We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.
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But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.
Here are the articles for the week ending 10 July 2022:
[00:08:44] Patrick: In the book and in the presentation you gave last week, there’s a key central example that, like you said, you stumbled upon via some of your own research. I would like to walk through that story. I want to just plant the key idea before we do that with another quote from the book, which is that, “Almost no prerequisite to any major invention was invented with that invention in mind.” You used that term stepping stones, the things that we combine. You gave the example of vacuum tubes and computers. People working on vacuum tubes weren’t thinking about computers, and there’s a million examples like this. So I just want to plant that idea out there. The stepping stones thing not resembling the final invention is the reason why it can’t be so deterministic, and here’s our objective, set up the steps between now and there. Maybe you can start to introduce that concept via the Picbreeder example that I think was the way that you originally alighted upon this idea in your research.
[00:09:31] Ken: It’s neat because, in a way, this is a story of serendipity, which is about serendipity. I mean, basically, this pic breeder just serendipitously led to this insight. Picbreeder was an experiment that I was running with my lab. I was a professor at the time at the University of Central Florida, where we allowed people on the internet to go and breed pictures. I know this is a major digression from what we were just discussing. We were discussing all these important things and we’re talking about breeding pictures. So how do these things connect? Breeding pictures, it is a little esoteric from general societal concerns perspective, but it’s basically about searching through a space in a way. This was an opportunity for us when we were doing artificial intelligence research to crowdsource. Crowdsourcing is really interesting. Let’s say you to take people on the internet because you’ve got access to potentially thousands, millions of people and have them try to do something collectively. Wouldn’t have been possible in the past if you didn’t have access to the internet. What we wanted to do was to crowdsource people, to search through the space of images or pictures and what that meant. So we used breeding. So basically, what it meant was that you could take an image, say a blob or something, and in fact, the site would start you off with random blobs if you started from scratch and you could say, “Look at some blobs and you could pick the one you like the best,” just like you might if you were breeding horses or dogs, “Pick the one you like the best.” You might have different reasons or criteria, but whatever your criteria is it’s fine, and then it would have children.
So it’s a little strange. It sounds strange. The picture has children, but this is inside of a computer. So if you think about it, why not? The picture can have children. The children or the offspring of the picture are like any other children. They look like it. They’re not exact duplicates just like if you have children, they look a little bit like you. They’re not exact duplicates of you or your spouse either. That’s the case here. So then what’s cool is that then you can see that if your picture that you chose has children, then you can look at the children and then you can pick from those children which one you like the best. You can see that this is in effect breeding. So then out of those, you pick your favorite there. It has children, and then you get to choose from those, and then so on and so forth. You’re basically iterating generations of breeding, where it goes depends on what you choose up to you. To tie this back quickly, what does this have to do with anything? If you think about those images, they’re basically a metaphor for discovery in general. If you think about like what you said about vacuum tubes and computers, computers are a discovery, vacuum tubes are a stepping stone on the road to that discovery. So somebody chose to use those vacuum tubes to try to build a computer. When it comes to image breeding, if I see an image that looks like something interesting, and then I choose to breed it further and then I get something else, maybe a picture of a skull, which actually was discovered, then I basically used that stepping stone to get to a discovery. So somehow, there’s a metaphor, an analogous metaphor here.
What’s cool about this site, what made it, I think, compelling to me was that because it’s crowdsourced, what we allowed people to do was to come in and look at what other people had bred. So there’s this big database and it’s being displayed in a natural way, a way that makes it easy for people to see what’s been discovered to surface things that are interesting. So those you can think of as stepping stones. You might see a butterfly or a face or something like that. Then someone who sees that is allowed to instead of starting from scratch, instead of starting from blobs like you would if you were starting from scratch, they can start from your discovery. If you found a butterfly and somebody wants to breed new butterflies, then no problem. They don’t have to start from scratch and get to a butterfly. They can start from your butterfly and then breed from there. It’s called branching. So that means that people are building off of the discoveries of their predecessors or you could think of as standing on the shoulders of their predecessors, which is, again, it’s a really nice analogy, I think, to how human innovation proceeds in general, where someone invents something, discovers something, comes up with an idea, and then someone else that they might not even know later in the future goes back in history and sees that thing and realizes this could be used for that, and it transfers that idea over and it becomes a stepping stone to something else. This has been going on for as long as civilization, basically is civilization. That’s what basically causes civilization to happen. So pic breeders are a microcosm of that, but here’s where the thing that leads to the insight that’s profound and to me was shocking was that after running this site for a couple years, so this is a long time, and letting people just breed and discover things and they discovered all kinds of things, butterflies and cars and planets.
[00:14:01] Patrick: We’ll put a link in and a collection to some of these. It’s really staggering, the things that you see that started with black blobs.
[00:14:07] Ken: Yeah. Yeah. So you’ll get a chance to see it. They found all this stuff after a couple years of watching this. Then what we found was that underneath the hood, we were able to look at how. If you think about just for a second, just think about why Picbreeder is fascinating. At first, it might seem like a toy or something. What is it actually for? People are playing around and breeding images, which have no purpose other than just that they’re images, but actually, what is, I think, profound about having something like that is that it is basically a history of discovery in all of its minute detail. Every little thing that everybody decided to do throughout the history is recorded. We don’t have artifacts like that. We don’t know every step of every invention that’s ever been made. A lot of it just happened inside of someone’s head. So this is not recorded, but Picbreeder is one of the few things, maybe the only thing where every single step of everything is recorded completely. So that meant that after a couple years, we could go back and find out what actually explains how everything was discovered, and I turned out to be, I think, shocking. The shocking revelation was that in almost every single case, more than 99% of cases, if you looked at something interesting, like a car, for example, or a butterfly or a bird or whatever it might be, if you go back in its history and you look at what were the steps that led to that thing, the steps look nothing like it at some point back. Right before you get to it, it might look like it, but if you go back far enough, you will find a stepping stone that looks absolutely nothing like it in 99.9% of cases.
Why is that a revelation? Well, the problem is that if you think about it, what that means is that the only way to discover any of these things was to not be trying to discover them. Now, usually if you say things like that, that sounds like some new age statements, discover things by not trying to discover, and that’s mystical or something. Now, think about this. I’m not talking in the new age perspective. This is an empirical observation. This is actually what happened. The people who discovered these things who are responsible for the stepping stones that led to the discoveries were not actually trying to discover those things because if they had been, then they wouldn’t have chosen the things when they had their selections. They had these blobs they could look at. They could choose one of them. They wouldn’t have chosen the ones they chose if they were trying to get the final product. For example, you have a case where there was an alien face that led to a car. Who would choose an alien face if they want a car? That would not be a good idea, but what happened was the wheels of the car, which was depicted from the side, actually derived from the eyes of the alien face. Again and again and again, you see this phenomenon that in hindsight, you can see what happened, but looking forward, you would never imagine that these connections could be made. This shows, in fact, it’s true in Picbreeder that you can only find things in the long run by not looking for them. You need to take your eyes off the ball in order to be able to accept the stepping stones that ultimately make finding the ball possible, which I think is totally contrary to our culture, to our way of making discovery, the way we think things should be done, which is always objectively driven. So the connection that I need to make, I think, beyond that is to justify why I would extend from that discovery to real life.
[00:17:21] Patrick: If you think about the power of these images, most of them were achieved across what I’ll call a modest amount of generations. We’ll talk about AI and machine learning a little bit later on, which is so interesting because almost all of it has an objective function. It’s almost all objective-based. So that’ll be an interesting part of our conversation, but when you put up the number of generations of breeding to get from a blob to a clear bird, let’s say, it was only 80, 90, 40, 100. It wasn’t that many iterations. Then you showed us a skull, a picture of a skull, and really drove the point home by describing, “Okay. Now, let’s imagine this specific skull or one very close to it is our objective.” Could we get close to it across way, way, way more generations and actually targeting it? Maybe you can describe that experience because I found that to be a powerful nail in the coffin.
[00:18:10] Ken: So basically, we took this and we said, “Let’s try to drive the point home and also just see if we can validate this hypothesis that you can only find things by not looking for them by actually looking for them explicitly.” Just to make it fun, I think this twist makes it fun, let’s look for things that we already saw were discovered. That makes this crazy because it’s like we know that these can be discovered in this space. Like you said, I think it is an important point that these things were not discovered with a lot of compute, so to speak. If I recall, I think it’s 72 generations, might be 74, 72, 74 steps or iterations. That is just ridiculously low. When you think about it in terms of compute, of course, these are humans making these selection steps, but in machine learning, modern machine learning, it’s pretty reasonable to have millions of iterations to get to something meaningful. Here, we’re talking about dozens. In some way, that says these are easy. These are not hard discoveries. In some sense, they’re still impressive because of the fact if I just randomly choose blobs in blob space, in the space of the Picbreeder, you’ll never find anything. 99.99999% of the space is just garbage blobs. So these are still needles in haystack, but what’s weird is that the needles in the haystack are discoverable within a few dozen steps. One conclusion you might draw naively would be that, “Oh, they can’t be that hard to find.” The skull is let’s say 74 steps trivial, basically, from a compute perspective. So let’s set up an experiment and see. So what we can do is we can say, “Let’s get an image matching algorithm,” which are available, which basically tells me if I show this algorithm a blob, I input this blob and I ask it to compare it to the skull, it’ll tell me how far away we are, how close is this image to a skull.
That comparison will help me because when I show a bunch of blobs, I can just have it automatically pick the one that’s closest to the skull. It’s really simple. Then every iteration can be done now by the computer instead of by a human. So we can automate it. Good old fashioned machine learning here. We just automate Picbreeder. No more humans in the loop, and we’ll just automate it to go to the skull. I think to me, this sounds like a worthy adversary. I would be worried this might actually work. It shouldn’t work though our hypothesis is correct because our hypothesis here is that you can only find things by not looking for them. Now, this is explicitly looking for the skull. This is a metaphor for how we do things in our culture. So we say, “This is our goal. This is our OKR. This is what we’re going to achieve this quarter, and now we’re going to work towards it. You’re going to give me a metric. In this case, it’s skull matching. Let’s match the skull picture,” and then you’re going to cut off branches that don’t seem to be maximizing that metric and go by the branches that do seem to be maximizing the metric and just move towards the skull. We’re going to do that now explicitly. We gave it 30,000 steps. This takes about 74 steps, let’s say, for the first discovery by a human. Now, we’re giving an automated algorithm, 30,000 steps, just for fun, just in case, I don’t know, it needs extra time. We’ll give it way extra time, orders of magnitude. What happens? Failure every single time. We ran this dozens of times. It’s every single time failure.
It’s also fun to look at the failures because you can see it’s trying. You see it shadows. It’s like somebody stumbling, almost getting there but not quite. Well, it’s not even close, but it’s like getting the silhouette shadow of what it wants, but it can’t get even close. It’s just fascinating. That’s much more compute. It should be able to eventually overcome it, but the thing is that it highlights the reason that this is happening in if you look at it. Why are all discoveries happening this way in Picbreeder? It’s actually because the world is deceptive, which means that the things that lead to skulls don’t look like skulls. This is the fundamental insight, which is not being recognized across society. It’s that the things that lead to the things you want don’t look like the things that you want. There’s actually a name for this in philosophy. It’s called the like causes like fallacy. I think it’s from Mills. We all seem to assume. It seems to be almost like built in to us biologically that the things that lead to what we want are going to resemble what we want. I don’t know why we all believe this, but it’s not how the world works. If you think about it, that makes total sense. If the world actually worked that way, if the like causes like fallacy was actually true, actually things do resemble where you want to go, we would solve all that problems…
…[00:24:01] Patrick: There’s one piece of this that I love that hasn’t been mentioned yet, which is the role of the individual and their decisions relative to I’ll call this heterogeneous decision making versus homogeneous ruling by committee or something or making choice by committee. Talk about the importance of the individual and their choice in this web of invention and disruption.
[00:24:20] Ken: Yeah. This is a funny thing. It’s true. This is another very popular mythology, I think, in our culture is let’s get together and collaborate, bring all the smart people into the room. It’s not just like, “Let’s get interdisciplinary collaboration. Let’s get the computer scientists sitting there with the economist.” All these things are very exciting to us. I just want to say I’m not saying we shouldn’t have collaboration. That again would be this crazy cranky thing to say. What I do want to get to what you’re asking about is that collaboration itself also is subject to a number of caveats because of the insight about the paradox, the objective paradox, and that means there’s a right way and a wrong way to think about collaboration. It’s quite dangerous. We tend to do it the wrong way. The issue that comes up here is that if you look at Picbreeder, I think something that’s very intriguing about what happens in it is that once somebody sees a stepping stone on the site, so if you recall, like I said, all the discoveries that other people had made are made available for you. So what it means is you are seeing a history of stepping stones when you go to this site. You don’t have to start from scratch. If somebody found a butterfly, you can start from their butterfly.
When you come in and see that butterfly, that is a point of collaboration. It’s implicit collaboration, but it is collaboration because somebody else did work, they found the butterfly, and now you’re building off of that work. So collaboration is happening. However, the moment you choose to continue or what we call branch from the butterfly to breed it further, you are on your own. This is a very unique thing. At first, it sounds like, “Oh, well, what’s the big deal? You’re on your own, okay,” but think about it. We almost never allow people to do that in collaborative situations in our culture. We always bring people together and move towards consensus almost immediately, but in Picbreeder, it’s not like that. Instead, you choose the thing you think is interesting and it was your choice and nobody else was involved in that choice. Now, think about this compared to, for example, I was a professor for a long time. So I think a lot about asking for grants, science grants. That’s like picking an image. It’s like what project do I want to pursue. You come in and you see a butterfly and you want to pursue the butterfly. It’s like you’re sending a grant proposal to the NSF. You think something interesting will happen if you choose this butterfly, but the thing about the NSF is now it’s going to go to a committee. I am not allowed to just go off on my own and work on that butterfly now. There’s going to be a committee that thinks about the decision that I’m making, and I have to justify usually objectively in the sense that I’m going to have to say where it’s going to lead.
What are you going to get by doing this butterfly? That is not how Picbreeder is. You are on your own completely, and not only are you on your own by choosing the butterfly, you’re on your own every single step of the way until you publish the thing you discovered. So there’s no interference, whatsoever, and you’re just on your own. Think about the difference between that and the way we run things where it’s basically you come into a room with all these people, you bring up these ideas, you have this discussion, you try to come to consensus. All the crazy things you would’ve done are basically cut off at the start by this surge towards consensus, which is going to lead to what I would call convergent consensus because we’re trying to move toward convergence very quickly. What you’d understand from Picbreeder is the proliferation of the stepping stones that gives the power to the process. The reason that I can get to cars, there was a discovery of a car which came from an alien face, was because of the discovery of the alien face. No one would ever think that you needed an alien face to get to a car, but the alien face is there not because somebody was thinking about cars, but because there is a general culture inside of Picbreeder of proliferating stepping stones.
This is not generally how we run collaborative systems because we run them by consensus, which is the exact opposite. That’s about pruning out stepping stones. People start generating things and then we start saying, “No, no, no. Committee doesn’t like this. Committee doesn’t like that.” We then converge to the thing, which is basically the consensus basis of current thinking, which tends to be dogmatic and tends to be status quo and everything that we basically want to get away from, and then all these radical stepping stones, which are the interesting things which could lead to places we’re not expecting for the very reason that the things that we’d want to get to don’t look like them so we need the radical stepping stones are the things that we cut out. You can see from this theory or philosophy way of looking things that a lot of the way we run collaborative systems is just totally kneecap at the start, and also should, I think, be rethought.
[00:28:31] Patrick: Can you describe when you put a consensus mechanism into this experiment, the outcome falling to all this? I promise we’re going to get to some of the bigger implications here in a minute, but this simplified example is this so damn powerful for how we all are going to spend our time in our lives. Maybe just describe the outcome when you insert consensus mechanism into how these generations progress.
[00:28:54] Ken: This is super interesting, and it’s funny because it’s just a coincidence that this happened because there was another project that was launched around the time of Picbreeder called the living image project. It had nothing to do with me other than it used basically the same in coding under the hood as Picbreeder. This is nice because it creates a controlled experiment by accident because both Picbreeder and this other thing, the living image project, have this underlying coding that’s the same. So what that means is in principle, they can achieve the same thing. They could find similarly cool stuff in principle, but there’s this one difference, which makes this very interesting as a comparison, which is this living image project did work by consensus. I mean, the reason it did is because I think it’s because there’s this cultural assumption just like riding on top of that. They’re like, “This is a good way to do things. Let’s have a vote.”
So basically what they said is, “Okay. Here’s what we’re going to do. Just like Picbreeder, there’s these blobs, they’re arranged on the screen, you can see all these blobs, and we’re going to pick one of them. That’ll be the parent of the next generation of blobs.” However, the difference from Picbreeder is that the choice will be made by a vote. So over the course of a week, people will come in and it would turn out basically hundreds of people would come in, and they would vote on their favorite blob and then we’ll choose the one that gets the most votes. To a lot of people, this is really intuitive. More opinions are better than one. Let’s use the crowd to decide what to do, but consistently with what I just argued, the result are starkly different and terrible in comparison. I don’t mean to cast any dispersion on the living image project. I think it was a cool idea to try it. It really helps to illustrate. The problem here is that you get a washout effect. Imagine you come in, okay? There’s hundreds of people coming in. Imagine you like butterflies and I like cars. Now, what’s going to happen when we vote and we’re just looking at blobs? The blobs don’t look like butterflies yet and they don’t look like cars, and you want a butterfly and I want a car. What is going to happen? Complete washout is what’s going to happen.
There’s no way you’re going to get enough people on your side. You don’t even know. We don’t even know what each other are doing or have understanding of how you even get these things. So what’s going to happen is you get this mildly aesthetic blobby pattern type of consensus. We get the mildly, most pleasing blob aesthetic, and then that’s going to happen at every iteration because there’s another few hundred people voting at the next iteration and another few hundred, and after thousands and thousands of, I think it was 25,000 votes, you can look at the top ranking, all you have are amorphous rainbowy blobs every single thing. I think it’s just stark and shocking. Even though it’s in this totally obscure genre of stuff like breeding pictures, I think it should give us all heart palpitations because we’re running our culture this way…
...[01:08:29] Patrick: It’s incredible, and I think demands one last question. This idea that you’ve referenced over and over again that no one is telling people how they have to behave in something like pick breeder. There’s a permissionless nature to it. There’s a individuality and individual interpretation of events. With all that in mind, for those whether it’s running a grant organization or running a labs, an AI labs or innovation labs inside of a company or anyone that has resources like Ed did that want to deploy those resources in service of disruption and innovation, either generative or protecting against it or whatever, you’ve already talked about what they do wrong. If you were in-charge of one of those, an allocator of resources to create innovation, how would you do it?
[01:09:14] Ken: I think if you’re in a position like that, you’re a gatekeeper. So you are responsible for the perpetuation or not of this objective culture. It’s especially relevant if you’re purportedly involved in fostering innovation because that’s where this gatekeeper has a huge influence. Yeah. I would recommend doing things differently. You probably exist in their framework where that’s very difficult because you answer to somebody. They don’t understand where you suddenly say, “Well, I’m not assessing things in this normal objective way anymore.” They’re like, “What the heck are you doing? How do we know this is working?” So this takes some courage, I think. The first thing I would say, get the courage because there’s nothing we can do about that. You have to explain to them, “If we’re not going to follow the usual security blanket rooted things, the people in the chain are going to have to be convinced and that’s hard work.” That’s why I think it’s worth having a conversation like this show. That’s why we wrote the book. It’s like we wanted to start people having these conversations. So get the courage to have the conversations and really fight because it’s not going to happen if you don’t. You’re just going to shut down. You’re going to think, “I want to do this, but, eh. On the other hand, my boss wants this. His boss wants that. There’s a funding agency out there or we have investors.” You’re like, “Forget it. It’s too complicated.” Somehow you got to fight this.
Now, in terms of actually practical implementation, what should you do? What I would say is you should be maximizing stepping stones in the pursuit of innovation, not maximizing an objective performance. There’s two things, maximizing stepping stones and maximizing exposure to stepping stones. The thing that makes innovation work is that the people who could run with something are exposed to the thing that they could run with, and that is what’s missing I think from a lot of these organizations is that we have these filters, which are extremely narrow, which decide what comes through, and they end up pruning out things. It’s the conversion consensus problem. Things don’t get exposed to a person who would react dramatically if they were exposed to that thing. What we should do is greatly broaden the filters that go from idea to exposure to the people who could run with the ideas and then also change the criteria for what should be pursued. You have to recognize that if you pursue something that requires investment, so it costs money. So we’re not talking about decisions that can be made lightly. Nobody can say, “Well, everything will pursue because now we’re all going to be open-minded. We’re just going to do everything everybody wants.” That cannot happen. Some things have to not happen, but the way that we decide what happens, I think the criteria should be quite different.
It should not be trying to move to consensus, get a committee to agree with something, get the most vote, something like that. It should be many people within the context of the organization, whatever many means. Many people are exposed to the ideas that are being generated, and that basically only one or two need to trigger the success of that idea or to say, “This is worth investing,” but then you say, “Well, how can that be?” Then every idea would have to be invested because somebody might want to invest in everything. The reason I think it can make sense is if there’s skin in the game for the people who are validating the ideas. If I see something that is so exciting to me that I’m personally willing to pursue it that I didn’t come up with myself, just like the alien face that led to the car in Picbreeder, then I’m actually willing to spend my time on what you did. I’m actually giving something away. I could have had that time. I could have invested in something else. What should make the confirmation of something meaningful and really worth investment is if the person who’s confirming it is giving something away. Maybe they lose their right for some period of time to have their idea even considered or they give away the resources that they were giving for some project that they had. There’s obviously finite resources, but if someone’s willing to do that, that means that this thing means a lot to them, and it only takes one person, magic connection, electric connection to happen, and we have to somehow create those connections. It’s not going to be consensus matter. It’s going to be a niche thing. When there’s something incredible, it’s not going to be tons of people see, it’s going to be one out of a hundred see it, and that has to be honored somehow. We have to find a way to do that.
PS: In fairness to Russell, it was in my introduction to Russell’s question [i.e., not in Russell’s question itself] that I said the economy is mechanical and that’s the definition of mainstream economics. Russell and I do not necessarily agree on that. But to continue on mechanical economics as a theory: In your memo On the Couch, you talk about your own early exposure to the efficient-market-type classes. For the audience, EMH is based on the rational expectations hypothesis; EMH states that markets are rational because any pockets of irrationality are averaged away [i.e., the errors made by the group become smaller than those made by individuals]. In contrast, you also highlight the reality of irrationality that can be observed in markets, something that both Alan Greenspan and Robert Shiller called “irrational exuberance.” Later, the GFC, or the Global Financial Crisis, painfully hit home that what seems rational for an individual can be dangerously irrational if done collectively. So my first question is, can we square this circle? For example, is irrationality just about semantics, or is it something real that not only exists, but because of the collective dynamic, can actually threaten the economic system and may thus not necessarily be averaged away?
HM: To me, Patrick, the answer lies in my view of the efficient market hypothesis. Again, the efficient market hypothesis says that due to the concerted actions of so many investors, who are intelligent and numerate and computerized and informed and highly motivated and rational and objective and willing to substitute A for B, prices for securities are right, such that they presage a fair risk-adjusted return. I believe that’s the definition.
But you get into a problem, because when I listed off the qualities that are necessary for a market to be efficient, I snuck in there the economist’s notion of the perfect market and its requirement that the participants be rational and objective. And in investing, they’re not. That’s really the point.
“Economic man” is supposed to make all these decisions in a way that optimizes wealth. But she often doesn’t, because she’s not always objective and rational. She has moods. And those moods interfere with this arriving at the right price. So my definition of the efficient market hypothesis is that because of the concerted efforts of all the participants, the price at a given point in time is as close to right as those people can get. And because it’s as close to right as most of them can get, it’s very hard to outperform the market by finding errors – what theory calls “inefficiencies” and I just think of as “mistakes.”
Sometimes prices are too high. Sometimes prices are too low. But because the price reflects the collective wisdom of all investors on that subject, very few of the individuals can identify those mistakes and profit from them. And that’s why active investing doesn’t consistently work, in my opinion. I think my version of the efficient market hypothesis makes it roughly just as hard for active managers to beat the market as does the strong form of the hypothesis, that everything’s always priced right. But I think mine is more reflective of reality. I wrote in one of my memos – maybe it was What’s It All About, Alpha? – about a stock that was $400 in 2000 and $2 in 2001. Now it’s possible – but to me it’s unlikely – that both of those observations were “right.” Rather, I think they merely reflected the consensus of opinion at the time.
This business – I shouldn’t say “this business”; that sounds derogatory – the idea that inefficiencies will be arbitraged away by the operations of the market ignores one of the key elements that I think describes reality, and that is mass hysteria. And I think the markets –economies too, but more importantly the markets – are subject to mass hysteria.
I think it was in On the Couch that I said, “in the real world, things fluctuate between pretty good and not so hot. But in the markets, they go from flawless to hopeless.” Just think about that one sentence. If it’s true – and I believe it’s true – that shows you the error, because nothing is flawless and nothing is hopeless. But markets, I believe, treat things as flawless and hopeless, and there’s the error.
The book I mentioned, Mastering the Market Cycle (I’m going to keep repeating the title in the hope that everybody will buy a copy) . . . You know, I’m a devotee of cycles. I’m a student of cycles. I’ve lived through a half a dozen important cycles in my career. I’ve thought about them. I think they dominate what I do. And I got about two-thirds of the way through writing that book and something dawned on me, a question: Why do we have cycles?
The S&P 500 – I mentioned Jim Lorie – the Center for Research in Security Prices told us almost 60 years ago, that from 1928 to ’62, the S&P 500 had returned an average of 9.2% a year. Things have been better since then and I think if you go back and look at the whole last 90 years, it’s 10½% a year, the return on the S&P 500.
Here’s a question: Why doesn’t it just return 10½% every year? Why sometimes up 20% and sometimes down 20%, and so forth? In fact – and I included this factoid in one of my memos – it’s almost never up between 8% and 12%. So if the average return is 10½%, why isn’t the return clustered around 10½%? Why is it clustered outside the central range? I think the answer is mass hysteria.
And by the way, the same is true of the economy and mainstream economics, which of course you described as mechanical, and I think that many people would describe as mechanical. But, certainly, economics is driven by decisions made by people, who are not always rational and objective. Maybe in theory they’re closer than investors to being rational and objective, but still they’re not always.
But anyway, my explanation for the occurrence of cycles is “excesses and corrections.” You have a secular trend or a “normal” statistic. Let’s say it’s the secular trend of the S&P 500. Sometimes, people get too excited. They buy the stocks too enthusiastically. The prices rise. They rise at more than a 10½% annual rate until they get to a price that is unsustainable. And then everybody says, “No, I think they’re too high.” So then they correct back toward the trendline. But, of course, given the nature of psychology, they correct through the trendline to an excess on the downside. And then people say, “No, that’s too low,” so then they bring it back toward the trendline and through it to an excess on the high side.
So excesses and corrections: that’s what cycles are about, in my opinion. Where do the excesses come from? Psychology. People get too optimistic, then they get too pessimistic. They get too greedy, then they get too fearful. They become too credulous, then they become too skeptical, and so forth. Oh, and the big one: they become too risk-tolerant, and then they become too risk-averse.
PS: If I can just follow up on that – particularly for our cognitively inclined audience – implied in this you suggest that there might be mental causality, and my next questions are basically also to motivate future research as part of economics revision. But during your September podcast, in which you revisit the On the Couch memo, you talk about causality and how complex it can be. And we agree and highlight this in our work.
For example, when Alan Greenspan, in that famous ’96 “irrational exuberance” speech, mentions the complexity of the interactions of asset markets and the economy, and I’m quoting him now: “It chiefly concerns, at least in our view, this dualism of the psychological of the former and the physical of the latter.” Now, saying this, mental causality is highly controversial and complex in cognitive science, but cognitive science is the area that really studies this. So, you also specifically refer to Soros’s reflexivity in that context, and as you already indicated just now, but also in your memo, you equate prices almost to psychology. And finally, we’ve all experienced this dangerous – to the point of existential – tail-wagging-the-dog dynamic surrounding Lehman’s collapse. So my first question is, if we agree that we will not gain much by identifying yet another behavioral bias, nor by running yet another regression, what would you like to see investigated by cognitive scientists that could potentially lead to more important insights, especially regarding our understanding of the interaction between these two domains of the real and financial economies?
HM: Well, the people at this symposium know much more than I do about how to get to the bottom of these things. But clearly there’s so much grist for this mill. Now, exactly how you quantify mood, and so-called animal spirits and irrational exuberance, is beyond me. I always say, Patrick, and I think I said it in Mastering the Market Cycle, that if I could know just one thing about every security I was thinking about buying, it would be how much optimism is in the price.
When you watch TV and you hear the newsreaders talking about what happened in the stock market today, you get the impression that prices are the result of fundamentals and changes in prices are the result of changes in fundamentals. And that is vastly inadequate. (By the way, they always say, “The market went up today because of X” or “The market went down today because of Y.” I always say, “Where do they go to find that out, because I haven’t found it yet?” I haven’t found where you go to get an explanation of the market’s behavior, even after the fact.) But it’s not true that it’s all about fundamentals. The price of an asset is based on fundamentals and how people view those fundamentals. And a change in an asset price is based on the change in fundamentals and the change in how people view those fundamentals. So, facts and attitudes. Any research that could capture changes in attitudes, I think is important.
Now, what about quantifying these animal spirits? In one of the more jocular portions of my first book, The Most Important Thing, I include something I called “the poor man’s guide to market assessment.” I have a list of things in one column, and I have a list of things in the other column, and whichever list is more descriptive of current conditions tells you whether it’s optimism or pessimism that’s governing the market. There are things like, do deals get sold out or do they languish? Are hedge fund managers being welcomed on TV or not? Who does the crowd form around at cocktail parties? What is the media saying: “We’re going to the moon” or “We’re cratering forever”? I don’t know how to quantify these things. But these are among the very important things that I listen to in order to figure out where we stand in the cycle. And I believe where we are in the cycle plays a very strong role in figuring out where we’ll go next. (In fact, take the title of my second book, Mastering the Market Cycle. When I was thinking about writing it, it was called Listening to the Cycle. “Listening” in the sense of taking our signals from where we are in the cycle. “Listening” also in the sense of obeying. The publisher thought we’d sell more books if the title implied the book would help you master the market cycle.) But I, as a practical investor, try to figure out what’s going on around me.
Now let’s go back. I didn’t do what I should have, because I didn’t answer Russell Napier’s real question: can I name two episodes that showed this kind of thing in action? I was glad to have the questions in advance, because it allowed me to think about the two episodes I want to propose.
In the spring of 2007, I wrote a memo called The Race to the Bottom. This was when the subprime mortgage mania was at its apex, I think, and when the logs had been stacked in the fireplace for the conflagration that became the Global Financial Crisis. It happens that I was driving around England in the fall of ’06 – maybe November or December ’06 –and I was reading the FT (I mean I wasn’t driving and reading; I was being driven so I could read), and there was an article in the FT that said that, historically, the English banks had been willing to lend people three-and-a-half times their salary in a mortgage. But now, XYZ Bank announced that it was willing to lend four times your salary, and then ABC Bank said, “No, we’ll lend five.” And that bidding contest – to make loans by lowering credit standards – seemed to me to be a race to the bottom. And I wrote that markets are an auction place where the opportunity to make a loan, or the opportunity to buy a stock or a bond, goes to the person who’s willing to pay the most for it. That is to say, get the least for his money, just like in an auction of a painting. And so, in this case, the bank that was willing to have the lowest credit standards and the weakest loans was likely to win the auction and make the loans: race to the bottom. And I said this is what happens when there’s too much money in the hands of providers of capital and they’re too eager to put it to work. Mood! And, of course, we all know the Global Financial Crisis ensued.
Now fast forward from February ’07 to October ’08: Lehman Brothers goes bankrupt on September 15, 2008, and now, rather than being carefree, the pendulum has swung, and people are terrified. Rather than seeing risk as their friend, as in, “The more risk you take, the more money you make, because riskier assets have higher returns,” now people say “Risk bearing is just another way to lose money. Get me out at any price.”
So the pendulum swung, and of course people’s optimism collapsed, the S&P 500 collapsed, and the prices of debt collapsed. So I wrote a memo right around October the 10th of ’08 – maybe that day was the all-time low for credit, I don’t know exactly – that was called The Limits to Negativism, based on an experience I had. I needed to raise some money to delever a levered fund that we had that was in danger of melting down due to margin calls, and I went out to my clients. I got more money. We reduced the fund’s debt from four times its equity to two times. Now we’re again approaching the point where we can get a margin call. Now I need to delever it from two times to one time. I met with a client who said, “No, I don’t want to do it anymore.” And I said, “You gotta do it. These are senior loans, and the default rate on senior loans has been infinitesimal over time. There’s potential for a levered return of 26% a year from what I consider incredibly safe instruments.”
This client – excuse me if I belabor this, but I think it’s interesting – this client said to me, “What if there are defaults?” And I said, “Well, our historical default rate on high yield bonds – which are junior to these instruments – is 1% a year. So if you start with 26% and you take off 1% for defaults, you still get 25%.” So she said, “What if it’s worse than that?” I said, “The high yield bond universe default rate has been 4% a year, so you’re still getting 22% net.” She says, “What if it’s worse than that?” And I said, “The worst five years in our default experience is 7½%, and if that happens, you’re still getting 19%.” She says, “What if it’s worse than that?”, and I said, “The worst year in history is 13%. If that recurs every year for the next eight years, you’ll still make 13% a year.” She says, “What if it’s worse than that?” And I said, “Do you have any equities?” She said, “Yes, we have a lot of equities.” I said, “If we get a default rate on high yield bonds of more than 13% a year every year into the future, what happens to your equities in that environment?”
I describe myself as having run back to my office after that meeting to write that memo, The Limits to Negativism. What I wrote there was that it’s very important when you’re an investor to be a skeptic and not believe everything you hear. And most people think being a skeptic consists of dealing with excessive optimism by saying, “That’s too good to be true.” But when it’s pessimism that’s excessive, being a skeptic means saying, “That’s too bad to be true.” That particular investor couldn’t imagine any scenario that couldn’t be exceeded on the downside. So, in other words, for that person, there was no limit to negativism.
And when I conclude that the other people in the market, the people setting the market prices, are excessively negative and excessively risk averse, then I – an inherently conservative person – and my partner, Bruce Karsh, who runs our distressed debt funds – also an inherently conservative person – we go crazy spending money when we conclude there’s excessive pessimism, fear, and risk aversion incorporated in asset prices [meaning they’re lower than they should be]. So it’s not just the mechanical aspects that determine market prices – it’s psychology. It’s mass hysteria, which comes in waves from time to time, that leads to market cycles that prove excessive.
That company is Aether, a lab-grown diamond startup that just raised $18 million in a funding round led by Helena, a “global problem solving organization” that includes both a for-profit investment and nonprofit action arm. Lab-grown diamonds are a hot market, and there’s no shortage of companies claiming that these synthetic gems are more ethical or environmentally friendly than their Earth-mined counterparts — and there are even other companies also focused on making diamonds using carbon dioxide from the air. But Aether’s claims are backed up by some ambitious facts about its operation: not only is it making diamonds in a process powered by clean energy — it’s pulling an additional 20 metric tons of CO2 out of the atmosphere per carat it produces.
While the cost of capturing all that carbon would be high for a company selling, say, cement, it’s one the luxury jewelry brand says it can easily absorb. And the world needs businesses that can pay for so-called direct air capture and still generate a profit if the nascent technology is ever going to make a dent in climate change…
…Aether, which also works with Climeworks, wouldn’t disclose how much it’s paying for direct air capture services. But it says it can transform one ton of captured CO2 into “millions of dollars’ worth of diamonds”. On a per carat basis, those diamonds, an ultra high-purity breed known as Type IIa diamonds that are difficult to find in nature, sell for anywhere from $4,900 to over $10,000. Shearman says this price range is higher than many competitors in the lab grown space and closer to that of mined diamonds because of the additional work that goes into making the fabrication process as clean as possible.
That process starts with Aether purchasing carbon dioxide from Climeworks’ facility in Switzerland and shipping it to the United States, where the diamonds are grown. Aether puts that CO2 through a proprietary process to convert it into high purity methane, or CH4. That methane is then injected directly into the company’s diamond reactors, where a method known as “chemical vapor deposition” is used to grow rough diamond material over the course of several weeks.
The chemical vapor deposition process involves heating gasses to very high temperatures under near-vacuum conditions, and considerable energy is required to do so. Shearman tells The Verge that this process and other manufacturing stages are powered entirely by carbon-free sources like solar and nuclear. Once the diamonds finish growing, they’re shipped to Surat, India, where they’re cut and polished before being sent back to New York City’s diamond district for sale…
…Aether only needs a relatively small amount of carbon dioxide to make the diamonds themselves — think fractions of grams rather than tons. Then, for every carat of diamond it sells, the company says it removes an additional 20 metric tons of carbon from the air, using a mix of direct air capture and other carbon removal methods that involve long-term carbon sequestration. Shearman says the company based this commitment on the fact that the average American has an annual carbon footprint of approximately 16 metric tons, meaning most customers can expect to roughly cancel a year’s worth of personal emissions by purchasing an Aether diamond. “It’s something that has proved to be difficult but doable, and we’re really proud to be able to do that,” he says.
Aether started shipping its first diamonds to customers in the middle of 2021. While Shearman wouldn’t offer specific sales figures, he says that the company produced “hundreds of carats” of diamonds last year, and this year plans to produce thousands. Shearman described the $18 million in Series A funds raised by Helena as “the fuel that’s going to enable us to increase our production footprint this year.”
Integrated Photonics (IP) is the use of light for applications traditionally tackled by electronics. It can be used in a wide range of areas including telecommunications such as 5G networks, biosensors for speeding up medical diagnosis, and in automotive where it is used in LiDAR. IP consists of integrating multiple photonic functions on a Photonic Integrated Circuit (PIC) fabricated using automated wafer-scale generic integration technology over silicon, silica, or Indium Phosphide (InP) substrates. Integrated photonics dramatically improves the performance and reliability of these photonic functions while simultaneously reducing the size, weight, and power consumption.
A good introduction to IP is by understanding its similarities and differences with traditional electronic circuits. Where electronics deal with the control of electrons on a chip, photonics does the same with photons. Photons are the fundamental particles of light.
Conventional integrated circuits (ICs) conduct electricity by allowing the flow of electrons through the circuit. Electrons are negatively charged subatomic particles that interact with both other electrons and other particles. These interactions slow electrons down as they move through circuits, this limits the amount of information that can be transmitted; it also generates heat, which in turn causes energy and information losses.
Photonic integrated circuits (PICs) use photons. Photons move at the speed of light with almost no interference from other photons. This greatly increases the bandwidth (the data transfer rate) and speed of the circuit, without big energy losses making PICs significantly more efficient than their IC counterparts.
Integrated photonic components use “waveguides”, which confine and direct the light in the desired directions (by total internal reflection), much the same way as metallic wires do for electrical signals. A PIC provides functions for information signals on optical wavelengths typically in the visible spectrum or near-infrared 850 nm-1650 nm.
The elements on a PIC are connected via waveguides. The chip elements can be both passive (e.g. couplers, switches, modulators, multiplexers) and active (e,g amplifiers, detectors, and lasers). These components are integrated and fabricated onto a single substrate, which creates the compact and robust photonic device.
A key difference between electronic circuits and PICs is in the primary device that is used for fabrication. In an electronic integrated circuit, the main device is the transistor. But, in PIC, there is no particular main device that dominates in the fabrication. According to its application, the PIC will be designed with a range of fabrication devices. This integration presents opportunities to reduce current bulky, complex, and expensive optical systems in an integrated chip-scale way that has increased stability and robust operation, reduced size and power consumption, and cost-effective large-scale fabrication of even complex circuits.
Here’s a surprisingly useful question to ask yourself next time you’re stumped by a problem, daunted by a challenge, or stuck in a creative rut: “What if this situation is even worse than I thought?”
This question, I admit, appeals to my taste for bloodyminded contrarianism. But its real value is that it expresses what I think of, more and more, as a fundamental truth about human psychology: that we often make ourselves miserable – and hold ourselves back from what we might be capable of achieving – not because we’re too pessimistic, but because, in a sense, we’re not pessimistic enough.
We think of certain kinds of challenges as really hard when they are, in fact, completely impossible. And then we drive ourselves crazy trying to deal with them – thereby distracting and disempowering ourselves from tackling the real really hard things that make life worth living.
A case in point: you feel overwhelmed by an extremely long to-do list. But it’s worse than you think! You think the problem is that you have a huge number of tasks to complete, and insufficient time, and that your only hope is to summon unprecedented reserves of self-discipline, manage your time incredibly well, and somehow power through. Whereas in fact the incoming supply of possible tasks is effectively infinite (and, indeed, your efforts to get through them actually generate more things to do). Getting on top of it all seems like it would be really hard. But it isn’t. It’s impossible…
…Anyway, you get the picture. And you probably get the point, too – which is that when you grasp the sense in which your situation is completely hopeless, instead of just very challenging, you can unclench. You get to exhale. You no longer have to go through life adopting the brace position, because you see that the plane has already crashed. You’re already stranded on the desert island, making what you can of life with your fellow survivors, and with nothing but airplane food to subsist on. And you come to appreciate how much of your distress arose not from the situation itself, but from your efforts to hold yourself back from it, to keep alive the hope that it might not be as it really was.
And then, crucially – because some people tend to mistake this for an argument for nihilism, or a life of mediocrity, when it’s really the opposite – that’s precisely when you can throw yourself at life’s real hard challenges: the impressive accomplishments, bold life choices, and deeply fulfilling relationships. You get to live more intensely, because you’re no longer making your full participation in life dependent on reaching some standard – of productivity, of certainty about the future, of competence, etcetera – that you were never going to reach in the first place.
[00:05:11] Patrick: Can you give an example that is not at all Shopify related on interoperability and the power of platforms from history that people might be familiar with?
[00:05:20] Alex: Let’s talk about interoperability for a second. People use this to mean a lot of things, but in general, what it means is that imagine that you have two levels of a system where one level of the system needs to interact with the other level, and you have n players on level one, and you have n players on level two, they both need to be able to work with each other in a way that just works fluently without really having to talk to each other very much. I’ll give you an example, which is the shipping container. I know you love talking about shipping containers on the show. I have a factory that makes inputs and you have a factory that takes those inputs and you build something value added out of them. And I need to ship it from me to you, how do we do this? Well, we could work together on figuring out, what is the shape of box that best fits this part? And how do I work with a shipper to make sure that box is going to go on their boat or on their plane effectively?
And how do we negotiate all these things? Or we could just put it all in the same, exactly standardized 40 foot box that goes on boats that know how to fit exactly that box on it, and through a supply chain that knows how to deal with this thing and then out the other side with neither of us ever having to even know about each other or what we’re putting in. This is this idea of a constraint that de constrains. It’s a very, very common motif that you see in interoperability, which is this idea, a free for all is actually no freedom at all. A very, very common lesson here. I can give you all sorts of examples throughout history of saying, if you give people no rules whatsoever, and then everybody tries to work with itself, that’s a mess, nothing ever gets done. However, if you have these really nice constraints or conventions or platforms or standards, many different angles of approaching this problem of interoperability, you can actually unlock something pretty magical, which is this community of n people on one side and this community of n people on the other side can actually create n times n different things without needing n times n different bits of glue stitching all of those things together…
…[00:12:05] Patrick: Can you think of an example where that’s violated, where someone’s trying to create a constraint standard but there is too many degrees of freedom and it failed?
[00:12:13] Alex: Sure, that’s almost every standard. Most standards do not succeed. And the reason why they do not succeed is because they just don’t grasp the problem entirely correctly. There’s that XKCD joke, which is like, “There are 12 standards, what we need is a common standard for how everybody represents this. The next day, there are 13 standards.” Standards work is very, very difficult to achieve because so many things have to go right. But if you look across, even in the history of computing, there are several incredible reference standards that are held up. Unix is one of them. The IP internet protocol is probably the greatest one of all of them, it is a very, very, very restricted way in which you can represent the information going through the internet, but what it means is that any webpage, any application, any whatever can submit something that can then get communicated over any kind of communications network. It could be sent over copper wire. It could be sent over ethernet. There are some aficionados that have sent message by carrier pigeon over internet protocol. You can do it. It doesn’t matter. As long as it runs through internet protocol, anything will work on either side. This overall design, I know you talked with Tobi the last time he came on the show, this overall design is something called hour glass architecture or narrow waist architecture. It’s one of the most powerful ideas in building things. This idea of, if you want many, many things to be able to inter-operate with many, many other things, there needs to be a narrow waist that is as constrained as possible between them.
A very, very important idea, and so Shopify really, really understands this, as evidence through how we built Liquid and how every app developer can make apps that works with every theme developer and they don’t talk to each other and you don’t need a piece of custom glue like you would with enterprise software, it just works. The same with anything can go into the internet protocol and it can be communicated over anywhere. Another good example of a narrow waist in computing is the X86 architecture, which Intel made. Anybody can submit instructions to this instruction set, and then it can be executed on any processor that knows how to deal with the X86’s instruction set, but there’s this common waist that it goes through. And I’m including Intel in there just to show that there are a couple of different ways that a narrow waist can come about. It could come about through a bunch of different academics getting together, it could come through with a standard body, but also it could come through when one monopoly says so. In the case with Intel, there’s not any one way to do this, but they’re hard to achieve, and when you do, you have something that’s going to last for a very long time.
[00:14:24] Patrick: It’s sort of obvious with the examples you’ve given, whether it’s the shipping container, or the internet itself, X86, ISO, whatever, that when you get one of these right, it crazy amount can be built on top of it in ways that you could never envision when you set the standard, the creativity that can exist on top of it is fast and unpredictable. That brings us to the topic at hand, which is tokengated commerce, maybe we need to start with why blockchains are potentially interesting narrow waists. But before we do that, tokengated is two parts, token and gated, give us a high level description of why you were doing this, why you were spending your time on it, why Shopify is heavily invested in this notion? This is a new idea, and I want to understand it at a high level.
[00:15:03] Alex: First, let me actually tell you what is tokengated commerce, because at its heart, it’s actually a very simple idea. Tokengated commerce means, here’s a product and I’m going to put a gate in front of it. And if you want to pass the gate, you need to show me a token that says I pass the gate. More generally speaking, what does this look like in practice? Well, what it looks like is, “Hey, I’m a brand. I have all these cool products. I want to make them very exclusive. If you want to unlock the product, you have to connect your wallet, a crypto wallet, sign a transaction showing that you own this wallet and this wallet owns,” let’s say, “the right NFT.” Because I own this NFT, I can unlock this product. Or it could be, because I own this NFT I unlock early access to a drop. I can get to the drop 15 minutes earlier than everybody else. Or because I own the rare version of this NFT, I’m able to get the rare version of the hoodie. Anybody can get the black version, but if I have the rare NFT, I can get the red version and that red version is cool. Or because I own this NFT, I’m able to buy this product and you can only buy one product per number of NFTs you own. These are all various ways of implementing this simple idea, which is, there is context somewhere. And that context is going to influence how my business wants to treat you. What if, as the buyer, I can bring that context with me and sign with it, proving I am me and here’s how I show I have some ownership over this bit of context?
And my storefront can respond to this and say, “Okay, now that I see that you’ve signed for this bit of context, my storefront is going to respond to that context by doing something appropriate.” Maybe it’s unlocking a product. Maybe it’s giving you really access to a drop. Maybe it’s letting you get into a party. It could be anything. It could be, “Here’s something live and in person. Here’s access to 15 minutes of a live stream with me.” It could be anything. It doesn’t just have to be products. This idea of token gating, defined it very, very simply is it’s a kind of behavior that is very, very natural. It’s how do I get into the exclusive thing? How do I show that I have done the challenge of gaining access? How do I get the thing that I want to get that is hard and feels like a reward? These are very, very old ideas in commerce, this idea of commerce is a challenge that the buyer and the merchant do together. And token gating, we are finding, is an incredible foundational piece of UX for the basic idea of the most meaningful kind of commerce is a challenge that you do together.
[00:17:08] Patrick: If you think about the many, many aspects of this, I want to start with the token itself, because if you think of non-fungible token, which have been popular, you own a Bored Ape, you own a Crypto Punk, you own whatever, piece of art, whatever, you could see a world where brands build specific product lines that tailored to you have to own one of these things that are already independently exclusive. So we’re sort of riding the scarcity of Bored Apes, let’s say, as a cool way to create something custom for them. Talk to me what you’ve learned here. Do you think that most merchants will outsource the scarcity function of the tokens themselves, or are you going to empower them to also create their own tokens that trade? It just seems like the world has coalesced around a small number of the most popular projects. Like all the examples you hear are, “If you’re an owner of one of those special things, we’re going to treat you differently, because it’s like you have a black card or something.” So start with the token piece. How do you think it will work?
[00:18:00] Alex: In that question, there were like three or four really good questions. So I want to try to answer them in the right order here. First, if you look at these NFTs, what are these things? What are they any good at representing? What do they all have in common? Let’s break down a couple of common aspects of these NFT projects. One aspect of them is these entities are owned by people, and the way that they own them is through their wallets. What is a wallet? Well, at a very basic level, a wallet is I have my public address and I have my private key and I sign my private key to show that I am who I am. My wallet address is associated with this token on this smart contract, which means I own this ape. First of all, let me just present a very basic observation, which is what are people doing with their wallets? Well, they’re connecting them everywhere. They’re connecting them to discords, to get into the discord. They’re connecting them to adapt. They’re connecting them to any kind of application that is asking them to authenticate in a certain kind of way. Now what we’re seeing is people want to connect these wallets to storefronts to say like, “Hey, I’m not a fungible buyer. I’m a non-fungible buyer because I have this token.” We really like saying NFTs aren’t a kind of product. They’re a kind buyer, a non-fungible buyer. I really want to get this into people’s minds. NFTs fundamentally to us are an input for commerce. They’re a piece of context that the buyer brings with them when they show up to the storefront. It can also be an outcome of commerce. We can do a commercial transaction where one of the outputs of this commerce as I’ve been to new NFT and give it to you. It doesn’t have to be an NFT either. It could be an ERC 20. It could be any number of other things.
These are very, very flexible ideas, but even this very basic thing of, “I have an NFT. I connect it to the storefront, it unlocks a product. Then I go check out. And maybe on the other end of the checkout, you want to sell me another NFT and I may buy that also. Then maybe I’ll use that somewhere else.” All of these are very, very interesting kinds of outputs and inputs to what we call commerce. But as you said before, I want to make sure that I’m answering your original question here, which is over the last year or two, there was this explosion of communities who were all issuing these tokens and everybody was getting in. “Oh, this is cool art.” These are going to have utility. Who are all these communities? And now what we’re seeing is yeah, a lot of these communities didn’t really have much of a game plan, but some of them do. And the ones that do are actually turning out to be formidably impressive media companies, because they have this fascinating way of creating fan bases. One way to look at NFT is this is a new way of creating a fan base, but it’s creating a fan base on the very beginning. You have to have some idea of what you’re doing with your brand. But nonetheless, the specific example of a merchant that we work with closely is Doodles. Doodles is one of the premier NFT brands. They understand fully that they are merchants and they are brands and they are media powerhouses. They understand that that’s the kind of business that they’re building. And they see these tokens as a new fundamental piece of what is it that their fans have that they can bring with them and connect into places in order to bring all that context with them…
…[00:28:14] Patrick: I’ve gone way too far into the conversation without asking what the literal mechanic that Shopify is building will let people do and won’t let them do. Is it as simple as saying, “If I’m a merchant, you can sign with whatever and I’m going to go through a menu and pick the tokens that I want to let and tie them to a certain thing and then you handle the rest”? What is literally going to be the thing that you offer?
[00:28:32] Alex: That’s actually a very good way to put it, which is that the number of things people want to do with token gating is very diverse and very hard to predict. We cannot know what all of them are. But you know what? We don’t have to. We’re a platform that is what app developers do. This is how Shopify is built. This is exactly like the problem of saying, “Well, there are many themes in the theme store and there are many apps that want to make mechanics. Do I have to think of every single thing that an app could do so that themes can know about it?” No. We just build our platform in a way where we present the right constraints and the right formats for saying, “Hey merchant, you want to do some token gating. Well, there are a lot of different ways that you might think your token gating wants to do. Some people want to token gate for discounts. Some people want to token gate around mechanics to do a cool sneaker drop. Some people want to token gate so that people can buy variants on a product to correspond to variants of their NFTs.” All of these are perfectly valid ways to do token gating and we’re not going to come up with what they are. What we are doing is we are creating a common platform for app developers to go make whatever kind of token gating rules you want to make in a way where those token gating rules can be presented in any selling surface where the merchant wants to go.
Maybe they want to sell on the online store, and that’s great. Maybe they want to sell at a retail point of sale environment. We have merchants doing this now. They’re doing a popup store and they’re an NFT brand. They’re like, “Oh, I only want to sell this thing in my popup store to people who have this NFT and can sign for it. And I want to do it on retail POS.” No problem. Some people want to buy things on mobile, and we have the shop app, which is our mobile app for shopping, and there’s some merchants who want to set up a little token gated store in the shop app that works really well on mobile. We have a product called GM shop that I’ll tell you about it in a minute that is exactly that. But your general question of what is the product that Shopify lets merchants do? It’s, well, you can do anything because we’re a platform. That’s the hard work of being a platform is coming up with what are exactly the right constraints that anybody can make inputs to it and anybody on the other side can read them and go carry out token gating instructions if we’ve come up with exactly the right constraints in the middle. The slogan I like to say when people say, “What are you doing with your life?” I say, “I’m making Shopify wallet aware.” That’s what I’m doing. Wallet awareness is not a single thing. It is an idea around everybody accepting a certain set of constraints that become deconstraining. They’re constraints that become liberating…
…[00:38:58] Patrick: You started to answer a key part, which is if all you wanted was an unlimited amount of people to have a certain access then pure text is great. If you want to limit it somehow obviously then the non fungible nature of the tokens becomes very important. So I get it. And you could certainly see the world normalizing too in your browser, you have a wallet, and you’re constantly like getting shit in your wallet from different people and they represent different things and blah, blah, blah. So now let’s talk about demand, this big topic of what is demand? Where does it come from? How does it tie into this whole story? And why is this new primitive for unlocking demand?
[00:39:32] Alex: Demand is one of my favorite topics because it’s simultaneously such a basic thing that everybody has opinions about. But also it’s one of the hardest things to conjure. You’re not a business until you have demand. A business plan is not demand. Nothing is a substitute for demand. Demand is the thing. Every business owner knows this. What is this mysterious thing, and how do I get it, and once I have it, how do I turn it into more? Before I was at Shopify, before I worked in DC, before we knew each other, long before that, I was in a band. I was in a band called The Fundamentals. We were on a label called Stomp Records out of Montreal, it’s a ska punk label. We never made it big, but we toured around. We had a record deal. We were trying to make it big. This is what we were doing with our lives.
And when you’re in a band, your business model is you lose money recording music, so that you can break even selling concert tickets, so that you can make money selling merch. That is how it works. You are a merchant. What you sell is apparel, basically, to your fans and you give them a reason to buy your stuff. Everything else is more or less a loss leader for your merch business when you’re at that size of band, anyway. I’m sure Taylor Swift makes money at all slices of the pie, but even like the Taylor Swift merch empire is, this is massive, massive, massive business, because there is demand for Taylor Swift merch. How do you make that demand and where does it come from is the question of being a retailer or the question of being a merchant. I can tell you honestly, when you’re a band, demand is something where it exists in two states. If I’m a band and I have these fans that are all out there in the world, they like me in a very sort of abstract way. They listen to my music. They’re thinking about me sometimes. The demand for them to buy my stuff is not really activated. It doesn’t exist in a more tangible form. The proof of this by the way is if you look at musicians merch businesses, let me ask you what percentage of a band’s merch do you think is sold online as opposed to at shows?
[00:41:08] Patrick: 50%.
[00:41:10] Alex: Almost none. So the rule of thumb is that no matter how big you are, your online merch business per year is about the same as two weeks of tour dates.
[00:41:19] Patrick: Oh, wow.
[00:41:19] Alex: Yeah. It is a very, very, very strong ratio. This is more or less universal whether you’re a small band or a big band or whoever you are. This is not to say that people don’t like Taylor Swift, unless they’re in the Taylor Swift concerts. No, they like Taylor the whole time. But you need there to be a precipitating event to cause people to be compelled to buy the merch now. The demand has taken a more meaningful form. It’s almost as if the demand isn’t like a gaseous state and then it becomes more active when certain things happen. And I want to tell you about those things because there are some universal rules to them in how culture works. When you’re a band, you have all these fans and they exist and they know who you are. But then when you come to town, what you do is you play a show. You sell tickets to the show, people buy the ticket, and they enter in this space, and this is very intimate space. And you do a challenge together called dance to the music. On completion of the challenge everybody lines up to go by the merch. This is a universal rule of music. There is a very, very specific orchestrated sequence of events that causes people to buy your stuff. Everybody who has active experience with being a certain kind of culturally cool merchant will recognize their version of this. Demand isn’t enough. It has to be activated demand. It has to be awakened by something. And the thing that awakens demand is a challenge of some sort. I think you were posting about this on Twitter or something. Challenges are the things that make life meaningful. They’re the thing that give us identity. They’re the thing that give us purpose. They’re the thing that makes us feel good about ourselves. Challenge and overcoming the challenge. Demand in absence of challenge is cheap and stupid.
It’s not necessarily stupid, but it’s baseload demand. I have baseload demand for paper towels. That’s fine. I can get them from the corner store. I can get them from Amazon. That’s fine. But the more meaningful kind of demand that actually is something meaningful to my life, that kind of demand is only awakened by a challenge. It might be the challenge of being in a particular store and really, really talking to a merchant and figuring out what I want. It could be the challenge of going to a show. It could be the challenge of being in a cool collab or whatever it is. But ultimately demand has to be activated by something and that thing is challenge. What kind of challenges are the things that people really care about? Well, the basic challenge that we care about is identity and group association. I’m a part of this group. I have these peers. I’m living up to a certain challenge that the peer group does. This is through the basis of all culture. That kind of culture is the basis of a certain kind of retailing called products that people buy to be cool or products that people buy to be a part of a group or products that people buy because they have some sort of meaning to them. The number of different kinds of products like this are quite varied. It’s not just t-shirts that bands sell. It could be memberships to something. It could be getting tattoos. Everybody has this thing that they’re really, really into. But ultimately demand, I want to bring this back to this sort of nebulous concept of demand, is something that people have understood as a part of commerce for thousands of years, but only up until recently that demand was always in person. There’s a challenge that the buyer and the merchants come together to flesh out what context is the buyer bringing with them? Under what circumstances does this demand unlock and activate the challenge? This is something that people naturally do face to face really well.
But online, it’s really hard to do this. It’s hard to show up to an online storefront and bring a vibe with you, do a challenge together, or engage in any of these things. I would say the first mechanic that people online came up with that actually activated this was the drop, the concept of, “Okay, at noon the sneakers are going to drop and you have to get them as fast as possible.” That’s fun. That is a great example of how you sell things. That’s how you get demand to actually convert into purchases is you do a drop or you make an exclusive thing or like you create a challenge and you motivate people to get behind the challenge. I believe it was Modest Proposal was on your podcast a long time ago, talking about eCommerce and this idea of getting all the friction out of commerce. That’s really not it. There’s some kinds of friction that are bad, but there are actually some kinds of friction that are really good. I talked to you about this in the Shopify podcast. This idea of a challenge is required to turn demand into buying. Different cultures do it in different ways, different kinds of retailers do it in different ways. A luxury brand like Gucci will do this in a very different way than a fast fashion brand like Forever 21 will do it. They’re obviously very, very different retailers. They move different kinds of merch for different kinds of price points. But they’re doing the same thing. Look at a really, really well run retailer like Aritzia. All of Aritzia is keyed into getting this latent demand to come in the door, activating it around this certain kind of challenge, and then converting it into incredible brand loyalty. That’s what really powers these businesses. Same on the merchant side, you have the challenge of tack. How do you convert that into something that will produce LTB for a very, very long time?
Trey Lockerbie (00:02:14):
So, we have a whole global monetary system right now that I think a lot of people would call a Petrodollar system, and we’re going to work a little bit backwards from what that means. There’s also the Eurodollar system in play that people may or may not be as familiar with. So, I want to actually start there with the Eurodollar. It’s a big loaded question, but going back to basics here, just simply tell us what is the Eurodollar?
Jeff Snider (00:02:39):
Well, technically speaking, and going back all the way to the beginning, Eurodollar refers to a very specific term, and it means US dollars on deposit outside the United States. In the early days, it actually took the form of actual cash deposit, physical Federal Reserve notes, bills, cash bills and things like that, that found their way mostly to Europe, but not just exclusively to Europe, thus the term Eurodollar. It doesn’t have anything to do with the European common currency. It is, again, the term Euro simply means offshore, because this goes way back to the 1950s and 1960s long before the European common currency was ever introduced. So, whenever you hear the term Euro and then attached to a currency denomination, what that simply means is money that the banking system uses outside the jurisdiction of the United States or even any of the other currency denominations that are floating around in it.
Jeff Snider (00:03:31):
So, there are things like Euroyen, for example, which means yen outside of Japan, that’s in this offshore currency system or even something like the Euroeuro, which is offshore euros. So, essentially, after beginning sometime in the 1950s and spreading through the 1960s, we have a huge, very much comprehensive global monetary system that undertook the roles of the reserve currency, global reserve currency, but it’s not actual cash. It’s not actual currency. There’s no money in it. It’s a virtual ledger system, a distributed ledger system that the global banking system operates and therefore has undertaken the roles of a reserve currency because banks have been able to flexibly and dynamically respond to the world in which they live in.
Jeff Snider (00:04:18):
So, for the last 60 years, this Eurodollar system has been essentially the global monetary reserve. And because it’s offshore, it’s outside the jurisdictions, not just the US, but pretty much anywhere, which is kind of a strange concept because these banks are located and doing business someplace. They’re physically located somewhere. But they have located and they have been able to take advantage of various regulatory blank spots, regulatory boundaries. So, this currency system has been able to grow and expand basically outside the reach of national governments, national regulators, bank regulators, whatever it may be and operate throughout the rest of the world. Again, so the point being to create this global reserve currency arrangement that goes back a long, long time.
Trey Lockerbie (00:05:05):
That last point there, what I hear you describing would maybe otherwise be called something like shadow banking, right? Or is that correct? And if not, what is a shadow bank and what is the shadow economic system?
Jeff Snider (00:05:16):
Well, shadow banking is part of it. That’s more about some of the non-bank participants who actually in this global monetary arrangement. I like to use the term shadow money, because they’re actually monetary forms that they don’t show up in any of the statistics. They don’t show up in any regulatory discussions. They’re not involved in any of the mainstream policy framework, because, again, this is outside the United States, it’s outside of every regulatory regime on earth and regulators are not too keen about people knowing about this vast, huge monetary system existing outside of their reach when their entire monetary policy and really political existence, it relies upon the idea that they are very much in control of this system and this arrangement.
Jeff Snider (00:05:57):
So, it’s outside of everyone’s reach, but also the ways in which these banks operate monetarily as well as credit has evolved and changed so that you have monetary forms like currency swaps, for example, that function every bit the same as cash would, except a currency swap doesn’t fit into a monetary aggregate, it doesn’t fit into any sort of quantitative measure, nor qualitative understanding. It doesn’t even fit into the bank balance sheets in a intuitive way. In essence, this is a virtual ledger money system, that’s a shadow money system because of the way the banks operate on their balance sheet.
Trey Lockerbie (00:06:32):
We’re going to explore the significance of that in a minute, but let’s keep with the basics for a minute. So, let’s say the US, we were on a gold standard for a very long time. We had to pay for some wars and stuff and we had to kind of break our promise that was the dollar was backed by gold, we kept changing the money multiplier over time. And at some point, it was unfeasible to continue on with the gold standard. So, like 71-ish, Nixon says, “Hey, you know what, we’re going off the gold standard into this fiat system.” And a lot of people said, “Okay, well,” there was this meeting with Saudi Arabia and we developed this agreement with them to now produce something called the Petrodollar system. And that’s what a lot of people believe we’re operating on today. But is that correct, Jeff? What’s your opinion?
Jeff Snider (00:07:12):
The short answer is no. And it’s a common misperception, because you can understand why. The Bretton Woods system, which was a quasi-gold-backed system, a commodity-based monetary system that grew out of World War II, in the ashes of World War II, where Harry Dexter White and John Maynard Keynes in particular said, “We can’t just have an international currency arrangement because nobody will accept it. So we need to tie this international currency to some national reserve.” And historically speaking, people wanted to use gold, because gold for various reasons that we don’t need to get into here.
Jeff Snider (00:07:40):
So, you had the Bretton Woods system 1944, which always had this inherent flaw or inherent tendency in it as Robert Triffin called it in the late 1950s, eventually become called the Triffin’s paradox or Triffin’s dilemma, which was that in order to operate a global reserve currency, you need to have enough currency floating around the world to be effective. Because what is a global reserve currency? It’s a mediating currency where vastly different systems can connect to each other through this third-party mediating system or mediating currency so that trade, financial flows, all of the free market capitalism that we’ve come to love and honor, those things can happen in a very efficient fashion so that we can have a globalized, highly efficient economic system.
Jeff Snider (00:08:24):
The problem was by tying this international currency and using, for example, the US dollar or the British pound and backing that currency with national stores of physical bullion, there was always going to be the problem where there’d be too much currency needed outside the US, which would then lead to anyone ending up with that currency, redeeming the paper for national reserves. Eventually the national reserves of gold would be drained from the system and Triffin’s paradox would be that once those reserves were drained, the whole thing would just fall apart, which by the way, came close to happening in the late 1950s.
Jeff Snider (00:09:00):
So, we’re talking about not even really 15 years into Bretton Woods, it was already falling apart. So, this is where the Eurodollar steps into it, because it divorces the national currency from the national store of reserves. So, long before 1971, you had this global monetary arrangement, because it was reserveless, because it was ledger money that it began to undertake the roles of the former Bretton Woods system as it broke apart. So, by the time you get to August of 1971 and President Nixon closing the gold window, the Eurodollar had long undertaken all of those roles of the reserve currency before that.
Jeff Snider (00:09:36):
So, August of 1971 represented nothing more than the symbolic end of Bretton Woods when the functional end started a decade and a half before that. So, in terms of the Petrodollar, it wasn’t like we moved from a commodity goal-based monetary system to a oil-based system in the 1970s. We moved off of the commodity-based monetary system long before that. And it had superseded the Petrodollar, the stuff that happened in 1973, for example, and basically all of the functions of the Eurodollar were up and running for more than a decade by then. And even the Eurodollar system itself had become absolutely huge and immense by the early 1970s.
Jeff Snider (00:10:15):
So, the transition took place into something that was a ledger of ledger virtual currency system long before then. And it took place into this offshore bank-centered sort of blank canvas where banks could experiment in all different types of money, so that we transitioned long before from a commodity gold exchange system, the Bretton Woods, to this virtual reserveless currency system under the Eurodollar over a long period of time before we even get to 1973…
…Trey Lockerbie (00:13:59):
So, how much of the narrative that we’re currently operating on comes to us from our actual own Federal Reserve, or even say the media or education around the system that we’re currently in? Because as I understand it, your research has led you to study papers from internal employees at the Fed and elsewhere. And some of them know what’s going on. Some of them are discovering what’s going on through their work. And others just have no clue maybe because they’re in the system and they have that kind of myopic view. So, from the research you’ve done, what’s the takeaway of how informed the people within the system even understand how the global system is operating?
Jeff Snider (00:14:36):
The funny thing is, we always think scientific progress is linear. It always goes in one direction. But here’s an example of how monetary scholarship, academic scholarship about money actually move backwards. When you go back in time to do the historical research, you see there’s much more awareness, much more understanding, not the whole thing, but much more understanding about at least the basics of the Eurodollar system in contemporary time. So, back in the 1960s, for example, it took international authorities and national authorities about a decade after the Eurodollar system began to really start investigating it, because it had become that big of an issue even for national authorities like the Federal Reserve.
Jeff Snider (00:15:11):
But when they did, they were sort of putting bits and pieces of it together through… I mean, which makes sense because it’s a brand new development banks were doing things, they were not sharing the information with anybody, which is, again, why we call it shadow money. So, there was a huge, huge blind spot for even regulators and officials to try to deal with. But at that time, they did attempt to try to understand this Eurodollar system. But then they just, they stopped and they gave up, which begs the question, what is it the Fed did? What does the Fed actually do now? Which goes back to one of the initial quote that you said at the top, when I say the Fed isn’t a central bank, this is the reason why, because what happened was in the 1960s and 1970s, Federal Reserve officials, Treasury officials, government officials, officials at the BIS, or the IMF realized this monetary and banking evolution that was going on through the Eurodollar system made it almost impossible to define, let alone measure and regulate and keep on top of the monetary system.
Jeff Snider (00:16:08):
And if you’re a central bank, if you’re a legitimate central bank, whose job it is to regulate the monetary system, as we all believe, going back to Walter Bagehot in the 19th century, how do you do that when the monetary system has evolved, and it has evolved in these offshore, outside of regulation spaces that make it almost impossible for you to have much of an influence, let alone direct relationship with the banks operating there? So, what ended up happening was around the turn of the decade in the 1970s and 1980s, central bankers decided they just kind of threw up their hands and said, “Well, the monetary thing, it’s too complicated. It’s outside our jurisdiction. So, we can’t really do money anymore. Instead, we’re going to try to make it so that people believe we do money, this expectations-based policy, where we’ll communicate to the public that we’re doing something and hope that the public and banking system and business people all around the world or inside the United States will behave in ways that we want them to behave.”
Jeff Snider (00:17:02):
For example, it became commonplace that, Alan Greenspan, for example, would raise or lower the federal funds rate whenever he wanted to do something. So, if he wanted to “tighten credit” and tighten the monetary system, would he actually tighten the monetary system? Would he go into the monetary system and take money out? No, he raised the federal funds rate, which was nothing more than a signal to the economy at large and try to get the economy and try to get the markets to tighten conditions based on that signal, based on expectations. As he said, during that time, as his predecessors said before, “We just can’t keep track of the monetary system. Therefore, this is what we have left to be able to do to try to get some form of control over the economy and the marketplace.”
Jeff Snider (00:17:44):
So, it’s really about this evolution in money in banking that took place outside of their purview, which left official scrambling to try to do something else to at least attempt to maintain the role of what a central bank used to do, but it’s not a monetary role. It’s not involved in the monetary system itself. So, once that happened, monetary scholarship simply dried up. The term Eurodollar kind of disappeared, not just from internal discourse, but from public discourse as well. So, you have a wealth of scholarship up to around early 1980s and then just nothing. Because what happened was we were told, we were all told, we were taught this in school. “At that point, don’t fight the Fed, just whatever the Fed says, whatever the Fed, they must know what they’re talking about when it comes to money, you don’t need to know. Just trust Alan Greenspan and Ben Bernanke. They’ve got it all covered.” So, once there was a vibrant monetary or debate and argument, it just kind of disappeared and dried up and went away.
Trey Lockerbie (00:18:40):
But it’s not all an illusion, is it? Because if we fast forward to today, we’re seeing it happen and play out in real time, where inflation is now high again as it hasn’t been for decades and they’re raising interest rates. And now we’re starting to see things like mortgage rates go up and home prices get underwritten in a new way. We’re seeing real economic impact from these decisions or actions from the Fed. So, where does the detachment actually occur in your opinion?
Jeff Snider (00:19:05):
Well, because that isn’t actually inflation. This isn’t due to money printing. This is sort of the federal… I mean, that’s why you didn’t see consumer prices react to QE6 back in 2020. Consumer prices didn’t start to skyrocket until March and April of 2021, which was coincident to the US treasuries helicopter drops. So, this wasn’t money printing, this wasn’t the Fed creating money. This wasn’t the Fed being a central bank. It was essentially a supply shock, which was the US government redistributed borrowing through the Treasury and mostly Treasury bills actually, the US government essentially redistributing cash into the pockets of consumers. And then consumers wind up spending that cash at a time when the ability of the global economic system to supply goods and then transport goods in particular was at its lowest point. So you see inventories of goods actually crash during these periods because we had essentially a supply shock.
Jeff Snider (00:19:59):
So, it isn’t inflation as much as it was consumer prices reacting to small E economics. Whenever you have a demand curve shift out to the right, especially when supply isn’t as any elastic as it was during that time, consumer prices have to react. I know most people are saying, “Who cares? Consumer prices went way up. What does it matter if it’s inflation? Or what does it matter if we call it inflation or not?” The issue is how it ends, because if it’s nothing more than a supply shock, it’s always going to be temporary and transitory rather than something like the 1970s, where you ignite the monetary spark of excessive currency, that leads to all sorts of, well, great inflation type of problems. So, how do we tell one from the other?
Jeff Snider (00:20:40):
And one of the things that consistent with excessive currency and money printing would’ve been destruction of the US dollar has been long proclaimed, long predicted, and long forecasted. But what you see ever since last year is the US dollars exchange value going up against almost every currency, because it wasn’t money that was printed. It was simply a supply shock. And because it wasn’t money printing, the way this is likely to end is in another bad way, which is a recession. That’s really what markets have been predicting over the last more than a year, actually, because the yield curve has been flattening. So, even as interest rates have been rising, the yield curve has been flattening. The Eurodollar futures curves have been flattening. All of the signals from the monetary system itself have been sending, “Hey, there’s no money here. This is not money printing. This is a supply shock and this is going to end predictably in something like a contraction or recession.” So, it was never inflation to begin with. It was simply small E economics of a supply issue.
Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Amazon and Shopify. Holdings are subject to change at any time..
