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 09 May 2021:
Fresh groceries have remained the holy grail of e-commerce. A task so daunting that even Amazon hasn’t been able to crack it. In China, the retail grocery market is estimated by McKinsey to be worth 5.2 trillion RMB (794bn dollars) in 2019, with only 10% of that currently online. In 2020, it seems like we’ve finally found a business model where the unit economics works at scale. Theoretically at least.
That’s 社区团购 or community group-buying, the least understood business model in online retail right now. In this edition, we’ll look at this business model’s innovations, its enabling factors, what will determine the winner’s success and ultimately its challenges…
…With community group buying, the format works like this:
- A self-designated community leader creates and maintains a WeChat group.
- Community leader sign-ups individuals from their local region (usually within their regular walking distance), each WeChat group is capped at 500 people.
- They maintain a weekly or daily schedule of posting a product selection to the group.
- The products are links to mini-programs where residents click through to place their orders. Residents do not have to order the same products and will only need to pay when their collective demand exceeds a designated value.
- The products are not limited to groceries but also include other life essentials like paper towels.
- Once the residents place their orders, the entire collated order is delivered in bulk to collection points the next day for the community leader to pick up.
- Community leader unpacks the bulk order and then organises this into the resident’s orders. They will either deliver the order, or the residents will come to this pick up themselves.
- In case of issues, the community leader is the first point of contact for the residents. They will escalate the problem to the platforms and handle the resolution on behalf of the residents.
- For their work, the community leaders get 10% commission from their group orders. Given the hands-on nature of the work, a community leader can typically only manage three WeChat groups well at any one point.
With the addition of community leaders into the supply chain, the unit economics for online groceries are fundamentally changed. Now CAC is lowered since community leaders are responsible for creating their own customer groups. Customer Life Time Value (CLTV) is extended since customers have more hands-on support and social buying promotes frequent purchases. Conversion rates are much higher – can reach 10% in WeChat community group buying rather than typical 2-3% e-commerce conversions. Community leaders and customers take care of the last-mile delivery, shaving off precious additional logistics costs (lowering logistics costs is often the sole driver of profitability in marketplaces). The platform can carry fewer SKUs, buying in large quantities directly from the source rather than through intermediaries and have higher pass-through rate, which means the produce stays fresh and contributes to a positive customer experience.
“The delivery cost per order for the home delivery mode is 7-10 RMB. This part of the cost is relatively rigid, and other fulfilment costs such as storage are about 1-2 RMB. The community group purchase model with a better order density can achieve less delivery cost of than 1.5 RMB per order” – Xingsheng Youxuan (One of the startup unicorn in the race)
The model is a win-win-win proposition for the consumer, community leader and the produce platform itself. The typical community group buying customer is price-conscious, often residing in third or fourth-tier cities, and frequently elderly (a population who find it hard to navigate the complicated purchasing consumer apps). For these consumers, they can access fresher, cheaper and potentially a wider range of goods (especially seafood in more remote regions). For the community leaders, who are typically local shopkeepers or stay-at-home mums, they can earn additional revenue while serving their community. For the produce platforms, they can run a streamlined operation with less spoilage and high volume throughputs. Ultimately, they can operate a profitable business at scale.
Car companies across the globe have had to idle production and workers because of a shortage of semiconductors, often referred to as microchips or just chips. They’re the tiny operating brains inside just about any modern device, like smartphones, hospital ventilators or fighter jets. The pandemic has sent chip demand soaring unexpectedly, as we bought computers and electronics to work, study, and play from home. But while more and more chips are needed in the U.S., fewer and fewer are manufactured here.
Intel is the biggest American chipmaker. Its most advanced fabrication plant, or fab for short, is located outside Phoenix, Arizona. New CEO, Pat Gelsinger, invited us on a tour to see how incredibly complex the manufacturing process is…
…Lesley Stahl: I’m wondering, if we’re going to continue to have shortages, not just in cars, but in our phones and for our computers, for everything?
Pat Gelsinger: I think we have a couple of years until we catch up to this surging demand across every aspect of the business.
COVID showed that the global supply chain of chips is fragile and unable to react quickly to changes in demand. One reason: fabs are wildly expensive to build, furbish, and maintain.
Lesley Stahl: it used to be that there were 25 companies in the world that made the high-end, cutting-edge chips. And now there are only three. And in the United States? – You.
GELSINGER HOLDS UP FINGER
Lesley Stahl: One. One.
Today, 75% of semiconductor manufacturing is in Asia.
Pat Gelsinger: 25 years ago, the United States produced 37% of the world’s semiconductor manufacturing in the U.S. Today, that number has declined to just 12%.
Lesley Stahl: Doesn’t sound good.
Pat Gelsinger: It doesn’t sound good. And anybody who looks at supply chain says, “That’s a problem.”…
…Pat Gelsinger: Well, they’re pretty happy to buy from some of the Asian suppliers.
Actually, they don’t always have a choice. For chips with the tiniest transistors – there is no “made in the U.S.” option. Intel currently doesn’t have the know-how to manufacture the most advanced chips that Apple and the others need.
Lesley Stahl: The decline in this industry. It’s kinda devastating, isn’t it?
Pat Gelsinger: The fact that this industry was created by American innovation–
Lesley Stahl: The whole Silicon Valley idea started with Intel.
Pat Gelsinger: Yeah… The company stumbled. You know, it’s still a big company – we had some product stumbles, some manufacturing and process stumbles.
Perhaps the biggest stumble was in the early-2000s, when Steve Jobs of Apple needed chips for a new idea: the iPhone. Intel wasn’t interested. And Apple went to Asia, eventually finding TSMC: the Taiwan Semiconductor Manufacturing Company – today, the world’s most advanced chip-manufacturer, producing chips that are 30% faster and more powerful than Intel’s.
Lesley Stahl: They’re ahead of you on the manufacturing side.
Pat Gelsinger: Yeah.
Lesley Stahl: Considerably ahead of you.
Pat Gelsinger: We believe it’s gonna take us a couple of years and we will be caught up…
…But TSMC is a manufacturing juggernaut worth over a half a trillion dollars. Collaborating with clients to produce their chip designs, it’s been sought out by Apple, Amazon, contractors for the U.S. military, and even Intel, which uses TSMC to produce their cutting-edge designs they’re not advanced enough to make themselves.
Lesley Stahl: How and why did Intel fall behind?
Mark Liu: It is surprising for us too…
…Pat Gelsinger: China is one of our largest markets today. You know, over 25% of our revenue is to Chinese customers. We expect that this will remain an area of tension, and one that needs to be navigated carefully. Because if there’s any points that people can’t keep running their countries or running their businesses because of supply of one critical component like semiconductors, boy, that leads them to take very extreme postures on things because they have to.
The most extreme would be China invading Taiwan and in the process gaining control of TSMC. That could force the U.S. to defend Taiwan as we did Kuwait from the Iraqis 30 years ago. Then it was oil. Now it’s chips.
Lesley Stahl: The chip industry in Taiwan has been called the Silicon Shield.
Mark Liu: Yes.
Lesley Stahl: What does that mean?
Mark Liu: That means the world all needs Taiwan’s high-tech industry support. So they will not let the war happen in this region because it goes against interest of every country in the world.
Lesley Stahl: Do you think that in any way your industry is keeping Taiwan safe?
Mark Liu: I cannot comment on the safety. I mean, this is a changing world. Nobody want these things to happen. And I hope– I hope not too– either.
[Patel] And so how does Shopify make money? You take a cut of every transaction, you charge a subscription fee. Where do you take your cut?
[Finkelstein] Yeah, so two sides. One is on the subscription side. So there’s a subscription fee. Starts at $29 a month, if you’re just getting started, and goes up to $2,000 a month for some of the larger merchants. But we also have a payments business. Shopify Payments powers a majority of, particularly in our main geographies, a majority of transactions. We have a capital business. We’ve now given out more than $2 billion of capital to small businesses. We have a fulfillment business and a shipping business. Actually, this is maybe a good point to pause on for a second.
If you were to pretend that Shopify was a retailer, we’re not a retailer, but pretend we were, we would be the second largest online retailer in America, after Amazon. The reason I say that is because the second largest online retailer in America, they’re entitled to massive economies of scale. And so what we try to do is, we try to go to the shipping companies and capital companies and the payment companies, and we negotiate as if we were the second largest retailer, except instead of keeping those economies of scale for ourself, we distribute those economies of scale and give those advantages to small businesses.
And we think what that does is a real leveling of the playing field so that these companies can get bigger, faster, at a pace that, frankly, we’ve never seen before. There’s rumors now that some of our biggest merchants are going public, are filing for IPOs. Some of them didn’t exist five years ago. In the history of commerce and retail, we’ve never seen that type of scale at that speed…
…[Patel] So I want to just pull back for one second, talk about Shopify as it’s something that you could look at as the second largest online retailer in America. You’re up against Google, Facebook, Amazon, Apple, the rest. This last quarter of earnings, these companies all did extraordinarily well. When I started Decoder, the question I would ask everybody is, “What are the trends you see in a pandemic? What’s going to snap back?”
Nothing’s snapping back, except maybe we’re not going to go work in offices the way that we used to. The economy has moved online in a real way. We are really dependent, in particular, on a handful of very large companies. I’ll pick on Apple because they have a lawsuit. They want to take a cut of every time you push a button on the iPhone.
Shopify enables small businesses to compete at that level. You have this economy of scale. You’re also partnered with those companies. You’re competitive with those companies. What is that relationship like? Where does Shopify slot in?
[Finkelstein] Shopify’s entire business model is predicated on: if small businesses do well, we do well. If they don’t do well, we don’t do well. And so the relationship we have, first of all, with small business, I think is very different than a lot of other technology companies where the small businesses, whether they sell a lot or not, they still need them for things like exposure and traffic and other all those things related to marketing and advertising. But the way we think about it is, the future of retail, in our view, is not going to be online, nor is it going to be offline. It’s not going to be on Instagram or TikTok or Facebook or Walmart.com, it’s going to be everywhere.
And the future of retail, in our view, is going to be about consumer choice. Now, that is very different. Commerce is about as old a construct as currency. We’re talking about since the beginning of time, you’ve had commerce and you’ve had currency, but it was always the retailer dictating to the consumer how to purchase.
So a great example is, go back when you were 10 years old or something and you wanted to go buy a video game at the video game store, There was a time it opened, at 9AM on a Saturday morning. Once you picked up the game on the shelf, you went into line. You had to use this credit card, but they didn’t accept that credit card. But basically, it’s always the same. It was always the retailer dictating to the consumer how to purchase.
The big shift that is happening that will exist long after the pandemic and, frankly, will be the future of retail, will be that consumers will simply say, “I want to buy however is most convenient for me.” And if you’re a really forward-thinking merchant like Allbirds, for example, and you know that it’s all about consumer choice, then you’re going to have a great physical store in San Francisco and New York City and a whole bunch of other places, you’re going to have a great online store, you’re going to cross-sell on things like Instagram and Facebook, you may also activate the TikTok ad channel because that’s when you can reach new potential customers. But what Shopify’s role in all that is, is that we want to integrate all of it into a centralized retail operating system.
So, think of Shopify as the hub of where you run your business day-to-day. When you say you’re going to work in the morning, you open up the Shopify admin, you have your inventory, your analytics, your reporting, you do fulfillment from there. One major spoke of that hub will be the online store. Another major spoke may be the offline store, but all the other spokes are going to be with Facebook and Google and Instagram and TikTok and all those companies.
And so our partnership with all these companies is predicated on this idea that we want to enable these merchants, these brands, to sell wherever they have customers. What is the modern-day town square? If you want to sell across a whole variety of age brackets, you need to sell everywhere. And that is really what Shopify’s role is, and that’s the reason why we partner with all these companies…
…[Patel] Oh, that’s really interesting. The reason I ask that is, Shopify is growing really fast. You were there in the early days. I keep coming back to this theme, you are now enabling companies to compete with the giants. You are yourself, in some ways competing with the giants. You are in some ways partnered with them.
As you have to make decisions there, you’re up against a lot of capital, a lot of market power, I’m definitely going to ask you about this Apple-Epic lawsuit. Sometimes you’re just up against other people controlling the interface, and just saying what you can and can’t do. How do you use your overall framework to make a decision, like we’re not going to have the Shop App become an actual marketplace for customers?
[Finkelstein] That’s actually an easier answer, because when you’re specific about that, you ask yourself, “What is best for the merchant?” Forget everything else. What is best for the merchant? During COVID, when COVID first hit, it hit hard in Canada around mid-March. We extended our trial from 14 days to 90 days. That’s a big change. There’s a real cost to moving a trial from 14 days to 90 days, nine zero.
But that was the right thing to do, even if it wasn’t the easy thing to do. Because it meant that more people that may have been on the fence about whether or not to digitalize their brick-and-mortar store, or to commercialize their hobby, or to enter the entrepreneurship ring, were able to do so with less risk, with less cost. That’s an easy decision, because you say, “What is best for the merchant there?”
The other thing is, we use a lens around Shopify, which is the idea of, we want to build a 100-year company. And we’re about 15 years in, so we have like 85 years left to go. When you use a long-term horizon of a 100-year company, you tend to not necessarily focus on short-term metrics or short-term results. You’re able to actually think a lot longer about what you’re trying to do here. And ultimately, just to be clear, what we’re trying to do here, is we want to be the world’s entrepreneurship company.
There is a company that owns search, and it’s Google, and they’ve done an amazing job organizing the world’s content and information. And there’s a company that owns social, and for the most part right now, it’s Facebook. But no company has yet to really own and make entrepreneurship something that is accessible by everyone, and we think we have the best shot at that.
So using that lens, it’s a lot easier to make decisions for the long run. It also means in some cases, that we will do something that maybe in the short run is not great for Shopify, but in the long run is great for the merchant. Or in the short run, it’s also great for the merchant, in the long run may eventually be good for Shopify. We can take these long-term bets, because we’re playing this ridiculously long game of a 100-year company.
Computers learned to see only recently. For decades, image recognition was one of the grand challenges in artificial intelligence. As I write this, I can look up at my shelves: they contain books, and a skein of yarn, and a tangled cable, all inside a cabinet whose glass enclosure is reflecting leaves in the trees outside my window. I can’t help but parse this scene—about a third of the neurons in my cerebral cortex are implicated in processing visual information. But, to a computer, it’s a mess of color and brightness and shadow. A computer has never untangled a cable, doesn’t get that glass is reflective, doesn’t know that trees sway in the wind. A.I. researchers used to think that, without some kind of model of how the world worked and all that was in it, a computer might never be able to distinguish the parts of complex scenes. The field of “computer vision” was a zoo of algorithms that made do in the meantime. The prospect of seeing like a human was a distant dream.
All this changed in 2012, when Alex Krizhevsky, a graduate student in computer science, released AlexNet, a program that approached image recognition using a technique called deep learning. AlexNet was a neural network, “deep” because its simulated neurons were arranged in many layers. As the network was shown new images, it guessed what was in them; inevitably, it was wrong, but after each guess it was made to adjust the connections between its layers of neurons, until it learned to output a label matching the one that researchers provided. (Eventually, the interior layers of such networks can come to resemble the human visual cortex: early layers detect simple features, like edges, while later layers perform more complex tasks, such as picking out shapes.) Deep learning had been around for years, but was thought impractical. AlexNet showed that the technique could be used to solve real-world problems, while still running quickly on cheap computers. Today, virtually every A.I. system you’ve heard of—Siri, AlphaGo, Google Translate—depends on the technique.
The drawback of deep learning is that it requires large amounts of specialized data. A deep-learning system for recognizing faces might have to be trained on tens of thousands of portraits, and it won’t recognize a dress unless it’s also been shown thousands of dresses. Deep-learning researchers, therefore, have learned to collect and label data on an industrial scale. In recent years, we’ve all joined in the effort: today’s facial recognition is particularly good because people tag themselves in pictures that they upload to social networks. Google asks users to label objects that its A.I.s are still learning to identify: that’s what you’re doing when you take those “Are you a bot?” tests, in which you select all the squares containing bridges, crosswalks, or streetlights. Even so, there are blind spots. Self-driving cars have been known to struggle with unusual signage, such as the blue stop signs found in Hawaii, or signs obscured by dirt or trees. In 2017, a group of computer scientists at the University of California, Berkeley, pointed out that, on the Internet, almost all the images tagged as “bedrooms” are “clearly staged and depict a made bed from 2-3 meters away.” As a result, networks have trouble recognizing real bedrooms…
…In his late twenties, Kambe came home to Nishiwaki, splitting his time between the lumber mill and a local job-training center, where he taught computer classes. Interest in computers was soaring, and he spent more and more time at the school; meanwhile, more houses in the area were being built in a Western style, and traditional carpentry was in decline. Kambe decided to forego the family business. Instead, in 1982, he started a small software company. In taking on projects, he followed his own curiosity. In 1983, he began working with NHK, one of Japan’s largest broadcasters. Kambe, his wife, and two other programmers developed a graphics system for displaying the score during baseball games and exchange rates on the nightly news. In 1984, Kambe took on a problem of special significance in Nishiwaki. Textiles were often woven on looms controlled by planning programs; the programs, written on printed cards, looked like sheet music. A small mistake on a planning card could produce fabric with a wildly incorrect pattern. So Kambe developed SUPER TEX-SIM, a program that allowed textile manufacturers to simulate the design process, with interactive yarn and color editors. It sold poorly until 1985, a series of breaks led to a distribution deal with Mitsubishi’s fabric division. Kambe formally incorporated as BRAIN Co., Ltd.
For twenty years, BRAIN took on projects that revolved, in various ways, around seeing. The company made a system for rendering kanji characters on personal computers, a tool that helped engineers design bridges, systems for onscreen graphics, and more textile simulators. Then, in 2007, BRAIN was approached by a restaurant chain that had decided to spin off a line of bakeries. Bread had always been an import in Japan—the Japanese word for it, “pan,” comes from Portuguese—and the country’s rich history of trade had left consumers with ecumenical tastes. Unlike French boulangeries, which might stake their reputations on a handful of staples, its bakeries emphasized range. (In Japan, even Kit Kats come in more than three hundred flavors, including yogurt sake and cheesecake.) New kinds of baked goods were being invented all the time: the “carbonara,” for instance, takes the Italian pasta dish and turns it into a kind of breakfast sandwich, with a piece of bacon, slathered in egg, cheese, and pepper, baked open-faced atop a roll; the “ham corn” pulls a similar trick, but uses a mixture of corn and mayo for its topping. Every kind of baked good was an opportunity for innovation.
Analysts at the new bakery venture conducted market research. They found that a bakery sold more the more varieties it offered; a bakery offering a hundred items sold almost twice as much as one selling thirty. They also discovered that “naked” pastries, sitting in open baskets, sold three times as well as pastries that were individually wrapped, because they appeared fresher. These two facts conspired to create a crisis: with hundreds of pastry types, but no wrappers—and, therefore, no bar codes—new cashiers had to spend months memorizing what each variety looked like, and its price. The checkout process was difficult and error-prone—the cashier would fumble at the register, handling each item individually—and also unsanitary and slow. Lines in pastry shops grew longer and longer. The restaurant chain turned to BRAIN for help. Could they automate the checkout process?…
…For the BRAIN team, progress was hard-won. They started by trying to get the cleanest picture possible. A document outlining the company’s early R. & D. efforts contains a triptych of pastries: a carbonara sandwich, a ham corn, and a “minced potato.” This trio of lookalikes was one of the system’s early nemeses: “As you see,” the text below the photograph reads, “the bread is basically brown and round.” The engineers confronted two categories of problem. The first they called “similarity among different kinds”: a bacon pain d’épi, for instance—a sort of braided baguette with bacon inside—has a complicated knotted structure that makes it easy to mistake for sweet-potato bread. The second was “difference among same kinds”: even a croissant came in many shapes and sizes, depending on how you baked it; a cream doughnut didn’t look the same once its powdered sugar had melted.
In 2008, the financial crisis dried up BRAIN’s other business. Kambe was alarmed to realize that he had bet his company, which was having to make layoffs, on the pastry project. The situation lent the team a kind of maniacal focus. The company developed ten BakeryScan prototypes in two years, with new image preprocessors and classifiers. They tried out different cameras and light bulbs. By combining and rewriting numberless algorithms, they managed to build a system with ninety-eight per cent accuracy across fifty varieties of bread. (At the office, they were nothing if not well fed.) But this was all under carefully controlled conditions. In a real bakery, the lighting changes constantly, and BRAIN’s software had to work no matter the season or the time of day. Items would often be placed on the device haphazardly: two pastries that touched looked like one big pastry. A subsystem was developed to handle this scenario. Another subsystem, called “Magnet,” was made to address the opposite problem of a pastry that had been accidentally ripped apart.
A major development was the introduction of a backlight—the forerunner of the glowing rectangle I’d noticed in the Ueno store. It helped eliminate shadows, including the ones cast by a doughnut into a doughnut hole. (One of BRAIN’s patent applications explains how a pastry’s “chromatic dispersion” can be analyzed “to permit definitive extraction of contour lines even where the pastry is of such hole-containing shape.”) At one point, when it became clear that baking times were never consistent, Kambe’s team made a study of the phenomenon. They came up with a mathematical model relating bakedness to color. In the end, they spent five years immersed in bread. By 2013, they had built a device that could take a picture of pastries sitting on a backlight, analyze their visual features, and distinguish a ham corn from a carbonara sandwich.
That year, BakeryScan launched as a real product. Today, it costs about twenty thousand dollars. Andersen Bakery, one of BRAIN’s biggest customers, has deployed the system in hundreds of bakeries, including the one in Ueno station. The company says it’s cut down on training time and has made the checkout process more hygienic. Employees are more relaxed and can talk to customers; lines have been virtually eliminated. At first, BakeryScan’s performance wasn’t perfect. But the BRAIN team included a feedback mechanism: when the system isn’t confident, it draws a yellow or red contour around a pastry instead of a green one; it then asks the operator to choose from a small set of best guesses or to specify the item manually. In this way, BakeryScan learns. By the time I encountered it, it had achieved an even higher level of accuracy…
…In early 2017, a doctor at the Louis Pasteur Center for Medical Research, in Kyoto, saw a television segment about the BakeryScan. He realized that cancer cells, under a microscope, looked kind of like bread. He contacted BRAIN, and the company agreed to begin developing a version of BakeryScan for pathologists. They had already built a framework for finding interesting features in images; they’d already built tools allowing human experts to give the program feedback. Now, instead of identifying powdered sugar or bacon, their system would take a microscope slide of a urinary cell and identify and measure its nucleus.
BRAIN began adapting BakeryScan to other domains and calling the core technology AI-Scan. AI-Scan algorithms have since been used to distinguish pills in hospitals, to count the number of people in an eighteenth-century ukiyo-e woodblock print, and to label the charms and amulets for sale in shrines. One company has used it to automatically detect incorrectly wired bolts in jet-engine parts. At the SPring-8 Angstrom Compact Free Electron Laser (sacla), in Hyogo, a seven-hundred-metre-long experimental apparatus produces high-intensity laser pulses; since reading the millions of resulting pictures by hand would be impractical, a few scientists at the sacla facility have started using algorithms from AI-Scan. Kambe said that he never imagined that BakeryScan’s technology would be applied to projects like these.
- That thing that made you weird as a kid could make you great as an adult — if you don’t lose it.
- If you have any doubt at all about being able to carry a load in one trip, do yourself a huge favor and make two trips.
- What you get by achieving your goals is not as important as what you become by achieving your goals. At your funeral people will not recall what you did; they will only remember how you made them feel.
- Recipe for success: under-promise and over-deliver.
- It’s not an apology if it comes with an excuse. It is not a compliment if it comes with a request.
- Jesus, Superman, and Mother Teresa never made art. Only imperfect beings can make art because art begins in what is broken.
- If someone is trying to convince you it’s not a pyramid scheme, it’s a pyramid scheme..
- …Train employees well enough they could get another job, but treat them well enough so they never want to.
- Don’t aim to have others like you; aim to have them respect you.
- The foundation of maturity: Just because it’s not your fault doesn’t mean it’s not your responsibility.
- A multitude of bad ideas is necessary for one good idea.
- Being wise means having more questions than answers.
- Compliment people behind their back. It’ll come back to you.
- Most overnight successes — in fact any significant successes — take at least 5 years. Budget your life accordingly.
- You are only as young as the last time you changed your mind..
- …When a child asks an endless string of “why?” questions, the smartest reply is, “I don’t know, what do you think?
- To be wealthy, accumulate all those things that money can’t buy.
- Be the change you wish to see
- When brainstorming, improvising, jamming with others, you’ll go much further and deeper if you build upon each contribution with a playful “yes — and” example instead of a deflating “no — but” reply.
- Work to become, not to acquire.
- Don’t loan money to a friend unless you are ready to make it a gift.
- On the way to a grand goal, celebrate the smallest victories as if each one were the final goal. No matter where it ends you are victorious.
- Calm is contagious.
- Even a foolish person can still be right about most things. Most conventional wisdom is true.
- Always cut away from yourself.
- Show me your calendar and I will tell you your priorities. Tell me who your friends are, and I’ll tell you where you’re going.
- When hitchhiking, look like the person you want to pick you up.
If Charles Ponzi were alive today, I have no doubt that he would be able to raise capital from investors, probably in the form of a SPAC. Many investors would laud him for being a genius as he bilked investors out of millions of dollars.
When I was researching the history of financial scams for Don’t Fall For It the one thing that jumped out above all else is how similar financial frauds are across time and place. They typically involve new technologies, people with extraordinary sales skills and the insatiable human desire for get-rich quick schemes.
Despite the fact that people have been getting duped by hucksters and charlatans for centuries, there was one period that kept coming up over and over again in my research — the 1920s.
It was the golden age of financial fraud.
The Roaring 20s had everything a con-artist looking to dupe people out of their money could ask for — innovation, new financial products, a booming economy, rising markets, new and exciting technologies, loose lending standards, new communication tools and people getting rich all over the place.
This period included Dr. John Brinkley, a fake doctor, who told people he could solve their fertility problems by implanting goat testicles into the male scrotum. He quickly became wealthy by promising to cure people’s ailments with his secretive medicines and procedures.
Then there was the match king, Ivar Kreuger, who used his match factories to create obscene amounts of leverage and offer insanely high rates of return to investors who put money into his ever-growing empire of new financial products. Kreuger created one of the biggest financial scams no one has ever heard of. It all fell apart in the Great Depression.
The Roaring 20s was a time of innovation in the financial markets but there were still bucket shops where people went to gamble their money on the markets. A scam artist nicknamed “The Kid” would set up fake bucket shops promising people the ability to buy $5 stock certificates for $1.
What was the catch?
Of course, those certificates were fake. He ran this same scam in multiple cities all over the country.
There are endless stories like this from that period.
The financial markets feel wonderful right now. It would have been nearly impossible to not make money over the past year or so. The economy could legitimately be setting up for our own version of the roaring 20s.
Yet these good times could also be setting us up for a new golden age of financial fraud.
You have new and exciting innovations happening all around us. A new asset class is being established right before our eyes in cryptocurrencies. Tens of thousands of people have become multi-millionaires in a matter of years.
All of the scam artists, hucksters and charlatans have to be licking their chops right now.
During bull markets and economic boom times people witness others becoming very wealthy. So they let their guard down, take more risk than they reasonably should and trust people they shouldn’t while chasing easy riches.
And the people most susceptible to financial fraud tend to be the more highly educated investors who have already made a ton of money.
One of the studies I reference in my book discovered people who were caught up in financial scams were actually more knowledgeable about markets and investing than people who weren’t involved in scams. This makes sense when you realize the people with the most money have the biggest target on their back.
Entertainment companies today don’t make movies or TV shows. They don’t even mainly “tell stories”. They manage the proprieties of those stories in such a way to create and sustain deep affinity, i.e., build love.
This is a very different rubric than the media industry is used to. It also suggests that many low-margin businesses, products, or titles create more value than an income statement might realize. Think about the correlation between the pajamas you wore growing up and the adaptations/films you deeply want to succeed, versus those you’re largely indifferent to. It’s doubtlessly true that the comics divisions of Warner Bros.’ DC, and Disney’s Marvel deliver minimal revenues and dilutive margins. But comics remain a low-cost channel for story and love building. Notably, almost all of the Marvel Cinematic Universe’s forthcoming series are from the last (and largely unknown) decade of comics. It doesn’t matter to maestro Kevin Feige that his films have eclipsed not just the comics by several literal orders of magnitude, nor even all of film history. These comics are where new stories are created, discovered, and refined. The now globally famous character of Miles Morales first appeared in 2011, Ms. Marvel (who has her own MCU TV series this year) comes from 2013. Riri Williams, who will take up Iron Man’s mantle in her own MCU TV series first appeared less than five years ago.
This trend also means that Hollywood needs to solve its video game problem. The category simply matters too much to audiences. It is also becoming more social, immersive, and narratively rich each day. Consider the evolution of TV/video versus games over the past fifteen years. The MCU films and series of 2021 are more interconnected, complex, and visually impressive than 2008’s Iron Man, but they’re still rather similar. Games, meanwhile, have been entirely reinvented for live services, social multiplayer, and UGC. Now, we’re only a few years from the point in which millions will come home to join a live event with a real-time motion capture hero like Tony Stark (who will likely not be performed by Robert Downey Jr., even though it will look like him) alongside their friends. Not long after, these will be integrated into the weekly release schedule a TV series, thereby enabling the audience to help the heroes as they watch them.
This also connects to Disney’s greatest love advantage: it’s theme parks. For all the success of Disney+, the strongest, most profitable, most defensible part of Disney’s business is its capex-heavy, physical theme parks. As I wrote in “Digital Theme Park Platforms: The Most Important Media Businesses of the Future”, “there is no simple way to quantify how important this business unit is to Disney… The financial role is obvious… [but] There is nothing that can compare to the impact of a child being hugged by her heroes. The ability to enjoy your favorite IP as “you” is unique and lasts a lifetime.” The problem with Disney’s parks, however, is that they can only ever reach a tiny portion of Disney’s fans (and rarely its lower income and foreign fans). And it takes tens of billions of dollars and close to a decade to reach more (which is why most of Disney’s competitors lack parks, despite their importance and profitability).
Digital theme parks, however, “are always ‘open’, ‘everywhere’, ‘full of your friends’, and impervious to COVID-19… They also boast an even larger (i.e. infinite) number of attractions and rides, none of which need be bound by the laws of physics or the need for physical safety, and all of which can be rapidly updated and personalized. These digital parks also allow for much greater self-expression (e.g. avatars, skins).” And soon, every fan will be able to receive a hug from the actual Iron Man.
This isn’t to say an IP holder needs to own a gaming studio, per se. Obviously that’s an advantage in a number of ways, but at minimum, every IP owners needs cohesive and comprehensive strategy for interactivity that goes beyond MGs, GGs, and avatar licensing.
What does all of this mean for the industry overall? Well, one of the key lessons over the past several decades in entertainment is one of “more”. We want more of the stories we love, more often, in more places, and more media, always. We might gripe about how Disney will never let Star Wars end or that endless sequels undermine the significance of any films that came before, but the truth is only we want something to “end” … until immediately after it does. Give us The Mandalorian, even as we tire of the sequel trilogy, and then second season of The Mandalorian one year later. We hated the prequels but delight at the idea of a spinoff of Ewan McGregor’s Obi-Wan. Two Star Wars games aren’t enough, nor is four. Just look at gaming over the past year and a half. Yes, the pandemic led us to play more games, but mostly we played our favorite games more.
If our biggest stories become bigger, and ultimately, we want endless amount of “more” from our favorite stories, then most of us will hit a sort of “Dunbar’s Number” for franchises. The bigger Marvel (or anyone) gets narratively, in love building, and in monetization, the harder it will be for a Power Rangers reboot or Dark Universe or Transformers Ecosystem to grow. Consider the mocap example. We’re not going to run home to mocap every hero we know of, even if we watch a diverse selection of hero movies. This means fewer stories will collect ever-more of the benefit.
There used to be a fight to be one of the winning comic books, video games, or film franchises. This meant there was room for many winners and that the reach of any winner was limited. Soon, it will be a fight for dominance between all franchises and across all mediums. The major stories will expand into all categories, from film to TV to podcasts, and be envisioned as interactive experiences. And as long as they continue to offer more “more”, there’s little reason for a fan to look (and invest) elsewhere.
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, Facebook, and Shopify. Holdings are subject to change at any time.
