[0:01:52] Thank you for joining us again. And I am so excited for this conversation with Dario and Daniela. Let's give them a round. 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.
[0:02:26] To be on the exponential. And so as two people who are definitely on the exponential, we've 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 going.
[0:02:57] 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.
[0:03:25] We're not totally sure that the operator of the roller coaster isn't like a 15-year-old doing a summer job of like questionable level of sound mind. 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...
[0:03:46] Me and the other co-founders who kind of, you know, first predicted it through the scaling laws, you know, over 10 years ago. And, you know, we wrote down these lines on graphs that say, like, well, first we're going to spend $1,000 in AMON, then $10,000, and then $100,000. You know, it's going to go all the way 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.
[0:04:13] 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 planet that's very close to a black hole. And so the planet has these waves that are like.
[0:04:42] You know, 2,000 feet high. And, you know, 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, you know, deeply strange and unsettling about seeing it actually happen. 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.
[0:05:11] We've seen it with us because we're so much in the spotlight. 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.
[0:05:40] 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. 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
[0:06:09] Compute deal. We're working as quickly as possible to provide more compute than we have in the past. We'll continue to do so. We'll pass that compute on to you as soon as we can do so. I guess I hope the ADX 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.
[0:06:38] 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 compute rate limits.
[0:07:07] 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 are 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
[0:07:29] Developers and builders. Maybe Danielle, I'd love to start with you. How do you think about supporting this ecosystem, supporting this community? 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. If you think about how we develop this technology, what we're building, we
[0:07:59] We learn so much from the developer community. It's the best partnership because first of all, I think it's a group that gives 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. Those are nice.
[0:08:22] The genuineness with which the developer community I think engages with us is something that is so special. We have tried really from day one, I think Anthropic has always primarily built for developers, for businesses. I think we're a little bit unique in the AI ecosystem 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. Developers are
[0:08:50] In my experience, they're very ecosystem and community oriented, which I think we are too. We're like, how do we build for this 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? There's been this renaissance of things in medicine and software development and
[0:09:14] It's like you pick the industry and there is an incredible developer or an incredible developer-based company that is transforming that industry, leveraging our tools sometimes. I think that's both a privilege and a responsibility that I think Anthropic holds to say developers are really the backbone of how we learn, how we build better tools for all of you.
[0:09:41] And I think that's a really special relationship that we feel 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. 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.
[0:10:11] And it helps us know what to do better. Sorry, Dario, 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, right? And I think there's a spectrum where the software engineers are the ones who are fastest to kind of adopt new, kind of 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.
[0:10:39] of how things are going to work across the economy, right? And how the economy is going to be transformed by AI. So I think, you know, getting this right and really, really making it work for this community, it's kind of like a microcosm of how we have to, you know, of how we have to make it work across the world. And I think one thing that, you know, a dynamic we should watch, it was about, I think it was roughly a year ago, there was an event.
[0:11:08] Like this, where Mike Krieger asked me, you know, when will there be the first billion dollar company with one person? And I said 2026. And I think we're 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. But like, you know, we got seven more months. So it's kind of...
[0:11:35] It's kind of... No, we do. Seven more months in 2026. Or eight. And, you know, 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, you know, before, if you just had an idea and you had a vision, like...
[0:12:04] 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. 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
[0:12:29] How can I build a business or an economic unit as a task? And so there's 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.
[0:12:58] 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...
[0:13:21] Going from single agents to kind of multiple agents. So the idea that, you know, you have a bunch of these Claude, it's like managing a team, right? You have a bunch of Claudes running and like, you know, you kind of farm a bunch of things out to your Claudes and maybe some of the Claudes farm things out to other Claudes with like different price. So you have a kind of whole hierarchy or we're gradually making our way to like the country of geniuses in the data center. You know, we're starting with like a team of smart people in a...
[0:13:48] 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 Claude Code is like.
[0:14:08] 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, you know, I think in this area, as with everywhere else,
[0:14:32] 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.
[0:14:55] As you could previously, you start to understand there are all these other things that are holding 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.
[0:15:21] 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
[0:15:51] How we build them. So when I talk about these things like, you know, kind of verification or kind of design quality or things like that, like one of the reasons training models for code and software engineering has gone so fast is that you have this verifiability, right? Where you train the model and it's like, you know, you're able to verify it by running unit tests. And so that has a lot of properties that make simplify the process of training. But what you discover is that there are these, of course, aspects of the job that are not verifiable.
[0:16:21] 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 less objective scientific tasks. So I think it's going to have benefits in many.
[0:16:49] In many other areas, but I think we find even within software engineering, these kind of soft or somewhat subjective skills and abilities 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?
[0:17:20] You know, I think when I think about what Anthropic is trying to do, there's 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...
[0:17:50] 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 technologies develop safely, that it's good for people. And I think Anthropic'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
[0:18:18] Cultural value called Hold Light and Shade. And I think that it is such a good encapsulation of 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 Glasswing are a great example of this. The potential...
[0:18:39] 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.
[0:19:10] I think that is 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...
[0:19:40] 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 product as 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
[0:20:08] It's 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 needs 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.
[0:20:37] But no, Ami is right. I think you have a hard job that you wear incredibly well. But I think in all seriousness, Dario and I both
[0:20:46] We care a lot about the product. 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. 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 bothering you is really, I think, our way of feeling like to the degree we
[0:21:15] We're standing up for our customers. We're 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 at other companies I've worked at is...
[0:21:36] Product is sort of one input and research is another. I'm sure you've felt this in your role. Sometimes we're like, man, 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,
[0:22:05] 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've worked well with, we've 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
[0:22:34] 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 want to 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,
[0:23:04] 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.
[0:23:30] 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 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 it, but relatively slow.
[0:23:57] 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 far enough along the exponential, then suddenly they light up. Suddenly they become possible. And so it puts a premium on internal experimentation.
[0:24:20] 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 work then when it didn't work before, which sounds a little bit crazy. But, you know, if we had tried to do Claude 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 interesting. But you couldn't actually derive value from it because the models were dumb.
[0:24:49] So they were. I've been training these models since 2015. They were really dumb. They were really dumb back then. The second thing is that products reach their saturation when models start to get too good. So I think this has happened with
[0:25:13] 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 Claude Code form factor and in more generally magentic form factor than they are on the chatbot code.
[0:25:33] 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 works 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.
[0:25:58] 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's true inside Anthropic, it's also true outside Anthropic. And so I actually think both within code as an application, but just coders writing
[0:26:26] Writing code with Claude for medical applications, for law, for finance. They're going to keep me new applications because the models get smarter and they enable them. So that's a building kind of in the age of AI. There's a newer thing we've seen maybe over the last
[0:26:48] 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 will 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.
[0:27:14] 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.
[0:27:43] 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.
[0:28:04] I've 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. 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 constantly scan for that. And then I also feel on a personal basis where your job...
[0:28:30] Just changes because you hit a new bottleneck. And the way you spend your day is just a little different. Maybe I'll just ask a couple more questions. 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? I would say this idea of...
[0:28:57] Thinking at the level of the organization rather than just one person. And again, it ties to this one person, billion dollar business, which maybe that will turn out to be an underestimate. But just kind of the idea that 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
[0:29:27] 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 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
[0:29:53] People using it for efficiency at places like Stripe doing major infra upgrades, but also people using it for these very personal and specific ways like connecting kids to foster families faster. And I would just love to hear, you know, what are some of your favorite examples of how you see people using the tools? Yeah, I mean, I think even going back to the days when I was at Stripe, something that I've just loved about the developer community in general is like for every
[0:30:21] Interesting challenge or problem there is in the world, there's an incredible dev somewhere who's trying to make that thing better. And I think getting to watch...
[0:30:29] 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 for me personally, a pilot that I've seen with some developers who are building these 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 50 miles from the nearest city, but people there
[0:30:59] We still encounter health challenges. How do you actually work with this really smart technology to build these interfaces where people can just ask you the question and can give you some medical advice in a way that's sanctioned? I'm also just in general blown away by the medical research.
[0:31:17] 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 individual examples. We have this user happiness channel at work. Some of my favorite ones, there's a developer who used
[0:31:34] 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 like 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
[0:32:03] 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.