The Munk Debates Podcast - Munk Dialogue with James Bessen: how AI will transform the labour market

Episode Date: June 19, 2023

Some experts believe that over 300 million jobs worldwide will be automated by AI, with white collar professions in advanced economies being most affected by AI bots like ChatGPT. Radiologists, lawyers, coders, you name it - if you sit at a computer for work, you can expect to have some of your tasks completed by artificial intelligence. To get a closer look at just exactly how AI will transform the labour market, we’re talking to James Bessen. James is the Executive Director of the Technology & Policy Research Initiative at Boston University, and an expert on how automation affects the workplace. We want to ask him: will robots steal our jobs?   The host of the Munk Debates is Rudyard Griffiths - @rudyardg.   Tweet your comments about this episode to @munkdebate or comment on our Facebook page https://www.facebook.com/munkdebates/ To sign up for a weekly email reminder for this podcast, send an email to [e-mail].   To support civil and substantive debate on the big questions of the day, consider becoming a Munk Member at https://munkdebates.com/membership Members receive access to our 10+ year library of great debates in HD video, a free Munk Debates book, newsletter and ticketing privileges at our live events. This podcast is a project of the Munk Debates, a Canadian charitable organization dedicated to fostering civil and substantive public dialogue - https://munkdebates.com/ Senior Producer: Ricki Gurwitz Editor: Kieran Lynch  Become a Munk Donor ($50 annually) to get 72-hour advanced access to the full length editions of Friday Focus and Munk Dialogues. Go to www.munkdebates.com to sign up. Hosted on Acast. See acast.com/privacy for more information.

When you're a journalist and people don't trust you, it's always your fault. These people need to be represented. They are Canadian. They deserve to have a voice and a seat at the table. It is time to go back to the office, and the time is now. Russia had reasons to be concerned. They had reasons to be fearful. We're at an absolute turning point in reproduction. This is the problem with realism. They just treat all countries the same. They don't distinguish between dictatorships and democracies. easy. Hi, monk listeners. Rudyard Griffiths here, your host and moderator. Welcome to this, our continuing conversations called the monk dialogues. These are in-depth questions and answers

with people grappling with some of the world's most important problems and issues. On each monk dialogue, we reflect on these problems and issues together, hopefully leaving you with some new analysis and insights. Today we're talking about the major concern that's on so many people's minds today. We're worried about our jobs, our social interactions, the future of our democracy, perhaps our very existence on planet Earth. I'm talking, of course, about artificial intelligence. In the lead up to our public monk debate on AI taking place in Toronto on June 22nd in front of a sold-out crowd at Roy Thompson Hall, we're hosting a series of interviews with experts in the field about how this fast-evolving technology could indeed change just about everything we take for granted in society today.

The focus of this monk dialogue, part of our AI series, is all about jobs. Some experts are forecasting that over 300 million jobs, yes, you heard that right, 300 million jobs worldwide, could be automated, made redundant by AI. White-collar professionals in particular are in the cross-hills. This could have huge effects for the economy and for how we think about our own lives and the goals that we seek to achieve in them through hopefully meaningful, enriching, personally satisfying employment. To get a closer look at exactly how AI will transform the labor market, we're talking today to James Besson. James is a fellow of the Berkman Center for Internet and Society and an expert on automation and its effects on the workplace. We're going to ask him today, are thinking machines about to steal our jobs?

James, welcome to the Monk Dialogues. Thank you for having me. Looking forward to our conversation today, the effects of AI, its impact on the workforce, on the jobs that we will have in the future has become a topic of hot conversation, a lot of speculation. I want to try to drill down into this topic with you to come away with some insights about just what is the scale of the transformation that we could be experiencing in the months and years to come? And how is it likely to affect each of us?

I've got two young kids. I'm kind of struggling to think of the parental advice I should be giving them who will grow up and work in a world of thinking machines. I want to get to that too with you. But let's just begin top level. why do you think some of our anxieties at this moment are overblown? I should first add a caveat that I feel comfortable talking about the next 10 or 20 years. If we're talking 50 or 100 years out, anything goes. And I don't think I have particular expertise to shed any light on that.

I think what we're seeing is a continuation of what information technology has been doing for 70-some years, maybe on steroids, but it's the same sort of thing. And if you look back, and it's important to understand why, but if you look back, the effects really, it has had some negative effects. It has had some definite positive effects as well. But it's never had anything close to the catastrophic effects that people have been regularly predicting, at least since the 1960s. As you may know, in the 1960s, there was a presidential

a commission looking into if information technology and computers we're going to destroy all our jobs. And there have been repeated cycles of this. Not to say that this time isn't different, it is different in some important ways, but when we drill down and look at why the dire predictions have consistently failed, you can go back to the Industrial Revolution, you know, when we look at why they have failed, I think we get a better understanding of why it's not likely. to be so different this time. Fascinating. You know the counter argument.

It's that what's different this time is that automation is coming for the white collar workforce. This isn't about the plant floor anymore. It's not about increasing productivity gains in manufacturing. This is about taking a lot of processes that we had once assumed were the kind of exclusive purview of the so-called laptop class and bringing machine learning powerful computation to those tasks, and therefore, as a knowledge-based society throughout most of the West,

which has an economy that relies on knowledge workers, why won't the disruption be bigger maybe than even, you know, some of the most skeptical critics of AI could suggest? Right. And there's two parts to that answer. One is, you know, the, the not. Knowledge worker jobs have been under attack from automation for over two decades. Some going further back, some going back to the 1950s.

And we haven't seen, you know, the bad effects. There is some difference. AI is going to affect more professional jobs, sort of the high end of the knowledge workers. But it's not like there have been some negative effects for the, you know, the lower end of the knowledge workers. But it's not, it hasn't been a devastating thing. But the other side of it is that automation doesn't necessarily mean a loss of jobs. And I think this is the big thing people get confused. Automation automates tasks, specific parts of a job.

And it's very rare that an entire job is eliminated by automation. That's what everybody thinks. Everybody thinks, you know, it's going to be just mass unemployment. And some people are arguing that. That's very rarely how it happens. And the reason is our jobs are very complex and they do lots of things. And I can give you some examples. So one is I looked at, I studied the jobs that were offered and were listed in the U.S. census in 1950.

And I tracked what happened to those jobs today. Some of those, you know, a fair number of those jobs went away for different reasons. Some went away because preferences changes. We don't have boarding houses anymore, so we don't have boarding housekeepers as an occupation anymore. Some went away because of technological obsolescence where the whole industry was wiped out. Telegraph, for instance. We don't have telegraph operators anymore. But it's not because their jobs were automated.

It's because their jobs disappeared. The industry disappeared. I only found one occupation where there's a clear-cut, case that automation eliminated the job, and that was elevator operators. I know there's still some around, but they've largely been eliminated because of the automatic elevator and the various safety things that came along with it. We face a similar thing today. So in a famous example, Jeff Hinton, the father of deep learning predicted in 2016 that we should, or not, it didn't predict he declared we should stop training radiologists because within five years, the AI is going to be so good that we won't need them anymore.

So AI systems back then and continuing today have been trying to develop a capability to look at x-rays, which is part of what a radiologist does, and identify various maladies, typically, you know, a malignant growth or something of that sort. And in some cases, they can do a good job at that. But it turns out that that's only, you know, one small thing that a radiologist does. And a radiologist has to combine that knowledge from the x-ray with other things. The patient's medical history and, you know, medications and other things going on. So it turns out that we haven't eliminated radiologists. We're in fact short of them still. And that's despite a large part of the radiology work.

being offshoreed to India. So it's, yes, we're seeing AI coming along, but it's coming along as a tool to help radiologists, not as something to replace them. And that's what so much of this automation is. It's going to change the nature of work. It's going to give you and I better tools to do our jobs that makes us more productive.

And in some cases, there may be elimination of some jobs, but it's also creating other jobs. I want to get to that in a sec, the idea that this isn't a zero-sum game and there can be other jobs that are the jobs of the future that we can't even anticipate now. Before I get there, let me just continue on this line a little bit further

and use those four dreaded words this time is different. The argument is that this is a new general purpose technology. This is not Fordism or Taylorism or some different way of organizing human or industrial capital. This is the introduction of a new general purpose technology on par with fire, the wheel, events in human history that have occurred from time to time, which truly are transformational. Sounds like you're skeptical about what is it.

Is it how smart these machines can or will become in the next five to ten, years, is it something else that you think human creativity and ingenuity applied to these machines will fashion new opportunities, larger amounts of productivity in society will create, who knows, maybe less requirements to work, more opportunities for leisure. I'm trying to understand, James, if you agree whether or not this is a general purpose technology, and if you do or don't, What are the implications of that? Yeah. No, I think it's definitely a general purpose technology.

It's something, a basic technology which can be applied to many different things. We've seen other general purpose technologies. The steam engine was one of them. The mechanical technologies of the Industrial Revolution were another. But just saying it's a general purpose technology that just all that means in, you know, in the context of what we're talking about is it's going to affect a lot of jobs. And absolutely, it will affect a lot of jobs. But just because it affects a job doesn't mean it's going to eliminate that job.

And that depends on the economics. And I think that's the key thing. So you can go back, I did some studies of weavers in the Industrial Revolution. So this is an occupation, and it was mind-blowing at the time, just what these automated looms could do. And they gradually improved over the course of the 19th century. but in the end, the machines could produce cloth. The amount that a weaver could produce with the machines was a hundredfold increase. That was phenomenal.

Now, you might think that enormous productivity led to the elimination of weaving jobs. And in fact, it led to tremendous growth in the weaving jobs. And the reason is people forget about economics. if you lower the cost of the cloth, you increase the demand for the cloth. And in the early 19th century, cloth was extremely expensive, very costly. Typical person had one set of clothing because, you know, it was that costly. When you start lowering the cost, people buy more, and they bought lots more, and they found all sorts of new uses for cloth. So for over 100 years, the number of weaving jobs and textile jobs generally increased

and, you know, was a powerful contributor, the growth of the middle class in this country. You come to the middle of the 20th century, that argument breaks down because at this point, you know, we have closets full of clothes. We've got draperies and all sorts of uses of cloth. Now the, you know, the technology is still reducing the cost of producing the cost of producing. the cloth, but there's just not much more demand. It's that thing that we keep missing. A more modern-day example was the automated teller machines. Everybody predicted we're going to lose bank tellers. So from the mid-90s to the mid-2000s, our banks in the U.S.

installed hundreds of thousands of ATMs, and the number of bank tellers actually went up. you know and the reason well it meant that the banks could open a new branch office much more cheaply they needed they needed fewer people to open a branch office and they were competing geographically so they wanted to open a lot more branch office and you may have noticed there's a hell of a lot more bank buildings in any neighborhood in the country than there were 20 years ago Well, you know, what happened is it reduced the cost. It made it easier to open or cheaper to open more offices, and they opened many more offices, and so the number of tellers didn't decline.

Reminds me of, you know, when we have efficiency gains in fuel standards for cars, people actually end up driving them further. When you're more efficient at cooling your house or heating it, you end up building a bigger house. So you may end up consuming exactly the same amount of energy, but you use those gains to, in a sense, get the things that you want. There is a view of thought out there that these machines are going to not only replicate or take on a lot of human tasks, possibly very mundane and boring ones, like writing tweets or press releases, which frankly we all might be spared, that drudgery. But they may go further. They may go further and begin to be able to create as some of the early bots that Google and others have demonstrated, synthetic video, audio, the extent to which, in a sense, out of whole cloth, they can produce a lot of cultural artifacts that we previously had really only associated with human hands, eyes, and ears.

Do you think that could be quite transformative and consequently disruptive if a lot of our culture from written letters, from emails, from business correspondence, to blockbuster movies, to videos that we're sharing on Instagram and TikTok are fabricated by machines, not humans. What does that mean? It's a good question. But again, we've seen very similar things before. So the camera, photography, people thought it was going to eliminate the artist, the visual artist. It didn't.

It created a new art. You know, for the most part, I think there's two issues. One is most of this stuff is basically providing better tools for artists. Some artists are going to like that and others aren't. But it's a whole new medium. It's a whole new way of working. That's great. Isn't the issue more that will be unable to distinguish between machine fabricated culture and artifacts and letters and emails and texts and human manufactured content?

So I think your analogy of the camera is an interesting one, but isn't this, again, and go back to those four fateful words, this time is different. I mean, you won't be able to distinguish if the photograph was taken by a person. You may not care. You may pick up a book and read it and be completely satisfied with your mystery novel, and it's an entirely synthetic creation of a large language learning model. I mean, first of all, just to be clear, that's a long way away, I believe. I think we're a little bit deceived by the remarkable progress has been made, but the difference between where we are now with chat GPT or whatever

and the ability to write a whole novel from whole cloth is something different. Obviously, there's a concern if we can't tell the nature of the author in terms of things like misinformation, but that's not what we're talking about here. I think basically if these things can produce great music, great books, and we don't need to necessarily care if it was done by a human or by somebody else. But I tend to think it's going to be humans in combination with the machines that are going to be producing the best work and where people have deployed these technology. Remember, AI has been out there in use since the late 80s. It was initially used in detecting fraud for credit cards.

But so we have some experience where it's been used. And generally in almost all cases, it's a combination of people. working with the machines that are getting the best results. And again, it's because these machines have very significant limitations, and they will continue to have very significant limitations for 10 or 20 years, despite how remarkable they are. Right. So I think that's a key point, James.

You think that the emergent aspect of this technology may not be as dynamic as a lot of people are assuming right now, because there is this idea, you know it, it's out there, that these machines are kind of like Schrodinger's cat in the box. They're doing things that we don't know. And even when we lift up the lid of the box and look inside, we're not sure why code that looks like X, Y, or Z is producing results A, B, or C. Some people have hypothesized that that suggests that this technology could end up developing

much more quickly than was even assumed six months or a few years ago. You feel, though, that this may be overestimating how fast this technology can evolve. If you look at the last 10 years, people have been heavily overestimating how rapidly things would evolve. So there was a famous paper in 2013, which predicted that in 10 or 20 years, which is now, 70% of the jobs in the U.S. were automatable. I don't think we're there by any stretch of the amount. imagination. We've had regular predictions that self-driving cars were a year away since 2016, I think. And, well, we have self-driving cars in some very limited circumstances. It's not the self-driving cars people who are predicting. That's for certain. You know, so people have tended to just,

I think, been, you know, the hype takes over. It's very understandable. To some extent, I think people are manipulating us a little bit. We've seen lots of claims about things that turn out to be, well, not quite so true. On the other hand, there's remarkable evidence of, you know, there's remarkable demonstrations that I think give us some reason to think this is, you know, this is tremendous, but it's important to put it in some sort of perspective and understand what it is and what it isn't. And I think, you know, what we're seeing for the most part is some very serious limitations at the same time as we're. some very serious progress.

Reminds me a part, you know, that craze about a decade ago where, you know, children had to learn Mandarin because China was going to take over the world. And if you didn't speak Mandarin, your child was going to be doomed to, you know, a life of working in salt mines in Uzbekistan or something. That didn't really kind of make a lot of sense as a parental investment. if you try to think now about the next five to ten years of people listening to this have children in high school, maybe university, what do you need to do to kind of future-proof yourself? You probably don't want to be again like a tweet writer or I recently read an

article, I think it was in New York Times about people in translation really getting whacked by AI to the extent to which translators are, if not completely redundant, have certainly been displaced in big ways by much more efficient and effective automated translation. So where do you think the dangers are? What are the zones you would recommend people stay away from in terms of career decisions and thinking about future proofing? And then where do you think the opportunities could lie? Yeah.

So I think the big, big lesson is what we need to do. to do now is not focus on a particular career, not focus on a, I mean, I think you do need to choose a career because that's the way that the system works, but it's really learning how to learn. You need to plan on having multiple careers over your lifetime. You need to expect that. And so you need to have the tools to be able to acquire new skills. That's the real impact. That's the real burden automation is putting on people. So it's not that the net number of jobs disappear. It's that some jobs are lost and new ones are created. And that means that in order to go from, you know, the old jobs to the new jobs, you need new skills. So we're just seeing very

strong evidence of that. That means people have to learn new things. They have to sometimes switch occupations, different careers. They may have to move. It's burdensome for many people. And the social safety net doesn't support those changes necessarily. And at the same time, we have an education system, which is very heavily geared to the idea that you learn everything you need to know by the time you're 20 and that's, you know, you're set until retirement. That world isn't there anymore. That's, I think, the key thing that a parent needs to think about is really providing sets of skills to learn how to learn, to learn on your own, to have that adaptability and flexibility. And that's how you'll thrive in the future. That's fascinating.

I think this idea of obviously lifelong learning, we've all kind of thought about that. Will there be, though, certain types of skills that may be in higher demand? I guess I kind of think, you know, the modern-day equivalent right now, Mandarin a decade ago, is you have to have your kids coding and programming.

And what we know is that a lot of these, LLMs, large language learning models are really effective and interesting as powerful coding tools that take away a lot of, not simply the drudgery, but in some cases already demonstrating that on a few word prompts, they can build a social media platform for you if you're so inclined. I mean, are we going to be in a future maybe where somehow softer skills are going to be in greater demand? because a lot of the hard skills, the computational skills that were needed in the past are really the ones that machine learning will displace first. I think there's something to that, and there's a lot of evidence that social skills

have been growing in importance as information technology, for 20 years, as information technology has become more important. Part of the reason for that is that the way information technology gets used is typically married to new sorts of organizations. So there are new ways of doing things. Things get reorganized. The people who can communicate well, who can work on a team,

that's going to be very important. That has been becoming more important, and it will continue to get ever more important. You know, on the one hand, you're seeing the LLMs taking over some coding tasks. On the other hand, they're creating a new sort of coding task, which is prompt design, right?

So maybe that will go away, but there seems to be a real art of, you know, learning how to give the machines the right prompts in order to get results. So there's going to be not only a set of those skills, but those skills married to particular applications. So knowing how, if I'm a marketing person, knowing how to design prompts that are going to let me solve marketing tasks.

You know, all of that, is going to be, is a newly emerging set of skills. Next week, June 22nd, the Monk Debate on AI is taking place in Toronto, Canada, in front of a live audience of 3,000 people. You can join as a virtual participant live in this debate via our online web stream of the entire debate proceedings. You'll have the opportunity to watch four of the world's leading experts on AI, debate the motion, be it resolved. AI research and development poses an existential risk. To watch the debate online, live and archived, you can do so by becoming a monk donor for as little as $25 a year. You can do that

right now on our website, triple w monk debates.com. Simply look for the donate button in the top right navigation. Become a donor and we'll send you a link next week to watch the debate live in the the comfort of your home. Thanks. Now, back to our program. James, things you'd possibly stay away from as career choices. I mean, I can see a lot of things that machines will have real problems with. We can see that, you know, self-driving cars have really stumbled because as soon as machines get into the physical world, it's incredibly difficult for them to do all the remarkably simple things that we take for granted every day, like using our index finger to touch our nose that from a computer science program to do that accurately time and time again and not break your nose is actually is super

tough. So if you think about some of these things that machines are good at and they're bad at, what things would you stay away from? And then I don't know, I just kind of think to myself, it's something like carpentry, plumbers, electricians. There's all kinds of potential tasks and products and services we need in society that revolve around this space. manipulation of things that I would think for years to come will be very difficult for artificial intelligence to solve. Yeah, if we're talking in the 10 or 20-year time frame, I think that's probably correct. I think, well, again, a lot of the social interpersonal skills, I was just completely

frustrated by dealing with a, you know, a voice system that was, you know, not. giving me the answer and wasn't, you know, wasn't picking up on my frustration, which a good human telephone operator is going to be able to do. You know, on the other hand, it's clear that there's lots of mundane tasks that I wouldn't want to bet a career on. I think coding is sort of a mixed bag, you know, and at this point at least, the tools are just making coders tremendously more productive, but at the same time, the demand for coders is going through the roof. So it's a little, you know, there's a demand story there where it may increase the number of coders. Plus, I think coding is an important introduction to understanding what the LLMs are doing. Maybe we'll see that

change in the future. But I think, you know, for now, it's probably important to have those sorts of, those aren't bad skills to have to understand the new machines. I wouldn't necessarily think they were a requirement. And things like, I don't know, physical therapists, people that, again, you know, work with other people to deal with physical things in the physical world that don't deal with zeros and ones. It would that be a safe place to try to. I think that's part of it. Part of it is a, so like the caring occupations, you know, it's not just that it's a physical thing. It's also the human interaction is important.

So, you know, nurses and school teachers. So, you know, we're going to have much better tools to assist school teachers in developing curricula and presenting materials. And people are going to be able to learn a lot more on their own. But teachers are still going to play a really important role. But they're just going to have better tools to do it. So what I'm hearing, Jim, is that you think this technology will be important. It'll have big changes.

There will be winners and users. But in a sense, it's like a sort. It's like a reshuffling of the deck. Certain cards move up or down, but very few are kicked out of the deck entirely. Is that a good analogy or a way to think about it? Yeah, I think that's a good analogy. Inevitably, when there are those sorts of frictions, you know, there's some people who just can't hack the transitions. And we have to think about a safety net for those folks.

there may be a variety of reasons, but that's an important part of it. And I think, frankly, the better safety net that we provide, the more easily society will be able to make these transitions. And it's going to, it will actually hasten the adoption of the technology. But we're not in a great shape right now, I think. So to think about that, when you say safety nets, is this and the idea that the displacement for some people isn't necessarily. going to lead to new opportunities for them? Is that a question of like retraining? Is that a question of like income supplements? I mean, I guess one might hope that if this technology is going to be so beneficial to productivity that you would, I guess, have a greater share possibly of social wealth

that could be allocated to manage a transition. Yeah, I think that's where I We did a study in the Netherlands. So it turns out that the Netherlands statistical agencies have been asking questions about automation in their annual census and surveys. So we were able to track which firms automated. We get a very interesting picture. It's not just manufacturing firms. Most of the automations outside of manufacturing. But we could also track, they have the ability to track what happened to these people.

And so what we see is that when a firm is automated, a plant is automated, there's a certain percentage of people who leave, an increased percentage. And they suffer an income loss because they're unemployed for a period of time. Most of them eventually do get jobs. When they do get new jobs, they haven't taken a cut and pay for the most part. For older workers, many of them go into retirement instead, so they have a more extended thing. But we also were able to look, you know, the Dutch Europe has, you know, has a safety net where they provide, we think, much, you know, a much bigger safety net than the U.S. often does. But we saw there that if we looked at the losses that people sustained during these transitions, the safety net, you know, the various different programs, welfare, unemployment, insurance, et cetera, they were only covering like 15% of the losses. So these were things, you know, just the economic losses alone were mostly born by the workers themselves.

And, you know, costs of retraining, a lot of that was born by the workers. And just, you know, the psychological costs of making those transitions, I think, was, you know, born by the workers. So there's a lot of room to improve. And I suspect in the U.S. even more so. So, you know, I think that the benefit of doing that is that we not only make it easier for those people to acquire new skills and move to new jobs. We help employers by, you know, employers are also short of being able to hire people who have the new skills to work with the new technology. So, you know, we just, if we can smooth those transitions, then we can start a virtuous cycle. where one thing improves and leads to other improvements.

Do you think, James, this comes about through regulation? Is this a question of like, we're going to have to act to affect these social goods during the transition? Do you think based on your experience of looking at other jurisdictions or examples in our recent history, is this something we can expect will just come about because people will demand it? I guess I think here in Canada, we've had a variety of industries that have simply gone away. And in some cases, we haven't managed those transitions well. Either communities and groups have been abandoned or we've created, unfortunately, subsistence cultures in their place where in the absence of allowing people to make a successful transition,

they become almost wholly and entirely dependent on the state. Yeah. So I think it's a little. bit of everybody needs to play a role. I should emphasize that the Dutch study where we looked at the impacts of automation specifically, the kind of effects we were seeing were different than the effects we saw when there were industry shutdowns or mass layoffs. So there's a literature there. There we find that the people who are affected have long sustained economic deterioration. They're, you know, 20 years later, they're not going to be making the same wages they made when they were employed before the layoffs.

So there's, I mean, the good news is automation so far at least seems to be a more modest impact. But regardless, getting back to your main question, I think there's a, you know, there's a role for everybody to play in these transitions. So we're seeing some companies stepping up and offering retraining and understanding that new technology is going to be accepted better. when it's assisted, when the company is playing a role in transitioning people. I think there's a role for government, obviously. There's obviously a role for the individual workers to acquire new skills on their own, and there are benefits for them to do that often. Everybody's got to play a role.

It's a social transition. Thank you, James. Great insights in analysis. As always, really appreciate your time today. And on behalf of the Monk Debates community, let's see, what happens. This is an exciting period, whether you're apocalyptic about AI or you can think that the glass is more than half full. You've given us some important things to think about and for that. We appreciate your time. Well, thank you. Thank you. Well, that wraps up our monk

dialogue. If you have your own feedback and reflections, we'd love to hear from you. Send us an email to podcast at monkdebates.com. That's MUNK DebateswithanS.com. And also just a reminder that our must-see debate on AI is fast approaching June 22nd. We're completely sold out, but we are making a live stream of the debate available to anyone who's a monk donor. You can become a monk donor right now for as little as $25 a year. If you're in Canada, you can even get a charitable tax receipt for that too. Grab your monk donorship and watch this important debate, June 22nd, live and archive. on our website, triplew monk debates.com. Thank you for lending your time and attention to our efforts

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