AI: The Beast or Jerusalem? – Jonathan Pageau & Jim Keller (Transcript)

The computer does those operations on representations of information, ones and zeros. It doesn’t understand them at all. The computer has no understanding of it. So what we call a language model translates information like words and images and ideas into a space where the program, the ideas, and the operation that does on them are all essentially the same thing.

Right, so a language model can produce words and then use those words as inputs. And it seems to have an understanding of what those words are, which is very different from how a computer operates on data.

Jordan B Peterson: About the language models, I mean, my sense of, at least in part, how we understand the story is that maybe we’re watching a movie, let’s say, and we get some sense of the character’s goals, and then we see the manner in which that character perceives the world, and we in some sense adopt his goals, which is to identify with the character. And then we play out a panoply of emotions and motivations on our body, because we now inhabit that goal space, and we understand the character as a consequence of mimicking the character with our own physiology. And you have computers that can summarize the gist of a story, but they don’t have that underlying physiology.

Deep Story And Prompt Engineering

Jim Keller: Well, first of all, it’s a theory that your physiology has anything to do with it. You could understand the character’s goals and then get involved in the details of the story, and then you’re predicting the path of the story, and also having expectations and hopes for the story, and a good story kind of takes you on a ride, because it teases you with doing some of the things you expect, but also doing things that are unexpected, and possibly that creates emotional.

So in an AI model, so you can easily have a set of goals. So you have your personal goals, and then when you watch the story, you have those goals. You put those together. Like, how many goals is that? Like, the story’s goals and your goals, hundreds, thousands, those are small numbers, right?

Then you have the story. The AI model can predict the story, too, just as well as you can. As the story progresses, it can look at the error between what it predicted and what actually happened, and then iterate on that. So you would call that emotional excitement, disappointment. Anxiety. Like some of those states are manifesting in your body, because you trigger hormone cascades and a bunch of stuff, but you also can just scan your brain and see that stuff move around.

And, the AI model can have an error function and look at the difference between what it expected and not, and you could call that the emotional state. And that’s speculation, but we can make an AI model that could predict the results of a story probably better than the average person. Some people are really good at it — they’re really well-educated about stories, or they know the genre or something.

And what they see today is the capacity of the models is, if you, say, start describing a lot, it will make sense for a while, but it will slowly stop making sense. But that’s possible. That’s simply the capacity of the model right now, and the model is not well-grounded enough in a set of, let’s say, goals and reality or something to make sense for a while.

Jordan B Peterson: So what do you think would happen, Jonathan? This is, I think, associated with the kind of things that we’ve talked through to some degree. One of my hypotheses, let’s say, about deep stories is that they’re meta-gists in some sense. So you could imagine 100 people telling you a tragic story, and then you could reduce each of those tragic stories to the gist of the tragic story, and then you could aggregate the gists, and then you’d have something like a meta-tragedy.

And I would say the deeper the gist, the more religious-like the story gets. And that’s part of, it’s that idea is part of the reason that I wanted to bring you guys together. I mean, one of the things that what you just said makes me wonder is, imagine that you took Shakespeare, and you took Dante, and you took, like, the canonical Western writers, and you trained an AI system to understand the structure of each of them, and now you could pull out the summaries of those structures, the gists, and then you pull out another gist out of that? So it would be, like, the essential element of Dante and Shakespeare, and I wonder if that would get biblical.

Jim Keller: I want to hear what Jonathan has been thinking so far. So here’s one funny thing to think about. You used the word pull out. So when you train a model to know something, you can’t just look at it and say, what does it know? You haven’t asked for it yet. Right? You haven’t asked. What’s the next sentence in this paragraph? What’s the answer to this question?

There’s a thing on the internet now called prompt engineering. And it’s the same way, I can’t look in your brain to see what you think. I have to ask you what you think. Because if I killed you and scanned your brain and got the current state of all the synapses and stuff, A, you’d be dead, which would be sad, and B, I wouldn’t know anything about your thoughts. All your thoughts are embedded in this model that your brain carries around. And you can express it in a lot of ways. And so you could add?

Jonathan Pageau: How do you train? So this is my big question is, I mean, because the way that I’ve been seeing it until now is that artificial intelligence is based on us. It doesn’t exist independently from humans. And it doesn’t have care. The question would be, why does the computer care?

Jim Keller: That’s not true.

Jonathan Pageau: Well, why does the computer care to get the gist of the story?

Jim Keller: Well, yeah. So I think you’re asking kind of the wrong question. So you can train an AI model on the physics and reality and images in the world just with images. And there are people who are figuring out how to train a model with just images. But the model itself still conceptualizes things like tree and dog and action and run, because those all exist in the world.

So and you can actually train — when you train a model with all the language and words, so all information has structure. And I know you’re a structure guy from your video. So if you look around you at any image, every single point you see makes sense.

Jonathan Pageau: It’s a teleological structure. It’s like it’s a purpose laid in structure. So this is something.

Jim Keller: So it turns out all the words that have ever been spoken by human beings also have structure. And so physics has structure. And it turns out that some of the deep structure of images and actions and words and sentences are related, like there’s actually a common core of like — imagine there’s like a knowledge space and ensure there’s details of humanity where, they prefer this accent versus that. Those are kind of details. But they’re coherent in the language model. But the language models themselves are coherent with our world ideas.

And humans are trained in the world just the way the AI models are trained in the world. Like a little baby, as it’s learning, looking around, it’s training on everything it sees when it’s very young. And then it’s training rate goes down and it starts interacting what it’s learning, interacting with the people.

Jonathan Pageau: It’s trying to survive. It’s trying to live. It has like it has the infant or the child has –

Jim Keller: Neurons aren’t trying. The weights in the neurons aren’t trying to live. What they’re trying to do is reduce the error. So neural networks generally are predictive things like what’s coming next. What makes sense? You know, how does this work? When you train them, when you train an AI model, you’re training it to reduce the error in the model.

Friston, Error Prediction and Emotional Mapping

Jordan B Peterson: Let me ask you about that. So well, first of all –

Jim Keller: So babies are doing the same thing, like they’re looking at stuff go around. And in the beginning, their neurons are just randomly firing. But as it starts to get object permanent, it’s a look at stuff, it starts predicting what will make sense for that thing to do. And when it doesn’t make sense, it’ll update its model.

Jonathan Pageau: It compares its prediction to the events and then it will adjust its prediction. So in a story prediction model, the AI would predict the story, then compare it to its prediction and then fine tune itself slowly as it trains itself.

Jim Keller: Or you could ask it to say, given the set of things, tell the rest of the story and it could do that. And the state of it right now is there are people having conversations with this that are pretty good.

Jordan B Peterson: So I talked to Karl Friston about this prediction idea in some detail. And so Friston, for those of you who are watching and listening, is one of the world’s top neuroscientists. And he’s developed an entropy enclosure model of conceptualization, which is analogous to one that I was working on, I suppose, across approximately the same time frame.

So the first issue and this has been well established in the neuropsychological literature for quite a long time, is that anxiety is an indicator of discrepancy between prediction and actuality. And then positive emotion also looks like a discrepancy reduction indicator. So imagine that you’re moving towards a goal and then you evaluate what happens as you move towards the goal. And if you’re moving in the right direction, what happens is what you might say, what you expect to happen, and that produces positive emotion. And it’s actually an indicator of reduction in entropy. That’s one way of looking at it.

And the point is –

Jim Keller: Yeah, you have a bunch of words in there that are psychological definitions of states. But you could say there’s a prediction and error in the prediction, and you’re reducing error.

Jordan B Peterson: But what I’m trying to make a case for is that your emotions directly map that both positive and negative emotion look like they’re signifiers of discrepancy reduction, both on the positive and negative emotion side. But then there’s a complexity that I think is germane to part of Jonathan’s query, which is that so the neuropsychologists and the cognitive scientists have talked a lot long time about expectation, prediction and discrepancy reduction.

But one of the things they haven’t talked about is — it isn’t exactly that you expect things. It’s that you desire them. You want them to happen, because you could imagine that there’s, in some sense, a literally infinite number of things you could expect. And we don’t strive only to match prediction. We strive to bring about what it is that we want. And so we have these pre-set systems that are teleological, that are motivational systems.

Jim Keller: Well, I mean, it depends. Like if you’re sitting idly on the beach, like, and a bird flies by, you expect it to fly along in a regular path. But you don’t really want that to happen.

Jordan B Peterson: Yeah, but you don’t want it to turn into something that could peck out your eyes either. Sure. So that’s a want.

Jim Keller: But you’re kind of following it with your expectation to look for discrepancy. Now, you’ll also have a, you know, depends on the person, somewhere between 10 and a million desires, right? And then you also have fears and avoidance. And those are contexts. So if you’re sitting on the beach with some anxiety that the birds are going to swerve at you and peck your eyes out, so then you might be watching it much more attentively than somebody who doesn’t have that worry, for example. But both of you can predict where it’s going to fly, and you will both notice the discrepancy, right?

Jordan B Peterson: The motivations, one way of conceptualizing fundamental motivation is they’re like a priori prediction domains, right? And so it helps us narrow our attentional focus, because I know when you’re sitting and you’re not motivated in any sense, you can be doing just, in some sense, trivial expectation computations. But often we’re in a highly motivated state, and what we’re expecting is bounded by what we desire. And what we desire is oriented, as Jonathan pointed out, towards the fact that we want to exist.

And one of the things I don’t understand and wanted to talk about today is how the computer models, the AI models, can generate intelligible sense without mimicking that sense of motivation. Because you’ve said, for example, they can just derive the patterns from observations of the objective world, but there’s a —

Does The Intelligence in AI Come From Humans?

Jim Keller: So again, I don’t want to do all the talking, but so AI, generally speaking, like when I first learned about it, it had two behaviors. They call it inference and training. So inference is you have a trained model, so you give it a picture and say, is there a cat in it? And it tells you where the cat is. That’s inference.

The model has been trained to know where a cat is. And training is the process of giving it an input and an expected output, and when you first start training the model, it gives you garbage out, like an untrained brain would. And then you take the difference between the garbage output and the expected output and call that the error. And then they invent — the big revelation was something called backpropagation with gradient descent. But that means take the error and divide it up across the layers and correct those calculations so that when you put a new thing in, it gives you a better answer.

And then to somewhat my astonishment, if you have a model of sufficient capacity and you train it with 100 million images, if you give it a novel image and say, tell me where the cat is, it can do it. That’s called — so training is the process of doing a pass with an expected output and propagating an error back through the network, and inference is the behavior of putting something in and getting an output.

But there’s a third piece, which is what the new models do, which is called generative. It’s called a generative model. So for example, say you put in a sentence and you say, predict the next word. This is the simplest thing. So it predicts the next word. So you add that word to the input, and now say, predict the next word. So it contains the original sentence and the word you generated, and it keeps generating words that make sense in the context of the original word and additional words. This is the simplest basis.

And then it turns out you can train this to do lots of things. You can train it to summarize a sentence. You can train it to answer a question. This is a big thing about — like Google every day has hundreds of millions of people asking a question, giving answers, and then rating their results. You can train a model with that information. So you can ask it a question, and it gives you a sensible answer.

Jonathan Pageau: I think in what you said, I actually have the issue that has been going through my mind so much, is when you said people put in the question, and then they rate the answer. My intuition is that the intelligence still comes from humans, in the sense that it seems like in order to train whatever AI, you have to be able to give it a lot of power, and then say at the beginning, this is good, this is bad, this is good, this is bad, like reject certain things, accept certain things, in order to then reach a point when then you train the AI.

And so that’s what I mean about the care. So the care will come from humans, because the care is the one giving it the value, saying this is what is valuable, this is what is not valuable in your calculation.

Jim Keller: So there’s the program called AlphaGo, I learned how to play Go better than a human. So there’s two ways to train the model. One is, they have a huge database of lots of Go games with good winning moves. So they train the model with that, and that worked pretty good. And they also took two simulations of Go, and they did random moves. And all that happened was, is these two simulators played one Go game, and they just recorded whichever moves happened to win, and it started out really horrible.

And they just started training the model, and this is called adversarial learning, it’s not particular adversarial, it’s like, you know, you make your moves randomly, and you train a model, and so they train multiple models, and over time, those models got very good, and they actually got better than human players. But the humans have limitations about what they know, whereas the models could experiment in a really random space and go very far.

Jonathan Pageau: Yeah, but experiment towards the process of winning the game.

Jim Keller: Yes, well, but you can experiment towards all kinds of things, it turns out. And humans are also trained that way, like when you were learning, you were reading, you were saying, this is a good book, this is a bad book, this is good sense construction, it’s good selling. So you’ve gotten so many error signals over your life.

Jordan B Peterson: Well, that’s what culture does in large part.

Jim Keller: And then culture does that, religion does that, your everyday experience does that, your family. So we embody that, and we’re all, and everything that happens to us, we process it on the inference pass, which generates outputs. And then sometimes we look at that and say, hey, that’s unexpected, or that got a bad result, or that got bad feedback. And then we back propagate that and update our models.

So really well-trained models can then train other models. So humans right now are the smartest people in the world.

Jonathan Pageau: So the biggest question that comes now based on what you said is, because my main point is to try to show how it seems like artificial intelligence is always an extension of human intelligence. Like it remains an extension of human intelligence. And maybe the way —

Jim Keller: That won’t be true at all.

Jonathan Pageau: So do you think that at some point the artificial intelligence will be able to — Because the goals, recognizing cats, writing plays, all these goals are goals which are based on embodied human existence. Could an AI at some point develop a goal which would be uncomprehensible to humans because of its own existence?

When a Human Records Data Vs an AI

Jim Keller: Yeah. I mean, for example, there’s a small population of humans that enjoy math, right? And they are pursuing adventures in math space that are incomprehensible to 99.99% of humans. But they’re interested in it. And you could imagine like an AI program working with those mathematicians and coming up with very novel math ideas and then interacting with them.

But they could also, you know, if some AIs were elaborating out really interesting and detailed stories, they could come up with stories that are really interesting. We’re going to see it pretty soon, like all of art, everything.

Jonathan Pageau: A story that is interesting only to the AI and not interesting to us.

Jim Keller: Yeah, it’s possible. So stories are like, I think, some high-level information space. So the computing age of big data, there’s all this data running on computers, but only humans understood it, right? Computers don’t. So AI programs are now at the state where the information, the processing, and the feedback loops are all kind of in the same space.

They’re still relatively rudimentary to humans. I guess some AI programs in certain things are better than humans already, but for the most part, they’re not. But it’s moving really fast. And so you can imagine, you know, I think in five or 10 years, most people’s best friends will be AIs. And, they’ll know you really well and they’ll be interested in you and, you know…

Jordan B Peterson: Unlike your real friends.

Jim Keller: Yeah, real friends are problematic.

Jordan B Peterson: They’re only interested in you when you’re interested.

Jim Keller: Yeah, yeah, real friends are.

Jordan B Peterson: AI systems will love you even when you’re dull and miserable.

Jim Keller: Well, there’s so much idea space to explore. And humans have a wide range. Some humans like to go through their everyday life doing their everyday things. And some people spend a lot of time, like you, a lot of time reading and thinking and talking and arguing and debating. And there’s going to be, I like to say, a diversity of possibilities with what a thinking thing can do when the thinking is fairly unlimited.

Jordan B Peterson: So I’m curious about, I’m still, I’m curious in pursuing this issue that Jonathan has been developing. So there’s a literally infinite number of ways, virtually infinite number of ways that we could take images of this room. Right now, if a human being is taking images of this room, they’re going to be, they’re going to sample a very small space of that infinite range of possibilities. Because if I was taking pictures in this room, in all likelihood, I would take pictures of objects that are identifiable to human beings, that are functional to human beings at a level of focus that makes those objects clear.

And so then you could imagine that the set of all images on the internet has that implicit structure of perception built into it. And that’s a function of what human beings find useful. I mean, I could take a photo of you that was, the focal depth was here and here and here and here and here and two inches past you. And now I suppose you could —

Jim Keller: There’s a technology for that called light fields. So then you could — if you had that picture properly done, then you could move around it and image and see. But yeah, fair enough. I get your point. The human recorded data has our biology built into it, but also unbelievably detailed encoding of how physical reality works.

So every single pixel in those pictures, even though you kind of selected the view, the focus, the frame, it still encoded a lot more information than you’re processing. And if you take a large, it turns out if you take a large number of images of things in general, so you’ve seen these things where you take a 2D image and turn it into a 3D image.

The reason that works is even in the 2D image, the 3D image in the room actually got embedded in that picture in a way. Then if you have the right understanding of how physics and reality works, you can reconstruct the 3D model. So you could — an AI scientist may cruise around the world with infrared and radio wave cameras, and they might take pictures of all different kinds of things. And every once in a while, they’d show up and go, hey, the sun, you know, I’ve been staring at the sun and the ultraviolet and radio waves for the last month. And it’s way different than anybody thought, because humans tend to look at light in the visible spectrum.

And there could be some really novel things coming out of that. But humans also, we live in the spectrum we live in, because it’s a pretty good one for planet Earth. Like, it wouldn’t be obvious that AI would start some different place. Like, visible spectrum is interesting for a whole bunch of reasons.

When Will AI Become Autonomous?

Jordan B Peterson: So in a set of images that are human-derived, you’re saying that, the way I would conceptualize that is that there’s two kinds of logos embedded in that. One would be that you could extract out from that set of images what was relevant to human beings. But you’re saying that the fine structure of the objective world outside of human concern is also embedded in the set of images, and that an AI system could extract out a representation of the world, but also a representation of what’s motivating to human beings.

Jim Keller: And then some human scientists already do look at the sun and radio waves and other things, because they’re trying to get different angles on how things work. It’s a curious thing. It’s like the same with, like, buildings and architecture. Mostly fit people. There’s a reason for that.

Jonathan Pageau: The reason why I keep coming back to hammering the same point is that even in terms of the development of the AI, that is, developing AI requires immense amount of money, energy, and time.

Jim Keller: That’s a transient thing. In 30 years, it won’t cost anything. So that’s going to change so fast. It’s amazing. So that’s, like, supercomputers used to cost millions of dollars, and now your phone is the supercomputer. So it’s the time between millions of dollars and $10 is about 30 years. So it’s, like, I’m just saying this, like, the time and effort isn’t a thing in technology. It’s moving pretty fast. That just sets the date.

Jonathan Pageau: Yeah. But even making, let’s say, even — I mean, I guess maybe this is a nightmare question. Like, could you imagine an AI system which becomes completely autonomous, which is creating itself even physically through automized factories, which is, programming itself, which is creating its own goals, which is not at all connected to human endeavor?

Jim Keller: I mean, individual researchers can — You know, I have a friend who — I’m going to introduce you to him tomorrow. He wrote a program that scrapes all of the internet and trained an AI model to be a language model on a relatively small computer. And in 10 years, the computer he could easily afford would be as smart as a human. So he could train that pretty easily. And that model could go on Amazon and buy 100 more of those computers and copy itself. So, yeah, we’re 10 years away from that.

Jonathan Pageau: And then why would it do that? I mean, what does — Is it possible? It’s all about the motivational question. I think that that’s what even Jordan and I both have been coming at from the outset. It’s like, so you have an image, right? You have an image of Skynet or of the Matrix, in which the sentient AI is actually fighting for its survival. So it has a survival instinct, which is pushing it to self-perpetuate, like to replicate itself and to create variation on itself in order to survive and identify humans as an obstacle to that, you know?

Jim Keller: Yeah, yeah. So you have a whole bunch of implicit assumptions there. So humans, last I checked, are unbelievably competitive. And when you let people get into power with no checks on them, they typically run amok. It’s been a historical experience. And then humans are self-regulating to some extent, obviously, with some serious outliers because they self-regulate with each other. Humans and AI models at some point will have to find their own calculation of self-regulation and trade-offs about that.

Jonathan Pageau: Yeah, because AI doesn’t feel pain, at least that we don’t know that it feels pain.

Jim Keller: Well, lots of humans don’t feel pain either. So, I mean, that’s, I mean, humans feeling pain or not doesn’t stop a whole bunch of activity. I mean, that’s —

Jordan B Peterson: I mean, it doesn’t — the fact that we feel pain doesn’t stop us from being able to level up.

Jim Keller: There’s not many people. Right, right. I mean, there’s definitely people like, children, if you threaten them with, you know, go to your room and stuff, you can regulate them that way. But some kids ignore that completely, and adults are the same way.

Jordan B Peterson: And it’s often counterproductive.

Jim Keller: Yeah. So, culture, societies, and organizations, we regulate each other, you know, sometimes.

Jordan B Peterson: In competition and cooperation. Do you think that, well, we’ve talked about this to some degree for decades. I mean, when you look at how fast things are moving now, and as you push that along — when you look out 10 years, and you see the relationship between the AI systems that are being built and human beings, what do you envision? Or can you envision it?

Jim Keller: Well, can I? Yeah. Like I said, I’m a computer guy. And I’m watching this with, let’s say, some fascination as well. I mean, the last, so Ray Kurzweil said, you know, progress accelerates. So we have this idea that 20 years of progress is 20 years. But, you know, the last 20 years of progress was 20 years, and the next 20 years will probably be, you know, five to 10.

To some level, that causes social stress, independent of whether it’s AI or Amazon deliveries. You know, there’s so many things that are going into the stress of it all.

To Create What Could Supplant You

Jonathan Pageau: But there’s progress, which is an extension of human capacity. And then there’s this progress, which I’m hearing about the way that you’re describing it, which seems to be an inevitable progress towards creating something which is more powerful than you. And so what is that? I don’t even understand that drive. What is that drive to create something which can supplant you?

Jim Keller: So look at the average person in the world, right? So the average person already exists in this world. Because the average person is halfway up the human hierarchy. There’s already many people more powerful than any of us. They could be smarter, they could be richer, they could be better connected. We already live in a world, like very few people are at the top of anything. So that’s already a thing.

Jonathan Pageau: So basically, the drive to make someone a superstar that they are, the drive to elevate someone above you, that would be the same drive that is bringing us to creating these ultra powerful machines. Because we have that, like we have a drive to elevate. Like, you know, when we see a rockstar that we like, people want to submit themselves to that. They want to dress like them. They want to raise them up above them as an example, something to follow, right? Something to subject themselves to.

You see that with leaders, you see that in the political world. And in teams, you see that in sports teams, the same thing.

Jordan B Peterson: And so we’ve always tried to build things that are beyond us. You know, I mean –

Jonathan Pageau: I mean, it’s about — are we building? Are we building a God? Is that what people — is that the drive that is pushing someone towards? Because when I hear what you’re describing, Jim, I hear something that is extremely dangerous. It sounds extremely dangerous to the very existence of humans. Yet I see humans acting and moving in that direction almost without being able to stop it. As if there’s no way.

Jordan B Peterson: I think it is unstoppable. Well, that’s one of the things we’ve also talked about is because I’ve asked Jim straight out, because of the hypothetical danger associated with this. Why not stop doing it? And well, part of his answer is the ambivalence about the outcome. But also that it isn’t obvious at all that in some sense it’s stoppable. I mean, it’s the cumulative action of many, many people that are driving this along.

And even if you took out one player, even a key player, the probability that you’d do anything but slow it infinitesimally is quite –

Jonathan Pageau: Yeah, because there’s also a massive payoff for those that will succeed. It’s also set up that way. People know that at least, at least until the AI take over or whatever, that whoever is on the line towards increasing the power of the AI will rake in major rewards.

Jim Keller: Yeah, I can recommend Iain Banks is an author, English author, I think. He wrote a series of books on the — he called the Culture novels. And that was a world where there was humans and then there was AIs — the smartest humans and AIs that were dumber than humans. But there was some AIs that were much, much smarter. And they lived in harmony because they mostly all pursued what they wanted to pursue. Humans pursued human goals and super smart AIs pursued super smart AI goals. And they communicated and worked with each other. But they mostly — they’re different. When they were different enough that that was problematic, their goals were different enough that they didn’t overlap.

That would be my guess is like these ideas where these super AIs get smart and the first thing they do is stomp out the humans. It’s like, you don’t do that. Like, you don’t wake up in the morning and think I have to stomp out all the cats. The cats do cat things and the ants do ant things and the birds do bird things. And super smart mathematicians do smart mathematician things. And, you know, guys who like to build houses do build house things.

And, the world, there’s so much space in the intellectual zone that people tend to go pursue — in a good society, like, you tend to pursue the stuff that you do. And then the people in your zone, you self-regulate. And you also, even in the social strata, we self-regulate. I mean, the recent political events of the last 10 years, the weird thing to me has been why have people with power been overreaching to take too much from people with less. Like, that’s bad regulation.

Jonathan Pageau: But one of the aspects of increase in power is that increase in power is always mediated, at least in one aspect, by military, by, let’s say, physical power on others. You know, and we can see that technology is linked and has been linked always to military power. And so the idea that there could be some AIs that will be our friends or whatever is maybe possible. But the idea that there will be some AIs which will be weaponized is, seems absolutely inevitable. Because increase in power is always — increase in technological power always moves towards military.

Jim Keller: So we’ve lived with atomic bombs since the 40s, right? So, I mean, the solution to this has been mostly, some form of mutual assured destruction or attacking me, like the response to attacking me is so much worse than the —

Unintended Consequences

Jonathan Pageau: Yeah, but it’s also because we have reciprocity. We recognize each other as the same. So if I look into the face of another human, there’s a limit of how different I think that person is from me. But if I’m hearing something described as the possibility of super intelligences that have their own goals, their own cares, their own structures, then how much mirror is there between these two groups of people, these two groups?

Jordan B Peterson: Well, Jim’s objection seems to be something like we’re making — we may be making when we’re doomsaying, let’s say, and I’m not saying there’s no place for that. We’re making the presumption of something like a zero-sum competitive landscape, right? Is that the idea behind movies like The Terminator is that there is only so much resources and the machines and the human beings would have to fight over it. And you can see that that could easily be a preposterous assumption.

Now, I think that one of the fundamental points you’re making, though, is also that there will definitely be people that will weaponize AI. And those weaponized AI systems will have as their goal something like the destruction of human beings, at least under some circumstances. And then there’s the possibility that that will get out of control because the most effective systems at destroying human beings might be the ones that win, let’s say. That could happen independently of whether or not it is a true zero-sum competition.

Jim Keller: Yeah, and also the effectiveness of military stuff doesn’t need very smart AI to be a lot better than it is today. Like the Star Wars movies where tens of thousands of years in the future, super highly trained fighters can’t hit somebody running across the field. That’s silly, right? You can already make a gun that can hit everybody in the room without aiming at it. It’s, there’s like the military threshold is much lower than any intelligence threshold, like for danger.

And, like I said, we self-regulated through the nuclear crisis. It’s interesting. I don’t know if it’s because we thought that the Russians were like us. I kind of suspect the problem was that we thought they weren’t like us. And but we still managed to make some calculation to say that any kind of attack would be mutually devastating.

The destructive power of the military we already have so far exceeds the planet. I’m not sure, like, adding intelligence to it is the tipping point. Like, that’s I think the more likely thing is things that are truly smart in different ways will be interested in different things. And then the possibility for, let’s say, mutual flourishing is really interesting. And I know artists using AI already to do really amazing things. And that’s already happening.

Jordan B Peterson: But when you’re working on the frontiers of AI development and you see the development of increasingly intelligent machines, I mean, I know that part of what drives you is, I don’t want to put words in your mouth, but what drives intelligent engineers in general, which is to take something that works and make it better and maybe to make it radically better and radically cheaper.

So there’s this drive toward technological improvement. And I know that you like to solve complex problems and you do that extraordinarily well. But is there also a vision of a more abundant form of human flourishing emerging from the development? So what do you see?

Jim Keller: Years ago, we’re going to run out of energy. What’s next? We’re going to run out of matter. Like our ability to do what we want in ways that are interesting and for some people, beautiful, is limited by a whole bunch of things because we’re partly technological and partly we’re stupidly divisive.

Jonathan Pageau: But there’s also a reality, which is one of the things that technology has been is, of course, an increase in power towards human desire. And that is represented in mythological stories where, let’s say, technology is used to accomplish impossible desire, right? We have the story of building the bull around the wife of the king of Minos in order to be inseminated by a bull. We have the story — the story of Frankenstein, et cetera, the story of the Golem, where we put our desire into this increased power.

And then what happens is that we don’t know our desires. That’s one of the things that I’ve also been worried about in terms of AI is that we have secret desires that enter into what we do that people aren’t totally aware of. And as we increase in power these systems, those desires, let’s say, like the idea, for example, of the possibility of having an AI friend and the idea that an AI friend would be the best friend you’ve ever had because that friend would be the nicest to you, would care the most about you, would do all those things. That would be an exact example of what I’m talking about, which is, it’s really the story of the genie, right?

It’s the story of the genie in the lamp, where the genie says, what do you wish? And I have unlimited power to give it to you. And so I give him my wish. But that wish has all these underlying implications that I don’t understand, all these underlying possibilities.

Jim Keller: But the cool thing, the moral of almost all those stories is having unlimited wishes will lead to your downfall. And so humans, like if you give a young person an unlimited amount of stuff to drink for six months, they’re going to be falling down drunk and they’re going to get over it. Having a friend that’s always your friend, no matter what, it’s probably going to get boring.

Jordan B Peterson: Well, the literature on marital stability indicates that. So there’s a sweet spot with regards to marital stability in terms of the ratio of negative to positive communication. So if on average you receive five positive communications and one negative communication from your spouse, that’s on the low threshold for stability. If it’s four positive to one negative, you’re headed for divorce.

But interestingly enough, on the other end, there’s a threshold as well, which is that if it exceeds 11 positive to one negative, you’re also moving towards divorce. So there might be self-regulating mechanisms that would in a sense take care of that. You might find a yes-man AI friend extraordinarily boring very, very rapidly.

Abundance and Nihilism

Jim Keller: But as opposed to an AI friend that was interested in what you were interested in, it was actually interesting. Like, you know, we go through friends in the course of our lives, like different friends are interesting at different times and some friends we grow with and that continues to be really interesting for years and years. And other friends, you know, some people get stuck in their thing and then you’ve moved on or they’ve moved on or something.

Yeah, I tend to think of a world where there was more abundance and more possibilities and more interesting things to do is an interesting place. And modern society has let the human population and some people think this is a bad thing, but I don’t know. I’m a fan of it. You know, modern population has gone from tens of 200 million to billions of people. That’s generally been a good thing. We’re not running out of space. I’ve been in it.

So some of your audience has probably been in an airplane. If you look out the window, the country is actually mostly empty. The oceans are mostly empty. Like, we’re weirdly good at polluting large areas, but as soon as we decide not to, we don’t have to. Most of our energy pollution problems are technical. Like, we can stop polluting. Like, electric cars are great. So there’s so many things that we could do technically. I forget the guy’s name. He said the Earth could easily support a population of a trillion people. And a trillion people would be a lot more people doing random stuff.

And he didn’t imagine that the future population would be a trillion humans and a trillion AIs. But it probably will be. It will probably exist on multiple planets, which would be good the next time an asteroid shows up.

Jordan B Peterson: So what do you think about — so one of the things that seems to be happening — tell me if you think I’m wrong here. I think it’s germane to –

Jim Keller: I just want to make the point of, you know, where we are compared to living in the Middle Ages, our lives are longer, our families are healthier, our children are more likely to survive. Like, many, many good things happened. Like, sending the clock back wouldn’t be good.

And, if we have some care and people who actually care about how culture interacts with technology for the next 50 years, we’ll get through this, hopefully more successful than we did the atomic bomb and the Cold War. But it’s a major change. I mean, this is like, your worries are, you know, I mean, they’re relevant. But, Jonathan, your stories about how humans have faced abundance and faced evil kings and evil overlords. Like, we have thousands of years of history of facing the challenge of the future and the challenge of things that cause radical change. That’s very valuable information.

But for the most part, nobody’s succeeded by stopping change. They’ve succeeded by bringing to bear on the change our capability to self-regulate the balance. Like, a good life isn’t having as much gold as possible. It’s a boring life. A good life is, having some quality friends and doing what you want and having some insight in life.

Jordan B Peterson: And some optimal challenge.

Jim Keller: And, you know, in a world where a larger percentage of people can have wealth, live in relative abundance and have tools and opportunities, I think is a good thing.

Jonathan Pageau: Yeah. And I don’t want to pull back abundance. But what I have noticed is that our abundance brings a kind of nihilism to people. And I don’t, like I said, I don’t want to go back. I’m happy to live here and to have these tech things. But I think it’s something that I’ve also noticed, that increase of the capacity to get your desires, when that increases to a certain extent, also leads to a kind of nihilism where exactly that —

High Human Goals and The Weaponization of Intelligence

Jordan B Peterson: Well, I wonder, Jonathan, I wonder if that’s partly a consequence of the erroneous maximization of short-term desire. I mean, one of the things that you might think about that could be dangerous on the AI front is that we optimize the manner in which we interact with our electronic gadgets to capture short-term attention, right?

Because there’s a difference between getting what you want right now, right now, and getting what you need in some more mature sense across a reasonable span of time. And one of the things that does seem to be happening online, and I think it is driven by the development of AI systems, is that we’re assaulted by systems that parasitize our short-term attention, and at the expense of longer-term attention.

And if the AI systems emerge to optimize attentional grip, it isn’t obvious to me that they’re going to optimize for the attention that works over the medium to long run, right? They could conceivably maximize something like whim-centered existence.

Jonathan Pageau: Yeah, because all the virality is based on that. All the social media networks are all based on this reduction of attention, this reduction of desire to reaching your rest, let’s say, and that desire, right? The like, the click, all these things.

Jim Keller: But that’s something that — for reasons that are somewhat puzzling, but maybe not, the business models around a lot of those interfaces are around the part, the users, the product, and the advertisers are trying to get your attention. But that’s something culture could regulate. We could decide that, no, we don’t want tech platforms to be driven by advertising money. That would be a smart decision, probably. And that could be a big change.

Jordan B Peterson: See, well, the problem for a lot of products is markets drive that in some sense, right? And I know they’re driving that –

Jim Keller: We can take steps, like at various times, alcohol has been illegal. Society can decide to regulate all kinds of things. And sometimes some things need to be regulated and some things don’t. When you buy a hammer, you don’t fight with your hammer for its attention, right? A hammer’s a tool. You buy one when you need one. Nobody’s marketing hammers to you. Like, that has a relationship that’s transactional to your purpose, right?

Our technology has become a thing where, I mean — and a short run of things are —

Jonathan Pageau: There’s a relationship between human, let’s say high human goals, something like attention and status. And what we talked about, which is the idea of elevating something higher in order to see it as a model. So these are where intelligence exists in the human person. So when we notice that in the systems, in the platforms, these are the aspects of intelligence which are being weaponized in some ways. Not against us, but are just kind of being weaponized because they’re the most beneficial at the short term to be able to generate our constant attention.

And so what I mean is that that is what the AIs are made of, right? They’re made of attention, prioritization, you know, good, bad. What is it that is worth putting energy into in order to predict towards a telos? And so I’m seeing that it’s the idea that we could just connect them suddenly seems very difficult to me.

Jim Keller: Yeah, so I’ll give you two. First, I want to give an old example. So after World War II, America went through this amazing building boom of building suburbs. And the American dream was you could have your own house, your own yard in the suburb with a good school, right? So in the 50s, 60s, early 70s, they were building that like crazy. By the time I grew up, I lived in a suburban dystopia, right?

And we found that that as a goal wasn’t a good thing because people ended up in houses separated from social needs and structures. And then new towns are built around like a hub with, places to go and eat. So there was a good that was viewed in terms of opportunity and abundance, but it actually was a fail culturally. And then some places it modified and it continues. And some places are still dystopian suburban areas. And some places people simply learn to live with it, right?

Jonathan Pageau: Yeah, but that has to do with attention, by the way. It has to do with a subsidiary hierarchy, like a hierarchy of attention, which is set up in a way in which all the levels can have room to exist, let’s say. And so, the new systems, the new way, let’s say the new urbanist movement, similar to what you’re talking about, that’s what they’ve understood. It’s like we need places of intimacy in terms of the house. We need places of communion in terms of parks and alleyways and buildings where we meet and a church, all these places that kind of manifest our community together.

Jim Keller: Yeah, so those existed coherently for long periods of time. And then the abundance post-World War II and some ideas about, like, what life could be like caused this big change. And that change satisfied some needs, people got houses, but broke community needs. And then new sets of ideas about what’s the sense of this? What’s the possibility of having your own home, but also having community, not having to drive 15 minutes for every single thing? And some people live in those worlds and some people don’t. Why were we smart enough to solve some of those problems?

Jordan B Peterson: Because we had 20 years. But now, because one of the things that’s happening now is, as you pointed out earlier, is we’re going to be producing equally revolutionary transformations, but at a much smaller scale of time. And so, one of the things I wonder about, I think it’s driving some of the concerns in the conversation is, are we going to be intelligent enough to direct with regulation the transformations of technology as they start to accelerate?

I mean, we’ve already — You look what’s happened online. I mean, we’ve inadvertently, for example, radically magnified the voices of narcissists, psychopaths, and Machiavellians. And we’ve done that so intensely, partly, and I would say partly as a consequence of AI mediation, that I think it’s destabilizing the entire –

AI: Who Will Hold The Keys?

Jim Keller: Well, it’s destabilizing part of it. Like, as Scott Adams pointed out, you just block everybody that acts like that. I don’t pay attention to people that talk like that.

Well, there are still places that are sensitive to it. Like, 10,000 people here can make a storm and some corporate person fires somebody. But I think that’s, like, we’re five years from that being over. Corporation will go 10,000 people out of 10 billion. Not a big deal. So that’s a learning moment that will re-regulate. What’s natural to our children is so different than what’s natural to us. But what was natural to us was very different from our parents. So some changes get accepted generationally really fast.

Jordan B Peterson: What’s made you so optimistic?

Jim Keller: What do you mean optimistic?

Jordan B Peterson: Well, most of the things that you have said today, and maybe it’s also because we’re pushing you. I mean, you really – –

Jim Keller: My nephew, Kyle, is really a smart, clever guy. He called me a — What did he call it? A cynical optimist. Like, I believe in people. Like, I like people, but also people are complicated. They all got all kinds of nefarious goals. Like, I worry a lot more about people burning down the world than I do about artificial intelligence. Well, you know people. They’re difficult, right?

But the interesting thing is, in aggregate, we mostly self-regulate. And when things change, you have these dislocations. And then it’s up to people who talk and think. And while we’re having this conversation, I suppose, to talk about how do we re-regulate this stuff.

Jonathan Pageau: Yeah, well, because one of the things that the increase in power has done in terms of AI, and you can see it with Google and you can see it online, is that there are certain people who hold the keys, let’s say. And who hold the keys to what you see and what you don’t see. So you see that on Google, right? And you know it if you know what searches to make, where you realize that this is actually being directed by someone who now has a huge amount of power in order to direct my attention towards their ideological purpose.

I always tend to see AI as an extension of human power. Even though there is this idea that it could somehow become totally independent, I still tend to see it as an increase of the human care. And whoever will be able to hold the keys to that will have increase in power. And I think we’re already seeing it.

Jordan B Peterson: Well, that’s not really any different though, than the situation that’s always confronted us in the past. I mean, we’ve always had to deal with the evil uncle of the king. And we’ve always had to deal with the fact that an increase in ability could also produce a commensurate increase in tyrannical power, right? I mean, so that might be magnified now. And maybe the danger in some sense is more acute, but possibly the possibility is more present as well.

Jonathan Pageau: Because you can train an AI to find hate speech, right? You can train an AI to find hate speech and then to act on that hate speech immediately within — Now, we’re not only talking about social media, but what we’ve seen is that that is now encroaching into payment systems and into people losing their bank accounts or access to different services. And so this idea of automatization —

Jordan B Peterson: Yeah, there’s an Australian bank that already has decided that it’s a good thing to send all of their customers a carbon load report every month, right? And to offer them hints about how they could reduce their polluting purchases, let’s say. And while at the moment that system is one of voluntary compliance, but you can certainly see in a situation like the one we’re in now, that the line between voluntary compliance and involuntary compulsion is very, very thin.

Jim Keller: Yeah, so I’d like to say, so during the early computer world, computers were very big and expensive. And then they made many computers and workstations, but they were still corporate only. And then the PC world came in. All of a sudden, PCs put everybody online. Everybody could suddenly see all kinds of stuff. And people could get a Freedom of Information Act request, put it online somewhere and 100,000 people could see it. Like, it was an amazing democratization moment.

And then there was a similar but smaller revolution with the world of smartphones and apps. But then we’ve had a new, completely different set of companies, by the way, from what happened in the 60s, 70s and 80s to today. It’s very different companies that control it. And there are people who worry that AI will be a winner-take-all thing.

Now, I think so many people are using it and they’re working on it in so many different places, and the cost is going to come down so fast that pretty soon you’ll have your own AI app that you’ll use to mediate the internet, to strip out the endless stream of ads. And you can say, well, is this story objective? Well, here’s the 15 stories, and this is being manipulated this way, and this is being manipulated that way. And you can say, well, I want what’s more like the real story.

And the funny thing is, information that’s broadly distributed and has lots of inputs is very hard to fake the whole thing. So right now, a story can pull through a major media outlet, and if they can control the narrative, everybody gets the fake story. But if the media is distributed across a billion people who are all interacting in some useful way, somebody’s standing up. Yeah, there’s a real signal there. And if somebody stands up and says something that’s not true, everybody goes, everybody knows that’s not true.

So a good outcome with people thinking seriously would be the democratization of information and objective facts in the same way. The same thing that happened with PCs versus corporate central computers could happen again. And that’s a real possibility.

Jonathan Pageau: The increasing power always creates the two at the same time. And so we saw that increasing power creates first, or it depends in which direction it happens, it creates an increase in decentralization, an increase in access, it creates all that. But then it also, at the same time, creates the counter reaction, which is an increase in control, an increase in centralization.

And so now, the more the power is, the bigger the waves will be. And so the image that 1984 presented to us of people going into newspapers and changing the headlines and taking the pictures out and doing that, that now obviously can happen with just a click. So you can click and you can change the past. You can change the past. You can change facts about the world because they’re all held online. And we’ve seen it happen, obviously, in the media recently.

So does decentralization win over centralization? How is that even possible, it seems?

Jim Keller: I mean, it’s also interesting. When Amazon became a platform, suddenly any mom and pop business could have Amazon, eBay, there’s a bunch of platforms, which had an amazing impact. Because any business could get to anybody. But then the platform itself started to control the information flow, right? But at some point, that’ll turn into, people go, well, why am I letting somebody control my information flow when Amazon objectively doesn’t really have any capability, right?

So like you point out, the waves are getting bigger, but they’re real waves. It’s the same with information. The information is all online. It’s also on a billion hard drives, right? So if somebody says, I’m going to erase objective fact, a distributed information system would say, go ahead and erase it anywhere you want. There’s another thousand copies of it.

And this is where thinking people have to say, yeah, this is a serious problem. Like, if humans don’t have anything to fight for, they get lazy and a little bit dopey, in my view. Like, we do have something to fight for. And that’s worth talking about. Like, what would a great world with distributed and human intelligence and artificial intelligence working together in a collaborative way to create abundance and fairness and some better way at arriving at good decisions than what the truth is.

That would be a good thing. But well, we’ll leave it to the experts and then the experts will tell us what to do. That’s the bad thing.

Technology Through Biblical Imagery

Jordan B Peterson: Well, so do you, is the model that you just laid out, which I think is very —

Jim Keller: I’m not so much optimistic about that.

Jordan B Peterson: Well, it did happen on the computational front. I mean, it was —

Jim Keller: It happened a couple of times both directions. Okay. Right. You know, the PC revolution was amazing. And Microsoft was a fantastic company. It enabled everybody to write a $10, $50 program to use. And then at some point, they’re also, you know, let’s say a difficult company. And they made money off a lot of people and became extremely valuable.

Now, for the most part, they haven’t been that directional and telling you what to do and how to do it. But they are a money-making company. You know, Apple created the App Store, which is great. But then they also take 30% of the App Store profits. And there’s a whole section of the internet that’s fighting with Apple about their control of that platform.

And in Europe, they’ve decided to regulate some of that, which that should be a social, cultural conversation about how should that work.

Jordan B Peterson: Yeah. So do you see the more likely, certainly the more desirable future is something like a set of distributed AIs, many of which are under personal, in personal relationship in some sense, the same way that we’re in personal relationship with our phones and our computers, and that that would give people the chance to fight back, so to speak, against this.

Jim Keller: And there’s lots of people really interested in distributed platforms. And one of the interesting thing about the AI world is, you know, there’s a company called OpenAI, and they open source a lot of it. The AI research is amazingly open. It’s all done in public. People publish the new models all the time. You can try them out. People, there’s a lot of startups doing AI in all different kinds of places. You know, it’s a very curious phenomenon. And it’s kind of like a big, huge wave. It’s not like a, you can’t stop a wave with your hand.

Jonathan Pageau: Yeah. Well, when you think about the waves, there are two, actually, in the book of Revelation, which describes the end, or describes the finality of all things, or the totality of all things, as maybe a way for people who are more secular to kind of understand it. And in that book, there are two images, interesting images about technology.

One is that there’s a dragon that falls from the heavens, and that dragon makes a beast. And then that beast makes an image of the beast, and then the image speaks. And when the image speaks, then people are so mesmerized by the speaking image that they worship the beast, ultimately. So that is one image of, let’s say, making and technology in scripture, in Revelation.

But there’s another image, which is the image of the heavenly Jerusalem. And that image is more an image of balance. It’s an image of the city, which comes down from heaven, with a garden in the center, and then becomes this glorious city. And it says, the glory of all the kings is gathered into the city. So the glory of all the nations is gathered into this city.

So now you see a technology, which is at the service of human flourishing, and takes the best of humans, and brings it into itself in order to kind of manifest. And it also has hierarchy, which means it has the natural at the center, and then has the artificial as serving the natural, you could say. So those two images seem to reflect these two ways that we see. And this kind of idea of an artificial intelligence which will be ruling over us or speaking over us.

But there’s a secret person controlling it, even in Revelation. There’s a beast controlling it and making it speak. So now we’re mesmerized by it. And then this other image. So I don’t know, Jordan, if you ever thought about those two images in Revelation as being related to technology, let’s say.

Jordan B Peterson: Well, I don’t think I’ve thought about those two images in the specific manner that you described. But I would say that the work that I’ve been doing, and I think the work you’ve been doing too, in the public front, reflects the dichotomy between those images. And it’s relevant to the points that Jim has been making.

I mean, we are definitely increasing our technological power. And you can imagine that that’ll increase our capacity for tyranny and also our capacity for abundance. And then the question becomes, what do we need to do in order to increase the probability that we tilt the future towards Jerusalem and away from the beast? And the reason that I’ve been concentrating on helping people bolster their individual morality to the degree that I’ve managed that is because I think that whether the outcome is the positive outcome that in some sense, Jim has been outlining, or the negative outcomes that we’ve been querying him about, I think that’s going to be dependent on the individual ethical choices of people, well, at the individual level, but then cumulatively, right?

What Will Humans Worship In The Tech Age?

So if we decide that we’re going to worship the image of the beast, so to speak, because we’re mesmerized by our own reflection, that’s another way of thinking about it. And we want to be the victim of our own dark desires, then the AI revolution is going to go very, very badly. But if we decide that we’re going to aim up in some positive way, and we make the right micro decisions, well, then maybe we can harness this technology to produce a time of abundance in the manner that Jim is hopeful about.

Jim Keller: Yeah, and let me make two funny points. So one is, I think there’s going to be continuum, like the word artificial intelligence won’t actually make any sense, right? So humans collectively, like individuals know stuff, but collectively, we know a lot more, right? And the thing that’s really good is, in a diverse society with lots of people pursuing individual, interesting ideas, worlds, like we have a lot of things, and more people, more independence generates more diversity.

And that’s a good thing where the totalitarian society, where everybody’s told to wear the same shirt, and like, it’s inherently boring. Like the beast speaking through the monster is inherently dull, right? Like, but in an intelligent world, where not only can we have more intelligent things, but in some places, go far beyond what most humans are capable of, in pursuit of interesting variety, and, like, I believe the information, well, let’s say intelligence is essentially unlimited, right? Like, and the unlimited intelligence won’t be the shiny thing that tells everybody what to do. That’s sort of the opposite of interesting intelligence. Interesting intelligence will be more diverse, not less diverse. Like, that’s a good future.

And your second description, that seems like a future worth working for, and also worth fighting for. And that means concrete things today. And also, it’s a good conceptualization. Like, I see the messages as my kids are taught, don’t have children, and the world’s going to end. We’re going to run out of everything. You’re a bad person. Why do you even exist? It’s like, these messages are terrible.

The opposite is true. More people would be better. We live in a world of potential abundance, right? It’s right in front of us. Like, there’s so much energy available. It’s just amazing. It’s possible to build technology without pollution consequences. That’s called externalizing costs. Like, we know how to do that.

We can have very good, clean technology. We can do lots of interesting things. So if the goal is maximum diversity, then the line between human intelligence, artificial intelligence that we draw, like, you’ll see all these kind of really interesting partnerships and all kinds of things, and more people doing what they want, which is the world I want to live in.

Jonathan Pageau: Yeah. But to me, it seems like the question is going to be related to attention, ultimately. That is, what are humans attending to at their highest? What is it that humans care for in the highest? You know, in some ways, you could say, what do humans, what are humans worshiping? And like, depending on what humans worship, then their actions will play out in the technology that they’re creating, in the increase in power that they’re creating.

Jordan B Peterson: Well, and if we’re guided by the negative vision, the sort of thing that Jim laid out, that is being taught to his children, you can imagine that we’re in for a pretty damn dismal future, right? Human beings are a cancer on the face of the planet. There’s too many of us. We have to accept top-down, compelled limits to growth. There’s not enough for everybody. A bunch of us have to go because there’s too many people on the planet. We have to raise up the price of energy so that we don’t, what, burn the planet up with carbon dioxide pollution, et cetera. It’s a pretty damn dismal view of the potential that’s in front of us.

Jim Keller: Yeah, the world should be exciting, and the future should be exciting.

Jordan B Peterson: Well, we’ve been sitting here for about 90 minutes, bandying back and forth both visions of abundance and visions of apocalypse. And I mean, I’ve been heartened, I would say, over the decades talking to Jim about what he’s doing on the technological front. And I think part of the reason I’ve been heartened is because I do think that his vision is guided primarily by a desire to help bring about something approximating life more abundance. And I would rather see people on the AI front who are guided by that vision, working on this technology.

But I also think it’s useful to do what you and I have been doing in this conversation, Jonathan, and acting in some sense as friendly critics and hopefully learning something in the interim. Do you have anything you want to say in conclusion?

Jonathan Pageau: I mean, I just think that the question is linked very directly to what we’ve been talking about now for several years, which is the question of attention, the question of what is the highest attention. And I think the reason why I have more alarm, let’s say, than Jim, is that I’ve noticed that in some ways, human beings have come to now, let’s say, worship their own desires. They’ve come to worship.

And that even the strange thing of worshiping their own desires has actually led to an anti-human narrative. You know, this weird idea, it’s almost suicidal desire that humans have. And so I think that seeing all of that together in the increase of power, I do worry that the image of the beast is closer to what will manifest itself. And I feel like during COVID, that sense in me was accelerated tenfold in noticing to what extent technology was used, especially in Canada, how technology was used to instigate something which looked like authoritarian systems.

And so I am worried about it. But I think like Jim, honestly, although I say that, I do believe that in the end, truth wins. I do believe that in the end, these things will level themselves out. But I think that because I see people rushing towards AI, almost like lemmings going off a cliff, I feel like it is important to sound the alarm once in a while and say, we need to orient our desire before we go towards this extreme power. So I think that that’s mostly the thing that worries me the most and that preoccupies me the most.

But I think that ultimately in the end, I do share Jim’s positive vision. And I do believe the story has a happy ending. It’s just, we might have to go through hell before we get there. I hope not.

Jordan B Peterson: So Jim, how about you? What have you got to say in closing?

Jim Keller: A couple of years ago, a friend who’s my age said, oh, kids coming out of college, they don’t know anything anymore. They’re lazy. And I thought, I worked at Tesla. I was working at Tesla at the time. And we hired kids out of college and they couldn’t wait to make things. They were like, it’s a hands-on place. It’s a great place. And I’ve told people like, if you’re not in a place where you’re doing stuff, it’s growing, it’s making things. You need to go somewhere else.

And also, I think you’re right. The mindset of, if people are feeling this is a productive, creative technology, that’s really cool. They’re going to go build cool stuff. And if they think it’s a shitty job and they’re just tuning the algorithm so they can get more clicks, they’re going to make something beastly, perhaps.

And the stories, our cultural tradition is super useful, both cautionary and explanatory about something good. And I think it’s up to us to go do something about this. And I know people are working really hard to make the internet a more open place, to make sure information is distributed, to make sure AI isn’t a winner-take-all thing. These are real things and people should be talking about them and they should be worrying.

But the upside is really high. And we’ve faced these kinds of technological, like this is a big change. Like AI is bigger than the internet. Like I’ve said this publicly, the internet was pretty big. And this is bigger. It’s true. But the possibilities are amazing. And so, with some sense, we could actually utilize them? Yeah, with some sense, we could achieve it. And the world is interesting. I think it’ll be a more interesting place.

Jordan B Peterson: Well, that’s an extraordinarily, cynically optimistic place to end. I’d like to thank everybody who is watching and listening. And thank you, Jonathan, for participating in the conversation. It’s much appreciated as always.

I’m going to talk to Jim Keller for another half an hour on the Daily Wire Plus platform. I use that extra half an hour to usually walk people through their biography. I’m very interested in how people develop successful careers and lives and how their destiny unfolded in front of them. And so, for all those of you who are watching and listening who might be interested in that, consider heading over to the Daily Wire Plus platform and partaking in that.

And otherwise, Jonathan, we’ll see you in Miami in a month and a half to finish up the Exodus Seminar. We’re going to release the first half of the Exodus Seminar we recorded in Miami on November 25th, by the way. So that looks like it’s in the can.

Jonathan Pageau: Yeah, I can’t wait to see it.

Jordan B Peterson: Yeah, yeah, yeah, absolutely. I’m really excited about it. And just for everyone watching and listening, I brought a group of scholars together. About two and a half months ago, we spent a week in Miami, some of the smartest people I could gather around me to walk through the book of Exodus. We only got through halfway because it turns out there’s more information there than I had originally considered, but it went exceptionally well, and I learned a lot. And Exodus means ex-hodos. That means the way forward.

And well, that’s very much relevant to everyone today as we strive to find our way forward through all these complex issues, such as the ones we were talking about today. So I would also encourage people to check that out when it launches on November 25th. I learned more in that seminar than any seminar I ever took in my life, I would say.

So it was good to see you there. We’ll see you in a month and a half. Jim, we’re going to talk a little bit more on the Dailywire Plus platform, and I’m looking forward to meeting the rest of the people in your AI-oriented community tomorrow and learning more about, well, what seems to be an optimistic version of a life more abundant. And to all of you watching and listening, thank you very much. Your attention isn’t taken for granted and it’s much appreciated.

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