We Study Billionaires - The Investor’s Podcast Network - TIP191: Jim Rickards (Part 2) Artificial Intelligence, Bitcoin, and Credit Cycles (Business Podcast)

Starting point is 00:00:00 You're listening to TIP. Hey, how's everyone doing out there? On today's show, we continue our conversation with New York Times bestselling author, Jim Rickards. If you missed our first part conversation, I highly recommend that you go back and listen to our first 45 minutes of that discussion. This episode picks up where Jim left off in the previous week, and he was describing his concerns with Bitcoin and how it fits into the modern credit-based financial system.

Starting point is 00:00:25 Additionally, Jim talks to us about a new venture he's starting that involves artificial intelligence. So without further delay, here's our second part interview with Jim Rickards. You are listening to The Investors Podcast, where we study the financial markets and read the books that influence self-made billionaires the most. We keep you informed and prepared for the unexpected. So let's just say that we fast forward a year, two years in advance, and let's say Bitcoin has another big resurgence and another big price movement. And at this point, let's say the U.S. government or any other major economy says, this is enough. We're going to shut this down. I personally feel that you've had so much entrenchment into the finance industry at that point.

Starting point is 00:01:20 I mean, we now have CME with derivatives wrapped around this. We're potentially going to have ETF vehicles now that the derivatives are in place that are then going to be wrapped around this. We're going to have options that are then wrapped around the ETF vehicles and everything else. So don't you kind of feel like the longer that the government doesn't make a decision on this, the harder it's going to be for them to actually shut it down? What's your thoughts on that? I think the decision's already been made. I mean, it's an interesting hypothetical, but the decision has already been made. And the fact that Bitcoin's way off the top, it's not just the price has dropped 70%, which it has, but the volumes have dropped.

Starting point is 00:01:55 So you're not seeing the transaction volume. A lot of exchanges have been shut down, more under scrutiny. But the point being a lot of people getting a very rude awakening about reporting their crypto profits. Some people are going to roll the dice and not report them. They have a good chance of landing in jail. Those who do report them are like, wait a second. I got my Bitcoin. I bought a thousand.

Starting point is 00:02:17 I sold it at 12,000. I swapped into ripple. I still have crypto, but you're trying to tell you. I owe 10,000 a coin. And a thousand coins is $10 million. I owe $4 million in tax when all I have is ripple. I mean, that's what's happening. You've got to pay the tax in dollars, even if you rolled into ripple or ether.

Starting point is 00:02:35 So people are just finding out the hard way that the rules apply to them. As far as the derivatives are concerned, I remember when late last summer, early fall, 2017, when the Chicago Board Options Exchange and NASDAQ were announcing all these derivatives and all the Bitcoin groupies were jumping out of the seas. Yay, look at us. We're real. We're respectable. These big established institutions are launching derivatives on our currencies.

Starting point is 00:02:58 that validates the use case in our currencies. I said, man, you don't know anything about derivatives. They're going to crush you. And if you look at when the Bitcoin future was launched and when the market broke, same day. In other words, one of the reasons it went up was because there was no way to short it. The many you could create short interest is like, hey, bring it on. So I just said, be careful what you wish for. And those derivatives have proved the undoing.

Starting point is 00:03:24 The Bitcoin might have gone to $40,000 if you had never invented a way to shorter. but they did and it went straight down after that. As far as the Chicago boys are concerned, they'll trade anything for a buck. I mean, they don't care. They're not Bitcoin supporters. They're supporters of commissions. You know, soybeans, lumber, pork bellies, Bitcoin,

Starting point is 00:03:41 it's all the same. If they can make a spread, if there's any vegan, they'll trade it. So the combination of fact that they did for reasons of their own, that had nothing to do with supporting the Bitcoin community. And B, the many of you could short it, it felt like a rock, popped off a high building. that tells you all you need to know about derivatives. So, Jim, I have to ask you, what's the bull case?

Starting point is 00:04:02 Like, I hear, like, all the bare arguments, and I think you have a lot of good arguments and they're well-research. I'm very, very curious now about why can you be wrong or why would Bitcoin be the currency or stores of value of the future? Well, you know, might outperform coal like the pet rock. I mean, I don't have a good bull case for Bitcoin. See, if you say what's the bull case, my question is, what's the use? case. Give me a use case that I can evaluate for better or worse. Now, I gave you a use case for

Starting point is 00:04:33 Lumen, which is it's a cheap, efficient micro-payments network in a bunch of countries that are four steps removed from the heart of the financial system. Some of these smart contracts have a use case, but Bitcoin doesn't. So the only use case I see is for criminals, terrorists, and tax evaders. But even there, you'd rather be in Monero or Spector, one of the other coins that has a little more stealthiness to it. By the way, I'm not counseling people to do any of those bad things, just to be clear. But if you happen to be a criminal, you're more likely to be in Spector or Monero than you are on Bitcoin. So I see no use case at all. I think, you know, it should win a place in the Smithsonian as the thing that increased consciousness and awareness of cryptocurrencies and

Starting point is 00:05:17 made an impact. It was innovative. By the way, Nakamoto made another mistake. See, look, I don't want to be too critical. The guy was a team. or woman or whoever we don't know but whoever it was could have been a team from the nsa building back doors with another shooter drop but who knows right but they didn't know that much about monetary economics there's no reason why a really smart developer really smart coder engineer should be an expert on monetary economics but if you are an expert on monetary economics you know that capping the number of coins is fatal that will prevent it from being widely used as a currency and here's why The criticism was the Fed printed $4 trillion QE.

Starting point is 00:05:59 I mean, Nakamoto left his clues. It was code about bank bailouts and he or she or she or it clearly had anonymous towards a lot of the bailouts and the money printing in QE and things that were done in 2008. Okay, that's a fair criticism. I share that criticism. But the solution was we're not going to be like a central bank. We're not going to have all this quantitative easing. We're going to cap the number of Bitcoin.

Starting point is 00:06:20 I believe the number is $21 million. And we're getting closer to that number. Now, we'll never get there, by the way, because of the exponential increase in the energy usage to make the next coin. But it'll be some natural level where it caps out. That's not good because it has an inherent inflationary bias. One of the attractions of gold, by the way, is that gold output expands at roughly the rate of the global economy. Not exactly. The global economy grows 2.9, 3% a year.

Starting point is 00:06:45 Gold output, mining output as a percentage of total stock above ground is about 1.6% a year. so it's not perfect, nothing is, but it sinks up pretty well. See, the money supply is to be elastic. If the economy is growing and the money supply is not growing, you have a deflationary bias. The money becomes worth more because if you have a fixed amount of money in a growing economy, then each unit of money buys more of that growing economy, which is deflationary.

Starting point is 00:07:14 The money is more valuable. Now you don't want the opposite. When you have too much money, now you don't have deflation, you have inflation and the money is less valuable. Deflation is bad. Inflation is bad. What you want is elasticity and money roughly in sync with the capacity of the real economy to grow.

Starting point is 00:07:33 That's what Milton Friedman advocated. That was part of the beauty of the gold standard, etc. Now, what happens when you have a deflationary money, which is what Bitcoin is? Because it can't grow beyond a certain point. Well, you don't have a credit market. Who in their right mind would borrow in money that is worth more when you have to pay back the debt. That means your debt is going up over and above interest. So I borrow in a certain amount of Bitcoin, but when I pay you back, the Bitcoin is worth a lot more. Well, that stinks for me because

Starting point is 00:08:00 my loan went up, doubled or tripled or whatever. So you'll never borrow in a deflationary currency, which means there's never a credit market. If you don't have a credit market, you can't have an economy. And it's what drives, you know, base money is important, M0 is important, M1's important, Absolutely. But the economy is driven on credit. Credit is a high multiple of the base money. It's credit in the form of bank credit and lending and increasing deposits. So a deflationary currency, which is what Bitcoin is doomed to fail because it's not elastic over and above all the other reasons I mentioned. I know you have a lot of contacts and the central bank, not just in the US, but all over the world. What do they say about Bitcoin specifically or about Crypto?

Starting point is 00:08:46 occurrences. Is that something that they are concerned about in any way? Well, it's what I described. And the closed door is becoming more of an open door because they have to act. It goes to your point. Preston, if they wait too long, you know, this cat's out of the bag, so to speak. There are some real Bitcoin millionaires out there, and there are some Bitcoin billionaires out there. And the math is simple. If you bought your Bitcoin for five bucks and you bought, you know, 100,000 coins for half a million bucks and it went to 20,000, you sold out. I mean, you're a billionaire. So I don't dispute that those cases exist,

Starting point is 00:09:19 but the point I make is that how did you make the money? You took it from suckers who were paying you $20,000 a coin. South Korean auto mechanics who hawk their inventory, guys in West Virginia who took out a home equity loan and bet the ranch at crazy prices because they thought this was easy money or a way to retire early. That's how you made your money. Is that the economic model you want?

Starting point is 00:09:43 There's no value created. There was no wealth created. There was no ingenuity. There was no Bill Gates. There was no Warren Buffett. It was just a wealth transfer from early adopters to suckers. Let's take a quick break and hear from today's sponsors. All right.

Starting point is 00:09:56 I want you guys to imagine spending three days in Oslo at the height of the summer. You got long days of daylight, incredible food, floating saunas on the Oslo Fjord. And every conversation you have is with people who are actually shaping the future. That's what the Oslo Freedom Forum is. From June 1st through the 3rd, 2026, the Oslo Freedom Forum is entering its 18th year, bringing together activists, technologists, journalists, investors, and builders from all over the world, many of them operating on the front lines of history. This is where you hear firsthand stories from people using Bitcoin to survive currency collapse, using AI to expose human rights abuses, and building technology under censorship and authoritarian pressures. These aren't abstract ideas. These are tools real people.

Starting point is 00:10:43 are using right now. You'll be in the room with about 2,000 extraordinary individuals, dissidents, founders, philanthropists, policymakers, the kind of people you don't just listen to, but end up having dinner with. Over three days, you'll experience powerful mainstage talks, hands-on workshops on freedom tech, and financial sovereignty, immersive art installations, and conversations that continue long after the sessions end. And it's all happening in Oslo in June. If this sounds like your kind of room, well, you're in luck because you can attend in person. Standard and patron passes are available at Osloof Freedom Forum.com with patron passes offering deep access, private events, and small group time with the speakers. The Oslo

Starting point is 00:11:27 Freedom Forum isn't just a conference. It's a place where ideas meet reality and where the future is being built by people living it. If you run a business, you've probably had the same thought lately. How do we make AI useful in the real world? Because the upside is huge, but guessing your way into it is a risky move. With NetSuite by Oracle, you can put AI to work today. NetSuite is the number one AI cloud ERP, trusted by over 43,000 businesses. It pulls your financials, inventory, commerce, HR, and CRM into one unified system. And that connected data is what makes your AI smarter. It can automate routine work, surface actionable, and insights and help you cut costs while making fast AI-powered decisions with confidence. And now with

Starting point is 00:12:13 the NetSuite AI connector, you can use the AI of your choice to connect directly to your real business data. This isn't some add-on, it's AI built into the system that runs your business. And whether your company does millions or even hundreds of millions, NetSuite helps you stay ahead. If your revenues are at least in the seven figures, get their free business guide, demystifying AI at net suite.com slash study. The guide is free to you at net suite.com slash study. NetSuite. When I started my own side business, it suddenly felt like I had to become 10 different people overnight wearing many different hats. Starting something from scratch can feel exciting, but also incredibly overwhelming and lonely. That's why having the right tools matters. For millions

Starting point is 00:13:02 of businesses, that tool is Shopify. Shopify is the commerce platform behind millions of businesses around the world and 10% of all e-commerce in the U.S. from brands just getting started to household names. It gives you everything you need in one place, from inventory to payments to analytics. So you're not juggling a bunch of different platforms. You can build a beautiful online store with hundreds of ready-to-use templates, and Shopify is packed with helpful AI tools that write product descriptions and even enhance your product photography. Plus, if you ever get stuck, they've got award-winning 24-7 customer support. Start your business today with the industry's best business partner, Shopify, and start hearing

Starting point is 00:13:44 sign up for your $1 per month trial today at Shopify.com slash WSB. Go to Shopify.com slash WSB. That's Shopify.com slash WSB. All right, back to the show. So, Jim, you've been working as a chief global strategist at Mirgleam Holdings, and you're working on a thing called Raven. Can you tell us a little bit more about what you're doing here? Sure, thank you.

Starting point is 00:14:15 I'd be glad to. This is a company I started with a partner, Kevin Massigil, about a year and a half ago. By the way, Kevin, like yourself, it's an army background. There's an Army Ranger, Mountain Division, and worked in Army Intelligence. We're off to a good start. And here's what we're doing. The company's Mirrenfell. Glenmar product is Raven and that was in honor of the Raven of Zurich which if you read my book

Starting point is 00:14:37 The Road to Ruin it was it started with the little bio in history of Felix Summary who was Austrian-born but lived most of his life in Switzerland banker who had an uncanny track record of predicting all the great events of the 20th century in advance so World War I coming sold all of his customer securities converted to gold moved the gold to Norway set out the war when war was over, everyone else was ruined, his clients were still rich. Saw the inflation of the 19s and 1923 period, saw the collapse of the gold exchange standard of the 1920s, 30s, and so forth throughout the century up until his death in mid-century. And he was called the Raven of Zorg because the Raven is associated with prophecy and prognostication and omens and so forth. So that was his nickname. So

Starting point is 00:15:26 we call our product Raven. And what we're doing is we are building a system for predictive analytics and capital markets. And you need to let that sink in a little bit because the capital markets are, you know, the biggest markets in the world. And we're going to tell you what's going to happen. That's what predictive analytics are using our technology. And a couple of things about that.

Starting point is 00:15:47 First of all, a lot of people will tell you that's impossible. They'll go, oh, that's interesting, Jim. Don't you know that that cannot be done? Well, the answer is it cannot be done using. the mainstream technologies and using the mainstream methodologies because those methodologies are badly flawed. It can be done using the right technology and the right branches of science. That's one of our innovations. We look all the time at competition. You know, great firm like Morgan Stanley, they spend $2 billion a year in R&D and the other big banks spend comparable amounts.

Starting point is 00:16:18 And you see Paul Jones, you know, Paul O Tudor Investments and Stevie Cohen at 0.72. these guys with practically unlimited resources. They're all, yeah, we're getting into this, you know, artificial intelligence doing this. And I look at that very closely because I'm like, man, if they're doing what we're doing, I'm not sure I want to be in this business because we're not spending $2 billion, but they're not. So we're very satisfied that we are unique in applying new branches of science, not science that we invented. Some of the science is a couple hundred years old, but the application of it to problems in capital markets.

Starting point is 00:16:50 And a lot of this in the case of myself and Kevin and Terry Rickard, who's our chief scientist and just absolutely brilliant, groundbreaking, applied mathematician. We all have a background in intelligence. Kevin worked in military intelligence. Terry did work for the Navy. I did a lot of work for the CIA. So we have that kind of mindset of how do you solve problems when you don't have all the data? I always say if you have all the data, a smart high school cake can solve the problem. How do you solve a problem?

Starting point is 00:17:18 How do you proceed? How do you make forecasts about, in my case, the next terrorist attack with very limited data? Well, there are ways to do it. One of the main ways to do it is something called Bay's Rule or Base theorem. So that's one of our inputs. The other one is complexity theory. We've talked a lot about that. It's amazing the application of the complexity theory all over the place.

Starting point is 00:17:38 And meteorology, seismology, volcanology, forest fire management, traffic management, so many areas where you see complex dynamic systems. and you can use the science to get better results, I'm dumbfounded that no one's applied it to capital markets, but we are. The other branch we're using is behavioral psychology. Now, this one has had a lot more take up on Wall Street, not as much as it should.

Starting point is 00:18:02 It's more in kind of government policy, you know, Cass Sunstein, Richard Thaler, Nudge, all this stuff. So you see it in the public policy realm. You know, it really goes back, I mean, Stanley Milgram in the 1950s, but prominently Daniel Kahneman, Amos Tversky, in the 1970s and 80s doing a very simple but ingenious experiments like they'll go to a group of

Starting point is 00:18:25 subjects and say I'm going to give you two choices I've got three dollars in my hand and I've got four dollars in the other hand you can have the three dollars with a hundred percent certain you can just come and take the three dollars you can have the four dollars with an 80 percent probabilities some risk you won't get it well Janet Yellen would do the math and say well $4 with an 80% probability. The expected value is $320, doing an inequality. 320 is greater than three. I'll take the four bucks.

Starting point is 00:18:51 Take my chances. When you do this experiment, overwhelmingly, people take the $3. So why do people take the lower expected return or what mathematicians would call the lower expected return? The answer is they don't like to lose. And that was the value of the possible loss, even at 20%, outweighs the smaller gain you're going to get by going with the short thing. So these are examples of what efficient market. market there is called irrationality, but they're actually rational if you put humans back in the ice age. And the risk is that, not that you won't get the four bucks, but that you'll be eaten by a saber two tiger.

Starting point is 00:19:25 And there are many, many, I just mentioned a couple confirmation bias, a recency bias, anchoring bias, etc. The last time I looked, there were 180 of these cognitive biases. We've taken a close look at that. And the fourth branch of science we use is history. And you won't find any economists using history. You'll find economic historians who are expert, but to say that we can look at the past and gain valuable lessons about what policymakers will do in the future, that's rare. So we're using base theorem, complexity theory, behavioral psychology, and history. So these are our applied sciences.

Starting point is 00:20:01 Now, how do we combine them? We combine them in a neural network, which, again, by themselves are not that unusual. We've all seen maps of neural networks, but you have nodes and edges and. Some are output nodes, some are input nodes, some are recursive functions where it's an output and then it becomes an input. Some of them are actionable. Some of them are exogenous. So, you know, what would a neural network look like for whether the Fed's going to raise interest rates in June? You know, you put in employment report, you put in disinflation, PC cord deflator and what's going on in the currency wars and who's the new Fed chairman. And you have all these nodes. So, okay, we said we got four branches of science. we process them through a neural network.

Starting point is 00:20:43 And who's our analyst? Our analyst is Watson. We've teamed with IBM. Watson can read 200 million Twitter feeds in real time with plain language comprehension. Or every page of every 10Q, 10K, you know, Fed speech. I'm a geek. I read a lot of them, but I can only read so many. But Watson can read them all.

Starting point is 00:21:04 And we're working with a team of a cognitive linguist in Finland who has some absolutely cutting edge. You know, the simple word association is, you know, I'm trying to find your website and I'm like, well, if I just put them pressed in a stick, I know I'll find it. Like Google's that smart, right? But okay, so that's word association. But there are more sophisticated, grammatical, syntactical ways of doing that. And Watson speaks about eight languages.

Starting point is 00:21:29 So we've got the four branches of science. We've got the way of combining them in a neural network. We can populate what's in the neural network with billions of people. pages of plain language comprehension by Watson. And then, of course, human oversight. Humans are important. They never go away. We tweak these things continually.

Starting point is 00:21:47 And what we're doing is we'll tell you where the euro is going to be in six months. Relative to the dollar. Now, this sounds funny, but believe it or not, it's actually easier to forecast six months than one day. I don't know what the euro is going to do tomorrow. It could go up or down. There's a lot of noise in the short, I could have a view. but my forecasting ability six months out is much, much greater, particularly with the tools we're talking about.

Starting point is 00:22:13 We'll tell you where the Chinese Yuan is going to be, where the euro is going to be, where the return your notes are going to be, et cetera. And again, with a three, six-month horizon using the method we described, much more accurate. 100%? Absolutely not. Nothing's 100%.

Starting point is 00:22:25 But in this game, if you can get to 70, 75, you are way ahead of the pack. Now, if you're a day trader, our system's not interesting to you. I mean, if I tell you the euro is going to be 1.30 by the end of the year and you bet the ranch on that and it goes down tomorrow, you just lost all your money. So it doesn't really work for day traders. Hedge funds have a tough time with this because they're on a mark to market basis. Again, you have to allow for the fact that you know, you could be right in the long run, but wrong in the short run and that could be closely.

Starting point is 00:22:54 But if you're an institution, if you're a sovereign wealth fund, if you're a college endowment, if you're an insurance company, if you're one of these very, very large portfolios where most of your money is actually managed by, third parties, that view that we offer you is very, very valuable because if we're telling you the euro is going to be higher at the end of the year and that is one of our outputs right now, and you look around to your managers and they're all short the euro, you better pick up the phone. You could lose a lot of money in the next six months. Forget about the daily market to market. So this is a tool that will be of the most value to the largest, the buy side institutions in the world, the largest money managers in the world.

Starting point is 00:23:30 We're not going to pick stocks. We're looking at the big macro tickers. So yielded maturity on 10-year-note, German-B, JGBB, cross rate, euro, US dollar, Euro, Swiss franc, yuan, yuan, U.S. dollar, yen, ruple, etc. Some of the big commodities, oil, gold, copper, a few others, and I'm sorry, central bank policy rates. So what's the Fed going to do and what's the ECB going to do? So that's our universe of tickers. I've described the science. No one else is doing this.

Starting point is 00:23:59 We're very far along. Couldn't be more excited. It's got a lot of a good investor uptake. Anyone's interested. They could just go to our website, miraglim.com, m-E-R-A-G-O-I-M-O-I-M-O-I-M-O-I-M to learn more. But this, by the way, this is a continuation of the work that my partners and I did at the CIA. This is Project Prophecy, third generation. So Project Prophecy was the predictive analytic engine that we built for the CIA to predict terrorist attacks

Starting point is 00:24:28 based on a strategic study that was done after 9-11. It worked so well that we were getting chastised by the general counsel because we kept finding insider trading that was not terrorist related. We were looking for terrorists, but we kept finding like normal crooks. We were telling the SEC, and they said, well, you can't do that because CIA is not a law enforcement agency. So we started our cash and release program. We let the sleaze balls go, but kept looking for terrorists. But the system worked very well. This is way beyond that.

Starting point is 00:24:57 This is third generation AI, again, combined with the police. power watts and so we're just having a lot of fun with it i'm curious do you guys already have a working prototype with this and this is absolutely fascinating by the way well the answer is yes and i was the one of the one of the those capsules on the london eye on june 20th 2016 three days before brexit in front of a camera telling people that the uk was going to vote for brexit the pound was going to collapse gold was going to soar in the days leading up to the 2016 election saying Donald Trump was going to win. I got left at, you know, ridiculed, whatever.

Starting point is 00:25:35 These were not lucky guesses. This was using exactly the science that I'm describing to you now, you know, with a lot of inputs and, you know, I'll give you a simple example. The polling, you know, Hillary was always ahead in the polls. Well, you looked at the polling methodology. The first thing you saw was that they were oversampling Democrats. So there are more Democrats than Republicans. So a fair poll would be about, you know, 53% Democrat,

Starting point is 00:25:59 47% Republican. That would be an honest poll because there are more Democrats. But they were sampling kind of 58, 42, 57, 43. So they were oversampling Democrats. And then within the oversample, they were oversampling African Americans who have a much higher capacity to vote for the Democratic candidate, 90% as opposed to maybe 70 or 80%. So that counted for another point.

Starting point is 00:26:22 So once you made those adjustments, Trump was always ahead. You know, he had to take the poll results, adjust them for the two things that I just, mentioned. And there was a lot else in the analysis. So we called Brexit. We called Trump in January and February 2017 using Fed Fund's futures, apply probability of rate hike. The market was giving a 30% probability that the Fed would hike rates in March of 2017. Our system was giving it an 80% probability. The Fed freaked out. The Fed said, wait a second, the market doesn't believe us. This was after, remember, they went through all of 2015 with one hike, all of 2016 with one hike.

Starting point is 00:27:02 And then in December 2016, the Fed says, we're going to hike three or four times. Our system said, yeah, you're right. You are going to hike in March. The market said, well, we don't believe you. So the Fed freaked out. And in three days at the end of February, yelling, Dudley, and Leo Briner all went out and gave speeches and practically yell, say, hey, we're going to raise rates. Wake up. And the market implied probability went from 30% to 80% in three trading days at the end of February 2017.

Starting point is 00:27:30 And it converged, we were already at 80%. The market was at 30%. In a couple of days, the market converged at 80. And then by the meeting date, which I think was March 13th or somewhere around there, everybody was at 100%. You knew they were going to raise it then. But these are real world cases. Let's take a quick break and hear from today's sponsors.

Starting point is 00:27:50 No, it's not your imagination. Risk and regulation are ramping up, and customers now expect proof of security just to do business. That's why VANTA is a game changer. VANTA automates your compliance process and brings compliance, risk, and customer trust together on one AI-powered platform. So whether you're prepping for a SOC 2 or running an enterprise GRC program, VANTA keeps you secure and keeps your deals moving. Instead of chasing spreadsheets and screenshots, VANTA gives you continuous automation across more than 35 security and privacy frameworks. Companies like Ramp and Writer spend 82% less time on audits

Starting point is 00:28:29 with Vantta. That's not just faster compliance, it's more time for growth. If I were running a startup or scaling a team today, this is exactly the type of platform I'd want in place. Get started at Vanta.com slash billionaires. That's Vanta.com slash billionaires. Ever wanted to explore the world of online trading, but haven't dared try? The futures market is more active now than ever before, and Plus 500 futures is the perfect place to start. Plus 500 gives you access to a wide range of instruments, the S&P 500, NASDAQ, Bitcoin, gas, and much more.

Starting point is 00:29:09 Explore equity indices, energy, metals, 4X, crypto, and beyond. With a simple and intuitive platform, you can trade from anywhere, right for from your phone. Deposit with a minimum of $100 and experience the fast, accessible futures trading you've been waiting for. See a trading opportunity. You'll be able to trade it in just two clicks once your account is open. Not sure if you're ready, not a problem. Plus 500 gives you an unlimited, risk-free demo account with charts and analytic tools for you to practice on. With over 20 years of experience, Plus 500 is your gateway to the markets. Visit Plus 500. to learn more. Trading in futures involves risk of loss and is not suitable for everyone. Not all

Starting point is 00:29:54 applicants will qualify. Plus 500, it's trading with a plus. Billion dollar investors don't typically park their cash in high-yield savings accounts. Instead, they often use one of the premier passive income strategies for institutional investors, private credit. Now, the same passive income strategy is available to investors of all sizes thanks to the Fundrise income fund, which has more than $600 million invested in a 7.97% distribution rate. With traditional savings yields falling, it's no wonder private credit has grown to be a trillion dollar asset class in the last few years. Visit fundrise.com slash WSB to invest in the Fundrise income fund in just minutes. The fund's total return in 2025 was 8%, and the average annual total return since inception is 7.8%.

Starting point is 00:30:47 Past performance does not guarantee future results, current distribution rate as of 1231, 2025. Carefully consider the investment material before investing, including objectives, risks, charges, and expenses. This and other information can be found in the income fund fund's prospectus at fundrise.com slash income. This is a paid advertisement. All right. back to the show. So where are you looking for two specific things? Is the dollar relative to other currencies? How is that looking in the next six months and then also oil? What's that looking like in the next six months? We're looking at a euro kind of 130 by the end of the

Starting point is 00:31:24 year. So that's a significant increase in the euro, a significant diminution in the value of the dollar. That's one of the ones who are mainly focused on. We've definitely got the fed hiking rates in June. And by the way, we're not, it's not that we're always out of consensus. We're not contrarians. Oh, the market says this. So we say the opposite. Sometimes the market's right with us.

Starting point is 00:31:45 Sometimes they're not. We don't care. I mean, we look at the market. We care in that sense. But we're going to go with our methodology because it's very solid and very proven. And I say with our team, we're building out these what are called maps or neural networks. And there are a lot of work. I mean, the engineering and the math.

Starting point is 00:32:04 inside is an awful lot of work in and of itself, and it's all proprietary. But just getting the subject matter expertise to identify the key knows and the key factors and inputs, that's a lot of work as well because it's easy to miss them. And we've developed them for, you know, for geopolitical events as well as the economic events I described. We've got the president pulling out of the joint comprehensive plan of action, the so-called JCPOA. This is our deal with Iran on nuclear weapons. It's not a treaty. I think everyone knows it's not a treaty. I'm not sure what it is. I don't think it was ever signed.

Starting point is 00:32:36 I don't think it was never ratified by any legislature. There was some backdoor ratification by the U.S. Senate, but it was never ratified by the Iranian parliament. It was never signed by anybody. It's just a piece of paper that John Kerry and Robson Johnny cooked up, and everyone, it's kind of a handshake deal between two parties who don't trust each other, but look for Trump to pull out of that in the months ahead. You know, we're getting a lot of output.

Starting point is 00:33:00 But just to be clear, this is still in development. we've got a really cool team. The people who are doing our user interface, these are called the UI, the user interface, but I've learned we call it the UX, which is the user experience. That's the new name for the user interface. But these are the folks who did Iron Man 2.

Starting point is 00:33:18 They did Black Panther title sequence. They're absolutely brilliant. We're very happy to be working with them. So we just got a lot of good people on the team having a lot of fun with it. So, Jim, it's really interesting studying the work that you guys do. It's a lot of different fields. It was actually kind of interesting how you talked about Keynes and Tversky in the 70s and people just puzzled that you would combine finance and economics with psychology,

Starting point is 00:33:44 and they even got a Nobel Prize for that because it was just mind-blowing for the establishment. Could you tell us your personal story about taking the road less traveled? Because this seems, I guess, for most people, like a very unorthodox way of thinking and perhaps not being more tempted to use conventional methods that, you know, you say they were laughed at whenever you said Trump would win and all the ridicule that you're not in here. So I hope I'm not saying something wrong. But like whenever you put yourself out there, say something like this and are so ambitious

Starting point is 00:34:20 in the way that you combine your fields, where do you really get your drive from? I was kind of a lawyer minded my own business. I worked at a major commercial bank. I worked at Citibank for 10 years. I worked at one of the major investment banks, a primary deal on government securities. I worked for what at the time was one of the biggest, most successful hedge funds in the world, long-term capital management. And then along came 1998, and long-term capital management not only failed spectacularly,

Starting point is 00:34:48 if it had just failed and we had just lost $4 billion in one month, which we did, that would have been a pretty big story. But we took global capital markets to the brink of collapse, And that is not an overstatement. I was in the room. We had the Treasury, the Fed, we were waking up the Italian finance minister in the middle of the night because long-term capital was the biggest trader and holder of Italian government bonds outside the Italian Treasury. The Italian Treasury was one of our investors.

Starting point is 00:35:15 So it was the Kumontang, you know, the Taiwanese Army. We were networked and we were in every market in size like you wouldn't believe. And I said, you know, if we had failed, I mean, we lost money, obviously, but failed in the sense of actually going Bangkok. I would have just slept in the next day. Like my job would have been over, but our $1.3 trillion of derivatives would have been instantaneously transferred to Wall Street and say with the counterparties,

Starting point is 00:35:39 we were no longer good for it. And they would have had to cover all of a sudden, two side of positions become one side of positions when your counterparty goes away and you have to cover. Imagine we had $15 billion in equity positions. We were the largest player in risk arbitrage on Wall Street, bigger than Goldman Sachs or, you know, Goldman Sachs or any of the hedge funds are specialized in this.

Starting point is 00:36:01 We were in every deal. We were in city group travelers, Lockheed Boeing, MCI Worldcom, I mean, name it. We and the cascade that would have followed the other banks that would have failed in our wake would have collapsed the entire global financial system. So as a lawyer, I negotiated that bailout. We got through it, closed up shop. People went on. Actually, some of the guys went on to make a billion dollars doing other things. our back office became a hedge fund servicing company called Globop that was sold for close to a billion dollars some years later.

Starting point is 00:36:32 So everybody got back on their feet. So as a lawyer, I felt, well, I had done my job. I mean, there were no enforcement actions, no, and there shouldn't have been. There were no penalties, no lawsuits, nothing. We all moved on. But I was very intellectually unsatisfied. I said, wait a second. We really did have 16 finance PhDs on our executive committee.

Starting point is 00:36:51 We did. We actually got complaints from Deans of Business Schools. who said we were depriving academia of the next generation of economic scholars because we were hiring so many bright PhD candidates. They said, who are you hiring all the geniuses? Who's going to be the faculty? That was a serious complaint from one of the Ivy League schools and two Nobel Prize winners. And they actually were that smart.

Starting point is 00:37:12 It wasn't like fake smart. They actually did have 160, 165 IQs. I knew them all good guys. So you get the finest, the absolute finest minds and finance. Some of the founders and inventors of modern financial theory. two Nobel Prize winners, and yet you fail that spectacularly. My takeaway was there must be something wrong with the theory. They're not dopes.

Starting point is 00:37:34 They weren't venal. They weren't bad people. There must be something really wrong with how they think about risk, because otherwise this couldn't have happened. So I set out on a personal odyssey to find the answer to that. What went wrong? And I did. And the main thing I found out was that the reliance on the normal distribution of risk,

Starting point is 00:37:52 the so-called bell curve, efficient market hypothesis, assumptions about risk-free rate, assumptions about prices moving continuously from point A to point B so you can transact smoothly at every point along the way, that every single one of those things was untrue. Markets are not efficient. Risk is not, the degree distribution is not a normal distribution. It's a power curve. Markets do not move smoothly and continuously. They gap up and gap down. Things come out of nowhere. Now, Nassim Taleb was doing similar work at the same time, and he came out with his book, Black Swan. but where, and I met him, he's a very funny guy, a nice guy, but where I parted it ways with

Starting point is 00:38:27 Taleb, Taleb demolished the bell curve, took a baseball bat and just bludgeoned it into like, you know, dust, and that needed to be done. But then he threw up his hands, he walked away, said, and by the way, you can't quantify this, it's just, just be long volatility, I'm a philosopher, have a nice life. So he criticized the existing paradigm, which is a good thing, but he didn't take it any further. I wasn't satisfied with that. I felt that, you know, okay, now I know what doesn't work, but what does work. There must be some quantitative scientific way of understanding this phenomena. And that's when I was introduced to complexity theory.

Starting point is 00:39:05 And I spent a long time doing complexity theory. And that was kind of had a bad reputation because a lot of people on Wall Street, you go back to the 90s, they were trying to use chaos theory. And there was a lot of confusion about the difference between chaos theory and complexity theory. chaos theory really doesn't work on Wall Street, but it's chaos theory is like a little branch of complexity theory. Complexity theory is a much bigger field, and it works extremely well when you set the dials in certain places. And what became apparent is that capital markets were complex systems. If I gave it, if you went to the University of Michigan, physics

Starting point is 00:39:42 department and took a course in complexity from a professor like Scott Page who doesn't know anything about finance. So brilliant physicists doesn't know. know anything about financing. He's just going to teach you physics. What is his definition of a complex dynamic system? He will say it has four characteristics. One, diversity, meaning the agents in the system have diverse views. If we all think alike, it's not complex.

Starting point is 00:40:06 It's boring. But if we have different opinions, pretty interesting. The second one is they have to be connected. What difference does it make if you have diverse views? If you're not connected through some channel, then that's not going to be an interesting system. Three, there has to be interaction. So what good does it do to have a connection?

Starting point is 00:40:22 Like we're connected right now over the Internet, but if we weren't talking to each other, this wouldn't be much of a podcast, so you have to interact. And the fourth is adaptive behavior. Based on what I'm hearing and learning and seeing, I might change my behavior or other people might change their behavior based on what I'm doing.

Starting point is 00:40:38 Well, look at capital markets. Diversity, absolutely. Bulls, bears, long, shorts, fear, greed, short-term, long-term. We've got lots and lots of diversity. Connectedness. We've got Bloomberg, Thompson-Royters, iPhones, email, CNBC, Fox business, etc. Probably overconnecting.

Starting point is 00:40:55 Interacting big time, trillions of dollars a day in stocks, bonds, derivatives, currencies, et cetera. Adaptive behavior, you know, Sarah Powell would say, you betcha, you know? If you're a hedge fund losing money and you don't adapt your behavior, you're going to go out of business. So, yeah, we do respond to what we see or other people respond to us. So capital markets are four for four. Once you realize the capital markets are complex dynamic systems,

Starting point is 00:41:17 you can now import this whole body of physics that's been around for 60, 70 years into capital markets and get enormous insights. And I started working with people at the Applied Physics Laboratory outside of Washington, D.C. and Los Alamos. And what I would say to the physicists, I say, hey, let's crack the code. Here's what we need to do. It's what we call team science. And this is what we're doing, Miracleon. So let's get a physicist, let's get an applied mathematician. Let's get a developer, an engineer, a behavioral psychologist, a lawyer, an economist, and a few other folks, and let's team up and crack the code.

Starting point is 00:41:54 And the physicist would say, that's great. What a great idea. Like, let's get the team. Let's get some funding. Let's do it. I would talk to PhD economists. They would say, why would we do that? You have nothing to teach us.

Starting point is 00:42:04 We know everything about economics. Why would we work with the physicists? In other words, physicists were more open to advancing the science of economics than economists were. So that was one of my discoveries. So we just solved that problem by putting our own team together. So the journey was as a lawyer, I felt that I did my job at Long-Term Capital. I brought the team through that. Everyone emerged without a scratch.

Starting point is 00:42:26 People were back on their feet. Reputations intact, got back in the business. But as a person and as just intellectually curious person, I was very, very dissatisfied that the smartest people in the world didn't get it right. And so that led me on an intellectual journey through the branches of science. And I really learned Bayes theorem at the CIA complexity theory I learned on my own. I'm a little bit of an autodidact, as you can probably tell. You know, I teach myself a lot of stuff. But Bayes is something I learned as CIA because we were trying to predict the next terrorist attack after 9-11.

Starting point is 00:43:01 Well, how many data points do we have? One. We had won. There's never been an attack like 9-11, 3,000 Americans dead. And so if you're Janet Yellen, you'd say, well, Well, okay, let's wait for 10 more attacks, 30,000 dead, then we'll have a time series, and then we can look for a correlation.

Starting point is 00:43:17 No, when it's life or death, you can't do that. When you're in the intelligence community, you don't have that luxury. You have to go tackle the problem with what you have, however, scant. And that's what Bayesville will let you do. You form a hypothesis based on whatever you have. Is it enough to satisfy a frequentist statistician like Janet Yellen? No, but it's the best you can do.

Starting point is 00:43:39 And sometimes when you have nothing to go on, you make a guess. But you're honest with yourself. You say, this is a guess. And it's 50-50. I could be wrong. But then what you do, you look at subsequent day. This is why it's called causal inference or inverse probability, because what you're doing is you're updating the hypothesis with subsequent information.

Starting point is 00:43:58 So when subsequent information comes along, you ask yourself a question. What is the probability that that subsequent thing would or would not have happened if my original hypothesis were true or false? In other words, what's the conditional probability of the second thing being true if the first thing was true? Well, if it's high, then you've now strengthened the original hypothesis with the subsequent data. If it's low, you lower the odds. So you might take that 50% that guess and you might upgrade it to 60, 65, 70 as this new stuff comes in. Or you might lower it, you might abandon it.

Starting point is 00:44:32 It might go to zero. Say, hey, none of these other things would be happening if I were right. So I'm probably wrong. So discard that and keep going. Jim, what a pleasure chatting with you. I mean, this was just fascinating stuff. We're just so thankful that you come back on the show. It's always so much fun to hear what you're up to because it's always something so fascinating.

Starting point is 00:44:52 Well, I always say great questions made for hopefully great answers. So this is absolutely one of my favorite shows. And this is the only one I'm doing. Like I say, I'm just absorbed in this book. But I was glad I had the opportunity to be with you guys. Well, thanks so much for joining us today, Jim. We just really appreciate it. And if people listening to this want to learn more about it,

Starting point is 00:45:09 about you or find you on the web, give them some information so that they know where to find you. Sure. For anyone interested in the predictive analytics, that's Miraglim, M-E-R-A-G-L-I-M-D-M-A-M-A-M-A-M-R-A-M-R-A-M-R-A-M-R-A-M-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-E-R-R-E-R-E-R-R-E. So if you want to have a look at Clyde, I have my own channel over on Collide, and I do weekly interviews. So, of course, my books and a new one coming out on October 30th. All right. So that concludes our episode for today, and we'll see everyone next week. Thanks for listening to TIP. To access the show notes, courses, or forums, go to theinvestorspodcast.com.

Starting point is 00:45:50 To get your questions played on the show, go to Ask theInvesters.com and win a free subscription to any of our courses on TIP Academy. This show is for entertainment purposes only. Before making investment decisions, consult a professional. This show is copyrighted by the TIP Network. Written permission must be granted before syndication or rebroadcasting.

We Study Billionaires - The Investor’s Podcast Network - TIP191: Jim Rickards (Part 2) Artificial Intelligence, Bitcoin, and Credit Cycles (Business Podcast)

There aren't comments yet for this episode. Click on any sentence in the transcript to leave a comment.