How Agentic AI is Reshaping Science, Education, and the Economy | Karthik Duraisamy at the University of Michigan

22:00Turner Novak:Are you kind of waiting for other people to be done with their phase and you're maybe working on multiple at a time? Yeah, so generally, these things were very sequential. Of course, some of that, even before AI, has been made more simultaneous and not completely sequential. But what AI has done, or AI has the promise of doing, it has done in some applications, and it presents promise in many applications, is to make all of this simultaneous.

22:36and especially with specialized agents, you can do decentralized science and you don't need to know everything about every domain. But I still think expertise matters, but it accelerates the whole process.

22:53Turner Novak:So how does that work that it makes it so these can all run at the same time and it's sort of decentralized? Like if I were to ask you just how does that actually work? versus like, how does AI enable that to happen? Like, what's the thing? Because did you need AI to do this? Like, what does it unlock? See, that's the thing. So people think in most of these cases, AI is an enabler, right? So think about AI having access to skills and tools, okay? Skills is, you know, my scientific expertise, I can encode as a set of rules, literally in a text file. Yeah, so you are an expert on, you know, aerospace physics.

23:39Turner Novak:Like, you know every single possible thing to know about how aerospace intersects with the world or something like that. And that's all you know, and you don't know anything else. Yeah, so that, plus I also have a certain kind of expertise and a certain kind of insight that I can also build into, you know, this database. So think of those as skills. You know, my flavor of the scientific method might be different from your flavor of the scientific method because we have different tastes, different perceptions, and we work in different fields. So all of that can be encoded, right? Think of those as skills.

24:16So agents have access to skills. And then think of tools. Tools would be like I have a simulation software that can, you know, take a real-world problem, turn it into a computation and help understand and manipulate the real world virtually. Tools could also be experimental facilities. You know, I could be in a lab setting. I could have a bunch of robots that do repeated experiments and they can also be connected, you know, and coordinated by AI. So I think human expertise and AI supplemented by tools is what makes this happen, right? It's not AI alone, right? But sometimes I think the reasoning capabilities of these AI models are so powerful, especially the last six months or so, that maybe many of the human skills are actually being identified by AI on its own, but I still think human expertise matters.

25:20So a short answer to your question is, AI with access to tools and skills is what makes all of this possible.

25:31Turner Novak:Okay, and this intersects with the Institute. You are essentially going to be doing a bunch of these experiments through the Institute or like what is the kind of the Institute going to be doing like practically on a daily basis? Yeah, so again, I think that at least at the beginning, right, the core job of the Institute is to make sure the open source project of developing OpenClaw is done and it's maintained in the best possible way, right? Because there are so many users and, you know, users need confidence that this is going to be open source and it's going to be developed, etc, etc, right?

26:10Turner Novak:And there's a couple, I think there's two other people that are running the institute with you. Yeah, I mean, for now, we have a leadership team. Myself, there is Professor Brad Orr from the University of Michigan and Kurt, whom you may know. Scott Skiftstad from engineering. So, I mean, we are the initial leadership team, right? But, so that's the leadership team. I think if you think about the core piece, there are the core developers and maintainers, as we call them. And then there are, there's the next layer of people, like people say, say my students, for instance, right? or my colleague students who want to apply this to their specific problems, not just taking the software and just using it, but also adapting that, right, for specific use cases.

27:03And the reason why, you know, this is, it's logical to center this in a university is because we have people in every possible discipline, right, like economics and sociology and medicine and engineering.

27:18Turner Novak:So you can kind of just say like, hey, we need a legal expert. It's like, oh, we've got a guy. Like there's just a guy who works for the school that's the expert on this thing. Probably in many fields, the world expert, you know, sitting right here. But the other way to, other important point to think about is it is not just about developing and applying software, right? It's not about software alone. there is also the physical piece right i mentioned robotics is an important piece and we have amazing robotics department but also to develop this in a in a very responsible way and in a sense even to prepare society for what is coming right because a lot of

28:06the apprehensions people have about AI is not even about the technology. It's about how fast it is moving and how it disrupts many existing structures. So, you know, can we design institutions that are ready for this, right? And can we design this responsibly? And I want to make it very clear, this is not just the OpenClaw Foundation and the University of Michigan. You know, this is the core. Think of it as a central node. But for this to be a success, we basically need pretty much representation from everywhere in the world, various different groups. So just think of it as a meeting place for that.

28:43Turner Novak:So could I get involved just as like a person that is not, I mean, I don't work at the university or OpenClaw in any way? Absolutely. I feel, like I said, there are 3 million users of OpenClaw right now, probably like 500 ,000 GitHub repositories. In a sense, they're already part of the ecosystem, right? So this centralizes some of the most important effort, but certainly this is pretty much a window to developers and various kinds of contributors around the world. And the other thing that I want to emphasize is nobody can predict with a great degree of confidence how the technology is going to evolve in the next year or two years.

29:25So we'll be very adaptive and dynamic to the changing scenario. But the goals are very clear.

29:31Turner Novak:Yeah, this might be completely outdated in six months. I don't think, you know, the core methods will be outdated in six months. Maybe a particular piece of software that you're using may be updated, might be outdated. But yeah, I think things do last for longer than that. But I think many other things will change in unpredictable ways. You mentioned that these institutes are almost like a VC investor, like VC fund in a way. Can you just explain that? Because I thought that was kind of interesting. Yeah. So, I mean, again, when I explain, you know, when people ask, what do you do as a professor?

30:10You know, people think, and it's fine, right? People think our entire job is teaching. It's certainly not because there is teaching in the classroom. And then you're mentoring research students, right? You're mentoring your PhD students to do original research, and then you do your own research. You lead a research group. You have a lab where you're doing some world-leading research in your particular domain. So one way to think about almost every professor in a top research university is like a startup founder, because you're recruiting some of the best students in the world, right? I just completed my PhD admissions.

30:51I got 200 applications for HTA. Maybe I accepted two or something, right? And you're fighting with MIT and Stanford and all these. So just like you're a startup, you're fighting other places to recruit top talent, you do that. And then all of what I said costs money. So you raise money, just like every startup founder does. It doesn't just show up? It does not show up, unfortunately. So how does that work at the University of Michigan,

31:19Turner Novak:that funding aspect? Yeah. So normally the largest portion of, you know, funding that any faculty member gets or the startup, so to speak, gets, we write proposals to the federal government. Like there is NASA, there is National Science Foundation, Department of Energy, Department of Defense, National Health Institute of NIH, et cetera. So we write proposals on interesting research ideas. And that is also very competitive. I would say maybe one in five proposals get accepted on average. I think at the University of Michigan, it's higher, but normally it's one in five. And this is basically saying like, hey, government, I have this idea.

32:05Turner Novak:Here's what it's going to, here's the impact it could have on the world. Give me$10 million to work on figuring this thing out. Yeah, pretty much, right? So you present some evidence. Hey, in my past research, I've done this. Here is some preliminary work that shows a new direction, and it is promising. And yeah, here is my idea. Here is my five-year plan. Then you're asking for funding. And it's very highly competitive, as you can imagine. What makes it so competitive? Well, there are so many excellent universities in the world, right? So it's not just the University of Michigan that is good at so many things.

32:41You know, there's so many other research groups around the country who are also pushing the envelope in their own fields. So anyway, so it's, again, coming back to the original question. Being a professor is like a startup founder. Yeah. Right. And in one sense, institutes that sit on top of departments and bring together different faculty can be thought of as an incubator or a VC. Because many times in, you know, MICTI, which is the institute that I direct, we identify an interesting direction of research that is not mainstream yet. So we bring together some faculty members and maybe students and postdocs and build some critical mass around that area.

33:34And then maybe we give some seed funds. We run something called the Catalyst Grants Program.

33:41Turner Novak:This is without going to the government. Exactly. This is a smaller amount of money. We're not giving 10 million. We give$100 ,000, for instance. You know, people can use that money to explore an idea. and then the institute will actually help those faculty members, professors to put together a proposal. You know, we recently won a$20 million center from the Department of Energy. We're very proud of. What was that? It's called the Predictive Science Program. So we started a new center called C-Prime, Center for Prediction, Reasoning, and Intelligence for Multiphysics Explorations. Pretty much, as I said, expertise, computations, AI coming together to address a very important problem.

34:26So yeah, you can think of institutes as incubators in that sense.

34:29Turner Novak:So what is a good kind of like research proposal or idea look like? Like you talked about, it's hard to get them approved. So how do you know if something is a good thing to even spend some time on? I mean, first of all, we don't send proposals completely in a blue sky sense. You know, there are some foundations that say, give me your best idea, right? But that is much more rare. So normally, the federal government will have requirements in a certain topic, okay? So they kind of have like a request for research that they put out. Yeah, it's called RFP request for proposals. Okay. So there, you know, somebody may be interested in, say, nuclear fusion.

35:17propose some ideas that can improve the efficiency of fusion.

35:21Turner Novak:This is someone at the government, federal government level that is in charge of dishing out these grants. Correct. They may say, we want some work to be done in nuclear fusion or fission or any category. Correct. We have 2 billion year marked and we might fund 20 projects or something like that. Yeah, something like that. Okay. Generally, it's not 2 billion, but in any case. As you can tell, I'm coming into this with no knowledge of how this works. Yeah. But anyway, I think the right order of magnitude for you to think is funding a PhD student for a year costs about$100 ,000, right? So in that sense, funding a full PhD student's time at a university is like half a million dollars over five years.

36:08So, and normally faculty members have five to 10 students. So that's the order of magnitude. So now you can do a little bit of order of magnitude math.

36:17Turner Novak:So each professor, each faculty member may be administering like a$500 million kind of budget per year. A median would be about$500. Again, to give you a sense, the University of Michigan is, I think, the third largest, has the third largest research program in the U.S. measured in terms of dollars. So our total research activity is about$2.2 billion per year. and I also don't want you to think that it's all federal government. A majority of that is from the federal government, I would say 60%. Our university particularly puts in quite a bit of its own money and there I think no other university does so much for research than, at least in terms of expenditures, I think about$700 million a year, the university spends towards research.

37:13And then there is state funding, industry funding, which we can't forget. It's not as much as federal government, but it's an important piece. So it's a whole range of things.

37:22Turner Novak:So this is basically like a corporation, big company that has, are they sort of outsourcing their R &D to a university in a sense? Like saying, you do this for us and we want to make products from it? Sometimes they're looking for a specific solution, like an outsourced thing. But sometimes they're looking for good ideas and sometimes they're looking for due diligence, right? Because we are experts and we know how to judge things possibly better than most. So it's a combination of things. And what's the benefit of that for the university? Like what do you get out of doing all this research that like some other company benefits from?

37:59Well, first of all, they pay us to do it.

38:01Turner Novak:Okay, fair. Well, that's a start. But don't you want to capture that economic value for the university instead of a different company? And that happens too, right? So again, I said our annual research expenditure is$2.2 billion or so at the University of Michigan. But we're also, I think, after Stanford and MIT, the third largest producer of spin-out startups out of university research. I think every year we do about 32 or 33 startups. So it's not like, you know, just money comes in and then we just produce research papers and graduate students. There are also like innovation that is happening.

38:42And yeah, and some of our colleagues have done really well in that area. But again, just to make sure I'm answering your question, you know, we don't take money just because it is money, right? so every faculty member or research group is interested in furthering boundaries of knowledge or innovation on certain problems and if the funding is aligned with that right so that's the way of having impact so that's training students is impact right training students to think training students a scientific method is impact solving problems that somebody cares about like a company or the government, right?

39:26That's impact. Purely pushing the boundaries of research for the sake of pushing the boundary of research, that is impact too, right? So that's what the university gets. And of course, then you also have startups and other things.

39:41Turner Novak:So with the case of a startup, when you're, so they say the university would own some of the equity in the startup and when there's some kind of an exit outcome, it goes back to the university, like to the endowment, to just like dumped into the budget for a year? How does that usually work? Yeah, correct. You know, if it is a startup that is spinning out of, directly spinning out of research that happened at the University of Michigan, funded by, say, the government or whatever, yes, then the university would take some equity and some part of the IP royalty, et cetera. But I must say, I mean, I have a startup myself, and the university is actually very fair.

40:23You know, they take, you know, it's not like they're here to like make money out of it. They truly want to help innovation grow. They want the faculty members' impact and students' impact to be higher. And of course, there's certainly some economic benefit too.

40:41Turner Novak:So I think it might be interesting now to kind of talk more about how the world is sort of changing because of AI. I think we're kind of, some people kind of see it, some people don't. I think an interesting way to start that is, you mentioned that there's a lot of academics that almost don't believe in AI. It seems like you're all in on it. You've basically created initiatives to lean into it, and you're using it to do research. But there's some people that almost think it's a fad. So do you know what is going on there? Yeah, first of all, I think, especially with something like AI, I mean, it's a general truth that we can hold in everyday life, but certainly applies more to AI, right?

41:26Multiple things that are seemingly contradictory can actually be true at the same time.

41:32Turner Novak:Okay, like how so? You know, you talked about, you know, some very learned professors, maybe they're not as much bought into AI, right? They say, okay, I asked AI this question, and it hallucinated a nonsensical answer. Hence, it doesn't work, right? Hey, but you did that two years ago, right? So many things have happened since then, you know, but I think the core thing remains, right? we gotta separate some of the marketing and publicity and certain kinds of people who run AI, AI companies, et cetera, from the actual model capabilities and the scientific value. And sometimes people mix these issues, right?

42:17If somebody's overselling something and people fail to see what is actually the most important thing. So yeah, I mean, the other way to think about it is in some domains, AI is already incredibly impactful and examples are certainly coding. I would say mathematics, maybe physical sciences, like theoretical physics and things like that.

42:45Turner Novak:So how is it good in those things? Like in your domain, were you seeing AI just being extremely useful. Yeah. So first of all, why, why are these particular domains, you know, more favorable for AI? It is because in coding and in math, especially if the AI did something wrong, it is immediately, you can immediately know that it is wrong. Right. And when that feedback goes back into the model, it can basically correct itself, right? These are called objective metrics because if it produced a little wrong piece of code, maybe it won't compile, so you know it's wrong. Or maybe you run the code and you get the wrong answer, then the feedback from the output of the model back to its thought generation, if that is very direct, then those are the domains where AI can be, AI is already very powerful.

43:40Turner Novak:So it's all rules. You can just say like two plus two equals five and say, nope, that's not correct because, you know, one, two plus one, two is like, you know, three, four. And you just keep doing that over and over until it knows all the rules, essentially. Correct. The thing is, it doesn't need to see all of the rules, right? That's the beauty of it. It sees enough rules that it is basically able to, you know, be intelligent in areas where it has not seen. So that's one thing. And the other thing, which has been pretty remarkable, in my opinion, about AI, at least these latest frontier models, is they're able to compose ideas from different fields, bring it together in a very seamless way, or even within a field, half-baked idea here, half-baked idea there, and they're able to see patterns and merge them.

44:34That is very powerful. And the other thing is, many people just use AI just in chatbot mode. They don't take advantage of the many tools that can be built around AI models. And if you're working in mathematics, there are infrastructures called interactive theorem provers. So AI can suggest something that can go into the theorem prover. And the theorem prover can basically use that information and feedback very important things back to the AI. So if people are not using this and they just use it as a chatbot, they're missing so many of these amazing things that can be. So again, we come back to human expertise plus AI plus tools.

45:23That is when you see much of the benefit to come out of AI.

45:27Turner Novak:So it's not just, you know, you say AI, like cure cancer and it just goes out and does it. Like that's not possible, right? Yeah, that is not possible. In some domains, like it is proving mathematical theorems that people hadn't touched before or people either for lack of attention or you know lack of patience or whatever you tell ai hey solve this thing it's actually doing it right but then of course there are things like cancer and fusion energy and many other practical problems that have to go through the full scientific process right you have to like if you're discovering a drug and AI gives you something that has to be verified using computation.

46:13It has to be verified using trials. So the way to think about it is bottlenecks, right? Whenever the bottleneck is completely cognitive, like math, math is all cognitive, right? AI can go all the way, I feel, soon. But in many problems, the bottleneck is not just cognitive. It is the fact that physics doesn't agree or you have to go build this very expensive experiment to test your idea and then give feedback to the model, right? So yeah, in those problems, anyway, always think of, again, human plus AI plus tools. And in some cases, AI can actually cover a lot of ground, like in math. In some places, pure cognitive intelligence maybe can address 30 % of the problem, but that 30 % can be cleaned up by AI, but you still have the other 70%.

47:13Turner Novak:So what are some of those other bigger bottlenecks that we're maybe running into? Like I said, I mean, first of all, I don't want to give you the impression that everything cognitive has been solved, right? Current AI models still have many limitations. You know, they're very good at taking language and reasoning. They're pretty good at taking math and reasoning, computations and reasoning. They're not so good, as good as humans at spatial reasoning or physical reasoning. Like that's where physical AI and robotics is lagging a little bit behind more language and math and these kinds of things.

47:51So I feel it's a matter of time before that gets conquered. But the bigger bottleneck in solving very hard problems, like truly discovering a superconductor that I can use tomorrow in this particular application is when you interact with the real world, you need to actually build something and you have to test it. And that's not something that AI does on its own. Maybe in the future, self-improving AI can get smarter and cover more of the scientific process. but true discoveries in most fields require you to run complicated computational solvers or run complicated experiments that's for the moment out of reach of ai so ai can has to work with all of these different things so there's something called the amdahl's law right so let's say you know there's 10 units you need to complete to complete your work.

48:57And AI completes eight of those units with no time. The other remaining units are going to choke you down. So just because you have identified 90 % of the work doesn't mean all of the work just happens on its own.

49:16Turner Novak:So then what are some ways you think that the world is going to change as AI gets better? and maybe there's timelines associated with these, maybe there's not, but what are some of the big things that you're thinking about, especially as you are building out this institute and running experiments and doing research? Like, what are you kind of expecting over time? Yeah, it's a very broad question, right? And like I said, if anyone answers that question with absolute certainty, I think they're probably not being honest, right? So you can only talk about likelihoods, you know, there is a possibility of this happening, there's a possibility of this happening.

49:50So under those constraints, I think, you know, what is AI doing, right, in the economy and in science and all of these things, right? So I feel like we've built the entire economy around scarcity and friction, right? Scarcity of resources, right? For instance, if you think of, you know, before the internet, information was scarce. It was a commodity.

50:16Turner Novak:Like you were an expert in how to do something in your town and you're the only one that knows how to do it. So you make economic value from knowing this thing. No one else knows how to even think about it. And no one else has even heard about it. You know, information, it's one example of scarcity of a resource, right? Information, knowledge, data, right? And if you think about some of the major inflection points in intelligence in history, like you had language. Before language, there's no transmitting ideas. Yeah. then writing came about. That was a big thing. Printing came about. That was a big thing.

50:54Internet came about. That's a big thing, right? And each of these things, those information barriers broke down and certain kinds of things became less scarce, right? But until this large scale AI hit, knowledge and cognition, those were still scarce. True intelligence, those were still scarce, right? You have to go to, you have to basically learn from your age five to age 22 to understand something right and to be competent in that field and now some device that's you know something that you can buy for the cost of a Netflix subscription is actually pretty much getting you not just the information

51:39Turner Novak:but the knowledge and intelligence right so so the scarcity and of certain kinds of things is, you know, getting wiped out. You know, we have more abundance of intelligence now. The other thing I mentioned is friction. Like I said, a lot of economy is built around the fact that, you know, moving this thing from here to here required somebody to pick it up and move. You know, we created friction. If you wanted to buy a house, you had to go to a realtor. You know, that's friction. you know you come to a university to learn things in a certain way to be able to know some things you know sometimes friction is good actually right but sometimes it was slowing things down now ai is removing all of those so yeah so the economies will economy will change in many different ways the value propositions that we used to place on different things is changing before our eyes, right?

52:45You know, we used to place, you know, people who are, say, extremely good at remembering things or extremely good at doing math or extremely good at a particular type of intelligence or a particular type of skill were very valued. I still think there is value to many of these skills, but I think value proposition will change. So.

53:09Turner Novak:And you gave a code red to your students, I think that's how you describe it. What was the code red you gave to your students? Yeah, so this was about a month or so ago when I brought all of my PhD students in a room, a dozen of them, and said basically models, some of these reasoning models, even I'm not even talking about agents, but agents are helping. They're able to like reason through things at the level of my expertise in areas that I am one of the world's experts in, right? And, you know, I had many weekend projects with some of these agents where, you know, I have a research idea and I'm not even describing it completely fully.

53:56I'm describing it to some sense. I'm talking to it. And it is doing research. It is exploring all kinds of configurations. It's writing code, testing ideas, coming back with results, writing reports, things that would have taken me four or five months getting done in a weekend right i don't want to give the impression that hence all of my research can be put into a weekend but many of these tasks are things that i could not give to a second year phd student at the university of michigan whom we select you know our our acceptance rate is like five percent or something. This is among the best in the world.

54:33And if some of the tasks that I give to AI, I mean, AI does as well a job as a second year PhD student, it raises a question.

54:43Turner Novak:So what's one of these tasks that AI is now doing at the same level or better than one of the PhD students? Just to give me an example, because I don't know if I know exactly what you're talking about. Yeah, I'll give you a specific example. But before that, I want to say a PhD student is not just about completing tasks. A PhD is not just a task completion, right? So I'm not... PhDs will still exist, right? PhDs will still exist. Original ideas exist. There is value in training people and people come up with ideas in a very different way. So there's all of that, right? So I don't want to equate a PhD student to just executing a set of tasks.

55:22But as a professor who wears many different hats, right? I'm a researcher, I teach, I have a startup, I run an institute, I, you know, I learn new things, et cetera, et cetera. So my time is very splintered. So generally, if I have a research idea, I would work on it over a weekend, for instance, or a few weekends. And then if it is, some of those will fail because they were bad ideas. Some of those are maybe reasonable. Then I say, hey, why don't you look at this? You know, this is how we used to do research until two, three years ago.

55:59Turner Novak:So you would come across something that maybe is worth spending a ton of time on and you'd suggest the idea to someone. Yeah. And sometimes the students themselves go through the process, right? They work on something over, you know, a month, month and a half, whatever. And they come to me and say, hey, what do you think about this? And I find that interesting. Sometimes their idea is better than mine, but whatever, right? Regardless, going from ideation to actually having that research idea makes sense. So that time, especially in fields like mine, I'm a computational scientist, right? Which means all of what I do, most of what I do, not all, involves basically wrangling equations and computing them and looking at physical phenomena and modeling them, etc.

56:49So many research ideas now in my field and many other fields, you can test out very quickly. And sometimes as the AI model or the agent is working through these, even if you're guiding these tools through our ideas, as they're working through the ideas, they can, you know, give you connections that steer your own thoughts, right? So long story short, I called my students to say, you know, until say December, 2025, these tools were, you know, kind of good and in some areas, not so good, but it was always, I always do talk about these tools in terms of future potential. You know, one year ago, it was absolutely horrible.

57:42Six months ago, it was less horrible. Now it's decent. You know, that is how I used to say. But now they've reached a threshold where some of the ideas that are coming out of these models and the way they're doing reasoning and the way these agents are reasoning, it is about as good as, you know, leading edge research. So if we are not using these tools at this point of time, then we may be missing out on something. But I also had another part of the code red.

58:18You develop a lot of intuition by doing it the slow way, the rigorous way. Students, there is value in having students not use AI and develop their own intuition and their own thinking. But then there is also a place to use these tools. So I don't know, you can't put things on stone, but I still want students to have original thinking, do things the hard way, make mistakes, learn from mistakes. mistakes, move ahead. So that's the friction. If you completely remove that friction, then you completely lack intuition on, you know, how things work. But at the same time, if you completely ignore these tools, somebody is going to get there faster than you.

59:02So it's a very hard problem and it's happening in all fields, right? So basically Code Red was to show them how powerful these tools are and be aware of, you know, what is happening. but at the same time not forget to you know do things thoroughly so it's not an easy balance

59:18Turner Novak:yeah so it's basically telling them they need to completely master AI and also master not using AI at the same time yeah so don't substitute AI so I mean maybe we gotta separate the learning phase from the creating phase okay and you know sometimes there is overlap between these two things right like you know the only way you learn physics is by doing problems, right? By doing the physics, by working through the problems. Of course, AI knows the answer, but if you substitute, but in the process of trying to solve that problem, there is some friction that's created that basically gives you intuition.

1:00:01So don't skip that. But at the same time, don't be oblivious to these tools, right? So they can be used in the right way.

1:00:10Turner Novak:Yeah. It reminds me in a way of, I don't know if you've ever come across the CFA charter for the CFA. It's kind of like a CPA, but for investments. It's like you learn a ton about all these different investment categories. So there's three different exams you have to pass. There's a level one, level two, level three. I think the level one is like an undergraduate degree in finance. It's just everything you learn in a finance undergrad. It's just one test and you got to know all of it. And then the second level is almost like a master's. So you get a master's in finance. And then the third is maybe, it's like a PhD in finance, like a little bit more theoretical.

1:00:45And at the end of it, you need to be able to say, answer a question, you know, Paul and Linda are in Canada and they have an investment portfolio in the United States.

1:00:55Turner Novak:But they have a couple investments in Bangladesh and France and it's in local currency and they want to hedge their bets in Mexican pesos. and they have a kid that's graduating college in 18 years or going to college and they need to be able to, you know, fund the college and this is the amount in their portfolio and they want to go to this. And you just have to be able to say, like give them a recommendation on what they need to do. And that is the most, I just described, like an absolutely insane situation. And you need to be able to do all these hand calculations to like calculate all these different, like what kind of hedges do they need?

1:01:32Turner Novak:They need to consider inflation, in the different returns of all these different potential asset classes. And at the end of the day, you're never going to actually go through and do all those things by hand. And like, you might just Google it, right? Or, or you would pay an expert to like, to figure these things out or something. And so, but it reminds me of, you know, I had to go through and learn all this stuff and it sucked. And I was like, I'm never going to actually do any of this. But it does give you some intuition on how to think about some of these things. And, you know, it's kind of a lot of the time in investing or finance or business, there's like a spreadsheet that someone's looking at to make a decision, but you need to know what goes into that spreadsheet.

1:02:12Turner Novak:It's in the same way of like a math problem. It's like, you need to know in physics, like what is influencing that outcome at the end of the day, but you don't actually have to solve it, but you do need to know how it's solved. Yeah, right. So you're absolutely right. It's the same thing. And this complex scenario that you described, I think it's a good example because by going through that process of doing it by hand and getting it wrong and your professor telling you or your tutor telling you or your colleague telling you, you missed this, that is how you actually get that intuition to go see a problem because I think the value shifts from, I mean, intuition is always useful, but I think that judgment that you need to give, you know, it can be informed differently.

1:03:02Turner Novak:So how in terms of, I know you said teaching classes is like a, it's a percentage, it's a smaller percentage of what you do, but how have you kind of seen the way that students are learning and how has that kind of changed over time? And then how do you think it's going to change going forward? Yeah, I mean, it's a pretty interesting scenario, right? So we have had, I would say, three shocks in the past few years, right? One is COVID. Oh, yeah. So that itself was big enough. I forgot about COVID. I know. People went online. Maybe high schools were not as rigorous in making them do the math. And you saw that show up at the university?

1:03:45Yeah, I mean, I could see in certain parts of the undergraduate class, I could see even right in 2022, the incoming class, I could see some mathematical deficiencies. I would even call it, at the time, I used to call it persistence. Like, keep going at a problem.

1:04:06Turner Novak:So the kids weren't as persistent. Yeah, I could see a bit of that. And you could say it's been happening over a longer period of time. When did it start, do you think? I think maybe Google and smartphones probably started that, right? I don't want you to think people are, students aren't great. They are, but certain level of mathematical skills, rigor and persistence, especially among the undergraduate crowd, we could see it dropping a bit. And then COVID really was a shock. and just as we were recurring from COVID we got Chad GPT November 2022 and but at the time the models were pretty bad right they were hallucinating like nobody's business but they could still do a bunch of things and to me the bigger one is the last six months where I think especially at the undergraduate level however hard you set a question or a problem I think the best AM models can actually do it in pretty much in any field.

1:05:12Turner Novak:So any kind of like take-home test or question situation, kids are just ace in everything. No, I mean, I'm actually pretty, I shouldn't say surprised because we are, you know, leaders and best or whatever, right? No, I think the honesty and integrity, I don't question as much. And we have a tradition. For instance, in the School of Engineering, for the past 150 years, no exam has ever been proctored. The professor gives the exam in class and then steps out of class, waits outside the classroom. We've done it for 150 years, you know. And I still see, like, much of that persisting. but without a doubt students are using AI tools to study and then to replace some parts of their thinking for sure and I'm sure I'm not naive we know human nature maybe some students are completely relying on those tools but it's a mix but what we again end up missing is yeah that persistence and that struggle you know you ask how my own teaching is has changed i remember i teach one of a class that students find to be one of the more difficult ones in my department maybe the most difficult but also the most enjoyable not just because of me the subject itself is beautiful what's the subject it's aerodynamics that's what i teach so what's the point of the class like what am i getting out I take in that class?

1:06:51Well, you're going to learn how, for instance, wings generate lift or how to design wings for certain properties. You know, how much power do you need to move an aircraft? And you learn it, you learn math, you learn physics, you learn engineering, and it all comes in a very special kind of way. So it's quite abstract and some students struggle, but everyone enjoys it. And I teach it. Some of my colleagues who teach the roles are excellent. So it's a hard class, but, you know, students learn a lot and they enjoy it. But anyway, you asked how is changing teaching? I remember six years ago, I give a homework problem and half the class would not even know how to start the problem.

1:07:36That would be me. Right? They're like, what is this? And, you know, I'm not, you know, I don't take pleasure in torturing students, but I knew it was excellent for learning.

1:07:50Turner Novak:So what made this problem so hard? Can you give me an example of something I might be getting from you and just I wouldn't know where to start? Yeah, maybe I teach how, you know, so you've seen these aircraft fly and then sometimes you see these trailing vortices. When an aircraft flies through clouds, you see these vortices that roll up behind the wing. I teach that. I teach how that happens for an aircraft. And then I say, in the homework, I would say, here are a flock of birds that are flying in a geese that are flying in a V formation. Tell me how efficient flying in this V formation would be, you know, without having mentioned many relevant details.

1:08:34So many students would not know where to start. I guess with Googling, you can get some amount of help. And there are some other problems that I Google and there is no way Google knows anything related. So then students take a few hours to even figure out how to start. But you learn a whole lot in that struggle. The hour and a half that you put in where you went nowhere is actually where you learn. And now that is completely removed.

1:09:03Turner Novak:Because you can just type it in a chat GPT. Whatever question anyone gives in any course, at any level, in any university in the world, including the most famous mathematician alive, AI at least, either will either completely solve it or it can recommend six or seven directions to start and then you follow. Right? So, yeah, you're certainly losing something. So this geese question, I'm just curious now because I'm thinking like, what would I even be trying to figure out? Like, how do I solve this? Okay, so basically I told you how, you know, a single aircraft flies and how those trailing vortices look like.

1:09:46Turner Novak:Do you give me the formulas of how to calculate this stuff ahead of time? For a single... The lecture would be going through the derivation for a very idealized airplane. Right? But then you basically use that particular information and then you treat each of those geese as different aircraft. and, you know, they have to support a certain amount of mass to be able to, you know, fly. And then you basically create a bunch of these little airplanes and the vertices corresponding to those, and then you'd optimize over that. And you're giving me the size of the geese in the description of the problem?

1:10:28Yeah, the spacing of the geese or something.

1:10:30Turner Novak:Okay. Yeah, I mean, maybe that's not the best example of problem that they don't know how to start, but, you know, there are other more complicated examples. But, you know, for some students, even that connection would be harder.

1:10:45But, yeah, in general, I think maybe, you know, moving away from the specifics, it is in that friction and the struggle that you actually learn. And if that is being replaced, then you don't develop as much intuition or that judgment, right? Like you described a scenario in your CFA where you have to think about 25 different things. but yes doing it persistently rigorously by hand has a lot of value and that's being replaced

1:11:15Turner Novak:yeah and then the interesting thing about where not even just ai but just computers and software comes into play like it's all these little mini calculations you kind of have to make and if you make a single mistake in any one of that chain even if you you know you knew it it's just you maybe if you're trying to do this all in your head it would take so long versus even using a calculator. Like it just changes that you know exactly the answer. So even just software speeds that up so much. And then with AI it's basically creating all those software calculator calculations, doing that all automatically for you.

1:11:51Turner Novak:So then the skill becomes instead of like how to use your calculator to solve a problem, the skill becomes knowing how to use AI and an agent to solve a problem. The meta, right? But what if one of these agents is actually doing the wrong thing? You have a sequence of these and one of those is wrong. So you still need to be able to understand what the agents are doing. Yeah. The other way to think about it is if everything can be automated, then what is the value you bring to the problem? Anyone can do it. Yeah. So do you think we're going to have to start learning how to create and manage and run agents?

1:12:24Turner Novak:Like that's a lot of what education is going to be turning into? I actually don't think so. Like did we need a lot of education to use chat GPT 3.5 when it came. No, right? But you needed a lot of common sense to be able to use it effectively. You know? So the other day, this was two weekends ago, my student came with a brilliant, brilliant idea. It's going to be published sometime soon. And I didn't quite get the intuition for it. he certainly understood more about it than I did. And then over the weekend, I was wrangling with that. And I asked AI to, I asked the Codex, the OpenAI agent to walk, I described a problem.

1:13:17I gave it all the context, it wrote code. And then I wanted it to create a visualization to help me understand what was going on. So it did everything beautifully, but then the visualization was kind of off. The angle at which it was showing me was off because it had no spatial reasoning. So I asked it a bunch of things. Hey, I don't, this does not make sense. I don't, there is no more clarity and it'd make it worse. So then I said, help me help you. I said, describe the view that you're showing with some numbers and help me and make the figure interactive so I can rotate it whichever way I want.

1:14:01And when I rotated, those numbers would change. So let me tell you what the best angle is by taking those numbers and giving it back to you. So anyways, human and AI, basically, without getting into details, I think people say, oh, the great education is how to use AI. No, I mean, you can use AI, these things, Anything that seems like a little bit of activation energy, like, oh, not everybody knows how to use an agent. I think in a couple of months, it's just going to be clicking a button. So it is more like, how do you synthesize what you know and how to use your common sense to wrangle with AI?

1:14:44So I think that's more important. Learning how to use AI, it's never going to be a big issue. Yeah. Well, so how does the role of the university change then with education becoming either less or more important?

1:15:02Turner Novak:Like, I don't actually know the answer to that. But how do you think the role of universities is going to change? Yeah. So, I mean, it has been changing, right, over the past few decades even, right? We used to be gatekeepers of information knowledge. Nobody else had, right? And then slowly, you know, the internet and large courses and all this content were eroding a little bit of that. So this would be like when MIT put the classes online? Yeah. So I would say, I mean, that is one of the most seminal moments. I think people should talk more about that moment, right? So what was that? When was this?

1:15:36Turner Novak:It was about 25 years ago? Yeah, around the early 2000s, MIT took all of their class notes and class videos and lectures and homeworks and just put it on the internet for free for anybody in any part of the world to access. So you could, in theory, anyone who's enrolled at MIT and they have to go to all these courses, anyone in the world could just get the same stuff. Same material, same lectures from the expert in the world, the videos, homeworks, everything. You know, that's amazing if you think about it. Did it change the world enormously? I mean, for certain people it did. It's an amazing thing.

1:16:13But again, so that was passive, right? It was not teaching you, it was just exposing. It was basically revealing information. So now it's basically giving you cognition and knowledge. So I think a lot of the things that universities used to do I think will be less valuable than before. As keepers of knowledge, you know, you had to go talk to this expert to know anything, right? And you had to go to this famous professor in Michigan Medicine to learn about that particular way of thinking or that scientific method. And professors who had, you know, maybe your Nobel Prize had a different flavor to it.

1:17:02So a lot of these things are now pretty much encoded. I mean, not all of it. A lot of it is now encoded in a skills.md file or, you know, identified in some way the models know it. So we have a keepers of knowledge for thousands of years. And now suddenly this box that you pay$20 a month for knows maybe not all of it, but most of it, right? But still universities have a big role to play because what are the other values we bring in, right? So just having access to content doesn't mean people learn.

1:17:40Turner Novak:I've never looked at any of those MIT videos. I mean, I think I've seen someone talk about it and went to like the YouTube page, but I've never actually watched any of them. So I might still use them once in a while. And I put my notes on, you know, some of my notes on the web and in YouTube. And, you know, I get a kick out of people watching it and learning and sending me emails. But in any case, so just because the materials are out there doesn't mean, you know, people learn. And just because knowledge is accessible for$20 doesn't mean people extract the most. So universities still add value in bringing, say, young people of a certain age together in a certain environment and, say, putting 25 people in a class and introducing, I mean, maybe the right way to say it is introducing friction in a certain way, introducing deadlines in a certain way, exams, making you think.

1:18:39And so that's, you know, one thing. And of course, people learn from people, right? And then of course, it's not just about the lecture room. There is other kinds of mentoring, personal mentoring happening, especially at the graduate level. and you have access to very specific facilities, right? Like, I don't know if you know, the University of Michigan has the world's, at least the nation's most powerful laser is right here. It's called Zeus.

1:19:14Turner Novak:What do you use it for? To study theoretical physics, you know, to study, say, plasmas or to study atomic properties, you know, material properties. Like, are you cutting things with it or are you just pointing it? You point it towards, say, a certain material. And the impact of the laser on that material basically can change some properties. You can get the spectrogram. And you can do a lot more with it, right? I'm just giving you one example. And a lot of theoretical physics you can do, theoretical developments based on that. So that doesn't exist inside ChatGPT. right and many again my work is computational but many of my colleagues have physical labs where you know they're building flexible aircraft you know aircraft that we fly on are not as flexible you know i have we have one of the best battery design facilities in in among any u.s university right here.

1:20:22These are specialized facilities. So anyway, you know, to summarize my answer to your question, yeah, bring people together in a certain way under certain constraints that they cannot sit at home and just get these kind of things. So there are still a lot of, and then of course, not just learning, but creating new things, right? So universities, many of the discoveries and innovations directly come from university labs. And, you know, the president of Arizona State, he has a very nice one-minute video where he picks up this iPhone and says, there are like 800 technologies here that were developed at a university lab, right?

1:21:08And what Apple does, of course, Apple does add value, you know, but they put things together in a certain way. So I think learning, creating, in introducing certain kind of friction, exposure to types of ideas. So I think all of those are still, still belong in a university and will belong in a university. And then the last thing, which maybe now becomes even more important is credentialing, filtering, right? Universities for better or worse, even to get into a place like Michigan, I don't know now what the, its acceptance rate is 10%. And then you come in, I mean, I'm not saying that's a good thing, right?

1:21:53We should be more accessible. That's a different topic for a different day. But after you come here, you know, you go through a program where you're credentialed, you're given grades. So I feel those will become more important now than before. because...

1:22:11Turner Novak:Because they prove that you know the topic or you have spent time running through the motions of learning. Yeah, first of all, it was, yeah, many times people recruit from top schools even for the reason that, oh, they got into the top school, so they must be good at something. Yeah, yeah. Right? So that is one, I would say, implicit credentialing. Yeah. Again, I didn't say it's good or bad. It is what it is. And then, of course, you go through the program at various levels of rigor, the university is credentialing, right? But I would say grade inflation is probably going to stop now. I've seen those charts where it's like, you know, the average grade used to be like a 2.3 GPA.

1:22:58Turner Novak:Now it's like four or something. But then like 3.6 or I don't know, something like that. You know, Harvard had a big, you know, it was about too many people are getting A's. So I feel these kind of distinctions will grow and we will take evaluation and credentialing more seriously. Also because there are easy ways to complete work. Yeah. Do you know what was going on there with grade inflation? Like from your perspective as the one who's giving out these grades, like why was it happening? Many things, right? So again, this is one of those things where, you know, people usually have one answer that, I don't know, prophesies the catering to customers or whatever, right?

1:23:42If you ask the students, they would say, we are working harder. One of those scenarios where all of these things may be true, certainly you can agree that students are better prepared to come into college now than they were, say, 20 years ago, right? That's one factor. And then, of course, I wouldn't say standards are dropping, but I think in most universities, the expectation is if I work hard, I get an A. And many times, that is actually correlated with outcomes. You really work hard, you're actually learning, but it's not always. And then, yeah, I mean, again, I don't want to be an idealist.

1:24:27I'm sure there is a little bit of, I'm paying$60 ,000 for a year for my degree. I'm not saying hence people give better grades, but if you dismiss that completely, then you're being naive. So as with any complex thing, there are many things that come together. I mean, something can be explained in many different ways. Like I said, I mean, students do work hard, right? Not everybody. They do work hard. and it's not like every profit is just giving out A's for free. But yeah, all of these are conflicted for sure.

1:25:04Turner Novak:And you think that the inflation is going to stop? Like you think it will come back down or just level out? I'm seeing a movement where things are either leveling or beginning to come back down a bit. How do you pull that off? Like bringing it back down? Because you have to be like stricter or something? Hardwood actually pulled it off. I mean, I think two years ago, three years ago, they actually showed the grades inflating. Like 70 % of people who went to Harvard got an A or something like that, some ridiculous number. And then the professors were, you know, said something and then they brought it down a bit.

1:25:43They plateaued and then they brought it down a bit. But now students are complaining. Students are like organizing and saying, oh, it's mental stress and we are working so hard. And again, it'd be stupid to dismiss that claim, but it'd also be stupid to completely overrule what the process are doing. So as with many things, maybe both are true.

1:26:04Turner Novak:And so I feel like for a lot of people, the reason you go to college and go to university, for most people do it because they want to get a job. I have to think it'll increase their chances of success to get the credential, which makes it easier to get that interview, the stamp of approval. What do you think is going to happen to the job market over the next, you know, decade or maybe it's happening today? Like, what are you kind of seeing, especially as it relates to kind of how AI is changing things? Yeah, so again, nobody can predict, but I think there are some things that are true. First of all, you are seeing in tech industry about 100K jobs, 100K layoffs or so in the last couple of years.

1:26:46And I think in 2026, we're already seeing 100K. right so that seems like a large number but I think we have about 7 or 8 million tech workers you know

1:26:59Turner Novak:so in the grand scheme of things it's like 3%, 2 % or something 1 % or something so I'm not dismissing I mean it's creating a lot of pain etc etc so I think there'll be especially in tech areas I feel there'll be more of these layoffs in the next few years but I don't think it's going to be as dramatic or drastic as what some of the talking heads are saying. Some people would say, based on what they're saying, you'd think massive loss of jobs, like no one's able to work, like AI just replaces everything and everyone's unemployed. Yeah, I think that's certainly probably going too far, at least in the short term, because the economy is built in a certain way that these kind of impacts will take longer time to like penetrate.

1:27:51But if you want to ask me what's going to happen in the next two to three years of which I have a reasonable handle off and not a great handle, I don't think like 20 % of the population will be unemployed. Maybe it'll be, what is it? 4.6, 4.5 or some percent unemployment right now. Maybe that'll go by a percent more, which is actually pretty bad. 20 % increase in... But I feel instead of having huge layoffs, I don't think there'll be massive layoffs. Maybe there'll be 100K-ish here and there. But I find it more concerning for fresh graduates to get into jobs and the code red that I described earlier has something to do with it because many of the entry-level skills in certain domains that actually can be automated.

1:28:53But again, if you don't have the entry-level skills, you don't actually become an expert. So it's a little bit of a chicken-egg situation. But I do believe the way the economy has been built, there's going to be a slower impact. Plus, you know, people talk about all kinds of things like, oh, everybody can have a billion dollar startup. Where is the market? Who's going to be buying your thing? You're saying the one person billion dollar startup? I'm sure there'll be a few one billion, there'll be a few one person unicorns. I think there is already one to my knowledge. Did you see the telehealth company?

1:29:34Yeah, that's right. I mean, there's some interesting stuff.

1:29:36Turner Novak:of those games about that. They may not be fully compliant in all the things they're doing. Yeah, I have no doubt about it. But anyway, I don't rule out that there may be a few single-person unicorns. And one of my colleagues, Jerry Davis, in the business school, he has very colorful remarks, thoughtful too, on zero-person unicorns. Like completely an agent running the whole thing. And I don't think it is completely out of question. But those will be exceptions. I think the economy is set up in such a way to blunt, you know, huge disruptions. And people, when they evaluate a technology, they always think linear.

1:30:17But things are very nonlinear. They can saturate. Then maybe they go. So, yeah, I think in summary, I feel people who already have, are in good jobs or most jobs, AI will help them be more productive and maybe they will do more.

1:30:44But then I worry more about entry-level jobs. It's just harder to get that first job.

1:30:51Turner Novak:Because maybe instead of someone who's a manager hiring a new person on their team, you're just able to use AI and software. Again, not in every profession and not completely, but again, you have to be naive to think that it won't have an impact. Well, I think one of the recent guests on the show, we talked a lot about the U.S. healthcare system and he just talks about how we've had like almost these like kind of job programs throughout the course of American history, like manufacturing was kind of built-in jobs mechanism for employing people. And healthcare is kind of that way right now where there's a lot of different cities and regions and states where some sort of healthcare system is the largest employer and it's just people who sit at a desk and you are doing things in the hospital or you're like a nurse.

1:31:43Turner Novak:There's a lot of people who help bring people around the hospital. They do some admin work. So there's a lot of these cases like that where those sort of exist to employ people. And even if AI makes their jobs more efficient, Like the government is kind of funding it and the government's just not going to say, let's not have this job anymore. So there's kind of like this multiple pieces of friction here. Yeah, certainly in the short term, I agree 100%, right? But I cannot look five years beyond the horizon and know how nonlinear the impact would be. So there are a lot of destabilizing things. but you're right there are many jobs where I think the way job is structured and the way incentives are structured I think will protect people a lot I mean there will still be layoffs but I don't think it's going to be as catastrophic as Dario Amadei talks about at least in the near term but as I said I do worry about entry level jobs I think that's the bigger concern So what advice are you giving your students like when it's like hey, you gotta, if you want a job, here's what you have to do.

1:32:55It's very hard advice, but the advice that I give is, you know, whatever you study, just get more rigorous about it and add some value, right? So just like get really good, get really good at what you do. I don't tell them, oh, use an AI tool. To me, this part is more important, right? Going through that process, building that intuition. And then I think using the tools can, you know, maybe just out of necessity or common sense. But then, you know, I'm of a certain age where many of my friends have now kids who are in like 10th grade and, you know, they're thinking about college and what should I do?

1:33:37People thought computer science was a sure shot, you know, to an amazing career.

1:33:44Turner Novak:Yeah, getting, you know, becoming like a millionaire, coasting forever. Yeah, so anyway, so I tell them, it doesn't matter what field you're in. Even if you study computer science, computer science is not just coding or software engineering. There is theoretical computer science. There is computer, there are so many other things beyond coding and software, right? In computer science also. But I think more fundamental advice, especially if they come to me with open advice, what should we study? Maybe I'm biased. I would say physics, chemistry, biology, mathematics. Those are all things AI can kind of do really well right now, right?

1:34:25AI can do very well to a certain degree, but most of the unsolved grand challenge problems in humanity involve those fundamental sciences, right? And then there are also questions, you know, what is the nature of life? You know, where did we come from? So, you know, nature of reality. So these are not things that we can answer right away. So there is, first of all, you study things for the intrinsic merit, but you have to do it really well and really build up your basics. And those problems are never going away, right? You're not going to be somehow tomorrow, somebody has this unified theory of physics and that's it.

1:35:09It's not going away. But the second advice I give is, yeah, get really deep, but also know how to synthesize things in the right way. But don't skip. This part is the most important. You need to know something really well so that you actually have expertise and not everything is AI-able. Sure, AI can do your maths, physics, and chemistry, biology, homeworks. But yeah, go to those fundamentals. I feel those are hard skills that will matter as long as you're able to synthesize information across different disciplines.

1:35:44Turner Novak:Yeah. One advice I always give people too is you probably want to niche down a lot more than you think. So if somebody just says to me, you know, like, I want to be a scientist or something and like I'm looking for a job as a scientist, that could be anything, right? But if you tell me, you know, and you might think that this is bad. As I say, I want to be like the, I want to be a scientist that works on like paper cup strength, being like more durable, holding water and like the strongest paper cups ever in the world or something like that. Like that's just super specific problem that you're really good at.

1:36:18Turner Novak:If I do ever come across something that requires that skill, like you are probably going to be the best person at that specific problem. So I may think of you. It's almost like when you're, we think about it as a sports team, like if you're a soccer coach and you're trying to make your team better and you're like looking for players and there's like all these people at the tryouts and you're thinking about who to add in the team and there's just like this person that's really good at throw-ins, right? like throwing the ball from the corner and like they may not be the best player at other aspects, but they have an insane ability to like, I don't even know how you'd be good at throwing in soccer.

1:36:51Turner Novak:They're just extremely good at it. You might make the team because you're just, you're good at that one skill or like corner kicks. You see a lot in soccer too, where someone's just so good at the corner kick aspect and you get to, you made the team because that's what you're good at. Right. Yeah, I think, you know, the way you're describing it, maybe the ideal profile is, you know, something like a V, So somebody who knows something about everything, you know, it's like just broad, it's like a rectangle, right? And then somebody who knows only one thing is just like this, very deep. I think it's like a T or yeah, that would be like an I.

1:37:24Yeah, maybe a T is better. You need to know obviously enough about how things connect, but you need to be really deep at some things. I don't think that value is going to go away. But all of that said, it also won't be simple. like I said a few minutes ago, just because you're so good at something and you're very smart, that alone used to be enough to make it in the world. You were good at math or you knew this really well. I think those skills are still useful but not sufficient. So I feel as a scientist and in general also, the value proposition will shift to people who solve real problems. You know, so maybe, you know, instead of saying, here is an idea that I generated, maybe the value of that idea itself is probably going to, you know, go down to zero, in my opinion, because AI can generate ideas.

1:38:33Maybe not all of them are good, but some are good. Cost of generating ideas is almost zero now. But to be able to take that and then keep going and going and going and actually solve a problem that people care about, I think that's where the value proposition is going to be. Earlier, it used to be, he's smart, he knows math, he can do counting in his head, he knows this, he can remember things, he or she, they. And yeah, now it's like, okay, so what? What are they doing with that skill? So I think, yeah. So going all the way, I think that's what Valley Propositions will go towards.

1:39:13Turner Novak:And you've actually seen, this is like a totally different topic, you've seen two different college basketball national championships as like first-hand members. So what is that like? Oh, amazing. So yeah, I'm a sports junkie and certainly enjoy college basketball more than most sports, not all, but it's in my top two or so. So what's your top sport? Top sport is actually soccer. Oh, really? Yeah, I'm incredibly passionate about it. That was actually a lucky guess then when I gave the soccer. Okay, I see. Anyway, so when I was a graduate student at the University of Maryland, we won the national championship in men's basketball and women's basketball.

1:39:52Oh, in the same year? Not the same year, a couple of years apart. Okay. And we also had the best stretch in history in our basketball history in women's and men's. the football team was good. But yeah, the distinct night in 2002, I remember when we won the championship. It was insane. It was insane. And I got to experience it again.

1:40:15Turner Novak:At Michigan, yeah. Yeah, at Michigan a few days ago. And, you know, I actually, a couple of my professors, I mean, colleagues, we were all in university, South University, along with all these writing kids. I'm sure they didn't know we were professors. But, you know, it was amazing. You know, to me, of course, being in a school where you want something, it's a very hard accomplishment, right? 64 teams, straight knockout, it's a big deal. But the other aspect is, you know, sports brings out certain kind of emotions that many other things don't. and what other event in life do we see 10 000 people completely happy come out of touch with reality deliriously happy forgetting all those things that are happening in the world all celebrating about a thing right so yeah to be part of that is amazing so yeah sports you know evoke certain kinds of wild emotions and you know sometimes i let them let them go wild.

1:41:23Turner Novak:Yeah, it's kind of one of those things. I've seen those videos of like, you know, robots playing sports and people are like, oh, soon they're going to be better than us at sports. I'm like, I don't really want to watch a robot play sports. Like, I don't know if they're, they will ever fully replace humans playing sports. Will they? Yeah, yeah. I think, again, well, no, I don't think, I mean, for the past 20, 30 years or so, computers are better than humans at chess. Doesn't mean, you know, we still, people like stream them live stream playing chess online. I think chess is more popular now than 30 years ago when, when they beat, when, when computers beat humans.

1:42:02There is some human, you know, pleasure involved, you know, to see people compete. So, yeah.

1:42:10Turner Novak:Well, there's this concept, I think it's called the bionic games. Have you heard about this? It's basically, you know how steroids and certain drugs are banned in the Olympics. It's like the Olympics, but you're allowed to cheat. Like you're allowed to take certain drugs and you're like allowed to have like, I'm assuming you'll be able to have like a robotic arm that like, like, let's say you're, you know, let's say you lost your arm in an accident, you get like a surgery to replace it. And like that arm is like somehow stronger than an actual human arm and be able to do better, you know, tasks or sports or games.

1:42:40Turner Novak:So I think that's kind of a thing that's maybe come in. Yeah, there is a market for everything. You know, one of the most popular sports in the world is motor racing. That's human and machine, right? So we'll probably get new sports. That's probably what's actually going to happen is there will be new sports that are created. Yeah, it's very possible for sure. And I think you actually told me one time you almost died once hiking in a national park. What happened? Yeah, that's, you know, one of our passion, my wife and I, we travel a lot. And at least there was a time when we used to do some interesting trekking and hiking, different parts of the world, in Argentina and here and there.

1:43:26There was this one time in the Grand Teton National Park when it was a snowy day, and somehow they gave us the pass to climb and pitch camp. It was maybe an overnight camping trip. and it was a beautiful setting. My wife and I were in the initial half an hour of the hike, a ranger was coming down and he said, I'm quite shocked that they gave you a permit because weather conditions are not so good. And then we said, yeah, I think we'll be okay. We've done a bunch of hikes. And then he said, don't make me come and rescue you up top.

1:44:14Turner Novak:yeah, we won't, nothing will happen. But then he said, you know, after a while you might not see the trail. So just keep to the right of it or, you know, or follow the footsteps. I don't know, he said some vague things. So it was snowing while this was happening? It was, looked like it was going to snow, but I'm sure at the elevation it was snowing and he was coming down. So anyway, we kept going and then there was a light snow and we enjoyed it. And then the snow got heavier and then the trails disappeared. and whatever instruction the ranger gave us, we were not able to follow. Oh, no. I think I fell into a hole.

1:44:54It was not too deep, but I fell into a hole. Then my wife fell into a hole. Like right next to each other? No, within maybe 20 minutes of each other.

1:45:03Turner Novak:Oh, so you fell and you got out. Yeah, yeah, yeah. Okay. And I think some of our backup socks got wet, whatever. And then it snowed heavily. And then somehow we found something that looked like a trail and we pitched camp. And it was pretty high up. And it snowed some more. And then because we had those falls, socks were wet. And the socks that were inside our backpacks were wet. And it was super freezing. And my wife's, especially my wife's hands were going numb. I think a few more hours, she would have probably have lost some things. and then the coyotes start howling. And she's like, I don't want to be, I don't want to die being eaten by coyotes or something.

1:45:49And this was, when was this? This was probably like 2005 or 2006. Cell phones were not very good and we couldn't call anybody. And we kept trying and trying. And somehow I reached, this was in Wyoming and somehow I was able to reach my friend in College Park, Maryland. And I think he was able to call the rangers And then they came up with some very hot stuff and they wound up our tent. It was actually the same guy who said, don't make me come and rescue you.

1:46:22Turner Novak:Oh, geez. How late? Like, was it the next day or like 12 hours later? Midnight or 2 a.m. or something like that. Geez. Actually, no, it was a little. Yeah, it was around that time. He said when he got the call, he was having dinner with his wife. so anyway so we've had some adventures I would say jeez any upcoming trips planned? yeah I mean we have some nothing big on hiking we have some trips to Japan and you know Europe and things like that more laid back more laid back yes yeah well this has been awesome thanks for taking the time to chat it was a lot of fun no it's a pleasure we touched upon so many different topics yeah and you were suddenly a good host There'll be a lot of data out there for the LLMs to train on.

1:47:08Turner Novak:Maybe we can teach the language model something. I don't know how much real insight I had, but I'm sure I accomplished a few things. Yeah. Well, it was a lot of fun. Yeah. Thank you for having me. And thank you for listening. Thanks again to this episode's sponsors, Flex. Upgrade to Flex Elite with the link in the description and get$1 ,000 after your first$10 ,000 to spend. Numeral, put your sales tax on autopilot at numeral.com. and Amplitude. For AI analytics, just ask Amplitude. If you enjoyed this conversation, please like, comment, subscribe and share this episode with a friend who still thinks AI is just a fad.

1:47:43Turner Novak:Make sure to check out the back catalog of over 100 episodes with founders of companies like Robinhood, Sweetgreen and Mercury. Tune in over the next few weeks for guests like Eric Israel and Metropolis, the parking company that's quietly pulled off one of the most successful versions of the AI enabled roll up strategy that everyone was talking about last year. and Jim Belosik at SendCutSend, making custom metal parts as one of the pioneers helping to reshore manufacturing back to the US. If you don't want to miss any of these, subscribe to my newsletter, The Split, linked in the description to get each episode plus a transcript emailed directly to your inbox every week.

1:48:19Turner Novak:Thanks again for listening. See you next time.