Harvard Professor: What Every MBA Student Now Learns About AI

Using AI every day doesn’t automatically make you better at your job. So how do you use it to build real expertise? In this episode, I talked to Karim R. Lakhani, a Harvard Business School professor whose research with BCG, Procter & Gamble, and H&M explores how AI changes the way we work. We talk about three ways people work with AI – cyborgs, centaurs, and self-automators – and why the way you delegate matters for what you actually learn. Karim shares why he never takes the first answer, how to make AI challenge your assumptions, and why “the AI gave me that” is no excuse for poor work. We also get into what Harvard MBA students now build with AI, how juniors can develop judgment and taste, why CEOs need hands-on experience with these tools, and how to set bigger business goals as AI gets more capable. And we end with a question every founder should be asking: when the next AI model launches, does your business become more valuable – or does the model absorb what you do? 📩 Follow my Newsletter (Future Proof): https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=description&utm_campaign=futureproof-sub&utm_term=karim-lakhani https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=description&utm_campaign=futureproof-sub&utm_term=karim-lakhani 📌 Help shape what’s next on Silicon Valley Girl! Take our quick survey: https://docs.google.com/forms/d/e/1FAIpQLSdQEqUy3wiE7TlzM_haWLEcgV8eJUeSBIVaK2QjDsAVEsXm3w/viewform?usp=publish-editor Links: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/𝕏 : ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://x.com/siliconvalleymm⁠⁠https://x.com/siliconvalleymm⁠⁠⁠⁠⁠⁠⁠⁠ https://x.com/siliconvalleymm 🔗 Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/⁠⁠https://www.instagram.com/siliconvalleygirl/⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/ 💼 LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko⁠⁠https://www.linkedin.com/in/marinamogilko⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko 📌 My Companies & Products: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://Marinamogilko.co https://Marinamogilko.co

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 0:31Speaker 01

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• 0:59Speaker 02

  I have fired people who have told me the AI gave me that. This is Kareem Lakani, a Harvard Business School professor. His research with BCG identified three ways people work with AI. Two groups developed their skills. One didn't. So how do you know which group you're in? In terms of learning, self automators do the worst because they have stopped thinking, they've stopped engaging in the activities. We can do way more complex things. We can push our expertise way further than before.

• 1:27Speaker 01

  Can you give me some practical tips how I can train those assistants? What are the prompts?

• 1:32Speaker 02

  I think the first is.

• 1:35Speaker 01

  You are the author of this famous phrase, you won't be replaced by AI, but humans with AI will replace humans without AI. And you helped launch the AI course every Harvard MBA student now has to take. And you also run these randomized experiments on thousands of workers at BCG, Procter and Gamble and H and M. So today I want to talk practical things, how people actually use AI and where it works and where it doesn't. Going back to your famous phrase, after your latest experiments, do you think it's tapping into replacing humans or it's just humans with AI replacing humans without?

• 2:11Speaker 02

  Look, I mean, I think this world is changing really fast in front of us. I still believe in the foreseeable future, it's humans with AI. Like AI are not going to independently start to figure out hypotheses and go solve those hypotheses. They need some direction. They need some, some curation. They need some taste on the way. A bunch of the execution tasks are getting done with the machines, but I think the, what we're in the middle of is a reconfiguration of both organizations and occupations at the same time. If you sort of go back a hundred years, when modern production systems are invented, we started to create new companies and new organization forms, but also new occupations were being set up as well. I believe the same thing is happening right now with, with AI today, that we're in the

• 2:55Speaker 02

  middle of us figuring out what does work look like? What does occupation look like? And what does an organization look like? But the world of machines completely replacing humans, I just don't, I just don't see it yet.

• 3:06Speaker 01

  Yeah. And you have this fascinating study where you studied 244 BCG consultants and some of them were cyborgs, 60%, meaning they were working together with AI. Then 14% centaurs, they split the work with AI for specific purposes. And then there were 27% self automators that delegated everything and didn't learn anything. Can you talk to me about prompting? Cause I know a lot of people are worried they might be delegating too much and some people are worried they're not delegating enough. What was the difference in prompting in these three groups?

• 3:37Speaker 02

  So this was a part of a larger experiment at BCG where we were able to get access to about 800 consultants and randomize them into different types of tasks and access to AI or not AI. What we observed very clearly was that those that got access to AI had three different genres of working. So cyborgs, they were intimately working with the AI and doing joint problem solving. Centaurs that separated themselves and said, I'm good at X, the AI is good at Y. I'm going to delegate some tasks to, to AI, but then I'm going to supervise when I get back. And then self automators who press play. In terms of learning, the self automators do the worst because they have stopped thinking, they've stopped engaging in the activities and they are just basically, I can be harsh

• 4:20Speaker 02

  sometimes with my students, like if all you are is a meat interface to the silicon, what's, what's your value?

• 4:26Speaker 01

  Yeah.

• 4:26Speaker 02

  Right? And so it's easy to fall into that trap. One of my former postdocs is a professor at Michigan now for Bizarre Delacqua, did this great study showing people can fall asleep at the wheel with AI. Good people with good AI fall asleep. So I think self automation is actually places where things can go bad quickly. I think the interesting part is going to be one about cyborg and centaurs. And so cyborgs are this, the folks that are intimately figuring out what AI is good at and what they're good at, and they work this way. What I observe in R and D tasks and sales tasks and marketing tasks is that the, the cyborgs actually have a lot, lot to learn and a lot to figure out. And then the, the centaurs who divide the work, the worry you would have is that they

• 5:10Speaker 02

  stay static in terms of the capabilities of the AI and what they can do. And because the AI capabilities are, are improving exponentially, we have to be paying attention to the fact that are you updating the separation between yourselves and, and the machine as well?

• 5:24Speaker 01

  And it gets really complicated, right? Cause you have this study about jagged frontier, right? And I have the graph here where sometimes you think the frontier is fully capable of doing the task because it's inside, but then there is a similar task that seems as hard and then suddenly it's outside the frontier. Is there a way for a normal human to differentiate these tasks?

• 5:47Speaker 02

  So I think this is such a great question. And by the way, like this came from this, this deep study we did, did actually on BCG as well. And what was fun about it was that we had a bunch of hypotheses that AI is going to be good at everything. And then we launch it and we discover that in fact, no, like for, for the job of a consultant, there are some things it's good at and some things it's not good at. The challenge for us now is we have this intelligence available to us, but the intelligence is not perfect, but it talks really well.

• 6:15Speaker 02

  And it can be very persuasive. And so the question is, how do you apply your skeptical thinking and high standards on the things that the AI is actually doing and not fall asleep at the wheel, not fall into the trap of just believing what the AI says? This is going to be an evergoing edge that I think humans will have to keep developing because the AI will keep getting better and better and better, at least for the foreseeable future. And we have to keep saying, under what circumstances is it break? And the only way that actually happens, even in my own personal practice, is to be using the most frontier tasks and, and doing the most frontier models along the way. That's the only way you'll actually discover and not to sort of say, I expect magic and rainbows and unicorns the first day. Engage with it and be critical about it along the way.

• 7:00Speaker 01

  Yeah. The problem is once you come to a conclusion like, oh, this task is good, this one is not, the next day, the new model comes out and you have to reevaluate again.

• 7:07Speaker 02

  So that's why like this constant change and the constant upgrading is something that we all just have to get comfortable with if we're going to be living in this world with AIs. And it's so damn difficult for all of us.

• 7:16Speaker 01

  It is. You're like, we're so used to workflows and we thrive on routines, but it's every day.

• 7:22Speaker 02

  That's out the window now, unfortunately. And I think, but I think it's, it's actually, I think for us, as AI does more of the execution, it does raise the question like, what are we really adding value for now? And so for me, I think it's a, it's a great place for humanity to be at to sort of say, let's not be comfortable with where the, what equilibrium right now, could we actually go, go solve bigger problems, go after bigger things in, for humanity?

• 7:46Speaker 01

  That's a very interesting question because in your persuasion bombing study, when BCG consultants pushed back on GPT-4, it often argued even harder, even when it was wrong.

• 7:57Speaker 02

  Yes, yes.

• 7:57Speaker 01

  How do you detect something like that in your?

• 7:59Speaker 02

  Yes. There's now ways around this. And so I recall on LinkedIn, Kai-Fu Lee had posted this thing on his system prompts for Claude. It starts with the counter argument first. So you start with like, you say, I want to figure out X, Y, or Z. AI starts with the counter argument first as maybe your, your perception about this problem is wrong in the first place. So it makes you not jump to conclusions that the AI is offering, but actually sort of argues a counter argument first. And then for every claim it makes, it gives it a sense of like, how much of this is based on its own training, how much of it is its own confidence? And, and also like what, where are things where the, the arguments are weak?

• 8:40Speaker 02

  I think there's enough best practice out there on how to have the AI not as somebody that kisses your butt, excuse my language, but actually is something in which there is a, a, almost an adversarial relationship with you because it's so easy for us to fall into it. And you see, you, you train the model and you train the system's prompt to, for it to push back at you from day one. But then you also figure out how to push back at it. And it's like a back and forth with the AI, which actually becomes, becomes critical. And so if you were naive about it, it's easy to fall into the trap that you just believe what the AI says. And many people fall into that trap. And I think that's dangerous. We have to learn to be a bit adversarial.

• 9:22Speaker 02

  And the, and the reason why I feel like I, I can do that is in my job, my job is to train people. So MBA students, PhD students, research associates, postdocs. And my job is to always be like, well, what if we were wrong? What if this thing is wrong? How would we go back? And I always want to sort of disprove myself over and over again, till I get to some, some degree of veracity of what we're trying to go after. But now all of us in, in a marketing job, in an R and D job, in a finance job, now have an array of assistants available to us to work for us. And we have to learn to now mentor them and to, to guide them along the way as much as I do with humans as well.

• 10:00Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 10:30Marina Mogilko?

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• 10:59Speaker 01

  Can you give me some practical tips on how I can train those assistants? What are the prompts?

• 11:04Speaker 02

  I think the first is like, like, don't believe what I'm saying either, like pushback. Then it's like, if a proposal is given in front of you, counter argue always like, like, give me a proposal and come up with three different arguments that would make this obvious or make this non-relevant as another thing. The other one is like, always do additional research. Like you've got this thing, but go ahead and go additional research and, and find me red flags or find me the sort of the, the black swans that it may not be happening. And so it's these types of, and depending on the task at hand, you can, you like, if it's a research agent, you can actually push today's models to do, they're going to be thrifty in tokens and you want to actually like, I'm always like, let's just squander

• 11:48Speaker 02

  tokens now in this space till we figure these things out, to actually go and go way more deeper than, than what the model will do itself. And don't, I never take the first answer. I never take the first answer. And either I've got the system prompt for it to go back and come up with alternatives or I'll just push back myself.

• 12:04Speaker 01

  I was trying Google's Argon latest model. So my whole process was let's give AI data, let's tell it how to use that data, let's describe a workflow. What's happening with the new models? You just give them access to whatever data and they figure out which data to use. Does that change the process you just described?

• 12:23Speaker 02

  I think it does, but I think it's dangerous. So here's, here's my perspective, not dangerous. I mean, I think I'm overstating it. I think it's worth paying attention to what's happening inside of these models, right? They are regressing to the mean. In the end, it's a statistical model that is predicting the next word overall. You know, everybody knows that that's in the audience here. But philosophically, what's going on is that if you let it choose the approach and you're not paying attention to the approaches choosing, then the worry is that it's going to pick the average approach and not the one that's going to differentiate you or find you the answer. So many people have said like prompting is becoming easier, models can prompt themselves. I go, I get that, but foundationally what's happening is that it's still going to go to the mean prompt and the average prompt.

• 13:05Speaker 02

  And the average prompts aren't going to be good enough for your particular use case. And so this is where I think we have a very interesting learning and growing part for humans in place. So the models we show in our research, the models allow you to do more and expand your role. Great. Now the question is, as you expand your role, like we've heard, for example, that product development teams are becoming one-person teams, right? So the engineer can also do design, can also do user interviews and can also do QA testing and because the AI helps you on the way. Great. But do you actually understand the, the principles of design? And do you actually have a sense of taste? Because that's what designers do. They have a sense of taste.

• 13:49Speaker 02

  You know, my daughter did four years of industrial design. She has way better taste in products than I do, right? And the AI can provide you with some sense of design, but is that going to be enough to differentiate you or not? And so will you get the average design or will you get the one based on expertise? And there the measure of taste will matter a ton because you will say, okay, like give me a design for this webpage or give me a design for this product and you get the average response. And even saying, give me a Steve Jobs one isn't enough. Like there's a sense of taste. So the question I would always have is like, you're better off like learning from the AI, but what the elements of design need to be first. And then you make the decision on the genre you want to go down under or the approach

• 14:31Speaker 02

  of the design instead of again, pressing play and getting it to do everything along. So to your question, you give a data set, it can figure out five different ways to do regression analysis is, you know, five different models. But I've had, you know, 20 years of research experience, five years of PhD training. Oftentimes the models it picks aren't the right models to go after because again, they're the average answers, not the ones for the task at hand. And this is where you can use the AI to help you educate to learn, but also push it into three different targets of models and then be adversarial about it and say, prove myself wrong, prove itself wrong, find me pros and cons along the way to get to the baseline that you're getting to.

• 15:11Speaker 01

  The problem I see here, if I'm a one-person team, for example, and I want to use AI for a design, how do I measure AI's taste and how to understand whether it's a world-class work or just very, very mediocre?

• 15:22Speaker 02

  Exactly. And that's where I think the responsibility doesn't go away because in the end you're going to put your name. I have fired people who have told me, well, the AI gave me that. Again, are you a meat interface to AI that I don't need you, right? You have to still be engaged. And I think that's the, that's the really tricky part we're in the middle of and which is becoming, it's important for me as an educator, as a researcher, I've always fallen that trap, but also for all of us, because what we see is that it becomes so easy to cut and paste. It's so easy to do that versus if you're going to be a one-person product team, then you need to be in the sense of, okay, I now need to build some knowledge about design and where do I learn that from? How do I bring that up?

• 16:06Speaker 02

  And how do I set up my AI system so then again, it's not just give me the first design, but I can actually prompt it to go in different directions. So the burden of learning and taste is back to the human.

• 16:17Speaker 01

  Yeah.

• 16:17Speaker 02

  Otherwise we're going to be back in the phase of what you said, the machines will replace humans if humans stop accessing their judgment.

• 16:23Speaker 01

  What do you tell to your students when they ask you, how do I learn taste? If all of the basic tasks are already taken by AI?

• 16:30Speaker 02

  Yes.

• 16:31Speaker 01

  Where is this place for, for the junior in the career ladder? How do they develop this taste?

• 16:36Speaker 02

  Look, again, I feel like I'm in the middle of that tsunami right now at Harvard Business School with, you know, 25 year olds that come, 27 year olds that come to our campus. I think there are three different perspectives I'll add to this and I have more questions than I have answers, but I have heuristics on this. The first is you can imagine this career ladder story. I think the interesting question is, are the bottom rungs of the career ladder being pulled out from juniors or is the AI a springboard for you to go higher? So my good colleague Mitch Weiss has sort of said, he feels like this is a springboard and not just a removal.

• 17:10Speaker 01

  But if taste is required.

• 17:11Speaker 02

  Yes, so let's get there, right? Exactly, right? So, so then it's like, okay, it's a springboard. So then the question becomes, how do you as a junior person develop the taste to design the best investment banking analysis, design the best consulting study, design the best product, design the best customer experience and so forth, right? That's where the burden of learning becomes even heavier on you. That's where you have to sort of take responsibility for the learning, where you say, if now I can do so many different things, what are the foundational pieces that I need to understand so I can learn to push back on this? And this is where my second aha has been. So I just watched this great YouTube conversation with Stephen Wolfram, who you might want to actually invite on your show.

• 17:53Speaker 02

  And in this interview, he said something that was, that has just, I heard it like 10 days ago and it's just been like rattling in my brain. You know, he designed mathematical languages to do integrals. And so he goes that you don't need to do integrals anymore by hand, you can just use integrals. But he saw his kids in school learning integrals and he's like, why I designed this, you know, decades ago, why now are you learning integrals? And what his story was, which is actually super interesting was that, look, you need to learn to do integrals by hand initially, just so you understand how the things work. But the next 50 integrals or the next 100 integrals, you should let the machine do it. Because actually, if you observe a thousand integrals being done by the machine, you

• 18:33Speaker 02

  will learn a lot more about integrals than you doing the next 40 by hand, the next 60 by hand, the next 80 by hand. And so what is interesting there for me, I think is a thesis about how do we deal with this automation, which is a foundational piece you need to learn and do by yourself first. But then you can give to the machine to do the rest and then you are now an observer of the underlying patterns that are becoming true to make this happen. And so I think for me, as we're thinking about this, this role expansion story, it behooves us when we're going to be responsible for these things to understand the fundamentals enough to be able to then use the machines in the proper way.

• 19:14Speaker 02

  And so it, and again, I'm, it's in a bit of ways, I self-centered because I'm an educator. I say people need to keep learning. I think the learning side keeps increasing in importance because it's no longer what I just learned in school or what I learned in university. It's going to be this continual things as capabilities expand of these models and we're going to use them for our things. We still have to understand the foundations pieces to be able to push forward.

• 19:39Speaker 01

  Yeah. So basically I see two challenges here. First challenge, don't jump to the machine too fast because what we see with kids, they're just using chat GPT for homework and you probably see it in specialists, right?

• 19:50Speaker 02

  Studies, totally. Like that's the cutting and pasting, you don't learn anything at all.

• 19:54Speaker 01

  Yeah. And the second, not danger, but challenge, you have to learn so much more in four years of college because it's the basics plus the whole junior layer of the career ladder.

• 20:04Speaker 02

  Yeah. And I think, I think the main thing is. It's learning how to learn, I think, is the biggest thing that I tell my students. Like, that's going to be the thing that unlocks them. You're not afraid of the challenges. You're actually thrilled by the challenges, but you have a routine to learn how to learn. And, and actually with AI now, learning how to learn is actually quite easy. Like, there's explanatory powers and different ways of teaching you things, quizzing you in those ways. That's all unlocked and available to us if we know how to use those, those capabilities.

• 20:33Speaker 01

  If you compare this year's students versus students two years ago, are the students now learning much more than they used to?

• 20:40Speaker 02

  Yeah, so look, in my MBA class, we, you know, we have a required course in AI, so everybody has to do it. And we've had to pivot the course, it's three years old now, and every year we have to pivot the course.

• 20:51Speaker 01

  Or every week.

• 20:52Speaker 02

  Yeah, exactly. In the, in the middle of teaching, we have to change stuff.

• 20:54Speaker 01

  Yeah.

• 20:55Speaker 02

  So one of our big challenges was like, last year, the, the clause showed up all of a sudden, and we're like, if you don't talk about clause in our, in our, in our course, we're dead. Like, we won't have any legitimacy. So how do we pivot rapidly to teach about clause and so forth? The aha moment for us was to say, we can't just talk about AI in business school, we have to do AI. So we've done this radical shift in our class, where half the class now coming, we're going to teach again in the, in the spring, is going to be building with AI. And so they actually have to learn to solve business problems with AI and build advanced AI tools. So they're going to be building rags, they're going to be doing evals, they're going to be building multi-genetic systems.

• 21:35Speaker 02

  They're going to be building, you know, whole ranges of things to solve business problems and to understand the nature of the technology itself and how it's changing. We had to throw out a final exam because how can we legitimately have a course on AI? And not use an AI tool to do the final exam. So what we said is, you actually have to now build projects. So you have to vibe code projects. So we had all 900 of our MBA students vibe code solutions, and some of them were spectacular. Like, I was blown away for what the students were able to build. So we pushed a ton of building as part of the AI curriculum. More to basically sort of push them to say, these capabilities are such where the only way

• 22:16Speaker 02

  you will actually understand what the, what the AI's impact on your job and your company and your future life is going to be, if you are fully immersed in it and you, you learn how to get the most out of the tools. But at the same time, we push the foundations of like, what are the economics behind it? What's the strategy behind it? What does the change management part look like? And we, we couple these exercises with the cases and the discussions to really get them to sort of see, I now can do all these magical things, but then what's the implication for me in terms of a company or a strategy or a job that I sort of see? And so that's been, for me, super challenging because, you know, for, for 17 years, I was just happy to just put a case in front of the students and go. And now we're, we're doing a lot of doing in the classroom.

• 23:01Speaker 02

  But also, it's a blessing because it really pushes me to the bleeding edge to sort of see where the edges are for both myself and our students on what needs to happen.

• 23:10Speaker 01

  When you say this, this really makes me want to take the course. Is there a way for someone outside Harvard Business School to take it?

• 23:16Speaker 02

  We are still sort of figuring that out. We do have some online courses. We have AI for Leaders, we have AI Essentials for Business that are being developed. They're online, but not to the depth of what, what we do when you're on campus with us and we're kicking butt with our students.

• 23:31Speaker 01

  I think everybody needs something like that because everyone's going through this. Every week on the Spotcast, I talk to AI founders, entrepreneurs and experts who actually build AI and ask them their secrets, prompts and actual workflows for using AI in real life. I put them and my own use cases into my free newsletter. It's called Future Proof. Subscribe using the link in the description and get my anti-AI writing guide as a thank you.

• 23:56Speaker 02

  When we do executive activations, for example, with C suites, it's funny, like every C suite, you know, chief of staff or executive members, oh, we're already doing lots of AI, we already know this. And I'm like, that's total BS. Like, I don't believe any of it, right? No, no, we've got it covered. I'm like, okay, whatever. But then you have to do AI for business. And I want the entire C suite vibe coding and not just trivial applications, but applications for themselves in their business, right? Like, don't make me like a sports betting app, but tell me if you're running production and operations inside of a major global conglomerate, what are the challenges you face and how would vibe coding a solution matter? There's always resistance, like, oh.

• 24:34Speaker 01

  The IT department?

• 24:35Speaker 02

  The IT department, oh, don't even start with the IT department. You guys, oof. Once we have the, the CIO, they'll say, look, just bring this in and just try, let's, it's just a trial. And what happens is, in most large organizations that I've encountered, the C suite and the senior leaders have, have, have been cushioned from the realities of what the models can do and what kind of work can happen. And so they have this third-hand view about what the AIs can actually do. But, and when we put them in front of keyboard to create an application in 90 minutes, all of a sudden their brains go on fire because two things happen. First, they yell at the CIO saying like, I've been asking for this for bloody five years. All of a sudden I built it. And the CIO has all these explanations for it.

• 25:20Speaker 02

  But secondly, the aha is, oh, if I'm not doing AI, how can I guide my teams to do AI as well? If programming is the leading indicator of what's going to happen in the world of AI, every other occupation is six months to two years behind. So now I have a sense of what is happening in one occupation, but finance, marketing, supply chain, sales, they're all going to get transformed in this way. And we're at this precipice moment for us as leaders in these companies to actually do the AI, to be able to, to be able to put that through. So then we force the doing as part of the executive activations that we do. And every single time, there's so much resistance against it.

• 26:03Speaker 02

  And every single time they come back and say, thank God we did this because now I have a much better appreciation of what, what needs to happen along the way.

• 26:11Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 26:41Marina Mogilko?

  Low energy, unfocused, foggy? You might be dehydrated. Gatorade Zero powders have been scientifically designed to help improve hydration balance in the body. With a clinically proven electrolyte blend and no artificial colors or flavors. Great tasting hydration, no matter where you're going. Shop now online or at retailers nationwide. Gatorade hydrates better than water. Is it in you? Symptoms noted are signs of mild to moderate dehydration. Consult a healthcare professional if symptoms persist.

• 27:10Speaker 01

  From what you're seeing, how far behind are these companies in terms of AI? Because I'm in Silicon Valley bubble. Everyone is white coding, everyone's building.

• 27:18Speaker 02

  Yes.

• 27:18Speaker 01

  But I'm talking to my friends who are building AI companies and they say, we hold conferences on very basic things and people are so excited. How far behind are those executives in corporations?

• 27:28Speaker 02

  I think, I think, you know, there's a William Gibson saying the future is already here, it's just not evenly distributed. It's the case here for sure. I think Silicon Valley and other, other pockets in the world are living a very different future than most companies. To be quite honest, I would say there are two things to pay attention to. Most companies are probably at GPT 4.5.

• 27:48Speaker 01

  It's basically search, advanced search.

• 27:50Speaker 02

  Yeah. But secondly, and this is where it's becoming a problem, is we've been able to get, again, because we build these close research relationships with companies, get access to prompting level data and AI use data and like many large organizations, hundreds of thousands of people. And we observe usage, you know, day by day, week by week, usage and model evolution. And what we see actually, this is again, one of my other postdocs that has done this research, has shown like, there's a real K curve inside of organizations where basically there's a set of people, if you look at the 95 percentile and 90 percentile of users, usage versus the average,

• 28:35Speaker 02

  the difference in usage is between 10X to 20X. 50th percentile, so that's not even, forget about the rest of the, there is that big a gap. And the, the rich get richer. So as the models improve in intelligence, those that have figured it out just skyrocket. And those that don't actually are staying like, it's just imagine, there's an escalator that keeps going up and up. And then there's a airport travel leader that's just going straight. The 50th percentile is just staying straight and these guys are just like, like it's, we hope to release some studies on this soon enough to show this. And so that's a problem. In our data so far, we don't have correlations to performance. But we've seen other data, especially in software, where we see this transformation of software.

• 29:20Speaker 02

  And what I would say is that what we've seen in, again, data and software and data in, from other companies is that the productivity differences are 10X to 20X for those that are the top of the game.

• 29:35Speaker 02

  And I just don't think we can run companies where the 50th percentile of an occupation is 20x less productive

• 29:44Speaker 01

  Yeah.

• 29:44Speaker 02

  than the top people. It's just not sustainable. And I think the software world where Silicon Valley has seen this and is figuring this out. But this is going to hit every company along the way. And this is where, again, we find that the C suites are blind to these to these questions and aren't seeing it thoroughly and not moving ahead faster on the change side.

• 30:05Speaker 01

  What's the way to solve it? Does it have to come from C suite? Or is there something an individual can do?

• 30:10Speaker 02

  No, I think the individual responsibility is like, gosh, if you're in the travel litter, you better figure out what practices you need to be doing to get up on the

• 30:18Speaker 01

  As soon as possible.

• 30:19Speaker 02

  And it's not an escalator, as you probably learned, like, just because Agentic is available to you today. If you haven't been through the cycles of prompting and learning the models and and knowing where it breaks and the tastes you've built and the heuristics you've built on how to use the models. You have to go on this journey yourself. So accelerating that journey will matter a ton personally. But from the large company perspective, it actually has to be top down. They have to set targets around this, not performative targets of AI. I often say that what you have to do is you to reimagine your business, value creation and value capture for your business and your operating model. But the the main thing is to targets that are actually about business results.

• 31:04Speaker 02

  How how more effective you want to be in terms of serving your customers? How more effective you want to be in terms of your R and D productivity? How more in terms of new product launches? How more effective you want to be in your operation side, for example? And set almost unreasonable targets in short amounts of time and make those targets business targets and customer targets. And then say, how does AI help us get there? Because then that enables the company to actually be able to to then align around of all the million things I could be doing with AI, or should I be focusing my attention?

• 31:37Speaker 01

  This is such a great question when it comes to targets and KPIs and AI impact on those. Because sometimes the target that you said doesn't really reflect the AI's input alone. Maybe there are other factors happening.

• 31:52Speaker 02

  Yes.

• 31:53Speaker 01

  Do you have any frameworks around that, even like for a small organization?

• 31:57Speaker 02

  So, look, I think I almost feel like we need to take a step back and imagine this world of exponential improvement in model capability. The trick for us is to say, how do I keep up with the new capabilities being unlocked on almost a daily basis now or weekly basis now to my essential business? Right? And so in a business perspective, even in small business, one of the things I've learned in studying business and teaching about business and doing research is that targets are sort of acts of imagination. And we can have little imagination or we can have a lot of imagination. One of the enduring facts is that when leaders set outlandish targets, which seem ridiculous,

• 32:40Speaker 02

  which seem kind of scary, which seem unachievable, sometimes, most of the times, they actually come true. Because if you just set an average target for yourself, guess what? You'll probably be below average by the time you achieve it. And there'd be many excuses to get there. But if you set like a target of like 10x and you achieve 7x instead of 10x, but you achieve 7x versus achieving only 2x, that changes the world. So I think obligation for leaders to use their imaginations and push the organizations. People are looking for leaders to set the to goalposts and then create the atmosphere to get there. And I really feel today with what AIs can do, the point is the models keep getting better and better and better.

• 33:24Speaker 02

  You can use these targets. Again, these are not AI targets. These are business results targets, revenue increase and so forth. And then work your way backwards. If this was going to be true, what else needs to be true? What else do we need to be true? And what else needs to be true?

• 33:37Speaker 01

  Before we keep going, I'd love to get to know you a little better. I share a lot about my work here and I'm curious about yours. What do you do and what you'd like more help with? We've put together a little survey. Your answers will help me decide what to cover in future conversations and what resources to put together for you. I'll leave the link in the description. Please, please, please take a couple of minutes and tell me about you, about your business, about your ambition, so I can make better content for you. I really, really appreciate you. Thank you so much for doing this. Yeah, it's the ambition question, right? And when you, this is interesting. I'm just remembering another study you did for H and M.

• 34:16Speaker 02

  Yes.

• 34:17Speaker 01

  And when AI created the solution, it was really hard for people to sell that, which kind of means that you can't use AI to work on your ambition, right? Because if AI sets a strategy for you, you won't be able to sell it to your

• 34:30Speaker 02

  100%, 100%. And so this is where, again, you're, you have to push first and the AI can help you then bolster it. It can't be the other way around.

• 34:39Speaker 01

  Okay, how do you teach your students to become more ambitious? That's the question.

• 34:44Speaker 02

  Look, there's a normal distribution of ambition as well in the world. And I think what we want is that for that normal distribution to move more to the right so people can be more ambitious. It gets deeply personal, you know, because it's like people have fear of failure. People have all these worries about what other people think and so forth. And of course, we live in a social setting. We worry about what other people think. But ambitious people care about something in the world that they want to change and are committed to it. And then will find ways to get others behind them and set big targets. And often worry I have often at our business school with our MBA students is that they're they've come from a setting where they've always been successful. To get into HBS, you had to have an impeccable CV, impeccable grades and so forth.

• 35:30Speaker 02

  And and so if you haven't encountered enough failures, then you'll be like, well, if I do this, if this is like, you're asking me to set a 10x goal, the mean outcome might be failure.

• 35:39Speaker 01

  Yeah.

• 35:39Speaker 02

  I don't want to do it. So I'll set a 2x goal instead or a half x goal instead, and that might be good enough. And so we always have to sort of bolster our students to say, look, like the, you will always be fine. You've been to the top schools, you're a Harvard graduate, you'll be fine. Have faith in yourself, but set some some incredible goals for yourselves and for the world. And you have the privilege to be at one of the best schools in the world. So let's use that for for for real good stuff instead of the average outcomes along the way. And so that's a constant for us as our faculty to keep pushing our students in that direction.

• 36:14Speaker 01

  Yeah, and I think thinking about AI as a team of experts that you have to help you achieve your goals is really empowering. But something you said made me a little bit scared. The marginal cost of expertise is going to zero. Can you talk about what that means for knowledge workers?

• 36:31Speaker 02

  Yes, 100%. So, look, this is I'm obsessed with. And in many ways, that's the thing that we're trying to figure out, both at our institute. And I have my own lab, the Laboratory for Innovation Science. At core, I'm still a nerdy researcher. I write obscure papers read by obscure people, you know.

• 36:47Speaker 01

  Your papers are amazing.

• 36:48Speaker 02

  Thanks. And I really want to get to like, show me data, let me run the experiment. Let me get causal about it instead of just like waving my hands and saying the world is ending or the world is all utopia. And by the way, the way I got into this, and I'll connect it back to what you said, is that I became an academic to actually understand open source software, like in the late 90s. Open source software was just taking off and it made no economic sense. Why would developers who are highly paid, who are rare in the world, highly needed, spend all their time writing free code? Like, it made no sense. And what we figured out was that, in fact, what was happening with open source was there's both an incentives and motivation story, and there was a clear information economics story.

• 37:30Speaker 02

  What happened? Well, on the incentives and motivation story, people aren't just motivated by money. They're motivated by the fun of problem solving. They're motivated by the the the social signals they might accumulate and so on and so forth. And those are not correlated with skill. So we could show that there is there's raw skill, which is not correlated with different differentiating motivations. And that explained why open source was working so well, because people had different motivations to do things. And the second thing that we had, which was this this this phrase that Eric Raymond had said about open source, that given enough eyeballs, all bugs are shallow. Meaning that if enough people are attacking the problem, right, you will find a solution. And that's what open source was in many ways. And we showed that clearly.

• 38:14Speaker 02

  Then when this AI shows up as, in many ways, an absorption of human expertise. We basically have this open source model at scale because what you observe is that if you the reason why open source works to develop, if you imagine a normal distribution of skills, right, or normal distribution of outcomes, what open source relies on is that you have multiple shots on goal, many different people are trying many different things, and you can take the best performing solution and make it available. So, open source, innovation contests, crowdsourcing works on the basis of the that the normal distribution is available to you. Most companies just get the average. In open source, you get the normal distribution and you can get the right tail.

• 38:53Speaker 01

  Yeah.

• 38:53Speaker 02

  That's the right tail story. Let's bring this now into the world of AI. What AI is doing is basically shifting the the the human plus AI expertise curve to the right. Just again, imagine a normal distribution and it's basically shifting it to the right. Wow, that's incredible because now all of us have these superpowers available. So our belief has been, and we've sort of seen this in our empirical studies at PNG, at H and M, at BCG, has been this view that you are just basically drawing from a distribution of expertise that the AI provides. This absorption of expertise is such that we all now need to understand, what have I developed myself individually? What does the AI have?

• 39:34Speaker 02

  And what's the differentiation of me versus AI? It can't not just be you without AI. It has to be always you with AI. And how are you better than just what the AI does by? I think that's going to be the edge that I think we all need to figure out. In CAD CAM design, I learned drafting by hand, and you could only do so much drafting by hand until CAD CAM came about, and you could do way more complex things. And I think that's the way we need to think about AI. You can do way more complex things. We can push our expertise way further than before. And that, but if you don't, then the machine will replace you.

• 40:10Speaker 01

  Which kind of makes me think that you either do something where you think you're going to be a top expert, or you don't do it at all. Is that correct?

• 40:18Speaker 02

  Expert with AI.

• 40:20Speaker 01

  Expert with AI. But yeah, if you're not top of the field, you can't compete with AI, meaning you don't go there at all. Like, how do you make decisions about your future?

• 40:27Speaker 02

  Yeah, no, good, good question.

• 40:29Speaker 01

  How do you, how do you know if you're going to be great at something? That's, that's the question.

• 40:33Speaker 02

  Do the work. It is really grinding it out. And like, I just feel like oftentimes grind always upperforms talent.

• 40:40Speaker 01

  Oh, I love that.

• 40:41Speaker 02

  Grind always up. And now with AI, you can grind more and you can learn more. And I think I just feel like I think people settle too early. Even our best students settle too early, and we have to keep reminding them. I've been vacillating about this, you know, I, you know, I finished my undergrad in the last century, 93, and the internet was showing up. And then we've been through the waves of the internet, mobile, machine learning and so forth. And the question I keep asking myself, is what we're seeing a difference of degree or a difference of kind? Right? Is this just the next wave of internet, blah, blah, blah? Or is this an compute? Or is it a difference of kind? I feel like I keep landing on this is a difference of kind. If it's a difference of

• 41:23Speaker 02

  kind, then all of us have to now keep thinking a lot about what is my expertise? How am I developing this expertise? And how does my expertise change with AI? And that's an experiential good. It's not something that I can just teach you. You have to keep working at these tools and keep figuring out how to do that. I see this, like, my wife's an artist, my daughter is a designer, and I see them struggling with this themselves in their work to say, how does an artist now use AI to help them go further than before? We've seen periods where technology has revolutionized the arts. You think about hip hop, right? That's all about technology, like two turntables and the mixer,

• 42:07Speaker 02

  new forms of expression. And if you just think about how much hip hop has taken over the world from, you know, French hip hop to Farsi hip hop to Chinese hip hop, you know, to Japanese hip hop, to sort of the hip hop here, that's because of technology, right? And so technology changed the art form. And I think this is across the board in all professions and all occupations. And so you can't be a hip hop artist without you mastering the two turntables, or having people around you that know how to master these turntables, and you know how to, how best to use them. The turntables have become digital now, of course, right? And so forth. But I think that's the precipice we're at, and it's a difference of kind than business as usual that we've had, we've seen.

• 42:50Speaker 00

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 43:20Marina Mogilko?

  Low energy, unfocused, foggy? You might be dehydrated. Gatorade Zero powders have been scientifically designed to help improve hydration balance in the body with a clinically proven electrolyte blend and no artificial colors or flavors. Great tasting hydration, no matter where you're going. Shop now online or at retailers nationwide. Gatorade hydrates better than water. Is it in you? Symptoms noted are signs of mild to moderate dehydration. Consult a healthcare professional if symptoms persist.

• 43:49Speaker 01

  Because you drew a parallel with creators. As a creator, the next question for me is, how much can people consume?

• 43:55Speaker 02

  Yes, yes.

• 43:56Speaker 01

  If we're developing new forms of art when it comes to business, if organizations become much more efficient and they're producing so much output, do you think we have capacity to consume all of that?

• 44:06Speaker 02

  This is such a cool question you're asking me because, and again, I don't have an answer. I just have questions. I'm like, I'm like trying to figure out like what the right study is on this. Because I'm always like, in the end, like, I'll make up some crap. I need, sorry, it's my take. I just always end up there. Again, let's go back to software. Again, we see this bleeding edge. The, if all of a sudden you have a doubling of the number of PRs you can push in your production in your organization, soon human review is going to be impossible.

• 44:35Speaker 01

  I was already being replaced by another model stepping in and doing the review, right?

• 44:40Speaker 02

  So now the question becomes, it goes back to what you asked, which is like, well, what does a human now do? What does a human now do? Because before, and I saw this in my open source studies, one of the reasons why people love to do open source is because they love coding. They love the grind of coding. I was a terrible coder, so I could never get into it, never understood it. But other people just do it. Now, when you take that away, where does meaning come from in your work? Now you're in this world of like, is it, is it all going to be about managing agents? Well, that may or may not be meaningful. And I think we're in the middle of all of us in all these occupations trying to figure out something new. We don't have the answers yet. And that's why it feels so uncertain

• 45:23Speaker 02

  and so like, we all feel so unmoored along the way. But to your question about how do I consume more? Well, there are two answers. One is that the models will be consuming our outputs. So part of what you will be thinking about is like, how many MD files are you also pushing out as much as you're pushing out all the content that you're doing? And, and if the models become, if I have my personal agent that is, is not going to watch your videos, and read your MD file, then say, go pay attention to these five segments in your show, right? And so that's the, I think that's part of the transition.

• 45:54Speaker 01

  I do care about human views though, right?

• 45:56Speaker 02

  Oh, of course you do. But I think, but I think this now going to be mediated through these tools. And so the question becomes, how do you do both? And how do you make them both available? Because you will care about human views, but if I will not be relying on my personal agent to tell me what to watch and where to watch.

• 46:10Speaker 01

  Yeah.

• 46:10Speaker 02

  And how do I discover you? It becomes, it becomes important.

• 46:13Speaker 01

  Yeah, such an interesting future. If you had to draw a picture of 2030, right?

• 46:18Speaker 02

  Oh, my goodness.

• 46:19Speaker 01

  Just, just like trying to understand with, with everything that we said. I didn't mention, you had another fascinating study where people were working with AI and the output of a person with AI equaled a two-person team output, right?

• 46:31Speaker 02

  Yeah. This is our part in gamble study.

• 46:33Speaker 01

  When this happens, when junior work is getting automated in 2030, what does an organization pay for?

• 46:41Speaker 02

  So let me step back first. I don't think junior work is being automated. So I don't, I don't think that question is settled, to be quite honest.

• 46:48Speaker 01

  Okay.

• 46:49Speaker 02

  If anything, people that may be at risk are more in the middle management layers than in the junior layers, from my perspective. Here's why. The junior people are fearless to absorbing AI and doing stuff. You've probably seen, I've seen high schoolers and undergrads. I don't see undergrads routinely as part of my graduate school work at HBS. But when I encounter them, I'm like, wow, you guys are a different set of human beings that are able to whip up all these things so quickly. So I would contend, and again, it's easy for us to say soft, there's multi-causal, the causal density is high in terms of why juniors, why the, why the early career market is soft right now. It may not just be AI. And my belief is that in fact, the middle layer is more at risk than the junior layer. Why? Because I observe inside of companies,

• 47:34Speaker 02

  AI usage, and the middle layer is the one that's resisting the most, doesn't want to change. So it's the 35 year olds to the 55 year olds that are pushing back against AI usage and not changing versus the more enlightened 55 year olds, and again, the K curve people with the junior people could run circles around most of the organization. So I think that's the first thing to say, like, I don't think it's settled at all. Other colleagues of mine, esteemed colleagues are worried about the junior side. I'm like, if you hit real organizations and see real workers with real data, you would be skeptical about if this is the case.

• 48:09Speaker 01

  But it's, it depends on the junior.

• 48:11Speaker 02

  Well, exactly right. So if, again, this is the pushback to the junior people. If all you are is a copy paste meat interface to the model.

• 48:18Speaker 01

  That job is gone, right?

• 48:19Speaker 02

  That job is gone. That job is completely gone. And by the way, that's the same question I ask law firms and every other CEO. I go, if all you are going to be is a thin wrapper around Anthropic at a law firm, or you're going to be a thin wrapper around OpenAI's model for a manufacturing firm, why should you exist? And that is very challenging for them to actually hear about. And so the question I ask is, how do we redefine jobs? And what does the nature of work now look like? Even in your setting, you could potentially be doing 10x more if you deployed AI even more at scale than what you're doing right now. And who are the right people to bring you along in that journey? Because you yourself can't do it all yourselves. And I think that's the question to be asking. So to your 2030

• 49:03Speaker 02

  question, I still feel that there's going to be a tale of two cities. I think there's going to be a set of firms that get it, that understand it, and there'll be both startups and large enterprises that do the heavy lifting to redefine the work. One of my colleagues, Rem Coning, has built up this frontier firm radar where he's looking at startups and their AI usage. And he's actually running experiments in actually showing them how to use AI better. And when he does that, they find faster ARR, faster launches of products and so forth. So even with startups, there is a learning curve of how to use AI better. And so I believe that there'll be a set of firms, a set of founders, a set

• 49:45Speaker 02

  of senior managers and people inside the firms that get it and start to accelerate. And then I think there'll be everybody else, and they will be plodding along. And the only question in my mind by 2030 is, if you again, go back to the late 90s and Amazon being established, and retailers sort of laughing at Amazon saying, oh, how cute. Right? But Bezos rethought retail commerce inside out versus Kmart and Sears just added a website to their database and weren't able to adjust. And Walmart struggled for a long time, then figured it out and is now as competitive as before. So the question I have for incumbent

• 50:27Speaker 02

  firms is, are you going to be Walmart or are you going to be Kmart? Now that the cost of intelligence, the cost of expertise has gone down to zero. Companies are nothing but bundles of expertise. And if that cost has gone down to zero, what do you do now? And how do you set yourself up now for your business? And for incumbents, it's really, you know, you have two, two futures ahead, a Kmart or a Walmart view versus the Bezos. And the question is, who's, who are going to be the Bezos in all these industries? Because I think all of them are up for grabs in professional services, in manufacturing, in consumer. The value destruction that people are anticipating quickly, I think will take longer than, than, just because again, I sort of see inertia in, in so many organizations

• 51:12Speaker 02

  and inertia in customer behavior, in buying behavior and so forth.

• 51:16Speaker 01

  We're still humans, we're not machines.

• 51:18Speaker 02

  Exactly.

• 51:19Speaker 01

  Okay. Well, I guess the last philosophical question is, how do you determine that you're not a wrapper?

• 51:26Speaker 02

  A wrapper around.

• 51:26Speaker 01

  A wrapper around a frontier model with them getting so advanced.

• 51:31Speaker 02

  Yeah, I think it's such a good question. I don't know the answer to that yet.

• 51:36Speaker 01

  You know what Craig Brockman told me, president of OpenAI, he told me, like, ask yourself whether your business is improving with a new frontier model or is it disappearing? This is how you figure out your mode and how it's affected by the new.

• 51:48Speaker 02

  Yeah. And I think, I think you can't outsource that thinking to somebody else outside of your organization or somebody junior in the organization. You actually have to do that thinking yourself. And it goes back to, look, you have to force yourself to do AI in your work. If you believe my thesis that these models are about expertise, they absorb expertise, and the expertise is, is improving exponentially in these models, and you became a leader in an organization because you have expertise. You have to play with them right now. And I'm reminded of the fact that when email came out in the last century, Stefan Bensalder-Moderan talks about this, I also experienced this, that

• 52:31Speaker 02

  our bosses' bosses would not do email. They would call in their assistant, they would read the emails to the boss, the boss would then provide the answers back, they would dictaphone this in, then they'd go back to the computer and put that in. And some of those bosses never did email or did the email 10 years later. Same things happened with AI, with leaders. Somebody else's job, not my job. I'm, I'm too busy thinking big thoughts. And let me just also add one more thing. In talking to CEOs that get it, they're spending between 20, these are of large multinational firms, those CEOs that get it, they're spending 20 to 30% of their time discussing AI issues. At one global firm, I heard this number and

• 53:14Speaker 02

  I'm like, are you sure this is right? They said 50% of senior meetings are talking about the impact of AI on their business and so forth. Just think how profound that has become.

• 53:23Speaker 01

  Yeah.

• 53:24Speaker 02

  And the doing part and the imagination part go hand in hand. And I think Brockman's view is correct. Like, like you have to be constantly thinking, it's not just me and my competitor, it's me with AI and the AI itself.

• 53:39Speaker 01

  100%. Karim, thank you so much.

• 53:42Speaker 02

  What a pleasure.

• 53:43Speaker 01

  Food for thought, right? We all need to be thinking about whether we're wrappers or companies are wrappers. And I think, yeah, it's a question that you keep thinking about every day.

• 53:51Speaker 02

  I do, I do.

• 53:52Speaker 01

  Thank you so much.

• 53:53Speaker 02

  Thanks a lot.

• 53:54Speaker 01

  It was amazing. Thank you.

• 53:55Speaker 04

  If you want to understand what AI is actually doing to jobs, watch my conversation with Stanford economist Eric Brynjolfsson. We get into the research on entry level work and why some jobs grow even as AI gets better. Click here to watch.

• 54:09Speaker 05

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