Your ‘AI crisis’ is totally normal but you’ll be fine

Note: Transcripts are automatically generated from episode audio, and are not fully corrected for spelling, grammar, and formatting.

ETHAN MOLLICK: 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 machines work cleverer 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 it mean that it does my job for me? What does it 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.

RANA EL KALIOUBY: 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 and 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, creative, and 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.

[THEME MUSIC]

EL KALIOUBY: Hi, Ethan, welcome to Pioneers of AI. I’m so excited to have you on.

MOLLICK: Thanks for having me. I’m excited to be here.

EL KALIOUBY: So anyone who’s followed your work or read your book, which I have, and I absolutely recommend it.

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 the 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 going to let ChatGPT ask you a question.

MOLLICK: All right. I’m ready. It depends on whether it likes me or not today. So I love that. I think you want to start with hard questions from an AI. I worry if I’m going to upset it, that the 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, 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.

I’ve done improv comedy.

EL KALIOUBY: That was mean, by the way. That was mean.

MOLLICK: Well, I check in frequently with Claude to make sure it’s not mad at me.

EL KALIOUBY: This is why I like talking with people like Ethan — Not only is he a superuser 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.

MOLLICK: So 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 I invented the transistor and all, and was the original lord of the dance. But I realized it was a problem because people now take me much more seriously. So people were like, I really love that you love Irish dancing. I’m like, where is that from? I’m like, oh no, I wrote a fictional LinkedIn category. So—

EL KALIOUBY: You can always blame it on the AI hallucinating, right?

MOLLICK: 100 percent at this point, it’s much easier.

Copy LinkHow playful experimentation unlocks better AI use

EL KALIOUBY: 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?

MOLLICK: 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 and launching a fake startup into a game.

There’s been a lot of those kind of projects of like, what happens if we try these six steps ahead and see what the world looks like?

Copy LinkWhat co-intelligence really means for human agency

EL KALIOUBY: Awesome. All right. So 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 love the idea of AI being a thought partner or a co worker or a tutor or a coach. But I’m curious, how would you define a co intelligence?

MOLLICK: 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 that, think of it like a thought partner, like teaming with another human being, that’s the kind of realm we’re thinking about.

EL KALIOUBY: One of my predictions for 2025 is we’re going to see a lot of embodied AI. Right. Do you think there’s a place for a co intelligence with this physical AI as well?

MOLLICK: 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 bodies. Agents, right? 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 this specific action. So that removes the co intelligence feature. So, there is some extent to which I think co intelligence is a limited paradigm.

Copy LinkWhy you should always invite AI to the table

EL KALIOUBY: How interesting. Well, 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?

MOLLICK: So, you write these books like months before they come out, right? 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 sometimes 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 to working with these systems. Some people embrace it, but a lot of people get very freaked out or 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 just read about it, but just to use it. And so about 10 hours has been my threshold for use. And 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 ask question. And the first thing that came to my mind is, okay, what else did you do with it? Did you ask for, like, 50 questions, then ask it to rate those 50 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.

EL KALIOUBY: Yeah. Are there examples where you would not invite AI to the table, AI today to the table?

MOLLICK: I’m sure. Where there’s ethical or legal restrictions, definitely. Like I know from our research that AI is a better grader than humans, definitely the 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 works and what 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 them 10 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.

Copy LinkHow to work around AI's jagged frontier

EL KALIOUBY: So I think we should actually double click on this on the jagged frontier a bit, because 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?

MOLLICK: That was a term we came up with for our paper on Boston Consulting Group, where 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 up 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.

EL KALIOUBY: And it’s also moving, right? It’s continuously.

MOLLICK: Expanding and changing, yeah.

EL KALIOUBY: Yeah, absolutely. What principle were you wrong about?

MOLLICK: I don’t think the principle’s wrong. I think the human in the loop principle gets taken the wrong way.

EL KALIOUBY: 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.

MOLLICK: 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 it. I think I meant it more of like, look, you’re going to learn that the AI does stuff that you do better than you. Not everything, but like, think about 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. Right. 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.

EL KALIOUBY: 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.

[AD BREAK]

Copy LinkWhy treating AI like a person improves results

Yeah, and 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, 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 20 people you’re hosting, or like, here’s the 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 dating coach, an assistant producer. Why is it important that we treat AI differently? As a person. And how does that maximize the value we get out of generative AI?

MOLLICK: That’s a great exercise. People should do that. There’s really a few levels to that question, right? One level to the question is the sort of big picture view about why you treat it like a person is that’s how it works best, right?

Simon Williamson, who’s an excellent AI coder and data journalist, he had this great example where he had Claude reading in political donation documents. He does a lot of stuff on data journalism, and 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. AI does, because people think of it like software, and software engineers often struggle the most with using AI successfully. It works really well like a person.

It BS’s you sometimes, it sometimes 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 have 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 it 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.

EL KALIOUBY: The context piece I think is actually really important. And in my former life at my company, Affectiva, we built emotion recognition technology and I’m convinced that the 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, maybe it’s connected to my whoop or it knows my emotional state slash mood for the day or how well I slept or, have I been eating well or not?

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

MOLLICK: 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 is 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, if you talk to Claude, which has computers, and say, hey, here’s the manual for my, 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. Like, so, as 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 a future thing. That’s just something no one’s bothered to do yet.

Copy LinkChoosing between centaurs cyborgs and agents

EL KALIOUBY: Yeah. Actually, if any of our listeners have tried this, please holler and reach out 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 Karim Lakhani, who’s a friend of both of us. He’s at the Harvard Business School. 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?

MOLLICK: Sure. So, a centaur is half person, half machine. It’s the most, basically, we say people using AI. In that case, you’re kind of dividing up the work, right? So, you might say, I hate, 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 AI as 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, I get stuck on a sentence. It can be 30 verses. How to end the sentence, read this academic paper, read this chapter to 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’s sort of cyborg work where you blend your work with the AI.

Self automator, I think, is now just a world that we call agent, right? Basically assign the AI to do a task and it just does the work for you.

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

MOLLICK: I mean, no, and I think there’s probably, but that’s not going to be a universal. And there’s probably like three or four reasons for this. One is, 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, yeah, we—

EL KALIOUBY: That is so cool.

MOLLICK: —stuff together. So, that is, one angle. The second is, that, I really enjoy interacting with the AI and 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, the third reason is like, I think of this like a tool in a paradigm. To me, it hopefully frees up more time to spend with my family and friends. 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.

Copy LinkWhat the homework apocalypse means for learning

EL KALIOUBY: Yeah. Let’s switch gears. So I want to talk about the role of AI in education. Kind of put your professor hat on. You talk about the homework apocalypse. What do you mean by that?

MOLLICK: I mean, it’s already happened. AI does all your homework, right? 70 percent of undergrads, 70 percent of K 12 students in the latest Walton survey. Using AI to do the work. So it’s already happening, right? And we haven’t adjusted to it. Like I was telling you early on, I built games for teaching.

One of the revelations of building games for teaching is that we can only get 70 percent fun. Like there are topics you need to learn about that you probably don’t like. People who love math, it’s really exciting. People who don’t love math, I cannot gamify it for you to a point where it’s going to be, 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 it’s true. It’s called desirable difficulty, 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 at Wharton have this great study in Turkey, where they did a randomized controlled trial and they found that students who are 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.

EL KALIOUBY: 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? Right.

MOLLICK: I mean, 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 hope something magical happens in the essay. And for writers, it does, right? You are struggling with this content, and you figure out a way to fit the pieces together, and you have a mental revelation.

Like, the best writing is, like, thinking. 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, right? You read this work, you go through it. 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 provide an outline, is that short circuiting the mental struggle? I don’t know. Like, 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.

EL KALIOUBY: Yeah. 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?

MOLLICK: My classes are 100 percent AI. I mean, I’m lucky enough to teach an entrepreneurship class, so it’s easy, but like, we’ve been publishing at the Generative AI Labs at Wharton, we’ve been publishing prompts to 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, my classes have 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 purposefully makes mistakes that you have to learn to correct and give it the right advice. Like, there’s lots of opportunities here, but you start from pedagogical grounding. You don’t just start with like, hey, is this cool?

EL KALIOUBY: So interesting. You also talk in your book about this idea of a flipped classroom where, the students are kind of, instead of this lecture where you’re just presenting material, the students can now go listen to this material on YouTube 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?

MOLLICK: So the thing that’s most important to know 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 in the 1970s with like calculators, like your class will look differently. If you are in an English composition class, you’re not going to do take home essays anymore. You’re going to be doing essays in class. Right? You’ll get writing coaching outside of class.

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, education doesn’t change instantaneously. There’s rear guard 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, 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 the job for three millennia, which is apprenticeship, right? You start working, let’s say you’re not that you’re a podcast host, but like imagine you’re 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-4 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 can 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.

EL KALIOUBY: Yeah, how do we solve that? 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.

MOLLICK: 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, what does it mean to be a consultant 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?

Copy LinkHow schools and teachers can prepare for AI

EL KALIOUBY: Yeah. Okay. I want to talk about K through 12 too, because I’m on the board of the school where my kids go. And we just launched a five to 10 year strategy — we kind of debated the length, 10 seems too long, but anyway — on how to reimagine what the school ought to look like in the next 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?

MOLLICK: Massive individual adoption by teachers, over 50 percent 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, that’s where we are. But, the surveys show 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.

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.

EL KALIOUBY: What’s your advice to educators who are listening to us on how to best leverage AI? How to get started?

MOLLICK: So I’ll go back to use it a lot. I mean, if it helps, we’ve at the Journal of AI, 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 experimenting in your 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.

EL KALIOUBY: 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.

[AD BREAK]

Copy LinkWhat business leaders get wrong about AI adoption

All right. Let’s talk about AI in business then. So you’ve consulted for some of the biggest companies and organizations out there like J. P. Morgan and Google and even the White House, advising them on how to best use AI in their organizations.

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

MOLLICK: On the tactical side, honestly, the executives need to use AI themselves. Like, the worst thing you could do is delegate this down to 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? Because they’re, like, I think there’s a problem with AI. 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 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 didn’t work 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 have to figure it out yourself.

EL KALIOUBY: Yeah, very interesting. 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 percent of employees have used AI in the work. And then another study in the U.S. 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?

MOLLICK: I mean, 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. And 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 HR 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. Don’t get ROI first, because you have to do R&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.

EL KALIOUBY: So how should organizations deal with this? Like how can leaders operationalize and almost give permission for their teams to play — I liked the word play that you kind of opened this interview with.

MOLLICK: 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 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, if it’s not like this, people aren’t thinking of them, right? Then you need to align incentive systems properly. Like, what happens if I come up with a good idea? I’ve seen companies slide, like, briefcases full of cash across the table to someone and be like, 10,000, whoever comes up with the best idea every week, right?

That’s cheap compared to an IT installation. So, you need to actually be doing stuff, right?

EL KALIOUBY: Yeah. Do you think AI is leveling the playing field?

MOLLICK: So, 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 temporary phenomenon. Because basically what that means is the AI is doing the work for them.

Right? What we’re finding is, in new studies, there’s a great study at MIT looking at generative AI use in material science, finding a 58 percent increase in patenting rates, but the scientists who gain the most were the best scientists because they could more easily use the AI to screen lots of ideas quickly.

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

MOLLICK: Yes, 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.

EL KALIOUBY: I’m an investor in AI companies and I’d be 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 a billion dollars. 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 the thesis here is that these companies are AI native, like with one person, you can leverage AI for a whole bunch of functionality.

MOLLICK: 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 enough to write exactly the right software, because you don’t want to, 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 it turns, 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 and having to do stuff. But I’m a little worried that this attitude of cut the people is the number one thing we always hear with technology. I think it’s a very bad way to start a industrial revolution. Like, if you were a brewery in the early 1800s 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 30 person company, or a 300 person company, or a 1,000.

EL KALIOUBY: Decacorn, right. Trillion.

MOLLICK: Amazing. Like, it makes no sense to me that, like, the first thing is how to be as efficient as possible.

EL KALIOUBY: Okay. Last question. If you could have AI do anything in the world for you, what would you have it do?

MOLLICK: 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, 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.

EL KALIOUBY: Okay. Oh, that is so cool. Well, Ethan, thank you so much for joining us. This—

MOLLICK: Thanks for having me. This was great. These were great questions. Thank you.

EL KALIOUBY: 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 is 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 Notebook LM. 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!