The Jay Kim Show #78: Kevin Massengill (Transcript)
Today’s show guest is Kevin Massengill, the founder and CEO of a fintech data analytics firm called Maraglim. Kevin has a unique background in military defense contracting and Wall Street, which led him down this journey to build a technology that uses predictive data analytics, which was originally conceived for use by the US intelligence community and then applying this data to identify global threats and opportunities to institutional investors and government agencies through warnings and signals.
A very interesting conversation we have today. We go pretty deep, but I think you guys will enjoy this one. Let’s get on to the show.
Jay: Kevin, how are you doing? Thank you so much for coming on the show. We’re very happy to have you on.
Kevin: Thank you, Jay. Another lovely day in Paradise. Thanks.
Jay: That’s right. I’m really excited actually to have you on the show because you are working on a very, very unique and special project, and you also have a very unique background. And you’ve kind of, I guess, married your previous experiences and career paths into what you’re working on now. So why don’t you give us a little bit of an introduction of who are and gives us some background on what you worked on in the past and how that led to what you’re working on now.
Kevin: Oh, boy. Okay. Sure. I’m a retired military officer, spent a misspent youth in the infantry and then as a Middle East foreign area officer bouncing around from various embassies and headquarters in the region. I had the dubious privilege of never having a single piece of advice ever taken, so I don’t know why they called me an advisor.
We really enjoyed where we were at in Abu Dhabi, and we had kids in high school and didn’t want to move them again, so we retired in place. I had opened a company and began doing capital introduction and general business development across a number of countries. We wound up opening offices in Doha and New Delhi, as well as Abu Dhabi in Dubai. And then I got recruited by Raytheon to take over the Middle East and did that for a number of years, wound up their head of international operations and then back to Capital Introduction again for a boutique investment bank in Manhattan. I got recruited back into the defense world again as s senior vice president for international for one of the defense contractors called Leidos. I used to be SAIC.
At some point, in the middle of all that, I ran into Jim Rickards. At the time, I was the ranking US defense executive living in the Middle East. My peers were all commuting in from Washington, D.C. And so I got invited to some really interesting conference, and Jim was a keynote speaker at one in Bahrain, hosted by the International Institute of Security Studies that was on geopolitics and currency as a weapon and that sort of thing.
There were about 60 of us in the room, and it was a bunch of ex-defense ministers and finance ministers. It was a really interesting, eclectic group. It turned out, out of that whole group, that Jim and I were pretty much the only two kind of Austrian, hard money, gold guys in the room. Everybody else were the normal Keynesian, the alchemy of our day, as I think of it. We were the only ones who would laugh at each other’s jokes, and the rest of the room would just stare at you stonily. Like, ah, I kind of like that guy. I had already read his book Currency Wars, so I went over and shook his hand. And it was the only one that was out at the time, so this was back a while.
And we talked a little bit about the project prophecy thing that he did for the agency. You may recall it was after 9-11. The agency wanted to know whether it was possible to build an algorithm that would scan open market, global financial data and look for the kind of anomalies that might signal another 9-11. And if so, could you get that information in enough time to get a warrant, kick down a door, stop the attack kind of thing. And Jim and some colleagues built it. And it worked really well.
For a host of domestic, political reasons, the agency closed it down, not because it didn’t work. It worked really well. And what Jim and I talked about doing was whether or not that could be used as the basis for something we could build upon, expand upon, and take that to global financial markets. Because if you’re running billions of dollars of capital, you care deeply about global macro events too. You don’t like surprises any more than the US government.
So what we’ve done is we’ve taken the Bayesian inference mathematical basis of the first thing he did for the agency, and that’s 200-year-old math. That’s just what we do if we’re intelligent analysts. We just posit our best hypothesis, and then we think as deeply as we can about what are the indications and warnings that would confirm or deny if you’re right or tell you you’re moving in the wrong direction. And then you constantly update your hypothesis with new data. Pretty simple 200-year-old math.
But then we added to that Bayesian inference…we added behavioral psychology, history, and complexity science, because in actual fact, capital markets are dynamic, adaptive systems. They are not machines. They don’t lend themselves to being tinkered with at the margins. Let me give you an example.
If you and I were somewhere, and we listened to a ranger get up in front of us and start talking about how they’re going to fix Yosemite, the national park… They’re going to dial up the number of badgers; they’re going to dial back the number of bears. They’re going to make it better… We would all just look at each other and think they were an idiot, because you have no idea what the outcome’s going to be of the changes you make to a complex, adaptive system. You’re going to get a whole host of unintended consequences you can’t begin to calculate because you’re not God. It’s Hayek’s Fatal Conceit on steroids. And yet, and yet, we, collectively, nod our heads and thoughtfully listen to chowder heads like Yellen or Bernanke or government officials or Christine Lagarde. They just pontificate on how they’re going to fix the world’s economy. They’re going to dial up a little inflation. They’re going to dial now some unemployment.
And instead of nodding thoughtfully, we should be getting them padded rooms and special jackets. These people are insane. And the hubris, the arrogance for them to think that they can manipulate the entire world’s economy, which they’re trying to do by manipulating the price signal in which everything else is measured — money itself, currency itself… It’s just a hubris beyond belief.
So what Jim and I have done is we’ve brought the right models to the right problem. Here’s another way to think about it. 400 years ago — maybe a little longer — mid-1500s you had the science of its day… Now, this was the most learned people, very long lead time to train for it. Long apprentice, journeyman guild, training, very much to memorize — the vast body of knowledge in alchemy. And it was all wrong. All of it. But they didn’t know it. They had no idea. They thought it was right. And they spent enormous amounts of effort and time and capital playing in this field that was completely… The models were all completely wrong. But all of a sudden, in the mid-1500s, somebody invents a thermometer. And all of a sudden, for the first time, you can begin to take actual measurements. You can begin to record what happens. You can begin to see what catalysts do at what temperatures on a repeatable, experiment basis.
Within 50 years, alchemy has completely transformed itself to become the foundations of scientific chemistry and modern metallurgy. In just 50 years, for the time these very bright people whose models were just wrong, got better data, got better ways of measuring data, got a better understanding of how the world actually worked. It’s not that they were bad people. It’s not they were stupid. Not at all. They were well-intended, very intelligent people, and their models were just wrong.
I think that is a perfect analogy of what we’re suffering right now in the economics profession in the Western world, in the modern world. And of course, government politicians don’t understand anything. They’re do whatever it takes to get them free chicks and… They don’t have a clue.
But the econ profession, we laugh at the physics envy. Economic textbooks are just loaded with elegant math equations, that anybody that knows anything about Austrian economics or anything about people in general, just know it’s all nonsense. It’s just all patent, silly nonsense.
But they’ve spent years learning that math, and it’s tough. It’s hard. You’ve got to be really smart. You’ve got to be really diligent. And so they’re not going to give that up lightly.
And so Jim and I feel like we are… He likes to use the analogy of Copernicus. I would use the analogy of the thermometer. We’re going to pull a branch of human knowledge forward violently toward reality. And we’re going to do it in a really short period time. I mean, they’d have gotten there eventually, maybe 100 years or so. But Max Planck used to say, “All knowledge advances with the death of one tenured professor at a time.”
Jay: There you go. Sorry to interject here. I just wanted to take a little bit of a step back for some of the listeners. We’re going pretty deep, and we’ve spoken before, so I know that you’re speaking at a pretty high level. So basically, I think that, first of all, you have a very unique background. So I think, again, the marriage of what you’ve done in the past has led you down this path. Obviously, you were fortunate to meet Jim Rickards. And for the audience listening in, you can literally just google Jim Rickard’s name. He’s a New York Times bestselling author. I believe here used to work at Long-Term Capital Management, so he’s very well…
Kevin: This about how competent a lawyer in a financial markets world he must have been for the Long-Term Capital Management… For your listeners who don’t remember that, they were the Olympic team. They have 14 economic PhDs, two Nobel Prize winners… They have some extraordinary brain power. And Jim Rickards was the gentleman they asked to be their attorney, their OGC.
Jay: Absolutely. So like you said, it’s the Olympic team, so Jim’s obviously seen this disconnect between academia and book smarts and the reality. And I love your analogy of central bankers being like park rangers trying to shift the Yosemite Park ecosystem. I actually agree with you. I think most people don’t actually know. We both know that the mainstream media is misleading, and what we hear and see on the news is not actually what’s going on or at the best interest of the population. So now with this backdrop — and thanks for the great detailed introduction on how you kind of went down this path — let us talk a little bit about the exciting new technology, which you started talking about but then specifically your company. So I want to get into that.
Kevin: Thanks. Sorry about going off track on you. So what we did was we took this company. We took a core analytic engine that we’ve already created, and what we decided was, the right way to do this is to blend human intelligence with machine intelligence. Artificial intelligence has largely been a disappointment, and it’s because people have asked it to do more than it could do. The old saw that machines are fast, accurate, and stupid, and humans can be quite slow, prone to error, but can be brilliant. It’s the marriage of the two that’s so powerful. It’s by any chess champion like Kasparov or any super blue chess computer, super computer, will always be defeated by three average human chess players paired with two average computers. That five unit collective of some average human brain power and some average computing power will defeat a supercomputer or even a Grand National Champion every time. It’s really curious. It’s the combination of the two that make it two powerful.
What we’ve done is we’ve taken the very best of the human intelligence world — I’ve spent some time in that world. Jim has. Our leadership all have, actually. We all had TS/SCI clearances at one point or another. And we’ve taken the very best of the intelligence world and your Bayesian process, and now imagine you’ve got this idea. You’ve posited your hypothesis, but now you turn to Watson, and you say, here’s all the notes. Here’s all the conceptual components. Here’s all the edges that connect them and the weightings we’ve given them.
Jay: That’s IBM’s Watson. Right?
Kevin: That’s right. IBM Watson’s our tech partner on this. In fact, their CTO of cognitive services, a charming lady who owns all of that, pulled me aside on our second day and said, “This is the coolest thing they’ve got to work on.” It’s because it’s not just elegant science, it solves a really big problem.
When you can see into the future three to six months… It looks like magic. And I’m reminded of Arthur C. Clarke, and I should probably put this on our website. Arthur C. Clarke, the brilliant science fiction writer, futurist, had a wonderful comment about that. He said, “Any sufficiently advanced technology is indistinguishable from magic.” Isn’t that a great line?
Jay: That’s a good one.
Kevin: It looks magical. It looks like we can see the future. And of course we can’t see the future. What we can see is the data. And because we’re getting the ingest from Watson — and it’s reading 9 million books, articles, transcripts a day.
Think about the average analyst. He comes back to work on a Monday morning. They’ve got two or three hundred single line headlines racked and stacked in their Bloomberg terminal or their Thompson and Reuters.
Jay: Exactly. That’s like every day for me.
Kevin: Exactly. And you’re supposed to open those up and figure out which ones matter to you and integrate what happened with everything else that happened and everything else you knew previously. It’s a ridiculously complicated task. We’re not God. It’s too much depth.
But imagine if you had a team of really smart people map out all the connections, all the nodes, everything that touches on something you care about, and then task Watson to evaluate the world’s inputs to each of those nodes and update them in real time. Now you come back on a Monday, you ask our system, RAVEN, what happened over the weekend. Now RAVEN knows what you care about. Let’s say it’s the Chinese yuan devaluation you and I talked about before. ” RAVEN, what’s happened over the weekend?”
It knows. It looks at the nodes that you care about. It says that there were four inputs to four different nodes. Would you like to hear them? Sure.
Jay: Raven is the name of this technology that you guys created.
Kevin: It is. We called it for that two reasons. One, it’s a core part of our logo. We chose the logo because a raven is fairly prominent in prophesy, both in Christianity and just in Western religions. The raven is what Noah sent out to look for land. A raven is what feeds Elijah. And yet a raven in the Norse Mythology, of course, is a major prophetic character. And then we named the company Meraglim. I don’t know if you and I have talked that.
Jay: I was just going to ask you because we did talk about, and I think it’s so cool who you guys came up with the name.
Kevin: We wanted to convey the idea of active reconnaissance, going out to look for risks and look for opportunities. And we spent a lot of time thinking about that. And we came across this wonderful world in Hebrew, and everybody has heard the story, and nobody has heard the word, or very few. And the Hebrew story comes from Numbers chapter 13 when Moses assembles a reconnaissance unit drawn from the 12 crown prices of the 12 tribes and send them into the Promised Land for 40 days. And they come out, and of course, 10 are famously panicked. There ain’t nothing but risk.
Then two, Joshua and Caleb, famously see the upside. And there’s a proper name in Hebrew called the meraglim, or anglicized, the meraglim. And that’s the proper name for that team. Ancient Talmudic scholars would translate that “the spies,” which may have been a good translation a thousand years ago. Today we know that’s not “spy” in the sense we use the word today. That’s reconnaissance. That’s a completely different function.
So the find that word, find that team, for u]]s is a perfect fit.
Jay: Yeah, a perfect fit. So let’s go back to the example you were giving. So I go to work Monday morning. I login to my Bloomberg terminal, and I’m specifically worried because, being based here in Hong Kong, I’m obviously worried about what’s going on in China. Obviously, there’s some tensions with the US. The currency is always a big asterisk or question mark. In 2015, we saw the yuan devalued which caused ripples throughout the world in global financial markets. So this is always in the back of my mind. I’m managing some money here for institutional investors, and obviously, all of these things matter. I have to process all this data — hundreds of lines of Bloomberg from brokers and news feeds and this sort of thing.
So let’s go back to that example. How does RAVEN help me?
Kevin: If you sit down and say, “So, RAVEN, what’s happened over the weekend?” Well, RAVEN knows you care about the four key… You’ve told us what you care about so we’re dragging on that for you because you’re a client, and RAVEN will tell you, “Okay, of the four things you’re tracking, two had notable impacts that we judge to be significant. Would you like to hear them?”
“Deputy central bank governor gave a speech in Beijing. We judged it supportive of the thesis. Would you like to hear the text?”
And it will read you that guy’s speech.
Even if you disagreed with us, even if you disagreed with our thesis of what we thought the outlook meant or what the data meant, you’re going to have so much better real-time intelligence at your fingertips to make your own decisions than you could possibly have. And it wouldn’t matter if you had a team of 100 analysts working that problem. They’re human, and it’s too much data to sift through, sort, assimilate.
And so when you’re looking at our product — and I don’t know if you saw the demo we did with IBM.
Jay: Yeah. Amazing. I’m going to get that linked up. Is that out there in the public domain? Can I link that up?
Kevin: It’s not, but you’re free to. Go ahead. And so it’s a constellation of ideas, and each idea, you could think of it as a point of light or a planetary body in a constellation, and as you’re looking at the whole, you can click on any one of them, and it will drill right down into it. You can click on any of the supporting notes. You can click on any of the edges between them, and it will tell you the weighting and the impact that we see from one to the other. And so you can drill down and understand exactly how we think the data all fits together and impacts each other.
And then, as a client, you can come back and tell us, “You know what else would be interesting to know? It would help me if you could track the Chinese-Australian cross rate. Can we add that?”
The client will tell us additional ideas.
One of the things that I’m most excited about is we’re going to do a lash up with the human intelligence side… So in the United States, you have something called DARPA, which is a pretty well known — Defense Advanced Research Projects Agency. It’s a really good think tank for US defense. A few years back, another one was created called IARPA. That’s the exact same function but now for the intelligence community, and Intelligence Advanced Research Projects.
And some years ago, they decided they would had a contest on prediction, and they wanted to know whether or not people could come up with or conceive of a methodology that would be of a higher predictive accuracy than the control group, and the control group were the intelligence community itself — the subject matter experts studying on a particular problem.
Five teams went at this.
I think all of them were led by different universities. The team led by Dr. Philip Tetlock and his colleague Dr. Mary — I’m going to screw her name up. My apologies. The two of them put a team together. And they not only outperformed all the other four teams four years running for all the years of the experiment, they beat the control. They beat the intelligence community, accuracy by some 50 to 60%.
That group we’re in talks with about tapping them, their people, and their methodology to be our initial human front-end that helps create what becomes the fuzzy cognitive map that you saw in the demo.
Imagine if you’re always one of these super forecasters that really are very good at this, and I come along and say, “Oh, by the way, not only are we going to leverage you into this and pay you, we will let you task Watson.” How fun would that be if you like doing this anyway? You tell me what data point do you think would help you in pinning down the accuracy of this prediction, and we’ll just add that to what we call a fuzzy cognitive map. But that becomes a tasking to Watson.
And so you as an analyst, you as a client are not only getting to do inputs to us of things you think would help, you’re getting the benefit of my human intelligence network that’s all doing this in a collaborative fashion as well.
So, again, we’re not invented water. Bayesian inferences has been around for 200 years. That’s 200-year-old math. Complexity science has been around for 60 years. If you and I worked at the National Weather Station tracking hurricanes, we’d be using complexity science every day. If we were research analysts at Los Alamos, we’d be using complexity science every day doing nuclear modeling. But for a host of reasons that I alluded to earlier, this science — which is actually much more germane or applicable to capital markets than anybody in the economics profession can admit — has been just let pass them by. They’ve just let it go by. And instead, they stuck their value-at-risk-equilibrium models, efficient market hypothesis — all these just nonsensical ideas that I literally liken to alchemy.
Here’s a great one. The Keynesian premise of animal spirits. We can just have every kite checks to each other, and everybody will get excited about that, assume that there’s real economic productivity behind all that and go out and just start spending money, and that will just magically cause the economy to take off again. And they call it animal spirits. I mean, really? Really? And yet serious thoughtful people from very good schools with higher IQs than mine will nod thoughtfully and stroke their chin and go, “Yes, of course. That’s right.”
Jay: Again, the central bankers, the park rangers. The animal spirits and the badgers. We’re running around… Listen, it’s a very interesting technology you guys have come up with. Let’s talk specifically about who are your ideal clients and what’s your business model. Is it subscription-based? It obviously sounds like, due to the complexity and the high level of technology that’s involved and the team and this sort of thing, it’s definitely an institutional-grade product. Tell us about the revenue model and the business itself.
Kevin: Sure. The revenue model is ridiculously high and for a couple of reasons. One, because we can. But the real reason is that I have this enormous wall of skepticism to get up over. If you go out to a hedge fund trader and say, “Hey, I can show you what’s happening in the world three to six months in advance,” his first reaction is going to be “Horse shit. No you can’t.” Because if you could, you wouldn’t be selling it to me. You’d be trading it.
Kevin: You’d go run money with it. Right?
Kevin: And that’s the logical first reaction. In fact, that was my first thought when Jim and I first conceived of doing this as a commercial product. My first assumption was I was just going to call some friends of mine, do a $2 billion fund and get at it. But here’s why we’re not going to do that, for two reasons. One, you can’t make anywhere near as much money running money as I can selling subscriptions at $5 million per year. And quite frankly, it’s worth way more than that. It’s just going to start at $5 million and go up. If you don’t want to pay me 5 million, actually I’m going to be fine with that. I’ll say that’s fantastic. That’s great. Give me 10% of everything you make off of my outlooks. I’m going to make way more than $5 million. You’ll wish you’d taken the first deal.
The other advantage of doing it the way we’re doing it is we’re running it as a business-to-business software as a service, to your point of subscriptions. The reason we chose that model is because in the business world right now — and these things change — but right now, the highest multiple business you can do is a B2B SaaS because that recurring revenue model is so steady, it’s so predictable, it can generate, on average — with nice growth story — it can generate, on average, ten time multiple to top-line revenue. Think of that.
So that means, for every subscription I sell, we’ve added another $50 million to our valuation. For every 20 subscriptions we sell, we’ve added a billion dollars to our valuation. Think of that. In a universe of 55,000 firms that we judge, between hedge funds, pension funds, sovereign wealth funds, insurance, asset management, trust companies — just in the financial services — we judge globally that’s about 55,000 firms managing about $380 trillion in assets. And all I need are 100 to be a $5-billion company.
So when you do the math, that helps me get over the wall of skepticism to the hedge fund operator who thinks, “Well this can’t possibly be real. Why would you sell it?”
Well, brother, because I can get a 10-time multiple top line revenue by selling it. That’s why. So there’s the first reason for the ridiculously high price.
The second reason is that is actually market normal. You can go in the market… In our deal documents you’ll find comps where we show published general services administration, government contracting catalogs where firms that are doing predictive analytics — not in capital markets but in social media and other things that matter for those clients — are charging roughly that price. So it’s not total lunacy.
And then again, the third point is it’s about value. It’s not about price. If I can show you a 20 to 1 ROI, you don’t give a damn what the price is.
Kevin: So my point… And so that answers your second question about who is our natural target. We’re going to go to firms, initially, that have $3 billion assets under management and above because I wanted firms whose minimum investment size — roughly 30 million — it’s inconceivable that if they execute on any one of our recommendations, they wouldn’t make at least 100 million. And by making 100 million, I’ve given them a 20 time ROI. So that’s how we backed into what size firm we wanted to focus on.
Jay: In addition to RAVEN, what else do you get for $5 million a year?
Kevin: Let me give you an example on some of the things that we’ve done. We called the English departure from Brexit months ahead of everybody else. And that was way out of consensus. We called the Trump election weeks before the election. Again, 99% out of consensus. If you’d have taken our advice and gone long gold and short the Mexican pesos, you made 4,250% return in 16 hours. You don’t have to do very many of those to have a really good year.
Conversely, your competitor doesn’t have to do very many of those to have a real good year, and you’ll be buying us just because they’ve got it.
Jay: That’s true.
Kevin: The third thing we did was we called the March interest rate hike four months early. And this is the entire world was giving that a… The future’s market was giving that a 20% probability. We said it’s 80%, plus. And then three weeks before the Fed meeting, the Fed kind of panics and goes, “Oh my goodness. These knuckleheads don’t listen. They’re not listening to us.” And so they did four speeches in four days in a row — from the only four voices that mattered. And all of a sudden, the market just — whoop — and does a parabolic curve like a bitcoin, and they come up and joined us at 90%.
Well there’s a lot of money to be made when the consensus is at 20% and you’re four months out and they’re just wrong. There’s a lot of money to be made.
Another part of our business model, I’m going to do a money-back guarantee because I don’t care. So if you’re not happy, I’ll give you your 5 million back. It means nothing to me. Obviously, I’ve got to set aside some funds for that. I’ll have an actuarial help run that up. But that float I’ll carry, we’re going to invest. We’re just going to divide the float into 10 chunks, and we’re just going to keep placing bets on our own outlooks because they’re such asymmetric trades. And I’m thinking about how I have either Accenture or Pricewaterhouse or somebody handle that or monitor it so that I’ve got a fantastic third-party credible track record.
Jay: And that’s a fund in the future on the back of that.
Kevin: Exactly. And it lets me… Eventually we’ll sail into really tough waters like we did in 2008. And then that happens, we plan to have a sufficient pool of capital that we wade in aggressively and start buying up firms. Anybody that touches complexity science or augmented intelligence and has an intelligent business model, we’ll roll up.
Jay: There you go.
Kevin: So that’s the plan.
Jay: That’s awesome. Thanks for the awesome overview. I mean, I think a lot of our listeners will be intrigued at what you guys are working on. Unfortunately, most of them probably can’t afford you.
Kevin: Most of them aren’t our intended clients, but they could certainly be investors. So if you’ve got investors, surf on over to either my website, Meraglim.com or go to Fundable, the largest crowd-sourced business fundraising. We put it on Fundable. Go to Fundable and just hit our name, Meraglim, or hit artificial intelligence. We’ll be the first thing that comes up. And just check out the offer, and see what you think. I can tell you that the head, the woman who owns Watson… Thank how cool the stuff she’s working on. This is the CTO for IBM’s cognitive services. She’s probably got a hundred peer-reviewed articles, 30 some-odd patents in the space — wicked smart, first Indian female named an IBM global fellow, ever, and a super nice lady. She looked at this and thought it was the coolest thing they had to work on. So if you’ve got people that like the space, like IBM, like Jim Rickards, kind of tend to agree with us that there’s just something a little bit off with governments and central planners, you should take a look at what we’re doing.
Jay: Look outside of the park, right? Thank you so much, Kevin. You’ve already answered what was going to be my last question, which is where to find you and follow you this sort of thing. I guess I want to just… Maybe you could tease the audience with a final freebie. Where should we be concerned about right now based on your models for 2018 or for the foreseeable future? Is there one area that you could just hint at that might potentially be a threat or a danger or a point?
Kevin: This isn’t a particularly novel insight. And they will have heard it probably ad nauseam. But if they’re caught up in cryptocurrencies, I would get out. There will be some winners, but it will take time to figure out who that’s going to be, and it will absolutely not be bitcoin because it’s a flawed technology platform. So bitcoin is the Neanderthal in this game. They’re already dead.
There are economic storms coming, and then that happens, the buildings are still there. The farms are still there. The income-producing real estate still exists. What gets wiped away are the tertiary assets, the paper assets, the 401Ks, anything tied to currency, tied to fiat currency gets devalued as these governments all destroy their debt obligations by destroying their currency. They don’t like it. They don’t want to do it. They don’t want to destroy the life savings of all their pensioners. But they don’t have any choice. They literally have no choice. And so they always do this.
So for your listeners, Jim would say have at least 10% of your assets in hard assets like gold and silver. I’m more the Nassim Taleb side. I say 80%. Now, he doesn’t think that gold necessarily, but he has said do a barbell strategy where you’ve got 80 to 90% rock solid safe, and then save 10 to 20% for the most high-flying speculations you can find. And for me — at the time he wrote that, maybe T-bills looked better than they do today — he said, like T-bills. I would say gold, and so that’s what I’ve done. I’ve got 80% of our family assets in precious metals and 20% reserved for the coolest multi-billion dollar project I could find that I’m doing with the Jim Rickards.
So for your investors, I would get away from tertiary assets. Look at the Exter pyramid. Look at the John Exter inverted pyramid he drew in the late ’60s. That’s a really good example. Just mentally draw that into thirds and recognize that the bottom third are primary assets, the middle third are these secondary assets. The top third are those tertiary assets. And they just get stripped away in the storms that come. It’s just inevitable. You can’t have a 40-year failed fiat currency experiment and gracefully… There’s never been a time in human history where the whole world has been in this monopoly money house of mirrors for so long. It’s never happened in human history, which is why we’re seeing things that’s never happened in history — whether that’s negative interest rates or 5,000-year lows in bond yields. And these things have never happened. Well, that’s not going to end well.
For your folks, I would say keep a monster box of silver in the house in case we… It doesn’t necessarily mean we’re going to have an EMP explosion overhead. Puerto Rico didn’t have an EMP explosion, and yet they can’t buy or sell because they have no electricity powers. What would 500 1-ounce silver coins do for you there? It could help keep your family alive. So throw a monster box of silver in the closet and forget about it. Get 10, 20% of your assets into gold and silver bullion, and just start getting away from tertiary assets as best you can. And I would think bitcoin would be the most fluffy, goofy… At least in the Tulip Bulb Scandal you got to keep the damn flower. What are you going to get…?
Jim has a beautiful formation for this. He says the Tulip Bulb Scandal was a bubble and a scam, but it wasn’t a fraud because you got to keep the flower. When I say fraud, I mean a Ponzi. And when you look at Bernie Madoff, his was a scam and a Ponzi and a fraud, but it wasn’t a bubble. And yet bitcoin is all three. It’s a Ponzi and a fraud and a bubble all at the same time. That is not going to end well for folks. So if you’ve got some assets in it, get them out. Get them out. Trust me on this and get some assets. And even if you don’t, even if you want to leave it in speculatively, fine. People have made a lot of money at it, and some people will make more, but get a chunk of your assets into primary wealth — gold and silver bullion or agriculture or multi-family apartment complexes — things that will spin off revenue for you. Real assets. And try, to the extent possible, to push away from, or get away from the derivatives contracts, the futures contracts, all the paper assets that are necessary in an advanced society, and they have their place. I don’t mean to minimize them. But in these great currency resets, these are the things that are just destroyed, and you don’t want to be caught in the gears of that.
Jay: Kevin, that’s sound advice. Thank you for sharing that, and I cannot help but agree with everything you said there in the last bit. But thanks so much for coming on the show. This is a really, really interesting episode. I think the listeners are going to really enjoy it, and I appreciate your time, and I’m looking forward to hearing more about you in the news and seeing how Meraglim does in the future. So thanks again, Kevin.
Kevin: Thank you, Jay. It’s a real pleasure, and it’s always fun to catch up with you. Thanks.
Jay: Alright. Take care. Bye.
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