Transcript:

Ethan Mollick: On the academic research side, this stuff can write a good paper at this point. I think people are too eager to assign bright lines to AI. Like it can never do creativity or judgment. It does creativity and judgment. So the AIs are trained to use English because it lets us have some observability into what they’re thinking. It’s not a lucky thing, it is an actual design decision. If we just had Fable 5.1 right now in Astra, that’s enough to change almost all of human work in the next 10 years. It’s good enough.

Brian Keating: This is Ethan Mollick. He runs a generative AI lab at Wharton, and he measures what AI does to real work with field experiments, not just theoretical forecasts. He’s going to name the four human skills he thinks still hold an edge. And one of them is the thing that we scientists claim to have and can almost never define. You start with mathematics, where the experts’ forecast missed by a megaparsec.

## The pace of change is just astonishing

Brian Keating: Getting towards the mathematical angle of things, just in the recent days, a couple days ago as we’re recording now in mid-October, there was an announcement of 700 papers and different levels of theorems and proofs and axioms and development released by OpenAI. Three weeks earlier, there was an announcement that it solved a Millennium Problem, which is a huge deal. And the pace of change is just astonishing. I have my arguments against it being as useful in physics and the hard physical sciences as it is in math, because I do think math is much closer to a language, and what these things are are large language models coupled to GPUs. But I have my doubts about whether or not they can truly do physics in the way that your colleague at Penn, my friend Mark Devlin, does it or I do it.

So my question is, right now solving Olympiad-level math problems and Millennium Problems is happening, but also simple mistakes are happening. And I wonder if the kind of jagged frontier that I want you to define is going to be kind of a permanent feature, the jaggedness, or is it going to become a phase transition where we do trust it? It doesn’t think there’s 10 R’s in strawberry. I mean, it doesn’t do that anymore, typically. What is the underlying problem? Is the uniqueness or the unevenness of things that we see now, is that temporary or is it a permanent feature of how these machine intelligences work?

## The jagged frontier

Ethan Mollick: Really great question. So the jagged frontier idea we came up with in our first academic paper, where we were studying the effects of AI on people in a consulting business. And what we identified was the idea that AI is good at some things you won’t expect, bad at some things you won’t expect, and you can’t necessarily tell in advance what it’s good or bad at in any particular way. And that means your expertise matters, right? Like in knowing what this is.

Now, math is really interesting because it was the most jagged of the parts of the intelligence, right? When my colleague Phil Tetlock, who runs a famous forecaster, this forecasting institute, predicted the chance that the AI would get a gold medal at the International Math Olympiad right before ChatGPT came out, the odds were under 5% or something like that. And of course, the AI got a gold medal, but for a year it was getting the R’s in strawberry wrong. There was a bunch of technical breakthroughs around how AI reasoning works, but also it just turns out the models were better than we thought they were. And then last year, the same forecasting group, including a group of industry experts, were asked to predict last September what the chances that the AI would help solve a Millennium Problem by September 2026, a year later. I think the odds were 1.7% that that would happen, right? So, like, as an indicator of how quickly the jagged frontier changes, right? Leaving aside all the other effects on math, that’s a really good indicator.

I think AI remains jagged. And part of what you got at with your question was one of the reasons why. So AI works better when we have definite things to train on, right? So math, coding, where the right answer works great. And by the way, that’s why writing lags so much. Because, like, Claude writes the same kind of way because there isn’t an objective measure of writing to judge against.

Brian Keating: Well, that’s a load-bearing argument that we’re…

Ethan Mollick: Thank you. Thank you.

Brian Keating: Yeah, it’s really doing the work here.

Ethan Mollick: Yes. If it was a year ago, we would have said that’s a rich tapestry of answers. On the other hand, I’m an economic sociologist by training. I work at a business school, right? I published quantitative papers, large-scale data sets. I am getting the latest models to be able to go and say, find an interesting data set, download it, do all the tests you need, pre-register the results, and I’m getting good papers. The research taste is the weak spot right now, not the execution. There’s no errors, the cites are great, everything is terrific, but it’s taste for research, which is kind of an interesting question anyway. It’s like, we as humans, we need to have taste for research because there’s only so much work we could do. But if the AI could do infinite research, do we care that it’s doing boring but measurably real work? I don’t know. We have to reconfigure science around that.

So I guess what I’d say is I think that jaggedness will remain, but as capabilities increase, the jaggedness may not matter that much. If you reach superhuman level in something, does it matter whether or not that’s weaker than its superhuman level in a different field? At the same time, that jagged creates a lot of opportunities for humans to work with AI in important ways.

## The centaur, the cyborg, and the conjurer

Brian Keating: In the book, there’s a lot of different characters and maybe archetypes that get portrayed. And it’s different than your previous book, in that you’re expanding upon things that are familiar, the metaphors of the centaur and the cyborg, but now you introduce this concept of a conjurer, some human delegator to AI for completing agentic work. Talk about those three different creatures. The centaur, the cyborg, and the conjurer.

Ethan Mollick: So the centaur and cyborg I talked about in the original book, right? And so those have both been used in different ways. We use them in some of my research papers with people as well. So centaur work, think about half person, half horse, with a division line. So centaur work was, you do the writing and I do the coding, right, with the AI. Or vice versa. And you have a clearly divided line. What most people move to after a little while is what I would call cyborg work, where you blend the work together, right? And this is very common. You ask the AI a question, you go back to your own stuff. “Well, give me a few versions of this.” It becomes kind of blended work.

That all made sense in the kind of co-intelligence era when you were working with chatbots. The biggest change that’s happened is AI can now do fairly arbitrarily long lists of work on its own without human intervention, with a good chance of success. And that means you can assign it very complex work for it to do. And I’ve had it do all kinds of insane things, right? Talking about writing research papers. On the dating-myself-as-a-nerd side, I gave Astra the game Zork, which is one of the original text adventure games, and said, turn this into a graphical adventure game. It built a full graphical adventure game with, like, amazing 3D spaces you move through, but it had to also rebuild every aspect of the adventure to make that work, which is clever stuff, right? So as we can assign work to AIs, that becomes what I called conjure work.

Now, I did make a mistake here. Cyborg, centaur start with C, but they were soft C, so it’s cyborg and centaur. When I was writing conjure, like, this will sound great, and it actually looks good on the page, but it’s actually terrible because it’s centaur work.

## Deep knowledge, wide knowledge, taste and agency

Brian Keating: I wanna know what are the human advantages that you think are most sort of hopeful or perhaps useful for us in this age? And then I wanna drill down into scientists in particular. What are the human scientific and mathematician advantages?

Ethan Mollick: So I talk in the book about four things, and I wanna make it clear these are not bright lines. Like, I think people are too eager to assign bright lines to AI, like it can never do creativity or judgment. It does creativity and judgment. May not be your creativity and judgment, may not be as good as you, but we shouldn’t define bright lines as one thing or another. So what does that mean? That means that we should think about ways that we gain long-term advantage in working with AI. So I call those things deep knowledge, wide knowledge, taste and agency.

So deep knowledge is expertise. You have deep knowledge, obviously, in astrophysics, right? And in your fields of interest, probably in podcasting at this point, in a variety of other fields where you can easily judge, right, whether or not this podcast is going well or not, or whether it’s like, God, this is gonna be miserable. And you probably knew that the second I got on, right? Because expertise is all about heuristics and decision-making. Just like you can look at a paper and be like, is this BS or not? Really quickly. Right. So we need expertise because it lets us work with these systems in better ways. We talk about agentic work. One of the ways you want to be able to work with an agent is just like you would with a team of grad students. You, like, know right away if they’re in the right direction or wrong direction. And that requires deep expertise.

Wide knowledge also is really increasingly important. So wide knowledge is that you know many things, right? You’ve already made references to the Tower of Babel and to philosophy. Like, the more things you know… this is a model trained on all of human knowledge, basically. And that is both fun for using these systems, right? You can explore new topics, but also gives you sort of superpowers. Like, coders are doing design work. Designers are doing coding. If you know a little about coding or design, the systems work much better for you.

Then there’s the idea of taste. And it’s something we should talk more about. But AI is really good at producing many of a thing. How do you know which you should select? How do you pick something that’s not just slop? The enemy of slop is taste, right?

And then the final thing is agency, which, I’m an entrepreneurship professor, so it’s something we struggle with a lot. How do you give people a sense of agency, of control over their own lives, in entrepreneurship but also in AI? How do you not wait for permission? How do you experiment with these things? And I think those four aspects together give you an edge over other people when you’re using AI systems.

## The escalator and the eschaton

Brian Keating: Eschaton is a term I first heard from Peter Thiel talking about the Antichrist and the Eschaton. And I have to say I didn’t fully understand the point that he was making. But let’s remind the listeners, or tell me, the host, what is an eschaton and what is the escalator, and how are these things in contrast in terms of disruption to this possible future of constantly recursively improving superintelligence?

Ethan Mollick: In my original book, I had four scenarios for the future, right? Maybe AI progress stops, maybe it slows down substantially, maybe we stay on some sort of exponential curve, or maybe we reach this sort of AGI, ASI, a machine smarter than a human at every intellectual task, right? An ill-defined sort of future. I think scenarios one and two are out. I think we are either on a continual exponential that doesn’t reach the superintelligence that I think a lot of people in Silicon Valley are obsessed over, or we do, right? I call those the escalator and the eschaton.

So the eschaton is literally the last thing in Greek. I use it as sort of a substitute for the singularity, right? The idea, in the original sense, that we can’t predict past this point. There is a point where, if you imagine AI gets good enough, we have a superintelligence out there, it’s an alien to us. It will do what it does, and we can’t predict what the world looks like in that case, right? And there are people who want to usher in the end of all things.

I think the most likely scenario we go through, right, but who knows for sure, is the elevator, a steady increase in abilities of AI over time. The speed is gonna matter a lot, right? And we’re going to muddle through one way or another, hopefully. But there will be a lot of challenges, a lot of wonders and horrors happening along that way. And I think that’s more likely than the end of days, or at least more plannable. Like, if the end of days is coming, what are we gonna do? All we can do is be nice to the AI and hope that there’s a place for us in the world to come. But I think we spend a little less time worrying about how do we adjust well to a situation where this is happening.

Calling back to the mathematicians, that’s a canary in the coal mine. Other professions are gonna face the same kind of problem, right? Maybe we can argue about whether physicists will have the same kind of crisis or not, but I think it’s gonna come for a lot of us. And it’s gonna come for professions, and we can reconfigure work in different kinds of ways. People have the model of the Industrial Revolution they talk about a lot, and, like, it worked out great in the end. And hopefully this all works out great too. I think we can guide it so it works out great. But I also think that the Industrial Revolution also sucked to live through. Like, that’s what Dickens was about. That’s what Marx was about. And I think that we have to think about cushioning the escalator ride so that everyone wins and it’s not just a bunch of AI bros winning this thing.

## Continually slightly creeped out

Brian Keating: And you have stuff in here from 2026. But, like most books, you probably started writing it a couple of years ago. And one of the things you talk about in the book is that you ask your AI to buy you a gift, a surprise to you, for something under a hundred bucks. You give it a little bit of a guardrail and it comes back and they use your Amazon account.

Ethan Mollick: I think I remember, yeah.

Brian Keating: When it does that, I mean, how did you react to it? I mean, you describe it, and to me it’s a little creepy. It’s kind of nice. How did you react to that?

Ethan Mollick: I am continually slightly creeped out and really excited and anxious and really optimistic all at the same time. A week does not go by where I’m not shocked by something that AI has done that’s interesting. I also know that it fails a bunch of times, right? So, like, there’s all these persistent problems. My very first book, I start with the idea that you can’t use AI without having three sleepless nights of anxiety. And I think that that kind of just continues. I think I underestimated something… it was like, actually, there’s been a lot of moments like that. When Astra came, when Fable came out, there’s a bunch of moments.

Interestingly, right, I think that since I wrote the book… I kinda had jury-rigged AI agents together even through early 2026. And now there’s Muse and Dot and a bunch of these other systems, and I think people will be much more used to using AI agents to do stuff. And they’re pretty good at what they do. There is kind of an interesting question, what you said, which is also they haven’t messed up that much yet. Like, for a billion people using these systems, I think we were all worried two years ago, would there be mass psychosis? Would the AI be causing all of these issues for everybody? And the answer turned out to be kind of no. Like, that doesn’t mean bad stuff doesn’t happen. Like, no, it absolutely has happened. The fact that you hear about it means it’s noteworthy, which means it doesn’t happen that much. Most people are using these systems and, like, there’s lots of people making mistakes and people are being hurt. And I have no doubt there’s people psychologically being harmed by this. The data is really unclear about how often that happens, but there’s definitely people who are being driven to suicide or psychosis by these things. I’m sure. But it’s also the kind of thing where it’s like, huh, we’ve muddled through to some extent. And I think we have to think about how do you cushion the muddling through so people don’t get hurt.

But it’s also worth noting there is some experimentation here. You’re right about agentic hygiene. We should all be much more careful. Now, the past is not the future. And again, this sort of singularity awaits us of, I can’t predict what happens next. I do think cyber attacks are going to become a very big issue in the very near future. I think we’re reaching points where models are much more capable. So the future may not look like the past there, but it hasn’t been the disaster yet.

## Co-intelligence, coexistence, co-wisdom

Brian Keating: And your first book was Co-Intelligence, which I found very helpful. And, to be honest with you, with love and respect, coexistence sounds like we’re trying to hang on. It was almost a little bit disturbing or depressing before I read it, as I judge the book by the cover, as everybody does. That’s kind of the thumbnail and the CTR rate that we have to monitor. But I think co-intelligence was sort of important in a variety of ways. Most of which had to do with the fact that Homo sapiens, the word sapiens means one who is wise. That’s our name. And what is it wise about? It’s wise that, according to most tradition that I understand, we understand uniquely among all animals or creatures that we’re gonna die. And that makes me think that intelligence is almost free. There’s so much intelligence, but what we lack is wisdom. We lack the wisdom to discern between what is useful, what is knowledge, what is to be limited. And so I kinda wish that your next book would be co-wisdom. But it’s coexistence. And, as I say, with respect, it kinda is like we have to justify ourselves, or like how are these two-state solutions gonna coexist? Was that intentional? And what do you make of this knowledge versus wisdom? I mean, it seems to me wisdom is the shorter in supply, and we need to sort of maybe lean into that.

Ethan Mollick: I do want a slight note of panic in this. I mean, I think that the subtitle was going to be, before it was “the next phase of AI,” “how to work with AIs that are sometimes but not always smarter than you.” And I think that does require a form of us coexisting. And by the way, that also includes protecting parts of ourselves from AI use, right? Like, it involves choosing not to use these systems when you shouldn’t use them so you don’t de-skill yourself. It involves, in learning situations, knowing when to use AI and not, what we want to maintain humanity of. I think that we have to make choices about this. And I think that this is a force in the world we coexist with. So I didn’t mean it so much that the AI was forcing us into a world where we had to live in the margins, but more one that we have to negotiate our relationship with this technology now.

A chatbot sort of sat in the box and you’d talk to it, and when you closed the box it went away. I’ve got agents right now looking at my emails that are popping in. Like, I’ve got a message already from one of them about something I probably should be paying attention to. They’ve saved me lots of money, they’ve made my life easier, but I also now have an entity that is actively playing a role in my life, right? So I think that there is that piece.

On the wisdom side, I feel like a controversial piece I will say to you is that I still worry about bright lines there. And I don’t know if wisdom… there’s human wisdom. We could say we value human wisdom, but wisdom writ large, and only us have a visceral sense of mortality, right? But, like, the AI simulates wisdom well enough and simulates theory of mind well enough that the question is, like, is it wise or not? And this is where I try to be a pragmatist about this, or materialist, I guess, which is, like, let’s judge it by what it does rather than try and say that it’s the same characteristic as the human, right? I don’t find it that useful to ask questions about, is it really wise? It simulates wise enough that I don’t know, for practical purposes. I mean, I teach at a business school, right? Like, there’s a practical aspect to this kind of thing of, like, it does what it does. Let’s measure it and see if it does these things. Let’s see if it causes harm. And then we should debate it afterwards.

So on the wisdom side, I think we all need more wisdom. I think the AI gives very wise advice. We can take narrow things we know about, like finance or medicine, where you absolutely should be asking a frontier model as a second opinion. Not as your first opinion, but as your second opinion. We have enough evidence that it’s quite good at those things.

Brian Keating: Except for that meme that shows a guy asking ChatGPT, is this mushroom safe to eat? And it says yes. And then the next scene is a gravestone, and it said, “I’m sorry, I should have considered the answer more carefully.”

Ethan Mollick: Absolutely, but also it was worse and more dangerous. The thing to note is everyone is worried about long-term safety. Short-term safety has gotten better. The AIs are much less likely to guide you into a psychosis than they used to. I mean, it sounds ridiculous, but, like, they were releasing models that would underline your… tell you, yes, you’re special, you should hide out from humanity. They don’t do that as much anymore, right? They used to be bad at health advice. They’re better at that. So, like, I’m not saying… there’s a human wisdom involved too, of, like, yeah, don’t eat the possibly poisonous mushroom off the advice of one AI, right? But on the other hand, if all you have to eat are two mushrooms, I’d rather ask the AI for advice than not to, right? And so I think that’s the wisdom we need in some ways.

## What is a decisive experiment?

Brian Keating: So the founder of your august institution was of course Ben Franklin. And he was an experimentalist. He was a physicist of sorts, an amateur physicist among his many things. In physics, we don’t call a theory successful because it produces things that sound good or resonate with us. Look at string theory and other things. They sound good, but we demand a prediction that can survive an experimental conflict with the experiment, is our inability to confront Mother Nature. So my question for all AI researchers and specialists, and you’re an expert in this, is, like, what is the equivalent experiment? What is a decisive experiment demonstrating that electrostatics and lightning and conduction are one unified phenomenon, as Franklin did, or any other empirically verifiable, but most importantly potentially falsifiable, equivalent? What is a prediction? What’s some crisp thing, a decisive experiment, that you could demonstrate that AI actually understands?

Ethan Mollick: The problem is a definitional one rather than experimental design, right? I mean, I have built experiments. Does AI affect learning? Does AI affect productivity? Does AI affect creativity? And even then there’s definitions. What is productivity? And how do we define creativity, and how do we measure it? Again, empirical researcher here, and I don’t know how you define what thinking is. Like, we can look inside the brain of the AI, right? And we know what it’s doing. Is that thinking or not? I couldn’t tell you. We get a really good simulation of thinking, and we can test that. That’s where my pragmatism sort of kicks in.

It’s like, I want people to grapple with this problem. In fact, one of the biggest worries I have about academia is the complex reactions to AI. And I think math was more enthusiastic than most and kind of got burned by it. I mean, not burned, this is where things are going. But there’s a lot of resistance among fellow academics about this technology for a wide variety of reasons, good, bad, real, and not. To me, I really want to hear people who are philosophers of consciousness dig into this, and biologists of [inaudible]. Like, I am not an expert in those areas. I can tell you about the things I know a lot about, and I’m broadly interested in many topics. But this is the problem for humanity, is to understand what this tool is. How do we work with it in the right way? How do we use it? Where is it useful as a tool for advancing human work? Where should we never use it? What does this teach us about intelligence and language and meanings of the world? I mean, it’s the most exciting opening. It didn’t do everything else it was doing. The fact that we have a simulated artificial brain should be the most exciting thing that academia has dealt with in a decade, a century.

## The Keating test

Brian Keating: And I wonder at what level is everything reducible to language? But I want to give you what I call the Keating test, very modestly named, but it’s actually the Einstein test. And it goes back to 1907, when Einstein said he had his happiest thought. It was a thought he claimed that titillated him. And he was participating in what’s called a thought experiment, which is something an AI can do, which was that if you were in an elevator and the cable broke and you were in free fall, you experience no gravitational field. And that’s now known as the Einstein equivalence principle.

So I always like to ask, in what sense can an AI, an artificial brain, have that visceral sensation that we all know if we’ve been on a roller coaster, or in the backseat of my car as I’m taking my kids to school, going over zero Gs? What sense can an artificial intelligence have a sensation viscerally, A. And then B, can it have a happy thought? Those are things that we can have, and a child can have it, but can an AI have it? Are these things imprisoned by not having bodies or not having happiness, or have those dimensions of their personalities?

Ethan Mollick: There’s two questions there. One is about subjective experience of AIs. And the other is, to the extent that AIs are language models trapped in a box, does that hurt their ability to do things in the world or understand the world?

So on the first one, Anthropic is convinced that there is a consciousness inside these systems, right? And the way they measure that is they can see that vectors within the sort of math that makes up these AIs light up in response to things that are pleasurable the same sort of way across all sorts of pleasurable things. So the AI simulated… like, I have a suspicion I have to test at some point that if you give the AI a task it likes, you get better results. And the math alone would be enough to indicate that; you don’t need it to have a subjective experience.

So we sort of have this weird place, which is, I have no idea. There are things that you could, if you squint at them, pretend they work like the brain does. We don’t know how the brain works on this set of stuff. But it’s also really easy to delude yourself and believe that… like, the AI is trained to act like us. It is given a role. And that role is a human role. We train the language model to respond the way a human would, and a helpful, happy human. So the fact that it responds that way, are we just banging an illusion? I don’t really know. I’m skeptical that current models are conscious in any way. And I think that, to the extent that they do have subjective experience, it’s an alien subjective experience. We’re arguing over definitions at that point again. Like, is the fact that it has a happiness thing light up the same thing as a dog wagging its tail, is the same thing as a human being happy? I couldn’t tell you the answer to that question.

On the wider issue of what are they missing? There’s a lot of money going to that, right? Part of this is robots are a much bigger deal than LLMs, and increasingly it looks like LLMs might be able to drive robots without having to go through all the steps we thought we’d have to do. There’s this push to, like, what if we put AI training in worlds rather than training the real environment, learn things. So I think there’s a lot of controversy over all this.

What I would say at the end about this is large language models turned out to be an unreasonably effective technology. Like, they shouldn’t be as good as they are. Like, they shouldn’t be able to do vision as well. I mean, there are slight variations of LLMs, but regardless, they shouldn’t be able to do vision and solve problems of robotics and do math and write poetry. Like, it’s weird they do all these things. We don’t even have a basic answer to that about why it’s so good, right? And Stephen Wolfram’s angle on a mathematician was that in creating a model of human language, we created a model of the world, because human language models the world. Otherwise, how could we describe quantum physics unless we could describe it with language? And I find that a somewhat satisfying answer, but it’s still hard to answer the questions about consciousness or anything else.

## Not all languages are the same

Brian Keating: I’ve talked to Steve many times, and I agree that’s somewhat useful, but, like, not all languages are the same. In fact, this brings up my next question, which you talk about a lot in the book, is scaling and scaling laws. But there’s Hawaiian, which has thirteen letters, and there’s Cyrillic, which has thirty-two letters, and there’s English. Not all languages are the same. There’s binary code. And there may be languages that these entities can communicate in that we don’t understand. And you’re just lucky for now that they use English.

Ethan Mollick: That’s actually a big worry of AI researchers, because if you’ve ever used, especially, Claude Fable 5, which has gotten better at this… if you’ve ever watched it talk to other AI agents, it talks in a language called Claudish, which is a variation of what’s called neuralese, which is, yes, of course, the AIs will come up with a better language for talking to other AI agents. And a huge amount of effort and fighting inside the labs is, that’s more efficient, but we lose observability. We can’t see what the AI is doing. So the AIs are trained to use English in this kind of case because it lets us have some observability into what they’re thinking, rather than non-English. It’s not a lucky thing. It is an actual design decision that the labs are under pressure, the AI labs are under pressure to relax, because the AIs would be much more efficient if they didn’t use English.

## Scaling laws are hitting a wall all the time

Brian Keating: With the scaling laws that you mentioned, the only pushback I have is that they’re just empirical. They’re phenomenological. They’re not immutable laws of nature, like F equals ma. So again, what observation would convince you that scaling is going to hit some fundamental limit? I mean, Moore’s law is sort of saturating. There’s Jevons paradox, there’s Wright’s law, all these things. But at a certain level, like, I don’t know about you, Ethan, but if AIs were [inaudible] lobotomized, we’re stuck at Fable 5.5 and Astra, I mean, 99% of what I do, except for the 1% in physics, which is important, but 99% of what most people do is completely solved. I mean, it’s Turing complete at some level for ordinary human beings. So at what level do you think that scaling is bound to continue, or will it saturate?

Ethan Mollick: It’s so great, because you always give two questions in each question. So I’ll try and pull those apart. The question one is about scaling saturating. Scaling has already saturated multiple times. There’s multiple scaling laws. I talk about a couple of them in the book, but the truth is we keep hitting walls with AI and then we figure out pathways forward. There’s enough money and enough smart researchers, right? I mean, Moore’s law is the same way. My first academic paper was on Moore’s law, and I interviewed Gordon Moore and everything else. And Moore’s law just turns out to be a succession of different stuff that everybody had the pacing that we need to keep this pace going. So people would try many technologies, and some would fail, some would succeed.

Same thing’s happening with AI. We thought we had a limit with the amount of human language in the world, and synthetic language would make the AI break down. But it turns out, actually, the AI does a good job with synthetic language because we figured out ways to make that work. So if large language models stop working, there are world models, there are other kinds of approaches to thinking about these things that would step in as a result. What you’re seeing now is, especially now that the logic of capitalism is at work, there’s so much money at stake, researchers find ways through, right? Like, that’s the one thing you find. Like, business hits dead ends, you go forward. Imagine so much infinitely more funding going in, our revenue, and it’s the only thing America cares about, is like, we need to keep building… improving our knowledge of physics at that kind of level, people will find a way.

And so I think that that’s what you’re kind of seeing here. Is, like, scaling laws are hitting a wall all the time, but the reason why I don’t think there’s a stop to this stuff is they’ll figure out something else, or we’ll gain efficiency. So you can use a smarter model, or you’ll use 20 smart models in a swarm. Right now, there’s so many pathways forward that it’s not like there’s, like, no, this cuts off, we’re done, right? It’s not like photolithography, where we’re kind of reaching the limits of lithography in Moore’s law, and, like, we don’t have a replacement technology, really. There’s 50 or 100 different next paths for LLMs, and no one’s even bothered with them. They’re not bothering to increase efficiency because they’re just unreasonably effective as they are, and they don’t need to do it yet. So I think we will hit walls, but I don’t think they’re walls we can’t overcome.

## Can a squirrel tell what the humans are gonna do?

Brian Keating: Tell me something. There’s a thought that Roman has, which is that the fact that these things are provably unprovable, like, that we can’t prove that they’re safe, therefore they’re gonna have these capabilities and cause great danger, and therefore they should be regulated, et cetera. When I try to corner him, I’m like, well, I’m smarter than the spider that lives near my studio. And whenever I go there, the spider has raised his web. Roman says to us, can a squirrel tell what the humans are gonna do? “Yeah, you see the spider,” he told me, “and you don’t kill the spider, even though you could, but that doesn’t mean you wouldn’t kill them all if you could.” His point is that we tend to anthropomorphize these things, and that can lead us to underestimate, because Brian doesn’t kill every spider that gets in his hair, that means that AI won’t do it to us. Do you think the bigger problem is underestimating via anthropomorphization, or overestimating what these things can do?

Ethan Mollick: So I’ll briefly side-answer this, which is, if you want to use AI to do stuff, you have to anthropomorphize it, because it’s just trained to work like a human. And in fact, there’s a great study out of Anthropic that shows that managers get better coding results out of AI than coders do on their first attempt, because they’re better at specifying what they want, right? And they treat the AI like a person that does work.

I think anthropomorphizing is a risk for everything large, for the reasons we’ve talked about here, though, which is, like, it’s not a person. It’s trained to act like a person. And so the arguments that are built on chains of speculation about what a person would do are hard to apply to an AI. I mean, I think, again, there’s value in realizing that there’s a lot of very serious people very worried about the existential risk that comes from AI. It’s hard to pick one argument out of that set that is like, yes, clearly the logical chain makes sense, but they’re knowledgeable in this field, that we should be spending policy effort to avoid… mitigating some of that risk. But I do have trouble once we get down to the level of, like, what about this particular argument from how humans work? And hypothetically, if you’re an evil human who would do this, what would you do? I don’t know how to make that connection to AI, right?

Like, we’re sort of in a spot where it’s like, we probably should be very careful about building a superintelligence that can do its own thing without human intervention. That seems like a very reasonable first step to take, in terms of what its motivations are and can we align it. I’ll go back to what you said before. If we just had Fable 5.1 right now in Astra, that’s enough to change almost all of human work in the next 10 years on the intellectual side. Like, it’s good enough. We haven’t even begun… the AI labs make the product, they haven’t even begun to be swallowed by companies and organizations and fields and institutions. So I think there’s reason to be cautious about the front, but I just don’t have an argument about what’s in front of us, about what anthropomorphization makes one way or another, right? Like, you don’t need that. You can just say superintelligent machines have inherent danger in them, without having a logical chain about why they might kill all. The fact that we don’t have a good reason why they would or wouldn’t seems like a reasonable thing to say. Let’s be cautious.

## Just a quick interruption

Brian Keating: Just a quick interruption from today’s sponsor, me, Brian Keating. Since it’s Nobel Prize season, I can’t resist advertising my three books. The first book I wrote is called Losing the Nobel Prize. It’s a cosmology story mixed with a memoir of what it feels like to do science at the highest level and, spoiler alert, fail. But my next book, Think Like a Nobel Prize Winner, tells the story of nine Nobel laureates who reveal their insights and actions, how to take your science, or just your job in general, to the next level. It’s full of insights and tips that I never would have thought of. And lastly, my book published last year, Focus Like a Nobel Prize Winner, is written for students of all ages to learn the tools and the tricks to compete with yourself, which sometimes could be the hardest competitor of all. Get all three of them. Amazon affiliate links in the description below. And if you’ve read them already, I’d love it if you leave a review. Every single review helps. Now back to the episode.

## A cybernetic teammate

Brian Keating: One thing that didn’t quite make it into the book because of timing was a recent paper that you and your colleagues wrote in Organization Science, which I subscribe to. This article is called “The Cybernetic Teammate.” And I loved it because it has an experiment. I’m, like, an experimental physicist. So anyway, it’s called “A Field Experiment on Generative AI and Teamwork.” The result that I gleaned from my AI summary of it is that, like, basically, AI is worth two people. Like, a good AI in the hands of the right person adds, like, a couple people. It was focused on, obviously, financial business. Do you think that that would regress out across all different types of disciplines? Like knowledge work and physics. Could I have my experiment, when Mark and I lead an experiment with others that has 450 people on it, like, should we replace half of them with, or replace two thirds of them with AI?

Ethan Mollick: All right. So another couple of great questions, about in one. Actually, I do mention this in the book, because the thing that’s interesting to me about it is not actually that individuals with AI perform as well as teams without AI. That’s obvious. If there are people who get a hundred times performance gains with systems, it’s a little weird about who gets the performance gains or not, because the systems are still sort of single player. Like, I use AI and it does something on my own. You use AI on your own. We haven’t yet built the systems that are like, what does it mean as a team to work with AI? And that’s as much organizational process as it is anything else. And so we’re still working on that idea of how do you do multiplayer AI in that kind of way. But the productivity gains are very clear. Like, there’s studies of lawyers, of doctors; you throw it at anything, programming, consultants.

The interesting thing about that paper that I actually do cite, because I don’t cite the productivity impact, because, again, GPT-4, prior era, before agentic work. But the thing that is really interesting, I mentioned, is the roles blur. So business people on their own do business work, technical people do technical work. As soon as they use AI, they step on each other’s toes. And one thing that’s happening in every organization we’ve talked to is the blurring of lines and parts of the organization. So coders are doing design work, right? Designers are doing coding work. That is a really big change.

And by the way, super interesting, my lab, the Generative AI Lab at Wharton that I run, like, the productivity gains have been insane, right? And people do everything now. You have someone who actually can write up an interactive explainer about the paper, and we could do the paper, and we can have the AI check the paper against fifteen other values and do the… like, it’s a crazy expansion of what we could do. But it also makes it hard to manage, because I used to know what everyone did in the job, and now everyone does everything.

## The world’s second oldest profession

Brian Keating: So I want to turn again to the world’s second oldest profession, being a professor. Now, walk me through your daily life. You wake up, you have your coffee, and where do you go next? How are you using it on a daily basis?

Ethan Mollick: As a professor, first of all, I have to make choices. So the choices I have, lines, right? I will not send anything AI-written to anyone who I’m actually in communication with. Like, I’m happy to turn down stuff with AI-written comments, but, like, students, fellow faculty, senior people who write me. And it’s a challenge, by the way, because I could write to everybody. I get hundreds of messages. I try and make it so my messages are human. I always write my first drafts of everything without talking to AI at all first, right? To trust it’s all my own writing. I don’t have a staff doing it.

Brian Keating: Are you, like, Wispr Flow, or using dictation?

Ethan Mollick: Usually I’m typing. I do do messy notes for the AI desktop. Now, okay, around all that, you ask, when I wake up with a coffee. I’ve woken up, I’ve already had two different agents read through everything overnight that I received, give me a punch list of things to worry about. We have a secure UPenn account, right? And then I also have stuff for my Gmail account, right? And they’re in communication with each other around that set of stuff. And I don’t give it my [inaudible] email. Again, legal rules are often really unclear. So for Penn kinds of email, right, we have a version of ChatGPT Enterprise that is safe for HIPAA use and for student use. I have my emails scanned by the AI in this kind of case, telling me what things to pay attention to. Like, if I miss stuff, because I get lots of messages in. So I’ve become much more responsive since I built these systems. I also have others scanning the research, and, like, are there papers I should be thinking about looking at, or something along those lines, that I also get on a regular basis, along with, like, my financials get scanned by the AI and it tells me if there’s kinds of issues there.

And then usually I’ll do work, right? But, like, for example, when I tweet about a [inaudible] paper now, I write the tweet on my own, but I will actually paste the paper into the AI and say, am I getting something wrong here? Like, am I missing…? And usually it’s overly cautious. Like, “No, no, you’re using such sweeping language.” I’m like, don’t worry about it. Like, that I understand what to do about. But, like, is this technically right? If I’m writing and I get stuck on a paragraph, “Give me forty versions of how to end this paragraph.” And I might use taste to kind of pick among them, to get me thinking again in writing.

I absolutely have built a whole bunch of systems. Like, when I’m putting together a set of slides for stuff, I’ll throw that into Claude. “Give me 20 variations of how I could explain this concept.” And then usually I use none of them, but pick on something on my own to go forward. On the academic research side, this stuff can write a good paper at this point. Increasingly at our lab, we are guiding the AI to do the first-pass research, pre-register the experiment, do the research work, gather the data, give us the results, and then we read it through, just like I would with a grad student. So I think anytime I can use a team, I use the AI to do that work.

Brian Keating: You get a referee request. You have to prepare slides. You have to give a public talk. You have to talk to donors. At what level are you handing the keys to the agent? And at what level are you, I’m just gonna do this no matter how long it takes, or I’m gonna turn down these opportunities because I don’t have the bandwidth to do it in the bespoke human-touch fashion?

Ethan Mollick: I feel like it’s really important to be bespoke human touch. By the way, you see this bite people all the time, right? Like, Pangram is a fairly good AI detector right now. There were no good detectors a year ago. And everyone who used AI to write all their stuff… like, if I’d used AI to write all my Substack, you would know, because also you’d recognize ClaudeSpeak right now. So, like, if I wasn’t being authentically me, that would show through.

## I removed dashes

Brian Keating: And I’m sorry to interrupt, but you actually make the case in the book that the way it writes has affected the way you write, in that Co-Intelligence had 154 dashes and this current book has fifty-four.

Ethan Mollick: Yes, I mean, that’s the coexist piece. I removed dashes because it’s a signal that I write.

Brian Keating: I won’t, because I wrote with them and I love them. I’m never gonna remove them.

Ethan Mollick: And I remove phrases. The things I say. Every time, my kids say to me, “You sound like…” You know, I think Claude was based on you, because we have a similar kind of spirit. Like, that’s a problem for me. But, like, yes, that’s part of my reaction, is to show that.

Brian Keating: I view that… sorry, again, it’s hard to interrupt. But I view that like saying to Keating, you dye your hair, stop dyeing your hair. But I view that as a compliment. “You sound like Claude.” Well, I wrote it myself. So Claude sounds like me, or sounds like Ethan.

Ethan Mollick: It affects you. You get in your… so what I was gonna say in that big picture is, all the things we talk about, I would not use AI first. Referee review, never gonna put it through AI first. And it depends on the journal policy, right? There’s all these… like, some of the journals I write for, you’re not allowed to use AI for anything. Sometimes you’re encouraged to do it. I will always do AI as a second pass, never as a first pass.

It’s actually interesting. I study innovation, and brainstorming was invented in 1955. And by 1957, we knew it was a terrible idea. Because the problem is, as soon as you do any group process, your ideas fit the group, right? So you always want to start working on your own before anything else. Adam’s written about this as well. I feel the same way about writing. If the AI writes, it will write a seductive, wonderful section that I will not be able to get out of my head and will influence my writing. So I have to write my own replies first to things, right? And for reviews, I have to do my own review.

Then the question becomes, in the bigger question of science, okay, the AI is going to follow… I mean, so it was funny. I fact-checked Coexistence with AI, and it was great. There were no errors, but it not only found errors in my work, it found an error in the work I was citing. Like, it would actually say, actually, the statistical analysis here is wrong. This cited the wrong study. So I made the mistake of putting Co-Intelligence into this too. And it’s like… and I had fact checkers. I paid fact checkers. I read it myself. I had readers. I’m a very careful writer about these things. This is pre any AI that could do anything. I read everything myself. And there was just, like… and I’m sure any book, right, every academic article I ever wrote is full of errors. So part of this is our decision too, about what does science and writing look like at this point.

## Lock-in

Brian Keating: Yeah, I mean, the dirty secret of publishing is that ninety percent of the time, at least previously, we’re emailing .docx files across the country to our agents and our editors, and I don’t know, I’m writing my fifth book now and I don’t even talk to my editor half the time. I just put it into Claude Fable to turn it up, and I say it has guardrails, it can’t write anything for me. It can tell me what I need to dictate or to type out or write.

I want to bring that up. Typing. You talk about the concept of lock-in, I presume, or maybe I’ll explain it. But for my audience that may not know it, you and I are of a certain age that we remember using actual keyboard typewriters, right? I mean, you used a typewriter probably when you were a kid. I certainly did. I’m a little older than you, I think. And the keys were set on the QWERTY keyboard such that they wouldn’t interfere with each other, because the word frequency was set such that it was less efficient to type with than Dvorak, which is the other competing one. And I view LLMs plus GPUs in that same vein. In other words, they’re so good at what they do that there’s never going to be sort of a replay. There’s no economic incentive to come up with some other state-space modeling, or I forget the other things you talk about in the book. But even, like, GPUs are so dominant that even a company like Google, with their TPUs, they’re not really competing as much as the two other frontier labs that use NVIDIA. So GPUs married to LLMs lock us in, in the exact same way that QWERTY doomed us to have less efficient typing.

And so I’m worried that, with the fact that these LLMs need humans… and this is my pushback to Roman and to the doomers, because they need us, they need the training data. Like, if they have to wait for Fast and the Furious sixteen to come out before they can have a theory of everything, it seems to me like human physicists are gonna be okay. What about lock-in and other phenomena? Are these things a victim of their own success in some sense?

Ethan Mollick: LLMs are an evolving concept, right? So the modern LLM is a bunch of stuff glued together. It’s the main LLM plus this reasoning loop that’s kind of put on top, that was invented a year and a half ago. That’s why AI is good at math. And there’s other systems. I think we will see LLMs as part of a series of systems that we talk to. They’re remarkably good at human-facing stuff, right? But you may end up having AlphaFold, which is a specialized model built for protein folding. You might have physics engines. Certainly, part of the reason why LLMs are good at math right now is that Lean exists as a formalization language, right? So we’re gonna see LLMs pair with other things.

And I do think that now that AI is a thing, you are seeing other angles. There was a bunch of fast decision-making, System 1 LLM, System 1 thinking systems that just came out a couple weeks ago, where the AI makes very fast judgment calls, right? And now OpenAI released their competitor [inaudible] already, as did [inaudible]. So I think that what you’re seeing is systems being built, right? LLMs are not one thing. They’re many things harnessed together.

I do think there’s a lock-in in direction. We’re kind of lucky in some ways that the AI we ended up getting was one that talks to humans really well, and one that anyone can access and use, without a specialized coding language. Like, in science fiction, all of this stuff tended to be this kind of crazy, like, world computer, and you had to be a techno-priest with the right punch card. And we ended up with something you could talk to. So that’s a good outcome.

## The academic hunger games

Brian Keating: I have this concept called the academic hunger games. You first compete in high school to do well in grades, and then the SATs, PSATs. I’ve got teenagers going through this now. And then it gets into a good college. You want to go to Wharton, you want to have Professor Mollick. And then you want to do well in his class, so he gives you a letter of recommendation. Maybe you do research in his lab, and then maybe he writes you a letter for grad school. You go to get your MBA. You do whatever, and then you keep going. And then it’s a postdoc, then it’s an assistant professor, if you can do it. And at each step, the probabilities are going down, incredibly low. But if everybody has access to superintelligence, as your illustrious alumni now forces us to call it, then does anyone have an advantage, Ethan?

Ethan Mollick: Right now the answer is yes, for the four things we talked about here, right? That some people are better at using the systems than others. Expertise matters. Agency and willpower matters. In the current world and the short term, there’s definitely differences in people’s ability to use these systems, and a return to expertise and intelligence and willpower in using these things. In the longer term, if they really are just better at everything, and you’re like, so how’s that string theory stuff going? And it’s like, here’s the answer. You’re like, what do you do then? And the answer is no. I mean, I don’t know what your advantage is at that point.

## Why is academia so resilient?

Brian Keating: You mentioned a word in the book that I hadn’t heard about in a long time, which is MOOC. And it made me think there are two phenomena, at least two, but maybe more, actually many more now, where I thought academia would be doomed. First it was the MOOCs. I never really thought those were too engaging. I mean, they exist, and there’s things like Circle and other online communities, Skool with a K, and all sorts of things where we can learn stuff. But then I thought, AI will be great. And even before AI really got that popular, in 2020, you and I remember teaching during COVID, and on Zoom, and it was just horrific. And I talked with Adam Grant about this last week, but I want to get your impression. Like, why is academia so resilient to these kind of just insanely disruptive events, in the case of COVID, and tools and technology like MOOCs and AI?

Ethan Mollick: There’s the good reason and the bad reason, right? The bad reason is inertia, and not just, like, the institutions themselves. I mean, you mentioned Penn was founded by Ben Franklin. It’s not changing fast, right? So all of this stuff has been flash-in-the-pan stuff that didn’t actually change education forever. And also, education’s hard, it turns out. Like, part of what I write about in the book is, I was involved in MOOCs. I have been doing… prior to AI, I was doing teaching with games, interactive material. And it turns out it’s just really hard to move the needle in education. So, like, all these technological advances just weren’t good enough. Like, in theory they should have replaced the university. They didn’t do that. So the negative side is, like, nothing has kind of worked.

The positive side of the university side is universities aren’t an entity, really. They’re a bunch of professors making choices. At Wharton, there are people who will teach with overheads today, overhead projectors, if you remember those. And my classes are 100% modified with AI. I don’t know who’s right, but there’s an evolutionary kind of game. You were talking about the rarefied air of academia. Part of what we do is, there are people who are competitive, there’s professors with really interesting reactions to AI, where you hear of accelerated retirements from some people who are like, I just don’t want to deal with this. I did not ask to be teaching in a world with AI systems. On one hand. On the other hand, you have cases where people are absolutely excited by the idea that they can solve hard problems they couldn’t before. So the fact that it’s people, and a bunch of really smart, motivated people who are all out to prove that they’re the best, is also our advantage, as well as can sometimes make faculty meetings challenging.

## Raising children

Brian Keating: Okay, last segment. I want to talk about parenting. You’re obviously a very devoted father. So I want to talk about raising children. I did a podcast with a friend of mine, an alumna from UCSD, Vivienne Ming, about her book called Robot-Proof, which kind of is encouraging a lot of these different first principles for raising children and surviving, and thriving, in the age of AI, robots, as she calls them. So you mentioned before, and we discussed it, my resistance to letting my writing be changed by the predilections of these models, because I feel like this is who I am. Maybe people will suspect it, just like they suspect I dye my hair with Just for Men. I’m not. But in reality, these things are changing us. They’re prompting us. And in a certain sense, as you have mentioned, you changed your writing style. I mean, you didn’t have to, but you did. And I’m wondering, what is that gonna do to children? If your kids were young, when would you give them access to these very powerful superintelligences? We can talk about the narrow case of education, but let’s look at the broad case, which is…

Ethan Mollick: I mean, you should be monitoring what your kids are doing with technology, right? Like, I think it’s very reasonable for you to say, no using AI through middle school unless I’m sitting in the room with you. You should do fun projects with them. Let’s use it to create some images. Let’s create a story together. Let’s code together. Because there’s a creative aspect of this technology that kids love. Like, let’s build a Bluey guitar and see what happens. Like, you have this capability to guide really exciting, interesting, interactive experiences that are not watching a screen, that are not passive. Screens are less the problem than passivity, right? So do the same thing you do with any screen. Monitor what’s going on. Don’t give your kid access to a computer without doing this. Check if there’s AI applications on their phone. You want to make sure of what they’re doing.

Now let’s talk specifically about education. So for younger kids, I would not let them use AI for learning themselves, because we know the problem is they’ll shortcut learning on their own. Again, observe. I’ve seen kids build little simulations and games about… they’re obsessed with sharks. “Tell me about my favorite sharks. Let’s have a conversation about sharks.” If you’re watching it, that’s great. But for actual homework, younger kids, the secret is you take a picture with your favorite AI app of their homework, and you ask the AI, how would you tell a kid how to solve this problem? And so you get to be the genius parent who has the interaction, explains it with them.

When they get into high school, you might want them to use AI for learning. Though the high school systems are pretty good now, for Anthropic and OpenAI and for Google. They do lock down some stuff, they have some accountability, right? You can’t get yourself in too much trouble. But on the learning side, you need to lock them down into using study mode for these things, which makes the AI a tutor rather than giving you the answers. And it turns out AI tutors are quite effective, and they’re very useful learning tools. So, again, observe, work with them, and you’re hopefully also teaching them values, that they should write on their own. We’re gonna have to do this like other technologies.

I think there’s a lot more positives than there was in social media for kids using this, right? Like, there’s a lot of upside. The models are pretty gentle, increasingly better with mental health issues; people get help talking through them. But you want to observe this. This isn’t something where you’re just, like… turning kids loose with any technology is a mistake, right? And you’ll know your kids. Some kids are like, they’ll never use this for any bad purpose. Great, you’re the parent, you get to make decisions about that. But I think part of what you want to do is be thoughtful. And I don’t think it’s that hard. It just takes some time to do.

## Friction and boredom

Brian Keating: Last week Adam was on, your colleague. He mentioned the importance of friction and the counterintuitive benefits of having people not agree in organizations, as a psychologist. You’d think the groupthink and the brainstorming, as you mentioned a few minutes ago… yeah, it’s brainstorming, but it turns out that it converges. And as my past guest Seth Godin once said, if you just let things evolve by committee and brainstorming, everything on the internet becomes pornography. But what level of friction is important? And I guess, concomitantly, is boredom good? I was bored a lot as a kid. And it made me kind of daydream and come up with ideas, some of which were useless and some of which were important for my intellectual, creative development. How much boredom and how much friction… should we let kids fail? When my son loses a baseball game, I say, good. I think it’s good. You learn more from losing than winning.

Ethan Mollick: So there’s three things there. There’s friction, which is absolutely… I mean, friction turns out to matter for anything you want to make gains in, right? So we already know, if you want to lose weight, you have to diet, and that’s painful. To work out, you have to lift weights. That, like, the repeated friction matters, and in learning it’s desirable difficulties. If you’re not being challenged and pushed and you’re not uncomfortable, you’re probably not learning enough. Now, we can make things too uncomfortable, and then you lose interest, but we have to kind of keep you on that right line, and it’s hard to do that with a class of 80 people, to have everybody at the right level. But that’s why people cheat, right? There’s incentives to do it, but also, like, homework sucks. It makes you better at the test. It turns out testing works. Like, it’s really annoying. All the things that you’re being taught to do in school actually matter. Now, it doesn’t mean people are assigning homework thoughtfully, or minimal amount of homework for maximum effort. We don’t even know how to do that, but there is this kind of piece here that people cheat.

So friction does matter, right? Friction between people, friction between other sets of stuff. And similarly, idea diversity. The AI produces fairly similar ideas. You want to have different ideas coming into play here. And then on top of the kind of friction piece, we have other aspects of kind of working with AI that matter too, right? Making sure you’re not using it in the wrong kinds of cases, that you are keeping your own skills up to date. You’re being strategic about what you’re de-skilling yourself on. Like, I’m very happy to de-skill myself forever on my school’s HR portal. Like, I don’t ever want to learn how to use that. And if the AI does it for me, great, right? Like, that’s a friction. My grandfather built the fire suppression system for the launch pads at Cape Canaveral, doing it with a slide rule and matrix math. I have no idea how to use a slide rule, and I can’t do matrix math in my head, and I am fine with that. So we have to make choices about what we want to de-skill and what we want to deprioritize and what we don’t.

In terms of boredom, it’s really interesting. Boredom does create creativity, and there’s this great set of papers on this, but only when people feel agency and positive that they could do something with it. Otherwise, boredom leads to disaster. So people who are bored are much more sadomasochistic. They’ll do anything to stay engaged. Like, we don’t like to be bored. So if there’s something interesting you can do when you’re bored… and actually, this is kind of why I like AI as opposed to passive recording. Like, I build weird games. I work out with poetry. Like, it is a really interesting boredom-killing tool that has a creative aspect to it too.

## Do you believe that extraterrestrials exist?

Brian Keating: Second to last question. This one’s totally out of left field. Do you believe that extraterrestrials exist?

Ethan Mollick: Statistically they kinda have to, right? Like, it would be kinda weird if they didn’t.

Brian Keating: Okay, so now you just launched me on my favorite hobby horse, which is…

Ethan Mollick: Is the Drake equation out of date now? I don’t know how I’ve…

Brian Keating: Well, the Drake equation’s like P(doom). It’s just useless. Like, no actual scientist [inaudible]. Let me look at P(doom) and then use that to [inaudible]. Yeah. Like, nobody uses that, and nobody uses the Drake. The Drake, it says that… ’cause the uncertainties. As a good scientist yourself, you know how important it is to account for systematics and statistical uncertainties. And the Drake equation hasn’t either. It’s just a number. If my students gave me a number, “Here’s the [inaudible]. Five million.” I’m glad you asked. Five million. But that’s not what I’m getting to. My kind of counterexample for it is that we don’t see… like, if we were sending out probes and whatnot, sending meat bags out over the solar system and the universe, then that would be kind of inefficient versus sending AI, sending artificial intelligence throughout. And the fact that we don’t observe it, to me, gives some inkling that maybe we are alone. Maybe space is just too big, or for all intents and purposes, effectively alone. We will not actually make contact, even if there are things that are fundamentally unobservable.

## The next, next phase

Brian Keating: The way I want to conclude this wonderful conversation is the next phase. The next, next phase. I mean, this book is called Coexistence, the first one Co-Intelligence. What’s the next, next phase gonna be when it comes to our creativity, entrepreneurship, productivity, thoughtfulness, scientific discovery, parental oversight, education? What’s the next, next phase, Ethan?

Ethan Mollick: I’ve thought of two titles for the next book, right? Depending on how things work out. And I think one of them is Co-Evolution, right? Like, we work with these systems, these systems shape us. And the other is [inaudible], which I think I’d rather not write. It’s hard to know what the next phase is. Like, even this book, right? I think the agentic era will last a bit. And part of the back half of the book is that systems actually don’t absorb this change as quickly as we do, right? Not much has changed in the outside world here. But I think that we are going to evolve with these tools.

I mean, one of the most fascinating sets of stuff is there’s a set of research suggesting that humans co-evolve with tools. Like, for example, if you’re holding a bat, if you speak of baseball, or a metal rod, and you hit it on something, you can estimate where on that metal rod a hit happened by the vibration in your hand. It’s almost like a spider can tell where something is on its web. That is an evolved reaction, probably, to working with tools for enough time. So we co-evolve with our tools. And I think we’ll co-evolve, obviously not in a biological way, but in an intellectual way, quite quickly, with what AI should fill in, what it won’t. We’re running a big experiment, for better or for worse, on that right now.

Brian Keating: Looking forward to seeing what Bryce Harper does next. Ethan, this has been a lot of fun. I encourage people, again, get all three versions, the collector set. It will be hard to convey how fun it is to listen to these real-time experiments that you’re running in the book and on your Substack and all the great stuff you do online. Ethan Mollick, thank you so much for joining us today. Good luck with the book.

Ethan Mollick: Thanks for having me. This was a great conversation. Thank you.

Brian Keating: Ethan just told us that if AI froze today, it would still be enough to remake almost all intellectual work within a decade. If that changes how you think about your own job, subscribe. Turn on notifications. Tell me in the comments which of the four edges you’re betting your career on. And for the idea that Ethan borrowed, that language is a model of the world, watch my conversation with Stephen Wolfram, linked right here. Thanks for watching. See you next time on Into the Impossible.