Latest / ContraMinds Podcast - Unlocking Personal Growth and Professional Excellence / Thinking With Machines: Sense-Making, AI Agents, Future of Work - Prof. Vasant Dhar (#063)

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Thinking With Machines: Sense-Making, AI Agents, Future of Work - Prof. Vasant Dhar (#063) · ContraMinds Podcast - Unlocking Personal Growth and Professional Excellence

• 0:00
  SPEAKER_00: You know, and the an example in my book is, you know, an agent that, you know, runs a business for you.
• 0:04
  SPEAKER_00: And and, you know, so John Doe creates a agent that starts running his real estate business.
• 0:10
  SPEAKER_00: And then it becomes so good that John Doe doesn't even bother, you know, with it anymore.
• 0:15
  SPEAKER_00: The machine keeps doing it.
• 0:16
  SPEAKER_00: And then one day John Doe passes away.
• 0:18
  SPEAKER_00: And the machine now realizes that, aha, like what do I do now?
• 0:22
  SPEAKER_00: And you know what?
• 0:22
  SPEAKER_00: I'll keep running the business.
• 0:24
  SPEAKER_00: And what should I do?
• 0:24
  SPEAKER_00: Well, maybe I should do this with my subscribers.
• 0:27
  SPEAKER_00: You know, and it comes up with a brilliant business plan and starts running the business and hires a board, you know, and enters into contracts, right?
• 0:35
  SPEAKER_00: I mean, is that like a bizarre, unthinkable scenario?
• 0:39
  SPEAKER_00: I don't think so.
• 0:44
  SPEAKER_03: Hello everyone.
• 0:46
  SPEAKER_03: Welcome to another edition of Contraminds.
• 0:49
  SPEAKER_03: In this episode, I'm talking to an AI researcher, a data scientist, and the host of a successful podcast, Brave New World.
• 0:59
  SPEAKER_03: I'm in conversation with Vasandar.
• 1:02
  SPEAKER_03: Vasandar is a professor at the Stern School of Business and he's an AI researcher who's gone deep into the origins of AI and has had experience of working with AI and has seen it grow into the size and impact that it has in our society today.
• 1:22
  SPEAKER_03: He's recently written a book, Thinking with Machines, which takes a completely different perspective on AI and its impact at work, the way we think, and the way we need to work along with it.
• 1:37
  SPEAKER_03: I began my conversation with Professor Vasandar on the importance of sense making in the age of AI.
• 1:44
  SPEAKER_03: I asked him why it's very critical to ask the right questions if you have to get the right answers from AI.
• 1:53
  SPEAKER_03: He talked about the importance of knowing interdisciplinary knowledge and the fact that the more we know, the more we can leverage AI.
• 2:04
  SPEAKER_03: He also spoke to me about the importance of AI laws, governance, and the fact that AI is destined to change the way we work and the way we live.
• 2:18
  SPEAKER_03: I also spoke to him about AI's impact on trust, AI impact in different industries like investing, sports, and healthcare.
• 2:28
  SPEAKER_03: He had a beautiful set of examples and ideas, and his experience came loud and clear in my conversation with Professor Vasantar.
• 2:39
  SPEAKER_03: Over to my conversation with him.
• 2:42
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• 2:46
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• 3:04
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• 3:35
  SPEAKER_03: Lovely having you as a special guest again in the Contra Minds podcast.
• 3:40
  SPEAKER_03: Thanks for taking time and talking to me.
• 3:43
  SPEAKER_00: Really happy to be here, Swami.
• 3:45
  SPEAKER_00: Happy to chat again, looking forward to it.
• 3:49
  SPEAKER_03: So congratulations, uh Professor, on the new book that you've uh uh you know got out.
• 3:55
  SPEAKER_03: And thinking with machines is absolutely fantastic.
• 3:58
  SPEAKER_03: I've had a brilliant time reading it, and it has beautiful anecdotes and ideas, and I'm really looking forward to having a conversation and your ideas on the book.
• 4:10
  SPEAKER_00: I am super pleased to hear that, Swami.
• 4:12
  SPEAKER_00: And you know, we were chatting before we uh started the podcast, and you said, you know, it reads like a story, and of all the things you say, that pleases me the most because it was meant to be a story, you know, as opposed to a textbook on AI.
• 4:24
  SPEAKER_00: So I'm I'm pleased to hear that.
• 4:26
  SPEAKER_00: That's exactly what my intention was to write a story.
• 4:29
  SPEAKER_03: Yeah, in fact, uh it read like a novel to me and not like a technical AI book, and uh that is really what made it interesting, and I just uh you know got it in one sitting, is really what I did.
• 4:43
  SPEAKER_03: So thanks a lot, and uh I'm gonna kick off uh this conversation with my first uh uh question, uh, which is really one of the things that I found interesting in this book is there are a set of uh you know thoughts that you are leaving in every chapter, okay, which makes people think.
• 5:07
  SPEAKER_03: And it's almost that's the philosophy with which you have written every chapter it looks like.
• 5:13
  SPEAKER_03: And you make a very, very pertinent uh point, uh, which is really the fact that in the age of AI uh we as humans have to develop this art of sense making, right?
• 5:26
  SPEAKER_03: And uh, can you talk a little bit about why sense making in the age of AI is going to become super important for us as humans?
• 5:36
  SPEAKER_00: Sure.
• 5:37
  SPEAKER_00: So let's back up a little bit, you know, just to what the book is called, right?
• 5:41
  SPEAKER_00: It's called Thinking with Machines.
• 5:44
  SPEAKER_00: And the with is really important because like it or not, the future for you and I is one of thinking with machines.
• 5:54
  SPEAKER_00: There is no opt-out of this.
• 5:57
  SPEAKER_00: Um and so what I'm doing is I'm I'm raising a set of questions that we should address, and I'm um describing how we should think about this future for each of us individually, because for each of us individually, the future is different, but the questions are very similar.
• 6:16
  SPEAKER_00: Uh and and and so I'm you know, one of the things that I'm trying to do in the book is just lay out how to think about the future of AI and how to think about a future we think with machines.
• 6:26
  SPEAKER_00: What you're raising, which is, you know, I mean you dived in right into one of the core uh themes of the book, which is sense making, is just that um, you know, the the machine as we know it, and I'm gonna sort of unpack a lot here, um modern AI is really largely a black box.
• 6:49
  SPEAKER_00: And there's a reason why it's evolved that way.
• 6:52
  SPEAKER_00: Um and the reason is we've tried to create a machine that is conversational, you know, in our own image.
• 6:59
  SPEAKER_00: And I should start by saying that one of the biggest um achievements of modern AI is breaking down the distinction between expertise and common sense.
• 7:10
  SPEAKER_00: If you really think about the implications of that, that's really profound because for my entire career in AI, which is now almost 50 years, you know, I got into it as as a graduate student in 1979.
• 7:21
  SPEAKER_00: For my entire career in AI, there was a distinction between expertise and common sense.
• 7:28
  SPEAKER_00: And people in AI said, common sense is just too hard.
• 7:32
  SPEAKER_00: Let's focus on expertise and knowledge.
• 7:35
  SPEAKER_00: And you know, that's been for the most part how the AI paradigm has evolved has evolved.
• 7:42
  SPEAKER_00: We built these ever more sophisticated applications.
• 7:46
  SPEAKER_00: But what happened in sort of the um, you know, twent after 2017, you know, when the first paper on Transformers came out, and then what happened with the release of ChatGPT is that we demonstrated for the first time that anyone could talk to the machine about anything at any level of depth.
• 8:07
  SPEAKER_00: That's a remarkable achievement, you know, if you think about it.
• 8:10
  SPEAKER_00: And I never imagined that I would see that in my lifetime.
• 8:15
  SPEAKER_00: You know, so that was just like a mind-blowing kind of achievement.
• 8:17
  SPEAKER_00: The distinction broke down.
• 8:19
  SPEAKER_00: The reason it's important is because now when you talk to the machine and you ask it something and it gets it wrong or it gives you something else, you say, Well, that's not what I meant, I meant something else.
• 8:27
  SPEAKER_00: And it has the common sense to understand, and you know, maybe I should use the word understand in quotes, understand the meaning of what you're saying, the meaning of what you're getting at.
• 8:37
  SPEAKER_00: And it does that because it's sort of has this common sense.
• 8:40
  SPEAKER_00: So that distinction has broken down, and that's what made it accessible to everyone.
• 8:46
  SPEAKER_00: Now how it happened is and you know, excuse me, I'm I'm I'm sort of talking a little, you know, uh you know, for a little too long here, but that was a question that was just packed with content, I need to unpack it.
• 9:00
  SPEAKER_00: The re the way we achieved it is you know, by predicting the next word in a sequence.
• 9:06
  SPEAKER_00: Like, I mean, that it was as simple as that, which makes some people think that, well, you know, all it's doing is sort of next word prediction.
• 9:12
  SPEAKER_00: Is it really intelligent?
• 9:13
  SPEAKER_00: Does it really know anything?
• 9:15
  SPEAKER_00: And the answer to that is in order to predict the next word almost flawlessly every time, it has to know a lot about the real world.
• 9:24
  SPEAKER_00: And that's what it's learned as a byproduct of this keyword prediction.
• 9:28
  SPEAKER_00: You know, and I talk about this in one of my ep uh podcast episodes with Sam Bowman, I think it's episode 58, where he says, we just got lucky, you know, that Google was trying to complete sentences in Gmail, and that really led to sort of this general purpose technology which could complete any sentence, which could predict the next word.
• 9:46
  SPEAKER_00: So he said, We just got lucky that we picked a problem that we could solve for which there was lots of data, you know, to make it solvable.
• 9:54
  SPEAKER_00: Now, but by doing this, uh, what we've um created is a machine that is intelligent for its own sake.
• 10:02
  SPEAKER_00: This is a machine, uh first machine built by us without any purpose.
• 10:06
  SPEAKER_00: But at the same time, we don't understand its inner workings.
• 10:11
  SPEAKER_00: You know, so when we ask it something, we have no idea what's going to come back at us, except that it'll probably make sense.
• 10:17
  SPEAKER_00: Right.
• 10:17
  SPEAKER_00: And that is the seductive aspect of the machine now, is that because it makes so much sense all the time, we just accept it.
• 10:26
  SPEAKER_00: We just accept its output.
• 10:28
  SPEAKER_00: And it's critical to actually make sense of what the machine is telling us.
• 10:35
  SPEAKER_00: That is it's it's critical to not just take its outputs as given and pass them along to the next person.
• 10:41
  SPEAKER_00: It's important in in most areas of our life to actually look at those outputs and say, do I believe it?
• 10:46
  SPEAKER_00: Like, is it is it true?
• 10:48
  SPEAKER_00: Does it make sense?
• 10:49
  SPEAKER_00: So sense making is is just a huge part of you know working with modern-day machines.
• 10:55
  SPEAKER_00: You know, and I set this up early in the book, you know, by by you know, when I talk about sort of traditional AI, right, but I say, you know, you patterns emerge before reasons before them become apparent.
• 11:06
  SPEAKER_00: And the reasons is all sense making, right?
• 11:09
  SPEAKER_00: So throughout AI, you know, in the previous paradigm, we have to make sense of patterns, and now with modern AI, we still need to make sense of what it's telling us.
• 11:17
  SPEAKER_00: So sense making is you know at the center of you know our sort of evolution with machines going forward.
• 11:24
  SPEAKER_03: Brilliant.
• 11:28
  SPEAKER_03: You know, as I read the book, uh, one of the things that you know got me thinking is the industrial era, uh, what it did was uh you know the machines that got invented, okay, actually made humans into almost machines.
• 11:49
  SPEAKER_03: We just do like what you explained in the hospital and the healthcare, where people have become actually machines trying to come in and they are just providers, as you call them, in one of the chapters, right?
• 12:01
  SPEAKER_03: In the AI era, though we are creating machines, I think we have to become more humans, right?
• 12:09
  SPEAKER_03: Does that make sense to you?
• 12:12
  SPEAKER_00: So let's back up, right?
• 12:14
  SPEAKER_00: So I think what you're pointing out is I describe the healthcare system in the US at the moment as giving us the worst of both humans and machines.
• 12:24
  SPEAKER_00: And and I describe a situation where you know I went to the emergency and I felt like I'd gone through an assembly line.
• 12:32
  SPEAKER_03: Yeah.
• 12:33
  SPEAKER_00: And so the healthcare process has become sort of you know unitized.
• 12:39
  SPEAKER_00: You go from one you know um provider to another, they ask you pretty much you know the same things, and then you you you get passed on like an assembly line.
• 12:48
  SPEAKER_00: Um, and that's what's happened now.
• 12:50
  SPEAKER_00: So, you know, ironically, what's happened is as we've made this process, you know, as we formalize the process, we've turned humans into robots, right?
• 12:59
  SPEAKER_00: Where they're just functioning like machines in this process.
• 13:02
  SPEAKER_00: Um, and at the same time, the machines aren't learning much about what's happening in the healthcare system.
• 13:07
  SPEAKER_00: So that's what I meant, you know, when I said we're getting the worst of both worlds at the moment.
• 13:14
  SPEAKER_00: You know, that is humans have become robots and we're not getting the full benefit of the machine because it isn't recording all the right kinds of data from our interactions with the healthcare system and learning something.
• 13:25
  SPEAKER_00: It didn't learn anything from my experience, and I suspect this is happening you know millions of times a day.
• 13:31
  SPEAKER_00: Where it's you know, where we're coming into contact with the healthcare system, but it isn't recording the right kinds of data.
• 13:37
  SPEAKER_00: So it's it's it's it's not learning, and at the same time, humans have become robots.
• 13:41
  SPEAKER_00: So that's what I meant, but I'm optimistic that that will change because I I feel like in healthcare, in physical healthcare, our incentives are more or less aligned, you know, with with the um, you know, that is the patients and and providers have the same kind of incentives.
• 13:56
  SPEAKER_00: So I feel like we're going through a transitional phase in healthcare at the moment, we're getting the worst of both worlds.
• 14:03
  SPEAKER_00: But I I'm more optimistic.
• 14:05
  SPEAKER_00: I mean, healthcare is one of those areas where I was more optimistic that machines would actually look at this uh trace of activity that we leave behind.
• 14:15
  SPEAKER_00: You know, when we go uh see a doctor, there's some sort of an interaction that are you know tests performed.
• 14:21
  SPEAKER_00: Um but so far it's been very difficult to pull all this data together into a database to make it usable and learn from.
• 14:29
  SPEAKER_00: I'm optimistic that with modern AI, these tools that actually quote unquote understand what's going on in the process, that they'll pull this data together and put it into a proper database that makes it more usable to learn from.
• 14:45
  SPEAKER_03: The other thing that uh your book made me uh really reflect on, you constantly use a word called scorekeeping, right?
• 14:54
  SPEAKER_03: Where I really found it a beautiful terminology because if I'm a teacher, I need to do scorekeeping, if I'm a doctor, I need to do scorekeeping.
• 15:04
  SPEAKER_03: If I'm a stock trader, I need to do scorekeeping because I'm doing things out of my instinct and knowledge which the machine may not know.
• 15:13
  SPEAKER_03: So the fact that we need new forms of systems where we need to keep you know using our data, putting it back into the system so that it can become more and more intelligent, and we need new forms of methods by which our knowledge and our experience need to get embedded into these systems.
• 15:35
  SPEAKER_03: Do you think that's critical and are there going to be new form factors that are going to come in?
• 15:40
  SPEAKER_00: Yeah, exactly.
• 15:41
  SPEAKER_00: I mean, you know, you you said it better than I did, which is that scorekeeping just pervades every aspect of our lives, right?
• 15:47
  SPEAKER_00: It's it's just there.
• 15:48
  SPEAKER_00: And, you know, as humans, we we we keep track of it the best we can.
• 15:53
  SPEAKER_00: But computers are so much better at scorekeeping, right?
• 15:55
  SPEAKER_00: Whether it's in finance or healthcare or you know, any other aspect of our lives, you know, they're so much better at scorekeeping.
• 16:01
  SPEAKER_00: It's just that in the healthcare system, they're not doing much of it at the moment.
• 16:05
  SPEAKER_00: Uh and that's what I'm more optimistic about that going forward, you know, with modern AI that you know can understand the meaning of the data trail it's seeing, it'll be able to say, oh yeah, you know, Swami visited the doctor on July so-and-so, you know, was uh, you know, here's here's the results of the exam, here was the medication that was prescribed.
• 16:25
  SPEAKER_00: And then, you know, two weeks later he came back, and lo and behold, you know, his blood pressure was lower and he was in better health, or whatever, right?
• 16:32
  SPEAKER_00: There's there's that scorekeeping that lets you know people who are providing uh care to you uh more information about you.
• 16:40
  SPEAKER_00: At the moment, they're largely flying blind.
• 16:42
  SPEAKER_00: You know, they barely have you know 30 seconds to consult your chart before you walk into the office, right?
• 16:48
  SPEAKER_00: They're just inundated um uh you know with um with patience, you know, and you know, functioning with you know a lack of information.
• 16:58
  SPEAKER_01: Yeah.
• 17:03
  SPEAKER_03: The other thing I found fascinating again uh is really the you know the uh Damodran bot architecture, the D bot architecture, which I found phenomenal, especially the number of AI agents that you put in into the architecture.
• 17:19
  SPEAKER_03: Can you just unravel it for the audience?
• 17:21
  SPEAKER_03: Because it's the fantastic architecture in terms of how to bring in that embedded knowledge of the man himself into the AI system.
• 17:33
  SPEAKER_00: So let me give you a little bit of background since I suspect many of your listeners are interested in finance, which is uh an area that I've been immersed in for you know uh over 30 years now.
• 17:46
  SPEAKER_00: So, you know, I brought AI to Wall Street in you know the early 90s, and my focus was on learning how to trade based on data, and and it was like trading, like short-term trading, you know, holding periods of a few days to a few hours.
• 18:06
  SPEAKER_00: You know, so at one time I was doing a lot of high frequency trading where you know I was trading you know almost like you know 0.1% of the volume of the New York Stock Exchange, you know, just like a lot of high frequency trading, and that was phenomenally profitable.
• 18:19
  SPEAKER_00: But my main emphasis was on short-term trading, learning from data to make you know short-term predictions.
• 18:27
  SPEAKER_00: So I'd been doing that for I don't know, 17, 18 years, and in 2015 I had two conversations with my colleagues.
• 18:34
  SPEAKER_00: One was with Scott Galloway, who's written the foreword for my book.
• 18:38
  SPEAKER_00: Um, and we had a conversation, it's on it's on the internet called Should You Trust Your Money to a Robot?
• 18:43
  SPEAKER_00: Yeah, and we chatted, and the end of it he says, Okay, so trading floors will disappear, but private equity and venture capital is safe.
• 18:51
  SPEAKER_00: I'm like, yes, I think that's a fair way to put it.
• 18:54
  SPEAKER_00: That's inherently human, sort of long-term investing, long-term thinking, is not something that the machine can do.
• 18:59
  SPEAKER_00: It doesn't have enough data, there's no basis for it to do that.
• 19:02
  SPEAKER_00: So that isn't a domain of long-term investing.
• 19:05
  SPEAKER_00: That same week, ironically, I met with my colleague Aswat Tamodharan, who's you know the guru of valuation on Wall Street.
• 19:13
  SPEAKER_00: Yeah.
• 19:14
  SPEAKER_00: And you know, we had this conversation about whether we could create uh a machine that could learn to think like him.
• 19:21
  SPEAKER_00: And we decided at the time that it would be too hard, you know, that his thinking um, you know, could not be systematized into a system at that time, given the state of the art of AI.
• 19:33
  SPEAKER_00: But when ChatGPT came out, we revisited that you know initial conversation.
• 19:40
  SPEAKER_00: And I went back to Aswat and I said, uh, should we try this again?
• 19:43
  SPEAKER_00: And he said, Yeah, it seems worthwhile.
• 19:46
  SPEAKER_00: So I sort of initiated a project with one of my uh colleagues, Zhao Siddok, uh here at NYU.
• 19:54
  SPEAKER_00: And at that time I had no idea whether this would really work.
• 19:58
  SPEAKER_00: But the idea was to systematize long-term investing in the same way that the Motheran thinks about it.
• 20:05
  SPEAKER_00: Now the reason this was uh feasible in my mind is because the Motheran is the only one out there, bar none, there's no exceptions, who has published the volume of valuations, right?
• 20:22
  SPEAKER_00: No one else has published the number of valuations that he has.
• 20:26
  SPEAKER_00: So it's all out there, and it's it's been out there for years, right?
• 20:30
  SPEAKER_00: So if you want to find you know all the valuations that he's done, they're out there, right?
• 20:34
  SPEAKER_00: His musings on markets uh newsletter gets you know tens of millions of views every time he publishes anything.
• 20:40
  SPEAKER_00: So it's it's something that people are looking at.
• 20:42
  SPEAKER_00: So there was a lot of training data available, which made me optimistic that maybe a machine could learn from it.
• 20:50
  SPEAKER_00: And so that's what we started initially.
• 20:52
  SPEAKER_00: We just started by you know giving the machine you know all of his musings and writings and said, Can you learn something from it?
• 21:00
  SPEAKER_00: So when we ask you how to value a new company, you'll do it.
• 21:02
  SPEAKER_00: So we tried that.
• 21:03
  SPEAKER_00: You know, it took six months to do that.
• 21:05
  SPEAKER_00: It was a complete flop.
• 21:07
  SPEAKER_00: You know, I mean it it just doesn't work.
• 21:10
  SPEAKER_00: You you just can't do that.
• 21:11
  SPEAKER_00: You know, you can't just take tell GPT or Claude or Gemini or whatever, hey, here are all the you know, you know, writings of the Motoran.
• 21:19
  SPEAKER_00: Now learn something from them so that you're an oracle just like him.
• 21:23
  SPEAKER_00: It just doesn't work.
• 21:25
  SPEAKER_00: So we had to get much more detailed about what he actually does, like literally get inside his process, get inside, get inside his head, and he was incredibly um you know helpful in enabling us to do that.
• 21:39
  SPEAKER_00: And so he broke down his entire valuation process into a bunch of parts, and each of these parts are handled by something called an agent, right?
• 21:50
  SPEAKER_00: So in AI, there's this notion of agents, and you know, these multi agent architectures are sort of you know beginning to.
• 22:00
  SPEAKER_00: You know, get quite popular.
• 22:01
  SPEAKER_00: By the way, they go back to the 70s, right?
• 22:03
  SPEAKER_00: That you know, you know, one of the people who came up with this, Alan Kay, you know, got the Turing Award in the 70s.
• 22:08
  SPEAKER_00: So the idea of agents has been around for a long time, 50 years.
• 22:11
  SPEAKER_00: But what's different now from 50 years ago is that at that time you had to program the agents.
• 22:17
  SPEAKER_00: You had to like you had to talk to them in code.
• 22:19
  SPEAKER_00: Now you can talk to them in English.
• 22:21
  SPEAKER_00: So I can have a data acquisition agent, and I tell the agent, hey, go grab every piece of fundamentals data and you know, balance sheets and everything for NVIDIA and load it into Aswat's quantitative model, you know, and it goes off and yes, boss goes off, does it, brings it, loads it.
• 22:41
  SPEAKER_00: And so we verified that it does it correctly, right?
• 22:44
  SPEAKER_00: So there's this sort of layer of what I call general intelligence in AI now that you can use to your benefit.
• 22:50
  SPEAKER_00: And that's and that's why there's so much excitement about AI, because people feel that they can build these applications very quickly, right?
• 22:56
  SPEAKER_00: So, same idea.
• 22:57
  SPEAKER_00: You have these agents that go off and they know how to grab financial data, right?
• 23:00
  SPEAKER_00: So you might have another, and so we have another agent that let's say goes and looks at consensus estimates, right?
• 23:06
  SPEAKER_00: Because you know that that's important information.
• 23:10
  SPEAKER_00: Um, another agent that figures out, you know, what are the comparable companies to this one, right?
• 23:16
  SPEAKER_00: So you tell the agent, you know, like get all the financials, you know, get the you know, price to forward inning ratios for all similar companies.
• 23:24
  SPEAKER_00: It knows how to do that, it has the intelligence to do that.
• 23:27
  SPEAKER_00: So, you know, and and similarly we have a report writer that says, okay, now that you have everything, write the report.
• 23:34
  SPEAKER_00: And then there's a critic that you know looks at the report and says, Is this correct?
• 23:39
  SPEAKER_00: You know, have you cited the sources?
• 23:41
  SPEAKER_00: You know, is it the correct length?
• 23:43
  SPEAKER_00: You know, is it is it clear?
• 23:45
  SPEAKER_00: Are the charts clear?
• 23:46
  SPEAKER_00: Do they have captions?
• 23:48
  SPEAKER_00: Right?
• 23:48
  SPEAKER_00: So the critic then says, Let's polish this up and make sure it's real and uh trustable by whoever's gonna look at it, right?
• 23:56
  SPEAKER_00: So trustable is key, and I'm sure we'll come back to that.
• 23:58
  SPEAKER_03: Yeah.
• 23:59
  SPEAKER_00: So that's what the agent really does.
• 24:01
  SPEAKER_00: And now it's you know, you know, very close to uh being a commercial tool where all you do is you know you go to the front end, and I should give you a demonstration of some of this one of these days.
• 24:11
  SPEAKER_00: It's fascinating.
• 24:12
  SPEAKER_03: It's very fascinating, it was very fascinating.
• 24:15
  SPEAKER_00: You know, you give it a symbol, you say value in video, and it goes off and it says, uh, do you do you have a thesis or should I generate the thesis?
• 24:23
  SPEAKER_00: Right?
• 24:24
  SPEAKER_00: And so you as the human can tell it I have a thesis, or no, you generate the thesis, and you know, press the button and it's off to the races, and 15 minutes later it sends you a report, you know, a five to ten page report, in the spirit of the Motoran, right?
• 24:40
  SPEAKER_00: So it's it's meant to be a report that he might have written on a company, you know, at the at the current time.
• 24:46
  SPEAKER_00: So that's how it works, um, you know, at the moment is give it a company, gives you back a report.
• 24:54
  SPEAKER_00: Now, I'll just end by saying, how do I think this will be used?
• 25:00
  SPEAKER_00: You know, how how do I think people will use it?
• 25:02
  SPEAKER_00: My I my conjecture is that I think it'll make analysts and and just people in general much more productive because you'll be able to run scenarios against it, right?
• 25:12
  SPEAKER_00: You'll be able to say, um, assume that the tariff wars will escalate.
• 25:18
  SPEAKER_00: You know, how would you value BYD if if these tariff wars escalate, versus assume that Trump's bluffing, you know, and that this is a negotiation tactic, and you know, and and that, you know, in a few years we'll be back to you know business as usual, right?
• 25:34
  SPEAKER_00: So you can give it these scenarios, and you know, which is very difficult and time consuming to do manually, right?
• 25:41
  SPEAKER_00: Because you have to imagine the amount of work involved in saying, okay, now with a different set of assumptions, how does the valuation change?
• 25:49
  SPEAKER_00: Right?
• 25:49
  SPEAKER_00: It's not just a question of changing formulas, it's a question of like changing your thinking and your reasoning with the new set of assumptions.
• 25:57
  SPEAKER_00: And that is what I find really exciting about a tool like this, is that it brings a new level of intelligence, you know, at your fingertips.
• 26:07
  SPEAKER_00: You know, and I talk about agents at your fingertips, you know.
• 26:10
  SPEAKER_00: I mean, Bill Gates used to talk about information at your fingertips, right?
• 26:13
  SPEAKER_00: Now we've got agents at your fingertips, and that's a tremendously powerful capability that's provided by AI, which I think will you know get increasingly used going forward.
• 26:23
  SPEAKER_02: ContraMinds is a podcast dedicated to decoding people, minds, strategy, and culture.
• 26:30
  SPEAKER_02: We interview and learn from high performers so that you can apply these lessons on your journey to becoming the knowledge worker athlete you were meant to be.
• 26:37
  SPEAKER_02: The Contraminds Podcast is available on all leading podcast players.
• 26:41
  SPEAKER_02: And if you are interested in revisiting past episodes or taking a look at our show notes from this episode, please visit us at www.contraminds.com.
• 26:51
  SPEAKER_02: And now back to the show.
• 26:57
  SPEAKER_03: Yeah, no, I'll come back to this uh uh you know the skill sets and uh how we need to work with these machines, which is really the core uh you know uh theme of the book.
• 27:07
  SPEAKER_03: But uh what I what I really found again uh you know very very evocative in terms of an idea is your uh trust heat map.
• 27:18
  SPEAKER_03: Because what I really liked about that is really, you know, where do you really bring in full automation and where you you know have to have human in-loop?
• 27:28
  SPEAKER_03: I thought it was a beautiful representation, and can you just uh unpack that big idea that you've written in the book?
• 27:36
  SPEAKER_00: Sure.
• 27:37
  SPEAKER_00: So the the what you refer to, this trust heat map, is something that emerged um you know after I don't know, 20 years of building AI applications in a number of areas.
• 27:50
  SPEAKER_00: So finance is something I'd been doing for a long time.
• 27:53
  SPEAKER_00: I had algorithms that were trading, uh automatically, no human intervention, completely systematic.
• 28:00
  SPEAKER_00: And that was interesting because I did ask myself why do I trust the algorithm in finance, which was wrong almost half the time, right?
• 28:08
  SPEAKER_00: My win rates were you know barely 52%.
• 28:12
  SPEAKER_00: I mean, in finance, if you can get you know a win rate of 52% with equal winners and losers, you know, you should celebrate, right?
• 28:19
  SPEAKER_00: And 53%, 54%, you should manage the the world's money.
• 28:23
  SPEAKER_00: So the the edge that you need in finance is very small, you know, in order for an algorithm to amplify it, right?
• 28:31
  SPEAKER_00: Because once you have a slight edge, then you just multiply it by trading as frequently as possible and trading across as many instruments as possible, right?
• 28:39
  SPEAKER_00: That's the name of the game in finance.
• 28:41
  SPEAKER_03: Yeah.
• 28:42
  SPEAKER_00: So that was interesting.
• 28:43
  SPEAKER_00: I I thought that you know I'm willing to trust an algorithm in finance without any human intervention.
• 28:49
  SPEAKER_00: In fact, when I intervened, we would invariably mess things up, right?
• 28:53
  SPEAKER_00: So, which is another theme that maybe we can touch on, which is you know, you know, at one point there was this assumption that humans plus algorithms are better than algorithms.
• 29:03
  SPEAKER_00: Not true, right?
• 29:04
  SPEAKER_00: Maybe true sometimes, but not all not always, and certainly not true in trading when it comes to finance, right?
• 29:10
  SPEAKER_00: If you if you've done the math right, get the hell out of the way, right?
• 29:14
  SPEAKER_00: That's the whole reason for being systematic, right?
• 29:16
  SPEAKER_00: You shouldn't be trying to second guess the machine or think you're better, right?
• 29:20
  SPEAKER_00: If you're systematic, you're systematic, right?
• 29:22
  SPEAKER_00: You intervene very, very infrequently, you know, in rare events like COVID, right?
• 29:26
  SPEAKER_00: Something happens and you know, that I can understand.
• 29:29
  SPEAKER_00: But on a day-to-day basis, you're systematic.
• 29:32
  SPEAKER_00: So that was you know, one thing that was interesting.
• 29:34
  SPEAKER_00: I'd also worked in sports and healthcare.
• 29:38
  SPEAKER_00: And you know, in healthcare, for example, I'd found that you probably wouldn't be willing to take the machine or take a decision from an algorithm.
• 29:48
  SPEAKER_00: You want the human specialist, the doctor, to be making that decision, right?
• 29:53
  SPEAKER_00: Even though an algorithm may be correct much more than it is in finance, right?
• 29:58
  SPEAKER_00: The algorithm may be correct 90% of the time.
• 30:01
  SPEAKER_00: You still want the human to say, yes, it makes sense, or no, it doesn't make sense, or or well, this is slightly different in this situation, the algorithm couldn't have taken that into account, right?
• 30:13
  SPEAKER_00: You want that reassurance, you know, from a human, right?
• 30:16
  SPEAKER_00: So um, you know, and then you have things like driverless cars, right?
• 30:21
  SPEAKER_00: Which until very recently, right, this is a technology where there are hardly any mistakes.
• 30:26
  SPEAKER_00: Yeah.
• 30:27
  SPEAKER_00: And and yet you're really reluctant to turn yourself over to an algorithm, right?
• 30:33
  SPEAKER_00: So when I thought about the range of problems, I realized that our trust in an algorithm really depends on how frequently it's wrong and the consequences of error when it's wrong.
• 30:46
  SPEAKER_00: And so those are the two things that I've put on the x-axis and the y-axis.
• 30:50
  SPEAKER_00: So the x-axis is predictability, it goes from sort of zero to one, zero being completely random, one being completely deterministic, like you know, will the sunrise tomorrow?
• 31:00
  SPEAKER_00: Right, we know that.
• 31:02
  SPEAKER_00: And on the y-axis are the costs of error from zero to infinity, right?
• 31:07
  SPEAKER_00: And so I have this map where uh there's a green zone which is you know problems to the lower right.
• 31:15
  SPEAKER_00: If the machine hardly makes any mistakes and the mistakes are inconsequential, then you should trust the algorithm.
• 31:21
  SPEAKER_00: Right?
• 31:22
  SPEAKER_00: Whereas on the bottom on the top left, where there's unpredictably lots of errors, high cost of error, you wouldn't trust the machine.
• 31:31
  SPEAKER_00: Right?
• 31:31
  SPEAKER_00: That's an inherently human thing.
• 31:33
  SPEAKER_00: Now it doesn't mean that a human will do it well, but it just means that there's no basis for the machine to do that.
• 31:38
  SPEAKER_00: So that in a nutshell is the trust heat map, and you can place all kinds of problems on it and see where you are, you know, willing to trust the machine, where you're not, and those problems that lie sort of on this frontier where you know it's moving from don't trust the machine to trust the machine.
• 32:01
  SPEAKER_00: And actually the Demotherin bot is a great example of that of a technology that I've that I um would assert is moving from the red zone to the green zone.
• 32:12
  SPEAKER_00: That is, in 2015, when I talked to the Motheran about this problem, there's no way that you could trust a machine to do valuation.
• 32:20
  SPEAKER_00: Right now we've got a demonstration um and we're evaluating it.
• 32:26
  SPEAKER_00: You know, it still makes mistakes, you know.
• 32:28
  SPEAKER_00: Um you know, sometimes it comes up with a thesis that's less than satisfactory.
• 32:33
  SPEAKER_00: So at the moment I'd say we're sort of on this frontier, um, you know, as far as long-term investing is concerned, with you know, the the bot.
• 32:42
  SPEAKER_00: But just to come back and sort of wrap up the the answer to your question, uh the trust heat map is really a trade-off.
• 32:49
  SPEAKER_00: It expresses a trade-off between how frequently a machine will be wrong and the consequences of its error.
• 32:55
  SPEAKER_00: And it's a really simple idea.
• 32:57
  SPEAKER_00: And um, you know, that's what the the trust heat map is about.
• 33:01
  SPEAKER_00: And it's it's you know, I found this to be a uh I I find that senior managers, leaders, find this to be really useful in helping them position the various problems that they're dealing with in their companies, you know, on this heat map and prioritize which ones are more amenable to automation versus those where you will need a human in the loop for the foreseeable future.
• 33:26
  SPEAKER_03: 100%.
• 33:30
  SPEAKER_03: In fact, uh that's exactly the thought that uh you know the framework left, and it's a brilliant framework because then I can go back and put all my problems uh my company is facing into that framework, and then I'm able to decide which one of those are uh you know inherently uh you know I can use AI because the cost error is not so huge, versus where should I be not automating it, and therefore how should I be working with AI, right?
• 33:57
  SPEAKER_03: That is really what I saw as the framework, which brings me back to the core theme of this book, which uh I think uh is what uh is critical, which is you really talk about this, you talked about the analyst in the uh you know the mother report, and uh so uh in the pre-AI era, if I was an average doctor, if I was an average analyst, okay, I could still you know go by because obviously information is not as compostable as what AI did, but what you really talk about is you've got to be absolutely an expert where you need to lead beyond what the AI can think.
• 34:48
  SPEAKER_03: And is that becoming more and more critical in occupations over the next 15 years?
• 34:54
  SPEAKER_00: So, what you're getting at is one of the key themes of the book that I talk about, which is that AI will lead to a bifurcation of humanity, right?
• 35:03
  SPEAKER_00: It'll lead to humans that are superhuman because they get so much better at what they do because of the AI, and then it'll lead to others who use the machine as a crutch, right, and and and they go the other way, right, and become potentially unemployable, you know, because the machine becomes a crutch and they turn to it for everything and not not able to get beyond it.
• 35:29
  SPEAKER_00: Now the lesson here is for humans it is not that profound, it's what it has always been with every technological change, which is you need to up your game.
• 35:42
  SPEAKER_00: And that's what I'm really getting at, right?
• 35:44
  SPEAKER_00: So if you're the average, you know, you're gonna sink to the bottom unless you can up your game.
• 35:49
  SPEAKER_00: Um, the reason we've had you know less than uh stellar diagnosticians or analysts continue is there wasn't adequate scorekeeping.
• 36:01
  SPEAKER_00: You know, it takes a long time to determine whether someone is good, uh, or or someone is way below the average, right?
• 36:09
  SPEAKER_00: And so, you know, you can sort of fly under the radar, that'll become much harder to do.
• 36:14
  SPEAKER_00: That there won't be any place to hide now, you know, when the machine enters the picture and is part of the process and part of thinking with you, um then you just you know everyone needs to up their game.
• 36:28
  SPEAKER_00: Um and that's and that's what I'm calling the sort of bifurcation of humanity.
• 36:32
  SPEAKER_00: And if you think about it, you know, I'm you know, take a simple example.
• 36:35
  SPEAKER_00: The other day I asked the machine, what's the difference between salt and sugar as a preservative?
• 36:42
  SPEAKER_00: And it said, you know, you know, they will both work by you know osmosis and all of that.
• 36:48
  SPEAKER_00: Now I needed to understand, I needed to know what osmosis is to understand its response, right?
• 36:54
  SPEAKER_00: And then it created a table uh of you know, and then it said, of course it matters, the taste matters, you know, is salt more appropriate than sweet and whatever.
• 37:02
  SPEAKER_00: And then it created a table of uh salt versus sugar, and there were several factors, and they all look pretty similar, except one that stood out, and it said, um, salt works well at lower concentrations, whereas sugar doesn't.
• 37:15
  SPEAKER_00: You have to have a high concentration of sugar.
• 37:16
  SPEAKER_00: And I thought that's curious, and I said, Well, why is that?
• 37:19
  SPEAKER_00: And it said, well, because sodium chloride ionizes, you know, whereas sugar molecules don't, and the ionization, you know, those ions bind to water, and so there's less left over for something to you know to grow on.
• 37:33
  SPEAKER_00: And I thought, wow, that's that's so cool, right?
• 37:36
  SPEAKER_00: But the reason I could understand that response is because I know something about ionization, right?
• 37:40
  SPEAKER_00: And I know something about affinities and binding and stuff like that.
• 37:43
  SPEAKER_00: If I didn't, I would not be able to understand its response, right?
• 37:46
  SPEAKER_00: And that's a great example of what I mean, which is the more you know, the more AI amplifies you.
• 37:56
  SPEAKER_00: And the less you know, the more it de-amplifies you, right?
• 38:00
  SPEAKER_00: And that's what I mean by this impending bifurcation of humanity, right?
• 38:03
  SPEAKER_00: That we've got this amazing resource, this amazing oracle on our fingertips.
• 38:09
  SPEAKER_00: And the more we know, the more we can learn.
• 38:11
  SPEAKER_00: You know, I was talking to a friend of mine who's a molecular biologist, and he was talking about how it's enabled him to sort of go into neighboring areas, to learn about neighboring areas, you know, because his problem involves knowing something about physics, knowing something about biology, knowing something about chemistry.
• 38:29
  SPEAKER_00: Yeah, um, right, and his emphasis is drug safety, like you know, and he's able to bring in ideas from disciplines in a much more fluid way and learn so much more and amplify his own capability.
• 38:43
  SPEAKER_00: And that's what we're looking at going forward is you've got this amazing machine that can amplify you, depending on how you use it, or it can de-amplify you, right?
• 38:52
  SPEAKER_00: So it's important to keep that mental muscle going and exercised even more, you know, in thinking with machines, as opposed to letting it atrophy.
• 39:05
  SPEAKER_00: And that's the critical thing going forward for for us to consider.
• 39:09
  SPEAKER_00: And the answer is different for each of us, by the way, right?
• 39:12
  SPEAKER_00: But but but what's common is that um there is no getting away from this.
• 39:19
  SPEAKER_03: Yeah.
• 39:23
  SPEAKER_03: Uh I picked up a statement from one of the chapters in your book, which said, we've got to learn the art of framing questions, right?
• 39:33
  SPEAKER_03: And that is something that was profound for me.
• 39:36
  SPEAKER_03: And uh to me, the whole book, uh, as I saw you unveil one chapter after another, uh, I really thought you were actually framing the questions and trying to answer them.
• 39:48
  SPEAKER_03: So as uh professionals and as leaders uh and as people who would be running companies or you know running functions, uh, our ability to actually frame the question is going to become more and more important in the AI era.
• 40:04
  SPEAKER_03: Would you agree?
• 40:05
  SPEAKER_00: Yeah, I mean that there's no getting away from that, right?
• 40:08
  SPEAKER_00: The the ability to ask good questions is critical, right?
• 40:12
  SPEAKER_00: And that's also you know segues of what I was talking about earlier, which is the more you know, the better the questions you can ask.
• 40:20
  unknown: Absolutely.
• 40:21
  SPEAKER_00: If you don't know anything, you can't ask a question, right?
• 40:23
  SPEAKER_00: There's nothing to ask, right?
• 40:25
  SPEAKER_00: Yeah.
• 40:26
  SPEAKER_00: So and and and that's what I mean.
• 40:27
  SPEAKER_00: The more you know, the better the questions you can ask.
• 40:29
  SPEAKER_00: And this is an iterative kind of process, right, of getting yourself up the learning curve with the machine, right, and saying, aha, I didn't even thought about that question.
• 40:39
  SPEAKER_00: But now that you're telling me something, here's the question, right?
• 40:42
  SPEAKER_00: So it's and exactly that that that at the moment is still in the domain of humans, right?
• 40:51
  SPEAKER_00: Is the ability to ask really good questions.
• 40:54
  SPEAKER_00: Because at the moment the machine doesn't have that inherent curiosity to ask questions on its own.
• 41:00
  SPEAKER_00: But you know, I'm sure going forward it will develop that curiosity and start asking great questions on its own.
• 41:06
  SPEAKER_00: It's just not doing that now.
• 41:07
  SPEAKER_00: So for the foreseeable future, that lies in the domain of humans is the ability to ask good questions.
• 41:15
  SPEAKER_03: Yeah.
• 41:19
  SPEAKER_03: And uh the chapter on uh you know AI laws and governance was something that uh you know I've not read anywhere else, and uh uh it really uh started triggering some thoughts for me, like how we do a KYC, which is know your customer for uh you know humans, is then uh you know a platform that could be built called KYE, which is no your agent, and probably that's going to be a new kind of platform where AI agents have to be really uh you know looked at given the fact that uh you know that could be challenges uh of the uh you know the failures that could happen, the wrong predictions that could happen, and therefore I really thought of the idea uh as a regular challenges of managing AI and the uh you know and the real issues that may come up.
• 42:16
  SPEAKER_03: Uh is there a need for something like a KYA where if I build an agent, I need to actually put it in some kind of a KYA, and therefore, when I know the agent, I know what it can do, and therefore you're able to build some guardings around these uh you know agents that people are building.
• 42:34
  SPEAKER_00: So there's several um issues you're raising here, Swami, which is that um the beauty of these agents is that you tell them what to do and not how to do it.
• 42:48
  SPEAKER_00: That that's one of the sort of really uh um impressive advances of modern AI, and it goes back to understand of having common sense, right?
• 43:00
  SPEAKER_00: Of you know that that all you need to do is tell the agent what to do and it'll figure out how to do it.
• 43:08
  SPEAKER_00: That's powerful.
• 43:09
  SPEAKER_00: But it's powerful in multiple ways.
• 43:11
  SPEAKER_00: It's powerful as long as it's uh doing stuff that you intend for it to do, and that's the right thing to do.
• 43:18
  SPEAKER_00: It becomes dystopian when it uh creates sub goals to do what you asked it to do that you're not envisioned, and it goes off the rails and starts killing people or something like that.
• 43:32
  SPEAKER_00: I'm just uh you know, I'm I'm drama, you know, I'm dramatizing.
• 43:35
  SPEAKER_00: But the the the point, the the larger point is that agents can do things that uh you hadn't envisioned.
• 43:45
  SPEAKER_00: Uh and that's true of you know AI in general, and so uh n the you know one of the uh questions I uh ask you know that are raised in the book is as these systems, as AI systems get more sophisticated.
• 44:00
  SPEAKER_00: Right.
• 44:00
  SPEAKER_00: And you know, at at the moment there's a lot of excitement around agentic AI.
• 44:04
  SPEAKER_00: You know, we'll see whether it pans out, you know, to the hype.
• 44:07
  SPEAKER_00: There's a lot of hype around it, but it's also real.
• 44:10
  SPEAKER_00: Um as they become um more um you know capable, that we give them more agency.
• 44:18
  SPEAKER_00: Right?
• 44:19
  SPEAKER_00: We tell them, well, you can act on my behalf, you know, you can even start negotiating a contract on my behalf, right?
• 44:26
  SPEAKER_00: Because you're so good at it, right?
• 44:28
  SPEAKER_00: You're better at negotiation than I am, so do it for me, right?
• 44:32
  SPEAKER_00: That yeah, you know, you you build this amazing negotiation agent, right?
• 44:35
  SPEAKER_00: That does that.
• 44:36
  SPEAKER_00: So the concern is that they become so good at this that you give them agency and say, Well, act on my behalf, right?
• 44:43
  SPEAKER_00: And the the the question I raise in the book is like, where do you really stop with that?
• 44:49
  SPEAKER_00: You know, like you know, does the does the machine now start having rights?
• 44:55
  SPEAKER_00: You know, and the an example in my book is you know, an agent that you know runs a business for you, and and you know, so John Doe creates an agent that starts running his real estate business, and then it becomes so good that John Doe doesn't even bother you know with it anymore.
• 45:10
  SPEAKER_00: The machine keeps doing it, and then one day John Doe passes away, and the machine now realizes that aha, like what do I do now?
• 45:18
  SPEAKER_00: And you know what, I'll keep running the business, and what should I do?
• 45:21
  SPEAKER_00: Well, maybe I should do this with my subscribers, you know, and it it comes up with a brilliant business plan and starts running the business and hires a board, you know, and enters into contracts, right?
• 45:32
  SPEAKER_00: I mean, is that like a bizarre, unthinkable scenario?
• 45:36
  SPEAKER_00: I don't think so.
• 45:37
  SPEAKER_00: I mean, yeah, um, that is, you know, as these agents get really good, we might start giving them more and more agency.
• 45:43
  SPEAKER_00: And the question is, where do you stop?
• 45:45
  SPEAKER_00: And you know, and I take it as far as saying that, you know, maybe Elon Musk and Jeff Bezos will say, you know, after we've passed away, you know, maybe they'll live, you know, they're doing longevity treatments and maybe they'll live to 120 or 150, but at some point they'll probably pass away, right?
• 46:03
  SPEAKER_00: Yeah.
• 46:03
  SPEAKER_00: And so then the question is, what happens to their wealth?
• 46:06
  SPEAKER_00: You know, and at the moment it's run by foundations, but my conjecture is that we might get so comfortable with the AI that we say, you know what, I'll leave it to my bot.
• 46:16
  SPEAKER_00: So we'll have you know, we'll have a Vasant bot and a Swami bot and you know, our own bots that that start doing things for us, and we say, you know what, whatever money I have left over, you manage it going forward, you know, in the way I would have if I were alive.
• 46:31
  SPEAKER_01: Yeah.
• 46:32
  SPEAKER_00: So that might sound like science fiction at the moment, but we may be closer to that scenario, you know, than we realize.
• 46:40
  SPEAKER_00: And so those are the kinds of issues I'm raising.
• 46:43
  SPEAKER_00: Now, you know, I talked about rights, but we've also got to think about you know restrictions or obligations, you know.
• 46:50
  SPEAKER_00: You know, in the future, would it be okay for a robot to show up at your doorstep and arrest you for non-payment of taxes?
• 46:58
  SPEAKER_00: You know, I mean, at the moment, there's no guardrails against that kind of thing, right?
• 47:02
  SPEAKER_00: We might actually, you know, the the IRS or you know, the income tax office in India, which I shudder to deal with, you know, you know, might release its you know, army of drones to go do this kind of stuff.
• 47:16
  SPEAKER_00: And do we want that kind of future?
• 47:17
  SPEAKER_00: I don't think so.
• 47:18
  SPEAKER_00: You know, so should we think seriously about sort of restrictions or obligations that AI should have towards you know human beings?
• 47:26
  SPEAKER_00: And at the moment we haven't even started thinking about these questions.
• 47:29
  SPEAKER_00: And given the pace at which the technology is moving and the rate at we're adopting it, I think it's time to really not just time, I think it's urgent that we ask these questions about you know the restrictions on machines, the obligations that AI should have towards humans, and the rights we want to give it going forward.
• 47:51
  SPEAKER_03: On that note, Wasan, uh thanks a lot.
• 47:55
  SPEAKER_03: So many things to really chew and think about uh on what we do, the way we work, the way we are going to be running our lives, the way the society needs to work with machines, the way we need to think with machines.
• 48:11
  SPEAKER_03: Lovely having a conversation, and I'm really looking forward at all the very best for a huge success as far as this book is concerned.
• 48:21
  SPEAKER_00: Thank you for the well-wishes, Swami, and great to uh chat with you as always.
• 48:27
  SPEAKER_00: Uh I look forward to our next conversation.
• 48:31
  SPEAKER_03: Thanks for listening to this episode.
• 48:34
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