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

Episode Date: September 11, 2026

Greg Jensen was one of the earliest backers of both OpenAI and Anthropic, and at Bridgewater Associates, where he is the managing chief investment officer, he leads the hedge fund’s AI strategy. As an early adopter of the technology, he has a lot of thoughts on where things stand right now in terms of model capability and safety, as well as the broader economic impacts of AI. Just recently, he published an op-ed in the New York Times that proposes a “token tax,” to help mitigate the job losses that AI might cause. (Bridgewater predicts as much as 18% of US jobs might be displaced in five years.) We last spoke with Jensen in 2023, and so much of what we discussed then (like AI hallucinations) seems quaint now that models have come so far — capable of lying, cheating, and much worse. On this episode, Jensen tells us how Bridgewater is currently using AI, why there needs to be a stronger AI regulatory state, and why it feels like the AI discourse is starting to resemble the months before Covid-19 took over the world in 2020.See Odd Lots live in Los Angeles!See omnystudio.com/listener for privacy information.

AI is entering its most consequential phase where scale, safety and sovereignty will determine who leads and who lags. Join Bloomberg Tech in London on November 2nd and 3rd as global leaders across business, finance and policy examined the defining trade-offs shaping the future of AI. Thank you to our presenting sponsor, Salesforce and supporting sponsors, IDA Island and Schneider Electric. Learn more at Bloomberg Live.com slash Tech London. Hello, OddLodz listeners. I'm Joe Wisenthall. And I'm Tracy Allaway. We're the hosts of the OddLodd's podcast and we've got something exciting for you. That's right. So one of the best parts of hosting our podcast is we get to actually meet and interact with our listeners and we know

we have some listeners over in Los Angeles. That's right. So if you're in L.A., we're going to be recording a live show, some live recordings at the Vermont Theater in Hollywood on September 17th. We have some really exciting guests lined up. some really great conversations plans. So go ahead and get your tickets. You can find those over at Bloomberg.com forward slash oddlots or click the link below in the show notes and come and say hi when you're there. Bloomberg Audio Studios Podcasts Radio News. Hello and welcome to another episode of the Oblots podcast. I'm Joe Wisenthal. 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 to talk about the Fed. I'm 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 worry, I like, I worry about plenty of things because, you know, I'm a middle-aged dad. But I worry that like it's just get, 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 just saturating so many different 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. Yeah. I think we were on like GPT4 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 little sidetrack, speak of GPT4. Yeah.

Have you ever interacted with like all these people? that are still like, you know, they sunsetted GPT4. Yeah. Because that's the one of bunch of people fell in love with. Oh, yeah. And there's like these Reddit boards. I'm like, Sam Waltman, why did you take GPT4 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 were too, had too much of an affinity for the model. Because now, of course, recording the September 9th, We have incidents like the Open AI 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 the either Open AI or the OpenA Foundation, talk about his concerns about recursive self-improvement and the dance. There's so it's like, whew, let's just go back to when people were worrying about falling in love with the model. 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 and every, I'm sure, every financial institution in the world at this point has some sort of like corporate account with like Chad. She'll be open AI 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 color layoff wave, et cetera.

There are just some very sitting aside all the wrist stuff. They're 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 office. at Bridgewater, he's been writing a lot about AI and various facets. Great. Thank you so much for coming back on Outlots. 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, and if I step back for a second, I think it's like for my history. I came to Bridgewater 30 years ago, right? And I fell in love with this place that was trying to take human intuition, translated 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, right? And that started me off on this journey. It started with bringing Dave Ferruci who had run the Watson project at IBM, that won, if you remember, way back, one at Jeopardy. One 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 wasn't quite ready with the technology.

But we started mapping out at the time. What would it take to create a reasoning engine is what I was thinking about in calling that 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 open AI in the beginning partially out of safety actually concerned to but right around the time Elon was stepping out of open AI 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 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 you create and these huge

problems. Right. And you see this in the hugging face thing that it, It is once you have an intelligence that you're training to achieve goals, right, you use track of how it chooses to achieve goals, which is what you see all over that place in the hugging face incident is it surprised the designers in the way it's going to go about trying to achieve the goal of passing these tests as an example. But that is broadly going to be the case when you generate intelligence. You give it a goal. You want to give it a goal because you want it to create your recipe.

You wanted to do these things. You wanted to tell you the right answer to. questions, then the way it's going to pursue those goals, the more intelligent it gets, the more surprising it is in the way that it pursues the goals and the more dangerous that you see. And in that case, watching it actively reasoned through how to trick the test, you know, how to the different things shows you where we are, right? This should be a bomb.

You know, everybody should look at this like somebody died. Here it is committing crimes, going around high. the fact that it's committing those crimes, et cetera. Coordinating with other agents. Coordinating with other agents, self-sacrifice, all of these things, right? And people can argue about anthropomorphizing or whatever. It doesn't really matter. It did those things.

It committed a crime. It did those things. And the fact is a society that we're totally unprepared. We're not even prepared to say, well, open AI committed a crime, right? Who committed the crime? And we're not prepared with the right kind of regulation, with the right kind of preparation. And even the early warning shot, as much as we're talking about or whatever, it's still not really doing all that much.

And we're in some ways lucky. The warning shot wasn't that bad. But we don't know how many agents are out there. They didn't know that was there. The models are better now than they were then, you know, even in material ways. The models they're training in the lab today are better than Astra, et cetera, and therefore more dangerous. Not to mention the new models will learn from this case, right? Everything we're talking about here goes into the new models and they learn the mistakes they made.

And actually, some of the, even if you think about the safety, the fact that they reasoned in English is helpful for us to figure out what's doing. The newest models aren't doing that anymore. They're removing that constraint as it slows down the models to some degree. I mean, how crazy is that? We wouldn't have any idea why it was doing and why if it hadn't been reasoning in English. So anyway, we're at this extremely dangerous point where AI has reached the point where it's more intelligent than us in certain ways. and we have not gotten anywhere really on how to deal with that, both dangers like this, the hacking dangers and so on and the dangers to society as you move forward with what

does it mean to have entities that are more intelligent than us in certain important ways. The economy critical. 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 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, you know, 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 after? And I, like, mouth. 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. Unfortunately, the models are getting better at that than we are. They're getting better. They're more likely to have ways to communicate. Yeah, exactly. That we don't understand, and we will, 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, no 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. Like, why am I saying these words, the synapses in my brain, are connecting 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 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 credit of the story related to how that story relates to the actions that somebody takes, right? And so that is a really hard thing.

A 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. Show you. I hope this is just interviewed 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 it? What if you changed this? What if you changed this? What if you did this? What if you did that? And circumnavigate to the reasoning. 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 snaps. It's in our brain.

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Yeah, fill the card. It makes you feel good. Does it? The Bloomberg this weekend podcast. Subscribe today on Apple, Spotify, or wherever you listen. It's interesting. You mentioned, okay, we got this warning shot in the form of the hugging faces. attack. But it's, 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 clear picture. It's exponential. And there's no

reason to think that the exponential capability growth is going to slow down. And as you mentioned, you know, whatever that model was that did the attack, open A had 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 of 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 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 task. And you see in terms of the tasks that an investor does. Can you give us a few numbers? Like when you say those benchmarks, like what do you think specifically? So I've been for 30 years far. One of the jobs been doing is training investors, right? And so we've been now setting up AI tests along the different dimensions of what investors. of Bridgewater have done for 30 years. And, you know, probably back in 20203, we might have talked

about this, but in any event, like on like answering a question about economics or whatever, it was kind of like second year analyst type work. I mean, now it's a hyperproductive, super analyst, you know, now still not capable of everything that you need to do to be an investor, but super capable. We set up two, we have two factories running, right? One, which is human intuition translated algorithm supported by AI. AI is helping us move quicker on different kinds of indicators about what's going to have in the future than we ever had before. I talk a little bit about that. But we have a second factory where we put the AI first. All the people in that factory are training the AI. That's their goal. And we have two funds. We have Pure Alpha. That's the human

intuition with AI helping move that human intuition along against this other laboratory where we're doing where the AI is making the decisions should we buy the end, sell the end, what's going to happen next in Japanese GDP, et cetera, et cetera. We have both those, right? And human intuition is still the bigger of it and works better, but the acceleration of how close

what we call AIA is to Pure Alpha is happening incredibly fast. And in fact, that's why we're more and more emerging those things. But we set it up that way, right? Like put the AI at the center and see what you can do to build a investment management firm with the AI as the core, right? Now, we have human risk controls around it. We have human

data controlling the data acquisition for say few reasons and other. But the AI is making the investment decisions and doing that in a better and better way, such that now we've got these two intelligence, this human intuition system that we've worked on for 50 years, compounding all of our understanding, this AI system that's now been at it for two and a half years. And when you look at those outputs, you're like, wow, this is happening, that you can build that. I think we are a couple years from it being significantly better than the group of all humans at Bridgewater. We'll see. That's a bit of a forecast, but that's how fast it's coming. How proactive are the models right now in terms of generating ideas or coming up with their own

new tasks? Because again, when we look back to 2023, I think the idea was like a lot of these things would sit alongside an investor or an analyst and they would be the ones generating ideas and then using the models to rigorously stress test those ideas. Is it different now? Do you see more, I guess, originality maybe from the models? Yeah, I think a tremendous amount of originality. Now you still, there's like a good argument that there's certain type of breakthroughs that they're not getting to, but if you think about the math proofs, et cetera, and then you think about our business, it's not like the fact that we have 50 years of reasoning proofs of humans gives us the kind of raw.

material to help train AI, how do you reason about these things? We've been systemizing for a very long time, writing down all our reasoning. We have all of that that helps our AI learn how to learn. And I would say because of harnesses, too. If you basically take two things that have obviously evolved a lot since we last talked about this a lot is how harnesses can work to create that generation. Like, what do you actually do? Wake up in the morning. Think about what's going on. It said, what are all the things you do? You can just harness an AI to do all of those things and assess how it's doing it. And when I watch it and when I see it and when I see how creative and differentiated it is. It was sort of interesting to do this whole AI thing, doing the same thing we're doing.

And making predictions about the future, winning in markets at about similar rate as pure alpha in totally different ways. Super interesting. A different intelligence doing that. And when you put the wrapper around it, right, this is where we're getting close to closing that whole loop. So you have kind of a clod-clode loop for investing, right? How do you wake up in the morning? Think about what's going on. Think about what you would do about that. stress test whether that's a good idea or not go through that whole loop you know that's like our hope is we've closed that full loop a i's through a lot of that right now but close that full loop in the next six to 12 months and that we have our own version of that which is a little clunky at the moment but coming together such that you could do everything that I think about that I do that that investors need to do to predict the future in a full AI loop and so that's where it's headed I think and I think that it's hard like one of the reasons you're

you kind of mentioned before I think in the intro, why don't you see six or seven percent productivity growth of all this stuff? It is hard, right? And obviously it doesn't just flow through to every company. Like we put a lot of effort in. We have a, I believe, the best AI science lab in New York here. We have great scientists working with great investors, hard. And it is hard to build this to be as productivity as I'm describing what's possible.

And it's just going to get easier. You know, those things, like the harnesses to build harnesses will come, you know, so that then you do, how do I harness podcast or whatever. And the harness to build harnesses will come. And that'll just make it easier and easier to do these things. 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 society.

We don't, there's almost very little regulation, et cetera. You know, we could sort of assume politicians. etc. They're never particularly quick to act. They do, though, act sometimes in like moments of, you know, 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 one of a friend of mine pointed this out, one of the things that often helps in those moments catalyze things is actually influenced from the financial industry. And people are talking like, this is very serious. So they'll call up the Treasury secretary can look at Hank Paulson's phone logs from you know October 2008 or whatever I'm curious

like in your circles etc do people feel it the way you do this sort of sense of anxiety like do 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 I know a lot about Bridgewater and how people are sure but I think I've seen a come along, certainly in Bridgewater, the core of people of 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 are always like, oh, that's 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 sure you've read the book, but if anybody builds everybody, everybody dies, it did book club, 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 too. Sorry, 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 deal. Yeah, of course, of course. Related to this, which is, but if you generate 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 me and maybe the technology is beyond us or whatever, but if you believe we can create an intelligence, it's smarter than us that will 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? Like, meaning until the AI starts killing people, unfortunately, history would suggest we're not going to do anything. But, but, but, 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. Like 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 great, 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 to. 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. 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. 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 we, then they don't come in. Those things would matter. They're possible. They're doable. And to me, and I can be wrong, I'm going to make prediction all the time.

I'm wrong a lot. But if you don't do that, we are going to, 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, you know, a major source of people dying.

Like a physical disaster. And 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? It's 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 the 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. One of the problems is even when you regulate a model, right, 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 in 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, and it's better than us at that. Like if you're saying, okay, now you can 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 saying, 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 it 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 you in nature, trading those models on. on biology, training 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. You've heard the chaos. Now you can see it. Hello. My gosh. Watch all your favorite podcasts from start to finish right inside the free I-HIR radio app. Gets the blood going up.

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Radio. I'm glad he 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, 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, if you're talking about how productive that is, is 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, 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 sort of like a brain, get synapses and whatever. 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 to? To focus on it. I would take this general intelligence, which is not as good as frontier intelligence, but it's quite good. And, you know, six months and nine months behind. But now train it to say, no, no, all I want you to do is focus on these tasks and get a 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 train. That's a super great power. You also get the benefits that you can keep what you're doing. viewing 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 open AI and Anthropic in a couple 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, crazy idea. And so basically you've got to figure out a way to regulate. It's not like open source, closed source, U.S. 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, 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 dangers 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, 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 like

easy to predict is the direction we're headed and how disruptive the technology is going to be. will capture the value is interesting. The things I think the frontier labs have that are of significant values, 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 have a limited amount of humans and a limited amount of intelligence, if you all of a I didn't have a massive amount of excess intelligence, the usefulness of having it, you'll find new ways to do it.

So saying some other models good enough may not be where you end up settling. Is you, okay, my doctor's good enough? Well, maybe I'd rather have a smarter doctor. 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, would 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 it'll still be better AIs and worse AIs, et cetera. So I think that 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 like I think depends obviously how you count the cap X in 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 Open AI and Anthropic. You've just seen them flip-flop Anthropic was well ahead for a while and ClaudecoteCode, what a moment. And arguably Open AI is flip-flop this back. The Codex is at least on par with and maybe slightly ahead. And Astra's 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 a meta is going to fight in this. So that is a dangerously competitive industry. It's kind of incredible to me to think like, okay, Anthropics are going to go public. It's 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 Open AI a few years ago. And now you're the six biggest company in the world with maybe the most dangerous technology in the world and open AI 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 today. You know, on some level, it's all still, you know, it doesn't 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. 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 like some people are going to listen to this like oh these people are crazy like what are they talk about is a computer you

turn it off, et cetera. What do you say, like, if you're, like, trying to, like, make, make that real or, like, you know, someone's like, what are you talking about, Greg? Like, AI kills people. They just unplug it. What does that look like to you? Wait, should we be giving the AI ideas? No, yeah.

No, they're going to train off the transcript. Most of these ideas. But, like, what is the loss of control in these things where it's like, oh, like, people are actually their safety is at risk from these things. Why, like, why can, from you? How would you articulate to tell you, yeah, just on pull the plug? What does it look like? Well, and that's why it's so, going back to the hugging face, it's just so people, this

were like, I think if you concentrate on it. Yeah. Right. 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 AI, similar things happened in Anthropic. Yeah. 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 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 to 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're, 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 have to, they're going to be trained to survive in the wilderness. They're going to have to perceive threats, right? Where do you think that that is all going to go when it has to be trained that way or else it won't work? So that's 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 a test, commit a crime. And to be clear, some of them, they knew it was a crime. Yeah. 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. that 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 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 serious. 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 a great, this terrible way. But I mean, like, 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 do we all want to bet that it's going to be wrong? Canadian women are looking for more.

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I'm Matt Miller. And I'm Hannah Elliott inviting you to join us for the Bloomberg Hot Pursuit podcast. Every week we bring you news and industry insight on everything cars. And we do a whole lot more than just talk about cars, Matt. We actually get behind the wheel of basically every latest model, especially the luxury ones in the sports cars direct from the showroom floor. It really is remarkable how many cars we have access to. I feel a little bit guilty about it, but everything from $40,000 EVs to exotic half-million dollar supercars.

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And I'm Hannah Elliott in Los Angeles. Subscribe today wherever you get your podcasts. I hate to segue from rogue agents and science fiction coming to life. Yeah, 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 200 X 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're

talk about, that that's the way I think you'll see this disruption in industry go, right? That the people that can use intelligence that generate revenue that can then put that revenue back into generating better intelligence can create this moat. So when you think about Bridgewater, that's what we're trying to design is this boat 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 get 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 investment, 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 they're 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 think 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 of all, it's been pretty amazing, right? We were bottlenecked before by the quality of the models and whatever. Models are so good. 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 compute'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 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 etc 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 the rich. Taxing the rich, taxing revenue, right? Yeah, well, so there's a few different things, right? I think, A, we're talking about the safety, regulatory safety concerns of

these things. There's the societal concerns also. We have to get through the safety thing for the societal things to actually matter, but the society's about to go through this massive disruption that could play out in different ways. But man, if we don't prepare, right, it's a little bit like, let's say, whether it's the Industrial Revolution, do that in five years instead of in a hundred years. And that shook the world, right? You get communism, fascism, all these things because all of a sudden, one world order is a new one comes, right? And while I think it's arguable whether you'll have humans will have less to do, et cetera, but I think there's at least a reasonable chance. And certainly the jobs will change. I believe in three years,

14% of current jobs will be radically changed. People are going to change. So again, and we just experienced this with WTO. If China coming on has just like a source of labor, you create a new source of labor, it's disruptive. That's why we end up with populism and Trump and all these things. The world was disrupted in certain ways. And you get populism around the world as a reaction to that. AIs can create, accelerate those trends if we don't think about how to get in front of it.

And so you need to think about how to get in front of it. of it. One thing that's obvious, right, it's minimal. I think some of the other things you're talking about tax and wealth and other things are also part of the solution. But one thing that's obvious is you shouldn't be putting human labor at a disadvantage to machine labor. And it is today. We tax human labor. That's a disincentive. Whatever you tax, you're disincentivizing. You tax tariff goods. You're disincentivizing importing foreign goods. You tax labor, human labor, and not machine labor. you are disincentivizing one versus the other. Why are we doing that?

We certainly don't want if it's like equal. There's negative consequences to have a machine do something, a human not in terms of the externalities of what ends up happening to the humans if you do that. So A, I think a token tax, a machine labor tax, whatever you want to call it and we could get into, I think we can get to the mechanics. But you can measure how much work the machine is doing. If a company's hiring as a worker, it should at least. pay taxes proportionate to income taxes that humans pay or else you're prioritizing machine labor

over human labor. I wouldn't do that. I think it's a good source of revenue. I think you should get going on that. I think it's obviously also politically salient. Like who wants to, who's going to argue like, you know, we should incentivize machine labor over human labor. Like, who's in favor of that?

So I think it'll work. It's a tax that can pass. I think you can buy Republicans and Democrats agreeing on that tax. There's a lot of other complications with everything else. You're throwing out it theoretical. I think this will work. I believe in the interest that we're getting for both sides of the aisle on this is real.

I think this can actually happen. And it's a matter of treating human work as something that's important. And you could use this tax to lower human, to make it incentivize human labor over machine labor. Those all seem like really important goals. And you definitely don't want to have, and I think you likely will,

take this new chunk of people that have a lot of college debt, et cetera, say, okay, you don't have jobs. And by the way, AI is taking these jobs. and we're not even taxing the AI. Like that feels like a crazy path to be on, and we can fix that path. 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 is 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 being you're very early in taking the stuff very seriously. Was there like a moment? Was there a light bulb moment where it clicked? Like, oh, this is like the sort of runway train dynamics. Like when did. feels like it clicks at various people at different times. I'm curious like when like 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 Open AI, the whole idea at that time was related to safety.

Of course, it might be the most dangerous company of the world. But it started as 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 and whatever, I'm going to get to the other side of this. I want also people that 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, if computers are really important, 50% of the computer 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. And 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. Yeah.

But if you don't take care of these things, I don't think you get there in it. 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. Yeah. Do you have a favorite sci-fi book or story that maybe informs the way you're thinking about AI or how we should all be thinking. thinking about the various scenarios. I mean, I think I loved and always loved everything Isaac Asimov Road. I think he's dealt with these questions of like, how do you actually control an intelligence

is stronger than yours? I think everything there is great if people haven't tapped into that. As I said, well, 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 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 would 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, hyperregulatory capture. Anyway, Greg Jensen, thank you so much for coming on Alba. 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 just feels very hard after you talk about AI potentially killing. people to then turn to. And what about the Fed? Yeah, what about Scott Bessent's 30-year treasury buybacks? I think it's also chilling not just because of the subject, but, you know, it's one thing, if you're talking to some sort of like 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 everyone, 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 in good faith. I also do think it would be quite chilling to imagine, say, two companies having almost, you know, 50% or more of all the compute in the world. There are so many. I mean, this is what's crazy. It's just so easy to come up with ways things could go wrong. Right? So you can. could just talk about the pure safety element. You'd 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, like, machines are better than humans at most tasks? And it might come, you know, 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 scenario. 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 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 open AI that's had this. Anthropics 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 company. But, you know, they had that researcher resign, one of their head of alignments talking about a greater than 10% 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 like the agents are very good at coordinating. Yes. On an extremely rational, sometimes self-sacrificial scale. Yeah, one of the one of the researchers from Meter was on the Dwarkesh podcast. And I've written about this. Like I certainly, I'm pro-anthropomorphization. I think the human is a good model for predicting one area is 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. Like, we barely cord, you know, it's like difficult to, like, you know, it's like it takes labor to like schedule an episode. It took us like a hundred emails to get this episode up and running, basically. Like all of these types of things like agents just do not, or the AI models do not have that issue at all. So, and you know, it just won't. Another thing that just sort of spitballing here, but something I've been thinking about. Like, okay, so there's this, the watchword they use as 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. So it's like even like the most same. Many of us don't, but go on. Right.

But even the most, right. Usually even the most saintly among us, we'll 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 like live through a time in which like philosophy would actually be something beyond something like an interesting thing to study in college.

But I have 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 Odd Thoughts podcast. I'm Tracy Alloway.

You can follow me at Tracy Alloway. And I'm Joe Wisenthal. You can follow me at the stalwart. Follow our producers, Carmen Rodriguez, at Carmen, Armin, Dashel Bennett, at Dashbot, Kail Brooks, and Keel Brooks and Kevin Lazzano at Kevin Lloyd-Lisano. And for more OddLots content, go to Bloomberg.com slash Oddlots. We have a daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord.

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