Jim Rickards: Will AI Destroy the World?

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1:36money gpt i mean we've heard about all the great things ai is going to do and in general i am extremely optimistic about ai and its future as you probably tell from the many podcasts i've done on it but our next guest jim records might have a more skeptical view he just wrote the book money GPT. And we, a few weeks ago, you might remember Jim, a few weeks ago, we talked an amazing discussion about the economy, everything that's going on in the economy, going from the history on to what's going to happen over the next four years. And now Jim is back to talk about what should we be careful of in terms of AI and the economy?

2:16A very interesting discussion. So here's Jim Rickards, the author of the new book, Money GPT.

2:26This isn't your average business podcast, and he's not your average host. This is the James Altucher Show.

2:42I do want to circle back to AI, which is the original reason we wanted to talk. But I feel like when you were writing MoneyGPT, you started off writing something else. You started off writing about money and the risks in the system and it seemed like what occurred to you. I'm just sort of imagining as I'm reading this how it's written and how it's structured. It seemed like AI started to really explode upwards and become a factor in every single industry. And the risks of AI, you sort of ran with that and how it could happen in the financial system. But it seemed like you started off writing almost like about the financial system first, and then, oh, hey, this AI stuff is going to be dangerous.

3:27And then the book kind of transformed. Well, that's true. There are five chapters, an introduction, conclusion, five chapters. I did want to stay in my lane, and let me be clear. This book does not bash AI. AI is powerful. It's huge. It's here. It's not a book. Oh, gee, guess what's coming? It's here. You open your refrigerator. There's a light that says, please change the water filter. Mine doesn't say please. It just says change the water filter. That's AI. I mean, there's a chip in there somewhere that's calculating moisture and has a clock on it. It's telling me what to do. It's on the dashboard of your car.

4:03It's on your iPhone or Android, whatever. It's everywhere and it's here. So start there. Number two, it can do enormous amounts of good. And I'll give you an example. There've already been some diseases where they've come up with treatments. as a result of AI with large language models and deep-layered algorithms finding molecular combinations that can treat certain diseases. Now, can a human do that? Yeah, but if you had a room, an auditorium full of geniuses working 24 hours a day, they would not be able to do it as quickly as a supercomputer with NVIDIA chips and the right algorithms and a large enough training set.

4:43So there's good stuff happening all over. And I just gave you one example. And I get it. I built AI models for the CIA. We built predictive analytics that would predict terrorist attacks based on capital markets information. They didn't say I didn't need me to jump out of a helicopter with a knife between my teeth. I was doing counterterrorism in terms of financial warfare. So, yeah, those models are powerful. But I'm not a doctor, and that wasn't my area of expertise. So you're right, James. I stayed in finance and national security because that's where my background, my training, my experience is, and I was kind of staying in my lane.

5:27I thought I had the most to offer there. But I do point out, you know, AI as a distinct scientific discipline has been around since the 1950s. going back to Alan Turing who actually, he's famous because of cracking the code on the Ultra machine the Enigma code rather but he was a pioneer in AI and wrote about it in the early 1950s but it goes back to Aristotle the father of deductive logic and logic generally, Plato might have been the father of philosophy but Aristotle was the father of logic and computer code is just logic I mean, it's encoded in certain languages. It's digitized. It's electronic.

6:09But it's still a logical process laid out by Aristotle. A lot of antecedents. My favorite, Mary Shelley's Frankenstein. Frankenstein's creature was AI. It was constructed artificially, and it learned French and read Shakespeare. And people always think of Boris Karloff. But the Frankenstein in the book is, or the creature in the book, actually, you got a pretty good self-education. Um, so yeah, there, there's all that, but, uh, so what's new, uh, what's new is that the processing power because of supercomputers, uh, and because of chips and particularly NVIDIA, but yeah, AMD and Intel, I mean, you know more about this than I do have some very powerful chips.

6:52Uh, so the processing power is greater. The computing power is greater and faster. Uh, the large language models, people have been working on those for a while, but what's new is the training set. The, the amount of material that you, where you can turn the, uh, the large language model loose on a training set and it can make associations. And this was what GPT was all about. GPT four, of course, late 2022 open AI. This was, you know, the breakthrough, uh, the, the headline news, you know, that thing got, I believe 200 million downloads in about 90 days. Fastest take up of any app in history, faster than Instagram or Tik TOK.

7:32or any of the others, again, from this company, OpenAI. So that's the revolution, and it does have the power I described. So what does that mean for – Do you think – just out of curiosity, and even though this is not really the top of your book, it seems like vision – like speech recognition had sort of been quote-unquote solved in the 90s. And then just as processors got faster, speech recognition got better. computer vision was sort of solved in the early 00s and by solved i mean like good enough to recognize the stop sign and then again as as processors got faster it got better right but but language really seemed to require it was sort of the first leap in the software that i had seen in like 30 years with the the you know the the adversarial the generative uh adversarial networks that were, that, that was happening.

8:27So that seemed that the data and the speed all seemed to converge at the same time. You know, language was like a harder problem than vision, which was interesting. Right. Well, you're exactly right. But then from that, you know, it's a good, good way to start the conversation from there. You have to say, okay, well, how does it actually work when you turn a large language model loose with the processing power we described in a training set? And you can buy the internet. I mean, you can buy the whole internet. It's billions of pages. And most users, they'll do a subset, maybe only 250 terabytes or whatever, because you have a specialized function or application.

9:06But you can pretty much buy the internet. So the training set is enormous. But I guess the first point I would make is it's not intelligent. There's nothing about artificial intelligence that's intelligent. It's not a brain. It's not a simulation of the human brain. The human brain works completely differently. um that so it's math it's just math now it's it's advanced it's it's uh um new branches mathematics applied mathematics faster process all the things we talked about but it's not actually intelligent it just creates association so it goes into a cloud and it it breaks words into clouds where they appear in some proximity to each other they don't call them words they call them tokens there's token a close to token b you know 95 of the time well maybe if i okay so if i use A, I better use B in the same sentence.

9:55So like extra innings and a baseball game. So that's how it works. And I did a, not a contest, but it was an example. I mentioned this in the book. Foreign Policy published two essays. They asked this bright high school student, high school senior, to write an essay, about 900 words on the causes of the war in Ukraine. And they went to GPT, GPT-4, and they gave it the same prompt, causes the war in Ukraine. And they produced two essays, about the same length. But they were published anonymously. And you were supposed to read them and see if you could pick the GPT-generated, the robot version, basically.

10:39I picked it in one sentence. I got one sentence. I said, that's the robot. I read the rest of it. I read the student's essay, et cetera. And I was right. But the way I was able to pick it was the overuse starting with the first sentence of cliches. Now, I don't like cliches. I use them. Every writer does. Every now and then it's helpful. It's a bit of a crutch, but sometimes they can make a point. But this thing was laden with cliches. And one after the other. I said only a robot would do that because a robot going through billions of pages, as we described, would encounter these cliches. And it would say to itself, huh, that must be how people write.

11:20So let's use that. But it was the overuse. It was precisely the overuse of something that a computer could spot, but a good writer would never do that said, that's the robot. And I was right. Which is really interesting. Let me just ask you briefly about this. So music, AI has been able to come up with sonatas that experts can't tell is that a Mozart sonata or not like they they fool the experts of Mozart sonatas hey this could have been composed by Mozart right I'm wondering why why is an AI as sophisticated with writing as it is for music but it makes sense in exactly what you just said music cliches are okay because that's a like a like an art form might be the sonata form, which is basically a cliche of like a certain type of music over and over and over again.

12:10And so you could, you know, if you fit inside a form, it's almost mathematical. You could say, okay, that's, it's a cliche. A sonata is a cliche, but that's okay because people are artistic within that cliche. Writing is, is different. You're, you know, cliches are punished correctly, I think, but, but it's not the same thing. I think that's exactly right. And you're right at that music has forms. I mean, I guess you can go kind of off the rail, who knows if it's music, but yeah, music has forms. You're exactly right. But every expert on music would say it's behind it is math. It's mathematically driven.

12:45It's scales, it's relationships, it's progressions, it's modulating bridges. It's, you know, various kinds of non-imitated polyphony. there's a lot to music, but it kind of can all be, and I love it and I'm a fan and I get 6 ,000 songs on my iPhone, but, but it is math. So the combination of the fact that there's mathematical scaling relationships behind it, and it is form driven, lends itself brilliantly to computer programming and computer development. And you write writing, who knows, you know, you, you can create formulas, but they're easy to spot, I guess is my point. take a quick break if you like this episode i'd really really appreciate it would mean so much to me please share it with your friends and subscribe to the podcast email me at alcatra gmail.com and tell me why you subscribed thanks

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15:47I don't see, in all these new versions of AI, I don't really see the writing getting better. Like I could always tell this feels weak as writing as opposed to like a real good writer. Well, you're right. By the way, the latest research says not only are you right, but not only is the writing not getting better, it's getting worse. And here's why. Because as more material is generated by AI and by GPT, as that begins to populate the training set. Now I come in with a new program and I'm looking at the training set. It's kind of polluted with a bunch of GPT. And the point is it's diluting itself.

16:24And so we're going to get a Kamala Harris campaign speech. I mean, we're basically taking away the creativity, taking away the originality, not only using cliches, which is a bad start, but diluting itself because it's going to be in a feedback loop. It's going to be self-referential. And that's already turning up. I've read some papers. There's scientists who have been able to quantify that and point to that as a problem. But that's to take everything we just said, and that's what's going on, and relate that to capital markets. And what's the danger? Well, a couple of things. Number one, there are some hedge funds, I've talked to some of the managers, who are using AI to pick their stocks.

17:03It's not like, oh, we're using it as a research tool. We want it to read a million, you know, 10Ks or footnotes and financials. to say, no, do all that. Yes, please. But pick the stocks. And they're just, they're robots, but they're having some success. It's actually, they've been outperforming some managers lately. Now you and I both know the problem with that, which is if you have that kind of success, it's really an arbitrage. More people jump in, they'll jump in, everyone will do the same thing. The returns will get reduced and all that. That's all fairly predictable. But what should not be overlooked is that the programs are all the same.

17:37I don't care who's developing them. I don't care. There's certainly differences, but they all work about the same. And the reason is when you're working in AI, you're trying to imitate the human brain. It's not a brain, but it's a mathematical imitation, if you want to put it that way. There's certain aspects of human nature that won't change. So a simple example. So let's say a stock market crash begins it doesn't matter why they they happen all the time there there's always a reason but that's not the point so march 2020 the stock market fell 30 in 30 days it was not the biggest crash in history but it was the fastest crash of that magnitude in history to go 30 30 days was light speed um and what happens in a situation like that well some people are kind of watching it's going down it's going down uh it'll go back whatever it doesn't and all of a sudden they're like they hit the panic button, they sell everything, go to cash, move to the sidelines and wait it out.

18:39And they wait it out. And then when things seem to stabilize, find the bottom, turn around, okay, tiptoe back in and build up your portfolio again. This lends itself to something called the fallacy of composition, which sounds fancy, but it's a pretty straightforward phenomenon. So you're at a baseball game. And let's say you can't see because the guy in front of you. He's got a big hat or he's too tall, whatever. And so you stand up and say, well, this is great. Now I can see the game. I get a really good view. But what happens next? The person behind you stands up and the person behind her stands up.

19:18And sooner than later, the entire stadium is on their feet. Nobody's better off because you're all standing on your feet. Sorry, nobody's better off because you all have the same lousy view and everybody's worse off because you're all on your feet. So there's an example of a strategy that works brilliantly at the individual level. The guy stands up, he does get a better view and it's cost-free, but it's catastrophic at scale. It doesn't continue. It basically reverses itself and ends up being enormously costly or dangerous. The same thing is true in stock markets. So the strategy I described, sell everything, go to cash, move to the sidelines, wait it out, tiptoe back in, that can be a very good strategy for an individual.

20:00But what happens if everyone does it? Well, we know what happens. Everybody's a seller. Nobody's a buyer. The market goes straight down. You blow through the circuit breakers. You go through the floor. And eventually, they have to close the markets. That's what happens. That is what we came very close to closing every market in the world with long-term capital management, by the way. People don't know that, but we were just hours away. We were trying to close the deal before Tokyo opened. um so that so that is what happens and uh why is that because all the programs are the same now in the old days you know maybe not not much later than 90s but certainly 80s or earlier there was a guy on the floor of the new york stock exchange there was a position you were called a specialist and the specialist had a privilege and a duty the privilege was you got to be the market maker in a particular stock.

20:50You were the market maker in IBM or ExxonMobil or whatever it might be. And you also got to see the back of the book. You didn't just see the best bid offer. You saw all the bids and offers behind it. You knew kind of where things were going. Your duty, however, was to stand up to the market. So if everybody was a seller, you had to be a buyer. You had to mitigate that damage a little bit and vice versa. If there was a buying frenzy, you wanted to be a seller and just kind of damp it down a little bit. Try to equilibrate up to a point. That system is long gone. A friend of mine is the director of floor operations in the New York Stock Exchange.

21:24I was down on the floor with him not long ago. And he turned to me and said, Jim, don't think for a minute that there's any liquidity on this floor. There isn't. I mean, we've got nice jackets. We do our jobs. We still make markets, but there's no liquidity here. And he was right. And of course, I understood that. So, and I talk in one of my other books, I talk about the rise of passive investing, index investing, ETFs, and the decline of active investing. It's another phenomenon, but that's highly prevalent today. So now let's take this toxic mix. You have almost everyone's a passive investor.

22:01All the big portfolios are relatively few active investors left. Everything's automated and all the automation is the same. There's no diversity in any of these programs. I just want to add a little bit to that because what you underline is the massive scam of the financial markets. Every big hedge fund out there owns Microsoft, Google, Exxon, Procter & Gamble. They all own the same things, but instead of an ETF, they're charging you 2 % of all the assets and 20 % of all the profits. Like it's a giant scam and they all own the same thing. You're exactly right. And the smaller version of what you described, you correctly described the giant scam, you know, upper middle class individual, retired, near retirement couple, whatever, they walk into a financial advisor and they sit down.

22:47It's a pleasant office. The person behind the desk appears very professional. And what does that consultation look like? Well, the guy said, well, OK, let's get started. How old are you? OK, got it. Married, you know, kids, yes, no, net worth. Give me your portfolio specifications. Thank you very much. What are your goals? Do you want to own a vineyard, sell around the world, pay for your grandkids, whatever? They ask you all these questions, and they put it into a system. All the systems are the same. They all give the same answer. They all give you the 60-40, but we'll turn it to 80-20 when you get a little bit older.

23:21Here's your ladder of municipal securities. It's all the same. And I'm not saying it's horrible. I'm just saying don't think for a minute that you're getting customized individual investment advice. I tell people getting in the business learn to play golf and give an attractive image because you don't need to know anything about finance it's all on a computer and they're all the same and that's the point there's nothing in any of the programs that says maybe this is a good time to buy maybe I ought to dip my toe maybe I ought to buy the dip the kinds of things that humans do don't exist in programs in these programs And they may be non-programmable, and I'll get to that in a second.

24:05But the point is, the idea of panic is as old as human nature. I could give you examples. I'm sure you're familiar with financial panics in the mid-14th century, 1350s. The House of Bardi and the House of Prusci in Milan went bankrupt. There was a financial panic all the way to London. So that's not new. What is new is the fact that we've combined it with AI. it's amplified it, accelerated it. We've outsourced it and we don't understand it. We over-rely on it. This will happen very, very quickly.

24:43So that's the danger. But let me ask you this, and I'm thinking of the examples in your book and you can explain to me how 1987 was a weird, I don't want to say it was AI, but the same thing was sort of happening where the kind of insurance products that were being sold on the stock market triggered ultimately the 1987 collapse in October of 1987. How is it really different now? Now we're just using a more sophisticated version of that, but how is it really different now, the way that software could trigger a panic? Because at least, you know, the Brady Commission did it after action report. They figured out what we just talked about and they made recommendations.

25:28By the way, a friend of mine was the head of that. I mentioned David Mullins. He was a good friend of Nick Brady, and he actually did that report. But that's when they introduced circuit breakers. They didn't have circuit breakers prior to 1987. And the circuit breaker is – it was viewed as a solution to what happened on October 1987, but it's a blunt instrument. And in the book, I talk about – I never like to give these warnings or dangers. and I have this scenario in the book, without giving some solutions. And one of the solutions I suggest is cybernetics. And cybernetics is a Greek word, but the origin actually means the helmsman, who's steering the boat.

26:10And just to give an example, James, so let's say you're driving on ice, and I have a house in the mountains, so I drive on ice and snow my share of the time. If you slam on the brakes on ice, the car doesn't stop. It keeps going and will probably spin out of control and maybe run off the road. If you're on ice and you need to slow the car down, you don't slam the brakes, you tap the brakes. Tap, tap, tap, tap. Keep two hands on the wheel. Keep it under control and eventually bring it to a halt. That's an example of cybernetics. Now, how would you apply that to the stock market? So let's say stocks are down 5%.