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A conversation with Dario Amodei & Daniela Amodei

A conversation with Anthropic Co-Founders Dario Amodei & Daniela Amodei, moderated by Chief Product Officer Ami Vora at the Code with Claude developer conference in San Francisco.

Ami VorahostDaniela AmodeiguestDario Amodeiguest
May 6, 202633mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 1:49

    Intro

    1. AV

      [upbeat music] [audience cheering]

  2. 1:49 – 3:41

    Riding the AI exponential: growth feels like an inflected rollercoaster

    1. AV

      All right. All right, everyone. Thank you for, um, for joining us again, and I am so excited for this conversation with Dario and Daniela. Let's give them a round. Whoo.

    2. DA

      [clapping]

    3. AV

      It is a delight to have you here at Code with Claude. We've been having a great day of sessions, demos, uh, 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, uh, what it feels like to be on the exponential. And so as two people [laughs] who are definitely on the exponential, um, we've talked a lot about growth and what it feels like. What does it feel like for you all?

    4. DA

      Well, first of all, it's great to be here. Thank you so much for having us. Um, you know, at Anthropic, we have this little Slack emoji of the rollercoaster. You guys know the one I'm talking about? Um, but it's like an inflected rollercoaster, 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-

    5. AV

      [laughs]

    6. DA

      ... of that rollercoaster. I don't know if you all have ever, maybe not recently, been on a rollercoaster. 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 w- a different type of whiplash depending on which side you're on. Um, 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. Um, we're not totally sure that the operator of the rollercoaster isn't like a 15-year-old [laughing] who's doing a summer job-

    7. AV

      Summer carnival ride

    8. DA

      ... of like questionable level of sounds mind. Um, but it's been great. It's fun. It's an adventure. There's a lot going on.

  3. 3:41 – 5:12

    Scaling-law predictions come true—yet still feel surreal in reality

    1. DA

      Yeah, you know, the way I always think about the exponential is, you know, it was, it was me and the other co-founders who kinda, you know, first predicted it through the scaling laws, you know, over, over ten years ago. And, you know, we wrote down these lines on graphs that say like, "Well, first we're gonna spend $1,000 in AMA, and then 10,000, and then 100." You know, it's gonna go all the way to, to, to hundreds of billions, and, you know, the model's gonna be this good at this task, and this good at this other task, and that good at, at, at, at coding. And, and it, it, 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, and yet the actual experience of seeing what it's like-

    2. AV

      Mm

    3. DA

      ... i- is, is, is just, it's, it's so crazy that you're shocked anyway, even though what you wrote down on the, on the graph is exactly what hap... I'm always reminded of like, you know, the, the-- there's this famous scene in the movie Interstellar where, where they go to this, you know, planet that's very close to a black hole. And, and so, you know, the planet has these waves that are like, you know, 2,000 feet high. And, and, you know, I, I was a physicist. I know, I know the math of general relativity, how much things can be, um, uh, uh, uh, sheared. But actually seeing it on human scale, like there's something deeply, you know, th- this kind of deeply strange and unsettling about seeing it actually happen. And, and, you know, that, that's what it felt like every year at Anthropic. And, and I feel like this is the first year where like the rest of the world is, is kind of, you know, is seeing us, seeing it with us-

    4. AV

      Mm-hmm

  4. 5:12 – 7:00

    When growth outruns planning: 80× annualized usage and compute constraints

    1. DA

      ... because we're so much in the spotlight. Um, you know, it applies to the internal growth of the company. It applies to our own work within the company. We're at the first time we've seen the number of internal PRs inflect upwards due to, due to the work that, that Claude is doing. And we've seen it externally because actually this is the first year we've grown faster than the exponential.

    2. AV

      Mm.

    3. DA

      So, you know, w- we tried to plan very well for a world of 10X growth per year. Um, in the first quarter of this year, we sawIf you were to annualize it, 80x growth per year-

    4. AV

      Ooh

    5. DA

      ... in, in, in, in, in, in, in, in, in, in, in, in revenue and usage. And, and, and so that, that, 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. Um, and, and, and so, you know, as you saw today with the, the, the, the SpaceX compute deal, we're working as quickly as possible to provide more, more compute than, uh, than, than, than we have in the past. We'll continue to do so. We'll pass that compute on to you as, as, as, as, as soon as we can, as soon as we can do so. Um, I, I, I guess I hope the 80X growth doesn't continue-

    6. AV

      [laughs]

    7. DA

      ... 'cause that's just crazy and it's too hard to, too hard to handle. I hope for some more normal, [laughs] more normal, uh, uh, more normal-

    8. AV

      AI normal

    9. DA

      ... numbers. A mere, a mere, a mere 10X. Um, uh, but we will, we will manage it, uh, absolutely as, uh, we will manage it absolutely as, uh, best we can and, uh, you know, we're, we're every day trying to, uh, trying to obtain even more compute that, that we can, uh, we can, we can, we can pass on to you. We're sorry if sometimes it takes some time, but, uh, we're gonna, we're gonna, we're gonna keep going to acquire as much as we can.

  5. 7:00 – 10:14

    Why developers are central: honest feedback and ecosystem partnership

    1. AV

      Awesome. Um. [clapping] Yes. Yay for compute 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 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 developers and builders. Um, maybe Daniela, I'd love to start with you. Like, how do you think about supporting this ecosystem, supporting this community?

    2. DA

      Yeah, I mean, I'll start out by saying I think in many ways, developers are the most important users of Claude, um, I think for a variety of reasons. You know, one is Anthropic ourselves are majority developers, right? If you think about, um, how we develop this technology, what we're building, we learn so much from the developer community. It's like the best, um, it's the best partnership-

    3. AV

      Mm

    4. DA

      ... because we-- It's-- First of all, I think it's, it's a group that gives, like, honest feedback.

    5. AV

      [laughs]

    6. DA

      Right? And so I think that, that is actually really hard to get.

    7. AV

      Yeah.

    8. DA

      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.

    9. AV

      Mm.

    10. DA

      Um, 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're like, they're 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, uh, software development and, you know, financial sur- 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-

    11. AV

      Mm

    12. DA

      ... um, help- you know, leveraging our tools sometimes. And I think, um, that's such a special... That's like a, that's both a privilege and a responsibility that I think Anthropic holds to say, you know, developers are really, um, the backbone of like how we learn, how we build better tools for all of you, and I think that's a, that's a really special relationship that, um, that we feel like pride in and also a responsibility towards.

    13. DA

      Yeah, I mean... [clapping]

    14. AV

      [laughs] And this is an example of the developer community. I think part of what Daniela 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, uh, uh, and I'm sure as you're talking to people around, we really value all of that, um, and it helps us know what to do better. Sorry, Dario, back to you.

  6. 10:14 – 12:41

    From dev adoption to economy-wide transformation—and the solo unicorn prediction

    1. DA

      Yeah. Uh, you know, one thing I would say is that technology doesn't diffuse at, uh, 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, that's, that's why there's so much, uh, 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, right, and how the economy is gonna be transformed by, by, by AI. So I think, you know, getting this right and, and really, really making it work for, 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, and I think one thing that, um, you know, a, a, a dynamic we should watch... Uh, it was about, uh, rough- I think it was roughly a year ago, there was an event, uh, uh, uh, like this, um, where Mike Krieger asked me, you know, "When will there be the first, uh, billion-dollar company with one person?" And I said, "Twenty twenty-six." Um, uh, and I, I think we're actually on track to achieve it. 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-

    2. AV

      So close

    3. DA

      ... several hundred million, million dollars. So but, but, like, you know, we got, we got seven more months. So it- it's kind of-It's, it's kind of-

    4. AV

      [laughs]

    5. DA

      No, we do. It's, so we see seven more months in twenty twenty-six, um, or eight. Um, uh, uh, and, you know, that's, that's, there's an eternity on, on the exponential. Um, uh, but what I'm trying to say with this though is that there's, there's an enormous ability for one person or a tiny set of people to, to do a set of things that are incredible, right? Where, you know, b-before if, you know, 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, and I think there's a very unique opportunity for single, single individuals or very tiny teams to, to do things that are incredible, right? Where I think we moved from the models are writing code to, you know, the, 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, as a, as, as, as a task. And so there's an extraordinary amount of opportunity for, for people in this room to, to, to, to kind of take advantage of that.

  7. 12:41 – 15:21

    What changes next for builders: multi-agent workflows and organization-level productivity

    1. AV

      Yeah. It really feels like it's, 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, an eight-month timer. [laughs] Um, and I'm excited to see what comes of it. Um, I would love to maybe hear what's gonna change for developers. So Dario, you talked a little bit about, uh, what they can do now, and you've talked, you know, in the past about how you expect, uh, Claude to build more with Claude. Can you just talk about how you expect things to move?

    2. DA

      Yeah. I mean, I, I, I think there's several like maybe, maybe trends. One is, uh, going from, uh, single agents to kind of multiple agents. So the idea that, you know, you have a bunch of these Claude, right? It's like managing a team, right? You have a bunch of Claudes running and like, you know, you kind of, you kind of farm a bunch of things out to your Claudes, and maybe some of the Claudes farm things out to other Claudes-

    3. AV

      [laughs]

    4. DA

      ... with like different pro- so you have a kind of whole hierarchy or wh- We're gradually making our way to like the country of geniuses in a data center. You know, we're starting with like a, a, a team of smart people in a, you know, in a room or something. We're working our way up to a city-

    5. AV

      It's a different exponential

    6. DA

      ... and then a country. Um, uh, uh, so, uh, I, I, 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, uh, you know, it, it, it helps kind of individuals to be more productive. But I think increasingly we're gonna start thinking at the level of whole teams and organizations-

    7. AV

      Mm

    8. DA

      ... and how can you make whole teams and whole organizations more, uh, more, uh, productive in a way that is kind of more than just the sum of its parts. Um, and then finally, um, you know, I think in this area, as with everywhere else, um, if you wanna 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, and so I think there are a bunch of things like, uh, 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 previ- 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.

    9. AV

      Mm.

    10. DA

      And, and so working to speed up those, those, those kind of, those kind of other things so that we can greatly increase people's productivity, but we can do it smoothly and, uh, uh, uh, you know, smoothly and productively and, and, and reliably, I think that's gonna be very important.

  8. 15:21 – 17:06

    How these bottlenecks shape model training: beyond unit-test verifiability

    1. AV

      Does that have any impact on how you think about training new models or, you know, what, what the future of models looks like?

    2. DA

      Yeah. I mean, you know, th-th-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, that's, uh, that's, uh, that's kinda al- that's kinda already, uh, um, uh, uh, you know, something that's, that's happening. Um, but I, you know, I also wonder if, if the things we're trying to do with the models could, um, uh, uh, could also influence, um, how we, 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, by running unit tests and so that, I mean, there has a lot of properties that make... simplify the process of training.

    3. AV

      Mm.

    4. DA

      But what you discover is that there are these, of course, aspects of the, of the job that are not verifiable, right? And, and, you know, some of the, some of the, you know, i- is this thing really right? Can we find errors? Are there security issues? Not quite as verifiable. Um, and, and so training, training the models to be better at that, which I think will also make the models better at, at, at other things where they haven't made progress as fast as coding, like their ability to write or their ability to kinda do, do, do, do, you know, to, to do, uh, you know, less, less objective scientific tasks. So I think it's gonna have benefits in many, in many other areas. But, you know, I think we find even, even within software engineering, this, uh, you know, this, these, these kind of, um, uh, uh, soft or somewhat subjective, um, uh, skills and abilities are become surprisingly important because of Amdahl's law.

  9. 17:06 – 19:41

    Anthropic’s mission: balancing transformative upside with real risks (“hold light and shade”)

    1. AV

      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, h-h-what should people know about our mission and about us as a company?

    2. DA

      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 ofClaude is this incredible tool. It has the power to really transform, you know, what people build and how they create, and the, 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, um, just labor disruption. There's risks to ensuring that the technology's developed 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, uh, 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, um, and, 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, um, to build something incredible with a model that capable is so vast, and we wanna 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 wanna 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, is sort of grounded in, in across those two pillars.

    3. AV

      I, 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, uh, the fact that we're kind of all building in the industry right now. It-- to me, it feels like w-we're getting the chance to have a vote in how everything unfolds, and the trade-offs you're describing, that's, that's just what I think of when I think of hold light and shade, is, um, as things move so rapidly, we get to build experiences that other people use to understand

  10. 19:41 – 22:59

    Building products in a world where model capabilities drive the roadmap

    1. AV

      what, what the future looks like. Um, so that's something I'm always really personally excited about. Um, maybe I'll ask a little bit about product. Both of you kind of lean in quite a lot on the product side. Um, one thing that we talk a lot about is building for the exponential. Um, 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 is changing much more rapidly than we're used to. Um, can you talk a little bit about how you think about product building in this world?

    2. DA

      So I love-- Ami, I love your way of putting that. It's like Dario and Daniela lean in a lot-

    3. AV

      [laughs]

    4. DA

      ... and as Chief Product Officer, what Ami means is like, "You guys are up in my business all the damn time."

    5. AV

      [laughs]

    6. DA

      "Can you please leave me alone and let me do my damn job?" [laughs]

    7. AV

      All feedback is a gift. [laughing]

    8. DA

      Um-

    9. AV

      I enjoy every perspective.

    10. DA

      But no, Ami is right. I think you, you have a, you have a hard job that you wear incredibly well. Um, but you know, I th- I think in all seriousness, you know, Dario and I both, we care a lot about the product, right? It's a, it's um, 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, um, you know, our bothering you is really-

    11. AV

      [laughs]

    12. DA

      ... I think our way of, you know, feeling like to the degree we can, 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, um, you know, at other companies I've worked at, is pr- like product is, 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, 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 gonna build a coding product." It was like once we saw that the models were able to write code at a reasonably, um, 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 y- are developers, right? And they're using it to write code. Um, this has always been a community that we've worked well with, we've wanted to lean in with, and, and engage and 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, 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 'cause nothing about Anthropic is traditional, but um, but I think there's a component that, that looks like a normal product organization, and then there's part of the organization that's like what is, what is new from the models? What's happening on the research side, and how do we kind of marry those two things together?

    13. AV

      Please.

  11. 22:59 – 28:35

    Two lenses: building products for AI vs building products with AI (and the tempo trap)

    1. DA

      Yeah. I, I, I would maybe, uh, take this from two lenses, which is, um, uh, uh, building products for AI and building products with AI. And, and I think, uh, uh, the internal experience at Anthropic month by month and week by week has given us lessons about both. Um, so I think over the last few years, learned a lot of lessons about, you know, building, building products with AI. And in some ways, it's been an advantage that, you know, I was, I was a researcher. I was never in the era of, you know, building products without AI, so it's like you can end up in a situation where you-- there are things you don't have to unlearn. You can just, you can just-

    2. AV

      Fresh eyes the whole way

    3. DA

      ... you can just learn the new world from scratch. Um, uh, I, I, I, I think you got the essential difference, which is that if you go back to the product era in the twenty tens, you had a slowly changing technological background, right? You were trying to do new things with, you know, the, the, the kind of technology that was present. And of course, every once in a while you'd have a new framework or a new way of doing, but relatively slow.AI is moving lightning fast. And so there are a few consequences of that. One is that, uh, 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-- f-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 di-didn't work, you want to revisit it a few months later because, you know, it, 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 Claude Code in, in like twenty twenty-two, 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 twenty twenty-two, and it was like, "Ah, this is inter--" But you couldn't, you couldn't actually derive value from it because the models were dumb. Uh-

    4. AV

      [laughing]

    5. DA

      So that's-- They were. Um-

    6. AV

      [laughing]

    7. DA

      I've been training these models since twenty fifteen. They, they were really, they were really dumb. They were really dumb back then.

    8. AV

      [laughing]

    9. DA

      Um, uh, I-- The, the second thing is that products reach their, uh, uh, products reach their, um, saturation when models start to get too good. So I think this has happened with chatbots, right? Like, you know, it's, it's, it's a big market. Lots of people use it. It's gonna, it's gonna stay around. But, like, you know, the ways in which we're making models smarter today are much more evident in, uh, you know, today's Claude Code fa- form factor and in more generally in the Gentic form factor than they are on the chatbot code factor. So it's ki- 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, isn't you make a product that becomes very big and then all the kind of stability sets in and you have to ask... You're, you're constantly n-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, it's kind of constantly an innovation laboratory. Um, and I think the other thing it, it means of, of particular relevance for developers and software engineers is API never really goes away as a market-

    10. AV

      Hmm

    11. DA

      ... um, uh, because it, it bec- the fact that it's always possible to build new, um, products, that's true inside Anthropic, it's also true outside Anthropic. And, and, and so, and so I, I, I, I actually think, you know, both, both within code as an application, but just coders writing, writing Code with Claude for, you know, medical applications, for, you know, law, for finance or, or, or, you know, there are gonna keep being new applications because the models get smarter a-and they enable them. Um, so that's, that's, um, uh, a 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-

    12. AV

      Mm

    13. DA

      ... like using AI to kind of, you know, to, to, to, to, to enable the, the process of, of doing, uh, uh, uh, product development, uh, faster. Um, that, that one is interesting. And again, we would go back to our old friend Amdahl's law, which is, you know, we've, we've found with the internal model acceleration, you can write two times as many, four times as many, five times as many. Y-you know, we-- you just-- you, you, you see this within the company, but then you see what breaks.

    14. AV

      Mm-hmm.

    15. DA

      We've been able to ship a, a lot more products, you know, than we could a year ago, and they're of pretty high quality, or, or, or I hope you think that. Um, uh, uh, uh, uh, uh-

    16. AV

      [laughing]

    17. DA

      But, but, but it, 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, like, undo that technical debt or keep track of what it is that we're doing? And, and then you learn, ah, the team has to work together in a totally different way. And these, these, these revelations come mon-month by month, and you kind of learn how to do things, um, uh, uh, in, in kind of a totally new way. So somehow it, 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.

    18. AV

      I c-- 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, you know. Like, we always-- we're gonna 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 it on a personal basis where your job just changes 'cause you hit a new bottleneck, you know, and, like, the, the way you spend your day is

  12. 28:35 – 29:37

    Near-term excitement: AI operating at the organization level

    1. AV

      just a little different. Um, maybe I'll just ask a couple more questions. Um, maybe Dario, v-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?

    2. DA

      Uh, you know, I, I would say this idea of, um, you know, thinking, thinking at the level of, uh, the organization rather than just-

    3. AV

      Mm

    4. DA

      ... uh, rather than just one person. Um, and, and, you know, a-again, it, it, it ties to this, um, uh, you know, one person billion-dollar business, which, you know, maybe that will turn out to be, to be a, to be an under, to be an underestimate. Um, uh, but, but, but, you know, just, just, just kind of the idea that, you know, you know, you can both have an 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, it does the work of many people many times over by operating within an organization of humans.

  13. 29:37 – 33:09

    Favorite real-world uses: global health, research acceleration, and personal victories

    1. AV

      Awesome. And maybe I'll just ask to close, like, Daniela, one thing we talked about is, um, some of the, the kind of developers or use cases we see that are just so inspiring. I talked a bit in, in the keynote about some of my favorites, whether that's, um, people using it for efficiency at places like Stripe, doing major infra upgrades, but also people using it for these very, um, 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 use the tools?

    2. DA

      Yeah, I mean, I think even going back to the days when I was at Stripe, some of the, the, um, something that I've just loved about the developer community in general is, like, for everyinteresting 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, um, 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, fi- 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 it a question, it can give you some medical advice, right? In a way that's like sanctioned. Um, I'm, 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. Some of my favorite ones, there's a developer who used, um, Claude to help retrieve their wedding photos-

    3. AV

      Oh

    4. DA

      ... that were corrupt, like on a corrupted hard drive. I thought that was so sweet. Um, and just like such a like personal use case, but so, like so meaningful. And then my last one, which is so random, is someone is using Claude to chart the growth of their tomato plants-

    5. AV

      [laughs]

    6. DA

      ... in their garden. And I was like, "Never in a million years would I have thought of it."

    7. AV

      [laughs]

    8. DA

      But it's like, it's so-- I like, was like, "Do you have a live cam, and can I subscribe to this?"

    9. AV

      [laughs]

    10. DA

      Um, because I would really love to see it. But I think just the range of things that people are able to do, um, is, is just astonishing.

    11. AV

      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 to these folks.

    12. DA

      Thank you. [audience applauding]

    13. SP

      [upbeat music]

Episode duration: 33:10

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