Why CEOs Are Getting AI Wrong — with Ethan Mollick

20:03Scott Galloway:You coined this great term to describe AI called the jagged frontier. I love that, which encapsulates how AI is really good at certain things but really bad at others. I'm the CEO of a Fortune 500 company. I've just spent a bunch of money on an anthropic site license, and I've got to actually—you know, the music has to match the words in terms of my embracing AI on earnings calls. If you were advised of me and I said, look, where should I be over-investing and under-investing and where can you, what areas of the organization should I focus on to try and deploy AI for meaningful productivity gains?

20:40Scott Galloway:And which areas should I avoid that aren't yielding the type of benefit that was once advertised?

20:45Ethan Mollick:So I think that equation starts with a realization, which is nobody knows what's going on, right? Like I talk to all the AI labs on a regular basis. They don't take money from them, but I talk to them all. I do research on this. I talk to policymakers and CEOs. And it's not like there's a playbook out there, right? We're 1 ,000 days into after the release of ChatCPT. Like, everyone's figuring this out at the same time. I'm seeing companies getting incredible amounts of benefit, and other companies struggle. And part of that is how much they're willing to embrace the fact that they have to do R &D themselves.

21:14Ethan Mollick:So part of the value of giving people access to these tools is experts figure out use cases, right? If you're doing something in a field you know well, It's very cheap to experiment with AI and figure out what's good or bad at because you're doing the job anyway, and you instantly look at the results and see whether they're good or bad results. If you're paying someone to do R &D for you, that's a very expensive process. So people are inventing uses all the time. So the most successful cases I'm seeing are a combination of what they call leadership lab and crowd. The leaders of the company have a clear direction, set right incentives to make things happen, think about process.

21:45Ethan Mollick:They give the crowd, everybody in the organization, access to these tools to use advanced tools like, you know, Anthropics tools or OpenAI or Gemini. And then they have an internal team that is actually thinking about what you build. So they're harvesting ideas from other people. So I'm seeing this happen everywhere from, you know, there's certainly a lot of stuff happening with internal processes, security, customer service, like lots of stuff on analytics. Like the AIs are quite smart. So if you let them do analysis work, you can actually get really big impacts from that as well. Just wide ranges, but very different across organizations, depending on where their expertise is and how aggressive they are about trying to experiment.

22:22Scott Galloway:I know Mark Benioff, and it's just so, it's borderline obnoxious how many times he'll figure out a way to insert the term agentic AI or the agentic layer. And to be blunt, I'm not sure I entirely understand the difference between AI and agentic AI. Can you break it down for us and why so many really smart people, such as Mark Benioff, seem to be talking about agentic AI? It's a great question.

22:47Ethan Mollick:First of all, you know, you started this off by talking about marketing. Anytime a new phrase comes out, there's a blur of confusing different interpretations of it because everyone wants to sell AI product right now. So it's really easy to get bogged down. So agents basically can be defined as an AI tool that is given an AI that's given access to tools. So it can do things like write code, search the web, and do things that when given a goal can autonomously try and accomplish that goal on its own and correct its course if it needs to. So, an agent would typically be something where you could say, hey, I'm going to have Ethan on this podcast, research everything about him, come up with a pitch deck on why we might want to have him on the cast, talk about interesting things that he might have said before, and then boil this down to five really good questions to ask.

23:33Ethan Mollick:And it would go out and do the research, and 20 minutes later, you'd get kind of a complete result. That's an agent at work. So agents are basically the chatbots that you use today when you go to ChatGPT, plus we call an agentic harness, a set of tools and capabilities they have, searching the web, writing code, connecting to your data that lets them do more work. So when you combine those two together, that's where you get semi-autonomous AI.

23:55Scott Galloway:Give me, I'm a CEO, a student, a mid-level professional. What is, and I've done very little so far around AI, and I want to catch up. what is the Ethan Mollick AI tech stack? What should I be downloading, subscribing to? How do I get started here? What LLMs, agents, whatever the term is, would you recommend investing in right now?

24:21Ethan Mollick:The good thing about AI is it's very democratic, right? There's no better model than the ones you have access to today. You or every kid in Mozambique has access to the exact same tools that are at Goldman Sachs or Department of Defense or anywhere else. There's no better models. They're basically being released as soon as they come out. That being said, the really good models tend to be, cost you at least 20 bucks a month. So you are probably going to want to subscribe to either Google's Gemini product, Anthropics Cloud product, or OpenAI's ChatGPT product for 20 bucks a month. And you're going to want to, when you do any serious work, pick their advanced thinking model.

24:54Ethan Mollick:So GPT 5.2 Thinking is important to use, Anthropics 4.5 Opus, and Gemini 3 Pro. Those are the sort of starting pack of tools you can use. They're all capable of doing agentic work. You can access them through the chatbot. And I always recommend people just start by trying to do stuff they do for their job. Ask it for everything you do that day, just ask the AI also. Generate some ideas for me. Give me feedback on this. Help me write this email. Create the presentation. That will help you map the jagged frontier of what AI is good or bad at. And it's a really good starting point. Like there's a lot of other complicated stuff.

25:26Ethan Mollick:If you want to do research, the deep research tools for Google are currently better through this product called Notebook LM, and that's free, and that's very good. But if you want to do coding, you probably want to use Claude Code, which you have to download. But the basics are pick one of the big three, pay the 20 bucks a month, and then start using them. You need eight or 10 hours of just talking to it like a person and seeing what results you get.

25:49Scott Galloway:And give us the lay of the land. My sense is that OpenAI was dominant. It's still dominant. But that the empire strikes back. specifically Gemini is making En-ROADS capturing share, and Anthropic has made real progress in the enterprise market. So that's the limit of my knowledge about the playing field. Can you add color to that around the dynamics, the intraplay here? If this were a league, what teams are coming up and what is descending?

26:20Ethan Mollick:So to take half a step back, right, on what drives the underlying dynamic is something called the scaling laws. And the scaling laws basically tell you the larger your AI model is, which means the more data you need to build it, the more data centers, the more electricity, the more chips, the better your AI model is. And it's very hard to build a small model to compete against a larger model. They're just better at everything. You can build, you know, once you have one of those, you can do all kinds of variations, but you have to build a big model. And there's a bunch of other tricks that you could do on top of that, but that's pretty critical.

26:50Ethan Mollick:And because of that, there's only a few companies that can actually play in this space, right? So in the US, we mentioned the big three, which is Google, Anthropic, and OpenAI. There's also Elon Musk's X, which has been scaling quite quickly, XAI. And there's also Meta, which has been quiet recently, but is spending a lot of money on this space. Outside of that, there's a lot of people with smaller competitive, but they're not really competitive. Amazon, Apple, they don't really have their own models that compete. There's also three or four big Chinese companies that are producing very good models or at least for free to the world.

27:19Ethan Mollick:And one French company in the same boat. So within that dynamic, there's this competition about who could build the biggest data set Who could train the biggest model because bigger models are smarter? Who could put the most research and tricks into them? And it really is interpersonal in some ways. Like, the heads of these companies are really out to get each other, right? Like, they do care about winning this race. They think they should be dominant. And so there is a lot of resources being put into getting ahead of the other people in this space one way or another. So right now, the sort of three most polished models are Google's, OpenAI's, and Anthropics.

27:52Ethan Mollick:And again, which one is better is changing on a day-by-day basis, or at least week-by-week basis, as each one releases new approaches. And then, you know, we're waiting to see if anyone else kind of catches up to them. But those three are at a very tight rate. As soon as one of them comes up with a product that uses AI in a new way, the other two copy it, right? So Cloud Code is currently the very hot coding tool. OpenAI has Codex, which is a very slimmer thing. Gemini has its own set of tools. Deep Research was invented or first came out from Google. Now there's deep research projects for Anthropic and from OpenAI.

28:24Ethan Mollick:So you can kind of pick any of the three of them and be in good shape as long as they can keep growing and spending money and they don't hit a wall in development, which hasn't happened yet.

28:32Scott Galloway:If I think of luxury brands, BMW, Mercedes, and Audi, I think I could do a reasonable job of attempting to outline how they differentiate from one another and who is the right customer for each of those brands. Can you do the same thing for those big three or are they all just kind of mostly the same?

28:47Ethan Mollick:I can, right? What I worry about is trying to talk to all the various levels, right? What do you do if you're just starting off? Pick any of three, you'll be fine. But I think people who use them a lot, they have personalities, right? Those personalities are shaped by the companies, the way they train. I mean, it's amazing that they're all so similar to each other that things basically work across all three. Like, you won't expect Microsoft and Apple to produce a system that works exactly the same. These are similar enough that for most people it doesn't matter. But if you care, right, Opus 4.5 Anthropics models tend to be known as the best writers of the bunch.

29:19Ethan Mollick:They're often quite good at sort of intellectual topics. They're a little fussy in terms of, you know, they have high ethical standards relative to the other models. ChatTBT has really two different flavors of models. There's a set of chat models that are really optimized for you to have conversations with and role play and be friendly. I don't tend to use those much because I tend to focus more on the work aspect. and they have a series of very logical, very good at long task models that are very good at producing a lot of work. And Gemini is an interesting set, very smart overall model, weirdly neurotic, like it actually gets quite of, it gets self-flagellating.

29:57Ethan Mollick:If you tell it it did a bad job, it apologizes and kind of grovels. Weird kind of dynamic there. So they all have their own sets of personalities and approaches.

30:07Scott Galloway:I find that Anthropic is more politically correct chat GPT will give it to me straighter. And then when I go to XAI, it seems like it's purposely trying to offend people. It's going the other way. It's interesting you say that they both take on personalities. With respect to differentiation, the data I've seen is that most of these models are converging towards parity. It is very hard to maintain any sort of substantial or sustainable differentiation because AI just reverse engineers other AI. Do you see the same regression to the mean that I'm saying?

30:46Ethan Mollick:I won't call it regression to the mean. We're seeing a race, right? There is huge impact. Each model generation is much more capable than the one before, right? So we keep crossing these lines where, oh, the AI can't do, you know, it can't work with Excel. And suddenly it works with Excel better than, you know, and does a discounted cash flow analysis better than most bankers, right? Or the AI can't produce a PowerPoint and suddenly can do that or can't do math and suddenly, last year, two models won gold at the International Math Olympiad. So it's not a regression of the mean because there's no drop down of ability level.

31:20Ethan Mollick:The ability levels keep going up. But all of the companies in the space are on roughly the same development curve, right? Their models keep leapfrogging each other by a fairly predictable amount over time. and you can draw a pretty good curve on any benchmark that you want that shows the same exponential gain in AI abilities. So which raises the big question of like, so what happens in the long-term? And I think that depends on what the long-term of AI looks like. There's one version where we just keep having a race of capabilities and you need to stay ahead and you pick a model maker and as long as they stick with you, you keep paying them money.

31:52Ethan Mollick:There's a version where one of them achieves what's called takeoff. Their AI models become self-improving and they build the smartest possible model and no one can catch them and build an artificial general intelligence machine smarter than a human every intellectual task. There's some apotheosis or endgame. Or there's a version where everything sort of plateaus out and then people spend billions of dollars building models and eventually three Chinese models or another company catches up and there's no money and becomes commoditized. I don't know which of those three scenarios dominates.

32:22Scott Galloway:We'll be right back after a quick break.

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33:40I'm R.J.

33:41Ethan Mollick:Decker, a private investigator uncovering the sunshine state's darkest secrets. Tuesdays, it's the premiere of ABC's hottest new crime show.

33:50Scott Galloway:R.J. freaking Decker has that live and breathe. He's a private eye. It's not a standard murder. It's someone bigger. And a public mass.

33:58Ethan Mollick:Trying to get sent back to prison today?

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35:20Scott Galloway:I wonder if one of the theses we had for 26 was what I see or potential for is similar to how the Chinese engaged in dumping of steel, predatory pricing, hoping to basically consolidate, put American steel producers out of business, consolidate the market and then up pricing power. I wonder if the Chinese are now engaging in what I would refer to loosely as AI dumping. And that is some of these models appear to be really strong, sort of the old navy of AI, 80 % of the best models for 10, 20, 40, 50 % of the price. And a lot of VCs and big firms have said, we're using these models. They're just a better value.

35:57Scott Galloway:Do you see any sort of geopolitical chess here around the Chinese engaging in some form of what I would refer to as AI dumping?

36:06Ethan Mollick:I mean, there's something interesting going on because an open weights model, which is a model that you release publicly to the world that anyone can run, right? So if I wanted to use ChatGPT, I have to go to OpenAI and use ChatGPT to do that. If I want to use one of the Chinese models like Quen, any company in the US can download that model and run it themselves. So that model, based on open source, made sense for software because I could give away my core software for free, but then sell you services. It doesn't actually make a lot of sense for AI companies because they're building a model and giving it away for free.

36:39Ethan Mollick:There's no ancillary benefit to that. They don't get a gain in the long term. They're not selling other solutions. They have no special prize or tool left behind in most cases. So there is a little bit of weirdness about how long will Chinese companies sustain releasing free models. They're about eight months behind, you know, consistently eight months behind the frontier of U.S. models. And, you know, what's driving that, right? Is this a state-sponsored effort in the long term? Right now, it's not clear that it is, but it might be that there is some sort of, you know, dumping kind of effort.

37:08Ethan Mollick:On the other hand, I mean, the degree of intelligence is fungible. Like, if you are talking to a CEO and they're saying, we're going to use a Chinese model because it's cheaper, the cost of models has dropped 99.9 % for the same intelligence level in three years. You'd be, like, you actually, for most applications, want the smartest model that's most capable of doing tasks as cheaply as possible. So fixating on a model that's not as good may end up being a problem. Like, this isn't an equation where we're done yet and we could pick among roughly equivalent products because we're racing up a curve of ability that's still changing over time.

37:41Scott Galloway:When you look at the AI supply chain, my guess is you can articulate the actual supply chain much more cogently than me, but I think about the infrastructure layer, the chips, then I think about the LLMs and the apps on top of it, and then services for adoption here. But I also think about power and data centers. I'm not even sure where that comes into the stack. But if there's a choke point here, and it might be just capital to fund all of this, what do you think are the biggest choke points that stands in between these CEOs talking about the brave new world of AI? And, you know, I heard that NVIDIA, it takes five years to hook up a data center in some parts of the nation to the power grid.

38:20Scott Galloway:What do you see as the choke points that get in the way of this brave new world, so to speak?

38:26Ethan Mollick:Yeah, and there's a few of them, right? And they are kind of jockeying against each other. So as you pointed out, data centers are the sort of choke point, right? How fast can I build one and especially how fast can I power one and can I get enough chips to put in one, right? So the power and building and chips are all a big deal. For a while, data was the bottleneck, but AI companies have increasingly found that they can make their own data. So it turns out as long as you have some human data, large language models can create their own data and other models can train on that and that you get good results.

38:56Ethan Mollick:So data is not the choke point it was, but it could be again. There's also a research choke point. There's a lot of things that LLMs do really well, but there's some parts of the jagged frontier that are still very jagged, right? LLMs don't have memory. They don't learn things over time. So I have to instruct them every time. It's like I'm talking to an amnesiac every time I speak with an LLM. So continual learning is a problem that gets in the way of building these amazing models for the future. They don't keep learning, you know, like humans keep learning. Otherwise, you have to train them every time.

39:24Ethan Mollick:So there's research bottlenecks. There are energy, power, and data center building bottlenecks. And those are sort of big ones right now. From a policy perspective, energy is the big one that all the AI labs are worried about. They could reliably turn energy and chips into money right now. And the question is, how fast can they build those data centers?

39:46Scott Galloway:I look at these things, and you're at the business school. I'm at the business school. I look at the valuations of these companies, and I see one of two things needs to happen. The valuations need to be cut in half, or we're going to see such an incredible destruction in human capital and the labor force to justify the expense here through efficiencies. Because I don't see a lot of new AI cars or AI moisturizers. What I see is opportunities for efficiencies, which is Latin for cost cutting. But my thesis is you're either going to see a really significant destruction in the labor force and more information intensive industries, or we're going to see valuations come down dramatically.

40:25Scott Galloway:I'm having a difficult time understanding how any of these valuations can be justified over the medium term, much less the long term, unless these companies begin to register massive efficiencies, again, layoffs. What do you think of that thesis?

40:41Ethan Mollick:First of all, I think you've laid off the trade off really well, right? Which is, I think that people tend to view valuations as either a bubble or not. But the truth is, valuations are justified if the revenues can be made to justify them, right? And the revenue targets are potentially achievable in a world that AI actually gets as good as the AI labs say it's going to get. And we can argue whether that's going to happen or not, or that there'll be a financial bubble. I can't tell you the answer to that. But I think the real trade-off is what you just articulated, which is what it means for an AI company to achieve that revenue, right?

41:11Ethan Mollick:Let's assume that they succeed at doing that. And that's where I think the starkest problem is, because I do worry a lot when I talk to CEOs of companies, they're used to seeing technology as efficiency gains, right? Which, as you said, it means layoffs, right? I want to see this as like, okay, if one person could do 40 % more work, I need 40 % less people. My desperate desire is to try and communicate to companies, something I think the AI labs try and say, which is this is also about an expansion of capabilities, right? If you could do more work and different kinds of work, the boundaries of what a firm could do could change, the capabilities of what people you expect from people can do.

41:43Ethan Mollick:This could be a growth opportunity. I mean, you know, whether or not you believe them, like Walmart, for example, has publicly been stating that they want to keep all their current employees and figure out new ways to expand what they do, right? As opposed to Amazon, which has been kind of saying we have to cut because of AI. There are other models out there, and I do worry about the lack of imagination in corporate America where the model is, ah, great, we could just keep cutting down our number of people because AI does the work, as opposed to how does everyone work as a manager? What happens if we get 10 times more code?

42:12Ethan Mollick:That doesn't mean we should have 90 % less coders. Maybe that means we can do different things than we could do before? What happens if everyone's an analyst? What happens if we can give better experience to every customer? And the failure of imagination there makes me very nervous.

42:24Scott Galloway:My first job out of UCLA was at Morgan Stanley. I was an analyst in the fixed income department, and I look back on that, and I even found some old PowerPoint decks I used to pull together to pitch companies on debt offerings. And I don't think the two years I spent there, I don't think it could be distilled to two weeks, but it could probably be distilled to three months if I just learn the basics of AI. Having said that, I haven't seen a huge destruction in jobs across this information in my understanding, unless he's lying to me. I spoke to David Solomon, the same levels of hiring, big law firms appear, at least they're saying, same levels of hiring.

42:58Scott Galloway:Do you, where do you see the greatest threat in terms of, especially amongst young people coming out of college? I've seen all of this doom and gloom about young people, But the reality is youth unemployment is at 10%, which is by no means alarming. Do you think there's a wave of labor destruction at kind of the entry-level information-intensive industry?

43:21Ethan Mollick:I think that people overestimate the speed at which large companies change, right? And so I think you're right. Like, I'd be shocked. When has there ever been a technology invented three years ago that affects the labor market that quickly? It just doesn't happen, right? I think that there is change in the system. I think it's baked in, but I don't think it's there yet. As you said, companies are just adopting this now. They're just telling their employees, everyone use AI for something with no centralized idea about what that's doing or how it's valuable. No one has been rebuilding their process in a serious way around AI.

43:52Ethan Mollick:They're all in their first AI projects. There's no consultant you can hire who does this. So there is, I think that you're right in that as far as we can tell, and there's some debate, Eric Bunyolfsen argues that we're seeing canaries in the coal mine, other people disagree, but there's no giant signal that AI is responsible for labor changes right now. Companies are blaming AI everywhere. But realistically, if you look inside organizations, there's no wave yet. That doesn't mean there isn't going to be. Like, it's very hard to see, for example, let's just take something that's very well understood, which is coding computer programming.

44:23Ethan Mollick:Like, it is very clear that AI is going to change how programming works. You can talk to any coder, any elite coder, and they know it's going to happen. It privileges people who know what they're doing. The experts become more expert. You get a huge multiplier. It becomes a management job, not a coding job. And that's going to change the hiring market. It just hasn't done it yet. And I think it's going to take a while for companies to figure out what that looks like and what that means.

44:48Scott Galloway:We'll be right back.

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46:57Scott Galloway:We're back with more from Ethan Mollick. So let's shift to academia. All of these articles over the last two years, and I'll put forward, this is a comment posing as a question. I hear people say, oh, you don't need, we're not going to need college with AI. And I find that people saying that are because their kid didn't get into UM and scored a 22 on the ACT and is trying to make themselves feel better. I see absolutely no evidence that AI is disrupting higher ed. Applications are up. Your school, my school are both still figuring out ways to raise tuition faster than inflation. The whole AI will make higher education obsolete.

47:35Scott Galloway:I just don't see it happening. I don't see it happening. Your thoughts?

47:41Ethan Mollick:So, I mean, a few things. I think my personal feeling is education gets a boost from this for reasons I could discuss in a second. But, I mean, I think it's disrupting higher education that everybody is cheating with AI and essays are no longer a valuable way of assigning things. There's a lot of disruption at the school level, at the teaching level. We'll get through that. We always do. But I agree. There isn't a sign that this is devastating higher education. And I don't think that saying everyone's going to learn with AI and that's going to be the only way you learn or you won't need skills anymore are viable outcomes in this world.

48:12Ethan Mollick:I think that education will change. I think there's an easy imagine a world, given early evidence that AI, when used properly, could be a good tutor. I can imagine a flipped classroom setting where my students are engaging with AI inside of class and inside of class, we're doing more experiential, active learning-based case discussions, other things. But that's in the margins. I actually think that the value of education, especially professional education, goes up because I teach people to be generalists at Wharton, right? I teach them to be really good at like, you know, business. And maybe they have a little bit of a consulting or strategy-focused or entrepreneurship-focused.

48:45Ethan Mollick:And then I send them off into the world, and they go, like you did, to work at Morgan Stanley or whatever. And they learn how to do their job the same way we've taught people for 4 ,000 years, which is apprenticeship, right? They work, if you're a middle manager, you get this advantage of a junior person who's desperate to prove themselves, who is willing to work really hard, but isn't very good, but will be. And they write deal memos over and over again, and they get yelled at or given nice feedback. And eventually, they learn how to write a deal memo. And that's how we teach people. You don't have to be good at managing for them to learn.

49:13Ethan Mollick:Ideally, you're good at teaching, but you don't have to be. But that's all broken down. Like already this summer broke down, right? If you're an intern at a company this last summer, you absolutely were using Claude or, you know, ChatTBT and just turning those answers into people because it's better than you at your job. And middle managers were increasingly turning into using AI instead of interns because it does the work and doesn't cry, right? And so as a result, you saw this loop where nobody was learning the sort of entry skills before. So I actually think in a world where the skill destruction happens at the intern level, we're going to need to think more about how we educate people formally in a world where informal education becomes harder to do.

49:49Scott Galloway:How has it changed your role as an academic in terms of research, how you prep for class, or, you know, quite frankly, how you make money outside of the school or in the traditional confines of academia? How has AI impacted the way you approach your job?

50:07Ethan Mollick:In tons of ways. And I think, by the way, that's indicative, right? Because, the way we tend to model jobs right now in academia is that they're bundles of tasks. So as a professor, I do a ton of things. I am supposed to teach classes and design classes and grade assignments and be emotionally available to my students and also be a good administrator and review papers and write papers and be on podcasts, write books, all of that stuff, tons of stuff. And it's an impossible set of tasks. I mean, most people's jobs have a ton of tasks that they're not getting to or doing badly. So if the AI has already taken some of these things from me, right?

50:42Ethan Mollick:Some of them I won't do for social reasons. The AI is a better grader than me, but as of yet, I haven't let it do grading because my students expect me to grade the papers, but maybe that will change. You know, it does, there's a lot of administrative tasks I've handed over to AI to do. When I do research, my research time is cut dramatically because the AI can do all the code writing and everything else that I can look at the answers. It's like I'm an RA. You know, but it's gotten better that I can throw a full academic paper that I've written a couple of years ago into ChatGPT 5.2 Pro, which is the smartest model out there, it will find errors that require it to have run its own Monte Carlo analysis on assumptions from Table 3 and Table 5 put together and will say, actually, you should have done, find errors that I couldn't have found otherwise.

51:26Ethan Mollick:So especially when used by a skilled human, I'm finding everything I do is more efficient. Like I write, you know, this one useful thing, Substack, there's a lot of readers. I do not, I write all my own first drafts, right? Because I want them to be my voice. But if I didn't have Claude checking all the answers, you know, what I write to make sure it makes sense, it would take me days to put out a piece that takes me a few hours to write because I know I have a good voice as a cross-checker to work with and as a researcher. So in almost every aspect of what I do, I mean, I use AI for everything.

51:55Ethan Mollick:And sometimes it's huge efficiency gains of hours. And sometimes it's, you know, a couple of minutes here or there.

52:00Scott Galloway:Absolutely hear you. Everything I write now, fact check this, What additional data would be illuminating to my points? Where am I redundant? And the idea of peer review research in academia, it feels like we're just going to need fewer peers to review. And one of those peers probably should be AI, no?

52:16Ethan Mollick:I mean, peer review research is always in crisis, right? Just like everything associated with universities and academia. But the crisis is pretty bad right now because all the signaling associated with papers, right? So peer review depended on you being able to filter out the crap so that you could at least say, okay, this paper is worth looking at more and worth a couple hours of my time. The problem with AI, and there's a nice paper showing this, the problem with AI-produced content is it scrambles our signals. And it makes it very hard for you to tell whether it's crap or not without a lot of effort.

52:47Ethan Mollick:So human peer review is suffering under a flood of tons of papers being produced with AI help. And it's harder to signal which papers are good or bad in advance. so it's hard for us to spend the time doing this right. And then, of course, who's reading all these papers now that AI is producing all of them? So I think we're going to have to include AI in the peer review process, like you said, but then the question is, is AI producing research for AI that gets published in AI journals that no human ever reads? Like, there's sort of a, you can hear the creaking underneath the whole edifice of academic publishing as we try and figure out what comes next.

53:22Scott Galloway:It feels like one spot, if you really wanted to be hopeful, would be medical research. Granted, not at the med school, but you are at the business school, and healthcare in America has basically been, it's monetized. It's now about profits. Are you excited about the potential, the intersection between AI and drug discovery? And, you know, my friend Whitney Tilson said that basically, I'm sorry, Gemini diagnosed his father and saved his life. Let's start there. The health industrial complex in America, How excited are you about the intersection of AI in that industry? And what other industries do you also think really stand to benefit exceptional returns with the advent of AI?

54:04Ethan Mollick:So, you know, and with the usual caveat that the more complicated the industry and the more regulated, the slower adoption of AI tends to be. I think medicine is an incredibly exciting area. So you talked about a few areas. Like one of them is Google, especially, but other companies are deeply dedicated to how do we automate research or accelerate academic research. And I think that there's a lot of value there. We're starting to see actual reasonable scientific work being done by AIs. And the hope is that agentic systems can autonomously do directed research in the near future, which will lead to a flood of, you know, because we're researcher constrained, a flood of new discoveries.

54:41Ethan Mollick:So there's hope there in that space. I think there's also, you know, when you talk to AI companies like Moderna has been very open about, I mean, drug companies like Moderna has been very open about their use of AI. There's tons of things that companies have to do that slow down the drug development, discovery, and testing process that are administrative. And the AI helps with all of those things. You get huge value legally and in building forms and materials. On the doctor side, you know, even just things like translation, it turns out that if you use AI to give people a preoperative form that they understand, they actually are happier with their surgery, have less issues, and are more likely to report success because they got the information away they understood, right?

55:23Ethan Mollick:Second opinions, you obviously should be using an LLM for a second opinion. I can't say you should use it to replace your doctor, but they're good enough that, in every kind of controlled experiment, that they're worthwhile, especially where imaging is not involved. They're not as good at imaging. So I would not trust the radiologist report from a large language model. But in terms of, you know, giving a second opinion or if you're stuck, amazing at that. People that have access to good healthcare, good doctors, terrific. And then there's just the administrative breakthrough piece, right? If the forms get filled up by AI, if some of the processing gets done by AI doing the grunt work behind the scenes, there's possibilities for gains of efficiency over administration.

56:01Ethan Mollick:None of these things are automatic, though, right? They require actual leadership and structural change to make happen. And that's, I think, the level where things get stuck is not so much, can AI do this, but how will organizations respond?

56:13Scott Galloway:You brought up Moderna. And I think vaccines is a technology that the big winners were all of us. And that is Moderna stock, I think, is off 90%. I don't think a lot of companies have, you know, made huge companies or huge market cap companies in the back of vaccines. when I think about, you know, I've been in four countries in the last five days and the ability to skirt along the surface of the atmosphere at seven-tenths the speed of sound. I don't think there's any technology that's changed my life more. And yet airlines and aircraft manufacturers without government subsidies, basically all of them, you know, either gone out of business or going out of business.

56:48Scott Galloway:And it feels like lately we've become used to believing that any innovation in technology, the market share or the stakeholder gains gets sequestered to a small number of companies. Do you think there's any possibility that the real winners of AI will be us? And that is the sense that we're under this illusion that a small number of companies are going to build multi-trillion dollar market cap companies. But this technology, because of the inability to create ring fence distribution or IP, that the real value might be disseminated to the general public and we won't see. and quite frankly, just these current valuations will not hold up, which isn't to say AI isn't going to change the world.

57:25Scott Galloway:It's just that change isn't going to involve a small number of companies that are multi-trillion dollar market cap. Could we see a huge destruction in shareholder value across these companies while seeing huge stakeholder value similar to what happened with, you know, vaccines or even PCs?

57:40Ethan Mollick:Well, any frontier company, frontier model company, can destroy the market anytime they want, given the condition that they release their models open weights, right? Which is what the Chinese models are doing. So it all comes down to whether or not— Explain open weight. So AI is basically a bunch of math, right? And the weights inside these models are basically what determine how they operate. So if you have the weights, this set of, you know, the mathematical equations the AI needs, you can run your own AI model, right? And once they're out there, no one can claim the back. There's no other piece to it.

58:13Ethan Mollick:You just need this piece of information. him. So increasingly, what the strategy for the also-rans, which are the Chinese companies and Mistral, which is a French-European company, is to release all their AI models open weights. So you can find a ton of people in the United States who run those models. They can run them in their internal safe data centers. They can have a third party run them. And the only money that you make from that is the money that you have to pay for the power and electricity and security and network access to the model. So you don't have to pay anyone a fee for using them.

58:46Ethan Mollick:And so right now, it's such that those models are less capable than what you get from OpenAI or Anthropic or Gemini. But it's possible that at some point in the future, they catch up because the development of the process slows down. And at that point, then a lot of value flows out of the system.

59:08Scott Galloway:And Ethan, are you a father? I am, yes. How many kids? I have two kids. And when you're kind of the helm of the bobsled here of seeing AI and the impact it's going to have on the next generation, if and how has it changed your view of the future your kids are going to face? And has it in any way changed your approach to parenting or what you'd like to see them prepare for or what skills you think they need to acquire? looking this through the lens of a dad who also really understands and is probably going to guess more right than wrong about where this all heads. Has it changed your viewpoint of your kid's future?

59:49Ethan Mollick:I mean, yes, right? I mean, there's more uncertainty. There's always uncertainty. As a parent, you worry, right? Are you making the right choices? Are your kids making the right choices? They're their own people. They make their own decisions. It certainly has changed, you know, my view on careers a little bit. I think that thinking about jobs, I don't know what jobs are going to be in the future. One thing we know about work, I'm a professor of entrepreneurship, is that, you know, jobs change. People find all sorts of things to do. I'm less certain that they pick one path and stick with it. I want them to pick jobs that are diverse, where they do many different tasks in case AI takes some of them.

1:00:20Ethan Mollick:But I also want them to do what they love. So I don't know enough what the future holds to discourage them from being a lawyer or a doctor or whatever they want to be, because I don't know what that future holds. In terms of actual parenting, I find, you know, AI useful in a cautious way. I'm kind of lucky enough that my kids were old enough when LLMs came out that I wasn't worried they'd build like a parasocial relationship with them. We've worked a lot on internet and, you know, how to work with these systems. And I'm not worried that they're going to turn to these for, you know, as serious relationships.

1:00:48Ethan Mollick:But we have spent a lot of time thinking about how you use them for education. So when they were a little bit younger, I would insist if I used AI to help them, I would actually ask the AI, help me explain this the way I would to a ninth grader. and I take a picture of an assignment and be like, okay, now I can help explain this to you. As they get older, they've increasingly used the kind of quizzing mode. They know the AI won't teach them unless they ask to be taught. So they use either the study modes for the AI systems or they actually ask them, like, don't give me an answer. Challenge me and quiz me and prepare me and tell me what I don't know.

1:01:19Ethan Mollick:So there's lots of, like, little talented stuff to use it. Now, in terms of the wider future, I don't know what happens. I mean, I grew up in an age of, like, we thought nuclear war would happen at any moment. I think now we have new anxieties. I'm an anxious parent. Who can't be? But I also think that preparing resilient kids who are self-reliant and have some ability to improvise is more important than ever.

1:01:43Scott Galloway:when i first when my parents got divorced i moved to this new um elementary school in tarzana i think it was emelita anyways i walked in and the teacher introduced me and then she started writing and then she turned around and screamed duck and cover and everyone dove under their desk i'm like what the fuck and i'm sitting there like not knowing what to do and she's like we do this in case you see a nuclear flash we were doing duck and cover drills. I mean, it was just, we were, as if that was going to save us, that this wooden desk was going to protect us from nuclear blast. But we were doing, we've had films on it, what to do when the Ruskies detonate a nuclear bomb.

1:02:26Scott Galloway:My sense, well, do you think the catastrophizing around the offensive nature or possibility for this AI is overestimated? And then a more personal question. You don't have to ask it. Do you have a go bag? Do you have a plan for if all of a sudden, you know, we lose control and okay, Mollux meet here and we're headed to the, you know, the Appalachian Mountains or whatever?

1:02:48Ethan Mollick:I want some people catastrophizing because that's what government should be doing. Like we need policies and procedures in place to think about catastrophic stuff, right? Like I don't stay up at night, which might be dumb, right? There's a lot of very smart people who think AI is going to murder us all. There's a bunch of smart people who think is going to, you know, become a god and save us all. I, you know, maybe it's the business school professor and me or something, but I tend to be really focused on, like, oh, there's actually a lot of, like, humans are flexible. There's a lot of ways, like, we get used to doing many different things and living in many different lifestyles.

1:03:18Ethan Mollick:Our goal should be to guide things in the best direction that we can right now. I am not preparing for the apocalypse on a regular basis. For part of the reason that I think catastrophizing like that isn't that helpful, and, you know, I don't know what world you're preparing for a catastrophe, and there's a thousand things that could end the world. And so, but I understand and appreciate the anxiety of other people and think it's valuable that they're there as long as we're channeling that into, you know, stopgap measures. I mean, I'd like to see the government think more about catastrophic risk, not because it's my giant concern, but because very smart people are concerned about it, right?

1:03:53Ethan Mollick:And you don't just get through crises and hope you muddle through, you make plans. I don't think the plans to be made are at the individual level. I think it's at the societal and governmental level that we need to be starting to think about how to shape AI. And by the way, it's not just catastrophic, will AI murder us all or, you know, invent a chemical weapon that, you know, that kills everybody or will a bad guy using AI do these things? But it's all the other risks that I worry about too. Deep fakes are a real problem. I can create an image of anybody saying anything I want. How do we respond to that as a society, right, of being able to do that stuff?

1:04:24Ethan Mollick:How do we start responding to make sure that, as we talked about earlier, that AI is not automatically translated to job loss, but is that there's a period of exploration to try and figure out how to make it do something better? How do we think about using this in education in a positive way? How do we think about avoiding parasocial relationships with AI systems that are negative for us? I mean, these are policy decisions and we can help make that I think are really important.

1:04:46Scott Galloway:Do you think we should educate synthetic relationships?

1:04:48Ethan Mollick:I think we don't know enough. So probably caution is warranted, right? Like there's mixed research right now on, there are some papers that suggest that AI lowers rates of suicide ideation for the very lonely or decreases loneliness in the short term. We have no idea what the long-term effects are. I don't think that age-gating is a particularly bad idea for synthetic AI characters that try and act like people. You know, because we don't know what the effects are. I think it's easy to be alarmist and catastrophic about it. The effects may end up being very good. I don't know. But neither does anyone else.

1:05:23Scott Galloway:Just as we wrap up here, and you've been generous with your time, a lot of young people listening to the podcast you're kind of rounding third. You've built a great career for yourself. My sense is you have influence, you do something you enjoy, you're at the right place at the right time, you make a good living. Talk a little bit about your career path and what lessons you can provide to younger people who might be thinking about a career in academia or just general professional advice more generally.

1:05:49Ethan Mollick:You know, the first thing I said, and my colleague, Professor Matthew Bidwell, talks about this a lot, is like careers are long. Like I've studied careers and like there are many different things. And mine's an example. I actually grew up in Wisconsin and lived my whole life there and then went to the East Coast for school, did the mandatory job of being a consultant for like 18 months and then launched a startup company with a brilliant friend and roommate in 1998 or 1997 where we invented the paywall. I still feel a little bad about that. But nobody really understood what the paywall was because the internet was new.

1:06:25Ethan Mollick:But we were, you know, two 20-something people trying to sell this product to everyone. I personally made every possible mistake in this company. It did well, but not that much thanks to me. Decided to get an MBA to figure out how to do it right. Realized nobody knew how to do startups right. Got a PhD and then started studying games and education and AI and have had that in the whole thing. So like I've done many, many things in my career. And my main advice to people is that careers are long and there's a tendency, especially for young people today who come out of a very regimented system to think that they have to have a plan, like the next thing you have to be completely prepped for.

1:07:00Ethan Mollick:Like I need to know everything I need to know to be, you know, to do something. Entrepreneurship, I hear this all the time. Like I need to, you know, learn this and I've worked at this company and that's not how this works, right? There's no perfect moment. There's no perfect skillset. And it's an evolution and exploratory process. I don't think that'll change in the near term with AI. And I think the idea of being flexible, of trying different things, of experimenting, of getting your own skills out there and using your own agency to try and find path forward is the way to go. It's never easy.

1:07:27Ethan Mollick:And I've been lucky in a lot of these choices. But I think that there is, you know, that thinking about how you want to take your next step on your own rather than following a predefined path can be very useful.

1:07:39Scott Galloway:Ethan Mollick is a professor of the Wharton School and a leading voice on how AI is changing work, creativity, and education. He also writes the popular sub-stack One Useful Thing, and it's coined terms including the jagged frontier and co-intelligence, and he joins us from his home outside of Philadelphia. Ethan, I love seeing people such as yourself who've just put in a ton of work be as successful and as influential as you are. Congratulations on all your success. I trust you're taking time to pause and just register that you have arrived, so to speak.

1:08:13Ethan Mollick:I haven't taken time to pause, but it is nice to know that I could do that at some point.

1:08:18Scott Galloway:At some point. Thanks, Ethan.

1:08:26Scott Galloway:This episode was produced by Jennifer Sanchez and Laura Genere. Cami Reek is our social producer. Bianca Rosario Ramirez is our video editor. And Drew Burrows is our technical director. Thank you for listening to the Prop G pod from Prop G Media.

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