Take a voyage through the technological tides with our guide Bob Tapscott, a tech sage with a compass always pointed to the future. His tales of shaping global banking systems and pioneering Wall Street's tech landscape form the backbone of our latest discussion. Bob's early tryst with the wonders of computer science and subsequent odyssey through a myriad of tech roles illuminates our chat, setting the stage for a deep exploration of the 'Trivergence'—the powerful confluence of AI, blockchain, and the Internet of Things. Together, we chart a course to navigate this convergence, uncovering strategies to harness its potential and steer toward success in the ever-changing waters of tomorrow.
This episode's narrative sails through the tech industry's frenzied gold rush, where NVIDIA emerges as the digital age's astute shovel seller, reaping success by equipping the miners of the virtual frontier. Bob sheds light on the immutable blockchain ledger, contemplating its role in redefining data control and the emergence of Web3 as a beacon of internet decentralization. The wind in our sails, AI's transformative gust, promises to revolutionize our professional and personal landscapes within a mere half-decade, challenging the status quo of technology centralization and stirring the waters of innovation.
Our conversation takes a sobering turn as we consider AI's rising tide on employment and education. We echo John Maynard Keynes's vision of a leisure-rich society, yet grapple with the ethical maelstroms AI might unleash. Bob emphasizes the importance of an informed public discourse and continuous learning to stay afloat in tomorrow's job market. As we anchor our discussion, we reflect on the lessons of the industrial revolution, contemplating the dual nature of AI as a tool for progress and disruption, and call on society to navigate these transformative times with wisdom and foresight.
To Learn more about AI: 1) https://www.coursera.org/learn/introduction-to-ai?action=enroll 2) https://www.zdnet.com/article/have-10-hours-ibm-will-train-you-in-ai-fundamentals-for-free/ 3) https://www.mygreatlearning.com/academy/learn-for-free/courses/chatgpt-for-beginners 4) https://www.mygreatlearning.com/academy/learn-for-free/courses/chatgpt-for-finance 5) https://app.pluralsight.com/ilx/video-courses/clips/7bcfc4f8-17e2-4616-bbdc-d4e636f8ae9d
To Learn more about Bob's Book Trivergence: 1) https://mitpressbookstore.mit.edu/book/9781394226610 2) https://www.amazon.ca/TRIVERGENCE-Accelerating-Innovation-Blockchain-Internet/dp/1394226616
Take a voyage through the technological tides with our guide Bob Tapscott, a tech sage with a compass always pointed to the future. His tales of shaping global banking systems and pioneering Wall Street's tech landscape form the backbone of our latest discussion. Bob's early tryst with the wonders of computer science and subsequent odyssey through a myriad of tech roles illuminates our chat, setting the stage for a deep exploration of the 'Trivergence'—the powerful confluence of AI, blockchain, and the Internet of Things. Together, we chart a course to navigate this convergence, uncovering strategies to harness its potential and steer toward success in the ever-changing waters of tomorrow.
This episode's narrative sails through the tech industry's frenzied gold rush, where NVIDIA emerges as the digital age's astute shovel seller, reaping success by equipping the miners of the virtual frontier. Bob sheds light on the immutable blockchain ledger, contemplating its role in redefining data control and the emergence of Web3 as a beacon of internet decentralization. The wind in our sails, AI's transformative gust, promises to revolutionize our professional and personal landscapes within a mere half-decade, challenging the status quo of technology centralization and stirring the waters of innovation.
Our conversation takes a sobering turn as we consider AI's rising tide on employment and education. We echo John Maynard Keynes's vision of a leisure-rich society, yet grapple with the ethical maelstroms AI might unleash. Bob emphasizes the importance of an informed public discourse and continuous learning to stay afloat in tomorrow's job market. As we anchor our discussion, we reflect on the lessons of the industrial revolution, contemplating the dual nature of AI as a tool for progress and disruption, and call on society to navigate these transformative times with wisdom and foresight.
To Learn more about AI: 1) https://www.coursera.org/learn/introduction-to-ai?action=enroll 2) https://www.zdnet.com/article/have-10-hours-ibm-will-train-you-in-ai-fundamentals-for-free/ 3) https://www.mygreatlearning.com/academy/learn-for-free/courses/chatgpt-for-beginners 4) https://www.mygreatlearning.com/academy/learn-for-free/courses/chatgpt-for-finance 5) https://app.pluralsight.com/ilx/video-courses/clips/7bcfc4f8-17e2-4616-bbdc-d4e636f8ae9d
To Learn more about Bob's Book Trivergence: 1) https://mitpressbookstore.mit.edu/book/9781394226610 2) https://www.amazon.ca/TRIVERGENCE-Accelerating-Innovation-Blockchain-Internet/dp/1394226616
Hey, what's going on, guys? We've got a special guest here today. We have Bob Tapscott. He's global, he's lived in so many different countries, he's been CIO of major different banks and right now he's really focused on the future. He's wrote an incredible book called the Trivergence, where we're going to talk about AI, blockchain, internet of Things and we're going to discuss how we can position ourselves to really excel in this future. So, bob, we're so grateful to have you here.
Speaker 2:
Thank you.
Speaker 1:
Where did your obsession with the future start? Where did that come up?
Speaker 2:
Because so many of us are focused on I just need to survive the here and now, and it's so hard to think so many years in the future and seeing all this happen Well, gee, I guess it would go back to university is that I got myself a part-time job teaching computer science to evaluate tests and help beginning programmers learn to program, and so I was just enthralled with computers and what you could do with them and the potential they had. So, as it turned out, I ended up in the industry, and a big part of developing any computer system is not just what the needs of today are, but what the potential needs are of tomorrow and what technology can do in terms of meeting those needs. And as the potential of what computers will do in the future constantly increases, I'm more fascinated about what the potential impacts of these changes will be.
Speaker 1:
Definitely, definitely. And what year was this that you were focusing on being a grad professor or helping out?
Speaker 2:
Oh, I wasn't a professor. I was just someone who sat in the lab and helped people with programmers. This goes back to the early 1970s.
Speaker 1:
I just want to preface that because just to show how early you really were on these ideas, because even in the tooth like yeah, so continue on.
Speaker 2:
Well, no, if you want to look at it in in a broader perspective, it was quite funny at the time. Is that? Um, um, I worked in the backups, the computer department of a corner store chain that had over 500 stores, oh wow, and I was able to automate the inventory process there from three months down to 45 seconds. But the irony of all ironies if I met, say, a woman somewhere, is that computers were so out back then I was better off saying that I worked in a corner milk store than I worked in the computer department at the back end of a corner milk store. So I was involved in technology far before it was actually cool.
Speaker 1:
Absolutely, absolutely. So you've seen the transition and where did that journey take your career? Because I know you have a super interesting career and I just want to share that before we hop into the divergence and discussing all of the important stuff there.
Speaker 2:
My background is crazy eclectic. I mean, my jobs have included water ski instructor, fighting forest fires. I worked in a mine in the Arctic. I was a nuclear medicine technician for a while. I eventually ended up in Brussels developing an international banking system for Citicorp. With like five other people I ended up the CIO of various Canadian banks and other financial institutions. From there I went to JP Morgan and worked on Wall Street in terms of complex derivatives and from there I was the chief designer architect for the system used today to direct aircraft by computer. That's used by Airbus and Boeing and the vast majority of the airlines in the world. So I have done many different things. I ran business intelligence for Quest as well at one point. So my career has been all over the place.
Speaker 1:
And just so the audience can get value here, how has that helped you in realizing what you want to do in your life? Because so many of us are like what do we want to be when we grow up? And a majority of the time it's always changing. A lot of the stuff we don't know about until it's finally there and we're actually doing it there and we're actually doing it. So how'd you always view these new opportunities and think about going in different directions instead of the normal? Hey, I'm climbing the corporate ladder. You just made your own ladder.
Speaker 2:
In some ways I wasn't trying to climb anything, it's just I was completely fascinated by the power of computers and what they could do. By the power of computers and what they could do, and as the power grew and the cost went down, the applications of what was possible have increased, and particularly in the last five years. What commuters can now do was unthinkable 10 years ago.
Speaker 1:
It really is. I mean, from my perspective. Uh, not everyone had a laptop. It was like premiere to have a lot like I'm 28 and people didn't have laptops. And you start looking at the phones, and the phones from like when I got mine and like when I was way younger to today, the computing power is insane, which you know much more about. So, for you, how has this transition? Has there been a time ever like this where it's just moved so quickly with AI, blockchain, internet of things, and we'll describe all, give definitions on all those after, but have you ever seen tech move this quickly?
Speaker 2:
Well, no, it is just exploding, and the reason for that is because of the divergence of these three different technologies. In a symbiotic fashion, each is accelerating the others, and so you see a rate of change that's going to have vast and profound implications for society has begun, and it's only going to accelerate, yeah, so let's just define each of these words so everyone understands.
Speaker 1:
So what is AI? What does that mean? What does that represent?
Speaker 2:
AI has been around for a very long time. Ai has been around for a very long time and it goes back in some ways, two or three centuries ago. In 2010, Professor Pedro Domingos identified as basically five major tribes to trying to get computers to be intelligent. The first were symbolists or rules-based systems, which were predominant in the 1970s and the 1980s. They were called intelligent or, sorry, they were called expert systems at the time, and they are still used in medicine and law today. The basic concept was that you would understand what an expert did to the best of the ability. You would hardwire, you would hard code a computer in such a way that it could emulate and demonstrate much of the expertise of the actual expert. So that was one of the techniques that was used. The second technique was called evolutionary genetic programs, inspired by the process of natural evolution. So what you would do is you'd develop a system that could create models and then, depending on how successful the models were, that the programmatic models that survived were the ones that would generate even smarter models to potentially survive survive.
Speaker 2:
The third approach is called Bayesian and it goes back to some math that Thomas Bayes did in 1763. And basically it's based on statistics, so it's used for making predictions, the most common of which would be political polling. The fourth approach were analogizers, or instance-based learners, and in that particular case you would look at a particular problem. You try to find something similar in a way, and learn from the closest analogy. But the one that is just taking off is a function of three different variables. Yes, there are advances in the approach that's transforming the world today, called neural networks, which is basically modeling a computer to work like the human brain, to work like the human brain. It was first envisioned back in the 1940s by Walter Pitts and Warren McCulloch and has been tried and failed and tried and failed about once a decade since the birth of the IBM personal computer in the 1980s. But thanks to Joshua Benjo, Jan LeCun and Jeff Hinton, which are three different professors at three different universities, neural networks have got smarter by going back and forward in terms of updating themselves to think more intelligently.
Speaker 2:
But the reason AI is actually exploding is a function of the divergence and if we can go back just a little bit here, about 10 or 11 or 12 years ago, the speed of a computer was a function of the chip on the computer. Call it the i3, the i5, the i9. And the increase in speed of those chips were limited by what's called Moore's Law, that since 1970 is predicted that the number of transistors on a chip would double about every two years. So for the last 50 years, Moore's Law has been a reasonable predictor of the speed and memory of computers, which is fairly powerful. What it basically says is that computers will increase a thousand times in power about once a decade. About once a decade.
Speaker 2:
But along came blockchain mining, which has transformed that. For mining, be it Bitcoin you know, Ether originally and other cryptocurrencies, it turns out that small, very tight, ie faster chips were much better at solving the particular problem. And what the problem is that basically, you have to, in crypto, find what's called a nonce to a process by trying trillions, if not quadrillions of possibilities and, as it turns out, to mine for crypto. You're much better off cost-effectively to have tens of thousands or hundreds of thousands of parallel, very simple chips looking for that particular number than it is to have one high-end chip like the i9 going through them, say, 20 at a time.
Speaker 2:
So, in the early days of the pandemic, with demand for business computing down and demand for crypto mining gear up exploding, a whole new computing architecture emerged that was cost-effective, that allowed throughput for massive parallels in mining. So as a result of that is that if you can do the task in parallel that if you can do the task in parallel is that Moore's law, which has a thousand time increase in speed per decade, it's now become a million times increase in speed in the last decade. So as it turns out is that NVIDIA, which was a company that had massive parallelism in terms of very simple chips, was originally for graphics. It morphed and was funded heavily for crypto mining, dramatically increasing the power of computer systems that could attack a task in parallel, of computer systems that could attack a task in parallel and, as it turns out, neural networks is a task that's perfect for massive parallelism. So suddenly in the last decade, moore's law has become irrelevant and that the speed of computers in the last 10 years, thanks to blockchain, has simply exploded. Now the thing about artificial intelligence with neural networks is, unlike other approaches to AI, they are not coded, they're techniques that are based on data, their techniques that are based on data and the more intelligent data they have, the more intelligent the system will be. So, for instance, chat GPT has basically scraped the internet and there's currently a lawsuit because obviously one of the sources they scraped was the New York Times. But the intelligence of neural networks is a function of two things the speed of the chips thank you, blockchain and the amount of data available of the last century.
Speaker 2:
I was working on evaluating the implementation of a maintenance system at North America's largest nuclear reactor complex and at the time they were installing tens of thousands of chips to monitor everything in terms of temperature, vibration and a whole bunch of other factors, so that they could glean any insights, potentially into better maintaining the reactor. You take a reactor down for a day or two days, three days unnecessarily. It costs you millions and millions of dollars. So back then the cost of these processors, these chips, was very expensive, but the benefit was so large it made sense to put them everywhere in a nuclear reactor and eventually they were called the internet of things. So now, today, everything is connected to the Internet and the Internet of Things is driving an incredible amount of data.
Speaker 2:
Is that we've got chips in people's bodies? We have chips in every conceivable imaging and monitoring and evaluating device and medicine. And so, based on all of that data from the Internet of Things, thank you to the speed increases from blockchain and the breakthroughs in terms of what's called back propagation in terms of artificial intelligence is suddenly the power of AI has gone up many, many, many orders of magnitude in the last 10 years. Many, many, many orders of magnitude in the last 10 years. So the transformation is going to be very profound, it's going to be very quick. It's going to be widely misunderstood. So I thought it made sense to write a book trying to explain what it is, where it came from and how this all fits together and what the potential implications are to the world and to society.
Speaker 1:
Wow. Well, first off, I'm just glad you're here explaining it, because, as you explained it, it really started to make sense about how there's basically different types of AI, how there's basically different types of AI. And then, essentially, the blockchain allows us to speed up, and if it wasn't possible by the internet of things like being placed on there, then none of this would be possible, which is super fascinating, cause a lot of us are just seeing chat, gbt, open AI, like we're just seeing the AIs pop up and we don't understand why it's all occurring. So do you think the blockchain is going to? Is it only crypto-related or it just increases everything in regard to the internet speed, Well, mining for cryptocurrency has dramatically increased the speed.
Speaker 2:
No-transcript For the gold rush of crypto, nvidia were the shovel manufacturer. If you read any books about the gold rushes, it's that very few people got lucky in terms of finding an actual vein. But the real money was made in terms of supplying the miners with the equipment, as opposed to mining yourself. And so NVIDIA, which was a company that 10 years ago was very close in market cap to Intel, has suddenly in the last six or seven years now has 10 times the market cap of Intel. Oh wow, so they're the shovel company, so they're a big winner in this. And, yes, there will be definite further advances in terms of massive parallelism driven by NVIDIA and many other companies that are aspiring to compete with NVIDIA. But is that? The funding now is for AI chips? Not so much in terms of mining, but in some ways, bitcoin is a fascinating architecture.
Speaker 2:
There's some pros and cons to it. One of the pros is that anything stored in it is immutable it can never be changed and one of the cons of it, of course, is that everything stored in it is immutable, it can't be changed, and if I can put that into perspective, I don't know how many times I shouldn't confess this, but on a Friday night, after a few beers, my beeper would go off and I'd go into the computers at Citicorp and put Humpty Dumpty back together again because something had gone wrong in the computer system and there was some bad data in the database. So one of the challenges of blockchains is you've got to be darn sure that what you write in there is indeed true. But once you have done that, it's immutable, it can't be corrupted, it can't be modified and it can be distributed. Now, as I've articulated, is that he or she who has the most data wins.
Speaker 2:
And so, in the history of computers, is that there is a trend towards centralization, is that WordPerfect was better than Word and Lotus was better than Excel? But what eventually happened is department standardized on Microsoft Office and, before you know what happens, the company and then the industry and then the world. And so they're in technology for compatibility, for interoperability I'm not quite sure I got that right, but there's a tendency to centralize and in terms of AI, that tendency is even magnified. So in some ways, distributed blockchains have the potential, through Web3, of taking control of our data back from these central conglomerates back to us and have the potential of one day of creating some form of semi-decentralized artificial intelligence where we could get value from the data we create, as opposed to Facebook or Google or whomever else. So, working with AI and blockchain in the future, driven by data produced by the IoT, there's the possibility that the massive kind of centralization we've seen historically in technology does not necessarily have to apply to the next wave. Oh, wow.
Speaker 1:
So instead of the centralization, it's the decentralization of the Internet and of all these different areas. So is this occurring globally? Because obviously the Internet's accessible globally, but it sounds like they're trying to control components of it. How do you even think of that?
Speaker 2:
How do you even think of that? It's a concept more than an implementation now, and if you want to know more about it, my brother's son, my nephew, wrote a book about it, alex Tapscott. It's called Web 3. I'm not saying that implementing distributed systems and taking out these massive conglomerates is going to be easy. The empire will certainly fight back, but it does lead to some intriguing possibilities.
Speaker 1:
Absolutely so. What are individuals who are listening to this? What are like? You're saying a lot of stuff right now. How is it going to impact people's lives? How will it impact someone's lives in the next five years? We can go 10 years and then maybe down the line even further but how do you think it's going to impact lives? But how do you think it's going?
Speaker 2:
to impact lives. Well, I think, for the next five years, is that AI is going to be part of everyone's life in the context of the work they do and in the context of, you know, helping around the house is that we've got to the point where robots are constructing robots driving down the cost of it. So we're going to see the trivergence materializing into the real world in terms of almost any job you do, from writing to medicine to composing, to almost anything, I think in the next five years is going to be assisted by artificial intelligence. So I think for the next five years, the path is pretty clear. Probably by the end of the decade is that what we should see is a dramatic increase in overall productivity, but somewhere towards the end of this decade is that AI, having been on your desk and learning and understanding and collaborating in the context of what you do in the context of what you do, is going to have the ability to move from your desk into your chair. So what happens then? There's some very tough and very real questions. Is that?
Speaker 2:
Mckinsey and others have predicted that it could easily result in billions of people being unemployed? Oh, wow, Now, if you want to look back 100 years. John Maynard Keynes predicted that by today we would not be defined by what our jobs are. Is that when we talk to other people, the answer to what do you do would be defined by our hobbies and our interests, because technology would have made for a much shorter work week. Turns out he was wrong on that particular prediction, but in the next five or 10 years that is a distinct possibility.
Speaker 2:
There are also Orwellian possibilities, where these centralized companies have made their founders or their key shareholders worth hundreds of billions of dollars can end up being worth trillions of dollars of dollars, can end up being worth trillions of dollars, and this disruption could have a profound and negative impact on society as a whole. So one of the things I hope to achieve from the book was to outline what issues are confronting and hopefully overcome the fear, uncertainty and doubt and help politicians and others take more responsible action in terms of allowing this technology the benefits to be accrued by everyone and end up with a much more beautiful world.
Speaker 1:
So what do you think that would entail? What would have to occur for people to have the beautiful world Like? What should the individual be doing? I understand at the political level it can definitely help, but what should the individual be doing?
Speaker 2:
Well, I mean, the first thing is the individual should become much more informed of what it is and what the potential is Over the next period. One of the major concerns is the thing about AI is that it fits the perfect definition of a psychopath about AI is that it fits the perfect definition of a psychopath in so much as it doesn't know when it's telling the truth and it doesn't know when it's lying. And when you scrape the internet, some of the conclusions you're going to come to are definitely false. It has no moral, it has no ethics, it doesn't know right from wrong, and so I think in the short term, is that we need to A understand that and B to some degree hedge the overall risks. I mean, the top leaders in AI calls for a six-month pause to help think through these issues, but the horse is out of the barn, the race is on. I think everyone in the industry knows that consolidation is an inevitability, and so everyone is racing hopefully to be the company, the person, the shareholder that wins that overall consolidation.
Speaker 1:
It's crazy how fast it's happening, because with Facebook it took many, many years and Google took many, many years to the point where they actually were mixed, mixed with politics, where they're like you can't do this, the data's bad. Here this is happening and with it happening with, like, open ai is only like a year old or a few years old since it came out. Yeah, and they're already dealing with these types of things. It's very, very fast and it's hard to even be prepared for these kinds of situations.
Speaker 2:
Sure, and the last release of chat GPT in the neural network went from, you know, a few billion nodes to trillions of nodes. So its size, its power and its intelligence is increasing at a startling rate. But I think we have to come to the understanding is that it is going to be able to do an ever-increasing slice of what people do today. That's an inevitability that we're going to have to accept.
Speaker 1:
So what would you know? Yeah, I mean it's. It's tough because, like, a lot of these jobs can be completely replaced, like you were mentioning. So what's the direction for people?
Speaker 2:
obviously become more informed, obviously formed, obviously learned, but like where, as an individual, is that if you educate yourself in terms of AI and how to use it and how to deploy it, you clearly have a future. If you don't, then you should be deeply concerned. I mean, that's a place to begin with, is that? Let's take radiology. As a nuclear medicine technician from many, many decades ago, is that reading, say, an MRI that can image tens of thousands of slices of, say, your brain, is almost as good as a radiologist who's top-notch with 20 years of experience, for the simple reason is that it can compare it with the reading of, you know, a hundred thousand previous MRIs, soon to be millions of previous MRIs, and what it turned out actually to be the case. And the other thing I can do is that an MRI might give you thousands and thousands of virtual slices of the brain, and an MRI can quickly look at every slice, while a human being will never have the time to do that.
Speaker 1:
The other factor is that the AI doesn't get offended. The AI doesn't get upset. The AI doesn't get upset. The AI doesn't get in fights Correct so many human ideas where, like, if the person's having a bad day or someone close to them dies, then obviously they're not going to look at as many data points as they even thought they would. Well, the AI will just turn through millions of data points and give you the exact results that was fed to them, which is really interesting.
Speaker 2:
And then the other factor, of course, is that you might tell an AI computer something that's a bit odd about your medical condition that you may not be willing or too embarrassed to share with a human being. Where, of course, it's going to have a profound impact is in the third world, because there are many people out there who are in extreme poverty, but they have smartphones for medicine or other things that can help them with everything from medicine to learning skills to get themselves out of poverty. Education is the idea of spending I don't know five years to become a geologist and then go out and practice geology for the rest of your life makes little or no sense, because education is going to have to go through a profound change, from learning a skill for life to learning a bit of the skill and then learning how to learn for life as you move forward, working with artificial intelligence in terms of doing whatever the task at hand may be, doing whatever the task at hand may be.
Speaker 1:
So that's going to be really interesting. Just because the schools are monetizing at rates Like everyone's seen it, how the price of college has gone up so fast in comparison to people's incomes, comparison to all these different things, and if now there's a school offering, hey, we only need six months instead of four years, becomes a very interesting dynamic for what happens with all of that. But if people become more efficient because they're actually focused on the learning, not on the socialization I mean they can get a mix of both it could be pretty interesting.
Speaker 2:
And both Canada and the United States. You know many, many years ago you could get a summer job that paid for your tuition, and the degree to which governments fund education has gone down dramatically, and so there are many people that come out of university so in debt. They'll never be able to dig themselves out of it. Wonderful new tools and techniques and industries never would have had the opportunity to do that if they'd been saddled with massive student debt when they got out of college. So in the US, for instance, the rate of people with degrees is going down.
Speaker 2:
So in a world in which we need to be much smarter is that I fear that we may end up with people that are less so.
Speaker 1:
Yeah, I mean it's a really fascinating one, because the thing about student loans is that that debt isn't forgivable. If you go through bankruptcy, it stays with you and it just becomes a scary place where the barrier to entry is just so high and it really shouldn't be for what people are getting and what they're getting.
Speaker 2:
It just doesn't make a whole bunch of sense. No, I mean, america used to be the number one country in the world for social mobility, but if you take a look on the Internet, it's now like 30th in the world in terms of social mobility.
Speaker 1:
And given the role of the US in the world, given the role of the US stabilizing the world, that may not be a good thing. Yeah, absolutely. And so what do you think is? Obviously, you've lived in many different countries. You've had a major global experience. What do you see happening with a lot of these countries utilizing the AI? Maybe some people are going to hold back on it until they fully understand it. Like, is government going to have a factor on this? How do you think about that?
Speaker 2:
um, the impact of AI in every country will be quite profound. I think in the third world it may be even more profound because suddenly being trained and being educated, having a constant companion in terms of helping you understand and learn math a constant companion in terms of helping you understand and learn math, as opposed just to a textbook that someone reads and misunderstands I think is going to dramatically increase education in the third world and I think that it has the potential to lift many people out of poverty. So it's going to have a profound and good influence there.
Speaker 1:
Yes, so you said that people should become familiar with AI. Understand that. If you were to really make it like the simplest first step, like what website is that going to? Where is that going Like? What are those action steps? The reason I say that is just because I know when you said that to me I thought free course on ai, that might be a good place to start right.
Speaker 2:
Um is that? Um is it is programming is becoming far less um important a skill in terms of interfacing with AI. So if you don't know how to write the code to interface with an AI system, you know what the AI system might write the code for you. So, yeah, I mean there are free courses out there that I think is a good place to start on all the major sort of online training systems, but it's going to impact your job one way or another. So you can be proactive or you can be reactive, and those that are reactive are the ones that are going to end up first to be unemployed absolutely.
Speaker 1:
And what was that 10-hour course? You who created that 10-hour course?
Speaker 2:
you said I was just looking at it the other day.
Speaker 1:
That's an ibm course oh, ibm okay yeah, okay I'm going to put that in the show notes for sure, cause I think that would be super valuable for everyone.
Speaker 2:
I can give you some URLs.
Speaker 1:
Um yeah that'd be awesome.
Speaker 2:
No problem.
Speaker 1:
Be happy to Cause. The major thing is, I really want people to understand, like, what you're saying is very, very interesting, and I just want people to. If they want to go further, I want them to have a place that they can go to, that it's like oh, this makes a lot of sense, I don't have to spend any money, I don't have to do anything. I just have to dedicate time and really go and learn that, because I think that's super valuable. To dedicate time and really go and learn that because I think that's super valuable. So for you, how did you even come up with this trivergence? How did you even realize this? Because I don't see many. I've never heard anyone talk about this before. In all honesty, a lot of different things.
Speaker 2:
Well, I mean, I've tried and failed with AI over my career a number of different times, so that's always fascinated me is that um, uh. My brother Don and my nephew Alex are among the top advocates of um, of uh blockchain based technologies as a whole. And um, the movement from uh, uh of blockchain-based technologies as a whole and the movement from complex instruction set computers to reduced instruction set computers has been predicted for 30 years, and I was just fascinated to see how blockchain accelerated that overall trend. So the term trivergence came out of a conversation I had with my brother Don about how all these three factors were converging, and then we realized that three things can't converge. Three things have to triverge. So that's where the term came up from.
Speaker 1:
That's awesome, that's awesome. And when did the book release and where can people find the book?
Speaker 2:
It was released in late February and it's available everywhere.
Speaker 1:
Awesome. I'm going to put that all in the show notes and can people? Is it on like Amazon or any of it? Yes, yes and yes, okay, every platform Fantastic.
Speaker 2:
Every platform I've checked is there.
Speaker 1:
Every platform, every place. We love that, and so, Bob. So we have. People should do AI courses. Is there anything else you'd recommend for them to do to increase their education on AI, blockchain, Internet of Things? Obviously, read the book. Is there anything else you'd recommend?
Speaker 2:
Well, I mean, once you read the book, I think you'll have a much better framework in terms of understanding what may happen and what should happen. That, for instance, I mean, there's been a lot of studies that say that a four-day work week is better than an exhausted five-day work week, and so hopefully, you know, we can move in the direction where everyone benefits from the massive potential of AI.
Speaker 1:
And why do you think a four-day work week is better?
Speaker 2:
Well, I mean just personally, better, right? Everybody's now got a 70 hour work week. Is that uh? Hopefully you know, as technology advances, that um, uh canes, ultimately may be right and we may end up with, uh, much more time on our hands to enjoy our lives as opposed to work for others.
Speaker 1:
With that concept, just cause I know we've got a few minutes left, and I'm just curious about your perspective. With you working so globally, uh, seeing how everyone lives their life, what was the best model you kind of found for work-life balance, in a way that allows you to get the most out of your life? Because I know you, you've lived in Brazil, europe, canada, I believe, the US, yes.
Speaker 2:
Three cities in the US. I think in some ways in terms of the divergence, is that the European model was one of the most enjoyable to me In some ways. I worked in a computer center that when the computer system crashed you could hear swearing in many different languages and I found working with people from different cultures had different perspectives. That allowed for much greater creativity. And in Europe I don't know if you know this, but you know if you have a low-end job, you get four weeks vacation. If you have a high-end job, you get eight weeks vacation. Job, you get four weeks vacation. You have a high-end job, you get eight weeks vacation. So we may be moving more towards that model, but I think to a great degree. One of the questions should be asked is how can the divergence of these technologies create greater social mobility within society as a whole?
Speaker 1:
Yeah, and what do you mean by social mobility?
Speaker 2:
within society as a whole. Yeah, and what do you mean by social mobility? Well, I mean the American dream work hard, you get ahead. Yeah, focus, you know if you're smart you can get ahead and I think in some ways the polarization, particularly in the US, and the anger of many is that the American dream to the younger generation is not as easily obtained as it was to my generation. That's clearly the case.
Speaker 1:
Yeah, 100%. I mean, I see it happening every single day where everyone, when I do the coaching with people and I do groups, and when I ask people what their five-year vision is, all of them say I want to get a home. And it's like it's still ingrained in everyone. But at the same time it's like is that even achievable at this point for a majority of people? Just because the prices are so high versus the income that is coming in, and it can be a very challenging place.
Speaker 2:
high versus the income that is coming in, and it can be a very challenging place. Sure, I mean, I work for a bank or I work for many different banks for the first 20 years of my life and after 20 years I was able to afford a large home in downtown Toronto with a swimming pool. And my daughter has now a similar title. She's a VP of a financial institution, and the dream of getting a home any home for her seems completely unattainable, let alone a large house with a pool. That's not good.
Speaker 1:
And the divergence could transform that dramatically yeah, I mean it really starts to affect people because even further along, as everything just keeps getting pushed up and pushed up, pushed up, then there's not even those opportunities to kind of buy in or get in at the lower prices or any of that, where that's where a lot of the upside could be for a lot of individuals if they invest and get the right company or whatever it is. But a lot of those opportunities haven't been available in a long time.
Speaker 2:
Yes, and soon we'll be able to produce robots that make, robots that can make housing yeah, it's really fascinating.
Speaker 1:
So what do you think the biggest thing to look out for for everyone in the in the last five minutes because I want to, like you say things and then I'm like wow, that is profound. Like robots making robots that make houses. Like that means no more contractors, that means no more gc license. That means, like, what do you think is going to be the most profound thing that's going to occur? Like, where do you see this going?
Speaker 2:
Well, I mean, when we talk about the industrial revolution, you know it was a wonderful thing. You know, overall we've all benefited from it today in terms of this generation. But when it first came on stream, it was Dickens. It was horrifically polluted, you know, seven-day work weeks with people, you know, living in extreme poverty. So the potential for negative disruption is large and I think people have to start thinking about this very quickly and understanding the issues very quickly and, through the democratic process, guiding society in such a way that we don't live through that period where AI is a highly negative and destructive force. Yeah, it's massive potential, that's it, okay. It's massive potential for good, it's massive potential for evil, and I think people have to be conscious of the issues and, through the democratic process, to drive society in such a way that it can benefit all absolutely, bob.
Speaker 1:
I want to thank you for taking the time to explain all this and take the time to write the book, and where can people find you and reach you if they want to learn more?
Speaker 2:
um well, my email is bob at tapscottcom and my site is tapscottadvisorscom.
Speaker 1:
Absolutely. I will put all of that in the show notes and I really appreciate the time, Bob. It's been a fascinating learning and just sharing with the audience because I think it's going to be a very helpful episode. So thank you.
Speaker 2:
Great, and if you want to know more, the book trivergence is generally available no-transcript.