The Motley Fool Interviews NYU Professor Vasant Dhar: Thinking With Machines

Motley Fool analyst Asit Sharma recently talked with Vasant Dhar about the brave new world of AI.

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A full transcript is below.

This podcast was recorded on Dec. 28, 2025.

Vasant Dhar: My fear is that we are slipping into a Huxleyan kind of world, perhaps even without our realization, that we are gradually disempowering ourselves in many areas of our life. The machine has become a gatekeeper of human activity in many ways.

Mac Greer: That was NYU professor Vasant Dhar, author of the new book, Thinking With Machines, The Brave New World of AI. I'm Motley Fool Producer Mac Greer. Motley Fool analyst Asit Sharma recently talked with Professor Dhar about that brave new world.

Asit Sharma: Greetings, Fools. I'm Asit Sharma, Senior Analyst and Lead Advisor at the Motley Fool. My guest today is Vasant Dhar, Robert A Miller Professor of Business at NYU's Stern School of Business. Professor Dhar is a pioneer in the field of artificial intelligence. In fact, he received his PhD from the University of Pittsburgh with a specialization in artificial intelligence in 1984. Among his many achievements, Professor Dhar is noted for bringing machine learning to Morgan Stanley's proprietary trading groups in the 1990s. You may have listened to the professor's popular Brave New World podcast, and he's out with a new book entitled, Thinking With Machines, The Brave New World of AI, which is the topic of today's discussion. Vasant Dhar, welcome to the Motley Fool.

Vasant Dhar: Thank you, Asit. Delighted to be a Fool.

Asit Sharma: Awesome. I wanted to start with your early childhood, which you recount in the introduction to Thinking With Machines. You were born in the 1950s in Kashmir, India, and you note that you rode to school in a horse drawn cart. You also moved around quite a bit in India, and by the time you were nine, your father was posted to Ethiopia on assignment as India's military attache to Africa. I wondered, Professor, can you tell us a little bit about these formative experiences and how they helped shape the person and scholar you became?

Vasant Dhar: All amazing experiences growing up, including what you mentioned in Ethiopia. I described this in my book in a humorous incident. My mother put me in the wrong grade. She put me in seventh grade instead of fourth grade by mistake and only realized her error six months later when it was too late to do anything about it. Here I was hanging around in class with 15, 16 year olds and I was like nine. That was a hell of an experience growing up. Then I went off to boarding school in India after that, which was also another so my trajectory was third grade, seventh grade, eighth grade, and then 6, 7, 8. You cannot make this up. That's what it happened. But it made me resilient, I guess, in some way, and it was a really unusual upbringing. I'm happy for it.

Asit Sharma: Fast forward to Pittsburgh, Pennsylvania, at a time where you were attending school and intersecting with a very exciting world, the very nascent world of artificial intelligence. You met an AI pioneer in Herbert Simon, who had received the Nobel Prize in economics for his work in revealing the limits of human rationality and decision making. Professor, I remember still in the early 90s, late 80s, early 90s, taking a college class in microeconomics in which rationality was still the governing principle or said to be the governing principle by which most humans make their economic decisions.

Vasant Dhar: That's right.

Asit Sharma: But Professor Simon had a different idea. He called it bounded rationality. I wondered if you could explain that to us.

Vasant Dhar: Essentially, what he said was that humans have limited cognitive resources that we are not able to enumerate all possible alternatives and evaluate them. That's just too taxing. We'd never get through the day if we did that and that our attention is limited, and then we tend to focus on the most plausible things to pursue and we do this through heuristics, that I learned through experience and so heuristics, actually focus our attention to the right parts of the problem. When we find an acceptable choice, we take it and we move on. That was his theory, which was called bounded rationality. I have to say that economists said, "Yeah, that's true, but let's just move on". For the most part they still because it doesn't lead to very good theories, it messes up nice mathematical model.

Asit Sharma: It's messy.

Vasant Dhar: It's messy, and economists don't like that. It was just, "Yes, it's true, but thank you very much". Whereas his ideas really took root in artificial intelligence which was really about, at that time, all about how do you represent knowledge and how do you traverse it intelligently and that was called heuristic search at the time. Heuristics became big in AI, and they were the primary paradigm at that time of expert systems where we tried to build these impressive applications in areas like medicine where you would extract knowledge from experts and use the heuristics that they require through experience to actually do medical diagnosis. That was my first real experience to AI, just watching this system called internist interact with an expert and elicit information and arrive at the correct differential diagnosis. I was just watching this, and it just blew my mind and that's when I decided, This is what I'd like to do with my life.

Asit Sharma: You posit that oftentimes, success in the markets or in other probabilistic endeavors is made up of small edges that compound, compounding small edges and you bring up the commencement address of tennis great Roger Federer last year to the graduating class of Dartmouth. Can you start with what interested you in that commencement address and explain the concept of compounding edges to us, please.

Vasant Dhar: The statistic that Federer said that really stayed with me and it's so similar to financial markets I view financial markets and sports as being two sides of the same coin. He said, "Over the course of 1,526 matches, I won 80% of them. What percentage of points do you think I won?" He paused and he says, "54%", barely better than even. In financial markets, you do 54%, you should be banned managing the world's money, as long as you're winners and losers of equal size. But what Federer was really saying is that, it's that little edge that just compounds over the course of the match. If the match was just one point long, then Federer would win 54% of his matches. But the fact that it goes on over time means that he's got time to regroup, even though he loses a point. Is that little edge that just keeps multiplying over time and so the longer the match the more matches he'll win, of course, as long as he doesn't get exhausted, so stamina also matters. Boris Becker, by the way, won almost 80% of his matches with only less than a little over 52% winning points because he had a tendency to win the really important ones like tiebreakers. But that's the point is that you don't need to be perfect. You don't even need to be really good. You need to be just slightly better than the average or some benchmark in order to be successful. That applies to almost everything in life as long as you're, slightly better, that edge will just continue to compound and that you'll get better and better and your outcomes.

Do you think that some of the same principles you've applied to systematic investing on a short term basis where you're looking for a higher probability trade with a shorter duration apply on the other side to long term investors like myself.

Vasant Dhar: They do and for the reasons that you pointed out that you need numbers. In fact in 2015, I went to my colleague Aswath Damodaran because I believe that machine learning and quantum methods really applied to short term trading, where you could identify an edge where there were lots of numbers involved. But it was hard to apply to long term investing where withholding periods of many months or even years. Because you just couldn't get enough sample size, you couldn't get enough training data. But I was really intrigued by my colleague Aswath Damodaran who's considered Mr. Valuation on Wall Street and so I went to him in 2015, and we had this conversation about whether it would be possible to create a bot of him. I had a similar conversation with my colleague Scott Galloway at that time should you trust your money to a robot? I'd just written this article. Should you trust your money to a robot? I made the case that you should, when it comes to high frequency trading in short term. But when it came to long term investing, that it was impossible to train a machine like you could with shorter duration stuff.

Remember, Scott, and at the end of that conversation saying, so what you're saying is that trading flows will disappear, but venture capital and private equity is safe. I said, "Yeah, that's pretty much it". My conversation with Damodaran was similar, that it would be too hard to actually try and replicate him. What's interesting is, post ChatGPT, we revisited that question. I went back to the Damodaran and I said do you think we could actually build a bat of you now given this new technology? He said, "Sure, let's give it a shot. You got all my training data", and so that's what I've been involved in for the last couple of years. We've built this bot that's designed to think like him, and my initial thinking was that we could apply that systematically, as well that we could just apply Damodaran to the S&P 500. It's impossible for him to do it because he can't evaluate 500 companies in a day or even in a week. It's just too much work. But my thinking was, if we can build a machine like him, why can't we just apply it to the entire index and then use it systematically? It's an interesting idea. It may actually work. But I've actually become intrigued with a different type of application of the bot, which is something that allows people to think and reason about companies in a deeper way to run scenarios and say "What if Trump escalates tariffs, what will valuation of Apple or Nvidia, look like, or what if this tariffs a head fake and we go back to the era of low trade barriers". This stuff is very laborious for people to do and it's very time consuming. I find it interesting that we can apply AI now systematically to long term investing as well.

Asit Sharma: What was the thing that surprised you most about the Damodaran bot? Basically, you had access to all the training materials, famously public materials. You also had access to Professor Damodaran's very elaborate write ups, his blog post, which they themselves, if you marry up the public spreadsheets that he has for investors, they're an object lesson in how you draw together numbers and narrative what surprised you most in this latest phase, post ChatGPT, where you took more modern tools, let's say, more contemporary tools and recreated the idea maybe the biggest success you had or the biggest pitfall that you didn't expect.

Vasant Dhar: When I started this two years ago with one of my colleagues, Joao Sedoc who's a LLM person, we had no idea whether this would work. It was a wild idea to build a bot like him we tried what most people might try, which is give all his valuations to an LLM, fine tune it, and then have it think about a new case. It just didn't work. It didn't sound like him. There was nothing deep about it. There was nothing profound about it. We just went back to the drawing board and said, just identify all components of his thinking fundamentally, he's got this quantitative model that he calls the Ginsu.

Asit Sharma: This is a spreadsheet.

Vasant Dhar: It's a spreadsheet.

Asit Sharma: I've used it.

Vasant Dhar: It's incredibly complex. It has all kinds of switches and contexts and all that kind of stuff. But at the end of the day, it's a quantitative model. Inputs gives you a valuation, you do a sensitivity, and there you have it. But the question is how do you marry a story to the numbers. What's the story that is consistent with the numbers? The story involves stepping back from the particular company. I'll give you a great example. When he evaluated Nvidia in 2023, the first question he asked, I called this a framing question was, is AI an instrumental or a disruptive technology. Why would you ask a question like that? You ask a question like that because the markets in those two scenarios tend to be very different. If it's incremental, it's pretty well defined you can put a boundary around it. If it's disruptive, it's much more uncertain. You need to think about what that really means. Disrupting what? Every industry is AI like electricity? Is it like the Internet. It makes you think about the problem in a really broad way. Then his subsequent question when disruptions happen, what's the distribution of winners and losers and he shows that you get a few winners and lots of losers, lots of wannabes. He says, "I think Nvidia is going to be a winner. They're going to have a dominant position", and then he goes about thinking about it like what are their margins going to be like? He says, "What are the margins of people in the semiconductor industry?" That's a good place to start and the work of Phil Tetlock, by the way, also applies here. He has this work on super forecasters what makes them good and what makes them good is that they anchor themselves in the right part of the problem as opposed to a biased part of the problem. They tend to be relatively unbiased. I realized that Aswath Damodaran was what I call a super forecaster. He just has those properties of what Tetlock calls super forecaster, the ability to really ask the right kinds of questions and insatiable curiosity of anchoring himself.

Asit Sharma: If you had a scale today, where would we be waiting more toward that we will govern AI or AI will govern us, and why?

Vasant Dhar: My fear is that we are slipping into a Huxleyan kind of world, perhaps even without our realization that we are gradually disempowering ourselves in many areas of our life. The machine has become a gatekeeper of human activity in many ways. You apply for a job, you're screened by the AI. You might even be interviewed by the AI increasingly these days. It's not a warm fuzzy feeling, when the machine has become a gatekeeper to human activity. My fear is that we might just slip into this without the machine having evil intentions or being programmed to do harm, that we just slip into this without our explicit realization. That's really my concern.

Asit Sharma: Which stakeholders do you think would be important to ensuring that we don't slip into such a future? Obvious answers would be, governments, perhaps we need regulations, academics, also big tech, but I don't know. What about people who use the machines as well? Who are the stakeholders that should put a voice forward in this decision?

Vasant Dhar: They more than anyone else, everybody. That's why I wrote the book for everyone. I meant for this book to be accessible to everyone because this applies to all of us. I tell my students this as well that it's easy to use this technology as a crutch. It is so tempting to use it as a crutch. But that'll in the long run, will be debilitating. You don't want to go down that road where you got a question and you just throw it to ChatGPT and say, what do you think? Because that's the surest way of going into cognitive decline. I can feel it, by the way, when I use maps. I don't think I navigate as well spatially as I used to. I think I've lost that facility by relying more and more on maps, and I'm aware of that. I now try and navigate myself manually sometimes just to keep that spatial mental muscle alive. That applies to all areas of our lives. Individuals, more than anything else, need to ask themselves how they're consuming this technology. As it is, my colleague Jonathan Haidt says that some of these social media platforms have caused tremendous harm to teenagers. We ain't seen nothing yet in terms of the potential harms that AI could cause if we just let it go unfettered. It's a tough area because as someone said, I was reading a piece by Ezra Klein this morning, where he said who are we to tell people what to consume? Sam Altman said, "We don't want to be the moral police of the world. We'll open ChatGPT to adult content". All true, all fair. But that's why it imposes the burden really on the consumer. Among all these people, the burden really is on the consumer to be aware of how you're consuming AI. As I say in my book, you can consume it to become super human. It can really serve to amplify your skills if you use it in the right way. But if you become dependent on it, it'll lead to cognitive decline, and that's no good. That's one of the points I try to make in my book is how to think about that, how to think about being on the right side of what I see as this impending bifurcation of humanity.

Asit Sharma: I think one of the clearest examples of all this is a choice you make that you describe in the book. Some people asked you why don't you use ChatGPT to write the book. You said, "Right now, machines don't write as well as us for now". I get that. But I think also underneath that is the desire to express yourself in your own unique style to make the points that you want to make and to have your expression, which is beautiful. By the way, it's a great expression and well written book to be the statement that you put into this work, not to rely on the crutch, just because it would be easy. To input some bullet points and perhaps spit out the product and you go talk about it. It's a world of ideas that you're putting forward. I really appreciated that part of your book, which was "Hey, there's a reason that I'm writing this myself".

Vasant Dhar: Exactly. By the way, thank you for that compliment. I really appreciate that. But in addition to the fact that I think I write better than ChatGPT and I want to express myself in my own style, it's also so much more fun to do that. At the end of the day, what's life about, if not for having fun? Life is about having fun, and this should be fun, and I had so much fun writing it. There's so much of sense of accomplishment and satisfaction from producing something good by yourself. That's what we should strive for.

Asit Sharma: Professor Vasant Dhar, this has been an extremely illuminating conversation. Above all things, it's been a lot of fun. I really appreciate your time today, and I hope that you will come back for another conversation at some point in the future.

Vasant Dhar: I'd be delighted. Thanks so much for this Asit. I really enjoyed it. Great questions, and I love the conversation. Thank you again.

Asit Sharma: Thanks.

Mac Greer: As always, people on the program may have interest in the stocks they talk about, and the Motley Fool may have formal recommendations for a gift, so don't buy yourself stocks based solely on what you hear. All personal finance content follows Motley Fool editorial standards and is not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only. To see our full advertising disclosure, please check out our show notes. For the Motley Fool Money Team, I'm Mac Greer. Thanks for listening, and we will see you tomorrow.

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