We recently published a Daily Observations from co-CIO Greg Jensen and CEO Nir Bar Dea titled “Our Thoughts on What Is Likely the Most Important Policy Decision of Our Lifetime.”
In that BDO, we shared our framework for how policy makers might want to consider navigating huge technological transformations like we’re seeing with AI, why it’s so important for policy makers to get this right today, and some specific policy proposals we would recommend—particularly around two areas: 1) AI safety, where Greg and Nir make the case that government rather than industry should set standards for how models are developed, released, and used, and 2) labor disruption, where they suggest that a token tax and citizen equity in the leading AI companies would help make society more resilient to the AI transition and give citizens a stake in it.
That Observations prompted excellent conversations internally at Bridgewater and with many of our clients about AI policy. To discuss some of the follow-up questions we’ve received and expand on the points laid out in that BDO—including why it’s so important to regulate AI development today and how to prepare for potential AI-driven labor displacement—Daily Observations editor Jim Haskel sat down for a conversation with Greg and Nir.
The goal of this podcast is to discuss the big-picture questions at a high level. We will follow up with more detailed work in coming Observations.
We hope you enjoy the conversation.
Transcript
Note: This transcript has been edited for readability.
“I think this is the most powerful technological revolution that we’ve ever been through, and I think that’s why this is so important to get right. I think you have a responsibility, particularly if you’re bullish on the power of AI, to see the risks and make sure we as a society mitigate them, because it’s the only way in the long run that we’re going to get the benefits. The two go hand in hand. I think if you believe it’s going to transform society, you have to know that’s a dangerous thing, that that happens very quickly, that you have to deal with both the good of AI and the danger of AI simultaneously, or you will get the danger and not the good.”—Co-CIO Greg Jensen
Jim Haskel
I’m Jim Haskel, editor of the Bridgewater Daily Observations. We recently put out a Daily Observations from co-CIO Greg Jensen and our CEO Nir Bar Dea titled “Our Thoughts on What Is Likely the Most Important Policy Decision of Our Lifetime.”
In that BDO, we shared our framework for how policy makers might want to consider navigating huge technological transformations like we’re seeing with AI today, and also why it’s so important for policy makers to get this right—now—and some specific policy proposals we would recommend, particularly around two areas: AI safety, where Greg and Nir make the case that government, rather than industry, should set standards for how models are developed, released, and used; and around labor disruption, where they suggest a token tax and citizen equity in the leading AI companies that would help make society more resilient to the AI transition and give citizens a stake in it.
That Observations got very broad readership and great feedback from our readers. It prompted excellent conversations, both internally at Bridgewater and with many of our clients around AI policy.
Based on the follow-up questions we’ve received, I asked both Greg and Nir to join me today to discuss many of those questions and to give them a chance to expand on some of the points laid out in the Observations.
And just to be clear, the goal both here and in the original BDO is to discuss the big-picture questions at the high level and not double-click into the details. We will follow up with more detailed work in coming Observations.
Chapter 1: Why We’re Commenting on AI Policy
Jim Haskel
First, let me welcome in both Greg and Nir. Thank you so much for joining.
I want to start with a direct question some people raised, which is: why comment on AI policy at all? Of course, we’ve been studying and using AI technology and we’re focused on its investment implications, but we’re not policy makers, so it’s really important to lay out what perspective you’re trying to add here.
Nir Bar Dea
First of all, Jim, on behalf of both Greg and I, we’re just so happy to be having this conversation. There’s really nothing more important for us to be discussing right now.
That leads me to the answer to your question, which I see as two parts. First of all, at Bridgewater, our goal has always been to understand how the world works, and we comment and try to wrap our head around the most important dynamics. Those are the things that are going to drive economies and markets, and whatever those dynamics are—when they are COVID, we wrap our head around pandemics; when there are wars, we wrap our head around geopolitics and acts of war; when it’s technologies, we wrap our head deeply around emerging technologies; and when it’s generational policy decisions throughout history, we do the same. We work hard to understand them, to understand how things work, the cause/effect linkages, and then we share our thinking with our clients and with policy makers.
That leads me to the second reason, which is, for many of us at Bridgewater—I know for Greg, for me—we are blessed with that position of putting the puzzle together, of having an objective, unbiased understanding of how things work. We do that with one of the most powerful teams out there and 50 years of compounded understanding. So, in some ways, I think we all believe we have to speak up, especially when things are this complicated.
If you look around right now and you look at this void that I think is a function of so many people looking at this reality and saying, “Well, it’s so complicated: you have to understand technology, you have to understand policy, you have to understand geopolitics.” Everybody feels unequipped to say something, and that void will be filled by either less-informed people or people who have biases. So, in many ways, I think it’s more important than ever for us to put our thoughts out there.
I’ll just finish with where you started, which is since we put this Observations out there, we’ve had great internal debates about these thoughts. We’ve had great back and forth with clients, great back and forth with policy makers, and, for both Greg and I, that is exactly what we wanted: smart people who care about how things actually work going back and forth trying to sharpen each other’s ideas, leading us to find the best solution.
At Bridgewater, we’ve been doing that for the last 50 years, and you can definitely expect us to keep talking without shying away about whatever the most important dynamics are that are driving the world’s economies and markets.
Greg Jensen
To add to what you’re saying, first off, I think the value of the Daily Observations is that you get an unvarnished look at what we’re thinking about. It’s quite a project to write every day, and I think it sharpens our thinking, which is why we do it. I think we’re better thinkers because we share our thinking every day. And what we intend that to be is a look over our shoulder to what actually matters.
We are at this point where AI in total is incredibly impactful. It’s kind of a third or a half of everything that’s going on, from how economic growth is being formed to equity markets, etc., and the regulation around it is going to be critical to whether it is sustainable or not. So, these questions are the questions we must answer.
Chapter 2: What Does It Mean to Win the AI Race?
Jim Haskel
A question for both of you is that one of the biggest themes that comes up when we talk to our clients about AI is, “What about China?” How are you guys thinking about that?
Nir Bar Dea
Great. I’m glad we’re taking this question up front because I think one of the most important things to lay out is to clarify how we believe the concept of winning actually works. What does it mean to win the AI race?
What Greg and I laid out in the Daily Observations is the argument that winning just the technological race while dismantling your society or political system isn’t winning. Even if you say, “Well, I think it is. I just want to win the technological race,” the process of unhinged pursuit of technological victory is so likely to cause either a security or political backlash that even for those who are saying, “Look, I really just want to focus on that one dimension,” ignoring the factors that we laid out in our write-up, is almost certainly a mistake and is also certainly going to hold you back from winning even just the technological arms race.
I think a good example that we’re living through right now is the wave of populism and mercantilism that is following the last, what I would call “unhinged,” pursuit of productivity growth from globalization. Because from a global perspective, I think people can look at the US and say, “Well, the US has won.” But when you look at the US domestically, internally, over the last several years, I think many or most would ask themselves, “Has the US won?”
This is not a political point. I think both on the left and the right, people look internally at the US and say that we’re at one of the lowest points that we’ve been in because the gutting of the manufacturing base, leading to a disenfranchised and divided society, and ultimately real pressures on institutions, real pressures on the ability to govern, have circled all the way back to destabilizing geopolitics.
These are lessons that are right in front of us, learned by other generations about the concept of winning, which we have to carry over to this next massive productivity leap.
Greg Jensen
To add on to what Nir has described about winning, I’d say the problem is a human problem. It’s across the world, and the US has a special responsibility and role being at the frontier of this technology. But it is correct that in the end, the only way for the world and humanity to be safe and to manage the disruption that’s likely to come is through global answers to these questions.
Now, how do we get started on that? We can’t get started by making this incredibly complex. I think starting with the US is super important, and I think the US moving a) will make it more likely that this works in a way that doesn’t tear US society apart or put us at unnecessary risk and b) will ripple into the rest of the world.
The US mandating that all models used in the US are regulated will put pressure on China, either to do similar regulation or to not get the capital benefits of being able to impact the US AI ecosystem, which is the biggest. So, a) it has some direct consequences there. And b) intellectual property from the US is slipping out across the world to China and other places, and by slowing down and carefully monitoring and regulating the security around AI models, you’ll slow down some of the distillation and some of the intellectual property theft. As a result, you’ll make the rest of the world safer, even if it’s uncooperative.
So, for those reasons, I think it’s important to a) see this as a global problem and b) see that the US can and should lead in changing the direction that the world is currently on in this race dynamic, and that by doing so, even without cooperation, which I still would like—and I expect that cooperation is more likely than pessimists expect—but even without it, you slow down the race in other countries because the slippage from the US is controlled.
Chapter 3: The Risks Around Open- and Closed-Source Models
Jim Haskel
Greg, you just discussed the importance of slowing down and regulating the security around AI models. But within this there are open-source models, there are closed-source models. How do you differentiate between the two and the risks that they pose?
Greg Jensen
There are risks to both open- and closed-source models, and I really think we need to change the language to “regulated” and “unregulated” models. We have to be very careful about what we leave unregulated, whether it’s closed-source or open-source. But let’s talk about the differences and then why both need to be managed.
The first is closed-source models. The danger here is that we’re putting so much power in so few hands. That needs to be regulated. We’ve seen the risk of that. We’ve seen the race dynamic as the different closed-source models—right now, OpenAI and Anthropic, but Google and Meta and so on—all race to win. The externalities of that are not being managed. The implications of that are not being managed. And they need to be. The risk of an unregulated closed-source model getting in control is both the types of risks that we’ll talk about of all the dangers AI might cause, but also the risk of the concentration of power.
The good news about open-source models is they are democratizing all of these powers. That’s a good thing. They allow you to reinforcement-learn on them. That can be used in many beneficial ways, and we do that here in our labs. The risk of it on open-source models is there’s even less control: how you reinforcement-learn, what you reinforcement-learn for them to do.
Secondly, when you ask a question, at least with the closed-source models, Anthropic and OpenAI check whether they should answer your question or not. There’s no equivalent way to do that with an open-source model, where you could get the weights, you could ask it anything that you want. It can’t control at the point of input.
Both paths—open-source models and closed-source models—have big risks, and both can cause societal disaster. So, both need to be regulated. We need to regulate which open-source models come into our ecosystem, and what closed-source models are able to do.
People can point accurately to how difficult that’s going to be, particularly open-source models. Preventing them from being utilized is difficult because they’re easily shared and so on, but the difficulty of it doesn’t change the fact that it’s necessary. And there are ways to handle these things if the government chooses and has the will to do it.
So, you’re going to need to separate—rather than open and closed—regulated and unregulated, and we’re going to have to do a lot to keep unregulated models out of the ecosystem, from doing grave harm, and on figuring out the very tough challenge of what regulations can be effective against the growing risks that we’re seeing in terms of these models.
Chapter 4: Safety Risks
Jim Haskel
Greg, one of the big themes of the Bridgewater Daily Observations you and Nir wrote is this nature of safety risk, and it’s so interesting because, for decades, we’ve been running huge risk with nuclear power and bad actors, biological risk, you name it, and yet, fortunately, nothing terrible has happened.
But what’s really interesting about your warning here is that the nature of this risk is unique and different, so I think it’s really important for you to expand on this.
Greg Jensen
I want to expand on why I believe a major accident is inevitable if we don’t change the path that we’re on.
We’re hurtling toward two different types of risks. The first is the acceleration of all the risks you just described. If you take cyber risk, if you take biological science risks, physical sciences—all of those cases, we are democratizing that intelligence. In a way, that’s great in some sense, that this power is getting handed out to people all around the world. Obviously, on the risk front, it’s accelerating the risks of all those things that you described.
Beyond that, and why the risks are different in kind, is that we’re generating an intelligence that pursues its goals in its own way. One of the powers of AI is that it could think for itself, decide how to achieve goals in ways that aren’t programmed in, and by doing that, what that means is that it could choose a path that’s highly dangerous in a way that nobody who built it intended. Not as an error, actually, but because it’s so smart it could find different ways to find goals. But sometimes it could do that in ways with unintended consequences.
If you look at what’s happened recently with the models breaking out of both OpenAI and Anthropic, going into other companies’ ecosystems, a) that’s a crime. The AI committed a crime, and right now we’re not even clear on who’s responsible when AI commits a crime; that’s something that obviously needs to get worked on in terms of the regulation around that. But b) the basic point is that was never designed. They were asked to answer these questions about cyberattacks and other things, and in order to do that, chose to break out of the sandbox they were in, answer that question, actually organize many AI agents to pursue getting those answers.
That level of thinking, however you want to describe what’s going on there—whether it’s agents scheming together or whether it’s just a series of stochastic parrots that have learned these things from all the texts that they’ve read—either way, it’s an incredible amount of danger when you think about what might happen when it tries to solve other problems, as they move it more into the material sciences, as they move it more into biological vaccine discovery, etc., and now it tries to answer questions and it chooses its own path on the way to answering those questions.
I think the dangers are clear; they’re imminent. And if we don’t change the way we’re regulating the labs, it’s inevitable that we’re going to have a very significant, disastrous outcome. Unfortunately, a lot of times that’s what it takes to create the kind of regulation that’s needed.
Nir Bar Dea
The only thing I would add to what Greg is saying—which, I completely agree with—is it’s in addition to the ability of technology because you have to contextualize this with where the world is, meaning we are on this trend of, first of all, from the existing nationalism, which is much more prone to confrontation, much more prone to where the ability exists for people to take advantage of that ability. Just look at what the last five years looked like, and if you subscribe to any of what we’re saying about potential societal changes, you have to add to that nationalism and geopolitical state that we’ve been living in for the last several years, the discontent of people within nations. So, in addition to just the capabilities being out there and in the hands of so many, you have to multiply that by, “Well, how many people are actually dissatisfied? How many people would actually like to put that to use?” That combination can paint a pretty scary future.
Chapter 5: Labor Displacement
Jim Haskel
I want to move our discussion here a little bit to the labor front and, particularly, labor displacement. There’s a wide range of opinions that AI may be more like a technology that will help create jobs to fears on the other side—that it will cause much more disruption and labor displacement. In the Observations that you both wrote, you mentioned up to 18% of current jobs could be displaced over a number of years. So, I just want to first start by walking us through how you’re assessing that risk.
Greg Jensen
I think it’s really important to take this risk head on. If the technology is successful, the basic idea is that you’re bringing an alien intelligence into the economy, and this artificial alien intelligence is going to change the way work is done, for sure, if there’s any chance of success. We’re using it in our labs at Bridgewater, and I can say it’s going to change a lot. It’s going to create a shock in Bridgewater in terms of what people do and how they operate. We can see in our labs that we’re so close to this major—what I think is a huge—productivity enhancement, but a big change in how people work, who we hire, etc. I think we’re on the cutting edge of that.
But society—like the rest of the companies—will come along in the coming years. The 18% number we put out there, the basic point is we’ve gone through the task ranking of what AI can do, what it’s on the edge of being able to do very quickly relative to the tasks necessary to add up to jobs. 18% of jobs are pretty fully disruptable, and you’re likely to see that rolling out as harnesses get better. You don’t even need AI to progress to see that happen soon. It’s like Nir said in the beginning about how if you had thought through better the implications of China entering the WTO and their mercantilist policies on US society, you obviously would want to handle that differently than we handled it. I think that’s easy in retrospect, and it’s going to be different this time, every time.
But here, my fear is that there’s a pretty good chance it’s a lot faster, a lot more disruptive. So, being preemptive and not waiting for that moment is even more critical. And we failed before. Learning from that failure to try to deal with these economic adjustments before you get there is super important.
I think you could size those adjustments, as we get into our policy decisions, you could size them to the actual effect. Size them to how big those companies that are creating this intelligence are. Size them to how big the job displacement is by how much actual AI is used. All of those things, I think, can help ensure you’re making a wise decision and you’re not overreacting.
But the basic point is that you want to start preparing society before the shock rather than after it because it’s so much more difficult and the problems become embedded in ways that are hard to break down if you haven’t given it some forethought.
So, we can hope—I certainly hope—that AI brings this intelligence, and it turns out that it’s complementary to human intelligence. I think we have to prepare for the fact that even if that’s the case, it’s going to be very disruptive, and it may be a replacement to human intelligence, which is what’s different about this invention relative to inventions of the past that weren’t intelligent. That’s why humans have been able to adjust so well as you get tools that are helpful.
The horse analogy is good. Horses were very economically valuable when carriages got better. If you made the wheel better, the horse was more valuable. But if you replace the horse with a car—where the car can do everything the horse can do better than the horse can do it—there isn’t the complementariness. And we may or may not be on that fringe. You don’t have to have a strong view on it to see how disruptive it is going to be in any reasonable case where the AI is successful and how potentially incredibly disruptive relative to anything it could be. So, being prepared for that labor displacement and that adjustment before it happens seems like the only real way to ensure you don’t get terrible outcomes.
Chapter 6: A Token Tax, and the Distribution of Equity to Citizens
Jim Haskel
Now, Greg, you and Nir propose a specific policy, a token tax, to address some of the risks from the labor displacement. Why do you see the token tax as the best policy option to address these issues?
Greg Jensen
I think the policy that the token tax most gets at is putting human labor and, potentially, replacement machine labor on similar terms. I think it just is common sense that we don’t want to incentivize machine labor over human labor. When you tax something, you’re creating incentives. There are a bunch of taxes, both that individuals and corporations pay when you’re talking about human labor; machine labor should be put on those same terms. So, that’s the basic idea, which is how do you get machine intelligence labor tax to work.
Today, I think the right way to handle that is a token tax. It’s basically the unit of input and output into machine models. You could tax it pretty directly by taxing the way AI labs and others charge for usage, so you could get to that fairly quickly.
Now, it’s important, as you set up that tax and set up a machine labor tax, that you actually create within the IRS a way to enforce that. So, people will object understandably with, “Well, what about tokens used overseas?” I would say that’s like labor used overseas. US entities need to pay when they work overseas, etc.; that if you’re benefiting from foreign tokens, you’re going to need to pay taxes on that. We will have to build up a way to collect those taxes as we do for all taxes, but I think that’s very manageable.
In an upcoming Observations, we’ll get more into the details of these things. But the basic point is we need to put human labor and machine labor on at least the same terms. You could argue—and I think I would—that you should put machine labor at a disadvantage to human labor because there are externalities to this transition. There are externalities in terms of the meaning, in terms of unemployment, and in terms of all of the regulations, of the safety issues we have to handle here. Those things have large costs, and, one way or the other, the actual beneficiaries should pay for the costs that are laid on society by the externalities of this machine intelligence.
So, those are the steps that we would take. I think you could get going today with a token tax and improve that process over time, as the way that machine labor is paid for changes.
Jim Haskel
You look at frontier labs like Anthropic or OpenAI, they both suggested reforming the tax code and introducing higher taxes, but on capital. How do you think about the token tax and the merits of that versus higher taxes on capital?
Greg Jensen
I would just separate the two things. I think higher taxes on capital, given the concentration of wealth in the US, has a lot of merit to it. But I would separate that point from the point I just made on both the externalities of potentially replacement technologies versus complementary technologies and say that you may well need both: higher taxes on capital and token taxes.
But there’s a purpose to the machine taxes that, if you just tax capital, you miss—which is, to say, there are a) externalities to having machine labor doing work instead of humans and b) why would you want to punish human labor? No matter what the capital gains tax rate is, why would you want to punish human labor versus machine intelligence labor?
So, those are the reasons that I’m focused on the token tax, and it’s a separate issue of whether you want to raise capital gains taxes, which I also think given everything that’s going on, putting capital gains taxes aligned to income taxes is totally reasonable policy. I’d be in favor of that as well, but it’s not the focus of this piece because it doesn’t deal directly with the issues raised by artificial intelligence.
Jim Haskel
The other suggestion that you both made was granting actual direct equity stakes. How do you feel about equity stakes as opposed to other ways of doing this, like universal basic income?
Greg Jensen
Well, I think there are a few things that matter here that you want to try to improve as a result of sharing the benefits of AI with everybody in society. I think you get them better by distributing that equity out. Now, there’s a lot of complexity to that, and I’ll talk about that in a second, but if you’re relying on the government for UBI or you’re relying on the government even for dividends from a wealth fund, the gap between the citizen and the money is large. You’re still dependent on a bureaucrat deciding how much and when you get your check, and so on.
If we actually distribute the equity to all citizens a) it once again takes the power away from the politicians to use that in ways that you might not want and it gets power directly to the citizens, and b) it puts everybody in society in the game.
One of the problems capitalism faces today is so many people don’t see it working for them. They see the negatives. They see that their job might be lost. They see other people getting wealthy and them not. By taking these steps, everybody having a stake in capitalism, you have at least increased the odds, in my view, that capitalism can be reformed and saved. I think if you don’t do that and more and more citizens see the costs and not the benefits, they will opt out and choose policies that move more in the direction of quashing productivity and more in the direction of moving away from capitalism, which, in some ways, is certainly needed. But capitalism is by far the best engine of productivity growth that we’ve created. So, trying to sustain it in a way that people feel tangibly the benefit of that system is why I like the idea of passing the equity stakes to individuals.
There are two complaints that, I think, are real and that we could deal with in more detail. One is you want to be careful about people getting those stakes and selling them right away. That would take away from the idea. I think that is controllable. You get into at what rate you’re allowed to sell those stakes if you get them. There are things like that that I think would need to be handled.
The second thing is, “Is what we’re talking about big enough to matter?” That’s where I think, particularly when you think about the externalities of all these things, I think the stakes, in the end, across companies that are using AI, should be large and you should think about how that’s going to occur as part of tax policy, etc. That if you use the AI, you owe back to society the direct costs of regulation and all of those things, and you owe the indirect costs of the externalities that this is having on society.
So, to me, I agree, I’d say starting this is really important and moving in the direction of it being meaningful in a way that protects capitalism and doesn’t move us away from capitalism.
Nir Bar Dea
Just to add to what Greg is saying. I think some of what’s tough to process—both on the token tax idea and the equity idea—is that they are not unlike what’s currently in the system. I think it’s worth reflecting on because, behind this, is the recognition—going back to the point that Greg made earlier that there’s a difference between the horse and the carriage when you improve the carriage versus when the car comes in—is that there’s a first-order change in how the system is going to work. If the system was completely centered around human work and now something else comes into the picture, we need to embrace the idea that that is going to require us to go back and have first-order-type changes in the system itself, which will look new to us, and it will take into account how to now coexist between humans and other types of work.
I think for a lot of people it’s trying to say, “Well, how do I take an entirely new ecosystem and just shove it into the same frameworks that have served us for 50 years of human work?” I think you have to make that leap and understand that organizations are going to just fundamentally look different and we have to adapt to new concepts.
Chapter 7: The Need for Policy Action
Jim Haskel
One final question I want to pose here and I’ll turn to you, Greg: you’ve been very bullish on AI, and internally we’re doing many things to restructure our entire business around AI. So, how do you square that bullishness with the regulations that you and Nir are proposing?
Greg Jensen
Well, I think this is the most powerful technological revolution that we’ve ever been through, and I think that’s why this is so important to get right. I think you have a responsibility, particularly if you’re bullish on the power of AI, to see the risks and make sure we as a society mitigate them, because it’s the only way in the long run that we’re going to get the benefits. The two go hand in hand.
I think if you believe it’s going to transform society, you have to know that’s a dangerous thing, that that happens very quickly, that you have to deal with both the good of AI and the danger of AI simultaneously, or you will get the danger and not the good.
So, to me, that’s how you square it, which is: the reason I’m so concerned about it is because it’s so powerful, and because of the way it’s designed, it makes its own choices to achieve its goals. And there isn’t a simple way to solve that problem. Intelligence by itself is very hard to constrain.
To me, the two pieces come together. I want AI to be successful. I see in our labs how much better it can make trying our pursuit of understanding the world, making predictions about it. In order to achieve the benefits of Bridgewater and, more importantly, the benefits across the world, we have to deal with those issues. So, to me, it’s hand in hand. If you want progress, you need to do it responsibly, and that’s what we’re talking about here.
Jim Haskel
Great. Greg Jensen, Nir Bar Dea, thank you so much for your time. I think this is a really important conversation to have. It was an important Observations and it’s important to keep the conversation going, and I think we’ll continue to do it into the future as required. So, thank you so much.
Greg Jensen
Thanks, Jim.
Nir Bar Dea
Thank you, Jim.
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