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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 15 May 2022:

1. An Interview with Coda Founder (and Bundle Expert) Shishir Mehrotra – Ben Thompson and Shishir Mehrotra

SM: Maybe we can start with some background on where I came from. Before I started Coda, I spent about six years running the YouTube products at Google. Most of that time, our presumption was that YouTube was going to be an ad-supported product. Obviously, this is still how the majority of YouTube is run, but we always thought that at some point we would add in this paid model, and we’d have some way for creators to make money from payments or subscriptions, or so on, but it wasn’t ever a top priority.

We tried things, but we always put our fifth or six priority on it, and they never really worked, I think over time I counted ten different experiments that we tried. One of my favorite ones was we spent nine months on this paid platform launch, it made a hundred dollars. Not a hundred dollars per day, or a hundred dollars per week, but all time, a hundred dollars. We bought pizza for the team, we shut it down.

Through this period, we keep trying these experiments, none of them work. At some point, I ended up having this conversation with a friend at actually one of the cable companies. We’re describing how this was working and he asked me how we felt about bundling. I said, “Oh yeah, we’ve tried everything, but we’re not going to do bundling. Bundling is evil.” He said, “What do you mean bundling is evil?” It was very interesting, it just stuck in my head.

This part, I definitely disagree with you, because I think bundling is the most amazing thing ever.

SM: For sure! That’s where I changed my mind. But it’s like, you ask anybody about bundling and first off, we say the word bundling, what do they think of? In the US, they think of Comcast and nobody really has positive interpretations of Comcast. In their head, they’re thinking bundling is cheating somebody, but we basically came to the conclusion that that is incorrect. So I developed, started doing all this research, and started coming up with this framework of what became this paper Four Myths of Bundling.

The core idea is that bundling is actually beneficial to all three parties. It’s beneficial to the consumer, to the providers, and to the bundler. This is because the heart of bundling is based off of balancing the needs of superfans and casual fans, I think this is one of the things that people often mistake about it. Of the four myths, the fourth one is the one that’s most cited, which is the reality that the best bundles are the ones that minimize superfan overlap and maximize casual fan overlap…

…I think it’s always been so important to have something physical — in the case of cable companies, they had a wire. That was an obvious bundling point that actually had nothing to do with the programming. You got the widest possible array of stuff that at the same time made total sense together.

SM: Yeah. When we talk about this at Spotify, we call this go-to-market alignment instead of superfan alignment, people mistake the two. A famous Spotify bundle that really worked was the student bundle. If you’re a student, you can get Hulu, Spotify, and Showtime for five bucks a month. Most people think that’s a big loss leader, or a marketing stunt, or so on — it’s not at all. It makes tons of money for all three parties, and has grown that business a lot. But one of the things that makes it work is that the wire, to use your analogy, is the student. What are all the things the student needs when they go to school? You can start stacking all these services into it. To pick something out of the B2B world, the most famous bundler in that world is Microsoft…

...As I understand it, it was kind of how you got connected with Daniel Ek and you ended up joining the Spotify board. I’m curious, to the extent you can talk about it, beyond that student bundle, how does your thinking about bundling impact the way Spotify is approaching things? Is podcasting a bundle play? How expansive is this?

SM: The student thing was probably the first step, but by far the biggest bundling experiment at Spotify is podcasting. I think the core idea — Daniel and I started riffing on this, to give lot of the credit, Daniel had basically the same ideas, we were very aligned on how to think about this. He just asked me to help formalize them and write them down, which turned into the Four Mythos of Bundling doc.

At that time, Spotify was synonymous with music and still is for a lot of people. One of the insights that Daniel had, in thinking about bundling, is what if we were to take something, let’s use your wire analogy, that still had a through line that people could understand, but drew a totally different group of superfans.

Daniel uses this line a lot that the video market, depending on how you size it is call it a trillion dollars in revenue a year — the audio market, radio and so on, is a tenth of that. He often talks about this idea that “Are your ears really worth the 10th of your eyes”? Of course not, it doesn’t make any sense, and he talks about how that market can grow. But all of that was I think Daniel did this genius job of saying, “Hey, this through line is going to be audio,” but fundamentally, what we’re going to do is we’re going to take products for which the superfans have incredibly distinct audiences. The set of people that care about listening to mystery podcasts, or to news bloggers, or to sports bloggers, or so on.

Or subscriptions to podcasts like Stratechery, available on Spotify.

SM: Exactly! We should talk more about that too; I think the fact that’s available on Spotify is amazing. But I think that idea of, “We’re going to pull this thing together”, is absolutely the idea of minimizing superfan overlap, maximizing casual fan overlap. You can actually see it, we have a team of economists at Spotify that try to measure that impact, there’s a part of the paper that talks about this concept we call Marginal Churn Contribution (MCC), which is, if you think about how you should divide up accountability or money in a bundle, we believe the right way to do it is something we call marginal churn contribution, which is if you were to remove this thing, how many people would churn from the product?

I love that. I’m going to completely steal that terminology, because you see this again and again when people are talking about sports, “Why does ESPN command so much money?”, or “Why do regional sports networks command so much money relative to their tiny audience sizes?” It’s this exact point. If you really like sports and your bundle does not have ESPN, you are going to leave. We saw this last year: Disney just put YouTube TV over the barrel because they tried to go one day without ESPN. It was like, “Nope, not going back going to happen.”

SM: Right. If you’re a Lakers fan, you’re going to end up getting that network. It works. The mistake many people make is they try to correlate usage with MCC, with marginal return contribution, and it’s generally wrong. In fact, if you were to take a graph and you plot on one axis you plot usage, on the other axis, you plot MCC, sometimes people call it anchor value, you could draw a diagonal line through it and everything below that line is things where usage matters more than anchor value, or more than MCC and above that line is things where your MCC matters more than usage. You get two completely different business models. For example, much of the content we had on YouTube at the time drove significantly more usage than it did MCC, if I removed any piece of it, you would still just come.

Which is sort of the UGC [user-generated content] idea in general, isn’t it? In that case, you’ve completely commoditized your suppliers because there is no special supply. There’s always other supply to put there.

SM: Well that’s not a very positive way to think about it! What it does do is it leads to an advertising-based business model. If usage is more important than MCC, then the right way to monetize that product is probably advertising. On the other hand, if you’re above that diagonal, and you have things where access is more important than usage, no matter how much I love your podcast and your newsletter, I can’t listen to them over and over again, I’m not going to read the newsletter over and over again.

Please don’t.

SM: You’re only going to get so much usage out of me, but I pay for it because why? I’m sure you’ve asked, but if you ask people, “Why do you pay for Stratechery”? I think it makes me feel smarter, I feel better informed when I’m in this other discussion. I think you know that you’re well read by some really important people out there. It creates a common understanding for us, but it’s uncorrelated to how much time they spend on it, it’d be a dumb way to measure it.

I get in trouble when I go too long.

SM: Right! Exactly. I don’t want that, I want synthesis out of you. One of the things that happens, one of the reasons I’m so excited about the bundling work, it’s a fun theory. People have all sorts of different hobbies, I have this weird one, I like bundling. (laughing) I have rather normal hobbies too!

(laughing) Theoretically, if this weren’t a podcast to talk about Coda, I’d be like, “Oh my God, I can talk about bundling for an hour. I’m ready to go.”

SM: That’s right. Well, I’ll tell you why the concept of bundling is relevant. We talk about it with some very literal examples, and you talk about product bundling, and Comcast, and so on. But the core idea of “people value access over usage” is a really interesting idea. This idea of marginal churn contribution actually applies to products in general. You’re building a product, and you like Coda, and you say, “Hey, what should I do next?” You kind of have two choices. You have things that are going to drive usage and things that are going to drive new users, you’re going to create MCC. You can apply the exact same philosophy the same way, “I’m going to add this thing, I think it’s going to add new users”, or prevent them from churning, versus things that are going to increase usage. When you use that framework, you see the world a little bit differently, you think about marginal impact, which is much more powerful than some of the other way of measuring success.

2. Terra Flops – Matt Levine

An “algorithmic stablecoin” sounds complicated, and there are a lot of people with incentives to pretend that it is complicated, but it is not. Here is how an algorithmic stablecoin works[1]:

1. You wake up one morning and invent two crypto tokens.

2. One of them is the stablecoin, which I will call “Terra,” for reasons that will become apparent.

3. The other one is not the stablecoin. I will call it “Luna.”

4. To be clear, they are both just things you made up, just numbers on a ledger. (Probably the ledger is maintained on a decentralized blockchain, though in theory you could do this on your computer in Excel.)

5. You try to find people to buy them.

6. Luna will trade at some price determined by supply and demand. If you make it up on your computer and keep the list in Excel and smirk when you tell people about this, that price will be zero, and none of this will work.

7. But if you do a good job of marketing Luna, that price will not be zero. If the price is not zero then you’re in business.

8. You promise that people can always exchange one Terra for $1 worth of Luna. If Luna trades at $0.10, then one Terra will get you 10 Luna. If Luna trades at $20, then one Terra will get you 0.05 Luna. Doesn’t matter. The price of Luna is arbitrary, but one Terra always gets you $1 worth of Luna. (And vice versa: People can always exchange $1 worth of Luna for one Terra.)

9. You set up an automated smart contract — the “algorithm” in “algorithmic stablecoin” — to let people exchange their Terras for Lunas and Lunas for Terras.[2]

10. Terra should trade at $1. If it trades above $1, people — arbitrageurs — can buy $1 worth of Luna for $1 and exchange them for one Terra worth more than a dollar, for an instant profit. If it trades below $1, people can buy one Terra for less than a dollar and exchange it for $1 worth of Luna, for an instant profit. These arbitrage trades push the price of Terra back to $1 if it ever goes higher or lower.

11. The price of Luna will fluctuate. Over time, as trust in this ecosystem grows, it will probably mostly go up. But that is not essential to the stablecoin concept. As long as Luna robustly has a non-zero value, you can exchange one Terra for some quantity of Luna that is worth $1, which means Terra should be worth $1, which means that its value should be stable

All of this is, I think, quite straightforward and correct, except for Point 7, which is insane. If you overcome that — if you can find a way to make Luna worth some nonzero amount of money — then everything works fine. That is the whole ballgame. In theory this seems hard, since you just made up Luna. In practice it seems very easy, as there are dozens and dozens of cryptocurrencies that someone just made up that are now worth billions of dollars. The principal ways to do this are:

• Collect some transaction fees from people who exchange Luna for Terra or Terra for Luna, and then pay some of those fees to holders of Luna as, effectively, interest on their Luna holdings. (Or pay interest on Terra, creating demand for Luna that people can exchange into Terra to get the interest.[3])
• Talk about building an ecosystem of smart contracts, programmable money, etc. on top of Terra and Luna, so that people treat Luna as a way to use that ecosystem — as effectively stock in the company that you are building and ascribe a lot of value to it.

These things reinforce each other: The more fees you collect and distribute to Luna holders, the more big and viable your ecosystem looks, so the more highly people value it, so the more Luna they buy, so the more activity you have, so the more fees you collect, etc.

But there is no magic here. There is no algorithm to guarantee that Luna is always worth some amount of money. The algorithm just lets people exchange Terra for Luna. Luna is valuable if people think it’s valuable and believe in the long-term value of the system that you are building, and not if they don’t.

The danger here is that Point 7 never goes away. Any morning, people could wake up and say “wait a minute, you just made up this all up, it’s worthless,” and decide to dump their Lunas and Terras. If people decide to dump their Lunas then the price of Luna goes down.

If people decide to dump their Terras — “wait,” you say, “there’s an algorithm; the price of Terra can’t go down.” If people decide to dump their Terras, then the price of Terra goes down from $1 to like $0.97, and arbitrageurs step in, buy Terras for $0.97 and exchange them for $1 worth of Luna.

Yeah. Well. The problem is that if people lose confidence in this system, they decide to dump both Lunas and Terras. Someone sells some Terras. Arbitrageurs step in, buy Terra for $0.99, and exchange it for $1 worth of Luna. Luna is at, say, $40, so each Terra gets you 0.025 Luna. Then the arbitrageurs sell their 0.025 Luna in the market, which drives down the price of Luna, which is falling anyway. Someone else sells some Terras, but now Luna is at $20, so each Terra gets 0.05 Luna, which arbitrageurs sell, and now Luna is at $10, so each Terra gets you 0.10 Luna, which then get sold, so Luna goes to $5, so each Terra gets you 0.2 Luna, etc. There is no natural stopping point for this process because Luna is just a thing you made up, and because it represents essentially confidence in your ecosystem, and as the price of Luna crashes that confidence ebbs away. And so eventually Luna trades at $0.0001 and you exchange one Terra for 10,000 Luna and you try to sell them and there are no buyers and so no one wants to arbitrage the price of Terra and so the price of Terra falls below $1 and everyone gives up on the stablecoin and the ecosystem and everything and it all goes to zero.

3. Jeff Jordan – Building & Investing in Marketplaces – Patrick O’Shaughnessy and Jeff Jordan

[00:05:31] Patrick: eBay, since it’s literally the perfect model of a marketplace is maybe the place to focus on for now. What kinds of actions did that mean when you were at eBay operating to try to promote price discovery or price equilibrium or something like that? What were you literally doing?

[00:05:46] Jeff: The most interesting thing is early on we try all these initiatives that we baked on our own and debuted the community. And we found out the leverage was way more to watch what the nascent behavior the community was doing and seek to amplify it. So the iconic thing there is Simon Rothman who’s bounced around. He’s a Valley veteran now. Early on in his career was just a early exec there and he has a very high interest in collectible cars. And one day I think he was searching for Maserati or Ferrari and expecting to see little replica cars and he found real ones. And it’s just like, “Why are people selling Lamborghini’s on eBay?” Well, it turned out Lamborghini’s are only sold on the coasts. And so if you’re in the middle of the country, it’s very hard to buy one typically and eBay entrepreneurs were figuring out, “Okay, here’s what we do.” So we took that nascent behavior and built eBay Motors, which then made it much easier to list and discover cars, generated the supply and created the awareness. The best actions we had was watch that nascent community behavior and amplify.

[00:06:51] Patrick: When you’re looking at a new marketplace for the first time, I’ll hold off on the discussion between horizontal and vertical marketplaces which we’ll come to at some point. But if you’re just looking de novo at a marketplace as an investor with your investor hat on, what are the features that you are zoomed in on most quickly that matter to you with all this experience?

[00:07:09] Jeff: Two main ones. One is fragmentation of the marketplace. I often have used the difference between OpenTable and Fandango in explaining this. OpenTable, the average restaurant owner on OpenTable owns one restaurant. And so aggregating them is a pain in the ass. But once you’ve aggregated them, it’s a very valuable thing. Whereas Fandango basically has deals with the five or six major theater chains and any one of them can have market power because if AMC pulls out of Fandango, I am motivated to go to amc.com and figure it out. When I was explaining this theory to a fellow board member and accolade Michael Klein and I explained the theory and he looks at me very quizzically and I go, “What?” He goes, “You do know I’m the founder of Fandango, right?” You’re like, “Oh crap.” So one is fragmentation.

The other is ideally lead gen. You’re creating relationships that otherwise wouldn’t have been created. The thing you try to avoid is “Okay, I have a relationship with my car repair man, my hair stylist, my whatever and it’s a frequent relationship.” Those don’t do well because the service provider, they’ll pay a little bit for convenience. They’ll pay a whole lot for a new customer. Ideally you have a combination of currently inefficient market that’s very fragmented and lead gen is a part of it. So Airbnb has lead gen. Hosts are being introduced to guests they never would’ve known. It’s spectacularly fragmented. The average host owns one property. It has those two characteristics.

[00:08:38] Patrick: Maybe we should just go read Andrew’s book to answer this question, but what have you seen in common amongst marketplace businesses that are especially good at thinking about that lead gen part of the equation? Because the fragmented supply side or the fragmented supplier base, like you said, it’s a pain in the ass to get them all, but it’s kind of straightforward, like you just got to go get them all. What about on that other side, what’s shared in common amongst the most talented people that you’ve seen thinking through this problem of lead gen?

[00:09:03] Jeff: The best models are ones that don’t really rely much on paid acquisition. The best entrepreneurs have figured out hacks to get user demand at scale through a user proposition. And one of the most brilliant hacks on this was the OpenTable hack that preceded me. The team figured it out ahead of time is they build a widget that restaurants could put on their own websites to empower online reservations, because the typical behavior at the time is “I want to go to The Slanted Door.” Okay, let me search on Google for the Slanted Door so I can find the telephone number. Go to the website and you see this widget that says make an online reservation. It’s just like, “Oh I’d rather do that than pick up the phone and have that experience of, ‘Can you hold sir?’ get back to you and then call multiple restaurants.” Just awful.

And so we put it on there and what it ended up doing, the diner would click on it and was redirected to The Slanted Door page on OpenTable. They would then discover, “Oh my God, I can make an online reservation at all these” and they’d come back to OpenTable. They wouldn’t go back to Google. They’d quickly learn a behavior to go do OpenTable. OpenTable was getting paid to acquire their restaurants consumers. While I was there, we didn’t spend a penny on demand acquisition and we’re growing very nicely based on that. So the best models don’t really rely on paid. They figured out some other way to get that distribution…

…[00:12:09] Patrick: Talent density. Obviously eBay is sort of like patient zero for this online digital marketplace concept. I’m sure working with Pierre there was a fascinating experience. You were there right in the thick of it. What stands out as the most important things that you learned as an operator at eBay?

[00:12:24] Jeff: I learned to be an operator. I’d only had a couple semi operating jobs up to that point. While I was a CFO at the Disney stores, I was also responsible for managing the Disney stores in Japan, but we had someone on the ground so I was kind of overseeing the person who was overseeing it. When I got to eBay, I’d never really run anything. And so I joined, Meg was building bench depth so she found a job for me and had me managing two people, one of whom promptly quit to go run a Baja Fresh franchise, which at that point might not have been the best financial decision unless he owns Baja Fresh at this point. I was managing one person, then a few months in she reorganized and gave me eBay North America, which was the ebay.com website. Seven years later, I was managing 5,000 people.

One of the blog posts that I get the most comments on is I think it’s titled Leaving It All On the Field. It brings a sports analogy to managing a hyper growth business. Because early on you’re the player, things are crashing around you and you’re making every call. And then there was a point where I remember one night when I go home, I get to work at 5:00 AM and it’s seven at night, there’s still a line outside my door waiting for me to make decisions. I go, “This is not scaling. I got to change something.”

And you become a coach. You hire a bunch of people. You try to get them into a place where they’d make most of decisions similar to how you would. And then the mode’s very different. You turn into a coach. At some point with hyper growth, they can’t make all the decisions. So they have to build a team. They become the coach and you become a general manager and you’re further and further from the action in the field each time.

And then take it to its logical conclusion, at PayPal with 5,000 people I was commissioner of the league. And it’s interesting, the job is fundamentally different. You’re not in the action. You are orchestrating it. I called it a bunch of -tions, organization, motivation, communication. And I didn’t like the job anywhere near as much. I was very gratified that I actually appeared to be pretty good at it. But my career was just, I continually went back to earlier stages. eBay grew, I went to PayPal, PayPal grew, I went to OpenTable, OpenTable grew… And there’s a point at which the good news is I got pretty good at that stage of growth, consumer marketplace businesses at that stage of growth.

The bad news is the learning curve just shallowed out like crazy. When I’m operating, I’m always on, always stressed, always tired. And then you throw on board on top of it and that was a pretty toxic combination…

…[00:22:08] Patrick: What were some of the early surprising aspects of coming at it from the investor side? I’m especially interested in the pricing of rounds. I was told to ask you about pricing Instacart, for example. What lessons did you learn on the investing side that were completely new and different in those early years?

[00:22:24] Jeff: The good news is I was looking for a steep learning curve and it was way steeper than I thought. I was like, “Wait, I’m in the same room. I’m just taking a different chair. How can it be that different?” And man, is it different. Lesson one was it is a steep learning curve. Some of the early lessons, and still learning them, which is the interesting part, 10 years in. You have to continue to be adopting your decision framework. One was whenever I saw a bargain, I should run. It’s a sign of no heat. Whenever I did a bargain, I regretted it later. Whenever I was forced to pay up, to date that has been a very good basket of companies. And you mentioned Instacart. I saw Instacart late when Apoorva was raising. I think he saw a blog I wrote on demand economy and just reached out. And he goes, “Listen, really late process but we’d would love to talk you.”

So we have this great conversation. And I think it was a Thursday or a Friday. And he goes, “Listen, I have to decide by the end of the weekend. I’m getting so much pressure.” I crunch away on the weekend, digging into the details. I want to do it. I get okay for my partners to go in with a number. And I think it was I go in with something that’s a 100x current GMV, like $90 million. And he goes, “Jeff, I’ve really enjoyed our conversations. I’d really love to work with you, but you’ve got to know you’re less than a third of any of my other term sheets. And by the way, I’m deciding tomorrow.” And so then do I want to play? If I want to play, I’ve got to triple. And so over a weekend… The interesting one, going back to your partners and saying, “You know I asked for $90? I need $300.” that was a gut check, but there was so much to like about it. They’re like, “Okay, I’m going to climb the ladder.” I’m glad I climbed the ladder on it. A lot of the very best deals have that kind of pricing pressure, and the pricing’s set by the market. It’s not set by metrics. So you have to figure out, “Okay, do you climb?” And I tend to climb if I think it’s legitimate heat…

…[00:26:56] Patrick: As you start to dig on the layers of what’s driving marketplace businesses, consumer ones specifically, what tensions are healthy? There’s a lot of stakeholders in marketplaces, and not everyone can get the best of everything all the time. How do you like to look and investigate tensions inside of a network?

[00:27:13] Jeff: Tensions are great because there’s two sides or three sides, and there’s always tensions. It started at eBay. The sellers paid us, and so the obvious thing, give the sellers what they want. But it turned out for me, what made eBay work was the buyers. Amazon and Yahoo both launched auction products early at eBay. By the way, they were the gorillas at the time. Particularly Yahoo. It was a $100 billion dollar market cap early. They launch auctions, they make it free. We charged a list. They made it free. Amazon made it free. And they quickly got millions of listings, but what they lacked were buyers. And so the sellers went there and it was like they put up billboards and no one walked by. And so they came running back to eBay and redoubled, their efforts on our platform. Long had the philosophy that why the sellers came is we had a robust buyer base, and so then growing the business requires optimizing the buyers’ base.

And so eBay and OpenTable, we did things that the sellers, the business side didn’t like. They viewed reviews on OpenTable. At OpenTable. I have four web windows open. I have the one for OpenTable, I have one for our map because we didn’t have a map. I had one for Zagat and Yelp because there were no reviews. And you’re like, “Okay, I think I see the path forward here, provide an integrated experience.” So we go to the restaurant and say, “Yeah, we’re going to debut reviews.” And they go, “You cannot publish a negative review from a customer I don’t know. You’re my technology provider. What are you doing?” Kind of thing. And you’re just like, “We did research. If a customer opened a review, they were twice as likely to make a reservation.”

You’re working with them. And then finally if we couldn’t convince them, we gave them the ability to opt out. “We will not show reviews on your page if you don’t want it. Just know that every other restaurant’s going to have reviews and you’re going to look pretty stupid.” So there are always those tensions. I almost always bias towards the buyer side of the equation. People come to Airbnb, hosts come to Airbnb because it has the largest guest network in the world. The more guests you have, the happier the host will be in the long term. You’re kind of optimizing for buyers, for diners, for guests, and in spite of the fact that the other side’s typically the one paying you, do

[00:29:22] Patrick: Do you have good examples of when the supply side is actually the harder side of the network? I remember talking to Gurley about this and saying, “Usually if you get all the buyers, the supply will show up.” But I’m sure there’s some examples where it’s different.

[00:29:35] Jeff: Airbnb’s been supply constrained almost since I got involved in the company. The supply’s expanding, but they believe they’d do more business if they had more supply, high quality supply. So it is interesting. Particularly in the unconventional businesses… I’ve said this probably. The first time I heard the Airbnb concept I said, “That’s the stupidest thing I’ve ever heard.”

[00:29:54] Patrick: So many, yeah.

[00:29:56] Jeff: I’m intensely private. I don’t want someone on my house, a stranger in my house. I don’t want to be in a stranger’s house. It was just like, “Oh.” When it’s that counterintuitive, the supply side, evangelical people to kind of say, “I see it, and I enjoy it.” And Airbnb was part economic empowerment but also part human relationships. They’re people who like meeting strangers and talking to them and learning about them and figuring out… There are multiple satisfactions involved in that experience. But there are a lot of marketplaces, particularly the weird ones, that can definitely use more supply…

…[00:35:35] Patrick: How far into the evolution of one of these marketplaces do you think it’s really important to start honing in on, I guess I’ll call it unit economics or margins, or something like on DoorDash or something? For a long time it was, “Well look, at scale these will be amazing.” And it’s kind of nebulous, what scale meant and when that would be. How much do you think about maybe the margin profile of a marketplace as you’re investing, especially if it’s early on?

[00:35:58] Jeff: I don’t not look at it if the margin’s not bad. An extreme case of this was Instacart. The time we invested, he was earning something like $12 a order in money to Instacart, and he was spending about $30. And so-

[00:36:16] Patrick: That’s a pretty bad margin.

[00:36:18] Jeff: That was pretty bad. And so the work I did that weekend was around profitability. And it turned out that he was just starting to do deals with grocery stores where the grocers would give him better pricing and share some of the incremental revenue from the economics. And that, at scale, would dramatically improve his economics. So one is you had to believe he’d get to the deals with the grocers. And then could he get to price parity? And he laid out this waterfall of, “This is how I’m going to make money.” And it was very detailed. Apoorva’s superpower is optimization. He’s just said, “These are the 19 things we need to accomplish to make the unit economics work. And I’m halfway on this one and just laid it out.” And I haven’t looked at that sheet in a while, but it largely came true. He made the unit economics work.

The big swing was he got the deals with the grocers and then the advertising business, I think Fiji, just announced it would be over $1 billion this year. Amazon showed that’s very high margin income. So the existence of that ad business means he can provide a very compelling value prop to the consumer because they don’t have to pay the full fare for the delivery. They get it partially subsidized through the advertising venue. And so that’s been key to the working, but the economics were awful when we invested. And so the leap of faith there wasn’t people would want groceries delivered to their homes. The leap of faith was he can make the economics work.

4. ‘Go for the Jugular’ – Sebastian Mallaby

On Tuesday, September 15, the pound took another beating. Spain’s finance minister telephoned Norman Lamont, his British counterpart, to ask him how things were. “Awful,” Lamont answered.

That evening Lamont convened a meeting with Robin Leigh-Pemberton, the governor of the Bank of England. The two men agreed that the central bank should buy the pound aggressively the next morning. As the meeting wound down, Leigh-Pemberton read out a message from his press office. Helmut Schlesinger, the president of the German Bundesbank, had given an interview to the Wall Street Journal and a German financial newspaper, Handelsblatt. According to a news agency report on his remarks, Schlesinger believed there would have to be a broad realignment of Europe’s currencies.

Lamont was stunned. Schlesinger’s remark was tantamount to calling for the pound to devalue. Already his public statements had triggered an assault on Italy’s lira. Now the German central banker  was attacking Britain. Lamont asked Leigh-Pemberton to call Schlesinger immediately, overruling Leigh-Pemberton’s concern that the punctilious Bundesbanker did not like to have his dinner interrupted.

After several conversations, Leigh-Pemberton reported that Schlesinger believed there was no cause for alarm. His comments were not “authorized,” and he would check the article and issue an appropriate statement when he reached his office in the morning. Lamont protested that this was a dangerously leisurely response. Schlesinger’s purported comments were already on news wires; traders in New York and Asia would react overnight; Schlesinger needed to issue a denial quickly. But Germany’s monetary master refused to be hurried. He was not going to adapt to a world of 24-hour trading.

That night, Lamont went to bed knowing that the next day would be difficult. But he could not imagine how difficult.

Stan Druckenmiller, the chief portfolio manager at George Soros’s Quantum Fund, read Schlesinger’s comments on Tuesday afternoon in New York. He didn’t care whether they were “authorized;” he reacted immediately. Schlesinger had made it obvious that the Bundesbank was not going to help the pound cling onto its position inside the exchange-rate mechanism by cutting German interest rates. The devaluation of sterling was now all but inevitable.

Druckenmiller walked into Soros’s office and told him it was time to move. He had held a $1.5 billion bet against the pound since August, but now the endgame was coming and he would build on the position steadily.

Soros listened and looked puzzled. “That doesn’t make sense,” he objected.

“What do you mean?” Druckenmiller asked.

Well, Soros responded, if the Schlesinger quotes were accurate, why just build steadily? “Go for the jugular,” Soros advised him.

Druckenmiller could see that Soros was right: Indeed, this was the man’s genius. Druckenmiller had done the analysis, understood the politics, and seen the trigger for the trade; but Soros was the one who sensed that this was the moment to go nuclear. When you knew you were right, there was no such thing as betting too much. You piled on as hard as possible.

5. Tracy Alloway — Understanding Financial Crises (EP.104) – Jim O’Shaughnessy, Jamie Catherwood, and Tracy Alloway

Jamie Catherwood: What’s your process for learning those new things in each kind of major crisis? How do you approach going from no knowledge of plumbing or commodities kind of nitty gritty details today to being able to talk about it?

Tracy Alloway: So this is one of the reasons I really like the podcast format. And this is one of the things that we do on all thoughts quite a lot is we try to go as micro as possible. So if we know that there are supply chain issues, we will talk to people who are into trucking, people who are into shipping, people who are experts in the world of wooden pallets, which I didn’t know we had experts on wooden pallets, but it turns out we do. We have on the economics of nails, experts on trust plates, lumber, the list goes on and on and on, but we’ll try to talk to those people as much as possible to get a handle of what’s going on in their individual markets so that we can connect that back to the macro.

Jim O’Shaughnessy: That’s nice try by the way there, Jamie, trying to get her to subscribe to Investor Amnesia. I like it, always, always

Tracy Alloway: I am subscribed.

Jim O’Shaughnessy: [crosstalk]. Look at that. I got the pull quote for you though, Jamie. I do a lot of research for our guests. And then I also have a couple of colleagues who do research as well. And I love this quote that somehow got connected to you. And it’s on this idea, it’s a tale quote, which it’s basically, it’s much easier to be a macro bullshitter than a micro bullshitter, right?

Tracy Alloway: I’m not sure why that quote’s connected to me, but I do like it. I like it a lot. I think there’s a kernel of truth there, which is, you see a lot of prognosticators, a lot of forecasters who will come out and say, “The economy’s going to do this, inflation’s going to do this.” I give this eventuality a 40% chance, which is the ultimate MBS of prognostication. And with the macro, it feels like there are so many variables swirling around that. You always have an excuse if you’re off, right? Well inflation, maybe it was transitory, but now there’s possibly World War III with Russia and that’s led to more supply shocks.

Tracy Alloway: So really I was right. It was going to come down, but no one could have predicted that Russia was going to invade Ukraine. You see that all the time. With the micro, it is the ultimate expression of individual expertise. And if someone is living and breathing a sphere like wooden pallets or nails, the economic contribution of nails throughout history, they know that market. And if they fail to predict which way it’s turning, I feel like that’s really like, they have skin in the for something like that. So that’s why we really enjoy talking to the micro people. We enjoy talking to the macro people too, but you get different things from each group…

…Jim O’Shaughnessy: Switching gears a little bit here. What can you tell me about fancy chickens?

Tracy Alloway: I have an inordinate amount of interest in the subject of fancy chickens. I don’t know, my dream is to, Jamie knows this, one day I will own so fancy backyard chickens and they’ll be beautiful. And my dog Pablo will chase them around. And Jamie, your dog is invited too. So the reason I took an interest in chickens is I’m just interested in chickens, but there’s actually a really interesting financial history nugget that comes out of reading and researching about chickens, which is, there is a massive –

Jamie Catherwood: Great pun by the way, [inaudible] nugget talking about chickens.

Tracy Alloway: There we go. So in the late 1800s or mid 1800s, there was a massive chicken bubble driven by this Victorian fashion for having chickens. So the world was opening up. There was lots of travel. There was lots of exploration. People started discovering that you can go to Indonesia and find this really cool looking chicken and bring it back to London and sell it for a lot of money. So this industry of collecting and breeding chickens became a thing. Price has became absolutely crazy. There are pamphlets written about this. People saying how ridiculous it was that people were spending money on birds and objectively, there are a lot of weird financial bubbles that have occurred throughout human history, but chickens is probably one of the weirder ones alongside maybe be rabbits in Japan and things like that, beanie babies.

Tracy Alloway: But there’s actually a really interesting outcome of the chicken bubble, which is that it gave the raw materials for research to Charles Darwin, right? When he was really starting to think about evolution. So suddenly he was surrounded by all these wild chickens that had been brought in from Indonesia or Asia. And he was able to breed them with domestic chickens in Europe and then say, well, they can breed together. So they must be related even though they’re from opposite sides of the world, there’s a link here. And that was one of the foundational pieces of research to his theory of evolution that would come out a few years later. So, whenever we talk about economic bubbles, we usually talk about the economic damage that they reek on the rest of the world. But in this one instance, we can say that actually something useful came out of the crazy Victorian chicken bubble.

Jamie Catherwood: Well, one thing I wanted to mention, I think it was in Liverpool or Manchester when they, in the 1830s or ’40s when they unveiled the first railroad mine in one of those cities. I think the mayor took the first trip and died on the ride. I feel like I’m missing [crosstalk].

Tracy Alloway: Great advertising.

Jamie Catherwood: Something along those lines of, because people were worried. And it wasn’t really until, I think Queen Victoria rode the train that people kind of really trusted that it wouldn’t kill them. Because there’s some quotes of scientist saying that, that wouldn’t work because people will die of asphyxiation just because you’re going at such high speed.

Jim O’Shaughnessy: The high speeds, like 40 miles an hour.

Jamie Catherwood: Exactly. So it was just funny, the grand unveil makes someone trust in the railroads and dies in railroad.

Jim O’Shaughnessy: Let me pick up on that because I think that is something I’d like Tracy’s viewpoint on. What Jamie just said basically was a well-known and well trusted personage in this instance, Queen Victoria associated herself with the railroad and then suddenly everyone is like, “Okay, that’s good.” Is that possible anymore? Or have we so atomized the world that the Queen of England associating herself with something we’d be just like, whatever.

Tracy Alloway: So this is something that I think about a lot, which is one of our very early episodes was with an archeologist called Arthur Demarest, who he’s often described as the real Indiana Jones of archeology, he’s out in Guatemala or wherever digging pits. And I don’t know, finding offs snakes and that sort of thing. But he came on a couple times really in the early days of Odd Lots to talk about his research into the collapse of civilizations. And the thing that he pinpoints a lot of collapses on, particularly in South America is this over extension into complexity.

Tracy Alloway: So the society has become too complex to function both on a sort of a societal level, the way people are interacting with each other, but also on a logistical and supply level. So the way the cities are actually supplied from outside and the difficulty of getting resources in as you get bigger and bigger, and this is something that I think about a lot. I think there’s a very fractious media environment. My dad’s American, I just got back from visiting him. We watched a lot of Fox News and other [inaudible] content. And I can tell you, it is polar opposite to what I’m seeing elsewhere and when you have an environment like that, it becomes very, very difficult to be on the same page and to have those conversations about what is possible and what’s reasonable.

6. A Few Beliefs – Morgan Housel

The worst financial decisions happen when people risk what they need in order to gain something they merely want.

Unsustainable things can last years or decades longer than people think.

Tell people what they want to hear and you can be wrong indefinitely without penalty…

…The luckier you are the nicer you should be.

Past performance increases confidence more than ability.

Define what you’re incapable of and stay away from it…

…Read fewer forecasts and more history…

…A lot of denial masquerades as patience.

A lot of people have a hard time distinguishing between what happened and what they think should have happened given their world view.

About once a decade people forget that bubbles form and burst about once a decade…

…With the right incentives, people can be led to believe and defend almost anything.

Expectations move slower than reality on the ground, so a lot of frustration comes from clinging to the trends of past eras…

…Progress happens too slowly to notice, setbacks happen too fast to ignore.

We are extrapolating machines in a world where nothing too good or too bad lasts indefinitely

Optimism and pessimism always overshoot because the only way to know the boundaries of either is to go a little bit past them.

The world is governed by probability, but people think in black and white, right or wrong – did it happen or did it not? – because it’s easier.

7. Twenty Lessons Learned – Michael Batnick

Nothing lasts forever. When growth stocks were going up every day, it felt like it would never end. Now that growth stocks are going down, it feels like it will never end. Everything ends, eventually…

…Risk management is most critical when it feels like you’re getting punished for managing risk.

Nothing is a perfect inflation hedge. Not gold, stocks, crypto, or cash…

…Diversification is the only answer to an unpredictable future. If everything is working, you’re not really diversified…

…Analogs are dangerous. We know how things played out in the past. That doesn’t tell us how things will play out in the future.

The more confident somebody seems, the more cautious you should be in taking their advice….

You didn’t know this was going to happen. You don’t know what’s going to happen next.

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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 Microsoft and PayPal. 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 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.

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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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