Your ‘AI crisis’ is totally normal but you’ll be fine, with Ethan Mollick

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For most people, there is a crisis that you have to have when you use AI. I absolutely believe that's true. Like, what does this mean? Oh my god, what's it mean that a machine's more clever than me about this? What's it mean that it seems so insightful? What's it mean that I enjoy talking to it? What does this mean that it does my job for me? What does this mean for my kids and for me? Like, we don't have answers to those questions yet.

Like, that's very exciting in a lot of ways. It's also very unnerving. And then you have to pick yourself up on the other side, and you will. Like, everyone gets through it and you're fine. But, like, I can't prevent the crisis from happening.

Three sleepless nights. That's how long Ethan Mollick says it takes to really get what AI can do. Ethan is a professor of management at the Wharton School of Business, an author of the book, Co-Intelligence: Living and Working with AI, which I read and highly recommend. He says that once you move past the realization that AI can be so smart, so creative, and so capable, you can actually start harnessing AI to your advantage, which is exactly what we're going to be focusing on in this episode.

Ethan shares his top advice for how to get started on your AI journey, why we should treat AI like a person, and how AI will revolutionize education globally. I'm Rana El Kaliouby, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Hi Ethan, welcome to Pioneers of AI. I'm so excited to have you on.

Thanks for having me. I'm excited to be here.

Anyone who's followed your work or read your book, which I have, and I absolutely recommend I have it all, like, marked up and stuff, which is always a good sign of a good read. You are just an amazing prompter. You have this knack for figuring out, like, the right prompt to give ChatGPT or Claude and whatnot to get, like, these amazing responses. So we thought it would be fun to start this interview by asking ChatGPT for how to kick off our conversation.

And so I'm actually gonna let ChatGPT ask you a question.

All right, I'm I'm ready. It's it depends on whether it likes me or not today.

Ethan, you've dedicated your career to studying innovation and entrepreneurship, but let's start with a curveball. What's the most innovative thing you've done outside of work?

So I I love that. I think I want to start with hard questions from an AI, and I worry if I'm going to upset it that future AIs will get mad at me. So I'm going to be trying to be cautious about this. I like part of I think why I'm good at AI stuff is like I do weird stuff all the time, right? Like that's sort of my like, a lot of the prompt stuff I do is just super strange, right?

Like, what happens if you remove the word squid from all Quiet on the Western Front, a book that has no squid in it, and ask the AI to do it and push it to do that? Um, I've done improv comedy, I have done like

That was mean, by the way. That was mean.

I hope not. Well, I I check in frequently with Claude to make sure it's not mad at me.

This is why I like talking with people like Ethan. Not only is he a super user who is immersed in all things AI, he's also pushing its boundaries in his teaching and beyond. And you can totally hear this passion in his voice.

I think there's a playfulness in a lot of what I try and do. My LinkedIn bio becomes entirely fictional halfway through. I think I claim that I invented the transistor and whole and was the original Lord of the Dance. But I realized there's a problem because people now take me much more seriously. So people are like, I really love that you love Irish dancing. I'm like, where's that from? I'm like, Oh no, I wrote a fictional LinkedIn category.

So

You you can always blame it on the AI hallucinating, right?

100%. At this point, it's much easier.

Ethan's bio is now all straight-laced and fact-based, by the way. Okay, but what is the most innovative thing you've done outside of work?

Before doing AI stuff, I was spending all my time trying to build games for teaching. So my idea was, how do we transform education by turning logic, a fake startup, into a game? And, you know, there's been a lot of those kind of projects of, like, what happens if we try and these six steps ahead and see what the world looks like?

Let's talk about your book, co-intelligence. I personally have this conviction that AI should be built in service of humanity, not to replace humans, but to augment and amplify our skills. So I I love your idea of AI being a thought partner or a coworker or a tutor or a coach. But I'm curious, how would you define a co-intelligence?

So the definition of this stuff is still kind of evolving, right? But a co-intelligence would be something that you work with yourself to both extend your own intelligence, fill in gaps that you have, but not take away your sense of agency. So the idea is to think of it like a thought partner, like a teaming with another human being. That's the kind of realm worth thinking about.

One of my predictions for 2025 is we're gonna see a lot of embodied AI. Do you think there's a place for a co-intelligence with this physical AI as well?

I don't know if we see a lot of embodied AI. I think we see AI with vision and voice. I don't think we see AI with body in 2025. I think robotics is harder than people think, and I think we'll see agents in the digital world long before we see physical agents. The embodiment, whether it's agents or physical stuff, does challenge the co-intelligence piece, right? The whole idea of an agent is it takes action without you requesting the specific action.

So that removes the co-intelligence feature. So, I mean, there is some extent to which I think co-intelligence is a limited paradigm.

That brings us to the four principles you outlined in your book about how to partner with AI, and the first one is always invite AI to the table. What do you mean by that?

So, you know, you write these books, like, months before they come out. So I think the book is still accurate for the current generation of AI systems. I don't feel like it's out of date yet. But, you know, sometimes, you know, you're wrong or right about things. And I feel like the thing I was most right about was that principle. And the reason why is because these systems are weird. There's no instruction manual. You can't ask OpenAI for their secret manual.

They don't have one. And I think that there's a natural aversion from people working with these systems. Some people embrace it, but a lot of people get very freaked out or are very happy that it fails and they just walk away because the AI failed. So now I don't have to worry about it anymore. And I think the most important thing you could do is not to delegate out learning AI, not to just read about it, but just to use it.

And so about ten hours has been my threshold for use. And how do you use it? So people ask, like, what do I do with it? Well, the easiest thing to do is just do your job with it, right? So you started this interview by playing me an AI-ass question. And the first thing that came to my mind is okay, well, you know, what else did you do with it? Did you ask for, like, fifty questions, then ask it to rate those fifty questions by quality and likeliness to do it.

Like, if you're just using it like a Google or query system, it's not as powerful. If you're interacting with it deeply, it's a very different experience.

Yeah, are there examples where you would not invite AI to the table? AI today to the table?

Sure, I mean, where there's ethical or legal restrictions, definitely. I know from our research that AI is a better grader than humans, definitely than TAs in most cases. I have not yet used AI to do actual grading because I feel like that's my obligation to my students, is that I read their papers and grade them, even if they think the AI might be better for it. So you have your own lines about what it works and where it doesn't.

The other reason you don't bring it to the table is like, once you use these systems, you know what they're good and bad for. Like, the reason to use the ten hours is to get the shape of the jagged frontier of AI ability. And once you do that, you're like, there's some stuff I would never ask an AI to do, and there's some stuff that I absolutely feel comfortable asking it to do.

We should actually double-click on the jagged frontier a bit, cause some of our listeners may not have heard this term before. So what do you mean by the jagged frontier, and how do we deal with it?

That was term we came up with for our paper on Boston Consulting Group. I was trying to come up with a way of describing the fact that the AI is really good at some stuff and really bad at other stuff, and it's hard to know what that is in advance. So I came with the idea of this jagged frontier of ability, that some things it's really good at, some things it's really bad at, it's hard to know beforehand.

And it's also moving, right? It's continuously moving.

Constantly expanding and changing. Yeah, exactly.

Absolutely. What principle were you wrong about?

I think the human in the loop principle, I think, gets taken the wrong way.

Human in the loop? This is about achieving what neither humans or AI can achieve on their own. It's about how humans and AI collaborate, with humans providing oversight, input, or even making the final decision.

I think that it's viewed as humans have to maintain control over AI in all circumstances, and that's clearly not true. Like we've already given up a lot of instances of making authority to AIs. I think I meant it more of like, look, you're going to learn that the AI does stuff you do better than you, not everything. But like, think about, you know, my job as a professor, right? What I have to be good at, I have to be good at doing research and running administration and teaching students and designing classes.

And, like, I can't be good at all these things. It's unlikely I'm good at all this. So imagine a doctor, right? Like, no one would have designed the job of doctor the way it exists today. You wouldn't expect the same person to be good at diagnosis and patient management and administration and hand skills. Like, that's an insane ask, right? So the AI does some of that work for you probably better than you do, right? And so part of being the human in the loop is actually building a full loop where it's not just human decision making, but it's the idea of building a system around you where AI supports you and helps you.

But building a system where AI supports you in all facets of your life means knowing how to use AI. Ethan's suggestion Treat AI like a human. We get to that in a minute after a short break. Stay with us. The companies winning right now aren't just building great AI. They're building the infrastructure to actually run a business on top of it, which is where Stripe comes in. Stripe is built for whatever comes next, with planetary scale financial infrastructure to help you power any business model.

As a matter of fact, 86% of the Forbes AI fifty run on Stripe, along with industry leaders like Salesforce and Pepsi. Last year, Stripe users processed $1.9 1 trillion combined. See how Stripe can power what's next for your business at stripe dot com. In prep for this interview and kind of related to one of your principles, which is treat AI as a person, I went back and asked ChatGPT to summarize all of our conversations over the last two plus years and kind of categorize them into themes or buckets.

And so here are the buckets. One, a lot of conversations around AI investing and how to set up a fund and like, right, like all of the mechanics of setting up a venture firm. Two, my podcast and just being an influential voice in AI. Three, a lot around personal and relational growth, so dating advice, parenting advice, a lot of self-reflection. And then we love to host as a family, and I'm a terrible cook, so there's a lot in there about, like, okay, here's like a menu for, like, you know, the twenty people you're hosting, or like, here's Mediterranean recipes and whatnot.

And so as I was thinking about it, I was like, oh my gosh, it's a business partner, it's a therapist, it's a, you know, a dating coach, an assistant producer. Why is it important that we treat AI as a person, and how does that maximize the value we get out of generative AI?

That's a great exercise. People should do that. There's really a few levels to that question, right? One level of the question is the sort of big picture view about why you treat like a person is that's how it works best. Simon Williamson, who's a excellent AI coder guru guy, he had this great example where he had Claude reading in political donation documents. He does a lot of stuff on data journalism, and that's the donations are all open information in the U.S.

You have someone's name and address. And Claude stopped working after a couple pages and said, I don't feel comfortable reading these addresses. I don't know what you're using them for, right? Like, software is not supposed to argue with you. It should be your therapist. It shouldn't be, you know. And so people have a lot of trouble sometimes working with AI because they think of it like software, and software engineers often struggle the most with using AI successfully.

It works really well like a person. It BSs you sometimes. It sometimes, like, cuts out a limb. It has moods, it has topics it wants to talk about, it has strengths and weaknesses. And some of the best prompt engineers on the planet are people who've never coded a day in their life. So the most practical view is like, if you treat it like a person, it feels much more open. It's not a person, but that gives you a lot more capability.

The more direct piece is, look, when you treat like a person and tell it what kind of person it is, you're also giving it context, which is one of the three or four things that actually make prompting better. And so when you give it all the information about you, it's better in that kind of way.

The context piece, I think, is actually really important. And in my former life at my company, Affektiva, we built emotion recognition technology. And I'm convinced that chatbots of the world need to also be paired with some sensing technology that doesn't just get the information from all your previous conversations, but it ought to know, you know, maybe it's connected to my Whoop, or it knows, you know, my emotional state slash mood for the day, or how well I slept, or, you know, have I been eating well or not.

Do you think this is gonna happen? Like this pairing of, like, more sophisticated sensing, human sensing technology with these large language models?

Right now we're at the early age of a new paradigm. And I think people are very used to having stuff handed to them on a plate, right? Like, where's the app that does this? No one's building the app that does this. The system just does it. So, like, if you say, hey, uh, you know, if you talk to Claude, which has computer use, and say, hey, you know, here's the manual for my whoop. Like, go and look it up and just incorporate that information into what you're doing.

Or tell me how I can incorporate that information into what you're doing. It'll help you. Context windows get longer as memory gets greater. Like, that's just not a problem, right? Like, you should give it all the information you can. That's context. So I don't think that's not a future thing. That's just a today thing that no one's bothered to do yet.

Yeah, if actually, if any of our listeners have tried this, please holler and reach out, and and maybe I'll try it too. It's super cool. Okay, so you also in the book, and you've published a paper with Kareem Lakani, who's a friend of both of us. He's at the Harvard Business School. You you outlined three different ways this collaboration could pan out between humans and AI. One, cyborgs, two, centaurs, and three, self-automators. What do you mean by each of these, and can you give us a quick example?

Sure, so a centaur is half person, half machine. It's the most basic way we say people using AI. In that case, you're kind of dividing up the work, right? So you might say, I love doing analysis, I hate writing emails. AI does the emails, I do the analysis. We have separate jobs. Cyborg is what happens, like you're kind of a nice example of doing this kind of work, right? Cyborg is you spend enough hours doing this, and it starts to become sort of a Swiss Army knife of the mind, an extension to what you do.

So, like in the book, right? I talk about how the book was written as a cyborg. I don't like the book. AI is a writer. I think I'm a much better writer than AI. I'm proud of my writing. But all the things that make writing a book suck, like how you know I get stuck on a sentence. It could be thirty versions at the end of the sentence. Read this academic paper, read this chapter with the perspective of a naive reader who's never heard about AI and tell me what you're confused by.

That stuff is gold, right? And that sort of cyborg work where you blend your work with the AI. Self automator, I think, is now just the world what we call agent, right? Basically assign the AI to do a task and it just go does the work for you.

Yeah. Do you find that in spending so much time with AI, does it take away from your human relationships?

I mean, no, and I think there's probably like three or four reasons for this. One is, um, I work with my wife on a lot of this stuff. We're co-directors of the AI lab together. So, like, we talk AI and do AI stuff together. So that is, you know, one angle. The second is that I really enjoy interact with the AI, treating it like a person, but I don't enjoy having deep conversations about meaning of life with the AI or anything.

For me, it doesn't substitute for human interaction. Some people it will, right? So people are going to have very different experiences. And then I think that, you know, the third reason is like, I mean, I think of this like a tool and a paradigm, but it's not necessarily a. I mean, to me, it hopefully frees up more time to spend with my family and friends. So, but again, everyone's mileage will differ on this, and I think it's important from a sociological perspective to realize not everyone's going to have the same kind of impact.

I mean, it is going to substitute for human behavior in some people.

Yeah, let's switch gears. So I want to talk about the role of AI in education. So kind of put your professor hat on. You talk about the homework apocalypse. What do you mean by that?

I mean, it's already happened. AI does all your homework, right? 70% of undergrads, 70% of K twelve students in, uh, the latest Walton survey using AI to do their work. So it's already happening, right? And we haven't adjusted to it. Like I was telling you earlier on, I build games for teaching. One of the revelations about building games for teaching is that we can only get 70% fun. There are topics you need to learn about that you probably don't like.

People who love math, math is really exciting. People who don't love math, I cannot gamify it for you to a point where you're going to love it. But you need to learn it. And the way we learn stuff, unfortunately, is grind, right? If you're not actually being challenged and you're not sweating, then you're not learning. And that's like there's enough research on this that is true. It's called, you know, desirable difficulty is the kind of level that you're aiming for.

As a result, the problem with AI is it can make you feel like you're learning when you're not. My colleagues have more done this great study in Turkey where they did a randomized controlled trial and they found that students who were using AI to get homework help did much better on homework but did much worse on the test. Even though they thought they were doing well because they explained stuff, but they didn't really absorb it because they didn't have to struggle with it.

Now, if you put a good tutor prompt in place, that changes things, right? So there are ways of using this in really valuable settings, but you can't just assume AI makes everything better. We have to actually work on it.

I want to come back to examples where we're like co-designing the class and the curriculum to kind of incorporate AI. But back to the example where students are already using AI. Like, how do you draw the line between AI help and just straight out cheating?

There's not an easy line. I mean, I think that's why we have to start paying attention to intent. Like a lot of homework, we give you an essay and we'd hope something magical happens in the essay. And for writers, it does, right? You are struggling with this content, you figure out a way to fit the pieces together, do you have a mental revelation? Like, the best writing is like thinking, right? We don't spend a lot of time trying to break that down to what part of the writing experience is necessary or not.

We assign an essay. So if the AI is helping you with your grammar, is that a problem? Probably not. But on the other hand, if we're trying to teach you grammar as part of this, maybe it is. If the AI helps write an outline, is that short circuiting the mental struggle? I don't know. Is the AI giving you advice cheating? Maybe not, right? Is it undermining the educational purpose? Very possibly, but it requires deliberate effort to figure out whether it does or does not.

So how have you adapted all of that to incorporate AI in a meaningful way? So that the assignments where the kids can just go and write an essay with AI, you know, you're not assigning those anymore.

My classes are 100% AI. I mean, I'm lucky enough to teach an entrepreneurship class, so it's easy. But, like, we've been publishing at the Journal of AI Lab Award at the Lab's Award. We've been publishing prompts that turn the AI into a tutor, into a mentor, into a student you have to explain stuff to. Like, there's a lot of solid pedagogical techniques you can apply to AI. You just have to start with pedagogy. So, like, you know, my classes have, you know, there are AI mentors to help you review information.

At one point, you have to co-create a case with the AI and make the case better. We have an AI that purposely makes mistakes that you have to learn to correct and give it the right advice. There's lots of opportunities here, but you start from pedagogical grounding. You don't just start with, like, hey, is this cool?

So interesting. You also talk in your book about this idea of a flipped classroom, where instead of this lecture where you're kind of presenting material, the students can now go listen to this material at you on YouTube, you know, at home. But a lot of the work happens in the classroom. I'm assuming it sounds like your classes have never been like this kind of lecture style anyway, but do you see this idea of a flipped classroom gaining more and more traction?

The thing that's most important to note about AI is that it doesn't fundamentally change the underlying pedagogy of how people learn. Classrooms have always struggled with new technologies and new approaches. It's absolute chaos right now. We're going to figure it out, right? Just like we did the nineteen seventies with, like, you know, like calculators. Your class will look differently. If you are in an English composition class, you're not going to be doing take-home essays anymore. You're going to be doing essays in class, right?

You'll get writing coaching outside of class, so you'll do inside class activities, flip classroom stuff. Some other cases it might be different, right? We're going to adapt how we learn to each of these circumstances. But right now, there's, you know, education doesn't change instantaneously. There's rearguard actions. People hope that AI detection tools work. They don't. Actually, my fear in education is not actually inside schools. My fear is post-graduation. Like, the whole idea of a place like Wharton is I teach people to be generalists, right?

Like, I teach you to be a strategist, or I teach you to be an entrepreneur, like, and then you go work for McKinsey or Goldman Sachs or Google. You learn to do the job the same way we've taught people how to do a job for three millennia, which is apprenticeship, right? You start working, you know, let's say your podcast, hopefully, but, like, imagine your podcasting. You start off by writing briefs over and over again for the interview you're about to have.

And then you, as an experienced host, would go take your intern and be like, No, this is bad, here's why this is bad, do it again, right? And they do this for a year and they learn the basics and then they move up a level. You have the advantage of having an intern who's super smart but doesn't know very much but is doing grunt work you didn't want to do, and they learn about the job from you, right?

And want to impress you. That's the core of how things work. Well, what just happened over the last summer now is GPT four is better than any intern you're going to hire, right? So no middle manager who's smart is delegating work to an intern when they could have the AI do it and it never complains and it's fast and, you know, you can yell at it. And every intern says, I want to impress people because I want a job and a raise.

How do I impress them? Well, I'm just getting started. I'm going to have the AI do the work for me. So nobody's learning anything anymore. They're just doing AI work and turning it into people who are using AI already. That, to me, is the real crisis.

Yeah, how do we solve that entry-level job, right? Where AI can do a lot of these tasks, but it's so key because that's how you grow.

So we're going to have to solve for that through formal training. Either companies will have to start taking very seriously learning and development in a way they don't right now, because mostly it's kind of a compliance thing right now. Or else we're going to have to move into universities, high-end vocational training essentially. Like, you know, what does it mean to be a consultant or a, you know, or a banker is going to have to be taught outside of schools.

I mean, ultimately, the question just gets to be how good is AI and how fast does it get that good?

Okay, I want to talk about K through twelve too, because I'm on the board of, uh, the school where my kids go. And we just launched how to, like, reimagine what the school ought to look like in the next, call it five years. And of course, AI is one of the main pillars of the strategy. So what are you seeing schools do with respect to AI?

Massive individual adoption by teachers. Over 50% of teachers have adopted, and massive confusion at the district and government level. Nobody knows anything. I mean, our perspective has been empowering teachers by giving them tools to help them educate better and giving them choice in decision making over whether AI should be used, shouldn't be used, is appropriate to use. Like, we need to, like, that's that's where we are. But, like, the surveys, teachers are universally asking for more training, and they're universally asking for actual policies from a district level.

And I also don't think enough people are skating to where the puck is going here. Like, this stuff's getting very good. You know, there are specialized learning LLMs coming out that are going to be very good teachers. We shouldn't be planning for a world where that isn't the case. So what does that look like? We need a lot of people thinking about that.

What's your advice to educators who are listening to us on how to best leverage AI? How to get started?

So I'll go back to use it a lot. I mean, if it helps, we've at the Journal of AI Lab, we have a bunch of free YouTube videos on teaching. There's a free Coursera course. We have tons of resources on how to use it. There's lots of other smart educators working on this. It has to be community-based. You have to be, like, experimenting in your own expertise and then working with other teachers who are interested to make this work.

Like, you shouldn't do this alone, but you need to start experimenting.

But obviously, this experimentation shouldn't stop with teachers. Everyone should be playing around with this technology, even you. After a short break, Ethan shares his two cents on AI and business. Plus, he offers a particular piece of advice for those sitting in the C-suite. Stick around to find out. All right, let's talk about AI and business then. So you've consulted for some of the biggest companies and organizations out there, like JP Morgan and Google and even the White House, advising them on how to best utilize AI in their organizations.

What's the first piece of advice you often give them?

Honestly, the executives need to use AI themselves. Like, the worst thing you could do is delegate this down to, you know, your IT department or your general counsel's office and ask for a report from a consulting company. Like, that's just a disaster. Like, the executives have to be using it, right? Like, I think there's a problem with the view of technology adoption, which is that the young people understand stuff and older people don't, so the younger people will teach you how to do it.

And we our research at BCG actually found that the younger consultants were actually much worse at figuring out how to use AI in organizations because they hadn't worked in organizations, right? So they're like, write me a memo, and the memo looks good to them, but their boss looks at it, like, this is a terrible memo. Don't wait for someone else to help you. You figure out yourself.

A lot of people are using AI already on the job, and you quoted some numbers in a recent article that you wrote. So a study from Denmark found that in some industries, upwards of 60% of employees have used AI in the work. And then another study in the US found that about a third of workers have used gen AI in the last week. How do we know if these AI tools are making people actually more productive?

Like, how do you measure ROI?

We know they're making people more productive. There are some studies inside companies that I know of, most are not public. And we've been doing controlled experiments, and every controlled experiment finds productivity boosts. Then the organization doesn't find it. Why not? Because people are hiding their AI use. First of all, everyone's gotten a lecture from their central, uh, HRs telling them that if they use AI badly, they get fired. So no one shows that they're using AI, or they're viewed as geniuses, and they don't want anyone to know that they're not.

So the problem actually starts inside organizations, and you don't get ROI first because you have to do R and D. Nobody can tell you how to use these systems. There's nobody who has a secret instruction manual. You can't buy an off-the-shelf product now and assume that there's going to be an ROI thing in it because nobody knows what they're doing.

How should organizations deal with this? Like, how can leaders operationalize and almost give permission for their teams to play? Like, I like the word play that you kind of open this interview with.

So, I mean, I think that you have to do a few things. One is you have to be a role model, so use AI publicly in front of people, talk about it a bunch, show them what you built. Second, you want to make sure that you are, um, aligning incentives properly. Like, your employees are going to be asking, What happens to my job? What's your long-term view? Like, if you don't answer those questions, it's not like these people aren't thinking of them, right?

Then you need to align incentive systems properly. Like, are you what's happens if I come with a good idea? You know, I've seen companies slide briefcases full of cash across the table to someone and be like, 10,000 for whoever comes with the best idea every week, right? That's cheap compared to an IT installation. So you need to actually be doing stuff.

Do you think AI is leveling the playing field?

The initial AI use is a pretty universal finding that the low performers benefit the most. They get the biggest boost for performance. That is a maybe a temporary phenomenon, because basically what that means is the AI is doing the work for them. What we're finding is in in new studies there's a great study at MIT looking at generative AI use in material science, finding 58% increase in patenting rates. But the scientists who gained the most were the best scientists because they could more easily use the AI to screen lots of ideas quickly.

Was that the same study where they also felt the least satisfied?

Yeah, so their job changed, right? And so that's going to be another issue. How do we work with that? Now, the changes were, like, not insanely large on the satisfaction changes, but people do get a shock out of using these systems. So we have to rethink a lot of parts of the organization, and I don't think people are willing to do that in every case.

I'm an investor in AI companies, and I'm, like, really intrigued by this idea of a one-person unicorn. So just like to unpack this, a unicorn is a privately held company with a valuation of over $1 billion. And so a one-person unicorn is basically this idea that one person can create a billion-dollar company. Do you think that is possible? And and and the thesis here is that these companies are AI native, like with one person, you can leverage AI for a whole bunch of functionality.

I mean, you can and you can do that, but when it becomes a one-person unicorn, I'm pretty convinced at that point, unless you get lucky and happen to write the exactly the right software, because you already like, you'd have to almost be there already. It's I think that that is a weird position to put people in because the AI is jagged. It's not equally good at everything. And, you know, at least right now, it can't handle all of the agentic tasks you need to do as an entrepreneur.

And when it can handle the agentic tasks of the entrepreneur, which maybe is coming very soon, then you don't need the entrepreneur, right? Because you're basically just giving instructions that have it do stuff. I'm a little worried that this attitude of cut the people is the number one thing we always hear with technology. Like, I think it's a very bad way to start a industrial revolution. Like, if you were a brewery in the early eighteen hundreds in England and you got steam power, you had two choices.

You could either fire most of your staff and go down to two people and make a lot more money per barrel of beer, or you could be Guinness and expand worldwide and add 100,000 people. I think the absolute wrong thing to do is how do I do a one person unicorn? Instead, it's how do I do a thirty person company or 300 person company or a thousand person company? But each of those thousand people is doing amazing.

It makes no sense to me that, like, the first thing is how to be as efficient as possible.

Okay, last question, if you could have AI do anything in the world for you, what would you have it do?

I mean, I'm constantly experimenting with this sort of stuff. I mean, I think that for me, as an educator, it's a moral imperative to figure out where and when AI tutoring is good and when it's destructive, and to start building systems to do that. Moral imperative, right? Probably the same thing with diagnostics. When should we be using AI for people who don't have access to doctors? When is that wrong to do? In terms of tools, like, I play a lot of games.

If I had an AI tool that could co-create games with me at a high level, I'd really enjoy that.

Well, Ethan, thank you so much for joining us.

Thanks for having me. This is great. These are great questions. Thank you.

The pace of innovation in AI is wild. It's hard to keep up. Every day there's a new product release or a new model. But if there's one thing I want you to take away from my conversation with Ethan, it's that it's never too late to experiment with AI. Approach these new technologies with curiosity and even a little bit of playfulness. Try things out and see how they can be helpful in your own professional and personal life.

For me, I recently tried out Google's NotebookLM. I actually had it create a podcast episode of an imaginary Rana in conversation with my executive coach. Honestly, I don't know if I'll be using it for Pioneers of AI, but it was insightful and I really enjoyed the process. Before you go, we have an ask. If you like what you heard on this episode, take a moment to rate and review us wherever you're listening to this podcast. We're still a new show, and your input can really help others find the pod.

Pioneers of AI is a Wait What original. Our executive producer is Eve Tro. Our producer is Rachel Ishikawa, and our associate producer is Jordan Smart. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pew. Original music by Ryan Holliday. Production support from Timothy Lu Lee, and our head of podcasts is Lital Molad. You can join the conversation on LinkedIn, Instagram, TikTok, YouTube, and X. Just search for at Pioneers of AI. Thanks so much for listening.