Are we betting too much on AI? | Nobel Laureate Daron Acemoglu

I'm still suspicious that we're going to have as much productivity growth as the industry tells us. And I'm certainly suspicious about some of the other beneficial claims that we get. It feels as though AI isn't working for society. People are pushing back on the data centers, a lot of fear about job apocalypse, a concentration of power. Do you think this moment, it's a result of the technologies themselves, it's a result of the companies building them, or this is kind of a failure on the institutions we have right now? We still have no idea what an AI economy will look like. I suspect there will be workers in there, but I don't know what they will be doing. Our approach to regulation is completely wrong. First, we give them a complete carte blanche. In four or five years' time, if some of the things they have done turns out to be sufficiently disastrous, then we put some backward-looking reactive regulation. That is a complete recipe for disaster. UBI is a very simplistic solution. First of all, it won't work. Even if it worked and if it was generous, it would not prevent what I've called a two-tiered society. If we generate a few more trillionaires and displace a lot of workers in the process, good luck to us. Today, I'm in conversation with Deron Akamoglu. He's an MIT professor and one of the world's leading voices on how technology, power, and institutions shape prosperity. In 2024, he won the Nobel Prize in Economics for his research on how institutions form and why they determine whether a society prospers. So today, I want to understand what do our institutions need to look like to govern power in the 21st century? I'm Sinead Bovell, and this is I've Got Questions. I want to start by taking stock on the current state of play, particularly with AI. So it feels as though AI isn't working for society in many ways. People are pushing back on the data centers. A lot of fear about job apocalypse. Concentration of power. And then this sits on top of an already wobbly tech stack with social communication technologies like social media, for instance. Do you think this moment that we're arriving in right now and the struggle that we see with technology, it's a result of the technologies themselves, it's a result of the companies building them, or this is kind of a failure on institutions and the shape of the institutions that we have right now? Thank you for that question, Sinead. It gets to the heart of it. That's a good answer, yes. Can we move on? Yes, no, I'm kidding. No, but it's absolutely right. It is the result of technology. It is the result of how the technology has been steered. And it's our responsibility because we've built the institutions and empowered politicians and bureaucrats who allow that to happen. So this is why the issue of tech, is intimately intertwined with democracy. What do we want our democracy to look like in the future? And what is it that we can do within the context of a democratic framework to interact with a technology that's going to shape every aspect of our lives? So let's make it clear, at least my view. AI is a very powerful technology. It's not a gimmick. It is truly a big innovation with far-reaching consequences. That does not mean that all of the things that the industry tells us are true. I would have been perhaps a little less suspicious if this had happened in 2005, before we saw what social media did. And I would have been less suspicious if it happened before we've seen what previous automation technologies have done to the working classes and middle classes in the United States and some of the other countries. So we have a track record of technology having a variety of negative effects, at least on some segments of society. And we, as a society, we have to be careful not to let our democratic system remain passive. So those are the issues that I think we have to grapple with. But do you think it's, because you said our democratic foundation, our democratic framework, isn't it possible that the mechanism through which we deliver on, let's say, liberal democracy, the values of it, can change? Because if you look at most of the institutions that we lean on today, they hardened in the second industrial revolution or in a post-war environment. So institutions aren't eternal. It's an adaptation. So it's, I think, one thing to make tweaks within them. But it's another thing to think, what do we have to invent the way we invented the Bureau of Labor Statistics or we invented the FDA? I mean, I feel as though maybe institutions have to be as innovative as the moment. One hundred percent. What could that look like? Yeah. First of all, I think labor voice was very important during the industrial age and remains very important today. But trade unions as constituted in the 19th century and the beginning of 20th century are no longer going to work. They have to represent workers with much more diverse backgrounds and not centered on blue-collar workers. They have to be much more, those labor organizations have to be much more conversant and even expert in AI. So we need a different set of labor organizations Labor voice is still critical. Same thing with self-government. I think self-government is the most important part of liberalism's ideas. And it's the foundation of democratic institutions. So what we mean by self-government may need to change. Much of the institutions that we rely on require regulatory muscle. You build that by exercising. Right now, in the United States, we have no antitrust left. Plain and simple. We have not exercised any antitrust oversight in the tech sector. We've allowed the biggest companies humanity has ever seen take over all of their rivals. So you have to build that regulatory muscle. We also don't have regulatory muscle when it comes to AI. We don't have that expertise. So all of those, you know, can happen should happen. But we also have to be realistic. Not every institution we wish can exist. And some of the institutions that could exist may not be feasible given the current polarized environment. And also not every adaptation that we wish may be feasible. Like for example, when I express worries about well, what about people lose their jobs and that sidelines them and they can't have any contribution to society and that's the source of their dignity. Some people in tech respond. Well, they can find other ways to find dignity. Well, perhaps that's a great idea, but we can't socially engineer people. And even if that's feasible, we're not going to transition to that immediately. So there are major things that we may get to in some point in the next 300 years. But our, not feasible in the near future. So we have to take those things as constraints in our thinking. Because you've mentioned self-government. So is there a potential idea of what that could look like? Because I think and so my first question is, does change happen inside the house or outside of the house? Because if you look at the history and I read your book quite closely, it looks as though change happens. That's great to hear. Yeah, I read every single page and it looks as though consistently change happened outside of the house. So labor organizing started. And then the institution responded by formalizing and internalizing labor relations. Then you had the same at the FDA. People were getting sick. There were scam medicines, scam everything when we went to mass production. So the FDA formed. So are we looking at the origin story of potentially a new institution form when we see people organizing around data centers and that's kind of the symptom of people changing the institution from the outside in? Or does this change happen inside the house? I think it happens both inside and outside. All of the examples you give, you know, the agitation also came from the inside, that they had to come up with new regulatory bodies and things like that. Sometimes people galvanize around a particular cause that in the grand scheme of things is actually secondary, but it might merge into something else. So for instance, if you look into US history, you see that there is the People's Party, the original populist movement, that had more rural agricultural and economic grievances, but then it merges with the progressive movement decades later. So data centers, look, to me, that's not where the big fight is. But if that's what exercises people, perhaps that gives an opportunity for organization. But it's also important, and that's where leadership comes in, that You have your eye on the big prize, and the big prize, I think, is not to stop AI, but That would be a waste at such a promising technology. It's not just making small changes around our social insurance system so that, you know, people don't starve when they lose their jobs to AI. It's really finding a more socially beneficial direction of AI. So that's the big prize. I do want to transition to AI because I read a post that you posted on X, and it was in response to the transition petition or kind of activist cause that I think you, Ajay Agrawal, there were quite a few economists that signed this. And you stated in your tweet, X post, I do not like the comparison of AI's impact on the economy to the industrial revolution. That feels like comparing apples to oranges to me. But it is true that AI will have complex effects on the economy. So my first question is, why don't you think the industrial revolution is a worthwhile comparison given we've seen, how poorly that went? I know we're all thriving because of it now, hundreds of years later, but it was terrible for the people in it. And then second, what is your forecast for how AI will impact the economy and therefore the labor market? Well, I think the specific reason why I don't like the comparison to the industrial revolution is that many people in the tech conversation are unfortunately not as erudite as you are, Shania. And when people compare it to the British industrial revolution, they are incidentally or purposefully forgetting the first 80 years. And they're actually referring to all the good things that the industrial revolution delivered. So I have written another book, The Power and Progress with Simon Johnson, to emphasize precisely that point. But, you know, that still gets ignored. So that's why I often, I fear, that the comparisons to the industrial revolution are a coded way of saying this is going to be just all wonderful. The second reason is that despite all that hardship that you very lightly put your finger on, and I've tried to emphasize as other economists and economic historians have documented as well, the industrial revolution was very slow and extremely localized. You know, the two sectors that completely differed, the first 80 years of the industrial revolution, are textiles and coal. Really just two sectors. Not much did happen in the other sectors of the economy. And that's the context in which the hardships that you mentioned transpired. Now imagine the speed with which AI is evolving. And I'm the first to say we have not seen, you know, huge job displacements yet. Whether we will see them, how we will see them, there's a lot of uncertainty. But imagine now that several major sectors start laying off workers at the same time. That would be just cataclysmic compared to the industrial revolution. So that's the sense in which the comparison is also strained in that way. We can still learn a lot from the past, absolutely. Right, so I almost see it as it is a telltale sign because not what happened after the industrial revolution where we are now thriving, but that 70, 80-year period was a nightmare and a localized nightmare. So imagine what it would mean to go through that at a wider scale. 100%. Maybe over a decade. And I think people also lose the plot of saying, well, maybe there's going to be no jobs and that's probably not going to happen. And so we get caught up in, are we going to have no jobs left? Versus recognizing we went from a pre-industrial economy to a post-industrial economy. That was a very strange transition. So we could go somewhere else. But what happens over that 10 to 15, 10 to 20 year time horizon. And that's where I think that the focus should be. And that's where I think the nightmare could actually be. So if we were to look at your projections then for the labor market, and I do want to get into a few different scenarios, but what do you see as the potential AI-first economy? I think we're in an internet-first economy right now and it still resembles the rhythms of the kind of post and the industrial age. You recognize your schedule. We still have something called weekends. We still go to markets. We still go to mass school. So we still recognize our lives. So I think people don't think that every general purpose technology is as big of a deal. But we forget that nobody, pre-industrial revolution and post it, nobody's lives, nobody cared about time, nobody had weekends. We invented all of this stuff. So do you think the post or the AI-first economy is as different and strangely shaped as the pre to the post-industrial revolution economy? Absolutely. Well, first of all, I think you're asking absolutely the right questions and you put them exactly the way-- I would put them as well. So first of all, thank you. Secondly, I actually think that the transition from the industrial to post-industrial economy, which took place very slowly, was hugely disruptive. That's what my book was about. So what happened to liberal democracy? In one word, the answer is post-industrial society. So that's big. If we lose-- by the way, if we lose liberal democracy, which I think we are at the cusp of doing, that is a disaster far worse than I could have imagined 10 years ago. Liberal democracy is really the apex of our achievements. Can you imagine, given our history of warfare, bans, just selfish behavior, conflict, that we would inequality hierarchy, that we will build societies consisting of millions and millions of people, of very complex interactions, that live peacefully, create conditions for shared prosperity, where the all-powerful states that we've built-- I mean, Canadian state is amazingly powerful. British state or a French state, let alone the American or the Soviet, I mean, the Russian one. I actually work, by and large, to provide public services and useful regulation to people, and everybody has a voice. I mean, that's just an amazing thing. And we're at the cusp of losing that. That's huge. So if we get something as disruptive as the transition to a post-industrial economy, that's big. We need a much better roadmap, much, much better guardrails. The internet-- I'm a huge fan of the internet, by the way. And I think we've misused some of it. Social media and online communication have been terrible. But other things have been wonderful with the internet. But the thing about the internet is that I think, by and large-- and I was there at the early stages as a user-- everybody more or less understood what the internet would do. All of that, the internet dot-com bust, the companies, that went bust, they all had the correct business model. Pets.com-- everybody laughs about the Pets.com. Well, they had the right business model. It's like you use the internet's new platform to provide more services and more varied goods more cheaply to replace and compete against existing stores. And you do that by providing more choice and better information to customers. That's the model that has endured on the internet. Compare that to AI. There's so much uncertainty. That really complicates that transition. So I cannot-- I would love to be able to answer your question, Sinead, that what would the AI economy look like in 20 years? I have no idea. Would they be performing social tasks? Will they be all engineers and technicians looking after the AI model? Will the AI models still be jagged so that we need a lot of handholding from human workers? Will the workers find new tasks and other things to do that contribute to innovation and productivity? There's just so much uncertainty. And then also, of course, as I said, there's so much uncertainty about what our social system will be like in 20 years' time. Mm-hmm. Right, and I think what you're highlighting, and what I try to get across, is technology doesn't happen in a silo, in a vacuum. How society organizes our governing structures, our economic structures, are all interconnected. So with these transitions, it's not just about jobs. How we govern could also change if we don't get this right. Absolutely, 100%. Do you think, then, it's possible that-- because I know you talk about pro-worker AI, and we need to start steering-- and I do want to hear your thesis, for that, so people can hear it straight from you-- and that we need to start steering AI in a bit of a different direction. And AI isn't a gift from the universe. We're building it. So there's no reason why, unless I missed a meeting, I don't think AI just landed upon us. And maybe that's true, but isn't it also-- do you have to understand the shape of the market structure of the future and the shape of the economy before you understand jobs? Isn't it possible that jobs don't happen? Let me give you some examples to just underscore your point. First of all, 100%, I say this very often, when I'm asked what will the future of the labor market would look like or inequality, well, I say, you know, forecasting the effects of AI is not like forecasting the weather because we control AI. It depends on what we do with it. Second, you know, you ask what would democratic governance look like in the age of AI. I think that's critical. I don't know the answer to that. My thinking there is very conventional in some sense. I think that Tocqueville had it right. Democracy is very much intertwined with local and national associations in which people participate and they form cross-cutting memberships and they provide information, public services, civic duties in the process. We've destroyed them over time. There isn't one guilty party, but many things, including social media, have contributed to it. Perhaps with AI, we'll build new ones. That would be amazing. That would be one way in which we can exercise. Democracy in the age of AI. But I don't know. I don't see any plans of that. You missed that meeting about the AI's arrival. I missed that meeting. So there's so much uncertainty. But the principle that you emphasize is absolutely central. There isn't a single direction of AI. We have to shape that direction. And the way we do that is via our institutions. Via the democratic process. And of course, we need to ensure that the firms, the companies that are at the forefront of AI, get on board. They will have an influence on this. It's not like a bureaucrat sitting in the White House or somewhere in Ottawa can decide what the direction of AI is going to be. The direction of AI is going to be whatever the leading companies decide. But we have huge influence on these companies, which so far we have not exercised. And so what is your thesis for pro-worker AI? How does that actually work in practice? And what would that mean from a company's standpoint to deliver on that? Okay, that's critical. And we will need to spend some time on that question because it's such an important question. Let me answer that at three different levels. Okay. The first is that contrary to the emphasis we have on AGI, artificial general intelligence and artificial superintelligence, which makes it sound like AI models are very, very, very similar to humans. And therefore, they should naturally and just legitimately do everything that humans can do. Artificial intelligence. Artificial intelligence is very different than human intelligence. So there are a lot of possibilities for complementarities. And in particular, AI is a very powerful technology for providing context-dependent, reliable, useful information to humans so that they can solve problems, they can perform new tasks, they can develop new expertise and capabilities. So that's one important observation. We have to understand the whole spectrum. We have to understand the whole spectrum of things that we can do with AI. A second element is that by pro-worker AI, what I mean is very specifically that end of the spectrum where AI becomes a tool for humans to expand what they are capable of doing and gain new expertise. That is critical for human contribution to the production process to increase. Rather than the humans being sidelined. And this is a very important point and needs to be tackled with care because sometimes we are told that AI is being useful to humans by open AI, Anthropic, other companies. Where what they mean is very different from pro-worker AI and from an economic standpoint would have very different consequences. So the examples that they give. And there's a lot of confusion on this is that if you happen to be one of the first few journalists to use AI or the first few authors to use AI, your productivity would increase and you can do things faster and more of them and you'll gain an advantage over your competitors. That's true. But that isn't pro-worker AI because for the workers. If you have a working class as a whole for the workers in your occupation, journalists or authors, it wouldn't be beneficial when ultimately more of the authors or journalists use AI that would commodify their skills. So that's very different from creating new expertise and new capabilities. And therein lies a lot of the confusion about when tech companies claim, oh, look, our models are already helpful. They're not helping workers. No, they're not. That's a transitional phase that are helping a few people at the expense of others and everybody is going to wake up and smell the napalm at some point. And so who would bear the cost for this design choice? Because we have designed our market structure where profits do matter. And I know that these particular companies you're mentioning are still, they're not public companies yet. But by and large, we have public markets. You have a fiduciary duty to your shareholders. So who is designed? Who is designing, making these design decisions? How do you factor in open source? And not even the U.S.-China dynamics, but just most models in America and Canada and Europe, most new companies are leaning on open source. So that's kind of, it comes to the diffusion level. And then the third thing, and this is why I was asking, do you have to think about the shape of the market and the future company? Then you think about the job. Because say if I'm a startup founder today, I'm going to be building AI first. I probably have some strange business model that looks like YouTube. YouTube would have in 2005. There isn't a job that people would recognize to even augment. And maybe if I have a more fluid org chart and org structure, I don't even need anybody full time because people can come in. And so it's just a very different type of economic unit in the economy. It's not even jobs. It's kind of evolving fluid companies and people. 100%. All of those are true. And all of those are very confusing. But right now. I'm not sure that, I mean, I'm happy to blame the market for many things. But I'm not sure that I would blame the market for the current direction of AI. After all, you know, all of these companies are losing more money than you can ever imagine. So the market isn't actually rewarding them. They're losing money. And that's part of the uncertainty. Right. Because I didn't want to go on and on on the uncertainty. But one other uncertainty. The other uncertainty is that even if what Anthropic, Google, OpenAI, Meta, whatever they're promising will be approximately true, that these AI models will become very, very valuable as production tools. That does not guarantee that they're going to make money. Because if there are two or three models. And if there are a few open source models. And if they have approximately the same capabilities. They're not going to be able to charge that much. It's going to be some other aspects of the economy. Some other part of the AI stack that would get the profits. So it is particularly jarring that venture capitalists, private wealth, sovereign funds are investing, you know, hundreds of billions of dollars. But there isn't a clear path to actually getting that money back. That adds to the uncertainty. That also creates a risk, which I wouldn't want to see realized, which is that at some point that money is going to dry up and we're going to have a recession. So all sorts of problems. But the key is exactly what you asked as your question. Where are these design choices coming from? Who's decided them? And how were they decided? I don't know. But my sense is that it's actually a very, very close-knit group of people. Who read them. Who read the same science fiction. Who had the same sensibilities. Who were trained or socialized in the same milieu that are all leading this charge. I mean, if you look at Google founders, Elon Musk, Sam Altman, Dario Amadei, some of their leading engineers, they were all part of the same milieu. They all read the same books. They were all friends at some point before becoming enemies. I've never seen any period in our other human history where such a close-knit group. has been in charge of so much. And that's why I think you write about it in your book. That part of the dilemma, challenge, complexity is also the ideological component of this moment. You have to recognize the ideology of AI or generative AI. And so if we were to place ourselves in two different futures. So let's say one of the futures is closure to what you think may transpire. AI is great. It drives productivity. it drives some GDP growth, nothing through the roof, nothing astronomical. Maybe it parallels more the computer age where we waited a long time to see it in the numbers. And now we can all kind of see it in the numbers. I know it was on my finance tests. So if scenario A looks like that, and we're still starting from this position of wealth inequality isn't looking too great, what institutional mechanisms or economic architecture would you want to see in that future where AI is helpful? We're driving GDP growth. It's not crazy though, but we do want to close that gap. What levers would you pull on? First of all, if AI goes in a pro-worker direction, then it will, I forecast, but of course I can't be sure, it will drive both productivity growth and wage growth. So it would be a engine of shared prosperity. And then what we would want is actually for it to spread rapidly. So one problem, for instance, is that AI is falling behind in terms of infrastructure education. That's another challenge because now pro-worker AI will drive growth in Canada and the US, but not in Mexico or not in Paraguay. So we have to worry about those things as well. But there isn't just even two, there is a continuum and a multidimensional continuum of futures. We talked about the pro-worker AI future. Another one is the Chinese future. We talked about the Chinese future. The Chinese future is where some aspects are better than the US, where they're actually much more insistent on integrating AI into the production process. So they may actually get more productivity gains faster than the US. On the other hand, they're copying a lot of the US technology. So that's not feasible in the long run. Then on the downside, AI is a powerful tool for surveillance that has really pacified the population. If we go the sort of the Elon Musk path, then I think we really have a private company's dominating AI as a centralizing technology. And the whole thing possibly leading to a two-tier society in which a lot of workers are sidelined. Or it could lead to a very non-democratic society because even though. we may want to keep democracy alive, either the foundations of it are shaken because of this two-tier structure, or because inequality reaches such levels that the powerful parties may decide, "Well, we need repression a la China." So there are so many possibilities. We also have one in which, as I mentioned, we get a big slowdown in investments and some companies go bankrupt, and then we have to re-constitute, like a pattern where we've had these AI springs and AI winters in the past, and this will be like the mother of all winters. There are just so many possible potentials. Some of them are under our control. That's where the democratic process comes in. But some of them are not under our control at all. And also, final point on this, our democracy is already ailing, obviously. That's why I wrote that book. So we have to be realistic about what we can achieve and do that before it's too late. So you made a few points there. The first is that, let's say we don't have this fast take-off and this really destabilizing transition where we wake up tomorrow and AI is super reliable and agents work, and then we have modest productivity gains. But if we have designed it and shaped it in such a way that it is pro-worker, workers could actually benefit, as they have historically, wages, productivity might be shared, and that's a pretty good future. So we don't even need to think about new redistribution mechanisms. And I know UBI is very linear thinking, but those types of ideas don't even need to come into the picture. We could all win. But then there's the other complexity of, well, there's the surveillance aspect of the technology. There's the fact that most of it is run by the private sector, which also goes back to taxation decisions to offload investment in R&D and all sorts of things to the private sector, but here we are. So then there's the surveillance complexity. And then you have, potentially there is a scenario B where it does take off. I mean, you can't predict a breakthrough, and it is possible that Transformers isn't where it ends. And something happened to the next two to three years where we do see a faster take-off and we get that wobbly transition for a decade or more. Is there a different economic structure? I know we hear about UBI, we've talked a lot about that in this podcast, but is there something different we should be doing to how income, tax, all of that system works right now? Because it seems to keep going like this. You're asking, again, fantastic questions, and I wish I had better answers, but I'm going to preface it with the same thing. Even pro-worker AI, I've been advocating it. I think it's our best chance, but there is a chance that it may not work. It may actually not be feasible. If indeed we transition very quickly to something like AGI, and it's a comprehensive AGI where AI models are better than us in everything, then you can't really have meaningful pro-worker things. Second, imagine we transition to something like pro-worker AI. We still need a lot of public support for workers. First, the labor market will be almost certainly different. Many more were in the 1980s, but still today are in routine jobs. Those jobs will be done by AI. So when I talk about pro-worker AI, I don't mean that AI should not be used for automation. It will be used for automation. It should be used for automation. We welcome automation if it's coupled with other things that creates jobs at the same time. So we need different skills. Where will people get those skills? Well, in vocational education programs, but mostly in schools. So our schools need to adapt. So that's a public education problem. Most likely, many jobs will be much more dynamic. So workers will need to be more flexible. Again, that is a very difficult skill, by the way, to teach, especially in low income schools and neighborhoods. But that flexibility isn't enough. We also need better social insurance programs. So we do need to adapt our institutions. And exactly in what way we're going to adopt them depends on how those uncertainties that I mentioned will be resolved. Yes, UBI is very linear thinking. I love that term. I had not heard it in this context. It's like a very simplistic solution. I mean, can you imagine us in our colored political economy to actually fund a decent UBI? I can't. The political economy of it doesn't work. Second, to me, it's like throwing the towel. It's saying AI is out of control. The only thing we can do is create our modern version of bread and circus. Third, it's actually even if it worked and if it was generous, we would not prevent what I've called a two tier society. Everybody would understand that 80 percent, 70 percent, however many people just live on the crumbs of the tech billionaires. And that would create a very big, very steep status hierarchy. And that would be very inconsistent with liberal democracy. Yeah, I think UBI, I know, depending on who's talking about it, for some people who bring it up, they mean it with the best intention. But to me, it takes us to a state of disempowerment and then asks, now what? Versus we're still here. The future hasn't happened yet. We just need to get a lot more creative in how we're thinking. And I get the intention behind we should stop this technology, but we need to. That's actually an easy scapegoat for companies. It is easy. It's impossible. And it's also, you know, look, I've spent as much of my career studying political economy and the history of political economy. As I've studied technology. And I would be the first one to tell you that almost every example of trying to stop or block technology in the past has been disastrous. So you really have to be very careful whenever you're asking people to resist technology. You need to have a positive, proactive plan. So the image that I have in my mind that I try to communicate, isn't that we should try to slow or stop AI. The image is we are in a fast car. We're driving 200 miles an hour towards a steep cliff. We need to steer away from it. You can't steer a 200 mile car. You might need to hit the brake a little bit and then do it gently. So that's the kind of slowdown that perhaps we need to think about that. Yeah, let's not put another trillion dollars into this race for AGI. Let's think how we can use both the engineering talent that we have, which is very scarce, and the funding and the entrepreneurial energy to develop applications that are going to be more useful for society and more workers. That's the redirection. Slow, gentle. We can't do a steep one. And I think some of this is also, we don't have a proper long-term vision from leaders. And I think some are. - Democracy is amazing, but some of the change The challenges are we think short term, we think in election cycles. And so we don't really hear from a leader in 12 to 14 years, here's why this technology is going to be worth it if we build it this way. So we're stuck in this kind of this presentism. We really should suffer from it. That's so important. This is what I've also emphasized almost in every conversation if the opportunity arises. The way we regulate tech right now is we have the most powerful corporations humanity has ever seen. They can do whatever they want. They have lawyers to enable them to do whatever they want. And then if four or five years time, if some of the things they have done turns out to be sufficiently disastrous, then we put some backward looking reactive regulations. That is. A complete recipe for disaster. Instead, we need to have two realizations at the same time. And you put your finger on both of them. First, we need a proactive regulation, meaning which starts with where we do we want to go and how we can go there and let's how can we set the institutions and the governance structure to achieve that. Second, recognize that you're dealing with the most powerful corporations you can imagine. Don't kid yourself that these are. Like small corporations in the local economy, they they they're going to be really powerful. They're they if they have their own agenda, they're going to push that agenda. You have to grapple with that fact. They're not our enemies. There's a lot of innovative talent there, but they have their own their own agenda and they're very powerful. So you have to take that into account. Yeah, I think there's so much with this technology that that is worth fighting for. And I mean, I wouldn't be able to to do the work I do in foresight if I didn't see visions of the future. That I thought worked for most of us, at least. But you do have to take people there and not all of it even has to be regulation. I mean, what we've done with accounting is so interesting where governments essentially outsource the regulation to private companies that can make money from ensuring people stay in check. We could have regulatory markets. I know Jillian had economist Jillian Hadfield talks about this a lot. We can get innovative and people can make money steering this in the right direction. This isn't a charity. So we just have to expand our how we're how we're thinking in our design. And the details really matter. The details really matter. Like, am I in favor of the military industrial complex? Hell no. I mean, I think we definitely spend too much on defense and there are so many things that go wrong there. But go to the first two decades after World War Two and DARPA, which came out of the defense sector. During those two decades was a very forward looking organization that not only had a clear vision of technology and how it should be steered, it was extremely open minded. It ventured into areas far beyond defense and killing machines and really laid the foundations of many of the technologies that came later. So the details of how you actually support different types of technology, even in the defense area, could have very different consequences. Yeah. I think a lot of the. The post-World War Two innovation and investment birth, the kind of modern progress that we all take for granted now, whether you're thinking about the Internet, computers, solar, if we can finally get on board with that, the list goes on and on and on. And even what came out of NASA, you also had international agreements and countries that work together that would have never even wanted to speak to one another from that science. So it is possible. This isn't just fantasy thinking. And I have two questions. Just on that point. Oh, yeah. It's a very important point. Yeah. Because AI is a global technology. It's spread. Its application is going to be global. So it's insane to think that we can just content ourselves with national policy. And the framing of race towards AGI has had another very pernicious effect. It has rather than enable us to work with China, it has created this oppression of a zero sum race with China and completely closed off all collaboration. Not just on AI, but on other things as well. And look, I am a very vocal critic of the Chinese system. It has a lot of problems. But when it comes to AI, when it comes to climate change, when it comes to nuclear nonproliferation and pandemics, we have to have communication and collaboration with China. And the AI race has also ruined that. Yeah. You can't just think national in this moment. It's not feasible. AI doesn't have a passport. Yeah. It's clearly already diffusing globally. And if we're going to be honest, I mean, America's focus on AGI, China's playing an entirely different game now. America's playing chess. China's playing checkers with diffusion. And so now they're not even on that same landscape. They don't necessarily see it as zero sum. They see an infrastructure play. So I think what people are measuring is also perhaps not even helping their own cause or their own race. It may actually be holding that same argument. It may actually be holding that same argument back if we're going to actually measure where this technology is and who's adopting what where. And so what do you think we should be measuring in this moment? So if we looked at the late 1800s, early 1900s, we started to measure employment, unemployment, product safety. As we start to move into this strange new era, what are some of the things you think institutions, people should start measuring? Because I think the metrics matter. That's a great question. I think some of the. Labor market outcomes we are measuring are still very central. What wages people receive, their options in terms of employment, mobility. But also probably we need to understand job satisfaction, where they find dignity and things like that a little bit better. But there's so much more that we need to measure in terms of. Like we need an auditing framework for AI models so that we know what they can be used for that's damaging for society. So sometimes some of these things are a little exaggerated, but I don't doubt that mythos, for example, did have some very powerful capabilities that could have been used for ill. So how can. How can regulatory agencies or government understand those things and start building the right guardrails ahead of time? I think that's part of the measurement problem. Right. I would agree. And I think even with mythos or whether people think it helped a pending IPO valuation, we're probably better off not. You're much more cynical than I am. Oh, no. I know. Who would have thought? I think we're better. We're probably better off not finding. Finding out if mythos could accurately hack the bank and just preparing for the worst, steering towards the best, but preparing against the worst case scenario. And we do have a really diverse set of listeners to this show. We have key decision makers in governments and global alliances, as well as civil society activists, the whole thing. So if you were to give two key takeaways from your book, the first would. What would be the message that you want policy. Policymakers to walk away with? Because I know a lot of them listen. And then the second is from the general public. How can we think non-linearly and see how much power we do have in this moment? Well, so actually it's easier for me to give. I mean, some of it is canned. I apologize in advance. But give advice to CEOs and managers and the civil society and policymakers, and I'll explain why. So let me start with CEOs. And my experience is that actually when I talk to. CEOs who know their business, this is an advice that they sympathize with. You shouldn't think of labor as a cost to be cut. I think especially in our age where we need more innovation, more new goods and services in a changing world, labor is your most important resource. And if you start with that mindset, you can articulate a demand for technology that's much more in line with increasing productivity and using your workers more productively. than just trying to automate, which is neither that easy nor often that profitable. For civil society, I think we just all need to get informed. I think that's the first important thing. And second, recognize that actually civil society as a whole has much, much more power than I could have even myself imagined, you know, 10, 15 years ago. If there is today no regulation. No regulations, no guardrails, no effort to steer AI, that's because the narrative has supported a vision of AI where everybody's going to benefit, AI is inevitable, and geniuses are leading the charge. So if we want a better future from AI, we need to form a different narrative, and that starts with civil society. For policymakers, and here is the difficulty, it would be an exaggeration, but it wouldn't be a massive exaggeration. To say that right now, AI has two beating hearts, one is in China, one is in the US. So I would have very clear advice to U.S. policymakers. But what about Canada? I think the issue here is that you can do things locally. You can encourage your companies to use AI the right way. You can encourage your talent to go into the right field of AI, but that's small. I think the bigger thing that policymakers can do is work towards an international alliance for building policy. At the end of the day, we need international policy. Countries like Canada, the U.K. have a lot more clout than first meet the eye. And they have expertise. They have more power if they can leverage a large number of countries coming together along well-defined aims for steering AI the right way. Yeah, and I loved your point. I mean, the first, especially about companies, if you're only thinking of your bottom line, and AI as an automation technology, that's an existential move because you're assuming your business model stays the same in the future. That's how you compete versus thinking of AI as a top-line technology. What can you do differently? And that's just the basic fundamentals of business. So I hope people can catch that finally. And I think civil society, it's an important one. I think we forget how much power we do have. And I know in your book you talk about it was easier for labor to organize when we were in manufacturing companies because we were in a central location and everyone was going to the same place 9:00 to 5:00 doing the exact same thing. But the irony of social media, which I think has given us all a massive headache, is that we're also all there. So maybe some of these technologies, and it doesn't have to be the platforms you see today. There's one other thing you can add. So it's not just proximity enables you to coordinate, it's not just proximity can give you more power. Solidarity. So workplaces were repositories of resistance organization because they built solidarity. So the question is, can we find online spaces in which we build solidarity? I don't think the answer is no, but right now I know of no example. And social media kills solidarity, doesn't build solidarity. So we have to find online interaction modes in which we can build solidarity. And I think at the end of the day, for solidarity, we need community. That's why a very large part of my book is about community and the role of community in freedom. And I think that there actually is one small example of how we could do the online of online spaces that work and then lead to people not talking to their neighbors for six weeks afterwards. If you look at Tokyo in their most recent election, there was a candidate, the first candidate under 40, to gain a meaningful share in the race. I think he only got, you know, 2.4, 2.5% of the vote, but came out of nowhere. And he followed Audrey Tang, which was Taiwan's Minister of Digital Innovation, I believe, or minister of the digital component. And Audrey had spoken about in their book, broad listening. So can we actually use these technologies to listen to what people are saying, what they need, the nuance of it? And he used technology to do that and actually played a significant role in the election. He's the fifth in line. And so there are these small examples. It's not going to work. That's a wonderful example. I didn't know about the Tokyo thing. But. I'll send you the paper after. It's really interesting. I would love that. I would love that. But yeah, I mean, power and progress, I give Audrey and her achievement in Taiwan as an example of how you could have used digital technologies more generally in AI in a more pro-democracy way. And absolutely, there's a lot more that can be done. It's just that we're not doing it. Right. Right. We're just not doing it. Okay. So we have, I think, 15 minutes and then you're out. So there are five questions, or maybe six, and we'll see how many we can get through that we had from our audience. They're really great questions. So I would love to hear your take on them. Please. So the first is from, and I really apologize in true Canadian fashion in advance if I get these names wrong, but there's Girish Meghalani. Their question, "Will AI enable us to improve wealth distribution and overcome this extreme concentration at the top?" No. No. I think, again, the future is very hard to know. And it may well be that all these AI investments go bust so badly that some trillionaires become millionaires. But here's the problem. If you get so much investment, right now, this year, probably about a trillion dollars, that's going to have returns. Right. And if you're not expecting anything like the returns that people are expecting, that's even more returns to capital and less to labor. So already we're on a path to expand inequality. The pro-worker direction would ameliorate that. Pro-worker models will be more domain specific, less centralized. But I think the centralizing tendencies of AI would persist even if we steered it in a somewhat more pro-worker direction. So we may need more other regulations, and that's where antitrust is really important to really deal with this. And I think that wealth inequality question should really be bundled with how centralized we want our economy to be. There is a tendency among some people to think, "Oh, centralized economy, that was the Soviet Union." But if one company plays a very, very important role, if one AI model plays a very, very important role, then it's going to play a very, very important role. If one company plays a very, very important role, then it's going to play a very, very important role, then it's going to play a very, very important role. If one AI model plays a very, very important role, that's also a centralizing model. And it will be associated with inequality, and it will be associated with imbalances of power. But can't we adjust the returns to capital and how much goes to labor? I know that- We can. That's why we need policy. Yes, yes. Absolutely. I mean, I answer this by saying if we don't take major policy decisions. So one of the policies that I have advocated for quite a while, both on fairness but also economic efficiency grounds, is get rid of the distortions we have in the tax system, whereby we don't tax capital. We tax labor, we don't tax capital. So we may need to tax capital returns much more. I think actually a feasible, effective, fair, and efficient model would be to tax all income the same, regardless of whether we label it capital income or not, and make sure that capital income cannot go to the Cayman Islands or hidden this way or that way. That would really change things. And I suppose maybe that feeds into the next question from Natasha Bowman. What is the best measure for reducing wealth inequality? I would start with three levels of inequality that we have to bear in mind. And we have to reduce all three of them. Labor income inequality, income inequality, and wealth inequality. Labor income inequality is what really drove the huge increase in inequality in the United States in the '80s, '90s, and '90s. Income inequality became even more as capital returns also increased later. And then because we did not tax capital and we also did not exercise antitrust, wealth inequality then ballooned, although I should also preface this by saying that we have very good measures of labor income inequality and income inequality. So when I say inequality increase in the United States, I'm 19 years old. I'm 99% not sure of that. Wealth inequality is really badly measured. So when I say wealth inequality increase, there is much more debate on that. But I think it seems pretty obvious that it did. So I think it would be far easier to deal with labor income inequality and income inequality by redirecting AI, creating more jobs, more opportunities, more wage growth. Right. Right. So I think that's where we're at. I think that's where we're at. Civic education, either we've tried to impose values top-down in schools or we've completely ignored the civic values. So that's another very important thing. It becomes even more important in the age of AI. And then finally, more directly to your question, I think flexibility skills are going to be very important because no specific area can be said to be completely immune to AI. But there are going to be many parts of skills that a particular occupation requires that will still need human input. But what those are is often not so clear. Yes, it won't be the most routine parts, but would it be the ones that require more experience, more expertise, more social interactions, more technical understanding? So you need to have a holistic enough understanding with a good grasp. And it's a grounding that you have that flexibility to shift across tasks and find where your contribution can be greatest. Yeah, I would totally agree. I think one thing, and I talk a lot about this on my channels, is we have to move on from the idea of static job titles and preparing for one lane. That was a chapter of history that worked for a certain economy. That economy is gone. What are the underlying skills in that job you aspire to hold or in the job that you hold today? And think of yourself as this kind of mini institution. That in your skills, you can evolve over time and you can move and work in different places. I think even the idea, I know we're all worried about the end of the career ladder. And until we get to whatever comes next, there's a lot to be concerned about. But the career ladder also accompanied a certain economy. And so we're going somewhere different. And education also needs to also get with the time. Education will have to look as different as the economy we're heading into. So everyone's job, everyone's institution, everyone's plan needs to change. If we're going to get there. That's right. I love the way you put it. That's 100%. What are the plans for dealing with a growing human population? And this is from Rukob always. Actually, that's one place where, depending on your perspective, the news is good or bad. World population will stop growing in about a decade or two. And it's already stopped growing in most of the developed world. Some people are very worried about that because they think that population declines would create macroeconomic problems. They would create innovation problems. They would create lack of young workers, would create lots of issues. Some people celebrate that. I think it creates risks. But my research also shows that labor scarcity that comes when entering cohorts are small are actually quite good for wage growth. And they're actually good for how we use technology. The reason being that when labor is scarce, companies really need to economize on labor. And that makes the automation more productive. But also it gives them incentive to use existing labor in the right way. That's the reason why, for example, German companies didn't lay off their blue-collar workers when they introduced robots. Because labor was so valuable and they had trained it. And so they retrained them to make them technicians. So we really need a different mindset. But it can work very well. And in history, it's actually worked reasonably well. Right. Yeah. I think the data does show we're going down in population over the medium to long term versus up. And you're totally right, depending on who you talk to. That's either a godsend for the planet or it's a nightmare for markets. So it totally depends on where your savings account is. And then, Sharnat Holmes. I like your summary. Thank you. Will the. Forty trillion dollar debt crisis, high energy costs, high inflation and stagnant job market cause a crash? Possible. I don't have a crystal ball. I think stock markets are very forward looking. So if there was a general consensus that there will be a crash in the next year's time, there will be a crash today. So I think. The way I would characterize it is that. There is certainly some hype in AI. And the investments are amazingly large. But so far, there is also a lot of private wealth. There's a lot of money in sovereign wealth funds, SoftBank, UAE, Saudi Arabia, U.S. trillionaires. So they can keep on financing hundreds of billions of dollars of losses for quite a while. So it's. Hard to know whether there will be a slowdown or a hard stop, but the risk is there. If you're squeamish about risks, you know, take that into account in your portfolio. The final question, and I know we chatted about it a little bit. This is from, I think her name is Nikki. What can everyday people do to enact change and to shape the future of how we use this technology, of how we use AI? Become informed, turn up to discussions. Be part of your community and try to improve the quality of the conversation. I am perhaps naive in my thinking, but I think if in late 2010s, if instead of this unproductive false dichotomy of AI is going to be our savior led by geniuses versus killer robots are going to come for us. If we had started having the conversations that we are having today, we would be in a much better place today. And it's the responsibility of all of us, not just all the journalists, to elevate the conversation to that level. I agree. I think the future is much more nuanced than jobs, no jobs. AI is amazing. AI is terrible. That's just not how society history has ever worked. So if we want to elevate and reach a different. From future, we have to meet the moment with the nuance that it requires. And you put it always much better than I do. Thank you.