Invest Like the Best with Patrick O'Shaughnessy
· Krishna Rao
[00:00:00] S1: The culture is a real unique aspect of Anthropic, and it is something that we do talk about externally, but it's different when you're in there living it. I can tell you a little bit about some of my observations. First of all, we have seven co-founders. That shouldn't work on paper, but it really does in practice. They've really set the example for the culture and the things that really matter to the company. We take culture extremely seriously. We do a culture interview, and it's not some pro-forma thing we do just to check a box. It is a real part of the evaluation process. Somebody could be flying colors on everything else and the smartest person you've met in this role — we won't hire them if they don't pass the cultural bar. And the way I would describe it — I like that framing of how would you describe it to your parents. It's incredibly collaborative. And this means that we don't really tolerate fiefdoms or sharp elbows or the "I need to take credit for this." It's incredibly humble. We have this sticker on our laptops internally — "Our competitors are incredibly capable, and success is far from guaranteed." That's part of how the company operates. If we reach a milestone and something good happens, there's not confetti on the floor. It's "What's next?" It's that focus on the mission and the alignment that is imbued throughout the culture of the company. The other thing is there's rigorous debate, and intellectual openness and intellectual honesty. That happens where people question things. People will express a point of view, but then there's dialogue around it that's productive, and then we'll decide on a path forward. After that happens, there's real alignment. In something like the compute allocation we were talking about before, people might have different perspectives on how to allocate that compute. They will engage in a thoughtful discussion about where the returns are the highest or the best. When they do that, and when we come to a decision, then there's alignment on it. There's not second-guessing. There's not politics or fiefdom. The other piece of it is, it's remarkably transparent. Dario gets up in front of the company every two weeks, usually writes a short document, and he talks about three or four topics, then takes open questions from the company. These are not softballs. They're not planted questions.
· Krishna Rao, Patrick O'Shaughnessy
They're real questions that are on people's mind, and he answers them the best that he can. It's not a decision-making forum, but it is a way for the company to get a window into how leadership is thinking, how he's thinking. There's debate and dialogue in that. That is something that people really value. It is a transparent culture. It is one where all seven of the co-founders are still at the company. The vast majority of the first 20 to 30 employees are still at the company. The culture underpins the reason why we've been able to attract and retain some of the best talent in the industry, because we don't always pay people the most. We have very competitive compensation packages, but when Meta and others were out with these huge packages for some of the technical talent across the large language labs, I think we lost two people, and other labs lost dozens. [00:00:00] S0: What parts of the business and the culture specifically for researchers — why do you think that is true?
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: What parts of the business and the culture specifically for researchers — why do you think that is true? [00:00:00] S1: It really is underpinned by the culture. That's not just something we feel. Empirically, when you talk to people, it's "I want to have the most impact possible." Again, this idea of talent density mattering more than talent mass. "I want to work in a place that is actually collaborative, versus I have to fight for this one thing and I feel like it wasn't discussed and debated in the right way," or there wasn't transparency around how a decision was made. I think it actually really matters, because most of our team just wants to do really good work, and they're attracted to the company for the mission. The idea of having an impact on a company like ours that is trying to develop this transformative technology, but to do it in a responsible way — I think that really matters to people, not just on the research team, but across the company. That is a real advantage for us, and it's not something that we take lightly. We have this concept of a race to the top. We don't always have all the right answers. We don't always do everything perfectly, but we want others to look at some of the things we do and maybe emulate some pieces of that, and actually have the technology be developed in a better way across the industry. People are also really attracted to that. Not that we have all the answers, but that we can be a part of contributing and leading to how this can go well for humanity.
· Patrick O'Shaughnessy
[00:00:00] S0: If we now think forward, as you're having conversations with people internally, what does the frontier feel like? I don't just mean the model frontier. I mean the next couple of rolls of the dice here in building AI in general. Everyone is kind of wise to, "These things are powerful, everyone's using them." What feels to you like the frontier from the inside?
· Krishna Rao
[00:00:00] S1: I think it's this idea — and again, because we're focused on enterprise and because we're really trying to change the productivity of knowledge work that's done in the economy — it is towards this vision or this goal of a virtual collaborator. Think of this as something that has context within your organization, that can use all of the tools that are specific to you, whether they be homegrown tools or tools that you purchase. That has memory and the ability to effectively learn from mistakes you've made, but also mistakes that it has made over time. The ability to work over a very long time horizon on not just a task, but an actual idea. What that means for us is the model capability has to continue to grow to support that. The products we build on top of it can unlock this virtual collaborator, that we think can really accelerate knowledge work. There's something like $40 trillion of knowledge work done annually in the world. We think that the productivity and the acceleration of it can happen, but you have to get it in the right form factor. This is where intelligence is not just a single dimension, but the virtual collaborator combines many of those things. Something that's not just generically smart but is smart for your use cases. What we're seeing in coding is something that we expect to see elsewhere. For us, Claude Code has led the way on that, as well as much of the businesses that — we have great customers that are pushing the coding frontier as well. You also see something like Cowork come along and start to unlock that. Cowork is growing faster than Claude Code was if you index them to the same point in time. That's kind of remarkable, because developers are really fast adopters of this technology. But it's because the model capabilities and the products are pushing towards this notion of a virtual collaborator, where even our product development today is not done by one product manager with two engineers shipping something over three months. It's shipping daily, and there's a fleet of agents that are working across the company on a specific task, so everyone becomes a manager. The implications of that and the productivity gain that can come from that when it's in the right form factor — we're very early in that, but the potential for it is incredible.
· Patrick O'Shaughnessy
[00:00:00] S0: I'm curious how you've had to personally evolve to be able to stay doing this. You hear a lot about these stories where the executives have to scale with the company. They're also getting new executives. The business that you were at prior to this was a great business, but it was a tiny, tiny fraction of the scale. You, like everyone, is in this new unprecedented thing. You talked about the example of getting out of linear into more exponential type thinking. That's one example of what I mean. But how have you managed it personally? What have you had to do? What's been the most painful? How do you manage your own ability to scale with this thing that's scaling faster than what we've seen before?