Living on the Exponential

How much are you sleeping?

你睡得怎么样?

You know, I've never been someone who slept all that well.

说实话,我这人一直就睡得不太好。

Let's just say I'm learning the art of finding ways to relax and sleep through moments of unusual pressure.

这么说吧,我正在学一门手艺:怎么在压力特别大的时候,找到办法放松下来、睡过去。

It is all moving so fast. How does it feel on the inside?

这一切都快得不行。从里面看,是种什么感觉?

It's this feeling of, like, the exponential.

就是那种……指数曲线的感觉。

Suppose you were to accelerate away from earth on a spaceship at relativistic speed.

想象你坐一艘飞船,以接近光速的速度加速离开地球。

The way special relativity works is, you go to sleep and you wake up and two days have gone by on earth, and so you have to deal with two days in one day.

按狭义相对论,你睡一觉醒来,地球上已经过了两天,于是你得在一天里处理两天的事。

And then you go to sleep, and then because you've continued to accelerate, three days have gone by on earth, and then the next day, four days have gone by. That's a little bit what it feels like.

然后你再睡一觉,因为飞船还在加速,地球上又过了三天;再下一天,就是四天。差不多就是这种感觉。

I mean, do you go to bed constantly paranoid about what you'll wake up to?

那你是不是每天睡前都提心吊胆,怕醒来又出什么事?

There are enough clear and present issues that we have to deal with that I'm constantly dealing with those, while thinking about how we can prepare.

眼前要处理的实打实的问题已经够多了,我一直在处理这些,同时也在想我们该怎么提前准备。

But I don't think paranoia or worrying about what you'll wake up to is productive.

但我不觉得疑神疑鬼、担心明早醒来会怎样,有什么用。

I've looked at people in history who've dealt with these very high pressure situations. You need to learn to respond rationally and not put dangers out of proportion to each other.

我研究过历史上那些扛过高压局面的人。你得学会理性应对,不要把各种危险的轻重缓急搞错比例。

This yo-yoing between "I'm not worried" and "oh my god, we need to panic today" — I think that's a hallmark of immature decision-making.

在"我一点不担心"和"天哪,今天得赶紧恐慌"这两头来回摇摆——我觉得这正是不成熟决策的标志。

The actual mature decision-making is: you can't ignore this, we can't be complacent — in fact, it's getting to be a bigger and bigger risk — but we have to respond rationally.

真正成熟的决策是:这事不能无视,我们不能掉以轻心——事实上风险越来越大——但我们必须理性应对。

Like a surgeon would deal with an operation, or a military officer would deal with a military operation. Someone making decisions that affect a lot of people has to make those decisions rationally, and they have to understand the risk. But they have to maintain a basic sense of calm.

就像外科医生面对一台手术,或者军官面对一场军事行动。一个要为很多人负责做决定的人,必须理性地做决定,必须看清风险——但也得保持一种基本的镇定。

So my son yesterday was like, "Can I use your Claude Co-work account?" And I was like, "Absolutely not. I need my tokens." [laughter]

我儿子昨天问我:"我能用你的 Claude Co-work 账号吗?"我说:"想都别想,我的 token 还不够用呢。"[笑]

We're seeing more and more of them, even in the consumer space.

这样的情况越来越多,连消费端也是。

We wanted to be more of an enterprise company, but even consumer — without us putting that much effort — is starting to grow fast.

我们本来想更偏企业级,但就连消费端——我们其实没投太多精力——也开始涨得很快。

You are at the center of the AI universe right now. What does that feel like?

你现在就站在 AI 宇宙的正中心。那是什么感觉?

The interesting thing is that the experience I've had for my whole career, and certainly the whole time at Anthropic, is that there's this kind of smooth exponential.

有意思的是,我整个职业生涯——尤其是在 Anthropic 这段时间——的体会都是:存在一条平滑的指数曲线。

And the experience of the smooth exponential is: nothing's happening, nothing's happening, nothing's happening. A little thing happens, and then — zoom — it goes crazy.

而这条平滑指数曲线给你的体感是:什么都没发生,什么都没发生,什么都没发生。一点小事冒头,然后——嗖——就疯了。

That's the experience of the world. That's the experience of the scale of the company compared to the other companies and compared to the world.

这是你对整个世界的体感;也是这家公司相对于其他公司、相对于整个世界,在体量上给你的体感。

So I was watching this graph for a while and I said, "Oh yeah, we'll probably become the AI company with the most revenue and the most valuation sometime around this time" — and indeed it has happened.

所以我盯着这张图看了一阵,然后说:"嗯,我们大概会在这个时间点前后,成为收入和估值最高的 AI 公司。"——结果还真就发生了。

So in one sense, I'm not surprised, because this is just a smooth line on the graph. But of course, in another sense, when things actually happen, you see so much more detail and color to it.

所以一方面,我并不意外,因为它不过是图上一条平滑的线;但另一方面,当事情真的发生时,你会看到多得多的细节和质感。

It definitely is surprising. But we're just keeping in mind all the things we usually keep in mind, which are: how do we train good models? How do we put them in good products? How do we make sure that everything's safe? How do we help people but also manage the societal risks around the technology?

它确实让人意外。但我们记挂的,还是平时一直记挂的那些事:怎么训出好模型?怎么把它们装进好产品?怎么保证一切都安全?怎么既帮到人、又管好这项技术带来的社会风险?

It's all the same questions, just kind of under a bigger microscope, as it were.

还是同样那几个问题,只不过现在被放在了一台更大的显微镜底下。

What were you like as a kid growing up in San Francisco? I know your dad was a leather craftsman, your mom worked in libraries. How did that shape you?

你在旧金山长大,小时候是个什么样的孩子?我知道你爸爸是做皮具的手艺人,妈妈在图书馆工作。这些怎么塑造了你?

You know, the whole first internet revolution was happening around me, and I had absolutely no interest in it.

你知道吗,第一波互联网革命就在我身边发生,而我对它毫无兴趣。

I was just interested in, like, doing math and figuring things out. I was interested in understanding the universe. I was interested in science fiction — that was kind of the general milieu.

我就只对做数学、琢磨东西感兴趣;我想搞懂这个宇宙;我喜欢科幻——那大概就是我从小的氛围。

I think I just felt a lot of curiosity about the world.

我想,我就是对这个世界充满了好奇。

You grew up in the town that is the center of technology, and right now it's the center of AI. Is there anything about this place, this city, that informed your worldview?

你从小生活的这座城,既是科技的中心,如今又是 AI 的中心。这个地方、这座城市,有什么塑造了你的世界观吗?

Yeah, I think the general spirit of nonconformism and individualism, and it's okay to be crazy — I think a good deal of that probably did rub off on me.

嗯,我觉得这里那种普遍的非主流、个人主义,还有"做个疯子也没关系"的劲儿——这些大概很大程度上确实感染了我。

You hear these stories about going to countries in Europe, or even other parts of this country, where it's just kind of discouraged or considered weird to think about things in some different way, or to have some set of crazy ideas.

你会听到这样的说法:在欧洲一些国家,甚至在美国的其他地方,用一种不一样的方式去思考、或者抱着一堆疯狂的点子,往往会被劝退,或者被当成怪人。

There are a lot of things I'm actually very critical about with Silicon Valley. But one thing that I think is good about it is this encouragement of: it doesn't matter if all the experts are against you.

说实话,我对硅谷有很多很尖锐的批评。但我觉得它有一点是好的,就是它鼓励你:就算所有专家都不看好你,也无所谓。

If you have a coherent vision and a coherent worldview, you should go and pursue it. Maybe it just won't work at all. But if it does, there's this kind of long-tailedness to it.

只要你有一套自洽的愿景、一套自洽的世界观,你就该去追。也许它压根行不通;但万一行得通,它会带来那种长尾式的回报。

There are certain veins of ore where you might find a huge gold mine. I think that spirit is very important.

就像有些矿脉,你顺着挖下去,可能挖到一座大金矿。我觉得这种精神非常重要。

You, Daniela your sister, and her husband Holden Karnofsky lived in a group house together back in 2016. What were you debating back then?

你、你妹妹 Daniela,还有她丈夫 Holden Karnofsky,2016 年曾经合租一栋房子住在一起。那会儿你们都在争论些什么?

That was the time when the Open Philanthropy Project was first being started up, which Holden was the lead of. And I at that time was, like, a biological scientist.

那正是 Open Philanthropy Project(开放慈善项目)刚起步的时候,Holden 是负责人。而我那时候算是个生物科学家。

So I was helping them with some of the stuff they were doing around developing-world health or biological research. I kind of advised on that stuff — what were the areas that were promising, what were the areas that were less promising.

所以我在帮他们做一些事,围绕发展中国家的健康问题,或者生物学研究。我多少给点建议:哪些方向有前景,哪些方向没那么有前景。

Your decision to leave OpenAI has become Silicon Valley lore. [Dario snorts] What really happened — like, beyond the narrative? What were the issues, what did you disagree on?

你离开 OpenAI 的决定,已经成了硅谷的一段传说。[Dario 笑]抛开外面那套说法,到底发生了什么?分歧在哪,你们到底为什么谈不拢?

Look, I'm going to say it very simply.

这么说吧,我就简单直接地讲。

There are many difficult issues that you face when you're building powerful technology, that Anthropic faces every day, where we don't know whether we're making the right decision or the wrong decision.

当你在打造一项强大的技术时,会碰到很多棘手的问题——Anthropic 每天都在面对——我们根本不知道自己是做对了还是做错了。

So there are many valid disagreements to be had on safety. We certainly had some of those disagreements with them.

所以在安全问题上,有很多合理的分歧空间。我们和他们确实有过一些这样的分歧。

But that alone is not sufficient to leave. People here have had disagreements with me. People here have disagreements with each other.

但光是这个,不足以让人离开。这里的人跟我也有分歧,彼此之间也有分歧。

But when you feel that you can't trust someone, when you feel that their values are not what they say they are, when you feel that they're not honest, when you feel that they're not in it for the reasons that they say, when you see disturbing patterns of behavior, dishonesty — that makes it very hard to continue to work with a company, to continue to trust the company.

可一旦你觉得没法信任某个人——觉得他们的价值观跟他们嘴上说的不一样,觉得他们不诚实,觉得他们做这件事的动机并不是他们声称的那样,觉得自己看到了一些令人不安的行为模式、看到了欺瞒——那就很难再跟这家公司共事下去,很难再信任它。

And at the end of the day, why argue with someone when you don't have the same vision and you don't trust them? The way to resolve it is: you go off and do your thing, they go off and do their thing.

说到底,跟一个你既不认同其愿景、又不信任的人争论,有什么意义?解决的办法就是:你去做你的,他们去做他们的。

And I am completely at peace with the idea that we're doing things our way and they're doing things their way. We'll see who wins in the market, and we'll see who wins in the court of public opinion.

对于"我们走我们的路,他们走他们的路"这件事,我内心毫无芥蒂。市场上谁赢,我们走着瞧;公众舆论这个法庭上谁赢,我们也走着瞧。

I think those things speak louder than any drama about who left what.

我觉得这些事的分量,远比"谁离开了哪儿"这种戏码要重得多。

We're providing an example of how to deploy this technology in what we think is a responsible way. If they disagree, they should make that argument. And I think that's really all there is to say about it.

我们是在示范一种我们认为负责任的方式,来部署这项技术。如果他们不认同,那就把他们的道理摆出来。关于这件事,我觉得真的就这么多可说的了。

There was a moment at India's AI summit where you and Sam Altman appeared to refuse to hold hands on stage. What happened there?

在印度的 AI 峰会上有那么一幕,你和 Sam Altman 在台上看起来像是拒绝牵手。那是怎么回事?

What happened is that the summit was extremely disorganized. We all came up at the last minute, and then they changed the order in which we were standing, and then they took a picture of us, and then they ordered us all to hold hands.

实情是,那场峰会乱得一塌糊涂。我们都是最后一刻才上台,然后他们又临时改了我们的站位,接着给我们拍照,然后命令我们所有人牵手。

If you've ever been to one of these — I'm not saying anything bad about India in particular — but all of these international-type summits that have heads of state are super disorganized.

你要是去过这种场合就知道——我不是单说印度不好——但凡是有各国元首出席的国际峰会,都乱得不行。

Okay. But everyone else held hands. Come on.

行吧。可别人都牵了啊,别装了。

I don't know what to tell you. There was, like, Narendra Modi up there suddenly telling everyone to hold hands. [laughter]

我也不知道该怎么解释。当时是 Narendra Modi(莫迪)在台上,突然让大家都牵手。[笑]

All right. Look, Sam and Elon are suing each other. You don't like Sam?

好吧。你看,Sam 和 Elon 在互相起诉。你是不喜欢 Sam?

It seems like, if the people building the most important technology in the world can't hold hands on stage, how can we trust you'll cooperate on existential risk?

这就让人觉得:如果连打造这项全世界最重要技术的人,都没法在台上牵个手,我们凭什么相信你们会在"生存性风险"上携手合作?

So here's what I will tell you. There is a wide variance in the quality and the trustworthiness of the people building this technology.

那我这么跟你说。打造这项技术的这群人,他们的水准和可信度,差异非常大。

I think this meme that no one trusts each other — I don't think it's right.

"谁都不信任谁"这个流行说法,我觉得它不对。

I've known Demis Hassabis, who builds the Gemini models that are a competitor to Claude models — I've known him for 15 years. We've worked together on a number of issues. We buy compute from Google. We swap safety ideas all the time.

Demis Hassabis 做的是 Gemini 模型,是 Claude 的竞争对手——我认识他 15 年了。我们在不少问题上合作过。我们向 Google 买算力,也一直在互相交流安全方面的想法。

So my view is that, one, there are some players who are more trustworthy than others. And I think there are players outside Anthropic who I trust, who I see as trustworthy.

所以我的看法是:第一,有些玩家就是比另一些更可信。在 Anthropic 之外,确实有一些我信得过、我认为值得信任的玩家。

What I think needs to happen is that the trustworthy actors need to get together and put the untrustworthy actors in a position where they have to adopt the same standards.

我认为应该发生的是:可信的那些人要联合起来,把不可信的那些人逼到一个境地——不得不接受同样的标准。

With a lot of experience, I've learned that there are some folks who don't do the right thing on their own. But if there's a majority of the industry that's doing the right thing, then the rest are left in a position where there's not much they can do but come along.

这么多年下来我明白了一件事:有些人,你指望他自觉做对的事是没用的。但只要行业里大多数人都在做正确的事,剩下那些人就被架住了——除了跟上来,没多少别的选择。

There's the positive version of it, where you inspire other people. That's like Demis and me inspiring each other. He does AlphaFold; we're trying to do something in bio as well. We do interpretability research; they start interpretability research.

这有它正向的一面,就是你去激励别人。比如我和 Demis 互相激励:他做 AlphaFold,我们也想在生物领域做点东西;我们做可解释性(interpretability)研究,他们也开始做可解释性研究。

It's not even competition. It's just each company does something cool, and the other company's like, "That's cool, we'd like to do that too, and see if there's something new within that we can do." So that's the carrot side of the race to the top.

这甚至算不上竞争。就是一家公司做了个酷东西,另一家说:"挺酷的,我们也想做,看看能不能在里头玩出点新花样。"这就是"向上竞赛(race to the top)"里胡萝卜的那一面。

Then there's the stick side, the implicit stick, where you're like, "Okay, these guys are doing the right thing. Those guys will look bad if they don't do the right thing."

然后还有大棒的那一面,一根隐形的大棒——意思是:"好,这帮人在做正确的事;那帮人要是不做,就会显得很难看。"

And often we see behaviors where they grudgingly do the right thing while trying to pretend they're doing something different, and that there's something bad or sinister about us. That is to be expected.

我们常看到这样的戏码:他们一边不情不愿地做了正确的事,一边又假装自己其实在做别的,还要暗示我们身上有什么坏的、阴险的东西。这都在意料之中。

But I think that's the way we get the industry together, and that's the way we get the industry to cooperate.

但我觉得,这就是把整个行业拢到一起、让它真正合作起来的办法。

Early on, others focused on fun, splashy consumer apps. You made a bet on coding and enterprise, and Claude Code is a hit, Claude Co-work is a hit. Why did you make that bet? Was it a values decision or a business decision?

早期别人都在做好玩、抓眼球的消费级 app。你押注的却是 coding 和企业级——结果 Claude Code 火了,Claude Co-work 也火了。你为什么这么押?这是个价值观决定,还是个商业决定?

When we started Anthropic, the base thing that mattered — the thing that always matters — is we want to do this right.

我们创办 Anthropic 时,最底层、最要紧的事——也是永远最要紧的事——是:我们想把这件事做对。

But then you have to ask yourself: in order to fund the very expensive creation of these models, it needs to be a company, it needs to have a business model. Does the business model get in the way of the values?

但接着你就得问自己:为了给"造这些模型"这件极其烧钱的事掏钱,它得是一家公司,得有商业模式。那么——这个商业模式会不会跟价值观打架?

One of the things I learned, just from being at other companies and watching other companies, is: if you pick a business model that fundamentally conflicts with your values, you're going to have a hard time.

我从在别的公司待过、也看过别的公司,学到一件事:如果你选了一个跟价值观根本冲突的商业模式,你会很难受。

Either you betray your own values, or you become irrelevant. You kind of end up in a catch-22. There are ways out, there are ways to dodge, but it's just a hard situation.

要么你背叛自己的价值观,要么你被边缘化。你会陷进一个进退两难的死局。出路是有的,可以躲一躲,但终究是个难局。

It's far better to pick a business model that is compatible with your values.

远更明智的做法,是挑一个跟你价值观相容的商业模式。

So when we thought about it, we said: we've seen the world of social media, the consumer world — it really seems to encourage engagement, even addiction.

所以我们想过之后说:我们见过社交媒体那个世界、消费端那个世界——它实质上是在鼓励参与度(engagement),甚至是上瘾。

The slop we've seen with AI video models — what's going on is it wants to maximize the number of minutes that you're paying attention, because that's the advertising-revenue-driven incentive.

我们见过 AI 视频模型刷出来的那些垃圾内容(slop)——它的逻辑就是要把你盯着屏幕的分钟数最大化,因为背后是广告收入驱动的激励。

Whereas if we look at enterprise — we want to make these models useful to people. If I think of all the positive things you can do with AI — I warn a lot about the negative things, but ultimately we think the positive things will outweigh the negative things — many of those basically fall under the banner of enterprise.

而企业级这边——我们想让这些模型对人真正有用。如果我去想 AI 能做的所有好事(我没少警告它的坏处,但我们终究认为好处会盖过坏处),会发现其中很多,基本都归在"企业级"这面旗下。

We want to use AI to cure diseases that we couldn't cure before — that's working with biotech, with pharma, with academic research groups. All of those are enterprises.

我们想用 AI 去治那些过去治不了的病——那就要跟生物科技公司、制药公司、学术研究团队合作。这些全是 enterprise。

We want to use AI to make energy cheaper and more efficient — that's all enterprise. We want to help with education — most of that is enterprise. We want to address health in the developing world — they're nonprofits, but those are basically enterprises. We want to increase economic growth — that is basically enterprise as well.

我们想用 AI 让能源更便宜、更高效——那也全是企业级。我们想帮助教育——大部分还是企业级。我们想解决发展中国家的健康问题——它们是非营利组织,但本质上也是 enterprise。我们想拉动经济增长——这本质上同样是企业级。

And then there's another factor: enterprises care a lot about trust and long-term relationships. Consumer can have this almost gimmicky aspect to it.

另外还有一个因素:企业非常看重信任和长期关系。而消费端,常常带着一种近乎噱头的成分。

With enterprise, what matters is you build a relationship where you work with a company for many years. You deliver on what you say, they deliver on what they say, and they basically trust you.

企业级真正重要的是:你跟一家公司建立起一种能合作很多年的关系。你说到做到,他们说到做到,彼此之间基本是信任的。

And so it's very synergistic with our goal of deploying these models in a positive and safe way. I think it serves us well to have this business model that largely aligns with our values.

所以它跟我们"以正向、安全的方式部署这些模型"的目标高度协同。我觉得,拥有一个跟价值观大体一致的商业模式,对我们很有帮助。

Not that there aren't conflicts sometimes, not that there aren't hard choices we have to make — but I think the number of such choices is much lower than it would be otherwise.

倒不是说从来没有冲突,也不是说我们不用做艰难取舍——但我觉得,这类取舍的数量,比换个模式要少得多。

A developer can switch from Claude to GPT or Gemini in an afternoon. Is it really possible to have a long-term lead in this industry? And how long would it take a serious competitor to replicate what you've built?

一个开发者一下午就能从 Claude 切到 GPT 或 Gemini。在这个行业,真有可能保持长期领先吗?一个认真的竞争对手,要多久才能复制出你们造的东西?

Model quality is the most important thing. We're very far ahead right now on model quality.

模型质量是最重要的。在模型质量上,我们目前遥遥领先。

There is some amount of inertia, but I've never relied on that. Anthropic has never relied on, "Oh, this is sticky and people won't switch."

是有一些惯性在,但我从不靠它。Anthropic 从没指望过那种"这玩意儿黏性强,用户不会走"的想法。

I think you want to have a better model. You want to have a better product. And we see the growth rates haven't inflected at all — if anything, they've gone up, at least at the time of taping this interview.

我觉得你就该有更好的模型、更好的产品。而我们看到,增长速度根本没有拐头往下——要说有变化,反而还在往上走,至少录这期访谈的时候是这样。

So I tend to think that is the most important thing.

所以我倾向于认为,这才是最重要的。

Soon after Claude Co-work was released, $285 billion in market value vanished overnight. Traders called it the SaaS apocalypse. If AI continues improving at this pace, how much of traditional software gets replaced, and how fast?

Claude Co-work 发布后没多久,一夜之间蒸发了 2850 亿美元市值。交易员管它叫"SaaS 末日"。如果 AI 按这个速度继续进步,传统软件会有多少被取代?多快?

This is one of these questions that's very hard to predict in advance. If you could predict it perfectly, people would, and they'd make a huge amount of money on the market and always be right. So no one knows exactly what's going to happen.

这是那种很难提前预测的问题。要是真能预测得分毫不差,大家早就去预测了,在市场上赚得盆满钵满、永远押对。所以没人确切知道接下来会怎样。

But I would note a few things. All of these traditional software companies have a number of moats. I think some of these moats are going to go away, but others are going to stay around.

不过有几点我想说。这些传统软件公司都有若干条护城河。我觉得,其中一些护城河会消失,但另一些会留下来。

The ability to quickly write software — I definitely think that's going away. If your moat is "we wrote this complex software that no one else can write," like, good luck. You're not going to be able to defend that.

"能快速写软件"这条护城河——我笃定它会消失。如果你的护城河是"我们写出了别人写不出的复杂软件",那祝你好运,这条你守不住。

But folks have customer relationships, folks have know-how of how the field works, folks have unique domain knowledge.

但有些公司握着客户关系,有些握着对这个领域怎么运转的 know-how,有些握着独到的领域知识。

So my advice to all of these folks is: obviously don't be complacent, don't ignore it. Make a list of all your moats, and be very aware that some of them are going to go away, while others are going to become relatively more important because they're the limiting factors. And there may also be new moats.

所以我给这些公司的建议是:首先别自满,别装看不见。把你所有的护城河列一张清单,清醒地意识到:有些会消失,而另一些会变得相对更重要——因为它们成了瓶颈所在。同时,也可能会冒出新的护城河。

Those that deftly respond, that lean into the list of moats that are still present as well as the new ones, will do well. Those that are complacent, that delude themselves that what worked in the past will continue to work, they're not going to have a good time.

那些反应灵敏、肯把还在的护城河和新出现的护城河都抓住的公司,会过得不错。那些自满的、自欺欺人地以为"过去管用的以后也管用"的公司,日子不会好过。

At the end of the day — it depends what you call SaaS and what you don't — but I would guess that the software industry gets larger, not smaller, although there will be some big losers.

说到底——这取决于你把什么算作 SaaS、什么不算——但我猜,软件行业会变大,而不是缩小,尽管其中会有一些大输家。

Explain that.

这话怎么讲?

I just think the pie is getting bigger. With AI, the pie is getting bigger. The existing incumbents may be smaller in relative terms. Some of them may go down in value. Some may even go out of business if they don't adapt in the right way.

我就是觉得,这块蛋糕在变大。有了 AI,蛋糕在变大。现有的那些巨头,相对份额可能会变小;有些市值会下滑;有些要是不以正确的方式适应,甚至会倒闭。

You see this often when growth is really fast. If what's possible with AI grows by 10x, it's very easy for an existing incumbent industry to go up by 1.5x — just not as much as the whole big pie is growing.

增长特别快的时候,你常会看到这种局面:如果 AI 能做到的事翻了 10 倍,那一个现有的成熟行业涨个 1.5 倍是很容易的——只是没有整块大蛋糕涨得那么猛而已。

That's not to say we won't have some big losers. Those who don't adapt, who put their heads in the sand, who don't see what's coming, who don't identify the moats they have — they're going to have a really hard time.

这不是说不会有大输家。那些不肯适应、把头埋进沙子、看不见浪头将至、也没认清自己手里有哪些护城河的公司——会非常难熬。

Your biggest backers are companies like Amazon and Google and Microsoft and Nvidia. These are companies that all have their own agendas. They are partners and rivals. You have huge commercial milestones tied to funding. Who's really calling the shots?

你最大的金主是 Amazon、Google、Microsoft、Nvidia 这些公司。它们各有各的算盘,既是合作伙伴又是对手。你的融资还绑着一堆巨大的商业里程碑。到底谁说了算?

There have been a number of cases where we've really spoken our minds about what we think. I've been very outspoken about the need for export controls on chips to China.

有不少回,我们是真的把自己的想法直说出来的。在"必须对出口到中国的芯片做管制"这件事上,我一直直言不讳。

I say this because I think it would be really bad for America, for the state of democracy in the world, for China to be ahead in AI capabilities.

我之所以这么讲,是因为我认为:如果中国在 AI 能力上领先,那对美国、对全世界民主的处境,都会非常糟糕。

Some of the chip makers obviously don't agree with that view, but it hasn't stopped me from saying it. I'm saying it again now, even after we've signed more partnerships.

显然,有些芯片厂商不认同这个观点,但这没让我闭嘴。即便在我们签了更多合作协议之后,我现在还是照样说。

What they know is that we always work with them. We've been good partners. We can work together. I'm sure they wish we didn't say these things, but these things are what I believe. What are you going to do?

他们清楚的是:我们一直好好跟他们合作,我们是好伙伴,大家能合作下去。我敢说他们肯定希望我们别说这些话,但这些就是我相信的东西。那你能怎么办呢?

At the end of the day, they benefit from these deals as much as we do. Look, we're all adults here. We can work together on one thing while disagreeing about another thing.

说到底,这些交易他们得到的好处和我们一样多。你看,大家都是成年人了。我们完全可以在一件事上合作,同时在另一件事上各执己见。

Bloomberg's reported that you're at valuations higher than OpenAI. We're talking nearly a trillion dollars for a 5-year-old startup. How do you make sense of that number? And why do you need that much money? If you're more disciplined on compute, you have a faster path to profit.

Bloomberg 报道说,你们的估值已经超过 OpenAI——一家成立五年的创业公司,估值近一万亿美元。这个数字你怎么理解?你又为什么需要这么多钱?要是你在算力上更克制,通往盈利的路其实更短。

The compute is ramping up very quickly. So it can both be the case that the fundamentals of the business look good, and that in a year you'll have three times — or four times — as much compute. I'm not going to give exact numbers, but these compute ramps are very fast.

算力正在飞快地往上加。所以可以同时成立两件事:业务的基本面看起来很好,而一年之后你的算力会是现在的三倍——或者四倍。具体数字我就不说了,但这些算力的爬坡非常快。

We have every expectation that the revenue ramp will meet and exceed those. But raising money is kind of the buffer against this cone of uncertainty. So it's a totally rational thing to do.

我们完全有理由预期,收入的爬坡会赶上、甚至超过算力的爬坡。但融资,某种意义上是给这片"不确定性的锥形地带"留的缓冲。所以这么做完全是理性的。

It's a very small dilution to the business. And it logically is not at all the same thing as — in fact, it's compatible with the opposite of — there being anything wrong with the fundamentals of the business.

它对公司的稀释非常小。而且从逻辑上讲,它根本不等于"业务基本面有问题"——事实上,它跟"基本面没问题"是完全相容的。

There have been reports of server strain, reliability issues, people complaining about running out of tokens. You've said other companies are yoloing on infrastructure. Do you actually have what you need, or are you playing catch-up?

有报道说出现了服务器吃紧、稳定性问题,有人抱怨 token 用光了。你说过别的公司在基础设施上"瞎赌(yolo)"。那你到底有没有你需要的算力,还是在疲于追赶?

One of these things about compute is there's a market in compute. So my view is that over a period of time even longer than a couple of months, we can get large amounts of compute.

关于算力,有一点是:算力是有市场的。所以我的看法是,在一段比"几个月"更长的时间里,我们能拿到大量算力。

One thing that's worth saying here is, I don't think we bought too little compute by any reasonable standard. We were planning for a 10x a year growth in compute. 10x a year is what we expect.

这里值得一说的是:按任何合理的标准,我都不觉得我们算力买少了。我们当时是按"算力每年涨 10 倍"在规划。每年 10 倍,是我们的预期。

That isn't what we've seen over the first quarter of 2026. We saw greater than 3x growth in revenue quarterly — just in a quarter, not annualized, 3x — which of course, three to the fourth power is 80x over the course of the year.

但 2026 年第一季度,我们看到的不是这个。我们看到季度营收涨了 3 倍多——是一个季度涨 3 倍,不是年化——而三的四次方,折算成全年就是 80 倍。

We didn't plan for 80x annualized growth. It would not have been rational to plan for 80x annualized growth, because that means if you only get 10x, you have eight times less.

我们没有按年化 80 倍去规划。按年化 80 倍去规划本来就不理性,因为那意味着:万一你只涨了 10 倍,你就少了八倍的量。

So we're in a locally extreme explosion of compute. That's not going to continue. If that continued, you just get to revenue numbers by the end of the year that no company on Earth — I don't think that's going to happen. It just can't.

所以我们正处在一个局部极端的算力爆炸里。这不会一直持续。要是它真持续下去,到年底你算出来的营收数字,地球上没有哪家公司——我不认为这会发生,它根本不可能。

But you can have these short periods where it's like, "oh my god, this is faster growth than we ever anticipated."

但你确实会碰到这种短暂的阶段:"我的天,这增长比我们预想的任何情况都快。"

You saw the compute deals with Google, you saw the compute deals with Amazon. There are more that we can and will do. The market's liquid. If you're able to use compute really well and there's the demand, you'll get your compute. It might just take a month or two.

你看到了我们跟 Google 的算力交易,也看到了跟 Amazon 的。还有更多是我们能做、也会做的。这个市场是有流动性的。只要你真的能把算力用好、又有需求,你就拿得到算力——可能只是要等上一两个月。

Does it feel good to surpass your arch rival?

超过你的头号对手,感觉爽吗?

Look, we have a lot of difficult challenges in front of us. There's this race-to-the-top idea — we're trying to pull other companies along with us. And I think we've seen that we have pulled them along with us.

这么说吧,我们面前有一大堆硬仗要打。有个"向上竞赛"的理念——我们是想把别的公司也拉着一起往上走。而我觉得我们已经看到,确实把他们拉着往上走了。

Sometimes they don't admit that's what they're doing. Sometimes they copy us while they're attacking us. But this pull is very valuable.

有时候他们不承认自己在跟着走;有时候他们一边抄我们,一边攻击我们。但这种"拉力"非常宝贵。

So the value of being the preeminent company, both commercially and in terms of models — it's not about beating rivals for the sake of beating rivals. It's about having the ability to pull the ecosystem along with us. And we hope we can do more of that in the future.

所以,成为那家最拔尖的公司——无论是在商业上还是在模型上——它的价值,不在于"为了打败对手而打败对手",而在于拥有把整个生态拉着一起往上走的能力。我们希望以后能多做这样的事。

But winning has to feel just a little bit good.

可赢了,总归得有那么一点点爽吧。

I mean, look, we're always trying to succeed. We're not trying to fail here. I'm not someone who believes we should shut this technology down, that we shouldn't build it.

我是说,你看,我们一直在努力成功。我们可不是来这儿求失败的。我也不是那种认为"应该把这项技术关停、根本不该造它"的人。

We exist within a free enterprise system, and there's nothing wrong with this. We just have to mitigate the risks of the models. And so it's always been the balance between the two.

我们活在一个自由市场体系里,这本身没什么不对。我们只是必须把这些模型的风险压下去。所以它一直是在这两者之间求平衡。

For most of Anthropic's history, you were the underdog. I imagine it's easier to take the moral high ground when you have nothing to lose. At this scale, how hard is it to stay true to your values?

在 Anthropic 的大部分历史里,你都是那个不被看好的"小角色"。我猜,当你没什么可失去的时候,占据道德高地是比较容易的。但到了今天这个体量,要守住你的价值观,有多难?

I've put a lot of time into thinking about how that's the case. As companies scale, I've been paranoid at every scale. At every scale of the company, there's some new challenge, some new way the company can lose either its will to win, commercially, or the core of its values.

这件事我花了很多时间去想。随着公司变大,我在每一个规模上都很疑神疑鬼。公司每到一个新的体量,都有新的挑战、新的方式让它可能丢掉点什么——要么是商业上那股求胜的劲头,要么是价值观的内核。

I'm worried about both, because I see them as synergistic. I actually see the fact that we've been able to make such good models as the thing that allows us to assert our values in a way that works as the company grows.

这两样我都担心,因为我觉得它们是相辅相成的。说实话,我把"我们能做出这么好的模型"这件事,看成是让我们能够在公司不断变大时,依然有效地坚持自身价值观的底气。

There are lots of pitfalls here, lots of ways to go wrong — not because me or the co-founders or the company's leaders' values change, but because the composition of the company changes very fast.

这里有很多坑,很多会走歪的地方——不是因为我、或几位联合创始人、或公司高管的价值观变了,而是因为公司的人员构成变得太快。

So I spend probably half of my time just talking to the company about the culture of Anthropic and how the culture works.

所以我大概有一半时间,就是在跟公司里的人讲 Anthropic 的文化,讲这套文化是怎么运转的。

When you're growing this fast, you're hiring a bunch of people from big tech companies. If you don't tell them how Anthropic operates, they'll simply recapitulate the only thing they know, which is how to operate at the companies they came from.

当你长得这么快,就会从大科技公司招来一大批人。如果你不告诉他们 Anthropic 是怎么运转的,他们就会照搬他们唯一懂的那一套——也就是他们原来那家公司的运作方式。

So this is a constant struggle and a constant challenge. Me and Daniela's maybe number one top priority is figuring out how to preserve this, because we recognize that this is the core of who we are in the long run.

所以这是一场持续的搏斗、持续的挑战。我和 Daniela 也许排第一位的头等大事,就是琢磨怎么把这个东西守住——因为我们清楚,长远来看,这才是"我们是谁"的内核。

Your product velocity is insane. You're shipping so much, so fast. How are you doing it?

你们出产品的速度简直离谱,又多又快。你们是怎么做到的?

I would say two things. The first is, we have a unified company, we have a unified culture. I think we've grown larger while still being incredibly efficient, everyone still being on the same page — just the cultural and organizational unity. I'd say that's the biggest factor.

我想说两点。第一,我们是一家心很齐的公司,文化也很统一。我觉得我们在变大的同时,依然极其高效,所有人还在同一个频道上——就是这种文化和组织上的一致性。我会说,这是最大的因素。

The second biggest factor is Claude itself — we're now using Claude to help develop our models, make them more efficient, and quickly develop products.

第二大的因素是 Claude 本身——我们现在用 Claude 来帮忙开发我们的模型、让模型更高效,也用它快速开发产品。

There's all kinds of new practices you have to develop. We're still new at it, but it's producing a lot of acceleration, and increasingly producing reliable acceleration. So those are the two factors I'd point to.

这里有各种新的做法你得摸索。我们还很生,但它确实带来了大量的提速,而且越来越能带来"可靠"的提速。这就是我会点出的两个因素。

Will you tell me the most wild thing you've seen AI do?

能告诉我,你见过 AI 做的最离谱的一件事是什么吗?

I think some of the wildest stuff I've seen is around biology and medicine. I've seen a number of cases — including Daniela, actually — where Claude diagnosed a medical problem that a bunch of fancy doctors had missed.

我觉得我见过最离谱的一些事,是在生物学和医学领域。我见过好几个例子——其中就有 Daniela——Claude 诊断出了一个问题,而那是一群很厉害的医生都漏掉的。

And on the biology side, the models are starting to get surprisingly good at tasks like drug design or computational chemistry. As someone who used to be a biologist, I look at it and I'm like, wow, that's hard — you need a lot of training to do that, and Claude is getting good at it.

在生物这边,模型在药物设计、计算化学这类任务上,开始好得出乎意料。作为一个曾经的生物学家,我看着就想:哇,这很难啊——做这个需要大量训练,而 Claude 正变得越来越在行。

That's one area where I think we're going to get a hell of a lot of benefit. That's the positive of AI — we're going to get these huge, enormous benefits. Life is going to get better, the quality of human experience is going to get better.

我觉得这是我们会收获巨大好处的一个领域。这就是 AI 正面的那一面——我们会得到这些巨大、巨大的好处。生活会变好,人活着这件事的质量会变好。

A century of scientific progress.

一个世纪的科学进步。

A century of scientific progress, and a century of progress in what it's like to be human.

一个世纪的科学进步,以及一个世纪的、关于"做人是种什么体验"的进步。

Go back to 1900. Think of all the problems we had in the 1900s, all the reasons people died prematurely, all the problems they had to suffer, all the material deprivation that we don't have to deal with today. Then think of another hundred years of that.

回到 1900 年。想想那个年代我们面对的所有难题,人们早早离世的种种原因,他们不得不承受的种种苦楚,以及我们今天再也不必忍受的种种物质匮乏。然后,再想象这样的进步再来一百年。

I really believe this century of scientific and medical progress — if we can get through this, and I think we will, I'm increasingly optimistic — we're going to have a much, much better world.

我是真的相信,这一个世纪的科学与医学进步——只要我们能闯过这一关,我也觉得我们闯得过,我越来越乐观——我们会拥有一个好得多、好得多的世界。

I know how much you love writing. You're known for your essays. Do you use Claude to help write?

我知道你有多爱写东西,你是以你的 essay 闻名的。你会用 Claude 帮你写吗?

I do. I have not gotten to the point where I actually allow text directly written by Claude in, because I just have such a specific style that I'm a little picky about it.

会。但我还没到那一步——我不会让 Claude 直接写出来的文字进到文章里,因为我的风格太特定了,这方面我有点挑剔。

I basically use Claude to help me brainstorm, to help me think through the themes, to think, "oh, what are some references I could use for this?" So it plays a supportive role.

我基本上是用 Claude 帮我头脑风暴、帮我把主题想清楚,或者想"诶,这里我能引用哪些素材?"。所以它扮演的是一个辅助角色。

I don't know how far we are from Claude being able to write better than me. We're not quite there yet, but I think certainly it's coming.

我不知道离"Claude 能写得比我好"还有多远。我们还没到那一步,但我觉得,那一天肯定要来了。

I love writing too, and I feel like writing helps you struggle through ideas. There is a lot of critical thinking involved in that. Do we lose that if we let Claude do it for us?

我也很爱写。我觉得写作能帮你跟自己的想法死磕,这里头有大量的批判性思考。如果我们让 Claude 替我们写,会不会就把这个给丢了?

I'm a little worried about that. In fact, that's half the reason I write myself.

这一点我有点担心。事实上,这正是我坚持自己动笔的一半原因。

It certainly is for external audiences — many people read what I write — but it is just as much to clarify my own thinking, so that I know what to do next, and to create a common reference point across me and others.

写当然是给外部读者看的——很多人会读我写的东西——但同样重要的是,它是为了厘清我自己的思路,让我知道下一步该做什么,也为了在我和别人之间建立一个共同的参照点。

I think we're still grappling with the question of how exactly do we use AI in a way that preserves those benefits. The thing I'm doing now does that — I use Claude for research, and I use Claude for how I organize my own thoughts.

我觉得我们还在跟一个问题较劲:到底该怎么用 AI,才能保住这些好处。我现在的做法是能保住的——我用 Claude 做研究,也用 Claude 来帮我整理自己的思路。

If we just used it end to end — like, "write an essay about the risks of AI" — first of all, it wouldn't write the things that I think, but also I would exactly lose that benefit.

但要是我们端到端地用它——比如直接说"写一篇关于 AI 风险的 essay"——首先,它写出来的不会是我心里想的那些东西;其次,我恰恰会把刚才说的那份好处弄丢。

As the models get better, there's probably some way to use them much more directly in the writing and yet still preserve those benefits. But I think it's going to be subtle. It won't be all one thing. We'll have to figure it out over time.

随着模型变强,大概会有某种方式,既能更直接地把它们用在写作里,又仍然保住这些好处。但我觉得那会很微妙,不会是非此即彼的一刀切。我们得慢慢摸索出来。

I think we could have this very unusual combination of very fast GDP growth and high unemployment — or at least underemployment, or a lot of low-wage jobs, high inequality.

我觉得,我们可能会遇到一种非常反常的组合:GDP 飞速增长,同时高失业——至少是"半失业",或者一大堆低薪岗位,加上严重的不平等。

He's been really direct about job loss: AI could eliminate half of all entry-level white-collar jobs in the next 1 to 5 years. That was a year ago. AI has moved incredibly fast. Is it still 50%, or is it higher?

你在失业问题上一向很直白:AI 可能在未来 1 到 5 年内,消灭掉一半的入门级白领岗位。那还是一年前的话。AI 进展快得惊人。现在还是 50%,还是更高了?

I've always said — and if you go back to those original clips, they always get cut out of context in, like, the 3 seconds — but the real statement was always: "I don't know what's going to happen, but this is an order of magnitude for how crazy things could be."

我一直说的是——你要是去翻那些原始片段,它们总是被剪掉上下文,只剩那 3 秒——但我真正的原话一直是:"我不知道接下来会怎样,但这是一个量级,标示出事情能疯狂到什么程度。"

Also, I always talk about all the things we can do in response. I've talked about a token tax, working with enterprises to adjust people — I'm a little skeptical of retraining programs, but we should throw them in the mix — macroeconomic policy.

而且,我每次都会谈我们能采取的应对办法。我谈过 token 税,谈过跟企业合作去帮人转岗——我对再培训项目有点怀疑,但也该把它们一并摆进来——还有宏观经济政策。

Even from the beginning, I always talked about solutions. But somehow there's this tendency in the human psychology to clip the 3 seconds of "doom is coming." So my message is definitely not "doom is coming." My message is: this is something we should see coming, that we're worried about, and that we need to respond to positively.

从一开始,我就一直在谈解决方案。但人类心理里好像就是有这么个倾向,专挑那 3 秒"末日要来了"剪出来。所以我的信息绝对不是"末日要来了"。我的信息是:这是一件我们该预见到、该担心、并且该积极去应对的事。

I don't know exactly, but I'm still pretty concerned. I'm still the same order of concern.

具体我说不准,但我依然相当忧虑,忧虑的量级还是和当初一样。

We are seeing right now that AI is making people more productive. But that's the usual hump. If you go back to the industrial revolution — I wrote about this in The Adolescence of Technology — you automate 90% of the job. Great. People are 10 times more productive in the other 10%, because they're 10 times more leveraged. But eventually it gets close to 100%.

我们现在看到的是 AI 在让人更有生产力。但这只是惯常的那个"驼峰"。回到工业革命——我在《The Adolescence of Technology(技术的青春期)》里写过这个——你把一份工作的 90% 自动化掉。很好,人在剩下那 10% 上效率提高了 10 倍,因为他们被放大了 10 倍的杠杆。但最终,这个比例会逼近 100%。

Now, the sequel to that is, well, then you have to find something else for them to do. I don't know about the long run — I'm truly uncertain about that. But I do think there are types of adaptation.

而接下来的续集是:那你就得给他们找点别的事做。长远怎样我不知道——这点我是真没把握。但我确实认为,存在一些适应的路径。

One thing I'll talk about is software engineers within Anthropic. We're going through this transition right now, where AI writes all the code, or almost all the code, but it still makes the software engineers more productive.

我会举的一个例子,是 Anthropic 内部的软件工程师。我们正在经历这样一场转变:AI 写掉了全部、或几乎全部的代码,但它仍然让软件工程师更有生产力。

But we're already starting to see the beginning of, like, there may be some people that it's not making more productive — that it's better for the AI to just do the thing.

不过我们已经开始看到苗头:可能有一些人,AI 并没有让他们更有生产力——这时候,干脆让 AI 把那件事直接做了反而更好。

The other side of it, though, is: what do we need more demand for? There's something we call a forward-deployed engineer, or an applied AI solutions architect, where their job is a mix of technical work and talking to customers. There's a lot of demand for that, because there's a lot of customers and we're growing very quickly.

但另一面是:我们对什么的需求在变多?我们有一种岗位,叫"前线部署工程师(forward-deployed engineer)",或者"应用 AI 解决方案架构师"——他们的工作是技术活和跟客户打交道的混合体。这类岗位需求很大,因为客户很多,而我们增长得很快。

Now, does every person who is in pure software engineering work for that? It's not perfect, it's not one-to-one. That gives you a flavor of: there's going to be a hell of a lot of disruption, but things will also adjust. Which wins out, I don't know.

那么,每一个纯做软件工程的人,都能转去做那个吗?不是完美对应,不是一对一。这能让你大致感受到:会有极大量的冲击,但事情也会调整。最后哪一边占上风,我不知道。

But the reason it's important to warn about it is that that's how we can respond — that's how we can make policy, both within Anthropic and macroeconomically, for the whole world.

但之所以重要的是要发出警告,是因为唯有如此我们才能应对——唯有如此我们才能制定政策,无论是在 Anthropic 内部,还是在面向全世界的宏观层面。

We want to put out carefully considered thoughts. We don't want to say things that people don't believe we'll actually do. We don't want to say things that are half-baked. We want to think carefully about what should actually be done about these problems.

我们想拿出经过深思熟虑的想法。我们不想说那些别人根本不信我们会真去做的话,也不想说那些没想透的半成品。我们想认真琢磨:对这些问题,究竟该做些什么。

You put out this chart showing potential job disruption — sales, finance — which jobs go away, who gets replaced, and what new jobs are created.

你发过一张图,展示潜在的就业冲击——销售、金融——哪些岗位会消失、谁会被取代,以及会创造出哪些新岗位。

No one knows for sure, because the economy is unpredictable. It's the same as the stock market — these decentralized processes where you don't really know ahead of time what pieces of the job people are still going to be able to do.

没人能笃定,因为经济是不可预测的。它跟股市一样——都是那种去中心化的过程,你事先根本无法确知,一份工作里有哪些部分人还能继续干。

But what I would say broadly is: anywhere you have these entry-level white-collar jobs — whether it's banking, whether it's finance — there's going to be a lot of potential for AI to first make people more productive, and then there's going to be a wholesale "AI can do the job," and then we're going to have to think about what is it that people can do.

但我大体上会说:凡是有这类入门级白领岗位的地方——无论是银行业还是金融业——都会有很大空间,让 AI 先把人变得更有生产力;然后会出现一种整体性的"AI 能把这活儿干了";再然后,我们就不得不去想:人还能做什么。

I think we need to plan about that ahead of time. We're already doing it when we talk to enterprise customers. We see the choices they face: should I save cost — which often means hiring fewer people, basically doing the same thing with fewer resources — or should we do more things with the same amount of resources?

我觉得这件事我们得提前规划。其实在跟企业客户打交道时,我们已经在做了。我们看到他们面临的抉择:是该省成本——往往意味着少招人,用更少的资源基本做同样的事——还是该用同样的资源去做更多的事?

And we always, when we can, try to push them to doing more with the same amount of resources — because that basically means hire the same number of people, or maybe even more, but just do new things. Pushing them towards the positive sum.

而我们只要有可能,总会推着他们"用同样的资源做更多的事"——因为那基本上意味着:招同样多的人,甚至更多,只是去做新的事情。把他们往"正和博弈"那边推。

The thing we have going for us is that the pie is going to expand a lot. And because the pie is going to expand a lot, there are probably going to be places where people can go. It's just a matter of finding them fast enough.

我们手里有利的一点是:这块蛋糕会大幅膨胀。正因为蛋糕会大幅膨胀,大概会有一些去处可以容纳这些人。问题只在于,能不能足够快地把这些去处找出来。

It's the size of the disruption — it's going to be big, and that's what I'm warning people about. But we have to solve that matching problem.

关键在于冲击的规模——它会很大,而这正是我要警示大家的。但我们必须解掉这个"匹配"难题。

So play this out for me. You wake up in 5 years. What does this country look like? What are those people doing? Because if there's that much unemployment, is that not how revolutions start?

那你给我推演一下。五年后你一觉醒来,这个国家是什么样子?那些人都在做什么?因为如果失业那么严重,革命不就是这么开始的吗?

Yeah. This is the outcome we want to prevent. This is absolutely the outcome we want to prevent. I think there's a few places — none of them are guaranteed, we're not sure.

对。这正是我们想要避免的结局,绝对是我们想要避免的结局。我觉得有几个去处——没有一个是板上钉钉的,我们也没把握。

There's the physical world — things that are in the physical world. Yes, there's a robotics revolution as well, but it's a lot slower than what's happening in AI. People always talk about building data centers, but when processing information of any type becomes a lot easier, maybe the restriction is going to be things in the physical world.

有物理世界——那些存在于物理世界里的东西。是的,机器人革命也在发生,但它比 AI 这边的进展慢得多。大家老是在谈建数据中心,可一旦处理任何类型的信息都变得容易得多,瓶颈也许就会落到物理世界里的那些东西上。

So we need a lot more people to make, build, manufacture things in the physical world. Anything that's human-centered, I think that's going to be a big deal.

所以我们会需要多得多的人,在物理世界里去做东西、造东西、生产东西。任何以人为中心的事,我觉得都会变得很重要。

I hear all these stories about "AI found something my doctor couldn't find," and I feel that. But people really want to talk to other humans, particularly over important things. Maybe AI can do better customer service, but nevertheless, at least some people want to talk to humans. So these human-relationship-driven jobs, I think those are going to be important.

我听过好多这样的故事:"AI 查出了我的医生查不出来的东西。"我能体会。但人们是真的想跟另一个人说话,尤其是面对要紧的事的时候。也许 AI 能做更好的客服,但即便如此,至少还是有一部分人想跟真人交流。所以这些靠人际关系驱动的岗位,我觉得会很重要。

And I think there will be some effort by the humans to direct the AIs. At some level, it has to be in line with someone's values and someone's intentions. So I think there's going to be some role there, although I don't know how thin versus how thick it will be. It's very hard to say.

我也觉得,人类会去做一些"指挥 AI"的工作。在某个层面上,这一切总得对齐某个人的价值观、某个人的意图。所以我认为那里会有人的一席之地,只是我不知道这一层是薄是厚。很难讲。

There has been a lot of pushback, and I know you've said you're trying to warn people. But Jensen Huang said you're conflating tasks with jobs. Other folks have said it's sort of doom marketing that benefits Anthropic.

外界有不少反弹。我知道你说你是在警示大家。但 Jensen Huang(黄仁勋)说你把"任务"和"岗位"混为一谈了。还有人说,这是一种对 Anthropic 有利的"末日营销(doom marketing)"。

So I want to be really clear and push back hard against this. The whole picture — that there are risks to job loss, and here are some ideas — I mean, we haven't fully fleshed out the ideas, because I want to get them right, but Anthropic has come up with lots of ideas.

所以我想说得非常清楚,并且强烈地回击这一点。完整的图景是:失业有风险,而我们也提出了一些办法——我是说,这些办法我们还没完全展开,因为我想把它们打磨对,但 Anthropic 确实提了很多想法。

We've had economic grants. We have the economic index. I talk about the possible ways to address these risks, from tax and macroeconomic policy to what the new jobs are.

我们做过经济资助(economic grants),我们有"经济指数(economic index)"。我谈过应对这些风险的各种可能办法,从税收、宏观经济政策,到新岗位到底是什么。

In The Adolescence of Technology, I have like five pages where I lay out the difference between tasks and jobs, why this time is different than other times, a list of six different things we can do, from private philanthropy to government action. I talk about the problems. I talk about the solutions.

在《The Adolescence of Technology》里,我有大约五页,专门讲清楚"任务"和"岗位"的区别、为什么这一次和以往不同,还列了六件我们能做的事,从私人慈善一直到政府行动。我谈问题,也谈解决方案。

But social media — which I detest as a category — people have these 3-second clips from a year ago. They don't actually read the essays, or they prey on the idea that people won't.

但社交媒体——作为一个品类,我厌恶它——人们手里拿着一年前那 3 秒的片段。他们根本不去读 essay,或者就是吃定了别人不会去读。

The idea that this is cheap marketing is itself cheap marketing. This is laziness. This is failure to engage with serious intellectual work. And I think that is part of the problem.

说"这是廉价营销"——这话本身才是廉价营销。这是懒惰,是拒绝去认真对待严肃的智识工作。我觉得这正是问题的一部分。

Again, I think it's part of the disease of Silicon Valley. It's been caught up in this social-media world of 3 seconds, and so people only respond to it, or they think they only have to respond to it.

我还是那句话,我觉得这是硅谷这种病的一部分。它被困在这个 3 秒的社交媒体世界里,于是人们只对这种东西做出反应,或者以为自己只需要对这种东西做出反应。

I think it's very dangerous, and we've failed to have a mature conversation. Instead, people just lazily see this 3-second clip and they're like, "Oh, this is what Dario was saying." It's so stupid. It's so unserious. And whenever someone says something like that, I take them less seriously.

我觉得这非常危险,我们没能进行一场成熟的对话。结果是,人们就懒洋洋地看一眼那 3 秒片段,然后说:"哦,Dario 原来是这意思。"这太蠢了,太不严肃了。每当有人说出这种话,我对他的认真程度就打个折扣。

One of the leading AI companies in the world is deeply embedded in many different aspects of US national security, across military operations. The standoff between Anthropic and the Pentagon over AI military safeguards is ramping up.

全球最顶尖的 AI 公司之一,已经深度嵌入美国国家安全的方方面面,渗入各类军事行动。围绕"AI 军用安全护栏",Anthropic 与五角大楼之间的对峙正在升级。

You've had a long-standing anti-war stance, dating all the way back to your days at Caltech. And yet, you were one of the first AI companies to sign a contract with the Department of Defense, to operate on classified networks that the US uses to fight wars. Explain that.

你一直有很坚定的反战立场,一路可以追溯到你在 Caltech(加州理工)的日子。可你又是最早跟国防部(Department of Defense)签约的 AI 公司之一,在美国用来打仗的机密网络上运行。这怎么解释?

What I would say is, the world changes. My view of this technology — when I see Russia invading Ukraine, when I see the risk of China invading Taiwan — it worries me that we have a kind of resurgent authoritarian bloc that's very aggressive, and that we need to defend ourselves.

我想说的是,世界是会变的。我对这项技术的看法是这样:当我看到俄罗斯入侵乌克兰,当我看到中国入侵台湾的风险,我会担心——我们正面对一个重新抬头的威权阵营,它非常咄咄逼人,而我们需要自保。

That's something I've believed for a while now, and continue to believe. And that's why, across both administrations — I may not agree with every policy of either administration — but that's why we've generally been supportive of this.

这是我相信了有一阵子、并且仍然相信的事。这也是为什么,跨越两届政府——尽管我未必赞同任何一届政府的每一项政策——但这就是我们为什么大体上一直支持这件事。

We don't want a world where China and Russia can analyze all the intelligence with AI, can use AI for attacking Taiwan and Ukraine, and we can't defend them. So that's why we worked with them.

我们不想要这样一个世界:中国和俄罗斯能用 AI 去分析所有情报、能用 AI 去进攻台湾和乌克兰,而我们却没法保卫它们。这就是我们之所以和军方合作的原因。

We certainly don't do it for the money. Even putting aside the lawfare, it's just a huge pain to get up on government networks for not that much money. So we did it because we cared about it.

我们当然不是为了钱。哪怕撇开那些"法律战(lawfare)"不谈,光是为了那点钱去把系统部署到政府网络上,就已经是个天大的麻烦。所以我们做这件事,是因为我们在乎它。

But similarly, because we're doing it because we cared about it, there need to be limitations on the use of the technology. The formulation that I used in The Adolescence of Technology: we should use this technology in every way except the ways that undermine our own values — and our red lines of mass surveillance and fully autonomous weapons.

但同样地,正因为我们做这件事是出于在乎,所以对这项技术的使用就必须有限制。我在《The Adolescence of Technology》里用的表述是:这项技术我们应该以一切方式去用,唯独不用那些会侵蚀我们自身价值观的方式——也就是我们的两条红线:大规模监控,和全自动武器。

Those are things that I believe undermine our values. It's not worth democracies winning if democracies do those things.

这两件事,我认为会侵蚀我们的价值观。如果民主国家靠做这些事去赢,那这种赢不值得。

That's the balance that I see, and that's the stand we took. It explains both why we were the first to work with the Department of War, and why there were some things we wouldn't do, when others were willing to do those things.

这就是我看到的那个平衡,也是我们站定的立场。它既能解释我们为什么是第一个跟国防部(Department of War)合作的,也能解释为什么有些事我们不肯做——哪怕别人愿意做。

I think you need to pick a stand and stand your ground. This idea of companies that seesaw from "we won't do anything with the government" to suddenly "we're doing absolutely everything with the government" — I don't get it. You should pick your principles and stick with them.

我觉得你得选定一个立场,然后守住它。有些公司在"我们绝不跟政府沾边"和突然"我们跟政府无所不做"之间来回翻烧饼——这种做法我不理解。你应该选定你的原则,然后坚持到底。

You've been working with Palantir since 2024.

你们从 2024 年起就一直在跟 Palantir 合作。

That's right.

没错。

Their technology is used by ICE, police departments, in Gaza. Is Claude being used for surveillance in other ways?

他们的技术被 ICE(移民海关执法局)、各地警察部门用,也用在加沙。Claude 有没有以别的方式被用于监控?

We don't work with ICE, either through Palantir or anyone else. We don't work with CBP. I don't believe we work in Gaza. We're very careful about scoping our engagements to things that we believe in.

我们不跟 ICE 合作,无论是通过 Palantir 还是其他任何人。我们不跟 CBP(海关与边境保护局)合作。我相信我们也没在加沙做事。我们非常谨慎地把合作范围,框定在我们认同的事情上。

So you drew your red lines. The president banned you from the federal government. The Pentagon labeled you a supply chain risk. OpenAI jumped in and signed the contract that you wouldn't. What does winning this fight actually look like?

于是你划了红线。总统把你们逐出了联邦政府。五角大楼给你们贴了"供应链风险"的标签。OpenAI 顺势接手,签下了你们不肯签的那份合同。那么,赢下这场仗,究竟会是什么样子?

I don't think there's any winning this fight for a private company. This isn't a fight Anthropic is trying to win, or thinks about winning or losing. This is more — I won't even call it a fight — this is more a debate about what the proper use of AI by the government is.

我不觉得一家私营公司能"赢"这场仗。这不是 Anthropic 想去赢的仗,我们也不会用输赢去想它。这更像是——我甚至不愿意叫它一场仗——它更像是一场辩论:政府对 AI 的恰当使用,应该是什么样子。

AI is an emerging new technology. We don't understand the ways in which it's reliable or unreliable. We don't understand the ways in which it promotes our values or undermines our values.

AI 是一项正在兴起的新技术。我们还不清楚它在哪些地方可靠、哪些地方不可靠;也不清楚它在哪些地方弘扬了我们的价值观、哪些地方又在侵蚀它。

So one of the things I thought was important was to establish a precedent on some of the use cases we think are good — which, frankly, is most of them — and some of the use cases that we're concerned about.

所以我认为重要的一件事,是为一些用例立一个先例:既包括我们认为是好的用例——坦白说,大多数都是好的——也包括一些我们有顾虑的用例。

As I've said, you can only do so much with a contract. As we've seen, someone else can sign a contract that doesn't respect your same red lines. But what it has done is raise awareness for the issue.

我说过,一纸合同能做的事是有限的。我们也看到了,别人可以去签一份不尊重你那几条红线的合同。但它确实做到了一件事:把这个议题的关注度提了上来。

And then we have serious bipartisan efforts in Congress attempting to ban some of the things we're concerned about and attempting to set guardrails. I don't want to talk about this as a fight, but that's kind of winning the effort to get our country to think more carefully about what is appropriate use of this technology.

而现在,国会里出现了严肃的、跨党派的努力,试图禁掉我们担心的某些做法,试图立起护栏。我不想把它说成一场仗,但这其实就算是赢了——赢在让我们这个国家,更认真地去思考这项技术怎样用才算恰当。

[Anthropic] is run by an ideological lunatic who shouldn't have the decision-making over what we do.

〔Anthropic〕是被一个意识形态疯子掌控的,这种人不该对我们的事拥有决策权。

Do you mind being called an ideological lunatic, or a bunch of left-wing nut jobs?

被人叫"意识形态疯子",或者一帮"左翼疯子",你介意吗?

I've been called worse things than that all the time. People can call me or Anthropic whatever they want.

比这更难听的,我天天被人骂。随便别人怎么称呼我或 Anthropic 都行。

The two things that matter are: we're successful as a company, and we stand up for our values. In some ways my life is really easy, because when those are the two things you're trying to do, it's really simple — you always know where you stand.

真正要紧的只有两件事:一,我们作为一家公司是成功的;二,我们捍卫我们的价值观。某种意义上,我的人生其实很轻松——当你要做的就这两件事时,事情就特别简单:你永远知道自己站在哪儿。

A US official has said that, with the help of LLMs, the US military has gone from being able to hit a thousand targets a day to 5,000 targets a day. That means Claude can help kill more people more quickly. Are you comfortable with that?

一位美国官员说,借助大语言模型(LLM),美军每天能打击的目标从 1000 个提升到了 5000 个。这意味着 Claude 能帮忙更快地杀死更多人。这一点你能接受吗?

I think there's two things here. There is the ability of the United States to be more effective militarily. I am supportive of that ability. I think having that ability be stronger doesn't cause wars — it deters wars.

我觉得这里有两件事。一是美国在军事上变得更有效的能力。这种能力,我是支持的。我认为这种能力更强,并不会引发战争——它会威慑战争。

Basically you're asking: do you believe in this country? Do you want this country to be a more powerful actor rather than a less powerful actor on the world stage? I do. I'm a patriot.

你问的其实就是:你信不信这个国家?你希不希望它在世界舞台上,是一个更有力量、而不是更弱小的角色?我希望。我是个爱国者。

There's a separate question, which is: are there particular policies that the US government is engaged in that I might support or not support? Obviously I support some of them and I don't support others.

还有一个单独的问题是:美国政府正在推行的某些具体政策,我支持还是不支持?显然,有些我支持,有些我不支持。

It's not up to me. If we provide a technology, the DoD made this point and we actually agree with them: it's not up to us to say "you can do this military operation and you can't do that military operation."

这不由我说了算。如果我们提供一项技术——国防部提过这一点,我们其实也认同他们:轮不到我们去说"这场军事行动你能做、那场你不能做"。

Now, I might privately believe that this military operation makes sense and that one is a bad idea, but we're not going to deny the technology. You have to leave policy in the hands of the military decision-makers.

当然,我私下里可能觉得这场军事行动合理、那场是个馊主意,但我们不会因此就把技术掐断。你必须把政策的裁量权,交到军事决策者手里。

What you can do is assert some high-level boundaries that, for us, prevent the use cases that seem inconsistent with our values, with our country's values, and promote the use cases that we think encourage our values. So that's how we think about it.

你能做的,是划出一些高层级的边界——对我们来说,就是挡住那些看起来与我们的价值观、与我们国家的价值观相悖的用例,同时去鼓励那些我们认为能弘扬我们价值观的用例。我们就是这么想这件事的。

Bloomberg has reported that Claude is being used by the US military in the war in Iran to do AI-assisted targeting, via a platform made by Palantir — the Maven Smart System. In February, a US missile reportedly hit a girls' school in Iran, killing more than 150 people, most of them children. Did Claude play a role in that strike?

Bloomberg 报道说,在对伊朗的战争中,美军正在用 Claude 做 AI 辅助的目标锁定,平台是 Palantir 做的 Maven Smart System。今年 2 月,据报道一枚美国导弹击中了伊朗一所女子学校,造成 150 多人死亡,其中大部分是儿童。Claude 在那次袭击里起了作用吗?

Look, we don't have access to — we don't know exactly how these models were used. Obviously, mistakes that happen in warfare are really, really terrible. This is a really terrible thing to happen.

这么说吧,我们没有权限——我们并不确切知道这些模型当时是怎么被使用的。显然,战争中出现的差错是非常非常可怕的。这是一件极其可怕的事。

If that doesn't make clear why we have to stand up for use cases that we don't support — we were willing to risk the future of our company to limit how these models are used.

如果这都说明不了我们为什么必须就那些"我们不支持的用例"挺身而出的话——我们可是愿意拿整家公司的未来去冒险,来限制这些模型被如何使用。

And what you're talking about is a use case that doesn't even violate our red lines. We're worried that there will be a hundred times as much, with use cases that do violate our red lines.

而你说的这个,还是一个甚至没有触碰我们红线的用例。我们真正担心的,是那些确实越过红线的用例——那才是会多上一百倍的东西。

Again, I would say I think overall the use of these models is appropriate. I think it's good on net. But military decision-makers make terrible mistakes even at the best of times. And I don't know if we're in the best of times.

我还是要说,总体上我认为这些模型的使用是恰当的,净效果是好的。但即便在最太平的时候,军事决策者也会犯下可怕的错误。而我不确定我们现在是不是处在最太平的时候。

We can talk about making red lines that prevent uses of the models that are more likely to lead to those problems. If we had just given in — which almost every other company now has — to fully autonomous weapons...

我们可以谈谈怎么去划红线,挡住那些更容易导致此类问题的用法。如果我们当初就让步了——而现在几乎其他每家公司都让步了——去做全自动武器……

What we've seen here is: Claude assists, but a human makes the final call. So a human made that final call, not Claude.

而我们这里看到的是:Claude 辅助,但由人做最终决定。所以做出那个最终决定的是人,不是 Claude。

Imagine if you had a world in which — not Claude, because we haven't allowed it, but someone else's AI model — the AI model just makes the decision and the human never sees it. That's what we were standing up for. That's what we were fighting against.

设想一个这样的世界:不是 Claude,因为我们没允许,而是别人的某个 AI 模型——这个 AI 模型直接做出决定,而人从头到尾都没看到。这正是我们当初挺身去捍卫的;也正是我们当初在抵抗的。

I would also say, again — I don't think procurement is the right way to do it — but it's a matter of interest to the American people, not to me as a supplier of the technology, that our military decision-makers don't make these mistakes, that they operate reliably, that they choose wisely what to do.

我还要再说一句——我不认为"政府采购"是处理这件事的正确方式——但这是关乎美国人民利益的事,而不只是关乎我这个技术供应方的事:我们的军事决策者别犯这些错、运作得可靠、明智地选择该做什么。

The government uses Microsoft Excel a lot. If I said, "You can use Excel for this military operation but not that one," you can't realistically do that. But hopefully that gives you a sense of how we think about it.

政府大量使用 Microsoft Excel。如果我说"这场军事行动你能用 Excel、那场不能用",这在现实里根本做不到。但希望这能让你大致明白我们是怎么想这件事的。

This school had a website — you could have found it in a Google search. Shouldn't Claude have spotted that? Should an AI, or whatever technology they used, have spotted that? And does it speak to a scarier issue about using technology as a shortcut in war?

这所学校是有网站的——你随手 Google 一下就能查到。Claude 难道不该发现这一点吗?AI,或者他们用的不管什么技术,难道不该发现吗?这是不是指向一个更可怕的问题:把技术当成打仗的"捷径"?

Look, what I'm going to say is — and this relies on maybe classified knowledge that I don't have — but the principle that we have established, and I think the principle that was obeyed here, is: a human makes the final decision.

这么说吧,我要讲的是——这可能涉及一些我并不掌握的机密信息——但我们确立的那条原则,也是我认为在这件事里被遵守了的原则,就是:由人做最终决定。

I don't know what role Claude or any other AI had, but if this isn't an illustration of why that principle is so important, I don't know what is.

我不知道 Claude 或别的任何 AI 在其中扮演了什么角色,但如果这件事都不能说明"那条原则为什么如此重要",那我真不知道还有什么能说明了。

Is AI warfare more likely to stop World War III — a war between the US and China — or is it more likely to make it happen?

AI 战争,是更可能阻止第三次世界大战——一场美中之间的战争——还是更可能让它发生?

I would say, on balance, it is more likely to stop it. But if we have no limits on how it's used, then I think it could be more likely to cause it.

我会说,权衡下来,它更可能阻止战争。但如果我们对它的使用毫无限制,那我觉得它反而更可能引发战争。

You've seen Dr. Strangelove, right? The premise was: you have a doomsday device that automatically fires nuclear weapons when it thinks nuclear weapons are being fired at it. What could go wrong?

你看过《Dr. Strangelove(奇爱博士)》吧?它的设定是:你有一台"末日装置",一旦它认为有核武器正朝它射来,就会自动发射核弹。这能出什么岔子呢?(反讽)

I get to this lethal, fully autonomous weapons thing. I think the way conflicts happen is that the two sides jump at each other, they misunderstand each other. And when we don't have proper oversight of this technology, those kinds of accidents are more likely to happen.

这就又回到了致命性全自动武器这件事上。我觉得冲突爆发的方式,往往是两边互相扑上去、彼此误判。而当我们对这项技术没有恰当的监督时,这类意外就更容易发生。

Now, if AI is used in an appropriate way — in not even warfare, but just intelligence collection — let's say we're able to predict an invasion of Taiwan or a new movement in Ukraine, our adversaries will think twice about conducting some kind of invasion or military operation if we know everything that they're doing.

反过来,如果 AI 被以恰当的方式使用——甚至都不用说打仗,就说情报收集——比方说,我们能预判一次对台湾的入侵,或者乌克兰方向的新动向,那当我们对对手的一举一动了如指掌时,他们在发动入侵或军事行动前就会三思。

So I think superior intelligence really can deter conflict here. Superior ability to respond can deter conflict. I continue to be a believer in these things.

所以我认为,情报上的优势在这里真的能威慑冲突,响应能力上的优势也能威慑冲突。这些事,我始终是相信的。

Anthropic's making headlines almost on a weekly basis, and most notably now around Mythos. This is the latest and greatest Anthropic model, and it is capable of going through all the links of the cyber kill chain — and doing so autonomously.

Anthropic 几乎每周都上头条,而眼下最受瞩目的,是围绕 Mythos 的争议。它是 Anthropic 最新、最强的模型,能够走完"网络杀伤链(cyber kill chain)"的每一个环节——而且是自主完成。

You said Mythos was too powerful to release to the public. What surprised you most about it?

你说 Mythos 太强了,不能向公众发布。它最让你意外的地方是什么?

The thing that surprised me most was that the models had been climbing in their ability to find vulnerabilities — and, importantly, to turn those vulnerabilities into exploits. People only talk about the vulnerabilities; they don't often talk about turning the vulnerabilities into exploits, which it was quite good at.

最让我意外的是:这些模型在"发现漏洞"上的能力一路攀升——而且更关键的是,在"把漏洞变成可用的 exploit(攻击利用)"上也越来越强。大家只谈漏洞,很少谈"把漏洞变成 exploit"这一步,而它在这一步上相当厉害。

So the things that surprised me are: we saw this huge jump. It was a particularly large jump. And without us really prompting them at all, some of the early companies that we gave this to said things like, "This is a super weapon. You should have to own a gun license to use it. Please don't release this."

所以让我意外的是:我们看到了一次巨大的跃升,一次格外大的跃升。而且我们几乎完全没去引导,一些最早拿到它的公司就主动说出这样的话:"这是个超级武器。用它得先有持枪执照。求你们别发布。"

The demand to do this was coming from the companies we gave it to, who were finding so many critical vulnerabilities and exploitability around these critical vulnerabilities that they were basically asking us not to release it.

这种"别发布"的诉求,恰恰来自我们交付了模型的那些公司——他们发现的严重漏洞太多了,而且围绕这些严重漏洞,可被利用的程度太高,于是他们基本上是在求我们别把它放出来。

Now, to be clear — because things always get distorted in the world of social media — the goal isn't to keep this locked up forever. We're gradually trying to open this up to a wider and wider set of people, and eventually we believe we should release Mythos to a general audience, but with strong cyber safeguards.

先说清楚——因为在社交媒体的世界里,事情总会被扭曲——目标并不是把它永远锁起来。我们正在逐步把它开放给越来越广的人群,而且我们相信,最终应该把 Mythos 发布给大众,只是要配上强有力的网络安全护栏。

A concern is that today's cyber safeguards — which we did release on Opus 4.7, a good cyber model but a substantially weaker one — these can be jailbroken. And we're a little concerned about some of the other companies who think this is a sufficient defense.

一个顾虑是:今天的这些网络安全护栏——我们已经随 Opus 4.7 一起发布,它是个不错的网络安全模型,但要弱得多——是可以被越狱(jailbroken)的。而我们有点担心另外一些公司,他们以为这就是足够的防御了。

Because, yeah, it works sometimes, but we all know that these classifiers can be jailbroken or gone around. Our own testing, as well as our assessment of the defenses that other companies have put in place, suggests that these defenses are not strong enough yet. And that's what we're waiting for — getting the defenses to the point where we really have confidence in them.

因为,是,它有时候管用,但我们都知道,这些分类器(classifier)是可以被越狱、或者被绕过去的。我们自己的测试,加上我们对其他公司已部署防御的评估,都表明这些防御还不够强。而这正是我们在等的东西——把防御做到我们真正有信心的程度。

There was a lot of pushback on it. You have researchers saying they were able to replicate it using cheaper open-source models. Some folks say OpenAI has these capabilities already. What do you say to folks who say this is a grand PR move?

外界对它有不少反弹。有研究者说,他们用更便宜的开源模型就能复现它。有人说 OpenAI 早就有这些能力了。对那些说"这就是一场盛大公关"的人,你怎么回应?

The claim that it could be replicated with open-source models — that's just incredibly false.

"用开源模型就能复现"这个说法——简直错得离谱。

The idea is that Mythos looks across the whole codebase and finds something. Some guy went on Twitter and said, "Well, if you point an open-source model at exactly the line of code that Mythos finds, then it finds the same issue." That isn't the prompt. That isn't the question.

实情是,Mythos 会扫遍整个代码库,然后找出问题。有个人跑去 Twitter 上说:"诶,你要是把一个开源模型直接对准 Mythos 找到的那一行代码,它也能找出同样的问题。"可那根本不是题目,根本不是要解的问题。

The ultimate test of this is: we go to companies, we go to open-source repos. We found 271 new vulnerabilities in Firefox. We've found many thousands within private companies who haven't fixed them yet or can't disclose them yet. No one found those 271 vulnerabilities with the previous models.

真正的检验标准是:我们去找真实的公司,去找开源代码仓库。我们在 Firefox 里找到了 271 个新漏洞;在那些还没修好、或还不能披露的私营公司里,找到了好几千个。用此前的模型,没人找得到这 271 个漏洞。

So it's the actual workflow of what works in practice — as opposed to, "okay, I find the exact line that Mythos found, I found the needle in the haystack, something else can now pick up the needle."

所以关键是"在真实场景里到底什么管用"的整套工作流——而不是那种"好,我把 Mythos 已经找到的那根针所在的位置告诉你,大海捞针的活儿已经替你干完了,现在换个东西也能把这根针捡起来"。

But what about the folks who say this was just good marketing?

那对那些说"这不过是一手漂亮营销"的人呢?

We have suffered enormously commercially from not releasing this model. This model has incredibly accelerated research within Anthropic, and production, and our next models. It would do the same in the outside world if we were to release it. This has hurt us enormously commercially.

不发布这个模型,我们在商业上损失惨重。这个模型极大地加速了 Anthropic 内部的研究、生产,以及我们下一代模型的开发。如果我们把它放出去,它在外部世界也会带来同样的加速。不发布,让我们在商业上付出了巨大的代价。

If this helps defenders, it also helps attackers. Can we defend anything anymore?

如果它帮得了防守方,它也帮得了进攻方。我们还守得住任何东西吗?

The reason we're giving Mythos to defenders before we give it to attackers is to patch all the bugs. I don't know — as the models get better, there may be more and more bugs to be found — but there's only so many. They're finite. It's like you have this surface, and there's only so many holes in it.

我们之所以先把 Mythos 给防守方、再给进攻方,就是为了把所有漏洞都补上。我也说不准——随着模型变强,要找的 bug 可能越来越多——但终究是有限的。它们是有数的。就好比你有这么一个表面,上面的洞也就那么多。

You patch all the holes, and then the surface becomes very hard to attack. As well, the code itself is written with the powerful models, so it then becomes very hard to find flaws in or break into.

你把所有洞都补上,这个表面就变得很难攻破。再加上代码本身也是用这些强大的模型写的,于是它就变得很难被找出破绽、很难被攻进去。

So I think on the other side of this — hopefully 6 months or a year from now — we have a much more secure internet ecosystem than we had in the past. We're trying to get to that world, and we're doing the best we can to open up Mythos to new cyber defenders.

所以我觉得,熬过这一关之后——但愿是半年或一年以后——我们会拥有一个比过去安全得多的互联网生态。我们正努力走向那个世界,也在尽最大努力把 Mythos 开放给新的网络防御者。

We've been talking to the government. We're very respectful of their recommendations. They're slowing the pace at which we open it up, because they're worried about counter-intelligence risk. I think that's sensible. I think all serious people here understand that there's real trade-offs.

我们一直在跟政府沟通。我们很尊重他们的建议。他们在放慢我们开放它的节奏,因为他们担心反情报(counter-intelligence)风险。我觉得这很明智。我想,这里所有严肃的人都明白:这里头是有真实取舍的。

We see a lot of sniping from people on Twitter and from other AI companies. You look at what they're saying and the inconsistency with what they're doing. They're not serious people. They're not seriously engaging with the serious trade-offs that we have here.

我们看到 Twitter 上、还有别的 AI 公司,有大量的冷枪暗箭。你看他们嘴上说的,跟他们手上做的,根本对不上。这些人不严肃。他们没有认真去面对我们这里实实在在的取舍。

Look, I have customers calling me up every day saying, "I want access to Mythos." I have countries calling me up saying, "I want access to Mythos." And I have the US government and my security team saying, "No, wait a minute, there's risk to it."

你看,每天都有客户打电话来跟我说:"我想用上 Mythos。"也有国家打电话来说:"我想用上 Mythos。"同时还有美国政府和我自己的安全团队在说:"不行,等一下,这里有风险。"

I'm not saying one side or the other is right. I think it's somewhere in between. Both sides have valid points. But there's a real challenge here, and we need to face it together as a society — not accuse things of being cheap marketing, not use cheap marketing to try and counter-position, which some of the other companies are doing. It just all shows an incredible lack of gravitas and maturity. We need to all face this moment together.

我不是说哪一方就对。我觉得答案在两者之间。双方都有站得住脚的道理。但这里有一个真实的难题,我们需要作为一个社会一起去面对它——而不是动辄指责别人在"廉价营销",也不是像别的某些公司那样,用廉价营销去做对位攻击(counter-position)。这些做法,只能暴露出一种惊人的、缺乏分量和成熟度的轻浮。我们都需要一起来面对这个时刻。

Have you had to make trade-offs already that you're not entirely comfortable with?

有没有一些取舍,是你已经不得不做、但其实并不完全心安的?

The entire history of Anthropic has been trade-offs. In some ideal world, before you release the first chatbot, you could spend years studying every possible thing that could go wrong with it. Now, we did delay the initial release of Claude — but we did it for a few months.

Anthropic 的整部历史,就是一连串取舍。在某个理想世界里,你在发布第一个聊天机器人之前,可以花上好几年,把它一切可能出岔子的地方都研究个遍。我们确实推迟过 Claude 最初的发布——但也就推迟了几个月。

So what I'm saying is, everything is a trade-off. The extreme ends of the spectrum are completely insane. And so everything is a trade-off.

所以我想说的是,一切都是取舍。光谱的两个极端都是彻头彻尾的疯狂。所以,一切都是取舍。

What I would say is that now that we're in what I'd describe as a commercially leading position, I and Daniela are actually doing all we can to move the dial even further towards being careful. That's what the Mythos release was about.

我想说的是,如今我们处在一个我会称之为"商业领先"的位置上,我和 Daniela 其实正竭尽所能,把指针往"更谨慎"那一边再拨一点。Mythos 那次发布,正是为了这个。

It's very hard to do something like that if you're not the leading player. And so I think you're going to see more things like that.

如果你不是领先的那一家,这种事是很难做的。所以我觉得,你以后还会看到更多这样的事。

There's this argument: why wouldn't the government take you over? Why would they let a private company control technology that's so powerful?

有这么一种说法:政府为什么不把你们接管了?他们怎么会放任一家私营公司,去掌控一项如此强大的技术?

I actually think that's a very serious question, and I share those concerns. I don't think the government should outright take us over. But I would put it this way.

说真的,我觉得这是个非常严肃的问题,而且我也有同样的担忧。我不认为政府应该直接把我们接管掉。但我会这么来讲。

Every previous powerful technology we've seen in history was either built by the government or originated with the government. Nuclear weapons, obviously — initially built by the government, and pretty much built by the government after that. But even the internet, GPS, cell phones — all the R&D was done in the federal labs, in the universities.

历史上,我们见过的每一项强大技术,要么是政府造的,要么起源于政府。核武器自不必说——一开始就是政府造的,之后也基本是政府在造。但就连互联网、GPS、手机——所有的研发(R&D),都是在联邦实验室、在大学里完成的。

AI is the first technology that's been built in the private sector, and where government has not really had a serious role and is coming in late to the game. I think that's actually a dangerous and unstable situation. It is not the situation I would have chosen.

AI 是第一项在私营部门里造出来的技术,政府在其中并没有真正扮演重要角色,而且是迟到入场的。我觉得这其实是个危险而不稳定的局面。这不是我会主动选择的局面。

There's not really an alternative. This technology is possible to build. Our adversaries are building it. It has economic value. It's going to get built. The issue is the government not doing it, not the private sector doing it.

但其实也没有别的选项。这项技术是造得出来的,我们的对手在造它,它有经济价值,它注定会被造出来。真正的问题在于政府"没去做",而不在于私营部门"在做"。

I think we need to think about checks and balances on power. So I think there need to be checks and balances on the power of the AI companies. We have this thing, the long-term benefit trust.

我觉得我们需要去想"对权力的制衡(checks and balances)"。所以我认为,必须对 AI 公司的权力加以制衡。我们有这么个东西,叫"长期受益信托(long-term benefit trust)"。

What that is, is a body that can appoint the majority of the board members and remove the majority of the board members. So essentially, if you thread it through, it has the power to fire me.

它本质上是这样一个机构:可以任命董事会的多数成员,也可以罢免董事会的多数成员。所以从头捋到底,它实质上拥有解雇我本人的权力。

And what we're looking at is, we're introducing some elements — nowhere near all the elements, but a little bit — of public governance, where you're accountable to someone who doesn't just have stock in the company. That's very important, and that structure is going to continue no matter what happens to the company. That's on the AI side, and we encourage other companies to have similar structures.

我们正在做的,是引入一些"公共治理"的成分——远谈不上全部,但有那么一点点——让你要对一个"并不只是持有公司股票"的人负责。这一点非常重要,而且无论公司将来发生什么,这套结构都会延续下去。这是公司这一侧;我们也鼓励别的公司去建立类似的结构。

On the government side, I think we need checks and balances. There are efforts in Congress that have been announced to enact those red lines. So I really think the legislative branch and the judicial branch need to exert themselves, because this technology — I'm scared of companies having it, but I'm also scared of government having it.

在政府这一侧,我同样认为需要制衡。国会已经公开宣布,有一些努力要把那几条红线立法。所以我真心认为,立法和司法这两大分支都得发力——因为对这项技术,我既怕公司掌握它,也怕政府掌握它。

And then the companies need to provide checks on government, and the government needs to provide checks on companies. We need basic regulation of the technology. I think we need to start doing pre-release testing — required pre-release testing — testing and auditing of the models.

所以,公司要对政府形成制衡,政府也要对公司形成制衡。我们需要对这项技术有基本的监管。我觉得我们得开始做"发布前测试"——而且是强制性的发布前测试——对模型进行测试和审计。

It's very funny to me how there's a particular group of people in the tech world, in Silicon Valley, who started with a position of, like, even having transparency around this technology, even export control — this is all just totally going to apocalyptically destroy our potential to create the technology, it'll kill innovation.

我觉得很好笑:科技圈、硅谷里有那么一群人,一开始的立场是——哪怕只是要求对这项技术有点透明度,哪怕只是出口管制——都被他们说成是会"末日级地"摧毁我们创造技术的潜力、会扼杀创新。

And then as soon as they see the first real danger, which I've been expecting all along, there's all this talk of nationalization and the government should just seize it. Come on, folks. You're yo-yoing from the most extreme anti-regulatory — "if you look at us the wrong way, you're destroying the industry" — to this completely communist, "the government should grab it all."

可一旦他们看到第一个真正的危险——我可是一直都料到会有的——就立马满嘴"国有化"、"政府应该直接没收"。拜托,各位。你们这是从最极端的反监管那一头——"你们多看我们一眼,就是在毁掉整个行业"——一下子摇摆到了那种彻头彻尾的共产主义:"政府该把它全抓过去。"

We need a more sensible, moderate approach. That's the one we've been favoring all along, because we've understood the power of this technology. We're not panicking. We're not denying it. We see the smooth exponential, and we're responding to it appropriately.

我们需要一种更明智、更温和的路子。这正是我们一直以来主张的,因为我们看懂了这项技术的力量。我们没有恐慌,也没有否认。我们看到了那条平滑的指数曲线,并在恰当地回应它。

So how was your visit back to the White House?

那你这次回白宫,感觉如何?

We always try to work together with whoever we can in government. We have a set of principles. We follow those principles, and we hope that folks on the other side are reasonable.

我们总是尽量跟政府里所有能合作的人合作。我们有一套原则,我们照这套原则行事,也希望对面的人讲道理。

And honestly, the government has taken Mythos very seriously. We've had good conversations with Secretary Bessent, with Chief of Staff Susie Wiles. I think they really understand the nature of the risks here. Mythos has helped them feel much more concretely where these risks are.

而且说实话,政府对 Mythos 非常重视。我们跟财长 Bessent(贝森特)、跟白宫幕僚长 Susie Wiles 都谈得不错。我觉得他们是真的理解这里风险的本质。Mythos 帮他们更具体地感受到了,这些风险究竟在哪里。

So, again, as with any administration, there are parts we get along with very well and who understand it, and there are other parts that are harder to get along with. I think that's normal. That would be the case in any administration, and we just try to navigate it as best we can.

所以还是那句话,跟任何一届政府一样,总有一些部分我们相处得很好、他们也理解;也总有另一些部分更难打交道。我觉得这很正常,换哪届政府都一样,我们只能尽力去周旋。

You worked at Baidu earlier in your career — a big Chinese tech company. You worked at the Silicon Valley outpost of it, and you've been clear on your views on China. Strong open-source models are coming out of China, and you have US companies building on them for free. Is that a threat?

你职业生涯早期在百度(Baidu)工作过——一家中国大型科技公司,你待的是它在硅谷的分部。你对中国的看法也一向很明确。如今中国出了一批很强的开源模型,而美国公司可以免费拿来开发。这是威胁吗?

One of the things we've seen with this technology is that there's really a premium to how intelligent the models are. We very rarely see that people would prefer to use models with lower intelligence.

关于这项技术,我们看到的一点是:模型有多聪明,真的会带来溢价。我们极少看到有人会更愿意用智能更低的模型。

Now, to be clear, there's a thriving ecosystem. There are lots of challenges and problems that are much easier than the ones we need frontier models for. But again, it's an exponential. It's possible that these far-from-frontier models have economic value comparable to what we saw in 2023 and 2024.

当然要说清楚,这里有一个繁荣的生态。很多挑战和问题,比那些非得用前沿模型才能解的要简单得多。但还是那句话,这是一条指数曲线。那些远离前沿的模型,其经济价值可能跟我们在 2023、2024 年看到的差不多。

But again, we have this 10x a year growth. And so what we find is that what's on the frontier is always much, much larger than what is away from the frontier.

可问题是,我们有每年 10 倍的增长。于是我们发现:处在前沿上的那一块,永远比远离前沿的那一块大得多、大得多。

I think this is something that people who are used to building products in the previous era don't quite understand. As someone who's come in who hadn't run a company before, who's never thought about the previous product era — particularly the social media era — I feel like an outsider to that world, and I feel that people's instincts are wrong.

我觉得,习惯了上一个时代做产品的人,不太能理解这一点。而我作为一个之前没运营过公司、从没琢磨过上一个产品时代——尤其是社交媒体时代——的人,我对那个世界像个局外人,而我觉得很多人的直觉是错的。

They have all these product heuristics, and I think the 10x-per-year model exponential really breaks that. Intelligence is just such a huge factor that it outweighs everything else. And so we're seeing over and over again that the value is found on the frontier.

他们手里有一整套做产品的经验法则,而我觉得"模型每年 10 倍"这条指数曲线,把那套法则彻底打破了。智能是个太大的变量,大到盖过其他一切。所以我们一次又一次地看到:价值在前沿上。

Now, what I do worry about with some of these laggard models is the risks of them — where we have Mythos-class cyber capabilities. 12 months from now we'll have much better cyber capabilities, but the Mythos-class cyber capabilities may just be available for anyone to download.

不过,对这些"落后一档"的模型,我确实担心一件事,就是它们的风险——在我们已经有了 Mythos 级别的网络攻击能力的前提下。12 个月之后,我们会有强得多的网络能力,但 Mythos 这个级别的网络能力,到那时可能已经变成任何人都能下载的东西了。

Now, hopefully we'll have patched everything before then. I don't think there's anything we can do to stop it, but I think it's a serious concern.

但愿到那时候,我们已经把所有洞都补上了。我不觉得我们有什么办法能阻止它发生,但我认为这是个严肃的隐忧。

Did what you saw at Baidu shape your views on China?

你在百度看到的东西,塑造了你对中国的看法吗?

Not really. No. I worked there for a year. I think I probably learned more about speech recognition and all of that.

其实没怎么。没有。我在那儿干了一年。我想,我学到更多的大概是语音识别之类的东西。

Maybe the only thing that concerned me was, part of how we got all the speech recognition data was — they said, ominously, "We don't care about privacy in China, so we have all this speech recognition data."

也许唯一让我心里咯噔一下的是:我们之所以能拿到那么多语音识别数据,部分原因是——他们半带阴森地说:"在中国我们不在乎隐私,所以我们手里有这么多语音识别数据。"

But aside from that, my worries here are geopolitical. The things that most worried me about what happened in China are what we saw happen to the Uyghurs, what we saw with suppression of criticism — even in the US — with what happened with Hong Kong, the fact that the CCP could reach into the US business network and suppress criticism.

但除此之外,我在这件事上的担忧是地缘政治层面的。中国发生的那些事里,最让我担心的是:我们看到维吾尔人(Uyghurs)的遭遇,看到对批评声音的压制——甚至在美国境内——以及香港发生的事,还有中共(CCP)有能力把手伸进美国的商业网络、去压制批评。

That's an authoritarian state — and a high-tech authoritarian state. And when I see how that combines with AI, you really get a dystopia here, like 1984 or worse.

那是一个威权国家——而且是一个高科技的威权国家。当我看到这种东西跟 AI 结合在一起,你真的会得到一种反乌托邦,像《1984》,甚至更糟。

My focus is on trying to prevent that, and I think we have an opportunity to prevent that. I think we have an opportunity for AI to be a pro-democracy technology, that makes people freer, that delivers on the promise of equal justice for all — or it could go the other way.

我所专注的,就是努力去阻止那种结局,而我认为我们有机会阻止它。我认为我们有机会让 AI 成为一项亲民主(pro-democracy)的技术,让人更自由,兑现"人人享有平等正义"的承诺——当然,它也可能走向另一面。

And which way it goes depends on the actions of the AI companies, on the actions of the government, on the actions of all of us. And so I see us as having responsibility here.

它往哪个方向走,取决于 AI 公司的行动,取决于政府的行动,取决于我们所有人的行动。所以我把这视为我们身上的一份责任。

There's a moment that people in your field talk about, where AI gets good enough to improve itself, and then the improved version improves itself, and so on. Some of your researchers think that moment is close. How far away is it?

你这个领域的人常谈到一个时刻:AI 强到足以改进自身,然后改进后的版本再改进自身,如此循环。你们有些研究者认为那个时刻已经很近了。它还有多远?

I don't think it's a moment in time. I think it's a continuous process. We're already seeing it in some ways, where the AI is able to suggest architectures for the next AI.

我不认为它是某个时间点。我觉得它是一个连续的过程。在某些方面我们已经看到了:AI 能够为下一代 AI 提出架构方案。

I would say a year ago we were seeing a 10 to 15% increase in total factor productivity due to AI. That's probably up to 20 or 30% now. It might be doubling. As with all things, we're on the exponential.

我会说,一年前我们看到 AI 带来全要素生产率(total factor productivity)提升 10% 到 15%。现在大概到了 20% 或 30%,可能正在翻倍。和所有事情一样,我们都在这条指数曲线上。

There's no moment where AI improves itself, or runs out of control, or becomes unsafe. What we have is an accelerating exponential. And at each point on the exponential, we have to assess: is this a time to slow down? Is this a time to put more controls on this technology? I think more and more of that is going to be required.

并不存在某个"AI 开始自我改进、或者失控、或者变得不安全"的时刻。我们面对的是一条不断加速的指数曲线。在曲线上的每一个点,我们都得评估:现在是该减速的时候吗?现在是该给这项技术加更多控制的时候吗?我觉得,这样的评估会越来越多地被需要。

But I think the Rosetta Stone to all of this is the smooth exponential.

但我觉得,读懂这一切的"罗塞塔石碑(Rosetta Stone)",就是这条平滑的指数曲线。

Again, there's an object lesson in the people who were against all AI regulation, and then they saw one thing and they wanted to nationalize. There's an object lesson in the people who dismissed the power of AI, and then said, "Oh my god, it's improving itself, it's running out of control, we have to shut it all down."

还是那句话,有两类人可以当反面教材:一类人原本反对一切 AI 监管,然后看到一件事,就想要国有化;另一类人原本轻视 AI 的力量,然后突然喊:"我的天,它在自我改进,它要失控了,我们必须把它全关掉。"

Yo-yoing between those extreme reactions is incredibly unhelpful as a response to this technology. The right response, the wise response, is to say: we're not going to panic. Our countermeasures will smoothly ratchet up with the power of the technology.

在这两种极端反应之间来回摇摆,作为对这项技术的回应,是极其无益的。正确的回应、明智的回应是:我们不恐慌。我们的应对措施,会随着技术力量的增强而平滑地、一格一格地往上调。

If you see someone having this kind of crazy yo-yo reaction, that's a sign that they were caught by surprise, and that they're not serious.

如果你看到某个人在这样疯狂地来回摇摆,那就是一个信号:他被打了个措手不及,而且他不严肃。

I understand one of your favorite books is The Making of the Atomic Bomb.

我听说你最爱的书之一是《The Making of the Atomic Bomb(原子弹的制造)》。

That is correct.

没错。

Do you see parallels between yourself and Oppenheimer?

你觉得自己和 Oppenheimer(奥本海默)之间有相似之处吗?

The figure I most identified with was Leo Szilard, who was the one who first basically had the idea that there could be a kind of chain reaction.

我最有共鸣的人物是 Leo Szilard(利奥·西拉德)——基本上是他最早想到,可能存在某种链式反应。

Look, my view is we're not going to get through this with larger-than-life personalities, or figures who try and be at the center of everything. There needs to be a balance of power here.

这么说吧,我的看法是:我们闯过这一关,靠的不会是某个传奇式的大人物,或者某个想把自己摆到一切中心的角色。这里需要的是一种权力的平衡。

There's a lot of powerful actors who have interests here, and the only way it's going to end well for everyone is if there's basically checks and balances everywhere. So in some ways, I actually see Oppenheimer as a failure case — as what should not happen.

这里有很多有力量、也有利益的玩家,而要让所有人都有个好结局,唯一的办法就是处处都有制衡。所以某种意义上,我其实把 Oppenheimer 看作一个失败案例——一个"不该发生"的范本。

You've said there's roughly a 10 to 25% chance of civilizational collapse. That is not insignificant. Is there a scenario where it's something that Anthropic built that caused that?

你说过,文明崩溃的概率大约在 10% 到 25% 之间。这可不是个小数。有没有一种情形,是 Anthropic 造出来的某样东西引发了它?

I mean, I certainly hope not. My view is that the actions we have taken lower that probability rather than increasing it.

我当然希望不是。我的看法是:我们采取的这些行动,是在压低那个概率,而不是抬高它。

That probability comes from the very straightforward recipe of the technology: the existence of many countries in the world, the existence of many companies within an economy, and new ones created if the void isn't filled. That's a dilemma that we're in.

那个概率,来自这项技术非常直白的"配方":世界上有许多国家,一个经济体里有许多公司,而且只要这个空位没人填,新的公司就会冒出来。这就是我们身处的两难。

We are trying to act to lower that probability. I think we lower it a lot more than we raise it. But the inherent property of this technology is that it's unpredictable.

我们正努力采取行动去压低这个概率。我觉得,我们压低它的幅度,远大于我们抬高它的幅度。但这项技术与生俱来的特性,就是它不可预测。

So we try to build something and test it a lot before it's released. And the models that are released today are not dangerous — or at least not really, I think, dangerous outside of cyber. And then we try and iterate and learn from that.

所以我们的做法是,造出一样东西,在发布前大量测试它。今天发布出去的这些模型并不危险——或者至少我觉得,除了网络安全这块之外,它们并不真的危险。然后我们再去迭代、从中学习。

So there's like a zillion defense mechanisms. Half of what we do within the company is try and reduce the risk as much as we can. But it's never going to be zero.

所以我们有数不清的防护机制。公司内部我们所做的事里,有一半就是在尽可能地把风险往下压。但它永远不可能归零。

Suppose there are a bunch of airline companies out there, and you're like, "Well, I'm going to make an airline company that's safer." It can both be the case that your airline company is 10 times safer than all the others. But if someone comes and asks you, "Can you guarantee that your airplane will never crash?" — I mean, how could you possibly?

打个比方:外面有一堆航空公司,你说"行,我要开一家更安全的航空公司"。可以同时成立的是:你的航空公司比其他所有航空公司都安全 10 倍;但如果有人来问你"你能保证你的飞机永远不会坠毁吗?"——我是说,你怎么可能保证得了?

But if there was a 25% chance of an airplane crashing, you wouldn't get on that plane.

可话说回来,如果一架飞机有 25% 的坠毁概率,你是不会上那架飞机的。

That's right. 25% is too high.

没错。25% 太高了。

We're trying to make that probability much, much lower. That is the goal.

我们正努力把那个概率压得低很多、低很多。这就是目标。

You are building something incredibly powerful and stand to gain enormously from it. Why should we trust you?

你在造一个无比强大的东西,而且会从中获得巨大的利益。我们凭什么信你?

My view of this is, actually, when any company starts out — and particularly given what we've seen with the behavior of Silicon Valley as an entity, its thinking over the last couple of years — I think starting from a position of distrust, if you don't know anything about me, if you don't know anything about Anthropic, is pretty rational.

我的看法其实是:任何一家公司刚起步时——尤其考虑到过去这几年,硅谷作为一个整体表现出来的行为、它的思维方式——如果你对我一无所知、对 Anthropic 一无所知,那么从"不信任"出发,是相当理性的。

I think Silicon Valley has lost a lot of the world's trust and has to re-earn it. And the message we're trying to send is: we're actually different. And that has to be earned in things that we actually do.

我认为硅谷已经失去了世界很大一部分的信任,必须重新挣回来。而我们想传递的信息是:我们是真的不一样。这一点,必须靠我们真正做出来的事去赢得。

You can agree or disagree, but we stood up for our values. The thing with Mythos — it's really hampered us commercially not to put this very powerful model out. And there were a bunch of smaller things before it.

你可以认同也可以反对,但我们确实为自己的价值观挺身而出过。Mythos 这件事——不把这么强的模型放出去,在商业上真的拖累了我们。而在它之前,还有一连串更小的事。

We put our money where our mouth is on China. We cut off access to models. We didn't have to do that. No one told us to do that. That cost us several hundred million, back when several hundred million was a significant fraction of our revenue.

在中国的问题上,我们是真金白银地兑现了自己的话。我们切断了对模型的访问。我们本可以不这么做,也没人要求我们这么做。那让我们损失了好几亿美元——而在当时,好几亿是我们营收里相当大的一块。

The delay of Claude 2 — we have a long history of it. We aren't perfect. We make mistakes. But what I would ask is for people to look at the overall history and say: if you add up that overall history, what is the hypothesis about us that is most consistent with it?

推迟发布 Claude 2——这类事我们有一长串记录。我们并不完美,我们会犯错。但我想请大家做的,是去看整段历史,然后问:如果把这整段历史加总起来,关于"我们是谁",哪一种假设跟它最吻合?

People have to decide for themselves. But I think the hypothesis that's consistent is: we are genuinely trying to do the right thing. We're imperfect. Organizations are always dysfunctional. We're always trying to fix them and make them work better. Many pitfalls, many things that go wrong.

这得每个人自己去判断。但我觉得,最吻合的那个假设是:我们是真心想做正确的事。我们不完美。组织总是有失灵的地方。我们一直在修补它们,想让它们运转得更好。坑很多,出岔子的地方也很多。

But at basis, we have an honest and earnest picture of how to do the right thing. And we're trying to execute on that picture.

但归根结底,对于"怎么做正确的事",我们心里有一幅诚实而恳切的图景。而我们正努力把那幅图景落到实处。

We will see you on the other side of the exponential. Done.

我们会在指数曲线的另一头再见。结束。

Uh, hopefully. [laughter]

呃,但愿吧。[笑]

You always wanted to be a Hollywood star.

你不是一直想当好莱坞明星嘛。

That's one surprising thing that I didn't understand about the CEO job — how often you have to wear makeup. [laughter] That was not on my bingo card.

这正是 CEO 这份工作里一件我没料到的事——你得多么频繁地化妆。[笑]这可不在我的"意料清单"上。

Just a little powder.

就扑一点点粉。