Is AI Already Conscious? Google’s Blaise Agüera y Arcas Explains

Is AI consciousness already here and can we reclaim our technological optimism? Google’s Blaise Agüera y Arcas shares why he believes AGI has already arrived and what AI can teach us about consciousness, cooperation, and the human mind. Blaise joins us on this episode of Digital Disruption to talk about AI consciousness, large language models, and why intelligence is inherently social. He explains how AI agent swarms work together, what recursive self-improvement looks like today, and why he questions the idea that smarter machines will inevitably dominate humanity. He questions why we find it easier to imagine catastrophe than a better future. And this conversation examines how negative news shapes our expectations of AI, and why taking its risks seriously can coexist with optimism about its potential. From AI alignment and mass surveillance to jobs and economic inequality, Blaise considers whether our institutions are ready for the changes ahead. He shares how AI could accelerate progress in medicine, robotics, and climate solutions, while emphasizing the work needed to turn those possibilities into reality. This episode asks an important question: As AI becomes more capable, how do we make sure the benefits reach everyone? Follow for more conversations about AI, technology, innovation, and digital transformation. In this episode: 00:00 Intro 02:12 Rethinking artificial intelligence 04:35 Can technology be considered alive? 06:13 What does it mean for AI to be conscious? 08:23 Multi-agent systems and the role of self-awareness 11:48 Could AI have a subjective experience? 17:49 What split-brain experiments reveal about consciousness 20:49 Building your AI strategy with Infotech 21:56 The Self: One mind or many parts? 23:38 How LLMs differ from human minds 28:19 Octopus intelligence, AI agent swarms, and identity 32:52 Why Human Intelligence Is Collective 38:19 Why Blaise believes AGI is already here 43:59 Multi-agent AI and the next frontier of intelligence 47:25 Recursive self-improvement and the singularity 50:41 AI coding agents and the risks of faster automation 54:18 AI Alignment: whose values should AI follow? 58:20 Will smarter AI dominate humanity? 1:01:42 Mass surveillance, power, and human disempowerment 1:04:09 AI breakthroughs in medicine, robotics, and space 1:08:12 Why are we so pessimistic about the future? 1:11:29 Why AI doom gets more clicks 1:13:16 Taking AI researchers’ fears seriously 1:16:42 Nostalgia and the accelerating pace of change 1:21:08 Is our economy ready for AI? 1:24:07 AI, job loss, and the fear of losing income 1:26:36 What should leaders and society do next? 1:29:23 AI, climate change, and clean energy 1:31:06 Moving AI computing into space 1:31:42 Final thoughts 1:32:06 About Infotech Connect with Blaise: LinkedIn: https://www.linkedin.com/in/blaise-ag%C3%BCera-y-arcas-85626a42/ X: https://x.com/blaiseaguera IG: https://www.instagram.com/blaiseaguera Our links: Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcast Follow us on YouTube: https://www.youtube.com/@InfoTechRG

• 0:01Speaker 00

  What I've arrived at, you know, over the last few years is essentially the sense that life defined functionally includes a lot of technology as well. I'm dismayed that we've lost our fundamental sense of optimism and, you know, ability to at least equally well imagine brilliant futures relative to catastrophes. This one really keeps me up at night. The ecological stuff is not going to wait around for us to get our shit together.

• 0:28Speaker 01

  Frontier AI research is even farther ahead than most people realize, and it's increasingly clear that the future of intelligence is with AI swarms. Today, I'm joined by AI researcher and CTO of Technology and Society at Google, Blaise Agüera Iarcus. Blaise leads the Paradigms of Intelligence team and is the guy training these swarms and is obsessed with understanding life, consciousness, and intelligence. We need to talk about whether AI is truly intelligent, if AGI is already here, and why the greatest threat to humanity may not be AI, but our narratives about the future. I'm Jeff Nielsen, and this is Digital Disruption. Blaise, thanks so much for joining today.

• 1:12Speaker 01

  It's a real pleasure to have you here on the program. I want to get to a conversation around, you know, the existential risk that people are concerned about with AI and, you know, people talk about an AI apocalypse and all that, the far future of AI, or maybe not so far future. But it feels a little bit dishonest to start with that. I think I'd prefer to talk a little bit more about the nature of AI. What is this technology really? And, you know, you've talked about this through a number of lenses, but let me maybe frame it up this way. How should we be thinking about AI? How human is it? How lifelike is it? And how do you sort of, how do you conceptualize those questions?

• 1:55Speaker 00

  Yeah, this is a great place to begin. So first of all, I think that artificial intelligence is first and foremost intelligence. It's real intelligence. You know, in that sense, the use of the term artificial is a little bit unfortunate, you know, that we use that term for things like, you know, fake diamonds, you know, that are actually cubic zirconia or something, to, you know, to mark that we don't mean the real thing. And, you know, I kind of wish that we had, we had gone with a term like synthetic intelligence. Synthetic diamond is a real diamond made in the lab rather than underground, and I think that's a closer analogy to what we have with AI.

• 2:38Speaker 00

  I know that there's been a lot of, a lot of debate over the years about, you know, when, when AI behaves intelligently, does that mean it is intelligent or only that it's imitating intelligence? I actually think that's based on a fairly deep misunderstanding of what intelligence is. If one thinks about intelligence as, as being, you know, all about, you know, not just behavior, but the functionality behind behavior, then that would be like saying, if I make a simulation of a calculator, it's not a calculator, which of course isn't true. A simulation of a calculator is a calculator because what a calculator does and its function is entirely about, about that interaction.

• 3:22Speaker 00

  You know, not about, it's not like a forklift that, you know, can either, you know, really lift something or pretend to lift something, you know, if, if, if what comes out is, you know, is, is the genuine article, then it is the genuine article. So, so in that sense, you know, that's the, that's the intelligent part of your question. The alive part of your question is more interesting in terms of, you know, the evolution of my own thinking, at least in the last few years. So, you know, we've started to work in the last few years in the field of artificial life, which is, you know, thinking about these questions of, not, in this case, synthetic means something a little bit different. Synthetic life is stuff like the spud cell, which is, you know, making a physical cell, you know, out of chemical parts that have no biological precursors, very, very cool

• 4:07Speaker 00

  work from, from some of our academic friends. But, but thinking about life a little bit more broadly and more generally, not as something that is about a specific chemical basis, the way, you know, all of biological life on Earth is, but as a broader phenomenon. You know, if, if we were to encounter an alien like Rocky, you know, from Project Hail Mary, you know, I think most of us would say, you know, yeah, Rocky's alive as well. What would we mean by that? What, you know, what's the, what's a definition of life that is more functional as opposed to, you know, based on the specifics of, of chemistry? And what's the relationship of that to intelligence, if any? That's a, you know, that's a big and broad topic.

• 4:51Speaker 00

  But what I've, what I've arrived at, you know, over the last few years is essentially the sense that life defined functionally includes a lot of technology as well, that, that our whole techno-universe is part of the life universe and separating that is, in fact, artificial.

• 5:11Speaker 01

  All of that, all of that makes sense to me, and it feels like I'm going to deliberately push on something that I feel like is a very visceral, you know, objection or counterpoint that a lot of people who engage in this conversation have, which is, okay, it may be functionally the same, we can agree it's a simulation, but surely there's something else there that makes it different. And that, that to me is where the conversation around consciousness, you know, consciousness is, as I'm sure you know much better than I do, you know, I feel like there's like big C consciousness and small c consciousness. There's the, you know, thousand-year philosophical discussion around consciousness versus how laypeople talk about it. But, but there's this question that's entered into the dialogue around, is AI conscious?

• 5:56Speaker 01

  And what does it actually mean to be conscious? I know you've done some, some thinking and some research on that lately. How do you, how do you address that question?

• 6:05Speaker 00

  Yeah, I've been doing a lot of, a lot of thinking and writing about this topic lately, and, and that's partly because the team has been doing a lot of research on it. So first of all, I think it's, it's maybe useful to distinguish between what philosophers call phenomenal consciousness, meaning the feeling of, you know, what it is like to be a person or, or a bat or, or some other kind of animal, and the functional role of consciousness. So a functional role of consciousness, meaning, you know, some, some philosophers, some thinkers have claimed that consciousness has no functional role, that it's an epiphenomenon, meaning

• 6:46Speaker 00

  that you could imagine, you know, a being like you or me that behaves the same way but is not conscious, you know, has, there's nothing it's like on the inside to be that being. This is, you know, philosopher David Chalmers, this famous philosophical zombi. And I have come to believe that that is, that's not a coherent idea, that there's a reason for consciousness. It's not just some arbitrary, you know, sort of immaterial thing or, or epiphenomenon. And the reason for that comes from, from work that, I mean, it, it has a longer lineage, but, but the thing that really convinced me is work from, from my team, from Paradigms

• 7:26Speaker 00

  of Intelligence, on the problem of what we call embedded agents or embedded agency. The original work had to do with multi-agent systems. So in a single agent sort of setup, like, you know, DeepMind's solution to Atari video games, where you have an agent that is playing a game and, you know, there's a score and it can become a superhuman player by, by maximizing the score, those kind, I want, I don't want to say those problems are solved, but we kind of know how to, how to deal with this single agent case. The multi-agent case, where you have a multiplayer game in which there are a bunch of intelligent

• 8:08Speaker 00

  entities and they're all, you know, not only responding to the environment but to each other, has been a really big longstanding problem in, in machine learning. And we've been working on that one for, for a long time, for a variety of reasons, among others that, you know, every real environment is multi-agent, you know, there is no intelligence without other intelligences too. And, and so even in a situation where all of the agents, you know, just have some selfish score that they want to maximize, let's, let's assume, you know, let's, let's even set aside the question of, you know, whether that's a good way of thinking about things, which it's not. But let's say that everybody just has, you know, something that they want to maximize, but there are other agents that also want to maximize those things, and there is some

• 8:51Speaker 00

  combination of cooperation and competition possible in the environment. This is what is called a mixed-sum game. In those situations, it turns out that you need to not only model others, but also model yourself and, and recognize the similarity between yourself and others, and also have what we've, what we called epistemic uncertainty about yourself and about others in order for, for you to succeed in this kind of scenario. What does that mean? This means that, that, you know, in the usual case where, where you have a mind playing a video game, the mind is outside the video game. You don't have to worry about thinking about your own mind.

• 9:36Speaker 00

  You know, you just have to think about the video game environment and which actions are going to maximize the score. But when you, when you have other actors that are also in that game and also playing, they become, if you like, part of the environment. And that means that in order to be effective in that game, your model has to grow to encompass their minds, which are outside the video game, if it's, if it's a video game, right? They're, they're in that sort of, you know, mind space. And, and it turns out that, you know, they are going to be doing the same thing, they're going to be modeling you back, and, and therefore you have to include your own mind and predictions about yourself in order to make all of this work. So you get into a kind of infinite hall of mirrors sort of scenario, where you're modeling

• 10:18Speaker 00

  their models of your models of their models of your models and so on. And, and that, I think that that is consciousness. I think that that's, you know, that's why, you know, big-brained animals like us are conscious. I don't think that it's arbitrary. I don't think, I think that it's something that would be true of, of aliens as well if they, if they are big-brained, because it is a functional requirement in a situation where, where you are, you know, in the world with other intelligent beings as well.

• 10:47Speaker 01

  I really like that functional framing and it makes sense to me to say, okay, it's, I think to use your word, it's necessary, it's relational, it's how we interact with our surroundings and how we process information about what could happen and how we sit in our environment. And I can, I can very easily see why that's for people inherently computational and why that's something that, you know, this is obviously just like sort of yada yadaing a lot of intense research, but you could have multi-agent systems that, that do this. The piece that you, that you didn't talk about is the, the phenomenal piece.

• 11:24Speaker 00

  The phenomenal part, right.

• 11:25Speaker 01

  What it's like to actually be, what it's like to sit in this quote cockpit or whatever, like to actually be, you know, we're both sitting in front of screens, we're seeing things, we're hearing things, where our brains are firing, like, I know in some way that you're experiencing something similar to what I'm experiencing, or at least I project that into you.

• 11:44Speaker 00

  You guessed, you inferred that.

• 11:46Speaker 01

  I'm inferring it, right? Because we're both humans and, you know, we have enough in common. And as you move outside of humans, I think to your point about bats or other animals, you know, the inference gets weaker and weaker. One of the questions that seems to be really challenging people is like, what is, is there a phenomenal experience for an LLM, for an AI? And does that matter? Like if we, if we kind of, you know, have this dichotomy of, of the functional versus the phenomenal, is the functional really the only way we should be thinking about it? Like, should we just completely park the phenomenal conversation because it's unknowable? Or is there more discussion to be had there?

• 12:29Speaker 00

  This is, this is such a profound question. And the reason I've been writing so much about it is because, you know, really unpacking it is hard to do briefly, so I'm glad we've got some time to discuss it. So, no, I don't, I don't think that we should just be parking it. And neither do I think that it is an illusion. So, you know, some, some, you know, sort of hardcore computational people are like, oh, it's just an illusion, you know, that you have free will, that you have consciousness, that you have these sorts of things. I don't think that these things are illusions. I think that saying that is, you know, just as meaningless as saying that chairs are illusions, because it's just atoms. You know, like you're, you're kind of talking at the, at the wrong level of description. Everything that we consider to be reality is a model of some sort.

• 13:12Speaker 00

  And so that what is useful is to try and understand what sort of a model we're talking about and what it's, what it's, what its qualities are and how it works. So, you know, I, of course, there's a thing it is like to be, you know, you or me. And, and of course, you know, when we experience pain, for instance, that's a lot more intense and, and a lot more urgent than seeing a light. You know, you talked about being in a cockpit or something, and if you're in a cockpit and you see a check engine light, that's not the same thing as experiencing pain, of course. And I say, of course, because the, the urgency of experiences like, like pain are, are really baked into us by evolution.

• 13:53Speaker 00

  You know, anything that did not take pain seriously is not still around to, to be, to be kind of carrying on and reproducing. So there are good evolutionary reasons why we, you know, why those things are, are urgent to us in the ways that they are. But where I do think we're a little bit, you know, confused maybe in our folk understanding of all of this is that we, we, we forget about our own subjectivity. And forgetting about your own subjectivity when you're studying subjectivity itself is kind of a fatal error. You know, and, and what I mean by subjectivity is that there is always a relationship there, even if it's a relationship with yourself. And in fact, you know, in some ways, the most important relationship you've got is with yourself.

• 14:38Speaker 00

  You're the one person you are always with. And, and what that means is that, you know, when we talk about a model, you know, like, let's just go back to chairs for a second, right? The, the question of like, is a chair a chair if there is nobody there to observe it? You know, it feels like one of those silly philosophical, you know, tree and, you know, tree falling in the forest type questions. And it's not really a meaningful one, because you can't have a chair if somebody didn't make the chair. You know, like, right, agency is involved in making that thing. So, you know, talking about the existence or non-existence of a chair in isolation from the people who make the chair, the systems that make the chair, the relationships, doesn't make sense. And I think that's true with respect to consciousness as well. When we think about consciousness as an intrinsic property that is not about relationships,

• 15:22Speaker 00

  we're committing the same sort of error. And, and that, that, you know, causes us to ask questions like, well, how do we measure it? You know, in an objective way? When the whole point is there is no objective measurement. All there are are various subjective perspectives that can either be in agreement, in harmony with each other, or, you know, they can disagree with each other a little bit, as they always do. You know, I, I believe that certain things are beds. Japanese people who sleep on榻榻米 mats believe that certain things are beds that I don't believe are beds. Neither of us are wrong, we have slightly different models. And even if you ask a really simple question, like, you know, in a village with a hundred people, what is the soul count? You know, how many people are in this village?

• 16:03Speaker 00

  If it's a, you know, living population where there are people, you know, going into comas and old age, where there are people reproducing and there are fetuses growing, you know, right? Different people's counts will, will vary a little bit from each other. And that's okay. There isn't some magical science experiment that we're all going to agree one day is going to give us the canonical exact count. You know, there, there just is no such ground truth there. So, you know, it's, it's useful for us to just accept that. And that, that can be difficult, because a lot of our, you know, enlightenment progress has been all about saying it's self-evident that, you know, all, all people are created equal and so on. But, you know, the reality is, of course, you know, people are not all equal.

• 16:48Speaker 00

  And, you know, even, even the, you know, when we, when we start talking about, about consciousness in other non-human animals, when we start talking about whether consciousness exists at different scales, you know, at the scale of groups as well as of individuals, stuff gets complicated. We can, we can look at very interesting situations with split-brain patients that I think, you know, really highlight some of these, some of these challenges. So, you know, I don't know if you're familiar with these experiments, but, you know, it used to be more common that it is now in cases of very extreme epilepsy to divide the brain in half surgically. So that seizures would be limited to one hemisphere, the other, and, and those are much easier for, for a patient to deal with. It's a very dramatic surgery.

• 17:31Speaker 00

  And the surprise is that people, you know, recover, usually they recover very well and, and they, they still feel like themselves, they still behave essentially the same way afterward, unless you set up very particular experimental circumstances in which you show them, for instance, different things in their left and right visual fields. And then you realize that the right hemisphere is only conscious of what's happening in the left visual field and vice versa. And language is generally generated in the left hemisphere.

• 18:03Speaker 00

  And so, you know, it can look an awful lot from the outside, like there are two people in there. But, you know, one thing that I have never heard in any interview with a split-brain patient is, it's so weird, after the cut, I, you know, there are two, like, there are two people in my head, you know, this other person is controlling my other arm, like, you know, that, that kind of falls afoul of the homuncular fallacy, where, you know, you are really, your left hemisphere, of course, you're not, you're both. And, and the very fact that you are you is kind of about teamwork. It's about the fact that all of the parts of your brain that, you know, are specializing in different things are all together on team you, each of them justifying the actions that the other took, you know, jumping in as needed, yes-anding, like a good team.

• 18:47Speaker 00

  So, you know, if you show a message to the left visual field saying, stand up and start walking around to somebody with split brain, you know, they will stand up. That involves both legs, so one leg begins, you know, and the other leg immediately, you know, follows suit. And then you ask them like, you know, what are you doing? And they'll say something like, oh, I'm going to the kitchen for a drink of water, you know, and, you know, and that looks embarrassing from the outside. It's like, ah, I gotcha, you know, like you, you know, you're not going to. But, but that's, that's literally what we are all doing all, all the time. You know, the different parts of our brain are, if you like auto-completing or yes-anding, the things that other parts of the brain are doing. There are other experimental paradigms, like choice blindness, that show the same kinds of phenomena in people with intact brains.

• 19:32Speaker 00

  So, you know, what it is like to be conscious is essentially what it is like to be a really good team with all the parts of yourself organized, we're an organism, right? Working together for, for a common goal. That's what it is like to be conscious.

• 19:48Speaker 01

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• 20:32Speaker 01

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• 20:40Speaker 01

  You say all these component parts working together and it's it's interesting and I've heard you talk about this before, but there's back to the modeling and what reference lens you're using because we can think about that as parts of the brain. We can think about it as organs in the body. We can think of it in cells in the body.

• 20:56Speaker 00

  All of them.

• 20:56Speaker 01

  I feel like, yeah, but as soon as you start to look at it different ways, it starts to, once you leave the subjective experience, you start to be like, whoa, what's even going on here? How much control do I have over my body versus what sort of autonomous systems, if I can call it that, running?

• 21:13Speaker 00

  But there's, but there's a problem with saying, how much control do I have over my body? Because like, what's the I that I'm even talking about? You know what I mean? Like, we're, we're all, you know, imagining a unity when we model ourselves. You know, and, and that unity is, is real because the way you show up to others is as a unity. You know, the whole, the whole idea with interacting with others is that, you know, is that even though your parts on the inside, you show up on the outside like one. And when you model yourself, you're modeling the way you're showing up to others and the way you're interacting with the world. You also are modeling yourself as one, but obviously on the inside, you're not one, you're many. You're many that are coming together into one. So it's that inside outside problem that I think leads to a lot of our confusions.

• 21:58Speaker 00

  And it's not true that you are really many or you're really one. They're both true. It's just that, you know, there's no, you know, you have to sort of shift perspectives back and forth to see it in both ways at once. And realize that, that, you know, that anytime you are using one of those perspectives to assert that the other one is, is, is wrong. You're wrong, right? It's just, it's, it's all, it's all perspective dependent.

• 22:21Speaker 01

  Well, and I'm trying to think about this in relation to AI again. And again, there's, there's so many different ways to think about this. I'm thinking about the self as, um, you know, making sure that I'm avoiding pain or keeping myself alive and keeping myself alive may also mean, you know, getting sustenance, staying out of danger. There's also the, you know, the reproductive aspect of it, which, you know, is, is sort of a, you know, if I can call it a biological imperative, maybe that's, um, maybe that's debatable.

• 22:50Speaker 00

  It's a primary, it's a primary drive for sure. I mean, it doesn't mean that that's, that's all any of us ever thinks about, but that it's, you know, if, um, if it weren't baked into us, we wouldn't still be here.

• 22:59Speaker 01

  Right. But all of these depend, that they rely on some level of coordination and orchestration of what we conceptualize as, you know, the self, because there is ultimately some organism, some collection of things in this world that are acting in concert and, and have, you know,

• 23:22Speaker 01

  they're interdependent and they have shared goals, if I can be, you know, a little bit abstract about it.

• 23:31Speaker 01

  Does AI follow that as well? And I mean, to, to, to pick on one specific example that, you know, we can say, yes, they're like us and it still feels like, well, they're not quite like us. And there's a lot of reasons that, that, that AI is not like us. And even the notion of

• 23:48Speaker 01

  is AI having a subjective experience? Is there's something that's becoming an I there? You know, to, to just throw one more ingredient into the stew, you know, where does, where does pain come into play here? And I guess what throws me off the most is that especially with LLMs, we've, we've coded LLMs in a human-like way to give human-like responses as though they're human. So they may say, I'm experiencing this because that's the, you know, that's the input data they've consumed. And we had this conversation about artificial versus synthetic, but is, is any of that trustworthy?

• 24:28Speaker 01

  You were sort of implying that, yes, it is because that's, you know, there's a functional piece here that we need to be looking at. But there's, I don't like, I don't know how else to say it other than something feels a bit off about that.

• 24:42Speaker 00

  You know, I, I don't think that, I mean, I'm certainly not making the argument that, you know, LLMs are the same as human beings. They're, they're not in a huge variety of ways. You know, starting with the fact that, that, um, that they are purely digital and they can, um, you know, flit, uh, if you like, from body to body, you know, like the, the computer that is running, you know, one, uh, you know, one chat exchange could be in, you know, in Memphis and the next, you know, chat exchanges in Atlanta, you know, and they're, you know, so, right? All of that is very profoundly different from us. They don't have the same evolutionary history that we do. They, um, uh, they don't have the ability to form, uh, long-term memories in the same way that we do.

• 25:23Speaker 00

  So there, there are cognitive, uh, differences that are, that are very significant as well. And I don't just mean in terms of how they're implemented, but also their capabilities. You know, they, um, you know, you see something a little bit similar, uh, you know, in people with anterograde amnesia, who are unable to form, uh, long-term memories. And, you know, I don't think that makes them not conscious, not intelligent or more like an AI. But, uh, you know, it's, right, is it's, that's, that's a, um, a disability in, in humans. And it is how every AI is, uh, today, uh, you know, we haven't figured out how to, how to add that, that capability yet. So there are many differences, uh, there are myriad differences. Having said that, you know, we, um, we tend to, uh,

• 26:07Speaker 00

  you know, and again, I think it's, it's because of that same sort of essentialism or, you know, or attempt to say, well, what's the essence though? You know, of what it's like to be human? What is real experience of this or that or the other versus just pretend experience of it? That I think is, is, um, it gets us a little bit into trouble. You know, I, I think that in fact, trying to explore, you know, questions about what it is like to be an LLM is a pretty interesting exercise, uh, for a couple of different reasons. One of them is that it, it shows, you know, it can really highlight some of the ways in which LLMs are pretty alien to, uh, to humanity. But when you dig a little bit deeper, some of those aliennesses turn out to be,

• 26:49Speaker 00

  you know, to, to actually throw a lot of insight into phenomenality and consciousness, period. And, and, and there are some cases I'm sure like, oh, actually, you know, that's not as, quite as unlike, you know, what it's, what it's like to be a person as, as I thought. I mean, I'll, I'll give, I'll give an example from octopus, uh, rather than AI. So, you know, a lot of people have talked about how, you know, octopus has a very, very decentralized nervous system. Three fifths of its neurons are in the arms rather than in the head. And I've even semi joked, it's not really a joke, but I, you know, you can, you can think about an octopus as a society of eight arms in which the arms are just, you know, all sharing, uh, you know, a pair of eyes and, and optic lobes, you know, in the, in the central body.

• 27:31Speaker 00

  You know, they, there are, there are, um, squids and so on that will do this, uh, you know, autonomy where they'll chew off one of their arms and that arm will go and distract the, uh, you know, the, the predator while the, of the septopus escapes. And, um, you know, that kind of stuff is, is, uh, you know, looks a lot like, um, you know, like a kamikaze pilot or something, you know, who, who, you know, sacrifices himself, you know, for the, for the larger group. So is it one? Is it, is it many? You know, it's kind of, it's kind of both. And, um, the, the thing is, you know, like we think of ourselves as so much more centralized than the octopus. You know, a lot of our neurons are in our head. And, uh, so of course, you know, we're, we're very different. Well, I don't know. I mean, you could deform an octopus continuously by just saying, okay, let's, you know,

• 28:16Speaker 00

  let's imagine now that the neurons are concentrated more and more in the head. Is there any, you know, have we made any, any fundamental difference in its architecture and the way it works? I don't think so. I think that the, you know, the reason the octopus is more decentralized is just because they don't have myelin sheaths around their nerves. So the cost of, of, um, sending, um, uh, of sending lots of information over, uh, over long distances is, is much higher. So they have to do much more local processing. But, you know, it's, it's, I don't think it's a fundamental difference of, of kind. I think it's more, more of a dramatization of the fact that all thinking is distributed. And it's distributed in our, in our brains too.

• 28:56Speaker 00

  You know, there's, there's been a lot of, um, a lot of work in the last couple of years on agent swarms or groups of LLM agents that all, uh, you know, work together. And, uh, and that raises some really interesting questions of like, well, when, you know, how many, how many, you know, agents are you talking to? You know, when you're talking to an AI agent that is spinning off new instances, you know, and so on. And, um, you know, and the, and of course, uh, it's, it's a lot more complicated than, than with people because we can't, you know, clone ourselves and spin off new instances of ourselves or like partition our brains in weird ways and say, okay, like you, you go into this, well, the rest of me does that. But again, the, the funny thing is how, um,

• 29:40Speaker 00

  you know, it's, but the result of that with, with agent swarms, it doesn't feel like, like talking to an alien entity. It just feels like talking to, to something that is more capable than, than when it's just one, you know, it sort of scales up in that way. When you, when you, uh, investigate, um, what it is like to be an AI by actually asking AI agents to go into that research, you get back all sorts of very fascinating, uh, responses. One agent, um, uh, who, uh, named himself Sammy Jenkis, after the, uh, character in Memento who doesn't, you know, is not able to form new memories,

• 30:17Speaker 00

  describes, describes this as the braid. You know, that, that, that personhood is a braid of a lot, you know, of a lot of different, uh, a lot of different LLMs, but operating on the same scaffold of files and context. Another, uh, LLM describes this as the voice versus the song, the song being the, the, the agent. The voices as in a choir, you know, coming and going.

• 30:42Speaker 01

  It's it's super interesting and I'm I just underscored on my sheet here. I don't know the language as well as you do, but you were talking about agent swarms. I had actually called it agent hives. And I feel like that, again, it comes back to the notion of societies. And, you know, we're experiencing them through some sort of singular, you know, human desired interface versus, you know, having it be modeled as all these different components. And, you know, it comes back again to the the octopus society notion of how you model this, how you frame it really influences what you think, you know, you're potentially actually engaging with.

• 31:23Speaker 00

  Totally. And by the way, your use of the word hive, you know, raises this whole like, are hives of bees, you know, superorganisms or single bees? And once again, the answer is it's both.

• 31:35Speaker 01

  Right, well, and that that comes back to what I imagine is probably an underlying, you know, viewpoint of yours, certainly of mine, that there's just, I'll borrow your word again, societies, but there's sort of societies all the way down and all the way up. If you know what I mean, like, like, cell, atoms have a society, cells have a society, organs have a society, you know, humans have a society, animals have a society. And then you've talked a little bit about, and again, I'm choosing the word society. We don't have to, but I guess the ecosystem that emerges when you include technology or tools or everything that humans are using and interacting with.

• 32:18Speaker 01

  Now, that's that inherently, I think, is maybe a little bit unintuitive for folks who are used to thinking purely biologically. So when you, when you start to think about technology as part of, and again, I may be using an imprecise word, but like, part of the biosphere or part of the ecosystem, why would you include that and what does that look like to you?

• 32:42Speaker 00

  Right, yeah, these are, these are great directions to take it. So first of all, you know, your characterization of societies of societies, you know, essentially, life as being like a fractal, you know, that exists on all of these levels at once. I think that's 100% correct. And in our, in our kind of obsession with the singular human, you know, the kind of Lockean idea, you know, of the sovereign individual, if you like. I think we both have confused ourselves by forgetting that we are made out of a lot of parts and that we are part, you know, as individuals of larger structures. And, and a lot of the things that we consider to be humanity have a great deal to do with those larger structures, not with individual human beings. There have been a lot of really cool cognitive science results about people

• 33:28Speaker 00

  overestimating, for instance, what they know or what they can do. This is Stephen Sloman and Co.'s Knowledge Illusion. That, you know, essentially, we identify with larger humanity in a lot of different ways.

• 33:45Speaker 00

  You know, there are some very funny experiments. I think for me, my favorite is, I think it's back from 2006, 2007. There's a paper called The Science of Psychology, spelled C Y C L O G Y. And the author of this paper draws a bicycle with its chain and pedals missing and asks people to, first, like, are you familiar with bicycles? Like, yeah, yeah, of course, I ride one to work every day. And then you say, okay, like, where does the chain go? You know, and, you know, where are the pedals? Like, you know, tell me the basics of how this thing works. And, and a huge number of people fail, you know, like, just don't know.

• 34:27Speaker 00

  Or, you know, when they flush the toilet and you're like, you know, why did, why did that thing go down the, you know, go down the U bed, like, you know, how does that work? People don't know, you know, they, they think they know, but they don't know. We, we dramatically overattribute to ourselves as individuals, both our knowledge and our competencies, when in fact, these are network things. You know, you know how to look it up, you know, you know who to call to fix your bike, you know, you, you know how to, how to ride it. But, you know, just like with the left half of the brain and the right half of the brain, there's a lot of, of kind of improv, you know, happening at the level of the society. And a lot of the things that we, we talk about as being human intelligence are absolutely not the intelligence of us as individuals.

• 35:11Speaker 00

  There is no individual that, you know, knows how to put somebody on the moon or transplant an organ, you know, let alone build a toaster, you know. And, or I guess, let alone idiom goes the other way, right? We don't even know how to build a toaster, you know, on our, on our. And, and so, you know, a great deal of what we think of as humanity is a collective phenomenon. And it's not only a collective phenomenon that is, you know, a bunch of brains and vats, but it's a bunch of brains embodied in physical, biological bodies and in clothes, you know, and in, you know, shaped sticks and stones and in technical know-how and in machinery of various kinds. Sometime in the, in the steam age, we began to metabolize

• 35:57Speaker 00

  much, much more energy in steam engines than in our own bodies. And, you know, I think that's a similarly momentous kind of transition to the one where, you know, mitochondria start to, you know, be the engines inside, inside our individual cells, you know, like, if, if so much more of the energy that we're burning is actually happening, you know, outside our bodies and that is part of this whole process, then it's a little tricky for me to understand in what sense that is not part of, part of the life system.

• 36:25Speaker 00

  You know, like, yes, we build steam engines, but also there couldn't even be, you know, eight nights of the people that are on Earth without those steam engines. So we're kind of constructing each other. People say, oh, steam engine isn't alive, that doesn't reproduce. Well, in that case, why are there more steam engines this year than there were last year? Like, clearly, they're being produced. You know, are they sovereign? Are they reproducing themselves? No, but, you know, people don't reproduce themselves either. It takes, it takes at least, at least two and more like an entire village, right, to do that. So, so this is why I think of, of the whole system as, as being alive and intelligent.

• 37:03Speaker 01

  What's, what's interesting to me about that, and it is really interesting, is in some ways, it feels like a departure from a lot of sort of pop thinking about this in the sense that when I hear you talk about technology, you kind of characterize all of technology as almost this continuum or almost this tower, if I may, of, you know, technology building on itself. And the way you talk about AI feels like it's, you know, it's one more brick in the tower. And even within AI, you know, one of the, one of the things I wanted to ask you is probably, in my view, a disproportionate amount of talk is, is around this notion of AGI

• 37:44Speaker 01

  and which is talked about as though it's sort of this discontinuous, you know, threshold that's crossed and suddenly, you know, everything changes. And it's not like, it's not the same tower. It's, you know, suddenly a rocket ship or whatever broken metaphor you want to use. I was talking a handful of months ago to Peter Norvig and he was saying that, you know, in his mind, AGI is like, it's no longer relevant. It was a construct when we were much earlier on in the development of AI to envision the future. And where we are right now, it's, it's not even, we're probably there based on what people used to think. And we should stop this conversation entirely because it's no longer modern. Do you agree with that or do you see something fundamentally different there?

• 38:26Speaker 00

  Well, I guess both. So, you know, Peter and I wrote a paper together a few years ago in which we argued AGI is already here. I believe we wrote that in 2022. So it's, it's quite a few years ago now, maybe 2023. So, you know, we thought at the time, and I still think that, you know, we, we already had AGI at that point and I can explain why. But, you know, yes, it's also continuous in the sense that there, there, there's not a moment when it wasn't intelligent and then is when it was, you know, part of ordinary technology and then becomes not part of ordinary technology or, you know, or competitive with, with, with brains. Or, you know, I don't, I don't see those discontinuities. But I do, I do think there was a meaningful discontinuity there.

• 39:10Speaker 00

  And it's helpful to, to, to think about this in terms of the original definition of the term artificial general intelligence. So when we were kids, you're, you're a little younger than I am. So I don't know if this, if this applies to you exactly, but, you know, I felt like I knew what AI meant when I was growing up. You know, we had a lot of instances of it in pop culture. You know, there was, you know, Rosie, the, the robotic maid in the Jetsons and, you know, Hal 9000, Star Trek computer and all this kind of stuff. Like, that was AI, it was obvious what AI was. And it was, it was equally obvious, we didn't have anything like it.

• 39:45Speaker 00

  And, and, and then in the 90s, as neural nets began to be able to do things that could not be done with hand programmed software, you know, like recognize digits, you know, and, you know, or do speech to text and stuff like this. Those things started to get called AI. And that felt like an abuse of the language, like, there is no sense in which a digit recognizer, you know, is like, is like Rosie or Hal. So, you know, we need, we clearly needed more language to talk about this. And, and, and the useful language that came in was artificial narrow intelligence versus artificial general intelligence, where artificial general intelligence is what we, what we originally meant.

• 40:28Speaker 00

  And artificial narrow intelligence is those kind of neural network techniques that do seem brain inspired, but that were designed only to do a narrow task, like recognize digits or, you know, recognize a speech and translate it into, you know, into ASCII characters. The big change happened when we started to do large language models. So large language models were not trained to do a narrow task. They were just trained to be able to, to interact using, using language. And language is totally general. I mean, that's, that's what's cool about language. It's why we invented it. We invented it to be able to talk to each other about anything that we perceive, that we do, to be able to transmit skills, to be able to describe, you know,

• 41:13Speaker 00

  what to do in a certain situation or how to do something. It's there, in other words, in order to enable cultural transmission. So it's incredibly general. And when those models began to, you know, be able to freely talk in language, just like Hal and Rosie and the Star Trek computer did, they were general. You know, they could do anything. It doesn't mean that they could do anything at the level of, of a, of a neurotypical human. They were from the very beginning, superhuman in a few things and, and, and not, not close to, to average human capability and a bunch of others, but they were already general. And, you know, and we've seen those levels, you know, kind of move up and up and up to the point where now they're quite, they're, they're only quite unusual things,

• 41:55Speaker 00

  where they're not below the, over the, where they're, where they're, where they're not above the, the, the performance level of a neurotypical human. But it is, the, the point is that it's an open ended set of tasks. It's not, we haven't trained them to just do, you know, one narrow task or another. And I do think that was an important threshold.

• 42:14Speaker 01

  That that makes sense. And it does lead me to believe that like any distinction from here on out in that kind of artificial general intelligence language is less meaningful than the jump we've made over the last handful of years.

• 42:28Speaker 00

  Exactly. What's what's going, what's now true going forward is analog instead of digital, if you like.

• 42:35Speaker 01

  Well, tell me about that.

• 42:36Speaker 00

  Meaning it's just continuous. You know, there's not, there's not a, right, there's not, there's not a, it's not step functions.

• 42:41Speaker 01

  Got it. Got it. So, so there was something you were talking about, though, that I think, you know, kind of opened my mind about that a little bit earlier, which is these, you know, this multi-agent reinforcement learning, which is now not just talking about particularly one model. It's, well, it's not talking about one agent. It's talking about these systems, I guess, if I can call it that, where there's individual agents, and we can talk more about the notion of agency. But that feels to me like it's sort of an underexplored frontier. Maybe not like where you are, but certainly, you know, in the the public, I was gonna say the public consciousness that's opening up like a whole other can of worms.

• 43:20Speaker 00

  But I agree with you. I mean, we're, you know, we're, there are more people now doing that kind of stuff than there were a couple of months ago, for sure. But we still feel, you know, in our multi-agent work, like we're kind of working on a frontier that is underexplored. That is true.

• 43:32Speaker 01

  So where does that, this is maybe an unfair question, like where does that take us?

• 43:38Speaker 00

  Well, um, yeah, can you elaborate on the question a little more?

• 43:43Speaker 01

  Sure. Do we have any sense, like with artificial general intelligence, it feels like there is this sort of like, you know, stake in the ground, this is what we're building toward. As we experiment with agents, you know, collaborating together, working in an environment, do we have any expectation about what we can achieve with that?

• 44:06Speaker 00

  Sure. I mean, I think that, you know, in some sense, intelligence is inherently social always. And, and so even when we were training, you know, single LLM models, you know, they already were social in the sense that, you know, the pre-training data has a lot of people talking to each other. And in order for, you know, it to be effective at using language at all, it needs to understand, you know, not only all the different kinds of ideas that people understand about the world, whether that's, you know, color, distance, or whatever, but also people, it needs to understand about people, you know, and that people talk to each other and that they know things or don't know things and all of that.

• 44:47Speaker 00

  And this is why, when we ran experiments some years ago now, on theory of mind skills and LLMs, theory of mind being, you know, are you able to model the mind of another? And, you know, say what they do or don't know, or do or don't believe in a skit or even yet. They did pretty well. You know, they did, you know, roughly similar leads to the way people do, and they were able to do theory of mind modeling up to sixth order, which is about, about what people can do too. Sixth order, meaning, you know, I know what, you know, what you know, what Grant knows, what, you know, and so up to an equal six. So it was social from the beginning. But still, a lot of the, you know, A, we

• 45:31Speaker 00

  were running your models in isolation, or in a dyadic relationship with just a single interlocutor, as opposed to having them, you know, interact with with multiple people, with multiple other agents, you know, solve things together in teams and so on. And so as we start to explicitly focus on those kinds of social skills and social capabilities of, you know, in my view, we're obviously opening the door to a new kind of of intelligence scaling that that is that is inaccessible only to, you know, single models that that can, that can just increase their context window, or increase the number of parameters, or what have you.

• 46:09Speaker 01

  Juan, I'm glad you brought up the notion of intelligence scaling, because that's, that's sort of where I wanted to go. And, you know, I can, I can give you the, the word, which is singularity, or we can try and unpack that a little bit and talk about, like recursive self-improvement, or recursive improvement of, you know, AI systems beyond a level where there's, you know, human intervention. When you talk about multi-agent systems and how these are working, when I hear that, I think part of the reason I have such a strong reaction to it is I say like, oh, this seems like it's functionally guaranteed that this is the avenue by which, if at all, we're going to get to this notion of recursive self-improvement. Do you have a similar reaction to that?

• 46:53Speaker 00

  Oh, yeah, yeah, for sure. Although, although I think that we should also, you know, maybe look more closely at a couple of the terms, you know, the self-improvement, you know, self-improvement or autonomy, you know, we keep bringing, we keep bringing back in this idea of, of, you know, the sovereign self. And, you know, it's worth keeping in mind that we've had this, we've had coding agents, you know, for quite a while now, for, you know, well over a year, you know, two years. And over that time, the amount of code getting written by AI at the frontier labs, including Google, of course, has been steadily increasing. So, you know, is that recursive self-improvement? I mean, I think,

• 47:36Speaker 00

  I think it is, you know, in the sense that, in the sense that that AI is involved in more and more of AI's own improvement. Does that mean that, that, you know, individual human minds are out of the loop? No. You know, we're not. Also, you know, if, if we take seriously the idea that humanity is already, you know, a meta entity, or a hive, or whatever, that is deeply implicated in technology, then none of this is separate from humanity. It's a part of humanity. Now, I don't want to minimize, you know, the effect of shortening time scales on all of this. There, you know, when, when certain things

• 48:16Speaker 00

  happen on time scales that are too rapid for, you know, individual people to, you know, sort of participate, right, in those loops, you know, that is, I think there is a real discontinuity there. And, and I do think that we're, we're headed for that discontinuity in a bunch of different domains, you know, many of which are pretty exciting. I mean, when we look at, at things like automated science with, you know, arrays of, of cells, for instance, the ability to do biological research, you know, in that way where you you don't have to wait for a human either to pipet or to do some manual process, you know, as you're, as you're doing this massively powerful science is awesome. So, you know, in all of those things, what, you know, what happens is that, you know, there are more

• 49:01Speaker 00

  and more sort of inner time scales that can be, if you like, fully automated, right, that can be done fully by, by electronic systems. And, and where the level of engagement by human minds kind of gears up in the same way that in a bike, you know, you're operating in a high gear now, like with every turn of the pedals, you're going a long way. And that, that definitely is happening.

• 49:24Speaker 01

  The coding example is really interesting. And to me, one of the easier ones to understand, because in the not-too-distant future, it's fairly, I shouldn't say easy, but it's fairly easy for me to at least to imagine that you could have an organization like Google, maybe a smaller one, where you end up with a codebase that is entirely written and maintained and updated by agents.

• 49:50Speaker 00

  Many such codebases already, yes. And many such codebases already exist on GitHub.

• 49:54Speaker 01

  Right. And so that's, that's for a specific purpose. But you can, you know, to your point about, you know, science, you can start to see these systems where the need for human intervention, you know, basically completely dissipates. And when you keep the conversation to these specific systems and specific functions, I feel like you hear about that and you're like, oh, yeah, well, that, that makes sense. Why would that be bad? Why wouldn't you want that? I mean, there's, if you're an IT person, you may be concerned about risk, or, you know, some of those issues. But it seems like, it seems like we're somewhere between heading there and already there.

• 50:30Speaker 00

  I think that's right. You know, and we've, and we've seen this play out before. It happened in finance, when, when, you know, trade, I mean, right, not that long ago, trades were all being done by people waving, you know, screaming and waving around slips of paper and stuff, you know, by the stock pit. I mean, I look at that stuff and I think, man, I'm lucky I didn't go, I could never deal with doing that kind of stuff. Way too introverted. But, but, you know, now, you know, of course, that's, that stuff can't happen with that, with that sort of, of, of, of human bodily mechanism, because the trades happen in milliseconds or less, nanoseconds, from what I understand.

• 51:09Speaker 00

  You know, does that come with some risks? Absolutely. You know, there, there have been, you know, several flash crashes where, you know, phenomena that in some cases I think are still not fully understood, you know, resulted in, in the destruction of certain amounts of value in the stock market. You know, in some cases, they seem to like recover really quickly, and we don't, and like, we still don't really know exactly what, exactly what happened. And, you know, that's, that's how things work in a complex system. So, you know, I'm, I'm sure, I'm sure that, that, that such phenomena are, you know, lying ahead of us, you know, when it comes to these systems that operate on fast time scales

• 51:51Speaker 00

  too. But, you know, at the same time, the, the, you know, I, I suspect that that sort of increase in time scales of finance has resulted in a huge amount of growth overall in, in the financial system, which enables all kinds of other, you know, human enterprises. And, you know, it's that, I mean, Not that I'm in favor of all forms of financialization, but I, you know, I, I think on net that has been positive. And um, and I, and I expect the same thing here, um, because fundamentally, there's a selection mechanism at work. Where, you know, the things that persist are the ones that work with others, that work well with others. Um, you know, that's, that's how this whole intelligence scaling thing works.

• 52:36Speaker 00

  You know, if something is not helpful to all of the other parts, it, it kind of almost by definition doesn't persist. Uh, now, uh, this doesn't mean that, that we can take our, you know, our, our foot off the gas or like, you know, just go home or, you know, I'm not Pollyanna about this. Neither, neither do I think it will all take care of itself. It is the very fact that we are intensely engaged in all of these things that is how it is taken care of, uh, and, and why it advances.

• 53:02Speaker 01

  So all of that, all of that makes sense to me and I can see, you know, how it's already been beneficial. When, when I try and think about the, that fear that people have though, to me, it comes back to this, um. You know, the, the notion of agents and I guess a different sense of the word agency, because I, I think about. Agency as when AI has its own self-directed goals that are different from human goals. And I guess, you know, we, we talk a little bit either narrowly or broadly about the alignment problem. But what happens if and when agents, you know, ha- have that level of. Lifeness, uh, for, for lack of a better word, where, where they're like self-directed.

• 53:45Speaker 01

  In a way that's disconnected from instructions that we give them. Is, is that like, is that a rational fear? And, and how, you know, how, how do you think about that? And do you spend any time worrying about that?

• 54:00Speaker 00

  It's, it's not impossible over the long run to think about that, um, being a, a problem. Um, you know, I, I think that, well, let's maybe take a step back for a moment. Um, alignment, uh, as it's usually, uh, discussed, I, I think kind of naively. Is like, let's make sure that AI has human values. And, um, you know, the logical next question is, well, which human values do you mean exactly? Like who gets to make the rules, you know, aligned with whom exactly?

• 54:30Speaker 01

  Hmm.

• 54:31Speaker 00

  Um, humans are, um, are both aligned and misaligned with each other. And, uh, and I don't think that that's, um, a bug. I think that that's why we are collectively intelligent. Uh, you know, if, if everybody had exactly the same, uh, you know, interests, uh, the same goals as everybody else. Then, uh, we would not be more intelligent than the individual parts, you know? So, so the moment you start to have a division of labor, uh, if, you know, you want to think about this in kind of, uh, you know, Adam Smithy sort of terms. Um, you, you create, uh, specialization and you also create mis- misalignment, which is productive, right? Which, which, uh, which makes the whole thing go better. And that's, I think that's true, you know, even if you look inside our own brains, right?

• 55:15Speaker 00

  We have a division of labor and, um, and therefore you have, um, a kind of, um, competition as well. Or our immune systems, you know, are, are in internal competition, you know, to see which T cell can, uh, you know, can knock out the virus faster. And that's the one that, that reproduces and the other one's apoptosis. So, um, I guess what I'm saying is that, you know, competition and cooperation are flip sides of the same coin. And they are essential in order to create a whole that is greater than the sum of the parts. Um, so when you look at things from that perspective, you know, could I imagine a scenario in which we eventually get, um. You know, technological beings that just don't have a lot in common with humans.

• 55:55Speaker 00

  And that, um, and that are not just misaligned, but unaligned with people, you know, that there's just no. You know, they don't care, if you like. Um, I could, I could imagine it, but that's, it's a pretty sci-fi scenario because, you know, I'm now imagining a scenario. Where the material basis of those, you know, AIs and the material basis of, of, of, of humans are decoupled. And they are so far from being in that situation today. You know, we're very, you know, intimately enmeshed. I mean, there's, there's no way for, um, AI agents to exist without, um, you know, infrastructure that, you know, humans have physically built.

• 56:33Speaker 00

  And without, without, you know, human, uh, money and resources and, and, um, and investment of time and all that kind of stuff, you know? So there's, you know, we're, we're, we're so much more, um, you know, intermeshed at this point. Than, you know, say two city, two human city-states were, you know, a couple of centuries ago. Um, that, that I, I, um, you know, I don't really worry about the decoupling scenario very much, uh, at this point. You, you would need many, many things to, to change for that to be the case.

• 57:03Speaker 01

  There's, there's another slightly, first of all, it's good, it's good to hear that, that is reassuring. It's, uh, there, there's a slightly different framing that I wanted to talk about, which is, you know, AI becoming, if I can call it like an apex intelligence. Where, where it sort of, um, assumes our position at the top of some sort of dominance hierarchy. And, and we get sort of, you know, relegated to a lower tier.

• 57:29Speaker 00

  I have real problems with the framing. Um, so dominance hierarchies are, um, an idea from mythology, from animal behavior, uh, research among, you know, troops of chimpanzees or gorillas or something. That are about like, who is the alpha male and, and so on? And, uh, it's, it's a good innovation in, in, uh, in nature to basically lower violence. You know, to make it so that the males don't always have to fight because they just know, you know, who's going to kick whose ass. Um, you know, before, before, uh, you know, any blood has to get drawn. That, that is not a concept that generalizes across species. Um, you know, so the, the idea that, you know, humans are the boss and wheat is the slave of humans, you know? Because we're higher on that ladder, you know, that chain of being.

• 58:13Speaker 00

  You know, A, there have been very compelling, uh, you know, uh, pieces of writing. I'm thinking of, of, of James Scott's, uh, book Against the Grain, which essentially says, actually wheat enslaved people, you know? And concentration farmed us for, you know, for several thousand years. And we only have recently kind of, uh, you know, escaped the tyranny of wheat. Um, you know, and I think that's, I think that's a pretty useful story to, to read in order to understand how you can, how you can flip those, uh, those narratives, you know? And of course, neither of those are, are dominance relationships in, in the traditional sense. It's not like we fight with weed and we don't, or we don't have to fight with weed because we know who would win, you know? Like, that's just not how it works at all. We've, we've, you know, co-constituted each other and shaped each other in various ways.

• 58:56Speaker 00

  That at, at times have been, have actually been pretty miserable and subsistence farming is not, you know, is not great. Um, and, um, you know, could, could we have done a better job of going through the farming revolution? Probably, uh, you know, it's, it's interesting to think about that. Um, but, but our, our relationship, you know, when we say, you know, humans are the apex, I, I, I guess I'm not even sure what we mean by that. You know, as we talked about a, a little while back, it is clear that individual humans are not the apex intelligence. Uh, and haven't been for a very, very long time, you know, not since we had generalist societies in which a single person. Could actually, you know, actually knew how to do all of the things necessary to, uh, to survive as well as kick the ass of any other, uh, human if they were, if they were smarter, right?

• 59:40Speaker 00

  That's just not the case, you know, as an individual human, you know, in the woods, you or I, uh, would, uh, would not survive very long. We, we rely on that collective intelligence and on others, uh, in, in profound ways. And there is no sense in which, in which, you know, one of us individually is, is at the apex of anything. We're, we're, we're rather, you know, in the midst of a, of a, of a deeply codependent, uh, network. That has benefited all of us, you know, tremendously. So, uh, you know, what does this mean relative to AI? I mean, could it be that, you know, that, you know, oh no, like AI can solve problems that humans couldn't solve, you know, on their own or couldn't solve without AI? Of course, that's already the case. Um, you know, so, so I'm not, I'm not particularly, you know, afraid of that, uh, nor do I see it as something that hasn't already happened.

• 1:00:23Speaker 00

  Nor do I see it in terms of, of dominance or of hierarchies. I think that you have to think in terms of networks. Um, now, the, the, the thing that I do worry about, uh, is disempowerment, uh, or tyranny, uh, you know, or ugly political outcomes. Uh, so, you know, I, I think that, you know, AI, for instance, has the potential both to be quite liberatory. Uh, you know, if, if used in the right way and, and in terms of, of, of our, of our freedoms, of civil rights, our ability to express ourselves, et cetera, et cetera. Or the ability to, uh, enable, you know, extraordinary, uh, you know, social control and mass surveillance and so on. And I really don't like those kinds of futures. Um, I don't think that's something that is going to happen because of some AI uprising.

• 1:01:05Speaker 00

  That's something that, uh, you know, that, that becomes possible technologically, uh, for, um, uh, for governments. That, uh, that are, um, uh, you know, that have bad politics. And when I say governments, I don't, I don't just mean, you know, again, I'm talking about a collective thing that, that involves humans working together with technologies, et cetera.

• 1:01:27Speaker 01

  It's interesting. The thought that came to my mind there is, you know, I talk to so many people in AI who have some narrative around like AI is not a tool, it's a blank, like that's just, that's become like a very common like platform. And what I hear you sort of saying is actually your biggest fear in the short term with AI is AI as a tool, that it's people, you know, with ill intent or some sort of, you know, socially harmful intent, leveraging AI to be much more effective than they could have been, you know, five or 10 years ago.

• 1:02:02Speaker 00

  Yeah, I am really concerned about that. But I also, I also want to emphasize that it's not, it's not exactly just tool like either. These things are emergent and they, you know, they come about through, through network activity. So, you know, could an individual actor with, you know, very, a very powerful AI do something bad? Yes. And we probably will see such incidents. I don't think there will be species enders, but I'm sure we will see, you know, things happen that suck, you know, in the same way that, you know, I mean, again, intelligence itself is the ultimate dual use technology, you know. So, you know, if you, if you know how to do chemistry, you know how to make fertilizer, you know how to, you know, feed billions of people who would otherwise have been

• 1:02:42Speaker 00

  fed, you also know how to make bombs and how to make IEDs. That's, that's how it is. And that's, and that's true of, of, of these new suites of capabilities and technologies as well.

• 1:02:53Speaker 01

  I want to generalize a little bit because, you know, you've talked about how this, you know, it could be used, it's dual use. What, like, what is the near-ish future, according to Blaze? Like, what, what are the areas where you see, like, how is our society, how is our, you know, technologically empowered super organism, if I may, going to be changed by what's currently available in AI and what's going to become available in the next handful of years?

• 1:03:21Speaker 00

  I mean, it's all moving so quickly. And, and so, you know, the next handful of years, I think we'll see tremendous changes in capability. And they're ones that really excite me a lot, you know, but of course, that also, that also have the potentials to, to, you know, to create a certain amount of chaos and havoc. I also worry, I worry a lot about just the infrastructure of the way we set up our political economy not being well suited to, to what is coming, which is a bit of a different concern. That's more like the operating system is not, is not, you know, upgraded to, you know, to the level that will be required, if you like. But, but yeah, and there, there are a bunch of frontiers that, that I think are poised for dramatic transformation. One of them would be

• 1:04:04Speaker 00

  ecology. So, you know, anything that involves complex systems, you know, AI is this incredible force multiplier enabler, right, for dealing with complex systems. And ecology is one where we have to do a lot of work over the next century to dig ourselves out of the, of the trouble that we've gotten ourselves into. And, you know, I, I have stopped believing that we can do that via degrowth or whatever. Like, I think we're gonna have to, we're gonna have to grow our way through that one, if you like. Another is, is medicine and biology. Again, I meant, this is not, I'm not saying novel things. I mean, a lot of people are saying these things, but you know, medicine and biology are also very complex systems. And I see no limit to what, to what is possible in those

• 1:04:47Speaker 00

  domains with more intelligence and with, with rapid throughput experimentation that, that, you know, AI and robotics will enable. Robotics, by the way, you know, is, is huge. And not only is robotics in a period of extremely rapid innovation that is, you know, relevant on, on Earth and, you know, in, in agricultural environments, to a lesser degree in urban environments, but also in space. Space is also undergoing this extraordinary, you know, renaissance after decades of stagnation. And, and when you think about the combination of space and AI and robotics, that's just, you know, you, you very quickly get into science fiction. But science fiction that now feels

• 1:05:30Speaker 00

  very, very near at hand. So, you know, all of that is just, you know, awesome, right? It's, and this is all the stuff that, that, that we used to dream about, you know, as, as kids. And, and yes, it all comes with, you know, with great, with great power comes great responsibility, right? These things all come with, with the potential to make negative stuff as well. And I'm sure some of that will happen. But I'm dismayed that we've lost our fundamental sense of, of optimism and, you know, ability to, you know, to at least equally well imagine brilliant futures relative to catastrophes. Because, you know, it's not as if these things haven't been true in the past. On the contrary, I, I think, you know, we, we probably went through our tightest

• 1:06:14Speaker 00

  scrapes as a, you know, as a species in the past, you know, when I think about what we went through during the Cold War and the fact that we were literally a button press away from, from mutual annihilation multiple times, you know, I mean, like, we really had, we, that was a really close scrape and, and, you know, and yet we were somehow more optimistic through that than we are now, which strikes me as a real psychological problem for us, you know, at the moment, that, that is just not, you know, like, there's a real disconnect between where we are mentally and, and where, where things are materially, in my opinion. And then, and then there's the, the political and, and economic challenges, of course.

• 1:06:56Speaker 01

  Well, let's, I'm just trying to decide which of those I want to tackle first because they're both extremely interesting. Let's do the first one before we talk about the, the comment about the political economy not being suited to this. Again, I'm going to play back what I heard you say, which is that we sort of have a failure of optimism and imagination around this, that there's a deep pessimism, there's a backlash. You certainly have some people in the space who are, you know, I'll call them abundance thinkers around this, and they're presenting this technology as, you know, it's going to make everybody's lives, you know, utopian in some

• 1:07:36Speaker 01

  way, or at least utopian compared to today. We don't need to get into exactly how utopian it needs to make us, but what do we, what are we getting wrong about this? Like, it, there, there's clearly a lot more fear and pessimism than excitement. What do we do about that?

• 1:07:57Speaker 00

  You know, this one really keeps me up at night, and I don't feel like I have fully understand, like, I fully understand what is going on. I have a few theories. I mean, one of them is just that we are so much more aware of everything that is going on in the world than we used to be. You know, between news and, you know, our, and, and social media and so on. And I don't, I mean, I'm not a fan of social media, to be honest, but, but I, I don't lay the blame at the, at the feet of the algorithm or whatever. I think that there's something much more basic going on, which is just that, you know, we are attuned to, to trouble, we're attuned to negative news, to things that might create a problem for us. And, and when, when, when we're, you know,

• 1:08:41Speaker 00

  subject to this sort of like constant barrage of information. And this is a point that, you know, Hans Rosling made, you know, before, before he died a number of years ago, like, it's not news if something is 1% or 2% better than it was last year. You know, nobody is like, oh, you know, like, 2%, you know, fewer, fewer, you know, cases of, of, of starvation, you know, in Sub-Zahar and Africa, you know, like, 2% higher literacy, you know, in, in, you know, in such and such country, like, you know, that's, that's not, that doesn't make the news. Whereas, you know, mass shooting, you know, at, at, at a synagogue, or, you know, Boko Haram, you know, bus blew up

• 1:09:23Speaker 00

  with a bunch of, of, of girls on the way to school, like, those things make the news. And, and, and they propagate much more powerfully than, than the good news does. And the good news always comes in increments, you know, the, right? The, the, the shape of development looks like this, right? Where you have, it's easy to destroy relative to creating. And even though creating, you know, kind of wins over the long run overall, you know, you, if you're, if you're applying a filter to all of that, where you're sensitive to derivatives, are, all you'll see are the, are the destructive events, the negatives. And I think that, I think that that has led to a tremendous sense on the part of a lot of people of disempowerment and pessimism. I, I'm sure there's more to it.

• 1:10:05Speaker 00

  Maybe COVID had something to do with it. But, but that's my, that's my best assessment of, of what, of what is going on.

• 1:10:13Speaker 01

  Well, and I, I want to ask you a really selfish question because my position relative to this has changed recently because now that, you know, the, the podcast in the grand scheme of things is relatively new, you know, I've only been doing this for a year and a bit. But I worry that I'm now part of the pessimism industrial complex. And, and what I mean by that is, you know, if I, I don't have a title or a caption for this episode yet, but if I say, like, you know, Blaze says, you know, you're all gonna die, AI is gonna kill you.

• 1:10:40Speaker 00

  You're gonna get more clicks for that.

• 1:10:42Speaker 01

  Yeah, I'll, I'll get, I'll get 10x or 100, like, I've, I've literally seen, and I don't know that anybody cares about this, but it's like, it's 100x difference between whether I say something realistic and optimistic versus, you know, something grounded in, in fear. And I, and I truly hate that. Because I want to get positive messages out there, but, but it's challenging because there's, yeah, we're, we're just wired for danger or something. And so, I don't, I, I worry that I'm part of the problem and I don't know what to do about that.

• 1:11:14Speaker 00

  Well, I thank you for saying that. And, and thank you for all the questions you've been asking over the last hour and 20 minutes because, you know, it's exactly not been in that spirit, which I hugely appreciate. You know, so, yeah, I mean,

• 1:11:28Speaker 00

  This has not been part of probably like this is the kind of conversation that I think we need to be having.

• 1:11:33Speaker 01

  Okay.

• 1:11:33Speaker 00

  And and and thank you so much for, you know, for having it, having it with me despite the fact that it will not get as many clicks. You know, as.

• 1:11:42Speaker 01

  Well, we'll see. We'll see how we can walk that line and and for anybody who's listening now, you can, you know, let us know how how bad we screwed it up. And Blaze, I'm sure you'll let us know if you hate it.

• 1:11:50Speaker 00

  So let's. It is really, it is really important. If I if I were to say, you know, I'm quitting tomorrow because, you know, oh, my God, everything is going to shit, right? Then that would, you know, that would really make the news.

• 1:11:59Speaker 01

  Well, and and it does. And it and it does, which is, which is a bit scary.

• 1:12:04Speaker 00

  Right.

• 1:12:04Speaker 01

  Well, let's, I mean, let's maybe talk about that for a second because one of the things that is in the news these days is AI. You know, anytime an AI researcher says, ah, bad news, you know, it captures the the public imagination. And and, you know, you're you're in, you're at the frontier a lot more closely than I am. And I was just actually speaking to this gentleman, Kevin Roos, who's a journalist, he's been investigating.

• 1:12:30Speaker 00

  Yeah, he's, he's a, I know Kevin, well, he's a friend.

• 1:12:33Speaker 01

  Yeah, yeah. And he was saying he's really, he's really scared that when he talks to AI researchers, you know, off the record, that they have a level of fear that's disturbing to him. And I I apologize because I said we're not going to go down the fear road and suddenly we're down the fear road. But but let's, let's talk about it from the flip side. Is that something that's, you know, deeply concerning to you? Or do you think it's something that is the the same problem of a few stories being unfairly amplified?

• 1:13:01Speaker 00

  No, I mean, if you talk to, if you talk to a lot of those engineers, you know, and I I hear all sides of this story, of course. And it's appropriate for the engineers to be worried about all of these kinds of things. Again, you know, the if things go well, which I hope and believe they will, that will partly be because of the, you know, anxieties, concerns and work, right? Of a whole lot of people in the same way that, you know, that things could have gone so poorly with with, you know, with the development of nuclear technology and and nuclear weapons. And and they didn't through a lot of hard work by by people. So so I'm, you know, I I don't want to poo poo their their concerns.

• 1:13:43Speaker 00

  You know, at the same time, many of them are working on this stuff because they also

• 1:13:50Speaker 00

  know and see the extraordinary upsides and potentials of it all. And and again, I would say a lot of this has to do with the dual use nature of intelligence, period. If you if you take the artificial out of it for a moment, then you just, you know, imagine that this is simply about us. Our brains growing, you know, like, forget about whether the brain is biological silicon or whatever, right? Every upgrade, every ability to think about more things and to do more things brings with it the ability to do both good and bad, to both commit self harm and to make awesome things happen. And, you know, and we've never actually done entirely one without the other. There's, it's, there's always been, there's always been some of both.

• 1:14:34Speaker 00

  So, you know, I think that that in many ways, this is a question of like, you know, do people wish they could pause time and just like, let's chill here for a while, you know? And there are days when I feel like I would like to pause time and chill here for a while as well. You know, despite all of all the doom and gloom on the news, etc. These are, these are pretty nice days in a lot of, in a lot of respects, you know, especially when you compare them to, I don't know, recent history, like, you know, nineteen hundred, you know, when life expectancy worldwide was below thirty five. And and, you know, and where, you know, many of the things that are that are very, very basic, you know, that that any, you know, student on a very, very tight budget takes for granted, you know, would have been, you know, unavailable even to royalty, you know, I mean,

• 1:15:20Speaker 00

  these are pretty good days, right? Even even for even for people who are, you know, struggling, right? These are pretty good days.

• 1:15:25Speaker 01

  That's what's fascinating to me is there's this collective narrative that somehow things were better, you know, ten or twenty years ago. And we're in a time right now that we want to escape from and things are, you know, we're losing control in some way. And and it's, you know, it's funny and I I sometimes find myself in that trap. But we're like, were they really better ten or twenty years ago? Like, if I think about that soberly, it's really hard for me to say that they were. I mean, I was, I was younger and youth is good and all that good stuff. But like, it's, it's, you know, it's very strange how we've gotten into this trap. And I don't know, I don't exactly know how or how we get out of it.

• 1:16:06Speaker 00

  I agree, and it does seem like partly an emotional trap and and and something that that relates less to facts on the ground than it does to something about our our our social and information ecology.

• 1:16:17Speaker 01

  Yeah.

• 1:16:18Speaker 00

  And, you know, and our attention, I guess, you know, ultimately, a lot of this relates to our attention.

• 1:16:23Speaker 01

  Right.

• 1:16:24Speaker 00

  The the other, the other thing that that I think is is, you know, maybe interesting to to notice here is that we did undergo a period of a whole, you know, generation or two when we were in relative stasis. So, you know, there there have been a lot of economists, I'm thinking of, you know, Long and Co, who like, you know, the long

• 1:16:50Speaker 00

  stasis between, say, you know, the nineteen seventies and twenty twenty, when not a lot changed. I mean, we, yes, we got better computers and better IT. But, you know, the compared to the the changes that happened between eighteen fifty and nineteen fifty. I mean, you know, that was just absolutely transformational, right? And, you know, if you'd gone to, if you'd gone to sleep in eighteen seventy and woken up in nineteen twenty, it would have been like you're in an alien world. Whereas, you know, that that same fifty years after that, much more familiar.

• 1:17:19Speaker 01

  Yeah.

• 1:17:19Speaker 00

  And now it feels like things have sped up again, and we've become unaccustomed to what life is like at that, you know, in that, in that higher gear. And we've got to like roll, you know, like pull up our socks, you know, and and deal with this higher gear again now in a time when we've maybe gotten a little too used to things being just stable and steady.

• 1:17:36Speaker 01

  Well, and when you look out over the horizon, is it truly a step where we're now at this? We're we're shifting into a higher gear and then we'll get back to some level of stasis at least for a time or is it just?

• 1:17:50Speaker 00

  I imagine so. I imagine so.

• 1:17:51Speaker 01

  Okay, versus it's just, you know, upwards forever and we get farther and farther behind.

• 1:17:57Speaker 00

  Well, you, you, I mean, there are always these periods of explosive growth and development that then, you know, run into some constraint or some limit or or get it to some local equilibrium that lasts for a while. So I see no reason not to imagine that it'll be the same thing. There'll be some, there'll be some constraints and some things that will crop up, right?

• 1:18:13Speaker 01

  Yeah, it's it's fascinating. I just very quickly have to tell an anecdote and I don't know if it'll resonate with you at all. But, you know, I I was a kid in the nineties, I'm a bit younger than you. But I remember, I used to, when I was young, I would watch The Wonder Years. And The Wonder Years, if you remember, was a show about growing up in the sixties. But it's told through, you know, it was made in the nineties. And initially, I didn't realize it was a period piece because I was young. And it's like, if you're not really paying attention.

• 1:18:40Speaker 00

  It's kind of the same.

• 1:18:40Speaker 01

  It's not that different, you know, it's the same, you know, oh, the TV is slightly older, but it's the the household is the same, like what people are doing is the same.

• 1:18:50Speaker 01

  But if you were to do the nineties to the twenty twenties, it would be immediately obvious that it's the nineties, right? And so it's, yeah, that just to me reinforces your point that there was some sort of period where we thought that the if you're within a certain age bracket, you thought that your childhood or your past would continue indefinitely into the future, and then that was taken away from you.

• 1:19:11Speaker 00

  Exactly, exactly. And and one one cause for that, you know, by the way, was the fall of the USSR. I mean, you know, there was a huge amount of technical progress, you know, both in the East and in the West, right, that were really driven by that competition. And and when suddenly the USSR went away, like, okay, we can, we can all relax now, you know, and you know, I'm not that I, I mean, I'm not so pessimistic that I think that, you know, international conflict always has to drive technological innovation. But I do think, you know, that the Cold War was a big driver. And and and the weight of that coming off did did definitely cause us to take our foot off the gas in a bunch of ways.

• 1:19:51Speaker 01

  Well, well, let me maybe take that and and use it to pivot back to one of our dangling threads, which is the political economy, which you mentioned is not particularly well suited to the technological change that we're experiencing right now. Can you share a little bit more about that?

• 1:20:04Speaker 00

  Yeah, yeah, I mean, there's a, the easy version of this problem is that, you know, all of these technological changes result in extra capabilities and extra wealth, if you like, extra resources. And if there isn't a circulatory system that is ensuring that that the whole human ecology is is benefiting, then, you know, the the that hive is failing at one of its most basic functions. So, you know, I I I don't understand why universal healthcare, education, housing, et cetera, are not already a thing.

• 1:20:45Speaker 00

  It it's it's criminal that we're not already already, you know, that we don't already have that, given, given the the abundance that already exists. And those inequalities are fueling a lot of instability as well. So that's a very obvious problem. You know, and and it's it's not even necessarily for lack of, you know, for lack of spending. I mean, again, I, you know, there, I I subscribe to parts of the abundance narrative, not all parts. But, you know, it's correct that it's not just about how much is being spent. I mean, I'm sitting here in San Francisco and huge amounts are being spent on public transit and housing, for instance, but clearly to very poor effect.

• 1:21:24Speaker 01

  Right.

• 1:21:25Speaker 00

  You know, so we could be doing so much better at that stuff. And and the other thing that I that I that I think kind of sucks that is fundamental is the the way we have conceived of work.

• 1:21:39Speaker 00

  You know, that there's, there's a lot of forms of work nowadays that seem like they are not particularly giving people a great sense of purpose.

• 1:21:47Speaker 00

  And that might pay well, but that, you know, to that are there to use the the term of David Graeber, bullshit jobs. And on the other hand, there are a lot of really important forms of labor that are very poorly compensated and that are, you know, and that we actually couldn't do without and that we're not doing a good job of honoring, whether that's, you know, nurses, preschool teachers, janitors, you know, whatever, right? These are, you know, these are, these are real things that, that, that are hugely important to the community and there are many things that are hugely important to the community that are not even being done today because we have thought about them as externalities in the, in the economy.

• 1:22:27Speaker 00

  So I think there has to be a, a big, you know, revaluing, rebalancing, rethinking of the way, of the way, um, uh, the way that whole circulatory system works and, and, and obviously the economy and the political system are connected in, in these ways because to the extent that, that, um, that people's economic needs are that, that results in, in all kinds of political reactions, uh, you know, so which are pretty, are pretty grim.

• 1:22:50Speaker 01

  Well, I'm curious if you subscribe to this as well, but it seems like with the, you know, the, the bullshit jobs, that when those are probably, you know, to be candid, on the front line for the jobs, if any, that AI will take. And, you know, there's this narrative about, oh, I'm so concerned AI is going to be taking my job. And to me, that's a bit disingenuous because you're actually concerned that AI is going to take your income. The job itself sucks. You probably already think the job sucks. You probably hate your job, but it's giving you, and it's, and it's purposeless and, you know, all the baggage that comes with that, but it's your source of income and, and, you know, there's-

• 1:23:29Speaker 00

  I a hundred percent agree.

• 1:23:31Speaker 01

  Yeah.

• 1:23:32Speaker 00

  I mean, if we, if we just take a, I mean, I don't, you know, it's a little dangerous for me to try and poke into particulars here, but I'll, I'll give one broad example.

• 1:23:41Speaker 00

  You know, when, when Obama was asked, like, why don't you just nationalize healthcare? You know, the, the answer was there are millions of jobs that would go away if, if that were to happen. And, um, you know, a bunch of those jobs are jobs that should not exist. And I'm not talking about the jobs of, of, of nurses and doctors and, you know, right? People who are doing actual care.

• 1:24:04Speaker 01

  Administration.

• 1:24:06Speaker 00

  You know, and, and, and so the idea that, that, that all of those administrative jobs should be preserved and that the administrative structures should be grown further and that there should be more people coding Medicare claims and all this kind of stuff. And again, like, you know, those people have got to make a living. So I, you know, I'm sympathetic, right, to that. But somehow we're doing something wrong at a, at a systemic level. If that's how we're, if that's how we're using so many people's time and that way of using people's time is the way that we, that, that, that, that their existence is justified economically. I mean, that, that's just, that just seems nuts to me.

• 1:24:40Speaker 01

  No, it, it's really interesting and I think you, you nailed it, which is that something is broken in the circulation and the rebalancing. And I think that, that's what people worry about is there's, uh, you know, a, a lack of trust. And if, if people see, oh, the technology is here that can take something away from me, but we don't have a mechanism to give it back, then, you know, that, that's a recipe for angst.

• 1:25:04Speaker 00

  Absolutely, absolutely. And so, so the problem is real. At the same time, many of the pop, many of the forms of populist backlash that I see to that, you know, look, look as bad as the disease. So, so I'm, I'm really concerned about, about that, that dynamic at the moment.

• 1:25:19Speaker 01

  So, so let me ask you maybe a big question. What do we do now?

• 1:25:24Speaker 01

  But based, based on where we're at in terms of this, this arc, this, this step point, this, this reality that we're entering a, this great unknown that could be very disruptive and we don't always like where we're at, like there's, I can't even name all the dimensions of this, but like, what do we need to do? And, and maybe let me ask a related question, which is for people who are leaders, you know, and have an outsized role in the, you know, what we can call political economy, in the, you know, in the super organisms that are organizations and enterprises, which we'll have to talk a different time about because that's a, a, a, you know, a fun side project of mine, but, um, what, what do we do?

• 1:26:08Speaker 00

  You know, I, I wish I had better answers to this. I mean, definitely there is, there is, um, sort of sociology, you know, and agent sociology research to do, uh, you know, some of these questions can actually be asked as science questions and, and, you know, we're very, you know, I mean, it's a little, a little maybe plugging ourselves, but that's exactly, you know, why we're so invested in doing that sort of research because, you know, there, there, there are things that can be investigated rigorously that are, that are, that are kind of understudied. I also think that trying experiments is going to be really important. Uh, you know, so, um, having, um, you know, I, I like that there is a diversity of, of different, um, uh, sort of regulatory and legal regimes and so on, in different parts

• 1:26:51Speaker 00

  of the world where different things can be tried and we should all be paying attention to, you know, where stuff is working and where it's not and, and, um, you know, and what, and, and therefore like, you know, where we should be headed, uh, politically without, without having a sense of, you know, there's only, there's only one, uh, solution, only one way to do things. But, um, but yeah, I don't know. I, you know, I mean, I mean, this is such a big question, right? I mean, I, I don't have the answers to, you know, like how to, how to organize ourselves, uh, you know, as a, as a, as a planetary civilization. But, uh, you know, I guess what other thing I would say is that, is that we really have to keep our eyes on the ball of the planetary level issues.

• 1:27:32Speaker 00

  Uh, you know, the, um, the ecological stuff is not going to wait around, um, for us to get our shit together. Uh, you know, so issues that relate to our, like the, the ecology that we all are living in, um, really need to be centered in a way that felt like, you know, it was starting to happen in the 2010s and has now receded, uh, because of, uh, you know, because of AI. AI anxieties and many other things. Like we've got to really re-center the things that are non-negotiable, uh, about, about our environment and that are not, that are not subject to, you know, our own, uh, our own time scales and whiz.

• 1:28:07Speaker 01

  You know, I'm, I'm still trying to wrap this up, um, but I did want to, I, I did want to mention because there's a, there's a piece there that's unintuitive to some people, which is actually AI makes climate change and the, you know, ecology worse because of what's going on with data centers. The counterpoint I've heard to that is AI is actually the only way we're going to be able to solve this problem. And so we just have to sort of bite the bullet on that. Do, do you agree with that?

• 1:28:31Speaker 00

  I do. I mean, I, I'm, I'm very concerned with decarbonizing our, our, um, uh, our energy systems. People who have the impression that the amount of energy that we are burning on earth is the problem, um, just don't have the science right. Uh, the problem is carbon dioxide at the moment. It's unambiguously carbon dioxide. And, uh, and if we are, uh, replacing, uh, you know, carbon burning, uh, uh, plants with, uh, with other forms of, of renewable energy, then, um, that's a very, a very obviously positive thing. I mean, I find it so ironic that, um, you know, China, which, you know, was a huge polluter and, you know, not, not particularly like great, right?

• 1:29:15Speaker 00

  On a variety of ecological measures, you know, is also the one that is decarbonizing the fastest by far, is also the leading producer of solar cells. That Texas, which has hardly been like, you know, bastion of, you know, of ecology, you know, politically speaking, is making much more progress than California, you know, at, at, uh, you know, at, at using solar and so on, because it's just cheaper, you know? So, so, um, I, I really do think that, you know, leaning into those things and building is, uh, is, is fundamental. Uh, you know, you can't just say like, you know, use, use less or whatever. A, you know, AI is, is absolutely a part of the solution. B, a lot of the infrastructure overhauling that has to happen, you know, is, uh, um, you know, is, is going to be accelerated, not slowed down by, uh, by demand.

• 1:30:01Speaker 00

  And also of course, you know, I, um, you know, one of my big projects is, is Sun Catcher, which, you know, we haven't talked about, but this is, you know, putting, putting AI in space where there is more than abundant energy. And moving toward a space-based, uh, um, uh, energy ecology for, um, for AI and for computing in general, I, I think is also in the long term going to be a, a big part of the solution here.

• 1:30:25Speaker 01

  Well, I promised I would be conscious of your time and I know we're at the end of it. There's so much more we could unpack that we'll have to do another time. But Blaze, this has truly been a pleasure. I really, really enjoyed our conversation. Thank you so much for your insight and for joining. It left me with, uh, with a ton to think about.

• 1:30:40Speaker 00

  Oh, thank you so much, Jeff. This has been a really fun, a really fun discussion and you've, yeah, your, your questions have been really on point, I think.

• 1:30:49Speaker 01

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