We were chatting about not so long ago when we first met and started covering Anthropic. But for those who aren't familiar, you and your brother Dario and a handful of other people left OpenAI to start Anthropic back in 2021. Now you employ thousands of employees, many of them in a reporting chain up to you in your role as president. I'm curious, what is the difference in the leadership style between you and Dario?
Well, first of all, thank you so much for having me. It's great to be here today. And, you know, Dario and I really think of how we sort of split duties. He's this incredible technical visionary. I think, you know, years before I was, he had this amazing idea that people were excited about around the world. They had this really strong conviction that artificial intelligence is going to be a big deal. This is like early 2010s. And I think Dario is constantly reminding the company about the scale, the ambition, sort of where the technology itself is going. I think of my role much more as actually running and managing the day-to-day of the organization. So I manage the executive team. We spend time making decisions about customers, about our products, about the research and how that applies to the people that are using artificial intelligence. And I like to think that the two of us are a great balance. And I have to say, like, I don't know how one CEO just runs a company by themselves. It seems like it's actually much easier to do with two people, particularly when they know each other really well.
I remember in one of our conversations earlier, are you telling me that you and Dario rarely fight? And I'm curious how you manage. What are some examples of places where maybe you have disagreed and have been able to settle it in a healthy way with regard to the business?
Well, as someone who also has a brother, I know, you know, I think we've had, like, you know, several decades to kind of practice fighting and getting and still loving each other. Right. So, as early as, like, why did you take my toy? Can I have that back, please? I think that has served us well. And being able to operate well together in Anthropic. I think what I would say is, you know, we do disagree sometimes, but I think how you disagree really matters. And I feel like there is always like an incredible mutual respect between us. So if we look at the same situation and we see something different, I like to think that what our default is, is to be curious, right? So I usually view it as like, oh, this felt like an obvious call to me. Like, why do you see it that way? Because usually he'll have a piece of information that I don't have or will have a different perspective. And I would say about 80 to 90% of the time, what ends up happening is we meet in the middle. And so we'll think like, oh, I think this is the right thing to do. You think this is the right thing to do? Actually, there's probably a third way to do this. That's the combination of what we both think. That's actually a little bit better. I think that's part of the strength of having both of us, the ability to practice looking at situations and decisions just from different points of view and perspective. And I think it helps us make better decisions.
So two years ago, when you were sitting on the Bloomberg Tech stage with Dario, Anthropic was still very much the underdog in the competition against OpenAI. And just in the race to AI generally. Now it's in a very different position. No longer the underdog, Claude is a runaway success, particularly among coders. Your company is projecting $47 billion in annualized run rate revenue, and the company has eclipsed OpenAI's valuation for the first time. Do you feel like you're definitively the front runner right now, and how does that change your approach?
It is something that I think both Dario and I, you know, really hammer at Anthropic is that that is just completely the wrong way to think. I think our view has always been, you know, this is really about how do we show up every day for our customers? How do we stay humble and make sure that we're really focused on the mission? Right. The whole reason we started Anthropic is we want to be able to build and develop this technology in a way that is ethical, that's responsible, that's fair. And I think it's really incumbent upon everybody at the company, but especially leadership to say, you know, all of these numbers, they're actually not the point, right. We are really here to do a job. We're here to support the businesses that rely on us every day. AI is increasingly a bigger part of everybody's workflow. Their experience at the office, their experience in their personal lives. That's a huge privilege and responsibility. And so I think if anything, you know, we work really hard every day to say, like, our job is to be the best version of who we can possibly be as a company every day. That takes work, that takes humility, that takes time, that takes focus and energy.
I want to talk about the big news earlier this week that Anthropic has filed confidentially for an IPO. SpaceX also filed, OpenAI is expected to file anytime now. How far back have you been planning to file? And do you think that there's a race here to be first among the AI companies?
So, yes. Correct, we confidentially, you know, filed our S-1. And that gives us the option, you know, to potentially go public after the SEC review. Unfortunately, that's all I can say about anything IPO related. I'm sure you understand.
I mean, if we can just maybe level up a little. What is the calculus for a kind of an AI company generally to go public or not in this moment? What are some of the pros and cons here? Because on the one hand, yes, you can access more capital if you're not a startup, but on the other, then you're beholden to, you know, quarterly results, shareholder calls, etc. So what are some of the pros and cons just generally for an AI startup, do you think, to go public in this moment?
So I think, you know, at least speaking for, you know, ourselves and I think I'd probably really for the AI industry more broadly. It's a very capital intensive business to train AI models. I think that's something we've been, you know, very open about from day one. I don't think that's, you know, a surprise in the industry. And I think this kind of concept of like, how do you access the level of capital that you need to train these models? Right. It's a really big kind of upfront cost to train the models and to sort of inference on them. And I think that is, you know, my guess is that, you know, over time, the sort of core set of companies that are working to advance the frontier are just going to need access to capital. And I think the public market is very well suited to that. So there's obviously, of course, trade offs. But my sense is there's just a fundamental structure of how training and kind of serving these models for customers works that it will require that level of access.
It's clear that many businesses can't get enough of Claude, but at the same time, we are hearing about some customers who are concerned about the pitfalls of so-called token maxing of, you know, companies making leaderboards of who uses the most AI tokens for using AI sake. Uber's, one of Uber's executives talked about it getting harder to justify why spend if there isn't a clear metrics backed ROI. Do you think there is truth to the idea that some businesses may be overspending on AI tools in this early experimental phase, and they may cut back as they move out of that phase?
So I think kind of two things are at once here. I think, you know, I like that you said, you know, experimental because I think the reality is it feels like these AI tools are so powerful and so capable, which is true. And comparing them, you know, particularly to, you know, even two years ago when I was on stage at Bloomberg with Dario, I think the models have just come so far in what they're able to do and the economic value that they provide. But I actually think there's a lot more distance to go still for what the models will be able to do, you know, four to six to eight years in the future. And I would suspect that we will see some, you know, experimentation in different fields to understand how do you kind of get the most value out of these models for what particular workflows? I think the second thing that's true is, you know, how businesses are choosing to use AI will change. So the use cases today, some of them, I expect will continue to be kind of the primary driver of, you know, efficiency or creativity or, you know, new approaches to doing things within businesses, whether that's coding or, you know, financial services, legal, health care. But I also think, and the industry as a whole, not just the AI industry, but really the business community gets more familiar with the tools we're all going to learn together. Like, what is the best way to apply these tools that also supports employees. And so I think today there's this feeling that it's like, oh, like, you know, the leaderboards and it's like, I have to use it and what do I even use it for? And I think my hope is that over time, it'll be more incorporated into the day-to-day of how humans do our work, how we communicate together, and that there will actually be a lot more value realized in a way that feels really good to people.
Does Anthropic have a leaderboard itself?
We don't have a leaderboard, no. Or at least not in the way you've described. We do track how much we're using Claude internally and for what use cases, because we really try to prototype all of the tools that we give to our customers. And so all of the products that, you know, you see, whether that's, you know, Claude Code, Claude Design, like all of these are things that we actually built at Anthropic first because we said, hey, this is a real need. We have. Could Claude help us with this? And so I think it's important for us to just have a kind of a metric for checking, like how are we using it and how much are we using it. But there's not a like you must use AI and you must use it, and it's better if you use it or not.
What type of job function outside of coding or research are using Claude most internally?
The entire company uses Claude for topics large and small. Probably the second biggest I would actually say is finance. And so a lot of the kind of back end financial planning analysis, you know, like just number analysis. I think Claude is really, really helpful at that. But honestly, like, our people team built an incredible tool internally for how we do performance reviews that leverages Claude. We're in the middle of performance review season, I have to say, like, the feedback from our team has been so positive. They're like, it just feels so much more fun to do this. It's so much more interesting, interactive. It's pulling in information about what I did over the course of the past six months when I'm assessing myself, and I think that it really just speaks to the generality of these tools, their ability to, I think, reach across the business and just take a lot of information in and help you as an employee, be more successful in your role.
