0:00On 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 or 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 a actual design decision. If we just had Fable 5.1 right now in House Strom, that's enough to change almost all of human work in the next 10 years. It's good enough. This is Ethan Mollick.
0:25He 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.
0:43We start with mathematics, where the experts forecast missed by a megaparsec.
0:48Getting towards the mathematical uh angle of things, you know, just in the recent days, a couple days ago as we're recording now in mid-October, there was an announcement of, you know, 700 papers in different levels of theorems and proofs and axioms development uh released by OpenAI. 3 weeks earlier, uh there was an announcement that it had solved the millennium problem, which is a a huge deal. And the the pace of change is is just astonishing. I have my arguments against it being as useful in
1:17physics 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 large language models, you know, coupled to coupled to GPUs, but I I have my doubts about whether or not they can truly do physics in the way that, you know, your colleague at at Penn, my friend Mark Devlin, does it or I do it. So, my question is, you know, right now solving Olympiad level math problems and and millennium problems is happening, but also simple mistakes are happening. And I wonder, you know, if
1:46the kind of jagged frontier that I want you to define is going to be, you know, kind of a permanent feature, the the jaggedness, or is it going to become a phase transition where we do trust it, it doesn't think there's 10 Rs in strawberry. And 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?
2:11Really great question. So, the jagged frontiers idea we came up with in our our first academic paper where we were studying the effects of AI on people in in a consulting business. And what we identified was the idea that AI is good at some things you wouldn't expect, bad at some things you wouldn'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 knowing what this is. Now, math is really interesting cuz it was the most jagged of the parts of the intelligence, right?
2:35When when my colleague Phil Tetlock, who runs a famous forecaster 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 something, you know, were were under under 5% or something like that. And of course, the AI got a gold medal, but for a year it was getting the hours of strawberry wrong.
2:54There was a bunch of technical breakthroughs that that around how AI reasoning works, but also it just turned 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 millennial 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
3:22at with your question was one of the reasons why. So, AI works better when we have definite things to train on, right?
3:28So, math, coding, where there's a right answer, works great. And by the way, that's why writing lags so much because good write like Claude writes the same kind of way because there isn't an objective measure of writing to to judge against.
3:41Well, that's a load-bearing argument that we can Thank you. Thank you. Yeah, it's really it's really doing the work here. Yes.
3:47It 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 publish 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 result, and I'm getting good papers. I'm not getting The research taste is the is the weak spot right now, not the execution. There's no errors, the sites are great, everything is terrific, but it's taste for research, which is kind of an interesting question anyway, is like should we as humans we need to have taste for research because there's only so much work we could do. But if the AI
4:17could 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 may not matter that much.
4:31If you reach superhuman level in something, does it matter whether or not that's weaker than a superhuman level in a different field? At the same time, that jaggedness creates a lot of opportunities for humans to work with AI in important ways.
4:42In the book, there's a lot of different characters and and maybe archetypes that get portrayed, and 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 but now you introduce this concept of a conjurer, you know, some human delegator to AI for completing agent agentic work. Talk about those three different, you know, creatures, the centaur, the cyborg, and the conjurer.
5:06So the centaur and cyborg I talked about in the original book, right? And so those have both been used in different ways. We define these 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 know, 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 know, you ask the AI a question, you go back to your own stuff.
5:33Well, yeah, give me a fifth version of this. It becomes kind of blended work.
5:37That all made sense in the kind of co-intelligence era when you work 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 could assign it very complex work for it to do. And I've had it do all kinds of insane things. Right? So I got to write a research papers on the dating myself as a nerd side. I gave Astra when I the game Zork, which is one of the original text adventure games. I said turn this into a graphical adventure game. It built a full graphical
6:05adventure game with like amazing 3D spaces you move through.
6:09But 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 conger work. Now I did make a mistake here. Cyborg centaur start with C, but they were soft C's. So it's cyborg and centaur. When I was writing conger, I was like, oh this will sound great. But it actually looks good on the page, but it's actually terrible because it's centaur work.
6:29I want to know, you know, what are the the the human advantages that you think are most sort of uh hopeful or perhaps useful for us in this age and and then I want to drill down into scientists in particular. What are the human scientific and mathematician advantages?
6:46So I talk in the book about about four things. And I want to 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, you know, in working with AI. So I 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
7:15and in your fields of interest. Probably in a 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, oh god this is going to be miserable. And you probably knew it the second I got on, right? Cuz expertise is all about heuristic and decision-making.
7:30Just like you can look at a paper and be like, is this BS or not really quickly, right? So we need expertise cuz it lets us work with these systems in better ways. And we're talking about agentic work. One of the ways you want to be able to work with agent is just like you would with a team of grad students. You know they'll right away if they're in the right direction or wrong direction.
7:44And that requires deep expertise.
7:46Wide 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, you know, to 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?
8:02You 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 bit of coding or design, these systems will work much better for you.
8:11Then there's the idea of taste. And something we should talk more about, but AI is really good at producing many of a thing. How do you know you should select? How do you pick something that's not just slop? The enemy of 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 the way that that, you know, 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.
8:40Eskaton is a term I first heard from Peter Thiel uh talking about the Antichrist and the Eskaton. And And I have to say I didn't fully understand the point that he was making. But let's let's remind the listeners or tell me, the host, uh what is an Eskaton? And what is the escalator? And how are these things in contrast in terms of disruption to this this possible future of constantly recursively improving superintelligence?
9:03In 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? It'll define sort of future. I I think scenarios one and two are out. I think we are either on a continual exponential that doesn't reach the level of superintelligence that I think a lot of people still in Silicon Valley are obsessed over, or we or we do, right? And I'll call those the escalator and the Eskaton. So, the Eskaton is literally the last thing in
9:32Greek. I use it sort of substitute for the singularity, right? The idea of in the original sense that we can't predict past this point. There is a point where a if you imagine AI gets good enough, we have a super intelligence out there, is an alien to us. It will do what it does and we don't can't predict what the world looks like in that 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 going to matter a lot, right? And we're going to muddle through one way or another
10:02hopefully, 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 planable. Like if the end of days is coming, you know, what are we going to do? You know, all we can do is be nice to the AI and hope that there's a place for us in the 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 going to face the same kind of problem, right?
10:25Maybe we can argue about whether physicists will have the same kind of crisis or not, but I think it's going to come for a lot of us. And it's going to come for professions and we can reconfigure work in different kinds of ways. People have the model of the industrial revolution talk about a lot and like it worked out great in the end.
10:39And 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 the escalator ride so that everyone wins and it's not just a bunch of AI bros winning this time.
10:56And you have stuff in here from 2026, but you know, like most books you probably started writing it a couple 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 100 bucks, you give it a little bit of a guardrail, and it comes back and it and it uses your Amazon account, I think I remember.
11:13Yeah. 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?
11:21I am continually slightly creeped out and really excited and anxious and really optimistic all at the same time.
11:28A 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. I start my 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 it kind of just continues. I think if I underestimated something it was like, "Actually, there's been a lot of moments like that." You know, and and when Astra came when Fable came out like there's there's a bunch of moments.
11:52Interestingly, right? I think that since I wrote the book, I kind of had jury-rigged AI agents together even through early 2026. And now there's Muse and Dot and a bunch of these other systems that I think people will be much more used to using AI agents to do stuff and they're pretty good at what they do. And there is kind of an interesting question what you said which is also they haven't messed up that much yet.
12:11Like for a billion people using these systems I think we were all worried 2 years ago would there be mass psychosis?
12:16Would 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 actually has happened.
12:23You're right. The fact that you hear about it is means it's noteworthy which means it doesn't happen that much.
12:28Most people are using these systems and not like and 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, you know, 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."
12:45And 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 like you know, 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. So, 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.
13:11And your first book was co-intelligence which I found very helpful. And you know, to be honest with you, you know, and with love and respect, you know, co-existence sounds like, you know, we're trying to hang on. It was almost a little bit disturbing or depressing before I read it as I judged the book by the cover as everybody does. It's kind of the thumbnail, the CTR rate that we have to monitor. But, I think, you know, co-intelligence was sort of important in the in the variety of ways. Most Most of which had to do with the fact that, you know, Homo sapien, the word sapien
13:40means, you know, one who is wise. That's our name. And And what is it wise about?
13:45It's It's wise that it's, you know, according to most tradition that I understand, is that we understand uniquely among all animals or creatures that we're going to die. And that makes me think that, you know, intelligence is almost free. You know, there's so much intelligence, but what we lack is wisdom. We lack the wisdom that to discern between what is what is useful, what is knowledge, what is what is to be limited. And so, I kind of wish that your next book would be, you know, co-wisdom, but but it's coexistence. And And as I say, you know, with with respect, it kind of is like we have to
14:14justify ourselves or like how are these two state solutions going to coexist?
14:18Was that intentional? And And what do you make of this knowledge versus wisdom? I mean, it seems to me wisdom is at the shorter in supply, and and we need to sort of maybe lean into that.
14:28I 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 that that does require a form of us coexisting. By the way, that also includes protecting parts of ourselves from AI use, right?
14:44Like it involves like choosing not to use these systems when you shouldn't use them so you don't de-skill yourself. It involves, you know, in learning situations, you know, knowing when to use AI or when not. What we want to maintain humanity up. I think that we have to make choices about this. And I think that this is a force in the world to coexist with. So, I didn't mean it so much 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 chatbot, you know, in the box, and you'd talk to it, and when you close the box, it went
15:13away. But, our agents right now looking at my emails and they're pop popping in.
15:16Like I've got a message already from one of them about like something I probably should be paying attention to. They save me lots of money. They make 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, you know, and we only have We only us have a, you know, visceral sense of mortality, right? But like the
15:45AI 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, you know, I try to be a pragmatist about this or, you know, materialist, I guess, which is like let's judge it by what it does rather than try and say that it's the same characteristics as a 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, you know, for practical purposes I mean I teach at a business school, right?
16:10Like this is, you know, 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, to have 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.
16:35Except for that meme that, you know, shows a guy asking Chat GPT, is this mushroom safe to eat? And then 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.
16:48But absolutely, but also it was worse and more dangerous. That like the thing to note is everybody's worried about long-term safety. Short-term safety has gotten better. The 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 like you would underline your, you know, 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.
17:10So, like I'm not saying, you know, there's a human wisdom involved too of like, yeah, don't eat the possibly poisonous mushroom off the advice of one 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 it's that's the wisdom we need in some ways.
17:27So, 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. You know, in physics we don't call a theory successful because it produces things that sound good or, you know, resonate with us. So, look at string theory and other things. They they sound good, but we demand a prediction that can survive an experimental conflict with uh with the experiment. There's our inability to confront Mother Nature. So, my question for all AI, you know, researchers and
17:57specialists, and you're an expert in this, is like what is the equivalent experiment? What is a decisive experiment demonstrating that, you know, electrostatics and and lightning and conduction are are one unified phenomenon as as as Franklin did or or or any other empirically verifiable, but most importantly falsifiable, potentially falsifiable equivalent. What is a prediction? What's some crisp thing, a decisive experiment that you could demonstrate that AI actually understands?
18:23The problem is a definitional one rather than experimental design, right? I mean, I haven't built experiments. Does AI affect learning? Does AI affect productivity? Does AI affect creativity?
18:32You know, and even then there's definitional issues. What is productivity and how do we define creativity and how do we measure it?
18:37Again, 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 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.
19:04I mean, not burned. This is where things are going. But, you know, 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 have got like I am not an expert in those areas. I can tell you about the things I know a lot about and I you know I'm I'm broadly, you know, 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
19:33never use it? You know, 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 or a century. And I wonder at what level is everything reducible to language. But I want to give you what I call the Keating test, you know, very modestly named, but it's 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
20:02claimed that titillated him. And he was participating 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.
20:19So I always like to ask, you know, 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 in the back seat of my car as I'm taking my kids to school, you know, going over, you know, zero Gs. What sense can an artificial intelligence, you know, 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
20:47happiness or have those dimensions of their personalities?
20:50There'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, you know, 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 the sort of the math that makes up these AIs that light up in response to things that are pleasurable the same sort of way across all sorts of
21:19pleasurable things. You know, 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 that you know, the math alone would be enough to indicate that you don't need it to have a subjective experience. So we sort of 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 in 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
21:49language 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 anyway and I think that to the extent that they do have a subjective experience it's an alien subjective experience. We're arguing over definitions is the point again.
22:07Like is is the fact that it has a happiness thing light up is the same thing as dog waving its tail 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 you know, what are they missing? There's a lot of money going into that, right?
22:21Part of this is robots are much bigger deal than LLMs and increasingly looks like LLMs might be able to drive robots without having to go through all the steps we thought we'd have to do.
22:30There's this push push to like what if we put AI training in worlds rather than training the real environment learns things. So I think there's a lot of controversy over all of this. What I would say at the end about this is large language models turn to be 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's slight variations in LLMs but regardless that you know, they shouldn't be able to do vision and solve problems in 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
22:59and a mathematician was that in creating model of human language we created a model of the world because human language models the world. Otherwise, how can 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.
23:16I talked to Steve many times and I I agree that's somewhat useful, but like not all languages are the same. In fact, this brings up my next question, which you you talk about a lot in the book is scaling and scaling laws. But, you know, there's Hawaiian which has 13 letters and there's, you know, Cyrillic which has 32 letters and there's English. Not all languages are the same. There's binary code and and there may be languages that these entities can can communicate in that we don't understand.
23:42And we've, you know, just lucky for now that they they use English.
23:45That's actually a big worry of AI researchers because if you've ever used especially Claude Fable 5 has gotten better at this. If you've ever watched it talk to other AI agents, it talks in a language that's called Claudish, which is a variation of what's called neurolease, which is yes, of course, the AIs will come with a better language for talking to other AI agents. And a huge amount of effort in 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
24:14observability into what they're thinking rather than not English. It's not a lucky thing. It is a actual design decision that the labs are under pressure. The AI labs are under pressure to relax because the AIs will be much more efficient if they didn't use English.
24:28With the scaling laws that you mentioned, the only pushback I have is that they're just empirical, you know, they're phenomenological. They're not immutable laws of, you know, 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.
24:43There's Jevons paradox. There's there's Wright's law, you know, all these things. But but at a certain level like I don't know about you, Ethan, but, you know, if AIs were future lobotomines, we're stuck at Fable 5.5 and and Astra.
24:58I mean, 99% of what I do, except for the 1% physics, which is important, but 99% of what most people do is is completely solved. I mean, it's Turing, you know, complete at some level for ordinary human beings. So, what level do you think that scaling is bound to continue or will it saturate?
25:14It's so great cuz you always give two two questions each question. So, I'll I'll try and pull those apart. The question one is that scaling saturate. Scaling's always saturated multiple times. There's multiple scaling laws. I talk about a couple of them in the book.
25:25But, 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. Uh and I interviewed Gordon Moore at the time and everything else. And Moore's law just turns 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'd hit a a limit with the amount of human language in the world and synthetic language would make the AI break down. And but it turns out, oh, actually it the AI does a good job
25:54with synthetic language because we figured out ways to make that work. So, if 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 the logic of, you know, of capitalism is at work, there's so much money at stake.
26:11Researchers find ways through, right?
26:13Like that's the one thing you find. Like physics has dead ends, you go forward.
26:16Imagine so much infinite more funding going in, or you know, revenue and it's the only thing America cares about is like we need to keep building, you know, 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.
26:32But, the reason why I don't think there'll be there's a stop to this stuff is so 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, oh no, if this cuts off, we're done, right? It's not like photolithography where we're kind of reaching the images of lithography and and Moore's law and like we don't have a a replacement technology really. There's a 50 or 100 different next paths for LLMs and no one's even bothered with them. They're not bothering to increase efficiency cuz they're just unreasonably effective as they are and they don't need to do it
27:02yet. So, I think we will hit walls, but I don't think they're walls we can't overcome.
27:11[music] [music] [singing] We need to judge this book by its cover. For the 10 people out there who don't know your work, I'd like to have you go through the title, the subtitle, and this cover art, which I can, you know, kind of guess at, but take us through it. And the one thing you can't say is my publisher chose everything.
27:34My publisher did not choose everything.
27:35My whole family did help me, right? My wife helped mark up some of the cover art cuz I'm really not being an artist. My sister helped me come up with the title. So, it was a family affair. I have to give credit where it's due.
27:44First of all, I wanted to avoid shining white robots, right? Like part of what I think we have a problem with AI is most of our views of AI are cliche. And most of the images we use of AI are either drawn from like utopian science fiction or, you know, Terminator, right? And like that's not what the AI we ended up with. We didn't end up with the shiny white robots and Wally glowing over things. Like so, I think that given that, right? Like what do we want to invoke? Something kind of human about our own sort of, you know, our relationship to something other other intelligence. So, first one hand
28:14reaching for the tree of knowledge from a classic painting. And this is obviously from uh from the Sistine Chapel, right? And close-up of the hands. Although, there is an Easter egg.
28:22I don't know if you spotted the Easter egg on the cover. The fingers. Extra digit. I actually debated over which hand to give the extra digits to, but it worked better. It's not supposed to be the biggest statement. But, the idea is like there is like something going on.
28:32We're contacting It's not fighting intelligence. I don't get too bogged down in questions of intelligence or consciousness, but there is an intelligence here that we're in contact with that's an other. And I think there's something interesting about we're creators, but we're also being shaped by it. And I think that's a important thing to keep in mind.
28:47Tell me something. There's a about that, you know, that Roman has, which is that you know, the fact that these things are probably, you know, unprovable. Like that we can't prove that they're safe, therefore they're going to have these capabilities and cause great danger and therefore they should be regulated, etc.
29:02When I try to, you know, 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, you know, can a squirrel tell what the humans are going to 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.
29:23His point is that we tend 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.
29:33Do you think that the the bigger problem is is underestimating the anthropomorphization or overestimating what these things can do?
29:40So, I'll briefly side answer this, which is if you want to use AI to do stuff, you have to anthropomorphize it because that it's just it's trained to work like a human and it 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 specifying what they want, right? And it 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
30:09arguments that are built on chains of speculation about what a person would do are hard to apply to an AI. I mean again, there's value in realizing that there's a lot of very serious people very worried about the risk that comes from existential risk that comes from AI. It's hard to pick one argument out of that set that is like, yes, this 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.
30:33But 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, you know, I'll go back to what you said before. If we just had Fable 5 right now, 5.1 right now in Astro, that's
31:02enough to change almost all of human work in the next 10 years on the intellectual like it's good enough. We haven't even begun to start. The AI labs make the products they haven't even begun to be swallowed by companies and organizations and fields and institutions. So, you know, 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 that anthropomorphization makes one way or another, right? Like you don't need that. You could just say superintelligent machines are have inherent danger in them without having a logical chain about why they might kill us all. The fact that we don't have a
31:31good reason why they would or wouldn't seems like a reasonable, you know, thing to say, let's be cautious.
31:36Just a quick interruption from today's sponsor, me, Brian Keating. Since it's Nobel Prize season, I can't resist advertising my three books. 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.
31:53But my next book, Think Like a Nobel Prize Winner, tells the story of nine Nobel laureates who will 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.
32:23Get 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.
32:31[music] Now, back to the episode. 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 organizational science, which I subscribe to. This article is called cybernetic teammate and I loved it because it has words experiment. I'm an experimental physicist. So anyway, it's called a field experiment on generative AI and teamwork. The result that you know, I can glean from my AI summary of it is that like basically AI's were two people. Like a good AI in the hands of the right person adds like a like couple
33:00people, you know, it was focused on obviously financial business. Do you think that that's sort of would regress out across all different types of discipline? Like knowledge work in physics that I have my experiment with Mark and I lead an experiment with with others that 450 people on it. Like should we replace half of them with you know, a third you know, replace two-thirds of them with AI?
33:19All right, so another couple great questions about in one. Actually, I do mention this in the book because the thing that's interesting to me that is not actually that individuals with AI performs well as teams without AI.
33:29That's obvious. If there people get a hundred times performance gains from 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 on my own. Use AI on your own. We haven't yet built the systems that are like what does it mean to work 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 there's studies in lawyers of doctors.
33:56You throw it at anything, programming, consultants. The interesting thing about that paper that I actually do cite because I don't cite the productive impact because again, GPT-4 prior era before agentic work. But the thing that I don't that is really interesting I mention is 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 every organization we talk to is the blurring of lines in parts of the organization.
34:21So coders are doing design work, right?
34:24Designers are doing coding work. That is a really big change. And by the way, super interesting in my lab at generative AI lab at Wharton told him I run, like the productivity has have insane, right? And people do everything now. You have someone who actually can write up an interactive, you know, explainer about about the paper. And we can do the paper, and we can have the, you know, the AI check the paper against 15 other values and do the like it's a crazy expansion of what we could do. But it also makes it hard to match cuz I used to know what everyone did in the job, and now everyone does everything.
34:53So, I want to turn again to the world's second oldest profession being a professor now. Walk me through your daily life, you know, you wake up, you you have your coffee, and where do you go next? How are you using it on daily basis?
35:06First of all, I have to make choices.
35:08So, the choices I have alliances, right?
35:10I will not send anything AI written to anyone 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 faculties, senior people who write me.
35:21And it's a a challenge by the way cuz I could write to everybody. I get hundreds of messages. I try and make it so my messages are are human. I always write my first drafts to everything without talking to AI at all first, right? To trust So, it's all my own writing. All my tweets are all my own. I never have a staff do it.
35:34Are you like WhisperFlow? Are you using a dictation?
35:36Usually I'm typing. I do do messy notes where the AI does stuff. Now, okay, around all that, I I wait You asked what 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 U Penn account, right? And then I also have stuff from my Gmail account, right? And they're in communication with each other around that set of stuff. And I don't give them my UCSD email.
35:59And again, legal rules are often really unclear. So, for Penn kinds of email, right? You want to like we have a version of ChatGPT Enterprise that is safe for HIPAA use and for student use.
36:10I have my email scanned by the AI, and it's kind of just telling me what things to pay attention to, like if I miss stuff cuz 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 how financials get scanned by the AI and it tells me if there's kinds of issues there. And then usually you know, I'll do work, right?
36:33And but like for example, when I tweet about a technical paper now, I write the tweet in my own, but I'll actually paste the paper into the AI and say, "Am I getting something wrong here? Like am I missing something?" And usually it's overly cautious. Like, "No, no, you're you're using such sweeping language."
36:45I'm like, "Don't worry about it." Like that I understand what to do about, but like is this technically right? Um I have like if I'm writing and I get stuck at a paragraph, give me 40 versions of how to end this paragraph. And I might use a taste to kind of pick among them to get me thinking again in writing. I absolutely built like a whole bunch of systems. Like, you know, I have like when I'm putting together a set of slides for stuff, I'll throw that into Claude. Like, "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
37:14this 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 of any time I can use a team, I use the AI to do that work.
37:29You get a referee request. You have to prepare slides. You have to give a public talk. You have to talk to donors.
37:35At what level are you handing the keys to the agent? And what level are you, you know, I'm just going to do this no matter how long it takes, or I'm going to turn down these opportunities cuz I don't have the bandwidth to do it in the, you know, bespoke human touch fashion?
37:47I feel like it's really important to be bespoke human touch. By the way, you see this bite people all the time, right?
37:51Like, you know, QuillBot is a fairly good AI detector for right now. There were no good detectors, you know, a year ago. And everyone who used AI to write all their stuff, like, you know, if I'd used AI to write all my stuff, you you would know because also you'd recognize Claude speak right now. So, like if I wasn't being authentically me, that would show through.
38:08And I'm sorry to interrupt, but but you actually make the case in the book that it's actually the way it writes is affected the way you write in that Oh Intelligence had 154 M-dashes, and this current book has a has 54.
38:20Yes, I mean, that's the co-existence piece. I removed m dashes because it's a signal I I won't I wrote with them and I love them. I'm never going to remove them.
38:28And I remove phrases. I things I say every for my my kids say to me, you sound like you know, I think Claude was based on you cuz we have a similar kind of speed like So like that's a problem for me, but like yes, that's part of my reaction is to shut I view that Sorry, again, it's hard to interrupt. I view that like saying to me, you know, Keaton, you dye your hair, you know, stop dying your hair. But I view that as a compliment. Oh, you sound like Claude. Well, I wrote it myself. So Claude sounds like me or sounds like Ethan.
38:49It affects you. You're getting your So what I was going to say in that big picture is all the things you talk about, I would not use AI first. Referee review, never going to put it through AI first. I would be in you know, it depends on the journal policy, right?
39:01There's all these like some of the journals I write for you don't let you use AI for everything. So they've actually encouraged to do it. I will always do AI as a second pass, never as a first pass. It's actually interesting. I I'm you know, I study innovation and brainstorming was invented in 1955 and by 1957 we knew it was a terrible idea.
39:15Because the problem is as soon as you do any group group process, your ideas fit the group. All 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, what are the I So it was funny. I fact
39:43checked coexistence with AI and it was great. There was no errors, but it also not only found errors in my work, but it found an error in the work I was citing.
39:51Like you would actually say that actually the statistical analysis here is wrong. This cited the wrong city. So I made the mistake of putting co-intelligence into this too and it's like, oh. Like I made all the like and I had a fact check I paid fact checkers. Like I I read it myself. I had readers.
40:04I I'm a very careful writer about these things. I read every article. 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?
40:12Every academic article I've ever wrote is full of errors. So part of this is our decision too about what is science and writing look like at this point?
40:18Yeah, I mean the dirty secret of publishing is that, you know, 90% of the time at least, you know, previously, we're emailing docx files across the country to our, you know, agents and our editors. And I don't know, you know, I'm writing my fifth book now and I don't even talk to my editor half the time. I just put into Claude Fable, turn it up and say, "Where I don't 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."
40:40Actually, I want to bring that up.
40:41Typing, you know about the concept of lock-in. I presume or maybe I'll explain it, but but for my audience, I mean, I know it. You and I are of a certain age that 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 cuz the word frequency was set such that it was less efficient to type with than the Dvorak, which is the other competing. And I view LLMs plus GPUs in that same vein. In other words, they're
41:11so good at what they do that there's never going to be sort of a 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 that even a company like Google with their TPUs, they're not really competing as as well as much as the two other frontier labs are using video. 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, you know, with with the fact that these LLMs need humans, and
41:41this 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 16 to come out before I can have a theory of everything, it seems to me like human physicists are going to be okay. What about lock-in and other phenomena? Are these things a victim of their own success in some sense?
41:58LLMs are a 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.
42:07That'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, you know, AlphaFold, which is a specialized model built for protein folding. You might have, you know, 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 going to see LLMs paired with other things. And I do think that now that AI is a thing,
42:37you are seeing other angles. There was a bunch of vast decision-making system one LLM uh system one thinking systems that just came out a couple weeks ago where the AI makes very fast judgment calls, right? And now OpenAI released a competitor jab already, as did Claude.
42:49Like, 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 specialized coding language. Like, science fiction, all of this stuff tended to be this kind of crazy like, the world's 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 that's a good outcome.
43:17I have this concept called the academic Hunger Games. You know, it's like you first compete in high school, you know, to do well in grades, and then 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 that Professor Moloch. And then you want to do well in his class. He gives you a letter of recommendation. Maybe you do research in his lab, and then maybe he writes you a grad school you go to get your MBA. You do whatever. And then you keep going. And then it's a post-doc. Then it's a assistant professor. If you can do it. And each step, the probabilities are going down incredibly low. But if everybody has
43:46access to superintelligence, as your illustrious alumni now forces us to call it, then does anyone have an advantage, Ethan?
43:53Right now, the answer is yes, for the four the things we talked about here, right? That some people are better using the systems than others. Expertise matters. Agency and willpower matters. There is a world of like, you know, in the current world in the short term, there's definitely differences in people's ability to use these systems.
44:07There's a return to expertise and intelligence and willpower in using these things. In the longer term, if they really are just better than us everything and you're like, you know, "So, how's that string theory stuff going?" And it's like, "Oh, here's the answer." You're like, you know, what do you do then? And the answer is no. I mean, you know, I don't know what your advantage is at that point.
44:22You men mentioned a word in the uh in the book that I hadn't heard about in a long time, which is MOOC. And it made me think, you know, there there two phenomena that I thought would be, you know, at least at least two, but maybe more actually many more now. But in instead I thought academia would be doomed. You know, first it was the MOOCs. I never really thought those were too engaging. I mean, they they exist and there's things like Circle and and other uh online communities, school with a K and all sorts of things where you can learn stuff. But then I thought,
44:51"Oh, 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 and it was just horrific.
45:00And I talked with with Adam Grant about this uh last week. But I want to get your impression like how Why is academia so resilient to these, you know, kind of just just insanely disruptive uh events in the case of COVID and tools and technology like MOOCs and AI?
45:17There'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. Like it's not changing fast, right? So, they tell you everything is a all of this stuff has been flash-in-the-pan stuff that didn't actually change education forever. And, you know, so and then also education's hard, it turns out. Like part of what I wrote about in the book is I was a you know, I was involved in MOOCs. I I've been doing AI you know, prior to AI I was doing teaching with games, interactive material. And it
45:47turns out it's just really hard to move the needle on education. So, like all these technological advances just weren't good enough. Like they seemed in theory they should replace 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 work and there are people who will teach with, you know, overheads today, overhead projectors, if you remember those. And all my classes are 100% modified with AI. I don't know who's right, but there's an evolutionary kind of game.
46:13You're talking about how the rarefied air of academia, part of what we do is there are people who are competitive. There's, you know, professors really interesting reactions to that AI where you hear 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.
46:28On one hand, on the other hand, you have cases where people are absolutely excited by the idea that they could solve hard problems 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.
46:46to 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, alumna from UCSD, Vivian Ming, about her book called Robot-Proof, which kind of is, you know, encouraging a lot of these different, you know, principles, first principles for raising children and and surviving, you know, and thriving in age of AI and 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
47:14models 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 I'm not talking.
47:21But in reality, these things are changing us. They're prompting us. And in a certain sense, as you as you have, you know, mentioned, you you change your writing style. You didn't have to, but you did. And I'm wondering, what is that going to do to children? You know, if your kids were young, when would you give them access to these very powerful superintelligences?
47:39We can talk about the narrow case of education, but let's look at the broad case, which is, I mean, you should be monitoring what your kids are doing with technology, right? Like, I mean, 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, you know, use it to create some images.
47:54Let's create a story together. Let's code together. Like, I think it like because there's a creative aspect to this technology that kids love, like let's build a you know a Bluey guitar and see what happen. Like, you can guide like you and somebody have this capability to guide really exciting, interesting, interactive experiences that are not watching a screen, but are not that are not passive. Screens are less the problem than passivity, right?
48:15So, do the same thing you do with any screen. Monitor what's being done. Don't give your kid access to a computer without doing this. Check if there if there's AI applications on their phone, you want to make sure that what they're doing. Now, let's talk specifically about education. So, for younger kids, I would not let them use AI to do for learning themselves because we know the problem is they'll shortcut learning on their own. Like again, observe. I've seen kids build little simulations and games like about they're obsessed with sharks. Tell me about my favorite sharks. Let's have a conversation about sharks. You're watching that's great.
48:43But, for actual homework, younger kids, the secret is you take a picture with your your favorite AI app of their homework and you ask the AI how would you tell a kid to how to solve this problem. And so, you get to be the genius parent who has the interaction explains with them. When they get into high school, you might want to be use AI for learning, but the high school systems are pretty good now for Anthropic and OpenAI and for Google.
49:05They do lock down some stuff. They have some accountability, right? You can't do any get yourself into 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 turns out AI tutors are quite effective and they're very useful learning tools.
49:19So again, observe, work with them, and you're hopefully also teaching them values that they should write on their own. They should do like we're going to have to do this like other technologies.
49:27I 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 to you know increasingly better in mental health issues. People get help talking through them, but you want to observe this. This isn't something where you like turning kids loose with any technology is a mistake, right? And and you'll know your kids. Some kids are like oh they won't like they'll never use this for any bad purpose. Great.
49:49You'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.
49:56Uh last week and 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 know, you think, "Oh, the group think and the brainstorming as you mentioned a few minutes ago." Yeah, all it's brainstorming, you know, but it turns out that it just converges and as my past guest Seth Godin once said, you know, if you just let things evolve by committee and brainstorming, every everything on the internet becomes pornography. But, what level of friction
50:24is important? And I guess concomitantly, you know, is boredom good? I was bored a lot as a kid and and it made me, you know, 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, you know, should we let kids fail?
50:40You know, when my son lose a baseball game, I say, "Good." I think it's good. You learn more from losing than winning.
50:45So, 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 if you want to lose weight, you have to diet and that's painful. To work out, you have to lift weights that, you know, that like that's not like that the repeated friction matters. And in learning is 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 teach it's hard to do that with a class
51:13of 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 other sets of stuff. And it's the way idea diversity that AI produces fairly similar ideas. You want to have
51:42different ideas coming into play here.
51:43And then on top of uh kind of friction piece, you know, we 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 and doing
52:13it with a slide rule and matrix math. I don't even know 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 do prioritize and what we don't. In terms of boredom, it's really interesting.
52:24Boredom does create creativity, and there's this great set of papers on but only when people feel agency and positive and 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.
52:39Like, we don't like to be bored. So, if there's something interesting you can do when you're bored, and actually this is why I kind of I like AI as opposed to passive recording. 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.
52:52Second to last question, this one's totally out of left field. Uh do you believe that extraterrestrials exist?
53:00Statistically, they kind of have to, right? Like, it would be kind of weird if they didn't.
53:04Okay, so now you're you just launched me on my favorite hobbyhorse, which is Is the Drake equation out of date now? I I don't know how I mean, I I've I've Well, the Drake equation's like P(Doom). It's just it's a useless like no actual scientist "Let me let me look at P(Doom)" and then use that to 40 out.
53:19Like, nobody use that, and nobody uses the Drake Oh, the Drake equation, you know, it says that cuz the uncertainties, you know, as a good scientist, you know, yourself, you know how important it is to account for systematics and statistical uncertainty. And the Drake equation has neither. It's just a number. If my students gave me a number, you know, "Here's the uh habitable rate It's 5 billion. I'm glad you asked. 5 billion.
53:38Uh but that's not what I'm getting to.
53:39My kind of counterexample for it is that we don't see, you know, like if we if we were sending out probes and whatnot, you know, sending meat bags out all over the solar system and the universe, then that would be kind of inefficient versus sending AI, you know, send some artificial intelligence throughout. And the fact that we don't observe it to me gives some, you know, inkling that maybe we aren't maybe we are alone. Maybe, you know, space is just too big or for all intents and purposes effectively alone.
54:06We don't we don't we will not actually make contact even if there are things there from fundamentally unobservable.
54:11The way I want to conclude with with this wonderful conversation is is the next phase, the next next phase. I mean, this book is called coexistence, first one co-intelligence. What's the next next phase going to be when it comes to our creativity, you know, entrepreneurship, productivity, thoughtfulness, scientific discovery, parental, you know, oversight, education? What's the next next phase, Ethan?
54:33I thought of two titles for the next book, right? Depending how things work out. And I think one of them is coevolution, right? Like we work with these systems, these systems shape us.
54:40And the other is co-oh no, which I think I'd rather not write. It's hard to know what the next phase is. Like even this book, right? Like I think the agentic era will last a bit. And part of the back half of the book is that our systems actually don't absorb this change as quickly as, you know, 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, you know, one of the most powerful things sets of stuff is there's a set of research suggesting that humans coevolved with tools. Like for example, if you hit a if you're holding a bat and
55:08you speak of baseball or you or metal rod and you hit it on some length, 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 net. That is an evolved reaction probably to our own, you know, to to working with tools for for enough time. So, we we coevolve with our tools. And I think we'll coevolve in a obviously not in a biological way, but in an intellectual way quite quickly with what AI should fill in and what it don't and what it won't. We're running a
55:37big experiment for better for worse on that right now. Looking forward to seeing what Bryce Harper does next.
55:43Uh, hi 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 really these real-time experiments that you're running in the book and on your Substack and all the great stuff you do on online. Ethan uh Mollick, thank you so much for joining us today. Good luck with the book.
56:03This is a great conversation. Thank you.
56:05Ethan 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.
56:27See you next time on Into the Impulse.
