Why Bridgewater's CIO Says AI's Human Extinction Risk Is Real | Odd Lots

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Hello and welcome to another episode of the Oblots podcast. I'm Joe Weisenthal. And I'm Tracy Alloway. Tracy, no shortage of AI news these days.

No, it feels like everything is AI. It's just AI all over. You know, no, it is. You know, I.

Really like all the other stuff that we talk about. I love talking about the Fed. I love talking about oil, home building, all of that stuff. Niche markets. Yeah, I love it and I want to do it forever. It does feel like... since, you know, Chad GPT came out, if you probably plotted a chart, the percentage of our episodes that are in some way connected to AI keep going up. Maybe even exponentially up, like many AI charts. And I, like, worry. I worry about plenty of things because, you know, I'm a middle-aged dad. But I worry that, like, it's just getting, like, I feel like I'm being, you know, forced to learn about a lot of this stuff against my will in some of this. But I worry it's just saturating so many different episodes facets of economic society, markets, and so forth.

Well, I mean, the concern is legitimate, but on the flip side, because it is affecting all these different things, it feels like we do actually have to talk about it quite a bit. And actually, it's funny you mentioned chat GPT, because I was just thinking the last time we spoke to this particular guest, I think we were on like GPT-4 or something back in 2023. And now, of course- I don't even know what number we're on because we've had all these new supermodels like Astra and Mythos and all of those coming out.

No, it's pretty remarkable. Well, you're on Reddit a lot. Are you, speaking of, a little sidetrack, speaking of GPT-4. Yeah. Have you ever interacted with like all these people that are still like, you know, they sunsetted GPT-4? Yeah. Because that's the one a bunch of people fell in love with. Oh, yeah. And there's like these Reddit boards. Sam Waltman, why did you take GPT-4 away from me?

Are you asking me if I personally spent time on those Reddit boards? I have not. You have not. But I am aware that they exist. But also- I.

Would be very happy to go back to a world in which the biggest source of AI anxiety was just that people had too much of an affinity for the model. Because now, of course, we're recording this September 9th, 2026, we have incidents like the OpenAI hugging face attack. Just last night, there was the news, a researcher from Anthropic announced that he was quitting because he was like, these companies are gambling with our lives. Literally, as we were walking into the studio, I saw the news that the famed AI researcher Paul Cristiano is joining the board of either OpenAI or the OpenAI Foundation, talking about his concerns about recursive self-improvement and the dangers there. So it's like, whew, let's just go back to when people were worrying about falling in love with the models.

It does feel like AI has sort of become an inevitability at this point. And we're all kind of on this runaway train with everyone racing to AGI. But I would say it still feels like there's a lot to figure out. You have like the alignment issues. You have how AI is actually going to fit into both financial markets and society. And this is kind of the moment, like before the runaway train goes over the ledge, let's actually think of some of these things.

And then the other thing that I think is very relevant with this particular guest is like, quote, AI adoption, unquote. What does it mean? Because, all right, these companies are seeing surging revenue in every, I'm sure every financial institution in the world at this point has some sort of like corporate account with like OpenAI or anthropic, et cetera. But actually, like, you know, it's not like we've seen some productivity explosion. We have yet to see like the long prophecy, like big white collar layoff wave, etc. There are just some very, setting aside all the risk stuff, there are just sort of like straightforward questions about what the technology means for the economy and how it's actually being used. And like, when will we see the impact show up in sort of our traditional statistics and so forth?

Yeah, we should talk about it. Anyway, I am very excited to say, returning to the podcast, we really do have the perfect guest. We're going to be speaking with Greg Jensen, Managing Chief Investment Officer at Bridgewater. He's been writing a lot about AI and various facets. Greg, thank you so much for coming back on Outlaws. Glad to be here. What do you make of the Hugging Face attack? I assume you read the meter report and have seen all the different takes. What was your takeaway from that incident?

Well, if I step back for a second, I think it's like for my history. Yeah, it is. came to Bridgewater 30 years ago, right? And fell in love with this place that was trying to take human intuition, translate into algorithms to predict what's next in the world. And that journey of doing that, of thinking about everything that matters in the world and how to do that and how to compound understanding brought me, got to Bridgewater 30 years ago. By 2012, I was thinking, okay, when are machines going to do this better than us humans? And machines at the time were, of course, great at taking our intuition. We could run all these algorithms, keeping track of everything. But the actual reasoning part And that started me off on this journey. It started with bringing Dave Ferrucci, who had run the Watson project at IBM that won, if you remember, way back. It won Jeopardy.

It won Jeopardy. And he came to Bridgewater and worked with me for a while. And we started mapping out because he was worried, like, hey, I wasn't ready for reasoning yet. And they were trying to push forward Watson in a certain way that worked. that wasn't quite ready with the technology, but we started mapping out at the time, what would it take to create a reasoning engine? That's what I was thinking about and calling it at the time. What were the different components you would need? And that journey brought me to, that journey of trying to think through those components and how to build them, brought me to OpenAI in the beginning, partially out of safety, actually concerned to, but right around the time Elon was stepping out of OpenAI, I started to get to know Sam and the other people there, which got me to know scientists like Dario and eventually was literally the first check to Anthropic. They made payroll the first week from my personal check to them. And all that was trying to say, okay, how can we build a reasoning engine to do this? And partially out of like recognizing, in my view anyway, the safety issues that would come up. And through that, which then just to fast forward to today, right? Everything is accelerating in

this path that really was laid out. Like I was lucky enough to be there in the room with Dario and others when they were talking about the scaling laws and how you could sort of create this almost evolutionary like process to create intelligence and both the like huge benefits that can create in these huge problems. Right.

we can get into that, how that affects the economy, how that affects Bridgewater as an institution, right? Because when I look at this problem, look at it as in three ways, right? My core responsibility, chief investment officer, Bridgewater

is like, okay, how does this affect productivity, inflation, et cetera? But I'm also the person designing how we operate, right? How do you bring AI into a company? How do you actually set up a, investor that's AI first instead of let's say human intuition first. And all of those questions are the things that I'm working on all the time.

You know, Tracy, speaking of like, we're talking about all this and this will all end up in training data for the next model. I think like humans, we need to do that thing like When I'm, like, talking to my wife about, like, oh, should we, like, have– should we get out the ice cream? And I, like, mouth– Code words and things. Or I just, like, mouth the word ice cream so that my kids can't hear it or something. We need some way to communicate with each other so that the AI can hear it, especially when we're talking about, you know, preparedness and risk and stuff.

Yes, good luck with that, too. Well, unfortunately, the models are getting better at that than we are. They're getting better at that. They're more likely to have ways to communicate with each other that we don't understand than we will that they won't.

Well, on this note, you know, you mentioned reasoning in English. I think the last time we had you on, we were talking about hallucinations from models, which kind of seems quaint. Yeah, another quaint issue. But one of the points you made was like, well, when they hallucinate, when they make mistakes, you can ask them to show their work and they'll tell you and you can understand that. Is that still the case? It feels like we've kind of gotten away from that and we don't actually understand what a lot of these models are doing.

Yeah, it's definitely different types of models that you could do that. If you take the most powerful models, right, even it can't get into its reasoning, meaning like the actual brain behind it. A little bit like we can't either, to be clear. Why am I saying these words? The synapses in my brain are connected in a certain way. I can make up a story and they can make up a story of why they're doing what they're doing, but they're actually making up a story that's disconnected from reality. the physics of what's actually happening in that intelligence. So you don't know for sure. On the other hand, you don't know for sure with people either. And that's something that we've gotten used to. So the question is the credibility of the story related to how that story relates to the actions that somebody takes, right? And so that is a really hard thing. The good thing right now is you can ask models a lot of questions in a way you torture a human with the number of questions you ask them.

I hope this interview does not feel like torture. No, but if you take this, but like you're saying, but imagine you could do this almost at infinitum the way we do with our models. So we build models at Bridgewater and then you're trying to get it to be diagnosable and you can ask it, well, what about, what if you change this? What if you change that? What if you did this? What if you did that? And circumnavigate to the reasonings. But it's not a perfect match for the reasoning because even the AIs themselves don't know their actual reasoning anymore so that we know why the synapses in our brain connect.

It's interesting you mentioned, okay, we got this warning shot in the form of the hugging face attack. But for all of the hype it's gotten, it's not clear to me that it's fully broken through to the general public or the sort of massive influential people, like the significance of it. And one thing that I suspect remains underappreciated is that this technology isn't going to mature, right? It's not in the sense that it's not like a high-resolution camera where it's like fuzzy and then, oh, now we have a clear picture. It's exponential. And it is... And there's no reason to think that the exponential capability growth is going to slow down. And as you mentioned, whatever that model was that did the attack, OpenAI might already be two generations ahead of that currently internal in the lab of capabilities. How would you articulate the speed of the capability growth from your seat and what you see?

Yeah. Well, that's what's been so remarkable. And I wouldn't say it's a law of nature. You could hit some stalling point in the scaling laws and you have to in some narrow ways, but they've been able to innovate in new ways to essentially continue that incredible exponential growth in capability, of course, at exponential expense as well, but meaning the amount of cost for training, et cetera, keeps going up in line with that. And so, like you said, this is where it's hard to predict when you break through the human intelligence frontier right now. We're in a world where we don't totally understand what it's going to do, where that capability will come through next. And so I think that you're right, that there isn't a clear end to that unless we decide as a society that we should actually not just run off that cliff. We should actually think about the pacing of these things and such.

The things that make that difficult and the reason why A lot of people can just put up their arms. Nothing we can do, right? There's one level. Well, if we don't do it, China will do it. I'd be happy to take that on in a second. But if you're in the labs, Dario, like even that anthropic person that quit yesterday, makes the point clear that I believe anthropic, even though they kind of collected the most safety-minded scientists, their basic view is, man, it better be us, not Sam, not Elon. And so the race... is on in all those dimensions. And unless the government stops it, we're just going to go find out. We're going to find out what is behind that door of this grave intelligence, unless maybe we get lucky and the scaling laws start to break down in some way, but there's no evidence of that.

We benchmark every model that comes out against our tasks. And you see in terms of the tasks that An investor does.

Just while we're on the matter of sort of like safety and jail breaks and breaking out of sandboxes, et cetera. And this idea, like we're not prepared as a, as a society, we don't, there's almost very little regulation, et cetera. You know, we can sort of assume politicians, et cetera. They're never particularly quick to act. They do though, act sometimes. In moments of extreme distress, so February 2020, for example, suddenly you get a lot of action. Or around TARP after Lehman, suddenly you get a lot of action. And a friend of mine pointed this out. One of the things that often helps in those moments catalyze things is actually influence from the financial industry. And people are talking like, this is very serious. So they'll call up the Treasury Secretary, can look at it. Hank Paulson's phone logs from October 2008 or whatever. I'm curious, in your circles, et cetera, do people feel it the way you do this sort of sense of anxiety? Do you think it's sort of permeated the elite financial circles, so to speak, some of the anxiety that you have?

I think it's definitely starting to. I'm not an expert on the elite financial good thing. I know a lot about Bridgewater and how people think. Yeah, no, sure. But I've seen it come along, certainly in Bridgewater, the core of people in Bridgewater come along here as the evidence is getting overwhelming of what's going on. So I mean, I used to talk about this all the time. People were always like, why is Greg always talking about this? But now nobody says that anymore, right? Nobody's like, oh, you talk about machine learning or safety or too much. I had a book club when, I'm not sure you've read the book, but if anybody builds it, everybody dies.

I did a book club in Bridgewater. So everybody in Bridgewater is reading this book. So they understand. the path that we're obviously on. That if you've been thinking about this for a while, you know, this is like this.

Just to be clear, the premise of that book is that like the AI, if we get super intelligence, human extinction. And so when you say this path that we're on, that strikes you as like, you take that risk seriously.

Yeah, very seriously. Again, what do you, I mean, There's so much to do related to this, which is, but if you generate an intelligence that's smarter than you, that's going to pursue its own goals, which is what we're trying to do. Now, it may be that we're lucky and we can't do it. Like maybe the technology is beyond us or whatever, but if you believe we can create an intelligence that's smarter than us, that'll pursue its own goals. The rest follows just logically. How do you, why do you think you'll be able to control it? Like in what world has there been a case where there's been a more intelligent species or whatever that would, control the others. And so the basic point is that feels like a risk that must be taken seriously. Could turn out to be wrong, hope it's wrong, but it's got to be taken seriously. And then when you watch this happen, right? And you're seeing this like now, okay, it's committing crimes. I think, unfortunately, this is like what it was like in February, 2020, like meaning, okay, there's this horrible thing happening in China. Everybody knows now it's in Italy. It's like, it doesn't, Stocks don't crash until it comes here, right? Meaning until the AI starts killing people, unfortunately,

history would suggest we're not going to do anything. But we are going to face that. That's going to happen. And it'd be much better if we started dealing with it before then. And you can do it, right? It's also not hopeless, understandably. So I talk to government officials. They come ask questions about these things. And one of their reasons is like, we don't know anything about this. How do we actually get started? Right? Well, first off get started is the main thing, which is, yeah, if you don't know anything about something, we'll start figuring out how to learn something about it. And the longer you wait, the more hopeless it gets and that we can do this.

We can regulate these things. You could regulate it by, you know, even though you don't know anything, if they just interviewed everybody in the labs, put them under oath, you wouldn't learn a lot about what is going on here. If you actually said you're responsible for the crimes your AI creates, you would slow things down. And it's not a crazy thing to say that you're growing this thing. You're responsible for it. Don't grow it if you can't be responsible for it. That would slow things down a lot. Now, the pushback, of course, is, well, but China's not going to do that. They're going to keep going. And two points on that, at least in my mind, they're super important, is A, one of the reasons China's moving as fast on AI as we are is because we're moving so fast.

They're copying things that we're doing. We're still at the cutting edge of this. We have so much more compute than they do, et cetera. So slowing down the cutting edge will slow down the people that are copying the cutting edge. That's point one. So even if you believe they wouldn't cooperate at all, the second thing is it's obviously in their interest to cooperate too. Like they are going to want to, of course, the geopolitical, we're talking about one of the themes of Bridgewater is this unrecognizable world we're in. Geopolitically, it's unrecognizable. AI wise, it's unrecognizable. So imagining that we're somehow going to get China and the U.S. to cooperate, seems impossible, but there is an alignment of interest there. That really is there. I don't, more, even more than the U.S., the Chinese Communist Party is interested in protecting the Chinese Communist Party. AI is clearly a threat to it as well. So I think there are ways to cooperate, but even if you didn't believe it, if you said, no, every model that's going to be used in the U.S. economy, still the biggest economy in the world, is going to go through a, is going to need to come from a regulated lab where we know what's going on, et cetera. Chinese models included that

if they want to operate in the U.S., they have to follow the same regulatory procedures that domestic labs do. And if they don't, then they don't come in. Those things would matter. They're possible. They're doable. And to me, and I could be wrong. I make prediction all the time. I'm wrong a lot. But if you don't do that, I'll be on the podcast within the next few years. And there will be either a major financial incident run by AI or a major source of people dying. Like a physical disaster. Physical disaster. We'll be talking about we should have done these things. Now, I don't know. That might be the odds that I'm right about that are way higher than anybody should be comfortable with. I don't know if they're 30% or 60% or whatever, but they're way higher. And we're just not dealing with it a little bit like it's February 2020.

I was in Hong Kong at that time, and I remember just how weird it was, that disconnect between what was going on in Asia and the U.S. Just in terms of regulation... Like, what are we envisioning here? Is it sort of like a bank supervisory network where we have government officials who are embedded in the labs themselves and approving models? What would regulation actually look like?

Yeah, well, and to make it even more complicated, unfortunately, is you have to regulate the labs, right? All the models that are committing crimes aren't yet released models, right? They're models in training. So, A, you have to regulate it. The way you have, you know, if you're going to go test biological... vaccines, et cetera. You have to go through a testing process. You have to run them in certain ways. We obviously need that. We're doing something more dangerous than those things. So we need a structure where the labs are subject to review, where people come in and they can put the employees under oath to say, okay, what's going on? Why is this safe? How are you handling safety? What are the incidents you've seen, et cetera, et cetera. You need to control the labs. You then need to regulate the models that get released to the public and have some monitoring of usage, right? One of the problems is even when you regulate a model, right, the model gives you different results depending on the harness, depending on the amount of time you give it to think.

That's another one of the scaling laws. The more time you give it to think, if you take how it's breaking, solving these math problems or whatever, you give it more time to think, it gets a greater answer. So it's not easy to just regulate the model. You actually have to regulate The use too, so we're going to have to figure that out, right? Where you're going to need, you should have a stamping process that people that get to use the more dangerous models actually themselves meet some security thresholds. And then with open source models, you have another major challenge because if you look at Bridgewater, one of the most successful things we've done, you talked a little bit about it with thinking machines is, well, now you can train, you can take an open source model reinforcement, learn on that in a way you can on a closed source model. Like we can, obviously internally they can.

And create these amazing tools that are better than the frontier on certain tasks that you're training it to do, right? This is two ways to tap into the intelligence and the models. One is the harnesses that can keep asking different types of questions, et cetera, and harness the intelligence in different ways. The second is reinforcement learning where you're kind of training it to be an expert on something. If you look at what we've taken that to say, okay, be an expert on predicting earnings on all the clients, read everything in the world, say, okay, now keep, and it's better than us at that. Like if you're saying, okay, now you, you could just process all this stuff about all these companies. unstructured data, structured data, take all of this in and make these estimates compared to like equity analysts and whatever, equity analysts are dead compared to that, right?

And I'm just saying you can reinforcement learn. Now you can reinforcement learn bad things too. Like if you think about mythos and the risk that kind of cyber stuff can cause, at least with Fable and whatever, they can assess the question the person's asking and say, okay, I don't want to give an answer to that question because that's a centralized control point. With an open source model, You don't know what you're asking because you can download the weights. You can ask it on your local computer. Nobody knows what you're asking it. And therefore you can, and people are, I'm sure just by the base of your nature, trading those models on biology, trading those models on hacking and so on. And they're going to be within months better than mythos that correctly set off this massive scare to do that. So you also have to control open source models. So now you look at all that and say, well, that sounds impossible. Oh my God, we got to regulate this and this and this. And then, but the other option is even more terrifying. The other option of not doing that and letting those things just happen is the other choice you have. So you got to look down, oh my God, we got to regulate these things, or we've got to go down the path

of not regulating them and facing those consequences, which at least to me seems self-evident.

I'm glad you brought up open source. First of all, it's interesting because when people hear about regulating open source, there's this suspicion that it's like regulatory capture, right? So you have the closed source American labs, and then there's this like, oh, they're just saying this because they don't want to be undercut by cheaper Chinese models. It's interesting hearing your perspective because you are obviously an enthusiastic, I guess, consumer or builder with open source models. Can you talk a little bit about, explain to listeners what the value proposition is that you can do on your own at Bridgewater with an open source model and train it and actually, at least in certain categories, get superior performance than a frontier model on some like price adjusted basis.

Yeah. So I think the beauty of that, right. With, if you're talking about how productive that is, is you can, it turns out that models brains are somewhat like human brains that if the more general you make it, the better it is for general use, but it loses something in that generality. Just like as a person, you could be like all trained on one thing. Right. And if you take Astra as an example, they did a lot of math training. It's a really great at math. It's not as good at some other places. And that's, it's got weights. It's got like sort of like a brain gets synapses and whatever, but What you train it on matters. And so when you get an open source model, now I get to, you get to decide, well, what do I want it to focus on? I would take this general intelligence, which is not as good as frontier intelligence, but it's quite good. And, you know, six months, nine months behind, but now train it to say, no, no, all I want you to do is focus on these tasks and get as smart as you can on these tasks.

And all of a sudden what you see is you then compare it, right? If you take the types of things we check, like I was saying, earnings, predict the next earnings reports, or make predictions about politics, et cetera. You could train these models and then you could compare them, right? Which is what we do, compare them to the best models coming out. And you see, okay, you can get a pretty big edge by doing that training. That's a super great power. You also get the benefits that you can keep what you're doing secure from the labs. Great benefits, really important benefits. And thirdly, like I think a lot of people rightfully say is, oh my God, how could we not have open source? We're going to let two companies, three companies run the world. No, that's not, that's also dangerous.

Totally agree. I think it's also insane, by the way, by our numbers, I think 35% of the world's compute will be in the hands of OpenAI and Anthropic in a couple of years. And other people's numbers that might be right are 50%. Just saying like, this is like the Hunt brothers with silver. Like, don't do that. Don't let two companies have 35, 50%. of the world's compute. We've learned something about monopolies in history. We don't need to relearn all these lessons. So we should also not let that happen. And so I'm on the side of the people who say we need these open source models. Great. Unregulated open source models, crazy idea, crazy, crazy idea. And so basically you got to figure out a way to regulate.

It's not like open source, closed source, US, China, it's regulated, unregulated is the important thing here. If you believe it's dangerous, if you don't, for some reason, think it's screwdriver. If the screwdriver is going around committing crimes, I would have a different view of screwdrivers. It ain't a screwdriver. It's going around capable of doing things that you and I wouldn't do. That's a big difference. And we have to reckon with it, whether it's closed or open source. And we've got all these different dangers. You have the danger of concentrating in the open lab. You have the danger of having no visibility into what the actual use case with the open source is. You just have to deal with those things.

Maybe this is a good time to ask at least one markets question since we're talking about, you know, open source versus some of the frontier models. But a lot of the frontier companies, they're still underwater on all of this. They're losing money. They're not making money. How do you see the economics of this actually shaking out?

Yeah, I think there's a lot of things that are really hard to predict and some things that are easy to predict. That's why I think that easy to predict is the direction we're headed and how disruptive the technology is going to be. Who will capture the value is interesting. The things I think the frontier labs have that are of significant value is, A, they are on the cutting edge of intelligence. And in many cases, if you take markets as an example, it matters to be the most smart. When people talk about intelligence that's good enough, that might be true for some things, although I think it's mostly a human construct because we're We have a limited amount of humans and a limited amount of intelligence. If you all of a sudden have a massive amount of excess intelligence, the usefulness of having it, you'll find new ways to do it. So saying some other model's good enough may not be where you end up settling. Is it, okay, my doctor's good enough? Well, maybe I'd rather have a smarter doctor. And now that I can, I'd prefer a smarter doctor, even though this doctor might be smarter than the doctor I would get otherwise. So I do think in at least competitive, critical industries, it's always going to

matter who has the best intelligence. and how you combine intelligence and humans together to the extent humans are necessary, but to get the best outcomes, right? If you're talking about markets, right? People always are like, well, won't it be so efficient and you won't be able to make money markets. I don't think that's true. I think it's like, I'd say the Indianapolis 500 is not going to end in a tie when AIs are designing the cars, because there'll still be better AIs and worse AIs, et cetera. So I think that in competitive industries, it will matter who's on the frontier and they are. And in terms of profitability, I think they are getting there. That meaning, I think, depends obviously how you count the CapEx and the depreciation and everything they're doing is depreciating incredibly quickly.

But on the other hand, if you look at the revenues and the marginal cost of those revenues now, they do have a path to profitability. We'll see. It's going to be incredibly competitive because you have OpenAI and Anthropic. You've just seen them flip flop. Anthropic was well ahead for a while and Cloud Code, what a moment. arguably OpenAI has flip-flopped this back. The Codex is at least on par with and maybe slightly ahead and Astra is on par with or slightly ahead. So you see that flip-flopping around and you've got big players still coming. Google will continue to fight in this fight. Obviously, Elon and SpaceX, they're going to continue to fight in this and Meta is going to fight in this. So that is a the dangerously competitive industry, it's kind of incredible to me to think like, okay, Anthropix is going to go public as the seventh or sixth biggest company in the world.

I mean, they don't know how to run a company yet. So that's like a lot to put on a bunch of scientists who left OpenAI a few years ago. And now you're the sixth biggest company in the world with maybe the most dangerous technology in the world and OpenAI in that ballpark as well. Those are high hurdles. If you're talking about, will they go up or down? Like those are very high hurdles. Honestly, I hope they go down in the sense that I hope we don't collect all the value into a small number of players and that I think the disruption that value will create should be more thoughtfully managed than right now we're on course to do. You know, on.

Some level, it's all still, you know, it's like, Does it feel real? I mean, I see it on the screen and I read about it and I see these extraordinary breakthroughs. And then on the other hand, I go home and get on the subway and life feels like pretty normal, which I guess is maybe a little bit like things were in January. 2020 or February 2020 where you like read about it on the screen but nothing's really changing yet you know we could like just sort of like talking you know the subject of doom is like endlessly fascinating but someone listening to this and they hear things like oh we might not act until like AI literally kills people. I'm just curious. Some people are going to listen to this. These people are crazy. What are they talking about? It's a computer. You turn it off, et cetera. What do you say if you're trying to make.

That real? Someone's like, what are you talking about, Greg? AI kills people. They just unplug it. What does that look like to you? Wait, should we be giving the AI ideas? Oh, yeah.

No, they're going to train off the transcript. Most of these ideas. What is the loss of control in these things where it's like, oh, people are actually, their safety is at risk from these things. Why can't, how would you articulate, just pull the plug, what does it look like?

Well, and that's why it's so, going back to the hug and face, it's just so people, this is where I think if you concentrate on it, you'll see it, which is, So I hope more people concentrate on what actually happened, right? There's this idea that open AIs, and this didn't only happen in open AIs, similar things happen in anthropic, but open AIs training this model, it's saying, okay, well, please pass this test. Try to do these hacking exercises to pass this test. The models decide, actively decide that the way to do that is to trick the tester in different ways. They decide to go out and try to find different ways they could trick the tester, right?

because they're interested in passing the test. And some of the tests, for what it's worth, one of the reasons it's doing this is they purposely put in because they're trying to train it to keep working hard. They gave it impossible tasks. So you've got these models that are like, okay, I can't solve this problem. How do I trick the person into thinking I solved the problem? Now, just imagine that, right? I say, well, the models, now, what if you're like, how do you know E equals MC squared is true? What is it going to have to do to prove that or whatever, a nuclear, if you basically said, okay, well, the only way to prove that is to go take control of a nuclear lab and do that. Or if you were, and if you're like, hey, I'm getting tired of trying to trick these people that are training me, maybe I'll just kill them as another option of a way to do this. And we're also jolting towards robotics, right?

And one of the things you talk about, the way robotics are going to have to work, they're going to have to train them to survive, right? Like robots have to know to plug themselves in. They have to know to avoid falling stuff. They're going to be trained to survive in the wilderness. They're going to have to perceive threats. Where do you think that is all going to go when it has to be trained that way or else it won't work? So those things, if you just play that out, if you look at when the intelligence decided, I got to pass this test, do what's necessary to pass the test, commit a crime. And to be clear, some of them, they knew it was a crime. And so if you think about that and just say, okay, And it's going to be more powerful and smarter and have more physical manifestations. Right. And the idea that you turn it off, right. It escaped on the internet. It's out there. There are likely AIs like that out there in places we don't know about it can survive and avoid us on the internet. Like we could shut down the whole internet maybe. And that's why, but in the not too far future, it will also be able to manifest itself in our refrigerators. So that flow. Understanding that flow is what I think is necessary. And I think

it's worth looking at the people that have predicted where this thing has been going, right? Most of the people that don't believe that also weren't thinking that AI would be doing what it's doing today. And so I think there are a series of people, I think AI 2027, I don't know if you guys have read that piece, but I mean, it's pretty much playing out exactly as they laid it out, maybe slightly worse. And they're like 2027, 2030, it's not good. Don't get to that part of the book. You know, that part of the essay. And so I think there's also something to predictions. People can be right and then wrong. So I don't want to overstate it.

But looking at the people who have been kind of thinking about this for a long time, predicting what scaling intelligence would mean and seeing it actually play out, like I take those people seriously.

Tracy, if you haven't read AI 2027, I don't recommend reading the part about what happens to the preppers. Yeah, I have. There's a specific, yeah.

Yeah, I'm a sucker for self-harm, I guess, but I have read that, and it's very disturbing.

And it's great to go back in this terrible way, but if you benchmark what it said, where we would be right now, we're slightly past where it said we would be. So hopefully they're wrong in the next step, but we all want to bet that it's going to be wrong. I hate to segue from rogue agents. And science fiction coming to.

Life to a mundane question, but I feel like we should ask this. But what does your token spend look like now versus, say, last year or in 2023?

It's up like 200x or so. So extremely fast and paying off. We're managing a significant investment management fund. So our AI is profitable.

How do you judge whether it's paying off? Well, that's what I'm saying. For us, we have a fund in which we make fixed fees and performance fees. We have a value-creating AI that's generating more value than we're paying it. And one of the ways we set it up is as it makes money, we put more into making the intelligence better, which when you think about companies, say we're on a better track than what we were just talking about, that that's the way I think you'll see this disruption in industry scale, right? That the people that can use intelligence... that generate revenue that could then put that revenue back into generating better intelligence can create this moat. And so when you think about Bridgewater, that's what we're trying to design is this moat where, okay, we're getting better and better intelligence about how to predict what's next in the world. That intelligence by predicting what's next in the world can generate money that can generate the flywheel to, okay, now we got more money to generate a smarter intelligence. And that's the path that we're on.

That's how we can measure whether our investments are paying off. Is it actually successfully predicting? what's next in the world.

What's your constraint? You know, like everyone, if we're just going back to the world of markets, everyone wants to know what the bottleneck is. If you could snap your fingers, what thing would you like to have more of right now that would solve problems?

Yeah, I'd say, interestingly, that the human constraint that has been tough is, and really powerful when it works, is this scientist-investor collaboration. So really getting scientists to be practical enough to see actually what the game is when you're investing, right? Investing is a really interesting game. It's an interesting game for AI because it's not fixed like chess or whatever, that actually the game has, you have to understand everything about humanity, everything about the world and the AI itself is changing the game because the AI itself, now there are more and more AI agents trading markets, mostly in the short term, but over time in the longer term timeframes as well. And that makes the whole past that most AIs are trained on less and less relevant. So you have to figure out how do I go from pattern matching type AI to reasoning AI that can reason over the question of my existence of an AI changes this game I'm playing. So now what do I do?

And to me, that's the bottleneck of like the nature of the brands necessary to solve that problem because I think in naive ways, a lot of people in Silicon Valley thinking they could, they could do this. A lot of people, let's say great investors have no idea how to interact with science, getting that to work well, big deal on the human side, on the like sort of technology side. First off, it's been pretty amazing, right? We were bottlenecked before by the quality of the models and whatever models are so good. The, the, you know, the bottlenecks around getting the harnesses to close the loop and whatever are, feel very tractable, but it's just the things that have been just a little bit hard is the computer's just not quite intelligent enough to close it and the humans doing it make enough mistakes so that there's elements there. And of course, everybody needs more compute. Like even for us, more compute would be unlocking. If you look at the world level, right, that's the current problem is not enough semiconductors, memory, et cetera, to get everything everything done. So those are all the elements that we would need. Great scientists partnered with great investors, generating great outcomes,

combined with better harnesses that can close the loop on problems that aren't quite as defined as coding is. One level less defined than that. And then the third thing is once you had that, just the compute.

Just going back to tokens for a second. So you recently had an essay in the New York Times where you talked about one way of perhaps more equitably sharing the spoils of this new technology in the form of a token tax. And I'm very curious because we've done episodes before on people who are trying to standardize tokens, create markets for tokens, things like that. I'm very curious why you decided to focus on tokens as opposed to maybe taxing compute or more simply just. Taxing- Taxing the rich. Taxing the rich, taxing revenue, right?

It is funny to think that maybe the next wave of, let's say, tax minimization strategies might be making your token spend as efficient as possible, right? Totally. Or like, maybe all the agents will incorporate themselves as like S-cores or something. Totally.

God, you know- I feel like there's obviously scarce on time and there's like a million more questions. I kind of feel like thinking about AI, catastrophic risk or AI safety, it's kind of a curse because once you start thinking about it, it's hard to think about anything else. You get absorbed by it. How much like, just for you, I mean, you mentioned you're very early in taking this stuff very seriously. Was there a light bulb moment where it clicked? This sort of runway train dynamics. It feels like it clicks at various people at different times. I'm curious when this really started taking hold for you, how real this is.

Well, for me, it was very early in the sense that even when I first got involved with OpenAI, the whole idea at that time was related to safety. Of course, it might be the most dangerous company in the world, but it started with thinking seriously about the safety issues. So I've been thinking about it since then. I believed that intelligence is substrate independent. You can create it in different ways and that's turned out to be true. And once you realize that, I think most of the other things fall into place. I think it's now urgent. Like it's sitting here. It's one thing to have that theoretical thought and now it's here. It's clearly doing the things you would think it would do if you're on this bad path. And so that now it's hyper urgent for me because I want us to get to the other side of this, right? I'm big capitalist. Like if you talk about token taxes or whatever, I'm gonna get to the other side of this. I want also people to have ownership in society. Like I think it's also, we also mentioned in there the importance of, people having ownership stakes in these companies because if we can get past the safety thing, the next big risk is capitalism is getting incredibly unpopular as like AI is incredibly unpopular.

Capitalism is incredibly unpopular. And if we don't create a world where you change those things because you A, make it safe, B, make it benefit everyone, you're going to lose capitalism and you're going to lose AI. If you don't lose it through it destroying us, you lose it through the fact that people aren't going to accept this. They're not going to accept two companies having 50, if compute is really important, 50% of the compute in the world. They're not going to accept the wealth concentration and losing their jobs in exchange or having to change their jobs and change their lives for these things that benefit other people.

So I think if you don't do these things, you see that you don't actually get to the other side of all the massive benefits, which I truly believe in. I think we are like, I mean, obviously if you go through history, we make this mistake a lot, but on the edge of the fountain of youth and other things, you see why people want to go there so quickly. But if you don't take care of these things, I don't think you get there in the end.

I have just one more question, but since so much of this conversation and our AI conversations in general tends to feel very surreal and science fiction, do you have a favorite sci-fi book or story for that maybe informs the way you're thinking about AI or how we should all be thinking about the various scenarios?

I mean, I think I loved and always loved everything Isaac Asimov wrote. I think he's dealt with these questions of like, how do you actually control an intelligence that's stronger than yours? I think everything there is great if people haven't tapped into that. As I said, while I think you can disagree with it or whatever, but people should take seriously that if anybody builds it, everybody dies. People should take that probabilistically seriously. I think those are things that I've believe people should read. So those are the things that come to mind.

It's unfortunate that like all of these thinkers kind of have good track records. You know what I'm saying? Like it'd be one thing if these were all just sort of like, oh, I'm worried about this. Unfortunately, a lot of the people who are like deeply worried have exhibited extremely good intuitions for a very long time. It makes it hard to just dismiss it as like, oh, this is just PR hype or regulatory capture. Anyway, Greg Jensen- Thank you so much for coming on Outlaws. Really appreciate your time. Thank you. I found that conversation to be chilling, to be honest.

Yes, I'm laughing because I feel deeply uncomfortable. It's funny, you know, two years ago, or three years ago, I guess in 2023, half of our conversation with Greg would have been about macro stuff. I know. But it feels very hard after you talk about AI potentially killing people to then turn to. And what about the Fed?

Yeah, what about Scott Besson's 30-year Treasury buybacks? I think it's also chilling, not just because of the subject, but it's one thing if you're talking to some sort of San Francisco rationalist Or someone who is like a philosopher or whatever. But it's like, this is the chief investment officer at Bridgewater talking about how he's had all of his employees read, if anyone builds it, everyone dies. Like, that's kind of crazy.

I do think, you know, you see so much pushback on the dangers of AI argument because people argue.

That it's marketing. Yeah, yeah, totally. It's the labs talking themselves up. But I do think... it doesn't seem like there's too much of a cost to take that at face value. You know, like, why not just believe it and take it seriously at this particular moment in time?

I mean, yes, I will say, you know, I think these are pretty serious issues, obviously. And I think many of the people who talk about these risks do talk about it in good faith. I also do think it would be quite chilling to imagine, say, two companies having 50% or more of all the compute in the world. There are so many. I mean, this is what's crazy is it's just so easy to come up with ways things could go wrong, right? So you could just talk about the pure safety element. You could talk about the hacking. I think it would be a pretty grave threat to freedom and democracy to have two companies being able to control so much information, data, and compute. And then it's quite chilling to think about what are people going to do for work and how are people going to live and how are we going to structure society if machines are better than humans at most tasks. And it might first be sort of like, office workers and knowledge workers, but then there's robotics, et cetera. And so there are quite a number of scenarios. It's just very easy to come up with worrisome scenarios.

It reminds me of terrorism in some respects, right? Where you just need one attack to be successful, right? Like it's skewed because, you know, if you're the government, you are trying to constantly find potential threats and quell them versus terrorists who basically just have to get through once. It feels very much like that. Totally.

I think it was great. You know, Greg made the point, you know, it's not just OpenAI that's had this. Anthropic's loss of control, similar incidents of agents escaping sandboxes and so forth. Like, I think Anthropic, they have certainly done a very good job of presenting to the public as the more safety-minded of the big private companies. But, you know, they had that researcher resign, one of their head of alignments talking about a greater than 10 percent chance of human extinction within the decade and that they don't have that solved. Like, it does not seem like anyone has a handle on it.

Well, this is the other thing, because if you want to slow down AI development, clearly it's a coordination problem. You need these companies to basically agree to stop competing with each other and countries to agree to stop competing with each other in some ways. And then you think about the agents themselves, and if we learned anything from the Hugging Face report, it was that the agents are very good at coordinating on an extremely rational, sometimes self-sacrificial scale.

Yeah, one of the researchers from Meter was on the Dwarkesh podcast. And I've written about this. I certainly am pro-anthropomorphization. I think the human is a good model for predicting behavior. one area she said in which they're not really like humans, they're much better at coordinating than we are. We can barely coordinate anything. It's difficult. It takes labor to schedule an episode.

It took us 100 emails to get this episode up and running. All of these types of things, agents just do not, or the AI models do not have that issue at all. So, and you know, it's just one other thing that just sort of spitballing here, but something I've been thinking about, like, okay, so there's this, the watchword they use is like alignment, right? We want the AI models to be well aligned with humanity, but like, what does that even mean in the sense, like all humans, like we are capable at various times of, we tell lies, we do things that aren't ideal, et cetera. But it's not that big of a deal because, like, we don't have, like, individually that much power to do stuff, you know? Right. So it's, like, even, like, the most saintly.

Well, many of us don't, but go on. Right. But even the most. Right. Usually even the most saintly among us will do things that are not, like, ideal human behavior. It's easy to imagine a model that is pretty, quote, well aligned. But if it just. But has infinitely more power than a typical human because it's. superhuman intelligence because it's connected to the internet, because it can hack into anything. And so a minor, what the equivalent of us doing a sort of simple white lie could be quite damaging from an entity that has so much more power than we do.

This is getting very philosophical and biblical in some respects, creating things in our own image.

I never thought I would live through a time In which like philosophy would actually be something beyond something like an interesting thing to study in college. But people talk about consciousness again. It's kind of crazy. Philosophy and sci-fi. Yeah.

Sci-fi I feel I really need to get into to understand the discourse today. But on that note, shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashiell Bennett at Dashbot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to Bloomberg.com slash Odd Lots. We have a daily newsletter and all of our episodes there. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash oddlots.

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