Anthropic CEO Dario Amodei on designing AGI-pilled products, model economics, and 19th-century vitalism

25:34It's like a mixture of true belief in the mission and belief in the upside of the equity. Like, I think, you know, we, andthropic has developed a reputation for doing what it says it will do for, in some cases, making less promises, but keeping those promises that we make. And, you know, being very clear on what we stand for and being consistent over the years and standing for it, that creates a unity around the company and I think it's a good guard against cynicism. When you're strong with the upside of the equity, when you're pitching investors or maybe Kenzitz, how do you pitch the anthropic business?

26:09Like we're building a very large business. That's a good start. Yeah. So often I'll talk about the platform and the importance of the models. For some reason, sometimes people think of the API business and they say, oh, it's not very sticky or it's going to be I like API business. I love API business. You know, exactly, exactly. And they're even bigger ones than both of ours. I would point to the clouds again. Yes. Those are $100 billion API businesses. And when the cost of capital is high, and there are only a few players, and relative to cloud, the thing we make is much more differentiated, right?

26:45Like these models have different personalities. They're talking to different people. A joke I often make is like, if I'm sitting in a room with like 10 people, does that mean I've been commoditized? Yes, yes. There's like nine other people in the room who have a similar brain to me, they're about the same height, they let you know. So, who needs me? But you know, we all know that human labor doesn't work that way. And so, so I feel the same way about this. So you know, I think the API business is a great business and then, but you know, we want to go broader than that. You know, the way I think about it is other players such as OpenAI and existing incumbents such as Google are very focused on the consumer side.

27:25The idea of providing AI to businesses is something that we are trying to get better and better at. And I think we're out to an early lead in that. I'm not sure because I don't know for sure what the revenues of the other players are, but I think we probably at this point have the plurality of the API market most likely and AI for business market perhaps. Yeah, it's funny to me to talk about the kind of the commodity argument where we obviously grew up facing this as a skeptical argument. And I remember finding it so striking when AWS finally had to break out their numbers in 2015. Remember, they used to be wrapped up in Amazon's numbers.

28:07And people had been talking about pundits had been saying, oh, cloud is a commodity, it's uninteresting. And then they broken out, you know, it was one of the greatest businesses of all time. And there's something where a business can have competitors and it can have buyers who care about price, but that's very different from being commodity. And as you say, yeah, yeah, yeah, yeah. Exactly. No, no, no, exactly. I mean, we're like one of the biggest customers of the clouds, right? And we use more than one of them. And like, you know, I can tell you, the clouds are much less differentiated than the AI models, right?

28:39For sure, because it feels like one the behavior is non -deterministic, which does, by design, trying to make it hard, but that just naturally means Oh, we get the customer service answer as we prefer with this model versus that model. You don't know why. You know, it's a little like baking a cake, right? It's like, you know, you put in the ingredients. It just, it kind of comes out a certain way. Yeah. And like, you know, one chef makes it this way and the other chef makes it this way. And you're, if you're like, making it exactly like that chef makes it, you can't, right? You just can't. And presumably it's striking to me, none of the AI products are that personalized right now.

29:10But it feels like personalization will be a huge deal. Well, it will be a huge deal. And it will be a big source of stickiness for the, like, you won't want to switch products. And I know exactly what that looks like, but given the amount of, for both the consumer and the business use case. Well, absolutely, absolutely. You know, I think we've just started to scratch the surface in terms of models that are customized in various ways for working with a particular business or a particular person within the business. So I think we're just seeing the beginning of the API business, but I don't think AI for business is just about API with things like Cloud Code.

29:46You know, we're selling that to not just individual developers, but enterprises as well. Yes. And they find it some useful. Cloud for enterprise. That is selling to a lot of enterprises. I actually see it and you see this with some of the clouds where they have a bunch of different services, right? Some of them are apps, some of it is the underlying cloud itself. And what they are is the way that AWS or GCP or Azure will present themselves, and the way that we are starting to present ourselves is, hey, we want to be your one -stop shop for AI or for cloud. And you can buy all of these things, and you can talk to us about which to use for what.

30:26And so I think that starts to create the outlines of a more more durable business. If you think about a typical Fortune 500 company, how, you know, they're probably playing with AI for customer service, their engineers maybe have AI powered coding tools, how AI adopted are they compared to how much they should be? Well, certainly much less than they should be. It's like 5%, 30%. So what I would say is there is almost, there is very often conviction at the top. You talk to the CEO, the CEO gets it. You talk to the CTO, the CTO gets it. The struggle they have is that they have a hundred thousand, take the company that has a hundred thousand people, who their job is to do something else, their job is to do banking or insurance or drug development, and they've heard about this AI stuff, but they're not like this, this is not what they're an expert in.

31:19And so the challenge is often we are working with the leadership of the company to get that the 100 ,000 people in the company really familiar with in using the technology. I think again, the code stuff goes the fastest because the developers are the ones who are most adjacent and most watching the trend. Some of the kind of customer service and process stuff is next to go, but you really have the instinct that even with today's models, it could be a hundred times bigger than it is. Like you really get that set. Yes. My intuition is sort of this. we will see the patterns of AI adoption from startups because they're unconstrained by existing organizations so they can kind of do whatever makes sense.

32:01Versus large organizations are somehow calcified because they have all these people whose job it is to do X and you need to be consulted and everything like that. So we'll see the new behaviors from the small startups and then large companies, as we say, the CEOs and CTOs are switched on and they're smart. They say, hey, we should be doing that and they'll kind of pour the new ideas from, like kind of like the adoption of we solve cloud or many of these other types of things. Yeah, so the new ideas from the small companies or the small companies will become threatening to them and disrupt them.

32:28Yeah, and that will give them the urgency to kind of drive things through and make them happen. Yes. A pattern I've seen that works pretty well that I actually actually recommend if you're a large company is to kind of make a strike team or strike force that's separate from the rest of the company and kind of develops these prototypes. And then basically you can get momentum behind something. And then there's always this hard work of integrating into the rest of the company. But if you have a lot of momentum and you've done the hard work and you've shown the thing works, then it's easier to do that.

33:01To work as did you read his recent blog post on his AI timelines? Oh, on continual learning, yeah. Yeah. And he talked about how his fundamental issue with many of the AI models for productivity is that they're like the super smart virtual co -worker who started five minutes ago, but they remain the co -worker that started five minutes ago. They don't learn over time. How will we solve that? Yes. So, the pattern that I've seen in AI on the kind of research and technical side is that what we've seen over and over again is that there's what looks like a wall. They're, you know, looks like AI models can't do this, right?

33:44It was like AI models can't reason. And recently there's this AI models can't make new discoveries. A few years ago it was like AI models can't write globally coherent text, which of course, now they obviously can. You go back a few more years and you know, it was like this chomsky thing of like, you know, they can get syntactics right, but they can't get semantics right. And every one of those has been blown through. Sir, what's, what have we done trying the new discoveries? This is a thing that people have said recently. Actually, my view on this, like many of the other things on new discoveries is that it's not really a binary.

34:20They don't get to have their name in the paper. Yeah, they don't get to have their name in the paper, but what is a new discovery? What is genius? I remember this developmental psychology book, but they were saying something like, we kind of lie in eyes genius, but let's say that a table's wobbly. And I'm like, oh, I take the coaster and I put it under the table and it's not wobbly anymore. That's an idea. In a way, that's like a new discovery. Even if I've never seen someone do that before, that's like a new discovery. And the difference between that and the Nobel Prize winning discovery, it's a matter of degree, not fundamentally different matter.

35:01And so I would say that the AI models make discoveries all the time. Right, I've had family members where they had a medical problem in the AI model, diagnosed, you know, called diagnosed to their medical problem when doctors missed it. Like, that's not a big new to it, but it, but it, like, that's a new discovery. Like, you know, they're, and you could say, oh, they're just pattern matching the things that, that happened before, but that new discoveries are like that you think of writers who have written novels or something that are totally new and you're like, well, what are your influences?

35:29And, you know, they're remixing together and adding a new element. So it's all more continuous. And that was the thing I was going to say about continual learning. I think this idea that it isn't present is, I would say it's present a little bit. Yes, and we're going to find a way to get more of it. So for instance, the models learn within the context. You talk to them and they absorb the context. Eventually the context is going to be 100 million tokens and maybe we'll train the model in such a way that it is specialized for learning over the context. You could even during the context update the model's weights.

36:06So there are lots of ideas that are very close to the ideas we have now that could perhaps do this. I think people are very attached to the idea that they want to believe there's some fundamental wall. There's something different, something that can't be done. It kind of reminds me of the coping mechanism deep down. Yeah. You know what it reminds me of? So you know the 19th century notion of vitalism. This was the idea that the human body, like organisms that are alive are made of a fundamentally different material than in Adam and Madder, which of course we know scientifically now is not true, but it's something people very much want to believe and your common sense seems to suggest it, like I'm not very much like a table.

36:50I've made very different materials than metal or glass or whatever, but when we actually go down to the fundamental units, of course, were all made of the same thing. But you think people now have this kind of modern concept of vitalism in whatever the fundamental humanity is, and they're saying, oh, you know, models can't do that? I think there's some tendency to believe it, and I think as with vitalism, the way around it is to recognize that a mind is a mind no matter what it's made of, the notion of the dignity or the specialness of cognition or sentience, it's not that it isn't special, It's that it can be made out of anything.

37:27You referenced the medical use case, which I think is a very cool use case. Obviously, one because all the people have fixed medical issues as a result. But another one is you talk to your machines of love and grace posts, which I really enjoyed. I thought it was very well done about the marginal returns to intelligence. You know, one of the places where intelligence is the limiting factor. And my read of the popular medical use case is obviously it's kind of a charismatic use case, but also for most normal people, they have like some kind of medical issue, low level or serious or something like that.

37:58And actually, society is very just intelligence limited there, not that you don't have access maybe to a smart doctor, hopefully you do, but they give you very limited time. You know, they think for 10 seconds about your problem. Yeah, yeah, yeah, exactly. You know, test time compute was actually what we needed there on the medical stuff, but is that going to your take on this? That is how I think about it as well. You know, I have talked to Nobel Prize -winning biologists to say, I will only, I mean, it sounds a little elitist, but they'll say I'll only go to the top one percent of doctors because the rest of the 99 percent I can get better advice from from from from an LLM.

38:33You know, it really is true doctors are busy, they're overworked, and just the nature of medical data and medical information, you know, it's a lot of pattern matching. It's a lot of the same things. The, you know, the level of consistency and the ability to put together many different facts. I think it's something that LLMs are quite good at. So you talked to this, Machines of Love and Grace, post about some of the big humanity level areas where we're intelligence limited. Again, the personal medical use case is a good example of one where society is intelligence limited. And if you give lots of people much more intelligence on their specific issues, it's very valuable.

39:13What are other areas either in the consumer use case or in the business use case where you think we're just very obviously intelligence limited? Yeah, the places where at least the AI models of today can help the most. The characteristic quality is something is repetitive, but every example is a little different, right? Automation before AI, if you could program exactly how it happened, you could do it. So if you were doing the same thing over and over again. But customer service is like, you know, just to take customer service as an example, there's like a long tail of stuff, but a lot of it is like you get a bunch of calls, each call But each call is basically about one of 10 things.

39:52And it's like a different person in a different voice, saying like basically one of these 10 things in a different way. And that situation, where things are repetitive and similar, but not the same and each has its own things. That's where AI can come in the most, I think. Yeah, yes. And Dworkhash had been the same blog post to prediction that You can't, yes, give an existing AI all of your financial data and forges all the emails and have it do your taxes and his prediction for the year in which you can plot as the year where your first tax return is done by just emailing everything to whatever AI you use.

40:32His prediction was 2028. What do you make of that prediction? Probably sooner than that. I don't know if it's 26 or 27. Some of that is model. mostly, mostly accuracy. I think the model could do that today, but it would make too many mistakes. And so working on ways to have the model check its own work and do less mistakes. There's one part, there's kind of an interface part of it as well, but I would be surprised if it takes that long. Okay, 26 or 27. At what you say about mistakes, actually, you're running through the list of things that people thought we would never solve in AI. It feels like hallucinations should be on that list.

41:06Not they're totally solved, but they've gotten a lot better. They've gotten a lot better, and I think people have gotten more used to, they kind of know what to trust the model for and what not to trust the model for. The models have also been grounded in citations. I mean, we've done that with Claude .ai, we've done that with Enterprise. Claude, so I think part of the solution to citation, part of the solution is algorithmically the models hallucinate less now. And part of the solution is people have adapted and understand the weaknesses of the model. My view on things like hallucinations has always been, there's a certain class of critic like who points to something where models are weird or worse than what humans do and say, see, they're not like us at all.

41:46Or they'll never get there. And I kind of get where the instinct comes from where like, maybe they're looking to, you know, to see if we've matched the human brain exactly. They're saying, oh, this is so different. It can't be like a human brain. But I basically, I just think it's a fallacy. There's a notion of kind of general intelligence, but it's made up of a bunch of different things. And you can simply have most of the things and be much worse on some and much better on others. Like if we look at humans that are, you know, have you met humans? Yeah, have you met humans, right? Like, you know, if you look at, you know, humans who are, who are, you know, autistic versus humans that are schizophrenic.

42:24If you look at the optical illusions that humans face, that machines are not fooled by, it's very clear that we have some of these weaknesses, just like, you know, just similar to the models hallucinations, It's just that they look very different and we're much more used to them because we're surrounded by humans all day. Yeah, the autonomous vehicle I had double standard. It feels like the kind of curious example of this. The clearest example of this, yes. Yeah, where people have much higher standards. People have much higher standards. But I think it's going to be a feature of this technology and it has implications on the business side.

42:57I think we're going to be in a world where the models will make mistakes much less often than humans, but they'll be stranger mistakes. And actually that takes some adaptation because imagine you're an end user. If you work with humans, you get used to it and you have some notion, right? So if a human makes a mistake 5 % of the time, you might have a good understanding of why. You know, like, let's say I'm talking to a customer service agent and they're kind of sounding coherent and they've flooring their speech. You know, they probably had too much of this and they're not doing their job very well.

43:32And you know, that's a bad mistake to happen, but also if I'm talking to this person, I kind of know what's going on and I know not to trust what they're saying. Whereas an LLM might make a mistake five times less often, but it's kind of, you know, it's more deceptive. If the model sounds just as erudite, just as coherent, as it does when it's saying something that's right, but that's not a, you know, that's an adaptation thing. That's a, you know, that's not a fundamental thing. And that's something that when we talk to our customers, we tell them about that. We tell them then you get used to that.

44:06So we need to invent slurring for all lens. Right, right, right, exactly. It's a,

44:13you know. You started out as a researcher, but now you're the CEO of a company and you're in the business of selling AI. And so what have you had to learn about go to Marcus, dealing with customers? Yeah, yeah, absolutely. I think my view on this was, I started a company not because I was initially excited about selling things or business or any of that. I'd seen the way that some of the other companies had run and the magnitude and gravity of what they were trying to build. And it was just a bit concerned that the people and the motivations were maybe not the best ones. And I knew that there would be a number of players in this space, but it felt like having at least one player that kind of had a strong compass and how we do things could have positive effects.

45:07On the ecosystem, we would build things in a different way. We would deploy them in a different way. And above all, we would have a, again, and sometimes short list of principles, but we would stick to them as well as well as we could. So I think that was the initial motive. And of course I was excited about building the technology. And I think as that has happened, of course, I and the other co -founders have kind of had to learn how to think about the business and the strategy. I think I've been very naturally interested in the business side of it. And actually I was surprised at how quickly I became interested.

45:45And actually the primary reason was that I was curious about all the industries that are customers of us, right? Somewhat like the clouds. And perhaps like your business, the businesses that we serve are, you know, they run across every possible industry. And so you know, you learn these things about parts of the economy that you've never thought about. And even in areas where nominally you know a lot like, you know, I used to be a biologist So I went away, I know a lot about the pharmaceutical business, but I never thought about it as a beyond the science. I never thought about the portfolio side of it.

46:19I never thought about how clinical trials work, and how they could be made cheaper. I never thought about the defense and intelligence business in any great detail. And so you run through those, and I just find it super interesting to understand what people's problems are, and how AI can help with those problems. So I feel like I took very naturally to that. Actually, the product side was one where I was initially more reluctant. I felt like I just had a natural interest in curiosity in the business side of it, but building apps, it was somehow initially, it was never a thing that drew me in, even after I started the company.

46:59But I think more recently, as I've seen what products have succeeded and what products haven't, I think this idea of how to design products so that there are, you know, what we call EGI -pilled, right? So that the direction of the product is durable and is kind of a bridge to things that are useful in the future, right? We've all heard this idea of wrapper companies or wrapper products. The idea is, you know, you make Claude N and, you know, someone makes a product that, you know, basically addresses the deficiencies of Claude N, but then you come out with cloud N plus one and it just kind of eats it.

47:34The advice I always give that I think all the folks that the AI companies give is like, don't make that. See the direction of the field and try to make something that's complementary. And I think thinking about how to make products in a new way in a kind of AGI -pilled way, that actually has caught my interest to Great Deal. Okay, so, glad you brought this up. Doesn't it feel like we have no AI UIs right now? Like we still enter text into text boxes, you know, literally same as terminals from the 1970s. I mean, a bit more random corner is in everything. Like we still talk into voice companion modes that are manually triggered, which is the same as pre -transformer series.

48:18So UIs are just completely same. Yeah, there's something not quite right about it. I basically agree with you. It reminds me a little bit of, in the early days of the internet, people would make these websites that had structures that looked like they were in the physical world and they clotted and like, do the... There's some term for this. I forget there's some word for this. I forget what it is. Skew a morphism. It feels like there's some of that going on here. The thing I would say is that as we move more towards agents, we're going to be in a world where the AI model can do something end to end.

48:59Like we're almost there with Claude, can do something end end and get it right most of the time. Yes. And a human's main job is to kind of check, right? Or check sometimes. But interestingly, checking often means getting really into the details of what happened. And so there's some kind of impedance mismatch here that some product or interfaces the solution to where you want something that says slick as possible and just goes off and does something. And you don't want to have to pay attention most of the time, but when something's wrong, you might actually need to get quite involved. Yes. And I don't feel like any products or interfaces operate on this principle now or handle this problem now.

49:42Yes. I don't know if that makes sense, but. No, it does. I agree. I think what you want is your agent to go away and do really good work for you. and then come back with its work product to let you review, steerers, decide. But you can't be overwhelmed because it's going to do so many more things than you have time to look at it. If you're always looking at it, it can be slower than if you just did it yourself. And so it actually strikes me as an interface problem. Yes, yes. The generalization of this is, it feels to me, one of the most exciting things about AI, is we have such an overhang of current capabilities, turning them into good products where even if AI progress was frozen right now, we'd have like 10 years of good products.

50:26Oh, oh, I completely agree. And actually, the way that products are being built, I think by everyone in the industry, but we've thought about it this way, is very different because the progress is continuing. If the progress in models stopped, the way we built products would change instantly. The reason is, I don't think we've ever had before a situation in which the technology is changing under you so fast as you're building the product. And so this idea of long -term product road maps or the usual way of product planning, I've started explicitly, again, early and anthropic, I was like, I don't know anything about product, I'm a doofus, but now I always try to talk to people when they come in and they say, this is not like building products in the non -AI space, right?

51:12Because they need to be more agile. Yeah, you may be the expert at building these, but the technology is moving under you, so these ideas about fast iteration, they're even more true than they are normally. What's a specific example of this? I think that if you're trying to make like, you're like, we're gonna make something and it's gonna be ready in six months. I think that makes even less sense here than it makes it, the building and isolation makes even... Tidership schedules and more. You need to have tider ship schedules, you need to try things. It's very hard to tell, even harder to tell what's gonna catch on.

51:47Because a new model may have come out and a new model may suddenly be good at something that makes a product possible. And so much more than anything else, you're trying something that's never been tried, right? There's a new model, it's only available within the company. So the thing you ought to do is like, just build something on it, let people internally try it. It's this like eternal September vibe to it, right? where it's like, it's, you know, it's as if you discovered database technology for the first time. And you're like, what could you build on this, right? And it's always, it's always the first day, right?

52:20That's what is different. You mentioned database technology, and maybe that provides an interesting analogy. And as we think about open source, the first relational databases that were successful in terms of adoption were proprietary, but then the open source guys caught up. How do you keep the gap with the open source up? Yeah, so open source I think has a different meaning in AI models than it has in other areas. For this reason some have called it like open weights models to distinguish. I think the main difference is that if you see the weights of the models and you look in, you can't understand what's actually going on.

53:01There's not that kind of composability. I can't read the source code. I can't, I can't. You can't present it for a really different purpose. I can't produce a trivially different version of it. Now, Anthropic is actually working on mechanistic interpretability, which allows you to see inside the models. We're actually working on things that would allow some properties, but we're not there yet. We're not anywhere close to there. There are some things you can do. For example, if you have access to the model, you can fine tune the model. We're now through interfaces, kind of allowing people to fine tune the model.

53:31So there is a question of how valuable access to the actual model weights is over and above some thick API that lets you do something. There's some question of economics, but note that it costs a significant amount to run the models on the cloud. Someone has to host it, someone has to run fast inference, and then you're back to the margin or some portion of the margin. So using open -weight models are not that useful and fully open source models, this is a big gap. I guess what I would say is that the analogy to the previous technologies is only partial, right? It's kind of a different thing that we're still discovering.

54:13But I can say from our perspective that when a new model comes out, when a competitor model comes out, we don't really think about whether it's an open weights model or not. We think about whether it's a strong model, right? So if someone makes a strong model that gets good at the things that we do, like that's that's competition, that's bad for us, whether it's an open weights model or not. Yes. There's there's not a huge difference between the two.

54:42How is ontropic more AGI -pilled than other organizations? So one is faster like a tighter product release cadence, but maybe more broadly across the organization, not just within product Yeah, so I mentioned this thing that every couple of weeks I get up in front of the organization and kind of describe my vision. And I think one of the purposes of that is to keep people kind of focused on the mission. It's a strange state of the world. And I always express uncertainty about it. But I say, if I were to bet, I would bet in favor of this, that in one or two or three years, I don't know exactly how long it's going to be, we'll have what I've described just like a country of geniuses in the data center.

55:24And like this is weird. Like it's gonna change the economy. It's gonna accelerate the pace of science. It's gonna pose global alignment and national security risks. It may pose economic problems. The upside is huge. The potential for disruption is also huge. And I think what I'm trying to fight against is the idea of employees who join and they're like, oh, I worked in this industry. I worked at this kind of company and I'm gonna work at an AI company and maybe a couple years later I'll go to the, this is like. This is very categorically different. This is a really different thing. And I think up and down the organization we want to make sure that when our finance people think about financial projections, they understand this, not that there's necessarily certainly going to be an exponential but like wild outcomes are possible, right?

56:17When our recruiting thinks they're like, like, oh yeah, you know, like this crazy cop stuff could happen because it had, and you know, and when the product people think they make a GI -pulled products, when the policy people interact, they understand the stakes of what may happen. And so I think a big part of my job is keeping the coherence of the organization around this central thesis. Not that everyone has to, you know, like believe the thesis, right? It's not like a, you know, there's not a indoctrination and people chanting with robes or anything, but like, But like the basic idea that the company is built around this hypothesis that it is possible and perhaps likely that these large chains will happen and every aspect of the business as well as the things the company is doing for social benefit should be constructed around strong possibility that this may happen.

57:11To put numbers on this you've talked about the potential for 10 % annually economic growth powered by AI. Doesn't that mean that when we talk about AI risk, it's often harms and misuses of AI? Isn't the big AI risk that we slightly misregulators or we slow down progress and therefore, there's just like a lot of human welfare that's missed out on? You can even have AI. I've had the experience where I've had family members die of diseases that were cured a few years after they died. So I kind of truly understand the stakes of not making progress fast enough. I would say that some of the dangers of AI have the potential to significantly destabilize society or threaten humanity or civilization.

57:59And so I think we don't want to take idle chances with that level of risk. Now, I'm not at all an advocate of like stop the technology, pause the technology. I think for a number of reasons, I think that's just, it's just not possible. Like we have geopolitical adversaries like they're not gonna not make the technology the amount of money. I mean, if you even, you know, propose even the slightest amount of like, you know, I have and, you know, I've gotten, I have many trillions of dollars of capital lined up against me for whom that's not in their interest. So that shows the limits of what is possible and what is not.

58:38But what I would say is that, you know, Instead of thinking about slowing it down versus going at the maximum speed, are there ways that we can introduce safety, security measures, think about the economy in ways that either don't slow the technology down or only slow it down a little bit? Instead of 10 % economic growth, we can have 9 % economic growth and buy insurance against all of these risks. Like I think that's what the trade -off actually looks like. And precisely because AI is a technology that has the potential to go so quickly, to solve so many problems, I see the greater risk as like, you know, the thing could overheat, right?

59:24And so I basically want, I don't want to stop the reaction, I want to focus it. That's how I think about it. You said, if we hit December 2025 and there's no AI law, I'll be really worried. How are you feeling? There is actually something in California. There's a bill out SB 53. Of course, you know, last year we had the whole SB 1047 thing. You know, we had mixed feelings on SB 1047. There was initial version that, you know, I think was too aggressive. And when I say that, what I mean is the technology is moving fast. And it's kind of unhelpful if you're too prescriptive about it, you know, it ends up actually not contributing to safety.

1:00:04And I was worried a little bit. And if something like this passes, it's like the tests that were prescribed to run will end up looking stupid. And then like all the people in the industry will be like, oh, this is what regulation for safety and security looks like. It's really stupid and they won't take it seriously. They'll kind of do everything they can to comply and letter and not inspire it. And so as an advocate of thoughtful regulation, I was actually a bit concerned about this. We offered some changes to the bill to a point where we felt good about it. and we tried to make a compromise between kind of industry and the safety advocates.

1:00:40We didn't really succeed as you saw, but this year I think we're making a bill that is something more moderate. It's focused particularly on transparency of practices. Transparency of safety and security practices, which is something that, andthropic has been very forward about, and that I think other companies are starting to do, but not all the companies do it. And there's no way to tell if folks are telling the truth about what they're revealing. I think California regulation is enough because all the companies have nexus here. Yeah, yeah, I mean, I think most of these bills are organized around doing business in California.

1:01:14And so it would be difficult to shut off. People are very AI -pilled here. People are very AI -pilled here. Yeah. So we'll see what happens. I'm not sure what's gonna happen, but we've always had this approach that we kind of are in favor of guardrails, including legislative guardrails on the technology, but we recognize the need to be careful. Like, we don't want to kill the golden goose. We just want to stop it from overheating or running off the road. Yeah, maybe something like modern bank regulation for all people complaining is a good example. Or there's an inherently very risky activity.

1:01:54Yeah, no, the dangers are pretty clear. I mean, you know, the bank runs are not working. Right, but it all works pretty well in the modern era. Once we figured out the regulatory environment. Last question, what is your personal AI stack? How do you use AI differently to maybe other people in tech? Yeah, interesting. I, you know, I basically write a lot. Hmm. Perhaps it's, I have too much pride in my own writing. I use Claude to generate lots of ideas. You know, I kind of use it as research. But so far I've done the writing myself. Claude is actually maybe closer than the other ones, but it's still not there.

1:02:32Like I'd be comfortable with it for business emails, but if I'm kind of like writing an essay or something that I want to really get right, it's not quite there yet, but maybe it will be in a year or so. Yeah, very cool. Well, this is awesome. Yeah, that's for coming online. Thank you for having me.