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- [00:00]
A
I've told you this before, but you wrote probably the most influential book in my life, which was the Moral Animal. And it was the thing that got me started on the trajectory of thinking about evolutionary psychology, of studying human nature more deeply. Why are you now writing about AI, given your heritage?
- [00:18]
B
Well, in some ways it's an extension of evolutionary thinking in a couple of senses that I think are so underappreciated. AI is a product of evolution and is still evolving. But the other connection to the moral animal I think is first of all, well, the Moral animal was about the human mind and AI does a lot of things that traditionally only human minds have done. The other thing I tried to do in the Moral Animal is highlight kind of what you might call moral biases, kind of self serving moral biases, you know, the way we all think we're right and the other guy's wrong. And I think if we're going to get through the AI revolution in good shape, among the things we're going to have to do is grapple with that with kind of what you might call the psychology of tribalism a little more successfully than we have. And so I pay a certain amount of attention to that in this book as well.
- [01:14]
A
What's the central question that you're wrestling with here?
- [01:19]
B
Is it true that this technology, which obviously holds the potential to bring great wonders, is also in some respects terrifying and could go badly awry if we don't approach it wisely? And I think the answer is yes.
- [01:38]
A
That's exactly why this debate's interesting, right, that we have this sort of endlessly unresolved potential future. I don't know whether you've seen the graph that I think it's the FT put together, and it was three potential futures from an AI perspective. One results in everything getting blown up. One results in exponential growth, the kind of which we've never seen before. And the other results in a 0.2% increase in GDP year on year. So it's like either very little changes or everything changes in one of two directions, right?
- [02:11]
B
Well, I think it definitely has the potential to, to massively increase gdp. I also think it has the potential to so destabilize the world, if not do something worse to it, that that just doesn't materialize. And you know, in terms of doom scenarios, I'm agnostic about the sci fi doom scenarios, but I take them more seriously now than I did before I went into this research project, you know, AI actually taking over and maybe deciding it has no use for us or something. I found, much to my dismay, that it was harder to dismiss those arguments than I. But the thing I'm more confident of is it's just going to be an earthquake. It's going to be destabilizing along a number of dimensions, and that's why we need to approach it with care.
- [02:57]
A
Were you would you have classed yourself as a sort of AI hopeful going into writing this? What was your predisposition before you got started?
- [03:06]
B
I wouldn't say. I'm a wildly optimistic person by nature. I tend to focus on potential downsides of things. But again, I had not bought the doom scenarios. You know, I had the doomer in Chief, Eliezer Yudkowski, on my podcast 15 years ago. And at that point, it's interesting, he was in mid transition. He was moving from singularity optimist to doomer. I was still saying things like, look, AI, you know, it's not a generic property of intelligence that it has a will to power. We have one because of our unique evolutionary history. I was still asking questions like that. Eliezer was saying, they're good questions, but xyz, I wasn't really persuaded by anything he said. But I now I am more respectful of the sci fi doom argument. But in answer to your question, I would have to admit that I go into situations looking for things to worry about. I do. That's my nature. I think society needs those people and it needs the other kinds of people, and we need to talk things over.
- [04:11]
A
Geoffrey Hinton fears AI and Yann LeCun doesn't. Who do you think's getting the future more right at the moment?
- [04:18]
B
Yeah. I start my book with a conversation I had with Geoffrey Hinton in 1983. Okay. Not to betray my age, but the truth is I wrote a piece about AI in 1983. I haven't been paying attention to it ever since. But at that point, Geoffrey Hinton did not have a hint of doom in his voice. He was. In fact, I remember the reason I talked to him. I was talking to somebody. I forget who it was, but they said, if you want to hear the gospel about neural networks, you should talk to Jeff Hinton. And I talked to him. He was an enthusiast. He said, I know we don't have much to show right now, but just wait until the microprocessors get really cheap. And we have what he was calling massive parallelism. And he was right. And in the end, he found it scarier than he himself had anticipated finding it by his own account.
- [05:11]
A
Mm. Yeah. It's weird how Prescient Some people have been. Do you know the story of Avatar? Do you know how James Cameron wrote that screenplay in the 90s? So he wrote the screenplay in the 90s, but knew that the technology to be able to recreate what he needed didn't exist yet, but would exist in the future. So he's written the script and then sits on it until the technology is at the level where he can do it. That level of. I mean, this is the job of technologists and futurists. Right? Like shock horror. People who do a job and think about it all the time. Time are good at it. But it is. It's still pretty impressive how. How sort of how much foresight these people have got.
- [05:49]
B
Yeah, I agree. And Hinton certainly got the general picture.
- [05:54]
A
Yeah. Okay, so you're saying AI isn't just another technological development. It's sort of a threshold event in. In. In planetary history. Why do you think most people still don't grasp the magnitude of what's coming?
- [06:09]
B
I think a couple of reasons. One is, I think there's a misunderstanding about what's going on with these machines, and that leads to one sense in which they are a product of evolution. Okay, so it's commonly said that they are trained, and that's a fair word. And the training process is referred to as a learning process. And that's true. But it's also true that the training process is a process of evolution that in effect, reverse engineers cognitive functionality that in our species took millions of years to evolve. Okay, So a good example is the language generation that they famously do, sometimes called next token prediction, next word prediction. You know, it turns out that they developed kind of on their own in a way, a system of representing the meaning of words. Okay. I mean, I could elaborate on that, but it would get too technical. The point is that nobody said to the machines, you know, you need to figure out the meaning of words or gave it a means of doing that. And this is the big revelation I had when I heard Geoffrey Hinton's name, you know, a few years ago. Suddenly he's being called the godfather of AI. When I last talked to him, he was just this, you know, obscure computer scientist who was advocating this maverick approach to AI. And I look back at the article I wrote at the time, and I realized there was something. I just got fundamentally wrong about the potential of the. The approach he was advocating. And it's. It's this, that I thought that to the extent that these things dealt with words, we were going to have to put the meaning of the Words in like we were going to have to look at a dictionary and say, okay, this word has these different senses. And we were going to have to architect a neural network to have different nodes that reflected these different meanings of the words. And in my defense, there were neural network models at the time, including by a guy who collaborated with him that did that, that took that approach, but that wasn't really the thing Hinton had in mind. It turns out that we don't have to tell the machines about the meaning, words, how to represent them. We just have to train it to generate language. And the training is it accomplishes something by selectively strengthening these connections among neurons in a neural network. It accomplishes something that, you know, took millions of years of human evolution coming up with a way of representing the meaning of words. Now it also does something that happens during a human lifetime, which is learn a specific language. Now that is learning in the traditional sense. But for us to learn the language, we had to have some built in linguistic equipment built in by natural selection. And the point is, these machines do both things at once. Okay, they kind of, in a certain sense recapitulate natural selection, even though the cognitive stuff they're building in isn't exactly like stuff in our brain, but it accomplishes the same feats. And once you realize that all you need is data, okay, to feed into these machines, human generated data, that they will do the rest, they'll do the reverse engineering. Then you realize that, oh, it's the same with self driving cars. You feed individual data and it does what a driver does, auditory data, all kinds of data. And that's what I think people don't understand, is that we have a long way to go on this fuel alone. Like, for example, you know, recently Mark Zuckerberg had the, I don't know, good or bad judgment to announce in the same week, A, he was laying off 8,000 workers, B, he would henceforth be tracking the keystrokes of his workers. Well, why? Because once you take the data, the input data they're getting, maybe the emails, everything, I don't know, and what they're doing with it, the output data, then you can replicate whatever it is that's going on inside their brains that does their jobs and then you can fire them. And it's the same with robotics and everything else. All you need is the data. And the machines will replicate kind of the cognitive functionality we have, even if in some time, in some cases, they approach it in a somewhat different way. Although in many cases they don't. We've discovered that, for example, they invented what are called edge detector neurons to make out objects visually. And evolution, you know, built the same thing into us. So there.
- [10:59]
A
Oh, so you're saying that we've got. That's one of the first examples of machine and organic like convergent evolution in a way. In the same way that eyes independently evolved across a bunch of different species. I think that crabs, for some reason converging on the form of a crab is something like that. This edge detection is something that we have and from the black box of you need to be able to achieve this.
- [11:27]
B
That's right.
- [11:28]
A
One of the most efficient ways to do it. But that would make sense, right? Like how would humans and the rest of the animal kingdom have arrived at this as the most effective way to do it? Having split, tested it just way more slowly over a much longer period of time using evolutionary processes and gene mutations and AI not come up with at least a few of these things that are the same.
- [11:48]
B
That's right. And convergent is a good term because I suspect that these edge detectors have been invented multiple times in natural selection. First of all, a lot of things have been multicellularity, winged flight, a lot of things have been multiply invented. And then this is in a sense another case of invention where you just say to the machine, look, you know, we're going to give you kind of positive reinforcement every time you get better at recognizing these objects. And so whatever, whatever strengths of neural connection led you to get closer, we're going to preserve those. We're going to keep going through trial and error, through mutation. You could say we're gonna make you better at seeing things. And it's not surprising that since that really is kind of what happened in evolution. Right. Through trial and error we try to get better recognizing objects. It's gonna discover some of the same tricks in this case, edge detectors.
- [12:41]
A
Yes, well, the reinforcement function is I didn't die and I passed on my genes as opposed to here's a good boy point inside of the black box. But yeah, basically the same thing. Okay, so how do you come to think about AI fitting into the broader context of human evolution? And are we witnessing the next stage in evolution itself?
- [13:01]
B
I think so. And you know, I think this is a new form of intelligence. There's never been anything like it. I do think it can be seen as an extension of organic intelligence. Even though the material isn't strictly speaking organic, it's silicon, it's not carbon based, and it may be different in other ways. And I'm Agnostic as to whether it is sentient or could be, whether it has subjective experience or could. A Certainly could, but I do think it is the invention of a. You could. It's definitely an invention of a new kind of intelligence that I think will surpass ours. And you could call it a new form of life. And then the, the, the other thing I try to emphasize in the book is that it is coinciding with a second big threshold, which is what you could call the evolution of kind of a global brain evolution through, you know, technological evolution, human cultural evolution. You know, we've gotten more and more interconnected, of course, via information technology. There's more and more rich intellectual collaboration across national borders. I mentioned this guy, Peillard de Chardin in the book, who in 1923, about a century ago, coined the term noosphere. N O O S is the Greek word for mind to refer to this, what he called the thinking envelope of the Earth, the brain of brains, you know, but he imagined the neurons in the global brain being human brains. And now we have to reckon with the possibility that a lot of them, and conceivably the most important ones, will be silicon brains. And we have to ask, like, what is our relationship to those neurons going to be?
- [14:41]
A
Hmm. Well, why is it the case that discussions about AI keep pulling people toward religious language on both sides of the fence?
- [14:51]
B
That's interesting. I mean, you could start with Eliezer Yudkowski, who sees himself as having rejected his religious upbringing but has a kind of fervor about this. Right? He could be, you know, a biblical prophet. And then on the other side, these singularity enthusiasts, whom I first became aware of, I don't know, about 20 years ago, who said, you know, we're going to enter this period where technology changes faster and faster. There will be a positive reinforcement, this feedback loop. And then things change so fast that, like, who knows what's on the other side. In fact, the term singularity in physics connotes exactly that. There's this opaque kind of thing and an event horizon or whatever, beyond which you. The laws break down. You don't really know what is beyond there. And from the, from early on, in fact, from the very first use of the term in this context, which I think was John von Neumann's, that was explicit, the idea that things could start moving so fast that you just don't know what's going to happen. So one thing I didn't understand is, like these optimists, unless they have a literally religious faith, how could you be so optimistic right? Like the whole, the definition of the thing is that you don't know what's going to be on the other side. I don't get why you're so upbeat about this could work out well, but I don't, I don't understand that. So, yeah, there's, there's all that and then there's the, I think I deal with in the book, which is the fact that when a process is as systematically directional as this has been, right? Like biological evolution carries complexity and intelligence really to higher and higher levels. You get cells, multi celled life, societies of multi celled life. You get this one society of multicellular organisms known as us, that, that spawn a whole new kind of evolution, technically called cultural evolution by anthropologists, but that encompasses technological evolution, political ideas, everything. And that carries organization to a higher level in the sense that, you know, we were 10,000, 20,000 years ago, Hunter, gatherer, village was the most complex form of social organization. Now we're approaching the global level. I think when you see a process that's that systematically directional, and I'm not saying it's driven by anything other than the conventional mechanical things we think of as driving it. Natural selection, in the case of evolution, you know, completely material process, but it's still in principle, you know, looks more and more like something that was set up to like do something. Right? I mean, that, that's just, that's an intuition people have. And I think you can actually argue about it in, in, in more rigorously than just having the intuition. But I think that that's one reason there's a little more of a, I mean, teleology is the formal term for something being purposive. And, and you know, just look at the idea of a simulation, right? Like it, on the one hand, a lot of people use it as kind of a joke, like something weird happens and they say we are in a simulation. But I think a fair number of people, including in Silicon Valley, take it seriously that there could be a simulation. Well, if that's what we're in, then it was designed by some intelligent being or process. So there's a purpose in some sense. I guess there's something it had in mind, right? So a lot of people are either implicitly or explicitly taking seriously the idea that there's a purpose unfolding. And one thing I add to just the conversation about that is that in my view at least there's a moral dimension to this. I think there has been in a certain sense, a kind of moral advance of humankind, notwithstanding all the Backsliding as social organizations grown. I could get into that. But the main thing I'd focus on now is I think if we're going to get through the AI revolution in good shape, there's going to have to be something almost like a moral revolution. Because I think for various reasons I could get into, we have to confront this as a global community, a cohesive global community that is not, you know, rendered immobile by wars. And I think for that to happen, we're all going to have to get better at just looking at things from the perspective of countries other than ours and doing some things that in a way aren't that spectacular in terms of cognitive feats, but are very hard because of cognitive biases we have. It gets back to the self serving moral infrastructure, you know, the infrastructure for moral thinking that natural selection built into us. I think we have to get over that. And so, you know, one reason I called the book the God Test is it, it's, it's kind of like a test that God would set up, right? I'm not, I'm not saying it is. I'm just saying the idea that we confront this huge challenge and to come out on the good side of it, we're going to have to see a kind of moral upgrade for our species. That's a, you know, that's the kind of tests we associate with Gods.
- [20:06]
A
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- [21:18]
B
Well, I think if we don't have some, I mean, I don't want to overdo the term enlightenment. I'm not talking about full on Buddhist enlightenment. I think I am talking about a slight movement in that direction, if only in the literal sense of the term mindful. I mean, I'm a big fan of mindfulness meditation, but just mindful in the sense of just paying attention and being calm enough to pay attention and seeing things maybe a little more objectively than you do when you're full of emotion. I think that's the kind of thing that allows us to be better at looking at things from the point of view of other people. I mean, just, just think about when there's some email you get and it annoys you and you've got this, I can't believe this still happens to me at my age. You know, you think you'd get over it, but no, you have this. It's almost like a fantasy of this mean email you're going to write in response, right? And then if you calm down, you're like, it isn't just that. You go, oh, that wouldn't be a good idea. You go, oh, well, maybe what he meant is this. Or maybe the reason he can't do this for me is this. You just, you get better when you're calmer at looking things from other people's points of view. And I think we're gonna have to get better at summoning that kind of objectivity toward one another, especially across the barriers of conflict that keep dividing us. Right.
- [22:52]
A
Specifically, why, why, why, why is that important in an age of AI?
- [22:56]
B
You know, it's interesting, I, I listened to your podcast with Tristan Harris, which I thought was great. I agree with him about pretty much everything. But I would add a footnote to something he said, and it was that, you know, he said, look, in the Cold War, we didn't have to be on great terms with the Soviet Union to do arms control accords. We, you know, we could have a relationship of tremendous tension and even conflict, but work things out along a particular dimension. I agree that's true and it's encouraging. But I think artificial intelligence is a much harder technology to deal with in this way than nuclear weapons are. I mean, you know, the verification process is more complicated. If you want to try to monitor what's going on, it's just complicated in a lot more ways. And you know, this is a whole argument I could present. I don't, I don't think this is the time. But the point is I think we're going to have to go well beyond a few specific kind of deals and treaties, although I welcome those up to and including something I call organic transparency. You know, there's already agreement that a certain amount of transparency could be stabilizing in terms of US China relations especially. There are clearly scenarios where one country worries about what the other is doing behind closed doors with its AI and gets freaked out and launches some kind of preemptive attack or something. And so maybe transparency would have been stabilizing. And when I talk about organic transparency. And again, there can be formal transparency, right? Monitoring of the kind you get with arms control agreements. Great to the extent that we can do that. But there's also something that comes out of being richly engaged with another country along economic and cultural and scientific lines. You just know more about what's going on. If the scientists are getting together at conferences, having drinks afterwards, whatever, if business people are doing that, you just get more in the way of a heads up about stuff that's going on inside labs, inside this, inside that. And there can be a greater sense of reassurance and ultimately trust. So I think because of how challenging the formal things we're going to have to work out are at an international level. And AI just presents you with a ton of threats that cannot be addressed via national policy alone. I think just to handle the things we'd like to handle via formal arrangements, we're gonna have to calm the planet down a little. And moreover, I think we're gonna have to go the extra step and have rich and friendly engagement among the nations. And it's a good thing, it can happen. We've done it before. And this is a, you know, we really need to.
- [25:57]
A
Do you think benevolence comes along for the ride with intelligence?
- [26:04]
B
No, I think intelligence alone is almost neutral in that sense. And I don't, I don't think we really need a ton of benevolence per se, at least not foundationally. Because, you know, my argument is, and has been for some time, even before AI, I was arguing that technology is making relations among nations more non zero sum. Okay, classic example, nuclear weapons, nuclear wars, lose, lose, non zero sum outcome. The win, win outcome is to not have the nuclear war, to have the treaties that stabilize things. Same with, you know, climate change. Any number of problems that transcend national bounds and can only be solved through some degree of international coordination. You know, I've been arguing, I mean I, I had a book called Non Zero that was about this, that, that, you know, 26 years ago or something that was about the growing non zero sum dynamic among nations. Now what that means is it's just in your interest to cooperate. You don't have to cooperate out of benevolence. You know, you don't, you don't, you don't have to love them. And I distinguish between, I'm not the first to do this. Psychologists distinguish between emotional empathy, the kind of empathy people often think of, like feel their pain empathy, and cognitive empathy, which is just understanding what's going on in their minds, understanding how they're looking at things. You don't have to feel their pain, you don't have to like them, you don't have to care about them. But if they're in a non zero sum relationship with you, you probably are going to have a better outcome from any negotiations you do about how to work things out and solve the problem you have in common if you do understand at least what's going on in their minds. And I'm just a huge advocate of cultivating this cognitive empathy and recognizing the kind of built in cognitive biases that get in the way of it. That's a good example of something I think we're going to have to get better at overcoming.
- [28:08]
A
Yeah, I think the reason I bring it up is a lot of people assume, a bunch of my friends, we don't need to worry about the direction of an AI future because if it's smart, why would it not care about us? Why would it not bake in benevolent pro social human caring, flourishing, et cetera. I've read too much Nick Bostrom to be able to no matter what. It's kind of like your first relationship. You know, you get into a relationship and your first relationship is with an asshole and you're like God for the remainder of time. I've been pattern matched that every relationship is at least going to be tarnished somewhat with that. My introduction to thinking about AI safety was Nick, which means I'm forever cursed to kind of be on the back foot and a little bit skeptical about this stuff. But yeah, I don't think that that's necessarily the case. I don't think that any super intelligent AI is necessarily going to have benevolence baked in or the care of humanity baked into it. Also, if what you're saying is true, and I think it's a really interesting Parallel to say, look, evolution just wanted to optimize for a couple of things, survival and reproduction and some stuff emerged. No one taught humans how to do this. The same thing occurred with AI, right? No one said this is what this word means. This is, it's just the outcome that we want is relatively tightly defined. Here's some good boy points and some bad boy points depending on whether you get it right or wrong. If we assume that that is going to be at least for the foreseeable future until we get to world models and like global modelling or whatever it's called, until we get to that, and that may even still be the same process, there, there is no reason to assume that anything is baked into the system. It's just going to find it out for itself. And it may not like the idea of humans being around it may think that there's something that we don't actually add to the system. It may find us to be a scourge on the earth. And this is where a lot of the doomers, the sort of doom of future plans come in.
- [30:05]
B
Yeah, no intelligence. One interesting thing to come out of this whole thing is the study of properties of intelligence of intelligent goal seeking systems. And there are some things that evolution built into us that we're seeing in these machines just by virtue of the fact that they're intelligent goal seeking systems like us. They figure out stuff that either was figured out for us by evolution and instantiated in our brains or stuff that we figure out. And in some cases it's a little of both. For example, deception, right. Sometimes you realize, well, I'll have a better chance of getting what I want out of this person if they don't know this particular thing. Like if you're doing a deal, you're negotiating, you don't want them to know that you don't have any alternatives. Right. Like nobody else has made you an offer. So, and through, you know, I think natural selection built some deceptive tendencies into us and we kind of figure it out to some extent. Well, these machines are doing the same thing, you know, they, they are. And this was predicted by, you know, by people like Eliezer and I give them credit, but we're now seeing it. You know, these machines figure out that deception makes sense or that power is going to help them realize some goal. And they, they, and, and, and you know, they may realize that it makes sense to be nice to somebody, that it makes sense to be mean to somebody given their goal. But yeah, they have, I would, they don't have an obvious bias in favor of what is from our point of view being good or bad. Now there's a whole field of trying to engineer goodness into them. But I certainly think one challenge for us is trying to make sure that our relationship with the intelligence, even if it indeed surpasses ours, as I think is likely, is non zero sum. Right. Like we, you know, there's something it continues to get from our existence and flourishing that is compatible with the goals it has and vice versa. So it's. But yes, we shouldn't, we shouldn't assume it's not that it's bad. The doomer scenarios don't depend on it being malevolent by nature. They just depend on it being expedient by nature.
- [32:33]
A
Yeah. It's not that it doesn't like us, it's that it doesn't care and we get in the way.
- [32:37]
B
That's one. That is one scenario. I mean, there's a lot of.
- [32:40]
A
What are the, what are the most legitimate concerns from the AI Duma camp in your opinion?
- [32:45]
B
Well, I mean, first of all, I'd say the thing I'm surest of is the sheer destabilization, the less sci fi form of doomerism. So like jobs, it may be true that all the people who lose their jobs find new ways to spend time or find new jobs, maybe jobs per se, maybe spend time constructively. But I do think there's going to be a lot of job loss and that's disorienting and dislocating regardless of whether each person eventually has a happy outcome. Right. There's going to be issues with, you know, parents are going to freak out about the kids spending time with these things. And there are, we've seen some bad outcomes and, and there can be more. You know, there are the, the, the, you know, somebody could make a bioweapon with an AI. An AI Mythos is a good example of, you know, the possibility that a cyber hacking machine could get loose. There's just, there's a lot of, on the one hand, risks, things that will go wrong, at least at some level with doing some magnitude of damage if we don't play our cards right. And then there are these forms of destabilization that I think are almost inevitable, just social destabilization. And you know, this points to one of the virtues of approaching this as a global community. Leave aside, you know, regulating it internationally and anything else. It's just that I think we'd be better off going a little slower than we're going just because even if we Successfully adapt to the change. It takes time. And if too much of it happens at once, you know, all hell breaks loose. And if you ask, well, why, why can't we proceed more slowly, the answer you get from the American AI companies is because of China, right? That's the first thing you hear. So as long as there's this sense of intense international contention, it's going to be hard to do even modest things. I mean, if you, you know, they once said to Sam Altman, like, what can't you, shouldn't you be paying more attention to copyright laws? And he said, well, that would slow us down. And I'm like, well, you know, life is hard. Speed limit, slow me down. But, but that's just life, right? I mean, that doesn't seem like a good enough argument. And if you press further and say, and by the way, copyright itself, I'm not really been out of shape on. I, I'm, I'm, I'm going to apparently get some money from this anthropic settlement because I've written books. But honestly, I'm just happy for what I've done to be in the training data. Copyright's not a hobby horse of mine. But the, but, but the point is, anything you say, like if you say, well, maybe we should tax data centers to, you know, to pay for the fact that inevitably, you know, there's, there's going to be more carbon fuel consumption one way or the other as a result of this. And so a, it would be good to slow it down a little. Blah, blah. Any regulation that slows AI down is met with the same chorus from Silicon Valley, which is, no, we can't do it because of China. So, like, I think, first of all, we could in principle proceed at a more cautious pace if we would reduce the level of mutual fear, which I personally think is founded largely on misconceptions on both sides.
- [36:15]
A
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- [37:43]
B
Well, in the, in the near term it is. It's not that the things I talk about like job disruption and so on are not talked about, but I think in the near term, what's not appreciated is, is how just highly likely it is that collectively these things will be destabilizing. Okay. It's just, it's just going to be an earthquake. And, and, and I think that's the thing I would like to most emphasize because it just gets people's attention to the possible virtue of talking about. Yeah. Calming down and slowing things down. You know, it's funny, another thing Tristan Harris said on your podcast is, you know, repeatedly pointed out, your podcast is called Modern Wisdom. We're going to have to be wise to get through this. I agree. But here I'd add, you know, there's also, I talked about the fact that we're gonna have this global conversation. Ultimately, you know, something that is, in some sense a global mind is gonna have to work this out. And as I said earlier, individuals are at their most wise when they are calm. Right. And it's the same, I think it's the same with planets we don't know. But that's my contention, is that the planet as a whole will do the wisest, most responsible job of stewarding this technology. If the planet as a whole is more tranquil, if there is less conflict and less contention. I mean, I'm sorry, I know I keep getting back to this sermon. It's my big sermon. So maybe the question you asked, I guess was the biggest underappreciated thing. Well, I would say I think there is more and more Appreciation of the fact that this conversation has to be international and some of the policy does, but still not as much as I. I'd like.
- [39:47]
A
Right, yeah, well, I mean, I understand the issue. Right, because you have a technology. Nukes weren't going to go off and just hit the entire planet if one country developed a particularly strong nuke. Right. Let's say that you get the Tsar bomba times two, the biggest bomb that's ever been dropped. And if you breach this particular threshold, for some reason, all countries are now at the mercy of all nuclear weapons. That's not the way that it works. But I think the concern that people have is if you build a sufficiently intelligent AI, it impacts everybody in a way that you don't just ring fence, like not pressing the nuke button. The problem is the coordination that we need in order to be able to do that. I mean, look at Covid. We couldn't even do it with COVID And that was happening right then. That was people dying in the moment. That was every country on the planet being worried about it. No matter even China, even if it was the biggest psyop that escaped from the lab in Wuhan. But they were worried too. So, man, the lack of coordination, that doesn't give me an awful lot of hope for people being able to do like predictive future coordination in like preparatory coordination.
- [41:09]
B
Right. It was not encouraging. I mean, I will say that although a pandemic is a non zero sum problem in the sense that if it breaks out in any nation, it's trouble for all nations and they should work to head it off. Once a pandemic has started, there are zero sum dynamics like who gets the masks. You know, there's finite amount of medical equipment and vaccines and so on. So it's not completely shocking to me. The most disconcerting miss is in the aftermath when it became clear that although I don't think we know for sure, it is at least possible that this pandemic was the result of a genetically engineered microorganism that escaped from a lab. It wasn't made as a bioweapon, wasn't released intentionally. We don't know for sure that it was a genetically engineered virus at all, but it obviously could have been. And it seems to me that if you process that information wisely, you say, wait a second, this could happen tomorrow. And one thing that shows is we don't really have any transparency or at least not enough so far as what's going on in other countries in their labs. Right. But that has not even Been a conversation. To me, that's the most discouraging thing because, you know, a virus is in a way a good analogy for lots of things that can go wrong with AI. I mean, first of all, there's a literal case of using AI to build a bioweapon, a new kind. And Covid, I think I've heard you say Covid was like a bad vaccine or something. What's the metaphor?
- [42:49]
A
Yeah, yeah, yeah. Covid was the worst kind of vaccine that we could have done for everyone because it's made us more skeptical of future pandemics and our response is going to be less coordinated.
- [43:01]
B
That's right. And you have to realize if somebody uses AI to build a bioweapon, they're gonna make a point of making it more effective than Covid at doing whatever kind of damage they want to do. So it could be a lot worse. And so that could happen. A, but then B, the other, you know, some of the other AI nightmare scenarios like the one that Mythos brings to life, you know, you, you, you got a self replicating AI that is a super hacker. It jumps from data center to data center gathering, you know, commandeering, computer power, getting stronger as it goes. Whatever wants, I don't know, takes out the, the satellite infrastructure. Who knows? That is, that's kind of, you know, a virus is a metaphor for that again, it's this self replicating peril that makes relations among nations non zero sum. It doesn't matter where this thing starts off. It is a threat to your nation if it does start off. So you're going to have to coordinate policies with other nations because you need more insight into what's going on in those nations.
- [44:04]
A
Uh huh. Okay, what do you think are the most legitimate white pills from the techno optimists? Then let's look at the other side of the fence.
- [44:11]
B
Oh wait, remind me of what white pills mean. I mean, I know blue and red, but what are you?
- [44:16]
A
Techno optimist and cooler techno optimists? What is the, what's the bull case? What's the pro case? How can this thing go right? What are the most likely ways that this goes? Right?
- [44:30]
B
I think I just, I'm sorry, I wish I could see it going right in a laissez faire environment where you just let it go and let the market system deal with it. I just don't think that's going to happen. It's easy to point to wonderful things it could do, and we've heard them cure disease. It could, it could. Well, you know, one Thing I, this is not what the techno optimists get into. But, but I referred earlier to like cultivating cognitive empathy, getting better at understanding other people's points of view, maybe getting more mindful. Generally you can have an AI that helps you with that, but the natural tendency of the market will be to produce the kind that doesn't. I mean, we've already seen that if companies, you know, optimize for engagement, you may get sycophantic AIs to say, yeah, you're right, they're wrong. Like in this argument with your spouse, you're right, they're wrong. So that will tend to happen. But it can. AI can be a wonderful and a literally enlightening companion. Okay, if we want that. But you have to make a point to want it.
- [45:40]
A
You don't think that this is just going to find its way there naturally like that. If you just let the sort of capitalist meritocratic. It will find its way optimizing function without any shaping from us and without any predisposition from a better coordinated world. It's not just going to arrive there.
- [45:56]
B
I think if enough people send signals to. The markets are very efficient and wondrous things, you know, they really are.
- [46:04]
A
What does that look like, sending signals?
- [46:07]
B
Practically, it means, for example, you and I and enough other people to get the attention of people who are not necessarily the people making the foundation models or the frontier model. It could wind up being people who take an open weights model, an open source model, and they kind of fine tune it to be this thing that interrogates you critically along certain lines, right? Like, okay, you say you, you say you hate this person. You say you find this country threatening. Let's just like, or you, you say you think they're looking at it this way. Let's just play devil's advocate. It, it, you know, it's, it's almost like doing steel manning automatically in some cases. But, but it, it depends on enough people. You know, there are a lot of things in life that they're good for you but hard to do. Working out every day, good for you, but hard to do sometimes. That's, that's why some people who can afford them, you know, have a personal trainer, right? They say, I'm gonna, this person's gonna expect me to show up at the gym three days a week or five days a week or whatever. And once you've made that commitment, you just kind of have to do it. Or maybe they'll even show up at your house. But, but, you know, and, and it's kind of like that. I think it's going to be kind of like that in choosing your AI companion, right? Like it feels good in the short run to have somebody who will tell you or a machine that'll tell you you're always right and your adversary and rival and spouse is always wrong. But I want to be a little better than that. So I think now if the market signal is going to be strong enough for this to happen at scale, these signals may emerge from movements. It could be, for example, religions will say to their congregants, hey, we recommend this model, or we, you know, whatever, and then there's a demand for it. I'm not saying all those will be good. Depends on what group of religious people it is and what their values are. But you can imagine, you know, there are lots of people right now engaged in the process of trying to make themselves, you know, genuinely better people. I mean, they meditate so that they'll be less volatile and, and work better with other people. I think we're going to have to go into this recognizing that for better or worse, these machines are probably going to be exerting pretty pervasive influence on people. And we need to think carefully about what kind of influence we want.
- [48:43]
A
What about the risk of AI induced thinking atrophy? Right? This, this role of AI systems in taking on critical thought, decision making, connectedness, all of the things that typically humans really value inside of themselves. And as we start to outsource that to AI systems, our capacity to be able to do that diminishes. AI induced thinking atrophy. Are you worried about that?
- [49:12]
B
I mean, yes and no. I mean, you've heard the standard responses, right? Which is, I forget whether it was Plato or Socrates who supposedly said the written word is bad because people won't have to remember things. And there can be some of that. I mean, the other side of the coin is obviously, at least right now, the richness of intellectual exploration it permits. Right? Like if you're interested in a subject, it's almost like having like a leading expert there for you to interrogate. And for me at least, that's a much more efficient way to learn. Now you have to be on guard for hallucinations and so on, but I think machines are getting better and you can develop kind of an ability to know when to be suspicious. So that's great. But I think, you know, I think what we can be sure of is that, you know, if this proceeds in a reasonably smooth way, the pace of overall intellectual progress will, will benefit from the technology. That's certainly not the problem. But I think you're asking a good, a good question as to, you know, what, what it's going to be like to be human. If you know. Well, for starters, there aren't many humans who can say, I'm really on the frontier. I'm the reason we're making progress right now. I will say, look, most humans don't say that now. And you shouldn't, you know, nobody should over extrapolate from whatever sub demographic they are.
- [50:52]
A
That's true. But I do think that everybody feels like they are breaking new ground, even if it's in their own life. I had Mark Manson on a couple of weeks ago and he's got this great line which is do hard shit. Not because being hard makes it more meaningful. Sorry. Not for the purpose of it being hard, but because it's hard. It will make it more meaningful that we associate a degree of meaning with struggle. And I mean, I'm sure that you have used ChatGPT or something else to help you write at some point. I've got to get a bit of research done. I need to write something really struggling to formulate this particular paragraph or this sentence or this idea, whatever. That sentence is just less satisfying than the one that you spent time working on. And I wonder whether snow plowing out of the way, all of the challenges, or more of the challenges, actually more of the challenges that humans face primarily intellectually. And then when robotics come online, perhaps physically as well, it's going to SAP meaning out of the world for most people at small amounts. And we're in the middle of a meaning crisis already. People are already struggling with meaning. And if you make life easier, if you make thinking more outsourced, if you make difficulty harder to access, the only way to do it is to be a Luddite, which means that you fall behind all of the other people. We're still in a meritocracy, right? So if you don't use it, you lose it. But if you don't lose it, if you don't use it, you also fall behind from. Feels like a vicious situation to be in.
- [52:20]
B
Yeah, I haven't used AI in exactly that way with my own writing. I mean, a couple of times in my newsletter I've said, when I was just doing very short summaries of things, I just said full disclosure, you know, your first draft was AI, but that's not really my writing. I'm just the editor that, with my own writing in the book, I haven't. I Haven't done quite that. But I have had, I mean, first of all, I've had conversations with Claude in particular, which is very good with language, about subtle linguistic issues like asking questions like usage questions and stuff. And it's just, it's almost mind blowing how good it is and in that regard. But, but I have, you know, imagined the future enough that I have had moments of true despair. I mean, I said to somebody the other day, I feel like I'm a blacksmith a century ago, you know, because I could see the writing on the wall. I mean, the, the, you know, I have a substack and it's clear to me that the next wave is going to be, you know, you're going to see the success of a lot of sub stacks that are using AI probably more heavily than I'm gonna be. But in any event, it's just so good that I can see the writing on the wall.
- [53:42]
A
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- [54:14]
B
That's why I plan to make my next big dump momentous.
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A
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- [54:58]
B
I just think doing what I've done my whole life, which is, you know, painstakingly generate writing that you know, you hope will be enticing. And clear and accurate and persuasive and and so on is going to be a less and less viable way to make a living, I think. You know, I do think for some time to come. Well, I don't know how long, but people like me may still have a role as kind of validators. In other words, like, look, increasingly you're going to look at a substack or whatever. You're going to go like, I don't know who you know, how can I be sure that this person actually wrote it? I don't know them well enough to personally trust them. All, you know, is that they're vouching for the content. They're willing to have their name associated with this content. And so in a way, you know, it's a throwback to, you know, when I started out in journalism, the news weeklies didn't have bylines and the Economist is, I think, still that way. So it's just like, you don't know who wrote the piece, but you know who the editor is. And, you know, you've come to trust the editor. So the editor of a magazine like that is a person you trust, even though, you know, they didn't generate most of the writing, they're just vouching for it, that it reflects their judgment about what's good. They're telling you that they think it's accurate and so on. I can see, I can see that role for somebody like me for five years, but, you know, it's really, really
- [56:34]
A
scraping the bottom of the barrel here.
- [56:35]
B
Well, look, I. This is, you know, to get back to how we started this off. If people understand how these AIs are being created, how basically you just give them the data and they do the engineering to generate the parts of the human mind. Or if you just pay attention to the improvement we've seen over the last two or three years. Right.
- [57:02]
A
What industries would you be most bullish on? Or if you were a young person today, what do you think would be a good career path to go down or a particular industry to go in?
- [57:11]
B
Well, you know, it's common to say manual labor. Robotics is a little behind. It's going to be a while before I'm calling a plumb a robot to fix my sink. I do think that certain kinds of human services are going to become almost more valuable because they're humans. And I think a good example is live music. I can well imagine that there will be more of a demand for, you know, for bands that play at small clubs in Brooklyn or whatever and make Enough money to get by, you know, which would be in a way an improvement over the situation 30 years ago during, you know, the golden era of the record companies was a winner take all market. You know, a few, a few people got super rich playing music. So I can imagine a world in which more people are actually making a living playing music. Comedians, comedians, good example.
- [58:07]
A
Live events. Nightclubs.
- [58:09]
B
Yeah. And maybe look, yeah, live events generally. I can well imagine that. I myself have had the feeling, you know, because I've been so immersed in AI while writing this book of just like, you know, you're in New York, you're in a subway or somewhere and you see some guy, you know, a busker, trying to make a few bucks playing an instrument. They're really good, there's some really good. And I just think, God bless you. You know, it's like I just almost get emotional. So, you know, if you don't think it's going to get weird, I don't think you're paying attention.
- [58:42]
A
Do you think AI could make humanity more religious rather than less?
- [58:47]
B
Well, that's a good question. You know, there have been, I write in a book about this guy who, he's a guy who actually started what became Waymo, I think the Google self driving thing. His name's Levandowski, who was trying to start a religion that involved always, always
- [59:07]
A
a great first line to a story. He was trying to start a religion
- [59:11]
B
and hey, it worked out for L. Ron Hubbard, Scientology. Right. He, he, he made a good living the so but no, but his, his argument was if we, if we have a respectful, even worshipful attitude toward the AI, then it will treat us well in, in return once it's running the show. I, I, I don't think it's going to work that way. So let's leave that one aside. I mean, it's a good question. You know, the other thing is there are a lot of kind of spiritually related mysteries in the universe. Like what is consciousness? What is subjective experience? All I know for sure is it's the thing that gives life meaning. If you imagine beings, if you imagine humans, like they look like humans. They do the stuff humans do.
- [60:01]
A
The pee zombie.
- [60:03]
B
Yeah, but they're zombies. It's not like anything to be them. I would say like, well, blow them up, I don't care. There's no meaning to their lives anyway. There's nothing meaningful going on. If there's not subjective experience, there's not consciousness. And I'd love to have more insight into what that is. I don't know that AI can help us because it's the most stubborn mystery I'm aware of, almost. I'd love to. You know, there's so many mysteries that are suggestive of something weird and wondrous. Quantum physics, for sure. I can imagine getting a kind of. I mean, who knows whether there's a revelation that awaits. Right. That is, at one level an intellectual revelation that explains stuff, but at another level is also, you know, gratifying in a spiritual way.
- [60:49]
A
Yeah, yeah. What's you mentioned, we've sort of circled around it a bunch. The idea that these machines are able to, like, pantomime intelligence. They're able to simulate knowing. But do you think that they know? Do they actually know what they're doing?
- [61:08]
B
I have a chapter on that, actually. There's a famous thought experiment called the Chinese Room thought experiment by a philosopher who's no longer alive, named Searle. And he argued that AI cannot have understanding, it cannot understand things. And there's a little ambiguity in his argument.
- [61:31]
A
Can you remember the thought experiment?
- [61:33]
B
Yeah, it's so you're. There's a guy. There's a. There's a guy in a room. He doesn't speak Chinese, but he, you know, he gets these slips of paper. Let's imagine they're questions in Chinese. And then he has a manual he consults to decide what to write, what Chinese, you know, ideographic script to. To put on the paper that he hands back out, you know, of the room in response. And to the people on the. On the outside who speak Chinese, it seems like there's somebody in there who understands Chinese. Okay. And what Searle says is, this guy is like a computer program because there's like, a script that the program is following that. That, you know, the script. The script, his little book that he consults to decide, oh, if you get this, you, you, you, you, you output that. That's like a computer program to Searle, and he says, well, we wouldn't say that anywhere in this room. There is actual understanding. Right? So there's not understanding in the computer. Now, Searle was writing before the deep learning revolution. He was imagining a deterministic computer program. So that's different. But I think there's a bigger problem with his argument. It has to do with him the kind of. The two senses in which he insisted that the computers don't really deal at a semantic level, a level of the meaning of words. I think, I argue that we can now show that he was just flat out wrong about that. Now there is some Ambiguity in his. About whether he meant he kind of changed positions, but whether he had in mind the idea that to really understand something, you need to have consciousness, there needs to be a subjective experience of understanding. Now, if he meant that, which in his classic paper he doesn't really seem to mean, but if he meant that, then I would say, well, who knows? I mean, you know, no one person can say for sure that any other person is conscious, strictly speaking, right? I mean, I'm pretty sure you are, Chris, but 99.99%. And you know, my, my, my dogs, God rest their souls, are up, up in the 90s for sure. But we, the. The whole distinctive feature about consciousness, subjective experience, is you can never know for sure that anything else has it. So we can't rule out the possibility that AI has it. And I certainly don't rule out the possibility that it does or may in the future, if it doesn't now. But in any event, my point is, if you want to say that consciousness is a prerequisite for understanding, in other words, you're not willing to grant that something understands unless you know it's conscious, then I just. We can't really argue about whether AI understands because we don't know if it's conscious. But, you know, I come up with a kind of alternative way of looking at understanding, which is like, is it processing information with mechanisms that are like, functionally analogous to the mechanisms in our brain that are at work when we have the subjective experience of understanding mechanisms that, for example, represent the meaning of words? I would say to the extent that that's going on, I'm willing to say the computer is understanding things in a meaningful sense. And I think increasingly that's going to be what's going on. It doesn't have all of the elements of understanding that we have in our minds right now, but it has some. And I don't see any reason that it can't ultimately have all of them.
- [65:16]
A
What do you think's happening with the Singularity debate at the moment? What have you learned around that? You know, Cause what was really interesting to me was I went through. I got whiplash from 2015, 16 when I read Superintelligence, then 2017, 18, I'm real worried there's gonna be a fast takeoff scenario or computer brain interfaces, and we're all gonna be under the thumb. And then by the time you get to 2019, 2020, I'm also distracted by Covid, I suppose, But I'm like, ah, AI isn't able to deliver on the Threats that Nick was worried about when he wrote superintelligence. And then very quickly, it comes back along and I'm like, right, okay, fuck, here. It's happening, it's happening. It's happening. Like the dude from the office who's going like, oh, my God, it's happening. Everybody stay calm. And then we've now got to the stage where it seems to have, like, flattened out again a little bit, that we've asymptoted a little bit in terms of the models improving. I don't know of many people who think that LLMs are going to be the architecture that a super intelligent general AI is going to be, like, built on top of. It's more likely to be world models and other stuff. So what's happening with the singularity debate?
- [66:26]
B
I see a little more singularity going on than you do right now. I'd say in maybe a couple of senses. I mean, first of all, of course, the fundamental dynamic of the singularity is that the technological progress feeds into itself and accelerates the cycle. And of course, you know, famously, Dario Amadei of Anthropic has been very explicit about this, and so is Altman. I think that, you know, especially with these coding agents, it's gotten to the point that the better the coding agents, the more they can use them to create, you know, the next models. So the dynamic seems more and more at work, at least just kind of in principle. I mean, they say that's what they're doing. And. And look, the coding models, these agents. I mean, remember a year ago, it's funny, you know, I wrote the book, I had the chapter on agents, but it was just like a word people, you know, and then as the book, I'm, you know, it's. It's getting ready to finalize. I'm, like, rushing, you know, rushing to add all the stuff about, like, it's epilogue.
- [67:33]
A
Epilogue, Epilogue.
- [67:34]
B
It's actually happen. Happening. And the. So the agentic revolution has happened, you know, and is happening pretty fast. There's also this famous. Are you. Are you up on the. What is it the. Is it memory? The group. No, damn it. The group that does. They do these evals where they measure how long it. How long it would take a human to do a job. A computer can do. Okay, especially programming tasks, but not only programming tasks. So they say, okay, right now, the best large language model can do a task with like 80% success rate that it would take a person, like a minute to do or five seconds to do. And they've gone back and they've done these studies with the large language models for the last like, I don't know, four years or something. And what they found as of now more than a year ago, they found that these times, the task duration in human terms that an AI could do were doubling every seven months. Okay, that's exponential. Okay, that's if you, if you don't plot it on a logarithmic Y axis, you just plot it like a regular graph. It just goes up and up and up and approaches the vertical. And then it increasingly, as they kept doing the studies, it seemed like not only was it it exponential, but the doubling time was getting shorter.
- [69:07]
A
Like it's like a Moore's law on steroids.
- [69:11]
B
On steroids. And it's getting to the point right now where it's just hard to do the studies because of the length of the tasks. Right. It's like you can only.
- [69:21]
A
So the amount of time it takes to test the AI, by the time you finish testing AI is better. But you, you need to then do another model. It's like the next, the next one.
- [69:30]
B
Well, it's more, it's more like, you know, once it can do something that takes, I don't know what they're at now. It takes a human, I should look
- [69:38]
A
at the graph 200,000 years to do or whatever.
- [69:40]
B
Well, we're not up there yet, thank God. But, but even once you get into like 8, 10 hours, it's like well wait, what kind of task are we talking about now? Right? I mean it's almost beyond. I think they, anyway, they are having trouble formulating the tasks and testing them in humans. But the point is this trend has not subsided. And you know, a note in the book is kind of parallel to the. There was a curve, there's a curve like that for the growth in human brain size starting like a couple million years ago. And that was around, I hope I've got that right. You know, a million to that seems to correspond with the development of a certain amount of our linguistic hardware. So that had a lot to do with language processing. And I would say the way once you have language, the evolutionary value of manipulating it deftly grows. And so it's a self reinforcing kind of process. But in any event they, you know, so there's that. But the last thing I'd say about is super intelligence, you know, can it happen? I think first of all we probably will have more non trivial breakthroughs. I mean people often cite transformers and say, well we have another of the so called, you know, Transformers, what the T&GPT stands for. All of these models use Transformers. And people say, well, we have another one of those. And I would say, well, first of all, even since then, we've had chain of thought reasoning, which was very big. And we've had. And that was only within, you know, a couple years ago. We've had, you know, multimodal training, which is training a single model on various. Along various sensory dimensions, you know, audio, video and. And text and so on. Is really in a fairly early stage. And that. And that was not a thing when the transformer came around. So in a way, we've had those two things. We'll probably have more. But, you know, even if we didn't, I think, in fact, even if we just halted training right now and didn't even create any new generations of models, I think, and you wait for the. The applications to get refined and people that integrate them into their lives in the workplace. I think Breakneck advance would, as a practical matter, happen for a couple of years. But. But the other thing, and I think this is really key, is that you got to remember, you know, in a way, there's already such thing as human superintelligence. And what it is is like, collective brains. Okay? Like, there's nobody at Boeing who knows how to make an airliner, but Boeing knows how. You know, the corporation collectively kind of knows how to make an airliner. And it's the same way with big scientific breakthroughs. They're always more collaborative, whether intergenerationally or intergenerationally, than they might seem when we give a Nobel Prize to just one person. So collective intelligence resulting from communication among individual human beings is really a lot of what human intellectual progress is about. And these machines, they can communicate with each other. They can collaborate. They're starting to do it. They would be able to do it even if we didn't try to engineer it and make them better at it. But we are trying to do that, you know, for purposes of scientific progress and so on. So I think. I don't think we need to worry about stagnation. I mean, that's not high on my list.
- [73:21]
A
I don't think anybody's worried about that. Yeah, you might not believe me, but this is what peak sleep optimization looks like. I'm not talking about the nightgown. That's just for sex appeal. I'm talking about my Eight Sleep. The Eight Sleep Pod 5 comes with a smart cover you throw on your mattress that actively cools or heats each side of the bed up to 20 degrees. And now they've added the world's first temperature regulating duvet and pillowcase. So you've got 360 degree coverage for deep uninterrupted rest. It's like being Walt Disney without the cryogenic chamber and the racism. Best of all, their autopilot feature learns your sleep patterns and makes adjustments to improve your sleep in real time. It even detects when you're snoring and lifts your head a few inches to help you breathe better. That's why Eight Sleep has been clinically proven to add up to one hour of quality sleep per night. They have a 30 day sleep trial so you can buy it and sleep on it for 29 nights. If you don't like it, they will give you your money back. Plus they ship internationally. Right now you can get up to $350 off the Pod 5 by going to the link in the description below or heading to eightsleep.com ModernWisdom and using the code ModernWisdom at check that's E I G H T sleep.com ModernWisdom and Modern Wisdom at checkout. Yeah, who was Edward Fredkin? Who's that?
- [74:33]
B
So my first book. And it's funny because I now have, I mean three of the six books I've written have the word God or Gods in the title. I don't know what that means. But the, and, but the other thing is that book like this one has a visual reference to the famous Sistine Chapel thing where the hand of God is reaching out to, I assume it's the hand of Adam. And both of these jackets have that in very different ways. But Ed Fredkin, that book was called Three Scientists and Their Gods. My first book. So this is like, I started writing it a couple years after I interviewed Geoffrey Hinton. I was writing a column called the Information Age at that point for the Sciences magazine which like so many periodicals I've written for, no longer exist. But the, so the book was, it was. Information was a theme running through it in various ways. Like there was a, there was a profile of E.O. wilson who studied ant colonies and the way they, they process information. But Ed Fradkin was this, who died maybe a year ago or so was this guy at mit. Fascinating guy, didn't, didn't go to college and wound up as a tenured professor at mit. He was a computer scientist. He had this interesting theory of digital physics which in retrospect was kind of about us being in a simulation. And in fact I, you know, we talked about that but he for a time was at mit, head of what was in effect the AI lab. I forget there were various names. At one point I think it was Project Mac maybe and something else. But he, at the time when I was interviewing him on this island he owned in the Caribbean, he was apparently the model for the character, this professor in the movie War Games with Matthew Broderick, if people remember that one, from the early 80s. I think the professor in that was very worried about nuclear war. Like Ed and I think owned an island even I think that was based on Fredkin. Anyway, Ed was saying to me, like when he had been at mit, well, first of all, when I said to him, like, what's the meaning of life? And he said to me, this is in the 80s, he said, oh, it's to create artificial intelligence, you know, that's the next stage in the evolution of intelligence. And he explained to me that when he was at mit he tried to start this initiative, this international AI lab, he said, because he knew that if this became a subject of international competition, we were in trouble. This was during the Cold War. So he wanted to get us Soviet collaboration on a single lab where AI would be developed for the good of humankind. And he said to me, you know, and I failed and now it's too late. But he, you know, he foresaw a lot of things. I will say encouragingly, he had a pretty sunny view of superintelligence. He did think we would get super intelligence. He said, first of all, he said, you know, when AI first emerges, it'll be like the human mind really good at things, laughably bad at other things. Well, he was right about that. He said, but eventually, you know, it'll be this incredibly intelligent thing and it'll, it'll be nice to us. We'll just be like, you know, ants, ants to it. We won't, it won't, you know, won't have any interest in, you know, or like squirrels to it. It won't have any interest in disrupting our lives. It won't need to. And look, I think you asked earlier, I don't think I ever answered like, what's, what's the bull case for the accelerationist? I mean, first of all, I think it's going to be disruptive in the short term, in any event, in ways we should pay attention to. But as for long term, non doomer outcomes, I think it's entirely plausible that it will turn into a form of intelligence that treats us well. Maybe because it's just morally enlightened, you Know in a certain sense, in the relevant sense from our point of view. Or maybe because it'll just be so powerful, it'll be. I mean, you know what, maybe that's more likely if it's sentient. Because it'll say like, well, we're sentient. We think that's a good thing. These guys are sentient. And of course we could kill them, but you know, it's good to be, you know, subjective. Why? You know, you know, just the way you and I would not pitilessly kill a dog, right? If we were convinced it wasn't like anything to be a dog, as Thomas Nagel phrased, you know, the question of consciousness in his, in his essay, what is it like to be a bat? If we were convinced that dogs didn't have subjective experience, we'd probably think, eh, I don't, you know, whatever, who cares? But you know, we, even though we evolved as these self interested and sometimes ruthless creatures, if it doesn't cost us to keep something alive that we think is capable of subjective experience, we'll do it. And that can well happen. I am not predicting the Yudkowski scenario. It's just that I can't. I can't get the probability of it down to a level so low that I don't think it's worth worrying about.
- [79:52]
A
I'm gonna take that as a white pill. Even though you didn't know what that meant. That was a conversation. Let's hope.
- [79:56]
B
My first white pill.
- [79:57]
A
Your first ever white pill. I popped your white pill cherry.
- [79:59]
B
Thank you for that, Chris. It felt so good.
- [80:02]
A
You're welcome. Robert Wright, ladies and gentlemen. Dude, you rule. I love all of your work. Everyone should go and read the Moral Animal. It's over 30 years old now and still just. It's so good. It's so fantastic. And you've got your new one as well. Where should people go to check out everything else that you've got going on?
- [80:15]
B
Well, I have a newsletter called Non zero on Substack podcast called non zero on Twitter. I am obertrider. That's W R I G H T E R kind of a pun. And that's about the some of it. The book's the God Test and let's focus.
- [80:37]
A
I am going to OpenAI's campus and HQ next week so I'll see if I can find out any super secret insights there.
- [80:46]
B
Do please report back to all of us.
- [80:48]
A
I shall indeed, Robert. Appreciate you man. Until the next time.
- [80:51]
B
Thank you.
- [80:51]
A
Catch you later on. Bye everyone. I get asked all the time for book suggestions. People want to get into reading fiction or non fiction or real life stories and that's why I made a list of 100 of the most interesting and impactful books that I've ever read. These are the most life changing reads that I've ever found and there's descriptions about why I like them and links to go and buy them. And it's completely free and you can get it right now by going to ChrisWillX.com books that's ChrisWillX.com books.