Anthropic Secures SpaceX Compute Deal After 80x Revenue Growth Surge

营收激增80倍，Anthropic达成SpaceX算力合作 联合创始人Dario Amodei预测，AI多智能体系统将消除小团队传统的执行障碍，很快催生全球首家十亿美元级的单人公司。 Anthropic联合创始人讨论了公司第一季度出人意料的80倍年化增长，这一激增促成了与SpaceX的算力扩展合作。他们探讨了AI从单智能体向多智能体系统的迅速演进，并预测到2026年将出现十亿美元级的单人公司。对话还重点指出了阿姆达尔定律（Amdahl's law）下产品构建的实际挑战——例如应对快速积累的技术债务以及针对主观任务训练模型——同时也展示了从全球南方医疗工具到受损硬盘数据恢复等一系列令人振奋的社区应用案例。 Anthropic 联合创始人透露，公司第一季度营收与使用量实现年化 80 倍增长，远超此前 10 倍的乐观预期并导致算力吃紧，目前已达成 SpaceX 算力协议以迅速扩充产能。 (5:00) Dario Amodei 预测首个估值达十亿美元的“单人公司”将在 2026 年诞生，并指出当前由两人创立的十亿美元级 AI 企业已真实存在。 (10:00) AI 开发正从单智能体系统转向层级化多智能体系统；援引阿姆达尔定律（Amdahl's law），代码生成的大幅加速使得安全、验证和设计质量等非客观环节成为新的组织瓶颈。 (12:00) 虽然借助 AI 使内部研发速度提升了 2 至 5 倍，但也带来了巨大的技术债务积累；未来最令人兴奋的技术演进在于让 AI 超越个体助手，在整个组织层面上实现深度协同。 (25:00)

Anthropic co-founders Dario and Daniela Amodei report that unexpected 80x annualized growth and rapid model evolution are forcing a fundamental shift in product design, developer tooling, and infrastructure management. They argue that as AI accelerates raw code generation, the primary engineering bottlenecks will shift toward verification, managing technical debt, and coordinating multi-agent organizational systems. Scaling Pressures and 80x Annualized Growth (5:00) Anthropic's operational demands have outstripped internal forecasts. While the company prepared for an aggressive 10x annual growth scenario, Dario Amodei reports that the first quarter saw an annualized 80x surge in revenue and usage. Although Dario and his fellow founders mathematically anticipated today's multi-billion-dollar compute trajectories over a decade ago via scaling laws, experiencing the physical inflection point remains jarring. This growth has created acute compute limitations, prompting Anthropic to partner on a SpaceX compute deal to rapidly expand infrastructure capacity. Daniela Amodei notes that developers represent the core user base and the majority of Anthropic's own workforce. She emphasizes that developer feedback drives model improvements, creating an ecosystem that supports use cases across biomedical research, financial services, and software engineering. Product Development in Shifting AI Landscapes (20:00) Dario Amodei distinguishes between building products for AI and building products with AI. In an environment of rapid capability changes, ideas that fail in one generation can suddenly become viable in the next; for example, internal experiments with Claude Code in 2022 produced poor results because the models lacked sufficient capability, whereas newer models make the tool effective. This dynamic requires software companies to operate as continuous research laboratories that frequently revisit abandoned concepts. Dario also observes that traditional chatbot interfaces are facing saturation because newer model capabilities are better showcased in agentic workflows. Despite evolving user interfaces, he projects that the underlying API market will remain permanent as external developers continually build specialized vertical tools. Multi-Agent Systems and Amdahl's Law Bottlenecks (10:00) Dario reiterates his forecast that the industry will see its first billion-dollar, one-person company by 2026, noting that two-person billion-dollar startups and individual founders worth hundreds of millions already exist. Looking forward, he anticipates a technical transition from single agents toward multi-agent systems—which he describes as a 'country of geniuses in a data center'—capable of multiplying the output of entire organizations rather than isolated workers. However, accelerating development introduces constraints dictated by Amdahl's law. Dario explains that while internal AI assistance lets Anthropic write code two to five times faster, it accelerates the accumulation of internal technical debt. Consequently, development bottlenecks shift away from code generation to harder-to-verify domains, including security auditing, design quality, and verification, requiring new training methods for subjective tasks. The Dual Challenge of Safety and Real-World Impact (15:00) Daniela Amodei frames Anthropic's operational philosophy around an internal value called 'hold light and shade,' which balances the imperative to deploy powerful tools against the risks of labor disruption and software vulnerabilities. Navigating this tension requires treating safety and security as active engineering priorities during model training. At the application level, Daniela highlights real-world developer implementations that reflect this deployment strategy. These range from high-impact medical solutions—such as offline interfaces enabling mobile doctors to deliver validated medical advice in remote areas of the Global South—to specialized tasks like scientific research acceleration and consumer data recovery from damaged storage drives. Anthropic faces the dual challenge of managing compute bottlenecks and mounting technical debt while developing models that operate across organizations, leaving open how effectively multi-agent architectures can overcome subjective engineering bottlenecks and mitigate wider economic disruptions.

Anthropic 联合创始人 Dario Amodei 与 Daniela Amodei 阐述了人工智能正在经历的超预期指数级跃迁，指出这一波技术浪潮正从单点效率提升演进为多智能体协同与组织层面的深刻重构。他们主张，在面对模型能力突增、算力紧缺与技术债务积累的同时，技术构建者必须直面阿姆达尔定律带来的验证瓶颈，并在技术普及与安全风险之间维持审慎平衡。 指数级增长冲击与算力基础设施扩张 (0:00) Dario Amodei 回顾指出，早在十多年前，他与创始团队便通过缩放定律（Scaling Laws）推演过 AI 支出的指数级轨迹，预测开销将从每月一千美元一路飙升至千亿美元规模。然而，尽管数学模型早已预见这一趋势，亲历这一过程依然令人震撼。Dario 将这种体验比作电影《星际穿越》中黑洞附近两千英尺高的巨浪，即便在物理理论上能够理解广义相对论和剪切力，但在人类尺度上亲眼目睹巨浪仍充满超现实的动荡感。Daniela Amodei 也通过内部 Slack 中近乎垂直上升的过山车表情符号，描述了身处指数级曲线顶端所承受的巨大推力与心理冲击。 在商业与使用层面，这种指数级爆发已超出团队的最乐观预期。Anthropic 原先设定了极具野心的十倍年度增长目标，但在今年第一季度，其营收与使用量实现了年化八十倍的爆发式增长。这一超常负荷直接导致了算力上限的紧张。为此，Anthropic 近期达成了与 SpaceX 的算力合作协议，以最快速度获取并向用户分配算力基础设施。Dario 坦言，八十倍的增长速度极难维系与应对，他更希望未来能回归到更为可控但依然迅猛的“仅十倍”年增长率。 阿姆达尔定律与不可验证任务训练 (15:00) 随着 AI 辅助编程使内部代码编写速度提升二至五倍，软件开发内部的瓶颈正根据阿姆达尔定律（Amdahl's law）发生转移。Dario 指出，当拉取请求（PR）数量激增三到四倍时，生成代码不再是阻碍，真正的阻碍转移到了代码安全性审计、系统验证与架构设计质量等环节。这种极高的生成速度导致团队内部积累了“极大量的内部技术债务”，促使团队探索如何利用 AI 模型自身来追踪与消除这些技术债务，重塑开发工作流。 这一工程瓶颈直接改变了 Anthropic 对模型的训练重点。Dario 解释称，编写代码之所以进步迅速，是因为其结果可以通过单元测试进行明确客观的验证；然而，发现隐蔽的安全漏洞或评估系统架构设计则高度主观且难以验证。Anthropic 正在探索如何训练模型应对这类“不可验证”任务。这一训练维度的突破不仅旨在消除软件工程领域的深层瓶颈，也将直接反哺模型在创意写作等非科学、主观领域的综合表现。 从单人独角兽到组织级智能体重构 (10:00) 技术扩散历来呈现不均衡性，软件工程师始终是最早拥抱变革的群体。Dario 回忆，约一年前 Instagram 联合创始人 Mike Krieger 曾向他询问世界上何时会出现首家估值十亿美元的单人公司，他当时预测这一节点将在 2026 年到来。Dario 当前认为这一预测正在按计划兑现：市场上已经出现了估值达十亿美元的双人 AI 企业，以及身价数亿美元的单人团队，随着距离 2026 年仅剩约八个月，在指数级曲线上这相当于漫长的时间跨度，AI 正在彻底破除传统创业的资源壁垒。 面向未来，Dario 勾勒了开发者生态与智能体发展的两大关键演进：首先是从单一智能体向多智能体系统的跨越，开发者将如同管理者一样指挥层级化的 Claude 协作集群，使数据中心逐步演变为汇聚无数高智商实体的“天才之国”；其次是从提升个体生产力转向赋能整个组织。尽管单人十亿美元企业备受瞩目，但 Dario 认为 AI 在组织层面的潜力更具变革意义，即让 AI 在人类团队中充当动态协作乘数，实现组织级工作产出的成倍放大。 动态产品构建与“兼顾光影”使命 (20:00) 在快速变化的技术背景下，Anthropic 的产品开发形成了不同于传统软件研发的独特逻辑。首席产品官 Ami 与创始人探讨了产品构建的双重维度：为 AI 构建产品（building for AI）与借助 AI 构建产品（building with AI）。Dario 指出，传统产品周期在指数级技术浪潮中不再适用，团队必须直面能力“突然点亮”的现象——过去因模型能力不足而失败的原型（例如 2022 年因模型太弱而表现糟糕的早期 Claude Code），在新一代模型下会瞬间具备商业可行性。同时，传统聊天机器人界面正面临承载力饱和，更前沿的模型智能正在向类似 Claude Code 的智能体形态转移，而满足专业领域深度定制的 API 市场则将长期存续。 针对业务爆发与技术迭代，Daniela 强调了 Anthropic 内部坚守的核心价值观——“兼顾光明与阴影”（hold light and shade）。这要求团队在追求将变革性技术尽快带给公众的同时，必须严肃承担起防范失业冲击与安全漏洞等潜在风险的责任。Daniela 指出，正是开发者社区的真实反馈构成了 Anthropic 改进工具的基石，驱动 Claude 在全球南方偏远地区的移动医疗诊断、前沿生物医药加速、硬盘损坏婚纱照数据修复以及日常农业追踪等广阔现实场景中发挥实际价值。 Amodei 夫妇的讨论表明，AI 发展已跨越纯粹的算法验证期，全面进入与工程瓶颈、商业现实及组织结构激烈碰撞的深水区。模型代码编写效率的指数级倍增，反向迫使企业面对更深层的系统验证瓶颈与技术债务管理；而在智能体从个体工具迈向组织级协同的过程中，如何在算力受限与高速竞争的现实下践行审慎安全责任，仍是 Anthropic 及整个行业长期面临的核心考验。

[0:00] Daniela Amodei: We're having a lot of fun. There's a ton of adrenaline [0:00] Dario Amodei: actually seeing it on human scale, like there's something deeply, it's kind of deeply strange and unsettling about seeing it actually happen. [5:00] Dario Amodei: I hope the 80x growth doesn't continue because that's just crazy and it's too hard to handle [10:00] Dario Amodei: We're gradually making our way to the country of geniuses in a data center [10:00] Dario Amodei: there's eternity on the exponential [15:00] Daniela Amodei: hold light and shade [20:00] Daniela Amodei: you guys are up in my business all the damn time. Can you please leave me alone and let me do my damn job? [20:00] Ami Vora: All feedback is a gift. I enjoy every perspective. [20:00] Dario Amodei: They were really dumb back then. [25:00] Dario Amodei: an extraordinary amount of internal technical debt. [25:00] Dario Amodei: thinking at the level of the organization rather than just rather than just one person.

[0:00] so Thank you. Thank you All right. All right, everyone. Thank you for joining us again. And I am so excited for this conversation with Dario and Daniela. Let's give them a round. Woo! It is a delight to have you here at Code with Claude. We've been having a great day of sessions, demos, customer sessions, all sorts of fun stuff. But I wanted to ask you maybe just a little bit zooming out we talked a lot about the exponential in some of our conversations this morning and what it feels like to be on the exponential And so as two people who are definitely on the exponential we talked a lot about growth and what it feels like What does it feel like for you all? Well, first of all, it's great to be here. Thank you so much for having us. You know, at Anthropic, we have this little slack emoji of the roller coaster. You guys know the one I'm talking about. But it's like an inflected roller coaster. So it's almost like it's like going straight up. And I think of me and Dario as riding at the front and the back of that roller coaster. I don't know if you all have ever, maybe not recently, been on a roller coaster. I'm not always sure which one of us is in the front and which one of us is in the back. But you get a different type of whiplash depending on which side you're on. And I think that's probably the best encapsulation of what it has felt like. It's like we're having a lot of fun. There's a ton of adrenaline. We're not totally sure that the operator of the roller coaster isn't like a 15 year old summer job of like questionable level of sounds minds. But it's been great. It's fun. It's an adventure. There's a lot going on. Yeah you know the way I always think about the exponential is you know it was it was me and the other co who kind of you know first predicted it through the scaling laws you know over over 10 years ago And, you know, we wrote down these lines and graphs that say, like, well, first, we're going to spend a thousand dollars in a month and then ten thousand and then hundred. You know, it's going to go all the way to to to hundreds of billions. And, you know, the model is going to be this good at this task and this good at this other task and that good at coding. And it is a remarkable experience to write down these lines on graphs and have the predictions come true. So in that sense, they are not surprising at all. And yet the actual experience of seeing what it's like is just, it's so crazy that you're shocked anyway, even though what you wrote down on the graph is exactly what happened. I'm always reminded of like, you know, there's this famous scene in the movie Interstellar where they go to this, a planet that's very close to a black hole. And so the planet has these waves that are like 2,000 feet high. And I was a physicist. I know the math, the general relativity, how much things can be sheared. But actually seeing it on human scale, like there's something deeply, it's kind of deeply strange and unsettling about seeing it actually happen. [5:00] And, you know, that's what it felt like every year at Anthropic. And I feel like this is the first year where, like, the rest of the world is kind of, you know, is seeing us, seeing it with us because we're so much in the spotlight. You know, it applies to the internal growth of the company. It applies to our own work within the company, where the first time we've seen the number of internal PRs inflect upwards due to the work that Claude is doing. And we've seen it externally because actually, this is the first year we've grown faster than the exponential. So we tried to plan very well for a world of 10x growth per year. In the first quarter of this year, we saw, if you were to annualize it, 80x growth per year in revenue and usage. And so that is the reason we have had difficulties with compute, right? We've planned for anything from it only grows a little to it grows 10x, and yet we saw 80x. And so as you saw today with the SpaceX compute deal we working as quickly as possible to provide more compute than we have in the past We continue to do so We pass that compute on to you as soon as we can do so I guess I hope the 80x growth doesn't continue because that's just crazy and it's too hard to handle. I hope for some more normal numbers, a mere 10x. But we will manage it absolutely as best we can, and we're every day trying to obtain even more compute that we can pass on to you. We're sorry if sometimes it takes some time, but we're going to keep going to acquire as much as we can. Awesome. Yes, yay for computer rate limits. You know, this is an audience of developers and builders and that's really what today is about, is about how we're making our platform better because developers who help us close the gap between what the models can do and what they're actually doing for real people out there. And you know both of you talk a lot internally about the importance of developers and builders Maybe Danielle I love to start with you How do you think about supporting this ecosystem supporting this community Yeah, I mean, I'll start out by saying I think in many ways developers are the most important users of Claude, I think for a variety of reasons. One is Anthropic ourselves are majority developers, right? If you think about how we develop this technology, what we're building, we learn so much from the developer community. It's like the best, it's the best partnership because we, it's, first of all, I think it's a group that gives like honest feedback, right? And so I think that is actually really hard to get. You know what I mean? It's like you build a product and you're like, I see some numbers, like those are nice. But like the genuineness with which the developer community I think engages with us is something that is so special. We have tried, you know, really from day one, I think Anthropic has always, you know, primarily built for developers, for businesses. I think we're a little bit unique in the AI ecosystem, you know for that reason And I think we have been very fortunate to be able to benefit from the feedback from the engagement from the community development You know developers are you know in my experience they like they very ecosystem and community oriented which I think we are too right We're like, how do we build for this sort of broader ecosystem of people who are developing, by the way, some of the most inspiring, transformative technologies and building the most incredible companies in the entire world, right? There's been this renaissance of things in, you know, medicine and software development and, you know, financial service. I mean, it's like you pick the industry and there is an incredible developer or an incredible, you know, developer based company that is transforming that industry. Help, you know, leveraging our tools sometimes. And I think that's such a special, that's like a, that's both a privilege and a responsibility that I think Anthropoc holds to say, you know, developers are really the backbone of like how we learn. how we build better tools for all of you. And I think that's a really special relationship that we feel like pride in and also a responsibility towards. Yeah, I mean. And this is an example of the developer community. I think part of what Danielle is saying is feedback is a gift. We hear the positive, [10:00] We also hear the negative. Please keep it coming. It is part of how we know what's working and what isn't. And so we really value and I'm sure as you're talking to people around, we really value all of that. And it helps us know what to do better. Sorry, Daria, back to you. Yeah. You know, one thing I would say is that technology doesn't diffuse at kind of an even pace across the economy. And I think there's a spectrum where the software engineers are the ones who are fastest to adopt new technology. That's why there's so much kind of focus on this area and it's the beginning of it. But it's like it's a foreshadowing of how things are going to work across the economy and how the economy is going to be transformed by AI. by AI. So I think getting this right and really making it work for this community, it's kind of like a microcosm of how we have to make it work across the world. And I think one thing that a dynamic we should watch, it was about, I think it was roughly a year ago, there was an event like this where Mike Krieger asked me when will there be the first billion dollar company with one person And I said 2026 And I think we actually on track to achieve It hadn't quite happened yet. There's been like two person companies that are one billion dollars built with AI. There's been like one person that's worth several hundred million dollars. So but but like, you know, we got we got seven more months. So it's kind of it's kind of no, we do it. Seven more months in 2026 or eight. And there's eternity on the exponential. But what I'm trying to say with this, though, is that there's an enormous ability for one person or a tiny set of people to do a set of things that are incredible, right? Where before, if you just had an idea and you had a vision, like there's so many resources you'd have to accumulate over several years in order to make that vision happen. And I think there's a very unique opportunity for single individuals or very tiny teams to do things that are incredible, right? Where I think we moved from the models are writing code to the models are helping us think of software engineering as a task to the models are helping us think of like how can I build a business or an economic unit as a task And so there an extraordinary amount of opportunity for people in this room to kind of take advantage of that Yeah, it really feels like it's kind of removing barriers. You know, there were all these barriers to like creating that kind of value in the world. And now, I mean, I guess the gauntlet has been thrown. We've got an eight month timer. and I'm excited to see what comes of it. I would love to maybe hear what's going to change for developers. So Dario, you talked a little bit about what they can do now, and you've talked in the past about how you expect Claude to build more with Claude. Can you just talk about how you expect things to move? Yeah, I mean, I think there's several, like maybe trends. One is going from single agents to kind of multiple agents. So the idea that you have a bunch of these clods, it's like managing a team, right? You have a bunch of clods running, and you kind of farm a bunch of things out to your clods, and maybe some of the clods farm things out to other clods with different products. So you have a kind of whole hierarchy We gradually making our way to the country of geniuses in a data center You know we starting with like a team of smart people in a you know in a room or something We're working our way up. It's a different exponential in the country. So I think that's one trend that we're kind of already starting to see. And we're already like offering tools that can help do that. I think a trend that's related to that is like, you know, what we've done so far with cloud code is like, you know, it helps kind of individuals to be more productive. But I think increasingly we're going to start thinking at the level of whole teams and organizations. And how can you make whole teams and whole organizations more productive in a way that is kind of more than just the sum of its parts. And then finally, I think in this area, as with everywhere else, if you want to think about what's next when something's working really well, you should always think about Amdahl's law, which is you speed one thing up. What are the things you're not speeding up? And so I think there are a bunch of things like security, like verification, like just if you're living in a world where you can, within an organization, write three or four times as many PRs as you could previously, you start to understand there are all these other things [15:00] you back or that will go wrong if you speed up just that and not everything else. And so working to speed up those kind of other things so that we can greatly increase people's productivity, but we can do it smoothly and productively and reliably, I think that's going to be very important. Does that have any impact on how you think about training new models or what the future of models looks like? Yeah, I mean, that's true on several levels. I mean, I've already said many times we're using Claude to speed up Claude, right? That's kind of already something that's happening. But I also wonder if the things we're trying to do with the models could also influence how we build them. So when I talk about these things like verification or design quality or things like that, one of the reasons training models for code and software engineering has gone so fast is that you have this verifiability, where you train the model, and it's like you're able to verify it by running unit tests And so that has a lot of properties that simplify the process of training But what you discover is that there are these of course aspects of the job that are not verifiable right And some of the, is this thing really right? Can we find errors? Are there security issues? Not quite as verifiable. And so training the models to be better at that, which I think will also make the models better at other things where they haven't made progress as fast as coding, like their ability to ride or their ability to kind of do to do less objective scientific tasks. So I think it's going to have benefits in many other areas. But I think we find even within software engineering, this kind of soft or somewhat subjective skills and abilities are become surprisingly important because of Amdahl's law. I would love to hear, you know, we talk a lot about our mission internally, like, Daniela, as we just keep growing and the stakes of this whole industry keep getting higher, what should people know about our mission and about us as a company? You know, I think when I think about what Anthropic is trying to do, there these sort of like two maybe two pillars right The first is around how do we develop this transformative technology in a way that is good for everybody right And I think this goal of, Claude is this incredible tool. It has the power to really transform, you know, what people build and how they create and the ambition level of what they can develop themselves. And I think there comes a huge amount of opportunity there, and there's also some risk, right? I think that we've talked about this a lot publicly. There's some risk to just labor disruption. There's risks to ensuring that the technology is developed safely, that it's good for people. And I think Anthropik's job, or what we try to do, is really think about how to balance these two things in equal measure. We have this internal cultural value called hold light and shade. And I think that it is such a good encapsulation of, you know, what we see about how the technology is being leveraged today. And also just our approach to putting the technology out into the world, right? I think Mythos and Glassman are a great example of this. The potential to build something incredible with a model that capable is so vast And we want to be a little bit careful about how we release it because of some of the security vulnerabilities right And I think that this is this kind of complicated dance that we do where we're like, we really want to get stuff out as quickly as we can. We're trying to build the best products and release the most powerful models, and we're just trying to do it responsibly. I think that is really the underpinning of the majority of actions that we take is sort of grounded across those two pillars. I think that is like one of the things I find most meaningful about getting to work here is just thinking about how much everything is changing. And the fact that we're kind of all building in the industry right now, to me, it feels like we're getting the chance to have a vote in how everything unfolds. and the trade-offs you're describing, that's just what I think of when I think of Hold Light and Shade, is as things move so rapidly, we get to build experiences that other people use to understand what the future looks like. So that's something I'm always really personally excited about. Maybe I'll ask a little bit about product. Both of you kind of lean in quite a lot on the product side. One thing that we talk a lot about is building for the exponential. I always think about progress [20:00] There's kind of a bridge between the technology that exists and the problems that people have. And it's just a very interesting time because the technology is changing much more rapidly than we're used to. Can you talk a little bit about how you think about product building in this world? So I love, Ami, I love your way of putting that. It's like, Dario and Daniela lean in a lot. And this chief product officer, what Ami means is like, you guys are up in my business all the damn time. Can you please leave me alone and let me do my damn job? All feedback is a gift. I enjoy every perspective. But no, Ami is right. I think you have a hard job that you wear incredibly well. But, you know, I think in all seriousness, you know, Dario and I both, we care a lot about the product, right? It's a representation of what we are trying to build at Anthropic. We want it to be useful for people. We want it to be accessible. we want the product to be good, right? Part of why I think we're leaned in so much is we feel very invested in ensuring that people that are using Claude are getting out of it everything that they can. And so I think our you know our bothering you is really I think our way of you know feeling like to the degree we can we standing up for our customers We standing up for our users who are building sometimes their whole business around the premise of what these AI models are capable of doing And I think the other thing, maybe the thing that makes product at Anthropic unusual or different than how I've seen it done, you know, at other companies I've worked at is like product is sort of one input and research is another. Like, I'm sure you've felt this in your role. Like, sometimes we're like, man, this is a clear, you know, this is a great place where we should just be able to build something better that's easier to use. Or here's a product idea, we really want to enable people to be able to access it right out of the gate. But a lot of the time, product innovation is driven by what new capabilities emerge in the model. And I think coding is actually a great example of this, right? We didn't just sit out and say, like, from day one, we're going to build a coding product. It was like once we saw that the models were able to write code at a reasonably accurate level, not perfect, we were like, huh, this is interesting. It seems like a lot of people that are kind of Claude files are developers, right? And they're using it to write code. This has always been a community that we worked well with we wanted to lean in with and engage in support Should we build a tool for them right Should we build something that actually is going to enable people to be better at doing their day-to-day work in this way? But I think that's just an interesting dance inside the company where there's a component that is sort of, I don't wanna say traditional product because nothing about Anthropic is traditional, but I think there's a component that looks like a normal product organization. And then there's part of the organization that's like, what is new from the models? what's happening on the research side, and how do we kind of marry those two things together? Yeah, I would maybe take this from two lenses, which is building products for AI and building products with AI. And I think the internal experience at Anthropic, month by month and week by week, has given us lessons about both. So I think over the last few years, learned a lot of lessons about building products with AI. And in some ways it's been an advantage that, I was a researcher, I was never in the era of building products without AI. So it's like, you can end up in a situation where there are things you don't have to unlearn. You can just learn the new world from scratch. I think you got the essential difference which is that if you go back to the product era in the 2010s you had a slowly changing technological background right You were trying to do new things with you know the kind of technology that was present And of course every once in a while you'd have a new framework or new way of doing, but relatively slow. AI is moving lightning fast. And so there are a few consequences of that. One is that there are new products that are not possible with a given capability of model. But then when you take the next step, when you go further far enough along the exponential, it then suddenly they light up suddenly they become possible. And so it puts a premium on internal experimentation, because you always want to be trying something even if you tried something that didn't work, you want to revisit it a few months later, because, you know, it might it might work then when it didn't work before, which sounds a little bit crazy. But you know, if we had tried to do quad code in like 2022, it wouldn't have worked because the models wouldn't have been strong enough. It was a frustrating experience. We did have some early things that were a little like Claude Code in 2022. And it was like, oh, this is intro. But you couldn't actually derive value from it because the models were dumb. So they were. I've been training these models since 2015. They were really dumb. They were really dumb back then. [25:00] The second thing is that products reach their saturation when models start to get too good. So I think this has happened with chatbots, right? Like, you know, it's a big market. Lots of people use it. It's going to stay around. But, like, you know, the ways in which we're making models smarter today are much more evident in, you know, today's cloud code form factor and in more generally, the Genetic form factor than they are on the chatbot code factor. So that's kind of the other side of the coin, which is that you always have to be thinking about what the new thing is, right? The way this business work isn't you make a product, it becomes very big, and then all the kind of stability sets in, and you have to ask. You're constantly not only are you able to make something new, you're constantly needing to make something new, or at least update the things that you've made. So it's kind of constantly an innovation laboratory. And I think the other thing it means of particular relevance for developers and software engineers is API never really goes away as a market. Because the fact that it's always possible to build new products that true inside Anthropic It also true outside Anthropic And so I actually think both within code as an application but just coders writing code with Claude for medical applications, for law, for finance, or they're going to keep me new applications because the models get smarter and they enable them. So that's building kind of in the age of AI. There's a newer thing we've seen maybe over the last year or six months, which is building with AI itself, like using AI to enable the process of doing product development faster. That one is interesting. And again, we would go back to our old friend Amdahl's law, which is we've found with the internal model acceleration, You can write two times as many, four times as many, five times as many. You see this within the company, but then you see what breaks. We've been able to ship a lot more products than we could a year ago, and they're of pretty high quality, or I hope you think that But it is possible to accumulate an extraordinary amount of internal technical debt when you ship that fast And so then you have to say well can we also use the AI models to undo that technical debt or keep track of what it is that we're doing? And then you learn, ah, the team has to work together in a totally different way. And these revelations come month by month, and you kind of learn how to do things. in kind of a totally new way. So somehow it increases the tempo, not just of building, but in which the way you have to change the way you build as a team. I really experienced that. I think the hardest part of it is you can get really familiar with the problems because the problems don't change that fast. The problems are about humans. You know, like we always, we're going to have similar problems. but a thing that's hard is to learn to be fresh-eyed all the time about what the technology can do and like constantly scan for that and then i also feel on a personal basis where your job just changes because you hit a new bottleneck you know and like the the way you spend your day is just a little different um maybe i just ask a couple more questions um maybe dario very quickly you talked a lot about model capabilities and how they change so often Is there one thing that makes you most excited in the models that are coming in the next call it six months You know, I would say this idea of, you know, thinking thinking at the level of the organization rather than just rather than just one person. And, you know, again, it ties to this, you know, one person billion dollar business, which, you know, maybe that will turn out to be to be to be an under to be an underestimate. But, you know, just kind of the idea that, you know, you can both have AI do the work of many people, but that when you have a team of humans, that AI is not just doing the work of many people working for one person, but that it does the work of many people many times over by operating within an organization of humans. Awesome. And maybe I'll just ask to close, like, Danielle, one thing we talked about is is some of the kind of developers or use cases we see that are just so inspiring. I talked a bit in the keynote about some of my favorites, whether that's people using it for efficiency at places like Stripe doing major infra upgrades, but also people using it for these [30:00] the personal and specific ways like connecting kids to foster families faster. And I would just love to hear, what are some of your favorite examples of how you see people use the tools? Yeah, I mean, I think even going back to the days when I was at Stripe, some of the, something that I've just loved about the developer community in general is like for every interesting challenge or problem there is in the world, there's like an incredible dev somewhere who's like trying to make that thing better. And I think getting to watch people build things with Claude that are just bringing so much utility and value and meaning to people around the world is really inspiring. I think, you know, for me personally, you know, a pilot that I've seen with some developers who are building these like interfaces basically for mobile doctors in the global south. So places where it's really hard to reach an actual doctor because of access issues, right? you're down a dirt road somewhere, you know, 50 miles from the nearest city, but people there still encounter health challenges. How do you actually like work with this really smart technology to build these like interfaces where people can just ask you the question, it can give you some medical advice, right? In a way that's like sanctioned. I'm also just like in general blown away by the medical research field and what is coming out of Claude being able to accelerate biomedical research across many of the developers that work with us, that build on us. There are also just some really heartwarming like individual examples. We have this user happiness channel at work So my favorite ones there a developer who used Claude to help retrieve their wedding photos that were corrupt like on a corrupted hard drive I thought that was so sweet and just like such a personal use case but so like so meaningful. And then my last one was just so random is someone is using Claude to chart the growth of their tomato plants in their garden. And I was like, never in a million years would I have thought of it. But it's like it's I like was like, do you have a live cam? and can I subscribe to this? Because I would really love to see it. But I think just the range of things that people are able to do is just astonishing. Well, Dario, Daniela, thank you both so much for spending time with us here. It's a pleasure to see you. Thank you to all of you for joining us. We've got a great rest of the day coming, but please join me in giving a round of applause. Thank you. Thank you. Thank you.