Awesome. So, super excited to be here with Dario Amodei, CEO of Anthropic. We have a really awesome partnership that we're going to talk about. Thanks for taking time. I wanted to ask you a little bit. You've talked a lot about your vision of the future for AI. You wrote Machines of Loving Grace. I thought if you could just a little bit summarize what's your vision of the future for AI.
Well, first of all, thanks for having me. In terms of the future of AI, yeah, I wrote this essay a few months ago called Machines of Loving Grace. And it was actually motivated by a bit of frustration where when people talked about the upsides of AI, I felt like they weren't being imaginative or specific enough. Lots of people were like, 'Yay, this is exciting. Let's do this.' But when you actually get into the specifics, I think the potential is incredible. So some of the things I wrote about, and it was colored a bit by the fact that I have been a biologist in the past. So I think of biomedical innovations as one of the areas where you can make the greatest progress. So I just went through the various areas of physical and mental health and a lot of the diseases that we face now are really complex diseases of complexity and I think AI is really going to help us to cure those diseases. You know, when I think of all the data, all the questions that say a pharmaceutical company has, I think there's really a potential for AI to address these issues. That's not to say that it won't do amazing things throughout the economy. If we look through the list of companies that we're working with or are going to work with in our partnership, I think it's going to be applied across the economy. But I think it's going to be an incredible change to society beyond even that where humanity gets healthier and much greater in its capacity to address problems that have plagued us for centuries.
That's super exciting. Looking forward to that future. What's your estimate? How far out is that?
I mean, it's all very uncertain. You know, I think the thing about AI is it's moving very fast, but there's no fundamental reason that it couldn't just stop. So, you know, my guess really is that the fundamental technology is going to be there in the next few years, maybe even next couple years. And there's a question of how quick is it to get those ideas and that technology out to society where it really produces all the benefits. And that honestly is going to come down to enterprise and go-to-market partnerships like the ones we have, getting the parts of the economy that in some cases are far away from this innovation to catch up to it and apply it to their areas of the world.
Yeah. So we have to get all these enterprises to build on that AI, leverage it in each of their industries and then they have to get also their customers to use it day-to-day, adopt it and then have the impact that it has inside. So it might take a little bit of time for all of those things to happen but hopefully we can accelerate it.
Accelerate as much as possible. Yes.
So how important is data in this, you know, for these enterprises?
So data is super important, right? You know, there's different kind of data. There's the data we use to train the models and that is obviously very important for making the base model. But then we get to data that is specific to a particular industry or a particular company or customer and I think that's super important as well. You know, there's a lot of stuff again if we go to our example of pharma companies, but it's just as true of finance, it's just as true of tech companies and productivity software. They have a lot of proprietary data where there's information that's simply not contained anywhere else, right? The model can't know facts that it doesn't have access to. And so I think it's important in a number of ways. Sometimes you'll want to fine-tune on the data. Sometimes you'll want the model to be able to act and operate on the data. And you know, sometimes you'll want to pull the data into context as is done with methods like RAG. Sometimes you'll want to have agents that operate on the data. So there's a whole bunch of different ways in which that data will really be essential and I think it'll be essential for building unique value between the AI companies like us and the enterprises which have that data. That's a way to build unique value that isn't substitutable on either side. And I think a lot of what our partnership is about is kind of helping to bring those two things together more efficiently.
Yeah, that makes a lot of sense. So in the future if we're going to have this much impact with AI, how important is it for organizations to have data? How should they be thinking about data themselves?
Yeah, again I think it's just absolutely essential, right? As the AI models get better at just about everything, you know, I think that data represents collected knowledge and wisdom from the company that, you know, it's probably one of the main things that is going to be synergistic with what the AI models are capable of doing. It doesn't matter how smart you are, if you don't know anything and you don't have access to the world, there's only so much you can do. So yeah, I think it's going to be absolutely critical.
Yeah totally. So you know I see a lot of use cases that enterprises are doing and they want to do so much with AI but the ones that I think are the most interesting are where they have some special data that's unique to their customers that they've been collecting. That's going to be really valuable and that's I think what protects them and that's where they should do the innovation. They should use the models that you guys provide together with their data and then come up with those innovations. I think that's where you're going to have this impact. You mentioned a bunch of things. You mentioned RAG, you mentioned agents. I'm curious what's the future of agents? What's that going to look like? And how can you get these agents to operate on this enterprise proprietary data that they have in Databricks?
Yeah, I mean my view is the future of AI mostly is agents. So you know we are increasingly going to have our models operate as agents, right? We've already released some early agents. There was computer use late last year where the models can take in screenshots from a computer and take actions. You know we recently released Claude Code which is an early agent that performs code. And of course a lot of the models we make via API can just be linked up and taught to operate as agents. So as the models get smarter and we build more of these specialized things I think agents are going to be much more of a thing. Agents are going to use tools and one of the tools they use can be thought of as accessing data in various ways. Accessing data through databases, accessing data through various kinds of search and you know I think there's going to be a lot of synergy in our partnership with that.
Yeah, super excited. And in particular, I know we have customers that are looking at us both together. AT&T is kind of exciting because they have I think 180 million subscribers on their wireless systems and they're using us to detect fraud. So, you know, that's an exciting use case.
That's a very exciting use case.
Another really awesome use case is Square or the company called Block and they used to have these UI interfaces where you have to configure how you want to set up your store and what you're buying. And now with generative AI they can just configure it all. You can just speak to the thing and it can just set itself up and makes it so much easier for their customers. So excited about all these use cases that we're going to see. So about this partnership, I'm super excited to natively let our customers access Claude's models inside Databricks. Of course governance security is super important for them. So they'll be using Unity Catalog and securing it. But I'm curious what do you think customers are saying about this and using us together?
Yeah. I mean, I think what we're hearing and probably you're hearing it as well is that there's this incredible convenience to having everything all in one place, right? So, the idea is, you know, you have these models and you have your data. And customers face this in a whole bunch of ways. You know, they might be on a different cloud or just these things are stored in a different way. And the idea that there's not one boundary where the data, the models that go into the data are all kind of contained within the boundary, it's just a huge headache to have to manage that. And so I think what you guys are doing is making it easier to take our model and fit it in with the other ingredients that are present and make everything run within this data boundary. So I think that's a huge benefit for folks.
Yeah, super excited to make that super seamless and simple going between the data and iterating because if you want to improve the quality, make the agents reason on that data, you probably want to iterate through that. So that inner dev loop...
It's the iteration loop, right? Right. It's the dev loop time. Every developer knows that, you know, you get that iteration loop time down and lots of things happen faster.
Exactly. Yeah. So super excited about that. So you guys just released Sonnet 3.7, Claude Code and lots of other innovations. Can you tell us a little bit about these?
Yes. So, Sonnet 3.7, we released it. I think as of time of release, it'll have been about two months since we released it. You know, the response has really been incredible. Both Sonnet 3.5 and the retroactively named Sonnet 3.6 represented a significant step up in coding and Sonnet 3.7 represented another significant step up. But we felt like with this step, the model had passed a certain threshold where it was able to do certain coding tasks end to end or on its own that was greater than what was possible before. And so we saw with a wide range of customers and with Claude Code which I'll get to, we saw huge upticks in usage as well as new use cases that hadn't been present before. You know it even led to the coining of this term by I believe it was Andrej Karpathy called vibe coding which is you just talk to the model and you let it code. So you know instead of having to be precise you give the vibes and you know I think there are still a number of imperfections to that and there's a large amount of improvement but I think the curve from this point onward is going to be pretty fast and people are going to be surprised at the power of the models that will come out soon.
Yeah, and we're seeing it right at... so tell us a little bit about Claude Code.
Yeah, Claude Code. So Claude Code is basically a particular way to use Claude 3.7 Sonnet. So, you know, it kind of goes back to the retro era of command line tools. So, the idea is that it's kind of a command line tool that's easy to integrate with things like Git and you just say, you know, I'm going to talk to Claude and basically ask it to write my PR and take these various actions. And it can just act. I mean, it'll ask for your permission to actually take action on the computer but it'll just act for you. And so you can think of it as a very early agent. And again, the interface is pretty simple, but we found very quickly within a few days of releasing Claude Code, we had nearly 100,000 people trying it out. And it's really been adopted. We've talked to enterprises who want to use it. So, it really shows the power both of coding models and of agents.
Yeah, it's super exciting. Yeah, we have these in Databricks as well and it's kind of changed how people use Databricks these days. They no longer have to write the code themselves. It's just an essential part. I mean I never these days, I leverage the assistant heavily for everything and you know it's things like Cursor, Cognition and so on. And I think a lot of these use Claude under the hood.
Most of them do. So we have these and you know alongside I think complementary to them we have now Claude Code. So there's probably 10 of these startups and you know some of them are bigger than others but all of them are showing explosive growth.
It'll be exciting years ahead. Yeah.
