Why the Fear of Displacement Might Be Overblown: AI as an Amplifier for Human Creativity

Most people think AI is just a recent phenomenon—Vasant Dhar reveals how the field’s ambitious goals from the 1950s are finally becoming true today. After decades of lofty promises, recent breakthroughs—like ChatGPT and advanced machine learning—are turning these dreams into reality. But what does this mean for the future of work, finance, healthcare, and human connection?

In this episode of The Most Important Thing, Dhar explains why regime shifts in finance challenge even the smartest models, and the profound implications for human judgment. Dhar warns of the dangers of dehumanization and reliance on machines—urging us to exercise our mental muscles instead of letting AI become a gatekeeper. Perfect for professionals, investors, policymakers, and anyone curious about the true potential—and peril—of artificial intelligence. If you've ever wondered how AI will reshape your career, your industry, or society at large, this episode is essential listening. We explore not just what AI can do today, but what it means for the future of human ingenuity and connection—leaving you with both excitement and necessary caution.

Vasant Dhar is a pioneer in AI research, known for his work on machine reasoning, prediction, and the social impact of technology. His insights draw from a career spanning over 40 years, including groundbreaking projects on AI-driven trading, healthcare, and economic modelling. Unlock the secrets behind AI’s rapid progress and learn how to harness its power—without losing sight of what makes us human.

Transcript

SPEAKER_01 0:00

That's a great question. Uh you know, that the previous technologies amplified brawn and this one amplifies brain, but it also potentially replaces that by by by making us um you know by doing many of the things that we used to do for a living and it just does them just so much better. It it it thinks um in in ways that were unimaginable. You know, my sort of larger fear is that that we might let this happen to us without our realization. So what I mean by that is that you know you can already see the machine is becoming a gatekeeper of lots of human activity. You know, you apply for a job, your CD is screened by an AI, you know, you're increasingly interviewed by an AI, right? So that human connection is what is disappearing, you know, or or getting weaker than that we used to have. It was much more, this used to be a face-to-face kind of world.

SPEAKER_00 1:00

Welcome to the Most Important Thing podcast, where we speak with the best and brightest in finance, economics, investment, and geopolitics to bring you the truth about what's driving the global economy and financial markets. Every week, our chief market skeptic, Dr. Alan O'Sullivan, and former government minister Ivan Yates sit down with a global expert to provide invaluable lessons from a lifetime of markets experience. Economic expansion or recession, market highs or underlying vulnerabilities, AI dystopia or just the next bubble, escalating geopolitical tensions, or simply the new global order. These are deep conversations searching for truth and practical investing strategies to improve your chances in this uncertain market environment. To optimize your learning experience, visit our learning hub at the mitpodcast.com, where you will find the latest guides, investment ebooks, and a community that focuses solely on expert insights. Enjoy the show.

SPEAKER_03 1:48

So today's episode is an amazing guy. He is an academic and a whole lot more. He started out in India but has really become an American uh citizen and a very influential person in the whole area and the history of artificial intelligence. His name is Professor Vassant Darr. Tell us all about why you picked him.

SPEAKER_02 2:12

Okay, so I was at the John McCarthy, Dr. John McCarthy Summer School in my native Kerry uh recently. And this gentleman, Professor Darr, was one of the guest uh lecturers. So he did it remotely. He's in NUI, NU NYU Stern in New York. Uh fascinating character in terms of began his career in AI and artificial intelligence before I was born.

SPEAKER_03 2:38

And I know the amazing thing is we think about artificial intelligence and generative AI and AI assist and is agenda and all these different things as a fairly recent phenomenon, Industrial Revolution 4.0. But he was talking about this four decades ago.

SPEAKER_02 2:55

He was talking about it four decades ago, but Dr. John McCarthy, who I want to go back to, was talking about this maybe seven or eight decades ago. Okay. John McCarthy's father was from a small village in County Kerry called Cremon, okay? And Dr. John McCarthy himself was born in Boston in 1927 and became a pioneer in artificial intelligence. And actually, the name artificial intelligence is accredited to Dr.

SPEAKER_03 3:22

John McCarthy, which is But was it in simple terms? Is AI just not a sexier, more sophisticated form of software? It goes much deeper than that.

SPEAKER_02 3:33

Okay, so if we go back to when we what we discuss in the interview is the evolution of AI. So Professor Darr wrote a very interesting book, which I know we'll get to, uh called Thinking with Machines. So he talks about how AI started as an expert system base. The problem was at the at the very start of AI that there wasn't the computer power, there wasn't the data there to generate the type of artificial intelligence we have through these large language models today. It quickly went as computing power increased, the ability of machines to learn independently became uh more profound into where we are today, where we have some real intelligence where potentially these machines could be autonomous. So that's the brief kind of evolution that we get into in detail in the interview.

SPEAKER_03 4:22

Okay. And and so basically uh moving in the direction of the most important thing. Uh, because I've I've watched the interview, and he talks about that the early iterations of AI had one flaw which made them not fit for general purpose, and that was the lack of common sense and how it's changed to include common sense. Explain that to me.

SPEAKER_02 4:47

Yeah, you you hit on the nub of it, Ivan. That's a very important point, okay? Because if we have if we are going to trust the output from these uh machines, we have it has to have some type of a human, innate human uh sensitivity, okay? So if we look at any task, right, it's it's not just um mechanic in nature, any type of inter interpersonal relationship, it has an element of nuance, of common sense, of emotional intelligence. So what the machines are actually moving towards now, or what AI is moving towards, would you believe is in recent uh research is has has associated smelling. Uh, so these machines can actually smell, right? As as mad as that seems, uh, and actually put output uh based from that.

SPEAKER_03 5:37

He gets into a bit of that uh in the interview. What I what I'm not quite clear on is I see so many societal and economic and commercial uses of AI. Where does it relate to investing? Whereby can it forecast, you know, whether gold is going to go up, shares are going to go down, tech stocks. Where does AI fit into investing?

SPEAKER_02 6:02

We held a Future of Work seminar very recently, right? And you you you In Galway. And you were involved in that, and it was very interesting because there's a lot of anxiety out there at the moment, a lot of anxiety with parents, young career professionals, they're not sure job displacement, job displacement, the job apocalypse they're talking about, right? But some of the best experts in the world at that um uh session actually said we the human will always have a place. And I was that was found that very refreshing myself. And the reason being is that if we look at financial markets and investing, nobody has a crystal ball. I've yet to see an algorithm or machine learning AI chatbot to use future data in its prediction, right? That's tongue-in-cheek statement, yeah. But nobody can see around corners, even the AI can can't see around corners, right? So, whereas it's very, very, very good at assimilating information and giving output, it's brilliant. It still is unable to forecast the future. And as long as we don't know the future, there'll be a place for human beings, in my opinion.

SPEAKER_03 7:08

Okay, and that's why the likes of Alan is an expert in wealth management and minding your money. So let's get into it. This is Alan in conversation with Professor Vassant Darr.

SPEAKER_02 7:27

The Most Important Thing podcast is sponsored by PRIA Wealth Management. For those watching or listening who are serious about building, protecting, and structuring wealth, professional advice can make a meaningful difference. At Priya Wealth Management, we work with individuals and families who want a considered, discrete, and highly personalized approach to investments, pensions, and long-term financial planning. If you would like to discuss your own circumstances in confidence, you can book a call directly with me using the details in the show notes. Now, back to the show.

SPEAKER_01 7:58

If you train a machine on that regime, then that's what it's trained for, and its predictions will work well as long as you're in that kind of regime. Now, what you're talking about is uh, you know, let's say a possible regime shift, you know, where you know, uh of higher inflation and maybe even interest rates rising. Um and if you were to be building a traditional, you know, uh uh machine learning program, you would probably then sample more data from those kinds of periods, you know, like maybe 1994 to 97 or you know, the early 2000s, you know, where we did have those kinds of regimes. That would be one way to do it. But to me, the sort of magic of the you know, I shouldn't use the word magic too much, but this capability of general intelligence is that it now has the ability to reason about these different regimes as well, you know. So it's it's it's learnt enough to know that markets have regimes and that this might be a you know a changing regime. And so it, you know, in theory, again, you know, practice is different, but in theory it should be able to sort of think about the new environment, you know, in a different way. Uh and s and and recognize that many m that much of its data from the past is not likely to apply to the future. So that's how I would think about it. Like that's how I would think about regime shifts in the current uh environment, right? So if I were building a bot, uh you know, like actually the Domoran bot, the D Moduran bot is a good example, right? That you can actually, you know, one of the interesting things about the bot is that you can give it a thesis. So you can say, okay, and and by the way, I did this, uh, I I ran it on NVIDIA pre-earnings, post-earnings, and I asked the Motheran for his input on it, which was really interesting. But, you know, um, you know, at at an event the other day, and I and I give live demos of it, I said, okay, evaluate Apple, and I said, and I gave it an investment thesis, which was assume that they need to come up with a new product, maybe in the you know, augmented reality, virtual reality space, but they need some innovation and value the company with that sort of in mind. And it did. It focused on its product lines and and you know uh broke down revenue and you know, applied the Motherin's discounted cash flow model and came up with a valuation. I then told it, I said, well, assume that Trump escalates tariffs, right, and that we get into an extended trade war with China, like how would that affect Apple's valuation? And you know, to my um uh delight, uh it started focusing, it it said, okay, you know, the total volume of business that Apple brings from China is about 150 billion, you know, with 60% tariffs, you know, 90 billion of this goes up in smoke. Um, you know, and there's going to be margin compression. So instead of focusing on product lines, it then started focusing on margin compression and who's gonna be able to absorb this margin, right? Will it be the producer, will it be the consumer? You know, you know, where how how how will these margins get absorbed? So it was a completely different type of a reasoning and thinking, you know, based on the investment thesis that I'd given it. And that's what I find sort of really exciting about modern AI is that uh it's it's not just like training a machine on past data and then hoping for the best, right? And hoping that you don't get too much of a distribution shift and that the future doesn't look like the past. You know, when you actually have a machine that can think and reason like humans can about these situations, right? How would you think about this situation? Well, you'd you'd know that it's a different regime and you should think about it differently. You know, you'd go to first principles, right? And so the machine can do that as well now. And that's what I find interesting. So uh, you know, what excites me about the modern thought is that it can do exactly the kind of thing you're talking about, where you give it an investment thesis and it has the knowledge, it has sufficient knowledge to be able to reason about valuation of a company given that thesis and given the current economic environment, which it snarfs up by looking at the news.

SPEAKER_02 12:22

Fascinating. I mean, you we get to the no, but the question then for Sanders is you know, somebody like me, I'm in the advice game, and hundreds of thousands more like me, and millions around the world. So we're all nervous, we're looking at all this going on, but I was reading your book and I was looking at the chapter that covered the the De Motoran bot, and I started thinking to myself, what makes a great investor? And to me, what makes a great investor, which that's what De Motoran, Professor De Moderan says, is an investor that knows the numbers but also knows the stories, the narratives. So great investors are able to look around corners. And then you got into Tetlock and super forecasters and this. So none of us can look around corners. So because I've yet to see somebody use future data in a forecasting exercise. My question is, do we end up with a smaller pool of really good people and we just get rid of a lot of the a lot of the average performers? And is that is that is that what we're looking at?

SPEAKER_01 13:31

Very possibly. That that that is what happens. Um because and and the reason I I see that happening is that by the way, no one can look around corners, right? And you know, and the Motoran is often wrong, as he you know admits freely. It's a question of narrative that goes with the numbers, but the narrative could be wrong, or maybe things play out differently. But it's still important to have the numbers and the narrative, because otherwise you're missing something, right? There's got to be a story that ties things together. Now, you know, you referred to the work of Tetlock, which I find fascinating because Tetlock has studied, you know, forecasting tournaments and you know long-term prediction. Um and I found his work fascinating because, and he was a guest on my podcast, you know, several years ago. You know, what I find fascinating about his sort of description of these super forecasters is that they have like an insatiable curiosity, right? It's it's just constant curiosity. They never are satisfied with their answers. There's always something missing. There's always that conditional they're looking for. And they also tend to sort of anchor themselves in the right place. I'll give you an example. Um, in my systematic investing class, one of the students asked me, you know, what's the probability, you know, prof, what do you think the probabilities of a recession next year? And I just sort of threw the question back at the class and said, What do you think? And by the way, this was last year. I repeated this a week ago. I gave a guest lecture in a colleague's class, and it was the same. I said, What do you guys think about this? How would you think about it? Right. And immediately these are business students, you know, they talk about the Fed, they talk about interest rates, they talk about employment, they talk about global trade, all that kind of stuff. And I'm like, wrong, wrong, wrong, wrong, wrong, wrong, wrong. Right. Uh, you know, if you want to know the probably recession next year, maybe the you know, i it's good to take what Tetlack calls the outside view of the problem. That is, without knowing anything about the problem at all, what's the question that I should be asking? And well, maybe the question that we should be asking is how many recessions have been have there been in the last hundred years, right? Because that establishes a base rate that you can then work with. If it's 5%, well, then chances are that the correct answer will be in that neighborhood, as opposed to starting with 50%, where you'd be completely biased, you know, and completely off the mark. So what distinguishes people who really kind of have this curiosity, you know, they they tend to anchor themselves in the right base rates and stuff like that, is that they just get better over time. And this is what I hypothesize is happening and will happen is that we'll get this bifurcation where people who are really good get even better. You know, they become superhuman in terms of their capabilities because they know the right questions to ask, they can evaluate the response of the machine, they can see whether it's interesting, they can nudge it in the you know, in in the right direction. They already have that ability and it just sort of amplifies them, it sort of supercharges them. Whereas people who use it as a crutch and just sort of take the answer and say, well, it must be right, it just knows a lot, you know, people like that will probably have a much harder time and sort of go into a cognitive decline because you're not exercising your mental muscle anymore. Right. And and to me, that's like the biggest challenge and biggest threat facing humanity in this age of AI is will we exercise our mental muscle? If we do, it's awesome news, you know, for all of us, because we'll we we will get better as well, along with the machine. Um on the other hand, there's this temptation for it to to to just rely on it and say, well, just like you know, do it for me, you know. And we fall into this Huxleyan world, you know, where we sort of let ourselves become gradually disempowered and we just sort of let you know the machine do the heavy lifting and call it a day.

SPEAKER_02 17:32

Yeah, and I I when I was teaching in an MBA class for the last few years, I was as AI and machine learning and this fear was becoming greater and greater and more influential, I was saying to students, do the work. You know, do the work because if you do the work, you can stand out a lot easier now. Because I find people are getting lazy and even even myself, um, because and the reason I say do the work is you have to know what questions to ask. If you don't un if you don't have a basic understanding of the type of questions that you need to ask, say in my field like finance in terms of investing, um then that's where you can be caught off side. Can I just go back to something something else that you that I I want to ask you? Are there particular there'll be some graduates listening to this, some young career professionals? You know, the bottom line, my own view is the horse is bolted with this, like the genie's out of the bottle, whatever you want to call it, right? This would this is the new reality. But are there certain sectors or industries that are less vulnerable? And even I think about investments, Professor Zarr, because I think investing is a zero-sum game. If I have to win, you have to lose. Uh and if we're all using AI bot or agent bots, I mean we can't all win. So, how would you square that circle?

SPEAKER_01 19:06

Well, you know, so uh I guess so I guess like futures uh trading is a zero-sum game, I guess in stocks, you know, as one of my friends used to say, everybody can be a winner, you know. Uh and and that's kind of the allure of stock markets, is that you know, there's an upward bias. And so you know, and that's what I tell my students as well, right? That if you don't know any better, right, and if you interest in finance, you don't know any better, it, you know, you'll you know, it doesn't excite you, you know, go along the index of some country you believe in, right? Uh you know, because that's that that's your bet then that the you know in in the equity markets of that country. And so in that sense, you know, everybody could be a winner. But what I tell people is that if you're really interested in it and excites you and it, you know, you like working with it and you want to get into active investing, you know, then that's what you should do because it's it, you know, it's a great time, but be mindful that you're competing against the best, the most motivated people. It's like, you know, it's like sports, you know, and I draw this analogy with sports and you know and finance. To me, they're two sides of the same coin, you know, where you know you're just like, you know, you're just looking for that really small edge, right? And Federer talks about this, you know, in his Dartmouth commencement speech, right, where he says, you know, I won 80% of my matches. You know, I paid I played 1,526 games and I won 80% of them. What percentage of points do you think I won? And the answer is like, you know, barely over 50%. You know, like in this case, 54%. You know, Boris Becker won something like 52% of his points and you know, almost 80% of his games because he was good at winning the really important points. Um but sports is a great metaphor because you know it's it's a it's a domain where everyone's got the best equipment, you know, they're motivated, they've got the best coaches, and you're just looking for that small edge, and over the course of a match, that edge multiplies, right? So if you're even slightly better than the other person, then the longer the match, the more likely that you're going to win the match, as long as you don't get exhausted. Um and to me, finance is the same way. And you know, and I realized this, by the way, after like many years of professional investing, of running my hedge fund, uh, was like, you know, how accurate did I need to be? Like how much better than random than did I need to be? And the answer was like very little, you know. And if you were like slightly better than random with every trade you did on average, then you know that's a multiplicative count and effect. And to me, financial markets are evolving, right? They're not a static thing. They're always evolving, things are changing. You know, stuff that used to work you know 20, 30 years ago may not work now. New patterns emerge in the market because new phenomena sort of you know come in. So to me, financial markets are an endless puzzle. Uh and it's not like we're done, right? It it it it doesn't work that way. It's it's always new constant stuff, and but it's also a treadmill. Um so you know, I I I don't think um you know we need to worry about like you know that that there'll be nothing for us to do because machines can do it all for us. Uh you know, I I don't I don't think machines are quite there yet. You know, I I think there's still a lot of scope and opportunity for humans to exercise their creativity. And I think this is true everywhere, you know, whether it's in um finance or healthcare. Healthcare is another area where I think at the moment we're getting the worst of both worlds. You know, humans are functioning like robots in an assembly line. Uh, but I think that'll change as you know, we begin to do better scorekeeping, um, and physicians will get better at what they do. You know, I mean, and I talk about this in my book, you know, I have a high PSA level, you know, prostate-specific antigen. A lot of older men get it. Um and you know, I've been seeing two renowned world-class urologists, and they can't figure out what's wrong. And that puzzles me as a data scientist, you know, because if I were them, I'd be, you know, my response would be, you know, Vasad, I've seen 1300 cases like yours exactly with the same trajectory of PSA levels, and here were the outcomes. But they're not able to do that because no one's collecting the data systematically. So, and there I see a lot of scope for AI to actually pour through electronic health records and stuff like that and actually construct these databases that are useful for diagnostic purposes. So I don't see, I don't necessarily see sort of the end of employment. You know, I mean, that could certainly happen, uh, you know, where you get mass displacement. And you know, with any technology, some people get displaced. But what I see happening is that the expectations of human performance will go up. And that's always been the case, right, with new technologies, right? So if you're an analyst, you're expected to do more, right? Maybe in the old days you could produce one report every month. Well, now you'll have 10 a day, right? With uh a tool like the the Mullerin bot. So the expectations go up, the expectations of people in healthcare will go up. So um, you know, will it be fewer people? Maybe. But that's something that really depends on us as well.

SPEAKER_02 24:11

Yeah, really interesting. I I I I interviewed Professor Robert Gordon, uh, Northwestern University. You you're probably familiar with his he he wrote that book, um, The Rise and Fall of American Growth. Um, but he is a very good TED talk for for listeners as well, where Professor Gordon basically said that peak growth was in in the US because of a number of headwinds, debt, demographics, very inequality. But he talks about you you mentioned general purpose technologies, and he's I know that you have um compared artificial intelligence to electricity. And electricity that that was amazing in terms of um improving manufacturing machinery, hand tools, and and the efficiencies that it produced. But is it fair to say, Professor Darr, that artificial intelligence is different insofar as it might be taking us out of the loop? Whereas we need we we're still in the loop with electricity. Um and are we going to be taken out of the loop if we look at some of the more darker elements of artificial intelligence?

SPEAKER_01 25:22

Indeed. I mean, that's a great question. Um, you know, that uh previous technologies amplified brawn, and this one amplifies brain, um, but it also potentially replaces that by by by making us um, you know, by doing many of the things that we used to do for a living, and it just does them just so much better. It it it thinks um in in ways that were unimaginable. Um you know, my sort of larger fear is that that we might let this happen to us without our realization. So what I mean by that is that you you you know you can already see the machine is becoming a gatekeeper of lots of human activity. You know, you apply for a job, your CV is screened by an AI, you know, you're increasingly interviewed by an AI, right? So that human connection is what is disappearing, you know, or or getting weaker that we used to have. It was much more, this used to be a face-to-face kind of world, you know. I mean, mostly local, but we interacted with people, we got a sense of them, you know, physically, um, and and you know, got a feeling of comfort or whatever. Now, you know, whether that was good or bad is a value judgment, right? Because we, you know, we were biased in some ways, you know, and more comfortable with people that thought like us, looked like us, etc. But it was a physical world. Whereas now it's become very distant, right? If if you're applying for a job and you know it takes you like five you know barriers before you can talk to a human, well, the whole process has become sort of dehumanized. You know, and so that's my fear is that um we just sort of lose that human connection uh because the machine gets in the way and becomes the gatekeeper of human-to-human activity as well. Um and you know, and and that's what sort of concerns me is that we not let that happen just as by default, you know, just by being so happy about the fact that these machines do a great job of screening resumes, and they even do a great job of interviewing, right? But what does that really mean for the human connection is is a big question that Conference says.

SPEAKER_02 27:39

As we draw to a close, this has been absolutely fascinating. I'll I'm very interested in in people learning more about your work. I know you have a very interesting podcast yourself, Brave New World, which I listen to. Uh, you have Substack as well, and you have your book, which we will put all that information uh for people. But can I ask you to look at your crystal ball, right, with all the caveats that are there? And for the people that are in the white-collar jobs, right? What level of disruption do you see there? And if it's enormous, which I think you will say it will be, um are there what kind of guardrails can can governments and policymakers put in place?

SPEAKER_01 28:28

So uh I'm gonna say two things that'll disappoint you. One is that I'm going to, you know, I asked Danny Kahneman a similar question, and he said, you know, there's an old Hebrew saying that says prophecies are for fools. So, you know, so I'm not gonna make that prophecy, was his response. But I will be a fool and say that I don't necessarily see you know large-scale disruption happening in white-collar jobs. Um I I think it sort of depends on whether those white-collar jobs whether those people in those white-collar jobs manage to upskill themselves and you know, see the writing on the wall that maybe older ways of doing things won't work, you know, that there are some things that are just done better with AI than you know, with human judgment. And to just sort of think through that clearly. And this is not, and this is for business leaders as well, by the way. You know, is you know, there's there's a lot of FOMO, there's a lot of um, you know, will we miss the bus? And I'm like, I don't think so. I mean, I I think there's plenty of time to think about this clearly, and one should be clear about what the business objectives are and where the in which areas and how business will get transformed because of AI. You know, I I think the leaders need to like think through that, and you know, people in managerial positions need to think through that carefully. It I don't think it'll it'll necessarily displace them. Um I think it might make them better and you know think about business in in in new kinds of ways. So that would be my um you know, thinking about the impact on you know knowledge work in general, is that just because you have a machine now that can do things better, many of the things that we used to do better doesn't mean that it's game over. It just means you gotta up your game, you know, as opposed to sort of you know fall over and and let it run over you.

SPEAKER_02 30:30

I think that's a great way to finish in terms of uh an uplifting, but I just have one last question, if you don't mind. In terms of advice that you would give, I mean, you did your PhD in AI in 1984, I think you defended your PhD. Yeah, it's incredible, right, that you've been in the industry and you continue to contribute positively. But if you were giving uh advice to a young graduate, yeah, a young career professional, what would you say to them?

SPEAKER_01 30:57

You know, it's funny you asked me this question because my l latest podcast release that just releases today is with Deepak Chopra. Um and you know, we talked about consciousness and reality and stuff, you know, fascinating podcast, by the way. I I would encourage people to listen to it, you know, even though it might sort of blow your mind, it's it's just like well worth a listen. Um and I asked him the same question, like, what's your advice to young people? You know, and his response was really interesting, which was, you know, he says, you know, I I find you know a lot of young people, and you know, and they'll say, you know, I made my fifth exit, you know, at a billion or whatever. And and and he says, I asked them, like, have you thought about your final exit? You know, like you know, like, what do you want to be when you make your final exit? Right. And so, you know, his his his summary response was like, do whatever brings you joy in life, you know, uh and and and and go for it, you know, without worrying about how much money it's gonna make or what kind of exits you're gonna have. I mean, I think those things will happen quite naturally if you like really pursue something you're passionate about. Um and that's what I tell people, you know, just like you know, life's too short, you know, to be worrying about how much money you're gonna make or you know, how you know what accolades you're gonna get or something like that. You know, just find something that really gives you joy. And that may not be obvious. You may have to search for it, uh, you know, or you know, to take what Scott Galloway says sometimes is you know, find something that you're good at and become like even better at it, you know, and then it'll give you joy, you know, once you achieve that sort of level of proficiency. So, you know, whether it's something like that's coming at you from deep inside, or whether it's something you you know you say pragmatically, I want to be really good at that, well, that would be like to me a great way to think about the future and you know, think about what brought brings you joy. And if you're doing that and having lots of fun, you know, I'm convinced that the other things will follow.

SPEAKER_02 32:57

Well, wonderful advice. And just for uh viewers to see again, this is the book uh Thinking with Machines by Professor Vicent Darr, Brave New World of AI. We'll put links to that, uh, the Professor Darr's uh podcast, more all the more information about him. He definitely is somebody you should be following and keeping close to. I really appreciate this, Professor Darr. Thank you for and I look forward to listening to that interview as well as the other interviews that you have coming up.

SPEAKER_01 33:26

Thank you, Alan. Really enjoyed the conversation.

SPEAKER_04 33:28

Great stuff.

SPEAKER_03 33:35

Okay, that was Alan speaking to Professor Vassant Darr. So many things to take about that, and we're gonna find out what is the most important thing. Let's talk first about his book, because he alluded to a number of times in the video, Thinking with Machines. I haven't read it. What does it say? I'll give you the cliff notes, Ivan.

SPEAKER_02 33:55

Okay. So the big thing about this book is, and the compliment I gave him was it's it's accessibility. So this is a professor of finance, N Uyu Stern, did his PhD in 1984, believe it or not, on artificial intelligence. So he could have made this very, very complicated, Ivan, okay? He made it accessible. He made it accessible for people with, you know, that don't have a background in artificial intelligence, uh, parents, people that want uh to improve their own insights. So that's the first thing. It is accessible. It talks about the evolution uh of AI, so how AI has moved from its very origins with Dr. John McCarthy in the 1950s and 1960s, all the way up to the present day. And the Eureka moment was the 22nd of November 2022, where ChatGBT was launched. And he believes, and he said in the interview, that there's a world before ChatGBT, and there's a world after ChatGBT.

SPEAKER_03 34:55

That's how profound he believes it is. Okay. I find, and I do a lot of conferences in relation to AI, you oscillate between hype and doom, and it's very hard to find some of the practical automations that are going to come. And I I actually did an award ceremony for CX, and they now have so your airline, your you know, big business, your bank, you're talking to a chatbot, which frees people up to deal with the more complex things. But the chatbot is Mary, and you wouldn't know this, but it's it's become so sophisticated, you actually believe it's it's Mary. So let's talk about AI and automation. What are the sectors that because you know, you often speak to me about the big thing over the decade is productivity gains. This economy will do well. Where are the productivity gains in automation, do you think, and how that might relate to investing?

SPEAKER_02 35:49

I I I do think we have to be honest with people, okay? And this is from my own perspective as well. I am a white-collar uh professional, okay. My my bit my career is is on advice. I'm paid from the neck up, as the man says. Yeah. Okay. So we have to get real and realise that the horse is bolted in terms of the application of this. You can't ignore it. There's the the the line that's thrown out a lot, Ivan, is uh advisors or lawyers or tax advisors that use AI will outlive those who don't. And I I be I strongly believe in that, right? Does it mean the the end of us as a career? Absolutely not, right? But where it is going to be most disruptive is in careers that are rule-based. You have to say anything that involves data entry, anything that is where the solution to your problem comes from statute, case study, law, legal text, legislation, you have to worry about that. Does that mean that lawyers, solicitors are going to be redundant? Absolutely not, because their expertise in terms of strategy, finding loopholes or finding ways around, is still going to be very, very valuable, perhaps even more valuable.

SPEAKER_03 37:02

There's two sort of questions I have for AI. One is trust, and the other is empathy. Will it be able to do those?

SPEAKER_02 37:11

Yeah, this is the big, this is the the big, big question. And what's really shocking as we talk today is that there's very little legislative control over the use of AI in terms of data. Okay. You think about what is ChatGPT, what it are these large language models doing? They're going out into the internet and hoovering up all this information, all this data, assimilating it, and just reading next word prediction. Now that's that's a very simplistic, but that is that is it. But so where is the where is the protection for artists? Where's the protection for authors, for writers, for people who wrote textbooks? Where is the protection there? And that is a big, big question that we haven't answered yet, okay? You talk about trust. You know, we innately trust human beings, okay? It's a big leap to make uh a trust decision on something like health, something like finances. And that is where the real challenge for AI will be. We can create avatars and we can have fun with AI, but when it comes to I make this decision, I could lose a million euros, I make this decision whether I get a stint or not. That's the crux of this, and that'll be the test.

SPEAKER_03 38:23

There's another aspect of AI which I think this particular audience will be very interested in. And that is that some people have said to me, if there's another stock exchange crash, it will become because the apples, the Metas, Amazons, and all of these really successful stocks have bet the bank, bet the farm on AI. And when I look at something like Chat GPT, and I use it a lot, it's free. So as you know, these tens of billions being invested in this, is this a potential like the early days of the internet? Could it be a dot bomb?

SPEAKER_02 39:04

There's there's two questions there, and you're you're spot on, okay. The first of all, the hyperscalers they're called, the big magnificent seven companies, they're spending hundreds of billions, silly money, really, in terms of uh their investment in artificial intelligence. It's an arms race for technology at the moment, okay? Is it a bubble? I think it probably is, okay. At some stage, but is it monetized if the if the people who use it are not paying for it? They're not paying yet. Okay. Yet. Okay. So what they do is it's the oldest trick in the book. Oh, yeah. They give you, they show you the efficiencies and then before you know the hello money. The hello money, it's 1999-99, and you you renew automatically, yeah, and that's it. Now, the problem with technology is technology eats technology, right? So they can't all win. Yeah. And that's what's good. That's so we've got an arms race in terms of technology, in terms of AI investment. Uh, but to get your question about is it a bubble, you need to look at the cash flows of the companies. These are real companies. Nvidia, you know, these companies are real, these earnings are real, but are they sustainable? And is the price you're paying for those those earnings justifiable? And that's the key key point.

SPEAKER_03 40:12

You asked uh uh Professor Darr, uh you posed the question about a zero-sum game. Could you just elaborate what you're getting at there?

SPEAKER_02 40:22

Yeah, so thankfully for me, in in terms of investing advice, investing is a zero-sum game. So, what that means, Ivan, is this if you take a position on Apple and I take a different position on Apple, let's say I think Apple is going to fall and you think Apple is going to go up in price, we can't but win. Okay. So if investing is a zero-sum game, whereby not I win, you lose. Yeah, yeah. Not everybody can win on the day, uh, that means that there is an advantage to having some insight. There's an advantage to having some alpha or outperformance still. And if all the machines predict one thing in a particular time, right, that could lead to even greater volatility in markets. Okay. So I'm not convinced that the machines are going to overtake human expertise in relation to investing.

SPEAKER_03 41:18

Okay. So finally, for Professor Vassantar, what was the most important thing?

SPEAKER_02 41:23

Believe it or not, his most important thing was something you touched on in relation to trust and risks associated with artificial intelligence. Obviously, he believes it's uh a general purpose technology like electricity, it's ubiquitous in nature. So once it becomes to a tipping point, it's you're not going back to what what we had previously. But there needs to be guardrails, there needs to be some uh risk management. And we have a new EU AI Act. We have, uh, and I know the EU generally are far more uh regulatory regulatory focused. Whereas the US are more innovative, more innovative, but there's consequences for uh in innovation without guardrails. So that in in fairness to him, when somebody with 40, 50 years of experience in this space is saying it's wonderful, but we need to be mindful of the risks, I think we should pay attention.

SPEAKER_03 42:18

Okay, if you're interested in more, thinking with machines, Vassantar, the brave new world of AI. Uh, my thanks to Alan, and that concludes this episode of The Most Important Thing. And I do want to allude to if you're interested in learning more, uh, there is a new series going to emerge from Alan called The Truth Series, which is an e-learning virtual model. Check it out. But from me, from Alan and myself, I want to thank you for joining us today, and I hope you found it as fascinating as I did. Goodbye for now.

SPEAKER_02 42:54

So thank you for listening or watching on YouTube. I sincerely hope you found this episode useful. This podcast is about slowing down the conversation, of focusing on first principles and long-term thinking and the ideas that shape outcomes over time. Because when everything feels important, knowing what matters the most is your edge. It is the most important thing. It should be said and important to say that this podcast is for information and educational uses only and does not constitute financial advice. All views expressed are those of the guests and the hosts. Although I am a qualified financial advisor and I am a certified financial planner, everyone's circumstances are unique. So before you make any decision in relation to your finances, investing, or financial planning, please seek a qualified financial advisor. There's loads more to come on the most important thing, and we can't wait to see you next time. Thank you.