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We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

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 08 May 2022:

1. An Interview with “Father of the iPod” Tony Fadell – Ben Thompson and Tony Fadell

Just to touch on that — I love that analogy, I’ll go back to it in a little bit — but the story of the iPod is so crazy. You weren’t even hired until April, yet you shipped in October. How was Apple able to move so quickly? Is there any company that could do that today? I doubt that even Apple could do that today with all their resources. How did that happen where you shipped this completely iconic product that didn’t even exist in the imagination of anyone, I guess in your imagination to an extent, but walk me through that process and how was that even possible?

TF: I think it was a coming together of a lot of things. The first one was experience. I and the people around me had experience for ten years. I pulled in a lot of people from either or General Magic or Philips or other people I just knew that I’d met around Silicon Valley over time. So one was having that network of being able to pull people in who knew what they were doing on this product, that was one thing.

Second thing was having a lot of failure before building these things, and they didn’t really necessarily become commercial successes, they might have been critical successes. So you had enough time doing this stuff. You’re like, “Okay, I’ve done this. I know to make boards. I know how to get software packages together, put all these things to happen.” So again, that was doing something totally new from a product perspective, but the process wasn’t necessarily new.

I think the other one was we had incredible leadership in Steve Jobs. He decreed from the minute after we gave the presentation to him in March of 2001, it was “Go!”. I had already been running it for a year before that doing MP3 players in my startup. So it was like, “Okay”, take all the latest knowledge I had gained during the contracting period, and ran with that.

And then the other one was we just cordoned off and it was, “Make it happen”. I saw so many projects that died at Philips because they didn’t happen fast enough, politics set in. So it was like, “Okay, we have to build this. We have to build it quickly. The holiday season’s coming around. This might be our one and only chance, because who knows when Sony’s going to come in and steal everything” because they were the number one in all audio categories. Every audio category Sony was number one in. So it was like, “Well they’re going to come for this”. So speed was everything.

So I had just been tempered all the time. One is technology changes, the market changes so quickly, you need to have the right experience and process, and we put it all together. Obviously, it was wonderful to have Apple in terms of the customer service angle, parts of the operations angle, but we had to do a lot of new stuff that Apple had never done before, and obviously the marketing, product marketing, pulling all that stuff together. So we got to pick the best bits of Apple and have them focused on us because of the leadership. Then we were able to build very quickly the new bits, throw them together, and just run like hell because at the end of the day, Apple isn’t the Apple you know it today. Twenty-one years ago, Apple was suffering. It had around barely 1% market share in the computer business, in just the US, that’s not worldwide, Apple wasn’t anywhere worldwide. It was only worth $4 or $5 billion, I think. maybe even $3 billion in total. Now it’s worth almost $3 trillion or $2 trillion, whatever it is this week.

So when you have leadership, when you have a competitor or at least you felt there was going to be a competitor coming very quickly, when the technology was there, right place, right time, and we had the right experience, and the company was at its wits end, because it had tried everything it could do to try to get the Mac to get back into the forefront of consumers’ minds with the iMac, whatever, and that wasn’t really going well. This was, “You’ve got to make it happen”, burn the boats, do whatever it takes to see this first product out there and even then it was a marginal success. It was a critical success. Everyone was like, “Wow!” but a lot of people were like, “I can’t buy it. It doesn’t work with my PC.” It didn’t work with Windows. We had to work really hard to make it a success.

It’s interesting that you list all those factors because in your book, when you talk about your experience at Nest, which I think the acquisition from Google maybe was a little more fraught and dramatic than you might have wished it could have been. But you had this contrast, you talk about Google antibodies resisting you and you said, “Oh, we had Apple antibodies resisting us, as well.” But the difference was Jobs protecting you and you also had this cultural bit where Apple needed a hit. To that end, I’m curious, are we reaching a point, a decade past his death, where Steve Jobs’ management abilities are actually becoming underrated? There are the scare stories that are still around. We all know he was this innovator in design, but just from being a manager and getting stuff out the door?

TF: Well, there’s one which is getting stuff out the door, and that’s a process and having good process. There’s another one, which is getting very innovative things out the door, things that are going against the grain of the internal business itself. The iPod was totally different than the computers at the time, “What? Apple’s making what? Stick to computers, Apple.” that’s what we heard from some people. So there’s leadership when you’re maintaining or when you’re operating something that’s already standing and working. And there’s another type of leadership when you’re trying to do something inside an organization that may be successful, may be not successful, but doing something very against the grain and seeing it through and saying, “We’re going to burn the boats. And this is the way it’s going to be.” That takes a different type of leadership and it takes what I always call is air cover. If we didn’t get what we needed, because we were on such a tight schedule, I could only call in the airstrike so often, but, “Steve. Need help.” and from above he would fly in and go, “Okay, what do I need to do?”

We do that a lot today with the businesses we work with, and we have to be the air cover for that and the investments we make and what have you. We fly in and go, “Okay, can we help you in some way? Where can we go to third parties or other ones to help you get what you need to start up this startup?” Leadership is really the key difference in all of this and understanding the difference between data-driven and opinion-based decisions. Steve was really great at understanding what were opinion-based decisions, and it was his opinion at the end of the day that was going to rule, and he was going to make sure everyone understood that “We’re going to do this. And yes, we don’t know if it’s going to be a success, but this is what I want done. Get it done, please.”

You talked about this in your book, actually, the opinion-driven versus data-driven decision making, and how to build the iPod in the first place was an opinion-driven decision, but to bring the iPod to Windows ended up being a data-driven decision. And in the case of an opinion, well it was Steve’s opinion that counted, but because it was a data decision, that’s how you were able to actually change Steve’s mind about going to Windows. Did I summarize that point properly?

TF: Yeah. It’s really correct. Look, Steve’s opinion specifically was at the beginning of the iPod project was, “We are going to make this amazing thing called the iPod” — we didn’t know it was called the iPod at that time — but “We’re going to make this thing, and this is going to drive Mac sales”. So to use the iPod, you’re going to have to buy a Mac, and that was his opinion.

Two years in, the numbers were okay for the Mac fanboys who had Macs, but no one else was interested in switching to a Mac just for an iPod. So we had that data and it showed very clearly that that original opinion or that hypothesis was that more people would buy Macs because the iPod was available was not right. We had a few people, but it was not this huge mass of people switching from Windows to the Mac because the iPod existed and you had to have one. So over time, and this is the third generation, we had to have the Windows connectivity, the Windows functionality, compatibility, to make sure it worked.

And then all of a sudden people were like, “Oh, this iPod thing is really cool. I’m using it on my Windows device, on my Windows laptop, or what have you. But I wonder what the full Apple experience would be?” And then people started buying Macs after they got a taste of the Apple experience with the iPod on their Windows based computer…

…A couple other points. Another interesting episode that we talked about privately was the bake-off at Apple when it came to the iPhone, if it was going to be iPod-based or if it was going to be touchscreen-based. One of the points you made in your book is that the bake-off was very short and it was resource constrained. You needed to make a decision, and then you invested in the right one. And I think this came up in the context of Facebook changing plans on their virtual reality OS. They had the Android one and they had their internal one, and it went on for years. Why do companies fall into this? Is it just that they’re too rich? They have too much money, and so they’re just undisciplined about this?

TF: Absolutely! That’s exactly the right thing, is when there’s too much money, there’s too many people saying that they can do it better, and there’s no time limit or other constraints, money limit, market constraints, what have you, these teams go at it. If you remember, there were two different operating systems going on at the time at Apple, before Steve got back,

That’s right. Yeah.

TF: There were pink and blue and all these things, IBM had it. So there were all these different kinds of in-fighting that happens, and it’s all based on constraints. When there’s a lack of constraints, that’s where all of these things bloom. At Google, when I was there, there were at least four different competing audio projects for audio in the home for playing music. There was four of them! I’m like, four? Why is there four? Everybody had a slightly different take and nobody was willing to go and kill them and prune them and say, “No, this is the right one,” and take all the pieces together, because they were too afraid, for whatever reason, I don’t know. It’s hard enough to have one great product that’s orthogonal to what the company does and saying, “Oh, this is an all new thing,” to have four of them, and say, “We’re going to launch all of them at some point?” That just doesn’t make any sense. Constraints are really key there.

2. Going Where Few Have Gone Before – Inside All Four Rolex Manufacturing Facilities – Benjamin Clymer

While Rolex’s manufacturing and design capabilities were (and still are) the reason that this company is so respected by its peers, it was Wilsdorf’s knack for storytelling that would would elevate Rolex to become the archetype of the luxury wristwatch not only for those within Switzerland, but also all over the world.

In 1927, Wilsdorf heard of a woman British woman named Mercedes Gleitze who had successfully swum the English Channel. Wilsdorf asked Gleitze to wear a Rolex Oyster watch around her neck as she swam. It should be noted that Gleitze had attempted this feat seven times before making it successfully, and then, due an attempt by another woman to steal the spotlight, was asked to swim it again. It was this last time that Gleitze wore the Rolex Oyster, not on her wrist but around her neck. 

She didn’t make it. After 10 hours in the freezing water, she was forced to abandon the attempt and be pulled into her trainer’s boat, because of numbness in her extremities. It didn’t matter, and Wilsdorf ran an ad in London’s Daily Mail citing not this most recent attempt, but Gleitze’s earlier successful attempt (which, of course, she swam without a Rolex). Still, her Oyster did withstand up to 10 hours in the bitter cold water of the English Channel, which was no small feat (you can read a detailed account here)…

…This, my friends, is where things get good. Plans-les-Ouates (an industrial park outside Geneva that’s also home to, among others, Piaget, Patek Philippe, and Vacheron Constantin) is where the Rolex of our collective imagination comes to reality – complete with robotic inventory machines straight out of Star Wars, a private gold foundry, and iris scanners. Built in 2006, Rolex Plan-les-Ouates is the largest of all Rolex facilities, comprising six different wings that are 65 meters long by 30 meters wide by 30 meters high, all linked by a central axis. I should also note that everything you can see from the outside of the building is actually less than half of what Rolex has here – the complex is 11 stories high, but you can only see five from the outside. The other six are underground and completely hidden from a casual observer’s eye, or the eyes of would-be competitors.

Here there are not only no cameras allowed inside, but we are also asked to surrender our mobile phones. This facility is, in my opinion, the core of Rolex’s competitive advantage and unlike any other Swiss (German, or Japanese) watchmaking facility on the planet. It may actually be completely unique in other industries too. I’ll explain why below.

Upon entry (and surrender of all digital device), we take a small elevator a few floors underground. The doors open to reveal what looks to be something akin to Dr. Evil’s underground lair, in the best possible way. The floor is cement, the hallways are wide. Access control points are everywhere – if someone doesn’t absolutely need to be in a particular room, then they simply do not have access to it. We immediately notice a gigantic elevator door – and when I say gigantic, I mean an elevator at a scale that I’ve never seen. I inquire about it – it can hold a load of up to five tons.

We are shuffled into a secure room – we are about to see the legendary Rolex automated stock system. Our guide places his eyes to the iris scanner (no lie) the doors slide open, and what we see is downright startling…

…Sorry guys. No photos allowed, nor provided. So what I will do is give you my best written description of what this absolutely extraordinary automated system looks like. There are two 12,000 cubic meter vaults, spliced by a network of rails totaling 1.5 kilometers, transporting over 2,800 trays of components per hour between the 60,000 storage compartments and the workshops upstairs. The view is straight out of Star Wars, minus the 1970s camp. This is efficiency defined.

Once someone within the workshops above requests a component, this incredible system takes just 6-8 minutes to retrieve it and deliver it to their work station. I remember when I was in undergraduate business school, our supply chain professional proclaimed Wal-Mart to be the model of professional logistics. I would almost guarantee you he said that because he’d never been to Rolex Plans-les-Ouates…

…Rolex owns its own foundry, where it creates its very own formulas for three different kinds of gold, and its own formulation of 904L stainless steel. Every single alloy used by Rolex is produced entirely in-house because, as they are quick to point out, the composition of the metal is the most important factor in determining a watch’s aesthetic, mechanical, and dimensional properties.

Rolex is able to make these special compounds because they have invested in something that few other watch companies would even dream of: a central laboratory with world-class experts in not only materials, but also tribology – the science of friction, lubrication, and wear – chemistry, and materials physics. This laboratory was truly extraordinary to see, and what was perhaps most impressive about the lab was not only the incredible testing going on, and the machines they’ve developed themselves (for example, Rolex invented a machine to open and close an Oyster bracelet clasp 1,000 times in a matter of minutes), but also the people who work there. I was asked not to mention from where Rolex has retained many of its top-tier scientists, but you can guess, and they are 100 percent not from the watch industry…

…I think what was perhaps most surprising about my visit to Chene-Bourg was the quality of gemstone and setting work Rolex does. I don’t really think of Rolex producing many watches with diamonds and stones, and they admit they don’t. But, this is Rolex and if they are going to do something, they are going to do it the Rolex way. This means 20 in-house gem setters, some of whom have names like Bulgari and Cartier on their resume. The stones they use? Only IF quality – otherwise known as “internally flawless” for those not familiar with jewelry-speak.

One of the coolest things I saw here was a machine that Rolex uses to filter the stones they receive for fakes, or anything that might not be what it’s supposed to be. One assumes that any supplier of Rolex understands just how big a business it is and might be tempted to take advantage of this, perhaps by including fake diamonds in with the real stones. Yes, well, Rolex has a machine in-house that can filter stones in mass to cull out anything that isn’t a real diamond. The machine costs tens of thousands of dollars so I asked how frequently they received a stone from a supplier that wasn’t an actual diamond. The answer? About one out of 10 million. They do it anyway, because this is Rolex.

3. Where Do Space, Time and Gravity Come From? – Steven Strogatz and Sean Carroll

Strogatz (02:56): It’s very exciting to me to be talking with the master of emergent space-time. Really mind-boggling stuff, I enjoyed your book very much. I hope you can help us make some sense of these really thorny and fascinating issues in, I’d say, at the frontiers of physics today.

Why are you guys, you physicists, worrying so much about space and time again? I thought Einstein took care of that for us a long time ago. What’s really missing?

Carroll (03:21): Yeah, you know, we think of relativity, the birth of relativity in the early 20th century, as a giant revolution in physics. But it was nothing compared to the quantum revolution that happened a few years later. Einstein helped the beginning of special relativity, which is the theory that says you can’t move faster than the speed of light, everything is measured relative to everything else in terms of velocities and positions and so forth. But still, there was no gravity in special relativity. That was 1905. And then 10 years later, after a lot of skull sweat and heavy lifting, Einstein came up with general relativity, where, he had been trying to put in gravity to special relativity, and he realized he needed a whole new approach, which was to let space-time be curved, to have a geometry, to be dynamical. It’s the fabric of space-time itself that responds to energy and mass, and that’s what we perceive as gravity.

(04:14) And as revolutionary as all that was, sort of replacing fundamental ideas that had come from Isaac Newton, both special relativity and general relativity were still fundamentally classical theories. You know, we sometimes prevaricate about the word “classical,” but usually what physicists mean is, the basic framework set down by Isaac Newton in which you have stuff, whether it’s particles or fields, or whatever. And that stuff is characterized by what it is, where it is, and then how it’s moving. So for a particle, that would be its position and its velocity, right? And then, from that, you can predict everything, and you can observe everything and it’s precise and it’s deterministic, and this gives us what we call the clockwork universe, right? You can predict everything. If you knew perfect information about the whole world, you would be what we call “Laplace’s demon,” and you’d be able to precisely predict the future and the past.

(05:08) But even general relativity, which says that space-time is curved, that still falls into that framework. It’s still a classical theory. And we all knew, once quantum mechanics came along, circa 1927, let’s say. It was bubbling up from 1900, and then sort of — it triumphed in 1927, at a famous conference, the fifth Solvay Conference, where Einstein and Bohr argued about what it all meant.

(05:32) But since then, we’ve accepted that quantum mechanics is a more fundamental version of how nature works. I know — you said this for all the right reasons, but it’s not that quantum mechanics happens at small scales. Quantum mechanics is the theory of how the world works. What happens at small scales is that classical mechanics fails. So you need quantum mechanics. Classical mechanics turns out to be a limit, an approximation, a little tiny baby version of quantum mechanics, but it’s not the fundamental one.

And since we discovered that, we have to take all of what we know about nature and fit it into this quantum mechanical framework. And we have been able to do that for literally everything we know about nature, except for gravity and curved space-time. We do not yet have a full, 100% reliable way of thinking about gravity from a quantum point of view…

…Strogatz (11:32): So I think that segues very nicely into the next thing I was going to ask you. We’re hoping, by the end of this episode, to give people a feeling of what it means for space-time to be emergent. But what would it mean for you, or anybody studying space and time, for them to be emergent?

Carroll (12:05): So I don’t think that there is any such thing as a position or a velocity of a particle. I think those are things you observe, when you measure it, they’re possible observational outcomes, but they’re not what is — okay, they’re not what truly exists. And if you extend that to gravity, you’re saying that what we call the geometry of space-time, or things like location in space, they don’t exist. They are some approximation that you get at the classical level in the right circumstances. And that’s a very deep conceptual shift that people kind of lose their way in very quickly.

(12:58) It’s a tricky word. We have to think about it. Emergence is kind of like morality. Sometimes we agree on it when we see it. But other times, we don’t even agree on what the word is supposed to mean. So, the physicists, and mathematicians, and other natural scientists tend to — but not always — rely on what a philosopher would call weak emergence. And weak emergence is basically a convenience, in some sense. The idea is that you have a comprehensive theory, you have a theory that works at some deep level. Let’s say, the standard example is gas in a box, okay? You have a box full of some gaseous substance, and it’s made of atoms and molecules, right? And that’s the microscopic theory. And you say that, okay, I could — in principle, I could be Laplace’s demon, I could predict whatever I want, I know exactly what’s going on.

(13:47) But, we human beings, when we look at the gas in the box with our eyeballs, or our thermometers, or whatever, we don’t see each individual atom or molecule, and its position and its velocity, we see what we call coarse-grained features of the system. So we see its temperature, its density, its velocity, its pressure, things like that. And the happy news — which is not at all obvious or necessary, it’s kind of mysterious when it happens and when it doesn’t — but the happy news is that we can invent a predictive theory of what the gas is going to do just based on those coarse-grained macroscopic observables. We have fluid mechanics, right? We can model things without knowing what every atom is doing. That’s emergence, when you have a set of properties that are only approximate and coarse-grained, that you can observe at the macroscopic level, and yet you can predict with them. And weak emergence just means, there’s nothing new that happened along the way. You didn’t say that, oh, when you go to the larger scales and you zoom out, fundamentally new essences or dynamics are coming in. It’s just sort of the collective behavior of the microscopic stuff. That’s weak emergence.

(15:01) There’s also strong emergence where spooky new stuff does come in. And people talk about the necessity of that when they think about consciousness or something like that. I’m not a believer in strong emergence at the fundamental level. So, to me, what the emergence of space-time means is that space-time itself is like, the fluid mechanics. It’s like gas temperature and pressure and things like that. It’s just a coarse-grained, high-level way of thinking about something more fundamental, which we’re trying to put our finger on.

Strogatz (15:34): Wow, as you’re describing the gas in a box, I happen to be sitting in a box. I’m in a studio that is kind of box-shaped. There is a gas in here, which is the air that I’m breathing.

So anyway, yeah, very vivid to me, the example you’re talking about. And it is amazing, isn’t it? That there are laws at that collective or emergent scale that work, that don’t — you know, like thermodynamics was oblivious to statistical physics. In fact, was discovered first, and only later, the microscopic picture came out. And so, I guess you’re saying something like that would be happening now with space and time and gravity, that we have the macroscopic theory that’s Einstein’s.

Carroll (16:14): When I’m not spending my research time studying quantum mechanics and gravity, I’m studying emergence. I think that there’s a lot to be done here, to be sort of cleaned up and better understood, in a set of questions that spans from philosophy to physics to politics and economics, not to mention biology and the origin of life. So, I think that these are deep questions that we’ve been kind of messy and sloppy about addressing, but I don’t think that the emergence of space-time is difficult for that reason.

(16:45) So, when you talk about, is the United States emergent from its citizens? Or is Apple Computer Company emergent from something? Those are hard questions. Those are like, tricky, like “where do you draw the boundary?”, etc. But for space-time, I think it’s actually pretty straightforward. The lesson, the important take-home point for the podcast is, you don’t start with space-time and quantize it, okay? Just like when you have the gas in the box, you’re trying to get a better and better theory of the gas in the box, but you realize that it’s made of something fundamentally different. And I think that’s what I’m proposing, and other people are proposing for space-time as well, that the whole thing that used to work for electromagnetism and particles and the Higgs boson and the Standard Model, where you started with some stuff and quantized it, that’s not going to be the way it’s going to happen for gravity and space-time. You’re going to have something fundamentally different at the deep micro-level, and then you’re going to emerge into what we know of as space-time.

Strogatz (17:46): Shouldn’t we start talking about entanglement, at this point, maybe?

Carroll (17:49): Never too early to start talking about entanglement.

Strogatz (17:51): Let’s talk about it. What is it? I hear it a lot. I hear quantum people talking about it. Nowadays, especially, with quantum computing, we keep hearing about entanglement. Why don’t you just start with telling us what it means, where the idea came from?

Carroll (18:04): Yeah, I mean, let’s think about the Higgs boson. We discovered it a few years ago, it’s a real particle, and I wrote a book about it, The Particle at the End of the Universe. The Higgs boson — one of the reasons why it’s hard to detect is that it decays. It has a very, very short lifetime, right? So, you can imagine if someone put a Higgs boson right in front of you, it would generally decay into other particles in about one zeptosecond. That’s 10-21 seconds. Very, very quickly.

(18:31) One thing it can do, it can decay into an electron and a positron, an antielectron. So it can decay into two particles, electron and positron. Now remember quantum mechanics. So, you can predict roughly how long it will take the Higgs boson to decay, but when it spits out that electron and positron, you can’t predict the direction in which they’re going to move.

(18:54) I mean, that makes perfect sense because the Higgs boson itself is just a point. It has no directionality in space. So there’s some probability of seeing the electron, in a cloud chamber or whatever, moving in whatever direction you want. Likewise, for the positron, there’s some probability, seeing it moving in whatever direction you want. But you want momentum to be conserved. So you don’t want the Higgs boson sitting there, stationary, to decay into an electron and a positron both moving rapidly in the same direction. That would be a shift in the momentum, right?

(19:26) So, even though you don’t know what direction the electron is going to move in, and you don’t know what direction the positron is going to move in — sorry, I’m already, I’m being, I’m being the person who I make fun of, I’m speaking as if these are real. Even though you don’t know what direction you will measure the electron to be moving in, and you don’t know what direction you will measure the positron to be moving in, you know that if you measure them both, they will be back to back. Because they need to have equal and opposite momentum, for those to cancel out.

(19:54) So what that means is, if you believe all those things, right away, this is why we believe there’s only one wavefunction for the combined system of the electron and the positron. It’s not an independent question, what direction are you going to measure the electron in? What direction are you going to measure the positron in? It’s a statement you need to ask at the same time. That’s entanglement, right there. Entanglement is the fact that you cannot separately and independently predict what the observational outcome is going to be for the electron and the positron.

(20:26) And this is completely generic and everywhere in quantum mechanics. It’s not a rare, special thing. Many things are entangled with many other things. It’s the unique and fun and very useful time when things are not entangled with each other. It took a long time — like, Einstein and his friends — Einstein, Podolsky and Rosen, EPR — published a paper in 1935 that really pointed out the significance of entanglement. Because it was sort of there, already, implicit in the equations, but no one had really shone a flashlight on it, and that’s what Einstein did. And the reason why it bothered him is because when that Higgs boson decays and the positron and the electron move off in opposite directions, you can wait a long time, let’s say you wait a few years before you measure what direction the electron is moving in.

(21:14) So, both particles are very, very far away from each other. And now when you measure the location of one, supposedly the location of the other one is instantly determined. And there’s no limit of the speed of light or anything like that. So for obvious reasons, Einstein, very fond at the speed of light as a limit on things, he didn’t like that. He never really quite thought that that was the final answer, he was always searching for something better.

Strogatz (21:39): And the argument goes nowadays that it’s okay, it’s no violation of special relativity, because you can’t use this to transfer any information or something? Is that the statement?

Carroll (21:39): Yeah, well, you know, there’s, there’s a whole bunch of statements that one can make. But the one that we absolutely think is true, is the one that you just made. If you imagine these two particles moving back-to-back, and one person detects one, and there’s another one, you know, a light-year away, who’s going to detect the other one, the point is that they don’t know what your measurement outcome is, you would have to tell them.

So even though in the global point of view, now, the location where the other particle is going to be detected is known to God, or to the universe, it is not known to any particular person sitting at any location within the universe. It takes the speed of light time to take a signal that would let you know that there is some now new fact about the matter, where you’re going to observe the positron. So, you cannot actually use this for signaling, you just don’t know what has happened when your other observer has measured something. And you can actually prove that, under reasonable assumptions, in the theory as we know it.

(22:43) So it seems as if this is the tension, that the way the universe works involves correlations that travel faster than the speed of light, but in some well-defined sense, information does not travel faster than the speed of light. That should worry you, that we didn’t define any of these words. So you know, what does that mean? You’re not going to build a transporter beam or anything like that out of this stuff.

(23:09) But — but let me just add one other thought, which I think, again, is a result of my quirky way of thinking about these things, which is not entirely standard, which is, people really like locality. Like, locality is a central thing. Locality is just the idea that if I poke the universe at one point in space-time, the effects of that poke will happen at that point, and then they will ripple out. But they will ripple out to other points no faster than the speed of light, okay? There’s nothing I can do to poke the universe here that will change the state of the universe in a tangible way very, very far away. And you can see how this entanglement thing is kind of on the boundary of that, like, the description of the universe changes instantly far away, but no information is traveling.

(23:51) So then, if you believe that locality is fundamental like that, then you’re sort of asking this question, why does the universe almost violate that but seem to not quite? That’s the puzzle that we have. And this is — a lot of ink has been spilled in the foundations of quantum mechanics.

(24:06) I think about it entirely the other way around, because I think of the wavefunction as the fundamental thing, right? I think that’s what exists in reality. And the wavefunction, like the wavefunction of this positron and electron is utterly nonlocal. It just exists all — it’s a, it’s a feature of the universe as a whole right from the start. So, I also have a mystery to be explained, but my mystery is the opposite way. It’s not “why is locality approximately or, you know, seemingly violated by entanglement?” It’s “why is there locality at all?” Like, that’s the puzzle to me.

4. Nvidia: The Machine Learning Company (2006-2022) – Benjamin Gilbert and David Rosenthal 

Ben: This was occurring to me as I was watching Jensen ensuring the omniverse vision for NVIDIA and realizing NVIDIA has really built all the building blocks—the hardware, the software for developers to use that hardware, all the user-facing software now, and services to simulate everything in our physical world with an unbelievably efficient and powerful GPU architecture.

These building blocks, listeners, aren’t just for gamers anymore. They are making it possible to recreate the real world in a digital twin to do things like predict airflow over a wing, simulate cell interaction to quickly discover new drugs without ever once touching a petri dish, or even model and predict how climate change will play out precisely.

There is so much to unpack here, especially in how NVIDIA went from making commodity graphics cards to now owning the whole stack in industries from gaming, to enterprise data centers, to scientific computing, and now even basically off-the-shelf self-driving car architecture for manufacturers.

At the scale that they’re operating at, these improvements that they are making are literally unfathomable to the human mind. Just to illustrate, if you are training one single speech recognition machine learning model these days—just one model—the number of math operations like adding or multiplying to accomplish it is actually greater than the number of grains of sand on the earth.

David: I know exactly what part of the research you got that from because I read the same thing and I was like, you got to be freaking kidding me.

Ben: Isn’t that nuts? There’s nothing better in all of the research that you and I both did to better illustrate just the unbelievable scale of data and computing required to accomplish the stuff that they’re accomplishing and how unfathomably small all of these are the fact that this happens on one graphics card.

David: Yeah, so great…

…Ben: It’s funny because that feels like that’s the killer use case, but that’s just the easiest use case. That’s the most obvious, well-labeled data set that these models don’t have to be amazingly good because they’re not generating unique output. They’re just assisting and making something more efficient.

Then flash forward 10 more years and now we’re in these crazy transform models with, I don’t know if it’s hundreds of millions or billions of parameters. Things that we thought only humans could do are now being done by machines and it’s happening faster than ever. I think to your point, David, it’s like, oh, there was this big cash cow enabled by neural networks and deep learning in advertising. Sure, but that was just the easy stuff.

David: Right. That was necessary though. This was finally the market that enabled the building of scale in the building of technology to do this. In the Ben Thompson, Jensen interview, Ben actually says this, when he realizing this talking to Jensen says, this is Ben talking, “The way value accrues on the internet in a world of zero marginal costs where there’s just an explosion in abundance of content, that value accrues to those who help you navigate the content.” He’s talking about aggregation theory.

Then he says, “What I’m hearing from you, Jensen, is that, yes, the value accrues to people to help you navigate that content, but someone has to make the chips and the software so that they can do that effectively. It used to be that Windows was the consumer-facing layer and Intel was the other piece of the Wintel monopoly. This is Google, and Facebook, and a whole list of other companies on the consumer side, and they’re all dependent on NVIDIA. That sounds like a pretty good place to be.” And indeed, it was a pretty good place to be.

Ben: Amazing place to be.

David: Oh my gosh. The thing is, the market did not realize this for years. I didn’t realize this and you probably didn’t realize this. We were the class of people working in tech as venture capitalists that should have.

Ben: Do you know the Marc Andreessen quote?

David: Oh, no.

Ben: Oh, this is awesome. Okay, it’s a couple years later, so it’s getting more obvious, but it’s 2016. Marc Andreessen gave an interview. He said, “We’ve been investing in a lot of companies applying deep learning to many areas, and every single one effectively comes in building on NVIDIA’s platforms. It’s like when people were all building on Windows in the ’90s we’re all building on the iPhone in the late 2000s.” Then he says, “For fun, our firm has an internal game of what public companies we’d invest in if we were a hedge fund. We’d put in all of our money to NVIDIA.”

David: It was a paradigm that called all of their capital in one of their funds and put it into Bitcoin when it was like $3000 a coin or something like that. We also have been doing this. Literally, NVIDIA stock—this is now 2012, 2013, 2014, 2015—doesn’t trade above $5 a share. NVIDIA today as we record this is I think about $220 a share. The high in the past year has been well over $300. If you realized what was going on, and again, in a lot of those years, it was not that hard to realize what was going on, wow, it was huge.

Ben: It’s funny. We’ll get to what happened in 2017 and 2018 with crypto and a little bit, but there was a massive stock run up to like $65 a share in 2018. Even as late as I think the very beginning of 2019, you could have gotten it. I tweeted this, and we’ll put the graph on the screen in the YouTube version here. You could have gotten it in that crash for $34 a share in 2019. If you zoom out on that graph, which is the next tweet here, you can see that in retrospect, that little crash just looks like nothing. You don’t even pay attention to it in the crazy run up that they had to $350 or whatever their all time high was.

David: Yeah. It’s wild. A few more wild things about this. AlexNet happened in 2012. It’s not until 2016 that NVIDIA gets back to the $20 billion market cap peak that they were in 2007, when they were just a gaming company. That’s almost 10 years.

Ben: I really hadn’t thought about it the way that you’re describing it. The breakthrough happened in 2010, 2011, 2012. Lots of people had the opportunity, especially because freaking Jensen is talking about it on stage. He’s talking about our earnings calls at this point.

David: He’s not keeping this a secret.

Ben: No, he’s trying to tell us all that this is the future. People are still skeptical. Everyone’s not rushing to buy the stock. We’re watching this freaking magic happen using their hardware, using their software on top of it. Even semiconductor analysts who are like students of listening to Jensen talk and following the space very closely think he sounds like a crazy person when he’s up there espousing that the future is neural networks, and we’re going to go all in. We’re not pivoting the business, but from the amount of attention that he’s giving in earnings calls to this versus the gaming. I mean, everyone’s just like, are you off your rocker?

David: I think people have just lost trust and interest. There were so many years, they were so early with CUDA and early takeout. They didn’t even know that AlexNet was going to happen. Jensen felt like the GPU platform could enable things that the CPU paradigm could not, and he really had this faith that something would happen. He didn’t know this was going to happen. For years, he was just saying that we’re building it, they will come.

Ben: To be more specific, it was that, well, look, the GPU has accelerated the graphics workload. We’ve taken the graphics workload off of the CPU. The CPU is great. It’s your primary workhorse for all sorts of flexible stuff. But we know graphics need to happen in its own separate environment, have all these fancy fans on it, and get super cooled. It needs these matrix transforms. The math that needs to be done is matrix multiplication.

There was starting to be this belief that like, oh, well, because the apocryphal professor told me that he was able to use this program that matrix transforms to work for him, baybe this matrix math is really useful for other stuff. Sure, it was for scientific computing. Then, honestly, it fell so hard into NVIDIA’s lap that the thing that made deep learning work was massively parallelized matrix math. NVIDIA is just staring down their GPUs like, I think we have exactly what you are looking for.

David: Yes. There’s that same interview with Bryan Catanzaro. When all this happened, he says, “Deep learning happened to be the most important of all applications that need high throughput computation.” Understatement of the century. Once NVIDIA saw that, it was basically instant. The whole company just latched on to it.

There are so many things to laud Jensen for. He was painting a vision for the future, but he was paying very close attention, and the company was paying very close attention to anything that was happening. Then when they saw that this was happening, they were not asleep at the switch.

Ben: Yeah, 100%. It’s interesting thinking about the fact that in some ways, it feels like an accident of history. In some ways, it feels so intentional, that graphics are an embarrassingly parallel problem because every pixel on a screen is unique. You don’t have a core to drive every pixel on the screen. There are only 10,000 cores on the most recent NVIDIA graphics cards, but there’s not, which is crazy, but there are way more pixels on the screen.

They’re not all doing every single pixel at the same time every clock iteration. But it worked out so well that neural networks also can be done entirely in parallel like that where every single computation that is done is independent of all the other computations that need to be done, so they also can be done on this super parallel set of cores.

You got to wonder, when you kind of reduce all this stuff to just math, it is interesting that these are two very large applications of the same type of math in the search space of the world of what other problems can we solve with parallel matrix multiplication? There may be more, there may even be bigger markets out there.

5. Twitter thread on an interview of Ted Weschler – Thomas Chua

1. Who is Ted Weschler? He was the founder and managing partner of Peninsula Capital Advisors. Between 1999 and 2011, the $2B fund returned 1,236% to its investors. He wanted to meet his hero and so he bided on the annual auction lunch with Buffett.

2. One fateful Tuesday morning, he received a phone call that changed his life. It was Buffett on the other end. He had won the annual charity auction lunch. Ted flew out to Omaha two days later to meet his hero. Everything clicked. Ted bid again the following year and won!

3. This time, Warren asked him:  “I think you’d be a pretty good fit out here. Would you have any interest in working at Berkshire?” He panicked. On one hand, he was running a successful fund and his family was in Charlottesville. But on the other, this is Warren Buffett!

4. He wrote Buffett a letter when he got back to Charlottesville explaining that it was difficult because his family was rooted here. Buffett replied “You can manage money from the moon as far as I am concerned.” Buffett was a real pioneer in the work from home trend 😂…

…7. Investing is a game of connecting the dots. We want to build up a lot of data in our minds and understand why the business will be vastly different five years from now than what the market perceives. He reads trade journals regularly to understand businesses…

…9. Why he always feel positive? United States has a system that works. There’s will be negativity every now and then. But if you take a long-term view, there’s innovation coming out every day and it keeps getting better. It’s hard not to be optimistic.

6. Sources of Enduring Business Success – John Huber

I recently read through the letters of Nick Sleep, who ran a very successful investment fund in the United Kingdom before closing it last decade. Sleep is a great thinker and I highly recommend his work. One thing Sleep wrote a lot about is how the average holding time period for many of the stocks he owned was around 50 days, whereas he planned to hold these stocks for more than 250 weeks (5 years). I think his key observation is important: The marginal buyer who is holding a stock for 2 months is not placing much emphasis on that company’s competitive advantage because that advantage won’t matter much at all over the next few months; what matters over that period of time are things like market perception, news flow, sentiment, and perhaps short-term business momentum…

…So what Sleep did is he decided to compete in a different game. Instead of attempting to determine how the crowd will react this quarter or how the trajectory of the business will fare this year, he wanted to focus on the factors that contributed to a business’s ultimate potential. What attributes give this company an advantage? What will lead this company to success through both good times and bad times (because if you’re a long-term shareholder, all companies face headwinds at some point).

…Sleep used the example of Walmart’s cost advantage. Walmart’s business model was to offer the lowest prices on everyday merchandise, and steadily gain scale advantages through larger and larger bulk purchases from suppliers at lower and lower unit prices, which meant further savings to customers, which led to more growth and more scale advantages. Sleep coined a term for this business model: “scaled economies shared”, meaning the business gained scale, but instead of keeping the excess profits for itself, it gave these scale advantages to the customer in the form of lower prices. This sacrificed near term profits but led to far greater future profits, which of course is where value comes from.

Walmart, Costco, and Amazon all exhibit this basic business model, and all have achieved great success. But what Sleep noticed is that investors — even when they understood this business model — still undervalued all of these companies because they placed too much emphasis on shorter term factors such as seasonal same-store sales trends, quarterly margins, or the business cycle. All of this focus came at the expense of what really mattered, which was the cost advantage that was so hard for competitors to replicate….

…Last summer, investors sold Amazon after its Q2 earnings report because the next few quarters would face tough comps from the gangbuster 2020; but Amazon’s value in 2032 has little to do with the comps it faces in 2022. It has a lot to do with the durability of its network, the economies of scale, the distribution advantages, the culture of operational excellence; none of that will likely drive the stock this quarter, but it’s what matters most to the stock over the next decade.

A mismatch of time horizons lead some investors to more heavily weight the short-term and deemphasize these sources of “enduring business success”.

7. Twitter thread on how company leaders handle crises – Dan Rose

I was at Amzn early ’00s when we lost 95% of our market cap. Later at FB I negotiated a down-round in ’09, and then in ’12 our stock dropped 50% post-IPO. I was on the board of a public company that went bankrupt (Borders) and a start-up that went under (Hello). Some lessons:

1/Raise capital when you can, not when you need it. Amzn tapped convert debt in Feb ’00 – if we had waited another month we would not have survived. 9 years later at FB we raised a 33% down-round despite having plenty of runway. Don’t wait until your back is up against the wall

2/Cash is king. Forget about valuation, dilution, etc – if you run out of cash, none of it matters. Borders used its free cash flow to buy back stock for yrs, ignoring the internet. By the time a PE firm fired the board and asked me to join in ‘09, we had no runway for turnaround…

…4/Change the tone. Amzn did a small but symbolic RIF in 2000. Around that time, Jeff was presented with a team t-shirt – he threw the team out of his office and banned all company swag. We even removed aspirin from the break rooms, served coffee and water. Small acts set the tone

5/It starts from the top. Zuck showed up to work in Jan ’09 wearing a tie, and he wore it every day for an entire year. His message to the company: “it’s time to get serious about our business.” Every time we walked into a meeting with Mark, we were reminded things had changed

6/Reset the team. In the middle of covid I addressed the exec team of a travel start-up whose revenue dropped to zero overnight. I encouraged them to re-evaluate their team. Some people step up in a crisis – they are your future leaders. Others will jump ship – good riddance…

…9/Communicate, a lot. When FB’s stock plummeted after our IPO, I addressed the issue with employees rather than pretending stock price didn’t matter. It’s tempting to go into a foxhole when times are tough. Don’t do that, your team needs you more than ever

10/Keep telling your story. I stayed at Amzn during this time because Jeff sold me on his vision. When GFC postponed FB’s IPO by 4 years, Zuck never stopped talking about the mission. Churchill taught the world the power of storytelling in a crisis

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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 Alphabet (parent of Google), Amazon, Apple, Costco, and Meta Platforms (parent of Facebook). Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

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 01 May 2022:

1. Henry Ward – Transforming Private Markets – Patrick O’Shaughnessy and Henry Ward

[00:15:45] Patrick: I remember reading about CartaX when it was first announced or posted online several years ago, and thinking, “Wow, what an interesting way to sit on top of the cap table infrastructure that you’ve built to now provide a real secondary exchange for private markets, and the possibilities that might unlock.” How would you grade yourself so far? Maybe describe CartaX. I think I kind of just have described it in its basic terms, but I’ll let you describe it how you see it. And I’d love to hear how you think it’s gone. Are you satisfied with the scale of it? What have been the lessons or the challenges you’ve learned building something notoriously hard to build? Because a huge defacto one doesn’t exist like the NASDAQ or the NYSE or something like this for this market.

[00:16:25] Henry: We’re definitely in the new market creation business. One question to ask is how you define a new market. And our definition is anything where you’ve made a way for money to exchange hands that hasn’t happened before. One example of that is having cap tables. We weren’t the first cap table provider. We were the first cap table provider to charge companies. And as an example, in CartaX we’re the first exchange where we’re charging buyers and sellers commissions to trade and provide crossing trades for them as a service. These things are really hard to get going. But when they go, they accelerate very, very quickly. If I were to grade ourselves, I’d give us a B minus on CartaX. I think we’re attracting a lot of supply, and now we’re building up the demand side of the equation. All marketplaces start this way. So if you’re an investor looking at marketplace companies, all marketplaces start with this thesis that there’s hidden embedded demand that you can’t see. And then the marketplace has somehow figured out how to unlock supply.

So if you look at Airbnb, there’s embedded demand that people wanted to sleep on people’s couches, and then Airbnb figured out how to unlock that supply and get people to do it. And the same for us. I think we have figured out how to unlock supply. I think companies are coming to us at scale. We run 20 liquidity events a month these days to unlock supply and create liquidity on their cap tables. The challenge now is, as supply starts to rise, every marketplace has this question. How do you then have demand rise as well? And it’s always that balancing act. And I think we’re in the demand side of this equation, how do we attract more investors to CartaX so that they can start buying into these pre-IPO liquidity trades?…

...[00:25:26] Patrick: How do you make those kinds of decisions faced with an infrastructure that, like you said gives you optionality, enables you to build other stuff? How do you decide what’s a good idea and what’s not? So I’ll leave it at that. I mean, it just seems like when there’s lots of options. Sometimes it’s very difficult to know what to focus on. So as a manager of a business now abstracting away from the specific problems you’re solving, what do you think are the right ways for other entrepreneurs to think about that problem of where to focus?

[00:25:53] Henry: I took something from our friend, Mark Andreessen where he talked to me about Andreessen Horowitz. There’s no bad ideas. It’s only timing. And if you have that belief system, he’ll walk you through this history of ideas that happened that were before their time, but then actually ended up being a good idea. I can do it for [Webend] versus Instacart, but the Andreessen people can do it for the like last 150 years. So we’ve taken that. What’s so great about that model is the question isn’t what’s a good idea, what’s a bad idea. The question to it comes is now the time for this idea. And that’s such a different way to think about investment decisions. I love that framework. We’re not investors, we’re operators. So we have our own framework, which is there’s no bad ideas, the question is which aperture you look at for this idea. So if you’re looking at an aperture saying, “Hey, we’re trying to solve this one problem for a user. It feels like we should do this for this user.” If that aperture is correct, when you’re a product manager focused on your user and the user wants feature X, we should do that. But then if you look at it through a different aperture, let’s say what is core to our mission over the next 10 years that feature may not actually be important to us. And we’re both right.

So the question is which aperture do we look through? I’ll give you one really good example. If you talk to some of our CEOs that are customers, some of them don’t like to give vesting email reminders to employees. This is a really weird one. But they don’t like it because they don’t employees worried about equity. It’s also sometimes they don’t want employees if they leave to know that their options can get exercised. There’s some weird dynamics that happen with some companies and investing email reminders for employees. So if you look through the aperture of what do I do to make my customer happy? You might say, “We should turn that off if a customer wants that.” If you look at through the aperture of our goal is to normalize equity as a means of compensation and educate the world about equity, we absolutely would send everybody vesting reminders and teach them about how important it is that they exercise their options. And both are correct answers. The question is which aperture do we want to look through today? And that’s how we look at everything is what’s the right aperture to look through a decision and then make a decision through aperture? And my job as a CEO is not to opine on yes, no versus good ideas, bad ideas, my job is to help the executive team to figure out what’s the right aperture for decision-making.

[00:28:07] Patrick: There’s this great idea, that idea of 1 of N versus N of 1 companies that I think I first became enamored with through David Haber, another mutual friend of ours, also at Andreessen. And I think he might have credited you with this model. Maybe talk about that concept a little bit and whether it also applies to this product, this decision framework, not just at the company level, but down at the feature level.

[00:28:28] Henry: I am a huge fan of the 1 of N versus N of 1 framework. And I just have to give credit, I probably talk about it more than anybody else because I’m such a disciple, but this actually came from Arjun at Tribe Capital who you may know. He told me this framework and I just ran with it. So an N of 1 business is one where the market structure allows for a monopolistic effect where there can be one and one winner. The N of 1 winner. A one event market is one where there’s lots of competition. You almost think of it like Peter Thiel’s competition is for losers, right? He has a very black and white view of the world. There’s either perfect competition or there’s monopoly and there’s nothing in between. And we subscribe to that view. Our job is to never enter 1 of N markets. Never enter anything where the end state of this marketplace has to be one with multiple competitors and only enter markets where we have a real chance at becoming the N of 1 player. And that actually makes it tricky because when you enter new markets to be an N of 1 player, by definition, you have to go to relatively small markets because large markets are really hard to become an N of 1 player. It takes a lot of time. You have to have the scale to take over these N of 1 markets. Like Amazon is still not N of 1, but boy, are they heading that direction. That is the balancing act where the investors that invest in Carta, the criticism might be, “Hey, they only go after small markets.” The bullish case is, “Well, hey, but they win all these markets. Each time they win a new market, it gives them optionality to build on top of that market and go into something bigger.” And so far we’ve been able to execute against that strategy.

[00:30:02] Patrick: What are some of the key principles of how you run the company that map back onto that idea of N of 1 market company, whatever? What is different do you think about running a company that, that explicitly is the goal or the strategy is to just be in markets that they can dominate?

[00:30:19] Henry: Yeah. I used to have this conversation a lot with candidates that I was trying to recruit. Back in the early days, especially, I’d compete against Instacart and I’d compete against MongoDB, and Gusto, and payroll companies and house tech companies. What I would always tell them is I would say, “Hey…” When I had a candidate that had an offer from a payroll type company and an offer from me and they were trying to figure out the two, and I would say to them, “Hey, there’s two types of businesses that you can pick from. One is a business like this payroll company we’re competing with that has line of sight to…” At that time, a billion in revenue seems crazy. Now, I would say 10 billion in revenue. But they had a line of sight to a billion in revenue when they were a series A or B company. The question was, could they out execute? There’s a billion dollars easily available in TAM for a payroll company or for a database company. The question is can they just execute better than incumbents and get there and build a better, faster, cheaper product?

For us, we’ve never seen line of sight to a billion in revenue or 10 billion in revenue in any one product line. We’re like that company that sort of has a machete and we’re hacking our way through a foggy jungle, and we’re building the path as we go. The first type of company, I would call an execution company. They know exactly what to do. The question is, can they organize a team and execute against that plan better than anybody else? For us, we don’t know what to do. We have to keep innovating and finding new markets because in any existing market, we’re going to run out of oxygen and we’ve raised venture capital. We’ve raised too much money to just flatline. And so we constantly have to innovate and find new paths. And the question is which company do you want to work for? High performance execution team or an innovation discovery company where we’re constantly beating our own path? And for some employees, it was better to go to an execution company. I would say everybody that comes to Carta is here for the journey, not the destination, because we don’t know what the destination is.

[00:32:11] Patrick: Let’s just imagine there was two classes of five amazing job candidates, a designer and engineer, whatever the lineup was. We could run a sliding doors experiment. So that five-person group went to payroll company in one world and they went to Carta in another world. In what ways are those two paths in the actual experience of doing the work the most different? I understand the concept, but in practice, like in literal terms, what is different about those two paths or those two kinds of companies and therefore those kinds of employees and how they operate?

[00:32:41] Henry: I would say that the experience of the employee is a top down versus bottoms up management style. If you’re an execution company like a database company or a payroll company, they know exactly what to do. The roadmap is defined from the top. Execution is measured and progress measured, OKRs, all this kind of stuff. So they’re given the thing. Here’s what you got to go do, and you just have to go do it. It’s great and you’ll do it really well, and all that kind of stuff. I think employees that come out of those companies become great executives. Because how do you become a great executive? I call it deterministic management. You know exactly what to do. You have a roadmap, you have a plan, and your job is to operationalize that plan. And they become great executives. If you work for a company like ours, we have no idea what to do. It’s very bottoms up. We intentionally organize that the best ideas come from the bottom. And my job is not to actually make decisions on what to do because I’m not close enough to the customer, to the markets, to all these things. My job is to give people the framework that they make the decisions on what to do.

So for example my framework is only N of 1 markets. We only do new market creations, so we’re not going to try to invade an existing market, we’re going to find a way for money to exchange hands that hasn’t happened before and make that true. I give all the frameworks for how to make these decisions, but you really push decision-making to the bottom. And it feels like for employees is it’s scrappy, it’s exciting. It’s also incredibly chaotic and they have no idea what’s going on half the time. And I would say the best thing, if you want to be a founder, Carta is the right place to do this. If you want to be an executive, this is a terrible place to learn to be an executive. But if you want to be a founder, this is how you do startups. We have the Carta cartel, we affectionately call it, early stage employees that have left to do startups, and there’s a dozen of them already. We breed founders. That’s what we do here.

[00:34:20] Patrick: What do you do to make that so true? What is the empowerment that’s happening? What is being pushed down, I guess, to that bottom that allows that experience to happen for them, deliberately from you and leadership team?

[00:34:33] Henry: A big part of it is roadmaps and decision making is pushed all the way down to the people that matter. So we’re very good at allowing experimentation to take place. I’ll give you a very practical example of this, which is really hard, hard to figure out. Let’s say a director level or senior director, something, their project tried a new product or new thing, and it didn’t work out. And now their performance review is coming up. We do a four point rating. Four is the best, one is the worst. Do you give them a two because it didn’t work out or do you give them a four because they tried. That question is it seems so simple, but it’s such a fundamental question because if you give them a two, nobody will ever take risks because they’ll only do things they know that work. And if you give them a four, people will want to take risks because they know that they’ll get rewarded for that effort. We’re a company that gives fours. Most companies won’t. If you ask most companies, what do you do when somebody tries something and they fail at it? They’ll say, “Well, we’re an outcome-based company. Results matter.” We’re an input based company, not an output based company. The results will be the results. What we question is do we do it the right way?…

…[00:45:02] Patrick: Maybe say a word of what you’ve learned about… You’ve given a lot of these interesting management concepts. I’d love any interesting, similar concepts on product you’ve got now beyond cap table, a number of different things that you do for your customer. What does great product, especially in the world of software, mean to you? What are the characteristics of a great product for Carta, but even more generally?

[00:45:22] Henry: There’s this great image I shared with all my product managers. I’ll try to describe it, hopefully, in words. There’s two styles of product management if you want to build a car and the first one was this, iteration of how to get to a car in pieces. You start with a chassis and then you start with a wheels and then you start with a steering wheel and then you put in a steering wheel and you put in seats and at the end of this, you get a car. Then the other style of product management was you start with a skateboard and then a scooter. Then you put a stick on it, it becomes a scooter. And then it becomes an electric scooter. And then it becomes a go-kart and then you get to a car. I love that one because what’s so powerful about that is, the first version of this product has utility in the second style, but not in the first. And so we talk a lot about… Everybody wants to build a car. We know that’s what we want to do, but that’s not the hard part in product management. The hard part in product management is the path to the car and how do you provide utility along the way? This is one of the things that big companies get wrong a lot, because they have so many resources. They’re like, “We’ll just go straight to the car. We’ll build a chassis, we’ll build this.”

We have executives that know how to operationalize this. We have a roadmap and a plan, but if you’re in a discovery company where you’re not sure what this car is going to look like, you have to start with utility early. And this is why it works really well for founder-led companies, because that’s what venture is like. Nobody gave me a billion dollars to start Carta. I started with 200K and then a million and a half. And each way I had to show utility, I had to show something that at that stage of the company was sustainable, we could build off of, and big companies don’t have that. And so they do these massive projects that often fail three years in where we instill that deeply into our product teams, even now we’re 2000 employees, that your job is to build a scooter first and not the chassis.

[00:47:10] Patrick: I love that. It reminds me of one of my favorite books by this guy, John Gall called The Systems Bible. And one of the principles in The Systems Bible is there’s no such thing as a complex system that’s just designed complex and implemented. Every complex system that works evolved from a simple system that worked first, and that really makes me think of that skateboard-scooter-car way of thinking about product. Same question for teams. Define what a great team looks like, especially given that it sounds like you are really pushing the fate of the company down onto relatively small teams at the edge of the business, not from the top down. What does a great functional team look like in your opinion?

[00:47:47] Henry: I talk a lot about this with my execs, where I have this interpersonal theory about how people talk and work together and I call it process and content. Process is how you work together. Content is what you’re talking about. Most teams and management C-level execs talk a lot about content. What’s the right budget here? What’s the right product here? What should we do here? All of it is around the decision making and what’s the right decision? And I spend a lot of time with them, especially with execs that come from bigger companies where vigorous debate is good because it gets you to the right answer. Another management maxim I can’t stand is, debate is good. And what I talk to them about is what I care most about this process, how we work together, how we talk to each other. You’ll give this example where two execs are arguing and not getting along and upset about something, but they ended up getting to the right decision, to the right outcome and agreeing on it.

They would consider that success. I would consider that failure. I use this phrase. Friction is failure, and most people think friction is good, because it shows a healthy debate. And so to me, it’s in a great exec team, works really well together and is okay if they don’t get the right answer. My favorite lines that I learned about partnerships is, great partnerships work when the relationship matters more than the answer, and I think that’s true for teams. How we work together matters more than the answer and we’re okay making mistakes to preserve the collaboration of what we’re working on together.

[00:49:13] Patrick: I like this line of questioning around aspects of the business so I’ll keep going. What defines great in go-to market, whether that’s marketing sales? You can tell me what matters more for Carta. What have you learned about what great means at doing this part of the company building motion really well?

[00:49:28] Henry: For us, I have a very specific answer. I don’t know that I can speak for all companies, but definitely for us, we are in this, I would say, later innings transition, the moving from a single product company to a multi-product company and the platform, yes. Multi-product and I would even say multi customer, because we both sell cap table software and compensation benchmarking software to companies, but we also sell back office and fund administration to venture funds. If you look at any life cycle of a company, obviously they start with an idea and they’re trying to get to product market fit. That’s stage one, is trying to get to product market fit. Then after product market fit, most companies die before that ever happens. That’s the first wave of death. The second wave that happens is they get to product market fit, but they can’t scale effectively, and that’s stage two, which is how do you scale this product that’s seems to be working? A lot of companies die there, but much, much fewer. That tends to be a little bit easier. Getting to product market fit is the hardest part.

And then at some point, unless your database is your payroll, you’re going to run out of oxygen there, you’re going to have to have a new product or a new customer, expand the market and then it becomes a multi-product, multi-customer company or platform. Vast majority of companies die there. That’s where you get the single digit billion outcomes, $2 billion market cap and always will be. But if you want to get the 10, 20, 100 billion in market cap, you have to become multi-product. Being in the midst of doing it right now, that’s actually really hard to do. It’s harder than I would’ve thought. And building a GTM motion that becomes the pipelines of distribution where we can invent a new product, we can acquire through M&A, Corp Dev a new product and then push that through the lines of distribution to our customers in a scalable way. That’s really challenging, but the teams that can do that’s incredibly valuable because now, if you get a good product market fit and a lot of that can be experimented with outside, you just look at these startups, you see which one’s getting traction and you buy it and you push it through your pipes. That is how you do Salesforce-level execution.

2. RWH005: Meet The Master w/ Aswath Damodaran – William Green and Aswath Damodaran

William Green (00:06:38):

You ended up at UCLA, you have multiple degrees if I remember rightly, and I wanted to get a sense of how you stumbled into teaching, because it seems like everything you do really is about teaching whether it’s being a professor at NYU, making videos for YouTube, writing your blogs, writing your books. And so, I’m curious how you actually discovered this lifelong passion which… What, you’ve been teaching now for 40 odd years?

Aswath Damodaran (00:07:02):

42 years now. No, it was accidental. Like so many things in so many people’s lives, it was just being at the right place at the right time. I came to UCLA to do my MBA. At that time, I’d already got a Master’s in Business in India, but because I had only 15 years of education, in India, school runs through quicker, US universities then required 16 years. So basically, I had to come back for a second Master’s. And my intent was to do what all MBAs do, which is to go work for some place which pay me a lot of money. When I started in 1979, that one might have been a consulting firm. But by the time I got towards 1981 and getting close to graduation, I was hitting the start of the growth of Wall Street exploding out, where you saw investment banks hiring.

Aswath Damodaran (00:07:47):

And I was on the verge of accepting that position at an investment bank when I realized I had run out of money and I needed to do something just to get enough funds to make it through when my job started. So I took a job as a TA, a teaching assistant, for an accounting class, a subject, as you might know, I don’t particularly care for. But I needed the money. So I remember I said, “I’ll get this done. It’s a quarter. How much pain can it be?” So I still remember that first day I walked into the class, and I was nervous. I mean, like everybody is when you’re in front of a big group of people. At about 15 minutes in, I don’t know what it was, but I realized that this was what I wanted to do with the rest of my life.

Aswath Damodaran (00:08:26):

I’m not a religious person, but I do believe that you get these moments of clarity when, I don’t know, some supreme being is saying, “Hey, listen, this is what you were meant to do.” I was lucky to be listening. And that moment changed my life because I said… And I remember right after that class, I marched up to the floor of the finance department, talked to professors there about, “Hey, how can I get into the PhD program? I want to be a teacher.” And luckily, that path opened up and I became a PhD. And the rest of my life has been all about teaching.

William Green (00:08:57):

I remember you once describing that as a [Godshot 00:09:00], which I thought was a wonderful phrase to describe that kind of 15 minutes that change your life. I am sort of a mystic who pretends to be rational because I cover the investing world where you’re supposed to be rational. So, I kind of love the idea that somehow there is some sense in which we’re being guided in life. I have no rational or objective basis to believe this, but it gives me pleasure to think it.

Aswath Damodaran (00:09:21):

And I believe we all get moments like that through our lives, but we’re so busy with our lives, we don’t listen. I tell my kids… They have social media, they’re constantly filling their days. And I still do this. Every day, I try to give myself some time. When I’ve nothing scheduled and I’ve open slots, it’s daydreaming time. I think we think about daydreaming as a waste of time. I think daydreaming is when you open your mind up to, “Hey, what can I do that’s different? What can I learn?” And I really value those moments because I think it makes a difference in my life…

…William Green (00:21:24):

But it also struck me that part of your skill was your willingness to provoke, to be a provocateur. And there was this wonderful beginning of the talk where, if I remember right, you said, “Basically, I sit at this nexus of these three really big, really badly run businesses of teaching, and writing, publishing, and finance. And they’re all begging to be disrupted and to be taken to the cleaners.” And I wondered if you had any advice for the rest of us on how to speak, how to communicate, because it seems to me that you’re really a master of this.

Aswath Damodaran (00:21:53):

I think that my two pieces of advice is don’t try to be somebody else. You got to be comfortable with your presence. And I’ll give you an example. I’ve never worn a suit to teach because when I started teaching, that was the standard. In business schools, people wore suits or [inaudible 00:22:09] ties when they walk to a classroom, because the view was students will not respect you if you’re not dressed up as if you’re an authority. And my view was, “Look, now if I bought a suit, I’m going to pay a few hundred dollars. My students are MBAs. They’re going to Barneys to get their suits for 3000 because they need to look good for investment banks. My suit is never going to look at as good as theirs and I hate wearing suits.” So I said, “Look, I don’t feel comfortable teaching in a suit. So, I’m going to teach in a T-shirt. I’ll teach in sweatshirts. Basically, I can teach in whatever makes me comfortable.” So, I had to pick something that made sense for me.

Aswath Damodaran (00:22:44):

Early on, I realized there’s some great teachers who were authoritarian teachers. I don’t know whether you remember the movie Paper Chase, I think where it’s about the Harvard Law School. And I don’t remember who it was, a great actor, maybe Gielgud was there playing the role. And he plays the role of a Professor of Law, and he intimidates. He has this immense presence in front of the classroom. But when he turns to a student, just the intimidation factor is enough to keep the class going. I realized very early that I was not in an intimidating person, that my presence couldn’t be built on, “I’m the authority figure, you’re not. And I’m going to tell you what to do.” So, I had to find a teaching style that fit me or a communication style that fit me. And my communication style is much more informal and much more open and much more willing to kind of accept the fact that there might be other people who push back. And over time, there are things I do better now than I did four years ago.

Aswath Damodaran (00:23:37):

One of the things I tell people is, “Look, there are days when you wake up and you get in front of a group, and you open your mouth and magical words come out. It’s like you can’t do anything wrong. You say, where did that come from?” It’s easy to teach when you’re in the zone, right? When baseball players are in the zone. When you’re in the zone, teaching is easy. Teaching or communication is difficult when you’re not in the zone. When you open your mouth and your tongue is getting in the way of your own words, it’s not your day. And I tell people, “You got to figure out ways to get into the zone when you’re not in the zone.” So, there are small tricks and I would suggest these. One is be well prepared. I’m prepared for my classes to the point I never have to look at my slides to know what’s on the slides.

Aswath Damodaran (00:24:18):

So I think that finding your zone when you’re not in the zone is something I do better now than when I started, because I’ve learned small tricks to bring myself back into the zone. Tricks like figuring out questions. One of the things you will notice in my slides is I’ve these questions asked or I give multiple choice answers and I put them up. So instead of throwing an open question to a group where nobody might react, I say, “Look, I’m going to throw this question up. I’m going to put five answers. None of the answers are going to be obviously wrong.” And I call for a minute of silence where people get to pick an answer. That minute actually helps me as much as it helps the students, because again, those moments allow you to gather your thoughts and say, “Okay, let me get back on track.” So, there are things I do now that keep me in the zone when I even…

Aswath Damodaran (00:25:03):[inaudible 00:25:00]. So there are things I do now that keep me in the zone even when I’m not feeling like I’m at my best. And being prepared, that I think is critical to teaching, but you’re right. One of the things I tell people is the biggest sin you can commit as a teacher is to bore people. I will provoke you. I will anger you. I’m willing to take any emotion over boredom. That doesn’t mean I’m going to prod at people just to make them mad. But it means that sometimes I would throw a question out that might be provocative because it challenges people’s beliefs.

Aswath Damodaran (00:25:32):

One of the first things I start my corporate finance class is I ask, “How many of you think markets are short-term?” Because that’s the conventional wisdom, at least is markets are short-term. We need to do other things to make them long-term. And about two-thirds of my class put up their hands and say, “Hey, I think markets are short-term.” And I say, “Can you give me a piece of evidence that backs up that view?” And it’s amazing how difficult it is to actually find actual evidence that markets are short-term.

Aswath Damodaran (00:25:57):

In fact, if you look at the actual evidence, you would conclude that markets are far too long-term. Otherwise, how can you explain the fact that you put $100 billion values on companies that haven’t figured out a business model yet? No short-term market would do that. So by opening up these questions where people have preset views and challenging those views, not because I want to change their views, that’s not my job, but to make them examine their own views. And if at the end they say, “I think markets there still short-term,” I’m perfectly okay with it. I’m not an evangelist when it comes to putting my views on others, but I want them to examine their own views…

…William Green (00:26:42):

One of the things I’ve particularly appreciated, and I’m agnostic about this. I don’t in any sense have the answer, but I really appreciate the way you’ve discussed ESG, the way you’ve been incredibly outspoken. This whole idea that companies should somehow be more environmentally and socially responsible and have better governance. And there’s obviously been a huge drive, commercially driven drive, I suspect from business leaders like Larry Fink, the CEO of BlackRock, to sell this idea to investors and to persuade everyone that it’s really beneficial for companies to do good, that it helps the bottom line and is profitable for shareholders.

William Green (00:27:14):

I think it’s fair to say that you are not convinced. And when I asked for questions on Twitter to ask you, there were several people who wrote to me about this. A listener named [Fabio Zugman 00:27:23], who I’m going to send a copy of my book, Richer Wiser Happier, to thank for his question, said to me, “You got to ask him about ESG.” And he said, “Do you think ESG will be a fad of the past? Or is it one of those things that will refuse to die as long as it serves as a marketing gimmick?” And so I wondered if you could talk us through this idea, why you’re so cynical about it, why you’re so skeptical.

Aswath Damodaran (00:27:44):

I first wrote about ESG in 2020, and I wrote about ESG because I’d never seen a concept explode that quickly out of nowhere to become the status quo. But usually concepts are the edges. No, the status quo had bought in, CEOs of companies. The corporate round table had bought this, signed the statement on stakeholders and how companies should be run for stakeholders. And the big investment funds led by BlackRock were pushing ESG to the forefront.

Aswath Damodaran (00:28:11):

But what made me suspicious was there seemed to be no trade-offs. So the sales pitch was you can have it all. You can do good and be more valuable. You can do good and earn higher returns. You can do good and you’ll have to sacrifice nothing. And through the history of humanity, being good has always been the more difficult choice. Being good has always cost you. In fact, if being good were the easier choice, we wouldn’t need religion in the first place, right? If the 10 commandments came to us as our natural choices, then why would we need religion?

Aswath Damodaran (00:28:41):

The nature of goodness is you got to have sacrifice. I’d have had a lot more respect for the ESG movement if they’d come up and said, “You know what, we need to make the world a better place. So companies have to accept that they will make less money and be less valuable in order to make the world a better place.” That investors have to accept lower returns because they want to be good.

Aswath Damodaran (00:29:01):

And if they’d made it a trade-off, I’d have said, “Okay, let’s talk. Let’s talk about what the trade-off is. Who’s making the trade-off? Who’s paying for this goodness?” And there’s still issues with ESG, but it would be an issue that you could talk about the trade-offs and say, “Does that make sense?”But the fact it was being sold as all good… It’s all cake, no calories. I said, “Somebody’s got to look under the hood.”

Aswath Damodaran (00:29:23):

So each of those in an area where I’ve seen this happen in the past, seen what happened. New concepts come up, which claim to be revolutionary, but really old wine in a new bottle claiming to be the magic way of coming up with a more valuable business. So it started with my favorite area, which is valuation. I said, “You guys keep telling me that ESG is good for value. Tell me where.”

Aswath Damodaran (00:29:46):

In my valuation class, I have a proposition called the It Proposition. If it does not affect the cash flows and it does not affect risk, let’s stop talking about it. So through time I’ve taken every buzzword in business and said, “Hey, whether it’s strategic considerations or China or cloud… Whatever that buzzword is, let’s talk about how it plays out in the cash flows and the risk because then we’re talking about something tangible.” Otherwise it just becomes this filler for whatever decision we want to make.

Aswath Damodaran (00:30:14):

So with ESG, that was my first reaction. Show me where. So I started looking at the evidence that ESG advocates were presenting. And I was horrified by the quality of research that passes for ESG research. Because, to be quite honest, it seemed to me that the research had many problems. One was, it was written by advocates, true believers. And they might have been deluding themselves saying, “I’m an objective researcher,” but when you start with a presumption or a prior that’s too wrong, it’s almost impossible to do clean research.

Aswath Damodaran (00:30:45):

The second was, they weren’t even sure what question they were answering. They were mixing up whether it was good for companies and whether it was good for investors in the same research. And the reason is very simple. One of the stories that has some backing to it is that ESG can make companies safer by protecting them from doing something stupid that can create a crisis.

Aswath Damodaran (00:31:05):

And I’m willing to listen to it. But if that story is true and ESG makes companies safer, those companies should have lower [inaudible 00:31:13], lower cost of equity, lower cost of capital. That’s good. But that means in the investors in those companies should earn lower returns as well. So what’s good for companies then can’t be good for investors as well. And much of this research was mixing up what was good for companies, what was good for… They weren’t sure what the question they were answering was.

Aswath Damodaran (00:31:31):

When I first started, very few people were pushing back. In the two years since, of course, the pushback has become much more tangible. And to be quite honest, I wrote a piece about ESG yesterday that I posted on my blog. I’m done with ESG, and I don’t want to re-fight. I’m going to move on to something else because I’m a dabbler. My interest has run out and I’ve pretty much said what’s on my mind. I’ve told people where I’m coming from and why I think what I do. I’ve no interest in forcing my thoughts on other people. And I will put out my views and if other people take strands of it and push back or make it their views, I’m completely okay with it. But I just wanted to make sure that people understood where I was coming from…

…William Green (01:01:23):

I was very struck by a wonderful line of yours that I think may have come from that Numbers and Stories book, which is a terrific book actually, where you wrote, “Humility as the single most important quality, you need to be a successful investor.” You also said hubris lies at the root of so much investing pain. Can you talk a bit more about how to guard against our own hubris and overconfidence? Because this is something that, particularly, for highly intelligent people who are used to being right and getting good marks at school and then they become investors, it’s an incredibly seductive mistake to make to assume that you’re going to be right in this game where you’re competing with other people who are equally brilliant and equally well qualified.

William Green (01:01:59):

So can you talk about that challenge of just dealing with overconfidence and hubris?

Aswath Damodaran (01:02:06):

The Buddhist are very fond of the word serene and the essence of serenity is when good things happen to you, don’t get over exuberant about what happened, and when bad things happened to you, don’t get down in the dumps, and investing is a lot of ups and downs. There are days you wake up and say, “That was an amazing day. My portfolio was up 8%.” Next day, you wake up and the end of the world is come, and recognizing that so much of what happens in markets has nothing to do with your great analysis or skill. It’s got to do with luck.

Aswath Damodaran (01:02:36):

This is a game where luck is the dominant paradigm, and it’s not like I tell people the difference between basketball and investing is you and I can go out there and try to shoot three pointers. Once in a while with luck, you might get one out of every 50, and I don’t even think I could get that, and as Steph Curry goes and do it, he does it 30 out of 50. Clearly, luck is not what’s explaining it. It’s skill. In investing though, you could get 30 hits in a row, and I can’t reject the hypothesis that he just got lucky 30 times in a row. It’s so difficult to separate.

Aswath Damodaran (01:03:08):

One of my favorite books, and I don’t know whether you’ve had Michael Mauboussin on your row, but you should definitely have him. He’s-

William Green (01:03:14):

He’s great.

Aswath Damodaran (01:03:14):

Separating out luck from skill in investing is how difficult it is to do, and that’s where humility comes from. It’s recognizing when you’re successful, how much of your success comes from luck. I still get asked by people, “What do you make around the market?” Usually, I don’t go around talking about my past performance because if I’m not asking for your money, really, it’s none of your business, whether I beat the market or not. But if I added up the returns, maybe they’re just curious. I might have made 3% or 4% more than the market going back over the last 30 years.

Aswath Damodaran (01:03:42):

Then they ask me, “Well, that must be payoff for you.” I say, “I have no idea what it is. I just might have gotten incredibly lucky at the right times.” I tell them about some of my successful investments. When I bought Apple in 1999, I bought it because I sorry for the company. Actually, I bought it as my charitable contribution. I’ve been an Apple user since 1981. Remember, ’99, Apple was facing a near-death experience. Their computers were not selling. It was just as Steve jobs was coming back, and they didn’t seem to be any way that you could recover from this crash.

Aswath Damodaran (01:04:12):

I bought Apple because I was I said, “You know what? They’ve been good to me, and I’m going to spend $5,000 buying Apple shares that I can write off.” Best investment I ever made, turned out to be a investment I made because I was feeling sorry for a company. The hubris, in my part, to go around starting with my return saying, “Look how great my investment in Apple was. “Without telling you that investment had nothing to do with doing full-fledge intrinsic valuation, and some are jumping in at exactly the right time. So it’s hard work though.

Aswath Damodaran (01:04:39):

I mean, it’s easy to let things go to your head, and the market, it’s just waiting for that to happen. It’s almost like markets are waiting and hiding for you to get all caught up in how good you are. So when I see these shooting stars the people who are lauded as the great investors because they’ve done well for two or three years, I say, “You know what? Just give it some time, because most of the time when you succeed, it goes to your head.

3. There’s a Piece of EV Tech Where the U.S. Has an Edge on China – Stephen Nellis and Gregg Lowe

hina dominates the electric vehicle supply chain, from processing raw minerals like lithium into chemicals for batteries all the way to building finished cars. But there’s one niche where America still has an edge: chips made from an exotic material called silicon carbide.

In EVs, these chips are used in inverters, which sit between the car’s battery and motors, converting the direct-current electricity the battery supplies into the alternating current the motors require. Such chips always lose some energy as heat, but silicon carbide chips lose far less than those made of conventional silicon. That difference can boost the range of an electric car 5% to 15%.

But the raw material for silicon carbide chips is difficult to manufacture. North Carolina–based Wolfspeed supplies about three-quarters of the world’s silicon carbide wafers—the thin discs on which chips are made, according to Piper Sandler analyst Harsh Kumar. Wolfspeed sells the wafers to major automotive chip firms including STMicroelectronics, Infineon and Onsemi, but also makes finished chips itself. In the coming weeks, Wolfspeed will open a $1 billion factory in upstate New York to boost its efforts to compete directly with those customers in making and selling the finished chips…

…Why should anybody care about something as esoteric as silicon carbide?

In a combustion engine car, think of your fuel lines going from your gas tank to your engine. With a silicon chip, it’s as if someone has poked a hole in it. As your fuel is coming to the engine, you’re just dumping a bunch out on the street. Your miles per gallon are going to be less because you’re losing some gallons as you’re driving. That doesn’t happen with silicon carbide.

​​This is a big deal for two reasons. One, the range of the car is longer, which is an important metric for people buying electric cars. Two, the amount of battery you need to drive a certain distance is less, and batteries are the most expensive thing in an electric car. So if you use fewer batteries, the car is going to be cheaper, which is another thing people care about…

…You’ve got a supply agreement with General Motors. Why are companies like GM coming directly to you?

The car companies have realized that they need to better understand their supply chains of semiconductors, and they need to get closer to the semiconductor manufacturers. I’ve been in this industry for 35 years, and never have I seen so many car factories being shut down because you can’t get a chip. So there’s been a wake-up call.

There’s a second element that is really important. The engine of a vehicle is the personality of the car. Some companies name their cars after the engine. For BMWs, the last two digits in the model name are the displacement of the engine in liters. A 525 is 2.5 liters, and a 550 is 5.5 liters, and so on. As technology goes from internal combustion engines to electric, the carmakers are trying to get their heads around it: How do we create our personality in this new engine, this inverter and the motor associated with it?…

…How are you thinking about China as a competitor? Are Chinese chipmakers also racing to develop silicon carbide manufacturing technology? And if so, how close are they to you?

They are, and so are our customers like the Infineons of the world. But this is a technology that’s really difficult to come after. Silicon carbide grows in a machine that operates at 2,500 degrees Celsius. That’s almost half the temperature of the sun. So this is not for the faint of heart.

You can’t buy that equipment on the open market. There’s not a vendor of silicon carbide machines. So that means you need to build it yourself. Well, to know how to build a machine like this, you need to know how to make silicon carbide. But to make silicon carbide, you need to know how to build a machine like this. There’s this whole startup process that takes many, many years. Our startup process began 35 years ago when the company was founded. And what we use today is dramatically different from what was used 35 years ago.

The game plan for typical Chinese companies is to take a bunch of capital and throw it at a problem. They can’t do that because you can’t just buy these machines. So I think that’s going to be a bit of a challenge. But the world’s supply of people that really understand this technology is pretty small.

We always have a healthy bit of paranoia around this. But it’s really tough.

4. TIP440: Beating The S&P500 Since 2004 w/ Bryan Lawrence – Stig Brodersen and Bryan Lawrence

Bryan Lawrence (00:17:22):

The second reason durable cash flows are great is that durable cash flows are more predictable. And the predictability of cash flows is a big advantage to a stock picker because they make valuing those cash flows more certain. And having certainty about valuation is a big advantage given how volatile share prices are, how volatile are share prices? This has amazed me since I started the business. When I started Oakcliff in 2004, I was lucky enough to find myself in a room with Warren Buffett and two dozen other aspiring stock pickers. We were very happy to ask him lots of questions, which pretty much all boiled down to, “How do we get to be like you but faster.” He very nicely broke to us the bad news that stock picking was a long game, but he said, “I do have a piece of good news for you, the average stock goes up and down by 80% in a year. And that’s an enormous advantage if you actually take the time to understand the underlying business because the stock price is not reflecting underlying value if it’s going up and down by 80%.”

Bryan Lawrence (00:18:17):

I said to myself, “80% in a year, he’s got to be out of his mind. He’s Warren Buffett, but he’s lost his mind.” I went back to New York, and I did the calculations he was suggesting, which was to compare the 52-week high to the 52-week low for every stock in the stock market and compare the percentage difference between those two things. And when I did the calculations, maybe not surprising because he is the Sage of Omaha, he was right. You can use Bloomberg and a computer to crunch these numbers for the thousands of companies. It’s about 4,000 companies in the US stock market going back 20 years. And if you do it, we do it about once a year, the answer is as astonishing now as it was in 2004 when I started.

Bryan Lawrence (00:18:55):

During a calm year like 2019, the average US stock price goes up and down by 50%, 5-0%. And in a crisis year, like the dot-com crash, we had in 2000 or the 08/09 financial crisis or the pandemic we just had in 2020, by up to 200%. Buffett, by saying 80%, was basically averaging a calm and a crisis year. That 50% in a calm year is also a median, and in a median year where it’s 50%, you have many stocks that are bouncing up and down by 80%. There’s no way that the intrinsic value of the average business is going up and down by so much each year, and this is a big advantage for a stock picker who’s done the work…

…Stig Brodersen (00:25:52):

Oakcliff’s net return to clients has underperformed the S&P 500 at eight out of 18 years, and yet your returns to clients outperformed the S&P 500 over time. I just wanted to mention some of those numbers. I also said it in the introduction before we kicked off this interview, Bryan, but I just can’t help but mention it because you’re too polite for you to say it yourself. But the S&P 500 with exception of Oakcliff Capital was 494.2% for the S&P 500, and net of fees is 718.3%. So, I mean, this is just an amazing track record. So bravo. You managed that impressive track record and at the same time, you underperformed the S&P 500 eight out of 18 years. I’m curious to hear your thoughts on that.

Bryan Lawrence (00:26:37):

Well, thank you, Stig. But we have had periods of underperformance, and those periods of underperformance have lasted for a year or more. This is not surprising. Warren Buffett gave a speech in 1984 about the super investors of Graham-and-Doddsville, which I would encourage your listeners to go find on the internet if they haven’t already. Just Google super investors of Graham-and-Doddsville and read Buffet’s speech and then the response by a professor at Columbia Business School, where he gave the speech. There are a couple of really interesting conclusions that can be drawn from that speech, basically, every concentrated value investor will underperform the market on an annual basis 30 to 40% of the time. It jumps out of the data. And this is data as of 1984, but you can carry this data forward and you’ll find it to be true.

Bryan Lawrence (00:27:29):

I think it’s an iron rule of underperformance. Joel Greenblatt talks about it. Warren Buffett talks about it. Here’s some data which is just fascinating. If you look at Berkshire Hathaway itself, okay, which is run by the patron saint himself, Warren Buffett, Warren has controlled Berkshire Hathaway for 57 years now, going back to 1965, and Berkshire Hathaway has underperformed the S&P 18 of those 57 years or 32% of the time. There’s that iron rule, 30 to 40%. You could say, “Oh, is that a function of the fact that he’s managing more and more money, making it more and more difficult for himself?” The answer would be no, because if you look at the first 25 years that he controlled Berkshire Hathaway, 1965 to 1990, he underperformed nine of those 25 years or 36% of the time.

Bryan Lawrence (00:28:21):

I think this is a reason why concentrated value investing, while it delivers great long-term results if it’s being done by people who actually have the ability and the temperament to handle it, why a lot of people kind of lose faith with it because you will find every practitioner of it having these periods of underperformance.

5. Quantum computing has a hype problem – Sankar Das Sarma

I am as pro-quantum-computing as one can be: I’ve published more than 100 technical papers on the subject, and many of my PhD students and postdoctoral fellows are now well-known quantum computing practitioners all over the world. But I’m disturbed by some of the quantum computing hype I see these days, particularly when it comes to claims about how it will be commercialized.

Established applications for quantum computers do exist. The best known is Peter Shor’s 1994 theoretical demonstration that a quantum computer can solve the hard problem of finding the prime factors of large numbers exponentially faster than all classical schemes. Prime factorization is at the heart of breaking the universally used RSA-based cryptography, so Shor’s factorization scheme immediately attracted the attention of national governments everywhere, leading to considerable quantum-computing research funding.

The only problem? Actually making a quantum computer that could do it. That depends on implementing an idea pioneered by Shor and others called quantum-error correction, a process to compensate for the fact that quantum states disappear quickly because of environmental noise (a phenomenon called “decoherence”). In 1994, scientists thought that such error correction would be easy because physics allows it. But in practice, it is extremely difficult.

The most advanced quantum computers today have dozens of decohering (or “noisy”) physical qubits. Building a quantum computer that could crack RSA codes out of such components would require many millions if not billions of qubits. Only tens of thousands of these would be used for computation—so-called logical qubits; the rest would be needed for error correction, compensating for decoherence.

The qubit systems we have today are a tremendous scientific achievement, but they take us no closer to having a quantum computer that can solve a problem that anybody cares about. It is akin to trying to make today’s best smartphones using vacuum tubes from the early 1900s. You can put 100 tubes together and establish the principle that if you could somehow get 10 billion of them to work together in a coherent, seamless manner, you could achieve all kinds of miracles. What, however, is missing is the breakthrough of integrated circuits and CPUs leading to smartphones—it took 60 years of very difficult engineering to go from the invention of transistors to the smartphone with no new physics involved in the process.

6. 103 Bits of Advice I Wish I Had Known – Kevin Kelly

• About 99% of the time, the right time is right now.
• No one is as impressed with your possessions as you are.
• Dont ever work for someone you dont want to become…
• …Ask funders for money, and they’ll give you advice; but ask for advice and they’ll give you money.
• Productivity is often a distraction. Don’t aim for better ways to get through your tasks as quickly as possible, rather aim for better tasks that you never want to stop doing.
• Immediately pay what you owe to vendors, workers, contractors. They will go out of their way to work with you first next time..
• …Speak confidently as if you are right, but listen carefully as if you are wrong…
• …The best way to get a correct answer on the internet is to post an obviously wrong answer and wait for someone to correct you. You’ll get 10x better results by elevating good behavior rather than punishing bad behavior, especially in children and animals…
• …Don’t wait for the storm to pass; dance in the rain…
• …When you have some success, the feeling of being an imposter can be real. Who am I fooling? But when you create things that only you — with your unique talents and experience — can do, then you are absolutely not an imposter. You are the ordained. It is your duty to work on things that only you can do….
• …Your best job will be one that you were unqualified for because it stretches you. In fact only apply to jobs you are unqualified for…
• …A wise man said, “Before you speak, let your words pass through three gates. At the first gate, ask yourself, “Is it true?” At the second gate ask, “Is it necessary?” At the third gate ask, “Is it kind?”…
• …. Getting cheated occasionally is the small price for trusting the best of everyone, because when you trust the best in others, they generally treat you best…
• …You see only 2% of another person, and they see only 2% of you. Attune yourselves to the hidden 98%.
• Your time and space are limited. Remove, give away, throw out things in your life that dont spark joy any longer in order to make room for those that do.
• Our descendants will achieve things that will amaze us, yet a portion of what they will create could have been made with today’s materials and tools if we had had the imagination. Think bigger.
• For a great payoff be especially curious about the things you are not interested in.
• Focus on directions rather than destinations. Who knows their destiny? But maintain the right direction and you’ll arrive at where you want to go.
• Every breakthrough is at first laughable and ridiculous. In fact if it did not start out laughable and ridiculous, it is not a breakthrough.

7. The Rich And The Wealthy – Morgan Housel

Cornelius Vanderbilt left his heirs the inflation-adjusted equivalent of something like $300 billion. Within 50 years it was gone.

In between sat three generations whose primary purpose was to compete on who could build the largest house and marry the bluest blood. The first heirs had some entrepreneurial sense of running the family business; over time the “family business” became insecurity and resentment.

In 1875 an op-ed said socialites “devote themselves to pleasure regardless of expense.” A Vanderbilt responded that actually they “devote themselves to expense regardless of pleasure.” It was a game that couldn’t be won, so everyone lost.

Reggie was one of the last Vanderbilts to inherit significant wealth. On his 21st birthday he received $12.5 million, or about $350 million in today’s dollars…

…Reggie’s two loves were brandy and gambling. The first left him dead at age 45, with cirrhosis so severe the blood flow from his liver was cut off and pushed up to his esophagus, where the veins abruptly ruptured and left him choking in a pool of blood. The latter left him broke – after repaying debts Reggie’s will was nearly irrelevant, as he had nowhere near the amount of money promised to his heirs.

Reggie’s grandson – Anderson Cooper – was one of the first Vanderbilts who was never promised dynastic wealth. It may have been a blessing. Cooper once said of inheritance: “I think it’s an initiative sucker. I think it’s a curse. From the time I was growing up, if I felt like there was some pot of gold waiting for me, I don’t know if I would have been so motivated.” It’s like he was the first Vanderbilt to be set free…

…I’m always interested in the difference between getting rich and staying rich. They are completely different things, and many of those skilled at the former fail at the latter.

Part of this topic is knowing the difference between rich and wealthy.

These definitions are my own, but here’s the distinction: Rich means you have cash to buy stuff. Wealth means you have unspent savings and investments that provide some level of intangible and lasting pleasure – independence, autonomy, controlling your time, and doing what you want to do, when you want to do it, with whom you want to do it with, for as long as you want to do it for.

What I find fascinating are stories like the Vanderbilts, who were the richest people on earth but, by my definition, some of the least wealthy. Money to them was less of an asset and more of a social liability, indebting them to a status-chasing life that left most of them seemingly miserable.

George Vanderbilt spent six years building the 135,000-square-foot Biltmore house – with 40 master bedrooms and a full-time staff of nearly 400 – but allegedly spent little time there because it was “utterly unaddressed to any possible arrangement of life.” The house nevertheless cost so much to maintain it nearly ruined Vanderbilt. Ninety percent of the land was sold off to pay tax debts, and the house was turned into a tourist attraction.

There are so many similar stories from the Vanderbilt family that you begin to ask, “What was the point?”

The point, as the New York Daily Tribune realized early on, was not to live a great life. It was to be rich – to be valued “upon no better basis than the possession of money.” Rather than using money to build a life, their life was built around money; rather than an asset, their inheritance was an insurmountable lifestyle debt, passed to the next generation until there was mercifully nothing left.

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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 Apple and Salesforce. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

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 24 April 2022:

1. Axie Infinity’s Financial Mess Started Long Before Its $600 Million Hack – Adi Roberston

Axie Infinity — whose creators refer to it as both a “nation” and “a bleeding-edge game that’s incorporating unfinished, risky, and highly experimental technology” — is sort of like hyper-financialized Pokémon. Players buy or rent three non-fungible tokens (or NFTs) linked to cartoon axolotls called “axies,” each of which has a set of associated stats and battle cards. Winning battles grants players a token called a “smooth love potion” or SLP, and axies can be “bred” with SLP and a third token called AXS to produce new NFTs.

Axie’s biggest selling point is the chance to turn these tokens into real money. Axies and SLP can be sold for cryptocurrency, and people can earn SLP by either playing the game directly or participating in the “scholarship” system, where they lend their axies to other players and receive a share of those players’ earnings. The result is a kind of tremendously popular in-game capital market, where axie-holders can earn currency through investment without necessarily playing the game.

The dream of Axie Infinity, like a lot of blockchain applications, is to get paid for something you currently do for free online. As Andreessen Horowitz partner Arianna Simpson told my colleague Casey Newton last October, “If I can play a game, and have an equivalent amount of fun, and also make money — well obviously I’d rather do that, right?” (We’ll leave aside the philosophical questions this raises about the nature of fun.)

But there is a fundamental problem: Axie Infinity’s in-game economy has so far relied on constant growth to keep it running, with inflation built into the mechanics. Even if the game can overcome the recent challenge of the hack, Sky Mavis hasn’t proven it can transition out of that phase.

Axie Infinity’s economy is built around three major resources: the in-game cryptocurrency SLP; the axies that live as in-game items as well as NFTs on Sky Mavis’ blockchain; and the “governance token” AXS. The game produces two of those resources in constantly increasing quantities. SLP is earned through player-versus-player battles, and, until recently it was also available by completing daily quests and grinding in single-player mode, the equivalent of printing money and handing it to players in large quantities. Axies can be bred several times to produce new creatures and are largely immortal, so the breeding mechanic increases the pool of NFTs.

Games often include economic “sinks” (like cosmetic items or in-game equipment maintenance costs) that burn resources without producing more. By contrast, Axie Infinity players had two main options: they could sell their SLP — which pumped it back into the ecosystem — or use it to breed axies whose main function is producing even more SLP. Either way, they were creating more resources and watering down the value of everything acquired in the game.

“From a macro[economic] perspective, you’ve created a positive feedback loop,” explains Mihai Gheza, the cofounder and CEO of Machinations, a consultancy that tests game economies with large-scale software simulations. Players (especially scholars) would use axies to produce SLP, the SLP would produce more axies, and the axies would bring even more SLP-producing players into the game. “It’s a guaranteed means of creating inflation.”

Sky Mavis said it needed a growing axie pool to let new players join Axie Infinity because, unlike a traditional game, the studio wasn’t supposed to simply create more characters out of thin air. Eventually it planned to introduce more sinks and hoped people would acquire axies for “the intrinsic value they can provide to players in the form of competitive, social, and progression-based fun and achievements.” In the short term, their primary use was generating currency that could create more NFTs for sale or rental, and that only worked if there were people around to buy. “By design the Axie economy will be dependent on new entrants,” Sky Mavis acknowledged.

But unless that intrinsic value materializes, the system requires players to keep joining up. In August, a cryptocurrency writer and decentralized autonomous organization operator who goes by M Goes wrote a widely cited Medium post calling Axie Infinity “the biggest Ponzi scheme in crypto.” He concluded that none of Axie Infinity’s potential long-term business models could support its biggest short-term selling point: letting a large number of people make a consistent full-time living playing games. The system was only sustainable with a huge demand for more SLP and axies, and maintaining that would require a functionally infinite number of new signups. “It is hard to predict when the collapse will happen,” he wrote. “But nevertheless, there are only so many daily players it can reach.”

As it turned out, Axie Infinity skeptics wouldn’t have to wait long. Around the end of 2021, the game suffered a dramatic decline in its token prices and sales volumes, with the SLP token crashing from an all-time high of 39 cents to a single penny. A report from research firm Naavik indicated that the typical player’s daily earnings had fallen below the minimum wage in the Philippines, Axie Infinity’s top market. Sky Mavis took drastic action, removing a large chunk of SLP-generating options and making player earnings dependent on winning competitive matches instead of just showing up to grind. “We know that this is painful medicine. The Axie economy requires drastic and decisive action now or we risk total and permanent economic collapse,” it warned. “That would be far more painful.”

2. Things not being said about Chinese tech management – Lillian Li

When Alibaba, Tencent and Baidu started in the 2000s, there was no concept of tech entrepreneurs. People have always started small businesses, but no one in living memory ever built a private business empire in China. VCs were mistaken for fraudsters — in fact anyone starting a business was mistaken for fraudsters. For a country undergoing the initial tremblings of liberalisation and digitalisation, two groups went to work for fledgling domestic startups— the crazy innovative self-starters and the people who couldn’t get a better job in either SOEs or MNCs. That’s a big gap in competence between the two.

This meant while the tech founders were impressive people, some of the early employees of these corporations were decidedly not. Talking to an early Tencent VP, he mentioned his co-workers did not have prestigious university degrees if they had university degrees. Before listing, the average coder in Tencent graduated from the Chinese equivalent of community colleges and was very average. This was not a localised phenomenon by any means. Some people get lucky by being at the right place at the right time. Their positions are more luck than merit. While this is also the unspoken rule in Silicon Valley much of the time, the difference is stark in China, given the heterogeneous distribution of education and the assumed inherent worth that accompanies education.

The early employees also tend to be missionaries relative to the mercenaries of the later cohort.2 Someone who joined Alibaba in 2012 joined an upstart on the cusp of changing the Chinese retail landscape. Someone who joins Alibaba in 2022 is entering an establishment potentially on the decline. The graduates who join tech firms are the best talent of their generation, but they join for the money and prestige more than the love of the mission. The intergenerational gap is stark.

Implications from these factors are numerous. First, there is a generational disconnect where older employees believe in the notion that tireless hard work yields rewards. After all, they experienced this with vested stock growth. The younger generation is there for a job, not a purpose. They want to know when they can afford a house. Second, early mediocre employees who made it to middle management oversee more qualified and talented underlings. People do not scale with organisations, but growth hides many ills. Insecurity abounds when managers, alongside their employees, realise that they aren’t as qualified to be holding the positions they do…

…Management and organisation excellence was a luxury for companies with stable growth and a longitudinal timeline. It was possible to brute force solve a problem with additional bodies in the early days. Hiring more people is still the default modus operandi of many firms when they encounter operational bottlenecks on tight deadlines (and deadlines are always tight, there are no prizes for being slow to a market). It also stroked the leader’s ego to be overseeing many people. After all, being in charge of such resources was a direct approximation of power.3

The very distinct problem of organisational bloat and diseconomy of scale with hiring people is apparent here. Instead of having 1:1s with their direct report, management tells employees to write daily, weekly, monthly, and quarterly reports listing what they’ve been doing. Not to mention the inefficiencies caused by hiring – it takes time to get new employees up and running, often dragging down the productivity of others during the ramp-up. Communication and coordination get harder as group size increases. The inability to attribute direct outcomes to individuals creates visible principal and agent problems. Entire work culture arises where employees slack off (touching fish culture) and management makes countermoves without addressing the real issues — firms overhired during product sprints then have to deal with excessive headcounts. 

The focus is for firms to get things done quickly, and the attitude is whatever that takes. Refinement of process and improving efficiency generally took a back seat. This approach afforded fluidity and agility. Work calls happen all day, every day. Project directions can change on a dime, and the teams will reorient. Less time is spent on strategy and more on execution and reiteration. Communication takes place over the fragmented synchronous WeChat more than email or work messaging platforms like DingTalk or Feishu. Calendar invites are getting wider adoption, meeting agenda-less so.

There seems to be a cultural background to the lack of optimisation. While Western firms credit their success to distilling and adopting industry best practices, Chinese firms credit their success to being one of a kind. Chinese management exceptionalism takes Western startup slogans like ‘move fast and break things’ and mixes them with the local customs of patronage linked to Jianghu culture. The assumption is that every firm’s process should be unique, and there is some resistance to change. This has been stalling the adoption of successful organisational processes like sales funnel across China, yet another reason why Chinese SaaS finds it hard to take off. 

3. Christopher Tsai, is investing an art? Insight of a good investor – Peridot Capital Management

[00:07:04] Tilman Versch: Maybe this is a question that’s quite broad. For your 25-year-old self, what knowledge and strength do you feel that looking back, you’ve missed as a 25-year-old and you had to acquire maybe also in a bit painful way over the years.

[00:07:23] Christopher Tsai: You asked me about curiosity before. I think that’s also at the root of this question. Being able to constantly think about the world in different ways and not get trapped using models that you might have used or other people use is so important. It’s not a lesson that you can just teach. It’s an experience that one has to go through.

I’ve been reading this book. I’m not finished with it, because it’s a long book. It’s Marcel Proust’s In Search of Lost Time. Proust says that the real voyage of discovery is not seeking new landscapes but having new eyes. There lies the curiosity that we’ve been speaking about. It’s important to look at the world constantly with new eyes, particularly because the world is changing very quickly, right? Businesses don’t have the lifespan that they used to have.

In 1958, McKinsey did this study. And McKinsey showed back then that the average lifespan of a company was 61 years. That’s incredible. Sixty-one years, six decades. But today, that number is 18 years. And one of the reasons, Tilman, that it’s 18 years is because technology is encroaching upon old business models. So, if you’re not thinking about the world in new ways, if you’re not curious, if you’re not constantly looking at the competitive threat that technologies posed to traditional businesses, you might find yourself in a business that’s going out of business.

So again, being curious is not something you can just teach. It’s something that you, I think, have, and it’s something that you can foster over time. Eleanor Roosevelt, by the way, said something really wonderful. She said, “I think at a child’s birth, if a mother could ask a fairy godmother to endow it with the most useful gift, that gift would be curiosity. I wish I was endowed with that gift and was able to foster it from the very beginning. I think that something in business that you learn, you either have it or not that curiosity. But I love looking at different businesses, different business models, especially today.

[00:10:29] Tilman Versch: You have close to 25 years of experience in managing your fund. Which topics have you worked on since these 25 years to get better at? Are there any consistent topics that have kept you up at night? Let’s say it makes you stay late because you’re still trying to achieve and get better with?

[00:11:00] Christopher Tsai: Let me draw a parallel to answer your question between investment management and the Michelin Guide. We know the Michelin Guide for restaurants. Chefs work their whole lives to get one star and then two stars and three stars. What gets them there? Well, creativity gets them there, pushing the boundaries and being the best at what they do. They’re not doing things like everybody else, by definition. There are only a handful of chefs in the world that have three Michelin stars. The problem is that for those few chefs that wind up getting those three stars, what do they then want? Well, typically, they want to maintain those three stars. And so, everything else becomes subordinate to keeping those three stars.

I think that investment management is, unfortunately, very similar to that. And so, when I started, I was inundated with the idea of structuring a portfolio in a way that would get you those three stars, if you will. So that meant looking at beta, looking at Sharpe ratios, looking at standard deviation. And what I found over time is that if you start to behave like everybody else, your performance is going to be like everybody else at best. So over time, I have refined our process. In fact, we moved away from trying to worry what other people thought about how the portfolios looked. We moved away from that a long, long time ago. Maybe three-four years into managing capital for outsiders.

So today, it’s all about structuring the portfolio in the most optimal way. What do I mean about that? I mean, it’s about structuring a portfolio to maximize return and minimize risk. And that’s pretty much all I think about in terms of managing the portfolio—maximize return, minimize risk. I don’t worry about so many items that institutional investors worry about that wind up restricting a manager’s ability to have the flexibility and create alpha. I don’t worry about what other people might think of the portfolio. The key is to manage portfolios as if nobody was looking. So that’s how I’ve moved things over time…

…[00:24:55] Tilman Versch: Are there lessons you’ve taken directly from your grandmother.

[00:24:58] Christopher Tsai: She had a saying, “Don’t be a square table when you can be around one.” She intuitively understood Dale Carnegie.

[00:25:08] Tilman Versch: This means?

[00:25:10] Christopher Tsai: It means that there’s no need to be abrasive in how you speak with other people. I think that Fred Rogers, I’m not sure if many of your viewers know who Fred Rogers is, but he was the character Mr. Rogers, a popular TV show in the states geared toward children. And he said, “There are three ways to ultimate success. To be kind, be kind, be kind.”

Everybody is going through the same kind of emotions. Everybody has difficulties. You don’t know what those difficulties are. Everybody has a bad day from time to time. Everybody has joys, desires, needs, wants to be loved. My grandmother understood that. She knew how to deal with people. Not to be abrasive, not to be square around the edges. Dale Carnegie espouses that way of behaving. And so did Fred Rogers, who was one of my mentors. I should say idols…

…[1:09:28] Tilman Versch: For the end of our interview, is there anything you would like to add that we haven’t discussed?

[1:09:34] Christopher Tsai: Was it Mark Twain that said, “It ain’t what you don’t know that gets you into trouble, it’s what you know for sure that just ain’t so.” I think we should all keep Mark Twain in our mind as we think about what we own, why we own it. And again, always remain curious, not judgmental. Try to understand that if something doesn’t make sense to us, or something doesn’t make sense on the surface, maybe it makes perfect sense, just not to us.

I’ll give you an example. If you go back to 2007, Apple had just launched its iPhone. Many investors at the time said, “Well, the market value of Apple makes no sense.” They said it didn’t make sense because Apple was worth more than Nokia, Palm, Research in Motion, all those companies combined. How can that be? And what people didn’t understand, or at least a lot of people didn’t understand, was that Apple was shifting to an entirely new business model. And it had a product that had tremendous network effects that were not understood. And the market didn’t understand that the other companies would actually be completely disrupted by this new business model, by this new product, by new technologies converging. So, the market as a whole got that.

And that’s why Apple was worth more than the competitors combined. But the short sellers didn’t get that. They were close-minded. They were looking at the world through a lens or with models that no longer made sense. They didn’t understand the changes. But if you think about it, if a company has a positive future, a bright future and its competitors don’t, the value of the company that is leading disruption in taking market share and growing profits will not just be worth one multiple of all its competitors combined, but as time goes on, it will be worth two times, five times, ten times, 100 times and ultimately an infinite number of times, as all the other businesses continue to lose cash flow, and the present value of those future cash flows decreased, and the intrinsic value of those businesses decrease.

We’re seeing that same argument today in certain areas, where the value of one company might be worth all the competitors combined. People are making the same mistake because ultimately, that one company boot will be worth two times, five times, and ten times and an infinite number of times of all the other companies combined that may no longer have any cash flow. It’s just mathematics. It’s a numerator over the denominator. But it’s catchy, right? It’s a very catchy thing when somebody says, “Heck, this makes no sense. This company is worth all the combined value of the other players.” It’s very catchy. And it’s powerful, for some reason. I haven’t figured out why that’s such a powerful argument, but it’s very powerful. We all have some cognitive bias there, or at least I have. It’s a powerful argument.

But if you actually break it down and you figure out, “Okay. What does that mean? What is the value of a company? How’s the math thing compared? Numerator over denominator. What’s happening to the denominators? What’s going down? What’s happening to the numerator? Well, it’s going up because the future cash flows are increasing. Right? The present value of those cash flows is increasing, and intrinsic value is increasing. So obviously, it becomes multiples, not just one or two times. So be curious, not judgmental, as Walt Whitman said, and always look at the world with new eyes.

4. TIP429: What Is Happening With Oil? w/ Josh Young – Trey Lockerbie and Josh Young

Trey Lockerbie (00:04:06):

One chart I noticed from your research shows global production and consumption rising together in this highly correlated fashion, basically since 2010. 2020 hits, and both declined dramatically from the pandemic, but similar to the stock market, they’re beginning to kind of bounce back. However, the chart indicates that the supply will severely lag demand moving forward. And I thought this was kind of interesting because I was curious as to why the supply wouldn’t bounce back just the same to where it was kind of in 2019 levels?

Josh Young (00:04:38):

I think there’s two different cycles that are happening simultaneously for oil. And I think that’s where a lot of the headlines have been kind of in reporters covering the space have been confused, along with a lot of the analysts that cover it. So there’s a long cycle, which is that oil has been in a bear market since roughly 2012. And really, oil never achieved the high price that was seen in 2008. And so arguably it’s been in a prolonged bear market, even since let’s say 2008. Then there was a shorter cycle boom and bust in shale investment that was primarily spurred by private capital, by endowments and pensions and whatever allocating to private equity funds and equity and debt, where they went and drilled shale which was a particular kind of oil field that has a very high initial production rate and very high decline and has been most economic here in the US.

Josh Young (00:05:29):

There was this mini cycle for oil shale here in the middle of this down cycle for oil. And so what you had happen was a lot of long cycle projects that take a while, but aren’t really low decline. They produce for a number of years without a lot of necessary reinvestment. And you had a prolonged and extended down cycle for conventional development for oil, partly because there was this shale boom and bust. And the boom and bust for shale has been heavily politicized. There’s lots of people that are anti-fracking. They don’t even understand what it is or what the real risks are. There’s a lot of people that are anti-pipeline and a lot of these things have gotten conflated.

Josh Young (00:06:09):

And I think when you remove the two and you understand kind of what’s happening, it becomes a lot clearer. And what we’re seeing is the impact of an arguably more than a decade downturn in long cycle oil investment, because we’ve been in this oil bear market, and that’s kicking in at the same time as this bust in shale where there had been three or 500 billion a year in, I think in some years, that had been spent, and in many cases lost, or much of the money was lost because of high declines of production at low prices.

Josh Young (00:06:43):

And so where you see those two meet, you end up with declining production, or at least production that’s not rising as much as you’d think, because you have this mini boom and bust along with this longer cycle. And it’s really, I think, messed up a lot out of the investment incentives. And I think it’s made this bull market for oil that we’re starting to see, way more powerful, as well as very misunderstood by many different sources.

Trey Lockerbie (00:07:09):

Well, on that note, maybe we just take a quick detour and debunk some of these things around fracking, because I’m not highly educated in it myself. And I could probably tell you that most people think fracking either creates dirty water because of the oil in the water, or the methane that could potentially come out of the fracking is bad, even worse for the environment than the carbon, et cetera. But I know there’s ways to burn off the methane now, even to say, power Bitcoin, which I think you have some familiarity with. So, what are some of these myths around fracking that we could debunk quickly?

Josh Young (00:07:39):

Yeah. Let’s address the two that you mentioned. So the first one is that fracking pollutes groundwater. And it’s hard to tell exactly where that started, but there was a famous movie, I think it was a decade ago called Gasland and they showed, I think it was Matt Damon going and finding tap water that was from a well in Pennsylvania. And they turned on the water on this one particular faucet and they lit it on fire. And this was a horrific misrepresentation of what’s happening. This was not at all related to fracking. There was zero relation. What happens in some places where there’s coal that’s naturally occurring near the surface is there’s a phenomenon called coal bed methane where if you pull enough water out from an aquifer that is surrounded essentially by coal, you end up de-pressuring the coal and you release natural gas from the coal.

Josh Young (00:08:32):

So they knew that. This was a total misrepresentation, but it looked really sexy and it fed into people’s fears, especially in New York City for their water system where they understand that there are some places historically where that water has come from that’s been really bad. Where there’s been all kinds of horrific industrial pollution and waste. Upstate New York there were historically all kinds of coverups and so there was a lot of sensitivity to this. But it’s also not a new thing. Fracking has been going on for decades and it’s been going on near population centers and near aquifers for decades. You look at near Dallas and you look in West Texas. This has been going on for a very long time in different forms, but essentially the same thing. And you can study these things and observe kind of the communication between different rock layers.

Josh Young (00:09:16):

And I think it was just this very easy kind of cheap hit. And unfortunately, as a society, we’ve been progressing from people that read books and long form essays, to seeing short kind of YouTube or Instagram or whatever clips. And it’s really hard to unsee the water being lit on fire, even though again, it’s totally unrelated. And maybe with like what’s happened with COVID and some other stuff, there’s more sensitivity to this where you see these videos of people vomiting blood and dying in China that were unrelated at all to COVID, but they were hard to unsee. So I think it’s a similar sort of thing. So I think that’s on the water pollution.

Josh Young (00:09:56):

And it’s not that it’s not affecting water at all. Any industrial process has externalities. So if you drill for oil or gas anywhere, you’re using stuff, you’re using equipment and supplying the equipment and running the equipment can cause small leaks. So you may have some engine oil that leaks. But it’s very similar to operating a commercial truck. And trucking, even trucking organic produce causes some amount of water pollution and some amount of emissions, but they’re not what they’re being described or being attacked or characterized. So there is a little bit of pollution, but it bears almost no resemblance to their critiques or the concerns that people have. And just the degree of risk versus the degree of concern is totally misplaced. And it’s really oriented towards anti-energy independence…

…Trey Lockerbie (00:19:53):

And as the price of oil increases, won’t that create a gold rush of producers to enter the market and with all these new rigs and eventually get enough supply so the price comes back down? Why would that not be the obvious case?

Josh Young (00:20:07):

Yeah. So that will eventually happen, but there’s a little bit of a couple of things going on that are going to make that hard. So one, we are at the tail end of a very long bear market for oil. We’re just starting this bull market. Prices, like you mentioned, in the last year have rocketed higher. And they’ve finally gotten to a point where it’s economic to start investing in these long lead time projects. The problem with long lead time projects is that they’re long lead. So in many cases you have to spend 10 years bringing your discovery onto production and developing it more. And there’s been too little activity in discovering oil fields. So you kind of need to start from the beginning. So in many cases you may need to spend 15 years in between now and bringing oil on. And oil prices have almost, I think they’ve doubled in the last year. So where do they go in between now and that kind of five to 15 year from now window for those long dated projects?

Josh Young (00:21:02):

And then on the short dated shale and other sort of conventional but short cycled projects, we’re just at the tail end of this giant boom and bust in that area too. And so there were many companies that misrepresented their economics and said, oh, we can break even at $30 oil or $40 oil or whatever their economics were. And many of those companies just reported their Q4 and they were profitable at $80 oil, but barely profitable. So it turns out that those companies require much higher prices too for their activity to be economic. And they’re only going to rush and drill a lot more if their activities are highly economic. So that whole, the setup both for the short cycle and long cycle, both of those are requiring much higher than historic prices in order to bring on new rigs.

Josh Young (00:21:51):

And then in the oil services industry, it’s even worse where there’s been even less capital available for even longer. I think people forget about this. They just kind of assume, oh, hey, there’ll be plenty of rigs. And there were more rigs running 10 years ago. The problem is that was 10 years ago and many of those rigs have been cannibalized. They’ve been scrapped. And many of the people that worked on them are no longer in the business. In many cases, they’re retired. And so getting the talented workforce, along with capable, additional rigs and frac stacks and other sort of equipment, it’s a real problem. And we’re not even at the point where it’s economic for those oil services companies to start. They’re starting to try to hire, but wages haven’t gone up enough yet, and they’re not even starting to build new rigs.

Josh Young (00:22:36):

So if you think about that from a lead time perspective, that’s a multi-year cycle on its own just for the short term stuff. So I think we’re set up for this multi-year bull market where the first thing you need to see oil services’ stocks go up 5 or 10 X. That way they can have an investment boom. That way they can go build over the next few years the equipment that’s necessary to have a drilling boom, to have drilling go way more than it needs over a multi-year period. And then you can have a big crash, but that might be coinciding with when these long lead time projects come on. So it’s really set up nicely I think for a very long, very strong bull market that’s really going to incent a lot of investment. But like you were saying, why can’t they do it? Well, there’s just all these logistical and investment problems that are keeping it from happening.

Trey Lockerbie (00:23:24):

Wow. Barely profitable at $80 a barrel. I find that very surprising. And especially when you’re considering the decrease in rigs, it’s not so much that the rigs are just going out of business and being scrap. They’re getting more efficient I think, say over the last 10 years. Or that would be the idea, right? Some innovation, they’re more efficient, and you would be able to run more profitably. So that kind of brings up for me, what is the actual marginal cost to produce oil today?

Josh Young (00:23:50):

So there is a cost curve. So it’s not like any one well. And I think that was the thing with shale where you had these various large cap or midcap companies with their CEOs getting on TV saying, oh, we break even at 25 or 30. They were talking about their very best well when they were drilling 500 wells and their 500th well was not economic at $150 oil. So there was a lot of this kind of snake oil-ish, charlatan-y, hey, we’re this, but we’re really that. And the truth was somewhere in the middle. And so I think it depends. I think the incremental well is going to be a lot less profitable than the average well. And since it’s a commodity, you really need that incremental well to be highly economic. So if there’s 500 rigs operating right now in the US for oil and gas drilling, to bring on that 501st rig needs to go somewhere. Needs to have a producer for whom the return is likely to be in excess of their cost of capital. And for producers right now, there’s huge pressure on them to return capital and not drill.

Josh Young (00:24:53):

Again, we’re at the tail end of this disaster where every company lost a ton of money that was active in the space. And so there’s this very, very high bar for them to bring on that rig. They have to find the rig, and there are some rigs left, and then they have to find the people for that rig. They have to find the oil field tubulars and other equipment, which is sold out in many cases. And then they have to have the drilling inventory. They have to have the rights to land that’s economic enough to exceed the costs of all of those different things.

Josh Young (00:25:23):

So on the marginal well, there’s a pretty good argument that you’re just getting your kind of 10% return on a cost of capital adjusted basis when you factor all that in. So the rig count is rising, because you are getting to that point, but you’re not so far beyond the point that you have these companies going and ordering more rigs or getting longer contracts from anymore. And in general, I think there’s this trajectory of a slow build, but it’s definitely not boom time, even with oil, as we’re talking around $96 WTI.

Trey Lockerbie (00:25:57):

So you have these green energy ESG initiatives underway, COVID shutdowns, labor shortages, as you mentioned, a lot of people exiting the space and it’s leading to this gap between drilled uncompleted wells, and completed wells. And this might sound very technical to a lot of people listening, but I’m having fun nerding out on this stuff with you. And I feel like it’s really setting up this bullish argument for this commodity here. So I want to kind of quickly walk us through what that means, the difference between the uncompleted and the completed wells, and why that is and the incentives driving these decisions from producers to kind of curb the investing in additional production.

Josh Young (00:26:35):

That’s a great, really kind of important choke point for the industry. And I guess I’ll just say it’s similar to the rigs where when you had under investment, you didn’t have, especially in the last couple years, you didn’t have companies building more rigs. And since they weren’t building more rigs, there’s a certain number of hours that a rig can work before you need to replace the engine, you need to replace various other components, you need to replace filters. And at some point you just hit your useful life on a rig and you’re done. And so that’s kind of an analogy to the process from a producer’s perspective, going from undrilled land to a producing well. And one of the steps is after you drill the well, then bringing the right equipment on to frack the well and tie it into a pipe and bring it on production.

Josh Young (00:27:21):

And so as a part of this giant boom and bust and the short cycle shale stuff, there were a lot of wells that were drilled that weren’t completed and brought on yet. I think some of it was a capital budgeting and timing thing. Some of it was some of these wells were not very good and they knew they weren’t good. And so they didn’t even bother fracking them and bringing them on. And what you’re pointing out is a white paper we talked about, and there’s various other sources that have been focusing on this because it is an issue, where we noticed that the number of these wells that were prepared to be fracked but hadn’t been fracked yet, was falling a lot. And what that told us and what it tells us in terms of why things are going to struggle to scale how you would expect in a boom, is that there’s been essentially this underinvestment, essentially burning the furniture where the wells that were drilled already are being completed faster than new wells are being drilled.

Josh Young (00:28:13):

And that means that you need to drill a lot more wells in order to be able to complete the same number of wells that you’ve been completing. So if you think about it, step one, step two, oil. Well, they did too many step ones to start, and now they’re doing too many step twos and you need to kind of coincide step one and step two in order to get to a completed well that’s on production. So it’s a sequencing issue, but it’s also a budgeting issue and we’re seeing many producers now subsequent to that white paper, we’re seeing them come out with guidance where they’re raising their capital budgets anywhere from 20% to 25% without raising their production guidance at all. Some of that’s cost inflation, but some of that’s also replacing. They’re recognizing they did not enough step one drilling wells. And so now they have to do more step one in order to catch up with the step two, which is completing wells.

5. Morgan Housel – The Best Story Wins (EP.100) – Jim O’Shaughnessy and Morgan Housel

Jim O’Shaughnessy:

That’s fantastic and it’s another thing we share. I generally think of myself as unemployable, other than by myself. Sometimes even I don’t want to hire me because I’m such a pain in the ass for everyone involved. But that’s a really cool situation to have and at least my impression is that your colleagues understand the new world we’re in. By that, I mean like some lawyers get when Patrick wanted to do, Invest Like The Best. They were like, “Yeah, but this isn’t OSAM business.”

Morgan Housel:

Of course, it is. It’s a key integral OSAM business.

Jim O’Shaughnessy:

Exactly.

Morgan Housel:

People don’t understand. The quirk that people don’t understand about what I do in Collaborative Fund too, is I never write about things we do at Collaborative Fund. I never say here’s the deals that we did, here’s why we’re so much better than everything else. I could, I have those stories that I genuinely believe but nobody wants to read that. That’s the truth. People don’t want to read what is clearly marketing but they will want to read and share with their friends and forward onto their coworkers an article about something that has to do with investing or history or psychology. I just want to write things that people will want to share. If I do that and gain the largest audience, cast the widest net, people will learn through osmosis about what Collaborative Fund is. That is so much more effective than force feeding them by saying here’s why we’re so great, here’s why we’re so great. I feel like a lot of asset managers that have finally woken up to we need to have a blog, we need to have a podcast. They still do it wrong because what they write about is how good they are and why you should give them their money. Nobody wants to read that.

Jim O’Shaughnessy:

I could not agree with you more. Luckily, Patrick and I are so simpatico on this. It’s just like you know what? Nobody gives a fuck about you. Really if they do, they want to know how can you help them? How can you give them something that’s interesting that might not be in their toolkit? You’ve got to be useful and the way to be useful, in my opinion, is to be an honest broker. About hey, have you thought about this, this or this? So whenever, for example, when I’m commenting on anything about OSAM, I always lead in with talking my book. I want people to know with that line, I’m going to throw a little marketing at you here…

…Morgan Housel:

No, I think it’s obvious too and I’m happy to admit this. There’s nothing new or groundbreaking in the slightest in the book. The book’s message is like don’t be greedy, compound interest is awesome, save some money. This is not rocket science stuff. But if I think why it may have connected, it’s because I tried to tell a story around that. A thing that I really believe is true for all, everything in the world is that the best story wins. It’s not the best ideas, it’s not the right ideas, it’s not the complex ideas. It’s just the best story wins. I’ve used this example before of Ken Burns, the documentarian. His documentary on the civil war came out in 1990. When it came out in 1990, it was such a success. More people watched the civil war documentary in 1990 than much the Super Bowl that year. It was just like a ridiculous blowout success.

This is a documentary on the civil war, which is like one of the most documented. How many books are there on the civil war? Thousands and thousands. There is nothing new in Ken Burn’s documentary, nothing new. This is not like he was the guy to uncover Gettysburg. There’s nothing new in there, he just told a really good story about it. An amazing story with captivating music and amazing editing. Because of that, he took an event that everyone had known about, and everyone has known the detail about. He got more Americans to tune in than watch the Super Bowl that year.

I think there’s so many examples of that, of things that everyone knows, have been discovered for centuries. Nothing’s new but if you can tell a good story about it, you’ll get people’s attention. That is what I think a lot of academics, in particular, miss. Is that they have all the right answers but they are the worst storytellers. I think a lot of the times they go out of their way to be bad storytellers. They want to use big words to fit in with their colleagues, to fit in with the academic tribe. I think there’s so much room to take what academics know and explain it to a layperson in a story that they’re likely to remember and likely to hook onto. There’s so much room doing that.

I think you could also write a book, not just Psychology of Money but you could write the Psychology of Medicine, the Psychology of Politics, the Psychology of Sports, the Psychology of Relationships. Just talk about things that people intuitively know and tell a story around it in a way that would really connect with them. So that’s what that I’ve always tried to do in my writing. Is like I don’t have the intelligence, the brain power, the education to discover new things in finance. Even for the people who do, I think there’s probably not that much to discover left. We’ve overturned almost all the rocks but I think there’s still a lot of room to be made and progress to be made. Connecting with these people by just doing a better job telling the story.

Morgan Housel:

I would, because this is the magic wand. I’ll make this ridiculous. I would show people exactly in their life when the things that they admired about themselves were actually due to luck. And I would show everyone a movie of like, “Hey, this point in your life that you think you did this.” Actually, here’s what happened behind the scenes. You didn’t know about that actually led to that thing. I think that would instill a degree of humility in people that would be so beneficial. It would help, it would not depress them. It would be so beneficial to know. And also I would, so this is a magic wand. I would show them every one else in the world’s movie too. I’d be like, “Here’s all the areas where Jim got lucky and Morgan got lucky.” And then they would stop idolizing people for just some level of success. And they would look at individual actions that led to what actually they did on their own volition to actually get to where they were.

Because I think one of the biggest problems in the world, not one of the biggest problems that’s exaggerating, but a problem in the world is that we underestimate the role of luck in a massive way. And even there’s that saying of like, “The harder I work, the luckier I get.” I think that’s bullshit too. I think luck is just luck. And I think if you are working hard to become luckier, then that’s actually a skill, luck is just luck. For you and I, you and I are white American males born in the latter, half of the 20th century, that’s just luck you. You and I did nothing to do that, it’s just what happened. And I think everyone has some story like that they under appreciate. And to make them aware of it would be a huge help in the world.

6. Amazon CEO Andy Jassy Speaks with CNBC’s Andrew Ross Sorkin on “Squawk Box” Today – Andrew Ross Sorkin and Andy Jassy

SORKIN: You talked about chips being a major issue. What do you think we should be doing here in the United States about manufacturing those chips and does Amazon have a role in that long-term you think?

JASSY: Well, I think it’s, it’s, it should concern people that so much of the chip production is concentrated in one place, and there’s, you know, there are a lot of geopolitical things that could happen. And so I think it’s quite wise for the US to be thinking about creating more production here and, you know, I’m very happy about the CHIPS act that we’ve been working on in the country. It’s a lot of money, it’s $35, $40 billion and yet, it’s probably not enough. I think we probably are going to need even more than that to have the ability to withstand some kind of shock to production in a particular part of the world. But I think it’s very important. I, you know, we design our own chips and we’re big buyers of chips and we’re big customers of some of the big chip companies as well as producers ourselves so there could be a role for us to play. We certainly want to help and we certainly want to partner.

SORKIN: Do we believe that the companies in America and I know Intel is trying to do this, but do we have enough know how in this in the country to actually do the manufacturing piece of this do you think?

JASSY: I think it’s a good question. I think we have a start. I mean, Intel obviously has been doing this for a long time. And you know, Pat Gelsinger has been a partner, you know, first on the VMware side now with Intel for a long time and I have confidence in their ability to produce and but they have work to do  as they know and and we’re going to need additional providers I think to be where we ultimately want to be…

…SORKIN: In that context, how do you see the union movement that’s taking place, frankly, around the country, but clearly aimed in certain places and I’m thinking about New York, where I’m from at Amazon?

JASSY: Well, I mean, I’d say a few things. You know, first of all, of course, it’s its employees’ choice whether or not they want to join a union. We happen to think they’re better off not doing so for a couple of reasons at least. You know, first, at a place like Amazon that empowers employees, if they see something they can do better for customers or for themselves, they can go meet in a room, decide how change it and change it. That type of empowerment doesn’t happen when you have unions. It’s much more bureaucratic, it’s much slower. I also think people are better off having direct connections with their managers. You know, you think about work differently. You have relationships that are different. We get to hear from a lot of people as opposed to it all being filtered through one voice. If you want to keep the construct that we’ve had for for this long, you have to have, you know, competitive and compelling benefits though for for employees and it’s why we champion the $15 minimum wage a few years ago and we’re up over $18 now. It’s why we have full insurance, why 401k, 20 weeks of paid leave and our Career Choice Program where in our fulfillment center for our employees who want to get a college education, we’ll pay for their full tuition, so those things really matter. The one thing regardless of how it all evolves is we just won’t compromise on the customer experience. That for us, you know, is paramount…

…SORKIN: When you look at one of the issues that the unions have raised as you know so well are safety issues, and you’ve addressed this to some degree in this morning’s letter. But I’m hoping you can speak to it because there was some data out just about two days ago that seemed to suggest and this was data put together by I think some of the Union advocates that there were more, even double the number of injuries at Amazon facilities relative to their peers.

JASSY: Well look, there’s a lot of ways you can spin the safety data and some special interests folks like you’re talking about with this case, will do it for their own interests. That that data is not really accurate. You know what I would say is a few things. You know, first of all, for anybody that had hired a lot of people during the pandemic like we did, and there are plenty of others who did as well, their incident rates, their recordable incidents which what OSHA asked everyone to report on, went up in 2021 versus 2020 because he had a lot of new people. In our case, we hired about 300,000 people just in 2021, most of whom had never worked in this type of manual and industrial space, and who had to be trained and all the data we have says that the incidence of injury in the first six months is always much higher than thereafter. So we have a lot of new people, you’ll have more incidents. But that said, if you if you look at the the injury data and safety data, you know, for us, we we have a few macro areas in which we do work. We have what OSHA calls warehousing. We have what OSHA calls messengers and couriers, messengers and couriers, and then we have grocery and if you look at the industry average versus our numbers, we’re a little bit higher than average in warehousing, we’re a little bit lower than average in both messenger and couriers and grocery. So we’re about average, which, frankly, I take no solace in. We don’t aspire to be average.

You know, we’re trying to be the best in the industry and it’s why we’re spending, you know, we have we spent about $300 million on safety last year alone. We have about 8,000 people who just work with safety and we’re trying all sorts of things and work in all sorts of all sorts of things. We have a rotational program we built where we’ve built sophisticated algorithms to try to predict when somebody’s doing something too, too frequently and rotate their jobs and rotate what they’re working on. We have wearables that we’re investing in that send haptic signals when we believe you’re making a dangerous movement. We have, you know, new shoes that we’ve had everybody wear that, you know, protect your toes and avoid slips. We do training on body mechanics and wellness. So we’re working on a lot of those things, but the reality is that we will not be happy until we’re the best in the industry and and even then, I won’t be happy because I’m gonna know there are things that we could be doing better. This is important to me, it’s important to—

SORKIN: How do you think about this? So one of the things that Jeff said in his letter last year was that one of the missions of Amazon now is to be Earth’s best employer and Earth’s safest place to work. How do you think about that relative to the priority of serving the customer?

JASSY: I don’t think they have to be at odds. And in fact, I think they’re very complimentary. When you take care of employees and employees are safe and they love working where they work, they stay longer. They tend to be happier, they tend to be more productive. And all those things improve the customer experience. So I see them as very complimentary…

…ORKIN: On the platform. Before we let you go, it’s been 10 months now in this new role. And I’m curious what the relationship is like with Jeff, how much time you guys spend together, what does he think of all of this? We were actually mentioning we thought your letter was a little Bezosian in some respects. What’s it been like?

JASSY: Well, I have a great relationship with Jeff and, you know, I’ve known him for a long time and I have an unbelievable amount of respect for him. And we talk regularly, we talk weekly and it’s great to have a sounding board and he’s got so much wisdom. And you know, I think both of us share a lot of excitement and optimism for the future. We’re so early in all of our businesses. I mean, even in our retail business, which people think as kind of our most mature business. You know, we’re about 1% of the worldwide retail market segment and 85% of retail still lives offline. So we’re so early in all of these areas. You know, AWS is a $70 billion revenue run rate business growing, you know, about 37% year over year in 2021. And still 95% of the world’s IT spend is on premises and not in the cloud. So, all of these areas you go through it with Alexa has the chance to be kind of, you know, the best personal assistant which changes your life. And entertainment as we just talked about. Our advertising business is early. Kuiper, you know, we’re building a low Earth orbit satellite. And Robotaxi business in Zoox. I mean, we’re so early in these areas that I think we both share a lot of optimism that there’s an opportunity to change a lot of customer experiences over a long period of time.

7. An Interview with Adam Mosseri About Creators, Blockchains, and TikTok – Ben Thompson and Adam Mosseri

What was this exactly, though? I mean, on one hand you are obviously the head of Instagram, so you don’t say anything publicly on accident. On the other hand, I don’t think that there was any sort of product announcement here. What was this talk, in the broader context of your day job?

AM: I believe that a lot of these conversations are going to happen with or without us. You see me out there a lot, probably on Twitter and elsewhere, doing talks sometimes but often engaging in other ways, because I just think it’s important to engage in the conversation because it’s going to happen with or without us.

I think one of the more interesting conversations over the next five to ten years is how power is going to continue to shift. I think technology has shown over and over, over centuries, that it tends to take power from the establishment and give it to people. It’s not a direct line, there are always detours, but if we assume that’s going to continue to happen, if you look at the fierce competition out there, particularly for creators, you assume more challengers are going to be interested or willing to hand more power over to creators. I assume the incumbents will follow.

Then, I think we should be part of the conversation of what that world looks like. I think, as uncomfortable as it might be, we should embrace it. I think ultimately, over the long run, we should take a view that what is best for creators is best for platforms, because there’s going to be more creativity in the world. There’s going to be more exchange of ideas, there’s going to be more art, there’s going to be more content, and we should try and figure out what that world looks like. The main idea here is just to throw out two longer-term ideas and hopefully influence that conversation…

…You talked at the beginning of your talk, and you reference it here, about how the Internet broke down gatekeepers but then “unexpectedly”, your words not mine, we ended up with even larger platforms like Instagram. Obviously that’s been the core thesis of Stratechery, is that actually all this stuff goes in the opposite direction people think.

AM: Aggregation Theory.

Yeah, exactly, that’s exactly what it is. Is Instagram a gatekeeper? Is it just a super-gatekeeper?

AM: I think that the Internet has very clearly pushed power into two directions. It’s pushed power into the hands of more and more people, not just creators, but I mean it’s enabled all sorts of businesses like yours, and it’s also pushed power up into really broad platforms like Instagram. I think the big companies, or what we used to think of as the big companies, have suffered the most. There’s just been these über-sized companies. I do think, though, that large platforms, if you look at the next ten or twenty years, they’re going to rise and they’re going to fall. When they fall, they’ll fall slowly, but I think they will fall. They’ll slowly lose cultural relevance, and —

But why? What’s going to be the driving factor? This is the big question. You talk about this as if it’s a law of nature, that creators are going to take over, but what’s the causal function here?

AM: Probably I think it’s really going to be competition. Take TikTok, for example. TikTok is a behemoth, I actually don’t think most people realize how big and relevant TikTok is, if you look at how much time people spend or how total minutes on TikTok in a day compare to most of the competition.

I was told you have no competition, you’ve killed it all.

AM: (laughing) Oh, yeah. Well, it doesn’t feel that way on my side! I know there are a lot of people who disagree with me, it certainly doesn’t feel that way over here! YouTube is also a behemoth. Actually I think’s TikTok’s a really good example, I’ll give a lot of credit to them for some things they’ve done well.

I think the newer platforms are going to see how important creators are. I’ll talk to a couple of reasons why I think creators are important. We’re in a world where clearly we’re inundated with more and more information, and there’s value in aggregators to help us find the most valuable information, that is sort of an adjacent concept to Aggregation Theory. One effect of that is, yes, aggregators have value, but another is that people are less and less interested in processed content, they want to get more of a sense of authentic content. I’m not saying creators are all authentic, obviously people bring a certain part of their identity, not their whole identity online, but people are much more interested — and we see this in engagement data — in seeing what it’s like to be in someone else’s shoes, seeing what it’s like to be backstage before a political debate or warming up before a football match or in a green room before a TV spot. They want to see the world through other people’s eyes and they’re more interested in creator-focused content, someone’s point of view, whether it’s you sharing your analysis on a business or The Rock pontificating or a small country artist from Nashville showing a song that she’s working on.

You’ve seen that one of TikTok’s strengths has been how strong they have been at breaking new talent, how well they have done by the little guy, the small creator. They have leaned much more into exploration-based ranking than pretty much all the competition, or least earlier, and they’ve helped new talent break. Now, it’s not all perfect over there, I think that there’s a lot of volatility and there’s a lot of downsides too, but they’ve done really well by particularly smaller creators and I think you’re seeing the competition follow. You’re seeing the other major platforms that you can think of, or you would’ve thought of two years ago as incumbents, which now I think you might actually even think of as challengers, follow, and I think you’re going to continue to see that.

My take is, and I could be wrong, but my take is that over the next five years, ten years, you’ll see more platforms, both challengers and incumbents, be willing to hand more power over to creators. I think that’s the causal relationship, is competition, but I think there’s also some extrapolation of existing trends…

…I think you just got into what I see as one of the issues here, because the Web2 people tend to have Web2 solutions, the Web3 people have Web3 solutions, when it’s always been at least clear to me that this token idea has always been the most attractive and interesting thing about blockchains. You can pay for a token with a credit card, there’s no reason why it has to be an all-in-one system, and this idea that it has to be full stack up and down all Web3 under the blockchain doesn’t make sense. Believe me, I know a lot about database performance — that’s why there’s been very little news about Passport over the last year, fixed now — but you’re not going to be doing a lot of this stuff, certainly not on a blockchain.

The idea that you could have all Web2 infrastructure, but this one piece that to your point, you can carry around from place to place, I mean, I’m not being a very good questioner here because I’m sort of making the point, I think this is what is very attractive, having this piece that no one controls. But to your point, someone needs to build it. You didn’t do a product announcement, you just painted a vision, is this though sort of a backdoor announcement of Meta’s new blockchain play? Are you going to help sort of build this infrastructure?

AM: I think we’re definitely interested in it. To be totally transparent, part of the reason why I want to talk about it publicly is to apply some pressure and get some excitement around the idea and build some momentum. I can’t talk about or I’m not going to talk about the specific companies I’ve been talking to, but I’ve been trying to talk to as many people as I can at all the different levels; at the payments level, at the authorization level, at the platform level. There’s a lot of interest, but to make this happen is far from a sure thing. It’s like you’re trying to align a bunch of different cultures and a bunch of different sort of philosophies around this idea.

The biggest risk to the idea, I think, is, is there enough of a market fit for creator subscriptions that this idea would create enough incremental value that those involved, particularly the platforms at the Instagram layer, not the sort of payments layer, will believe that it’s going to create enough incremental value that they won’t need to over-worry about their particular share.

It’s not lost on me that a lot of people don’t trust the company that I work for or me even. And so in all of these conversations, they’re trying to figure out what my angle is and I’m like, “No, no. I just think this should exist. I think it’ll be good for us indirectly over the long run.” I think if we get critical mass, if we get enough platforms to do this, then there’s pressure for the holdouts to do it because the creator community will put pressure on platforms to support this. But the question is, “Can you get to critical mass?” And I think the biggest risk with getting to critical mass isn’t the technical one. Like you said, we don’t have to build a whole thing on-chain, sure, you could pay with this with coin if you wanted to, but you could totally pay with it with fiat.

The stack should be 98% Web2 technologies and like 2%, or actually probably more like 0.2% on-chain. People, when they talk about blockchain, you only want to use it for what it is uniquely suited to do, and what it is definitely uniquely suited to do is to be a neutral arbiter between platforms where it is the one place you can go that no one controls, no one touches, and you can stick a token there and that token has the minimum amount of information necessary. Believe me, I’ve thought a lot about this, but no product announcements for Passport here either!

AM: The question is what’s the token? What’s that protocol? What exactly does that token entail and how do we make sure that it supports enough use cases that enough platforms and businesses will be interested in supporting it, right?

Let’s drill into this point because I think this is the biggest question. So from my perspective, the value that Instagram brings to the creator ecosystem is, Meta in general is by far the best customer acquisition platform, period. There’s no one even close. And I would say that’s the case, even post ATT. It’s instead of a thousand times better, maybe it’s a hundred times better, but it’s still really good. TikTok, you have discovery of new talent, Instagram, you can acquire customers, and YouTube is where you actually make money.

I think you talked about creators start on Instagram, and they go to platforms like YouTube, and to me this is because YouTube monetizes so well. One of the brilliant things that YouTube did and Google did, and it took many, many years to build up, is they shared a huge chunk of the revenue with creators, and every single creator in the Internet knows that outside of subscriptions, the way to make money is to get on YouTube.

And so the question is, given YouTube is so dominant here, to me, they’re the great white whale, I would love to have a Passport integration with YouTube, they’re so far ahead in this particular area, why would they ever want to partner with anyone, number one? Number two, that suggests that Instagram needs to get way better at monetizing its creators so it’s a competitive counterweight, but then we’re back in, “Well, you’re in your walled garden, they’re in their walled garden.” There’s a valley of disconnect here, and how do you think about crossing that chasm?

AM: So a few different things. On the Instagram side to start, I think there’s two ways it can benefit our business. Certainly we’re a customer acquisition channel and we’re good at that, but also, it’s the same idea, but not paid ads, we are a marketing channel for a lot of creators. Creators share a bunch of content and tell a story, build an audience, and then they monetize that audience, whether it’s through rev share on YouTube or branded content deals on Instagram or subscription on Twitch, and they drive a lot of impressions for us. So you don’t even need to pay us directly for you to create value for us and for us to create value for you. If we are a great platform for you to build an audience, then you’re going to be creating compelling content and we can advertise against that content the same way we advertise against everything else. So it doesn’t even have to be ads.

I agree. Just to say it’s just an ad platform — an ad platform only exists in the context of great organic reach.

AM: Well, I want to point out both because I think this is true for any of the platforms like us. I think YouTube is I think one of the big questions because they are — if TikTok is the best at breaking new talent, YouTube is the best at driving direct dollars into creators’ hands. I think if you look at the branded content ecosystem on Instagram, it’s probably about the same size. It’s many, many billions of dollars in your industry.

The same size as YouTube money or YouTube-branded content?

AM: I don’t know what YouTube pays out creators, I’m just talking about rev share. I don’t know what the total is because I don’t think they’ve released it, but I’m just saying, there’s other big things, but I don’t think anyone who’s a creator who sells branded content on Instagram thinks of that as Instagram service. They think of that as like, “No, that’s my deal. I made that happen on the side,” even if we help.

Even though it’s definitely an Instagram thing.

AM: Yeah. We’re just not going to get credit for that. 

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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, Apple, and Meta Platforms (parent of Facebook and Instagram). Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

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 17 April 2022:

1. RWH004: Intelligent Investing w/ Jason Zweig – William Green and Jason Zweig

Jason Zweig (00:06:24):

I think the other story about my dad that really sticks in my memory, William, is in 1981, when my dad was dying of cancer, I was home for a visit and the phone rang, and a voice said, “Is this the Zweig residence?” Very polite, formal sounding man. And I said, “Yes, can I help you?” And he said, “Is Irving there?” And I said, “Yes, but he’s not really able to come to the phone, can I take a message?” And as I recall the man’s name he said, “Well, could you tell him that Glen Irwin is on the phone?” And I knew everything about my parents’ business and a lot about their life history, I had never heard of this man. And I went and I told him. At that point it was very difficult for my dad to move around the house because his lung cancer had spread to his legs, but he looked at me and then a light came on in his eyes and he said, “Oh, I’ll speak to him.” And he, with a great deal of difficulty, came to the phone.

Jason Zweig (00:07:28):

If you’ve ever listened to a stunning conversation that you can only hear one half of, it always sticks with you. And my dad took the phone and he said, “Glen,” and after a long pause my dad said, “Yes, I remember.” And the person at the other end started telling my dad a story, and my dad kept nodding and saying, “Yes, I remember, I remember” and I saw something I had never seen, I saw my father cry, and I couldn’t hear almost anything of what Mr. Irwin was telling him, but they talked for about 10 minutes And at the end my dad said, “Thank you very much, I hope so,” which I immediately inferred, and I think correctly, that Mr. Irwin had said to my dad, “I hope I will get to see you while I still can.”

Jason Zweig (00:08:22):

And when he hung up I said to my dad, “Who was that?” And my dad proceeded to tell me the other half of the story, which is sometime around in the late 1930s, my dad was the student at Union College in Schenectady in New York, and he was walking to class one morning, and he was walking behind a student, and my dad noticed he was black. And at that time he was either the only black student, or one of maybe three black students, or a handful of black students at the time, and my dad had never seen him before. And they were both walking along, minding their own business, and suddenly from behind a few trees a bunch of white guys jumped the black student and started kicking him and beating him up. And my dad immediately dropped his books, or whatever he was carrying, and jumped in and fought back and took Glen Irwin’s side, even though he didn’t know who this kid was, but it was obvious to him who was right and who was wrong.

Jason Zweig (00:09:26):

And momentarily the campus security people came along and broke up the fight, and they all got dragged to the office of the president of the university, whose name was Dixon Ryan Fox, who was a very famous scholar. And of course the white kids who had jumped Glen Irwin all blamed him, and they said, “We were walking along, minding own business, and this N-word guy attacked us, so we had to fight back, and then this kid came along and made even more trouble, and that’s what happened.” And so Fox turned to my dad, and Glen Irwin, and said, “What’s your side of the story?” And Glen Irwin was so scared he couldn’t speak, and my dad said, “Well, President Fox, maybe you remember me from when I was admitted to Union College,” because my dad had gotten a rejection letter when he had initially applied that said, “You’re qualified for admission but the Jewish quota is filled,” because in the 1930s most elite educational institutions in this country had a policy that they would only admit so many Jews, and the Jewish quota had been filled.

Jason Zweig (00:10:37):

And so my dad immediately got in his family’s wagon, because in those days they didn’t have cars, and rode to Schenectady, which was probably about 25 miles away, 30 miles away, and he waited outside President Fox’s office all day long until his secretary said that he could go in. And he was admitted, and he said to the president of the college, “You sent me this letter, and it said the Jewish quota has been filled. Well, as you know, President Fox, the whims of war are gathering in Europe, and young American men may be called into military service. Should I tell the US Army that the Jewish quota has been filled when I’m drafted?” So he’s telling this story, and President Fox says, “I remember you young man, why don’t you tell me what really happened?” And so what happened in the end was the thugs who attacked Glen Irwin were expelled. Glen Irwin went on and, if I remember right, he became something like a chemical engineer, and became a senior executive at a major company in the US. And what to me was so striking about this story is that my dad had never told any of us about this. My mom had never heard the story, in fact, the day it happened my mom didn’t even hear about it, because all this happened between me and my dad, and that, I think, is really the definition of quiet courage, when you do something that noble and you never even talk about it. And he completely transformed this man’s life, and obviously Mr. Irwin was calling because somebody had told him, “Irving Zweig is very sick,” and they hadn’t spoken in over 40 years…

…William Green (00:31:20):

And I wondered if you could talk about the element of luck versus skill. Clearly these guys have to have skill. I remember people telling me that they had been in investment meetings with Peter Lynch at Fidelity, and they would say, “Look, I came out of the same meeting. I heard the same information from the same companies and he made more money than I did again and again.” So there was clearly something he had. And yet there is an amount of luck that I think we can’t deny. Can you unpack that a little for us?

Jason Zweig (00:31:47):

One way I like to think about it, is that there’s a skill to being lucky. And I know you’ve heard me tell this story before, William, and technically it has nothing to do with investment management. But people often ask me how I got to edit Graham’s book, The Intelligent Investor. And they expect me to say, “Oh, the publisher did a beauty contest and brought in 10 different writers and had each one write a sample chapter.” Or, “They interviewed people,” or whatever. And it’s like, “No, that’s not what happened at all.

Jason Zweig (00:32:19):

What happened is this. So I had read a book and then interviewed the author, a book called The Luck Factor by British psychologist named Richard Wiseman. And he had done a sort of big nationwide survey of people’s attitudes toward luck. And when all the surveys came back, he and his team were going through them. And there was one that really jumped out at him, which was, and I’m massively really paraphrasing. I’m going to get all the details wrong. But the essence of it is correct.

Jason Zweig (00:32:49):

This woman had said, “My husband died. Two of my kids have cancer. I lost my job. I got it back, but I’m a very lucky person.” And he said, “I really need to interview this woman.” So they brought her in and he said, “You described all these terrible things that happened to you and you say you’re lucky. Why do you say that?” And she proceeds to tell him this story. And she says that after her husband died and her kids got sick, she felt very depressed, as anybody would. And she was really struggling. And then she decided that she needed a rule. And the rule she came up with was whenever she’s about to go into a room full of people, she thinks of a color.

Jason Zweig (00:33:37):

Then she goes into the room and she walks up to the first person who’s wearing anything of that color and says, “Hello, my name is,” whatever her name was. And so she looks at professor Wiseman and he looks at her and he says, “Well, what does that have to do with luck?” And she says, “I always have a date on Saturday night.” So I have just read this and heard the story from him. And there was a huge party at Time Inc. where you and I think both were working there at the time. And hundreds of journalists were there. I forget what the occasion was.

Jason Zweig (00:34:10):

And I was talking with as usual, my closest friends and not really socializing with the group. But before I had walked in the room, I had said to myself, and I’m not sure which color it was, but I’m going to say blue. I had said blue. And so I looked across the room and there was somebody I knew wearing blue. And I said to my friends, “Excuse me, I really have to go talk to her.” And it was our mutual friend, Nina.

William Green (00:34:39):

This is Nina Munk who’s a wonderful writer.

Jason Zweig (00:34:43):

Yep. And so I lost her in the crowd. And I haven’t talked to her in like three years or four years or something. And I was like, “Ah, the heck with it. Forget it.” And then I was like, “No, I have to talk to her because she’s wearing,” whatever color it was, blue. And I found her because I was for the color and we had a wonderful talk about nothing in particular and life went on.

Jason Zweig (00:35:06):

And I went back to work the next day, et cetera, et cetera. But it turns out a couple days later, her book publisher takes her out to lunch to congratulate her for finishing her wonderful book on the merger, the takeover of Time Warner by AOL.

William Green (00:35:21):

Fools Rush In.

Jason Zweig (00:35:22):

Fools Rush In. And her publisher says to her, “Oh-

William Green (00:35:25):

And we were working for those fools.

Jason Zweig (00:35:26):

That’s correct. And her publisher says to her, “Oh, Nina, you could help me with one thing.We have this book by this guy who’s dead, Benjamin Graham, I think his name is. And it still sells, but it’s old and we need to update it. Who do you think would be good for that?” And she said my name. Now, she insists to this day that she would’ve said my name anyway, but I’m not so sure about that. I think she might have said, “Well, I don’t know. There’s like five different people you could try. One of them is Jason’s Zweig.”

Jason Zweig (00:35:58):

But instead, because I just so happened to run over to her because she was wearing the right color, she said my name. And that’s why they hired me. And so the thing is, that was despite the fact that I was trying to outwork everybody else in financial journalism, despite the fact that I had all these great contacts, despite everything I threw into my job, why did I get this in honor of a lifetime? Because Nina Munk happened to be wearing a dress whose color I had thought of because I had read a book.

Jason Zweig (00:36:34):

So skill is hugely important and it matters, but much of life, maybe most of life is shaped by just these weird moments of random chance. And the more professional you are, and the more intellectual effort is involved in what you do, the more vehemently you will deny the importance of the luck, but it affects everyone in every field. And it’s hugely important in asset management too. 

2. Alexandr Wang – A Primer on AI – Patrick O’Shaughnessy and Alexandr Wang

[00:14:29] Patrick: I couldn’t agree more on that point, maddening that we don’t become just the perfect beacon for all the most talented people. The interesting analogy that I’ve heard before just to wrap our minds around the sorts of things or tasks or functions or whatever that constantly improving AI/ML models can accomplish. One model that was funny and interesting was like anything that an intern could do for you, you might be able to scale up through one of these models. It’s complicated enough that a person’s on it now but it’s simple enough that you give it to an intern it’s sort of repetitive. I always kind of like that conception. What’s your way of thinking about how to communicate to your audience, other businesses using your tooling and just people general, what categories of things AI can do well, and maybe what categories of things we’re excited about but might be a very long time until AI can do well.

[00:15:19] Alexandr: I think this is one of the general misconceptions about AI and machine learning, which I think causes a great deal of FUD. Which is that the intuitive belief is that the things that are easy for humans to do are going to be the things that are easy for AI and machine learning to do, which is absolutely not the case and the things that are easy for algorithms are relatively orthogonal, frankly, to things that are easy for humans to do. One simple example here is I think that it’s going to be a very, very long time before we have home robots that can do things like fold your laundry and your dishes, but a much shorter time span/I think this is already today where you can have artificial intelligence systems that are world class copywriters and can write better rhetoric, better words than most people ever could. There’s probably a few frameworks I would assign to this. I think in a broad general sense, one way to think about the potential impact or lower bound potential impact of artificial intelligence is kind of as you mentioned, which is the ability to scale repetitive human tasks. So take repetitive human tasks instead of going from zero to one, go from one to N human work. And I think that this is a generally amazing thing to happen because I think that humans for the most part don’t enjoy repetitive tasks or generally find those relatively unpleasant and find it much more exciting to be creative and to constantly be creating.

This ability to scale human tasks from one to N, is going to be this incredible, not only economic good or economic enabler for the world, but also going to be a significant enabler for humans to be more leveraged, more happy, more creative, et cetera. I think that’s one way to sort of contextualize the broad impact that AI can have. And there’s a bunch of other nuances, which I’m sure we’ll get to. If you think about what tasks humans are good at versus what tasks algorithms are good at, generally that more or less boils down to data availability, which is that where there are large pools of digital data an algorithm can learn from, and those pools of digital data either have been collected in the past or easy to collect in the future. Those are going to be the problems which algorithms can do effectively and can learn to do effectively. And then areas where there does not exist digital data and is expensive to collect this digital data. Those are going to be the last things to be automated. So a great example is if you look at GPT 3 and these large language models, the real secret behind it is that it leverages two decades of Reddit data, which is two decades of humans using the internet and basically typing language into the internet in various forms for decades and decades and decades. And that is a pool of digital data that it used to be able to do these incredible things in writing long form text.

Then if you think about this parallel that I mentioned around home robot, there’s so little data about actual capture data of let’s someone folding a shirt, or somebody folding a towel, or going around and doing chores, the ability to actually collect and produce that level of digital data necessary to produce algorithms that can understand that and actually perform this task is an incredibly, incredibly, incredibly hard road. This extends by the way to things that are really unintuitive. So for example, DeepMind and OpenAI very recently released algorithms some of which are very good at … DeepMind released an algorithm that’s very good at competitive programming, OpenAI released an algorithm that can prove very difficult math problems or math theorems. And these are both things which are very, very challenging for humans to do. Very, very premium skill sets as far as humans go. But there are incredible pools of visual data as well as abilities to verify or simulate the outcomes here, which allow these algorithms to reform actually incredibly, incredibly well. There’s this very interesting process by which artificial intelligence will slowly automate or meaningfully change what human jobs that are primarily digital in context will look like. And then a lot of the physical work will I think be generally on touch for a very long time…

…[00:20:18] Patrick: Maybe it makes sense to help people understand the process of creating one of these models in the first place. I think the discreet steps, let’s say the outcome is a model that makes a useful prediction. Ultimately, this is all predictions instead of what’s being generated by the models in the first place. I don’t know where to start, whether it’s with raw data or annotation of data, and we’re starting to get into what Scale now provides for companies. But how do you think about explaining the discreet or the important stages of building one of these models in the first place? I think just understanding that architecture will let us dig into each piece a little bit more.

[00:20:51] Alexandr: Again, everything starts with the data. I often will analogize the data for these algorithms as the ingredients that you would make a dish with, or the ingredients that you would make something that you’d eat with. Is incredibly, incredibly important. We often say this thing, which is data is the new code. If you compare traditional software versus AI software, in traditional software, the lifeblood is really the code. That’s the thing that informs the system what to do. In artificial intelligence and machine learning, the lifeblood is really the data. That is certainly like one major change. That’s really important. The life cycle for most of these algorithms is a few fold. So first is this process of collecting large amounts of data. By collecting it could be data that is already sitting there. There’s a lot of software processes that already collect a bunch of data. There’s a lot of cameras in the world that already collect a bunch of data, but you need to get the raw data in the first place. Then it goes through this process of annotation, which is the conversion of this large pools of unstructured data to structured data that algorithms can actually learn from. This could be for example, in imagery or video from a self-driving car marking where the cars and pedestrians and signs and road markings, and bicyclists and whatnot are so that an algorithm can actually learn from those things. It could be for example, in large snippets of text, actually summarizing that text so that now we can understand and learn what it means to actually summarize text. So whatever that translation is from unstructured data to a structured format that these algorithms can learn from, then it goes through a training process.

So these algorithms basically look through these rims and rims of data, learn patterns and slowly train themselves so to speak, to be able to do whatever task is necessary on top of the data. And then you launch one of these algorithms in production and you run them on real world data, and they’re constantly producing as you mentioned these predictions. The very important piece is, this is not a sort of like one way process this is actually a loop. If you look at almost every algorithm that has launched out their own production, it is not a sort of you build the algorithm and then you’re done because these algorithms are generally very brittle and unless you’re constantly updating them and maintaining them, they will eventually do things that you don’t want them to do, or they’ll eventually perform poorly. There’s this critical process by which you are constantly then replenishing them. You’re constantly going and recollecting new data, annotating it, training the algorithm, launching that new algorithm onto production and you constantly undergo this process to create very high quality algorithms.

[00:23:19] Patrick: I want to make sure that this interesting point you made about data being the new code really hits home for people, and maybe even put that in a business context. So if the IP or the moat of a software company is this code base that takes a very long time to develop, has all sorts of dimensions to it. Maybe it’s microservices, maybe it’s some code monolith, it’s questionably like an incredibly valuable asset. It’s digital, but it’s an incredibly valuable asset to the company. And you’re talking about, I think, a transition where it’s something different where maybe, I don’t know, maybe Google’s data repository or something, is this unbelievable advantage that they have because no one else has access to all of their data. Is that kind of what you mean? That ultimately maybe something like Google, their data is worth a lot more than their code base. And that that would become a trend that we see sort of across industries?

[00:24:07] Alexandr: If you look at the highest performing algorithms across a variety of different domains, image recognition and speech recognition and summarizing texts and answering questions of texts. So these very different cognitive tasks, look under the hood, they actually all use the exact same code base. That’s been this very meaningful shift that’s happened over the past few years in artificial intelligence. We’re at this point where the code has become effectively the same and more or less a commodity so to speak when it comes to artificial intelligence and machine learning. The thing that enables the differentiation is really the data and the data sets that are used to power these algorithms. To your point, if you think about … One of the ways that we talk about this in a business context is if you think about what is your strategic asset? In general in business, your strategic assets are the things that allow you to differentiate yourself against your competition. In a world where 99.99% of the software in the world is sort of traditional software, and then only 0.01% is AI software, then you care the most about your code. Your code is what will differentiate your product versus your competitor’s product or your processes versus your competitors’ processes, et cetera. But then as more and more of the software in the world’s written, infused with AI, using AI or over time the interfaces shift to AI. Interfaces and Alexa like interface for example, as that shift happens, as you go from 99.99 to 90:10 or 80:20, or even 50:50 over time, the vector of differentiation totally shifts to data and the data sets that you have access to. And so that means is that your strategic differentiator to your point as a firm is going to be primarily based off of what are my existing data assets.

And then what is the engine by which I’m constantly producing new insightful differential data to power these core algorithms that are actually powering my business. And these algorithms at the core that will power the future of business, I think are relatively core. I think there’s definitely algorithms around automating business processes that are going to result in significantly more profitable firms over time. There’s going to be algorithms that are based around customer recommendations and customer life cycle, which is a lot of the algorithms that we’ve seen date. Imagine TikTok recommendation algorithm, but for like every economic interaction or every economic transaction in your life that is constantly identifying the perfect next thing that you may want to transact with. And that is going to exist across every firm or every industry is basically going have to build their version of that. And that’s going to result in significantly more efficient trade. The long-term impacts of that you could think of as like a general reduction in marketing expenses or sales and marketing expenses because the algorithm just does a better job at knowing what the user wants to do next and having to do all this marketing and all this very active sales. There’s a lot of very real changes to I think the physics of what the best businesses will look like in, let’s say a decade or two decades or three decades that come from artificial intelligence. If you think about what will allow me to do these things better than someone else, it’s the quality, efficacy and volume of the data that is used to power these algorithms…

…[00:41:11] Patrick: If we zoom out and go to the more market side of things and put my investor hat on and think about what drives enterprise value, value creation, the things that investors ultimately care about when they’re putting money into a business, they want to get a lot more money out. The world of software has obviously been a center stage for seven, 10 years now because they’ve tended to be very scalable, fairly high margin, incredibly fast growing businesses. And the word that you never want to hear as an investor is deceleration, in the growth world where maybe they’re reaching saturation points and software is no longer a new thing. It’s a fairly mature thing. How do you think about, you mentioned this concept of thinking about like an S curve and maybe we’re for software approaching the diminishing part of that S curve. Where is AI in that same thing and how might these two things intersect to form lots of new enterprise value in the future if software becomes overly saturated?

[00:42:04] Alexandr: One thing to think about software for a moment, the sort of alchemy or the magic of software is that A, you’re able to collect very large scale data sets in a very coordinated way, B that you’re able to build simple workflow tooling on top of these data sets, think about your traditional CRM or frankly the majority of SaaS tooling is workflows on top of these data sets that enable business value. Then three is basically infinite scalability of a lot of these systems. These are some of the like technological primitives that have enabled SaaS broadly speaking, or software in general to produce a lot of value for most enterprises, but these primitives or these forms of alchemy have some cap, that’s for the saturation of software that you’re mentioning. Well, then if you think about AI technology and you use this mental model that I mentioned before, which is the fundamental promise of AI technology is you can take repetitive task that people are doing, you go from one to N with those repetitive tasks so you can automate the Nth repetitive tasks rather than relying on humans for that. Well, if you look at the majority of fortune 500 businesses or the majority largest enterprise in the world, there are an incredible number of parts of their business, where they spend enormous amounts of money on large teams of people to do repetitive tasks.

The alchemy that is possible there is not only the automation of meaningful parts of that work, but also the ability to even go further than even the best trained humans could do in many of those tasks. There’s some value that potential economic value or the TAM so to speak of AI machine learning is just absolutely astronomical. I think that is at minimum 10X, probably 100X the total business value that has been generated by SaaS systems or software historically, I think if you think about it, you have this one S curve of the saturation of software. And then there’s this very, very early S curve that is being developing right now around the productization and productionization of large scale AI systems, let’s say in the enterprise, or let’s say across businesses. And the real question is, okay, what’s the pacing of that S-curve versus the pacing of the saturation and deceleration of the current software S-curve? And I’m an optimist in not too long, we’re going to have a massive proliferation of AI use cases within the enterprise that are going to be way more impactful than the use cases of software in the past. And the way you’ll see that the business ROIs generated from high quality AI systems are going to be 10X more than the business value generated by let’s say, deploying a CRM or deploying an ERP system.

3. Why Market Timing Is Near Impossible – Peridot Capital Management

Let’s assume for a moment that you, unlike most everyone else on the planet, have an uncanny ability to forecast when S&P 500 company profits are going to decline within the economic cycle. You surmise that the market should go down when profits are falling  so you will use this knowledge to simply lose less money during market downturns than the average investor.

The long-term data would support this strategy. Since 1960, the S&P 500 index has posted a calendar year decline 12 times (about 19% of the time). Similarly, S&P 500 company profits have posted calendar year declines 13 times during that period (21% of the time). This matches up with the often repeated statistic that the market goes up four years out of every five (and thus you should always be invested). But what if you can predict that 5th year? Surely that would work.

Here’s the kicker; while the S&P 500 index fell in value during 12 of those years and corporate profits fell during 13 of those years, there were only 4 times when they both fell during the same year. So, on average, even if you knew for a fact which years would see earnings declines, the stock market still rose 70% of the time.

So the stock market goes up 80% of the time in general and in years when corporate profits are falling the it goes up 70% of the time. And so I ask you (and every client who I discuss this with), how on earth can anyone expect to know when to be out of the market? 

4. There Is No Playbook – Christoph Gisiger and Gavin Baker

Mr. Baker, you are one of the very few tech investors who lived through the dotcom crash in the year 2000 firsthand and remained active in the sector afterwards. As a battle-hardened industry veteran, how do you assess today’s market environment compared to back then?

Here’s the big thing: A lot of non-profitable tech companies with under $100 billion market capitalization just experienced a similar crash in valuations as we saw in the year 2000. But from a fundamental perspective, I don’t think the burst of the dotcom bubble has many parallels to what’s happening today. At that time, after the bubble burst, the fundamentals of every tech company imploded, they missed their earnings numbers by thirty, forty or fifty percent. Many had significant year-over-year revenue declines, and then their stocks went down more.

And how do things look today?

I do not believe that the fundamentals are going to crash in a similar way. In the year 2000, nobody knew which business models were going to work on the internet. The buildout in telecom equipment, data centers and software was not based on a consumption basis. It was built in anticipation of demand that took much longer than expected to materialize. In fact, we added so much telecom capacity that it took 15 years to absorb the amount of fiber and optical components we put in the ground. Every bank, every retailer and almost every other company was in a huge hurry to go online. They spent all this money to put up a website, but then they were like: «Wow! Why did I do that?» Today, you don’t see that degree of overbuild or excess on the supply side, because it’s all sold on a consumption basis. I promise, if the big cloud hyperscalers stopped spending on CapEx, they would run out of capacity in twelve to eighteen months. It’s a very different environment.

What does this mean for tech stocks?

It creates opportunities because today’s unprofitable tech companies are so much better than the ones back then. Many of them are Software as a Service companies where we know that the business model works. They have immense control over their P&L. You have seen some of them take their free cash flow margins up 80-90% in two quarters. They can be profitable whenever they want. They’re making a conscious trade-off between growth and profitability, and when they tilt towards more profitability, they don’t stop growing, they just grow slower. So it’s wild to me that you’ve had a move comparable to the year 2000 crash in non-profitable tech companies. Their forward multiples have compressed at least as much if not more, but they are great businesses, they’re not missing their numbers.

However, many of these companies were notably highly valued. As interest rates have risen, their shares have now come under pressure.

If you’re unprofitable, you’re essentially a long-duration asset. Hence, it’s natural that you take some pain as interest rates go up. But I think you’ve taken all the pain in the terminal valuation now. I don’t see much more multiple compression. Thoma Bravo, a private equity firm, just took out a software company at 12x forward sales. Today, you can buy a lot of software companies at roughly half that multiple, and they are growing faster with better fundamentals than the asset acquired by Thoma Bravo. So if you’re a software company trading at 6x sales, and an inferior company just got bought out at 12x sales by a very knowledgeable private equity buyer, I think that’s enough of a discount. That’s why I don’t see much more multiple compression. What will drive performance is growth and the relative operational performance of these businesses, and they should do reasonably well in an inflationary environment…

…How does this affect the outlook for the tech sector?

For their research, a lot of people are going back to look at the 70s. That’s a great exercise for energy, materials or restaurant companies where the business models are stable. But it may lead you to terrible conclusions for companies and industries where the business models have drastically changed. That’s why looking at the 70s to understand how tech will do today is absurd. Today’s tech companies are totally different, their business model is completely different. They have much higher ROICs, they are less capital intensive, have much higher margins, more pricing power and more gross profit per employee than tech companies in the 1970s. There is no precedent, there is no playbook for these business models in a high inflation environment. In America and Europe, you have never seen how inflation impacts the business models of different software companies. You haven’t seen how it impacts different internet business models. So from first principles thinking, there is an exciting opportunity to reach very differentiated conclusions and first principles thinking suggests these business models should do well fundamentally in a high inflation environment…

…In today’s world, cyberattacks are also occurring more and more often. Does this speak in favor of cybersecurity companies?

During the first twenty years of my career, I was always very negative on cybersecurity because it was one of the very rare industries where scale was a massive disadvantage rather than an advantage. Before the rise of artificial intelligence, human beings were writing software, it was a very manual process. And, as a cybersecurity company got bigger, hackers would start to optimize more and more for hacking that particular cybersecurity company’s software. As a result, the performance of that company would go down and it would lose customers. AI changed all of that because if the AI learns from the attacks, then you get better with scale. So that’s another area I’m excited for. Antonio Gracias, a fantastic thinker, has this great phrase «pro-entropic». To me, cybersecurity companies are pro-entropic. They benefit from rising chaos in the world.

5. Deep Roots – Morgan Housel

Forecasting, “If this happens, then that will happen,” rarely works, because this event gives rise to another trend, which incentivizes a different behavior, which sparks a new industry, which lobbies against this, which can cancel that, and so on endlessly.

To see how powerful these chain reactions can be, look at history, where it’s easy to skip the question, “And why is that?”

Take the question, “Why are student loans so high?”

Well, in part because millions of people ran to college when job prospects were dim in the mid-2000s.

Why were job prospects dim?

Well, there was a financial crisis in 2008.

Why?

Well, there was a housing bubble.

Why?

Well, interest rates were slashed in the early 2000s.

Why?

Well, 19 hijackers crashed planes on 9/11 that spooked the Fed into action to prevent a recession.

Why? Well …

You can keep asking, why? forever. And when do so you get these crazy connections, like a terrorist attack leading to student debt a decade later.

Every current event has parents, grandparents, great grandparents, siblings, and cousins. Ignoring that family tree can muddy your understanding of events, giving a false impression of why things happened, how long they might last, and under what circumstances they might happen again. Viewing events in isolation, without an appreciation for their deep roots, helps explain everything from why forecasting is hard to why politics is nasty.

Japan’s economy has been stagnant for 30 years because its demographics are terrible. Its demographics are terrible because it has a cultural preference for small families. That preference began in the late 1940s when, after losing its empire, its people nearly starved and froze to death each winter when the nation couldn’t support its existing population.

It was almost the opposite in America. The end of wartime production in 1945 scared policymakers, who feared a recession. So they did everything they could to make it easier for consumers to spend money, which boosted the economy, which inflated consumers’ social expectations, which led to a household debt boom that culminated with the 2008 crash.

No one looking at the last decade of economic performance blames Harry Truman. But you can draw a straight line from those decisions to what’s happening today.

6. “Ignoring the Possibility of Progress Is a Sure Method of Destroying Ourselves” – Rafaela von Bredow, Johann Grolle, and David Deutsch

DER SPIEGEL: Professor Deutsch, you believe that mankind, after billions and billions of years of absolute monotony in the universe, will now reshape it to their liking, that a new cosmological era is coming. Are you serious?

Deutsch: I am not the first to propose this idea. The Italian geologist Antonio Stoppani wrote in the 19th century that he had no hesitation in declaring man to be a new power in the universe, equivalent to the power of gravitation.

DER SPIEGEL: And fly to distant planets? Tap energy from black holes? Conquer entire galaxies?

Deutsch: I am not saying that we will necessarily do all this. I am only saying that, in principle, there is nothing to stop us. Only the laws of physics could prevent us. And we do not know a law of physics that forbids us, for example, from traveling to distant stars.

DER SPIEGEL: Theoretically, the colonization of the galaxy may be possible. But how would this work practically?

Deutsch: Human brains, assisted by our computers, can create the necessary knowledge for this – even though we do not yet know how.

DER SPIEGEL: Your late colleague Stephen Hawking did not have such high hopes for Homo sapiens. He thought we were “just a chemical scum on a moderate-sized planet, orbiting around a very average star in the outer suburb of one among a hundred billion galaxies.” Was Hawking wrong?

Deutsch: Well, it’s literally true. Just as it is true in a sense that the war in Ukraine was caused by atoms. It’s factually true, but it doesn’t explain anything. What we need to understand the world and our role in it are explanations, not empty statements.

DER SPIEGEL: Even among your fellow researchers, it might be hard to find many who grant us humans such a godlike role in the universe as you do.

Deutsch: Science is currently in a deplorable state. I’m reluctant to diss my colleagues, but, unfortunately, there’s a sort of cult of the expert. Accordingly, many researchers remain narrowly focused on their particular field, and even within that they are focused on creating usefulness rather than finding explanations. This is a terrible mistake.

DER SPIEGEL: What is so terrible about useful science?

Deutsch: All usefulness, every prediction, comes from understanding. However, if you no longer strive for fundamental explanations, but believe that it is sufficient to generate something useful, then you will merely move incrementally from one decimal place to the next, and even then, only in areas that are already well studied. This tendency has dramatically slowed down progress.

DER SPIEGEL: There are photos of a black hole, we can genetically modify people and develop a vaccine against a new pathogen within months. All this is not progress?

Deutsch: Yes, it is, but it is going slower than it could.

DER SPIEGEL: Biologist Richard Dawkins believes that this is perhaps because our brains are insufficient to comprehend the increasingly complex world. After all, it evolved to deal with problems on the African savanna. Now, however, we have to deal with stars, quantum and nuclear reactions.

Deutsch: Dawkins overlooks the fact that there is basically only one kind of computer. Whether it’s your laptop, or a supercomputer for modeling the climate, any computer can run the same computations. And our brain is nothing more than a universal computer. Its hardware can run any program, and we can use extra memory in our computers if necessary; therefore, it can run any explanation. There is no such thing as a computer that’s suitable for understanding the savanna, but not the sky. We couldn’t build one if we tried. It violates the laws of physics.

7. DALL•E 2 – Sam Altman

1) This is another example of what I think is going to be a new computer interface trend: you say what you want in natural language or with contextual clues, and the computer does it. We offer this for code and now image generation; both of these will get a lot better. But the same trend will happen in new ways until eventually it works for complex tasks—we can imagine an “AI office worker” that takes requests in natural language like a human does…

…3) Copilot is a tool that helps coders be more productive, but still is very far from being able to create a full program. DALL•E 2 is a tool that will help artists and illustrators be more creative, but it can also create a “complete work”. This may be an early example of the impact AI on labor markets. Although I firmly believe AI will create lots of new jobs, and make many existing jobs much better by doing the boring bits well, I think it’s important to be honest that it’s increasingly going to make some jobs not very relevant (like technology frequently does).

4) It’s a reminder that predictions about AI are very difficult to make. A decade ago, the conventional wisdom was that AI would first impact physical labor, and then cognitive labor, and then maybe someday it could do creative work. It now looks like it’s going to go in the opposite order.

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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. We have no vested interest in any company mentioned. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

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 April 2022:

1. Antonio Gracias – Pro-Entropic Investing – Patrick O’Shaughnessy and Antonio Gracias

[00:02:52] Patrick: Antonio, I was trying to figure out where to begin this conversation. I always like to dive straight into the meat of some interesting idea, not do the childhood background stuff, we’ll get to that later. And because of our conversations over the last many months, I think this idea of pro-entropic companies is one of the most interesting ideas that I have personally encountered maybe ever in investing, and an ideal place to start. I would love you to walk us through this concept, because I think it really defines your style of investing and is quite a bit different of a lens than I’ve heard from other investors investing at a similar stage.

[00:03:25] Antonio: Sure, Patrick. Thank you. It’s a word we use here internally in our investment framework, and we think about – there are lots who use the word resilient. And to us, resilient things, resilient companies are things that recover quickly. So when you talk to neuroscientists about the word resilient, they define it as you come out of homeostasis, something happens to you, the adrenaline goes up, cortisol, whatever, and then you recover quickly, you go back to homeostasis and make a good decision. If you don’t recover quickly, then you can’t make a good decision.

Pro-entropic, as you think about a company, if a company’s resilient, it means that it recovers quickly when something happens. There’s a crisis, management’s good, they figure out a pivot, they figure out what to do. For us, pro-entropic, it really is a company that is good at chaos. So this kind of comes from our trying to understand and learn how to get better at not making mistakes and make great investments, right?

So, how do we figure out how to lower our errors and improve our successes? And we started to identify this idea that the world’s going to be more chaotic, a lot more chaotic – almost 10 years ago now. And the chaos was driven by the idea that de-globalization was changing the world, technological disruption was changing the world, climate change was changing the world, politics was changing the world, demographics are changing the world. So much was changing all at once. And if you think about the second law of thermodynamics, it tells us that everything tends towards entropy anyway, and entropy being chaos, it felt to us that the world was getting more chaotic. And we wanted to find things that were good that got better when chaos went up. And one of the reasons we invested so heavily in a company like SpaceX is because whatever we could imagine was happening in the world, this company just got better, and it was actually good for it.

And I think how we really think through it is thinking about all of the probabilistic outcomes of the world. We’re Bayesian thinkers here, we think a lot about Bayesian probability trees and Bayesian updating. And as you think about all the probabilities that might occur to a company – pandemic, wars, recession, whatever might happen, how will that company respond if nothing changes? So it’s strategy itself. That’s how we differentiate from resilient. If something’s resilient, resilience is great too, we invest in resilient companies as well. But every once in a while, we’ll find something that we think is really pro-entropic. And that for us is the holy grail of investing; a company that gets better no matter what, just by virtue of what it’s actually doing. It’s certainly true of SpaceX. We have a company called Gopuff in our portfolio, which also actually is, I think, heavily pro-entropic. And believe it or not, these guys are disrupting 7-Eleven, so it’s really convenience stores. Convenience stores do well in recessions. They do well in pandemics, they deliver convenience store stuff. It does well in almost any environment you can imagine, and it did extremely well through COVID. They’re still growing very quickly now. We think they’ll grow through a recession. Believe that’s the case, because usually convenience stores do. So it’s not just the super high tech stuff. It could also be things like Gopuff, where you’re providing a very valuable service to people no matter what, and they need it no matter what. That’s how we think about it.

And then when we find a company where the strategy is pro-entropic, we think about, are the managers pro-entropic? The people running this company, are they really good at understanding chaos in the world? And the way we think about this, and I’ll differentiate again for resilience, is that a great pro-entropic thinker is someone who is able to keep their probability state, their Bayesian probability state, open on options. So you’re running a company. You always want to have optionality in what’s going to happen and how you’re going to build the company. They will be able to keep that probability state open to the appropriate moment and then close it for execution, because great managers, great CEOs, great executives are great at strategy, and they’re great at keeping options open. But then when you watch what happens, moments will be like, “Nope, we’re going to close that probability set. We’re going to do that thing. We’re choosing one of those paths or maybe two of those paths. We can’t have 20 of them all the time.” If you have somebody who’s always open state, they don’t get anything done, and you’ve probably seen that too. We have too many ideas and we don’t go anywhere.

Executives that are really good at doing both of those things, opening the probability tree and then knowing when to close it and execute really hard and then reopen it; we define that as pro-entropic thinkers. And they rarely get knocked out of homeostasis. They’re resilient too. They’re usually really good at recovering, but they get knocked out of, I’d say their regular homeostasis by events less than someone who’s resilient because they’re already predicting it. They’ve got a probability tree they’re running already about what the world’s going to look like. And their inference is running against what the world’s going to look like. They’re making decisions about that, and they’re deciding when to toggle down and close off and when to reopen. It’s been really valuable to us…

...[00:15:08] Patrick: One term you used earlier that I want to make sure I understand as contrasted against resiliency and pro-entropic is durable. What do you mean by durable? And what is the full spectrum of companies? If we got pro-entropic at one end, what’s the other end of the spectrum, and where does durable fit in that?

[00:15:22] Antonio: Our framework is pro-entropic, resilient, durable, cyclical, and then fragile. And we do not do fragile. The word durable, think of durable as almost the little boat in the ocean and the ocean’s going up and down, a big storm comes, and it survives. It’s battered and beaten, but it came out the other end and it’s okay. And we have done some of that in the past. In the more traditional growth, the GARP kind of investing, I think that’s what you end up doing a lot of. These are companies that are probably growing nicely in good economic cycles, and they do okay. They survive bad cycles, and the timing of that investment matters a lot. Unless you really like to manage it a lot, probably don’t invest in those kinds of companies.

And a durable thinker is someone that is not resilient, and we use a neuroscience construct here. You have something happen to you that’s an event. Knocks you out of homeostasis, your limbic system fires off. It impacts your prefrontal cortex, your ability to make decisions. So we call that the limbic hijack. You can’t make a good decision because you’re in an emotional state. Recovering emotionally, recovering your physiological homeostasis quickly makes you resilient. There are lots of people in the world that don’t necessarily recover quickly, but they’re really good at just compartmentalizing it, sort of shove it down. And what happens when you do that? When you talk to neuroscientists about this, they call it allostatic load, which is the load of stress on your brain. Think about the number of windows open in the background of your brain. We think about your brain as a neural network. The number of windows just keeps going up. They’re open and open. Your processor’s spinning on this stuff in the background. Your decision quality goes down over time.

So it’s both true for companies that have a strategy where they’ll survive the crisis, they’ll come through it, they’ll still be there. But man, they’ll be a little battered, beaten. And then for decision makers who will survive the crisis, they’ll come through it, but they will probably be battered, beaten, and their coping strategy will not be to resolve and recover. It will be to compartmentalize and move on. And this idea of, the windows are still open and processing in the background means they probably won’t have as much processing power available for the next thing that happens, and the next thing that happens. And over time, that just adds up. And I don’t want to say that I’m a neuroscientist here, but I studied it very carefully, so maybe an amateur neuroscientist. I think about it as, as that stress builds up over time and what they call allostatic load is going up, eventually it can lead to PTSD. You get that from one event that’s super terrible or from lots of events that are bad that you don’t resolve…

...[00:22:05] Patrick: I want to come back to the transition into early Valor, but you mentioned the theory of constraints. And I want to talk about operations and the operations work that Valor does, I think completely uniquely, relative to peers, discuss that concept. what is the theory of constraints? Why is it so powerful? Do you think it’s universally applicable in business, or is it more applicable to certain kinds of businesses?

[00:22:25] Antonio: I think it’s universally applicable. I recommend to anyone listening to this podcast, “The Goal”. When I was trying to figure out how to run this factory, and I didn’t know what to do, I went to a friend of mine who was at business school and said, “What should I read?” And he told me to read this book called “The Goal”, which was great. And it really comes down to physics. So the laws we’re talking about, the second law of thermodynamics, the theory of constraints, these all are concepts that exist inside of physics. And it’s simply that a system operates at its slowest limiter. In the plating context, an example, we had big, long lines, hundred-foot long lines that had plating of nickel, gold, et cetera, going onto beryllium-copper substrates. And the slowest thing in that line to plate was nickel. And I asked the question one day of one of our platers, “Is there a way to make this thing go faster, please?”

He was a second shift guy. And he said, “Oh yeah, there’s a way to do it. The guy that ran the company before you, he had three nickel baths, and now we have one, and we go one third as fast.” So I went to go talk to the chemist and I asked him the question, “What’s going on?” And he’s describing Faraday’s law. If you have an aqueous solution and you put a current through it, basically the current dissipates in the center. So if you make the bath longer, you’ve got to slow the whole thing down to get the right amount of plating on the part. And we went, let’s take over three nickel baths. Well, it takes one extra chemist, it’s going to cost pretty much nothing. Okay, let’s do that. That’s an example. It’s a closed system and, in a closed system like this, the thing that limits its speed is the slowest element.

That was really how I started thinking about closed versus open systems theory. And first I started thinking about entropy, which is when you’re thinking about entropy, it’s actually an open system. If the world tends towards more entropy, it’s because the system is open. When you have these very closed systems, like in a manufacturing facility, you don’t have as much entropy inside, what you have is this idea of constraint-based thinking. In this case, it was driven by Faraday’s law, but it happens in pretty much any business I’ve seen.At our business here, we’re in the asset management investment business, we think about it this way. We know that our constraints should always be in our operations teams. We’re continuing to hire operations people. We could invest more capital, there’s more good opportunities, than we have operations people to help them. And so we think about this as our portfolio size, the amount of capital we’re managing, is driven by our ability to serve our companies. In our business here, we define our customer as the company we’re investing in. Our operations work, the lean stuff we do for them, is the valuable service we provide them. And so we know that that’s our primary constraint and everything else revolves around actually being able to make investments that are really, really good. But the thing that limits how many investments we’re making here, is can we serve them?

I have yet to run into a good business where, if we really thought through it with the executives, they weren’t constraint-based thinking. And it’s one of the things, by the way, I really enjoyed about working with Elon over the years, because he has a physics background, he 100% thinks this way. And most of the executives I’ve met, whether they know they’re doing it or not, actually think this way. What part of my business should I work on today to make the whole system go faster? And if things are high quality and high speed, typically the product is good. The customer’s happy. The velocity’s going up, and if you’re improving velocity quality, your business is getting better…

…[00:32:51] Patrick: It’s a great excuse, the concept of risk, to talk probably for a while about one of my favorite things that you and I have talked about, which is the evaluation of people. And this really gets into the world of psychology. You’ve mentioned your foray into neuroscience a couple of times already, but I think that the ability to understand people, their motivations, the type of person they are, the type of leader they’ll be, is incredibly important. Certainly in pro-entropic companies, it probably drives the outcomes. I would love you to take us down this road that you’ve been on for 15 years or so into the world of psychological research, the theories or ideas that you’ve found to actually be helpful in investing and why. I just think this is such a rich area of understanding. And I know it’s a passion area for you.

[00:33:31] Antonio: Part of this starts at the story of how I started thinking about this. In the same fund that we did Tesla and SpaceX, we had actually, what turned out to be a Medicaid fraud, and I was just tortured by this. These were things I did, okay, so I’m not blaming anybody but me, I made these decisions. How could it be that I could make some sets of decisions that are just great, and others that are just terrible? And in very close proximity in time. And so I actually went and found a psychologist and theologian named Galen Buckwalter, who thinks about some of these questions, and asked him to help me figure it out. We started thinking about, could we create a cultural test to help us understand people better? And one of the things he studies is people that have a brain anomaly that their amygdala doesn’t fire properly and clinically we call these people psychopaths.

The reality is that I want to be careful with that word, because it’s got a very negative connotation. But if you watch the movie Free Solo, you see Alex Honnold, he just doesn’t feel risk and anxiety the same way that people might. And some of these folks go on to do great things, some of them can be criminals. And one of the things he pointed out to me is, look, when you have someone who might commit a fraud, and they’re high performing, there’s no way to figure this out ex ante. You really have to have known them and watched them, because they’re really good at it. And if they’re clinical, they’re probably already in jail, but if they’re high IQ and subclinical, they’re probably in your office, and that’s the kind of people we try to sort out.

And so he took us through trying to learn how to think about this problem of sorting out people that really didn’t fit for us in terms of values, and sorting in people that were really good. And I think the key learning that I would share with you is this: he told us about 5% of the human population has this brain anomaly and they concentrate in areas like finance, law, entrepreneurship, politics, et cetera, all the power professions. And that we should think about it as probably 10% of the people we deal with are going to do things that we don’t like, other people might like them, we don’t like them. They don’t fit for our values just because of the nature of how they think and the way their brain operates, their actual, physical brain operates.

And so we changed our base rate forecast. We do base rate forecasting here on our investments all the time. What’s the probability of a 3x, what’s the probability of a 5x? We weren’t base rate forecasting people. Most people I talk to, if I said to them, “Hey, what percent of people do you think walk in your office, sit down for a meeting, just are not telling you the truth and think it’s okay? Not that they’re not telling the truth and think it’s not okay, but they’re not telling the truth and think it is okay?” Most people would say none. I would’ve said zero before I heard this. And so we raised our base rate forecast to 10% and it’s actually helped a lot, because you’d say, think about it as a base rate toward 10%. It should take you six or nine months to figure it out.

And so, one of the reasons that six month thing happened is, as we get to know people, and they get to know us, one of the questions we’re asking ourselves, “Are our values aligned?” That doesn’t mean someone’s bad or good. We have our set of values, other people their values. And if our values are very misaligned, we’ve found over the years that we’re just not going to enjoy the experience, which means you won’t be great partners, they won’t be good partners for us, and that’s not good for anyone. And our values, they’re here up on the wall in the conference room, humility, integrity, responsibility, and excellence. These are the things you believe in, we believe in it for ourselves; we hold ourselves accountable to it and we want to work with people that do the same thing.

It takes us about six to nine months to figure out whether or not we match up on that stuff, and that’s why this small check goes in first then we get to know each other, and once we match up, I think we’re great partners. We will go to bat for you, we will go to war for you. We will, 100%. But we’ve got to believe that the values matched up. And then it continued and we went deeper in the neuroscience, we brought in a woman, Laura Harrison, who’s been working on some of the stuff we’re talking about here. She’s got a PhD in the neuroscience of emotion from CalTech, and she’s brilliant. The things that I’m telling you about with decision-making have come from the work that we’ve had with this team of Galen and Laura and some of our folks internally, thinking about how we are understanding decision-making, and how we make better decisions, and how we analyze other people making decisions. And soon, how we can help train other people to make better decisions…

...[00:45:45] Patrick: This concept of identity that’s related to ego has always interested me. With a background in philosophy I always loved the Paul Graham essay, I think the title was Keep Your Identity Small. The idea that when you establish an identity, things you identify as or with, you’ll do irrational things to align with that view of your own identity. You’ll act to protect this identity you’ve built up for yourself, even though it’s BS typically. Identities are sort of illusory. What have you done personally, to not make those kinds of mistakes? To not make the affirmation ego related? Is it Atul Gawande checklist manifesto type stuff? What are the tools in that toolkit that are actually effective at improving this category of error?

[00:46:25] Antonio: I’ll start with internal things, then the external things. So internally a meditative practice I started several years ago as a young man, then dropped it for a while, then picked it back up again now a little over a decade ago. I started doing TM, transcendental meditation, which is a useful mantra. And I found that that gave me space between my limbic brain and my prefrontal cortex to make even a millisecond decision about how I’m going to respond, how I’m going to feel. And that was very useful to me. After practicing that for a long time, I was able to actually get myself to a place where I could use a mantra to replace the dialogue in your head that’s always going. And one thing that’s important about the dialogue, just to tell you, we tell people a lot, it’s not you thinking. It might be you but it’s often the source of emotional bias or cognitive bias. It’s important to know this, unless we direct it, it’s not really always thinking.

Thinking is occurring in the background and when you are asleep, and meditation, and TM in particular, opened a space between my limbic system and my prefrontal cortex so I could make a millisecond decision. That is a response that is correct, if it’s mediate to the environment, it makes sense or if it doesn’t, I should just do nothing and be calm. Over time, what happened to me, I got more deeply into it. I had done some Zen Buddhist meditation when I was in Japan when I was very young. I was able to return to a breathing practice, a meditative practice that is without a mantra, where I could reduce the voice. And this happened to me just a few years ago, I went to Japan with my family, and I went back to my very favorite place on earth, a temple called Ryoanji, outside Kyoto. It’s a beautiful zen temple.

And I was meditating there on New Year’s Day, and it was the first time in my life that I could take the voice down to zero, be in a state of awareness without hearing the voice and just be. This has allowed me to, I think, mediate between my sense of ego and what is really happening in the environment, because there’s a space there for me now. It’s been really very useful to me, just made my life more pleasant frankly.

The other thing I’ve done here is, I have some wonderful, wonderful partners I work with, very smart people and really great humans. And it’s part of our process. If you go look at our underwriting documents, it has bias, it’s got cognitive, behavioral and emotional bias in it. And we literally sit around and talk about it. And there’s a culture here where I want people to check me and I check them, why are we doing this? Does it really make sense? What are you feeling about it?

And having a conversation about feelings around an investment table, I know it sounds a little crazy, when it’s so data driven, but it’s important because we want our people to be passionate. We want to be passionate about what we’re doing, that’s the whole point, we like doing it. We want to make the world better, but at the same time we want to make sure it doesn’t override our cognitive systems. And so we have external checks as well, which is more of the checklist manifesto thing, where we’re actually just checking off and saying, yeah, we thought about it, we talked about it.

We don’t even say they don’t exist actually, we just identify them. And when we go back in time and look at our errors, cause we still make errors, we will look at the underwriting, look at the numbers, all the qualitative, quantitative stuff we do, we’ll look at the bias states; we’ll look and see if we made a mistake. If we miss a bias, did something happen there?

2. As Russia Plots Its Next Move, an AI Listens to the Chatter – Will Knight

A radio transmission between several Russian soldiers in Ukraine in early March, captured from an unencrypted channel, reveals panicked and confused comrades retreating after coming under artillery fire…

…As the soldiers spoke, an AI was listening. Their words were automatically captured, transcribed, translated, and analyzed using several artificial intelligence algorithms developed by Primer, a US company that provides AI services for intelligence analysts. While it isn’t clear whether Ukrainian troops also intercepted the communication, the use of AI systems to surveil Russia’s army at scale shows the growing importance of sophisticated open source intelligence in military conflicts.

A number of unsecured Russian transmissions have been posted online, translated, and analyzed on social media. Other sources of data, including smartphone video clips and social media posts, have similarly been scrutinized. But it’s the use of natural language processing technology to analyze Russian military communications that is especially novel. For the Ukrainian army, making sense of intercepted communications still typically involves human analysts working away in a room somewhere, translating messages and interpreting commands.

The tool developed by Primer also shows how valuable machine learning could become for parsing intelligence information. The past decade has seen significant advances in AI’s capabilities around image recognition, speech transcription, translation, and language processing thanks to large neural network algorithms that learn from vast tranches of training data. Off-the-shelf code and APIs that use AI can now transcribe speech, identify faces, and perform other tasks, often with high accuracy. In the face of Russia’s numerical and artillery advantages, intercepting communications may well be making a difference for Ukrainian troops on the ground.

Primer already sells AI algorithms trained to transcribe and translate phone calls, as well as ones that can pull out key terms or phrases. Sean Gourley, Primer’s CEO, says the company’s engineers modified these tools to carry out four new tasks: To gather audio captured from web feeds that broadcast communications captured using software that emulates radio receiver hardware; to remove noise, including background chatter and music; to transcribe and translate Russian speech; and to highlight key statements relevant to the battlefield situation. In some cases this involved retraining machine learning models to recognize colloquial terms for military vehicles or weapons.

The ability to train and retrain AI models on the fly will become a critical advantage in future wars, says Gourley. He says the company made the tool available to outside parties but refuses to say who. “We won’t say who’s using it or for what they’re using it for,” Gourley says. Several other American companies have made technologies, information, and expertise available to Ukraine as it fights against Russian invaders.

3. RWH003: How To Win The Investing Game w/Joel Greenblatt – William Green and Joel Greenblatt

William Green (00:07:38):

When you think of the biggest mistakes that you made, not just in those early years. I remember you saying to me that actually, you didn’t have that many disasters other than obviously, the wonderfully amusing story of Florida, Cypress Gardens. Well, you tell the story, what happened to Florida, Cypress Gardens?

Joel Greenblatt (00:07:53):

Well, the interesting thing, a Harcourt Brace Jovanovich, which was a publisher, but also owned amusement in parks in Florida, believe it or not, went to buy a very small company called Florida Cypress Gardens, which I remembered as a kid going to, and they had water skiing Santa Claus, during Christmas time, and all kinds of water shows and beautiful gardens. It was a very unique, interesting, and very memorable place to visit when you’re five or six years old.

Joel Greenblatt (00:08:18):

When I saw they were getting taken over, and this was literally in the first month I went into business for myself. I was pretty nervous. I was 27 and I had gotten money from a very famous guy and I want to do a good job. I saw this opportunity where Florida Cypress Gardens was being taken over, and there was a nice spread in that deal where I could make a lot of money if it went through. I thought the deal made a lot of sense at the time. I was able to have a big smile on my face and buy Florida Cypress Gardens as one of the first investments I made when I went out on my own.

Joel Greenblatt (00:08:48):

A few weeks before the deal was supposed to close, unfortunately, Florida Cypress Gardens fell into what’s called a sinkhole, meaning the main pavilions of Florida Cypress Gardens literally fell into a hole that appeared out of nowhere. Apparently that happens a lot in Florida, I wasn’t that familiar with it, and thank God I wasn’t at Florida Cypress Gardens when it happened, but the wall street journal wrote a real humorous story about it. I was like, “Why is this funny? I’m about to lose my business. I had taken a pretty decent sized bet in the a deal.”

Joel Greenblatt (00:09:15):

It just tells you, things can happen that you don’t anticipate, that it’s not really your fault. I’d never even heard of a sinkhole before I read about this happening, so it’s a risk that I… When you’re doing a merger deal, you’re not really saying risk of sinkhole is in your checklist of things to look for, so stuff happens, less kind words for that. It’s a good lesson to learn, especially out of the box. I was sweating pretty good. They ended up re-cutting the deal at a lower price and I lost money, but not that terrible. I got… Howard Marks, my favorite line from Howard Marks is always, “Experience is what you got when you didn’t get what you wanted.” I always loved that line and that’s what I got in Florida Cypress Gardens, some good experience…

…William Green (00:18:29):

Have you ever figured out ways to handle your emotions and to become more emotionally resilient? Because I think of someone like Howard Marks, who we talked about before. Howard, I think is… He says that he’s a worrier, but I think also he’s not super emotional. I always felt… When I was with him, it felt like being in the presence of a most superior machine, with about 50 more IQ point than I had. When I think of someone like Charlie Munger, who said to me at the bottom tick, in March 2009, when he was buying Wells Fargo, he didn’t feel any emotion, any fear. I was wondering if you were wired that way yourself, not to be too anxious, focused on odds, or if there were things that you had to do to get your emotions under control during these very rocky periods?

Joel Greenblatt (00:19:12):

Yeah. I think what you’re alluding to is, to be a really good investor and have a strong enough stomach, do you have to have a screw loose someplace to be able to handle it? I think the answer is, yes. You have to have a little bit of a screw loose to be able to take those risks. On the other hand, I do feel the kick in the stomach when I lose a lot of money, but I usually adjust to it in two or three days, try to get my wits about me to take advantage of the opportunity. I think I’m human, at least in that part, where the kick in the stomach, but you kind of get used to it. I think different parts of your career are different. When you’re young, you figure you have time to make it back. When you’re older, you maybe have the experience to know that it will come back. I’ve seen this before and I’ve seen it not only once but many times, so what do you do here?

Joel Greenblatt (00:19:57):

I’m not saying you can completely defeat the emotions that are involved and those emotions are very strong. But I do think, at least for me, being able to adjust and count your blessings fairly quickly and say, “Okay, can I live with where I am now? Yes, let’s move forward and try to do it the right way.” One of the best experiences I had, was when I had a summer job and a friend of mine was working for, actually, the head of risk arbitrage at [Drexel 00:20:26]. The guy running that department was about 72 at the time, which I thought was ancient, now I think he was a youngster.

Joel Greenblatt (00:20:32):

But I forgot why I had an opportunity to talk to him, but either way, we were taking a walk someplace or whatever and I was saying, I was so upset that I lost money in this thing and how unfair it was, and this thing came out of the blue. This gentleman turned to me and he says, “Well, have you ever made money where you were kind of lucky and it turned out better than you expected.” I said, “Yeah, that happens a lot.” He said, “Well, does it happen more than when the bad things happen?” I said, “Yeah.” And he said, “Well, stop complaining.” It’s a good way to contextualize, if you didn’t take risk, you couldn’t make extra money. You can put your money in the bank and only take inflation risk or whatever that might be, but at least you know what you have. But one of the reasons you’re able to make money is that the stock market gets very emotional sometimes, creates these opportunities, but it also comes along with pain. If it didn’t, everyone would do it and you wouldn’t have this opportunity.

Joel Greenblatt (00:21:27):

Of course, I’m saying something now, not in the heat of the moment, that sounds very logical, but eventually you get there. Eventually when bad things happen in a few days, if you can get your sea legs back and start thinking, “Okay, where are my opportunities? What can I do? Can I trade around in my portfolio? Is there a new opportunity that came up that’s maybe better than what I have?” That’s been the case. Good investors maybe still get kicked in the stomach, but then come back soon enough to take advantage of the opportunities that come there. I think big, big picture, you have to have a little bit of a screw loose to take the pain, especially with a very concentrated portfolio that a number of people I know, pursue.

Joel Greenblatt (00:22:06):

I did it for a number of reasons. When I’m looking for really, what I would call unfair bets, I don’t have 50 or a hundred unfair bets at a time to take, so by necessity, I have to, when I was running a very concentrated portfolio, take six or eight of them. Just have a very fine… I think you have to have a very high hurdle. Meaning, to get into the portfolio, it has to be really great. If you own six or eight great things, or at least great bets, that’s more comforting if you actually know what you own. If you don’t know what you own, if you don’t know how to value a business, you’re just going to react to the emotions, because you don’t actually understand what you own. But if you actually understand what you own, and the premise that you bought those things with is still intact, that’s actually the only way I think you can deal with the emotion, because you realize what you own is still good…

…William Green (01:08:00):

I wonder if I could talk to you a bit about Success Academy? Because obviously it’s an extraordinary thing. This network of, I think 46, 47 charter schools in New York City, that you helped to set up. That have had incredible results in turning around the lives of low income and minority kids in particular. This is a subject close to my heart, because my son Henry is an English teacher at Success Academy in New York City at the moment. Teaching 6th grade. So I get the inside dope on how well the system works. So I wondered [crosstalk 01:08:27]

Joel Greenblatt (01:08:26):

He’s really the one you should interview, by the way.

William Green (01:08:29):

Exactly.

Joel Greenblatt (01:08:29):

Because that’s a hard job. That’s a hard job.

William Green (01:08:31):

It’s a challenging job. This is something I never really included in the book, but it really struck me when I interviewed you about Success Academy. That your thought process in solving the problem of education was remarkably similar to your thought process in solving the problem of investing. That you went to about it in a similar way. I wondered if you could talk us through how you looked at the problem of, okay, here’s this existing school system that isn’t working. Let me figure out what might work well and solve this puzzle. How in a…

William Green (01:09:03):

It worked well and solve this puzzle. And how, in a sense, part of what you were doing was finding a simple idea that was very powerful, a simple strategy that was very powerful and replicable because that strikes me as in some ways, not dissimilar to your approach with something like the magic formula, where you said, okay, let me distill this very complex game of investing to it assets, of here’s, how you buy cheap and good stocks. And in some ways I see a real similarity in the way that you’ve tackled the education problem. Without you saying, this is the best way, you’re saying this is a really good solution.

Joel Greenblatt (01:09:32):

Well, I appreciate that. Together with my partner, John Petry, we really took a business approach to the way we wanted to tackle this. We’re not education experts, but to some extent we see what businesses work. And so first off, there are a lot of good one off schools. If you get the best teachers and you give them enough resources, you can have a really good school. But the real challenge is to scale a really good school and then also scale to kids who probably need more help than others, because they have less resources when they come in. And so what we knew from a business standpoint was that if you just rely on the top 1% of teachers, you’re going to run out of those. And so can you make an average teacher? Can you give them a model that works for them to be great and really teach those kids?

Joel Greenblatt (01:10:20):

In addition, you don’t want to scale a model that doesn’t work and you have to be willing to make errors. So you want to first come up with a prototype that works and then expand that, and it has to work because it’s replicable, whatever you do has to be replicable. And so if you start with that concept, what happened with successes? We had a school, we hired most brilliant women I’ve ever met named Eva Moskowitz to sort of follow with this strategy, try to design a school that was replicable. Of course we weren’t looking for bad teachers and we weren’t even looking for average teachers. So that’s part of it. But we were looking for a prescriptive model, which could help any teacher become much better and started with one school. And when it started to doing pretty well, a couple years later, we opened three more schools.

Joel Greenblatt (01:11:07):

And the only question I asked was not, are these schools great, but how much ahead are these three schools than the first school? How are the kids doing? And most of schooling is done with inputs, meaning, well, if we get this teacher and they have this much experience and we get whatever, but if you’re measuring outputs, which is, are the kids learning? It’s a very different, and we’re agnostic of how that happens. We want to figure out something where the kids are learning and that’s, it’s the output that matters to us, not the input. We have a theory of teachers have to do this, or the curriculum has to do this, but how do we get the outputs? Putting all those principles together actually lets you scale. And so each time we opened more schools and now there are, I think 47 schools and 23,000 kids, we make sure we’re making progress on all those elements.

Joel Greenblatt (01:11:54):

And there was a lot of trial and error what worked or what worked better and what’s the best way to recruit teachers and what’s the best way to train them and all those other things and all these things that I give total credit to Eva Moskowitz has been incredible. And I think she’s the only part that’s not replicable, but she has created a system that I think other people can take a lot from and copy. And so she’s done an amazing job and we really used our business sense as to get to a replicable model. And so I think a lot of that is based on not obviously being education experts, but being business experts and just saying, instead of profits, our profits are kids learning at a high rate and at a good level and measuring it that way. All those things together, we’re sort of the basis of how Success got started. And you know, I think part of why it’s been successful.

4. Nvidia: The GPU Company (1993-2006) – Benjamin Gilbert and David Rosenthal

David: Yup. NVIDIA at this point, they’re halfway down the road of developing the next chip that they think Sega is going to adopt for what ultimately would become the Dreamcast. NVIDIA was calling the NV2. When Sega comes back and says, we’re switching horses, we’re not going to do this, they’re screwed.

For so many reasons, everything we’ve discussed, there’s also in the interim year-and-a-half since NVIDIA started, the price of memory dropped because, thank you, Moore’s Law. NVIDIA’s chips were designed to be super, super tight on memory. The memory cost about $200 in component parts to go into their chips. Their competitors have more memory that’s costing them $50.

Ben: That was just in that one iteration. It’s interesting to note that NVIDIA, by being first and not projecting out the exponential change that would come from Moore’s Law, was actually at a disadvantage. Because they didn’t get a chance to watch and see where the standards were adopted, so they picked their own lane and went off in their own direction, which ended up not being what everyone else picked, which put them at a disadvantage. But second of all, everyone else’s cost structure was way lower or at least everyone else could see that the cost structure was getting way lower. NVIDIA designed for a constraint that was no longer true by the time everyone else came out with their stuff.

At this point, Jensen and his co-founders had to look at each other and say, okay, do we scrap everything we did? And if so, how do we not make this mistake again? How do we make sure that in future generations, we premeditate the exponential curve of Moore’s Law and prices coming down and design for things that are two, three, four generations beyond what we actually have available to hardware right now?

David: When all this goes down, the company has about nine months of runway left. Literally anybody else, you pull the plug. It’s over. Everything in the deck is stacked against you, like your F’d. I can’t imagine sitting there dreaming up a way out of this. But Jensen, God, he’s such a G. He’s like, no, we’re not going out like this.

When you hear Jensen talk today about NVIDIA’s culture, he says that intellectual honesty is the cornerstone of NVIDIA’s culture. This is what he’s freaking talking about. He sits down with Curtis and Chris. Remember, they’re engineers.

They’ve recruited NVIDIA a hundred-plus engineers into the company at this point and sold them on this technological vision of how we’re going to define the industry, we set the standards. We’re not going to use some off-the-shelf stuff. It’s all toast. Jensen’s like, guys, this is a pipe dream. We need to throw it all out if we’re going to survive.

The only thing we can do is standardize on the same Microsoft Direct3D as everyone else, same architecture, and our only shot is just to compete on performance and try to become the best chip out there in this now sea of commodity chips. His co-founders don’t want to do this. This is not an exciting vision for a Silicon Valley engineer.

Ben: When your CEO comes to you and says that, what they’re basically saying is, look, if my job was strategy and your job is execution, the strategy failed, so we just now need to literally out-engineer all of our competitors. We need to be smarter at engineering decisions, so we can be more performant at a lower price point using less energy than our competitors.

Microsoft being Microsoft had all the developer attention. And because Microsoft set a standard, NVIDIA realized, look, we have no ability to uniquely get our own developers, at least at that point in the company’s history. So we must just on our left, look and see all the developers are coming from Microsoft using this API, on our right is all the same consumers. We have to compete just head to head on raw engineering ability with everyone else.

David: You’re saying engineering ability. But remember, this is essentially a commodity at this point. Really, it’s not just engineering ability. It’s how fast you can ship. How fast can you design the next generation of chips? And can you ship it before everybody else? Because everybody knows what’s going to be on that ship.

Ben: And why is it? What fundamentally was it about graphics cards that made it a commodity?

David: At this point, all the other peripherals—and we’re going to get into this in a sec—there was nothing that special about it. They all did the same thing, which was take polygon-level, 3D graphics processing out of the CPU and onto this other chip on the motherboard. Just like sound cards were doing the same thing for sound, just like networking cards were doing the same thing for networking.

It was just like, what’s the price performance ratio of doing that? The interfaces and the programming language, that’s all standardized by Microsoft. You’re just a commodity hardware.

Ben: What GPUs actually do or did, at least in this point in time, say, okay, the system is going to feed me in basically point clouds, like vertices that make polygons that represent like a 3D world and my job as the GPU is to, as fast as I can, in the highest resolution that I can or I suppose a standard predetermined resolution, output a 2D thing that goes on the screen?

I turned 3D stuff into 2D stuff. I have to do that better than other things that I’m competing against, where basically all of us are. When you say commodity, you mean limited by Moore’s Law and doing right up to the edge of what integrated circuit manufacturing techniques enable us to do.

David: Yup. Everybody knows what this means. They got to ship faster than their competitors. They also got to ship faster than their competitors because they’re about to go bankrupt. They draw up this plan. They’re trying to thread the tightest needle possible here.

They have to lay off 70% of the company, which they do. They go down to about 35 people. Everybody who’s staying knows we now have to design from scratch and ship a new chip before our runway runs out, which is nine months. You can’t do that on a normal chip design cycle.

Ben: It takes two years, right?

David: Yeah. With these fabless chip companies, the way they would design chips is they would work on the design, they would send them over to the fabless company, the fabless company would produce some prototypes, they’d send them back, they test them, they go back and forth a few times.

Ben: You mean the foundry would produce some, like the TSMC, or the Samsung, or the GlobalFoundries.

David: Now importantly, NVIDIA is not using TSMC at this point because they can’t. TSMC only works with the best and NVIDIA is not the best. They’re using secondary foundries. That process takes a long time. Then at the end of it, when you’re sure you got the design right, then you do what’s called a tape-out of the chip.

Ben: I love this term, by the way.

David: It harkens back to literally when you used to tape masks to do the photolithography on the chip back in the day, but it just means finalizing the design.

Ben: But you actually do run it on some prototypes first. The foundry sends back some, hey, thanks for the designs, here’s the chip, run your tests on it, and make sure everything does what you think it does. That process takes two years to get a full iteration on.

David: Yup. They’re like, we can’t do this. Jensen’s like, here’s what we’re going to do. I’ve heard about these new technologies, some new machines out there that enable emulation of chips. In our case, we’re going to use it to emulate the graphics chip that we’re designing. It’s all in software and it works.

Ben: They’re startups, but they exist.

David: The problem is, when you emulate it in software, it’s really slow. When you play a game, when you’re looking at your computer monitor or whatever, it’s refreshing 30 to 60 times a second. If you’re a professional gamer, you probably have a go on it, like 120 times frames per second. This emulator runs at one frame every 30 seconds. They’re going to have to debug this thing in software to save this time going at one frame every 30 seconds.

Ben: It’s just insane.

David: That’s brutal.

Ben: They’re basically making this trade-off of, okay, if we want to ship something in nine months, we don’t have time to actually have it execute on the hardware. We are going to make the trade off of our testing being mind-numbing, like running whatever our graphics tests are, where we’re looking for this certain specified output. We need to plant someone in front of a screen to watch the new frame render once every 30 seconds and look again some tests to verify that the output is correct. If it is and this person does that mind numbing work, and sits there just observing, and observing, and observing, then we will go right to manufacturing without ever producing a physical prototype and ship that.

David: That is exactly what they did. They had spent a million dollars just to get the emulator hardware and software to do this.

Ben: I think they had generated some revenue, but it was still a third of the cash that they had in the entire bank account.

David: They go down to six months until they cash out in the company. They get it done in a few months and then they call up their foundry. I don’t know if they’re using United or one of the other foundries in Taiwan, not TSMC. They’re like, all right, we tape this thing out and send it to production. The foundries were like, you guys sure about that? They’re like, yup, we’re sure. Make 100,000 units.

Ben: If I’m remembering right, I think NVIDIA basically was the only customer of that emulation software. That was a startup that really wasn’t fully proven yet. NVIDIA was like, look, we literally have no options.

David: Yeah, they were the only customer and then that company went out of business after. The chip they designed is now the advantage. This is lunacy, what they’re doing. Obviously, they have to do it because their back is against the wall.

The advantage of this, though, is they are now designing this chip with the same set of assumptions about what technology is available as all their competitors, but their competitors are working on those designs. They’re not going to be able to get them out for 18 to 24 months. NVIDIA is going to get the same generation of design out in six months. This chip is called the RIVA 128. It’s what they call it. It is a freaking beast in every sense of the word.

Ben: It’s big.

David: It’s big. It’s extremely powerful relative to anything else on the market.

Ben: More powerful than any customers are telling them they want.

David: Yeah, way, way more powerful. But it comes with some downsides. With great power comes great responsibility. Because they built this thing in such a manner, it barely works. There are a lot of stuff wrong with it. I forget the exact number of this, but essentially, Direct3D at the time had something like 24 or 25 different ways and techniques.

Ben: These are the blend modes?

David: Yeah. I think that’s what it was, blend modes. The RIVA only works about two-thirds. One-third of it just freaking crashes. It doesn’t work.

Ben: I thought even worse than that. Basically, I think NVIDIA had to launch a campaign, going around to all the different developers and being like, come on, what do you really need more than these eight for? What are you really going to do where you need to use that fancy stuff? Do us a favor. For this generation of the chip, these eight work great. You’re going to love them. They’re so good. Just use those.

David: This is so, so great because people do it. They learn about the market. In the first iteration of NVIDIA, we’re going to build all this technology. We’re going to drive the market. They didn’t know anything about the market. They were just making all these assumptions about what people wanted.

But now, Jensen’s actually going into these developers trying to convince them to do this. They all do it. Why did they do it? Because the only thing that matters is performance. Consumers are going to buy hardware and games based on the quality of the graphics. This is being discovered for the first time. People are willing to make a lot of compromises in service of performance. NVIDIA’s the first one that figured this out because they have to go around and do this, and developers all get on board.

Ben: To be clear, it’s because the consumers are making the buying decision on what graphics card they buy.

David: It’s a completely interrelated system where the consumer is making all of the decisions. That’s where the demand is, the consumer is deciding what hardware to buy. That’s what NVIDIA’s business is.

Ben: Whether they’re buying it as a fully built computer from the OEM or whether they’re buying the card put in later themselves, they’re making a decision on what graphics card goes in the computer.

David: Exactly. The game developers are making decisions on what graphics cards to support and how to build their games with the assumption of what’s my target market of consumers? Who do I think will this game run on? You need to have at least an X-level performance rig in order to run my game in its fullest form.

Ben: The developers are premeditating what graphics cards are going to be out in the market when their games launch. They’re saying it’s going to be the most performant one at the right price point, so whatever the mass market is, we have to target that. If you’re telling us that we’re going to test it and it turns out that yours is the best performance per price, performance per watt, or whatever, if it’s the most efficient card, then people are going to buy that one, so we must target that card.

David: And they’re going to buy my game. I remember that this is a few years later. This is a trope that happened. There was a game called Crysis. Do you remember this?

Ben: Oh, yeah. What’s the relationship between Crysis and Far Cry?

David: Far Cry was the first game, the Crysis Engine, and then Crysis also. It was super convoluted. Basically, my perception of this thing was when Far Cry came out—this was mid-2000s—the graphics were unbelievable. If you had a rig powerful enough to run it, just unbelievable. The game itself was total crap. I don’t think I ever played more than 10 minutes of it.

Ben: I’m pretty sure if your computer didn’t support it, there were all these videos that people would record of building a tower of a thousand gasoline barrels and then shooting it. Because it was too complex for their graphics card to handle, their computer would just freeze. That was the failure mode of Far Cry with non-performant chips.

David: This is how the hardcore gaming industry evolves. Far Cry sold so much software and so much hardware just because people wanted to attempt to experience that level of graphics. That’s what the developers are starting to figure out. They’re like, all right, well, you can ship this thing. We’ll use only those eight blend modes whatever it takes because graphical performance is the most important thing.

It works. They sell one million units of the RIVA 128 within four months. I should have looked at what the MSRP was, but that is a lot of revenue.

5. An Interview with Dan Wang about COVID, Chinese Manufacturing, and China’s Response to Ukraine – Ben Thompson and Dan Wang

That’s good! I’m happy for you. What are you hearing, though, from people who are there? The views we have from the outside, whether that be news reports or what gets out under Western social media, it’s pretty scary. People struggling to get food, even water. Kids separated from their parents. What’s the view on the ground. Is it as bad as it seems from the outside?

DW: I think it might be quite a bit worse than what it seems than the outside. Now, Shanghai started to lock down about a month ago when it started posting a few cases, and at the time very few of us had been terribly concerned. What was surprising about this wave is that the bad news just kept piling on and on, such that we saw just a steady increase of new restrictions. At one point we woke up and figured out that all the schools had now been closed. Steadily the grocery delivery and eCommerce delivery platforms like Hema for groceries and JD.com for e-commerce had been slightly breaking down. So to have this series of steady escalation has been very surprising indeed.

Now for those of our listeners who don’t know, Shanghai has been doing very, very well in China, and has been known for having a light touch on COVID throughout the entire pandemic. Beijing, where I used to live, would lock down tight every single time, every other city would lock down for a few cases, Shanghai had a pretty light touch, it never really locked down. So we all feel that Shanghai is now guilty of quite a lot of hubris and is locking down in the most severe way; I think the really comparable event here is Wuhan in February 2020, or March 2020, when most people were confined to a space, and when Beijing deployed the People’s Liberation Army to really try to control the pandemic. So the situation is now pretty serious indeed.

I asked you this the last time you were on, but is zero-COVID sustainable? I know that was the message from Xi Jinping, so of course the Shanghai situation is going to be blamed on the local leaders, but is that messaging going to carry the day, that Shanghai should have done better? Or is there going to start to be some realization that this is impossible for the long run?

DW: We’re probably getting a little bit closer to the realization that zero-COVID is not possible, especially given the variant on top of Omicron which makes it far more transmissible. I think the way to think about this is that Beijing has deployed the People’s Liberation Army, again, for only the second time throughout this pandemic, and if you deploy the army to try to control this virus, well, then it becomes very slightly more complicated to say, “A lot of this is just local incompetence, the rules were not properly enforced,” because if the army is involved, then things are much more challenging to pass the buck.

It really is pretty surprising how poorly the Shanghai government has managed things. Shanghai is known throughout the rest of the country for being the most progressive, for having the greatest resources, for being the richest jurisdiction in the country, for doing a lot of the nicest things. I’ve written recently about how pleasant a city Shanghai really is. Every time we Shanghaiers would have to go to Beijing, we’re asking ourselves, “Why do we, who are living in mini New York, have to visit mini Pyongyang again?” So it’s been pretty shocking to see how poorly Shanghai’s government has bungled things.

In my view, the most stark issue here is that for a lot of people, Shanghai is out of food. So the grocery delivery platforms have been mostly shut down for the last few days, even over the last few weeks. It’s become quite a bit more difficult to buy groceries. Now that everyone is confined to home, the government is now sending rations of packs of vegetables to different people. Now, some of these rations are fairly generous. You have people getting some fish, you have people getting some shrimp and an assortment of things, but a lot of these ration packets are a head of cabbage, a few potatoes, carrots, and that is what you have to live on for the next while. This is the big surprise to all of us, that the government has looked fairly hesitant, uncertain, even incompetent in this case, and not correctly managing what should be a more straightforward affair with delivering food to most people.

I think you mentioned your annual letter in passing, referring to talking about Shanghai versus Beijing, and we’ll have a link to it, as it’s always an amazing read. But I think I would gauge that letter and our last interview as being pretty optimistic about China, I think particularly relative to maybe broader opinion. Over the last few months, though, you not only have this COVID situation coming back to the fore, but you also seem to have some backtracking around initiatives like reforming the real estate market or Common Prosperity. Are things looking a little bit stormier than they were even a few months ago?

DW: Things are absolutely more stormy over the last few months in China than even just three months ago when I published my letter. I think there are four big risks this year that’s going to make 2022 a very, very special year indeed. The first big risk, as you put it, is COVID. Right now the biggest economic city in the economic center in the country is under the most severe lockdown, demanding the army. That is a pretty big thing, and the danger here is that even if Shanghai is back to normal in something like two or three weeks, which I think is the most optimistic scenario, we don’t know if we have to do these rolling lockdowns throughout the rest of the country throughout the rest of the year. So that is the first big risk.

Second, if there are huge lockdowns of something like, let’s say, 10% of the country going dark for any given point, then the economy cannot do well. The National People’s Congress this year set a target of 5.5% GDP growth. It was ambitious in the best of times when there wasn’t quite a bit of COVID running around, but to have something like 5.5% growth while shutting down 10% in the country at any given point, that is looking far more difficult indeed.

The third big risk is this geopolitical uncertainty as it relates to Russia. It’s hard to figure out what exactly is going on here, but at least we can acknowledge that there are a lot of unknown unknowns.

And finally, the major event that will be the controlling force for this year is going to be the 20th party Congress held sometime in the fall, probably September or October of this year, when General Secretary Xi Jinping, is going to very likely almost certainly seek a third term as China’s top leader. And so, having this Party Congress in place, I think, is a very big reason that the country is pursuing zero-COVID, to make sure that COVID is not running rampant over what is the most important political event in China every five years, especially for Xi Jinping on a personal level; and then also, to set this fairly ambitious growth target so that people are feeling pretty good.

I think one of these things are going to have to give. Even in the other previous few party Congress years, we have a lot of politicians being purged for the sin of joining the wrong political faction, and when you add up all of these four factors together: COVID, the economy, geopolitics, as well as the Party Congress, this is going to be just a really weird brouhaha and I am not sure how things are going to shake out this year in China.

The level of uncertainty is really striking. You said that “almost certainly” Xi Jinping will be re-elected. Is there any thought or any discussion that that wouldn’t happen? From the outside, it’s certainly just been assumed that will be the case.

DW: The conventional wisdom in China is that Xi Jinping will have a third term, but what we don’t know is the shape of his responsibility. Now, there are a lot of tea leaf watchers in Beijing who are following these things much more closely than I am, but there is some speculation that he resurrects the title of Party Chairman, which previously only Mao Zedong held, and then appoints a new General Secretary, which would be his successor. Also, the other major uncertainty is the composition of the Politburo, and is there some sense that the Politburo will be stacked a little bit more evenly between different factions within the party? Or is he going to have a run of the table and have many of his own people in place at this most senior leadership positions?…

…One final question. How, if at all, has your view of the Taiwan risk changed? I have to ask you this because I was traveling over the last couple weeks, and as you can imagine, I got asked this constantly, so I will put it to you.

DW: Ben, I want to hear your answer first.

My answer is I think it has decreased the risk in the short-term, because I think that China is probably surprised by the unanimity and vigor of the Western response, and it’s probably a bit of a wake up call that the West still can get its crap together to a certain extent, but it’s probably increased the medium-term, in that China now knows what it has to do, what issues it has to overcome, what preparations it has to make. It, to your point, focuses minds on doing that. I do also think the clear trepidation in the West about Russia using nuclear weapons might also weigh into this, in that China may realize they have a bit of a trump card, which is, “Hey, if we do an embargo, and say we’re going to respond with nuclear, the West isn’t going to do anything.” That adds an additional layer of ambiguity into the question. Basically, short-term risk, I would say, is down a bit, but medium-to-long term risk is probably up a bit. I’m not sure what exactly the time periods are with that, but that’s my answer.

DW: Yeah, that’s exactly my view, that in the short-term at least, Beijing certainly knows that whatever lack of vigorous response it can hope for from the West, probably is now put to bed because of all of these different actions from the West. The major uncertainty that we have here, is that none of us can define a timeline. If we don’t know what the medium-term is, if we don’t know what the long-term is, then it becomes much more difficult to say when exactly these things will happen. My sense has always been that Beijing has never laid out a clear deadline of when it would really like to liberate Taiwan Island.

Just teasing you here, Ben.

6. Can Matt Mullenweg save the internet? – David Pierce

Most of Mullenweg’s time is spent as CEO of Automattic, one of the web’s largest platforms. It’s best known as the company that runs WordPress.com, the hosted version of the blogging platform that powers about 43% of the websites on the internet. Since WordPress is open-source software, no company technically owns it, but Automattic provides tools and services and oversees most of the WordPress-powered internet. It’s also the owner of the booming ecommerce platform WooCommerce, Day One, the analytics tool Parse.ly and the podcast app Pocket Casts. Oh, and Tumblr. And Simplenote. And many others. That makes Mullenweg one of the most powerful CEOs in tech, and one of the most important voices in the debate over the future of the internet…

…In every way that matters, Automattic is a reflection of Mullenweg (you could say he puts the “Matt” in Automattic). He started building web software because he wanted a place to store and share his photos; he’s a blogger to the core, and loves anything that aids in the free expression of ideas on the internet. He loves jazz, which is why WordPress releases are named for jazz musicians. He loves to read and write and work from anywhere, so he turned Automattic into a company that supports bloggers and promotes remote work. He buys companies that make products he likes, and companies that have missions he believes in. Most of all, he believes that open-source software is the future of everything. And he’s betting on it every way he can.

Eighteen years after he first started working on WordPress, Automattic is more powerful than ever. It’s a $7.5 billion company, one of the biggest private companies in the industry. And yet its founding idea — that software should be available to everyone and editable by anyone, that communities can build great things together, that walled gardens always eventually fall — seems more tenuous than ever. There’s another 17-year-old company named Facebook that flies in the face of everything Mullenweg believes in, and is threatening to own the future of the internet…

…The first time Mullenweg and I spoke for this story, I asked him what he thought about the state of the tech industry. It was early September, and conversations were raging about antitrust, misinformation, surveillance capitalism, Big Tech’s overreach, Facebook’s effect on democracy and in general the society wrought by the tech industry.

Before he answered, Mullenweg changed the frame of the question. This happened constantly in our conversations: I’d ask about Instagram or the iPhone, he’d respond with Plato or Camus. Once, when I asked him about Facebook, he responded with a story about the printing press. In this case, he simply urged me to think more broadly. “I don’t think you have to limit yourself to looking at technology,” he said. “Zoom out to human history, or look at the current state of the world, and look at the tension and the pendulum swing between freedom and authoritarianism.” That back and forth has always existed, he said, and to expect a bunch of companies to suddenly fix it is unrealistic.

The cycle plays out the same in tech, he said. Take the internet: built as an open platform, eventually colonized by a handful of dictatorial players. To them, Mullenweg says: Congratulations on all your accomplishments, but you’ll lose in the end. “You get folks who want to ride that openness, but then close people off,” he said. “Like Facebook using your contact books or your email to bootstrap its growth, but then not allowing anyone to do the same on Facebook.” That can work, Mullenweg acknowledges. Sometimes really, really well. “But it also contains the seeds of its own demise.” Users inevitably begin to feel hemmed in and controlled by the closed platforms, and yearn for open pastures. Then they go build something better. Something open. “People’s natural desire for freedom starts to get more and more of the best and brightest in the world working on open, distributed, decentralized systems.”

The seeds of this change are already everywhere, he said. Tesla has open-sourced its patents in an effort to speed up innovation in electric vehicles, because as Elon Musk said, the company’s goal is not just to sell cars but “to accelerate the advent of sustainable transport.” There’s also the whole decentralized, Web3, blockchain community, which excites Mullenweg every time it comes up. “There’s an inevitable gravitational pull towards open source affecting literally every field: finance, health, politics,” he said. “All the things that currently happen in closed ways, what if they were open? What if they were transparent? What if you could copy and paste it? Do your own version? Remix it?”

And then he offered the closest thing you’ll find to a Unified Theory of Matt Mullenweg. “As more and more of our lives start to be run and dictated by the technology we use, it’s a human right to be able to see how that technology works and modify it. It’s as key to freedom as freedom of speech or freedom of religion. So that is what I plan to spend the rest of my life fighting for.”…

…There’s just no hurrying Mullenweg, it seems. Even as the tech industry swirls around him, with regulatory fights and social media backlashes and the seemingly hourly shift in priorities, Mullenweg remains steadily on course. “We aspire to create the layer that every other application on the web can run on,” he said. “Hopefully one day, 85% or 90% of all websites have WordPress as their base layer.” Right now, the web operates largely on top of closed platforms owned by companies like Amazon and Facebook. “But to truly be a platform,” Mullenweg said, “it has to be open. Otherwise it’s more like a trap.”

He plans to spend the rest of his career building the web’s one true platform, the open system the internet deserves. What exactly does that look like? Who knows. Mullenweg is increasingly fascinated by all things Web3 and crypto, and sees in that space much of the collaboration and community he loves about WordPress and open source in general. He proudly reminded me that WordPress.com began accepting bitcoin in 2012, and that Vitalik Buterin, who eventually created Ethereum, wrote about Automattic for Bitcoin Magazine the same year.

“To me, what Web3 embodies is two essential ideas: decentralization and individual ownership,” Mullenweg said at his recent annual State of the Word speech, where he updates the WordPress community on the year that passed. He preceded that by saying he didn’t really know how to define Web3 at the moment — who does, really? — but supported the belief in an internet that anyone can help build, tweak to fit their own needs, and own themselves without paying rent to some large tech giant. He did issue a warning, though: “For every project which is asking for your money, dollars, for you to pay the cost of a house for a picture of an ape, you should ask: Does it apply the same freedoms which WordPress itself does? How closely does it apply to increasing your freedom and agency in the world?”

7. The Metaverse Has Bosses Too. Meet the ‘Managers’ of Axie Infinity – Edward Ongweso Jr

It’s only in the past year, however, that games have begun to not only shoehorn cryptocurrency into their rewards systems but also fully build themselves around crypto-tokens and digital assets like NFTs. What’s emerging is an ecosystem known as “play-to-earn,” where the players can generate revenue directly from playing video games, harvesting digital assets, and trading them…

…Axie Infinity is arguably the industry standard-bearer for play-to-earn games, and it’s a deceptively simple one at that. Axie, developed by Vietnam-based studio Sky Mavis, centers on NFTs of monsters called Axies that form a team whose battles earn players Smooth Love Potion (SLP) tokens. The game features its own blockchain, named Ronin, to facilitate faster and cheaper transactions for SLP, Axie’s governance token (AXS), and Ronin’s native token (RON). Battling is basic, akin to if your entire team of Pokémon battled at once. Axies, as well as other in-game items, are represented by NFTs which can be bought or sold on an in-game market. 

The rise of play-to-earn games, however, has not been as clear-cut as some suggest. The prices of Axie Infinity’s core tokens as well as trading of its Axie NFTs have consistently fallen since their peak last year. A recent hack threatens even greater downward pressure on prices. Questionable economics and labor dynamics have risen up from the froth: Play-to-earn is not just giving rise to a new class of digital workers who see a fraction of the total earnings from their efforts, but bosses, too. 

Getting started in Axie isn’t like other games, however. To play Axie at the highest level of the game—to be, effectively, a boss—you need start-up capital. 

To start playing Axie Infinity, you need to buy three Axie NFTs—an investment that, when crypto markets were stronger, cost well over a thousand U.S. dollars. Today, the cost hovers around $300. Axies can also be bred―for a fee that grows each time―using a basic tripartite genetics system allows for selection for different traits as well as random permutations. These newer and potentially stronger Axies NFTs can be minted for use in teams, or sold on marketplaces. 

Not everyone has enough money IRL to be their own boss in the metaverse, however. That’s why you can also loan your Axies to players unable to afford their own in exchange for a cut of any profits they generate, which can run from 20 to 50 percent. Manager cuts can go even higher, with “ranges from 30% – 75% cut per scholar based on their monthly rewards”  according to Axie Infinity’s co-founder and chief operating officer Aleksander Larsen (also known as “Psycheout” online). These players are known as “scholars,” and their benefactors—who may employ a few scholars or run massive operations with dozens or hundreds of scholars toiling away—widely refer to themselves as “managers.”…

…”I can’t specifically call it a boss/employee. It’s more of a partnership, or let’s call it a joint venture. One party puts up the capital and the other puts up the time,” Conor Kenny, an Axie Infinity manager and a YouTuber who documents crypto trades, told Motherboard. “The scholar grinds daily, you split the profits. Everybody wins!”

In his YouTube videos, however, Kenny strikes a different tone. “I employ them,” he enthusiastically says of his scholars in a September video agonizing over whether Axie Infinity was still a worthwhile investment for managers. “As we stand right now, Axie Infinity is a Ponzi scheme. It’s built on new players coming into the world,” Kenny added in the video…

…“Everything in life is a Ponzi,” said one Venezuelan manager who goes by Iguano and directs five scholars, reflecting the widespread idea that Axie Infinity’s buy-in requirement and diminishing returns as its token sinks in value make it similar to a Ponzi or pyramid scheme. “The first bunch of people who invested in the game have better profit than the people who invest at the end. The economy of Axie needs new people to join to provide gains to the people who were there before.”…

…Rafar’s scholarship program is a comprehensive one: It includes a one-month trial to maintain a competitive ranking, a daily quota of 75 SLP, an onboarding system complete with Google Doc guides, playtesting, live feedback, and a community Discord to ask questions and get further help.

“I also see Axie as a gateway for Filipinos to be educated about crypto which I believe will help advance them in the future,” he told Motherboard. “I’ve set up crash courses for them about the basics of crypto (how to create wallets, trade, and how to avoid scams)—basically making them literate in the crypto world.”

When someone is in desperate need of funds, Rafar said, he has in the past offered Axie scholarships as a way for them to solve their problems themselves. 

“For example I had one family member that is a scholar earning maybe $250 a month from their job,” Rafar told Motherboard. “They are having a kid and need money to pay the hospital bills, estimated to be about $500. I offered them an Axie account and held on to their SLP till the baby’s birth month. They are about to receive about $600 from earnings, which they wouldn’t have had otherwise.”…

…All this may be even further complicated by a recent major hack that rocked the Axie Infinity ecosystem: 173,600 ETH (about $588 million) and 25.5 million in a stablecoin called USDC was stolen from the Ronin Network on March 23, but was only noticed on March 29. Ronin is a so-called “sidechain” designed to allow people to use Axie Infinity without incurring expensive fees on Ethereum for every action. The hackers drained the liquidity from the Ronin “bridge,” which allowed for assets to be transferred between Ethereum to Ronin, leading to the bridge and its affiliated Katana decentralized exchange to both be deactivated. This means deposits and withdrawals are paused, including on Binance, stranding the funds of those who have wagered that this game will make them money, even as its core tokens have shed most of their value over the past year….

…Before the reforms announced in February, the most SLP an incredibly skilled player could expect daily was somewhere north of 324 tokens per day in Season 19 if they had 20 or more Axies, played at the highest levels of Adventure Mode, and got a 60 percent win rate while battling in higher ranks of PvP battling. At the current SLP price of $0.02 that’s a daily wage of $6.48, excluding the manager’s cut, which could be half of those earnings. In the Philippines, where nearly half of all players reside, the average minimum wage is about 366 pesos or $7.13. 

A research report from gaming research and consulting firm Naavik affirms as much: For low-level players or those just starting out, playing Axie Infinity each day earned less than a minimum wage job in the Philippines, the research concludes. That was in November, when the price oscillated between $0.06 and $0.08.

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