Dario Amodei i Anthropic: feina, guerra i poder de la IA

How much are you sleeping? You know, I've never been someone who slept all that well. Let's just say, I'm learning the art of finding ways to relax and sleep through through moments of unusual pressure. It is all moving so fast. How does it feel on the inside? It's this feeling of like the exponential. Suppose you were to accelerate away from Earth on a spaceship at relativistic speed. The way special relativity works is you go to sleep and you wake up in two days of gone by on earth and so you have to deal with two days in one day. You go to sleep and then because you continue to accelerate, three days have gone by on earth and the next day and four days have gone by. And that's a little bit what it feels like. Do you go to bed constantly paranoid about what you'll wake up to? There are enough clear and present issues that we have to deal with that I'm constantly dealing with those while thinking about how we can prepare. But you know, I don't think paranoia or worrying about what you'll wake up to is productive. I've looked at people in history who've dealt with these very high pressure situations and you need to learn to respond rationally and not put dangers out of proportion to each other. This yo-yo in between, I'm not worried and oh my god, we need to panic today. I think that's a hallmark of immature decision making. And the actual mature decision making is can't ignore this. We can't be complacent in fact it's getting to be a bigger and bigger risk. But you know we have to respond rationally, you know like a surgeon would deal with an operation or you know like a military officer would you know deal with a military operation or you know someone making decisions that affect a lot of people has to make those decisions rationally and they have to understand the risk but they have to maintain a basic sense of calm. So my son yesterday was like can I use your Claude Co. work account? And I was like absolutely not any my tokens. We're seeing more and more of them even in the consumer space. We wanted to be more of an enterprise company but you know it's even even even consumer without us putting that much effort is starting to go fast. You are at the center of the AI universe right now. What does that feel like? The interesting thing is that the experience I've had for my whole career and certainly the whole time in and thropic is that there's this kind of smooth exponential. And the experience this smooth exponential is nothing's happening, nothing's happening, nothing's happening, little things happen and then zoom it goes crazy. That's the experience of the world, that's the experience of the scale of the company compared to the other companies and compared to the world. So you know I was watching this graph for a while and I said oh yeah we'll probably become the AI company with you know the most revenue and most valuation sometime around this time and indeed indeed it has happened. So in one sense I'm not surprised because this is smooth line on the graph. But of course in another sense when things actually happen you just you see so much more you know detail and color to it. And it you know it definitely is surprising and but we're just keeping in mind all the things we usually keep in mind which are just you know how do we train good models how do we put them in good products how do we make sure that everything safe. How do we help people but also manage this is I to risk around the technology it's all the same questions just kind of under a bigger under a bigger microscope as it were. What were you like as a kid growing up in San Francisco? I know your dad was a leather craftsman your mom worked in libraries how did that shape you? You know the whole you know like first you know internet revolution was happening around me and I had absolutely no. Interest in it. I was just interested in like doing math and like scroll you know scrolling things I was interested in like understanding the universe. So it's interested in science fiction like that was that was kind of the you know that was that that was the general. That was the general milieu I think I just felt a lot of curiosity about the world. You grew up in the town where you know that is the center of technology and right now it's the center of AI you know is there anything about this place the city here that informed your world view. Yeah, I mean I think the general you know the general spirit of kind of you know non-conformism and individualism and it's okay to be crazy. I think I think a good deal of that probably did probably did probably did rub off against me. You know you hear these stories about you know you go to countries in Europe or you know even other parts of this country where it's it's just you know it's just kind of discouraged or considered weird to like. Think about things in some different way right or have some set of some set of crazy ideas and you know there's a lot of things I'm actually very critical about with with Silicon Valley but one thing that I think is good about it is this this encouragement of like. You know it doesn't matter of all the experts are against you it doesn't matter you know if you have a can coherent vision and coherent where you are the world you should go and pursue it. Maybe just won't work at all but but if it does there's this kind of long tailedness to it where you know there are certain places you can you know you can you can search certain veins of war where you know you might you might find a huge gold mine there. I think that spirit is very important. You Danielle your sister and her husband pulled in carnovsky lived in a group house together back in 2016. What were you debating back then that was I think the time when you know open philanthropy project was was you know first being startup which you hold in. I was the lead of and I was at that time you know like a biological scientist so you know I was helping them with some of the stuff they were doing around kind of developing world health through biological research.

So you know I kind of advised on that stuff and you know what were the areas that were promising what were the areas that were less promising. Your decision to leave open AI has become Silicon Valley lore. What really happened like beyond the narrative what were the issues what did you disagree on. Look I'm going to say it I'm going to say it very simply. You know there are many difficult issues that you know you face when you're building powerful technology that inthropic faces everyday where we don't know whether we're making the right decision or the wrong decision. So you know there are many valid disagreements to be had on safety. We certainly had some of those disagreements with them but you know people that that that alone is not sufficient to leave. People here have had disagreements with me people here have disagreements with each other but when you feel that you can't trust someone. When you feel that their values are not what they say they are when you feel that they're not honest when you feel that they're not in it for the reasons that they say. When you see disturbing patterns of behavior dishonesty that makes it very hard to you know to continue to work with a company to continue to trust the company and look at the end of the day. Why argue with someone when you don't have the same vision and you don't trust them like the way the way to resolve it is you go off and do your thing they go off and do their thing. And I am completely at peace with the idea that we're doing things our way and they're doing things their way we'll see who wins in the market and we'll see who wins in the court of public opinion. I think those things speak louder than any drama about why who left what you know we're providing an example of how to deploy this technology. You know in what we think is a responsible way if they disagree they should make that argument and you know that I think that's really all there is to say about it. There was a moment at India's AI summit where you and Sam Alman appeared to refuse to hold hands on stage what happened there. What happened is that the summit was extremely disorganized. We all came up at the last minute and they like changed the order in which we were standing and then like they took a picture of us and then they ordered us all to like hold hands. If you've ever been to one of these summit and I'm not saying anything bad about Indian particular but like all of these kind of international types of it that have like heads of state are like super disorganized. Okay, but everyone else held hands come on. I don't know I don't know what to tell you okay there was like you know in a rendromote up there suddenly telling everyone to like. Telling everyone to hold hands. All right well okay look. Sam and you on are suing each other you don't like Sam. It seems if the people building the most important technology in the world can't hold hands on stage. How can we trust you'll cooperate on existential risk. So here's what I will tell you there is a wide variance in the quality and the trustworthiness of the people building this technology. I think this mean that you know different that no one trust each other I don't think it's right. You know I've known Dennis is obyss who builds the Gemini models better competitor to call models I've known him for 15 years we've worked together on like you know number of issues we buy compute from Google we swap safety ideas all the time. So you know my my view of this is that one there are some players who are more trustworthy than others and you know I think there are players outside and drop it who. You know who I trust who I see as trustworthy what I think needs to happen is that the trustworthy actors need to. Need to get together and and put the untrustworthy actors in a position where they kind of have to adopt the same standards with a lot of experience I've learned that there are some folks who don't do the right thing on their own. But if there's a majority of the industry that's doing the right thing then I think the rest of the industry is is kind of. They're left in a position where there's not much they can do that that then come along there's like the positive version of it where you inspire other people that's like them as to me inspire each other you know he does alpha fold we're trying to do something and bio as well right we do interpretability research they start an interpretability research it's not even competition it's just. You know each company does something cool and the other companies like that's cool we'd like to you know do that too and see if there's something new within that we can do so that's the kind of. You know the carrot side of the race to the top then there's the stick side or the implicit stick where you're like okay these guys are doing the right thing. Those guys will look bad if they don't do the right thing and often we see behaviors where they kind of. But you can't really do the right thing while trying to pretend they're doing something different and there's something bad or sinister about us that is to be expected but I think that's the way we get the industry together and that's the way we get the industry to cooperate. Now early on others focused on funds flashy consumer apps you made a bet on coding and enterprise and cloud code is a hit cloud code work is a hit. Why did you make that bet was it a values decision or a business decision? The thing that the the base thing that matter the thing that always matters is we want it we want to do this right but then you have to ask yourself. Okay in order to fund the very expensive you know creation of these models it needs to be a company that needs to have a business model was the business model get in the way the values there's always this question. But I think one of the things I learned is you know just from being another companies and watching other companies is look if you pick a business model that fundamentally conflicts with your values you can have a hard time right either you be trade your own values or you become irrelevant.

You kind of end up in a catch 22 situation and there are ways out there ways to dodge but it's just it's just a hard situation it's far better to pick a business model that is compatible with your values. And so when we thought about it we said look you know we've seen the world of social media the consumer world it really seems to you know encourage engagement even addiction you know the swap we've seen with AI video models it's like what's going on is it maximize the number of minutes that you're. Your pay attention to because that's the advertising revenue driven incentive. Whereas if we look at enterprise look I mean you know we want to make these models useful to people if I think of all the positive things you can do with AI right I warn a lot about the negative things but ultimately we think the positive things a lot way the negative things. Many of those are basically fall under the banner of enterprise you know we want to use AI to you know cure diseases that we couldn't cure before right well that's working with biotech it's working with pharma it's working with. Academic research groups all of those are enterprises right we want to use AI to like you know to make energy cheaper and more efficient that's that's all enterprise. You know we want to use AI to help with education most of that is enterprise you know we want to use. To address you know health and developing world well and nonprofits but those are basically enterprises we want to increase economic growth that that is basically. Enterprise as well and then I think there's another factor which is that enterprises care a lot about trust and long term relationship right consumer can have this. So almost this gimmicky aspect to it right where with enterprise like what matters is you build a relationship where you know you work with you work with a company for many years you know you deliver on what you say they deliver on what they say and they basically trust you and so. It's very synergistic with our goal of you know deploying these models in a positive and safe way and so I think it's served us well to have this business model that largely aligns with our values not that there aren't conflict sometimes not that there aren't hard choices we have to make. But I think the number of such choices it's much lower than it would be otherwise. A developer can switch from cloud to GPT or Gemini in and afternoon is it really possible to have a long term lead in this industry and you know. How long would it take a serious competitor to replicate what you've built model quality is the most important thing like we're we're very far ahead right now on model quality there is some amount of inertia but I've never relied on that right I've never relied on like the. The you know the and thropic has never relied on like oh this is sticky and people won't switch I think you want to have a better model you want to have a better product and you know we see the growth rates haven't reflected at all if anything they've gone up at least at the time of taping this interview. So you know I think I I tend to think that is the most important thing soon after cloud co-work was released. To $285 billion dollars in market value vanished overnight traders called it the SAS Pocalypse if AI continues improving at this pace. How much of traditional software gets replaced and how fast. Yeah so you know this is this is one of these questions that it's kind of very hard to predict in advance right if you could predict a perfectly advanced than. People would and they'd make a huge amount of money on the market and they'd always be right so you know no one no one knows exactly. What's going to happen but I would know a few things right all of these traditional software companies have a number of modes. I think what's going to happen is some of these modes are going to go away but others are going to stay around right the ability to quickly write software. I definitely think that's going away right if your mode is we've wrote this complex software that no one else can write like. Good luck you're not going to be able to defend that but I think folks have customer relationships folks have know how of how you know of how the field works folks have unique domain knowledge. So I think my advice to all of these folks is obviously you know don't be complacent don't ignore it. Make a list of all your modes and be very aware that some of them are going to go away. While others are going to become relatively more important because they're living factors and they're may also be new modes. And I think those that definitely respond that you know lean into the list of modes that are still present as well as a new ones will do well. I think those that are complacent that kind of you know just to loot themselves that what worked in the past will will continue to work. They're they're not going to have a good time. So that is that is the advice I would give and you know I think at the end of the day I would guess I mean depends what you call SAS and what you don't call SAS. But like I would guess that the software industry gets larger not smaller although there will be some big losers explain that. I just think the pie is getting bigger right like I think I think with AI like the pie is getting bigger the existing incumbents may be smaller and relative terms. Some of them may may go down in value some of them may even may even go out of business if they don't adapt in the right way. But I you know I think you I think you see this often when growth is really fast right if the you know if if if what's possible they are grows by 10x. It's very easy for an existing incumbent industry to go up by 1.5x right just just you know not as much as the whole big pie is growing. So I think that may happen that that's not to say we won't have such some big losers. Like those who don't adapt to put their heads in the sand who don't kind of see what's coming who do identify the modes they have they're going to have a really hard time.

Your biggest backers are companies like Amazon and Google and Microsoft and Nvidia. These are companies that all have their own agendas they are partners and rivals you have huge commercial milestones tied to funding. Who's really calling the shots? There have been a number of cases where we've really spoken our minds about what we think you know I've been very outspoken about the need for export controls on ships to China right. I I say this because I think it would be really bad for America for you know the state of democracy in the world for you know China to be ahead in a iK abilities and you know it's it's like some of the chip makers obviously don't agree with that view. I'm not saying that I'm saying it again now even after we've signed more partnerships what they know is that we always work with them we've been good partners. You know we can work together I'm sure they wish we didn't say these things but these things are what are what I believe. What are you going to do you know they're they're at the end of the day they want the you know they benefit from these deals as much as we do. Look we're all adults here we can work together on one thing while this agreeing about another thing Bloomberg reported that your at valuations that are higher than open a i we're talking nearly a trillion dollars for a five year old start up. How do you make sense of that number and why do you need that much money if you you know you're more disciplined on compute you have a faster path to profit. Computers ramping up very quickly right so it can both be the case that the fundamentals of the business look good. But in you know in a year you'll have three times as much compute as you know three times or four times right I'm not going to give exact numbers but like these compute ramps are very fast. We have every expectation that the revenue you know ramp will meet and exceed those but raising money is is kind of the buffer against this cone of uncertainty so it's a totally rational thing to do it's it's a very small dilution to the business. And it logically is not at all the same thing in fact is compatible with the opposite as you know that there's anything wrong with the fundamentals of the business. There've been reports of server strain reliability issues people complaining about running out of tokens you said other companies are yellowing on infrastructure. Do you actually have what you need or are you playing catch up so one of these things about computers there's a marketing compute right so you know my view is that over a period of time even longer than a couple months. Like you know we can get large amounts of compute one thing that's worth saying here is you know I don't think we bought too little compute by any reasonable standard so you know we were planning for a 10x a year growth in compute 10x a year is what we expect. That isn't what we've seen over the first quarter of 2026 we saw a greater than three x growth in revenue quarterly just in a quote not annualized three x in the quarter which of course three to the fourth power is 80x over the course of the year. We didn't plan for 80x annualized growth. It would not have been rational to plan for 80x annualized growth because that means if you only get 10x you know that you have eight times less so we're we're in a locally extreme you know explosion of compute. That's not going to continue if that continued you know you just get to revenue by the end of the year you get to revenue numbers that no company on earth. I don't think that's going to happen it just it just can't but you can have these short periods where it's like oh my god like you know this is faster growth than we ever ever possibly anticipated but I don't know you saw the compute deals with Google you saw the compute deals with Amazon. You know there are more that we kind of can and will do like you know the markets liquid like if if you know if you're able to use compute really well and there's the demand you'll get your compute it might just take a month or two. It's a good to surpass your arch rival look I we have a lot of difficult challenges in front of us there's this race to the top idea that we're trying to pull other companies along with us and I think we've seen that we have pulled them along with us sometimes they don't admit that that's what they're doing. Sometimes they copy us while they're attacking us but but this pull is very valuable and so I think the value of being the preeminent company will commercially and in terms of models you know it's it's not about. We're dealing rivals for the sake of being rivals it's it's about having the ability to pull the ecosystem along with us and we hope that we can do more of that in the future. But winning has to feel just a little bit good. I mean look we're always trying to succeed right like we're always trying to you know we're not we're not trying to fail here right like I'm not someone who believes. We should shut this technology down we shouldn't build it like you know we we we we you know we exist within a free enterprise system and and you know there's there's nothing. There's nothing wrong with this we just have to mitigate the risks of the models right and and so it's always been the balance between the two. Now for most of anthropics history you really underdog. I imagine it's easier to take the moral high ground when you have nothing to lose. At this scale how hard is it to stay true to your values. What I would say is that you know I've put a lot of time into thinking about how that's the case you know as as company scale you know I've been paranoid at every scale. At every scale of the company there's some new challenge there's some new way the company can lose either it's. It's kind of will to win just commercially or kind of the core of its values. I'm worried about both because I see them as synergistic I actually see the fact that we've been able to make such good models.

As the thing that that allows us to assert our values in a way that works as the company grows as it gets bigger. There are lots of pitfalls here there are lots of ways to go wrong not because me or the co-founders of the company's leaders values change but because the composition of the company changes very fast so. I spend probably half of my time just talking to the company about the culture of andthropic and how the culture works right when you're growing this fast you're hiring a bunch of people from. I think that's the way that companies if you don't tell them how andthropic operates they'll simply recapitulate the only thing they know which is how to operate at the companies that they came from. And so this is a constant struggle and a constant challenge and you know it's like you know me and Daniela is maybe number one top priority. Is is figuring out how to preserve this because we recognize that this is the core of who we are in the long run. I think that's the way that we're doing this. You're doing it. I would say two things the first is you know we have a unified company we have a unified culture you know I think we've gotten grown larger while still being incredibly efficient everyone still being on the same page. Like just the cultural and organizational unity I would say that's the biggest factor and I would say the second biggest factor is clawed itself. That we're now using clawed to help you know develop our models and you know make them more efficient and quickly develop products. There's all kinds of new practices you have to develop you know we're still new we're still new at it but you know it's producing it's producing a lot of acceleration and increasingly producing reliable acceleration. And so those are the two factors I would point to. Will you tell me the most wild thing you've seen a I do. I think some of the wildest stuff I've seen is around biology and medicine. I've seen a number of cases including Danielle actually where clawed diagnosed a medical problem that you know a bunch of fancy doctors had missed. And on the biology side like the models are starting to get surprisingly good at like you know. You know tasks like drug design or you know computational chemistry or things like and I'm just like wow it you know as someone who used to be a biology I look at it and I'm like wow. That's hard like you need a lot of training to do that and like clawed is getting good at it and that's one area where I think we're going to get a hell of a lot of benefit like that's the positive for AI we're going to get these huge enormous benefits. Life is going to get better the quality of human experience is going to get better. A century of scientific progress. A century of scientific progress and a century of progress and what it's like to be human like go back to 1900 think of all the problems we had in 1900. All the reasons people died prematurely. All the problems they had to suffer all the material deprivation that we don't have to deal with today. Then think of another hundred years of that. I really believe this century of scientific and medical progress. If we can get through this and I think we will, I'm increasingly optimistic, we're going to have a much much better world. I know how much you love writing your known for essays. Do you use clawed to help write? I do. I have not gotten to the point where I actually allow text directly written by Claude because I just have such a specific style that I'm a little picky about it. But I basically use Claude to help me brainstorm, to help me think through the themes to help me kind of, oh, you know, what are some references I could use for this. So it kind of plays a supportive role. I don't know how far we are from Claude being able to write better than me. We're not quite there yet, but you know, I think certainly it's coming. I love writing to and I feel like writing helps you struggle through ideas. There is a lot of critical thinking involved in that. Do we lose that if we let Claude do it for? I'm a little worried about that, and in fact that half the reason I write myself. It certainly is for external audiences, many people read what I write, but it is just as much to clarify my own thinking, so that I kind of know what to do next and to create a common reference point across me and others. I think we're still grappling with the question of how exactly do we use AI in a way that kind of preserves those benefits? I think the thing I'm doing now does that where I use Claude for research, and I use Claude for kind of, you know, how do I help organize my own thoughts? I think if we just used it end-hand, like write an essay about the risks of AI, first of all it wouldn't write the things that I think, but also I would exactly lose that benefit. There's some way as the models get better, I think probably to use them directly much more directly in the writing, and yet still preserve those benefits, but I think it's going to be a subtle thing. It won't be all one thing, and we'll have to kind of figure it out over time. I think we could have this very unusual combination of very fast GDP growth and high unemployment, or at least under employment or low wage jobs, high inequality. You've been really direct about job loss, AI could eliminate half of all entry level white-collar jobs in the next one to five years. That was a year ago, AI has moved incredibly fast. Is it still 50% or is it higher? I've always said, and if you go back to those original clips, they always get cut out of context in like the three seconds, but like, you know, the real statement was always, I don't know what's going to happen, but this is in order to magnitude for how crazy things could be. Also, I always talk about all the things we can do in response to this, right? I've talked about token tax, and working with enterprises to adjust people, and I'm a little skeptical of retraining programs, but like we should throw them in the mix,

macroeconomic policy, even from the beginning, I always talked about solutions, but you know, somehow there's this tendency in the human psychology to clip the three seconds of like, doom is coming. So my message is just definitely not doom is coming. My message is like, this is something, you know, that we should see coming, that we're worried about, and that we need to actually respond to positively. You know, I don't know exactly, but I'm still pretty concerned. I'm still the same order of concerns. You know, we are seeing right now that AI is making people more productive, but that's the usual hump. If you go back, you know, to the kind of industrial revolution, you know, I wrote about this in adolescence of technology. You automate 90% of the job. Great. People are 10 times more productive in the other 10% because they're 10 times more leverage, but eventually it gets close to 100%. Now, the sequel to that is, well, then you have to find something else for them to do. I don't know about the long run. I'm truly uncertain about that, but I do think their types of adaptation. Like one thing I'll talk about is, you know, software engineers within Enthropic. We're going through this transition right now, where, you know, right now AI makes a software engineers more productive, even though AI writes all the code or almost all the code. But still it makes people more productive, but we're already starting to see the beginning of like, you know, there may be some people that it's not making more productive, that it's better for the AI to just do the thing. So that's one side of it. The other side of it, though, is, what do we need more demand for? Something we call a forward deployed engineer or like a plight AI solutions architect, where their job is a mix of technical work and talking to customers. There's a lot of demand for that because there's a lot of customers and we're growing very quickly. Now, does every person who is in the pure software engineering quite work for that, they're certain, you know, it's not perfect, it's not one to one. That gives you a flavor of, there's going to be a hell of a lot of disruption, but things will also adjust which wins out. I don't know, but the reason it's important to warn about it is that that's how we can respond. That's how we can make policy, right, both within and thenthropic and macroeconomically for the whole world. We want to put out carefully considered thoughts. We don't want to say things that people don't believe will actually do. We don't want to say things that are half-baked. We want to think carefully about what should actually be done about these problems. You put out this chart showing potential job disruption like sales, finance, which jobs go away, who gets replaced and what new jobs are created. So no one knows for sure because the economy is unpredictable. Right, the same is the stock market, right? They're these kind of decentralized processes that you don't really know ahead of time, what are the pieces of the job that people are still going to be able to do? But what I would say broadly is that anywhere that you have, you know, these kind of entry-level, white collar, whether it's banking, whether it's finance, whether it's, you know, there's there's going to be a lot of potential for AI to first make people more productive. But then there's going to be a whole sale AI can do the job. And then we're going to have to think about, well, what is it that people can do? And I think we need to plan about that ahead of time. We're already doing it and we talked to enterprise customers. We see choices that they face. They face the choice of, you know, should I save costs, which often means hiring less people, basically do the same thing with less resources, or should we do more things with the same amount of resources? And we always when we can try to push them to doing more with the same amount of resources. Because basically that means like hire the same number of people or maybe even more people, but just do do kind of do new things, pushing them towards the positive sum. The thing that we have going for us here is the pie is going to expand a lot. And so because the pie is going to expand a lot, there are probably going to be places where people can go. It's just a matter of finding them fast enough. It's the size of the disruption. It's going to be big and that's what I'm warning people about. But we kind of have to solve that matching problem. So we'll play this out for me a little bit. You know, you wake up in five years. What is this country look like? What are those people doing? Yeah. Because if there's that much unemployment, is that not how revolutions start? Yeah, no, this is the outcome we want to prevent. This is absolutely the outcome we want to prevent. You know, I think there's, I think there's a few places. None of them are guaranteed, we're not sure. But there's the physical world, right? Like things that are in the physical world, yes, there's a robotics revolution as well. But it's a lot slower than what's happening in AI. People always talk about building data centers. But like when processing information of any type becomes a lot easier, maybe the restriction is going to be things in the physical world. And so we need a lot of more people to make build, manufacture of things in the physical world. Anything that's human centered. I think that's going to be a big deal, right? I hear all these stories about AI found something that my doctor couldn't find. And I feel happy, but like, but there's a people really want to talk to other humans, particularly over kind of important things, right? Maybe AI can do better customer service, but nevertheless, people are at least some people want to talk to humans. So these kind of human relationship driven jobs, like I think those are going to be important, right? And I think there'll be some effort by the humans to kind of direct to the AI's, right? At some level, it has to be in line with someone's values and someone's intentions. And so I think there's going to be some role there, although I don't know how thin versus how thick it will be.

I think it's very hard to say. There has been a lot of pushback, and I know you've said you're trying to warn people, but that you know, your, you know, Jensen Wong said you're conflating tasks with jobs. Other folks have said this, you know, it's sort of do marketing. That benefits and throttling. So I want to be really clear and push back hard against this. The whole picture of there are risks to job loss and here are some ideas. I mean, we haven't fully fleshed out the ideas because I want to get them right, but anthropic has come up with lots of ideas. We've had economic grants. We have the economic index. I talk about the, um, the possible ways to address these risks from tax and macroeconomic policy to what the new jobs are in the adolescence of technology. And so, you know, I have like five pages where I lay out the difference between tasks and jobs, why this time is different than other times. A list of six different things we can do from private philanthropy to government action. I talk about the problems. I talk about the solutions. But social media, which I'd to test, which I'd to test as a category, people have these three second clips from, you know, from a year ago. They don't actually read the essays or they pray on the idea that social media, I've, I've written much more carefully about these things where I talk about the risks. The idea that this is cheap marketing is itself cheap marketing. This is, this is laziness. This is failure to engage with serious intellectual work. Um, and, and I think that is part of the problem. Again, I think it's, it's part of the disease of Silicon Valley. It's been caught up in this social media world of of of three seconds. And so, people only respond to it or they think they only have to respond to it. Again, I think it's very dangerous, and we fail to have a mature conversation. Instead, people just lazily see this like three second clip. And, and, and, and, and, they're like, oh, this is what Dari was saying. It's, it's, it's so stupid. It's so unserious. And whenever someone says something like that, I take them less seriously. One of the leading AI companies in the world is deeply embedded in many different aspects of US national security across military operations. It stand off between anthropic and the Pentagon of AI Ministry of Safety, and its ramping up. You've had a long standing anti-war stance, dating all the way back to your days at Caltech. And yet, you were one of the first AI companies to sign a contract with the Department of Defense to operate on classified networks that the US uses to fight wars. Explain that. Yeah, so, you know, what I would say is, is look, I mean, the world changes. Like, you know, my, my view of this technology, you know, when I see Russia invading Ukraine, when I see the risk of China invading Taiwan, it worries me that we have a kind of resurgent authoritarian block, that they're very aggressive, and that we need to defend ourselves. That is something that I, you know, have believed for a while now, continue to believe, and that's why across both the ministrations, you know, you know, I may not agree with every policy of either a administration, but, you know, that's why we've generally been supportive of this. We don't want a world where China and Russia can build, you know, can analyze all the intelligence with AI, can, you know, can use AI for, you know, for attacking Taiwan and Ukraine, and we can't defend them. So that's why we worked with them. We certainly don't do it for the money. It's a huge pain, you know, even, even putting aside the, the warfare, it's just a huge pain to get up on government networks for not that much money. So we did it because we cared about it. But similarly, because we're doing it because we cared about it, there need to be limitations on the use of the technology. And the formulation that I used in adolescents of technology, we should use this technology in every way, except the ways that undermine our own values, right? And our red lines of mass surveillance and fully autonomous weapons, those are things that I believe undermine our values. It's not worth democracies winning if democracies do those things. And, and so that's the, that's the balance that I, that I see, and that's the stand that we took, and it explains both why we were the first to work with the Department of War and why there were some things we wouldn't do when, when others were willing to do those things. I think you need to pick a stand and stand your ground. This idea of, you know, companies that see saw from, we won't do anything with the government to suddenly we're doing absolutely everything with the government. I don't, I don't get it. You should pick your principles and stick with them. You've been working with Palantir since 2024. That's right. There are technologies used by ICE, police departments in Gaza is clod being used for surveillance in other ways. We don't work with ICE either through either through Palantir or anyone else. We don't work with CBP. I don't believe we work in Gaza. You know, our, our, we're very careful about, you know, scoping our engagements to things that we believe in. So, you know, you drew your red lines, the president banjo from the federal government, the Pentagon labeled you a supply chain risk, open AI, jump in and sign the contract that you wouldn't. What is winning this fight actually look like? You know, I don't think there's any winning this fight for a private company like this isn't a fight. And Thropic is trying to win or thinks about winning or losing. This is more a, I won't even call it a fight. This is more a debate about what the proper use of AI by the government is. And AI is an emerging new technology. We don't understand the ways in which it's reliable or unreliable. We don't understand the ways in which it promotes our values or undermines our values. And so one of the things that I thought was important was to establish a precedent on some of the, some of the use cases we think are good, which frankly is most of them. And some of the use cases that were concerned about.

And as I've said, we've already seen, you know, you can only do so much with a contract, right? As we've seen someone else can sign a contract that doesn't respect your, your same red lines. But what it has done is raised awareness for the issue. And then we have serious bipartisan efforts in Congress attempting to ban some of the things that were concerned about and attempting to set guardrails. Again, I don't want to talk about this as a fight, but that's kind of winning the effort to get our country to think more carefully about what is appropriate use of this technology. And traffic is run by an ideological lunatic, who should have a, So that's not my question. My question is, A, I'm making a bridge. Do you mind being called an ideological lunatic or a bunch of left wing nut traps? You know, I've been called worse things than that all the time. You know, people can call me or anthropic, you know, people can call me or anthropic, whatever they want. The two things that matter are we're successful as a company and, you know, we stand up for our values. Like, I actually, in some ways, my life is really easy because when those are your, you know, those are the two things you're trying to do. It's, it's really simple, right? Like it, you know, you just, you always know where you stand. A U.S. official has said, with the help of L.L.M. The U.S. military has gone from being able to hit a thousand targets a day to five thousand targets a day. That means, Claude can help kill more people more quickly. Are you comfortable with that? I think there's two things here, right? There is, there is the ability of the United States, you know, to be more effective militarily. I am supportive of that ability. I think having that ability be stronger. Doesn't, flaws wars, it deters wars, like, you know, basically you're asking, like, you know, do you believe in this country, right? Do you want this country to be a more powerful actor rather than a less powerful actor on the world's stage? I do. I'm a patriot. There's a separate question, which is, you know, are there particular policies that the U.S. government is engaged in that I might support or not support? Obviously, I support some of them and I don't support others of them. It's not up to me if we provide a technology, you know, the D.O.W. made this point and we actually agree with them. If we provide a technology, it's not up to us to say, you can do this military operation and you can't do that military operation. Now, I might privately believe that this military operation makes sense and that military operation is a bad idea. But we're not going to deny the technology. You have to leave policy in the hands of the military decision makers. What you can do is to assert some high-level boundaries that, you know, for us, prevent the use cases that seem inconsistent with our values, with our country's values, and promote the use cases that we think we think encourage our values. So that's how we think about it. Bloomberg has reported that Claude is being used by the U.S. military in the war and Iran. To do AI assisted targeting, the platform made by Palantir, Mavon Smart System. In February, U.S. missile reportedly hit a girl school in Iran, killing more than 150 people, most of them children. Did Claude play a role in that strike? We don't have access to, you know, we don't know exactly how these models were used. You know, obviously like, you know, these things that, you know, mistakes that happen in warfare are really, really terrible. Like, this is a really terrible thing to happen. If that doesn't make clear why we have to, you know, stand up for use cases that, you know, we don't support, like, you know, we, we were willing to risk the future of our company to like limit how, you know, these models are used. And, you know, what you're talking about is a use case that doesn't even violate our red lines. We're worried that there will be a hundred times as much, you know, with use cases that do violate our red lines. Now, you know, again, again, I would say, I think overall, the use of these, the use of these models is appropriate. I think it's good on net. You know, but military decision makers make terrible mistakes, even even at the best of times. And I don't know if we're in the best of times. Like, there are several things we can talk about. We can talk about making red lines that, you know, prevent uses of the models that are more likely to lead to those problems. Right. If we had allowed, you know, fully at high, if we had just given in with almost every other company now has to fully autonomous weapons. Right. This is like a human. You see here is Claude assists, but a human makes the final call. So a human made that final call, not Claude. Imagine if you had a world in which not Claude, because we haven't allowed it, but someone else's AI model, the AI model just makes the decision in the human never sees it. That's what we were standing up for. That's what we were fighting against. I would also say, you know, there's a separate thing here. Again, I don't think procurement is the right way to do it. Like, you know, we need to make sure that, you know, it's a matter of interest to the American people, not to me as a supplier of the technology, but to the American people that are military decision makers don't make these mistakes that they operate reliably that, you know, they choose wisely what to do. Again, you know, that's, that's of concern to me as a citizen. As a supplier of the technology, like, you know, the government uses Microsoft Excel a lot. You know, if I said, micro, you can use Excel for, you know, this military operation, but not that you can't, you can't realistically do that. But hopefully that gives you a sense of how we think about it. This school had a website you could have found it in a Google search. Like, shouldn't Claude have spotted that? Should an AI or whatever technology they use, have spotted that? And it does it speak to a scarier issue about using technology as a shortcut in war. Look, look, what I mean, you know, what I'm going to say is, you know, and I don't know this relies on, you know, maybe classified knowledge, I don't have.

But, you know, the principle that we have established, and I think the principle that was obeyed here is a human makes the human makes the final decision. I don't know what role Claude or any other AI had, but like, if this isn't an illustration, why that principle is so important, I don't know what it is. Is AI warfare more likely to stop World War III or between the US and China, or is it more likely to make it happen? I would say on Balon, it's more likely to stop it. But, if we have no limits on how it's used, then I think, you know, it could be more likely to cause it. You know, you've seen Dr. Strange Love, right? The premise of it was like, you have a doomsday device that automatically fires nuclear weapons when it thinks nuclear weapons are being fired at it. What could go wrong, right? Again, I get to this lethal, you know, fully autonomous weapons thing. I think the way conflicts happen is that, you know, the two sides jump at each other. They misunderstand each other. And when we don't have proper oversight of this technology, I think those kinds of accidents are more likely to happen. Now, I think if AI is used in an appropriate way, and in not even warfare, but think of just intelligence collection, you know, let's say we're able to predict an invasion of Taiwan or a new movement in Ukraine. Like, you know, our adversaries will think twice about, you know, about conducting some kind of invasion or military operation. If we know everything that they're doing. And so I think superior intelligence really can deter a conflict here. Superior ability to respond can deter a conflict. I continue to be a believer in these things. I think it's making headlines on what's on a weekly basis. Most most of us have been around mythos, of course. This is the latest and greatest anthropic model, and it is capable of going through all the links of the cyber kill chain and doing so autonomously. You said mythos was too powerful to release to the public. What surprised you most about it? I think the thing that surprised me most about it was the models had been climbing in their ability to find vulnerabilities. And importantly, turn those vulnerabilities into exploits, which people only talk about the vulnerabilities they don't often talk about, turning the vulnerabilities into exploits, which it was quite good at. So the things that surprised me are we saw this huge jump. It was a particularly large jump. And without us really prompting them at all, some of the early companies that we gave this to said things like, this is a super weapon. You should have to own a gun license to use it. Please don't release this. Like the demand to do this was coming from the companies we gave it to who were finding so many critical vulnerabilities and exploitability around these critical vulnerabilities that, you know, they were basically asking us not to not to not to release it. Now to be clear, because things always get distorted in the world of social media, the goal isn't to keep this locked up forever. We're kind of gradually trying to open this up to a wider and wider set of people. And eventually we believe that we should release mythos to, to, you know, two-age general audience, but with kind of strong cyber safeguards. Now, a concern is today's cyber safeguards, which we did release on Opus 4.7, which is a good cyber model, but a substantially weaker one. These can be jailbroken and we're a little concerned about some of the other companies who think this is a sufficient defense. Because, yeah, it works sometimes, but, you know, we all know that these classifiers can be jailbroken or gone around and are own testing as well as frankly our assessment of the models that other, the defenses that other companies have put in place, suggest that these defenses are not strong enough yet. And that's what we're waiting for, getting the defenses to the point where we really have confidence in them. There was a lot of pushback on it. You know, you have researchers saying they were able to replicate it using, you know, cheaper open source models. Some folks say, open AI, you know, has these capabilities already, you know, what do you say to folks who say this is a grand PR plan? The claim that could be replicated with open source models that's just incredibly false. So the idea is, mythos looks across the whole code base and finds something. Some guy went on Twitter and said, well, if you point an open source model exactly the line of code that mythos finds, then it finds the same issue. That isn't the, that isn't the, that isn't the problem. That isn't the question, right? Like, that is not the same thing. The ultimate test of this is like we go to companies, we go to open source reports. We found 271 new vulnerabilities in Firefox. We've found many thousands within the private, you know, companies who haven't fixed them yet or can't disclose them yet. Like, no one found those 271 vulnerabilities with the previous model. The actual workflow of what actually works in practice as opposed to, you know, okay, I find the exact line that mythos found, you know, I found the needle in the haystack, something else can now pick up the needle. But what about the folks who say this was just good marketing? You know, we have suffered enormously commercially from not releasing this model. This model has incredibly accelerated research within and thropic and production in the next models. It would do the same in the outside world if we were to release it. This has heard us enormously commercially. If this helps defenders, it also helps attackers. Can we defend anything anymore? What I would say is that the reason that we're giving mythos to defenders before we give it to attackers is to patch all the bugs. I don't know, as the models get better, there may be more and more bugs to be found, but there's only so many, they're finite, right? It's like you have this surface and there's only so many holes in it. You patch all the holes and the surface becomes very hard to attack as well as the code itself is written with the powerful model, so it then becomes very hard to find flaws in or break into. So I think on the other side of this, hopefully six months or a year from now, we have a much more secure internet ecosystem than we had in the past.

We're trying to get to that world. And we're doing the best we can to open up mythos to new cyber defenders. We've been talking to the government. We're very respectful of their recommendations. They're slowing the pace at which we opened it up because they're worried about counterintelligence risk. I think that's sensible. I think all serious people here understand that there's real trade offs here. We see a lot of sniping from people on Twitter and from other AI companies. You look at what they're saying and the inconsistency with what they're doing. It's not, they're not serious people. They're not seriously engaging with the serious trade offs that we have here. Look, I have customers calling me up every day saying, I want access to mythos. I have countries calling me up saying, I want access to mythos. And I have the US government and my security team saying, no way to minute, there's risk to it. You know, I'm not saying one side or the other is right. I think it's somewhere in between. Both sides have valid points. But there's a real challenge here. And we need to face it together as a society, not accused things of being cheap marketing, not used cheap marketing to try and counter position, which some of the other companies are doing. It just, it just all shows an incredible whack of gravitas and maturity. We need to all face these moments together. Have you had to make trade offs already that you're not entirely comfortable with? Throughout the entire history of endropic has been trade offs. Right? The entire history of endropic, right? Where, you know, in some ideal world, you would prefer to before you release the first chatbot. You know, you could spend years studying every possible thing that could go wrong with it. Now, we did delay. We did delay the initial release of Cod, but you know, we did it for a few months. So what I'm saying is everything is a trade off. You know, the extreme ends of the spectrum are completely insane. Right? And so everything is a trade off. What I would say is that now that we're in, you know, what I would describe as a commercially leading position, I'm actually, and Dunyella are actually doing all we can to move the dial even further towards towards being careful. That's what the mythos release was about. Right? It's very hard to do something like that if you're not the leading player. And so I think you're going to see more things, more things like that. You know, there's this argument why wouldn't the government take you over? Why would they let a private company control technology that's so powerful? So I actually think that's a very, that's a very serious question. And I share those concerns. I don't think the government should outright take us over, but I would put it this way. I would say just to back up and describe the situation, every previous powerful technology we've seen in history was either built by the government or originated with the government. So nuclear weapons, obviously, you know, initially built by the government and pretty much built by the government after that. But even like the internet, GPS, cell phones, all the R&D was, you know, was done in the labs and federal labs and the universities. AI is a first technology that's been built in the private sector. And where a government has not really had a serious role and is coming in late to the game. I think that's actually a dangerous and unstable situation. It is not the situation I would have chosen. It's not really an alternative. Like, you know, this technology is possible to build or adversaries or building and has economic value. Like, it's going to get built. The issue is the government not doing it, not the private sector doing it. I think we need to think about checks and balances on power. So I think there need to be checks and balances on the power of the AI companies. We have this thing, the long-term benefit trust. What that is is it's a set of basically, it's a body that can, appoint the majority of the board members and remove the majority of the board members. So it basically, essentially, if you throw it through, it has the power to fire me. And what we're looking at is we're introducing some elements. You know, nowhere near all the elements, but we're introducing a little bit of the elements of like public governance, right? Where it's like, you know, you're accountable to someone who just doesn't, he doesn't just have stock in the company. So that's, that's very important and that structure is going to continue no matter what happens to the company. That's on the AI kind. We encourage other companies to have similar structures. On the government side, I think we need checks and balances. You know, there are efforts and Congress that have been announced to enact those red lines, right? So I really think the, you know, the legislative branch and the judicial branch need to exert themselves. Because this technology, I'm scared of companies having it, but I'm also scared of government having it. And then the companies need to provide checks on government and the government needs to provide checks on companies. You know, we need basic regulation of the technology. You know, I think we need to start doing pre-release testing, required pre-release testing, testing, and auditing of the models. You know, it's very funny to me how there's a particular group of people in the tech world in Silicon Valley who started, you know, they started with the position of like, even having transparency around this technology, even export control, you know, this is all, you know, just totally, it'll, it'll apocalypticly destroy our potential to create the technology, it'll kill innovation. And then as soon as they see the first real danger, which I've been expecting all along, there's all this talk of like nationalization in the government should just seize it. Come on folks here, your, your yo-yo in from like, the most extreme anti-regulatory, you know, if you, if you look at us the wrong way or destroying the industry to, you know, this completely communist the government should grab it all. We need a more set, we need a more sensible moderate approach. That's the one we've been favoring all along because we've, we've understood the power of this technology. We're not panicking. We're not denying it. We see this move exponential and we're responding to it appropriately. So how was your visit back to the White House? You know, we always try to work together with whoever we can in government, you know,

I said we have the simple approach like we have a set of principles. We like follow those principles and we hope that folks on the other side are reasonable. And you know, honestly, the government has taken mythos very seriously. Like we've had good conversations with Secretary Bessent with the chief of staff, Susie Wiles. I think they really understand, you know, the nature of the risks here. And the most of those has, I think, you know, helped them to feel much more concretely where these risks are. So, you know, again, as with any administration, there are parts who we get along with very well and who understand it. And, you know, there are other parts that are harder to get along with. I think that's normal. That would be the case in any, in any administration. And we just try to navigate it as best we can. You worked at Bidu earlier in your career, big Chinese tech company. You worked at the Silicon Valley outpost of it. And you've been clear on your views on China. Strong open source models are coming out of China. And you have US companies building on them for free. Is that a threat? So, you know, one of the things we've seen with this technology is that there's really a premium to how intelligent the models are. We very, very rarely see that people would prefer to use models with lower intelligence. Now, to be clear, there's a thriving ecosystem. There are lots of challenges and problems that are much easier than, you know, the ones we need frontier models for. But again, it's an exponential. Right? Like, it's possible that like these far from frontier models have economic value comparable to what we saw in 2023 and 2024. But again, we have this 10x a year of growth. And so, what we find is that what's on the frontier is always much much larger than what is, what is away from the frontier. I think this is something that people who are used to building products in the previous era don't quite understand. Right? As someone who's come in, who, you know, hasn't run a company before, who's like, you know, has never thought about the previous product era, particularly to test the social media era. I feel like an outsider to that world. And I feel that people's instincts are wrong. They have all these kind of producturistics. And I think the 10x per year model exponential really breaks that. Like intelligence is just such a huge factor that it outweighs everything else. And so we're just seeing over and over again that, you know, the value is found on the frontier. Now, what I do worry about with some of these laggard models is the risks of them, where we have mythos class cyber capabilities. 12 months from now, we'll have much better cyber capabilities, but the mythos class cyber capabilities may just be available for anyone to download. Now, hopefully we'll have patched everything before then. I don't think there's anything we can do to stop it, but I think as a serious concern. Did what you saw it by do shape your views on China? Not really, no. I worked there for, I worked there for a year. You know, I think I probably learned more about like speech recognition and, you know, all, all of that. Maybe the only thing that concern me was, you know, part of how we got all the speech recognition data was, you know, they're like, they said ominously, though, we don't care about privacy in China. So we have all this, this speech recognition data. But I think I think aside from that, my worries here are geopolitical. You know, I think the things that most worried me about what happened in China are, you know, what we, what we saw happen to the weegers, what we saw with suppression of criticism even in the US with what happened with Hong Kong, right? The fact that CCP could reach into the US business network and, you know, and suppress criticism, that's an authoritarian state. And high tech authoritarian state. And when I see how that combines with AI, you really get a dystopia here, like 1984 or worse. And my focus is on trying to prevent that. And I think we have an opportunity to prevent that. I think we have an opportunity for AI to be a pro-democracy technology, you know, that kind of makes people freer, that delivers on the promise of equal justice for all, or it could go the other way. And, and which way it goes depends on the actions of the AI companies, it depends on the actions of the government, depends on the actions of all of us. And so I see us as having responsibility here. There's a moment that people in your field talk about where AI gets good enough to improve itself. And then the improved version improves itself and so on. Some of your researchers think that that moment is close. How far away is it? I don't think it's a moment in time. I think it's a continuous process. We're already seeing it in some ways, where the AI is able to suggest our protection for the next AI. You know, I would say a year ago we were seeing 10 to 15% kind of increase in total factor productivity due to AI. Like that's probably up to 20 or 30% now. You know, it might be doubling. Like as with all things, we're on the exponential. There's no moment where AI improves itself or runs out of control or becomes unsafe. What we have is an accelerating exponential. And at each point on the exponential, we have to assess is this a time to slow down. Is this a time to put more controls on this technology? I think more and more of that is going to be required. But I think the Rosetta stone to all of this is the smooth exponential. Again, I think there's an object lesson in the people who are against all AI regulation. And then they saw one thing and they wanted to nationalize. I think there's an object lesson in the people who dismissed the power of AI and then said, oh my god, it's improving itself. It's running out of control. We have to shut it all down. Yo-yo in between those extreme reactions is incredibly unhelpful as a response to this technology. The right response, the wise response is to say, we're not going to panic. Our countermeasures will smoothly ratchet up with the power of the technology. If you see someone having this kind of crazy yo-yo reaction, that's a sign that they were caught by surprise and that they're not serious.

I understand one of your favorite books is the making of the atomic bomb. That is correct. Do you see parallels between yourself and Oppenheimer? You know, the figure I most identified with was Leo Zillard, who was the one who first basically had the idea that there could be a kind of chain reaction. Look, my view is we're not going to get through this with larger than life personalities or like figures who try and be at the center of everything. Right, there needs to be a balance of power here. Right, there's a lot of powerful actors who have interest here. And the only way it can end well for everyone is if there is some there's basically checks and balances everywhere. So in some ways actually see Oppenheimer as a failure case as what should not happen. You've said there's roughly a 10 to 25% chance of civilizational collapse. That is not insignificant. Is there a scenario where it's something that anthropic built that caused that? I mean, I certainly hope not. My view is that, you know, the actions that we have taken lower that probability rather than increasing it. Right, that probability comes from the, you know, the very straightforward recipe of the technology, the existence of many countries in the world, the existence of many companies within an economy and new ones created if the void isn't filled. Like that's the dilemma that we're in. We're trying to act to lower that probability. I think we lower it a lot more than we raise it. But, you know, the inherent property of this technology is that it's unpredictable. And so, you know, we try to build something and test it a lot before it's released and then the models that are released today are not dangerous or at least not, you know, really, I think dangerous outside of cyber. And then we try and iterate and learn from that. So there's like a zillion defense mechanisms, you know, half of what we do within the company is trying and you know, reduce the risk as much as we can. But, you know, it's never going to be zero. I guess what I would say is, you know, suppose there are a bunch of like, you know, airline companies out there and you're like, well, I'm going to make an airline company that's safer. It can both be the case that, you know, your airline company is 10 times safer than all the other airline companies. But, you know, if someone comes and asks you, like, can you guarantee that your airplane will never crash? I mean, how could you, how could you possibly? But if there was a 25% chance of an airplane crashing, you wouldn't get on that plane. That's right, 25% is too high. We're trying to make that probability much much lower. That is the goal. You are building something incredibly powerful and stand to gain enormously from it. Why should we trust you? My view of this is actually when any company starts out and, and particularly, you know, what we've seen with the behavior of just silica valley as an entity. It's thinking over the last couple of years. I think starting from a position of distrust, you know, if you don't know anything about me, if you don't know anything about it, anthropic is pretty rational. I think Silicon Valley has lost a lot of the world's trust and kind of has to re-earn it. And the message, you know, we're trying to send is, or actually different, and that has to be earned in things that we actually do. You can agree or disagree, but we start up for our values. The thing with, you know, mythos like it's, it's really hamper us, especially, not to put this very powerful model out. And there are a bunch of smaller things before it. You know, where, where, where money where a mouth is on, you know, China, we cut off access to models, we didn't have to do that. No one told us to do that, you know, that cost of several hundred million dollars, back when several hundred million dollars was the big was the significant fraction of our revenue. You know, the delay of clawed too, like we have a long history of it. We aren't perfect. We make mistakes. But, you know, what I would ask is for people to look at the overall history and say, if you add up that overall history, what is the hypothesis about us that is most consistent with that overall history? People have to decide for themselves, but I think the hypothesis that's consistent is we are genuinely trying to do the right thing. We're imperfect. Organizations are, you know, always dysfunctional. We're always trying to, you know, fix them and make them work better. Many foot faults, many things that go wrong. But at basis, we have a honest and earnest picture of how to do the right thing and we're trying to execute on that picture. We will see you on the other side of the exponential then. Hopefully. Well, he's wanted to be a Hollywood star. Right. That's a one surprising thing that I didn't understand about the CEO job is how often you have to wear makeup. So that was not on my bingo car. Just a little powder.