Welcome to the Outthinkers podcast. Plug into fascinating minds and breakthrough ideas that are transforming industries and the world. I'm your host, Ken Krypidor, founder of Outthinker Networks, a global think tank comprised of corporate strategists, innovators, and entrepreneurs that are shaping the future of business. If this describes you, join us at outthinker.com. Now, let's dive into this week's episode.
This is inherently a human technology. If you think of it like software, you're in trouble because it doesn't work like software. Software shouldn't argue with you. Software shouldn't make the right... and coders are often really bad at using AI and they have very kind of unimaginative uses. It works like working with people. So where the benefit comes from — what happens if you have 10,000 PhD analysts? And if you don't have an answer to what to do with 10,000 PhD analysts, I'd be thinking really hard about that, because o1 is a pretty good PhD analyst in lots of fields. What can you do with that? That's a fundamentally different question than how do I improve performance 12% by firing people and replacing them with AI tools.
That was Ethan Mollick, the Ralph J. Roberts Distinguished Faculty Scholar, Rowan Fellow, and associate professor at one of my alma maters, the Wharton School at the University of Pennsylvania, where he studies the effects of artificial intelligence on work, entrepreneurship, and education. Ethan's most recent book, and the topic of our conversation today, is Co-Intelligence: Living and Working with AI. It's one of today's most popular among the sea of books on AI. It is the culmination of a life's work that spans research in entrepreneurship, gaming, and innovation. While AI is a topic that is at this point practically talked to death, Ethan flips the typical conversation to focus not on if and how AI will replace which workers and tools today, but instead how to think of it as an accelerator to our current way of working and a tool for abundance. In this episode, we discuss what people get wrong when thinking about how AI will shape the workforce, what he calls the centaur versus cyborg approach, the misconception that work must be divided between humans and AI rather than completed in unison, what preliminary studies the past few years between organizations like BCG, Harvard, and MIT have to tell us about how AI powers human productivity and work itself, what history reveals about the cycle of adopting a new technology in our modern times, AI to keep your competitive advantage. If you want to understand the current state of AI, which goes way beyond what most of us think is even possible, Ethan unpacks that for us. Ladies and gentlemen, Ethan Mollick.
Ethan, it is great to have you here. We have tried for quite a while to get you on the podcast. I know you're in high demand, and thank you for being here in person. I'd like to start with the same two questions that we ask all our guests. The first one is just to get us to know you a little bit more personally. Could you complete a sentence for me? If you really know me, you know that I'm actually this enthusiastic in real life and genuinely weird about the stuff I do in reality, just like I do online and on podcasts.
Yeah. And it's interesting because AI is kind of what you're known for currently, but it's not AI that brought you here, right? Like you had this great book on entrepreneurship and what it takes companies to scale, and you have courses on that. And that's kind of, as I understand it — I got to participate in one of your classes a long time ago, and it was an entrepreneurship class. So I don't know if you don't mind just telling us a little bit of the pre-AI journey.
I mean, sure. I'm a professor of entrepreneurship, former entrepreneur myself with a college roommate. I helped start a company that helped invent the paywall, which I still feel kind of bad about trying to get over in the late 90s. And I started working on AI stuff at the MIT Media Lab as like the non-technical person on the media lab group, Marvin Minsky on AI stuff. At the same time, I also started working on my other passion project, which has been about using games for education. So they've kind of run in parallel, which is how do we educate at scale, and using games, techniques, simulations. And I've been building a lot of organizations around that and kind of waiting for AI to take off. And when it did, I happened to be really well positioned for that.
That's awesome. Yeah. People can't see your visual right now, but I see — and I've seen this background on other podcasts — you've done a lot of games in your background. They're all ones I've mostly played and books I've read. Amazing. Amazing. Amazing. And we're talking physical games. We're talking board games.
Yes. I'm a big video gamer too. But yes, these are board games.
Got it. Okay. Awesome. My son's a big gamer and just started at the University of Pennsylvania, and I think there's so much value in kids gaming and what they can learn. Next question: What's your definition of strategy?
So, you know, this is always a rough question to ask because I am a business school academic. So I have been in rooms where debates over what strategy is take 90 minutes or so. Right? So I will not — I'm going to do an off-the-cuff definition rather than try and do something where I'm going to argue what a bunch of different thinkers have thought strategy is. But I think it is the art and science of anticipating potential futures and taking action for the best possible outcome across them.
I love that. Potential futures. I love that. Great. Okay. All right. We won't debate it. It's perfect. That's kind of part of the point is that we have these people who spend their lives thinking about strategy coming and talking here, and they all have different definitions of it. There is not yet one.
I am very much a pragmatist in both AI and strategy, which I think comes out of entrepreneurship, which is: if it works, it's worth working with, and we'll figure out the definition later.
Yeah. Perfect. Great. All right. So I've got a list of questions here that I created myself, and they're in a random order, and I'm going to sort of jump around and we'll just sort of see where it goes, because you've got great work. You've got the book, you've got all kinds of podcast and other content. So people that want to just know your content step by step, they can do that. So I'm just going to jump around a little bit, and I'd love to just start first by the future of work, the shape of work. What do people get wrong when they think about how AI is going to shape the workforce?
So, I mean, I think part of what we get wrong is thinking that we have an answer right now, which we absolutely do not have. And so it is — we don't have answers, right? I think that the reshaping of the workforce is going to happen. It's already happening. I think probably a good model to follow is the current sort of economic view of thinking about a job as a bundle of tasks, some with different levels of relatedness and importance. That the task bundle is going to change more than the jobs are going to change.
Got it. Yeah. So right now a job or a role is a bundle of tasks that has one title, but they can be decoupled. Is it?
Well, I mean, yes. Or they're shuffled, right? I mean, they already are decoupled, right? But like you would never have had — for example, a doctor would never — you'd never give them the role that they have right now. You wouldn't have a doctor where you'd expect someone to be good at hand skills and diagnosis and running a business and empathy, and like that's just too many things in a bundle, right? So that bundle gets reconfigured. It changes part of the meaning of being a doctor. But is that always a bad thing? Do most doctors want to do all of that? I'm a professor, right? I'm on podcasts like this. I also talk to companies. I write academic papers. I create pedagogical materials. I run committees. I teach. But like that's a bundle. It's going to be changed by AI too.
Yes. Well, let's dig into that a little bit then. You know, there's some things in that bundle that people have thought are human — just, you know, creativity, for example, the human interaction, the empathy. What do you think people misunderstand about what AI is capable of as compared to kind of what is the uniquely human domain of humans?
So I think that it is hard to draw golden lines like that, you know, bright shining lines no one crosses. And I think that most attempts to do that will result in you feeling bad, right? So your creativity, persuasion, apparent empathy — if not real empathy, because these are not humans — management judgment, you know, like things that you would call a human's quite good at.
Yes. Right. I mean, at least just me personally, I think the experience of the creativity that I can — what I would call creativity that I see in AI is remarkable. And yet I would think maybe like six months ago, kind of the narrative was, oh, well, humans, we will always be the creative portion. Can you give us — like tell us a little bit about AI being creative and what does that tell us about what creativity is?
I mean, the problem when you talk about AI stuff is that we have all of these tests that were good enough when humans were the only intelligence in the room that no longer make sense now that there's other forms of intelligence available. We have very mediocre tests of creativity, of divergent thinking, of convergent thinking, of ideation, and the AI beats all those tests right now. You know, we can argue is that true creativity or not. I don't know, right? Because we didn't have to operationalize it, just like we didn't have to operationalize theory of mind, which is, you know, can you take someone else's perspective? The AI apparently does that. We don't have a definition that separates out the ability for humans to do it from the AI to do it. You know, there's the famous Turing test, whether an AI can fool a human to think it's human. That was a mediocre test, but it didn't matter because it was the best test we had. So on the creativity side, you obviously should be using AI for idea generation. We have objective data on that. You should be a little cautious when you use it for idea generation that you also generate your own ideas outside of it so that you come with your own set of stuff. But it beats most humans in ideation, and good prompting makes the ideas it generates even more diverse.
Would you suggest people use AI before they introduce their own ideas in parallel or after?
I would generally introduce your own ideas first and then have the AI generate some, because for the same reason why brainstorming is a terrible idea. I mean, we've known brainstorming — I think 1955, by 1958 it was clearly a bad idea, and we keep doing it because it feels like fun. But as soon as you're in a room, people start yelling ideas, your ideas become bound to their ideas. So you should always start by writing your ideas down individually, in the same way I would recommend starting the same way with the AI side.
Great. So let's just double-click or add the other components. If we say strategy is sensing possible futures and making choices now to get to those possible futures, probably the steps within that are like thinking about what part of the system to look at, ideating options, and then selecting options. If we think about the different — it does all of those really well. Like we have paper, like it does good idea selection, it does good ideation, it does good variance, it does good variation. Like it's good at all those things. I haven't personally experienced them all except for the idea selection. Can you tell us a little bit about that?
Well, we've got, you know, a bunch of research work that suggests that the AI is pretty good at selecting ideas. You give it some criteria, even better if you tell it what kind of ideas you like, and you say rate the ideas according to this rubric, and it aligns with how humans would rate those ideas according to a rubric.
Amazing. Amazing. I hadn't seen that. And so then where would you see AI fitting into the process of strategy inside a corporation?
So, I mean, we also know AI does good strategy work, right? Like there's a nice paper showing you ask the AI a few times, you get a good answer. As a strategy professor, we know that if you're a high-performing small business owner in Kenya and you get advice from the AI, your profits go up 18%. Like, I mean, you know, to the — like, I mean, a direct advisory role, yes. Simulating potential futures, yes. Giving you alternatives, sure. Role-playing against you, definitely. You know, integrating knowledge as a second check on your own perspectives or ideas, yes, do that too. Like, you know, like there isn't a piece — like watching your, you know, the situation happening in your environment and telling you whether or not it's, you know, literally watch with cameras a scene and telling you what it thinks of it. Like all of those things.
