Krishna Rao - Anthropic's CFO on Compute, Scaling to $30B ARR, and the Returns to Frontier Intelligence - [Invest Like the Best, EP.472]

频道: Invest Like the Best with Patrick O'Shaughnessy
视频: https://colossus.com/episode/cone-of-uncertainty/
原文语言: en
统计: 共 110 轮 · Patrick O'Shaughnessy 34 · Krishna Rao 75

[0:00] Patrick O'Shaughnessy

Ramp is the only platform built to make your finance team leaner, faster, and better,saving businesses 5% annually on average so you can stay focused on growth.Ramp customers grew revenue 3.2 times faster than the average American business.Visa, Vercel, Cursor, Stripe, Notion, 11Lab, Shopify, and 70,000 other businesses all runon Ramp.Mine does too, and so should yours.Learn more at ramp.com slash invest.OpenAI, Cursor, Anthropic, Perplexity, and Vercel all have something in common.They all use WorkOS.To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO,SCIM, RBAC, and Audit Logs.Instead of spending months building these mission-critical capabilities yourself, you can just use WorkOSAPIs to gain all of them on day zero.That's why so many of the top AI teams you hear about already run on WorkOS.WorkOS is the fastest way to become enterprise-ready and stay focused on what matters most,your product.Visit WorkOS.com to get started.Felix by Rogo is a personal finance agent that turns a single prompt into finished,client-ready work using your firm's own templates, context, and standards.Send Felix an email like,take these comments and turn them for me,

Ramp 是唯一一个专为让你的财务团队更精简、更快速、更出色而打造的平台，平均每年为企业节省 5% 的开支，让你可以专注于增长。Ramp 的客户收入增速比美国企业的平均水平快 3.2 倍。Visa、Vercel、Cursor、Stripe、Notion、ElevenLabs、Shopify，以及另外 70,000 家企业，全都跑在 Ramp 上。我自己的公司也是，你的也应该是。访问 ramp.com/invest 了解更多。OpenAI、Cursor、Anthropic、Perplexity 和 Vercel 有一个共同点：它们都在用 WorkOS。要想大规模拿下企业客户，你必须交付 SSO、SCIM、RBAC、审计日志（Audit Logs）这些核心能力。与其花好几个月自己去搭这些关键功能，不如直接用 WorkOS 的 API，第零天就把它们全部收入囊中。这正是为什么你听说过的那么多顶尖 AI 团队早已跑在 WorkOS 上。WorkOS 是最快让你具备企业级交付能力、并把精力留给最重要之事——你的产品——的路径。访问 WorkOS.com 即可开始。Rogo 出品的 Felix 是一个个人金融智能体，它能把一条提示词变成可直接交付客户的成品，全程使用你所在机构自己的模板、上下文与标准。给 Felix 发一封邮件，比如「把这些批注帮我落实进去」，

[1:11] Patrick O'Shaughnessy

or update my tracker with the context of these emails.And Felix sends back finished PowerPoint decks, Excel models, and sourced research.Felix works the way your team already does,delivering work quickly and accurately around the clock.Learn more at rogo.ai slash Felix.Hello and welcome, everyone.

或者「用这些邮件里的上下文更新我的追踪表」。Felix 就会把做好的 PowerPoint 演示文稿、Excel 模型和标注了出处的研究材料发回给你。Felix 的工作方式和你的团队本来就一致，全天候快速而准确地交付成果。访问 rogo.ai/Felix 了解更多。大家好，欢迎收听。

[1:30] Patrick O'Shaughnessy

I'm Patrick O'Shaughnessy, and this is Invest Like the Best.This show is an open-ended exploration of markets, ideas, stories, and strategiesthat will help you better invest both your time and your money.If you enjoy these conversations and want to go deeper,check out Colossus, our quarterly publication with in-depth profilesof the people shaping business and investing.You can find Colossus along with all of our podcasts at Colossus.com.Patrick O'Shaughnessy is the CEO of Positive Sum.All opinions expressed by Patrick and podcast guests are solely their own opinionsand do not reflect the opinion of Positive Sum.This podcast is for informational purposes onlyand should not be relied upon as a basis for investment decisions.Clients of Positive Sum may maintain positions in the securities discussed in this podcast.To learn more, visit PSUM.vc.My guest today is Krishna Rao, the CFO of Anthropic.The center of our conversation is how he navigates the decision around procuring and allocating compute,which he describes as the canvas on which everything else gets built.We talk about what he calls the cone of uncertainty,the three-chip platforms Anthropic uses fungibly across Tranium, TPUs, and GPUs,

我是 Patrick O'Shaughnessy，这里是《Invest Like the Best》。本节目是对市场、思想、故事与策略的开放式探索，帮助你更好地投资自己的时间与金钱。如果你喜欢这些对话、想走得更深，欢迎关注我们的季刊 Colossus，里面有对那些正在塑造商业与投资的人物的深度特写。Colossus 与我们所有的播客都可以在 Colossus.com 找到。Patrick O'Shaughnessy 是 Positive Sum 的 CEO。Patrick 及播客嘉宾表达的一切观点均为其个人观点，不代表 Positive Sum 的立场。本播客仅供参考，不应作为投资决策的依据。Positive Sum 的客户可能持有本播客中讨论的证券。了解更多请访问 PSUM.vc。今天的嘉宾是 Anthropic 的 CFO Krishna Rao。这场对话的核心，是他如何决策算力（compute）的采购与分配——他把算力称作「万物据以建造的那块画布」。我们聊到他所说的不确定性锥（cone of uncertainty），聊到 Anthropic 互换着使用（fungible）的三种芯片平台——Trainium、TPU 和 GPU，

[2:38] Patrick O'Shaughnessy

and the daily meetings they run to allocate compute between model development,internal use, and serving customer demand.He explains where the returns to frontier intelligence keep getting higher,especially in enterprise,and how Anthropic thinks about the line between platform and application,and why they choose to build their own products like CloudCode.Krishna has such a unique seat watching one of the fastest-growing businesses in history,and he is generous in sharing what he has already learned since joining the company two years ago.Please enjoy my conversation with Krishna Rao.Krishna, I have been so excited for this conversation

也聊到他们每天召开的会议，用来在模型研发、内部使用与服务客户需求之间分配算力。他解释了前沿智能的回报在哪些地方还在持续走高——尤其是在企业市场；Anthropic 如何看待平台与应用之间的那条界线；以及他们为什么选择自己做 Claude Code 这样的产品。Krishna 拥有一个极为独特的观察席位，看着史上增长最快的生意之一，而他也毫无保留地分享了自己加入公司两年来学到的东西。请欣赏我与 Krishna Rao 的对话。Krishna，我对这场对话期待已久，

[3:08] Patrick O'Shaughnessy

because you get to see from the insideone of the most interesting businesses in world historyat maybe the most interesting time in world history,at least if you're a technologist or care about technology.One of the things that fascinates me most is this question of computethat you have to deal with all day, every day.It's a key part of what you do.It's a key part of what these companies are doing.And there's just this whole revolution happening.I'd love you to just start by explaining what it's like to have to deal with that.I understand at one point you were having like a daily meetingabout how to allocate to compute and to who and why.Bring us into that part of your lifebecause I think it's right at the cutting edge of what's going on.The compute that we procure is the lifeblood of our business.It is the most important thing in the company.It's like the canvas on which everything else gets built.And so the decisions we make and how much compute to buyare some of the most consequential and hardest decisions to make in the entire company.Think of it this way.If you buy too much compute, you go out of business.If you buy too little compute, you can't serve your customersand you're not at the frontier.

因为你能从内部看到世界历史上最有意思的生意之一，而且是在也许是世界历史上最有意思的时刻——至少对技术从业者、或者关心技术的人来说是这样。最让我着迷的问题之一，就是你每天从早到晚都要面对的算力问题。它是你工作中的关键一环，也是这些公司正在做的事情的关键一环。而这中间正发生着一整场革命。我很想请你先讲讲，要天天处理这件事究竟是什么体验。我听说你们一度每天都要开一次会，讨论算力怎么分、分给谁、为什么这么分。带我们进入你生活的这一部分，因为我觉得它正处在当下这场变革的最前沿。我们采购的算力是这门生意的命脉，是公司里最重要的东西，就像万物据以建造的那块画布。所以我们在「买多少算力」上做的决定，是整个公司里后果最重大、也最难做的决定之一。你可以这样理解：算力买多了，公司会倒闭；算力买少了，你既服务不了客户，也站不到前沿。

[4:12] Krishna Rao

Same thing.So we talk a lot about this cone of uncertainty,but the idea of just these purchases that have these real world implications.You can't just go out and buy a gigawatt of compute and have it delivered next week.You have to really think ahead to plan for this.And so we really take a very disciplined approach to how we think about it.So we look bottoms up.We model what we think demand will be.Obviously, we sometimes get that wrong.We think about the compute we need to stay at the frontier.And we really look ahead and try to estimate that.And then as we go out and do these deals to procure compute,flexibility is really important to us.And so we build that flexibility into the deals themselves.We build that flexibility into how we use the compute as well.Because the way in which we bridge from a position we are today to where we want to gowhen the business is growing exponentiallyis to use that compute as efficiently as possible.I would say I spend 30 or 40% of my time on compute even today.What does flexibility mean in that example?

结果一样糟。所以我们经常谈到这个不确定性锥（cone of uncertainty），谈到这些采购决定有着实实在在的现实后果。你没法今天出门买下一吉瓦（gigawatt）的算力、下周就交付到位，你必须提前很久去筹划。所以我们采取了一套非常有纪律的方法：自下而上地看，把我们预判的需求建成模型——当然，有时候我们会算错；我们也会推演，为了留在前沿我们需要多少算力，认真往前看、尽量估准。然后当我们真正出去谈这些算力采购交易时，灵活性对我们非常重要，所以我们把灵活性写进交易条款本身，也把灵活性做进我们使用算力的方式里。因为在业务指数级增长的情况下，我们要从今天所处的位置架桥通向想去的地方，靠的就是尽可能高效地使用这些算力。哪怕到今天，我大概仍有 30% 到 40% 的时间花在算力上。在你说的这个例子里，灵活性具体指什么？

[5:07] Krishna Rao

It means a couple of different things.Number one, we use three different chip platforms.So we are customers of Amazon's Tranium chip, Google's TPUs, and NVIDIA's GPUs.We use these chips fungibly.If you think about the compute we buy, we're using it for model development.We're using it internally to speed up our own product and model development.We're also using it obviously to serve customers.Across those three chip platforms, we're using compute for all of those internal and externaluses.And that flexibility took us a long time to be able to do that.We've invested in that over multiple years to be what I believe the most efficient usersof compute amongst any of the frontier labs.And that's not something that just happened overnight.When we started using TPUs, I think it was third generation TPUs was the first one we used.At scale, people thought, oh, well, you're crazy.Everyone's using GPUs.Why aren't you using GPUs?

它包含几层意思。第一，我们使用三种不同的芯片平台：我们是亚马逊 Trainium 芯片、谷歌 TPU 和英伟达 GPU 的客户。我们把这些芯片互换着用。想想我们买来的算力：一部分用于模型研发，一部分在内部用来加速我们自己的产品与模型开发，当然还有一部分用来服务客户。在这三种芯片平台上，我们都同时承载着这些内部和外部的用途。这种灵活性我们花了很长时间才做到，投入了好几年，才成为我认为在所有前沿实验室里算力使用效率最高的一家。这不是一夜之间发生的。我们开始用 TPU 的时候，我记得最早用的是第三代 TPU。当时大规模这么做，人们会觉得：你们疯了吧，所有人都在用 GPU，你们为什么不用 GPU？

[5:59]

And we've invested very heavily to be able to use that compute incredibly flexibly.And then we look across the different generations of those chip platforms and use each generationfor the best workload internally.And so we really built this orchestration layer that gives us that flexibility to use alldifferent types of compute.And in doing so, we also are able to get the most value out of it.Am I thinking about this in the right way that something like CUDA that has been a part ofNVIDIA's story for a long time now, that allows you to do a lot with the underlying actualhardware, that you want to sort of eke your way into being as close to the bare metal aspossible.That's part of this flexibility and being able to control as many of the variables as youcan.Is that the journey that you've been on?

而我们投入了非常大的力气，才能把算力用得极其灵活。然后我们会横向比较这些芯片平台的不同代次，把每一代用在内部最适合它的工作负载上。我们真正搭起了一个编排层，让我们有能力灵活调度各种类型的算力，也因此能从中榨出最大的价值。我这样理解对不对：像 CUDA 这种长期以来构成英伟达故事一部分的东西，让你能对底层实际硬件做很多操作，而你希望尽可能贴近裸机（bare metal）去做——这也是这种灵活性的一部分，是尽可能多地掌控变量。这就是你们一路走来的历程吗？

[6:40] Krishna Rao

That's part of the journey for sure.But it's also been actually pretty collaborative.So we work really closely with the Annapurna Labs team at Amazon to help to influence the roadmapof these chips because we believe what we're doing is really stressing the limits of whatthese chips are capable of.And that means that like a dollar of compute inside our organization goes further than Ithink it does anywhere else.Importantly, we basically want to utilize each chip to its best purpose within the company.So that does mean that we're building our own compilers.We're really building things from the chip level up in order to have that customization andthat flexibility to use it internally the way we think will generate the most ROI.Can you explain this cone of uncertainty thing?

这确实是历程的一部分，但它其实也相当具有协作性。我们和亚马逊的 Annapurna Labs 团队合作非常紧密，去影响这些芯片的路线图，因为我们相信自己正在做的事情真正挑战了这些芯片能力的极限。这也意味着，同样一美元的算力，在我们组织内部能走得比我认为在其他任何地方都更远。重要的是，我们本质上希望让每一颗芯片都用在公司内部最适合它的用途上。所以这确实意味着我们在自己写编译器，我们真的是从芯片这一层往上搭，为的就是获得那种定制化与灵活性，让我们能按自己认为 ROI 最高的方式在内部使用它。你能解释一下这个不确定性锥是怎么回事吗？

[7:20] Patrick O'Shaughnessy

I want to ask about all the component parts of this, but that feels like a really key startingpoint or overall frame for how to think about both sourcing and then the uses of compute.Can you just explain what that concept is?

这里面每一个组成部分我都想问，但那个概念感觉像是一个非常关键的起点，或者说是理解算力来源与算力用途的总体框架。你能先解释一下这个概念吗？

[7:31] Krishna Rao

When you're building and growing a business exponentially, really small movements in monthlyor weekly growth rates result in compounding very, very different outcomes.And so as we're thinking ahead, even with our revenue growth, it's really hard to predictthis business.And it's really hard.I think humans mostly think linearly and you think incrementally.I've been at the company for two years.That's something that's a paradigm I've had to break for myself to stop just thinking linearlyand think on this exponential.When you're on this exponential, again, the range of outcomes starts to be really, reallywide.We look at a range of scenarios and we look at different points in that cone of uncertaintyover a one to two year period.And then we kind of work backwards from that.And what we want to do is we want to be at a place where we can obviously still be at thefrontier.That's the most important thing to be able to serve customers and then to be able tohave enough internal compute to accelerate our employees.It's interesting.If we were to say to our employees, you can't use our models anymore.We could serve billions of dollars of revenue with that compute that we allocate to employeesinternally.

当你在指数级地建设和扩张一门生意时，月度或周度增长率上极小的差别，复利下来会导向截然不同的结果。所以当我们往前看时，哪怕是我们自己的收入增长也非常难预测，非常难。我认为人类大多是线性思考、增量式思考的。我在公司待了两年，这是我必须为自己打破的一个思维范式——不要再线性地想，要按指数去想。而当你身处这条指数曲线上，结果的分布区间会变得非常非常宽。我们会看一组不同的情景，看这个不确定性锥上的不同位置，时间跨度是一到两年，然后从那里倒推回来。我们希望达到的状态是：首先当然仍要站在前沿，这是最重要的；其次要有能力服务客户；再次要有足够的内部算力去加速我们的员工。有意思的是，如果我们对员工说「你们不能再用我们的模型了」，那我们内部分配给员工的这部分算力，可以支撑起数十亿美元的收入。

[8:31] Krishna Rao

But we want to take a long term view and a long term perspective on that cone of uncertaintybecause we want to range towards the top end of these outcomes.But we have to plan for that.And as we go, that's how we think about buying compute in a disciplined way.The most important thing is what happens if you are at one point in the cone of uncertainty,but you've only bought compute for a different point.That's where this compute efficiency is something that really has helped us out.Can you bring us into the room for the conversations around the trade-offs between those?

但我们希望对这个不确定性锥采取长期视角、长期立场，因为我们想朝着结果分布的上限去够。可我们必须为此提前规划。就这样一步步走，这就是我们有纪律地采购算力的思路。最要命的问题是：万一你实际落在不确定性锥的某个位置上，而你买的算力只对应另一个位置，那怎么办？正是在这里，算力效率真正帮了我们大忙。你能带我们走进那些讨论取舍的房间里吗？

[8:59] Patrick O'Shaughnessy

I'm so interested by those three buckets of training, research, internal use, broadlyspeaking, and then serving customer demand.Naively, you might think, okay, it's a third, a third, a third allocation or something.How much does that range around?

我对那三个大类特别感兴趣：训练、研究、内部使用（大致这么划分），再加上服务客户需求。天真地想，你可能会觉得，那就三分之一、三分之一、三分之一这样分吧？这个比例的浮动范围有多大？

[9:11] Krishna Rao

What are the trade-offs?What is that discussion like on an ongoing basis?In addition to meeting about compute procurement, we meet a lot about compute allocation.I think what's important is it starts with a place where our culture is one that's incrediblycollaborative and that informs how this conversation happens.So there's not fiefdoms.It's done in a very collaborative, not a zero-sum way.There's a level of compute for model development that we will not go below.Even if it means it's harder to serve customers or we have to do unnatural things when it comesto that, we want to continue to make that long-term investment in developing the best models becausewe think the returns to frontier intelligence are extremely high.And it's extremely high, especially in enterprise.That kind of puts a floor on the compute that's allocated to model development.And then as we think about the internal use of compute, it really helps us to speed up that model development and to speed up finding those compute efficiencymultipliers that really get us more from each dollar of compute.So when we're talking about it, each team is kind of representing what they would do with that compute.And then we have a really open and frank discussion about how we think about ROI.

取舍在哪里？这个讨论在日常是什么样的？除了开会讨论算力采购，我们也经常开会讨论算力分配。我认为重要的一点是，这一切的起点是我们的文化极度重视协作，而这决定了这场对话会怎么进行。这里没有山头，讨论是以非常协作、而非零和的方式展开的。有一条模型研发的算力底线，我们绝不会跌破。哪怕这意味着服务客户会更吃力，或者我们得在这件事上做一些不那么自然的动作，我们也要继续在打造最好的模型上做长期投入，因为我们认为前沿智能的回报极高——尤其是在企业市场，回报极高。这就为分配给模型研发的算力设了一个下限。再看内部使用的算力，它其实帮助我们加速模型研发，也帮助我们更快找到那些算力效率的乘数，让每一美元算力产出更多。所以我们讨论的时候，每个团队都会陈述他们拿到这些算力会做什么，然后我们非常开放坦率地讨论各自的 ROI 该怎么看。

[10:16] Patrick O'Shaughnessy

And because we can allocate that compute so dynamically, we can make changes.We can make adjustments in that on a relatively short time horizon.The efficiency thing is so interesting to me.I'm curious if you have a sense of how much more efficient you are versus your own internal benchmarks from a year ago or something,or versus others that you have some sense of how efficient they are.How do you measure what efficiency means?

而且因为我们可以如此动态地分配算力，我们随时可以调整，在相对较短的时间尺度上做出改变。效率这件事我特别感兴趣。我很好奇你有没有一个概念：跟你们自己一年前的内部基准（benchmark）相比，你们的效率提升了多少？或者跟你们多少有所了解的其他公司相比呢？你们是怎么衡量「效率」这件事的？

[10:38] Krishna Rao

There's a couple of different ways I would think about it.From a model perspective, I think the analogy people have when these new models come out is they're kind of like cars.You had a sedan before, and then you might have the higher inversion of that sedan, and you're moving up the chain.And I think that is true in terms of model intelligence.The place that analogy kind of breaks down a little bit is people think, okay, I'm going from the sedan to the sports car.We get much less fuel efficiency.I'm not going to buy the sports car for the gas mileage.In our case, we actually see huge improvements in capability, but also in model efficiency.And so if you look at going from Opus 4 to 4.5, 4.6, and now 4.7, they're not equal.But each one of those leaps to a new model has a multiplier in terms of how much more efficient it is at processing tokens effectively.And that just doesn't serve customers.That also helps us internally as well.Because if you think about if we're doing reinforcement learning on the model,it's basically inference within a sandbox with a reward function.And so if the model is better at more efficient inference, that RL is more efficient as well.We're able to do this kind of win-win where the customer is getting more capability when we release a new model.

我会从几个不同的角度来想这件事。从模型的角度看，我觉得人们在新模型发布时常用的类比是汽车：你原来有一辆轿车，然后可能换成同一款轿车的高配版本，一级一级往上走。就模型智能而言，这个类比是成立的。它稍微失效的地方在于，人们会想：好，我从家用轿车换到跑车，那燃油经济性肯定差多了——反正我买跑车又不是图省油。但在我们这里，我们实际看到的是能力大幅提升的同时，模型效率也在大幅提升。所以你看从 Opus 4 到 4.5、4.6，再到现在的 4.7，它们并不等价：每一次跃迁到新模型，在实际处理 token 的效率上都是成倍数的提升。而这不只是服务客户，对我们内部同样有帮助。因为你想，如果我们在对模型做强化学习（RL），那本质上就是在一个沙盒里带着奖励函数做推理（inference）。所以如果模型的推理更高效，那 RL 也就更高效。我们因此能做到一种双赢：我们发布新模型时，客户拿到了更强的能力，

[11:45] Krishna Rao

And then we're able to serve that model, sometimes, again, a multiple more efficient than the prior generation.And then when we're in between generations, we're dynamically deploying efficiency improvements in between these more step function model changes.It is always getting more efficient over time.And what fuels that is the research team.So if you think about it, all these things are very connected.These various tasks and workloads that we have internally all fit together in this way of doing R&D for model capabilities,for compute efficiency, for serving customers, and then having internal workloads that can be sped up by using the best models,sometimes models that we haven't released.You said something really important before, which is the returns to being at the frontier are really high.Can you just explain that in as much detail as you can?

而我们提供这个模型的效率，有时又比上一代高出好几倍。而在两代之间的间隙里，我们还会动态地部署效率改进，穿插在这些阶跃式的模型更新之间。它一直在随时间变得更高效。而驱动这一切的是研究团队。所以你想想，这些事情全都是相互连着的：我们内部的各种任务和工作负载，最终都拼进同一件事——为模型能力做研发、为算力效率做研发、服务客户，同时让内部的工作负载被最好的模型加速，有时候用的还是我们尚未发布的模型。你刚才说了一句非常重要的话：站在前沿的回报非常高。你能尽可能详细地解释一下这句话吗？

[12:33] Krishna Rao

It sounds obvious when you say it, but there's certainly been some camps.I can use the six-month-old model, and it's a fraction of the cost, and I'll just use that, and that'll be catching up all the time.And that just hasn't been the case.The second Opus 4.7 comes out, even me as a consumer, the thing you do is you switch it on, or GPT 5.5 comes out, you switch on the new one right away.Like, I want the best.Talk about the returns to being on the frontier and why it's so high.I think it's a couple things.It's every time we have a new model, there's a set of capabilities that are different.People tend to think about model intelligence as IQ.It's a single number.Okay, this model was at 110, and then it goes to 125.We think of it differently.Intelligence for us is multidimensional.It's not just a score.In fact, we find that, yes, everyone publishes their model benchmark cards.We find that a lot of those benchmarks are saturated.We publish it too, but what our measurement is what the customers tell us.What is the real-world capability of this model?

这话说出来好像理所当然，但确实一直有另一派看法：我可以用半年前的模型，成本只有一个零头，我就用它好了，反正它一直在追赶。可事实并非如此。Opus 4.7 一出来，哪怕作为一个普通消费者，我做的第一件事就是切换过去；GPT 5.5 一出来，我也立刻切到新的。我就是想要最好的。请讲讲站在前沿的回报，以及为什么它这么高。我觉得有这么几点。每次我们出新模型，都会带来一组不一样的能力。人们倾向于把模型智能理解成 IQ——一个单一的数字：好，这个模型原来是 110，现在到了 125。我们的看法不同。对我们来说，智能是多维的，不是一个分数。事实上我们发现，是的，大家都会发布自己模型的基准测试成绩卡，我们也发，但我们发现其中很多基准测试已经饱和了。我们真正的衡量标准，是客户告诉我们的东西：这个模型在真实世界里的能力究竟如何？

[13:27] Krishna Rao

As we've released better and better models, what we've seen is it's not just the outright intelligence.It's also the ability to do long-horizon tasks.It's the ability to use tools or computer use.It's the ability to do things for agentic tasks that have specific value even faster.In some sense, if you have two employees and they're maybe both equally capable, someone takes a week to do an assignment, someone does it in a day.Well, that second person, if they're continuing to do that, can be seven times better.They might be equally capable at something, maybe just take longer times to do it.All of those factor in to then how customers experience it.And what we found very consistently is by releasing new models, the TAM is unlocked in a unique way.More TAM gets unlocked, more use cases are possible.And a good illustration of that is this last four months that we've had at the company.We started the year with about $9 billion of run rate revenue, and we ended the quarter with worth of $30 billion of run rate revenue.That kind of a change is really enabled by these model intelligence leaps and then the products that we build around them.And so that's what I mean by the returns to frontier intelligence are really high.

随着我们发布越来越好的模型，我们看到的不只是纯粹的智能提升，还有完成长周期任务的能力，使用工具或电脑操作（computer use）的能力，以及更快完成那些有特定价值的智能体式（agentic）任务的能力。某种意义上，如果你有两个员工，两人能力也许相当，但一个人要花一周才能完成一项任务，另一个人一天就做完了——那第二个人如果能持续这样，就等于强出七倍。他们在某件事上也许能力相当，只是花的时间不同。所有这些因素最终都会汇入客户的实际体验。而我们非常一致地发现：每发布新模型，潜在市场规模（TAM）都会以一种独特的方式被打开——更多的 TAM 被释放，更多的用例成为可能。一个很好的例证就是公司过去这四个月：年初时我们的年化收入（run rate revenue）大约是 90 亿美元，而这个季度结束时，年化收入已经超过 300 亿美元。这样的变化，正是由这些模型智能的跃迁、以及我们围绕它们打造的产品所驱动的。这就是我说「前沿智能的回报非常高」的意思。

[14:36] Krishna Rao

I think that's unique to enterprise.In consumers, sometimes you don't see that as readily, that consumers really are pushing the limits of what the models can do.Whereas in enterprise, our customers are always now, it started with coding, but it's really expanded beyond that very meaningfully.But each model generation gives you the chance to do more with it, to do it better, to do it more efficiently.And customers see that, and then they invest really heavily in more tokens with the newer models.And we just have seen that cycle play out again and again.And that's a core thesis of our business that, especially in enterprise, the returns to frontier intelligence are not slowing down.Vanta automates security and compliance for over 16,000 fast-moving companies like Ramp, Cursor, and Harvey, keeping them audit-ready around the clock.It's the number one agentic trust platform, and it now helps companies like yours watch for the risks that show up between audits, across your vendors, your AI tools, and your whole environment.Every new tool your team signs up for, every vendor that turns on AI features, is an opportunity for something to go wrong.And most security programs weren't built for AI's pace of growth.

我认为这一点在企业市场尤其独特。在消费端，你有时候没那么容易看到消费者真的在把模型能力推到极限。而在企业市场，我们的客户如今始终在这么做——一开始是从写代码开始的，但现在已经非常显著地扩展到了编码之外。每一代模型都给你机会用它做更多的事、做得更好、做得更高效。客户看得到这一点，于是他们在新模型上大手笔投入更多的 token。我们看到这个循环一遍又一遍地上演。这也是我们生意的一个核心论点：尤其在企业市场，前沿智能的回报并没有放缓。Vanta 为超过 16,000 家快速成长的公司（比如 Ramp、Cursor、Harvey）自动化安全与合规，让它们全天候处于随时可接受审计的状态。它是排名第一的智能体式信任平台，如今还能帮你这样的公司盯住两次审计之间冒出来的风险——覆盖你的供应商、你的 AI 工具和你的整个环境。你的团队每新注册一个工具、每一家供应商每打开一项 AI 功能，都可能成为出事的口子。而大多数安全体系当初并不是为 AI 这种增长速度设计的。

[15:41] Patrick O'Shaughnessy

The Vanta agent works like a 24-7 GRC engineer in the background, finding issues, drafting fixes for you, and cutting vendor assessment time by up to 50%.Whether you're a fast-growing startup or a global enterprise, Vanta helps you earn and prove trust.Invest like the best listeners get a special offer for $1,000 off at vanta.com slash invest.Ridgeline is the first end-to-end system of record with embedded AI for investment management firms, running portfolio accounting, reconciliation, reporting, trading, and compliance on one unified platform.Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software.Which is why I believe that firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform.If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation.You can request a demo at ridgeline.ai.The things that push that frontier is like a sci-fi story or something from books I was reading when I was growing up.

Vanta 的智能体就像一位 7×24 小时在后台工作的 GRC（治理、风险与合规）工程师，帮你发现问题、起草修复方案，并把供应商评估所需的时间最多缩短 50%。无论你是高速成长的初创公司还是全球性大企业，Vanta 都能帮你赢得并证明信任。《Invest Like the Best》的听众可享专属优惠，在 vanta.com/invest 立减 1,000 美元。Ridgeline 是首个面向投资管理机构、内嵌 AI 的端到端记录系统，在一个统一平台上运行组合会计、对账、报表、交易与合规。各家机构正在从旧有技术迁移到 Ridgeline，因为相比投资管理软件领域的任何其他产品，Ridgeline 的 AI 功能都遥遥领先。这也是为什么我相信，在 AI 时代能够胜出的机构，将是那些跑在 Ridgeline 统一平台上的机构。如果你认真对待自家机构的 AI 战略，Ridgeline 就应该进入这场讨论。你可以在 ridgeline.ai 申请演示。推动那道前沿向前的这些东西，简直像科幻小说，或者像我小时候读的那些书里的情节。

[16:47] Patrick O'Shaughnessy

It seems as though in the major labs, we've reached this point, someone on your team said it recently, of recursive self-improvement where the models themselves are building and doing a lot of the research to do the next generation of improvement.If I think about the frontier that you're pushing and OpenAI is pushing and compare that to the open source models, that maybe the gap will widen as a result of you getting there first to this like recursive thing.How do you think about that?

看起来在几家主要实验室里，我们已经走到了这样一个节点——你团队里有人最近就这么说过——即递归式自我改进：模型本身在构建、并在很大程度上承担着下一代改进所需的研究工作。如果我想想你们在推的前沿、OpenAI 在推的前沿，再对比开源模型，那么由于你们率先抵达了这种递归状态，差距也许会因此拉大。你怎么看这件事？

[17:15] Krishna Rao

Tell us how we should think about this idea of recursive self-improvement in the models themselves because it seems like getting there first is incredibly important because then you just can continue to separate yourself versus those that haven't reached it yet.We do see progress accelerating.I can't speak for other companies, but for us, the scaling laws are alive and well, and we're seeing that with releases more recently like Mythos.But right now within the company, 90 plus percent of our code is actually written by Cloud Code.A lot of Cloud Code's code is written by Cloud Code.And so you think of this as why do we allocate compute internally?

请讲讲我们该如何理解「模型自身的递归式自我改进」这个概念，因为看起来率先抵达这一点极其重要——一旦到了，你就能持续把自己和那些还没到的人拉开距离。我们确实看到进展在加速。我不能替别的公司说话，但对我们而言，规模定律（scaling laws）依然活得好好的，我们在 Mythos 这类较近期的发布上也看到了这一点。而现在在公司内部，我们 90% 以上的代码实际上是由 Claude Code 写的，而 Claude Code 自己的代码，也有很大一部分是 Claude Code 写的。所以你可以由此理解：我们为什么要把算力分配到内部？

[17:48] Krishna Rao

Why would we forego revenue for it?It's because the models themselves are helping us to build that next generation of models.In addition to this capability leap that you would have from the scaling laws, talent is really important.And that talent with the best models can really accelerate the development of the capabilities.And we're really seeing that.We don't really think about models as closed or open.We think of them as frontier or not.The ones that are at the frontier clearly are capturing this economic value, driving meaningful ROI for customers.We are just investing behind that thesis.And that means both compute, but it also means talent to use that compute and use our own models to really accelerate the development.And that's been something we've been doing for a long time.It's not a new phenomenon, but we're seeing the fruits of that in everything we've done.The other piece of it is to, it's not just the models, it's the products that get built on top of them.We had 30 different product and feature releases in January.The pace of that has accelerated as well.And that's enabled in part by utilizing the models with the talent that we have to accelerate ways to access this underlying intelligence.

我们为什么愿意为此放弃收入？因为模型本身正在帮我们打造下一代模型。除了规模定律带来的这种能力跃迁之外，人才也非常重要。而这些人才配上最好的模型，能真正加速能力的开发。我们确实正在看到这一点。我们其实不太用「闭源还是开源」来看待模型，我们看的是「在不在前沿」。那些站在前沿的模型，显然正在捕获这份经济价值，为客户带来实打实的 ROI。我们就是在这个论点后面下注。这既意味着算力投入，也意味着人才投入——用好这些算力、用好我们自己的模型来真正加速开发。这件事我们已经做了很久，不是什么新现象，但我们在做过的每一件事上都看到了它结出的果实。另一面是，不只是模型，还有建在模型之上的产品。我们光是一月份就发布了 30 项不同的产品和功能更新，这个节奏同样在加快。而这在一定程度上，正是因为我们用模型加上我们的人才，去加速找到访问这份底层智能的各种途径。

[18:54] Patrick O'Shaughnessy

That's kind of our theory of the case on the product side.How do you think about this weird world where you mentioned the talent and the leverage and they're not writing code themselves and CloudCode's writing its own code?

这大致就是我们在产品这一侧的判断逻辑。你怎么看这个有点诡异的世界——你提到了人才和杠杆，可他们自己并不写代码，是 Claude Code 在写自己的代码？

[19:03] Patrick O'Shaughnessy

It seems like the last step of that would be you don't even need the talent to tell the thing what to do.It just figures out what to do on its own.And that's like the ultimate thing then just runs and is only constrained by computer or something.Am I being too crazy about that?

看起来这条路的最后一步会是：你甚至不再需要人才去告诉它该做什么，它自己就能想清楚该做什么。那这东西就成了终极形态，一直跑下去，只受算力（compute）之类的东西约束。我这么想是不是太疯了？

[19:15] Krishna Rao

Or is that future possible, do you think?The core of our company is still a research lab.I think it's maybe not as well understood.Maybe it's getting more understood from the outside.But we're doing experiments.We are doing things that push the limits of what our models can do.And that research and that engine is upstream of everything else that we've talked about.That is enabled by the models today.It's not entirely done by the models.Over time, we think that the models will get better.They'll be more helpful in that process.But having the best talent to set the direction and to help effectively guide that process, not just the priorities, but some of the new areas of discovery, it just actually makes that research talent even better.I think of it as accentuating and accelerating the talent that we already have.We talk a lot about how talent density beats talent mass.And I think that's true here.Like we want the densest collection of AI research talent and inference engineering talent.And that enabled with the best models is a really winning combination.How are scaling laws talked about internally?

还是说你认为这个未来是可能的？我们公司的内核仍然是一家研究实验室。我觉得这一点可能还不太被理解，也许从外部看正在被越来越多的人理解。但我们在做实验，我们在做那些把模型能力推到极限的事。这套研究、这台引擎，位于我们刚才聊到的一切的上游。它今天是被模型赋能的，但并非完全由模型完成。随着时间推移，我们认为模型会越来越好，在这个过程中也会越来越有帮助。但拥有最好的人才去设定方向、去有效地引导这个过程——不只是定优先级，还包括开辟一些新的探索领域——这实际上会让研究人才本身变得更强。我把它理解成对我们已有人才的放大与加速。我们经常讲人才密度胜过人才规模，我认为这在这里同样成立。我们想要的是密度最高的一群 AI 研究人才和推理工程人才，而这样一群人再配上最好的模型，就是非常能赢的组合。在公司内部，你们是怎么谈论规模定律的？

[20:20] Patrick O'Shaughnessy

The sort of consensus has been you've got different components of them.You've got pre-training, you've got post-training, you've got reasoning.And that all of these are kind of moving at different paces.And to hit a true wall, they would all need to fall down.That's sort of like how the world is now conceptualizing scaling laws.How are they talked about internally?

外界大致的共识是：规模定律有几个不同的组成部分——预训练（pre-training）、后训练（post-training），还有推理能力（reasoning）——而这几块推进的速度各不相同；要真的撞上一堵墙，得这几块同时塌掉才行。这大概就是当下世界对规模定律的理解方式。在你们内部，它是怎么被谈论的？

[20:36] Krishna Rao

How do you think about them?We look at models at various points in their development.So we can see during a pre-training run, how does this model compare to a prior model that we did on these kind of loss curves?

你自己是怎么看的？我们会在模型发展的不同阶段去观察它。比如在一次预训练运行的过程中，我们能看到这个模型在这类损失曲线上，跟我们之前做的某个模型相比处在什么水平。

[20:49] Krishna Rao

And that gives us a sense for model capability.You can do the same thing as you think about RL.Probably as importantly is when customers get their hands on it.What are they seeing?Where are they identifying pain points?

这就能让我们对模型能力有一个判断。做 RL 的时候你也可以做同样的事。可能同样重要的是，当客户真正上手用起来之后：他们看到了什么？他们在哪里发现了痛点？

[21:02] Krishna Rao

And those pain points become like training targets for us.We don't train on customer data in the enterprise side, on the prosumer side.It's only if you opt in.But customers tell us things like, hey, I wish the model were better at this.Or I had this particular place where it got stuck.And I could build this other product, but the capability needs to be further than that.What we usually tell them is build your product for that because we're going to, on the R&D side, improve that over time.And so there is this connected loop.But internally, we're always looking at different models that are being trained, different snapshots that we have,and comparing them internally and to a lesser extent externally against our own measure and then ultimately how our customers view them as well.And it feels like there's just no slowdown in the scaling laws themselves.Is that a fair characterization?

这些痛点就成了我们的训练目标。企业侧我们不拿客户数据训练，消费者与准专业用户（prosumer）侧也只有在用户主动选择加入（opt in）时才会用。但客户会告诉我们这样的话：我希望模型在这件事上更强；或者我在某个具体环节卡住了；又或者我本可以再做一个产品，但模型能力还得往前再走一步。我们通常会跟他们说：就照着那个目标去做产品，因为我们在研发（R&D）这一侧会随时间把那部分能力提上去。所以这里存在一个连通的闭环。而在内部，我们一直在观察正在训练的不同模型、手上的不同快照（snapshot），拿它们相互比较，也在较小程度上跟外部比较，用我们自己的衡量标准，最终还要看客户怎么评价它们。感觉规模定律（scaling laws）本身完全没有放缓的迹象，这个说法公平吗？

[21:46] Krishna Rao

For us, that's a fair characterization.Obviously, a bunch of the authors of the scaling laws papers are amongst our founders.Notwithstanding that, we can be a bit of a skeptical bunch.We hold ourselves to a really high standard.It's very kind of scientific method and people are constantly challenging previously held assumptions.But from what we see, the scaling laws are not slowing down.So if that's true, you said before it's hard for humans to be exponential in their thinking and not linear.That continues to be true for however many more turns of the crank here.How do you do that thing of not thinking linear and thinking exponential yourself in your job and for the business?

对我们来说，这个说法是公平的。显然，规模定律那几篇论文的一批作者就在我们的创始人当中。尽管如此，我们这群人还是可以相当怀疑主义的。我们对自己的要求非常高，非常讲科学方法，大家不断挑战此前持有的假设。但就我们看到的情况，规模定律并没有放缓。既然如此——你前面说过，人很难用指数思维去想问题，容易只想成线性。不管接下来还要摇多少轮曲柄，这一点都还会成立。那在你自己的工作里、在经营这门生意时，你是怎么做到不按线性、而按指数去思考的？

[22:18] Patrick O'Shaughnessy

The implications are really hard to reason through.Exponential growth rate is one thing, but exponential growth of capability.I don't even know how to get my head around it.How do you get your head around it?

它的种种含义真的很难推演清楚。指数级的增长率是一回事，但能力的指数级增长——我压根不知道该怎么把它装进脑子里。你是怎么想明白的？

[22:27] Krishna Rao

We think about the world as scenarios.It's very hard to have a point estimate in this business.And then having a very low bar for updating your current priors, basically, or your current perspective.Because it could be the case that something a month ago was true that's just not true today.And that breaks your model.And you have to go back and update it.And so this old like, well, we'll forecast once a quarter and we'll revisit this in three months at the next board meeting.That doesn't work for our business.It's so dynamic that we have to always think about, oh, our models couldn't do this before and they could do this now.What does that mean for the TAM?

我们把世界当成一组情景（scenario）来看。在这门生意里，很难给出一个点估计（point estimate）。然后就是把更新自己当前先验、当前判断的门槛放得非常低。因为很可能一个月前还成立的事情，今天就不成立了，那你的模型就崩了，你得回过头去更新它。所以那种老一套——我们一个季度做一次预测，三个月后下次董事会再来复盘——在我们这门生意里行不通。它太动态了，我们必须时刻在想：哦，我们的模型以前做不到这件事，现在能做到了，那这对潜在市场规模（TAM）意味着什么？

[23:02] Krishna Rao

We've seen this in coding first.Starting with around Sonnet 3536, we started to see this really remarkable jump in capability, which was then followed by adoption and usage and revenue.It was a little hard to predict that, but now we can use coding as an analog for a lot of what's happening elsewhere in the economy and elsewhere in our business.We kind of look at pattern recognition in our own business to try to predict what's going to happen in the future.Literally 15 minutes before you got here, the news came out about your partnership with XAI in the Tennessee facility.But it makes me curious about how you are canvassing the world.That is an opportunity you decided to do.I'm sure there's a universe of things that you've explored.What is the strategy for trying to get more in creative ways?

这一点我们最先是在编程上看到的。大概从 Sonnet 3.5、3.6 开始，我们看到能力出现了非常惊人的跃升，紧随其后的是采用、使用量和收入。当时这有点难预测，但现在我们可以把编程当作一个类比，去看经济中其他领域、以及我们业务其他部分正在发生的事。我们靠在自己业务里做模式识别，来试着预测未来会发生什么。就在你到这儿之前 15 分钟，你们和 xAI 在田纳西那座设施合作的消息刚出来。这让我很好奇你们是怎么在全世界范围内做地毯式排查的。这是你们决定去做的一个机会，我相信你们探索过的东西还有一大堆。用更有创造性的方式拿到更多算力，这里的策略是什么？

[23:46] Krishna Rao

Bring us a little bit more into that.We announced a partnership with SpaceX for their Colossus facility in Memphis.We're really excited about that.It's going to allow us to continue to expand, especially on the consumer and prosumer side.But that's just one example of us just, as you said, looking for near-term compute wherever we can get it.As the compute base grows, that near-term compute becomes a smaller and smaller fraction of what's available and what's out there.We look at it as, can we deploy that compute that's available productively?

能不能多带我们进去一点。我们宣布了与 xAI 的合作，使用他们位于孟菲斯的 Colossus 设施。我们对此非常兴奋。它会让我们能够继续扩张，尤其是在消费者和准专业用户这一侧。但这只是一个例子，正如你说的，我们在到处寻找能拿到的近期算力（near-term compute）。随着算力基数变大，这类近期算力在可获得总量里占的比重会越来越小。我们看待它的方式是：这些能拿到的算力，我们能不能有产出地部署出去？

[24:15] Krishna Rao

Sometimes the answer is yes and sometimes it's no.But if we can, then we look at the economic return on it based on what its price, what duration we have it for, where it's located, what type of compute it is, and how efficiently we can run it.So we have kind of a process to assess.And that same process, by the way, we use to assess longer-term deals as well.So last month, we signed a 5-gigawatt deal with Google and with Broadcom for TPUs starting in 2027.We also signed a deal with Amazon for Tranium for up to 5-gigawatts as well.It was an over $100 billion commitment.And a lot of that compute is actually already landing and will land in the rest of this year and next year.And so if you think about it, it's a bit of this layer cake of compute that's starting at different times with different capabilities.And we're very dynamically comparing that compute.It's price performance over time.That's really, really important to us.When it lands and what we think we can do with it internally in the business.And so there's so many different variables you have to optimize for around what compute it is at what cost and over what time horizon.But we have a pretty dynamic way of looking at near-term compute and then medium to long-term compute.

有时答案是能，有时是不能。但如果能，我们就会看它的经济回报——基于它的价格、我们能用多久、它在什么位置、是哪一类算力、以及我们能多高效地把它跑起来。所以我们有一套评估流程。顺带一提，同一套流程我们也用来评估更长期的交易。比如上个月，我们和谷歌、博通（Broadcom）签了一笔 5 吉瓦（gigawatt）的 TPU 交易，2027 年开始。我们还和亚马逊签了一笔最高 5 吉瓦的 Trainium 交易，是一笔超过 1000 亿美元的采购承诺。其中很大一部分算力其实已经在陆续到位，今年剩下的时间和明年还会继续落地。所以你可以把它想成一个算力的千层蛋糕，不同层在不同时间点开始，带着不同的能力。我们非常动态地在比较这些算力：它随时间变化的性价比（price performance），这对我们真的非常非常重要；它什么时候落地，以及我们认为在业务内部能拿它做什么。所以围绕「是哪种算力、什么成本、什么时间跨度」，你要优化的变量非常多。但对于近期算力和中长期算力，我们有一套相当动态的看法。

[25:23] Patrick O'Shaughnessy

But the things we're assessing are largely the same.What is different is just the time horizon.What about the tradeoff use of price per performance?Tradeoff between like cost per token or something throughput and speed.Both are important, of course.From the customer perspective, they care about both.With speed, it probably unlocks some capability and use cases that are really interesting that we don't know about yet as these things get faster.Can you talk a little bit about that tradeoff in compute as you're assessing it?

但我们评估的东西大体是一样的，不同的只是时间跨度。那性价比里的取舍呢？比如每 token 成本与吞吐量、速度之间的权衡。当然两者都重要。从客户视角看，他们两个都在乎。随着这些东西变快，速度大概会解锁一些我们现在还不知道的、非常有意思的能力和用例。你能不能讲讲你们在评估算力时的这个取舍？

[25:48] Krishna Rao

As we look across three different chip platforms, we also have multiple generations of chips within it.It could be TPU, V5E, and V6, and V7, and Trainium 2, Trainium 3.All of them are at different places on the price performance curve.And then we importantly look at how we will utilize it.Price performance is important because of efficiency.Speed is also important for certain use cases as well.So we look at the compute down to a very granular level in terms of what it can deliver for us and when.And that's something that we do.Again, our compute team leads that, but we closely collaborate across the business to say like,where do we need this compute and for what?

我们要在三种不同的芯片平台之间做比较，而且每种平台里还有多个世代的芯片。可能是 TPU 的 v5e、v6、v7，也可能是 Trainium 2、Trainium 3。它们全都处在性价比曲线上的不同位置。然后很重要的一点是，我们会看自己将如何利用它。性价比重要，是因为它关乎效率；速度对某些用例同样重要。所以我们会把算力拆到非常细的颗粒度，去看它能在什么时候为我们交付什么。这是我们一直在做的事。同样，这由我们的算力团队牵头，但我们会跨业务紧密协作，去问：我们在哪里需要这块算力、拿它做什么？

[26:24] Krishna Rao

Okay, we might need CPUs for RL.Okay, how much of that do we need and where and what's the nature of it?We might need this more leading edge compute and we're going to deploy it for our best and fastest models or for training them.From our perspective, it's customer demand, but it's also really down quite granular in terms of what is each chip best for?

好，我们可能需要 CPU 来跑强化学习（RL）。那需要多少、放在哪里、性质是什么？我们可能需要更前沿的算力，用来部署我们最好、最快的模型，或者用来训练它们。从我们的视角看，一方面是客户需求，另一方面也确实要细到每一款芯片最适合干什么。

[26:44] Patrick O'Shaughnessy

And then what will we have when?I'm always so curious by the metabolism of, in this case, Anthropic for new compute.How fast you could take if I airdropped on you twice the compute that you have tomorrow?

以及我们在什么时候会拿到什么。我一直特别好奇——就以 Anthropic 为例——一家公司消化新算力的「代谢率」有多高。如果我明天空投给你现在两倍的算力，你能多快吃下去？

[26:57] Patrick O'Shaughnessy

Like, would you consume that in?How fast would you consume that?If I airdropped 10 times the compute on top of you, how fast would you consume it?Can you calibrate us on that?It feels like demand's unlimited between these three uses, training, internal, customer demand.Everyone's saying the same thing.Shortages everywhere.Memory stocks, mooning.Is it that extreme that like if you 2x or 5x or 10x the amount available to you tomorrow, you just like more or less instantly consume it?

你会把它消耗掉吗？多快消耗掉？如果我空投给你 10 倍的算力，你多快能消耗完？能不能给我们校准一下？感觉在训练、内部使用、客户需求这三种用途之间，需求是无限的。所有人都在说同一件事：到处都缺货，存储芯片股票一路飙升。真的这么极端吗——如果明天你手上可用的算力翻 2 倍、5 倍、10 倍，你基本上会立刻把它消耗掉？

[27:21] Krishna Rao

This goes back to like how we use it and the fungibility of it.So the answer is we're constrained across those use cases.A year or two ago, it would have been harder to consume, especially like a heterogeneous compute drop in your example.Really quickly because these chip platforms are different and they are different.Some are harder to operate.Some of them have idiosyncrasies in terms of how we use it.I would say today that getting a bunch more compute, I think it would be deployed very rapidly across those different use cases.We probably have the same allocation or calibration that we do with compute today.But it's become a lot easier for us to spin up very quickly and deploy almost any type of compute.And that's something, again, we think is a real advantage.One of the interesting tensions and trade-offs that I'm fascinated to hear how you think through is between the platform approach where I build my business on top of Claude and it powers my thing versus you doing the thing that I wanted to build.This is like the classic Claude design versus Figma or something like this.How do you think about the right balance of how deep into the application layer you should go versus just being a pure enabling layer of we're going to provide the reasoning engine and the intelligence and world go forth and build whatever you want, pay us through the API or whatever.

这又回到我们怎么用它、以及它的可互换性（fungibility）。答案是：在这几类用途上我们都受限。一两年前要消化掉会更难，尤其是像你举的例子那样，突然空投来一批异构算力——想很快消化掉会更难，因为这些芯片平台各不相同，确实有差异：有些更难运维，有些在使用方式上有自己的怪脾气。我要说，今天如果一下子拿到大量算力，我认为它会非常迅速地部署到这些不同用途上。我们大概会沿用今天在算力上的同一套分配或校准。但对我们来说，快速启用、部署几乎任何类型的算力，已经容易得多了。这一点我们同样认为是一项真正的优势。有一组很有意思的张力和取舍，我特别想听你怎么想：一边是平台路线——我在 Claude 之上建自己的生意，由它驱动我的产品；另一边是你们自己去做那个我本想做的东西。这就像经典的 Claude Design 对 Figma 这类问题。你怎么看这个平衡点——你们该往应用层走多深，还是就做一个纯粹的赋能层：我们提供推理引擎和智能，世界上的人尽管去建你们想建的东西，通过 API 之类的方式付钱给我们。

[28:34] Krishna Rao

That seems like a fascinating internal discussion and tension to some degree.The way I would think about it is most of what we're building is platform.We think that there's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value.We think that's what we're setting up for today.It's maybe akin to the early days of AWS.If you think about the Claude platform and all the tools and services that are now built into it, because it's not just the raw model access, it is prompt caching and the ability to use virtual machines and Claude code being called within there or dispatch on and on.The Claude agents SDK, managed agents, all of these are effectively, I think of as vectors to access that model intelligence for other companies to build into their own products.That's most of what we're focused on and really most of where we think the business is going from where we are today.We will also build our own applications on that same platform where a couple of things are true.Number one, if we feel like we have a vision into where the models are going and we can kind of demonstrate that and create customer value in that, that might be something like Claude code.

这在内部想必是一场很有意思的讨论，也带着某种程度的张力。我的想法是：我们在建的东西绝大部分是平台。我们认为，有太多例子表明平台能积累很多价值，但在这个平台上做东西的客户其实能积累更多价值。我们认为这正是我们今天在铺设的局面，可能有点像 AWS 的早期。想想 Claude 平台以及现在内建其中的所有工具和服务，因为它不只是裸模型访问，还有提示缓存（prompt caching）、使用虚拟机的能力、可以在里面调用 Claude Code 或做任务分派，等等等等。Claude Agent SDK、托管智能体（managed agents），所有这些在我看来实际上都是别的公司把模型智能接进自己产品的通道。这才是我们主要的发力点，也是我们认为业务从今天出发的主要走向。我们也会在同一个平台上建自己的应用，条件是有几件事同时成立。第一，如果我们觉得自己看得到模型正在往哪里走，并且能把它演示出来、在其中创造客户价值，那可能就是像 Claude Code 这样的东西。

[29:41] Krishna Rao

We are able to say, actually, a lot of what's out there in the market was developer led.Claude code is a platform that's Claude led.And we think the models can't quite do that today when it was launched, you know, a little over a year ago.We think they'll get there and they have.One is kind of building ahead to model capabilities.The second is thinking about ways to like demonstrate value for the ecosystem that others might emulate.If you think about Claude for financial services or Claude for life sciences or even something like Claude security, these are ways in which we've composed the platform.Again, we're building on the same platform as our customers.That creates a level playing field.We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well, which is why you've seen as we've launched some of these products, we've done them in a collaborative partnership oriented way.Whether that be on the security side or sign or financial services, we've partnered with the ecosystem.So I think of our strategy as mostly horizontal.We'll build vertical where we think we have some value to add or a perspective that's useful or a way to demonstrate to the market how we think about our platform adding value.

我们能够说：市场上很多东西是开发者驱动的，而 Claude Code 是一个由 Claude 驱动的平台。它一年多前发布的时候，我们认为模型当时还不太做得到，但我们相信它们会做到——现在也确实做到了。所以第一条是「往模型能力的前面建」。第二条是想办法为生态演示价值，让其他人可以效仿。比如 Claude for Financial Services（金融服务）、Claude for Life Sciences（生命科学），甚至像 Claude 安全这样的东西，都是我们把平台组合起来的方式。同样，我们和客户是在同一个平台上开发，这创造了一个公平的竞技场。我们也认为这些领域会沉淀出巨大价值，客户能赢，我们也能赢——所以你会看到我们在推出其中一些产品时，是以协作、伙伴导向的方式做的。无论是在安全方向、生命科学还是金融服务，我们都和生态伙伴一起做。所以我把我们的策略理解为主要是横向的。在我们认为自己能加点价值、或者有个有用的视角、或者能向市场演示我们怎么理解平台创造价值的地方，我们才会做纵向。

[30:46] Krishna Rao

A lot of the value is going to accrue to the customers that are building on top of it.Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers.How much do you care that it's just a reality that people are scared of you?

很多价值会沉淀到在它之上做东西的客户那里。我们的目标是造出最好的模型，然后做出让这份智能在客户内部扩散开来的产品、工具和服务。有一个现实是——人们害怕你们。这件事你在多大程度上在意？

[31:02] Patrick O'Shaughnessy

There is a sense that because you control the most essential piece of these new applications, the underlying intelligence reasoning engine, that may totally be true and maybe already is true that more of the value is accruing on top of the Anthropic platform than is being captured by it.But nonetheless, it's still scary to imagine.And I guess maybe you could say something similar about cloud and AWS or something like that.But how much do you think and care about the fact that some of your would-be customers or existing customers are in fact scared of you as a competitor?

有一种感觉是：因为你们掌握着这些新应用里最本质的那一块——底层的智能与推理引擎。可能完全属实、甚至现在就已经属实的是，沉淀到 Anthropic 平台之上的价值，比平台自己捕获的还要多。但即便如此，想想还是让人害怕。我猜你也可以对云和 AWS 之类的说类似的话。但对于「你们的潜在客户或现有客户其实把你们当成一个令人生畏的竞争对手」这个事实，你到底想得多不多、在意得多深？

[31:32] Krishna Rao

Part of what is hard in this business is it's changing so quickly.So the model capabilities sometimes even surprise us.And so when we release models or products on top of that, there is an element of what's happened in prior waves over the course of 5 years, 10 years, 20 years.It's happening in months now.When we release things, people are also surprised by it in some ways in the same way that we were surprised by it.But I think fundamentally, what we are trying to do is be very partner-oriented towards the ecosystem.And that means that we have early access programs.We work very closely with customers.We listen to them about what capabilities they want.That doesn't mean that the things we release are sometimes not moments where you're like,wow, that's way more powerful than I thought it would be.Or I didn't realize the models would be able to do that this quickly.Part of that is a reality of where we are in this cycle, in this development of intelligence.Part of it is also like we want to make those capabilities really accessible.And that should accrue a lot of value to customers as well.And customers that are front-footed on that and adopt and also ones that are building and using the tools that we offer on our platform,

这门生意里难的一点是它变化太快。模型能力有时连我们自己都会感到意外。所以当我们发布模型、或基于模型的产品时，会有这样一种成分：过去 5 年、10 年、20 年里一波波发生过的事情，现在压缩到几个月里发生。我们发布东西的时候，别人在某种程度上也会感到意外——就像我们自己当初也被它惊到一样。但我认为根本上，我们想做的是对生态非常伙伴导向。这意味着我们有早期访问计划，我们和客户合作得非常紧密，我们听他们讲想要什么能力。这并不代表我们发布的东西就不会出现那种时刻——「哇，这比我以为的强太多了」，或者「我没想到模型这么快就能做到这个」。这一部分是我们所处的这个周期、这段智能发展进程的现实。另一部分也是因为我们想把这些能力做得非常可及，而这同样应该给客户沉淀出很多价值。那些在这件事上抢先一步、积极采用，同时也在用我们平台上提供的工具去建东西的客户，

[32:40] Patrick O'Shaughnessy

we think we can actually accelerate them.And so I think some of it's a reality of frontier model development,but our approach to it is probably a little different and more partner-oriented.You said before going 9 to 30 in the first quarter, the pace is so insane, which makes me wonder about pricing.The dynamic of how to price tokens or use of the system is so fascinating to me because I think a lot of people a year ago would say price is going to constantly fall.But actually what's happening is it's going up in many cases.This is true at different levels, whether it be the mythos pricing that is quite high because it's so powerful,the cost of an H100, you know, the rental price of a cost of an H100 looks like a smile curve.I'm very curious why if everyone is compute constrained,why everyone doesn't just raise prices a lot to try to find what the right equilibrium is.And so I'd love you to just like riff on pricing, how you think about it, what the trade-offs are, why not raise prices a lot.The company is only a little over five years old.This past March was the third anniversary of the first dollar of revenue into the business.And we only had a frontier model for real for the first time in March of 2024.

我们认为其实是能被我们加速的。所以我觉得，一部分是前沿模型开发的现实，但我们的做法可能有点不一样，更偏伙伴导向。你前面说过第一季度就从 90 亿做到 300 亿，这个节奏疯狂到让我开始想定价的事。怎么给 token、或者对系统的使用定价，这个动态我觉得特别迷人，因为我想一年前很多人会说价格会持续下跌。但实际发生的是，很多情况下价格在上涨。这在不同层面都成立：可能是 Mythos 那种因为太强所以相当高的定价，也可能是 H100 的成本——H100 的租赁价格走出了一条微笑曲线。我很好奇，如果所有人都受算力约束，为什么大家不干脆大幅提价，去试出正确的均衡点在哪里。所以我特别想请你就定价即兴聊聊：你怎么看它、其中的取舍是什么、为什么不大幅提价。这家公司才刚过五年出头。刚过去的这个三月，是这门生意收到第一美元收入的三周年。而我们真正意义上第一次拥有前沿模型，是在 2024 年 3 月。

[33:46] Krishna Rao

So the timescale of these things, it's an important backdrop as I think about it.Our pricing has been relatively stable across Haiku, Sonnet and Opus.And now Mythos is obviously newer.We've made very few pricing changes.The biggest pricing change we made was to bring down the price of the Opus family when we launched Opus 4.5.If we thought about why did we do that, it's really because we found that Opus class models were underutilized relative to their capability.People were trying to often fit an Opus problem into a Sonnet.We were able to serve that very efficiently from our perspective, but actually bring down the price, which made it more accessible to customers.We want our customers to generate a lot of value from it.And they're generating a ton of ROI from our models today.We want that to just continue because our goal is to proliferate this throughout the ecosystem.We think we're in the very, very early innings on all of these use cases.The best way to do that is get this intelligence in the hands of as many businesses from startups to digitally native businesses to the largest companies in the world.Some of that means that you have to make it in a price point that's accessible and that allows them to get a lot of value from it.

所以这些事情的时间尺度，是我思考这个问题时很重要的背景。我们的定价在 Haiku、Sonnet 和 Opus 之间一直相对稳定，而 Mythos 显然更新一些。我们做过的定价调整非常少。最大的一次定价调整，是在发布 Opus 4.5 时把 Opus 系列的价格降下来。为什么这么做？真正的原因是我们发现，相对于 Opus 级模型的能力，它被使用得不足。人们常常想把一个本该用 Opus 的问题硬塞进 Sonnet。从我们的角度看，我们能非常高效地把它服务出去，同时还能把价格降下来，让客户更容易用得起。我们希望客户能从中产生大量价值。他们今天已经从我们的模型里拿到了巨大的投资回报（ROI），我们希望这件事继续下去，因为我们的目标是让它在整个生态里扩散开。我们认为在所有这些用例上，我们都还处在非常非常早的阶段。做到这一点最好的办法，就是把这份智能交到尽可能多的企业手上——从初创公司，到数字原生企业，再到世界上最大的公司。这其中一部分意味着，你必须把它定在一个可及的价位上，让他们能从中获得大量价值。

[34:59] Krishna Rao

And changing the pricing for Opus, you see this Jevons paradox.We lowered the price of it, but the consumption went up way, way more than what you would have expected.And so because we hit that sweet spot for customers, they were able to use it a lot more.We had the efficiency to be able to serve it to customers at scale.And then they were able to build that into their workload such that when we released Opus 4.6, it's a model improvement.They can slot it in.We didn't change the price.So we think pricing stability is important.And we also think that pricing to get that value and to see that kind of Jevons paradox happen is really important.The other component of this is margins and how you think about margins as a business.Again, because this is so unbelievably capital intensive to build these frontier labs, you've got the levers we talked about, which is efficiency, price.Both those things relate to margin.And given how much capital you need, why not just say we want to have a healthy margin and set the price accordingly and maybe that price can come down if efficiency is better or whatever.And I'm just curious how you think about margins as it relates to pricing in the business.We think about what is the return on our compute spend writ large.

而在给 Opus 改定价这件事上，你会看到杰文斯悖论（Jevons paradox）。我们把价格降了下来，消耗量却涨得远远超出你原本的预期。因为我们踩中了客户的那个甜蜜点，他们就能用得多得多；我们也有足够的效率能大规模地把它服务给客户。然后他们就能把它嵌进自己的工作负载里，以至于我们发布 Opus 4.6 时，那是一次模型升级，他们可以直接换上去，而我们没有改价格。所以我们认为定价的稳定性很重要；我们同样认为，用定价去释放价值、去看到杰文斯悖论那样的效应发生，也非常重要。这里另一个组成部分是毛利率，以及作为一家企业你怎么看毛利率。同样，因为建这些前沿实验室是难以置信地资本密集，你手上有我们刚才说过的那些杠杆——效率、价格，这两者都和毛利率相关。既然你需要这么多资本，为什么不干脆说：我们要一个健康的毛利率，然后照着定价，如果效率变好了价格也许还能降下来。我就是很好奇，在这门生意里你怎么看毛利率和定价的关系。我们想的是：我们整体算力支出的回报是多少。

[36:05] Krishna Rao

So that is all of the different workloads that we've talked about, whether it's serving customers.If you think of all of those are kind of in support of revenue over different timescales.If I serve inference, it's in support of revenue today.If I do model development, it might help for a capability that unlocks TAM that drives revenue six months from now.And everything in between.If I do internal acceleration to launch a new product, all of these things are in support of that.Our returns on that compute expense today are robust.We think of it as what is the return on that full envelope of compute.We feel really good about where we are from that perspective.And we're balancing delivering value to customers with also seeing a really, really strong return on that compute ourselves.If you think about when revenue grows, as we mentioned, kind of in Q1, it's not like we onboarded a bunch of new compute in that time period.We talked about compute comes based on a ramp that might have been determined 12 months ago.This idea of a variable cost that's like on the incremental to serve a customer, it doesn't really fit our business.It tries to maybe fit our business into like a software paradigm.

也就是我们前面聊过的所有那些工作负载，无论是服务客户还是别的。你可以把它们都看成是在不同时间尺度上支撑收入。如果我做推理（inference），那是在支撑今天的收入；如果我做模型开发，它可能会催生一项能力，解锁潜在市场规模，从而带来六个月后的收入；以及介于两者之间的一切。如果我做内部加速去推出一个新产品，所有这些也都是在支撑收入。我们今天在那笔算力开支上的回报是稳健的。我们的思路是：整个算力盘子的回报率是多少。从这个角度看，我们对现在的位置感觉非常好。我们在给客户交付价值、和自己看到非常非常强的算力回报之间做平衡。想想收入增长的时候——就像我们提到的第一季度——并不是说那段时间我们接入了一大批新算力。我们说过，算力是按一条可能 12 个月前就定好的爬坡曲线到位的。所以「服务一个客户的边际可变成本」这种概念，其实并不太适用于我们这门生意。它是想把我们的生意硬塞进软件的范式里。

[37:09] Krishna Rao

That's not the case.In actuality, compute is supporting all of these activities.We're really generating a robust return on that compute.And that's our measuring stick.I think it's something where we think of the compute envelope that we have as the thing that is able to govern how much we're able to drive revenue, both over the short term and the long term.If you're this great customer of the compute providers, what does that group need to do to be a great provider to you to help you drive that return?

但事实并非如此。实际情况是，算力在支撑所有这些活动。我们确实在这笔算力上产生了稳健的回报，这就是我们的衡量尺。我认为我们把手上的算力盘子看成那个决定我们能把收入推到多高的东西——无论短期还是长期。既然你是算力供应商的重要客户，那这群供应商要做到什么，才算是能帮你拿到那个回报的好供应商？

[37:38] Krishna Rao

We're fortunate in that we have really great partners in Amazon, in Google, in Microsoft, but also with Broadcom and NVIDIA as well.Our ecosystem, we are the only model that's on all three clouds.Today, we're the only model that has large language lab that's using all three of these chip platforms.These collaborations are much deeper than just procurement.I think that's something that's often lost.If you think about our relationship with Amazon, our teams are deeply embedded with the Annapurna Labs team.We are good users of Tranium.We've spent a lot of time and energy and worked closely with the team and internally.And that's something where we plan capacity together.We do that with the other hyperscalers as well.If you think about the three clouds, they're great distribution engines for us too.We have a really, really robust first party business as well.But these are multifaceted partnerships, whether it be on developing the chips themselves, landing that capacity, serving it, and then ultimately distributing it to the customers.Each one of them has that element to it.They're different and each of them has relative strengths, but we've been able to work really well across those clouds.

我们很幸运，有非常好的合作伙伴——亚马逊、谷歌、微软，还有博通和英伟达（NVIDIA）。在我们的生态里，我们是唯一一个上了全部三家云的模型。今天，我们是唯一一家同时在用这三种芯片平台的大语言模型实验室。这些协作远比单纯的采购更深，我认为这一点常常被忽略。以我们和亚马逊的关系为例，我们的团队和 Annapurna Labs 团队深度嵌合。我们是 Trainium 的重度好用户，我们在这上面花了大量时间和精力，和对方团队以及内部都紧密合作。我们会一起做产能规划，和其他超大规模云厂商也是这么做的。而如果你看这三家云，它们对我们来说也是很好的分发引擎。我们自己的第一方业务同样非常非常稳健。但这些都是多维度的伙伴关系：从芯片本身的开发，到产能落地、到把算力服务出去，再到最终分发给客户。它们每一家都有这些元素。它们各不相同，各有相对优势，但我们在这几家云上都能配合得非常好。

[38:42] Patrick O'Shaughnessy

And then obviously across Broadcom and NVIDIA as well.I'm thinking about your function, like the finance team.I'm picturing this like ROI on compute thing on different horizons with all these complex variables, which makes me wonder, how do you use these powerful tools yourself internally to run your group and the business?

当然，在博通和英伟达那边也一样。我在想你负责的这块职能，也就是财务团队。我脑子里浮现的是那个「算力投资回报」的东西，跨不同时间跨度、带着这么多复杂变量。这让我很好奇：你们自己在内部怎么用这些强大的工具来运转你的团队和整个业务？

[39:00] Krishna Rao

What is the deployment of CloudCode and Cloud in general on the finance team at Anthropic?This is really interesting because we were using CloudCode about a year ago.And, you know, I started asking people, like, is everyone just kind of taking up vibe coding or what is it?

Claude Code 以及 Claude 整体在 Anthropic 财务团队里的部署情况是怎样的？这个真的很有意思，因为我们大概一年前就在用 Claude Code 了。当时我开始问大家：是不是所有人都在搞氛围编程（vibe coding），还是别的什么？

[39:15] Krishna Rao

And we started to use CloudCode as almost like an assistant, a digital coworker, not just for coding tasks.And that actually was early in what eventually became coworker.That was kind of an extension of CloudCode to say what it's done for agentic software development, it should do for all of knowledge work.But then we started to productionize that.And I'm actually really proud.We spend a lot of time with our product team, too.They kind of see how we use it and get input and feedback from that.But today, all of our legal entities, we can produce the statutory financial statements using Cloud.And yes, the human checks it, but all of those financial statements are produced with Cloud.We also have a more real-time platform called AntStats.And it used to take a lot of time to sift through all the data, get to conclusions, write a memo about it, or publish a regular report on what's happening over the course of the day, what's driving it.We now have a library of skills for Cloud that are specific to finance.Last I checked, there were over 70 of them that everyone can kind of access through this common repository.And on top of that, we built an MFR, a monthly financial review skill.And it can produce our monthly financial review.

我们开始把 Claude Code 当作一个助手、一个数字同事来用，不只是用于编程任务。而那其实就是后来 Cowork 的雏形——它算是 Claude Code 的一种延伸：它为智能体式（agentic）软件开发做到的事，也应该为所有知识工作做到。之后我们开始把它产品化。我其实挺自豪的。我们也和产品团队花了大量时间，他们能看到我们怎么用它，并从中拿到输入和反馈。而到今天，我们所有法律实体的法定财务报表都能用 Claude 生成。是的，人还是要复核，但所有这些财务报表都是用 Claude 产出的。我们还有一个更实时的平台，叫 AntStats。以前要把所有数据筛一遍、得出结论、写成一份备忘录，或者定期发布一份关于这一天里发生了什么、由什么驱动的报告，非常费时间。现在我们有一个专门面向财务的 Claude 技能（skills）库，我上次看的时候有 70 多个，所有人都能通过这个公共仓库访问。在此之上，我们还建了一个 MFR，也就是月度财务复盘（monthly financial review）技能，它能直接生成我们的月度财务复盘。

[40:24] Krishna Rao

It's 90% to 95% ready.And then all of our discussion becomes about what do we do?What are the implications?Not what exactly happened because Cloud is not just reporting the weather.It's also helping to think about drivers.Why did the number change in the way it did?

完成度能到 90% 到 95%。然后我们所有的讨论就变成：我们该做什么？这意味着什么？而不是到底发生了什么——因为 Claude 不只是在播报天气，它还会帮着思考驱动因素：这个数字为什么会这样变化？

[40:40] Krishna Rao

And that gives you tremendous insight into the business, both in terms of this like MFR that we do, but also on a daily basis.And so what used to take hours to produce a weekly report for what's driving revenue or what's driving our compute utilization now comes down to 30 minutes.And then we can spend our time on the actual strategic implications of the business.We can also get it in the hands of business leaders much more quickly.The insight engine is a lot faster.I have a dashboard.I look at token usage.Leaderboard?

这会给你对业务极大的洞察，既体现在我们做的这个 MFR 上，也体现在日常。过去要花好几个小时才能做出的周报——关于收入由什么驱动、算力利用率由什么驱动——现在缩短到 30 分钟。剩下的时间我们就能花在业务真正的战略含义上，也能更快把它送到业务负责人手里。这个洞察引擎快多了。我有一个仪表盘，会看 token 使用量。排行榜？

[41:10] Krishna Rao

Yeah, we don't compensate people on it.No one's trying to token max for that.But it's really interesting because some of the most senior people within the finance team are actually the biggest users of tokens.So it is not just the 22-year-old who joined and has a coding background and was doing that on the weekends and brought it to work.It's also people using the tools to change how they're working.Like I think our number one user is our head of tax and he's focused on tax policy engines and automating large parts of the workloads that are happening within the team.So I love seeing that.I tell people, if we're not super users of this, if we're not pushing the limits of it, how can you expect customers to do that?

是的，我们不按这个给人发薪酬，没人会为了它去刷 token 量。但很有意思的是，财务团队里一些最资深的人反而是 token 用量最大的。所以这不只是那个 22 岁、有编程背景、周末自己玩然后把它带到工作里的年轻人。也有人是在用这些工具改变自己的工作方式。比如我们的头号用户是我们的税务负责人，他专注在税务政策引擎、以及把团队内部大量工作负载自动化上。所以我特别喜欢看到这个。我跟大家说：如果我们自己都不是这东西的超级用户，如果我们都不去把它推到极限，你怎么能指望客户去这么做？

[41:46] Patrick O'Shaughnessy

Just as a human, does it freak you out at all?I've heard so many examples like this.It starts to feel like we just start doing the stuff that AI tells us to do in the sales example or the calendar or whatever.And maybe that's great.Maybe it's just such a better coordinator and wide thinker and optimizer than we ever could be that we should do what it tells us to do.But it feels ever so slightly dystopian to me that that reality is coming quickly.I've had examples of it too.It feels kind of cool.Like it's helpful.But at the same time, if I really close my eyes, I'm like, oh, I'm just like doing what it tells me versus me telling it what to do.So it's a really interesting just human dynamic that I'm curious for your take on.I maybe have a slightly different view on it in that I think we've been able to hire great people at the company, but it has made even those incredibly talented people so much more productive.There's a little bit of this.I think of it again, like Jevin's paradox, but for labor, which is that we have people who become incredibly more productive.We've hired a lot more people because of that, because there's no shortage of work to do.And now with the assistance of Claude, people are spending less time in that MFR trying to reconcile some number, but they're actually thinking, oh, how do we reinvest this in the business?

作为一个普通人，这会不会让你有点发怵？这类例子我听过太多了。感觉我们开始变成 AI 让我们干什么就干什么——不管是销售那个例子，还是日历安排，或者别的什么。也许这挺好。也许它就是比我们更会协调、想得更宽、更懂优化，所以我们本来就该照它说的做。但这个现实来得这么快，我总觉得有那么一丝反乌托邦的味道。我自己也遇到过这样的例子，感觉挺酷的，确实有帮助。可与此同时，如果我真的静下心来想，就会意识到：我只是在照它说的做，而不是我在告诉它该做什么。所以这是一种非常有意思的人性层面的互动，我很想听听你的看法。我的看法可能略有不同。我认为我们公司确实招到了很优秀的人，而这项技术让这些本就极有才华的人生产力提升了太多。这里面有点像——我还是那个说法——杰文斯悖论（Jevons paradox），只不过是劳动力版本的：我们的人生产力大幅提升，正因如此我们反而招了更多人，因为要做的事永远不缺。现在有了 Claude 的协助，大家在月度财务复盘（MFR）里花在核对某个数字上的时间变少了，转而真的在思考：我们该怎么把这些资源重新投回业务里去？

[42:54] Krishna Rao

How do we think about dynamically allocating resources?Whereas before I'm working to tie out a number or I'm in that accounting example, taking a long time to close the books.So I actually think of it as even more optimistically that it is an accelerant to our productivity.And that actually means that we can get a lot more done.And that even as we grow the team, those people are more productive as well as they come up the curve on how to use Claude within our company.And I think that's starting to be true across many companies as well.I'd love to talk about investors and capital formation.Of course, you've had to raise tons and tons of capital.At the same time, it seems as though if I just squint my eyes and think about the multiple on current revenue, it's like not that crazy in terms of like, where are you raising money?

我们该怎么动态地配置资源？而在此之前，我要么在忙着对平一个数字，要么就像会计那个例子里一样，花很长时间才能关账。所以我其实更乐观地看待它：它是我们生产力的加速器，这意味着我们能做成多得多的事。而且即便团队在扩张，随着这些人在公司内部逐渐学会怎么用 Claude、爬上那条学习曲线，他们的生产力也在提高。我觉得这一点在很多公司都开始成立了。我很想聊聊投资人和资本形成。你们当然融了非常非常多的钱。但与此同时，我要是眯起眼睛、按当前收入的倍数算一算，会觉得其实并没有那么离谱——你们究竟是在什么水平上融资的？

[43:36] Patrick O'Shaughnessy

I'm so curious for you to teach us about what it's been like to interact with investors, how you've seen their understanding of the company evolve and mature.Where do you think the investors as a group understand it now?

我非常好奇，想请你讲讲和投资人打交道是什么体验，你如何看着他们对这家公司的理解一步步演进、成熟。你觉得投资人作为一个群体，现在理解到什么程度了？

[43:46] Krishna Rao

Where are their misunderstandings about Anthropic?Tell us that side of your life.And I don't know what percentage it is of your job, but it's an important part.So I joined the company about two years ago.We were closing our Series D at the time.That was not a straightforward fundraising.The company really only had a frontier model in the middle of that fundraising.Towards the tail end of it, the FTX transaction was happening, which liquidating a bunch of Anthropic shares.That was kind of the starting point.And at that point, the questions were around, why do you need to have a frontier model?

他们对 Anthropic 的误解又在哪里？跟我们讲讲你生活中的这一面。我不知道它占你工作的百分之多少，但这是很重要的一部分。我大约两年前加入公司，当时我们正在关闭 D 轮融资。那不是一次一帆风顺的融资。在那轮融资进行到中途时，公司才刚刚拥有一个前沿模型。到融资接近尾声的时候，FTX 那笔交易正在发生，要清算掉一大批 Anthropic 的股份。那大概就是我的起点。而在那个时点，投资人的问题是：你们为什么需要拥有一个前沿模型？

[44:17] Krishna Rao

What's the returns to this?They're also around our mission and how we approach things.People said, well, AI safety and building a really big business, aren't those things at odds?And there were also a lot of other misconceptions on your Salesforce is really small.Don't you need to scale it like all these enterprise software companies?

这么做的回报是什么？还有一些问题是围绕我们的使命和做事方式的。有人说：AI 安全和做成一门非常大的生意，这两件事难道不是互相矛盾的吗？另外还有很多别的误解，比如你们的销售团队太小了，难道不该像那些企业软件公司一样把销售规模铺开吗？

[44:33] Krishna Rao

There was just a bit of a paradigm around trying to fit us into a particular mold that it existed before.Over time, it's evolved.At the end of 2024, we raised the Series E.The business had scaled to close to a billion dollars of run rate revenue.But the day of our first close was the day the DeepSeek news came out.We got the close done, but certainly a ton of volatility.As people then said, wait a minute, should I just totally re-underwrite how I think about AI in the total?

当时存在一种思维定式，总想把我们套进一个此前已经存在的模子里。随着时间推移，这一点在演变。2024 年底我们融了 E 轮。那时业务的年化收入（run rate revenue）已经接近 10 亿美元。但我们首次交割的那一天，正好是 DeepSeek 消息爆出来的那一天。交割是完成了，但波动性肯定极大。因为人们当时会说：等一下，我是不是该把我对整个 AI 的看法彻底重新评估一遍？

[45:01] Krishna Rao

That was the Series E.Obviously, we brought on great investors across all of these.But people still had some of those questions.But they looked at our forecast and they thought, I get it.You've grown to a billion dollars of run rate revenue so quickly.But there's no way you're going to be able to keep up.Yeah, that's just not possible.There's laws of physics.You're in enterprise, which is great.But the adoption is going to take so much longer.I mean, look at how long it took with cloud and how many people are still on-prem.The business continued to prove out the thesis that the return to frontier intelligence is really high.We are really focused on what's really happened is model-led growth, enabled byproducts and our go-to-market team and our distribution.What they also saw was that this thesis of, hey, it's really important to build this transformative technology, but to do it in the right way and do it responsibly.That had this really interesting interlink with our business that most people didn't really understand or really believe,which was that we invest in research, not just in model development, but also in AI safety research.We pioneered interpretability, which is, think of it as like an MRI for the model to see inside the neural network how it works.

那就是 E 轮。显然，这几轮我们都引入了很棒的投资人。但人们心里还是有那些疑问。他们看着我们的预测会想：我明白了，你们这么快就做到了 10 亿美元的年化收入，但你们不可能一直跟得上这个节奏。对，那根本不可能，物理定律摆在那儿。你们做的是企业市场，这很好，但采用周期会长得多。你看云计算当年花了多长时间，到现在还有多少人在用本地部署（on-prem）。而业务持续验证了那个论点：前沿智能的回报真的非常高。我们真正专注的、也真正发生了的，是模型驱动的增长（model-led growth），由产品、我们的市场推进团队和我们的分发能力来加持。他们同时还看到了另一个论点：构建这项变革性技术非常重要，但要用正确的方式去做、负责任地去做。这一点和我们的业务之间有一种非常有意思的联结，而多数人当时并不真正理解、也不真正相信——那就是我们在研究上的投入不只在模型开发上，也在 AI 安全研究上。我们开创了可解释性（interpretability）研究，你可以把它想象成给模型做核磁共振（MRI），看清神经网络内部是怎么运作的。

[46:08] Krishna Rao

We pioneered alignment science, which is you want the model to do what you tell it to do.And how often does it do that?And how often does it stray from that?Those things are important for our mission.And that's why we did them.They had these downstream effects where it turns out if you can look inside the model, you're better at building them.And then the last linkage, if you're selling to enterprises, we now sell to nine of the fortune 10.All of those enterprises are entrusting us with customer information, their data, they're interacting with their employees, sometimes even interacting with their customers as well.Those are the most sensitive workloads.More and more of these businesses are running on Claude and our Claude platform.When you have this investment that we made and will continue to make in safety, interpretability, alignment,that actually enures to the benefit of the enterprise customers as well.And all of our customers, because they want that.If they're going to entrust us with all that access and all that data and the ability to work in the most sensitive workflows within their company, they want a company that they can trust.That's not why we invested in it, but it did have this downstream effect that we've really seen prove out again and again to be a company that is both at the frontier,

我们也开创了对齐科学（alignment science）：你希望模型照你说的去做，那它有多大比例真的做到了？又有多大比例偏离了？这些事对我们的使命很重要，我们做它们就是因为这个。而它们带来了下游效应——事实证明，如果你能看进模型内部，你也就更擅长把模型造出来。还有最后一层联结：如果你是卖给企业客户的——我们现在已经服务了《财富》10 强中的 9 家——所有这些企业都把客户信息、把他们的数据托付给我们，模型在和他们的员工打交道，有时甚至直接和他们的客户打交道。这些是最敏感的工作负载。越来越多这样的企业跑在 Claude 和我们的 Claude 平台上。当你在安全、可解释性、对齐上做了这样的投入并会持续投入，这些投入其实同样惠及企业客户，也惠及我们所有的客户，因为他们要的正是这个。如果他们要把这么大的访问权限、这么多数据、以及在公司内部最敏感流程中工作的能力都托付给我们，他们想要的是一家自己信得过的公司。我们当初投入并不是为了这个，但它确实产生了这样的下游效应，而且我们一次又一次看到它被验证：成为一家既站在前沿、

[47:16] Krishna Rao

but one that is investing in safety and that you can trust.We've raised $75 billion since I joined the company.We have another $50 billion that will come in into the future from the Amazon and Google deals that we inked last month.And so that's a tremendous amount of capital, but it's a capital intensive business and we need this capital to support that growth.It all goes to the fact that the business is running very efficiently.And so the reason we raise this capital is more because of that cone of uncertainty than it is to fund losses in the business.How is your own perception of this perspective for a 10x growth of the business?

又在安全上持续投入、并且值得信赖的公司。自我加入公司以来，我们已经融资 750 亿美元。未来还会有另外 500 亿美元进来，来自我们上个月和亚马逊、谷歌敲定的交易。这是一笔极其庞大的资本，但这是一门资本密集型的生意，我们需要这些资本来支撑增长。这一切都指向一个事实：业务本身运转得非常高效。所以我们融这些钱，更多是因为那个不确定性锥（cone of uncertainty），而不是为了填补业务上的亏损。对于业务 10 倍增长这个前景，你自己的看法是怎样的？

[47:49] Patrick O'Shaughnessy

The first time that happened, did you personally believe that it was possible?Did that seem absurd?Now that it's becoming consistent, maybe it's becoming more commonplace to you or something,but what was your own view staring at this cone about the odds of hitting a 10x type of growth so many years in a row?

第一次发生的时候，你个人相信这是可能的吗？当时觉得荒唐吗？现在它变得反复出现，也许对你来说已经习以为常了。但当你盯着这个锥形图时，你自己对连续这么多年实现 10 倍级增长的概率是怎么判断的？

[48:07] Krishna Rao

When I joined the business, it had about $250 million of run rate revenue and the plan was to get to a billion.And I said, great, in what year?That was linear thinking.Consistently, Dario has been a much better predictor of the revenue than I have.I think we're going to close the gap over time as we get better at forecasting and understanding the business.But definitely the first time I saw it, you have all these arguments about the laws of physics and law of large numbers and this can't.Where is the revenue coming from and how can it be added this quickly?

我加入这家公司时，它大约有 2.5 亿美元的年化收入，计划是做到 10 亿美元。我说，很好，哪一年做到？——那是线性思维。始终如一地，Dario 对收入的预测都比我准得多。我想随着我们在预测和理解业务上越来越娴熟，这个差距会慢慢缩小。但我第一次看到那个数字时，肯定会有各种反驳：物理定律、大数定律、这不可能。收入从哪儿来？怎么可能加得这么快？

[48:33] Krishna Rao

And how can customers move this quickly?And is this even possible in enterprise?All of those things start to get broken down over time as you see how the business works internallyand you see how the adoption curves and the exponentials that are happening.Again, we have the exponential that's happening on revenue,but that's underlies it or these many other exponentials that support that.You start to see and believe in that more.Now, that doesn't mean we're not disciplined and thoughtful about the forecast and how we think about the range of scenarios.But it does mean that my thinking has at least shifted a lot more from linear and incremental towards leaning into this exponentialand really believing in its potential and how this is just different than how other businesses have evolved.As you've talked to investors at every stage, I'm sure every stage, every round that you've raised,there's something that is like the most common or hardest thing to explain to investorsor that they're struggling the most to understand and get their heads around.What is that today?

客户怎么可能动得这么快？这在企业市场里可能吗？随着你看到业务内部是怎么运转的，看到那些采用曲线和正在发生的指数增长，所有这些质疑都会一点点被瓦解。我们在收入上有一条指数曲线，但支撑它的底下还有许多条别的指数曲线。你会越来越看清、也越来越相信这一点。当然，这不代表我们在预测和情景区间的思考上就不再自律、不再审慎。但它确实意味着，我的思维方式已经从线性和渐进，大幅转向拥抱这种指数增长，真正相信它的潜力，相信这和其他生意的演进方式就是不一样的。你在每个阶段、在你们融的每一轮里都跟投资人打过交道，我相信总有一件事是最常被问到、或者最难向投资人解释的，或者说他们最难理解、最转不过弯的。今天这件事是什么？

[49:29] Krishna Rao

I think it is this paradigm of how compute is used.Thinking of it as not just something that is a variable cost over some time period,but really this resource that's so fungibly utilized.We run workloads on one day in the morning on a chip for inference.And in the afternoon or evening, we use it for model development.That paradigm does not exist in a company like a software company or a factory.You can't repurpose if you have a bunch of people doing R&D and that's your R&D expense.They can't go and become COGS and vice versa in most traditional companies.Here, you really have that fungibility that's possible.And I think that's where the return on compute is so important.And I think people are beginning to understand that.But there's still a tendency towards treating it like,oh, I have to separate these two costs.When in actuality, they're very self-reinforcing.And that flexibility is actually what helps to drive revenue short-term and long-term.If I was to force you out of your role and into an investor seat at a great big investing firm,and then I said, your job is to go grill these companies and invest in the best ones.What questions would you be asking of the labs or companies that are building models

我认为是算力（compute）使用方式的这套范式。不能只把它当成某个时间段里的一项可变成本，而要看到它其实是一种可以高度互换使用（fungible）的资源。同一天早上，我们在一块芯片上跑推理（inference）的工作负载；到了下午或晚上，我们又用它做模型开发。这种范式在软件公司或工厂那样的企业里是不存在的。如果你有一批人在做研发，那就是你的研发费用；在多数传统公司里，它没法转成销售成本（COGS），反过来也不行。而在这里，这种可互换性是真实存在的。我认为这正是算力回报之所以如此重要的原因。我觉得人们开始理解这一点了。但仍然有一种倾向，觉得必须把这两块成本分开来看。而实际上，它们之间是高度自我强化的。而这种灵活性恰恰是短期和长期推动收入的东西。如果我把你从现在的岗位上拽出来，塞到一家顶级投资机构的投资人位置上，然后跟你说：你的工作是去拷问这些公司、并投中最好的那几家。你会向这些实验室、向这些做模型的公司问哪些问题，

[50:45] Patrick O'Shaughnessy

to really get at the heart of the points of uncertainty, of skepticism,of things that might not make these the best businesses of all time?Curious from that angle, how you would approach it.First, what is the ROI on compute all up?

才能真正切中那些不确定、值得怀疑、可能让它们成不了史上最好生意的要害？我很好奇从这个角度你会怎么做。第一，算力整体的投资回报率（ROI）是多少？

[51:00] Krishna Rao

How are you utilizing it?What return are you seeing today?And how is that coming over time?That's certainly a question I get asked a lot by investors,but I think it's a really important question.These are the massive, unprecedented investments that companies like us are making.What's the return that you're generating on that?

你们是怎么使用它的？今天看到的回报是多少？这个回报随时间又是怎么演进的？这确实是投资人经常问我的问题，但我认为它是个非常重要的问题。像我们这样的公司正在做的，是规模巨大、前所未有的投资。你在这上面产生的回报是什么？

[51:16] Krishna Rao

And when does it come?And what is the shape of it?So I think that's one.A second one is how your customers see ROI.I get asked this question a lot as well.Like, are people just using this for testing?

它什么时候到来？它的形态是什么样的？这算第一个。第二个是，你的客户如何看待他们自己的投资回报。这个问题我也经常被问到。比如：人们只是拿它做测试吗？

[51:28] Krishna Rao

Are they actually deploying this at meaningful scale?For our business, we're seeing that in spades.Our net dollar retention rate is over 500% on an annualized basis.Nine out of the fortune 10.These are real customers making significant buying decisions.These aren't pilots anymore.Exactly.On the way here, I was in an Uber and I signed two double digit million dollar commitsin the car ride, which was like 20 minutes.So from that perspective, we're seeing it when we're now being judged by some of the biggestcompanies in the world, the most sophisticated buyers and startups.They have choice in the market and they're choosing us.But I think one question I get a lot is, or I would ask from the investor seat,the skeptical investor seat is, how are your customers getting returned from this?

他们真的在有意义的规模上部署了吗？就我们的业务而言，我们看到的证据非常充分。我们的净美元留存率（net dollar retention rate）按年化计算超过 500%。《财富》10 强里有 9 家是我们的客户。这些是真实的客户，在做重大的采购决策。——这些已经不再是试点了。——正是如此。来这儿的路上，我在一辆 Uber 车里就签下了两笔两位数百万美元级（即千万美元量级）的采购承诺，那趟车程大概只有 20 分钟。所以从这个角度看，我们现在是在被世界上最大的一批公司、最挑剔成熟的买家以及创业公司来检验。他们在市场上是有选择的，而他们选择了我们。但我常被问到的一个问题——也是我如果坐在投资人、尤其是持怀疑态度的投资人位置上会问的——是：你的客户从中获得了什么回报？

[52:09] Krishna Rao

Maybe a third one is, how do you think about compute in the future and where does it comefrom?Because obviously some of the places that we buy compute from, they sell the compute toothers.They may use the compute internally.What is the balance of that over time?

也许第三个问题是：你如何看待未来的算力，以及它从哪里来？因为很显然，我们采购算力的一些来源，他们自己也把算力卖给别人，也可能在内部自用。随着时间推移，这中间的平衡会是什么样？

[52:25] Patrick O'Shaughnessy

So your philosophy there is be involved with great players and have flexibility.That's right.There's this crazy stat about AI, just the generic concept being less popular than Congressamongst like the general populace.And it's like kind of funny when you first hear it, but when you really think about it,you're like, this is kind of fucked.We need to solve this problem.It doesn't seem like the general world that isn't in technology, doesn't live in the BayArea or New York does not yet feel or understand why this is good for them, just as measuredby their opinion of it.What do you think we need to do as an industry about that problem?

所以你在这件事上的原则是：和最强的玩家合作，同时保持灵活性。——正是如此。关于 AI 有一个很离谱的数据：在普通民众里，AI 这个笼统概念的受欢迎程度比国会还低。第一次听到会觉得有点好笑，但你真的细想，会觉得这挺操蛋的，我们必须解决这个问题。看起来，那些不在科技行业、不住在湾区或纽约的普通人，还没有感受到、也没有理解这东西对他们有什么好处——至少从他们对它的看法上看是这样。你觉得作为一个行业，我们该对这个问题做点什么？

[52:59] Krishna Rao

If we think about the transformation that's happening, there's been other transformative waysbefore, all the way back to the industrial revolution, the internet, cloud, et cetera.One of the things that's different about AI is it's all happening so quickly.You can have years or decades of progress that are being compressed into months.Going back to humans thinking in terms of exponentials versus linear, that can be jarring.We are very optimistic generally about the potential for this technology.We as an industry can continue to do a better job of articulating, you know, Dario wrotethis essay, Machines of Love and Grace.It's all about the potential for this technology to transform the way that we live, whether thatbe in drug development and curing diseases that are more mainstream, but also rarer diseases,the ability to accelerate that biological progress.Number two, in healthcare and how healthcare is delivered to raise our standard of livingin the developing world and in places where resources are not as plentiful.How do we actually make sure that the economic gains are also accruing to not just a smallnumber of people in the world, but across the world?

如果我们看这场正在发生的变革，此前也有过其他变革性的浪潮，一路回溯到工业革命、互联网、云计算等等。AI 不一样的一点在于，它发生得太快了。原本需要数年甚至数十年的进展，被压缩进几个月。回到人类习惯用线性而非指数方式思考这一点，这会让人很不适应。我们总体上对这项技术的潜力非常乐观。作为一个行业，我们可以在阐述这件事上做得更好。Dario 写过一篇文章《Machines of Loving Grace》（爱之恩典机器），通篇讲的都是这项技术改变我们生活方式的潜力：其一是药物研发，治愈那些较为常见的疾病，也包括更罕见的疾病，以及加速生物学进展的能力；其二是医疗健康领域，以及医疗服务如何递送，从而提高发展中国家、提高那些资源没那么充裕的地方的生活水平。我们又该如何确保经济收益不只落到世界上一小部分人手里，而是惠及全世界？

[54:03] Krishna Rao

I think that all of those things are part of the promise and the potential of AI.We could probably do a better job of painting that picture.And we want to show more tangible results for that over time.I think that is coming.And that's one of the things I'm most optimistic about.I think on the other side, though, we do want to articulate the risks.I don't think we should just tell everyone everything's going to be great.There are likely to be bumps on the road.And so I think people generally gravitate towards more honest and balanced assessments.If I feel like somebody is just telling me all the good news and none of the bad news,then I'm like, okay, do I really trust this perspective?

我认为所有这些都属于 AI 的承诺和潜力。我们大概可以把这幅图景描绘得更好一些。我们也希望随着时间推移拿出更多切实可见的成果。我觉得这些正在到来，这也是我最乐观的事情之一。不过另一方面，我们确实也想把风险讲清楚。我不认为我们应该只告诉所有人一切都会很好。路上很可能会有坎坷。我认为人们总体上更愿意接受那种更诚实、更均衡的判断。如果我觉得某个人只跟我讲好消息、不讲坏消息，我就会想：等等，我真的信得过这个视角吗？

[54:39] Krishna Rao

That's where there's a need for balance.Look, these are some of the things that happen when change is compressed over a short amountof time.How do we work across commercial and government to actually come up with some of the solutionsets?

这就是需要平衡的地方。听着，当变化被压缩在很短的时间里，这类事情就是会发生。我们又该如何在商业界和政府之间协作，真正拿出一些解决方案？

[54:51] Krishna Rao

So I think it's about a clear articulation of the opportunities.It's about thinking about what those solutions may be.And that's not any one company that can come up with it.We don't have this blueprint that's going to solve everything.But to at least have that dialogue about some of the risks and downsides and what we cando to address it.And then I think it's being transparent about both of those things when we talk about it.I do think that over the long term, the opportunity is going to be significantly higher and greaterthan some of the risks and the downsides that will happen.But that doesn't mean it's going to be perfectly smooth on the curve.The release of Mythos was such an interesting moment.It was the first time many people, friends of mine that are careful watchers of this stuff,said something like, this one kind of makes me scared.So it relates back to the safety question.It's also the first example of you coming out and saying, we want to make sure this isn'tused for bad.And it's maybe the first one that we are worried could be used for bad.I'm curious what that discussion was like internally before the world heard about it,the decision-making process around it, and just using that as an example to talk about

所以我认为，一是要把机会清晰地讲出来；二是要去思考那些解决方案可能是什么，而这不是任何一家公司能独自想出来的，我们并没有一份能解决一切的蓝图，但至少要就其中的风险与负面影响、以及我们能做什么来应对，展开对话；三是我认为在谈论这两方面时都要保持透明。我确实认为，长期来看，机会会显著大于那些将会出现的风险和负面影响。但这并不意味着这条曲线会一路平滑。Mythos 的发布是一个非常有意思的时刻。那是很多人——包括我那些长期密切关注这类事情的朋友——第一次说出类似这样的话：这一个有点让我害怕。所以它又回到了安全那个话题。这也是你们第一次公开站出来说：我们要确保这东西不被用于作恶。而且它可能是第一个你们真正担心会被用于作恶的模型。我很好奇，在外界知道之前，内部的讨论是什么样的，围绕它的决策过程是怎样的。也想以它为例，聊聊

[55:51] Krishna Rao

the things that do scare you as we continue to advance and the scaling laws continue tohold.One of the things about Mythos is that people may be misconstrued as just a cyber model.It is a incredibly capable model across many, many different dimensions.What we found was that cyber in particular was a place where it spiked.This was the first model that we decided to release in a different way.The way in which we did that, again, is consistent with our mission, our principles.We wanted to do it in that way.We have this phased approach to it because we think that when a model is as capable, andagain, cyber is the thing that people focused on, but there are other things as well.We think it can be used in a positive way to patch code bases.You've seen these examples where we had an open source code base that a prior model found22 security vulnerabilities in, and Mythos then found 250.That is kind of scary, but that informed the way in which we released it.So we didn't say we're never going to release it.We said, let's do it in a phased way.Let's do it to a group that will expand over time where we can focus on this one cyber capabilityand how it can actually be used positively in a defensive way as opposed to in an offensive

随着我们继续推进、随着规模定律（scaling laws）继续成立，哪些事情是真正让你害怕的。关于 Mythos，有一点是人们可能会误以为它只是一个网络安全（cyber）模型。它其实是一个在非常非常多维度上都极其强大的模型。我们发现的是，网络安全恰恰是它能力尖峰突出的一个领域。这是我们决定用不同方式发布的第一个模型。我们采取的那种方式，同样与我们的使命和原则一致，我们希望那样做。我们对它采用了分阶段的路径，因为我们认为当一个模型有这么强的能力时——再说一次，网络安全是人们关注的点，但还有别的方面——我们认为它可以被用在正面的用途上，比如修补代码库。你已经见过那些例子：在一个开源代码库上，上一代模型找出了 22 个安全漏洞，而 Mythos 找出了 250 个。这确实有点吓人，但它也决定了我们发布它的方式。所以我们没有说我们永远不发布它，我们说：让我们分阶段来做，先发给一个群体、这个群体会随时间扩大，让我们能聚焦在网络安全这一项能力上，看它如何真正被用于防御性的正面用途，而不是用于进攻性的

[56:59] Patrick O'Shaughnessy

way.And we think that's a template that could be used for the future, but because of this oneparticular area, we wanted to be cognizant of that in how we released it.You're so big now that you run into everything and everyone.And one example of this is the government just a couple of days ago said, maybe there'd bethis new system where you have to sort of pre-approve the release of a new model withthe government before it was released to the public.Obviously you had the crazy experience with the Department of War, which I'm really curiouswhat that was like as you went through it.Now everyone cares about this company and this technology and a couple other companies thatare building it.How do you navigate that stuff?

用途。我们认为这可以成为未来的一个模板，但正是因为这一个特定领域，我们希望在发布方式上对它格外有意识。你们现在体量太大了，什么事、什么人都会撞上。一个例子是，就在几天前政府表示，也许会有一套新制度：新模型在向公众发布之前，必须先经过政府某种形式的预先批准。当然，你们还有过和战争部（Department of War）打交道的那段疯狂经历，我特别好奇你们经历那件事时是什么感受。现在所有人都关心这家公司、关心这项技术，也关心另外几家在做这件事的公司。你们是怎么应对这些的？

[57:32] Krishna Rao

Some of it is just, I guess, beyond your control, but I'm sure you're trying to work with peopleas best you can.Maybe talk about those two examples of the government now as a very relevant partner,player, overseer, et cetera.We prioritize having a strong relationship on this because we do think that regulationhas a role to play.We've been pretty vocal about that.We are very America first in our approach.We want the technology to support the U.S. as well as democratic countries around the world.And that's one of the reasons why we've been working closely with the administration.I do think that there's a balance, right?

其中有些大概不在你们的掌控之内，但我相信你们在尽力和各方合作。也许可以聊聊这两个例子——政府现在作为一个非常重要的合作方、参与者、监督者等等。我们把维护这方面的良好关系放在很高的优先级，因为我们确实认为监管有它该起的作用，我们在这一点上一直表达得很明确。我们的立场是非常「美国优先」的。我们希望这项技术能支持美国，也支持全球的民主国家。这也是我们一直和本届政府紧密合作的原因之一。我确实认为这里存在一个平衡，对吧？

[58:06] Krishna Rao

You want to be able to have innovation happen really quickly and have that not be slowed down,but you also want to have this responsibility framework for how these things are deployed.Because we've long said that this technology has implications and we should have an honestconversation about them.And that includes with the government.And so I think the mythos process is a good example of that.Can you teach us a bit more about how you would describe their cultural tenets to yourparents or something like this?

你希望创新能够飞快地发生、不被拖慢，但你同时也希望有一套关于这些东西如何被部署的责任框架。因为我们很早就说过，这项技术是有影响的，我们应该就此展开诚实的对话，这也包括与政府的对话。所以我认为 Mythos 的这个流程就是一个很好的例子。你能再多讲讲，你会怎么向你父母之类的人描述你们的文化信条吗？

[58:32] Patrick O'Shaughnessy

What feels like it really drives most of the culture?I'm especially curious about the writing.You hear often that Dario publishes these long essays every so often externally.My understanding is he does that way more frequently and there's a lot of writing cultureinternally.I'm trying to get a sense of what the culture is like to be in and what makes it the mostdistinctive from other companies maybe that you've worked at or from other companies thatare trying to do the same thing.What's your sense of the differences and the distinctiveness?

究竟是什么在真正驱动这套文化？我尤其好奇写作这件事。人们常听说 Dario 每隔一段时间会对外发表这些长文。据我了解，他在内部写得频繁得多，公司内部有很强的写作文化。我想搞清楚身处其中的文化是什么感觉，以及是什么让它与你待过的其他公司、或者其他试图做同一件事的公司最不一样。你觉得差异和独特之处在哪里？

[59:00] Krishna Rao

The culture is a real unique aspect of Anthropic and it is something that we do talk aboutexternally, but it's different when you're in there living it.I can tell you a little bit about some of my observations.First of all, there's seven co-founders that shouldn't work on paper, but it really doesin practice.And I think they've really set the example for the culture and the things that really matterto the company.We take culture extremely seriously.We do a culture interview and it's not some pro forma thing we do just to check a box.It is a real part of the evaluation process.So somebody could be flying colors on everything else and really, really the smartest personyou've met in this role.We won't hire them if they don't pass the culture bar.And the way I would describe it, I like that frame.How would you describe it to your parents?

文化确实是 Anthropic 一个非常独特的方面，我们对外也会谈它，但当你真的身处其中去经历，感受是不一样的。我可以讲一点我自己的观察。首先，公司有七位联合创始人——这在纸面上本不该行得通，但在实践中它真的行。我认为他们确实为公司的文化、为公司真正在乎的东西立下了标杆。我们对文化极其认真。我们有一轮专门的文化面试，它不是走个过场、打个勾的形式，而是评估流程中实打实的一环。所以哪怕一个人在其他所有方面都表现亮眼，是你在这个岗位上见过的最聪明的人，只要他过不了文化这一关，我们就不会录用。至于我会怎么描述它——我喜欢你这个提法：你会怎么向你父母描述它？

[59:47] Krishna Rao

It's one incredibly collaborative.And this means that we don't really tolerate fiefdoms or the sharp elbows or the, I needto take credit for this.It's incredibly humble.We have this sticker on their laptops internally.Our competitors are incredibly capable and success is far from guaranteed.And I think that's part of how the company operates.If we reach a milestone and something good happens, there's not confetti on the floor.It's like, what's next?

第一，它极其协作。这意味着我们基本不容忍山头主义、不容忍那种硬碰硬的排挤，也不容忍「这件事的功劳得算我头上」。它极其谦逊。我们内部把这样一句话做成贴纸贴在笔记本电脑上：我们的竞争对手极其强大，成功远未被保证。我认为这就是公司运作方式的一部分。如果我们达成了某个里程碑、发生了什么好事，地上不会撒满彩带，大家的反应是：下一件事是什么？

[1:00:14] Krishna Rao

And I think it's just that focus on the mission and the alignment that is imbued throughoutthe culture of the company.The other thing I would say is there's rigorous debate and intellectual openness and intellectualhonesty that happens where people question things.People will express a point of view, but then there's dialogue around it that's productive.And then we'll decide on a path forward.And then after that happens, there's real alignment.So in something like compute allocation, we were talking about before, people might havedifferent perspectives on how to allocate that compute.They will engage in a thoughtful discussion about where the returns are the highest orthe best.And when they do that, you know, and we come to a decision, then there's alignment on it.There's not second guessing.There's not politics or fiefdom.The other piece of it is it's remarkably transparent.Dario gets up in front of the company every two weeks, usually writes a short document, andhe talks about usually three or four topics and then takes open questions from the company.These are not softballs.They're not planted questions.They're just real questions that are on people's mind.And he answers them the best that he can.

我觉得起作用的正是这种对使命的专注，以及贯穿公司文化的目标一致（alignment）。另一点我想说的是，公司里有严格的辩论、思想上的开放和智识上的诚实，大家会质疑各种事情。有人会表达一个观点，然后围绕它展开富有成效的对话，之后我们再决定往哪个方向走。而一旦决定下来，就有真正的一致。所以像我们之前聊到的算力（compute）分配，大家对怎么分配这些算力可能有不同看法，他们会就「哪里的回报最高、最好」展开深思熟虑的讨论。讨论完、我们做出决定之后，大家就统一了：不会有事后质疑，没有政治斗争，也没有山头。另一部分是，公司透明得惊人。Dario 每两周会站到全公司面前，通常先写一份简短文档，讲三四个话题，然后接受全公司的开放提问。这些不是送分题，也不是安排好的托儿问题，就是大家心里真正在想的问题，他会尽自己所能回答。

[1:01:17] Krishna Rao

It's not a decision making forum, but it is a way for the company to get a window intohow leadership is thinking, how he's thinking.And there's debate and dialogue in that.That is something that people really value.It is a transparent culture.It is one where all seven of the co-founders are still at the company.The vast majority of the first 20 to 30 employees are still at the company.The culture underpins the reason why we've been able to attract and retain some of thebest talent in the industry.Because we don't always pay people the most.We have very competitive compensation packages.But when Meta and others were out with these huge packages for some of the technical talentacross the large language labs, I think we lost two people.And other labs lost dozens.What parts of the business and the culture, specifically for researchers, why do you thinkthat is true?

这不是一个决策场合，但它让全公司有一扇窗口，看到领导层在怎么想、他本人在怎么想。里面有辩论、有对话，这是大家非常珍视的东西。这是一种透明的文化，七位联合创始人至今全都还在公司，最早的 20 到 30 名员工里绝大多数也还在。这种文化正是我们能吸引并留住业内最优秀人才的原因。因为我们并不总是给最高的薪酬——我们的薪酬包很有竞争力，但当 Meta 等公司拿着巨额待遇包去各大语言模型实验室挖技术人才时，我记得我们只走了两个人，而其他实验室走了几十个。具体到研究员这个群体，业务和文化里的哪些部分让你觉得这件事是成立的？

[1:02:08] Krishna Rao

It really is underpinned by the culture.That's not just something we feel.It's like empirically, when you talk to people, it's, I want to have the most impact possible.I want to work in a place where, again, this idea of talent density mattering more thantalent mass.And I want to work in a place that is actually collaborative versus I have to fight for thisone thing.And I feel like it wasn't discussed and debated in the right way, or there wasn't transparencyaround how a decision was made.I think that actually really matters because most of our team just wants to do really, reallygood work.And they're attracted to the company for the mission.The idea of having an impact on a company like ours that is trying to develop this transformativetechnology, but to do it in a responsible way.I think that really matters to the people, not just on the research team, but across thecompany.And that is a real advantage for us.And it's not something that we take lightly.We have this concept of a race to the top.We don't always have all the right answers.We don't always do everything perfectly, but we want others to look at some of the thingswe do and maybe emulate some pieces of that and actually have the technology be developed

这真的是由文化支撑的。这不只是我们自己的感觉，而是有实证的——你跟人聊，他们会说：我想产生尽可能大的影响力；我想在一个「人才密度胜过人才规模」的地方工作；我想在一个真正协作的地方工作，而不是我得为某一件事去争抢，还觉得它没有被以恰当的方式讨论和辩论过，或者决策过程不透明。我认为这真的很重要，因为我们团队里大多数人只是想做非常非常好的工作。他们是被使命吸引到公司来的。能在一家试图开发这种变革性技术、并且要以负责任的方式去做的公司里产生影响——我觉得这对大家真的很重要，不只是研究团队，而是全公司。这对我们是实实在在的优势，也是我们不敢轻慢的东西。我们有一个概念叫「向上竞赛」（race to the top）：我们并不总有正确答案，也并不总能把每件事做到完美，但我们希望别人看到我们做的某些事，或许能效仿其中一部分，让这项技术在整个行业里

[1:03:13] Krishna Rao

in a better way across the industry.I think people are also really attracted to that as well.Again, not that we have all the answers, but that we can be a part of contributing andleading to how this can go well for humanity.If we now think forward, as you're having conversations with people internally, what does the frontierfeel like?

以更好的方式被开发出来。我想大家也很被这一点吸引。再说一次，不是说我们掌握了全部答案，而是我们能参与其中、并引领这件事朝着对人类有利的方向发展。如果我们往前看，在你和内部同事的交流中，「前沿」是什么感觉？

[1:03:30] Patrick O'Shaughnessy

I don't just mean the model frontier.I mean, the next couple of rolls of the dice here in building AI in general.Everyone is kind of wise to like, these things are powerful.Everyone's using them.What feels to you like the frontier from the inside?

我指的不只是模型的前沿。我是说，在整体构建 AI 这件事上，接下来的几次掷骰子会是什么样。现在所有人都已经明白这些东西很强大，所有人都在用。从内部看，什么东西让你觉得那才是前沿？

[1:03:45] Krishna Rao

I think it's this idea.And again, it's because we're focused on enterprise and because we're really trying to change theproductivity of knowledge work that's done in the economy.I think it is towards this vision or this goal of like a virtual collaborator.And so think of this as something that has context within your organization that can useall of the tools that are specific to you, whether they be homegrown tools or tools thatyou purchase that has memory and the ability to effectively learn from mistakes you've made,but also mistakes that it's made over time.The ability to work over a very long time horizon on not just a task, but an actual idea.What that means for us is the model capability has to continue to grow to support that.The products we build on top of it can unlock this virtual collaborator that we think canreally accelerate knowledge work.There's something like $40 trillion of knowledge work done annually in the world.We think that the productivity and the acceleration of it can happen, but you have to get it inthe right form factor.This is where like intelligence is not just a single dimension, but the virtual collaboratorkind of combines many of those things.Something that's not just generically smart, but it's smart for your use cases.

我想是这样一个想法。再强调一次，因为我们聚焦企业客户，因为我们真正想改变的是经济体中知识工作的生产率。我认为它指向的是「虚拟协作者」（virtual collaborator）这样一个愿景或目标。你可以把它想成：它掌握你所在组织内部的上下文，能使用所有专属于你的工具——无论是自研工具还是你采购的工具；它有记忆，能有效地从你犯过的错误、也从它自己长期以来犯过的错误中学习；它能在非常长的时间跨度上工作，处理的不只是一个任务，而是一个真正的想法。这对我们意味着：模型能力必须继续增长来支撑这一点，我们在它之上构建的产品才能解锁这个我们认为可以真正加速知识工作的虚拟协作者。全世界每年的知识工作大约有 40 万亿美元的体量。我们认为生产率的提升和加速是能发生的，但你必须把它做成正确的形态。这里的关键是——智能不是单一维度的，而虚拟协作者把很多维度结合在了一起：它不只是泛泛地聪明，而是针对你的使用场景聪明。

[1:04:58] Krishna Rao

What we're seeing in coding is something that we expect to see elsewhere.For us, Cloud Code has led the way on that as well as much of the business that we havegreat customers that are pushing the coding frontier as well.But then you also see something like Co-Work come along and start to unlock that.Co-Work is growing faster than Cloud Code was if you index them to the same point in time.That's kind of remarkable because developers are really fast adopters of this technology.But I think it's because the model capabilities and the products are pushing towards this notionof a virtual collaborator where even our product development today is not done by likeone product manager with two engineers shipping something over three months.It's shipping daily and there's a fleet of agents that are working across the companyon a specific task.So everyone kind of becomes a manager.I think the implications of that and the productivity gain that can come from that when it's in theright form factor, we're very, very early in that.But the potential for it is incredible.I'm curious how you've had to personally evolve to be able to stay doing this.You hear a lot about these stories.The executives have to scale with the company.

我们在编程领域看到的一切，我们预期会在其他领域也出现。对我们来说，Claude Code 在这方面走在了前面，我们业务中也有很多正在推动编程前沿的优秀客户。但你还会看到像 Cowork 这样的产品出现，并开始解锁这件事。如果把两者对齐到同一时间点来看，Cowork 的增长比当年的 Claude Code 还快。这挺不可思议的，因为开发者本来就是这项技术最快的采用者。但我认为原因在于，模型能力和产品都在朝「虚拟协作者」这个概念推进——就连我们今天的产品开发，也不再是一个产品经理带两个工程师、花三个月发布一个东西，而是每天都在发布，公司里有一支智能体（agent）舰队在同时处理某个具体任务。于是每个人某种程度上都变成了管理者。我认为这件事的含义、以及形态对了之后能带来的生产率提升，我们还处在非常非常早期，但它的潜力大得惊人。我很好奇，你个人必须做出哪些进化，才能一直干下去。这类故事你听过很多：高管必须随公司一起扩张。

[1:06:08] Patrick O'Shaughnessy

They're all still getting new executives.The business that you were at prior to this was a great business, but it was a tiny, tinyfraction of the scale.You, like everyone, is in this new unprecedented thing.You talked about the example of getting out of linear into a more exponential typethinking.That's one example of what I mean.But how have you managed it personally?

他们全都还在不断引进新高管。你在此之前所在的那家公司是一门很好的生意，但规模只是现在的极小极小一部分。你和所有人一样，身处一件前所未有的新事物里。你讲过那个从线性思维跳到指数思维的例子，那就是我说的其中一例。但你个人是怎么应对的？

[1:06:25] Krishna Rao

What have you had to do?What's been the most painful?How do you manage your own ability to scale with this thing that's scaling faster thanwhat we've seen before?It's really hard.I think the important thing is to think in first principles.Everyone has priors when they come to something new.Thinking in first principles and having like intellectual openness.I spent a lot of time with Tom Brown, our chief compute officer.He was actually one of the first people to interview me at the company.And I remember we went on a walk and he started to tell me abouthis vision for the future of the company.And this is in 2024, early 2024.And I'll be honest, it sounded crazy.You walk me all the way home.I remember I came in and I told my wife, I was like, this is going to be wild.If even 10% of that is true, this is going to bend all paradigms of not just things I'veseen, but what most people have seen.And it turns out that a lot of what Tom said during that walk has come to fruition.I remember that as like an early formative thing.This walk I took with him and coming home and being like, holy shit, this is going to betotally different and new, really incredible experience, but also really challenging.

你必须做些什么？最痛苦的是什么？面对这个扩张速度超过我们以往所见的东西，你怎么管理自己跟上它的能力？真的很难。我认为重要的是用第一性原理思考。每个人面对新事物时都带着先验。第一性原理思考，加上智识上的开放。我花了很多时间和我们的首席算力官（chief compute officer）Tom Brown 在一起。他其实是公司里最早面试我的人之一。我记得我们一起去散步，他开始跟我讲他对公司未来的构想，那是在 2024 年，2024 年初。说实话，当时听起来简直像疯了。他一路把我送回家。我记得进门后跟我太太说：这事要疯了。哪怕其中只有 10% 成真，它都会颠覆所有范式——不只是我见过的范式，而是大多数人见过的范式。结果 Tom 在那次散步中说的很多事，后来都成真了。我把它记成一件很早期的、塑造性的事：那次和他的散步，回到家心想「我的天，这将会是彻底不同的、全新的东西」，那是一段非常了不起的经历，但也非常有挑战。

[1:07:32] Krishna Rao

And then that's what it's been.The other piece of this is just hiring great people.I try to hire people and I tell people during the interview process, I'm like, I'm not reallyhiring you as like a direct report of mine.I'm hiring you as a partner and I want you to treat it as a partnership, which means thatthere might be things that you and I disagree on.I want to hear that.I want to whiteboard it.I want to understand we've hired people from some of the best companies in the world.They come to this from a different perspective.They might come to it from a hyperscaler or a large software company or from financial services.In another lifetime, you know, I worked at Blackstone in the private equity group.That training is really valuable and thinking about things at a granular level and not losingthat.I'm not somebody who is comfortable at 50,000 feet.That's just not me, but you can't be at 500 feet at everything in this business.There's too much surface area.And so having people who can be partners in that is really, really critical.And I found we've been able to attract really great people and they've pushed my thinkingquite a bit.I think the last piece is to think about how the business evolves over time and where there

后来事情也确实如此。另一部分就是招到优秀的人。我在面试过程中会告诉他们：我不是把你当作我的直接下属来招，我是把你当作合伙人来招，我希望你也把它当成一种伙伴关系——这意味着有些事情你我会有分歧，我想听到这些分歧，我想跟你在白板上把它画出来，我想把它弄明白。我们从世界上一些最好的公司招人，他们带着不同的视角过来，可能来自超大规模云厂商（hyperscaler），可能来自大型软件公司，或者来自金融服务业。我上辈子曾在 Blackstone 的私募股权部门工作过，那种训练非常有价值——凡事在很细的颗粒度上思考，并且不丢掉这种能力。我不是那种待在 5 万英尺高空还觉得舒服的人，那不是我；但在这门生意里，你也不可能对每件事都下沉到 500 英尺，面太广了。所以能有一群人在这件事上做你的伙伴，就非常非常关键。我发现我们确实能吸引到非常优秀的人，他们把我的思考推进了很多。最后一点，是去思考业务如何随时间演化，以及哪里

[1:08:37] Krishna Rao

might be moments or analogs to things that have happened in the past.I helped lead the financing that Airbnb did in the middle of the pandemic.Very different situation, right?The business lost 70% of its revenue in seven weeks.And O'Brien did a show with you.That was a harrowing time, but it was also a time kind of without precedent where you hadto think about things with a clear perspective when it was rapidly changing and there wasnot a good template or pattern to match.And then on a personal level, look, it is hard to balance everything.Family and friends and this job takes a big bite out of all that.But what I do try to do maybe once a week is in a quiet moment, just think, wow, this isreally cool.It's an incredible opportunity to work with this group of people on this problem at thiscompany at this moment in time.Maybe it's in a car ride, maybe it's late at night or something like that.Having that recognition and that appreciation is really important.So I try to bring that to at least my life for a couple of minutes once a week.What did Tom tell you on the walk that sounded most crazy?

可能出现与历史上发生过的事情相似的时刻或类比。疫情当中 Airbnb 那笔融资是我帮忙牵头的。情况当然很不一样——那家公司在七周里损失了 70% 的收入。Brian 还上过你的节目聊过这段。那是一段惊心动魄的时期，但也是一段没有先例的时期：局面在飞速变化，没有好的模板或可匹配的模式，你必须用清晰的视角去思考。至于个人层面，说实话，要平衡一切很难，家人、朋友和这份工作互相挤占。但我尽量每周至少做一次的事，是在某个安静的时刻想一想：哇，这真的很酷。能在这个时间点、在这家公司、和这群人一起做这个问题，是一个了不起的机会。可能是在车里，可能是深夜之类的时候。有这份意识和这份感恩非常重要，所以我每周至少给自己的生活留几分钟去做这件事。Tom 在那次散步里跟你说的哪件事最像疯话？

[1:09:42] Krishna Rao

We talked a lot about the scale of the compute infrastructure, what models could do in a shortamount of time.I think he described a world that I would have said is kind of sci-fi.A lot of what we're experiencing here and now have really roots in that conversation.And so there's even more things that he talked about that are probably beyond where we are today.But I think the commonality of it was that everything is going to happen much quicker than wethink and that both the implications, but also the capabilities of that can change.And then he also had like a really incredible optimism about the future that I think we talkabout internally, holding light and shade.That's one of the things we say.I came from that conversation with just a bunch of questions, but also a sense of positivity aboutwhat could happen in the future.It seems like we've spent most of our time talking about because it's been the reality thatwe exist at the high end of that cone.What can you imagine that would cause that to change to the low end of that cone?

我们聊了很多关于算力基础设施的规模，以及模型在很短时间内能做到什么。我觉得他描述的那个世界，我当时会说那有点科幻。我们此时此地正在经历的很多东西，根源其实都在那次谈话里。而他谈到的还有更多事情，可能超出了我们今天所处的位置。但我觉得共通点是：一切发生的速度会比我们以为的快得多，而它带来的影响和能力都会随之改变。另外他对未来还有一种非常了不起的乐观，这也是我们内部常谈的——「同时握住光与影」（holding light and shade），这是我们常说的一句话。我从那次谈话里带走了一堆问题，但也带走了对未来可能性的一种积极感受。看起来我们大部分时间聊的都是「我们身处那个锥形的高端」，因为现实确实如此。你能想象什么样的情况会把它推向锥形的低端？

[1:10:41] Patrick O'Shaughnessy

If we were to do like some sort of premortem on a year from now, we're like, wow, actuallywe didn't need nearly as much compute as we thought or something like that.What can you imagine that would shift us meaningfully in that cone?

如果我们对一年之后的情况做某种「事前验尸」（premortem），说「哇，其实我们根本不需要当初以为的那么多算力」之类的，你能想象什么会让我们在那个锥形里发生实质性的位移？

[1:10:52] Krishna Rao

The capability and the use cases are playing catch up to where the model is.We are talking about humans in large organizations with a set of tools and practices and things thatthey've been doing for a really long time.Change is hard.To the extent that that diffusion hits a wall or slows down or something like that, that couldaffect the rate of change in terms of revenue growth.The scaling laws slowing down or not holding.We don't see that, but can't say that with 100% certainty.I think that would be silly.We certainly believe in the trajectory, but the model capabilities leveling off would beanother thing.Third is how we think about being at the frontier.Today, we're at the frontier.I think we're defining the frontier of agentic AI.We need to stay there.It's a competitive market and we're going to continue to invest in the technology andthe compute and the go-to-market that's required to be there, but that's not guaranteed either.To finish on an optimistic note, if those are the things that could cause us to go to thelow end, what are you most excited about?

能力和使用场景正在追赶模型所在的位置。我们面对的是大型组织里的人类，他们有一套工具、惯例和已经做了很久的事情，改变是困难的。如果这种扩散（diffusion）撞上墙，或者慢下来之类的，就会影响收入增长的变化速率。第二是规模定律（scaling laws）放缓或不再成立。我们没有看到这种迹象，但也不能说有 100% 的确定性，那样讲就太傻了。我们当然相信这条轨迹，但模型能力见顶也是另一种可能。第三是我们怎么看待「处在前沿」这件事。今天我们在前沿，我认为我们在定义智能体式（agentic）AI 的前沿，我们必须留在那里。这是一个竞争激烈的市场，我们会继续在技术、算力和为此所需的市场开拓（go-to-market）上投入，但这同样没有保证。用一个乐观的调子收尾吧：如果这些是可能让你们走向低端的因素，那你最兴奋的又是什么？

[1:11:49] Patrick O'Shaughnessy

You have a privileged seat.You sort of get to literally see the future because it's happening inside the business beforethose outside the business see it.With that perspective and in that seat, what are you most excited about?

你坐在一个得天独厚的位置上，某种意义上你真的能看到未来，因为它先发生在公司内部，外面的人还没看到。以这个视角、坐在这个位置上，你最兴奋的是什么？

[1:12:00] Krishna Rao

I really think that the biotechnology and healthcare outcomes that can come from this technologyare the things that I'm most optimistic about it.We may live in a world where you're diagnosed with a disease that is not curable, but inyour lifetime, that cure can be found much more rapidly and you might not die of that disease.A lot of what we're doing today is helping to speed up the drug development process.A lot of the paperwork and clinical studies reports and things like that that are neededto be done, AI and our solutions in particular are helping to rapidly accelerate that.I'm really most optimistic and excited about when it goes further back into drug developmentand drug discovery because humans are capable at research.But if you think about these molecules and proteins, they're so complex and such small changeshave such big implications for the outcomes.AI is perfect for that.What can happen when the lab's throughput goes up 10x or 100x and we can run that many moreexperiments, probably get better results faster.And that can be something that helps people around the world.And it doesn't have to be limited to a small set of diseases or disorders that can reallygo much further down the chain.

我真的认为，这项技术能带来的生物技术和医疗健康成果，是我最乐观的部分。我们可能会生活在这样一个世界：你被诊断出一种目前无法治愈的疾病，但在你有生之年，治愈方法能被更快地找到，你可能不会死于那种疾病。我们今天做的很多事情是帮助加速药物研发流程——那些必须完成的文书工作、临床研究报告之类的，AI、尤其是我们的解决方案，正在帮助大幅加速这些环节。而我最乐观、最兴奋的，是它进一步往前追溯到药物开发和药物发现阶段的时候。因为人类在做研究上是有能力的，但如果你想想这些分子和蛋白质，它们如此复杂，如此微小的变化就会对结果产生巨大影响——AI 特别适合这个。当实验室的吞吐量提高 10 倍或 100 倍、我们能多做那么多实验时，会发生什么？大概率能更快得到更好的结果。而这可以帮到全世界的人，也不必局限于一小类疾病或病症，能沿着链条走得更远。

[1:13:11] Patrick O'Shaughnessy

And so I think that has the potential to greatly alter the way that we live and the way thatwe interact.And that's really exciting to me.Sure hope you're right.It sure seems like we're on that trajectory and it's quite a future to imagine.This is so much fun.I feel like we covered so many interesting aspects of the business.When I do these, I ask the same traditional closing question.What is the kindest thing that anyone's ever done for you?

所以我认为这有潜力极大改变我们的生活方式和互动方式，这让我非常兴奋。真希望你是对的。看起来我们确实在那条轨迹上，那是个值得想象的未来。这太有意思了，我觉得我们聊到了这门生意里非常多有趣的方面。我做这些访谈时，最后都会问同一个传统的收尾问题：有人为你做过的最善意的一件事是什么？

[1:13:34] Krishna Rao

I have a brother who's five and a half years older than me.We lived in California when he went to college and he got into everywhere he applied to andhe was going to go to medical school after that.And I didn't know any of this at the time.So he ended up going to college in state.He did exceptionally well.It's kind of years later that I kind of had to pull this out of him.But in deciding where to go to college, we were solidly middle class.This was like 25, 30 years ago.The financial aid packages weren't as robust as they are today.A big factor in his decision, I found out, was wanting to give me the opportunity to gowherever I wanted.That was six years out and who knows how I would turn out.A big part of his decision, a big factor in his decision was giving me the opportunityto have choice.I didn't know that 12-year-old me or 13-year-old me would have never understood.Many years later, I think that's something that was incredibly kind and is something thatI still kind of hold with me today.I've done this like 600 times or something.I've never heard an answer of that type.That's awesome.Amazing.Love to meet your brother.Krishna, thanks so much for doing this with me.Thanks for having me, Patrick.

我有一个哥哥，比我大五岁半。他上大学时我们住在加州，他申请的学校全都录取了他，之后他还打算读医学院。当时这些我一概不知道。他最后去了本州的大学，读得非常出色。这是很多年以后我才从他嘴里一点点问出来的：在决定去哪里上大学时——我们家是标准的中产，那还是 25 年、30 年前，助学金方案不像今天这么慷慨——我后来才知道，他决定里的一个重要因素，是想给我留下「想去哪儿就去哪儿」的机会。那是六年之后的事，谁知道我会长成什么样。他那个决定里很大一部分、很重要的一个考量，就是给我选择的机会。我当时并不知道，12 岁或 13 岁的我也根本不可能理解。很多年之后，我觉得那是一件极其善意的事，也是我至今仍带在身上的东西。这个问题我大概问过 600 次，从没听过这种类型的答案。太棒了，了不起。真想见见你哥哥。Krishna，非常感谢你来做这期节目。谢谢你邀请我，Patrick。

[1:14:38] Patrick O'Shaughnessy

Really enjoyed it.If you enjoyed this episode, visit Colossus.com.You'll find every episode of this podcast complete with hand-edited transcripts.You can also subscribe to Colossus, our quarterly print, digital, and private audio publicationfeaturing in-depth profiles of the founders, investors, and companies that we admire most.Learn more at Colossus.com slash subscribe.You know how small advantages compound over time?

我很享受这次对话。如果你喜欢这一期节目，请访问 Colossus.com，那里有本播客的每一期，并配有人工精校的文字稿。你也可以订阅 Colossus——我们的季度出版物，包含印刷版、数字版和私享音频，深度描摹我们最欣赏的创始人、投资人和公司。到 Colossus.com/subscribe 了解更多。你知道微小的优势会如何随时间复利累积吗？

[1:15:22] Patrick O'Shaughnessy

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