Speaker 1: Bloomberg Audio Studios, podcasts, radio news. We were chatting about
Speaker 1: not so long ago when we first met and started
Speaker 1: covering Anthropic. But for those who aren't familiar, you and
Speaker 1: your brother Dario, and a handful of other people left
Speaker 1: Opening Eye to start Anthropic back in twenty twenty one.
Speaker 2: Now you employ thousands.
Speaker 1: Of employees, many of them in a reporting chain up
Speaker 1: to you and your role as president. I'm curious what
Speaker 1: is the difference in the leadership style between you and Dario.
Speaker 3: Well, first of all, thank you so much for having me.
Speaker 3: It's great to be here today. And you know, Dario
Speaker 3: and I I really think of how we sort of
Speaker 3: split duties. Dario is this incredible technical visionary. I think,
Speaker 3: you know, years before AI was this amazing idea that
Speaker 3: people were excited about around the world. We had this
Speaker 3: really strong fiction that artificial intelligence is going to be
Speaker 3: a big deal. This is like early twenty tens, and
Speaker 3: I think Daria is constantly reminding the company about the
Speaker 3: scale the ambition, sort of where the technology itself is going.
Speaker 3: I think of my role much more as actually running
Speaker 3: and managing the day to day of the organization. So
Speaker 3: I managed the executive team. We spend time making decisions
Speaker 3: about customers, about our products, about the research, and how
Speaker 3: that applies to the people that are using artificial intelligence.
Speaker 3: And I like to think that the two of us
Speaker 3: are a great balance. And I have to say, like,
Speaker 3: I don't know how one CEO just runs a company
Speaker 3: by themselves. It seems like it's actually much easier to
Speaker 3: do with two people, particularly when they know each other
Speaker 3: really well.
Speaker 1: I remember in one of our conversations earlier, are you
Speaker 1: telling me that you and Daria rarely fight? And I'm curious
Speaker 1: how you manage What are some examples of places where
Speaker 1: maybe you have disagreed and have been able to settle
Speaker 1: it in.
Speaker 2: A healthy way with regard to the business.
Speaker 3: Well As someone who also has a brother, I know,
Speaker 3: you know, you know. I think we've had, like you know,
Speaker 3: several decades to kind of practice fighting and getting and
Speaker 3: still loving each other right, so as early as like
Speaker 3: why did you.
Speaker 4: Take my toy? Can I have that back? Please?
Speaker 3: I think that has served us well in being able
Speaker 3: to operate well together. In anthropic I think what I
Speaker 3: would say is that you know, we do disagree sometimes,
Speaker 3: but I think how you disagree really matters. And I
Speaker 3: feel like there is always like an incredible mutual respect
Speaker 3: between us. So if we look at the same situation
Speaker 3: and we see something different, I like to think that
Speaker 3: what our default is is to be curious, right, So
Speaker 3: I usually view it as like, oh, this about like
Speaker 3: an obvious call to me, Like why do you see
Speaker 3: it that way? Because usually he'll have a piece of
Speaker 3: information that I don't have, or we'll have a different perspective.
Speaker 3: And I would say about eighty to ninety percent of
Speaker 3: the time what ends up happening is we meet in
Speaker 3: the middle, and so.
Speaker 4: We'll think like, oh, I think this is the right
Speaker 4: thing to do. You think this is the right thing
Speaker 4: to do.
Speaker 3: Actually, there's probably a third way to do this that's
Speaker 3: the combination of what we both think that's actually a
Speaker 3: little bit better. I think it's part of the strength
Speaker 3: of having both of us the ability to practice looking
Speaker 3: at situations and decisions just from different points of view
Speaker 3: and perspective, and I think it helps us make better decisions.
Speaker 1: So two years ago, when you were sitting on the
Speaker 1: Bloomberg Tech stage with Dario, Anthropic was still very much
Speaker 1: the underdog in the competition against open AI and just
Speaker 1: in the race to AI generally. Now it's in a
Speaker 1: very different position. No longer the underdog. Claude is a
Speaker 1: runaway success, particularly among coders. Your company is projecting forty
Speaker 1: seven billion dollars in annualized run rate revenue and the
Speaker 1: company has eclipsed Opening Eyes valuation for the first time.
Speaker 1: Do you feel like you're definitively the front runner right
Speaker 1: now and how does that change your approach it.
Speaker 3: It's something that I think both Dario and I, you know,
Speaker 3: really hammer at Anthropic is that that is just completely
Speaker 3: the wrong way to think. I think our view has
Speaker 3: always been and you know, this is really about how
Speaker 3: do we show up every day for our customers, how
Speaker 3: do we stay humble and make sure that we're really
Speaker 3: focused on the mission. Right The whole reason we started
Speaker 3: Anthropic is we want to be able to build and
Speaker 3: develop this technology in a way that is ethical, that's responsible,
Speaker 3: that's fair. And I think it's really incumbent upon everybody
Speaker 3: at the company, but especially leadership, to say, you know,
Speaker 3: all of these numbers, they're actually not the point, right.
Speaker 3: We are really here to do a job. We're here
Speaker 3: to support the businesses that rely on us every day.
Speaker 3: AI is increasingly a bigger part of everybody's workflow, their
Speaker 3: experience at the office, their experience in their personal lives.
Speaker 3: That's a huge privilege and responsibility. And so I think,
Speaker 3: if anything, you know, we work really hard every day
Speaker 3: to say, like our job is to be the best
Speaker 3: version of who we can possibly be as a company
Speaker 3: every day. That takes work, that takes humility, that takes time,
Speaker 3: that takes focus and energy.
Speaker 1: I want to talk about the big news earlier this
Speaker 1: week that Anthropic has filed confidentially for an IPO SpaceX
Speaker 1: soulso filed Opening Eyes expected to file anytime now. How
Speaker 1: far back had you been planning to file and do
Speaker 1: you think that there's a race here to be first
Speaker 1: among BAA companies.
Speaker 3: So, yes, you're correct, we confidentially you know, filed our
Speaker 3: S one and that gives us the option you know,
Speaker 3: to potentially go public after the SEC review. Unfortunately, that's
Speaker 3: all I can say about about anything IPO related.
Speaker 4: I'm sure you understand.
Speaker 2: I mean, if we can just.
Speaker 1: Maybe level up a little, what is the calculus for
Speaker 1: a kind of an AI company generally to go public
Speaker 1: or not in this moment? What are some of the
Speaker 1: pros and cons here? Because On the one hand, yes,
Speaker 1: you can access more capital if you're not a startup,
Speaker 1: but on the other then you're beholden to you know,
Speaker 1: quarterly results, shareholder calls, et cetera. So what are some
Speaker 1: of the pros and cons just generally for an AI startup?
Speaker 2: Do you think to go public in this moment?
Speaker 1: So?
Speaker 3: I think, you know, at least speaking for you know, ourselves,
Speaker 3: and I think ideas probably really for the AI industry
Speaker 3: more broadly.
Speaker 4: It's a very.
Speaker 3: Capital intensitive business to train AI models. I think that's
Speaker 3: something we've been you know, very open about from from
Speaker 3: day one. I don't think that's you know, a surprise
Speaker 3: in the industry. And I think this kind of concept
Speaker 3: of like how do you access the level of capital
Speaker 3: that you need to train these models? Right, it's a
Speaker 3: it's a really big kind of upfront cost to train
Speaker 3: the models and to sort of inference on them. And
Speaker 3: I think that is you know, my guess is that
Speaker 3: you know, over time, the sort of core set of
Speaker 3: companies that are working to advance the frontier are just
Speaker 3: going to need access to capital, and I think the
Speaker 3: public market is very well suited to that. So there's obviously,
Speaker 3: of course, trade offs. But my sense is there's just
Speaker 3: a fundamental structure of how training and kind of serving
Speaker 3: these models for customers works that it will require that
Speaker 3: level of access.
Speaker 1: It's clear that many businesses can't get enough of Claude.
Speaker 1: But at the same time, we are hearing about some
Speaker 1: customers who are concerned about the pitfalls of so called
Speaker 1: token maxing of you know, companies making leaderboards of who
Speaker 1: uses the most AI tokens for using AI's sake Ubers,
Speaker 1: one of ers executives talked about it getting harder to
Speaker 1: justify AI spend if there isn't a clear metrics backed
Speaker 1: ROI do you think there's truth to idea that some
Speaker 1: businesses may be overspending on AI tools in this early
Speaker 1: experimental phase and they may cut back as they move
Speaker 1: out of that phase.
Speaker 4: So I think kind of two things are are true
Speaker 4: at once here.
Speaker 3: I think, you know, I like that you said, you know, experimental,
Speaker 3: because I think the reality is it feels like these
Speaker 3: AI tools are so powerful and so capable, which is true,
Speaker 3: and comparing them, you know, particularly to you know, even
Speaker 3: two years ago when I was when I was on
Speaker 3: stage at Bloomberg with Dario, I think the models have
Speaker 3: just come so far in what they're able to do,
Speaker 3: in the economic value that they but I actually think
Speaker 3: there's a lot more distance to go still for what
Speaker 3: the models will be able to do, you know, two
Speaker 3: to four to six to eight years in the future,
Speaker 3: and I would suspect that we will see some you know,
Speaker 3: some experimentation in different fields to understand how do you
Speaker 3: kind of get the most value out of these models
Speaker 3: for what particular workflows.
Speaker 4: I think the second thing.
Speaker 3: That's true is, you know, how businesses are choosing to
Speaker 3: use AI will change, So the use cases today, some
Speaker 3: of them, I expect, will continue to be kind of
Speaker 3: the primary driver of you know, efficiency or creativity or
Speaker 3: you know, new approaches to doing things within businesses, whether
Speaker 3: that's coding or you know, financial services, legal, healthcare. But
Speaker 3: I also think as the industry as a whole, not
Speaker 3: just the a the AI industry, but really the business
Speaker 3: community gets more familiar with the tools.
Speaker 4: We're all going to learn together like what is the.
Speaker 3: Best way to apply these tools that also supports employees.
Speaker 3: And so I think today there's this there's this feeling
Speaker 3: that it's like, oh, like AI, you know, the leaderboards,
Speaker 3: and it's like I have to use it and what
Speaker 3: do I even use it for? And I think my
Speaker 3: hope is that over time it'll be more incorporated into
Speaker 3: the day to day of how humans do our work,
Speaker 3: how we communicate together, and that there will actually be
Speaker 3: a lot more value realized in a way that that
Speaker 3: feels really good to people.
Speaker 2: Does Xanthropic have a leaderboard itself.
Speaker 3: We don't have a leaderboard, no, or at least not
Speaker 3: not in the way you've described. We do track how
Speaker 3: much we're using Claude internally and for what use cases,
Speaker 3: because we really try to prototype all of the tools
Speaker 3: that we give to our customers, and so all of
Speaker 3: the products that you know you see, whether that's you know,
Speaker 3: Claud code, Claud design, cowork, like all of these are
Speaker 3: things that we actually built Adanthropic first because we said, hey,
Speaker 3: this is a real need we have, could Claud help
Speaker 3: us with this?
Speaker 4: And so I think it's important for.
Speaker 3: Us to just have a kind of a metric for
Speaker 3: checking like how are we using it and how much
Speaker 3: are we using it? But there's not like you must
Speaker 3: use and you must use Claud and it's better if
Speaker 3: you use it or not.
Speaker 1: What type of job function outside of coding or research
Speaker 1: are using Claude most internally.
Speaker 3: The entire company uses Claude for topics large and small.
Speaker 3: Probably the second biggest, I would actually say is finance
Speaker 3: and so a lot of the kind of back end
Speaker 3: financial planning analysis, you know, like just number analysis. I
Speaker 3: think claud is really really helpful at that. But honestly,
Speaker 3: like our people team built an incredible tool internally for
Speaker 3: how we do performance reviews that leverages Claude run in
Speaker 3: the middle performance review season. I have to say, like
Speaker 3: the feedback from our team has been so positive. Claude either,
Speaker 3: like this just feels so much more fun to do this.
Speaker 3: It's so much more interesting, interactive. It's pulling in information
Speaker 3: about what I did over the course of the past
Speaker 3: six months when I'm assessing myself, and I think that
Speaker 3: it really just speaks to the generality of these tools
Speaker 3: their ability to I think, reach across a business and
Speaker 3: just take a lot of information in and help you,
Speaker 3: as an employee be more successful in your role.
Speaker 1: Dario Anthropics leadership has been vocal about the need for
Speaker 1: more compute. Your company recently made a major deal with
Speaker 1: Xai to least compute.
Speaker 2: There are others. Opening Eye made a.
Speaker 1: Big splash sort of earlier on with securing huge data
Speaker 1: center deals and spending you know, up to a trillion
Speaker 1: on infrastructure. Why did Anthropic take a little bit of
Speaker 1: a different approach, And in hindsight, do you think you
Speaker 1: should have.
Speaker 2: Done more earlier on on the compute front.
Speaker 3: We've talked publicly about this concept of sort of the
Speaker 3: cone of uncertainty related to compute, and I think something
Speaker 3: that's important to know is the sort of structure of
Speaker 3: these deals is you have to commit to a certain
Speaker 3: amount of compute you know, reasonably far in advance. And
Speaker 3: so I think in thropics view has always been we
Speaker 3: are we are wanting to, you know, plan for the
Speaker 3: best outcome, but not over extend ourselves such that we're
Speaker 3: buying more compute than we could productively use. It's really
Speaker 3: hard to predict that, you know, perfectly, and I think
Speaker 3: we would much prefer to be on the side of
Speaker 3: having a little bit more demand for the product then
Speaker 3: we're able to serve than the inverse where you overshoot
Speaker 3: and then you're actually like, not in a great situation
Speaker 3: because you've bought something you can't pay for it down
Speaker 3: the road.
Speaker 4: It is really hard to get this right.
Speaker 3: I think the industry as a whole is still grappling
Speaker 3: with how to kind of structure these deals. But I
Speaker 3: think my sense is it's impossible to know what the
Speaker 3: future will look like. On balance, I continue to think
Speaker 3: it's better to be fiscally responsible and think you know
Speaker 3: carefully about how much you're going to buy, make sure
Speaker 3: that you have the ability to actually use all of
Speaker 3: that compute in the future, and we'll probably undershoot or
Speaker 3: overshoot at some point A little bit.
Speaker 1: In the announcement with SpaceX, Anthropic express preliminary interest in
Speaker 1: using potential data centers in space.
Speaker 2: What do you think about that? Is that a real
Speaker 2: and how far out are we for that.
Speaker 3: I don't think that data centers in space are something
Speaker 3: that will be on our RTD list in twenty twenty seven,
Speaker 3: but I will say AI is the field that has
Speaker 3: surprised me the most. It's probably surprised the world the
Speaker 3: most in terms of just what new things are possible
Speaker 3: are made possible by the advent of this technology. So
Speaker 3: no immediate immediate plans for working with the astronauts to
Speaker 3: get space center data centers going, but you never know.
Speaker 1: A few months ago Anthropic had a very public battle
Speaker 1: with the Pentagon over restrictions on.
Speaker 2: Its AI software.
Speaker 1: Are you more or less optimistic today about finding the
Speaker 1: solution to that?
Speaker 3: Anthropic has been really leaned in from day one and
Speaker 3: talked very publicly about our commitment to national security, and
Speaker 3: we were the first AI company available on the top
Speaker 3: secret cloud. I think our commitment to these values, in
Speaker 3: these principles are also very very old, right.
Speaker 4: It's sort of not not a new not a new
Speaker 4: thing for us.
Speaker 3: But I have to say I've been very impressed the
Speaker 3: degree to which we've been able to work productively, you know,
Speaker 3: with the administration around a wide variety of topics, and
Speaker 3: I think that's actually been the been the much bigger
Speaker 3: story over a long period of time is our ability
Speaker 3: to partner productively with government at all kinds of levels,
Speaker 3: because fundamentally, I think artificial intelligence is and will be
Speaker 3: a geopolitical issue, and in order to be an ethical
Speaker 3: and responsible lab we need to work with governments around
Speaker 3: the world, in the US but also in partner countries
Speaker 3: to say, how can we how can we roll this
Speaker 3: technology out in a way that's safe, in a way
Speaker 3: that's good for people, that's going to protect democracy, and
Speaker 3: so I do really feel optimistic about this in the
Speaker 3: long run.
Speaker 1: Do you think other companies like Open Eye, Google have
Speaker 1: been able to negotiate better deals with the government because
Speaker 1: of Anthropics sticking to its position.
Speaker 3: You know, I think every individual jewel company is going
Speaker 3: to have their own stance and sort of their own
Speaker 3: principles about like what their redlines and what their values are.
Speaker 4: And my sense is Anthropic.
Speaker 3: Will always be as open as we can be about
Speaker 3: what our principles and values are why we have those principles.
Speaker 4: But ultimately, I think it's.
Speaker 3: Important that whatever those values are for you as a company,
Speaker 3: that you are true to them, that you feel like
Speaker 3: you can explain them to employees and to the world
Speaker 3: more broadly and fundamentally. You know, we can't really control
Speaker 3: what other businesses are going to do. I think anthropics
Speaker 3: role is to be the sort of best version of
Speaker 3: itself again, to say, like, if we were the only
Speaker 3: organization in this, you know, in this situation, how would
Speaker 3: we want to behave I think that's that's really the
Speaker 3: best way We've been able to navigate many difficult decisions
Speaker 3: at the company over over the years.
Speaker 1: I think philanthropy it seems to be a big part
Speaker 1: of anthropics culture. I know you and many Anthropic co
Speaker 1: founders have pledged to give away half of your equity
Speaker 1: eighty percent, sorry, eighty percent of your equity. I'm curious
Speaker 1: would you be in support of something like the proposed
Speaker 1: billionaire tax ballot in California that would impose a one
Speaker 1: time tax on individuals whose networth exceeds one billion.
Speaker 3: So anthropic has I think, again, like pretty publicly talked
Speaker 3: about the fact that we think AI is going to
Speaker 3: create a huge amount of wealth. We're already seeing that,
Speaker 3: and I think we've been pretty open about this idea
Speaker 3: that that should be redistributed in some fashion. Both the
Speaker 3: pledge and then a lot of sort of views of
Speaker 3: anthropic when we're talking about, you know, potential for labor displacement,
Speaker 3: are really grounded on this belief that if we don't
Speaker 3: take intentional actions, AI, like many technologies, will probably widen
Speaker 3: the gap of inequality and disparity.
Speaker 4: It's not what we want to see.
Speaker 3: So I think there are a lot of interesting suggestions
Speaker 3: kind of floating around for how to approach that.
Speaker 4: Of course, the part we can control is.
Speaker 3: You know, what do we as a company and as
Speaker 3: individuals as co founders sort of choose to do with
Speaker 3: you know, any you know, economic benefits that we get
Speaker 3: from AI. But I think this is a really big
Speaker 3: and important question that actually goes beyond the walls of
Speaker 3: any individual company.
Speaker 1: There's a lot of potential upside with AI to cure
Speaker 1: disease or lower the cost of consumer goods, but also
Speaker 1: real fears that.
Speaker 2: You know, Dario is validated saying last year that AI.
Speaker 1: Could potentially wipe out half of all white collar work.
Speaker 1: Do you agree with that, and if so, what's the solution?
Speaker 1: Is it something like more taxes? We talked about basic income,
Speaker 1: job retraining, and how do you pay for all of that?
Speaker 4: Sorry?
Speaker 3: I think AI is We're already seeing the ways that
Speaker 3: it's disruptive. I don't necessarily think we know the future.
Speaker 3: So exactly what the type of disruption that might happen,
Speaker 3: you know, a year or three years or five years.
Speaker 4: From now is unknown.
Speaker 3: I do think it's important for companies to be open
Speaker 3: about and study what we're seeing today, which is part
Speaker 3: of why we publish our Societal Impacts research. We say, hey,
Speaker 3: here is how people are using AI. Is it displacing jobs?
Speaker 3: Is it supplementing jobs. What we found so far is
Speaker 3: that in twenty twenty five and twenty twenty six, replacement
Speaker 3: is a tiny, tiny, tiny.
Speaker 4: Fraction of what AI is doing.
Speaker 3: And really where we see that is mostly in jobs
Speaker 3: that are overseas and mostly in customer support, and so
Speaker 3: these are jobs that are sort of already being automated
Speaker 3: by more traditional mL non generative AI systems. Could that
Speaker 3: change in the future, absolutely, And I think we're a
Speaker 3: little bit unusual in the sense that we talk about
Speaker 3: it a lot. We say, hey, this might happen. But
Speaker 3: I think there is this just broader again sort of
Speaker 3: societal challenge. AI is going to be able to do
Speaker 3: so many productive things that humans can do today.
Speaker 4: What does that mean.
Speaker 3: For how we find meaning, how we earn income, how
Speaker 3: we relate to one another? And again, I think the
Speaker 3: default will be to treat it like past technologies and say, Okay,
Speaker 3: we're going to integrate this into existing workflows and that
Speaker 3: sort of the end of the story. I personally believe
Speaker 3: there's a real opportunity here for us to say, how
Speaker 3: do we accelerate and sort of accentuate the parts of
Speaker 3: doing work and finding meaning that only humans can do?
Speaker 3: And this very fundamental belief that humans like to spend
Speaker 3: time with other humans. We like to create things together,
Speaker 3: we like to relate to one another. Sometimes we like
Speaker 3: to disagree with each other. And I don't think AI
Speaker 3: will fundamentally take that away from us. But we have
Speaker 3: to figure out how we apply that within the existing
Speaker 3: economic infrastructure so that people can still find meaning in
Speaker 3: their work and so that people have a way to
Speaker 3: earn a livelihood. Again, I think there could be a
Speaker 3: lot of different ways to structure that, but I think
Speaker 3: this is an important moment for us generally as a
Speaker 3: society to say, like, what are the lives that we
Speaker 3: want humans to be able to live, and how do
Speaker 3: we work closer towards that future.
Speaker 1: Two months ago, Anthropic unveiled and restricted access to Mythos,
Speaker 1: your most powerful model right, citing significant concerns about its
Speaker 1: potential to wreak havoc on critical software by spotting and
Speaker 1: exploiting security vulnerabilities. Now, Anthropic plans to release these Mythos
Speaker 1: level AI models more widely. Can you help us understand
Speaker 1: what's changed in the past few months. Are you confident
Speaker 1: that these models are different now in any meaningful way
Speaker 1: from the version before are they safer?
Speaker 4: This is a great question.
Speaker 3: We actually on Tuesday just expanded the set of customers
Speaker 3: that have access to Mythos. I think it's about an
Speaker 3: additional one hundred and fifty organizations around the world in
Speaker 3: fifteen different countries. And really our approach to Mythos has
Speaker 3: always been there's a time component to it. So we
Speaker 3: released it initially to cyber defenders, so some nonprofit groups,
Speaker 3: some government, some organizations that are critical infrastructure for protecting,
Speaker 3: you know, against potential cyber attacks. And what we found is,
Speaker 3: just like in any kind of security vulnerability situation, you
Speaker 3: have to give the defenders.
Speaker 4: A head start.
Speaker 3: The technology AI models are going to keep advancing. If
Speaker 3: it's not us one day releasing a Mythos level model,
Speaker 3: another AI company will. But it actually matters who you
Speaker 3: give access to first and.
Speaker 4: How long they have to patch some.
Speaker 3: Of the vulnerabilities that Mythos was capable of revealing. And
Speaker 3: so we're taking this very cautious, very tiared approach, which
Speaker 3: I know in some ways is frustrating because people really
Speaker 3: want access to the model. But I think again, as
Speaker 3: a company that's founded on these principles of being ethical,
Speaker 3: being responsible. We felt it was important to give access
Speaker 3: to the organizations that were capable of really helping us
Speaker 3: defend against some of these risks, and then slowly widening
Speaker 3: that circle to more and more critical infrastructure until eventually
Speaker 3: we feel it's safe to release it more widely.
Speaker 1: So far, Anthropic has been very focused on enterprise even
Speaker 1: and how you're talking about that rollout, but could we
Speaker 1: see a push into consumer this year?
Speaker 3: So we do have a consumer product. It's it's claud
Speaker 3: dot AI. And you know, I think we have always,
Speaker 3: really from from day one, felt that you know, enterprise
Speaker 3: and and business is the best kind of spiritual fit
Speaker 3: for Anthropic and our and our values. I think this
Speaker 3: focus on trust, on responsibility, reliability, transparency, these are just
Speaker 3: so baked into the DNA of Andropic the company, and
Speaker 3: I think, you know, we have a you know, increasingly
Speaker 3: growing consumer base. It mostly looks like you know, professionals,
Speaker 3: U and individuals that are using AI for productive uses.
Speaker 3: So it doesn't necessarily have to be you know, work,
Speaker 3: sometimes it is, but it's often for you know, advancing
Speaker 3: your own skills and knowledge, whether that's in a hobby
Speaker 3: or helping you know, I'm a mom right, helping me
Speaker 3: like organize applications for preschool when my son was younger.
Speaker 3: I mean, Claude is so powerful in being able to
Speaker 3: just help help abstract away some of the administrative duties
Speaker 3: of just being a person. But I think the difference
Speaker 3: to us in our consumer product, maybe compared to competitors,
Speaker 3: is it's not an entertainment tool. We're not using it
Speaker 3: for people to sort of have fun and have it
Speaker 3: be this like, it can be fun to use it,
Speaker 3: but it's really for kind of productive activities, whether those
Speaker 3: are at work or at home. And I think I
Speaker 3: believe that is going to continue to be a reasonably
Speaker 3: large number of people that wants to use that product
Speaker 3: and service, even though enterprises are a primary focus.