B (3:32)

Resist and unsubscribe.

A (3:34)

Resist and unsubscribe. Explain to me why I should unsubscribe from Amazon Prime. If you really want to hurt or send a message to the President, what he does listen to is the following. If you look at the times when he has really checked back, immediately responded and pulled back, it's been when one or two things has happened. The bond market yields have spiked where the S and P has gone down. This is when he backed off of his plans to annex Greenland. It's when he's backed off of tariffs. When you go after big tech platforms with just a small decline in spending, this is what moves the market. I think the string we can pull here is to go after the subscription revenues of big tech that now represents 40% of the S and P. You're hitting them with a $10,000 decrease in market cap with just one subscription cancellation. So this is a chance to go after the soft tissue of big tech whose leaders the President appears to be listening to. Anyways, we've got literally millions of views from these and they get circulated. And there's something about traditional media that still has a halo effect. And that is Jessica Yellen, who by the way was on with this week, who I adore, pointed something out that was really important. And that is the economic model of traditional media is in collapse, but its relevance is still pretty substantial. And that is if you look at where people are getting all their news online, nothing influences online content. Or if you look at the stuff that really gets broad distribution online or a lot of clicks, it's a snippet usually from traditional media. And so while traditional media economic model is in serious decline, its relevance in some ways gets more and more, if you will, relevance. So what has begun as an idea has turned into measurable action. And that is since February, more than or almost 600,000 people have visited resistanceunsubscribe.com and the campaign has generated over 16 million views across social platforms, with 14.7 million on Instagram and Facebook alone, plus over a million on threads. Thousands of People have publicly posted using our sticker template signaling something important. This isn't passive, outrageous, its economic coordination. The question isn't whether this works, it's how we scale it and what are the metrics for success here? And to be blunt, this wasn't as much a coordinated effort as it was an attempt to have action absorb anxiety. And my team was on board with it. I have a group of very talented people. But what we've done here is the following. I did not want to coordinate with other groups. People have been pinging me. Talk to these people at this union or this activist group and I'll talk to anybod. But the idea of getting on the phone, I've heard from a lot of kind of celebs and journalists who said, I wish you'd called me. The idea of getting on the phone with a bunch of activists and people wearing Birkenstocks with viewpoint on which big tech platforms we should subscribe from or not subscribe from, and people masturbating over every word on the site. That sounds like my worst fucking nightmare. So while I realize greatness is in the agency of others, the greatness I'm leveraging is the people within our circle. And to give you a sense for the metrics. So we're getting upwards or near 100,000 unique visits a day. Now, if you ask ChatGPT or Claude what would be required to put up a site and get 100,000 uniques, what would the cost be? Say you were building an E commerce site or a political action committee site and you were asking for an action, a call to action to drive people to the site, and then you were asking for another action at the site. Both ChatGPT and Cloud came back and said, all right, the site would be about 100 to 200 grand, right? That's the cheap part. What's interesting is if you wanted sustained traffic of 100,000 plus unique visitors each day, it estimates you would need between get this and monthly budget of 4 to 5 million dollars across Alphabet, Instagram, Facebook ads, earned media, et cetera. So the way I see it is the following. The metrics I'm tracking are the following. What would this cost? This is like a chaser effect. I'm going to spend a lot of time, treasure and talent trying to get democrats elected in 26 and trying to find someone more reasonable to take on or to occupy Pennsylvania Avenue. I'm going to spend a lot of money on Democratic politics. If one man can spend $300 million, then we need 100 of us at least to spend $3 million or more to push back on this. The way I see it is this effort is kind of doubling or tripling every dollar that I'm going to commit to trying to get moderates back in the house. In addition, the math I'm doing is the following. You know, would I love it if all of a sudden Sam Altman and Tim Cook were saying, you know, no masked agents, and it was a clear sign that this was working and the Trump White House had to respond? Yeah, that has not happened. As a matter of fact, I asked ChatGPT to summarize the effort so far, and it said the product management teams are talking about it, and that is people in companies are talking about it, and we've got a lot of media exposure, but executives are not talking about it, meaning that if the stated goal is some sort of action on the part of the companies or the White House, that just hasn't happened so far. But the way I see it is if I can sustain 100,000 uniques a day to a site, these are people not being driven by Facebook or Google Ads, but they're intentionally deciding to go to this site. I used to be in the world of E commerce. You hope for a conversion rate of 2 to 4%. I think I'll get at least 3%, because these are people who are coming of their own volition who've decided consciously to come to a URL. So let's walk through the math. 100,000 visitors a day, 3% unsub, subbing an average of three platforms. So let's call it. Let's be generous and call it 10,000 unsubs each day, right? That's 300,000 unsubs. Through the month of February, 300,000 unsubs average dollar value, $100. So that comes to $30 million less in unsubscription revenue. The average multiple on revenues is 10x. So that is a $300 million market cap hit, notional hit to these firms. Does that make any difference in the big picture? Probably not. But if we can get a bunch of people to figure out a way to ding big tech by a third of a trillion dollars, something is going to happen. And that's the whole point here. The signal we're trying to send is that one person with a footprint and it could be your parish, it can be your sports league, it can be your friends, maybe you have a little bit of a following online, can take action with fairly little effort. And this has been an effort more so for My team than me. But more than anything, what is required to have a voice in a chorus of pushback? It's the following. An absence of fear of public failure. That was really the only thing getting in the way of me doing this was the fear of public failure. The fear that you were gonna throw a party and no one showed up. The fear that, oh, maybe I'd be a good sophomore class president, but I don't wanna risk public failure. The fear of reaching out to someone who you're impressed by and saying, let's get together for the game. Fear that they wouldn't be friends with you cause we think they're much cooler than the air. The fear of applying for a job that you feel you're not qualified for. The fear of living the life you want to. Who do we respect the most? I'll shift that. Who do I really admire? At the end of the day, the best example I can use is occasionally I'll find myself in a situation when I'm on vacation and people start getting drunk and someone gets up and starts dancing as if no one's watching. Some dude who has no rhythm is just having a great time. And then inevitably, and this is more fun, some exceptionally hot person gets on a table and starts dancing as if no one's watching them. That's how you want to live your life. You want to live your life as if what's important to me, how can I make a difference and just pretend or just imagine that no one's watching? Because here's the bottom line. In a hundred years, nobody you care about and nobody who cares about you is going to remember you or anyone they knew. So here's the key. Here's the key to taking action. Here's the key to having an impact. Here's the key to living a self actualized life is recognizing that every obstacle that is in your way, nothing is as big as the obstacle of the following. And that is your fear of public failure. And your fear of public failure is a barrier, but it's a 2 inch high curb in your brain. It just doesn't matter. And the people who punch above their weight class, economically, psychologically, romantically, are the ones who have decided that the risk of public failure is a much smaller risk than everybody else thinks. If something goes wrong, if I started this movement and nobody showed up and it was a hit to my credibility, okay, then everyone goes back to thinking about them fuck themselves. So the fact that it's worked is really reinforcing. But more than anything, I want it to be a signal to people to say, hey, take action, do something. But more than anything, if there's a lesson in any, any of this that I could communicate to young people, said the only thing or the biggest thing between you and having relevance and meaning and living the life you want to live is the dancing as if nobody is watching you. Moving on. In today's episode, we speak with Ethan Mollick, professor at the Wharton School and author of Cointelligence. Ethan is the leading voice on how AI is changing work, creativity and education. He also writes the popular substack One Useful Thing. So with that, here's our conversation with Ethan Mollick. Where does this podcast find you, Ethan?

B (13:34)

I'm outside of beautiful Philadelphia, Pennsylvania.

A (13:37)

There you go. Are you at school or is that your home or.

B (13:40)

I'm at my home. Yeah, that's my game collection back there, so.

A (13:43)

Oh, I like it. So let's bust right into it. Anthropic CEO Dario Amode recently released a 38 page essay in which he delivers a very ominous warning about AI and the threat it poses to our society. Why do you think the CEO of one of the largest AI companies in the world seems to be so pessimistic about AI and what do you make of this view? Is it more of this kind of virtue signaling and not meaning it, or do you think he's generally trying to build a better AI?

B (14:09)

So I think that there's always debates, right? There's like external facing, but like when you talk to these people internally, I think Anthropic is fairly sincere about their view views about how AI works. You may or may not agree with them. He actually has a pair of essays, one on like the bright future ahead of all of us and the other about our potential doom and pointing out issues that may actually occur. So, you know, it always is a question of weirdness that you're building this thing if you're so worried about it. But I think it is a sincere anxiety.

A (14:38)

A quote from the essay, Humanity is about to be handed almost unimaginable power, and it's deeply unclear whether our social, political and technological systems possess the maturity to wield it. Do you agree with that? And also, what does Ethan Malik think are the biggest dangers of AI or what are you worried about?

B (14:54)

You know, I'm in kind of a weird boat here, which is, I think that there's a lot of worries about the existential risks of AI and so people leap ahead five years, assume the current path continues, and there's no sign yet by the way that AI is slowing down development. But there's a move towards sort of existential. You know, Dario in that essay talks about what would a group of geniuses in a data center that are smarter than any human, what would they do? I'm actually much more concerned in thinking about how we guide the next few years to make AI help people thrive and succeed rather than the negative consequences that could happen. How do we mitigate those negative risks? So I think there's a nitty gritty path between here and some imagined future. We don't know if AI is going to get there to sort of super powerful and autonomous. But we do know it's disruptive today. So I worry a lot about how do we model the right kinds of work so that when we start using AI at work that we do it in ways that empower people rather than fire people. How do we think about AI energy education so that it helps students learn rather than undermines learning? How do you think about using a society in ways that don't lead to deep fakes and dependencies? I think there's two sides to each of these coins and we need to get very nitty gritty about which things we care about.

A (16:02)

Well, I'll put forward a thesis and you tell me where I've got it right or wrong. I'm actually an AI optimist and I think it's easy. You just sound smarter when you catastrophize and I do a lot of that. But the. The existential risk of it turning into a sentient being and deciding that in a millisecond that we should no longer exist or self healing weapons. I don't see any reason why AI couldn't be used as much for defensive measures as offensive inequality. That's already here. We've opted for that. But what I see, I'm an investor in a company called Section AI that helps corporations upskill the enterprise for AI. And what we have seen and what they have seen is that the adoption is woefully under penetrated within the actual organizations, at least in the enterprise, individuals are using AI for therapy or how to reduce their workload on a Friday. I mean, is all of this quite frankly. And also I Wonder if the CEOs have a vested interest in catastrophizing because it makes it sound like the technology is world changing and that much more powerful. And please sign up for my $350 billion round at Anthropic. Is some of this, quite frankly, just. Is some of the dread and doom just quite is just inflated.

B (17:17)

I mean, sure, I mean, but some of it is also they drink their own Kool Aid. Like they believe this stuff, whether that serves marketing or not. But I do want to take a step back. I'm a Bisco professor, right, like you. And so I've been doing a lot of work with my colleagues on impacts of AI at work. And there's a few things. One is there are fairly large impacts in any randomized controlled trial. We did early experiment with my colleagues at Harvard, MIT and University of Warwick at Boston Consulting group. We found 40% improvements in quality using the now obsolete GPT4 with people who weren't even trained. 26 grand faster work penetration rates are up there. It's interesting. Companies people are using AI, but they're not talking about it. They're not using the corporate AI. So about 50% of American workers use AI. They report, by the way, three times productivity gains on the tasks they use AI for. They're just not giving that to companies. Right. Because why would you like, you're worried you'll get fired if AI shows that you're more efficient. You look like a genius right now, and maybe, you know, the AI is the genius. You're doing less work. So I think that there's a difference between what companies are seeing about adoption, what's actually happening with adoption.

A (18:17)

The CEO section says that right now it's being used at work for therapy. And so someone who understands AI is giving themselves another day off that. Why sharpen the sword publicly that you cut your own head off with? What specific tasks at work have you seen in your studies and your research have registered the greatest increases in productivity? What's been overestimated and what's underestimated in terms of the disruption or the improvements in productivity at the workplace.

B (18:45)

So I think the big picture, overestimation and underestimation is work is complicated and organizations are complicated. Right? So you can get lots of individual productivity gain. But if that's producing 10 times more PowerPoints than you did before, that's not necessarily going to translate to any actual benefit for the company. So leadership needs to start thinking about how do you build organizations around this. At the individual level, though, huge impacts and, you know, coding especially has taken this massive leap. We have earlier evidence that you saw about a 38% improvement in the amount of code people were writing once they started using agentic coding tools with no increase in error rates. But that's even increased further. The newest coding tools, both the people in charge on the research level at OpenAI and Anthropic have said 100% of their code is now written by AI. That's actually quite believable given how good these tools have become. We're seeing similar things, you know, managerial tasks, medicine. We're seeing impacts in scientific publication. Super interesting area. People who started using AI early to write scientific papers. And we know this because there's a great study that looks at when they started using the word delve, which was a dead giveaway. You were using AI back in 2023. If you use Delve a lot in 2023, then you actually publish about a third more papers in higher quality journals afterwards. Now the question is, is that good for science to have more AI writing? Separate issue. So that's the sort of process versus detail problem, right? People are becoming individually more productive. The system isn't built to handle a mix of high quality and low quality and just more work. And that's where the bottleneck often is.

A (20:10)

You coined this great term to describe AI called the jagged Frontier. I love that. Which encapsulate. 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. Actually the music has to match the words in terms of my embracing AI on earnings calls. If you were advising 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? And which areas should I avoid that aren't yielding the type of benefit that was once advertised.

B (20:52)

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? This is a. We're a thousand days into after the release of ChatGPT. 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 and D themselves. 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 good or bad results. If you're paying someone to do R and 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. They give the crowd, everybody in the organization, access to these tools to use advanced tools like, you know, anthropic 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.

A (22:29)

I know Marc 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 Marc Benioff seem to be talking about agentic AI?

B (22:52)

It's a great question. 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 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, you know, 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 the down to five really good questions to ask and it would go on and do the research and 20 minutes later you 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 it 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.

A (24:02)

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?

B (24:28)

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, Anthropic's quad 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. So GPT 5.2 thinking is important to use. Anthropic 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 if you want to do research. The deep research tools for Google are currently better through this product called NotebookLM and that's free and that's very good. 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 ten hours of just talking to it like a person and seeing what results you get and.

A (25:57)

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 inroads, 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 interplay here? If this were a league, what teams are coming up and what is, what is descending?

B (26:27)

Yeah. 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 could build. Once you have one of those, you can do all kinds of variations, but. 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. And because of that, there's only a few companies that can actually play in this space. Right. So in the US we've 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 in 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, releasing them for free to the world, and one French company in the same boat. So within that dynamic, there's this competition about who could build the biggest data set or 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. 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 we're waiting to see if anyone else kind of catches up to them. But those three are in a very tight race. 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 Claude Code is currently the very hot coding tool. OpenAI has Codex, which is a very similar 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. 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.

A (28:39)

If I think of luxury brands, BMW, Mercedes and Audi, I think I could do a reasonable job of attempting to outline how they're. 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?

B (28:54)

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 the 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 wouldn't expect Microsoft and Apple to produce a system that works exactly the same. These are similar enough that for most people doesn't matter. But if you care. Right. Opus 4.5 anthropics models are tend to be known as the best writers of the bunch. 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. ChatGPT is, has, is really two different flavors of models. There's a set of chat models that are really optimized for you to have conversations with and roleplay 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. It actually gets quite, it gets self flagellating. If you tell 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.

A (30:15)

I find that anthropic is More politically correct. ChatGPT 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, that 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 seeing?

B (30:53)

Well, I wouldn't call it regression to the mean. We are 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 it can do that or can't do math and suddenly last year both two models won gold, the International Math Olympiad. So like there is not, it's not a regression of the mean because there's no dropdown of ability level. The ability levels keep going up. But all of the companies in the space are on roughly the same development curve, right? Their models are keep leapfrogging each other by a fairly predictable amount of 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. 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 artificial general intelligence machines 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 have spent billions of dollars building models and eventually free 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.

A (32:29)

We'll be right back after a quick break.

B (32:31)

Foreign.

A (32:37)

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B (33:51)

And this week on my podcast on.

A (33:52)

With Kara Swisher, I'm interviewing defense attorney Abby Lowell.

B (33:55)

Last year he left one of the.

A (33:56)

Country'S premier law firms and went independent so he could defend clients targeted by the Trump administration. People like Don Lemon, Federal Reserve Governor Lisa Cook, and New York Attorney General Letitia James. Here's a snippet from our conversation. It's not random, it's not ad hoc, and it's not an outlier that their first attack after they had already neutered the Congress and they had politicized the courts, was to go after the lawyers and to go after the journalists. The full interview is out now and you can find it anywhere. You listen to podcasts and of course on YouTube. Be sure to follow on with Kara Swisher for great conversations like this. Support for today's show comes from hungry Root habits are hard to change and oftentimes it's not about a lack of motivation, but more about not having the right options at your disposal. Like if you're looking to change up your diet, you can't expect to make the move if all you have are snacks and junk food. That's why there's hungryroot. For those of you looking to up your nutrition and eat healthier, hungryroot basically works like a personal nutrition coach and shopper in one by planning, recommending and shopping everything for you. They take care of the weekly grocery shopping, recommending healthy groceries tailored to your tastes, nutrition preferences and health goals. We've gotten to try Hungryroot on the Profg team and people reported back that it was surprisingly easy and good tasting and people were able to spend less time worrying about grocery shopping. Take advantage of this exclusive offer for a limited time get 40% off your first box plus get a free item in every box for Life. Go to hungryroot.com profg and use code propg that's hungryroot.com propg code propg to get 40% off your first box and a free item of your choice for life. I Wonder if for 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 have 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. 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?

B (36:31)

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 Quinn, 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. 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 value 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 a month behind, you know, consistently eight months behind the frontier of US 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. On the other hand, I mean, the matter, 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. 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.

A (38:06)

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, and 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. What do you see as the choke points that get in the way of this brave new world, so to speak?

B (38:51)

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. 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 that humans keep learning. Otherwise you have to train them every time. 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. And the question is, how fast can they build those data centers?

A (40:11)

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. 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?

B (41:06)

First of all, I think you've laid off the trade off really well, 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 could 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, what it means for an AI company to achieve that revenue. Right? 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, 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, 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. 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 working as a manager. What happens if we get 10 times more code? 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.

A (42:50)

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 index 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 learned the basics of AI. Having said that, I haven't seen a huge destruction in jobs across those 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. 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?

B (43:46)

I think that people overestimate the speed at which large companies get 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. 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 Brynjolfsson 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. Like, it is very clear that AI is going to change how programming works. You could 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.

A (45:14)

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A (47:08)

We're back with more from Ethan Moloch. 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 the 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. I just don't see it happening. I don't see it happening. Your thoughts?

B (47:52)

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, 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 of in this world. I think that education will change. I think there's an easy. Imagine a world given early evidence that AI, when used properly, can be a good tutor. I can imagine a flipped classroom setting where my students are engaging with AI outside 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 business and maybe they have a little bit of consulting or strategy focus or entrepreneurship focus 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, they. 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. 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 ChatGPT 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.

A (50:00)

How has it changed your role as an academic in terms of research, how you prep for class, or 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?

B (50:19)

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, right? So as a professor, I do a ton of things, right? 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, right? 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 the AI has already taken some of these things from me, right? 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. There's a lot of administrative tasks I've handed over to AI to do. When I do research, my research time is cut traumatically because the AI can do all the code writing and everything else, and I can look at the answers. It's like I'm in ra, but it's gotten better. I can throw a full academic paper that I've written a couple 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 a table, 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. 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 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. And sometimes it's huge efficiency gains of hours, and sometimes it's a couple minutes here or there.

A (52:11)

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.

B (52:27)

No, I mean peer reviewed research is in a mailbox, is always in crisis, right? Just like everything associated with universities, 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 crisis 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. So human peer review is suffering under a flood of tons of papers being produced with AI help and 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 we, 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 it 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.

A (53:33)

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. ChatGPT. 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.

B (54:15)

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. 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 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, we even just things like translation. It turns out that if you use AI to give people a pre operative form that they understand they actually are, are happier with their surgery, have less issues and are more likely to report success because they got the information away. They understood. Right. 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 health care, good doctors, terrific. And then there's just the administrative breakthrough piece, right? If the, 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. 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 we're thinking get stuck is not so much can AI do this, but how will organizations respond?

A (56:25)

You brought up Moderna and I think of 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 on or huge market cap companies on 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 7:10, 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 have basically all of them either gone out of business or going out of business. 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, 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, 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 value across these companies while seeing huge stakeholder value similar to what happened with vaccines or even PCs?

B (57:51)

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.

A (58:04)

Or not explain open weight.

B (58:05)

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? So and once they're out there, no one can claim them back. There's no other piece to it. You just need this piece of information. 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. And so right now it's such that those models are much 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 a process slows down and at that point then a lot of value flows out of the system.

A (59:19)

And Ethan, are you a father?

B (59:22)

I am, yes.

A (59:24)

How many kids?

B (59:24)

I have two kids.

A (59:26)

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 gonna guess more right than wrong about where this all heads, has it changed your viewpoint of your kid's future?

B (60:00)

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 my view on careers a little bit. I think that thinking about jobs, I don't know what jobs are gonna be in the future. One thing we know about work, I'm a professor of entrepreneurs, 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. 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 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 a parasocial relationship with them. We've worked a lot on Internet and how to work with these systems, and I'm not worried that they're going to turn to these as serious relationships. 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 9th grader, and I'd take a picture of an assignment and be like, okay, now I could help explain this to you. As they get older, they've increased the use the kind of quizzing mode they know the AI won't teach them unless they ask every Time. 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. 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 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 shoot, to improvise is more important than ever.

A (61:54)

When I first, when my parents got divorced, I moved to this new 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 even had films on it. What to do when the Ruskies detonate a nuclear bomb. My sense, well, do you think the catastrophizing around the offensive nature, possibility for this AI is overestimated? And then. And 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 we lose control and okay, Molochs meet here and we're headed to the Appalachian mountains or whatever.

B (62:59)

I want some people catastrophizing because that's what governments should be doing. We need policies and procedures in place. Catastrophic stuff, right? 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 it's going to know, become a God and save us all. I, you know, maybe it's the business school professor in 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 way like we get used to doing many different things, living in many different lifestyles. 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 Katava or something like that isn't that helpful. And, you know, I don't know what world you're preparing for a catastrophe in. There's a thousand things that could. That could end the world. 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 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. 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 at the individual level. I think it's at 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 invent a chemical weapon that kills everybody, or will a bad guy using AI do these things? But it's all the other risks they worry about too. Deepfakes are a real problem. I can create an image of anybody saying anything I want. How do we respond to that as a society of being able to do that stuff? How do we start responding to make sure that as we talked about earlier, that that AI is not automatically translating to job loss, but 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.

A (64:57)

You think we should age gate synthetic relationships?

B (65:00)

I think we don't know enough. So probably caution is warranted, right? Like there is mixed research right now on. There are some papers that suggest that AI lowers the, you know, rates of suicide ideation for the very lonely or decrease of 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 know, but neither does anyone else.

A (65:34)

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, 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.

B (66:01)

You know, the first thing I said, and my colleague, Professor Matt 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 went, 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, you know, 18 months, and then launched a startup company with brilliant friends and roommate in 1998 or 1997, where we embedded the paywall. I still feel a little bad about that, but nobody really understood what a paywall was because the Internet was new, but we were through 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 Ph.D. and then started studying games and education and AI and, 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. 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, perfect skill set. And it's an evolution 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, 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. Useful.

A (67:50)

Ethan Mollick is a professor at the Wharton School and a leading voice on how AI is changing work, creativity and education. He also writes the popular substack One Useful Thing and has coined terms including the jagged Frontier 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, you know, you have arrived, so to speak.

B (68:24)

I haven't taken time to pause, but it is nice to know that I could do that at some point.

A (68:29)

At some point. Thanks, Ethan. This episode was produced by Jennifer Sanchez and Laura Gennar. Cammie Reek is our social producer. Bianca Rosario Ramirez is our video editor editor, and Drew Burroughs is our technical director. Thank you for listening to the propg pod from PropG Media.