Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: If you're this great customer of the compute providers, what does that group need to do to be a great provider to you to help you drive that return? [00:00:00] S1: We're fortunate iwn that we have really great partners in Amazon, Google, and Microsoft, but also with Broadcom and NVIDIA as well. We are the only model on all three clouds today. We're the only large language lab using all three of these chip platforms. These collaborations are much deeper than just procurement. I think that's something that's often lost. If you think about our relationship with Amazon, our teams are deeply embedded with the Annapurna Labs team. We are good users of Trainium. We've spent a lot of time and energy and worked closely with the team internally, and we plan capacity together. We do that with the other hyperscalers as well. If you think about the three clouds, they're great distribution engines for us too. We have a really, really robust first-party business as well, but these are multifaceted partnerships, whether it be on developing the chips themselves, landing that capacity, serving it, and then ultimately distributing it to the customers. Each one of them has that element to it. They're different, and each has relative strengths, but we've been able to work really well across those clouds and obviously across Broadcom and NVIDIA as well.
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: We're fortunate iwn that we have really great partners in Amazon, Google, and Microsoft, but also with Broadcom and NVIDIA as well. We are the only model on all three clouds today. We're the only large language lab using all three of these chip platforms. These collaborations are much deeper than just procurement. I think that's something that's often lost. If you think about our relationship with Amazon, our teams are deeply embedded with the Annapurna Labs team. We are good users of Trainium. We've spent a lot of time and energy and worked closely with the team internally, and we plan capacity together. We do that with the other hyperscalers as well. If you think about the three clouds, they're great distribution engines for us too. We have a really, really robust first-party business as well, but these are multifaceted partnerships, whether it be on developing the chips themselves, landing that capacity, serving it, and then ultimately distributing it to the customers. Each one of them has that element to it. They're different, and each has relative strengths, but we've been able to work really well across those clouds and obviously across Broadcom and NVIDIA as well. [00:00:00] S0: I'm thinking about your function — the finance team. I'm picturing this ROI-on-compute thing on different horizons with all these complex variables. Which makes me wonder, how do you use these powerful tools yourself internally to run your group and the business? What is the deployment of Claude Code and Claude in general on the finance team at Anthropic?
· Krishna Rao
[00:00:00] S1: This is really interesting. We were using Claude Code about a year ago, and I started asking people, "Is everyone just taking up vibe coding, or what is it?" We started to use Claude Code as almost like an assistant, a digital coworker — not just for coding tasks. That was early in what eventually became Cowork. That was an extension of Claude Code — to say, what it's done for agentic software development, it should do for all of knowledge work. Then we started to productionize that, and I'm actually really proud. We spend a lot of time with our product team too, they see how we use it and get input and feedback from that. Today, all of our legal entities — we can produce the statutory financial statements using Claude. Yes, a human checks it, but all of those financial statements are produced with Claude. We also have a more real-time platform called Ant Stats. It used to take a lot of time to sift through all the data, get to conclusions, write a memo about it, or publish a regular report on what's happening over the course of the day, what's driving it. We now have a library of skills for Claude that are specific to finance. Last I checked, there were over 70 of them that everyone can access through this common repository. On top of that, we built an MFR — a monthly financial review — skill. It can produce our monthly financial review. It's 90 to 95% ready. And then all of our discussion becomes about — what do we do? What are the implications? Not what exactly happened, because Claude is not just reporting the weather. It's also helping to think about drivers. Why did the number change in the way it did? That gives you tremendous insight into the business, both in terms of this MFR that we do, but also on a daily basis. What used to take hours to produce a weekly report for what's driving revenue or what's driving our compute utilization now comes down to 30 minutes, and then we can spend our time on the actual strategic implications of the business. We can also get it in the hands of business leaders much more quickly. The insight engine is a lot faster. I have a dashboard, I look at token usage.
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: A leaderboard? [00:00:00] S1: Yeah. We don't compensate people on it. No one's trying to token maxx for that. But it's really interesting because some of the most senior people within the finance team are actually the biggest users of tokens. So it is not just the 22-year-old who joined and has a coding background and was doing that on the weekends and brought it to work. It's also people using the tools to change how they're working. Our number one user is our head of tax, and he's focused on tax policy engines and automating large parts of the workloads happening within the team. I love seeing that. I tell people, "If we're not super users of this, if we're not pushing the limits of it, how can you expect customers to do that?" [00:00:00] S0: Just as a human, does it freak you out at all? I've heard so many examples like this. It starts to feel like we just start doing the stuff that AI tells us to do — in the sales example or the calendar or whatever. Maybe that's great. Maybe it's just such a better coordinator and wide thinker and optimizer than we ever could be that we should do what it tells us to do. But it feels ever so slightly dystopian to me, that that reality is coming quickly. I've had examples of it too. It feels kind of cool. It's helpful, but at the same time, if I really close my eyes, I'm like, "Oh, I'm just doing what it tells me, versus me telling it what to do." It's a really interesting human dynamic that I'm curious for your take on.
· Krishna Rao
[00:00:00] S1: I have a slightly different view on it. I think we've been able to hire great people at the company, but it has made even those incredibly talented people so much more productive. I think of it again like a Jevons paradox, but for labor, which is that we have people who become incredibly more productive. We've hired a lot more people because of that, because there's no shortage of work to do. Now with the assistance of Claude, people are spending less time in that MFR trying to reconcile some number, but they're actually thinking, "Oh, how do we reinvest this in the business? How do we think about dynamically allocating resources?" Whereas before, I'm working to tie out a number, or in that accounting example, taking a long time to close the books. I actually think of it even more optimistically — that it is an accelerant to our productivity, and that actually means we can get a lot more done. Even as we grow the team, those people are more productive as well as they come up the curve on how to use Claude within our company. That's starting to be true across many companies as well.
· Patrick O'Shaughnessy
[00:00:00] S0: I'd love to talk about investors and capital formation. Of course, you've had to raise tons and tons of capital. At the same time, it seems as though if I just squint my eyes and think about the multiple on current revenue, it's not that crazy in terms of where you're raising money. I'm so curious for you to teach us about what it's been like to interact with investors — how you've seen their understanding of the company evolve and mature. Where do you think the investors as a group understand it now? Where are there misunderstandings about Anthropic? Tell us that side of your life. I don't know what percentage it is of your job, but it's an important part.