Transcript


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Our first guests are siblings and co-founders of Anthropic and A.I.
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Research Lab that combines machine learning, neuroscience and cognitive
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science. Everyone, please join me in welcoming to
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the stage Dario and Daniela. I'm a day.
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All right. Thank you, guys.
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It's so exciting to have you here. I mean, first, perhaps for the
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uninitiated who may have heard about Jackie Beattie or the result that Google
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serves up in Google Search Search Results for Microsoft Copilot.
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Tell us about Claude and how it's different from the wave of large
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language models or chat bots that have emerged over the last year.
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Yeah. So if we go back to kind of the founding
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of Anthropic, you know, right. When a group of us left open the AI to
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start Anthropic, we had two real theses and one of those theses is something
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that I think is shared among, you know, three or four or five companies that
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have been very successful in this space. And one was very unique to anthropic.
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The first one was, you know, what's come to be called the scaling hypothesis,
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which is the idea that if you scale these models up a huge amount, then, you
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know, you add more data, you add more computing parameters in the model, they
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perform better and better on a wide range of tasks.
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I was we were early advocates of that. But, you know, that that I think is
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common to all of the companies. The second was that along with scaling
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these models up, making them like a very powerful rocketship, you need to steer
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the rocket ship. And so we've worked on various things
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like constitutional A.I., which is a method for explicitly specifying the
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values of an AI system, interpretability which is looking inside in the AI
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system, a bunch of things around AI security and resistance to jailbreaks.
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And so I really think of our work as the combination of the pure scaling up with
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this sort of steering and safety component.
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I guess let me ask you to answer that question too, because, you know, if
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you're going to distill it down for consumers who now can download a client
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iOS app or enterprise users who have access to your APIs on Amazon's cloud or
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Google's cloud, but they have other options.
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So why Anthropic? So really, I think there's a few
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reasons. The first is that today, Claude, three
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OS, which is our largest model, is the most powerful, capable model available
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anywhere in the world today. And so for businesses that are really
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looking to transform some of the work that they do, particularly highly
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complex cognitive tasks. Claude three Opus is a great choice.
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Another reason is we really provide enterprise choice and sort of business
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choice. And the reason that we do that is we
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heard this feedback from our customers. You know, sometimes you need really the
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kind of, you know, the Ferrari, like the best sort of fastest, smartest model
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available. But sometimes you actually just need a
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model that is, you know, cheaper, quicker.
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Cloud three Haiku, which is our smallest model, is a great choice for businesses
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that are using an LM for something like customer support, right?
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Something where you need a really, really fast model that is, you know,
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price competitive. And I think more broadly, something
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we've found is that businesses in particular, but also consumers really
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vibe, I think with our approach around trustworthiness and safety and
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reliability of these models. Ultimately, when you're using a tool
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like Clyde for so many different facets of your work, you really have to feel
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confident that what it's returning is something that is, you know, accurate,
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reliable isn't going to go off script. And I think entropic has really been
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known for prioritizing that type of of work.
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Dario Chaput says we're experiencing them now as consumers.
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Do you think that's a novelty like the first demonstration of this novel
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technology? And does it go away?
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Does it sort of fade into the background of the tools that we end up using?
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I think actually sort of both. So I think there's two directions the
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technology is going to take. I think One Direction is that A.I.
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systems, you know, potentially through through APIs and this is more of the
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enterprise side of things are going to start suffusing all of kind of our
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existing technological infrastructure in the world.
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So you can see the beginnings of it with kind of, you know, integrating models
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into into, for example, ease or code completion.
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You know, you look at what Intuit has done with, you know, kind of
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answering questions about about TurboTax, extending products with with
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A.I. systems and large language models.
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I think there's going to be a lot of that.
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A second thing that I think is going to happen is that the chat bot is is also
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going to be extended by kind of reaching into a lot of the tools and being able
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to do things for you. Right?
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This is the personal assistant that takes actions, that interacts with all
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the stack of existing technology for you.
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So I think both of those things will exist.
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They may correspond roughly to enterprise and consumer, or it may be a
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little bit more blurry than that. We, of course, are more focused on the
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enterprise, but really we deploy A.I. and, you know, in every every surface we
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can. I have a suspicion that next week at
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Google's event, Google IO and next month at Apple's event WW DC, we're going to
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hear a lot from those companies about integrating AI into their into their
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software, into their operating systems. I'm curious like what kind of lane
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exists for the kind of third party app, like the cloud app that you just
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introduced on iOS. So I don't know.
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I think our our general our general perspective is it takes a lot of effort
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and a lot of resources to build the model.
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And I think once having once having built that model, you know, we want to
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deploy it on every possible surface that we can.
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And we find a lot of folks find it useful to use the model on mobile just
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as they use it on web. One thing I would also hint at that
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we've we've kind of started to move towards is, you know, we recently
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released a teams product and of course, you know, that's going to be on mobile
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as well. And the direction we're going in that is
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is towards a more enterprise direction. If we think of the current chat bots,
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you know, when people try and deploy the current chat bots in enterprise setting,
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it's a bit like I took a human off the street, the smart human, and then kind
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of asked them to work with a coworker at a particular company, right?
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They don't necessarily have all the context.
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So I think we're working in the direction of trying to make it more like
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an informed employee, hooking it up to databases, hooking it up to company
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specific information. And I think the mobile app is going to
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be part of that in addition to having a consumer experience.
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So you and your co-founders are known as the team that spun out of Openai to
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develop models that are aligned with humanity, that don't develop unforeseen
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risks, that are that are safe. How did you come to develop your kind of
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concerns about the potential direction of AI?
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So I think something that has always been this founding principle for us is,
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you know, we see an incredible amount of potential benefit from this technology,
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right? Something that Darwin I talk about a
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lot, you know, together with the company, with our co-founders, is.
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This technology to transform sectors like health care, right.
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Or even science. I think some customers that have worked
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with us, you know, Pfizer, the Dana-Farber Cancer Institute, the type
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of work that they're doing is truly amazing.
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And we're proud to be able to support businesses like that.
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I also think we have felt very strongly that there are some lessons to be
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learned from kind of prior technological waves or innovation.
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And this is not that, you know, we're so incredibly prescient.
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We've just had, you know, several decades now of time to kind of look back
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on things like social media and say, wow, you know, there was a lot of good
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that came out of this, but there were also a lot of harms that were kind of
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caused by this technology. Whether or not you could have
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anticipated those in advance, you know, hard to say, but I think we are trying
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to be the most responsible actors that we can be and think about what are the
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potential externalities that could be caused kind of accidentally by this
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tech. And we really try and take a very broad
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approach to how we think about that. Of course, we don't have all the
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answers. We try to be humble, but we've tried
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very, very hard to sort of think about what are the areas where something kind
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of could go wrong here and how do we act as responsible leaders in this space.
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Dario, I don't know if you caught the introductory video before we came up on
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stage, but there was a little clip of Emily last year at this conference
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asking Sam Altman with kind of wonderful simplicity, Why should we trust you?
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And after a bit he goes, You shouldn't know.
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And so I want to ask you the same question.
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Why should we trust you? Yeah, I mean, my my view has always been
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you shouldn't trust people. You should trust institutions.
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And, you know, I think I think my view is, you know, on one hand, we've tried
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to build you know, we tried to build an institution and a reputation that's, you
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know, trustworthy, both in a societal sense and then a, you know, companies
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can trust us with art, with art, with with with their with their data sense.
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I think in the end, you know, like, the proof is just the proof is just in the
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pudding, right? You have to develop a reputation of
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doing what you say and seeing what you do.
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And, you know, Anthropic hasn't been around for that long.
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But that's that's what we've tried to develop over time.
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I think there are broader societal questions about if the technology
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becomes truly powerful, you know, to to to to a level that, you know, that we
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haven't we haven't seen in a century or so, then we start to ask questions about
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kind of democratic legitimacy. It's not so much about trust.
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It's about, you know, is this is this is this so big that, you know, there needs
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to be some democratic, you know, democratic mandate and involvement of
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the government? I don't think we've reached those
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questions yet, but I think I suspect they're closer than we think.
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And, you know, then then that, again, we should think about mechanisms of
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democratic accountability. You guys have raised over $7 billion in
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part from Amazon and Google helping to accelerate this arms race.
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I guess I wonder, like, is the world safer now than it was three years ago
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before we started down this path? I think something that is very kind of
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challenging about a field like AI is just in some ways like the genie is out
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of the bottle, right? I think something that is that is, you
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know, we we think about a lot is how do we in the kind of environment that we're
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in. Right.
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And the circumstances that sort of everyone is experiencing where A.I.
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is is taking off. Right.
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How do we act as kind of the best, most responsible actors we can?
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And I think we have this sort of vision of not just, you know, anthropic and the
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decisions, you know, we make impacting the technology we develop.
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But how can we put sort of positive pressure on the ecosystem, Right.
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Something that we hear from our customers a lot, which we which we like
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to hear is, hey, we're we're choosing anthropic, we're choosing Clod because
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of your commitment to, you know, safety and security and reliability, because we
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also know that is what they're saying to our competitors, right?
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They're saying, hey, we don't feel confident that your models are as safe
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as anthropic. That's actually a great market signal,
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right? I think that there is a way in which we
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view our impact not just from the work that we do directly, but really from how
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we impact the markets. You know, policy, right?
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We try as much as possible to be very open about trends we're seeing so that
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policymakers can be informed about how to set thoughtful regulation in the
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industry. I would I would add, you know, we have
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this notion called the Race to the Top, right?
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So what you're referring to with, you know, all these different players you
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might call a race to the bottom, and I think that's real.
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But we often try to counteract that with a Race to the Top, which is we try to
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set a good example and get others to follow it.
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And in order to set that good example, we do have to have enough weight that
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that others actually respond to our signal and get move by our signal.
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Example of this is, you know, we pioneered this this concept of a
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responsible scaling plan. We put this out last September, and
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within a few months opening, I put out a similar thing.
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And, you know, it really seemed to us that that us putting ours out helped to
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spur the efforts by others. Now, we've heard that Google is doing
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the same thing on podcasts, and I've heard similar,
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you know, at least whisperings of other companies doing the same thing.
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I think in areas like interpretability, you know, we developed the notion of
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constitutional AI. And, you know, I think just yesterday
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one of our competitors put out a concept very similar to it.
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And you guys clearly left open air in part because of some concerns, right.
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About the direction of the company and whether your values were aligned.
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Are any of the fears that you had back then, now, now being manifested by what
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you're seeing at open air? I think what I would say here is
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anthropic really has always aimed to have, you know, kind of our own vision
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and our own story. Right?
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We saw this opportunity to go out and really build something where everybody
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was kind of a line from the start about about what our values were.
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We've always had this very strong kind of moral compass around wanting to
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develop these tools in a way that is, you know, responsible and safe and
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reliable. All the words I've been using.
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And I think that it just sort of felt to us like this was this was the best way
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to do it. Right.
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We had this opportunity to go off on our own and really sort of set this vision
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from from day one. There was a very similar response, very.
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Is there anything that you want to add? I mean, I don't know.
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I would just I would just add the notion of trust.
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Like we have seven we have seven co-founders, you know, like three and a
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half years later, all of us are still at the company.
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We have an incredible amount of, you know, trust for each other.
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We you know, we start we start we started this company because we believe
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that we all had the same values and we trust each other.
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I want to run a poll. What is the most urgent issue raised by
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AI? And you're going to have some options.
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So please weigh in. I'd love to know what you all think.
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And while we wait for results, I think feel like I have an opportunity here
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talking to you both your brother and sister onstage at the same time.
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Danielle, what is it like starting a company with your brother?
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I mean, if it was my brother's, we would, you know, we'd be in probably in
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court, or at least there'd be a struggle for control.
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So how are you guys managing to do it? You know, I think something that is a
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little unusual about us is Darren have always been really close.
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We grew up in San Francisco. It's like a fun fact about us.
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We actually grew up like 10 minutes in the mission.
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In the mission. That's right.
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And I think we I don't know, Dario and I it's just sort of hard to explain.
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Right? Like folks who knew us when we were
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little to folks that, you know, know us now and our co-founders, they've always
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kind of described this like very strong, you know, special bond between us.
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And I think since we were really little kids, we always had this desire to,
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like, make things better. I mean, I know that sort of sounds like
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a little corny or basic, but we we like it was a very deep thing.
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Like we really felt like the world, you know, there's many problems in the
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world. We really want to try and help and make
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things go better. I think getting to do that together,
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like in a company that we lead, is is the sort of fullest expression and form
15:30
that we've been able to do. And I don't know, it's great.
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I mean, maybe how I would encapsulate it is I you know, I remember when when
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Daniela was in high school and, you know, every week we'd play the original
15:42
Final Fantasy seven, unlike, you know, the original PlayStation in 1997 or
15:47
something like that. And now we do the same thing every week,
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you know, playing, you know, the, like, new remake on the Ps5, like 30 years
15:56
better, 30 years, better graphics. And it's basically the same thing,
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except we're running this company. Was there anything about the
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circumstances of your upbringing or the family dynamic, do you think that pushed
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you to become entrepreneurs? I think.
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I mean, I'm curious for driver's too, but I think I something I think about a
16:12
lot is I don't think we recognized it at the time, but growing up in, you know,
16:18
S.F. in the nineties, we really saw the sort
16:22
of first tech boom like up close, right? We didn't have the language yet for sort
16:26
of what was what was happening. And of course a lot was sort of going on
16:30
down the peninsula. But I vividly remember, you know,
16:34
walking around and sort of seeing all of these, you know, very well-dressed young
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people like going into these kind of swanky office buildings and just
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thinking like, what do you do? Right.
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Like still kind of a mystery at this point.
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But but I had a concept of like, what's a doctor?
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You know, what's what's a lawyer? What's a teacher?
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And I think there was, at least for me, sort of this spark of like, who are
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these people? What do they work on?
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Like what? What is this?
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And that sort of just like percolated in the background as we were going off and
17:05
doing. You know, Daria was obviously getting a
17:08
Ph.D. and I was working in global health, but
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there was always sort of this globe, this backdrop of of these young people
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walking around, sort of having these cool jobs that I think there was
17:17
something in there that that stuck with me.
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I was actually entirely uninterested in the first tech boom in the nineties.
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It seemed it all seemed really boring to me.
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I was like, interested in being, you know.
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Inventing new theories of like the universe.
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And, you know, I thought of I is kind of in that in that bucket.
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But, you know, so I worked on neuroscience for a while.
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And then as I moved into a I, you know, it kind of became increasingly
17:45
interlinked with the tech world. And it became clear to me that if you
17:48
wanted to actually make these issues of both scientific discovery and social
17:53
responsibility real and bring them out into the world, you know, you know, like
17:59
the the devil the devil is in the details.
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And it would really come down to the details of the technology and how it's
18:06
integrated into the economy and that the only way you could really, really hope
18:10
to influence that or the best way you could hope to influence that was, you
18:13
know, actually buy, buy, buy, buy. Being involved in, you know, running one
18:17
of these companies, I feel like there's more to explore here, but we're running
18:21
out of time. And I want to get to three questions.
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We have to kind of do two rapid fire considering the extraordinary expense
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and resources that have to go into training and operating these large
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language models. Do you do you worry that it won't be
18:34
feasible anymore to keep showing these step improvements in the way that we
18:39
have historically been seeing them these past few years?
18:41
Yeah. So I think I think we have quite, quite
18:44
a ways up to go. Right.
18:45
The current generation of models that we're seeing out there is still in the
18:48
$100 million range. You know, I think all of this can scale
18:52
to the $100 billion range at the end of that range with some difficulty in terms
18:56
of power and chip provision. But we're going to we're going to get
18:59
there in a few years. So that's a factor of a thousand more.
19:02
And then I think the algorithms are going to get more efficient and we're
19:05
going to see things, some things on top of post training.
19:07
At some point we're going to run out. But but I think there's there's lots of
19:11
things stacked on top of things to keep making models.
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Do you guys worry about the carbon footprint of these models?
19:16
So something that we have invested in from, you know, early on IT anthropic is
19:20
actually doing carbon offsets. Of course, you know, it's a complicated
19:23
issue. There's no perfect answer, but it is
19:25
something that is top of mind for us and that we invest in.
19:28
The FTC is examining the relationship that companies like Anthropic have with
19:32
the big tech companies like Google and Amazon.
19:35
Is there an argument here that these relationships forestall competition?
19:39
So, I mean, I can only speak for anthropic, but I think one of the things
19:43
about us is we have relationships with two different cloud providers.
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And so, you know, I can't go into the details, but but in many cases, having
19:52
those two different partnerships makes it, you know, not not, not not plausible
19:57
that, you know, that that various things could be could be exclusive.
20:01
Right. And so, you know, I think that that
20:04
independence and that choice is one thing that I think differentiates
20:08
Anthropic from some of these other deals.
20:11
I mean, just to build on that, I know we're out of time, but I really think,
20:14
you know, this sort of current state is that it is it is quite expensive to
20:18
train these models. Right.
20:19
And so I expect we'll see similar structure deals, you know, to come.
20:22
But strongly agree with Dario. We're an independent company.
20:25
Okay. I'm going to I'm going to ask one quick
20:26
question and we'll leave you. Just considering that you're saying that
20:30
we're going to continue to see the same pace of improvement, make the argument
20:34
for us that we should be excited about this a future instead of slightly
20:38
fearful about it? My my honest answer is that you should
20:41
be both at the same time. You know, people people have asked me,
20:45
you know, how how excited are worried already will scale 1 to 10.
20:48
And I said, I'm ten of ten excited that I'm ten out of ten worried, worried as
20:52
well. And so we try to we try to handle handle
20:54
both of it. But let's what's actually you know, I'll
20:57
just discuss the positives for for like 20 or 30 seconds, which is I think a
21:02
biomedical right. You know, I go back to my work as a
21:05
neuroscientist and computational biologist.
21:08
And, you know, I you know, I really look at diseases like cancer, Alzheimer's.
21:14
We're getting to this range of diseases that that are super biologically
21:19
complicated and that that humans are running up against their limits of what
21:23
they can, what they can understand. Right.
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What can fit in one person's understanding.
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And, you know, I think that I computational chemist, which were
21:32
already beginning to see the little bits of with you know, work we're doing with
21:36
with companies like like like like Pfizer.
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I think there's a lot of potential there.
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And that field could be very surprised by how fast things go and, you know, the
21:47
real improvements in quality of life that come from it.
21:50
All right. Well, Dario and Daniela Amodei, thank
21:53
you, guys. Thank you for having us.

Extract all the points  as per transcript above.