Wharton Entrepreneurship Professor: What I Push My Students to Build Now That They Have AI

We can easily give up skills to the AI, and we may never get those skills back.

You don't want the AI making those choices, even if they're good ones. You want to be inserted into that process. I'm deciding when to insert my own agency into the system rather than just sending off agents that do work. The adaptation piece has to happen from scratch every two weeks. That's what I'm feeling.

I wouldn't say never delegate, but...

That is always a hard question, right? Ambition looks like — we call this entrepreneurial efficacy in the entrepreneurship world, right? Same kind of thing, agency. And it's actually quite tough to sort of do. We don't have like a playbook for it. But I actually find that people create barriers that you can kind of help them overcome by getting them to — by just asking them to be more ambitious and being in a room where peers are more ambitious. So a lot of what I just asked — it's really easy now — is like, what could you do more than that? And that was a hard question before AI because you stopped because you had the ability to only get so much done. And now as the frontier moves out, pushing for ambitions is a much bigger deal.

So it's funny because like the last version I taught was six months ago, and so already that was like the beginning of agentic work. My next semester starts in January. So I actually — when after ChatGPT came out, I had been following, you know, obviously models for a while, but after DaVinci, which came out right a couple days before ChatGPT, and then a couple days later I had a class and I basically just dropped the entire class and said I'm going to show you how to use ChatGPT. And by the end of the class, one of my students had actually stopped paying attention and had coded up his entire project for the semester during the class period and showed me the results at the end. And so every year since then, the ambition level has gone up further and further. What was most interesting this time was all of these people who are subject matter experts. I've got surgeons and I have biomedical device people and I've got finance people and I have, you know, NGO and humanitarian people, soldiers all in this class, and suddenly they were all building real stuff, right? Like, you know, and so there were really interesting ideas: how do we track our entire supply chain, because a lot of companies don't actually know their entire supply chain, and how we figure out where's the financial weakness in that chain that we might have to watch out for. Really interesting set of ideas. There was a bunch of really interesting stuff around like specific problems in medicine about how do you actually give information to patients in ways they understand as opposed to just forms. So I mean, just really interesting, but very narrow, right? Focused on something people know. Part of what is interesting is like software can be many things, and if you're trying to think about solving a problem that you have deep experience in, suddenly there's a lot of capabilities there that are really exciting.

There — among those of us who teach entrepreneurship at some of the leading business schools, we, you know, we used to exchange notes about the topics we see people address over and over again that we know will never work out, but like we're always conflicted: should we tell them it's not going to work out or let them try and figure it out for themselves? So there's a few that are sort of standard problems that are actually kind of a good example of where technology is limited. One of the most common is we are going to replace brokers for houses, right? It seems like a really big — like you're paying a couple percent to these people who like are doing increasingly less work for you. This is obviously a case of disintermediation, and every time the idea fails because it turns out there's a deep social integration, a political integration, and legal integration of brokers into the process that you just can't dislodge. So sometimes the reality of the world is quite different than what people think. And then the other is students thinking too narrowly about their student problems. So everyone always wants, you know, better ways to like get homemade food to them, or better ways to, you know, hold stuff between semesters that probably aren't as universal once you're out of college.

Well, I don't know, right? That's why we don't tell people, right? Like these are the highest failure rate ideas, but obviously because they want to do it, right? Because they keep coming up. They're not bad ideas. The whole idea is you can't filter them out initially. Like there should be a better way to buy and sell a house, right? And you know, people do want home-cooked meals more easily than a meal delivery service, which is sort of what they're asking for now. It's like, how do I have my mother cook a meal for me, or father, is essentially what they're asking. And so there should be a way to crack this. There's demand for it, but they're harder problems than people think. And in some ways, AI makes it more ambitious, right? Because it's no longer the technical piece, but the numbers look really good, and then they just hit a point where it's like, oh, there is a challenge here. So whoever cracks it makes a lot of money, but it is also an idea that everyone — we've seen people throw themselves against over and over again. So maybe some people who are watching or listening will be the ones to break the real estate broker experience, but it's not as easy as it appears, I guess.

In general, like, I mean, I think if you're trying to build a business from scratch, right, aim big because you may not get there anyway, right? And also ambition helps motivate people. It gets you the best people to work for you. It creates a real self-fulfilling prophecy, right? But I also think that there are phases to a business, right? So that initial phase might be difficult no matter what. Scaling presents its own challenges versus running a small business, right? So they're not quite equivalent, right? Like trying to get to the next level. If you're going to raise a Series B and a Series C, that's very different than running, you know, a local business of some sort, right? So I think different skill set.

Yeah. Yeah. I mean, if you're starting from a blank slate, right, and you want to take over the world, go fully ambitious. And but I also, by the way, I think that, you know, we keep talking about ideas. I think people aren't being ambitious enough about the kinds of ideas they pursue, partially because they're sort of in a Silicon Valley mindset about software, right? And I think a lot about — I study the history of technology among other things, and I've done work on it. And if you look at who got like really rich during the Industrial Revolution, right, it wasn't people selling steam engines, even though this keeps being the analogy in Silicon Valley is like, I want to sell picks and shovels. You don't want to sell picks and shovels. You want to dig a gold mine with new gold mine technology. And I feel like we're kind of missing that point here. If you can make a business more efficient, just do it yourself right now, right? Overcome those barriers directly.

So I think, you know, it's good news for entrepreneurship overall. Like the early data looks really good, and you can understand why it's a really good idea. I think everyone's a little too, like, SaaS-brained at this point, right? Which is like, what do you do to make money? Software as a service. And I think that that is under threat, right? Like it's a different sort of environment than it was before. I spend a lot of time with big companies now as well as entrepreneurs, and there are people getting it. There's a lot of conflict over what the future of software looks like. On the other hand, I think software is increasingly becoming something that's table stakes for doing a lot of other interesting work. So I kind of would love to see more entrepreneurs branch out from software being the thing they sell to actually making or doing something, because there's never been a better time to do that.

So it's kind of interesting, right? You have a bunch of stuff happening at once. On one hand, everybody's a coder now. I mean, and that's not just a theoretical thing, right? So there's this great set of studies out of Anthropic suggesting that actually managers now write better code than coders, at least in first passes, right? Because it's about giving clear instructions more than being ready to code. And things are transforming quite quickly, right? You've got a hundred times more productive people. So companies are also completely in a mess as a result, but they all have their targets on, you know, SaaS vendors, right? There's not a large company out there that doesn't want to think about how do we become more independent, how we build our own tools. And so it'll be a while before they get that all right. And a lot of them are still very happy. I mean, you know, as far as I know, you know, both OpenAI and Anthropic still use Slack, you know, from Salesforce, they still probably use Workday. So like there'll be more choices being made about what kind of software vendor to go with. But I think the more interesting thing is that companies are realizing that a lot of the startups that were selling them stuff are just selling them things they could build already, right? They don't have their own LLMs, so they're selling LLM capabilities to them that they already have. And that's creating a really interesting dynamic.

I think that software will be democratized, right? So like the whole thing — I teach at Wharton, I teach MBA students, right, and I teach undergrads, and a lot of the MBA students aren't technical. So like the key to success for, you know, the last 10 years has been find a technical person who can code for you. I think it's incredibly freeing to view this like other kinds of fields where you can approach it in different kinds of ways, right? There'll be software-heavy companies where you really need top engineers working with AI to succeed. And then there's going to be like software as a toolkit that we get things done. And I think democratizing that's incredibly helpful, right? I mean, you don't want to be a software company. You want to make your newsletter. You want to make your videos. And you know, there were things standing in your way. And software, by the way, was one of them, right? It was also you needed to be able to do your own finance. I mean, when I did my first startup company, I did not realize — it was late '90s, first internet bubble — but I did not realize that you could pay somebody a couple cents a pay period to handle taxes for your employees. So I would calculate taxes in Excel every week for the first few weeks, not realizing you could just do that. So even just advice, all of the kind of pieces you get make this much easier now than it ever was.

Yeah. So it was an interesting study. It was GPT-4, right? Long obsolete system. There's now 180 models better than GPT-4, 19 of which can run on your phone. So, you know, big difference. And it was only giving advice. So the fact that I think the number was 18% profit increase for the best entrepreneurs — like that's a crazy number. Like we do studies all the time. We try and move the needle in entrepreneurship. That is a crazy number. But it only helped entrepreneurs who were already being successful because it helped them expand in the sort of way that you're talking about. Entrepreneurs who were already failing couldn't implement the advice. They were already going down the hole, and there was no way to kind of pull back. So we know this stuff can be effective. That was before coding, right? That was literally what should I do now? And entrepreneurship — like it turns out, by the way, about 50% of Americans have had a good idea, or they think they have had a good idea for a business in the last five years, and almost nobody pursues it because of all these barriers. So even just the advice to overcome those barriers is a real golden age for entrepreneurship compared to what we've had before.

I talk about this in the new book, but I think there are sort of four things you need to use AI successfully, and that's deep knowledge, right? Some expertise in something. And that's, by the way, like entrepreneurship is all about getting disciplined expertise, right? You go to industry or you interview people, you know, you learn about the industry, you test ideas out. I mean, that is building expertise, and I think that's every bit as important as it was, right? Otherwise, you're just sharing Claude ideas, and Claude ideas all look very similar to each other, right? Second is wide knowledge. Increasingly important for entrepreneurship is knowing a little bit about the fields you're not in, right? So if you're in software, you should know a little bit about design and a little bit about accounting because that helps you apply the AI more widely. And then agency, which we've been discussing a little bit here. And then finally, some sense of taste, like selecting good ideas out of the slop pile, is also really helpful. So I think that what we're seeing with the more agentic systems is they can run longer periods of time, right? GPT-4 could do four minutes of work independently with 50% success rate. Now we're up well over 17 hours of work. But you still need to be able to have some guidance in these systems. And even as they get really good, it turns out the human touch actually does matter because variation is what makes entrepreneurs successful.

Yeah. I mean, it's kind of funny because you realize that people are more important than we thought, or that some people thought, right? So everybody who was early on to using Claude as a good writer back, you know, when, you know, Opus 4.5 or whatever, they wrote really well, and for a while nobody knew what Claude writing sounded like. And now if you open like LinkedIn or X, it's just like nauseating, right? It's like, what's carrying the weight? What is it? Right? It's just all the same stuff. And even not just the little phrases, like the way it approaches things, the kind of language it uses. And you're like, well, if you didn't hire a marketer, you're probably making a mistake. Now the marketer can do a lot more work than they could do before, but their human variation actually matters quite a bit. And I think we're seeing that in a lot of other cases, which is the AI pushes people towards a sort of consensus strategy or view. We have some experiments where we found that we can make the ideas almost as diverse as a group of people, but if you map the ideas that the AIs come up with and map the ideas that a group of humans come up with, the AI still doesn't cover the full range of human ideas, even if you're pushing it really hard. So this variation piece is going to become increasingly important. Like, what are you adding to the system to guide or steer it so you're not just Claude-ing about everything? And that would be okay. Like, it's okay if you're using Claude for finance, and that's not your competitive advantage. But in the area where you are narrowly aiming, you need to stand out, you need a differentiator, you need a moat that has to be human to some extent, even as the models get incredibly capable.

We know a little about teaching people taste, right? Like you need to see good and bad examples of things. So like you want people to explain to you how to pull something apart. So I bring in an art professor actually in my classes now who talks about sort of thinking about like how does a designer see the world, right? So you get this kind of perspective like what makes a font good or bad? What makes an image interesting or not? And so I think we need language to discuss things for taste, right? If you talk to somebody who really understands movies, they have a taste for movies that's individual, but they can probably talk widely about that. In the same way like venture capital — like venture capitalists are all about taste, right? They develop a sense of taste for a particular kind of problem space or not. So I think that seeing a lot of variation is really important. Learning to be disciplined about this is another example of why I think education has some value here. Being exposed to lots of options — I mean, those help give you a sense of taste that you need.

I don't like the idea of judgment per se because AI is obviously doing judgment. You can't do a 17-hour task without the AI making judgment calls. Just like we don't have creativity in the list because the AI is obviously making creative decisions. The question is whether they match the kind of judgment and creative decisions you would make, right? So we can call that under taste or knowledge, but it's a kind of a different question, right? So judgment is this hard thing. We don't really know how to teach, and we don't really have a good example of it. But some people, you know, in what context is that judgment happening becomes a question. So it's a hard thing to ask, right? Like, what is judgment? And so the AI is exercising some kind — there's obviously a human judgment element that's different, but we haven't had to build the language around differentiating these things. I kind of think about what's happening in math right now, right, which is this sort of explosion of proofs generated by AI. And one of the things mathematicians will tell you when you talk to them is a bunch of revelations about their field, or maybe clarifications about their field, that outsiders didn't know, right? There's an aesthetic element to math that actually matters, right, to a mathematician, that if the proof doesn't help teach you something beautiful or interesting, there's no value in it for the field of math. Even if we've solved this problem, who cares? And so we're starting to differentiate things that we didn't have to differentiate before because the only kind of judgment intelligence we have is human. So we didn't have to come up with a different category.

There's a couple of things happening at one time, sort of when you think about markets and education, right? So first of all, we are temporarily in a crisis in universities. I mean, we're always in a crisis in universities, so this is not new. Anyone who's been in education knows this, but we are in a temporary AI crisis, right? And we already kind of know the outlines of that solution, which is AI is an incredibly good tutor when you use it as a tutor, and it's very bad if you're just cheating and getting answers from it. Even if you think you're learning, you're not, right? So eventually we'll have AI tutors outside of class. Inside of class, we'll do more active learning. I mean, if you've been to business school, law school, you've already done these kind of case studies and discussions and activities, and, you know, we'll do in-class tests. We'll figure it out. The real problem is where people actually have developed taste and judgment is in the world, right? And we had this great deal for the last few thousand years, which is I would send to you, you know, a newly trained recruit who didn't know anything. And we had apprenticeship, right? That's how the last 4,000 years of white-collar training have operated. So you'd get a really junior person who was desperate to show you they were good. They'd work for cheap because they wanted to move up the rungs, and they would do the work that a middle manager didn't want to do. And in return, they would get a little bit of pay, they'd get to show off their stuff, and they'd learn the ropes, and the middle manager would get their work passed off to somebody who could at least do some of the work for them and make their lives easier, right? And that's how they moved up the management chain. And that all broke, right? Because every — you know, your incentives as an intern or as a first-year associate or whatever you are, your incentives are 100% deliver the best product you can. Who's better than you, Claude or ChatGPT or Gemini? So you will deliver Claude or ChatGPT or Gemini answers. And this is not like being digitally native because you don't know what you're delivering. You're just handing stuff over, and every manager has realized, why would I give feedback to a human who takes a lot of time to do the work? I could just have Claude do it myself. So we've just broken the entire system that forms professional taste and judgment, and we haven't figured out a full replacement yet.

So I think that there are a couple of things. One is at the individual level, we now have to start being really disciplined about when we're going through cognitive surrender, when we're giving stuff up to AI, and that's really hard to do. We're built — like first of all, entrepreneurship, like which is what I teach, right, and what we've been talking about here, like that's about trying to make your life as easy as possible so you can focus on the hard stuff, right? And you know, but then you learn nothing, right, as a result of that. But it's also true in every other aspect of life, like we can easily give up skills to the AI, and I think we all feel tempted every day to do this, and we may never get those skills back. So we have to purposely add friction to our lives in certain key areas so we maintain an advantage, but we're not incentivized to do so. At the individual level, it's going to be making choices. At the organizational level, companies are going to do one of two things. They're going to either follow the easy path or the hard path. And I worry about the easy path, which is only hire senior people. And we're seeing some signs of that. I know you've had some economists on talk about this. There's some early signals that junior jobs might be threatened. We're not 100% sure yet. Or the other option, the harder option, is that we actually make training a serious part of jobs, right? So you actually have to give people time to be inefficient and learn on the job. And rather than learning be sort of an accidental thing, we'll have to teach them. Maybe you have to actually add an extra year to universities that we teach you to be, you know, a Google person or a McKinsey person or, you know, whatever it is, and there's a year of customized training for you to teach you, because we know how to teach and assess people in classrooms that we don't know how to do in the real world anymore.

So everything is too early to tell anything yet, right?

Yeah, I mean, I — but also how long is good enough good enough, right? Like the issue is that's also right, like I mean that's progressing too. So you know, this concept that we developed in our first paper where we examined this, we called the jagged frontier, that AI is good at some stuff you won't expect and bad at some stuff you won't expect, and it's hard to know in advance what that is as long as the frontier remains pretty jagged. There are always bottlenecks that humans kind of need to fill in, and those can be really weird ones. Like if you think about like AI replaces a lawyer, well it turns out a lawyer does lots of things with friction in the world that the AI can't necessarily do, even if you had a robot doing the work, right? Because there's human systems they interact with, there's legal requirements they interact with. Some people want to talk to a human. So it creates these bottlenecks that actually reward different talents than before. You see this happen in coding too, right? Like what makes a good coder now is completely different than three years ago because you're not writing code anymore. So I think one thing I would say is good enough is a bundle of skills, and it may very well be the people who are like mediocre coders but really good at the vision about building software.

Yeah. Or communication, and those will change too over time, right? I'm not going to say the AI will never do that task. It probably will. So I think we're going to see ourselves ping-ponging across what skills are valuable in a really rapid way without an easy answer.

Well, one of my favorite phenomena is watching people who know nothing about a field getting the AI to do like impossible things, and you're like, 'Wait, how'd you do that?' You're like, 'I just asked.' You're like, 'You can't ask the AI for that.' And they did, and it like works, and you're like, 'Oh, great.' You know, what are we doing here? So there — I mean, there is this sort of weird phenomenon there. I mean, there's two things that we know make you better at AI. It used to be AI was mostly a leveler, right? If you're a bad performer, you got a boost. It still is that way, but what we're increasingly seeing — and there's some nice results of Anthropic as well as some other studies — that suggest that the people who have the most expertise get the most gains from AI at this stage, and that wasn't true until agents kind of came around in the last year or so. But the other thing we know is that there's some AI whispering ability that is like knowing how the AI thinks, that might be sort of theory of mind. Like if I know people really well and I get to know the mind of Claude or ChatGPT or Gemini or whatever I'm using, I become more effective in working with it. And so those are sort of two different skill sets. And then there's like a mysterious third category, which is like some people seem to be good at this in ways we don't know. There's this great study by Alex Imas and a few others. He was at Chicago, now he's at DeepMind. But they measured people creating agents for negotiation, and a couple of interesting things. One thing they found is your skill multiplier becomes much higher because the agent engages in many negotiations. So a small difference in skills in creating good agents has a huge impact on bottom line.

The second thing they found was that the people who are good at creating agents are not necessarily people who would be the best negotiators in real life. I mean, there was even a gender divide. So one of the things that we find repeatedly in studies, for a wide variety of reasons, is that women tend to underperform in negotiation, and there's been a lot of work on this. It's been a big topic of concern, but women made much better negotiation agents than men did. And you know, and again, we don't actually know — like I don't even know how to teach you how to do that necessarily.