学习冲动，它们还有想要实现的资本主义冲动——除非配给它们的是糟糕产品或糟糕销售。
**English / Original**
A way to kind of open the aperture and let the exponential happen. It's like the models want to learn, the models want to be extraordinarily successful in the market. Yes, right, in addition to having this learning impulse, the models have this capitalistic impulse that they want to embody, unless they're given a bad product or bad sales to go with them.
## 00:15:33
**中文**
因为这些智能真的对人有用，所以价值会自然被释放出来。没错，也可以这样理解。这个市场最终会形成什么结构？会是少数几个大规模玩家，还是会不断有针对特定用例的新公司出现？
**English / Original**
Because they're really useful, that intelligence is really useful to people, and so it kind of gets pulled out of you. Yes, yes, yes. That is a way to think about it. What is the terminal market structure here? Like, is there a few large scaled players, or do we kind of keep seeing new upstarts for kind of specific use cases?
## 00:15:55
**中文**
很难确定。两三年前不确定性很大，但我认为我们可能已经相当接近最终的玩家集合，尽管未必接近最终市场结构或各玩家的角色。取决于如何计算，大概会有三到六家；它们有能力构建前沿模型，也有足够资本让自己持续滚动发展。
**English / Original**
It's very hard it's hard to tell for sure, and I think there was quite a lot of uncertainty two or three years ago. But I think we might be relatively close to the final set of players, if not necessarily the final market structure or the roles of the players. I would say there's probably somewhere between three and six players, depending on how you count, and those are the players that are capable of building, the players that are capable of building frontier models and have enough capital to plausibly bootstrap themselves.
## 00:16:32
**中文**
我很想理解模型业务的运作：先投入大量资金训练，得到一项贬值相当快、但也许有长期用途的资产，再期待收回投资。外界目前看到的是不断增大的资本开支，以及这些钱似乎被不断烧掉。
**English / Original**
I would love to understand how the model business works, where you invest a bunch of money upfront in training, and then you have this fast-ish depreciating asset, though maybe with kind of a long tail of usefulness, and hopefully kind of you pay that back, Thus far, like, I think the image people have from the outside world is ever larger amounts of CapEx and how does all— Get kind of burned.
## 00:16:56
**中文**
可以用两种不同方式描述当前的模型业务。假设 2023 年花 1 亿美元训练模型，2024 年部署后产生 2 亿美元收入；与此同时，由于缩放定律，2024 年又花 10 亿美元训练下一代模型。
**English / Original**
There's kind of two different ways you could describe what's happening in the model business right now. So let's say in 2023, you train a model that costs $100 million, and then you deploy it in 2024 and it makes $200 million of revenue. Meanwhile, because of the scaling laws, in 2024, you also train a model that costs $1 billion.
## 00:17:19
**中文**
2025 年，这个耗资 10 亿美元的模型带来 20 亿美元收入，而你又花 100 亿美元训练新模型。按传统公司损益表看，第一年亏 1 亿、第二年亏 8 亿、第三年亏 80 亿，似乎越来越糟。
**English / Original**
And then in 2025, you get $2 billion of revenue from that $1 billion, and you spend $10 billion to train the model. So, if you look in a conventional way at the profit and loss of the company you've lost $100 million the first year, you've lost $800 million the second year, and you've lost $8 billion in the third year, so it looks like it's getting worse and worse.
## 00:17:43
**中文**
但如果把每个模型都视为一家公司，2023 年训练的模型是盈利的：投入 1 亿，收入 2 亿。当然还有推理成本，不过在这个极度简化的示意例子里，即便加上推理成本，情况仍然不错。
**English / Original**
If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish cartoon example that even if you add those two up, you're kind of in a good state.
## 00:18:07
**中文**
所以若每个模型都是一家公司，那么例子中的每家公司其实都盈利。只是你在收获一家公司的收益时，又创办了一家贵得多、需要更大前期研发投入的新公司。这个过程会一直持续，直到数字变得非常大、模型无法再扩大，届时它会成为庞大且高利润的业务；或者某个时点模型停止进步。
**English / Original**
So, if every model was a company, the model is actually, in this example is actually profitable. What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's much more expensive and requires much more upfront R&D investment. And so the way that it's gonna shake out is this will keep going up until the numbers go very large and the models can't get larger, and, you know, then it'll be a large, very profitable business, or at some point the models will stop getting better, right?
## 00:18:44
**中文**
通向 AGI 的进程可能因某种原因停下。届时也许会有一次过度投入：“天啊，我们花了很多钱却什么也没得到。”随后业务回到当时的规模。换种说法，风险投资支持的企业通常先大量花钱、之后开始赚钱，而在这个领域，同一家公司内部反复经历这一过程。
**English / Original**
The march to AGI will be halted for some reason, and then perhaps it'll be some overhang, so there'll be a one-time, "Oh man, we spent a lot of money and we didn't get anything for it," and then the business returns to whatever scale it was at. Maybe another way to describe it is, the usual pattern of venture-backed investment, which is that things cost a lot and then you start making it, is kind of happening over and over again in this field within the same companies.
## 00:19:12
**中文**
我们现在处于指数曲线上，最终会达到均衡。关键问题只是：均衡发生在多大规模，以及途中是否会过冲。你以云公司作类比，但云公司的数据中心资本开支似乎更连续，它们一直新建数据中心；而模型代际更离散，也许更像发动机制造商不断开发新技术，或像 F-16 这类项目，也可能有点像药物研发。
**English / Original**
And so we're on the exponential now. At some point, we'll reach equilibrium. The only relevant questions are, at how large a scale do we reach equilibrium, and is there ever an overshoot? Right, right. You referenced the cloud companies as a point of comparison, but I don't know, there's something about the cloud companies where it feels like their data center CapEx is more continuous, they're just always doing new data centers, whereas there's something about how discrete these generations are that maybe it's like the way the engine manufacturers, they keep coming up with new technologies— Yeah, yeah, it's like the F-16 or something, orit might be a little bit like drug development.
## 00:19:47
**中文**
没错，是研发密集型业务。那什么时候才值得真正训练一个模型？它几乎像制药公司：开发一种药成功后，再开发十种；再成功，就开发一百种。药物研发市场在数字上不是这样运作的，但这里仿佛如此。
**English / Original**
Exactly. Kind of an R&D heavy thing. Yes, and when do you actually go to the effort of training a model? Yeah, yeah it's almost like a drug company, where it's like you develop one drug, and then like if that works, you develop 10 drugs, and then if that works, you develop 100 drugs. The drug development market does not work like that numerically, but it is as if it did.
## 00:20:05
**中文**
因此可以把每个模型看成独立项目，分别看其损益。你的意思是，至少就行业迄今的模型而言，投资回收并不困难。获取客户若九个月就能回本，企业会一直愿意做。
**English / Original**
Right, so we can look at each of these models as individual programs and look at their individual P&Ls, and you're saying that the payback math on those, at least in the models we've seen to date in the industry, is not actually that challenging. Where I think most... When you're acquiring a customer, if you have a nine-month payback on acquiring a customer, you'll do that all day long.
## 00:20:24
**中文**
九个月的回收期非常容易评估承保。你说模型的回收期大约九至十二个月，因此也很容易评估。当然我不想做具体断言。但定性而言，逐个模型看，这项业务非常可行。没错，不断增长的资本开支掩盖了底层模型业务的质量。
**English / Original**
That's a very easy payback to underwrite. And you were saying the paybacks are kind of nine months, 12 months, so like, they're very easy to write. I don't wanna make any specific claims— Sure, yeah, yeah. But qualitatively, if you look at the business this way, model by model, it looks very viable. Yes, because the ever-growing CapEx is masking the underlying quality of the model businesses.
## 00:20:47
**中文**
2023 年人人都在谈“数据墙”。我们是这样突破数据墙的吗？公众讨论中有时只是传言或推测，我甚至不一定接受存在数据墙这个前提。但用强化学习的想法早已有之。
**English / Original**
Yes. In 2023, everyone is talking about the data wall. Is this how we solved our way out of the data wall? Yeah, so I don't know, people talk about things in public, and sometimes they're rumors or suppositions or whatever. I wouldn't even necessarily assume that there's a data wall. One thing I will say is that the idea of using RL has been around for a while, right?
## 00:21:11
**中文**
回到 Google DeepMind 用 AlphaGo 击败世界围棋冠军时，首先采用的就是强化学习。后来我们构建语言模型，现在又通过在语言模型之上加入强化学习，把两者结合起来。所谓思维链或推理，本质上只是强化学习的华丽说法：在强化学习环境里，模型先写出一系列内容，再给出答案。
**English / Original**
If we go all the way back to when Google DeepMind won, beat the world Go champion with AlphaGo, it was RL first. And then we built these language models, and now we're kind of uniting the two together by putting RL on top of the language models. That's all chain of thought or reasoning is, it's just a fancy way of saying RL, where the RL environment is that the model writes a bunch of things and then gives an answer.
## 00:21:35
**中文**
本质没有别的，只是名字听起来高级。我认为这是两种关键学习方式：基础大语言模型训练是模仿学习，强化学习是试错学习。人类儿童也有这两种学习方法。
**English / Original**
There's nothing more to it than that. It just kind of has a fancy name. And so I think of these as kind of the two key ways of learning, right? I think of base LLM training as learning by imitating, and RL as learning by trial and error. I think those are the two styles of learning. If I'm like a child, there's two ways for me to learn.
## 00:21:55
**中文**
我可以观察父母做事并模仿，也可以在世界中尝试、从结果中学习。发展心理学明确表明人会同时使用两者。现在语言模型正在重现这一点：一个阶段做模仿学习，一个阶段通过试错学习。
**English / Original**
I look at my parents and I'm like, "Oh, they do something," and I try and learn what they do, or I can just kind of experiment with the world and learn things, and it's very clear in developmental psychology that people use both. And so we're now seeing that recapitulated in the language models, and so we have a stage where we do the imitative learning, and we have a stage where we learn by trial and error.
## 00:22:18
**中文**
所以这在我看来非常自然。AI 行业外的人还会注意到激烈的人才争夺，以及知识产权每晚都会随着员工走出公司。你最近一次采访提到，有些价值 1 亿美元的秘密只是几行代码。你当时谈的是国家安全，但人才竞争中同样如此。
**English / Original**
So it seems very natural to me. The other thing that's obviously notable to people not in the AI industry looking at it, is all of the talent wars and the fact that your IP walks out the door each evening. You referenced in a recent interview you gave, $100 million secrets that were a few lines of code. And obviously I think you were talking about that in a national security context, but you could also think about it in a talent context.
## 00:22:45
**中文**
当然。那么该怎么办？制药业用专利保护秘密；华尔街也有价值 1 亿美元却只是简单想法的秘密，例如对冲基金 Renaissance Technologies 就非常成功地留住并约束员工。在当前 AI 环境下，怎样才能维持商业领先？
**English / Original**
Of course. And so how does one... Like, in the pharma industry, they protect their secrets with patents. In Wall Street, where also they have $100 million secrets that are just a very simple idea, Renaissance Technologies, the hedge fund, just very successfully locked up its employees. How do you make keeping a commercial lead work in kind of the current AI environment?
## 00:23:07
**中文**
确实有些东西是那样，但随着领域成熟，优势越来越来自诀窍以及构建复杂系统的能力。有些想法很简单，例如稍微调整 Transformer 的某个部分；但这些通常会被独立发现，或很快变成人尽皆知。
**English / Original**
One thing I will say is that there are some things that are like that, but I think more and more as the field matures, it starts to be more about know-how and ability to build complex kind of objects. Some of the ideas we work with are simple, but I would say the simple ideas, the ones that are like, oh, yeah, twiddle this element of the transformer or something, those tend to be independently discovered or anyone knows them before too long.
## 00:23:39
**中文**
另一些东西在工程上真的很难实现，而我们已经实现；或者做起来非常麻烦，需要特定诀窍。这些通常是更集体性的能力，更难泄露，因此也更具防御性。
**English / Original**
But there are things like, oh man, this thing is actually really hard to implement from an engineering sense, and we have it implemented, or this thing is just kind of a pain to do, or there's a know-how to doing it. And those tend to be more collective things that are more difficult to leak. And so I think those things are substantially more defensible.
## 00:23:58
**中文**
尽管如此，泄密仍会发生，我们不希望再发生，无论从商业竞争还是国家安全看都是问题。我们采取几项措施，其中之一是信息分隔。任何情报机构都按这种方式运作。
**English / Original**
That said there's still leakage, and we still don't want it to happen again, both for commercial competitive reasons and for national security reasons. Both are problems. Yes. And so a few things we do. One is, you know, we tend to compartmentalize information. So if you talk to any intelligence agency, that's how they operate.
## 00:24:18
**中文**
每个人只知道自己需要知道的内容，Anthropic 内部也是如此。但这大概与普通硅谷文化很不同，后者信息在公司里到处流动。我们一方面这样做，另一方面仍保持非常开放的文化。我对全公司讲话时会直说，换个人可能会包装成公关话术。
**English / Original**
You're only told what you need to know, and I think everyone within Anthropic. But that's probably quite different to a normal Silicon Valley culture, where everything's just flying around the company. Yes, we actually do that at the same time as we have a very open culture. I say things to the company that maybe another person would put it in kind of PR speak or, you know what I mean?
## 00:24:37
**中文**
但真正需要保密时，这种坦率反而让人相信它确实属于“按需知悉”。最后，更高的留任率、减少人员流失也是关键。我们在所有 AI 公司中留任率最高。差距实际上更明显，因为每家公司都有大致恒定的“非遗憾流失”，扣除那部分后，差异更大。
**English / Original**
But when there is a secret, then I think that actually leads to people trusting that it's something that you actually need to know. And then finally, having better retention rates and losing less people is one of the most important things here. We have the highest retention rate of all the AI companies. I think the differences are even starker, because everyone has kind of a non-regretted attrition rate that's maybe constant, so if you subtract that off, then the difference is even larger.
## 00:25:08
**中文**
员工离开后有时还会回来。最近确实有这种例子。公开名单显示，有人去了 Meta 超级智能实验室；即便按公司规模校正，我们流失的人也不多，而且许多人拒绝了对方。因此在众所议论的疯狂“1 亿美元薪酬大战”中，你们应对得不算困难。
**English / Original**
Sometimes when people leave, they come back. Yes. I saw that recently. If you look at you can see publicly the list of people who went to the Meta Superintelligence lab. Even if you normalize for our size, it's not, and many turned them down. So in the crazy $100 million comp wars that everyone's been talking about, you guys have not had too hard a time of that.
## 00:25:30
**中文**
与其他公司相比，我们做得不错，甚至相对占优。这既来自对使命的真诚信念，也来自对股权上行空间的信心。Anthropic 逐渐树立了说到做到的声誉：有时承诺较少，但会兑现作出的承诺。
**English / Original**
I think relative to other companies, we've done well. We even have been relatively advantaged. It's like a mixture of true belief in the mission and belief in the upside of the equity. Anthropic has developed a reputation for doing what it says it will do for, in some cases, making less promises, but keeping those promises that we make.
## 00:25:52
**中文**
我们清楚表明自己的立场，多年来保持一致并坚守它。这使公司形成团结，也能很好抵御犬儒主义。谈到股权上行空间时，你如何向投资者或候选人介绍 Anthropic 的业务？
**English / Original**
And being very clear on what we stand for, and being consistent over the years and standing for it. That creates a unity around the company, and I think it's a good guard against cynicism. When you're talking about the upside of the equity, when you're pitching investors, or maybe candidates, how do you pitch the Anthropic business?
## 00:26:09
**中文**
比如“我们正在打造一家非常大的企业”，这是不错的开头；还会说什么？我通常会谈平台和模型的重要性。不知为何，有时人们觉得 API 业务“黏性不足”或“会商品化”。我经营 API 业务，而且非常喜欢 API 业务。
**English / Original**
Like, we're building a very large business. That's a good start, but what else goes into it? Often I'll talk about the platform and the importance of the models. For some reason, sometimes people think of the API business and they say, "Oh," you know, "It's not very sticky," or, "It's gonna be commoditized." I run an API business, I love API businesses. No, no, exactly, exactly.
## 00:26:28
**中文**
还有比我们两家都更大的 API 业务，比如云服务，那是千亿美元级的 API 生意。当资本成本高、玩家只有少数时尤其如此；而且相较云服务，我们做的东西差异化大得多。这些模型有不同的个性，与人交流的方式也不同。
**English / Original**
There are even bigger ones than both of ours. I would point to the clouds again. Those are $100 billion API businesses, and when the cost of capital is high and there are only a few players... and relative to cloud, the thing we make is much more differentiated. These models have different personalities. They're talking to different people.
## 00:26:50
**中文**
我常开玩笑：如果我和另外九个人坐在房间里，是不是就说明我被商品化了？那九个人的大脑和我差不多、身高也差不多，那还要我做什么？但我们都知道，人类劳动并非这样运作；模型也是一样。
**English / Original**
A joke I often make is, if I'm sitting in a room with 10 people, does that mean I've been commoditized? There's like nine other people in the room who have a similar brain to me, they're about the same height, so who needs me? But we all know that human labor doesn't work that way. And so I feel the same way about this.
## 00:27:10
**中文**
API 是很好的业务，但我们希望做得更广。OpenAI 等玩家以及 Google 等既有巨头非常专注消费者市场；我们则努力不断提升为企业提供 AI 的能力。我认为我们已取得早期领先。
**English / Original**
I think the API business is a great business, but we wanna go broader than that. The way I think about it is other players such as OpenAI and existing incumbents such as Google are very focused on the consumer side. The idea of providing AI to businesses is something that we are trying to get better and better at. I think we're out to an early lead in that.
## 00:27:34
**中文**
我不能确定，因为不知道其他玩家的确切收入，但现在我们很可能占 API 市场的相对多数，在企业 AI 市场也可能如此。谈到商品化论点很有意思：我们成长过程中也常面对这种质疑。我记得 2015 年 AWS 首次被迫单独披露数据时，令人印象深刻。
**English / Original**
I'm not sure, because I don't know for sure what the revenues of the other players are, but I think we probably at this point have the plurality of the API market, most likely, and AI for business market, perhaps. It's funny when you talk about kind of the commodity argument, where we obviously grew up facing this as a skeptical argument, and I remember finding it so striking when AWS finally had to break out their numbers in 2015.
## 00:28:04
**中文**
此前 AWS 一直并在 Amazon 总数据里，后来才拆分。评论者曾说“云是商品，没什么意思”，结果数据一拆出来，它竟是史上最优秀的业务之一。一个行业可以有竞争者，也可以有在乎价格的买方，但这与商品化完全不同。
**English / Original**
Remember they used to be wrapped up in Amazon's numbers, and they broke them up? Yes, yes, yes. And people had been talking about, pundits had been saying, "Oh, cloud is a commodity, it's uninteresting," and then they broke it out and it was one of the greatest businesses of all time. And there's something where a business can have competitors and it can have buyers who care about price, but that's very different from being a commodity, and as you say— Yeah, yeah, yeah, exactly.
## 00:28:26
**中文**
这些产品的工作方式都不一样。我们是云服务最大的客户之一，而且使用多家云。我可以告诉你，云服务之间的差异远小于 AI 模型。模型行为并非确定性的——这并非刻意增加难度，而是它的自然属性。因此客户可能偏爱某个模型给出的客服答案，却说不清原因。
**English / Original**
All these products work differently. No, exactly. I mean, we're like one of the biggest customers of the clouds. And we use more than one of them. I can tell you, the clouds are much less differentiated than the AI models. For sure. Because it feels like, one, the behavior is non-deterministic, which not by design trying to make it hard, but that just naturally means that, oh, we get the customer service answers we prefer with this model versus that model, and I don't know why. No, no, no, exactly.
## 00:28:50
**中文**
你不知道原因。这有点像烤蛋糕：把配料放进去，成品自然呈现某种样子；一个厨师这样做，另一个那样做。你要求“做得和那位厨师一模一样”，其实做不到。现在 AI 产品几乎都还没有高度个性化，但个性化显然会非常重要。
**English / Original**
You don't know why. It's a little like baking a cake, right? You put in the ingredients— It just works. It just, it kind of comes out a certain way. One chef makes it this way, and the other chef makes it this way. And if you're like, "Make it exactly like that chef makes it," you can't, right? You just can't. And presumably, it's striking to me, none of the AI products are that personalized right now, but it feels like personalization will be a huge deal.
## 00:29:14
**中文**
个性化会非常重要，也会形成很强的黏性，因为用户不愿更换产品。我还不知道具体形态，但无论消费者还是企业用例，都存在这种趋势。我们才刚刚开始探索：让模型以各种方式针对某家企业或企业中的某个人定制。
**English / Original**
Will be a huge deal. And will be a big source of stickiness for the... Like, 'cause you won't want to switch products. And I don't know exactly what that looks like, but given the amount of... For both the consumer and the business use case. Absolutely, absolutely. You know, I think we've just started to scratch the surface in terms of models that are customized in various ways for working with a particular business or particular person within the business.
## 00:29:37
**中文**
因此 API 业务才刚刚起步。不过企业 AI 不只等于 API。Claude Code 不仅卖给个人开发者，也卖给企业，而且非常有用；Claude for Enterprise 同样被许多企业采用。它有点像云厂商提供的一系列不同服务。
**English / Original**
So I think we're just seeing the beginning of the API business. But I don't think AI for business is just about API. With things like Claude Code, we're selling that to not just individual developers, but enterprises as well. And they find it some useful. Claude for Enterprise, that is selling to a lot of enterprises. I actually see it, and you see this with some of the clouds, where they have a bunch of different services, right?
## 00:30:03
**中文**
其中有应用，也有底层云本身。AWS、GCP 或 Azure 会把自己呈现为云的一站式商店；我们也开始把自己呈现为 AI 的一站式商店：客户可以买到这些不同产品，并与我们讨论何种场景该用哪个。
**English / Original**
Some of them are apps, some of it is the underlying cloud itself, and what they are is the way that AWS or GCP or Azure will present themselves, and the way that we are starting to present ourselves is, hey, we wanna be your one stop shop for AI or for cloud, and you can buy all of these things, and you can talk to us about which to use for what.
## 00:30:26
**中文**
这开始勾勒出一种更持久的业务。想想典型的《财富》500 强公司：它们可能在尝试用 AI 做客服，工程师也许在用 AI 编程工具。相较于应有水平，它们的 AI 采用程度如何？肯定低得多。
**English / Original**
And so I think that starts to create the outlines of a more durable business. If you think about a typical Fortune 500 company, how... They're probably playing with AI for customer service or engineers maybe have AI powered coding tools, how AI adopted are they compared to how much they should be? Well, certainly much less than there should be.
## 00:30:47
**中文**
那是 5% 还是 30%？我会说，最高层通常已有信念。CEO 和 CTO 都明白。但难点在于，一家十万人的公司里，这些人的本职工作是别的：银行、保险或药物开发。
**English / Original**
Yeah. But is it like 5%, 30%? What I would say is there is almost, there's very often conviction at the top. You talk to the CEO, the CEO gets it. You talk to the CTO, the CTO gets it. The struggle they have is that they have 100,000... Pick a company that has 100,000 people, who their job is to do something else. Their job is to do banking or insurance or drug development.
## 00:31:13
**中文**
他们听说过 AI，却不是这方面专家。因此挑战往往是，我们要与公司领导层合作，让十万名员工真正熟悉并使用这项技术。编程推进得最快，因为开发者与趋势最接近、也最关注。
**English / Original**
And they've heard about this AI stuff, but they're not like— It's not what they're an expert in. And so the challenge is often we are working with the leadership of the company to get the 100,000 people in the company really familiar with and using the technology. I think, again, the code stuff goes the fastest, because the developers are the ones who are most adjacent and most watching the trend.
## 00:31:37
**中文**
客服和流程类工作会紧随其后。但即使只用今天的模型，应用规模也可能扩大一百倍。我直觉上认为，新型 AI 使用方式会先从初创公司出现，因为它们不受既有组织限制，可以按最合理的方式做事；大型组织则因大量员工已有固定职责、所有人都需被征询而趋于僵化。大公司 CEO 和 CTO 也很敏锐聪明，
**English / Original**
Some of the kind of customer service and process stuff is next to go, but you really have the instinct that even with today's models, it could be, 100 times bigger than it is. You really get that sense. My intuition is sort of that we will see the patterns of AI adoption from startups, because they're unconstrained by existing organizations, so they can kind of do whatever makes sense, versus large organizations are somehow calcified, because they have all these people whose job is to do X and need to be consulted and everything like that, so we'll see the new behaviors from the small startups, and then large companies, as you say, the CEOs and CEOs are switched on and they're smart,
## 00:32:14
**中文**
他们会说“我们也应该这样做”，再把小公司产生的新想法移植过来，就像当初采用云及其他技术一样。你观察到的是这样吗？他们会移植小公司的新想法，或者小公司会变成威胁并颠覆它们。
**English / Original**
they say, "Hey, we should be doing that," and they'll kind of port the new ideas from... Kinda like the adoption we saw of cloud or many of these other techs. Is that what you're seeing? Is that your intuition? They'll port the new ideas from the small companies, or the small companies will become threatening to them and disrupt them.
## 00:32:28
**中文**
这会给大公司足够紧迫感，推动改变真正发生。对大型公司，我推荐一个相当有效的模式：组建独立于其他部门的突击小组，先开发原型。
**English / Original**
Sure, yeah. And that will give them the urgency to kind of drive things through and make them happen. A pattern I've seen that works pretty well that I actually recommend if you're a large company is to kind of make a strike team or strike force that's separate from the rest of the company and kind of develops these prototypes.
## 00:32:48
**中文**
这样可以先形成势能。之后当然仍要完成整合进整个公司的艰苦工作，但如果已有很大势能、完成了难事并证明它有效，整合就会更容易。你读过 Dwarkesh 最近那篇讨论 AI 时间线的博客吗？
**English / Original**
And then basically, you can get momentum behind something, and then there's always this hard work of integrated into the rest of the company, but if you have a lot of momentum and you've done the hard work and you've shown the thing works, then it's easier to do that. Dwarkesh, did you read his recent blog post on his AI timelines?
## 00:33:04
**中文**
是关于持续学习的那篇吗？他认为，许多生产力 AI 模型的根本问题是：它们像五分钟前刚入职、聪明绝顶的虚拟同事，而且永远停留在“刚入职五分钟”的状态，不会随时间学习。
**English / Original**
Oh, on continual learning, yeah? Yeah. He talked about how his fundamental issue at many of the AI models for productivity is that they're like the virtual co... They're like the super smart virtual coworker who started five minutes ago, but they remain the coworker that started five minutes ago. You know, they don't learn over time.
## 00:33:27
**中文**
怎样解决？在 AI 研究和技术发展中，我们一再看到：某件事看起来像一堵墙，仿佛模型做不到。过去是“AI 模型不能推理”，最近则是“AI 模型不能做出新发现”。
**English / Original**
How will we solve that? Yeah, so, you know, the pattern that I've seen in AI on the kind of research and technical side is that what we've seen over and over again is that there's what looks like a wall. You know, it looks like AI models can't do this, right? It was like, "AI models can't reason." And recently, there's this, "AI models can't make new discoveries."
## 00:33:50
**中文**
几年前还有人说“AI 模型写不出整体连贯的文本”，如今显然可以。再早些，则是乔姆斯基式的论断：它们能处理句法，却不懂语义。这些所谓障碍全都被突破了。
**English / Original**
A few years ago, it was like, "AI models can't write globally coherent text," which of course now they obviously can. You go back a few more years, and you know, it was like this Chomsky thing of like, you know, they can get syntactics right, but they can't get semantics right. And every one of those has been blown through.
## 00:34:07
**中文**
等等，在“新发现”上我们突破了什么？这是近来人们常说的一点。我的看法和其他问题类似：新发现并非二元状态。模型不能在论文上署名。是的，但究竟什么算新发现？
**English / Original**
Sorry, what have we blown through on the new discoveries? This is a thing that people have said recently. Actually, my view on this, like many of the other things, on new discoveries, is that it's not really a binary, right? They don't get to have their name in the paper. Yeah, yeah, they don't get to have their name in the paper, but like, what is a new discovery?
## 00:34:26
**中文**
什么是天才？我记得一本发展心理学书说，我们往往把天才神化。假设桌子摇晃，我拿一个杯垫垫到桌脚下，桌子就稳了；这在某种意义上也是一个想法、一项新发现。
**English / Original**
What is genius? You know, I think I remember this developmental psychology book, but they were saying something like we kind of lionize genius, but let's say that a table's wobbly, and I'm like, "Oh," I take the coaster and I put it under the table, and it's not wobbly anymore. That's an idea in a way that's like a new discovery.
## 00:34:46
**中文**
即使我从未见别人这样做，它也是新发现。它与诺贝尔奖级发现的差别是程度，而非本质。AI 模型其实一直在做发现。我的家人曾遇到医疗问题，医生没诊断出来，AI 模型却诊断出来了。
**English / Original**
Even if I've never seen someone do that before, you know, that's like a new discovery. And you know, the difference between that and the Nobel Prize winning discovery, it's a matter of degree, not a fundamentally different matter. And so I would say that the AI models make discoveries all the time. I've had family members where they had a medical problem, and the AI model diagnosed their medical problem when doctors missed it.
## 00:35:12
**中文**
那不是什么宏大突破，但确实是新发现。你可以说“它只是匹配过去的模式”，可新发现本就如此。作家写出全新小说，也仍有各种影响来源；他们把既有元素重新组合，再加入新元素。
**English / Original**
That's not a big, new, but that's a new discovery. And you could say, "Oh, they're just pattern matching to things that happened before," but new discoveries are like that. You think of writers who have written novels or something that are totally new, and you're like, "Well, what are your influences?" And they're remixing together and adding a new element.
## 00:35:32
**中文**
这一切更像连续谱。持续学习也是如此：说它完全不存在并不准确，我会说现在已经有一点。我们会找到办法把它增强。例如模型会在上下文中学习；你与它交谈，它会吸收上下文。
**English / Original**
It's all more continuous. And that was the thing I was gonna say about continual learning. I think this idea that it isn't present is... I would say it's present a little bit. Yes. It's comfortable practice. We're gonna find a way to get more of it. So for instance, the models learn within the context. You talk to them and they absorb the context.
## 00:35:53
**中文**
未来上下文可能达到一亿 token，我们还可能专门训练模型，使其擅长在上下文中学习，甚至可以在上下文交互期间更新模型权重。很多与现有思路很接近的方案都有可能实现这一点。人们很执着于相信存在某种根本高墙、某种本质差异或不可做到的事情。
**English / Original**
Eventually, the context is gonna be 100 million tokens, and maybe we'll train the model in such a way that it is specialized for learning over the context. You could even, during the context, update the model's weights. There are lots of ideas that are very close to the ideas we have now that could perhaps do this. I think people are very attached to the idea that they want to believe there's some fundamental wall, that there's something different, something that can't be done.
## 00:36:20
**中文**
这让我想到——你觉得这其实是一种心理应对机制吗？它让我想到 19 世纪的“活力论”：认为人体和生命体由一种与无生命物质根本不同的材料构成。如今科学已证明这不正确，但人们非常愿意相信它，常识直觉似乎也支持它。
**English / Original**
It kind of reminds me of— Do you think it's a coping mechanism deep down? Yeah. You know what it reminds me of? So, you know the 19th century notion of vitalism? This was the idea that the human body and organisms that are alive are made of a fundamentally different material than inanimate matter. Which of course we know scientifically now is not true, but it's something people very much want to believe, and your common sense seems to suggest it.
## 00:36:48
**中文**
我看起来确实与桌子很不同，好像与金属或玻璃由完全不同的材料构成。但深入到基本单元，我们当然都由同样的东西组成。如今人们似乎对某种“人之所以为人”的本质抱有现代版活力论，于是说“模型做不了那个”。
**English / Original**
I'm not very much like a table. I'm made of very different materials than metal or glass or whatever, but when we actually go down to the fundamental units, of course, we're all made of the same thing. But you think if people now have this kind of modern concept of vitalism in whatever the fundamental humanity is, and they're saying, "Oh, you know, models can't do that."
## 00:37:11
**中文**
确实有这种倾向。破解之道和反驳活力论一样：承认心智就是心智，无论由什么材料构成。认知或感知能力的尊严与特殊性并未消失，只是它可以由任何材料实现。你刚才提到医疗用例，我认为非常酷。
**English / Original**
I think there's some tendency to believe it. And I think as with vitalism, the way around it is to recognize that a mind is a mind no matter what it's made of. The notion of the dignity or the specialness of cognition or sentience, it's not that it isn't special, it's that it can be made out of anything. You referenced the medical use case, which I think is a very cool use case.
## 00:37:31
**中文**
首先，它确实帮助很多人解决了医疗问题；另外，你在《Machines of Loving Grace》一文中谈到智能的边际回报，我很喜欢那篇文章。哪些领域真正受限于智能？大众医疗用例之所以引人注目，不只是因为故事性强，还因为几乎每个普通人都有某种轻微或严重的健康问题，而社会在这方面确实受制于智能供给。
**English / Original**
Obviously, one, because of all the people who have fixed medical issues as a result, but another one is you talked in your "Machines of Loving Grace" post, which I really enjoyed, I thought it was very well done, about the marginal returns to intelligence. What are places where intelligence is the limiting factor? And my read of the popular medical use case is obviously it's kind of a charismatic use case, but also, for most normal people, they have some kind of medical issue, low level or serious or something like that, and actually, society is very just intelligence limited there.
## 00:38:02
**中文**
你也许能见到聪明医生，但他们给你的时间非常有限，可能只思考十秒钟。没错。原来医疗问题真正需要的是测试时计算。你的看法也是如此吗？
**English / Original**
Not that you don't have access maybe to a smart doctor, hopefully you do, but they give you very limited time. You know, they think for 10 seconds about your problem. Yeah, yeah, yeah, exactly. And it turns out test-time compute was actually what we needed there on the medical stuff. But is that kinda your take on this?
## 00:38:16
**中文**
我也是这样理解。我认识一些诺贝尔生物学奖得主，他们说：“我只去找最顶尖的 1% 医生。”听起来有点精英主义，但他们认为，从其余 99% 医生那里得到的建议还不如大语言模型。这确实是真的：医生很忙，工作过载。
**English / Original**
That is how I think about it as well. I have talked to Nobel Prize winning biologists who say, "I will only"... I mean it sounds a little elitist, but they'll say, "I'll only go to the top 1% of doctors, because the rest of the 99%, I can get better advice from an LLM." It really is true. Doctors are busy, they're overworked.
## 00:38:37
**中文**
医疗数据与信息本身也大量依赖模式匹配，很多情况反复出现。大语言模型擅长保持一致，也擅长综合许多不同事实。你在《Machines of Loving Grace》中谈到人类层面受智能限制的领域；个人医疗正是一个例子。如果给许多人更多用于处理自身具体问题的智能，价值会很大。
**English / Original**
And just the nature of medical data and medical information, it's a lot of pattern matching. It's a lot of the same things. The level of consistency and the ability to put together many different facts, I think it's something that LLMs are quite good at. So you talked in this "Machines of Loving Grace" post about some of the big humanity-level areas where we're intelligence-limited, but again, the personal medical use case is a good example of one where society's intelligence-limited, and if you give lots of people much more intelligence on their specific issues, it's very valuable.
## 00:39:13
**中文**
还有哪些消费者或企业领域显然受智能限制？至少对今天的 AI 而言，最能提供帮助的事情通常具有一个特征：任务会重复，但每个实例又略有不同。AI 之前的自动化要求你能精确编程定义过程。
**English / Original**
What are other areas, either in the consumer use case or in the business use case where you think we're just very obviously intelligence limited? Yeah, the places where at least the AI models of today can help the most, the characteristic quality is something is repetitive, but every example is a little different. Automation before AI, if you could program exactly how it happened, you could do it.
## 00:39:37
**中文**
也就是完全重复同一件事。以客服为例，问题有很长的长尾，每通电话都不同，但大多数本质上围绕十类事情之一，只是不同的人用不同声音、不同说法表达这十类问题之一。
**English / Original**
So if you were doing the same thing over and over again. But customer service is like, you know, just to take customer service as an example, there's like a long tail of stuff, but a lot of it is like, you get a bunch of calls, each call is different, but each call is basically about one of 10 things, and it's like a different person in a different voice saying, basically, one of these 10 things in a different way.
## 00:40:00
**中文**
任务重复、相似却不完全相同，每件又有自身细节——这是 AI 最能发挥作用的地方。Dwarkesh 在同一篇博客中说，现在还不能把所有财务资料和转发邮件交给 AI，让它替你报税。他预测首次只需把一切邮件发给 AI 就能完成报税的年份是 2028 年。
**English / Original**
And that situation, where things are repetitive and similar but not the same, and each has its own thing, that's where AI can come in the most. Dwarkesh had in the same blog post the prediction that you can't yet give an existing AI all of your financial data and forge all the emails and have it do your taxes, and his prediction for the year in which you can, what is the year where your first tax return is done by just emailing everything to whatever AI you use, his prediction is 2028.
## 00:40:34
**中文**
你怎么看？可能会更早。我不知道是 2026 还是 2027 年。部分取决于模型，但主要是准确性。今天的模型或许已经能做，只是错误太多。让模型检查自身工作、减少错误是一部分，接口也是一部分；若真要等那么久，我会惊讶。
**English / Original**
What do you make of that prediction? Probably sooner than that. Okay. I don't know if it's '26 or '27. Some of that is model, mostly accuracy. I think the model could do that today, but it would make too many mistakes. And so working on ways to have the model check its own work and do less mistakes is one part. There's kind of an interface part of it as well, but I would be surprised if it takes that long.
## 00:40:58
**中文**
所以是 2026 或 2027 年。谈到错误，回看人们曾认为 AI 永远解决不了的问题，幻觉似乎也应该上榜。虽然尚未完全解决，但已经改善许多。是的，而且人们也更习惯了。
**English / Original**
Okay, '26 or '27. And when you say about mistakes, actually, you're running through the list of things that people thought we would never solve in AI. It feels like hallucinations should be on that list. Not that they're totally solved, but they've gotten a lot better. They've gotten a lot better, and I think people have gotten more used to.
## 00:41:13
**中文**
人们逐渐知道哪些事情可以信模型，哪些不行。模型输出也开始用引文来落地；Claude.ai 和企业版 Claude 都这样做。因此解决方案一部分是引用，一部分是算法进步使模型减少幻觉，另一部分是人类适应并理解模型弱点。
**English / Original**
They kind of know what to trust the model for and what not to trust the model for. The models have also been grounded in citations. I mean, we've done that with Claude.ai, we've done that with Enterprise Claude. So I think part of the solution is citation, part of the solution is algorithmically, the models hallucinate less now, and part of the solution is people have adapted and understand the weaknesses of the model.
## 00:41:34
**中文**
我一直认为，对于幻觉这类问题，有一类批评者会指出模型某处很怪或不如人类，然后说：“看，它们与我们根本不同”或“它们永远达不到人类水平。”我理解这种直觉，也许他们是在检验模型是否精确复制了人脑。
**English / Original**
My view on things like hallucinations has always been, there's a certain class of critic who points to something where models are weird or worse than what humans do, and say, "See, they're not like us at all," or, "They'll never get there." And I kind of get where the instinct comes from, where, maybe they're looking to see if we've matched the human brain exactly.
## 00:41:56
**中文**
他们会说：“差异这么大，不可能像人脑。”但我认为这是谬误。所谓通用智能本来就由多种能力组成；一个系统完全可能拥有其中大多数能力，却在少数能力上差很多、另一些能力上强很多。看看人类就知道——你见过人类吗？
**English / Original**
They're saying, "Oh, this is so different. It can't be like a human brain." But I basically, I just think it's a fallacy. There's a notion of kind of general intelligence, but it's made up of a bunch of different things, and you can simply have most of the things and be much worse on some and much better on others. If we look at humans that are— Yeah, have you met humans?
## 00:42:14
**中文**
对，你见过人类吗？比较自闭症患者与精神分裂症患者，再看一些能欺骗人类、却骗不过机器的视觉错觉，就会发现人类也有各种弱点，与模型幻觉类似。只是表现形式不同，而且我们整天被人类包围，早已习惯。
**English / Original**
Yeah, yeah, have you met humans, right? Like if you look at humans who are autistic, versus humans that are schizophrenic. If you look at the optical illusions that humans face that machines are not fooled by, it's very clear that we have some of these weaknesses, just like just similar to the model's hallucinations, it's just that they look very different and we're much more used to them because we're surrounded by humans all day.
## 00:42:41
**中文**
自动驾驶汽车所面对的双重标准，大概是最清楚的例子：人们对它要求高得多。这会是该技术的一项特征，也会影响商业。我认为未来模型犯错频率会远低于人类，但错误会更奇怪。
**English / Original**
The autonomous vehicle double standard feels like the kind of clearest example of this. Clearest example of this, yes. Yeah, where people have much higher standards. People have much higher standards. But I think it's gonna be a feature of this technology, and it has implications on the business side. I think we're gonna be in a world where the models will make mistakes much less often than humans, but they'll be stranger mistakes.
## 00:43:07
**中文**
这需要人适应。终端用户与人类共事时，知道如何判断。例如某个人有 5% 概率犯错，你通常能理解为什么。假如客服说话前言不搭后语、口齿含糊，
**English / Original**
And actually, that takes some adaptation, because imagine you're an end user. If you work with humans, you get used to it, and you have some notion, right? So if a human makes a mistake 5% of the time, you might have a good understanding of why, you know? Like, let's say I'm talking to a customer service agent, and they kind of sound incoherent and they're slurring their speech.
## 00:43:28
**中文**
他大概喝多了，工作状态不好。这当然是糟糕错误，但交谈时你能判断发生了什么，也知道不能相信他的话。大语言模型可能少犯五倍错误，却更具迷惑性：说错话时听起来仍和正确时一样博学、连贯。
**English / Original**
They've probably had too much of this and they're not doing their job very well. That's a bad mistake to happen, but also, if I'm talking to this person, I kind of know what's going on and I know not to trust what they're saying. Whereas an LLM might make a mistake five times less often, but it's more deceptive. The model sounds just as erudite, just as coherent as it does when it's saying something that's right.
## 00:43:56
**中文**
不过这只是适应问题，并非根本障碍。我们也会向客户说明这一点，让他们习惯。所以你的意思是，我们需要给大语言模型发明“口齿不清”。没错。你最初是研究员，如今是公司 CEO，还要销售 AI。
**English / Original**
But that's an adaptation thing. You know, that's not a fundamental thing. And that's something that when we talk to our customers, we tell them about that, we tell them they need to get used to that. So we need to invent slurring for LMS, is what you're saying. Right, right, right, exactly. So, you started out as a researcher, but now you're the CEO of a company and you're in the business of selling AI.
## 00:44:22
**中文**
关于市场进入、客户沟通及其他商业事务，你学到了什么？我创办公司，最初并非因为热衷销售或商业，而是看到其他一些公司的运作方式，以及它们试图构建之物的规模和分量。我有些担心，掌舵者及其动机未必最合适。
**English / Original**
And so what have you had to learn about go-to-market or dealing with customers, all the rest of the stuff? Absolutely. I think my view on this was I started a company not because I was initially excited about selling things or business or any of that, I'd seen the way that some of the other companies had run, and the magnitude and gravity of what they were trying to build, and I was just a bit concerned that the people and the motivations were maybe not the best ones.
## 00:44:56
**中文**
我知道这一领域会有多个玩家，但若至少有一个玩家在做事方式上有坚定指南针，可能会给生态带来正面影响。我们会用不同方式构建和部署技术；更重要的是，我们会有一份也许很短的原则清单，并尽最大努力坚守。
**English / Original**
And I knew that there would be a number of players in this space, but it felt like having at least one player that kind of had a strong compass in how we do things could have positive effects on the ecosystem. We would build things in a different way, we would deploy them in a different way, and above all, we would have a, again, sometimes short list of principles, but we would stick to them as well as we could.
## 00:45:20
**中文**
这就是最初动机。当然，我也对构建技术本身很兴奋。随着公司发展，我和其他联合创始人自然必须学会思考业务与战略。我发现自己很自然地对商业侧产生了兴趣。
**English / Original**
So I think that was the initial motive. And of course, I was excited about building the technology. And you know, I think as that has happened, of course I and the other cofounders have kind of had to learn how to think about, kind of the business and the strategy. I think I've been very naturally interested in the business side of it.
## 00:45:41
**中文**
事实上，我对自己这么快产生兴趣也很意外。主要原因是，我对客户所在的所有行业都很好奇。像云服务，也许也像你的公司一样，我们服务的企业遍布所有行业，因此会了解到从前从未想过的经济领域。
**English / Original**
Actually, I was surprised at how quickly I became interested in it. And actually, the primary reason was that I was curious about all the industries that are customers of us. Somewhat like the clouds, and perhaps like your business, the businesses that we serve, they run across every possible industry. And so you just learn these things about parts of the economy that you've never thought about.
## 00:46:06
**中文**
即使在我名义上很了解的领域也是如此。我曾是生物学家，对制药科学了解很多，却从未从科学之外思考制药业务：没想过产品组合，也没想过临床试验如何运作、如何降低成本，对国防和情报业务也从未做过深入了解。
**English / Original**
And even in areas where I nominally know a lot. I used to be a biologist, so in a way, I know a lot about the pharmaceutical business, but I'd never thought about it beyond the science. I never thought about the portfolio side of it. I never thought about how clinical trials work and how they could be made cheaper. I never thought about the defense and intelligence business in any great detail.
## 00:46:30
**中文**
逐一接触这些行业后，我觉得理解人们的问题以及 AI 如何帮助解决，非常有趣，我很自然就投入其中。产品侧则是我起初更抗拒的领域。我对商业天然好奇，但即使创办公司后，开发应用也一直不太吸引我。
**English / Original**
And so you run through those, and I just find it super interesting to understand what people's problems are and how AI can help with those problems. I feel like I took very naturally to that. Actually, the product side was one where I was initially more reluctant. I felt like I just had a natural interest and curiosity in the business side of it, but like, building apps, somehow, initially it was never like, a thing that drew me in even after I started the company.
## 00:46:59
**中文**
但最近看到哪些产品成功、哪些失败后，我对如何设计所谓“具备 AGI 思维”的产品越来越感兴趣：产品方向要能长期成立，并成为通往未来有用能力的桥梁。大家都听过“套壳公司”或“套壳产品”。
**English / Original**
But I think more recently, as I've seen what products have succeeded and what products haven't, I think this idea of how to design products so that they're, what we call AGI pilled. So that the direction of the product is durable and is kind of a bridge to things that are useful in the future. We've all heard this idea of wrapper companies or wrapper products.
## 00:47:22
**中文**
意思是：你发布 Claude N，有人做了个产品专门弥补 Claude N 的缺陷；可当 Claude N+1 发布，新模型直接把它吞掉。我以及其他 AI 公司的人通常都会建议：不要做这种产品。要看清领域发展方向，做与之互补的东西。
**English / Original**
The idea is you make Claude N, and someone makes a product that basically addresses the deficiencies of Claude N, but then you come out with Claude N+1 and it just kind of eats it. The advice I always give that I think all the AI, all the folks at the AI companies give, you know, don't make that. See the direction of the field and try to make something that's complementary.
## 00:47:45
**中文**
思考如何以一种新的、具备 AGI 思维的方式做产品，确实让我产生了很大兴趣。很好。你不觉得现在其实还没有真正的 AI 用户界面吗？我们仍把文字输入文本框，和 20 世纪 70 年代的终端完全一样，只不过圆角更多。
**English / Original**
And I think thinking about how to make products in a new way, in a kind of AGI pilled way, that actually has caught my interest a great deal. Okay, so, glad you brought this up. Doesn't it feel like we have no AI UIs right now? Like, we still enter text into text boxes, literally same as terminals from the 1970s. I mean, a bit more rounded corners and everything.
## 00:48:10
**中文**
语音陪伴模式仍要手动触发，与 Transformer 之前的 Siri 一样；界面基本毫无变化。确实有哪里不对，我基本同意。我们可以有生成式界面。这让我想到早期互联网：人们做的网站会模仿现实世界的结构，比如打开一个柜子再执行某个操作。
**English / Original**
Like, we still talk into voice companion modes that are manually triggered, which is the same as pre-transformer Siri. UIs are just completely the same. Yeah, yeah, there's something not quite right about it. I basically agree with you. Could have generative UIs. It reminds me a little bit of, you know in the early days of the internet, it was like people would make these websites that had structures that looked like they were in the physical world, open the closet and do the...
## 00:48:39
**中文**
这属于“无马马车”时代的思维，有个术语我忘了。拟物化？对，拟物化。如今也存在类似现象。随着我们走向智能体，AI 模型会越来越能端到端完成事情。
**English / Original**
They're from the horseless carriage era. There's some term for this, I forget. There's some word for this, I forget what it is. Skeuomorphism? Skeuomorphism, yes. It feels like there's some of that going on here. A thing I would say is that as we move more towards agents, we're gonna be in a world where the AI model can do something end to end.
## 00:48:59
**中文**
Claude 几乎已经可以端到端完成任务，并在大多数时候做对；人的主要工作变成检查，或偶尔检查。但有趣的是，检查往往意味着深入了解实际发生的细节。这里存在一种阻抗不匹配，需要产品或界面来解决：你希望它尽可能顺滑、自行完成任务，大多数时候无需关注；可一旦出错，你又可能必须深度介入。
**English / Original**
Like, we're almost there with Claude. Can do something end to end and get it right most of the time. And a human's main job is to kind of check. Or check sometimes. But interestingly, checking often means getting really into the details of what happened. And so there's some kind of impedance mismatch here, that some product or interface is the solution to, where you want something that's as slick as possible and just goes off and does something, and you don't wanna have to pay attention most of the time, but when something's wrong, you might actually need to get quite involved.
## 00:49:36
**中文**
现在似乎没有任何产品或界面按这一原则运行、解决这一问题。我理解。你希望智能体离开去替你做好工作，再带着成果回来，让你审阅、引导并作决定。
**English / Original**
And I don't feel like any products or interfaces operate on this principle now or handle this problem now. I don't know if that makes sense, but— No, it does. Like, agreed. I think what you want is your agent to go away and do really good work for you, and then come back with its work product to let you review, steer it, decide everything.
## 00:49:56
**中文**
但不能让人被信息淹没，因为智能体完成的事情会远多于你有时间查看的数量；若一直盯着它，甚至比自己做还慢。因此这确实像一个界面问题。更广泛地说，AI 最令人兴奋的一点，是当前能力还有巨大产品化余量。即使 AI 进步今天冻结，凭现有能力也足够开发十年好产品。
**English / Original**
But you can't be overwhelmed, because it's gonna do so many more things than you have time to look at, and if you're always looking at it, you know, it can be slower than if you just did it yourself. And so it actually strikes me as an interface problem. Yes, yes. The generalization of this is it feels to me, one of the most exciting things about AI is we have such an overhang of current capabilities, turning them into good products, where even if AI progress was frozen right now, we'd have like 10 years of good products, because— Oh, oh, I completely agree.
## 00:50:28
**中文**
完全同意。整个行业今天构建产品的方式之所以不同，正因为模型仍在持续进步。如果模型停止进步，我们构建产品的方式会立刻改变。此前从未有过一种技术，在你开发产品时，其底层本身以如此快的速度变化。
**English / Original**
And actually, the way that products are being built, I think by everyone in the industry, but we've thought about it this way, is very different because the progress is continuing. If the progress in models stopped, the way we built products would change instantly. The reason is I don't think we've ever had before a situation which the technology is changing under you so fast as you're building the product.
## 00:50:53
**中文**
因此长期产品路线图或传统产品规划方式都不太适用。Anthropic 早期我会说：“我不懂产品，我是个门外汉。”但如今遇到新同事，我总会强调：“这和非 AI 领域做产品不一样。”
**English / Original**
And so this idea of long-term product roadmaps or the usual way of product planning, I've started explicitly... Again early in Anthropic, I was like, "I don't know anything about product. I'm a doofus." But now I always try to talk to people when they come in and they say, "This is not like building products in the non-AI space," right?
## 00:51:12
**中文**
因为产品要更具 AGI 思维？是的。你也许是产品专家，但脚下的技术在不断移动，因此快速迭代比平时更加重要。具体例子是什么？如果你说“我们要做一个东西，六个月后上线”，这在 AI 领域甚至比闭门开发一般产品更不合理。
**English / Original**
Because they need to be more AGI pilled? Yeah, you may be the expert at building these, but like, the technology is moving under you, so like, these ideas about fast iteration, they're even more true than they are normally. What's a specific example of this? I think that if you're like, "We're gonna make something, and it's gonna be ready in six months," I think that makes even less sense here than it makes, the building in isolation.
## 00:51:37
**中文**
所以要缩短发布周期、增加迭代？是的，要更紧密地发布并不断试验。什么会流行更加难以判断，因为新模型随时可能出现，突然擅长某件事，从而使一种产品成为可能。你所尝试的事情前所未有。
**English / Original**
So you need to just have tighter ship schedules and more iteration? You need to have tighter ship schedules, you need to try things. It's very hard to tell, even harder to tell what's gonna catch on, because a new model may have come out, a new model may suddenly be good at something that makes a product possible, and so much more than anything else, you're trying something that's never been tried.
## 00:51:58
**中文**
有了一个公司内部才能用的新模型，你最应该做的就是基于它快速构建东西，让内部人员试用。这里有种“永恒九月”的感觉：仿佛第一次发现数据库技术，然后问“我们能基于它构建什么？”
**English / Original**
There's a new model. It's only available within the company. So the thing you ought to do is just build something on it, let people internally try it. There's this eternal September vibe to it, right? Where it's like, it's as if you discovered database technology for the first time, and you're like, "Well, what could you build on this?"
## 00:52:17
**中文**
每天都像第一天，这就是不同。既然提到数据库，也许它是个有趣类比：最早成功普及的关系型数据库是专有的，后来开源产品追了上来。你们如何保持与开源选项的差距？
**English / Original**
And it's always the first day. That's what is different. You mentioned database technology, and maybe that provides an interesting analogy, and as we think about open source, the first relational databases that were successful in terms of adoption were proprietary, but then the open source guys caught up. How do you keep the gap with the open source options?
## 00:52:41
**中文**
AI 模型中的“开源”和其他领域含义不同，因此有人称其为“开放权重模型”以作区分。主要差别在于，即使看到模型权重、向内部查看，你也无法理解它究竟如何运行，并不具备传统开源软件那种可组合性。
**English / Original**
Yeah, open source I think has a different meaning in AI models than it has in other areas, right? And for this reason, some have called it open-weights models to distinguish. I think the main difference is that if you see the weights of the models and you look in, you can't understand what's actually going on. There's not that kind of composability.
## 00:53:02
**中文**
你无法阅读源代码，也无法轻易产出略有不同的版本。Anthropic 正在研究机制可解释性，使我们能看到模型内部，从而有望获得部分相关能力，但目前还做不到。
**English / Original**
I can't read the source code, I can't— You can't produce a trivially different version. Yeah, yeah, you know, I can't produce a trivially different version of it. Now, you know, Anthropic is actually working on mechanistic interpretability, which allows you to see inside the models, and so we're actually working on things that would allow some properties, but we're not there yet.
## 00:53:21
**中文**
我们离那个目标还很远。现在能做一些事，例如拿到模型后进行微调；我们也开始通过接口允许用户微调。但真正拿到模型权重，相比一个功能丰富、允许你完成各种操作的 API，究竟多大程度更有价值，仍是个问题。
**English / Original**
We're not anywhere close to there. There are some things you can do. For example, if you have access to the model, you can fine-tune the model. We're now through interfaces kind of allowing people to fine-tune the model. There is a question of how valuable access to the actual model weights is over and above some thick API that lets you do something.
## 00:53:41
**中文**
这还涉及经济性。请注意，在云上运行模型本身成本很高：总要有人托管、进行高速推理，而这又会回到利润率或其中一部分。因此你的看法是，开放权重模型并没有那么有用，而真正全开源的模型仍存在巨大差距？
**English / Original**
There's some question of economics, but note that it costs a significant amount to run the models on the cloud. Someone has to host it, someone has to run fast inference, and then you're back to the margin or some portion of the margin. So you think open-weight models are not that useful, and fully open source models, there's just a big gap?
## 00:53:58
**中文**
我会说，与以往技术的类比只部分成立。这是一种不同的事物，我们仍在探索。但从我们的角度看，当竞争者发布新模型时，我们并不太在意它是否开放权重，只看它是不是强模型。
**English / Original**
I guess what I would say is that the analogy to previous technologies is only partial, right? It's kind of a different thing that we're still discovering. But I can say from our perspective that when a new model comes out, when a competitor model comes out, we don't really think about whether it's an open-weights model or not, we think about whether it's a strong model.
## 00:54:26
**中文**
若有人做出在我们所做之事上很强的模型，那就是竞争、对我们不利，无论它是否开放权重，两者差异并不大。Anthropic 如何比其他组织更具“AGI 思维”？一个表现是速度更快、产品发布节奏更紧，但也可以从整个组织而不只是产品开发来看。
**English / Original**
If someone makes a strong model that's good at the things that we do, that's competition, that's bad for us, whether it's an open-weights model or not. There's not a huge difference between the two. How is Anthropic more AGI pilled than other organizations? So one is faster, like a tighter product release cadence. But maybe more broadly across the organization, not just within product development.
## 00:54:53
**中文**
我提过，每隔几周我会在全公司面前讲话，阐述自己的愿景，目的之一是让大家聚焦使命。世界处在奇特状态，我总会强调不确定性；但若必须下注，我会押注一两年或三年后——具体多久不知道——数据中心里会出现我所说的“天才之国”。
**English / Original**
I mentioned this thing that every couple of weeks, I get up in front of the organization, and kind of describe my vision, and I think one of the purposes of that is to keep people kind of focused on the mission. It's a strange state of the world, and I always express uncertainty about it, but I say if I were to bet, I would bet in favor of this, that, in one or two or three years, I don't know exactly how long it's gonna be, we'll have what I've described as like a country of geniuses in the data center.
## 00:55:24
**中文**
这很奇特。它会改变经济、加快科学进步，也会带来全球对齐和国家安全风险，还可能引发经济问题。上行空间巨大，潜在扰动也巨大。我想抵抗的是那种普通职业心态：员工加入后想“我以前在这个行业、这类公司工作，现在去一家 AI 公司，过几年也许再去另一家”。
**English / Original**
And like, this is weird. It's gonna change the economy, it's gonna accelerate the pace of science. It's gonna pose global alignment and national security risks. It may pose economic problems. The upside is huge. The potential for disruption is also huge. And I think what I'm trying to fight against is the idea of employees who joined and they're like, "Oh, you know, I worked in this industry, you know, I worked at this kind of company, and I'm gonna work at an AI company, and maybe a couple years later, I'll go to this," you know?
## 00:55:56
**中文**
这与以往经历有着类别上的根本不同。我们希望组织上下都理解：财务人员做预测时，不是必须认定指数增长一定发生，而是要明白极端结果确实可能出现。
**English / Original**
This is very categorically different from previous experiences that have happened. This is a really different thing, and I think up and down the organization, we want to make sure that when our finance people think about financial projections, they understand this, not that there's certainly going to be an exponential, but like, wild outcomes are possible.
## 00:56:16
**中文**
招聘团队也要明白某些看似疯狂的薪酬情形可能发生；产品团队要做具备 AGI 思维的产品；政策团队与外界互动时也要理解潜在事态的分量。因此，我工作的重要部分就是让整个组织围绕这一核心论点保持一致。
**English / Original**
When our recruiting thinks they're like, "Oh, yeah, this crazy cop stuff could happen because"... You know, and when the product people think they make AGI pilled products, when the policy people interact, they understand the stakes of what may happen. And so I think a big part of my job is keeping the coherence of the organization around this central thesis.
## 00:56:39
**中文**
并非每个人都必须相信这个论点；这不是灌输，更没有人穿长袍一起吟诵。但公司建立在一个假设之上：这些巨大变化有可能、甚至很可能发生；业务的每个方面以及公司为社会福祉所做的事，都应围绕这种强可能性来构建。
**English / Original**
Not that everyone has to believe the thesis. There's not an indoctrination and people chanting with robes or anything. But like, the basic idea that the company is built around this hypothesis that it is possible and perhaps likely that these large changes will happen, and every aspect of the business, as well as the things the company is doing for social benefit, should be constructed around strong possibility that this may happen.
## 00:57:11
**中文**
具体到数字，你谈过 AI 可能推动经济每年增长 10%。那么我们谈 AI 风险时，通常想到伤害和滥用；但最大的 AI 风险会不会其实是监管略有偏差或拖慢进步，导致大量本可获得的人类福祉因 AI 延迟而丧失？
**English / Original**
To put numbers on this, you've talked about the potential for 10% annual economic growth powered by AI. Doesn't that mean that... When we talk about AI risk, it's often harms and misuses of AI, but isn't the big AI risk that we slightly misregulate it or we slow down progress, and therefore there's just like a lot of human welfare that's missed out on because you don't have AI?
## 00:57:36
**中文**
我的家人曾死于某种疾病，而几年后这种病就有了疗法，所以我切实体会到进步不够快的代价。但 AI 的一些危险也可能严重动摇社会，或威胁人类与文明。
**English / Original**
I've had the experience where I've had family members die of diseases that were cured a few years after they die, so I kind of truly understand the stakes of not making progress fast enough. I would say that some of the dangers of AI have the potential to significantly destabilize society or threaten humanity or civilization.
## 00:57:59
**中文**
面对这种级别的风险，不能随意碰运气。不过我完全不主张停止或暂停技术。出于多种原因，那不可能。我们有地缘政治对手，他们不会停止开发；其中牵涉的金钱也太多。
**English / Original**
And so I think we don't want to take idle chances with that level of risk. Now, I'm not at all an advocate of like, stop the technology, pause the technology. I think for a number of reasons, I think that's just... It's just not possible. We have geopolitical adversaries. They're not gonna not make the technology, the amount of money.
## 00:58:22
**中文**
哪怕只提议最轻微的监管，也会有数万亿美元资本站到我的对立面，因为监管不符合它们的利益。这显示了现实中什么可行、什么不可行。与其把选择理解成“减速”或“全速前进”，不如问：能否引入安全和安保措施、考虑经济影响，同时不拖慢技术，或只让它稍微放慢？
**English / Original**
I mean, if you even propose even the slightest amount of reg... I have, and you know, I have many trillions of dollars of capital lined up against me for whom that's not in their interest, so that shows the limits of what is possible and what is not. But what I would say is that instead of thinking about slowing it down versus going at the maximum speed, are there ways that we can introduce safety security measures, think about the economy, in ways that either don't slow the technology down or only slow it down a little bit?
## 00:59:01
**中文**
如果把经济增长从 10% 调到 9%，就能为所有这些风险购买保险，我认为真实的权衡更像这样。正因为 AI 可能进展如此迅速、解决如此多问题，我更担心它会过热。
**English / Original**
If instead of 10% economic growth, we could have 9% economic growth and buy insurance against all of these risks. I think that's what the trade off actually looks like. And precisely because AI is a technology that has the potential to go so quickly, to solve so many problems, I see the greater risk as like the thing could overheat.
## 00:59:24
**中文**
我不想停止反应，而是想把反应聚焦起来。我就是这样理解的。你曾说：“如果到了 2025 年 12 月仍没有 AI 法律，我会非常担忧。”你现在感觉如何？加州确实有一项法案 SB 53；去年则有 SB 1047。
**English / Original**
And so I basically want... I don't wanna stop the reaction. I wanna focus it. That's how I think about it. You said, "If we hit December, 2025, and there's no AI law, I'll be really worried." How are you feeling? There is actually something in California. There's a bill out, SB53. Last year, we had the whole SB1047 thing.
## 00:59:45
**中文**
我们对 SB 1047 看法复杂。初版过于激进。技术变化很快，规定得太具体反而无益，最后甚至不能真正促进安全。我担心一旦通过，其中规定的测试会显得荒唐，AI 行业的人便会说：“原来安全监管就是这种愚蠢东西”，从而不再认真对待。
**English / Original**
We had mixed feelings on SB1047. There was initial version that I think was too aggressive. And when I say that, what I mean is the technology is moving fast, and it's kind of unhelpful if you're too prescriptive about it. It ends up actually not contributing to safety. And I was worried a little bit, if something like this passes, it's like the tests that were prescribed to run will end up looking stupid, and then all the people in the AI industry will be like, "Oh, this is what regulation for safety and security looks like, it's really stupid," and they won't take it seriously.
## 01:00:17
**中文**
他们会竭尽所能只遵守条文、不遵守精神。因此作为审慎监管的支持者，我反而有所担心。我们提议修改法案，直到自己认为可以接受，并试图在行业与安全倡导者之间达成妥协。
**English / Original**
They'll kind of do everything they can to comply in letter and not in spirit. And so, as an advocate of thoughtful regulation, I was actually a bit concerned about this. We offered some changes to the bill to a point where kind of we felt good about it, and we tried to make a compromise between kind of industry and the safety advocates.
## 01:00:39
**中文**
如你所见，最终没有成功。但今年我们正推动一项更温和的法案，尤其聚焦实践透明度及安全、安保实践透明度。Anthropic 一直在这方面走得很前；其他公司也开始做，但并非全部，而且没有办法判断各方披露时是否说了实话。
**English / Original**
We didn't really succeed, as you saw, but this year, I think we're making a bill that is something more moderate. It's focused particularly on transparency of practices, transparency of safety and security practices, which is something that Anthropic has been very forward about, and that I think other companies are starting to do, but not all the companies do it, and there's no way to tell if folks are telling the truth about what they're revealing.
## 01:01:03
**中文**
加州监管就足够，因为所有公司都与这里有联系。没错，大多数法案以“在加州开展业务”为适用基础，因此很难完全切断服务。这里的人非常认同 AI。我们看看会发生什么。
**English / Original**
And California regulation is enough, because all the companies have a nexus here. Yeah, yeah, I mean, I think most of these bills are organized around, doing business in California, and so it would be difficult to shut off. People are very AI-pilled here. People are very AI-pilled here. So, you know, we'll see what happens.
## 01:01:24
**中文**
我不确定结果。但我们始终支持为技术设置护栏，包括立法护栏，同时也承认必须谨慎。我们不想杀死下金蛋的鹅，只想防止它过热或冲出道路。现代银行监管也许可作类比：银行业务本质上风险很高。
**English / Original**
I'm not sure what's gonna happen. But we've always had this approach that we kind of are in favor of guardrails, including legislative guardrails on the technology, but we recognize the need to be careful. We don't wanna kill the golden goose, we just wanna stop it, from overheating or running off the road. Yeah maybe something like modern bank regulation for all the people complained is a good example, where there's an inherently very risky activity, you know?
## 01:01:53
**中文**
银行挤兑等危险非常明确；但找到合适监管环境后，现代银行体系总体运行良好。最后一个问题：你个人的 AI 工具栈是什么？你使用 AI 的方式与其他科技从业者有何不同？我主要写很多东西。
**English / Original**
Yeah, no, no, the dangers are pretty clear. I mean it's like the bank runs are not- Right, but it all works pretty well in the modern era once we figured out the regulatory environment. Last question. What is your personal AI stack? How do you use AI differently to maybe other people in tech? Yeah, interesting. You know, I basically write a lot.
## 01:02:14
**中文**
也许我对自己的写作太自负。我会用 Claude 生成很多想法，也把它当研究工具；但到目前为止，文章还是自己写。Claude 也许比其他模型更接近我的要求，但仍没到位。商务邮件可以放心交给它，可若是想真正写好的文章或随笔，它还差一点。
**English / Original**
Perhaps I have too much pride in my own writing. I use Claude to generate lots of ideas. You know, I kind of use it as research. But so far, I've done the writing myself. Claude is actually maybe closer than the other ones, but it's still not there. I'd be comfortable with it for business emails, but if I'm kind of, like, writing an essay or something that I wanna really get right, it's not quite there yet.
## 01:02:41
**中文**
不过也许一年左右就可以了。非常精彩。达里奥，今天聊得很棒，感谢你来。谢谢邀请。
**English / Original**
But maybe it will be in, you know, a year or so. Yeah, very cool. Well, Dario, this was awesome. Thanks for coming by. Thank you for having me.