Professor Vasant Dhar (3:04)

Delighted to join you, Chris.

Chris Staigle (3:05)

Yeah, we're very excited to have you here. You and I had the opportunity to have kind of like a pre interview and that opened the, I guess my curious nature. So I started doing some research and one of the things that I most recently found was a TEDx talk that you did eight years ago. And in that talk it was about can we trust the machines? And it was AI and you were way ahead of the conversation as it, as it stands today and, and current commerce where the general conversation tends to be artificial, narrow intelligence, generative AI and those sorts of things. So if you don't mind, maybe recap some of the thesis that you shared in that talk and I'd like to explore how, how those have aged eight years on in the AI game.

Professor Vasant Dhar (3:51)

Sure. So the title of that talk, if I remember correctly, was you know, when should we trust machines? Right. So when should we trust AI? And let me just give you a little bit of the genesis for that question. Right. Because I've been in AI since 1979. You know, I got into the field as a PhD student. You know, I did my thesis in an area called planning and then I brought machine learning to Wall street in the, you know, early to mid-90s. And I created a hedge fund, like the first machine learning based hedge fund that continues to operate, you know, trades every day. One of the questions that intrigued me because I'd been doing, you know, prediction financial markets, but I'd also done stuff in the healthcare arena, various other business applications. And the question that really intrigued me was why was I willing to trust my algorithm in finance? That was wrong almost half the time. And yet I wouldn't trust it, let's say in healthcare, where it could be right much more often or for a driverless car where I'm really hesitant to take my hands off the wheel. I still don't trust driverless cars on highways. I mean I would gladly take a Waymo in San Francisco, but I'm not going to take my hands off the wheel. On the highway at 70 miles an hour. So that was the question, why? And the answer now in retrospect seems mind numbingly simple, which is that it depends on two things. It depends on how often the algorithm will be wrong and the consequences of its error. So the way I saw it was that if a machine is never wrong, then you should absolutely trust it. It never gives a wrong answer. You should trust it, right? So on this, so I created this sort of spectrum of predictability from 0 to 1, right? 0 being completely random or some benchmark, and 1 being it's never wrong. And so that was one axis. And the way I saw it, you could position problems on that axis. So predicting financial markets is about as close to random as you get. It's a very difficult problem to predict. You know, you're not going to do better than, you know, 52, 53% accuracy, you know, on a daily basis, like not happening. It's just like too noisy a problem. Whereas, you know, in travel, the scars, you're, you know, raw. You're right, like 99.999997% of the time, like extremely accurate. But the problem is that when that driverless car makes a mistake, the consequences are catastrophic or can be right, it's death. Whereas if my trading algorithm makes a mistake, I lose a lot of money. It's not the end of the world if I've got lots of positions on. So essentially my cost of error is low in finance and trading, whereas cost might. Cost of error in driverless cars is extremely high. So these two axes essentially create a map of trust, right? On the horizontal axis there's predictability, on the vertical axis there's cost of error. And so on the bottom right, you should always trust the machine. And on the top left, you should never trust the machine because the cost of error is too high and it's going to make lots of mistakes. So I labeled these like trust the machine zone versus don't trust the machine zone. And they're separated by this automation frontier, which is something that AI enables us to cross. So sports refereeing has crossed the automation frontier, right? Machines make calls about whether in tennis, whether a ball is out or in. In baseball, it's increasingly the case. And cricket, increasingly the referees are becoming automated, right? Urban taxis have crossed that automation frontier into the trust zone, right? We begin to trust them, right? High frequency trading, same thing, right? You know, you trust the algorithm, you know, because the cost of error is low. So that's sort of the map of trust that I presented eight Years ago. And your question about how, how has it aged? Well, it's relevant more than ever today, right. So in 2018, when I first presented it, you know, ChatGPT was three years from coming out, right. I was talking about sort of traditional AI at that time. Right. But now the problem has become even more important. We're using it for all kinds of applications. Use an LLM to check a legal contract, for example. But the question becomes, can you trust it? Let's say you have a legal contract, you give it to the machine and say, check this for me and the machine says it's okay. Would you trust it? So that's the kind of question that that trust heat map is implying. And the answer is, if it's a simple legal contract, chances are I would trust it. It's a few pages, it's seen lots of them, what could go wrong? But it's a complex one. Then I'm not going to just trust the machine that says, hey, it's good, I'm going to have to double check it and verify it. In which case the question is, what good did the AI do you in the first place? Right. If you're going to check it anyway, did it save you any effort at all? So these are the questions I see as being like very front and center now to the future of work. Now that AI is becoming pervasive. And in my book I talk about sort of this general intelligence paradigm where the machine has learned something about everything and that something is getting deeper and everything is getting wider.

Chris Staigle (9:07)

As I'm talking to business leaders, the kind of the three things that are preventing them from feeling comfortable about going all in on their efforts to just remain viable in the space with, with introduction of generative AI at all levels of the business. There's three things. One, I don't understand the risk and therefore I can't really do much with AI because saying that I like is a confused mind always says no. It's safer for the business owners at this point to simply say no because they don't truly understand. How can my people screw this up? How can I screw this up? Right? The second thing that I'm hearing a lot is we'd love to use AI more, but we don't know where to start. So kind of that, that use case identification. If it's something like, like we just talked about, if the risk is very high and I'm not aware of how to judge that risk as a business owner, big mistake. And then the third thing is we'd love to do more with AI, but we don't have anybody to help us. I don't trust a YouTube video or a TikTok reel.

Professor Vasant Dhar (10:09)

Yeah.

Chris Staigle (10:10)

Considering this, this evaluation of this, this risk line. This, this.

Professor Vasant Dhar (10:17)

Yeah.

Chris Staigle (10:19)

How would you counsel a business leader who's maybe those are the three main things that are keeping them from really embracing this in their business.

Professor Vasant Dhar (10:28)

Yeah.

Chris Staigle (10:28)

And getting benefit of what we know is possible.

Professor Vasant Dhar (10:31)

Yeah. I mean, you know, you hit the nail on the head, Chris. These are the three most important problems, right? So if you start with the last of these, which is like, do I have the talent to implement it? This is a huge barrier to entry, Right. Because unless you're working with a team of people who have a demonstrated track record of success, right. That they've actually done something successful, chances are you should be skeptical. That is, you can't just hire talent off the street, you know, and say, all right, guys, you know, you guys know a lot about. You guys are PhDs in AI. You know, go do it. Right. That's a risky proposition. Right. So you need people who have, like, some sort of a demonstrated record of successful projects in this space. Right. And these days, it's particularly important because lots of wannabes, you know, lots of people sort of saying, yeah, I can do it. Right. You should be skeptical. Right. So talent number one, very important. The two other things you talked about was like, where to start and the risk, Right.

Chris Staigle (11:37)

Yeah.

Professor Vasant Dhar (11:38)

So the where to start is. You know, you're probably familiar with this MIT study that showed that 95% of the projects failed to provide any kind of value. And that's a great example of a number of things, because to me, that's not the problem with AI. It's a problem with the choice of problem. Right. You're just choosing the wrong problem, or you're letting someone choose the problem for you. Equally bad. And to me, that's a leadership issue. As a business leader, you have to be capable of asking the right questions and identifying those projects that are associated with those questions. So if I'm the CEO of an insurance company, right. The question I need to ask him to need to ask myself is for, for example, is how can AI make my underwriting process better? That's a fundamental question. There's no doubt in my mind that that's a question that there could be others. But that's one question that a leader should be asking right now. As it turns out, I've been through this exercise with an insurance company where the head of the business says, this is what we want to do. And I said, what does that mean? I. And the answer was, I don't know. I don't know what it means. I need you to help me figure out what this really means. And it took us three to six months to actually figure it out. But we did figure it out. In this particular case, what it boiled down to was, so we get prospects as an insurance company, we get prospects from brokers. And so the question, one of the questions was, are we getting better prospects from some channels than others?

Chris Staigle (13:25)

Fair question.

Professor Vasant Dhar (13:27)

And that question had not been asked. That question had not been asked. You know, big miss right? Now, the data for that did exist within the company because they've had lots of leads from lots of brokers, right? But no one had answered this particular question, which was, is there a difference in the levels of risk that we're getting from different brokers? Are there certain brokers that exert tremendous market power and force us to take on contracts that we shouldn't be taking on? And so that was the question that we discovered as being direct, directly related to how do I improve my underwriting process? Well, one of the reasons, one of the ways to do it is to sort risks and take on better risk. And now there are other issues like, is my process, is my internal process good, or can I improve the internal process? Right? But to me, this is a leadership question as to where to start. And to me, the MIT study demonstrates or suggests that people just don't know where to start. Maybe there was fomo. We need to get started. Let's try something, right? And that's not usually a good idea, right? So that's an extremely important problem is, you know, a question where to start? Which problems do I start with? And that's fundamentally a leadership issue, starts with the CEO, right? That's it. The third, the first point you raised about risk, that plays directly into my trust framework, right? Because if you're going to take on problems where there's a high degree of predictability, right. Chances are you'll succeed, right? Conceptually your odds of success are higher. So this sort of ties into that second question, which is, where do I start? And where you start is a function of those projects where the likelihood of success is high, right? So you have to estimate that, and that varies within each organization. So you have to, like, say, okay, There are these 50 possible projects we could do. Can we rank them by probability of success? And you should be able to rank them right? Now, this isn't an easy exercise because it might involve asking yourself, do we even have the right data to answer this question or might we have to acquire data? And so there's a lot of sort of data uncertainty involved. So the more the data uncertainty involved, the higher the likelihood of failure. The lower the data uncertainty involved, the higher the chance that you'll succeed. So the example I gave you earlier about am I getting better risks from some channels than others? You have the data for that, right? That question is actually answerable from the data. So that's the way I would look at these three questions of risk of where do I start? And of talent. Because the talent part is sort of the technical uncertainty, right? Do I have the chops to solve this problem? Even if I have the right data and I have everything else, do I have the right people? Do I have the right talent? So there's this sort of data uncertainty, talent uncertainty. And so what's the likelihood of success? And then the last piece of this is estimating what the payoff is. Like what's the size of the opportunity, right? So if you multiply the probability of success by the size of the opportunity, that gives you some sort of an expectation, a number that you can use to rank this potential project relative to others.

Chris Staigle (16:59)

So for any of those, those of you who are listening who are a little savvier with using the models, it we didn't give you the A plus B equals C formula here, what I would suggest is you re listen to this or take this transcript, plug that into the model, ask it to evaluate the perspectives that were just shared through the lens of your business or your situation. Because I think what was just outlined was it's a two sided coin, it's not easy. But there is a way for you to mitigate the risk of the introduction of generative AI in your business significantly by looking at it through the lens of the perspectives that were just shared by Professor Darr. So that was, that's fantastic. I was hoping that there might be a silver bullet, but doesn't sound like there is at this point. And for those of you that were, if you're not familiar with the MIT research that was discussed, I think it came out maybe in the fall. MIT and their study that, that got a lot of headlines was that 95% of pilot projects were failing. So I can tell you that the response that you gave just then, Vasant, was, was spot on. It had a lot to do with probably the, the pilots that were selected and the lack of talent that was available to execute those pilots within those businesses. So again, dear listener, don't fear we're not seeing those results when we're working with clients. So all is not lost. You just need to do it the right way. So, Professor Doer, what are some of the trends, maybe, that you're seeing? Because you're not just working with obviously ambitious students at Stern School and the Data Science center, but you're also obviously communicating with, I guess, business leaders, for sure, considering your location, what are some of the trends that you're seeing today when it comes to, I guess, green shoots of opportunity for businesses of all size?

Professor Vasant Dhar (18:50)

You know, the trends I guess I'm seeing today are that leaders are beginning to really sort of engage with this problem. Right. What I'm seeing is like, a serious engagement with this problem because I think people do recognize that this is a pivotal technology. It's here to stay. You know, it ain't going anywhere. It's here to stay. It is likely to be transformative. And, you know, the question is, how is it, you know, how is it likely to transform my business? And I've got a question.

Chris Staigle (19:33)

Is the sentiment that you're seeing, is it fear or greed?

Professor Vasant Dhar (19:38)

I would say it's mostly fear.

Chris Staigle (19:40)

Okay.

Professor Vasant Dhar (19:42)

If you were to ask me which of those two dominates, I think at this point, what is dominating is fear. There's fomo, but there's also, like, a fear of, like, genuinely, like, missing the boat. Because, you know, like I said, this is transformative, and it really involves, in many cases, just rethinking the way things are done, right? And that's. I mean, that's easier said than done. You know, that. How do you rethink something that, you know, that you're doing? Because organizations get into modes of sort of operating procedures, right? For good reason. You need these operating procedures, but then those really get sort of ossified into the way you do things. It's very difficult to, like, dig your head out of the sand and say, could we be doing that thing completely differently? Why do we need 50 people to do that? Right? I mean, maybe we need five. You know, maybe the 50 people should be doing so much more than they're capable of doing. You know, I was talking to the chief AI Officer of Morgan Stanley recently, and he said, you know, what this has enabled us to do is take on projects that we couldn't take on earlier, right? So if we had, like, 200 potential projects to do, you know, in the ideal situation earlier on, we could only do 50 of them, you know, for budget reasons or whatever, that's. That's all we could afford. Whereas now we can go much further down that list of things. Right? So our ambitions have increased. Right? Right. So with the same number of people, we can do a lot more. Right. So that's the kind of thinking I'm seeing in organizations that are sort of really kind of pushing the frontier. And I'd say Morgan Stanley is probably one of them, that they are sort of pushing the frontier and saying, like, how can we do more with less? Right. Because software is the killer app of generative AI. And so how can we leverage this? Now? By the way, I spoke at an event last night which was about using generative AI for creating software or enabling programmer productivity. And the interesting thing there was that again, the issue of trust comes up. So on my trust heat map, I said, where does software lie on this trust heat map? Right. Would you blindly trust a machine that generates the software? And the answer is, well, maybe sometimes I would, but sometimes I wouldn't. Like, if it's mission critical software, I want a human looking at it. Right? In which case, how much of a productivity gain am I getting? Probably not orders of magnitude, right? Maybe the machine can generate stuff, but I still need humans to look at it and verify it and run unit tests and run regression tests and all that kind of stuff. That ain't going away. On the other hand, if it's a low risk kind of application where it's internal and it doesn't really matter if the machine makes mistakes, I still get a lot of productivity enhancement, then I can probably use it, like right out of the box, generate the software, run stuff. Yeah, it makes errors occasionally, but I can tolerate them. Like the cost of error is low. That's the kind of stuff I'm seeing across the board. But the issue of trust is like front and central to all of these applications. You know, like, when should I trust the machine? What sort of human involvement do I need to verify the outputs? Like, how easily verifiable are the outputs? You know, the more easily verifiable they are, the easier it becomes, you know, for the human to trust it. So, you know, to sort of summarize where I'm coming from. Right. You know, you asked me, like, what are the green shoots? What are you seeing? Seeing fear. I'm seeing real movement. I'm seeing higher ambitions. Like, we can do more than we used to be able to do because this thing is a real productivity enhancer, so we can do more. And then other questions around can we trust this thing or do we need humans in the loop to verify its outputs so broadly Speaking those are the kinds of trends I'm seeing out there.

Chris Staigle (24:03)

Yeah, interesting, because you're talking to a different audience than I typically do. I'm usually talking to lower middle market sized businesses. And you've got the opportunity to speak to Wall street and you know, known brands for sure. And I was looking for maybe what are the parallels that are being that are concerns at one end of the spectrum versus the other. And it sounds like there's a lot of similarity. Now you mentioned something and I want to just kind of take it, not a quick tangent, but you mentioned that they have some concern about missing the boat. I'd like to see if you have any prognostications on the timeline that exists before there really will be a, an obvious have and have not in the business world with those who adopted, went through the pain of growing, testing, shifting culture, all that stuff. And those who said, oh, we'll just wait and see what happened. What do you think that, how long do those guys have?

Professor Vasant Dhar (24:53)

You know, that's a great question. And one could sort of come down on either side of this, right, which is that, you know, look before you leap versus he who hesitates is lost.

Chris Staigle (25:07)

Yeah,

Professor Vasant Dhar (25:10)

there's a truth to both of those things. And I would say that in this case the look before you leap is probably more important than he who hesitates is lost. You know, that it's important to engage in sort of what Kahneman called slow thinking, you know, that just like, you know, take your time, figure it out properly. Right? Figure out, you know, what the most important sort of questions are that you should be asking, you know, figure out, you know, which other projects you should be taking on and why and be really clear about those, you know, be clear about the uncertainties involved and how you're going to navigate them. To me, those are the important questions.

Chris Staigle (25:50)

I like it.

Professor Vasant Dhar (25:51)

I don't think you're going to get left behind if you wait a week or a month or whatever. In fact, the argument I've seen for delay is that, hey, the speed at which things are moving is very relevant. That six months from now you're going to have capabilities in AI in these tools that you don't even have now. Right. We've seen this even in the progression of these chatbots becoming, let's say, multimodal, right. Initially it was just text, then they became multimodal. They could idle images and numbers and spreadsheets and all that kind of stuff. Right? Now, if you had started early, if you started way too early, you'd be like, Reinventing the wheel to a large degree. Right. You'd be trying to do this. And I did do that, by the way. I did some projects before LLMs came out where it was harder. You could still succeed, but you had to build a lot of the machinery yourself. In retrospect, that was like effort down the drain. If you had just waited, we could have done so much better. So there's a case to be made for that for multiple reasons, which is look before you leap, because the cost of error of like, choosing the wrong projects is way too high.

Chris Staigle (27:12)

Yeah.

Professor Vasant Dhar (27:13)

You know, so take your time, be clear about what you want to do and why. And in a sense, time is on your side. Right. It's your friend. Because the technology is improving and you're sort of, you know, you get more for the, you know, for your buck the later you start.

Chris Staigle (27:28)

Yeah. You know, in our pre interview, you mentioned this concept, and my perspective was, hey, I was an early adopter of generative AI. Pretty much as soon as it came out, we were doing, we were trying to break it, not GPT1.2, but 3.5 and beyond. So since November of 2022. And I thought that I had an advantage in the space because I had been playing with it, working with it, using it since then, as compared to somebody who was just getting started today. But I have to agree with you, and I want the listeners to know there's hope for you if you haven't gotten started. As the technology improves, it gets easier to use. Now, there's a couple things that you said there that I think are important. You didn't say, oh, just wait till it's easy. You said, continue to think about it, but think slowly about it. So for those of you that are listening, if it's like, yeah, we need to do something, we need to do something. And you've been saying that for several quarters now. You need to do something, even if it's just starting to really take a serious cogent, like, what are we, what does this mean for our business? But at least start thinking, even if it's thinking slowly. I think that's great advice for our audience. And secondly, for those of you that are feeling that fomo, the, the perspective that was just shared was those who hesitate is lost is not the approach. So, you know, it's maybe not what you're hearing from me because I'm a go, go, go kind of guy, but I think that that advice that we just got is, should certainly be considered. And if you're doing it, you're in motion, you're at least off the fence, so.

Professor Vasant Dhar (29:02)

Exactly. And by the way, it's essential to get started because, you know, you may not. So one of the realities of organizations is that leaders are very far removed from the details of their data. Right. They don't have a clue. Right. And very often they're not going to realize that you can't get there from here because you don't have the right data or you're, you know, and, and I, and I, by the way, I went through that with this insurance company. That is when we actually started looking at that data. Right. And, you know, if you've dealt with data at all, you know that it's a messy endeavor. You know that there are fields missing, you know, some of the values are wrong. You know, this is just part and parcel of dealing with data. And, and leaders need to get on board with really understanding what they have and the quality of it. And they don't do it unless they actually start asking questions. Right. It's only the question asking that leads to some kind of learning where people say, oh, like, you know, our customer database is pretty solid or it's pretty messed up, or we need to acquire external data to augment what we have. Right. These are steps that are essential. There's no getting around them. So in that sense, you need to get started.

Chris Staigle (30:21)

Yeah, yeah. Because you won't discover those things until you get started.

Professor Vasant Dhar (30:24)

Until. Yeah, until you get started and until you start asking the right questions.

Chris Staigle (30:28)

And even if you're not planning on jumping right in, you're at least preparing the playing field for when that time is right. You're not starting from scratch. You've already set the conditions of the company beginning its engagement with. With Generative AI. And again, on this podcast, we don't really talk a lot about data science and things like that. I would consider that like a level two discussion for most of the people listening to this. They just want to get started. But that's, that's an interesting question. How does, how does somebody who's been doing this since 1979, when AI was a different definition of what most people think of when they talk about AI today? What do you think when you see, like, the new person that's like, oh, I'm an AI expert because I've been using chat GPT for. Is it two different paths of AI and one is not gonna say independent from the other, but is it necessary that a business leader. Because I know that there's remote learning classes even from perhaps Stern, but I know that MIT has one, Chicago's Booth School has one, Miami's Howard School. They've got these Chief AI Officer certifications. And when I talk to individuals who have done those, they were attracted by the pedigree, but they leave the class with an understanding of things that aren't necessarily practical for, let's call it transformation. They understand the mechanics of machine learning, they understand what a neural network is and those sorts of things. But they don't learn how to do what we call, at least in our organization, think in AI. I'm encountering an issue with the business. I don't know what to do. My reflex has now become let me go to the models to ideate a solution or to get closer to a decision.

Professor Vasant Dhar (32:09)

So I'm going to say something and I apologize if it sounds self serving because I don't mean it to be as such. But one of the reasons I wrote this book and the way I wrote it was to be accessible to everyone, to students, teachers, parents, grandma, policymakers, my colleagues. Smart. Because there's, I mean, I'm a big fan of Bob Marley. And one of my favorite sort of lines of his is if you know your history, then you'll know where I'm coming from. And I think it's important for people to understand the history of AI that this isn't something that has emerged in the last three years. It's been around since 1956. I mean, I only got into it in 1979, 23 years into its existence. But our ambitions at that time were really high. One of the things we were trying to do was get a machine to write programs. Automatic programming, it was called. Sound familiar? Right. And that vision has been realized. So the vision of AI right from the 70s was about telling the machine what to do without telling it how to do it. That's been the vision of AI for a very long time. Just declare what you want and the machine will figure it out. You know, agents have been around since the 70s, but the reason we're so excited about it now is because you just tell an agent what to do and it figures out how to do it. Right? It writes the program, it knows how to communicate with other applications. You don't have to like do the messy job of writing code and verifying it, all that kind of stuff. It does that for you. So the ambitions were there way back then, but why couldn't we achieve them? Like what were the barriers? Right. And so one thing I do is I walk people through the sort of begin with the history of AI, the scientific history of AI, right? From the paradigm of expert system of specification to machine learning to deep learning, to what I call general intelligence. And so it's important to understand this evolution. It's important to understand what the goals of the field have been, what the barriers were, what was achievable, what wasn't, and what's led us here, you know, so it's important to understand the history because without that, you're functioning at a very superficial level, right? You're just saying, oh yeah, these deep nets, you know, they're amazing, right? But you don't really get beyond that, right? You don't really understand why they're so good, right? And one of the points I make is that the fundamental difference between modern AI and AI that existed for the 67 years preceding that was that this boundary between expertise and common sense has broken down, right? In my 40 plus years in AI, we artificially created a boundary around expertise and said, this is medical knowledge. Guess what? Medical experts don't just use medical knowledge, they use common sense all the time. You walk into your doctor's office, he or she is using a lot of common sense in addition to that domain knowledge, right? These things like, blend seamlessly into each other. And that is the nature of human activity and human thinking, is that we don't even think about it. We're unaware of it. And this is what modern AI has done. It just sort of broke down this barrier. And in one of my podcast episodes, on my podcast Brave New World, I asked my guest, Sam Bowman, I said, what happened? Like, you know, you know, what was the difference between pre chatgpt and post? And he said, serendipity. Essentially what he said was Google wanted to do sentence completion in Gmail, and that required predicting the next word in a sentence and the word after that and the word after that. And that was a problem at just the right level of difficulty for which there was lots of data available to solve that problem, right? And it was basically like a prediction problem. Just predict what's going to come next. Predict what's going to come next. Some people say, well, these machines aren't really intelligent because they're. All they're doing is like predicting the next thing and sort of generating a sequence. But I think that's a little uncharitable because in order to speak flawlessly, that is, you know, always generate stuff that makes sense, you have to know a lot about the world, right? So the serendipity was that in order to do sentence completion, we had to force the machine to learn about the world in general based on the collective expression of humanity on the Internet. And that's been the big deal. That's been the sort of leap forward in modern AI is this sort of dissolution of the boundary between common sense and expertise. Because the LLM has no idea whether it's talking to you about a deep medical case or whether it's talking about something frivolous or. Or whether it's talking to you about shows on Broadway. It doesn't know, it doesn't care. And so people really need to understand AI not in terms of the technical details, but sort of functionally, why have we gotten to where we are? And if you understand that and you understand what's similar to the kinds of things we were doing in the 80s and the 70s and what's different, then you have a much better perspective on how to think about AI and how to think about its applicability to business.

Chris Staigle (37:30)

This is interesting because we have a chief AI officer at our company. At chief AI officer, his name is Adam Lyons. And Adam, when he's talking to people, he tells them, well, we don't have artificial intelligence yet. And one of his perspectives is that Xai, they don't call their product Grok AI, their product is Grok. OpenAI is the company, but the product is Chat GPT, Claude AI or Anthropic AI is the company, but the product is called Claude. So to him, it's an indication that these companies that we haven't achieved true artificial intelligence yet, because if it was, they would include that in the product name. But being as litigious as the US is that false advertising could be an issue. If they were calling it Grok AI or ChatGPT AI, what are your. How do you take that?

Professor Vasant Dhar (38:19)

I'm not sure I fully agree with that. I agree with his premise that. Well, part of his premise that AI isn't that we have not reached the promised land yet, whatever that is. Right.

Chris Staigle (38:37)

Yeah.

Professor Vasant Dhar (38:39)

Now, I'll just sort of backtrack a little. If you think about AI, it's the only technology that has been designed without any purpose in mind. Every other machine has a purpose.

Chris Staigle (38:51)

Sure. Okay.

Professor Vasant Dhar (38:52)

You want to make a car, you want to drill a hole. It has a purpose. AI has no purpose. It has no purpose other than to be intelligent. And in my history of AI in the field over the last 45 years, the one thing I can tell you is that the goalpost keeps moving. Right. Anytime the machine does something, people say, oh, yeah, but that's, you know, it can't do this yet. Right. So, you know, when I got into the FIELD in the 70s, there was a philosopher called Stuart Dreyfus who said, look, machines will never really be intelligent because they're not going to be able to do even basic things that humans do so easily, like drive a car. And at that time, it was inconceivable to think that a computer could, like, drive a car. You know, it just wasn't in people's conception, right now that computers can drive cars. We say that, okay, that's good, but it doesn't do this. So we keep sort of shifting the goalpost. And I would say that in this paradigm of what I call general intelligence, the machine is incredibly intelligent about some things and not so on others. Right. So the intelligence is, is very uneven. But that's okay, you know, I mean, that's what we should expect of machines because they are not like us, Right?

Chris Staigle (40:13)

Yeah.

Professor Vasant Dhar (40:14)

You know, as Jeff Hinton says, you know, it's like an alien has descended on the planet, but we're having a hard time taking it in because they speak such good English. Right. So, you know, but, but that, that. But that's what we have. We've got this alien species cohabiting the planet with us, you know, of our own creation. But the species thinks very differently from us. Why should we expect it to be like us in every way? Right. So I'm a little bit sort of, you know, when I think about, like, AGI, right, this sort of obsession with AGI, it baffles me because I don't see why machines should be like us in every way. In some ways they're going to be so much better. In other ways, they're not going to be as good as us. Right? They're not going to feel emotion. I'm quite sure about that. You know, unless they sort of become maybe biological, they'll be able to simulate it, but they're not going to, like, really feel emotion the way we do. So they're going to be different from us. But are they intelligent? I'd say yeah. You know, I think we have achieved artificial intelligence to a large degree. And especially now because of this sort of dissolution of the boundary between expertise and common sense. Like, to me, this is like a, you know, an indication that this sort of, you know, that AI has gone from an application to a general purpose technology that I view AI now as much more akin to electricity, you know, than I do to an application. Because general purpose technologies have, like, widespread impact throughout every industry. You know, in the Economy. And that's what we're seeing with AI, Right. So this is, you know, to me it's exceeded its expectations as far as I'm concerned. Right. So through most of my career, AI over promised and under delivered, and now it's doing exactly the opposite. Right. That it seems to be like surprising us. I never imagined that I would see a machine in my lifetime that you could talk to in English. Right. If 10 years ago you told me this, I'd tell you you were smoking something, you know, that's like no way, right. That this was like beyond my comprehension. So in that sense it has over delivered, you know, in the last few years and we're just sort of caught by surprise at how fast it's, you know, going, you know, which is why I sort of end my book with like, you know, are we going to govern AI or will it govern us? Right. And I start with the sort of the 2001 Space Odyssey, you know, example of HAL, the famous line, open the part bay doors, Halloween. And I sort of conjecture whether HAL was actually doing an experiment on the humans, whether it created an edge case to test them and they failed the test, so it killed them. So I know I've gone a little far afield of your original question.

Chris Staigle (43:01)

No, no, it was always awful.

Professor Vasant Dhar (43:04)

But I feel that in some ways AI is actually over delivering now relative to its history.

Chris Staigle (43:10)

Yeah, I like it. So you mentioned your book a few times and I want to make sure that we do talk about some of the key concepts in there. And the book is called Thinking with Machines, the Brave New World of AI. And one of the key concepts that we had kind of talked about in our pre interview was this concept of the bifurcation of humanity with AI splitting us, the listeners, into two groups. Those with deep domain knowledge who use AI to accelerate or augment or amplify, and then those who are the opposite, I guess, the other side of that coin. So I'd like to kind of dig in on that a little bit because I think that that's important for any individuals who are discovering or they're on the AI journey where you see them falling into like which group over the next coming months and years.

Professor Vasant Dhar (43:55)

Yeah, that's a, that's a really big point in, in my book, you know, someone called it the bfi and you know, the, the thinking here is. And so I'll give you an analogy, right. I talk to a lot of parents who express concern. How should I be thinking about it? And my question and my answer to them is that the question you should be asking is, is AI making my kids smarter or dumber? And I do believe that question is answerable. It probably depends on each family. Like when do you talk to your kids? Is it around the dinner table or when you drive them to school or walk them to school or whatever, right? So when do you really engage with your kids? Because it's important to engage. And the thing you want to be figuring out is how, how, you know, can they hold their own in a conversation without the AI? Because if you've got the AI, you can say, hey, answer this question for me. But then you're really using it as a crutch, right? You're using it as a question answering tool and it's doing stuff for you as opposed to sort of making you think in new ways and making you learn things that you never even dreamed about. And so I talk about this bifurcation of humanity where I say it's going to be sort of the rich get richer kind of phenomenon. The more you know, the more AI can teach you, you know, and to be honest, I find myself learning all kinds of new things with AI. Right? That. Because I can ask it the right question, right? I know, I know the question to ask, right. So it's important to be able to ask the right question. And then it's also important to be able to gauge its responses and say, well, is that really true? You know, to push back against it or ask it to verify something. Right. So to have the chops to know whether what it's giving you makes sense and then to sort of nudge it in promising directions, right? Now someone's an expert who's really good at what they do, is able to do this. You know, a biologist might be able to learn something about the physics of, you know, things and vice versa.

Chris Staigle (46:05)

Yes.

Professor Vasant Dhar (46:06)

Because they already know a lot about, you know, their domain. Right. They can now amplify the knowledge in their domain, they can go to adjacent domains, they know the right, you know, what questions to ask, they know how to evaluate the outputs. Whereas people who just say, give me the answer are basically becoming disempowered and entering into a potential cognitive decline. You know, and so the question I get from people sometimes is like, what do you think the relative proportion is going to be, you know, of people who get amplified versus disempowered? And that's a great question that I try not to answer because, you know, as Yogi Berra said, it's very difficult to make predictions, especially about the future. But what I do sort of suggest is guidelines for staying on the right side of this divide. Right. Because you don't want to be on the wrong side of this, you know, and get disempowered. So how do you really stay on the right side of this divide? Right. And this applies to everyone, not just kids. I mean, I used kids as an example because that's sort of the obvious sort of use case. But it applies to all of us, you know, and I suspect that. Sorry, go ahead.

Chris Staigle (47:14)

I was just gonna say that's another concept in the book about the. You, you don't have a choice here. You have to make. You have, you have to opt in. The future is not right. AI versus humans, but humans thinking with machines. So like I.

Professor Vasant Dhar (47:26)

There's no, there's no opting out here.

Chris Staigle (47:28)

No, no, I think that that's. So everybody listening. That is a critical point. This isn't something that you get to say, oh, well, others do it and I don't. Because if you are on the other side of that coin of the others do it. As you said, the opportunities for individuals who opt out or consider that they're not going to participate in this, there's not a lot of options for them and there aren't.

Professor Vasant Dhar (47:48)

And like all other technologies, this really ups the bar for humans. You know, it ups the expectations of everyone. You know, one of the things I talk about in this book, in my book is this. The mother and bot that I've built to value companies in the same way that my colleague Aswath, the mother and values companies considered a valuation expert on Wall Street. Now that I've built the system, we're testing it and debugging it and everything. I'm realizing that I'm thinking about its use case and how it's going to impact work. And the way I see it is it'll amplify people who are really good at doing valuation because suddenly now they have the mother on their fingertips. They have this machine that has a tremendous degree of knowledge about how this valuation guru does valuation. And now you have that available to you, right? It can generate reports for The S&P 500, all companies, at the press of a button. It wasn't possible to do this earlier on, but what this does, it sort of ups the expectations that people will have from you. You're going to be required to be doing so much more because you've now got this sort of really powerful amplifier, right? The expectation isn't going to be that you produce, you know, one report or you evaluate one company every two weeks or a month. You can now evaluate so many more companies. Right. You're able to do stuff that was just not feasible before that.

Chris Staigle (49:16)

Kind of like that Morgan Stanley example, where now, because of the amplification from the tools, they're able to address a lot more of that wish list than they were able to prior.

Professor Vasant Dhar (49:25)

Exactly. Yeah.

Chris Staigle (49:26)

So for the business owners listening to this, the encouraging part is that nobody knows your business as well as you do. And as we just talked about, this idea of the, you know, the experts will get smarter because they know that they know the domain well enough to ask those nuanced or very high leverage questions. That's you, dear listener. You are that person as soon as you start using the tools and thinking in AI. And. And if you're not doing that now, what I would suggest is you. You dedicate some time on a daily basis, 10 minutes minimum. And I think by the third exercise, you're going to have a breakthrough and you're going to go, oh, my gosh, like, this needs to be on my phone. This needs to be what I. What I talk to when I'm walking the dog. I think you're really going to see the value of this, if you're not already there. You mentioned your podcast. I would love for. Because the conversation has been fascinating, and again, I'm in a bubble. You're in a different bubble. And I enjoy the. The being able to see, well, what's going on in that bubble. So this has been great for me personally. But for your podcast, what's the name of that show?

Professor Vasant Dhar (50:30)

So my podcast is called Brave New World. I'm a big Aldous Huxley fan, and to some extent, I feel that the world is becoming Huxleyan, you know, so Aldous Huxley, you know, essentially, you know, for those of you that haven't read the book, I would highly recommend it. Right? But it's this sort of, you know, he paints a dystopian future in a technology, in a society with really advanced technology. So it's called Brand New World, and it's really a podcast about the future that the world that our future selves would like to inhabit. Right? So it's a really broad canvas. I've had some amazing guests, you know, some Nobel laureates, Turing laureates, you know, you know, like, really amazing guests that I've been privileged to have, and I've had some, you know, fantastic conversations with them on topics ranging from, you know, business impact to consciousness to law, economics, you know, just like, you know, the philosophy AI, you know, just like the whole gamut of topics, you know, related to AI.

Chris Staigle (51:38)

Very cool. So for the audience here, I just want you to know the kind of. The idea behind this episode today was to introduce you to Professor Dar's positions on how he's thinking about AI ultimately leading to, if you want to do a deeper discovery. Getting a hold of a copy of the book that was published by Wiley and Sons in November of 2025. And again, I didn't give you the full title earlier, but the full title is Thinking with Machines the Brave New World of AI, which feeds into the podcast as well. Outside of that, where are you kind of sharing your thoughts outside of the podcast and being.

Professor Vasant Dhar (52:11)

Yeah, so I have, I have a newsletter on Substack also Brand New World, which is vasantar substack.com I have a column in Psychology Today where I've started posting on a regular basis, so you can find me in a number of places. The newsletter follows the podcast. So every. I release a podcast monthly. So I'll be releasing it next week. And the newsletter follows the podcast. It describes, summarize the podcast in one paragraph and then know talks about, you know, something else. So the next, my next episode is about fiction machines, which is what AI machines really are, right? They generate fiction. And one, one of the questions I consider is, you know, and I was talking about this last night to a bunch of executives, you know, I said I'm actually surprised at how often it is truthful and correct, considering that AI machines are not designed to be truthful. And this is something that people should be aware of. So when people say, oh, machines hallucinate and that's not a good thing. Well, actually they don't hallucinate, they confabulate everything. So everything the machine generates is a confabulation. And one of the things I talk about is I'm amazed at how often these confabulations turn out to be truthful and correct. Right. That's what amazes me considering that they've been trained on everything, you know, truth, lies, opinions, everything. And yet somehow, you know, we've, through this reinforcement, learning, human feedback, we've kind of fine tuned them or channeled them to generate outputs that we find good or acceptable or useful. But there's a lot of stuff like lurking under the surface there, you know, that, you know, that can erupt. Right? It's all there. It's all there. It's learned everything about us, including some of our undesirable aspects, such as our ability to lie to deceive, to manipulate. Right? These machines have learned everything.

Chris Staigle (54:20)

We'll have to have an episode on that at some point in the future. Hasan, thank you so much for taking your time and for the audience. All those the link to the sub stack, the link to the podcast, all those will be in the show notes and I encourage you to to plug in. This is again, we had the opportunity to get the perspectives of somebody who's been paying attention to AI and its impact in business and in the world for 40 years plus. So not very often you don't run into too many AI experts these days who had such a long track record. So passant, thank you so much for being a guest and I look forward to continuing to read the the sub stack and look forward to any future writings that you're going to be doing. Thank you so much, Chris.

Professor Vasant Dhar (55:02)

I really enjoyed the show. Great questions and I hope to keep in touch. Good stuff.

Chris Staigle (55:07)

Awesome. Thanks everybody. We'll see you on the next episode. Thanks for tuning in to Using AI at Work. Don't forget to subscribe for more conversations about how to use AI at work and a special thank you to our sponsor, Chief AI Officer for empowering businesses with AI education and training. Visit their website for a free AI readiness assessment and AI strategy guide to help you get started using AI at work. That's www.chiefai officer.com. follow us on Twitter at the handle using aiatwork and visit www.usingaiatwork.com for free resources to help you harness AI in your role.