Daniela Amodei Says Curiosity Is Underrated

Note: This transcript was generated by an automated system and has been lightly edited for clarity. It may contain errors or omissions. 

Gintare Zukauskaite: Welcome to View From The Top: The Podcast. I’m Gintare Zukauskaite, an MBA student of the Class of 2026.

Michael McDowell: And I’m Michael McDowell, a producer at Stanford Graduate School of Business. Gintare, who’s on View From The Top today?

Gintare Zukauskaite: Today we’re hosting Daniela Amodei, who is co-founder and president of Anthropic, one of the leading AI frontier companies in the world.

Michael McDowell: Very exciting stuff. For those who may be unfamiliar, tell us a little bit about Anthropic.

Gintare Zukauskaite: So Anthropic is an AI safety company founded in 2021, by Dario and Daniela Amodei, along with five other former OpenAI colleagues. It’s structured as a public benefit corporation, meaning, it’s legally required to balance shareholder interests with a broader mission, in their case, developing AI responsibly.

Michael McDowell: And I guess the second question I have to ask you is, what is Claude?

Gintare Zukauskaite: Claude is their main product that millions of people currently use.

Michael McDowell: Yes.

Gintare Zukauskaite: Especially here on Stanford campus. It helps you to work, to do life better, to study better, to code better. It’s a versatile tool that truly, in the last couple of years, changed a lot in how we live, work, and study.

Michael McDowell: So Claude is one of the big LLMs, Anthropic is one of the massive, potentially future shaping companies, of this moment. Why did you want to talk with Daniela, and what did you want to ask her about?

Gintare Zukauskaite: I think there is just so much excitement about AI, but also a lot of uncertainty and fear, about where is this going? Things are changing so fast, and she’s at the frontier. She is the one that started it all, and she was there at the beginning, beginning at OpenAI, with the first GPT models. Talking to someone who doesn’t come from a technical background is so relatable for this Stanford GSB crowd, where many of us here are about to go back to the workforce. We want to be leaders in AI, but we also see us not having a technical background as the main obstacle. And I think Daniela embodies all of that combined, and could share a very interesting take, on what does it take to lead at the frontier? How do you get to lead at the frontier? And how do you lead responsibly, given that it’s so uncertain where is this going to go?

Michael McDowell: On that note, should we roll the tape?

Gintare Zukauskaite: Let’s go.

Gintare Zukauskaite: Daniela, welcome to Stanford and View From The Top.

Daniela Amodei: Thank you so much for having me.

Gintare Zukauskaite: We’re so excited to be with you here today. As you can see, we have a full house, and I want to start by asking everyone in the audience. Please raise your hand if you’re a user of Claude.

Daniela Amodei: Oh, wow. Okay. But I feel like if you had Sam Altman visit, and you asked about ChatGPT, would you all have raised your hands? Let’s be real. Okay.

Gintare Zukauskaite: Still, you have a very supportive crowd down here, and you and your brother helped build one of the most important AI companies in the world. Yet neither of you grew up planning for any of this. Your background is in the arts, you studied English literature, and your early career was in politics. Can you share more about, what was your original career plan?

Daniela Amodei: Oh my God. First of all, so nice of you to use the word plan in there. I don’t know that I necessarily would have described it that way, at any point in time, but I really think… This is sort of a story that I hear from a lot of people, who either wind up founding a company, or just doing something a little bit unexpected or unconventional with their life. It was following what was most interesting to me at the time, and the intersection of what was I good at, what was I interested in, and what was going to have a big impact in the world.

So for me, I think coming out of college that looked like… By the way, I graduated in 2009, which was not the most fun year to be a graduate. You were like, “I have a literature degree and no skills. Who will hire me?” But at the time, I felt this very strong pull towards wanting to make the world better. I think that was always a sort of defining feature, of both me and Dario, from a young age. And where that started out for me was in international development, working in global health. And I think my desire there was really to figure out, how do we build a world that is fair, where everybody has access to basic things like food and water and medicine?

And even though that’s not what I directly work on now, I think that early grounding sort of gave me this foundation for thinking about, how do you do good in the world? How do you build something that is of consequence, and has a real purpose behind, where you’re spending 50 or 60 of your hours per week, what you’re working on. And so, it was kind of a winding journey from there.

I worked on Capitol Hill after that. I worked on a campaign, and eventually I ended up coming back to Silicon Valley, I’m originally from San Francisco. And started working at Stripe, which was this tiny company that at the time nobody had heard of. My friends on Capitol Hill were like, “You’re leaving to go do what? Payments?” Now it looks like a great decision, but at the time, it was about 40 people. And then from there, things really just snowballed into working at OpenAI, and then co-founding Anthropic.

Gintare Zukauskaite: You’ve moved across fields without being constrained by what you studied, or had done before. Where does that mindset come from, that your background doesn’t have to define your next move?

Daniela Amodei: So it’s really interesting. I think in some ways, I really think of myself as a generalist. If you were to sort of look through my background, you would be like, “What is this lady actually good at? She doesn’t have a law degree. She’s not a computer scientist,” but I think this concept that the ability to be curious and learn, across a lot of disciplines. To have a strong foundation of wanting to have impact, regardless of the area that you’re working on, I think that’s an underrated quality. And I see it a lot in the people that we hire at Anthropic, and the really talented people that I’ve worked with in the technology industry, more broadly. People who are curious and smart, and they want to learn, and they want to be helpful. That is the, first of all, the description of every role except for engineer, at a startup. It’s like, whatever, people that are like, “I have this degree, I have that degree,” and those are sort of the qualities that you’re looking for.

But I think for me, it was always very interest and impact driven. So I was like, “Okay, there’s something that feels really wrong to me about the fact that I was born in America, I had access to all of the sort of basic things that we take for granted in life.” Some people around the world just weren’t, that’s just not where they were born, that’s not what they were born into. “How do I make that more fair? How do we make that better as a broader community of the world?”

And from there, I was like, “I’m just not having the level of impact that I want to. I need some skills.” And so I went and I worked on a campaign, and I was like, “Wow, a small number of people that work really hard, who are young and driven, can really change the world.” Not that surprising that eventually that led me to Silicon Valley, where like, you can do that, but it turns out that at a startup, you have a lot more money and it’s a lot easier going than the kind of 80 hours a week of working on a campaign. But I think those kind of core qualities around really following your passion, because you just want to do more when you care about the thing that you’re working on, either intellectually or from an implicit sense of meaning.

Gintare Zukauskaite: And your career in AI started when you joined OpenAI in 2018, when it was still a relatively small research lab. Suddenly you were in rooms where people were talking about neural networks, and transformers, and scaling laws. How did you learn to speak that language?

Daniela Amodei: So I think I was well-trained on two dimensions. I think the first is, I had already spent almost six years at Stripe, and so I had worked with a lot of engineers. Obviously, research and engineering are different, but I think there was some overlap and some grounding.

The second is that I grew up with a very talented, technology-oriented physicist, who’s my sibling and obviously my co-founder at Anthropic. Among five others, who are also all engineers, or researchers. But I think the number one thing I would say is, both of those experiences just instilled in me the sense to just not be afraid of technology. It was ultimately, it’s a set of skills that are really highly prized, I think. But it’s something that anybody can learn, and the sort of basics behind it, I think the terminology and the jargon can feel overwhelming at first. But if you just ask enough questions, and if you have people who are kind enough to be patient with you, which I had in my life, I was very lucky for that. I just kept asking questions until I felt like I could understand it.

And I think the second part of it was also, just knowing my lane, and their lane. So there were a lot of things that the researchers, I was like, I probably couldn’t have trained, I certainly couldn’t have trained GPT-2 or GPT-3, but I brought things to the table that they didn’t know how to do as well. And so I really think understanding what your comparative advantage is, and knowing how you fit into the broader ecosystem, that takes a lot of skills that are kind of interpersonal. Curiosity, I think is a very inherent skill, but one that you can learn and train. And I think all of those sort of put together really put me in this position to be able to be successful in that type of role.

Gintare Zukauskaite: And in December of 2020, you, your brother, along with a group of colleagues, left OpenAI. Why did you and Dario decide to start Anthropic?

Daniela Amodei: I’m gonna take a big sip of water, for that one.

Gintare Zukauskaite: Take your time.

Daniela Amodei: I’ll take one more, actually.

So there were seven of us that originally left, me and Dario and our five co-founders, and then a number of people that kind of came over shortly thereafter. And I think for us, it really came down to just the focus on what it was we really wanted the ultimate impact of the technology to be. And I think, in our own very different ways, all seven of us are people that have a lot of integrity. We’re people who care a lot about the impact of what it is that we build. And I think eventually it felt like it was just easier for us to sort of create the type of vision that we saw, outside of the company we were in, and in a new company.

And I frequently say, because I know it’s shocking I’m actually asked this question, not infrequently. And we really, I think we’re running towards something, versus running away from something. We had this vision in our heads of wanting to create an organization where the values that matter to us around safety, around responsibility, were the kind of forefront of what we were doing. That’s why we chose to incorporate as a public benefit corporation. That took a while to figure out, what was the right kind of form factor to express, “Look, we are going to be a commercial entity. We think there’s going to be a lot of economic value that’s going to be created by artificial intelligence, but it’s really important to us that we do this the right way.” And I think that was something that kind of united the seven of us. We had all worked on a combination of both capabilities, and safety, and policy work when we were at OpenAI. And it felt like it was just easier to kind of create this structure in a new form.

Gintare Zukauskaite: And you’re building Anthropic not only with your brother, but with five other co-founders. Many of us here are about to choose co-founders for the first time, and we all know how often that ends badly. What does it take to make it work?

Daniela Amodei: So I think of myself as just extremely lucky. The seven of us, I think, are a very special group on a couple of dimensions. I think the first thing I would say is, the interpersonal relationships, I know this is going to sound not that surprising, matter a lot more than you think. How do you guys have conflict together? So for example, Dario and I have been fighting and getting over it for almost 40 years. Because he’s my brother, and I used to steal his toys, right? So we know how to work through conflict together, and there’s no question we will love each other at the end of that, no matter what.

With our co-founders, it’s like, I’ve known Jared for like 15 years. I’ve known Chris for 15 years. Tom and Sam were roommates. Jared and Sam worked together at Stanford, when they were in their PhD program. So there was this kind of long history. Both Dario and I actually managed all of the other co-founders, and I think they had either reported to one of us or both of us, I think the majority of them reported to both of us at OpenAI. So we already had this kind of existing structure and framework of, we knew how to give each other feedback, we knew how to work together. We’d like understood who we were, as people.

And I think the other really important thing is just making sure you have a very strong sense of what it is you’re trying to do, and that that picture is the same. Like if you locked yourself and your co-founder in another room, and you wrote down or drew a picture of what it is you’re trying to build, you’re not going to walk out and one has drawn a unicorn and the other has drawn a platypus. That’s the type of situation, where you think you’re doing the same thing, but I think it just doesn’t end well.

And I think for us, this kind of vision of what it is we wanted to build, in some ways we were lucky because we had been in an environment where we were like, “Ooh, it’s not quite this. We want to do this other thing,” but we were in the same zone already. So we were sort of pre-selected for that level of interest. But I think really being able to pressure test to the degree that you can, like instead of starting a company together, go on vacation together. Just see how that goes. Share a room with them. Be like, “How did that go?” And if you’re like, “Man, all I want to do is spend more time with you,” great. If you’re like, “Really going to need a vacation to recover from my vacation,” it might be the wrong choice.

Gintare Zukauskaite: I want to circle back to what you just said a few moments ago. Anthropic is deeply associated with AI safety, but I want to make sure that everyone understands what that actually means. So when you say AI safety, what do you mean?

Daniela Amodei: Yeah, this is a great question. And I think this is a term that’s gotten almost a little bit overloaded in the past few years, because it’s kind of a catchall that I think has been reached for in a lot of contexts.

But I think to us, the kind of highest level of framing, is just taking a form of radical responsibility for the technology that we’re developing. And the analog that we often use, or that we often point to is, social media companies. Which, it’s so in vogue to crap all over them in public now, so I’m going to do that. But basically, if you imagine going back in time, I actually think developers who created these technology companies, they weren’t like, “I’m setting out to cause a pandemic of eating disorders for teenage girls.” That was not their intention. But they were like, “What are the metrics that I’m trying to optimize for? I’m trying to build a company. I would like to see rapid scale growth. Let’s just build towards that.”

And there wasn’t this sort of… At the time, it just wasn’t necessary, because we’d never seen something on the level of scale that we have seen today, how quickly these companies grew, how many people adopt the technology so quickly. But if you could imagine, you could go back in time and say like, “Wow,” you’re starting Facebook, or Instagram, or Snapchat, or Twitter. And you’re like, “What if I really tried to think through all of the ways that this could go wrong? What if I could think about what all the unintended externalities are, and really just kind of tried in advance to prevent some of those from happening?”

And it’s a little bit unfair, because in AI, we’ve had this whole generation of technologies before us, where they’ve gotten to [expletive] up. And we’ve gotten to be like, “Ha ha, we’re not going to do that thing again.” But that is a huge privilege. We’re able to say, “Okay, you guys made this mistake. We are not going to make that mistake this time. We’re going to be careful, and we’re going to say, ‘How do we make sure to think about the other things that might not go wrong, because we understand the technology better? How can we imagine a world where everything goes right, but also a world where everything goes wrong?’”

And I think for us, safety means all of the big stuff, so preventing chemical and biological weapons from being developed, using our technology. Which, by the way, they could have the potential to do. But also cyber, right? Cyber warfare, we’ve been in the news a lot lately about our decision to not release our Mythos-class model, because of the potential for cyber warfare. There’s also a lot of work that happens around things like user wellness, child safety. A lot of misinformation, election integrity work. This is not something new that we invented. We’ve been able to stand on the shoulders of previous safety and security teams who worked on this at some of the other most consequential technology companies in history, and say, “How do we learn from you? How do we do this better?”

Gintare Zukauskaite: Anthropic is an AI safety company that also has to generate revenue. How do you manage the tension between the two?

Daniela Amodei: We’re asked this question a lot too, and I think in general, the two don’t come into conflict as much as you would expect. So what I would say is, most businesses in particular, which is by the way, the majority of our revenue comes from businesses, are not looking to have models that are unsafe. They’re not like, “Wow, we would love for Claude to hallucinate more,” or, “It would be great if Claude just produced harmful outputs when you’re asking it a question.” And so, I think until pretty recently, these things were just 100% in alignment. You’re like, “It’s actually really good for business to be safe.” Because businesses are, correctly, they’re risk averse. Right? They’re like, “We don’t want AI technologies that are going to be super unpredictable or unreliable.”

That said, I think we’re now entering an era where the capabilities of the models are developing so rapidly, that the tension is about time. So it’s not necessarily the case that the models can’t do amazing things, it’s just we don’t fully understand at this stage, and I think it will be more the case going forward, how serious are the risks? What are all of the risks, and how do we help mitigate them? That sometimes means that we take slightly unusual actions, like we did with Project Glasswing and say, “This new class of model, it would be great if we could just release this to all of our customers. They all would love to use it,” but we’re just not confident enough yet. We need a little more time to do some of the work to make the models safer to use, but that’s uncomfortable. It’s uncomfortable to say that to your customers, right? They’re like, “Look, we all believe in cyber defense, but I really want access to that model.”

And I think this is the place where we just come back to the mission. We say, “Okay, we understand that desire. We want to get this technology to you as quickly as possible, but it is irresponsible of us to release it until we are confident that all of the patching that needs to be done has been done.”

Gintare Zukauskaite: We cannot deny that when it comes to AI, there’s a lot of fear, fear that AI means fewer jobs because of less need for human judgment. Is that fear valid?

Daniela Amodei: I think this is actually a very complicated question.

My sense is that, this probably will not surprise people here, AI is going to change what types of jobs are available and what types of jobs people do. So today I think there are jobs that exist, that did not exist five years ago, because of AI. I also think there will be some jobs that will not exist in the future, because of AI. But what we have seen so far today is that, according to our economic index where we study how are people actually using artificial intelligence technology right now, it mostly looks like complimentary skills. So you have artificial intelligence as an enabler of work, you don’t have it as a replacer of work. Except in a very vanishingly small number of cases, which is mostly customer service. Sorry, if you have to email Comcast, it will never be a human again. Probably. But I don’t know that that was actually different five years ago, so.

I think in reality, what I expect is, there will be a number of types of work that will feel a lot like they rhyme with a job that exists today, but they’re not necessarily the same as a job that exists today. And I think we just don’t know the shape of what all of that is. Today, I think the thing that’s most talked about is coding, right, software developers? I always, in business meetings, people will say to me, we’re talking shop about Claude, and then two thirds of the way through the conversation, a CEO will kind of conspiratorially lean across the table and say, “My daughter is a sophomore at Stanford. What should she study? She was going to be a CS major, should she not major in computer science?”

And I think the truth is, we don’t know, but my guess is software developers will still exist. But they won’t write as much code. A lot of what software developers do is much bigger than just hands-on-keyboard. They’re talking to product managers, they’re working closely with customers, and I think the percentage of that work is going to expand, and I think the sort of things that can be more easily done by AI, that will contract. But my sense is that is going to create a very different scope of what’s possible.

Gintare Zukauskaite: But what needs to happen in education, leadership, society, that people feel prepared and excited, and not just anxious?

Daniela Amodei: So I think there’s a few things here. I think the first one is, we need to start and lead from a place of humility, and not knowing the answers, but doing the research. And I think at Anthropic, something that we’ve always aimed to do is be as radically transparent about all of this as we possibly can be. We’ve always said, “Look, we don’t have all the answers. We do need to study this, so that we’re able to tell people what we see coming.” And I think, sometimes fairly, people can say like, “Wow, you guys are just, this is like a lot of negativity.” You’re like, “Here’s what we think might happen in the future.” But I think it’s more important that we start the conversation sooner, because we don’t want people to be caught off guard. We’re publishing the economic index to say, “Here’s how people are using artificial intelligence today,” because we want people to have an understanding of, “Where do we think this is going?” So I think that’s step one is, we have to actually all agree on what reality is, to the degree that’s possible.

I think step two is, we have to be creative and experimental at many different layers. So how do we have artificial intelligence really be something that is a grounding and a unifying force for people, outside of just like, “Oh, I’m using it at my kind of job,” which is really important. But I think in some ways, we need to sort of be rethinking the paradigm of this connection between work, and meaning, and social life. All of these things I think are going to look very different in the future, and we need to practice.

And then I think the third one, which is really outside of the realm of what a technology company can do alone, is this is going to become a social and political issue. People are going to care if it feels like their jobs are being displaced by AI. People already care about this, right? It comes up in polling. People are like, “I have anxiety about what artificial intelligence is going to mean for my future, for my kid’s future.” That CEO that’s leaning across the table from me, it’s not just them. And so, I think there is a broader discussion that needs to happen at many different levels of government, with civil society, with universities, by the way. To say like, “What does this mean? What is the type of world that we want to be able to build, where artificial intelligence is capable of doing many of the things that humans do today?”

Gintare Zukauskaite: At the core, it is all about adoption. Here at Stanford, we live and breathe AI, but Stanford and Silicon Valley are not the whole world. What currently hinders AI adoption outside of this bubble?

Daniela Amodei: You know, I think this is a really, really great call out. It’s so interesting because it feels like, certainly to us at Anthropic, and I’m sure at Stanford too, it’s like the only thing people want to talk about is AI. Certainly the only thing people want to talk to me about is AI, but that’s probably a me problem. But I think that feeling, you’re totally right. That even in other parts of America, it’s not something that people are comfortable with yet, on the whole, and it’s not even something that people necessarily know how to use with very high fluency. So you read these really astounding, super impressive numbers about how many people are using AI tools, but there’s a demographic component to that. So it’s generally people who are college educated, not uniquely. It’s more men than women. There’s racial demographics involved, there’s wealth demographics involved. And if you look around the world, it’s really not equally distributed.

And so I think what’s interesting is that you pair that with some other data that we’ve collected, which is that people in developing countries are much more optimistic about AI than people in higher income countries. So the Global South is almost universally like, “Wow, this is a huge opportunity for us. This is the moment where, perhaps, we could have an equalizing force that will make things more fair.” But I think in the US, and in Europe, and in parts of Asia, people have a lot more anxiety. They’re like, “I like things the way they are. I don’t want AI to come in and disrupt that. That doesn’t sound as good to me.” What do we do with this information? I have no idea.

But I think it’s really interesting to say there are different questions of access and adoption around the technology, and I think we are actually still very early in the game, and that’s the thing that I think can be missed in Silicon Valley, in our bubble. That we’re like, it’s already, everybody who’s a software engineer is like, “I’m using Claude Code. I’m using Codex.” That is not the vast majority of developers in the world at large, and so I think the race is still like the gun just went off, to start the race. And I think there’s a lot of opportunity to still positively shape how this technology is going to be used and developed, what access looks like, and just what the values that are baked into it are going to ultimately be.

Gintare Zukauskaite: We’ll be back with more of Daniela Amodei after this.

Gintare Zukauskaite: So let’s forward to the future, where AI is far more widely adopted. What are the things we risk losing if we start delegating too much to AI?

Daniela Amodei: Yeah. We did this very large qualitative survey at Anthropic. I think it’s the largest qualitative study ever done, that we know about. We talked to 81,000 people about their use of artificial intelligence. And so, some of them were Claude users, some of them were users of other AI tools. And what was interesting is, people have a lot of different feelings about AI. And again, it cuts differently, depending on where you are and what you do. But some people are like, “I’ve never been able… It enabled me to do things that I never thought I could do.”

Right, I think, for myself as an example, I didn’t think I could build a website. And now, using Claude I’m like, “Oh man, that’s so easy. I just click a couple buttons and Claude builds a website for me.” What? That would have taken me probably a year, if I tried to do it by myself, and it would not have been a very good website.

There’s some people though, who express a feeling of… There’s not a specific term for it, but I think there might be, or there might be in another language one day. Like, “I don’t engage my brain because I don’t have to.” So it’s not the same feeling as scrolling on your phone, but it’s like, “I could have reached for this idea, could have thought through it. But it was so much easier to not do it, and to just trust what the AI tool was giving me.” And I think this is the source, I actually believe, of a lot of the anxiety around AI. As humans like to, I think, have an inherent desire to learn, to be curious, to want to expand the aperture of things that they know about. And AI, in some ways, enables that. But if used incorrectly, can sort of disable that.

I’ve done this sometimes. I was like, “Oh, I could look this up and figure it out myself, but I’ll just ask an AI tool, and then I’ll blindly trust that what it says is correct. It’s not always correct, by the way. Sometimes Claude is wrong. Heretical to say, but a fact. And I think the anxiety there is around, how do we actually set some guardrails in place, so that it’s not impossible to do that, but you actually have to really be trying to do it. I think some of the work that we do with universities is maybe an interesting microcosm for this. We have this concept of learning mode. Maybe some of you even use it, I don’t know if we’re at the GSB yet. But faculty and professors and students, one version of this is, you put your homework in ChatGPT, I’m going to use that one instead. And you’re like, “Ha ha, it just gave me the answer.” There’s a word for that. It’s called cheating. And you’re like, “That was great.”

There’s another version where you use Claude in learning mode and you’re like, “I’m stuck. I’m trying to write this essay, and there’s something about the format that doesn’t feel right to me.” And Claude is this sort of patient tutor. It’s almost like you have an individualized professor who knows you, and understands what you most want to learn, and why this class is important to you. And it’s like, “Let me help get you unstuck. Do you want to go back and read this section together? Could we talk through this?” I think that’s the version where these tools can make you smarter, they can make you expand the set of things that you think you can learn.

And then I think there’s the version that’s just, turn your brain off. And I’m hoping that as an industry, we’re going to choose to do the second versus the first.

Gintare Zukauskaite: So if you had to prioritize, what human skills, most likely going to be more important in an AI driven world?

Daniela Amodei: So I have my own views on this, which is, I think like we talked about. A lot of specific, task oriented things. Like, I’m a financial analyst, or I’m a developer, or I’m a copy editor. Those jobs are going to change a lot, and a lot of that work I think will be able to be done by AI tools. But I think, ultimately, there’s this very real phenomenon, which is that humans like to be with other humans. We like to spend time together with each other. We like to learn from each other, we like to be creative, we like to spend time understanding the other person. And we’re social creatures.

And I think I’m imagining that in a world where AI is able to do a lot more of the productive day-to-day work that we do, those skills are going to become a lot more important and much more prized. Because ultimately, if you’re in a work environment and you’re like, “Well, I could just ask Claude to write a bunch of code for me,” you are going to choose to talk to the developer that’s going to explain to you why something broke. Or why we chose to build a tool the way that we did.

And I think sort of expanding this outside of the realm of just the technology industry, the example I often use is in medicine. Today, we hire doctors who are really good diagnosticians. We’re like, “Hey, can you tell me what’s wrong with me? I don’t feel well.” And you’re basically paying this doctor to say, “Here’s a set of things that could be wrong with you. This is the one that’s the most likely, let me run some tests.” Guess what? AI is going to get really good at doing that. But the thing that an AI tool can’t do is actually look at you, and examine you, and also help understand how you’re feeling and help you feel better. There’s a reasonable body of medical literature that indicates that people have a good relationship with their doctor, they just like their doctor, have better clinical outcomes than people who do not like their doctor.

That’s really hard to explain, but what’s probably going on, probably the doctor tries a little bit harder to understand what’s wrong with you. Maybe they run a set of tests that were unexpected. And I think those skills, that bedside manner, is going to be five times more important in a world where you’re not trying to cram that into one of seven things that you’re looking for, to make a doctor qualified to treat you.

Gintare Zukauskaite: And when you’re thinking about the future, what AI use cases you’re most personally excited about?

Daniela Amodei: Oh, man. I think for me personally, I mean, I’m a career manager. So I spend most of my time with people, and we have this sort of, I don’t know, again, it’s maybe one day there’s going to be a word for it. But there’s this phenomenon where everybody thinks that AI is not going to come for their job, because they’re so special, and I am extremely guilty of this. I was like, “People love people. People are going to want to report to me. They’re not going to want to report to Claude, obviously.” But I actually think Claude is incredibly powerful as a management coach, and as a person to help you be a better leader. So I use Claude, we write performance reviews at Anthropic, and I’ve uploaded a lot of the people that I work with have reported to me for like three or four years. So in general, it’s like you’re the same person, they’re the same person, you give them feedback. But how much has really changed in the past six months?

But I think Claude has been really powerful in helping me to spot patterns about somebody. So more data is better, but if you look back over the course of three to four years of time of working with somebody and you’re like, “Wow, you guys have been circling around this topical issue for the past three to four years, maybe they need some additional coaching. Or maybe they need somebody sort of outside of you.” It’s just the type of thing that I think tends to get missed, because you’re just in it day to day.

And in the opposite direction, Claude is great at giving you feedback. So I upload all of my reports, upward feedback for me, and sometimes Claude will kind of very kindly be like, “It sounds like you haven’t improved on this in the past year. Maybe you should get some extra coaching, Daniela.” But I think Claude’s ability to kind of coach and help people be the best versions of themselves, I think there’s a version of that that makes sense in the workplace, in people’s personal lives, that I think could be done quite carefully but that I think could be really powerful.

And then the second is, I have two little kids. So I have an almost-five-year-old, and an almost-one-year-old. And I have to tell you, number one, best thing Claude has ever done is help me through potty training. That was not a fun experience, and Claude made it just a little bit, it was empathetic, very actionable. There were some diagrams, I don’t need to tell you guys, but it was really, really useful. And I think Claude’s ability to help, in particular overwhelmed parents, is going to be really powerful. Because there’s so much bad information. It’s like every time you Google, is something wrong with your kid, the answer is yes. And I think Claude is a lot more measured, and can be interactive in a way that I think is really, really helpful.

Gintare Zukauskaite: Daniela, before we turn to student questions, when you think about the next generation of AI leaders and builders who are with us here today in this room, what’s the one thing you hope they take from your journey?

Daniela Amodei: I would say… Can I do two?

Gintare Zukauskaite: Go ahead.

Daniela Amodei: Okay. I would say the first is, it sounds so trite and so I almost won’t say it, but I truly think following something that you really care about, something that you are passionate about, is the most important thing you can do. There are so many great ideas, and if you don’t feel the sort of burning feeling of like, “This is a thing that needs to exist in the world, and I will just run through walls to be able to do it.”

It’s just, it matters for the times when it’s not fun, and when it sucks. You just have to be able to say, “I remember why this matters to me. I remember why it’s important, whether it’s to me personally or it’s because of a type of change that I want to see happen in the world.” There will be times, there certainly have been, at Anthropic. Times where we’re like, “This is not the most fun part,” right? There are parts that are tough. And so being able to relate it back to why you decided to do this in the first place, and why it matters to you, I think is so important.

And then the second I would say is, I think for this generation in particular, and I think really in the past five to 10 years, this concept that being in business doesn’t have to be in tension with doing good. I think that is a very new idea, and I think it is really special. And I have been so impressed at the sort of generation of founders, and just creators, who are thinking in that way. There’s this kind of marriage of innovation and social impact. I think Stanford has always been exceptional at this, but I think that is a very new concept, and I think there’s more appetite for it today. I think there can sort of be this feeling of, only the kind of mean, sucky people can build a business. I just don’t think that’s true, and I think increasingly, I feel that the desire to do good is a strong correlate with actually doing well.

Gintare Zukauskaite: Let’s turn to the students.

Student: Hi, Daniela. My name is Brandon. I’m a second year MBA student here. Thank you for joining us. There’s a debate about whether we’re in an AI bubble, and people usually mean three different things when they say that. Company valuations, how much companies are spending on infrastructure, or whether the pace of AI progress is actually sustainable. Which of those three are you most worried about, and which do you think people are most wrong about?

Daniela Amodei: That’s a great question. Yeah, we need different, we need like air bubbles and glass bubble. They’re very different. I see what you’re saying.

I think the one that probably, I don’t know if I would say I’m the most worried about, but I think is valid as a concern about this industry in general, is it is a high capital expenditure business. And that inherently brings some risks along with it. This is probably stuff you all already know, but it is really expensive to train these models. It takes a lot of compute, and that compute is in scarce supply. And when you put those things together, there’s a lot of demand, there’s not a lot of supply. I’m not an economics professor, but I think that means the price goes up.

So the compute, that is sort of the lifeblood of these companies, you have to buy really far in advance. And so you’re essentially making a bet on the future. You’re like, “We think we’re going to need this much compute at this period of time.” That’s a really big expenditure to make, and it is a little bit harrowing to work at any of these companies. I think if someone doesn’t tell you that, maybe with the exception of Google, because they’re a public company and have so much money. But I think certainly for Anthropic, for OpenAI, you’re kind of making a calculated bet that you’re going to be able to pay that money back over time.

We obviously are very bullish on this. I think the revenue from both of those companies is unbelievable, it’s something that I think we hear all the time. Venture capitalism, nothing like this has ever happened before. It’s impossible to imagine a business getting to the kind of revenue numbers that are being talked about on such a short time scale. And, if that ever were to change, there would be a problem. Both of these companies have bought a lot of compute for the future. It’s very expensive.

And so, I think that is probably the risk that feels, it’s not crazy to be worried about that. We obviously think we’re in a very good position, I think the industry as a whole is, but that could change any time. And I think it’s important to remember that this is ultimately a bet. It’s ultimately the industry thinking, “Hey, this is going to have a lot of returns to it,” but we could absolutely be wrong.

Student: Hi, Daniela. I’m Yash, MBA student. My question is, what does Anthropic believe is the right balance between government regulation and AI innovation? And what do you wish governments around the world were doing differently?

Daniela Amodei: Great question. So I think this is an area where, I think the conversation about it has been, sadly as sort of is the case I think today, just in the sort of political climate, it’s hard to have a nuanced discussion. And I think that’s a shame, because I think it is a really nuanced question. I think sensible regulation will need to be part of the story for artificial intelligence. It’s a very different technology than any other technology that’s ever been built before, and even the last generation of technologies probably could have benefited from slightly more regulation, in my personal opinion.

That being said, we’re not blind to the fact that you need to have some room to maneuver as a company to be able to try things, if you want to come up with the next generation of incredible products, that people want to use and that are adopted. And I think this is one of these things that, my actual most kind of critical hope for the conversation, is that it doesn’t become politicized. Which I fear it already has. It’s sort of like, “Oh, regulation, bad. Innovation, good.” Or, “Innovation bad, regulation good.” I think it’s just really complicated.

I think there are areas of regulation that just don’t make a lot of sense. And then I think there are areas of regulation that are absolutely critical to make the technology be developed in a way that is good for people, and that will prevent bad things from happening to the people that rely on it every day, and to the broader world. My hope is that, in an ideal world, what that would look like, is technology companies and regulators working hand in hand. Because we have the information about how the technology can be abused, because we see it every day. We have safeguards teams and security teams that look at, “How are people poking on this, and what are the actual risks?” But regulators know how to provide a framework and a system that can actually be followed and enforced.

And so, maybe it’s overly optimistic, but I still hold out hope that that future’s possible, where I think the two sides can find common ground and say, “How do we ensure that we’re able to develop the next amazing technology that doesn’t even exist yet, or the next company that’s going to be,” whatever, the next Google or the next Meta, but that we just put some common sense regulations in place that help protect people.

Student: Hi, Daniela. Thanks for being here. My name’s Jackie Kimmel. I’m an MSx student. My question is, AI is gaining access to increasingly sensitive personal data, such as our health data. What do you think individuals should actually be doing to protect their privacy?

Daniela Amodei: I think this is also an excellent question.

The first thing that I’ll say is, you would be surprised at how common of a use case it is to ask Claude medical questions. I think it’s one of our most common, just casual use cases. People, I use it all the time. I’m like, “This is wrong with my kid. This is wrong with me. Help me.” I think there’s two sides to this. I think the first is, it’s actually the companies’ jobs to use and protect your data with care. I think it’s extremely important. I bluntly think people should hold companies accountable for using their data carefully, because it is very personal. And I think, for example, our decision to not put ads in Claude is partly predicated on this belief that AI technology is just different. People have conversations with AI tools that are much more personal, than even what you would put on an Instagram account, or on social media of any form. And so I think the first is, with that knowledge comes more responsibility for the technology companies to actually protect your data.

The second is, I think from a personal perspective, I don’t know that I have the perfect answer. I can certainly tell you, a lot of people use it for medical questions. I would really think about safety from the perspective of, don’t take the models on faith about medical things. In my own experience, Claude has been right more often than my doctors about complex medical cases, and I would never do something without checking with a licensed medical professional. We are very open about the fact that the models make things up sometimes. They get confused. They don’t know you, they can’t examine you, right? Just like I was saying on stage.

So I think having some healthy skepticism is extremely correct, but think of it as, if you had a friend who was a really good doctor, who maybe wasn’t a specialist. So you’re like, “I’m seeing a specialist, and I would like help kind of being guided in this conversation with my doctor.” I think Claude is a great tool for that. It’s great for helping you think of things that might be going on, that you might not know. But I think my number one recommendation is please do not just do medical things that any… I mean, I know you’re all too smart to do this, but any AI tool just says like, “Go do X.” Look at it with some skepticism, and actually talk to a professional.

Gintare Zukauskaite: Thank you to the students, and Daniela, before we let you go, we have to do our View From The Top rapid fire tradition.

Daniela Amodei: Love it.

Gintare Zukauskaite: Are you ready?

Daniela Amodei: I’m so ready.

Gintare Zukauskaite: Okay, let’s go. What would you major in if you were back in college?

Daniela Amodei: If I say business, does that just get me out of this entirely? What would I major in? I would probably major in literature again. I know that sounds crazy. I like to read.

Gintare Zukauskaite: What’s your favorite thing about working with your brother?

Daniela Amodei: Oh, Thanksgiving dinner. No. I would say that we know so much about each other, and we’re able to have… Sometimes we’re able to say things to each other that nobody else in the company can. Like sometimes we can get away with something that no one else feels like they could.

Gintare Zukauskaite: What about least favorite thing about working with your brother?

Daniela Amodei: Thanksgiving dinner. No. I think just the needing to also have separation between our personal relationship and our work relationship, so we build in time every week when we hang out outside of the office. But I think that’s hard, just… Like we were siblings for a long time before we were co-founders, we will be siblings for a long time after we’re co-founders, and making sure to just continue to water that relationship outside of work.

Gintare Zukauskaite: Favorite book you found at the library in your office?

Daniela Amodei: Ooh, oh man. I don’t know if I’ve discovered a new book, which maybe means I should get… I was like, “I like reading.” Just kidding. I should probably go look more, but I was reminded of a favorite book there. Maybe that counts. There’s a book called The Guns of August. I don’t know if there’s any World War I, I’m seeing some blank faces, so maybe not. It’s a great book if you’re curious about World War I.

I picked it up from the library and actually reread it, because I read it I think maybe right after college, or something. But I think it’s a really important study of just the individual people and personalities that resulted in, and sort of led to, the beginnings of what became World War I. And just how much so many individual events, and sort of people and personalities, hinged on this really, ultimately, sort of tragic and terrible thing that happened. So anyway, blanket shout out of, read that book. It’s great.

Gintare Zukauskaite: Amazing. And if Anthropic had ended up with a different name, what would it be?

Daniela Amodei: Oh, man. Wow. We went through some truly tragic ideas before we came up with Anthropic. I think, for some reason we were very into birds, at the time. We were like, “Sparrow Systems.” No idea where that one came from. But I think actually, now that I’m remembering, I think some of the model names from the early days were all birds. So there was BERT, which then became, we had a [inaudible], I guess that’s not a bird. But anyway, we had this kind of like, bird thing. I don’t know. There were some terrible bird names. Thankfully, better decisions prevailed, and we named it Anthropic, which is now impossible to imagine as not our name.

Gintare Zukauskaite: Imagine. And finally, not a rapid fire question. Best advice you ever received?

Daniela Amodei: Ooh. Drop the ant. No, I’m trying to think of… Best advice I’ve ever received. I think probably, I will say, when we were thinking about leaving. And it’s now, in retrospect, people are like, “Of course you guys, you all left, and you founded Anthropic.” It didn’t feel that way at the time. We were like, “This is kind of a crazy thing to do. Maybe we should stay. Maybe we could make this work.” But we talked, I talked to one sort of friend and mentor outside of work, and she was like, “Honestly, I don’t think you really need to be on the phone with me. You already know what the right answer is.” And I think in general, when you’re in a moment of, “Is this the right thing for my life?” Often you actually know what the right answer is. And I think that was really good advice.

Gintare Zukauskaite: Daniela, it’s been a pleasure.

Daniela Amodei: Thank you.

Gintare Zukauskaite: Thank you so much.

Daniela Amodei: Thanks for having me.

Michael McDowell: So The Guns of August, published in 1962. Gintare, tell me about Anthropic’s library.

Gintare Zukauskaite: Yes. So it’s not a very well-known fact, but at Anthropic’s office here in San Francisco, they have a huge library full of books. And not just technical books, but also, fiction books. So my rapid fire question, I knew that the audience going to be like, “Well, it makes sense. She’s an English literature major, so of course, makes sense why she would ask about books.” But I knew that she’s going to get the cue that I was talking about the library in their office, because of how well-read everyone at Anthropic is. And it was a very, very interesting book option. It was a very interesting book choice that she gave.

Michael McDowell: Yeah. And what does it mean for an AI company to have, at its headquarters, a massive library?

Gintare Zukauskaite: I think it shows what Anthropic stands for, given… Even Daniela, I think is a standing example. That we’re trying to make AI understand more on how to help humans, and I think books is the artifact that’s been with us for now hundreds of years. And that’s how we transfer knowledge, that’s how we transfer customs, culture, stories. And I think it’s a very interesting symbol.

Michael McDowell: There’s something distinctly human about books.

Gintare Zukauskaite: Correct.

Michael McDowell: Let’s jump right into the big existential question. You asked Daniela about the tension between safety and revenue, and how she manages that at Anthropic. What did you make of her answer?

Gintare Zukauskaite: This is a question she mentioned she gets asked a lot, and yes, I saw her answering that question a lot of times in her previous interviews. But I knew that the crowd that was at CEMEX probably never heard that answer. So I really wanted to bring that out, because I think the question is, great, you’re focusing on AI safety, but your competitors might not be. And you have many competitors who are going for the same type of mission and goal, to dominate this industry.

Michael McDowell: That’s right.

Gintare Zukauskaite: And if you are making decisions where you’re prioritizing safety over speed, then you cannot really influence the rest of them.

Michael McDowell: Are you going to be left behind?

Gintare Zukauskaite: Exactly. And I think her answer kind of showed where they got conviction to really educate their own customers, and get the validation in their turn that yes, no one wants models that hallucinate.

Michael McDowell: Right.

Gintare Zukauskaite: Everyone wants to make sure that you get out of AI what you think you’re going to get out of it, which is productivity. I hope it brought clarity to the audience on what that mission statement actually means in practice.

The other part that I really wanted to kind of, maybe we didn’t have time to touch it, but I think it’s also about the differentiation, the differentiation of what your offering is, especially when the LLM models converge so rapidly. But I think in that answer, she kind of covered exactly where Anthropic’s strength is, that it is really about building its safety responsibly, and thinking about the consequences, and mapping out the worst case scenarios before they happen. So that you’re able to make sure that things don’t go unexpected.

Michael McDowell: Fingers crossed.

Gintare Zukauskaite: Fingers crossed.

Michael McDowell: Let’s shift over to consequences to the future of work. It seems like for Daniela, the question isn’t what AI might automate, or what it might make obsolete. It’s, do we permit ourselves to stop thinking, and doing the kind of thinking that isn’t necessarily easy or fun? What are your thoughts there?

Gintare Zukauskaite: That’s why I put that question in. We still know the world, and understand the world, without AI. We had to do math. We had to learn languages. We had to learn everything analog, before we went digital. And I think the next generation, already, like there will never going to be a world without AI again. And if from the beginning you start outsourcing everything, will we be able to foster critical thinking, ability to connect different fields? Or will AI dictate everything? Like what should we be eating, how should we should be dressing, what we should be doing? And I think that’s the risk, and it was very clear from her answer, that we really need to think about it. And make sure that that doesn’t happen.

Michael McDowell: Along those lines, Gintare, are you an optimist or a pessimist regarding our AI future?

Gintare Zukauskaite: I’m definitely an optimist.

Michael McDowell: That’s good.

Gintare Zukauskaite: Because just like Industrial Revolution took many jobs away, and new ones came, I think exactly same will happen with AI. We have to take responsibility, individual responsibility, to make sure that we also leap ahead.

Michael McDowell: Yeah.

Gintare Zukauskaite: I think that’s the secret.

Michael McDowell: I like that. Yeah. One of your peers in the audience asked Daniela about the bubble. She did acknowledge that AI is ultimately a bet, and quote, “We could absolutely be wrong.” I was surprised. Were you surprised?

Gintare Zukauskaite: No. And let me tell you why. You cannot really develop this technology in the way how previous technologies got developed, and I think it would be foolish to pretend that we know where all of this is heading, and what the outcome will be. It’s ultimately a bet, and a bet on the future, and I think that’s why there’s so much excitement about this industry overall. Because right now is the time when there’s a technology inflection point that will unlock many great things, and we don’t know how it’s going to play out. It’s going to be a very, very interesting decade or so, of seeing how the story plays out.

Michael McDowell: What did you find most surprising about this conversation, and why?

Gintare Zukauskaite: I would say Daniela herself. I have never met a leader who is so humble, so warm, so open. And she’s truly one of the most powerful women in the world, but she’s leading with humility, she’s leading with openness. And I absolutely love that. I felt it did not matter the topics we’re going to cover, or what we’re going to talk about. Just her openness was enough for me to be sure that this conversation will make the audience happy, excited, and optimistic about what’s to come.

Michael McDowell: Your last question was about her advice. “You already know what the answer is,” to quote. Why is that both so obvious and so important to hear?

Gintare Zukauskaite: Because I think it’s relatable, and there’s never 50-50. There’s always 51-49. And I think the way she put it in her own words was, encouragement to be okay with going the option where you know it’s 51, and not 49.

Michael McDowell: Letting go of that 49.

Gintare Zukauskaite: Letting go of that of 49.

Michael McDowell: Gintare, thank you so much. This was fantastic.

Gintare Zukauskaite: Thank you, Michael.

Gintare Zukauskaite: You’ve been listening to View From The Top: The Podcast, a production of Stanford Graduate School of Business. This interview was conducted by me, Gintare Zukauskaite, of the MBA Class of 2026. Michael McDowell is our managing producer, and Veronica Simonetti edited and mixed this episode. Special thanks to Liz Walker.

View From The Top is the dean’s premier speaker series. It was started in 1978, and is supported in part by the F. Kirk Brennan Speaker Series Fund. During interviews led by students, leaders from around the world share insights on effective leadership, core values, and lessons learned along the way. You can find more episodes of View From The Top on our website, gsb.stanford.edu/business-podcast. Don’t forget to rate and subscribe, and follow us on social media, @StanfordGSB.

And see you next time, on View From The Top.