Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Krishna Rao
[00:00:00] S1: I joined the company about two years ago. We were closing our Series D at the time. That was not a straightforward fundraising. The company only had a frontier model in the middle of that fundraising. Towards the tail end of it, the FTX transaction was happening, which was liquidating a bunch of Anthropic shares. That was the starting point. At that point, the questions were around, why do you need to have a frontier model? What are the returns to this? They were also around our mission and how we approach things. People said, "Well, AI safety and building a really big business, aren't those things at odds?" There were also a lot of other misconceptions, like "Your sales force is really small. Don't you need to scale it like all these enterprise software companies?" There was a bit of a paradigm around trying to fit us into a particular mold that had existed before. Over time, it's evolved. At the end of 2024, we raised the Series E. The business had scaled to close to a billion dollars of run-rate revenue. But the day of our first close was the day the DeepSeek news came out. We got the close done, but certainly a ton of volatility as people then said, "Wait a minute, should I just totally re-underwrite how I think about AI in total?" That was the Series E — obviously, we brought on great investors across all of these, but people still had some of those questions. They looked at our forecast and they thought, "I get it. You've grown to a billion dollars of run-rate revenue so quickly, but there's no way you're going to be able to keep up. That's just not possible."
· Patrick O'Shaughnessy
[00:00:00] S0: Can't 10x again.
· Krishna Rao
[00:00:00] S1: "There's laws of physics. You're in enterprise, which is great, but the adoption's gonna take so much longer. Look at how long it took with cloud and how many people are still on-prem." The business continued to prove out the thesis that the return to frontier intelligence is really high. What's really happened is model-led growth, enabled by products and our go-to-market team and our distribution. What they also saw was that this thesis of, "Hey, it's really important to build this transformative technology, but to do it in the right way and do it responsibly." That had this really interesting interlink with our business that most people didn't understand or really believe, which was that we invest in research, not just in model development, but also in AI safety research. We've pioneered interpretability. Think of it as an MRI for the model to see inside the neural network, how it works. We pioneered alignment science — you want the model to do what you tell it to do, and how often does it do that, and how often does it stray from that? Those things are important for our mission, and that's why we did them. They had these downstream effects where it turns out if you can look inside the model, you're better at building them. The last linkage is, if you're selling to enterprises — we now sell to nine of the Fortune 10. All of those enterprises are entrusting us with customer information, their data. They're interacting with their employees, sometimes even interacting with their customers as well. Those are the most sensitive workloads. More and more of these businesses are running on Claude and our Claude platform. When you have this investment that we've made and will continue to make in safety, interpretability, alignment — that actually inures to the benefit of the enterprise customers as well, and all of our customers. Because they want that — if they're going to entrust us with all that access and all that data and the ability to work in the most sensitive workflows within their company. They want a company they can trust. That's not why we invested in it, but it did have this downstream effect that we've really seen prove out again and again — to be a company that is both at the frontier, and one that is investing in safety and that you can trust. We've raised $75 billion since I joined the company.