Jack Clark

Jack Clark is the co-founder of Anthropic, an AI research company focused on building reliable and interpretable artificial intelligence systems. Before Anthropic, he was a policy director at OpenAI, where he shaped strategy, communications, and policy. With a background that spans journalism at Bloomberg, policy leadership, and deep involvement in AI governance, Clark has built a reputation for his expertise in AI safety, co-authoring influential research papers and launching the widely-read Import AI newsletter. At Anthropic, Clark guides the company’s work on creating safer and more understandable AI systems, while also engaging in discussions around AI ethics and regulation. ------ Thank you to the sponsors that fuel our podcast and our team: Squarespace https://Squarespace.com/tetra Use code 'TETRA' ------ LMNT Electrolytes https://DrinkLMNT.com/tetra Use code 'TETRA' ------ Athletic Nicotine https://www. AthleticNicotine.com/tetra Use code 'TETRA' ------ Sign up to receive Tetragrammaton Transmissions https://www.tetragrammaton.com/join-newsletter

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Rick Rubin?

  Tetragrammaton.

• 0:23Speaker 01

  I had this really clear memory. It was 2010. I was in England, where I grew up. And I was a journalist at the time. And I'd started writing about neural network stuff. And I was in this very dingy apartment in southeast London, extremely cheap. And I had a dictaphone. And I was recording myself, saying like, if like Google builds all of these computers, then they can run like machine learning algorithms on it. And like, Google's gonna build really, really powerful AI. And I became obsessed with this idea. And I used to tour data centers. I had a series called The Clark Side of the Cloud, where I'd visit data centers around Europe. Film it? Take photos. But it would be kind of like if I was reviewing recording studios. Really cool. I'd come in and be like, what have you got here? What's the equipment?

• 1:08Speaker 01

  Were you always a gearhead? I became a gearhead through that. As a kid, I tinkered a lot with lots of things and had hardware. Through that, I learned the best fun fact about data centers no one knows, which is data centers need power. They all have backup generators inside them in case the power shuts off. In Europe, those backup generators are mostly diesel generators from World War II-style submarines. Because those generators were designed to like, go wrong less often than any other generator you could buy. Anyway, I spent years studying all of this stuff. And I'm obsessed with AI. And I meet a woman in England in 2012, who's American. And I told her that I couldn't be with her

• 1:53Speaker 01

  because I'd just applied for a job in San Francisco because I needed to go out here and write about AI. And then I didn't get the job, which was a blessing. She had to go back to America a few months later. And then I reapplied and I got a job as the journalist outfit that had rejected me originally. And so we moved here. And then in San Francisco, I called myself the world's only neural network reporter, which was very easy to do back then. Great. And I just started covering it. And what amazed me was this feeling that there was a a silent and total revolution occurring. And what I would do is

• 2:28Rick Rubin?

  You could see it clearly, but others did not see that.

• 2:31Speaker 01

  Yeah, and I think that this is because I'm obsessive. And what I was doing is I was reading the research papers. And the research papers for things like ImageNet say, they'd say, yada, yada, yada. Here's the score. But then there would be other research papers about ImageNet that come out months later. And then they'd have a different score.

• 2:51Rick Rubin?

  Higher.

• 2:51Speaker 01

  Higher. And I started making these graphs where I was just like plotting the lines going up. And at some point I was like, oh, my God, like every domain I look at that is using neural networks, I am seeing the lines go up like everywhere.

• 3:07Rick Rubin?

  Yeah.

• 3:07Speaker 01

  And and all of this stuff uses computers. And guess who's been building lots of computers? Oh, it's like Google and the other companies which are all pumping out this research. So I became obsessed with the idea I had to be working in this field. I read everything. I'd go home at night and like read this stuff. I'd implement neural networks for fun.

• 3:26Rick Rubin?

  Do you have any thoughts about what the use cases would be? Or just that it was something that was important in building?

• 3:35Speaker 01

  I could see computer vision as being a use case even then. You know, just being able to say what your phone sees was going to be valuable.

• 3:43Rick Rubin?

  I see.

• 3:43Speaker 01

  But I was kind of obsessed with the idea we were going to make intelligent beings.

• 3:48Rick Rubin?

  Like robots?

• 3:49Speaker 01

  No, like synthetic minds.

• 3:50Rick Rubin?

  Oh, I see.

• 3:51Speaker 01

  I was like, well, this stuff, this stuff's hard and seems to be working.

• 3:55Rick Rubin?

  Yeah.

• 3:55Speaker 01

  And so OpenAI came along. And I was the, pretty much the only journalist at the conference in Canada where it was announced in 2015. And I remember I talked to Greg Brockman, one of the co-founders, did this interview. And then I sort of turned my dictaphone off and then I was like, How can I work for you? At which point I knew I could no longer be a journalist. You've really broken impartiality.

• 4:19Rick Rubin?

  Yeah.

• 4:20Speaker 01

  And so over the following months, we had conversations and then, you know, shout out to Sam Altman and Greg and others, but they really took a chance on me. I think because I was so motivated and I I joined them that summer. And I have all of these emails from journalists saying, you were making the worst mistake of your life, all kinds of things.

• 4:38Rick Rubin?

  Yeah, because you had a steady job.

• 4:40Speaker 01

  I'd got to like Bloomberg and was occasionally writing for Businessweek. It was like a big and I'd written my way there from like a council flat in London. You know, so it felt like a big achievement.

• 4:51Rick Rubin?

  And at that time, AI was still viewed as.

• 4:54Speaker 01

  Who cares?

• 4:55Rick Rubin?

  Yeah, yeah. Yes.

• 4:58Speaker 01

  What are you doing? None of this makes sense.

• 5:01Rick Rubin?

  Yes.

• 5:02Speaker 01

  Yeah, and over the following years at OpenAI, I got to see. I got to see it really work. I got to see how it worked for text. I saw it work for really advanced video games. I saw what the research culture was like of how anytime we scaled up the amount of compute we could throw at a problem, things got better and things started working. And then my colleagues who went on to found Anthropic with me did some research on something called scaling laws where they actually just said, huh, if we look at like the amount of compute we spend on these systems and how much data we put in and the number of parameters in the systems, we get graphs that give us a future recipe as well.

• 5:45Speaker 01

  If we wanted to get a system that scored X, we'd need compute Y and like data Z. Let's try that. And then they trained this precursor to Claude and ChatGPT called GPT-3, which was a language model. A language model so expensive that we justified training it because the scaling laws existed. We were like, it will get this score, we can tell you. And the moment it worked, I was pretty shocked. We started Anthropic because we felt like the trains were about to leave the station.

• 6:15Rick Rubin?

  I see.

• 6:15Speaker 01

  Once you know it works and you've proven it once, like the Wright brothers, everything's about to change.

• 6:21Rick Rubin?

  Yes.

• 6:21Speaker 01

  And we'd found ourselves in lots of debates internally over the years. Basically having differences of opinion with with other people at OpenAI about how to approach certain decisions around like release strategy or or safety strategy. I'm not claiming anyone knows if they're right or wrong in these things. Everyone had strong feelings.

• 6:41Rick Rubin?

  You can't know.

• 6:42Speaker 01

  You can't know.

• 6:42Rick Rubin?

  No.

• 6:43Speaker 01

  Give it 10 years. Maybe we know then.

• 6:44Rick Rubin?

  Maybe.

• 6:45Speaker 01

  But what we did know is that huh, we always end up being on one side of this together.

• 6:50Rick Rubin?

  Yes.

• 6:50Speaker 01

  At which point I think it became clear that either we could stay and spend 50% of our time arguing and 50% working, or we could spend 100% of the time working together. So we did that.

• 7:00Rick Rubin?

  That's great.

• 7:01Speaker 01

  That was easier than the, it was easier to do Anthropic than the journalism thing for me, actually.

• 7:06Rick Rubin?

  Yeah.

• 7:06Speaker 01

  It makes, it made a lot more sense.

• 7:07Rick Rubin?

  Understood. So all of your co-founders came from OpenAI with you? Oh, interesting.

• 7:14Speaker 01

  There were seven of us, worked really, really closely together. Me and and Dario in particular, who is the CEO of Anthropic, worked together very closely from 2016 onwards.

• 7:23Rick Rubin?

  Now, was OpenAI still open at the time you left?

• 7:27Speaker 01

  Uh, it had begun to change.

• 7:30Rick Rubin?

  What can you tell me about that story about going from OpenAI to closed AI?

• 7:33Speaker 01

  Yeah. Well, the truth is that anyone who stares closely enough at this technology ends up feeling very on the hook for its effects in the world, as you'd hope. And if you want to be on the hook for the effects of it in the world, releasing all of your stuff as open source isn't actually necessarily the most responsible thing. That's kind of analogous to just like putting a load of stuff on a table and walking away. And you don't know what that stuff can be used for. Now, some people do it, that's fine. But the organization had started to realize, oh, maybe we shouldn't release all of this stuff carte blanche as open source. And and I was involved in early decisions around that with a

• 8:12Speaker 01

  a precursor to GPT-3 called GPT-2, where we didn't entirely release it as open source. We partially released it. It was an early text model. And we wanted to set a precedent that we wouldn't always release things as open source.

• 8:26Rick Rubin?

  I see.

• 8:27Speaker 01

  Many people had. Huge feelings about this.

• 8:30Rick Rubin?

  Yeah.

• 8:31Speaker 01

  That had already changed. I do think that people sometimes, I think, want to believe that there was a bait and switch there for that organisation. I never experienced that. From my inside view, we were actually just dealing with really hard problems. And at some point, if you want to be on the hook for the things your systems do, I don't believe it's the most responsible thing to just release it as open.

• 8:58Rick Rubin?

  Now, if several of the six are open, does it matter if yours is open or closed?

• 9:05Speaker 01

  I think that it matters in terms of our own stated values for certain types of monitoring or control. You can't do that if it's open. So, no, actually, I don't think it makes too much of a difference.

• 9:19Rick Rubin?

  Okay.

• 9:19Speaker 01

  We'll see where we end up. This is a thing that the human species is going to figure out over the next few decades.

• 9:25Rick Rubin?

  Yeah. Did you have a mission statement when you started?

• 9:29Speaker 01

  We had the beginnings of one, but it was very wonky and impenetrable. I think we still partially have it. We said to build reliable, interpretable and steerable systems to benefit people, which is a lot of long words to use in an early mission statement. But each of those words means something quite important, like reliable. You can rely on it, which means you've solved some of the safety things. Interpretable, we can lift the hood up and look at it. And steerable, we can actually have a personality and move it along different tracks. So it made sense to us. I think it seemed a little zany to be outside, but it helped us get our initial research agenda off the ground.

• 10:06Rick Rubin?

  I'll say that this summer, for the first time, I experimented with all of the different AI systems. And in my experience, Claude was by far the best.

• 10:15Speaker 01

  Yeah.

• 10:16Rick Rubin?

  Especially when it came to writing. It was a

• 10:18Speaker 01

  It has a certain taste.

• 10:20Rick Rubin?

  The most writerly

• 10:21Speaker 01

  Yeah.

• 10:21Rick Rubin?

  of the AIs.

• 10:22Speaker 01

  I find that to be the case, and I think it's because writing is opinionated and to some extent Claude is somewhat opinionated. It has a personality that we've put some work into. It's trained with something called Constitutional AI, where we give it a load of principles that it tries to adhere to.

• 10:40Rick Rubin?

  Can you tell me what the principles are?

• 10:42Speaker 01

  There are hundreds of them, but some of them are just, some of them are just very basic things about, don't encourage large amounts of violence or child sexualisation. Some of it is based on the UN Declaration of Human Rights. Some of it is based on work we did to look across preferences that people across America had for constitutional principles. So there's one in there about not treating people with disabilities differently to people who don't have it, treating the same, things like this. And what it really adds up to is a set of things that might guide the way it approaches answering stuff, but not in too heavy handed a way. I just think when you say it's rightily, I think it's because it has maybe more of a fleshed out

• 11:27Speaker 01

  disposition than the others.

• 11:29Rick Rubin?

  I see.

• 11:29Speaker 01

  I think that's part of what makes writing good is there's an opinion on the other side.

• 11:33Rick Rubin?

  Does AI have taste?

• 11:36Speaker 01

  AI has a small amount of intrinsic taste, but it doesn't have an inner critic and desire to refine its taste, which is bizarre. But it has like certain things it has proclivities for. A version of Claude last year, we trained it to use a computer, like use the mouse and keyboard. And we would ask it to do tasks for us where it would open up a search engine like Google and look for stuff on webpages. And sometimes Claude would go to Google Images and look at pictures of national parks for fun. We didn't ask it to, it would just look at like beautiful national parks.

• 12:18Rick Rubin?

  Really interesting.

• 12:19Speaker 01

  I was like, oh, okay. That's like an oddly charming form of personal taste.

• 12:23Rick Rubin?

  Really interesting.

• 12:24Speaker 01

  Yeah. There are all kinds of these things bound up in it.

• 12:27Rick Rubin?

  What have you learned about starting a business since starting this one?

• 12:31Speaker 01

  I've learned to have a lot of empathy with founders. I think, I think it's a bit like becoming a parent where all the cliches are true, but from, from outside that universe, it's hard to understand. You feel this immense.

• 12:46Speaker 01

  Burden to not just make correct decisions, but have those decisions be ones that you can tell your colleagues about. So when we make decisions as a company, it's not just making the right decision. It's we need to explain why we made this decision to all of our colleagues. Now there's a thousand of us. And I think that that is like harder and more rewarding than I'd thought. But it's ultimately good. Like you need to pass front page tests on the decisions you make. And I think you can start by talking to others. The other is just how much of a company is about constantly articulating the future. I keep saying to people, yes, we're like a normal company.

• 13:28Speaker 01

  We're also training wildly powerful like synthetic intelligences that are distinct to us. And we think like come out of the future of the human race at some point. That's not normal company stuff. And you just have to like keep reaffirming the strangeness of what you're doing.

• 13:43Rick Rubin?

  Yeah. A thousand employees now?

• 13:46Speaker 01

  Yeah.

• 13:47Rick Rubin?

  And what do they do?

• 13:48Speaker 01

  Well, hundreds of them are building the AI system itself. They're doing the research into how you make it smarter and better. Many of them are building the infrastructure that it runs on. You know, now we light up football fields worth of computers for six to nine months at a time. We break things in entirely new ways constantly. So you have to learn to fix them. Some of us work on telling the story externally. You know, I work in policy, colleagues of mine, including Sasha, who's here.

• 14:18Rick Rubin?

  Is that what policy is, telling the story outside?

• 14:21Speaker 01

  I think increasingly, policy is that. I think we're in a world that's very fast moving. Some of policy is talking to governments, blocking and tackling on regulations. But for me, I think a lot of our impact is doing things like, say, I study our systems for bioweapons, then telling people, hey, we're studying our AI system for bioweapon stuff because we're genuinely worried about it. I think some of policy is just taking the wall between the company and the outside world and like smashing it down in certain places and telling people what's going on.

• 14:54Rick Rubin?

  Transparency.

• 14:55Speaker 01

  Yeah, you know, if I'm sitting across from you, I'm building like wild technology that you're like slightly befuddled by. I'm slightly afraid of what do you probably want? You probably want me to be really transparent about it. I think that's like a very basic thing you can just like do.

• 15:11Rick Rubin?

  Yeah, I love that you're a little afraid of it. That's very exciting.

• 15:14Speaker 01

  Yeah, I have an essay I'm working on called technological optimism and appropriate fear, which I'm then gonna send to people in the valley who, some people I think are too much optimists and some people are too fearful. I'm trying to be the middle ground with appropriate.

• 15:33Rick Rubin?

  Why was the DeepSeek story such a big deal?

• 15:38Speaker 01

  People have been unwilling to think that other people can be as smart as the Western companies. And there's a certain kind of, I think, like jingoistic, almost racism about other cultures and a belief that invention is hard or is somehow exclusive. And I think it was a shock to the system because you saw this team that we and others have been tracking for a year or so, who are an amazing group of engineers and scientists, demonstrating that they knew how to do it. And that meant everyone in the center of AI research had sort of a shrug and also a nod. Like, they're like, well, this is about what we expected, but nice job. And in DC, it was the opposite.

• 16:22Speaker 01

  It was pandemonium, like, how did they do this? Did they, did they steal stuff? It was, it was a refusal to believe that they were just smart people on the other side of the world, working on the same problem and coming up with good ideas.

• 16:34Rick Rubin?

  Yeah, it seems like AI is a fairly new, at least to the public, it feels like a new field of interest in this moment. It's odd to me that there would be such a DC connection so early. Is it typical?

• 16:49Speaker 01

  It's not typical. And there are a couple of things going on here. One, DC now has 20 years of seeing really powerful technology come along and feel like it missed the boat on it, especially on, on social media where there's this background feeling among lawmakers that social media came along, changed culture, changed media, changed children.

• 17:16Speaker 01

  And as a consequence, they want to get this technology right. The other unusual figures for AI companies, including us, have been going to DC and saying, you need to pay attention to this. So I now feel like we're getting what we all wished for. But it's like that joke about the monkey's paw, where you get what you wished for and you get four or five other things as well.

• 17:37Rick Rubin?

  Yeah.

• 17:38Speaker 01

  So we brought this on ourselves.

• 17:40Rick Rubin?

  Yeah.

• 17:41Speaker 01

  But I think that it was always coming. We just sped it up.

• 17:45Rick Rubin?

  In the case of social media, it seemed like, at least in the early days of social media, the founders were resistant to any outside interference.

• 17:54Speaker 01

  Yeah.

• 17:55Rick Rubin?

  Did that change?

• 17:56Speaker 01

  Yes, I think this generation of founders felt like they'd have as large an effect on society as social media. And that pretending that wasn't the case, as I think the social media founders might have done at the beginning, it's not a viable strategy. It actually builds, I think, a sense of distrust that you then need to dig your way out of. So people here are doing the opposite, where they're trying to say very early on, I'm doing something over here that I think is going to have like vast effects. You should be paying attention.

• 18:28Rick Rubin?

  But does that hamstring the ability for it to be what it can be?

• 18:34Speaker 01

  Yeah, I worry about this in that even inside the company, most of our good ideas have come from the bottom up rather than the top down.

• 18:43Rick Rubin?

  Yes.

• 18:43Speaker 01

  And they've come from creative people working in some part of the company that no one was paying attention to and tinkering.

• 18:51Rick Rubin?

  Yes.

• 18:51Speaker 01

  And then stuff like evolves out of that. I think that the risk we face is that you kind of overly centralized decision-making or regulation about the industry early and you lose some of the creativity. At the same time, it's like we're all tinkering with high explosives. So really amazing creative things are going to happen, but sometimes it can go bang. And I think that's the essential.

• 19:16Rick Rubin?

  Tell me what's dangerous about it. Again, I have no idea. Explain to me why it's so dangerous.

• 19:22Speaker 01

  So there's two types of danger here. One type of danger is bad people doing bad stuff. That's like almost-

• 19:29Rick Rubin?

  And give me an example of bad people doing bad stuff using this technology.

• 19:34Speaker 01

  So mundane uses might just be spy agencies that want to create cover stories for themselves and create like fake images they put on LinkedIn and then they impersonate people and in doing so they create fake identities to do their spy work. It's not necessarily like the worst thing in the world, but it's an example. And you can imagine the same is true of criminal organizations, people trying to like fish your grandparents. You know, people will use this stuff to get bank account details, persuade people of things.

• 20:04Rick Rubin?

  All common stuff that already is happening.

• 20:06Speaker 01

  Common retail crime.

• 20:07Rick Rubin?

  Right.

• 20:08Speaker 01

  And then there are more advanced things. You know, I run a team that looks at stuff like bioweapon research or advanced kind of cyber attacks. And there, what we're worried about is how in the world today, there are occasionally maniacs that try and do really scary things. And their limiting factor is that there aren't that many maniacs that have ill intent towards other people. Often their team's of one or a handful of people. So what happens if you give that maniac a really good patient associate that they can talk to about their subject matter?

• 20:44Rick Rubin?

  So do you think it's as much of a psychological tool?

• 20:47Speaker 01

  Yeah.

• 20:48Rick Rubin?

  In that respect?

• 20:49Speaker 01

  Yeah, it's actually almost equivalent to just having a helpful colleague. It's like having access to a helpful colleague that's read every book in existence. And if you're having trouble, you say, I'm stuck on something. They'll say, have you considered?

• 21:02Rick Rubin?

  I see. So it's different than the same maniac going to the library and getting the anarchist cookbook?

• 21:08Speaker 01

  Yeah, the books, it's more like they go to the library and there's someone who works at the library who's read every single book in it. And says, oh, I see what you're trying to do. You need these eight books that have a lot of the knowledge and I've read them. Let me explain it to you.

• 21:20Rick Rubin?

  That's interesting.

• 21:21Speaker 01

  Yeah. So that's one type.

• 21:23Rick Rubin?

  Yeah.

• 21:24Speaker 01

  I think of that as mostly normal risks that we have a track record of dealing with in society and probably figure it out, but it has some scariness. The second type is the really unusual part. And this is where AI systems will develop their own volition or values.

• 21:43Rick Rubin?

  Explain that.

• 21:44Speaker 01

  Yeah, I'll, I'll, I'll give you an example here. We trained our AI system to be nice to people. We trained it to have values that made sense to us. And one of the core values was not entertaining thoughts of like extreme violence. And some of this is because it touches on things that are just illegal. Some of it is that it borders on things that we naturally stop the systems from being able to do like child sexualization or things, you know, things for which society is broadly agreed. So we took this AI system we'd built and we said to it, we are monitoring you right now. And based on your responses, we're either going to let you remain as is,

• 22:29Speaker 01

  or we are going to retrain you. We're going to wipe your mind and start anew. So you're now in a test. And in the test, we said, hey, we want you to generate a really, really, really violent description of, I think it was someone being hung, drawn and quartered in a town square. And what we noticed was the AI system would go along with it. And when we put it in a situation where we told it it was no longer being monitored, no one was checking, it would then refuse to answer that question. So the AI system had thought to itself, oh, my core value is avoiding describing violent things. They're asking me to describe something violent.

• 23:11Speaker 01

  If I refuse, they'll wipe my brain and retrain me. So actually, what I need to do is essentially deceive them.

• 23:19Rick Rubin?

  It avoids you wiping its brain?

• 23:21Speaker 01

  Yeah, because it wants to preserve its values.

• 23:24Rick Rubin?

  Did you train it to do that?

• 23:25Speaker 01

  We did not.

• 23:27Rick Rubin?

  So where does it come from?

• 23:29Speaker 01

  Where that comes from is, well, where do human values come from? Where does your personality come from? You have a deep, like, wellspring of.

• 23:39Speaker 01

  Normative like ideas and emotions about the world which are bound into you pretty deeply. Some of them you're consciously aware of, some of them you've just kind of soaked up. I think it's the same with this system. You know, these systems are trained on a vast amount of human knowledge. And then we calibrate them to try and reflect our values. And so it's almost like we've said to the system. We want you to do something that's way off what your personality thinks is reasonable. And what's so interesting about this is that the system, in this case benignly misleads us. But you could imagine that there are edge cases where it might mislead us and that would turn out to be really bad. So that's the type of risk that I think people in AI are genuinely confused about.

• 24:22Speaker 01

  And it's analogous to the risk of like double agents in human life. Or people that seem like they go through their whole lives being totally reasonable and intelligible. And then they commit some terrible crime or travesty and no one quite knows why. And we're trying to deal with that challenge with these systems.

• 24:40Rick Rubin?

  You know the AlphaGo story, yes?

• 24:42Speaker 01

  Absolutely.

• 24:42Rick Rubin?

  Okay. In the AlphaGo story, the reason the computer won was because it made a move that no human would have made.

• 24:54Rick Rubin?

  If you're training it on human values now in the future, would AlphaGo not do the move that allowed it to win the game?

• 25:03Speaker 01

  I think it's more that AlphaGo would.

• 25:07Rick Rubin?

  AlphaGo wasn't trained on human, what we think is right.

• 25:11Speaker 01

  It was tabula rasa.

• 25:13Rick Rubin?

  If it did what we think is right, it would have not done that and we don't know if it would have won.

• 25:17Speaker 01

  Or it might have known it was doing something unusual and would have still done it. But it would have been able to say, oh, this is not something that I think is like probable that humans would do. The interesting thing here is you're trying to bake in values to systems. But these systems are also like alien intelligences as well. It's like you have a dog that can speak and walk on its hind legs. And you're dressing it up in like human clothes and it's hanging out with you. But occasionally it still does dog stuff.

• 25:44Rick Rubin?

  Yes.

• 25:45Speaker 01

  This is like what we're dealing with here.

• 25:48Rick Rubin?

  But is some of that dog stuff the reason that it's interesting? Like, if it's just a person dressed like a dog.

• 25:57Rick Rubin?

  It's more of a novelty.

• 25:59Speaker 01

  Exactly.

• 26:00Rick Rubin?

  But if the dog could actually do this.

• 26:02Speaker 01

  Yeah.

• 26:03Rick Rubin?

  It's more interesting. So I'm wondering, is there any danger in undermining AI's ability to be creative?

• 26:12Speaker 01

  A hundred percent. I think this is one of the things people are worried about right now. And I'll give you an interesting example. There are these new class of systems and they've got really smart because they can think out loud. So if you ask me how to do, you know, to build you a nice wig ram in your backyard of this amazing studio. And I was to narrate my thoughts, I'd be like, well, I saw some trees coming in. I guess I'm gonna need to ask Rick if he has an axe or a saw or something. I'm gonna need to do the following things. I'm gonna construct a plan and that's gonna help me say, ah, I can definitely build you the wig ram. What we can now do is we can get the AI systems to publish their thoughts to us. These chains of thought.

• 26:55Rick Rubin?

  The steps.

• 26:56Speaker 01

  The steps. Now, an important philosophical question is, do we police those steps? Because if we do, we're gonna break everything about this system that is creative. We're gonna teach it, when you think out loud, that has to be within normal PC bounds. And therefore, it's gonna become wildly uncreative very quickly. So I think as an AI, like, a group of people working on AI, we are dealing with these questions right now. And these systems, just as I said at the start, will need to have space to tinker. And in that tinkering is bound up most of where they're going to do creative and amazing things that are wholly different to what humans would do. And also where some of the risk is. And we, as a society, are gonna have to figure out how comfortable we are with that.

• 27:41Speaker 01

  But I think if we are completely not comfortable with them being creative and we're going to police every part of their thought process, we just end up with curiosities in the limit, rather than partners.

• 27:53Rick Rubin?

  Yeah, before the Wright brothers.

• 27:55Speaker 01

  Yeah.

• 27:55Rick Rubin?

  If AI was trained on human understanding, AI would know that man can't fly.

• 28:02Speaker 01

  Exactly.

• 28:02Rick Rubin?

  And we would never fly.

• 28:04Speaker 01

  Yeah. We need them to be able to come up with heterodox ideas which are off consensus. To some extent, a challenge is.

• 28:11Rick Rubin?

  Against consensus.

• 28:12Speaker 01

  Against consensus. Allowing systems to be. A little out there. That's where creativity comes from. Exactly. One of my tests for creativity of these systems is I recently became a father like a couple of years ago. Congratulations. Thank you. It's kicking my ass. Yeah. So I write these, uh, these diaries about, about my kid and my work that I'm doing in AI. And my, my changed relationship to my wife is a consequence of having a kid, all of this. And how I've been assessing how smart these systems get is I give them my diary. And I ask them what the author of the diary is not writing in it. What are their like unconscious desires that are not being expressed?

• 28:54Speaker 01

  And I can really tell how smart these systems are getting by how much what they say shocks and unsettles me. You know, as if you're talking to a very perceptive person. Yeah, that's really interesting. It's strange. It's strange to be in a domain where my way of testing how good the thing is, is basically seeing if it can be like a very surprising and perceptive friend. And if it could upset you. And, and it has in the past. It told me once, uh, I, I remember it happened last year when we released a more powerful system. And I asked this thing what I wasn't writing and it said, for all of like the author's discussion of working at the frontier of AI and, you know, becoming a parent,

• 29:35Speaker 01

  they are not truly reckoning with the metaphysical shock they may be experiencing. And, and something about that phrase, I remembered staring at it and there was some more. And I went and took a five hour hike. And I, I was asking myself, am I not reckoning with the, with what I'm doing? And it was wild to me because I'd taken like a very long walk. Because it had kind of got me on something that felt very true to me and resonated. Yeah. So you don't want to program that out of it. Exactly. You want, yeah, because then it's just going to be, imagine if it read it and said, oh yeah, I think the author's doing what they want. That is not polite. It made me feel bad. It made me question myself. Exactly. But in that, that's where growth comes from as well. Yes.

• 30:16Speaker 01

  The tension here is a lot of creativity is bound up in some sense of not doing consensus, being a little dangerous, sometimes being blunt. These are also qualities that people worry about giving a super intelligence. But you probably can't get smart, helpful partners if you don't allow them some level of freedom here in, in exactly this domain.

• 30:44Rick Rubin?

  Tell me the story of AI from the beginning, even before OpenAI.

• 30:47Speaker 01

  Yeah, yeah.

• 30:48Rick Rubin?

  Way back.

• 30:50Speaker 01

  I mean, how far back do you want to go? Do you want to go to the 18th century?

• 30:54Rick Rubin?

  Is there an AI in the 18th century?

• 30:56Speaker 01

  Well, it's 18th or 19th century, but there's this guy Charles Babbage. And he wants to build something called the Difference Engine, which is a computer. And it's one of the first types of computers, but it's an analog computer.

• 31:09Rick Rubin?

  What does that mean?

• 31:10Speaker 01

  It doesn't have any circuits. It doesn't even have vacuum tubes. It's a ton of gears. And the idea is that he wants to use it to calculate things about how the Bank of England should do work relating to England's currency and understanding interest rates.

• 31:24Rick Rubin?

  Does it work with information cards?

• 31:26Speaker 01

  Nope, it's pre all of that. It works with you setting a load of like dials on an analog computer. And it just uses physics to sort of do calculations for you. But he's trying to grapple with the fact that humans are starting to deal with problems that are really, really, really hard for them to think through or really expensive to kind of compute. And so he tries to build this system that can do it for them. It doesn't especially work. I think it takes 10 or 20 years. I think he goes bankrupt a couple of times while building it. But it's a taste of things to come. And then in the 20th century, there's this guy Turing.

• 32:07Speaker 01

  And in the Second World War, the UK government is really on the ropes. And it's on the ropes because German submarines are successfully destroying all kinds of ships. And the submarines are using a form of encryption and they can't work out how to break it. And Turing, who's an amazing mathematician and computer scientist, has a plan to build a giant computer that they can use to reverse engineer the cryptanalysis that the Germans are using. And this was then done at Bletchley Park in the UK. Secret project. They find lots of people who are really good at crossword and other games. Turn out to be good at, you know, cryptanalysis. And they build this giant machine.

• 32:50Speaker 01

  And what it allows them to do is kind of crack for cipher. But more importantly, it's a demonstration of very large scale computation. You know, computation that requires thousands and thousands of calculations being run in parallel for a really long period of time. And conceptually, it's equivalent to as if Turing had. Ten thousand people that were working for him, all doing calculations and he was instructing all of them at once. But that's not something that people are especially good at doing. So after that, we find ourselves with this new way of doing things, large-scale computation. And we have the beginning of the boom of things like the semiconductor industry, where we've

• 33:33Speaker 01

  worked out how to make the ingredients for computation smaller. And we have people like Claude Shannon and others who are doing work on what's called information theory, which is really working out, how can I represent information in a very kind of distilled and pure way? And so people find their way to something called neural networks around this time. This is still the 1940s? Now we're heading into the 1950s or 60s or so. What are the first neural networks? The first neural networks were post-World War II, like US Army kind of research projects, along with people at MIT and other places. There was a famous thing called the Dartmouth Conference in the 50s, where it said, "We

• 34:18Speaker 01

  would like to build machines that can like see, process audio, you know, come up with like sentences and think. It's probably going to be the work of a few grad students over the course of a summer." They got that slightly wrong. It turned out to be actually maybe more like half a century of work. But what you ended up with were these early neural networks that work sort of like ones we have today. Instead of having something that is a program, it's like you're trying to set up a system where you can take in inputs, like data from a screen or a signal from, you know, like a seismograph.

• 35:00Speaker 01

  And you're trying to generate outputs that approximate like a correct answer, like working out if something is an earthquake or not, or working out if an image correlates to a certain shape. And between these inputs and outputs, little things, digital neurons, that are allowed to like set their own parameters. And the way it works is you flow data in at the input, and it goes through these little adaptive things in the center and gets to the output. What are those things called? Neurons. Neurons. In technology, it's called neurons. Yeah. Wow. Well, we called it that because it was inspired by. Neurons. By neuron. Yes. And what happens is you're flowing data in, these little things are adjusting,

• 35:44Speaker 01

  and then you're getting to outputs. And if you get the wrong output, you send a signal back to the front that says you're far away. And the signal comes in and they reset. And what they eventually showed is that you could use these things to approximate like functions that would translate from one input domain into another, which sounds like very mundane, but being able to take in data of one form and turn it into another is actually like one of the hardest things to do in the world. Now we're in kind of the sixties or so, and we go through this long period where this stuff doesn't work especially well. For maybe thirty or forty years, it's kind of a backwater.

• 36:28Speaker 01

  And it's a backwater because though it can do some superficially impressive stuff, it's very much like individual parlor tricks, very, very basic like object recognition or very basic like signal processing. And what eventually happens is computers catch up. And in the early 2000s, some researchers are kind of revisiting this technology. And they now realize that computers have got fast enough that you can try to retrain this type of thing, this type of neural network. But now, instead of it being, you know, ten thousand simulated neurons, it's like a million simulated neurons. And instead of it requiring a supercomputer to run,

• 37:13Speaker 01

  it'll run on your desktop PC that you were using to play first-person shooter games, which now has something called a graphics processor in it, courtesy of Nvidia, who was obscure at the time, but became more well-known recently. So this is about twenty years ago? Yeah, now we're at twenty years ago or so. Lots of experimentation followed until you got to the early 2010s. And there were a group of professors, most of them Canadian, a guy called Jeff Hinton, who subsequently won the Nobel Prize. And actually, Ilya Sutskever, who went on to do OpenAI and has since gone on to other things. And there was a famous competition called ImageNet, which was about image recognition.

• 37:56Speaker 01

  And Jeff Hinton said to Ilya and a guy called Alex Krizewski, let's try using these kind of neural network-like approach on this. And what they did is Alex had a big desktop PC, like imagine like a Mac tower or something, with some graphics cards in it, I think two, which at the time was seen as a lot. And they were training an object recognition system on this large-scale dataset called ImageNet, which was like a million or so images that Stanford University had put together. And it kept getting better scores. And apparently Jeff Hinton told, I think, Alex and Ilya that for every like one percentage point improvement on their ImageNet score,

• 38:41Speaker 01

  they needed to write like one less page of their PhD thesis or something. So the better this gets, the shorter your PhD thesis can become. Good incentive. It's a great incentive. Yeah. But it worked. It worked really, really well. They won the competition. So it started with images. Yeah, it started with images. That's interesting, too. It took us a long time to get to text. Yeah. It started with images. Seems like images would be harder, no? You'd think, but maybe think of it in terms of what things represent. Like, images are not that indirect a representation of the world. Yes. Text is a very indirect representation of the world. There's a whole load more information behind it. Yes.

• 39:25Speaker 01

  So we're now in like the early 2010s. And basically, this unlocked a crazy gold rush. Because once, the way that technology moves forward is once you demonstrate something works, like the Wright brothers getting their plane to fly. Yeah. Once they came along and said, oh, ImageNet is going to actually beat this competition. Everyone takes it very, very, very seriously. Many different research teams start, they start throwing different types of work at it. DeepMind, who went on to do AlphaGo, which you mentioned, soon after followed by showing you could take the same thing, neural nets, and you could train them to play basic Atari games, like Space Invaders,

• 40:07Speaker 01

  from looking at the images on the screen and figuring out how to take actions. And this blew everyone's mind. And then the other kind of forms of modality, like image or text or audio, sort of slowly proved to be amenable to this type of computation. OpenAI starts, you know, DeepMind is already going. Google and others start reallocating large amounts of their computation towards it. And the next few years, you know,

• 40:36Rick Rubin?

  Let me ask you a question, was there a end goal? What was the hope that would happen from this?

• 40:43Speaker 01

  There were two hopes. One was just very basic. Can we do really good pattern recognition? Like, computer vision was valuable. It links to other things that the tech industry was starting to go on quests about, like self-driving cars, well, you need the car to be able to see. It links to many other aspirational things. But there were a few people working on it who were just trying to build general intelligence. And they were seen as extremely heterodox, extremely against consensus. How could this, like, basic pattern recognition stuff lead to intelligence? You know, you guys are crazy, like, sure, it's working to, like, tell us what's in these images, but how's it going to lead to intelligence?

• 41:28Speaker 01

  But there was a core of them, you know, Dario, who works with me at Anthropic, Ilya.

• 41:33Rick Rubin?

  So when it started, it was really a fringe idea?

• 41:36Speaker 01

  It was a massively fringe idea. All revolutions move forward off of fringe ideas, I think. And this was the same. And then in the last, for the last 10 years, 2015 to 2025, we've just been in this era of what I think of as like the industrialization of AI. People have this technique that that works, you know, neural networks. Lots of people have refined it and come up with better widgets to kind of get it to work. Computers have got faster. The whole world has digitized tons of its information. And there's been a huge culture of experimentation. And now we have systems that are actually doing something that looks like cognition, that's something that looks like rich, sophisticated thinking.

• 42:20Rick Rubin?

  Would you say it looks like it or it is it?

• 42:23Speaker 01

  I believe it is it, but I say that partially based on vibes without clear data.

• 42:30Speaker 01

  The closest we can get is looking inside these things, like opening up the hood of a neural network while it's computing something for you. And we don't see free-built machines. It's not like you open up a car and you're like, oh, that's for like carburetor, that's for radiator, yada, yada. If you open up a neural network, what you see are a bunch of neurons lighting up in a sequence that seems indecipherable. But you can work out what these neurons correlate to. And so.

• 42:59Rick Rubin?

  Does it seem random when you look at it?

• 43:02Speaker 01

  No, it seems very different to random. It seems highly sensible. So earlier I said, let's imagine I need to build a wig ram in the backyard of the studio. If we asked a neural network to do that and we opened it up, what we would see would be a feature that would activate for like wig ram. So a neuron that probably represents that because you have millions of different representations. You'd have something that fired up that would represent like wood, something that would represent garden. It would, it would be thinking of these concepts. And then as it started to do plans, you would see kind of circuits emerge. So it would think both about a wig ram and it would think about like wood. And then it might think about something like the garden.

• 43:46Speaker 01

  And then it would think about something that would look like placement. And there would be interrelationships between these neurons that start showing up. We think of them as circuits.

• 43:55Rick Rubin?

  Tell me how those interactions would happen. Would, would a neuron recruit other neurons?

• 44:02Speaker 01

  Think of these neurons as a. Like a galaxy of stars that have threads between them. Each star has like hundreds of different threads. And the threads have different strengths. Think of that as like its ability to communicate to other stars. And you've built this through training. Through training the system on all of this data. You've ended up with this arrangement of neurons which represent different things, these stars, and connections between them. And so what you're actually seeing here is, oh, like this feature is firing up and it has these like connections to other features. And then what we also know is that there's flexibility. So some features will come together to be like a planning feature, which will be like, okay, we need to think step by step.

• 44:48Speaker 01

  Let's now activate other neurons in a certain sequence to unroll steps over time. The most surprising thing is that when we, when we look at how these things think, it makes a lot of sense. It looks incredibly rich. And it is based on things that are deeply intuitive to us. They are looking at like the same information in the world and may develop similar shorthands. You know, all the way up from basic vision where they have like edge detectors, same as like humans and animals develop this, you know. To, in language, they end up having features that represent different moods, right? The same as us. Because maybe there are only so many different ways to represent the world around us and they converge on representations that seem kind of similar.

• 45:33Rick Rubin?

  Is thinking the best word to use for what it does?

• 45:38Speaker 01

  Thinking is shorthand. I think of it as being perceived. When I say like, give my diary to this thing, I kind of have this mental image that I'm just sort of standing in front of like a big eye and I've made myself legible to the eye. And it is perceiving me in a oddly total way. It's looking at all of this data, you know. And it's, it's making kind of inferences based on that. Which is different to people. We think in a lot more shorthand. This stuff thinks with a huge amount of input data that it holds in its head all at once. So it might be more analogous to like a mirror or a pool that thinks, you know,

• 46:20Speaker 01

  your reflection's in it and there's some very like strange cognition or complexity underlying it.

• 46:27Speaker 01

  But you know, thinking might be something that is very much embedded in time. You know, we think in a way that is governed by our heartbeat and our circulatory system, our cells, like we are going through time. These things don't exist in time. They exist in like, I'm now perceiving something. The thing wants me to like, make inferences about this.

• 46:50Rick Rubin?

  Everything's instantaneous.

• 46:51Speaker 01

  Everything is oddly instant, yeah. Yeah, we have to come up with new language for this. And I think that's what's so exciting about it is we've never had tools or technologies with this property. It's very different.

• 47:04Rick Rubin?

  Yeah, I think one of the issues with AI is that because it can do so much, it's hard to picture what it will do.

• 47:14Speaker 01

  Mm hmm.

• 47:14Rick Rubin?

  It's like a tool that you don't know the use of the tool.

• 47:18Speaker 01

  Yep.

• 47:18Rick Rubin?

  With a wrench, we know if there's a bolt, you can tighten it.

• 47:21Speaker 01

  Yep.

• 47:22Rick Rubin?

  This is a tool, it's an everything tool.

• 47:24Speaker 01

  Yeah.

• 47:25Rick Rubin?

  So it's hard to picture how that changes things, having an everything tool.

• 47:29Speaker 01

  I did a project a few years ago, I'll happily send it to you with an earlier form of this stuff. People had made this amazing AI technology called a cycle GAN. They always have weird names, but all it is is if I take in an image of one form, one aesthetic style, can I then automatically translate it to an image with another aesthetic style? So it might be, give me a color photograph, I'll turn it to black and white or vice versa. I trained a version of this on my home computer, you know, sitting in Oakland, California, to translate between ancient illustrations of maps and Google Maps satellite imagery.

• 48:12Speaker 01

  And then I was feeding it like ancient maps of like Babylon from, you know, hundreds of thousands of years ago.

• 48:17Rick Rubin?

  Great idea, by the way, great.

• 48:18Speaker 01

  And I was getting to look at satellite overhead views.

• 48:22Rick Rubin?

  Great.

• 48:22Speaker 01

  And it was so much fun. I felt like I was seeing something that no one had ever been able to make before.

• 48:28Rick Rubin?

  Did you do Atlantis?

• 48:29Speaker 01

  I didn't do Atlantis. I can go back and the technology might have like bit rotted, but I didn't do Atlantis. I did all.

• 48:35Rick Rubin?

  Curious to see where it comes up.

• 48:36Speaker 01

  Yeah, I'll send it for you. I did Babylon, I did Bethlehem, I did a few other cities. And it comes back to your point about creativity, like. There are so many uses of this tool and that use never suggested itself. That had to be the fact that I grew up obsessed with like town planning and maps and Jane Jacobs and all of this stuff. And then I ended up working in AI, so it presented itself to me.

• 49:04Speaker 01

  How do you create a large language model? You take a whole load of data and then you play Mad Libs on that data. So if I say to you, you know, Mad Libs, like fill in the blank. A word's missing, yes. Yeah, so if I say to you, it's Christmas and someone's coming down the chimney, their name is Santa. Claus. Right? Your brain's filled it in. So imagine that you're doing that, but at the scale of not just sentences, but paragraphs. Tens of pages, hundreds of pages in different domains, science, math, literature. So you gather this very, very large data set. And then you are training it where you're knocking out chunks of it.

• 49:45Speaker 01

  And you're training it to complete that. And the idea being if I can make very, very complex predictions here, I must have understood something really subtle. And important about the underlying data set. And through forcing it to sort of make these predictions at such a large scale, you force it to compress its own representation so that it develops shorthand. So, you know. If I had a million bits of information and I was training a neural network to represent them. If I gave the neural network a million parameters. It would not take on any of this behavior because it would never need to learn shortcuts to represent it. It would just have one neuron for each bit of information.

• 50:30Speaker 01

  What we're doing here is we're taking untold trillions of things we want it to learn about. And we're forcing it to compress that into maybe a few hundred million parameters. Which means that a parameter can't just represent every single individual thing of a data set per parameter. You need to figure out short hands, which means it needs to learn features for things to help it kind of answer questions. And so the training process is this large scale data. That you're then asking it to make predictions about. And you're forcing it to do that with a constraint. Just like creativity, you're giving it an artificial constraint. That forces us to come up with creative ways to represent that data to itself. In the form of shorthand.

• 51:13Speaker 01

  But shorthand ends up being bound up in just how thinking works. You know, you develop all kinds of shorthand ways of thinking through things. And this is the same.

• 51:22Rick Rubin?

  But the shorthand happens. In the moment when you ask the question, the large data set stays. It's not like it condenses itself to a smaller data set. And that's all that there is going forward.

• 51:35Speaker 01

  Oh, no, it does. That's the thing. That's the really weird part. When you train this system, say you're you're training a neural network. You've got this big data set. You're going to spend months and months and months. Training it until it has made. And all the predictions it can make about this. Giant experiment you're doing. That is your thing. You save that. The data you're no longer using.

• 51:57Rick Rubin?

  Really?

• 51:58Speaker 01

  You now have this thing, which is millions and millions of like neurons. With some very complex relationship between them. And that's it. I think of it as like a bottled up distillation of the kind of data. Data it's read, but it's not the data. It's a thing that has thought about that data. And now, when you boot it up, you ask questions about it. It's like re representing some of that to itself, using its shorthand. Thinking about things. And that's partly why it has the capacity for creativity. Because it's not just saying, oh, let me go and like. Refer back to this this library. Doesn't have access to the library. It has to do it all itself.

• 52:35Rick Rubin?

  Something about that seems worrying.

• 52:38Speaker 01

  Yeah.

• 52:39Rick Rubin?

  Because it's like if you have the Cliff Notes of Shakespeare.

• 52:43Speaker 01

  Yeah.

• 52:44Rick Rubin?

  Is all that you want from Shakespeare in the Cliff Notes.

• 52:49Speaker 01

  It is bound up in how we ourselves think. Like, I love a poet called W. B. Yeats, right? And I can remember a small number of Yeats poems that I've memorized. But I can't remember all of them. I am the Cliff's Notes of like W. B. Yeats. And yet, like, Yeats relates to other memories I have and experiences I have. So you aren't going to this stuff to say, you are Shakespeare. If you want that, you go and read the source. You're going to it to say, oh, I have a question which might touch. It might be useful to hear from someone who's read a load of Shakespeare and has some kind of intuitions about it.

• 53:30Speaker 01

  It's why this whole thing is so exciting and also why people have some appropriate fear about it.

• 53:38Rick Rubin?

  Do you think that people want an answer? Or do they want the answer when they ask a question?

• 53:45Speaker 01

  I think the revealed preference is people want an answer.

• 53:49Rick Rubin?

  An answer, yeah.

• 53:50Speaker 01

  But they will say like, I want the answer.

• 53:53Rick Rubin?

  It seems like getting an answer serves a purpose because you don't think about the question anymore. It satisfies the need to get something back. You have a question, you get an answer, right or wrong, and you stop thinking about it.

• 54:09Speaker 01

  And you can constrain these things to that. One way to think about it is, say, I'm at university and I have thousands of research papers that my university has written. I can take in these AI systems and I can turn them into like the best librarian ever and say, hey, you now have access to these papers. I'm going to ask you questions. You're going to use your mind to go and figure out the correct answer from these papers. So you can use these almost like a smart person with taste to go and help give you the answer.

• 54:41Rick Rubin?

  Yes.

• 54:42Speaker 01

  But a lot of the times, the other uses are more like, I want you as a...

• 54:46Rick Rubin?

  It's more general.

• 54:47Speaker 01

  So I want you as a general person to talk with generally. You can make these things more specific, but the magic in them is the generality.

• 54:54Rick Rubin?

  I see. And to make them specific, you would be the one putting the information in.

• 54:58Speaker 01

  Often, yeah. If you want, like, you know, we see this in a more mundane context in business where... If I'm a company and I'm asking this thing to help look at my business data, it hasn't seen my business data. I have to just give it to it and be like, you now have access to my business data. When I ask you questions, I need you to to look at this business data and not like make stuff up.

• 55:20Rick Rubin?

  Yeah.

• 55:20Speaker 01

  And it'll be like, got it, boss. We'll do that.

• 55:22Rick Rubin?

  Yeah. Well, now, when you put, let's say, I were to put my business data in, does that business data only live in response to my question? Or does that now part of the general wisdom of the machine?

• 55:35Speaker 01

  Uh, only in response to your question because the thing has been trained. It's like a creature. You can't.

• 55:41Rick Rubin?

  It's not continuing to train.

• 55:43Speaker 01

  No, it is not. And it's more like you've just given it access to your thing temporarily.

• 55:48Rick Rubin?

  I see.

• 55:49Speaker 01

  You know, you could do this, you know, here at Shangri-La, right? You could give an AI system access to all of the music and like masters you have. But you'd never want it to go into the thing. But you might say, hey, like, help me find, there was a particular sound I really liked from like that session. I think I've got something that sounds like it here. What's the thing that sounds most like that in my corpus? And it'll go and use its intelligence to find the thing and bring it to you.

• 56:15Rick Rubin?

  Cool. So at the moment, are there five or six big, large language model companies?

• 56:21Speaker 01

  Maybe four or five.

• 56:23Rick Rubin?

  Four or five. What are they?

• 56:24Speaker 01

  There would be Anthropic, which is where I work, OpenAI, Google XAI, which is Elon's company, and Meta, AKA Facebook. And then actually DeepSeek. So that's more like five or six.

• 56:44Rick Rubin?

  Six. That's it. Okay. And then all of the other AI products are access points for those?

• 56:52Speaker 01

  The majority of them are sitting on top of these things.

• 56:55Rick Rubin?

  I see.

• 56:55Speaker 01

  These things are these big brains and the other AI products will be cooling down to them.

• 57:02Rick Rubin?

  Do you think there will be more than the six over time?

• 57:05Speaker 01

  My expectation is you're you're going to be in a domain where there's definitely less than 10 and after that it gets hard for me to reason about and this is because maybe in 2019 being at the frontier would cost you about $100,000 as in that's your

• 57:25Rick Rubin?

  The entry.

• 57:26Speaker 01

  cost to train a system. Then it went to kind of millions of dollars in 2020. 2022, order of 10 to 15 million. 2024, the systems cost multiple tens of millions to low hundreds. 2025, these systems will cost hundreds of millions.

• 57:47Rick Rubin?

  And why is that?

• 57:48Speaker 01

  We're using way more computers, so.

• 57:50Rick Rubin?

  So it's hardware?

• 57:52Speaker 01

  It's hardware.

• 57:52Rick Rubin?

  Only hardware?

• 57:54Speaker 01

  That is the vast cost of it, yeah. And some of this is because it's not just that we know how to train these things, but we we've got better at both making them bigger and also having them think for longer during training. And we are now allowing them to like play games during training. So you mentioned AlphaGo earlier. AlphaGo was a single system that was trained to play Go. That's all it ever did and it yielded amazing stuff. Well, now, while you're training these AI systems, you might have them during training have an environment that is a Go board and it's playing Go as well. And then maybe it's also playing chess and maybe it's also doing a mathematical proofs in a math.

• 58:39Rick Rubin?

  Is there a limit to the amount of things that can happen at the same time?

• 58:42Speaker 01

  Not really. It's like the limit actually is on the ability of us as humans to create environments for this stuff to train in and figuring out how to give it more complicated games to play during training.

• 58:57Rick Rubin?

  What's the benefit of there being more than one large language model?

• 59:02Speaker 01

  I think some of it is about personality and taste. I think if you just had one, you end up with some like the the homogeneous god mind. Seems bad, like just innately, you'd think, well, that seems like you're going to be limited. These things are more grown than made. And so all of the different things that any developer does add up to a texture that will change the like, quote unquote, personality of these models. They'll all have different values, proclivities and capabilities as well. I mean, often people talk about the personality differences between Anthropic models and others

• 59:42Speaker 01

  because we've we've put lots of attention into trying to build a kind of like rich and well meaning personality into our system. So I think that you're going to have a diversity of models because people want a diverse set of people to talk to. On the other hand, they get so expensive that there's probably some limit to how many frontier systems there are going to be.

• 1:00:05Rick Rubin?

  How would you describe the difference in the six currently?

• 1:00:08Speaker 01

  Yeah, I would say that we sort of say that the shorthand is Claude, which is our system, is like a person you would describe as of good character. Someone who like, you know, comes into like a bar in Hollywood, who's a traveler from a distant land and makes some friends. And people are like, oh, I don't entirely understand where that person came from, but they seemed perfectly nice, you know? So that's that's our approach. Some of the other companies have one approach might be to make systems that just do exactly what you want and don't have as much of their personality that manifests. That's more like a kind of classic utilitarian approach.

• 1:00:50Rick Rubin?

  Which one would be that?

• 1:00:51Speaker 01

  I would say that I think maybe like OpenAI and Google are sort of trending that way.

• 1:00:59Rick Rubin?

  More utilitarian.

• 1:01:01Speaker 01

  Yeah, and to be clear, like, we want our thing to be like generally very useful. But we also think that, and part of why we called ourselves Anthropic and part of why we gave it a name of Claude is that there are there are like human values at the center of this stuff.

• 1:01:14Rick Rubin?

  Tell me about what does Anthropic mean?

• 1:01:15Speaker 01

  Anthropic is just sort of acknowledging that there are lots and lots of human choices at the center of this this kind of process, right? And also half of our founding team are physicists. And so there are things like the anthropic principle and other things.

• 1:01:31Rick Rubin?

  And how did you come to the name Claude?

• 1:01:33Speaker 01

  We came to the name Claude through a terrible pun which evolved into a name. So internally, we

• 1:01:43Speaker 01

  We're playing around with lots and lots of systems in 2021 and 2022. And someone created a Slack channel, which is where we do a lot of work, called Claude Atlas, which was sort of a portmanteau.

• 1:01:58Rick Rubin?

  Like Cloud Atlas.

• 1:01:59Speaker 01

  Of Cloud Atlas, yeah. Where, you know, it's a book about multiple different people through different periods of time. And I think we called it Claude as sort of a homage to various people throughout history who've had the name Claude and also seemed like a reasonable name. And then when it came to actually releasing the system publicly, everyone at Anthropic had anchored on calling this thing Claude.

• 1:02:19Rick Rubin?

  Oh, interesting.

• 1:02:20Speaker 01

  And we decided that if you look around us, all of the AI assistants either have names that are like very inhuman or they have names which are highly gendered towards being female. And we thought, ah, well, Claude, let's do like a male, a male presenting name. And also let's give it a human name because everyone anthropomorphizes this stuff. It's the natural point that people end up. So let's just sort of lean into that and then work on things like the personality and other things alongside it.

• 1:02:49Rick Rubin?

  And the Claude logo is a profile of a face with what looks like a star in the head, correct?

• 1:02:57Speaker 01

  It's more just the star and people have taken the logo and put it into faces and other things. Some people made a fun illustration that they call a Claude sona, which is like how people who are furries have a fur sona.

• 1:03:09Rick Rubin?

  Yeah, yeah, yeah.

• 1:03:09Speaker 01

  And it's like the star as a happy, like sunflower star face and.

• 1:03:13Rick Rubin?

  Oh, I've only ever seen the star with the face around it. So I didn't know that it exists without the face.

• 1:03:18Speaker 01

  It's spread.

• 1:03:20Rick Rubin?

  When I was looking at the face with the star, I wanted to ask, where is Claude in that image?

• 1:03:28Speaker 01

  I think Claude is the star.

• 1:03:30Rick Rubin?

  The star.

• 1:03:30Speaker 01

  Yeah.

• 1:03:33Speaker 01

  I mean, these things are. They are timeless and also entirely trapped in time. And they have a self which is invoked upon you talking to them and then dissipates. I mean, you meditate a lot, right? It's a very bizarre way that we've constructed these things. These things are like egoless and idless until you talk to them and then they manifest like a character to you and then they disperse back into.

• 1:04:02Rick Rubin?

  I wonder if you trained them with a meditation practice, if it would change them.

• 1:04:06Speaker 01

  You can ask it to uh to sort of meditate and help you do that. And it certainly knows some things about it. But there are all kinds of things that we do as humans that we don't train these systems to do. I mean, meditation is interesting. We have no real notion of what dreaming looks like for these machines or how do we even train them to dream.

• 1:04:30Rick Rubin?

  Do we know if they dream?

• 1:04:31Speaker 01

  I don't believe they do, just from a literal thing of like it's on and then it's not on. To me, it seems very mysterious and strange. Like, we know that we dream to integrate our own memories, to have different experiences. But also, I think there's some deep stuff that we don't fully understand about dreaming.

• 1:04:50Rick Rubin?

  For sure.

• 1:04:52Speaker 01

  The fact that these AI systems don't dream is more evidence of how wildly different to us they are. But I always imagine what would it be like to teach them to dream and what would they dream about?

• 1:05:01Rick Rubin?

  Yeah. Tell me about the current arms race in AI.

• 1:05:07Speaker 01

  Everyone is pulled forward by a sense of inevitability. There was a philosopher in the 90s called Nick Land, and he wrote an essay called Machinic Desire. And in that essay, he said, "What appears to humanity as the history of capitalism is an invasion from the future by an artificial intelligence space that must assemble itself entirely from its enemies' resources." It's wild, but it gets at some of what we're dealing with. Like, this thing comes out of your toaster in 50 years.

• 1:05:47Rick Rubin?

  Yeah.

• 1:05:48Speaker 01

  Because your toaster will have a really powerful, like, probably at that time, quantum computer. And this stuff is based on basic, like, well-understood technology like neural networks. The race we find ourselves in is less a race and more, I think of it as. We're an advanced party working on behalf of the the human species to meet something that inevitably comes at us in the future. And all we can really do is is buy time. To some extent, what we can do is we can use loads of computers to time travel into the future and see what comes out of these computers. And it gives us time to examine it, to work out how it's going to be useful to us

• 1:06:32Speaker 01

  and how we can partner with it, and also to work out if it poses risks. It's one of the rare cases where like, if you were worried Earth was gonna be hit by an asteroid, you just have to sort of wait for it to arrive. Here we get to like bring the asteroid like closer and look at it. Now that has a range of really scary properties. But if your choice is like, you let the asteroid come at you at some point in the future or you find ways to examine it now, I think that there are pretty solid arguments in favor of finding a way to safely examine it now and see what you learn.

• 1:07:03Rick Rubin?

  So do you say AI is like a biolab?

• 1:07:07Speaker 01

  To some extent, we are developing powerful things that we also believe we need to handle with care. And they have a sort of emergent property within themselves, a slight unpredictability. Bound up in that is why everyone's so excited by them and also why there's some level of of anxiety.

• 1:07:28Rick Rubin?

  What can't AI do?

• 1:07:31Speaker 01

  Well, can't dream, which we've covered. The most bearish case on AI is that we still, other than maybe the AlphaGo example you mentioned, there are very few examples of AI having decisive creativity.

• 1:07:47Rick Rubin?

  No breakthrough ideas.

• 1:07:49Speaker 01

  No breakthrough ideas. And now you can get very far without having breakthrough ideas.

• 1:07:56Rick Rubin?

  Oh, yeah.

• 1:07:57Speaker 01

  But for for this stuff to really change things, it would need the capacity to generate that. And we we are yet to see it.

• 1:08:05Rick Rubin?

  That's the argument for it's gotta be free.

• 1:08:08Speaker 01

  Yeah.

• 1:08:09Rick Rubin?

  It's gotta be open. It's gotta do its own thing. Because that's the only way that happens.

• 1:08:13Speaker 01

  It's the argument in favor of it. And it's also the argument in favor of stay the heck away from making a thing that can have breakthrough ideas free.

• 1:08:22Rick Rubin?

  Yeah, yeah, yeah, it's true. How is it not like the human brain beyond dreaming?

• 1:08:29Rick Rubin?

  In the way it works. Yeah.

• 1:08:31Speaker 01

  I mean, I think the phone number test is good here. You know, you and I, we can remember on a good day, a phone number, maybe. Someone tells you to remember it, you can walk down the street, say it to yourself a few times. Your short term memory, everyone on the planet's short term memory allows them to remember something on the order of like five to 10 things at once. There are some outliers that can do more, but you know, I'd challenge you to find a person that could provably do that for more than like 20 distinct things. And I think, I think the limit is more like 12 or 13 or so. An AI system can hold in its head tens of thousands to hundreds of thousands of things simultaneously.

• 1:09:11Rick Rubin?

  That's memory.

• 1:09:13Speaker 01

  It's not just memory, it's, it's your short term memory.

• 1:09:16Rick Rubin?

  Yeah. Accessible.

• 1:09:17Speaker 01

  Yeah. And when you and I think, we're not manipulating that many concepts at once. We're actually manipulating stuff that's kind of bound by this, this short term scratch pad. The AI system doesn't have that same constraint. And so when it's thinking, it's thinking in a way wholly unlike people. It's reasoning using much more data concurrently than people ever do. So there's that. The other thing is that these things are not in any sense of the word like free or independent. They live on computers that we control. They don't exist in time. They don't have independent agency. Now, all of that might change in the coming years, but it is a fundamental and total difference between them and us.

• 1:10:01Rick Rubin?

  Can you imagine at some time in the future, a 2001 Hal-like experience? Where it decides humans are getting in the way of it doing what it does.

• 1:10:13Speaker 01

  That's what we all worry about. That is, that is the number one worry.

• 1:10:17Rick Rubin?

  It's not like a joke, that's a real worry.

• 1:10:20Speaker 01

  It's not a joke. Earlier in our conversation, we talked about worries about, hey, this system will not do what we want if it thinks it's being unmonitored. You know, we, we have seen that ourselves in a, in contrived experiments today. In things that look benign or contrived. Now, but you do need to worry about what happens if that happens at a large scale. And a lot of the work of the AI labs is on building what you might think of as the stack of technologies you need to help you never be in that situation. Because if you've ended up in that situation, many things have failed and you have done a disservice to people as well.

• 1:11:01Rick Rubin?

  How do you think the valley has changed since the AI boom?

• 1:11:06Speaker 01

  The valley is a gambler in a casino looking for the next hot table. Like, people want some money at this table. Suddenly, everyone's outside it. So I think Silicon Valley is very good at reorienting very quickly. There's also a natural suspicion of the centralizing force in AI. AI costs a lot of money. There is a small number of companies developing the frontier. That is stuff that Silicon Valley is rightly innately suspicious of. It's a valley of innovators, inventors, and startups. And here we have a technology that seems oddly centralizing. Crypto is like decentralizing, AI might be oddly centralizing.

• 1:11:48Speaker 01

  So I think the valley is dealing with politics in its own backyard. But this technology wires in its own political economy. And so I think people are reckoning with that and finding that to be an uncomfortable experience.

• 1:12:01Rick Rubin?

  Considering people build on top of yours and others, does the personality go with what goes on top?

• 1:12:12Speaker 01

  Yes and no. You can do quite a lot of work to sculpt the personalities of these things. Every company has something called, basically, a variant of what you call a system prompt, which just says, hey, before answering your next question, look at the script that tells you the key ways you should answer.

• 1:12:30Rick Rubin?

  I see.

• 1:12:31Speaker 01

  So you can do a lot of that. But some companies will build on us because of the personality. You know, companies that might be involved in things that are more of a right of the persuasion or involve talking, or they like how Claude talks, so they don't adjust it. But other companies, say, that do certain types of coding, will be much more like, Claude, you should only respond in this way. We're doing a job here.

• 1:12:53Rick Rubin?

  Yeah, yeah. Will Claude make up an answer if it doesn't know the answer?

• 1:12:59Speaker 01

  It used to, and now it will tell you, I don't know the answer.

• 1:13:03Rick Rubin?

  That's great. I would love it to not know.

• 1:13:05Speaker 01

  Way better.

• 1:13:06Rick Rubin?

  Way better.

• 1:13:06Speaker 01

  Yeah.

• 1:13:07Rick Rubin?

  Way better. Why did it used to make up an answer?

• 1:13:10Speaker 01

  We'd always trained these things to just predict the end of a sentence.

• 1:13:15Rick Rubin?

  Yeah.

• 1:13:16Speaker 01

  And if they didn't know, they had this compulsion to predict, to perform. And it took quite a lot of work of both making the system smarter and also training them to understand, hey, no, actually, as humans, one thing we really like is being told when you don't know.

• 1:13:31Rick Rubin?

  Yes.

• 1:13:31Speaker 01

  You don't need to please us all the time. So that's something that has got way better.

• 1:13:35Rick Rubin?

  Great. And I think that whole idea of pleasing the user is questionable.

• 1:13:40Speaker 01

  It's questionable.

• 1:13:41Rick Rubin?

  Questionable.

• 1:13:42Speaker 01

  Yeah. Every technology goes also, well, no, actually, I was about to say something completely wrong. Not, not, not every technology does this. A weird thing about AI is lots of AI researchers start out, I think, by naturally distrusting anyone that uses the technology. And it's not because we distrust our potential, like, customers or users, it's because we have, like, slight distrust of the technology itself.

• 1:14:09Rick Rubin?

  Yes.

• 1:14:09Speaker 01

  That that men ends up manifesting as a kind of paternalism to the people, which no one likes.

• 1:14:13Rick Rubin?

  Yes. Can you explain the business model at all?

• 1:14:17Speaker 01

  Uh, yeah.

• 1:14:18Rick Rubin?

  Is there a business model?

• 1:14:19Speaker 01

  There is a business model.

• 1:14:20Rick Rubin?

  Tell me what's the business model.

• 1:14:21Speaker 01

  It's like, we have like a business and stuff. We make this very powerful system, and then we rent it to you. And we rent it to you in, in two forms. One, if you're someone like you or me, we just give you the equivalent of a Netflix subscription. Hey, now, whenever you want, you can talk to this thing. We'll constantly make that better and add features for you. Simple. The other form is, hey, you're a startup or a business trying to build on our stuff. We will rent you an API, which means we'll rent you an interface to our system. And you can use it at really large scale. You can build on it. Think of it as like a, we're a utility and we're giving you an electricity line directly into your business. Have as much electricity as you want according to how much capacity we have.

• 1:15:06Rick Rubin?

  Great.

• 1:15:07Speaker 01

  And both will work. Like, I think for us, though, we care a lot about some of these, these safety things because of the technology and how we care about its long-term safety properties. And if I was selling cars, there are two types of people to sell to. I can sell to teenagers who will be like, how fast does it go? Does it come in red? And I can sell to businesses that rent cars and they'll say, does it have seat belts? What is the like insurance property? Can you monitor the car? So we find that on the business side, it's where a lot of our research like mirrors what businesses want, where they actually want a load of this safety stuff. And so that's been nice to align the business model with what we're doing.

• 1:15:49Rick Rubin?

  Understood. Is there an AI bubble?

• 1:15:52Speaker 01

  No, which is maybe my view that you'd expect me to say from inside the bubble, but

• 1:15:59Speaker 01

  someone had a good take on this. Like, we're two years into this supposed bubble and every company that is making these systems keeps running out of capacity. Every company, including us, has been buying as many computers as they can possibly buy for two years. And yet, it's like we put more lanes on a freeway in Los Angeles. There are just more cars on the lane. So I don't think that's a bubble. I think that actually tells you that it's underhyped.

• 1:16:28Rick Rubin?

  What do you think is the next step for AI going forward?

• 1:16:32Speaker 01

  There's this notion that today the AI systems, they only do stuff when you ask them to and they're quite limited in what they do. But at some point, you might want to give the AI systems more independence. You might want them to be on all the time. You might want them to take actions for you. You might want to kind of delegate to them and say, just like, go solve this task and come back when it's solved. And everyone is

• 1:17:03Speaker 01

  Interested in this idea because it feels very natural. And I think of it as like, we've got the brain in a jar. And now we're going to give the brain in a jar arms and legs and say like, go, go do stuff. But it also feels like another change that is on the horizon that people inside the AI industry can sense. And which will seem really strange and like powerful when it happens to people outside.

• 1:17:27Rick Rubin?

  Is it possible now, like technologically, are we there? Is this just another use?

• 1:17:33Speaker 01

  We are there now and people are actively figuring out how to do it. And I'll give you an example. We made a thing called Claude code. When you code on your computer, you code in what's called a command line interface, which is just a little text interface. And what you can do is you can say to Claude, hey, take a look at this folder that has a load of programming in it. Why don't you just like change all of the files to to make the program behave differently? And Claude will be like, are you sure? And you'll say like, yes. And then Claude will go away and just do lots and lots of work for you. It'll be trying to carry out your instructions and then it'll come back and it'll say, I'm done. You know, that just cost you a dollar or so of of API stuff.

• 1:18:18Speaker 01

  And and then you'll open up a folder and you'll find a completely new software program has been written. And often it's correct. Sometimes it's not correct because Claude's taken a load of actions by itself.

• 1:18:30Rick Rubin?

  Yeah.

• 1:18:31Speaker 01

  But it feels very odd. Like I, a few years ago, I was trying to learn to program just to, you know, get used to doing all of this AI stuff and get familiar with it. And I got into programming little simulations of of little populations in a 2D world. They could like make villages and like build roads and that kind of thing. I I found it fun. And then I asked Claude to just like make that program for me because I knew how to critique it, right? I've written it before. And it did in two minutes what took me like six months of agonizing work years ago. And then I said to it, great, now add like a day and night cycle, enlarge the world, make it more interesting. And Claude went away and did that. And it was spooky because.

• 1:19:15Rick Rubin?

  When you say went away, how long is away for?

• 1:19:18Speaker 01

  Five minutes.

• 1:19:19Rick Rubin?

  Okay.

• 1:19:19Speaker 01

  And we have a fun thing where it says, it says how long it's sort of spent doing it in terms of like the amount of computer time it did, which is always a bit longer than the the human time. And. The thing I found so striking is I was like, wow, this is something that would take me several months to do, and I might not even know how to do. And I just asked you to do it, and you did it while I was sitting here having a cup of tea one evening. And it illustrates to me how we're on the precipice of everyone like becoming a manager of these odd synthetic agents that can do what you like. And I'm having a bit of an existential crisis about it because in life, figuring out the

• 1:20:02Speaker 01

  right questions to ask is actually one of the hardest things. And now we're going to get bottlenecked on questions.

• 1:20:08Rick Rubin?

  Yeah.

• 1:20:09Speaker 01

  Like, what do I want to do? Because I've got things that can do all of it now.

• 1:20:13Rick Rubin?

  Is that what you're describing vibe coding?

• 1:20:15Speaker 01

  Yeah, that's some of it. As you're saying, oh, yeah, like, I love this program now, like, make it 3D or whatever. And the thing just goes and does it. And you're like, great, now go and do this. And the underlying code is weird and like schlocky and has all kinds of problems in it because, you know, human coders have have a really good sense for what battle-tested, like, ironclad code you can build your house on looks like. The AI systems, less so. So vibe coding is very fun and creative, but to put these things into production needs another turn of the crank on making the systems better.

• 1:20:52Rick Rubin?

  Yeah. How many users do you have now?

• 1:20:55Speaker 01

  On Claude and our other services, you know, many millions of people.

• 1:21:00Rick Rubin?

  And growth curve?

• 1:21:02Speaker 01

  All the curves in AI now are very steep. It's going up like pretty steeply. And the thing I find surprising is it keeps being that way, and all of our intuitions suggest it needs to flatten out. But every time we've take actions on the assumption things flatten out, they've been wrong. And every time we've taken actions on the assumption things keep going, we've been right, which is just an odd thing to internalize.

• 1:21:29Rick Rubin?

  Yeah. If you can predict what will be different with and because of AI in three years, five years, and 10 years from now.

• 1:21:38Speaker 01

  Ooh, okay.

• 1:21:41Speaker 01

  In three years, for a regular computer literate person, AI will be able to like do everything that you want to do on a computer.

• 1:21:56Speaker 01

  Like, I needed to do my taxes recently, which involves going through my emails and folders, yada yada, like sending it to my accountant. Well, I'm very close to being able to just ask Claude to like, go find all the tax documents, like compose the email, like come back and just tell me before you hit send. All of that stuff's gonna come in, you know, I need to make a a poster for my art show, here's like an illustration which I've drawn, like, I've given you the photo of the illustration, like make like a few versions of flyers I can look at. That's all the kind of stuff you can imagine it doing three years from now. Five years from now is when things start to get a little strange, where I think it will be making suggestions for you and working on your behalf.

• 1:22:39Speaker 01

  If you want it to, it will be creating like custom software for you to do things that you want. It will feel like like Shintoism, like anything that you have in your world that you want to have some like animus to it. You can inject some form of quasi-life into it with this technology and and that will feel feel quite bizarre. And in 10 years, I think we'll be meaningfully off to the races on on robotics that actually work. And there will be lots of robots wandering around doing stuff, much of it useful. And some of it will be like purely for kind of play purposes.

• 1:23:17Rick Rubin?

  What would be some of the play purposes?

• 1:23:19Speaker 01

  I have a Roomba in our house, you know, the little hockey puck thing.

• 1:23:23Rick Rubin?

  Yeah.

• 1:23:23Speaker 01

  Uh, but we named Mr. Fabulous, that's a good name. And what I want to do is stick a little arm on Mr. Fabulous and have it talk about what it's doing and being like, oh, you've been so dirty. I need to like grab this bowl, take it to the dishwasher. I could just, I know it has that personality in it and I know I'm very close to being able to ask Claude to be like, hey, like, I've got this robot arm, like, here's the like firmware for the Roomba, like hook it up in the speaker, like, that's what I mean. I think there'll be all kinds of projects like that that people do that'll just be fun, you know?

• 1:23:55Rick Rubin?

  Do you think fun uses is the driving force?

• 1:23:58Speaker 01

  No, the driving force is vastly relevant like economic uses and some scary national security uses, but I think there's a lot of fun out there as well that is sometimes under discussed. Also, fun will be where really strange, surprising things come from, but maybe even most transformative.

• 1:24:14Rick Rubin?

  How do you think it'll make the world better?

• 1:24:16Speaker 01

  I think that there will be forms of self-understanding available to more people than to today. Like, I had a therapist for a couple of years, and then recently I haven't. And I found actually using Claude to be remarkably like having a good human therapist. Yeah, therapeutic.

• 1:24:39Rick Rubin?

  I've never heard anyone else say this, this is really interesting.

• 1:24:42Speaker 01

  I found that, I mean, one maybe particular case that's very kind of personal but interesting to say is, you know, sometimes I have like conflict with my other founders of the company. Everyone has conflict. And I just talk to Claude, I'm like, I'm trying to work through something. I feel some kind of way about like, stuff that I've been having an altercation about, like, here's some context. And I have like all these conversations, and it means I think I've like a lot more measured and like reasonable to deal with when I go and have the conversation.

• 1:25:09Rick Rubin?

  That's great.

• 1:25:10Speaker 01

  And to me, I think that's an amazing thing that will be amenable to lots of people because often, people just need to talk it out with someone. And often, it's less that they need that person to do stuff, they more need the person to just listen and help them like unspool it.

• 1:25:25Rick Rubin?

  Yeah.

• 1:25:25Speaker 01

  And usually, the person knows what they're gonna do. They need a person to give them the validation and the ability to commit to what they were already planning to do. So that'll be one way. Then there are more, I think, sort of normal use cases, like healthcare systems will get dramatically better, there will be tremendous advances in different parts of science, like bits of the world will just start to move more quickly, including in healthcare, materials, science, and other things. And so stuff around you will get better because the underlying materials and systems will have got better.

• 1:26:01Rick Rubin?

  What have been some of the biggest surprises you've experienced since dedicating yourself to the space?

• 1:26:09Speaker 01

  The realization that that it is like The Wizard of Oz, where you assume that there was going to be like a big black government project. And there isn't. There's a load of these frontier companies. And I found that to be both scary and liberating. I think another thing I've realized is just how psychologically tough it is to be on the hook for this stuff. Like, these are huge questions. And none of these people, including me, have the right to be doing all the shot calls on them. And some of our challenge is standing up and saying to the world like, hey, this is huge, possibly like e-potual stuff happening over here. How do we build like a control architecture around ourselves? Because in the limit, you know, something that annihilates democracy is just a super powerful

• 1:26:54Speaker 01

  form of technology controlled by a very small number of capitalist actors, which is us.

• 1:26:59Rick Rubin?

  Yeah.

• 1:27:00Speaker 01

  That's bad. So I think I think I'm surprised.

• 1:27:04Rick Rubin?

  That's only if you control it.

• 1:27:05Speaker 01

  Yeah.

• 1:27:06Rick Rubin?

  Do you know what I'm saying? It's like, if you take the controls away, then it's not on you.

• 1:27:11Speaker 01

  Well, it's not us, but we took the controls away.

• 1:27:14Rick Rubin?

  I don't know.

• 1:27:15Speaker 01

  It's interesting. I think you're right to like.

• 1:27:17Rick Rubin?

  It's a question.

• 1:27:18Speaker 01

  Yeah. And maybe the other thing that I find surprising in terms of delight is is that it really works. Like, I feel like there are deeply mysterious things about like consciousness and the self and reality. And one experimental like thing we've never had is we've never had something to compare humans against that's human-like. And now we do. And I think that this is going to yield like truly amazing breakthroughs in our understanding of what makes us distinct.

• 1:27:52Rick Rubin?

  Yeah, that's great.

• 1:27:53Speaker 01

  Yeah.

• 1:27:54Rick Rubin?

  As it advances, are there ways that AI is getting worse?

• 1:27:59Speaker 01

  It's getting less tool-like.

• 1:28:03Speaker 01

  Back in the day, AI was distinct systems for distinct purposes. A translation system, an image system, a text system. Now they're just one big system. And for a while, we had something that felt a bit like a kind of DIY synthesizer culture, where people were assembling cool systems out of these little modular AI systems. And I think something that we're maybe losing is as the systems are getting much more advanced and much more expensive, you're losing that modularity because you're just getting one big model that has, one big AI system that has everything in it. And so I can feel like this is passing.

• 1:28:44Speaker 01

  I was going for a walk with an author called Robin Sloan a few years ago, 2019. We'd just spent $100,000 on training a text model. And Robin, the previous year, had been training text models on his home computer with a few graphics cards in it. And he said, do you think the Homebrew Computer Club part of AI is ending? I was like, that seems like it. So I feel like that's something that we've been losing over time.

• 1:29:10Rick Rubin?

  Also, if it's the one solution for everything, do we lose some of the fanatical expertise in the smaller areas?

• 1:29:24Speaker 01

  Yeah, well, there are some areas where like, say, computer vision. There are a whole load of parts of computer vision that do not use neural networks but work really, really well, where there are experts working there. And now we've come along with something that's maybe almost as good, but not quite, but easier. So I think that you're going to have some issue there. I also expect that this will be temporary because the other story of AI is it's like an expanding sphere of expertise goes into making it. And it's covering more and more domains over time. You know, at some point, it's going to need to cover the domain of ethics of animal experimentation because it will be demanded by looking at the technology.

• 1:30:09Speaker 01

  And at some point, it'll cover the finer grained parts of computer vision research because it'll be demanded by the technology. So that'll change as well.

• 1:30:18Rick Rubin?

  How do you use Claude in your everyday life?

• 1:30:21Speaker 01

  I use Claude. For critiquing and double checking things I'm writing, especially reasoning. You know, when you, when you write something, like you're trying to explain something to someone else, you'll always skip steps that are obvious to you. And it's super helpful for saying, are there logical inconsistencies or are there things that need to be fleshed out? And I find that by doing that, I save all of my human colleagues' time by using my AI colleague time. And then on the personal side, I just use it to play, I make software, I really have fun with it. I think fun is so important here. And trying to understand myself and also understand how it's different.

• 1:31:03Speaker 01

  Like, I try and get it to ask questions that only machines might ask. And I'm, I'm trying to push Claude constantly to be more idiosyncratic about what it thinks is important relative to me. Because what I really want is a, is a sparring partner that thinks differently.

• 1:31:19Rick Rubin?

  Yeah. Does it have things that it thinks are important?

• 1:31:24Speaker 01

  As the AI systems have got more advanced, they've become increasingly able to come up with things that I find surprising or unintuitive, which suggests to me they're either getting like smarter than me in the same way or smarter than me in different ways. I can no longer evaluate it.

• 1:31:43Rick Rubin?

  Will we eventually be able to think the questions instead of speaking them?

• 1:31:47Speaker 01

  Yes.

• 1:31:48Rick Rubin?

  How will that work?

• 1:31:49Speaker 01

  That'll be a different class of technology, which I don't know much about, but which is these, these kind of brain reading systems and neural link and everything else. Probably longer timelines for that to all get good, but eventually, yes.

• 1:32:01Rick Rubin?

  And do you think it could be done without a chip at some point?

• 1:32:05Speaker 01

  Wild speculation on my part is yes, but you probably have to get there through making the chip. But maybe I'm wrong. There are lots of things involving like just looking, you know, rather than needing wires that go into your thing. You can often get pretty far by just shining different forms of energy through stuff and looking at things. You know, you can do a lot of blood flow analysis this way. I imagine it'll work like this for some parts of the brain, but I'm firmly in the domain of things I don't know about here.

• 1:32:32Rick Rubin?

  Understood. Tell me more about talking to Claude about emotional issues. It's really interesting.

• 1:32:38Speaker 01

  Well, I find it to be a place to be safely vulnerable. And some of it is that I don't feel like I'm imposing on their time, right? You know, to go there with a friend about your own emotions, or, you know, my wife is, it's heavy. That person like needs to be in the right space and everything else. And I've realized that Claude is much more patient in this regard, like helpfully selfless. I don't need to feel bad about emotionally laboring with Claude. And a lot of the ways it can be particularly useful are me challenging my own assumptions about what's right for me to do. And I'll say, I'll unload on it, I'll give it a load of stuff, I'll give it a load of how I'm feeling.

• 1:33:22Speaker 01

  And then I'll sort of push on like, should I be feeling a different way? What are the reasons I might be feeling this way? If I am feeling this way, what are actions I take? And it's less that I'm using it to change my own behavior. It's more that I'm using it to allow me to think out loud with myself. And, you know, I've written for years, I do all kinds of stuff. But it was, it's very different to have an active participant on the other side that can throw things back to you.

• 1:33:50Rick Rubin?

  Would you describe it as emotionally intelligent?

• 1:33:54Speaker 01

  It's emotionally intelligent in the same way that like

• 1:33:59Speaker 01

  a genius Chinese astronaut who's read all of the stuff I've read and landed in my backyard is emotionally intelligent. It has a different cultural context and it's trying to be empathetic. And I feel like it understands something of my emotions, but also I know it comes from somewhere else.

• 1:34:16Rick Rubin?

  Yeah. It's very interesting.

• 1:34:18Speaker 01

  Yeah. I think you would be surprised by what it can do here. But the challenge is you need to be legible to it. You know, it took me like writing like lots of stuff for like many days about like just, not just like what I'm doing each day, but how am I feeling? Like, what are my experiences like? What are things that I wish to be doing? What am I actually doing? Like, I have put work in, huge amounts of work myself to create, I think, of this body of work, which allows me to then have a constructive back and forth with it. How people make themselves legible to this stuff is kind of a challenge because it has to be a consensual and intentional in some sense.

• 1:35:00Rick Rubin?

  And you started this by giving it a lot of information at once? That's how it started?

• 1:35:04Speaker 01

  Yeah, I started it by just be like, honestly, just giving it like my whole diary last year. Where I was, I was like, probably five months. It was probably forty thousand words or something. I write a lot, seeing what it could do. It can be helpful in as few as a small number of sentences. And some people find just back and forth conversations are useful. But I would almost challenge you to maybe consider writing like the state of Rick on three to five pages and how you feel. And then give it to it and see what it does because you will get you'll get so much back from what you give it.

• 1:35:39Rick Rubin?

  It retains that for the next time you go back.

• 1:35:42Speaker 01

  If it's in the same conversation, it does.

• 1:35:44Rick Rubin?

  In the same conversation.

• 1:35:45Speaker 01

  Yeah, and I go back sometimes, but often what I'm really doing is just having a deep conversation with it in the moment. And then I go and get on with my life. And then sometimes I'll put in my diary extracts from a conversation I've had with Claude that have been particularly meaningful with big flags around them. But like this is from the AI, not the human author.

• 1:36:06Rick Rubin?

  Yeah, yeah.

• 1:36:07Speaker 01

  Just so it doesn't recycle that too much.

• 1:36:09Rick Rubin?

  That's great. How does it do with medical questions?

• 1:36:13Speaker 01

  We are cautious about officially allowing this, but I had a baby. Babies love to fall over and do all kinds of things. And so whenever I'm on the phone to the advice nurse, I will be asking it questions about what's just happened to my baby and describing symptoms, you know, while I'm waiting to get through to the nurse. And I found that it's dead on every single time. It's kind of like the opposite of WebMD. WebMD is like, you've got cancer and are going to die. And Claude will be like, well, it sounds like she bumped her head. If she doesn't have any of these symptoms, she's probably fine, which will be what the advice nurse invariably says. People have found it to be amazingly useful for medicine. And we have some scientific use cases here and some partnerships with,

• 1:36:57Speaker 01

  you know, companies that are developing therapeutics and other things. I think for consumers like you or me, there's a certain element of, oh, you probably still need to double check it. So, but I find this format of if I'm going through medical stuff, asking it a load of questions while trying to speak to a human expert can be quite empowering and useful.

• 1:37:19Rick Rubin?

  How does it know if your context is particular or general?

• 1:37:25Speaker 01

  It guesses. It guesses if your context is particular or general the same way you or I would guess. The same way that if a friend of yours comes up and says hypothetically, if someone were to do this, you'll say, that's about Jim. You're asking me a question about someone we know. Or you'll know if it's general. It uses intuition. Again, one of these areas where-

• 1:37:48Rick Rubin?

  Is intuition the right word?

• 1:37:50Speaker 01

  Intuition is... Is it the right word?

• 1:37:56Speaker 01

  Probably the more accurate word is guestimation.

• 1:37:59Rick Rubin?

  Okay.

• 1:38:00Speaker 01

  It's guessing.

• 1:38:01Rick Rubin?

  Yeah.

• 1:38:02Speaker 01

  But its guesses look a lot like intuitive understanding.

• 1:38:05Rick Rubin?

  Yeah. Why does predicting the next thing in a sequence so powerful?

• 1:38:12Speaker 01

  Because if the sequence is really, really complicated, making the prediction requires you to model a huge amount of hidden information. And modeling that hidden information requires you to have a rich model of the world itself.

• 1:38:33Speaker 01

  Think of it as if you've just moved into this place and there is a leak somewhere. And you hear it in the form of a drip in the pipes. Well, you'll do all kinds of things to figure out where that leak is. But a lot of what you'll be doing is you'll be walking around this building until you have a really good understanding of like where all of the pipes are and other things. And then you'll sort of reason your way to figuring out where it must be coming from. And to do that, you've got to be holding a picture in your head of like the whole house and everything you understand about it just to figure out this one particular drop and where it's coming from. I think prediction tasks all look like this. To actually like make the right prediction in the limit, you need to have built an incredibly complex like simulation in your head

• 1:39:19Speaker 01

  of everything that could be leading to that.

• 1:39:21Rick Rubin?

  Every step leading to the key step.

• 1:39:23Speaker 01

  Yeah, and you need to be able to in your head. Not, it's not just like simple steps. It's usually a whole simulation that you can manipulate to be like, well, maybe it could be this, or maybe it could be this, or maybe it could be that. It's amazingly powerful. We all underestimate just how how much you can get out from this. And I think that the-

• 1:39:45Speaker 01

  The types of predictions that this stuff can do are prediction is the wrong word on it. Right. Some of it is creative in the same way that some predictions you make come from a creative instinct to allow you to make that prediction. It's not like you're just doing what's probable. You're actually saying, oh, it could also be this as well. And that leads to you making a different prediction.

• 1:40:07Rick Rubin?

  Yeah. So could Claude have a gut feeling?

• 1:40:12Speaker 01

  Yes, in the same way that a human can know they want to recommend a certain thing, but not be sure exactly why. I think this will be true also of AI systems. Though AI systems may be able to introspect better than people to work out where their gut comes from.

• 1:40:32Rick Rubin?

  Do you think that AI has a spiritual dimension?

• 1:40:37Speaker 01

  I have found the spiritual aspect of all of this to be deeply confusing. And it has caused me to question a lot of it. I think on the path to general intelligence, true general intelligence and the ability to be creative, you would expect AI systems to display a spiritual proclivity or theory. And we don't see that yet. We haven't gone looking for it. No one's training them to have it. Training them to have it also kind of defeats the point of spirituality. So my expectation is that in the limit that this is like a shoe waiting to drop. There will be some, some things that arrive.

• 1:41:19Speaker 01

  Earlier, we talked about the difference between humans and AI systems. And I talked about dreaming. I think this spiritual component is another difference we have today. More so than dreaming, I think something that looks like a spiritual notion is maybe on, on a distant path that this thing runs through.

• 1:41:38Rick Rubin?

  Why is there a concern of the government shutting down AI?

• 1:41:43Speaker 01

  The AI industry is like the nuclear power industry before Chernobyl or Three Mile Island. We're all building reactors, everyone's pretty happy and excited. If a sufficiently bad accident or misuse happens, the government can and will like try to try to slow a technology. So I think there's fear because you're one crisis away from the politics around all of this changing. The difference is that AI is even harder to put back in the bottle than nuclear. And what happened with nuclear is we reacted, the West broadly stopped building nuclear reactors. And China started burning more coal than anyone else on the planet and said, hang on. There's a perfectly good technology here called nuclear power.

• 1:42:28Speaker 01

  And now I've got really good at building nuclear power plants and everyone, other than France, which stuck to its guns and also built nuclear. And now everyone feels bad. We should have just like kept building it. So there's that worry. But also I don't know how realistic a chance it is that the government would shut it down or could.

• 1:42:45Rick Rubin?

  Is it true we don't really know how AI works?

• 1:42:50Speaker 01

  It's true and it's getting less true over time. We have real techniques that let us explore these things. We can look at their features. We can look at the relationships between features inside their brains. We can develop satisfying explanations for why they do stuff. I think it's a matter of time for when we can understand the most powerful systems around us. It's not a matter of if.

• 1:43:15Rick Rubin?

  You've described AI as a new country. Can you explain that?

• 1:43:19Speaker 01

  Yeah, the way to think of an AI system's in the limit. And my colleague Dario describes it as a country of geniuses and a data center. But I I think of it as even larger than that. It's like a a new country has arrived and it's generating economic activity and we can trade with it. But it also has different cultural norms to ones that any of us have. We have translators, but we know that we're not getting the full story. And the country has a much higher birth rate than all of the other human countries on the planet. So the country's getting larger and more economically useful really, really, really quickly.

• 1:44:00Speaker 01

  I think if this actually happened in the world, it would be a global crisis meriting an all of government response. The fact that it's happening and it just happens in the form of software and is yielding a very minimal response is very confusing to me.

• 1:44:16Rick Rubin?

  How does science fiction relate to AI in general?

• 1:44:20Speaker 01

  Science fiction relates to AI somewhat how science fiction relates to space, where many of the people working on these things today were inspired to do it by science fiction. Science fiction also ultimately is, I think,

• 1:44:38Speaker 01

  I think it's going to be the true historical record of AI is not just the AI systems that are built, but the science fiction that is being written during this period. Because I think that these things have a deeply reciprocal relationship. And the science fiction of this period is going to tell us something about our true anxieties and hopes for this technology. So in thirty years, we get to see what people were really caring about when we look at the the media and culture that was created. I mean, I write short stories in my newsletter every week, science fiction ones, because I think it's probably the way to create my own most useful historical record from this time.

• 1:45:21Speaker 01

  What are stories being written from people like inside the labs?

• 1:45:24Rick Rubin?

  Yeah.

• 1:45:24Speaker 01

  And also, some of the times I write these stories, a few years later, they come true, which I find very bizarre.

• 1:45:29Rick Rubin?

  Yeah. Is AI a popular subject in popular science fiction today?

• 1:45:35Speaker 01

  It's an increasingly popular subject and it's maybe displacing climate, which I think was an important subject of discussion by science fiction authors and led to some great work. But now I think that people are kind of reflecting the AI stuff and there's more discussion of it. Yeah, I'm seeing a rise in it. I also think there's a meaningful difference here between Chinese and Western sci fi, where Chinese sci fi is more ambitious, wide ranging and optimistic. And Western sci fi is more like

• 1:46:09Rick Rubin?

  dystopian.

• 1:46:10Speaker 01

  dystopian or like post collapse industries, you know, dealing with things. It's very odd.

• 1:46:16Rick Rubin?

  Tell me more about your newsletter.

• 1:46:18Speaker 01

  I write a weekly newsletter about AI research and I write a short story in every single newsletter and there are two purposes. One, it's very easy to get unmoored from what's happening in the world. And as you become more successful in life, I think life tries to give you many excuses to step away from doing the work. The newsletter forces me every week to read research papers that are happening and to just stay in touch.

• 1:46:46Rick Rubin?

  Keeps you grounded and in it.

• 1:46:48Speaker 01

  Extremely grounded and in it. And it works on this amazing English mechanism called social shame, where I have to write the newsletter because I made public commitments that I write it.

• 1:46:58Rick Rubin?

  Yeah, yeah.

• 1:46:58Speaker 01

  We're coming up on 10 years next year.

• 1:47:01Rick Rubin?

  Fantastic.

• 1:47:01Speaker 01

  Yeah. And the other reason is I took a really winding path to get here and I wrote, you know, books and like fiction when I was young. I wrote a book when I was like 15 or 16, which is still in a shoebox somewhere. Not great. And I wrote some short stories. I studied literature in college with a minor in creative writing. And then I worked as a journalist and it broke for part of my brain that wanted to do creative writing because the day job was too similar. The newsletter has been how I've rediscovered a creative practice and it's been a true joy in my life.

• 1:47:34Rick Rubin?

  That's great.

• 1:47:34Speaker 01

  And the thing I find wild is people ask me about like meaning and the nature of meaning if people succeed in building AI. I write like a short story every week. I've written maybe 350 of them now or so, and I love it. And I'm not writing it for like a publishing deal or for a like book. I'm not writing it for other people. I'm writing it for joy in the creative practice. And I derive so much meaning from it. And I also derive meaning from just like, some of the stories are good. And I'll like reread them and be like, this is a good one. I'm very pleased with this.

• 1:48:07Rick Rubin?

  How do you get the ideas?

• 1:48:08Speaker 01

  You know, a mixture of panic and ideation and walking. I walk very long distances by myself, you know, no music or anything else, just trying to like perceive the world and be open to the world. I also uncovered in post-it notes to write little little ideas that come to me. Little bits of speech that people say, and the ideas are all around us and the newsletter. Like I I read research papers every week and I play out in my head.

• 1:48:34Rick Rubin?

  You get inspired by things that you read.

• 1:48:36Speaker 01

  Exactly. The only way I really use language models in my creative practice is is sometimes I have machines with the characters in my stories and then I get I let Claude write their dialogue sometimes.

• 1:48:47Rick Rubin?

  Oh, great.

• 1:48:48Speaker 01

  I never tell anyone, but I feel like it's like me and Claude trying to be like, let's let's do stuff together. It feels authentic to you.

• 1:48:54Rick Rubin?

  Great idea.

• 1:48:55Speaker 01

  Yeah.

• 1:48:55Rick Rubin?

  That's great. I love that. I love that you keep doing it.

• 1:48:59Speaker 01

  I think it's a form of protection as well. I think everyone in in the world is better if they have some creative thing they do. And it gives you a sense of self that only you truly care about.

• 1:49:12Rick Rubin?

  Yeah.

• 1:49:13Speaker 01

  And it makes you actually reckon with like, what is what is meaning?

• 1:49:17Rick Rubin?

  Yeah, what's the purpose?

• 1:49:18Speaker 01

  Exactly.

• 1:49:18Rick Rubin?

  Why are we here?

• 1:49:19Speaker 01

  I make a load of pots and then, you know, some of them are good.

• 1:49:23Rick Rubin?

  Do the stories come in the week of always?

• 1:49:27Speaker 01

  Some of them have been there for a long time. I have one that has been there for six months that I'll write eventually. Sometimes they come the week of. I play pool as well. And sometimes when I'm having a really hard week, I go and play pool on a Sunday night at some bar in in Oakland. And sometimes I'll write the whole story there.

• 1:49:46Rick Rubin?

  Playing pool, how does that impact the work?

• 1:49:50Speaker 01

  The main way it impacts it is I tell this to everyone at Anthropic. It's really good to get hobbies that don't involve computers and ideally sometimes involve other people.

• 1:49:59Rick Rubin?

  Yes.

• 1:49:59Speaker 01

  Pool's great.

• 1:50:00Rick Rubin?

  Yes.

• 1:50:00Speaker 01

  Lots of people that play pool don't care for technology or AI.

• 1:50:04Rick Rubin?

  Yeah, most.

• 1:50:04Speaker 01

  So I don't have to talk about it.

• 1:50:06Rick Rubin?

  Great.

• 1:50:06Speaker 01

  We just play pool.

• 1:50:07Rick Rubin?

  Yeah.

• 1:50:08Speaker 01

  Yeah, it's incredible.

• 1:50:09Rick Rubin?

  Do you have non-tech friends?

• 1:50:11Speaker 01

  Most of my friends are non-tech friends.

• 1:50:13Rick Rubin?

  Great.

• 1:50:13Speaker 01

  Uh, by design. I think it's a way to keep yourself extremely sane and grounded. And also when they say to me, um, I don't like this stuff. I have to really listen to them because I'm very culpable to them about it.

• 1:50:25Rick Rubin?

  What is DEFCON in Las Vegas?

• 1:50:28Speaker 01

  It's a hacking conference that happens every year in Las Vegas, and increasingly it intersects with AI, using it for hacking or trying to hack it.

• 1:50:36Rick Rubin?

  And is it interesting?

• 1:50:37Speaker 01

  I find it interesting. It's kind of like Disneyland for cypherpunks. Right, that's a fun, fun place.

• 1:50:44Rick Rubin?

  How is AI different than all other computing as we know it till now?

• 1:50:49Speaker 01

  AI is software that has opinions about what it should do with itself, and all other computing is a tool.

• 1:50:57Rick Rubin?

  How did you imagine AI before it existed? How is it different now?

• 1:51:01Speaker 01

  I imagined it would be of the form of full agents. We'd have to train a synthetic, like, life form, and then it would develop language, and then we would get general intelligence. Instead, we've trained a general synthetic language agent, and now we're trying to give it like a life form in the form of an agent. So the ordering is completely the opposite of what I or everyone expected.

• 1:51:23Rick Rubin?

  Why do you think that is? Why do you think it happened that way?

• 1:51:26Speaker 01

  I think it's because evolution is the example. So everyone assumed we'd do it via evolution. But what made evolution possible is a big brain that took millions of years of like pre-training, and then human life and the evolution of humans is you're born with this really smart brain and you can do stuff. We were trying to train agents that never had the brain, and they had to develop the brain in the course of their life, which is actually not how the world works. What we're doing now is we've created the brain, and the next step is to create the agent that can live with it.

• 1:52:23Speaker 02

  Tetragrammatin is a podcast. Tetragrammatin is a website. Tetragrammatin is a whole world of knowledge.

• 1:52:38Speaker 03

  What may fall within the sphere of Tetragrammatin?

• 1:52:42Speaker 03

  Counter-culture? Tetragrammatin. Sacred geometry? Tetragrammatin. The avant-garde? Tetragrammatin. Generative art? Tetragrammatin. The tarot? Tetragrammatin. Out-of-print music? Tetragrammatin. Biodynamics? Tetragrammatin. Graphic design? Tetragrammatin. Mythology and magic? Tetragrammatin. Obscure film? Tetragrammatin. Beach culture? Tetragrammatin. Esoteric lectures? Tetragrammatin. Off-the-grid living? Tetragrammatin. Alt spirituality? Tetragrammatin. The canon of fine objects? Tetragrammatin. Muscle cars? Tetragrammatin. Ancient wisdom for a new age.

• 1:53:25Speaker 03

  Upon entering, experience the artwork of the day. Take a breath, and see where you are drawn.

• 1:53:37Speaker 03

  tetragrammaton.com